![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1872759b-c9e3-4d4e-a3cf-30813840b049",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 1: Orienting inside a \"Climate Solution\" Simulator\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Maximilian Puelma Touzel, Paul Heubel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2d2b006-8493-451b-a75e-797d902a0fcd",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "During the first week of the course, you applied computational tools to climate data (measurements, proxies, and model output) to characterize past, present, and future climate. During day one of this second week (W2D1), you began to explore climate model data from Earth System Model (ESM) simulations conducted for the recent Climate Model Intercomparison Project (CMIP6) that are presented in the report from the Intergovernmental Panel on Climate Change ([IPCC](https://www.ipcc.ch/)). However, the dominant source of uncertainty in those projections arises from how human society responds: e.g. how our emissions reduction and renewable energy technologies develop, how coherent our global politics are, how our consumption grows (cf. [Rogelj et al. (2018)(Global Warming of 1.5°C. An IPCC Special Report \\[...\\])](https://doi.org/10.1017/9781009157940.004)). For these reasons, in addition to understanding the physical basis of the climate variations projected by these models, it's also important to assess the current and future socioeconomic impact of climate change and what aspects of human activity are driving emissions.\n",
- "\n",
- "This day's tutorials focus on the socioeconomic projections regarding the future of climate change and are centered around the Shared Socioeconomic Pathways (SSP) framework used by the IPCC. However, in this first tutorial, you will use the [En-ROADS](https://www.climateinteractive.org/en-roads/) simulator to get some intuition about different socioeconomic variables and their consequences for climate. Additionally, you will analyze potential economic and population scenarios to learn about the complex and intertwined dynamics of socioeconomic variables, as well as how this model informs modern-day climate challenges.\n",
- "\n",
- "Unlike the rest of the days of this course, this day will be highly conceptual and discussion-driven, and focus much less on analyzing datasets.\n",
- "\n",
- "In this tutorial, you will:\n",
- "- Get familiar with the interface of the simulator named En-ROADS\n",
- "- Explore the impact of different growth variables on the temperature increase by 2100 projected from the En-ROADS model.\n",
- "- Understand why it is necessary to implement various actions against climate change, not a single one.\n",
- "- Explore the assumptions and limitations of this model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "368d0c90-ae7e-43d9-ada7-ad42c75f5f95",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6db786f8-e5c4-42a9-9837-e41027b9ad4d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e11e3bbb-ce8f-4404-8d04-b6f86d4a9ea9",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ea9d553f-c3eb-4723-a922-e81f97e1e3ba",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting inside a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52ca5582-816c-423c-9ed7-cda1c7319731",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57265af6-b3ab-41e4-82a1-6c4b63a0c321",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Exploration of a Climate Solution Simulator"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "042a83d3-2e94-4092-a289-2430d349966c",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The following introductory video gives a quick overview of the En-ROADS simulator, a simple climate model (SCM), developed by [Climate-Interactive](https://www.climateinteractive.org/) for teaching purposes. It is used in policy workshops, role-plays, and other activities to explore the possibilities and obstacles of scenarios and human solutions to climate change.\n",
- "\n",
- "To get familiar with modeling societal and economic mechanisms in combination with climatic variables, the so-called **socio-economic model**, you will examine its 'control knobs', limitations, and assumptions. In later tutorials you will compare these findings with those of Integrated Assessment Models (IAMs) which are state-of-the-art models for projecting scenarios and those used in the socioeconomic pathways of the IPCC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9c6b2260-bdb0-4d7b-a07d-41551d5ccf92",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 2: Overview of the En-ROADS Climate Solutions Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'Py_qIgcZxKg')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "718606a5-1239-414b-84fc-f9c0a18924d6",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Exercise 1: Can you limit human-caused global warming to \"well-below 2ºC\"?\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "We jump right in with an exercise, adapted from [En-ROADS](https://www.climateinteractive.org/guided-assignment/), that allows you to explore the Climate Solution Simulator.\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Don't worry if you don't find a scenario that works right away - keep exploring. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) to help you get started. Have fun!\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* How many variables did you have to adjust to reach the \"well-below 2ºC\" target?\n",
- "* Which variables had the most individual impact? Did the magnitude of impact surprise you for any variables?\n",
- "* How feasible is this scenario? That is, what actions would have to be taken by governments, businesses and people over the next few years to make the proposed scenario possible?\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30a81fb8-c21b-490c-9297-a6705f114957",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "3. One scenario to try based on the goals of the energy transition is the combination of:\n",
- "(1) full electrification of the energy supply, i.e. 'very highly taxed' fossil fuel emissions (Coal, Natural gas, and Oil),\n",
- "(2) 'highly subsidized' renewables, and\n",
- "(3) a 'very high' carbon price,\n",
- "as well as the 'highly reduced' emissions of Methane and other Gases\n",
- "which gets us to a maximum increase of 2°C by the end of the century\n",
- "(cf. this example scenario https://en-roads.climateinteractive.org/scenario.html?v=24.3.0&p1=100&p7=85&p10=5&p16=-0.05&p23=-1&p39=250&p59=-64&p67=2&g0=2&g1=62).\n",
- "\n",
- "Nevertheless, other scenarios are possible, either by involving more parameters or by focusing on carbon removal.\n",
- "There are (at least!) two important observations to make. First, actions vary in leverage, in other words, some actions are more helpful than others.\n",
- "Second, to make a difference and reach an ambitious goal like the 2°C degree target, many actions in many sectors are required.\n",
- "Sometimes one refers to this circumstance by calling it a 'Silver Buckshot' instead of a 'Silver Bullet' approach (cf. e.g. https://www.washingtonpost.com/archive/opinions/2006/05/27/welcome-to-the-climate-crisis-span-classbankheadhow-to-tell-whether-a-candidate-is-serious-about-combating-global-warmingspan/26b2ac5a-a4a3-46ff-b214-3fc07a3a5ab3/).\n",
- "Furthermore, people might be surprised by the fact that some actions may be much lower leverage, while others like carbon pricing and energy efficiency might be higher leverage than people expect.\n",
- "'''"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05df13b5-14ce-4515-aaae-7cc9e3765445",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "#link_id = \"gwr8h\"\n",
- "\n",
- "download_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "render_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download a cheatsheet for the En-ROADS Control Panel:\\n{download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f5a0c7f-8fd3-4c89-b25d-8070ef2fdd4b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 2: Limitations of the En-ROADS Model Approach\n",
- "\n",
- "We conclude this tutorial by stepping back and discussing the limitations of En-ROADS.\n",
- "\n",
- "### Exercise 2: Limitations\n",
- "*Estimated timing:* 5 minutes\n",
- "\n",
- "1. Think about limitations that arise from the En-ROADS model approach. List a few mechanisms that seem oversimplified or phenomena that might be not or misrepresented. Discuss with your pod.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b17595a7-b326-4edb-9f80-b8e21f1d4e5e",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "1. At first, En-ROADS is a world model, not an optimal planning model so it is unlike any IAM used e.g. for the IPCC reports.\n",
- "Controls are somewhat limited (DAC/CCS, soil, BioChar, and mineralization are not covered).\n",
- "It allows only for single parameter changes - in reality, there will be correlations, e.g. between carbon pricing and renewables due to market pressure.\n",
- "Some feedbacks are also missing, besides the above-mentioned damage from climate change on GDP, land use, etc.,\n",
- "climate change harms the human population by shortening lives.\n",
- "Although this happens already in the current 1°C increase reality, this harm is difficult to quantify and hence not implemented.\n",
- "Furthermore, it is a fully aggregated model, no spatial/regional or income resolution, and corresponding interdependency exists.\n",
- "An action/ policy is assumed to be executed globally, which is a utopia so far.\n",
- "It hence remains important to consider the implications of heterogeneity across different countries and their interactions.\n",
- "Last but not least, tipping points such as the thawing of the permafrost are represented in a very simplified manner only, although they probably have strong implications.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "51aac287-f6d0-479e-be4b-87f557c32214",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, you got an intuition for various 'control knobs' that can be turned in a socio-economic model environment. We discussed why no policy alone can be a silver bullet to solve all problems but a mixture of many actions, in particular, the energy transition to renewables and carbon pricing. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c949bc5-e5ef-4d07-b379-122072ef6271",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial1",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb
deleted file mode 100644
index b3219f7bc..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb
+++ /dev/null
@@ -1,508 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "873927b3-ad55-40a2-ab6a-de2af3c09cc8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8eb286c6-dd69-440b-bc14-e6c7f20517bf",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 2: Fossil Fuel Emissions, Growth, and Damage\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d86fc935-8b60-4fcf-83d7-1e9c78ca6b84",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "This tutorial explores the impact of population and economic growth on the global temperature and how a changing climate due to fossil fuel emissions damages our economy. You model these scenarios by 'controlling the knobs' of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* discuss the impact of a growth-based economy on future fossil-fuel emissions along the Kaya identity\n",
- "* qualitatively describe the impact of population and economic growth on the global temperature within the En-ROADS model environment.\n",
- "* (explain qualitatively how a different damage function formulation impacts the economic activity)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8dc93386-4bde-4e22-b7eb-174133f57351",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9398705-5441-4ed3-980d-c6339cb30696",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3342114d-deee-4d5b-afde-d4852bac2352",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2ca0746a-edad-45f7-8ad5-1b4ec1e7c546",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting in a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d45c633-1daa-4408-ab8c-18969fff8bde",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4cb055f0-0f58-4c8c-9e11-a7a16f0b6e98",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Quantification of Fossil Fuel Emissions and Its Dependency on Growth\n",
- "\n",
- "We discussed in the slides how economics represents the human population as a source of labor needed to produce goods and services. A by-product of this production are fossil fuel emissions, which increase atmospheric greenhouse gas (GHG) concentrations, significantly changing the earth's climate. Let us take a look at the relationship between people, production, and emissions in more detail using the En-ROADS simulator.\n",
- "\n",
- "You might already have clicked through the toggles at the top of the control panel of En-ROADS and tried the different *Views* (Main graphs, Kaya graphs, Miniature graphs). Select now the **Kaya graphs** view and reset the simulator (click on the anti-clockwise circular arrow to 'Reset all policies & assumptions' or just reload the page/model [here](https://en-roads.climateinteractive.org/)).\n",
- "\n",
- "The Kaya graphs depict four drivers of growth in carbon dioxide emissions from energy use, which reflects about two-thirds of all greenhouse gas emissions (the remaining third of emissions are from land use changes and other gases, such as methane (CH4) and nitrous oxide (N2O), which are not considered here). The corresponding equation, the so-called [***Kaya identity***](https://en.wikipedia.org/wiki/Kaya_identity) below was developed by Yoichi Kaya:\n",
- "\n",
- "***CO$_2$ Emissions from Energy = Global Population × GDP per Capita × Energy Intensity of GDP × Carbon Intensity of Energy***\n",
- "\n",
- "Where [gross domestic product](https://en.wikipedia.org/wiki/Gross_domestic_product) (GDP) is a measure of economic activity or health. \n",
- "\n",
- "In a more mathematical form, this equation is\n",
- "\n",
- "$$\n",
- "F = P \\times \\left[\\frac{G}{P}\\right] \\times \\left[\\frac{E}{G}\\right] \\times \\left[\\frac{F}{E}\\right]\n",
- "$$\n",
- " - $F$ (CO$_2$ emissions from energy), \n",
- " - $P$ (global population), \n",
- " - $G$ (world GDP), and \n",
- " - $E$ (global energy consumption).\n",
- "\n",
- "Decomposing emissions in this way helps to see key contributions and, if we assume that the factors are independent of each other (which is not fully true), distinct ways that we can lower emissions. Each factor in this model is important and extreme reductions in one can offset increases in the others. In our case, our emissions $F$ are increasing over time because the Energy Intensity of GDP $\\left[\\frac{E}{G}\\right] $ and the Carbon Intensity of Energy do not together offset enough of our growing GDP ($G$), which is by definition necessary for a growth-based economy.\n",
- "\n",
- "The first four graphs in the Kaya view display these four variables $\\left(P, \\frac{G}{P}, \\frac{E}{G}, \\frac{F}{E}\\right)$, with the fifth plot showing Emissions, $F$. This allows us to compare the importance of, say, economic and population growth on the CO$_2$ emissions and subsequent temperature rise by 2100."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fd232a4d-c350-4e7b-9519-921800d81e20",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Population vs. Economic Growth\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: Turn two *growth* 'control knobs' of En-ROADS, which are the sliders *Economical growth* and *Population growth*. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* What can you observe within the *Kaya graph* view? Describe the graphs and interpret them.\n",
- "\n",
- "Read the following dropdown accessible box if you need more information to interpret the Kaya graphs. \n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c61eb29-ab14-4445-915e-5d39abab9a73",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53e42e1c",
- "metadata": {
- "execution": {},
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "source": [
- "# Tutorial 3: The Temporal Dimension of Actions\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87ef2f44-fb74-4664-8472-4c97158a8d17",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 15 minutes\n",
- "\n",
- "The last tutorials covered the necessity for an energy transition to tackle the climate emergency and many solutions at once. As emissions accumulate, it becomes substantially harder to succeed the longer we take to make big changes. This tutorial explores the temporal dimension of action, here policies, by using the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* exemplify along the carbon price, why policy-making has to consider the temporal dimension and the needs of the most vulnerable parts of society"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "062c4b1a-6239-4644-a0dd-03fe1342fe99",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b4722fe-cbd5-484a-be6d-96ed7aafbf62",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "053ee0ea-d704-45c8-8b4b-aad17216710d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b50c362",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: The IPCC's Transition Narratives and Project Modelling\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "703ee8f2",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "29cf9a43-2478-4d14-9faf-193e54378a04",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: When and How Do You Introduce a Carbon Price?\n",
- "\n",
- "You might have recognized that a high ***carbon price*** by default has a strong impact on the temperature increase within the En-ROADS model as it both reduces the carbon intensity of the energy supply and reduces the energy demand. However, the carbon price itself could be introduced now or in 50 years and at different amounts, depending on for example the social cost of such a policy, which makes energy supply more expensive depending on its emissions. Energy producers could pass additional costs to their customers, so policy could be designed to minimize the impacts on the poorest, e.g. by introducing a [Carbon fee and dividend](https://en.wikipedia.org/wiki/Carbon_fee_and_dividend). \n",
- "\n",
- "To account for their temporal evolution, many variables in En-ROADS allow for modifications of the timing when something is introduced, stopped, in or de-creased, and so on."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d29fb31c-84ab-4705-8678-87f8274f6500",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Implications of Actions and Their Timing\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- " \n",
- "2. **Develop a scenario**: Click on the ellipsis of the 'Carbon price' slider, and a widget-info box opens that allows for finetuning of your carbon price policy.\n",
- " 1. Click on the title of the top right graph *Greenhouse Gas Net Emissions*, select the 'Financial' toggle, and select the 'Market price of electricity'.\n",
- " 2. Turn the most upper knobs/ vary the sliders of the 'Carbon price' and slide through different carbon prices.\n",
- " 3. Move the sliders to answer the following question. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed.\n",
- "\n",
- "\n",
- "3. **Answer the following questions**:\n",
- " * What do you observe in the 'Market price of electricity' graph after varying the carbon price?\n",
- " * Now, select a high carbon price and increase the 'Year the carbon price starts to phase in'. How does the carbon price change the temporal evolution of the 'Market price of electricity'. \n",
- " * How could this be of consequence for a low-income household?\n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c97e6d3d-1718-479b-a19a-ffc6a082347b",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "3. The earlier the carbon price starts to phase in the more effective it is regarding the temperature increase.\n",
- "However, the earlier the larger the disturbance of the market price of electricity, emphasized by a strong increase in the price at first\n",
- "and a strong drop in the price after the first few years.\n",
- "This might lead to economic disruptions as energy supply becomes very expensive, therefore low-income households,\n",
- "which usually spend large parts of their income on energy, would need compensation like a 'carbon dividend' to avoid precariat.\n",
- "In summary, actions and their temporal implementation always need to be evaluated in various aspects to be a successful and\n",
- "societal least disruptive action against climate change.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3562deb9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, we discussed the temporal dimension of the carbon price implementation in order to understand why no policy might be a silver bullet to solve all problems but comes with various ethical and political implications. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6d0c2c37-0e64-4734-8776-1f455a20987f",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27636b92",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial3",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial4.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial4.ipynb
deleted file mode 100644
index 417f5abdc..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial4.ipynb
+++ /dev/null
@@ -1,773 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial4.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 4: The Shared Socio-economic Pathways\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "In this tutorial, you will learn about Integrated Assessment Models (IAMs), a class of models that combine climatology, economics, and social science, reflecting the intertwined nature of these domains in addressing climate change. Based on these models the IPCC established the socioeconomic pathway framework. You are going to learn how these pathways differ from one another in both climate and socioeconomic variables as well as assumptions.\n",
- "\n",
- "After finishing this tutorial, you will know how to \n",
- "\n",
- "- filter data series of interest from a rich `pandas` data frame that contains various variables for different SSPs,\n",
- "- tell what the abbreviation SPA stands for,\n",
- "- explain the differences and similarities of the SSP1-26 and SSP5-85, and \n",
- "- sketch the modeling approach of IAMs."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# installations ( uncomment and run this cell ONLY when using google colab or kaggle )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {},
- "executionInfo": {
- "elapsed": 1460,
- "status": "ok",
- "timestamp": 1682429063027,
- "user": {
- "displayName": "Maximilian Puelma Touzel",
- "userId": "09308600515315501700"
- },
- "user_tz": 240
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# imports\n",
- "import warnings\n",
- "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import pooch\n",
- "import os\n",
- "import tempfile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "code_folding": [
- 0
- ],
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file\n",
- "\n",
- "\n",
- "def legend_without_duplicate_labels(ax):\n",
- " handles, labels = ax.get_legend_handles_labels()\n",
- " unique = [(h, l) for i, (h, l) in enumerate(zip(handles, labels)) if l not in labels[:i]]\n",
- " ax.legend(*zip(*unique))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Transition Goals and Integrated Assessment Models\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "# TODO add Bilibili link\n",
- "video_ids = [('Youtube', 'TqpPDz2kfWc'),\n",
- " # ('Bilibili', '')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "pycharm": {
- "name": "#%%\n"
- },
- "tags": [
- "remove-input"
- ]
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"ujprw\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Shared Socio-economic Pathways"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {},
- "jp-MarkdownHeadingCollapsed": true
- },
- "source": [
- "In this, and subsequent, tutorials, you will explore Integrated Assessment Models (IAMs) which are the standard class of models used to make climate change projections. IAMs couple a climate model with an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. IAMs can also account for changes that result from mitigation efforts, which lessen anthropogenic emissions.\n",
- "\n",
- "Let's start by investigating some IAM model output.\n",
- "\n",
- "The simulations are labeled by both the Shared Socioeconomic Pathway (SSP1, SSP2, SSP3, SSP4, and SSP5) and the forcing level (greenhouse gas forcing of 2.6, 7.0, 8.5 W/m2 etc. by 2100).\n",
- "The 5 SSPS are: \n",
- "- SSP1: Sustainability (Taking the Green Road)\n",
- "- SSP2: Middle of the Road\n",
- "- SSP3: Regional Rivalry (A Rocky Road)\n",
- "- SSP4: Inequality (A Road divided)\n",
- "- SSP5: Fossil-fueled Development (Taking the Highway)\n",
- "\n",
- "We select two SSPs to exemplify how these scenarios differ from each other. \n",
- "To get a strong contrast, we select SSP1 and SSP5. \n",
- "\n",
- "Let's load the data and describe their features along a few plots.\n",
- "\n",
- "Like in other tutorials, we provide a `.csv` file that is stored in the cloud and was downloaded beforehand from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# Load SSP data from .csv file\n",
- "filename_SSPs = 'SSP_IAM_V2_201811.csv'\n",
- "link_id = \"2uwr4\"\n",
- "url_SSPs = f\"https://osf.io/download/{link_id}/\"\n",
- "\n",
- "df = pd.read_csv(pooch_load(url_SSPs, filename_SSPs))\n",
- "# get a summary of the resulting pandas dataframe\n",
- "df.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "We further explore our data frame by printing categories that are used to tag the numeric data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "print(df.SCENARIO.unique()) # print all scenarios\n",
- "print(df.VARIABLE.unique()[:10]) # print the first 10 variables\n",
- "print(df.REGION.unique()) # print all regions\n",
- "print(df.MODEL.unique()) # print all IAMs\n",
- "print(df.UNIT.unique()) # print all units"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "This file contains much data we are not interested in at the moment. To filter our example scenarios, region, and variables, we use the convenient [`.query()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html) method from `pandas`. The `VARIABLE`s of interest are those we already touched on in Tutorials 1 to 3: \n",
- "\n",
- "- **economic growth** (`'GDP|PPP'`),\n",
- "- **energy use** (`'Primary Energy'`),\n",
- "- **emissions** (`'Emissions|Kyoto Gases'`),\n",
- "- and **forcing** (`'Diagnostics|MAGICC6|Forcing'`).\n",
- "\n",
- "- As a `REGION`, we choose the `'World'`,\n",
- "- and our `SCENARIO`s are called `'SSP1-26'` and `'SSP5-85'`.\n",
- "- The model of choice for the former scenario is by convention `'IMAGE'` and `'REMIND-MAGPIE'` for the latter, respectively.\n",
- "\n",
- "A function named `get_SSPs_for_variable()` applies all this generally and is hidden in the next cell. Please execute it, such that the subsequent cells can make use of it. If you are interested in its procedure and want to adjust it, don't forget to save a copy beforehand."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown *Execute this cell to enable the dataframe filter function: `get_SSPs_for_variable`*\n",
- "\n",
- "def get_SSPs_for_variable(df,scenario,variable,region='World'):\n",
- " '''\n",
- "\n",
- " Function that filters IIASA's SSP database that is stored in a data frame 'df'\n",
- " and was loaded before from the 'SSP_IAM_V2_201811.csv' file.\n",
- " It returns a data frame with selected columns depending on scenario, variable and region input.\n",
- " For a given SSP scenario it chooses the conventional model for the respective scenario\n",
- " (cf. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about#v2).\n",
- "\n",
- " Args:\n",
- " scenario: string in \"SSPX-XX\" with X=1,...,5\n",
- " variable: string in df.VARIABLE, e.g. 'Population' or 'GDP|PPP'\n",
- "\n",
- " Returns:\n",
- " SSP data for selected columns for a given SSP scenario\n",
- "\n",
- " Example:\n",
- " dd = get_SSPs_for_variable(df,'SSP1-26','Population')\n",
- "\n",
- " '''\n",
- " ssp_model_conv = {\"SSP1-Baseline\" : \"IMAGE\",\n",
- " \"SSP1-26\" : \"IMAGE\",\n",
- " \"SSP2-Baseline\" : \"MESSAGE-GLOBIOM\",\n",
- " \"SSP3-Baseline\" : \"AIM/CGE\",\n",
- " \"SSP4-Baseline\" : \"GCAM4\",\n",
- " \"SSP5-Baseline\" : \"REMIND-MAGPIE\"}\n",
- " model = ssp_model_conv[scenario]\n",
- " ds = df.query(\n",
- " f'(VARIABLE == \"{variable}\") & (SCENARIO == \"{scenario}\") & (MODEL == \"{model}\") & (REGION == \"{region}\")'\n",
- ")\n",
- " return ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Let's plot our variables of interest and compare the respective features of the scenarios."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['GDP|PPP','Emissions|Kyoto Gases', 'Primary Energy','Diagnostics|MAGICC6|Forcing']\n",
- "# create new names for structured data series and axes labels\n",
- "val_name = ['GDP (billion US$/yr)', 'Emissions (Mt CO$_2$/yr)', 'Energy use (EJ/yr)', 'Forcing (W/m$^2$)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,2)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich data frame\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0,0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The projections in the plots you just created show changes in **GDP** (billion US\\$/yr), **fossil fuel emissions** (Mt CO$_2$/yr), **energy use** (EJ/yr), and **forcing** (W/m$^2$) across the two very different scenarios SSP1 and SSP5, computed at their baseline forcing level, which are each represented by a distinct color in each plot.\n",
- "\n",
- "Our plots show that the SSP5-Baseline scenario exhibits very high levels of energy use, and emissions (due to fossil fuel exploitation), it marks the upper end of the scenarios in several dimensions (cf. [Kriegler et al. (2014)](https://doi.org/10.1016/j.gloenvcha.2016.05.015)). \n",
- "\n",
- "The SSP1-26 scenario contrarily caps the increase of energy use by 2030, combined with other actions leading to decreasing emissions and subsequently a decreasing forcing for the second half of the century. However, economic growth continues with half the slope of SSP5-Baseline. In summary, it is the most optimistic projection: we transition to a global society of sustainability-focused growth."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.1: SSP Creation via IAMs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The underlying modeling of Integrated Assessment Models (IAMs) works roughly as follows:\n",
- " \n",
- "All SSP projections are created by optimizing economic activity within the constraint of a given level of greenhouse gas forcing at 2100 (bottom right in the above plot). This activity drives distinct temperature changes via the emissions it produces (top right), which are inputted into a damage function to compute economic damages. These damages feedback into the economy model to limit emissions-producing economic activity (top left).\n",
- "*Note that we already explored these damage functions along our En-ROADS climate solution simulator in Tutorial 2*.\n",
- "\n",
- "The forcing constraint ensures the amount of emissions produced is consistent for that particular scenario. In other words, the projected temperature change under different scenarios is fed to a socioeconomic model component in order to assess the socioeconomic impacts resulting from the temperature change associated with each SSP. *For examples of such impacts check out today's Tutorial 2 and W2D4.*\n",
- "\n",
- "Not every variable in IAMs is endogenous (i.e. determined by other variables in the model). Some variables, like population or technology growth, are exogenous (i.e. variables whose time course is given to the model). In this case, the time course of, e.g., population and economic growth, are derived from simple growth models. These exogenous variables are assumed to be under our society's control (e.g. via mitigation).\n",
- "\n",
- "To understand how our plotted scenarios compare with all other scenarios, we first have a look at the underlying policy assumptions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.2 Shared Climate Policy Assumptions (SPAs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "All pathways have common Shared Climate Policy Assumptions (SPAs) like \n",
- "- limits to how fast we can respond based on where we are now,\n",
- "- what kinds of policies can be implemented, and how\n",
- "\n",
- "which constrain the modelers that create scenarios and their narratives.\n",
- "\n",
- "Our example scenarios hence share the above SPAs and vary in their narrative:\n",
- "\n",
- "In the hypothetical world of SSP5, no climate policies or climate impacts exist, which in other words effectively implies that the carbon price is zero (cf. [Ch.3.2.1.1 of IPCC's report on 'Climate Change 2022: Mitigation of Climate Change')](https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter03.pdf).\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.3: Similarities of SSP1 and SSP5\n",
- "\n",
- "When you compare the two scenarios, in particular, the evolution of the population and the GDP shows how similar these scenarios are in their optimistic view on the development of humanity. We all learn to get along, both within and across countries and more equal development naturally stems from population growth through well-known mechanisms like access to conception. The following figure emphasizes this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['Population', 'GDP|PPP']\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ['Population\\n(millions)', 'GDP (billion US$/yr)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Both GDP and population increase."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1.3: Differences of SSP1 and SSP5\n",
- "\n",
- "Major differences are visible when you contrast emissions and assume direct causation with ecosystem health. Increasing emissions then translate into decreasing ecosystem health."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Coding exercise 1.3\n",
- "\n",
- "1. Choose two variables to emphasize ecosystem health differences in the SSP1 and SSP5 scenarios and assign them to `vars`. Then assign axis labels with the correct units for plotting to the `val_name` variable.\n",
- "2. Explain to your pod why the chosen variables emphasize a difference in the scenarios and describe this difference based on your current knowledge of the narratives."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put two variables of interest in a list\n",
- "vars = ...\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ...\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "#################################################\n",
- "## TODO for students:\n",
- "## Put two variables of interest in a list and assign it to 'vars'.\n",
- "## Create new names for the structured data series and axes labels,\n",
- "## put them in a list and assign it to 'val_name'.\n",
- "## Remove the following line of code once you have completed the exercise:\n",
- "raise NotImplementedError(\"Student exercise: Put two variables of interest in a list and assign it to vars. Create new names for the structured data series and axes labels, put them in a list and assign it to val_name.\")\n",
- "#################################################\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove solution\n",
- "\n",
- "# put two variables of interest in a list\n",
- "vars = ['Emissions|Kyoto Gases', 'Land Cover|Forest']\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ['Emissions\\n(Mt CO$_2$/yr)','Land covered by\\nforest (million ha)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "'''\n",
- "2. We choose 'Emissions' and 'Land Cover|Forest' as variables of interest to contrast ecosystem health between the two scenarios.\n",
- "First, note that the land model components from the IMAGE and REMIND-MAGPIE estimate different initial land cover areas, if we assume however that their trend is reasonable, we can conclude the following:\n",
- "For high emissions, forcing rises and hence temperature, we know that precipitation patterns change with higher temperatures such that forests,\n",
- "here our indicator for ecosystem health, are dying.\n",
- "In turn the land cover by forests is decreasing in the SSP5 scenario.\n",
- "In contrast, the SSP1 reduces emissions and also exploits resources like wood only sustainably due to afforestation.\n",
- "As afforestation is also used to capture carbon, while land use is reduced in general,\n",
- "the scenario shows an increase in land cover by forests within the current century.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "In this tutorial, you've gained a first understanding of the Shared Socioeconomic Pathways and their creation, the application of Integrated Assessment Models in climate economics. You've learned how SSPs share policy assumptions. Furthermore, you compared SSP1 and SSP5 with respect to their view on the development of humanity and their ecosystem health.\n",
- "\n",
- "In the next tutorial, you dissect and analyze the SSP narratives in more detail."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "It is possible to download the SSP data used in this tutorial, when you provide an email address, from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available.\n",
- "\n",
- "Find a summary of all SSP narratives in this paper by [Oneill et al. (2017)](https://doi.org/10.1016/j.gloenvcha.2015.01.004).\n",
- "\n",
- "Find even more information in\n",
- "\n",
- "- [IIASA's introduction to SSPs](https://iiasa.ac.at/models-tools-data/ssp)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial4",
- "provenance": [],
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb
deleted file mode 100644
index 9b234f6e5..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3d4298c9-a204-48a4-b408-90d0403d4950",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c55c3b25-2613-4f34-8d4a-ccc417f24708",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 5: Mapping the Narrative Space\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c8af3c8-42a4-45ba-a16f-be86198e23e8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Tutorial objectives\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1af3e766-b9c3-4bf8-815c-4087e7811925",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "#import dicelib # https://github.com/mptouzel/PyDICE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7392de8-f946-453b-bedf-8a07c705ee7f",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c10210c-a3d9-4996-a3d3-4bafa4b72637",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "80412217-b5a7-4fed-b29e-b8751eb8c847",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Mapping the Narrative Space\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '9ytkpO_pbWE')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2dc2f6c1-637b-4506-a667-45750dd7fbe5",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"cyavb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c7ce6e92-a46d-437a-9e03-c5b80eb63474",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Background on IAM Economics\n",
- "\n",
- "The last tutorial gave us a glimpse of Integrated Assessment Models (IAMs), a class of models economists use to inform policy decisions. Recall that IAMs couple a climate model to an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. \n",
- "\n",
- "Let's begin with a brief description of IAMs:\n",
- "\n",
- "- IAMs resolve the spatially, in contrast, the toy model En-ROADs for example, which we applied in Tutorial 1 to 3, aggregates all variables and is non-spatial.\n",
- "- Like En-ROADS, the world models used in IAMs usually have *exogeneous* (externally set) times series for variables, in addition to fixed world system parameters. These exogenous variables are assumed to be under our society's control (e.g. mitigation). \n",
- "- IAMs come equipped with an objective function (a formula that calculates the quantity to be optimized). This function returns the value of a projected future obtained from running the world model under a given climate policy. This value is defined by the time series of these exogenous variables. In this sense, the objective function is what defines \"good\" in \"good climate policy\". \n",
- "- The computation in an IAM is then an optimization of this objective as a function of the time series of these exogenous variables over some fixed time window.\n",
- "\n",
- "In En-ROADS, there are exogenous parameters, in particular:\n",
- " - **$\\mu(t)$**: time-dependent mitigation rate (i.e. emissions reduction), which limits warming-caused damages\n",
- " - **$S(t)$**: savings rate, which drives capital investment \n",
- "\n",
- "Most IAMs are based on *Neo-classical economics* (also referred to as \"establishment economics\"). This is an approach to economics that makes particular assumptions. For example, it is assumed that production, consumption, and valuation of goods and services are driven solely by the supply and demand model. To understand this approach and how it is used, it is important to begin with a brief overview of some fundamental concepts. One such concept is **utility** (i.e. economic value), which is not only central to economics but also to decision theory as a whole, which is a research field that mathematically formalizes the activity of *planning* (planning here means selecting strategies based on how they are expected to play out given a model that takes those strategies and projects forward into the future)."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "28c0a077-c9a0-42d9-ada6-e8b835668c3b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9e904e57-b6b9-4ee8-aefb-140dce3c93c2",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial5",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb
deleted file mode 100644
index 944aeb1a3..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "a4589a1d-0f80-45e7-aec3-3513172e617b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d93b742-06f3-4a0b-9a1d-043397878e48",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Bonus Tutorial 6: Create your Socio-economic Scenario\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b5c4ba1-fad0-4d38-b3b2-875a67549624",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 min\n",
- "\n",
- "In this tutorial, you will model your own simplified Socioeconomic Pathway, a scenario of actions to limit global warming below 2°C. You take action against climate change within the socioeconomic model environment of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/). To examine its characteristics you address its challenges, winners/losers, political feasibility, narrative, and current trends. \n",
- "\n",
- "You learn how to\n",
- "\n",
- "- apply the knowledge you gained in last tutorials to design actions against climate change\n",
- "- map your scenario narrative in the SSP \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b591486d-dff5-4dc6-b83b-1125977a7758",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1:\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '_Y_eLlExwxI'),\n",
- " #TODO('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "616428a4-3db2-4e56-af30-6c56aaf66204",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"gwr8h\" #TODO\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a516e969-8b67-4f8a-9d39-061770ffb4c9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Create your Solution Scenario and Evaluate it\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders and changing the assumptions, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- " 1. **Your Plan**: What are the top 3-5 most important policies in your strategy? (For example, the most important sliders that you moved). Share your scenario link and a screenshot of your final En-ROADS dashboard.\n",
- " \n",
- " *If you want to save your scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the panel, and select 'Copy Scenario Link'*.\n",
- " \n",
- " 2. **Political Feasibility**: To implement your proposals, what actions and priorities are needed over the next two years in businesses, civil society, government, and the public?\n",
- " 3. **Winners & Losers**: Who would be the biggest winners and losers globally in your proposed future? Create a table with two columns for winners and losers. \n",
- " 4. **Narrative**: Map your ideas in the narrative space, introduced in Tutorial 5. \n",
- " 5. **Hope**: What trends in the world give you hope that your proposals are possible?\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fadd5652-5db9-475e-a072-f204358ade8a",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this last tutorial, you went from model to action by designing your own socioeconomic modeling scenario to limit global warming below 2°C by the end of the century. You reflected on its challenges, political feasibility, inherent winners and losers of your approach, and finally, the narrative that you chose. At the end of the day, you collected observations of actions and trends you are making in your region, globally, and in your daily life that give hope. That hope and various solutions exist, which one has to combine, and that only action is needed, might be the most important lesson of today.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a6aaa44b-0fc2-4249-91c4-589aac662188",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)\n",
- "\n",
- "**A Planetary Crisis Planning Computer Game**\n",
- "- [Half Earth Socialism](https://play.half.earth/).\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial6",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/chapter_title.md b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/chapter_title.md
deleted file mode 100644
index ec8f9f7ef..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/chapter_title.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Future Climate - IPCC II & III Socio-Economic Basis
-
- ````{div} full-height
- 
-````
-
-*Artwork by Sloane Garelick*
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/dicelib.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/dicelib.py
deleted file mode 100644
index bbc544141..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/dicelib.py
+++ /dev/null
@@ -1,538 +0,0 @@
-import seaborn as sns
-import matplotlib.pyplot as pl
-import pandas as pd
-import numpy as np
-import scipy.optimize as opt
-from matplotlib.ticker import EngFormatter
-
-
-class DICE():
-
- def __init__(self):
- self.time_step = 5 # Years per Period
- # Set
- self.min_year = 2000
- self.max_year = 2500
- self.TT = np.linspace(
- self.min_year, self.max_year, 100, dtype=np.int32)
- self.NT = len(self.TT)
- self.t = np.arange(1, self.NT+1)
-
- def init_parameters(self, a3=2.00, prstp=0.015, elasmu=1.45):
-
- # Maximum cumulative extraction fossil fuels (GtC); denoted by CCum
- self.fosslim = 6000
- self.ifopt = 0 # Indicator where optimized is 1 and base is 0
- self.elasmu = elasmu # Elasticity of marginal utility of consumption
- self.prstp = prstp # Initial rate of social time preference per year
-
- self.init_pop_and_tech_parameters()
- self.init_emissions_parameters()
- self.init_carboncycle_parameters()
- self.init_climatemodel_parameters()
- self.init_climatedamage_parameters(a3)
- self.init_abatementcost_parameters()
-
- # ** Scaling and inessential parameters
- # * Note that these are unnecessary for the calculations
- # * They ensure that MU of first period's consumption =1 and PV cons = PV utilty
- # Multiplicative scaling coefficient /0.0302455265681763 /
- self.scale1 = 0.0302455265681763
- self.scale2 = -10993.704 # Additive scaling coefficient /-10993.704/;
-
- # * carbon cycling coupling matrix
- self.b11 = 1 - self.b12
- self.b21 = self.b12*self.mateq/self.mueq
- self.b22 = 1 - self.b21 - self.b23
- self.b32 = self.b23*self.mueq/self.mleq
- self.b33 = 1 - self.b32
-
- # * Further definitions of parameters
- self.a20 = self.a2
- self.sig0 = self.e0/(self.q0*(1-self.miu0)) # From Eq. 14
- self.lam = self.fco22x / self.t2xco2 # From Eq. 25
-
- self.init_exogeneous_inputs()
-
- def init_pop_and_tech_parameters(self, gama=0.300, pop0=7403, popadj=0.134,
- popasym=11500, dk=0.100, q0=105.5,
- k0=223, a0=5.115, ga0=0.076, dela=0.005):
- self.gama = gama # Capital elasticity in production function /.300 /
- # Initial world population 2015 (millions) /7403 /
- self.pop0 = pop0
- self.popadj = popadj # Growth rate to calibrate to 2050 pop projection /0.134/
- # Asymptotic population (millions) /11500/
- self.popasym = popasym
- # Depreciation rate on capital (per year) /.100 /
- self.dk = dk
- # Initial world gross output 2015 (trill 2010 USD) /105.5/
- self.q0 = q0
- # Initial capital value 2015 (trill 2010 USD) /223 /
- self.k0 = k0
- self.a0 = a0 # Initial level of total factor productivity /5.115/
- self.ga0 = ga0 # Initial growth rate for TFP per 5 years /0.076/
- self.dela = dela # Decline rate of TFP per 5 years /0.005/
-
- # ** Emissions parameters
- def init_emissions_parameters(self, gsigma1=-0.0152, dsig=-0.001, eland0=2.6,
- deland=0.115, e0=35.85, miu0=0.03):
- # Initial growth of sigma (per year) /-0.0152/
- self.gsigma1 = gsigma1
- # Decline rate of decarbonization (per period) /-0.001 /
- self.dsig = dsig
- # Carbon emissions from land 2015 (GtCO2 per year) / 2.6 /
- self.eland0 = eland0
- # Decline rate of land emissions (per period) / .115 /
- self.deland = deland
- # Industrial emissions 2015 (GtCO2 per year) /35.85 /
- self.e0 = e0
- self.miu0 = miu0 # Initial emissions control rate for base case 2015 /.03 /
-
- # ** Carbon cycle
- def init_carboncycle_parameters(self, mat0=851, mu0=460, ml0=1740, mateq=588, mueq=360, mleq=1720):
- # * Initial Conditions
- # Initial Concentration in atmosphere 2015 (GtC) /851 /
- self.mat0 = mat0
- # Initial Concentration in upper strata 2015 (GtC) /460 /
- self.mu0 = mu0
- # Initial Concentration in lower strata 2015 (GtC) /1740 /
- self.ml0 = ml0
- # mateq Equilibrium concentration atmosphere (GtC) /588 /
- self.mateq = mateq
- # mueq Equilibrium concentration in upper strata (GtC) /360 /
- self.mueq = mueq
- # mleq Equilibrium concentration in lower strata (GtC) /1720 /
- self.mleq = mleq
-
- # * Flow paramaters, denoted by Phi_ij in the model
- self.b12 = 0.12 # Carbon cycle transition matrix /.12 /
- self.b23 = 0.007 # Carbon cycle transition matrix /0.007/
- # * These are for declaration and are defined later
- self.b11 = None # Carbon cycle transition matrix
- self.b21 = None # Carbon cycle transition matrix
- self.b22 = None # Carbon cycle transition matrix
- self.b32 = None # Carbon cycle transition matrix
- self.b33 = None # Carbon cycle transition matrix
- # Carbon intensity 2010 (kgCO2 per output 2005 USD 2010)
- self.sig0 = None
-
- # ** Climate model parameters
- def init_climatemodel_parameters(self):
- # Equilibrium temp impact (oC per doubling CO2) / 3.1 /
- self.t2xco2 = 3.1
- # 2015 forcings of non-CO2 GHG (Wm-2) / 0.5 /
- self.fex0 = 0.5
- # 2100 forcings of non-CO2 GHG (Wm-2) / 1.0 /
- self.fex1 = 1.0
- # Initial lower stratum temp change (C from 1900) /.0068/
- self.tocean0 = 0.0068
- # Initial atmospheric temp change (C from 1900) /0.85/
- self.tatm0 = 0.85
- self.c1 = 0.1005 # Climate equation coefficient for upper level /0.1005/
- self.c3 = 0.088 # Transfer coefficient upper to lower stratum /0.088/
- self.c4 = 0.025 # Transfer coefficient for lower level /0.025/
- # eta in the model; Eq.22 : Forcings of equilibrium CO2 doubling (Wm-2) /3.6813 /
- self.fco22x = 3.6813
-
- def init_climatedamage_parameters(self, a3=2.00):
- # ** Climate damage parameters
- self.a10 = 0 # Initial damage intercept /0 /
- self.a20 = None # Initial damage quadratic term
- self.a1 = 0 # Damage intercept /0 /
- self.a2 = 0.00236 # Damage quadratic term /0.00236/
- self.a3 = a3 # Damage exponent /2.00 /
-
- def init_abatementcost_parameters(self):
- # ** Abatement cost
- # Theta2 in the model, Eq. 10 Exponent of control cost function / 2.6 /
- self.expcost2 = 2.6
- self.pback = 550 # Cost of backstop 2010$ per tCO2 2015 / 550 /
- self.gback = 0.025 # Initial cost decline backstop cost per period / .025/
- self.limmiu = 1.2 # Upper limit on control rate after 2150 / 1.2 /
- self.tnopol = 45 # Period before which no emissions controls base / 45 /
- # Initial base carbon price (2010$ per tCO2) / 2 /
- self.cprice0 = 2
- self.gcprice = 0.02 # Growth rate of base carbon price per year /.02
-
- def init_exogeneous_inputs(self):
- NT = self.NT
-
- self.l = np.zeros(NT)
- self.l[0] = self.pop0 # Labor force
- self.al = np.zeros(NT)
- self.al[0] = self.a0
- self.gsig = np.zeros(NT)
- self.gsig[0] = self.gsigma1
- self.sigma = np.zeros(NT)
- self.sigma[0] = self.sig0
- # TFP growth rate dynamics, Eq. 7
- self.ga = self.ga0 * np.exp(-self.dela*5*(self.t-1))
- self.pbacktime = self.pback * \
- (1-self.gback)**(self.t-1) # Backstop price
- # Emissions from deforestration
- self.etree = self.eland0*(1-self.deland)**(self.t-1)
- self.rr = 1/((1+self.prstp)**(self.time_step*(self.t-1))) # Eq. 3
- # The following three equations define the exogenous radiative forcing; used in Eq. 23
- self.forcoth = np.full(NT, self.fex0)
- self.forcoth[0:18] = self.forcoth[0:18] + \
- (1/17)*(self.fex1-self.fex0)*(self.t[0:18]-1)
- self.forcoth[18:NT] = self.forcoth[18:NT] + (self.fex1-self.fex0)
- # Optimal long-run savings rate used for transversality (Question)
- self.optlrsav = (self.dk + .004)/(self.dk + .004 *
- self.elasmu + self.prstp)*self.gama
- self.cost1 = np.zeros(NT)
- self.cumetree = np.zeros(NT)
- self.cumetree[0] = 100
- self.cpricebase = self.cprice0*(1+self.gcprice)**(5*(self.t-1))
-
- def InitializeLabor(self, il, iNT):
- for i in range(1, iNT):
- il[i] = il[i-1]*(self.popasym / il[i-1])**self.popadj
-
- def InitializeTFP(self, ial, iNT):
- for i in range(1, iNT):
- ial[i] = ial[i-1]/(1-self.ga[i-1])
-
- def InitializeGrowthSigma(self, igsig, iNT):
- for i in range(1, iNT):
- igsig[i] = igsig[i-1]*((1+self.dsig)**self.time_step)
-
- def InitializeSigma(self, isigma, igsig, icost1, iNT):
- for i in range(1, iNT):
- isigma[i] = isigma[i-1] * np.exp(igsig[i-1] * self.time_step)
- icost1[i] = self.pbacktime[i] * isigma[i] / self.expcost2 / 1000
-
- def InitializeCarbonTree(self, icumetree, iNT):
- for i in range(1, iNT):
- icumetree[i] = icumetree[i-1] + self.etree[i-1]*(5/3.666)
-
- """
- Emissions of carbon and weather damages
- """
-
- # Retuns the total carbon emissions; Eq. 18
- def fE(self, iEIND, index):
- return iEIND[index] + self.etree[index]
-
- # Eq.14: Determines the emission of carbon by industry EIND
- def fEIND(self, iYGROSS, iMIU, isigma, index):
- return isigma[index] * iYGROSS[index] * (1 - iMIU[index])
-
- # Cumulative industrial emission of carbon
- def fCCA(self, iCCA, iEIND, index):
- return iCCA[index-1] + iEIND[index-1] * 5 / 3.666
-
- # Cumulative total carbon emission
- def fCCATOT(self, iCCA, icumetree, index):
- return iCCA[index] + icumetree[index]
-
- # Eq. 22: the dynamics of the radiative forcing
- def fFORC(self, iMAT, index):
- return self.fco22x * np.log(iMAT[index]/588.000)/np.log(2) + self.forcoth[index]
-
- # Dynamics of Omega; Eq.9
- def fDAMFRAC(self, iTATM, index):
- return self.a1*iTATM[index] + self.a2*iTATM[index]**self.a3
-
- # Calculate damages as a function of Gross industrial production; Eq.8
- def fDAMAGES(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * iDAMFRAC[index]
-
- # Dynamics of Lambda; Eq. 10 - cost of the reudction of carbon emission (Abatement cost)
- def fABATECOST(self, iYGROSS, iMIU, icost1, index):
- return iYGROSS[index] * icost1[index] * iMIU[index]**self.expcost2
-
- # Marginal Abatement cost
- def fMCABATE(self, iMIU, index):
- return self.pbacktime[index] * iMIU[index]**(self.expcost2-1)
-
- # Price of carbon reduction
- def fCPRICE(self, iMIU, index):
- return self.pbacktime[index] * (iMIU[index])**(self.expcost2-1)
-
- # Eq. 19: Dynamics of the carbon concentration in the atmosphere
- def fMAT(self, iMAT, iMU, iE, index):
- if (index == 0):
- return self.mat0
- else:
- return iMAT[index-1]*self.b11 + iMU[index-1]*self.b21 + iE[index-1] * 5 / 3.666
-
- # Eq. 21: Dynamics of the carbon concentration in the ocean LOW level
- def fML(self, iML, iMU, index):
- if (index == 0):
- return self.ml0
- else:
- return iML[index-1] * self.b33 + iMU[index-1] * self.b23
-
- # Eq. 20: Dynamics of the carbon concentration in the ocean UP level
- def fMU(self, iMAT, iMU, iML, index):
- if (index == 0):
- return self.mu0
- else:
- return iMAT[index-1]*self.b12 + iMU[index-1]*self.b22 + iML[index-1]*self.b32
-
- # Eq. 23: Dynamics of the atmospheric temperature
- def fTATM(self, iTATM, iFORC, iTOCEAN, index):
- if (index == 0):
- return self.tatm0
- else:
- return iTATM[index-1] + self.c1 * (iFORC[index] - (self.fco22x/self.t2xco2) * iTATM[index-1] - self.c3 * (iTATM[index-1] - iTOCEAN[index-1]))
-
- # Eq. 24: Dynamics of the ocean temperature
- def fTOCEAN(self, iTATM, iTOCEAN, index):
- if (index == 0):
- return self.tocean0
- else:
- return iTOCEAN[index-1] + self.c4 * (iTATM[index-1] - iTOCEAN[index-1])
-
- """
- economic variables
- """
-
- # The total production without climate losses denoted previously by YGROSS
- def fYGROSS(self, ial, il, iK, index):
- return ial[index] * ((il[index]/1000)**(1-self.gama)) * iK[index]**self.gama
-
- # The production under the climate damages cost
- def fYNET(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * (1 - iDAMFRAC[index])
-
- # Production after abatement cost
- def fY(self, iYNET, iABATECOST, index):
- return iYNET[index] - iABATECOST[index]
-
- # Consumption Eq. 11
- def fC(self, iY, iI, index):
- return iY[index] - iI[index]
-
- # Per capita consumption, Eq. 12
- def fCPC(self, iC, il, index):
- return 1000 * iC[index] / il[index]
-
- # Saving policy: investment
- def fI(self, iS, iY, index):
- return iS[index] * iY[index]
-
- # Capital dynamics Eq. 13
- def fK(self, iK, iI, index):
- if (index == 0):
- return self.k0
- else:
- return (1-self.dk)**self.time_step * iK[index-1] + self.time_step * iI[index-1]
-
- # Interest rate equation; Eq. 26 added in personal notes
- def fRI(self, iCPC, index):
- return (1 + self.prstp) * (iCPC[index+1]/iCPC[index])**(self.elasmu/self.time_step) - 1
-
- # Periodic utility: A form of Eq. 2
- def fCEMUTOTPER(self, iPERIODU, il, index):
- return iPERIODU[index] * il[index] * self.rr[index]
-
- # The term between brackets in Eq. 2
- def fPERIODU(self, iC, il, index):
- return ((iC[index]*1000/il[index])**(1-self.elasmu) - 1) / (1 - self.elasmu) - 1
-
- # utility function
- def fUTILITY(self, iCEMUTOTPER, resUtility):
- resUtility[0] = self.time_step * self.scale1 * \
- np.sum(iCEMUTOTPER) + self.scale2
-
- def init_variables(self): # TODO: add full variable names as comments
- NT = self.NT
- self.K = np.zeros(NT)
- self.YGROSS = np.zeros(NT)
- self.EIND = np.zeros(NT)
- self.E = np.zeros(NT)
- self.CCA = np.zeros(NT)
- self.CCATOT = np.zeros(NT)
- self.MAT = np.zeros(NT)
- self.ML = np.zeros(NT)
- self.MU = np.zeros(NT)
- self.FORC = np.zeros(NT)
- self.TATM = np.zeros(NT)
- self.TOCEAN = np.zeros(NT)
- self.DAMFRAC = np.zeros(NT)
- self.DAMAGES = np.zeros(NT)
- self.ABATECOST = np.zeros(NT)
- self.MCABATE = np.zeros(NT)
- self.CPRICE = np.zeros(NT)
- self.YNET = np.zeros(NT)
- self.Y = np.zeros(NT)
- self.I = np.zeros(NT)
- self.C = np.zeros(NT)
- self.CPC = np.zeros(NT)
- self.RI = np.zeros(NT)
- self.PERIODU = np.zeros(NT)
- self.CEMUTOTPER = np.zeros(NT)
-
- self.optimal_controls = np.zeros(2*NT)
-
- self.InitializeLabor(self.l, NT)
- self.InitializeTFP(self.al, NT)
- self.InitializeGrowthSigma(self.gsig, NT)
- self.InitializeSigma(self.sigma, self.gsig, self.cost1, NT)
- self.InitializeCarbonTree(self.cumetree, NT)
-
- def get_control_bounds_and_startvalue(self):
-
- NT = self.NT
- # * Control variable limits
- MIU_lo = np.full(NT, 0.01)
- MIU_up = np.full(NT, self.limmiu)
- MIU_up[0:29] = 1
- MIU_lo[0] = self.miu0
- MIU_up[0] = self.miu0
- MIU_lo[MIU_lo == MIU_up] = 0.99999*MIU_lo[MIU_lo == MIU_up]
- bnds1 = []
- for i in range(NT):
- bnds1.append((MIU_lo[i], MIU_up[i]))
-
- lag10 = np.arange(1, NT+1) > NT - 10
- S_lo = np.full(NT, 1e-1)
- S_lo[lag10] = self.optlrsav
- S_up = np.full(NT, 0.9)
- S_up[lag10] = self.optlrsav
- S_lo[S_lo == S_up] = 0.99999*S_lo[S_lo == S_up]
- bnds2 = []
- for i in range(NT):
- bnds2.append((S_lo[i], S_up[i]))
- bnds = bnds1 + bnds2
-
- # starting values for the control variables:
- S_start = np.full(NT, 0.2)
- S_start[S_start < S_lo] = S_lo[S_start < S_lo]
- S_start[S_start > S_up] = S_lo[S_start > S_up]
- MIU_start = 0.99*MIU_up
- MIU_start[MIU_start < MIU_lo] = MIU_lo[MIU_start < MIU_lo]
- MIU_start[MIU_start > MIU_up] = MIU_up[MIU_start > MIU_up]
- x_start = np.concatenate([MIU_start, S_start])
-
- return x_start, bnds
-
- def fOBJ(self, controls):
- self.roll_out(controls)
- resUtility = np.zeros(1)
- self.fUTILITY(self.CEMUTOTPER, resUtility)
-
- return -1*resUtility[0]
-
- def roll_out(self, controls):
- NT = self.NT
-
- iMIU = controls[0:NT]
- iS = controls[NT:(2*NT)]
-
- for i in range(NT):
- self.K[i] = self.fK(self.K, self.I, i)
- self.YGROSS[i] = self.fYGROSS(self.al, self.l, self.K, i)
- self.EIND[i] = self.fEIND(self.YGROSS, iMIU, self.sigma, i)
- self.E[i] = self.fE(self.EIND, i)
- self.CCA[i] = self.fCCA(self.CCA, self.EIND, i)
- self.CCATOT[i] = self.fCCATOT(self.CCA, self.cumetree, i)
- self.MAT[i] = self.fMAT(self.MAT, self.MU, self.E, i)
- self.ML[i] = self.fML(self.ML, self.MU, i)
- self.MU[i] = self.fMU(self.MAT, self.MU, self.ML, i)
- self.FORC[i] = self.fFORC(self.MAT, i)
- self.TATM[i] = self.fTATM(self.TATM, self.FORC, self.TOCEAN, i)
- self.TOCEAN[i] = self.fTOCEAN(self.TATM, self.TOCEAN, i)
- self.DAMFRAC[i] = self.fDAMFRAC(self.TATM, i)
- self.DAMAGES[i] = self.fDAMAGES(self.YGROSS, self.DAMFRAC, i)
- self.ABATECOST[i] = self.fABATECOST(
- self.YGROSS, iMIU, self.cost1, i)
- self.MCABATE[i] = self.fMCABATE(iMIU, i)
- self.CPRICE[i] = self.fCPRICE(iMIU, i)
- self.YNET[i] = self.fYNET(self.YGROSS, self.DAMFRAC, i)
- self.Y[i] = self.fY(self.YNET, self.ABATECOST, i)
- self.I[i] = self.fI(iS, self.Y, i)
- self.C[i] = self.fC(self.Y, self.I, i)
- self.CPC[i] = self.fCPC(self.C, self.l, i)
- self.PERIODU[i] = self.fPERIODU(self.C, self.l, i)
- self.CEMUTOTPER[i] = self.fCEMUTOTPER(self.PERIODU, self.l, i)
-# self.RI[i] = self.fRI(self.CPC, i)
-
- def optimize_controls(self, controls_start, controls_bounds):
- result = opt.minimize(self.fOBJ, controls_start, method='SLSQP', bounds=tuple(
- controls_bounds), options={'disp': True})
- self.optimal_controls = result.x
- return result
-
- def plot_run(self, title_str):
- Tmax = 2150
- NT = self.NT
- variables = [self.optimal_controls[NT:(2*NT)], self.optimal_controls[0:NT], self.CPRICE, self.EIND, self.TATM, self.DAMAGES, self.MAT,
- self.E]
- variables = [var[self.TT < Tmax] for var in variables]
- variable_labels = ["Saving rate",
- "Em rate", # 'Carbon emission control rate'
- "carbon price",
- "INdustrial emissions",
- # Increase temperature of the atmosphere (TATM)
- "Degrees C from 1900",
- "Damages", # 'trillions 2010 USD per year'
- "GtC from 1750", # 'Carbon concentration increase in the atmosphere'
- "GtCO2 per year" # Total CO2 emission
- ]
- variable_limits = [[0, 0.5], [0, 1], [0, 400], [-20, 40],
- [0, 5], [0, 150], [0, 1500], [-20, 50]] # y axis ranges
- plot_world_variables(self.TT[self.TT < Tmax], variables, variable_labels, variable_limits,
- title=title_str,figsize=[4+len(variables), 7],
- grid=True)
-
-
-def plot_world_variables(time, var_data, var_names, var_lims,
- title=None,
- figsize=None,
- dist_spines=0.09,
- grid=False):
- prop_cycle = pl.rcParams['axes.prop_cycle']
- colors = prop_cycle.by_key()['color']
-
- var_number = len(var_data)
-
- fig, host = pl.subplots(figsize=figsize)
- axs = [host, ]
- for i in range(var_number-1):
- axs.append(host.twinx())
-
- fig.subplots_adjust(left=dist_spines*2)
- for i, ax in enumerate(axs[1:]):
- ax.spines["left"].set_position(("axes", -(i + 1)*dist_spines))
- ax.spines["left"].set_visible(True)
- ax.yaxis.set_label_position('left')
- ax.yaxis.set_ticks_position('left')
-
- ps = []
- for ax, label, ydata, color in zip(axs, var_names, var_data, colors):
- ps.append(ax.plot(time, ydata, label=label,
- color=color, clip_on=False)[0])
- axs[0].grid(grid)
- axs[0].set_xlim(time[0], time[-1])
-
- for ax, lim in zip(axs, var_lims):
- ax.set_ylim(lim[0], lim[1])
-
- for axit, ax_ in enumerate(axs):
- ax_.tick_params(axis='y', rotation=90)
- ax_.yaxis.set_major_locator(pl.MaxNLocator(5))
- formatter_ = EngFormatter(places=0, sep="\N{THIN SPACE}")
- ax_.yaxis.set_major_formatter(formatter_)
-
- tkw = dict(size=4, width=1.5)
- axs[0].set_xlabel("time [years]")
- axs[0].tick_params(axis='x', **tkw)
- for i, (ax, p) in enumerate(zip(axs, ps)):
- ax.set_ylabel(p.get_label(), rotation=25)
- ax.yaxis.label.set_color(p.get_color())
- ax.tick_params(axis='y', colors=p.get_color(), **tkw)
- ax.yaxis.set_label_coords(-i*dist_spines, 1.01)
- axs[0].set_title(title)
-
-
-def hello_world():
- dice = DICE()
- dice.init_parameters()
- dice.init_variables()
- controls_start, controls_bounds = dice.get_control_bounds_and_startvalue()
- dice.optimize_controls(controls_start, controls_bounds)
- dice.roll_out(dice.optimal_controls)
- dice.plot_run()
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/further_reading.md b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/further_reading.md
deleted file mode 100644
index cc972adcd..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/further_reading.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# **Tutorial 2 Further Reading**
-
-## **IAM Model Summary**
-The economy model in most IAMs is a capital accumulation model.
-- Capital combines with a laboring population and technology to generate productivity ($Y$) that is hindered by climate damage.
-- A savings fraction of production drives capital accumulation, while the rest is consumed. Welfare is determined by consumption.
-- Climate action is formulated by a mitigation rate, $\mu$, which along with the savings rate are the two control parameters in the model.
-- These are used to maximize welfare.
-
-The climate model in DICE could be improved (c.f. [this study](https://www3.nd.edu/~nmark/Climate/DICE-simplified_2019.pdf)). We only summarize here how it interacts with the economy model:
-- Productivity generates industrial emissions, $$E_\mathrm{ind}=(1-\mu)\sigma Y,$$ where the $1-\mu$ factor accounts for reduced carbon intensity of production, $\sigma$, via supply-side mitigation measures (e.g. increased efficiency).
-- The productivity $Y$ rather than output production ($Q$) see model is used here because damages aren't included.
-- Namely, the emissions produced in the process of capital production occur before climate change has a chance to inflict damage on the produced output.
-- These emissions combine with natural emissions to drive the temperature changes appearing in the damage function, closing the economy-climate loop.
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Exogeneous Control Variables**
-There are two exogeneous variables in the model:
-- mitigation (emissions reduction) rate $\mu_t$, and
-- the savings rate $S_t$.
-
-There exists a mitigation cost associated with a specific mitigation rate, presented as a fraction of productivity, $\Lambda_t=\theta_1\mu^{\theta_2}$. This implies that increased mitigation efforts correspond to elevated costs.
-
-The savings rate $S_t$ extracts a portion of the total production to invest, thereby boosting capital. The rest of the production is then allocated for consumption.
-
-## **Economy Model Summary**
-The essence of the economy model in DICE is a capital accumulation model. Existing capital depreciates at rate $\delta$ and new capital arises through investment,
-$$K_{t+1}=(1-\delta)K_t+I_t$$
-where the invested capital $I=SQ$ is determined by a chosen fraction $S$ of the production $Q$ that is "saved" rather than consumed. Production is given as the productivity $Y$ reduced by damages and mitigation cost, $$Q=(1-\Omega)(1-\Lambda)Y.$$ Productivity, $$Y=A K^\gamma L^{1-\gamma},$$
-is determined by the technology conversion $A$ operating on a combination of capital $K$ and labor $L$ whose relative contributions are set by the capital elasticity parameter $\gamma$. Labor is population which set to saturate over the 2nd half of the 21st century. Technology conversion is only weakly sigmoidal in time, deviating slightly from linear growth.
-
-The remaining production is consumed $$C:=(1-S)Q$$ producing utility $$U(C,L)=Lu(c)=Lu(C/L),$$ using the isoelastic utility function, $$u(c)=\frac{(c+1)^{1-\alpha}-1}{1-\alpha}.$$ The overall value of a projected future is then $$V=\sum_{t=1}^{\infty}\gamma^t U(C_t,L_t),$$ where $\gamma=1/(1+\rho)$ for discount rate $\rho$ and we use the population level utility function $U(C,L)=Lu(C/L)$.
-
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Optimal Planning**
-The unconstrained problem aims to maximize $V$ within the bounds of $\mu_t$ and $S_t$ time courses (while considering constraints on $\mu$ and $S$). Why is there a "sweet spot"? Increasing savings boosts investment and productivity, but higher production leads to emissions, resulting in increased temperature and damages that reduce production. Mitigation costs counterbalance this effect, creating a trade-off. As a result, there typically exists a meaningful joint time series of $\mu_t$ and $S_t$ that maximizes $V$. Due to the discount factor $\gamma$, $V$ depends on the future consumption sequence (non-invested production) within a few multiples of the horizon, approximately $1/(1-\gamma)$ time steps into the future.
-
-Constrained formulations introduce an additional constraint to limit industrial emissions below a certain threshold.
-
-## **Social Cost of Carbon**
-A definition for the social cost of carbon (SCC) is
-
-- *the decrease in aggregate consumption in that year that would change the current...value of social welfare by the same amount as a one unit increase in carbon emissions in that year.* ([Newbold, Griffiths, Moore, Wolverton, & Kopits, 2013](https://www.worldscientific.com/doi/abs/10.1142/S2010007813500012)).
-
-The $SCC$ quantifies how much consumption is lost with increased emissions, using changes in welfare to make the connection. In technical terms:
-
-- the marginal value with respect to emissions relative to the marginal value with respect to consumption, $$SCC_t\propto\frac{\partial V/\partial E_t}{\partial V/\partial C_t}=\frac{\partial C_t}{\partial E_t}.$$
-This is usually expressed by multiplying by a proportionality factor of $-1000$ that converts the units to 2010 US dollars per tonne of CO2.
-
-# **Tutorial 4 Further Reading**
-
-## **Vectorization Methods for Creating Word Clouds**
-
-Let's write down what they compute by denoting the index, $d$, over the $D$ documents and the index, $w$, over the $W$ words in the vocabulary (the list of all the words found in all the tweets, which we'll call documents):
-- term frequency, $\mathrm{tf}(w,d)$. The frequency of a word $w$ in a document $d$ is $$\mathrm{tf}(w,d):=\frac{n(w,d)}{n(d)},$$ where $n(w,d)$ is the number of times term $w$ is in document $d$ and $n(d)=\sum_{w=1}^W n(w,d)$ is the total number of words in document $d$. The term frequency over all the documents is then, $$\mathrm{tf}(w):=\frac{\sum_{d=1}^D n(d)\mathrm{tf}(w,d)}{N},$$ where the denominator $N=\sum_{d=1}^D n(d)$ is just the total word count across all documents.
-- term frequency-inverse document frequency, $\mathrm{Tfidf}(w,d):=\mathrm{tf}(w,d)\mathrm{idf}(w)$. Here, $$\mathrm{idf}(w)=\frac{\log(D+1)}{\log(n(w)+1)+1},$$ where $n(w)$ is the number of documents in which term $t$ appears, i.e. $n(w,d)>0$. Idf is like an inverse document frequency. The `sklearn` package then uses $$\mathrm{Tfidf}(w)=\frac{1}{D}\sum_{d=1}^D \frac{\mathrm{Tfidf}(w,d)}{||\mathrm{Tfidf}(\cdot,d)||},$$ where $||\vec{x}||=\sqrt{\sum_{i=1}^Nx^2_i}$ is the Euclidean norm.
-
-$\mathrm{Tfidf}$ aims to add more discriminability to frequency as a word relevance metric by downweighting words that appear in many documents since these common words are less discriminative.
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/images/world3_major_flows.png b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/images/world3_major_flows.png
deleted file mode 100644
index 354cf6ee3..000000000
Binary files a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/images/world3_major_flows.png and /dev/null differ
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_DaySummary.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_DaySummary.ipynb
deleted file mode 100644
index b3059d2de..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_DaySummary.ipynb
+++ /dev/null
@@ -1,49 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3b83976b",
- "metadata": {},
- "source": [
- "# Day Summary"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f2b474b6",
- "metadata": {},
- "source": [
- "This day's tutorials focused on the socioeconomic projections regarding the future of the climate emergency. The content centered around the shared socioeconomic pathways framework used by the IPCC and is more knowledge-based than skills-based compared with other days.\n",
- "\n",
- "The day began with a tutorial that summarizes the socio-economic history of growth & limits to physical growth as a parent problem within which the climate emergency arises. You explored the consequences of resource limitations in the computational part of this tutorial through the world3 model.\n",
- "\n",
- "The next tutorial dove into a formulation of the economics of climate change beginning with goal setting and then the main concepts and methods to finding optimal economic plans. You learned about the economic models used in the SSP framework, integrated assessment models (IAMs), and the modeling practice that uses them to make projections. The computational part of the tutorial first walked you through some graphical exercises that illustrate basic economic concepts before running a simple IAM (the RICE model) under various parameter settings.\n",
- "\n",
- "The third tutorial presented the SSP framework in which IAMs are used. You learned how the five SSP narratives were constructed and integrated with physical modeling efforts to define the full set of SSP pathways. You then got a glimpse of what an IAM used in this project consists of and saw the results through important metrics like energy use and greenhouse gas emissions. The computational part of the tutorial took off from a critique of the SSP framework to motivate playing with MARGO, a simple model with more, and more dynamic mitigation controls.\n",
- "\n",
- "Finally, the fourth tutorial on public opinion focussed on climate action psychology and values-based communication. The computational exercise had you analyze public sentiment through analysis of a large dataset of Twitter messages."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Intro.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Intro.ipynb
deleted file mode 100644
index bc9b42b91..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Intro.ipynb
+++ /dev/null
@@ -1,66 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "df1a0a21",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Intro"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6013efcc",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Overview"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dfaee097",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Up to today, you have studied models of our climate system. Today, we will begin to model the (in many ways more complex!) system of society’s economy and how climate impacts them. "
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Intro",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Outro.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Outro.ipynb
deleted file mode 100644
index b707d8c54..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Outro.ipynb
+++ /dev/null
@@ -1,56 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "175cc467",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Outro"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2e5c1afd",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Now that you have a sense for the complexity nestled within Integrated Assessment Modeling and socioeconomic phenomena, you will look at how we try to coarse grain that complexity when applying our understanding of climate to assess extreme events and vulnerability. "
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Outro",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb
deleted file mode 100644
index 749e30970..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial1.ipynb
+++ /dev/null
@@ -1,469 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "25d9555a-5485-42c3-81cf-4851396a643f",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial1.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1872759b-c9e3-4d4e-a3cf-30813840b049",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 1: Orienting inside a \"Climate Solution\" Simulator\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Maximilian Puelma Touzel, Paul Heubel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2d2b006-8493-451b-a75e-797d902a0fcd",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "During the first week of the course, you applied computational tools to climate data (measurements, proxies, and model output) to characterize past, present, and future climate. During day one of this second week (W2D1), you began to explore climate model data from Earth System Model (ESM) simulations conducted for the recent Climate Model Intercomparison Project (CMIP6) that are presented in the report from the Intergovernmental Panel on Climate Change ([IPCC](https://www.ipcc.ch/)). However, the dominant source of uncertainty in those projections arises from how human society responds: e.g. how our emissions reduction and renewable energy technologies develop, how coherent our global politics are, how our consumption grows (cf. [Rogelj et al. (2018)(Global Warming of 1.5°C. An IPCC Special Report \\[...\\])](https://doi.org/10.1017/9781009157940.004)). For these reasons, in addition to understanding the physical basis of the climate variations projected by these models, it's also important to assess the current and future socioeconomic impact of climate change and what aspects of human activity are driving emissions.\n",
- "\n",
- "This day's tutorials focus on the socioeconomic projections regarding the future of climate change and are centered around the Shared Socioeconomic Pathways (SSP) framework used by the IPCC. However, in this first tutorial, you will use the [En-ROADS](https://www.climateinteractive.org/en-roads/) simulator to get some intuition about different socioeconomic variables and their consequences for climate. Additionally, you will analyze potential economic and population scenarios to learn about the complex and intertwined dynamics of socioeconomic variables, as well as how this model informs modern-day climate challenges.\n",
- "\n",
- "Unlike the rest of the days of this course, this day will be highly conceptual and discussion-driven, and focus much less on analyzing datasets.\n",
- "\n",
- "In this tutorial, you will:\n",
- "- Get familiar with the interface of the simulator named En-ROADS\n",
- "- Explore the impact of different growth variables on the temperature increase by 2100 projected from the En-ROADS model.\n",
- "- Understand why it is necessary to implement various actions against climate change, not a single one.\n",
- "- Explore the assumptions and limitations of this model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "368d0c90-ae7e-43d9-ada7-ad42c75f5f95",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6db786f8-e5c4-42a9-9837-e41027b9ad4d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e11e3bbb-ce8f-4404-8d04-b6f86d4a9ea9",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ea9d553f-c3eb-4723-a922-e81f97e1e3ba",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting inside a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52ca5582-816c-423c-9ed7-cda1c7319731",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57265af6-b3ab-41e4-82a1-6c4b63a0c321",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Exploration of a Climate Solution Simulator"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "042a83d3-2e94-4092-a289-2430d349966c",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The following introductory video gives a quick overview of the En-ROADS simulator, a simple climate model (SCM), developed by [Climate-Interactive](https://www.climateinteractive.org/) for teaching purposes. It is used in policy workshops, role-plays, and other activities to explore the possibilities and obstacles of scenarios and human solutions to climate change.\n",
- "\n",
- "To get familiar with modeling societal and economic mechanisms in combination with climatic variables, the so-called **socio-economic model**, you will examine its 'control knobs', limitations, and assumptions. In later tutorials you will compare these findings with those of Integrated Assessment Models (IAMs) which are state-of-the-art models for projecting scenarios and those used in the socioeconomic pathways of the IPCC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9c6b2260-bdb0-4d7b-a07d-41551d5ccf92",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 2: Overview of the En-ROADS Climate Solutions Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'Py_qIgcZxKg')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "718606a5-1239-414b-84fc-f9c0a18924d6",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Exercise 1: Can you limit human-caused global warming to \"well-below 2ºC\"?\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "We jump right in with an exercise, adapted from [En-ROADS](https://www.climateinteractive.org/guided-assignment/), that allows you to explore the Climate Solution Simulator.\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Don't worry if you don't find a scenario that works right away - keep exploring. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) to help you get started. Have fun!\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* How many variables did you have to adjust to reach the \"well-below 2ºC\" target?\n",
- "* Which variables had the most individual impact? Did the magnitude of impact surprise you for any variables?\n",
- "* How feasible is this scenario? That is, what actions would have to be taken by governments, businesses and people over the next few years to make the proposed scenario possible?\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30a81fb8-c21b-490c-9297-a6705f114957",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "3. One scenario to try based on the goals of the energy transition is the combination of:\n",
- "(1) full electrification of the energy supply, i.e. 'very highly taxed' fossil fuel emissions (Coal, Natural gas, and Oil),\n",
- "(2) 'highly subsidized' renewables, and\n",
- "(3) a 'very high' carbon price,\n",
- "as well as the 'highly reduced' emissions of Methane and other Gases\n",
- "which gets us to a maximum increase of 2°C by the end of the century\n",
- "(cf. this example scenario https://en-roads.climateinteractive.org/scenario.html?v=24.3.0&p1=100&p7=85&p10=5&p16=-0.05&p23=-1&p39=250&p59=-64&p67=2&g0=2&g1=62).\n",
- "\n",
- "Nevertheless, other scenarios are possible, either by involving more parameters or by focusing on carbon removal.\n",
- "There are (at least!) two important observations to make. First, actions vary in leverage, in other words, some actions are more helpful than others.\n",
- "Second, to make a difference and reach an ambitious goal like the 2°C degree target, many actions in many sectors are required.\n",
- "Sometimes one refers to this circumstance by calling it a 'Silver Buckshot' instead of a 'Silver Bullet' approach (cf. e.g. https://www.washingtonpost.com/archive/opinions/2006/05/27/welcome-to-the-climate-crisis-span-classbankheadhow-to-tell-whether-a-candidate-is-serious-about-combating-global-warmingspan/26b2ac5a-a4a3-46ff-b214-3fc07a3a5ab3/).\n",
- "Furthermore, people might be surprised by the fact that some actions may be much lower leverage, while others like carbon pricing and energy efficiency might be higher leverage than people expect.\n",
- "'''"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05df13b5-14ce-4515-aaae-7cc9e3765445",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "#link_id = \"gwr8h\"\n",
- "\n",
- "download_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "render_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download a cheatsheet for the En-ROADS Control Panel:\\n{download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f5a0c7f-8fd3-4c89-b25d-8070ef2fdd4b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 2: Limitations of the En-ROADS Model Approach\n",
- "\n",
- "We conclude this tutorial by stepping back and discussing the limitations of En-ROADS.\n",
- "\n",
- "### Exercise 2: Limitations\n",
- "*Estimated timing:* 5 minutes\n",
- "\n",
- "1. Think about limitations that arise from the En-ROADS model approach. List a few mechanisms that seem oversimplified or phenomena that might be not or misrepresented. Discuss with your pod.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b17595a7-b326-4edb-9f80-b8e21f1d4e5e",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "1. At first, En-ROADS is a world model, not an optimal planning model so it is unlike any IAM used e.g. for the IPCC reports.\n",
- "Controls are somewhat limited (DAC/CCS, soil, BioChar, and mineralization are not covered).\n",
- "It allows only for single parameter changes - in reality, there will be correlations, e.g. between carbon pricing and renewables due to market pressure.\n",
- "Some feedbacks are also missing, besides the above-mentioned damage from climate change on GDP, land use, etc.,\n",
- "climate change harms the human population by shortening lives.\n",
- "Although this happens already in the current 1°C increase reality, this harm is difficult to quantify and hence not implemented.\n",
- "Furthermore, it is a fully aggregated model, no spatial/regional or income resolution, and corresponding interdependency exists.\n",
- "An action/ policy is assumed to be executed globally, which is a utopia so far.\n",
- "It hence remains important to consider the implications of heterogeneity across different countries and their interactions.\n",
- "Last but not least, tipping points such as the thawing of the permafrost are represented in a very simplified manner only, although they probably have strong implications.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "51aac287-f6d0-479e-be4b-87f557c32214",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, you got an intuition for various 'control knobs' that can be turned in a socio-economic model environment. We discussed why no policy alone can be a silver bullet to solve all problems but a mixture of many actions, in particular, the energy transition to renewables and carbon pricing. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c949bc5-e5ef-4d07-b379-122072ef6271",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial1",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb
deleted file mode 100644
index b3219f7bc..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial2.ipynb
+++ /dev/null
@@ -1,508 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "873927b3-ad55-40a2-ab6a-de2af3c09cc8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8eb286c6-dd69-440b-bc14-e6c7f20517bf",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 2: Fossil Fuel Emissions, Growth, and Damage\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d86fc935-8b60-4fcf-83d7-1e9c78ca6b84",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "This tutorial explores the impact of population and economic growth on the global temperature and how a changing climate due to fossil fuel emissions damages our economy. You model these scenarios by 'controlling the knobs' of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* discuss the impact of a growth-based economy on future fossil-fuel emissions along the Kaya identity\n",
- "* qualitatively describe the impact of population and economic growth on the global temperature within the En-ROADS model environment.\n",
- "* (explain qualitatively how a different damage function formulation impacts the economic activity)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8dc93386-4bde-4e22-b7eb-174133f57351",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9398705-5441-4ed3-980d-c6339cb30696",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3342114d-deee-4d5b-afde-d4852bac2352",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2ca0746a-edad-45f7-8ad5-1b4ec1e7c546",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting in a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d45c633-1daa-4408-ab8c-18969fff8bde",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4cb055f0-0f58-4c8c-9e11-a7a16f0b6e98",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Quantification of Fossil Fuel Emissions and Its Dependency on Growth\n",
- "\n",
- "We discussed in the slides how economics represents the human population as a source of labor needed to produce goods and services. A by-product of this production are fossil fuel emissions, which increase atmospheric greenhouse gas (GHG) concentrations, significantly changing the earth's climate. Let us take a look at the relationship between people, production, and emissions in more detail using the En-ROADS simulator.\n",
- "\n",
- "You might already have clicked through the toggles at the top of the control panel of En-ROADS and tried the different *Views* (Main graphs, Kaya graphs, Miniature graphs). Select now the **Kaya graphs** view and reset the simulator (click on the anti-clockwise circular arrow to 'Reset all policies & assumptions' or just reload the page/model [here](https://en-roads.climateinteractive.org/)).\n",
- "\n",
- "The Kaya graphs depict four drivers of growth in carbon dioxide emissions from energy use, which reflects about two-thirds of all greenhouse gas emissions (the remaining third of emissions are from land use changes and other gases, such as methane (CH4) and nitrous oxide (N2O), which are not considered here). The corresponding equation, the so-called [***Kaya identity***](https://en.wikipedia.org/wiki/Kaya_identity) below was developed by Yoichi Kaya:\n",
- "\n",
- "***CO$_2$ Emissions from Energy = Global Population × GDP per Capita × Energy Intensity of GDP × Carbon Intensity of Energy***\n",
- "\n",
- "Where [gross domestic product](https://en.wikipedia.org/wiki/Gross_domestic_product) (GDP) is a measure of economic activity or health. \n",
- "\n",
- "In a more mathematical form, this equation is\n",
- "\n",
- "$$\n",
- "F = P \\times \\left[\\frac{G}{P}\\right] \\times \\left[\\frac{E}{G}\\right] \\times \\left[\\frac{F}{E}\\right]\n",
- "$$\n",
- " - $F$ (CO$_2$ emissions from energy), \n",
- " - $P$ (global population), \n",
- " - $G$ (world GDP), and \n",
- " - $E$ (global energy consumption).\n",
- "\n",
- "Decomposing emissions in this way helps to see key contributions and, if we assume that the factors are independent of each other (which is not fully true), distinct ways that we can lower emissions. Each factor in this model is important and extreme reductions in one can offset increases in the others. In our case, our emissions $F$ are increasing over time because the Energy Intensity of GDP $\\left[\\frac{E}{G}\\right] $ and the Carbon Intensity of Energy do not together offset enough of our growing GDP ($G$), which is by definition necessary for a growth-based economy.\n",
- "\n",
- "The first four graphs in the Kaya view display these four variables $\\left(P, \\frac{G}{P}, \\frac{E}{G}, \\frac{F}{E}\\right)$, with the fifth plot showing Emissions, $F$. This allows us to compare the importance of, say, economic and population growth on the CO$_2$ emissions and subsequent temperature rise by 2100."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fd232a4d-c350-4e7b-9519-921800d81e20",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Population vs. Economic Growth\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: Turn two *growth* 'control knobs' of En-ROADS, which are the sliders *Economical growth* and *Population growth*. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* What can you observe within the *Kaya graph* view? Describe the graphs and interpret them.\n",
- "\n",
- "Read the following dropdown accessible box if you need more information to interpret the Kaya graphs. \n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c61eb29-ab14-4445-915e-5d39abab9a73",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53e42e1c",
- "metadata": {
- "execution": {},
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "source": [
- "# Tutorial 3: The Temporal Dimension of Actions\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87ef2f44-fb74-4664-8472-4c97158a8d17",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 15 minutes\n",
- "\n",
- "The last tutorials covered the necessity for an energy transition to tackle the climate emergency and many solutions at once. As emissions accumulate, it becomes substantially harder to succeed the longer we take to make big changes. This tutorial explores the temporal dimension of action, here policies, by using the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* exemplify along the carbon price, why policy-making has to consider the temporal dimension and the needs of the most vulnerable parts of society"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "062c4b1a-6239-4644-a0dd-03fe1342fe99",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b4722fe-cbd5-484a-be6d-96ed7aafbf62",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "053ee0ea-d704-45c8-8b4b-aad17216710d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b50c362",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: The IPCC's Transition Narratives and Project Modelling\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "703ee8f2",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "29cf9a43-2478-4d14-9faf-193e54378a04",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: When and How Do You Introduce a Carbon Price?\n",
- "\n",
- "You might have recognized that a high ***carbon price*** by default has a strong impact on the temperature increase within the En-ROADS model as it both reduces the carbon intensity of the energy supply and reduces the energy demand. However, the carbon price itself could be introduced now or in 50 years and at different amounts, depending on for example the social cost of such a policy, which makes energy supply more expensive depending on its emissions. Energy producers could pass additional costs to their customers, so policy could be designed to minimize the impacts on the poorest, e.g. by introducing a [Carbon fee and dividend](https://en.wikipedia.org/wiki/Carbon_fee_and_dividend). \n",
- "\n",
- "To account for their temporal evolution, many variables in En-ROADS allow for modifications of the timing when something is introduced, stopped, in or de-creased, and so on."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d29fb31c-84ab-4705-8678-87f8274f6500",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Implications of Actions and Their Timing\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- " \n",
- "2. **Develop a scenario**: Click on the ellipsis of the 'Carbon price' slider, and a widget-info box opens that allows for finetuning of your carbon price policy.\n",
- " 1. Click on the title of the top right graph *Greenhouse Gas Net Emissions*, select the 'Financial' toggle, and select the 'Market price of electricity'.\n",
- " 2. Turn the most upper knobs/ vary the sliders of the 'Carbon price' and slide through different carbon prices.\n",
- " 3. Move the sliders to answer the following question. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed.\n",
- "\n",
- "\n",
- "3. **Answer the following questions**:\n",
- " * What do you observe in the 'Market price of electricity' graph after varying the carbon price?\n",
- " * Now, select a high carbon price and increase the 'Year the carbon price starts to phase in'. How does the carbon price change the temporal evolution of the 'Market price of electricity'. \n",
- " * How could this be of consequence for a low-income household?\n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c97e6d3d-1718-479b-a19a-ffc6a082347b",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "\n",
- "'''\n",
- "3. The earlier the carbon price starts to phase in the more effective it is regarding the temperature increase.\n",
- "However, the earlier the larger the disturbance of the market price of electricity, emphasized by a strong increase in the price at first\n",
- "and a strong drop in the price after the first few years.\n",
- "This might lead to economic disruptions as energy supply becomes very expensive, therefore low-income households,\n",
- "which usually spend large parts of their income on energy, would need compensation like a 'carbon dividend' to avoid precariat.\n",
- "In summary, actions and their temporal implementation always need to be evaluated in various aspects to be a successful and\n",
- "societal least disruptive action against climate change.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3562deb9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, we discussed the temporal dimension of the carbon price implementation in order to understand why no policy might be a silver bullet to solve all problems but comes with various ethical and political implications. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6d0c2c37-0e64-4734-8776-1f455a20987f",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27636b92",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial3",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.jl b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.jl
deleted file mode 100644
index e3c0be61d..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial3.jl
+++ /dev/null
@@ -1,2138 +0,0 @@
-### A Pluto.jl notebook ###
-# v0.19.24
-
-using Markdown
-using InteractiveUtils
-
-# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
-macro bind(def, element)
- quote
- local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
- local el = $(esc(element))
- global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
- el
- end
-end
-
-# ╔═╡ 1c8d2d00-b7d9-11eb-35c4-47f2a2aa1593
-begin
- import Pkg
- ENV["JULIA_MARGO_LOAD_PYPLOT"] = "no thank you"
- Pkg.activate(mktempdir())
- Pkg.add([
- Pkg.PackageSpec(name="Plots", version="1"),
- Pkg.PackageSpec(name="ClimateMARGO", version=v"0.3.3"),
- Pkg.PackageSpec(name="PlutoUI", version="0.7"),
- Pkg.PackageSpec(name="HypertextLiteral", version="0.9"),
- Pkg.PackageSpec(name="Underscores", version="2"),
- ])
-
- using Plots
- using Plots.Colors
- using ClimateMARGO
- using ClimateMARGO.Models
- using ClimateMARGO.Optimization
- using ClimateMARGO.Diagnostics
- using PlutoUI
- using HypertextLiteral
- using Underscores
-
- Plots.default(linewidth=5)
-end;
-
-# ╔═╡ 9a48a08e-7281-473c-8afc-7ad3e0771269
-TableOfContents()
-
-# ╔═╡ 94415ff2-32a2-4b0f-9911-3b93e202f548
-const initial_1 = Dict("M" => [2090, 6]);
-
-# ╔═╡ 50d24c91-61ae-4544-98fa-5749bafe3d41
-# md"""
-# ## Overview of the climate problem: from greenhouse gas emissions to climate suffering
-
-# Human emissions of greenhouse gases, especially Carbon Dioxide (CO₂), increase the stock of greenhouse gases in the atmosphere. For every molecule of CO₂ emitted, about 50% are taken up by plants, soils, or the ocean within a few years, while the rest remains in the atmosphere. (The effects of other greenhouse gases, such as Methane and CFCs, and other forcing agents, can approximately be converted into the "CO₂-equivalent"– or CO₂ₑ– concentrations that would lead to the same climate forcing).
-
-# Greenhouse gases get their name because they trap invisible heat radiation emitted by Earth's surface and atmosphere from escaping to space, much like greenhouses trap hot air from rising when it is warmed by the Sun. This "greenhouse effect" causes the temperature to rise globally, although some places warm *more* and *faster* than others. Warmer temperatures exacerbate both the frequency and intensity of "natural" disasters, such as heat waves, coastal flooding from major hurricanes, and inland flooding from torrential rain. These climate impacts lead to enhanced climate suffering, which economics typically attempt to quantify suffering in terms of lost money or welfare.
-
-# In the interactive article below, we invite you to explore the benefits of emissions mitigation and carbon dioxide removal in reducing climate suffering, and the trade-offs with their costs.
-# """
-
-# ╔═╡ e810a90f-f964-4d7d-acdb-fc3a159dc12e
-const initial_2 = Dict("M" => [2080, .7]);
-
-# ╔═╡ a3422533-2b78-4bc2-92bd-737da3c8982d
-const initial_3 = Dict("M" => [2080, .7]);
-
-# ╔═╡ 51037451-0fea-4021-8824-56911970b97b
-const initial_x = Dict("G" => [2030, 1]);
-
-# ╔═╡ 3094a9eb-074d-46c3-9c1e-0a9c94c6ad43
-blob(el, color = "red") = @htl("""| Enabled? | Cost multiplier | - - - -|
|---|---|---|
| $(longname.M) | -$(@bind enable_M_9 CheckBox(;default=true)) | -$(@bind cost_M_9 multiplier(default_cost.M, 4)) | -
| $(longname.R) | -$(@bind enable_R_9 CheckBox(;default=true)) | -$(@bind cost_R_9 multiplier(default_cost.R, 4)) | -
| $(longname.G) | -$(@bind enable_G_9 CheckBox(;default=false)) | -$(@bind cost_G_9 multiplier(default_cost.G, 4)) | -
| $(longname.A) | -$(@bind enable_A_9 CheckBox(;default=false)) | -$(@bind cost_A_9 multiplier(default_cost.A, 4)) | -
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 4: The Shared Socio-economic Pathways\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "In this tutorial, you will learn about Integrated Assessment Models (IAMs), a class of models that combine climatology, economics, and social science, reflecting the intertwined nature of these domains in addressing climate change. Based on these models the IPCC established the socioeconomic pathway framework. You are going to learn how these pathways differ from one another in both climate and socioeconomic variables as well as assumptions.\n",
- "\n",
- "After finishing this tutorial, you will know how to \n",
- "\n",
- "- filter data series of interest from a rich `pandas` data frame that contains various variables for different SSPs,\n",
- "- tell what the abbreviation SPA stands for,\n",
- "- explain the differences and similarities of the SSP1-26 and SSP5-85, and \n",
- "- sketch the modeling approach of IAMs."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# installations ( uncomment and run this cell ONLY when using google colab or kaggle )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {},
- "executionInfo": {
- "elapsed": 1460,
- "status": "ok",
- "timestamp": 1682429063027,
- "user": {
- "displayName": "Maximilian Puelma Touzel",
- "userId": "09308600515315501700"
- },
- "user_tz": 240
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# imports\n",
- "import warnings\n",
- "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import pooch\n",
- "import os\n",
- "import tempfile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "code_folding": [
- 0
- ],
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file\n",
- "\n",
- "\n",
- "def legend_without_duplicate_labels(ax):\n",
- " handles, labels = ax.get_legend_handles_labels()\n",
- " unique = [(h, l) for i, (h, l) in enumerate(zip(handles, labels)) if l not in labels[:i]]\n",
- " ax.legend(*zip(*unique))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Transition Goals and Integrated Assessment Models\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "# TODO add Bilibili link\n",
- "video_ids = [('Youtube', 'TqpPDz2kfWc'),\n",
- " # ('Bilibili', '')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "pycharm": {
- "name": "#%%\n"
- },
- "tags": [
- "remove-input"
- ]
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"ujprw\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Shared Socio-economic Pathways"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {},
- "jp-MarkdownHeadingCollapsed": true
- },
- "source": [
- "In this, and subsequent, tutorials, you will explore Integrated Assessment Models (IAMs) which are the standard class of models used to make climate change projections. IAMs couple a climate model with an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. IAMs can also account for changes that result from mitigation efforts, which lessen anthropogenic emissions.\n",
- "\n",
- "Let's start by investigating some IAM model output.\n",
- "\n",
- "The simulations are labeled by both the Shared Socioeconomic Pathway (SSP1, SSP2, SSP3, SSP4, and SSP5) and the forcing level (greenhouse gas forcing of 2.6, 7.0, 8.5 W/m2 etc. by 2100).\n",
- "The 5 SSPS are: \n",
- "- SSP1: Sustainability (Taking the Green Road)\n",
- "- SSP2: Middle of the Road\n",
- "- SSP3: Regional Rivalry (A Rocky Road)\n",
- "- SSP4: Inequality (A Road divided)\n",
- "- SSP5: Fossil-fueled Development (Taking the Highway)\n",
- "\n",
- "We select two SSPs to exemplify how these scenarios differ from each other. \n",
- "To get a strong contrast, we select SSP1 and SSP5. \n",
- "\n",
- "Let's load the data and describe their features along a few plots.\n",
- "\n",
- "Like in other tutorials, we provide a `.csv` file that is stored in the cloud and was downloaded beforehand from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# Load SSP data from .csv file\n",
- "filename_SSPs = 'SSP_IAM_V2_201811.csv'\n",
- "link_id = \"2uwr4\"\n",
- "url_SSPs = f\"https://osf.io/download/{link_id}/\"\n",
- "\n",
- "df = pd.read_csv(pooch_load(url_SSPs, filename_SSPs))\n",
- "# get a summary of the resulting pandas dataframe\n",
- "df.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "We further explore our data frame by printing categories that are used to tag the numeric data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "print(df.SCENARIO.unique()) # print all scenarios\n",
- "print(df.VARIABLE.unique()[:10]) # print the first 10 variables\n",
- "print(df.REGION.unique()) # print all regions\n",
- "print(df.MODEL.unique()) # print all IAMs\n",
- "print(df.UNIT.unique()) # print all units"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "This file contains much data we are not interested in at the moment. To filter our example scenarios, region, and variables, we use the convenient [`.query()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html) method from `pandas`. The `VARIABLE`s of interest are those we already touched on in Tutorials 1 to 3: \n",
- "\n",
- "- **economic growth** (`'GDP|PPP'`),\n",
- "- **energy use** (`'Primary Energy'`),\n",
- "- **emissions** (`'Emissions|Kyoto Gases'`),\n",
- "- and **forcing** (`'Diagnostics|MAGICC6|Forcing'`).\n",
- "\n",
- "- As a `REGION`, we choose the `'World'`,\n",
- "- and our `SCENARIO`s are called `'SSP1-26'` and `'SSP5-85'`.\n",
- "- The model of choice for the former scenario is by convention `'IMAGE'` and `'REMIND-MAGPIE'` for the latter, respectively.\n",
- "\n",
- "A function named `get_SSPs_for_variable()` applies all this generally and is hidden in the next cell. Please execute it, such that the subsequent cells can make use of it. If you are interested in its procedure and want to adjust it, don't forget to save a copy beforehand."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown *Execute this cell to enable the dataframe filter function: `get_SSPs_for_variable`*\n",
- "\n",
- "def get_SSPs_for_variable(df,scenario,variable,region='World'):\n",
- " '''\n",
- "\n",
- " Function that filters IIASA's SSP database that is stored in a data frame 'df'\n",
- " and was loaded before from the 'SSP_IAM_V2_201811.csv' file.\n",
- " It returns a data frame with selected columns depending on scenario, variable and region input.\n",
- " For a given SSP scenario it chooses the conventional model for the respective scenario\n",
- " (cf. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about#v2).\n",
- "\n",
- " Args:\n",
- " scenario: string in \"SSPX-XX\" with X=1,...,5\n",
- " variable: string in df.VARIABLE, e.g. 'Population' or 'GDP|PPP'\n",
- "\n",
- " Returns:\n",
- " SSP data for selected columns for a given SSP scenario\n",
- "\n",
- " Example:\n",
- " dd = get_SSPs_for_variable(df,'SSP1-26','Population')\n",
- "\n",
- " '''\n",
- " ssp_model_conv = {\"SSP1-Baseline\" : \"IMAGE\",\n",
- " \"SSP1-26\" : \"IMAGE\",\n",
- " \"SSP2-Baseline\" : \"MESSAGE-GLOBIOM\",\n",
- " \"SSP3-Baseline\" : \"AIM/CGE\",\n",
- " \"SSP4-Baseline\" : \"GCAM4\",\n",
- " \"SSP5-Baseline\" : \"REMIND-MAGPIE\"}\n",
- " model = ssp_model_conv[scenario]\n",
- " ds = df.query(\n",
- " f'(VARIABLE == \"{variable}\") & (SCENARIO == \"{scenario}\") & (MODEL == \"{model}\") & (REGION == \"{region}\")'\n",
- ")\n",
- " return ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Let's plot our variables of interest and compare the respective features of the scenarios."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['GDP|PPP','Emissions|Kyoto Gases', 'Primary Energy','Diagnostics|MAGICC6|Forcing']\n",
- "# create new names for structured data series and axes labels\n",
- "val_name = ['GDP (billion US$/yr)', 'Emissions (Mt CO$_2$/yr)', 'Energy use (EJ/yr)', 'Forcing (W/m$^2$)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,2)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich data frame\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0,0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The projections in the plots you just created show changes in **GDP** (billion US\\$/yr), **fossil fuel emissions** (Mt CO$_2$/yr), **energy use** (EJ/yr), and **forcing** (W/m$^2$) across the two very different scenarios SSP1 and SSP5, computed at their baseline forcing level, which are each represented by a distinct color in each plot.\n",
- "\n",
- "Our plots show that the SSP5-Baseline scenario exhibits very high levels of energy use, and emissions (due to fossil fuel exploitation), it marks the upper end of the scenarios in several dimensions (cf. [Kriegler et al. (2014)](https://doi.org/10.1016/j.gloenvcha.2016.05.015)). \n",
- "\n",
- "The SSP1-26 scenario contrarily caps the increase of energy use by 2030, combined with other actions leading to decreasing emissions and subsequently a decreasing forcing for the second half of the century. However, economic growth continues with half the slope of SSP5-Baseline. In summary, it is the most optimistic projection: we transition to a global society of sustainability-focused growth."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.1: SSP Creation via IAMs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The underlying modeling of Integrated Assessment Models (IAMs) works roughly as follows:\n",
- " \n",
- "All SSP projections are created by optimizing economic activity within the constraint of a given level of greenhouse gas forcing at 2100 (bottom right in the above plot). This activity drives distinct temperature changes via the emissions it produces (top right), which are inputted into a damage function to compute economic damages. These damages feedback into the economy model to limit emissions-producing economic activity (top left).\n",
- "*Note that we already explored these damage functions along our En-ROADS climate solution simulator in Tutorial 2*.\n",
- "\n",
- "The forcing constraint ensures the amount of emissions produced is consistent for that particular scenario. In other words, the projected temperature change under different scenarios is fed to a socioeconomic model component in order to assess the socioeconomic impacts resulting from the temperature change associated with each SSP. *For examples of such impacts check out today's Tutorial 2 and W2D4.*\n",
- "\n",
- "Not every variable in IAMs is endogenous (i.e. determined by other variables in the model). Some variables, like population or technology growth, are exogenous (i.e. variables whose time course is given to the model). In this case, the time course of, e.g., population and economic growth, are derived from simple growth models. These exogenous variables are assumed to be under our society's control (e.g. via mitigation).\n",
- "\n",
- "To understand how our plotted scenarios compare with all other scenarios, we first have a look at the underlying policy assumptions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.2 Shared Climate Policy Assumptions (SPAs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "All pathways have common Shared Climate Policy Assumptions (SPAs) like \n",
- "- limits to how fast we can respond based on where we are now,\n",
- "- what kinds of policies can be implemented, and how\n",
- "\n",
- "which constrain the modelers that create scenarios and their narratives.\n",
- "\n",
- "Our example scenarios hence share the above SPAs and vary in their narrative:\n",
- "\n",
- "In the hypothetical world of SSP5, no climate policies or climate impacts exist, which in other words effectively implies that the carbon price is zero (cf. [Ch.3.2.1.1 of IPCC's report on 'Climate Change 2022: Mitigation of Climate Change')](https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter03.pdf).\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.3: Similarities of SSP1 and SSP5\n",
- "\n",
- "When you compare the two scenarios, in particular, the evolution of the population and the GDP shows how similar these scenarios are in their optimistic view on the development of humanity. We all learn to get along, both within and across countries and more equal development naturally stems from population growth through well-known mechanisms like access to conception. The following figure emphasizes this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['Population', 'GDP|PPP']\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ['Population\\n(millions)', 'GDP (billion US$/yr)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Both GDP and population increase."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1.3: Differences of SSP1 and SSP5\n",
- "\n",
- "Major differences are visible when you contrast emissions and assume direct causation with ecosystem health. Increasing emissions then translate into decreasing ecosystem health."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Coding exercise 1.3\n",
- "\n",
- "1. Choose two variables to emphasize ecosystem health differences in the SSP1 and SSP5 scenarios and assign them to `vars`. Then assign axis labels with the correct units for plotting to the `val_name` variable.\n",
- "2. Explain to your pod why the chosen variables emphasize a difference in the scenarios and describe this difference based on your current knowledge of the narratives."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "```python\n",
- "# put two variables of interest in a list\n",
- "vars = ...\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ...\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "#################################################\n",
- "## TODO for students:\n",
- "## Put two variables of interest in a list and assign it to 'vars'.\n",
- "## Create new names for the structured data series and axes labels,\n",
- "## put them in a list and assign it to 'val_name'.\n",
- "## Remove the following line of code once you have completed the exercise:\n",
- "raise NotImplementedError(\"Student exercise: Put two variables of interest in a list and assign it to vars. Create new names for the structured data series and axes labels, put them in a list and assign it to val_name.\")\n",
- "#################################################\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove solution\n",
- "\n",
- "# put two variables of interest in a list\n",
- "vars = ['Emissions|Kyoto Gases', 'Land Cover|Forest']\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ['Emissions\\n(Mt CO$_2$/yr)','Land covered by\\nforest (million ha)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# to_remove explanation\n",
- "'''\n",
- "2. We choose 'Emissions' and 'Land Cover|Forest' as variables of interest to contrast ecosystem health between the two scenarios.\n",
- "First, note that the land model components from the IMAGE and REMIND-MAGPIE estimate different initial land cover areas, if we assume however that their trend is reasonable, we can conclude the following:\n",
- "For high emissions, forcing rises and hence temperature, we know that precipitation patterns change with higher temperatures such that forests,\n",
- "here our indicator for ecosystem health, are dying.\n",
- "In turn the land cover by forests is decreasing in the SSP5 scenario.\n",
- "In contrast, the SSP1 reduces emissions and also exploits resources like wood only sustainably due to afforestation.\n",
- "As afforestation is also used to capture carbon, while land use is reduced in general,\n",
- "the scenario shows an increase in land cover by forests within the current century.\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "In this tutorial, you've gained a first understanding of the Shared Socioeconomic Pathways and their creation, the application of Integrated Assessment Models in climate economics. You've learned how SSPs share policy assumptions. Furthermore, you compared SSP1 and SSP5 with respect to their view on the development of humanity and their ecosystem health.\n",
- "\n",
- "In the next tutorial, you dissect and analyze the SSP narratives in more detail."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "It is possible to download the SSP data used in this tutorial, when you provide an email address, from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available.\n",
- "\n",
- "Find a summary of all SSP narratives in this paper by [Oneill et al. (2017)](https://doi.org/10.1016/j.gloenvcha.2015.01.004).\n",
- "\n",
- "Find even more information in\n",
- "\n",
- "- [IIASA's introduction to SSPs](https://iiasa.ac.at/models-tools-data/ssp)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial4",
- "provenance": [],
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb
deleted file mode 100644
index 9b234f6e5..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial5.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3d4298c9-a204-48a4-b408-90d0403d4950",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c55c3b25-2613-4f34-8d4a-ccc417f24708",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 5: Mapping the Narrative Space\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c8af3c8-42a4-45ba-a16f-be86198e23e8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Tutorial objectives\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1af3e766-b9c3-4bf8-815c-4087e7811925",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "#import dicelib # https://github.com/mptouzel/PyDICE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7392de8-f946-453b-bedf-8a07c705ee7f",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c10210c-a3d9-4996-a3d3-4bafa4b72637",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "80412217-b5a7-4fed-b29e-b8751eb8c847",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Mapping the Narrative Space\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '9ytkpO_pbWE')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2dc2f6c1-637b-4506-a667-45750dd7fbe5",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"cyavb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c7ce6e92-a46d-437a-9e03-c5b80eb63474",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Background on IAM Economics\n",
- "\n",
- "The last tutorial gave us a glimpse of Integrated Assessment Models (IAMs), a class of models economists use to inform policy decisions. Recall that IAMs couple a climate model to an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. \n",
- "\n",
- "Let's begin with a brief description of IAMs:\n",
- "\n",
- "- IAMs resolve the spatially, in contrast, the toy model En-ROADs for example, which we applied in Tutorial 1 to 3, aggregates all variables and is non-spatial.\n",
- "- Like En-ROADS, the world models used in IAMs usually have *exogeneous* (externally set) times series for variables, in addition to fixed world system parameters. These exogenous variables are assumed to be under our society's control (e.g. mitigation). \n",
- "- IAMs come equipped with an objective function (a formula that calculates the quantity to be optimized). This function returns the value of a projected future obtained from running the world model under a given climate policy. This value is defined by the time series of these exogenous variables. In this sense, the objective function is what defines \"good\" in \"good climate policy\". \n",
- "- The computation in an IAM is then an optimization of this objective as a function of the time series of these exogenous variables over some fixed time window.\n",
- "\n",
- "In En-ROADS, there are exogenous parameters, in particular:\n",
- " - **$\\mu(t)$**: time-dependent mitigation rate (i.e. emissions reduction), which limits warming-caused damages\n",
- " - **$S(t)$**: savings rate, which drives capital investment \n",
- "\n",
- "Most IAMs are based on *Neo-classical economics* (also referred to as \"establishment economics\"). This is an approach to economics that makes particular assumptions. For example, it is assumed that production, consumption, and valuation of goods and services are driven solely by the supply and demand model. To understand this approach and how it is used, it is important to begin with a brief overview of some fundamental concepts. One such concept is **utility** (i.e. economic value), which is not only central to economics but also to decision theory as a whole, which is a research field that mathematically formalizes the activity of *planning* (planning here means selecting strategies based on how they are expected to play out given a model that takes those strategies and projects forward into the future)."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "28c0a077-c9a0-42d9-ada6-e8b835668c3b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9e904e57-b6b9-4ee8-aefb-140dce3c93c2",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial5",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb
deleted file mode 100644
index 944aeb1a3..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/W2D3_Tutorial6.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "a4589a1d-0f80-45e7-aec3-3513172e617b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d93b742-06f3-4a0b-9a1d-043397878e48",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Bonus Tutorial 6: Create your Socio-economic Scenario\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b5c4ba1-fad0-4d38-b3b2-875a67549624",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 min\n",
- "\n",
- "In this tutorial, you will model your own simplified Socioeconomic Pathway, a scenario of actions to limit global warming below 2°C. You take action against climate change within the socioeconomic model environment of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/). To examine its characteristics you address its challenges, winners/losers, political feasibility, narrative, and current trends. \n",
- "\n",
- "You learn how to\n",
- "\n",
- "- apply the knowledge you gained in last tutorials to design actions against climate change\n",
- "- map your scenario narrative in the SSP \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b591486d-dff5-4dc6-b83b-1125977a7758",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1:\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '_Y_eLlExwxI'),\n",
- " #TODO('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "616428a4-3db2-4e56-af30-6c56aaf66204",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"gwr8h\" #TODO\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a516e969-8b67-4f8a-9d39-061770ffb4c9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Create your Solution Scenario and Evaluate it\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders and changing the assumptions, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- " 1. **Your Plan**: What are the top 3-5 most important policies in your strategy? (For example, the most important sliders that you moved). Share your scenario link and a screenshot of your final En-ROADS dashboard.\n",
- " \n",
- " *If you want to save your scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the panel, and select 'Copy Scenario Link'*.\n",
- " \n",
- " 2. **Political Feasibility**: To implement your proposals, what actions and priorities are needed over the next two years in businesses, civil society, government, and the public?\n",
- " 3. **Winners & Losers**: Who would be the biggest winners and losers globally in your proposed future? Create a table with two columns for winners and losers. \n",
- " 4. **Narrative**: Map your ideas in the narrative space, introduced in Tutorial 5. \n",
- " 5. **Hope**: What trends in the world give you hope that your proposals are possible?\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fadd5652-5db9-475e-a072-f204358ade8a",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this last tutorial, you went from model to action by designing your own socioeconomic modeling scenario to limit global warming below 2°C by the end of the century. You reflected on its challenges, political feasibility, inherent winners and losers of your approach, and finally, the narrative that you chose. At the end of the day, you collected observations of actions and trends you are making in your region, globally, and in your daily life that give hope. That hope and various solutions exist, which one has to combine, and that only action is needed, might be the most important lesson of today.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a6aaa44b-0fc2-4249-91c4-589aac662188",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)\n",
- "\n",
- "**A Planetary Crisis Planning Computer Game**\n",
- "- [Half Earth Socialism](https://play.half.earth/).\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial6",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/dicelib.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/dicelib.py
deleted file mode 100644
index bbc544141..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/dicelib.py
+++ /dev/null
@@ -1,538 +0,0 @@
-import seaborn as sns
-import matplotlib.pyplot as pl
-import pandas as pd
-import numpy as np
-import scipy.optimize as opt
-from matplotlib.ticker import EngFormatter
-
-
-class DICE():
-
- def __init__(self):
- self.time_step = 5 # Years per Period
- # Set
- self.min_year = 2000
- self.max_year = 2500
- self.TT = np.linspace(
- self.min_year, self.max_year, 100, dtype=np.int32)
- self.NT = len(self.TT)
- self.t = np.arange(1, self.NT+1)
-
- def init_parameters(self, a3=2.00, prstp=0.015, elasmu=1.45):
-
- # Maximum cumulative extraction fossil fuels (GtC); denoted by CCum
- self.fosslim = 6000
- self.ifopt = 0 # Indicator where optimized is 1 and base is 0
- self.elasmu = elasmu # Elasticity of marginal utility of consumption
- self.prstp = prstp # Initial rate of social time preference per year
-
- self.init_pop_and_tech_parameters()
- self.init_emissions_parameters()
- self.init_carboncycle_parameters()
- self.init_climatemodel_parameters()
- self.init_climatedamage_parameters(a3)
- self.init_abatementcost_parameters()
-
- # ** Scaling and inessential parameters
- # * Note that these are unnecessary for the calculations
- # * They ensure that MU of first period's consumption =1 and PV cons = PV utilty
- # Multiplicative scaling coefficient /0.0302455265681763 /
- self.scale1 = 0.0302455265681763
- self.scale2 = -10993.704 # Additive scaling coefficient /-10993.704/;
-
- # * carbon cycling coupling matrix
- self.b11 = 1 - self.b12
- self.b21 = self.b12*self.mateq/self.mueq
- self.b22 = 1 - self.b21 - self.b23
- self.b32 = self.b23*self.mueq/self.mleq
- self.b33 = 1 - self.b32
-
- # * Further definitions of parameters
- self.a20 = self.a2
- self.sig0 = self.e0/(self.q0*(1-self.miu0)) # From Eq. 14
- self.lam = self.fco22x / self.t2xco2 # From Eq. 25
-
- self.init_exogeneous_inputs()
-
- def init_pop_and_tech_parameters(self, gama=0.300, pop0=7403, popadj=0.134,
- popasym=11500, dk=0.100, q0=105.5,
- k0=223, a0=5.115, ga0=0.076, dela=0.005):
- self.gama = gama # Capital elasticity in production function /.300 /
- # Initial world population 2015 (millions) /7403 /
- self.pop0 = pop0
- self.popadj = popadj # Growth rate to calibrate to 2050 pop projection /0.134/
- # Asymptotic population (millions) /11500/
- self.popasym = popasym
- # Depreciation rate on capital (per year) /.100 /
- self.dk = dk
- # Initial world gross output 2015 (trill 2010 USD) /105.5/
- self.q0 = q0
- # Initial capital value 2015 (trill 2010 USD) /223 /
- self.k0 = k0
- self.a0 = a0 # Initial level of total factor productivity /5.115/
- self.ga0 = ga0 # Initial growth rate for TFP per 5 years /0.076/
- self.dela = dela # Decline rate of TFP per 5 years /0.005/
-
- # ** Emissions parameters
- def init_emissions_parameters(self, gsigma1=-0.0152, dsig=-0.001, eland0=2.6,
- deland=0.115, e0=35.85, miu0=0.03):
- # Initial growth of sigma (per year) /-0.0152/
- self.gsigma1 = gsigma1
- # Decline rate of decarbonization (per period) /-0.001 /
- self.dsig = dsig
- # Carbon emissions from land 2015 (GtCO2 per year) / 2.6 /
- self.eland0 = eland0
- # Decline rate of land emissions (per period) / .115 /
- self.deland = deland
- # Industrial emissions 2015 (GtCO2 per year) /35.85 /
- self.e0 = e0
- self.miu0 = miu0 # Initial emissions control rate for base case 2015 /.03 /
-
- # ** Carbon cycle
- def init_carboncycle_parameters(self, mat0=851, mu0=460, ml0=1740, mateq=588, mueq=360, mleq=1720):
- # * Initial Conditions
- # Initial Concentration in atmosphere 2015 (GtC) /851 /
- self.mat0 = mat0
- # Initial Concentration in upper strata 2015 (GtC) /460 /
- self.mu0 = mu0
- # Initial Concentration in lower strata 2015 (GtC) /1740 /
- self.ml0 = ml0
- # mateq Equilibrium concentration atmosphere (GtC) /588 /
- self.mateq = mateq
- # mueq Equilibrium concentration in upper strata (GtC) /360 /
- self.mueq = mueq
- # mleq Equilibrium concentration in lower strata (GtC) /1720 /
- self.mleq = mleq
-
- # * Flow paramaters, denoted by Phi_ij in the model
- self.b12 = 0.12 # Carbon cycle transition matrix /.12 /
- self.b23 = 0.007 # Carbon cycle transition matrix /0.007/
- # * These are for declaration and are defined later
- self.b11 = None # Carbon cycle transition matrix
- self.b21 = None # Carbon cycle transition matrix
- self.b22 = None # Carbon cycle transition matrix
- self.b32 = None # Carbon cycle transition matrix
- self.b33 = None # Carbon cycle transition matrix
- # Carbon intensity 2010 (kgCO2 per output 2005 USD 2010)
- self.sig0 = None
-
- # ** Climate model parameters
- def init_climatemodel_parameters(self):
- # Equilibrium temp impact (oC per doubling CO2) / 3.1 /
- self.t2xco2 = 3.1
- # 2015 forcings of non-CO2 GHG (Wm-2) / 0.5 /
- self.fex0 = 0.5
- # 2100 forcings of non-CO2 GHG (Wm-2) / 1.0 /
- self.fex1 = 1.0
- # Initial lower stratum temp change (C from 1900) /.0068/
- self.tocean0 = 0.0068
- # Initial atmospheric temp change (C from 1900) /0.85/
- self.tatm0 = 0.85
- self.c1 = 0.1005 # Climate equation coefficient for upper level /0.1005/
- self.c3 = 0.088 # Transfer coefficient upper to lower stratum /0.088/
- self.c4 = 0.025 # Transfer coefficient for lower level /0.025/
- # eta in the model; Eq.22 : Forcings of equilibrium CO2 doubling (Wm-2) /3.6813 /
- self.fco22x = 3.6813
-
- def init_climatedamage_parameters(self, a3=2.00):
- # ** Climate damage parameters
- self.a10 = 0 # Initial damage intercept /0 /
- self.a20 = None # Initial damage quadratic term
- self.a1 = 0 # Damage intercept /0 /
- self.a2 = 0.00236 # Damage quadratic term /0.00236/
- self.a3 = a3 # Damage exponent /2.00 /
-
- def init_abatementcost_parameters(self):
- # ** Abatement cost
- # Theta2 in the model, Eq. 10 Exponent of control cost function / 2.6 /
- self.expcost2 = 2.6
- self.pback = 550 # Cost of backstop 2010$ per tCO2 2015 / 550 /
- self.gback = 0.025 # Initial cost decline backstop cost per period / .025/
- self.limmiu = 1.2 # Upper limit on control rate after 2150 / 1.2 /
- self.tnopol = 45 # Period before which no emissions controls base / 45 /
- # Initial base carbon price (2010$ per tCO2) / 2 /
- self.cprice0 = 2
- self.gcprice = 0.02 # Growth rate of base carbon price per year /.02
-
- def init_exogeneous_inputs(self):
- NT = self.NT
-
- self.l = np.zeros(NT)
- self.l[0] = self.pop0 # Labor force
- self.al = np.zeros(NT)
- self.al[0] = self.a0
- self.gsig = np.zeros(NT)
- self.gsig[0] = self.gsigma1
- self.sigma = np.zeros(NT)
- self.sigma[0] = self.sig0
- # TFP growth rate dynamics, Eq. 7
- self.ga = self.ga0 * np.exp(-self.dela*5*(self.t-1))
- self.pbacktime = self.pback * \
- (1-self.gback)**(self.t-1) # Backstop price
- # Emissions from deforestration
- self.etree = self.eland0*(1-self.deland)**(self.t-1)
- self.rr = 1/((1+self.prstp)**(self.time_step*(self.t-1))) # Eq. 3
- # The following three equations define the exogenous radiative forcing; used in Eq. 23
- self.forcoth = np.full(NT, self.fex0)
- self.forcoth[0:18] = self.forcoth[0:18] + \
- (1/17)*(self.fex1-self.fex0)*(self.t[0:18]-1)
- self.forcoth[18:NT] = self.forcoth[18:NT] + (self.fex1-self.fex0)
- # Optimal long-run savings rate used for transversality (Question)
- self.optlrsav = (self.dk + .004)/(self.dk + .004 *
- self.elasmu + self.prstp)*self.gama
- self.cost1 = np.zeros(NT)
- self.cumetree = np.zeros(NT)
- self.cumetree[0] = 100
- self.cpricebase = self.cprice0*(1+self.gcprice)**(5*(self.t-1))
-
- def InitializeLabor(self, il, iNT):
- for i in range(1, iNT):
- il[i] = il[i-1]*(self.popasym / il[i-1])**self.popadj
-
- def InitializeTFP(self, ial, iNT):
- for i in range(1, iNT):
- ial[i] = ial[i-1]/(1-self.ga[i-1])
-
- def InitializeGrowthSigma(self, igsig, iNT):
- for i in range(1, iNT):
- igsig[i] = igsig[i-1]*((1+self.dsig)**self.time_step)
-
- def InitializeSigma(self, isigma, igsig, icost1, iNT):
- for i in range(1, iNT):
- isigma[i] = isigma[i-1] * np.exp(igsig[i-1] * self.time_step)
- icost1[i] = self.pbacktime[i] * isigma[i] / self.expcost2 / 1000
-
- def InitializeCarbonTree(self, icumetree, iNT):
- for i in range(1, iNT):
- icumetree[i] = icumetree[i-1] + self.etree[i-1]*(5/3.666)
-
- """
- Emissions of carbon and weather damages
- """
-
- # Retuns the total carbon emissions; Eq. 18
- def fE(self, iEIND, index):
- return iEIND[index] + self.etree[index]
-
- # Eq.14: Determines the emission of carbon by industry EIND
- def fEIND(self, iYGROSS, iMIU, isigma, index):
- return isigma[index] * iYGROSS[index] * (1 - iMIU[index])
-
- # Cumulative industrial emission of carbon
- def fCCA(self, iCCA, iEIND, index):
- return iCCA[index-1] + iEIND[index-1] * 5 / 3.666
-
- # Cumulative total carbon emission
- def fCCATOT(self, iCCA, icumetree, index):
- return iCCA[index] + icumetree[index]
-
- # Eq. 22: the dynamics of the radiative forcing
- def fFORC(self, iMAT, index):
- return self.fco22x * np.log(iMAT[index]/588.000)/np.log(2) + self.forcoth[index]
-
- # Dynamics of Omega; Eq.9
- def fDAMFRAC(self, iTATM, index):
- return self.a1*iTATM[index] + self.a2*iTATM[index]**self.a3
-
- # Calculate damages as a function of Gross industrial production; Eq.8
- def fDAMAGES(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * iDAMFRAC[index]
-
- # Dynamics of Lambda; Eq. 10 - cost of the reudction of carbon emission (Abatement cost)
- def fABATECOST(self, iYGROSS, iMIU, icost1, index):
- return iYGROSS[index] * icost1[index] * iMIU[index]**self.expcost2
-
- # Marginal Abatement cost
- def fMCABATE(self, iMIU, index):
- return self.pbacktime[index] * iMIU[index]**(self.expcost2-1)
-
- # Price of carbon reduction
- def fCPRICE(self, iMIU, index):
- return self.pbacktime[index] * (iMIU[index])**(self.expcost2-1)
-
- # Eq. 19: Dynamics of the carbon concentration in the atmosphere
- def fMAT(self, iMAT, iMU, iE, index):
- if (index == 0):
- return self.mat0
- else:
- return iMAT[index-1]*self.b11 + iMU[index-1]*self.b21 + iE[index-1] * 5 / 3.666
-
- # Eq. 21: Dynamics of the carbon concentration in the ocean LOW level
- def fML(self, iML, iMU, index):
- if (index == 0):
- return self.ml0
- else:
- return iML[index-1] * self.b33 + iMU[index-1] * self.b23
-
- # Eq. 20: Dynamics of the carbon concentration in the ocean UP level
- def fMU(self, iMAT, iMU, iML, index):
- if (index == 0):
- return self.mu0
- else:
- return iMAT[index-1]*self.b12 + iMU[index-1]*self.b22 + iML[index-1]*self.b32
-
- # Eq. 23: Dynamics of the atmospheric temperature
- def fTATM(self, iTATM, iFORC, iTOCEAN, index):
- if (index == 0):
- return self.tatm0
- else:
- return iTATM[index-1] + self.c1 * (iFORC[index] - (self.fco22x/self.t2xco2) * iTATM[index-1] - self.c3 * (iTATM[index-1] - iTOCEAN[index-1]))
-
- # Eq. 24: Dynamics of the ocean temperature
- def fTOCEAN(self, iTATM, iTOCEAN, index):
- if (index == 0):
- return self.tocean0
- else:
- return iTOCEAN[index-1] + self.c4 * (iTATM[index-1] - iTOCEAN[index-1])
-
- """
- economic variables
- """
-
- # The total production without climate losses denoted previously by YGROSS
- def fYGROSS(self, ial, il, iK, index):
- return ial[index] * ((il[index]/1000)**(1-self.gama)) * iK[index]**self.gama
-
- # The production under the climate damages cost
- def fYNET(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * (1 - iDAMFRAC[index])
-
- # Production after abatement cost
- def fY(self, iYNET, iABATECOST, index):
- return iYNET[index] - iABATECOST[index]
-
- # Consumption Eq. 11
- def fC(self, iY, iI, index):
- return iY[index] - iI[index]
-
- # Per capita consumption, Eq. 12
- def fCPC(self, iC, il, index):
- return 1000 * iC[index] / il[index]
-
- # Saving policy: investment
- def fI(self, iS, iY, index):
- return iS[index] * iY[index]
-
- # Capital dynamics Eq. 13
- def fK(self, iK, iI, index):
- if (index == 0):
- return self.k0
- else:
- return (1-self.dk)**self.time_step * iK[index-1] + self.time_step * iI[index-1]
-
- # Interest rate equation; Eq. 26 added in personal notes
- def fRI(self, iCPC, index):
- return (1 + self.prstp) * (iCPC[index+1]/iCPC[index])**(self.elasmu/self.time_step) - 1
-
- # Periodic utility: A form of Eq. 2
- def fCEMUTOTPER(self, iPERIODU, il, index):
- return iPERIODU[index] * il[index] * self.rr[index]
-
- # The term between brackets in Eq. 2
- def fPERIODU(self, iC, il, index):
- return ((iC[index]*1000/il[index])**(1-self.elasmu) - 1) / (1 - self.elasmu) - 1
-
- # utility function
- def fUTILITY(self, iCEMUTOTPER, resUtility):
- resUtility[0] = self.time_step * self.scale1 * \
- np.sum(iCEMUTOTPER) + self.scale2
-
- def init_variables(self): # TODO: add full variable names as comments
- NT = self.NT
- self.K = np.zeros(NT)
- self.YGROSS = np.zeros(NT)
- self.EIND = np.zeros(NT)
- self.E = np.zeros(NT)
- self.CCA = np.zeros(NT)
- self.CCATOT = np.zeros(NT)
- self.MAT = np.zeros(NT)
- self.ML = np.zeros(NT)
- self.MU = np.zeros(NT)
- self.FORC = np.zeros(NT)
- self.TATM = np.zeros(NT)
- self.TOCEAN = np.zeros(NT)
- self.DAMFRAC = np.zeros(NT)
- self.DAMAGES = np.zeros(NT)
- self.ABATECOST = np.zeros(NT)
- self.MCABATE = np.zeros(NT)
- self.CPRICE = np.zeros(NT)
- self.YNET = np.zeros(NT)
- self.Y = np.zeros(NT)
- self.I = np.zeros(NT)
- self.C = np.zeros(NT)
- self.CPC = np.zeros(NT)
- self.RI = np.zeros(NT)
- self.PERIODU = np.zeros(NT)
- self.CEMUTOTPER = np.zeros(NT)
-
- self.optimal_controls = np.zeros(2*NT)
-
- self.InitializeLabor(self.l, NT)
- self.InitializeTFP(self.al, NT)
- self.InitializeGrowthSigma(self.gsig, NT)
- self.InitializeSigma(self.sigma, self.gsig, self.cost1, NT)
- self.InitializeCarbonTree(self.cumetree, NT)
-
- def get_control_bounds_and_startvalue(self):
-
- NT = self.NT
- # * Control variable limits
- MIU_lo = np.full(NT, 0.01)
- MIU_up = np.full(NT, self.limmiu)
- MIU_up[0:29] = 1
- MIU_lo[0] = self.miu0
- MIU_up[0] = self.miu0
- MIU_lo[MIU_lo == MIU_up] = 0.99999*MIU_lo[MIU_lo == MIU_up]
- bnds1 = []
- for i in range(NT):
- bnds1.append((MIU_lo[i], MIU_up[i]))
-
- lag10 = np.arange(1, NT+1) > NT - 10
- S_lo = np.full(NT, 1e-1)
- S_lo[lag10] = self.optlrsav
- S_up = np.full(NT, 0.9)
- S_up[lag10] = self.optlrsav
- S_lo[S_lo == S_up] = 0.99999*S_lo[S_lo == S_up]
- bnds2 = []
- for i in range(NT):
- bnds2.append((S_lo[i], S_up[i]))
- bnds = bnds1 + bnds2
-
- # starting values for the control variables:
- S_start = np.full(NT, 0.2)
- S_start[S_start < S_lo] = S_lo[S_start < S_lo]
- S_start[S_start > S_up] = S_lo[S_start > S_up]
- MIU_start = 0.99*MIU_up
- MIU_start[MIU_start < MIU_lo] = MIU_lo[MIU_start < MIU_lo]
- MIU_start[MIU_start > MIU_up] = MIU_up[MIU_start > MIU_up]
- x_start = np.concatenate([MIU_start, S_start])
-
- return x_start, bnds
-
- def fOBJ(self, controls):
- self.roll_out(controls)
- resUtility = np.zeros(1)
- self.fUTILITY(self.CEMUTOTPER, resUtility)
-
- return -1*resUtility[0]
-
- def roll_out(self, controls):
- NT = self.NT
-
- iMIU = controls[0:NT]
- iS = controls[NT:(2*NT)]
-
- for i in range(NT):
- self.K[i] = self.fK(self.K, self.I, i)
- self.YGROSS[i] = self.fYGROSS(self.al, self.l, self.K, i)
- self.EIND[i] = self.fEIND(self.YGROSS, iMIU, self.sigma, i)
- self.E[i] = self.fE(self.EIND, i)
- self.CCA[i] = self.fCCA(self.CCA, self.EIND, i)
- self.CCATOT[i] = self.fCCATOT(self.CCA, self.cumetree, i)
- self.MAT[i] = self.fMAT(self.MAT, self.MU, self.E, i)
- self.ML[i] = self.fML(self.ML, self.MU, i)
- self.MU[i] = self.fMU(self.MAT, self.MU, self.ML, i)
- self.FORC[i] = self.fFORC(self.MAT, i)
- self.TATM[i] = self.fTATM(self.TATM, self.FORC, self.TOCEAN, i)
- self.TOCEAN[i] = self.fTOCEAN(self.TATM, self.TOCEAN, i)
- self.DAMFRAC[i] = self.fDAMFRAC(self.TATM, i)
- self.DAMAGES[i] = self.fDAMAGES(self.YGROSS, self.DAMFRAC, i)
- self.ABATECOST[i] = self.fABATECOST(
- self.YGROSS, iMIU, self.cost1, i)
- self.MCABATE[i] = self.fMCABATE(iMIU, i)
- self.CPRICE[i] = self.fCPRICE(iMIU, i)
- self.YNET[i] = self.fYNET(self.YGROSS, self.DAMFRAC, i)
- self.Y[i] = self.fY(self.YNET, self.ABATECOST, i)
- self.I[i] = self.fI(iS, self.Y, i)
- self.C[i] = self.fC(self.Y, self.I, i)
- self.CPC[i] = self.fCPC(self.C, self.l, i)
- self.PERIODU[i] = self.fPERIODU(self.C, self.l, i)
- self.CEMUTOTPER[i] = self.fCEMUTOTPER(self.PERIODU, self.l, i)
-# self.RI[i] = self.fRI(self.CPC, i)
-
- def optimize_controls(self, controls_start, controls_bounds):
- result = opt.minimize(self.fOBJ, controls_start, method='SLSQP', bounds=tuple(
- controls_bounds), options={'disp': True})
- self.optimal_controls = result.x
- return result
-
- def plot_run(self, title_str):
- Tmax = 2150
- NT = self.NT
- variables = [self.optimal_controls[NT:(2*NT)], self.optimal_controls[0:NT], self.CPRICE, self.EIND, self.TATM, self.DAMAGES, self.MAT,
- self.E]
- variables = [var[self.TT < Tmax] for var in variables]
- variable_labels = ["Saving rate",
- "Em rate", # 'Carbon emission control rate'
- "carbon price",
- "INdustrial emissions",
- # Increase temperature of the atmosphere (TATM)
- "Degrees C from 1900",
- "Damages", # 'trillions 2010 USD per year'
- "GtC from 1750", # 'Carbon concentration increase in the atmosphere'
- "GtCO2 per year" # Total CO2 emission
- ]
- variable_limits = [[0, 0.5], [0, 1], [0, 400], [-20, 40],
- [0, 5], [0, 150], [0, 1500], [-20, 50]] # y axis ranges
- plot_world_variables(self.TT[self.TT < Tmax], variables, variable_labels, variable_limits,
- title=title_str,figsize=[4+len(variables), 7],
- grid=True)
-
-
-def plot_world_variables(time, var_data, var_names, var_lims,
- title=None,
- figsize=None,
- dist_spines=0.09,
- grid=False):
- prop_cycle = pl.rcParams['axes.prop_cycle']
- colors = prop_cycle.by_key()['color']
-
- var_number = len(var_data)
-
- fig, host = pl.subplots(figsize=figsize)
- axs = [host, ]
- for i in range(var_number-1):
- axs.append(host.twinx())
-
- fig.subplots_adjust(left=dist_spines*2)
- for i, ax in enumerate(axs[1:]):
- ax.spines["left"].set_position(("axes", -(i + 1)*dist_spines))
- ax.spines["left"].set_visible(True)
- ax.yaxis.set_label_position('left')
- ax.yaxis.set_ticks_position('left')
-
- ps = []
- for ax, label, ydata, color in zip(axs, var_names, var_data, colors):
- ps.append(ax.plot(time, ydata, label=label,
- color=color, clip_on=False)[0])
- axs[0].grid(grid)
- axs[0].set_xlim(time[0], time[-1])
-
- for ax, lim in zip(axs, var_lims):
- ax.set_ylim(lim[0], lim[1])
-
- for axit, ax_ in enumerate(axs):
- ax_.tick_params(axis='y', rotation=90)
- ax_.yaxis.set_major_locator(pl.MaxNLocator(5))
- formatter_ = EngFormatter(places=0, sep="\N{THIN SPACE}")
- ax_.yaxis.set_major_formatter(formatter_)
-
- tkw = dict(size=4, width=1.5)
- axs[0].set_xlabel("time [years]")
- axs[0].tick_params(axis='x', **tkw)
- for i, (ax, p) in enumerate(zip(axs, ps)):
- ax.set_ylabel(p.get_label(), rotation=25)
- ax.yaxis.label.set_color(p.get_color())
- ax.tick_params(axis='y', colors=p.get_color(), **tkw)
- ax.yaxis.set_label_coords(-i*dist_spines, 1.01)
- axs[0].set_title(title)
-
-
-def hello_world():
- dice = DICE()
- dice.init_parameters()
- dice.init_variables()
- controls_start, controls_bounds = dice.get_control_bounds_and_startvalue()
- dice.optimize_controls(controls_start, controls_bounds)
- dice.roll_out(dice.optimal_controls)
- dice.plot_run()
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/further_reading.md b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/further_reading.md
deleted file mode 100644
index cc972adcd..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/instructor/further_reading.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# **Tutorial 2 Further Reading**
-
-## **IAM Model Summary**
-The economy model in most IAMs is a capital accumulation model.
-- Capital combines with a laboring population and technology to generate productivity ($Y$) that is hindered by climate damage.
-- A savings fraction of production drives capital accumulation, while the rest is consumed. Welfare is determined by consumption.
-- Climate action is formulated by a mitigation rate, $\mu$, which along with the savings rate are the two control parameters in the model.
-- These are used to maximize welfare.
-
-The climate model in DICE could be improved (c.f. [this study](https://www3.nd.edu/~nmark/Climate/DICE-simplified_2019.pdf)). We only summarize here how it interacts with the economy model:
-- Productivity generates industrial emissions, $$E_\mathrm{ind}=(1-\mu)\sigma Y,$$ where the $1-\mu$ factor accounts for reduced carbon intensity of production, $\sigma$, via supply-side mitigation measures (e.g. increased efficiency).
-- The productivity $Y$ rather than output production ($Q$) see model is used here because damages aren't included.
-- Namely, the emissions produced in the process of capital production occur before climate change has a chance to inflict damage on the produced output.
-- These emissions combine with natural emissions to drive the temperature changes appearing in the damage function, closing the economy-climate loop.
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Exogeneous Control Variables**
-There are two exogeneous variables in the model:
-- mitigation (emissions reduction) rate $\mu_t$, and
-- the savings rate $S_t$.
-
-There exists a mitigation cost associated with a specific mitigation rate, presented as a fraction of productivity, $\Lambda_t=\theta_1\mu^{\theta_2}$. This implies that increased mitigation efforts correspond to elevated costs.
-
-The savings rate $S_t$ extracts a portion of the total production to invest, thereby boosting capital. The rest of the production is then allocated for consumption.
-
-## **Economy Model Summary**
-The essence of the economy model in DICE is a capital accumulation model. Existing capital depreciates at rate $\delta$ and new capital arises through investment,
-$$K_{t+1}=(1-\delta)K_t+I_t$$
-where the invested capital $I=SQ$ is determined by a chosen fraction $S$ of the production $Q$ that is "saved" rather than consumed. Production is given as the productivity $Y$ reduced by damages and mitigation cost, $$Q=(1-\Omega)(1-\Lambda)Y.$$ Productivity, $$Y=A K^\gamma L^{1-\gamma},$$
-is determined by the technology conversion $A$ operating on a combination of capital $K$ and labor $L$ whose relative contributions are set by the capital elasticity parameter $\gamma$. Labor is population which set to saturate over the 2nd half of the 21st century. Technology conversion is only weakly sigmoidal in time, deviating slightly from linear growth.
-
-The remaining production is consumed $$C:=(1-S)Q$$ producing utility $$U(C,L)=Lu(c)=Lu(C/L),$$ using the isoelastic utility function, $$u(c)=\frac{(c+1)^{1-\alpha}-1}{1-\alpha}.$$ The overall value of a projected future is then $$V=\sum_{t=1}^{\infty}\gamma^t U(C_t,L_t),$$ where $\gamma=1/(1+\rho)$ for discount rate $\rho$ and we use the population level utility function $U(C,L)=Lu(C/L)$.
-
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Optimal Planning**
-The unconstrained problem aims to maximize $V$ within the bounds of $\mu_t$ and $S_t$ time courses (while considering constraints on $\mu$ and $S$). Why is there a "sweet spot"? Increasing savings boosts investment and productivity, but higher production leads to emissions, resulting in increased temperature and damages that reduce production. Mitigation costs counterbalance this effect, creating a trade-off. As a result, there typically exists a meaningful joint time series of $\mu_t$ and $S_t$ that maximizes $V$. Due to the discount factor $\gamma$, $V$ depends on the future consumption sequence (non-invested production) within a few multiples of the horizon, approximately $1/(1-\gamma)$ time steps into the future.
-
-Constrained formulations introduce an additional constraint to limit industrial emissions below a certain threshold.
-
-## **Social Cost of Carbon**
-A definition for the social cost of carbon (SCC) is
-
-- *the decrease in aggregate consumption in that year that would change the current...value of social welfare by the same amount as a one unit increase in carbon emissions in that year.* ([Newbold, Griffiths, Moore, Wolverton, & Kopits, 2013](https://www.worldscientific.com/doi/abs/10.1142/S2010007813500012)).
-
-The $SCC$ quantifies how much consumption is lost with increased emissions, using changes in welfare to make the connection. In technical terms:
-
-- the marginal value with respect to emissions relative to the marginal value with respect to consumption, $$SCC_t\propto\frac{\partial V/\partial E_t}{\partial V/\partial C_t}=\frac{\partial C_t}{\partial E_t}.$$
-This is usually expressed by multiplying by a proportionality factor of $-1000$ that converts the units to 2010 US dollars per tonne of CO2.
-
-# **Tutorial 4 Further Reading**
-
-## **Vectorization Methods for Creating Word Clouds**
-
-Let's write down what they compute by denoting the index, $d$, over the $D$ documents and the index, $w$, over the $W$ words in the vocabulary (the list of all the words found in all the tweets, which we'll call documents):
-- term frequency, $\mathrm{tf}(w,d)$. The frequency of a word $w$ in a document $d$ is $$\mathrm{tf}(w,d):=\frac{n(w,d)}{n(d)},$$ where $n(w,d)$ is the number of times term $w$ is in document $d$ and $n(d)=\sum_{w=1}^W n(w,d)$ is the total number of words in document $d$. The term frequency over all the documents is then, $$\mathrm{tf}(w):=\frac{\sum_{d=1}^D n(d)\mathrm{tf}(w,d)}{N},$$ where the denominator $N=\sum_{d=1}^D n(d)$ is just the total word count across all documents.
-- term frequency-inverse document frequency, $\mathrm{Tfidf}(w,d):=\mathrm{tf}(w,d)\mathrm{idf}(w)$. Here, $$\mathrm{idf}(w)=\frac{\log(D+1)}{\log(n(w)+1)+1},$$ where $n(w)$ is the number of documents in which term $t$ appears, i.e. $n(w,d)>0$. Idf is like an inverse document frequency. The `sklearn` package then uses $$\mathrm{Tfidf}(w)=\frac{1}{D}\sum_{d=1}^D \frac{\mathrm{Tfidf}(w,d)}{||\mathrm{Tfidf}(\cdot,d)||},$$ where $||\vec{x}||=\sqrt{\sum_{i=1}^Nx^2_i}$ is the Euclidean norm.
-
-$\mathrm{Tfidf}$ aims to add more discriminability to frequency as a word relevance metric by downweighting words that appear in many documents since these common words are less discriminative.
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/requirements.txt b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/requirements.txt
deleted file mode 100644
index 8f92038e4..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-wordcloud
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_b2906775.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_b2906775.py
deleted file mode 100644
index 254d0948d..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_b2906775.py
+++ /dev/null
@@ -1,16 +0,0 @@
-
-'''
-3. One scenario to try based on the goals of the energy transition is the combination of:
-(1) full electrification of the energy supply, i.e. 'very highly taxed' fossil fuel emissions (Coal, Natural gas, and Oil),
-(2) 'highly subsidized' renewables, and
-(3) a 'very high' carbon price,
-as well as the 'highly reduced' emissions of Methane and other Gases
-which gets us to a maximum increase of 2°C by the end of the century
-(cf. this example scenario https://en-roads.climateinteractive.org/scenario.html?v=24.3.0&p1=100&p7=85&p10=5&p16=-0.05&p23=-1&p39=250&p59=-64&p67=2&g0=2&g1=62).
-
-Nevertheless, other scenarios are possible, either by involving more parameters or by focusing on carbon removal.
-There are (at least!) two important observations to make. First, actions vary in leverage, in other words, some actions are more helpful than others.
-Second, to make a difference and reach an ambitious goal like the 2°C degree target, many actions in many sectors are required.
-Sometimes one refers to this circumstance by calling it a 'Silver Buckshot' instead of a 'Silver Bullet' approach (cf. e.g. https://www.washingtonpost.com/archive/opinions/2006/05/27/welcome-to-the-climate-crisis-span-classbankheadhow-to-tell-whether-a-candidate-is-serious-about-combating-global-warmingspan/26b2ac5a-a4a3-46ff-b214-3fc07a3a5ab3/).
-Furthermore, people might be surprised by the fact that some actions may be much lower leverage, while others like carbon pricing and energy efficiency might be higher leverage than people expect.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_bbf54df1.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_bbf54df1.py
deleted file mode 100644
index 6bef8614b..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_bbf54df1.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-'''
-1. At first, En-ROADS is a world model, not an optimal planning model so it is unlike any IAM used e.g. for the IPCC reports.
-Controls are somewhat limited (DAC/CCS, soil, BioChar, and mineralization are not covered).
-It allows only for single parameter changes - in reality, there will be correlations, e.g. between carbon pricing and renewables due to market pressure.
-Some feedbacks are also missing, besides the above-mentioned damage from climate change on GDP, land use, etc.,
-climate change harms the human population by shortening lives.
-Although this happens already in the current 1°C increase reality, this harm is difficult to quantify and hence not implemented.
-Furthermore, it is a fully aggregated model, no spatial/regional or income resolution, and corresponding interdependency exists.
-An action/ policy is assumed to be executed globally, which is a utopia so far.
-It hence remains important to consider the implications of heterogeneity across different countries and their interactions.
-Last but not least, tipping points such as the thawing of the permafrost are represented in a very simplified manner only, although they probably have strong implications.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_1425c24a.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_1425c24a.py
deleted file mode 100644
index 6435f43d1..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_1425c24a.py
+++ /dev/null
@@ -1,13 +0,0 @@
-'''
-1. There are two options. We could change one boring parameter and a more exciting one.
-The former has a strong impact. By turning off the 'Climate change slows economic growth' button, we make the strong assumption that there is less severe damage as asked because there is NO damage at all.
-In other words, we decoupled GDP and temperature increase which results in even more GHG emissions than in the baseline scenario.
-To get a more relevant and exciting result, it is better to change the 'Economic damage formulation' instead.
-
-2. The default formulation by 'Burke 2018' serves as the baseline.
-In order to have a less severe damage formulation we could have a look at the 'Reduction in GDP vs. Temperature' plot.
-However, all other formulations show a more or equal severe damage function than 'Burke 2018' with respect to temperature.
-Another view, i.e. the 'Gross World Product' in contrast shows that the 'Howard & Sterner' formulation results in less economic damage,
-which might have been covered in the 'Reduction in GDP vs. Temperature' graph as the scale was going up to 5°C,
-which we do not reach until 2100 in our simple baseline-like scenario.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_c67833bd.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_c67833bd.py
deleted file mode 100644
index 46db77624..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_c67833bd.py
+++ /dev/null
@@ -1,11 +0,0 @@
-'''
-1. In En-ROADS the population growth has a small effect on the temperature increase by 2100. On the left, the global population graph emphasizes the development until 2100, resulting in an expected population of 8.8 billion in the lowest growth case and an expected population of 12.56 billion in the largest growth case, respectively.
-The former leads to a 3.2°C increase and the latter to a 3.5°C which is only a $\pm$ 0.2°C change due to population growth.
-In contrast, economic growth has a much larger impact: high growth (+30.000 $/person/year in 2100) leads to a temperature increase of 0.4°C by 2100 and low growth (-20.000 $/person/year in 2100) decreases it by 0.3°C, making discussions of overpopulation rather irrelevant as the decisions around family choice are personal decisions and efforts to shift these decisions have many ethical implications.
-It is instead raising the question of the necessity to end economic growth or at least to discuss its current coupling to resource exploitation.
-Note that lower population growth takes a long time to affect emissions because global population shifts do not occur quickly and instead play out over many decades.
-
-2. All possible combinations of pop.-econ. -- low-low: 2.9°C, low-no: 3.2°C, low-high: 3.6°C, no-no: 3.3°C, no-low: 3.0°C, no-high: 3.7°C, high-low: 3.1°C, high-no: 3.5°C, high-high: 4.0°C.
-In terms of an anticipated minimal temperature increase, the best (worst) case is to have a low (high) growth in population and economy. It is better to have a decreasing economy and a growing population than vice versa (increasing economy + shrinking population).
-Having high population growth and economic growth add up to a larger temperature increase (+0.7°C) than their individual contribution (high pop. growth +0.2°C, high econ. growth +0.4°C) which indicates that these variables are not fully independent in the En-ROADS model.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_efff427c.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_efff427c.py
deleted file mode 100644
index 9fcb077f6..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial2_Solution_efff427c.py
+++ /dev/null
@@ -1,3 +0,0 @@
-'''
-Other potential examples are human health, extreme weather events, sea-level rise, desertification, flooding, species migration.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial3_Solution_2aba4d73.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial3_Solution_2aba4d73.py
deleted file mode 100644
index 1cc5f7ad8..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial3_Solution_2aba4d73.py
+++ /dev/null
@@ -1,10 +0,0 @@
-
-'''
-3. The earlier the carbon price starts to phase in the more effective it is regarding the temperature increase.
-However, the earlier the larger the disturbance of the market price of electricity, emphasized by a strong increase in the price at first
-and a strong drop in the price after the first few years.
-This might lead to economic disruptions as energy supply becomes very expensive, therefore low-income households,
-which usually spend large parts of their income on energy, would need compensation like a 'carbon dividend' to avoid precariat.
-In summary, actions and their temporal implementation always need to be evaluated in various aspects to be a successful and
-societal least disruptive action against climate change.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_4240ed7b.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_4240ed7b.py
deleted file mode 100644
index 278c4e8b0..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_4240ed7b.py
+++ /dev/null
@@ -1,10 +0,0 @@
-'''
-2. We choose 'Emissions' and 'Land Cover|Forest' as variables of interest to contrast ecosystem health between the two scenarios.
-First, note that the land model components from the IMAGE and REMIND-MAGPIE estimate different initial land cover areas, if we assume however that their trend is reasonable, we can conclude the following:
-For high emissions, forcing rises and hence temperature, we know that precipitation patterns change with higher temperatures such that forests,
-here our indicator for ecosystem health, are dying.
-In turn the land cover by forests is decreasing in the SSP5 scenario.
-In contrast, the SSP1 reduces emissions and also exploits resources like wood only sustainably due to afforestation.
-As afforestation is also used to capture carbon, while land use is reduced in general,
-the scenario shows an increase in land cover by forests within the current century.
-'''
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py
deleted file mode 100644
index f6f1f4a52..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py
+++ /dev/null
@@ -1,34 +0,0 @@
-
-# put two variables of interest in a list
-vars = ['Emissions|Kyoto Gases', 'Land Cover|Forest']
-# create new names for structured data series and plot labels
-val_name = ['Emissions\n(Mt CO$_2$/yr)','Land covered by\nforest (million ha)']
-# choose scenarios of interest and a color for plotting
-scenarios = ['SSP1-26', 'SSP5-Baseline']
-colors = ['darkblue','darkorange']
-
-
-# init figure and axis
-fig, axs = plt.subplots(2,1)
-# loop over all variables and new names
-for var, val, ax in zip(vars,val_name, axs.flatten()):
-
- # loop over scenarios and their color
- for sc, col in zip(scenarios, colors):
- # retrieve SSP for the respective variable from rich dataframe
- ds_unstrct = get_SSPs_for_variable(df,sc,var)
- # restructure dataframe for plotting
- ds_strct = pd.melt(ds_unstrct, id_vars=["MODEL"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name="YEAR", value_name =val)
- #print(ds_strct)
- # plot variable vs. time, add label incl. scenario and model
- ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\n{ds_strct.MODEL[0]}', color=col)
- # altern. plotting procedure w/o the color distinction
- #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')
-
- # aesthetics
- ax.set_ylabel(fr'{val}')
- ax.set_xlabel('Time (years)')
- plt.setp(ax.get_xticklabels(), rotation=45)
- plt.setp(ax.get_xticklabels()[::2], visible=False)
- ax.grid(True)
- axs[0].legend()
\ No newline at end of file
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png
deleted file mode 100644
index 83b04acb1..000000000
Binary files a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/static/W2D3_Tutorial4_Solution_a4ef74b1_0.png and /dev/null differ
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_DaySummary.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_DaySummary.ipynb
deleted file mode 100644
index b3059d2de..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_DaySummary.ipynb
+++ /dev/null
@@ -1,49 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3b83976b",
- "metadata": {},
- "source": [
- "# Day Summary"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f2b474b6",
- "metadata": {},
- "source": [
- "This day's tutorials focused on the socioeconomic projections regarding the future of the climate emergency. The content centered around the shared socioeconomic pathways framework used by the IPCC and is more knowledge-based than skills-based compared with other days.\n",
- "\n",
- "The day began with a tutorial that summarizes the socio-economic history of growth & limits to physical growth as a parent problem within which the climate emergency arises. You explored the consequences of resource limitations in the computational part of this tutorial through the world3 model.\n",
- "\n",
- "The next tutorial dove into a formulation of the economics of climate change beginning with goal setting and then the main concepts and methods to finding optimal economic plans. You learned about the economic models used in the SSP framework, integrated assessment models (IAMs), and the modeling practice that uses them to make projections. The computational part of the tutorial first walked you through some graphical exercises that illustrate basic economic concepts before running a simple IAM (the RICE model) under various parameter settings.\n",
- "\n",
- "The third tutorial presented the SSP framework in which IAMs are used. You learned how the five SSP narratives were constructed and integrated with physical modeling efforts to define the full set of SSP pathways. You then got a glimpse of what an IAM used in this project consists of and saw the results through important metrics like energy use and greenhouse gas emissions. The computational part of the tutorial took off from a critique of the SSP framework to motivate playing with MARGO, a simple model with more, and more dynamic mitigation controls.\n",
- "\n",
- "Finally, the fourth tutorial on public opinion focussed on climate action psychology and values-based communication. The computational exercise had you analyze public sentiment through analysis of a large dataset of Twitter messages."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Intro.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Intro.ipynb
deleted file mode 100644
index bc9b42b91..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Intro.ipynb
+++ /dev/null
@@ -1,66 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "df1a0a21",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Intro"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6013efcc",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Overview"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dfaee097",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Up to today, you have studied models of our climate system. Today, we will begin to model the (in many ways more complex!) system of society’s economy and how climate impacts them. "
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Intro",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Outro.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Outro.ipynb
deleted file mode 100644
index b707d8c54..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Outro.ipynb
+++ /dev/null
@@ -1,56 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "175cc467",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Outro"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2e5c1afd",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Now that you have a sense for the complexity nestled within Integrated Assessment Modeling and socioeconomic phenomena, you will look at how we try to coarse grain that complexity when applying our understanding of climate to assess extreme events and vulnerability. "
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Outro",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb
deleted file mode 100644
index 3c7745274..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial1.ipynb
+++ /dev/null
@@ -1,440 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "25d9555a-5485-42c3-81cf-4851396a643f",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial1.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1872759b-c9e3-4d4e-a3cf-30813840b049",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 1: Orienting inside a \"Climate Solution\" Simulator\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Maximilian Puelma Touzel, Paul Heubel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2d2b006-8493-451b-a75e-797d902a0fcd",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "During the first week of the course, you applied computational tools to climate data (measurements, proxies, and model output) to characterize past, present, and future climate. During day one of this second week (W2D1), you began to explore climate model data from Earth System Model (ESM) simulations conducted for the recent Climate Model Intercomparison Project (CMIP6) that are presented in the report from the Intergovernmental Panel on Climate Change ([IPCC](https://www.ipcc.ch/)). However, the dominant source of uncertainty in those projections arises from how human society responds: e.g. how our emissions reduction and renewable energy technologies develop, how coherent our global politics are, how our consumption grows (cf. [Rogelj et al. (2018)(Global Warming of 1.5°C. An IPCC Special Report \\[...\\])](https://doi.org/10.1017/9781009157940.004)). For these reasons, in addition to understanding the physical basis of the climate variations projected by these models, it's also important to assess the current and future socioeconomic impact of climate change and what aspects of human activity are driving emissions.\n",
- "\n",
- "This day's tutorials focus on the socioeconomic projections regarding the future of climate change and are centered around the Shared Socioeconomic Pathways (SSP) framework used by the IPCC. However, in this first tutorial, you will use the [En-ROADS](https://www.climateinteractive.org/en-roads/) simulator to get some intuition about different socioeconomic variables and their consequences for climate. Additionally, you will analyze potential economic and population scenarios to learn about the complex and intertwined dynamics of socioeconomic variables, as well as how this model informs modern-day climate challenges.\n",
- "\n",
- "Unlike the rest of the days of this course, this day will be highly conceptual and discussion-driven, and focus much less on analyzing datasets.\n",
- "\n",
- "In this tutorial, you will:\n",
- "- Get familiar with the interface of the simulator named En-ROADS\n",
- "- Explore the impact of different growth variables on the temperature increase by 2100 projected from the En-ROADS model.\n",
- "- Understand why it is necessary to implement various actions against climate change, not a single one.\n",
- "- Explore the assumptions and limitations of this model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "368d0c90-ae7e-43d9-ada7-ad42c75f5f95",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6db786f8-e5c4-42a9-9837-e41027b9ad4d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e11e3bbb-ce8f-4404-8d04-b6f86d4a9ea9",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ea9d553f-c3eb-4723-a922-e81f97e1e3ba",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting inside a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52ca5582-816c-423c-9ed7-cda1c7319731",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57265af6-b3ab-41e4-82a1-6c4b63a0c321",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Exploration of a Climate Solution Simulator"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "042a83d3-2e94-4092-a289-2430d349966c",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The following introductory video gives a quick overview of the En-ROADS simulator, a simple climate model (SCM), developed by [Climate-Interactive](https://www.climateinteractive.org/) for teaching purposes. It is used in policy workshops, role-plays, and other activities to explore the possibilities and obstacles of scenarios and human solutions to climate change.\n",
- "\n",
- "To get familiar with modeling societal and economic mechanisms in combination with climatic variables, the so-called **socio-economic model**, you will examine its 'control knobs', limitations, and assumptions. In later tutorials you will compare these findings with those of Integrated Assessment Models (IAMs) which are state-of-the-art models for projecting scenarios and those used in the socioeconomic pathways of the IPCC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9c6b2260-bdb0-4d7b-a07d-41551d5ccf92",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 2: Overview of the En-ROADS Climate Solutions Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'Py_qIgcZxKg')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "718606a5-1239-414b-84fc-f9c0a18924d6",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Exercise 1: Can you limit human-caused global warming to \"well-below 2ºC\"?\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "We jump right in with an exercise, adapted from [En-ROADS](https://www.climateinteractive.org/guided-assignment/), that allows you to explore the Climate Solution Simulator.\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Don't worry if you don't find a scenario that works right away - keep exploring. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) to help you get started. Have fun!\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* How many variables did you have to adjust to reach the \"well-below 2ºC\" target?\n",
- "* Which variables had the most individual impact? Did the magnitude of impact surprise you for any variables?\n",
- "* How feasible is this scenario? That is, what actions would have to be taken by governments, businesses and people over the next few years to make the proposed scenario possible?\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30a81fb8-c21b-490c-9297-a6705f114957",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "[*Click for solution*](https://github.com/neuromatch/climate-course-content/tree/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_b2906775.py)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05df13b5-14ce-4515-aaae-7cc9e3765445",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "#link_id = \"gwr8h\"\n",
- "\n",
- "download_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "render_link = f\"https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download a cheatsheet for the En-ROADS Control Panel:\\n{download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f5a0c7f-8fd3-4c89-b25d-8070ef2fdd4b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 2: Limitations of the En-ROADS Model Approach\n",
- "\n",
- "We conclude this tutorial by stepping back and discussing the limitations of En-ROADS.\n",
- "\n",
- "### Exercise 2: Limitations\n",
- "*Estimated timing:* 5 minutes\n",
- "\n",
- "1. Think about limitations that arise from the En-ROADS model approach. List a few mechanisms that seem oversimplified or phenomena that might be not or misrepresented. Discuss with your pod.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b17595a7-b326-4edb-9f80-b8e21f1d4e5e",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "[*Click for solution*](https://github.com/neuromatch/climate-course-content/tree/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial1_Solution_bbf54df1.py)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "51aac287-f6d0-479e-be4b-87f557c32214",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, you got an intuition for various 'control knobs' that can be turned in a socio-economic model environment. We discussed why no policy alone can be a silver bullet to solve all problems but a mixture of many actions, in particular, the energy transition to renewables and carbon pricing. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c949bc5-e5ef-4d07-b379-122072ef6271",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial1",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb
deleted file mode 100644
index 6e25a5756..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial2.ipynb
+++ /dev/null
@@ -1,481 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "873927b3-ad55-40a2-ab6a-de2af3c09cc8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial2.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8eb286c6-dd69-440b-bc14-e6c7f20517bf",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 2: Fossil Fuel Emissions, Growth, and Damage\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d86fc935-8b60-4fcf-83d7-1e9c78ca6b84",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 minutes\n",
- "\n",
- "This tutorial explores the impact of population and economic growth on the global temperature and how a changing climate due to fossil fuel emissions damages our economy. You model these scenarios by 'controlling the knobs' of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* discuss the impact of a growth-based economy on future fossil-fuel emissions along the Kaya identity\n",
- "* qualitatively describe the impact of population and economic growth on the global temperature within the En-ROADS model environment.\n",
- "* (explain qualitatively how a different damage function formulation impacts the economic activity)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8dc93386-4bde-4e22-b7eb-174133f57351",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9398705-5441-4ed3-980d-c6339cb30696",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3342114d-deee-4d5b-afde-d4852bac2352",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2ca0746a-edad-45f7-8ad5-1b4ec1e7c546",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Orienting in a 'Climate Solution' Simulator\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d45c633-1daa-4408-ab8c-18969fff8bde",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4cb055f0-0f58-4c8c-9e11-a7a16f0b6e98",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Quantification of Fossil Fuel Emissions and Its Dependency on Growth\n",
- "\n",
- "We discussed in the slides how economics represents the human population as a source of labor needed to produce goods and services. A by-product of this production are fossil fuel emissions, which increase atmospheric greenhouse gas (GHG) concentrations, significantly changing the earth's climate. Let us take a look at the relationship between people, production, and emissions in more detail using the En-ROADS simulator.\n",
- "\n",
- "You might already have clicked through the toggles at the top of the control panel of En-ROADS and tried the different *Views* (Main graphs, Kaya graphs, Miniature graphs). Select now the **Kaya graphs** view and reset the simulator (click on the anti-clockwise circular arrow to 'Reset all policies & assumptions' or just reload the page/model [here](https://en-roads.climateinteractive.org/)).\n",
- "\n",
- "The Kaya graphs depict four drivers of growth in carbon dioxide emissions from energy use, which reflects about two-thirds of all greenhouse gas emissions (the remaining third of emissions are from land use changes and other gases, such as methane (CH4) and nitrous oxide (N2O), which are not considered here). The corresponding equation, the so-called [***Kaya identity***](https://en.wikipedia.org/wiki/Kaya_identity) below was developed by Yoichi Kaya:\n",
- "\n",
- "***CO$_2$ Emissions from Energy = Global Population × GDP per Capita × Energy Intensity of GDP × Carbon Intensity of Energy***\n",
- "\n",
- "Where [gross domestic product](https://en.wikipedia.org/wiki/Gross_domestic_product) (GDP) is a measure of economic activity or health. \n",
- "\n",
- "In a more mathematical form, this equation is\n",
- "\n",
- "$$\n",
- "F = P \\times \\left[\\frac{G}{P}\\right] \\times \\left[\\frac{E}{G}\\right] \\times \\left[\\frac{F}{E}\\right]\n",
- "$$\n",
- " - $F$ (CO$_2$ emissions from energy), \n",
- " - $P$ (global population), \n",
- " - $G$ (world GDP), and \n",
- " - $E$ (global energy consumption).\n",
- "\n",
- "Decomposing emissions in this way helps to see key contributions and, if we assume that the factors are independent of each other (which is not fully true), distinct ways that we can lower emissions. Each factor in this model is important and extreme reductions in one can offset increases in the others. In our case, our emissions $F$ are increasing over time because the Energy Intensity of GDP $\\left[\\frac{E}{G}\\right] $ and the Carbon Intensity of Energy do not together offset enough of our growing GDP ($G$), which is by definition necessary for a growth-based economy.\n",
- "\n",
- "The first four graphs in the Kaya view display these four variables $\\left(P, \\frac{G}{P}, \\frac{E}{G}, \\frac{F}{E}\\right)$, with the fifth plot showing Emissions, $F$. This allows us to compare the importance of, say, economic and population growth on the CO$_2$ emissions and subsequent temperature rise by 2100."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fd232a4d-c350-4e7b-9519-921800d81e20",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Population vs. Economic Growth\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: Turn two *growth* 'control knobs' of En-ROADS, which are the sliders *Economical growth* and *Population growth*. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- "* What can you observe within the *Kaya graph* view? Describe the graphs and interpret them.\n",
- "\n",
- "Read the following dropdown accessible box if you need more information to interpret the Kaya graphs. \n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1c61eb29-ab14-4445-915e-5d39abab9a73",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "53e42e1c",
- "metadata": {
- "execution": {},
- "vscode": {
- "languageId": "plaintext"
- }
- },
- "source": [
- "# Tutorial 3: The Temporal Dimension of Actions\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "87ef2f44-fb74-4664-8472-4c97158a8d17",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 15 minutes\n",
- "\n",
- "The last tutorials covered the necessity for an energy transition to tackle the climate emergency and many solutions at once. As emissions accumulate, it becomes substantially harder to succeed the longer we take to make big changes. This tutorial explores the temporal dimension of action, here policies, by using the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/).\n",
- "\n",
- "After finishing this tutorial you can\n",
- "\n",
- "* exemplify along the carbon price, why policy-making has to consider the temporal dimension and the needs of the most vulnerable parts of society"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "062c4b1a-6239-4644-a0dd-03fe1342fe99",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b4722fe-cbd5-484a-be6d-96ed7aafbf62",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "053ee0ea-d704-45c8-8b4b-aad17216710d",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b50c362",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: The IPCC's Transition Narratives and Project Modelling\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', 'g5VRHCcIyxk'),\n",
- " #('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "703ee8f2",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"mtyrb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "29cf9a43-2478-4d14-9faf-193e54378a04",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: When and How Do You Introduce a Carbon Price?\n",
- "\n",
- "You might have recognized that a high ***carbon price*** by default has a strong impact on the temperature increase within the En-ROADS model as it both reduces the carbon intensity of the energy supply and reduces the energy demand. However, the carbon price itself could be introduced now or in 50 years and at different amounts, depending on for example the social cost of such a policy, which makes energy supply more expensive depending on its emissions. Energy producers could pass additional costs to their customers, so policy could be designed to minimize the impacts on the poorest, e.g. by introducing a [Carbon fee and dividend](https://en.wikipedia.org/wiki/Carbon_fee_and_dividend). \n",
- "\n",
- "To account for their temporal evolution, many variables in En-ROADS allow for modifications of the timing when something is introduced, stopped, in or de-creased, and so on."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d29fb31c-84ab-4705-8678-87f8274f6500",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Implications of Actions and Their Timing\n",
- "*Estimated timing:* 10 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- " \n",
- "2. **Develop a scenario**: Click on the ellipsis of the 'Carbon price' slider, and a widget-info box opens that allows for finetuning of your carbon price policy.\n",
- " 1. Click on the title of the top right graph *Greenhouse Gas Net Emissions*, select the 'Financial' toggle, and select the 'Market price of electricity'.\n",
- " 2. Turn the most upper knobs/ vary the sliders of the 'Carbon price' and slide through different carbon prices.\n",
- " 3. Move the sliders to answer the following question. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed.\n",
- "\n",
- "\n",
- "3. **Answer the following questions**:\n",
- " * What do you observe in the 'Market price of electricity' graph after varying the carbon price?\n",
- " * Now, select a high carbon price and increase the 'Year the carbon price starts to phase in'. How does the carbon price change the temporal evolution of the 'Market price of electricity'. \n",
- " * How could this be of consequence for a low-income household?\n",
- "\n",
- "*Be warned that a reset of all your previous changes might be necessary before. If you would like to save your previous scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the Panel, and select 'Copy Scenario Link'*.\n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c97e6d3d-1718-479b-a19a-ffc6a082347b",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "[*Click for solution*](https://github.com/neuromatch/climate-course-content/tree/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial3_Solution_2aba4d73.py)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3562deb9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this tutorial, we discussed the temporal dimension of the carbon price implementation in order to understand why no policy might be a silver bullet to solve all problems but comes with various ethical and political implications. At last, we discussed a few limitations of the En-ROADs model approach."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6d0c2c37-0e64-4734-8776-1f455a20987f",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27636b92",
- "metadata": {
- "execution": {}
- },
- "source": [
- ""
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial3",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.jl b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.jl
deleted file mode 100644
index e3c0be61d..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial3.jl
+++ /dev/null
@@ -1,2138 +0,0 @@
-### A Pluto.jl notebook ###
-# v0.19.24
-
-using Markdown
-using InteractiveUtils
-
-# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
-macro bind(def, element)
- quote
- local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
- local el = $(esc(element))
- global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
- el
- end
-end
-
-# ╔═╡ 1c8d2d00-b7d9-11eb-35c4-47f2a2aa1593
-begin
- import Pkg
- ENV["JULIA_MARGO_LOAD_PYPLOT"] = "no thank you"
- Pkg.activate(mktempdir())
- Pkg.add([
- Pkg.PackageSpec(name="Plots", version="1"),
- Pkg.PackageSpec(name="ClimateMARGO", version=v"0.3.3"),
- Pkg.PackageSpec(name="PlutoUI", version="0.7"),
- Pkg.PackageSpec(name="HypertextLiteral", version="0.9"),
- Pkg.PackageSpec(name="Underscores", version="2"),
- ])
-
- using Plots
- using Plots.Colors
- using ClimateMARGO
- using ClimateMARGO.Models
- using ClimateMARGO.Optimization
- using ClimateMARGO.Diagnostics
- using PlutoUI
- using HypertextLiteral
- using Underscores
-
- Plots.default(linewidth=5)
-end;
-
-# ╔═╡ 9a48a08e-7281-473c-8afc-7ad3e0771269
-TableOfContents()
-
-# ╔═╡ 94415ff2-32a2-4b0f-9911-3b93e202f548
-const initial_1 = Dict("M" => [2090, 6]);
-
-# ╔═╡ 50d24c91-61ae-4544-98fa-5749bafe3d41
-# md"""
-# ## Overview of the climate problem: from greenhouse gas emissions to climate suffering
-
-# Human emissions of greenhouse gases, especially Carbon Dioxide (CO₂), increase the stock of greenhouse gases in the atmosphere. For every molecule of CO₂ emitted, about 50% are taken up by plants, soils, or the ocean within a few years, while the rest remains in the atmosphere. (The effects of other greenhouse gases, such as Methane and CFCs, and other forcing agents, can approximately be converted into the "CO₂-equivalent"– or CO₂ₑ– concentrations that would lead to the same climate forcing).
-
-# Greenhouse gases get their name because they trap invisible heat radiation emitted by Earth's surface and atmosphere from escaping to space, much like greenhouses trap hot air from rising when it is warmed by the Sun. This "greenhouse effect" causes the temperature to rise globally, although some places warm *more* and *faster* than others. Warmer temperatures exacerbate both the frequency and intensity of "natural" disasters, such as heat waves, coastal flooding from major hurricanes, and inland flooding from torrential rain. These climate impacts lead to enhanced climate suffering, which economics typically attempt to quantify suffering in terms of lost money or welfare.
-
-# In the interactive article below, we invite you to explore the benefits of emissions mitigation and carbon dioxide removal in reducing climate suffering, and the trade-offs with their costs.
-# """
-
-# ╔═╡ e810a90f-f964-4d7d-acdb-fc3a159dc12e
-const initial_2 = Dict("M" => [2080, .7]);
-
-# ╔═╡ a3422533-2b78-4bc2-92bd-737da3c8982d
-const initial_3 = Dict("M" => [2080, .7]);
-
-# ╔═╡ 51037451-0fea-4021-8824-56911970b97b
-const initial_x = Dict("G" => [2030, 1]);
-
-# ╔═╡ 3094a9eb-074d-46c3-9c1e-0a9c94c6ad43
-blob(el, color = "red") = @htl("""| Enabled? | Cost multiplier | - - - -|
|---|---|---|
| $(longname.M) | -$(@bind enable_M_9 CheckBox(;default=true)) | -$(@bind cost_M_9 multiplier(default_cost.M, 4)) | -
| $(longname.R) | -$(@bind enable_R_9 CheckBox(;default=true)) | -$(@bind cost_R_9 multiplier(default_cost.R, 4)) | -
| $(longname.G) | -$(@bind enable_G_9 CheckBox(;default=false)) | -$(@bind cost_G_9 multiplier(default_cost.G, 4)) | -
| $(longname.A) | -$(@bind enable_A_9 CheckBox(;default=false)) | -$(@bind cost_A_9 multiplier(default_cost.A, 4)) | -
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 4: The Shared Socio-economic Pathways\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "In this tutorial, you will learn about Integrated Assessment Models (IAMs), a class of models that combine climatology, economics, and social science, reflecting the intertwined nature of these domains in addressing climate change. Based on these models the IPCC established the socioeconomic pathway framework. You are going to learn how these pathways differ from one another in both climate and socioeconomic variables as well as assumptions.\n",
- "\n",
- "After finishing this tutorial, you will know how to \n",
- "\n",
- "- filter data series of interest from a rich `pandas` data frame that contains various variables for different SSPs,\n",
- "- tell what the abbreviation SPA stands for,\n",
- "- explain the differences and similarities of the SSP1-26 and SSP5-85, and \n",
- "- sketch the modeling approach of IAMs."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# installations ( uncomment and run this cell ONLY when using google colab or kaggle )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {},
- "executionInfo": {
- "elapsed": 1460,
- "status": "ok",
- "timestamp": 1682429063027,
- "user": {
- "displayName": "Maximilian Puelma Touzel",
- "userId": "09308600515315501700"
- },
- "user_tz": 240
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# imports\n",
- "import warnings\n",
- "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import pooch\n",
- "import os\n",
- "import tempfile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "code_folding": [
- 0
- ],
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file\n",
- "\n",
- "\n",
- "def legend_without_duplicate_labels(ax):\n",
- " handles, labels = ax.get_legend_handles_labels()\n",
- " unique = [(h, l) for i, (h, l) in enumerate(zip(handles, labels)) if l not in labels[:i]]\n",
- " ax.legend(*zip(*unique))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "tags": []
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Transition Goals and Integrated Assessment Models\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "# TODO add Bilibili link\n",
- "video_ids = [('Youtube', 'TqpPDz2kfWc'),\n",
- " # ('Bilibili', '')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {},
- "pycharm": {
- "name": "#%%\n"
- },
- "tags": [
- "remove-input"
- ]
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"ujprw\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Shared Socio-economic Pathways"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {},
- "jp-MarkdownHeadingCollapsed": true
- },
- "source": [
- "In this, and subsequent, tutorials, you will explore Integrated Assessment Models (IAMs) which are the standard class of models used to make climate change projections. IAMs couple a climate model with an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. IAMs can also account for changes that result from mitigation efforts, which lessen anthropogenic emissions.\n",
- "\n",
- "Let's start by investigating some IAM model output.\n",
- "\n",
- "The simulations are labeled by both the Shared Socioeconomic Pathway (SSP1, SSP2, SSP3, SSP4, and SSP5) and the forcing level (greenhouse gas forcing of 2.6, 7.0, 8.5 W/m2 etc. by 2100).\n",
- "The 5 SSPS are: \n",
- "- SSP1: Sustainability (Taking the Green Road)\n",
- "- SSP2: Middle of the Road\n",
- "- SSP3: Regional Rivalry (A Rocky Road)\n",
- "- SSP4: Inequality (A Road divided)\n",
- "- SSP5: Fossil-fueled Development (Taking the Highway)\n",
- "\n",
- "We select two SSPs to exemplify how these scenarios differ from each other. \n",
- "To get a strong contrast, we select SSP1 and SSP5. \n",
- "\n",
- "Let's load the data and describe their features along a few plots.\n",
- "\n",
- "Like in other tutorials, we provide a `.csv` file that is stored in the cloud and was downloaded beforehand from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# Load SSP data from .csv file\n",
- "filename_SSPs = 'SSP_IAM_V2_201811.csv'\n",
- "link_id = \"2uwr4\"\n",
- "url_SSPs = f\"https://osf.io/download/{link_id}/\"\n",
- "\n",
- "df = pd.read_csv(pooch_load(url_SSPs, filename_SSPs))\n",
- "# get a summary of the resulting pandas dataframe\n",
- "df.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "We further explore our data frame by printing categories that are used to tag the numeric data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "print(df.SCENARIO.unique()) # print all scenarios\n",
- "print(df.VARIABLE.unique()[:10]) # print the first 10 variables\n",
- "print(df.REGION.unique()) # print all regions\n",
- "print(df.MODEL.unique()) # print all IAMs\n",
- "print(df.UNIT.unique()) # print all units"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "This file contains much data we are not interested in at the moment. To filter our example scenarios, region, and variables, we use the convenient [`.query()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html) method from `pandas`. The `VARIABLE`s of interest are those we already touched on in Tutorials 1 to 3: \n",
- "\n",
- "- **economic growth** (`'GDP|PPP'`),\n",
- "- **energy use** (`'Primary Energy'`),\n",
- "- **emissions** (`'Emissions|Kyoto Gases'`),\n",
- "- and **forcing** (`'Diagnostics|MAGICC6|Forcing'`).\n",
- "\n",
- "- As a `REGION`, we choose the `'World'`,\n",
- "- and our `SCENARIO`s are called `'SSP1-26'` and `'SSP5-85'`.\n",
- "- The model of choice for the former scenario is by convention `'IMAGE'` and `'REMIND-MAGPIE'` for the latter, respectively.\n",
- "\n",
- "A function named `get_SSPs_for_variable()` applies all this generally and is hidden in the next cell. Please execute it, such that the subsequent cells can make use of it. If you are interested in its procedure and want to adjust it, don't forget to save a copy beforehand."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown *Execute this cell to enable the dataframe filter function: `get_SSPs_for_variable`*\n",
- "\n",
- "def get_SSPs_for_variable(df,scenario,variable,region='World'):\n",
- " '''\n",
- "\n",
- " Function that filters IIASA's SSP database that is stored in a data frame 'df'\n",
- " and was loaded before from the 'SSP_IAM_V2_201811.csv' file.\n",
- " It returns a data frame with selected columns depending on scenario, variable and region input.\n",
- " For a given SSP scenario it chooses the conventional model for the respective scenario\n",
- " (cf. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about#v2).\n",
- "\n",
- " Args:\n",
- " scenario: string in \"SSPX-XX\" with X=1,...,5\n",
- " variable: string in df.VARIABLE, e.g. 'Population' or 'GDP|PPP'\n",
- "\n",
- " Returns:\n",
- " SSP data for selected columns for a given SSP scenario\n",
- "\n",
- " Example:\n",
- " dd = get_SSPs_for_variable(df,'SSP1-26','Population')\n",
- "\n",
- " '''\n",
- " ssp_model_conv = {\"SSP1-Baseline\" : \"IMAGE\",\n",
- " \"SSP1-26\" : \"IMAGE\",\n",
- " \"SSP2-Baseline\" : \"MESSAGE-GLOBIOM\",\n",
- " \"SSP3-Baseline\" : \"AIM/CGE\",\n",
- " \"SSP4-Baseline\" : \"GCAM4\",\n",
- " \"SSP5-Baseline\" : \"REMIND-MAGPIE\"}\n",
- " model = ssp_model_conv[scenario]\n",
- " ds = df.query(\n",
- " f'(VARIABLE == \"{variable}\") & (SCENARIO == \"{scenario}\") & (MODEL == \"{model}\") & (REGION == \"{region}\")'\n",
- ")\n",
- " return ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Let's plot our variables of interest and compare the respective features of the scenarios."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['GDP|PPP','Emissions|Kyoto Gases', 'Primary Energy','Diagnostics|MAGICC6|Forcing']\n",
- "# create new names for structured data series and axes labels\n",
- "val_name = ['GDP (billion US$/yr)', 'Emissions (Mt CO$_2$/yr)', 'Energy use (EJ/yr)', 'Forcing (W/m$^2$)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,2)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich data frame\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0,0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The projections in the plots you just created show changes in **GDP** (billion US\\$/yr), **fossil fuel emissions** (Mt CO$_2$/yr), **energy use** (EJ/yr), and **forcing** (W/m$^2$) across the two very different scenarios SSP1 and SSP5, computed at their baseline forcing level, which are each represented by a distinct color in each plot.\n",
- "\n",
- "Our plots show that the SSP5-Baseline scenario exhibits very high levels of energy use, and emissions (due to fossil fuel exploitation), it marks the upper end of the scenarios in several dimensions (cf. [Kriegler et al. (2014)](https://doi.org/10.1016/j.gloenvcha.2016.05.015)). \n",
- "\n",
- "The SSP1-26 scenario contrarily caps the increase of energy use by 2030, combined with other actions leading to decreasing emissions and subsequently a decreasing forcing for the second half of the century. However, economic growth continues with half the slope of SSP5-Baseline. In summary, it is the most optimistic projection: we transition to a global society of sustainability-focused growth."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.1: SSP Creation via IAMs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "The underlying modeling of Integrated Assessment Models (IAMs) works roughly as follows:\n",
- " \n",
- "All SSP projections are created by optimizing economic activity within the constraint of a given level of greenhouse gas forcing at 2100 (bottom right in the above plot). This activity drives distinct temperature changes via the emissions it produces (top right), which are inputted into a damage function to compute economic damages. These damages feedback into the economy model to limit emissions-producing economic activity (top left).\n",
- "*Note that we already explored these damage functions along our En-ROADS climate solution simulator in Tutorial 2*.\n",
- "\n",
- "The forcing constraint ensures the amount of emissions produced is consistent for that particular scenario. In other words, the projected temperature change under different scenarios is fed to a socioeconomic model component in order to assess the socioeconomic impacts resulting from the temperature change associated with each SSP. *For examples of such impacts check out today's Tutorial 2 and W2D4.*\n",
- "\n",
- "Not every variable in IAMs is endogenous (i.e. determined by other variables in the model). Some variables, like population or technology growth, are exogenous (i.e. variables whose time course is given to the model). In this case, the time course of, e.g., population and economic growth, are derived from simple growth models. These exogenous variables are assumed to be under our society's control (e.g. via mitigation).\n",
- "\n",
- "To understand how our plotted scenarios compare with all other scenarios, we first have a look at the underlying policy assumptions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.2 Shared Climate Policy Assumptions (SPAs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "All pathways have common Shared Climate Policy Assumptions (SPAs) like \n",
- "- limits to how fast we can respond based on where we are now,\n",
- "- what kinds of policies can be implemented, and how\n",
- "\n",
- "which constrain the modelers that create scenarios and their narratives.\n",
- "\n",
- "Our example scenarios hence share the above SPAs and vary in their narrative:\n",
- "\n",
- "In the hypothetical world of SSP5, no climate policies or climate impacts exist, which in other words effectively implies that the carbon price is zero (cf. [Ch.3.2.1.1 of IPCC's report on 'Climate Change 2022: Mitigation of Climate Change')](https://www.ipcc.ch/report/ar6/wg3/downloads/report/IPCC_AR6_WGIII_Chapter03.pdf).\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Section 1.3: Similarities of SSP1 and SSP5\n",
- "\n",
- "When you compare the two scenarios, in particular, the evolution of the population and the GDP shows how similar these scenarios are in their optimistic view on the development of humanity. We all learn to get along, both within and across countries and more equal development naturally stems from population growth through well-known mechanisms like access to conception. The following figure emphasizes this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put variables of interest in a list\n",
- "vars = ['Population', 'GDP|PPP']\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ['Population\\n(millions)', 'GDP (billion US$/yr)']\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "Both GDP and population increase."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1.3: Differences of SSP1 and SSP5\n",
- "\n",
- "Major differences are visible when you contrast emissions and assume direct causation with ecosystem health. Increasing emissions then translate into decreasing ecosystem health."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Coding exercise 1.3\n",
- "\n",
- "1. Choose two variables to emphasize ecosystem health differences in the SSP1 and SSP5 scenarios and assign them to `vars`. Then assign axis labels with the correct units for plotting to the `val_name` variable.\n",
- "2. Explain to your pod why the chosen variables emphasize a difference in the scenarios and describe this difference based on your current knowledge of the narratives."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# put two variables of interest in a list\n",
- "vars = ...\n",
- "# create new names for structured data series and plot labels\n",
- "val_name = ...\n",
- "# choose scenarios of interest and a color for plotting\n",
- "scenarios = ['SSP1-26', 'SSP5-Baseline']\n",
- "colors = ['darkblue','darkorange']\n",
- "\n",
- "#################################################\n",
- "## TODO for students:\n",
- "## Put two variables of interest in a list and assign it to 'vars'.\n",
- "## Create new names for the structured data series and axes labels,\n",
- "## put them in a list and assign it to 'val_name'.\n",
- "## Remove the following line of code once you have completed the exercise:\n",
- "raise NotImplementedError(\"Student exercise: Put two variables of interest in a list and assign it to vars. Create new names for the structured data series and axes labels, put them in a list and assign it to val_name.\")\n",
- "#################################################\n",
- "\n",
- "# init figure and axis\n",
- "fig, axs = plt.subplots(2,1)\n",
- "# loop over all variables and new names\n",
- "for var, val, ax in zip(vars,val_name, axs.flatten()):\n",
- "\n",
- " # loop over scenarios and their color\n",
- " for sc, col in zip(scenarios, colors):\n",
- " # retrieve SSP for the respective variable from rich dataframe\n",
- " ds_unstrct = get_SSPs_for_variable(df,sc,var)\n",
- " # restructure dataframe for plotting\n",
- " ds_strct = pd.melt(ds_unstrct, id_vars=[\"MODEL\"], value_vars=['2010','2020','2030','2040','2050','2060','2070','2080','2090','2100'], var_name=\"YEAR\", value_name =val)\n",
- " #print(ds_strct)\n",
- " # plot variable vs. time, add label incl. scenario and model\n",
- " ax.plot(ds_strct['YEAR'],ds_strct[val],label=f'{sc},\\n{ds_strct.MODEL[0]}', color=col)\n",
- " # altern. plotting procedure w/o the color distinction\n",
- " #sns.lineplot(ds_strct, x='YEAR', y=val, hue='MODEL', ax=ax, palette='flare')\n",
- "\n",
- " # aesthetics\n",
- " ax.set_ylabel(fr'{val}')\n",
- " ax.set_xlabel('Time (years)')\n",
- " plt.setp(ax.get_xticklabels(), rotation=45)\n",
- " plt.setp(ax.get_xticklabels()[::2], visible=False)\n",
- " ax.grid(True)\n",
- " axs[0].legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "[*Click for solution*](https://github.com/neuromatch/climate-course-content/tree/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_a4ef74b1.py)\n",
- "\n",
- "*Example output:*\n",
- "\n",
- "
\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "colab_type": "text",
- "execution": {}
- },
- "source": [
- "[*Click for solution*](https://github.com/neuromatch/climate-course-content/tree/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/solutions/W2D3_Tutorial4_Solution_4240ed7b.py)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "In this tutorial, you've gained a first understanding of the Shared Socioeconomic Pathways and their creation, the application of Integrated Assessment Models in climate economics. You've learned how SSPs share policy assumptions. Furthermore, you compared SSP1 and SSP5 with respect to their view on the development of humanity and their ecosystem health.\n",
- "\n",
- "In the next tutorial, you dissect and analyze the SSP narratives in more detail."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "It is possible to download the SSP data used in this tutorial, when you provide an email address, from [this IIASA database](https://tntcat.iiasa.ac.at/SspDb/dsd), where all data from the main simulations of the IAMs used in the [IPCC reports](https://www.ipcc.ch/reports/) is freely available.\n",
- "\n",
- "Find a summary of all SSP narratives in this paper by [Oneill et al. (2017)](https://doi.org/10.1016/j.gloenvcha.2015.01.004).\n",
- "\n",
- "Find even more information in\n",
- "\n",
- "- [IIASA's introduction to SSPs](https://iiasa.ac.at/models-tools-data/ssp)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial4",
- "provenance": [],
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb
deleted file mode 100644
index 9b234f6e5..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial5.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "3d4298c9-a204-48a4-b408-90d0403d4950",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial5.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c55c3b25-2613-4f34-8d4a-ccc417f24708",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial 5: Mapping the Narrative Space\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5c8af3c8-42a4-45ba-a16f-be86198e23e8",
- "metadata": {
- "execution": {}
- },
- "source": [
- "### Tutorial objectives\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1af3e766-b9c3-4bf8-815c-4087e7811925",
- "metadata": {
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# import\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "#import dicelib # https://github.com/mptouzel/PyDICE"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7392de8-f946-453b-bedf-8a07c705ee7f",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Figure settings\n",
- "import ipywidgets as widgets # interactive display\n",
- "\n",
- "%config InlineBackend.figure_format = 'retina'\n",
- "plt.style.use(\n",
- " \"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c10210c-a3d9-4996-a3d3-4bafa4b72637",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Helper functions\n",
- "\n",
- "def pooch_load(filelocation=None, filename=None, processor=None):\n",
- " shared_location = \"/home/jovyan/shared/Data/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis\" # this is different for each day\n",
- " user_temp_cache = tempfile.gettempdir()\n",
- "\n",
- " if os.path.exists(os.path.join(shared_location, filename)):\n",
- " file = os.path.join(shared_location, filename)\n",
- " else:\n",
- " file = pooch.retrieve(\n",
- " filelocation,\n",
- " known_hash=None,\n",
- " fname=os.path.join(user_temp_cache, filename),\n",
- " processor=processor,\n",
- " )\n",
- "\n",
- " return file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "80412217-b5a7-4fed-b29e-b8751eb8c847",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1: Mapping the Narrative Space\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '9ytkpO_pbWE')\n",
- " # , ('Bilibili', 'BV1bj411Z739')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2dc2f6c1-637b-4506-a667-45750dd7fbe5",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "# TODO update\n",
- "link_id = \"cyavb\"\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c7ce6e92-a46d-437a-9e03-c5b80eb63474",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Section 1: Background on IAM Economics\n",
- "\n",
- "The last tutorial gave us a glimpse of Integrated Assessment Models (IAMs), a class of models economists use to inform policy decisions. Recall that IAMs couple a climate model to an economic model, allowing us to evaluate the two-way coupling between economic productivity and climate change severity. \n",
- "\n",
- "Let's begin with a brief description of IAMs:\n",
- "\n",
- "- IAMs resolve the spatially, in contrast, the toy model En-ROADs for example, which we applied in Tutorial 1 to 3, aggregates all variables and is non-spatial.\n",
- "- Like En-ROADS, the world models used in IAMs usually have *exogeneous* (externally set) times series for variables, in addition to fixed world system parameters. These exogenous variables are assumed to be under our society's control (e.g. mitigation). \n",
- "- IAMs come equipped with an objective function (a formula that calculates the quantity to be optimized). This function returns the value of a projected future obtained from running the world model under a given climate policy. This value is defined by the time series of these exogenous variables. In this sense, the objective function is what defines \"good\" in \"good climate policy\". \n",
- "- The computation in an IAM is then an optimization of this objective as a function of the time series of these exogenous variables over some fixed time window.\n",
- "\n",
- "In En-ROADS, there are exogenous parameters, in particular:\n",
- " - **$\\mu(t)$**: time-dependent mitigation rate (i.e. emissions reduction), which limits warming-caused damages\n",
- " - **$S(t)$**: savings rate, which drives capital investment \n",
- "\n",
- "Most IAMs are based on *Neo-classical economics* (also referred to as \"establishment economics\"). This is an approach to economics that makes particular assumptions. For example, it is assumed that production, consumption, and valuation of goods and services are driven solely by the supply and demand model. To understand this approach and how it is used, it is important to begin with a brief overview of some fundamental concepts. One such concept is **utility** (i.e. economic value), which is not only central to economics but also to decision theory as a whole, which is a research field that mathematically formalizes the activity of *planning* (planning here means selecting strategies based on how they are expected to play out given a model that takes those strategies and projects forward into the future)."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "28c0a077-c9a0-42d9-ada6-e8b835668c3b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9e904e57-b6b9-4ee8-aefb-140dce3c93c2",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial5",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb
deleted file mode 100644
index 944aeb1a3..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/W2D3_Tutorial6.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "a4589a1d-0f80-45e7-aec3-3513172e617b",
- "metadata": {
- "execution": {}
- },
- "source": [
- "[](https://colab.research.google.com/github/neuromatch/climate-course-content/blob/main/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/W2D3_Tutorial6.ipynb) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3d93b742-06f3-4a0b-9a1d-043397878e48",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Bonus Tutorial 6: Create your Socio-economic Scenario\n",
- "\n",
- "**Week 2, Day 3: The Socioeconomics of Climate Change**\n",
- "\n",
- "**Content creators:** Paul Heubel, Maximilian Puelma Touzel\n",
- "\n",
- "**Content reviewers:** Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan\n",
- "\n",
- "**Content editors:** Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Production editors:** Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan\n",
- "\n",
- "**Our 2024 Sponsors:** CMIP, NFDI4Earth"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b5c4ba1-fad0-4d38-b3b2-875a67549624",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Tutorial Objectives\n",
- "\n",
- "*Estimated timing of tutorial:* 30 min\n",
- "\n",
- "In this tutorial, you will model your own simplified Socioeconomic Pathway, a scenario of actions to limit global warming below 2°C. You take action against climate change within the socioeconomic model environment of the Climate Solution Simulator named [En-ROADS](https://www.climateinteractive.org/en-roads/). To examine its characteristics you address its challenges, winners/losers, political feasibility, narrative, and current trends. \n",
- "\n",
- "You learn how to\n",
- "\n",
- "- apply the knowledge you gained in last tutorials to design actions against climate change\n",
- "- map your scenario narrative in the SSP \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b591486d-dff5-4dc6-b83b-1125977a7758",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @title Video 1:\n",
- "\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import YouTubeVideo\n",
- "from IPython.display import IFrame\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "class PlayVideo(IFrame):\n",
- " def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
- " self.id = id\n",
- " if source == 'Bilibili':\n",
- " src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
- " elif source == 'Osf':\n",
- " src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
- " super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
- "\n",
- "\n",
- "def display_videos(video_ids, W=400, H=300, fs=1):\n",
- " tab_contents = []\n",
- " for i, video_id in enumerate(video_ids):\n",
- " out = widgets.Output()\n",
- " with out:\n",
- " if video_ids[i][0] == 'Youtube':\n",
- " video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
- " height=H, fs=fs, rel=0)\n",
- " print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
- " else:\n",
- " video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
- " height=H, fs=fs, autoplay=False)\n",
- " if video_ids[i][0] == 'Bilibili':\n",
- " print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
- " elif video_ids[i][0] == 'Osf':\n",
- " print(f'Video available at https://osf.io/{video.id}')\n",
- " display(video)\n",
- " tab_contents.append(out)\n",
- " return tab_contents\n",
- "\n",
- "\n",
- "video_ids = [('Youtube', '_Y_eLlExwxI'),\n",
- " #TODO('Bilibili', 'BV1nN411U75L')\n",
- " ]\n",
- "tab_contents = display_videos(video_ids, W=730, H=410)\n",
- "tabs = widgets.Tab()\n",
- "tabs.children = tab_contents\n",
- "for i in range(len(tab_contents)):\n",
- " tabs.set_title(i, video_ids[i][0])\n",
- "display(tabs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "616428a4-3db2-4e56-af30-6c56aaf66204",
- "metadata": {
- "cellView": "form",
- "execution": {}
- },
- "outputs": [],
- "source": [
- "# @markdown\n",
- "from ipywidgets import widgets\n",
- "from IPython.display import IFrame\n",
- "\n",
- "link_id = \"gwr8h\" #TODO\n",
- "\n",
- "download_link = f\"https://osf.io/download/{link_id}/\"\n",
- "render_link = f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\"\n",
- "# @markdown\n",
- "out = widgets.Output()\n",
- "with out:\n",
- " print(f\"If you want to download the slides: {download_link}\")\n",
- " display(IFrame(src=f\"{render_link}\", width=730, height=410))\n",
- "display(out)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a516e969-8b67-4f8a-9d39-061770ffb4c9",
- "metadata": {
- "execution": {}
- },
- "source": [
- "## Exercise 1: Create your Solution Scenario and Evaluate it\n",
- "*Estimated timing:* 20 minutes\n",
- "\n",
- "1. **Open En-ROADS** [here](https://en-roads.climateinteractive.org/). *(Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show \"English\".)*\n",
- "\n",
- "2. **Develop a scenario**: By moving the sliders and changing the assumptions, find a scenario (i.e. a combination of slider positions of different variables) that results in *less than 2°C* of temperature increase by the end of the century. Use the following [cheatsheet](https://img.climateinteractive.org/2019/09/EnROADS-one-page-guide-to-control-panel-v11-dec-2021.pdf) if needed. \n",
- "\n",
- "*Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.*\n",
- "\n",
- "3. **Answer the following questions**:\n",
- "\n",
- " 1. **Your Plan**: What are the top 3-5 most important policies in your strategy? (For example, the most important sliders that you moved). Share your scenario link and a screenshot of your final En-ROADS dashboard.\n",
- " \n",
- " *If you want to save your scenario, export it via a click on the* ***Share your scenario*** *button in the top right of the panel, and select 'Copy Scenario Link'*.\n",
- " \n",
- " 2. **Political Feasibility**: To implement your proposals, what actions and priorities are needed over the next two years in businesses, civil society, government, and the public?\n",
- " 3. **Winners & Losers**: Who would be the biggest winners and losers globally in your proposed future? Create a table with two columns for winners and losers. \n",
- " 4. **Narrative**: Map your ideas in the narrative space, introduced in Tutorial 5. \n",
- " 5. **Hope**: What trends in the world give you hope that your proposals are possible?\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fadd5652-5db9-475e-a072-f204358ade8a",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Summary\n",
- "\n",
- "In this last tutorial, you went from model to action by designing your own socioeconomic modeling scenario to limit global warming below 2°C by the end of the century. You reflected on its challenges, political feasibility, inherent winners and losers of your approach, and finally, the narrative that you chose. At the end of the day, you collected observations of actions and trends you are making in your region, globally, and in your daily life that give hope. That hope and various solutions exist, which one has to combine, and that only action is needed, might be the most important lesson of today.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a6aaa44b-0fc2-4249-91c4-589aac662188",
- "metadata": {
- "execution": {}
- },
- "source": [
- "# Resources\n",
- "\n",
- "This tutorial is inspired by teaching material from [Climate Interactive](https://climateinteractive.org/) and other documents. \n",
- "A few important resources are linked below:\n",
- "\n",
- "- [En-ROADS documentation](https://docs.climateinteractive.org/projects/en-roads/en/latest/index.html)\n",
- "- [En-ROADS User Guide PDF](https://docs.climateinteractive.org/projects/en-roads/en/latest/en-roads-user-guide.pdf)\n",
- "- [Guided Assignment - Simulating Climate Futures in En-ROADS: Short Version](https://www.climateinteractive.org/guided-assignment/)\n",
- "\n",
- "**A Planetary Crisis Planning Computer Game**\n",
- "- [Half Earth Socialism](https://play.half.earth/).\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [],
- "include_colab_link": true,
- "name": "W2D3_Tutorial6",
- "toc_visible": true
- },
- "kernel": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/dicelib.py b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/dicelib.py
deleted file mode 100644
index bbc544141..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/dicelib.py
+++ /dev/null
@@ -1,538 +0,0 @@
-import seaborn as sns
-import matplotlib.pyplot as pl
-import pandas as pd
-import numpy as np
-import scipy.optimize as opt
-from matplotlib.ticker import EngFormatter
-
-
-class DICE():
-
- def __init__(self):
- self.time_step = 5 # Years per Period
- # Set
- self.min_year = 2000
- self.max_year = 2500
- self.TT = np.linspace(
- self.min_year, self.max_year, 100, dtype=np.int32)
- self.NT = len(self.TT)
- self.t = np.arange(1, self.NT+1)
-
- def init_parameters(self, a3=2.00, prstp=0.015, elasmu=1.45):
-
- # Maximum cumulative extraction fossil fuels (GtC); denoted by CCum
- self.fosslim = 6000
- self.ifopt = 0 # Indicator where optimized is 1 and base is 0
- self.elasmu = elasmu # Elasticity of marginal utility of consumption
- self.prstp = prstp # Initial rate of social time preference per year
-
- self.init_pop_and_tech_parameters()
- self.init_emissions_parameters()
- self.init_carboncycle_parameters()
- self.init_climatemodel_parameters()
- self.init_climatedamage_parameters(a3)
- self.init_abatementcost_parameters()
-
- # ** Scaling and inessential parameters
- # * Note that these are unnecessary for the calculations
- # * They ensure that MU of first period's consumption =1 and PV cons = PV utilty
- # Multiplicative scaling coefficient /0.0302455265681763 /
- self.scale1 = 0.0302455265681763
- self.scale2 = -10993.704 # Additive scaling coefficient /-10993.704/;
-
- # * carbon cycling coupling matrix
- self.b11 = 1 - self.b12
- self.b21 = self.b12*self.mateq/self.mueq
- self.b22 = 1 - self.b21 - self.b23
- self.b32 = self.b23*self.mueq/self.mleq
- self.b33 = 1 - self.b32
-
- # * Further definitions of parameters
- self.a20 = self.a2
- self.sig0 = self.e0/(self.q0*(1-self.miu0)) # From Eq. 14
- self.lam = self.fco22x / self.t2xco2 # From Eq. 25
-
- self.init_exogeneous_inputs()
-
- def init_pop_and_tech_parameters(self, gama=0.300, pop0=7403, popadj=0.134,
- popasym=11500, dk=0.100, q0=105.5,
- k0=223, a0=5.115, ga0=0.076, dela=0.005):
- self.gama = gama # Capital elasticity in production function /.300 /
- # Initial world population 2015 (millions) /7403 /
- self.pop0 = pop0
- self.popadj = popadj # Growth rate to calibrate to 2050 pop projection /0.134/
- # Asymptotic population (millions) /11500/
- self.popasym = popasym
- # Depreciation rate on capital (per year) /.100 /
- self.dk = dk
- # Initial world gross output 2015 (trill 2010 USD) /105.5/
- self.q0 = q0
- # Initial capital value 2015 (trill 2010 USD) /223 /
- self.k0 = k0
- self.a0 = a0 # Initial level of total factor productivity /5.115/
- self.ga0 = ga0 # Initial growth rate for TFP per 5 years /0.076/
- self.dela = dela # Decline rate of TFP per 5 years /0.005/
-
- # ** Emissions parameters
- def init_emissions_parameters(self, gsigma1=-0.0152, dsig=-0.001, eland0=2.6,
- deland=0.115, e0=35.85, miu0=0.03):
- # Initial growth of sigma (per year) /-0.0152/
- self.gsigma1 = gsigma1
- # Decline rate of decarbonization (per period) /-0.001 /
- self.dsig = dsig
- # Carbon emissions from land 2015 (GtCO2 per year) / 2.6 /
- self.eland0 = eland0
- # Decline rate of land emissions (per period) / .115 /
- self.deland = deland
- # Industrial emissions 2015 (GtCO2 per year) /35.85 /
- self.e0 = e0
- self.miu0 = miu0 # Initial emissions control rate for base case 2015 /.03 /
-
- # ** Carbon cycle
- def init_carboncycle_parameters(self, mat0=851, mu0=460, ml0=1740, mateq=588, mueq=360, mleq=1720):
- # * Initial Conditions
- # Initial Concentration in atmosphere 2015 (GtC) /851 /
- self.mat0 = mat0
- # Initial Concentration in upper strata 2015 (GtC) /460 /
- self.mu0 = mu0
- # Initial Concentration in lower strata 2015 (GtC) /1740 /
- self.ml0 = ml0
- # mateq Equilibrium concentration atmosphere (GtC) /588 /
- self.mateq = mateq
- # mueq Equilibrium concentration in upper strata (GtC) /360 /
- self.mueq = mueq
- # mleq Equilibrium concentration in lower strata (GtC) /1720 /
- self.mleq = mleq
-
- # * Flow paramaters, denoted by Phi_ij in the model
- self.b12 = 0.12 # Carbon cycle transition matrix /.12 /
- self.b23 = 0.007 # Carbon cycle transition matrix /0.007/
- # * These are for declaration and are defined later
- self.b11 = None # Carbon cycle transition matrix
- self.b21 = None # Carbon cycle transition matrix
- self.b22 = None # Carbon cycle transition matrix
- self.b32 = None # Carbon cycle transition matrix
- self.b33 = None # Carbon cycle transition matrix
- # Carbon intensity 2010 (kgCO2 per output 2005 USD 2010)
- self.sig0 = None
-
- # ** Climate model parameters
- def init_climatemodel_parameters(self):
- # Equilibrium temp impact (oC per doubling CO2) / 3.1 /
- self.t2xco2 = 3.1
- # 2015 forcings of non-CO2 GHG (Wm-2) / 0.5 /
- self.fex0 = 0.5
- # 2100 forcings of non-CO2 GHG (Wm-2) / 1.0 /
- self.fex1 = 1.0
- # Initial lower stratum temp change (C from 1900) /.0068/
- self.tocean0 = 0.0068
- # Initial atmospheric temp change (C from 1900) /0.85/
- self.tatm0 = 0.85
- self.c1 = 0.1005 # Climate equation coefficient for upper level /0.1005/
- self.c3 = 0.088 # Transfer coefficient upper to lower stratum /0.088/
- self.c4 = 0.025 # Transfer coefficient for lower level /0.025/
- # eta in the model; Eq.22 : Forcings of equilibrium CO2 doubling (Wm-2) /3.6813 /
- self.fco22x = 3.6813
-
- def init_climatedamage_parameters(self, a3=2.00):
- # ** Climate damage parameters
- self.a10 = 0 # Initial damage intercept /0 /
- self.a20 = None # Initial damage quadratic term
- self.a1 = 0 # Damage intercept /0 /
- self.a2 = 0.00236 # Damage quadratic term /0.00236/
- self.a3 = a3 # Damage exponent /2.00 /
-
- def init_abatementcost_parameters(self):
- # ** Abatement cost
- # Theta2 in the model, Eq. 10 Exponent of control cost function / 2.6 /
- self.expcost2 = 2.6
- self.pback = 550 # Cost of backstop 2010$ per tCO2 2015 / 550 /
- self.gback = 0.025 # Initial cost decline backstop cost per period / .025/
- self.limmiu = 1.2 # Upper limit on control rate after 2150 / 1.2 /
- self.tnopol = 45 # Period before which no emissions controls base / 45 /
- # Initial base carbon price (2010$ per tCO2) / 2 /
- self.cprice0 = 2
- self.gcprice = 0.02 # Growth rate of base carbon price per year /.02
-
- def init_exogeneous_inputs(self):
- NT = self.NT
-
- self.l = np.zeros(NT)
- self.l[0] = self.pop0 # Labor force
- self.al = np.zeros(NT)
- self.al[0] = self.a0
- self.gsig = np.zeros(NT)
- self.gsig[0] = self.gsigma1
- self.sigma = np.zeros(NT)
- self.sigma[0] = self.sig0
- # TFP growth rate dynamics, Eq. 7
- self.ga = self.ga0 * np.exp(-self.dela*5*(self.t-1))
- self.pbacktime = self.pback * \
- (1-self.gback)**(self.t-1) # Backstop price
- # Emissions from deforestration
- self.etree = self.eland0*(1-self.deland)**(self.t-1)
- self.rr = 1/((1+self.prstp)**(self.time_step*(self.t-1))) # Eq. 3
- # The following three equations define the exogenous radiative forcing; used in Eq. 23
- self.forcoth = np.full(NT, self.fex0)
- self.forcoth[0:18] = self.forcoth[0:18] + \
- (1/17)*(self.fex1-self.fex0)*(self.t[0:18]-1)
- self.forcoth[18:NT] = self.forcoth[18:NT] + (self.fex1-self.fex0)
- # Optimal long-run savings rate used for transversality (Question)
- self.optlrsav = (self.dk + .004)/(self.dk + .004 *
- self.elasmu + self.prstp)*self.gama
- self.cost1 = np.zeros(NT)
- self.cumetree = np.zeros(NT)
- self.cumetree[0] = 100
- self.cpricebase = self.cprice0*(1+self.gcprice)**(5*(self.t-1))
-
- def InitializeLabor(self, il, iNT):
- for i in range(1, iNT):
- il[i] = il[i-1]*(self.popasym / il[i-1])**self.popadj
-
- def InitializeTFP(self, ial, iNT):
- for i in range(1, iNT):
- ial[i] = ial[i-1]/(1-self.ga[i-1])
-
- def InitializeGrowthSigma(self, igsig, iNT):
- for i in range(1, iNT):
- igsig[i] = igsig[i-1]*((1+self.dsig)**self.time_step)
-
- def InitializeSigma(self, isigma, igsig, icost1, iNT):
- for i in range(1, iNT):
- isigma[i] = isigma[i-1] * np.exp(igsig[i-1] * self.time_step)
- icost1[i] = self.pbacktime[i] * isigma[i] / self.expcost2 / 1000
-
- def InitializeCarbonTree(self, icumetree, iNT):
- for i in range(1, iNT):
- icumetree[i] = icumetree[i-1] + self.etree[i-1]*(5/3.666)
-
- """
- Emissions of carbon and weather damages
- """
-
- # Retuns the total carbon emissions; Eq. 18
- def fE(self, iEIND, index):
- return iEIND[index] + self.etree[index]
-
- # Eq.14: Determines the emission of carbon by industry EIND
- def fEIND(self, iYGROSS, iMIU, isigma, index):
- return isigma[index] * iYGROSS[index] * (1 - iMIU[index])
-
- # Cumulative industrial emission of carbon
- def fCCA(self, iCCA, iEIND, index):
- return iCCA[index-1] + iEIND[index-1] * 5 / 3.666
-
- # Cumulative total carbon emission
- def fCCATOT(self, iCCA, icumetree, index):
- return iCCA[index] + icumetree[index]
-
- # Eq. 22: the dynamics of the radiative forcing
- def fFORC(self, iMAT, index):
- return self.fco22x * np.log(iMAT[index]/588.000)/np.log(2) + self.forcoth[index]
-
- # Dynamics of Omega; Eq.9
- def fDAMFRAC(self, iTATM, index):
- return self.a1*iTATM[index] + self.a2*iTATM[index]**self.a3
-
- # Calculate damages as a function of Gross industrial production; Eq.8
- def fDAMAGES(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * iDAMFRAC[index]
-
- # Dynamics of Lambda; Eq. 10 - cost of the reudction of carbon emission (Abatement cost)
- def fABATECOST(self, iYGROSS, iMIU, icost1, index):
- return iYGROSS[index] * icost1[index] * iMIU[index]**self.expcost2
-
- # Marginal Abatement cost
- def fMCABATE(self, iMIU, index):
- return self.pbacktime[index] * iMIU[index]**(self.expcost2-1)
-
- # Price of carbon reduction
- def fCPRICE(self, iMIU, index):
- return self.pbacktime[index] * (iMIU[index])**(self.expcost2-1)
-
- # Eq. 19: Dynamics of the carbon concentration in the atmosphere
- def fMAT(self, iMAT, iMU, iE, index):
- if (index == 0):
- return self.mat0
- else:
- return iMAT[index-1]*self.b11 + iMU[index-1]*self.b21 + iE[index-1] * 5 / 3.666
-
- # Eq. 21: Dynamics of the carbon concentration in the ocean LOW level
- def fML(self, iML, iMU, index):
- if (index == 0):
- return self.ml0
- else:
- return iML[index-1] * self.b33 + iMU[index-1] * self.b23
-
- # Eq. 20: Dynamics of the carbon concentration in the ocean UP level
- def fMU(self, iMAT, iMU, iML, index):
- if (index == 0):
- return self.mu0
- else:
- return iMAT[index-1]*self.b12 + iMU[index-1]*self.b22 + iML[index-1]*self.b32
-
- # Eq. 23: Dynamics of the atmospheric temperature
- def fTATM(self, iTATM, iFORC, iTOCEAN, index):
- if (index == 0):
- return self.tatm0
- else:
- return iTATM[index-1] + self.c1 * (iFORC[index] - (self.fco22x/self.t2xco2) * iTATM[index-1] - self.c3 * (iTATM[index-1] - iTOCEAN[index-1]))
-
- # Eq. 24: Dynamics of the ocean temperature
- def fTOCEAN(self, iTATM, iTOCEAN, index):
- if (index == 0):
- return self.tocean0
- else:
- return iTOCEAN[index-1] + self.c4 * (iTATM[index-1] - iTOCEAN[index-1])
-
- """
- economic variables
- """
-
- # The total production without climate losses denoted previously by YGROSS
- def fYGROSS(self, ial, il, iK, index):
- return ial[index] * ((il[index]/1000)**(1-self.gama)) * iK[index]**self.gama
-
- # The production under the climate damages cost
- def fYNET(self, iYGROSS, iDAMFRAC, index):
- return iYGROSS[index] * (1 - iDAMFRAC[index])
-
- # Production after abatement cost
- def fY(self, iYNET, iABATECOST, index):
- return iYNET[index] - iABATECOST[index]
-
- # Consumption Eq. 11
- def fC(self, iY, iI, index):
- return iY[index] - iI[index]
-
- # Per capita consumption, Eq. 12
- def fCPC(self, iC, il, index):
- return 1000 * iC[index] / il[index]
-
- # Saving policy: investment
- def fI(self, iS, iY, index):
- return iS[index] * iY[index]
-
- # Capital dynamics Eq. 13
- def fK(self, iK, iI, index):
- if (index == 0):
- return self.k0
- else:
- return (1-self.dk)**self.time_step * iK[index-1] + self.time_step * iI[index-1]
-
- # Interest rate equation; Eq. 26 added in personal notes
- def fRI(self, iCPC, index):
- return (1 + self.prstp) * (iCPC[index+1]/iCPC[index])**(self.elasmu/self.time_step) - 1
-
- # Periodic utility: A form of Eq. 2
- def fCEMUTOTPER(self, iPERIODU, il, index):
- return iPERIODU[index] * il[index] * self.rr[index]
-
- # The term between brackets in Eq. 2
- def fPERIODU(self, iC, il, index):
- return ((iC[index]*1000/il[index])**(1-self.elasmu) - 1) / (1 - self.elasmu) - 1
-
- # utility function
- def fUTILITY(self, iCEMUTOTPER, resUtility):
- resUtility[0] = self.time_step * self.scale1 * \
- np.sum(iCEMUTOTPER) + self.scale2
-
- def init_variables(self): # TODO: add full variable names as comments
- NT = self.NT
- self.K = np.zeros(NT)
- self.YGROSS = np.zeros(NT)
- self.EIND = np.zeros(NT)
- self.E = np.zeros(NT)
- self.CCA = np.zeros(NT)
- self.CCATOT = np.zeros(NT)
- self.MAT = np.zeros(NT)
- self.ML = np.zeros(NT)
- self.MU = np.zeros(NT)
- self.FORC = np.zeros(NT)
- self.TATM = np.zeros(NT)
- self.TOCEAN = np.zeros(NT)
- self.DAMFRAC = np.zeros(NT)
- self.DAMAGES = np.zeros(NT)
- self.ABATECOST = np.zeros(NT)
- self.MCABATE = np.zeros(NT)
- self.CPRICE = np.zeros(NT)
- self.YNET = np.zeros(NT)
- self.Y = np.zeros(NT)
- self.I = np.zeros(NT)
- self.C = np.zeros(NT)
- self.CPC = np.zeros(NT)
- self.RI = np.zeros(NT)
- self.PERIODU = np.zeros(NT)
- self.CEMUTOTPER = np.zeros(NT)
-
- self.optimal_controls = np.zeros(2*NT)
-
- self.InitializeLabor(self.l, NT)
- self.InitializeTFP(self.al, NT)
- self.InitializeGrowthSigma(self.gsig, NT)
- self.InitializeSigma(self.sigma, self.gsig, self.cost1, NT)
- self.InitializeCarbonTree(self.cumetree, NT)
-
- def get_control_bounds_and_startvalue(self):
-
- NT = self.NT
- # * Control variable limits
- MIU_lo = np.full(NT, 0.01)
- MIU_up = np.full(NT, self.limmiu)
- MIU_up[0:29] = 1
- MIU_lo[0] = self.miu0
- MIU_up[0] = self.miu0
- MIU_lo[MIU_lo == MIU_up] = 0.99999*MIU_lo[MIU_lo == MIU_up]
- bnds1 = []
- for i in range(NT):
- bnds1.append((MIU_lo[i], MIU_up[i]))
-
- lag10 = np.arange(1, NT+1) > NT - 10
- S_lo = np.full(NT, 1e-1)
- S_lo[lag10] = self.optlrsav
- S_up = np.full(NT, 0.9)
- S_up[lag10] = self.optlrsav
- S_lo[S_lo == S_up] = 0.99999*S_lo[S_lo == S_up]
- bnds2 = []
- for i in range(NT):
- bnds2.append((S_lo[i], S_up[i]))
- bnds = bnds1 + bnds2
-
- # starting values for the control variables:
- S_start = np.full(NT, 0.2)
- S_start[S_start < S_lo] = S_lo[S_start < S_lo]
- S_start[S_start > S_up] = S_lo[S_start > S_up]
- MIU_start = 0.99*MIU_up
- MIU_start[MIU_start < MIU_lo] = MIU_lo[MIU_start < MIU_lo]
- MIU_start[MIU_start > MIU_up] = MIU_up[MIU_start > MIU_up]
- x_start = np.concatenate([MIU_start, S_start])
-
- return x_start, bnds
-
- def fOBJ(self, controls):
- self.roll_out(controls)
- resUtility = np.zeros(1)
- self.fUTILITY(self.CEMUTOTPER, resUtility)
-
- return -1*resUtility[0]
-
- def roll_out(self, controls):
- NT = self.NT
-
- iMIU = controls[0:NT]
- iS = controls[NT:(2*NT)]
-
- for i in range(NT):
- self.K[i] = self.fK(self.K, self.I, i)
- self.YGROSS[i] = self.fYGROSS(self.al, self.l, self.K, i)
- self.EIND[i] = self.fEIND(self.YGROSS, iMIU, self.sigma, i)
- self.E[i] = self.fE(self.EIND, i)
- self.CCA[i] = self.fCCA(self.CCA, self.EIND, i)
- self.CCATOT[i] = self.fCCATOT(self.CCA, self.cumetree, i)
- self.MAT[i] = self.fMAT(self.MAT, self.MU, self.E, i)
- self.ML[i] = self.fML(self.ML, self.MU, i)
- self.MU[i] = self.fMU(self.MAT, self.MU, self.ML, i)
- self.FORC[i] = self.fFORC(self.MAT, i)
- self.TATM[i] = self.fTATM(self.TATM, self.FORC, self.TOCEAN, i)
- self.TOCEAN[i] = self.fTOCEAN(self.TATM, self.TOCEAN, i)
- self.DAMFRAC[i] = self.fDAMFRAC(self.TATM, i)
- self.DAMAGES[i] = self.fDAMAGES(self.YGROSS, self.DAMFRAC, i)
- self.ABATECOST[i] = self.fABATECOST(
- self.YGROSS, iMIU, self.cost1, i)
- self.MCABATE[i] = self.fMCABATE(iMIU, i)
- self.CPRICE[i] = self.fCPRICE(iMIU, i)
- self.YNET[i] = self.fYNET(self.YGROSS, self.DAMFRAC, i)
- self.Y[i] = self.fY(self.YNET, self.ABATECOST, i)
- self.I[i] = self.fI(iS, self.Y, i)
- self.C[i] = self.fC(self.Y, self.I, i)
- self.CPC[i] = self.fCPC(self.C, self.l, i)
- self.PERIODU[i] = self.fPERIODU(self.C, self.l, i)
- self.CEMUTOTPER[i] = self.fCEMUTOTPER(self.PERIODU, self.l, i)
-# self.RI[i] = self.fRI(self.CPC, i)
-
- def optimize_controls(self, controls_start, controls_bounds):
- result = opt.minimize(self.fOBJ, controls_start, method='SLSQP', bounds=tuple(
- controls_bounds), options={'disp': True})
- self.optimal_controls = result.x
- return result
-
- def plot_run(self, title_str):
- Tmax = 2150
- NT = self.NT
- variables = [self.optimal_controls[NT:(2*NT)], self.optimal_controls[0:NT], self.CPRICE, self.EIND, self.TATM, self.DAMAGES, self.MAT,
- self.E]
- variables = [var[self.TT < Tmax] for var in variables]
- variable_labels = ["Saving rate",
- "Em rate", # 'Carbon emission control rate'
- "carbon price",
- "INdustrial emissions",
- # Increase temperature of the atmosphere (TATM)
- "Degrees C from 1900",
- "Damages", # 'trillions 2010 USD per year'
- "GtC from 1750", # 'Carbon concentration increase in the atmosphere'
- "GtCO2 per year" # Total CO2 emission
- ]
- variable_limits = [[0, 0.5], [0, 1], [0, 400], [-20, 40],
- [0, 5], [0, 150], [0, 1500], [-20, 50]] # y axis ranges
- plot_world_variables(self.TT[self.TT < Tmax], variables, variable_labels, variable_limits,
- title=title_str,figsize=[4+len(variables), 7],
- grid=True)
-
-
-def plot_world_variables(time, var_data, var_names, var_lims,
- title=None,
- figsize=None,
- dist_spines=0.09,
- grid=False):
- prop_cycle = pl.rcParams['axes.prop_cycle']
- colors = prop_cycle.by_key()['color']
-
- var_number = len(var_data)
-
- fig, host = pl.subplots(figsize=figsize)
- axs = [host, ]
- for i in range(var_number-1):
- axs.append(host.twinx())
-
- fig.subplots_adjust(left=dist_spines*2)
- for i, ax in enumerate(axs[1:]):
- ax.spines["left"].set_position(("axes", -(i + 1)*dist_spines))
- ax.spines["left"].set_visible(True)
- ax.yaxis.set_label_position('left')
- ax.yaxis.set_ticks_position('left')
-
- ps = []
- for ax, label, ydata, color in zip(axs, var_names, var_data, colors):
- ps.append(ax.plot(time, ydata, label=label,
- color=color, clip_on=False)[0])
- axs[0].grid(grid)
- axs[0].set_xlim(time[0], time[-1])
-
- for ax, lim in zip(axs, var_lims):
- ax.set_ylim(lim[0], lim[1])
-
- for axit, ax_ in enumerate(axs):
- ax_.tick_params(axis='y', rotation=90)
- ax_.yaxis.set_major_locator(pl.MaxNLocator(5))
- formatter_ = EngFormatter(places=0, sep="\N{THIN SPACE}")
- ax_.yaxis.set_major_formatter(formatter_)
-
- tkw = dict(size=4, width=1.5)
- axs[0].set_xlabel("time [years]")
- axs[0].tick_params(axis='x', **tkw)
- for i, (ax, p) in enumerate(zip(axs, ps)):
- ax.set_ylabel(p.get_label(), rotation=25)
- ax.yaxis.label.set_color(p.get_color())
- ax.tick_params(axis='y', colors=p.get_color(), **tkw)
- ax.yaxis.set_label_coords(-i*dist_spines, 1.01)
- axs[0].set_title(title)
-
-
-def hello_world():
- dice = DICE()
- dice.init_parameters()
- dice.init_variables()
- controls_start, controls_bounds = dice.get_control_bounds_and_startvalue()
- dice.optimize_controls(controls_start, controls_bounds)
- dice.roll_out(dice.optimal_controls)
- dice.plot_run()
diff --git a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/further_reading.md b/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/further_reading.md
deleted file mode 100644
index cc972adcd..000000000
--- a/tutorials/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis/student/further_reading.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# **Tutorial 2 Further Reading**
-
-## **IAM Model Summary**
-The economy model in most IAMs is a capital accumulation model.
-- Capital combines with a laboring population and technology to generate productivity ($Y$) that is hindered by climate damage.
-- A savings fraction of production drives capital accumulation, while the rest is consumed. Welfare is determined by consumption.
-- Climate action is formulated by a mitigation rate, $\mu$, which along with the savings rate are the two control parameters in the model.
-- These are used to maximize welfare.
-
-The climate model in DICE could be improved (c.f. [this study](https://www3.nd.edu/~nmark/Climate/DICE-simplified_2019.pdf)). We only summarize here how it interacts with the economy model:
-- Productivity generates industrial emissions, $$E_\mathrm{ind}=(1-\mu)\sigma Y,$$ where the $1-\mu$ factor accounts for reduced carbon intensity of production, $\sigma$, via supply-side mitigation measures (e.g. increased efficiency).
-- The productivity $Y$ rather than output production ($Q$) see model is used here because damages aren't included.
-- Namely, the emissions produced in the process of capital production occur before climate change has a chance to inflict damage on the produced output.
-- These emissions combine with natural emissions to drive the temperature changes appearing in the damage function, closing the economy-climate loop.
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Exogeneous Control Variables**
-There are two exogeneous variables in the model:
-- mitigation (emissions reduction) rate $\mu_t$, and
-- the savings rate $S_t$.
-
-There exists a mitigation cost associated with a specific mitigation rate, presented as a fraction of productivity, $\Lambda_t=\theta_1\mu^{\theta_2}$. This implies that increased mitigation efforts correspond to elevated costs.
-
-The savings rate $S_t$ extracts a portion of the total production to invest, thereby boosting capital. The rest of the production is then allocated for consumption.
-
-## **Economy Model Summary**
-The essence of the economy model in DICE is a capital accumulation model. Existing capital depreciates at rate $\delta$ and new capital arises through investment,
-$$K_{t+1}=(1-\delta)K_t+I_t$$
-where the invested capital $I=SQ$ is determined by a chosen fraction $S$ of the production $Q$ that is "saved" rather than consumed. Production is given as the productivity $Y$ reduced by damages and mitigation cost, $$Q=(1-\Omega)(1-\Lambda)Y.$$ Productivity, $$Y=A K^\gamma L^{1-\gamma},$$
-is determined by the technology conversion $A$ operating on a combination of capital $K$ and labor $L$ whose relative contributions are set by the capital elasticity parameter $\gamma$. Labor is population which set to saturate over the 2nd half of the 21st century. Technology conversion is only weakly sigmoidal in time, deviating slightly from linear growth.
-
-The remaining production is consumed $$C:=(1-S)Q$$ producing utility $$U(C,L)=Lu(c)=Lu(C/L),$$ using the isoelastic utility function, $$u(c)=\frac{(c+1)^{1-\alpha}-1}{1-\alpha}.$$ The overall value of a projected future is then $$V=\sum_{t=1}^{\infty}\gamma^t U(C_t,L_t),$$ where $\gamma=1/(1+\rho)$ for discount rate $\rho$ and we use the population level utility function $U(C,L)=Lu(C/L)$.
-
-
-Here are a list of variables used:
-- $K$ capital
-- $Y$ productivity
-- $Q$ production
-- $A$ technology conversion
-- $S$ savings rate
-- $\mu$ mitigation rate
-- $\Lambda$ mitigation cost
-- $\Omega$ damage fraction of productivity
-- $\sigma$ carbon intensity of production
-- $E$ emissions
-
-## **Optimal Planning**
-The unconstrained problem aims to maximize $V$ within the bounds of $\mu_t$ and $S_t$ time courses (while considering constraints on $\mu$ and $S$). Why is there a "sweet spot"? Increasing savings boosts investment and productivity, but higher production leads to emissions, resulting in increased temperature and damages that reduce production. Mitigation costs counterbalance this effect, creating a trade-off. As a result, there typically exists a meaningful joint time series of $\mu_t$ and $S_t$ that maximizes $V$. Due to the discount factor $\gamma$, $V$ depends on the future consumption sequence (non-invested production) within a few multiples of the horizon, approximately $1/(1-\gamma)$ time steps into the future.
-
-Constrained formulations introduce an additional constraint to limit industrial emissions below a certain threshold.
-
-## **Social Cost of Carbon**
-A definition for the social cost of carbon (SCC) is
-
-- *the decrease in aggregate consumption in that year that would change the current...value of social welfare by the same amount as a one unit increase in carbon emissions in that year.* ([Newbold, Griffiths, Moore, Wolverton, & Kopits, 2013](https://www.worldscientific.com/doi/abs/10.1142/S2010007813500012)).
-
-The $SCC$ quantifies how much consumption is lost with increased emissions, using changes in welfare to make the connection. In technical terms:
-
-- the marginal value with respect to emissions relative to the marginal value with respect to consumption, $$SCC_t\propto\frac{\partial V/\partial E_t}{\partial V/\partial C_t}=\frac{\partial C_t}{\partial E_t}.$$
-This is usually expressed by multiplying by a proportionality factor of $-1000$ that converts the units to 2010 US dollars per tonne of CO2.
-
-# **Tutorial 4 Further Reading**
-
-## **Vectorization Methods for Creating Word Clouds**
-
-Let's write down what they compute by denoting the index, $d$, over the $D$ documents and the index, $w$, over the $W$ words in the vocabulary (the list of all the words found in all the tweets, which we'll call documents):
-- term frequency, $\mathrm{tf}(w,d)$. The frequency of a word $w$ in a document $d$ is $$\mathrm{tf}(w,d):=\frac{n(w,d)}{n(d)},$$ where $n(w,d)$ is the number of times term $w$ is in document $d$ and $n(d)=\sum_{w=1}^W n(w,d)$ is the total number of words in document $d$. The term frequency over all the documents is then, $$\mathrm{tf}(w):=\frac{\sum_{d=1}^D n(d)\mathrm{tf}(w,d)}{N},$$ where the denominator $N=\sum_{d=1}^D n(d)$ is just the total word count across all documents.
-- term frequency-inverse document frequency, $\mathrm{Tfidf}(w,d):=\mathrm{tf}(w,d)\mathrm{idf}(w)$. Here, $$\mathrm{idf}(w)=\frac{\log(D+1)}{\log(n(w)+1)+1},$$ where $n(w)$ is the number of documents in which term $t$ appears, i.e. $n(w,d)>0$. Idf is like an inverse document frequency. The `sklearn` package then uses $$\mathrm{Tfidf}(w)=\frac{1}{D}\sum_{d=1}^D \frac{\mathrm{Tfidf}(w,d)}{||\mathrm{Tfidf}(\cdot,d)||},$$ where $||\vec{x}||=\sqrt{\sum_{i=1}^Nx^2_i}$ is the Euclidean norm.
-
-$\mathrm{Tfidf}$ aims to add more discriminability to frequency as a word relevance metric by downweighting words that appear in many documents since these common words are less discriminative.
\ No newline at end of file