From 2d27e812494ad828c5cf22e1d462e7a64b39c39f Mon Sep 17 00:00:00 2001 From: Paul Heubel Date: Fri, 12 Jul 2024 22:08:24 +0200 Subject: [PATCH] Exercise update. --- .../W2D2_Tutorial4.ipynb | 2 +- .../W2D2_Tutorial5.ipynb | 90 ++++++++++++++----- 2 files changed, 69 insertions(+), 23 deletions(-) diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb index c0c11f540..e29c69ea3 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb @@ -825,7 +825,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial5.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial5.ipynb index 2e65488b6..c3bc8888c 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial5.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial5.ipynb @@ -47,9 +47,7 @@ "cell_type": "code", "execution_count": null, "id": "1af3e766-b9c3-4bf8-815c-4087e7811925", - "metadata": { - "execution": {} - }, + "metadata": {}, "outputs": [], "source": [ "# import\n", @@ -63,8 +61,7 @@ "execution_count": null, "id": "bf05ea3d-be91-4007-b4dd-210179c2f6f1", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -93,8 +90,7 @@ "execution_count": null, "id": "e7392de8-f946-453b-bedf-8a07c705ee7f", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -112,8 +108,7 @@ "execution_count": null, "id": "6c10210c-a3d9-4996-a3d3-4bafa4b72637", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -141,8 +136,7 @@ "execution_count": null, "id": "80412217-b5a7-4fed-b29e-b8751eb8c847", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -201,8 +195,7 @@ "execution_count": null, "id": "ddbb472a-a055-4f48-800b-ec9ac36cb3c6", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -215,8 +208,7 @@ "execution_count": null, "id": "2dc2f6c1-637b-4506-a667-45750dd7fbe5", "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -241,18 +233,72 @@ "\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", + "Let's begin with a brief recall of the IAM features:\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", + "- IAMs resolve the economy 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", + "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. One fundamental 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).\n", + "\n", + "As we want to discuss the background of IAM economics, you are going to reflect on these particular *Neo-classical* assumptions." + ] + }, + { + "cell_type": "markdown", + "id": "fccdeb9d-f825-415f-a947-b51230e0c331", + "metadata": {}, + "source": [ + "## Questions 1\n", + "\n", + "As a follow-up to its presentation in the tutorial video, try now to place all 5 SSP narratives in the three-dimensional feature space yourself. You can use the SSP narrative names as a hint.\n", + "\n", + "* SSP1: Sustainability\n", + "* SSP2: Middle of the road\n", + "* SSP3: Regional rivalry\n", + "* SSP4: A road divided\n", + "* SSP5: Fossil Fueled development\n", + "\n", + "Which were easy to place? Which were harder? What made them easy or hard to place? Discuss with your pod.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7b00ec8-495f-4e07-ab13-989dd26b2c92", + "metadata": {}, + "outputs": [], + "source": [ + "# to_remove explanation\n", + "'''\n", + "Guidance for TAs: The solution is already given in the slides, to further discuss these slides, please read the detailed SSP summaries in the 'SSP narratives' section from the carbon brief website under https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change/\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "id": "35fa283c-f214-45a4-9b45-bf263d989f74", + "metadata": {}, + "source": [ + "## Questions 2\n", + "\n", + "The SSP framework. Take a minute to list the strengths and weaknesses of the scenario approach to socio-economic climate projections. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8f40968-8d7a-487f-aeb3-b901ebfe53fd", + "metadata": {}, + "outputs": [], + "source": [ + "# to_remove explanation\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)." + "'''\n", + "Strength: SSPs are an approach that nicely balances (biased) expert opinion with more objective (but perhaps inaccurate) models.\n", + "Weakness: even larger set of assumptions/complexity gives over confidence?\n", + "'''" ] }, { @@ -303,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.18" } }, "nbformat": 4,