Tutorial 5: Mapping the Narrative Space#
Week 2, Day 2: The Socioeconomics of Climate Change
Content creators: Paul Heubel, Maximilian Puelma Touzel
Content reviewers: Jenna Pearson, Ohad Zivan
Content editors: Paul Heubel, Jenna Pearson, Ohad Zivan
Production editors: Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan
Our 2024 Sponsors: CMIP, NFDI4Earth
Tutorial objectives#
Estimated timing of tutorial: 25 minutes
In this tutorial, you discuss the narratives of the SSP framework and step back to reflect on the framework’s assumptions and their consequences for modeling socioeconomics and climate futures. It further emphasizes how equity aspects need to be thought of when solving the climate crisis.
After working through this Tutorial you know how to
map Shared Socioeconomic Pathways in a feature space of equality and environmental health,
reflect on narratives when discussing future scenarios and their socioeconomic implications.
# import
import matplotlib.pyplot as plt
Install and import feedback gadget#
Show code cell source
# @title Install and import feedback gadget
!pip3 install vibecheck datatops --quiet
from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
return DatatopsContentReviewContainer(
"", # No text prompt
notebook_section,
{
"url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
"name": "comptools_4clim",
"user_key": "l5jpxuee",
},
).render()
feedback_prefix = "W2D2_T5"
Figure settings#
Show code cell source
# @title Figure settings
import ipywidgets as widgets # interactive display
%config InlineBackend.figure_format = 'retina'
plt.style.use(
"https://raw.githubusercontent.com/neuromatch/climate-course-content/main/cma.mplstyle"
)
Helper functions#
Show code cell source
# @title Helper functions
def pooch_load(filelocation=None, filename=None, processor=None):
shared_location = "/home/jovyan/shared/Data/tutorials/W2D2_TheSocioeconomicsofClimateChange" # this is different for each day
user_temp_cache = tempfile.gettempdir()
if os.path.exists(os.path.join(shared_location, filename)):
file = os.path.join(shared_location, filename)
else:
file = pooch.retrieve(
filelocation,
known_hash=None,
fname=os.path.join(user_temp_cache, filename),
processor=processor,
)
return file
Video 1: Mapping the Narrative Space#
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Mapping_the_narrative_space_Video")
If you want to download the slides: https://osf.io/download/cyavb/
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Mapping_the_Narrative_Space_Slides")
Section 1: Background on IAM Economics#
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.
Let’s begin with a brief recall of the IAM features:
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.
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).
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”.
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.
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).
Six of the most well-established IAMs have been chosen to represent a certain SSP narrative as discussed by Max in the video. Their simulations result in energy use and emissions characteristics for the respective SSP. These narratives try to describe broad socioeconomic trends that are plausible while reflecting worlds in which mitigation and adaptation challenges vary from low to very high.
Questions 1#
As a repetition and a follow-up of the tutorial video, try now to place all 5 SSP narratives in the three-dimensional feature space of Intra-nation equality, Inter-nation equality, and Environmental Health yourself. You can use the SSP narrative names as a hint.
SSP1: Sustainability
SSP2: Middle of the road
SSP3: Regional rivalry
SSP4: A road divided
SSP5: Fossil Fueled development
As an additional hint, you might want to check out detailed SSP summaries in the ‘SSP narratives’ section in this Carbon Brief article.
Which were easy to place? Which were harder? What made them easy or hard to place? Discuss with your pod.
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Questions_1")
This feature space is one way to dissect the narratives that underlie the SSPs.
A consideration of the future socio-economic situation in the light of the resulting challenges is also fruitful. For example, low challenges to mitigation and adaptation due to rapid technological development, relative global equality of income and focus on environmental sustainability arise in SSP1. SSP4, on the other hand, features similarly low challenges to mitigation due to its rapid technological development, but high challenges to climate adaptation due to persistent inequality and poverty in many parts of the world (cf. Carbon Brief Explainer, Hausfather, 2018). Note that mitigation and adaptation responses are not implemented in the SSPs, such that other studies can assess the effects of policies and of impacts by comparing outcomes to those in the reference SSPs. Hence, a large number of studies investigate a variety of policies and projected risks (cf. O’Neill et al., 2020).
Let us finally summarize a few advantages and disadvantages of the SSP framework in general.
Summary#
In this tutorial, you discussed the narratives of the SSP framework and stepped back to reflect on the framework’s assumptions and their consequences for modeling socioeconomics and climate futures. It helps to prepare a thoughtful development of your own climate scenario via En-ROADS in the next tutorial. Equality aspects, prerequisites and socioeconomic necessities as well as other implications need to be thought of when solving the climate crisis.