Tutorial 5: Mapping the Narrative Space#
Week 2, Day 3: The Socioeconomics of Climate Change
Content creators: Paul Heubel, Maximilian Puelma Touzel
Content reviewers: Jenna Pearson, Chi Zhang, Ohad Zivan
Content editors: Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan
Production editors: Wesley Banfield, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan
Our 2024 Sponsors: CMIP, NFDI4Earth
Tutorial objectives#
# import
import matplotlib.pyplot as plt
import numpy as np
#import dicelib # https://github.com/mptouzel/PyDICE
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/W2D3_FutureClimate-IPCCII&IIISocio-EconomicBasis" # 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
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 description of IAMs:
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.
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.
In En-ROADS, there are exogenous parameters, in particular: - \(\mu(t)\): time-dependent mitigation rate (i.e. emissions reduction), which limits warming-caused damages - \(S(t)\): savings rate, which drives capital investment
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).