Tutorial 3: The Temporal Dimension of Actions#
Week 2, Day 2: The Socioeconomics of Climate Change
Content creators: Paul Heubel, Maximilian Puelma Touzel
Content reviewers: Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Jenna Pearson, Abel Shibu, Ohad Zivan
Content editors: Paul Heubel, Jenna Pearson, Chi Zhang, Ohad Zivan
Production editors: Wesley Banfield, Paul Heubel, Jenna Pearson, Konstantine Tsafatinos, Chi Zhang, Ohad Zivan
Our 2024 Sponsors: CMIP, NFDI4Earth
Tutorial Objectives#
Estimated timing of tutorial: 20 minutes
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.
After finishing this tutorial you can
exemplify along the carbon price, why policy-making has to consider the temporal dimension and the needs of the most vulnerable parts of society
# 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_T3"
[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: pip install --upgrade pip
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: Orienting inside a ‘Climate Solution’ Simulator#
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Orienting_inside_a_Climate_Solution_Simulator_Video")
If you want to download the slides: https://osf.io/download/mtyrb/
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Orienting_inside_a_Climate_Solution_Simulator_Slides")
Section 1: When and How Do You Introduce a Carbon Price?#
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.
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.
Exercise 1: Implications of Actions and Their Timing#
Estimated timing: 10 minutes
Open En-ROADS here. (Note the control panel is available in various languages - check the left of the panel of the simulator that should by default show “English”.)
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.
Click on the title of the top right graph Greenhouse Gas Net Emissions, select the ‘Financial’ toggle, and select the ‘Market price of electricity’.
Turn the most upper knobs/ vary the sliders of the ‘Carbon price’ and slide through different carbon prices.
Move the sliders to answer the following question. Use the following cheatsheet if needed.
Answer the following questions:
What do you observe in the ‘Market price of electricity’ graph after varying the carbon price?
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’.
How could this be of consequence for a low-income household?
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’.
Note that your changes are reflected in the light blue graph, while the baseline scenario remains a black line.
Submit your feedback#
Show code cell source
# @title Submit your feedback
content_review(f"{feedback_prefix}_Exercise_1")
Summary#
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.
Resources#
This tutorial is inspired by teaching material from Climate Interactive and other documents. A few important resources are linked below: