Energy and Climate Policy – Lecture 12 Policy Analysis Uncertainties, Review and Summary

Sample analytic questions

How do I know if I should trust policy analysis results?
What are the methods to address uncertainties?
What are the emerging topics to improve modeling analysis?
How to interpret modeling results to communicate effectively?
Where to use model? How to build your analysis?

Projections went off

IEA underestimates solar and wind

Science models are in a better position

Conditions for model robustness

Models can be accurate when they describe systems that

are observable and measurable
exhibit constancy of structure over time
exhibit constancy across variations in conditions not specified in the model
permit the collection of ample data

Social economic structural changes

Core data and assumptions, which drive results, are based on historical experiences, which can be off if structural conditions change
The exact timing and character of pivotal events and technology changes is unpredictable

Changing landscape of new power generation capacities

Shale gas revolution

Renewable costs

Mission impossible

Predict the unpredictable
Price the priceless

Better modeling uncertainties

Define an endpoint of analysis
List all uncertain parameters
Specify maximum range of values
Specify subjective probability distribution for values within the range
Determin and account for correlations
Probability distribution of model predictions
Derive quantitative uncertainties
Obtain additional data and repeat analysis if needed

Incoporating political economy

Examples

Risk averse investors
Subsidies or taxes
Carbon lock-in
Unequal costs and benefits of climate polices accrue to different groups
Public opinion
Confidence in political institutions
Trade and investments
Competence of goverment

Why a model in the first place

Description
Explanation
Experimentation
Providing sources of analogy
Communication/Education
Providing focal objects or centerpiece for scientific dialogue
Thought experiment
Projection

Modeling for insights

Modeling based on research questions
Modeling elements, structure, relations
Do not over-interpret
Do not misinterpret
“All models are wrong, but some are useful”. Avoid useless models.

Focusing on structures and relations

Knowing limitations

Known unknowns
Unknown unknowns
“Garbage in, garbage out”

Interpreting results

Results are outcomes of assumptions and models
Results does not automatically translate to policies
Communicating the limitations and uncertainties

Open source

Open model (code)
Open data
Open results
Open validation

In Open we Trust!

Ensure model for society

Mind the assumptions
Mind the hubris
Mind the framing
Mind the consequences
Mind the unknowns
Questions not answers

Summary

Know the limitation of models
Use model appropriately
Interpret/communicate the results effectively
Models for social good

Review and summary

What have we talked and learned?

Energy grand challenges: net-zero

A five points “climate haiku”

It’s warming
It’s us
We’re sure
It’s bad
We can fix it

Energy vs power

Energy is the ability to do work. Energy is power integrated over time.
Basic unit: joule = watt·second

Power is the rate at which work is done, or energy is transmitted.
Basic unit: watt = jourle/second

We need to know what we are talking about!

Common sources of energy and climate data

Energy:
- IEA (OECD)
- EIA
- UN
- WB
- BP Statistical Review of World Energy

Climate:
- NASA
- NCAR
- EUCCI
- UNEP
- NOAA

Carbon:
- CDIAC (Carbon Dioxide Information Analysis Center)
- EDGAR (emissions database for global atmospheric research system)
- Carbon Budget Project
- Carbon Monitor

Human component is the major driver of warming

Social cost of carbon

Marginal cost of carbon
Cost included:
- Net agricultural productivity
- Human health
- Property damage
- Energy system costs
Cost not included:
- Unknown impact: physical, ecological, economic
- Unknown cost: information

Kaya identify

\(F=P \times \frac{G}{P} \times \frac{E}{G} \times \frac{F}{E}\)

Where:

F: global CO2 emissions from human sources
P: global population
G: world GDP
E: global energy consumption

And:

G/P: GDP per capita
E/G: energy intensity of the GDP
F/E: emission intensity of energy

Risks and resilience

Energy systems

Energy project economics

NPV, discounting, LCOE, learning rate
Project economics is useful for basic cost-benefit analysis
Getting the price (discounting) right
Understanding technology dynamics will help to model future projections
Aware of the limitations

Thermodynamics

Thermodynamic efficiency
Comparing different technologies
Thermodynamics provides physic limits

Brayton cycle vs. Rankine cycle

Brayton Cycle	Rankine Cycle
Jet, Gas turbine	Steam turbine
Open	Open/closed circuits
Working fluid in gaseous phase	Working fluid phase change

Defining a Rosenfeld Plant

500 MW
c.f.: 70%
T&D losss: 7%

Results:

3 TWh/year
3 MtCO2/year
NYC electricity use: ~4 TWh/year

P-N Junction

Wind

\(P=\frac{1}{2}\rho \pi r^2 v^3\)

Where,
\(\rho\) = Air Density (\(kg/m^3\))
\(A\) = Swept Area (m2) = \(\pi r^2\)
\(v\) = Wind Speed (m/s)
\(P\) = Power (W)

Nuclear

Nuclear fission

Nuclear fussion

Hydro

Hydropower

Pumped storage hydropower (PSH)

\(E=\rho mg(h_2-h_1)\)

Energy demand

Energy demand is shaped by dynamic factors: populations, GDP, technology, regulation, and human behavior
Understanding those drivers will help us learn the magnitude and profile of our demand
Load forecast integrates all insights to the show

Energy efficiency

There are barriers for energy efficiency and inforamtion and standards can help address the energy efficiency gap
Human behavior is complicated, and understanding it will help us to design policy
Rebound is real but might be overplayed, rebound could be good if improve welfare
Sustainable consumption and production within the limit

Energy access and energy justice

Electricity/energy access is fundamental to other modern services: education, health, and information
Energy access also has gender equity, health cobenefits, and other implications
Energy justice is important to achieve just transition
An reflection on our approaches to energy justice

Energy, environment, and human health

Environmental and health impacts are the externalities of energy systems
Energy-air-pollution-human health framework of analysis
Energy-water-carbon nexus
System impacts show the constraint in systems modeling

Energy transition

Diverse factors that drive energy transition
Common and differentiated solutions to energy
Leverage the advantages
Addressing uncertainties: geopolitics, wars, pandemic, and more

Power sector decarbonization

Power sector’s central role in decarbonization
Capacity expansion model to analyze the optimized investment decisions
Decisions in the real world is much more complicated
Emerging trends in the power sector

Big data and AI

AI and big data has transformed how we live, and how can we save the planet
AI does not automatic add new insights, but it provides powerful tools to analyze increasingly available big data
Learning those new tools and skills becomes necessary to work in the field
The potential of using AI/big data to analyze our understanding of energy supply/demand and human behavior is enomorous.

Uncertainies and limitation

Know the limitation of models
Use model appropriately
Interpret/communicate the results effectively
Models for social good

References

Craig, Paul P, Ashok Gadgil, and Jonathan G Koomey. 2002. “What Can History Teach Us? A Retrospective Examination of Long-Term Energy Forecasts for the United States.” Annual Review of Energy and the Environment 27 (1): 83–118. https://doi.org/10.1146/annurev.energy.27.122001.083425.

Hausfather, Zeke, Henri F Drake, Tristan Abbott, and Gavin A Schmidt. 2020. “Evaluating the Performance of Past Climate Model Projections.” Geophysical Research Letters 47 (1): e2019GL085378. https://doi.org/10.1029/2019GL085378.

Hodges, James S, James A Dewar, et al. 1992. “Is It You or Your Model Talking?: A Framework for Model Validation.” Rand Santa Monica, CA. https://www.rand.org/pubs/reports/R4114.html.

IEA. 2021. “Net Zero by 2050.” International Energy Agency. https://www.iea.org/reports/net-zero-by-2050.

Kaya, Yoichi, and Keiichi Yokobori, eds. 1997. Environment, Energy, and Economy: Strategies for Sustainable. Tokyo: United Nations Univ. https://archive.unu.edu/unupress/unupbooks/uu17ee/uu17ee00.htm.

Koomey, Jonathan, Hashem Akbari, Carl Blumstein, Marilyn Brown, Richard Brown, Chris Calwell, Sheryl Carter, et al. 2010. “Defining a Standard Metric for Electricity Savings.” Environmental Research Letters 5 (1): 014017. https://iopscience.iop.org/article/10.1088/1748-9326/5/1/014017.

Macknick, Jordan. 2011. “Energy and CO2 Emission Data Uncertainties.” Carbon Management 2 (2): 189–205. https://doi.org/10.4155/cmt.11.10.

Peng, Wei, Gokul Iyer, Matthew Binsted, Jennifer Marlon, Leon Clarke, James A Edmonds, and David G Victor. 2021. “The Surprisingly Inexpensive Cost of State-Driven Emission Control Strategies.” Nature Climate Change 11 (9): 738–45. https://doi.org/10.1038/s41558-021-01128-0.

Peng, Wei, Gokul Iyer, Valentina Bosetti, Vaibhav Chaturvedi, James Edmonds, Allen A Fawcett, Stéphane Hallegatte, David G Victor, Detlef van Vuuren, and John Weyant. 2021. “Climate Policy Models Need to Get Real about People—Here’s How.” Nature Publishing Group. https://doi.org/10.1038/d41586-021-01500-2.

Saltelli, Andrea, Gabriele Bammer, Isabelle Bruno, Erica Charters, Monica Di Fiore, Emmanuel Didier, Wendy Nelson Espeland, et al. 2020. “Five Ways to Ensure That Models Serve Society: A Manifesto.” Nature Publishing Group. https://doi.org/10.1038/d41586-020-01812-9.

Supran, G., S. Rahmstorf, and N. Oreskes. 2023. “Assessing ExxonMobil’s Global Warming Projections.” Science 379 (6628): eabk0063. https://doi.org/10.1126/science.abk0063.