Lecture 1 Introduction: Grand Challenges

Gang He

September 9, 2024

Welcome and icebreaker

• Rose: “something that is working well or something positive”
• Thorn: “something that isn’t working or something negative”
• Bud: “an area of opportunity or idea yet to be explored”

What brings you here?

• Concerned topics?
• Motivations?
• Dream jobs?

Not fast enough

How the exponential growth of solar will change the energy sector and the economy?

Why PAF9199?

• Policy matters
  • Technology innovation
  • Social change
  • Research
  • Jobs
• Clean energy transition to achieve climate goals

Organization

• Syllabus
• Brightspace
• Course website: https://drganghe.github.io/energy-climate-policy/
• Policy responses (10): 30%
• Assignments (4): 20%
• Proposal: 10%
• Final presentations: 20%
• Policy memo: 20%

Climate guest speakers

• 9/16 – Richard Morse: Renewable energy investments
• 9/23 – Shirley Gallagher: My Sustainable Journey and the skills used to contribute to climate solutions
• 9/30 – Kaifang Luo: Clean technology deployment and impact
• 10/7 – Simi George: Storage and regulation
• 10/15 – Maryam Mozafari: Transitioning from fossil fuels to renewable energy
• 10/21 – Fabio Passaro: Steel & cement sectors’ decarbonization
• 10/28 – Liqun Peng: Power sector decarbonization
• 11/4 – Samuel Vaillancourt: Gold mining decarbonization
• 11/11 – Bharathi Esakkimuthu: Climate career options and essential skills
• 11/18 – Mohsen Fatemi: City government implementation

Contents

• Climate change
  • Impact
  • Mitigation
  • Adaption
  • Governance

• Energy systems
  • Supply
  • Demand
  • Economics
  • Technology

• Tools/skills
  • Data analysis
  • Economic analysis
  • Energy-economy-environment (nexus)

A five points “climate haiku”

1. It’s warming
2. It’s us
3. We’re sure
4. It’s bad
5. We can fix it

An analytical approach

Data-driven, evidence-based, energy and climate policy

Using analytic tool to answer questions such as:

• What will be the climate future if we do nothing?
• What technology to invest to achieve our climate goal?
• What will be the impact to carbon emission of IRA?
• How NYS/NYC could achieve its climate goals?

Two quotes about models

George Box:

All models are wrong, but some are useful.

Albert Einstein:

Everything should be made as simple as possible, but not simpler.

Models can be useful

• Prediction/Projection
• Simulation
• Optimization
• Control
• Stochastic/dynamic
• Policy: Scenarios

Models help us think

Modeling is hard

Why?

• Risk: System structural change could break our core data and assumptions that are based on historical experience. Example: shale gas revolution.

• Uncertainty: The exact timing and character of pivotal events and technology changes is unpredictable. Example: renewables cost changes, fussion.

Why: climate and energy systems are both complicated

The Energy Trillemma: Balancing trade-offs

Energy security: Ensure reliable energy supply

Energy equity: Provide universal access to reliable, affordable, and abundant energy

Environmental Sustainability: Avoid environmental harm or climate impact

Those goals sometimes conflict with each other, and decisions has to make trade-offs between them

Energy, economy, and environment

Economy: Decent living? Growth? Degrowth?

Environment: Emissions, ecosystems constraints/goals

Energy: Work within constraints

Energy great achievement

Energy grand challenges: SDGs

Energy grand challenges: net-zero

Modeling can be useful/insightful (an example)

• Build the structure
• Demonstrate the relation
• Visualize the changes
• Inform the impacts

Renewables are achieving grid parity: a structural change

Implications are profound

End with two quotes

Bill Hogan:

It is not the individual results of a model that are so important; it is the improved user appreciation of the policy problem that is the greatest contribution of modeling.

Huntington, Weyant, Sweeney “Modeling for insights, not numbers”:

The primary goal of policy modeling should be the insights quantitative models can provide, not the precise-looking projections –i.e. numbers – they can produce for any given scenario.

References

Grant, Neil, Adam Hawkes, Tamaryn Napp, and Ajay Gambhir. 2021. “Cost Reductions in Renewables Can Substantially Erode the Value of Carbon Capture and Storage in Mitigation Pathways.” One Earth 4 (11): 1588–1601. https://doi.org/10.1016/j.oneear.2021.10.024.

He, Gang, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar, and Amol Phadke. 2020. “Rapid Cost Decrease of Renewables and Storage Accelerates the Decarbonization of China’s Power System.” Nature Communications 11 (1): 2486. https://doi.org/10.1038/s41467-020-16184-x.

Hogan, William W. 2002. “Energy Modeling for Policy Studies.” Operations Research 50 (1): 89–95. https://doi.org/10.1287/opre.50.1.89.17803.

Huntington, Hillard G, John P Weyant, and James L Sweeney. 1982. “Modeling for Insights, Not Numbers: The Experiences of the Energy Modeling Forum.” Omega 10 (5): 449–62. https://doi.org/10.1016/0305-0483(82)90002-0.

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

IRENA. 2021. “Renewable Power Generation Costs in 2021.” Abu Dhabi: International Renewable Energy Agency. https://www.irena.org/publications/2022/Jul/Renewable-Power-Generation-Costs-in-2021.