The Pace and Impact of Deep Decarbonization: Ways to Make Model Simulations More Useful and Realistic | 9am PT, Tues Feb 10, 2025

[Grigory Bronevetsky]

Modeling Talks

The Pace and Impact of Deep Decarbonization:  Ways to Make Model Simulations More Useful and Realistic David Victor, UCSD Tues, Feb 10, 2026 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/the-pace-and-impact-of-deep-decarbonization-ways-to-make-model-simulation More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: The bread-and-butter of integrated assessment modeling has been the study of deep decarbonization needed to meet widely discussed climate policy goals such as stopping the rise in temperatures or cutting net emissions to zero.  A troubling element of these models is their lack, to varying degrees, of proper representation of how policies are formulated and the impacts of policies and changes in technology on patterns of investment in clean technologies.  In some ways, the models have yielded abundant elegant insights into deep decarbonization strategies that, increasingly, don’t reflect reality.  This talk will outline some ways to improve the models and show suggestive results from a decade of collaboration with different modeling teams.  The net effect of adding more realism is to increase pessimism about meeting global climate goals in the near term and more optimism about the long term.  More realistic model assessments suggest that carbon removal technologies and climate resilience (and possibly geoengineering) need more policy priority.  Realism also has implications for the geography of climate policy action and investment—with Europe occupying a more central role and the United States becoming less reliably relevant.   Bio:  David G. Victor is a distinguished professor of innovation and public policy at the School of Global Policy and Strategy at UC San Diego and also a professor in Climate, Atmospheric Science & Physical Oceanography at the Scripps Institution of Oceanography. He directs the UCSD-wide Deep Decarbonization Initiative (D2I) where his research focuses on the engineering, economic and political challenges associated with slashing emissions of warming gases and removing the gases that have already accumulated in the atmosphere and oceans.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/RxU1M3kWQQc

Slides:

- pdf: https://drive.google.com/file/d/11Q34s1QUueOy5ajW4CIGurE1QCNDxMQd/view?usp=sharing
- pptx: https://docs.google.com/presentation/d/1FzjfPXJtL1rBls70agElobtXYDB-aQNm/edit?usp=sharing&ouid=110584304018462442605&rtpof=true&sd=true

Summary:

• Focus: how to improve Integrated Assessment Models (IAMs) so that they are more predictive and informative

• IAMs are a key tool for assessing climate policy and produce many forecasts that look at climate, energy, agriculture, investment, etc.

• E.g. global uniform carbon tax is more tractable but highly unlikely

• Modeling Strategy: focus on most important and tractable phenomena

• Carbon caps and taxes

• Huge variation in Policy challenges across the world and states within US

• Question: how inefficient is it for states within a country to set their own climate policies vs a single national one?

• IAMs suggest that the inefficiency is not high as long as there is smooth trade of power and po

• Decarbonization via electrification / More capital-intensive energy system

• small differences in capital costs will inform whether investments are/aren't profitable

• Cost of capital varies across the world (higher in more risky countries, lower in more stable ones); makes it harder to upgrade infrastructure in developing world

• This raises the carbon price needed to motivate investment

• Indeed, we now see that most infrastructure investment in green tech is happening in stable, developed countries

• Investors have limited visibility into the future

• Modeled by having the modeled agents anticipate the future with varying levels of precision or time horizon (e.g. 0-5y, ~8y, 15+y)

• New tech: by assumption and learning-by-doing efficiency improvements

• Tech adoption: S-shaped curve: 

• Slow development and unpopular

• Fast adoption and politically popular (lots of investment)

• New tech firms have money use it to change competitive landscape

• Lobbying for more favorable laws

• Maturity, replacement by next tech

• Thus, once decarbonization technologies take off, it will get encoded in public policy

• Stringent climate goals: carbon removal deployed globally

• The longer you wait to stop polluting, the more the carbon stocks rise

• As such, if the policy goal is to keep warming below a certain temperature, carbon removal is required

• Big Question: how do we get from today’s small carbon removal industry/research to the global multi-GT CO2 deployment we need by 2040-2050

• Example: growing more row crops with deeper/more durable roots; better carbon storage (decades to centuries)

• How quickly can this be put into practice?

• Looking back on the adoption of GMO crops: change happens over 5-10 years (where legally allowed)

• Suggests that this transition can remove a few GTons/year and is quickly deployable

• Example: increasing ocean alkalinity

• How quickly can this be deployed

• Analogues?

• Crushed stone production (sand, gravel, lime)

• Aquaculture, desalinization

• Took a few decades to deploy

• Example: direct air capture

• Analogues: chemical synthesis projects, fertilizer plants

• These technologies have taken decades in the past

• Recommendation for IAMs: make adoption of carbon removal slower and different adoption curves for each tech

• Recommendation for decarbonization: focus on investor, developer incentives for executing deployment and profiting from it

• Collaboration in emissions

• There are many externalities: cutting emissions has a private cost and public benefit

• Best-case scenario: global enforcement of agreements and high joint gains

• In a more chaotic world: little global enforcement, while maintaining joint gains

• Europe is developing decarbonization clubs

• Can bring in China

• US is outside of this process because it is currently unreliable

• Capital is key: need to pay for the power grid to decarbonize; can only be done in lower risk/cost-of-capital scenarios

• Roles in decarbonization for government

• Direction/orchestration

• Payment

• Chiuna: govt does both

• Europe: govt directs, private industry pays

• US (old): govt pays, private directs

• US (today): all private

• IAM literature over-focuses on high-confidence results since they’re inter-comparable

• E.g. IPCC has many more high-confidence statements than med-low confidence

• This short-changes important results that are harder to model

• Makes it hard to motivate work on resolving phenomena that have low confidence that are