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Paying for Drawdown: the Value of Commitment on the Cost of Ending Global Warming

Paying for Drawdown: the Value of Commitment on the Cost of Ending Global Warming

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Authors

John F. Raffensperger 

Abstract

This paper develops a mechanism to pay for drawing down excess atmospheric carbon dioxide while avoiding third party payments. Assuming this mechanism, the paper investigates the costs of reversing global warming under different levels of commitment. The costs are based on simulated auctions for emissions and carbon removal to reach a climate goal by a particular date. The paper describes a method to model and price carbon removal contracts, and estimates the value of commitment to strong versus weak contracts. The least cost trajectory requires long commitments to emissions reductions and carbon removal. The paper estimates the value of long-run versus short-run commitments and the value of the ability to manage revenue across decades. The models constrain warming robustly under different discount rates. Hotelling’s rule does not apply because carbon removal makes the atmosphere a renewable resource. For reducing temperature 1.4°C by 2125, estimates range from $20.4 trillion down to $10.84 trillion (present value over 100 years at 3%) depending on commitment. These estimates for reversing global warming are far lower than other researchers’ estimates simply for keeping temperature to 1.5°C. The models could be used in trading. The paper shows that drawdown costs less when emitters pay for it than when a third party pays for it.

DOI

https://doi.org/10.31223/X5XN1Q

Subjects

Environmental Studies

Keywords

climate change, auctions, linear programming

Dates

Published: 2025-11-13 09:21

Last Updated: 2025-11-13 09:21

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The open-source Hector climate simulator is available at https://jgcri.github.io/hector/. Python code to pull the warming matrix from Hector, the SMDAMAGE family of models, the calibration model, all data, and full output is available at https://github.com/JohnFRaffensperger/SMDAMAGE. All data is taken from the literature and is referenced in the paper.

Conflict of interest statement:
None