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Causal Carbon: Baselines and Additionality with Potential Outcomes

Author

Listed:
  • Ayers, Megan
  • Sanford, Luke
  • Gardner, Will
  • Kuebbing, Sara

Abstract

Recent work has questioned the credibility of forest carbon offsets as an environmental intervention and nature-based solution for mitigating climate change. Despite some updates to carbon credit methodologies and advice to purchase only high-integrity or high-quality credits, it is not clear which carbon offsets meet these standards under which conditions. In this paper, we draw on the fields of statistics and causal inference to develop a generalized framework for analyzing carbon offset protocols. We show that strategic enrollment combined with even seemingly innocuous measurement errors in carbon stocks can lead to market distortions and that there is an inherent tradeoff between minimizing these distortions and broadening enrollment. The provided framework clarifies what purchasers of carbon offsets must believe about the world in order for purchased credits under each protocol to accurately reflect the impact of crediting programs and builds common ground on which more fruitful engagement between different sectors of the carbon market can build agreement.

Suggested Citation

  • Ayers, Megan & Sanford, Luke & Gardner, Will & Kuebbing, Sara, 2025. "Causal Carbon: Baselines and Additionality with Potential Outcomes," OSF Preprints 5pcuh_v2, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:5pcuh_v2
    DOI: 10.31219/osf.io/5pcuh_v2
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    References listed on IDEAS

    as
    1. Delacote, Philippe & Le Velly, Gwenolé & Simonet, Gabriela, 2022. "Revisiting the location bias and additionality of REDD+ projects: the role of project proponents status and certification," Resource and Energy Economics, Elsevier, vol. 67(C).
    2. Philippe Delacote & Tara L’Horty & Andreas Kontoleon & Thales A. P. West & Anna Creti & Ben Filewod & Gwenole LeVelly & Alejandro Guizar-Coutiño & Ben Groom & Micah Elias, 2024. "Strong transparency required for carbon credit mechanisms," Nature Sustainability, Nature, vol. 7(6), pages 706-713, June.
    3. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
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