Learning lessons from over-crediting to ensure additionality in forest carbon credits

IPCC. Global Warming of 1.5 °C: IPCC Special Report on Impacts of Global Warming of 1.5 °C above Pre-Industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. (Cambridge University Press, 2022).

Edwards, D. P. et al. Conservation of tropical forests in the Anthropocene. Curr. Biol. 29, R1008–R1020 (2019).

Wright, S. J. & Muller-Landau, H. C. The future of tropical forest species. Biotropica 38, 287–301 (2006).

Vieilledent, G. et al. Spatial scenario of tropical deforestation and carbon emissions for the 21st century. bioRxiv https://doi.org/10.1101/2022.03.22.485306 (2023).

Vancutsem, C. et al. Long-term (1990-2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

UN Environment. State of Finance for Forests 2025. http://www.unep.org/resources/report/state-finance-forests-2025 (2025).

West, T. A. P., Börner, J., Sills, E. O. & Kontoleon, A. Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon. Proc. Natl. Acad. Sci. USA 117, 24188–24194 (2020).

Guizar-Coutiño, A., Jones, J. P. G., Balmford, A., Carmenta, R. & Coomes, D. A. A global evaluation of the effectiveness of voluntary REDD+ projects at reducing deforestation and degradation in the moist tropics. Conserv. Biol. 36, e13970 (2022).

West, T. A. P. et al. Action is needed to make carbon offsets from forest conservation work for climate change mitigation. Science 381, 873–877 (2023).

Probst, B. S. et al. Systematic assessment of the achieved emission reductions of carbon crediting projects. Nat. Commun. 15, 9562 (2024).

West, T. A. P., Bomfim, B. & Haya, B. K. Methodological issues with deforestation baselines compromise the integrity of carbon offsets from REDD + . Glob. Environ. Change 87, 102863 (2024).

Jones, J. P. G. Scandal in the voluntary carbon market must not impede tropical forest conservation. Nat. Ecol. Evol. 8, 1203–1204 (2024).

Forest Trends’ Ecosystem Marketplace. State of the Voluntary Carbon Market 2025. Washington DC: Forest Trends Association. https://www.ecosystemmarketplace.com/publications/2025-state-of-the-voluntary-carbon-market-sovcm/ (2025).

Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).

Ferraro, P. J., Sanchirico, J. N. & Smith, M. D. Causal inference in coupled human and natural systems. Proc. Natl. Acad. Sci. USA 116, 5311–5318 (2019).

Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2019).

Burivalova, Z., Miteva, D., Salafsky, N., Butler, R. A. & Wilcove, D. S. Evidence types and trends in tropical forest conservation literature. Trends Ecol. Evol. 34, 669–679 (2019).

Ribas, L. G. S., Pressey, R. L. & Bini, L. M. Estimating counterfactuals for evaluation of ecological and conservation impact: an introduction to matching methods. Biol. Rev. Camb. Philos. Soc. 96, 1186–1204 (2021).

Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62, 467–475 (1994).

Imbens, G. W. & Wooldridge, J. M. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47, 5–86 (2009).

Pearl, J. & Mackenzie, D. The Book Of Why: The New Science Of Cause And Effect. (Basic Books, Inc., 2018).

Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).

Tang, Y. et al. Tropical forest carbon offsets deliver partial gains amid persistent over-crediting. Science 390, 182–187 (2025).

Balmford, A. et al. PACT tropical moist forest accreditation methodology. Cambridge Open Engage. https://doi.org/10.33774/coe-2023-g584d-v2 (2023).

Guizar-Coutiño, A. et al. Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects. OSF https://osf.io/azkub_v1 (2025).

Tosteson, J., Mitchard, E. & Pauly, M. REDD+ Baselines Revised: A 20-Year Global Analysis of Carbon Crediting from Avoided Deforestation. https://everland.earth/new-research-crediting-from-redd-projects-systematically-robust/ (2024).

Pauly, M. et al. A holistic approach to assessing REDD+ forest loss baselines through ex post analysis. Environ. Res. Lett. 19, 124096 (2024).

Verra. Technical Review of West et al. 2020 and 2023, Guizar-Coutiño 2022, and Coverage in Britain’s Guardian. https://verra.org/wp-content/uploads/2023/03/Technical-Review-of-West-et-al.−2020-and-2023-Guizar-Coutino-2022-and-coverage-in-Britains-Guardian-Verra.pdf (2023).

Mitchard, E. et al. Serious errors impair an assessment of forest carbon projects: a rebuttal of West et al. https://doi.org/10.48550/arXiv.2312.06793 (2023).

Haya, B. K. et al. Quality Assessment of REDD+ Carbon Credit Projects. https://policycommons.net/artifacts/4824016/quality-assessment-of-redd-carbon-crediting/5660732/ (2023).

Blanchard, L. et al. Funding forests’ climate potential without carbon offsets. One Earth 7, 1147–1150 (2024).

Macintosh, A. et al. Carbon credits are failing to help with climate change - here’s why. Nature 646, 543–546 (2025).

West, T. A. P. et al. Demystifying the romanticized narratives about carbon credits from voluntary forest conservation. Glob. Chang. Biol. 31, e70527 (2025).

Balmford, A. et al. Credit credibility threatens forests. Science 380, 466–467 (2023).

Swinfield, T., Shrikanth, S., Bull, J. W., Madhavapeddy, A. & zu Ermgassen, S. O. S. E. Nature-based credit markets at a crossroads. Nat. Sustain. 7, 1217–1220 (2024).

Delacote, P. et al. Restoring credibility in carbon offsets through systematic ex post evaluation. Nat. Sustain. 8, 733–740 (2025).

Bos, A. B. et al. Global data and tools for local forest cover loss and REDD+ performance assessment: accuracy, uncertainty, complementarity and impact. Int. J. Appl. Earth Obs. Geoinf. 80, 295–311 (2019).

McNicol, I. M., Ryan, C. M. & Mitchard, E. T. A. Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands. Nat. Commun. 9, 3045 (2018).

Sannier, C., McRoberts, R. E. & Fichet, L.-V. Suitability of Global Forest Change data to report forest cover estimates at the national level in Gabon. Remote Sens. Environ. 173, 326–338 (2016).

Verra. VT0007, Unplanned Deforestation Allocation (UDef-A), v1.0. https://verra.org/methodologies/vt0007-unplanned-deforestation-allocation-udef-a-v1-0/ (2023).

Delacote, P. et al. Strong transparency required for carbon credit mechanisms. Nat. Sustain 7, 706–713 (2024).

Delacote, P., Le Velly, G. & Simonet, G. Revisiting the location bias and additionality of REDD+ projects: the role of project proponents' status and certification. Res. Energy Econ. 67, 101277 (2022).

Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).

Pfaff, A. & Robalino, J. Protecting forests, biodiversity, and the climate: predicting policy impact to improve policy choice. Oxf. Rev. Econ. Pol. 28, 164–179 (2012).

Sloan, S. & Pelletier, J. How accurately may we project tropical forest-cover change? A validation of a forward-looking baseline for REDD. Glob. Environ. Change 22, 440–453 (2012).

Mertz, O. et al. Uncertainty in establishing forest reference levels and predicting future forest-based carbon stocks for REDD + . J. Land Use Sci. 13, 1–15 (2018).

Verra. Verra Program Fee Schedule. https://verra.org/wp-content/uploads/2024/10/Verra-Program-Fee-Schedule-v1.0.pdf (2024).

Verra. Methodology overview. Verra https://verra.org/program-methodology/vcs-program-standard/overview/ (2024).

Cinelli, C. & Hazlett, C. Making sense of sensitivity: extending omitted variable bias. J. R. Stat. Soc. Ser. B Stat. Methodol. 82, 39–67 (2020).

Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. Am. Econ. Rev. 111, 4088–4118 (2021).

Ben-Michael, E., Feller, A. & Rothstein, J. The augmented synthetic control method. J. Am. Stat. Assoc. 116, 1789–1803 (2021).

Garcia, A. & Heilmayr, R. Impact evaluation with nonrepeatable outcomes: The case of forest conservation. J. Environ. Econ. Manag. 125, 102971 (2024).

Rau, E.-P. et al. Strengthening the integrity of REDD+ credits: objectively assessing counterfactual methods using placebos. Environ. Res. Lett. 20, 114051 (2025).

Weiss, D. et al. Global maps of travel time to healthcare facilities. Nat. Med. 26, 1835–1838 (2020).

Balmford, A. et al. Time to fix the biodiversity leak. Science 387, 720–722 (2025).

Verra. VM0048 Reducing Emissions from Deforestation and Forest Degradation, v1.0. https://verra.org/methodologies/vm0048-reducing-emissions-from-deforestation-and-forest-degradation-v1-0/ (2023).

Architecture for REDD+ transactions (ART). The REDD+ Environmental Excellence Standard (TREES). (2021). Version 2.0. Washington, DC: ART. Available at: https://www.artredd.org/trees.

Teo, H. C. et al. Uncertainties in deforestation emission baseline methodologies and implications for carbon markets. Nat. Commun. 14, 8277 (2023).

Oeko. The ICVCM approval of three REDD methodologies presents risks to the integrity of the initiative. oeko.de https://www.oeko.de/blog/the-icvcm-approval-of-three-redd-methodologies-presents-risks-to-the-integrity-of-the-initiative/ (2024).

Badgley, G. et al. Systematic over-crediting in California’s forest carbon offsets program. Glob. Chang. Biol. 28, 1433–1445 (2022).

Zu Ermgassen, S. O. S. E. et al. Evaluating the impact of biodiversity offsetting on native vegetation. Glob. Chang. Biol. 29, 4397–4411 (2023).

Hazlett, C. & Xu, Y. Trajectory balancing: A general reweighting approach to causal inference with time-series cross-sectional data. SSRN Electron. J. https://doi.org/10.2139/ssrn.3214231 (2018).

Battocletti, V., Enriques, L. & Romano, A. The voluntary carbon market: market failures and policy implications. U. Colo. L. Rev. 95, 519 (2024).

Verra. VM0047 Afforestation, Reforestation, and Revegetation, v1.1. https://verra.org/methodologies/vm0047-afforestation-reforestation-and-revegetation-v1-1/ (2023).

Verra. VM0045 Methodology for Improved Forest Management Using Dynamic Matched Baselines from National Forest Inventories, v1.2. https://verra.org/methodologies/methodology-for-improved-forest-management/ (2020).

Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190120 (2020).

Beerling, D. J. et al. Challenges and opportunities in scaling enhanced weathering for carbon dioxide removal. Nat. Rev. Earth Environ. 6, 672–686 (2025).

Pirard, R., Philippot, K. & Romero, C. Estimations of REDD+ opportunity costs: Aligning methods with objectives. Environ. Sci. Policy 145, 188–199 (2023).

Imbens, G. W. & Rubin, D. B. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. (Cambridge University Press, 2015).

Souza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sens. 12, 2735 (2020).

Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).

Austin, P. C. & Small, D. S. The use of bootstrapping when using propensity-score matching without replacement: a simulation study. Stat. Med. 33, 4306–4319 (2014).

Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

Jarvis, A., Reuter, H. I., Nelson, A., Guevara, E. & Others. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database. Preprint at https://srtm.csi.cgiar.org (2008).

Rosenbaum, P. R. & Rubin, D. B. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am. Stat. 39, 33 (1985).

Geo-referencing and digitization of scanned maps. ArcGIS API for Python https://developers.arcgis.com/python/latest/guide/geo-referencing-and-digitization-of-scanned-maps/

Dales, M., Ferris, P., Message, R., Holland, J. & Williams, A. Tropical Moist Forest Accreditation Methodology Implementation. Zenodo. https://doi.org/10.5281/ZENODO.18712812 (2026).