Search

Menu
Search
in Ecology

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

169 Views


Abstract

Independent evaluations have shown substantial over-issuance of REDD+ (Reducing Emissions from Deforestation and Degradation) credits traded on the voluntary carbon market. We synthesise these evaluations to estimate the additional forest conservation achieved by first-generation REDD+ projects and to identify mechanisms underlying over-crediting. We combine six independent ex post evaluations of avoided deforestation covering 44 REDD+ projects. These evaluations show that most projects reduced deforestation, but that they claimed an aggregate of 10.7 times more avoided deforestation than is justified by independent estimates. This discrepancy is not driven by the choice of forest cover data, but by selection bias in projects’ control areas and modelling approaches. Although recent initiatives that transfer assessment to unconflicted parties and restrict methodological flexibility are critical, they are insufficient. Ex post certification against credible counterfactuals is necessary if carbon markets are to represent causal reductions in deforestation.

Similar content being viewed by others

Evaluating the impacts of a large-scale voluntary REDD+ project in Sierra Leone

Article
Open access
04 January 2024

Modest forest and welfare gains from initiatives for reduced emissions from deforestation and forest degradation

Article
Open access
22 July 2024

Little evidence of management change in California’s forest offset program

Article
Open access
21 September 2023

Data availability

All data generated in this study have been deposited in the Zenodo database under accession code zenodo.org/records/18715093. Avoided deforestation area calculations, compound annual deforestation rates and above-ground biomass densities for all the projects included in the relevant analyses are provided in the supplementary information.

Code availability

All analyses were undertaken in R (v4.2.1) using Terra (v1.7.65), Simple Features (v1.0.15) and Raster (v3.6.26) for geospatial processing; Vegan (v2.6.4) for ordination analysis; Tidyverse (v2.0.0) for data manipulation; and Natural Earth Data (v1.0.0) for country borders. In an effort to contribute to improved transparency, we have made the code necessary to run the PACT evaluations (github.com/quantifyearth/tmf-implementation and https://zenodo.org/records/1871281279) and our analysis (github.com/quantifyearth/REDD-Over-Credit-Reasons and zenodo.org/records/1871509380).

References

  1. 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).

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

    Google Scholar 

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

    Google Scholar 

  4. 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).

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

    Google Scholar 

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

  7. 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).

    Google Scholar 

  8. 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).

    Google Scholar 

  9. 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).

    Google Scholar 

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

    Google Scholar 

  11. 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).

    Google Scholar 

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

    Google Scholar 

  13. 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).

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

    Google Scholar 

  15. 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).

    Google Scholar 

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

  17. 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).

    Google Scholar 

  18. 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).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

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

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

  26. 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).

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

    Google Scholar 

  28. 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).

  29. 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).

  30. 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).

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  35. 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).

    Google Scholar 

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

    Google Scholar 

  37. 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).

    Google Scholar 

  38. 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).

    Google Scholar 

  39. 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).

    Google Scholar 

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

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

    Google Scholar 

  42. 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).

    Google Scholar 

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

    Google Scholar 

  44. 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).

    Google Scholar 

  45. 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).

    Google Scholar 

  46. 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).

    Google Scholar 

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

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

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

    Google Scholar 

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

    Google Scholar 

  56. 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).

  57. 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.

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

    Google Scholar 

  59. 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).

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

    Google Scholar 

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

    Google Scholar 

  62. 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).

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

    Google Scholar 

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

  65. 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).

  66. 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).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

  70. 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).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  73. 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).

    Google Scholar 

  74. OSM-Boundaries. https://osm-boundaries.com/.

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

    Google Scholar 

  76. 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).

  77. 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).

    Google Scholar 

  78. 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/

  79. 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).

  80. Swinfield, T. et al. Data for ‘Learning lessons from over-crediting to ensure additionality in forest carbon credits.’ Zenodo https://doi.org/10.5281/ZENODO.18715093 (2026).

Download references

Acknowledgements

This study was funded with support from the Tezos Foundation (grant NRAG/719; AEW, TS, JHolland, JHartup, MOKL, PF), John Bernstein (donation; SJ) and Tarides (donation; MD). The authors thank 4 anonymous reviewers and Lynsey Stafford for their valuable comments on the manuscript. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

Author information

Authors and Affiliations

  1. Department of Zoology, University of Cambridge, Cambridge, UK

    Tom Swinfield, Abby Williams, James Hartup, Jody Holland, Miranda O. K. Lam & Andrew Balmford

  2. Conservation Research Institute, University of Cambridge, Cambridge, UK

    Tom Swinfield, Abby Williams, David Coomes, Michael Dales, Patrick Ferris, Alejandro Guizar-Coutiño, James Hartup, Jody Holland, Sadiq Jaffer, Miranda O. K. Lam, Srinivasan Keshav, Anil Madhavapeddy, Eleanor Toye-Scott & Andrew Balmford

  3. Department of Biology, University of Oxford, Oxford, UK

    Abby Williams & Alejandro Guizar-Coutiño

  4. Department of Plant Sciences, University of Cambridge, Cambridge, UK

    David Coomes & Alejandro Guizar-Coutiño

  5. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Michael Dales, Patrick Ferris, Sadiq Jaffer, Srinivasan Keshav, Anil Madhavapeddy & Eleanor Toye-Scott

  6. School of Environmental Sciences, University of Liverpool, Liverpool, UK

    James Hartup

  7. School of Environmental and Natural Sciences, Bangor University, Bangor, UK

    Julia P. G. Jones

  8. Department of Biology, Utrecht University, Utrecht, the Netherlands

    Julia P. G. Jones

  9. Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Thales A. P. West

  10. Centre for Environment, Energy and Natural Resource Governance, University of Cambridge, Cambridge, UK

    Thales A. P. West

Authors

  1. Tom Swinfield
    View author publications

    Search author on:PubMed Google Scholar

  2. Abby Williams
    View author publications

    Search author on:PubMed Google Scholar

  3. David Coomes
    View author publications

    Search author on:PubMed Google Scholar

  4. Michael Dales
    View author publications

    Search author on:PubMed Google Scholar

  5. Patrick Ferris
    View author publications

    Search author on:PubMed Google Scholar

  6. Alejandro Guizar-Coutiño
    View author publications

    Search author on:PubMed Google Scholar

  7. James Hartup
    View author publications

    Search author on:PubMed Google Scholar

  8. Jody Holland
    View author publications

    Search author on:PubMed Google Scholar

  9. Sadiq Jaffer
    View author publications

    Search author on:PubMed Google Scholar

  10. Julia P. G. Jones
    View author publications

    Search author on:PubMed Google Scholar

  11. Miranda O. K. Lam
    View author publications

    Search author on:PubMed Google Scholar

  12. Srinivasan Keshav
    View author publications

    Search author on:PubMed Google Scholar

  13. Anil Madhavapeddy
    View author publications

    Search author on:PubMed Google Scholar

  14. Eleanor Toye-Scott
    View author publications

    Search author on:PubMed Google Scholar

  15. Thales A. P. West
    View author publications

    Search author on:PubMed Google Scholar

  16. Andrew Balmford
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualisation: A.B., A.M., AEW, D.C., S.K. and T.S. Investigation: A.B., A.M., AEW, JHolland, JHartup, M.D., M.O.K.L., P.F., S.J., S.K. and T.S. Visualisation: AEW, JHolland, JHartup, S.J., and T.S. Funding acquisition: A.B., A.M., S.K., T.S. Project administration: A.B., A.M., E.T.S., D.C., S.J., S.K. and T.S. Writing—original draft: A.B., JHolland, J.P.G.J. and T.S. Writing—review and editing: A.B., AEW, A.G.C., A.M., D.C., E.T.S., JHolland, JHartup, M.D., P.F., J.P.G.J., M.O.K.L., S.J., S.K., T.A.P.W. and T.S.

Corresponding author

Correspondence to
Tom Swinfield.

Ethics declarations

Competing interests

The Cambridge Centre for Carbon Credits (4 C) has no commercial interest in carbon credits. T.S. has an advisory position with Symbiosis, an initiative to finance carbon storage in nature. The remaining authors declare no competing interests. A.B., A.M., D.C. and S.K. are trustees and E.T.S. and T.S. are founders of Canopy PACT, an initiative to integrate science into nature-based credit markets.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information (download PDF )

Reporting Summary (download PDF )

Transparent Peer Review file (download PDF )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Swinfield, T., Williams, A., Coomes, D. et al. Learning lessons from over-crediting to ensure additionality in forest carbon credits.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-71552-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41467-026-71552-3

Source: Ecology - nature.com

Investment in coastal wetland restoration yields high returns in blue carbon and ecosystem services in China

Microplastic contamination in tadpoles (Anura) in the Brazilian Amazon

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close