in Ecology

Ecological integrity of avoided deforestation projects

95 Views


Abstract

Reducing deforestation is proposed as a global climate action, yet it remains unclear whether carbon projects based on such interventions also maintain forests’ ecological conditions. Here we evaluate 133 projects against matched controls using five ecological-integrity indicators, to show that most projects have mixed, negligible or negative impacts relative to control areas. Results highlight fundamental shortcomings of these climate solutions that limit their ability to safeguard healthy ecosystems and sequester carbon.

Access through your institution

Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Ecological integrity of avoided deforestation projects relative to unprotected matched areas nearby.
Fig. 2: Protected projects are compared with both unprotected controls and protected-area controls.

Similar content being viewed by others

Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation

Article
Open access
04 October 2022

Hedging our bet on forest permanence for the economic viability of climate targets

Article
Open access
27 March 2025

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

Article
Open access
22 July 2024

Data availability

The datasets used to support the findings of this study are available for download by request from their respective providers. The spatial boundaries of avoided deforestation projects were obtained from publicly accessible carbon registry platforms as listed in Extended Data Table 1 and are available via Zenodo at https://doi.org/10.5281/zenodo.19726735 (ref. 38). Protected area boundaries were sourced from the World Database on Protected Areas (WDPA, https://www.protectedplanet.net). Covariates used in propensity score matching were obtained from publicly accessible global datasets, as listed in Extended Data Table 2. All ecological-integrity indicators—biodiversity intactness index, forest landscape integrity index, forest fragmentation index, canopy height and forest GHG net flux—were obtained from publicly available datasets as listed in Extended Data Table 3. We make all processed data (in the form of SMD values from propensity score matching) freely available. Similarly, processed project-level results generated in this study, including treatment effect estimates for each ecological-integrity indicator across projects and comparison groups, are provided. Project-level sensitivity analysis results for unobserved confounding are available via Zenodo at https://doi.org/10.5281/zenodo.19726735 (ref. 38) (Extended Data Table 4). Source data are provided with this paper.

Code availability

Codes used in this Brief Communication are available via Zenodo at https://doi.org/10.5281/zenodo.19726735 (ref. 38).

References

  1. State of the Voluntary Carbon Market 2025. Forest Trends’ Ecosystem Marketplace https://www.forest-trends.org/publications/2025-state-of-the-voluntary-carbon-market/ (2025).

  2. 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).

    Article 

    Google Scholar 

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

    Article 
    CAS 

    Google Scholar 

  4. Kwan, V., Friess, D. A., Sarira, T. V. & Zeng, Y. Permanence risks limit blue carbon financing strategies to safeguard Southeast Asian mangroves. Commun. Earth Environ. 6, 57 (2025).

    Article 

    Google Scholar 

  5. Greenfield, P. Revealed: more than 90% of rainforest carbon offsets by biggest certifier are worthless, analysis shows. The Guardian https://www.theguardian.com/environment/2023/jan/18/revealed-forest-carbon-offsets-biggest-provider-worthless-verra-aoe (2023).

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

    Article 
    CAS 

    Google Scholar 

  7. Ferreira, J. et al. Carbon-focused conservation may fail to protect the most biodiverse tropical forests. Nat. Clim. Change 8, 744–749 (2018).

    Article 
    CAS 

    Google Scholar 

  8. Wurtzebach, Z. & Schultz, C. Measuring ecological integrity: history, practical applications, and research opportunities. BioScience 66, 446–457 (2016).

    Article 

    Google Scholar 

  9. Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  11. Ian, G. B. Reduced emissions from deforestation and forest degradation (REDD) and access and exclusion: obstacles and opportunities in Cambodia and Laos. Southeast Asian Stud. 3, 643–668 (2014).

    Article 

    Google Scholar 

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

    Article 
    CAS 

    Google Scholar 

  13. Phelps, J., Friess, D. A. & Webb, E. L. Win–win REDD+ approaches belie carbon–biodiversity trade-offs. Biol. Conserv. 154, 53–60 (2012).

    Article 

    Google Scholar 

  14. Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Change Biol. 27, 1518–1546 (2021).

    Article 

    Google Scholar 

  15. Lou, J., Hultman, N., Patwardhan, A. & Qiu, Y. L. Integrating sustainability into climate finance by quantifying the co-benefits and market impact of carbon projects. Commun. Earth Environ. 3, 1–11 (2022).

    Article 

    Google Scholar 

  16. Michaelowa, A., Hermwille, L., Obergassel, W. & Butzengeiger, S. Additionality revisited: guarding the integrity of market mechanisms under the Paris Agreement. Clim. Policy 19, 1211–1224 (2019).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  18. Maron, M. et al. The many meanings of no net loss in environmental policy. Nat. Sustain. 1, 19–27 (2018).

    Article 

    Google Scholar 

  19. Bull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).

    Article 

    Google Scholar 

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

    Article 

    Google Scholar 

  21. Sunderlin, W. D. et al. How are REDD+ proponents addressing tenure problems? Evidence from Brazil, Cameroon, Tanzania, Indonesia, and Vietnam. World Dev 55, 37–52 (2014).

    Article 

    Google Scholar 

  22. Schmid, D. V. Are forest carbon projects in Africa green but mean?: a mixed-method analysis. Clim. Dev. 15, 45–59 (2023).

    Article 

    Google Scholar 

  23. Furtado, F. P., Gibson, M. L., Junior, O. A. d. B. & Torres, P. P. In the name of the climate: extractive capitalism and the forest carbon offsets market in the Amazon. Ambiente Soc. 27, e00170 (2024).

    Google Scholar 

  24. Lau, Y. et al. Challenges in forest carbon governance: insights from Southeast Asia. WIREs Clim. Change 16, e70018 (2025).

    Article 

    Google Scholar 

  25. Börner, J., Schulz, D., Wunder, S. & Pfaff, A. The effectiveness of forest conservation policies and programs. Annu. Rev. Resour. Econ. 12, 45–64 (2020).

    Article 

    Google Scholar 

  26. Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).

    Article 

    Google Scholar 

  27. Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A. & Betts, M. G. A forest loss report card for the world’s protected areas. Nat. Ecol. Evol. 5, 520–529 (2021).

    Article 

    Google Scholar 

  28. Flammer, C., Giroux, T. & Heal, G. M. Biodiversity finance. J. Financ. Econ. 164, 103987 (2025).

    Article 

    Google Scholar 

  29. Kunming–Montreal Global Biodiversity Framework. CBD https://www.cbd.int/doc/c/e6d3/cd1d/daf663719a03902a9b116c34/cop-15-l-25-en.pdf (2022).

  30. Karnik, A., Kilbride, J. B., Goodbody, T. R. H., Ross, R. & Ayrey, E. An open-access database of nature-based carbon offset project boundaries. Sci. Data 12, 581 (2025).

    Article 

    Google Scholar 

  31. Ho, D. E., Imai, K., King, G. & Stuart, E. A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit. Anal. 15, 199–236 (2007).

    Article 

    Google Scholar 

  32. Sze, J. S. et al. Indigenous Peoples’ Lands are critical for safeguarding vertebrate diversity across the tropics. Glob. Change Biol. 30, e16981 (2024).

    Article 

    Google Scholar 

  33. Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).

    Article 

    Google Scholar 

  34. Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC, IUCN, 2025).

  35. Hanson, J. O. wdpar: interface to the World Database on Protected Areas. J. Open Source Softw. 7, 4594 (2022).

    Article 

    Google Scholar 

  36. Crombach, L. & Smits, J. The Subnational Corruption Database: grand and petty corruption in 1,473 regions of 178 countries, 1995–2022. Sci. Data 11, 686 (2024).

    Article 

    Google Scholar 

  37. Smits, J. & Permanyer, I. The Subnational Human Development Database. Sci. Data 6, 190038 (2019).

    Article 

    Google Scholar 

  38. Ong, K. Ecological integrity of avoided deforestation projects: code & supplementary data. Zenodo https://doi.org/10.5281/zenodo.19726735 (2026).

Download references

Acknowledgements

We thank R. Sreekar, A. Lamba and S. Ng Jing Wen for their contributions to improving the manuscript. Y.Z., K.O., Z.C. and T.C. are supported by the Ministry of Education (MOE), Singapore, under its MOE Academic Research Fund Tier 3 Award (MOE-MOET32022-0006). Y.Z. and K.O. are supported by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 1 Award (04MNP004127C210) and the Nanyang Technological University Start-Up Grant (03INS002001C210). Z.C. is supported by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 1 Award (#024891-00001). T.C. is supported by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 1 Award (RG46/24) and the Nanyang Technological University Provost’s Chair Professorship.

Author information

Authors and Affiliations

  1. Asian School of the Environment, Nanyang Technological University, Singapore, Singapore

    Kelly Ong & Yiwen Zeng

  2. Nanyang Business School, Nanyang Technological University, Singapore, Singapore

    Tao Chen & Zhimin Chen

  3. World Bank, Tower 2 Marina Bay Financial Centre, Singapore, Singapore

    Carlos T. Pérez-Brito

  4. School of Social Sciences, Nanyang Technological University, Singapore, Singapore

    Yiwen Zeng

Authors

  1. Kelly Ong
    View author publications

    Search author on:PubMed Google Scholar

  2. Tao Chen
    View author publications

    Search author on:PubMed Google Scholar

  3. Zhimin Chen
    View author publications

    Search author on:PubMed Google Scholar

  4. Carlos T. Pérez-Brito
    View author publications

    Search author on:PubMed Google Scholar

  5. Yiwen Zeng
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.Z., T.C., Z.C., C.T.P.-B. and K.O. conceived the study. K.O. conducted spatial and statistical analyses and interpreted results. Y.Z. contributed initial discussions and modelling insights. K.O. and Y.Z. wrote the initial draft of the manuscript. All authors contributed subsequent discussions and improvements to the manuscript.

Corresponding authors

Correspondence to
Kelly Ong or Yiwen Zeng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Alex Caruana and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Table 1 Voluntary carbon project registries used for project data extraction
Full size table
Extended Data Table 2 Environmental and socio-economic variables used as covariates in propensity score matching
Full size table
Extended Data Table 3 Variables representing indicators of ecological integrity
Full size table
Extended Data Table 4 Summary of sensitivity analysis statistics for unobserved confounding across comparison-indicator pairs
Full size table

Extended Data Fig. 1 Associations between ecological integrity outcomes and project, socio-economic and governance characteristics.

Heatmap showing Spearman’s rank correlation coefficients between mean standardized ecological-integrity effect sizes and project characteristics, project-level socio-economic factors and subnational governance factors across the four treatment-control comparison types. Factors include project size, project start date, gross domestic product (GDP), population density, travel-time accessibility to urban centres, the Subnational Corruption Index (SCI), and the Subnational Human Development Index (SHDI). Colored cells indicate statistically significant correlations (p < 0.05), while non-significant associations are shown in white. Overall, correlations are weak and largely non-significant, with a small number of context-specific associations, including negative correlations with SCI (lower SCI values indicate higher corruption) and SHDI for avoided deforestation projects that do not overlap protected areas relative to unprotected areas, and associations with GDP and travel-time accessibility for avoided deforestation projects that overlap protected areas relative to protected areas. Data sources: SCI and SHDI were obtained from the Subnational Corruption Database36 and the Subnational Human Development Database37. All other variables are described in Extended Data Table 2.

Source data

Extended Data Fig. 2 Associations between ecological integrity outcomes and project status.

Distribution of mean standardized ecological-integrity effect sizes by project status for each treatment–control comparison. Differences across status categories were assessed using Kruskal–Wallis tests (two-sided), with test statistics and sample sizes reported beneath each panel. Boxes show the interquartile range (25th–75th percentiles), the centre line shows the median, whiskers extend to the most extreme values within 1.5 × the interquartile range, and points represent individual projects. For unprotected projects relative to unprotected controls, we find a significant overall association between project status and ecological integrity outcomes. However, post-hoc Dunn’s tests indicate that pairwise differences are not statistically significant after adjustment for multiple comparisons.

Source data

Extended Data Fig. 3 Ecological integrity of avoided deforestation projects relative to protected-area controls.

Project-level standardized effect sizes are shown for each ecological-integrity indicator, with columns representing individual projects grouped by country and region. Effect sizes were estimated using linear regression models; statistical significance was assessed using two-sided tests (p < 0.05). Positive values (blue) indicate higher biodiversity intactness, greater forest landscape integrity, lower fragmentation, taller canopy height, and lower greenhouse gas (GHG) emissions in project areas relative to controls; negative values (red) indicate the opposite. Color intensity reflects effect magnitude. White indicates no significant difference (p ≥ 0.05), and grey indicates indicators not evaluated due to insufficient data or failed model diagnostics. Country abbreviations follow ISO 3166-1 alpha-3 codes. No adjustments were made for multiple comparisons.

Source data

Supplementary information

Peer Review File (download PDF )

Supplementary Table 1 (download XLSX )

Summary of avoided deforestation projects and SMD from propensity score matching.

Source data

Source Data Fig. 1 (download XLSX )

Statistical source data.

Source Data Fig. 2 (download XLSX )

Statistical source data.

Source Data Extended Data Figs. 1 and 2 (download XLS )

Statistical source data.

Source Data Extended Data Fig. 3 (download XLSX )

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Ong, K., Chen, T., Chen, Z. et al. Ecological integrity of avoided deforestation projects.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02657-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41558-026-02657-2

Source: Ecology - nature.com

Estimating high-resolution albedo for urban applications

A hybrid deep learning approach for winter wheat yield prediction: evidence from leveraging multi-source data