This study re-evaluates the impacts of the twelve voluntary REDD+ (Reducing Emissions from Deforestation and forest Degradation, plus other forest-related activities) projects in the Brazilian Amazon from West et al. (2020) on avoided deforestation and carbon credit generation. Although these initiatives are widely regarded as important tools for climate change mitigation, their effectiveness has come under increasing scrutiny. To shed light on this issue, we employ three causal inference methods—the original synthetic control method (SCM), Augmented SCM (ASCM), and Bayesian SCM (BSCM)—to construct counterfactual scenarios for the REDD+ projects and estimate their impact on avoided deforestation. Next to placebo tests, we employ conformal inference to evaluate statistical significance. In addition, we investigate adverse selection by developing two measures of deforestation threat and comparing the distribution of these between projects and control areas. Our objectives are three-fold: to compare the performance of the three methods, evaluate the robustness of the results across them, and to analyze potential location bias using our deforestation threat measures. Our findings corroborate the mounting literature indicating that most REDD+ projects overstate their climate impacts, consequently claiming more carbon credits than warranted. Moreover, the analysis reveals that most projects did not significantly reduce deforestation, with several more closely associated with increases in post-intervention deforestation than with decreases. Our findings also indicate that REDD+ projects are disproportionately located in areas facing relatively low deforestation pressure. These results highlight the need for more rigorous and transparent impact assessments of REDD+ projects to strengthen the credibility of carbon offsetting and have important implications for the integration of voluntary projects into regulated carbon markets under the Paris Agreement.
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Wijler et al. (2026) studied this question.
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