The effect of payments for ecosystem services on forest cover, land use, and capacity building in northern Mexico

Segura-Millan, Karla; Pérez-Verdín, Gustavo

doi:10.1016/j.tfp.2023.100387

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Considering the climate finance invested in REDD+ projects and the application of VERs towards corporate claims, the endeavor has been under intense scrutiny. Recent academic studies have tested the application of statistical techniques (difference-in-differences and synthetic controls) to estimate the effect of REDD+ interventions on land use 8 – 11 . While evaluating the accuracy ex-post facto is important, recent attempts 11 , 12 have been applied in such a way to elicit highly uncertain conclusions 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

A novel deforestation risk and baseline allocation model for the next generation of nested REDD+ projects

Freund,

Pauly,

Gochberg

et al. 2024

Sci Rep

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Nature-based solutions that use a counterfactual scenario depend heavily on the methodology used to determine the business as usual (BAU) case, i.e., the “baseline.” Reducing emissions from deforestation and forest degradation (REDD+) projects traditionally set baselines using a “reference area” as a control for estimating BAU deforestation and emissions in the treatment (project) area. While the REDD+ market is shifting from project-based to nested approaches as countries increase their efforts to meet nationally determined contributions (NDCs) to the Paris agreement’s global climate target, methodologies for allocating national baselines are not yet formalized and tested, despite an urgent need to scale the market. We present a novel method for mapping deforestation risk and allocating national forest reference emission levels (FREL) to projects: baseline allocation for assessed risk (BAAR). This approach provides a spatial predictor of future deforestation using a dynamic vector, and a method for allocating a FREL to differentiated risk areas at the project level. Here, we present BAAR using 34 REDD+ projects in the Democratic Republic of the Congo (DRC). We demonstrate the importance of risk-based FREL allocations to balance fitness for purpose and scientific rigor. We show how BAAR can be used by governments to focus voluntary carbon market finance in areas at highest risk of imminent deforestation, while maintaining alignment with nationally determined contribution (NDC) goals.

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