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Abstract
Reducing Emissions from Deforestation and Forest Degradation (REDD+) projects are essential in combating climate change, but current and next-generation certification methods rely on counterfactuals developed before an intervention occurs (ex ante). These may struggle to account for temporal variance, weakening credit integrity. To compare the robustness of different methods for producing counterfactual estimates, we used placebo projects - areas analogous to existing REDD+ projects but without experiencing REDD+ activities. We designated 27 such placebo areas across the wet tropics, and compared their observed deforestation rates with three ex ante forecasts and one ex post estimate. We found that ex post (85% goodness-of-fit) was more precise than ex ante (13% - 51% goodness-of-fit), adding to concerns around existing and proposed crediting mechanisms. We argue that a standardised placebo approach can facilitate the development of new methods for counterfactual estimation, strengthening the reliability and transparency of future credit claims.
Supplementary materials
Title
Supplementary Information for “A placebo approach to evaluate methods of counterfactual estimation for REDD+”
Description
This Supplementary Information contains two sections: (1) a mathematical representation of the forecasting procedure, and (2) the exploratory analysis of an hybrid ex ante forecasting method
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Supplementary weblinks
Title
Research data supporting "A placebo approach to evaluate methods of counterfactual estimation for REDD+"
Description
Shapefiles (*.geojson, compressed in a zip file): boundary of all 27 placebo projects. Parquet files (*.parquet): sampled project pixels and matched pixels of each placebo. Each row represents a sampled pixel, and each column containing the k_luc prefix indicates the JRC-TMF land use class of the pixel in a given year. The proportional change of pixels classified as undisturbed forests over a given time interval is used to calculate annual compound deforestation rates.
[ID]_regional.parquet contain sampled pixels in the surrounding landscape of each project, which are used to calculate ex ante forecasts using the regional method; [ID]_expost_matches.parquet contain sampled pixels in the project area, which are used to calculate ex ante forecasts using the project method, as well as the ex post estimates; [ID]_matches.parquet contain matched pixels of the sampled project pixels, which are used to calculate ex ante forecasts using the time-shifted matching method.
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GitHub Repository: Tropical Moist Forest Accreditation Methodology Implementation
Description
This repository contains an implementation of the PACT Tropical Moist Forest Accreditation Methodology (https://www.cambridge.org/engage/coe/article-details/64621025fb40f6b3eea0642f). Version used: commit 7f15246.
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GitHub Repository: Evaluating ex ante and ex post methods of counterfactual estimation for REDD+ projects using a placebo approach
Description
The repository contains the project that evaluates various ex ante and ex post methods of counterfactual estimation for REDD+ projects (Reducing Emissions from Deforestation and forest Degradation in Developing countries), using a set of placebo projects randomly selected across the wet tropics with comparable characteristics to existing REDD+ projects. Because of the absence of project activities, project and counterfactual deforestation should follow the identical trend in these placebo projects, allowing us to evaluate the predictive performance of a method by comparing predictions against observed deforestation rates in the placebo projects.
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