Abstract
The financing of tropical forest conservation projects through the sale of carbon credits remains a key opportunity to curb forest loss. REDD+ projects generate carbon credits by reducing deforestation and degradation within the project area compared with a counterfactual area that faces similar pressures (known as “additionality”). Several methods are available for constructing counterfactuals, but comparing their effectiveness is challenging. Here, we present an evaluation approach based on the creation of placebo projects, in which there are no REDD+ activities and in which we would not expect any carbon credits to be generated. We compare four methods based on pixel matching to estimate counterfactual deforestation rates. Using 40 placebo projects spread across the tropics, we found that pixel-matching is a reliable way of calculating a key element of additionality (i.e. deforestation in counterfactual areas) when based on data gathered at the end of an accounting period (i.e. ex post estimation). However, forecasting additionality from information available at the start of projects (i.e. ex ante estimation) is much less reliable, reinforcing existing concerns about ex ante crediting mechanisms. We argue that systematic application of the placebo approach can accelerate the development and adoption of more credible counterfactual-estimating methods. As counterfactuals are the basis which underpins the validity claims of most nature credits, strengthening the credibility of counterfactuals will enhance the effectiveness of conservation finance, helping REDD+ and other nature-based solutions realise their full potential in delivering real, measurable benefits.
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
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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
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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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