Generating ex ante forecasts for planned REDD+ projects

This project generates ex ante forecasts of annual carbon credit generation rates (MgC ha^-1 yr^-1, how many carbon credits can be generated per year per hectare) of planned and uses 20 ongoing REDD+ projects to evaluate the forecasting method. As input, it ingests the parquet files of sampled pixels in the project area and parquet files of matched pixels. The matching procedures were conducted using the Tropical Moist Forest Accreditation Methodology Implementation code[1], which implements the Canopy PACT 2.0 methodology[2][3]. The matched pixels are used to constrcut counterfactuals used to estimate carbon credits (additionality) ex post, and the regional pixels are used to estimate historical regional carbon loss rates. The code adopts two simple approaches using historical within-project and regional carbon loss rates (MgC ha^-1 yr^-1), as well as a mixed approach combining project and carbon rates, an approach explored in Rau et al. (2025)[4] (https://github.com/epingchris/placebo_evaluation), and compares them against ex post estimates of carbon credits.

Requirements

This project is developed under R 4.2, and requires the packages tidyverse, magrittr, units, sf, arrow, MatchIt, boot, , Metrics, and patchwork. The sf package runs on GDAL 3.10.

Structure

The project contains three core scripts:

1. forecast_1_setup.r: selecting the projects to be analysed, their basic project-level variables and paths for directories containing results
2. forecast_2_estimate_carbon.r: reading parquet files generated by the implementation code and calculating (1) ex post additionality, (2) historical project carbon loss rates, and (3) historical regional carbon loss rates
3. forecast_3_forecast.r: constructing forecasts with two simple and one mixed approaches based on different historical periods to predict ex post carbon credit genration rates in different target periods and comparing predictive performance

References

[1] Dales M, Ferris P, Message R, Holland J, and Williams A (2023). GitHub Repository: Tropical Moist Forest Accreditation Methodology Implementation, https://github.com/quantifyearth/tmf-implementation. commit:7f15246

[2] Balmford, A et al. (2023). PACT Tropical Moist Forest Accreditation Methodology v2.0. Cambridge Open Engage, https://www.cambridge.org/engage/coe/article-details/657c8b819138d23161bb055f.

[3] Swinfield, T and Balmford, A (2023). Cambridge Carbon Impact: Evaluating carbon credit claims and co-benefits. Cambridge Open Engage, https://www.cambridge.org/engage/coe/article-details/6409c345cc600523a3e778ae.

[4] Rau, E et al. (2025). A placebo approach to evaluate methods of counterfactual estimation for REDD+. Cambridge Open Engage, https://www.cambridge.org/engage/coe/article-details/67fe2c2072da395cef4519d0.