Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.05.22.595326

Sensitivity of estimates of the effectiveness of REDD+ projects to matching specifications and moving from pixels to polygons as the unit of analysis

Guizar-Coutino, A.·Coomes, D.·Swinfield, T.·Jones, J. P.
View original source ↗Export metadata ↓

Abstract

There is a substantial interest in the potential of carbon credits generated by Reducing Emissions from tropical Deforestation and Degradation (REDD+) and traded on the voluntary carbon market for generating the finance needed to slow forest loss. However, such credits have become marred in controversy. Recent global-scale analysis using a range of methods for estimating the counterfactual rate of deforestation ex post suggest that many REDD+ projects have overestimated their effectiveness at reducing deforestation and consequently issued more credits than can be justified. All such methods include potentially arbitrary choices which can affect the estimate of the treatment effect. In addition, using pixels as the sampling unit, as some of the studies do, can introduce biases. One study which has been widely cited in the debate (Guizar-Coutino et al. 2022) estimated avoided deforestation using statistical matching of pixels and a single set of matching options. We estimate avoided deforestation from the same set of projects using 7-hectare plots rather than pixels to sample deforestation and explore the sensitivity of the results to matching choices (exploring 120 matched sets in total). We filtered the results on three criteria: 1) post-matching covariate balance, 2) proportion of REDD+ samples that were successfully matched, and 3) similarity of trends in deforestation rates prior to REDD+ implementation (parallel trends). While one of the 44 REDD+ projects failed these quality control process, we estimate treatment effects for the remaining 43 projects. There was a substantial correlation between our new estimates and those published in Guizar-Coutino et al. 2022 (0.72 measured in annual percent change, and 0.9 measured in total area change) and our headline estimate of 0.22% per yr (95% CI: 0.13 to 0.34) is essentially unchanged. At a time when confidence in the voluntary carbon markets is low, we hope these results provide reassurance that ex-post counterfactual estimates of avoided deforestation are consistent, helping accelerate their widespread adoption and rebuild trust in nature-based climate solutions.

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guizar-Coutino, A., Coomes, D., Swinfield, T., Jones, J. P.. 2024-05-26. Sensitivity of estimates of the effectiveness of REDD+ projects to matching specifications and moving from pixels to polygons as the unit of analysis. https://doi.org/10.1101/2024.05.22.595326

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗
bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗
bioRxiv2026

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗