Dataset Information

A global evaluation of the effectiveness of voluntary REDD+ projects at reducing deforestation and degradation in the moist tropics.

ABSTRACT: Reducing emissions from deforestation and forest degradation (REDD+) projects aim to contribute to climate change mitigation by protecting and enhancing carbon stocks in tropical forests, but there have been no systematic global evaluations of their impact. We used a new data set for tropical humid forests and a standardized evaluation approach (based on pixel matching) to quantify the performance of a representative sample of 40 voluntary REDD+ projects in 9 countries certified under the Verified Carbon Standard (VCS). In the first 5 years of implementation, deforestation within project areas was reduced by 47% (95% confidence interval [CI]: 24-68) compared with matched counterfactual pixels, and degradation rates were 58% lower (95% CI: 49-63). Reductions were small in absolute terms but greater in sites located in high-deforestation settings and did not appear to be substantially undermined by leakage activities in forested areas within 10 km of project boundaries. At the 26th Conference of the Parties of the United Nations Framework Convention on Climate Change, the international community renewed its commitment to tackling tropical deforestation as a nature-based solution to climate change. Our results indicate that incentivizing forest conservation through voluntary site-based projects can slow tropical deforestation and highlight the particular importance of prioritizing financing for areas at greater risk of deforestation.

SUBMITTER: Guizar-Coutino A

PROVIDER: S-EPMC10086997 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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A global evaluation of the effectiveness of voluntary REDD+ projects at reducing deforestation and degradation in the moist tropics.

Guizar-Coutiño Alejandro A Jones Julia P G JPG Balmford Andrew A Carmenta Rachel R Coomes David A DA

Conservation biology : the journal of the Society for Conservation Biology 20220908 6

Reducing emissions from deforestation and forest degradation (REDD+) projects aim to contribute to climate change mitigation by protecting and enhancing carbon stocks in tropical forests, but there have been no systematic global evaluations of their impact. We used a new data set for tropical humid forests and a standardized evaluation approach (based on pixel matching) to quantify the performance of a representative sample of 40 voluntary REDD+ projects in 9 countries certified under the Verifi ...[more]

PMID: 35713105

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Project description:Protected areas (PAs) have been established to conserve tropical forests, but their effectiveness at reducing deforestation is uncertain. To explore this issue, we combined high resolution data of global forest loss over the period 2000-2012 with data on PAs. For each PA we quantified forest loss within the PA, in buffer zones 1, 5, 10 and 15 km outside the PA boundary as well as a 1 km buffer within the PA boundary. We analysed 3376 tropical and subtropical moist forest PAs in 56 countries over 4 continents. We found that 73% of PAs experienced substantial deforestation pressure, with >0.1% a(-1) forest loss in the outer 1 km buffer. Forest loss within PAs was greatest in Asia (0.25% a(-1)) compared to Africa (0.1% a(-1)), the Neotropics (0.1% a(-1)) and Australasia (Australia and Papua New Guinea; 0.03% a(-1)). We defined performance (P) of a PA as the ratio of forest loss in the inner 1 km buffer compared to the loss that would have occurred in the absence of the PA, calculated as the loss in the outer 1 km buffer corrected for any difference in deforestation pressure between the two buffers. To remove the potential bias due to terrain, we analysed a subset of PAs (n = 1804) where slope and elevation in inner and outer 1 km buffers were similar (within 1° and 100 m, respectively). We found 41% of PAs in this subset reduced forest loss in the inner buffer by at least 25% compared to the expected inner buffer forest loss (P<0.75). Median performance (P) of subset reserves was 0.87, meaning a reduction in forest loss within the PA of 13%. We found PAs were most effective in Australasia (P = 0.16), moderately successful in the Neotropics (P = 0.72) and Africa (p = 0.83), but ineffective in Asia (P = 1). We found many countries have PAs that give little or no protection to forest loss, particularly in parts of Asia, west Africa and central America. Across the tropics, the median effectiveness of PAs at the national level improved with gross domestic product per capita. Whilst tropical and subtropical moist forest PAs do reduce forest loss, widely varying performance suggests substantial opportunities for improved protection, particularly in Asia.

Project description:BACKGROUND: To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. RESULTS: We observed over 80,000 ha of forest cover lost from 2000-2009 (0.21% annual loss), representing over 39 million Mg CO2. The rate increased rapidly following the enhancement of the Inter Oceanic Highway in 2005. Accessibility and distance to previous deforestation were strong predictors of deforestation risk, while land use designation was less important. The model performed consistently well (AUC?>?0.9), significantly better than random when we compared predicted deforestation risk to observed. If past deforestation rates continue, we estimate that 132,865 ha of forest could be lost by the year 2020, representing over 55 million Mg CO2. CONCLUSIONS: Maxent provided a reliable method for identifying areas at high risk of deforestation and the major explanatory variables that could draw attention for mitigation action planning under REDD+. The tool is accessible, replicable and easy to use; all necessary for producing good risk estimates and adapt models after potential landscape change. We propose this approach for developing countries planning to meet requirements under REDD+.

Dataset Information

A global evaluation of the effectiveness of voluntary REDD+ projects at reducing deforestation and degradation in the moist tropics.

Publications

A global evaluation of the effectiveness of voluntary REDD+ projects at reducing deforestation and degradation in the moist tropics.

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