Dataset Information

Evidence that a national REDD+ program reduces tree cover loss and carbon emissions in a high forest cover, low deforestation country.

ABSTRACT: Reducing emissions from deforestation and forest degradation (REDD+) is a climate change mitigation policy in which rich countries provide payments to developing countries for protecting their forests. In 2009, the countries of Norway and Guyana entered into one of the first bilateral REDD+ programs, with Norway offering to pay US$250 million to Guyana if annual deforestation rates remained below 0.056% from 2010 to 2015. To quantify the impact of this national REDD+ program, we construct a counterfactual times-series trajectory of annual tree cover loss using synthetic matching. This analytical approach allows us to quantify tree cover loss that would have occurred in the absence of the Norway-Guyana REDD+ program. We found that the Norway-Guyana REDD+ program reduced tree cover loss by 35% during the implementation period (2010 to 2015), equivalent to 12.8 million tons of avoided CO₂ emissions. Our analysis indicates that national REDD+ payments attenuated the effect of increases in gold prices, an internationally traded commodity that is the primary deforestation driver in Guyana. Overall, we found strong evidence that the program met the additionality criteria of REDD+. However, we found that tree cover loss increased after the payments ended, and therefore, our results suggest that without continued payments, forest protection is not guaranteed. On the issue of leakage, which is complex and difficult to quantify, a multinational REDD+ program for a region could address leakage that results from differences in forest policies between neighboring countries.

SUBMITTER: Roopsind A

PROVIDER: S-EPMC6900633 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evidence that a national REDD+ program reduces tree cover loss and carbon emissions in a high forest cover, low deforestation country.

Roopsind Anand A Sohngen Brent B Brandt Jodi J

Proceedings of the National Academy of Sciences of the United States of America 20191118 49

Reducing emissions from deforestation and forest degradation (REDD+) is a climate change mitigation policy in which rich countries provide payments to developing countries for protecting their forests. In 2009, the countries of Norway and Guyana entered into one of the first bilateral REDD+ programs, with Norway offering to pay US$250 million to Guyana if annual deforestation rates remained below 0.056% from 2010 to 2015. To quantify the impact of this national REDD+ program, we construct a coun ...[more]

PMID: 31740591

Similar Datasets

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+.

Project description:Studying land use change in protected areas (PAs) located in tropical forests is a major conservation priority due to high conservation value (e.g., species richness and carbon storage) here, coupled with generally high deforestation rates. Land use change researchers use a variety of land cover products to track deforestation trends, including maps they produce themselves and readily available products, such as the Global Forest Change (GFC) dataset. However, all land cover maps should be critically assessed for limitations and biases to accurately communicate and interpret results. In this study, we assess deforestation in PA complexes located in agricultural frontiers in the Amazon Basin. We studied three specific sites: Amboró and Carrasco National Parks in Bolivia, Jamanxim National Forest in Brazil, and Tambopata National Reserve and Bahuaja-Sonene National Park in Peru. Within and in 20km buffer areas around each complex, we generated land cover maps using composites of Landsat imagery and supervised classification, and compared deforestation trends to data from the GFC dataset. We then performed a dissimilarity analysis to explore the discrepancies between the two remote sensing products. Both the GFC and our supervised classification showed that deforestation rates were higher in the 20km buffer than inside the PAs and that Jamanxim National Forest had the highest deforestation rate of the PAs we studied. However, GFC maps showed consistently higher rates of deforestation than our maps. Through a dissimilarity analysis, we found that many of the inconsistencies between these datasets arise from different treatment of mixed pixels or different parameters in map creation (for example, GFC does not detect reforestation after 2012). We found that our maps underestimated deforestation while GFC overestimated deforestation, and that true deforestation rates likely fall between our two estimates. We encourage users to consider limitations and biases when using or interpreting our maps, which we make publicly available, and GFC's maps.

Project description:Intense and chronic disturbance may arrest natural succession, reduce environmental quality and lead to ecological interaction losses. Where natural succession does not occur, ecological restoration aims to accelerate this process. While plant establishment and diversity is promoted by restoration, few studies have evaluated the effect of restoration activities on ecological processes and animal diversity. This study assessed herbivory and lepidopteran diversity associated with two pioneer tree species growing in 4-year-old experimental restoration plots in a tropical dry forest at Sierra de Huautla, in Morelos, Mexico. The study was carried out during the rainy season of 2010 (July-October) in eleven 50 x 50 m plots in three different habitats: cattle-excluded, cattle-excluded with restoration plantings, and cattle grazing plots. At the beginning of the rainy season, 10 juveniles of Heliocarpus pallidus (Malvaceae) and Ipomoea pauciflora (Convolvulaceae) were selected in each plot (N = 110 trees). Herbivory was measured in 10 leaves per plant at the end of the rainy season. To evaluate richness and abundance of lepidopteran larvae, all plants were surveyed monthly. Herbivory was similar among habitats and I. pauciflora showed a higher percentage of herbivory. A total of 868 lepidopteran larvae from 65 morphospecies were recorded. The family with the highest number of morphospecies (9 sp.) was Geometridae, while the most abundant family was Saturnidae, with 427 individuals. Lepidopteran richness and abundance were significantly higher in H. pallidus than in I. pauciflora. Lepidopteran richness was significantly higher in the cattle-excluded plots, while abundance was significantly higher in the non-excluded plots. After four years of cattle exclusion and the establishment of plantings, lepidopteran richness increased 20 -fold in the excluded plots compared to the disturbed areas, whereas herbivory levels were equally high in both restored and disturbed sites. Restoration with plantings and exclusion of cattle and plantings was shown to be a successful strategy for attracting lepidopterans and cattle exclusion was the main factor explaining lepidopteran diversity.

Dataset Information

Evidence that a national REDD+ program reduces tree cover loss and carbon emissions in a high forest cover, low deforestation country.

Publications

Evidence that a national REDD+ program reduces tree cover loss and carbon emissions in a high forest cover, low deforestation country.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets