Using publicly available remote sensing products to evaluate REDD+ projects in Brazil
The perpetuity and improvement of REDD+ projects for curbing deforestation re- quire rigorous impact evaluations of their effectiveness.Today, a number of global and regional remote sensing (RS) products are publicly available for detecting changes in forest cover world wide. In this study, we assess the suitability of using these readily available products to evaluate the impact of REDD+ local projects targeting smallholders in the Brazilian Amazon. Firstly, we reconstructed forest loss for the period between 2008 and 2018 of 21,492 farms located in the Transamazonian region, using data derived from two land-cover change datasets : Global Forest Change (GFC) and Amazon Deforestation Monitoring Project (PRODES). Secondly, we evaluate the consistency between the two sources of data. Lastly, we estimate the long-term impact of a local REDD+ initiative using both datasets. We found that the estimates of deforestation at the farm level vary considerably from one dataset to another. However, using microeconometric techniques that use pre-treatment out comes to construct counterfactual patterns of participants in the REDD+ program, we found that an average of about 2 hectares of forest were saved on each of the 348 participating farms during the first years of the program, regard-less the source of data used. More over, we found that de forestation decreased on plots surrounding participating farms, suggesting that the program had a positive spillover effect on neighbouring farms. Finally, we showed that the participants returned to the business-as-usual pattern at the end of the program. The environmental gain generated during the four years of the program was, however, not offset by any catch-up behaviour. By evaluating the monetary gain of the delayed carbon dioxide emissions, we found that the program's benefits were ultimately greater than its costs.
| Main Authors: | , , , |
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| Format: | monograph biblioteca |
| Language: | eng |
| Published: |
INRA, Montpellier SupAgro
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| Online Access: | http://agritrop.cirad.fr/598315/ http://agritrop.cirad.fr/598315/1/Demarchi_Subervie_Catry_Tritsch_2020_CEE_Working_paper.pdf |
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| Summary: | The perpetuity and improvement of REDD+ projects for curbing deforestation re- quire rigorous impact evaluations of their effectiveness.Today, a number of global and regional remote sensing (RS) products are publicly available for detecting changes in forest cover world wide. In this study, we assess the suitability of using these readily available products to evaluate the impact of REDD+ local projects targeting smallholders in the Brazilian Amazon. Firstly, we reconstructed forest loss for the period between 2008 and 2018 of 21,492 farms located in the Transamazonian region, using data derived from two land-cover change datasets : Global Forest Change (GFC) and Amazon Deforestation Monitoring Project (PRODES). Secondly, we evaluate the consistency between the two sources of data. Lastly, we estimate the long-term impact of a local REDD+ initiative using both datasets. We found that the estimates of deforestation at the farm level vary considerably from one dataset to another. However, using microeconometric techniques that use pre-treatment out comes to construct counterfactual patterns of participants in the REDD+ program, we found that an average of about 2 hectares of forest were saved on each of the 348 participating farms during the first years of the program, regard-less the source of data used. More over, we found that de forestation decreased on plots surrounding participating farms, suggesting that the program had a positive spillover effect on neighbouring farms. Finally, we showed that the participants returned to the business-as-usual pattern at the end of the program. The environmental gain generated during the four years of the program was, however, not offset by any catch-up behaviour. By evaluating the monetary gain of the delayed carbon dioxide emissions, we found that the program's benefits were ultimately greater than its costs. |
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