|Title||Are REDD+ interventions targeting the right drivers on the ground? Insights from space and below|
|Author(s)||Bos, A.B.; Sy, V. de; Duchelle, Amy E.; Herold, M.|
|Event||FLARE (Forests & Livelihoods: Assessment, Research, and Engagement) 4th Annual Meeting 2018, Copenhagen, 2018-10-17/2018-10-20|
Laboratory of Geo-information Science and Remote Sensing
|Publication type||Abstract in scientific journal or proceedings|
|Abstract||Efforts to reduce emissions from deforestation and forest degradation and enhancing forest carbon stocks (REDD+) have evolved over the past decade. Through early subnational REDD+ initiatives, various interventions (i.e. enabling measures, disincentives and incentives) have been implemented by non-government, private sector and government actors. Ultimately these interventions aim at the common goal of reducing emissions, but it remains understudied to what extent they address site-specific drivers of deforestation and forest degradation(DD).
As part of CIFOR’s Global Comparative Study on REDD+, we assessed DD drivers and REDD+ interventions at four sites in Brazil, Indonesia, Peru and Vietnam. Our study design triangulates observed, reported and modelled drivers. First, we used observed follow-up land use from high resolution imagery as proxy for direct deforestation drivers. Second, reported direct and indirect DD drivers were based on semi-structured interviews with REDD+ implementers, and village-level (mixed-gender and women’s) and household surveys. The surveys were implemented before and after the start of REDD+ initiatives and targeted approximately 480 households in 16 villages. Data gathered included forest regulations; perceived causes of forest cover/quality change; income (including cash and subsistence components); perceived tenure security; and involvement in REDD+. Third, spatial modelling of DD drivers allowed for influence quantification of biophysical and socioeconomic variables. While we found general alignment between observed, reported and modelled drivers locally, some drivers were not adequately addressed by interventions. Furthermore, drivers differed among villages even in the same study sites, which underscores the importance of having local information on drivers and locally tailored REDD+ interventions.
With this interdisciplinary study, we provide insights in the complexities of DD driver identification at the local scale considering the interplay between direct and indirect drivers, and concerning differences between observed, reported and modelled drivers. This information is relevant for practitioners and policy makers interested in enhancing the effectiveness of REDD+ at the local level. A better understanding of the alignment of deforestation drivers and REDD+ interventions is vital for REDD+ performance assessments, since addressing the right drivers is the first requirement for success.