Abstract:
Approximately 13 million hectares of tropical forests are cut down annually, contributing 12-20% of global greenhouse gas emissions. REDD+ (Reduction in Emissions from Deforestation and Degradation) is designed to tackle this challenge by providing economic incentives for conservation, restoration and sustainable management of forests. In addition to emissions reductions, REDD+ also has the capacity to deliver co-benefits including biodiversity conservation and rural development by conserving species-rich forests and providing poor forest communities with alternative livelihoods. The Philippines, a biodiversity hotspot with the seventh highest deforestation rate in the world seeks to maximize these co-benefits during project planning and site selection. The 2010 Philippine National REDD-plus Strategy outlines the rationale for targeting project sites, however, a national-scale spatial prioritization assessment has yet to be conducted.
This study performs a multi-attribute utility theory (MAUT) analysis using five environmental and socio-economic indicators (carbon stocks, deforestation rate, biodiversity, indigenous community areas, and poverty incidence). The model is then applied in geographic information system (GIS) to spatially identify potential priority areas (by province and municipality) for REDD+ investment in the Philippines. Having a clear, publicly available prioritization method ensures that REDD+ funds are allocated efficiently, and that the overall environmental and social objectives of the program are being effectively conveyed on the ground. It can also provide informed accounting of the trade-offs made between carbon, co-benefits and costs.
The results from the MAUT decision framework and subsequent spatial analysis show four geographic scenarios coinciding with various possible stakeholder priorities. Areas with high utility scores were located primarily in the uplands, with some scenarios overlapping with the existing REDD+ pilot sites. While carbon, biodiversity and socioeconomic indicators proved to be spatially correlated, deforestation rates were not and thus require higher trade-offs. Additional, more detailed maps and studies on indicators are needed for the Philippines, however, this spatial prioritization assessment represents a compilation of the best available data and can be used as a stepping stone and resource until more robust analyses can be completed.
