| Summary: | <p>Reducing deforestation and degradation (REDD&plus;) can lower anthropogenic carbon emissions by 9-11% and is therefore integral to mitigating climate change. REDD&plus; is a proposed scheme in which developed countries pay developing countries for reduced deforestation. Kenya is one such country participating in REDD&plus;. As such, Kenya must establish a reference emission level (REL) against which it can compare avoided emissions from reduced deforestation. Developing a spatial model of future deforestation will determine the REL for Kenya and provide complementary information on where forests are at risk and drivers of deforestation. REDD&plus; also offers benefits of poverty alleviation and biodiversity conservation, referred to as "co-benefits." Since the spatial distributions of carbon and co-benefits may not be congruent, there may be trade-offs among them whereby increasing one decreases others. To assist Kenya in its REDD&plus; preparations, I build the first spatially explicit deforestation model for the whole of Kenya parametrized with national data. I apply a novel Bayesian inference method which propagates uncertainty in the estimated parameters to the predictions. In addition, I conduct a novel analysis of trade-offs among carbon and co-benefits in three dimensions. I find that predicted forest loss is 1.68% ± 0.06% from 2013 to 2018 (cross-validated AUC = 0.8). There is high deforestation in the Rift Valley and coastal regions attributed to drivers related to accessibility, extraction, and agriculture. Analysis of three-dimensional trade-offs reveals that REDD&plus; sites could simultaneously include 86% of the maximum possible number of people in poverty and 88% of the maximum possible average proportion of ranges of rare species with the smallest 25% of global ranges with a 10% reduction in carbon. The results presented here may help Kenya spatially target REDD&plus; interventions and address particular deforestation drivers as well as plan for securing high levels of carbon stocks and co-benefits.</p>
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