title: A Spatial Probit Econometric Model of Land Change: The Case of Infrastructure Development in Western Amazonia, Peru.
abstract: Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.
authors.affiliation: Department of Geography and the Environment-University of Texas at Austin, 305 E 23rd St, Austin, Texas, 78712, United States of America.
authors.forename: E Y
authors.initials: EY
authors.lastname: Arima
mesh_terms: D001499:Bayes Theorem; D002244:Carbon; D057231:Climate Change; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring / Q000379:methods; D065928:Forests; D005843:Geography; D017059:Models, Econometric; D015233:Models, Statistical; D000067936:Natural Resources; D010568:Peru; D011336:Probability; D012044:Regression Analysis; D062206:Spatial Analysis; D013269:Stochastic Processes; D014186:Transportation
chemical_list: D002244:Carbon