A Bayesian network approach to modelling land-use decisions under environmental policy incentives in the Brazilian Amazon

ABSTRACT

Deforestation driven by agricultural expansion is a major threat to the biodiversity of the Amazon Basin. Modelling how deforestation responds to environmental policy implementation has thus become a policy relevant scientific undertaking. However, empirical parameterization of land-use/cover change (LUCC) models is challenging due to the high complexity and uncertainty of land-use decisions. Bayesian Network (BN) modelling provides an effective framework to integrate various data sources including expert knowledge. In this study, we integrate remote sensing products with data from farm-household surveys and a decision game to model LUCC at the BR-163, in Brazil. Our ‘business as usual’ scenario indicates cumulative forest cover loss in the study region of 8,000 km² between 2014 and 2030, whereas ‘intensified law-enforcement’ would reduce cumulative deforestation to 1,600 km² over the same time interval. Our findings underline the importance of conservation law enforcement in modulating the impact of agricultural market incentives on land cover change.