Abstract
Abstract
This article contributes a machine learning approach to detecting environmental crimes in Brazil. Detecting these crimes is important for the health of the environment and public safety. The measurement of environmental crimes is challenging, in part due to efforts of those involved to remain hidden. However, administrative data on embargoes—legal liens on parcels of land—identify locations where the government documented some of these crimes. Using these data, the analysis develops a method for detecting yet-unidentified locations of these crimes. Embargo data are merged with a wide range of predictors that capture features of the physical environment, as well as demographic, socioeconomic, political, and public safety attributes. After training a series of models on these data, the random forest (RF) model performs best (AUC = 0.92). Overall, a combination of changes in vegetation, homicide rates, the prevalence of young men, human development, employment, the presence of a poverty reduction program, and political factors that capture local electoral competition and participation are important predictors of illegal deforestation. These findings—some unsurprising and some surprising—show the value of multicausal, cross-disciplinary approaches to understanding and detecting environmental crime. The practical gains from integrating remote sensing, a wide range of administrative data, and machine learning help build more effective crime detection, enhancing the government’s ability to conduct forest enforcement more safely and efficiently. Future research is promising as more and better data become available, and the approach can be extended to detect other illicit behavior, in Brazil and elsewhere.