Modeling illegal logging in Brazil

Bohan Chen,Tara Lyn Slough,Andrea L Bertozzi,Christian Parkinson,Kaiyan Peng,Johannes Urpelainen

doi:10.1007/s40687-021-00263-6

Bohan Chen, Tara Lyn Slough + Show 4 more

Open Access

https://doi.org/10.1007/s40687-021-00263-6

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Abstract

Deforestation is a major threat to global environmental wellness, with illegal logging as one of the major causes. Recently, there has been increased effort to model environmental crime, with the goal of assisting law enforcement agencies in deterring these activities. We present a continuous model for illegal logging applicable to arbitrary domains. We model the practice of criminals under influence of law enforcement agencies using tools from multiobjective optimal control theory and consider non-instantaneous logging events and load-dependent travel velocity. We calibrate our model using real deforestation data from the Brazilian rainforest and demonstrate the importance of geographically targeted patrol strategies.

Highlights

Deforestation, and in particular, illegal logging causes some of the most damaging effects to the world’s forests
Pfaff [29] has validated the correlation between certain parameters and deforestation in tropical regions such as Brazil
Companies operating in Brazil, under such concessions, fell an average of only one tree per acre instead of clearcutting to allow tree regrowing [37]

Summary

Introduction

Deforestation, and in particular, illegal logging causes some of the most damaging effects to the world’s forests. Pfaff [29] has validated the correlation between certain parameters and deforestation in tropical regions such as Brazil. An effort to control deforestation in Brazil, while exploiting timber in a sustainable way, is through allowing legal concessions for industrial timber harvest in public forests [4]. The government’s failure to detect and punish illegal loggers leads to even more rampant organized crime and more severe deforestation. It is an essential and urgent task to work out effective tactics to combat illegal loggers. Effective deterrence can lead to spatial spillovers, as loggers move away from areas with heavy monitoring.

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