Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon

Author(s)

Morello, Thiago Fonseca ^[1] ; Ramos, Rossano Marchetti ^[2] ; Anderson, Liana O. ^[3] ; Owen, Nathan ^[4] ; Rosan, Thais Michele ^[5] ; Steil, Lara ^[2]

Affiliation

^[1] Fed Univ ABC, Alameda Univ S-N, BR-09606045 Sao Bernardo Do Campo, SP - Brazil

^[2] Natl Ctr Prevent & Suppress Forest Fires PREVFOGO, SCEN Trecho 2, Edificio Sede, Brasilia, DF - Brazil

^[3] Brazilian Ctr Monitoring & Early Warnings Nat Dis, Estr Doutor Altino Bondman, BR-12247016 Sao Jose Dos Campos, SP - Brazil

^[4] Univ Exeter, Business Sch, Land Environm Econ & Policy Inst, Xfi Bldg, Rennes Dr, Exeter EX4 4PU, Devon - England

^[5] Brazilian Inst Space Res INPE, Av Astronautas 1-758, Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: ECOLOGICAL ECONOMICS; v. 169, MAR 2020.
• Web of Science Citations: 7

Abstract

The positioning of federal fire brigades in the Brazilian Amazon is based on an oversimplified prediction of fire occurrences, where inaccuracies can affect the policy's efficiency. To mitigate this issue, this paper attempts to improve fire prediction. Firstly, a panel dataset was built at municipal level from socioeconomic and environmental data. The dataset is unparalleled in both the number of variables (48) and in geographical (whole Amazon) and temporal breadth (2008 to 2014). Secondly, econometric models were estimated to predict fire occurrences with high accuracy and to infer statistically significant predictors of fire. The best predictions were achieved by accounting for observed and unobserved time-invariant predictors and also for spatial dependence. The most accurate model predicted the top 20% municipal fire counts with 76% success rate. It was over twice as accurate in identifying priority municipalities as the current fire brigade allocation procedure. Of the 47 potential predictors, deforestation, forest degradation, primary forest, GDP, indigenous and protected areas, climate and soil proved statistically significant. Conclusively, the current criteria for allocating fire brigades should be expanded to account for (i) socioeconomic and environmental predictors, (ii) time-invariant unobservables and (iii) spatial auto-correlation on fires. (AU)

• FAPESP's process: 16/15833-6 - Economic analysis of land use change in the Amazon: accounting for the externalities of fires
• Grantee: Thiago Fonseca Morello Ramalho da Silva
• Support Opportunities: Scholarships abroad - Research