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

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  • Fonseca Morello, Thiago
  • Marchetti Ramos, Rossano
  • O. Anderson, Liana
  • Owen, Nathan
  • Rosan, Thais Michele
  • Steil, Lara

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 autocorrelation on fires.

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  • Fonseca Morello, Thiago & Marchetti Ramos, Rossano & O. Anderson, Liana & Owen, Nathan & Rosan, Thais Michele & Steil, Lara, 2020. "Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon," Ecological Economics, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:ecolec:v:169:y:2020:i:c:s0921800918312023
    DOI: 10.1016/j.ecolecon.2019.106501
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    1. Selene Cerna & Christophe Guyeux & Guillaume Royer & Céline Chevallier & Guillaume Plumerel, 2020. "Predicting Fire Brigades Operational Breakdowns: A Real Case Study," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
    2. Thiago Fonseca Morello Ramalho da Silva & Paula Carvalho Pereda & Ana Carolina M. Pessoa & Liana O. Anderson, 2024. "Unveiling the Dynamic Impact of Protected Areas: An Event Study Analysis to Assess Conservation Effectiveness," Working Papers, Department of Economics 2024_02, University of São Paulo (FEA-USP).
    3. Marcus V. F. Silveira & Caio A. Petri & Igor S. Broggio & Gabriel O. Chagas & Mateus S. Macul & Cândida C. S. S. Leite & Edson M. M. Ferrari & Carolina G. V. Amim & Ana L. R. Freitas & Alline Z. V. Mo, 2020. "Drivers of Fire Anomalies in the Brazilian Amazon: Lessons Learned from the 2019 Fire Crisis," Land, MDPI, vol. 9(12), pages 1-24, December.

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