@Article{MorelloRaAnOwRoSt:2020:ImAcFi,
author = "Morello, Thiago Fonseca and Ramos, Rossano Marchetti and Anderson,
Liana O. and Owen, Nathan and Rosan, Thais Michele and Steil,
Lara",
affiliation = "{Universidade Federal do ABC (UFABC)} and {Instituto Brasileiro do
Meio Ambiente e Recursos Naturais Renov{\'a}veis (IBAMA)} and
{Centro Nacional de Monitoramento e Alertas de Desastres Naturais
(CEMADEN)} and {University of Exeter Business Schoo} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Brasileiro do Meio Ambiente e Recursos Naturais Renov{\'a}veis
(IBAMA)}",
title = "Predicting fires for policy making: Improving accuracy of fire
brigade allocation in the Brazilian Amazon",
journal = "Ecological Economics",
year = "2020",
volume = "169",
pages = "e106501",
month = "Mar.",
keywords = "Amazon, Fire, Land use, Panel data, Spatial econometrics.",
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.",
doi = "10.1016/j.ecolecon.2019.106501",
url = "http://dx.doi.org/10.1016/j.ecolecon.2019.106501",
issn = "0921-8009",
language = "en",
targetfile = "morello_predicting.pdf",
urlaccessdate = "21 maio 2024"
}