@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 = "28 abr. 2024"
}