Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.ecolecon.2019.106501 |
Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon | |
Morello, Thiago Fonseca1; Ramos, Rossano Marchetti2; Anderson, Liana O.3; Owen, Nathan4; Rosan, Thais Michele5; Steil, Lara2 | |
2020-03-01 | |
发表期刊 | ECOLOGICAL ECONOMICS |
ISSN | 0921-8009 |
EISSN | 1873-6106 |
出版年 | 2020 |
卷号 | 169 |
文章类型 | Article |
语种 | 英语 |
国家 | Brazil; England |
英文摘要 | 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. |
英文关键词 | Amazon Fire Land use Panel data Spatial econometrics |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000510953600034 |
WOS关键词 | SLASH-AND-BURN ; PANEL-DATA ANALYSIS ; LAND-USE ; DEFORESTATION SLOWDOWN ; EASTERN AMAZON ; CLIMATE-CHANGE ; DATA MODELS ; FUTURE ; FORESTS ; AGRICULTURE |
WOS类目 | Ecology ; Economics ; Environmental Sciences ; Environmental Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Business & Economics |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/278962 |
专题 | 资源环境科学 |
作者单位 | 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 |
推荐引用方式 GB/T 7714 | Morello, Thiago Fonseca,Ramos, Rossano Marchetti,Anderson, Liana O.,et al. Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon[J]. ECOLOGICAL ECONOMICS,2020,169. |
APA | Morello, Thiago Fonseca,Ramos, Rossano Marchetti,Anderson, Liana O.,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,169. |
MLA | Morello, Thiago Fonseca,et al."Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon".ECOLOGICAL ECONOMICS 169(2020). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论