Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon
Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon
复制标题
DOI：
10.1016/j.ecolecon.2019.106501
复制
发表时间：
2020-03-01
影响因子：
7
通讯作者：
Steil, Lara
中科院分区：
文献类型：
--
作者：
Morello, Thiago Fonseca;Ramos, Rossano Marchetti;Steil, Lara
巴西亚马逊地区联邦消防队的部署是基于对火灾发生的过于简单的预测，不准确的预测会影响政策的效率。为了缓解这个问题，本文试图改善火灾预测。首先，从城市一级的社会经济和环境数据建立面板数据集。该数据集在变量数量（48）以及地理（整个亚马逊）和时间宽度（2008年至2014年）方面都是无与伦比的。其次，估计经济计量模型预测火灾发生的准确性高，并推断统计上显着的预测火灾。最好的预测是通过考虑观察到的和未观察到的时不变预测因子以及空间依赖性来实现的。最准确的模型预测了前20%的城市火灾数量，成功率为76%。它在确定优先城市方面的准确性是目前消防队分配程序的两倍多。在47个可能的预测因素中，毁林、森林退化、原始森林、国内生产总值、土著和保护区、气候和土壤证明具有统计意义。最后，分配消防队的现行标准应扩大到考虑（一）社会经济和环境预测因素，（二）时不变的不可观测因素和（三）火灾的空间自相关性。
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.