{"title":"Australia’s Black Summer wildfires recovery: A difference-in-differences analysis using nightlights","authors":"Sonia Akter","doi":"10.1016/j.gloenvcha.2023.102743","DOIUrl":null,"url":null,"abstract":"<div><p>This study examines how communities of New South Wales (NSW), Australia, recovered from the extreme wildfire event of 2019–2020 (i.e., the Black Summer fires). Using monthly night-time radiance as an indicator of economic activity in a geographic area (i.e., a mesh block) from January 2017 to June 2021, I conducted a spatio-temporal and socio-economic analysis of economic recovery after the 2019–2020 wildfires using the difference-in-differences method. This is the first study to examine the intersectional role of space with time and socio-economic characteristics for extreme wildfire recovery. The findings reveal that wildfire-affected locations had about 0.038σ and 0.026σ lower night-time radiance in major cities and rural hinterlands (i.e., inner regions), respectively, than the unaffected areas. These numbers translate to approximately 30% reduction in economic activities in both areas. The findings remain consistent when using <em>Facebook</em>’s movement range data. The pace of recovery varied spatially across time and socio-economic groups. In rural hinterlands of NSW, wildfire-affected communities, both poor and non-poor, followed a slower recovery trajectory than wildfire-affected city dwellers. In major cities, the economic recovery of poor communities lagged behind non-poor communities. Accounting for such spatial, temporal and socio-economic heterogeneity in the natural hazard recovery process can support the design of equitable wildfire risk reduction and management strategies and programs. If unaddressed, gaps in wildfire recovery can increase location and economic group specific vulnerabilities to future wildfires. Note that nightlights are not a good proxy for economic activity in heavily forested remote and rural areas; thus limiting the application of the use of high frequency satellite data for wildfire recovery analysis only in major cities and rural hinterlands.</p></div>","PeriodicalId":328,"journal":{"name":"Global Environmental Change","volume":"83 ","pages":"Article 102743"},"PeriodicalIF":8.6000,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Environmental Change","FirstCategoryId":"6","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959378023001097","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study examines how communities of New South Wales (NSW), Australia, recovered from the extreme wildfire event of 2019–2020 (i.e., the Black Summer fires). Using monthly night-time radiance as an indicator of economic activity in a geographic area (i.e., a mesh block) from January 2017 to June 2021, I conducted a spatio-temporal and socio-economic analysis of economic recovery after the 2019–2020 wildfires using the difference-in-differences method. This is the first study to examine the intersectional role of space with time and socio-economic characteristics for extreme wildfire recovery. The findings reveal that wildfire-affected locations had about 0.038σ and 0.026σ lower night-time radiance in major cities and rural hinterlands (i.e., inner regions), respectively, than the unaffected areas. These numbers translate to approximately 30% reduction in economic activities in both areas. The findings remain consistent when using Facebook’s movement range data. The pace of recovery varied spatially across time and socio-economic groups. In rural hinterlands of NSW, wildfire-affected communities, both poor and non-poor, followed a slower recovery trajectory than wildfire-affected city dwellers. In major cities, the economic recovery of poor communities lagged behind non-poor communities. Accounting for such spatial, temporal and socio-economic heterogeneity in the natural hazard recovery process can support the design of equitable wildfire risk reduction and management strategies and programs. If unaddressed, gaps in wildfire recovery can increase location and economic group specific vulnerabilities to future wildfires. Note that nightlights are not a good proxy for economic activity in heavily forested remote and rural areas; thus limiting the application of the use of high frequency satellite data for wildfire recovery analysis only in major cities and rural hinterlands.
期刊介绍:
Global Environmental Change is a prestigious international journal that publishes articles of high quality, both theoretically and empirically rigorous. The journal aims to contribute to the understanding of global environmental change from the perspectives of human and policy dimensions. Specifically, it considers global environmental change as the result of processes occurring at the local level, but with wide-ranging impacts on various spatial, temporal, and socio-political scales.
In terms of content, the journal seeks articles with a strong social science component. This includes research that examines the societal drivers and consequences of environmental change, as well as social and policy processes that aim to address these challenges. While the journal covers a broad range of topics, including biodiversity and ecosystem services, climate, coasts, food systems, land use and land cover, oceans, urban areas, and water resources, it also welcomes contributions that investigate the drivers, consequences, and management of other areas affected by environmental change.
Overall, Global Environmental Change encourages research that deepens our understanding of the complex interactions between human activities and the environment, with the goal of informing policy and decision-making.