McConnell Kathryn, Braneon Christian V
Population Studies and Training Center, Brown University.
Columbia Climate School, CUNY Institute for Demographic Research (CIDR), Carbon Direct, USA.
Landsc Urban Plan. 2024 Jul;247. doi: 10.1016/j.landurbplan.2023.104997. Epub 2024 Mar 26.
As the number of highly destructive wildfires grows, it is increasingly important to understand the long-term changes that occur to fire-affected places. Integrating approaches from social and biophysical science, we document two forms of neighborhood change following the 2018 Camp Fire in the United States, examining the more than 17,000 residential structures within the burn footprint. We found that mobile or motor homes, lower-value residences, and absentee owner residences had a significantly higher probability of being destroyed, providing evidence that housing stock filtering facilitated socially stratified patterns of physical damage. While the relationship between building value and destruction probability could be explained by measures of building density and distance to nearby roads, building type remained an independent predictor of structure loss that we could not fully explain by adding environmental covariates to our models. Using a geospatial machine learning technique, we then identified buildings that had been reconstructed within the burn footprint 20 months after the fire. We found that reconstructed buildings were more likely to have been owner-occupied prior to the fire and had higher average pre-fire property value, suggesting an emerging pattern of cost-burden gentrification. Our findings illustrate the importance of examining the built environment as a driver of socially uneven disaster impacts. Wildfire mitigation strategies are needed for mobile and motor home residents, renters, low-income residents, and dense neighborhoods.
随着极具破坏性的野火数量不断增加,了解受火灾影响地区发生的长期变化变得越发重要。我们整合社会科学和生物物理科学的方法,记录了美国2018年营火事件后社区变化的两种形式,研究了过火区域内17000多座住宅建筑。我们发现,移动房屋或房车、低价值住宅以及业主不在当地的住宅被摧毁的可能性显著更高,这证明住房存量筛选促成了物质破坏的社会分层模式。虽然建筑价值与破坏概率之间的关系可以通过建筑密度和到附近道路的距离来解释,但建筑类型仍然是结构损失的一个独立预测因素,我们在模型中添加环境协变量后仍无法完全解释这一因素。然后,我们使用地理空间机器学习技术,识别了火灾发生20个月后在过火区域内重建的建筑。我们发现,重建的建筑在火灾前更有可能是业主自住,且火灾前的平均房产价值更高,这表明出现了一种成本负担型绅士化模式。我们的研究结果说明了将建筑环境视为社会不均衡灾害影响驱动因素进行研究的重要性。对于移动房屋和房车居民、租户、低收入居民以及密集社区,需要制定野火缓解策略。