Harvard Graduate School of Design, Department of Architecture, Cambridge, MA, USA.
Harvard Graduate School of Design, Department of Architecture, Cambridge, MA, USA.
Sci Total Environ. 2020 Jun 10;720:137296. doi: 10.1016/j.scitotenv.2020.137296. Epub 2020 Feb 19.
Municipalities use Heat Vulnerability Indices (HVIs) to quantify and map relative distribution of risks to human health in the event of a heatwave. These maps ostensibly allow public agencies to identify the highest-risk neighborhoods, and to concentrate emergency planning efforts and resources accordingly (e.g., to establish the locations of cooling centers). The method of constructing an HVI varies by municipality, but common inputs include demographic variables such as age and income - and to some extent, metrics such as land cover. However, taking demographic data as a proxy for heat vulnerability may provide an incomplete or inaccurate assessment of risk. A critical limitation in HVIs may be a lack of focus on housing characteristics and how they mediate indoor heat exposure. To provide an objective assessment of this limitation, we first reviewed HVIs in the literature and those published or commissioned by municipalities. We subsequently verified that most of these HVIs excluded housing factors. Next, to scope the potential consequences, we used physics-based simulations of housing prototypes (46,000 housing permutations per city) to estimate the variation in indoor heat exposure within high-vulnerability neighborhoods in Boston and Phoenix. The results show that by excluding building-level determinants of exposure, HVIs fail to capture important components of heat vulnerability. Moreover, we demonstrate how these maps currently overlook important nuances regarding the impact of building age and air conditioning functionality. Finally, we discuss the challenges of implementing housing stock characteristics in HVIs and propose methods for overcoming these challenges.
各城市使用热脆弱性指数 (HVI) 来量化和绘制热浪事件中人类健康风险的相对分布情况,并将其绘制成图。这些地图表面上可以让公共机构识别出高风险社区,并相应地集中进行应急规划工作和资源分配(例如,确定冷却中心的位置)。HVI 的构建方法因城市而异,但常见的输入变量包括年龄、收入等人口统计学变量,以及一定程度上的土地覆盖等指标。然而,将人口统计学数据作为热脆弱性的代理可能会导致风险评估不完整或不准确。HVI 的一个关键局限性可能是缺乏对住房特征以及它们如何调节室内热暴露的关注。为了客观评估这一局限性,我们首先回顾了文献中的 HVI 以及各城市发布或委托制作的 HVI。随后,我们验证了这些 HVI 大多排除了住房因素。接下来,为了确定潜在的后果,我们使用基于物理的住房原型模拟(每个城市有 46,000 种住房组合)来估计波士顿和凤凰城高脆弱性社区内的室内热暴露变化。结果表明,HVI 通过排除暴露的建筑层面决定因素,无法捕捉热脆弱性的重要组成部分。此外,我们展示了这些地图目前如何忽略了有关建筑年龄和空调功能影响的重要细微差别。最后,我们讨论了在 HVI 中实施住房存量特征的挑战,并提出了克服这些挑战的方法。