This study develops an integrated, intersectional climate vulnerability assessment for Greensboro, North Carolina, a midsize city in the rapidly changing American Southeast. Moving beyond generalized mapping, we combine demographic, socioeconomic, health, and environmental data at the census tract level to identify neighborhoods where flood exposure, chronic health burdens, and social disadvantage spatially converge. Through k-means and hierarchical clustering, we identify four distinct neighborhood typologies, including a critically high-risk cluster characterized by high flood exposure, extreme poverty, poor respiratory health, and aging housing. The findings demonstrate that climate-related risks are not randomly distributed but systematically cluster in historically marginalized communities, revealing a clear environmental justice disparity. This place-based typology approach provides a targeted framework for policymakers to design integrated interventions that bridge flood management, public health, housing, and social services to build equitable urban resilience

翻译：本研究为美国东南部快速变化的中型城市——北卡罗来纳州格林斯伯勒，开发了一种综合的、交叉的气候脆弱性评估方法。我们超越了广义的制图分析，在人口普查区层面整合了人口、社会经济、健康和环境数据，以识别洪水暴露、慢性健康负担和社会劣势在空间上汇聚的社区。通过k-means和层次聚类分析，我们识别出四种不同的社区类型，其中包括一个以高洪水暴露、极端贫困、呼吸健康状况差和住房老化为主要特征的极高风险集群。研究结果表明，气候相关风险并非随机分布，而是系统性地聚集在历史上被边缘化的社区中，揭示出明显的环境正义差异。这种基于地域的类型学方法为政策制定者提供了一个有针对性的框架，用以设计综合干预措施，将洪水管理、公共卫生、住房和社会服务联系起来，从而构建公平的城市韧性。