Study on Climate-Related Risks Examined City Surfaces and Social Vulnerability

Findings Highlight Need for Evidence-Based Decisions in Selecting Materials to Build More Equitable and Sustainable Cities

Cities are increasingly becoming the epicenter of climate-related risks, with research showing that impervious surfaces (e.g., roofs, streets, sidewalks, parking lots) are a major driver of urban climate impacts because they disrupt the natural surface energy balance, increase stormwater runoff, and intensify urban heat island effects.

In a new study, researchers used high-resolution remote-sensing imagery classification in ArcGIS Pro to map surface characteristics in Pittsburgh, Pennsylvania, and examine their relationship with land surface temperatures and social vulnerability. The study’s findings shed light on urban thermal inequities and emphasize the need for evidence-based decision making in choosing surface materials.

Conducted by researchers at Carnegie Mellon University (CMU), the study is published in Sustainable Cities and Society.

“Although research on the impact of impervious surfaces on urban heat has grown recently, most studies overlook variations in surface characteristics, such as surface color, which influence urban heat,” explains Suzy (Zekun) Li, PhD graduate at CMU, who led the study. “Consequently, citywide data on subcategories of impervious surfaces are limited.”

In this study, researchers developed a machine learning-based classification approach that categorizes and maps urban surfaces in more intuitive, publicly understandable categories, then integrates them with socioeconomic data to generate actionable insights for evidence-based equitable policy decisions and climate mitigation strategies related to urban surfaces.

Although most studies on urban heat have focused on areas with hot climates, such as Los Angeles and Phoenix, it is increasingly important to understand how temperate cities are also affected as climate change intensifies, according to the authors. In addition, Pittsburgh’s population changes and socioeconomic disparities made it ideal for investigating social vulnerability in relation to historical redlining (redlined neighborhoods are those with significant numbers of racial and ethnic minorities) and current levels of heat exposure.

Based on the study’s results, the authors conclude that in Pittsburgh, impervious surfaces cover 55% of the city, including 22% of roofs, 30.7% of roads and 2.3% of parking lots, with 52% of these surfaces classified as dark; darker roofs are more likely to retain heat, which can harm the quality of life in neighborhoods with those roofs. The study also found that on average, historically redlined neighborhoods are 2.6 ◦C (4.7 ◦F) hotter and contain a higher proportion of dark surfaces than other neighborhoods.

Because the study looked at just one city, the authors say it is unclear whether their findings represent a general trend across cities or a context-specific phenomenon in Pittsburgh. They suggest further investigations of other cities with different climate conditions.

“Our findings emphasize the importance of evidence-based decision-making in the selection of city surface materials and the implementation of green infrastructure strategies,” says Kristen Kurland, professor of architecture, information systems, and public policy at Carnegie Mellon Heinz College and School of Architecture, who coauthored the study. “They also support the data-driven prioritization of interventions to reduce urban heat, such as adopting reflective surfaces and expanding the canopy of urban trees.

“Integrating this approach into city planning processes can help cities design more equitable, resilient, and sustainable urban environments for the future.”

The research was funded by the China Leon Group.

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Summarized from an article in Sustainable Cities and Society, Assessing Social Equity and Urban Heat Risks with Machine Learning of Remote Sensing Imagery: A Pittsburgh Case Study, by Li, Z (Carnegie Mellon University), Vasanthawada, SRS (Carnegie Mellon University), Chai, K (Carnegie Mellon University), Luo, L (Carnegie Mellon University), Xu, X (Carnegie Mellon University), Zhang, Y (Carnegie Mellon University), Kurland, K (Carnegie Mellon University), and Loftness, V (Carnegie Mellon University). Copyright 2025 The Authors. All rights reserved.

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