Cool it: AI-driven solutions to lower urban heat need community buy-in to work
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A new framework to cool urban areas which integrates AI-driven technical solutions with cooling schemes that are acceptable and doable for the host community, has been proposed by QUT Urban AI Hub researchers.
Publishing in the journal Urban Climate, the researchers argue that their Cooling with Consent framework addresses the fact that communities differ in which cooling initiatives they will accept and that AI-driven heat adaptation solutions cannot be imposed on residents without their buy-in.
First author Professor Tan Yigitcanlar, from QUT's School of Architecture and Built Environment, said urban heat is intensifying around the world. However, AI's precision in identifying heat hotspots does not guarantee public support, local readiness or administrative feasibility.
"A growing disconnection is emerging between technical capacity and implementation on the ground," Yigitcanlar said.
"The Climate AI framework we propose takes account of the fact that people's response to heat risks is shaped by their lived experiences, cultural norms and immediate priorities.
"Many communities do not see mitigation of increasing urban heat as urgent, especially when extreme heat has become normalized or overshadowed by more pressing concerns.
"If they have a strong connection to their neighborhood, they may resist visible interventions such as shade structures or tree planting if these are seen as altering their local identity or routines."
Yigitcanlar said that even when cooling initiatives gained community license, they could be stymied by budget constraints, lack of coordination among various agencies, political priorities such as elections and regulatory approvals.
"Thus, we advocate shifting from conventional risk-based approaches to a readiness-based framework that prioritizes where interventions are most socially and institutionally feasible.
"Our framework repositions AI systems as decision-support tools that are socially aware and procedurally grounded by using behavioral and institutional proxies to enhance planning, guide resource allocation and strengthen the legitimacy of the process.
"It is designed to build on data streams commonly used in urban planning, such as census data, administrative records, mobility patterns and structured or unstructured public feedback.
"Our framework focuses on equitable deployment of community-approved cooling systems by incorporating vulnerable groups using census data and socioeconomic datasets.
"Community readiness and legitimacy can be estimated using natural language processing (NLP) of citizen feedback and public discourse.
"Attachment to place may be inferred from geospatial behavior, such as long-term residency or frequent public space use.
"Institutional inertia can be estimated using network analysis or lag metrics in policy implementation. Resource allocation should use indicators of institutional feasibility such as governance structure, interagency coordination and procedural readiness.
"Incorporating such indicators enables models to rank not only areas of high thermal stress but also locations where adaptation is most feasible, equitable and inclusive, and therefore more likely to succeed."
More information
Tan Yigitcanlar et al, Cooling with consent: Toward equitable and readiness-based AI-guided heat adaptation, Urban Climate (2026). DOI: 10.1016/j.uclim.2026.103003
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Citation: Cool it: AI-driven solutions to lower urban heat need community buy-in to work (2026, July 30) retrieved 30 July 2026 from https://phys.org/news/2026-07-cool-ai-driven-solutions-urban.html
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