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AI applications in urban climate mitigation and adaptation

Source article: Artificial Intelligence for Net-Zero and Climate-Resilient Sustainable Cities: Technologies, Challenges, and Future Perspectives

Urbanization, climate change, increasing energy demand, and rapid infrastructure development have created significant challenges for achieving sustainable and resilient cities. Artificial intelligence (AI) has emerged as a transformative technology that enables data-driven decision-making, intelligent automation, and efficient resource management, making it an important enabler of net-zero and climate-resilient urban systems. This narrative review examines AI applications relevant to urban climate mitigation and…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1659· cite & verify
Artificial Intelligence for Net-Zero and Climate-Resilient Sustainable Cities: Technologies, Challenges, and Future Perspectives
World Trade Center, New York. Exterior. View from below with St. Nicholas Greek Orthodox Church in foreground - LCCN2021636614 by Balthazar Korab. Public domain
Trace impact readingContested
P 71The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 67The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

A narrative review drew on literature found through Google Scholar and ScienceDirect to examine how AI tools are applied to urban climate mitigation and adaptation. It covered machine learning, deep learning, IoT, big data analytics, cloud computing, digital twins and generative AI across energy, transport, monitoring, infrastructure and governance.

The review matters because cities face urbanization, rising energy demand and climate pressure, and AI is presented as a possible enabler of emission cuts and resilience. Its conclusions are qualitative, so they indicate potential rather than measured outcomes, and success is said to depend on ethical governance and public participation.

Main points

  1. The review covers AI in urban energy systems, sustainable transportation, environmental monitoring, infrastructure management and smart governance.
  2. Identified benefits include carbon emission reduction, renewable energy integration, climate adaptation, disaster preparedness and efficient urban planning.
  3. Challenges include privacy, cybersecurity, algorithmic bias, interoperability, AI's own energy consumption, digital inequality and governance.
  4. Emerging directions include explainable AI, green AI, AI-enabled digital twins, federated learning and human-centered smart governance.

The gain

The review concludes AI has potential to speed urban transitions to low-carbon, climate-resilient systems through uses such as energy systems, transport, monitoring and disaster preparedness.

The problem

The same urban AI deployments face obstacles including data privacy, cybersecurity, algorithmic bias, poor interoperability, the high energy use of AI systems, digital inequality and governance gaps.

The rundown

The paper is a narrative review that draws on literature identified through Google Scholar and ScienceDirect. It evaluates how several AI technologies support both climate mitigation and adaptation in cities, rather than reporting a new experiment or measured deployment.

Based on the reviewed literature, the authors propose research priorities: interdisciplinary collaboration, responsible AI governance, standardized urban data frameworks and sustainable policy development. They tie successful implementation to ethical governance, public participation and long-term sustainability planning aligned with the United Nations Sustainable Development Goals.

What this doesn’t fix

The synthesis is qualitative and non-exhaustive, and original search and screening records are unavailable, with differences between study settings and outcome measures limiting comparability.

Sources

  1. Peer-reviewedArtificial Intelligence for Sustainable Cities2026-10-15

The debate