Strategies to accelerate climate risk management in the building sector using data-driven methods and tools

Abstract Data-driven methods and tools are becoming vital for facilitating all stages of climate risk management, including risk analysis, assessment, and management. Nevertheless, current research often focuses on a specific stage or single tool, limiting their effective deployment. Here, we intersect data-driven tools, geographical contexts, social and physical dimensions to examine how these tools improve climate risk management in the building sector. We perform bibliometric analysis, machine learning, and qualitative analysis on large text data to show and evaluate applications of various tools, highlight opportunities and challenges, and propose targeted strategies for its effective implementation. Our analysis shows trends toward the integration of Artificial Intelligence. Despite the recent policy emphasis on social dimensions of climate risks, implementation of these methods and tools remain low in this area, particularly in developing countries, due to various barriers. Facilitating these methods and tools is essential to support climate risk management in the building sector.