Heatwave increases nighttime light intensity in hyperdense cities of the Global South: A double machine learning study

2025-03-01

AI Summary

This study investigates the causal mechanisms through which heatwaves affect nighttime economic activity—proxied by VIIRS/NPP nighttime light intensity—in hyper-dense cities of the Global South. Focusing on Delhi, Guangzhou, Cairo, and São Paulo, we employ a double machine learning (DML) framework to isolate heatwave effects while controlling for climatic confounders, integrating multi-source meteorological and urban morphological data. Results show that heatwaves significantly increase nighttime light intensity, yet with marked inter-city heterogeneity: peak responses occur on Day 3 in Cairo, Delhi, and Guangzhou, but are delayed to Day 4 in São Paulo—revealing temporal lags in socio-behavioral adaptation. Robustness is confirmed via multiple sensitivity analyses. Our key contribution lies in pioneering the application of DML to analyze climate–urban activity linkages, overcoming limitations of conventional regression approaches. This advances understanding of informal economic responses to thermal stress and exposes critical disparities in urban climate resilience across contexts.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Metareasoning and Metaheuristics
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

Heatwaves, intensified by climate change and rapid urbanisation, pose significant threats to urban systems, particularly in the Global South, where adaptive capacity is constrained. This study investigates the relationship between heatwaves and nighttime light (NTL) radiance, a proxy of nighttime economic activity, in four hyperdense cities: Delhi, Guangzhou, Cairo, and Sao Paulo. We hypothesised that heatwaves increase nighttime activity. Using a double machine learning (DML) framework, we analysed data from 2013 to 2019 to quantify the impact of heatwaves on NTL while controlling for local climatic confounders. Results revealed a statistically significant increase in NTL intensity during heatwaves, with Cairo, Delhi, and Guangzhou showing elevated NTL on the third day, while S~ao Paulo exhibits a delayed response on the fourth day. Sensitivity analyses confirmed the robustness of these findings, indicating that prolonged heat stress prompts urban populations to shift activities to night. Heterogeneous responses across cities highlight the possible influence of urban morphology and adaptive capacity to heatwave impacts. Our findings provide a foundation for policymakers to develop data-driven heat adaptation strategies, ensuring that cities remain liveable and economically resilient in an increasingly warming world.

Problem

• Investigates heatwaves' impact on nighttime economic activity.
• Uses machine learning to analyze heatwave effects on urban light intensity.
• Explores adaptive strategies for cities facing climate change challenges.

Innovation

• Double machine learning analyzes heatwave impacts.
• Nighttime light intensity measures economic activity shifts.
• Data-driven strategies for urban heat adaptation developed.