mlsanityData to 7 Oct 2026

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

arXiv 2503.00557 ↗First posted 1 Mar 2025cs.LG cs.CYcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 86 references that are in the map. First posted on arXiv 1 Mar 2025.

Builds on

5 of 86 references.

References · 5

  1. 2013Program Evaluation and Causal Inference with High-Dimensional Datacited by 29
  2. 2024Estimating Causal Effects with Double Machine Learning -- A Method Evaluationcited by 6
  3. 2023Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learningcited by 1
  4. 2023Econometrics of Machine Learning Methods in Economic Forecastingcited by 1
  5. 2024Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimatescited by 1

Built on by

Not cited yet.