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April 30, 2026Energy Economics0 citationsOpen Access

Weathering the past: Firms' risk responses to climate shocks

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FHFangmin HaoZYZhijian Yu

Key Points

  • This research aims to understand how firms react to past climate extremes, specifically focusing on cash holdings as a risk management indicator.
  • Analyzed over 700,000 firm-year observations of global listed firms over 34 years.
  • Utilized machine learning to project cash reserve gaps in developing countries.
  • Examined interactions between lagged extreme weather and current non-extreme conditions.
  • Firms increase cash holdings after extreme heat and heavy rainfall, but decrease them following cold weather and droughts.
  • Market responses penalize cash increases after extreme heat and excessive rainfall, and reductions after rainfall deficits due to misperception of climate risks.
  • Firms in developing countries maintain cash reserves significantly below those in advanced economies, highlighting a preparedness gap.

Abstract

This paper investigates firms' risk reactions to lagged weather extremes, using corporate cash holdings as an indicator of perceived risk. Analyzing more than 700,000 firm-year observations of global listed firms over 34 years matched with high-resolution weather data, we document significant heterogeneity and asymmetry in these reactions. Firms tend to increase cash holdings following lagged extreme heat and excessive rainfall, while decreasing them after extreme cold and rainfall deficits. Mechanism tests reveal that these adjustments are fundamentally driven by attention-based behavioral biases rather than traditional corporate finance motives, with internal cash serving as the strictly preferred adjustment margin over alternative financial instruments. Furthermore, capital markets selectively discipline these behaviors by penalizing cash increases following extreme heat and excessive rainfall, as well as cash reductions after rainfall deficits. However, this investor penalization stems primarily from an initial market-wide misperception of climate risks rather than managerial agency conflicts. Finally, machine learning projections demonstrate that firms in developing countries maintain cash reserves significantly below advanced benchmarks, revealing a critical preparedness gap that exacerbates their vulnerability to climate-induced risks. • Isolates lagged effects via (Past Extreme) × (Current Non-Extreme) interactions. • Firms raise cash after lagged heat and heavy rain, but cut it for cold and droughts. • Behavioral biases, rather than standard corporate motives, drive these adjustments. • Markets penalize these cash adjustments due to limited climate risk awareness. • Machine learning reveals large financial preparedness gaps in developing regions.

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Cite This Study

Hao et al. (2026) studied this question.

synapsesocial.com/papers/69f2f0991e5f7920c6386cd6https://doi.org/10.1016/j.eneco.2026.109376
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