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February 28, 2026Systems2 citationsOpen Access

Climate Risk Perception and Firms’ Energy Productivity: Evidence from China

JWJue WangGachon UniversityCNCong NieGachon UniversitySJShanyue JinGachon University

Key Points

  • This research aims to explore the relationship between climate risk perception and energy productivity in non-financial firms in China.
  • Analyzed panel data from non-financial Chinese firms between 2012 and 2023.
  • Constructed climate risk perception using a term frequency–inverse document frequency method.
  • Measured energy productivity as a ratio of operating revenue to total energy consumption.
  • Employed two-way fixed-effects models for robust statistical analysis.
  • Investigated the mediating role of digital transformation and various factors influencing the CRP–EE relationship.
  • Identified a positive association between climate risk perception and energy productivity.
  • Found that digital transformation partially mediates this relationship.
  • The association is stronger with higher analyst coverage and weaker under severe financing constraints.
  • Results are consistent across different proxies of climate risk perception and various statistical specifications.

Abstract

Whether firms translate climate risk perception into energy-related operational productivity remains unclear. Panel data on non-financial Chinese firms (2012–2023) are used to examine the association between climate risk perception (CRP) and energy productivity (EE). Firm-level CRP is constructed from management discussion and analysis (MD&A) sections using a term frequency–inverse document frequency (TF–IDF)-weighted, Word2Vec-expanded climate-risk lexicon. Energy productivity (EE) is measured as the natural logarithm of operating revenue per total energy consumption unit converted into tons of coal equivalent, capturing the economic value generated per energy input unit. Two-way fixed-effects models with firm-level clustered standard errors show a positive CRP–EE association. Digital transformation, proxied by an annual report text-based index across five digital technology domains, partially mediates this association, which is stronger when analyst coverage is higher and weaker when financing constraints are more severe. The results are robust to an alternative CRP proxy based on raw keyword frequency, dynamic specifications, and an instrumental-variable approach exploiting province-year extreme-weather exposure (share of days meeting extreme temperature or precipitation thresholds), using leave-one-province-out aggregation as the instrument and systematic heterogeneity across state ownership, pollution intensity, and high-tech status. This study extends CRP research from disclosure-oriented to energy-productivity outcomes, and highlights how digital capabilities, information scrutiny, and financial friction shape climate-aware energy productivity improvements.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69a286600a974eb0d3c014a8https://doi.org/10.3390/systems14030238
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