Tech sector carbon emissions continued their rise in recent years, fueled by rapid advances in artificial intelligence (AI) and data infrastructure, according to the ITU-WBA Greening Digital Companies 2025 report.

Abstract

Purpose – This study aims to investigate the relationship between climate policy uncertainty (CPU) and corporate environmental, social and governance (ESG) performance. We attempt to uncover the underlying rationale of how CPU influences corporate ESG performance and provides empirical evidence for companies’ strategic enhancement of ESG performance with risk reduction objectives.

Design/methodology/approach –We conduct a regression analysis using panel data from 4,490 Chinese listed companies spanning the period from 2011 to 2022. In addition, we use propensity score matching analysis (PSM), two-stage least squares (2SLS), system generalized method of moments (sys-GMM) and difference-indifferences (DID) methods to analyze the enterprise systematic risk.

Findings – The empirical findings reveal a positive correlation between CPU and corporate ESG performance, with a stronger effect observed in non-state-owned enterprises, heavy-polluting industries and those facing fierce market competition and strict environmental regulation. Mechanism analysis suggests that as CPU increases, companies with higher systemic risk tend to improve ESG performance more significantly, highlighting risk mitigation as a primary motive. Robustness tests further validate the consistency of our conclusions. Additionally, we find that enhancing ESG performance helps mitigate the risks and improve total factor productivity arising from the increased CPU.

Originality/value – This study examines the impact of CPU on the ESG performance of Chinese listed companies and its underlying logic. The conclusions of this paper provide important policy references for coordinated development and security, as well as for effectively mitigating the adverse impact of CPU. We hope to offer insights for companies to identify potential risk factors, thereby enhancing their level of sustainable development and sense of environmental responsibility.

Abstract

Purpose – This study aims to examine the impact of climate-related risks on cryptocurrency volatility during crisis periods, focusing on the physical risk index (PRI)and transition risk index(TRI). It investigates how acute and chronic climate events, alongside regulatory and technological changes, influence market dynamics in major cryptocurrencies, including Bitcoin, Ethereum, Litecoin and Ripple.

Design/methodology/approach – A fuzzy logic model is employed to evaluate the effects of PRI and TRI on cryptocurrency volatility. The model’s accuracy is validated using root mean square error (RMSE) metrics to ensure reliability.

Findings – The results reveal that acute events (e.g. hurricanes and wildfires) and chronic risks (e.g. long-term environmental disruptions) significantly heighten cryptocurrency volatility. Transition risks, including regulatory and technological shifts, also play a pivotal role. Bitcoin and Ethereum exhibit the highest sensitivities, reflecting the critical influence of climate risks on market stability.

Research limitations/implications – This study enriches the literature by integrating climate risk factors into cryptocurrency market analysis and advancing fuzzy logic models to assess non-linear interactions in financial markets. It provides a novel framework for evaluating external shocks’ impact on digital assets.

Practical implications – Investors and market participants can use these findings to incorporate climate risks into their investment strategies, diversify portfolios and anticipate periods of instability. The insights also guide policymakers in developing resilient frameworks that align cryptocurrency regulations with environmental goals.

Social implications – By linking climate risks to cryptocurrency market behavior, this research emphasizes the need for sustainable investment practices and collaborative policy efforts. It advocates for integrating environmental sustainability into financial systems to mitigate systemic risks and promote economic resilience. Originality/value– This research is among the first to apply PRI and TRI within a fuzzy logic framework to cryptocurrency markets, offering new insights into how climate risks drive financial volatility during crisis periods.