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<p>Climate change introduces a dual challenge to the financial sector, characterized by physical risks from direct climate events such as floods and droughts, and transition risks from the adaptation to a low-carbon economy. This paper introduces Climate Uncertainty (CU) index to quantify physical risk and Climate Policy Uncertainty index to measure transition risk, and utilizes the time-varying parameter vector autoregressive (TVP-VAR)&#xa0;model and the dynamic conditional correlation&#xa0;-&#xa0;generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to analysis mean spillover and volatility spillover of climate risk, in order to analyze the impact mechanism of climate change on China’s stock market. The study concludes that: (1) This paper identifies three periods (2010–2012, 2015–2016, and 2020–2022) with significant risk spillovers associated with climate uncertainty. (2) Physical risks have a persistent impact on return spillovers, whereas transition risks have a transient impact on volatility spillovers. (3) The energy sector demonstrates heightened sensitivity to physical risks, which paradoxically leads to a positive market impact and benefit from physical risks. Conversely, sectors such as agriculture and public utilities show less sensitivity to transition risks, which exert a negative influence. (4) The transmission mechanisms indicate that income factors are predominantly affected by physical risks, while cost factors, notably financial and administrative expenses, are significantly affected by transition risks. The paper recommends targeted policy interventions to mitigate the financial risks posed by climate change.</p>

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Research on the impact of climate change on China’s stock market

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Haiqin Ouyang,
  • Bo Yu,
  • Jianbin Deng,
  • Chao Guan

摘要

Climate change introduces a dual challenge to the financial sector, characterized by physical risks from direct climate events such as floods and droughts, and transition risks from the adaptation to a low-carbon economy. This paper introduces Climate Uncertainty (CU) index to quantify physical risk and Climate Policy Uncertainty index to measure transition risk, and utilizes the time-varying parameter vector autoregressive (TVP-VAR) model and the dynamic conditional correlation - generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to analysis mean spillover and volatility spillover of climate risk, in order to analyze the impact mechanism of climate change on China’s stock market. The study concludes that: (1) This paper identifies three periods (2010–2012, 2015–2016, and 2020–2022) with significant risk spillovers associated with climate uncertainty. (2) Physical risks have a persistent impact on return spillovers, whereas transition risks have a transient impact on volatility spillovers. (3) The energy sector demonstrates heightened sensitivity to physical risks, which paradoxically leads to a positive market impact and benefit from physical risks. Conversely, sectors such as agriculture and public utilities show less sensitivity to transition risks, which exert a negative influence. (4) The transmission mechanisms indicate that income factors are predominantly affected by physical risks, while cost factors, notably financial and administrative expenses, are significantly affected by transition risks. The paper recommends targeted policy interventions to mitigate the financial risks posed by climate change.