As climate change accelerates worldwide, government responses aimed at reducing emissions and adapting to environmental risks are increasingly shaping economic expectations and financial market behaviour. In China, this influence is powerful. As the world’s second-largest economy and a central player in global climate governance, China’s transition towards carbon neutrality requires frequent policy adjustments and regulatory experimentation. These evolving signals generate Climate Policy Uncertainty (CPU), a growing source of financial risk. While CPU has attracted increasing attention, its effects on how different Chinese economic sectors contribute to systemic financial risk have not been fully explored, despite the potential consequences for market stability.
This gap matters because heightened uncertainty can delay investment, intensify investor anxiety, and disrupt the efficient allocation of capital. In sectors closely tied to emissions, regulation, or infrastructure, unclear policy direction may slow restructuring and amplify financial stress. At the same time, uncertainty can spill over into the broader financial system, increasing the likelihood that shocks in one sector trigger wider instability. Against this background, the study examines how the CPU influences the systemic risk profiles of 11 major Chinese sectors, providing insights directly relevant to policymakers, investors, and regulators managing the risks of a low-carbon transition.
To capture these complex dynamics, the research applies an advanced mixed-frequency modelling framework that links low-frequency policy uncertainty with high-frequency financial data. The core approach allows the relationship between sectors and the overall market to change over time and to behave differently during market booms and crashes. Crucially, the model also accounts for extended memory, meaning that past periods of stress continue to influence current risk conditions. Systemic risk is measured using a forward-looking indicator that shows how much each sector is expected to contribute to overall market losses when conditions deteriorate.
The analysis covers eleven key sectors, including Energy, Materials, Industrials, Real Estate, Consumer Staples, Healthcare, and Finance, over the period from 2008 to 2023. Climate policy uncertainty is measured using a text-based index derived from major Chinese newspapers that captures shifts in policy-related discussion around China’s carbon goals. This approach allows the study to reflect not only formal policy changes but also evolving expectations and debates, which often matter just as much for financial markets as concrete regulations.
The findings show that sectors tend to move much more closely together during market downturns than during upswings, highlighting the importance of focusing on downside risk. Real estate stands out as particularly persistent in its links to overall market stress, while materials show a long-lasting positive dependence during favourable periods. Sectoral contributions to systemic risk also change across crises. Energy and finance were key risk drivers during the global financial crisis. In contrast, industrial firms played a larger role during the 2015–2016 Chinese market crash, reflecting different sources of vulnerability over time.
Most importantly, the effects of CPU vary across sectors and market conditions. During moderate downturns, policy uncertainty increases risk volatility in carbon-intensive and regulation-sensitive sectors, while more defensive sectors appear relatively stable. However, during severe market crashes, CPU amplifies risk across almost all sectors, weakening the protective role of traditional safe havens. These results underline that climate policy uncertainty is a meaningful financial risk factor. Understanding how it affects different sectors is essential for maintaining financial stability as China advances its low-carbon transformation.
More information: Kun-Liang Jiang et al, Does CPU impact systemic risk contributions of Chinese sectors? Evidence from mixed frequency methods with asymmetric tail long memory, China Finance Review International. DOI: 10.1108/CFRI-05-2025-0281
Journal information: China Finance Review International Provided by Shanghai Jiao Tong University Journal Center