Background and Motivation
As climate change intensifies globally, national policies aimed at mitigation and adaptation have become a significant, yet volatile, factor influencing financial markets. In China—the world's second-largest economy and a key player in global climate governance—the path toward carbon neutrality involves substantial policy adjustments, creating what researchers term Climate Policy Uncertainty (CPU). While CPU is recognised as an emerging source of financial risk, its specific impact on the systemic risk contributions of different economic sectors within China has remained underexplored. This gap is critical because heightened uncertainty can delay industrial restructuring, trigger investor panic, and disrupt resource allocation, ultimately threatening financial stability. Against this backdrop, a new study investigates how CPU shapes the systemic risk profiles of 11 major Chinese sectors, offering timely insights for policymakers, investors, and regulators navigating the low-carbon transition.
Methodology and Scope
The research employs an innovative mixed-frequency econometric framework to capture the complex, time-varying relationships between CPU and sectoral risk. The core of the analysis is a newly proposed TVM-MIDAS Copula model, which integrates a time-varying mixture copula with Mixed Data Sampling (MIDAS) techniques. This model uniquely accounts for asymmetric tail dependence with long memory—meaning it can distinguish how sectors correlate with the overall market during booms versus crashes, and how past dependencies influence current risks. Using this model, the authors compute the Marginal Expected Shortfall (MES), a forward-looking measure of a sector's contribution to systemic risk when the market is under stress.
The study examines 11 sectors—including Energy, Materials, Industrials, Real Estate, Consumer Staples, Healthcare, and Finance—from January 2008 to April 2023. The CPU index is constructed from textual analysis of six major Chinese newspapers, reflecting policy-related uncertainty around China's "dual carbon" goals. To assess CPU's impact, the authors also develop a GARCH-MIDAS-CPU model, which allows low-frequency policy uncertainty to directly affect high-frequency sector risk dynamics without losing informational richness.
Key Findings and Contributions
- Sector-Market Dependence is Asymmetric and Persistent: All sectors show stronger lower-tail dependence (linked to market downturns) than upper-tail dependence. The real estate sector exhibits the most persistent tail dependence with the market, while the materials sector shows the longest memory in upper-tail dependence. The consumer discretionary sector, by contrast, displays no significant long memory, suggesting it may serve as a potential hedge.
- Sectoral Systemic Risk Contributions Vary During Crises: During the 2008 global financial crisis, the Energy and Financial sectors contributed most to systemic risk. Conversely, in the 2015–2016 Chinese stock market crash, the Financial sector acted as a stabiliser, while Industrials contributed the most—likely due to excessive pre-crash leverage.
- CPU Has Heterogeneous and Market-State-Dependent Effects:
During moderate market declines, CPU amplifies risk contribution volatility in Energy, Materials, Industrials, and Real Estate—sectors directly exposed to carbon regulation. However, it reduces volatility in defensive sectors like Consumer Staples, Healthcare, and Finance, as investors flock to these perceived safe havens.
During extreme market crashes, CPU increases risk volatility in nearly all sectors except Healthcare, overwhelming even defensive sectors' ability to hedge against policy-driven uncertainty.
- Modelling Advantage: Incorporating low-frequency information and long memory into dependence modelling significantly improves the accuracy of systemic risk forecasts, especially during periods of high market stress.
Why It Matters
Climate policy is no longer just an environmental or regulatory issue—it is a material financial risk factor with cross-sector spillovers. This research provides the first comprehensive evidence of how China's CPU differentially affects sectoral stability, highlighting that policy uncertainty does not impact all industries equally. The findings underscore that during market stress, even traditionally "safe" sectors may become vulnerable to climate policy shocks. For a country steering a major economic transformation under its "dual carbon" framework, understanding these dynamics is essential to prevent systemic financial disruptions and to ensure a stable transition.
Practical Applications
- Regulators & Policymakers can use the findings to design targeted monitoring mechanisms for high-exposure sectors (e.g., Energy, Real Estate), improve climate risk disclosure requirements, and develop contingency plans for government-led projects during policy shifts.
- Investors & Portfolio Managers can adjust sector allocations based on CPU phases, for instance, increasing exposure to defensive sectors during moderate downturns, but preparing for reduced hedging effectiveness during severe crashes.
- Corporate Strategists in high-carbon sectors can better anticipate and mitigate policy-driven volatility through scenario planning, supply chain diversification, and early adoption of green technologies.
- Financial Risk Controllers can integrate CPU indicators into early-warning systems and stress-testing models to capture policy-induced tail risks.
Discover high-quality academic insights in finance from this article published in China Finance Review International . Click the DOI below to read the full-text!
