Global Climate Policy Uncertainty and Green Risk Premia in China: A QVAR-DY/BK Connectedness Analysis

The continued advancement of global climate governance and frequent adjustments to climate policies in major economies have made climate policy uncertainty (CPU) an important source of risk affecting green asset pricing and financial market stability. This study develops a multi-source global climate policy uncertainty (GCPU) framework comprising the CPU of China, the United States, and Europe, and incorporates China’s stock green risk premium (SGRP) and bond green risk premium (BGRP) into a unified system. Using the QVAR-DY/BK connectedness approach, we examine the dynamic risk connectedness between GCPU and China’s green risk premia (GRP) across market states, transmission directions, and investment horizons. The results show that: (1) pronounced risk spillovers exist between GCPU and both types of GRP. The total connectedness of the system exhibits an asymmetric U-shaped pattern, with the strongest risk connectedness in the upper-tail state. (2) Overall risk spillovers are predominantly driven by long-term, low-frequency components, although their receivers vary across investment horizons. Short-term spillovers are concentrated mainly in BGRP, whereas long-term spillovers involve both SGRP and BGRP. Major events, including the COVID-19 pandemic and the European Union’s Carbon Border Adjustment Mechanism (CBAM), are associated with stronger system-wide connectedness and changes in the structure of risk transmission. (3) Spillovers between GCPU and the environmental, social, and governance component green risk premia exhibit marked heterogeneity, with all three component premia acting as net risk receivers in the upper-tail state. These findings extend the literature on the cross-border transmission of CPU and green asset pricing and provide empirical evidence to help emerging markets identify external climate policy risks, implement differentiated regulation of green assets, and improve cross-market risk early-warning mechanisms.