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Determining the influence of climate policy uncertainty (CPU), fossil fuel dynamics, and renewable energy (REE) adoption on carbon market volatility (CTM) is essential for ensuring its stability and sustainable development. Therefore, this study captures the dynamic relationships among CTM, CPU, crude oil (COP), coal (COA) and REE across different time horizons utilizing a Time-Varying Parameter Structural Vector Autoregression with Stochastic Volatility (TVP-SVAR-SV) model. Results reveal that in the short term, shocks from CPU, COP, COA, and REE all significantly intensify carbon price fluctuations. In the medium term, the carbon market exhibits heightened sensitivity particularly to CPU and COA shocks, while the effects of all factors diminish over the long term. Furthermore, the analysis confirms pronounced time-varying characteristics, with the influence of oil prices on carbon price volatility notably strengthening over time. By comparing influence degree, CPU and COP emerge as the more influential and volatile drivers, whereas the impact of COA remains more stable. Finally, all shocks are significantly amplified during periods of major external disruption, especially during the Russia-Ukraine conflict. These findings highlight the importance of maintaining clear climate policy signals and stabilizing energy market dynamics to enhance the resilience and efficiency of carbon markets. • CPU, fossil fuels, and renewables shocks drive short-term carbon market volatility. • Medium-term volatility is dominated by CPU and coal market shocks. • Oil price influence on carbon volatility strengthens significantly over time. • CPU and oil emerge as the most volatile and influential drivers of CTM. • External shocks, like the Russia-Ukraine conflict, amplify all market effects.
Wang et al. (Sat,) studied this question.