Results of bootstrap mediation test.

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict—financial market pressure and energy market—China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China’s carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale.本研究基于2015年1月至2022年12月的月度经济数据，构建了一套分析框架以考察“俄乌冲突—金融市场压力与能源市场—中国碳排放交易价格”的作用机制。为达成该研究目标，我们构建了金融体系压力、能源市场及投资者情绪三类指数，并运用中介效应模型验证其传导路径。随后，我们采用时变参数随机波动率向量自回归（TVP-SV-VAR）模型，考察俄乌冲突对中国碳排放权估值的非线性影响；该模型整合了时变参数（time-varying parameters, TVP）与随机波动率（stochastic volatility, SV），并借助马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）技术完成参数估计。最后，我们运用包括连续小波变换、交叉小波变换及小波相干谱在内的多种小波分析技术，将时序数据分解为不同的时频尺度，以此分析各时序序列间的领先滞后关系。本研究结果可为维护交易主体权益、完善碳市场结构以及防范全球系统性风险提供重要决策参考。