This paper employs a GARCH-MIDAS framework integrated with machine learning to investigate the impact of climate-related uncertainties on the volatility of the China's green bond market (GBM). By combining high-frequency financial data with multi-source, low-frequency climate uncertainty indicators, we examined how macro-financial conditions and climate risks jointly affect the dynamic changes of the GBM in China. Machine learning methods were used to identify and rank the key drivers of volatility. The research results indicate that traditional macro-financial variables remain the main determinants of the volatility in the green bond market, among which the impact of government bond yields is the most significant. Climate uncertainty information also has a significant impact on the volatility of green bonds. Moreover, incorporating climate uncertainty into the GARCH-MIDAS model significantly enhances its explanatory power, highlighting the importance of considering mixed-frequency risk factors in understanding China's green bond market dynamics. These findings underscore the crucial role of climate uncertainty in green bond pricing and indicate that combining machine learning with mixed-frequency volatility modeling can provide a more comprehensive framework for understanding the dynamics of the green bond market.

Amid intensifying geopolitical fragmentation, growing energy security uncertainty, and increasingly complex external constraints on low-carbon transformation, advancing urban energy transition (UET) through the coordination of multiple environmental regulations has become a critical issue in green development. This study exploits the overlapping implementation of the Low-Carbon City Pilot policy and the Carbon Emissions Trading Pilot policy as a quasi-natural experiment. Drawing on panel data for 283 Chinese cities from 2006 to 2024 and a comprehensive UET index, we employ a multi-period difference-in-differences model to identify the effect of the dual-pilot policies (DPPs) on UET. The results show that the DPPs significantly promote UET, and this finding remains robust across a series of robustness checks. Mechanism analyses indicate that the policy effect operates primarily through improved resource allocation and innovation upgrading, with both channels becoming more pronounced in cities facing higher geopolitical risk exposure. Further analysis shows that the DPPs have stronger effects in non-resource-based, non-old-industrial, non-transportation hub, and weakly regulated cities. In addition, geopolitical risk, industrial foundations, and innovation talent reserves shape the extent to which the DPPs are translated into actual transition performance. We also find that, while the DPPs improve the energy transition performance of pilot cities, they generate negative spatial spillover effects in neighboring areas, manifested in industrial relocation, higher industrial electricity consumption, and greater pollution emission pressure. Overall, the synergy among multiple environmental regulations improves UET, but its effects are context-dependent and may involve spatial reallocation. Stronger policy coordination and regional governance are therefore needed to enhance the resilience of UET.

Global climate change continues to intensify, with frequent extreme climate events and abnormal climate patterns profoundly altering humanity's living environment. As a rapidly urbanising developing nation, China's practices in building resilience against extreme climate hold significant global reference value. This research constructs an Urban Comprehensive Resilience Index and an Extreme Climate Index based on Chinese urban panel data, systematically examining the impact of extreme climate on urban resilience. Findings reveal that both extreme climate and urban resilience exhibit a spatially distinct "eastern high, western low" gradient pattern, with fluctuating temporal trends. Among the four resilience dimensions, social and economic resilience levels have strengthened, while engineering resilience has weakened, and ecological resilience remains largely stable. Extreme climate events exert a significant inhibitory effect on urban resilience, a conclusion upheld after rigorous endogeneity and robustness tests employing instrumental variables, double machine learning, and spatial econometric analysis. Further investigation indicates this suppression primarily manifests as extreme heat and drought weakening urban economic and social resilience. Through an analysis of moderating mechanisms, this paper further reveals that fiscal flexibility, industrial diversity, and technological innovation can effectively mitigate the negative impacts of extreme climate on urban resilience. Furthermore, this inhibitory effect exhibits marked heterogeneity across different levels of urban development, geographical locations and policy interventions; whilst it remained significant throughout the period 2006–2021, the intensity of its effect has generally shown a gradual decline. This research provides empirical evidence on the link between extreme climate and urban resilience, and offers scientific guidance for differentiated enhancement policies.