As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets.

The rapid expansion of artificial intelligence is tightening the interdependence between digital infrastructure and energy systems, making the linkages between technology and energy increasingly important for understanding energy security. This study examines how major events reshape risk spillovers between China’s technology and energy sectors, with particular attention to the asymmetric effects of policy shocks, external technological shocks, and domestic technological breakthroughs. Focusing on four events, namely the announcement of the carbon peaking and carbon neutrality goals, the launch of the East Data and West Computing project, the release of ChatGPT 3.5, and the release of DeepSeek R1, the study explores whether different shocks alter risk transmission between sectors in distinct ways. Using interval data for 16 representative listed firms from 2021 to 2025, the analysis combines firm-level spillover networks with sector-level robustness tests. The results suggest that policy-driven events are associated with stronger systemic connectedness and changes in the direction of predictive spillovers between the technology and energy sectors, although the Dual Carbon result is sensitive to the selected event window, whereas ChatGPT 3.5 is associated with short-term risk decoupling under the baseline specification, although this result also shows some sensitivity to the event horizon. DeepSeek R1 shows the largest baseline increase in overall spillovers and a relatively stable positive pattern across alternative event windows. The precision of the estimated changes also varies across events. The findings suggest that domestic advances in large language models are not only innovation milestones, but also potential sources of persistent pressure on energy security.