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A scientific assessment of the sensitivity of the Chinese coal market has become an important research topic. This paper combines Gaussian Process Regression (GPR) and Sobol sensitivity analysis to construct a GPR–Sobol hybrid model innovatively applied to the Chinese coal market, thus addressing a gap in the economic applications of this method. The model is used to analyze the sensitivity of factors influencing coal prices in China. The GPR–Sobol model effectively handles nonlinear relationships, enabling an in-depth exploration of key factors affecting price volatility and quantifying their impacts, thus overcoming the limitations of traditional econometric models in nonlinear data processing. The results indicate that economic growth, energy prices, interest rates, exchange rates, and uncertainty factors exhibit high sensitivity and significantly impact coal price fluctuations in China. Coal prices in northwest China are more sensitive to interest rates and geopolitical risks, while prices in east and south China are more responsive to exchange rates but less so to economic policy uncertainty. Additionally, coal prices in north, south, and east China are highly sensitive to international energy prices, indicating that coal prices are dominated by the global energy market, yet their weak response to macroeconomic indicators suggests these regions is still insufficiently mature. [ABSTRACT FROM AUTHOR]

As China's financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China's financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China's financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China's financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China's financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes. [ABSTRACT FROM AUTHOR]

Developing new productivity adapted to local conditions and actively promoting the green transformation of industries is the key to improving environmental pollution control levels. Based on data from 697 listed companies in heavily polluting industries nationwide from 2012 to 2022, this paper empirically examines the impact mechanisms, paths, and heterogeneity of technological finance, technological innovation, and financing constraints on environmental pollution control levels. The study shows that technological finance significantly improves the environmental pollution control levels of heavily polluting enterprises. However, there is some greenwashing behavior in the utilization of technological finance funds. Technological innovation and financing constraints play a partial mediating role in the process of improving environmental pollution control levels through technological finance. Compared with non-state-owned enterprises, state-owned enterprises are more sensitive to the enhancement of environmental pollution control through technological finance. To improve the environmental pollution control levels of heavily polluting enterprises, those located in strictly regulated "dual control areas" are less influenced by technological finance compared to those outside these areas. This paper also attempts to explore the factors affecting the sustainable green development of heavily polluting industries and provides policy recommendations for improving the environmental pollution control levels of heavily polluting enterprises. [ABSTRACT FROM AUTHOR]