As a core component of the digital economy, digital financial technology has a complex interactive and interdependent relationship with ecological efficiency. From the holistic spatial interaction perspective, this paper uses spatial simultaneous equations and generalized spatial three-stage least squares (GS3SLS) to analyze the spatial interaction spillovers between digital financial technology and urban ecological efficiency based on data from 284 Cities in China from 2008 to 2018. The results show that: (1) Digital financial technology and urban ecological efficiency promote each other, and the latter is relatively dominant. (2) Both digital financial technology and urban ecological efficiency have significant spatial spillover effects. (3) Digital financial technology in surrounding cities has a restraining effect on local ecological efficiency, and the improvement of ecological efficiency in surrounding cities has a siphon effect on local digital financial technology. (4) There is spatial and period heterogeneity in the intensity of the spatial interaction spillover effect between the two. With resources and environment increasingly becoming rigid constraints on economic growth, these findings help identify new drivers of regional ecological efficiency and promote the coordinated development of digital finance and green ecology.

We study the time-frequency dynamics of stochastic volatility spillovers between international crude oil markets and China's commodity sectors in the spectral representation framework of generalized forecast error variance decomposition (GFEVD). We find evidence that international crude oil markets has significant volatility spillover effects on China's bulk commodity markets, and the volatility spillovers are sensitive to extreme geopolitical or financial events. The net spillovers of international oil markets are almost positive and driven mainly by short-term components (within a week). However, uncertain financial factors from China such as the market-oriented reform in 2013 and the stock disaster in 2015 adversely affect the net oil-commodity volatility spillovers through the medium-term components (week to a month) and long-term components (month to a year). Moreover, the volatility spillover effects of crude oil prices on different commodity sectors in China are heterogeneous. Metal, coal coke, and steel ore and energy commodity sectors are more affected by crude oil prices, whereas nonmetal building materials and agricultural commodities are less affected. These outcomes implement necessary implications for investors and policymakers.

We utilized a high dimensional financial network to investigate the systemic risk contagion between different industries in China and to explore the impacts of monetary policy and industry heterogeneity factors. The empirical results suggest that the total level of systemic risk increased quite significantly during the 2008 global crisis and the 2015–2016 Stock Market Disaster. The energy, material, industrial, and financial sectors are the top systemic risk contributors. Industry heterogeneity variables such as the leverage ratio, book-to-market ratio, return on assets (ROA) and size have significant impacts on the systemic risk, but their effects on the systemic risk contribution are more pronounced than those on the systemic risk sensitivity. Moreover, monetary policy can effectively suppress the systemic risk diffusion derived from the leverage ratio. These results are essential for investors and regulators of risk management.

This paper investigates the impact of green credit policy (GCP) on the green technological innovation capacity of heavily polluting enterprises (HPEs) from the perspectives of external financing and internal concerns. Using data from companies in China’s A-share market from 2008 to 2021, we study the shocks of Green Credit Guidelines to the green technological innovation in HPEs based on the difference-in-differences (DID) model. The baseline regression result reveals that the GCP significantly motivates HPEs to engage in green technological innovation. Moreover, the efficiency of capital utilization has an adverse moderating effect on the impact of GCP, while commercial credit financing has a positive moderating effect. Mechanism analysis shows that the GCP stimulates green technological innovation in HPEs by reinforcing short-term loan dependence and strengthening executive green awareness. In a further study, the policy effects are heterogeneous for enterprises with different characteristics. Regionally, green credit policy affects enterprises in China’s eastern region more strongly. The effect is also more pronounced for Chinese domestic enterprises and those with low financial background heterogeneity within top management teams. Overall, the findings in this study have important implications for policymakers in implementing green finance policies.