China’s main energy structure is dominated by coal. The burning of coal is a major source of greenhouse gas emissions, making China the largest carbon emitter. Facing double pressure on ecological protection and economic development, improving energy efficiency is more practical than reducing coal utilization. In this context, digital finance can be a vital engine that supports a transition to a low-carbon economy. Based on panel data for 2011 to 2019 of 30 provinces in China, this study probes the effect of digital finance on the total factor energy efficiency and constructs a moderated mediating effect model to analyze the mechanism of action. The results show that: (1) digital finance is able to improve the total factor energy efficiency, (2) the industrial structure plays a mediating effect, which is regionally heterogeneous, and (3) the above transmission path is affected by the degree of regional resource dependence. With the deepening of resource dependence, the role of digital finance in driving energy efficiency through the industrial structure is enhanced. This research demonstrates the effectiveness of digital finance in energy efficiency improvement and develops ideas for ecological governance and sustainable development.

Green innovation integrates innovation-driven and green development strategies, which helps to realize the green transformation of production and life in the Yangtze River Delta region, and promote high-quality economic development. Based on the multidisciplinary cross attribute of system dynamics (SD), the boundary and influencing factors of the green innovation system are defined, and the system dynamics model of green innovation ability of science and technology enterprises is constructed. With the help of statistical data from 2010 to 2020, the model is simulated to explore the change trend and law of elements. The results show that: (1) The green innovation ability of science and technology enterprises is composed of three stages, knowledge innovation, technological innovation, and innovation application, which are interconnected and progressive. The change trend of each variable is conducive to the improvement of green innovation competitiveness, and the green innovation benefits are significant. (2) Green innovation is driven by multidimensional factors such as R&D investment, technological innovation investment, knowledge innovation ability, and the conversion rate of scientific research achievements. The improvement of the conversion rate of scientific research achievements has the greatest impact on the enterprise’s green innovation ability, and the change trend is more obvious. (3) Positive and negative two-direction sub-mode regulation of R&D investment, technological innovation investment, and scientific research achievement conversion coefficient will affect the speed of green innovation accumulation of enterprises, and this increment is marginally increasing with the increase of the coefficient in the short term. Finally, some suggestions are put forward to promote the green innovation ability of science and technology enterprises in the Yangtze River Delta.

Mining cities in China are vital energy bases that contribute greatly to the country’s development. With the pressure of resources depletion and environmental destruction, the sustainable development of these cities is increasingly concerned by the government. This study chooses typical mining cities in Anhui Province of China as the case studies, then selects ten indicators from four dimensions of natural, economic, social, and environmental resources and improves the relative resources carrying capacity (RRCC) model. The dynamic changes of RRCC of these cities from 2007 to 2017 are analyzed. Then the predicted values in 2018-2023 were gotten based on GM (1, 1) model. The results indicate that: (1) Overall, the RRCC of these cities are overload and the overload population has slightly decreased. (2) The primary resources carrying the population are natural resources, but their carrying capacity shows a declining trend. The carrying capacities of economic, social, and environmental resources are much less than that of natural resources, although they have increased a lot in these years. (3) The RRCC and its variation tendency of each mining city are different. (4) The predicted RRCC of these cities would become stronger and the population overload would decrease in the next six years.

The evaluation of environmental status and the analysis of the influencing factors are significant to the transformation and high-quality development of mature coal cities. Choosing five typical mature coal cities in China as the case study, this study constructed the comprehensive evaluation system of ECC (environmental carrying capacity). It assessed the temporal variation of ECC from 2010 to 2019 with normal cloud mode. Then the main influencing factors were identified by analyzing the interaction mechanism between factors via BP-DEMATEL mode. The results indicated that: (1) The ECC level of most cities improved significantly. (2) The ECC levels of resource-environment subsystem were higher than that of society-economy subsystem. (3) Proportion of environmental protection spending, comprehensive utilization rate of industrial solid and atmospheric pollutants emission intensity were the main driving influencing factors, while the proportion of subsidence area and urbanization rate were the main characteristic influencing factors of most cities. Meanwhile, each city has some main influencing factors which are different from each other. This study compares the changing trend of ECC in different mature coal cities and analyzes the reasons, which provides scientific basis and decisionmaking reference for the formulation of relevant policies.

Resource-based cities generally have large carbon-emission, and their carbon balance status is receiving more attention. Land use is a key factor in regulating regional carbon balance. To explore the relationship between land use patterns and carbon balance in resource-based cities, we selected nine cities in Anhui, a major energy province, as the research object. Based on the land use data (2000–2020) and the carbon emission coefficient method, we calculated the carbon emissions, carbon sequestration, and net carbon emissions to show their spatiotemporal evolution. The Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving factors of carbon emissions. The results indicated the following: (1) Net carbon emissions increased by 149.60%, and the growth rate had slowed down since 2015. Forestland constituted the primary carbon sink, whereas cropland was the dominant carbon source. The spatial distribution of carbon emissions and carbon sequestration was uneven. (2) The economic development level and energy consumption density were the principal factors of emission increases. Conversely, carbon emission intensity and land use economic efficiency served as the key mitigating factors.