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.

As the major energy bases, numerous coal cities in China are facing severe challenges in terms of resources and environment. In order to overcome the disadvantages of static evaluation, this study selected Huainan city, a typical coal city in China, as the case, and combined with the improved SD (system dynamics) model, analyzed its RECC (resource and environmental carrying capacity) systematically and dynamically. Firstly, a SD model of RECC system including resource-environment and society-economy subsystem was constructed. Then, the control parameters were determined objectively according to the analysis results of BP-DEMATAL model. Thirdly, we designed 18 simulation scenarios based on orthogonal test to dynamically predict the development trend of RECC in different conditions. Results show that: (1) From 2019 to 2030, the RECC of Huainan is generally on the rise. (2) In all simulation scenarios, test 12 is the most effective way of improving RECC. (3) The factors with the greatest influence on the simulation results are GDP, output value of secondary production, total expending on environmental protection, and coal production. This study provides a reference for the analysis method of RECC and the sustainable development of coal cities.

Coal is the main energy source in China. In 2010-2020, raw coal accounts for more than 70% and 56% of its production and consumption of primary energy resources respectively, and the status that taking coal as the main energy source will not change for a long time. According to the guarantee term of coal resources and the accumulation degree of resistance in development, coal cities in China can be divided into four types: growing coal city, mature coal city, declining coal city and regenerating coal city [1]. Growing coal cities take 22% of all prefecture-level coal cities. Their coal resources reserves are large but the mining modes may not be standardized. The economic development

Considering the regional economic and social characteristics and high-quality development goals, this paper improves the calculation method of green efficiency of water resources and adopts the SBM-DEA model to calculate the green efficiency of water resources in 35 cities of 4 provinces in the Huaihe River Basin. The results show that (1) the green efficiency of water resources in the Huaihe River Basin decreased first and then increased from 2011 to 2020. (2) In terms of spatial distribution, the provincial-level divisions of green efficiency of water resources in the Huaihe River Basin from high to low are Shandong, Henan, Jiangsu, and Anhui. The elliptical area of green efficiency of water resources keeps expanding, and the efficiency value radiates outward along ‘Yangzhou-Zhengzhou’. (3) At the level of total-factor productivity decomposition, TC (technological change) has a more noticeable impact on factors than EC (efficiency change). TC scale technology changes mainly come from the improvement of MATC (magnitude technical change). (4) Industrial output value has a positive driving effect on the green efficiency of water resources. Government participation, resource endowment, and economic growth rate reflect China's current incongruity between economic development and water resources green efficiency improvement.

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