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.

The convergence of China’s digital economy and green finance holds great significance for fostering a sustainable and high-quality developmental path. However, existing studies have not explored the coupling coordination development between these two crucial subsystems. To bridge this gap, this paper employs a modified coupling coordination degree (CCD) model to assess and affirm the coupling coordination degree between the digital economy and green finance across 30 provinces in China from 2015–2021. Based on degree results, provinces are classified into three clusters by using K-means and hierarchical clustering algorithm. Our findings unveil that the current level of coupling coordination development in China is at a primary coordination stage. Although regional disparities significantly exist, the overall level of coordination remains steadily increasing, with the eastern region outperforming the western region. Additionally, we determine that the COVID-19 pandemic’s disruption on the coupling coordination development of these systems has been limited. This research sheds light on the evolution of coupling systems and offers practical recommendations for strengthening the coordinated development of the digital economy and green finance.

A comprehensive survey of the development trends, trend evolution, and spatial non-equilibrium characteristics of the intelligent smart medical industry in the Yangtze River Economic Belt could provide significant policy implications for optimizing the spatial layout of the integrated development of the smart medical industry in this region. Using the Criteria Importance Though Intercriteria Correlation objective evaluation method for a study period from 2016 to 2020, 11 provinces and cities along the Yangtze River Economic Belt were quantitatively evaluated in relation to the development of the smart medical industry. Accordingly, the application of exploratory spatial data analysis, the kernel density estimation, and the Dagum Gini coefficient and its decomposition method were used to comprehensively evaluate the trends in the Yangtze River Economic Belt's smart medical industry regarding trend evolution and unbalanced spatial characteristics. The overall level of development of the smart medical industry in the Yangtze River Economic Belt was not good. It showed an increasing spatial pattern from the western inland to eastern coastal regions. The development of the artificial intelligence industry in the Yangtze River Economic Belt showed a positive spatial autocorrelation with significant “spatial spillover effects.” The local agglomeration mode was mainly high (a high cluster). In addition, industrial development showed a multi-polarization trend. Although the degree of spatial disequilibrium in the artificial intelligence industry development along the Yangtze River Economic Belt has decreased in recent years, the degree of spatial disequilibrium remains significant.

Carbon emission trading emerges as a pivotal instrument in propelling China’s commitment to green development. With the nation’s carbon market in its formative years and its mechanisms yet to fully mature, unraveling its influence on green innovation efficiency becomes increasingly vital. This study utilizes the SBM-DEA model to evaluate green innovation efficiency across 30 provinces from 2005 to 2020, uncovering the following notable findings: (1) Carbon emission trading schemes significantly enhance green innovation efficiency in China, a finding that remains robust under various checks. (2) In the context of China’s emerging carbon market, government intervention, rather than market mechanisms, plays the most crucial role in enhancing green innovation efficiency. (3) A threshold regression analysis discloses a dual-threshold effect of government intervention on the relationship between market mechanisms and green innovation efficiency. (4) The impact of policies varies across different geographical areas, cost pass-through capabilities, and levels of energy consumption. These findings provide new insights into refining strategies for carbon market development and improving green innovation efficiency, offering a fresh perspective on navigating the complexities of environmental policy and economic growth.

In the digital economy era, there's a notable trend of merging technology with the financial sector, a concept known as sci-tech finance. This fusion is part of the broader industrial finance category. The advancement of technology plays a pivotal role in enabling innovation and growth within this sci-tech financial sector. This study applies Principal Component Analysis (PCA) and factor analysis, both dimensionality reduction techniques from machine learning, to compute the development index of Sci-tech finance for Anhui Province. Utilizing this index, the research then employs unsupervised learning algorithms, namely K-means and hierarchical clustering, to classify the development levels of Sci-tech finance in different cities within the province. The findings reveal marked variations in fintech development across these cities, essentially dividing them into three distinct developmental categories.