The Yangtze River Delta Urban Agglomeration (YRDUA) is the core area for the development of China’s digital economy. This paper constructed a five-dimensional evaluation index system and measured the digital economy development level of the YRDUA. A modified gravity model social network analysis was applied to analyze the structure of the digital economy network. The results show that (1) the level of digital economy in the YRDUA has increased during 2011–2021, with Hangzhou having the highest digital economy development level of 0.513 in 2021. (2) The digital economy network density of the YRDUA increased from 0.155 in 2011 to 0.158 in 2016, and then decreased to 0.151 in 2021, showing an “inverted U-shaped” trend of first increasing and then decreasing. (3) Shanghai, Suzhou, Nanjing, Hangzhou, and Wuxi play the role of “bridge” in the digital economy network, and the pattern of linkage development of neighboring cities driven by the central cities of Shanghai, Nanjing, Suzhou, Hangzhou, Ningbo, and Hefei has basically formed. These findings of the study provide data support and reference for the coordinated development of the digital economy in the YRDUA and are of theoretical and practical significance for the synergistic development of cities.

In China, coal security issues are strongly linked to national energy security and economic and social stability. Facing environmental protection constraints, research on China’s coal security should analyze both supply and demand security. In this study, 19 criteria were selected for four subsystems (coal supply chain, coal market, economy and demographics, and social ecology) to construct an evaluation system for China‘s coal supply and demand security. Entropy and TOPSIS methods were used to evaluate coal security for 2002–2019. The obstacle factors affecting coal security for each subsystem were determined, and the grey model was used to predict the security and obstacle degree for each subsystem for 2020 and 2021. The results indicate that (1) China’s coal supply and use safety level in the period 2002–2019 was below a relatively safe level, i.e., at a safe early warning level during the period 2010–2014 and at a general safety level in the remaining years. (2) The basic coal reserves, the reserve-production ratio of the basic reserves, the balance of social coal stocks at the beginning and the end of the year, and the proportion of coal imports to consumption, urbanization rate, carbon dioxide emissions, and coal consumption in thermal power generation are the main obstacle factors that affect the safety of coal supply and use in China. (3) The obstacle degree of the coal production and supply evaluation subsystem is higher than that of the other three sub-systems, and the overall change trend during the study period showed a downward trend at first, followed by an upward trend.

The synergistic development of economic construction and low-carbon transformation of energy systems must be promoted for building a green, low-carbon, and cyclic economic system and achieving the “double carbon” goal in China. Based on the panel data of 30 provincial-level administrative regions in China from 2015 to 2019, the global entropy method, coupling coordination degree model, and spatial statistical analysis methods are applied to analyse the factors affecting the coupling effect. The coordination degree increased in the study period, with Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang being the regions with the highest values. The spatial distribution of the coupling coordination degree is strongly positively correlated with the eastern provincial-level administrative regions located in the high–high concentration area of the Moran scatterplot and western provincial-level administrative regions concentrated in the low–low concentration area. The spatial association pattern is stable in the study period, with only two provinces exhibiting a transition: Shandong province made the transition to high–high agglomeration areas, and Liaoning province made the transition to low–low agglomeration areas. The level of regional economy, urbanization process, energy consumption structure, and level of investment in science and innovation enhance the coupling coordination degree, whereas the industrial structure deteriorates this degree.