Xiaochang Lu

Economic decarbonization has gained attention as a viable way to achieve carbon neutrality without sacrificing high-quality economic development. Regional integration (RI), technological innovation (TI), and the digital economy (DIE) are important decarbonization driving forces. To analyze how they impact carbon reduction and the evolutionary patterns observed in urban development, this study focuses on the urban agglomeration of the Yangtze River Delta (YRD) and uses the spatial Durbin model (SDM) to decompose the carbon reduction effects into spatial spillover effects and short-term and long-term effects. The research findings include the following: (1) The decarbonization driving forces have promoted economic decarbonization in the YRD and exhibit distinct evolutionary patterns. (2) The decarbonization effects exhibit spatiotemporal heterogeneity depending on the economic situation, period, and energy status of the cities. Over time, TI and DIE demonstrate better decarbonization effects. Economically developed cities are more likely to expand their carbon reduction advantages, while cities with dense energy supplies struggle to achieve their full carbon reduction potential due to traditional energy structures. (3) Industrial structure upgrading (UIS) is a critical pathway through which TI and DIE reduce carbon emissions. These findings enrich existing research on decarbonization drivers and offer valuable insights for governments to formulate further policies for economic decarbonization.

The coordinated development of resource–environment–economy is the strategic choice to realize sustainable development. To explore the historical pattern of the coordinated development of resource–environment–economy, showing the logic of the spatiotemporal evolution of the system in China, this paper conducts a measurement study. Based on the actual data of 30 provinces in China from 2005 to 2019, the paper constructs an evaluation index system for the coordinated development of resource–environment–economy and establishes a coupling coordination degree (CCD) model and a spatial autocorrelation analysis model. The results show that the mean value of the coupled coordination of the three systems (resource–environment–economy) gradually increased from the stage of near dissonance (0.479) in 2005 to the stage of good coordination (0.853) in 2019. The global Moran’s I was 0.349, indicating that there is a certain spatial aggregation of resource–environment–economy at the province level. Coastal areas have a higher degree, while inland areas have a lower degree. In the spatial correlation analysis, the resource–environment–economy coupling coordination degree of 30 provinces in China is significantly positively correlated. Low–low clusters are found mainly in the Northwest (e.g., Xinjiang, Qinghai). Furthermore, the findings provide some targeted international recommendations. Relevant policies should encourage sustainable development and promote green transformation of industrial structure.