Study on the measurement and driving mechanisms for coordinated development level of digital economy and low-carbon economy: evidence from China

The progress of the digital economy and low-carbon economy (hereinafter “both economies”) in China currently shows a digitalization trend and decarbonization urgency, and their intrinsic connections are becoming increasingly evident. Study on coordination of both economies is of crucial importance for China. In this study, an index system was constructed to measure the development level of both economies, and the entropy method was employed to calculate the index based on the data panel of China’s 282 prefecture-level cities during 2012–2021. Then, the coordinated development level of both economies (CDL) was assessed by the coupling coordination degree model. In addition, we conduct a sensitivity test on the weight setting of the composite index in the coupling coordination degree model to verify the robustness of CDL measurement to alternative weight specifications. Results indicate that CDL has steadily improved annually and is generally in the basic coordination stage. Cities in the basic maladjustment stage and the basic coordination stage still show considerable room for further improvement. The CDL across China’s four regions exhibits a pattern of “Eastern > Northeastern > Central > Western”. Next, the regional differences of CDL were decomposed by the Dagum Gini coefficient, revealing a decreasing trend in overall differences. Within-regional differences is the primary source of regional differences. More specifically, within-regional disparities rank from high to low as Western, Eastern, Northeastern, and Central; and the between-regional disparities, from high to low, are Western–Northeastern, Eastern–Western, Central–Western, Eastern–Central, Eastern–Northeastern, and Central–Northeastern. Accordingly, further empirical analysis using the spatial Durbin model identified several driving mechanisms for CDL. In the spatial econometric setting, we further perform robustness checks with alternative spatial weight matrices, including a geographic adjacency matrix, an economic distance matrix, and a gravity-model nested matrix, and we also report the decomposition of spatial effects. Moreover, we introduce a one-period lag of the dependent variable to estimate a dynamic spatial Durbin model, capturing path dependence in CDL and testing the robustness of the conclusions. Results demonstrate that government guidance, market regulation, technological innovation, and structural optimization mechanisms all promote CDL significantly, while the openness mechanism has a significant inhibiting effect. The direct effects are consistent with the above results, while the indirect effects indicate positive spatial spillovers from government guidance and market regulation, and a significant negative spatial spillover from openness; these findings remain broadly stable after replacing spatial weight matrices and after introducing the dynamic term, suggesting strong robustness in identifying the driving mechanisms. These findings can be interpreted through the technology–institution–structure analysis framework. Specifically, technological integration highlights digital technology empowering low-carbon technology innovation; institutional guarantee emphasizes synergy between government policies and market mechanisms; and structural transformation underscores the synergistic upgrading of industrial digitalization and low-carbonization.