Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.

Drawing upon a quasi-natural experiment, this research investigates the influence of China’s National Digital Economy Innovation Development Pilot Policy on urban energy intensity. By examining a sample of 282 Chinese cities with the difference in differences (DID) approach, the findings provide robust empirical support for the proposition that digital economy pilot policies substantially reduce urban energy intensity. Furthermore, the policy’s effectiveness in lowering urban energy intensity differs across cities with varying administrative levels and population scales. The results suggest that the policy’s impact is more pronounced in ordinary cities (non-provincial capitals/municipalities) and in those with smaller populations. An examination of the underlying mechanisms reveals three principal pathways through which the policy affects energy consumption: (1) digital economic development, which promotes optimal resource allocation and enhanced energy intensity; (2) technological innovation, driving advances in green technologies and supporting sustainable industrial upgrades; and (3) economic agglomeration, which leverages economies of scale and industrial clustering to bolster energy efficiency. The conclusions underscore the necessity of expanding digital economy pilot zones, strengthening investments in digital infrastructure, and fostering greater technological innovation to sustain improvements in energy efficiency and environmental performance.

In the context of China’s “dual carbon” strategy, carbon productivity serves as a central in dicator for coordinating economic development with carbon emissions. While the digital economy reshapes spatial economic configurations and affects regional carbon productivity, its spatial interdependence and spillover effects remain insufficiently explored. Our study constructs composite indicators to measure both digital economy development and carbon productivity, examining 30 Chinese provinces from 2011 to 2022 using the super-efficiency SBM model and exploratory spatial data analysis. Spatial regression is applied to assess the spatial influences of the digital economy and the moderating role of industrial structure transforming. Results reveal that: (1) China’s carbon productivity has improved overall but with notable regional disparities; (2) a U-shaped linkage between digital development and carbon productivity is confirmed, with early-stage suppression and later environmental benefits; (3) industrial rationalization and upgrading significantly enhance this relationship, though structural frictions remain obstacles.