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Digital Innovation

Comprehensive Evaluation, Spatial Correlation Network, and  Driving Mechanism of Digital-green Integration in China 

Nie Changfei1 , Yuan Yongwen1 , Feng Yuan2

(1.School of Economics and Management, Nanchang University, Nanchang 330031, China; 2.College of City  Construction, Jiangxi Normal University, Nanchang 330022, China)

Abstract: In the context of the coordinated development of the in-depth advancement of the  "dual carbon" goals and the full-scale implementation of the Digital China initiative, the integra⁃ tion of digital and green development has become a key engine for fostering new high-quality  productivity, promoting high-quality development, and advancing Chinese modernization. Based  on the panel data of 30 provinces (autonomous regions and municipalities) in China from 2012 to  2022, this paper breaks through the traditional coupling coordination analysis framework, fo⁃ cuses on the "full-cycle" perspective of digital and green integration, and innovatively con⁃ structs a progressive evaluation index system from three dimensions: "integration foundation— integration depth—integration performance". The entropy method is used to measure the level of  digital and green integration, and the modified gravity model is used to construct the spatial cor⁃ relation matrix. The social network analysis method is combined to analyze the spatial network  structure characteristics, and the quadratic assignment procedure (QAP) is adopted to deeply ex⁃ plore the driving mechanism of network formation. The results show that: ① Spatial evolution trend: The level of digital and green integration in China has generally shown a steady upward  trend, with an average annual growth rate of 6.80%, but regional differentiation is significant, presenting a spatial pattern of "high in the east and low in the west, high in the south and low in  the north". The eastern coastal areas have formed a core growth pole, while the central and west⁃ ern regions, although having improved, still lag behind the east due to factors such as a lack of  talent and a weak innovation ecosystem. ②Spatial correlation network characteristics: The spa⁃ tial network structure of digital and green integration in China has initially taken shape, but the  overall network density is low, and regional cooperation is weak, presenting a "core-periphery"  structure. Beijing, Shanghai, Jiangsu, and Guangdong in the east are at the core of the network, while the participation of the central and western regions has improved but is still relatively  weak, and the participation of the northwest and northeast peripheral regions is low. Block model  analysis divides different regions into four major blocks: net spillover, two-way spillover, main  beneficiary, and isolated. The cross-block spillover effect is significant. ③Driving mechanism: The level of industrial structure, the level of data elements, spatial adjacency relationships, and  differences in economic development levels are the core driving forces for the formation and evo⁃ lution of the spatial correlation network of digital and green integration. Among them, the level of  data elements and economic development levels have a significant positive promoting effect on  the formation of the spatial correlation network. The industrial structure reduces cross-regional  transaction risks by optimizing resource allocation, and spatial adjacency relationships  strengthen inter-regional cooperation through the flow of factors and industrial collaboration. However, insufficient policy coordination and regional imbalance in scientific and technological  talents to some extent restrict cross-regional integration. Finally, based on the above conclu⁃ sions, three policy implications are drawn from three dimensions: First, rely on spatial adjacency  advantages to build regional collaborative clusters to achieve deep integration and balanced re⁃ gional development; Second, break regional barriers, build cross-regional collaboration plat⁃ forms, and enhance the radiation and driving role of the east on the central and western regions; Third, optimize policy coordination and market environment, and improve the coverage of digital  infrastructure. 

Key words: integration of digital and green development; spatial correlation network; social  network analysis; driving mechanism; QAP regression analysis; regional coordinated develop⁃ ment; spatial spillover effects; digital economy