Spatial spillover effects and driving factors of regional green innovation efficiency in china from a network perspective

Abstract

The spatial spillover effect of regional green innovation efficiency (GIE) is a heated issue of academic research; however, it has rarely been discussed from a network perspective. It is pretty meaningful to clarify its spatial association network’s evolutionary rules and driving factors. To fill the lack of research, this study measures the regional GIE in China from 2010 to 2019 using an epsilon-based metric (EBM) model that considers undesirable outputs. A modified gravity model and social network analysis (SNA) method are used to analyze the evolutionary rules and spatial spillover effects of the network structure of GIE, and a quadratic allocation process (QAP) was employed to identify its driving factors. The findings reveal that: 1) China’s regional GIE has a geographic correlation network structure with a low network density (peaking at 0.210 in 2018) and an annually increasing slow trend. 2) The network structure is relatively loose and has a certain hierarchical gradient, with “dense in the eastern” and “sparse in the western” characteristics. 3) The eastern provinces are at the relative center position and play a leading role in the network; the central, western, and northeastern regions are relatively inferior and play a fulcrum and conduction role. 4) Spatial adjacency, the differences in infrastructure, urbanization, and economic development level positively affect the spatially correlated regional GIE. In contrast, differences in environmental regulations and differences in science and technology innovation (STI) have negative effects. Finally, from the perspectives of national, regional, block, and driving factors, several recommendations are made to enhance the overall improvement and balanced development of regional GIE in China.

The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces

“…The regional differences gradually decreased from the eastern coastal areas to the central, western, and northeastern regions. Zhuang et al (2022) used the EBM model to measure the GIE of 30 provinces in China [22]. Miao et al (2021) constructed a two-stage SBM-DEA model including energy and non-performing output to measure the GIE, and the results show that the GIE in eastern China has been higher than that in other regions [4].…”

“…Capital investment: Concerning the research of Zhuang et al (2022), this paper uses R&D expenditure as a proxy for R&D investment [ 22 ].…”

“…Therefore, this paper chooses the following six control variables according to the previous relevant literature and data availability, namely, the level of economic development (lnED), the level of foreign trade (lnFT), the level of research and development (lnRD), the degree of marketization (lnMA), urban-rural income gap (lnURI) and the level of financial development (lnFD). The level of economic development is expressed by GDP per capita [ 21 ], and the level of foreign trade is measured by the ratio of total imports and exports to GDP [ 22 ]. The level of R&D is measured by the ratio of internal expenditure of R&D funds to GDP, and the level of marketization is measured by the general index of China’s marketization [ 68 ].…”

Spatial and temporal evolution characteristics of urban green innovation efficiency in Northeast China

“…Wang et al used kernel density estimation, cold hotspot analysis and standard deviation ellipsometry to visually analyze the spatial-temporal pattern evolution characteristics of green innovation efficiency in 285 cities in China from 2004-2018 (Wang et al, 2023). Zhuang et al used an improved gravity model and social network analysis to study the spatial spillover effects of regional green innovation efficiency in China from a network perspective (Zhuang et al, 2022). Most of the research results show that the regional differences of urban green innovation efficiency in China are obvious, decreasing from the eastern coastal areas to the central, western and northeastern regions.…”