Corporate Co-Agglomeration and Green Economy Efficiency in China

Abstract

This paper uses panel OLS, IV, and system GMM methods to empirically study the effects of manufacturing and producer service corporate co-agglomeration on green economy efficiency (GEE) in China. Chinese panel data from 2000 to 2019 are collected to assess the GEE and co-agglomeration degrees. The regression results show that there is an “inverted U-shaped” relationship between co-agglomeration and GEE. However, regional heterogeneity is found in the effects of corporate co-agglomeration on GEE. The mediating analysis indicates that corporate co-agglomeration could increase GEE through business entrepreneurship and innovation entrepreneurship. Variables such as transportation infrastructure, human capital, foreign direct investment, and environmental regulations are also found to have an elevating effect on GEE, whereas local fiscal expenditure on environmental protection has little effect. The findings in this paper indicate that entrepreneurship plays an important role in the process of co-agglomeration impacting GEE which differs in different regions and thus provide references for corporate and regional sustainable development.

Industrial Synergy Agglomeration, Urban Innovation Capacity, and Advanced Manufacturing Development

“…Consequently, this leads to increased economic benefits (Klein and Crafts 2020). The optimization of spatial layout in these two industries engenders the effects of concentrated governance and economies of scale, curbing carbon emissions and environmental pollution , thereby enhancing the green growth efficiency of the manufacturing sector (Lv and Lu 2022;Zhu et al 2022). In comparison to the above-mentioned existing studies that focus on the effects of industrial synergy agglomeration on advanced manufacturing development from singular perspectives such as technology, management, or environmental sustainability, this study employs a comprehensive index to measure the level of advanced manufacturing development, ensuring a more scientific and comprehensive approach.…”

“…Following the introduction of the "Carbon Peaking and Carbon Neutrality Goals", the green transformation of the manufacturing industry has become a research hotspot. Scholars have empirically analyzed the efficiency of green growth in the manufacturing industry, highlighting the significant influence of industrial clustering (Lv and Lu 2022;Zhu et al 2022).…”

“…From the perspective of production efficiency and economic benefits, industrial synergy agglomeration enhances the overall factor efficiency of the manufacturing industry through green technological innovation (Yang et al 2022). It also exerts a significant positive impact on China's Green Economic Efficiency (GEE) through firm-level innovation and entrepreneurial innovation (Zhu et al 2022). Expanding the perspective to the manufacturing industry's value chain, research has found that industrial synergy agglomeration strengthens the resilience of the manufacturing industry's value chain through technological innovation.…”

A Study on the Impact of Digital Finance on the Efficiency of Urban Green Economy Development

“…Prior research has assessed the efficiency of green economies and investigated the factors that impact green economy efficiency, including policies related to new energy, environmental regulations, investments in science and technology, industrial clustering, environmental variables, urban land development intensity, and more [24][25][26]. Luo [27] suggests that the digital economy enhances the efficiency of green development.…”

Entrepreneurship and Export Product Quality Improvement

“…Systematic GMM is a common approach to addressing the endogeneity problem. By using the differential lag term of the endogenous explanatory variables as the instrumental variables, this method can solve not only the endogeneity problem but also the issues of autocorrelation in the time series and heteroskedasticity in the cross-section [42,43]. Based on this, the lagged one period of the dependent variable (qua) is put into the independent variable here, and the dynamic panel data are composed to be regressed using the systematic GMM method.…”