Yali Yang

We investigated the impact of environmental regulation on total factor productivity (TFP) based on a panel dataset of 284 cities at the prefecture-level and above in mainland China from 2006 to 2020 and examined whether environmental regulation had a resource reallocation effect and thus affected TFP. The results showed that there was an “inverted U-shaped” pattern in the impact of environmental regulation on TFP in China and a moderate strengthening of environmental regulation helped to increase TFP, which still held after endogeneity treatment and robustness tests. The “inverted U-shaped” relationship between environmental regulation and TFP in eastern, central, and western cities still held, while environmental regulation did not produce significant effects on TFP in the northeast. The effect of environmental regulation on TFP in large, medium, and small cities tested in groups by city size was consistent with the full sample findings, but the effects decreased in a gradient with city size. The analysis of the impact mechanism showed that environmental regulation had a suppressive effect on resource misallocation and could generate a positive resource reallocation effect and enhance city TFP. The labor reallocation effect of environmental regulation for TFP was stronger than the capital reallocation effect. The findings of our study are of policy reference value for optimizing resource allocation through environmental regulation and thus promoting high-quality city development in China.

Improving green total factor productivity (GTFP) is the inherent requirement for practicing the philosophy of green development and achieving regional high-quality development. Based on panel data for 68 prefectural-level-and-above cities in the Yellow River Basin of China from 2006 to 2019, we measured their GTFPs and degrees of productive-services agglomeration using the non-radial directional distance function and industrial agglomeration index formulas, respectively. Furthermore, we empirically investigated the interactive relationship between agglomeration of productive services, industrial-structure upgrading, and GTFP using the dual fixed-effects model, the mediating-effect model, and the moderating-effect model. The findings were as follows. (1) Both specialized and diversified agglomeration of productive services significantly improved the GTFPs of cities in the Yellow River Basin, and the promoting effect of specialized agglomeration was stronger than that of diversified agglomeration. (2) The diversified agglomeration of productive services (hereinafter referred to as diversified agglomeration) made a significant contribution to GTFP in all sample cities of the Yellow River Basin, while the specialized agglomeration of productive services (hereinafter referred to as specialized agglomeration) only significantly improved GTFP in the upstream cities and had no significant effect on the midstream and downstream cities. (3) When examined according to city size, specialized agglomeration was found to have a positive impact on the GTFPs of small and medium-sized cities in the Yellow River Basin but a non-significant negative impact on large cities, while the effect of diversified agglomeration on GTFP was found not to be significant. (4) Industrial-structure upgrading played partially mediating and negative moderating roles in the process of specialized agglomeration affecting the GTFPs of cities in the Yellow River Basin, but it did not become a mediating channel and moderating factor that influenced diversified agglomeration in relation to GTFP.

Green technology innovation, containing economic, social and ecological triple value effects, plays an important role in promoting regional high-quality development. In this paper, we take the Central Plains city cluster, one of China’s top ten national city clusters, as the research object and use the super-efficiency SBM-DEA model to measure and analyze its green technology innovation efficiency. The panel spatial Durbin model (SDM) is used to empirically investigate the market-based, policy and social factors that affect green technology innovation efficiency in the Central Plains city cluster. The main findings are as follows: (1) The green technology innovation efficiency in the Central Plains city cluster shows a fluctuating upward trend from 2009 to 2019, and the spatial differences are obvious, but this spatial difference has converged somewhat over time; (2) Economic development and industrial structure upgrading are the dominant market forces driving green technology innovation efficiency in the Central Plains city cluster, while opening up and enterprise performance hurt the efficiency of green technology innovation; (3) By strengthening environmental regulation and fiscal expenditures on science and technology, the government plays a guiding role in promoting green technology efficiency; (4) Human capital can provide talent support for green technology innovation to effectively promote the efficiency of green technology innovation in the Central Plains city cluster, while the impact of urbanization on green technology innovation efficiency is not significant; (5) In addition to urbanization, the market-based, policy, and social factors that affect green technology innovation efficiency in the Central Plains city cluster also present significant spatial spillover effects. To further promote green technology innovation efficiency in the Central Plains city cluster in the future, we should significantly promote the green transformation and upgrading of industrial structure, improve the quality of opening up to the outside world, strengthen environmental supervision and optimize its governance model, increase government support for green innovation, improve the talent cultivation and introduction system, and mobilize enterprises’ enthusiasm for green technology innovation.