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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.

A vast theoretical and empirical literature has been devoted to exploring the relationship between environmental regulation and total factor productivity (TFP), but no consensus has been reached and the reason may be attributed to the fact that the resource reallocation effect of environmental regulation is ignored. In this paper, we introduce resource misallocation in the process of discussing the impact of environmental regulation on TFP, taking China’s provincial industrial panel data from 1997 to 2017 as a sample, and the spatial econometric method is employed to investigate whether environmental regulation has a resource reallocation effect and affects TFP. The results indicate that there is a U-shaped relationship between environmental regulation and industrial TFP and a negative spatial spillover effect of environmental regulation on industrial TFP at the provincial level in China. Both capital misallocation and labor misallocation will lead to the loss of industrial TFP. Capital misallocation has a negative spatial spillover effect on industrial TFP, while labor misallocation is just the opposite. Environmental regulation can produce a positive resource reallocation effect, which in turn promotes the industrial TFP in the range of 28% to 33%, while capital misallocation and labor misallocation are only partial mediator.

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.