“The deep integration of the innovation chain, industry chain, capital chain, and talent chain” is a major national strategy at present, and how to achieve deep integration among these different chains has important theoretical and practical value. However, empirical frameworks and measurement methods for quantitatively analyzing the industrial chain are still lacking. Building on the approach of Acemoglu et al. (2012), this study uses artificial intelligence to gather related transaction and equity investment data from AiQiCha, ShangShangCha, regional tendering and bidding public service platforms, and the annual reports of listed enterprises, and then employs a depth-first search algorithm to analyze the industrial chain positions of enterprises. On this basis, combining data from input-output tables and the theory of directed graphs, this study calculates the industrial chain linkage, and further integrates the industrial chain centroid degree to measure the degree of industrial chain integration. The empirical results demonstrate that enterprises’ integration into the industrial chain significantly improves their innovation performance via knowledge spillover and scale effects, and this improvement exhibits significant heterogeneity due to enterprise characteristics. Our analytical process and measurement results offer a relatively scientific quantitative analysis model to construct a modern industrial system and advance industrial chain development.

Environmental decentralization (ED), or the allocation of environmental protection affairs and responsibilities among various administrative authorities, affects the effectiveness of environmental regulation in promoting green total factor productivity (GTFP). Based on panel data of 30 Chinese provinces from 2001 to 2015, this paper employs dynamic panel models to test the effects of environmental regulations (environmental protection investment, ENV; pollutant discharge fees, PDF) on GTFP, with or without being influenced by ED. Without the impact of ED, GTFP is significantly inhibited by ENV while significantly promoted by PDF. Considering the impact of ED, with the strengthening of ED, the negative effects of ENV on GTFP is significant; contrarily, the positive effects of PDF on GTFP is significant; improving provincial ED adds negative effects of ENV, while reduces the positive effects of PDF; increasing prefectural ED reduces negative effects of ENV; expanding county-level ED adds the positive effects of PDF. Therefore, to boost GTFP growth, prefectural environmental protection authorities should have more autonomy in ENV, while the county-level should have more autonomy in PDF.

Environmental decentralization (ED), as an institutional factor, impacts the effectiveness of environmental regulation to achieve green productivity (GTFP). Based on panel data of 30 Chinese provinces from 2001 to 2015, this paper assessed GTFP, whose value is higher than 1. A two-step generalized method of moments (GMM) was employed to test the effects of environmental regulations (environmental protection investment (ENV) and pollutant discharge fees (PDF)) on GTFP with or without being influenced by ED. Without the impact of ED, GTFP is significantly inhibited by ENV, while it is significantly promoted by PDF. Under the influences of environmental decentralization from the central to the local authorities (TED), ENV has insignificantly negative effects on GTFP; contrarily, PDF have positive effects on GTFP. As for moderating effects of environmental decentralization at different administrative levels within a province, the degree of provincial environmental decentralization (PTED) should be decreased, while the degrees of prefectural ED (UTED) and county-level ED (CTED) should be increased. Generally, rationally allocating environmental management power among various administrative levels in a province increases the effectiveness of PDF to achieve green productivity while decreases the negative effects of ENV.