Based on the cooperation patent data between 31 provinces in China from 1985 to 2017, this paper portrays the topological structure, spatial pattern and proximity mechanism of China’s inter-regional innovation cooperation network by the graph theory, spatial statistical methods and the quadratic assignment procedure model. The conclusions are as follows: (1) the topological structure of China’s inter-regional innovation cooperation forms a hierarchical network with Beijing as the core and it exhibits a typical small-world property and ‘core–periphery’ structure. (2) In terms of spatial distribution characteristics, the ‘quadruple’ with the Beijing–Tianjin region, Yangtze River Delta, Pearl River Delta and Sichuan as the peaks has become the backbone of the inter-regional innovation cooperation network in China. At the same time, the spatial differentiation of the three indicators of degree centrality, median centrality and intensity centrality is obvious. (3) In terms of proximity mechanism, geographical, institutional and cognitive proximity exert a significant effect on China’s inter-regional innovation cooperation network linkage. However, there are significant differences in the effects of various proximity elements on China’s regional innovation network.

Purpose: Environmental regulation policy and cleaner production technology innovation are the key links to achieve sustainable economic development. This paper tests the impact of Two Control Zone (TCZ) environmental regulation policy on cleaner production technology innovation and explains the heterogeneity effect between them from the perspective of regional pollution intensity and R&D investment scale.Design/methodology/approach: This paper takes TCZ policy as an environmental regulation policy and collects the patent data related to coal desulfurization cleaner production technology innovation in prefecture-level cities from 1994 to 2002 in China. This paper also tests the relationship between TCZ environmental regulation policy and cleaner production technology innovation based on difference in difference (DID) model. Take regional R&D investment scale and pollution intensity as category variables and analyze the heterogeneity effect of TCZ environmental regulation policy on cleaner production technology innovation based on difference-in-difference-in-difference (DDD) model.Findings: TCZ environmental regulation policy effectively promotes China's cleaner production technology innovation, but it is more conducive to cleaner production technology innovation in heavy pollution areas. With the increasing of R&D investment scale, the positive effect of TCZ environmental regulation policy on cleaner production technology innovation is stronger.Research limitations/implications: On the basis of this study, the authors should further explore the regulatory factors of the relationship between TCZ environmental regulation policy and cleaner production technology innovation and further expand the research object, so as to make the research conclusions more practical and instructive.Originality/value: This paper tests the impact of TCZ environmental regulation policy on cleaner production technology innovation based on the prefecture city level data and DID model, and it handles the endogenous problem caused by the missing variables and provides the accurate conclusions. Moreover, this paper examines the heterogeneity effect of TCZ environmental regulation policy on cleaner production innovation from regional R&D investment scale and pollution intensity two hands and expands the existing theoretical research.

Purpose: The purpose of this paper is to add to the existing research about how corporate performance is influenced by their basic research (BR) investment. On this basis, the authors examined the moderating effect of human capital structure (HCS) on the relationship between BR investment and corporate performance. Design/methodology/approach: Hypotheses were tested using static and dynamic models to analyze a large-scale data of Chinese A-share listed companies. Findings: This study provides empirical evidence that contributes to the research about how private BR investment influences corporate performance in the digital age. In addition, human resource is an important dynamic ability for enterprise development. Based on the dynamic capability theory, further research finds that the human resources practice on the knowledge stock can enhance the company’s dynamic capability, thereby enhancing the company’s core competitiveness. Research limitations/implications: The results may be affected by the context of the data set. This study considers the influence of private research investment type on corporate performance, further studies considering the influence of specific contextual variables, such as corporate industry differences, could yield richer insights that would help validate the results of this study. Practical implications: This study provides useful information for managers. As well as increasing the investment in the BR of enterprise and creating the necessary conditions to increase the competitiveness of enterprise, they should strive to adjust the structure and quality of researchers involved in BR projects. Originality/value: This research contributes to the enterprise’s BR investment and the management of human capital resource. It points that the investment of BR positively influences the corporate performance. In addition, the increasing of high-skilled labor’s proportion positively promotes the promotion of BR investment on corporate performance.

Owing to the externality of corporate eco-investment (CEI) behavior and serious climate policy uncertainty (CPU), CEI is insufficient, and its structure is distorted. The innovative development and application of the new generation of twin transition (TT) represented by digital technology and green technological innovation, enhances the practice of CEI and has become an important technological force and operational strategy in promoting the development of sustainable corporate transformation. In contrast to existing research that aims to analyze the relationship between CPU, digital transition, and sustainable transformation, this study uses China's A-share listed companies in the heavy-pollution industry from 2010 to 2020 as research samples and empirically examines how corporate TT affects the relationship between CPU and CEI. The main conclusions reveal that CPU has a significant negative effect on CEI. The levels of digitization, green technology innovation, and TT positively moderate the negative effects of CPU on CEI. Entrepreneurial carbon information disclosure, enterprise internal control capability, and regional environmental pollution are the main factors influencing the moderate effect of TT on the relationship between CPU and CEI. This study's findings are helpful in further exploring the potential value of TT in addressing the negative impact of CPU. They are significant in supporting entrepreneurs in implementing CEI practices and promoting sustainable development.

Based on provincial panel data from 2000 to 2014, this paper measured the total factor energy efficiency by adopting the dynamic slacks-based measure model, and then employed the spatial econometric model to analyze the impact of regional mitigation of carbon dioxide on energy efficiency from both intra-regional and inter-regional perspectives in China. The results show the following: first, the average of total factor energy efficiency presents a rising trend from 2000 to 2014, and it shows a significant difference among regions. Second, the direct effect of carbon productivity on energy efficiency is positive, but the indirect effect is negative. The share ratio of direct and indirect effects is 1: −0.96; the total effect is positive but insignificant. Based on this conclusion, the paper provides some relevant policy suggestions and measures for regionally saving energy resources and relieving environmental constraints.