Background: The transfer of pollution-intensive industries in China accounts for an increasing proportion of industrial transfer, and related studies emerge endlessly. Fully exploring its research and development breadth and depth will help clarify the development trend in this field and point out the direction for future research.Method/Process: From the perspective of bibliometric analysis, with keywords as the core and cluster analysis of research hotspots as the basis, the keywords of pollution-intensive industry transfer in CNKI database are analyzed by CiteSpace software and divided into five categories. Established the text corpus model, and the network analysis transformed into a visual form. Due to the diverse research hotspots in pollution-intensive industry transfer, this article analyzes the evolution of research hotspots in this field to predict its future development trend.Conclusion/Significance: China, as the world’s factory, is affected by relevant policies, and industrial transfers have generally occurred. Industrial transfer, especially the transfer of pollution-intensive industries, has gradually attracted the attention of academic circles and has become a hot topic. When dealing with the transfer of pollution-intensive industries, industrial transfer only transfers pollution across regions. If we want to reduce pollution from the origin, innovation is an essential means. In retrospect, there were rare articles concerning the emerging polluting industries, however, recently since the emerging polluting industries have already constituted as the main source of pollution, more academic attentions are definitely needed. Although the mainstream measurement methods, the related share index method, and the input-output table have their weaknesses, respectively, the deviation share method can overcome the shortcomings of both. Therefore, it can be used as a reference for scholars to measure the transfer of pollution-intensive industries in the future.

CO2 emissions have become a topical issue worldwide, but few studies have explored the relationship between CO2 emissions and income by establishing direct, indirect and total environmental Kuznets curves (EKCs). Using an annual panel dataset collected over the 1997-2017 period in China, this study first analyzed the spatiotemporal evolutionary process of CO2 emissions and subsequently developed direct, indirect and total EKCs based spatial Durbin model (SDM) and partial derivative approach. These results indicate that, first, CO2 emissions have characteristic positive spatial autocorrelation, with gravity centers that have shifted westward. Second, the direct EKC forms a line, while the total EKC resembles a lying-S shape as well as the total EKC, which indicates that compared to local economic growth, neighboring growth plays a very different role in impacting local CO2 emissions. Furthermore, neighboring economic growth seems to have stronger impacts on local emissions, and the turning point of the total EKC comes much earlier than that of the conventional EKC due to the spillover effects of economic growth. Finally, the growth of the population, as well as the rise of energy intensity, can stimulate CO2 emissions in both local and neighboring regions. Industrialization seems to have a nonsignificant impact on emissions changes due to the offsetting effects of the positive direct and negative indirect impacts of the share of secondary industry. Improvements in local urbanization may lead to an increase in emissions, while neighboring improvements may have stronger restricting effects; thus, urbanization improvement is beneficial to emissions reduction. This study provides more scientific information from both local and neighboring perspectives, which may differ from conventional results but still be beneficial for emissions reduction policy makers to introduce corresponding measures.

With the tendency toward economic and strategy decoupling between China and the United States and amidst the anti-globalization trend, enterprises are facing unprecedented challenges and opportunities. In this study, we reveal how the agile intuition (AI) of top managers with respect to the external environment affects enterprise innovation behavior (IB) based on the cognition–behavior framework. Strategic learning (SL) is considered a moderator, and knowledge sharing (KS) is considered a mediator. The survey sample consists of 305 managers from 47 enterprises in China during the COVID-19 period. The empirical results show that top management agile intuition significantly promotes enterprise IB; knowledge sharing (KS) partially mediates the relationship between top manager AI and enterprise IB; and SL suppresses the promotion effect of top manager AI on enterprise IB to a certain extent, hindering blind innovation. In a surprising result, we find that strategic guidance by an external consultant does not significantly affect the enterprise IB in China.

The environmental pollution in the Beijing–Tianjin–Hebei region is of serious concern, and the environmental impact of dispersing Beijing’s non-capital functions and promoting industrial transfer in an orderly manner cannot be ignored. Based on the spatial panel model, the environmental impact effect of industrial transfer on pollutants was analyzed using the panel data of 13 regions in Beijing–Tianjin–Hebei Province from 2004 to 2018, and the total effect EKC curve was decomposed into direct and indirect effect EKC curves. The results showed the following: (1) The total effect of industrial transfer had a restraining effect on the emission intensity of three types of industrial pollutants. The direct and indirect effects of industrial transfer can significantly inhibit the emission intensity of industrial wastewater, whereas only the indirect effect of industrial transfer can reduce the emission intensity of industrial SO2 and SO2 in the region. (2) The EKC of the indirect and total effects of industrial SO2, wastewater, and dust was an inverted u-shape, and the EKC of the direct effect of industrial wastewater was a positive u-shape. Except for industrial dust, industrial SO2 and wastewater have exceeded the inflection point. With the development of per capita GDP, the emission intensity of industrial pollutants is showing a downward trend. Therefore, the Beijing–Tianjin–Hebei region should gradually transfer pollution-intensive industries, jointly protect the environment, prevent and control pollution, adjust the industrial structure, optimize the industrial layout, promote the development of a circular economy, and promote high-quality development.