Liyuan Zhang

The increase in carbon emissions year by year poses a severe challenge to the high-quality development and sustainability of China’s economy. How to reduce the intensity of carbon emissions has become a prominent issue to promote green growth. Based on the provincial panel data from 2011 to 2020, this paper uses Exploratory Spatial Data Analysis (ESDA), the spatial econometric model and intermediary effect test as analysis methods. The following results are drawn. Firstly, China’s industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west of China, followed by the middle of China and is the lowest in the east of China. Secondly, the distortion of the industrial structure will not only lead to the increase in local carbon emission intensity but also produce reverse spillover to adjacent areas. Thirdly, the results of intermediary effect analysis show that industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI. This paper has a profound practical significance for promoting the process of industrial upgrading by insisting on developing foreign trade to achieve carbon emission reduction. The main innovation of this paper is to put forward the concept of industrial structure distortion and bring it into a unified research framework with two-way FDI and carbon emission intensity.

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

This study examines the impact of top management team (TMT) media exposure on corporate social responsibility (CSR) and the moderating effect of TMT characteristics based on the upper echelons theory and stakeholder theory. Based on the observations of 5,352 firms between 2010 and 2019, multiple regression analysis is conducted to empirically test whether TMT media exposure can promote CSR. TMT media exposure is further divided into paper media and online media to reveal the impact of different types of TMT media exposure on CSR. Some robustness tests are also conducted to strengthen the regression results. The results found that a high level of TMT media exposure promotes social responsibility. In addition, the TMT power and political connections negatively moderate the relationship between TMT media exposure and CSR. The main contribution of this study is to develop a TMT media exposure model to assess the impact of TMT media exposure on CSR, providing a theoretical contribution to the existing literature and enriching the research in the CSR context from the perspective of the TMT characteristic moderating role.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Green innovation has become an important combination of high-quality economic growth and ecological sustainability. In this paper, the super-efficiency network SBM model was used to measure the two-stage green innovation efficiency of the industrial technology research and development (R&D) stage and achievement transformation stage in China (30 provinces and cities) from 2009 to 2019. The results show the following points. Firstly, in terms of temporal series, the efficiency of technology R&D and achievement transformation has experienced three stages of “upward-declining-revitalized period”. Secondly, in terms of spatial trend, the industrial green innovation efficiency gradually increases from northwest to southeast. The high-efficiency areas are still concentrated in the eastern coastal region, with a clear trend towards balanced development in the central and western regions. Finally, openness, industrial structure, government technical expenditures, enterprise scale, and environmental regulation all have different degrees of impact on the efficiency of green innovation in the two stages. Based on the above, this paper is helpful for the government to formulate laws and regulations and coordinate the level of regional economic development and clarify the spatio-temporal characteristics and influencing factors of the efficiency of green innovation.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super-efficient SBM model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing of the industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, the industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: " stationary period", " recession period " and "growth period ". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.