This study examines the spillover effects of foreign direct investment (FDI) on green technology progress rate (as measured by the green total factor productivity). The analysis utilizes two measures of FDI, labor-based FDI and capital-based FDI, and separately investigates four sets of industry classifications—high/low discharge regulation and high/low emission standard regulation. The results indicate that in the low discharge regulation and low emission standard regulation industry, labor-based FDI has a significant negative spillover effect, and capital-based FDI has a significant positive spillover effect. However, in the high-intensity environmental regulation industry, the negative influence of labor-based FDI is completely restrained, and capital-based FDI continues to play a significant positive green technological spillover effects. These findings have clear policy implications: the government should be gradually reducing the labor-based FDI inflow or increasing stringency of environmental regulation in order to reduce or eliminate the negative spillover effect of the labor-based FDI.

Many researchers have studied the relationships among heterogeneous foreign direct investment (FDI), environmental regulation, and green total factor productivity. However, no research has been done on how different types of FDI can result in green technology spillover under different levels of environmental regulation intensity. To address this research gap, in this paper, we build a static linear panel model, a static panel threshold model, and a dynamic panel threshold model to investigate the environmental regulatory threshold effect of labor-based FDI and capital-based FDI in terms of their green technology spillover. Based on the measurement of green total factor productivity (GTFP) of 36 industry sectors in China from 2003 to 2015, we first compare the threshold effects of environmental regulation on green technology spillover between labor-based FDI and capital-based FDI with a static linear model and a static threshold model. The results show that environmental regulation is unable to significantly promote the green technology spillover of labor-based FDI. However, intensifying environmental regulation can reduce the negative impact of labor-based FDI on GTFP. The effect of environmental regulation on green technology spillover of capital-based FDI is more complex. In the static linear model, environmental regulation can significantly promote the green technology spillover of capital-based FDI. In the static threshold model, the green technology spillover of capital-based FDI exists only when the environmental regulation intensity is sufficiently low or sufficiently high. Finally, the dynamic threshold model is adopted for robustness check. The results show when the environmental regulation intensity is higher than a threshold, both types of FDI can indeed result in green technology spillover. In short, our results prove that to ensure that FDI results in green technology spillover, it is necessary to continue to strengthen environmental regulation.

In this paper, we conduct an empirical study on the impact of CEOs’ environmental awareness on technological innovation. To this end, we obtain a large sample with 7615 observations from Chinese A-share listed firms between the years of 2003 and 2014. Our empirical results show that a CEO’s environmental awareness has a significant positive impact on technological innovation of his/her enterprise. Environmentally conscious CEOs will invest more in R&D and obtain more patents. This will help their enterprises achieve higher efficiency of technological innovation. Furthermore, the environmental awareness of a CEO has a more significant impact on technological innovation if his/her enterprise is subject to a higher level of monitoring. We also conduct robustness check of our findings and provide managerial insights and proactive government policies to raise the environmental awareness of CEOs and improve the innovation vitality of enterprises.

In this paper, a nonparametric method is used to measure the total factor productivity (TFP) growth index in Chinese agriculture from 1981 to 2017, and the factor bias of technological progress is identified based on the theory of induced technological progress. Then, according to the degree of dependence of technological progress on fertiliser, biased technological progress is divided into green-biased technological progress and pollution-biased technological progress, and then empirical test the factors allocation structure that induce and promote green-biased technological progress. The results show that China's agricultural TFP has undergone three stages of accelerated growth, negative growth and fluctuation, and the growth momentum has undergone three transformations, which are jointly driven by technological efficiency and technological progress, dominated by technological progress and dominated by technological efficiency. Biased technological progress has contributed to the long-term growth of agricultural TFP in most regions of China, but it is mainly biased towards capital-using and fertiliser-using. The labour/capital ratio and the capital/fertiliser ratio are increased, reducing the capital/soil ratio, which can induce and promote green-biased technological progress while suppressing pollution-biased technological progress. The mechanism test results show that increasing labour input can indirectly promote green technological progress by reducing mechanisation.

Sustainable agriculture and good governance are part of the UN Sustainable Development Goals (SDGs), which have attracted great attention from all nations around the world. A scientific metrological and knowledge map analysis was conducted on the spatial–temporal evolution, collaboration network, research hotspots, cluster labels, frontier detection, and evolution path of 2350 pieces of data in this paper. The main results show that research hotspots such as sustainable development, rural development, agriculture, and others have influenced the development of the entire research process and have evolved into larger topic cluster groups such as ecosystem service, sustainable agriculture, land consolidation, and agricultural intensification. Research frontiers such as agriculture, integrated systems, smallholder systems, rural sustainable development, and land consolidation play key roles. Based on the findings, it is necessary to focus on the UN 2030 SDGs, combine the countries’ regional development needs and reality, and further clarify and refine the topics that need to be studied and the problems that need to be solved. More scientific demonstration and more feasible measures should be adopted to jointly deal with and enhance awareness of the current problems and practical challenges and further promote practical development by cohering academic consensus and expanding and innovating the governance models from the comprehensive dimensions of economy, politics, society, culture, and ecological environment so as to achieve good agricultural and rural governance.