Previous studies have pointed out that Industry-University-Research Institution (IUR) collaborative innovation is an important means to ensure the sustainable development of regional innovation, and there may be spillover effects among different regional innovation systems. However, the impact of regional spatial correlation and IUR collaborative innovation synergy degree on regional innovation performance is not that clear. Based on the panel data of 31 regions in China from 2006 to 2015, we construct static and dynamic spatial econometrics models to analysis the relationships among regional innovation performance, IUR collaborative innovation and spatial correlation. The research results show that there are significant positive spillover effects among different regions, indicating that the dynamic flows of innovation elements among regions is conducive to improve the regional innovation performance. In addition, IUR collaboration innovation also has a positive impact on regional innovation performance: the current period of IUR synergy degree has a negative impact, while the lagged one has a positive impact. It means that it will take a while for IUR collaborative innovation to be effective and it will have far-reaching contributions to long-term improvements rather than short-term benefits in social development. The results are significant for both static and dynamic spatial econometrics models. The conclusions of this paper have important policy significance to fully understand the coordination of innovative elements and promote the sustainable development of regional innovation systems.

The emission trading scheme (ETS) has become a significant tool to solve the climate change problem. China has built domestic carbon trading pilots to control energy consumption and reduce emissions. This paper explores the linkage between the carbon market and covered corporate value in China. To address the relationship, this paper estimates the impact that the carbon prices of different pilots in China have on the value of thermal listed enterprises and the extent of this impact. By using weekly data from July 2014 to June 2017, we analyze the overall effect and perform a comparative study of influences of the three trading years. Moreover, we test if the effect of carbon trading pilots on electricity corporate value is market-specific. The results demonstrate that carbon prices have a significantly negative impact on stock value when looking at the full sample and the effects vary between markets.

In this paper we study and compare the environmental efficiency of 118 photovoltaic (PV) plants in China. Drawing on the nonparametric data envelopment analysis (DEA) method, our study takes the initiative to take the insolation, annual sunshine duration, and covering area as input variables into account, as well as the installed capacity, annual electricity generation, CO₂ emission reduction, and coal saving as output variables, to provide a unified measure of environmental efficiency of PV plants in China. We find widespread inefficiencies in roughly 95% of the PV plants, and the performance of different economic zones and types of PV plants are quite different. Specifically, those PV plants in eastern China are the least satisfying performers among three different economic zones. The surprising result indicates that eastern China has room for improvement by overcoming the inefficiencies caused by serious aerosol pollution and the high urbanization rate. We also find rooftop PV plants have the highest efficiencies among the four types of PV plants due to very little power loss. However, complementary PV plants have the lowest efficiencies most likely because of high operating temperatures during the process of power generation.