To alleviate climate change and environmental issues, China has implemented many environmental regulation policies. This paper takes the SO2 and carbon emission trading pilots (SETP, CETP) in China as the quasi-experiment and, under the difference-in-difference framework, studies whether the market-based environmental regulation (MER) policy promotes green technology innovation. The investigation is conducted employing patent data with the “IPC Green Inventory” on the panel data of China’s 278 prefectural-level cities over the period 2003–2017. We found that 1) as for a single policy, SETP successfully promoted green technology innovation, but failed in CETP, which shows that not all MER policies can play a positive effect on green technology innovation. Meanwhile, SETP and CETP did not change the direction of technology innovation and had no impact on total technology innovation. 2) For the combination policy, SETP and CETP failed to jointly promote green technology innovation, and with the current MER policy in China, it is difficult to realize the policy combination effect. This result implies that repeated implementation of similar environmental policies failed to stimulate innovation. 3) Heterogeneity analysis shows that the promotion effect of SETP on green technology innovation, mainly in the eastern region, and the promotion effect on invention patents is more prominent than utility model patents, which shows that green technology has improved not only in quantity but also in quality. These findings provide empirical evidence and policy implication for the efficient implementation of environmental regulation.
As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co‐opetition matrices of Chinese provinces, then builds the co‐opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co‐opetition DEA cross‐efficiency model that integrates haze's feature of cross‐border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision‐making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co‐opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co‐opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results.
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