## 1 Introduction
### Literature Review and Contribution
[15] analyzed the relationship between environmental regulation and green development by the spatial Dubin model and the panel model with Chinese provincial-level data from 2005 to 2016. The results display that there is a \"U\" shaped relationship between environmental control and green energy efficiency. [PERSON] et al. [16] used the environmental policy strictness index issued by the OECD as an indicator of environmental policy to analyze the impact of environmental regulations on TFP with the panel quantile regression model. They found that more stringent environmental regulations can promote cleaner production processes and energy efficiency. [PERSON] and [PERSON] [17] took China's non-ferrous metal industry as a sample to discuss the relationship between environmental regulation and energy environmental performance, which is represented by the competitiveness index constructed with DEA in two stages. It concluded that there is a significant U-shaped nonlinear relationship between environmental regulation and EEPT. [PERSON] et al. [18] used various methods to measure environmental productivity and conducted econometric analysis on long macroeconomic panel data sets to evaluate the impact of environmental policies. They found that environmental policies adopted by different countries, directly and indirectly, promoted the performance and environmental productivity at various environmental levels through innovation. [PERSON] et al. [19] used policy and patent data from 194 countries and regions to study how different renewable energy support policies affect the impact of solar and wind energy technology innovation, and found that the combination of renewable energy support policies increases the number of patents for solar and wind energy-related technologies. [PERSON] et al. [20] used the DID estimation method to study the relationship of the carbon intensity constraint policy proposed by the Chinese government in 2009 on green production performance. They found that there is a non-linear inverted U-shaped effect on the green productivity of Chinese industry, and after a brief ascent, industrial GPP showed a circuitous downward trend. The fourth is to study the impact of environmental policy on green total factor productivity. For this aspect of research, scholars hold different views. Firstly, some scholars believe that environmental policy can promote the improvement of _GTFP_. [PERSON] et al. (2017) used the DID model to study the impact of LCCP policies implemented in China in 2012 on urban _GTFP_, recognizing that LCCP policies do have a significant positive impact on the _GTFP_ of pilot cities. The promoting effect of policies on _GTFP_ in different types of cities is heterogeneous. Based on provincial panel data from 2007 to 2017, [PERSON] et al. (2017) used the DID estimation method to analyze the impact of the APCP policy implemented in 2013 on the _GTFP_ of China's chemical industry. They found that the policy significantly increased China's chemical industry _GTFP_ and technological progress is the major driving factor. Some scholars have found that the impact of environmental policy on _GTFP_ is nonlinear and heterogeneous. Based on the TFP data of 273 cities in China from 2003 to 2013, [PERSON] and [PERSON] (2014) calculated the ML index and adopted the spatial Dubin model to study the impact of environmental regulation on _GTFP_, and found that due to different political attributes of cities, the impacts of environmental regulation on _GTFP_ in different regions are significantly different. [PERSON] et al. (2017) measured _GTFP_ with the SBM-DDF method and analyzed the effect of environmental regulatory policies on _GTFP_, verifying the Porter Hypothesis that environmental policy has a positive impact on _GTFP_ within a certain degree of strictness, but the impact becomes negative when the environmental policy exceeds a certain degree.