## 2 Materials and Methods
### Variables Description and Data Sources  
#### 2.2.1 Variable Description  
In the process of regional sustainable economic development, the green total factor productivity (_GTFP_) is a concept with green background based on TFP and considering the undesirable output of pollutants. The _GTFP_ is generally measured by input indicators such as labor, capital, and energy consumption, desired output indicators such as GDP, and undesired output indicators such as environmental pollution. _GTFP_ emphasizes the coordinated development of economic growth and ecological environment, which can reflect the quality of economic growth. Therefore, it has become one of the core indicators for supervising regional sustainable development. The major explained variable in this paper is the provincial _GTFP_, which is calculated by the SBM-GML model. See Appendix A.1 for the detailed calculation process. Table 1 is a description of the variables used to calculate _GTFP_. According to the research and empirical analysis of other scholars [30; 31; 32], _GTFP_ measured by the GML can used in the panel regression analysis. The core explanatory variable of this paper is the dummy variable Policy [11]. On 13 September 2013, the State Council of China issued the APCP Action Plan, which presents specific requirements for regional remediation of fine particulate matter concentrations in the Beijing-Tianjin-Hebei Region in northern China, the Yangtze River Delta in central China and the Pearl River Delta region in southern China. Thus, the pilot regions of the APCP Action Plan policy are specific. Therefore, in this paper, the pilot provinces and cities (11 in total) (There are Beijing, Tianjin, Hebei, Shanxi, Shandong, Inner Mongolia Autonomous Region, Shanghai, Jiangsu, Zhejiang, Anhui and Guangdong provinces.) in the three target regions are selected as the treatment group affected by the policy. Other provinces (19 in total) are classified as the control group that did not receive significant policy effect. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Type**}} & \multicolumn{2}{c}{**Input-Output Indicator**} \\
**Indicator Name** & **Index Calculation Method** \\ \hline \multirow{3}{*}{Input indicators} & Capital & \multirow{3}{*}{Number of employees in each province at the end of the period} \\  & Labor & & \\ \cline{1-1}  & Energy consumption & Total energy consumption per province \\ \hline \multicolumn{1}{c}{Output indicators} \\ \hline \multicolumn{1}{c}{Desired output} & Gross regional product & Provincial GDP for the year \\ \hline \multirow{3}{*}{Undesired output} & Industrial wastewater discharge & Provincial industrial wastewater discharge \\ \cline{1-1}  & Industrial fixed waste generation & Industrial fixed waste production in each province \\ \cline{1-1}  & Industrial SO2 emissions & Provincial industrial sulfur dioxide emissions \\ \cline{1-1}  & Carbon dioxide emissions & Provincial carbon dioxide emissions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of input–output indicators. The moderating variables of this paper are rationalization of industrial structure (RIS) and optimization of industrial structure (OIS1, OIS2). The detailed calculation formula and process of moderating variables are given in Appendix A.2. The rationalization of industrial structure can effectively reflect the increasing degree of correlation and coordination between industries and the increasing level of effective utilization of resources, that is, to measure the coupling degree of factor input structure and output structure. In this paper, the Theil index is used to measure the rationalization of the industrial structure of each province. The index not only retains the advantage of the deviation of the industrial structure, but also measures the structural deviation between the output value of different industries and the employment. It also reflects the different economic status of each industry through the weighted output value.