## 3 Empirical Results and Analysis
### Heterogeneity Test  
In the process of the DID regression model, the econometric test and robustness test of the benchmark model indicate that the conclusion of this paper is significant and robust. In this paper, the APCP Action Plan significantly promotes the improvement of green total factor productivity. After the analysis of the above two parts, it is necessary to further explore the heterogeneity effect of the APCP Action Plan on \(GTFP\) in different pilot regions of China through the heterogeneity test, which will help the government to make further improvement plans for the implementation effect of policies in different regions, which is also the importance of the heterogeneity test. Based on Model (2), the Panel OLS-DID model is used to analyze the impact of the policy on \(GTFP\) in different regions. According to the principle of geographical proximity of Chinese provinces and the three key pilot areas specified in the APCP Action Plan, the full sample of China is divided into the Jing-Jin-Ji, Jin-Lu-Meng region in northern China (Beijing, Tianjin, Hebei, Shanxi, Shandong and Inner Mongolia autonomous region are in the treatment group, and Henan, Heilongjiang, Jilin, Liaoning, Shaanxi, Gansu, Ningxia, Xinjiang, Qinghai are in the control group;), the Yangtze River Delta region (with Shanghai, Zhejiang, Anhui, Jiangsu in the treatment group, and Jiangxi, Hunan, Hubei, Fujian, Sichuan and Chongqing in the control group;) in central China, the Pearl River Delta region (with Guangdong in the treatment group and Jiangxi, and Fujian, Hunan, Guangxi and Hainan are in the control group) in southern China. The empirical results of the regional heterogeneity test [37] are displayed in Table 7.  
The estimated regression coefficients for the variable policy are significantly positive between the full sample and the southern China sample. However, the estimated coefficients of the variable policy between northern China and central China are not significant and negative, which indicates that the APCP Action Plan policy has heterogeneous effects on northern China, central China and southern China. The APCP Action Plan has contributed significantly to the \(GTFP\) improvement in southern China, while its impact on the \(GTFP\) of northern China and central China is not significant. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Full Sample** & **Northern China** & **Central China** & **Southern China** \\ \hline  & _InGTFP_ & _InGTFP_ & _InGTFP_ & _InGTFP_ \\ \hline Policy & \(0.0152\) * & \(0.0053\) & \(-0.0022\) & \(0.0114\)* \\  & \((1.74)\) & \((0.52)\) & \((-0.17)\) & \((1.70)\) \\ treated & \(-0.0084\)* & \(-0.0013\) & \(-0.0017\) & \(0.0187\) \\  & \((-1.95)\) & \((-0.25)\) & \((-0.20)\) & \((0.80)\) \\ time & \(-0.0261\)** & \(-0.0205\)** & \(-0.0306\)** & \(-0.0065\)** \\  & \((-6.40)\) & \((-4.55)\) & \((-3.00)\) & \((-1.96)\) \\ Control variable & YES & YES & YES & YES \\ Individual effect & YES & YES & YES & YES \\ Time effect & YES & YES & YES & YES \\ \_cons & \(-0.0319\)*** & \(-0.0562\)*** & \(-0.173\)*** & \(-0.0547\)*** \\  & \((-2.93)\) & \((-3.48)\) & \((-3.30)\) & \((-4.37)\) \\ \hline \hline \end{tabular} Note: *, ** and *** represent passing the significance test of 10%,5% and 1%, respectively, the same below.  
\end{table} 
Table 7: Regression results of the full sample and regional samples in China. Based on the empirical analysis of the benchmark model of subsamples in China, it is found that the impact of this policy on _GTFP_ in different regions is heterogeneous. Through the empirical regression results in Table 7, it is clear that the policy has significantly enhanced the _GTFP_ in southern China, and the other two regions have not been significantly affected by the policy. This may be due to the fact that the APCP Action Plan has different regulatory powers to different regions.