## 4 Empirical Results
### Heterogeneity Impact on Policy Effect
#### 4.5.1 Economic Development Differences  
According to Equations (3) and (5), it can be found from Table 7 that the interaction coefficients of model m1 without the control variables according to the DID and DDD estimations are not significant. The DDD estimation is \(-0.209\), which indicates that the ETS policy may have a lower emissions effect on the provinces with higher economic development rates than the provinces with lower economic development rates. Thus, the pilot policy strongly inhibits the carbon emissions effect in the provinces with higher economic development rates. The R-squared in model m2 with control variables increases significantly from 0.520 to 0.957, and except for the research structure, the coefficients of the other eight control variables are significant and have better statistical significance. The economic significance analysis is as follows. (1) The p value of the DID interaction term Z passes the t test at the 10% significance level. Thus, we can conclude that there may be time trend differences between the control group and treatment group, and the results of the DDD method are more robust than those of the DID method. (2) The coefficient of the DDD interaction term Z*if_econ is significant (\(-0.370\)), indicating that the ETS pilot policy has strong carbon emission inhibitory effects on the provinces with higher economic development rates than the provinces with lower economic development rates. (3) The mechanism effect, population, per capita GDP and emissions intensity have major positive effects on carbon emissions. The environmental pollution control intensity and research intensity have negative effects on carbon emissions (the industrialization rate, the proportion of coal consumption, and the urbanization rate also have positive effects). The results of models m3 and m4 studying the economic effect show that the coefficient of the interaction term of the DDD estimation is small and the P-value is not significant, regardless of whether or not control variables are included, indicating that the level of economic development has no significant effect on the economic effect of the policy. Compared with the results of this study and the related literature [7; 34], we can find that the ETS policy has a more significant effect on promoting the development of regions and enterprises with higher development  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dynamic Effect**} & \multicolumn{3}{c}{**Regional Placebo Test**} \\ \hline  & **m1** & **m2** & **m3** & **m4** & **m5** & **m6** \\ \hline \(Z\) & \(-0.100\) * (1.80) & \(0.064\) * (1.85) & & & & \\ Zif\_2010 & \(-0.052\) (\(-0.68\)) & \(0.071\) * (2.44) & & & & \\ Zif\_2011 & \(-0.065\) (\(-1.17\)) & \(0.048\) (1.64) & & & & \\ Zif\_2012 & \(-0.087\) (\(-1.53\)) & \(0.108\) (1.66) & & & & \\ Zif\_2013 & \(-0.092\) (\(-1.64\)) & \(0.077\) (1.07) & & & & \\ Zif\_2014 & \(-0.107\) * (1.91) & \(0.061\) * (2.31) & & & & \\ Zif\_2015 & \(-0.097\) * (1.86) & \(0.069\) * (1.94) & & & & \\ Zif\_2016 & \(-0.092\) * (1.96) & \(0.062\) * (1.82) & & & & \\ Zif\_2017 & \(-0.101\) * (1.90) & \(0.063\) * (1.90) & & & & 0.051 \\ Z\_district & & & 0.051 (0.30) & 0.057 (1.24) & 0.039 (0.23) & (1.44) \\ control & yes & yes & no & yes & no & yes \\ province & no & no & yes & yes & yes & yes \\ time & no & no & yes & yes & yes & yes \\ R-square & 0.832 & 0.904 & 0.351 & 0.942 & 0.238 & 0.944 \\ \hline \hline \end{tabular}  
* \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001. 2 2009 as the base period of 2010–2013, 2013 as the base period of 2014–2017.  
\end{table}
Table 6: Dynamic effect test and placebo test.  
levels since these entities are better than the regions and enterprises with lower development levels in terms of investment intensity and environmental regulation investments, thus achieving better \"environmental welfare\" and \"economic welfare.\"