*4.3. Further Analysis*

In this study, the regression of the model of Equation (7) was performed using Stata15SE software, and the results are shown in Table 4. This study screens out regions with high economic levels in eastern China by comparing the averages of regional GDP from 2011– 2017. Two groups of regions were screened using "expectation + 1 times standard deviation" and "expectation" as the bounds. A-group: Beijing, Tianjin, Shanghai, Nanjing, Hangzhou, Guangzhou, Shenzhen and Foshan, a total of eight prefecture-level cities. B-group: on the basis of A-group, 10 prefecture-level cities, Tangshan City, Wuxi City, Changzhou City, Suzhou City, Ningbo City, Xiamen City, Jinan City, Qingdao City, Zibo City and Dongguan City, are added, to a total of 18 prefecture-level cities. Table 4 reports the regression results for both groups and it can be found that the third column has the best regression effect in both Agroup and B-group. In Table 4(III), the coefficient of Land\_EcoE is significantly positive, indicating that for most regions in eastern China, the increase in land economic efficiency promotes carbon emissions and pollutes the environment. However, the coefficients of (D\_A \* Land\_EcoE) and (D\_B \* Land\_EcoE) are both negative and extremely significant, suggesting that the cities in eastern China with the most prosperous economies have been able to reduce carbon emissions and thus improve environmental pollution by increasing land economic efficiency. Therefore, hypothesis H2 holds.


**Table 4.** Further regression for the spatio-temporal fixed effects of land economic efficiency.

\*\*\*, \*\* and \*, respectively, indicate statistical significance at the 1%, 5% and 10% levels. Obs (A-group) and Obs (B-group) refer to observations where the dummy variable is 1.
