3.2.2. Control Variables

In this study, the explained variable is carbon emissions and the explanatory variable is land economic efficiency (Land\_EcoE). To increase the validity of the empirical results and to avoid endogeneity problems arising from omitted variables, the following control variables are added: (I) Chen and Ouyang et al. both found that foreign investment has a significant impact on environmental improvement [60,61]. Therefore, the foreign capital utilization intensity (amount of actual foreign investment utilized/land area) is used, controlling for the impact from abroad. (II) There is no doubt that innovation can have a relationship with the environment [62–64]. This study uses innovation intensity (number of non-descript patent applications/employment), controlling for the impact from domestic innovation.

#### *3.3. Data Resource and Processing*

This study uses the eastern region of China as the sample for this study. Considering the statistical caliber and completeness of the data, Taiwan Province, Hong Kong and Macau are excluded from this study. Hainan Province is excluded due to its relatively underdeveloped economy and because it does not meet the requirement of being a more economically developed region. The initial time point for this study is set at 2011 as China has developed and implemented a rich and stringent environmental governance policy since the starting point of the 12th Five-Year Plan (2011). As the latest city-level carbon emission data were only updated to 2017, the end point of this study is set at 2017. In summary, this study uses data from 2011–2017 for 84 prefecture-level cities in eastern China. The main data for this study were obtained from the China City Statistical Yearbook, and all of them were from the statistical scope of municipal districts. City-level carbon emissions data were obtained by aggregating county-level data from Carbon Emission Accounts & Datasets (CEADs) [65]. For some of the missing data, the moving average method was used to complete the study. Table 2 reports the descriptive statistics of the main variables in the study. In this case, all values are in logarithmic form, except for land economic efficiency. It can be found that the standard deviations of the variables are small and there are no extreme values that are several orders of magnitude higher than the other variables, indicating that the data are suitable for use in the regression model.


**Table 2.** Variable description.

*3.4. Analysis of Spatio-Temporal Evolution of Key Variables*
