*3.2. Explanatory Variables*

#### 3.2.1. Core Explanatory Variable

Land economic efficiency (Land\_EcoE) is the core explanatory variable in this study. Most scholars use comprehensive indicators to measure Land\_EcoE, for example, traditional data envelopment analysis (DEA), the slacks-based measure (SBM) model [50], land and output intensity [51] and principal component analysis [52]. As a matter of course, different measurement methods and research objectives yield different results [53,54]. Among them, DEA and SBM methods can measure input–output values better, but cannot show the different dimensions of indicators better. Principal component analysis can show multiple dimensions of indicators, but its explanatory power of different dimensions is weaker. The entropy method is based on the degree of variation among variables to different dimensions of indicators. The entropy method is able to explain the importance of each dimension of an indicator and extract the maximum information from the variables. Therefore, this study uses Matlab 2019a software to measure Land\_EcoE using the entropy method.

The aim of this study is to investigate whether there is a win-win situation between the "economy" and the "environment" in the development process of sustainable cities in eastern China, i.e., whether an increase in Land\_EcoE can improve environmental pollution. Therefore, this study constructs land economic efficiency indicators only at the level of economic development. Economic development generally consists of three dimensions: the economic growth dimension, the economic structure dimension and the economic quality dimension. Based on this, the indicator evaluation system of Land\_EcoE is described as follows (Table 1):



 **Table 1.** Indicator evaluation system of land economic efficiency.
