## 4 Regression Variables, Data Sources, and Econometric Framework  
To conduct empirical estimations, \"urban population (% of total population)\" (URB) is employed to measure urbanization, \"renewable energy consumption (% of total final energy consumption)\" (REN) is employed to measure renewable energy consumption, \"services, value added (% of GDP)\" (SER) is employed to measure service industries, and \"individuals using the internet (% of population)\" (INT) is employed to measure internet usage. For the control variables, \"mean years of schooling (years)\" (MYS) is employed to measure education, and \"gross national income per capita, PPP (current international $)\" (GNIPC) is employed to measure income. Abbreviations of all of the regression variables are listed in Table 2.  
Data of URB, REN, SER, INT, and GNIPC were obtained from the World Bank Indicators. Data of MYS were obtained from the Human Development Reports of the United Nations Development Program. All of the data used were obtained from public and reliable data sources, which could guarantee that the regression estimations are repeatable and testable. After sorting out all of the data of the seven variables and based on the criteria of selecting the sample countries, we finally obtained three panel datasets, i.e., all sample countries (90), the developed countries \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Variable** \\ \hline EF & Ecological footprint \\ URB & Urban population (\% of total population) \\ REN & Renewable energy consumption (\% of total final energy consumption) \\ SER & Services, value added (\% of GDP) \\ INT & Individuals using the internet (\% of population) \\ MYS & Mean years of schooling \\ GNIPC & Gross national income per capita \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of abbreviations of the regression variables. Based on panel datasets, the ordinary least square (OLS) was employed to conduct the estimations. We employed Equation (1) to estimate the impacts of the influencing factors and control variables on ecological consumption. Equation (2) is the specification of Equation (1). \[EF=f(\mathit{URB},\mathit{REN},\mathit{SER},\mathit{INT},\mathit{MYS},\mathit{ GNIPC}). \tag{1}\]  
\[EF_{i,t}=\alpha+\beta_{1}\mathit{URB}_{i,t}+\beta_{2}\mathit{REN}_{i,t}+\beta_ {3}\mathit{SER}_{i,t}+\beta_{4}\mathit{INT}_{i,t}+\beta_{5}\mathit{MYS}_{i,t} \tag{2}\]  
\[+\beta_{6}\mathit{Ln}(\mathit{GNIPC})_{i,t}+\beta_{7}\mathit{Ln}(\mathit{ GNIPC})^{2}_{i,t}+\varepsilon_{i,t}\]  
\(EF\) was the dependent variable and \(\mathit{URB}\), \(\mathit{REN}\), \(\mathit{SER}\), \(\mathit{INT}\), \(\mathit{MYS}\), \(\mathit{Ln}(\mathit{GNIPC})\), and \(\mathit{Ln}(\mathit{GNIPC})^{2}\) were the independent variables. \(\mathit{Ln}(\mathit{GNIPC})\) is the natural log form of \(\mathit{GNIPC}\). \(\mathit{Ln}(\mathit{GNIPC})^{2}\) is the square of \(\mathit{Ln}(\mathit{GNIPC})\). Because marginal impacts of income on ecological consumption are supposed to be diminished, \(\mathit{Ln}(\mathit{GNIPC})\) rather than \(\mathit{GNIPC}\) is used in Equation (2). Relative to \(\mathit{GNIPC}\), \(\mathit{Ln}(\mathit{GNIPC})\) could minimize the potential estimation bias caused by extreme income values. \(\alpha\) represents the intercept term. \(\beta_{1},\beta_{2},\beta_{3},\beta_{4},\beta_{5},\beta_{6}\), and \(\beta_{7}\) represent the slope coefficients of \(\mathit{URB}\), \(\mathit{REN}\), \(\mathit{SER}\), \(\mathit{INT}\), \(\mathit{MYS}\), \(\mathit{Ln}(\mathit{GNIPC})\), and \(\mathit{Ln}(\mathit{GNIPC})^{2}\), respectively. \(i\) represents the sample countries (cross-section), which indicates the country-specific effects. \(t\) denotes the time period (years), which indicates the time series effects. \(\varepsilon_{i,t}\) is the stochastic error term, which captures the impacts of all unobserved variables on \(EF\). According to the hypotheses in Section 3, \(\beta_{1},\beta_{2},\beta_{3}\), and \(\beta_{4}\) are expected to be negative.