## 4 Analysis of Results  
Analysis of the Results of Measurement and Spatial-Temporal Evolution of Agricultural Green Development Level  
Dynamic Analysis of the Measurement Results of Agricultural Green Development Level  
In this study, the gray correlation analysis method was used to calculate the level of agricultural green development in 30 provinces in China based on the data published in the China Statistical Yearbook from 2006 to 2019. Due to limited space, this paper only presents data from 2006, 2013 and 2019, as shown in Figure 3. Secondly, seven \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Variable** & **Variable Definition** & **Average** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Minimum** & **Maximum** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Explained \\ variable \\ \end{tabular} } & \multirow{2}{*}{InGrad} & \multirow{2}{*}{Agricultural green development level value} & \multirow{2}{*}{3.8192} & \multirow{2}{*}{0.0724} & \multirow{2}{*}{3.6763} & \multirow{2}{*}{4.0270} \\ \cline{6-6}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Core explanatory \\ variable \\ \end{tabular} } & \multirow{4}{*}{Inast} & \multirow{4}{*}{Inast} & \multirow{4}{*}{6.2525} & \multirow{4}{*}{1.2669} & \multirow{4}{*}{2.7726} & \multirow{4}{*}{8.8009} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Control variables \\ \end{tabular} } & \multirow{4}{*}{Innsa} & \multirow{4}{*}{6.9155} & \multirow{4}{*}{1.0493} & \multirow{4}{*}{3.6636} & \multirow{4}{*}{8.5957} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Control variables \\ \end{tabular} } & \multirow{4}{*}{Innisa} & \multirow{4}{*}{1.7077} & \multirow{4}{*}{0.4325} & \multirow{4}{*}{0.7020} & \multirow{4}{*}{2.7314} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Incrs \\ \end{tabular} } & \multirow{4}{*}{Innci} & \multirow{4}{*}{Sown area of crops/cultivated area} & \multirow{4}{*}{4.7764} & \multirow{4}{*}{0.3077} & \multirow{4}{*}{3.7543} & \multirow{4}{*}{5.3898} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variable description and descriptive statistical results. geographical regions of China, namely, north China, northeast China, east China, central China, south China, southwest China and northwest China (hereinafter referred to as the seven regions), were selected as the study regions. MATLAB software was used to analyze the temporal variation trend of the agricultural green development level evaluation index in the seven regions (excluding Tibet, Taiwan, Hong Kong and Macao) from four dimensions: economic and social development, resource-saving input, resource recycling and resource and environment friendliness, as shown in Figures 4-8. As can be seen from Figure 3, the agricultural green development level of 30 provinces in China was generally stable at 0.35-0.60. The average agricultural green development level of some provinces, such as Beijing and Fujian, is higher than 0.5, indicating a high agricultural green development level. The average value of the agricultural green development level in Shanxi and Chongqing is lower than 0.4, indicating that the development level of agricultural green in Shanxi and Chongqing is low. A possible reason is that Beijing and Fujian have a high level of economic and social development and have advanced crop management concepts, which have a positive role in promoting agricultural green development.