## 3 Empirical Results
### Heterogeneity Analysis
#### 3.5.2 Regional Heterogeneity Analysis  
As previous research generally divided samples into the east, central and west regions with regional heterogeneity analysis, which would cut off the connection between adjacent provinces in the same region. Therefore, this paper refers to relevant research [46] and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{6}{c}{**Dynamic Dubin Model**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Long-Term Effects**} & \multicolumn{6}{c}{**Long-Term Effects of Interaction Relationship**} \\ \cline{2-7}  & **Direct Effect** & **Indirect Effect** & **Direct Effect** & **Indirect Effect** & **Direct Effect** & **Indirect Effect** \\ \hline \multirow{2}{*}{Iner} & \(-0.0799\) *** & \(-0.1224\) *** & \(-0.2024\) *** & \(-0.0794\) *** & \(-0.1178\) ** & \(-0.1972\) *** \\  & (\(-5.47\)) & (\(-2.35\)) & (\(-3.37\)) & (\(-5.39\)) & (\(-0.26\)) & (\(-3.28\)) \\ \multirow{2}{*}{Infiscal} & \(0.2851\) * & \(1.8118\) *** & \(2.0970\) *** & \(0.3289\) * & \(2.3221\) *** & \(2.6510\) *** \\  & (\(1.74\)) & (\(3.93\)) & (\(3.89\)) & (\(1.80\)) & (\(3.97\)) & (\(4.05\)) \\ \multirow{2}{*}{Iner \(\times\) Infiscal} & & & & \(-0.0207\) & \(-0.2006\) ** & \(-0.2214\) * \\  & & & & (\(-0.53\)) & (\(-1.97\)) & (\(-1.95\)) \\ \multirow{2}{*}{Instruc} & \(0.2698\) *** & \(-0.2973\) *** & \(-0.0274\) & \(0.2676\) *** & \(-0.2857\) *** & \(-0.0180\) \\  & (\(8.11\)) & (\(-2.79\)) & (\(-0.23\)) & (\(0.81\)) & (\(-2.95\)) & (\(-0.17\)) \\ \multirow{2}{*}{Inagrimech} & \(-0.0109\) & \(0.0154\) & \(0.0044\) & \(-0.0099\) & \(0.0219\) & \(0.0119\) \\  & (\(-0.85\)) & (\(0.47\)) & (\(0.12\)) & (\(-0.78\)) & (\(0.64\)) & (\(0.31\)) \\ \multirow{2}{*}{Inedu} & \(0.0256\) & \(0.9742\) *** & \(0.9998\) ** & \(0.0335\) & \(0.9961\) *** & \(1.0296\) ** \\  & (\(0.20\)) & (\(2.57\)) & (\(2.15\)) & (\(0.26\)) & (\(2.69\)) & (\(2.23\)) \\ \multirow{2}{*}{Inscale} & \(0.0123\) & \(-0.0025\) & \(0.0097\) & \(0.0097\) & \(-0.0040\) & \(0.0056\) \\  & (\(0.19\)) & (\(-0.01\)) & (\(0.05\)) & (\(0.15\)) & (\(-0.02\)) & (\(0.03\)) \\ \hline \hline \end{tabular} Note: Standard errors in parentheses, ***\(p<0.01\), **\(p<0.05\), *\(p<0.1\).  
\end{table} 
Table 5: _Cont._divides the samples into the east-central region, east-west region and middle-west region for further search, the results are shown in Table 6.  
From the result of stage heterogeneity analysis in Table 6, it is seen that the spatial spillover effect on the systematic stage of agricultural green development in the years 2015-2020 is obviously more significant than that in the initial stage of agricultural green development in the years 2000-2014, and the influence coefficient is lager, indicating that agricultural green development has experienced a process of continuous improvement. Furthermore, the coefficient of financial support for agriculture on agricultural green development increases from 0.78 to 3.39. In contrast, the coefficient of environmental regulation on agricultural green development decreases from \(-\)0.005 to \(-\)0.127. Additionally, the negative coefficient of the interaction term between environmental regulation and financial support for agriculture on agricultural green development shows a shrinking trend, indicating that local government constantly adjusts the intensity of environmental regulation and financial support for agriculture according to their local situation to meet the requirements of agricultural green development. From the perspective of spatial characteristics, there are significant differences in environmental regulation and financial support for agriculture among the east-middle region, east-west region and middle-west region. Financial support for agriculture could significantly promote agricultural green development with a spatial spillover effect in the east-middle region and east-west region while showing no significant effect in the middle-west.