## 3 Empirical Results
### Spatial Dubin Model Regression Analysis
On the other hand, under high environmental pressure, the local government might overuse environmental regulation, which leads to the cost increase of agricultural green development of agricultural operators and lowers their willingness [44]. According to model 6 and model 7 in Table 4, the variable of a spatial lag term is significant, indicating that there is a spatial spillover effect of agricultural green development. Compared with the static panel model, the dynamic panel model considering the first-order lag term of agricultural green development has a better fit, and the coefficient sign of the estimated results is consistent with the significance, which has better stability. That means there is a significant time lag effect and space spillover effect of agricultural green development. Moreover, the results show that agricultural green development has a strong dependence on the time dimension, and the level of agricultural green development in the previous period would also affect the accumulation of agricultural green development in the next period. The estimated coefficients of environmental regulation and financial support for agriculture are in the same direction as those of the static panel model. However, the negative inhibitory effect of environmental regulation is gradually decreased, while the positive promoting effect of financial support for agriculture is constantly enhanced. This is because the local government would also increase taxes on non-agricultural sectors while providing \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Static Dubin Model Model (4)** & \begin{tabular}{c} **Static Dubin Mode** \\ **of Interaction Model (5)** \\ \end{tabular} & \
\begin{tabular}{c} **Dynamic** \\ **Dubin Model Model (6)** \\ \end{tabular} &
\begin{tabular}{c} **Dynamic** \\ **of Interaction Model (7)** \\ \end{tabular} \\ \hline \multirow{2}{*}{\(W^{\text{*}}\)Inscale Log-L \(\rho\)} & \(0.0507\) & \(0.0407\) & \(0.0060\) & \(-0.0055\) \\  & \((0.43)\) & \((0.34)\) & \((0.05)\) & \((-0.44)\) \\  & \(-2859.27\) & \(-2859.27\) & \(-580.44\) & \(-533.07\) \\  & \(0.1907\) *** & \(0.1823\) *** & \(0.1341\) ** & \(0.1335\)** \\  & \((3.62)\) & \((3.46)\) & \((2.10)\) & \((2.09)\) \\ R\({}^{2}\) & \(0.3638\) & \(0.3965\) & \(0.5932\) & \(0.5838\) \\ N & \(651\) & \(651\) & \(620\) & \(620\) \\ Control & & & & \\ variables and & YES & YES & YES & YES \\ spatial terms & & & & \\ Individual fixed & YES & YES & YES & YES \\ effects & YES & YES & YES & YES \\ Time fixed effect & & & & \\ \hline \hline \end{tabular} Note: Standard errors in parentheses, *** \(p<0.01\), ** \(p<0.05\), * \(p<0.1\).  
\end{table} 
Table 4: _Cont._subsidies to agricultural sectors and regulating the agricultural environment by means of incentive environmental regulation, which is similar to the find of [PERSON] et al. (2019). Moreover, the coefficient of the interaction term between environmental regulation and financial support for agriculture varies from the range of \(-0.12\) and \(-0.13\) in the static panel model, indicating that the dynamic panel model could effectively reduce the bias of the regression coefficient, which is an optimized model compared with the static panel model. In view of control variables, the results of Table 4 show that industrial structure has positive effect on local agricultural green development, while shows spatial negative spillover effect on adjacent agricultural green development. The reason may be that different provinces have a different mode of agriculture industry structure, and the agricultural industry structure efficiency shows overlapping among regions resulting in negative spillover effect with adjacent regions ([PERSON] et al., 2019).