## 3 Empirical Results
### Spatial Dubin Model Regression Analysis  
On the basis of the above spatial Dubin model, this paper uses Stata16.0 software for empirical estimation. Table 4 shows the regression results of environmental regulation and financial support for agriculture on agricultural green development from Model 4 to Model 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Static Dubin 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** \\ **Dubin Model** \\ **of Interaction** \\ **Model (7)** \\ \end{tabular} \\ \hline L.Inagtfp & & & & \(0.1912\) * & \(0.1723\) * \\  & \(-0.0780\) *** & \(-0.0786\) *** & \(-0.0719\) * & \(-0.0720\) *** \\  & \((-5.58)\) & \((-5.58)\) & \((-5.12)\) & \((-5.08)\) \\ lnfiscal & \(0.2001\) & \(0.2524\) & \(0.1580\) *** & \(0.1738\) \\  & \((1.27)\) & \((1.42)\) & \((0.97)\) & \((0.94)\) \\ lner \(\times\) Infiscal & & \(-0.0232\) & \(-0.0052\) \\  & \((-0.58)\) & & \((-0.13)\) \\ lnstruc & \(0.2806\) *** & \(0.2776\) *** & \(0.2881\) *** & \(0.2846\) *** \\  & \((8.82)\) & \((8.74)\) & \((8.59)\) & \((8.50)\) \\ lnagrimech & \(-0.0146\) & \(-0.0138\) & \(-0.0118\) & \(-0.0111\) \\  & \((-1.17)\) & \((-1.11)\) & \((-0.90)\) & \((-0.85)\) \\  & \(-0.1742\) * & \(-0.1572\) & \(-0.0396\) & \(-0.0316\) \\  & \((-1.66)\) & \((-1.47)\) & \((-0.34)\) & \((-0.27)\) \\ lnscale & \(0.0059\) & \(0.0032\) & \(0.0133\) & \(0.0106\) \\  & \((0.10)\) & \((0.05)\) & \((0.21)\) & \((0.17)\) \\ W*Iner & \(-0.0919\) *** & \(-0.0935\) *** & \(-0.0628\) * & \(-0.0673\) ** \\  & \((-2.60)\) & \((-2.65)\) & \((-1.71)\) & \((-1.84)\) \\ W*Infiscal & \(1.1103\) *** & \(1.4735\) *** & \(1.2089\) *** & \(1.6199\) *** \\  & \((3.61)\) & \((3.94)\) & \((3.83)\) & \((4.20)\) \\ W*Iner \(\times\) & & \(-0.1266\) * & & \(-0.1393\) * \\  & \((-1.69)\) & & & \((0.85)\) \\ W*Instruc & \(-0.2389\) *** & \(-0.2253\) *** & \(-0.3069\) *** & \(-0.2908\) *** \\  & \((-3.41)\) & \((-3.21)\) & \((-4.15)\) & \((-3.91)\) \\ W*Inagrimech & \(0.0217\) & \(0.0232\) & \(0.0163\) & \(0.0177\) \\  & \((0.96)\) & \((1.03)\) & \((0.69)\) & \((0.75)\) \\ W*Inedu & \(0.4119\) ** & \(0.4656\) ** & \(0.6944\) *** & \(0.7473\) *** \\  & \((1.94)\) & \((2.17)\) & \((2.86)\) & \((3.07)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of spatial Dubin model regression. According to the results of model 4 and model 5 in Table 4, both the regression coefficient and the spatial lag term of environmental regulation are negative. The spatial lag term is significant at the level of 1%, which supports H1. The reason is that although the government has invested much manpower, materials and governance costs to protect the environment, it has not reversed negative externalities of environmental governance as agricultural operators need to invest more cost to meet the requirements of environmental regulation. Thus, there is an asymmetry between the benefit and costs of agricultural operators, which leads to their unwillingness to agricultural green development [42; 43]. Financial support for agriculture has a significant positive effect on agricultural green development, which supports H2. This is mainly because local governments have strong autonomy and a considerable amount of financial support for agriculture, which promotes agricultural green development. However, the interaction term between environmental regulation and financial support for agriculture has a negative impact on agricultural green development, which supports H3. This is because, on the one hand, under the existing local interest incentive system, local governments are more willing to offer economic support while ignoring the reasonable use of environmental regulation means, which leads to poor environmental awareness and low attention to the agricultural environment of agricultural operators.