4.2. Economic Effect of AI
To ensure the robustness of the GTWR estimation results, OLS estimation was first performed, and the results are shown in
Table 5, below. The degree of AI shows a significant positive correlation with farmers’ income and regional economic development. The estimation results of the OLS model indicate that H2 holds. Since OLS estimation does not consider spatial distance and the variability among observations, the results fail to reflect the spatial instability.
When using the GTWR model to analyze the economic effects of agricultural information, both the spatial correlation of informatization and the heterogeneity of individual cities are included in the research framework. The estimated results are listed in
Table 6. Compared with the OLS estimation results, the models estimated by GTWR all achieve a fitting degree of over 0.8, which is much higher than that of the model R
2 estimated by OLS. To further illustrate the applicability of the GTWR model, the estimated residuals were tested for spatial correlation. The estimated residuals of both Model 3 and Model 4 fail the spatial correlation test, which means that the omitted variables are in random distribution rather than spatially correlated, which increases the reliability of the GTWR estimation results.
From the above estimation results, it can be seen that AI exerts a positive effect on enhancing farmers’ income and regional economic development during 2001–2020. Hence, H3 and H4 are correct. In other words, AI enhances farmers’ income and regional economic development. By comparing the estimated coefficients of AI variables in Model 3 and Model 4, it can be seen that the effect of AI on farmers’ income is more pronounced. Moreover, its interquartile range is relatively small, indicating that this effect of increasing farmers’ income appears to be concentrated. Although AI also plays an incentive role for regional economic development, its estimated coefficient interquartile range is relatively large, indicating that the effect of this factor is more dispersed. Therefore, the economic effects of AI are analyzed from the time dimension and the regional dimension, respectively.
Spatial and temporal differences exist in the impact of AI on farmers’ income and regional economic development. In order to analyze the temporal characteristics of this impact,
Figure 2 and
Figure 3 show box plots of the changing trends of the estimated results of AI variables over time in Model 3 and Model 4, respectively. On the whole, the economic enhancement effect of AI varies widely from year to year due to influence factors such as labor force, investment and degree of industrialization in each province.
As illustrated in
Figure 2 and
Figure 3, the economic effects of AI fluctuate as time goes by. The average level of AI’s effect on increasing farmers’ income remains stable during the sample observation period. From 2001 to 2009, the positive contribution of AI to farmers’ income increased unevenly, and the mean value of the regression coefficient reached a maximum value of 1.031 in 2009. After 2009, the role of AI on farmers’ income decreased until the coefficient reached 1.017 in 2020. The relationship between AI and farmers’ income fluctuated significantly at the beginning of the study, and then the dispersion of the coefficients gradually decreased, indicating that the spatial variability of the impact of AI on farmers’ income decreased between 2001 and 2010. The coefficient dispersion showed a weak increase in the later part of the study.
In terms of the average effect, the positive contribution of AI to the regional economy shows a decreasing trend with the deep diffusion of telecommunication technology, i.e., the average level of this estimated coefficient gradually decreases from 2001 to 2020. The average enhancement coefficient of AI for regional economic development decreases from 1.215 to 0.779 in 2020. This indicates that the development of AI has had a diminishing effect on the average improvement of the national regional economy. In addition, the dispersion of the regression intensity coefficients of AI on regional economic development in each province gradually decreased from 2001 to 2012, and the dispersion of the coefficients gradually increased since 2013, indicating that the spatial variability of AI on regional economic development is more obvious. This indicates that although AI still shows a positive enhancing effect on regional economic development, the actual effect has a large gap due to spatial differences.
The concentration degrees of the estimated results of AI are different when these samples are under observation, which indicates that significant geographical differences exist in the promoting effect of AI on farmers’ income and regional economic development. In order to present directly the spatial and temporal differences in the impact of transportation on the economy of each province, the spatial heterogeneity of the estimated coefficients of AI variables was explored by combining spatial visualization (
Figure 4 and
Figure 5).
The effect of AI on increasing farmers’ income is stronger in developmentally backward regions than in more developed regions. For example, AI increases farmers’ income more notably in Heilongjiang, Jilin, Shaanxi, Gansu, Qinghai, Xinjiang and Yunnan than in provinces such as Liaoning, Inner Mongolia, Hebei, Beijing, Jiangsu, Anhui, Zhejiang and Shanghai. This means that for regions with a backward economic development, deepening AI is an important way to enhance farmers’ income and thus lift them out of poverty. In provinces such as Heilongjiang, Gansu and Guangdong, AI’ beneficial effect on regional economic growth is more prominent, followed by coastal provinces such as Shandong, Jiangsu, Zhejiang, Shanghai and Fujian.
Synthesizing the results of the above analysis, we believe that the findings are similar to those of Almalki et al. [
34] and Nukala et al. [
35], that is, information technology has led to an accelerated modernization process in the agricultural sector. IoT platforms help farmers to predict farm environmental data, and this information can effectively improve crop productivity and help to enhance farmers’ income and regional economic development.
4.3. Mediating Effect in Agricultural Industry Structure Upgrading
The estimation results of the GTWR model indicate that AI has a positive impact on both farmers’ income and regional economic development in China. So, what are the reasons for this phenomenon? In other words, what is the transmission mechanism by which AI exerts its economic effects? Based on the previous analysis, this section will investigate this transmission mechanism from the perspective of the upgrading path of the agricultural industry structure. To verify H5 and H6, a spatial mediating effect model was established with farmers’ income and regional economic development as the explained variables, AI as the core explanatory variable and agricultural industry structure upgrading as the mediating variable.
Table 7 reports the estimation results of the model of farmers’ income growth effect of AI with agricultural industry structure upgrading as the mediating variable. In these results, the coefficient of the agricultural industry structure upgrading variable in Model 5.2 is significantly positive, which indicates that agricultural industry structure upgrading contributes the farmers’ income growth effect; the coefficient of the AI variable in Model 5.3 is significantly positive, which indicates that AI accelerates, to a considerable extent, agricultural industry structure upgrading. By comparing the estimation results of Models 5.1–5.3, it is found that the process of AI accelerates the upgrading of agricultural industrial structure, which then exerts a significant effect on the growth of farmers’ income, so this empirical result verifies H5.
Table 8 reports the estimation results of the model of regional economic development effect of AI with agricultural industry structure upgrading as a mediating variable. In these results, the coefficient of agricultural industry structure upgrading variable in Model 6.2 is significantly positive, which indicates that agricultural industry structure upgrading greatly contributes to the local economy. A comparison of the estimation results of Models 6.1–6.3 reveals that AI relies on the industrial structure upgrading to play its role in enhancing regional economies, so H6 is correct.
In China, the emergence of information technology has blurred industrial boundaries, and there is even a situation where information technology dominates the development of the agricultural sector, which has promoted the upgrading of the agricultural industrial structure. AI is characterized by high growth, high efficiency and high value-added, which has a strong correlation with the upgrading of the industrial structure. Moreover, the advantages of a strong penetration and deep impact of AI can also strongly promote the upgrading of the industrial structure [
36,
37,
38]. Similar to Yu [
39], the same phenomenon of information technology accelerating industrial structure upgrading exists in China. Therefore, while AI plays a direct role in promoting farmers’ income and regional economic development, it will also play an indirect role by promoting the upgrading of the agricultural industry structure.