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The Effect and Mechanism of Agricultural Informatization on Economic Development: Based on a Spatial Heterogeneity Perspective

Sustainability 2022, 14(6), 3165; https://doi.org/10.3390/su14063165

by Tian Tian¹, Li Li² and Jing Wang^3,*

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Sustainability 2022, 14(6), 3165; https://doi.org/10.3390/su14063165

Submission received: 7 February 2022 / Revised: 26 February 2022 / Accepted: 5 March 2022 / Published: 8 March 2022

(This article belongs to the Section Sustainable Agriculture)

Round 1

Reviewer 1 Report

The article is written on the original topic. The study of the impact of agricultural informatization (AI) on the economy of China is interesting. There are a number of comments on the article:
1. In the "Introduction" section, 4 important Questions are formulated. The authors stated that they would look for answers to them. These answers are interesting. At the same time, 4 Hypotheses were formulated in the "Literature review and hypothesis" section. It is logical to assume that these Hypotheses should coincide (be close in essence) with the Questions. But it's not. It is required to coordinate Hypotheses and Questions.
2. In the "Conclusion" section, it is necessary to disclose the answers to the Questions posed in the "Introduction" (the formulated Hypotheses). This must be done in a free form. Conclusions must be specific. They should correlate with Questions/Hypotheses.
3. Clarification of the title of the article is required. Now it is called "Economic Effect of Agricultural Informationization..." From such a name it follows (and this is described in the text of the article) that, for example, regional economic growth is a consequence of AI. But this fact has not been proven. We can only speak of a correlation between these two phenomena. Causal relationships have not been proven. Perhaps, on the contrary, there is an inverse relationship (regional growth increases welfare and makes AI development possible). It should be more correct, in this regard, to describe the results of the author's research.
The reviewer did not doubt the value of the results. The authors will be able to quickly correct these comments.

Author Response

Dear reviewers of Sustainability,

Thanks a lot for having reviewed our manuscript. We acknowledge your comments and constructive suggestions, which are valuable in improving the quality of our manuscript. We have revised the manuscript according to the reviewers’ comments. We hope, with these modifications and improvements based on the reviewers’ comments, the quality of our manuscript would meet the publication standard of Sustainability. All the revisions are in the manuscript. The explanations regarding the revisions of our manuscript are as follows. In addition, all the revisions are in the manuscript and highlighted in yellow. If you have any more questions about this paper, please contact us without hesitation.

Title

Exploring the effect of urban traffic development on PM_2.5 pollution in emerging economies: Fresh evidence from China

Comments and responses

Reviewer:

The article is written on the original topic. The study of the impact of agricultural informatization (AI) on the economy of China is interesting. There are a number of comments on the article:

Comment 1

In the "Introduction" section, 4 important Questions are formulated. The authors stated that they would look for answers to them. These answers are interesting. At the same time, 4 Hypotheses were formulated in the "Literature review and hypothesis" section. It is logical to assume that these Hypotheses should coincide (be close in essence) with the Questions. But it's not. It is required to coordinate Hypotheses and Questions.

Answer to the reviewer:

Thank you very much for valuable comment. According to your advice, we have reworked the question in the Introduction and these hypothesis in the Literature review and hypothesis.

Action:

Selected paragraphs in Introduction (see Para.3, Section 1, Page 2)

Although informationization has been massively penetrating China’s agriculture as it develops, there is still a lack of a convincing and comprehensive review to assess the impact of AI on farmers’ income and regional economic development, especially in the context of the widely discussed the “productivity paradox of information technology”. Therefore, this study will answer the realistic questions that plagues the development of modern agriculture from two dimensions: the effect of increasing farmers’ income and the effect of enhancing regional economic growth. More specifically, this study is going to answer the questions of: Whether AI spatially dependent? Whether AI has positive effects on economy? Are these effects on farmers’ income or on regional economic development? And in what ways do these effects work?

Selected paragraphs in Literature review and hypothesis (see Para.4, Section 2, Page 3)

Based on the above findings, the following research hypotheses are proposed in this study and will be tested in the subsequent analysis.

Hypothesis 1 (H1): China's AI development has spatial relevance.

Hypothesis 2 (H2): AI technology can exert a positive economic enhancement effect.

Hypothesis 3 (H3): AI improves farmers' income, that is, AI has the effect of increasing farmers' income.

Hypothesis 4 (H4): AI promotes regional economic development, that is, AI exerts a regional economic growth effect.

Hypothesis 5 (H5): AI plays its effect of increasing farmers' income through the upgrading factor of agricultural industry structure.

Hypothesis 6 (H6): AI exerts its regional economic growth effect through agricultural industrial structure upgrading factors.

Comment 2

In the "Conclusion" section, it is necessary to disclose the answers to the Questions posed in the "Introduction" (the formulated Hypotheses). This must be done in a free form. Conclusions must be specific. They should correlate with Questions/Hypotheses.

Answer to the reviewer:

Thank you very much for your careful comment. Based on your suggestions, we have revised the "Conclusion" section by adding specific statements that point out the conclusions of these hypotheses presented in the text.

Action:

Selected paragraphs in Conclusions (see Section 5, Page 16)

At present, China’s agriculture is transcending its traditional form to a modern one. Information technology, which is scientific, fast-evolving, highly penetrative and influential, provides support for the development of modern agriculture. Exploring the economic effect of AI from the perspectives of enhancing farmers’ income and regional economic development is conducive to the sound development of digital agriculture. In this study, the entropy method is adopted to establish AI indicators. Considering the spatial and temporal heterogeneity of AI, the GTWR model is constructed to analyze the effect of AI on farmers’ income and regional economic development. Besides, the transmission mechanism of AI is explored from the perspective of agricultural industry structure upgrading. The estimation results of these empirical models confirm that all six hypotheses hold, and the following conclusions are drawn.

First, during 2001-2020, the level of AI in China increased significantly and showed typical spatial agglomeration characteristics, with spatial correlation strengthening year by year. Meanwhile, there is significant spatial heterogeneity in the impact of AI on economic development.

Second, AI has shown significant economic enhancement, specifically, AI has enhanced farmers' income while also promoting regional economic growth. This suggests that hypotheses 2, 3, and 4 are all valid.

Third, agricultural industry structure upgrading is one of the important ways for AI to play its economic role. Specifically, AI not only directly affects farmers’ income and regional economic development, but also indirectly promotes the upgrading of agricultural industry structure. That is, H5 and H6 are valid.

Comment 3

Clarification of the title of the article is required. Now it is called "Economic Effect of Agricultural Informationization..." From such a name it follows (and this is described in the text of the article) that, for example, regional economic growth is a consequence of AI. But this fact has not been proven. We can only speak of a correlation between these two phenomena. Causal relationships have not been proven. Perhaps, on the contrary, there is an inverse relationship (regional growth increases welfare and makes AI development possible). It should be more correct, in this regard, to describe the results of the author's research.

The reviewer did not doubt the value of the results. The authors will be able to quickly correct these comments.

Answer to the reviewer:

Thank you very much for valuable comment. First, we have revised the title of the manuscript. Based on your suggestion, we revised the title to " The effect and mechanism of agricultural informatization on economic development: Based on spatial heterogeneity perspective". Secondly, the whole text was carefully reviewed to revise misunderstood phrases.

Author Response File: Author Response.pdf

Reviewer 2 Report

The author has constructed models to analyse the effects of Agricultural Informationization on farmers’ income and regional economic development.

1. Table 3, the legend is the Global Moran index of AI in China in 2001-2020. However, data is showing up to 2010.
2. Authors should define Agricultural Informationization at the beginning of the Introduction.
2. Results and discussion; the results is described technically, however interpretation of results is not clear and not discussed, citing literature. The discussion has no supporting literature and comparison of previous related studies.
3. Conclusion is also written like results. The conclusion should highlight the main finding and its future application of the research.

Author Response

Dear reviewers of Sustainability,

Title

Exploring the effect of urban traffic development on PM_2.5 pollution in emerging economies: Fresh evidence from China

Comments and responses

Reviewer:

The author has constructed models to analyse the effects of Agricultural Informationization on farmers’ income and regional economic development.

Comment 1

Table 3, the legend is the Global Moran index of AI in China in 2001-2020. However, data is showing up to 2010.

Answer to the reviewer:

Thank you very much for valuable comment. In fact, we have provided all the data of the Global Moran index of AI in China from 2001-2020 in the table, but we have not specifically labeled it, making this confusing for you. So, we have recompiled Table 4 (original Table 3).

Action:

Selected paragraphs in Results and discussion (see Table 4, Section 4.1, Pages 8-9)

Table 4. Global Moran index of AI in China in the period 2001-2020.

Year	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010
Moran’s I	0.105*	0.106**	0.114*	0.114*	0.114*	0.118**	0.118**	0.121**	0.122**	0.125**
Z value	1.796	1.811	1.909	1.906	1.918	1.965	1.963	2.010	2.044	2.053
Year	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Moran’s I	0.125**	0.130**	0.135**	0.135**	0.138**	0.138**	0.138**	0.140**	0.143**	0.143**
Z value	2.107	2.167	2.176	2.189	2.226	2.231	2.246	2.291	2.309	2.401

Comment 2

Authors should define Agricultural Informationization at the beginning of the Introduction.

Answer to the reviewer:

Thank you very much for your careful comment. According to your suggestions, we have added definition of Agricultural Informationization in the Introduction.

Action:

Selected paragraphs in Conclusions (see Section 5, Page 16)

Comment 3

Results and discussion; the results is described technically, however interpretation of results is not clear and not discussed, citing literature. The discussion has no supporting literature and comparison of previous related studies.

Answer to the reviewer:

Thank you very much for valuable comment. We have added discussion section to the Results and discussion, and have added six classical literatures for comparison.

Action:

Selected paragraphs in Results and discussion (see Section 4.2 and 4.3, Page 5-6)

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 informationization 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 R² 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 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, Figures 2 and 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 Figures 2 and 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 is decreasing until the coefficient reaches 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.

Table 5. OLS estimation results.

Variables	Model 1		Model 2
	LnFI		LnRED
	Coefficients	p-value	Coefficients	p-value
LnAI	1.029	0.000	0.964	0.000
LnIND	-0.015	0.000	0.045	0.097
LnEE	0.003	0.426	-0.143	0.000
LnINV	0.013	0.000	0.122	0.000
LnEP	0.015	0.000	0.422	0.000
C	1.093	0.000	3.939	0.000
R²	0.564		0.536
R² Adjusted	0.561		0.533

Table 6. Estimated results of economic effects of AI based on the GTWR model.

	Variables	Average		Min	25%		50%			75%	Max	IQR
LnFI Model3	LnAI	1.024		0.896	1.018		1.027			1.032	1.069	0.014
	lnIND	-0.010		-0.061	-0.024		-0.013			0.003	0.043	0.027
	lnEE	0.008		-0.059	-0.006		0.005			0.022	0.085	0.028
	lnINV	0.011		-0.039	-0.002		0.009			0.018	0.089	0.020
	lnEP	0.021		-0.071	0.007		0.019			0.032	0.143	0.025
	C	0.000		0.114	-0.004		0.000			0.004	0.028	0.008
	Bandwidth		0.115			Sigma			0.010		R²	0.999
	Residual Squares		0.055			AICc			-3660.86		R² Adjusted	0.999
	Spatio-temporal Distance Ratio							0.542
	Variables	Average		Min	25%		50%			75%	Max	IQR
LnRED Model 4	LnAI	0.970		0.533	0.807		0.963			1.110	1.535	0.303
	lnIND	-0.043		-0.444	-0.176		-0.061			0.055	0.922	0.231
	lnEE	-0.320		-1.376	-0.515		-0.286			-0.082	0.200	0.433
	lnINV	0.073		-0.322	-0.025		0.071			0.168	0.477	0.193
	lnEP	0.400		-0.593	0.299		0.392			0.472	1.196	0.173
	C	0.011		-0.290	-0.028		0.008			0.053	0.220	0.081
	Bandwidth		0.115			Sigma			0.072		R²	0.992
	Residual Squares		3.138			AICc			-1224.61		R² Adjusted	0.992
	Spatio-temporal Distance Ratio							0.642

Note: IQR indicates interquartile range.

Figure 2. Variation trend of estimated coefficients between AI and farmers’ income over time during 2001-2020.

Figure 3. Variation trend of estimated coefficients between AI and regional economic development over time during 2001-2020.

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 (Figures 4 and 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 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.

Figure 4. Spatial distribution of the average effect of AI on farmers’ income.

Figure 5. Spatial distribution of the average effect of AI on regional economic development.

Synthesizing the results of the above analysis, we believe that the findings are similar to 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 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 6. AI and farmers’ income: transmission mechanism of agricultural industry structure upgrading.

Variables	Model 5.1	Model 5.2	Model 5.3
Variables	LnFI	LnFI	LnISU
LnAI	1.034***	1.034***	0.045**
	(684.73)	（680.41）	（2.25）
LnISU		0.005***
		(4.78)
lnIND	-0.006**	-0.006**	-0.238***
	(-3.62)	(-3.73)	（-4.50）
lnEE	0.002*	0.002*	0.034
	(2.98)	(2.99)	（0.65）
lnINV	0.012***	0.012***	-0.010
	(4.61)	(4.59)	（-0.27）
lnEP	-0.003**	-0.003**	0.160***
	(-3.75)	(-3.84)	（3.37）
C	1.021***	1.020***	-0.539**
	(57.13)	(56.70)	（-2.28）
N	600	600	600
R²	0.998	0.997	0.308

Table 6 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 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 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 7. AI and regional economic development: transmission mechanism of agricultural industry structure upgrading.

Variables	Model 6.1	Model 6.2	Model 6.3
Variables	LnRED	LnRED	LnISU
LnAI	1.075***	1.072***	0.045**
	（93.26）	（94.63）	（2.25）
LnISU		0.112***
		（4.91）
lnIND	0.485***	0.518***	-0.238***
	（15.86）	（16.93）	（-4.50）
lnEE	-0.198***	-0.208***	0.034
	（-6.64）	（-7.09）	（0.65）
lnINV	0.114***	0.113***	-0.010
	（5.58）	（5.66）	（-0.27）
lnEP	0.229***	0.209***	0.160***
	（8.32）	（7.65）	（3.37）
C	3.093***	3.131***	-0.539**
	（22.61）	（23.26）	（-2.28）
N	600	600	600
R²	0.816	0.801	0.308

Table 7 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 local economy. A comparison of the estimation results of Models 6.1-6.3 discloses 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 industrial structure. Moreover, the advantages of strong penetration and deep impact of AI can also strongly promote the upgrading of industrial structure [36-38]. 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.

Comment 4

Conclusion is also written like results. The conclusion should highlight the main finding and its future application of the research.

Answer to the reviewer:

Thank you very much for your careful comments and suggestions. Following your suggestion, we have rewritten the conclusions and added the study outlook.

Action:

Selected paragraphs in Conclusions (see Section 5, Page 16)

Conclusions

Selected paragraphs in Policy implications and Outlook (see Section 6.2, Page 17)

6.2. Research Outlook

Our study constructs AI indicators with China as an example, and uses the GTWR model to verify the positive relationship between AI with farmers' income and regional economic development, and further explores the mechanisms. This study provide new paths for emerging economies similar to China to explore rural economic development, while providing fresh and reliable evidence for the development of informatization. Not only that, this paper provides new ideas for other scholars' research around the field of agricultural informatization development.

Author Response File: Author Response.pdf

Reviewer 3 Report

1-the proposed work is not clear in abstract

2-the literature lack of discussion, so I suggest authors improve it with a more recent paper published in 2020, 2021, 2022 such as Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities, A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs and etc. furthermore, authors should create a table to summaries related works

3-tables and figures should be fixed according to paper formate

4- figures 2 and 3 need more explanation

5-how it comes equation 10

6-conclusion must be separated section summaries your work and results

Author Response

Dear reviewers of Sustainability,

Title

Exploring the effect of urban traffic development on PM_2.5 pollution in emerging economies: Fresh evidence from China

Comments and responses

Reviewer:

Comment 1

the proposed work is not clear in abstract

Answer to the reviewer:

Thank you very much for valuable comment. We have rewritten the Abstract in accordance with your comments.

Action:

Selected paragraphs in Abstract (see Abstract, Page 1)

Abstract: As the future direction of modern agriculture, agricultural informationization (AI) and its economic effects are worth exploring because it can well promote digital agriculture. Based on the existing research results, we propose six hypotheses around the economic benefits of AI and construct the geographically and temporally weighted regression (GTWR) model with a sample of 30 Chinese provinces over the period 2001-2020 for validation. Specifically, starting from farmers' income and regional economic growth, the entropy value method is used to construct the AI indicators and GTWR model is constructed to analyze the effect of AI. Furthermore, the transmission mechanism of AI was explored from the perspective of agricultural industry structure upgrading. The following conclusions were concluded. First, the level of AI in China has increased significantly, and meanwhile its spatial correlation has also strengthened year by year. Second, AI demonstrates a positive correlation with farmers’ income growth and regional economic development, which means that it has become an important contributing factor of rural economic output. Third, agricultural industry structure upgrading is one of the important ways for AI to leverage its economic effect. Hence, improving informationization level in rural and agricultural sector through multi-dimensionality is of positive and pragmatic significance for rural economy.

Comment 2

the literature lack of discussion, so I suggest authors improve it with a more recent paper published in 2020, 2021, 2022 such as Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities, A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs and etc. furthermore, authors should create a table to summaries related works

Answer to the reviewer:

Thank you very much for your careful comment. According to your suggestions, we have added a table to summaries related works and cited the high quality literature you mentioned.

Action:

Selected paragraphs in Introduction (see Para.1, Section 1, Page 1)

The information revolution, which began in the 1940s and 1950s, has intensified in recent years, driving profound changes in agriculture and rural areas and forming many new industries and new forms and models of business. Indeed, informationization has affected agriculture and many other industries [1]. Agricultural informatization (AI) refers to the development and application of modern information technology in agriculture in a comprehensive manner, so that it penetrates into the whole process of agricultural production, market, consumption and all specific aspects of rural society, economy and technology. Apart from providing services for agriculture, AI has also enhanced the value of agricultural information resources and has played an important role in narrowing the digital divide between urban and rural areas [2-3]. As a major component of modernization, exploring the economic effects of AI and its mechanism is highly important, which is especially true in China. The Chinese government has always put emphasis on AI. After the victory in poverty reduction, the focus of the “three-rural-issue” work has shifted to rural revitalization, which is also the general grasp of the “three-rural-issue” work in the new era [4]. Moreover, AI is an important direction for rural revitalization. The report of the 19th National Congress of China proposes to promote the simultaneous development of new industrialization, informationization, urbanization and agricultural modernization. The Strategic Plan for Rural Revitalization (2018-2022) clearly proposes to improve the level of AI and consolidate the infrastructure for rural informationization so as to implement the digital rural strategy. In addition, in the Outline of Digital Countryside Development Strategy, it is clearly put forward that the digital countryside will be taken as an important aspect of constructing digital China, and that the development of informationization will be accelerated so as to drive the modernization of agriculture and rural areas.

Selected paragraphs in Literature review and hypothesis (see Para.2, Section 2, Page 3)

Subsequently, the development of AI started to gain attention from researchers. Grimes believed that ICT can help reduce the rural-urban divide, bringing both development and challenges to rural economies [11-12]. Whether rural areas can benefit from telecommunications technology is a question worth thinking about. Malecki found that only some rural areas gained economic development through AI, and that informationization is not a quick solution for rural development in the United States [13]. Some studies in China pointed out that AI will have a direct impact on agricultural production. For example, Zhang and Zhang analyzed China’s rural information and rural economic growth from 1993 to 2002, and concluded that the level of rural informationization directly affects rural economy and serves as a powerful drive for growth [14]. In addition, Zhao and Wen found a positive correlation between rural informationization index and agricultural economic growth in Ningxia irrigation area, China, suggesting that rural informationization in Ningxia is gradually showing an economic promotion effect [15]. The classic literature on the role of information technology for economic development is summarized as Table 1.

Table 1 Summary of existing literature studies

Ref.	Country	Year	Result
[16]	22 OECD countries	2002-2007	There is a significant causal positive link between informatization and economic growth.
[17]	OECD	1996-2007	For every 10 percentage point increase in information technology infrastructure development, the per capita economic growth rate will increase by 0.9-1.5 percentage points
[18]	U.S.	1999-2006	There is a positive relationship between informatization and local economic growth.
[19]	201 countries	1988-2010	A 10 percentage point increase in information technology penetration rate raises real GDP per capita by 0.57 to 0.63 percentage points.
[20]	U.S.	2001-2010	High levels of information adoption are causally associated with higher incomes.
[21]	12 countries	2006-2014	Informatization has a significant positive effect on employment rates, thus increasing the average income of society as a whole.

Selected paragraphs in Results and discussion (see Para. 9, Section 4.2, Pages 13-14)

Comment 3

tables and figures should be fixed according to paper formate

Answer to the reviewer:

Thank you very much for valuable comment. We have modified the format of the figures and tables to conform to the journal's requirements.

Comment 4

figures 2 and 3 need more explanation.

Answer to the reviewer:

Thank you very much for your careful comments and suggestions. Following your suggestion, we have added an explanation of figures 2 and 3 in the text.

Action:

Selected paragraphs in Results and discussion (see Para. 5-6, Section 4.2, Pages 6-8)

Comment 5

how it comes equation 10

Answer to the reviewer:

Equation 9 involves the output share and labor productivity. Since labor productivity has a scale, while the output share has no scale. Therefore, it is necessary to standardize the labor productivity. We choose the "minimum-maximum standardization" method to standardize the labor productivity index, the specific calculation process is shown in Equation 10. This is the reason for showing Equation 10.

Comment 6

conclusion must be separated section summaries your work and results

Answer to the reviewer:

Thank you very much for your careful comment and very helpful suggestion. Based on your suggestion, we have reorganized the conclusion section into two chapters.

Action:

Selected paragraphs in Section 5 (see Section 5, Page 16)

Conclusions

Selected paragraphs in Section 6 (see Section 6, Page 16-17)

Policy implications and Outlook

6.1. Policy implications

Along with the rapid development of China’s agriculture and rural economy, the demand for information in rural areas is growing stronger and stronger. Hence, increasing the informationization level in rural areas through various methods is of positive and pragmatic significance for rural economy. Based on the above findings, the following policy suggestions are proposed.

(1) To improve the efficiency of investment and allocation of AI infrastructure. Demonstration bases of AI should be created. Governments at all levels and agricultural science and technology departments should act on the principle of “pilot, demonstrate and then promote” to build rural informationization demonstration bases, and create a hierarchy of informationization demonstration bases according to local conditions for the goal of sustainable development.

(2) To train technical talents for AI. Farmers are the main force of agricultural production. We make efforts to provide basic skills training and fundamental education to ensure that farmers learn the knowledge of informationization, which then enables them to use informationization platform to inquire information related to the production and sale of agricultural products. Moreover, informationization can also be utilized to transfer the knowledge and skills of rural production and life, cultivate new farmers for a new era, provide more jobs, ensure that all the remaining rural laborers are fully employed, and gradually improve farmers’ own informationization awareness and ability.

(3) To strengthen the integration of information technology and regional special agriculture. It is suggested to 1) take advantage of local features and leverage information technology to pave the road of special AI; 2) use advanced information technology such as 5G technology, cloud computing and big data to build “smart agriculture” and achieve on-demand agricultural production; 3) develop “Internet + rural E-commerce” mode to further expand the sales channels of agricultural products; 4) create an intelligent system for product quality supervision; and 5) connect “the last mile” in rural areas with the help of intelligent logistics technology, thus solving the difficulty in transporting agricultural products.

6.2. Research Outlook

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have addressed all comments. I feel that this manuscript is now acceptable for publication.

Reviewer 3 Report

Accepted in current form. Authors addressed my comments very well

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