Introduction

As one of the core sectors responsible for ensuring national food security and sustaining socioeconomic development, agriculture exerts a substantial impact on the national economy, individual livelihoods, and national security (Dhillon et al., 2010; Winters, 1990)1,2. China is one of the world’s largest agricultural countries, with a long history of farming and a significant contribution to global food production. Although there has been significant growth in agricultural output value and grain production, challenges such as inefficient production processes and severe environmental pollution continue to persist. Agricultural pollution primarily arises from two key factors. Firstly, activities such as the utilization of mechanical energy and the burning of grasslands result in the emission of greenhouse gases， predominantly consisting of CO2. Secondly, there exists non - point source pollution resulting from 10 types of agricultural activities such as chemical fertilizer applications. Existing research indicates that China is the country with the largest pesticide usage globally, however, the effective utilization rate of these pesticides remains below one - third (Sun et al., 2012)3, posing a significant threat to the environment (Chen et al., 2021)4. Meanwhile, according to FAOSTAT, China is the world’s second - largest emitter of agricultural greenhouse gases (GHG), releasing approximately 650 million tons (Mt) of carbon dioxide emissions annually. Therefore, agricultural greenhouse gas emissions and other non - point source pollution have generated considerable negative externalities in China (Xiong H, et al., 2023)5. The development of green agriculture in China is constrained by thecurrent level of the rural labor force and the capabilities for technological innovation. Agricultural production in China is primarily characterized by small - scale farming operations. Traditional small - scale farmers have limited awareness of the potential risks and uncertainties in the use of new factors. They continue to adhere to traditional factor-input methods inherited from their predecessors, resulting in extensive production processes and inefficient pesticide utilization, which collectively impede the advancement of green agriculture development(AGD). Moreover, the large - scale outflow of young and middle - aged rural labor force has resulted in agricultural workforce that is is increasingly involved in part - time employment. Under such circumstances, the gradually decline in the technological innovation capabilities of the agricultural labor force hinders the promotion and application of green production technologies, thereby serving as a practical constraint on the green transformation of agricultural production.

With the development and application of next-generation information technologies, the contribution rate of scientific and technological progress to agriculture has increased significantly. As a driving force of the Fourth Industrial Revolution, artificial intelligence(AI), through its techno - economic characteristics such as permeability, substitutability, synergy, and innovativeness (Liu S Y, 2020), is profoundly reshaping the transformation of the agricultural sector6. Existing research suggests that AIplays a substantial role in reducing carbon emission intensity (Ding T. et al, 2023)7. Through the widespread adoption and application of industrial robots,  advancements in industrial intelligence, , and the integration of smart agriculture, there has been a notable enhancement in labor productivity as well as energy utilization efficiency (Liu J. et al, 2022; Li Y. et al, 2022)8,9. This has mitigated the significant aging of the rural labor force and the low level of technical skills. The extensive adoption of industrial robots has significantly reduced energy consumption, consequently reducing the carbon emission associated with production (Meng X. et al, 2022; Lv H. et al, 2022), and fostering advancements in the AGD10,11. Meanwhile, AI not only addresses the challenge of inadequate human capital in rural areas and transforms the traditional farming practices of small - scale farmers, but it has also gradually evolved into a new driving force for advancements in green tetechnology. This development paves the way for a novel pathway toward green economic growth (Zhao P. et al, 2022)12. Through the application of AI, precise irrigation techniques, and intelligent detection systems, the utilization rates of pesticides and fertilizers have been significantly improved (AlZubi A A. et al, 2023), thereby reducing pollution associated with agricultural production 13. Consequently, smart agriculture has emerged as a crucial component of the AGD .

Regrettably, data from the “2023 Report on the Development of China’s New - Generation AI Technology Industry” indicate that the proportion of AI enterprises within the tertiary industry is as high as 75.79%. In contrast,those in the secondary industry account for 23.82%, while the proportion in the primary industry relatively small. Existing research and applications also predominantly focus on the manufacturing and service sectors. Currently, there exists a notable deficiency in research and applications that directly connect AI with agriculture. This is particularly concerning given that the deep integration of AI into agriculture is regarded as a crucial approach to addressing future challenges within the sector. Consequently, investigating how AI can facilitate agricultural development to achieve the intelligent and green transformation of agricultureis of paramount importance. This endeavor aims to meet the objectives of “carbon peak” and “carbon neutrality”, while also realizing high - quality development, thereby holding significant meaning and value.

Literature review

AI, exemplified by industrial robots, represents a novel resource and technology that is grounded in significant technological advancements. Through the deep learning, AI has found extensive use across a variety of economic and social contexts. The scope and depth of its integration within the economic field have been continuously expanding. The notion of AI was formally presented at the Dartmouth Conference in 1956. This conference characterized AI as the scientific and engineering discipline focused on the development of intelligent machines, especially intelligent computer programs. This definition subsequently catalyzed research within this domain. (Moor, 2006)14. Existing literature has discussed the economic consequences of artificial intelligence. On one hand, AI facilitates enhancements in agricultural productivity (Lakshmi V. et al, 2020; Hamed M A, 2020), contributes to the advancement of digital rural construction (Huang R, 2022), ensures food security (Lee C C. et al, 2024; Ikram A. et al, 2024; How M L. et al, 2020), and promotes rural revitalization as well as the modernization of governance capabilities in rural areas (Gan L.. et al, 2023; Xiao M. et al, 2024). By substituting rural labor with AI, agricultural production efficiency can be significantly improved while concurrently addressing challenges such as labor shortages and environmental sustainability. In rural applications, AI technology has the potential to enhance digital rural construction and promote the modernization of agriculture and rural areas (Huang R, 2022)15. Moreover, AI also plays a crucial role in facilitating food production, ensuring food and food quality (Ikram A. et al, 2024), and alleviating the aging of the rural population (Lee C C. et al, 2024), thereby enhancing the resilience of food security (How M L. et al, 2020)16,17,18. With the conditions for integrating AI into rural construction gradually being established and the technical pathways becoming increasingly clear, Gan L, Xiao M, and colleagues have concluded that,given the rapid development of AI technology, a collaboration between technologies and traditional applications, AI can empower the modernization of rural governance, promote rural revitalization, and achieve sustainable rural development. On the other hand, AI plays a significant role in reducing carbon emission and fostering green development. From a micro - perspective, the dual pressures of government environmental protection and market competition significantly influence enterprises’ motivations to adopt AI technology in the realm of carbon emissions. The stronger this motivation, the greater their capabilities for technology innovation capabilities, ultimately leading to more effective outcomes in utilizing AI to reduce carbon emissions. Meanwhile, green technology innovation exerts a moderating influence on the relationship between AI and corporate carbon emission performance (Chen Y. et al, 2023; Liang P. et al, 2022)19,20. From a macro - perspective, the application of AI promotes the reduction of carbon emission by improving energy efficiency (Ding T. et al, 2023)7. At the same time, the modernization of the industrial structure, the intelligentization of industry, and the development of information infrastructure are critical factors influencing the improvement of the energy efficiency in AI technology innovation (Chen P. et al, 2022)21.

Since the United Nations Development Programme proposed the concept of “green development” in 2002, it has been regarded as an essential pathway for achieving a harmonious integration of environmental protection and economic development (Adam, 2009)22. Agriculture has consistently served as the cornerstone of the national economy. The concept of green agriculture emerged in Europe in 1924, initially referring solely to the green agricultural products that were spontaneously produced by the market to satisfy the needs of a small number of people. American researcher J. I. Rodale was proposed the replacement of traditional agriculture with green agriculture.He emphasized the adoption of new technologies and methods to supplant traditional production, thereby aiming to establish a scientific model of natural circulation. David M. and Blaylock (1986) conducted a pioneering study examining the relationship between the consumption demands for conventional food and green food. They analyzed the factors influencing demand for green food, as well as the cost and expenditure levels that consumers are willing to incur for such products. Michael (1988) examined the cost implications associated with green agricultural production , as well as the risks incurred by exclusively focusing on traditional agricultural industries without engaging in green agricultural development. With the acceleration of China’s industrialization process, attention has increasingly focused on agricultural development and green agriculture within the context of a dual economic structure. While domestic scholars have focused on measuring agricultural carbon emissions and assessing the degree of agricultural green development, they have also analyzed the factors influencing agricultural green development from various perspectives. First, from the perspective of agricultural product circulation, according to the trade environmental effect theory, it is posited that agricultural product trade can influence both the quality of the agricultural environment and the advancement of agricultural green development, and it is a key factor influencing the agricultural green total factor productivity (Xinyu Z, 2024)23. Second, from the perspective of agricultural transformation and upgrading, agricultural digitalization (Shen Z. et al, 2022) and agricultural mechanization (Luo X. et al, 2016) can facilitate the intelligent transformation of this sector. This is achieved by enhancing the quality of the labor force, streamlining logistics development, and promoting industrial integration24,25. This can increase agricultural productivity while simultaneously reducing environmental pollution, thereby serving as crucial driving forces for the advancement of agricultural green development. In addition, from the perspective of agricultural production factors, rural human capital (Ma G. et al, 2023) and agricultural technological innovation (Zhang M. et al, 2023)26,27; Chen Y. et al, 2023) have a significant impact on reducing agricultural carbon emissions28. The aforementioned research provides a valuable reference for constructing the mechanisms through which AI influences green development in agriculture.

Overall,researchers have devoted significant attention to AI and the AGD, and there exists a substantial body of research concerning the related economic impacts. Existing research have confirmed the positive impact of AI on agricultural productivity and rural development, thereby establishing a robust theoretical framework for this paper. Furthermore, from the perspective of production factors, human capital and technology represent two critical elements that significantly influence agricultural development. As a component and important driving force behind the new-quality productive forces, AI can promote the innovative allocation of production factors. Therefore, the potential of AI to promote the AGD, particularly through the enhancement of rural human capital and the ability of technological innovation, presents topics that merit thorough discussion. However, currently, there is a paucity of evidence directly investigating the impact of AI on AGD, and the influencing channels need to be further explored. Therefore, this paper intends to conduct an in-depth analysis and provide an objective assessment of the mechanisms, spatial effects, and heterogeneous impacts of AI on the AGD. This not only aids in understanding the economic effects of AI but also contributes new evidence for relevant research fields. Furthermore, it provides policy references for implementing the fundamental principles of AGD and solving the critical problem of agricultural production pollution.

The marginal contributions of this paper can be Firstly, in the existing literature, green development especially the AGD, is predominantly examined as a mechanism influencing high-quality development. Given China’s extensive cultivated land area and substantial agricultural output, this paper directly investigates the impact of AI on the AGD. This study not only provides a novel perspective on the economic implications of AI development but also contributes to the understanding of technological progress as a pathway for advancing agricultural development. Secondly, this paper a model to assess the impact of AI on the AGD and analyzes the effects in the surrounding areas beyond the local region,thereby exploring its spatial spillover effects, which provides a reference for future research on the AGD. Thirdly, in examining the influencing mechanisms, this paper respectively discusses the interrelationships among labor, technology, and government expenditure from the perspective of production factors,within the context of the relationship between AI and the AGD. Through a mechanism analysis from the perspectives of human capital levels and technological innovation capacity, this study elucidates the underlying mechanisms through which AI influences the ADG. Moreover, this study further elucidates the threshold effect of financial support levels on AGD, thereby providing novel insights into promoting agricultural technological innovation and green development. Fourthly, from the perspectives of major grain-producing areas, the upper, middle, and lower reaches of the Yangtze River, and different geographical regions, this study further clarifies the regional heterogeneity issues of the role of AI in the AGD, thereby providing empirical evidence to support the formulation of targeted policies for promoting the AGD in China.

The remainder of this study is structured as follows. Section two analyzes the internal mechanisms underlying the impact of AI on the AGD and presents the proposed research hypotheses. Section three elaborates expounds on the research methods, model construction, and variable selection. Section four examines the current situation of the AGD. Section five, the empirical results are discussed and analyzed, with further exploration of the spatial spillover effects. Section six presents the conclusions and corresponding policy recommendations. The overall framework of this study is illustrated in Fig. 1.

Fig. 1
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Framework diagram of the article.

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Theoretical analysis and research hypotheses

The channel of “human capital level”

AI exhibits a higher degree of automation compared to traditional machinery, resulting in a more pronounced substitution effect on labor (Chen Yanbin, 2019)29. With the application of AI, the phenomenon of “employment polarization” has emerged which has led to an increased demand for high - skilled labor capable of effectively utilizing the technology (Acemoglu, 2020)30. Simultaneously, AI influences existing domains of human labor through four mechanisms: substitution, enhancement, adjustment, and reconstruction. These influences lead to labor force reallocation across employment sectors and require workers to improve their human capital level in order to meet elevated skill requirements (Chowdhury S, 2023)31. As AI technology becomes deeply integrated into the production process, it can promote the transformation of the labor force structure from physical to cognitive labor, from low - skilled to high - skilled roles, and from regular to irregular tasks. The society’s demand for senior, composite, and cross - border talents has increased, which directly and efficiently promotes the improvement of the human capital level, facilitates the realization of individual value, and thereby contributes to the achievement of strategic results (Brown S, 2024)32. Rural human capital constitutes a critical determinant in driving the growth of agricultural total factor productivity (Wang M, 2021)33. Human capital equipped with corresponding technologies can accelerate the transformation of agricultural scientific and technological achievements into tangible productive forces. Moreover, human capital formed through rural education can effectively facilitate the dynamic matching between agricultural technology choices and factors (Czapiewski K, 2019), thereby improving agricultural technical efficiency and promoting growth in agricultural economy34. In terms of environmental protection, agricultural producers with a higher level of human capital demonstrate greater environmental awareness, pay more attention to environmental pollution problems caused by agricultural production, and are more inclined to choose green production factors, thereby contributing to AGD (Aldieri L, 2019)35. In addition, agricultural producers with a relatively high cultural quality are more likely to realize that the premium associated with improved agricultural product qualityresulting from green production methods outweighs the losses in production profits caused by the reduction of chemical factors (Ren J, 2022)36. They tend to produce green and high - quality agricultural products, which contributes to enhancing the level of agricultural green development (Cai Q, 2024)37. Based on this, the following research hypotheses are proposed:

Hypothesis 1: AI enhances the accumulation of human capital, thereby serving as a key driver of AGD.

The channel of “technological innovation capacity”

Within the “techno - economic” paradigm theory proposed by Freeman and Perez, technological progress is regarded as the fundamental driver of economic structural transformation.AI has already demonstrated the ability to facilitate the transformation of key production factors (Xia L, 2024)38. Firstly, technological innovation depends on the absorption of external knowledge and the creation of novel insights through the integration of internal and external knowledge. It represents the result of the co - evolution between a firm’s external knowledge and its internal knowledge accumulation (Cano et al., 2016)39. AI technology plays a facilitating role in the process of knowledge dissemination and release (Stalidis G. et al., 2015), thereby contributing to the enhancement of technological innovation capabilities40. The application of generative AI, such as large language models, can help agricultural practitioners break through information barriers,  thereby enabling more cost-effective access to high-quality external knowledge. Meanwhile, within an enterprise, AI technology supports the enterprise in analyzing and summarizing the acquired knowledge, and establishing an internal organizational knowledge repository. The innovation, integration, application, and re - creation of knowledge can be multiplied through intelligent methods (Liu Q. et al., 2022)41. Moreover, intelligent technology enhances the synergistic effect of innovative factors during the process of knowledge flow, promotes the diffusion of the knowledge network, realizes knowledge interaction, and drives technological innovation through knowledge platform sharing (Colombelli A. et al., 2018)42. In addition, while AI enhances technological innovation capabilities, it can also promote the improvement of green technology and sustainable technology innovation capabilities (Govindan K. et al., 2022)43. Therefore, AI serves as an important driving force in enhancing technological innovation capabilities. Secondly, AI and the Internet of Things represent the main potential forces driving agricultural transformation and productivity improvement. Technology and innovation (TI) play a crucial role in promoting the improvement of agricultural production efficiency and reducing greenhouse gas emissions (Qayyum M. et al., 2023)44. Additionally, agricultural technological innovation significant spillover effects. With the continuous improvement of agricultural technology levels, the dissemination of agricultural technological innovation among regions has been significantly accelerated, thereby facilitating regional coordinated AGD and promoting the overall improvement in the green level of agriculture (Rong J, 2023)45. Building upon this foundation, the following hypotheses are proposed:

Hypothesis 2: AI effectively promotes the AGD by improving technological innovation capacity.

The threshold effect of the level of financial support for agriculture

Due to the unique characteristics of China’s basic economic system, the impact of fiscal support levels for agriculture on agricultural green development should be further considered. Under the dual - economic structure, agricultural production technology remains relatively backward and production efficiency is low. Fiscal support, as an important policy instrument of promoting economic development, plays a crucial role in promoting agricultural development and narrowing the gap. Therefore, when exploring the impact of AI on agricultural green development, the influence of fiscal support for agriculture cannot be ignored. In the process of promoting agricultural green development, effectively leveraging the benefits of AI technology while reasonably controlling the level of fiscal support for agriculture will enhance the contribution of AI to agricultural green development. On the one hand, fiscal support for agriculture can improve agricultural production conditions and the agricultural environment (Deng H, 2023)46. For example, the subsidy policy for agricultural machinery purchase can generate both an income effect and a multiplier effect. Through the use and popularization of smart agricultural machinery, the combination of factors such as labor and land can be optimized, the efficiency of resource allocation and the level of information utilization can be improved, and agricultural productivity can be enhanced. On the other hand, excessive agricultural fiscal expenditure may lead to over - reliance on government support, thereby reducing the enthusiasm for innovation and self - restraint. Agricultural support funds may incentivize producers to increase the input of chemical - based factors, thereby exerting adverse effects on the environment. For instance, government regulatory and fiscal subsidy policies for the fertilizer industry have distorted the fertilizer factor market, significantly stimulating the emission of non - point source pollutants from fertilizers (Wang X, 2022)47. Therefore, the issue of the “degree” of agricultural fiscal expenditurerepresents a key factor affecting the impact of AI on the level of agricultural green development. Based on this, the following hypotheses are put forward:

Hypothesis 3: The higher the level of agricultural financial support, the weaker the positive promoting effect of AI on the AGD, and a threshold effect exists.

The spatial spillover effect of AI on the AGD

The endogenous growth theory posits that technological innovation functions as a crucial source of knowledge creation and spillover (Romer, 1990). Meanwhile, technological innovation exhibits typical public - good characteristics. It generates spillovers effects through economic interactions, thereby demonstrating a positive externality in enhancing the overall quality of regional economic development (Khezri M, 2021)48. As a major product of technological innovation in the Fourth Industrial Revolution, AI empowers various industries through knowledge recombination and creation. It realizes knowledge - geographical spillover across different domains through the “compound effect” generated by knowledge flow and knowledge spillover (Zhou W. et al., 2024)49. Therefore, it is necessary to conduct spatial analysis. Moreover, environmental pollution exhibits a strong propensity for inter-regional diffusion, a characteristic that is also evident in agricultural production. Areas that are geospatially adjacent are more likely to form pollution agglomerations. The ecological environmental pollution of agriculture in a certain area can exert a diffusion effect on adjacent regions. For example, regions with high fertilizer application intensity not only affect the local agricultural production environment but also impact the agricultural production environment of adjacent regions (Hou M Y, 2021)50. In addition, the policy effects and behaviors impacts in agricultural production areas utilizing AI can provide a basis and reference for the policy implementation in surrounding areas, thereby facilitating the implementation of relevant policies and expanding the promotion and application of AI in the field of agricultural green development. In conclusion, the impact of AI on agricultural green development exhibits a spatial spillover effect.

Hypothesis 4: AI not only promotes the improvement of the local level of AGD, but also contributes to the level of AGD in neighboring regions through spatial spillover effects.

Research methods and research design

Model construction

Benchmark regression model

In order to test the impact of the development of AI on the AGD, a dual fixed-effects model is constructed. The specific model is as follows:

$$\begin{aligned} ADG_{it} = \beta _0 + \beta _1 AI_{it} + \beta _2 Control_{it} + \mu _{it} + \gamma _{it} + \varepsilon _{it} \end{aligned}$$
(1)

Among them, ADG represents the AGD, and AI represents AI.\(\beta _1\) represents the marginal impact of AI on the AGD.\(Contorl_{it}\)are control variables.i and t represent provinces and years respectively. \(\mu _{it}\)is the individual fixed effect, \(\gamma _{it}\)is the time fixed effect, and \(\varepsilon _{it}\)is the random disturbance term.

Mediating effect model

In order to explore the influence mechanism of AI on the AGD, the following model is constructed:

$$\begin{aligned} Z_{it}&= \beta _0 + \beta _1 AI_{it} + \beta _2 Control_{it} + \mu _{it} + \gamma _{it} + \varepsilon _{it} \end{aligned}$$
(2)
$$\begin{aligned} ADG_{it}&= \beta _0 + \beta _1 Z_{it} + \beta _2 AI_{it} + \beta _3 Control_{it} + \mu _{it} + \gamma _{it} + \varepsilon _{it} \end{aligned}$$
(3)

Among them, Z represents the mediating variable, which specifically includes the level of human capital and the ability of technological innovation.

Threshold effect model 

In order to explore the non-linear influence of the level of financial support for agriculture in the impact of the development of AI on the AGD, the following model is constructed:

$$\begin{aligned} ADG_{it} = \beta _0 + \beta _1 AI_{it} I(q_{it} \le \gamma ) + \beta _2 AI_{it} I(q_{it} > \gamma ) + \beta _3 Contorl_{it} + \varepsilon _{it} \end{aligned}$$
(4)

Among them, \(q_{it}\) is the threshold variable, \(\gamma\) is the threshold value.When \(q_{it} <= \gamma\),\(I(q_{it} > \gamma )\)=0,\(I(q_{it} <= \gamma )\)=1;When \(q_{it} > \gamma\),\(I(q_{it} > \gamma )\)=1,\(I(q_{it} <= \gamma )\)=0.

Spatial durbin model

In order to test the spatial spillover effect of AI on the level of green development in agriculture, after a series of tests, a Spatial Durbin Model is constructed. The specific model is as follows:

$$\begin{aligned} ADG_{it} = \beta _0 + \rho \sum _{j=1}^{30} w_{it} ADG_{it} + \beta _1 AI_{it} + \beta _2 \sum _{j=1}^{30} w_{it} AI_{it} + \beta _3 Contorl_{it} + \mu _{it} + \gamma _{it} + \varepsilon _{it} \end{aligned}$$
(5)

Among them, \(\sum _{j=1}^{30} w_{it} ADG_{it}\) represents the spatial spillover effect of the current level of green development in agriculture, that is, the impact of the AGD in the local region on the surrounding areas. In the above formula, \(w_{it}\) is the spatial weight matrix, which is used to represent the degree of connection between regions. The geographical distance matrix is adopted for estimation, and is the impact result of the spatial spillover effect.

Variable selection and data explanation

Explained variable

Agricultural Green Development (AGD), referring to the idea of constructing indicators proposed by Chen Y (2021)4,  is operationalized through an indicator system developed from three: input, expected output, and unexpected output. All the data are sourced from the website of the National Bureau of Statistics and the China City Statistical Yearbook. The indicator system for AGD is shown in Table 1.

Table 1 Indicator system for green development in agriculture.
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The input indicators include the number of agricultural employees, the total sown area of crops, the total power of agricultural machinery, the effective irrigation area, and the net quantity of agricultural fertilizers applied. The output indicator is defined as the proportion of the total agricultural output value of each city relative to its GDP. The unexpected output is represented by agricultural carbon emissions. For the unexpected output of carbon emissions, the IPCC carbon emission coefficient method is adopted. The carbon emissions are calculated by multiplying the specific quantity of each type of carbon source by the corresponding carbon emission coefficient. The specific calculation formula is as follows:

$$\begin{aligned} E = \sum E_i = \sum T_i \cdot \delta _i \end{aligned}$$
(6)

In the above formula, E represents the total amount of agricultural carbon emissions; \(E_i\) represents the carbon emissions of each carbon source; \(T_i\) represents the quantity of each carbon emission source; and \(\delta _i\) represents the carbon emission coefficient of each carbon source.

Explanatory variables: artificial intelligence development (AI)

At present, academia has not reached a unified standard for measuring the development level of artificial intelligence.The primary measurement approaches include the following. First, industrial robot installation data obtained from reports published by the International Federation of Robotics (IFR) are employed as an indicator to assess the application level of artificial intelligence. However, due to the potential lack of overlap between industrial robots and agricultural AI applications, this approach entails significant errors. Second, the policy of innovative application demonstration zones for artificial intelligence is adopted as an indicator of AI application. Nevertheless, within the variables of this study, the number of regions covered by the policy is limited; meanwhile, the subjective selection of policy-implemented regions may violate the quasi-natural experiment assumptions underlying the difference-in-differences method. Third, comprehensive indicator systems are used to measure AI development levels, however, academia has not reached a consensus on the indicator system for AI development. In light of the limitations of the aforementioned measurement indicators, this study measures the level of AI development based on the logarithm of the number of AI enterprises in each city. Additionally, the sum of authorized AI invention patents and utility model patents in each city is used as an alternative indicator for robustness testing.

Mediating variables

The technological innovation capability (Inno) of each province is assessed based on the expenditure on science and technology. The level of human capital (plel) is assessed based on the number of students enrolled in regular institutions of higher learning.

Threshold variables

The level of agricultural financial support is selected as the threshold variable, and it is measured by the ratio of the expenditure on agriculture, forestry, and water affairs to the added value of agriculture, forestry, animal husbandry, and fisheries.

Control variables

According to existing literature, the number of employees in urban units of agriculture, forestry, animal husbandry, and fishery (People), agricultural machinery intensity (AgriM), electricity consumption in rural areas (Elec), the proportion of the primary industry (Prim), population density (PeopD), and the degree of greening (GrenC), fiscal spport level for agriculture (FinaA),are selected as control variables. Among them, agricultural machinery intensity is quantified as the ratio of the total power of agricultural machinery to the sown area of crops. The proportion of the primary industry is quantified as the ratio of the added value of the primary industry to the regional gross domestic product. The population density is quantified as the number of individuals per square kilometer. The degree of greening is assessed based on the green coverage rate of the built-up area. The data on the number of employees in urban units of agriculture, forestry, animal husbandry, and fisheries, electricity consumption in rural areas, and it can be directly obtained.The fiscal support level for agriculture is quantified as the ratio of expenditure on agriculture, forestry and water affairs to the added value of agriculture, forestry, animal husbandry and fisheries.

Descriptive statistics of variables

Considering the availability of data, panel data from 104 cities in the Yangtze River Economic Belt from 2011 to 2023 are selected for regression analysis. The data utilized are obtained from the International Federation of Robotics, the National Bureau of Statistics, the China Labor Statistical Yearbook, the China Rural Statistical Yearbook, and the “2006 IPCC Guidelines for National Greenhouse Gas Inventories (2019 Revision)”. To address missing values, linear interpolation and the average growth rate method are employed for supplementation. The descriptive statistical results of the main variables are shown in Table 2.

Table 2 The results of descriptive statistics.
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Analysis of the current situation of AGD

Selection of methods

In recent years, the Data Envelopment Analysis (DEA) model has been widely applied in the research of ecological efficiency due to its advantages, including the requirement of fewer indicators, higher sensitivity and reliability, and the ability to avoid subjective bias associated with manually determined weights in the final results. However, the DEA model still belongs to the radial and angular DEA measurement methods, focusing solely on a single dimension from the input and output perspectives. When non - zero slacks exist in inputs or outputs, the radial DEA will overestimate the productivity of the evaluated objects, thereby leading biases into the calculation results (Huang Y, 2021)51. Therefore, to address this limitation, the Slack - Based Measure (SBM) model proposed by Tone is adopted. This model not only solves the problem of input - output slackness in traditional DEA but also addresses the issue of evaluating ecological efficiency when considering undesirable outputs (Tone, 2002)52. This approach ensures the accuracy of the resulting measurements.

Analysis of the current situation

To further reveal the changing trend of the AGD in the Yangtze River Economic Belt, the level of AGD is calculated based on the original data of 104 prefecture-level cities in the region spanning the period from 2011 to 2023. According to the standards of the National Bureau of Statistics, the data of the three major regions in the upper, middle, and lower reaches of the Yangtze River Economic Belt are categorized and organized. The specific information is shown in Table 3, Fig. 2, and Fig. 3.

Analysis of the changes in the levels of AGD in each city

Acigis is used to visualize the levels of AGD in each city of the Yangtze River Economic Belt in 2011 and 2023, and the spatiotemporal evolution of each city is analyzed. As illustrated in the following figures, the green development levels of agriculture across cities in the Yangtze River Economic Belt display distinct regional characteristics. During the period from 2011 to 2023, except for a slight decline in the green development level of agriculture in Tongling City, Anhui Province, the green development levels of agriculture in other cities have shown a consistent upward trend.

Fig. 2
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Map of the green development level of agriculture in 2011.

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Fig. 3
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Map of the green development level of agriculture in 2023.

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Analysis of the levels of AGD in each province

Based on data from 104 cities and their corresponding provinces between 2011 and 2023, with each province’s annual data serving as a decision-making unit, the agricultural green development level of each province in each year was measured, and the basic situation is shown in Table 3. From the time-series data, the agricultural green development level in each province has demonstrated a consistent upward trend over the past decade. In terms of the average value of agricultural green development, the 13-year average values of Chongqing, Sichuan, Jiangsu, Hubei, Jiangxi, Yunnan, and Guizhou are relatively high, all exceeding the regions average of the Yangtze River Economic Belt. In contrast, the values for Shanghai, Anhui, Hunan, and Zhejiang are comparatively lower. Meanwhile, the results show that none of the provinces in the Yangtze River Economic Belt have achieved a relatively complete efficiency state. Except for Jiangxi Province with a value of 1.0679 and Guizhou Province with a value of 1.0664 in 2023, which are at relatively high levels, there is still a certain gap between the agricultural green development levels of the remaining provinces and the achievement of complete efficiency.

Table 3 Level of AGD by province.
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Comparative analysis of the level of AGD in the upper, middle and lower rraches

The current situations of the three major regions, namely the upper, middle and lower reaches of the Yangtze River Economic Belt, are illustrated in Fig. 4. From a temporal distribution perspective, the agricultural ecological efficiency of the Yangtze River Economic Belt showed a steady upward trend from 2011 to 2016, although the increase was relatively slow. This can be attributed to the fact that during this period, China actively promoted the construction of agricultural modernization and the improvement of the green development level of agriculture. However, during this process, the diversified governance pattern of the ecological environment in the Yangtze River Economic Belt had not yet been fully established. The traditional agricultural development mode still existed in large numbers. The level of talent education remained relatively low, and the technical ability of agricultural labor force was limited, resulting in a slow growth rate of the green development level of agriculture. From 2017 to 2023, the green development level of agriculture in the Yangtze River Economic Belt generally exhibited a rapid upward trend, accompanied by a significant increase in the growth rate. The reason lies in the fact that during this stage, China gradually implemented a series of major measures to accelerate the high-quality development of the Yangtze River Economic Belt, while placing great emphasis on talent cultivation and scientific and technological innovation. These efforts collectively contributed to an accelerated increase in the green development level of agriculture.

Fig. 4
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The green development levels and growth rates of agriculture in the upper, middle and lower reaches of the Yangtze River Economic Belt.

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From the perspective of regional distribution, as shown in Fig. 5, the levels of AGD in the upper, middle and lower reaches of the Yangtze River Economic Belt are all showing an upward trend. However, as can be observed from Fig. 4, it is not difficult to find that the levels of AGD in both the upper and middle reaches are above the overall average, and exceed those in the lower reaches. This indicates that significant regional disparities exist in the levels of AGD in the Yangtze River Economic Belt. Through an analysis in combination with the actual situation, it can be observed that the degree of agricultural development in the upper reaches remains relatively low,influenced by resource endowments and the level of economic development. The terrain is not conducive to the operation of large-scale machinery,thereby constraining the investment in the total power of agricultural machinery. Therefore, both energy consumption and carbon emissions remain at relatively low levels.Meanwhile, the lower reaches are predominantly plains, with a large area of agricultural planting. A substantial quantity of chemical fertilizers and mechanized production are used to increase the total agricultural output value. The production mode continues to rely on high input and low output, resulting in a relatively low level of AGD in this region, with an insignificant upward trend.

Fig. 5
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The changing trends of the green development level of agriculture in the Yangtze River Economic Belt from 2011 to 2023.

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Analysis of empirical results

Results of the benchmark regression

The panel data of 104 cities in the Yangtze River Economic Belt from 2011 to 2023 were regressed using a double fixed-effect model, and the results are shown in Table 4. After controlling for individual fixed effects, time fixed effects, and variables including the number of employed persons in agricultural, forestry, animal husbandry, and fishery urban units (People), agricultural machinery intensity (AgriM), rural electricity consumption (Elec), the proportion of primary industry (Prim), population density (PeopD), and greenness level (GrenC), fiscal spport level for agriculture (FinaA),the regression results remain significant when both time and individual effects are controlled. The results indicate that the number of employed persons, agricultural machinery intensity, and rural electricity consumption exert negative impacts on agricultural green development, whereas the proportion of primary industry and greenness level exert positive influences. Meanwhile, the significant positive coefficient of artificial intelligence (AI) suggests that AI serves as a key driver in promoting agricultural green development.

Table 4 Results of the benchmark regression.
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Robustness tests

Replacement of explanatory variables

Although the application of AI, such as industrial robots, can improve efficiency and reduce labor intensity, its limitations remain notably pronounced. First, the application of AI in agriculture extends beyond industrial robots. Technologies such as intelligent irrigation systemsexemplify other AI implementations that may offer higher efficiency or lower costs. Focusing solely on robots overlooks these alternative AI technologies. Furthermore, agricultural green development increasingly depends on data-driven decision-making, such as precision fertilization algorithms, which can be realized through non-robotic AI solutions. To address this limitation, this study utilizes the number of authorized AI patents as a proxy for AI application levels in a robustness test. Using Python web crawler technology, we collected AI patents from China’s patent database and categorized them at the city level, yielding the number of authorized AI patents in different cities. This includes both AI invention patents and utility model patents authorized in the current year. By reintroducing the patent count as an explanatory variable into the regression model, the results (as shown in Column 1 of Table 5) show that the coefficient remains significantly positive at the 1% level, thereby validating the robustness of the original findings.

Changing the estimation method

Considering that the AGD may exhibit persistence and hysteresis, a dynamic panel model is adopted to re-examine the sample. The AGD is lagged by one period, and the impact of AI on the lagged AGD is further examined. In the regression results, AR(1) is 0.000, and AR(2) is 0.473. There is no second-order auto-correlation in the random disturbance term. In the results of the Hansen test, the P-value is 1.000, indicating that there is no over-identification problem, and the model specification is reasonable. The coefficient of the first-order lag term is significant at the 1% level, further confirming the validity of the null hypothesis, which indicates that AI has a significant promoting effect on the AGD, and also shows that the original results are robust.

Adding control variables

Fixed asset investment (FAI) and rural consumption level (Consum) are incorporated into the set of control variables. At the same time, the total retail sales of social consumer goods are employed as a proxy for the rural consumption level. The results show that the impact coefficient of the development level of AI on the AGD is still significantly positive at the 1% level, proving that the original results are relatively reliable.

Winsorization treatment

In order to eliminate the possible outliers in the variables, a two-sided winsorization treatment is applied to all control variables, and the results are shown in Table 5. As presented in Table 5, following the winsorization treatment of the variables, the regression coefficients of AI are all significantly positive at the 1% level, and the original regression results still hold.

Table 5 Robustness test results.
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Tests of the influence mechanism

Tests of the mediating effect

Based on the theoretical analysis and research hypotheses presented earlier, both the level of human capital and the ability of technological innovation emerge as important mediating variables that affect the AGD. The regression results of the tests of the mediating effect are shown in columns (2)-(5) of Table 6. The coefficient of AI in column (2) of Table 6 is significantly positive at the 1% level, indicating that AI can promote the improvement of the level of human capital. In column (3), the coefficients of human capital level and artificial intelligence are both significantly positive at the 1% level. Meanwhile, compared with column (1), the coefficient of artificial intelligence becomes smaller (0.1896 < 0.372), indicating that the mediating effect of AI in achieving the AGD through improving the level of human capital is significant, and Hypothesis 3 is verified. This is because AI enhances the ability of farmers and agricultural professionals to adapt to new technologies and knowledge by improving the human capital in the agricultural field, thereby promoting more efficient and environmentally friendly agricultural practices, which in turn strongly advance the AGD. Hypothesis 1 is verified. As shown in column (4) of Table 6, AI demonstrates a significant positive impact on the technological innovation ability of each province. Column (5) indicates that AI improves the level of AGD by promoting the improvement of the technological innovation ability. This is because AI promotes the transformation of agriculture into a more efficient, environmentally friendly, and sustainable model through improving the innovation level in the agricultural field, implementing precision agriculture, and intelligent pest and disease management. It is an important driving force for achieving the AGD, and Hypothesis 2 is verified.

Table 6 Mediating effects test results.
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Threshold effect test

In the benchmark regression, the level of fiscal support for agriculture exhibits a significantly positive effect on agricultural green development,with a coefficient of 0.0168. This indicates that a one-unit increase in fiscal support for agriculture is associated with a 0.0168-unit increase in the level of agricultural green development. To verify Hypothesis 3, the fiscal support level for agriculture was used as a threshold variable, and a panel threshold regression model was employed for empirical testing. Before estimating the threshold model, a panel threshold existence test was first conducted. Table 7 presents the results of the threshold effect test with the fiscal support level for agriculture (FinaA) as the threshold variable. The results show that, after 1,000 bootstrap replications, the fiscal support level for agriculture, serving as a threshold variable, passed the significance test for a single threshold at the 5% significance level,whereas the double-threshold and triple-threshold tests were not passed. Moreover, the threshold estimate fell within the confidence interval. Therefore, adopting a single-threshold regression model is more appropriate in this study. The LR diagram is shown in Fig. 6.

Fig. 6
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Graph of the results of the single-threshold effect test.

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Table 7 Single-threshold effect test for the level of fiscal support to agriculture.
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The regression results of the threshold effect are shown in Table 8. By calculating the threshold value of the level of financial support for agriculture, this study obtains the single - threshold regression results regarding the effects of financial support for agriculture and AI development on the AGD . When the level of financial support for agriculture is below 0.4120, the coefficient of AI on the AGD is 15.12349; when it exceeds 0.4120, the marginal coefficient decreases to 12.98642, and both estimates are significant at the 1% level. According to the results of the threshold regression, when the level of financial support for agriculture reaches the threshold, the marginal effect on the AGD decreases instead. The reason may be that an appropriate level of financial support for agriculture contributes to infrastructure improvement. However, a high - level of financial support for agriculture not only causes excessive resource allocation and waste of resources, but may also lead to excessive dependence on government subsidies in the agricultural sector, reducing the enthusiasm of farmers and enterprises and weakening innovation ability, thereby reducing the impact of AI on the AGD. Therefore, the policy of financial support for agriculture should emphasize strategic planning to ensure they promote both short - term growth and long - term green and sustainable development in agriculture.

Table 8 Single-threshold regression results.
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Further analysis

Test of spatial spillover effects

Spatial auto-correlation test

The existence of spatial correlation in the AGD is a necessary condition for the regression of the spatial econometric model. The Moran’s I index is employed to assess the presence of spatial auto-correlation among the core variables. Table 9 shows the test results based on the geographical distance matrix. It can be seen that the Moran’s I indices from 2011 to 2023 are consistently positive, and in most years, they are statistically significant at the 1% level. Generally, there exists a significant positive spatial correlation between the AGD and the development of AI. Provinces are not independent of each other but have spatial dependence. That is, the AGD in a region is affected by that in neighboring regions. Therefore, neglecting the spatial influencing factors of the AGD in this study may lead to biases in model estimation and empirical conclusions. Hence, it is more appropriate to select the spatial econometric model.

Table 9 Global Moran index for core variables 2011-2023.
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Selection of spatial econometric models

In order to verify the effectiveness of the model specification, a series of tests were carried out on the spatial panel data. The test results based on the economic distance matrix are reported here, and the results are shown in Table 10. The Moran’s I test is significant at the 1% level, indicating that the spatial econometric model is more appropriate for analyzing this panel data compared to the OLS model. In addition, the selected model is determined according to the results of the Lagrange Multiplier (LM), Robust LM, Wald, and LR tests. It can be seen that the LM-error test, LM-error (robust) test, LM-lag test, and LM-lag (robust) test are all significantly positive at the 1% level, suggesting that the spatial Durbin specification is more reasonable. Therefore, the SDM (Spatial Durbin Model) is selected. Subsequently, according to the results of the Hausman test, the two-way fixed effects model is chosen, and the findings indicate that the model must account for both individual and time effects simultaneously. Finally, the statistical values of the LR test and the Wald test are both significantly positive, indicating that the SDM will not degenerate into a spatial lag model or a spatial error model. Therefore, the SDM with two-way fixed individual and time effects is selected for regression.

Table 10 Model applicability tests.
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Decomposition of spatial effects

Table 11 reports the impact of AI on the AGD and the corresponding spatial effect results under the economic distance matrix. The first column of Table 11 shows the impact of the development of AI in the local area on the level of AGD. The results show that the coefficient of AI is significantly positive at the 1% level, indicating that, from a national perspective, the development level of AI in each province exerts a certain impact on on local AGD. As the level of AI continues to expand, the level of AGD also increases steadily. The results in the second column show that the coefficient of the interaction term of AI is significantly positive at the 1% level, indicating that the presence of a spatial spillover effect of AI on the AGD. That is, the higher the development level of AI in adjacent regions, the higher the level of AGD in the local area. This is because AI applications in adjacent regions can generate a technology spillover effect and play a demonstrative role, thereby promoting the application of AI in the local area. Moreover, the cooperation and communication in agricultural AI technology between adjacent regions can enhance interpersonal exchanges, effectively solve the problems related to green agriculture, and promote the transformation of local agriculture towards green development to meet the demand for green agricultural products.

Columns (3) - (5) of Table 11 explore the spatial spillover characteristics of the core explanatory variables, presenting the specific estimation results of the direct effect, indirect effect, and total effect based on the geographical distance matrix. Among them, the direct effect reflects the impact of the development level of AI on the AGD in the local area,while the indirect effect reflects the impact of the development of AI on the AGD in adjacent regions. The results show that both the direct effect and the indirect effect of the application level of AI on the AGD are significantly positive at the 1% level, indicating that the application of local AI not only enhances the level of green development of local agriculture but also significantly enhances the level of AGD in adjacent regions. Judging from the magnitude of the coefficients, the indirect effect is greater than the direct effect, which suggests that the application level of AI promotes the AGD in both the local area and adjacent regions. At the same time, it indicates that spatial spillover effects, such as knowledge diffusion, learning and imitation, are also relatively strong.

Table 11 Impacts and spatial effects of AI on the greening of agriculture.
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Analysis of regional heterogeneity

In fact, due to differences in resource endowments and development stages, both the development of AI and the level of AGD exhibit obvious characteristics of regional heterogeneity in terms of spatial distribution. Therefore, the impact of AI on the AGD may also show regional heterogeneity, necessitating an in-depth discussion on this issue. The analysis primarily proceeds from three perspectives: the three major regions, the functional areas for grain production, and the upper, middle, and lower reaches of the Yangtze River Basin.

Table 12 presents the heterogeneity regression analysis of the three major regions. The results of Model (1), Model (2), and Model (3) show that artificial intelligence (AI) has a significant promoting effect on agricultural green development in central and western regions, with the strongest impact observed in the western region. However, the result of Model (1) indicates that the influence of AI on agricultural green development is not obvious in the eastern region. This result may be attributed to the following aspects: although the eastern region surpasses the central and western regions in terms of economic development, infrastructure, policy support, and technological innovation, its relatively limited land resources and constrained degree of large-scale agricultural operation have restricted the extensive application of AI in agricultural green development. In contrast, the central and western regions are abundant in agricultural resources and characterized by flat terrain, making them suitable for large-scale mechanized operations and intelligent transformation, thereby enhancing the popularity of AI in the agricultural sector.Additionally, the economic structure of the eastern region is dominated by industry and service sectors, whereas those of the central and western regions are dominated by agriculture and animal husbandry. This difference in industrial structure has led to a low degree of agricultural scale in the eastern region, making it difficult to highlight the “marginal effect” of technological applications. As a result, the impact of AI on agricultural green development in the eastern region is not significant. Furthermore, compared with the central and western regions, the eastern region enforces stricter environmental regulations. Even in the absence of extensive application of AI, traditional agriculture remains constrained by environmental protection policies to some extent, thereby limiting the marginal impact of AI on agricultural green development. Therefore, the influence of AI application on agricultural green development is comparatively weaker in the eastern region.

Table 12 Heterogeneity analysis.
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Furthermore, the geographical regions are categorized based on the functional areas for grain production and the upper, middle, and lower reaches of the Yangtze River Basin to examine the impact of AI on the AGD. Columns (1) - (3) of Table 13 show the differences in the effects brought about by AI within different functional areas for grain production. The results indicate that the impact of AI on the AGD is significantly positive at the 1% level in major grain-producing areas, while it is not significant in non-major grain-producing areas. This phenomenon may be attributed to the fact that major grain-producing areas receive greater policy support and financial investment, possess more advanced agricultural technologies, and exhibit a larger agricultural scale and a higher degree of organization development. These factors collectively contribute to the effective implementation of AI technologies, thereby improving both production efficiency and environmental sustainability. In contrast, in other regions, due to limitations in resources, technology, infrastructure, and other aspects, the application and impact of AI technologies are relatively insignificant. Therefore, to promote the widespread application of AI in agriculture, it is necessary to comprehensively consider regional differences, enhance policy and financial support, strengthen infrastructure, and cultivate professional talents in order to achieve the comprehensive AGD.

Columns (4) - (6) of Table 13 show the differences in the impact of AI on the level of AGD within the upper, middle, and lower reaches of the Yangtze River Basin. In the middle and lower reaches of the Yangtze River, AI demonstrates a significantly positive effect on AGD,with a larger impact coefficient observed in the lower reaches. However, in the upper reaches of the Yangtze River, the relationship between AI and AGD is not significant. The primary reason for this phenomenon may lie in the fact that regions in middle and lower reaches of the Yangtze River pay more attention to ecological protection, water resources management, industrial transformation, and scientific and technological innovation. Moreover, policy support and financial investment are relatively concentrated in these river basins, which facilitates the application of AI technologies in agriculture. At the same time, the market demand in these river basins exhibits a stronger preference for green agricultural products, which further drives the application of AI technologies in agriculture.

Table 13 Heterogeneity analysis.
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Conclusions and recommendations

panel data from 104 cities in China between 2011 and 2023, this study examines the impact effect and spatial spillover effect of AI on the AGD from the perspective of production factors. At the same time, it focuses on analyzing the action mechanisms of two production factors, human capital and technology, as well as the threshold effect of the level of financial support for agriculture. The following are the key findings of this study: (1) AI exerts a significant positive influence on improving the green level of agriculture. It not only affects the green level of agriculture in the local area but also generatesbeneficial spatial spillover effects on neighboring areas. (2) The development of AI effectively drives the AGD through twopathways: improving the level of human capital and enhancing the ability of technological innovation. (3) Financial support for agriculture contributes to promoting the AGD. However, when the level of financial support exceeds a certain threshold, the positive moderating effect of AI on the AGD diminishes.(4) The impact of artificial intelligence on agricultural green development in China exhibits significant regional heterogeneity, with more significant effects observed in major grain-producing areas and the middle and lower reaches of the Yangtze River. Furthermore, the application of artificial intelligence demonstrates a spatial spillover effect on agricultural green development. Drawing upon the aforementioned research findings, the following policy recommendations are proposed:

First, encourage the development and promotion of AI technologies in the agricultural field. In the process of future technological progress, the large-scale and intelligent development of agriculture represents an inevitable trend. To align with the current economic development context, at a stage where the intelligence level of agricultural machinery and equipment in China remains relatively low, it is both necessary and significant to develop intelligent agricultural machinery technologies related to intelligent decision-making and precise operation.This will facilitate the intelligent and green development of the agricultural field and support the key development directions of information and intelligence of agricultural machinery and equipment advocated in “Made in China 2025”.

Second, strengthen investment in rural human capital and encourage innovation in green agricultural technologies. Strengthen the training and guidance of green technologies for agricultural producers, enhance the green and scientific production concepts of farmers, and effectively disseminate both technologies and awareness through rural networks to standardize agricultural production operations. At the same time, improve the investment mechanism for relevant scientific research funds in the agricultural field and increase the encouragement and support for scientific research talents. With a secured talent supply, encourage and support the promotion of green agricultural technologies, realize the application of technologies such as waste recycling, and promote the AGD.

Third, appropriately adjust the level of financial support for agriculture and establish financial support policies related to agricultural pollution reduction. The government should appropriately adjust the proportion of financial support based on regional disparities in AI development levels,attract enterprise investment, introduce capital flows, and encourage both enterprises and farmers to engage in green technology innovation. Implement targeted financial incentives to motivate farmers to use environmental protection technologies, strengthen the promotion and application of agricultural environmental protection technologies, stimulate the AGD, and create a good production environment and policy atmosphere.

Fourth, apply AI technologies in a region-specific manner to enhance spatial effects while balancing cooperation and win-win outcomes. On the one hand, encourage the application of AI technologies tailored to the specific conditions of different regions to enable localized and data-driven decision making. On the other hand, strengthen the spatial effects of agricultural green development by promoting regional collaborative cooperation, facilitating complementary utilization of resource advantages, and fostering mutual learning of development experiences. This should cultivate a sound interactive development pattern, encourage provinces to jointly assume responsibilities for environmental maintenance, and promote equitable sharing of the benefits of green ecology.