How Artificial Intelligence Empowers Rural Industrial Revitalization: A Case Study of Hebei Province

Abstract

Industrial revitalization is the foundation and top priority of rural revitalization, and artificial intelligence (AI) serves as a core driver of industrial revitalization. This study analyzes how AI empowers the rural industrial revitalization, and it measures the comprehensive development level of AI and rural industrial revitalization using the entropy-weighted TOPSIS method. Utilizing data on prefecture-level cities in Hebei Province from 2003 to 2023, this research empirically investigates the impact of AI on rural industrial revitalization through a two-way fixed-effects model and a mediation effect model. The findings reveal that AI development significantly promotes rural industrial revitalization, a conclusion that holds after robustness tests. Mechanism analysis indicates that AI facilitates rural industrial revitalization by promoting agricultural technological innovation and driving industrial structural upgrading. Heterogeneity analysis shows that the empowering effect of AI on rural industrial revitalization is more pronounced in areas lagging in technological innovation and in the Functional Expansion Zone of Central and Southern Hebei.

1. Introduction

Rural revitalization is a critical topic in the socioeconomic domain, and it is closely intertwined with sustainable development [1,2]. Industrial prosperity is the foundation for comprehensive rural revitalization [3]. Rural industries include agriculture, rural secondary and tertiary industries and various integrated new forms of business. The focus of its development is to build a modern agricultural industry system; promote the integrated development of primary, secondary and tertiary industries in rural areas; and increase agricultural efficiency, farmers’ income, and all-round rural development. Under the guidance of the new development philosophy and sustainable development theory, China attaches great importance to rural industrial revitalization, which is of great significance to the sustainable development of the rural economy and increasing farmers’ income [4,5]. Therefore, accelerating the transformation of rural industries from traditional extensive, inefficient, and small-profit industries to digitalization, intelligence, and industrial diversification [6,7], constantly improving agricultural total-factor productivity and market competitiveness, and comprehensively promoting high-quality and efficient agriculture, livable and suitable rural work, and common prosperity for farmers have become important tasks to realize the rural revitalization strategy and build an agricultural power. However, to accelerate the revitalization of rural industries, it is far from sufficient to rely only on traditional production methods and conventional industrial structures. At the 20th Collective Study Meeting of the Political Bureau of the 20th CPC Central Committee, it was emphasized that artificial intelligence is a strategic technology leading a new round of scientific and technological revolution and industrial transformation, with a strong “head goose” effect of spillover and driving [8,9,10]. Artificial intelligence, a general-purpose technology that simulates human intelligence [11], is “penetrating” into all walks of life and accelerating industrial progress [12,13,14], and it has brought a positive impact on agriculture and related industries. Application scenarios such as unmanned harvesters, intelligent greenhouses, picking robots, and rural e-commerce show the great potential of artificial intelligence in the revitalization of rural industries [15]. For example, machine learning and computer vision technology can accurately monitor crop growth and environmental changes, achieve precise fertilization and irrigation, and improve crop yield and quality. Shandong Shouguang Vegetable High-Tech Demonstration Park is equipped with intelligent sensors to collect data in greenhouses, and it uses artificial intelligence algorithms to automatically its control equipment, achieving the intelligent management of agricultural production processes [16,17]. Longnan, Gansu Province, has created a “smart agriculture + e-commerce” system, using artificial intelligence to analyze consumer preferences for precision marketing, combined with intelligent logistics system to optimize distribution routes, as well as expanding the sales and market of agricultural products [18]. It can be seen that artificial intelligence has become an important driving force to promote the revitalization of rural industries.

Technological innovation theory holds that technological innovation is an important driving force for economic development [19]. New technologies are characterized by creative destruction and qualitative change, which will bring profound changes to traditional industries and markets. From the perspective of system theory and industrial ecology, the development of artificial intelligence in a region can be regarded as the collaborative progress of core elements such as infrastructure capacity, innovation momentum and application transformation efficiency in a multivariate coupling network. Artificial intelligence (such as machine learning, computer vision, and other technologies) will have a significant impact on agricultural production, processing, marketing, and other areas through data-driven decisions and automated operations [20]. Artificial intelligence technology not only significantly improves agricultural production efficiency but also shapes new forms of modern agriculture through its unique diffusion and systematic characteristics [21,22], ultimately promoting the comprehensive development of rural industries. Therefore, determining how to effectively unleash AI’s potential to boost rural industrial revitalization and clarifying its effects and mechanisms within this context have become crucial and pressing research questions. In light of this, this study takes Hebei Province as a case study to explore the impacts of AI on rural industrial revitalization, posing the following questions: Does AI drive rural industrial revitalization in Hebei? If such an impact exists, what are its underlying mechanisms? Furthermore, taking into account the distinct characteristics of technological innovation levels and functional zones, this study delves into its heterogeneous impacts. This study aims to address these questions, providing valuable insights for comprehension of the development of rural industries in the intelligent era.

This study makes several contributions. (1) Innovation of Research Perspective: Unlike previous studies, which mostly emphasized the impact of the digital economy, industrial integration, human capital, and other factors on rural industrial revitalization, this study innovates to conduct research on artificial intelligence as the core driving force, providing a new perspective for research in the field of rural revitalization. (2) This study abandons the previous single and scattered research ideas, systematically constructing a theoretical framework of artificial intelligence directly driving rural industrial revitalization from the three closely related and progressive dimensions of “resource acquisition”, “processing and transformation”, and “value output”. (3) In measuring the development level of artificial intelligence, this study breaks through the past limitation of using only the installation density of industrial robots as a single index, and it constructs a comprehensive index evaluation system from three dimensions: basic support of artificial intelligence, innovation ability of artificial intelligence, and application transformation of artificial intelligence. (4) Through the heterogeneity analysis of Hebei Province, this study puts forward some suggestions for characteristic measures that can be imitated. Based on the level of regional scientific and technological development, the targeted strategy of “pioneer breakthrough, later transplantation” is proposed. In addition, based on the pattern of functional zones in Hebei Province, closely combined with the development direction of each region, this study proposes the rural industry development strategy of functional fit and dislocation complementarity, which is more targeted and operable than the conventional policy suggestions.

The remainder of this paper is structured as follows: Section 2 systematically reviews the existing literature on AI and rural industrial revitalization. Section 3 elaborates on the theoretical mechanisms and explores the research hypotheses. Section 4 details the design of the econometric models, variable definitions, and data sources. Section 5 employs a two-way fixed-effects model and a mediation effect model to empirically test the hypotheses and conducts a heterogeneity analysis. Section 6 summarizes the main conclusions, proposes policy recommendations, and discusses the limitations of this study.

2. Literature Review

The findings in the existing literature that are closely related to this study can be summarized into three broad categories: The first category focuses on the impact of AI. Existing research indicates that AI’s impact on economies, industries, and socio-cultural aspects exhibits duality; its enabling effects and associated challenges have continued to evolve through dynamic interplay [23]. At the economic level, AI drives growth across sectors by increasing productivity, fostering innovation, and optimizing resource allocation [24]. In the employment domain, some scholars believe that AI enhances labor productivity [25] and creates new employment opportunities [26,27]. However, other scholars believe that, due to the substitution effect of AI on traditional repetitive jobs, it may lead to the disappearance of some low-skilled workers’ positions in the short term and exacerbate income inequality [28]. In China, AI’s contribution to economic growth is higher in the eastern regions compared to the central and western regions. This regional disparity stems from cumulative advantages in digital infrastructure investment, talent density, and industrial ecosystem maturity [29]. At the industry level, AI fuels business model innovation [30,31]. While the existing research often focuses on traditional sectors such as manufacturing, healthcare, and education, AI’s disruptive nature has a particularly significant impact on agriculture and adjacent fields, which has catalyzed trends such as smart agriculture, Agriculture 4.0, and digital twins [32]. In social and cultural aspects, while AI has enhanced the convenience of daily life, it has still raised ethical concerns, including privacy protection, algorithmic bias, cultural homogenization, and potential issues of social equity [33,34]. In general, AI’s positive contributions to economic development, production efficiency enhancement, and improvement of people’s livelihoods are generally considered to outweigh its risks and drawbacks.

The second category of research focuses on rural industrial revitalization. Through sorting out the relevant literature, factors influencing rural industrial revitalization are analyzed at both the macro and micro levels. At the macro level, diverse rural development models and strategic approaches from various countries offer rich experiences and insights. The UK has addressed issues such as rural population outflow and infrastructure deficits through its Rural Planning System [3]. France has established a long-term rural revival mechanism via territorial planning (Aménagement du Territoire) to tackle regional development imbalances [35]. The US has ensured rural economic development through a series of agricultural and rural legislation [36]. South Korea has promoted rural industrialization and alleviated rural poverty through the “Saemaul Undong” (New Village) Movement [37]. These international experiences show that rural industrial revitalization is not a fixed and unified development path but, rather, needs to be based on national conditions and combined with their own rural development situation, with policy planning, institutional system guarantees, industrial cultivation, and other multi-dimensional comprehensive strategies. At the micro level, digital economy, industrial integration, agricultural technology, and human capital are important factors to promote rural industrial revitalization. The digital economy enables agricultural production and sales, optimizes resource allocation, and enhances market competitiveness [38,39]. Industrial integration promotes the agricultural industrial chain, enhances rural economic resilience [40], and can also improve the total-factor productivity of agriculture, which has a positive impact on sustainable agricultural production [41]. The progress of agricultural technology relies on innovative research and development, which significantly improves the efficiency and quality of agricultural production [42]. Equally important, under the “dual carbon” target, it is very important to integrate green technology into rural environmental construction to promote the green and sustainable development of rural industries [43,44]. However, the scarcity of professionals skilled in integrating new technologies within rural areas limits their widespread application. Therefore, cultivating and introducing relevant people is the key guarantee to achieve the revitalization of rural industries [45].

The third category of research focuses on the relationship between artificial intelligence and rural development. Existing studies indicate that AI can boost rural economic growth, ecological governance, service quality, and administrative efficiency. By deploying intelligent automation systems, AI technology significantly enhances agricultural productivity and resource utilization. Expert systems monitor climate and market risks in real time, providing precise early warnings and preventive recommendations to minimize agricultural losses [46]. AI applications in environmental monitoring and water management optimization further promote sustainable ecological development [47]. Furthermore, AI’s implementation in the e-commerce, smart education, and healthcare sectors delivers enhanced public services for farmers [48,49,50]. In rural governance, big data analytics and AI algorithms can optimize resource allocation, while smart village administration systems ensure transparent decision-making processes and improved grassroots governance effectiveness [51].

In summary, while the academic community has conducted considerable research on AI and rural industrial revitalization separately, the following gaps remain: First, regarding the research focus, direct studies examining the relationship between AI and rural industrial revitalization are scarce. Some research explores the application value of AI from an agricultural perspective, offering insights for this study, but it often relies on qualitative research methods, remaining predominantly theoretical. Empirical research, robust testing, and mechanism exploration are notably insufficient. Second, in terms of research objects, there is inadequate attention paid to the Hebei region. Existing studies often concentrate on the national level, lacking specific regional perspectives. As one of China’s significant agricultural production areas, Hebei possesses unique resource endowments, industrial foundations, and development needs. Research specifically targeting rural industrial revitalization in Hebei holds important demonstration significance. Currently, the absence of dedicated studies hinders the full exploration of Hebei’s vast rural industrial development potential under AI empowerment. Therefore, this study argues for the necessity of conducting empirical research to deeply investigate the impact of artificial intelligence on rural industrial revitalization in Hebei and clarify its underlying mechanisms.

3. Theoretical Analysis and Research Hypotheses

The mechanism of artificial intelligence’s role in rural industrial revitalization can be divided into direct and indirect effects, as shown in Figure 1. Direct impact: Artificial intelligence directly promotes the revitalization of rural industries in the three dimensions of “resource acquisition”, “processing and transformation” and “value output”. Indirect impact: AI promotes the revitalization of rural industries through agricultural scientific and technological innovation and industrial structure upgrading, respectively.

3.1. The Direct Impact of AI on Rural Industrial Revitalization

Rural industries rely on agricultural and rural resources, including agricultural production, agricultural processing, and agricultural sales. The primary sector covers farming, forestry, animal husbandry, and fisheries; the secondary sector involves the processing and manufacturing of agricultural products; and the tertiary sector includes distribution services, as well as cultural and tourism industries. Innovation theory emphasizes that the introduction of new technologies can play a revolutionary and propulsive role in traditional industries [19]. Under this theoretical lens, rural industries constitute a significant part of traditional industries. AI, as a key new technological element characterized by its intelligence, can be applied within the practical context of rural industries and integrated into each phase of their value chain [52]. When AI is involved, each link will have a significant positive impact. Therefore, this study explains the direct impact of AI on rural industrial revitalization from three dimensions: “resource acquisition”, “processing and transformation”, and “value output”.

In the resource acquisition phase, rural industries are rooted in traditional agriculture (farming, forestry, animal husbandry, fisheries), utilizing natural resources for activities such as crop cultivation, livestock breeding, and fishing to obtain primary crops. Traditional agriculture is highly reliant on manual experience and natural conditions, suffering from inefficiency and weak risk resilience [53]. The introduction of AI technology provides effective solutions to these problems. Precision agriculture, through the combination of the Internet of Things and deep learning models, can monitor and diagnose crops in real time according to soil moisture, crop growth, and pest conditions, and can regulate the planting process accurately. Drones and autonomous agricultural machinery are based on path optimization AI algorithms to achieve targeted pesticide spraying, as well as precision sowing and harvesting [54,55]. This process replaces traditional, extensive, and inefficient agricultural production and management models, increasing labor productivity, land output rate, and resource utilization efficiency, thereby promoting sustainable agricultural development and enhancing agricultural output.

In the processing and transformation phase, rural industries focus primarily on the processing and manufacturing of agricultural products, converting the primary agricultural raw materials obtained in the resource acquisition phase into products with higher added value. Traditional agricultural processing and manufacturing often rely on manual operations and simple machinery, involving cumbersome processes, inefficiencies, inadequate nutritional and functional properties, imprecise quality control, and potential environmental harm. Computer vision- and reinforcement learning-driven intelligent sorting systems, such as intelligent sorting robots, can accurately sort fruits based on size, color, ripeness, and other indicators, ensuring that only fruits meeting quality standards enter the production line [56]. In rice and wheat food processing, artificial neural networks predict drying curves, precisely controlling temperature, humidity, wind speed, and air volume to optimize drying processes during flour production [57], which can not only reduce energy consumption but also stabilize product quality and achieve green and intelligent production. This intelligent processing approach not only significantly increases processing efficiency and product profitability while reducing labor costs and ensuring food safety [58], it also effectively guarantees the transformation into higher-quality, more diverse processed agricultural products that better meet market demand, adding value to agricultural products.

In the value output phase, the main focus is the sale of agricultural products and the transformation of market value. This link is the key step for rural industries to achieve economic benefits, which are directly related to the rate of return on agricultural production and the income level of farmers. Traditional agricultural products are often faced with problems such as information asymmetry, with single sales channels and marketing means when they are sold, resulting in insufficient market competitiveness of high-quality agricultural products. The intelligent e-commerce platforms, however, leverage big data analytics to analyze consumer purchasing behavior, preferences, and demand trends, enabling precision marketing and helping agricultural products reach more accurately targeted customer groups [59,60]. Simultaneously, the AI-based agricultural product quality traceability system provides consumers with the complete chain information of agricultural products, such as their origin, planting process, and processing [61], so as to enhance the brand of agricultural products and consumer trust therein. This new model of marketing in the sales of agricultural products by artificial intelligence has effectively improved the market conversion rate of agricultural products and promoted the increase in agricultural income.

Based on the above analysis, this study proposes the following research hypothesis:

H1. 

The development of AI can significantly promote rural industrial revitalization.

3.2. The Indirect Impact of AI on Rural Industrial Revitalization

3.2.1. AI Promotes Rural Industrial Revitalization Through Agricultural Technological Innovation

The application of artificial intelligence in rural industry essentially plays the role of a “general-purpose technology”, which has the characteristics of self-learning, strong computing, and rapid iteration. Endogenous growth theory posits that technological progress is an endogenous variable in economic growth [19]. Agricultural technological innovation, characterized by information and biotechnology, encompasses the cultivation of new agricultural varieties, mechanization of agricultural production, and agricultural informatization. AI can significantly elevate the efficiency of agricultural R&D and accelerate the transformation of research outcomes, propelling rural industries toward intelligent, green, and higher-value-added development [62]. On the one hand, the widespread application of artificial intelligence demonstrates technological diffusion capabilities. This enables advanced agricultural technologies to reach broader rural areas and farming communities, while intelligent auxiliary functions lower the barriers to adopting these innovations [63,64]. On the other hand, data accumulated during AI operations—such as land, meteorological, and crop information—can be transformed into shareable and reusable research assets [65,66,67]. This accelerates the development cycles for new agricultural varieties and equipment, thereby advancing the digital transformation of agricultural data assets. The iterative upgrading of agricultural technology is not merely a tool innovation but a systemic transformation encompassing production methods, organizational models, and resource optimization, crucial to elevating rural industries to higher levels. At the micro level, technology suppliers and demanders achieve mutual benefits through new technology transactions [68], enabling the application of innovations into agriculture, and boosting productivity. At the macro level, governments coordinate R&D and promotion based on developmental goals, fostering synergy between technological innovation chains and agricultural industrial chains. This allows agriculture to achieve value-added growth through technological innovation [69], driving rural industrial revitalization.

Accordingly, this study proposes the following hypothesis:

H2. 

AI promotes rural industrial revitalization through agricultural technological innovation.

3.2.2. Artificial Intelligence Promotes Rural Industrial Revitalization Through Industrial Structural Upgrading

A rational and advanced industrial structure is fundamental to sustainable industrial development [70]. With support from artificial intelligence, industries have progressed toward sophistication and diversification. Artificial intelligence promotes the refinement and optimization of the rural industrial division of labor. On the one hand, traditional rural industries primarily focus on the primary sector. The application of artificial intelligence can significantly enhance production efficiency in these sectors, freeing up substantial labor resources. These labor factors are released from the traditional, inefficient agricultural production. This breaks the singular “crop-and-livestock-dependent” model, redirecting resources toward higher-value-added secondary and tertiary industries in agriculture [5,71], thereby advancing rural industrial revitalization. On the other hand, AI technology has catalyzed the emergence of new industries and business models. AI-driven rural tourism, wellness services, cultural innovation, and other emerging sectors have deeply integrated with agriculture [72,73], creating new economic growth points for rural industries. Innovative intermediaries such as “agricultural technology management” and “AI diagnostic services” have diversified rural economic activities. This industrial restructuring not only boosts the overall competitiveness of rural industries but also lays the foundation for sustainable rural economic development.

Accordingly, this study proposes the following hypothesis:

H3. 

AI promotes rural industrial revitalization through industrial structural upgrading.

4. Research Design

4.1. Model Specification

This study employs a two-way fixed-effects model to analyze the impact of AI on rural industrial revitalization in Hebei Province. It is mainly based on the following theoretical basis and empirical adaptability: When studying the relationship between artificial intelligence and rural industrial revitalization, prefecture-level cities in Hebei Province often differ due to their geographical location, endowment of resources, and other differences, i.e., regional characteristics that do not change with time. These characteristics may indirectly affect the revitalization of rural industries by affecting agricultural foundations and other conditions. The use of individual fixed effects can effectively control the heterogeneity in such regions, reduce the estimation bias caused by missing variables, and meet the conditional independence assumption of causal inference. At the same time, rural industrial revitalization is dynamically affected by time-domain factors such as policy regulations and economic environment, i.e., common characteristics that do not change with regions. By introducing dummy variables, time fixed effects can control the common interference of macro policy changes and economic cycle fluctuations faced by different regions at the same time, so as to more clearly show the real effects of artificial intelligence. Therefore, this study constructs the following baseline regression model: rural industrial revitalization (Rural) as the dependent variable and artificial intelligence development level (AI) as the core independent variable, with economic level, fiscal expenditure, resource consumption, urban infrastructure, and openness to the external world as control variables. The model is specified as follows:

$${\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{A}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+\mathsf{\lambda }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\mathrm{u}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

where i denotes the region and t denotes the year; ${\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ represents the level of rural industrial revitalization in prefecture-level city i in year t, ${\mathrm{A}\mathrm{I}}_{\mathrm{i}\mathrm{t}}$ represents the AI development level in prefecture-level city i in year t; ${\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ denotes the control variables, ${\mathsf{\alpha }}_0$ is the intercept term, and ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ is the stochastic disturbance term. Individual fixed effects and time fixed effects are included in the regression to control for city-specific factors that do not change over time and time-specific factors that do not vary across regions, respectively.

Based on the preceding theoretical analysis, and considering the mediating effects of agricultural technological innovation and industrial structural upgrading, mediation effect models are constructed based upon Model (1) to examine the mechanisms through which AI influences rural industrial revitalization in Hebei. The models are specified as follows:

$${\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{m}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{A}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+\mathsf{\lambda }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\mathrm{u}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

$${\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathsf{\delta }}_0+{\mathsf{\delta }}_1{\mathrm{A}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+\mathsf{\delta }{\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{m}}_{\mathrm{i}\mathrm{t}}+\mathsf{\lambda }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{v}}_{\mathrm{i}}+{\mathrm{u}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

where ${\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{m}}_{\mathrm{i}\mathrm{t}}$ represents the mediating variables, specifically including agricultural technological innovation and industrial structural upgrading. All other indicators retain the same definitions as in Model (1).

4.2. Variable Selection

4.2.1. Dependent Variable

Rural Industrial Revitalization (Rural): Different scholars have constructed different evaluation systems of rural industrial revitalization indices, according to their different understanding of the connotations and the needs of their research purposes. Tian Ye believes that the core of rural industrial revitalization lies in agriculture and constructed an indicator system from three dimensions: agricultural output increase, value increase, and income increase from the perspective of high-quality agricultural development [74]. Huang Dunping et al. constructed an indicator system from three dimensions—the agricultural product industry system, multi-functional agricultural industry system, and agricultural support industry system—based on the scientific connotations and practical requirements of rural industrial revitalization [75]. Liu et al. selected their indicator system from three dimensions: rural industrial production system, functional system, and support system [76]. Based on the existing research, and in accordance with the requirements of the Rural Comprehensive Revitalization Plan (2024–2027), this study holds that rural industrial revitalization aims to build a stable agricultural foundation, efficient output, and a multi-functional and sustainable modern rural industrial system, which specifically includes four aspects: agricultural production scale, agricultural output benefit, rural industrial function, and industrial chain extension. Agricultural revitalization is the basic connotation of rural industrial revitalization. As a basic dimension, the agricultural production scale provides basic support for rural economy and is related to food security and the foundation of economic stability. Agricultural production volume and scale are measured by indicators such as total agricultural output value and sown area. The key task of agricultural industry is to improve the efficiency of production processes and resource utilization, and to improve the output effect. Therefore, the dimension of agricultural production efficiency is measured by agricultural mechanization rate and yield per unit area. The multifunctionality of rural industry can promote increases in farmers’ income, maintain ecological balance, and promote social equity. The multi-dimensional contribution of rural industry can be comprehensively evaluated based on rural residents’ income, fertilizer application intensity, and urban–rural income gap. The extension dimension of the industrial chain focuses on the sustainable development of rural industries; by expanding the development space of the agricultural industry chain, it promotes the long-term development of the rural economy, which is measured by the level of agricultural service industry and the industrial structure of rural areas. According to the above content, and in accordance with the principles of hierarchical variable selection, scientificity, and data availability, this study constructs an index system for rural industrial revitalization based on four dimensions: agricultural production scale, agricultural output benefit, rural industrial function, and industrial chain extension (as shown in

Table 1. Evaluation system for rural industrial revitalization.

Target	System	Indicator	Calculation Formula	Attribute	Weight
Agricultural Production Scale	Agricultural Output Scale	Gross Agricultural Output	Gross Output Value of Farming, Forestry, Animal Husbandry, and Fishery	Positive	0.095
		Share of Agricultural Economy	Gross Agricultural Output/Regional GDP	Positive	0.066
	Crop Planting Scale	Total Crop Sown Area	Sown Area of Major Crops	Positive	0.092
		Per Capita Sown Area	Total Crop Sown Area/Total Population	Positive	0.099
Agricultural Production Efficiency	Agricultural Equipment Level	Agricultural Mechanization Rate	Total Agricultural Machinery Power/Total Crop Sown Area	Positive	0.055
		Comprehensive Irrigation Capacity	Effective Irrigated Area/Total Crop Sown Area	Positive	0.024
	Agricultural Output Efficiency	Grain Yield per Unit Area	Grain Output/Grain Crop Sown Area	Positive	0.024
		Cash Crop Yield per Unit Area	Oil Crop Output/Oil Crop Sown Area	Positive	0.037
Rural Industrial Functionality	Economic Function	Rural Disposable Income	Per Capita Disposable Income of Rural Residents	Positive	0.14
	Ecological Function	Fertilizer Intensity per Unit Area	Pure Fertilizer Application/Total Crop Sown Area	Negative	0.04
	Social Function	Urban–Rural Income Gap Ratio	Urban Disposable Income/Rural Disposable Income	Negative	0.016
Industrial Chain Extension	Agricultural Service Level	Share of Agricultural Services	Output Value of Agri-Support Services/Gross Agricultural Output	Positive	0.202
	Industrial Structure Level	Primary Industry Output Share	Added Value of Primary Industry/Regional GDP	Positive	0.075
		Primary Industry Growth Rate	Index of Primary Industry Added Value	Positive	0.034

).

When assessing the level of rural industrial revitalization, the entropy-weighted TOPSIS method was adopted to ensure objective and comprehensive measurement. This method integrates the advantages of both the entropy weight method and TOPSIS. The entropy weight method objectively determines indicator weights based on the degree of data variability, effectively avoiding subjective weighting bias. The TOPSIS method comprehensively evaluates the relative merits of all alternatives by calculating their closeness to the ideal solution through objective ranking. The specific implementation steps are as follows:

Step 1: Standardize the data first.

$${\mathrm{Z}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-\min \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}{\max \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)-\min \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}}$$

(4)

Step 2: Calculate the proportion of item j in year i.

$${\mathrm{p}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{z}}_{\mathrm{i}\mathrm{j}}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}}}}$$

(5)

Step 3: Calculate the information entropy of each index and the redundancy of information entropy of each index.

$${\mathrm{e}}_{\mathrm{j}}=-{\displaystyle \frac{1}{\ln (\mathrm{n})}}\sum _{\mathrm{i}=1}^{\mathrm{n}}\left[{\mathrm{p}}_{\mathrm{i}\mathrm{j}}\times \ln \left({\mathrm{p}}_{\mathrm{i}\mathrm{j}}\right)\right]$$

(6)

$${\mathrm{d}}_{\mathrm{j}}=1-{\mathrm{e}}_{\mathrm{j}}$$

(7)

Step 4: Calculate the weight of indicators.

$${\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{d}}_{\mathrm{j}}}{\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{d}}_{\mathrm{j}}}}$$

(8)

Step 5: Calculate the weighted matrix.

$${\mathrm{r}}_{\mathrm{i}\mathrm{j}}={\mathrm{z}}_{\mathrm{i}\mathrm{j}}\cdot {\mathrm{w}}_{\mathrm{j}}$$

(9)

Step 6: Calculate the positive and negative ideal solutions.

$${\mathrm{V}}_{\mathrm{j}}^{+}=\max \left\{{\mathrm{r}}_{1\mathrm{j}},{\mathrm{r}}_{2\mathrm{j}},\dots ,{\mathrm{r}}_{\mathrm{n}\mathrm{j}}\right\};{\mathrm{V}}_{\mathrm{j}}^{-}=\min \left\{{\mathrm{r}}_{1\mathrm{j}},{\mathrm{r}}_{2\mathrm{j}},\dots ,{\mathrm{r}}_{\mathrm{n}\mathrm{j}}\right\}$$

(10)

Step 7: Calculate the Euclidean distance.

$${\mathrm{s}}_{\mathrm{i}}^{+}=\sqrt{{\sum _{\mathrm{j}=1}^{\mathrm{n}}\left({\mathrm{V}}_{\mathrm{j}}^{+}-{\mathrm{r}}_{\mathrm{i}\mathrm{j}}\right)}^2};{\mathrm{s}}_{\mathrm{i}}^{-}=\sqrt{{\sum _{\mathrm{j}=1}^{\mathrm{n}}\left({\mathrm{V}}_{\mathrm{j}}^{+}-{\mathrm{r}}_{\mathrm{i}\mathrm{j}}\right)}^2}$$

(11)

Step 8: Calculate the overall score

$${\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\displaystyle \frac{{\mathrm{s}}_{\mathrm{i}}^{-}}{{\mathrm{s}}_{\mathrm{i}}^{+}+{\mathrm{s}}_{\mathrm{i}}^{-}}}$$

(12)

4.2.2. Independent Variable

Artificial Intelligence (AI): Currently, academia often measures the development level of AI using industrial robot density [77,78]. However, due to its limitations in narrow statistical scope and single-dimensionality, relying solely on this indicator fails to comprehensively reflect the multi-dimensional characteristics of AI technology. Therefore, building on existing research, this study constructs an evaluation system for AI development across three dimensions: AI foundational support, AI innovation capability, and AI application transformation (as shown in

Table 2. Evaluation system for the development level of AI.

Dimension	Indicator Description/Explanation	Measurement Method	Weight
AI Foundational Support	Level of New Digital Infrastructure	Ratio of keywords related to new digital infrastructure in government work reports	0.142
AI Innovation Capability	Number of AI Patent Applications	Count of AI patent applications	0.392
AI Industrial Scale	Number of AI Enterprises	Count of enterprises with AI-related business scopes	0.467

). The calculation method is similar to that of rural industrial revitalization.

The dimension of AI infrastructure support is measured by the level of new digital infrastructure. New digital infrastructure (e.g., 5G, cloud computing, big data platforms) is the underlying support for the development of artificial intelligence, and the powerful computing power, data transmission, and resource allocation required for the development of artificial intelligence all depend on it. The level of new digital infrastructure reflects the basic conditions for the development of artificial intelligence. On the one hand, it can improve data acquisition, processing, and storage capabilities; on the other hand, it can also promote the deep integration of AI and the real economy, providing a solid technical foundation and operating environment for the development of AI. Therefore, this study takes the level of new digital infrastructure as an indicator to measure the basic support level of AI development. This study draws on the research of Chao XiaoJing and Wu Fei [79,80], and the specific methods are as follows: First, obtain the municipal government work report and convert its text content into a txt file with UTF-8 encoding. Next, a keyword txt file is constructed using the new digital infrastructure-related words summarized by Chao Xiao Jing. Then, the Jieba word segmentation is performed on the government work report txt file using Python 3.12.4, in which punctuation marks are removed and individual words are not counted, and new digital infrastructure-related words and total word frequency are counted. Finally, the ratio of keyword frequency to total word frequency is used to measure the level of new digital infrastructure.

The dimension of AI innovation capability is measured by the number of artificial intelligence patent applications. Patents represent an important index to evaluate innovation ability and technological progress, and their quantity and quality directly reflect regional innovation ability and technological activity. From the perspective of the development of artificial intelligence, AI patents reflect not only the phased technological innovation achievements but also the communication and application potential of AI among different subjects. On the one hand, each AI patent represents a technological advance. On the other hand, the accumulation of a large number of patents provides knowledge reserves and technical references for the subsequent development of artificial intelligence technology, accelerating the iteration and upgrading of technology. Therefore, this study refers to the index of Jing Xiao to measure local AI innovation ability based on artificial intelligence patents in various cities [81]. The specific approach is as follows: First, the patent numbers of artificial intelligence in the “Classification System of Key Digital Technology Patents (2023)” are sorted out, and the duplicates are removed. Then, the patent retrieval service of the State Intellectual Property Office is used to retrieve patents according to the application time range, the province and city of the applicant, and the patent number under the advanced retrieval column.

The dimension of AI application transformation is measured by the number of artificial intelligence enterprises. Enterprises are the main body of technology transformation and the key promoter of market applications, and artificial intelligence enterprises are an important bridge for artificial intelligence technology, from research and development to practical application. The number of AI enterprises reflects the acceptance and promotion of AI technology in the market. In addition, the agglomeration of AI industry can produce scale and knowledge spillover effects, accelerate the adaptation of AI technology to different industrial scenarios, and promote the efficient application of AI technology in more fields. Therefore, this study refers to the research of Wang Linhui and takes the number of AI enterprises as a measure of the transformation level of AI application [82]. The specific approach is as follows: Using the advanced search feature in Tianyancha’s enterprise information database, users can enter keywords to search for companies. When a company’s business scope involves AI-related terms such as chips, image recognition, computer vision, speech recognition, or sensors, it will be identified as an AI enterprise. Subsequently, users can further filter by location and establishment date to narrow down their research targets.

4.2.3. Mediation Variables

Agricultural Technological Innovation

This study measures agricultural technological innovation using the number of agricultural technology patent applications (retrieved via patent classification code A01). Innovation in agricultural science and technology is one of the core driving forces for promoting rural industrial revitalization. Patent applications need to go through strict and rigorous examination procedures, and only technologies with certain innovativeness and practicability can obtain patent authorization. Therefore, the number of agricultural technology patent applications can relatively objectively reflect the actual quality of innovation in agricultural science and technology.

Industrial Structural Upgrading

Essentially, industrial structural upgrading is a process in which the industries of a region transform from low added value to high added value, and from a low technical level to a high technical level. Compared with the primary industry, the secondary and tertiary industries usually have higher added value and technical content. Therefore, this study selects the ratio of the sum of the added values of the secondary and tertiary industries to the added value of the primary industry to represent industrial structural upgrading.

Limitations and countermeasures: Due to data availability and differences in index selection, there are limitations in mediating variables. For example, although the number of agricultural technology patent applications can reflect the explicit results of regional technological innovation, it may be difficult to comprehensively consider the transformation and application effect and comprehensive influence of agricultural science and technology in actual production. The ratio of the sum of the added value of the secondary and tertiary industries to the added value of the primary industry to represent the upgrading of the industrial structure can directly reflect the overall trend of the industrial focus shifting from agriculture to industry and service industry, but it may not accurately reflect the degree of optimization of the internal structure of the industry and the quality of collaborative development among industries. Future research can try to integrate multiple indicators such as R&D expenditure and the proportion of employees, so as to more comprehensively describe agricultural scientific and technological innovation and industrial structure upgrading.

4.2.4. Control Variables

To address potential omitted-variable bias that could affect the baseline regression results, the following variables potentially influencing rural industrial revitalization are controlled for: economic level, fiscal expenditure, resource consumption, urban infrastructure, and openness to the external world; these are measured as follows: the logarithm of per capita GDP for economic development level, the logarithm of the share of fiscal expenditure in GDP for government fiscal expenditure, rural electricity consumption for resource consumption level, the logarithm of the ratio of total road length within the jurisdiction to permanent residents for urban infrastructure, and the logarithm of the ratio of foreign direct investment (FDI) to GDP for the degree of openness to the external world.

4.3. Data Sources and Descriptive Statistics

The study sample comprises 11 prefecture-level cities in Hebei Province, focusing on the level of rural industrial revitalization, AI development, and their interrelationship within Hebei from 2003 to 2023. Data were sourced from statistical yearbooks, the EPS database, government work reports of Hebei cities, the Tianyancha enterprise information database, and the National Intellectual Property Administration. Among them, the variables of artificial intelligence were derived from the government work reports of various cities in Hebei Province, the enterprise information database of Tianyancha, and the State Intellectual Property Office. The specific methods have been explained in the Explanatory Variables section of the previous article, so they will not be repeated here. For very few missing data, for example, the missing proportion of the government work report is 4.76%, linear interpolation is used. Quantitative variables were treated with logarithms. The comprehensive level of artificial intelligence and rural industrial revitalization was measured by the entropy-weighted TOPSIS method. Descriptive statistics are shown in

Table 3. Data sources and descriptive statistics.

	Obs	Mean	Std. Dev.	Min	Max
Rural Industrial Revitalization	231	0.253	0.113	0.088	0.552
Artificial Intelligence	231	0.055	0.087	0.002	0.725
Economic Level	231	7.490	0.767	5.460	9.172
Fiscal Expenditure	231	8.328	0.896	6.283	9.746
Resource Consumption	231	12.740	0.794	10.586	14.242
Urban Infrastructure	231	3.892	0.440	3.264	4.904
Openness to External World	231	8.090	1.018	3.879	9.573

.

5. Results

5.1. Baseline Regression

This study uses a two-way fixed-effects model to conduct an empirical analysis on the panel data of 11 prefecture-level cities in Hebei Province from 2003 to 2023, aiming to explore the direct impact of the development level of AI on rural industrial revitalization. Prior to the use of bidirectional fixed effects, multiple collinearity tests and Hausman tests were performed. According to the results of the multiple collinearity tests, the variance inflation factor of each variable is less than 10, and the average variance inflation factor is 4.62, which is less than 5, indicating that there is no serious multiple collinearity. According to the multiple Hausman test results, the P value is less than 0.05, which further verifies the rationality of using a fixed-effects model. The baseline regression results are shown in

Table 4. Baseline regression results.

	(1)	(2)	(3)
AI	0.492 ***	0.448 ***	0.448 ***
	(0.096)	(0.095)	(0.132)
Economic Level		−0.059	−0.059
		(0.065)	(0.113)
Fiscal Expenditure		−0.074	−0.074
		(0.058)	(0.098)
Resource Consumption		−0.077 ***	−0.077 *
		(0.027)	(0.035)
Urban Infrastructure		0.594	0.594
		(1.086)	(1.308)
Openness		0.003	0.003
		(0.014)	(0.028)
Time FE	YES	YES	YES
Province FE	YES	YES	YES
_cons	0.226 ***	−0.070	−0.070
	(0.007)	(4.309)	(4.711)
N	231	231	231
R2	0.587	0.626	0.626

Note: *, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Values in parentheses in Columns (1) and (2) are standard errors; Column (3) reports clustered robust standard errors.

. Column (1) presents the regression results with fixed effects controlled, and the results indicate that the regression coefficient of the AI index is significantly positive at the 1% significance level. Column (2) shows the regression results after adding control variables. Since clustered standard errors can adjust the correlation of error terms and provide more robust regression results, Column (3) uses clustered robust standard errors after adding control variables. The regression coefficients of the artificial intelligence index in Columns (2) and (3) are still significantly positive at the 1% significance level, which suggests that artificial intelligence can significantly promote rural industrial revitalization in Hebei, thus verifying Hypothesis H1 proposed in this study.

5.2. Robustness Tests

5.2.1. Modifying Regression Models

To verify the robustness of the baseline regression results, regression analysis was re-conducted by modifying the regression model. The regression results of the Tobit model in Column (1) and the CLAD model in Column (2) of

Table 5. Robustness Tests Results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Tobit	CLAD	AI-PCA	AI-Entropy	Excl. Pandemic	DV Winsorized	IV Winsorized	Both Winsorized
AI	0.343 ***	0.346 ***	0.529 ***	0.240 ***	0.956 ***	0.426 ***	0.921 ***	0.854 ***
	(0.094)	(0.012)	(0.198)	(0.081)	(0.177)	(0.091)	(0.179)	(0.173)
Economic Level	−0.054 **	−0.015 ***	−0.060	−0.064	−0.089	−0.062	−0.075	−0.077
	(0.026)	(0.004)	(0.068)	(0.065)	(0.065)	(0.063)	(0.065)	(0.063)
Fiscal Expenditure	−0.016	−0.000	−0.089	−0.071	−0.070	−0.058	−0.044	−0.030
	(0.015)	(0.003)	(0.061)	(0.058)	(0.063)	(0.056)	(0.058)	(0.056)
Resource Consumption	0.030 *	0.046 ***	−0.084 ***	−0.086 ***	−0.060 **	−0.079 ***	−0.066 **	−0.069 ***
	(0.018)	(0.003)	(0.028)	(0.026)	(0.029)	(0.026)	(0.027)	(0.026)
Urban Infrastructure	0.086 ***	0.076 ***	0.118	0.256	−0.155	0.585	0.256	0.254
	(0.031)	(0.006)	(1.119)	(1.075)	(2.493)	(1.046)	(1.067)	(1.032)
Openness	−0.004	−0.024 ***	−0.001	−0.000	0.009	0.003	0.006	0.006
	(0.015)	(0.003)	(0.015)	(0.014)	(0.013)	(0.014)	(0.014)	(0.014)
Time FE			YES	YES	YES	YES	YES	YES
Province FE			YES	YES	YES	YES	YES	YES
_cons	0.088	−0.377 ***	1.745	1.402	2.755	−0.114	0.925	0.887
	(0.235)	(0.042)	(4.445)	(4.258)	(9.643)	(4.149)	(4.238)	(4.095)
N	231	231	231	231	198.000	231	231	231
R2			0.598	0.606	0.688	0.629	0.633	0.634

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Values in parentheses are clustered robust standard errors. This notation convention applies consistently to subsequent tables.

show that the impact coefficients of the core independent variable “AI” on rural industrial revitalization are both positive and significant at the 1% level. This indicates that AI has a significant promoting effect on rural industrial revitalization, which is consistent with the direction of the baseline regression results.

5.2.2. Replacing Indicator Measurement Methods

Different indicator measurement methods may lead to differences in research results. To further ensure the reliability of the research results, this study replaces the measurement method of AI level. Columns (3) and (4) of Table 5 adopt principal component analysis and the entropy method, respectively, to recalculate the AI level index before conducting regression analysis. The results show that the impact coefficient of the independent variable “AI” on rural industrial revitalization remains positive and reaches the 1% significance level, indicating that the conclusion is still robust.

5.2.3. Excluding Samples from Specific Time Periods

The COVID-19 pandemic may have impacted the rural industrial ecology. During the pandemic, lockdown measures and traffic controls led to poor circulation of agricultural materials and restricted cross-regional mobility of the labor force. To eliminate the interference of data during the pandemic in the overall regression results, this study excluded sample data from the period after 2019 (the pandemic period) and re-conducted the regression analysis. The regression results in Column (5) of Table 5 show that, after excluding the special factor of the pandemic, the coefficient of the independent variable “AI” is still significant, and the research conclusion remains valid.

5.2.4. Data Winsorization

Outliers may cause bias in estimation results. To reduce the unreasonable impact of potential outliers in the data on the regression results, this study conducts 2% winsorization on the independent variables and dependent variables before performing the regression. As shown in Table 5, Columns (6), (7), and (8) represent dependent-variable winsorization, independent-variable winsorization, and double winsorization, respectively. The results show that, except for certain fluctuations in the coefficient values due to different winsorization methods, the regression coefficients remain positive and significant at the 1% level, which is highly consistent with the baseline regression results, indicating that the original regression results are not affected by extreme values.

5.3. Endogeneity Test

Although a range of control variables have been considered in this study, endogeneity issues may still exist. On the one hand, key control variables may be omitted, which may interfere with the research results. On the other hand, there may be reverse causality among the variables, leading to bias in the estimates. In this study, the one-period-lagged data of artificial intelligence were selected as instrumental variables, and then the two-stage least squares method was used for regression. The regression results are shown in

Table 6. Endogeneity test.

	(1)	(2)
	stage I	stage II
AI		0.533 ***
		(0.094)
L.AI	1.221 ***
	(0.040)
Economic Level	−0.020	−0.121 *
	(0.022)	(0.064)
Fiscal Expenditure	−0.007	−0.045
	(0.019)	(0.054)
Resource Consumption	0.013	−0.085 ***
	(0.009)	(0.025)
Urban Infrastructure	−0.697 **	0.743
	(0.336)	(0.968)
Openness	−0.001	−0.004
	(0.005)	(0.014)
Time FE	YES	YES
Province FE	YES	YES
_cons	2.303	0.056
	(1.129)	(3.253)
N	220	220
R2		0.630
Stage I F value	916.83

. The results show that the regression coefficient of the artificial intelligence variable lagged by one period is still positive and significant at the level of 1%, which effectively alleviates the possible endogeneity problem.

5.4. Mechanism Analysis

5.4.1. Mediation Effect of Agricultural Science and Technology Innovation

Based on Column (1) of

Table 7. Mechanism test results.

	(1)	(2)	(3)	(4)
	Agricultural Sci-Tech Innovation	Rural Industrial Revitalization	Industrial Structural Upgrading	Rural Industrial Revitalization
AI	0.445 ***	0.274 **	6.533 ***	0.366 ***
	(0.025)	(0.137)	(1.542)	(0.097)
Agricultural Sci-Tech Innovation		0.491 *
		(0.280)
Industrial Structural Upgrading				0.013 ***
				(0.004)
Economic Level	−0.039 **	−0.042	8.365 ***	−0.163 **
	(0.017)	(0.066)	(1.066)	(0.074)
Fiscal Expenditure	0.037 **	−0.086	2.760 ***	−0.109 *
	(0.015)	(0.058)	(0.952)	(0.059)
Resource Consumption	−0.010	−0.072 ***	−1.688 ***	−0.056 **
	(0.007)	(0.027)	(0.436)	(0.027)
Urban Infrastructure	0.893 ***	0.151	−37.625 **	1.065
	(0.283)	(1.110)	(17.718)	(1.078)
Openness	0.001	0.003	−0.103	0.004
	(0.004)	(0.014)	(0.232)	(0.014)
Time FE	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
_cons	−3.351 ***	1.567	90.045	−1.199
	(1.122)	(4.386)	(70.288)	(4.246)
N	231	231	231	231
R2	0.901	0.632	0.820	0.642
Sobel	2.394 ** [0.017]		2.783 ** [0.015]

Note: In [] is the p-value of Sobel’s test.

, when agricultural science and technology innovation serves as the dependent variable, the regression coefficient of AI on agricultural science and technology innovation is 0.445, significant at the 1% level. This indicates that the development of AI has a significant promoting effect on agricultural science and technology innovation. Column (2) of Table 7 presents the results of the baseline regression incorporating the mediating variable of agricultural science and technology innovation. The impact coefficient of agricultural science and technology innovation on rural industrial revitalization is 0.274, significant at the 5% level, suggesting that agricultural science and technology innovation exerts a partial mediating effect.

5.4.2. Mediation Effect of Industrial Structural Upgrading

As shown in Column (3) of Table 7, when industrial structural upgrading serves as the dependent variable, the regression coefficient of artificial intelligence on industrial structural upgrading is 6.533 and is significant at the 1% level. This indicates that the development of artificial intelligence drives the upgrading of rural industrial structure. Column (4) of Table 7 presents the baseline regression results incorporating the mediating variable of industrial structural upgrading. The impact coefficient of industrial structural upgrading on rural industrial revitalization is 0.366, which is also significant at the 1% level. This demonstrates that artificial intelligence indirectly promotes rural industrial revitalization by facilitating industrial structural upgrading. In addition, it can be seen from the Sobel test reported in Table 7 that the mediating effect of agricultural scientific and technological innovation and industrial structural upgrading passed the Sobel test, indicating the robustness of the empirical results and verifying that artificial intelligence drives rural industrial revitalization through the dual path of agricultural scientific and technological innovation and industrial structural upgrading.

5.5. Heterogeneity Analysis

5.5.1. Heterogeneity Analysis Based on Sci-Tech Innovation Levels

The essence of AI is intrinsically linked to technological innovation. The differences in the foundation of scientific and technological innovation in different regions will affect the depth and scope of artificial intelligence technology’s integration into rural industries, thereby leading to different effects on rural industrial revitalization. This study employs the logarithm of local fiscal expenditure on science and technology to measure regional innovation capacity. This index can directly reflect the regional investment in science and technology innovation, and this investment is an important support to promote the development of science and technology innovation, which is closely related to the regional level of science and technology innovation. Regions were classified into Sci-Tech-Innovation Frontrunners (above-average innovation level) and Sci-Tech Innovation Laggards (below-average) to examine the heterogeneous effects of AI on rural industrial revitalization across these subgroups. The regression results in

Table 8. Heterogeneity analysis results.

	(1)	(2)	(3)	(4)	(5)	(6)
	Sci-Tech Innovation Frontrunners	Sci-Tech Innovation Laggards	Coastal Pioneering Development Zone	Core Functional Zone around Beijing–Tianjin	Functional Expansion Zone of Central and Southern Hebei	Ecological Conservation Zone of Northwest Hebei
AI	0.223 **	1.765 ***	1.025 **	−0.337	0.570 ***	−2.442 **
	(0.091)	(0.270)	(0.421)	(0.557)	(0.084)	(0.867)
Economic Level	−0.024	0.019	0.079	−0.538 ***	−0.035	−1.100 ***
	(0.153)	(0.073)	(0.159)	(0.168)	(0.142)	(0.347)
Fiscal Expenditure	−0.034	−0.052	0.151	−0.097	−0.324 **	−0.299 *
	(0.061)	(0.083)	(0.123)	(0.216)	(0.121)	(0.143)
Resource Consumption	−0.100 **	−0.060 **	0.032	−0.140	−0.052	0.243 **
	(0.050)	(0.028)	(0.047)	(0.086)	(0.067)	(0.090)
Urban Infrastructure	−0.096	−7.500 **	0.919	3.809	3.262	2.332
	(0.996)	(3.304)	(1.764)	(2.866)	(2.583)	(6.787)
Openness	0.061	0.006	0.140 *	0.037	0.050 *	−0.034
	(0.048)	(0.013)	(0.081)	(0.056)	(0.025)	(0.023)
Time FE	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES
_cons	1.867	31.364 **	−6.792	−6.827	−8.452	−2.963
	(3.650)	(13.312)	(7.351)	(9.714)	(9.518)	(32.454)
N	79.000	152.000	63.000	42.000	63.000	42.000
R2	0.909	0.747	0.863	0.790	0.845	0.961

(Columns (1) and (2)) show that, in frontrunner regions, AI’s coefficient for rural industrial revitalization is 0.222 (significant at the 5% level), while in laggard regions, the coefficient is larger and more statistically significant, indicating stronger AI effects in innovation-weak regions. This likely arises because, under catch-up effects, laggard regions start from lower development baselines with entrenched traditional industries and slower technological updates, and their factor efficiency is low. At this time, the rapid introduction of artificial intelligence technology can directly bring new technologies and new methods. Artificial intelligence can directly replace inefficient labor and capital, form a large impact and change, and bring “leapfrog” benefits, so as to promote the revitalization of rural industries more significantly. However, the innovation-intensive regions themselves have a good scientific and technological foundation and active innovation, and they have accumulated rich innovation resources and technological advantages in terms of long-term development. Although AI is an important force to promote the development of rural industries, it is only one of many factors; its individual role is partially diluted, and its marginal contribution to the revitalization of rural industries is relatively small.

5.5.2. Heterogeneity Analysis Based on Functional Zones

Hebei Province’s “Four Zones” spatial layout comprises the Core Functional Zone around Beijing–Tianjin, the Coastal Pioneering Development Zone, the Functional Expansion Zone of Central and Southern Hebei, and the Ecological Conservation Zone of Northwest Hebei, as shown in Figure 2. The zoning of Hebei is scientifically planned and divided according to various factors, such as the geographical location, resource endowment, industrial foundation, and development demand of each region in Hebei Province. Different functional areas have a clear functional positioning and development direction, which will affect the application effect of artificial intelligence in rural industries, leading to different impacts on rural industrial revitalization. The Beijing–Tianjin Core Functional Zone primarily serves the capital region, undertaking industrial relocation and functional decentralization tasks while maintaining coordinated development with Beijing and Tianjin. The Coastal Pioneering Development Zone leverages world-class port clusters including Tangshan Port and Huanghua Port, along with marine economic advantages, to focus on port-related industries and the marine economy. As a key agricultural production base in Hebei Province, the Central–South Development Zone boasts abundant agricultural resources and solid industrial foundations, driving deep integration between advanced manufacturing and modern agriculture. The Northwest Ecological Conservation Zone prioritizes ecological protection, water conservation, and green development, forming an ecological security barrier for the Beijing–Tianjin–Hebei region.

The regression results in Table 8 (Columns (3)–(6)) show that AI significantly promotes rural industrial revitalization in Central and Southern Hebei’s Functional Expansion Zone and the Coastal Pioneering Development Zone, while exerting negative effects in Northwest Hebei’s Ecological Conservation Zone. Notably, the Coastal Pioneering Development Zone demonstrates the largest and most significant AI coefficient. This likely arises because, in the Functional Expansion Zone of Central and Southern Hebei, Shijiazhuang, as the provincial capital, boasts strong economic strength and extensive radiation capabilities; its technological talent and information resources can be disseminated to surrounding areas, providing support for rural industrial revitalization. Moreover, this region serves as a crucial grain production base with profound agricultural foundations. Artificial intelligence-assisted precision and intelligent management upgrades in agriculture enhance production efficiency and quality, making rural industrial revitalization more effective. The Coastal Pioneering Development Zone, leveraging its port advantages and open geographical position, demonstrates robust economic vitality and competitiveness; it demonstrates strong adaptability to emerging technologies such as artificial intelligence. AI applications in marine fisheries, agricultural processing, and logistics distribution can drive high-end and intelligent industrial development, significantly boosting rural revitalization. The functional orientation of the Ecological Conservation Zone of Northwest Hebei Province is mainly ecological protection, and its industrial development is limited due to the strict control of high-pollution and high-energy-consumption industries. At the same time, due to the influence of plateau and mountain terrain, the climate, ecology, and other characteristics are obviously different from those of other regions, and the population density and industrial level are low, limiting the promotion of artificial intelligence in rural industries. As a result, the development of artificial intelligence has a negative impact on the revitalization of rural industries in this region.

6. Conclusions and Recommendations

This study utilized panel data from 11 prefecture-level cities in Hebei Province (2003–2023) to construct two-way fixed-effects baseline regression models and mediation effect models. Robustness was verified through alternative regression specifications and winsorized data processing, with heterogeneity analysis conducted to examine AI’s impact on rural industrial revitalization and its mechanisms. Our findings reveal the following: (1) AI development significantly promotes rural industrial revitalization. (2) Mediation tests confirm that AI indirectly facilitates revitalization by advancing agricultural sci-tech innovation and industrial structural upgrading. (3) Heterogeneity analysis reveals significant regional disparities in AI’s impact on rural industrial revitalization. The promotive effect of AI proves particularly pronounced in regions with lagging sci-tech innovation capacity and the Functional Expansion Zone of Central and Southern Hebei, as compared to other innovation-tiered areas and functional regions.

Under the national policy framework that actively promotes rural revitalization and artificial intelligence development, existing policies have laid the foundation for the development of rural industries and AI. Based on the empirical results, this study puts forward the following more targeted suggestions to provide ideas for the specific implementation of the policy:

(1)

Increasing investment in the construction of new digital infrastructure is in line with the requirements of the digital agricultural and rural development plan. Provincial communications authorities play a leading role in working with communications enterprises to formulate special plans for rural broadband improvement. Priority should be given to areas with weak digital infrastructure, such as rural areas with low broadband coverage, to increase the scale and intensity of optical-fiber network laying, achieve full coverage of rural broadband and 5G networks, and consolidate the foundation for artificial intelligence support. In order to reduce the use cost and increase the penetration rate of intelligent agricultural machinery, special funds should be set up to subsidize enterprises and farmers who purchase intelligent agricultural machinery. This proposal is expected to improve the level of digitization and intelligent production in rural areas, as well as increasing productivity and output.

(2)

Improve the digital skills of the people employed in supporting rural industries. On the one hand, digital skills should be improved for existing rural industry practitioners. Provincial education departments take the lead in carrying out science and technology training activities for the rural labor force, and they jointly promote them with scientific research institutions and vocational colleges. “AI New Farmer” training programs should be developed, tailored to local agricultural characteristics. Priority should be given to foundational AI knowledge in regions with underdeveloped technological innovation, focusing on agricultural big data analysis and smart equipment operation/maintenance in major grain-producing areas, and emphasizing digital rural tourism and live-streaming sales in ecological conservation zones. Effectiveness could be enhanced through a combination of centralized lectures, online learning resources, agricultural expert platforms, and field guidance. On the other hand, we should strengthen the mechanism of introducing professional talents. Provincial human resources and social security departments formulate policies for talent introduction, offering housing subsidies, research start-up funds, and other preferential treatment to high-level artificial intelligence talents introduced. At the same time, emphasis should be placed on the cultivation of reserve talents in artificial intelligence. Special activities integrating artificial intelligence with teaching should be carried out for teachers in rural schools to cultivate the scientific and technological literacy and innovative thinking of rural students.

(3)

Promote cooperation among industry, academia, and research institutions and the transformation of research results. Provincial science and technology departments should take the lead in building a platform for cooperation among industry, academia and research in rural industries, giving full play to the role of research institutions and enterprises in innovation themes, while encouraging enterprises, research institutions, and universities to release their cooperation demands and achievements on the platform, and regularly organizing matching and exchange activities to promote in-depth cooperation among all parties and the matching of supply and demand for agricultural science and technology applications. Provincial financial departments have established special funds to support projects that integrate artificial intelligence with technological innovation in rural industries and implement the “Artificial Intelligence +” initiative. An incentive mechanism should be established for the transformation of scientific research achievements, and rewards should be offered to enterprises and teams that successfully transform scientific research achievements and achieve significant economic benefits. The wide application of artificial intelligence technology in rural industries should be accelerated. This suggestion is conducive to accelerating the application of artificial intelligence technology in rural industries and optimizing the industrial layout.

(4)

Implement targeted strategies based on differences in regional technological development levels. For regions with relatively advanced technology, the key lies in leveraging technological advantages to break through into more cutting-edge agricultural scenarios. A cross-disciplinary fund should be established in the frontier fields of agriculture, with a focus on supporting innovative and forward-looking projects such as biological breeding and the research and development of intelligent agricultural machinery and equipment. For regions with relatively backward technologies, the focus lies in the transplantation of mature technologies and the reference and application of successful experiences. Provincial agricultural departments can establish regional collaborative development teams for agricultural science and technology. Before transplanting mature technologies from advanced regions, they should conduct comprehensive research on the actual conditions of the local soil, climate, distribution of industrial resources and industrial structure, etc., in order to ensure that the technologies match the local agricultural production and industrial development needs, thereby reducing the cost of technological trial and error, and quickly introducing the technologies. After the technology is transplanted, a long-term technology tracking service mechanism should be established to promptly solve various problems arising in the application of the technology. At the same time, efforts should be made to promote regional coordination; encourage the dissemination of achievements from advanced regions to backward regions through training, sharing, and cooperation; and facilitate overall development. This suggestion is conducive to balancing regional development and narrowing the technological gap between regions.

(5)

Implement targeted strategies based on differences in functional areas. There are certain differences in resource endowments and the level of rural industrial revitalization between different regions. For instance, the development route of the Functional Expansion Zone of Central and Southern Hebei may not be suitable for the Ecological Conservation Zone of Northwest Hebei. Each region should implement differentiated and local rural industrial revitalization paths based on its own conditions. Specifically, the Functional Expansion Zone of Central and Southern Hebei and the leading development zone along the coast will leverage their economic, technological, and industrial advantages to establish innovation in demonstration zones and offer preferential policies, carry out innovative research and application of intelligent agriculture, and develop modern agriculture. The Ecological Conservation Zone of Northwest Hebei Province is rich in natural resources such as grassland and forest scenic spots, as well as unique agricultural products. We suggest cultivating regional characteristic agricultural product brands and developing cultural tourism industries, following the path of branding and ecologicalization. Zhangjiakou is building itself into a “computing power capital” in the Beijing–Tianjin–Hebei region, with huge potential for future development. However, there is still room for further exploration in terms of technology. The core functional area around Beijing and Tianjin is advised to enhance its cooperation with Beijing and Tianjin, actively introduce and undertake industrial transfer projects from the Beijing–Tianjin region, strengthen talent and technology exchanges, promote the transformation and upgrading of local rural industries, and attract more investment and advanced technologies. This suggestion is conducive to giving full play to the advantages of each region, achieving differentiated development and reducing regional development inequalities.

This study has empirical limitations. First, Hebei-specific data may limit generalizability to other regions. Second, although this study constructed an index system of explanatory variables from multiple dimensions, the measurement data may have a certain degree of subjectivity, and the long span of panel data makes it somewhat challenging to find the optimal proxy variables and instrumental variables. Meanwhile, relying solely on local fiscal expenditure on science and technology as an indicator of innovation capacity may fail to capture the complete picture. In addition, in terms of the intermediary analysis, this study regards the intermediary mechanisms related to agricultural technology innovation and industrial upgrading as independent operating mechanisms, without fully considering their potential interactions, which may have a chain of intermediary relationships. That is, artificial intelligence may first drive agricultural technology upgrading (such as intelligent equipment research and development), and then promote the evolution of industrial structure to the direction of high added value through technology upgrading (such as intensive processing of agricultural products), and finally form a progressive promotion effect.

Future Research: In view of the limitations of data acquisition in measuring the rural use of AI in Hebei Province, future research should obtain first-hand information through field investigation and questionnaire distribution to more accurately grasp the actual application status of AI in rural Hebei Province.