Introduction

Since the 2018 No. 1 Central Document proposed the digital countryside development strategy, the No. 1 Central Document has mentioned the digital countryside strategic development initiative for five consecutive years, providing important support for the development of digital village. Nevertheless, while endeavors are underway to cultivate a harmonious and aesthetically pleasing countryside, the excessive utilization of pesticides, fertilizers and other chemicals has rendered Chinese agriculture vulnerable to pollution. Moreover, as China transitions to a new phase of development, the prevailing social contradictions have evolved into a discord between the populace’s mounting aspirations for an enhanced quality of life and the prevailing imbalance and inadequacy in developmental processes. The objective of this initiative is to promote the coordinated development of the digital village, green agriculture and the enhancement of farmers’ well-being. The Third Plenary Session of the 20th Central Committee of the Communist Party of China identified the necessity of enhancing the ecological civilization system, with a view to promoting carbon reduction, pollution reduction, green expansion and growth in a synergistic manner. Achieving sustainable development in agriculture and rural areas is a key commitment on the part of the Party and the State, insofar as it pertains to the synchronized construction of an ecological civilization centered on human beings. The combined effect of digital village construction, green agricultural development and the improvement of farmers’ well-being is identified as the key path to achieving modernization of agriculture and rural areas and a strong agricultural country. It is therefore imperative to achieve the coordinated development of digital village, green agriculture and farmers’ well-being.

Literature review

The first is the relationship between digital village and farmers’ well-being levels. Existing literature indicates that digital village and farmers’ well-being levels mutually reinforce each other. On one hand, the development of digital village helps enhance farmers’ well-being levels. The development of digital village represents the digital transformation of rural areas1; in the process of this digital transformation, digital elements penetrate the agricultural sector, improving crop production efficiency and increasing farmers’ economic income2. Meanwhile, the leap in quality of life brought about by the digital village strategy helps stimulate farmers’ sense of well-being3. On the other hand, the improvement of farmers’ well-being levels can facilitate the development of digital village. The improvement of well-being levels involves not only the fulfillment of spiritual values but also the satisfaction of objective living conditions. Therefore, it will prompt the government to adjust the economic structure and develop infrastructure, thereby facilitating the development of digital village4. The second is the relationship between digital village and green agriculture. Scholars have found through research that digital village and green agriculture promote each other. On one hand, digital village empowers green agriculture. In the development process of China’s agriculture, the reliance on digital development has contributed to the advancement of agricultural production5.At the same time, speedy communication helps to mitigate information barriers and eliminate information gaps6,7,athus enabling farmers to streamline production and adopt advanced environmental technologies for mass production of agricultural products. With the further promotion of the digital village strategy, the integration of digital technology and rural areas has gradually become a new-quality productive force for green agriculture8,9. On the other hand, the development of green agriculture can drive the upgrading of digitalization. Green development is an important model for economic and social development; it can balance the relationship between the development of digital village and environmental protection and enable green agricultural production through the application of cloud computing, biotechnology, and other technologies, which in turn enhances the penetration of digital technology in various fields10. The third is the relationship between green agriculture and farmers’ well-being levels. Some scholars argue that the development of green agriculture and the improvement of farmers’ well-being levels complement each other. On one hand, the development of green agriculture can boost the improvement of farmers’ well-being levels. In the production process, farmers can adopt green practices to increase their income, thereby enhancing their sense of happiness11. Meanwhile, the adoption of green practices can bring about a potential increase in farmers’ prestige and social status12, which in turn enhances their sense of happiness. On the other hand, the improvement of farmers’ well-being levels can drive the development of green agriculture. A sound ecological environment is of great significance to farmers’ well-being; therefore, in the process of pursuing better well-being for farmers, it can promote the formation of “environment-friendly behaviors,” which in turn advances the development of green agriculture13.

To sum up, existing studies mainly focus on the pairwise correlations between digital village, green agriculture, and farmers’ well-being levels; however, systematic research on the coupling coordination evaluation of these three elements has not yet attracted scholars’ attention. Based on this, this paper takes Digital Village as the entry point, constructs a “Digital Village-Green Agriculture-Farmers’ Well-being” Evaluation Index System of Coupling and Coordination System using panel data from 2011 to 2021, applies the TOPSIS Entropy Method and the coupling and coordination degree model to study the comprehensive development level of the three systems, and simultaneously establishes a spatial correlation model to analyze the spatial spillover effect of the coupling coordination degree among digital village, green agriculture, and farmers’ well-being. It is expected to provide quantitative evidence for advancing agricultural modernization and building a strong agricultural country. .

Research framework

The complex adaptive system theory holds that a system is composed of multiple interacting subsystems, which jointly drive the evolution of the system through continuous learning and adaptation. The degree of interdependence and the level of positive interaction among subsystems can be measured by the coupling coordination degree. In today’s digital era, digital village, green agriculture, and farmers’ well-being can be regarded as three subsystems within a complex giant system of rural development. Among them, digital village serves as the technology driver, green agriculture as the ecological foundation, and farmers’ well-being as the development goal. Digital technology empowers agricultural data collection and the precise management of agricultural inputs such as water, fertilizers, and pesticides; Green Agriculture reduces non-point source pollution to improve the rural environment and achieve sustainable development; meanwhile, green agriculture and digital platforms promote the premium of agricultural products and increase farmers’ income. The three subsystems—digital village, green agriculture, and farmers’ well-being—form interactions and achieve coupling through information flow, resource circulation, and value sharing, thereby promoting the efficient and orderly operation of the giant rural development system and accelerating the modernization of agriculture and rural areas. Based on the complex adaptive system theory, this paper constructs a coupling coordination degree analysis model for digital village-green agriculture-farmers’ well-being levels (see Fig. 1) to assess whether these three subsystems promote each other and develop in a coordinated manner (high coordination degree) or constrain each other and develop in an imbalanced way (low coordination degree).

Fig. 1
Fig. 1
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Technology roadmap.

This paper defines the coupling relationship between digital village, green agriculture, and farmers’ well-being levels as the mutual promotion and coordinated development among these three subsystems within the giant system of rural development. Specifically: First, the development of digital village promotes the advancement of green agriculture and the improvement of farmers’ well-being levels. The construction of rural digital infrastructure, the emergence of new digital industry models, and the innovation of digital application scenarios can utilize technologies such as ground observation, sensors, remote sensing, and geographic information systems to enhance the collection, aggregation, and correlation analysis of perceived data related to the agricultural production environment, production facilities, and the organisms of plants and animals. This helps improve the intelligent monitoring system for agricultural production progress, strengthen the real-time monitoring and analysis of data concerning agricultural conditions, plant protection, soil fertility, pesticides, feed, vaccines, and agricultural machinery operations, and enhance the data support capacity for agricultural production management and command scheduling, thereby promoting the green and high-quality development of agriculture. At the same time, the development of digital village can effectively narrow the urban-rural digital divide. By providing convenient digital services and infrastructure, it improves the convenience and quality of life for rural residents, enabling farmers to gain a stronger sense of happiness. Second, the development of green agriculture not only enhances farmers’ well-being levels but also puts forward higher requirements for the development of digital village. The development of green agriculture helps increase the added value of agricultural products and build livable and business-friendly beautiful villages. This provides farmers with a better living environment and more income-increasing opportunities, thereby improving their well-being levels. The development of green agriculture requires the establishment of long-term fixed-point and location-based monitoring systems for agricultural biological resources, agricultural product origin environments, and agricultural non-point source pollution, so as to provide data support for the implementation of the “One Control, Two Reductions, and Three Basics” initiative (“One Control” refers to strictly controlling the total amount of agricultural water use and vigorously developing water-saving agriculture; “Two Reductions” refer to reducing the use of chemical fertilizers and pesticides and implementing the zero-growth campaign for chemical fertilizers and pesticides; “Three Basics” refer to the basic resource utilization of livestock and poultry manure, crop straw, and agricultural plastic film). This requires the development of a higher-level digital village, such as establishing data sharing mechanisms with meteorological, water conservancy, land and resources, and environmental protection departments, building a basic database of agricultural resources and environments, and developing data products for water conservation, fertilizer reduction, pesticide reduction, and agricultural meteorological forecasting.

Third, the improvement of farmers’ well-being levels places higher demands on the development of digital village and green agriculture. Currently, the principal contradiction in China’s social development is the contradiction between the people’s ever-growing needs for a better life and unbalanced and inadequate development. Enabling all people to share the achievements of digital society and ecological civilization development is the value goal of China’s current social development. Enjoying the convenience of production and life brought by digitalization locally, just like urban residents, is a new requirement of farmers for the development of digital village. Greener mountains, clearer waters, and a more beautiful living environment are farmers’ new expectations for the development of green agriculture. The growing demand of farmers for a better life will drive the more balanced and adequate development of digital village and green agriculture.

In summary, the three subsystems of digital village, green agriculture, and farmers’ well-being are mutually coupled and effectively coordinated, which jointly promote the healthy and sustainable development of China’s giant system of rural development and accelerate the modernization of agriculture and rural areas in China.

Indicator system

Table 1 Indicator system for digital village, green agriculture and farmers’ well-being in China.
Full size table

Digital village rely on information technology to achieve digital governance and services, providing an important driving force for the development of “agriculture, rural areas, and farmers”. While the construction of digital villages is accelerating, green agriculture and the farmers’ well-being are also among the Focused areas of national development, ultimately jointly contributing to the comprehensive revitalization of rural areas. Guided by the principles of being scientific, systematic, and rational, this paper selects relevant indicators and constructs a comprehensive indicator system consisting of three subsystems: digital village, green agriculture, and farmers’ well-being.(Table 1). The Digital rural indicator system is based on the key questions in the Outline of the Digital Rural Development Strategy and references the indicator systems constructed by scholars14, with the ingestion of data such as rural Networking sales and rural Networking procurement amounts, to build the Comprehensive Evaluation Indicator System for digital rural areas in this paper, which includes a total of 13 indicators. For the 2 dimensions of Green Agriculture, based on the metrics system constructed by scholars such as Wang Yan, metrics such as Pure amount of agricultural chemical fertilizer application are ingested to reflect the Comprehensive Development Level of Green Agriculture. Farmers’ Well-being is based on the overall requirements of the “Rural Comprehensive Revitalization Plan (2024–2027)”, and at the same time, drawing on the research of scholars1516, metrics such as Township cultural station are ingested to construct 3 comprehensive dimensions of farmers’ well-being.

Materials and methods

Empirical research

This study uses the Coupling Coordination Degree Model to parse the interaction relationships between the Digital Village system, the Green Agriculture system, and the Farmers’ Well-being system. The Coupling Coordination Degree Model can assess the degree of interaction between composite systems and is widely applied in realms such as agriculture, tourism, and ecological environment, etc17,18,19., which are different but influence each other and function together. Therefore, this paper adopts the Coupling Coordination Degree Model, convergence coefficient, Spatial Correlation Model, Theil index, etc. to explore the coordinated development situation and spatial effects of the three. The specific model formula is as follows:

Comprehensive development level model

The steps for calculating the entropy method are as follows:

  1. 1)

    When\(\:{\chi\:}_{ij}\)is a positive indicator:

$$\:{y}_{ij}=\frac{{x}_{ij}-{min}{x}_{ij}}{{max}{x}_{ij}-{min}{x}_{ij}}$$
(1)
  1. 2)

    When\(\:{\chi\:}_{ij}\:\)is a negative indicator:

$$\:{y}_{ij}=\frac{max{x}_{ij}-{x}_{ij}}{max{x}_{ij}-min{x}_{ij}}\:$$
(2)

In the formula,\(\:{\mathcal{X}}_{ij}\)represents the value of the j indicator in indicator system \(\:{i}\) and \(\begin{array}{c}{y_{ij}}\end{array}\) is the standardized value.

$$\:{\text{e}}_{\text{j}}\text{=-k}\sum\nolimits_{\text{i}\text{=1}}^{\text{m}}{\text{f}}_{\text{ij}}\text{ln}{\text{f}}_{\text{ij}}\text{,}\text{i}\text{=1,2,...,}\text{n;j}\text{=1,2,...,m}$$
(3)
$$\:{W}_{j}={d}_{j}/\sum\nolimits_{j=1}^{m}{d}_{j}\:\:\:\:\:\:\:\:\:\:$$
(4)
$$\:{S}_{i}=\sum\nolimits_{j=1}^{m}{W}_{j}{X}_{ij}^{{\prime\:}}$$
(5)

Coupling coordination degree model

$$\:\text{}\text{}\text{}\:\:\:\:\:\:\:\:\:\:\:\:\:C=\frac{\sqrt[3]{{U}_{1}\bullet\:{U}_{2}\bullet\:{U}_{3}}}{\frac{1}{3}\left({U}_{1}+{U}_{2}+{U}_{3}\right)}$$
(6)
$$\:\text{}T={\upalpha\:}{U}_{1}+{\upbeta\:}{U}_{2}+{\upgamma\:}{U}_{3}$$
(7)
$$\:D=\sqrt{C\cdot\:T}$$
(8)

In the formula, C represents the coupling degree; U1, U2, and U3 denote the levels of Digital Village, Green Agriculture, and Farmers’ Well-being. T stands for the comprehensive coordination index among the three systems. Based on the discussion, the author regards these three as subsystems of China’s giant rural development system and uses their coupling coordination degree to measure the development level of this giant system. Although the three differ in their roles, to better analyze the coupling coordination degree among them, this paper, following the common practice in relevant studies, assumes that these three systems are equally important, each accounting for one-third. D represents the coupling Coordination degree among the three systems, ranging from 0 to 1. A higher D value indicates a higher coupling coordination degree and more balanced development among the three systems, and vice versa. The range for each level is provided in Table 2.

Table 2 Ranking and developmental stages of coupled coordination degrees.
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Convergence coefficient

To measure the convergence characteristics of the coupled coordination level of China’s digital countryside, green agriculture and farmers’ well-being development, this paper introduces the σ-convergence test, which observes the trend of the coefficient of variation of the coupled coordination level of 30 provinces in China over time and determines whether there is any convergence within the region. The formula is as follows:

$$\:{{\upsigma\:}}_{t}=\sqrt{\frac{1}{n}\sum\nolimits_{i=1}^{n}{\left(\text{ln}{D}_{it}-\frac{1}{n}\sum\nolimits_{i=1}^{n}\text{ln}{D}_{it}\right)}^{2}}$$
(9)

\(\:{\sigma\:}_{t}\)indicates the convergence coefficient in year t, \(\:{\text{D}}_{\text{i}\text{t}}\)indicates the degree of coupling and harmonization of digital village, green agriculture and farmers’ well-being in region i in year t, and n indicates the 30 provinces under study.

Theil index

In recent years, scholars have applied the Thiel index to the study of regional development. In this paper, we use the Thiel index to calculate the inter- and intra-regional differences in the level of coupled coordination of digital village, green agriculture,  and farmers’ well-being in China, with the following formula:

$$\:\text{Theil=}\frac{\text{1}}{\text{n}}\sum\nolimits_{\text{i}\text{=1}}^{\text{n}}\frac{{\text{D}}_{\text{i}}}{\overline{\text{D}}}\text{ln}\left(\frac{{\text{D}}_{\text{i}}}{\overline{\text{D}}}\right)$$
(10)

Theil is the Thiel index of the coupled development of China’s digital village, green agriculture and farmers’ well-being, \(\:{D}_{i\:}\)indicates the coupled and coordinated development level of region i, and \(\:\overline{D}\) indicates the average value of coupled coordination. The specific decomposition is as follows:

$$\:Theil={T}_{b}+{T}_{w}$$
(11)
$$\:{T}_{b}=\sum\nolimits_{q=1}^{Q}\frac{{D}_{q}}{D}\text{ln}\left(\frac{{D}_{q}/{n}_{q}}{D/n}\right)$$
(12)
$$\:{T}_{w}=\sum\nolimits_{q=1}^{Q}\:\frac{{D}_{q}}{D}{T}_{q}=\sum\nolimits_{q=1}^{Q}\:\frac{{D}_{q}}{D}(\frac{l}{{n}_{q}}\sum\nolimits_{i\in\:{g}_{q}}\:\frac{{D}_{i}}{{D}_{q}/{n}_{q}}ln(\frac{{D}_{i}}{{D}_{q}/{n}_{q}})$$
$$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:=\sum\nolimits_{q=1}^{Q}\frac{{D}_{q}}{D}\left(\sum\nolimits_{i\in\:{g}_{q}}\frac{{D}_{i}}{{D}_{q}}\text{ln}\left(\frac{{D}_{i}}{{D}_{q}/{n}_{q}}\right)\right)$$
(13)

Where, Tb table the inter-regional differences, Tw table the intra-regional differences, Tq table the intra-group differences of group q within the group, q = 1,…,Q, nq denotes the group q contains Dq denotes the sum of coupling harmonization values of group q, and D denotes the sum of the coupling harmonization values of all the regions, i.e., Dq/D denotes the value of coupling harmonization development of group q in the total coupling harmonization development value of the total coupled coordinated development value. On this basis, the contribution rates of intra-regional differences and inter-regional differences are calculated:

$$\:C{R}_{q}={D}_{q}\times\:\frac{{T}_{q}}{Theil},q=1,\cdots,Q$$
(14)
$$\:C{R}_{b}=\frac{{T}_{b}}{Theil}$$
(15)

Spatial correlation analysis

Global and local spatial correlation analyses were used to identify spatial agglomerations of China’s digital countryside and the coupling and coordination of green agriculture and farmers’ well-being. Global spatial correlation analysis identified spatial effects, while local spatial correlation analysis further explored spatial agglomeration characteristics between cities. Global and local Moran’s indices were calculated, respectively. The Moran index ranges from [−1 to 1]. When the value is 0, there is no spatial autocorrelation. The closer the value is to 1, the stronger the spatial dependence. Conversely, the closer the value is to −1, the greater the spatial variability.

Moran’s I is calculated as:

$$\:I=\frac{n\sum\nolimits_{i=1}^{n}\:\sum\nolimits_{j=1}^{n}\:{w}_{ij}\left(\sum\nolimits_{i=1}^{n}\:\right({D}_{i}-\overline{D}{)}^{2}}{\left(\sum\nolimits_{i=1}^{n}\:\sum\nolimits_{j=1}^{n}\:{w}_{ij}\right)\sum\nolimits_{i=1}^{n}\:({D}_{i}-\overline{D}{)}^{2}}$$
(16)

Localized Moran’s I is calculated as:

$$\:L{I}_{i}=\frac{{D}_{i}-\overline{D}}{\frac{l}{n}\sum\nolimits_{i=1}^{n}\:({D}_{i}-\overline{D}{)}^{2}}\sum\nolimits_{j\ne\:i}^{n}\:{w}_{ij}({D}_{i}-\overline{D})$$
(17)

Among them, wij stands for the spatial weight value, n is 30 provinces, Di and Dj respectively represent the coupling coordination values, and \(\:\overline{D}\) stands for the average value of coupling Coordination degree.

Study area

This article is based on Major regional strategies. Since the regions covered by the Yangtze River Delta Integration Development Strategy can be fully included in the Yangtze River Economic Belt Development Strategy, the Yangtze River Delta Integration Development Strategy among the “Five major strategies” will not be studied separately. Due to data availability, under the guidance of the four Major regional development strategies, the research area of this article is divided as shown in Fig. 2: the Beijing-Tianjin-Hebei Region refers to the three provinces of Beijing, Tianjin and Hebei; Yangtze River Economic Belt refers to 11 provinces: Anhui, Guizhou, Hubei, Hunan, Jiangsu, Jiangxi, Shanghai, Sichuan, Yunnan, Zhejiang and Chongqing; the Yellow River Basin covers eight provinces, including Shandong, Henan, Shanxi, Shaanxi, Inner Mongolia, Ningxia, Gansu and Qinghai, and the Guangdong-Hong Kong-Macao Greater Bay Area (hereinafter referred to as the “Greater Bay Area”) researches only Guangdong Province. Other regions refer to seven provinces, including Fujian, Guangxi, Heilongjiang, Hainan, Jilin, Liaoning and Xinjiang, which are set up as blank control groups to verify regional development (Fig. 2).

Fig. 2
Fig. 2
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Study Area(Software version: ArcMap 10.8.1,URL:https://www.esri.com/en-us/arcgis/products/index. This map is based on the standard map with the review number GS(2024)0650 obt-ained from the National Platform for Common Geospatial Information Services (https://www.tianditu.gov.cn/). The base map has not been modified.).

Data source and processing

China’s agricultural data were obtained from China Statistical Yearbook, China Rural Statistical Yearbook, China Urban and Rural Construction Statistical Yearbook, and other statistical reports (2011–2021) and other databases, with individual missing values completed by linear interpolation, and the sample of this study did not include Tibet, Hong Kong, Macao, and Taiwan due to missing data in some studies.

Results

Comprehensive development level

As shown in Figs. 3 and 4, the overall score increased from 8.75 in 2011 to 14.44 in 2021. It also shows the development level of the eight secondary indicators of the three systems coordinated development. Except for the slight fluctuation of the ecological environment (C4), the digital village infrastructure development (C1), digital rural industrial environment (C2), digital villagel application environment (C3), ecological governance (C5), quality of life (C6), educational level (C7), and joint construction and sharing (C8) have grown steadily. This indicates that the systems are interacting more and developing in a more coordinated manner.

Fig. 3
Fig. 3
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B1-B3 integrated development levels (B1 = Digital Village; B2 = Green Agriculture; B3 = Farmers’ Well-being).

Fig. 4
Fig. 4
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C1-C8 Integrated development levels of the secondary system (C1 = digital village infrastructure development; C2 = digital rural industrial environment; C3 = digital village application environment; C4 = ecological environment; C5 =  ecological governance; C6 = quality of life; C7 = educational level; C8 = joint construction and sharing).

The comprehensive development index (B1) of the digital countryside system increased steadily from 2011 to 2021, rising from 5.86 to 12.66. This increase is attributed to the National Informatization Development Strategy (2006–2020), issued by The State Council, which emphasizes the importance of promoting information services in the three rural areas. The growth is also attributed to the National Informatization Development Strategy (2006–2020), which was issued by the State Council and emphasizes the importance of promoting information services for the “three rural areas.” During the study period, the total growth was 6.8. This growth can be divided into two phases: the first (2011–2016) with relatively fast growth, and the second (2017–2021) with moderate growth. Meanwhile, the digital village was further refined into three subsystems (Fig. 3) to study its dynamic progress. Regarding digital countryside infrastructure construction (C1), an upward trend is evident during the study period. The index rose from 0.075 in 2011 to 0.28 in 2021. Notably, it surged to 0.23 in 2018, potentially due to the Communist Party of China (CPC) Central Committee and The State Council release of the Plan of Rural Vitalization Strategy (2018–2022) and other policies that guided the consolidation of foundations as a solid strategy. The digital countryside industrial environment (C2) showed a steady upward trend during the study period. However, the sharp increase from 2020 to 2021 may be due to the implementation of the Outline of the Digital Countryside Development Strategy, issued by the General Office, CPC Central Committee and General Office of the State Council of the People’s Republic of China. This strategy has boosted China’s digital countryside industrial environment, leading to increased regional support for agriculture and rural areas. From 2011 to 2021, the digital countryside application environment (C3) grew in an orderly manner. The goal of this study is to promote comprehensive agricultural upgrades.

The Green Agriculture System Composite Development Index (B2) shows an increasing trend from 15.06 in 2011 to 16.13 in 2021.This growth is divided into two phases, a slow growth in the first phase (2011–2019) and a moderate growth in the second phase (2020–2021). In this paper, green agriculture is divided into 2 subsystems (Fig. 3) to further analyze the internal dynamics. The ecological environment (C4) showed steady improvement during the study period, with a slight decline in 2019. This fluctuation or temporary reversal may stem from increased extreme weather events coinciding with rapid agricultural economic growth, which raised farmers’ use of pesticides, chemical fertilizers and other agrochemicals, thereby intensifying pressure on the ecological environment. Eco-governance (C5) generally showed an upward trend, with slight fluctuations in 2013 followed by annual increases, which may be attributed to the implementation of the National 12th Five-Year Plan for Eco-Protection issued by the Ministry of Environmental Protection, which strengthened China’s eco-protection and construction, improved the relevant policies, and increased the investment to reverse the trend of ecological deterioration from the source.

The farmers’ well-being (B3) has shown a significant upward trend, increasing from 7.83 in 2011 to 15.2 in 2021. This increase is mainly attributed to the“No. 1 Central Document”, which, from 2011 to 2021, stated that farmers who stayed in the countryside would be able to live and work in peace and contentment. This growth can be divided into two phases: the first (2011–2014) with slow growth, and the second (2015–2021) with moderate growth. This analysis covers changes in three subsystems (Fig. 3). Quality of life (C6) fluctuated during the study period but generally showed a slow upward trend. Literacy (C7) fluctuated and then rose but declined in 2020 due to the epidemic’s impact. Sharing (C8) increased from 0.25 in 2011 to 0.48 in 2021. The green agriculture system developed relatively slowly during the study period compared to the rapid development of the digital village system and the farmers’ well-being standard system. However, its starting point was higher than that of the other systems. This trend can be attributed to several factors. First, prior to the implementation of the National 12th Five-Year Plan for Ecological Protection, China’s agricultural infrastructure was weak and susceptible to natural disasters and other external factors, which may have impeded the sustainable development of green agricultural systems. Since implementing the plan, China has actively promoted ecological governance. Subsequently, the Chinese government issued the Technical Guidelines for Green Agricultural Development (2018–2030). These guidelines aim to promote green agricultural development technology as an important strategy for sustainable development and a fundamental approach to addressing the pressing issues of resources and the environment in China’s agriculture and rural areas. Transform the crude approach into a new scenario that aligns with the carrying capacity of agricultural productivity through sustainable development strategies. Realizing the green development of agriculture inevitably requires that agriculture and rural areas take the path of coordinated development of the “three lives” of production development, affluent living and ecological livability.

Analysis of coupling coordination degree

The coupling coordination degree model was used to calculate the coupling degree (C), coordination degree (T), and coupling coordination degree (D) among China’s digital village, green agriculture, and farmers’ well-being levels. To better compare the research results, data from 2011, 2016, and 2021 were selected. According to Fig. 5, first, the coupling degree of the three systems gradually increased from 2011 to 2021, indicating that the interaction among the systems became closer over time. Second, in 2021, the coupling degree between China’s “green agriculture-farmers’ well-being” system was relatively high at 0.86; meanwhile, the T value increased from 0.38 to 0.52, indicating a simultaneous improvement in the coordination and adaptability between the two systems. Next was the coupling degree between the “digital village-farmers’ well-being” system: it stood at 0.84 in 2011 and remained at 0.86 in 2021, but the T value only ranged from 0.23 to 0.47. This shows that there was a strong correlation between the systems, yet the coordination degree was insufficient. Finally, the coupling degree between the “digital village-green agriculture” system was 0.79 in 2021, showing an upward trend, which indicates that the interaction between these two systems was also deepening. Overall, from 2011 to 2021, the coupling coordination degree among the three systems—digital village (B1), green agriculture (B2), and farmers’ well-being (B3)—showed an overall upward trend, but its value was lower than the coupling degree. This suggests that the interaction among the systems was strong, while the degree of collaborative adaptability needs further improvement.

Next, as shown in Fig. 6, we used the ArcGIS tool to visualize the coupling coordination degree in 30 Chinese provinces. As shown in Fig. 5, from 2011 to 2021, the three coupling coordination situations developed positively. Some provinces developed from moderate dysfunction to intermediate coordination. Combined with the 2011 situation, 10 provinces, including Jilin, Heilongjiang, Jiangxi, Hainan, Chongqing, Guizhou, Yunnan, Gansu, Qinghai, and Ningxia, were in moderate disorder. In 2016, 93% of them were in a dysfunctional state. Beijing and 11 other provinces stepped into the forced coordinated sequence, with Jiangsu taking the lead in achieving intermediate coordination. In 2021, Jiangsu, Shandong, and Sichuan achieved intermediate coordination, leading the nation in coordinated development. The provinces of Beijing, Hebei, Zhejiang, Anhui, Henan, Hubei, Hunan, Guangdong, and Guangxi had also achieved primary coordination. The remaining four provinces were borderline disorder. This shows that, although China’s digital countryside, green agriculture, and farmers’ well-being and living standards tend to improve, there is still room for improvement.

Fig. 5
Fig. 5
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Calculation Results of the Coupling Degree, Coupling Coordination Index, and Coupling Coordination Degree among China’s Digital Village, Green Agriculture, and Farmers’ Well-being (B1 = Digital Village; B2 = Green Agriculture; B3 = Farmers’ Well-being).

Fig. 6
Fig. 6
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Time-series evolution of the coupling coordination degree(Software version: ArcMap 10.8.1,URL:https://www.esri.com/en-us/arcgis/products/index.This map is based on the standard map with the review number GS(2024)0650 obtained from the National Platform for Common Geospatial Information Services (https://www.tianditu.gov.cn/). The base map has not been modified.).

Analysis of convergence coefficient

Figure 7 shows the trend in convergence coefficients for coupling coordination levels over time in the Yangtze River Economic Belt, the Beijing-Tianjin-Hebei Region, the Yellow River Basin, and other regions. From 2011 to 2021, all 30 Chinese provinces showed convergence. The provinces in the Yangtze River Economic Belt had the largest convergence coefficients, suggesting small differences in coupling coordination levels within the region.

Since the research scope of this paper is based on Major regional strategies, focusing primarily on the impacts of strategic development, meanwhile, other regions (including 7 provinces directly under the Central Government in Fujian and Xinjiang) exhibit significant differences in factors such as geographical characteristics and economic development. Comparing the convergence coefficients of these regions with those of other strategic zones would lack practical significance, so a comparative study is temporarily not conducted.

Fig. 7
Fig. 7
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Timing diagram for convergence of coupled coordination levels(YEB = Yangtze River Economic Belt).

Analysis of theil index

Figure 8 shows the overall, inter-regional, and intra-regional contribution rates based on the Thiel index calculation method. Since the study of the Guangdong-Hong Kong-Macao Greater Bay Area is currently limited to Guangdong, it is not possible to assume that there are spatial differences within the Greater Bay Area.

The overall disparity has gradually narrowed, dropping from 0.0319 to 0.0088, indicating a trend of coordinated development across regions. Specifically, the disparity within the Yangtze River Economic Belt has gradually decreased; the regional disparity in the Beijing-Tianjin-Hebei Region has shown an overall fluctuating downward trend; although the Theil index of the Yellow River Basin is significantly higher than that of other regions, it has generally exhibited a downward trend. At the same time, it can be observed that the disparity in the Yangtze River Economic Belt is significantly greater than that in the Beijing-Tianjin-Hebei Region. This is because the Yangtze River Economic Belt spans China’s three major regions (eastern, central, and western China) and covers 11 provinces, resulting in large gaps in digital infrastructure construction among provinces, weak inter-provincial coordination mechanisms, and inconsistent implementation standards for green agriculture development policies. For instance, Jiangsu Province, located in the eastern part of the Yangtze River Economic Belt, had the largest number of rural broadband subscribers in China in 2021, with full 5G network coverage in all towns and townships; in contrast, the 5G availability rate in administrative villages in Guizhou Province (in the western part) was only 38% in the same year. The coordinated development of the Beijing-Tianjin-Hebei Region is a major national strategy. Under this strategy, the construction of digital infrastructure is advanced in a coordinated manner, regional resources are allocated with rational flow, and a highly efficient and collaborative inter-provincial cooperation mechanism has been established. In terms of inter-regional differences, the overall level of inter-regional differences is relatively low and shows a fluctuating downward trend. Overall, within the research sample period, intra-regional differences are greater than inter-regional differences, indicating that intra-regional differences are the main source of overall differences.

According to Fig. 9, the spatial differences mainly come from intra-regional differences, which are at 75.7% to 86.63%, indicating that there are large differences in China’s intra-regional development. The internal differences of the Yangtze River Economic Belt, the Beijing-Tianjin- Hebei Region, the Yellow River Basin and other regions have all gradually decreased, and the contribution of the Beijing-Tianjin-Hebei Region , the Yellow River Basin and other regions to the whole has increased. Overall, the coordinated development of the Yellow River Basin and other regions has played a positive role.

Fig. 8
Fig. 8
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Theil’s exponential time series evolution(YEB = Yangtze River Economic Belt).

Fig. 9
Fig. 9
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Time-series evolution of differential contribution rates(YEB = Yangtze River Economic Belt).

Analysis of spatial autocorrelation

Overall spatial correlation analysis

As shown in Table 3, the global Moran index of the coupled coordination of the digital village, green agriculture, and farmers’ well-being in China from 2011 to 2021 is positive and significant at the 1% level, indicating significant spatial dependence in this coordination. The Moran index fluctuates over time during the study period. It is highest in 2018 and steadily decreases by the end of the period. This indicates a decline in the degree of spatial agglomeration of China’s digital countryside, green agriculture, and farmers’ happiness.

Table 3 China’s digital Countryside, green agriculture and farmers’ happiness level moran’s I Index.
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Local spatial correlation analysis

According to Table 4, it is clear that the specific locations of the development of digital village, green agriculture and farmers’ well-being coupled with the degree of coordination in China are clustered in the Moran index scatter quadrant distribution, and the first to the fourth quadrant are the spatial correlation patterns of high-high (HH), low-high (LH), low-low (LL), and high-low (HL), respectively.

Further measuring the local Moran index, the coupling coordination coefficients are divided into four spatial autocorrelation patterns through Moran scatter plot. The HH provinces are mainly concentrated in the Yangtze River Economic Belt and the Yellow River Basin, and most of the LL are concentrated in other regions, which indicates that spatially China’s coordinated development of digital village, green agriculture and farmers’ well-being is in an uneven state. Combined, the HH mode of the Yangtze River Economic Belt is basically distributed in the middle and lower reaches of the Yangtze River, of which Anhui and Jiangsu passed the significance test, representing that it can also drive the coupled and coordinated development of the neighboring regions based on the passing of the significance and comparing with the upper Yangtze River region, only the economically strong province of Sichuan is in the HL mode and passed the significance test, and the rest of the provinces are in the LH mode, which reveals the imbalance of the development within the region of the Yangtze River Economic Belt. Beijing and Hebei in the Beijing-Tianjin Wing are in the HL quadrant, indicating that they are highly coupled but influenced by neighboring regions. Guangdong in the Greater Bay Area is in the HL quadrant, indicating that Guangdong is a strong leader in coupled and coordinated development. Shandong and Henan in the Yellow River Basin belong to the HH quadrant and are significant, which indicates that the region has a high degree of coupling and its neighboring regions are better developed; Shanxi, which also belongs to the Yellow River Basin, is in the LH quadrant, which indicates that it is poorly coupled but driven by the growth of its neighboring regions; and the Yellow River Basin and the rest of the world, including Inner Mongolia Autonomous Region, Heilongjiang, and Jilin, are significant in the LL pattern. Therefore, based on the data, this paper argues that it is possible to govern from these three provinces (autonomous regions), carry out internal optimization, and ultimately achieve the reversal of the low coupling coordination aggregation in northwest China (Fig. 10).

Table 4 Localized Spatial autocorrelation results.
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Fig. 10
Fig. 10
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Coupled and coordinated development of spatial agglomeration types(Software version: ArcMap 10.8.1,URL:https://www.esri.com/en-us/arcgis/products/index. This map is based on the standard map with the review number GS(2024)0650 obt-ained from the National Platform for Common Geospatial Information Services (https://www.tianditu.gov.cn/). The base map has not been modified.).

Discussion

Research progressiveness and enlightenment

Compared with existing literature20,21,22,23,this study makes the following contributions: First, we construct a comprehensive evaluation index system for the digital village, green agriculture, and the farmers’ well-being. We use the entropy value method, coupled coordination degree model, Theil index, and spatial autocorrelation model. Second, we measure and analyze the spatiotemporal evolution of the relationship between the coupled coordination degree of the digital countryside, green agriculture, and the living standard of farmers’ happiness in China. The evaluation indicators cover digital village infrastructure construction, the ecological environment, and quality of life. Finally, this paper proposes policy recommendations to promote sustainable development in China, which is crucial for the coupled and coordinated development of the digital village, green agriculture, and the farmers’ well-being. In summary, we conclude that digital villages have greater development potential than traditional villages. From an overall optimization perspective, promoting the development of the digital countryside will directly or indirectly affect green agriculture and farmers’ well-being. Similarly, promoting the development of green agriculture or farmers’ well-being will contribute to the development of the digital countryside. Together, these factors will help realize a strong agricultural country.

Countermeasures and suggestions

First, strengthening digital supply to advance agricultural and rural modernization. To continuously enhance the driving role of digital villages, it is essential to further improve new digital infrastructure in rural areas, effectively enhance farmers’ literacy and digital skills, and accelerate the implementation of the “Digital Commerce for Rural Revitalization” initiative. The development of green agriculture should focus precisely on addressing weak links in the agricultural sector, vigorously promoting ecological and circular agriculture, and expediting a comprehensive green transformation across all stages of agricultural production. To enable farmers to live and work in greater comfort and contentment, it is also necessary to enhance their sense of happiness and ensure that they jointly build and share the achievements of reform and opening up, while continuously improving the quality of rural living environments.

Second, enhancing regional coordinated development and consolidating the foundation based on local conditions. The research findings indicate that intra-regional disparities are the primary factor influencing the coupling and coordinated development among China’s digital villages, green agriculture, and farmers’ well-being. It is therefore recommended that the Yangtze River Economic Belt and the Beijing-Tianjin-Hebei region leverage their regional advantages and solid economic foundations to further strengthen the development of digital villages, establish special funds dedicated to green development, and promote the construction of 5G base stations as well as the popularization of intelligent agricultural equipment. By doing so, these regions can play a leading and demonstrative role in promoting the coordinated development of digital villages, green agriculture, and farmers’ well-being. Other regions should optimize spatial layouts, make full use of relevant policies, and address existing shortcomings according to their local conditions.

Thirdly, promoting regional linkage and achieving collaborative development for mutual benefits. Anhui Province and Jiangsu Province should take the lead in demonstrating best practices, cultivating a “Fengqiao Experience” model for coordinated regional development. Regions that are currently less developed should seize the opportunities brought by the new round of national policies, remove institutional barriers, and facilitate the smooth flow of talent. By doing so, these regions can benefit from the spillover effects of more developed areas, forming a virtuous cycle of coordinated and shared growth.

Insufficiencies in research and prospects

Although this paper attempts to explore the three through a combination of different methods, it still has limitations. The data on farmers’ well-being used in this paper is derived from macro panel data. In the future, integrating micro and macro data with β-convergence analysis could enable a more comprehensive investigation of happiness and Agricultural greening. Additionally, it should be noted that the three systems have different dimensional definition criteria, and the comprehensiveness of the indicator system needs to be enhanced.

Conclusions

First, this paper focuses on three core dimensions: digital village, green agriculture, and farmers’ well-being. The research examines factors including digital village Infrastructure construction, ecological environment, and quality of life, with each indicator influencing the others to varying degrees. During the study period, digital village exerted an increasingly significant impact on both green agriculture and farmers’ well-being. Concurrently, as national emphasis on developing green agriculture and improving farmers’ well-being grew, these two areas reciprocally fueled digital village development, collectively advancing Agricultural Power. Consequently, the three systems exhibit strong interdependence. The main conclusions are as follows: First, the composite development index of China’s digital village, green agriculture, and farmers’ well-being has increased annually; meanwhile, the coupling degree, coordination degree, and coupling coordination degree of these three subsystems show an overall positive trend. This progress follows an evolutionary trajectory: “moderate disruption-borderline imbalance - forced synchronization -primary synchronization -intermediate synchronization” with substantial positive temporal trends. Geographically, development has dynamically expanded from the Yangtze River Economic Belt and Yellow River Basin to encompass the entire nation. Second, the coupling Coordination degree level among China’s digital villages, green development, and farmers’ well-being shows a converging trend over time. Third, there are significant regional disparities in the coupling coordinated development of the three systems, primarily driven by intra-regional differences. Fourth, the spatial correlation among the three systems exhibits positive Spillover effect. These findings provide quantitative support for promoting regional coordinated development.