The Impact of Digital–Green Synergy on Agricultural New Quality Productive Forces in China

Abstract

The synergy between agricultural digitalization and greening is an inherent requirement for high-quality agricultural development and is a vital pathway for cultivating agricultural new quality productive forces (ANQPFs). Based on 2012–2023 provincial-level data from 30 Chinese provinces, this study constructs comprehensive evaluation index systems for agricultural digitalization, greening, and ANQPFs. A coupling coordination model is applied to measure the degree of digital–green synergy, and a two-way fixed effects model is employed to test its impact on ANQPFs, along with the underlying mechanisms and regional heterogeneity. The results indicate that digital–green synergy significantly enhances ANQPFs. A 1% increase in the synergy index improves ANQPFs by 29.6%, primarily through industrial structure optimization, technological innovation stimulation, and resource allocation efficiency improvement. The positive effect is most prominent in the central region, after Digital Village Strategy implementation, and in major grain-producing areas. This study innovatively integrates digitalization and greening into the analytical framework of agricultural productivity, expanding the theoretical understanding of how synergistic transformation drives high-quality agricultural development. Regarding policy, governments should strengthen coordination between digital and green policies, promote the integration and innovation of related technologies, and foster an enabling environment that supports the formation and evolution of new quality productive forces in agriculture.

1. Introduction

A strong nation must first have strong agriculture; only when agriculture is strong can the nation be strong. In the context of a new era of dynamic globalization and technologicalization, the international community has established unprecedented new expectations and modern requirements for the development of new and high-quality productive forces [1]. In recent years, scientific and industrial innovation have continually advanced, and the development of agriculture has been associated with new opportunities and challenges. Under the new development paradigm, promoting new quality productive forces in agriculture is a strategic choice for China to proactively adapt to the context of the new era. In 2025, the No. 1 Central Document explicitly proposed the development of new quality productive forces in agriculture, prioritizing agricultural and rural development to provide fundamental support for the development of an agricultural powerhouse [2]. This strategic focus is further reinforced by the “15th Five-Year Plan,” which emphasizes accelerating this very development. From both the theoretical and practical perspectives, developing new quality productive forces in agriculture is an essential requirement for innovation-driven agricultural operations and provides fresh momentum for building an agricultural powerhouse.

New quality productive forces represent an advanced form of productivity dominated by innovation. In the agricultural domain, from a historical perspective, this emerging form of productivity is reflected not only in the innovation of individual elements but also in the optimized reconfiguration of production factors [3], prominently manifested in the rapid development of digital and green technologies. At present, the advancement of new quality productive forces in agriculture still faces practical constraints such as the imbalanced allocation of resources in factor markets, a shortage of scientific and technological innovation talent, an insufficient supply of interdisciplinary professionals, and the ageing of grassroots agricultural extension teams, leading to gaps in technology adoption, resource waste, and ecological degradation; Thus, new drivers characterized by “high technology, high efficiency, and high quality” are urgently needed to foster these productive forces [4]. Agricultural digitalization and greening are two major trends in current technological and industrial innovation [5], and they serve as key drivers for the development of new quality productive forces in agriculture [6]. Therefore, investigating how to enhance the coupled synergy and transformation effectiveness of digitalization and greening is necessary to promote the development of new quality productive forces [7].

From a policy perspective, the CPC Central Committee and the State Council issued the Overall Layout Plan for Digital China Construction, emphasizing the acceleration of the coordinated transformation of digitalization and greening (hereafter referred to as “dual transformation synergy”). In August 2024, the Cyberspace Administration of China, the National Development and Reform Commission, and eight other departments jointly issued the Implementation Guidelines for the Coordinated Development of Digital and Green Transformation, emphasizing the need to “promote the comprehensive integration of digitalization and greening and to accelerate the development of new quality productive forces.” From an academic perspective, studies have focused mostly on the effects of either agricultural digitalization or greening in isolation, while research on the relationship between their synergy and new quality has provided the foundational capabilities and efficiency improvements required for new quality productive forces [8]. Chrysomallidis et al. argued that new technologies create conditions for sustainable agricultural production by establishing relational and network-based mechanisms that contribute to the “greening” of the agricultural sector [9]. Similarly, Hussain et al. used provincial panel data from Pakistan (2014–2024) to examine how digital financial inclusion influences sustainable agriculture and identify a double-threshold effect—when digital inclusion exceeds a critical level, it significantly strengthens sustainable farming practices [10]. Complementing these findings, a review highlighted the role of Agriculture 4.0 technologies—including precision farming, the IoT, and data analytics—in enhancing sustainable agriculture in Australia [11]. These findings further highlight the global relevance of digital–green interaction mechanisms for promoting sustainable and innovation-driven agricultural development.

Agricultural digital transformation primarily advances through the empowerment and iterative upgrading of various elements, such as media, technologies, actors, values, and scenarios [12], to promote technological progress, industrial upgrading, effective resource allocation, and the development of new quality productive forces. With respect to agricultural green transformation, the academic focus has been on the relationship between green technologies and new quality productive forces, with the prevailing view that agricultural green technological innovation and its application are essential for advancing new quality productive forces [13].

In the current context of accelerating the development of new quality productive forces in agriculture, research has focused primarily on the level of digital–green synergy and the degree of policy support for this synergy [14]. In summary, a gap remains in research regarding the theoretical mechanisms and implementation pathways through which agricultural digital–green synergy empowers new quality productive forces. Therefore, in this study, the coordinated transformation of digitalization and greening is incorporated into the development system for new quality productive forces based on the basis of its theoretical connotation, and three questions are addressed: Can digital–green synergy in agriculture empower new quality productive forces? What are the internal mechanisms of this empowerment? What are its implementation pathways? By answering these questions, this study seeks to provide theoretical references and policy insights from the perspective of agricultural digital–green synergy for empowering new quality productive forces.

This paper contributes to existing research in three ways. First, an empirical analysis is conducted on the relationship between digital–green synergy and agricultural new quality productive forces (ANQPFs) in the Chinese context. China’s unique regional structure, agricultural resource endowment, and policy environment make it an ideal setting for examining how the integration of digitalization and greening drives new forms of agricultural productivity. This analysis extends the theoretical and empirical scope of research on agricultural modernization research. Second, a mediation model combined with an instrumental variable (IV) approach is employed to identify the mechanisms through which digital–green synergy influences ANQPFs and to address potential endogeneity problems caused by reverse causality and omitted variables. This methodological design enhances the robustness and credibility of the results. Third, the heterogeneous effects of digital–green synergy on ANQPFs are explored across different regions, revealing spatial disparities and providing policy implications for developing new quality productive forces in line with local conditions.

2. Theoretical Analysis and Research Hypotheses

2.1. Agricultural Digital Transformation and the Development of New Quality Productive Forces

Agricultural digital transformation has been widely recognized as a crucial driving force for accelerating the development of new quality productive forces in agriculture. Centred on digitalization and intelligentization, this transformation actively integrates technological innovation resources, promotes deep structural upgrades in agricultural systems, and achieves qualitative improvements in traditional productivity frameworks. The mechanism through which agricultural digital transformation fosters new quality productive forces lies in the application of digital–intelligent technologies, such as artificial intelligence (AI), big data, and the Internet of Things (IoT),to enhance production efficiency and optimize resource allocation. As emphasized by Ghazal et al., the integration of digitization and automation has triggered a profound transformation in farming practices, driven by advances in computer vision and AI technologies. This process not only increases productivity and economic efficiency but also contributes to addressing global challenges such as food security and agricultural sustainability [15]. From an international perspective, studies have shown that digital transformation reshapes the structure and performance of agricultural systems by fostering innovation networks and enhancing technological absorptive capacity [16]. Empirical research has further demonstrated that this digital agriculture, supported by data-driven technologies and precision management, significantly improves total factor productivity and resource efficiency [17]. These findings reinforce the theoretical view that agricultural digitalization contributes to sustainable intensification and the evolution of new quality productive forces through technological innovation and knowledge diffusion.

Specifically, when labourers, labour methods, and labour objects are transformed through intelligent digital technologies, digital agricultural talent is cultivated, data are integrated into production processes, and the development of new quality productive forces is promoted [18]. Agricultural digital transformation can reduce information asymmetry among agricultural production organizations, decrease transaction costs, promote the scaling-up of productivity organizations, and accelerate the transformation towards high-efficiency, innovation-driven agricultural development.

2.2. Agricultural Green Transformation and the Development of New Quality Productive Forces

Based on the fundamental connotation of new quality productive forces in agriculture, the goal of digital transformation is to improve short-term efficiency to foster these new productive forces, while the aim of green transformation is to realize their environmental sustainability attributes. The essence of agricultural green transformation lies in the application of green technologies to utilize natural resources efficiently and rationally, reducing the negative externalities of agricultural production. Through a sustainable technological system composed of low-carbon agricultural technologies and ecological recycling methods, it overcomes the limitations of traditional agricultural techniques, shifts away from high-consumption and high-pollution development models, builds a green and low-carbon agricultural industrial chain, and establishes a high-tech, high-efficiency, high-quality, and environmentally friendly production system. These transformations jointly enhance green total factor productivity (GTFP) and advance the development of new quality productive forces in agriculture [19]. At the same time, the concept of GTFP has become a central analytical framework for evaluating environmentally sustainable growth [20]. In this framework, agricultural productivity is assessed by incorporating both economic output and environmental performance, thereby providing a more comprehensive understanding of sustainable efficiency. Studies on agricultural eco-efficiency, such as those by Staniszewski & Matuszczak [21], have further emphasized that achieving eco-efficient agriculture requires optimizing the use of natural resources, minimizing environmental externalities, and integrating ecological sustainability into productivity evaluation. These theoretical perspectives collectively underpin the understanding of how agricultural greening fosters the development of new quality productive forces, linking productivity enhancement with ecological sustainability.

Unlike agricultural digital transformation, green transformation tends to have more clearly defined objectives. Through agricultural green transformation, sustainable agricultural production can be achieved, thereby meeting the green development goals that are essential to new quality productive forces in agriculture.

2.3. The Impact of the Synergistic Effects of Agricultural Digitalization and Greening on New Quality Productive Forces

New quality productive forces represent a significant facet of advanced productivity, stemming from revolutionary advancements in technology, innovative resource allocation, and comprehensive industrial transformation and upgrading. This concept encapsulates the optimization of labour, materials, and tools, emphasizing enhanced total factor productivity as its core tenet [22]. From a theoretical perspective, new quality productive forces can be viewed as an evolutionary extension of classical total factor productivity (TFP), incorporating both the efficiency dimension of green productivity and the innovation-driven mechanism of digital transformation [23]. Traditional TFP emphasizes output gains from technological progress, whereas green TFP frameworks integrate environmental constraints and ecological efficiency into productivity measurement. In this study, agricultural new quality productive forces are conceptualized as the combined outcome of technological innovation, ecological optimization, and resource reconfiguration. Therefore, the digital–green synergy provides a theoretical and practical foundation for enhancing ANQPFs through two channels: efficiency improvement and innovation activation.

Both agricultural digital transformation and green transformation are intrinsic requirements for developing new quality productive forces in agriculture. New quality productive forces are driven by deep industrial transformations and upgrades, revolutionary technological breakthroughs, and innovative configurations of production factors. Although digital and green transformations differ significantly in terms of connotation and characteristics, they share a common underlying logic as transformation processes—namely, an emphasis on innovation—thus providing a basis for synergy [24].

From the perspective of synergy theory, digitalization and greening exist within the complex system of developing new agricultural productive forces, where the components of both subsystems must continually integrate to generate synergy and drive the growth of new productive forces.

Specifically, the logic of dual transformation synergy lies not only in achieving a win-win outcome for economic and ecological benefits but also in the deep integration of development factors, thereby advancing new quality productive forces in agriculture through a balance of development and protection, ultimately contributing to the construction of an agricultural powerhouse [25].

In summary, studies have preliminarily shown that both agricultural digital and green transformations play critical empowering roles in fostering new quality productive forces, with regional-level evidence partially revealing the relationship between the two transformation processes. At the same time, current policies and the literature emphasize the need to explore, from an industrial level, how the “dual transformation” in agriculture can empower new quality productive forces. However, research still reveals significant gaps in this area.

First, although studies have highlighted the importance of synergy between digital and green transformations for the development of new productive forces, the literature has not clearly explained the internal mechanisms of this process. Second, most current research on “dual transformation” focuses on the regional level, and further clarification of the specific connotations and operational logic of dual transformation from the perspective of the agricultural industry is needed. Finally, the research lacks systematic strategic guidance and process interpretation with regard to how to realize the dual transformation of digitalization and greening to empower new productive forces in agriculture. Therefore, this paper aims to address the core research question of “how to enhance the effectiveness of synergistic digital–green transformation in agriculture to develop new quality productive forces” by applying synergy empowerment theory to open the “black box” of how the dual transformation empowers the development of new productive forces in the agricultural sector.

Traditional economic theory suggests that rapid economic growth often comes at the expense of excessive resource consumption and environmental degradation [26], However, digital and green transformations offer a pathway to break this pattern.

On the one hand, digital technologies improve production efficiency and optimize resource allocation, thereby reducing energy consumption and emissions per unit of output and achieving simultaneous increases in economic and ecological benefits [27]. On the other hand, green transformation emphasizes reducing pollution and resource consumption at the source, and promoting mutual reinforcement between economic growth and environmental protection through the adoption of advanced technologies such as clean energy and digital platforms.

This transformation model aligns not only with the concept of sustainable development but also with the requirements for cultivating new quality productive forces. Especially in the development of traditional industries, upgrading through the application of digital and green technologies for high-end, intelligent, and green transformation can significantly increase new quality productive forces in agriculture [28]. Thus, the following hypothesis is proposed:

H1. 

The synergistic transformation of agricultural digitalization and greening can promote the development of new quality productive forces.

In the context of rapid digital economic development, the synergy between digitalization and greening has become a key factor in developing new quality productive forces. This synergy optimizes and unleashes the multiplier effect of empowerment in advancing new quality productive forces in agriculture [29]. The essence of new quality productive forces lies in their advancement, with innovation being the core driving force. The synergy of digital and green transformations—through their deep integration—embodies the innovation-driven development concept. This synergy addresses the needs of new quality productive forces for high technology, high efficiency, and high quality by driving industrial upgrading, revolutionary technological breakthroughs, and optimized resource allocation. This synergistic development model is not only a theoretical necessity but also a practical lever, laying a solid foundation for exploring new paradigms of future economic development. Therefore, this paper analyses the internal transmission logic of how the synergy of agricultural digitalization and greening promotes new quality productive forces in three dimensions: industry, technology, and resources.

First, in terms of the industrial structure, the synergy between digitalization and greening fosters new quality productive forces by optimizing the agricultural industrial structure. Compared with traditional productivity, the cultivation of new agricultural quality productive forces places greater emphasis on restructuring the agricultural industry and extending the value chain. This emphasis is achieved through the deep integration of digitalization and greening by fostering new agricultural business entities, innovating industrial forms, and expanding industrial chains, thereby driving agriculture towards intelligent and diversified upgrading [30]. The synergy between digitalization and greening drives structural transformation, forming new industrial forms. Through digital transformation, agricultural production methods are modernized, promoting the green upgrading of the agricultural industrial structure, accelerating the cultivation of new agricultural business entities, and empowering new quality productive forces. Specifically, synergy empowers agricultural green development through digital technologies, leveraging the multiplier effect of the internet, big data, and other tools. As a result, new agricultural industries such as smart agriculture and rural tourism have emerged. These emerging industrial models are characterized by high added value, low energy consumption, and controlled pollution. Moreover, by extending the multifunctionality of agriculture, these models broaden the industrial dimension of green development and generate a compound effect of factor agglomeration and industrial synergy, thus building a sustainable industrial support system for new quality productive forces in agriculture.

Second, technological innovation plays a crucial role in the synergy of agricultural digitalization and greening, and it is a core component of new quality productive forces in agriculture [31]. The synergistic combination of digital and green technologies breaks the linear iteration logic of single technologies and forms a symbiotic technology system [32], facilitating revolutionary breakthroughs that advance agricultural production. This process not only improves agricultural productivity but also significantly reduces environmental pollution and degradation, promotes low-carbon agricultural practices, and fosters the development of new quality productive forces. Specifically, the key mechanism through which digital and green technologies empower agricultural new quality productive forces lies in their spillover and diffusion into the agricultural sector [33]. These outcoms occur through digitalization, networking, and intelligent approaches that comprehensively promote the development of rural economies and societies by improving quality and efficiency across multiple fields, levels, and scenarios. This promotion helps to build a digital resource-sharing model for green agricultural production [34], facilitates the reduction and efficiency improvement of inputs such as fertilizers and pesticides [35], and accelerates the integration of digital and green technologies with rural and agricultural development. It enhances total factor productivity across diverse agricultural actors and promotes the formation and advancement of new quality productive forces in agriculture.

Third, regarding resource allocation, efficient allocation plays a significant role in upgrading agricultural production methods and is vital for developing new quality productive forces in agriculture [36]. On the one hand, the synergy between digitalization and greening promotes the establishment of digital infrastructure in agriculture and widespread internet adoption It reduces information asymmetry in agricultural factor markets, fosters effective market competition, and facilitates the efficient supply-demand allocation of agricultural resources. On the other hand, data resources generated through agricultural digitalization are integrated with other production factors. Through the multiplier effect of data [37], such resources improve the efficiency of green agricultural production and drive the growth of new quality productive forces. On this basis, the following hypotheses are proposed. This paper presents a mechanism map to illustrate the relationship between Digital–Green Synergy and Agricultural New Quality Productive Forces, as shown in Figure 1.

H2a. 

Digital–green synergy empowers new quality productive forces in agriculture through industrial structure upgrades.

H2b. 

Digital–green synergy empowers new quality productive forces in agriculture through technological innovation.

H2c. 

Digital–green synergy empowers new quality productive forces in agriculture through optimized resource allocation.

3. Research Design

3.1. Variable Selection and Data Description

3.1.1. Explained Variable: Agricultural New Quality Productive Forces

Based on the theoretical analysis and the study by Zhu and Ye [38], an evaluation index system for the development level of agricultural new quality productive forces is developed from three aspects—high-quality agricultural labourers, new media-based agricultural means of production, and new material agricultural labour objects—anchored in Marx’s three elements of productivity theory. The specific selected indicators are shown in

Table 1. Indicator system for agricultural new quality productive forces.

Target Layer	Criteria Layer	Indicator Layer
Workers	Scientific and Technical Personnel	Number of Agricultural Science and Technology Personnel
	Rural Labour Force Educational Level	Average Years of Education of the Rural Labour Force
	Worker Structure	Outbound Migrant Workers/Rural Employed Population
	Household Entrepreneurship Activity	Rural Entrepreneurship Index
Labour Objects	Agricultural Machinery and Equipment	Total Agricultural Machinery Power/Total Sown Area
	Grain Yield Level	Total Grain Output/Grain Sown Area
	New Business Models	Facility Agriculture Level
Labour Resources	Technological Progress Level	Contribution of Agricultural Technological Progress
	Technological Innovation Support	R&D Expenditure/Total GDP of Agriculture, Forestry, Animal Husbandry, and Fishery

.

3.1.2. Core Explanatory Variable

Based on the core concepts of agricultural digitalization and agricultural greening, an indicator system is constructed to measure their respective development levels. Drawing on previous studies [39,40], and considering indicator comprehensiveness, scientific validity, data availability, and comparability, the digitalization dimension is evaluated from three aspects: digital infrastructure, digital financial services, and the digital transformation of the agricultural industry. Similarly, the greening dimension is assessed from three aspects: efficient resource utilization, green production methods, and the abundance of ecological products.

To ensure objectivity in the evaluation, the entropy weight method is employed to measure the levels of agricultural digitalization and greening across provinces. Considering that digital–green synergy involves the mutual interaction and coordinated transformation of both systems, the coupling coordination degree model is applied to calculate the level of synergy between agricultural digitalization and greening. This model captures not only the degree of interconnection between the two subsystems but also the strength of their dynamic interaction, thereby reflecting the overall level of synergy of the dual transformation process. This approach has been widely adopted in recent studies on the coupling of digital and green development in agriculture [41]. The specific indicator system is shown in

Table 2. Evaluation index system for digital–green synergy.

Target Level	Criterion Level	Indicator Level	Indicator Description
Digitalization	Digital Infrastructure	Rural Internet Popularization Level	Rural Internet Penetration Rate
		Internet Users	Number of Rural Broadband Internet Accesses
		Optical Cable Quantity	Length of Optical Cable per Square Metre
	Digital Financial Services	Digital Inclusive Finance—Coverage Breadth	Coverage Breadth of Digital Inclusive Finance
		Digital Inclusive Finance—Usage Depth	Usage Depth of Digital Inclusive Finance
	Digitalization of Agriculture	Ecommerce Development Level	Proportion of Enterprises Engaged in Ecommerce Activities
		Digital Village Pilot	Number of Digital Village Pilot Projects
Greening	Efficient Resource Utilization	Agricultural GDP per Unit Area	Agricultural GDP/Total Sown Area
		Proportion of Effectively Irrigated Area	Effectively Irrigated Area/Total Sown Area
	Green Production Methods	Pesticide Use per Unit Sown Area	Pesticide Usage/Total Sown Area
		Fertilizer Use per Unit Sown Area	Fertilizer Usage/Total Sown Area
	Abundance of Ecological Products	Green Agricultural Cooperatives	Number of Green Agricultural Cooperatives
		Agricultural Product Quality	Number of National Agricultural Product Quality–Safety Counties

.

In this paper, the entropy weight method is employed to comprehensively measure the levels of agricultural new quality productive forces, agricultural digitalization, and agricultural greening. The specific steps are as follows. First, all indicators are standardized, and both positive and negative indicators are adjusted according to their directional attributes to ensure comparability across dimensions.

$$X_{ij}={\displaystyle \frac{z_{ij}-\min \left(z_{ij}\right)}{\max \left(z_{ij}\right)-\min \left(z_{ij}\right)}}$$

(1)

$$X_{ij}={\displaystyle \frac{\max \left(z_{ij}\right)-z_{ij}}{\max \left(z_{ij}\right)-\min \left(z_{ij}\right)}}$$

(2)

where $z_{ij}$ is the original data, $i$ is the region, $j$ is the indicator, $X_{ij}$ is the result of standardization, $\min \left(z_{ij}\right)$ is the minimum value of the indicator, and $\max \left(z_{ij}\right)$ is the maximum value of the indicator.

Next, we calculate the weight of the $i$ sample in the indicator $j$, $p_{ij}={\displaystyle \frac{x_{ij}}{{\sum }_{i=1}^n x_{ij}}}$, where $n$ is the number of samples.

Next, we calculate the information entropy of the indicator $j$, $e_j=-k{\sum }_{i=1}^n p_{ij}\ln \left(p_{ij}\right)$, where $k$ = 1/$\ln (n)$.

Then, we calculate the information entropy redundancy, $d_j$ = 1 − $e_j$, and the weight of each indicator $w_j={\displaystyle \frac{d_j}{{\sum }_{i=1}^n d_j}}$, where $m$ is the number of indicators.

Finally, we calculate the score $h_{ij}$, $h_{ij}=\sum _{i=1}^n w_j{p}_{ij}$.

Mechanism Variables

• Industry (AIS): This variable is measured by multiplying the total agricultural output value by the proportion of specialized and auxiliary activities in agriculture, forestry, animal husbandry, and fishery, serving as a proxy for agricultural industrial structure optimization [42].
• Technology (DGT): This variable is measured by the number of valid granted invention patents. Given that invention patents are highly innovative and that there is a time lag from application to authorization, this study considers authorized valid patents to be a better reflection of patent quality and technological level. The integration of digital and green technologies is captured through these two dimensions as indicators of technological innovation and is used to assess the mediating effect of technology [43].
• Resource (RA): Following the approach of Tang et al. and Wang [44,45], the ratio of agricultural fixed asset investment to the output value of the primary industry is used as a proxy for resource allocation.

Control Variables

To ensure the accuracy of the estimation, this study also controls for other variables that may influence agricultural new quality productive forces.

• Unemployment level (Unemp): This variable is measured as the ratio of the registered unemployed population in urban areas at year end to the total population at year end.
• Education (Ed):This variable is measured as the ratio of governmental expenditure on education to the total general budgetary expenditure of local government.
• Economic development level(NGDP): This variable represents per capita regional GDP.
• Fiscal support intensity(ngov): This variable is measured as the ratio of general government fiscal expenditure to regional GDP.
• Scientific and technological support (kgov): This variable is measured as the ratio of science and technology expenditure to general budget expenditure.
• Urban-rural income gap (cxi): This variable is measured as the income ratio between urban and rural residents. The descriptive statistics for all of the variables are shown in

  Table 3. Variable definitions and descriptive statistics.

  Variable Name	Mean	Standard Deviation	Minimum	Maximum
  Agricultural New Quality Productive Forces	0.226	0.093	0.070	0.541
  Level of Digital–Green Synergy	0.430	0.153	0.111	0.824
  Unemployment Level	0.690	0.381	0.130	3.140
  Education	0.163	0.027	0.099	0.222
  Economic Development Level	6.487	3.212	1.971	20.028
  Fiscal Support Intensity	0.107	0.031	0.038	0.186
  Scientific and Technological Support	0.022	0.016	0.005	0.068
  Urban-Rural Income Gap	2.532	0.407	1.820	3.930

  .

3.2. Data Sources

Following established research practices and constrained by data accessibility, the dataset for this study was compiled from official statistical publications, including” China Statistical Yearbook,” “China Rural Statistical Yearbook,” “China Tertiary Industry Statistical Yearbook,” provincial statistical yearbooks, and the official websites of the National Bureau of Statistics. The inclusive finance data come from the “China Digital Inclusive Finance Index”, which is jointly compiled by the Peking University Digital Finance Research Centre and Ant Group. For some of the missing data, linear interpolation is used to fill in the blanks.

3.3. Econometric Model Specification

3.3.1. Baseline Regression Model

Based on the theoretical framework and research hypotheses [46], this study employs provincial panel data to empirically examine the influence of digital–green synergy transformation on ANQPFs. Considering that provinces may vary in terms of their resource endowments, industrial structures, and policy environments, such heterogeneity may bias the estimation results it is not appropriately controlled for. To address these potential confounding factors, this study adopts a two-way fixed effects model as the baseline estimation framework to identify the effect of digital–green synergy on ANQPFs.

This model simultaneously controls for individual (province specific) and time-fixed effects, thereby accounting for unobserved heterogeneity that is constant over time within provinces and common shocks that vary over time but are constant across provinces. The specific econometric model is constructed as follows: The specific model construction is as follows:

$$ANQ{PF}_{it}={\alpha }_0+{\alpha }_{1}DG{S}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

where $i$ and $t$ represent the province and year, respectively. $ANQPF$ represents the level of agricultural new quality productive forces; $DGS$ represents the digital–green synergy level; $X$ represents the control variables; ${\mu }_i$ and ${\lambda }_t$ represent regional and time fixed effects, respectively; and ${\epsilon }_{it}$ is the random disturbance term. This study focuses on estimating parameter ${\alpha }_1$, which reflects the magnitude and direction of the impact of agricultural digital–green synergy on agricultural new quality productive forces.

3.3.2. Mediating Effect

In this study, the Jiang [47] mediation test method is used to verify the relationship between agricultural digital–green synergy and the mediator variables and the mediating effect is further explored. By constructing the following mechanism test model, this study examines the ‘industry–technology–resource’ pathway through which digital–green synergy influences agricultural new quality productive forces. The specific model specification is as follows:

$${AIS}_{it}={\mathsf{\omega }}_0+{\mathsf{\omega }}_{1}DG{S}_{it}+{\mathsf{\omega }}_{i}contro{l}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(4)

$${DGT}_{it}={\mathsf{\omega }}_0+{\mathsf{\omega }}_{1}DG{S}_{it}+{\mathsf{\omega }}_{i}contro{l}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(5)

$${RA}_{it}={\mathsf{\omega }}_0+{\mathsf{\omega }}_{1}DG{S}_{it}+{\mathsf{\omega }}_{i}contro{l}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(6)

${AIS}_{it}$ is the level of upgrading of the agricultural industrial structure of region $i$ in period $t$, ${DGT}_{it}$ is the technological innovation of region $i$ in period $t$, ${RA}_{it}$ is the level of agricultural resource allocation efficiency of region $i$ in period $t$, ${\mathsf{\omega }}_0$ is the intercept term, and ${\mathsf{\omega }}_1$ and ${\mathsf{\omega }}_i$ are the coefficients of the variables. All other specifications are consistent with those in Equation (3).

4. Empirical Results and Analysis

4.1. Baseline Regression Results

The results of the impact of digital–green synergy on agricultural new quality productive forces are shown in

Table 4. Baseline regression results.

Variable	(1) Agricultural New Quality Productive Forces	(2) Agricultural New Quality Productive Forces	(3) Agricultural New Quality Productive Forces
Digital–Green Synergy	0.297 ***	0.240 ***	0.296 ***
	(0.028)	(0.044)	(0.051)
Unemployment Level			−0.014 **
			(0.007)
Education			0.349 ***
			(0.113)
Economic Development Level			0.012 ***
			(0.001)
Fiscal Support Intensity			−0.014 ***
			(0.005)
Scientific and Technological Support			0.453 **
			(0.227)
Urban-Rural Income Gap			−0.006
			(0.010)
Constant Term	0.098 ***	0.127 ***	0.122 ***
	(0.013)	(0.011)	(0.046)
Region	Controlled	Controlled	Controlled
Year		Controlled	Controlled
Observations	360	360	360
R2	0.238	0.615	0.717

Note: The values in parentheses represent standard errors. ** and *** denote significance at the 1% and 5% levels, respectively. The same applies below.

. Columns (1) and (2) represent the model results with regional fixed effects and two-way fixed effects but no control variables, whereas Column (3) represents the model results with two-way fixed effects and control variables included. The results show that, regardless of whether control variables are included in the model, the digital–green synergy level is positive and significant at the 1% level, and the coefficient is positive. These results indicate that digital–green synergy can significantly improve the improvement of agricultural new quality productive forces, confirming Hypothesis 1. Without control variables, when the digital–green synergy level increases by 1 unit, agricultural new quality productive forces increase by 0.297 units. With control variables included, when the digital–green synergy level increases by 1 unit, agricultural new quality productive forces increase by 0.296 units.

4.2. Mechanism Test

4.2.1. Industry (AIS)

Drawing on the approach of Jiang et al. [42], the total agricultural output value multiplied by (the value of agricultural, forestry, animal husbandry, and fishery specialized and auxiliary activities/the total output value of agriculture, forestry, animal husbandry, and fishery) is used to calculate the total agricultural service industry output value as a proxy for the optimization of the agricultural industrial structure. According to the results in Column (1) of

Table 5. Mediating effect test results.

	(1) AIS	(2) DGT	(3) RA
Digital–Green Synergy	0.597 ***	0.970 **	0.378 ***
	(0.075)	(0.416)	(0.060)
Control Variables	Controlled	Controlled	Controlled
Fixed Effects	Controlled	Controlled	Controlled
_cons	0.105	7.813 ***	0.037
	(0.064)	(0.357)	(0.052)
N	360	360	360
R2	0.720	0.950	0.602

Notes: Standard errors are in parentheses. *** and ** denote significance at the 1% and 5% levels, respectively.

, improvements in digital–green synergy significantly promote agricultural industrial structure upgrading. Coordinating the development of the primary, secondary, and tertiary sectors of agriculture is key for achieving agricultural new quality productive forces. The synergy between digitalization and green development promotes the integration of different agricultural industries, and digital–green synergy creates new conditions for agricultural industrial structure upgrading. The application and development of agricultural digital technology create conditions for agricultural greening, and the demand for agricultural greening further drives the development of agricultural digitalization. Therefore, the synergy between the two creates a foundation for upgrading the agricultural industrial structure, which promotes the development of agricultural new quality productive forces. Overall, digital–green synergy promotes agricultural new quality productive forces by optimizing the agricultural industrial structure, confirming H2a.

4.2.2. Technology (DGT)

Effective invention patents are highly innovative. The number of effective digital and green agricultural invention patents is used as a measure of technological innovation. As shown in Column (2), the level of digital–green synergy has a significant positive effect on technological innovation, indicating that digital–green synergy can drive the technological innovation of agriculture. Through digital transformation and the demand for green development, the agricultural sector precisely controls various costs, directing resources towards invention patent activities, which further promotes the development of agricultural new quality productive forces. Therefore, H2b is confirmed, indicating that digital–green synergy promotes technological innovation and drives agricultural new quality productive forces.

4.2.3. Resources (RA)

Capital is an important manifestation of the efficient resource allocation As shown in Column (3), the regression coefficient of the effect of digital–green synergy on resource allocation is positive and significant at the 1% level. Therefore, digital–green synergy can optimize capital allocation and promote agricultural new quality productive forces, confirming H2c. The reason for this relationship is that digital–green synergy closely aligns with the key elements of digitalization and greening, enabling the realization of a bidirectional relationship between them, promoting the rapid flow of agricultural capital, optimizing resource allocation, and facilitating the development of agricultural new quality productive forces. This process improves agricultural industries, expands the effective allocation of agricultural digital and green production factors, and accelerates the development of agricultural new quality productive forces.

4.3. Robustness Tests

To guarantee the dependability of the empirical findings, this paper performs a range of robustness tests. Three methods are mainly used: lagging one period, changing the model estimation method, and excluding directly controlled municipalities.

4.3.1. One-Period Lag

The endogeneity problem caused by bidirectional causality is an important issue that must to be addressed. Therefore, this paper uses a one-period lag of the digital–green synergy level as a new explanatory variable for regression, with control variables consistent with those in the baseline regression. This approach can partially alleviate the endogeneity problem caused by bidirectional causality. The results are shown in Column (1) of

Table 6. Robustness tests.

	(1) Lagged X by One Period	(2) Alternative Estimation Method	(3) Excluding Municipalities
Dependent Variable (y)	y	y	y
L.x	0.369 ***
	(0.054)
x		0.052 ***	0.257 ***
		(0.004)	(0.066)
L.y		0.684 ***
		(0.034)
Control Variables	Controlled	Controlled	Controlled
Time Fixed Effects	Controlled	Controlled	Controlled
Region Fixed Effects	Controlled	Controlled	Controlled
R2	0.723	0.963	0.953
Sample Size	330	300	312

Notes: Standard errors are in parentheses. *** denotes significance at the 1% level

. The regression results with a one-period lag show that digital–green synergy has a positive effect on agricultural new quality productive forces, which is consistent with the baseline regression results.

4.3.2. Change in the Model Estimation Method

Considering that using only the fixed effects model for estimation may not fully eliminate historical behaviour errors, this paper changes the estimation method and adopts the two-stage system generalized method of moments (GMM), adding a one-period lag of the dependent variable. The impact of digital–green synergy on agricultural new quality productive forces is re-examined. The regression results are shown in Column (2) of Table 6, revealing that the coefficient of the impact of digital–green synergy on agricultural new quality productive forces is positive and significant at the 1% level. These findings indicate that the regression results are robust and reliable.

4.3.3. Excluding Directly Controlled Municipalities

Beijing, Tianjin, Shanghai, and Chongqing are the four directly controlled by the central government; they are the highest-level cities in China and have the best economic foundations and greatest policy dividends. Including them in the model may lead to biassed regression results. Therefore, these four municipalities are excluded, and the regression analysis is reconducted. The results, presented in Column (3) of Table 6, show that digital–green synergy still significantly promotes agricultural new quality productive forces at the 1% level, further confirming the robustness of the baseline regression results.

4.4. Endogeneity Test

Generally, regions with a higher level of agricultural new quality productive forces development tend to have higher-quality labour, more abundant and diverse labour resources, and higher levels of application and popularization of digital technologies such as the internet, artificial intelligence, and big data. As a result, the level of digital–green synergy has a certain first-mover advantage. This situation means that agriculture may reverse the influence of the level of digital–green synergy, i.e., the two exist in a bidirectional causal relationship, leading to an endogeneity problem. To address this problem, this paper uses the instrumental variable method to mitigate the endogeneity issue. An exogenous shock is selected, namely, the dual transformation synergy pilot policy dummy variable, with China’s “Digital Transformation and Green Development Demonstration Zone” as an instrumental variable for the level of digital–green synergy in agriculture.

The two-stage least squares method is used for estimation, and the results are shown in Columns (1) and (2) of

Table 7. Instrumental variable results.

Variable	X	Y
	(1)	(2)
iv	0.086 ***
	(0.017)
x		0.188 ***
		(0.069)
Control Variables	Controlled	Controlled
Time Fixed Effects	Controlled	Controlled
Region Fixed Effects	Controlled	Controlled
Sample Size	360	360

Note: The F statistic is 23.40; the Kleibergen-Paap LM statistic for the under-identification test is 12.56; and the Cragg–Donald Wald F statistic for the weak identification test is 20.32. *** denotes significance at the 1% level.

. Both tests for instrument validity yield strong results. The Kleibergen–Paap rk LM statistic (12.56) allows for a clear rejection of under-identification, while the Cragg–Donald Wald F statistic (20.32), which is well above the common threshold of 10, confidently rejects the presence of weak instruments. The first-stage regression results reveal a positive correlation between the instrumental variable and the explanatory variable, indicating that the instrumental variable satisfies the correlation condition with agricultural digital–green synergy. The second-stage regression results show that, after considering the endogeneity issue is considered, digital–green synergy still promotes agricultural new quality productive forces, indicating that the baseline regression model has no severe endogeneity problem.

4.5. Heterogeneity Analysis

4.5.1. Geographical Location Heterogeneity

China has a vast east–west span, and the geographical differences among regions are significant. To further explore the heterogeneous effects of digital–green synergy on agricultural new quality productive forces, the sample is divided into eastern, central, western, and northeastern regions. The regression results are presented in

Table 8. Heterogeneity analysis results.

	Geographic Area Heterogeneity				Temporal Heterogeneity		Heterogeneity of Functional Agricultural Areas
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	East	Central	West	Northwest	2012–2018	2019–2023	Major Agricultural Region	Major Food Marketing Area	Food Production and Marketing Balance Area
Digital–Green Synergy	0.309 ***	1.185 ***	0.311 ***	−0.697	0.329 ***	0.424 ***	0.364 ***	0.670 ***	0.279
	(0.081)	(0.226)	(0.110)	(0.456)	(0.105)	(0.067)	(0.066)	(0.096)	(0.173)
Control Variable	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled	Controlled
Sample Size	120	72	132	36	210	150	156	84	120
R2	0.697	0.914	0.759	0.732	0.508	0.670	0.834	0.833	0.638

Notes: Standard errors are in parentheses. *** denotes significance at the 1% level.

. The findings show that digital–green synergy in agriculture significantly promotes ANQPFs in the eastern, central, and western regions, with a “central > western > eastern.” This pattern suggests that the effect of digital–green synergy on agricultural new quality productive forces varies across regions because of geographical and institutional heterogeneity. Specifically, most central provinces are major grain-producing areas with abundant natural resources, a solid agricultural foundation, and a higher degree of agricultural scale and mechanization. Additionally local governments in these regions bear a strong political responsibility for ensuring national food security, which motivates them to implement proactive agricultural modernization policies. By constructing precision agricultural service systems and cultivating new-type agricultural business entities, these governments provide crucial organizational and institutional support for enhancing ANQPFs through digital–green synergy.

In contrast, the northeastern region does not show a significant effect. This finding may be related to its sparse population, vast land area, and ongoing industrial transformation from heavy industries to modern sectors, limiting the integration of digital and green development in agriculture. Moreover, the relatively slow diffusion of digital technologies and lagging institutional adaptation have weakened the synergistic effect in this region. These results confirm that regional heterogeneity is an important factor influencing the strength and direction of the synergistic effect.

4.5.2. Temporal Heterogeneity

In 2018, the central government’s “No. 1 Document” explicitly proposed the “Digital Rural Strategy.” Therefore, this paper uses 2018 as a temporal base from which to study the temporal heterogeneity of the impact of digital–green synergy on agricultural new quality productive forces. Table 8 (2) reports the impact of digital–green synergy on agricultural new quality productive forces during two periods: 2012–2018 and 2019–2023. The results show that from 2012 to 2018, the diminishing effect of digital–green synergy on agricultural new quality productive forces was positive and significant at the 1% level. From 2019 to 2023, the increasing effect of digital–green synergy on agricultural new quality productive forces was significant, and the coefficient was greater than that in the 2012–2018 period. Therefore, the expanding effect of digital–green synergy on agricultural new quality productive forces became more pronounced from 2019 to 2023. These results indicate that the introduction of the “Digital Rural Strategy” and policies such as the “Digital Agriculture and Rural Development Plan (2019–2025)” have helped to gradually release digital dividends to rural areas, with the cumulative effect of rural digital transformation, thus enhancing the impact of digital–green synergy on agricultural new quality productive forces.

4.5.3. Heterogeneity of Agricultural Functional Zones

To further establish the impact of digital–green synergy on new productivity in different regions, this paper divides the 30 provinces of China according to the State Council’s “Guiding Opinions on Establishing Grain Production Functional Zones and Important Agricultural Products Protection Zones,”based on their functional roles in terms of grain production. The results in Table 8 show that in grain-producing areas, the estimated coefficient of the impact of agricultural digital–green synergy on new productivity is 0.364 and significant at the 1% level. In grain-selling areas, the estimated coefficient is 0.670 and significant at the 1% level However, in areas with balanced grain production and sales, the impact of digital–green synergy on agricultural new quality productive forces is not significant. These results may be due to differences in resource endowments and policy support. Grain-producing areas typically have greater agricultural infrastructure, large-scale planting, and stronger policy support, such as digital and green subsidies, digital technology, and green promotion. As a result, digital–green synergy can be effectively implemented. In grain-selling areas, the economy is more developed, and the level of marketization is greater, resulting in a more efficient application of digital and green technologies, such as smart agriculture and precision fertilization. In contrast, in balanced grain production and sales areas, insufficient resource input or low policy attention may lead to inadequate technology application. The industrial structure might be in a transitional state, lacking both the economies of scale found in production areas and the market-driven momentum present in selling areas.

5. Conclusions, Policy Recommendations and Limitations

5.1. Conclusions

Digital and green synergy in agriculture is a key driving force for developing agricultural new quality productive forces. The effective synergy between the two not only breaks through the growth path of traditional agricultural productivity but also embodies the efficient and green characteristics of agricultural new quality productive forces. Through a theoretical analysis and empirical testing, the impact of digital–green synergy on agricultural new quality productive forces and the corresponding mechanisms are explored. The research conclusions are as follows. The baseline regression results show that digital–green synergy significantly enhances agricultural new quality productive forces. The “Dual Synergy Comprehensive Pilot Work” in 10 provinces, proposed in January 2023, was used as an instrumental basis for robustness testing, and consistent conclusions were drawn. The results of the mediating effect test indicated that digital–green synergy promotes agricultural new quality productive forces through industrial optimization, technological innovation, and efficient resource allocation. The heterogeneity analysis revealed that the promoting effect of digital–green synergy on agricultural new quality productive forces is most prominent in the eastern, central, and western regions as well as in grain-producing and grain-consuming areas. Moreover, after the digital rural pilot was introduced, the promoting effect of digital–green synergy on agricultural new quality productive forces became more significant.

5.2. Policy Recommendations

Based on the findings of this study, the following policy recommendations are proposed to strengthen agricultural modernization and promote new quality productive forces through digital–green synergy.

First, an enabling policy environment should be established to promote the coordinated development of agricultural digitalization and greening. Relevant ministries—such as those related to agriculture, ecology, and information—should form cross-ministerial coordination frameworks to jointly design and implement integrated digital–green policies. A national data-sharing platform can be created to link agricultural, environmental, and market information, enabling evidence-based and timely decision-making. Governments can enhance incentive-based mechanisms, including digital–green innovation funds, carbon-credit rewards, and preferential tax policies, to encourage farmer and enterprise participation. To overcome implementation barriers—such as uneven infrastructure and institutional inertia—capacity-building programmes and pilot demonstrations should be promoted to strengthen local adaptability and ensure consistent policy execution.

Second, it is crucial to leverage the digital–green synergy to optimize industrial structure, foster technological innovation, and improve resource allocation. Efforts should prioritize the construction of modern agricultural industrial chains that integrate digital and green elements to enhance resilience and extend value creation. Governments efforts can be enhanced by supporting R&D in key dual-transformation technologies—such as AI-based precision farming, smart irrigation, and low-carbon production—and reform talent training and utilization mechanisms to support the development of agricultural new quality productive forces (ANQPFs). Establishing cross-disciplinary innovation platforms and pilot digital–green technology bases in regions like the Yangtze River Delta and Greater Bay Area can accelerate technology transfer and diffusion. Moreover, expanding green finance channels and setting up agricultural innovation funds can attract private capital, forming a self-sustaining system that integrates industrial upgrading, technological progress, and efficient resource allocation.

Third, context-sensitive policy adaptation to regional conditions remains crucial for balanced agricultural modernization.The eastern region can capitalize on its digital infrastructure to develop smart, low-carbon agriculture; the central region is well-positioned to demonstrate integrated digital–green transformation; the western region may focus on improving rural infrastructure and promoting ecological farming; and the northeastern region could strengthen link agricultural revitalization with industrial restructuring through “industry supports agriculture” initiatives. Maintaining policy continuity under the Digital Rural Strategy, establishing dynamic monitoring systems, and creating a national coordination platform linking eastern technological advantages with western resource endowments will further ensure synergistic, inclusive, and sustainable agricultural development.

To visually illustrate how digital–green synergy promotes agricultural new quality productive forces and guides policy implementation, Figure 2 presents the conceptual mechanism linking digital–green synergy with agricultural modernization and new-quality productivity development pathways. This framework integrates the interactive dynamics between digital and green transformations, their empowerment mechanisms through industrial upgrading, technological innovation, and resource allocation, as well as the supporting policy environment that sustains and reinforces these processes. The figure highlights that coordinated digital and green transformations serve as the core driving forces of agricultural modernization—enhancing productivity, sustainability, and innovation capacity—while policy coordination, institutional support, and regional adaptation provide the systemic foundation for realizing the sustainable development of agricultural new quality productive forces.

5.3. Limitations

Although this paper employs a scientifically rigorous methodology to examine the relationship and mechanisms between digital–green synergy and agricultural new quality productive forces, several limitations remain.

First, the available data are limited, and the granularity is relatively coarse. This study mainly relies mainly on provincial-level data, which may mask important variations at the municipal or county level. Future research could integrate multi-level or micro-level data to capture local dynamics and regional heterogeneity more accurately. Second, owing to data accessibility constraints, this study focuses solely on China. While China provides a representative case for exploring digital–green synergy in agriculture, future studies could extend the analysis to other emerging economies, enabling cross-country comparisons and enhancing the generalizability of the conclusions. Third, although this study employs an instrumental variable (IV) approach to mitigate reverse causality, omitted variables and measurement errors may still bias the estimates. Future research may employ quasi-natural experimental approaches to enhance causal inference in this domain.