Internal Mechanism and Empirical Analysis of Digital Economy’s Impact on Agricultural New Quality Productive Forces: Evidence from China

Abstract

Agricultural new quality productive forces (ANQPFs) signify the progressive trajectory of modern agriculture. However, their development encounters significant challenges in many nations. The digital economy, characterized by its strong innovative capacity, offers continuous impetus for advancing agricultural new quality productive forces (ANQPFs). Based on panel data from 30 Chinese provinces (2014–2023), this study employs a two-way fixed-effects model, mediation and threshold effect analyses, and a spatial Durbin model to comprehensively assess the influence of the digital economy (DE) on agricultural new quality productive forces (ANQPFs). The findings reveal that (1) the digital economy (DE) significantly enhances the advancement of agricultural new quality productive forces (ANQPFs); (2) while its positive effect is pronounced in eastern, central, and western China, the impact is weaker in the northeastern region; (3) rural financial development (RFD) acts as a mediator in the relationship between digital economy (DE) growth and agricultural new quality productive forces (ANQPFs); (4) the digital economy (DE)’s contribution to agricultural new quality productive forces (ANQPFs) demonstrates non-linear trends; and (5) spatially, while the digital economy (DE) boosts the local agricultural new quality productive forces (ANQPFs), it exerts a negative spillover effect on neighboring areas. This research offers fresh empirical insights into the determinants of agricultural new quality productive forces (ANQPFs) and suggests policy measures to support agricultural modernization.

1. Introduction

Agricultural new quality productive forces (ANQPFs) constitute an advanced extension of new quality productive forces within the agricultural domain. They reflect a significant evolutionary leap in adapting to shifts in agricultural production relations and epitomize the progressive trajectory of modern agricultural development [1]. As a critical indicator of sustainable agriculture, ANQPFs leverage cutting-edge technologies and novel production factors to catalyze systemic transformations in agricultural labor, labor inputs, and production methods. This, in turn, elevates agricultural efficiency, stimulates economic growth in the sector, and generates robust impetus and safeguards for long-term agricultural sustainability [2].

Recent years have witnessed global initiatives in developing ANQPFs. Nations with advanced economies, particularly in Europe and North America, have implemented policy innovations and allocated substantial funding to stimulate corporate research expenditures, foster technological advancements in agriculture, and facilitate industrial transformation [3]. A case in point is the American agricultural sector, where satellite imaging, unmanned aerial vehicles, and data analytics have been extensively deployed to enable precision farming practices, resulting in enhanced productivity and optimized resource allocation. Meanwhile, numerous emerging economies are actively adopting sophisticated agricultural techniques and modern production approaches from industrialized nations to elevate both service-oriented and industrial dimensions of their agricultural sectors. Illustratively, several African nations have achieved remarkable improvements in cereal output through the adoption of advanced wheat farming techniques and modern water management systems.

The global advancement of ANQPFs continues to encounter multiple obstacles that constrain sustainable agricultural development [4]. First, technological progress faces substantial constraints, as agricultural innovation efforts often mismatch market requirements, creating barriers to technology adoption. Many countries also experience deficiencies in indigenous core technological capabilities. Second, uneven geographical distribution of resources leads to simultaneous occurrences of scarcity and inefficient utilization, while data accessibility limitations restrict the broad implementation of digital solutions. Moreover, insufficient policy frameworks and institutional support systems, including underdeveloped property rights regimes and inadequate incentive structures, negatively impact the motivation of innovation stakeholders [5]. As the most populous developing nation globally, China is actively pursuing the modernization of its agricultural sector. Farm equipment is progressively evolving toward smart technologies, while the agricultural sector itself requires comprehensive structural transformation.

The emergence of the digital economy (DE) presents innovative approaches to overcome obstacles in developing ANQPFs while offering comprehensive strategies for sustainable farming advancement. Digital innovations effectively dismantle information asymmetries between agricultural research and market needs, facilitating accurate demand–supply alignment and expediting both technological breakthroughs and research commercialization. Utilizing advanced analytics and AI-driven solutions enables more efficient distribution of resources, enhancing productivity in land and water usage while mitigating regional disparities. Furthermore, the DE enhances institutional frameworks by strengthening property rights and incentive structures through blockchain-based solutions that ensure data protection and fair benefit-sharing, consequently boosting engagement among innovation stakeholders [6].

This study systematically investigates the correlation between the DE and ANQPFs, seeking to offer valuable insights and practical guidance for advancing sustainable agriculture in China and globally. Accordingly, grounded in theoretical examination, the research analyzes the DE’s direct influence on ANQPFs, including its mediating mechanisms, threshold effects, and geographical spillover impacts. The findings subsequently inform relevant policy suggestions.

This research makes three key academic advances that meaningfully extend current understanding: (1) It develops an integrated evaluative system for assessing the DE and ANQPFs that employs more comprehensive metrics compared to existing studies, significantly expanding the analytical approaches available for investigating these interrelated domains. (2) A multi-level investigation is conducted into geographical variations, intermediary channels, and critical threshold effects in the DE–ANQPFs nexus, thoroughly exploring not only their immediate and mediated connections but also uncovering complex nonlinear relationships and spatial interaction patterns, thereby providing a unified conceptualization of the DE’s effectiveness-enhancing properties. (3) Implementation of spatial Durbin modeling technique enables the identification of location-specific policy insights concerning the DE’s role in ANQPF improvement, producing territorially-aware results with greater analytical rigor.

2. Literature Review

Contemporary scholarly investigations concerning the digital economy (DE) predominantly concentrate on two principal aspects: conceptual definitions and consequential effects. Regarding conceptual explorations, researchers have adopted diverse analytical frameworks. Certain academics adopt a descriptive approach, characterizing the DE as an innovative economic paradigm propelled by digital innovations [7,8]. Alternative studies highlight its distinctive features compared to conventional economic systems, particularly in terms of driving mechanisms and factor compositions [9,10,11]. Another research strand employs structural methodologies, emphasizing measurable dimensions of the DE [12]. Concerning the DE’s socioeconomic impacts, extant literature has yielded substantial findings, generally categorized into beneficial and adverse consequences. The advantageous effects encompass the following: The DE’s permeation into rural economies enables synergistic urban–rural resource allocation, diminishes transactional expenses, stimulates environmentally sustainable industrial growth [13], expedites technological advancements, and enhances urban–rural connectivity [14,15]. Conversely, potential drawbacks involving expansion may precipitate challenges including technological disparities and workforce skill inadequacies [16].

Contemporary scholarly discourse primarily focuses on examining the theoretical foundations and operational implications of ANQPFs. Concerning conceptual definitions, the academic consensus positions ANQPFs as multifaceted constructs with stratified meanings, demonstrating several distinctive attributes in their operational manifestation. These include dependence on highly specialized human capital as its foundation, characterized by transformative technological advancements, emphasizing coordinated multifactor allocation [17], and displaying dual structural properties encompassing both industrial diversification and value chain extension [18], with its most salient features manifesting through digitalization processes and sustainable development practices [19,20,21,22]. Regarding functional impacts, from the standpoint of domestic agricultural advancement requirements, ANQPFs possess the capacity to restructure production frameworks in agriculturally developing nations, modernize smallholder farming operations, and enable their assimilation into contemporary agricultural paradigms [23]. Simultaneously, within the context of resource constraints and growing environmental regulations, the modernization of agricultural production necessitates fundamental transformations in production factors to achieve balanced economic, social, and ecological outcomes [24]. From a global agricultural perspective, international competition in agricultural technology is escalating dramatically. Developing innovative agricultural productivity through technological breakthroughs has become crucial for maintaining national food security sovereignty [25]. ANQPFs, with scientific innovation at their core, not only accelerates agricultural technological advancement and strengthens food security frameworks but also enhances sectoral resilience and improves international market competitiveness of domestic agricultural products [26]. Scholars in China have undertaken substantial empirical investigations regarding ANQPFs. Notably, Wang et al. [27] and colleagues applied the entropy-weighted TOPSIS approach to assess ANQPF development levels, subsequently implementing an obstacle diagnosis model alongside Dagum Gini coefficient decomposition to examine regional disparities and constraining factors. Parallel research by the Yang et al. [28] team systematically investigated the relationship between ANQPFs and rural household earnings. Furthermore, Li et al. [29] leveraged machine learning techniques to quantify ANQPF metrics while clarifying how provincial-level ANQPFs affect income inequality between urban and rural areas.

Current academic research exhibits a notable gap in studies specifically investigating the correlation between the DE and ANQPFs. The predominant scholarly attention has been directed toward examining the DE’s influence on new quality productive forces (NQPFs). Illustrative of this focus, Wang and Wang [30] posit that within digital economic contexts, the advancement of NQPFs is fundamentally contingent upon optimal data element utilization. Complementary findings by Fu et al.’s [31] research team reveal that capitalizing on market-oriented data allocation mechanisms significantly stimulates enterprise-level progression in NQPFs. Further scholarly contributions include Liu and Yang’s [32] demonstration that DE expansion facilitates greater specialization in labor distribution while simultaneously optimizing production relationships for enhanced compatibility with NQPFs. Luo and Song [33] contend that fostering NQPFs requires strategic integration of digital ecosystem components with complementary institutional factors, proposing implementation models spanning from state-directed approaches to collaborative multi-sector frameworks. Methodologically, the academic community predominantly employs fixed-effects regression analyses to assess direct DE–NQPF linkages, supplemented by mediation modeling techniques to elucidate potential secondary causal pathways.

In conclusion, prior studies have established a conceptual framework for investigating the interconnection between the DE and ANQPFs. Nevertheless, several research gaps remain evident: The academic discourse currently lacks a unified understanding regarding the conceptual parameters and measurable constructs of NQPFs. Divergent perspectives exist, with certain scholars maintaining that technological and digital advancements form the core of NQPFs, whereas others advocate for a multidimensional framework encompassing innovations in labor components, production materials, and instrumental resources, particularly noting the scarcity of focused investigations on ANQPFs. Existing scholarship examining the DE–ANQPFs nexus has predominantly concentrated on establishing either direct or mediated associations, leaving significant gaps in understanding their potential causal linkages and the fundamental processes governing these interactions. Furthermore, the field suffers from inadequate quantitative exploration of DE–ANQPF dynamics. While theoretical conceptualizations abound, substantiating empirical evidence remains limited, underscoring the critical necessity for more robust, data-driven investigations in this domain. Building upon this foundation, the current investigation systematically examines the fundamental principles through which the DE facilitates ANQPF advancement. Utilizing multiple econometric approaches, this research empirically explores the causal mechanisms linking these two constructs, thereby contributing to enhanced scholarly comprehension of both research fields.

3. Theoretical Analysis

3.1. The Direct Impact of the DE on ANQPFs

China is presently experiencing an accelerated phase of digital innovation, which plays a pivotal strategic role in advancing ANQPFs. Firstly, the DE serves as a catalyst for upgrading agricultural labor capabilities. The extensive adoption of digital solutions enables farmers to access cutting-edge production techniques and managerial expertise, consequently elevating their professional competencies [34]. Illustratively, e-learning platforms, remote education systems, and smart farming tools have democratized access to agricultural knowledge, facilitating the adoption of contemporary farming methods [35]. Concurrently, digital transformation has revolutionized agricultural supply chains through intelligent systems, compelling workers to adapt to technological advancements while strengthening their innovative capacities.

Secondly, digital technologies significantly contribute to optimizing agricultural production inputs. Digital tools facilitate precision resource management through coordinated allocation mechanisms [36]. Practical applications include IoT sensors, data analytics, and satellite imaging that enable real-time monitoring of soil conditions, microclimates, and crop development. Such technologies optimize input utilization, minimize chemical applications, and reduce environmental contamination [37,38]. Moreover, the convergence of digital platforms with agriculture has spawned innovative business paradigms, exemplified by digital integration across livestock, aquaculture, and crop production sectors. These developments have fostered novel value chains combining production, processing, distribution, and ancillary services, as well as interdisciplinary models blending technology, commerce, and education [39].

Thirdly, digital advancement modernizes agricultural production means. By integrating information technologies with conventional farming resources [40], smart agricultural equipment has emerged. Automated systems, including pesticide-dispensing drones, autonomous transport vehicles, and robotic processing facilities, not only augment human labor but also dramatically enhance operational efficiency [41]. Furthermore, digital transformation has reshaped agricultural value chains through comprehensive data platforms, enabling end-to-end digital management from production to marketing. These developments collectively enhance supply chain performance and value creation.

Accordingly, we postulate the following hypothesis:

H1. 

The DE positively influences ANQPF development.

3.2. The Indirect Effects of the DE on ANQPFs

This study identifies rural financial development (RFD) as a mediating factor through three principal justifications. Initially, financial accessibility represents a critical limiting factor for implementing modern agricultural technologies and expanding operational scale, aligning with the established theoretical framework linking technological advancement, financial mechanisms, and productivity enhancement. Secondly, China’s rural financial system has historically suffered from structural imbalances between supply and demand, with digital transformation now positioning it as a pivotal conduit for agricultural modernization through DE interventions—a relationship that remains under-investigated in current scholarship. Conclusively, national policy directives have specifically designated “digital inclusive finance” as a strategic instrument for rural development, with our research offering quantitative support for evidence-based policy improvements.

The DE has substantially advanced rural financial development (RFD) through multiple channels [42]. Firstly, technological innovations have enhanced financial inclusion by facilitating easier access to banking services for rural populations. Mobile payment systems and digital banking platforms, for instance, eliminate geographical barriers, allowing rural residents to conduct financial operations remotely [43]. Secondly, data analytics and machine learning applications have strengthened financial institutions’ risk evaluation capacities, mitigating credit risks and expanding loan accessibility for rural businesses and farmers. Additionally, the DE has stimulated the creation of innovative financial instruments [44], including blockchain-enabled supply chain financing solutions that offer streamlined funding options for rural SMEs [45].

The progression of RFD actively contributes to ANQPF enhancement [46]. Financial mechanisms facilitate agricultural modernization by funding technological innovations and equipment acquisition [47], thereby accelerating the sector’s transition toward smart and digital practices. Specific initiatives include preferential loan programs supporting farmers’ investments in intelligent farming equipment and digital infrastructure development. Moreover, financial interventions assist in restructuring agricultural production systems [48], encouraging the cultivation and processing of premium agricultural commodities [49]. These measures not only boost sectoral competitiveness but also stabilize farmers’ incomes through risk management tools like agricultural insurance.

Accordingly, we postulate the following hypothesis:

H2. 

The DE facilitates ANQPF advancement through its positive influence on RFD.

3.3. The Nonlinear Relationship Between the DE and ANQPFs

ANQPFs encompass multiple interdependent dimensions, including technological innovation, human capital, and industrial transformation, whose development trajectory may demonstrate non-linear patterns due to complex factor interactions [50]. The initial phase of DE development demonstrates substantial capacity to elevate ANQPFs, largely attributable to digital technologies’ rapid mitigation of traditional agriculture’s systemic constraints. In this early stage, cost-effective digital solutions (e.g., e-commerce platforms and mobile payment systems) effectively address information asymmetries and lower transactional barriers in agricultural operations, generating prompt efficiency gains [51]. Beyond the threshold point, however, the enabling impact tends to display progressively reduced marginal benefits. This phenomenon represents not a detrimental outcome but rather an expected transition as the DE evolves from rapid adoption to comprehensive assimilation. Several factors contribute to this pattern: Primarily, as digital penetration deepens, the most readily adaptable production processes have already undergone digitization, leaving remaining optimizations constrained by inherent agricultural biological properties that substantially complicate technological adaptation [52]. Additionally, heightened coordination demands emerge between digital technologies and complementary production factors (including land consolidation and workforce skills), where asynchronous development of these elements may limit the DE’s advanced potential. Ultimately, institutional and market infrastructure constraints become pronounced when digital advancement surpasses existing organizational frameworks’ adaptive capacity; the marginal utility inevitably attenuates [53].

Accordingly, we postulate the following hypothesis:

H3. 

The facilitating effect of the DE on ANQPFs demonstrates phase-dependent variations in its magnitude and characteristics across different developmental stages.

3.4. The Spatial Spillover Effects of the DE on ANQPFs

The DE may generate distinct spatial diffusion patterns in ANQPFs. When the DE facilitates ANQPF enhancement in neighboring regions, this constitutes positive spatial spillover; conversely, it represents negative spatial spillover.

The DE contributes to beneficial spatial externalities for ANQPFs through several mechanisms. Firstly, digital technologies eliminate physical constraints by facilitating information exchange and data flows, allowing progressive agricultural methods and managerial knowledge to propagate efficiently across geographical boundaries [54,55]. For instance, online platforms enable farmers to acquire cultivation techniques, market intelligence, and operational know-how nationwide, consequently boosting productivity and output quality. Secondly, digital advancement fosters agricultural value chain expansion and convergence [56]. E-commerce systems and temperature-controlled logistics solutions permit seamless distribution of farm products to urban markets, broadening commercial opportunities and enhancing agricultural profitability. Furthermore, the DE stimulates rural digital infrastructure development and technological adoption [57], creating an enabling ecosystem for ANQPF maturation. The deployment of 5G networks and precision farming technologies, for example, has elevated production automation and smart agriculture implementation.

The DE could potentially generate adverse spatial externalities for ANQPFs. Firstly, accelerated DE advancement may exacerbate existing disparities in digital access between urban and rural regions [58]. Metropolitan areas, benefiting from superior infrastructure and skilled workforce concentration, demonstrate faster adoption and implementation of digital solutions [59]. Conversely, rural communities often face implementation delays due to inadequate connectivity and limited digital literacy programs, potentially constraining technological deployment and industrial transformation essential for ANQPF advancement. Secondly, DE-driven market intensification may result in disproportionate resource accumulation within select high-performance zones or corporations. Rural smallholders and agricultural businesses, frequently constrained by limited financial and technological capacities, may encounter survival challenges in increasingly competitive markets, consequently impeding ANQPF development. China’s regional heterogeneity in economic and digital advancement generates distinct spatial dynamics, where technologically advanced “core” areas frequently draw critical resources (including skilled labor and capital investments) from adjacent “peripheral” zones, consequently producing adverse spatial externalities.

Accordingly, we postulate the following hypothesis:

H4. 

The spatial spillover effect of the DE on neighboring ANQPFs is contingent on competing forces. While knowledge diffusion may create positive spillovers, resource competition for capital, talent, and markets is likely to create a stronger negative siphoning effect, leading to a net negative spillover.

Building on the preceding theoretical discussion, this study establishes an analytical framework to examine how the DE influences ANQPFs, as shown in Figure 1.

4. Research Design

4.1. Data Sources

This study employs longitudinal data spanning 2014–2023 across 30 Chinese provincial divisions (excluding Tibet, Hong Kong, Macao, and Taiwan). The dataset draws from multiple authoritative sources. Among the indicators in the ANQP, “Environmental protection fiscal expenditure” and “Agricultural ammonia nitrogen emissions” are sourced from the China Environmental Statistics Yearbook, while all other indicators are derived from the China Rural Statistics Yearbook. In the DE indicators, “Number of mobile phone base stations per permanent resident,” “The ratio of mobile phone usage to the permanent resident population,” and “E-commerce sales” are sourced from the China Statistical Yearbook, while all other indicators are sourced from the China Internet Development Statistics Report. Where data points were unavailable, linear interpolation methods were applied to ensure dataset completeness. The only missing data in this paper is from 2013, as the data for “Agricultural ammonia nitrogen emissions” could not be found. Therefore, linear interpolation was used to supplement this data, which is consistent with the application of linear interpolation.

4.2. Variable Description

(1) Explained variables. This research examines ANQPFs as the primary outcome variable. While fundamentally representing productive capacity, ANQPFs specifically emphasize innovative characteristics that demonstrate qualitative advancements over conventional productivity systems. This study establishes a more robust ANQPF indicator system that surpasses prior research frameworks through the inclusion of enhanced metrics with greater explanatory power. Specifically, within the agricultural workforce dimension, we introduce “Number of agricultural technology training graduates/Rural population” as a novel proxy for regional agricultural human capital quality, given its capacity to directly capture workforce technical competency levels. Furthermore, our environmental dimension now integrates “Agricultural ammonia nitrogen emissions/Primary industry output” as a diagnostic measure, since this variable effectively mirrors the ecological sustainability aspects inherent in ANQPF’s developmental progress. Three defining components characterize this construct: agricultural workers, agricultural labor objects, and agricultural means of production. Following established methodological approaches in prior research [60], we developed a multidimensional assessment framework incorporating these three aspects—agricultural workforce, agricultural labor objects, and agricultural means of production—measured through 19 distinct metrics. Weight assignments were determined through entropy weighting methodology. The complete measurement framework with detailed specifications appears in

Table 1. Evaluation index system for ANQPFs.

Indicator Category	Definition and Description	Expected Sign	Information Entropy Value	Information Utility Value	Weights
Agricultural workforce	Average years of education of rural labor force	+	0.991	0.009	0.009
	Number of agricultural technology training graduates/Rural population	+	0.854	0.146	0.143
	Primary industry output/Primary industry employment	+	0.967	0.033	0.033
	Per capita disposable income of rural residents	+	0.966	0.034	0.034
	Total number of migrant workers/Rural labor force	−	0.999	0.001	0.002
Agricultural labor objects	Forest coverage ratio	+	0.962	0.038	0.037
	Environmental protection fiscal expenditure/Government public fiscal expenditure	+	0.983	0.017	0.016
	Agricultural ammonia nitrogen emissions/Primary industry output	−	0.997	0.003	0.003
	Number of green agricultural cooperatives/Number of people employed in the primary industry	+	0.869	0.131	0.129
	Number of national key leading enterprises in agricultural specialization	+	0.969	0.031	0.030
	Agriculture, forestry, animal husbandry, and fishery services output value	+	0.925	0.075	0.074
Agricultural means of production	Rural road mileage/Rural population	+	0.947	0.053	0.052
	Per capita electricity consumption in rural areas	+	0.826	0.174	0.171
	Number of rural broadband access users/Number of rural households	+	0.960	0.040	0.040
	Rural Digital Financial Inclusion Index	+	0.986	0.014	0.013
	Comprehensive mechanization rate of grain cultivation	+	0.983	0.017	0.017
	Number of agricultural technology patents	+	0.938	0.062	0.061
	Number of agricultural science and technology professionals	+	0.936	0.064	0.063
	Agricultural R&D expenditure	+	0.926	0.074	0.073

. The comprehensive entropy-weighted scores of ANQPFs development across provinces are presented in

Table 2. Entropy-weighted provincial ANQPFs index scores.

Province	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Beijing	0.165	0.175	0.182	0.191	0.196	0.189	0.195	0.205	0.193	0.190
Tianjin	0.094	0.105	0.108	0.110	0.111	0.131	0.126	0.134	0.131	0.127
Hebei	0.160	0.173	0.181	0.194	0.198	0.211	0.226	0.231	0.232	0.243
Shanxi	0.102	0.104	0.109	0.117	0.116	0.121	0.131	0.138	0.141	0.143
Inner Mongolia	0.097	0.103	0.106	0.108	0.114	0.119	0.129	0.140	0.147	0.155
Liaoning	0.165	0.169	0.165	0.170	0.158	0.155	0.160	0.175	0.174	0.173
Jilin	0.106	0.113	0.116	0.112	0.114	0.127	0.136	0.142	0.146	0.149
Heilongjiang	0.138	0.155	0.157	0.168	0.168	0.178	0.181	0.190	0.188	0.196
Shanghai	0.253	0.254	0.309	0.330	0.341	0.365	0.217	0.229	0.251	0.273
Jiangsu	0.373	0.400	0.407	0.417	0.418	0.442	0.421	0.436	0.431	0.426
Zhejiang	0.225	0.251	0.265	0.284	0.334	0.362	0.368	0.383	0.396	0.409
Anhui	0.118	0.146	0.159	0.173	0.186	0.184	0.204	0.225	0.222	0.236
Fujian	0.159	0.181	0.196	0.207	0.230	0.246	0.247	0.260	0.260	0.270
Jiangxi	0.096	0.112	0.130	0.140	0.155	0.167	0.182	0.195	0.204	0.213
Shandong	0.244	0.277	0.281	0.288	0.285	0.280	0.307	0.347	0.373	0.398
Henan	0.189	0.199	0.206	0.218	0.218	0.225	0.240	0.260	0.262	0.286
Hubei	0.139	0.157	0.170	0.184	0.210	0.228	0.247	0.271	0.291	0.312
Hunan	0.132	0.141	0.157	0.172	0.185	0.208	0.236	0.254	0.271	0.291
Guangdong	0.222	0.243	0.255	0.288	0.307	0.328	0.346	0.372	0.360	0.380
Guangxi	0.102	0.123	0.130	0.147	0.151	0.153	0.160	0.177	0.183	0.195
Hainan	0.080	0.093	0.104	0.111	0.121	0.130	0.139	0.156	0.170	0.186
Chongqing	0.104	0.119	0.130	0.141	0.139	0.138	0.145	0.157	0.170	0.183
Sichuan	0.158	0.182	0.196	0.214	0.215	0.204	0.239	0.256	0.270	0.284
Guizhou	0.109	0.109	0.129	0.141	0.141	0.145	0.148	0.156	0.150	0.150
Yunnan	0.206	0.222	0.230	0.239	0.249	0.249	0.249	0.255	0.255	0.255
Shaanxi	0.125	0.139	0.150	0.151	0.155	0.168	0.177	0.183	0.195	0.206
Gansu	0.061	0.071	0.074	0.084	0.092	0.104	0.111	0.123	0.126	0.137
Qinghai	0.071	0.085	0.093	0.115	0.124	0.134	0.145	0.152	0.167	0.182
Ningxia	0.055	0.067	0.073	0.081	0.093	0.099	0.111	0.121	0.124	0.127
Xinjiang	0.127	0.140	0.151	0.159	0.171	0.162	0.161	0.189	0.205	0.221

.

(2) Core explanatory variable. This investigation employs the DE as its principal explanatory factor. Scholarly consensus has reached relative maturity regarding assessment frameworks for DE advancement, with predominant methodologies utilizing multi-criteria evaluation systems. The existing literature on DE measurement frameworks has predominantly focused on metrics pertaining to digital infrastructure. In contrast, our proposed evaluation system expands this conventional approach by integrating novel indicators that capture digital transaction dynamics, including “Number of enterprises engaged in e-commerce transactions” and “E-commerce sales”, which serve as robust proxies for assessing regional digital transformation. These supplementary metrics provide complementary perspectives on digitalization processes, thereby enhancing both the comprehensiveness and analytical rigor of our DE assessment framework. For our analysis, we developed a provincial-scale measurement framework for China’s DE by integrating three critical aspects: digital infrastructure, digital network development, and digital transaction development [61]. This tripartite structure, comprising 11 specific metrics (detailed in

Table 3. DE evaluation index system.

Indicator Category	Definition and Description	Expected Sign	Information Entropy Value	Information Utility Value	Weights
Digital infrastructure	Long-distance optical cable line length	+	0.895	0.105	0.144
	Number of mobile phone base stations per permanent resident	+	0.970	0.030	0.041
	Total number of broadband ports	+	0.954	0.046	0.063
Digital network development	Total telecommunications revenue	+	0.906	0.094	0.128
	Total mobile Internet access traffic	+	0.880	0.120	0.164
	Number of broadband Internet users	+	0.947	0.053	0.072
	The ratio of mobile phone usage to the permanent resident population	+	0.978	0.022	0.031
Digital transaction development	Number of websites owned by every 100 enterprises	+	0.987	0.013	0.018
	Number of enterprises engaged in e-commerce transactions	+	0.911	0.089	0.122
	E-commerce sales	+	0.877	0.123	0.168
	Number of computers used per 100 employees	+	0.964	0.036	0.049

), was formulated after a thorough examination of the DE’s developmental context and socioeconomic impacts, building upon established scholarly work [62,63]. To ensure objective weight determination and precise evaluation of the study subjects, we employed the entropy weighting approach to quantify DE levels (detailed in

Table 4. Entropy-weighted provincial DE index scores.

Province	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Beijing	0.168	0.187	0.201	0.217	0.254	0.298	0.325	0.361	0.392	0.424
Tianjin	0.063	0.074	0.082	0.087	0.101	0.131	0.150	0.162	0.177	0.192
Hebei	0.068	0.084	0.106	0.123	0.153	0.185	0.215	0.241	0.261	0.281
Shanxi	0.047	0.057	0.065	0.075	0.095	0.112	0.131	0.152	0.172	0.191
Inner Mongolia	0.040	0.048	0.059	0.070	0.086	0.102	0.115	0.128	0.140	0.153
Liaoning	0.067	0.085	0.096	0.109	0.128	0.149	0.164	0.181	0.197	0.213
Jilin	0.039	0.044	0.054	0.064	0.078	0.089	0.103	0.112	0.120	0.128
Heilongjiang	0.040	0.046	0.056	0.068	0.082	0.097	0.112	0.119	0.129	0.139
Shanghai	0.203	0.228	0.251	0.264	0.293	0.340	0.370	0.407	0.459	0.512
Jiangsu	0.167	0.206	0.218	0.240	0.294	0.347	0.397	0.444	0.511	0.579
Zhejiang	0.164	0.205	0.224	0.240	0.282	0.339	0.373	0.420	0.468	0.517
Anhui	0.065	0.091	0.105	0.121	0.155	0.192	0.214	0.243	0.269	0.295
Fujian	0.085	0.101	0.112	0.125	0.149	0.176	0.194	0.218	0.242	0.266
Jiangxi	0.040	0.060	0.066	0.083	0.106	0.133	0.153	0.177	0.196	0.214
Shandong	0.111	0.140	0.181	0.209	0.271	0.289	0.324	0.388	0.460	0.533
Henan	0.071	0.097	0.119	0.135	0.177	0.206	0.241	0.289	0.333	0.377
Hubei	0.066	0.085	0.100	0.111	0.135	0.163	0.187	0.216	0.242	0.268
Hunan	0.059	0.073	0.087	0.101	0.134	0.166	0.200	0.232	0.263	0.294
Guangdong	0.201	0.242	0.271	0.315	0.406	0.486	0.537	0.630	0.723	0.817
Guangxi	0.040	0.043	0.054	0.066	0.098	0.131	0.161	0.188	0.215	0.243
Hainan	0.055	0.061	0.069	0.076	0.081	0.094	0.098	0.107	0.116	0.125
Chongqing	0.054	0.068	0.084	0.099	0.124	0.148	0.166	0.188	0.227	0.265
Sichuan	0.071	0.099	0.122	0.142	0.183	0.223	0.263	0.309	0.348	0.386
Guizhou	0.038	0.048	0.063	0.076	0.101	0.129	0.148	0.170	0.199	0.229
Yunnan	0.049	0.063	0.070	0.083	0.107	0.138	0.166	0.191	0.211	0.232
Shaanxi	0.057	0.069	0.084	0.098	0.124	0.147	0.169	0.192	0.212	0.232
Gansu	0.028	0.038	0.048	0.060	0.075	0.090	0.104	0.120	0.131	0.142
Qinghai	0.033	0.041	0.046	0.051	0.061	0.071	0.079	0.088	0.092	0.097
Ningxia	0.034	0.039	0.047	0.054	0.064	0.068	0.076	0.084	0.090	0.096
Xinjiang	0.035	0.043	0.052	0.057	0.075	0.092	0.110	0.133	0.148	0.163

). Furthermore, stringent quality control measures were implemented, including comprehensive indicator screening to eliminate potential outliers that might lead to disproportionate weight allocation. The validity of both indicator selection and final weight distribution was subsequently verified through structured consultations with domain experts, leveraging their specialized knowledge to validate the methodological robustness.

(3) Mediating variable. Drawing upon the preceding theoretical framework, this study identifies rural financial development (RFD) as its mediating variable [64,65,66]. Adopting the measurement approach established by Sun Q and Zhou B [67], we quantify regional RFD levels using agricultural loan balances per capita, specifically, the logarithmic transformation of agricultural loan amounts relative to rural population size. This indicator reflects the intensity of urban-to-rural capital mobility, with elevated values signifying greater financial resource transfers between urban and rural sectors, enhanced inter-regional economic connectivity, and, consequently, more advanced RFD.

(4) Control variables. To mitigate potential estimation bias arising from omitted variables, this investigation incorporates four control variables following established research practices [68,69]. First, economic development (ED) is proxied by provincial GDP per capita. Second, human capital (HC) is measured through enrollment rates in tertiary education institutions relative to the total population. Third, government intervention (GI) is quantified as the proportion of fixed-asset investments in agriculture to GDP. Fourth, external openness (EO) is evaluated based on the trade-to-GDP ratio (sum of imports and exports relative to regional GDP).

(5) Descriptive statistics. The analysis incorporates seven directly relevant variables, with their complete descriptive statistical characteristics presented in

Table 5. Descriptive statistics of variables.

Variables	Obs	Mean	Std. Dev.	Min	Max
ANQPF	300	0.192	0.082	0.055	0.442
DE	300	0.170	0.125	0.028	0.817
RFD	300	1.866	0.530	0.689	3.823
ED	300	0.672	0.329	0.251	1.931
HC	300	0.225	0.059	0.092	0.447
GI	300	0.250	0.101	0.107	0.643
EO	300	0.251	0.250	0.008	1.216

.

4.3. Model Construction

To examine the impact of the DE on ANQPFs, we establish the following baseline regression model:

$$ANQP{F}_{it}={\alpha }_0+{\alpha }_{1}D{E}_{it}+{\alpha }_{2}Control{s}_{it}+{\delta }_i+{\tau }_t+{\epsilon }_{it}$$

(1)

In the specified econometric Formulation (1), α₀ denotes the intercept term, while ANQPF_it and DE_it stand for ANQPF and DE indicators, respectively. The subscripts i and t identify provincial and temporal dimensions. Controls_it encompasses control variables, with α₁ and α₂ representing parameter estimates. δ_i and τ_t capture regional and time-specific fixed effects, correspondingly, and ε_it signifies the stochastic error component.

To investigate the potential mediating effect of RFD in the relationship between the DE and ANQPFs, we employ a mediation analysis model. The analytical models are specified as follows:

$$ANQP{F}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}Control{s}_{it}+{\delta }_i+{\tau }_t+{\epsilon }_{it}$$

(2)

$$RF{D}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}Controls+{\delta }_i+{\tau }_t+{\epsilon }_{it}$$

(3)

$$ANQP{F}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}RF{D}_{it}+{\beta }_{3}Control{s}_{it}+{\delta }_i+{\tau }_t+{\epsilon }_{it}$$

(4)

Within this econometric framework, RFD_it serves as the mediation variable under investigation, β₀ constitutes the intercept term, while β₁, β₂, and β₃ represent the regression coefficients. All other variables retain their original definitions provided earlier.

To investigate potential nonlinear relationships between the DE and ANQPFs, we employ a threshold regression specification with the following functional form:

$$ANQP{F}_{it}={\phi }_0+{\phi }_{1}D{E}_{it}\times I(D{E}_{it}\le \theta )+{\phi }_{2}D{E}_{it}\times I(D{E}_{it}>\theta )+{\phi }_{3}Control{s}_{it}+{\delta }_i+{\epsilon }_{it}$$

(5)

In the specified threshold model (5), φ₀ denotes the intercept term, while φ₁, φ₂, and φ₃ represent the parameters requiring estimation. The critical threshold is symbolized by θ, with I(·) designating the indicator function. All remaining variables maintain their previously defined interpretations.

To investigate the potential spatial diffusion impacts of the DE on ANQPFs, we establish the following spatial econometric specification:

$$ANQP{F}_{it}={\gamma }_0+\rho W\times ANQP{F}_{it}+{\gamma }_{1}D{E}_{it}+{\gamma }_{2}W\times D{E}_{it}+{\displaystyle \sum {\gamma }_3}Control{s}_{it}+{\displaystyle \sum {\gamma }_{4}W\times }Control{s}_{it}+{\delta }_i+{\tau }_t+{\epsilon }_{it}$$

(6)

In the spatial econometric specification (6), γ₀ represents the intercept, while γ₁ through γ₄ denote the estimable parameters. ρ indicates the spatial lag coefficient and W corresponds to the predefined spatial weights matrix.

5. Analysis of Empirical Results

5.1. Benchmark Regression

Prior to conducting the primary regression analysis, results from the Hausman specification test indicated that the fixed-effects specification demonstrated superior performance compared to its random-effects counterpart at the 1% significance threshold. Consequently, our empirical analysis employed the fixed-effects framework. The estimation outcomes are displayed in

Table 6. Results of the benchmark regression.

Variables	ANQPF
	(1)	(2)	(3)	(4)	(5)
DE	0.321 *** (20.020)	0.248 *** (8.120)	0.193 *** (6.240)	0.185 *** (5.970)	0.188 *** (6.020)
ED		0.044 ** (2.760)	0.018 (1.130)	0.025 * (1.550)	0.028 * (1.680)
HC			0.321 *** (5.340)	0.330 ** (5.490)	0.310 *** (4.840)
GI				0.093 * (1.920)	0.091 * (1.880)
EO					0.023 (0.880)
Time fixed effects	Yes	Yes	Yes	Yes	Yes
Province fixed effects	Yes	Yes	Yes	Yes	Yes
N	300	300	300	300	300
R2	0.599	0.610	0.647	0.652	0.653

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% probability thresholds, correspondingly, while the parenthetical figures represent t-statistic values.

. The initial findings in Column 1, examining the direct DE–ANQPF relationship, yielded a statistically significant coefficient of 0.321 for the DE (p < 0.01), providing initial support for Hypothesis 1. The subsequent columns (2) through (5) systematically incorporated additional control variables, including economic development (ED), human capital (HC), government involvement (GI), and external openness (EO). Notably, the positive association between the DE and ANQPFs persisted at the 1% significance level across all model specifications, offering robust confirmation of Hypothesis 1.

5.2. Robustness Test and Endogenous Treatment

(1) Alternative measurement approach: To verify robustness, we employed principal component analysis (PCA) as an alternative to the entropy weight method for evaluating DE development. The original 11 indicators were reduced to three principal components through dimensionality reduction, which were then aggregated using appropriate weighting. Subsequent regression analysis using this alternative measure (

Table 7. Results of the robustness test.

Variables	(1)		(2)		(3)		(4)		(5)
	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.	Coef.	S.E.
DE	0.032 ***	0.010	0.216 ***	0.036	0.157 ***	0.036	0.351 ***	0.070	0.165 ***	0.026
Control variable	Yes		Yes		Yes		Yes		Yes
N	300		300		210		120		300
R2	0.621		0.653		0.682		0.848		0.794

Note: *** denotes statistical significance at the 1% probability threshold, while the parenthetical figures represent t-statistic values.

, Column 1) demonstrates that the DE maintains a statistically significant positive coefficient (p < 0.01), confirming the stability of our baseline findings.

(2) Outlier treatment: To address potential outlier effects, we performed 1% winsorization on both the DE and ANQPF variables before re-estimating the model. As shown in Table 7, Column 2, the direction and statistical significance of the DE’s coefficient remain unchanged from our primary results, providing additional evidence for the robustness of our conclusions.

(3) Exclude outliers: To account for the unique economic disruptions caused by the COVID-19 pandemic from 2020 to 2022—a period characterized by disproportionate impacts on traditional sectors alongside accelerated digital adoption—we deliberately excluded these years’ observations to preserve analytical integrity. As evidenced in Table 7’s third column, the modified specifications continue to demonstrate statistically meaningful positive relationships, with the DE maintaining both its positive coefficient and 1% statistical significance in promoting ANQPFs. These consistent outcomes across alternative specifications substantially reinforce the credibility of our baseline regression estimates.

(4) Control for policy shocks: To examine potential policy-induced structural changes, we considered the implementation of China’s Digital Rural Strategy following the January 2018 policy directive (“Opinions of the Central Committee of the Communist Party of China and the State Council on Implementing the Rural Revitalization Strategy”). Recognizing this potential inflection point in DE development patterns, we restricted our analysis to pre-2018 data to isolate policy effects. The subsequent regression outcomes (Table 7, Column 4) demonstrate remarkable consistency with our primary findings—both the statistical significance and directional influence of the DE remain unchanged, thereby confirming the robustness of our conclusions against temporal policy variations.

(5) Endogenous Treatment: To mitigate potential endogeneity issues that could compromise the analytical outcomes, this investigation reassesses the DE and ANQPF relationship by eliminating observational data susceptible to bidirectional causation, thus enhancing the validity of the conclusions. The four Chinese directly controlled municipalities (Beijing, Shanghai, Tianjin, and Chongqing) possess superior economic and infrastructural development relative to other regions, consistently exhibiting more pronounced agricultural modernization initiatives. Such exceptional circumstances might inversely influence DE progression, creating potential reciprocal causal relationships. Consequently, the analysis omitted these four special administrative regions when performing model re-estimation to ensure result robustness.

5.3. Heterogeneity Analysis

Considering substantial regional disparities in development levels and resource endowments, the effect of the DE on ANQPFs demonstrates notable geographical variations. Following established academic conventions, we categorize China’s 30 provinces into four geographical groups: eastern, central, western, and northeastern regions. As presented in

Table 8. Results of the heterogeneity analysis.

Variables	Eastern	Central	Western	Northeastern
DE	0.187 ** (3.570)	0.337 *** (3.760)	0.136 *** (2.760)	−0.329 (−1.55)
Control variable	Control	Control	Control	Control
Time fixed effects	Yes	Yes	Yes	Yes
Province fixed effects	Yes	Yes	Yes	Yes
N	100	60	110	30
R2	0.618	0.920	0.879	0.829

Note: ** and *** denote statistical significance at the 5% and 1% probability thresholds, correspondingly, while the parenthetical figures represent t-statistic values.

, our regional heterogeneity analysis reveals differential impacts of the DE on ANQPFs across these regions. Notably, the DE shows statistically significant positive coefficients (p < 0.01) of 0.187, 0.337, and 0.136 for eastern, central, and western regions, respectively, suggesting an effectiveness ranking of central > eastern > western regions. However, the northeastern region displays statistically insignificant results, indicating limited DE-driven ANQPF enhancement.

Several factors may explain these findings:

The central region has prioritized digital infrastructure development (5G networks and IoT systems), creating favorable conditions for agricultural digitalization and smart farming adoption.

Eastern provinces, as national leaders in digital infrastructure, have successfully implemented IoT and big data technologies to achieve precision agriculture.

Western areas have focused on developing interdisciplinary professionals skilled in both agriculture and digital technologies while leveraging comparative advantages in agricultural digital transformation.

Conversely, the northeastern region faces challenges including brain drain, fragmented agricultural value chains, and underutilization of smart farming data, hindering effective technology integration across agricultural production systems.

5.4. Mechanical Analysis

The mediation analysis results examining RFD are presented in

Table 9. Results of the mediation analysis.

Variables	(1)	(2)
	RFD	ANQPF
DE	1.578 *** (7.88)	0.110 *** (3.35)
RFD		0.049 *** (5.42)
Control variable	Control	Control
Fixed effects	Yes	Yes
N	300	300
R2	0.856	0.688

Note: *** denotes statistical significance at the 1% probability threshold, while the parenthetical figures represent t-statistic values.

. Specification (1) demonstrates that the DE exhibits a statistically significant positive coefficient, confirming its substantial contribution to RFD advancement. Column 2 reveals that both RFD and the DE maintain significant positive coefficients, though the magnitude of the DE’s effect diminishes relative to the baseline estimates, suggesting RFD partially mediates the DE–ANQPF relationship and thereby supporting Hypothesis 2.

One plausible explanation is that the DE has substantially advanced RFD by broadening its scope through increased financial service accessibility, improved risk evaluation methods, innovative financial instruments, and the growth of rural e-commerce. Conversely, rural finance contributes to the emergence and expansion of ANQPFs by providing funding, facilitating industrial restructuring, mitigating risks, and fostering financial innovations.

5.5. Further Analysis

(1) Nonlinear relationship analysis. This study investigates potential nonlinear dynamics in the DE–ANQPF relationship by implementing threshold regression analysis with the DE as the threshold variable. The empirical testing reveals statistically significant evidence for a single threshold (p < 0.05) at 0.381, as reported in

Table 10. Threshold value.

Variable	Threshold Type	Threshold Value	F Value	p Value
DE	Single threshold	0.381	32.960	0.040

, while failing to identify significant double or triple threshold effects. The likelihood ratio test statistics visualized in Figure 2 provide additional confirmation of this single-threshold structure. These findings collectively demonstrate that the DE influences ANQPFs in a nonlinear manner, thereby supporting Hypothesis 3 regarding threshold effects.

Table 11. Results of the threshold effect analysis.

Variables		ANQPF
Threshold value	DE ≤ 0.381	0.372 ***
	DE > 0.381	0.270 ***
Control variable		Control
Time fixed effects		Yes
Province fixed effects		Yes
N		300
R2		0.689

Note: *** denotes statistical significance at the 1% probability threshold, while the parenthetical figures represent t-statistic values.

presents the threshold regression estimates, revealing distinct patterns of the DE’s influence on ANQPFs across threshold boundaries. Below the identified threshold of 0.381, the DE demonstrates a strong positive coefficient of 0.372 (p < 0.01). Above this critical value, while the coefficient magnitude moderates to 0.270, it maintains statistical significance at the 1% level. These results collectively indicate that while the DE’s effect displays nonlinear variation across development phases, its positive contribution persists throughout all stages.

This nonlinear pattern may be explained by developmental dynamics: In initial phases, the DE drives substantial productivity gains through transformative technologies (IoT, big data, and AI) that revolutionize conventional agricultural practices, yielding maximal impact during periods of systemic modernization. As development progresses, though digital technologies continue supporting ANQPF advancement, their relative contribution diminishes as other factors (institutional reforms and human capital) become increasingly important, resulting in moderated but still significant effects.

(2) Spatial effect analysis. This study extends its examination of the DE–ANQPF relationship by incorporating spatial econometric analysis. Prior to assessing spatial spillovers, we conduct spatial dependence diagnostics using both global and local Moran’s I indices for ANQPFs. The global Moran’s I results (

Table 12. Moran’s Index result.

Year	ANQPF
	Moran’s I	Z Value
2014	0.325 ***	3.078
2015	0.338 ***	3.192
2016	0.405 ***	3.720
2017	0.401 ***	3.656
2018	0.435 ***	3.896
2019	0.455 ***	4.090
2020	0.269 ***	2.528
2021	0.285 ***	2.638
2022	0.314 ***	2.868
2023	0.332 ***	2.990

Note: *** denotes statistical significance at the 1% probability threshold, while the parenthetical figures represent t-statistic values.

) reveal consistently significant positive values throughout the 2014–2023 study period, demonstrating pronounced spatial interdependence in ANQPF distribution. Complementing these findings, the local Moran’s I analysis (Figure 3) identifies predominant high–high and low–low spatial clustering configurations, providing additional evidence of positive spatial autocorrelation patterns across regions.

This research employs a systematic model selection approach for spatial econometric analysis, performing sequential LM, LR, and Wald tests (

Table 13. Results of the LM, LR, and Wald tests.

Type of Test	Test Statistical Values	p Value
LM-Error	5.360	0.021
Robust LM-Error	0.567	0.451
LM-Lag	10.523	0.001
Robust LM-Lag	5.730	0.017
LR-ind	23.570	0.023
LR-time	569.400	0.000
Wald-sem	51.410	0.000
Wald-sar	67.090	0.000

). Diagnostic results indicate that while the spatial error specification satisfies only one LM test, the spatial lag alternative meets both LM test requirements. Following Elhorst’s methodological framework, when LM diagnostics confirm spatial dependence, the spatial Durbin model (SDM) warrants consideration as a more comprehensive specification.

Further examination reveals statistically significant outcomes for both LR tests evaluating regional and temporal fixed effects (p < 0.01), supporting the inclusion of bidirectional fixed effects. The Wald test results, showing 1% level of significance, confirm that the SDM specification maintains its distinct structure without degenerating into simpler spatial error or lag formulations. Consequently, our final analytical framework employs a bidirectional fixed-effects SDM to rigorously assess the DE’s influence on ANQPFs, ensuring methodological robustness in capturing spatial interdependencies.

Employing partial differential decomposition techniques, this study analyzes the spatial diffusion mechanisms of the DE by disaggregating them into three distinct components: (1) direct effects measuring local agricultural impacts, (2) indirect effects capturing cross-regional influences, and (3) total effects representing combined spatial impacts. As evidenced in

Table 14. SDM model regression results.

Variables	LR-Direct (3)	LR-Indirect (4)	LR-Total (5)
DE	0.123 *** (4.160)	−0.060 *** (−1.230)	0.063 (1.000)
Control variable	Control	Control	Control
Province fixed effects	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
N	300	300	300
R2	0.691

Note: *** denotes statistical significance at the 1% probability thresholds, while the parenthetical figures represent t-statistic values.

, the decomposition yields statistically significant coefficients for both direct (0.123) and indirect (−0.060) effects. These estimates reveal that while DE development substantially enhances local ANQPFs (p < 0.01), it concurrently creates negative spillovers that hinder neighboring regions’ productivity growth. This spatial competition pattern provides empirical confirmation for Hypothesis 5 regarding the DE’s divergent regional impacts.

6. Conclusions and Recommendations

This investigation employs longitudinal data from 30 Chinese provinces (2014–2023) to examine the DE’s impact on ANQPF formation through multiple econometric approaches, including bidirectional fixed effects, mediation analysis, threshold regression, and spatial Durbin modeling. The empirical results demonstrate the following: (1) The DE exerts a statistically significant positive influence on ANQPF advancement, with findings robust to alternative specifications, sample adjustments, temporal variations, and endogeneity controls. (2) Regional variations emerge, with the DE substantially enhancing ANQPFs in eastern, central, and western provinces while showing limited efficacy in northeastern areas. (3) RFD serves as a critical mediator, where DE-driven technological innovations stimulate financial sector development that subsequently fosters ANQPFs through capital infusion and institutional modernization. It should be emphasized that RFD represents merely one of several potential mediating channels through which the DE may influence ANQPFs and, therefore, this study cannot fully encapsulate the complete transmission mechanism. Future research should incorporate additional mediating factors to provide a more comprehensive understanding of the multifaceted DE–ANQPF relationship. (4) A nonlinear threshold relationship exists, with maximal DE impacts occurring during early adoption phases before moderating, yet remaining positive, at advanced development stages. (5) Spatial analysis reveals localized benefits coupled with adverse neighboring effects, as the DE stimulates local ANQPF growth while inadvertently constraining proximate regions’ productivity development.

First, to facilitate agricultural modernization and establish a robust technological foundation for NQPFs, the following strategic priorities should be implemented. Enhance digital infrastructure development in rural areas by deploying advanced information and communication technologies across critical agricultural zones while improving digital farmland management capabilities. Foster comprehensive integration of cutting-edge innovations like artificial intelligence into farming systems, with particular emphasis on advancing breakthrough technologies, including agricultural intelligent decision-making and precision farming solutions. Expand professional training programs to develop a skilled workforce proficient in smart agricultural equipment operation and data analytics. Develop a structured agricultural data marketplace to enable efficient data flow and collaborative utilization throughout the entire agricultural value chain, from production to management. Second, implement region-specific development strategies tailored to local conditions. Eastern areas should capitalize on their economic and technological strengths to facilitate the convergence of advanced digital solutions with agriculture, emphasizing high-tech domains such as AI-driven farming and blockchain-based supply chain tracking to boost agricultural value chains. Meanwhile, central, western, and northeastern regions should prioritize upgrading digital infrastructure, including 5G networks, IoT systems, and cloud computing, while expanding the adoption of inclusive digital services like e-commerce and smart logistics in rural sectors.

Third, accelerate RFD. Governments should enhance policy incentives, offering fiscal grants and tax relief to stimulate agricultural investments from financial institutions. Upgrading rural internet and transportation networks will improve financial service accessibility. Promoting financial innovation to design tailored rural financial products is essential. Additionally, robust risk management frameworks should be established, incorporating diverse risk mitigation instruments.

Fourth, to address the adverse spatial externalities associated with DE development, comprehensive regional coordination frameworks should be implemented through multi-dimensional interventions. This involves creating a graduated investment strategy for digital infrastructure that ensures core urban centers strategically allocate resources to neighboring underdeveloped areas, with particular emphasis on deploying next-generation technologies, including 5G networks and cloud-based computing facilities. Cross-jurisdictional digital governance innovations should be pursued through integrated regulatory systems enabling data interoperability and collaborative enforcement, thereby preventing digital inequality. Industrial chain integration can be enhanced by leveraging digital platforms to facilitate knowledge transfer from industry leaders to regional small and medium enterprises. Furthermore, equitable development outcomes should be ensured through balanced interest distribution mechanisms incorporating fiscal incentives, technological diffusion, and institutional innovations that collectively foster inter-regional DE harmonization.