The Impact Mechanisms of New Quality Productive Forces on Rural Transformation: Evidence from Shandong Province, China

Abstract

New quality productive force is a crucial driver for rural transformation. Exploring the impact of this new quality productive force on rural transformation in Shandong Province and enhancing the positive role of regional new quality productive force are significant in promoting high-quality development in this area. Based on urban panel data from 16 prefecture-level cities in Shandong Province, China, spanning from 2010 to 2022, the levels of new quality productive force and rural transformation in Shandong Province are measured separately and an econometric model is constructed to analyze, in depth, the impact of new quality productive force on rural transformation and its mechanism of action. The results show the following. (1) New quality productive force can significantly increase the level of rural transformation in Shandong Province. (2) The urbanization rate of new quality productive force significantly promotes rural transformation, but increases in the average wage of urban workers and the over-advancement of industrial structure significantly inhibit rural transformation. (3) New quality productive force significantly affects the level of rural transformation, mainly by improving the quality of the population. (4) There is regional heterogeneity in the impact of new quality productive forces on rural transformation in the three economic circles of Shandong Province. New quality productivity force provides new dynamic energy for rural transformation in Shandong Province, which can provide new research perspectives and practical guidance for better rural development in China and the rest of the world.

1. Introduction

Rural areas constitute distinct spatial–territorial systems that fundamentally differ from urban landscapes [1,2]. Under the institutional constraints of the urban–rural dual structure, global rural regions exhibit a relative decline compared to urban development trajectories. This decline is manifested in rural recession, the erosion of traditional cultures, ethics, and social orders, and the pervasive phenomenon of rural hollowing [3,4,5,6]. Rural transformation (RT) is a pivotal mechanism for achieving sustainable rural development, observed in rural regions across Western Europe, North America, and low- and middle-income countries. Governments worldwide implement initiatives to advance RT, such as Germany’s Village Renewal Program and Japan’s One Village, One Product (OVOP) initiative. These efforts establish distinct transformation pathways tailored to regional contexts [7,8,9,10]. The Third Plenary Session of the 20th Central Committee of the Communist Party of China underscored the pivotal role of cultivating new quality productive forces (NQPFs) in activating rural development potential and promoting industrial upgrading, which are critical components in achieving comprehensive rural revitalization. NQPF represents a form of productivity characterized by innovation as its hallmark and quality development as its essential attribute. By improving workers, means of work, subjects of work, and optimizing their configurations, NQPF drives technological breakthroughs that drive economic progress [11]. Through intelligent and informatized means, the extensive application of intelligent technology has transformed the traditional production mode, and NQPF has been deeply integrated into the field of agriculture and the countryside, empowering the construction of the countryside in the new era and providing a brand new development idea for the transformation of the countryside [12]. In this context, to explore the role of NQPF in the RT mechanism, how to promote the level of RT with NQPF has become a major practical problem.

NQPF is the core driver of quality economic development and has become the focus of academic attention in recent years. Existing studies have explored multiple dimensions, such as connotation definition, indicator construction, influence mechanism, industrial application, and policy path, and gradually built a research framework that emphasizes both theory and practice. As shown in

Table 1. Research directions of NQPF in different research fields.

NQPF	Field of Political Economy	Marxist Political Economy
	Field of Geography	Rural digital transformation
		Agricultural quality development
		Urban–rural integration

, In the field of political economy, NQPF is regarded as a development and innovation based on the law of contradictory movement of productive forces and relations of production, which is an important theoretical proposition in the Sinicization and modernization of Marxist political economy [13,14,15]. In the field of geography, NQPF, and rural digital transformation, agricultural quality development and urban–rural integration are combined to explore the impact mechanisms, industrial applications, and policy paths of NQPF. In the context of rural digital transformation, there have been studies that have found a well-fitting relationship between NQPF and rural digital development. In terms of theoretical logic, NQPF can create conditions for the realization of the high-quality development of digital village construction by playing an instrumental role in development concepts, development kinetic energy, balanced development, and social foundation. In terms of practical solutions, NQPF can effectively improve the level of digital village construction by enhancing the role of innovation drive, cultivating rural professionals, and popularizing digital infrastructure [16,17,18,19]. In terms of the quality development of agriculture, existing empirical studies have found that NQPF in agriculture has made a significant contribution to the modernization of China’s agriculture, as well as the quality development of agriculture. As an advanced agricultural productivity model driven by scientific and technological innovation and capable of enhancing production efficiency, the development of agricultural NQPF is a pivotal strategic choice for China-style modernization and a crucial step in achieving common prosperity in both urban and rural regions [20,21,22,23]. Urban–rural integration is an important approach through which NQPF promotes rural development. NQPF can facilitate better factor flow between urban and rural areas through urban–rural integration. Digital finance can bridge the capital gap between urban and rural regions, and the flow of capital to rural areas serves as the core driving force for the transformation of rural areas from production spaces into consumption spaces [24,25]. In the context of urban–rural integration, NQPF, as an advanced productive force that propels high-quality development and a beautiful productive force that fosters the construction of a beautiful China, possesses the capacity to catalyze urban–rural integrated development through the reallocation of urban and rural production factors, the restructuring of the urban–rural industrial system, the reconfiguration of the urban–rural spatial pattern, and the rehabilitation of the urban–rural ecological environment [26,27,28]. Existing studies have constructed a three-dimensional indicator system, mainly including labor materials, labor objects, and laborers. These methods have been combined with the entropy method and others to achieve dynamic assessment and cross-regional comparison.

As an important topic of global sustainable development, RT has received extensive attention in academic research and policy practice in recent years. RT is frequently characterized as a multifaceted process of transitioning from a conventional agriculture-dominated model to a diversified economic and social structure [29]. This transformation encompasses a range of dimensions, including economic restructuring, the restructuring of social relations, the optimization of land use, and innovation in the application of technology. The construction of RT indicators has primarily focused on the following three analytical perspectives: the functional perspective, encompassing economic, social, and ecological–environmental dimensions [30,31], the factor-based perspective, centered on demographic dynamics, land use patterns, and industrial structures [32,33], and the developmental perspective, incorporating agricultural input–output efficiency, rural revitalization progress, and urban–rural integration levels [34,35]. Extant empirical analyses demonstrate that the driving mechanism of RT is diversified, with institutional reforms, policy interventions, urbanization, and technological innovation constituting the primary driving forces. For instance, China’s reform of the land system and the transfer of residential land policy have significantly promoted rural non-farm employment and high-value-added agriculture [36]. Similarly, in Bangladesh, smallholder farmers invested in cash-crop cultivation through the land mortgage mechanism, reflecting the joint role of market forces and institutional innovation [37]. In recent years, the proliferation of digital technologies has profoundly impacted rural production and lifestyles. Research on digital rural transformation has seen a surge, with extant studies affirming that the emergence of rural e-commerce has enhanced the market participation of agricultural products and, in a broader sense, contributed to economic transformation through the optimization of the consumption structure [38].

A review of the existing literature reveals that the measurement of NQPF is predominantly focused on the provincial level. The mechanism through which NQPF influences RT and the development law at the prefecture level remains to be elucidated. Shandong, a high-income province situated along China’s eastern coast, encompasses the economically thriving Jiaodong Peninsula, the more developed Luzhong region, and the less developed southwestern Lu. This geographical diversity serves as a microcosm of China’s spatial pattern of economic and social development, encompassing both natural conditions and socio-economic aspects. Shandong is a paradigmatic province and region for the study of China’s rural transformation and development [39]. Consequently, this study contributes to the existing body of knowledge by expanding upon and innovating extant theories, while also taking into account the unique context of Shandong Province. A rural transformation indicator system will be constructed at the prefecture-level city level, encompassing the four dimensions of population, land, industry, and society. Concurrently, an NQPF indicator system will be developed, comprising the three dimensions of laborers, labor materials, and labor objects. Utilizing econometric and mediated effect models, the relationship between the impact of NQPF on rural transformation in Shandong Province and its development law from 2013 to 2022 will be analyzed in depth. The objective is to provide a scientific decision-making basis for Shandong Province and other global regions to promote RT and rural prosperity. This will establish a scientific foundation for promoting RT and prosperity in Shandong Province and, indeed, in all regions of the world.

2. Theoretical Analysis

2.1. Direct Effects of NQPF on RT

NQPF is a technology- and innovation-driven, high-performance, high-quality form of productivity [40]. Three key areas through which NQPF can promote rural development and enhance RT have been identified. The primary objective is to cultivate a high-quality labor force. The agricultural labor force has been identified as a primary catalyst for enhancing productivity [41]. The formation of a talent team with a specialization in rural areas has been identified as a key strategy for facilitating the development of RT. This team is expected to provide a consistent and reliable source of support for the ongoing development of rural areas [42]. Additionally, the need to modernize labor tools has been emphasized as a crucial element in enhancing the effectiveness and efficiency of agricultural operations. NQPF has the capacity to facilitate the development of advanced and intelligent agricultural equipment, thereby overcoming limitations imposed by time and space. This development has the potential to significantly extend the conventional boundaries of agriculture and enhance its growth potential. Thirdly, the object of labor should be expanded. Subversive technological innovation will, ineluctably, engender an extensive extension of the object of agricultural labor, thereby giving rise to new forms of agriculture, forms of production organization, and product form [43]. NQPF can alleviate the urban–rural digital divide, promote the improvement of urban–rural integration, and drive the digital transformation of rural areas by strengthening the construction and development of digital infrastructure in rural areas [44]. The logic through which NQPF drives RT and development lies in its capacity to channel the traction of technological innovation into rural industrial systems, diffuse the radiating effects of green development into rural ecosystems via shifts in production and lifestyles, and transmit the enabling force of innovative factor allocation into rural distribution systems through optimized resource distribution [45,46]. This tripartite synergy, spanning technological advancement, ecological modernization, and institutional restructuring, ultimately catalyzes comprehensive RT by reconfiguring socio-economic ecological dynamics [47]. The development of NQPF has the potential to promote the optimization and upgrading of the rural industrial structure, raise the income level of farmers, and promote the high-quality development of the rural economy [48]. Accordingly, this study proposes the following hypothesis:

Hypothesis 1.

NQPF can contribute to the level of rural transformation.

2.2. Mediating Effects of NQPF on RT

NQPF has emerged as a catalyst for rural talent development, with the cultivation and recruitment of skilled professionals becoming the cornerstone of rural revitalization [16]. NQPF, as represented by intelligence and digitalization, has reconfigured the logic of agricultural production factors, forcing the rural labor force to upgrade in the direction of high-skill and composite types. An important aspect of rural development is to narrow the urban–rural gap and promote urban–rural integration, while the flow of talent to rural areas serves as a key driving force [49]. The industrial integration pattern generated by NQPF provides a career path for introducing talents and attracting external technologists, management talents, and creative teams to the countryside. Rural capable people function as key intermediary variables, serving as the “engine” and “locomotive” to promote the transformation process. They also act as the “paver” for resource integration and the “bridge builder” for urban–rural linkage. Through technology diffusion, organizational mobilization, and model innovation, they have transformed NQPF into concrete practices of rural industrial quality improvement, governance optimization, and ecological improvement [50]. As the primary carrier of NQPF, they facilitate the exchange of knowledge, collaborative innovation, and the absorption of capital, thereby accelerating the modernization of traditional agriculture and reshaping production relations at the institutional level. This provides a theoretical foundation and a practical approach for the Chinese-style modernization of agricultural and rural areas [51]. NQPF has the capacity to facilitate the triple change of “technology–people–system” by empowering the upgrading of the labor structure, thus systematically promoting the comprehensive transformation of the countryside. Accordingly, this study proposes the following hypothesis:

Hypothesis 2.

NQPF can contribute to the level of rural transformation through the quality of the population.

2.3. Heterogeneous Effects of NQPF on RT

The heterogeneous effect of NQPF on regional rural transformation stems from the imbalanced distribution of regional resource endowment, industrial base, and scientific and technological innovation capacity. NQPF has technological innovation as its core driving force, but the release of its effectiveness is constrained by regional factor structure constraints. The adaptability of the policy support system to local needs further strengthens differentiation, and the public sector promotes a coupling effect between NQPF and local resources through differentiated technology supply and institutional innovation. From the perspective of regional factor structure, the Provincial Capital Economic Circle and Jiaodong Economic Circle have a strong economic foundation. NQPF in these areas is primarily concentrated in urban high-end manufacturing and service industries, with weaker linkages with rural economies. For example, Jinan focuses on quantum communication and biomedical industries, Qingdao prioritizes electronic information industries, and Yantai centers on commercial aerospace industry clusters. As a traditional agricultural region, Lunan Economic Circle‘s NQPF development places a greater emphasis on agricultural modernization and industrial chain upgrading, which is highly aligned with rural transformation needs. Theoretical logic suggests that NQPF is not homogenized for RT, and that different regions in Shandong Province can choose the optimal transformation path based on their own conditions, ultimately forming a multi-level and differentiated pattern of NQPF-driven RT. Accordingly, this study proposes the following hypothesis:

Hypothesis 3.

The impact of NQPF on rural transformation exhibits significant regional variations within the province’s three economic zones.

2.4. Mechanisms of NQPF Influence on RT

In accordance with the aforementioned assumptions and extant research, as shown in Figure 1, this paper puts forth the following proposed impact mechanisms:

3. Research Design

3.1. Model Setup

3.1.1. Basic Regression Model

This study constructs a baseline regression model to examine the impact mechanism of NQPF on RT. The specification of the baseline model is as follows:

$${RT}_{it}={\alpha }_0+{\alpha }_1{NQPF}_{it}+{\alpha }_c{Z}_{it}^q+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

In this model, ${RT}_{it}$ denotes the rural transformation index for city i in year t and ${NQPF}_{it}$ represents the new quality productive forces index. $Z_{it}^q$ refers to a series of control variables, where q indexes the corresponding control variable. ${\alpha }_0$, ${\alpha }_1$, and ${\alpha }_c$ are coefficient vectors to be estimated. ${\mu }_i$ captures regional fixed effects, ${\lambda }_t$ accounts for time-fixed effects, and ${\epsilon }_{it}$ denotes the random error term (below).

3.1.2. Mediation Effect Model

We construct the following mediation effect model based on Model (1), adding population quality as a variable to test the mediation effect influence mechanism of NQPF on RT, see Models (2) and (3), as follows:

$$M_{it}^k={\gamma }_0^k+{\gamma }_1^k{NQPF}_{it}+{\gamma }_q^k{Z}_{it}^q+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

$${RT}_{it}={\beta }_0+{\beta }_1{NQPF}_{it}+{\beta }_2^k{M}_{it}^q+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

In the model, M denotes the mediating variable, K denotes population quality, and $\gamma$ and $\beta$ denote the vector of parameters to be estimated.

3.2. Variables and Data

3.2.1. Dependent Variable

RT can be defined as the reconstruction of the socio-economic and spatial patterns of rural areas. This reconstruction is characterized by the transformation of demographic, economic, spatial, and territorial structures, as well as the improvement of infrastructure. Essentially, RT encompasses the transformation of people’s way of life from rural to urban areas [52]. It is evident that RT is a multifaceted process of metamorphosis, encompassing various aspects of the countryside, including population, land, industry, and society. Consequently, this study synthesizes the extant research bases of scholars to construct an evaluation index system for the level of RT in Shandong Province from the four dimensions of population, land, industry, and society, as illustrated in

Table 2. Indicator system for evaluating the level of RT.

Dimension	Primary Indicators	Indicator Description	Trend
Demographic Transition	Ratio of urban to rural income	Ratio of disposable income per urban resident to disposable income per rural resident	-
	Proportion in basic education	Number of high school graduates proportion of rural population/total rural population at the end of the year	+
Land Use Transition	Proportion of arable land area	Cultivated land area as a proportion of the total area of the municipality	-
	Housing area per capita	Ratio of total rural housing area to total rural inhabitants	-
	index of replanting	Grain sown area as a proportion of total cultivated area	+
Industrial Transition	Rate of change in industrial structure	Share of agricultural, forestry, livestock and fishery output in regional GDP	-
	Rate of change in area of non-food crops	Share of non-food crops in total sown area	+
Societal Transition	Per capita electricity consumption in villages	Ratio of rural electricity consumption to rural population	+
	Total postal operations	Total postal operations	+
	Hospital beds per 1000 rural population	Hospital beds per 1000 rural population	+

[29,53]. The crux of RT is the optimization of the population structure and the enhancement of quality. The urban–rural income ratio can reflect the gap between urban and rural areas. A larger value indicates a weaker attractiveness of rural areas and more serious population loss. Specifically, the urban–rural income ratio can reflect the direction of population flow [54]. The proportion of the population receiving basic education mainly refers to the population with a high school education. This is because very few people with a university degree choose to stay in rural areas, and the diffusion of technologies related to new productive forces requires a certain level of cultural literacy. Relatively speaking, the population with a high school education has a stronger influence on rural areas. Secondly, land constitutes the material foundation of rural development, and RT necessitates the intensification and sustainability of land use. The proportion of cultivated land area can reflect the changes in traditional agriculture in the region. The multiple cropping index, which refers to the number of times the same piece of cultivated land is replanted with crops within a year, can measure the utilization efficiency of cultivated land resources and the degree of agricultural intensification. Per capita housing area can reflect the transformation of living space for rural populations. Industrial upgrading is a significant driving force for RT. The rate of industrial structure change is represented by the proportion of the output value of agriculture, forestry, animal husbandry, and fishery in the regional GDP, which can directly capture the weight change of the primary industry and reflect the degree of agricultural transformation. The rate of change in the proportion of cash-crop area reflects the degree of agricultural diversification and is closely related to agricultural modernization [55]. The modernization of social services is a critical factor in facilitating RT. Consequently, the following three indicators are employed to assess the level of public services and infrastructure: the total volume of postal services, per capita electricity consumption in rural areas, and the number of hospital beds per 1000 rural population. In order to circumvent the potential pitfalls of the entropy weight method, such as the generation of localized high index weights that lack practical significance, a combination of the entropy weight method with expert opinion is employed. This approach utilizes the subjective–objective combination of the assignment method to determine the index weights, thereby ensuring the robustness and relevance of the resulting indices.

3.2.2. Core Explanatory Variable

NQPF represents an advanced paradigm of high-efficiency productivity, embodying a novel configuration distinct from conventional productivity frameworks [56]. NQPF is fundamentally characterized by the synergistic optimization of the following three core components: laborers, labor objects, and labor materials. Collectively, these elements constitute its essential theoretical connotation [46]. Therefore, drawing on extant scholarship and methodologically aligned with established agricultural NQPF indicator systems [57,58], this study constructs an evaluation framework for assessing NQPF development levels through the following tripartite analytical dimensions: laborer capacity enhancement, labor object innovation, and labor material optimization. Specifically as shown in

Table 3. Indicator system for evaluating the level of development of NQPF.

Dimension	Primary Indicators	Indicator Description	Trend
Laborers	Human Capital Quality	R&D personnel converted full-time equivalent	+
	Innovation-Driven Vitality Index	China Agricultural Innovation and Entrepreneurship Index	+
	Knowledge-Intensive Employment Ratio	Number of employed persons in scientific research and technology services/total employed persons	+
	Employment Paradigm Shift Coefficient	Number of employed persons in private sector/total employed persons	+
Labor Objects	Industrial Intelligence Index	Number of Artificial Intelligence enterprises	+
	Corporate Digital Maturity Score	Percentage of number of enterprises with e-commerce trading activities	+
	Growth Rate of Agricultural Factor Productivity	Ratio of output value of agriculture, forestry, animal husbandry, and fishery to sown area of crops	+
	Industrial Structure Sophistication Index	Value added of tertiary industry as a share of regional GDP	+
	Per Capita Green Space Coverage	Green covered area/total population	+
	Agricultural Carbon Productivity	Total output value of agriculture, forestry, animal husbandry, and fishery/carbon emissions from agriculture	+
	Input–Output Efficiency Ratio of Chemical Fertilizers	Fertilizer use/agriculture, forestry, animal husbandry, and fishery output value	-
	Pesticide Application Efficiency Coefficient	Pesticide use/agriculture, forestry, animal husbandry, and fishery output value	-
	Agricultural Film Utilization Efficiency Index	Amount of agricultural film used/output value of agriculture, forestry, animal husbandry, and fishery	-
	Digital Platform Adoption Rate per 100 Enterprises	Number of websites per 100 enterprises	+
Labor Resources	Digital Financial Inclusion Index	Digital Inclusive Finance Index	+
	Internet Broadband Penetration Rate	Internet broadband access users	+
	Agricultural Mechanization Composite Index	Total power of agricultural machinery/cultivated land area	+
	Scientific Expenditure Ratio in Local Fiscal Budgets	Share of science expenditures in local fiscal expenditures	+

. In the laborer dimension, the manifestation of NQPF primarily occurs through the intellectual transformation of the workforce, necessitating innovative literacy and knowledge-based competencies. Consequently, this study operationalizes these theoretical constructs through the following four measurable indicators: human capital quality (full-time equivalent of R&D personnel), innovation-driven vitality (China Agricultural Innovation and Entrepreneurship Index), employment paradigm shift (proportion of private sector employment), and knowledge-intensive engagement (research service sector employment ratio), collectively reflecting intellectualization and innovation-driven development trajectories. At the level of the labor object dimension, the emergence of NQPF necessitates the intelligent and sustainable evolution of production elements. Therefore, this study quantifies efficiency enhancement and green transition through the following four operationalized metrics: industrial intelligence and digitalization (number of AI enterprises and corporate digital platforms), agricultural eco-efficiency (carbon productivity and input–output ratios of fertilizers and pesticides), industrial structure sophistication (tertiary sector share), and resource utilization efficiency (agricultural output per unit cultivated area). These metrics systematically reflect the dual imperatives of technological advancement and ecological sustainability. This study posits that the modernization of production means constitutes the material foundation for NQPF. The operationalization of this framework is achieved through the use of the following four validated indicators: the Digital Financial Inclusion Index, the internet broadband penetration rate, the ratio of scientific expenditure to local fiscal budgets, and the level of agricultural mechanization. The collective significance of these metrics is in their ability to underscore the digital transformation and technology-driven empowerment of production means within the NQPF paradigm.

3.2.3. Control Variables

In consideration of extant studies, the following control variables are incorporated into the model. The first component is the logarithm of the urbanization rate (Ur), which is expressed as the logarithm of the proportion of the resident population in urban areas to the total resident population in the area. The second component is the Average Wage of Employees (Awe), expressed as the logarithm of the average wage of urban and private units. The third component is the Industrial Structure Level (Indus), expressed as the proportion of the sum of the value added of the secondary industry and the value added of the tertiary industry to the regional GDP. The fourth component is the logarithm of the average wage of the private sector. The Average Wage of Employees (Awe) is expressed as the logarithm of the average wage in the urban and private sectors. The Industrial Structure Level (Indus) is expressed as the sum of the added value of the secondary and tertiary sectors as a proportion of the GDP of the region.

3.2.4. Intermediary Variables

In light of the viability of data acquisition and the prevailing circumstances in Shandong Province, as well as in consideration of extant studies, the following three domains are identified for the assessment of Population Quality (PQ): human capital level, scientific and technological innovation capacity, and employment structure. Specifically as shown in

Table 4. Indicator system for intermediary variables.

Primary Indicators	Indicator Description	Trend
Level of human capital	Number of students enrolled in general undergraduate and specialized schools/total population at the end of the year	+
	Number of students enrolled in secondary vocational schools/total population at the end of the year	+
	Ratio of basic education for rural residents	+
Scientific and technological innovation capacity	Full-time equivalent of R&D personnel	+
	Innovation and Entrepreneurship Index	+
Employment structure	Employed persons in scientific research and technology services/total employed persons	+
	Number of employed persons in private sector/total employed persons	+

.

3.3. Data Source and Statistical Description

Considering the availability of data, this study adopts the panel data of 16 prefecture-level cities in the Shandong Province region from 2013 to 2022 for analysis.

Table 5. Descriptive statistics for variables.

Variable Types	Variable Name	Variable	Sample Number	Mean Value	Standard Deviation	Minimum Value	Maximum Values
Explanatory variable	Rural Transformation	RT	160	0.411	0.057	0.246	0.662
Core explanatory variables	New Quality Productive Forces	NQPF	160	0.394	0.147	0.121	0.778
Control variables	Urbanization Rate	Ur	160	−0.514	0.138	−0.877	−0.257
	Average Wage of Urban Workers	Awe	160	10.932	0.276	10.370	12.587
	Level of Industrial Structure	Indus	160	0.921	0.028	0.856	0.971
Mediating variables	Population Quality	PQ	160	0.333	0.122	0.083	0.682
Instrumental variables	Civilian Car Ownership	CO	160	1,286,685	757,168	348,567	3,548,205

presents the descriptive statistical results of the variables. The data are mainly from Shandong Provincial Statistical Yearbook, China Agricultural Statistical Yearbook, China Urban Statistical Yearbook, and the statistical yearbooks and statistical bulletins of each prefecture-level city The data on agricultural carbon emissions are from the EDGAR_2024_GHG database; the data on innovation and entrepreneurship levels are from the China Rural Innovation and Entrepreneurship Index measured by the Center for Agricultural and Rural Development, Zhejiang University [59]. For the number of AI companies, we refer to Wang Linhui’s research, which identifies a company as an AI company when its business scope involves keywords related to AI such as chips, image recognition, computer vision, speech recognition, sensors, etc. [60]; we use the Digital Inclusive Finance Index from the Peking University Digital Inclusive Finance Index published by the Peking University Digital Finance Research Center [61].

4. Empirical Results Analysis

4.1. Basic Regression Analysis

The impact of NQPF on RT is measured by Equation (1), with columns 1 and 2 indicating the impact of NQPF on RT and its impact with the inclusion of control variables. In

Table 6. Baseline regression.

Parameters	RT
	(1)	(2)
NQPF	0.201 *** (0.052)	0.360 *** (0.041)
Ur	—	0.366 *** (0.118)
Awe	—	−0.021 *** (0.006)
Indus	—	−1.089 ** (0.484)
Constant	0.332 *** (0.020)	1.688 *** (0.467)
City	Yes	Yes
Year	Yes	Yes
N	160	160
R2	0.778	0.818

Note: **, *** indicate significance at the 5%, and 1% levels, respectively, with standard errors in parentheses.

, The results show that an increase in the level of NQPF in the Shandong Province region can promote RT and is statistically significant at the 1% level, with each unit increase in NQPF increasing the level of RT by about 0.2010 units. The contribution of NQPF to RT is equally significant after the inclusion of control variables, at which point the level of RT increases to about 0.3600 units, verifying that Hypothesis 1 holds. This study shows that NQPF itself brings less inhibition than facilitation to RT, and that NQPF is able to promote rural and agricultural progress while promoting urban and industrial development, which provides new perspectives and ideas for promoting RT.

The incorporation of control variables reveals that Ur exerts a significant positive effect on RT. Urban population growth and developmental processes elevate regional economic levels, while the diffusion of advanced urban production paradigms and lifestyle concepts enhances the quality of rural laborers and modernizes rural labor materials. The implementation of innovative labor materials, encompassing intelligent agricultural equipment, IoT monitoring systems, and big data analytics, has been demonstrated to result in significant enhancements in agricultural productivity. Consequently, this development establishes an enhanced mechanism for propelling RT. A rise in Awe and the level of Indus structure has a significant dampening effect on RT. A rise in urban wages is expected to hasten the migration of young and middle-aged rural workers to urban areas, thereby leading to the hollowing out of the rural workforce. The enhancement of the industrial structure is frequently accompanied by a relocation of the economic focal point to urban centers. Rural areas are often regarded as “raw material supplying places” or “ecological protection zones”, resulting in their inability to partake in the advantages brought about by urbanization to the same extent. Consequently, the role of rural areas is marginalized in urban planning, thereby illustrating the discrepancy between the affluence of urban areas and the relative decline of rural regions. The present study will examine the relative decline of the countryside [62]. Consequently, the inhibitory effect of Awe, as well as the over-advancement of the industrial structure, is fundamentally attributable to resource mismatch and policy lag within the framework of the urban–rural dual system. This outcome is analogous to that reported in Li et al.’s study, which determined that industrial agglomeration exerts substantial negative direct and indirect influences on the advancement of new and high-quality productivity in agriculture [63].

4.2. Robustness Analysis

In order to assess the reliability of the results obtained from the benchmark regression model, a robustness test is conducted, and the findings are presented in (1) to (3) in

Table 7. Robustness tests.

Parameters	RT
	Explanatory Variables Lagged First Order (1)	Variable Indentation Processing (2)	System GMM Estimation (3)
NQPF	—	0.361 *** (0.041)	0.359 *** (0.093)
L.NQPF	0.232 *** (0.073)	—	—
Controls	—	Yes	Yes
L. Controls	Yes	—	—
City	Yes	Yes	Yes
Year	Yes	Yes	Yes
N	160	160	160
R2	0.628	0.637	—
AR(2) P	—	—	0.120
Hensen test P	—	—	0.129

Note: L. denotes the first-order lag of the corresponding variable. *** indicate significance at the 1% level, respectively, with standard errors in parentheses.

. Firstly, the core explanatory variables, as well as the control variables, are introduced into the model with first-order lagging to measure, to a certain extent, the effect of excluding the current period. The impact of the lagged first order of NQPF on RT remains positive at the 1% significance level. Secondly, the two sides of the 1% shrinking tails are processed to avoid the possibility that extreme values of the independent variables will affect the regression results. NQPF maintains a positive impact on RT at the 1% significance level. Finally, the lagged terms of CO and NQPF are introduced into the system GMM model to test the consistency and reliability of the regression results under different setting conditions. The p-values of the AR(2) and Hansen tests are both greater than zero. The first result indicates that the model dynamics are set more reasonably, and the instrumental variables are not related to the error terms.

The findings of the robustness test demonstrate that the coefficient of NQPF is consistently and significantly positive at the 1% level across various model configurations. This suggests that the contribution of NQPF to RT remains unaffected by extreme value disturbances or the endogeneity of variables, thereby ensuring the reliability of the results.

4.3. Endogeneity Analysis

In light of the potential challenges of omitted variables and two-way causality in constructing the NQPF model for RT, the model undergoes a secondary regression using the IV-2SLS instrumental variable method combined with two-way fixed effects, calculating the Kleibergen–Paap rk LM statistic, Cragg–Donald Wald F statistic, and the p-value of the over-identification test. The Kleibergen–Paap rk LM statistic is used to test the correlation strength between instrumental variables and endogenous variables, with the null hypothesis being that instrumental variables are unrelated to endogenous variables. The Cragg–Donald Wald F statistic ensures that instrumental variables have sufficient explanatory power for endogenous variables; when its value is lower than the critical value at the 10% level, this indicates that instrumental variables are invalid, leading to estimator bias in the IV-2SLS method greater than that in OLS analysis. The over-identification test verifies the exogeneity of instrumental variables, whether instrumental variables are uncorrelated with the error term or not. As shown in

Table 8. Heterogeneity tests.

Parameters	RT
NQPF	0.595 *** (0.104)
Controls	Yes
City	Yes
Year	Yes
N	160
R2	0.836
Kleibergen–Paap rk LM	30.052 ***
Cragg–Donald Wald F	35.199 [19.93]
Hansen J	0.1649

Note: Values in [ ] are critical values for the Stock–Yogo weak identification test at the 10% level. *** indicate significance at the 1% levels, respectively, with standard errors in parentheses.

, the p-value of the Kleibergen–Paap rk LM statistic is also significant at the 1% level. Additionally, the Cragg–Donald Wald F statistic is 35.199, far exceeding the critical value of 19.93 at the 10% level. Meanwhile, the p-value of the over-identification test is 0.1649, indicating that instrumental variables are free from issues of unidentifiability, weak instrumental variables, and over-identification. After all three indicators meet the standards, NQPF still exhibits a significantly positive impact on RT at the 1% significance level, fully verifying the validity of the conclusions in this paper.

4.4. Impact Mechanism Analysis

The above test results confirm that NQPF has a significant positive effect on RT. To further explore the underlying mechanism, we employed the mediation model constructed using Equations (2) and (3). Both the Sobel test and Bootstrap test are critical for validating the mediating role of the intermediate variable [64]. The Sobel test, with its computational simplicity, is suitable for theoretical derivations or preliminary analyses. In contrast, the Bootstrap test constructs confidence intervals for the indirect effect through non-parametric resampling, making it robust to distributional assumptions and applicable across various sample sizes and data distributions [65]. Using both methods enhances the reliability of the mediation effect verification. The results are presented in

Table 9. Intermediary mechanism test.

Parameters	PQ	RT
	(1)	(2)
PQ	—	0.197 **
NQPF	0.478 **	0.266 ***
Controls	Yes	Yes
City	Yes	Yes
Year	Yes	Yes
N	160	160
R2	0.942	0.827
Sobel	2.521 **
Bootstrap	95%CI [0.005, 0.346]

Note: **, *** indicate significance at the 5%, and 1% levels, respectively, with standard errors in parentheses

. When PQ was introduced as the mediating variable in the model, controlling for individual and time effects along with other covariates, we found that NQPF significantly and positively affects PQ, and PQ, in turn, significantly and positively affects RT, both at the 5% significance level. The Sobel test confirmed the significance of the mediation effect at the 5% level. Furthermore, the more rigorous Bootstrap test (with 5000 resamples) showed that the 95% confidence interval for the indirect effect excluded zero and contained only positive values, indicating a significant and positive mediation effect. These findings support the conclusion that PQ acts as a significant mediator through which NQPF positively influences RT at the 5% significance level, thus validating Hypothesis 2.

4.5. Analysis of Heterogeneity

The total sample is divided into three regions, namely, the Provincial Capital Economic Circle, the Lunan Economic Circle, and the Jiaodong Economic Circle, according to the Shandong provincial government plan, to examine, in depth, the heterogeneous impacts of NQPF on RT in different regions. The results are shown in

Table 10. Heterogeneity analysis.

Parameters	Provincial Capital Economic Circle	Lunan Economic Circle	Jiaodong Economic Circle
	(1)	(2)	(3)
NQPF	−0.069 (0.0700)	0.264 *** (0.080)	0.093 (0.086)
Ur	0.426 *** (0.083)	−0.123 (0.108)	−0.160 * (0.091)
Awe	0.020 (0.042)	0.005 (0.026)	0.060 * (0.033)
Indus	0.107 (0.275)	−0.032 (0.510)	0.489 ** (0.209)
Constant	0.328 (0.617)	0.232 (0.508)	−0.816 * (0.407)
N	70	40	50
R2	0.746	0.248	0.337

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively, with standard errors in parentheses

. On the premise of adding control variables and fixing time effects and individual effects, the results show that the impact of NQPF on RT is significant in the Lunan Economic Circle at the 1% significance level, while it is insignificant in the other two economic circles. This indicates that the impact of NQPF on RT varies across different economic circles, confirming Hypothesis 3. Combined with the actual conditions of the three major economic circles, this is because the stronger the economic strength of a city, the weaker the correlation between new productive force elements and rural areas. This leads to the effectiveness of new productive forces being restricted by industrial orientation, resulting in a weak influence on agriculture and rural areas.

5. Conclusions and Discussion

5.1. Conclusions

As the latest form of productivity development, NQPF, with its strong scientific and technological innovation and empowerment, has become a key force in promoting sustainable rural development. This study empirically analyzes the mechanism of NQPF’s impact on RT from a multidimensional perspective based on panel data from 16 prefecture-level cities in Shandong Province, China, from 2013 to 2022. This study obtains the following conclusions through empirical analysis. First, the development of NQPF can significantly promote RT, and the conclusion still holds after a series of robustness tests. Second, Ur significantly promotes RT, but an increase in the Awe of urban workers and a change in the industrial structure can significantly inhibit RT. Third, NQPF can indirectly promote the level of RT through promoting PQ. Fourth, the impact of NQPF on RT in Shandong Province is regionally heterogeneous due to regional resource endowment and other factors, and the impact of NQPF on RT in Shandong Province is most significant in the Lunan Economic Circle, which has poorer economic conditions.

5.2. Discussion

The analysis revealed that there is still room for further expansion of this study. Firstly, with the passage of time and an increase in sample size, examining and analyzing in a larger regional scope can be considered; secondly, due to the different development status of each region, which leads to differences in the selection of indicators, future research should take into account these regional differences and carry out the construction of indicators in a more scientific way; lastly, in subsequent research, the multidimensional relationship between science and technology innovation and sustainable rural development can be further elucidated.

Sustainable rural development has attracted significant attention as a critical global issue. As the largest middle-income country, China’s substantial rural population constitutes a key factor in national development. Since implementing reform and opening-up policies, the vigorous development of the market economy has significantly accelerated urbanization. Analyzed from a geographic and spatial perspective, market economy elements exhibit clear urban agglomeration tendencies—cities efficiently absorb capital, technology, human resources, and material resources through well-developed infrastructure systems, with this agglomeration effect serving as the core driver of China’s rapid economic growth. Concurrently, rural areas face a sustained outflow of labor, capital, and resources to urban centers, resulting in stagnating endogenous development momentum. Currently, China’s economic development has entered a phase of growth rate transition, necessitating profound transformations in rural areas to achieve the “from affluence to strength” shift. The new development paradigm centered on domestic circulation recognizes the pivotal role of rural development. It should be emphasized that while rural prosperity relies on urban radiation effects, its fundamental development impetus originates from endogenous systems. Rural development models dependent entirely on external inputs lack sustainability. Empirical evidence indicates that rural elite leadership and government policy interventions can effectively activate sustainable development potential. As a strategic policy instrument, NQPF injects new momentum into RT by channeling capital and technology downward. However, its functional scope requires clarification: NQPF operates as an exogenous driver primarily serving as a sustainable development “catalyst”, while sustainable development’s essence resides in rural structural transformation. Cultivating rural communities’ autonomous development capacity constitutes a prerequisite for sustainability, with external policy resources providing essential safeguards. The decisive factor remains whether rural actors can effectively integrate into market economic systems and secure commensurate development agency and resource allocation rights.

6. Recommendations

Based on the findings of this study, the following policy recommendations are made:

(1) Strengthen the central driving role of NQPF through deepening technological innovation to drive the transformation of the countryside’s “factor–structure–function”, the use of digitalized and intelligent means of labor to empower traditional agriculture, the construction of an intelligent agricultural production system, the promotion of the Internet of Things, big data, and other technologies, and the deep integration of the entire agricultural industry chain. Establish a linked skills training system for urban and rural areas, improve support policies for returning to the countryside to start businesses, and provide talent attraction to the countryside through industrial integration, as well as new businesses. To stimulate the dynamics of endogenous development in the countryside, realize the RT of the countryside from blood-supply development to blood-supply development, and enhance the capacity for sustainable development in the countryside. For example, Zhejiang Province of China has built an agricultural big data platform to integrate data from planting, processing, and circulation links, so as to provide farmers with market trend prediction [66]. Shandong Province of China has piloted variable fertilization technology by unmanned aerial vehicles.

(2) Integrate urban–rural relations and narrow the gap between urban and rural areas. Improve the system of equal exchange of urban and rural factors, and break down institutional barriers to the flow of technology, capital, talent, and other factors. Build a platform for the synergistic development of urban and rural industries, promote the docking of urban innovation resources with rural production resources, deepen the reform of the land system, explore the path of marketization of rural collectively operated construction land, and establish a sound mechanism for the distribution of factor returns that takes into account both efficiency and fairness. Promote the equalization of the layout of basic public services and the extension of high-quality resources, such as education and medical care, to the counties. Through the development of NQPF, we can promote the integrated development of urban and rural areas, thereby resolving the problem of the relative decline of the countryside brought about by the urban–rural dichotomy. For example, pilot projects for the “marketization of collectively owned commercial construction land” can be carried out in suburban areas, allowing rural land to directly enter the market for transaction, drawing on the pilot experience of Chengdu [67]. Encourage the flow of talent to rural areas by learning from Zhejiang Province’s policies and practices in introducing rural professional managers.

(3) Stimulate the indirect promotion of population development and industrial development. Establish a tiered and categorized mechanism for talent cultivation and a whole-chain cultivation system linking vocational initiation in basic education, skill enhancement in vocational education, and innovation leadership in higher education. Innovate the mechanism for realizing the value of skills and broaden the development channels of talents. Improve urban and rural talent mobility policies, establish incentive mechanisms, and guide the flexible flow of urban intellectual resources to the countryside. Strengthen the effective connection between education investment, skills training, and market demand, break down the systematic barriers to technology diffusion and talent flow between urban and rural areas, and enhance the continuity and stability of the transmission of NQPF to RT goals. For example, the Chinese government is currently implementing the “New Farmer Program” in rural areas, which aims to enhance farmers’ capacity to adopt new technologies through vocational training.

(4) Build a differentiated regional policy system. A differentiated policy system has been established and a distinctive development path has been formulated on the basis of regional resource endowments and stages of development. For peri-urban areas with significant factor siphoning effects, innovative urban–rural industrial community models have been developed; for remote areas with outstanding ecological functions, sound mechanisms for realizing the value of ecological products have been developed. Improve the cross-regional factor compensation system and balance factor flow benefits through tax sharing and indicator trading. Construct a county cooperative development network, promoting the linked development of strong and weak villages through industrial co-construction, facility sharing, and other mechanisms and enhancing the overall resilience of the rural system. In regions with better economic conditions, while encouraging the development of high-tech industries, attention should also be paid to the development of enterprises related to new productive forces in agriculture.