New Quality Productivity of Agriculture and Rural Areas at the Provincial Scale in China: Indicator Construction and Spatiotemporal Evolution

Abstract

New quality productivity in agriculture and rural areas serves as a critical foundation for addressing the development needs of the times, advancing the comprehensive revitalization of rural regions, and overcoming the urban–rural dual structure. This paper studies the spatial changes and evolutionary trends of new quality productivity in agriculture and rural areas in 31 provinces of mainland China. The main conclusions are as follows: (1) At the provincial level, the development of China’s new quality productivity exhibits a spatial gradient, with a decreasing trend from east to west. (2) At the national scale, while significant spatial autocorrelation exists in the new quality productivity of agriculture and rural areas, internal disparities are gradually narrowing. (3) The eastern region demonstrates significant advantages, the central region is making steady progress, the western region is rapidly catching up, and the northeastern region faces increasingly significant development pressures. This paper extends the research boundary of new quality productivity to the field of agriculture and rural areas, and we introduce a variety of spatial analysis methods to depict its distribution characteristics.

1. Introduction

The current global economic situation is complex and volatile. Factors such as the adjustments in global trade patterns, the rapid advancement of technological revolutions, climate change, and geopolitical instability have introduced significant uncertainties to global economic growth [1]. Amid these profound changes, unseen in a century, nations worldwide are striving to identify new drivers of economic growth and development. While the Chinese economy has demonstrated a stable and positive trajectory, challenges such as lagging productivity and insufficient development momentum persist [2]. In September 2023, the concept of “New quality Productive Forces” was first introduced, aligning with the demands of high-quality development in the current era, and marking a new stage in the evolution of China’s productive forces. Against the backdrop of the comprehensive rural revitalization strategy outlined in the No.1 Central Document of 2024, the concept of “New quality productivity of agriculture and rural areas” has emerged.

For decades, rural areas in China have predominantly relied on traditional farming practices, including conventional planting and breeding methods characterized by low technological content and production efficiency. This reliance has resulted in sluggish rural economic growth and has posed significant challenges to the sustainable development of agriculture. These issues have constrained the economic advancement of agriculture and rural areas, rendering them unable to meet the increasing demands of the population for an improved quality of life. Consequently, it is of critical importance to scientifically assess the emerging quality and productivity of agriculture and rural areas, thoroughly analyze their evolutionary characteristics and driving mechanisms, and explore effective development strategies. Such efforts are essential for achieving revolutionary breakthroughs in production technologies, fostering innovative allocation of production factors, and promoting the comprehensive transformation and upgrading of agricultural and rural industries.

1.1. Theoretical Research Related to the New Quality Productivity of Agriculture and Rural Areas

In the mid-to-late 18th century, Western countries entered the era of classical political economy, during which theories related to productivity began to attract significant attention. The French economist François Quesnay was the first to introduce the concept of “productivity”, proposing the idea that “agriculture is the natural source of production”, which sparked widespread discussions [3]. The British economist Adam Smith introduced the concept of “labor productivity”, arguing that the division of labor and cooperative efforts could significantly enhance labor efficiency [4]. Jean-Baptiste Say and David Ricardo further integrated the concept of productivity into political economy, emphasizing its variability and adaptability [5]. Subsequent theoretical developments expanded the understanding of productivity from various perspectives. Karl Heinrich Marx’s political economy, Franz Liszt’s national economy, economic growth theory, and economic structural change theory discussed productivity from different perspectives. Marxist political economy emphasized the law of the contradictory movement between productive forces and production relations, becoming the basic theory for the study of productive forces [6,7]. Friedrich List’s national economy emphasized the crucial role of national protection in the development of productive forces in late-developing countries [8]. The economic growth theory introduced the efficiency of factor combination into the analysis of productive forces [9]. Economic structural change theory focused on the issue of industrial forms in growth [10]. At this point, the theoretical system of productive forces was initially formed.

Building on these historic achievements, China has continuously deepened its understanding of the development patterns of productive forces. As the primary contradiction in the socialist stage has shifted from “the people’s ever-growing material and cultural needs” to “the people’s ever-growing needs for a better life”, there is an urgent need to move away from dependence on traditional productive forces. In response, a new form of quality productivity, characterized by higher levels of integration, innovation, and technological advancement, has emerged [11]. This concept of new quality productivity advances Marxist theory on productive forces and deepens the understanding of the laws governing their development [12]. In the context of liberating and enhancing the productivity of agriculture and rural areas, scholars have conducted extensive research from multiple perspectives, including the role of information technology [13], system innovation [14], the elimination of the urban–rural dual structure [15], and moderate-scale operation. These efforts have yielded significant research outcomes. Furthermore, scholars have explored various aspects of new quality productivity, such as its connotation features [16], realization pathways [17], and practical applications [18].

In the theoretical framework of the new quality productivity of agriculture and rural areas, precision, digital, and smart agriculture technologies are the core elements of modern productive forces, reconstructing the traditional agricultural production model. Precision agriculture relies on the Internet of Things and other technologies to collect data, achieve precise fertilization and irrigation, and provide early warning of pests and disease, thus improving resource utilization efficiency [19]. Digital agriculture, supported by big data and cloud computing, builds a digital management platform for the entire industrial chain, optimizing every link from planting decisions to market circulation [20]. Smart agriculture integrates artificial intelligence and automated equipment, driving the transformation of agriculture from experience-driven to data-driven [21]. These technologies not only increase labor productivity but also contribute to the sustainable development of rural areas [22]. The evolution of the new quality productivity conforms to Marxist theory and provides a technological path for rural revitalization.

However, as a relatively new concept, limited research has been conducted on integrating new quality productivity with agricultural and rural development. In particular, systematic analyses of the connotations and characteristics of new quality productivity in the context of agriculture and rural areas remain scarce. This paper holds that the new quality productivity of agriculture and rural areas represents an advanced state of productive forces. It is characterized by a high-quality labor force as the main feature, disruptive innovation as the technological feature, the infiltration and integration of multiple factors as the allocation feature, the breakthrough and mutual integration of the boundaries between agriculture and rural areas as the structural feature, and digital intelligence and green development as the morphological features.

1.2. Research on the Index Construction and Spatiotemporal Evolution

Currently, numerous studies focus on the theoretical framework, connotations, and significance of new quality productivity. However, research involving quantitative analysis, such as the evaluation and spatiotemporal evolution of new quality productivity, remains relatively scarce. Some scholars used the improved entropy weight TOPSIS method to measure the new quality productivity in China from 2012 to 2021. The results showed that the level increased year by year, with an average annual growth rate of 21% [19]. Some other scholars constructed an index system to evaluate the new quality productivity of prefecture-level cities and found that the eastern region was leading in development, while the western and northeastern regions lagged behind, with significant differences between the regions [20]. In addition, some scholars used the Dagum Gini coefficient method to analyze new quality productivity, finding spatiotemporal differences between the north and south of China, among the four major regions and the five major economic belts, and that the development among provincial regions is unbalanced [21]. These studies provide important references for the quantitative analysis of new quality productivity. However, currently, there are relatively more studies on productivity at the provincial and urban scales, and these studies suffer from the problem of “overemphasizing theory and neglecting practice”.

In conclusion, this study integrates multiple theoretical perspectives and employs an interdisciplinary approach to assess the new quality productivity of agriculture and rural areas while analyzing its spatiotemporal evolution characteristics. This paper has three research objectives: constructing a scientific and comprehensive evaluation index system suitable for the new quality productivity in agriculture and rural areas; accurately depicting its distribution characteristics from the dual perspectives of time and space; and, based on the actual conditions of various regions, proposing targeted and practical development measures tailored to local conditions to promote its efficient development. This study’s innovations are reflected in three aspects: Firstly, it broadens the research scope of new quality productivity. On the one hand, it refines the research focus by applying the concept of new quality productivity specifically to the field of agriculture and rural areas. On the other hand, it moves beyond theoretical exploration by incorporating empirical analysis, thereby enriching the quantitative research on new quality productivity. Secondly, it advances the research methods for studying new quality productivity in agriculture and rural areas. This study employs a comprehensive approach, integrating hierarchical evaluation methods with various spatial analysis techniques across multiple disciplines, enhancing the methodological framework for this field of research. Finally, it contributes to the policy implications of new quality productivity in agriculture and rural areas. This study provides a rich body of research that offers scientific support for identifying key areas, primary models, and innovative strategies for the development of new quality productivity. These recommendations are tailored to regional and phased development needs and emphasize the importance of adapting to local conditions.

This paper is characterized by a rigorous logic, with its content presented in six parts that progress in a hierarchical manner. The Introduction analyzes the research background and motivation. The second part elaborates on the data sources and the construction of the index system. The third part introduces the research methodology. The fourth part presents the research findings. The fifth part conducts an in-depth discussion centered around the findings. Finally, the research achievements are summarized.

2. Data Sources and Construction of Index System

The data utilized in this study were sourced from the China Industrial Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Rural Statistical Yearbook, China Population and Employment Statistical Yearbook, China Urban and Rural Construction Statistical Yearbook, and the statistical yearbooks of 31 provinces in mainland China. The dataset spans the period from 2010 to 2022.

The index system is structured into four hierarchical levels, comprising a total of 23 detailed indicators. These indicators comprehensively and systematically reflect the performance and development trends of new quality productivity in agriculture and rural areas. Based on the principle of the three elements of productive forces, the new quality productivity in agriculture and rural areas is categorized into three dimensions: new quality laborers, new quality objects of labor, and new quality means of labor (Table 1). The dimension of new quality laborers is evaluated through laborer skills, labor productivity, and agricultural scientific and technological innovation capability [22,23].

Among them, the two indicators of forest coverage and urbanization rate were retrieved using 30 m resolution data obtained from the Landsat 8 satellite, with a timespan from 2010 to 2022. We integrated Band 5, Band 4, and Band 3 to obtain the standard false-color composite image. Then, the support vector machine method was used to conduct supervised classification on land-use types. Verification was carried out by establishing the confusion matrix for classification. It was found that the annual average value of the overall accuracy was 89%, and the annual average value of the kappa coefficient was 0.89, which met the requirements for the classification accuracy of the article. The standard false-color composite image of the original data is shown in Figure 1. In the figure, (a) is an example of Jiamusi, (b) is an example of Jinan, and (c) is an example of Tieling. According to the classification results, the corresponding indicators can be obtained by calculating the proportions of forest land and construction land in their respective provincial regions.

The dimension of new quality means of labor is divided into tangible and intangible means of production [24]. Tangible means of production encompass agricultural development and rural infrastructure construction. Specific indicators for agricultural development include the per capita total power of agricultural machinery, the effective irrigation area, and the application amount of pesticides and fertilizers. Rural infrastructure is characterized by indicators such as rural digital bases, fiber optic length, and Internet penetration rate. Intangible means of production primarily refer to the scale of digital elements, with the digital economy is measured by fixed asset investment in the digital industry and fixed asset investment in digital services.

3. Methodology

3.1. Index Weight Calculation Method

The weights of the indicators in this paper were calculated by using the entropy method. The entropy method completely determines the weight of indicators based on the degree of dispersion of the data themselves, avoiding subjective interference from human factors [25]. This method can objectively reflect the relative importance of each indicator, can fully utilize the information contained in each indicator, and is sensitive to reflecting the degree of variation in the indicator data. For comprehensive evaluation problems with multiple indicators and multiple objects, the entropy method can play a role in the fields of economy, management, or other fields [26]. Suppose the data are a matrix of m rows and n columns; the specific steps are as follows:

• 1. Data standardization:

(1)$\mathrm{Positive} \mathrm{indictors}: x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-x_{j\left(min\right)}}{x_{j\left(max\right)}-x_{j\left(min\right)}}} j=\mathrm{1,2}\dots n$

(2)$\mathrm{Negative} \mathrm{indictors}: x_{ij}^{\prime }={\displaystyle \frac{x_{j\left(max\right)}-x_{ij}}{x_{j\left(max\right)}-x_{j\left(min\right)}}} j=\mathrm{1,2}\dots n$

where $x_{ij}$ and $x_{ij}^{\prime }$ are the original data and normalized values, respectively; $x_{j\left(max\right)}$ represents the maximum value of the j-th indicator datum; and $x_{j\left(max\right)}$ represents the minimum value of the j-th indicator.

• 2. Calculate the proportion of the i-th datum in the j-th indicator $w_{ij}$:

  (3)$w_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\sum }_{i=1}^m{x}_{ij}^{\prime }}} j=\mathrm{1,2}\dots n$

• 3. Calculate the entropy value ($e_j$) of each indicator:

  (4)$e_j=-{\displaystyle \frac{1}{\mathit{ln}\left(m\right)}}{\sum }_{i=1}^m(w_{ij} \ast ln{w}_{ij}) j=\mathrm{1,2}\dots n$

• 4. Calculate the information entropy residue $d_j$:

  (5)$d_j=1-e_j$

• 5. Calculate the weight $W_j$ of the j-th indicator:

  (6)$W_j={\displaystyle \frac{d_j}{\sum _{j=1}{d}_j}}$

3.2. Spatial Pattern Analysis Method

This paper uses Moran’s I in exploratory analysis to study the spatial pattern mode of new quality productivity in agriculture and rural areas. It is mainly used to study whether the distribution of the research object in space has aggregated or dispersed characteristics [27]. If I is positive and significant, it indicates the existence of positive spatial autocorrelation; that is, similar values tend to cluster in space. If I is negative and significant, it means that there is negative spatial autocorrelation; that is, high values and low values tend to be dispersed in space. If I is close to zero, it indicates that the spatial distribution is random and there is no obvious spatial autocorrelation. When studying the new quality productivity of agriculture and rural areas across the country, Moran’s I can detect whether there is obvious spatial clustering. Its calculation formula is as follows:

(7)$I={\displaystyle \frac{n{\sum }_{i=1}^n\sum _{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{\sum }_{i=1}^n\sum _{j=1}^n{w}_{ij}{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2}}$

where $\overline{x}$ is the expectation of the spatial unit, and $w_{ij}$ represents the spatial weight matrix. Its significance level is usually determined by the p-value test. If the p-value is less than the given significance level $\alpha$, then the null hypothesis is rejected. Otherwise, the null hypothesis is accepted. In the analysis of practical problems, the significance level $\alpha$ is usually 0.05.

3.3. Spatiotemporal Process Analysis Method

Standard deviational ellipse (SDE) is a method used to describe the directionality and degree of dispersion of the spatial distribution of geographical elements [28,29]. It intuitively shows the distribution characteristics of geographical elements in space by calculating parameters such as the centroid of the ellipse, the length of the x-axis, the length of the y-axis, and the direction angle. The centroid of the SDE represents the average central position of the distribution of geographical elements. The length of the x-axis and the y-axis represents the degree of dispersion in the horizontal and vertical directions, respectively. The longer the x-axis or y-axis, the more dispersed the distribution in that direction. The direction angle indicates the main trend direction of the distribution. The formulae are as follows:

(8)${SDE}_x=\sqrt{{\displaystyle \frac{\sum _{i=1}^n{\left(x_i-\overline{X}\right)}^2}{n}}}$

(9)${SDE}_y=\sqrt{{\displaystyle \frac{\sum _{i=1}^n{\left(y_i-\overline{Y}\right)}^2}{n}}}$

(10)$tan\theta =\left[\left({\sum }_{i=1}^n{\widetilde{x_i}}^2-{\sum }_{i=1}^n{\widetilde{y_i}}^2\right)+\sqrt{{\left({\sum }_{i=1}^n{\widetilde{x_i}}^2-{\sum }_{i=1}^n{\widetilde{y_i}}^2\right)}^2+4{\left({\sum }_{i-1}^n\widetilde{x_i}\widetilde{y_i}\right)}^2}\right]+2{\sum }_{i-1}^n\widetilde{x_i}\widetilde{y_i}$

where ${SDE}_x$ and ${SDE}_y$ represent the lengths of the x-axis and y-axis of the ellipse, respectively, $tan\theta$ is the direction angle of the ellipse and $\widetilde{x_i}$ and $\widetilde{y_i}$ are the deviation values of the coordinates of element $i$ from the centroid of the ellipse.

4. Results

4.1. Results of Spatial Pattern Analysis

At the provincial scale, significant disparities exist in the development levels of new quality productivity across China. Except for the northeast region, there is a general decreasing trend in new quality productivity from east to west. In 2022, the levels of new quality productivity in China ranged from 2.66 to 38.46, with an average value of 13.40 (Figure 2). Provinces with relatively high new quality productivity in agriculture and rural areas include Guangdong (38.46), Zhejiang (29.53), Shandong (23.49), Jiangsu (22.76), Hebei (18.70), Sichuan (18.15), and Henan (17.86). These demonstrate outstanding performances and possess advantages in aspects such as agricultural scientific and technological innovation, rural industry integration, and the cultivation of new agricultural business entities. Notably, Guangdong significantly outpaces other regions and serves as an important growth pole for the development of new quality productivity in China. Regions with medium levels of new quality productivity of agriculture and rural areas include Hubei (16.01), Hunan (15.17), Fujian (14.79), Heilongjiang (14.13), Shanghai (13.93), Anhui (13.49), Beijing (13.38), Guangxi (13.37), Chongqing (12.12), Yunnan (11.96), Jiangxi (11.68), Shaanxi (10.99), Liaoning (10.76), Gansu (10.18), Xinjiang (10.12), Inner Mongolia (9.76), and Guizhou (9.65). Most of these regions are at a critical stage of agricultural modernization. There is a need to further strengthen agricultural infrastructure construction, the application of agricultural science and technology, and the cultivation of rural talents. For instance, as a major agricultural province, Heilongjiang has an advantage in the promotion of agricultural mechanization but still holds great potential for the development of smart agriculture. The remaining seven provinces, due to factors such as geographical limitations, weak economic foundations, and singular industrial structures, lack comprehensive agricultural infrastructure and advanced agricultural technology application conditions.

There is a sharp contrast in the performance of new quality productivity between Sichuan and Tibet. In the western region, Sichuan performs relatively outstandingly. In terms of the industrial structure, its agricultural industry has developed diversely and formed a complete industrial chain. Meanwhile, its investment in scientific research is considerable. Numerous scientific research institutions and universities have facilitated the development of emerging models such as smart agriculture. In contrast, due to geographical constraints, Tibet has low agricultural production efficiency. Its relatively lagging economic development has led to insufficient investment in agriculture.

At the national scale, significant spatial autocorrelation exists in the new quality productivity of agriculture and rural areas; however, its significance is gradually weakening, and the internal disparities are diminishing. In 2010, the national Moran’s I was 0.35, with a Z-score of 1.86 and a p-value of 0.049. In 2016, the national Moran’s I decreased to 0.33, the Z-score to 1.51, and the p-value increased to 0.082. In 2022, the national Moran’s I further declined to 0.30, the Z-score to 1.09, and the p-value rose to 0.134 (Figure 3). Moran’s I passed the significance test at a 10% confidence level in 2010 and 2016, but not in 2022, indicating that while spatial autocorrelation has persisted, it has been weakening over time. The decreasing Moran’s I and increasing p-value suggest a diminishing level of significance, which implies that the imbalance in new quality productivity among provinces is decreasing.

4.2. Results of Time Trend Analysis

The average level of new quality productivity in agriculture and rural areas across China’s provinces has shown an increasing trend; however, the growth rate has fluctuated over time. The highest growth rate occurred between 2011 and 2012, reaching 8.77%. In 2011, the central government allocated 988.45 billion RMB for expenditures on agriculture, rural areas, and farmers, an increase of 130.48 billion RMB over the previous year, representing a growth of 15.2%. Subsequently, the new quality productivity in agriculture and rural areas initially declined, then experienced fluctuations, and eventually tended toward stability (Figure 4).

The level of new quality agricultural productivity in all four major regions of China has shown a yearly upward trend, with the northeast region exhibiting the slowest growth rate. Notably, the new quality productivity in the eastern region is significantly higher than the national average, rising from 9.36 in 2010 to 18.69 in 2022 (Figure 5). Regarding average annual growth rates, the eastern region leads, with 7.68%, followed by the western region at 6.67%, the central region at 6.27%, and the northeastern region at 3.19% (Table 2). The new quality productivity level in the central region is slightly above the national average, while the western region remains significantly below it. The development in the northeastern region has been relatively uneven, with its new quality productivity increasing from 7.73 to 10.04.

The disparities in the levels of new quality productivity among the four major regions are relatively pronounced. The eastern region demonstrates significant advantages, the central region is making steady progress, the western region is continuously catching up, and the northeastern region faces greater development pressure. From 2010 to 2014, the level of new quality productivity in the northeastern region was slightly higher than the national level. However, after 2014, the gap between the northeastern region and the national level gradually widened, eventually approaching the development level of the western region (Figure 5). Although the western region started from a relatively low baseline, it has shown continuous growth. Before 2018, the growth in the western region was relatively slow. However, after 2018, the growth rate accelerated, with the level of new quality productivity increasing from 8.28 to 9.53 in 2022.

The growth of new quality productivity in the northeast region is slow, and it faces numerous difficulties. Most of the labor force engaged in agricultural production is elderly. They have difficulty adapting to the development of smart agriculture, which restricts the improvement of agricultural production efficiency. Recently, large-scale development has led to a decline in soil fertility in some areas, affecting the yield and quality of crops, and weakening the foundation of agricultural development. The northeast region has a high proportion of traditional industries. In recent years, it has faced great pressure in transformation and upgrading, and its driving effect on agricultural and rural development has weakened. At the same time, rural industries lack innovative vitality, further limiting the improvement of new quality productivity.

4.3. Results of Spatiotemporal Trend Analysis

The spatiotemporal changes observed in new quality productivity during the development process of the eastern region highlight the continuous optimization and transformation of its economic structure. The relative stability of the lengths of the x-axis and y-axis indicates that the core area and diffusion range of new quality productivity remain relatively stable. This stability can be attributed to the formation of a mature industrial cluster and economic development model in the eastern region (Table 3). The growth in new quality productivity relies more on technological innovation and industrial upgrading rather than large-scale spatial expansion. The change in the rotation angle reflects the directional adjustment of regional economic development, resulting in a certain degree of rotation in the spatial distribution of new quality productivity.

The stable spatial distribution pattern of new quality productivity in the central region reflects its important position and consistent role in the national economic development (Figure 6). Serving as a crucial hub connecting the eastern and western regions, the central region’s economic development is relatively balanced, and its industrial structure remains stable. The constancy of the axis lengths and rotation angle indicates that there is no significant expansion or contraction trend in the spatial distribution of new quality productivity within the central region. This phenomenon suggests that the economic development of the central region relies primarily on the upgrading and transformation of traditional industries rather than large-scale spatial expansion.

The spatial changes in new quality productivity within the western region are closely linked to resource exploitation and economic development in this region. The shift in the centroid is attributable to the continuous advancement of resource development and infrastructure construction, leading to a displacement of the center of gravity of agricultural and rural economic activities. Fluctuations in axis length and changes in rotation angle indicate that the spatial distribution of new quality productivity in the western region is constantly adjusting (Figure 7). This phenomenon occurs because the economic development of the western region is influenced by multiple factors, such as resource distribution, policy support, and market demand. Consequently, the growth of new quality productivity exhibits a certain degree of uncertainty.

The spatial variations in new quality productivity within the northeastern region reflect its efforts and exploration during industrial transformation. The relatively small change in axis lengths indicates that the core area of new quality productivity in this region remains stable. Moreover, the decrease in the rotation angle suggests that the economic development direction of the northeast region is gradually adjusting, placing greater emphasis on cooperation and coordinated development with neighboring regions.

5. Discussion

Research on new quality agricultural and rural productivity is vital for food security, rural economic development, and urban–rural integration. With rising food demand, traditional agriculture faces resource and environmental constraints [30,31,32,33,34]. New quality productivity can drive rural economic growth and narrow the urban–rural gap [35].

The utilization of entropy and spatial analysis methods allows for a comprehensive analysis of the spatiotemporal evolution trends of new quality productivity in agriculture and rural areas. The weights determined by the entropy method can help accurately measure the comprehensive levels of new quality productivity and identify key influencing factors. The spatial agglomeration characteristics revealed by exploratory spatial analysis provide a basis for formulating regional coordinated development policies and promoting the optimal allocation of resources [36]. Additionally, the distribution pattern shown by the standard deviational ellipse assists in planning infrastructure construction, optimizing the industrial layout, and promoting the balanced development of new quality productivity in agriculture and rural areas.

This study reveals that the development of new quality productivity in China’s agriculture and rural areas is characterized by spatial heterogeneity and dynamic evolution. In combination with the rural revitalization policy, to promote its balanced development, in terms of policies, it is necessary to strengthen regional coordination to facilitate the cross-regional flow of technological factors. Differential investments should be made in infrastructure to address the development conditions of regions with weaknesses. By optimizing fiscal and financial policies, the endogenous innovation momentum can be stimulated. A two-way talent flow mechanism should be established to break the dilemma of the “siphon effect”. Ecological protection and productivity improvements should be coordinated to avoid extensive development. The coordination of multi-dimensional policies can not only provide strong support for the implementation of the rural revitalization strategy but also achieve the coordinated development of new quality productivity.

Due to limitations in workload and time, this paper has not explored the spatial imbalances and its influencing mechanisms of new quality productivity in agriculture and rural areas. In future work, we intend to utilize the spatial Gini index and the geographically weighted regression model to further examine these characteristics.

6. Conclusions

This study evaluated the spatiotemporal evolution of new quality productivity in agriculture and rural areas across 31 Chinese provinces. Our key findings include the following:

At the provincial level, the development level of new quality productivity in China shows a decreasing trend from east to west. Economically, eastern provinces usually have a more developed economic foundation, advanced industrial structures, and comprehensive infrastructure, which provide favorable conditions for the development of new quality productivity. In contrast, western provinces have relatively low levels of economic development, more singular industrial structures, and less developed infrastructure construction, leading to numerous challenges in the advancement of new quality productivity.

There is significant spatial autocorrelation of new quality productivity in agriculture and rural areas across the country; however, this significance is gradually weakening, indicating that internal disparities are diminishing. With the continuous increase in policy support and proactive efforts in industrial transformation and upgrading, western provinces are expected to gradually narrow the gap with the eastern region in the development of new quality productivity.

Although the new quality productivity of agriculture and rural areas in China’s four major regions has continued to grow, regional disparities remain significant. The eastern region has outstanding advantages, the central region is making steady progress, the western region is continuously catching up, and the northeastern region is facing greater development pressure.

Author Contributions

Conceptualization, Xiangyang Cao; methodology, Xiangyang Cao; software, Jiahui Lei and Donghui Shi; validation, Wenlong Yu; formal analysis, Tianhui Tao; investigation, Xun Zhang; resources, Xiangyang Cao; writing—original draft preparation, Xiangyang Cao; writing—review and editing, Wenlong Yu; supervision, Wenlong Yu and An Wang; funding acquisition, Xiangyang Cao and Wenlong Yu. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available due to data confidentiality and privacy concerns, but they are available from the corresponding author upon reasonable request.

Conflicts of Interest

All authors of this article declare that there is no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Examples of the standard false-color composite images of the original data; Jiamusi region (a), Jinan region (b) and Tieling region (c).

Figure 2. Calculation results of new quality productivity in each province.

Figure 3. Spatial autocorrelation test.

Figure 4. Annual average value and growth rate of China’s new quality productivity.

Figure 5. Development trend of new quality productivity in four major regions.

Figure 6. Spatiotemporal change trends in the eastern and central regions.

Figure 7. Spatiotemporal change trends in the western and northeastern regions.

Table 1

Evaluation index system of new quality productivity in agriculture and rural areas.

Criterion Level	First-Level Indicator	Second-Level Indicator	Measurement Method
New quality laborers	Laborer’s skills	Per capita education level	Worker’s education level
		Rural cultural facilities	Number of rural cultural stations
	Labor productivity	Economic income	Net income
			Revenue growth rate
		Happiness index	Engel coefficient for rural residents
		Entrepreneurial concept	Farmers’ entrepreneurial activity
	Agricultural innovation capability	Innovation achievements	Number of valid patent applications
New quality objects of labor	Agricultural development ability	Agricultural production capacity	Overall grain production capacity
		Agricultural production efficiency	Agricultural labor productivity
		Agricultural production value	Value added from agriculture, forestry, animal husbandry, and fisheries
	Ecological environment	Ecological environment governance	Area of soil erosion control
			Forest coverage
		Urbanization development	Urbanization rate
New quality means of labor	Tangible means of production	Agricultural development situation	Per capita power of agricultural machinery
			Irrigated area
			Application amount of pesticides and fertilizers
		Infrastructure	Rural digital base
			Fiber length
			Internet penetration
	Intangible means of production	Digital economy	Rural power consumption
			Agricultural fiscal expenditure
			Digital fixed assets investment
			Digital service fixed assets investment

Table 2

Average growth rate of new quality productivity in major regions of China.

Area	Contained Cities	Average Growth Rate(%)
Eastern region	Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan	7.68
Central region	Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan	6.27
Western region	Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang	6.67
Northeast region	Liaoning, Jilin, Heilongjiang	3.19

Table 3

Changes in the properties of standard deviational ellipses in four major regions.

Region	Year	Centroid (x)	Centroid (y)	Axis (x)	Axis (y)	Rotation Angle
Eastern	2010	117.43	31.86	3.61	8.82	10.87
	2016	117.45	31.43	3.61	8.74	12.1
	2022	117.31	31.25	3.57	8.86	11.95
Central	2010	113.69	31.4	2.78	4.55	177.25
	2016	113.74	31.31	2.84	4.48	177.54
	2022	113.77	31.28	2.81	4.42	177.99
Western	2010	104.68	32.4	12.03	8.69	113.28
	2016	104.36	32.22	12.19	8.56	113
	2022	104.4	32.07	12.25	8.5	113.77
Northeastern	2010	125.37	44.34	0.79	5.23	38.1
	2016	125.53	44.53	0.8	5.2	37.78
	2022	125.68	44.72	0.79	5.18	37.46

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