The Impact of New Quality Productive Forces on Common Prosperity: Evidence from Chinese Cities

Abstract

As a pivotal engine driving China’s economic development, new quality productive forces are profoundly shaping the pathways for realizing common prosperity and Chinese modernization. The study constructs multidimensional evaluation frameworks for new quality productive forces and common prosperity, respectively, measures the development levels of new quality productive forces and common prosperity across 277 prefectural-level and above cities in China from 2013 to 2022, and analyzes the spatial and temporal evolution characteristics of China’s new quality productive forces over the past decade using ArcGIS 10.8.1. Meanwhile, the two-way fixed model and the spatial Durbin model are used to analyze the impact of new quality productive forces on common prosperity and its spatial spillover effect. The study finds the following: (1) China’s new quality productive forces development levels generally show a spatial pattern of “high in the east and low in the west”, in which cities located in the Yangtze River Economic Belt and the eastern coastal strip have a higher level of new quality productive forces than other cities, with significant inter-regional differences. (2) New quality productive forces exhibit a robust and significant promoting effect on common prosperity. Mechanism analysis reveals that this effect operates through three channels: enhancing economic agglomeration, advancing industrial structure upgrading, and improving labor misallocation. (3) Regional heterogeneity shows that the promotion effect of new quality productive forces on common prosperity is particularly prominent in Northeast China and Eastern China. Structural heterogeneity reveals that labor materials and objects of labor exhibit more pronounced effects in enhancing common prosperity compared with laborers. (4) Spatial econometric analysis confirms that the new quality productive forces have a significant spatial spillover effect on common prosperity. The findings provide theoretical support for advancing common prosperity while contributing to China’s approach to addressing developmental imbalances among developing countries within the global community with a shared future.

1. Introduction

Global modernization has led to profound societal transformations across nations. Despite variations in developmental stages, historical contexts, and institutional frameworks among countries, wealth polarization has emerged as a ubiquitous phenomenon accompanying these modernization processes. Currently, China continues to face prominent challenges in achieving balanced urban–rural and regional development. Additionally, critical tasks such as economic restructuring and transitioning from old to new growth drivers remain unresolved, significantly impeding sustainable development and modernization [1]. Modernization is a comprehensive and multidimensional process. It is revolutionary in nature, systemic in structure, global in scope, long-term and phased in progression, and marked by characteristics such as irreversibility, homogenization, and continuous advancement [2]. This transition represents an essential and universal trajectory for the development of all countries and regions. China, as a latecomer to modernization, faces a distinct set of structural conditions. Its unique national context—including economic configuration, political institutions, cultural traditions, and historical legacies—shapes a modernization pathway that differs significantly from that of earlier industrialized nations. Consequently, the Chinese path to modernization can be conceptualized as a distinctive and complex “long march” toward modernity. While diverse models of modernization have emerged across different national contexts, they share one fundamental feature: the sustained and continuous development of productive forces remains at the core of modernization processes. In this context, the General Secretary Xi Jinping has proposed the novel concept of “new quality productive forces” during his inspection and research trips to Sichuan, Heilongjiang, and other places since July 2023, emphasizing the need to “accelerate the formation of new quality productive forces (NQPF) and enhance new drivers of development” [3], and to break the current impasse in China’s development by developing new quality productive forces. In January 2024, he reiterated the significance of this initiative, underscoring its role in propelling high-quality development and ushering in a new era of productivity. He expounded on the intertwined relationship between new quality productive forces and the pursuit of common prosperity, emphasizing the need for enhanced support measures to advance this shared well-being. The crux of this new paradigm lies in the “new” and the “quality”. The “new” is reflected in new laborers, new objects of labor, and new labor materials, and from the perspective of Marxist political economy, it represents a productivity leap characterized by high technology, high efficiency, and high quality [4]. The “quality” emphasizes innovation-driven development and marks an improvement in TFP. New quality productive forces transcend dependence on traditional productivity development and represent a more advanced state of productivity [5]. Furthermore, they foster systemic shifts in both supply and demand, and induce endogenous transformation in modes of economic development. As such, they serve as a primary source of growth in total factor productivity. Moreover, NQPF is a high-level summary of humanity’s entry into the digital economy era. In an environment characterized by rapid technological advancement, data has become the most essential factor of production. Unlike traditional production inputs, data possesses distinct characteristics. First, it exhibits inherent liquidity and shareability, enabling efficient allocation across spatial and temporal boundaries. Second, it demonstrates decreasing marginal costs and increasing returns to scale, making it possible to generate exponential value through cross-organizational utilization and exchange. This transformation in factor endowments is reshaping the global industrial structure and altering the configuration of international economic competitiveness. Common prosperity means that all people work together to create increasingly advanced and world-leading productivity levels and share an increasingly happy and beautiful life. It is not about everyone becoming wealthy at the same time, but rather something that needs to be achieved in stages through dynamic development. See

Table 1. Conceptual comparison of NQFP, TFP, and digital economy.

Dimension	NQPF	TFP	Digital Economy	Interconnections
Connotation	A productivity leap characterized by innovation-driven development, technological revolution, and reorganization of factors.	The part of output growth that cannot be explained by capital and labor inputs.	An economic form characterized by data as a key element and digital technology as the core driving force.	The digital economy serves as an important carrier of NQPF, and its development directly enhances TFP; NQPF optimizes TFP through the application of digital technologies.
Driving factors	Technological innovation, high-quality talents, green technologies.	Technological progress, efficiency of resource allocation, scale effect, management optimization.	Data, algorithms, computing power, digital infrastructure (such as 5G, cloud computing).	The digital economy provides technological tools (such as AI) and new factors (data), empowering NQPF and jointly driving the growth of TFP.
Mechanism	Reconstruct the production function through industrial digital transformation and intelligent upgrading.	Measuring the net contribution of technology.	Reduce information asymmetry, optimize resource allocation, and give rise to new business forms and models.	The digital economy enhances NQPF by increasing the penetration rate of technology, and this is manifested as the continuous improvement of TFP.

for details. The development of NQPF should be in line with the principle of common welfare, which corresponds to the objectives of equitable distribution in production relations and inclusive economic growth that benefits all societal groups. To advance this agenda, a rigorous examination of how these productive forces influence common prosperity is warranted, particularly regarding their potential to mitigate regional development disparities.

A comprehensive understanding of NQPF can be traced back to the construction and development of new quality combat effectiveness (NQCF). In January 2019, the General Secretary Xi Jinping highlighted the importance of strengthening the construction of new-type combat forces and increasing the proportion of NQCF at the Military Work Conference of the Central Military Commission. The 20th National Congress of the Communist Party of China also clearly indicated that the proportion of new-domain and new quality combat forces should be increased. The “new” in new combat effectiveness is reflected in new developments in the field of space, new mutations in science and technology, new modes of weaponry, etc. These emerging quality productive forces highlight the significance of technological innovation. They underscore the transformative impact brought about by contemporary societal development as well as successive scientific and technological revolutions [6]. Since its introduction, NQPF has been a subject of considerable interest within the domestic academic community, with the primary focus being on the logic and connotative characteristics [7] of the concept. Further elaboration has been given to the practical path [8], key focus [9], and functional orientation [10]. In brief, NQPF refers to advancements in technology, the emergence of alternative energy sources, and the rise of novel industries. These elements collectively contribute to the development of the digital economy, which represents an integrated and synergistic evolution of the components [11]. Building upon the theoretical framework of NQPF, researchers have developed quantitative measurement systems to assess new quality productive forces development levels (NQPF-DL) [12,13,14,15]. Subsequent empirical studies have further validated these measurement approaches [16,17,18]. In the context of research on common prosperity, the focus is primarily on three perspectives: the urban–rural gap, the regional gap, and the group gap [19]. A key starting point for promoting common prosperity is the narrowing of the urban–rural income gap [1]. Existing scholarship on pathways to common prosperity has predominantly examined conventional approaches, including Chinese-style modernization, digital economy development, and rural revitalization, while largely overlooking the catalytic role of NQPF. These forces, conceptualized as advanced formations emerging from the evolutionary upgrading of traditional productive forces, integrate scientific–technological innovation with industrial transformation. The current literature identifies them as fundamental drivers of high-quality economic development [20]. As a result, they have the potential to become a significant impetus for advancing the achievement of common prosperity.

Many scholars have given positive theoretical affirmation to the role of NQPF in promoting the achievement of common prosperity. Dunford (2022) [21] believes that new quality productive forces and common prosperity are both people-oriented and committed to achieving Chinese-style modernization. Xu et al. (2024) [22] propose that it can promote common prosperity through internal driving forces such as economic empowerment, full employment, and regional linkage. Therefore, as a new development of productive forces, they are the fundamental support and guarantee for achieving common prosperity [23]. Building upon this theoretical framework, researchers have undertaken quantitative analyses demonstrating that new quality productive forces contribute significantly to common prosperity through multiple pathways. Empirical evidence reveals three key findings: (1) on the analysis of the urban–rural gap, these forces effectively reduce urban–rural income disparities [24,25]; (2) on the analysis of the region, their impact on common prosperity is particularly pronounced in more developed eastern regions [26]; and (3) on the analysis of the group, they help mitigate intra-firm wage inequality between management and workers, facilitating more equitable distribution of productivity gains [27].

While the existing literature has begun examining NQPF, significant gaps remain that warrant further scholarly investigation. First, extant studies have predominantly focused on conceptual analyses and theoretical deductions regarding these two aspects, while quantitative investigations remain notably scarce. This methodological limitation undermines the scientific rigor and objectivity of the existing conclusions. Second, in terms of data collection, most scholars use provincial panel data for research, which is relatively macroscopic, and the accuracy and scientificity of the empirical results are difficult to guarantee. To address this research gap, this study develops a comprehensive evaluation index system for new quality productive forces and common prosperity. Utilizing panel data from 277 Chinese cities (2013–2022), we conduct empirical analyses to examine both the impact mechanisms and pathways through which new quality productive forces influence common prosperity. The findings provide an evidence-based foundation for policy formulation aimed at fostering balanced development of new quality productive forces and advancing common prosperity across China.

This study makes three principal contributions to the existing literature: First, the academic community has not yet formed a unified consensus on the construction of the indicator system for new quality productive forces and common prosperity. Existing evaluation frameworks remain incomplete and require further refinement. This paper systematically interprets the core concepts of them, reveals their intrinsic compatibility at the level of value orientation, analyzes the theoretical logic through which new quality productive forces promote common prosperity, and develops a scientifically grounded evaluation index system to strengthen the theoretical foundation. Second, existing studies on new quality productive forces primarily concentrate on the macro level, with limited attention paid to the urban scale and insufficient exploration of regional heterogeneity. This paper addresses these gaps by empirically investigating the impact of new quality productive forces on common prosperity using data from 277 prefecture-level and above cities in China, thereby enriching the spatial granularity of current research. Third, most existing research emphasizes the temporal relationship between new quality productive forces and common prosperity, while overlooking the spatial dimension. Considering the significant spatial spillover effects of new quality productive forces, this study employs the spatial Durbin model (SDM), incorporating the adjacency weight matrix, the geographical distance weight matrix, and the economic distance weight matrix to systematically examine spatial interactions. By empirically analyzing both spatial and temporal effects, the study broadens the analytical framework of the existing literature and offers new theoretical insights and policy implications for advancing regionally coordinated and inclusive development.

The paper is structured as follows: Section 2 explains the theoretical hypotheses. Section 3 presents the model, framework, variables, and data sources. Section 4 and Section 5 present the empirical results. Section 6 provides conclusions, respectively.

2. Theoretical Analysis and Hypotheses

2.1. Analysis of the Direct Effect of New Quality Productive Forces Empowering Common Prosperity

Marx and Engels repeatedly stressed that in future societies “production will be directed to the wealth of all” [28]. The masses of people are the creators of social wealth. Modernization with Chinese characteristics should serve the broad masses, development should be based on the masses, and the fruits of development should be shared by the masses [29]. During the Southern Tour in 1992, Deng Xiaoping proposed the following: “The essence of socialism is to liberate the productive forces, develop the productive forces, eliminate exploitation, eliminate polarization and ultimately achieve common prosperity” [30]. The fundamental driving force behind rapid economic development and the achievement of social fairness is the liberation and development of the productive forces, with common prosperity as the goal. With the advent of a new technological revolution, highly developed productive forces have become a necessary condition for the achievement of common prosperity [31]. New quality productive forces not only emphasize the pursuit of value above people, but also demonstrate the concept of sharing fairness and inclusiveness [32], which helps promote the “bigger” and “better” economy. They are an important driving force for common prosperity and Chinese-style modernization [33]. Driven by the advancement of digitalization, intelligent systems, and environmentally sustainable technologies, the emergence of new quality productive forces is fundamentally restructuring the logic of factor endowment allocation. These forces enable the decoupling of economic growth from traditional resource-intensive development models, laying the material and structural foundation for achieving the objective of common prosperity. Realizing this objective necessitates the dual optimization of allocative efficiency and distributive justice. Enhancing the efficiency of factor allocation increases the overall productivity of the economy, while a robust and equitable income redistribution mechanism ensures that the benefits of growth are broadly shared, facilitating a dynamic interaction between efficiency and equity. New quality productive forces contribute to the reconfiguration of the production function by enhancing allocative efficiency through both technological innovation and institutional reform. On the supply side, digital technologies improve the precision of factor matching, thereby reducing allocative distortions and transaction costs within the factor markets. Concurrently, the integration of emergent production factors gives rise to new industrial ecosystems and organizational models. These innovations extend the frontier of the production possibility set, leading to a higher marginal productivity of inputs and fostering endogenous growth dynamics [34]. Green technologies, as part of the broader technological paradigm shift, facilitate structural transformation by improving energy efficiency and reducing environmental externalities, particularly in high-emission sectors. This transition supports the shift toward sustainable, innovation-led growth paths consistent with long-term development objectives. High-quality economic development, characterized by capital deepening, knowledge accumulation, and technological upgrading, enhances TFP and generates incremental social surplus. This surplus provides the material preconditions for implementing redistribution mechanisms that support social inclusion. Equitable redistribution, when effectively institutionalized, can mitigate the negative externalities of income polarization, strengthen aggregate demand through consumption smoothing, and create fiscal space for further investment in human capital and infrastructure. In contrast, distributional asymmetries may lead to underconsumption, social unrest, and macroeconomic instability, thereby undermining the sustainability of growth. Hence, the promotion of high-quality development requires the coordinated governance of the tripartite income distribution system: primary distribution, which is grounded in market efficiency and return to factors; secondary distribution, which is executed through fiscal and social policy instruments; and tertiary distribution, which is driven by philanthropic and voluntary mechanisms. The synergy among these layers can simultaneously enhance economic dynamism and social welfare, ultimately converging toward the policy goal of common prosperity.

H1. 

New quality productive forces can significantly promote common prosperity.

2.2. Analysis of the Intermediary Mechanism of New Quality Productive Forces Empowering Common Prosperity

Developing new quality productive forces helps cities create an environment conducive to economic agglomeration, which in turn can promote common prosperity. Economic agglomeration serves as both a fundamental mechanism for developing regional growth poles and a critical driver of comprehensive regional development, thereby facilitating the attainment of common prosperity [35]. The essence of new quality productive forces lies in advancements in science and technology. Enterprises depend on innovation in these areas to develop industrial chains that are both high-end and intelligent. This process motivates firms to cluster within specific geographic regions, which in turn significantly enhances production efficiency and gives rise to agglomeration economies. As a result, social production activities tend to concentrate and expand in these regions, thereby stimulating regional economic growth and contributing to increased household incomes. Ultimately, it supports the broader goal to achieve common prosperity. In parallel, the spatial concentration of enterprises reduces the cost of job searching and lowers living expenses. Additionally, technological innovation produces spillover effects, which further attract both labor and capital to these areas, creating a more dynamic environment for innovation-driven development. Moreover, improvements in inter-regional connectivity and rising population mobility serve to reinforce the agglomeration advantages of economically developed areas, particularly metropolitan regions, and national-level central city clusters [36]. By capitalizing on these agglomeration effects, it becomes possible to foster a more sophisticated labor division and intensify industrial competition. This process continuously strengthens economic entities, raises both labor productivity and household income, and promotes a more efficient alignment between supply and demand. This marketing process contributes to regional income growth. Importantly, the spatial spillover effects from developed regions to their peripheries substantially narrow inter-regional income disparities, thereby advancing common prosperity within the framework of high-quality economic development [37].

H2. 

New quality productive forces promote common prosperity by increasing economic agglomeration.

The development of new quality productive forces will accelerate the process of achieving common prosperity by promoting the transformation and upgrading of traditional industries, while giving rise to a series of new industries and models. First, traditional industries, with their large scale, broad market demand, and strong employment absorption capacity, are the dominant force behind China’s economic development. Among them, the intelligent upgrading of manufacturing, a typical representative of traditional industries, is an inevitable trend. The integration of advanced technologies enables enterprises to implement intelligent processing systems, predictive maintenance protocols, and automated quality monitoring. This technological transformation fosters the digitalization, network integration, and automation of production processes, thereby creating optimal conditions for accelerating the transformation and upgrading of traditional industries. Such technological advancements significantly enhance overall production efficiency, consequently establishing a robust material foundation for advancing common prosperity [38]. The second is to focus on giving full play to the “leadership effect” by cultivating emerging industries. These innovation-driven industries exhibit high-growth potential and substantial value-added characteristics. Within the industrial ecosystem, emerging and future-oriented sectors function as “leading geese,” steering the entire system toward greater innovation and environmental sustainability. This dynamic facilitates industrial structure optimization and upgrading, thereby enhancing national competitiveness [21]. New quality productive forces place a strong emphasis on sustainability. The integration of green technologies, such as clean energy systems and circular economy practices, has encouraged industries to concentrate in regions characterized by high environmental carrying capacity and robust policy support. A typical example is the emergence of new energy industrial clusters in areas endowed with rich natural resources or designated as policy pilot zones. Through mechanisms of technological transfer and diffusion, cutting-edge innovations originating in these clusters can gradually extend to less developed areas. This facilitates industrial upgrading and stimulates economic development in regions with relatively weak technological foundations. As a result, regional disparities can be narrowed over time. Moreover, by cultivating new drivers of economic growth and broadening the aggregate stock of societal wealth, green innovation contributes to the sustainable expansion of the economy. These processes also support the structural optimization and equitable distribution of wealth, thereby advancing the overall objective of common prosperity [39].

H3. 

New quality productive forces promote common prosperity through industrial structure upgrading.

The development of new quality productive forces can promote the rational flow of factors between regions and improve the degree of labor misallocation, thereby promoting the achievement of common prosperity. The degree of deviation between the skill level of workers and the average skill level of their occupations is defined as labor misallocation [40]. The advancement of new quality productive forces is frequently accompanied by a transformation in the economic structure. This process tends to attract highly skilled labor and high-quality capital to regions with stronger innovation capabilities and higher levels of competitiveness. Such spatial concentration facilitates the conversion of emerging technologies into practical applications. It also enhances firms’ innovation potential and production efficiency by capitalizing on network externalities and economies of scale. Simultaneously, the diffusion of technological knowledge generates spillover effects that accelerate the cross-regional flow of production factors, including capital, land, and labor. This dynamic contributes to the alleviation of resource misallocation and promotes a more efficient allocation of inputs across regions [41]. Furthermore, the multiplier and synergistic effects arising from the free movement of production resources can help reduce the marginal cost of production entities. These effects, in turn, stimulate economic growth, raise household income levels, and promote greater balance in regional development. By narrowing disparities both across regions and between urban and rural areas, such mechanisms contribute to improving the overall equity of socio-economic development [36]. Many scholars have used data from different years in China to empirically demonstrate the relationship between labor misallocation and TFP. Labor misallocation leads to a gap between actual and potential output [42]. Improving the efficiency of labor allocation can significantly improve TFP, maximize total social output, and promote economic growth [43,44,45,46]. The improvement in total factor productivity is the fundamental source of high-quality economic development [36] and can provide a solid material foundation for the realization of common prosperity.

H4. 

New quality productive forces promote common prosperity by improving the degree of labor misallocation.

3. Indicators, Data, and Methods

3.1. Construction of the Indicator System

3.1.1. New Quality Productive Forces

Following the approach of Wang et al. (2024) [47], this paper selects three primary indicators: laborer, labor materials, and objects of labor to construct an evaluation indicator system for NQPF. The endogenous growth theory holds that the driving force of economic growth comes from the upgrading of factors and innovation activities within the economic system, rather than the traditional accumulation of exogenous factors. This theoretical framework provides an important perspective for constructing an indicator system of new quality productive forces. New quality productive forces focus on improving total factor productivity, and their essence is to achieve a leap in economic quality through endogenous forces such as technological innovation, human capital accumulation, and organizational change. In this paper, the measurement of laborers is mainly carried out from three levels: laborers’ skills, laborers’ productivity, and laborers’ concept. The measurement of labor resources is carried out from two aspects: infrastructure and technological innovation. Finally, the object of labor is measured from two perspectives: emerging industries and green ecology. Building upon this methodological framework, we employ the entropy method to determine the weighting scheme for each tier of indicators, subsequently computing the NQPF-DL for 277 Chinese cities from 2013 to 2022. The detailed index system is shown in

Table 2. Evaluation index system of new quality productive forces.

Primary Index	Secondary Index	Tertiary Index	Explanation	Direction
Laborer	Laborer skills	Human capital	Number of full-time teaching staff in regular higher education (persons)	+ (positive)
		Technological research and development	R&D personnel (persons)	+ (positive)
	Laborer productivity	Average Wage	Average wage of employees (CNY)	+ (positive)
	Laborer concept	Innovation and entrepreneurship	Regional Innovation and Entrepreneurship Index	+ (positive)
Labor Materials	Infrastructure	Information penetration	Mobile phone users at year-end (10,000 households)	+ (positive)
		Internet penetration	Broadband internet access users (1000 households)	+ (positive)
		Ease of transport	Road passenger transport (10,000 persons)	+ (positive)
	Scientific and technological innovation	R&D intensity	R&D expenditure/GDP (%)	+ (positive)
		Innovation and R&D	Number of patents granted (cases)	+ (positive)
		Share of science expenditure	Share of scientific expenditure in local financial expenditure (%)	+ (positive)
Objects of Labor	Emerging industries	Telecommunications	Total business volume of telecommunications (100 million CNY)	+ (positive)
		Artificial Intelligence	Number of artificial intelligence companies (units)	+ (positive)
	Green ecology	Waste treatment	Household waste treatment rate (%)	+ (positive)
		Environmental management	Investment in environmental protection (100 million CNY)	+ (positive)
		Waste gas emissions	Industrial sulphur dioxide emissions (tons)	− (negative)
		Wastewater discharge	Industrial wastewater discharge (10,000 tons)	− (negative)

.

3.1.2. Common Prosperity

In a basic economic sense, common prosperity means the shared prosperity of all people and the sharing of the fruits of development, but it does not mean equal prosperity. A gap in living standards within a reasonable range is a normal phenomenon [48]. Current scholarship exhibits notable variations in the operationalization of common prosperity indicators. The prevailing approach involves constructing dual-dimensional measurement systems grounded in the theoretical foundations of common prosperity. Representative frameworks include the following: (1) material and spiritual wealth dimensions [49], and (2) evaluation systems derived from fundamental conceptual attributes [50]. Second, some scholars use a three-dimensional construction method. For example, the indicator system constructed by Wang et al. (2024) [51] includes the three dimensions of development, sharing, and sustainability; Wei et al. (2024) [52] believe it is composed of three aspects: wealth, sharing, and urban development. In addition, some scholars have proposed more dimensional indicators to measure common prosperity. For example, Wu et al. (2024) [53] constructed a seven-dimensional indicator system, including high-quality economic development, innovation and entrepreneurship, and social governance. While multidimensional indicators may enhance the explanatory power of common prosperity measurements, they inevitably introduce complexity in interpreting inter-indicator relationships. More critically, the selection of indicators should specifically address the mechanistic role of new quality productive forces in advancing common prosperity. Therefore, this paper constructs a common prosperity evaluation index system from the three dimensions of development, sharing, and sustainability [54,55], and establishes a comprehensive index system of three first-level indicators, eight second-level indicators, and 19 third-level indicators [56,57] to measure the overall level of common prosperity. See

Table 3. Evaluation index system of common prosperity.

Primary Index	Secondary Index	Tertiary Index	Direction
Development	Prosperity	Per capita disposable income (CNY/person)	+ (positive)
		Engel coefficient (%)	− (negative)
		Per capita consumption expenditure	+ (positive)
	Coordination	Urbanization rate (%)	+ (positive)
		Urban–rural income gap (%)	− (negative)
Sharing	Infrastructure	Gas connection rate (%)	+ (positive)
		Road area per capita (m2/person)	+ (positive)
		Green area per capita (m2/person)	+ (positive)
	Medical and health	Practicing (assistant) physicians per 1000 population (person/1000 people)	+ (positive)
		Number of beds in medical and health institutions per 1000 population (beds/1000 people)	+ (positive)
	Culture and education	Average years of schooling (years)	+ (positive)
		Library holdings per capita (volumes/person)	+ (positive)
	Social security	Registered urban unemployment rate (%)	− (negative)
		Proportion of social security and employment expenditure in local fiscal budget expenditure (%)	+ (positive)
Sustainability	Ecological environment	Energy consumption per unit of GDP (tce/10,000 CNY)	+ (positive)
		Wastewater treatment rate (%)	+ (positive)
		Environmental protection expenditure/budget (%)	+ (positive)
	High-quality development	Regional GDP per capita (CNY/person)	+ (positive)
		Number of invention patents granted per 10,000 inhabitants (units/10,000 inhabitants)	+ (positive)

for a detailed list of indicators.

3.2. Research Methods

3.2.1. Entropy Method

This paper uses the entropy method of the objective weighting method to determine the weight of each indicator layer. Its advantage is that it avoids the interference of subjective judgment factors in the subjective weighting method, and it is more scientific and effective. Following the established methodology, this paper first normalizes all indicators using the range standardization method. Subsequently, we apply the entropy method to derive normalized weight coefficients. Finally, these weighted measures are aggregated to compute both NQPF-DL and common prosperity indices for each research unit. See the attachment for specific analysis steps (Supplementary Materials).

3.2.2. Measurement Model Construction

In order to verify the impact of the new quality productive forces on common prosperity, this paper constructs the following regression model:

$${com}_{it}={\alpha }_0+{\alpha }_1{new}_{it}+{\alpha }_2{control}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(1)

where i and t are the city and year, respectively. The core explanatory variable is the new quality productive forces level of city i in year t. The explained variable is the common prosperity level of city i in year t. ${\mu }_i$ and $v_t$ represent the city fixed effect and the year fixed effect, respectively. ${\epsilon }_{it}$ is the random error term. A series of control variables that may affect common prosperity are included, including the following: education investment (Edu), measured by the proportion of fiscal education expenditure in general budget expenditure; financial development level (Fin), measured by the total amount of loans from financial institutions as a proportion of GDP; population size (Pop), measured by the total population at the end of the year; economic growth (Gdp), measured by the GDP growth rate; and transportation facilities (Tra), measured by the number of road miles.

In addition, economic activities across regions are often highly interconnected and exhibit significant spillover effects. As a result, new quality productive forces and common prosperity in cities demonstrate strong spatial correlation. Under such conditions, relying solely on a standard benchmark regression model may yield biased or inaccurate estimates. Therefore, this study adopts a spatial analytical approach. Specifically, the spatial Durbin model (SDM) is employed, as it accounts for potential autocorrelation in the error term and effectively captures spatial dependence in the data. By incorporating both direct and indirect spatial effects, the SDM offers a more comprehensive assessment of regional economic linkages. To enhance the objectivity and robustness of the empirical analysis, this paper incorporates spatial factors into the baseline regression model to construct the following spatial Durbin model:

$$\begin{array}{c}{com}_{it}={\beta }_0+{\beta }_1{new}_{it}+{\beta }_2{control}_{it}+\rho W{com}_{it}+\theta W{new}_{it}\\ +\gamma W{control}_{it}+{\mu }_I+v_t+{\epsilon }_{it}\end{array}$$

(2)

When using a spatial econometric model regression, common matrices include the adjacency weight matrix, geographical distance weight matrix, and economic distance weight matrix. The adjacency weight matrix is defined as w {i j} = 1 if cities i and j share a border, and w {i j} = 0 otherwise. When constructing a geographical distance weight matrix, a reverse distance spatial weight matrix is constructed using the square of the distance between city i and city j. Compared with a spatial weight matrix that only uses distance values, the spatial effect decays more rapidly as the distance increases. The constructed economic distance weight matrix (We) is based on the absolute difference in GDP between cities, and its value is the reciprocal of the absolute value of the difference in GDP between cities. To study accurately, the geographical proximity and mutual influence between regions are taken into consideration more comprehensively. Therefore, this paper uses an adjacency weight matrix for measurement analysis. With the help of the adjacency weight matrix, the transmission effect of new quality productive forces between different regions can be analyzed more accurately, providing more robust and reliable empirical results for the study. This reveals the spatial distribution characteristics of new quality productive forces on common prosperity and provides strong support for a deeper understanding of its impact mechanism. Finally, the geographical distance weight matrix and economic distance weight matrix are used as supplementary results. $\rho$ is the spatial autocorrelation coefficient, and the control variables are all consistent with Equation (1).

In addition, based on the theoretical analysis, NQPF may affect common prosperity through three paths: economic agglomeration, industrial structure upgrading, and labor misallocation. Therefore, in order to further discuss the mechanism of new quality productive forces promoting common prosperity, this paper constructs the following mediating effect model with reference to Jiang’s research [58]:

$${mediator}_{it}={\delta }_0+{\delta }_1{new}_{it}+{\delta }_2{control}_{it}+{\mu }_I+v_t+{\epsilon }_{it}$$

(3)

Among them, mediator is the mediating variable, including economic agglomeration (app), industrial structure upgrading (stru), and labor misallocation (abstaul). The measurement of each mediating variable is as follows:

(1) Economic agglomeration (app): Following the approach of Hao et al. (2023) [59], the degree of regional economic agglomeration is measured by the ratio of non-agricultural industrial added value to built-up area.

(2) Industrial structure upgrading (stru): Following the approach of Li et al. (2018) [60], the proportion of the added value of the tertiary industry to the added value of the secondary industry is used to measure the regional industrial structure upgrading.

(3) Labor misallocation (abstaul): Following the approach of Bai et al. (2018) [61], the labor price distortion coefficient is used to measure the degree of labor misallocation. The specific formula is as follows:

$$\left\{\begin{array}{c}{\tau }_{Li}={\displaystyle \frac{1}{{\gamma }_{Li}}}-1\\ {\widehat{\gamma }}_{Li}=\left({\displaystyle \frac{L_i}{L}}\right)/\left({\displaystyle \frac{s_i{\beta }_{Li}}{{\beta }_L}}\right)\end{array}\right.$$

(4)

In Formula (4), is the absolute distortion coefficient of the labor price andis the labor misallocation index, which is taken as an absolute value. A higher value indicates a higher regional labor misallocation and vice versa.

4. Spatial–Temporal Development Characteristics of China’s NQPF-DL

4.1. The Development Trend of China’s NQPF-DL

This paper uses the entropy method to calculate the NQPF-DL from 2013 to 2022, and then sorts it to obtain the annual average value of new quality productive forces and its components, as shown in Figure 1. Overall, from 2013 to 2022, China’s NQPF-DL continued to grow; the constituent elements basically maintained a growth trend within ten years. Moreover, judging from the growth rates of the various components: objects of labor > labor materials > laborer, the laborer index has maintained a slowest growth, probably because the market demand for labor does not match the supply of the quality and skill level of existing laborers, and the laborer training and education system has not kept pace with technological development; the labor materials index has achieved steady growth, which includes two major categories: infrastructure and technological innovation. The reason for this growth may be that the country has vigorously developed innovation in recent years, unleashing a new wave of “mass innovation” and “grassroots entrepreneurship”, achieving breakthroughs in key core technologies such as 5G and ECMO, and other important breakthroughs in core technologies. The work object index mainly examines the two aspects of emerging industries and green ecology and has now become an important growth point for China’s NQPF.

This article refers to national standards and the related literature [62] and divides the country into four main regions: Eastern China, Central China, Western China, and Northeast China. The average level of new material productivity in each region is then calculated. As can be seen in Figure 2, the level of new material productivity in the four main regions is as follows: Eastern China > Central China > Western China > Northeast China. (1) The level of new material productivity in the eastern region has always been at the top. Due to its geographical proximity and preferential policies, the eastern region opened to the outside world relatively early, attracting a large amount of foreign capital and technology. In addition, due to its comfortable living conditions, the eastern region has good infrastructure and innovative resources, and a relative concentration of high-end factors such as finance, talent, and information. (2) The changes in the NQPF-DL of the central and western regions tend to be consistent, with both maintaining steady growth, but the growth rate is second to that of the eastern region. The central region serves as a bridge between the east and the west and has benefited from the implementation of the national strategy of “rising in the central region”, with the manufacturing and service industries developing rapidly. The western region is rich in mineral resources, but industrialization started late and infrastructure development has lagged behind. However, based on the national strategy for the development of the western region implemented in recent years, the potential development is huge in the western region, but it will take time to show. (3) The NQPF-DL in the northeast is growing slowly, and the development of innovation is relatively backward. Compared with other regions, Northeast China, as a traditional old industrial base, is dominated by heavy industry, and there are problems such as resistance to the transformation of traditional industries, a relative lag in the transformation of old and new kinetic energy, and the underdevelopment of the private economy. In

Table 4. NQPF-DL in selected cities.

City	Year_2014	Year_2016	Year_2018	Year_2020	Year_2022
Beijing	0.3215 (1)	0.3855 (1)	0.4008 (1)	0.4438 (2)	0.6098 (1)
Shanghai	0.2673 (2)	0.3004 (2)	0.3439 (2)	0.4439 (1)	0.6022 (2)
Guangzhou	0.2233 (3)	0.2471 (3)	0.3014 (3)	0.3937 (3)	0.5234 (3)
Shenzhen	0.1692 (5)	0.1847 (5)	0.2746 (5)	0.3496 (4)	0.5106 (4)
Chongqing	0.1888 (4)	0.2239 (4)	0.2773 (4)	0.3217 (5)	0.4021 (5)
Hangzhou	0.1179 (12)	0.1479 (8)	0.1821 (8)	0.2450 (6)	0.3492 (6)
Chengdu	0.1418 (6)	0.1665 (6)	0.2031 (6)	0.2325 (8)	0.3155 (7)
Suzhou	0.1034 (15)	0.1206 (13)	0.1676 (12)	0.2331 (7)	0.3057 (8)
Xi’an	0.1305 (9)	0.1456 (10)	0.1694 (11)	0.1996 (12)	0.2887 (9)
Nanjing	0.1222 (11)	0.1404 (11)	0.1717 (9)	0.2231 (9)	0.2796 (10)
Wuhan	0.1352 (7)	0.1474 (9)	0.1833 (7)	0.1969 (13)	0.2768 (11)
Tianjin	0.1090 (13)	0.1561 (7)	0.1699 (10)	0.2130 (11)	0.2658 (12)
Zhengzhou	0.1253 (10)	0.1228 (12)	0.1505 (13)	0.2144 (10)	0.2544 (13)
Hefei	0.0830 (23)	0.1179 (14)	0.1301 (14)	0.1676 (14)	0.2457 (14)
Jinan	0.0902 (19)	0.1146 (15)	0.1134 (18)	0.1575 (16)	0.2289 (15)
Qingdao	0.1037 (14)	0.1014 (17)	0.1093 (20)	0.1493 (17)	0.2201 (16)
Changsha	0.0944 (18)	0.1026 (16)	0.1198 (16)	0.1641 (15)	0.2057 (17)
Foshan	0.0698 (30)	0.0829 (25)	0.1143 (17)	0.1401 (18)	0.1827 (18)
Ningbo	0.0832 (22)	0.0960 (19)	0.1129 (19)	0.1398 (19)	0.1762 (19)
Dongguan	0.1329 (8)	0.0888 (21)	0.1248 (15)	0.1352 (21)	0.1734 (20)

, most of the top 20 cities in China’s four regions in terms of new quality productive forces in 2022 are municipalities directly under the central government and provincial capitals, and they are also first-tier and new first-tier cities. Among them, the NQPF-DL in “Beijing, Shanghai, Guangzhou and Shenzhen” have always been among the highest over the past decade and lead other cities by a wide margin.

4.2. Spatial Analysis of China’s NQPF-DL

In order to explore the spatial distribution pattern of the NQPF-DL, this paper traces the spatial–temporal development characteristics of China’s NQPF-DL by ArcGIS 10.8.1 in 2013, 2016, 2019, and 2022, as shown in Figure 3. Through the GIS natural break method, the minimum and maximum values of the NQPF-DL are equally divided into four levels.

In Figure 3, during the period under study, China’s new quality productive forces showed a spatial distribution pattern of “the east leads, the west develops rapidly”. With the promulgation of documents such as the “Guidelines for Building Innovative Cities” (Guo Fa [2016] No. 370) and the “Opinions of the State Council on Promoting the High-Quality Development of Innovation and Entrepreneurship and Building an Upgraded Version of ‘Double Innovation’” (Guo Fa [2018] No. 32), various provinces have actively implemented the guiding opinions, transforming the development of industries from factor-driven to innovation-driven, and the NQPF-DL in various cities have improved significantly. However, as shown in the figure, the NQPF-DL in cities along the Yangtze River Economic Belt and the eastern coastal strip is higher than in other cities, with a pronounced pattern of high in the east and low in the west. The regional differences are still significant, which shows that regional innovation and development are related to the foundation of their own economic development.

4.3. Moran’s Index-Based Spatial Correlation Analysis

In this paper, a spatial correlation test of China’s new quality productive forces is conducted by constructing an adjacency weight matrix, a geographical distance weight matrix, and an economic distance weight matrix. The Moran’s I of new quality productive forces for each year is calculated using the global autocorrelation method, as shown in

Table 5. Moran’s I of NQPF-DL in China, 2013–2022.

Year	Adjacency Weight Matrix		Geographical Distance Weight Matrix		Economic Distance Weight Matrix
	Moran’s I	Z-Value	Moran’s I	Z-Value	Moran’s I	Z-Value
2013	0.018 ***	3.520	0.035 ***	6.430	0.051 ***	8.299
2014	0.017 ***	3.280	0.025 ***	4.771	0.031 ***	5.402
2015	0.012 ***	2.601	0.020 ***	3.975	0.023 ***	4.103
2016	0.014 ***	2.813	0.025 ***	4.768	0.031 ***	5.337
2017	0.018 ***	3.456	0.033 ***	6.050	0.043 ***	7.257
2018	0.021 ***	3.934	0.035 ***	6.366	0.047 ***	7.702
2019	0.021 ***	3.993	0.032 ***	5.952	0.043 ***	7.211
2020	0.022 ***	4.176	0.035 ***	6.365	0.047 ***	7.781
2021	0.020 ***	3.872	0.034 ***	6.225	0.047 ***	7.694
2022	0.020 ***	3.888	0.032 ***	5.872	0.043 ***	7.095

Note: *** indicate significance at the 1% levels, respectively.

. The empirical results demonstrate consistently positive and statistically significant (p < 0.01) Moran’s I value across all study years under the three spatial weighting matrices. This robust spatial autocorrelation pattern reveals a distinct clustering effect of new quality productive forces, with economically developed regions and their adjacent areas exhibiting significantly higher development levels compared to less developed regions.

5. Empirical Results

5.1. Baseline Regression

Table 6. Benchmark regression results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
New	0.683 ***	0.433 ***	0.430 ***	0.430 ***	0.449 ***	0.449 ***	0.458 ***
	(54.425)	(13.808)	(13.621)	(13.588)	(11.841)	(11.862)	(12.365)
Edu			0.021	0.020	0.025	0.026 *	0.028 *
			(1.328)	(1.283)	(1.596)	(1.659)	(1.818)
Fin				−0.085	−0.088	−0.088	−0.098
				(−1.256)	(−1.259)	(−1.256)	(−1.368)
Pop					−0.277	−0.272	−0.269
					(−1.497)	(−1.464)	(−1.475)
Gdp						0.012 *	0.011 *
						(1.919)	(1.760)
Tra							−0.070 ***
							(−4.793)
_cons	0.117 ***	0.129 ***	0.126 ***	0.128 ***	0.140 ***	0.138 ***	0.148 ***
	(124.150)	(81.038)	(43.338)	(36.941)	(16.942)	(16.717)	(17.316)
Year FE	NO	YES	YES	YES	YES	YES	YES
City FE	NO	YES	YES	YES	YES	YES	YES
N	2770	2770	2770	2770	2770	2770	2770
r2	0.517	0.957	0.957	0.958	0.958	0.958	0.958

Note: the values between parentheses are the t-values. * and *** indicate significance at the 10% and 1% levels, respectively.

shows the baseline regression results of new quality productive forces on common prosperity. Column (1) excludes control variables and fixed effects, while columns (2) to (7) gradually add control variables and two types of fixed effects: time and space. The results show that the regression coefficient of new quality productive forces on common prosperity is always positive and significant at the 1% level, which verifies H1.

5.2. Endogeneity Test

To address possible omitted errors, reverse causality, and other endogeneity issues, the following methods are used in this paper to ensure the reliability of the model estimates: (1) Instrumental variable method. In this paper, following the methodology of Wu et al. (2023) [63] and Huang et al. (2019) [64], this paper employs two instrumental variables: the lagged value of NQPF and the number of landline telephones per 100 people in 1984, to re-estimate the baseline model. The rationale for selecting this instrumental variable is twofold. First, the emergence and evolution of NQPF are primarily driven by digital technologies and intelligent systems. Their sustainable development is closely linked to the historical accumulation of information and communication infrastructure. The number of landline telephones per 100 people in 1984 is an indicator of traditional communication levels. Although it cannot directly measure modern new quality productive forces, it reflects the regional information infrastructure and historical endowments. Coastal areas with high fixed telephone penetration rates often have a first-mover advantage in the subsequent development of the internet and digital economy, as they cultivated technical talent and accumulated communication network resources early on. However, the explosive growth of NQPF relies more on new generation technologies such as mobile internet and big data. The direct impact of traditional fixed telephones is limited, but the historical information infrastructure still indirectly shapes the differences in regional innovation capabilities today through the path dependence effect, thereby ensuring instrument relevance. Second, with the declining use of traditional communication tools such as fixed-line telephones, their direct impact on contemporary economic development is minimal, thus satisfying the exogeneity condition. However, since it belongs to time-invariant cross-sectional data, it cannot be directly used in panel data econometric analysis. Drawing on the approaches of Nunn & Qian (2014) [65] and Zhao et al. (2020) [66], this paper constructs an interaction term between the number of landline telephones per 100 people in 1984 and the national number of internet users in the preceding year, serving as an instrument for NQPF. The model is then estimated using the two-stage least squares (2SLS) method. (2) System GMM estimation method. Common prosperity at the city level has a certain degree of continuity, that is, serial correlation. Therefore, this paper re-estimates the model by lagging common prosperity by one period and using the system generalized moment estimation method.

The regression results are shown in

Table 7. Results of instrumental variable estimation.

Variables	Lnew		Inter		GMM
	New	Com	New	Com	Com
New		0.474 ***		0.612 ***	0.106 *
		(10.471)		(4.980)	(1.728)
IV	1.050 ***		0.368 ***
	(25.132)		(4.135)
Control variables	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	46.739 ***		12.948 ***
Kleibergen–Paap Wald rk F statistic	631.61		17.099
AR(1)					0.071
AR(2)					0.130
Hansen test p value					0.155
Observations	2493	2493	2770	2770	2493
R-squared		0.786		0.794

Note: the values between parentheses are the t-values. * and *** indicate significance at the 10% and 1% levels, respectively.

. The Kleibergen–Paap rk LM statistic, Kleibergen–Paap Wald rk F statistic, AR(1), AR(2), and Hansen p value all indicate that the instrumental variables successfully pass the identifiability test and weak instrument test. The regression coefficients of new quality productive forces are all significantly positive, which is consistent with the benchmark regression analysis.

5.3. Robustness Tests

The following methods were used for robustness tests in this paper, and the results are shown in

Table 8. Results of robustness tests.

Variables	Exclude Municipalities	Crest-Tail Treatment		Fixed Effects Replaced		Exogenous Shocks
	Com	Com_w (1%)	Com_w (5%)	Com	Com	Com
New	0.455 ***	0.346 ***	0.394 ***	0.458 ***	0.392 ***	0.491 ***
	(12.842)	(11.457)	(9.707)	(12.365)	(11.339)	(9.137)
Control Variables	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Province FE	NO	NO	NO	YES	NO	NO
Province-Year FE	NO	NO	NO	NO	YES	NO
N	2730	2770	2770	2770	2700	1939
r2	0.956	0.959	0.953	0.958	0.966	0.968

Note: the values between parentheses are the t-values. *** indicate significance at the 1% levels, respectively.

. First, the four municipalities were excluded, and the data for Beijing, Tianjin, Shanghai, and Chongqing were removed and re-estimated. Second, the data was censored. To avoid the adverse effects of outliers and non-randomness in the sample, the NQPF-DL were censored by 1% and 5%, respectively. Third, replacing fixed effects, introducing province fixed effects, and “province-time” level cross-fixed effects based on two-way fixed effects in time and space prevented province-level factors from interfering with the estimated results. Fourth, exogenous shocks, considering that the outbreak of the new crown epidemic in 2020 may have an impact on NQPF, the sample data for the three years from 2020 to 2022 are removed to generate a new sample time interval.

After conducting the above tests, the results all indicate that new quality productive forces can promote common prosperity at the 1% significance level, which is consistent with the core conclusion of this paper, and the results are basically robust.

5.4. Mechanism Test

According to the theoretical analysis in the previous section, new quality productive forces can affect common prosperity through economic agglomeration, industrial structure upgrading, and labor misallocation. The results are shown in

Table 9. Mechanisms of the role of new quality productive forces in promoting common prosperity.

Variables	(1)	(2)	(3)	(4)
	App	Stru	Abstaul	Com
New	0.282 ***	1.046 ***	−3.217 ***	0.458 ***
	(3.662)	(2.868)	(−3.359)	(12.365)
Control Variables	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
City FE	YES	YES	YES	YES
N	2770	2770	2770	2770
r2	0.890	0.880	0.614	0.958

Note: the values between parentheses are the t-values. *** indicate significance at the 1% levels, respectively.

. (1) Column (1) shows that the coefficient of economic agglomeration is significantly positive, indicating that NQPF can increase the degree of economic agglomeration. According to theoretical analysis, for regions with better locational advantages, sustained economic growth can be brought about through economic agglomeration, which in turn will lead to an increase in income levels. For regions with less advantageous locations, income can be increased by either the movement of local populations to high-income regions or by receiving industrial support from advantageous regions, thereby narrowing regional income gaps and further promoting the achievement of common prosperity. H2 is verified. (2) The coefficient of industrial structure upgrading in column (2) is significantly positive, indicating that new quality productive forces can accelerate industrial structure upgrading. Theoretical analysis suggests that technological upgrading of traditional industries through advanced and applicable technologies facilitates their transformation toward digitalization, intellectualization, and integration. This industrial evolution engenders sustained productivity enhancement throughout the upgrading process, leading to improved aggregate production efficiency. Consequently, it contributes to the accumulation of new economic growth drivers while establishing a robust material foundation for advancing common prosperity. H3 is verified. (3) The coefficient of the labor misallocation index in column (3) is significantly negative, indicating that new quality productive forces can improve labor misallocation. According to theoretical analysis, the labor mobility guided by efficiency and price mechanisms significantly reduces search and matching costs associated with inter-firm and inter-occupational transitions. Labor misallocation is improved, which in turn brings about economic benefits and an increase in social welfare, providing strong support for achieving the goal of common prosperity. H4 is verified.

5.5. Heterogeneity Analysis

5.5.1. Regional Heterogeneity

This paper divides the country into Eastern China, Central China, Western China, and Northeast China for regression estimation, and the results are shown in

Table 10. Results of regional heterogeneity in the impact of new quality productive forces on common prosperity.

Variables	(1)	(2)	(3)	(4)
	Eastern China	Central China	Western China	Northeast China
New	0.512 ***	0.465 ***	0.182 ***	0.539 ***
	(8.549)	(10.200)	(5.010)	(2.857)
Control Variables	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
City FE	YES	YES	YES	YES
N	860	800	780	330
r2	0.956	0.957	0.969	0.908

Note: the values between parentheses are the t-values. *** indicate significance at the 1% levels, respectively.

. It can be found that the regression coefficients of the NQPF-DL in the four major regions are all significantly positive, among which the degree of influence on promoting common prosperity is “the Northeast China > the Eastern China > the Central China > the Western China”.

The regional disparities in the contribution of new quality productive forces to promoting common prosperity may stem from several factors: (1) Different industrial bases. The northeast region is China’s traditional industrial base, with a relatively good industrial foundation and technological reserves. Compared with Eastern China, Central China, and Western China, it started the industrialization process earlier and has a good manufacturing foundation. It has promoted industrial upgrading through technological innovation and driven economic development. Western China started later in terms of technological innovation and industrial transformation and upgrading, and the cultivation and development of new quality productive forces have lagged. There is also an obvious gap in the layout of emerging industries and high-tech industries. (2) Different resource endowments. Eastern China has a superior geographical location, abundant marine resources, and an advanced manufacturing base, and has attracted a large number of high-quality talent and foreign investment. With these advantages, the NQPF-DL in the eastern region has developed rapidly, production efficiency and economic benefits have been improved, and residents’ income has increased substantially, supporting the achievement of common prosperity. (3) Policy tilt. In recent years, the state has promulgated a series of policies to revitalize the old industrial base in the northeast, which has prompted the new quality productive forces in the northeast to have greater development potential. In contrast, the policy support in the other regions is relatively scattered, so the northeast has a clear advantage in promoting the development of common prosperity.

5.5.2. Structural Heterogeneity

This paper analyzes the structural heterogeneity of the new quality productive forces’ impact on common prosperity from the perspective of their constituent elements. As can be seen from

Table 11. Structural heterogeneity results for the impact of new quality productivity on common prosperity.

Variables	(1)	(2)	(3)
	Com	Com	Com
Laborer	0.176 ***
	(5.511)
Labor Materials		0.381 ***
		(10.303)
Objects of Labor			0.281 ***
			(9.802)
Control Variables	YES	YES	YES
Year FE	YES	YES	YES
City FE	YES	YES	YES
N	2770	2770	2770
r2	0.945	0.955	0.955

Note: the values between parentheses are the t-values. *** indicate significance at the 1% levels, respectively.

, the laborer, the labor materials, and the objects of labor contribute to common prosperity to varying degrees.

Among them, labor materials and labor objects exhibit a more significant driving effect, not only enhancing production efficiency and reducing costs but also facilitating the optimal allocation and efficient utilization of resources. Emerging industries, including renewable energy, biomedicine, and AI, have created significant opportunities for economic and social development due to their rapid growth and innovative potential. These sectors not only generate substantial employment opportunities but also contribute to increased societal output. Consequently, the strategic development of key industries such as AI and the Internet of Things can help reduce income disparities across regions and sectors, foster regional coordination, and ultimately advance the realization of common prosperity.

5.6. Spatial Spillover Effect

Table 12. Results of the spatial Durbin model.

Variables	Adjacency Weight Matrix	Geographical Distance Weight Matrix	Economic Distance Weight Matrix
Direct effect	0.445 ***	0.422 ***	0.422 ***
	(29.638)	(23.306)	(28.473)
Indirect effect	0.155	4.859 *	1.490 ***
	(1.014)	(1.676)	(6.270)
Total effect	0.600 ***	5.281 *	1.913 ***
	(3.887)	(1.815)	(8.014)
Control variables	YES	YES	YES
rho	−0.083	0.688 ***	0.300 ***
	(−0.558)	(8.679)	(2.880)
Year FE	YES	YES	YES
City FE	YES	YES	YES
N	2770	2770	2770
r2	0.472	0.399	0.476

Note: the values between parentheses are the t-values. * and *** indicate significance at the 10% and 1% levels, respectively.

shows the regression results of new quality productive forces on common prosperity based on the spatial Durbin model, and the results are decomposed using the partial differential form [67] to explore the spatial spillover effect of new quality productive forces on common prosperity. Additionally, a likelihood ratio (LR) test was performed to assess the fixed effects model specification. The results indicated that a two-way fixed effects model was the most appropriate for empirical analysis. Across three distinct spatial weight matrices, new quality productive forces demonstrated statistically significant direct, indirect, and total effects on common prosperity. The findings demonstrate that new quality productive forces not only directly contribute to common prosperity but also exhibit substantial spatial spillover effects. Specifically, enhancing new quality productive forces in each city reduces the urban–rural income disparity in neighboring cities, thereby advancing common prosperity at the regional level. This phenomenon may be attributed to the capacity of new quality productive forces to enhance cross-regional factor mobility, strengthen inter-regional industrial linkages, generate additional employment and entrepreneurial opportunities, and ultimately expand income-generating channels for residents.

The results of the hypothesis testing are summarized in

Table 13. Results of the hypothesis testing.

Code	Hypothesis	Results
H1	New quality productive forces can significantly promote common prosperity.	Accepted
H2	New quality productive forces promote common prosperity by increasing economic agglomeration.	Accepted
H3	New quality productive forces promote common prosperity through industrial structure upgrading.	Accepted
H4	New quality productive forces promote common prosperity by improving the degree of labor misallocation.	Accepted

, confirming that all proposed hypotheses are accepted.

6. Discussion

Despite differences in developmental stages, historical trajectories, and institutional structures across countries, wealth polarization has become a pervasive phenomenon accompanying the process of modernization. Our study shows that new quality productive forces significantly promote common prosperity. Further analysis uncovers an indirect transmission mechanism, wherein new quality productive forces advance common prosperity by enhancing economic agglomeration, upgrading the industrial structure, and reducing labor misallocation. This indicates the development of new quality productive forces, accelerating industrial transformation and improving labor resource allocation efficiency, narrow income distribution gaps to facilitate the achievement of common prosperity. Moreover, because of new quality productive forces’ strong spatial autocorrelation, it provides a theoretical foundation for promoting regionally coordinated development by facilitating the flow of regional factors, deepening industrial interactions with adjacent regions, generating employment and entrepreneurial opportunities, and expanding new income channels.

There are still some limitations that point to future research directions. First, from a theoretical perspective, in constructing the indicator system for new quality productive forces and common prosperity, the absence or unavailability of core indicators in certain years may affect the empirical results to some extent. To address this limitation, future studies should consider developing a more comprehensive and robust evaluation system to enhance the accuracy and credibility of empirical findings. Second, from an empirical perspective, this study selects 277 Chinese cities from 2013 to 2022 as the research sample. Due to limitations in data availability and completeness, only ten years of panel data are used, resulting in a relatively short time span. Extending the study period in future research could improve the robustness of the analysis and enrich the empirical understanding of the relationship between new quality productive forces and common prosperity.

7. Conclusions and Policy Implications

7.1. Conclusions

Based on panel data from 277 Chinese cities from 2013 to 2022 and the construction of an evaluation index system for new quality productive forces level and common prosperity, this study uses various econometric models to analyze the mechanism of action of new quality productive forces on common prosperity, its spatial spillover effects, and its heterogeneity. The study found that first, from 2013 to 2022, China’s NQPF-DL achieved steady growth, and its components also showed a growth trend. The analysis revealed significant regional disparities in NQPF-DL, exhibiting a distinct east–west spatial gradient. Notably, Beijing, Shanghai, Guangzhou, and Shenzhen consistently demonstrated the highest NQPF-DL nationwide, maintaining their leading positions throughout the study period. Second, the development of new quality productive forces can promote common prosperity, and this conclusion remains valid after considering the issue of endogeneity and conducting a robustness test. Further analysis shows that new quality productive forces can promote common prosperity by increasing economic agglomeration, promoting industrial structure upgrading, and improving labor misallocation. Third, the impact of new quality productive forces on common prosperity is heterogeneous in different regions and the internal structure of new quality productive forces. The driving effect is more significant in Northeast China and Eastern China, and the empowering role of labor materials and objects of labor is particularly prominent. Fourth, the development of new quality productive forces has a strong spatial autocorrelation, which not only directly promotes common prosperity but also has significant spatial spillover effects.

7.2. Policy Implications

First, focus on the empowering effect of new quality productive forces on common prosperity. According to the findings, China’s NQPF-DL exhibits a spatial pattern characterized by higher concentrations in the eastern region and lower levels in the western region. Mega-cities such as Beijing, Shanghai, Guangzhou, and Shenzhen take the lead in areas such as high-end manufacturing, thereby reinforcing their roles as innovation hubs and fostering regional growth poles with radiation-driven effects. Drawing on China’s experience, other countries may consider cultivating one or two “innovation center cities” (e.g., Bangalore in India, São Paulo in Brazil, Lagos in Nigeria), concentrating resources on the development of emerging industries such as AI and green energy. Technology sharing and knowledge overflow will accelerate the transformation of scientific and technological achievements into real productivity and inject new momentum into global economic growth.

Second, improve the efficiency of labor allocation and accelerate economic agglomeration. According to the findings, new quality productive forces can contribute to common prosperity by improving labor misallocation and increasing economic agglomeration. Dismantling institutional obstacles that hinder the free mobility of production factors is essential for optimizing resource allocation across regions. By promoting the construction of an open, inclusive, and interoperable innovation ecosystem, it becomes possible to enhance the spatial matching efficiency between technological elements and a heterogeneous labor force. This process entails a shift in the paradigm of regional competition from a zero-sum game centered on TFP toward a more cooperative model based on networked innovation. In such a framework, Eastern China can serve as a hub for technological diffusion, while Central China and Western China strengthen their capacity for factor absorption and adaptive innovation. Through this coordinated mechanism, technological spillovers and factor mobility generate a cumulative effect that narrows inter-regional development disparities. Over time, this facilitates a bidirectional flow of resources between urban and rural areas, thereby contributing to balanced and inclusive regional development. Provide skills-appropriate training, targeting job requirements that are compatible with the development of new quality productive forces, and launch a “digital artisan” program, focusing on training migrant workers and transforming workers in traditional industries. By optimizing the primary distribution system and improving redistribution and tertiary distribution mechanisms, economic growth can be more effectively translated into household income growth. Especially by expanding the size of the middle-income group, and ultimately, it could narrow the gap between the rich and the poor. The goal is to transform the efficiency dividend of new quality productive forces into an inclusive development dividend. This path is consistent with economic laws and provides a practical framework for global sustainable development.

Third, activate latent inter-industrial complementarities for sustainable structural evolution. To improve economic resilience, industrial and supply chains require systematic optimization and upgrading, with particular emphasis on fostering coordination between upstream and downstream sectors to ensure chain stability. China should dismantle trade barriers and facilitate cross-border flows of production factors, including technology, capital, and human resources. International collaboration on major scientific research initiatives and industrial upgrading best practices should be enhanced. Concurrently, policy instruments should be strategically employed to support the development of high-tech industries while guiding the upgrading of traditional sectors. Emphasis should be placed on fostering innovation-driven transformation within legacy industries to enhance their technological content, value-added capacity, and adaptability. This coordinated industrial evolution is essential to meeting the structural and technological demands of high-quality development in the new era.

Fourth, develop new quality productive forces according to local conditions. Given that different regions are at varying stages of development, significant regional disparities exist in the formation of new quality productive forces. Therefore, it is essential to uphold the principle of localization and formulate differentiated regional development strategies based on each region’s resource endowments and industrial structure. For example, suggest that Eastern China focus on digital technology, Northeast China promote the intellectualize of traditional industries, and Western China invest in infrastructure and education.