The New Quality Productive Force, Science and Technology Innovation, and Optimization of Industrial Structure

Abstract

With the rapid development of information and communication technology, the new quality productive force has gradually become one of the key factors to promote scientific and technological innovation and promote the transformation and upgrading of industrial structure. Based on the essential connotation and three key characteristics of the new quality productive force, this study constructs a comprehensive evaluation index system and measures the development level of the new quality productive force by the entropy method, while the industrial structure advancement coefficient is employed to assess provincial industrial upgrading. This study examines the relationship and transmission mechanisms between the new quality productive force (NQPF), scientific and technological innovation (STI), and industrial structure optimization (ISO) using China’s provincial data (2012–2022) from the National Bureau of Statistics of China through the stepwise regression method. Based on a balanced panel of 330 province-year observations, the results show that NQPF significantly enhances STI, with stronger effects in regions with higher NQPF development. However, regional heterogeneity exists, with the eastern and central regions benefitting more than the western region. Additionally, mediation tests reveal that the NQPF fosters STI, thereby driving industrial upgrading. These findings highlight NQPF’s role in regional innovation and structural transformation.

1. Introduction

In September 2023, President Xi Jinping mentioned the NQPF for the first time when he visited Heilongjiang. The proposal of new quality productivity has historical inevitability. China’s economy has undergone a transformation from large-scale economic expansion focused on scale to high-quality development, emphasizing quality and efficiency. Traditional extensive development models have led to resource waste and environmental pollution, prompting a shift in development philosophy towards green, innovative, and sustainable directions. Against this backdrop, the NQPF has emerged, driving socioeconomic progress towards greater efficiency and sustainability through technological innovation and industrial upgrading. It is an important support and key driving force for the development of new quality productive force. Essentially, the NQPF represents advanced productivity, with its essence and characteristics formed on the basis of traditional productivity. In addition to new quality productivity, the NQPF is a new form of productivity emerging from traditional productivity through technological innovation, management innovation, and system innovation. Its core includes a leap in the optimization of laborers, means of production, objects of labor, and their combinations, as well as significant improvements in total factor productivity [1].

In today’s fast-changing technological era, the importance of the NQPF is increasingly emphasized [2]. The NQPF not only represents a technological leap but is also an important driving force for social and economic development. With the deepening of the application of information technology, artificial intelligence, and cloud computing in production and life, the NQPF is rapidly promoting the optimization of the economic structure and the change in social production mode. Through industry chain extension and business model innovation, the NQPF promotes the transformation and upgrading of traditional industries, making the industrial structure more reasonable and efficient [3]. At the same time, resource allocation optimization is also an important means for the NQPF to promote the upgrading of industrial structure. By optimizing the allocation of resources, production efficiency can be improved, and production costs can be reduced, thus enhancing the competitiveness of industries [4]. Further, the development of the NQPF promotes the technological innovation of frontier technologies, such as information technology, artificial intelligence, and cloud technology, which changes the production mode of traditional industries, realizes the transformation and upgrading of a large number of traditional industries, and injects a new impetus for economic development. The NQPF promotes technological innovation in the field of high and new technology, which not only improves the production efficiency but also promotes the development of emerging industries and provides strong technical support for the optimization and upgrading of industrial structure [5]. The NQPF promotes technological innovation in the field of advanced tech, which not only improves the production efficiency but also promotes the development of new industries and provides powerful technical support for the optimization and upgrading of the industrial structure [6,7].

Therefore, this study focuses on the impact mechanism of the NQPF on industrial structure upgrading (ISO), with particular emphasis on addressing the following two core research questions: (1) how to construct a scientific evaluation index system for the NQPF based on its characteristics of “newness, quality, and power”; and (2) how the NQPF promotes ISO through both direct effects and the mediating path of scientific and technological innovation (STI). The research aims to establish a quantitative measurement system and reveal the transmission mechanism of “the new quality productive force–scientific and technological innovation–industrial upgrading”, thereby providing empirical evidence for the theoretical development of the NQPF and offering scientific references for industrial policy formulation [8,9]. The research of this paper is not only of great theoretical significance but also of great practical significance for realizing the optimization and upgrading of industrial structure and promoting the optimization of China’s economic structure.

2. Literature Review

At present, research on new quality productivity, industrial structure, and their inter-relationships in the academic world is mainly carried out in the following aspects: Firstly, research focuses on the connotation of new quality productivity [10]. New quality productivity is a brand new form of productivity formed on the basis of traditional productivity through STI, management innovation, institutional innovation, and so on. Its connotation mainly includes the leap of workers, labor materials, labor objects, and their optimal combination, as well as the significant increase in total factor productivity. Seeking to truly understand the connotation of the new quality productivity, we should firmly grasp the key features of new quality productivity. Through the continuous improvement of scholars, this paper concludes that the new quality of productivity is encompassed by the following three elements: workers, labor materials, and labor objects in the role of the three major characteristics of the new quality of productivity, “new”, “quality”, and “force”. Under the role of the three characteristics of new productivity, “new”, “quality” and “power”, new vitality is exerted, thus giving birth to new quality productivity in the context of the new era.

Secondly, research focuses on the measurement of new quality productivity. On top of the theoretical foundation, some scholars put forward the idea of constructing the evaluation index system of new quality productivity. On the one hand, the evaluation index system of new quality productivity can be established from the three dimensions of STI, industrial upgrading, and factor transformation by articulating the essence and characteristics of new quality productivity [11]. In addition, the evaluation index system can also be constructed from the four dimensions of new industries, new kinetic energies, new modes, and new factors based on the connotation of new quality productivity [12]. It can be seen that most of the indicator systems of new quality productivity constructed at this stage are only based on the basic connotation of new quality productivity but neglect the three major characteristics of new quality productivity, “new”, “quality”, and “power”, in which they play a key role [13]. However, the key role played by the three main characteristics of new productivity, namely, “new”, “quality”, and “power”, has been neglected, so that the index layer indicators in the current evaluation index system are not very close to the new connotation of new quality productivity.

Thirdly, research focuses on analyzing the role of the NQPF on the optimization of industrial structure. From the point of view of existing studies [14], there are few studies on the relationship between the NQPF and the optimization and upgrading of China’s industrial structure [15,16,17]. In addition, scholars generally believe that the development of the NQPF is conducive to promoting the optimization and upgrading of China’s industrial structure, which lays the foundation for further research in this paper. On this basis, this paper analyzes the impact of the NQPF on the optimization and upgrading of China’s industrial structure and its role mechanism from the perspective of science and technology innovation.

The marginal contribution of this study is reflected in two aspects: Firstly, from the connotation of the new quality productive force, this paper combines the three major characteristics of the new quality productive force, namely, “new”, “quality”, and “power”, and constructs a NQPF evaluation index system to measure the development level of the new quality productive force. This paper starts from the connotation of the new quality productive force, combines the three characteristics of new, quality, and force, constructs the evaluation index system of the new quality productive force, and measures the development level of the new quality productive force. Secondly, this paper analyzes the inherent conduction mechanism among the three from both theoretical and empirical levels and takes STI as the entry point, tries to explore the conduction effect of the NQPF on the industrial structure through the STI, and digs out the chain transmission relationship that may exist among the three. Through these studies, this paper aims to provide new perspectives and methods for the theoretical research and practical application of the new quality productive force, and to provide a scientific basis for policy makers, so as to promote the optimization and upgrading of China’s industrial structure and the sustainable development of the economy.

3. Theoretical Analysis and Research Hypothesis

The importance of the NQPF is becoming more and more significant in today’s global economic pattern, which not only represents a leap in productivity but also the key force to promote sustainable and healthy economic and social development. This paper takes science and technology innovation as an entry point to deeply analyze the intrinsic connection between the new quality productive force, science and technology innovation, and industrial structure optimization, which is of great significance for understanding the law of economic development and guiding the practice of industrial upgrading [18]. Firstly, the direct influence of new high-quality productivity on ISO is expounded theoretically. Secondly, it explores the relationship between the new quality productive force, science and technology innovation, and ISO, which helps to reveal the inherent logic of innovation-driven development. Moreover, this paper will conduct an in-depth analysis of the spatial spillover effect of the NQPF on ISO and upgrading. At the same time, according to the differences in economic development level, resource endowment, and industrial structure of different regions, this paper will systematically study the heterogeneous impact of the NQPF on the advanced and rationalized industrial structure of China’s eastern, central, and western regions and put forward corresponding hypotheses and countermeasure proposals.

As a product of the in-depth integration of modern science and technology and productivity, the NQPF not only marks a great leap in technology but is also an indispensable and important driving force for socioeconomic development [19]. Under the continuous penetration and wide application of information technology, artificial intelligence, cloud computing, and other cutting-edge technologies, the NQPF is leading the optimization of economic structure and the profound change in social production mode at an unprecedented speed [20]. In terms of industry chain extension, through technological innovation and integration, the NQPF has continuously broadened the boundaries of traditional industries, giving rise to a series of new business forms and value-added services, and opening up a broad space for ISO. This process not only enriches the industrial structure but also promotes inter-industry synergy and complementarity, making the whole economic system more rational and efficient [21]. Meanwhile, business model innovation has become another key path for the NQPF to promote the ISO. With the help of big data, the Internet of Things, and other advanced technologies, enterprises can more accurately grasp the market demand and realize personalized customization and flexible production, thus effectively improving the added value of products and services and enhancing market competitiveness. At the level of resource allocation optimization, the NQPF realizes accurate scheduling and efficient use of production resources through intelligent and automated means. This not only significantly improves production efficiency and reduces production costs but also lays a solid foundation for the sustainable development of the industry [22]. In addition, the optimization of resource allocation also promotes the wide application of environmental protection and energy-saving technologies, which helps build a green and low-carbon industrial development model.

Hypothesis 1. 

The new quality productive force has a direct contribution to the optimization of industrial structure.

The development of the NQPF exhibits uneven diffusion patterns at the regional level, primarily attributable to disparities in infrastructure, talent pools, industrial foundations, and policy support across different areas [23]. For instance, eastern coastal regions, benefiting from well-established digital infrastructure and abundant innovation resources, demonstrate greater agility in adopting emerging technologies like smart manufacturing and industrial internet, thereby accelerating the transformation of traditional manufacturing toward high-end production. In contrast, the central and western regions, constrained by technological absorption capacity and supporting industrial maturity, show relatively slower penetration rates and shallower transformation depths of the new quality productive force. This regional divergence manifests not only in technological application efficiency but also in the diversity of industrial upgrading pathways. The eastern regions tend to prioritize knowledge-intensive service industries and advanced manufacturing, while the central and western areas predominantly leverage local resource endowments to develop specialized digital industries. Consequently, the impact of the NQPF on STI varies significantly, according to regional development levels [24,25,26,27].

Hypothesis 2. 

There is a positive spatial spillover effect of the impact of the development of the NQPF on the STI.

The development of new quality productivity has significantly driven innovation in cutting-edge technologies, such as information technology, artificial intelligence, and cloud computing [28]. These technologies have not only transformed production methods in traditional industries but also facilitated the transformation and upgrading of numerous conventional sectors [29]. For instance, in manufacturing, the widespread adoption of automated production lines and intelligent robots has markedly improved production efficiency, while reducing costs and labor demands. Moreover, new quality productivity plays a pivotal role in technological innovation within high-tech industries. Such innovations not only enhance production efficiency but also propel the growth of emerging industries. For example, in smart manufacturing, AI-based predictive maintenance technology—by monitoring equipment conditions in real time, analyzing historical data, and forecasting potential failures—has reduced factory equipment downtime by over 30%, substantially improving overall production line efficiency. In summary, through both technological innovation and breakthroughs, new quality productivity fosters advancements in science and technology, thereby promoting the modernization of traditional industries and the rise of emerging sectors. This dual effect drives the optimization and upgrading of industrial structures [30,31].

Hypothesis 3. 

The NQPF can indirectly promote the optimization and upgrading of industrial structure through the intermediary of science and technology innovation.

The transmission mechanism between the new quality productive force, science and technology innovation, and industrial structure optimization and upgrading is shown in Figure 1.

4. Modeling and Data Sources

4.1. Model Setup

The above theoretical hypotheses constitute the theoretical basis for this paper to examine the relationship between the new quality productive force, science and technology innovation, and industrial structure. Based on this, so as to test the impact of the development of the NQPF on science and technology innovation in Hypothesis 1, this paper constructs the following basic econometric model:

$${patent}_{it}={\alpha }_0+{\alpha }_1{nqpf}_{it}+{\alpha }_n{X}_{it}+{\lambda }_i+{\delta }_{it}$$

(1)

In Equation (1), ${patent}_{it}$ represents the science and technology innovation indicator of province i in period t, ${nqpf}_{it}$ represents the indicator of the development level of the NQPF of province i in period t, ${\lambda }_i$ represents the unobservable individual fixed-effect of province i, and ${\delta }_{it}$ is the random perturbation term. ${\alpha }_0$ denotes the model intercept term, and ${\alpha }_1$ is the coefficient of the new quality productive force, reflecting its impact on science, technology, and innovation. The vector X represents other variables that may affect the science and technology innovation at the provincial level.

On the basis of the modeling of the impact of the NQPF on science and technology innovation, aiming to further analyze the transmission mechanism between the new quality productive force, science and technology innovation, and industrial structure optimization, this paper tests Hypotheses 2 and 3 by drawing on the three-stage mediation effect model. In this study, based on the previous mechanism analysis, the explanatory and interpreted variables in this part are the NQPF and ISO, respectively, while the mediating variable is science and technology innovation. Therefore, based on model (1), the following model is added:

$${ind}_{it}={\beta }_0+{\beta }_1{nqpf}_{it}+{\beta }_n{X}_{it}+{\theta }_i+{\epsilon }_{it}$$

(2)

$${ind}_{it}={\gamma }_0+{\gamma }_1{nqpf}_{it}+{\gamma }_2{patent}_{it}+{\gamma }_n{X}_{it}+{\eta }_i+{\sigma }_{it}$$

(3)

Among them, according to the mediation effect model constructed in this paper, the total effect coefficient of the NQPF on ind is ${\beta }_1$, the mediation effect coefficient of the NPQF to promote ISO through promoting STI is ${\beta }_1+{\gamma }_2$, and the direct effect coefficient of the influence of the NQPF on the ISO is ${\gamma }_1$, then the size of the mediation effect in this paper can be expressed as ${ \beta }_1\times {\gamma }_2={\beta }_1-{\gamma }_1$.

4.2. Variable Selection

4.2.1. Explained Variable: Industrial Structural Optimization (ind)

This paper measures the level of ind in each province through the industrial structure hierarchy coefficients, which are calculated as follows:

$$ind=\sum _{j-1}^3\left({\displaystyle \frac{Y_J}{Y}}\right)\times j j=1, 2, 3$$

(4)

In Equation (4), Y denotes the value added of output, and j denotes the industry. The larger the ind, the larger the proportion of the tertiary industry, indicating the higher degree of upgrading of the industrial structure [32,33].

4.2.2. Core Explanatory Variable: New Quality Productive Force (NQPF)

The NQPF is essentially advanced productivity; its connotation and characteristics are formed on the basis of traditional productivity, so when measuring this advanced form of productivity, we naturally focus on the following three core elements of its composition: workers, labor objects, and labor materials. Based on this, this paper takes the three core elements of productivity, workers, labor objects, and labor materials, as the core of the construction of the NQPF evaluation index system and integrates them into the construction of the subsequent specific index system.

The new quality productive force, as a representative of the advanced productive forces, compared with the traditional productive forces, has significant differences and advantages that are centered on the following three unique major characteristics: “new”, “quality”, and “power “. These three features not only constitute the core characteristics of the new quality productive force but also provide us with an important dimension for a comprehensive and in-depth evaluation of the new quality productive force. ”Newness” is the primary characteristic of new quality productive force, emphasizing innovation leadership. This includes continuous innovation at the technical level, pursuing the breakthroughs and applications of cutting-edge technologies to improve production efficiency and product quality; increasing research and innovation capabilities, increasing investment in scientific research, establishing an efficient research system, and solving industry problems; and significantly increasing entrepreneurial activity, encouraging innovation and entrepreneurship, and providing a fertile ground for new technologies and new forms of business. These “new” elements together constitute the unique charm of the new quality productive force, which not only promotes the change in production mode but also leads the upgrading of industrial structure and the transformation of economic growth mode. Therefore, when evaluating the new quality productive force, the breadth and depth of “newness” has become an indispensable criterion. The core connotation of “quality” as a distinctive feature of the new quality of productivity is deeply reflected in the fundamental transformation of production efficiency. This transformation does not only seek to increase speed but also focuses on the optimization of both quality and efficiency. “Power”, as an important feature of the new quality of productivity, connotes “arithmetic power”. As an important pillar of the NQPF, the core of “power” lies in “arithmetic”, which is the core driving force for the development of modern science and technology. Arithmetic power, in short, is the ability to process and compute data, which is the key to promoting the development of the digital economy, smart manufacturing, smart cities, and other cutting-edge fields. Under the framework of new quality productive force, arithmetic power is not only a breakthrough at the technical level but also a core force to promote industrial upgrading, enhance production efficiency, and optimize resource allocation. Its importance is self-evident, and it is an important indicator of a country or region’s scientific and technological strength and innovation capability. To measure the development level of “arithmetic power”, it is necessary to comprehensively consider a number of dimensions. Another important criterion for the measurement of the NQPF is green development. The environment places higher demands on the NQPF. It includes indicators, such as the share of environmental protection expenditure, the share of wastewater, the share of waste gas, and the area of forests. Green development as an important measurement criterion for the NQPF highlights the importance of environmental protection and sustainable development. In the context of the new quality productive force, green development not only revolutionizes the traditional production mode but also leads the future development direction [34,35].

The measurement of the NQPF should be based on its basic connotation and take into account the three main features of the new quality productive force, while not ignoring the requirements of green development. On the one hand, the NQPF relies on the three basic connotations of new types of workers, new types of labor materials, and new types of labor objects; On the other hand, the three basic characteristics of the new quality productive force, namely, “new”, “quality”, and “power”, should be integrated into the selection of indicators; in addition, the importance of environmental protection and sustainable development requires that the evaluation index system of the NQPF should not ignore the criterion of green development. Based on this, this paper constructs the evaluation index system of the new quality productive force, including the development level of the NQPF in the target layer, as follows: four in the criterion layer, namely, the innovation level, quality level, arithmetic level, and green level, and 18 specific indicators in the indicator layer. Details are shown in

Table 1. NQPF evaluation indicator system.

Target Level	Standardized Layer	Indicator Layer	Note
New mass productivity	Innovation level	New product development capability	+
		Technological innovation capacity	+
		Funding for research	+
		Research staff inputs	+
	Quality level	Percentage of people with advanced degrees	+
		Number of employees in high-tech industries	+
		(Generated) electrical energy	+
		Product quality qualification rate	+
	Arithmetic level	Telecommunications communications capacity	+
		Fiber optic line length	+
		Internet penetration	+
		Technology market size	+
	Green level	Percentage of expenditure on environmental protection	+
		Percentage of wastewater	−
		Percentage of exhaust gas	−
		Forest area	+

Note: “+” indicates a positive indicator; “−” indicates a negative indicator.

.

Based on the evaluation index system of the NQPF constructed above, the entropy weight method was employed to calculate the weight values of various indicators in the metaverse industrial development evaluation system, thereby measuring the development level of the new quality productive force. The specific procedures are as follows:

Dimensionless normalization processing, $a_{ij}(i=1, 2,\dots ,m;j=1, 2,\dots ,n)$ represent raw data, and $x_{ij}(i=1, 2,\dots ,m;j=1, 2,\dots ,n)$ represent standardized data.

For positive indicators:

$$x_{ij}={\displaystyle \frac{a_{ij}-\min \left(a_{ij}\right)}{{\max (a}_{ij})-\min (a_{ij})}}$$

(5)

For negative indicators:

$$x_{ij}={\displaystyle \frac{\max \left(a_{ij}\right)-a_{ij}}{{\mathrm{m}\mathrm{a}\mathrm{x}(a}_{ij})-\mathrm{m}\mathrm{i}\mathrm{n}(a_{ij})}}$$

(6)

Calculate the proportion of the i indicator value under the j criterion $p_{ij}(i=1, 2,\dots ,m;j=1, 2,\dots ,n)$

$$p_{ij}={\displaystyle \frac{x_{ij}}{\sum _i^m{x}_{ij}}}$$

(7)

Compute the entropy value $e_j(j=1,2,\dots ,n)$ of the j indicator

$$e_j={\displaystyle \frac{1}{\ln m\sum _{i=1}^m{p}_{ij}\ln p_{ij}}},e_j\in \left(0, 1\right)$$

(8)

Determine the divergence coefficient $g_j(j=1,2,\dots ,n)$ of the j indicator

$$g_j=1-e_j$$

(9)

Calculate weights for all indicators $w_j(j=1,2,\dots ,n)$

$$w_j={\displaystyle \frac{g_j}{\sum _j^n{g}_j}}$$

(10)

Compute comprehensive scores of the NQPF for each sample ${nqpf}_i(i=1,2,\dots ,m)$

$${nqpf}_i={\sum }_{j=1}^n{w}_j{x}_{ij}$$

(11)

4.2.3. Mediating Variable: Science, Technology, and Innovation (Patent)

Considering that the number of patents granted can reflect the innovation input in the process from R&D to direct output and is also the inevitable result of STI, the number of domestic invention patent applications granted is used as a proxy variable for STI [36,37].

4.2.4. Control Variable

Based on the existing related literature, this paper introduces the following control variables: (1) openness to the outside world (open), which is measured by the ratio of total imports and exports to regional GDP; (2) government intervention (gov), which is measured by the ratio of the government’s public financial expenditure to the regional GDP; (3) social security (secur), measured using the proportion of pension and social security contributions to the total number of people; and (4) infrastructure level (infra), which reflects the level of regional infrastructure in terms of urban road space per capita.

4.3. Data Sources and Descriptive Statistics

This study employs quantitative research methods to systematically analyze the impact of the NQPF on ind through econometric modeling. Specifically, descriptive statistics and correlation analysis are first applied to examine the distribution characteristics of core variables, ensuring data quality and providing preliminary insights into variable relationships. The research then constructs a multiple regression model using the stepwise regression method, which effectively addresses multicollinearity issues, while verifying the robustness of the NQPF indicators through control variables. To further enhance the reliability of conclusions, comprehensive robustness tests are conducted, including the instrumental variable approach, core variable substitution, and sample period adjustments. These rigorous quantitative methods collectively establish a solid methodological foundation for accurately identifying the causal relationship between the NQPF and ISO.

In this paper, a study is conducted for 30 provinces in China (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2012 to 2022, resulting in a balanced panel of 300 province-year observations. The raw data are all obtained from the National Bureau of Statistics (NBS), and for individual cases of missing data, the missing values are replaced by the corresponding trend values obtained through trend analysis based on the characteristics of data changes. The descriptive statistics of the main indicators are detailed in

Table 2. Results of descriptive statistics.

Variant	Number of Observations	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Value
ind	330	2.407	0.121	2.132	2.835
nqpf	330	0.478	0.208	0.035	0.884
patent	330	10.390	1.419	6.219	13.680
open	330	0.283	0.282	0.007	1.476
gov	330	0.260	0.111	0.105	0.758
secur	330	0.349	0.136	0.0295	0.575
infra	330	0.304	0.127	0.0697	0.712

.

To mitigate potential reliability issues in empirical results caused by excessive correlations among variables, this study first conducted correlation analysis for all variables, with the specific results presented in

Table 3. Correlation analysis.

Variant	ind	nqpf	patent	open	gov	secur	infra
ind	1
nqpf	0.3059 ***	1
patent	0.4409 ***	0.3847 ***	1
open	0.5060 ***	−0.0877	0.4934 ***	1
gov	−0.2155 ***	−0.0508	−0.7992 ***	−0.4384 ***	1
secur	−0.5697 ***	0.0452	−0.1351 **	−0.6698 ***	0.0777	1
infra	0.3310 ***	0.4137 ***	0.3615 ***	0.3512 ***	−0.2228 ***	−0.4888 ***	1

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, ** p < 0.05.

.

The analysis revealed generally low correlations among the variables, with one notably higher value of −0.7992. To eliminate potential multicollinearity effects and ensure the reliability of empirical results, variance inflation factor (VIF) tests were conducted for the key variables, as presented in

Table 4. Multicollinearity diagnostics.

Variable	VIF	1/VIF
nqpf	5.11	0.195703
patent	3.62	-
open	3.04	0.329348
gov	2.59	0.385952
secur	2.05	0.486752
infra	1.84	0.543960
Mean VIF	3.04

.

As shown in the table above, VIF = 3.04 < 10, indicating the absence of multicollinearity issues among the variables.

According to Table 2, it can be seen that the maximum value of ind (ind) is 2.835, and the minimum value is 2.132, which indicates that there is a certain difference in the optimization of industrial structure in 2012–2022, and it is initially judged that the industrial structure is upgraded year by year and is in a fluctuating upward state; furthermore, with the help of the stata 18 software, this paper adopts the equidistant grading method to carry out the spatial grading, and the visualization map (with 2015 and 2022 as an example) is shown in Figure 2. Further, with the help of stata software, this paper adopts the isometric grading method to carry out spatial grading, and the visualization map (taking 2015 and 2022 as an example) is shown in Figure 2. By analyzing the spatial evolution pattern of the degree of optimization of provincial industrial structure in 2015 and 2022, we can see that there exists a certain large gap in optimization of the industrial structure among provinces, and the degree of optimization of the industrial structure in the eastern part of China is obviously higher than that in the western part and the central part of China.

5. Empirical Results and Analysis

5.1. Analysis of Benchmark Regression Results

Seeking to study the relationship between the regional NQPF and science and technology innovation, the selection of an econometric model should be carried out in advance through relevant tests. Based on the balanced panel data, this paper conducts an F-test to determine whether to choose the mixed regression model or the fixed-effect model, and the regression results find that the mixed regression model is excluded. Then, the Hausman test is conducted to determine whether to choose the fixed-effect model or the random effect model, and the final results show that it is more scientific to choose the fixed-effect model. Based on this, this paper selects the fixed-effect model to analyze the impact of the NQPF on science and technology innovation, and the estimation results are shown in

Table 5. Regression results of the impact of the NQPF on science and technology innovation.

Variant	Model (1)	Model (2)
nqpf	2.628 *** (7.55)	2.532 *** (12.96)
open		1.577 *** (8.01)
gov		−8.301 *** (−21.90)
secur		1.174 *** (2.88)
infra		0.094 (0.25)
constant term (math.)	9.130 *** (50.37)	10.450 *** (35.38)
sample size	330	330
${\mathrm{R}}^2$	0.148	0.804
F-value	56.97	266.31

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01.

. From model (1) and model (2), it can be seen that before and after adding control variables, the NQPF has a significant positive impact on science and technology innovation, and the coefficients are all significant at a 1% significance level, which also verifies the assertion of hypothesis 1 that the NQPF has a facilitating effect on science and technology innovation. It can be seen that the NQPF is indeed a key factor in promoting regional science and technology innovation, and improving the development level of the NQPF is conducive to the realization of regional science and technology innovation. From the regression results of control variables, it is worth noting that government intervention has a significant negative impact on science and technology innovation, indicating that, in the process of promoting science and technology innovation, the government should reduce excessive intervention in the market mechanism, optimize the efficiency of policy implementation, and strengthen the decisive role of the market in resource allocation. At the same time, it should increase its support for basic research and long-term innovation, and build a more open, flexible, and efficient innovation ecosystem, so as to stimulate the vitality of independent innovation of enterprises and scientific research institutions and promote the self-reliance of science and technology.

5.2. Heterogeneity Test

In order to test whether there are regional differences in the impact of the NQPF on science and technology innovation, 30 provinces in China were divided into three major regions, namely, east, central, and west, according to the traditional regional division, so as to analyze the heterogeneous impact of the NQPF on science and technology innovation. The specific regression results are shown in

Table 6. Subregional regression results.

Variant	Eastern Part	Central Region	Western Region
	Model (1)	Model (2)	Model (3)
npqf	0.135 *** (3.68)	0.183 *** (7.53)	0.076 * (1.86)
open	−0.170 *** (−5.28)	−0.609 *** (−5.12)	0.169 *** (2.67)
gov	−0.473 *** (−3.85)	0.545 *** (4.52)	0.120 ** (2.37)
secur	−1.108 *** (−16.24)	0.242 *** (3.02)	0.279 *** (3.02)
infra	−0.091 (−1.57)	0.241 *** (3.35)	0.112 (1.30)
constant term (math.)	2.932 *** (49.49)	2.073 *** (35.43)	2.123 *** (41.04)
sample size	110	66	154
${\mathrm{R}}^2$	0.783	0.775	0.212
(be) worth	75.21	41.25	7.94

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

, which gives the regression results of the grouping of the three major regions of east, central, and west. From the results, it can be found that the development of the NQPF shows a significant positive promotion effect on science and technology innovation in the eastern, central, and western regions, which is consistent with the full-sample estimation results in the previous section. However, it is noteworthy that the estimated coefficients of the impact of the NQPF on science and technology innovation in the eastern, central, and western regions are 0.135, 0.183, and 0.076, respectively, i.e., “central > eastern > western”. This indicates that, although other conditions remain unchanged, the incentive effect of science and technology innovation generated by the development level of the NQPF in different regions will be the largest in the central region, followed by the eastern region, and the smallest in the western region for every 1 unit increase in the level of development of the new quality productive force. The phenomenon is illustrated. This paper believes that the main reasons for this are the following aspects: Firstly, the central region of the latecomer advantage is significant. The central region has a certain gap in economic development and science and technology level relative to the eastern region, but it has a strong latecomer advantage. With the tilting of national policies and the promotion of industrial transfer, the central region can absorb and utilize advanced technology faster, while undertaking industrial transfer from the east, thus realizing the rapid growth of STI in a shorter period of time. In addition, the industrial structure of the central region is relatively balanced, with a certain manufacturing base as well as the gradual development of high-tech industries, which provides a good soil for the role of the new quality productive force. Secondly, the mature economy and innovation saturation in the eastern region limit the marginal contribution of the new quality productive force. As the most economically developed region in China, the eastern region has a better foundation for STI but at the same time faces the problem of diminishing marginal effect of innovation. Since the level of science and technology in the eastern region is already relatively high, the marginal contribution of the further improvement of the NQPF to science and technology innovation is relatively small, so its estimated coefficient is lower than that of the central region. In addition, the industrial structure of the eastern region is relatively mature, and the transformation and upgrading of some traditional industries is slow, which may limit the further contribution of the NQPF to S&T innovation. Finally, the resource and foundation constraints in the western region affect the effect of the new quality productive force. Although the western region is rich in resources, its foundation for STI is relatively weak due to limitations in geographic location, infrastructure, and scientific and technological talents. Although the enhancement of the NQPF has a positive effect on STI in the western region, the effect of the NQPF is relatively small due to the backwardness of its overall scientific and technological level and economic foundation. In addition, the industrial structure of the western region is dominated by resource-based industries, and the development of high-tech industries is relatively lagging behind, which also limits the role of the NQPF in promoting S&T innovation.

Further analysis reveals that the level of openness significantly inhibits STI in eastern and central regions, while promoting it in western regions, indicating regional differences in technological absorption capacity during the opening-up process. The impact of government intervention also shows regional divergence; in the more market-oriented eastern region, government intervention negatively affects innovation, whereas it plays a positive role in central and western regions. This suggests that government-led resource allocation is more effective in less developed areas. Similarly, the influence of social security exhibits heterogeneity. The comprehensive welfare system in eastern regions may reduce risk-taking incentives for innovation, while improved social security in central and western regions helps stabilize the talent pool for innovation activities.

5.3. Analysis of Transmission Mechanisms

Based on the previous theoretical analysis, this paper adopts econometric models (1), (2), and (3) to test the direct effect of the NQPF on ind, as well as the indirect effect of promoting ISO through promoting STI, and the relevant regression results are shown in

Table 7. Conduction mechanism regression results.

Variant	ind	patent	ind
	Model (1)	Model (2)	Model (3)
npqf	0.250 *** (9.40)	2.532 *** (12.96)	0.151 *** (4.79)
patent			0.039 *** (5.42)
open	0.087 *** (3.25)	1.577 *** (8.01)	0.025 (0.89)
gov	−0.118 ** (−2.29)	−8.301 *** (−21.90)	0.208 *** (2.67)
secur	−0.486 *** (−8.76)	1.174 *** (2.88)	−0.532 *** (−9.88)
infra	−0.200 *** (−3.94)	0.094 (0.25)	−0.204 *** (−4.18)
constant term (math.)	2.524 *** (62.68)	10.450 *** (35.38)	2.113 *** (24.81)
sample size	330	330	330
${\mathrm{R}}^2$	0.500	0.804	0.542
F-value	64.78	266.31	63.60

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, ** p < 0.05.

. Specifically, model (1) tests the total effect of the development of the NQPF on ISO, and the results show that the estimated coefficient of the NQPF is 0.250. It is significant at 1% level, indicating that the improvement of the development level of the NQPF in the region can have a positive impact on ISO, and it verifies the assertion of Hypothesis 2 that the NQPF promotes the upgrading of industrial structure. It is also in line with the reality of economic development.

Based on the above significant positive correlation between the NQPF and ind, this paper further tests whether there is a mediating effect. Model (2) is the effect of the NQPF on the intermediary variable of science and technology innovation, and the results are positive and significant; model (3) is the effect of the NQPF and science and technology innovation on ISO, and the results show that the estimated coefficients of the effect of the NQPF and science and technology innovation on ISO are both positive and pass the test of significance at the level of 1 percent. Combining models (1), (2), and (3), the results show that there is a partial mediation effect, which verifies the assertion of Hypothesis 3 that the NQPF can promote ISO through science and technology innovation. From the regression results of the transmission mechanism, under the condition that other factors remain unchanged, for every 1 unit increase in the development level of the new quality productive force, science and technology innovation will increase by 2.532 units, and the ISO will be directly increased by 0.151 units, which will lead to an indirect improvement of ISO by 0.099 units, and the total effect will be 0.250 units, that is, the sum of the direct effect and indirect effect, and the mediating effect accounts for the total effect. The total effect is 0.250 units, i.e., the sum of direct effect and indirect effect, and the proportion of intermediary effect in the total effect is 39.60%. It should be noted that part of the mediation effect also indicates that the development of the NQPF may also promote ISO through other channels, such as government intervention and human capital mentioned above.

5.4. Robustness Analysis

5.4.1. Endogeneity Test

Endogeneity problems arise for a number of reasons, and some basic treatment of possible problems has been provided in the previous section, specifically, (1) avoiding endogeneity problems arising from data measurement errors. Official NSO data are selected to minimize the possible impact of data quality on the estimation results. (2) Avoiding endogeneity problems due to omitted variables. Control variables such as openness to the outside world, government intervention, social security, infrastructure level, etc., are included in the estimation process of the econometric model, while the fixed-effect model is used for regression analysis.

To further avoid estimation bias due to endogenous problems in the development of the new quality productive force, this paper adopts the Internet penetration rate in each province as an instrumental variable for the composite index of the NQPF development. On the one hand, the Internet penetration rate is highly correlated with the new quality productive force, because the Internet, as an important carrier of information technology, can promote the flow of information, resource sharing, and technology diffusion, which can significantly improve the production efficiency and technological innovation capacity and satisfy the correlation requirements; on the other hand, the direct correlation between the Internet penetration rate and the optimization of industrial structure, STI, as well as other control variables, is weak, and the role of which is mainly achieved indirectly by influencing the NQPF rather than directly determining other control variables. On the other hand, the direct correlation between the Internet penetration rate and ISO, science and technology innovation, and other control variables is weak, and its role is mainly realized indirectly by influencing the NQPF rather than directly determining the changes in other variables, which meets the requirement of exclusivity. Therefore, it is reasonable to take the Internet penetration rate as an instrumental variable of the new quality productive force. Based on this, this paper adopts the two-stage least squares method to re-test the underlying regression and the transmission mechanism.

Table 8. Endogeneity test: instrumental variables.

Variant	Phase I	Phase II
	Model (1)	Model (2)	Model (3)	Model (4)
	xpqf	ind	patent	ind
xqpfv	0.233 *** (4.61)
xpqf		11.265 *** (8.06)	1.658 *** (17.85)	1.479 *** (14.96)
patent				0.016 *** (4.45)
control variable	containment	containment	containment	containment
constant term (math.)	−0.430 ** (−2.18)	5.005 *** (7.46)	1.615 *** (36.18)	1.535 *** (32.71)
sample size	330	330	330	330
${\mathrm{R}}^2$	0.061	0.165	0.493	0.522
F-value	21.23	64,96	318.57	178.32

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, ** p < 0.05.

gives the regression results after using instrumental variables to solve the endogeneity problem. Based on the test results of the first-stage instrumental variables, it can be found that the F-statistic value is greater than 10 and passes the significance test at the 1% level, which indicates that there is no weak instrumental variable problem. From the regression results of benchmark regression and transmission mechanism regression, the impact of the NQPF on science and technology innovation is still positive and significant, and the NQPF can also promote ISO through the promotion of science and technology innovation, and the sign, direction, and significance of the NQPF variables have not changed significantly, which supports the robustness of the core conclusions of this paper.

5.4.2. Robustness Check

In this paper, robustness tests are conducted through the following three methods: (1) Split-sample regression. As shown in the results above, the 30 provinces are regressed on the sample to test the impact of the NQPF on science and technology innovation. (2) Adjusting the sample interval for regression. The NQPF has not entered the gestation period before 2015, so this paper adjusts the sample interval to the development level of the NQPF in each province from 2015 to 2022 as an explanatory variable for regression analysis. By testing the transmission mechanism above, the results are obtained (see

Table 9. Robustness test (I): adjusted sample interval.

Variant	ind	patent	ind
	Model (1)	Model (2)	Model (3)
npqf	0.178 *** (4.80)	2.216 *** (8.48)	0.108 *** (2.61)
patent			0.032 *** (3.49)
open	0.091 ** (2.58)	1.874 *** (7.50)	0.032 (0.84)
gov	−0.152 ** (−2.57)	−7.976 *** (−19.10)	0.099 (1.08)
secur	−0.505 *** (−8.13)	1.018 ** (2.32)	−0.537 *** (−8.75)
infra	−0.238 *** (−4.45)	−0.323 (−0.85)	−0.228 *** (−4.36)
constant term (math.)	2.600 *** (55.07)	10.715 *** (32.10)	2.262 *** (21.10)
sample size	240	240	240
${\mathrm{R}}^2$	0.510	0.817	0.534
F-value	48.72	209.45	44.56

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, ** p < 0.05.

), and the regression results of the mechanism test are consistent with the above.

(3) Regression with transformed variables. This paper adopts different measurement methods to measure the upgrading of industrial structure. Drawing on the understanding of studies on ind, the ratio of the output value of tertiary industry to that of secondary industry can better reflect the upgrading of service-oriented economic structure, and the specific formula is as follows:

$$\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{t}={\displaystyle \frac{Y_3}{Y_2}}$$

(12)

Y denotes the value added of output. Therefore, this paper uses the ratio of value added of the tertiary industry to that of the secondary industry to measure ind. By testing the transmission mechanism above, the regression results are obtained (see

Table 10. Robustness test (II): transformed variables.

Variant	indt	patent	indt
	Model (1)	Model (2)	Model (3)
npqf	1.303 *** (7.07)	2.532 *** (12.96)	1.058 *** (4.67)
patent			0.097 * (1.85)
open	−0.807 *** (−4.34)	1.577 *** (8.01)	−0.960 *** (−4.74)
gov	−0.515 (−1.44)	−8.301 *** (−21.90)	0.287 (0.51)
secur	−4.480 *** (−11.66)	1.174 *** (2.88)	−4.594 *** (−11.84)
infra	−1.490 *** (−4.23)	0.094 (0.25)	−1.499 *** (−4.27)
constant term (math.)	3.147 *** (11.28)	10.450 *** (35.38)	2.137 *** (3.49)
sample size	330	330	330
${\mathrm{R}}^2$	0.376	0.804	0.383
F-value	39.11	266.31	33.41

Note: t-statistics for estimated coefficients are in parentheses; *** p < 0.01, * p < 0.1.

for details), and the regression results of the mechanism test are consistent with the above.

By analyzing the robustness test above, it can be found that the conclusions obtained in the previous section have good robustness.

5.5. Results Analysis

The findings of this study systematically corroborate the theoretical hypotheses, with key discoveries articulated across three dimensions, as follows.

First, regarding the direct effect of the NQPF on ISO (Hypothesis 1), the empirical results validate theoretical expectations. The study reveals that the NQPF significantly propel industrial structure advancement toward higher-end development through technological innovation and production mode transformation. This finding aligns with the theoretical framework of innovation-driven development, demonstrating that the NQPF serves as a pivotal driver in reshaping industrial development dynamics.

Second, the research confirms the existence of regional heterogeneity in the impact of the NQPF (Hypothesis 2) but reveals a spatial pattern distinct from conventional understanding. While the NQPF exhibits significantly positive effects on STI across regions, the magnitude of these effects displays marked regional heterogeneity, specifically following a “central > eastern > western” gradient.

Finally, the verification of technological innovation’s mediating role (Hypothesis 3) elucidates the intrinsic mechanism through which the NQPF influences industrial structure. The study identifies a chain transmission mechanism among the new quality productive force, scientific/technological innovation, and ISO; the NQPF not only exerts direct effects on industrial upgrading but also indirectly promotes it through stimulating scientific and technological innovation, confirming the presence of significant mediation effects.

6. Discussion

The authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.

7. Conclusions and Policy Recommendations

Based on the provincial panel data from 2011 to 2020, this paper empirically examines the impact of the NQPF on science and technology innovation and further explores the transmission mechanism of the NQPF in promoting ISO. The findings show that the development level of the regional NQPF has a significant role in promoting science and technology innovation, and the significance results remain at the 1% significance level before and after the introduction of control variables, indicating that the conclusions have a high degree of robustness. In terms of regions, the NQPF in the eastern, central, and western regions has a significant positive effect on science and technology innovation, but the promotion effect shows obvious regional heterogeneity, which is specifically manifested as “central > eastern > western”. This indicates that compared with the western region, the NQPF has a stronger role in promoting science and technology innovation in the central and eastern regions. In addition, the study also found that there is a chain transmission mechanism among the new quality productive force, science and technology innovation, and industrial structure optimization; the NQPF not only has a direct effect on ISO but also indirectly promotes ISO through the promotion of science and technology innovation, i.e., there is a significant intermediary effect.

Based on the aforementioned research findings, the following region-specific policy recommendations are proposed:

(1)

For the central region with significant late-mover advantages, it is recommended to prioritize the implementation of an industrial integration development strategy. Specifically, the “new quality productive force–industrial innovation” pilot zones could be established in cities with strong STI foundations. This would involve creating industrial collaborative innovation centers to facilitate targeted R&D cooperation between leading enterprises and research institutions. Concurrently, intelligent transformation incentive policies should be implemented, and regional industrial internet platforms should be developed to enable manufacturing resource sharing. To optimize the allocation of innovation factors, a “dual-appointment” system for scientific and technological talent could be introduced, allowing university researchers to hold concurrent positions in innovative enterprises, along with establishing specialized technology transfer funds focusing particularly on pilot-scale testing.

(2)

For the eastern region with robust innovation foundations but facing diminishing marginal returns, efforts should concentrate on transforming innovation paradigms. On the one hand, there should be a shift from follow-up innovation to original innovation, with major scientific and technological infrastructure deployed in cutting-edge fields, such as artificial intelligence and quantum information. On the other hand, the research evaluation system should be reformed to establish long-term assessment mechanisms for major original achievements. Simultaneously, the innovation ecosystem needs optimization, including streamlining approval processes for new technologies and products, establishing “regulatory sandbox” mechanisms, improving venture capital systems, and developing international science and technology cooperation parks to attract world-class R&D institutions.

(3)

For the western region with relatively weak innovation foundations, the primary task is to strengthen basic innovation capacity. It is recommended to implement a “digital infrastructure improvement” special project, prioritizing the construction of 5G private networks and edge computing nodes in industrial parks. Through “science and technology commissioner” initiatives, talent from eastern regions could be organized to provide on-site services at western enterprises. Meanwhile, leveraging regional resource advantages, emphasis should be placed on cultivating specialized innovation clusters, such as establishing new energy technology application demonstration bases based on clean energy advantages and developing comprehensive innovation systems for specialized agricultural deep processing.

(4)

To promote regional coordinated development, it is suggested to establish multi-level cooperation mechanisms. These include forming a NQPF Development Alliance to regularly organize technology matching activities, implementing a cross-regional universal redemption system for science and technology innovation vouchers, building a national technology trading market to enhance technology transfer efficiency, and improving benefit sharing mechanisms for cross-regional industrial transfers. These measures not only account for the differentiated characteristics of each region but also focus on key aspects of NQPF development, effectively promoting the virtuous cycle of “NQPF-STI-industrial upgrading”.

The research in this paper has the following limitations: (1) The development of the NQPF from its introduction to the present time is relatively short, so the sample interval of this paper is chosen to be 2011–2020, which is a relatively limited time span. Future research can consider extending the sample interval to capture the long-term dynamic effects of the development of the new quality productive force. (2) Due to the availability of the indicator data, the sample in this paper only considers the provincial level and fails to explore in-depth the heterogeneity at the city level or county level. Future research can start from more refined geographic units (e.g., cities and counties) to further reveal the micro-influence mechanism of the NQPF on science and technology innovation. (3) This paper mainly focuses on the direct impact of the NQPF on S&T innovation and its mediating effect on ind through S&T innovation but does not fully consider other possible moderating or mediating variables (e.g., institutional environment, degree of marketization, etc.). Future research can introduce more moderating or mediating variables to reveal the complexity of the mechanism of the role of the NQPF more comprehensively. (4) The empirical analysis in this paper is mainly based on the static panel model, and future research can try to adopt the dynamic panel model or other more advanced econometric methods to better deal with the endogeneity issue and capture the dynamic relationship among variables. By remedying the above limitations, future research can further deepen the understanding of the NQPF and its economic effects and provide a more precise theoretical basis for policy formulation.