New Quality Productivity and Industrial Structure in China: The Moderating Effect of Environmental Regulation

Abstract

To explore the connotation and development level of China’s new quality productivity, this paper constructs an index system based on innovation, greenness, and productivity. This system is used to describe the development level of China’s new quality productivity. Using relevant data from 30 provincial administrative regions in China from 2011 to 2021, the entropy weight-TOPSIS method was employed to measure the index system. The development level of new quality productivity in China and its four major economic regions was analyzed through the three dimensions of the index system. Additionally, this paper examines the impact of new quality productivity on China’s industrial restructuring and integrates environmental regulation to elucidate the interaction mechanisms among these factors. An econometric regression model is further constructed to verify the effect of new quality productivity on industrial structural change and to examine the moderating role of environmental regulation. The results of this study show that there is a regional imbalance in the level of development of new quality production in China, with the level of development of new quality productivity in the eastern region being significantly higher than that in the central, western, and northeastern regions. However, on the whole, the new quality productivity of the four major regions has been in a state of continuous improvement during the period under investigation, and the spatial gap has been constantly decreasing. The benchmark regression coefficients, sys-GMM regression coefficients, and diff-GMM regression coefficients for new quality productivity and industrial rationalization are −0.6228, −0.1121, and −0.0439, respectively, and they are negatively correlated. The regression coefficients of the sys-GMM and diff-GMM of the interaction terms of environmental regulation and new quality productivity are −0.0051 and −0.0045, and there is a negative moderating effect of environmental regulation between new quality productivity and industrial structure rationalization. The benchmark regression coefficient, the sys-GMM regression coefficient, and the diff-GMM regression coefficient of new quality productivity and industrial upgrading are 2.5179, 0.7525, and 0.3572, respectively, and there is a positive correlation between the two. The regression coefficients of sys-GMM and diff-GMM for the interaction terms of environmental regulation and new quality productivity are 0.0380 and −0.0167, and there is a positive moderating effect of environmental regulation between new quality productivity and industrial structure upgrading.

1. Introduction

Productivity, throughout the industrial revolution, has undergone transformative changes and transitions, driving the progress of human society and the evolution of industrial systems. With the advent of the Fourth Industrial Revolution, productivity has acquired a richer connotation and deeper meaning. In September 2023, China introduced the concept of new quality productivity, emphasizing the proactive cultivation of future industries, the acceleration of new quality productivity formation, and the enhancement of new growth drivers. In December 2023, China’s Central Economic Work Conference reiterated the need to leverage disruptive and cutting-edge technologies to generate new industries, new modes, and new kinetic energy, thereby fostering new productivity and building a modern economic system. The formation of new quality productive forces not only symbolizes the transformation of traditional productive forces to advanced productive forces but also represents a revolutionary change in the mode of production as well as the achievement of efficiency and quality enhancement, providing a new engine power for the construction of the modern industrial system.

The modern industrial system forms the foundation for constructing a modern economic system. Compared to advanced countries, China faces more pronounced issues of industrial structure imbalance, with significant challenges in transforming and developing its industrial framework. The healthy development of the industrial structure profoundly impacts economic growth and social employment. New quality productivity, as a crucial force driving China’s high-quality development, focuses on the advancement of green innovative technology and the widespread application of digital technology. This new quality productivity provides core momentum for adjusting and optimizing the industrial structure, acting as an important driver for aligning with contemporary trends, enhancing the quality of economic development, and improving the industrial framework. However, it is crucial that industrial structure adjustments align with the concept of sustainable development. China’s extensive growth model since the Reform and Opening up is no longer viable and must be replaced by a specialized division of labor and an innovative development model. “Green development” and “digital development” have become contemporary standards for assessing the quality of an economy’s development. Environmental regulation, an essential tool for the government to address environmental pollution [1], is indispensable for ensuring the green transformation of industries. It plays a significant role in the adjustment of the industrial structure.

This paper aims to explore several key questions: What is the connotation of China’s new quality productivity? How can the development level of China’s new quality productivity be quantitatively measured? Does new quality productivity contribute to the adjustment and upgrading of the industrial structure? Additionally, what role does China’s environmental regulation play in the relationship between new quality productivity and industrial structure? This research will address these critical issues and will provide a conceptual understanding of the new quality productivity proposed by China’s Central Economic Work Conference, which is of great significance for China to enter a new stage of development, transform the extensive production mode, absorb new productivity, optimize the future industrial layout of China, and achieve high-quality economic and social development.

The full text is divided into six sections. Section 1 is the introduction. Section 2 reviews and summarizes previous research. Section 3 presents the theoretical analysis, examining the mechanisms underlying the relationships between new quality productivity, industrial structure, and environmental regulation. Section 4 details the methodology and data, explaining the measurement methodology, econometric methods, and data selection used in this paper. Section 5 presents the results and discussion, showing and analyzing the measurement results and econometric estimations. Finally, Section 6 provides the conclusion and implications, summarizing the findings of the preceding chapters and offering policy recommendations based on the analyzed results. The goal is to provide insights that can promote new quality productivity, accelerate industrial structure adjustment, and optimize industrial layout.

The specific contribution of this study is as follows: First, it expounds the meaning of new quality productivity from a new perspective. Secondly, quantitative methods are used to measure the development level of the new quality productivity in China as a whole and the four regions, and to measure the spatio-temporal evolution of the new quality productivity in the four regions. Third, this paper explores the mechanism of the relationship between China’s new quality productivity, industrial structure, and environmental regulation and takes environmental regulation as a moderating variable to verify the relationship between the three using an econometric model. Fourth, it provides policy recommendations for China to improve new quality productivity, accelerate industrial restructuring and upgrading, and ultimately achieve high-quality development.

2. Literature Review

2.1. Literature on New Quality Productivity and Industrial Structure

Classical economics posits that labor materials and labor are the two main inputs to the productive forces, which are used as cost inputs for the use and development of productivity. The “real man”, the laborer, has an important role to play in the dynamics of the development of productivity, and the entirety of the elements required for the labor process is nothing other than the “material and human elements”. With the increasing complexity of the social division of labor, Marx expanded this notion in “Capital” by identifying laborers, labor objects, and labor materials as the three elements of productivity. Since the concept of new quality productivity was introduced, many Chinese scholars have actively explored and analyzed its connotations from various dimensions and perspectives. Historical materialism believes that material productivity is the source and support of human life and production, and the total social production relations that match the development of productive forces constitute an economic foundation. In the process of promoting high-quality development, new-quality productivity promotes the achievement of high-quality green life for the people [2,3,4]. Looking into the future, the new quality productivity is a kind of high-level, leapfrog, revolutionary, and highly intelligent productivity that meets the requirements of China’s high-quality development. In the emerging industry revolution with the strategic window period of “curve overtaking” China, accelerating the formation of new productivity is helpful for the development of development initiatives [5,6,7]. New quality productivity is a result of the sinicization and modernization of the Marxist political economy. Driven by information technology and scientific and technological innovation, the elements of laborers, labor objects, and labor materials are continuously optimized and upgraded, integrating into more efficient factors of production. This integration promotes high-quality economic development through the principles of green development and advanced technology [8]. From the perspective of China’s manufacturing industry transformation and upgrading, Xu Zheng and Zhang Jiaoyu [9] discussed the logical mechanisms by which new quality productivity drives this transformation. As a key sector of the national economy, the transformation and upgrading of the manufacturing industry significantly impact the modern industrial structure system. The formation of new quality productivity supports the transformation of traditional manufacturing industries and the construction of a modern industrial system. It also accelerates the innovation and development processes of emerging and future industries [10]. Moreover, integrating new quality productivity with digital inclusive finance can facilitate the convergence of industrial, financial, and technological chains. This integration enhances the industry’s basic capacity by strengthening the resilience and modernization of the industrial chain, thereby supporting the construction of a modern industrial system [11,12]. Therefore, there is an inextricable link between new quality productivity and industrial structure. However, the logical mechanisms underlying this relationship require quantitative research for thorough examination.

2.2. Literature on Environmental Regulation and Industrial Structure

In the academic community, it is widely recognized that environmental regulation serves as a governmental constraint to protect the environment and ecological resources from pollution [13]. To measure the intensity of environmental regulation, researchers commonly employ the single-indicator approach, the composite-indicator approach, and the assignment approach [14,15,16]. Different types of environmental regulation can have varying effects on economic and social development [17,18]. Consequently, there are diverse perspectives on the impact of environmental regulation on industrial structure.

Some scholars argue that environmental regulation imposes additional costs on enterprises. To comply with environmental regulations, companies must divert development funds from projects and products to green technologies and manage pollutants produced by their operations, known as end-pollution treatment. This internalizes external costs, increases expenses, reduces corporate income, and diminishes competitiveness, which hinders industrial transformation and upgrading [19,20]. Conversely, other scholars contend that strict environmental regulation can positively impact the optimization and upgrading of industrial structures. Environmental constraints can compel high-energy-consuming and high-polluting enterprises to innovate green technologies. The subsequent benefits of these technological innovations can offset the initial costs [21,22,23]. Environmental regulation can also help these enterprises screen investment projects, directing funds towards initiatives that align with environmental standards, reducing unnecessary resource waste, and promoting green transformation and industrial upgrading. Additionally, some scholars suggest a non-linear relationship between environmental regulation and industrial structure. The impact of environmental regulation on industrial structure may exhibit threshold effects, vary with economic development, or follow an inverted “U”-shaped curve [24,25].

The above literature provides valuable inspiration and reference for this paper. Currently, there is limited literature that quantitatively analyzes the relationship between new quality productivity and industrial structure, and almost no literature integrates environmental regulation, new quality productivity, and industrial structure into a single system. This paper aims to fill this gap by analyzing the mechanisms and conducting quantitative research to demonstrate the interaction between these three elements.

3. Theoretical Analysis and Hypothesis Development

3.1. The Connotation of New Quality Productivity

In Marxian political economy, productivity refers to the human capacity to transform and utilize nature. The concept of new quality productivity acts as an accelerator of economic development in the modern era. It is a novel concept tailored to China’s national conditions and contemporary characteristics, emphasizing the qualitative transformation and enhancement of the three elements: labor, labor materials, and labor objects [26]. This approach aims to achieve a revolutionary advancement in productive forces. To further understand the connotation of new quality productivity, this paper interprets it through three dimensions: innovation, greenness, and productiveness. “New” emphasizes the innovation in science and technology. “Quality” focuses on whether productivity can contribute to the coordinated development of the economy, society, and environment, highlighting the levels of greenness and sustainability. “Productivity” is rooted in factor endowment and factor combination, driving the concurrent development of economic progress, social advancement, and nature conservation.

Innovation is the core of new quality productivity [27], aligning with the principle that “scientific and technological innovation is the primary driving force”. Innovation plays a crucial role in the development of science and technology, enabling the transformation of productivity from quantitative to qualitative changes, thus serving as the internal driving force of new quality productivity [28]. Continuous innovation is essential for moving beyond traditional paths of economic growth and productivity development. As the scientific and technological revolution progresses, cutting-edge technology has become a major driving force for social and economic reforms. Additionally, the cultivation of high-tech and high-quality talent represents a key aspect of innovative production factors.

Greenness. The core value of green productivity is “low carbon and green”, and the essence of new quality productivity is green productivity [29]. Guided by the new development concept, the enhancement of productivity and quality improvement must be synergistically promoted. Quality improvement emphasizes the sustainable use and development of the ecological environment and natural resources in economic operations. This approach aims to achieve coordinated progress in economic growth, social advancement, and environmental harmony, driving high-quality economic and social development with high efficiency and low energy consumption.

New quality productivity redefines traditional labor productivity, yet it remains fundamentally a form of “productive forces”. From an economic perspective, the development of productive forces must evolve with the times. As the advantage of traditional labor factors gradually diminishes, new quality productivity no longer relies solely on these traditional factors. Instead, it incorporates elements of digital technology, particularly the rapid development of the digital economy, which has significantly transformed the economy and society. Therefore, the driving force of new quality productivity in economic and social production includes both traditional infrastructure and new business models represented by the digital economy. Together, these material and intangible means of production enhance social production efficiency.

3.2. Analysis of the Mechanism between New Quality Productivity and Industrial Structure

The total productivity achieved by human beings determines social conditions, serving as the foundation for economic and social progress. The external manifestations of this progress are the development of science and technology and the transformation of industrial systems. As the main driving force behind the high-quality development of China’s economy, new quality productivity has profoundly influenced the adjustment and evolution of China’s industrial structure.

New quality productivity can accelerate industrial rationalization by enhancing the efficiency of production factors and optimizing resource allocation. Traditionally, labor and capital have been the fundamental factors of production driving economic and social development. However, with the advancement of science and technology, knowledge and technological factors have become increasingly significant. New quality productivity encompasses both primitive production factors and these advanced elements, representing a deepening of the traditional concept of productivity. The integration of knowledge and technology with traditional production factors can create new combinations and structures, leveraging the strengths of knowledge and technology to improve the productivity of labor and capital [30]. This integration provides enterprises with more efficient production drivers and helps them identify their strengths and weaknesses, enabling them to adjust their internal product structures and future development directions, thereby expediting the optimization of industrial structures. Moreover, knowledge and technology typically exhibit positive spillover effects [31], and their dissemination is not constrained by geographical conditions. The diffusion of knowledge and technology enhances the mobility of factor resources and reduces information asymmetry [32]. This optimization in the allocation and utilization efficiency of factor resources contributes to correcting irrational industrial structures. Based on the above analysis, we propose Hypothesis 1:

H1: 

New quality productivity can improve the irrational situation of industrial structure and speed up the process of industrial structure change.

New quality productivity can promote industrial transformation and upgrading through the upstream and downstream linkages within the industrial chain and the development of high-technology industries. On the one hand, the linkage effect enhances resource utilization efficiency. The development of new quality productivity enables upstream enterprises to produce intermediate products with higher technological content and environmental sustainability. When downstream enterprises utilize these advanced intermediate products, they can reduce energy consumption and pollution emissions during subsequent processing and manufacturing, facilitating their green transformation and upgrading [33]. On the other hand, new quality productivity can drive the growth of high-tech industries, such as the pharmaceutical, aerospace, and electronic communication industries in China. These industries are technology-intensive and generate added value, not through labor accumulation but through innovation and advanced production processes. New quality productivity provides the core driving forces for these high-tech industries: innovation and production efficiency. High-technology industries, in turn, strengthen their interactive association and integration with productive service industries, significantly contributing to industrial upgrading [34]. Therefore, new quality productivity plays a pivotal role in promoting the upgrading of industrial structures. Therefore, we propose the corresponding Hypothesis 2:

H2: 

The new quality productivity can promote the upgrading of industries and thus promote the change of industrial structure.

3.3. Analysis of the Mechanism of New Quality Productivity, Environmental Regulation and Industrial Structure

Environmental regulation is a crucial means of government intervention designed to reduce environmental pollution and resource consumption caused by enterprises or individuals. Government departments typically set environmental regulations of varying intensity based on local development goals and the actual performance of enterprises, thereby imparting regional attributes to the intensity of these regulations. The external effects of environmental regulation are generally understood through two primary perspectives: the crowding-out effect, based on the compliance cost hypothesis, and the innovation compensation effect, based on the Porter hypothesis [35,36]. These effects can influence new quality productivity and industrial structure through these distinct pathways.

The “compliance cost hypothesis” posits that environmental regulations increase investment costs for enterprises. Businesses must not only control pollution at the end of the production process but also invest in the research and development of new technologies and processes to mitigate the environmental impact from the production’s onset. Consequently, environmental regulations can exert a “crowding out effect” on R&D and innovation. Additionally, small- and medium-sized enterprises that cannot withstand the constraints of environmental regulations may opt to relocate. Such industrial relocation can disrupt the balance of the original industrial structure, and the reduction in competition may indirectly affect the innovation and competitive vitality of local enterprises. Since innovation is a core component of new quality productivity, environmental regulations may slow its development through the crowding out effect, thereby impacting the evolution of industrial structure. Based on the above analysis, we propose Hypothesis 3:

H3: 

Environmental regulation plays a negative moderating effect between new quality productivity and the rationalization of industries through the “compliance cost effect”.

From the perspective of the “innovation compensation effect”, although environmental regulations increase the financial burden on enterprises and may slow the development of new quality productivity in the short term, they ultimately compel enterprises to undertake technological reforms and innovations [37]. In the long run, this can confer competitive advantages and generate greater profits for enterprises. These additional profits can offset the increased costs, leading to long-term positive effects. Environmental regulations can enhance new quality productivity by driving the development of the digital economy and innovative technologies, which in turn promote industrial upgrading. The relocation of high-polluting enterprises can directly reduce local industrial pollution levels, thereby improving the green aspect of new quality productivity. This promotes the green transformation of industries, boosts enthusiasm for the development of the local service sector, and effectively supports industrial upgrading. Therefore, we propose Hypothesis 4:

H4: 

Environmental regulation plays a positive moderating effect role between the new quality productivity and the upgrading of industries through the “innovation compensation effect”. The mechanism of the role of new quality productivity, environmental regulation, and industrial structure is shown in the figure below.

Based on the above analysis and hypothesis, we got the mechanism analysis diagram of new quality productivity, environmental regulation and industrial structure, as shown in Figure 1, and there was a close relationship among the three.

4. Methods and Data

4.1. Construction and Measurement of New Quality Productivity Index System

4.1.1. Construction of New Quality Productivity Index System

(1)

Construction of the index system

Building on a deep understanding of the connotation of new quality productivity, this paper constructs an index system for new quality productivity based on three key characteristics: innovation, greenness, and productivity. The system is structured around three dimensions: innovation driving force, green driving force, and production driving force. The specific ideas for selecting indicators are as follows.

Indicators for the innovation driving force primarily measure the level of innovation inputs and the effectiveness of innovation outputs within a given economy. Regarding innovation input, aside from the research funds already invested and the existing research talents in the market, the enrollment of students in colleges and universities is considered advanced human capital. This group represents the future generation of innovative talents, and their proportion in the workforce reflects the optimization of the labor market in the region [38]. Regarding innovation output, the number of domestic patent applications granted serves as an indicator of the dynamism of China’s innovation output. Additionally, high-technology output is used to characterize the overall development level of innovative industries. The higher the innovation capacity of the employed population, the greater the marginal contribution of total employment to socio-economic development. Labor productivity is the corresponding indicator for measuring this marginal contribution.

The indicators in the green driving force typically measure the depletion of ecological resources by human social activities and the level of terminal pollution control adopted to promote coordinated economic, social, and environmental development. The pressure on land resources arises from population growth, while the pressure on energy resources primarily stems from the intensity of energy consumption on the supply side. Atmospheric environmental pollutants are mainly measured by SO₂ emissions. Sustainable economic development necessitates the prevention of environmental pollution at the source, such as increasing forest coverage, investing in urban environmental construction, and reducing greenhouse gas emissions. For existing pollution, it is essential to use current treatment methods to purify and manage pollutants, including the treatment capacity of domestic waste, solid waste, and sewage.

Indicators in the productivity driving force measure the level of production factors that achieve economic and social progress. Traditional infrastructure reflects the economic development degree of an economy or region, while transportation resources indicate the frequency of material exchange and information symmetry between the region and the outside world. Education and medical resources directly reflect the living standards of regional residents. In the information age, the digital economy has significantly contributed to promoting regional economic development, representing new productivity content endowed by the times. Drawing on Zhao Tao [39], this paper selects indicators such as internet-related output, employment levels in the digital economy, internet penetration rate, and digital inclusive finance to reflect the digital economy’s impact.

(2)

Sample Selection and Data Description

This paper utilized panel data from 30 provincial-level administrative regions in China (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2011 to 2021. The relevant data for research funding, research talents, and patent output were sourced from the China Science and Technology Statistical Yearbook across various years. Data for labor market sophistication, labor productivity, population density, internet-related output, and employment levels in the digital economy were obtained from the China Population and Employment Statistical Yearbook. High-technology output data were derived from the China High-Technology Industry Statistical Yearbook. Data related to energy consumption intensity, air resources, domestic waste treatment capacity, solid waste treatment capacity, and sewage treatment capacity were sourced from the China Environmental Statistical Yearbook. Greening rate and urban environmental protection data were sourced from the China Urban Construction Statistical Yearbook. Carbon emissions data were obtained from the CEADs (Carbon Emission Accounts and Datasets). Digital inclusive finance was measured using the Digital Inclusive Finance Index, jointly compiled by the Digital Finance Research Center of Peking University and Ant Financial Services Group [40]. The total telecommunication business in 2021 was calculated based on growth rates. Data for other secondary indicators are taken from the China Statistical Yearbook over the years. The final constructed indicator system for new quality productivity, and a measurement of each indicator was shown in

Table 1. Description of the new quality productivity index system and measurement method.

Indicator Dimension	Primary Indicator	Secondary Indicator	Measurement Method	Direction of Effect
Innovation driving force	Innovation input	Scientific research fund	Internal expenditure on R&D/GDP	+
		Scientific manpower	R&D full-time personnel	+
		The optimization of the labor market	Number of students in colleges and universities/total employment population	+
	Innovation output	Patent output	Number of domestic patent applications granted	+
		High-tech industry output	High-tech industry new product sales revenue/GDP	+
		Labor productivity	GDP/total employment population	+
Green driving force	Resource consumption	Energy intensity	Energy consumption/GDP	−
		Land resources	Population density	−
		Atmospheric resources	SO2 emissions	−
	Green and environmental protection	Greening rate	Forest coverage rate	+
		Urban environmental protection	Investment in the urban environment	+
		Greenhouse effect	CO2 emissions	−
	Terminal pollution control	Domestic garbage disposal capacity	Domestic garbage harmless treatment rate	+
		Solid waste treatment capacity	Common industrial solid wastes utilized/common industrial solid wastes generated	+
		Wastewater Treatment capacity	Daily treatment capacity of Wastewater	+
productivity driving force	Traditional infrastructure	Transportation resources	(Highway Miles + Railroad Miles)/Jurisdictional Area	+
		Educational resources	Number of colleges and universities per 10,000 people	+
		Medical resources	Number of beds in medical and health institutions	+
	Digital economy development	Internet-related output	Total telecommunications business per capita	+
		Digital economy employment level	Number of employees in the information transmission, software, and information technology services industry/employed population in urban organizations	+
		Internet Penetration Rate	The number of Internet users per 100 people	+
		Digital Inclusive Finance	Digital Inclusive Finance index	+

.

4.1.2. Measurement of New Quality Productivity Index System

The current measurement methods for indicator systems primarily include the entropy weight method, the comprehensive index method, and principal component analysis (PCA). The comprehensive index method involves expert scoring to assign weights to each evaluation index. These weights are then used to linearly combine the data in the indicator system, with the development level assessed based on the aggregated results. However, this method is susceptible to subjectivity in weight assignment and may not objectively reflect the development level of new quality productive forces in each region. The PCA method provides a degree of objectivity in weight assignment and simplifies various indicators in the evaluation system through dimensionality reduction by assigning weights to indicators based on the characteristics of the original data. It retains only the indicators with higher weights, which does not encompass all the indicator data in the index system we constructed. However, the entropy weight method can assign weights to all the indicators in the index system, and some relatively unimportant indicators can be given a smaller weight without losing the indicator; therefore, the entropy weight method is comparatively more comprehensive and scientific. Nevertheless, when factor loadings have negative signs, the meanings of the principal components can become ambiguous. In addition, the premise of principal component analysis is that the data of the investigated objects are independent of each other during the study period. The index data of 30 provinces in China adopted in this paper have a longitudinal correlation in time and horizontal influence in space, so they cannot be guaranteed to be independent data. The entropy weight method assigns weights based on the dispersion characteristics and information content of the original data within the evaluation indexes. This approach is more objective, reflecting changes in weights over time, and is one of the most widely used measurement methods currently.

Hwang and Yoon proposed the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution) in 1981 to address multiple decision-making problems [41]. This method involves calculating the positive and negative ideal solutions for an indicator system. By measuring the Euclidean distance between each indicator and these ideal solutions, the degree of fit to the ideal solution can be compared, allowing the evaluation objects to be ranked in order of merit. A crucial step in using the TOPSIS method is determining the weights of the indicators. The entropy weight method effectively eliminates the influence of subjective factors in the weight assignment process. Therefore, this paper employs the entropy weight-TOPSIS method to measure the new quality productivity index system accordingly.

In addition, due to the variability of the indicators in terms of attributes, dimensions, and units, this paper adopts the range method to standardize the secondary indicators. For the secondary indicators with positive and negative effects, the following formula was adopted for standardization:

$$X_{ij}={\displaystyle \frac{x_{ij}-\min [x_{ij}]}{\max [x_{ij}]-\min [x_{ij}]}}$$

(1)

$$X_{ij}={\displaystyle \frac{\max [x_{ij}]-x_{ij}}{\max [x_{ij}]-\min [x_{ij}]}}$$

(2)

where $x_{ij}$ is the original data of the $j$ ($j=1,2,3,\dots ,n$) indicator of the $i$ ($i=1,2,3,\dots ,m$) decision unit, $\max [x_{ij}]$ and $\min [x_{ij}]$ are the maximum and minimum values in the raw data, respectively, and $X_{ij}$ is the normalized data.

Calculation method: Based on the standardization of the index system mentioned above, in the new quality productivity measurement index system with $m$ research objects and $n$ evaluation indicators, the information entropy of the $j_{th}$ indicator was as follows:

$$E_j=-k{\displaystyle \sum _{i=1}^m{V}_{ij}\ln V_{ij}}$$

(3)

In Formula (3), $k={\displaystyle \frac{1}{\ln m}}$, $V_{ij}=X_{ij}/{\displaystyle \sum _{i=1}^m{X}_{ij}}$.

Based on the information entropy contained in each indicator, the weight $W_j$ of each indicator in each year was calculated, as shown in Equation (4):

$$W_j=(1-E_j)/{\displaystyle \sum _{j=1}^n\left(1-E_j\right)},(0\le W_j\le 1)$$

(4)

The weighted evaluation matrix was calculated as $R={(r_{ij})}_{m\times n}$, where $r_{ij}=W_j\cdot X_{ij}$. The positive and negative ideal solutions $r_j^{+}$ and $r_j^{-}$ in the evaluation matrix were determined as follows:

$$r_j^{+}=\max \left(r_{1j},r_{2j},\dots ,r_{nj}\right)$$

(5)

$$r_j^{-}=\min \left(r_{1j},r_{2j},\dots ,r_{nj}\right)$$

(6)

The Euclidean distance between each indicator in the evaluation matrix to the positive and negative ideal solutions was calculated as follows:

$$d^{+}=\sqrt{{\displaystyle \sum _{j=1}^n{(r_j^{+}-r_{ij})}^2}}$$

(7)

$$d^{-}=\sqrt{{\displaystyle \sum _{j=1}^n{(r_j^{-}-r_{ij})}^2}}$$

(8)

Finally, the fit degree $D$ between each evaluation object and the ideal solution was calculated according to the obtained Euclidean distance, as shown in Formula (9), where $0\le D\le 1$; the larger $D$ is, the closer it represents to the optimal level.

$$D={\displaystyle \frac{d^{-}}{d^{+}+d^{-}}}$$

(9)

4.2. Variable Selection Specification

To explore the relationship between new quality productivity, industrial structure change, and environmental regulation, this paper selected relevant data from 30 provincial administrative regions in China from 2011 to 2021 for empirical research. Additionally, the relationship between the three dimensions of new quality productivity and industrial structure was analyzed empirically.

Table 2. Statistical description of variables.

(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Types	Variables	Indicators	Obs	Mean	SD	Min	Max
Explained variables	rat	Rationalization of industries	330	0.1524	0.0937	0.0082	0.4515
	upg	Ppgrading of industries	330	1.3415	0.7320	0.5271	5.2440
Core explanatory variables	nqp	New quality productivity	330	0.3883	0.0775	0.2288	0.6418
	ino	Innovation driving force	330	0.2406	0.1254	0.0275	0.6757
	gre	Green Driving Force	330	0.4776	0.0814	0.2977	0.7161
	pro	Production Driving Force	330	0.3359	0.0993	0.1133	0.6121
Moderating variable	evi	Environmental Regulation	330	11.3379	12.0869	0.0860	110.3389
Control variables	cos	Consumption level	330	0.3801	0.0683	0.2220	0.5384
	fdi	Foreign investment	330	0.8381	0.8092	0.0003	3.5760
	tra	Foreign trade	330	0.2653	0.2908	0.0076	1.5482
	gov	Government intervention	330	0.2487	0.1025	0.1066	0.6430
	tax	Tax burden level	330	0.0819	0.0293	0.0443	0.1997

presents the relevant variables and their statistical characteristics.

Explained variables: In order to reflect the status of China’s industrial structure adjustment, this paper adopts industrial rationalization (rat) and industrial upgrading (upg) as the explanatory variables. Drawing on the article of Gan Chunhui [42], this paper redefined the Thiel index for the measurement of industrial structure rationalization, with the formula of $ra{t}_t={\displaystyle \sum _{i=1}^3\left(\frac{Y_{it}}{Y_t}\right)}\ast \ln \left({\displaystyle \frac{Y_{it}}{L_{it}}}/{\displaystyle \frac{Y_t}{L_t}}\right)$, where $i$ represents the type of industry, $t$ is the year, $Y$ is the output value, and $L$ is the number of employees. The higher the index, the greater the deviation of the industrial structure of the economy. Conversely, a lower index indicates that the industrial structure tends towards equilibrium. In measuring industrial upgrading, the ratio of the output value of the tertiary industry to the output value of the secondary industry is used as the measurement index of industrial upgrading. The formula is as follows: $up{g}_t=Y_{3t}/Y_{2t}$, where $t$ is the year, $Y_{3t}$ is the value added of the tertiary sector in year $t$, and $Y_{2t}$ is the value added of the secondary sector in year $t$. The higher the index, the more the industrial structure of the economy is upgraded during the period of study, which is characterized by a “service-oriented economy” [43]. As shown in Table 2, the average value of industrial rationalization was 0.1524, with a maximum value of 0.4515. This indicates that there are still significant deviations in the industrial structure across different regions in China during the investigation period, suggesting that the industrial structure is not entirely rational. The average value of industrial upgrading was 1.3415, with a minimum value of 0.5271 and a maximum value of 5.2440. This suggests that while China’s industrial structure is generally undergoing an upgrade, some regions exhibit signs of industrial degradation, highlighting a considerable disparity in the upgrading trends among regions. The relevant data were sourced from the China Statistical Yearbook and the China Population and Employment Statistical Yearbook over the years.

Core explanatory variables: This paper constructs an index system for measuring new quality productivity by selecting corresponding indicators from the three dimensions of innovation driving force, green driving force, and production driving force. The entropy weight-TOPSIS method was used to measure new quality productivity and these three dimensions. Based on the measurement results, new quality productivity (nqp), innovation driving force (ino), green driving force (gre), and production driving force (pro) were identified as the core explanatory variables of this study. Table 2 shows that the average value of China’s new quality productivity was 0.3883, with a difference of 0.4130 between the maximum and minimum values, indicating an unbalanced development level of new quality productivity among Chinese provinces. Additionally, the difference between the maximum and minimum values of the innovation driving force was more pronounced than those of the green driving force and production driving force, suggesting that the level of innovation driving force varies more significantly among Chinese provinces.

Moderating variable: This paper uses environmental regulation as a moderating variable between new quality productivity and industrial structure. Since environmental regulation may initially increase the cost of pollution treatment for enterprises [44] and given that environmental protection-related policies and the frequency of relevant terms in government documents do not accurately reflect the strength of policy implementation, this paper calculates the ratio of industrial pollution treatment investment to GDP. This ratio reflects both the initial costs of environmental regulation and the intensity of local environmental regulation. The relevant data are sourced from the China Environmental Statistics Yearbook over various years.

Control variables. This paper uses consumption level (cos), foreign investment (fdi), foreign trade (tra), government intervention (gov), and tax burden level (tax) as control variables for regression estimation. The consumption level was measured by the ratio of retail sales of consumer goods to GDP, and foreign investment was measured by the total foreign investment. Foreign trade, government intervention, and tax burden level were measured by the ratio of the amount of goods imported and exported to GDP, the ratio of local government general public budget expenditures to GDP, and the ratio of tax revenues to GDP, respectively. The relevant data were obtained from the China Statistical Yearbook over various years.

4.3. Econometric Modeling

To verify the effect of new quality productivity and its various dimensions on industrial structure, as well as the moderating role of environmental regulation, this paper constructs a dynamic panel econometric model. This model aims to examine the relationship between these three factors. The formula is presented below:

$$ra{t}_{i,t}=\alpha +{\beta }_1{X}_{i,t}+{\beta }_2{Z}_{i,t}+{\beta }_{3}ev{i}_{i,t}+{\mu }_{i,t}+{\epsilon }_{i,t}$$

(10)

$$up{g}_{i,t}=\alpha +{\beta }_1{X}_{i,t}+{\beta }_2{Z}_{i,t}+{\beta }_{3}ev{i}_{i,t}+{\mu }_{i,t}+{\epsilon }_{i,t}$$

(11)

$$ra{t}_{i,t}=\alpha +{\beta }_1{X}_{i,t}+{\beta }_{2}ev{i}_{i,t}+{\beta }_{3}evnq{p}_{i,t}+{\beta }_4{Z}_{i,t}+{\mu }_{i,t}+{\epsilon }_{i,t}$$

(12)

$$up{g}_{i,t}=\alpha +{\beta }_1{X}_{i,t}+{\beta }_{2}ev{i}_{i,t}+{\beta }_{3}evnq{p}_{i,t}+{\beta }_4{Z}_{i,t}+{\mu }_{i,t}+{\epsilon }_{i,t}$$

(13)

In the above model, $\alpha$ is the constant term, ${\beta }_i$ is the regression coefficient of each variable, $i$ is the sample individual, $t$ denotes the time cross-section, $rat$ is the rationalization of industrial, and $upg$ is the upgrading of industrial. $X$ is the core explanatory variable, which is measured by new quality productivity, innovation driving force, green driving force, and production driving force in the regression model. $Z$ is the control variable, which denotes the consumption level, foreign investment, foreign trade, government intervention, and tax burden level, respectively, and $evi$ is environmental regulation. In Formulas (12) and (13), $evnqp$ is the interaction term between environmental regulation and new quality productivity, and if its estimated coefficient ${\beta }_3$ is significant, it indicates that environmental regulation can influence the relationship between new quality productivity and industrial structure and play a moderating role. ${\mu }_{i,t}$ is the individual fixed effect, and ${\epsilon }_{i,t}$ is the random disturbance term.

5. Results and Discussion

5.1. Analysis of the Results of the Neoplasm Productivity Measurements

5.1.1. Overall Analysis of New Quality Productivity Measurement Results

To measure the development level of China’s new quality productivity, this paper utilized the indicator system and measurement method constructed in the previous section. Based on the three dimensions of innovativeness, greenness, and productivity, along with 22 secondary indicators, the entropy weight-TOPSIS method was employed to assess the development level of new quality productivity across 30 provinces from 2011 to 2021. China is divided into four economic regions: eastern, central, western, and northeastern. The measurement results were then analyzed. The results of measuring the level of new quality productivity in each province are presented in

Table 3. Measurement results of new quality productivity during 2011–2021.

Province	District	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Beijing	1	0.4444	0.4734	0.4946	0.5188	0.5202	0.5322	0.5375	0.5503	0.5648	0.5556	0.5691
Tianjin	1	0.3986	0.4093	0.4180	0.4232	0.4308	0.4326	0.4336	0.4416	0.4498	0.4801	0.4988
Hebei	1	0.2549	0.2585	0.2683	0.2781	0.2951	0.3209	0.3427	0.3515	0.3732	0.4070	0.4226
Shanxi	2	0.2288	0.2566	0.2579	0.2629	0.2650	0.2830	0.3032	0.3210	0.3352	0.3520	0.3640
Inner Mongolia	3	0.2671	0.2722	0.2926	0.2989	0.3021	0.3140	0.3257	0.3272	0.3360	0.3507	0.3671
Liaoning	4	0.3145	0.3329	0.3422	0.3408	0.3422	0.3694	0.3813	0.3967	0.4033	0.4235	0.4448
Jilin	4	0.3079	0.3199	0.3416	0.3452	0.3477	0.3651	0.3735	0.3930	0.4089	0.4234	0.4378
Heilongjiang	4	0.2977	0.3159	0.3334	0.3347	0.3369	0.3499	0.3476	0.3648	0.3770	0.3924	0.4047
Shanghai	1	0.3857	0.3986	0.4140	0.4167	0.4249	0.4248	0.4400	0.4640	0.4789	0.4984	0.5115
Jiangsu	1	0.4024	0.4282	0.4501	0.4583	0.4657	0.4690	0.4806	0.5137	0.5268	0.5684	0.5825
Zhejiang	1	0.4079	0.4358	0.4549	0.4640	0.4798	0.4867	0.4986	0.5310	0.5498	0.5809	0.5933
Anhui	2	0.3357	0.3524	0.3697	0.3755	0.3898	0.3938	0.4130	0.4275	0.4405	0.4755	0.4960
Fujian	1	0.3695	0.3960	0.4092	0.4125	0.4161	0.4201	0.4298	0.4524	0.4600	0.4802	0.4991
Jiangxi	2	0.3225	0.3372	0.3421	0.3508	0.3599	0.3629	0.3826	0.4101	0.4397	0.4517	0.4748
Shandong	1	0.3516	0.3715	0.3969	0.4028	0.4101	0.4257	0.4480	0.4649	0.4671	0.4991	0.5218
Henan	2	0.2672	0.2878	0.3101	0.3201	0.3357	0.3564	0.3965	0.4151	0.4343	0.4706	0.4960
Hubei	2	0.3468	0.3598	0.3745	0.3890	0.3938	0.4189	0.4255	0.4471	0.4679	0.4819	0.5010
Hunan	2	0.3271	0.3415	0.3545	0.3651	0.3785	0.3874	0.4052	0.4293	0.4455	0.4715	0.4839
Guangdong	1	0.4061	0.4235	0.4427	0.4566	0.4736	0.4833	0.5078	0.5572	0.5811	0.6211	0.6418
Guangxi	3	0.3288	0.3427	0.3559	0.3600	0.3678	0.3731	0.3828	0.3970	0.4104	0.4350	0.4380
Hainan	1	0.3342	0.3537	0.3647	0.3641	0.3744	0.3779	0.3794	0.3930	0.4093	0.4227	0.4352
Chongqing	3	0.3407	0.3529	0.3730	0.3826	0.3974	0.4049	0.4173	0.4380	0.4534	0.4785	0.4937
Sichuan	3	0.3007	0.3180	0.3357	0.3493	0.3768	0.3868	0.4010	0.4339	0.4480	0.4732	0.4956
Guizhou	3	0.2420	0.2617	0.2746	0.3087	0.3243	0.3419	0.3545	0.3802	0.4015	0.4279	0.4437
Yunnan	3	0.2879	0.2962	0.3254	0.3338	0.3460	0.3524	0.3673	0.3862	0.4057	0.4233	0.4386
Shaanxi	3	0.2992	0.3165	0.3341	0.3460	0.3648	0.3852	0.3814	0.3945	0.4016	0.4305	0.4244
Gansu	3	0.2516	0.2611	0.2774	0.2828	0.2903	0.3074	0.3171	0.3379	0.3667	0.3719	0.3814
Qinghai	3	0.2742	0.2779	0.2810	0.2927	0.2933	0.3047	0.3165	0.3338	0.3461	0.3524	0.3677
Ningxia	3	0.2873	0.2996	0.3114	0.3257	0.3194	0.3209	0.3294	0.3457	0.3371	0.3555	0.3713
Xinjiang	3	0.2372	0.2455	0.2507	0.2524	0.2779	0.2867	0.3037	0.3165	0.3178	0.3406	0.3494

. Figure 2 illustrates the development trend of new quality productivity in the four major regions of China from 2011 to 2021. Figure 3 shows the line chart of the average score of new quality productivity and the average annual growth rate for each province in China. The kernel density curve of the new quality productivity level is depicted in Figure 4.

(1)

Analysis of new quality productivity at the national and sub-regional levels

As shown in Table 3 and Figure 2, China’s new quality productivity level exhibited an overall upward trend from 2011 to 2021, with higher scores mainly concentrated in the eastern region. On average, the eastern region outperformed other regions, with an average new quality productivity value of 0.4468 during the investigation period. In comparison, the central and northeastern regions had average values of 0.3791 and 0.3640, respectively, while the western region had the lowest average value of 0.3468. Notably, in the northeastern region, the growth trend of new quality productivity slowed between 2013 and 2015. Since 2014, the central region’s growth rate accelerated, gradually pulling ahead of the northeastern region. This disparity may be attributed to factors such as brain drain, technological backwardness, and the rigidity of state-owned enterprise institutional mechanisms in the northeastern region in recent years. Consequently, in the latter part of the investigation period, the development strength of new quality productivity in the northeastern region fell below the national average. In contrast, the release of the “Opinions on Vigorously Implementing the Promotion of the Rise of Central China” by the State Council in 2012 provided clear direction for the development of advanced technology and key areas in the central region, creating favorable conditions for the enhancement of new quality productivity.

Figure 3 visually presents the average score and average annual growth rate of new quality productivity across various provinces in China during 2011–2021. According to Table 3, the average value of new quality productivity in China during the investigation period was 0.3883. Thirteen provinces, accounting for 43.33% of all provinces surveyed, had scores higher than this average. Among these, eight provinces were located in eastern China, with Beijing and Guangdong achieving scores of 0.5237 and 0.5086, respectively. In the central region, Hubei, Anhui, and Hunan had scores above the national average, while Chongqing and Sichuan represented the western provinces in this group. The bottom five provinces were Xinjiang, Shanxi, Qinghai, Gansu, and Inner Mongolia. Except for Shanxi, which belongs to the central region, the other four provinces were in the western region. Xinjiang had the lowest score, with an average new quality productivity value of 0.2889. This was a difference of 0.2348 from the highest score of 0.5237 held by Beijing. These findings indicate that the development level of new quality productivity is uneven across China’s provinces, with significant disparities.

In terms of the average annual growth rate, all provinces experienced positive growth during the investigation period, indicating an overall increase in the level of new quality productivity. However, the pace of development varied significantly across provinces. At the regional level, the central and western regions had average annual growth rates of 4.9102% and 4.2418%, respectively, both higher than the national average annual growth rate of 4.0912%. In contrast, the eastern and northeastern regions had lower growth rates, with averages of 3.6810% and 3.6279%, respectively. At the provincial level, the top five provinces with the fastest growth rates were Henan, Guizhou, Hebei, Sichuan, and Shanxi. Henan and Guizhou both had average annual growth rates exceeding 7%, while the other three provinces had growth rates above 5%. Conversely, Tianjin, Beijing, Ningxia, Hainan, and Shanghai had the lowest average annual growth rates, all below 3%. The relatively low growth rates in Beijing, Shanghai, and Tianjin may be attributed to their already high levels of new quality productivity at the beginning of the investigation period, resulting in less pronounced growth rates.

(2)

Analysis based on kernel density curve

To more comprehensively analyze the overall pattern of China’s new quality productivity and to understand the dynamic characteristics of its spatial distribution, this paper employed the Gaussian density function and kernel density estimation method to map the evolutionary trend of new quality productivity in China and its four regions.

Figure 4 presents the results of the kernel density estimation for new quality productivity across 30 provinces in China during the investigation period. Throughout this period, the kernel density curve for China’s new quality productivity shifted to the right, indicating a gradual increase in productivity levels, consistent with previous analyses. The kernel density curve’s center was initially between 0.3 and 0.4 and slowly moved to the right over time. Notably, the wave peak was higher than the average during the 2014–2016 period, suggesting a decrease in the spatial gap of new quality productivity levels among Chinese provinces compared to the early and later stages of the investigation. The number of wave peaks throughout the 2011 to 2021 period remained unimodal, indicating that the development of China’s new quality productivity did not become multipolar.

From the perspective of the four regions in China (the density diagram of the new product of the four major regions in China is not reflected in the text, and it is necessary to ask the author.), the kernel density curve centers in the eastern, central, western, and northeastern regions all showed varying degrees of rightward shifts during the investigation period. This indicates that the level of new quality productivity improved across all regions, though the extent of improvement differed slightly. Analyzing the wave crest morphology, the eastern region’s density curve shifted rightward the most significantly, with shorter wave crests observed in 2012–2013 and 2016–2019. This suggests that the spatial disparity of new quality productivity among provinces in the eastern region was more pronounced during these years. The central and northeastern regions displayed similar trends in wave crest changes, but the wave crest in the northeastern region was higher than in the central region, indicating a smaller spatial gap in the development levels of new quality productivity in the northeast. In the western region, the wave peak decreased in 2018–2019, suggesting that the spatial gap increased during this period before showing a downward trend. Regarding the number of wave peaks, the kernel density curves for all four regions remained unimodal throughout the investigation period, indicating that there was no multi-polarization phenomenon in the development of new quality productivity.

5.1.2. Analysis of New Quality Productivity Measurement Results in Different Dimensions

Table 4. New quality productivity and its dimension index ranking of 30 provinces in China during 2011–2021.

Province	New Quality Productivity Index	Ranking	Innovation Driving Force Index	Ranking	Green Driving Force Index	Ranking	Production Driving Force Score	Ranking
Beijign	0.5237	1	0.3726	5	0.6062	2	0.5203	1
Tianjin	0.4379	6	0.4242	4	0.4840	15	0.3752	8
Hebei	0.3248	25	0.1864	19	0.3838	27	0.3281	15
Shanxi	0.2936	29	0.1853	20	0.3384	30	0.3029	20
Inner Mongolia	0.3140	26	0.1642	23	0.3650	28	0.3307	14
Liaoning	0.3719	17	0.2491	12	0.4429	20	0.3544	12
Jilin	0.3694	20	0.1994	17	0.4792	16	0.2935	24
Heilongjiang	0.3505	22	0.1950	18	0.4332	22	0.3213	17
Shanghai	0.4416	5	0.3687	6	0.5082	11	0.3847	5
Jiangsu	0.4860	4	0.4965	1	0.5473	5	0.3767	7
Zhejiang	0.4984	3	0.4508	3	0.6169	1	0.3580	11
Anhui	0.4063	11	0.2400	13	0.5314	8	0.3189	18
Fujian	0.4314	8	0.2777	8	0.5570	4	0.3148	19
Jiangxi	0.3849	14	0.2247	14	0.4951	13	0.2943	23
Shandong	0.4327	7	0.3463	7	0.4935	14	0.4016	4
Henan	0.3718	18	0.2191	15	0.4332	23	0.3815	6
Hubei	0.4188	9	0.2530	10	0.5261	9	0.3750	9
Hunan	0.3990	12	0.2141	16	0.5063	12	0.3595	10
Guangdong	0.5086	2	0.4767	2	0.6012	3	0.4063	3
Guangxi	0.3810	16	0.1316	26	0.5352	6	0.2580	28
Hainan	0.3826	15	0.1379	25	0.5247	10	0.2497	29
Chongqing	0.4120	10	0.2634	9	0.5336	7	0.2977	21
Sichuan	0.3926	13	0.1765	21	0.4679	18	0.4177	2
Guizhou	0.3419	23	0.1274	27	0.4496	19	0.2921	25
Yunnan	0.3603	21	0.0877	29	0.4727	17	0.3270	16
Shaanxi	0.3707	19	0.2519	11	0.4384	21	0.3474	13
Gansu	0.3132	27	0.1484	24	0.3980	26	0.2623	27
Qinghai	0.3128	28	0.0816	30	0.4046	24	0.2485	30
Ningxia	0.3276	24	0.1696	22	0.4012	25	0.2814	26
Xinjiang	0.2889	30	0.1010	28	0.3523	29	0.2961	22

presents the average scores and rankings of new quality productivity level, innovation driving force index, green driving force index, and production driving force index for 30 provinces in China during the period 2011–2021. Figure 5 illustrates the evolution trend of the total score of new quality productivity and each dimension’s index over the years.

The innovation driving force primarily reflects the innovation capabilities and awareness within an economy or region. During the inspection period, the innovation driving force index exhibited a continuous upward trend, with a notable acceleration in growth since 2017. At the provincial level, Jiangsu, Guangdong, Zhejiang, Tianjin, and Beijing were the top five provinces in terms of innovation driving force, while Qinghai, Yunnan, Xinjiang, Guizhou, and Guangxi were the bottom five, all located in the western region. This disparity may be attributed to the relatively low investment in R&D funding in the western region and the challenging transportation infrastructure due to geographical factors, which hinder the attraction of talent from other regions. Additionally, these provinces have a smaller pool of research talent and less advanced educational facilities compared to other regions, resulting in less developed labor markets and subsequently lagging innovation driving force.

The higher the green driving force index, the more harmonious the relationship between the economy and the environment in the region. Throughout the inspection period, China’s green driving force index demonstrated a slow but steady increase. At the provincial level, Zhejiang, Beijing, Guangdong, Fujian, and Jiangsu ranked as the top five provinces. A comparative analysis revealed that Fujian’s high ranking in green driving force significantly contributed to its new quality productivity. This was mainly due to the province’s high greening rate and low carbon dioxide emissions. Conversely, Shanxi, Xinjiang, Inner Mongolia, Hebei, and Gansu ranked lowest in green driving force. Inner Mongolia, Shanxi, and Hebei, in particular, had high energy consumption intensity due to their rich mineral resources, resulting in low scores for terminal pollution control indicators.

The production driving force is a key component of enhancing national economic productivity. During the inspection period, the production driving force index of all provinces in China showed continuous improvement, with an average annual growth rate of 10.98%. This growth rate was significantly higher than the average annual growth rate of new quality productivity, likely due to the continuous improvement of China’s digital infrastructure in the new era. At the provincial level, Beijing, Sichuan, Guangdong, Shandong, and Shanghai ranked among the top in production driving force. Notably, Sichuan is the only province in the western region to be in this top group. Specific indicators contributing to Sichuan’s high score include its transportation resources, digital economy employment level, and internet penetration rate. The other four provinces, all located in the eastern region, benefit from robust traditional infrastructure and promising digital economy development prospects. The provinces with the lowest production driving force scores were Qinghai, Hainan, Guangxi, Gansu, and Ningxia. Among these, only Hainan is in the eastern region. Hainan’s lower ranking may be attributed to its low per capita output value and the geographic limitations of Hainan Island, which results in relatively scarce railway and highway resources. Consequently, Hainan’s traditional productivity levels lag behind those of other regions.

5.2. Benchmark Regression and Robustness Test Results

Table 5. Benchmark regression results of new quality productivity on the rationalization of industries.

	(1)	(2)	(3)	(4)
	Rat	Ino	Gre	Pro
nqp	−0.6228 ***
	(0.0884)
ino		−0.4507 ***
		(0.0706)
gre			−0.7016 ***
			(0.0929)
pro				−0.4172 ***
				(0.0518)
cos	−0.2001 **	−0.2640 **	−0.1745 **	−0.1560 *
	(0.0868)	(0.0979)	(0.0803)	(0.0936)
fdi	−0.0031	−0.0012	−0.0096	−0.0073
	(0.0079)	(0.0085)	(0.0077)	(0.0071)
tra	−0.0830 ***	−0.0724 ***	−0.0888 ***	−0.0882 ***
	(0.0272)	(0.0241)	(0.0268)	(0.0331)
gov	−0.3002	−0.4197 *	−0.2931 *	−0.1048
	(0.2139)	(0.2130)	(0.1730)	(0.1527)
tax	0.7976 **	1.0145 **	0.9029 **	0.2676
	(0.3363)	(0.3760)	(0.3740)	(0.2652)
evi	0.0003	0.0004	0.0008**	0.0002
	(0.0003)	(0.0004)	(0.0003)	(0.0003)
_cons	0.5009 ***	0.3984 ***	0.5756 ***	0.3836 ***
	(0.0530)	(0.0452)	(0.0614)	(0.0389)
Hausman	14.35	14.70	−148.48	6.23
p-Value	0.0259	0.0401	-	0.5126
R2	0.650	0.580	0.586	0.665
N	330	330	330	330

Note: Standard errors in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.

and

Table 6. Benchmark regression results of new quality productivity on the upgrading of industries.

	(1)	(2)	(3)	(4)
	Upg	Ino	Gre	Pro
nqp	2.5179 ***
	(0.4119)
ino		1.4677 ***
		(0.4265)
gre			2.5862 ***
			(0.4729)
pro				1.7481 ***
				(0.2264)
cos	0.4155	0.7269 **	0.4388	0.2349
	(0.3237)	(0.3295)	(0.3400)	(0.3142)
fdi	−0.0066	−0.0027	0.0080	−0.0029
	(0.0493)	(0.0674)	(0.0546)	(0.0464)
tra	−0.8545 ***	−0.9306 ***	−0.8993 ***	−0.8746 ***
	(0.2848)	(0.3059)	(0.2951)	(0.2339)
gov	3.7884 ***	4.4497 ***	4.3143 ***	3.1945 ***
	(0.8260)	(0.786)	(0.8920)	(0.7256)
tax	−5.8453 **	−7.5855 ***	−7.8955 ***	−4.0714 **
	(2.1639)	(2.3196)	(2.1473)	(1.8121)
evi	0.0007	−0.0001	−0.0008	0.0018 **
	(0.0009)	(0.0014)	(0.0011)	(0.0008)
_cons	−0.0336	0.4769 ***	−0.2454	0.4177 ***
	(0.1572)	(0.1430)	(0.2065)	(0.1196)
Hausman	294.44	178.05	459.34	3428.56
p-Value	0.0000	0.0000	0.0000	0.0000
R2	0.740	0.669	0.705	0.776
N	330	330	330	330

Note: Standard errors in parentheses. *** and ** denote significance at the 1% and 5% levels, respectively.

present the benchmark regression results of new quality productivity on the rationalization and upgrading of industries, respectively. To explore the impact of the three dimensions of new quality productivity on industrial structure, the benchmark regression results of the innovation driving force, green driving force, and production driving force on the rationalization and upgrading of industries are reported in columns (2)–(4) of Table 5. These results enhance the robustness of the econometric model between new quality productivity (NQP) and industrial structure. The regression results further reinforce the robustness of the relationship between new quality productivity and industrial structure.

In Table 5, columns (1) and (2) passed the Hausman test at the 5% significance level, indicating that the fixed effects regression model was appropriate for these columns. Conversely, columns (3) and (4) did not pass the Hausman test, so a random effects regression model was used for these columns. The regression coefficient for new quality productivity in column (1) was −0.6228, which was significant at the 1% level. This indicates that new quality productivity has a negative effect on the rationalization of industries. However, since the method for measuring the rationalization of industries is the improved Thiel index—an inverse index where a higher value indicates a larger gap between industrial structures—the negative regression coefficient suggests that the improvement of new quality productivity can reduce the gap between industrial structures, thereby promoting the rationalization of industrial adjustments. The development of new quality productivity can integrate innovative resources, leverage modern technological advantages, and achieve the integrated development of technology-driven industries. This promotes collaboration within the industrial chain, facilitates the redistribution of traditional factor resources and digital economy resources across the industrial chain, and enhances the synergistic effects of these resources. Consequently, it improves the allocation of resources between different industries, addressing and reducing the irrational distribution of resources. Based on the above results, it can be seen that H1 is valid.

The regression results in columns (2)–(4) show that the regression coefficients for the innovation driving force, green driving force, and production driving force are −0.4507, −0.7016, and −0.4172, respectively, all of which are significant at the 1% level. This indicates that both the overall new quality productivity and its three dimensions negatively impact industrial rationalization. The direction of the regression coefficients for the control variables aligns with those in column (1), and their significance is roughly the same, verifying the robustness of the model.

In Table 6, columns (1)–(4) all passed the Hausman test at the 1% significance level, so they were all estimated using the fixed effects regression model. The estimation results in column (2) of Table 6 show that the regression coefficient of new quality productivity on the upgrading of industries is 2.5179, which is significant at the 1% level. This indicates that improvements in new quality productivity promote the upgrading of industries. One characteristic of new quality productivity is innovation, which is driven by scientific and technological advancements. This drives the development of new and emerging industries, and the new generation of the digital industry has infused vitality into the upgrading of industrial structures [45]. The green driving force enhances the efficient use of resources, promotes the transformation of traditional industrial enterprises, and facilitates the elimination of outdated enterprises, thereby achieving the upgrading of the secondary industry. Therefore, new quality productivity fosters the vigorous development of the tertiary industry and the renewal and upgrading of the secondary industry, playing a positive role in industrial upgrading. Based on the above results, it can be seen that H2 is valid.

The regression results in columns (2)–(4) similarly confirm the robustness of the relationship between new quality productivity and the upgrading of industries. The regression coefficients for innovation driving force, green driving force, and production driving force are 1.4677, 2.5862, and 1.7481, respectively, all significant at the 1% level. These positive coefficients indicate that each dimension of new quality productivity positively influences industrial upgrading. Additionally, the direction and significance of the regression coefficients for the control variables remain consistent across the models, further verifying the robustness of the findings.

5.3. Endogeneity Issues and Robustness Tests

To address the endogeneity problem that may exist in the constructed econometric model, Arellano and Bond [46] proposed the differential generalized method of moments (diff-GMM) method to estimate dynamic panel models. This method’s advantage is that it can solve endogeneity problems without relying on external instrumental variables. However, it is susceptible to the influence of weak instrumental variables, which can bias the estimation results. To increase the validity of instrumental variables and improve model estimation accuracy, Blundell and Bond [47] proposed the system generalized method of moments (sys-GMM) method. Both the diff-GMM and sys-GMM methods include one-step and two-step estimation methods. The two-step estimation method is less susceptible to heteroskedasticity and produces more accurate results [48]. Since the lags of the explanatory variables are included in the estimation of the diff-GMM and sys-GMM, to reduce the bias caused by possible extreme values, improve the robustness of the model, and solve potential endogeneity problems, this paper uses the two-step diff-GMM and two-step sys-GMM for the estimation of the sample data after winsorization. The estimation results are shown in the following table.

Columns (1) and (2) of

Table 7. GMM regression results on the impact of new quality productivity on industrial structure.

	(1)	(2)	(3)	(4)
	Sys-GMM		Diff-GMM
	Rat	Upg	Rat	Upg
L.rat	0.7200 ***		0.7552 ***
	(0.0204)		(0.0187)
L.upg		0.9922 ***		0.6629 ***
		(0.0399)		(0.0390)
nqp	−0.1121 ***	0.7525 ***	−0.0439 ***	0.3572 **
	(0.0187)	(0.1096)	(0.0161)	(0.1772)
cos	−0.0121	0.4463 ***	−0.0130 *	0.2589 ***
	(0.0087)	(0.0779)	(0.0067)	(0.0392)
fdi	−0.0120 ***	−0.0889 ***	−0.0051 ***	−0.0206 **
	(0.0036)	(0.0212)	(0.0014)	(0.0084)
tra	−0.0740 ***	−0.1777 ***	−0.0001	−0.6128 ***
	(0.0194)	(0.0393)	(0.0077)	(0.1028)
gov	−0.3254 ***	1.3933 ***	−0.3838 ***	2.2454 ***
	(0.0288)	(0.3295)	(0.0234)	(0.1483)
tax	0.3911 ***	−0.3031	0.7349 ***	−2.6063 ***
	(0.1013)	(0.5166)	(0.0933)	(0.4328)
evi	−0.0001 ***	−0.0001	−0.0001 ***	0.0005
	(0.0000)	(0.0004)	(0.0000)	(0.0003)
_cons	0.1501 ***	−0.2537 ***	0.0862 ***	0.0341
	(0.0102)	(0.0974)	(0.0100)	(0.0636)
AR(1)-P	0.0135	0.0431	0.0176	0.1144
AR(2)-P	0.0564	0.1158	0.1069	0.2759
Sargan	28.0614	26.0649	26.9213	23.6456
p-Value	0.7911	0.9807	0.3598	0.8570
N	263	263	223	223

Note: Standard errors in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.

present the results of the sys-GMM regressions of new quality productivity on the rationalization and upgrading of industries, respectively. Columns (3) and (4) report the results of the diff-GMM estimation for the same relationships. According to Table 7, the AR(1) test was not effective in column (4), but the other three columns all passed the AR(1) and AR(2) tests, indicating that the disturbance term does not exhibit second-order autocorrelation. Additionally, the p-values of the Sargan test were all greater than 0.05, suggesting that the instrumental variables for the dynamic panel model were appropriately selected and the model estimation results were credible. By comparing the estimation results of sys-GMM and diff-GMM, it can be seen that, except for some differences in the regression coefficients of the control variables, the direction and significance of the regression coefficients for the other variables are roughly the same. This consistency verifies the robustness of the regression results presented in Table 5 and Table 6. Overall, the sys-GMM regression model test results are superior, indicating that the sys-GMM effectively addresses the endogeneity problem in the regression model. Therefore, this paper chooses the sys-GMM regression results for specific analysis.

The regression results in columns (1) and (2) of Table 7 show that the regression coefficients of the first-order lag term of the rationalization of industries and the first-order lag term of the upgrading of industries are both significantly positive at the 1% level. This indicates that the previous period’s rationalization and upgrading of industries are positively correlated with the current period’s rationalization and upgrading, respectively. Since the rationalization of industries index is an inverse indicator in this paper, a decrease in its first-order lag term implies a decrease in the current period’s rationalization, meaning the gap between the industrial structures is reduced. Conversely, the upgrading of industries is a positive indicator; a 1% increase in its first-order lag term resulted in a 0.9922% increase in the current period’s industrial upgrading. This demonstrates that improvements in industrial structure in the previous period can accelerate future upgrades, indicating that both the rationalization and upgrading of industries have a significant cyclical cumulative effect.

The regression coefficients of new quality productivity on the rationalization of industries and upgrading of industries were −0.1121 and 0.7525, respectively, both significant at the 1% level. These results were consistent with the direction and significance level of the regression coefficients in Table 5 and Table 6, verifying the robustness of the model and showing again that H1 and H2 are valid. However, the absolute values of the estimated coefficients show varying degrees of reduction, likely due to the sys-GMM regression model incorporating the lagged terms of the explanatory variables. The lagged terms are highly significant, explaining part of the changes in the rationalization and upgrading of industries, which in turn affects the extent to which new quality productivity impacts the GMM regression results on industrial structure. The significance and magnitude of the regression coefficients of the control variables also show some variability, but this does not affect the overall robustness of the model in this paper.

5.4. Moderating Effects Test of Environmental Regulation

To investigate whether environmental regulation has a moderating effect on new quality productivity and industrial structure and to address potential endogeneity problems, this paper includes environmental regulation as a moderating variable. The interaction term between environmental regulation and new quality productivity is incorporated into the model. Regression estimations are conducted using the benchmark model, the sys-GMM model, and the diff-GMM model, with the results reported in

Table 8. Tests for the moderating effect of environmental regulation.

	(1)	(2)	(3)	(4)	(5)	(6)
	Benchmark Regression		Sys-GMM		Diff-GMM
	Rat	Upg	Rat	Upg	Rat	Upg
L.rat			0.7125 ***		0.7287 ***
			(0.0205)		(0.0211)
L.upg				0.9823 ***		0.6250 ***
				(0.0379)		(0.0360)
nqp	−0.5742 ***	2.6335 ***	−0.1051 ***	0.7579 ***	−0.0550 ***	0.4162 **
	(0.0767)	(0.4859)	(0.0300)	(0.1588)	(0.0186)	(0.1982)
evi	0.0065 ***	0.0090	0.0015 ***	−0.0131 ***	0.0013 ***	−0.0049 **
	(0.0020)	(0.0095)	(0.0003)	(0.0026)	(0.0003)	(0.0025)
evnqp	−0.0194 ***	−0.0260	−0.0051 ***	0.0380 ***	−0.0045 ***	0.0167 **
	(0.0062)	(0.0301)	(0.0009)	(0.0084)	(0.0009)	(0.0074)
cos	−0.1557 **	0.4530	0.0187 **	0.3827 ***	−0.0108	0.3762 ***
	(0.0776)	(0.3144)	(0.0087)	(0.0917)	(0.0097)	(0.0643)
fdi	−0.0026	−0.0047	−0.0111**	−0.0546**	−0.0030	−0.0231
	(0.0083)	(0.0468)	(0.0053)	(0.0265)	(0.0018)	(0.0150)
tra	−0.0943 ***	−0.8557 ***	−0.0806 ***	−0.1629 ***	−0.0001	−0.5476 ***
	(0.0276)	(0.2899)	(0.0203)	(0.0560)	(0.0084)	(0.1183)
gov	−0.1855	3.8440 ***	−0.3345 ***	1.4725 ***	−0.3579 ***	2.3434 ***
	(0.1589)	(0.8234)	(0.0402)	(0.3534)	(0.0327)	(0.1488)
tax	0.7018 **	−5.6357 **	0.5115 ***	0.1629	0.7793 ***	−3.3264 ***
	(0.2858)	(2.2077)	(0.0860)	(0.7969)	(0.0980)	(0.4858)
_cons	0.4536 ***	−0.11657	0.1423 ***	−0.2424 *	0.0849 ***	0.0267
	(0.0444)	(0.1971)	(0.0167)	(0.1279)	(0.0155)	(0.1119)
Hausman	12.34	316.71
p-Value	0.0900	0.0000
R2	0.683	0.742
AR(1)-P			0.0115	0.0461	0.0157	0.1737
AR(2)-P			0.0760	0.1498	0.1446	0.2314
Sargan			28.0984	23.4695	27.5126	22.5209
p-Value			0.9615	0.9933	0.3308	0.6055
N	330	330	263	263	223	223

Note: Standard errors in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.

. According to the results of the Hausman test, random effects and fixed effects were used for regression in columns (1) and (2), respectively. In columns (3) to (6), all models passed the AR and Sargan tests, except for column (6), where the AR(1) test was not effective. This indicates that the selection of instrumental variables for the dynamic panel model is generally valid and reliable. Considering that GMM effectively addresses endogeneity problems, the regression results are more robust. Among these, the sys-GMM model demonstrated better testing results. Therefore, this paper primarily analyzes the regression results using the sys-GMM model.

The regression results in columns (3) and (4) show that the regression coefficients of new quality productivity on the rationalization of industries and the upgrading of industries were −0.1051 and 0.7579, respectively, both significant at the 1% level. These findings further validate the robustness of the regression results presented in the previous section. Additionally, the regression coefficients for the interaction terms between environmental regulation and new quality productivity were −0.0051 and 0.0380, respectively, both significant at the 1% level. This indicates that environmental regulation has a negative moderating effect on the relationship between new quality productivity and the rationalization of industries, while it has a positive moderating effect on the relationship between new quality productivity and the upgrading of industries.

In areas with greater environmental regulatory intensity, high-polluting and high-energy-consuming industrial enterprises face increased pressure. On the one hand, pollution treatment requires the purchase of extensive equipment and significant investment in environmental protection funds. These funds can have a “crowding out effect” on product innovation and production technology, potentially reducing the speed of mechanization and automation. As a result, high-polluting enterprises may attract cheaper labor to replace machine work, diminishing the benefits of new quality productivity for industrial restructuring and exacerbating the irrational allocation of labor factors within the industry. On the other hand, due to the high costs of sewage treatment, some energy-intensive and high-polluting small firms may be unable to afford these expenses and may relocate to regions with relatively weaker environmental regulations [49]. This relocation often involves the movement of capital, manpower, and technology, which slows the development of new quality productivity and disrupts the balanced allocation of local inter-industry resources. Overall, environmental regulations weaken the negative correlation between new quality productivity and the rationalization of industries, playing a negative moderating role between the two. It can be argued that environmental regulation has a negative moderating effect between new quality productivity and rationalization of industries through the “compliance cost effect”, and H3 is valid.

Environmental regulation has an “innovation compensation effect”. Under the influence of environmental regulations, high-polluting and high-energy-consuming enterprises must be more cautious in technological innovation and process improvement. By setting environmental protection thresholds, eliminating inefficient technological practices, and promoting the development of cutting-edge green technologies aligned with the values of “innovation” and “greenness”, these enterprises can enhance their new quality productivity through green innovation, thereby accelerating the process of “economic servitization” [42]. Furthermore, while environmental regulations may cause the geographical relocation of some industries, they also create opportunities for the development of emerging enterprises. To achieve “environmental performance”, governments often support and provide tax incentives to green emerging enterprises. The relocation of such enterprises can stimulate the competitive vitality of local businesses of the same type, encouraging them to actively develop new quality productivity to gain competitive advantages. This, in turn, promotes the development of the local tertiary industry. Therefore, environmental regulation can strengthen the positive correlation between new quality productivity and the upgrading of industries, playing a positive moderating role. Environmental regulation has a positive moderating effect between new quality productivity and upgrading of industries through the “innovation compensation effect”, and H4 is valid.

6. Conclusions and Prospect

6.1. Conclusions

To analyze the connotation and development status of China’s new quality productivity and its effect on industrial structure under the constraints of environmental regulations, this paper selected relevant data from 30 provincial-level administrative regions in China (excluding Hong Kong, Macao, Taiwan, and Tibet) for the period 2011 to 2021. Utilizing the entropy-weight-TOPSIS method, the paper calculated the development index of new quality productivity and measured its scores in the three dimensions of innovation driving force, green driving force, and production driving force. Additionally, this paper examined the influence mechanism of new quality productivity on China’s industrial restructuring and integrates environmental regulation into the research framework to clarify the relationships among these factors. An econometric model was constructed to verify the moderating effect of environmental regulation between new quality productivity and industrial structure. Based on this research, this paper draws the following conclusions:

Firstly, based on the measurement results from the entropy weight-TOPSIS method and the kernel density curve, it is evident that the level of new quality productivity in China’s provincial-level administrative regions has steadily improved during the investigation period, and the spatial gap has narrowed. However, from the perspective of regional synergistic development, there remains an issue of unbalanced development. The new quality productivity level in the eastern region is significantly higher than in the other three regions. Since 2015, the growth rate of new quality productivity in the northeast has gradually slowed, and the gap with the western region, which has the lowest level of new quality productivity, has been decreasing over time.

Secondly, regarding the different dimensions that constitute the new quality productivity index system, the development level of the innovation-driving force needs improvement. The green driving force has developed more steadily, and the production driving force has grown the fastest during the examination period.

Thirdly, according to the econometric regression analysis, there was a negative correlation between new quality productivity and its three dimensions and the inverse indicator of industrial rationalization. This indicates that new quality productivity can narrow the gap between industrial structures and positively promote industrial upgrading. Comparing the fixed effects (FE) and generalized method of moments (GMM) estimation results, it was found that the sys-GMM better addressed the endogeneity problem in the model, resulting in more robust regression outcomes and improved significance of the variables.

Fourthly, environmental regulations exhibited a negative moderating relationship between new quality productivity and the rationalization of industries, and a positive moderating effect between new quality productivity and the upgrading of industries. As the intensity of environmental regulation increases, the negative correlation between new quality productivity and industrial rationalization weakens, while the positive correlation between new quality productivity and industrial upgrading strengthens.

Based on the above analysis and conclusions, this paper makes the following recommendations:

Firstly, given the significant regional disparities in the development of new quality productivity across China, local governments should implement policies tailored to their specific conditions. By leveraging their unique factor resource endowments, they can accelerate the development of region-specific new quality productivity. The eastern region should be encouraged to fully utilize its advantages in independent innovation. The central region should capitalize on the positive spillover effects of knowledge and technology from the eastern region. Additionally, policy support should be strengthened for the western and northeastern regions to promote balanced development.

Secondly, optimizing the allocation of resources, synergizing the development of high and new technologies, and leveraging new quality productivity should occur to narrow the gap between industrial structures. Compared to traditional production factors, technology and human resources play a more crucial role in promoting the development of new quality productivity. The eastern coast can more easily acquire and implement advanced technology from abroad and is relatively rich in scientific research and innovation resources. To address regional disparities, China should increase its investment in scientific research in the central and western regions, establish scientific research bases, and attract high-quality talent. This would enable the optimal allocation of factor resources, promote the synergistic development of high and new technologies, and reduce the industrial structure gap.

Thirdly, the next recommendation is to strengthen industry-university-research cooperation, deepen resource sharing, promote industrial transformation and upgrading, and provide a solid foundation for constructing a modernized industrial system. New quality productivity is grounded in new technology and guided by the principle of green development, aiming to enhance the production efficiency of factors. Colleges and universities should focus on the development and study of cutting-edge technologies while strengthening communication with enterprises. The government should stimulate scientific research enthusiasm and promote the development of new technologies by providing subsidies and rewards. This approach will facilitate continuous progress in new quality productivity and provide technical support for the emergence and growth of new industries.

Fourthly, local governments should consider adopting a graduated approach when formulating environmental policies. It is crucial to account for the size and type of enterprises to avoid imposing excessive environmental regulations on those with weaker technological innovation capacities, which could lead to an undue cost burden and dampen their enthusiasm for transformation and upgrading. For large heavy industry enterprises, the government should strictly enforce environmental regulations, strengthen supervision, and set pollutant emission ceilings based on the enterprises’ production and operational conditions and the characteristics of their products. This approach will guide the deepening of industrial structural reform, align with the development pace of local, new quality productivity, and accelerate industrial upgrading and the transition from old to new energy sources.

6.2. Prospect

As the concept of new quality productivity is relatively new, the academic community is still in the initial stages of exploring its connotation. The index system of new quality productivity established in this paper is also exploratory, and the selection of various indicators within the system requires continuous refinement in subsequent studies. Additionally, due to data limitations, this study used 30 provincial-level administrative regions in China for sample selection. Future research could refine the sample to the city level, which would help obtain more robust conclusions as the sample size expands. Moreover, considering that environmental regulation can be categorized into different types, future studies should differentiate between formal and informal environmental regulations. This differentiation will allow for an exploration of whether there is heterogeneity in the interaction mechanisms and whether a threshold effect exists.