New Quality Productivity and Industrial Structure in China: The Moderating Effect of Environmental Regulation (2024)

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.

3.1. 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:

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:

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:

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:

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.

4.1. Construction and Measurement of New Quality Productivity Index System

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 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

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.

5.1. Analysis of the Results of the Neoplasm Productivity Measurements

5.2. Benchmark Regression and Robustness Test Results

Table 5 and Table 6 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 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. 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.

6.1. 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.