Research on the Impact of the New Quality Productive Force on Regional Economic Disparities

Abstract

The New Quality Productive Force (NQPF) serves as a key driver in narrowing regional economic disparities and promoting sustainable development. Clarifying the mechanism through which it affects regional economic disparities not only facilitates coordinated regional development but also provides critical insights for synergizing high-quality economic growth with ecological and environmental sustainability. Based on panel data from 30 Chinese provinces between 2012 and 2022, this study systematically examines the impact of New Quality Productive Forces on regional economic disparities, analyzing the mediating role of scientific and technological innovation as well as the nonlinear moderating effect of urbanization rate on this relationship. The findings reveal: First, NQPF significantly contributes to narrowing regional economic disparities overall, but its effects exhibit notable regional heterogeneity—widening disparities in eastern regions while demonstrating significant convergence effects in central and western regions. Second, mechanism analysis indicates that scientific and technological innovation is a critical transmission channel through which NQPF reduces regional disparities, as NQPF indirectly promotes coordinated regional development by fostering technological innovation. Third, threshold effect tests show that the convergence effect of NQPF varies nonlinearly with urbanization levels, and its enabling effect weakens once urbanization exceeds a specific threshold. Based on these findings, policy recommendations are proposed, including continuously nurturing New Quality Productive Forces, strengthening the drive of scientific and technological innovation, and coordinating urbanization with ecological civilization construction. These measures aim to provide new momentum for achieving high-quality regional economic development and sustainable transformation.

1. Introduction

Under the new development paradigm, regional economic disparities remain a critical issue that China must urgently address, with the NQPF emerging as a potential breakthrough solution [1].

In the context of building a new development pattern, coordinated regional economic development has always been an essential component of national strategy. The report of the 20th National Congress of the Communist Party of China explicitly called for “thoroughly implementing the strategy of coordinated regional development, major regional strategies, and functional zone strategies, while optimizing the layout of major productive forces.” The Outline of the 14th Five-Year Plan for National Economic and Social Development and Long-Range Objectives Through 2035 further emphasized “improving institutions and mechanisms for coordinated regional development and promoting optimized adjustment of productive forces distribution.” These top-level designs clearly demonstrate that narrowing regional economic disparities is both an inherent requirement for achieving high-quality development and a necessary element in advancing Chinese modernization [2]. However, China’s regional economic development still faces significant imbalance issues [3,4]. According to National Bureau of Statistics data, in 2022 the per capita GDP of eastern regions reached 1.8 times that of central and western regions, while the urban-rural income gap expanded by 12.3% in absolute terms compared to a decade ago. Such development disparities not only constrain the formation of a unified national market but may also lead to multiple issues including social equity concerns and inefficient resource allocation [5]. Consequently, exploring convergence mechanisms for regional economic disparities has become an urgent research priority for both academia and policymakers.

Against this backdrop, the proposition of NQPF offers a fresh theoretical perspective and practical approach to addressing the imbalance in regional development [6,7]. Historically, the emergence of NQPF has also aligned with the intrinsic requirements of China’s economic transformation, which is shifting from scale expansion toward quality and efficiency [8]. The traditional extensive development model, which relies on factor input, has faced challenges such as tightened resource and environmental constraints and diminishing marginal returns. In contrast, NQPF, driven by technological innovation as its core force, achieves a leap in total factor productivity through enhancing the quality of the workforce, intelligentizing production materials, and expanding the scope of production objects, thereby injecting new impetus into high-quality development [9,10].

This article argues that there is a profound mechanism connection between NQPF and the coordinated development of regional economies [11,12]. Firstly, it directly narrows the development gap through the efficient cross-regional allocation of production factors. For instance, the “digital equality effect” brought about by digital technology can significantly reduce transaction costs in underdeveloped regions. Secondly, technological innovation, as the core driving force, plays a key mediating role, generating an “innovation trickle-down effect” through technology spillover and industrial synergy, facilitating the diffusion of advanced technological achievements to surrounding areas in a gradient manner [13]. More importantly, the convergence effect of NQPF on regional economies exhibits a significant non-linear characteristic: when the regional urbanization level is below a certain threshold, NQPF can effectively promote regional coordinated development through factor reorganization and innovation diffusion; however, when the urbanization rate exceeds the critical value, due to the decline in factor allocation efficiency and the “crowding effect” of innovation resources caused by the transformation towards service-oriented industries, its marginal convergence effect will weaken [14,15]. This “direct effect—mediating transmission—threshold regulation” compound mechanism provides a new path for coordinating regional development in the new stage.

This study is based on provincial panel data from 2012 to 2022 and employs three econometric methods for comprehensive analysis: (1) A dual fixed-effects model is utilized to identify the net impact of the NQPF on regional economic disparities, while controlling for temporal and individual heterogeneity; (2) A mediation effect model is constructed to examine the bridging role of scientific and technological innovation in the “NQPF → regional convergence” transmission mechanism; (3) Hansen’s threshold regression is applied to reveal the nonlinear moderating characteristics of urbanization rate on this relationship. Through this rigorous research design, we aim to address three fundamental questions: Can NQPF reduce regional economic disparities and through what mechanisms? What specific role does technological innovation play in this process? How does the impact effect vary across different stages of urbanization development?

The theoretical value of this research lies in its breakthrough beyond the linear analytical framework of traditional convergence theory, through the construction of a “technology-space-institution-sustainability” quadruple interactive explanatory model that incorporates environmental carrying capacity and green innovation into the theoretical framework. From an empirical perspective, its practical significance is demonstrated by the proposal of a differentiated set of regional sustainable development policies: in areas with higher urbanization rates, the focus should be on strengthening green innovation sourcing and low-carbon transition functions, while regions in the early stages of urbanization should prioritize ecological resource capitalization and the cultivation of green human capital. This targeted policy approach not only avoids the efficiency losses and ecological costs associated with one-size-fits-all policies but also, as empirical evidence suggests, enables the NQPF to decouple economic growth from carbon emissions, thereby achieving deep integration between regional coordinated development and sustainability goals. It provides an academic foundation for implementing the Party’s 20th National Congress report’s call to “build a regional economic layout characterized by complementary advantages and high-quality development,” while also aligning with the essential requirement of Chinese modernization: “harmonious coexistence between humanity and nature.”

2. Literature Review

The literature closely related to this article mainly falls into three aspects: first, research on the connotation and measurement of the NQPF; second, research on regional economic disparities; and third, research on the impact of NQPF on regional disparities. As a core concept in the new stage of China’s economic development, the theoretical origin of NQPF can be traced back to the theory of productivity in Marxist political economy [16].

NQPF represents an innovative productivity model that evolves from traditional frameworks through advancements in technology, management practices, and institutional systems. It embodies a fundamental enhancement in the optimal integration of labor, tools of production, and objects of labor, leading to a substantial rise in total factor productivity [17,18]. To truly understand the connotation of NQPF, it is essential to firmly grasp its key characteristics [19,20]. Some scholars believe that to accurately understand the connotation and characteristics of NQPF, it is necessary to grasp it from the two aspects of “new” and “quality”. Some scholars point out that NQPF is essentially a new form of productivity represented by “computing power”, which is the economic foundation for achieving Chinese-style modernization. Other scholars propose that the connotation and characteristics of NQPF are at least reflected in three dimensions: “new”, “quality”, and “power”. Among them, the change in “new” mainly manifests in the emergence of new starting points such as strategic emerging industries and future industries as the carriers and manifestations of productivity; the change in “quality” mainly manifests in transcending the traditional category of “material transformation”; the change in “power” mainly manifests in the upgrade from thermal power, electric power, and network power to computing power. Through the continuous improvement by scholars, this article summarizes and proposes that NQPF is the new vitality exerted by the three elements of productivity—laborers, means of labor, and objects of labor—under the influence of the three characteristics of NQPF: “new”, “quality”, and “power”, thus giving birth to NQPF in the context of the new era.

However, the current research has not yet reached a consensus on the measurement of NQPF [21,22,23]. Some scholars, based on the definition of the connotation of NQPF, have respectively constructed a comprehensive evaluation index system for NQPF from the three dimensions of laborers, objects of labor, and means of production; some scholars have constructed a comprehensive evaluation system for NQPF from three first-level indicators: scientific and technological productivity, green productivity, and digital productivity; and other scholars have constructed an evaluation index system for NQPF from the perspectives of penetrating factors and substantive factors. Such differences in concepts and measurements limit the comparability among different studies [24,25,26].

Regarding the causes of regional development disparities in China, existing research has achieved significant results [27,28]. Specifically, current studies primarily attribute regional development gaps to factors such as technological talent, digital development levels, new infrastructure construction, degree of openness to the outside world, policy interventions, geographical constraints, and impacts of globalization. In terms of theoretical research on regional economic disparities, academia has long been divided between convergence theory and polarization theory [29,30]. Convergence theory posits that under market mechanisms, less developed regions can gradually narrow the gap through technology imitation and capital flows, while polarization theory emphasizes that cumulative causation effects will amplify regional differences [31]. It is noteworthy that most of these studies are based on traditional analytical frameworks of production factors, paying less attention to how the NQPF reshapes regional economic patterns, resulting in diminished explanatory power of traditional theories in the digital economy era [32,33,34].

Research on the impact of NQPF on regional disparities remains relatively scarce. Existing studies examining NQPF’s influence on regional economic disparities from the perspectives of industrial structure advancement and rationalization generally support its role in reducing regional economic gaps. Current research demonstrates clear divergences in viewpoints. Studies supporting the convergence effect emphasize the role of technology diffusion effects and the rise of remote economies, noting that digital technologies have lowered market entry barriers for less developed regions, while technologies like remote work during the pandemic have created new opportunities for inland areas. Conversely, studies suggesting divergence effects focus on digital divides and innovation polarization phenomena, arguing that developed regions’ leading advantages in new infrastructure and innovation resources may reinforce the Matthew effect. These disagreements may stem from methodological differences in research samples, measurement indicators, or time spans.

To address the gaps in existing research, this paper makes marginal contributions in three main aspects: First, its theoretical value lies in breaking through the linear analytical framework of traditional convergence theory and constructing a “technology-space-institution-sustainability” quadruple interactive explanatory model. Second, it analyzes both theoretically and empirically the impact mechanisms of NQPF on regional economic disparities, particularly examining the transmission mechanism through scientific and technological innovation, aiming to explore how NQPF directly or indirectly affects regional economies through technological innovation and uncover potential chain transmission relationships among these factors. Third, it investigates the nonlinear effects of NQPF on regional economic disparities and proposes differentiated regional policy packages.

3. Theoretical Analysis and Research Hypothesis

3.1. The NQPF and Regional Economic Disparities

The development of the NQPF breaks the geographical constraints of traditional production factors and provides new impetus for coordinated regional economic development. With digitalization and intelligence at its core, NQPF can effectively promote the optimal allocation of factors such as technology, capital and talent across regions, thereby driving faster development in relatively underdeveloped areas. Specifically: the widespread application of digital technologies has significantly reduced the cost of technology diffusion, enabling innovation achievements from advanced regions to be transferred to less developed regions more quickly; the development of new-generation information technologies has broken the dependence of traditional industries on geographical location, creating conditions for less developed regions to undertake industrial transfer and cultivate emerging industries; the rise of the digital platform economy has helped remote areas better access the national unified market and expand development space. At the same time, the improvement in production efficiency brought by NQPF has also enhanced the endogenous growth momentum of underdeveloped regions. Under the combined effect of these factors, the economic development gap between regions shows a gradual convergence trend. The effectiveness of this process depends on the improvement level of digital infrastructure, the optimization level of institutional environment, and the accumulation of human capital.

Hypothesis H1:

The NQPF can significantly narrow regional economic disparities.

3.2. The NQPF, Scientific and Technological Innovation, and Regional Economic Disparities

Beyond its direct impact on the regional economy, NQPF also indirectly influence regional economic disparities through scientific and technological innovation. The development of NQPF provides a new pathway for coordinated regional economic development by accelerating scientific and technological innovation as a key mediating variable. Characterized by innovation-driven development, NQPF can significantly enhance the scientific and technological innovation capabilities of various regions, while the diffusion and application of innovation outcomes become an important mechanism for narrowing regional development gaps. Specifically: the frontier technological breakthroughs driven by NQPF provide opportunities for less-developed regions to catch up technologically, enabling them to leapfrog traditional development stages; industrial upgrading brought about by technological innovation promotes the reallocation of production factors across regions, optimizing the spatial pattern of economic development; the enhanced knowledge spillover effects facilitate technological exchanges and cooperation between advanced and lagging regions. In this process, technological innovation not only directly improves production efficiency across regions but also indirectly promotes the convergence of regional economic disparities by altering regional comparative advantages and development pathways. The effective operation of this mechanism depends on the maturity of regional innovation systems, the efficiency of transformation of scientific and technological achievements, and the smooth flow of innovation factors.

Hypothesis H2:

NQPF can indirectly narrow regional economic disparities by promoting scientific and technological innovation.

3.3. The Nonlinear Characteristics of the NQPF in Empowering Regional Economic Disparities

However, the aforementioned impact of the NQPF on regional economic disparities is not linear and may vary with changes in the urbanization rate. The convergence effect of NQPF on regional economic disparities is nonlinearly moderated by the level of urbanization development. When a region’s urbanization rate exceeds a specific threshold, the marginal effect of NQPF in promoting coordinated regional development shows a diminishing trend. This phenomenon stems from the interaction between urbanization processes and NQPF’s mechanisms: in the early stages of urbanization, improved infrastructure and factor agglomeration create favorable conditions for the diffusion of NQPF, significantly accelerating technology spillovers and industrial upgrading; however, when urbanization reaches a relatively high level, the crowding effects caused by urban expansion gradually emerge, the allocation efficiency of innovation factors declines, and interregional industrial homogenization intensifies, weakening the differentiated development advantages that NQPF should bring. Particularly noteworthy is that excessive urbanization may solidify the “digital divide,” strengthening advanced regions’ monopoly on new technologies and hindering the gradient transfer of technologies to surrounding areas. The specific level of this threshold depends on the synergistic effects of multiple dimensions, including regional innovation capacity, industrial structure characteristics, and institutional environment quality.

Hypothesis H3:

When the urbanization development level exceeds a certain threshold, the empowering effect of NQPF on coordinated regional economic development diminishes.

Integrating the above analysis, the theoretical framework is constructed as shown in Figure 1.

4. Modeling and Data Sources

4.1. Model Specification

4.1.1. Fixed Effects Model

To examine the provincial-level impact of the NQPF on regional economic disparities and test the underlying mechanisms, we establish the following baseline econometric model:

$$\begin{array}{c}{\mathrm{g}\mathrm{a}\mathrm{p}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{n}\mathrm{q}\mathrm{p}\mathrm{f}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}\end{array}$$

(1)

In Equation (1), ${gap}_{it}$ represents the regional economic disparity indicator for province i in year t; ${nqpf}_{it}$ is the core explanatory variable, measuring NQPF development level for province i in year t; $Z_{it}$ denotes the set of control variables; ${\mu }_i$ and ${\gamma }_t$ represent time-invariant individual fixed effects and individual-invariant time fixed effects, respectively; ${\alpha }_0$ is the constant term; ${\alpha }_1$ is the regression coefficient for the core explanatory variable (a key focus of this study); ${\alpha }_2$ represents the coefficients for control variables; ${\epsilon }_{it}$ is the random error term; Subscripts i and t denote province and year, respectively.

4.1.2. Mediation Effects Model

To further examine whether scientific and technological innovation serves as a mediating mechanism, we construct the following mediation effects models:

$$\begin{array}{c}{patent}_{it}={\beta }_0+{\beta }_1{nqpf}_{it}+{\beta }_2{Z}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}\end{array}$$

(2)

$$\begin{array}{c}{gap}_{it}={\gamma }_0+{\gamma }_1{nqpf}_{it}+{\gamma }_2{patent}_{it}{+\gamma }_3{Z}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}\end{array}$$

(3)

In Equations (2) and (3), ${patent}_{it}$ is the mediating variable (scientific and technological innovation); Other variables retain their original meanings as defined above.

4.1.3. Threshold Effects Model

As theoretical analysis suggests that NQPF’s effects may exhibit nonlinear characteristics at different urbanization levels, we employ a panel threshold model:

$${\mathrm{g}\mathrm{a}\mathrm{p}}_{\mathrm{i}\mathrm{t}}={\mathsf{\phi }}_0+{\mathsf{\phi }}_1{\mathrm{n}\mathrm{q}\mathrm{p}\mathrm{f}}_{\mathrm{i}\mathrm{t}}{\times \mathrm{I}\left({\mathrm{q}}_{\mathrm{i}\mathrm{t}}\le \mathsf{\lambda }\right)+{\mathsf{\phi }}_2{\mathrm{n}\mathrm{q}\mathrm{p}\mathrm{f}}_{\mathrm{i}\mathrm{t}}\times \mathrm{I}\left({\mathrm{q}}_{\mathrm{i}\mathrm{t}}>\mathsf{\lambda }\right)+\mathsf{\phi }}_3{\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

In Equation (4), $q_{it}$ represents the threshold variable (urbanization rate); $\lambda$ is the threshold value; $I\left( \right)$ is an indicator function that equals 1 when the condition in parentheses is satisfied and 0 otherwise.

4.2. Variable Selection

The definitions and descriptions of the various variables involved in the empirical analysis in this paper are shown in

Table 1. Variable Description.

Variable Type	Variable Name	Variable Symbol	Description
Dependent Variable	Regional Economic Disparity	red	The economic gap between cities and the most developed sample cities under the condition of national average deviation
Core Explanatory Variable	The New Quality Productive Force	nqpf	The New Quality Productive Force development level
Mediating Variable	Scientific and Technological Innovation	patent	The number of patents
Threshold Variable	Urbanization Rate	urban	The ratio of urban population to total population
Control Variables	Government intervention	gov	Measured by the ratio of government public fiscal expenditure to regional GDP
	Human capital	human	Measured by the ratio of the number of enrolled college students to the resident population.
	Openness to the outside world	open	Measured by the ratio of total import and export volume to regional GDP
	Infrastructure level	infra	Reflected by the per capital urban road area, indicating the regional infrastructure development level.

.

4.2.1. Dependent Variable: Regional Economic Disparity (Red)

It should be noted that this study focuses on examining the economic gap between cities and the most developed sample cities under the condition of national average deviation, rather than intra-city economic disparities. Following the relative disparity approach, we measure regional economic disparities using the following formula [35]:

$$\begin{array}{c}{\mathrm{r}\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{t}}=\left|{\displaystyle \frac{{\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}-\max \left({\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}\right)}{{\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{t}}-\max \left({\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}\right)}}\right|\end{array}$$

(5)

In Equation (5), ${\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}$ represents the per capita GDP of city i in year t; $\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}\right)$ represents the maximum per capital GDP among all sample cities in year t; ${\mathrm{p}\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}$ represents the average per capital GDP of all sample cities in year t. This method not only considers the direct gap between individual cities and the most developed city, but also reflects the gap between the national average and the most developed city. The ratio reveals the relative degree of regional economic disparity. When ${\mathrm{r}\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ > 1, it indicates that the relative gap between the city and the most developed city is larger than the national average economic disparity. This method can reasonably characterize inter-city economic disparities and reveal their dynamic evolution, while the results are suitable for empirical analysis using panel data models [36].

To test the robustness of NQPF’s impact on regional economic disparities, this study also employs an alternative measurement method:

$$\begin{array}{c}{\mathrm{r}\mathrm{e}\mathrm{d}1}_{\mathrm{i}\mathrm{t}}={\displaystyle \frac{{\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{c}\mathrm{t}}}{{\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}}}\end{array}$$

(6)

In Equation (6), ${\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{c}\mathrm{t}}$ represents the per capital GDP of central city c in year t; ${\mathrm{g}\mathrm{d}\mathrm{p}}_{\mathrm{i}\mathrm{t}}$ represents the per capital GDP of city i in year t; ${\mathrm{r}\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{t}}$ measures the economic development gap between peripheral cities and central cities.

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

The core explanatory variable in this study is The New Quality Productive Force (nqpf). Drawing on previous research and considering NQPF’s fundamental connotation and three key characteristics while incorporating green development requirements, we establish a comprehensive evaluation index system to measure NQPF development level. This system includes multiple dimensions such as innovation level, quality level, computing power level, and green level (as shown in the figure). Using the entropy method, we process these 4 first-level indicators and 16 second-level indicators to obtain regional NQPF development levels, which are then standardized, demonstrating good representativeness (

Table 2. NQPF Evaluation Indicator System [37].

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

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

) [37].

4.2.3. Mediating Variable: Scientific and Technological Innovation (Patent)

Considering that the number of patents granted can reflect innovation inputs throughout the R&D-to-output process and represents an essential outcome of technological innovation, this study uses the number of domestic invention patents granted as a proxy variable for scientific and technological innovation [38,39,40].

4.2.4. Threshold Variable: Urbanization Rate (Urban)

This study measures the urbanization rate of each region using the ratio of urban population to total population.

4.2.5. Control Variables

For the selection of control variables, based on existing literature on regional economic disparities and the research focus of this paper, the following control variables are introduced: (1) Openness to the outside world (open): Measured by the ratio of total import and export volume to regional GDP. (2) Government intervention (gov): Measured by the ratio of government public fiscal expenditure to regional GDP. (3) Human capital (human): Measured by the ratio of the number of enrolled college students to the resident population. (4) Infrastructure level (infra): Reflected by the per capital urban road area, indicating the regional infrastructure development level.

4.3. Data Sources and Descriptive Statistics

This research is based on a balanced panel dataset comprising 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan) over the period from 2012 to 2022, yielding a total of 300 province-year observations. All raw data were sourced from the National Bureau of Statistics. In cases where data were missing, trend analysis was applied according to the attributes of the available data to estimate and impute the missing values.

Table 3. Results of descriptive statistics.

Variant	Number of Observations	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Value
red	330	1.0000	0.2380	7.73 × 10−10	1.3220
nqpf	330	0.1910	0.1120	0.0363	0.7140
patent	330	8.4300	1.4340	4.5110	11.6500
urban	330	0.6070	0.1170	0.3630	0.8960
gov	330	0.2600	0.1110	0.1050	0.7580
human	330	0.0213	0.0057	0.0085	0.0436
open	330	0.2830	0.2820	0.0074	1.4760
infra	330	0.3040	0.1270	0.0697	0.7120

provides detailed descriptive statistics for the key variables.

To ensure the robustness of the empirical findings and reduce the risk of multicollinearity, a correlation analysis was performed among all variables prior to further modeling. The results of this analysis are provided in

Table 4. Correlation Analysis.

	Red	npqf	Patent	Urban	Open	Gov	Human	Infra
red	1
npqf	−0.2639 ***	1
patent	−0.6266 ***	0.6911 ***	1
urban	0.8524 ***	0.1178 **	0.5620 ***	1
open	−0.5260 ***	0.3533 ***	0.4998 ***	0.6656 ***	1
gov	0.4311 ***	−0.5394 ***	−0.7868 ***	−0.3333 ***	−0.4384 ***	1
human	−0.4303 ***	−0.1589 ***	0.4020 ***	0.5524 ***	0.1538 ***	−0.3447 ***	1
infra	−0.4955 ***	0.2606 ***	0.3421 ***	0.6232 ***	0.3512 ***	−0.2228 ***	0.5311 ***	1

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

.

The analysis revealed generally low correlations among the variables, with one notably higher value of −0.8524. To eliminate potential multicollinearity effects and ensure the reliability of empirical results, variance inflation factor (VIF) tests were conducted for the key variables, as presented in

Table 5. Multicollinearity Diagnostics.

Variable	VIF	1/VIF
npqf	4.21	0.237269
patent	6.50	0.153831
urban	4.88	0.204733
open	2.54	0.393579
gov	3.19	0.313631
human	3.22	0.310900
infra	2.81	0.355476
Mean VIF	3.91

.

As shown in the table above, the variance inflation factor (VIF) is 3.91, which is below the threshold of 10, indicating no significant multicollinearity among the variables.

According to Table 2, the maximum value of NQPF reaches 0.714, while the minimum is 0.0363, reflecting considerable variation in NQPF optimization during the period 2012–2022.This suggests a year-by-year enhancement of NQPF, characterized by a fluctuating yet overall upward trend.

Furthermore, analysis of the spatial evolution patterns illustrated in Figure 2 for the years 2015 and 2022 reveals notable disparities in NQPF optimization across provinces. Specifically, the eastern region of China demonstrates a significantly higher level of NQPF development compared to the central and western regions.

5. Empirical Results and Analysis

5.1. Analysis of Benchmark Regression Results

To investigate the relationship between regional New Quality Productive Forces (NQPF) and economic disparities, preliminary tests were conducted to select the appropriate econometric model. Based on balanced panel data, an F-test was performed to determine whether a pooled regression model or fixed effects model should be adopted. The regression results rejected the pooled regression approach. Subsequently, a Hausman test was conducted to choose between fixed effects and random effects models, with the results conclusively favoring the fixed effects model. Consequently, this study employs a two-way fixed effects model to analyze the impact of NQPF on regional economic disparities. The estimation results are presented in

Table 6. Empirical Results and Analysis.

Variant	(1) Red	(2) Red	(3) Red	(4) Red	(6) Red
npqf	−0.602 *** (−12.33)	−0.242 *** (−6.04)	−0.301 *** (−7.45)	−0.217 *** (−5.42)	−0.234 *** (−5.22)
open		0.332 *** (8.60)	0.332 *** (9.07)	0.208 *** (8.16)	0.201 *** (8.07)
gov			0.337 *** (3.35)	0.357 *** (3.27)	0.374 *** (3.60)
human				20.021 *** (11.14)	18.944 *** (12.27)
infra					0.319 *** (6.22)
constant term (math.)	1.115 *** (119.74)	0.952 *** (55.90)	0.876 *** (30.45)	0.464 *** (15.45)	0.390 ** (11.78)
area	YES	YES	YES	YES	YES
year	YES	YES	YES	YES	YES
N	330	330	330	330	330
${\mathrm{R}}^2$	0.961	0.967	0.968	0.977	0.978

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

.

Column (1) of Table 6 presents the regression results controlling only for regional and time effects, showing that the estimated coefficient of the NQPF is −0.602, which is significant at the 1% level. In columns (2) through (7) of Table 6, we progressively incorporate a series of control variables including openness to trade, government intervention, human capital, and infrastructure development. The sign and statistical significance of NQPF’s coefficient remain fundamentally unchanged throughout these specifications, demonstrating that NQPF can significantly reduce regional economic disparities with considerable robustness. These findings provide validation for Hypothesis H1.

5.2. Endogeneity Test

Endogeneity issues may arise from multiple sources. The study has implemented several fundamental treatments to address potential problems: (1) To mitigate measurement error endogeneity, we exclusively use official data from the National Bureau of Statistics to ensure data quality; (2) To control for omitted variable bias, the econometric model incorporates control variables including openness to trade, government intervention, human capital, and infrastructure level, while employing a two-way fixed effects specification.

To further address potential endogeneity in NQPF development, this study utilizes provincial internet penetration rates as an instrumental variable (IV) for the NQPF composite index. This choice satisfies the necessary conditions for a valid IV: First, internet penetration strongly correlates with NQPF development because internet infrastructure, as a key carrier of information technology, facilitates information flow, resource sharing and technology diffusion, thereby significantly enhancing production efficiency and innovation capacity (relevance condition). Second, internet penetration demonstrates weak direct correlation with regional economic disparities, technological innovation, and other control variables, primarily influencing them indirectly through NQPF rather than directly determining their variation (exclusion restriction). These properties establish the internet penetration rate as a theoretically sound instrumental variable.

Table 7. Endogeneity test: instrumental variables.

Variant	(1) Phase I npqf	(2) Phase II Red
npqf		−1.581 *** (−14.19)
IV	0.430 *** (17.86)
control variable	containment	containment
constant term (math.)	0.129 *** (37.42)	1.301 *** (61.24)
area	YES	YES
year	YES	YES
N	330	330
${\mathrm{R}}^2$	0.970	0.974
F	318.82	201.22

Note: t-statistics are reported in parentheses; *** p < 0.01.

presents the IV regression results addressing endogeneity. The first-stage tests confirm the instrument’s validity: the F-statistic exceeds 10 and shows significance at the 1% level, indicating strong positive correlation between the IV and NQPF. The instrument also passes both the underidentification and weak instrument tests, satisfying all relevance and exogeneity requirements. Column (2) demonstrates that even after accounting for endogeneity, NQPF’s estimated coefficient remains significantly negative at the 1% level, robustly confirming the reliability of our baseline regression findings.

5.3. Robustness Analysis

5.3.1. Alternative Measures of the Dependent Variable

To assess the robustness of our findings, we employ different measurement approaches for regional economic disparities. Column (1) of

Table 8. Robustness test.

Variant	(1)	(2)	(3)
	Red	Red	Red
npqf	−1.318 *** (−3.65)	−0.228 ** (−3.65)	−0.572 *** (−3.65)
open	−0.263 ** (−2.32)	0.217 * (2.27)	−0.014 (−0.66)
gov	3.920 *** (8.95)	0.166 (1.19)	0.368 *** (4.32)
human	−19.014 *** (−3.24)	17.953 *** (8.29)	10.631 *** (8.54)
infra	0.494 *** (3.77)	0.310 *** (4.02)	−0.010 (−0.18)
constant term (math.)	2.188 *** (21.76)	0.396 *** (7.65)	0.864 *** (32.01)
area	YES	YES	YES
year	YES	YES	YES
N	330	240	286
${\mathrm{R}}^2$	0.980	0.986	0.973

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

shows that the estimated coefficient of the NQPF remains statistically significant at the 1% level with consistent sign, confirming the robustness of our baseline regression results.

5.3.2. Adjusted Sample Period

Given that the NQPF had not yet entered its incubation phase prior to 2015, this study adjusts the sample period to 2015–2022 and uses provincial NQPF development levels as the explanatory variable for regression analysis. The results of re-estimating the baseline regression (shown in Column (2) of Table 8) remain consistent with the main findings.

5.3.3. Exclusion of Municipalities

Considering the significant differences between municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing) and other provinces in terms of policy support, new urbanization construction, and resource concentration, we re-estimate the model after excluding these four municipalities. The results in Column (3) of Table 8 demonstrate that both the sign and statistical significance of NQPF’s coefficient maintain strong robustness, further confirming the reliability of the baseline regression results.

5.4. Heterogeneity Analysis

Given significant differences across regions in terms of economic development status, digital infrastructure construction, and resource endowments, the enabling effects of the NQPF may also vary. To examine whether regional differences exist in NQPF’s impact on regional economic disparities, this study divides the total sample into eastern and central-western regions based on geographical location, then conducts separate regressions to empirically test whether NQPF’s enabling effects differ across geographical locations. Results in columns (1) and (2) of

Table 9. Subregional regression results.

Variant	Eastern Part	Central and Western Region
	Red	Red
npqf	0.165 ** (2.25)	−0.498 *** (−7.59)
open	0.165 *** (4.45)	−0.065 (−0.83)
gov	1.329 *** (5.54)	0.386 *** (6.82)
human	38.311 *** (13.06)	6.292 *** (6.30)
infra	0.322 *** (3.94)	0.184 ** (2.60)
constant term (math.)	−0.564 *** (−5.04)	0.896 *** (23.75)
area	YES	YES
year	YES	YES
N	110	220
${\mathrm{R}}^2$	0.985	0.966

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

show that the regression coefficients of NQPF in eastern and central-western regions are 0.165 and −0.498, respectively, both statistically significant at the 1% level. This indicates that in eastern regions, NQPF promotes the expansion of regional economic disparities, while in central-western regions, the development of NQPF facilitates the narrowing of regional economic disparities, demonstrating regional heterogeneity. Moreover, the disparity-reducing effect of NQPF in central-western regions is stronger than its disparity-expanding effect in eastern regions. Possible reasons include:

For central-western regions: In recent years, through national strategies, these regions have received increased infrastructure investment and policy support for technological innovation. NQPF in central-western regions benefits from policy advantages, accelerating technology diffusion and efficiency improvements, thereby narrowing the development gap with eastern regions. These regions can directly apply mature advanced technologies from eastern regions, reducing trial costs and achieving development leaps.

For eastern regions: NQPF concentrates in core cities, leading to intensified development within the region while non-core cities lag behind due to resource outflows, widening intra-regional economic disparities. As traditional industries relocate, some areas fail to transform timely, while high-tech industries cluster in developed cities, exacerbating internal disparities.

Nationally: NQPF promotes synergy between eastern and central-western regions. The stronger absolute effect in central-western regions combined with their growing economic share leads to overall disparity reduction, reflecting diminishing marginal effects in developed regions versus greater improvement potential in less-developed regions.

5.5. Mediation Effect Analysis

Theoretical analysis indicates that NQPF promotes convergence of regional economic disparities indirectly through facilitating scientific and technological innovation. To verify this hypothesis, scientific and technological innovation is used as the mediating variable, and the mediation effect model is employed for testing. Results in column (2) of

Table 10. Mediation Effect Test Results.

Variant	(1)	(2)	(3)
	Red	Patent	Red
npqf	−0.413 *** (−10.15)	1.933 *** (6.25)	−0.347 *** (−7.37)
Patent			−0.034*** (−4.31)
urban	1.311 *** (9.35)	2.829 *** (6.48)	1.406 *** (10.46)
open	0.025 (0.75)	0.363 ** (2.61)	0.037 (1.14)
gov	0.473 *** (6.28)	−0.682 (−1.67)	0.450 *** (6.91)
human	13.592 *** (12.49)	−17.761 * (−2.09)	12.992 *** (11.07)
infra	0.269 *** (5.73)	−0.880 * (−2.19)	0.240 *** (4.66)
constant term (math.)	−0.219 ** (−2.82)	7.063 *** (28.93)	0.020 (0.19)
area	YES	YES	YES
year	YES	YES	YES
N	330	330	330
${\mathrm{R}}^2$	0.981	0.991	0.981

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

show the estimated coefficient of NQPF is significantly positive, indicating NQPF can effectively enhance the level of scientific and technological innovation. Results in column (3) of Table 10 show the regression coefficients of both NQPF and scientific and technological innovation are significantly negative.

Combining models (1), (2) and (3), the results demonstrate the existence of partial mediation effects, meaning NQPF can promote convergence of regional economic disparities through scientific and technological innovation. From the regression results of transmission mechanisms, holding other factors constant, for each 1-unit increase in NQPF development level, scientific and technological innovation increases by 1.933 units, regional economic disparities directly decrease by 0.347 units, leading to an indirect reduction in regional economic disparities by 0.066 units. The total effect amounts to 0.413 units, comprising both direct and indirect effects, with the mediation effect contributing 15.98% of this total. It is worth noting that the presence of partial mediation implies that NQPF development may also facilitate industrial structure upgrading through additional mechanisms—such as government intervention and human capital, as previously discussed.

5.6. Threshold Effect Analysis

The preceding analysis shows that NQPF can directly promote the convergence of regional economic disparities. This leads to the question: does the enabling effect of NQPF exhibit nonlinear characteristics? Would the empowering effect of NQPF be enhanced when the urbanization rate exceeds a certain threshold? To examine this, we use a panel threshold model with urbanization rate as the threshold variable. First, we conduct significance tests for threshold effects using Hansen’s method.

Table 11. Significance test results of threshold effect.

Threshold Variable	Threshold Number	F	p	Critical Value			Threshold
				10%	5%	1%
urban	single threshold	265.60	0.0000	36.1033	43.1820	61.1255	r = 0.7478
	double threshold	52.09	0.2233	367.1357	451.7974	530.9216	non-existent
	Triple threshold	37.44	0.4167	477.7671	542.9399	649.4823	non-existent

results show the p-values for both double and triple thresholds of urbanization rate fail to pass significance tests, while the single threshold test’s p-value is significant at the 1% level. This confirms a single threshold effect of urbanization rate on the NQPF-regional disparity relationship. We therefore construct a single threshold regression model for detailed analysis. Based on the threshold value, we divide urbanization rates into low urbanization ($\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}\le 0.7478$) and high urbanization ($\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}>0.7478$) regimes.

Table 12. Regression results of threshold effect.

Variant	(1) Red	Threshold Variable Interval
nqpf	−0.688 *** (−4.80)	$\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}\le 0.7478$
	−0.575 *** (−3.89)	$\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}>0.7478$

Note: t-statistics are reported in parentheses; *** p < 0.01.

presents the threshold regression results examining the impact of NQPF on regional economic disparities. During the low urbanization phase, the coefficient of NQPF’s effect on regional economic disparities is −0.688, statistically significant at the 1% level. In the high urbanization phase, the coefficient becomes −0.575, also significant at the 1% level. These results confirm a single threshold effect of urbanization rate on the NQPF-disparity relationship, showing that NQPF’s enabling effect weakens when urbanization exceeds a certain threshold.

In the early urbanization stage, continuous infrastructure improvements and rapid factor agglomeration create ideal conditions for NQPF diffusion. Enhanced transportation networks and communication facilities significantly reduce technology diffusion costs, while concentrated populations and industries generate scale effects that facilitate rapid innovation application. This is particularly evident in central-western regions, where rising urbanization rates coincide with large-scale digital infrastructure construction, creating favorable conditions for NQPF penetration and significantly promoting regional disparity reduction.

At higher urbanization levels, cities face diminishing marginal returns. Over-agglomeration leads to crowding effects like rising land costs, worsening pollution, and traffic congestion, all reducing innovation factor allocation efficiency. Simultaneously, intensified regional industrial homogenization emerges as different areas blindly pursue similar emerging industries, weakening NQPF’s potential for creating differentiated competitive advantages. Multiple cities simultaneously developing big data and AI industries without distinctive positioning results in resource dispersion and redundant construction.

More fundamentally, excessive urbanization may reinforce the “digital divide” effect. Advanced regions consolidate their first-mover technological advantages through institutional barriers and talent attraction, particularly at high urbanization levels. This not only hinders technology transfer to surrounding regions but may also widen innovation gaps between core cities and peripheral areas. Additionally, highly urbanized regions often experience painful industrial restructuring periods, where productivity gains from NQPF may temporarily weaken during transitions between traditional industry decline and emerging industry cultivation.

6. Conclusions and Policy Recommendations

6.1. Conclusions

This study utilizes panel data from 30 Chinese provinces (autonomous regions, and municipalities directly under the central government, excluding Tibet, Hong Kong, Macao, and Taiwan) from 2012 to 2022, employing three econometric methods for comprehensive analysis: (1) a dual fixed-effects model to identify the net impact of the NQPF on regional economic disparities while controlling for temporal and individual heterogeneity; (2) a mediation effects model to examine the bridging role of technological innovation in the “NQPF → regional convergence” transmission mechanism; and (3) Hansen’s threshold regression to reveal the nonlinear moderating characteristics of urbanization rate in this relationship. The study provides an in-depth analysis of the intrinsic relationship between NQPF and regional economic disparities, with further investigation into the influencing roles of scientific and technological innovation and urbanization rate.

The main conclusions are as follows: (1) NQPF significantly promotes the narrowing of regional economic disparities, indicating its substantial role in fostering coordinated regional economic development. From an empirical perspective, the convergence effect observed in regional economic disparities can be structurally attributed to the dimensions of quality and green development. Empirical findings indicate that quality improvement facilitates industrial development in less-developed regions through the diffusion of standards and industrial processes, while green transformation supports leapfrog development in underserved areas via energy structure optimization and eco-economic initiatives. These two dimensions exhibit a statistically significant synergistic effect. In contrast, innovation and computing capacity demonstrate a discernible polarizing effect in initial stages, particularly in eastern regions. Benefitting from pre-existing technological accumulation and infrastructure advantages, these areas further strengthen their leadership in innovation and computing, thereby empirically exacerbating inter-regional disparities in the short run. (2) NQPF’s impact on regional economic disparities exhibits regional heterogeneity: in eastern China, the NQPF coefficient is 0.165, showing a significant expansion effect on regional disparities; in central and western China, the coefficient is −0.498, demonstrating a significant reduction effect. (3) The mediation mechanism tests confirm the significant existence of scientific and technological innovation’s intermediary effect, meaning NQPF can indirectly narrow regional economic disparities and promote coordinated regional economic development by advancing scientific and technological innovation. Further dimensional analysis reveals that this mediating pathway is primarily driven by the promotion of technological diffusion and efficiency enhancement through the levels of innovation and computing power, while the quality and green dimensions contribute to regional coordination partly through non-technical pathways. (4) Threshold effect tests reveal a significant single-threshold effect of urbanization rate, indicating that when urbanization exceeds a certain level, NQPF’s enabling effect diminishes. This phenomenon may stem from the relative stabilization of industrial structures in highly urbanized areas, where the marginal driving effect of New Quality Productive Forces—particularly in its innovation and computing dimensions—has diminished. Empirically, these findings demonstrate that the impact of New Quality Productive Forces on regional economic disparities exhibits multidimensional and heterogeneous characteristics. The net effect observed is the balance between the “polarizing” effect driven by innovation and computing capabilities and the “convergence” effect contributed by quality and green dimensions.

This study employs multiple econometric methods to empirically analyze the relationship between New Quality Productive Forces and regional economic disparities. However, due to limitations in research conditions and data availability, there is still room for further improvement. First, although the model controls for province and year fixed effects, there may still be influences from other unobserved variables. Future research could consider incorporating more control variables or adopting new identification strategies to enhance the accuracy of the estimates. Second, while this study primarily focuses on the mediating mechanism of technological innovation, New Quality Productive Forces may also affect regional economic disparities through other channels such as industrial structure and human capital. Subsequent studies could conduct more comprehensive analyses of these mechanisms. Additionally, constrained by data availability, this research uses provincial-level data, which may not capture heterogeneity at more granular regional levels. Future studies could collect prefecture-level or county-level data to deepen the conclusions. Lastly, the threshold effect analysis of urbanization rate identifies only a single threshold, while the actual impact may involve more complex nonlinear characteristics. Further research could explore multiple thresholds or continuous moderating effects to more comprehensively reveal the underlying mechanisms. The above aspects will be further refined and expanded in future studies to enrich empirical evidence in this field.

6.2. Policy Recommendations

Based on these findings, the study proposes the following policy recommendations:

(1)

Continuously Strengthening NQPF Infrastructure to Promote High-Quality and Sustainable Development. The primary task in advancing the NQPF to foster coordinated regional economic development and sustainable growth is to consolidate its green and intelligent infrastructure. In eastern regions, emphasis should be placed on developing AI computing centers, industrial internet platforms, and 6G communication networks, while enhancing low-carbon operation and energy efficiency to strengthen global green competitiveness. Central and western regions should prioritize the construction of 5G base stations, data centers, and smart logistics networks, actively adopting clean energy and energy-saving technologies to reduce both the energy consumption and costs of digital transformation, thereby effectively narrowing the digital and green divides. It is also essential to optimize the spatial distribution of regional innovation platforms: international green sci-tech innovation hubs shall be established in eastern metropolitan areas to form dual-driven sources of the NQPF and sustainable development, whereas national computing hubs and industrial innovation centers shall be constructed in central and western regions with strengthened renewable energy adoption and ecological carrying capacity adaptation. These measures will facilitate the spillover of green technologies from the east and foster local low-carbon innovation ecosystems. Furthermore, enhancing the coordinated development of data factor markets and green element markets is critical. Efforts shall be made to advance the integrated national big data center system and clean energy coordination mechanisms, and to establish cross-regional data and carbon emission trading platforms. These will significantly improve resource utilization efficiency and sustainable development capabilities in less-developed regions.

(2)

Developing the NQPF Based on Local Conditions to Promote Coordinated and Green Regional Development. The development of the NQPF must adhere to the principles of local adaptation and ecological priority, promoting regional economic coordination and green transformation in a steady and orderly manner. Eastern regions should focus on high-end innovation and green upgrading, shifting core cities toward high-value-added industries such as R&D, fintech, biomedicine, and low-carbon services. Metropolitan coordination mechanisms should be utilized to extend green industrial chains and prevent imbalanced development within provinces due to polarization effects. Central and western regions should leverage comparative advantages in renewable energy and ecological resources to develop distinctive industries such as new energy, digital economy, eco-agriculture, and green tourism, avoiding high-carbon homogeneous competition. Green industrial gradient transfer and technical cooperation can be facilitated through “flywheel economy” models and ecological compensation mechanisms. Regional interest-sharing mechanisms—such as cross-regional eco-product accounting and green tax distribution—should be established to incentivize eastern green enterprises to invest and transfer low-carbon technologies to central and western regions. A dedicated Regional Coordination and Green Development Fund for the NQPF shall be set up to support key green technology innovation and low-carbon industry cultivation in central and western regions, effectively promoting balanced and sustainable regional development.

(3)

Increasing Green Research Investment to Advance Science and Technology Innovation for Sustainable Development. Enhancing investment in sustainability-oriented research and elevating the level of green technology innovation are crucial pathways through which the NNQPF can enable regional coordination and low-carbon transformation. Greater efforts should be devoted to basic research and core technology development in low-carbon technologies, circular economy, and ecological restoration. National major S&T programs in frontier areas such as hydrogen energy storage, carbon capture and utilization, and AI-enabled energy conservation should be established to overcome key green technology bottlenecks. Additionally, increasing the share of green R&D funding in universities and research institutions in central and western regions will help foster local green innovation capacity. Incentive mechanisms for green technology innovation should be optimized, including strengthening protection of green intellectual property and implementing fast-track review mechanisms for ecological patents. Enterprises should be encouraged to increase green R&D investment, with tax reductions and targeted subsidies offered particularly to SMEs for green innovation. Deep integration among industry, academia, research, and application should be promoted. Eastern research institutions are encouraged to establish joint green laboratories and low-carbon engineering technology centers in collaboration with central and western enterprises. For talent development, eastern regions may implement global recruitment programs for top green technology experts, while central and western regions can alleviate talent shortages through initiatives such as “talent flywheel” programs and “green expert workstations.” Strengthening local green vocational education and low-carbon skills training will provide sustained momentum for sustainable technological innovation.

(4)

Optimizing Green Urbanization Pathways to Enhance the Synergy Between the NQPF and Sustainability Goals. Urban development strategies should systematically incorporate sustainability concepts to fully leverage the synergistic effects of the NQPF in green transformation. In highly urbanized eastern regions, the focus should be on improving urban quality by promoting smart city development, low-carbon buildings, and new energy transportation systems, avoiding environmental pollution and ecological pressure caused by over-concentration of resources. Central and western regions need to balance new urbanization with ecological conservation, improving digital infrastructure and green public services to enhance urban capacity for supporting the green NQPF. The moderating effect of urbanization rate green thresholds should be taken into account. Eastern cities that have exceeded critical urbanization thresholds should diffuse green technologies to surrounding areas through policy guidance, promoting regional environmental collaborative governance and circular metropolitan development. For small and medium-sized cities in central and western regions with lower urbanization rates, priority should be given to cultivating local green industries and low-carbon innovation ecosystems, avoiding ecological degradation and rapid carbon emission increases resulting from low-quality urbanization.

(5)

Establishing cross-regional green collaborative innovation systems is essential for promoting the sustainable development of the NQPF and enhancing low-carbon technology spillover effects. Green technology cooperation platforms between eastern and western regions should be actively promoted. Eastern research institutions and enterprises are encouraged to collaborate with central and western regions in building joint green laboratories, clean technology incubators, and carbon-neutral demonstration zones to facilitate cross-regional transformation of green technological achievements. Successful models such as “Eastern Data Western Computing” and “Eastern Energy Western Transmission” should be expanded. Moreover, market mechanisms for the flow of green production factors should be improved. Administrative barriers and institutional obstacles hindering the movement of data, carbon sinks, green technologies, and talent must be eliminated. Policies such as green tax incentives and eco-compensation fiscal subsidies can guide the orderly flow of green NQPF resources toward central and western regions. Regional green innovation alliances shall be established to promote collaborative development of clean energy (e.g., wind, solar, hydro), energy consumption data sharing, and low-carbon technology cooperation, ultimately forming a new pattern of regionally complementary and mutually beneficial green innovation development.