Research on the development of marine new quality productivity in China: level measurement, dynamic evolution and regional differences Open Access

General Secretary Xi Jinping first introduced the pivotal concept of “new quality productive forces” during a symposium on the comprehensive revitalization of Northeast China in September 2023. He has since repeatedly emphasized the central role that innovation plays in driving the development of productive forces. Unlike traditional productive forces that rely on expansion of factor inputs, new quality productive forces are led by technological innovation, emphasizing technological breakthroughs and innovative allocation of production factors to achieve productivity improvement and transformation of the development model. Their main characteristics are manifested in the accelerated emergence of new industries, new business forms, and new models, as reflected in the digital, intelligent, and green transformation of traditional industries, the development of emerging industries such as artificial intelligence, new energy, and biomedicine, and the forward-looking deployment of future industries including quantum information and brain-computer interfaces. In terms of connotation, new quality productive forces represent a holistic leap in the optimized combination of laborers, means of labor, and objects of labor, and are essentially advanced productive forces.

From the perspective of economic growth measurement, traditional productivity indicators differ fundamentally from new quality productive forces. Traditional productivity indicators mainly include single-factor productivity indicators represented by labor productivity and capital productivity, as well as total factor productivity indicators that reflect technological progress and resource allocation efficiency. These indicators focus on the quantitative representation of economic growth outcomes. New quality productive forces are not a simple substitution for the above indicators; rather, by promoting total factor productivity improvement, they shape the quality and efficiency of economic growth at a deeper level, thereby facilitating the transformation of economic development from scale expansion to quality improvement.

Given the critical role of new quality productive forces in promoting high-quality economic development, their importance in national development strategies is increasingly prominent. The Proposals of the CPC Central Committee on Formulating the 15th Five-Year Plan for National Economic and Social Development—which were reviewed and adopted at the Fourth Plenary Session of the 20th CPC Central Committee—further articulated the strategic imperative of leading the development of new quality productive forces. From the standpoint of future market competition, China has entered a stage of high-quality development, with the cultivation of new quality productive forces serving as both an inherent requirement for and a critical lever in advancing this high-quality development. From the perspective of international competition—amidst “profound changes unseen in a century” and the convergence of numerous global challenges—developing new quality productive forces represents a key avenue for enhancing comprehensive national strength, forging new competitive advantages on the global stage, and addressing shared human challenges such as climate change, resource constraints, and unbalanced development.

Guided by the “Building a Maritime Power” strategy, the scale of China's marine economy has continued to expand. In 2024, the country's Gross Marine Product (GMP) surpassed the 10-trillion-yuan mark for the first time; notably, the marine tertiary sector accounted for 59.6% of the GMP, while the value added by emerging marine industries rose by 7.2% year-on-year, and the marine science and technology innovation index grew by 2.6% ^{[1] [2] [3]}. This indicates that, alongside its continuous expansion in scale, China's marine economy is undergoing a transition in its growth drivers—shifting from factor-driven to innovation-driven growth—with emerging industries and technological innovation playing an increasingly supportive role in the sector's overall expansion (Wang, 2025). The role of ‘new quality productive forces'—characterized primarily by innovation-driven development and industrial upgrading—has become increasingly prominent. However, in terms of both overall level and spatial distribution, China's Marine new quality productivity (MNQP) still exhibits significant disparities: on the one hand, compared with developed nations, China's total factor productivity in 2023 amounted to less than half that of developed countries such as the United States and Germany ^[4]. On the other hand, high-level regions are primarily concentrated along the eastern coast, while low-level cities are predominantly located in the western regions ^[5], exhibiting distinct characteristics of regional differentiation ^[6]. Meanwhile, issues such as the homogenization of the marine industrial structure, insufficient capacity for technological innovation, and inefficient allocation of production factors (Xu and Gao, 2022) have, to a certain extent, constrained the further enhancement of MNQP.

Existing research has conducted systematic explorations regarding the conceptual definition, indicator construction, and economic effects of “new quality productive forces”; however, studies specifically focusing on the marine sector remain relatively limited—particularly concerning quantitative measurement, the sources of regional disparities, and dynamic evolutionary characteristics, areas that still require further in-depth investigation. Against this backdrop, this paper adopts a perspective based on the three fundamental elements of productivity to construct an evaluation indicator system for MNQP. It measures the development levels of these forces across China's 11 coastal provinces and employs Gini coefficient decomposition and kernel density estimation methods to analyze their regional disparities and dynamic evolutionary characteristics. Compared with existing literature, the primary contributions of this study are as follows: (1) Grounded in the practical realities of marine economic development, this paper constructs an evaluation system for MNQP from the perspective of production factors, thereby deepening and expanding the conceptual scope of “new quality productive forces”; (2) Adopting a dynamic perspective, it systematically reveals the spatiotemporal evolutionary trajectory of China's MNQP, enriching the body of relevant empirical research through an in-depth analysis of development trends and spatial distribution patterns; and (3) By integrating Gini coefficient decomposition with kernel density estimation, it conducts a systematic analysis of the sources of regional disparities and the evolution of their distribution, thereby effectively overcoming the limitations inherent in existing studies that tend to rely predominantly on single statistical indicators or parametric econometric models.

The remainder of this paper is structured as follows: Section 2 reviews the relevant literature; Section 3 outlines the research methodology and data sources; Section 4 presents the measurement results of MNQP; Section 5 investigates regional disparities and dynamic evolutionary characteristics; and Section 6 provides conclusions and policy recommendations.

The concept of “new quality productive forces” was first introduced by General Secretary Xi Jinping and has since garnered widespread attention and usage within both political and academic circles. In his article titled Developing New Quality Productive Forces Is an Intrinsic Requirement and Key Focus for Promoting High-Quality Development—published in the journal Qiushi—General Secretary Xi provided the first systematic exposition of the scientific connotations, formation mechanisms, and profound significance of these new quality productive forces (Xi, 2024). The article characterizes new quality productive forces as an advanced form of productive forces—distinguished by high technology, high efficiency, and high quality—that is driven primarily by innovation and transcends the traditional reliance on factor inputs and extensive growth models; furthermore, this concept is fully aligned with the New Development Philosophy. This definition serves as a foundational guiding principle and theoretical starting point for subsequent research.

Based on this, scholars have expanded the connotation of new quality productive forces from various perspectives (Zhou and Xu, 2023) (Gao, 2023). One strand of research begins with the factors of productivity, asserting that new quality productive forces result from the optimization, reorganization, and qualitative leap of laborers, means of labor, and objects of labor, driven by technological innovation (Wang et al., 2025a) (Zhu et al., 2025); another strand of research starts from the development concept, incorporating characteristics such as innovation, green development, digitalization, and others into the analytical framework (Liu and Li, 2025); and some studies adopt a multi-dimensional integration perspective, unifying factor structure, industrial form, and economic form into the new quality productive forces system (Lu et al., 2025). Furthermore, some literature proxy's new quality productive forces using indicators such as total factor productivity (Wang et al., 2025b). Overall, although existing research has defined new quality productive forces from different perspectives, a unified measurement framework has yet to be established.

Empirical research on new quality productive forces primarily focuses on the construction of indicator systems, the analysis of influencing factors, and their economic effects. Regarding indicator systems, most studies develop evaluation frameworks based on the three fundamental elements of productivity (Shan et al., 2025), while expanding their scope through dimensions such as technological advancement, digitalization, and green development (Liu and Li, 2025), or factor restructuring (Lu et al., 2025). Concerning influencing factors, existing literature analyzes the subject from various perspectives—including the digital economy (Guan and Pan, 2025), digital inclusive finance (Shen et al., 2025), green finance (Zhu et al., 2025), technology finance (Wang et al., 2025b), and the allocation of higher education resources—with a general consensus that technological innovation, industrial structure optimization, and factor allocation play significant mediating roles in this process (Mao and Lu, 2026). As for economic effects, studies indicate that new quality productive forces can significantly enhance total factor productivity (Chin et al., 2025), facilitate industrial structure upgrading (Li and Du, 2025) and drive high-quality regional economic development (Yang et al., 2025); furthermore, they exert positive impacts on areas such as coordinated urban-rural development (Gu, 2026), energy transition (Li and Liu, 2025), and economic resilience (Shi et al., 2024). Overall, while existing research has explored the formation mechanisms and economic effects of new quality productive forces from multidimensional perspectives, it tends to concentrate heavily on identifying causal relationships, leaving a gap in the comprehensive characterization of their intrinsic distributional features and dynamic evolution.

MNQP represent an extension of the broader concept of new quality productive forces into the maritime domain. At the theoretical level, existing research has systematically explored the theoretical connotations, constituent elements, and realization pathways of these marine-specific productive forces. Specifically, relevant studies characterize them as an advanced form of productive forces—driven by marine technological innovation—that emerge through the restructuring of marine production factors and the transformation and upgrading of marine industries (Liu and Li, 2025); furthermore, these studies emphasize the pivotal role these forces play in promoting green development and enhancing marine governance (Feng et al., 2025). On the empirical front, scholars have conducted analyses focusing on the construction of indicator systems (Wang et al., 2025c), regional disparities (Sun et al., 2025) (Li et al., 2025), and economic impacts (Dai et al., 2025) (Du et al., 2025). Employing methodologies such as the Gini coefficient, kernel density estimation, and spatial econometric models, these studies reveal that MNQP exhibit a general upward trend—albeit with significant regional variations—and exert a positive catalytic effect on the high-quality development of the marine economy in China.

Overall, as a specific extension of new quality productive forces within the marine domain, research on MNQP remains in its nascent and rapidly developing stages. Current research limitations are primarily manifested in the following three aspects: (1) Although new quality productive forces have emerged as a crucial theoretical tool for driving high-quality economic development, systematic studies on the measurement of MNQP remain relatively limited; (2) Existing research on MNQP tends to focus on theoretical aspects—such as conceptual definitions, underlying theoretical logic, and influencing factors—while lacking quantitative empirical studies; and (3) Current studies tend to prioritize the identification of causal relationships, yet offer relatively insufficient characterizations of the intrinsic distributional structures, sources of regional disparities, and dynamic evolutionary processes of MNQP themselves. Therefore, to enrich the body of research on MNQP, deepen our understanding of their spatial distribution characteristics, and foster the high-quality development of the marine economy, it is imperative to adopt a holistic perspective and employ scientifically sound statistical methodologies to characterize and analyze the regional disparities and dynamic evolutionary processes of MNQP within China.

MNQP across China's 11 coastal provinces, autonomous regions, and municipalities exhibit distinct characteristics of spatial development imbalance, with significant disparities observed among the three major coastal regions. To more effectively characterize these regional disparities—as well as their underlying sources—this study employs the Gini coefficient decomposition method to analyze the MNQP of these 11 coastal jurisdictions over the period from 2011 to 2022. Compared to traditional Gini coefficients and the Theil index, the Dagum Gini coefficient method not only accurately delineates the magnitude and evolutionary trends of overall regional disparities but also effectively addresses issues regarding the cross-overlapping distribution of samples across subgroups. By decomposing the overall Gini coefficient into three components—intra-regional disparity contributions, inter-regional disparity contributions, and contributions from “super-variable density”—this method enables a more precise identification of the core sources of disparity. Consequently, it is particularly well-suited to provide robust empirical support for analyses regarding the spatial imbalances of MNQP, as well as for research focused on their coordinated development (Wang et al., 2025d). The formulas for the Gini coefficient decomposition method proposed by Dagum (Dagum, 1998)—specifically Formulas (1) and (2)—are presented below:

Here, $k$ represents the number of regions into which the country is divided; in this paper, $k=3$⁠. $j$ and$h$ denote distinct areas within these $k$ regions, where $j=1,⋯,k,h=1,⋯,k$⁠,and $j≠h.n$ represents the number of provinces (or municipalities) included in the study sample; as this paper focuses primarily on China's 11 coastal provinces, $n=11.nj(nh)$ denotes the number of provinces within region $j(h)$⁠, $yji(yhr)$ represents the level of MNQP in province $i(r)$ within region $j(h)$⁠; and $y$ denotes the mean level of MNQP. The overall Gini coefficient is composed of three components: the contribution of intra-regional disparities $Gw$⁠, the contribution of inter-regional disparities $Gnb$ and the contribution of transvariation density $Gt$⁠; this decomposition allows for an analysis of the primary sources underlying the overall disparities in MNQP levels.

Kernel density estimation is a non-parametric statistical method that primarily utilizes kernel functions to provide a smoothed estimate of the probability density of a random variable, thereby revealing the shape of its distribution; it is widely employed in the analysis of regional disparities. Let $X1,X2,⋯,Xn$ be a sample from a univariate continuous population; then the kernel density estimate is:

Equation (3) represents the estimate of the density function $f(x)$ at any given point $x$⁠. Here, $f(x)$ denotes the density function, $K(·)$ is the kernel function, and $h$ is the bandwidth−also known as the smoothing parameter; the magnitude of $h$ influences the degree of smoothness of the kernel density estimate.

This paper employs the commonly used Gaussian kernel function to estimate the kernel density curves characterizing the distributional patterns of MNQP across 11 coastal provinces, autonomous regions, and municipalities in China. Based on sample data, it further describes the dynamic evolutionary trends of this distribution over time.

The game theory-based combined weighting method, centered on the principles of game theory, is a weighting technique that balances both scientific rigor and rationality. This method treats various individual weighting techniques as decision-making agents participating in a game; by constructing a combined optimization model, it derives the optimal combination coefficients—based on the principle of minimum deviation—to yield comprehensive weights that synthesize information from multiple sources. In this study, the game theory-based combined weighting method is employed to integrate the Entropy Weight Method with the CRITIC method, thereby generating comprehensive weights that effectively capture both the information regarding data dispersion and the structural characteristics of indicator correlations.

Let $w1,w2$ denote the weight vectors derived from the CRITIC weighting method and the entropy weighting method, respectively. To synthesize the information from these two weighting methods, a linear combination of the two sets of weight vectors is performed to construct a combined weight vector:

Here, $α1$⁠, $α2$ are combination coefficients to be determined. To ensure that the combined weights collectively approximate the weights derived from each individual method as closely as possible, a deviation-minimization objective function is constructed to optimize for $α1$⁠, $α2$⁠, specifically:

Based on the principles of matrix differentiation, by computing the first-order derivative of the aforementioned objective function and setting it to zero, the optimal condition equations for the combination coefficients can be derived:

Solving Equation (6) yields the optimal combination coefficients $(α1,α2)$⁠. Upon normalization, the weight allocation coefficients $(α1*,α2*)$ are obtained. Substituting these into the combination expression yields the final optimal combination weights:

The Marxist theory of the three elements of productive forces explicitly defines productive forces as comprising laborers, objects of labor, and means of labor; these constitute the fundamental basis upon which production activities are carried out. Laborers serve as the subjects of production, objects of labor are the entities upon which productive action is directed, and means of labor act as the instruments connecting laborers to the objects of labor. When these fundamental elements undergo renewal and upgrading, productive forces develop accordingly—a process that aligns precisely with the developmental objective of new quality productive forces, which seeks to achieve enhanced levels of productivity.

Drawing upon the preceding analysis of the characteristics of MNQP—as well as the challenges encountered in their development within China's coastal regions—this paper deconstructs the influencing factors into three distinct dimensions: new types of laborers, new objects of labor, and new means of labor. Based on this framework, the study further refines specific influencing variables and indicators, thereby laying the foundation for constructing an indicator system to measure the level of MNQP across China's coastal regions.

1. New-Type Workers. New-type workers are gradually assuming a pivotal position within the modern system of productive forces. This study identifies expenditure on marine education, the number of master's graduates in various marine disciplines, and the ratio of personnel employed in marine research and development institutions to the total workforce in coastal regions as key factors influencing worker skills. Increased expenditure on marine education serves to enhance workers' theoretical foundations and practical competencies in marine disciplines, thereby building a reserve of specialized talent for the marine sector's new-type workforce. The number of master's graduates in marine disciplines augments the supply of workers endowed with advanced marine-specific knowledge and scientific research capabilities. Furthermore, fluctuations in the ratio of marine R&D personnel to the total coastal workforce impact the capacity of new-type workers in these regions to integrate marine scientific research with industrial applications. The per capita Gross Marine Product (GMP)—calculated relative to the number of employed persons in coastal regions—is designated as a critical factor influencing worker productivity. Changes in this per capita GMP metric serve as a feedback mechanism, incentivizing workers to upgrade their specialized skills related to marine production and prompting them to seek out more efficient production models.

2. Novel Objects of Labor. The 14th Five-Year Plan for Marine Economic Development highlights the optimization of energy structure and the governance of the production environment; marine new energy, marine biological resources, and the marine ecological environment all serve as key manifestations of novel objects of labor within the marine sector. Variables such as energy consumption per unit of marine GDP, wind power generation volume, and marine natural gas output are treated as factors influencing the energy structure. Energy consumption per unit of marine GDP serves as an indicator of energy utilization efficiency; fluctuations in wind power generation volume reflect the extent to which wind energy—a form of clean energy—has been developed; and changes in marine natural gas output reflect shifts in the scope of development and utilization of deep-sea energy resources, which constitute a novel object of labor. Variables such as the proportion of nearshore waters with excellent water quality, the volume of sewage discharged directly into the sea by coastal provinces, and the direct economic losses caused by marine disasters are treated as factors influencing the ecological environment. The proportion of nearshore waters with excellent water quality reflects biodiversity and ecological issues within the marine domain, thereby indicating the impact on the developmental value of marine biological resources and coastal tourism resources; the volume of sewage directly discharged into the sea by coastal provinces reflects issues regarding marine water quality, and consequently, the available space for the development of novel objects of labor; finally, the direct economic losses resulting from marine disasters reflect the degree of damage sustained by the marine ecosystem, thereby influencing the risk costs associated with the development of novel objects of labor.

3. New Types of Means of Labor. The Decision of the CPC Central Committee on Further Comprehensively Deepening Reform and Advancing Chinese Modernization—adopted at the Third Plenary Session of the 20th CPC Central Committee—proposes “improving the institutional mechanisms for developing new quality productive forces in light of local conditions,” emphasizing the need to drive the upgrading of productive elements—such as the means of labor—through technological breakthroughs and the innovative allocation of factors. New types of means of labor encompass both various forms of digitized and intelligent material means of labor, as well as non-material means of labor, such as algorithms, computing power, and new work platforms (Yu and Zhang, 2024). In this study, we identify two key factors influencing material means of labor: the ratio of long-distance fiber-optic line length to the year-end population in coastal regions, and the ratio of internet broadband access ports to the year-end population in coastal regions. The former metric—long-distance fiber-optic line length per capita—serves as a measure of the per capita supply level of the region's long-distance communication backbone network, given that long-distance fiber optics constitute a critical transmission channel for new types of means of labor. The latter metric—internet broadband access ports per capita—reflects the capacity for per capita service provision enabling end-users in coastal regions to access the internet; a higher value indicates that material means of labor can connect to the network more rapidly, thereby reducing the operational and maintenance costs associated with these assets. Furthermore, we designate the internal R&D expenditure of marine research institutions, the number of patents granted to marine research institutions, the number of R&D projects undertaken by marine research and development organizations, and the Digital Economy Index as key factors influencing non-material means of labor. Fluctuations in the internal R&D expenditure of marine research institutions can impact both the quality and quantity of non-material means of labor—such as marine technology patents and research outcomes—thereby providing core technical support for new types of means of labor, such as marine big data platforms. An increase in the number of patents granted to marine research institutions establishes a patent-based technological barrier that supports and protects new types of marine-related means of labor. The number of R&D projects undertaken by marine research and development organizations reflects the vibrancy and scope of scientific research activities, thereby serving to broaden the application scenarios for new types of means of labor. Finally, the Digital Economy Index serves as a comprehensive metric for assessing the level of digital economic development within a specific region or nation; by enhancing the circulation efficiency of non-material means of labor—such as data resources and digital algorithms—it fosters a favorable digital ecosystem for the development and deployment of new types of means of labor.

This study utilizes a sample encompassing 11 coastal provinces, autonomous regions, and municipalities in China, covering the period from 2011 to 2022. The data primarily originates from sources including the China Marine Statistical Yearbook, the China Marine Economic Statistical Yearbook, the China Statistical Yearbook, the China Energy Statistical Yearbook, as well as the annual statistical yearbooks of each respective province, historical reports on China's marine disasters, bulletins concerning the state of China's marine ecological environment, and the Wind database. To address the issue of missing data for specific indicators, methods such as interpolation and linear regression were employed to complete the dataset.

The three fundamental elements of Marxist productive forces—labor power, objects of labor, and means of labor—constitute the core structural framework to which new quality productive forces, as an advanced form of productive forces, adhere. Building on this Marxist foundation, this paper develops an indicator system to measure the level of MNQP, structured across three dimensions: new types of laborers, new types of objects of labor, and new types of means of labor, as shown in Table 1. Drawing from relevant literature and considering data availability and practical feasibility, this paper outlines the constructed indicator system for China's MNQP. The Entropy Weight Method and the CRITIC method are employed individually to determine initial weights for the indicators. These weights are then aggregated using a game theory-based combined weighting method to quantitatively assess the development levels of MNQP across China's 11 coastal provinces from 2011 to 2022.

1. Time evolution

Building upon the calculations of the level of MNQP across China's 11 coastal provinces—as presented in Section 4.3—this section analyzes the temporal evolution of these forces, dissecting their underlying patterns and growth trends. Figure 1 illustrates the characteristics of this temporal evolution for the 11 coastal regions collectively, as well as for the three major marine economic circles. Overall, the average index of MNQP across these 11 coastal regions exhibited a general upward trend—albeit with fluctuations—during the period from 2011 to 2022; specifically, the mean value rose from 0.1916 in 2011 to 0.4039 in 2022.

At the inter-provincial level, the development of MNQP across China's 11 coastal provinces exhibits considerable disparities. Guangdong has consistently maintained a leading position, with its MNQP increasing significantly from 0.3411 in 2011 to 0.7051 in 2022—a growth of more than twofold. Closely following are Shanghai and Shandong, both of which surpassed the 0.5 threshold for MNQP by 2022, forming a cluster of high-level provinces alongside Guangdong. Notably, while Shandong's MNQP grew relatively slowly between 2011 and 2017, its growth rate accelerated notably after 2017, surpassing Shanghai in that year. In contrast, Shanghai, with a relatively high baseline for MNQP, experienced slower growth—and even a slight decline—between 2015 and 2019, before witnessing a marked acceleration post-2019. Beyond these top performers, Jiangsu, Tianjin, Liaoning, Zhejiang, and Fujian generally fall within the medium-level range. Despite differences in their developmental foundations, these provinces displayed relatively steady growth, with their MNQP levels ranging between 0.3 and 0.5 by 2022, yet they have not yet reached the high-level tier. Meanwhile, Guangxi, Hainan and Hebei occupy lower positions, with their MNQP showing limited improvement, slower developmental progress, and a noticeable gap in comparison to the high-level provinces.

From a regional perspective, the average level of MNQP within the Eastern Marine Economic Circle consistently surpasses that of the 11 coastal provinces, as well as the Southern and Northern Marine Economic Circles. Furthermore, the Southern Marine Economic Circle's overall level is slightly below the aggregate average of the 11 coastal provinces. In general, the development trajectories of the three major marine economic circles align closely with the overall trend observed across the 11 coastal provinces, all exhibiting a steady upward trajectory.

1. Spatial evolution

To more accurately capture the spatial evolution of new marine productive forces, this study employed ArcGIS software to generate spatial distribution maps illustrating the levels of MNQP for the years 2013, 2016, 2019, and 2022. Utilizing the natural breaks method, the levels of MNQP across China's 11 coastal provinces were categorized into four distinct grades: High (0.476233–0.705130), Relatively High (0.307054–0.476233), Relatively Low (0.167973–0.307054), and Low (Minimum Value–0.167973), as shown in Figure 2.

Figure 2 effectively illustrates the spatial evolution of MNQP across China's 11 coastal provinces. In 2013, only Shanghai and Guangdong were classified within the “relatively high” tier, while most other provinces were categorized in the “relatively low” or “low” tiers, exhibiting clear regional differentiation. By 2016, Guangdong retained its position in the “relatively high” tier, while Shanghai's MNQP level declined to the “relatively low” tier. Meanwhile, Zhejiang and Fujian advanced from the “low” tier to the “relatively low” tier. Despite some provinces showing progress, the overall distribution was still predominantly characterized by low-to-medium levels. After 2019, Guangdong ascended to the “high” level tier, and Shandong, Jiangsu, and Shanghai gradually entered the “relatively high” tier, marking a shift from a single-core dominance to a multi-core development pattern. By 2022, Guangdong, Shandong, and Shanghai had collectively formed a high-level development cluster, while Liaoning, Jiangsu, Tianjin, and Zhejiang joined the “relatively high” tier, making the regional hierarchical structure even more pronounced.

Overall, the spatial distribution of MNQP across China's coastal provinces exhibits a clear gradient pattern, characterized by “the East leading, the South following, and the North lagging behind.” Regions with high to relatively high levels of development are predominantly concentrated in the core provinces of the Yangtze River Delta and the Pearl River Delta. Leveraging their strengths in intensive investment in marine R&D and high efficiency in the commercialization of research outcomes, these regions provide strong support for the agglomeration of factors essential to MNQP. In contrast, the peripheral provinces in the northern and southern coastal regions, constrained by traditional industrial structures and insufficient momentum for technological innovation, are experiencing a relatively slower pace in the advancement of their MNQP.

As shown in Table 2 and Figure 3, the overall Gini coefficient for MNQP across the 11 coastal provinces demonstrates a fluctuating downward trend, decreasing from 0.2213 in 2011 to 0.1895 in 2022. This indicates a convergence of regional disparities, with a gradual alleviation of development imbalances. Possible reasons for the narrowing of regional disparities include the following. First, the technology spillover and radiation-driven effects of high-level provinces have gradually manifested. The continued increase of marine new quality productivity in core provinces such as Guangdong, Shanghai, and Shandong may promote the development of neighboring provinces through industrial chain collaboration and technology transfer. Second, the further implementation of the national regional coordinated development strategy may optimize, to a certain extent, the allocation of marine innovation resources among the three major marine economic circles, thereby driving the gradual convergence of disparities between the southern and eastern regions and between the southern and northern regions.

Regarding internal disparities within the three major marine economic circles, the pattern of intra-regional imbalance exhibits distinct characteristics of staged evolution. Between 2011 and 2015, the internal regional disparity followed the order of “South > East > North,” with the Southern Marine Economic Circle exhibiting the highest degree of internal imbalance, while the Northern region remained relatively low. Starting in 2016, the ranking of internal disparities shifted to “South > North > East.” Notably, the internal Gini coefficient of the Eastern Marine Economic Circle experienced a significant decline—from 0.1680 in 2011 to 0.0520 in 2018. From 2016 onward, it fell below that of the Northern Marine Economic Circle, establishing the East as the region with the most balanced internal development among the three major marine economic circles. However, a temporary rebound occurred after 2019, with the coefficient rising to 0.1060 in 2022, indicating that the level of internal balance remains subject to some fluctuation.

Internal disparities within the Southern Marine Economic Circle have consistently remained at a high level. Although there was a slight moderation in the later stages of the period, the overall degree of imbalance continues to be the most pronounced. The internal disparity within the Northern Marine Economic Circle reached a low point of 0.1040 in 2017. Following this, it generally trended upward, rising to 0.1470 in 2022, reflecting certain pressures associated with the adjustment of its internal development structure. Overall, internal disparities within the Eastern Marine Economic Circle exhibited a general trend of convergence throughout the sample period. However, the temporary rebound observed in the later stages indicates that, while its level of internal balance has improved, it is still accompanied by structural fluctuations. In contrast, the levels of internal disparity within the Northern and Southern Marine Economic Circles either remain relatively high or are trending upward, suggesting that there is still considerable potential for further improvement in internal structural optimization and the efficiency of factor allocation.

Based on Table 2 and Figure 3, an analysis of changes in inter-regional disparities reveals that, from 2011 to 2022, differences among the three major marine economic circles generally exhibited a fluctuating pattern. Viewed holistically, the inter-regional Gini coefficients for the East-South and North-South pairings remained at consistently high levels over the long term—substantially higher than the disparity level between the North and the East—indicating that the development gap between the Southern Marine Economic Circle and the other two major economic circles is relatively more pronounced. Regarding evolutionary trends, the disparities between the East-South and North-South pairings both demonstrated a phased downward trajectory: the East-South inter-regional Gini coefficient declined from 0.2418 in 2011 to 0.2061 in 2022, while the North-South coefficient dropped from 0.2291 to 0.2050. The trajectories of these two pairings were largely consistent, suggesting that the gap between the South and the other economic circles is gradually narrowing. In contrast, the inter-regional disparity between the North and the East remained at the lowest overall level; it declined from 0.1712 in 2011 to 0.0922 in 2017, exhibiting a distinct trend of convergence. However, since 2019, this disparity has exhibited a degree of rebound, rising from 0.1039 to 0.1381 in 2022. This suggests that the North and the East may be experiencing a phased divergence regarding their development pathways for MNQP, as well as their growth drivers. Furthermore, during the 2015–2016 period, disparities across all three pairings—North-East, North-South, and East-South—witnessed significant declines, reflecting a phase during which the regional development landscape underwent a certain degree of structural reconfiguration.

Figure 4 illustrates the respective contributions of inter-regional disparities, super-variable density, and intra-regional disparities to the overall disparity. The decomposition results reveal that super-variable density consistently serves as the primary source of disparity; its contribution rate has long remained above 50%, even briefly exceeding 60% during the 2017–2019 period. This suggests that the various marine economic circles have not yet formed a fully stratified structure, but rather exhibit a distinct pattern of overlapping distribution. In contrast, the contribution rate of net inter-regional disparity generally follows a “decline-then-rebound” trend: it fell from 18.303% in 2011 to 6.504% in 2018, before recovering to 14.688% in 2022. This reflects that, following a period of convergence, the gap in mean values between regions has subsequently widened. Intra-regional disparity has generally remained around 30%, indicating that the degree of internal imbalance has remained relatively stable. Consequently, the formation of overall disparities in China's MNQP is primarily attributable to the overlapping distribution patterns among regions. In the process of fostering the advancement of these MNQP, efforts should focus on simultaneously narrowing inter-regional development gaps while strengthening internal structural optimization and factor integration within each region, thereby promoting overall coordinated development and mitigating spatial imbalances.

To further investigate the time-varying characteristics and evolutionary patterns of disparities in MNQP, this study employs Kernel Density Estimation (KDE) to analyze the distributional features and dynamic evolutionary processes of these forces across 11 coastal provinces, as well as the Northern, Eastern, and Southern Marine Economic Circles. Kernel Density Estimation has become widely utilized in the analysis of dynamic evolution due to its core advantage: the ability to fit sample data through smooth peak functions. This fitting approach effectively preserves the intrinsic characteristics of the data while mitigating the influence of extreme values, thereby providing intuitive and reliable support for the analysis of dynamic trends. Based on the Gaussian Kernel Density Estimation method, this paper estimates the density distribution morphology of China's MNQP and analyzes the dynamic evolutionary patterns of their distribution.

To visually illustrate the spatial distribution characteristics and dynamic evolutionary processes of these forces in China, this study constructs 3D kernel density plots for the period from 2011 to 2022, covering the 11 coastal provinces and the Northern, Eastern, and Southern Marine Economic Circles. Additionally, by selecting 2013, 2016, 2019, and 2022 as specific observation points, the study generates 2D kernel density plots depicting the levels of MNQP for the 11 coastal provinces and the three major marine economic circles.

The 3D kernel density plots, illustrating the levels of MNQP across the 11 coastal provinces as well as the Northern, Eastern, and Southern Marine Economic Circles from 2011 to 2022, are presented in the figure.

1. The overall dynamic evolution of MNQP across the 11 coastal provinces. As depicted in Figure 5a, the primary peak of the overall level of MNQP in these 11 coastal areas initially shifted to the right, then to the left, and subsequently back to the right. This indicates that the overall level of MNQP follows a fluctuating evolutionary trajectory, characterized by a “rise—slight decline—subsequent rise” pattern, which aligns with the trend observed in the mean value curve of MNQP for these regions discussed earlier. From 2011 to 2017, the kernel density peaks exhibited increased concentration within the 0.25–0.3 range; however, from 2017 to 2022, these peaks dispersed. This dispersion signifies a shift in the MNQP of China's 11 coastal provinces from a concentrated development model to one characterized by diversified development.

2. The Dynamic Evolution of MNQP in the Northern Marine Economic Circle. As illustrated in Figure 5b, the primary peak position in the Northern Marine Economic Circle follows a fluctuating trend of shifting to the right, indicating a steady overall improvement in the level of MNQP. However, during the period from 2017 to 2022, the peak gradually dispersed, losing intensity, suggesting that the development of MNQP in the Northern Marine Economic Circle is marked by a certain degree of stratification.

3. The Dynamic Evolution of MNQP in the Eastern Marine Economic Circle. As illustrated in Figure 5c, the primary peak within the Eastern Marine Economic Circle demonstrates phased changes. From 2011 to 2019, the primary peak contracted and converged toward the 0.3–0.4 range on the MNQP scale, while its peak intensity consistently increased; during the 2016–2019 period, the primary peak was particularly concentrated. After 2019, the primary peak continued to shift to the right, while the width of the curve simultaneously broadened; this indicates that, in recent years, alongside the overall enhancement of MNQP within the Eastern Marine Economic Circle, the degree of internal regional dispersion has increased. Within the kernel density curves, the high-value region surrounding the peak has, in recent years, gradually extended from its originally compact and concentrated state across a wider range of productivity levels. This shift suggests that, within the Eastern Marine Economic Circle, disparities—such as the pace of development—among various entities concerning MNQP have broadened to some degree.

4. The Dynamic Evolution of MNQP in the Southern Marine Economic Circle. As illustrated in Figure 5d, the position of the main peak in the Southern Marine Economic Circle demonstrates an evolutionary trend of shifting rightward, indicating an overall upward trend in the level of MNQP. The kernel density curve for the Southern Marine Economic Circle reveals a distinct bimodal distribution; while the secondary peak remains relatively low, the kernel density estimate is centered around 3.3. This indicates that MNQP in the Southern Marine Economic Circle exhibit distinct stratified characteristics.

To more clearly characterize the distributional features and dynamic evolution of MNQP in China, this paper selects 2013, 2016, 2019, and 2022 as the sample observation years and constructs 2D kernel density plots for the 11 coastal provinces and the three major marine economic circles—Northern, Eastern and Southern.

As shown in Figure 6, the distribution patterns of the kernel density curves for the level of MNQP differ across the 11 coastal provinces and the three major marine economic circles of the North, East, and South:

1. The Kernel density estimation curves for China's 11 coastal provinces exhibit a multi-modal structure, reflecting significant disparities in the development levels across these regions. The 2022 curve demonstrates a general rightward shift compared to the 2013 curve, indicating an upward trend in the level of MNQP across these regions. Over the years, the primary peak exhibits a consistent rightward movement, signaling a steady improvement in the overall level of marine-based MNQP.

2. Compared to other coastal provinces, the curve representing the Northern Marine Economic Circle exhibits a more concentrated peak shape, indicating that the overall development disparity within this region is smaller than that of other coastal provinces. Specifically, the Kernel density estimation curve for the Northern Marine Economic Circle displays a relatively distinct bimodal pattern, suggesting a certain degree of stratification in MNQP within the region. As the years progress, the highest peak of the curve exhibits a rightward shift, indicating that a significant number of areas within the Northern Marine Economic Circle are experiencing gradual improvements in their MNQP. The trends observed in the curves for 2013 and 2016 are largely consistent; however, the Kernel density curve for 2016 is slightly shifted to the right compared to that of 2013, signifying an overall enhancement in MNQP. Furthermore, from 2013 to 2022, the Kernel density estimation value corresponding to the highest peak gradually declined over time, suggesting that the degree of provincial-level clustering regarding MNQP within the Northern Marine Economic Circle has weakened, and the distribution has tended toward greater dispersion.

3. The Kernel density estimation curves for the Eastern Marine Economic Circle exhibit significant variation, displaying a trend of continuous fluctuation. Specifically, the curves for the Eastern Marine Economic Circle present a distinct multi-modal pattern, with side peaks distributed within the medium-to-high value range and varying in height—some high, some low. This indicates that the MNQP in the Eastern Marine Economic Circle are characterized by evident stratification and differentiation. In 2016, the center of the Kernel density estimation curve shifted slightly to the right, and the curve narrowed; this suggests that the level of MNQP in the Eastern Marine Economic Circle improved in 2016, and that development disparities showed a tendency to diminish. In 2019, the center of the curve shifted further to the right, indicating a continued improvement in the level of MNQP. By 2022, the center of the curve had shifted markedly to the right, while the curve's width had increased significantly; this implies that, alongside an overall improvement in performance, internal disparities within the region have widened.

4. The width of the curve for the Southern Marine Economic Circle is broader than that of the Eastern Marine Economic Circle, indicating a relatively higher degree of internal disparity within the region. The MNQP curve for the Southern Marine Economic Circle exhibits a distinct bimodal pattern, with the secondary peak predominantly situated within the medium-to-high value range. This suggests a pronounced phenomenon of hierarchical differentiation regarding MNQP within the region: while the MNQP in certain provinces tend to cluster at a high level, those in others tend to cluster at a medium-to-low level. Compared to 2013, the median of the 2016 MNQP curve shifted slightly to the right, while its width increased significantly; this indicates that, in 2016, as the MNQP in the Southern Marine Economic Circle improved, internal regional disparities also widened substantially. The median of the 2019 curve shifted further to the right relative to 2016, accompanied by a significant increase in width, signifying that during the 2016–2019 period, the level of MNQP continued to rise, as did the internal regional disparities. Finally, the MNQP curve for 2022 continued its rightward shift, demonstrating that the level of MNQP has sustained its upward trajectory.

Building upon the Marxist theory of the three elements of productive forces, this paper constructs an indicator system for MNQP, organized into three dimensions: new types of laborers, new objects of labor, and new means of labor. Employing a combined weighting method based on game theory, the study quantifies the development levels of MNQP across China's 11 coastal provinces from 2011 to 2022. Furthermore, utilizing various methodologies—including the Gini coefficient and kernel density estimation—the paper analyzes regional disparities and the dynamic evolutionary characteristics of MNQP. The main conclusions are as follows:

First, from the perspective of spatiotemporal evolution, MNQP exhibit an overall fluctuating upward trend. Spatially, they follow a gradient distribution pattern characterized by “the East leading, the South following, and the North lagging behind.” While regions with high levels of MNQP are transitioning from a single-core to a multi-core structure, the characteristics of regional stratification remain relatively distinct. Guangdong has long been leading. Meanwhile, Shandong accelerated and surpassed Shanghai in the middle period, while Shanghai recovered its growth in the later period. High-level regions have expanded from a single core (Guangdong) to multiple cores, namely Guangdong, Shandong, and Shanghai. However, peripheral provinces have long trailed behind, and regional stratification remains evident.

Second, from the perspective of regional disparities, the overall Gini coefficient exhibits a fluctuating downward trend, indicating convergence in regional disparities. A regional breakdown reveals that intra-regional disparities are minimal in the East but widened in the later period; the degree of intra-regional imbalance is consistently highest in the South; and in the North, disparities, after periodic improvement, increased again. There exists an obvious cross-distribution phenomenon among the three major marine economic circles, rather than a simple hierarchical structure. The results of disparity decomposition indicate that transvariation density is the primary source of overall disparity.

Third, with respect to distributional evolution, MNQP are transitioning from a state of periodic concentration to one of diversified differentiation. This process is characterized by the “concurrent advancement of horizontal levels and structural differentiation,” with varying degrees of stratification emerging within each distinct marine economic circle. Kernel density estimation shows that the overall distribution has shifted from concentrated development to diversified development. Both the northern and southern regions exhibit a bimodal distribution, with evident stratification characteristics. The eastern region exhibits a multimodal distribution; in recent years, an expansion in internal dispersion has accompanied the improvement in its overall level. For the three major marine economic circles, alongside the improvement in their overall levels, an intensification of internal differentiation is a typical feature.

Overall, from 2011 to 2022, the general level of MNQP across China's 11 coastal provinces demonstrated steady growth, accompanied by continuous optimization in their spatial distribution. However, issues of uneven development—both inter-regionally and intra-regionally—persist, and the structure of these disparities exhibits dynamic evolution. Based on these findings, the following policy implications can be derived:

1. Promote the differentiated development of MNQP by adapting to local conditions, and strengthen region-specific policies and functional positioning. Currently, the overall level of MNQP in China's coastal regions is steadily rising; however, distinct gradient disparities persist among these regions. The eastern region—leveraging its relatively mature industrial system and concentrated innovation resources—holds a leading position in high-end marine industries and technological innovation. The southern region is experiencing rapid growth, yet it exhibits a pronounced trend of internal structural differentiation. The northern region remains relatively weak in terms of industrial foundation and innovation capacity, leaving ample room for improvement in its overall development level. Therefore, differentiated development pathways should be formulated based on the specific development stages and resource endowments of each distinct marine economic circle. The eastern region should further reinforce its technological leadership and strategic layout of high-end industries, thereby enhancing its capacity to drive and influence surrounding areas. The southern region should optimize its internal structure while simultaneously expanding its scale, thereby bolstering the stability of its development. The northern region, for its part, should consolidate its industrial foundation, increase investment in innovation, and enhance both its capacity to absorb external resources and its internal endogenous growth drivers, with the goal of gradually narrowing regional disparities. Thus, at the national level, the functional positioning and development priorities of the three major marine economic circles should be clearly defined, with region- and category-specific guidance. The eastern region—leveraging advantage of agglomerated innovation resources—should focus on marine high-end manufacturing, marine information technology, and other fields, tackle key core technologies, and, through construction of national-level marine science and technology innovation platforms and industrial clusters, enhance technology spillover capacity to surrounding areas. The southern region—based on its “bimodal distribution and stratification” characteristics—should establish a stratified and category-specific mechanism, strengthening innovation guidance for high-level areas and intensifying industrial cultivation and technology introduction for medium- and low-level areas to promote internal structural optimization. The northern region should focus on enhancing foundational capacity, by improving marine infrastructure and establishing special funds to support technological transformation of traditional industries, to enhance capacity for industrial transfer absorption and transformation, and gradually narrow regional development disparities.

2. Improve cross-regional coordination mechanisms to facilitate orderly and efficient allocation of innovation factors. Research findings indicate that, regarding the overall variation in MNQP, there is a pronounced phenomenon of overlapping distribution across regions; this suggests that a stable gradient pattern—in terms of both development levels and structural sophistication—has not yet emerged among the various regions. While certain regions have achieved an overall improvement in their development levels, internal disparities within these regions have widened, implying that the dividends of development have not been distributed equitably. Therefore, it is imperative to strengthen cross-regional coordination mechanisms to facilitate the orderly flow and efficient allocation of innovation factors—such as talent, capital, and technology. Meanwhile, through data sharing platforms and joint research and development mechanisms, redundant investment and homogeneous competition should be reduced, overall resource allocation efficiency should be improved, and regional development should be transformed from decentralized competition to collaborative advancement.

3. Emphasize the simultaneous advancement of structural optimization and coordination to enhance the quality of development. While MNQP are undergoing overall enhancement, they also exhibit characteristics of phased differentiation; specifically, internal disparities have widened in certain regions amidst the process of high-level expansion. This indicates that the mere pursuit of scale expansion cannot achieve high-quality, coordinated development. Moving forward, greater emphasis must be placed on structural optimization and substantive enhancement; while expanding the scale of high-end marine industries, efforts must be intensified to support innovative small and medium-sized entities, thereby fostering technology diffusion and the sharing of achievements. Concurrently, a normalized, dynamic monitoring and policy evaluation mechanism should be established to enhance the foresight and precision of policy regulation, prevent the further widening of intra-regional disparities, and achieve a harmonious integration of efficiency gains and coordinated development. Therefore, while promoting scale expansion, greater emphasis should be placed on development quality and structural balance. On one hand, differentiated regulation policies should be implemented for regions at different development levels. Specifically, support for the transformation of innovation achievements and industrial chain extension should be strengthened for high-level regions, while technology introduction and industrial cultivation should be intensified for medium- and low-level regions, thereby promoting gradient upgrading. On the other hand, a dynamic monitoring and evaluation system for marine new quality productivity should be established to regularly track changes in regional disparities, serving as an important basis for fiscal support and industrial policy adjustments. Meanwhile, special funds for regional coordinated development should be set up to channel resources toward structurally weak regions, preventing the further intensification of a polarization pattern characterized by “high-level agglomeration and low-level lock-in,” and achieving an organic integration of efficiency improvement and regional coordinated development.