The Impact of High-Quality Development of Foreign Trade on Marine Economic Quality: Empirical Evidence from Coastal Provinces and Cities in China

Abstract

Against the backdrop of a complex global economic landscape, foreign trade serves as a critical link integrating China’s marine economy with the global market, playing an indispensable role in advancing high-quality marine economic development in China and realizing the strategic goal of building a strong maritime nation. Utilizing panel data covering 11 coastal provinces and municipalities in China from 2013 to 2022, this research adopts a double machine learning approach to examine the effects and mechanisms through which the high-quality development of foreign trade (HQD) shapes high-quality marine economic development (THQ) in China. The empirical results demonstrate that (1) high-quality development of foreign trade significantly promotes high-quality marine economic development in China, with a 1-unit increase in the former corresponding to a 1.437-unit rise in the latter. This finding withstands multiple robustness checks. (2) Mechanism analysis indicates that this promotion occurs through three channels: strengthening marine environmental regulation, enhancing marine labor productivity, and upgrading the marine industrial structure. (3) Heterogeneity analysis shows that the effect of high-quality foreign trade is stronger in China’s eastern marine economic region. Simultaneously, the trade development environment emerges as a key factor exerting a significantly positive influence on marine economic quality during China’s foreign trade advancement. The empirical findings propose the following optimization countermeasures for high-quality marine economic development: strengthening marine environmental regulation, enhancing marine labor productivity, and promoting the upgrading of the marine industrial structure.

1. Introduction

The marine economy encompasses the economic activities of marine industries and the assets, goods, and services derived from marine ecosystems [1]. It represents the sustainable utilization of ocean resources to achieve economic growth, improve livelihoods, and create employment while preserving the health of marine ecosystems [2]. In 2024, the global value of coastal and marine ecosystem services exceeded USD 20.4 trillion and USD 27 trillion annually, respectively [3]. Consequently, within the contemporary global economic landscape, the marine economy has emerged as a strategic priority for high-quality development worldwide, serving as a new growth engine for the global economy [4,5,6]. China’s modernization drive has progressively elevated the marine economy to a position of strategic primacy and core value [7]. In 2022, the Chinese government articulated its strategic commitment to “advancing the marine economy, preserving marine ecological environments, and expediting the development of a maritime power.” In July 2024, the “Decision of the Central Committee of the Communist Party of China on Further Comprehensively Deepening Reforms and Advancing Chinese Modernization,” adopted at the Third Plenary Session of the 20th Central Committee, reiterated major initiatives such as “Optimizing governance frameworks for marine economic advancement.” This reaffirms the marine economy’s critical function in actualizing China’s strategic maritime power aspiration. Following the systematic implementation of China’s national maritime power enhancement doctrine, the marine-based economy has transitioned into a novel developmental paradigm. Data released by China’s Ministry of Natural Resources shows that from 2001 to 2023, China’s gross marine product surged from CNY 0.95 trillion to CNY 9.91 trillion, with a stable 10.6% yearly growth trend over the period, solidifying its position as a key driver of China’s high-quality economic development. However, China’s marine economy is currently transitioning from scale expansion to quality-driven growth, confronting challenges such as inadequate land–sea coordination, suboptimal industrial structure, imperfect regional coordination mechanisms, and low self-sufficiency rates in the R&D and application of core marine technologies [8,9]. There is an urgent need to explore new growth potentials to propel high-quality marine economic development in China.

Against the backdrop of deepening economic globalization, achieving high-quality development has become a pivotal focus for nations worldwide. In 2017, China officially introduced the concept of “high-quality development,” emphasizing the centrality of quality in its economic advancement. This paradigm addresses both domestic developmental imperatives in China and aligns with global sustainable development trends, resonating with the green growth principles advocated by Organisation for Economic Cooperation and Development (OECD) members [10]. It also corresponds with the European Union’s circular economy model [11] and the United Nations’ goals of inclusive and sustainable growth. Currently, high-quality development of foreign trade constitutes a pivotal objective for national economic advancement [12,13]. It facilitates not only the free flow of goods, services, and capital but also exchanges of technologies, knowledge, and cultures [14]. Oceans, termed the “blue circulatory system” of the global economy, hold strategic significance in foreign trade. Empirical evidence indicates that oceans facilitate up to 75% of global merchandise trade [15], stemming from the energy-efficient operations of seaborne logistics and the global dependence on freight transported via waterways [16]. This underscores trade’s function as a catalytic mechanism for latent expansion across maritime industries, establishing it as imperative developmental leverage for emerging economies [17]. China’s 75 Years Since New China’s Founding: Series Report on Economic and Social Development Achievements, issued by the Chinese government, documents that national merchandise trade volume has ballooned from CNY 8.35 trillion in 1978 to CNY 41.76 trillion in 2023 following the reform and opening-up policy, consolidating its status as the world’s largest merchandise trader. Concurrently, China’s foreign trade exhibits characteristics of high-quality development, manifested through sustained optimization of trade structure, innovation-driven upgrading of trade models, diversification of export markets, and progressive improvement in the trade business environment [18,19]. These developments have established foreign trade as a key growth engine for China’s high-quality economic development.

Within this context, critical questions arise as follows: Can high-quality development of foreign trade propel marine economic quality, thereby accelerating the construction of a strong maritime nation? What specific mechanisms underlie this potential influence? Moreover, does this impact of foreign trade’s high-quality development exhibit heterogeneity? Through rigorous investigation of these issues, this study aims to provide theoretical guidance and empirical references for China’s coastal regions, enabling them to advance high-quality foreign trade development. Such progress will enhance high-quality marine economic development and contribute to building a maritime power.

2. Literature Review

2.1. High-Quality Development of Foreign Trade

Foreign trade facilitates economic growth by disseminating knowledge and advancing technological capabilities, while also enhancing a nation’s competitiveness in international trade through the stimulation of market competition, which further refines production processes [20]. Recent years have witnessed a resurgence of trade protectionism despite increasing trade liberalization worldwide. Notably, the U.S.–China tariff war has substantially undermined international trade stability. Reciprocal tariff measures between these economic powerhouses have not only disrupted bilateral merchandise flows but also generated ripple effects impacting third-party economies like Indonesia [21]. Even nations not directly engaged in trade disputes face adverse consequences, including contracted export demand, heightened market uncertainty, and global supply chain disruptions [22]. Consequently, against this backdrop, pursuing high-quality trade development constitutes an imperative path for nations to adapt to evolving global markets and advance high-quality economic development.

Currently, most scholars have examined trade quality through isolated dimensions such as trade sustainability, international trade competitiveness, trade policy, and trade liberalization and facilitation [23,24,25,26,27,28]. As “high-quality development” establishes itself as the defining feature of China’s economic paradigm shift, numerous studies have investigated the conceptualization and measurement of the high-quality development of foreign trade. Regarding conceptual interpretation, the high-quality development of foreign trade is highly aligned with Chinese-style modernization in terms of value orientation and conceptual pursuit, both adhering to a “people-centered” core principle and integrating the new development concept throughout [23,29]. For measurement approaches, scholars have explored diverse methodologies. Xu and Zhao [30] developed an evaluation index system grounded in China’s new development philosophy, comprehensively assessing high-quality foreign trade development through five dimensions: innovation, coordination, green development, openness, and sharing. Bertsatos and Tsounis [31] assessed China’s high-quality development of foreign trade level by calculating total factor productivity in trade. Additionally, Wang et al. [32] and Wacke et al. [33] developed specialized indices at the product level to measure trade quality. These studies collectively establish multidimensional analytical frameworks and empirical evidence for evaluating China’s high-quality development of foreign trade.

2.2. High-Quality Development of the Marine Economy

Sustainability constitutes an inherent characteristic of the marine economy. The United Nations Economic Commission for Africa (UNECA) defines the marine economy as “a sustainable and equitable economic growth model” [34]. In the Declaration on a New Approach to Align Development Cooperation with the Goals of the Paris Agreement on Climate Change, the OECD Development Assistance Committee (DAC) commits to “promoting a resilient and sustainable marine economy” [35]. Le Gouvello and Simard conceptualize the marine economy as an economic paradigm that integrates rigorous and effective regeneration and conservation of marine, oceanic, and coastal ecosystems with sustainable, low-carbon or carbon-free economic activities, ensuring equitable prosperity for present and future generations of humans and the planet [36].

Some existing studies have centered on foundational theories of the marine economy, with sustainability in marine social–ecological systems, green total factor productivity, and economic inclusiveness constituting critical research domains [37,38,39]. As academic understanding of the conceptual framework of high-quality economic development has matured, scholars have constructed evaluation index systems to measure high-quality marine economic development. Zhang et al. [40] established an evaluation index system for high-quality marine economic development based on the quintuple development philosophy, specifically addressing innovation, coordination, green development, openness, and sharing, with defined metrics across these five dimensions. Ji et al. [41] evaluated high-quality marine economic development across four dimensions: economic efficiency, resource endowment, industrial structure, and innovation capacity. Andruseac [42] and Li et al. [43] expanded this paradigm by incorporating security into the five-sphere integrated plan, proposing six fundamental principles. Furthermore, some studies have identified key determinants influencing high-quality marine economic development, highlighting the significant roles of new quality productive forces, the digital economy, and technological innovation [44,45,46].

2.3. Foreign Trade and Marine Economy

Regarding the impact of foreign trade on the marine economy, most scholars focus on factors such as China’s pilot free trade zone (FTZ) development, openness level, and maritime trade. Wang et al. [47] quantitatively assessed the interactive relationship between free trade pilot zone development and marine economic advancement through coupling coordination modeling. He et al. [48] contend that FTZs propel marine economic growth by facilitating global technology exchange and enhancing foreign capital utilization. Wang et al. [49] examined how the openness level affects the economic efficiency of marine industries. Zhao et al. [50] demonstrated a significant positive association between trade openness and marine economic efficiency through dynamic panel modeling of economies along the Maritime Silk Road. This finding indicates that foreign trade facilitates economic advantage transformation in resource-abundant regions and stimulates export-led economic expansion. Chen et al. [51] applied Tobit regression to demonstrate that China’s foreign trade expansion significantly enhances marine ecological welfare. The mechanism lies in developed foreign trade leveraging locational advantages to increase coastal residents’ disposable income, generating dual benefits for economic growth and ecological conservation. Guo et al. [52] employed spatial Durbin modeling to empirically demonstrate significant positive spatial spillovers from foreign trade openness on marine economic efficiency across China’s coastal regions. Chen [53] proposed that China–ASEAN trade would foster bilateral cooperation in the marine economy and related fields.

2.4. Research Gaps

Collectively, existing studies provide valuable references for investigating how high-quality development of foreign trade influences marine economic quality. However, three research gaps persist. Firstly, a critical research gap persists as follows: the scant literature empirically investigates the relationship between foreign trade and the marine economy through an evaluation index system methodology from a high-quality development perspective. Secondly, comprehensive analyses are scarce, particularly regarding the transmission mechanisms linking these two dimensions. Thirdly, conventional causal inference models encounter methodological constraints including functional misspecification, dimensionality-induced limitations, and multicollinear disturbances. This study’s innovative aspects include the following: (1) investigating the impact of high-quality development of foreign trade on marine economic quality across 11 coastal regions, offering policy insights for China’s marine economic advancement; (2) elucidating transmission channels through marine environmental regulation, marine labor productivity, and marine industrial structure; (3) and employing double machine learning for regression analysis addresses methodological limitations inherent in conventional causal inference approaches, thereby enhancing estimation reliability.

3. Theoretical Analysis and Research Hypotheses

3.1. Promoting the Effect of High-Quality Development of Foreign Trade on Marine Economic Quality

In the context of globalization, foreign trade has shifted from “quantitative expansion” to “structural optimization and efficiency enhancement.” As a new engine of growth, the marine economy sees its synergistic relationship with foreign trade decisively shaping a nation’s growth potential. Specifically, high-quality development of foreign trade can attract high-end technology, professional talent, and advanced management experience, which are high-quality production factors globally, to converge in the marine economy sector [54]. The integration of these elements enhances production efficiency and technological capabilities in marine industries, enabling traditional sectors to upgrade from extensive growth patterns to intensive and refined operational models [55]. Concurrently, it empowers marine enterprises to access international markets, overcome trade barriers, and strengthen the global competitiveness of marine products and services, thereby expanding development horizons and catalyzing quality-driven advancement in the marine economy [56]. Consequently, this study proposes Hypothesis 1:

Hypothesis 1.

High-quality development of foreign trade significantly promotes high-quality marine economic development.

3.2. Mechanisms Underlying High-Quality Foreign Trade’s Impact on Marine Economic Development

3.2.1. Environmental Regulation

Environmental regulation functions as an essential leverage for orchestrating green industrial transformation and underpinning sustainable pathways. As high-quality foreign trade advances globally, heightened environmental awareness and stringent requirements have intensified marine environmental regulations. On the one hand, escalating green trade barriers and environmental standards in international trade compel marine industries to adopt eco-friendly technologies and processes across production, processing, and logistics operations, thereby minimizing ecological damage to marine ecosystems [57]. Concurrently, rigorous regulations incentivize marine enterprises to increase environmental investments, phase out obsolete capacities, and stimulate innovation in developing greener and more efficient production methods [58]. Structurally, enhanced marine environmental regulation steers industrial restructuring toward low-carbon, sustainable trajectories. This facilitates the transition of resource-intensive and polluting marine sectors toward eco-friendly, high-value-added industries [59,60]. Such synergistic progress establishes a virtuous cycle where economic growth and environmental protection mutually reinforce, laying the foundations for sustainable marine economic development. Consequently, this study proposes the following:

Hypothesis 2.

High-quality development of foreign trade facilitates high-quality marine economic development by strengthening marine environmental regulation.

3.2.2. Marine Labor Productivity

Labor productivity constitutes a pivotal metric for marine economic efficiency and a key driver of its quality advancement [61]. High-quality foreign trade introduces diversified advanced technologies, management expertise, and expanded market access, collectively elevating marine labor productivity. Technologically, imported production equipment and innovative concepts directly enhance operational efficiency in marine production processes [62,63]. Managerially, adopting international best practices in organizational governance optimizes internal workflows and resource allocation, while mobilizing workforce potential to improve human capital utilization [64]. Furthermore, the expansion of foreign trade exposes marine enterprises to diversified market demands and competitive environments, stimulating continuous innovation in products and services [65]. This drives sustained growth in marine labor productivity, injecting robust momentum into high-quality marine economic development. Consequently, this study proposes Hypothesis 3:

Hypothesis 3.

High-quality development of foreign trade advances high-quality marine economic development through enhanced marine labor productivity.

3.2.3. Upgrading of Marine Industrial Structure

Industrial restructuring is essential for high-quality marine economic development [66]. High-quality development of foreign trade plays a pivotal role in facilitating cross-regional resource flows, guiding technological innovation, and expanding market access, thereby providing robust support for marine industrial upgrading. From a resource allocation perspective, it attracts premium domestic and international capital, technologies, and talent toward dominant and emerging marine industries, accelerating the transformation of traditional sectors while fostering high-end marine equipment manufacturing, marine information industries, and marine renewable energy [67]. Regarding technological advancement, the cutting-edge technological achievements and innovative concepts brought by foreign trade provide abundant references and ideas for technological innovation in the marine industry. This accelerates the structural realignment of marine industries toward technology- and knowledge-intensive paradigms, elevating value-added creation and core competitive advantages within the oceanic economy [68]. In market orientation, growing global demand for high-value marine products and services drives enterprises to restructure their offerings, steering marine industries toward high-end and diversified development trajectories [69]. Accordingly, this study proposes Hypothesis 4:

Hypothesis 4.

High-quality development of foreign trade drives high-quality marine economic development through marine industrial structure upgrading.

Synthesizing the preceding theoretical foundations, Figure 1 graphically depicts the conceptual framework constructed in this study:

4. Research Design

4.1. Model Specification

This study examines the effects of high-quality foreign trade development on high-quality marine economic development. The existing literature predominantly relies on conventional causal inference methods for parameter estimation. Traditional linear regression models, however, remain vulnerable to functional form misspecification even with polynomial and interaction terms, potentially inducing endogeneity through model specification bias [70]. Conventional linear approaches also encounter dimensionality curses and multicollinearity when handling high-dimensional control variables [71]. These limitations extend to difference-in-differences designs with multiple periods [72]. Propensity score matching introduces subjectivity through variable selection, where differing matching algorithms and bandwidth choices generate inconsistent estimates and sample attrition. The synthetic control method proves effective primarily for policy evaluations with limited treated units; its performance deteriorates as the number of treated units increases due to compromised synthetic control fit [73,74]. To address these methodological constraints, scholars are increasingly adopting double machine learning (DML) for causal inference applications.

In 2018, Chernozhukov proposed the DML methodology, which has since evolved into two primary research streams. The first strand employs DML to evaluate causal relationships in economic phenomena [75]. Farbmacher et al. [76] integrated DML with causal mediation analysis using National Longitudinal Survey of Youth data, quantifying both the direct causal effect of health insurance coverage on youth health outcomes and the indirect mechanism mediated by regular medical checkups. Jiang and Sun [77] applied DML to panel data from 282 Chinese prefecture-level cities, establishing causal links between smart city development and urban green growth while elucidating underlying mechanisms through industrial upgrading, resource allocation, information infrastructure, and technological innovation. The second strand focuses on methodological innovations in DML. Chiang et al. [78] enhanced multi-directional cross-fitting DML estimators to simultaneously compute doubly clustered robust standard errors and output high-dimensional regression results, significantly improving estimation efficiency for multi-clustered sampling structures. Michela et al. [79] addressed discrete treatment evaluation under sample selection or outcome attrition by incorporating observable selection and instrumental variable assumptions into the DML framework to control high-dimensional covariates.

Machine learning leverages algorithms to autonomously identify complex nonlinear patterns within large-scale, high-dimensional data without requiring a predefined functional form, demonstrating superior performance over traditional statistical methods in terms of accuracy and efficiency [80]. DML is a nonparametric regression model capable of automatically selecting high-dimensional control variables and forming high-precision ensembles. It mitigates model misspecification and redundancy, thereby enhancing the reliability of research outcomes and addressing limitations inherent in traditional causal inference methods [75,81]. The random forest algorithm, an ensemble machine learning technique, aggregates predictions from multiple weak classifier decision trees, significantly boosting predictive accuracy and robustness [82]. Particularly suited for datasets with complex nonlinear relationships and high-dimensional features, this study employs random forests for predictive estimation in both primary and auxiliary regressions. Following Chernozhukov et al. [75], this study implements the DML framework specified as follows:

$${\mathrm{H}\mathrm{Q}\mathrm{D}}_{\mathrm{i},\mathrm{t}+1}={\mathsf{\theta }}_0{\mathrm{T}\mathrm{H}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+\mathrm{G}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)+{\mathrm{U}}_{\mathrm{i},\mathrm{t}}$$

(1)

$$\mathrm{E}\left({\mathrm{U}}_{\mathrm{i},\mathrm{t}}\right|{\mathrm{T}\mathrm{H}\mathrm{Q}}_{\mathrm{i},\mathrm{t}},{\mathrm{X}}_{\mathrm{i},\mathrm{t}})=0$$

(2)

In the equation, i denotes the city, t represents the year, HQD_i,t+1 indicates the high-quality development level of the marine economy in region i at year t + 1, THQ_i,t signifies the high-quality development level of foreign trade in region i at year t, θ₀ is the coefficient of the focal treatment variable, X_it denotes a set of control variables requiring estimation via the machine learning algorithm to determine the functional form G(X_i,t), and U_i,t represents the error term.

Direct estimation using Equations (1) and (2) would yield biased estimates of ${\widehat{\mathsf{\theta }}}_0$. This occurs because DML models introduce regularization bias when handling high-dimensional or complex specifications. While regularization reduces estimator variance, it simultaneously induces functional misspecification. Consequently, $\widehat{\mathsf{\theta }}$ fails to converge to ${\mathsf{\theta }}_0$. Therefore, following Jiang et al. [83], this study constructs auxiliary regressions for parameter estimation as specified below.

$${\mathrm{T}\mathrm{H}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}=\mathrm{M}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)+{\mathrm{V}}_{\mathrm{i},\mathrm{t}}$$

(3)

$$\mathrm{E}\left({\mathrm{V}}_{\mathrm{i},\mathrm{t}}\right|{\mathrm{X}}_{\mathrm{i},\mathrm{t}})=0$$

(4)

In the equation, M(X_i,t) represents the conditional expectation function of the treatment variable given high-dimensional control variables, which requires derivation through algorithmic estimation. The procedure unfolds as follows: Firstly, estimate M(X_{i,t) using} a machine learning model to obtain the predictor $\widehat{\mathrm{M}}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)$. Then, compute the residual $\widehat{\mathrm{V}}$_it = THQ_i,t$-\widehat{\mathrm{M}}$(X_i,t). Next, employ ${\widehat{\mathrm{V}}}_{\mathrm{it}}$ as an instrumental variable for THQ_i,t. Finally, continue to use machine learning algorithms to estimate the function G(X_i,t) to obtain the estimator G$\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)$, yielding the unbiased estimator ${{\widehat{\mathsf{\theta }}}_0=\left({\displaystyle \frac{1}{\mathrm{n}}}\sum _{\mathrm{i}\in 1,\mathrm{t}\in \mathrm{T}}{\widehat{\mathrm{V}}}_{\mathrm{i},\mathrm{t}}{\mathrm{T}\mathrm{H}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}\right)}^{-1}{\displaystyle \frac{1}{\mathrm{n}}}\sum _{\mathrm{i}\in 1,\mathrm{t}\in \mathrm{T}}{\widehat{\mathrm{V}}}_{\mathrm{i},\mathrm{t}}\left({\mathrm{H}\mathrm{Q}\mathrm{D}}_{\mathrm{i},\mathrm{t}+1}-\widehat{\mathrm{G}}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)\right)$.

Building on Chernozhukov et al. [75] and Farbmacher et al. [76], this research develops a semi-parametric instrumental variable specification embedded in the double ML architecture, formally expressed as follows:

$${\mathrm{H}\mathrm{Q}\mathrm{D}}_{\mathrm{i},\mathrm{t}+1}={\mathsf{\theta }}_0{\mathrm{T}\mathrm{H}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+\mathrm{G}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)+{\mathrm{U}}_{\mathrm{i},\mathrm{t}}$$

(5)

$${\mathrm{I}\mathrm{V}}_{\mathrm{i},\mathrm{t}}=\mathrm{M}\left({\mathrm{X}}_{\mathrm{i},\mathrm{t}}\right)+{\mathrm{V}}_{\mathrm{i},\mathrm{t}}$$

(6)

In the equation, IV_i,t is the instrumental variable for THQ_i,t.

4.2. Variable Selection

4.2.1. Dependent Variable: High-Quality Marine Economic Development (HQD)

In the context of high-quality marine economic development, innovation serves as the catalytic element energizing development, propelling marine economies to new heights while sustaining prosperity and progress; coordination emphasizes multidimensional synergy across marine economies, industrial structures, resource allocation, and land–sea coordination to establish balanced and sustainable development patterns; green development constitutes the fundamental criterion for high-quality objectives and a key measure of ecological civilization advancement; openness represents the essential pathway for sustainable marine economic growth, where deepening coastal openness enhances developmental quality; and sharing, as the ultimate value pursuit, underscores inclusive benefit distribution to ensure development dividends reach all citizens, achieving comprehensive economic–social–environmental sustainability [40]. Consequently, building on prior theoretical analysis and adopting the indicator selection methodology of An et al. [7] and Li et al. [84], this study constructs a high-quality marine economic development evaluation system. Aligned with the five development philosophies, the system encompasses marine economy, marine society, and marine environment dimensions. Considering data availability and reliability, the final framework comprises 3 primary indicators, 7 secondary indicators, and 23 tertiary indicators.

While the evaluation index system approach remains susceptible to expert experience bias in indicator selection and weight allocation, its hierarchical structure effectively integrates subjective and objective data, comprehensively covering multidimensional evaluation objectives to enhance assessment robustness [85,86]. Given the inaccuracies inherent in factor analysis for weight determination and the elevated subjectivity and uncertainty associated with the analytic hierarchy process, this study adopts the entropy weight method (EWM) following Li and Huang [87] to measure indicator weights and compute composite indices. The EWM’s core advantages lie in its objective weight determination based exclusively on information entropy derived from raw data, effectively circumventing human bias. By quantifying indicator variability, the EWM ensures weights align intrinsically with data patterns. Additionally, its broad applicability, operational efficiency, and flexibility in handling multivariate indicators and diverse data types establish significant reliability and universality for cross-domain comprehensive evaluations. Weight calculation formulas are presented in Equations (7) and (8):

$${\mathrm{E}}_{\mathrm{j}}=\ln {\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{Y}}_{\mathrm{ij}}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{ij}}}}\ln {\displaystyle \frac{{\mathrm{Y}}_{\mathrm{ij}}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{ij}}}}$$

(7)

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{1-{\mathrm{E}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^{\mathrm{m}}\left(1-{\mathrm{E}}_{\mathrm{j}}\right)}}$$

(8)

In the equation, E_j denotes information entropy, Y_ij represents the input variable where i and j indicate secondary and primary indicators, respectively, and n signifies the number of secondary indicators. W_j denotes the weight value, while m represents the number of primary indicators. Detailed evaluation indicators and corresponding weights are presented in

Table 1. Evaluation index system for high-quality marine economic development.

Primary Indicators	Secondary Indicators	Tertiary Indicators (Attribute)	Concept	Data Source	Weights
Marine Economy	Overall Scale	Per Capita Marine GDP (+)	Coordination	China Marine Economy Statistical Yearbook	0.0399
		Contribution to Marine GDP (+)		China Marine Economy Statistical Yearbook	0.0424
	Structural Optimization	Non-Fishing Industry Structure Index (+)		China Fisheries Statistical Yearbook	0.0573
		Optimization Degree of Marine Industry Structure (+)		China Marine Economy Statistical Yearbook	0.0671
	Level of Openness	Coastal Port Cargo Throughput (+)	Openness	China Statistical Yearbook	0.0413
		Coastal Port Passenger Throughput (+)		China Statistical Yearbook	0.0367
		Coastal Port International Container Throughput (+)		China Statistical Yearbook	0.0332
		Actual Direct Utilization of Foreign Capital in Coastal Areas (+)		China Statistical Yearbook	0.0327
		Outward Direct Investment Volume in Coastal Areas (+)		China Statistical Yearbook	0.0347
Marine Society	Marine Education	Number of Marine Higher Education Institutions (+)	Innovation	China Marine Economy Statistical Yearbook	0.0368
		Number of Full-time Marine Higher Education Teachers (+)		China Marine Economy Statistical Yearbook	0.0342
		Number of Marine Professional Master’s and Doctoral Graduates (+)		China Marine Economy Statistical Yearbook	0.0399
	Marine Achievements	Number of Marine Research Projects (+)		China Marine Economy Statistical Yearbook	0.0365
		Number of Marine Research Papers (+)		China Marine Economy Statistical Yearbook	0.0340
		Number of Marine Research Publications (+)		China Marine Economy Statistical Yearbook	0.0364
		Number of Marine Patent Authorizations (+)		China Marine Economy Statistical Yearbook	0.0491
Marine Environment	Resource Endowment	Marine Biological Resource Ownership per 10,000 People (+)	Sharing	China Marine Economy Statistical Yearbook	0.0426
		Marine Non-biological Resource Ownership per 10,000 People (+)		China Marine Economy Statistical Yearbook	0.0392
		Per Capita Marine Rights Area (+)		China Marine Economy Statistical Yearbook	0.0315
		Marine Product Supply Capacity (+)		China Marine Economy Statistical Yearbook	0.0282
	Pollution Prevention and Control	Wastewater Emission per Unit of Marine-related Output (−)	Greenness	China Environmental Statistics Yearbook	0.0659
		Waste Gas Emission per Unit of Marine-related Output (−)		China Environmental Statistics Yearbook	0.0690
		Solid Waste Emission per Unit of Marine-related Output (−)		China Environmental Statistics Yearbook	0.0713

Note: Marine GDP contribution rate measures the contribution of marine economic activities to regional economic growth; non-fishery industrial structure index represents the output value of secondary and tertiary marine industries per employed person; marine industrial structure optimization degree is quantified as the ratio of tertiary to secondary marine industry output value; seawater product supply capacity denotes the total output of mariculture and marine fishing.

.

Simultaneously, based on the measured composite indices, this study employs Python 3.10 software to generate a three-dimensional stacked bar chart illustrating high-quality marine economic development, as depicted in Figure 2. The results indicate Guangdong Province maintains persistently leading HQD values with significant growth, while Shandong, Jiangsu, and Shanghai form a second-tier cohort. Following temporary declines in 2016, several provinces resumed upward trajectories.

4.2.2. Treatment Variable: High-Quality Development of Foreign Trade (THQ)

High-quality foreign trade development entails qualitative optimization and leaps beyond quantitative expansion. The trade development environment reflects the foundation for stable foreign trade growth, serving as a critical safeguard against trade disruption risks and ensuring international circulation quality, thereby underpinning sustained trade advancement. Trade development conditions encompass policy support and infrastructure, functioning as external enablers that inject vitality into foreign trade innovation. Trade development capabilities involve enterprise competitiveness and industrial advantages, representing core determinants of trade competitiveness and forming the foundation of high-quality foreign trade. Trade cooperation levels indicate the depth and breadth of bilateral and multilateral engagement, mirroring optimization potential in trade structures and economic benefits while aligning intrinsically with the fundamental pursuit of economic efficiency. Therefore, extending the analytical frameworks of Fu [88] and Bao et al. [89], this research develops a multidimensional assessment architecture spanning the following: (1) trade development environment; (2) trade development conditions; (3) trade development capabilities; and (4) trade cooperation levels. The resultant framework integrates 8 secondary metrics and 25 tertiary indicators to quantify high-quality foreign trade progression. The composite index is similarly quantified using the entropy method. Detailed indicators are presented in

Table 2. Evaluation index system for high-quality development of foreign trade.

Primary Indicators	Secondary Indicators	Tertiary Indicators (Attribute)	Data Source	Weights
Trade development environment	Economic and social environment	GDP per capita (+)	China Urban Statistical Yearbook	0.0269
		GDP growth rate (+)	China Urban Statistical Yearbook	0.0207
		Consumer price index (−)	China Urban Statistical Yearbook	0.0147
		Registered urban unemployment rate (−)	China Urban Statistical Yearbook	0.0176
	Infrastructure environment	Percentage of highway mileage (+)	China Urban Statistical Yearbook	0.0194
		Proportion of railway operating mileage (+)	China Urban Statistical Yearbook	0.0191
		Commodity circulation efficiency (+)	China Statistical Yearbook	0.0176
		Number of Internet users (+)	China Statistical Yearbook on Science andTechnology	0.0281
		Freight turnover (+)	China Statistical Yearbook on Science and Technology	0.0351
Trade development conditions	Feature allocation efficiency	Labor productivity (+)	China Statistical Yearbook on Science and Technology	0.0195
		Level of education spending (+)	China Urban Statistical Yearbook	0.0188
		Number of students enrolled in higher education per 10,000 people (+)	China Urban Statistical Yearbook	0.0107
	Efficiency of scientific and technological innovation	Number of patent applications per 10,000 people (+)	China Statistical Yearbook on Science and Technology	0.0384
		Level of spending on science and technology (+)	China Statistical Yearbook on Science and Technology	0.0255
		Intensity of R&D spending (+)	China Statistical Yearbook on Science and Technology	0.0197
Trade development capacity	The size of the trade	Dependence on foreign trade (+)	General Administration of Customs of China	0.0361
		Export dependence (+)	General Administration of Customs of China	0.0330
	Trade competitiveness	Trade import and export balance (+)	General Administration of Customs of China	0.0293
		Export growth advantage index (+)	General Administration of Customs of China	0.0293
		Trade competitiveness index (+)	General Administration of Customs of China	0.0656
Trade cooperation levels	Trade partnerships	Number of foreign-invested enterprises (+)	National Bureau of Statistics of China	0.0673
		Registered capital of foreign-invested enterprises (+)	National Bureau of Statistics of China	0.1213
	Benefits of trade cooperation	Number of FDI projects (+)	National Bureau of Statistics of China	0.1963
		Number of foreign technology import contracts (+)	National Bureau of Statistics of China	0.0754
		The contract amount of foreign contracted projects (+)	National Bureau of Statistics of China	0.0145

Note: Commodity circulation expense ratio denotes the ratio of total distribution costs to total sales revenue; freight turnover represents the product of transported cargo volume and transportation distance; labor productivity is measured by industrial value added to the average number of employed persons; education expenditure intensity indicates the proportion of education spending to total expenditures; science and technology expenditure level reflects the share of S&T expenditures in total outlays; R&D investment intensity refers to R&D expenditure as a percentage of regional GDP; foreign trade dependence ratio measures total import–export value relative to regional GDP; export dependence ratio signifies export value accounting for regional GDP; export growth advantage index compares export growth rate to import growth rate; trade competitiveness index calculates the trade balance (exports minus imports) as a proportion of total regional trade volume.

. Furthermore, kernel density estimation is applied to the composite index for representative years (2013, 2018, 2022) across 11 coastal provinces and municipalities using Stata 18 software. Figure 3 illustrates the evolutionary trends, indicating an upward trajectory in most regions. Notably, Shanghai, Jiangsu, and Guangdong consistently exhibit higher kernel density values, while Guangxi and Hebei demonstrate relatively lower levels.

4.2.3. Control Variables

This study selects the following control variables to mitigate confounding effects on high-quality marine economic development: (1) Human capital (HC). Measured by average years of education. Human capital constitutes a core determinant of technology absorption and industrial upgrading, directly influencing innovation efficiency in the marine economy. Specialized human capital serves as the critical driver for enhancing total factor productivity in marine sectors [90]. (2) Informatization level (INT). Quantified as per capita postal and telecommunications services volume. Informatization functions as a fundamental productive power in the digital economy era, directly affecting intelligentization levels and resource allocation efficiency in marine industries [91]. (3) Consumption level (CL). This metric is quantified as the quotient of regional final consumption expenditure to gross domestic product. Domestic demand represents a crucial engine for high-quality marine economic development. Rising consumer demand drives transformation from resource extraction to high-value-added processing in marine industries [92]. (4) Transport infrastructure (TI): Calculated as road mileage per capita. Infrastructure provides the physical foundation for marine economic operations, where existing stock decisively shapes trade facilitation and industrial efficiency [93]. (5) Government intervention (GI). Defined as the ratio of governmental spending to regional gross domestic product. Government subsidies, tax incentives, and industrial funds simultaneously influence foreign trade upgrading and marine industry expansion, establishing bidirectional causal channels [94]. (6) Urbanization rate (URB). This metric quantifies urbanization as the ratio of officially registered urban inhabitants to the total population documented at year-end. Urbanization directly propels high-quality marine economic development while exhibiting endogenous linkages with foreign trade [95]. Following Guo and Small [96], quadratic terms of these controls are incorporated to capture potential nonlinear effects.

4.2.4. Mechanism Variables

This study examines the transmission mechanisms through which high-quality development of foreign trade enhances marine economic quality, focusing on three pathways: strengthening environmental regulation, elevating marine labor productivity, and upgrading marine industrial structure. (1) Environmental Regulation Intensity (ER). Following Zhang et al. [97], ER is measured as the ratio of total environmental pollution control investment to regional GDP. (2) Marine labor productivity (LP). Adopting Wei et al.’s [98] methodology, LP is calculated as the regional gross marine product divided by ocean-relevant employment. (3) Marine Industrial Structure Advancement (MIU). Utilizing Gao et al.’s [99] approach, MIU is quantified via the marine industrial structural hierarchy coefficient: MIU = 1 × N₁ + 2 × N₂ + 3 × N₃, where N₁, N₂, and N₃ denote the percentage contributions of China’s primary, secondary, and tertiary marine industries to marine GDP, respectively.

4.3. Study Area and Data Sources

Coastal regions function as primary development zones for the marine economy, constituting strategically pivotal core areas within China’s maritime economic framework. These regions occupy a decisive position in national strategic planning, serving as indispensable catalysts for sustaining nationwide economic growth and advancing regional coordinated development. Guided by data completeness and accessibility principles, this study examines 11 coastal areas from 2013 to 2022: Liaoning Province, Tianjin Municipality, Hebei Province, Shandong Province, Jiangsu Province, Shanghai Municipality, Zhejiang Province, Fujian Province, Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province (Figure 4) (Shanghai and Tianjin, as municipalities directly administered by China’s central government, constitute key study regions in this research. According to the Chinese Constitution, these municipalities hold provincial-level administrative status alongside provinces, autonomous regions, and special administrative regions).

The dataset for this study primarily originates from the China Statistical Yearbook, China Marine Statistical Yearbook, China Marine Economic Statistical Yearbook, and China Customs Database across successive annual editions. To address anomalies within the dataset, rectifications were implemented based on regional statistical yearbooks, statistical communiqués on national economic and social development, and supplementary online sources. Residual anomalies were rectified using linear interpolation and exponential smoothing techniques.

Table 3. Descriptive statistical analysis of variables.

Variable	N	Mean	SD	Min	Max
HQD	110	1.010	0.402	0.433	1.948
THQ	110	0.160	0.087	0.060	0.324
PGDP	110	9.626	0.405	8.947	10.48
FDI	110	0.027	0.024	0.004	0.096
HC	110	9.619	0.730	8.714	11.17
INT	110	0.060	0.045	0.020	0.176
CL	110	0.374	0.052	0.240	0.455
TI	110	0.011	0.009	0.002	0.029
GI	110	0.193	0.059	0.121	0.321
URB	110	0.677	0.113	0.494	0.892

presents descriptive statistics for all variables.

5. Spatial Distribution Characteristics of High-Quality Marine Economic Development in China

This study categorizes China’s high-quality marine economic development levels into five tiers—high, relatively high, medium, relatively low, and low—using the natural breaks classification method. Spatial–temporal evolution patterns are visualized through ArcGIS 10.8 software for the years 2013 and 2022, revealing characteristic trajectories of marine economic quality advancement. Figure 5 presents the spatial distribution patterns of high-quality marine economic development levels across China in 2013 and 2022.

In 2013, high-quality marine economic development predominantly demonstrated high or relatively high levels in eastern coastal regions, attributable to robust economic foundations, strong innovation capacity, comprehensive policy support, and highly open economic environments. Conversely, northern and southern coastal regions exhibited lower developmental tiers, with Hebei, Guangxi, and Hainan registering the lowest levels—a pattern linked to constrained economic development, industrial stratification, and limited openness. By 2022, high-quality development extended from eastern to southern coastal zones, while northern regions persistently remained at lower tiers.

Comparative analysis of marine economic quality across coastal regions reveals dynamic evolutionary trends between 2013 and 2022. While most coastal areas exhibited progressive enhancement in development levels, marked regional disparities persisted among the Northern, Eastern, and Southern Marine Economic Rims. Notwithstanding these variations, the national maritime power strategy has driven measurable quality improvements across the majority of coastal zones.

6. Empirical Analysis Results

6.1. Baseline Regression

This study employs a DML model to estimate the impact of the high-quality development of foreign trade on marine economic quality enhancement, with a sample splitting ratio of 1:4. Random forest algorithms are implemented for predictive solutions in both primary and auxiliary regressions. As presented in

Table 4. Baseline regression results.

Variant	(1)	(2)	(3)
	HQD	HQD	HQD
THQ	2.268 ***	1.601 ***	1.437 ***
	(0.463)	(0.622)	(0.611)
Control variable linear term	No	Yes	Yes
Control variable quadratic term	No	No	Yes
Time fixed effects	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes
Sample size	110	110	110

Note: Robust standard errors in parentheses *** p < 0.01.

, columns (1) to (3) report the results without control variables, with linear control terms, and with quadratic control terms, respectively. The estimated coefficients for the treatment variable THQ are 2.268, 1.601, and 1.437, all statistically significant at the 1% level. This demonstrates that high-quality development of foreign trade significantly promotes high-quality marine economic development, with higher foreign trade quality corresponding to elevated marine economic performance. These empirical findings align with the theoretical expectations, thereby validating Hypothesis 1.

6.2. Robustness Tests

6.2.1. Endogenous Issues

Acknowledging potential dynamic endogeneity between high-quality development of foreign trade and marine economic quality, this study leverages methodologies from Emran et al. [100] and Xia et al. [101] by selecting market proximity (MP) as an instrumental variable. Market proximity is defined as the inverse of the shortest distance from provincial capitals or municipalities to the coastline, designed to capture exogenous geoeconomic attributes. Given the time-invariant nature of coastal distances during the sample period, this study constructs the instrumental variable IV₁ as the interaction term between the 2013–2022 average exchange rate (ER) and regional market proximity (MP). This interaction effectively captures the heterogeneous transmission of external shocks.

To further verify the robustness of endogeneity tests, this study employs the establishment of free trade zones (FTZs) in coastal regions as a policy-based instrumental variable (IV2 = Treat_i × Post_t), following the methodologies from Dharma [102], Song et al. [103], and Zhou et al. [104]. Here, Treat_i equals 1 if region i hosts an FTZ and 0 otherwise; Post_t takes the value 1 for post-establishment years and 0 otherwise. This specification satisfies two critical conditions: Firstly, FTZs’ designation decisions, made exclusively by China’s central government, remain exogenous to marine economic quality development factors. Secondly, FTZs directly enhance regional trade quality through trade cost reduction and foreign investment attraction, ensuring strong relevance to the core explanatory variable. Estimation via double machine learning’s partially linear IV model (

Table 5. Instrumental variable regression results.

Variant	(1)	(2)
	IV = ER × MP	IV = Treati × Postt
THQ	2.705 **	1.748 **
	(1.284)	(0.549)
Control variable linear term	Yes	Yes
Control variable quadratic term	Yes	Yes
Time fixed effects	Yes	Yes
City fixed effects	Yes	Yes
Sample size	110	110

Note: Robust standard errors in parentheses ** p < 0.05.

, columns 1–2) yields statistically significant coefficients for foreign trade THQ at the 5% level (2.705 and 1.748), confirming the absence of endogeneity concerns.

6.2.2. Replacement of Treatment Variable

To fortify the statistical reliability of core regression specifications, this study recalculates the composite index for the high-quality development of foreign trade using the entropy-weighted TOPSIS method following Li et al. [105]. Subsequent model re-estimation yields the results presented in

Table 6. Robustness check results.

Variant	(1)	(2)		(3)	(4)
	Replacement of Treatment Variable	2013–2019	2020–2022	1% Tail Reduction Treatment	5% Tail Reduction Treatment
THQ	1.981 **	2.647 **	2.876 ***	1.669 ***	1.962 ***
	(0.960)	(1.120)	(0.902)	(0.627)	(0.590)
Control variable linear term	Yes	Yes	Yes	Yes	Yes
Control variable quadratic term	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes	Yes	Yes
Sample size	110	77	33	110	110

Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

column (1): The estimated coefficient for the foreign trade variable (THQ) is 1.981, statistically significant at the 5% level, demonstrating the robustness of the benchmark regression results.

6.2.3. Select Some Samples

Accounting for structural breaks induced by the pandemic, this research applies Li’s temporal stratification methodology to estimate distinct models for the 2013–2020 baseline and 2020–2022 crisis intervals [106]. As shown in Table 6, column (2), the THQ maintains statistically significant positive coefficients (p < 0.05) throughout both observational periods, validating the baseline model’s resilience.

6.2.4. Outlier Elimination

To avoid biases in the regression results caused by outliers in the sample, this paper refers to the study by Brogaard et al. [107] to perform 1% and 5% winsorization on all variables in the sample (except for the disposal variable). Values above the highest percentile and below the lowest percentile are replaced, as shown in columns (3) and (4) of Table 6. After excluding outliers, the research conclusions remain unchanged.

6.2.5. Robustness Check: Alternative Machine Learning Models

To avoid the influence of DML model specification bias on the conclusions, this paper employs various methods of replacing DML to test the robustness of the results. Firstly, change the sample split proportion of the DML model, modifying the baseline regression from 1:4 to 1:2 and 1:7, to explore the potential impact of sample split proportion on the conclusions of this paper. Secondly, the base random forest algorithm is substituted with Lasso regression (LR), Gradient Boosting (GB), Support Vector Machine (SVM), and Neural Network (NE) approaches to assess algorithm dependency. SVM, GB, and NE effectively handle high-dimensional and complex data through kernel function mapping, weak learner integration, and computational modeling, respectively; meanwhile, LR mitigates estimation distortion by imposing regularization constraints on high-dimensional variables. The regression outcomes in

Table 7. Robustness test results with alternative machine learning models.

Variant	(1)	(2)	(3)	(4)	(5)	(6)
	(1:2)	(1:7)	Lasso Regression	Gradient Boosting	Support Vector Machine	Neural Network
THQ	1.981 **	1.646 ***	1.322 ***	1.972 ***	1.893 ***	2.014 ***
	(0.960)	(0.456)	(0.313)	(0.421)	(0.411)	(0.447)
Control variable linear term	Yes	Yes	Yes	Yes	Yes	Yes
Control variable quadratic term	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Sample size	110	110	110	110	110	110

Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

columns (1)–(6) demonstrate that the foreign trade coefficient (THQ) remains statistically significant and positive at the 5% level across all alternative specifications, confirming the robustness of core findings.

6.3. Mechanism Analysis

The preceding regression results demonstrate that high-quality foreign trade significantly promotes marine economic development. To further identify the transmission channels, this study employs a causal mediation analysis via double machine learning, following Farbmacher et al. [75]. Utilizing random forest algorithms, this study examines how core explanatory variables influence mediating mechanisms. The empirical findings are presented in

Table 8. Mechanism test results.

Variant	(1)	(2)	(3)	(4)	(5)	(6)
	ER	HQD	LP	HQD	MIU	HQD
THQ	0.301 ***		1.291 ***		0.483 ***
	(0.0954)		(0.175)		(0.145)
ER		0.344 ***
		(0.0889)
LP				0.793 ***
				(0.135)
MIU						0.868 ***
						(0.222)
Control variable linear term	Yes	Yes	Yes	Yes	Yes	Yes
Control variable quadratic term	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Sample size	110	110	110	110	110	110

Note: Robust standard errors in parentheses *** p < 0.01.

.

6.3.1. Environmental Regulation (ER)

Theoretical analysis indicates that foreign trade influences marine economic quality by shaping environmental regulation. To validate this mechanism, this study separately examines foreign trade’s impact on environmental regulation and the latter’s effect on marine economic development. Columns (1) and (2) in Table 8 show the inspection results. The results show significantly positive coefficients for both high-quality foreign trade and environmental regulation at the 1% level, confirming that foreign trade advances marine economic quality through strengthened environmental oversight. This result is consistent with Hypothesis 2. This compels domestic regulatory upgrades due to international environmental standards and competitive pressures introduced via trade. Enterprises respond with technological innovation and industrial restructuring, driving green transitions in marine industries and enabling synergistic economic–ecological development, thereby fostering sustainable, high-quality marine growth.

6.3.2. Marine Labor Productivity (LP)

To examine whether high-quality foreign trade enhances marine economic development by boosting marine labor productivity, this study replicates the methodological approach. The results in Table 8, columns (3)–(4), show significantly positive coefficients for both foreign trade and marine labor productivity at the 1% level, validating Hypothesis 3. This occurs through technology spillovers and competitive mechanisms introduced via trade, which accelerate technological upgrading and optimize resource allocation in China’s marine industries. Consequently, industrial clustering synergies emerge, elevating labor productivity and driving sustainable industrial advancement.

6.3.3. Marine Industrial Structure Upgrading (MIU)

This study further tests whether foreign trade quality influences marine economic development by promoting industrial structure upgrading. Applying the same methodology, Table 8, columns (5)−(6), reveals significantly positive coefficients for foreign trade and marine industrial structure upgrading at the 1% level, confirming Hypothesis 4. Foreign trade facilitates technology diffusion and factor mobility, enabling China’s marine industries to transition toward high-value-added, technology-intensive sectors. This optimizes specialization within global industrial chains, thereby enhancing the marine economy’s overall efficiency and competitive capacity.

6.4. Heterogeneity Analysis

Given regional disparities in openness levels, resource endowments, and policy orientation, this study examines geographic diversity and trade structure variation to dissect heterogeneous effects of high-quality foreign trade on marine economic development, providing empirical grounding for differentiated policy design.

6.4.1. Regional Heterogeneity Test

Drawing upon the regionalization framework established by Ni et al. [108], and consistent with the coastal zoning in China’s 14th Five-Year Plan (2021–2035), the 11 coastal provinces/municipalities are classified into three maritime economic zones: the Northern Marine Economic Rim (Liaoning Province, Hebei Province, Tianjin Municipality, and Shandong Province), Eastern Marine Economic Rim (Jiangsu Province, Shanghai Municipality, and Zhejiang Province), and Southern Marine Economic Rim (Fujian Province, Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province). The results of group regression are presented in

Table 9. Regional heterogeneity test results.

Variant	Northern Marine Economic Rim	Eastern Marine Economic Rim	Southern Marine Economic Rim
THQ	5.931 **	3.949 ***	2.137 ***
	(2.524)	(1.309)	(0.830)
Control variable linear term	Yes	Yes	Yes
Control variable quadratic term	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes
Sample size	40	30	40

Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

. High-quality development of foreign trade exerts positive effects on marine economic quality across all rims, with the most pronounced impact observed in the Eastern Marine Economic Rim, where a 1% increase in foreign trade quality corresponds to a 1.995% average improvement in marine economic quality. The Eastern Marine Economic Rim demonstrates significant industrial structure optimization with a high proportion of emerging industries. Its strategic location, robust policy support, and abundant scientific talent resources efficiently convert foreign trade advantages into drivers of high-quality marine economic development, yielding pronounced promotional effects. The Southern Marine Economic Rim, while geographically favorable, exhibits a relatively homogeneous industrial composition dominated by traditional marine sectors. With less prominent policy backing and scientific resources compared to the East, the foreign trade’s catalytic effect on the marine economy ranks secondary. The Northern Marine Economic Rim faces structural constraints: homogeneous industries dominated by traditional marine activities, geographically disadvantaged positioning, and a limited economic hinterland, resulting in comparatively weaker trade-driven marine economic advancement.

6.4.2. Heterogeneity in Foreign Trade Structure

This study examines the impact of foreign trade structural heterogeneity (across trade development environment, conditions, capacity, and cooperation level dimensions) on high-quality marine economic development.

Table 10. Heterogeneity test results for foreign trade structure.

Variant	Trade Development Environment	Trade Development Conditions	Trade Development Capacity	Trade Cooperation Level
THQ	1.348 ***	0.992 **	0.968 **	0.390 ***
	(0.208)	(0.502)	(0.439)	(0.0526)
Control variable linear term	Yes	Yes	Yes	Yes
Control variable quadratic term	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes
City fixed effects	Yes	Yes	Yes	Yes
Sample size	110	110	110	110

Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05.

demonstrates that all four dimensions significantly promote marine economic quality, with trade development environment, conditions, and capacity exhibiting stronger effects than trade cooperation level. The pronounced impact of the trade development environment stems from institutional openness dividends generated by policy innovations like pilot free trade zones. These institutional advantages in investment access, trade facilitation, and financial liberalization create superior policy conditions for marine economic agglomeration, generating a “reform multiplier effect” that amplifies marginal outputs from traditional factor inputs. Trade development conditions and capacity establish fundamental support systems for the marine economy through two dimensions: hardware infrastructure and industrial competitiveness. Modernized port infrastructure and optimized logistics networks reduce trade costs, while enhanced industrial innovation capacity and digitalization strengthen value creation capabilities—forming a synergistic mechanism that propels marine economic development. In contrast, the trade cooperation level demonstrates relatively limited effects due to structural constraints in international collaboration. Geopolitical factors undermine cooperation stability, while institutional disparities in marine industry standards and environmental regulations across nations increase coordination costs, thereby diminishing cooperative effectiveness.

7. Discussion

Regarding the impact effects, existing studies have predominantly focused on the aggregate promoting effect of trade openness on the marine economy. For instance, Li et al. [109] emphasized its short-term role in enhancing the green efficiency of the marine economy, while Liu et al. [110] found that seafood trade promotes the marine fishery economy by raising environmental awareness. Guo et al. [52] further verified the positive spatial spillover effects of foreign trade in coastal regions. These findings provide a foundation for this study; however, most prior analyses are not systematically grounded in the “high-quality development” perspective, nor do they construct a comprehensive evaluation system to fully capture the relationship between these factors. Starting from the core connotations of high-quality development, this study develops a comprehensive evaluation index system, thereby overcoming the limitations of earlier work that focused only on single-dimensional impacts.

At the mechanism level, the existing literature has identified pathways such as environmental regulation [111,112], labor productivity [113,114], and industrial structure upgrading [45,115], which collectively offer a theoretical basis for the mechanistic analysis in this research. Nevertheless, most previous studies emphasize single mechanisms and lack a systematic examination of multidimensional parallel pathways. This study simultaneously investigates three transmission pathways: marine environmental regulation, marine labor productivity, and marine industrial structure upgrading. Our empirical results identify marine labor productivity as the core mechanism.

Most importantly, this study reveals significant heterogeneous effects, providing new insights into the complexity of the relationship. The heterogeneity test results indicate that the promoting effect of high-quality development in foreign trade is not uniform but is moderated by regional characteristics and trade structure. This finding suggests that one-size-fits-all trade policies may fail to maximize effectiveness in promoting the high-quality development of the marine economy. Instead, policy design should be more targeted.

8. Conclusions and Policy Implications

8.1. Research Findings

This study employs panel data from 11 coastal provinces and municipalities in China (2013–2022) to construct comprehensive evaluation index systems for high-quality foreign trade development and high-quality marine economic development, utilizing the entropy weight method to compute composite indices for both variables. Through DML models, this study empirically examines the impact, transmission mechanisms, and heterogeneous characteristics of high-quality foreign trade development on China’s marine economic advancement. Key findings include the following: (1) The high-quality development of foreign trade has exerted a significant positive driving effect on the high-quality development of China’s marine economy during the study period. For every 1-unit increase in the high-quality development of foreign trade, the high-quality development of China’s marine economy increases by 1.437 units. This conclusion has withstood multiple robustness tests. (2) Mechanism analysis reveals that the high-quality development of foreign trade promotes the high-quality development of China’s marine economy through three pathways: strengthening marine environmental regulations, enhancing marine labor productivity, and upgrading the marine industrial structure. Among these, the improvement in marine labor productivity constitutes the most significant channel, accounting for 62.1% of the indirect effects; marine industrial upgrading and marine environmental regulations contribute 25.3% and 12.6%, respectively. (3) Heterogeneity analysis indicates that the high-quality development of foreign trade has a more substantial promoting effect on the high-quality development of the marine economy in China’s eastern marine economic circle, with a significant coefficient of 1.995. The trade development environment, as a critical factor, also demonstrates a significant positive impact on the high-quality development of the marine economy, with a coefficient of 1.348.

8.2. Policy Implications

8.2.1. Strengthening Marine Environmental Regulation for Sustainable Marine Economy

Firstly, establish internationally aligned environmental standards. The Chinese government should develop industry and region-specific standards defining environmental thresholds and emission requirements for the marine sectors. Implement full-process supervision with dynamic monitoring systems to ensure corporate compliance. Firms meeting high environmental standards should receive preferential treatment, including priority access to international trade exhibitions and targeted subsidies for overseas market expansion. Secondly, create multi-agency coordination mechanisms. The Chinese government should establish cross-departmental platforms integrating oversight from environmental, maritime, and transportation authorities. Enhance information-sharing systems for interconnected regulatory data. Develop adaptive policy frameworks with regular evaluation cycles to optimize regulatory effectiveness. Thirdly, integrate environmental criteria into trade policy. The Chinese government should incorporate ecological requirements in trade negotiations to align domestic and international standards. Establish green trade promotion channels supporting environmentally compliant firms in global markets. Formulate conflict-resolution mechanisms balancing ecological protection and trade growth.

8.2.2. Enhancing Marine Labor Productivity for High-Quality Marine Economic Development

Firstly, advance international technology collaboration. The Chinese government should create screening mechanisms for importing critical marine technologies and establish technology absorption–reinnovation systems. Foster global talent exchange through open innovation networks. Secondly, optimize industrial organization. The Chinese government should implement modern enterprise management to improve operational efficiency. Develop supply-chain coordination frameworks, enhancing upstream–downstream synergy. Introduce adaptive labor organization models for optimal human resource allocation. Thirdly, reform talent development. The Chinese government should strengthen vocational training through multi-tiered skill cultivation systems. Cultivate high-end specialists via industry–academia–research–application integration. Enhance training relevance through demand-driven programs.

8.2.3. Accelerating Marine Industrial Upgrading for Global Competitiveness

Firstly, nurture emerging industries. The Chinese government should prioritize support for technology-intensive and knowledge-intensive industries such as marine biotechnology and renewable energy. Establish innovation incubators and accelerate core technology breakthroughs. Secondly, modernize traditional industries. The Chinese government should drive the digital and green transformation of fisheries and maritime transport. Facilitate value-chain ascension through smart manufacturing integration. Implement orderly phase-out mechanisms for obsolete capacities. Thirdly, optimize spatial distribution. The Chinese government should develop specialized industrial clusters based on regional comparative advantages. Establish cross-regional coordination mechanisms for efficient factor allocation. Upgrade marine industrial parks to enhance agglomeration economies.

9. Limitations of the Study and Future Research Directions

This study constructs a comprehensive evaluation index system for high-quality development in both foreign trade and the marine economy, analyzing their interactions and impact mechanisms while proposing potential policy implications. However, certain limitations should be acknowledged. Firstly, the foreign trade and marine economy indices rely on the selection and weighting of multiple sub-indicators, which may introduce some subjectivity. Secondly, constrained by data availability, the study covers only eleven coastal regions over ten years, potentially limiting the generalizability of the findings and long-term inferential capacity. Furthermore, while the article examines terminal transmission mechanisms such as environmental regulation, productivity, and industrial structure, it provides insufficient exploration of potential mechanisms like technology spillover and infrastructure. Lastly, the research operates at a macro level and does not disaggregate analysis into specific marine sectors such as maritime transport or services, thereby failing to capture heterogeneity across sectors. Future research will endeavor to develop a more objective indicator weighting system, expand the sample scope and time span, investigate the role of mechanistic variables such as technology spillover and infrastructure, and examine trade elasticity in marine-related sectors and its impact on macro-level relationships to refine the theoretical framework and policy implications.