Marine Resources and Tourism Industry in China’s Coastal Areas: Coupling Coordination, Driving Mechanism and Compensation Path

Abstract

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction.

1. Introduction

The 21st century is widely recognized as the “Ocean Century”. Advancing China’s maritime power strategy, marine tourism development and ecological civilization construction constitute indispensable components of the country’s high-quality development [1,2]. Endowed with abundant marine natural resources, cultural assets, spatial resources and ecological endowments, coastal provinces and municipalities have evolved into core carriers for agglomeration, transformation and upgrading of the tourism industry [3,4]. Marine resources lay unique resource endowment foundations and expand development space for the tourism sector; in return, the tourism industry facilitates the revitalized utilization of marine resources, inheritance of marine culture and materialization of ecological values, forming an inherently symbiotic and coupled nexus between the two subsystems [5,6].

In recent decades, alongside intensified intensive exploitation of coastal marine resources and the high-quality, diversified upgrading of the tourism industry, continuous interactions have taken place between the marine resource subsystem and the tourism industry subsystem in terms of factor matching, functional complementarity and value conversion [7,8]. Nevertheless, remarkable bottlenecks still hamper their coordinated development. From the perspective of marine resource exploitation, excessive exploitation at the expense of conservation prevails in certain coastal regions, accompanied by prominent problems including overloaded nearshore aquaculture and imbalanced sea area utilization structure. Such issues trigger aggravated ecological disturbances, occupation of coastal shoreline space and accelerated resource depletion, eroding the resource base for sustainable tourism development [9,10]. From the tourism industrial dimension, insufficient excavation of local marine culture, shortage of specialized professionals and inadequate industrial feedback capacity hinder effective marine resource conservation and rational exploitation. From the external institutional environment, imperfect policy matching systems, market supervision frameworks and cross-regional coordination mechanisms further impede the coordinated advancement of the two systems [11]. Moreover, marine resources feature quasi-public-good attributes, ecological externalities and ambiguous property rights. The costs arising from resource depletion and ecological degradation can hardly be fully internalized, leading to widespread mismatches between resource protectors and benefit gainers as well as incongruence of costs and revenues, which obstruct the transition from extensive loose linkage to high-quality coordinated development between marine resources and the tourism industry [12].

Existing literature has delivered insightful explorations covering marine tourism development, resource and environmental constraints, coupling-coordination measurement and ecological compensation mechanism formulation [13,14,15]. However, compared with practical demands for high-quality coordinated development of the marine resource–tourism industry nexus, three major research gaps remain. First, most extant studies construct evaluation frameworks centering on general tourism systems, marine economic systems or eco-environmental systems, while few specifically target the dual system of marine resources and the tourism industry; characteristic marine tourism indicators such as marine cultural resources, island resources and biodiversity are insufficiently incorporated into evaluation systems. Second, prior scholarship predominantly focuses on static coupling-coordination measurement or single-subsystem deficiencies, with inadequate elaboration on long-term temporal evolution, spatial neighborhood spillover effects, regional differentiation and phased shifts of restrictive obstacles underlying the coupled systems. Third, current ecological compensation research mostly concentrates on general ecological preservation or damage indemnification, lacking quantifiable criteria and implementable pathways for compensation mechanisms tailored to marine cultural tourism that integrate resource restoration, spatial regulation, tourism revenue feedback and community benefit sharing.

To fill the aforementioned research gaps, this paper addresses three core research questions: (1) What spatiotemporal differentiation patterns and evolutionary trajectories characterize the coupling-coordination level of marine resources and the tourism industry from 2008 to 2024? (2) What are the internal restrictive obstacles and external driving determinants affecting their coupled coordination, and do such factors feature evident regional heterogeneity? (3) How can a targeted marine resource compensation mechanism adapted to marine cultural tourism scenarios be formulated based on empirical findings? To answer these questions, this study selects 11 Chinese coastal provincial-level regions as research samples and establishes a specialized evaluation index system for the marine resource–tourism industry coupling coordination. By adopting a multi-model empirical framework consisting of the coupling coordination degree model, spatial Markov chain, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model, this paper systematically identifies spatiotemporal disparities, restrictive constraints and driving mechanisms of the dual-system coupling and further proposes a differentiated and synergistic marine resource compensation mechanism.

Three-fold marginal contributions are expected from this research. In terms of research scope, this study unifies marine resources and the tourism industry within a consistent analytical framework and develops a customized dual-system evaluation index system oriented toward marine tourism realities, filling the research gap of insufficient attention to unique marine resource indicators in conventional marine economy or tourism-related studies. Methodologically, the combined application of coupling coordination assessment, spatial Markov chain analysis, obstacle diagnosis, fixed-effect estimation and GTWR enables multi-dimensional identification of coupling mechanisms from temporal evolution, spatial transition, internal constraint and external driving perspectives. From a policy design perspective, the proposed marine resource compensation mechanism rooted in quantified obstacle and driver identification specifies measurable compensation benchmarks, financing modes, interregional resource allocation, concrete compensation approaches and institutional safeguards, delivering actionable policy implications for coordinated sustainable development of marine resource conservation and high-quality coastal tourism.

2. Literature Review

Extant literature has accumulated abundant research findings concerning marine resources and tourism industrial development. Relevant studies are theoretically underpinned by synergy theory, system theory, spatial economics and public goods theory. Synergy theory highlights the coordinated evolution of diverse subsystems via factor flow, functional complementarity and structural adjustment; system theory focuses on holistic correlations among resource, industrial, ecological and social elements; spatial economics provides analytical perspectives for interpreting spatial differentiation, neighborhood effects and regional development lock-in of coupling coordination across coastal zones; public goods and externality theories lay theoretical foundations for marine resource conservation, tourism revenue feedback and compensation mechanism formulation [16]. From the above theoretical perspectives, the nexus between marine resources and the tourism industry goes far beyond a simplistic relationship of resource provision and industrial exploitation; instead, it constitutes a compound systematic linkage covering resource carrying capacity, spatial allocation, ecological constraints, industrial transformation and benefit distribution [17]. Accordingly, it is essential to conduct research within a dual-system analytical framework.

First, in terms of coupling coordination measurement, existing research mostly constructs indicator systems from resource, ecological, industrial and social dimensions and adopts coupling coordination models alongside spatial econometric approaches to explore the spatiotemporal evolution, influencing factors and spatial heterogeneity of tourism systems. Such studies verify that resource endowment, economic development level, transportation infrastructure and policy environment are core determinants shaping the coordinated development of regional tourism [18,19]. From the perspectives of coastal tourism, blue economy, marine ecosystem service, marine spatial planning and sustainable tourism governance, numerous scholars have elaborated the ecological constraints and spatial coordination between marine resource exploitation and tourism development. Nevertheless, most existing literature focuses on coupling analyses of general tourism systems, marine economic systems or eco-environmental systems, while targeted quantitative evaluation focusing exclusively on the dual system of “marine resources–tourism industry” remains insufficient. In particular, characteristic indicators with unique marine tourism attributes, including biodiversity, marine folk customs, marine cultural heritage and island resources, are inadequately incorporated into evaluation frameworks. Consequently, most established indicator systems lean toward generalized resource assessment and fail to precisely reflect the actual dependence and transformation demands of the tourism industry on marine resources [20,21,22].

Second, regarding the identification of obstacle factors, prior studies generally discuss restrictive conditions including institutional arrangement, industrial scale, product supply, public infrastructure and ecological depletion [23,24]. Several papers analyze internal bottlenecks of marine tourism from the resilience and sustainable development perspective, confirming that resource utilization efficiency, industrial format innovation, auxiliary public services and ecological carrying capacity serve as pivotal constraints hindering systematic optimization [6,16]. However, relevant research is largely confined to obstacle identification for a single subsystem or static description of developmental drawbacks, with scarce discussions on structural discrepancies, phased evolution and regional divergence of obstacles across the marine resource and tourism dual system. Given substantial interregional gaps in coastal resource exploitation modes, marine space utilization and tourism industrial foundations, identifying constraints only from a single subsystem cannot accurately capture core restrictive factors during coupled coordination, nor can it deliver targeted evidence for differentiated compensation mechanism design.

Third, concerning the dissection of driving mechanisms, previous literature has acknowledged the impacts of economic growth, industrial composition, transportation facilities, financial support, digital infrastructure and policy regulation on marine tourism expansion [25]. Still, most empirical works estimate average effects and insufficiently explain differentiated influences of driving factors across disparate regions, developmental stages and spatial neighborhoods. Noticeably, coastal China features striking discrepancies among the Bohai Rim, Yangtze River Delta and Southeast Coastal regions in resource exploitation patterns, industrial structure, marketization level and ecological governance efficiency [26], making uniform econometric models incompetent to interpret heterogeneous driving paths across regions. Therefore, exploring spatial heterogeneity of driving forces by combining fixed-effect models, spatial transition features and geographically and temporally weighted regression (GTWR) remains a promising research frontier requiring further refinement.

Fourth, on ecological compensation and benefit coordination mechanisms, grounded in theories of public goods, externality, property right and benefit sharing, existing studies investigate ecological compensation, cultural compensation and trans-regional horizontal ecological compensation [27,28,29]. Some scholars propose frameworks for cross-administrative horizontal marine ecological compensation to mitigate ecological externality and mismatches between costs and benefits [30]; other attempts link ecological compensation with tourism gains to build a closed-loop “conservation-benefit acquisition” mechanism. Even so, prevailing compensation research concentrates predominantly on conventional ecological preservation, with inadequate attention paid to an integrated mechanism tailored for marine tourism scenarios that integrates resource restoration, spatial governance, community benefit sharing, tourism revenue feedback and market-oriented financing. Specifically, operable schemes matching empirical outcomes are still lacking in terms of quantified compensation criteria, diversified financing channels, transboundary benefit allocation, supervision assessment and long-term benefit-sharing systems [3,4,7].

In summary, existing literature provides a solid theoretical basis for understanding the interactive relationship between marine resources and the tourism industry yet contains four prominent research deficiencies. First, current coupling coordination indicator systems lack pertinence for marine tourism, neglecting core characteristic variables such as marine cultural resources, biodiversity and island resources. Second, obstacle diagnosis is dominated by static or single-system analyses, failing to reveal inter-system structural gaps, phased evolution and regional differentiation of restrictive factors, which restricts evidence-based formulation of differentiated compensation policies. Third, studies on driving mechanisms pay insufficient attention to regional and spatiotemporal heterogeneity and cannot explain divergent developmental trajectories among coastal provinces. Fourth, existing compensation frameworks center on general ecological compensation, without quantifiable standards, financing design, horizontal benefit distribution and supervisory rules customized for marine tourism contexts. Against such research gaps, this paper constructs an integrated analytical framework of “coupling measurement—spatiotemporal heterogeneity—obstacle diagnosis—driving mechanism—exclusive marine compensation” and advances relevant research by improving indicator system suitability, dynamically identifying obstacle factors, unpacking the spatial heterogeneity of driving forces and operationalizing marine resource compensation targeting tourist destinations.

3. Methodology

3.1. Construction of Indicator System

This study focuses on the coupled and coordinated development of the marine resource system and the tourism industry system. Following the principles of scientificity, systematicness, quantifiability and data availability and drawing on the research findings of relevant scholars [5,15,17], this paper constructs an evaluation index system for the coupling coordination of the two major systems, as detailed in

Table 1. Index system for coupling coordination between marine resources and the tourism industry.

System Layer	Primary Indicator	Secondary Indicator	Direction	System Layer	Primary Indicator	Secondary Indicator	Direction
Marine Resource System	Natural Marine Resources	O1 Number of islands (unit)	+	Tourism Industry System	Industrial Basis	Q1 Number of hotels (unit)	+
		O2 Output of marine mining industry (10,000 tons)	-			Q2 Number of travel agencies (unit)	+
		O3 Mariculture yield (tons)	-			Q3 Number of A-grade scenic spots (unit)	+
		O4 Marine biodiversity (%)	+			Q4 Number of cultural heritage sites (unit)	+
	Cultural Marine Resources	O5 Number of marine cultural heritage parks (unit)	+		Human Capital	Q5 Employment in star-rated hotels (person)	+
		O6 Number of marine folk culture museums (unit)	+			Q6 Employment of A-grade scenic spots (person)	+
		O7 Number of marine R&D institutions (unit)	+			Q7 Graduates from tourism-related tertiary institutions (person)	+
	Spatial Marine Resources	O8 Sea area utilization rate (%)	-		Market Benefits	Q8 Domestic tourism revenue (CNY 100 million)	+
		O9 Length of coastline (km)	+			Q9 Number of domestic tourist arrivals (10,000 persons)	+
		O10 Number of dock berths (unit)	+			Q10 Proportion of tourism industry in tertiary industry (%)	+
	Ecological Marine Resources	O11 Area of marine protected zones (km2)	+			Q11 Investment in tourism and related industries (CNY 100 million)	+
		O12 Total wetland area (1000 ha)	+
		O13 Proportion of seawater reaching Grades I and II water quality standards (%)	+

. The marine resource subsystem is built upon four dimensions: natural resources, cultural resources, spatial resources and ecological resources. Natural marine resources depict the resource endowment and exploitation foundation of islands, marine minerals and fishery resources, which provide material prerequisites for the tourism industry to develop differentiated tourism products. Cultural marine resources cover marine cultural heritage, folk customs and cultural exhibition carriers and constitute a vital source of cultural connotation and experiential value for marine tourism. Spatial marine resources reflect sea area exploitation, coastline occupation and spatial carrying capacity, representing a fundamental spatial support for tourism development. Ecological marine resources embody marine environmental quality, biodiversity and ecological bearing capacity and serve as the ecological baseline underpinning sustainable marine tourism. The tourism industry subsystem is composed of three dimensions: industrial foundation, human capital and market benefit. Industrial foundation characterizes tourism supply capacity and reception infrastructure, consisting of travel agencies, star-rated hotels and A-grade scenic spots. Human capital stands for tourism service capability and industrial specialization level, supported by tourism practitioners and specialized service talents. Market benefit measures industrial scale, economic gains and market transformation capacity, mainly proxied by inbound tourist receptions and total tourism revenue. This study avoids subjective assignment of indicator weights; all weights are objectively computed with the entropy weight method based on the dispersion degree of original data.

On this basis, eight external influencing factors are further selected, namely the level of opening up, marketization index, economic strength, industrial structure, infrastructure, fixed asset investment, enterprise loan balance and digital infrastructure. From the perspectives of institutional environment, economic development, infrastructure construction and financial support, this paper fully reveals the mechanism of macro conditions affecting the coupled and coordinated development of the two systems.

3.2. Data Sources

This study takes 11 coastal provinces and municipalities in China as the research samples, including Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Shanghai, Guangdong, Guangxi, Hainan and Tianjin. The research period spans from 2008 to 2024, forming a panel dataset with a total of 187 observed values. The original data are mainly derived from the China Statistical Yearbook, China Marine Statistical Yearbook, China Tourism Statistical Yearbook, provincial and municipal statistical yearbooks, national economic and social development statistical communiques and the official website of the National Bureau of Statistics of China.

Specifically, data from 2008 to 2023 are official figures released in published statistical yearbooks and statistical communiqués. As complete official statistical yearbooks for 2024 have not yet been fully published, the 2024 data are primarily derived from the 2024 provincial/municipal economic and social development communiqués and annual preliminary statistical bulletins issued by the National Bureau of Statistics and competent administrative authorities. For a small number of 2024 indicators lacking finalized official statistics, values are supplemented via trend extrapolation based on adjacent-year data or preliminary official bulletins, with consistent statistical criteria maintained throughout data collation.

Indicators concerning marine cultural heritage parks and marine folk custom museums are compiled with reference to the research of Montenero et al. [18]. Relevant data are sorted retrospectively from annual museum directories issued by the National Cultural Heritage Administration, publicly announced documents on archaeological site parks and cultural and tourism archives of coastal provinces and cross-verified against successive editions of the China Marine Culture Development Report.

Linear interpolation is applied to fill sporadic missing values to guarantee the continuity of panel data. In detail, missing early-year data of cultural-resource indicators including the number of marine cultural heritage parks and marine folk custom museums across certain provinces are supplemented through linear interpolation using adjacent-year observations. Missing values caused by adjustments of provincial statistical standards for individual cultural indicators in specific years are also imputed in line with the annual variation trend of the same indicator within the corresponding province.

3.3. Research Methods

To systematically reveal the temporal-spatial evolution law, driving mechanism and heterogeneous characteristics of the coupling coordination between marine resources and the tourism industry, this paper comprehensively adopts five methods: the coupling coordination degree model, spatial Markov chain, fixed-effect model, obstacle degree model, and geographically and temporally weighted regression model (GTWR). The specific methods are described as follows.

3.3.1. Coupling Coordination Degree Model

The coupling coordination degree model is applied to measure the coupling level and coordinated development degree of the two systems. By combining coupling degree and coordination degree, this model overcomes the limitation that a single coupling degree cannot distinguish low-level coupling from high-level coordination and accurately characterizes the dynamic evolution process of the marine resource system and tourism industry system from disorder to order. Coupling degree reflects the interaction intensity between two systems. For the coupling of two systems, the calculation formula of coupling degree C is presented as follows [31]:

$$C =\sqrt{{\displaystyle \frac{U_1·U_2}{{\left(\frac{U_1+U_2}{2}\right)}^2}}} = {\displaystyle \frac{2\sqrt{U_1·U_2}}{U_1+U_2}}$$

(1)

where U₁ denotes the comprehensive development index of the marine resource system, and U₂ represents the comprehensive development index of the tourism industry system; both are within the range of [0, 1]. C refers to the coupling degree with a value range of [0, 1]. A larger value of C indicates stronger interaction and higher coupling degree between the two systems, and vice versa.

The formula for coupling coordination degree is as follows:

$$D = \sqrt{C·T}, T = \alpha U_1 + \beta U_2$$

(2)

where D is the coupling coordination degree ranging from 0 to 1; a higher D value means a better coordinated development level of the two systems. T is the comprehensive coordination index reflecting the overall development level of the two systems. α and β are undetermined coefficients. Given that the marine resource system and tourism industry system occupy an equal position in coupled development, this paper sets α = β = 0.5.

3.3.2. Spatial Markov Chain

To identify the spatiotemporal transition characteristics of coupling coordination degree between marine resources and the tourism industry, this study adopts the spatial Markov chain model and incorporates a spatial lag term to quantify the neighborhood spillover impacts from adjacent regions on local state transitions [16]. Distinct from the conventional Markov chain that only estimates intra-regional state transition probabilities, the spatial Markov chain classifies transition probabilities conditional on regional neighborhood contexts to reveal spatial spillover effects and regional convergence patterns of coupled coordination across coastal provinces.

For the specification of spatial weight matrix, a geographic contiguity matrix is employed. Specifically, w_ij = 1 if two provincial-level regions share a common land border or are geographically adjacent along the coastline; otherwise w_ij = 0. Row normalization is implemented for the weight matrix to eliminate scale bias and ensure comparability of spatial impacts across regions. The spatial lag term is defined as the weighted average of coupling coordination values of neighboring regions, expressed as follows:

$$L_i={\sum }_j{w}_{ij}{D}_j$$

(3)

where L_i denotes the spatial lag value of region i, w_ij refers to the row-normalized spatial weight and D_j represents the coupling coordination degree of adjacent region j. All regions are grouped by the tier of their corresponding spatial lag values to form differentiated neighborhood environments. Subsequent calculations of tier transition probabilities enable identification of leapfrogging, lock-in and convergence features of coupling coordination under varied spatial surroundings.

3.3.3. Obstacle Degree Model

To further identify the core restricting factors and key bottlenecks affecting the coupled and coordinated development of marine resources and the tourism industry and clarify the main constraints on the systematic collaborative evolution in different provinces, municipalities and years, this paper constructs an obstacle degree model for quantitative analysis. The model is set as follows:

Firstly, the standardized indicator data of marine resources and the tourism industry are processed by the extremum method to calculate the indicator deviation degree I, which reflects the gap between the actual indicator value and the ideal value of coupling coordinated development. The formula is as follows:

I = 1 − X_ij

(4)

where X_ij represents the standardized matrix data within the range of [0, 1], and I is also between 0 and 1. A larger I value implies a stronger hindrance effect of the indicator on coupling coordinated development.

On this basis, the indicator obstacle degree model is established to quantitatively measure the obstacle contribution of each indicator:

$$H_{ij} = {\displaystyle \frac{W_j{I}_{ij}}{{\sum }_{j = 1}^n{W}_j{I}_{ij}}}$$

(5)

where H_ij denotes the obstacle degree of the j-th indicator in the i-th year, ranging from 0 to 1; a higher H_ij indicates a more significant hindrance effect. W_j denotes the weight of the j-th indicator (derived from the entropy weight method); I_ij is the deviation degree of the j-th indicator in the i-th year; n is the total number of indicators.

The obstacle degree of upper-level indicators is obtained by weighted summation of the obstacle degrees of subordinate indicators. On this basis, the main obstacle factors under the marine resource system, tourism industry system and each primary dimension are traced layer by layer, providing a quantitative basis for targeted policy formulation.

3.3.4. Fixed-Effect Model

The fixed-effect model is used for benchmark regression analysis. By controlling individual regional effects and adopting cluster robust standard errors at the regional level, this model effectively solves the problems of omitted variables and heteroscedasticity. It empirically tests the overall average impact of opening-up, marketization index, financial support and other factors on coupling coordination degree to ensure the robustness and reliability of empirical results.

3.3.5. Geographically and Temporally Weighted Regression Model (GTWR)

Based on the benchmark regression, the core variables with the strongest explanatory power and highest significance are selected to construct the geographically and temporally weighted regression (GTWR) model. Incorporating both temporal and spatial effects into the analytical framework, the GTWR model captures the spatial-temporal heterogeneity of influencing factors across different regions and years and reveals the temporal-spatial evolution characteristics of driving factors. It makes up for the deficiency of global regression in reflecting local differences and improves the pertinence and practical explanatory power of research conclusions [32].

4. Spatiotemporal Differentiation Characteristics of Coupling Coordination Degree

4.1. Temporal Evolution of Coupling Coordination Degree

Based on the panel indicator data spanning 2008 to 2024, this study calculates the annual average values of marine resource evaluation index, tourism industry evaluation index, coupling degree and coupling coordination degree for the 11 coastal provincial regions, as plotted in Figure 1.

From the perspective of temporal evolution, the coupled coordination between marine resources and the tourism industry evolves through three distinct developmental stages: the initial development stage (2008–2013), the rapid improvement stage (2014–2019) and the shock and recovery stage (2020–2024). Detailed interpretations are as follows. During 2008–2013, the tourism industry expanded rapidly, driven by market demand, with remarkable growth in its composite index. By contrast, marine resource exploitation covers multiple elements including sea areas, coastlines, islands, fishery resources and marine ecology, whose growth is restricted by long exploitation cycles, capital input limits, sea-use approval administration, coastline regulation and ecological conservation requirements, thus presenting a relatively moderate growth rate. Accordingly, the dual system evolves under a development pattern of “tourism taking the lead with marine resources following up”. Despite a mild decline in coupling degree, the overall coupling coordination degree rises steadily, and the loose extensive linkage between the two systems gradually shifts toward high-quality coordinated interaction.

In the period of 2014–2019, fueled by China’s Maritime Power Strategy, marine resource exploitation transforms from blind extensive expansion to an integrated mode featuring intensive utilization, ecological restoration and comprehensive development. The marine resource system achieves accelerated quality improvement and synchronizes robust growth with the tourism industry, leading to more balanced inter-system interaction. The coupling degree remains persistently high, while the coupling coordination degree steps into the primary coordination interval, marking a substantial qualitative upgrade of synergistic development.

During the shock and recovery phase of 2020–2024, the COVID-19 pandemic triggers a sharp drop in the tourism industry index alongside a mild increase in the marine resource index. Such asynchronous development of the two subsystems induces a temporary decline of coupling coordination degree. Starting from 2023, the tourism sector rebounds, and the dualcsystem returns to the track of high-quality coordination with a continuous pickup in coupling coordination degree. Throughout this period, the coupling degree stays at a high level, verifying strong systemic resilience and anti-interference capacity.

4.2. Spatial Distribution of Coupling Coordination Degree

4.2.1. Spatial Differentiation Analysis

Referring to the classification criteria of coupling coordination degree in SPSS Statistics 26.0 and previous research experience, the coupling coordination degree between marine resources and the tourism industry is divided into five grades: severe disorder [0.00, 0.30), mild disorder [0.30, 0.50), barely coordinated [0.50, 0.65), basically coordinated [0.65, 0.80), and well coordinated [0.80, 1.00]. To explore its spatial evolution process, this paper selects the coupling coordination values of 2008, 2016 and 2024 and conducts spatial visualization analysis via ArcGIS 10.5 (Figure 2).

In terms of spatial pattern, the coupling coordination across coastal regions maintains a persistent stratified layout featured with “higher coordination in southern coast versus lower coordination in northern coast with three-tier differentiation”, accompanied by remarkable and persistent spatial inequality. Benefiting from superior marine resource endowments, sophisticated tourism industrial chains and sound collaborative governance, the Yangtze River Delta and Southeast China coastal areas have prominent advantages. Specifically, Guangdong and Zhejiang sustain long-term favorable coordination and serve as two core growth poles for nationwide synergistic development. Shandong, Jiangsu, Fujian and Liaoning achieve steady improvement and are transitioning from moderate toward high-quality coordination, functioning as the backbone of regional integrated development. Hebei, Guangxi and Hainan remain at the preliminary coordination stage with sluggish growth rates. The Bohai Rim suffers from insufficient growth momentum largely because its marine economy has long prioritized productive sectors including port logistics, coastal heavy industry, marine mineral exploitation and inshore aquaculture. The supply of tourism services, marine cultural consumption and eco-leisure products remains inadequate; additionally, massive coastline and sea space are occupied by ports, industrial facilities and aquaculture, squeezing land for coastal tourism and lowering the conversion efficiency from marine resources to tourism output.

By dynamic growth patterns, provincial divergence in coordination evolution is grouped into three categories: high-level stable type, accelerated catch-up type and slow-growth type. Guangdong and Zhejiang fall into the high-level stable group. Relying on mature coastal tourism portfolios, digital service infrastructure, robust tertiary industry foundation and strong domestic consumption capacity, the two provinces facilitate in-depth integration of marine resources and tourism, thus maintaining persistently high coordination with mild volatility. Shandong, Fujian and Liaoning belong to the accelerated catch-up group. Optimized resource exploitation modes and upgraded tourism formats fuel their rapid progress, as emerging integrated businesses such as coastal tourism, recreational fishery, marine ranch sightseeing, island tourism and cultural tourism keep expanding and lift bilateral coordination continuously. Tianjin, Guangxi and Hebei are classified as the slow-growth type. Restricted by underdeveloped tourism foundations, excessive coastline occupation for ports and manufacturing, insufficient development of marine cultural commodities and fragmented cross-regional coordination institutions, these regions are trapped in low coordination tiers and constitute major bottlenecks constraining the overall nationwide coordination improvement.

4.2.2. Spatial Evolution of Coupling Coordination Degree

Based on the Markov chain method, the spatial transition matrix of coupling coordination degree from 2008 to 2024 is obtained, as shown in

Table 2. Markov transition matrix.

ti/ti + 1	n	Serious Imbalance	Mild Imbalance	Barely Coordinated	Basically Coordinated	Well Coordinated
Serious Imbalance	55	0.836	0.163	0.000	0.000	0.000
Mild Imbalance	36	0.083	0.638	0.277	0.000	0.000
Barely Coordinated	26	0.076	0.153	0.615	0.153	0.000
Basically Coordinated	25	0.000	0.000	0.040	0.760	0.200
Well Coordinated	34	0.000	0.000	0.000	0.058	0.941

. The diagonal probabilities are generally at a high level, indicating that the coupling coordination degree of the 11 coastal provinces and municipalities presents strong overall stability, with obvious characteristics of club convergence and path dependence within each grade. Meanwhile, grade transitions only occur between adjacent levels, and there is no cross-grade leap across two or more levels. This suggests that the improvement of coupling coordination degree follows a gradual evolutionary pattern: low-level regions can hardly achieve leapfrog upgrading, while high-level regions exhibit obvious locking effects. In addition, the diagonal probabilities of the mild disorder and barely coordinated grades are relatively lower, implying that regions at the medium coordination level possess greater potential for improvement. By contrast, the polarization and solidification characteristics are most prominent in the severe disorder and well-coordinated grades.

The spatial Markov chain introduces the spatial lag effect to reveal the influence of neighborhood conditions on local evolutionary trends, as presented in

Table 3. Spatial Markov transition matrix.

Spatial Lag	n	ti/ti + 1	I	II	III	IV	V	Spatial Lag	n	ti/ti + 1	I	II	III	IV	V
1	17	I	1.000	0.000	0.000	0.000	0.000	4	12	I	0.666	0.333	0.000	0.000	0.000
	0	II	0.000	0.000	0.000	0.000	0.000		5	II	0.200	0.400	0.400	0.000	0.000
	0	III	0.000	0.000	0.000	0.000	0.000		15	III	0.066	0.000	0.666	0.266	0.000
	5	IV	0.000	0.000	0.000	0.600	0.400		9	IV	0.000	0.000	0.111	0.888	0.000
	1	V	0.000	0.000	0.000	1.000	0.000		2	V	0.000	0.000	0.000	0.000	1.000
2	9	I	0.888	0.111	0.000	0.000	0.000	5	7	I	0.571	0.428	0.000	0.000	0.000
	5	II	0.000	0.600	0.400	0.000	0.000		18	II	0.111	0.722	0.166	0.000	0.000
	2	III	0.000	0.500	0.500	0.000	0.000		4	III	0.000	0.250	0.750	0.000	0.000
	9	IV	0.000	0.000	0.000	0.666	0.333		2	IV	0.000	0.000	0.000	1.000	0.000
	14	V	0.000	0.000	0.000	0.000	1.000		0	V	0.000	0.000	0.000	0.000	0.000
3	10	I	0.900	0.100	0.000	0.000	0.000
	8	II	0.000	0.625	0.375	0.000	0.000
	5	III	0.200	0.400	0.400	0.000	0.000
	0	IV	0.000	0.000	0.000	0.000	0.000
	17	V	0.000	0.000	0.000	0.058	0.941

. The results indicate that the development level of neighboring regions exerts a significant impact on the transition probability of coupling coordination degree. When surrounded by low-level neighborhoods of severe disorder (Grade I), the maintenance probability of local severe disorder reaches 1.000, representing complete status locking and a negative locking effect of adverse neighborhood constraint. When located in medium–low-level neighborhoods of mild disorder (Grade II), the retention probability of the severe disorder grade is 0.888, and the upward transition probabilities of mild disorder and barely coordinated (Grade III) increase moderately, demonstrating a weak positive driving effect of medium–low-level neighborhoods on low-level regions. In medium–high-level neighborhoods of barely coordinated status, the retention probability of severe disorder remains 0.900, and the retention probability of the well-coordinated grade (Grade V) stays consistent with the traditional Markov matrix at 0.941. This indicates that medium–high-level neighborhoods can hardly drive the upgrading of low-level regions, nor can they break the stable locking state of high-level regions. When adjacent to high-level neighborhoods of basically coordinated status (Grade IV), the retention probability of severe disorder drops sharply to 0.666, with the upward transition probability rising to 0.333; meanwhile, the upward transition probability of the barely coordinated grade also increases remarkably. It is verified that high-level neighborhoods generate strong positive spatial spillover effects on low- and medium-level regions, effectively breaking path dependence and promoting grade upgrading. When surrounded by extremely high-level neighborhoods with well-coordinated status, the retention probability of severe disorder is only 0.571, and the upward transition probability reaches 0.428; the upward transition probability of the mild disorder grade also rises significantly. This further confirms the trickle-down effect of high-level neighborhoods, which acts as the core driving force for the overall improvement of regional coupling coordination degree.

A comparison between the traditional Markov matrix and the spatial Markov matrix shows that the higher the neighborhood level, the higher the local upward transition probability and the lower the downward transition probability. This fully verifies significant positive spatial correlation and interactive coordinated development characteristics among the 11 coastal provinces and municipalities. Furthermore, the feature of spatial club convergence is further strengthened: high-level regions mutually reinforce their advantages with high-level neighbors and form a stable high-quality agglomeration belt, while low-level regions are enclosed by low-level neighborhoods, making status upgrading increasingly difficult. The spatial differentiation pattern of the high remaining high and the low remaining low is gradually solidified.

5. Influencing Factors of Coupling Coordination

5.1. Internal Influencing Factors

To explore the internal influencing factors of the coupling coordination system, this paper calculates the obstacle degrees of the main factors affecting the development of marine resources and tourism industry using the obstacle degree model. Due to space limitations, only the ranking of obstacle degrees of the main factors for the Bohai Rim, Yangtze River Delta, Southeast Coastal regions and the whole country in 2008, 2016 and 2024 are listed (

Table 4. Major obstacle factors of coupling coordination.

Region	Year	Ranking of Major Barrier Factors in the Marine Resource System					Ranking of Major Barrier Factors in the Tourism Industry System
		1st	2nd	3rd	4th	5th	1st	2nd	3rd	4th	5th
Bohai Rim	2008	O2	O8	O1	O6	O7	Q10	Q8	Q4	Q7	Q9
	2016	O2	O8	O1	O10	O3	Q10	Q4	Q7	Q5	Q8
	2024	O8	O2	O1	O9	O4	Q7	Q10	Q5	Q4	Q8
Yangtze River Delta	2008	O2	O1	O8	O3	O7	Q10	Q8	Q7	Q2	Q9
	2016	O2	O8	O1	O3	O4	Q10	Q7	Q4	Q5	Q2
	2024	O2	O8	O3	O1	O5	Q7	Q5	Q10	Q4	Q1
Southeast Coastal	2008	O2	O8	O1	O3	O4	Q4	Q8	Q7	Q2	Q9
	2016	O2	O8	O1	O9	O3	Q4	Q7	Q2	Q8	Q5
	2024	O2	O8	O1	O4	O7	Q7	Q4	Q5	Q10	Q8
National	2008	O2	O8	O1	O3	O7	Q8	Q10	Q4	Q7	Q2
	2016	O2	O8	O1	O3	O4	Q7	Q10	Q4	Q5	Q2
	2024	O2	O8	O1	O3	O5	Q7	Q5	Q10	Q4	Q8

), with little change in other years.

5.1.1. Analysis of Obstacle Degree of Marine Resource Subsystem

The obstacle degree pattern of the marine resource subsystem is generally stable, showing the characteristics that traditional development constraints dominate in the long term, spatial resource constraints are gradually strengthened and regional differences emerge with the development stage. The core obstacle factors have long focused on the dimensions of marine natural resources and spatial resources.

At the national level, O₂ (marine mining output) has always been the primary obstacle factor from 2008 to 2024. Marine mining development causes a strong disturbance in the ecology, which inherently conflicts with the ecological development of marine tourism and forms an obvious squeeze on the tourism development space. O₈ (marine area utilization rate) has long ranked second, reflecting the high development intensity and single utilization mode of coastal marine areas; the expansion of industrial and port shorelines has continuously occupied high-quality tourism shorelines. O₁ (number of islands) has stably been the third obstacle, and the insufficient endowment of island resources directly restricts the supply of marine tourism products, becoming a structural shortcoming.

From a regional perspective, the obstacle degree pattern of the Bohai Rim region has undergone phased changes over time: from 2008 to 2016, its obstacle ranking was consistent with that of the whole country, with O₂ (marine mining output), O₈ (marine area utilization rate) and O₁ (number of islands) as the core constraints; in 2024, O₈ (marine area utilization rate) surpassed O₂ (marine mining output) to become the first obstacle, reflecting that the problem of tight spatial resources caused by port development and industrial shoreline expansion in the Bohai Rim region became more prominent in the later period, and spatial resource constraints surpassed traditional mining development to become the primary shortcoming of regional coupling development. The obstacle structure of the Yangtze River Delta and Southeast Coastal regions has been long-term stable, with O₂ (marine mining output), O₈ (marine area utilization Rate) and O₁ (number of islands) always ranking as the top three; the constraint of O₃ (marine aquaculture output) in the Yangtze River Delta region has gradually strengthened, reflecting the pressure of aquaculture expansion on ecological and tourism resources; the obstacle degrees of O₄ (marine biodiversity) and O₇ (number of marine R&D institutions) in the Southeast Coastal region have increased, indicating that the problems of insufficient biodiversity protection and scientific and technological support have become increasingly prominent. On the whole, the obstacle characteristics of the three regions are highly consistent with their respective resource development modes and industrial structures.

5.1.2. Analysis of Obstacle Degree of Tourism Industry Subsystem

The obstacle degree of the tourism industry subsystem presents clear phased evolution characteristics. From 2008 to 2024, it experienced a fundamental transformation from “market scale constraint” to “factor supply constraint”, and the change trend of obstacle factors is highly consistent with the phased characteristics of China’s tourism industry from scale expansion to high-quality development.

Around 2008, the obstacles of the tourism industry in the whole country and the three regions were mainly concentrated in the dimensions of market benefits and industrial foundation: at the national level, Q₈ (domestic tourism income) and Q₁₀ (tourism share in the tertiary industry) were the core obstacles, reflecting that the tourism industry at that time was small in scale, insufficient in market competitiveness and low in status in the regional economy, and market scale constraint was the main factor restricting coupling development; the first obstacle in both the Bohai Rim and Yangtze River Delta regions was Q₁₀ (tourism share in the tertiary industry), consistent with the national trend; the Southeast Coastal region showed unique regional characteristics, with Q₄ (number of cultural relics sites) as the first obstacle, reflecting that although the region was rich in marine cultural resources, its development and utilization were insufficient in the early stage, and the shortage of cultural resource endowment became the primary factor restricting the development of the tourism industry.

In 2016, the obstacle degree pattern of the tourism industry subsystem underwent a key turning point, and the restrictiveness of human capital indicators began to be prominent: at the national level, the obstacle degree of Q₇ (number of graduates from higher tourism colleges) increased significantly, replacing market benefit indicators as the core obstacle; Q₇ also entered the top three obstacles in the Bohai Rim and Yangtze River Delta regions, reflecting that with the upgrading of the tourism industry, the demand for professional talents became increasingly urgent, the contradiction of insufficient market scale was gradually alleviated, and the shortage of talent supply began to appear. In 2024, human capital constraint became a common core constraint in the whole country and the three regions: Q₇ (number of graduates from higher tourism colleges) was the first obstacle in the whole country and in the Bohai Rim, Yangtze River Delta and Southeast Coastal regions, and Q₅ (number of employees in star-rated hotels) became the second obstacle, which together constituted the dual human capital constraints of “professional talents + front-line employees”, clearly reflecting the mismatch between the quality of talent supply and the level of industrial development after the tourism industry entered the stage of high-quality development. At the same time, the restrictiveness of market benefit indicators decreased significantly, Q₁₀ (tourism share in the tertiary industry) retreated to the third place and beyond and Q₁ (number of hotels) in the Yangtze River Delta region entered the top five obstacles, indicating that the constraint of the shortcoming of tourism-supporting facilities gradually emerged. On the whole, the evolution trend of the obstacle degree of the tourism industry subsystem is highly consistent with the actual development stage, and the regional differences are also consistent with the characteristics of resource endowment.

5.2. External Influencing Factors

To further understand the external driving factors of China’s coupling coordination degree system, this paper adopts the fixed-effect model and geographically and temporally weighted regression (GTWR) analysis. Referring to previous research experience [33,34], the following external influencing factors are selected: ① Economic Development: As the material foundation for the coordinated development of marine resources and tourism industry, economic development can provide capital, technology and market support for the development of marine resources and the upgrading of tourism industry, which is represented by per capita GDP. ② Capital Investment: Capital investment is mainly used for the construction and upgrading of hardware projects such as marine infrastructure, tourism-supporting facilities and industrial parks, which directly determines the intensity of regional marine development and tourism reception capacity and is represented by the amount of fixed asset investment. ③ Industrial Structure: The optimization of industrial structure is the key to promoting the integrated development of marine tourism. The increase in the proportion of the tertiary industry can promote the supply of tourism services and the efficient allocation of resources, which is represented by the proportion of the added value of the tertiary industry in GDP. ④ Financial Support: Financial support is a key support to alleviate corporate financing constraints and activate the vitality of industrial operation, ensuring the daily operation, expanded reproduction and format innovation of enterprises, which is represented by the balance of corporate loans. ⑤ Digital Empowerment: The construction of digital infrastructure can promote the digital transformation of marine resource management and tourism services, improving the efficiency of industrial integration and service experience, which is represented by the level of digital infrastructure. ⑥ Institutional Environment: The institutional environment is a key factor affecting the efficiency of resource allocation and the vitality of industrial development. The improvement of marketization can optimize resource allocation and stimulate market vitality, which is represented by the marketization index. ⑦ Market Openness: The level of opening up is an important indicator to measure regional market vitality and external linkage, which has an important impact on the market expansion and resource utilization of the marine tourism industry and is represented by the level of opening up. ⑧ Infrastructure Construction: Transportation infrastructure is an important guarantee for connecting tourism resources and improving accessibility, which can promote the flow of factors between regions and the development of the tourism industry and is represented by the density of the highway network. Firstly, the fixed-effect regression is carried out on the influencing factors, and the regression coefficients are positively significant in most years and regions. Then, the most significant influencing factors are selected for geographically and temporally weighted regression, and the average value of regression coefficients is calculated (Figure 3).

5.2.1. Fixed-Effect Regression Results

The unit root test results show that the dependent variable, namely the coupling coordination degree, is stationary, while some explanatory variables exhibit first-order integration characteristics. Considering that this study mainly focuses on the statistical association between external factors and the coupling coordination level and that both regional fixed effects and year fixed effects are controlled in the fixed-effect model, the influence of time-invariant regional characteristics and common time trends can be mitigated. Therefore, the benchmark regression is estimated using the original variables. To determine the form of the panel data model, this paper conducts the F-test and Hausman test successively. The F-test indicates that there are significant individual fixed effects, which means that regional differences must be controlled; the Hausman test further rejects the null hypothesis of random effects, and the two tests jointly support the final adoption of the fixed-effect model. To avoid the interference of multicollinearity, this paper conducts multicollinearity tests via the variance inflation factor (VIF) and the correlation coefficient matrix. The VIF values of all explanatory variables are lower than the critical value of 10, suggesting no serious multicollinearity among variables and reasonable selection of indicators.

Based on the panel data of 11 coastal provinces and municipalities in China from 2008 to 2024, this paper uses the fixed-effect model to empirically test the impact of various factors on the coupling coordination degree between marine resources and the tourism industry, and the regression results are shown in

Table 5. The results of fixed-effect model.

Influencing Factor	Explanatory Variable	Path Coefficient
Constant	C	0.617 *** (10.633)
Market Environment	Opening-up Level	−0.032 *** (−4.265)
Institutional Environment	Marketization Index	−0.119 *** (−2.295)
Economic Development	Per Capita GDP	0.089 (0.598)
Capital Investment	Fixed Asset Investment	0.056 (1.005)
Infrastructure	Highway Network Density	0.029 * (1.205)
Digital Empowerment	Digital Infrastructure	0.082 * (1.899)
Industrial Structure	Proportion of Tertiary Industry Value-added in GDP	0.105 *** (3.948)
Financial Support	Corporate Loan Balance	0.110 *** (2.528)

Notes: * and *** represent significance at the 10% and 1% levels respectively.

.

Empirical regression results reveal that the share of tertiary industry value added in the GDP and outstanding corporate loans exert significantly positive effects on the coupling coordination between marine resources and the tourism industry. Such findings indicate that the optimization of the service industry structure and effective financial support can boost tourism supply and inter-industry synergy, which is largely consistent with prior research concluding that industrial upgrading and financial underpinning advance high-quality tourism development [14]. Marketization index and opening-up level show statistically negative coefficients, yet this adverse correlation does not imply that marketization and opening up per se hinder marine tourism development. Instead, it can be attributed to the fact that capital in coastal regions predominantly flows into port logistics, coastal manufacturing, real estate and conventional resource exploitation sectors, crowding out investment for coastal tourism space construction, public services and ecological conservation [15]. This interpretation coincides with the viewpoint of conditional impacts of marketization and opening up proposed in existing literature. The positive coefficient of digital infrastructure empirically confirms the proposition that digital economy enables the integrated development of marine tourism [30]. In contrast, road network density, per capita GDP and fixed-asset investment yield insignificant impacts. It demonstrates that amid diminishing marginal returns of traditional transportation construction and extensive scale expansion across coastal China, the coordinated coupling of marine resources and tourism relies more on industrial restructuring, rational financial resource allocation, digital empowerment and improved ecological governance.

This result profoundly reveals that the coupled and coordinated development of marine tourism is a multi-dimensional and collaborative systematic project. It does not simply rely on marketization expansion, opening up or economic scale but requires the joint efforts of service-oriented upgrading of industrial structure, precise financial empowerment, digital technology innovation-driven development and reasonable marketization guidance. In the future, coastal regions need to optimize the direction of market-oriented allocation and guide factors to tilt towards marine tourism and ecological fields; promote the transformation and upgrading of opening-up and develop high-end marine cultural and tourism formats; accelerate the construction of digital infrastructure and empower industrial integration with digital technology; increase financial support and set up special credit for marine tourism, so as to truly realize the in-depth coupling and high-quality development of marine resources and the tourism industry.

5.2.2. GTWR Analysis Results

Based on the results of the fixed-effect regression, combined with the three principles of statistical significance, economic theoretical rationality and model applicability, this paper finally selects four core variables, namely marketization index, digital infrastructure, the proportion of the added value of the tertiary industry, and the proportion of enterprise loans, to carry out the geographically and temporally weighted regression (GTWR) analysis, as shown in Figure 3. On the one hand, all the above variables show significant statistical correlation in the benchmark regression, which can stably reflect the core driving and constraint mechanisms of regional development; on the other hand, the four variables correspond to the four dimensions of institutional environment, digital empowerment, industrial structure and financial support, which are highly consistent with the research theme and have clear economic significance. At the same time, considering the sensitivity of the GTWR model to the number of variables, streamlining the core variables can effectively reduce the risk of multicollinearity, improve the efficiency of model estimation and the accuracy of spatial-temporal heterogeneity analysis and make the empirical results more robust, reliable and regionally policy-oriented.

From the perspective of core driving factors, explanatory variables feature multi-dimensional driving effects and prominent regional heterogeneity. The proportion of tertiary industry and digital infrastructure exert positive impacts across most coastal provinces, demonstrating that the optimization of service industrial structure and improvements in digital facilities facilitate the upgrade of marine tourism service provision, resource management efficiency and industrial synergy, which aligns with existing studies regarding industrial upgrading and digital economy empowering marine tourism development [12]. The impacts of outstanding corporate loans are conditional: this indicator generates favorable effects in regions with mature marine tourism industries such as Guangdong and Hainan, while its contribution remains limited in areas dominated by industry and port economy. Such a discrepancy can be explained by the diversion of financial capital toward coastal manufacturing, conventional infrastructure and traditional resource exploitation sectors. The marketization index also presents divergent spatial performances; it tends to produce positive outcomes in regions with sound market institutions and developed service sectors, whereas its effectiveness is restricted by capital allocation bias, industrial composition and ecological governance constraints in other coastal zones [17]. Overall, GTWR estimation results verify significant spatial heterogeneity among exogenous driving factors. Accordingly, the coordinated coupling of marine resources and the tourism industry requires localized policy portfolios covering industrial upgrading, digital empowerment, targeted financial support and institutional optimization.

From the perspective of spatial heterogeneity characteristics, there are significant differences in development foundation, factor allocation and driving mechanism among different regions, forming a spatial distribution pattern with significant differentiation. Relying on the mature industrial structure and improved digital facilities, the Yangtze River Delta region has formed a dual-wheel drive advantage of “industrial upgrading + digital empowerment”. Among them, Shanghai presents the multi-dimensional positive effects of marketization, digital infrastructure and financial support, while Jiangsu shows the strong driving characteristics of digital infrastructure and the tertiary industry, with the overall development quality and resource allocation efficiency at the leading level. The Southeast Coastal region presents a development trend of multi-dimensional empowerment and prominent characteristics: Guangdong forms a finance-driven development model with financial empowerment as the core, Guangxi relies on the joint efforts of the tertiary industry and digital infrastructure, and Hainan relies on financial support and policy support but has shortcomings in the service industry structure. The Bohai Rim region has significant internal differences: Shandong and Hebei take the upgrading of the tertiary industry and digital infrastructure as the core driving forces, Hebei and Tianjin are constrained by factor misallocation in the process of marketization and Liaoning shows a development characteristic completely different from the multi-dimensional empowerment of the Southeast Coastal region due to insufficient digital resource transformation, with obvious overall power differentiation and strong constraints.

6. Marine Compensation Mechanism in Tourism Destinations

6.1. Basis for Constructing Marine Compensation

The coupled and coordinated development of marine resource and tourism industry subsystems is highly dependent on the ecological authenticity of marine ecosystems, the spatial integrity of coastlines, seawater environmental quality, and the sustainability of island resources and biodiversity [22]. The empirical results of this study indicate that excessive marine mineral exploitation, high sea area utilization rates, insufficient island resource endowments and shortages of tourism human capital constitute the core long-term obstacles restricting the coordinated improvement of the two subsystems. Meanwhile, the coupling coordination degree across coastal China presents a spatially heterogeneous pattern of “higher levels in the south and lower levels in the north with three-tier hierarchical differentiation”, with significant disparities in obstacle structures, driving mechanisms and coordination levels among the Bohai Rim, Yangtze River Delta and southeast coastal regions. Due to the quasi-public-good attributes and strong ecological externalities of marine resources, ecological degradation, spatial occupation and restoration costs induced by tourism development cannot be automatically internalized through market mechanisms, resulting in widespread misalignment between resource protectors and benefit recipients, as well as mismatches between development gains and ecological costs. Accordingly, based on the empirically identified obstacle factors and regional heterogeneity characteristics, the establishment of a quantifiable, implementable and supervisable marine resource compensation mechanism is essential to address regional development bottlenecks and promote the transformation of the two subsystems from extensive linkage to high-quality coupled coordination.

6.2. Division of Rights and Responsibilities for Marine Compensation

Marine tourism development is accompanied by prominent ecological externalities and imbalanced benefit distribution, necessitating the construction of a long-term, stable marine compensation framework with clear rights, responsibilities and multi-stakeholder participation. This study adheres to the fundamental principles of “responsibility for sea utilization, compensation for benefits, and rewards for ecological protection” [24], forming a multi-dimensional collaborative governance pattern featuring government overall planning, market entity accountability and public participation. Tourism enterprises and various marine business operators bear primary compensation responsibilities for directly exploiting and utilizing marine ecology, coastline and landscape resources. Governments at all levels undertake institutional design, fund coordination and interregional balance regulation to provide overall guarantee support. Tourists, as direct beneficiaries of marine tourism, participate in compensation through ecological fees and special funds. Compensation targets focus on marine ecological environments, resource-damaged areas and coastal communities and business entities that incur costs for marine ecological protection, with priority given to groups suffering interest losses due to coastline regulation, aquaculture withdrawal and development restrictions [29]. This design effectively enhances grassroots protection enthusiasm and facilitates the transformation of marine resource and tourism development from one-way exploitation to two-way coordinated and sustainable development.

6.3. Methods and Paths of Marine Compensation

The quantitative design of compensation standards is directly based on the diagnostic results of the obstacle degree model. The model quantifies the restrictive intensity of each indicator within the marine resource subsystem on coupled coordinated development, clarifying regional compensation priority and targeted obstacle mitigation needs. Given that the obstacle degree analysis is conducted at the refined indicator level, the quantitative compensation standards are formulated targeting empirically identified core obstacle factors. To balance the empirical pertinence and general applicability of the model, a generalized compensation demand intensity model is constructed as follows:

$$C_i={\displaystyle {\sum }_{j=1}^m}{w}_j{O}_{j,i}$$

(6)

where $C_i$ denotes the theoretical compensation demand intensity of region $i$; $O_{j,i}$ represents the obstacle degree of the $j$-th core marine resource obstacle factor in region $i$; and $w_j$ refers to the policy adjustment weight corresponding to each obstacle factor, determined by policy priorities, expert weighting or equal weighting methods. This formula is applicable to the selection of core obstacle factors across different regions and years.

$$\begin{array}{c}{C}_i=w_2{O}_{2,i}+w_8{O}_{8,i}+w_1{O}_{1,i}+w_3{O}_{3,i}+w_5{O}_{5,i}\end{array}$$

(7)

where $O_{2,i}$, $O_{8,i}$, $O_{1,i}$, $O_{3,i}$ and $O_{5,i}$ represent the obstacle degrees of marine mineral output, sea area utilization rate, island quantity, mariculture output and number of marine cultural heritage parks in region $i$, respectively; $w_2$, $w_8$, $w_1$, $w_3$ and $w_5$ are the corresponding policy adjustment weights. The model indicates that regions with higher obstacle degrees of core marine resource factors possess stronger theoretical compensation demand. Different from equal compensation distributed by administrative divisions, the proposed standards are determined by the contribution of specific regional obstacle factors, realizing the transformation from “average compensation” to “obstacle-oriented targeted compensation”.

From a dimensional perspective, the refined obstacle factors correspond to distinct types of marine resource constraints. O₁ island quantity, O₂ marine mineral output and O₃ mariculture output reflect constraints on natural marine resources, requiring compensation prioritizing resource loss restoration, island resource protection, and carrying capacity improvement. O₈ sea area utilization rate represents spatial marine resource constraints, with compensation focusing on coastline protection, low-efficiency sea-use withdrawal and marine spatial governance. O₅ number of marine cultural heritage parks indicates insufficient marine cultural resource endowments, including inadequate heritage protection, exhibition carriers and cultural tourism transformation capacity, necessitating compensation for heritage maintenance, park construction, cultural space optimization and cultural tourism resource activation.

Furthermore, regional compensation priorities are determined by theoretical compensation demand intensity. Regions with high $C_i$ values are prioritized for compensation funding based on their specific obstacle structure. Regions with moderate overall obstacle degrees but prominent single core obstacles adopt special targeted compensation to address specific developmental shortcomings. Regions with continuously declining obstacle degrees and improving coupling coordination levels receive reduced basic compensation and increased performance rewards and market-oriented compensation proportions. This design enables dynamic adjustment of compensation standards across regions, obstacle types and developmental stages.

6.4. Compensation Fundraising and Regional Allocation

The fixed-effect model and GTWR regression results guide the optimization of compensation tools, fundraising modes and regional allocation pathways. Empirical findings reveal that tertiary industry proportion, digital infrastructure and corporate loan balances positively promote coupled coordination, while marketization and opening-up levels exert restrictive effects in partial regions. Significant regional heterogeneity is observed in driving mechanisms. The Bohai Rim benefits from tertiary industry development but suffers from insufficient marketization, financial support and opening-up dividends, coupled with severe spatial pressure and resource exploitation constraints, making government fiscal appropriation, special ecological funds and operator payment the dominant financing modes. The Yangtze River Delta features sound market conditions, mature tertiary industries and advanced digital infrastructure, supporting tourism revenue feedback and digital governance-based financing. The southeast coastal regions possess superior tourism benefits, financial resources and market vitality, suitable for market-oriented financing tools such as green finance and blue carbon trading. Therefore, differentiated financing mechanisms are formulated to adapt to regional heterogeneous driving effects rather than adopting a unified financing model.

The total compensation fund pool is defined as follows:

$$\begin{array}{c}F=F_g+F_e+F_t+F_f+F_m+F_p\end{array}$$

(8)

where $F$ denotes the total compensation fund pool; $F_g$ represents government fiscal funds; $F_e$ represents corporate contribution funds; $F_t$ represents funds extracted from tourism revenue; $F_f$ represents green finance funds; $F_m$ represents market-oriented funds including blue carbon trading and ecological product value realization; and $F_p$ represents ecological fees paid by tourists and the public. Regions adjust the proportion of each funding source according to the positive and negative effects of local external driving factors.

Based on regional differences in external driving forces, targeted funding sources and allocation priorities are clarified for differentiated operational arrangements, translating empirical driving heterogeneity into actionable regional financing strategies, as shown in

Table 6. Design of differentiated financing channels.

Region	Characteristics of External Factors	Priority Financing Channels
Bohai Rim	Tertiary industry provides partial support, while marketization, financial support and opening-up effects are insufficient; prominent constraints from spatial pressure, excessive resource exploitation and inadequate island resources	Government fiscal funds ($F_g$), special ecological restoration funds ($F_g$), marine enterprise contributions ($F_e$), tourist ecological fees ($F_p$)
Yangtze River Delta	Sound market conditions, developed tertiary industry and advanced digital infrastructure, limited financial support; coexisting pressures from resource exploitation, spatial occupation and inshore aquaculture	Tourism revenue extraction ($F_t$), local fiscal funds ($F_g$), digital governance funds ($F_g$), ecological restoration funds ($F_g$), ecological product value realization ($F_m$), tourist ecological fees ($F_p$)
Southeast Coastal Regions	Superior marketization, financial support and digital infrastructure, ordinary tertiary industry contribution; prominent constraints from resource exploitation, spatial occupation and insufficient island resources	Tourism revenue extraction ($F_t$), green finance ($F_f$), blue carbon trading ($F_m$), ecological product value realization ($F_m$), tourism enterprise contributions ($F_e$), ecological restoration funds ($F_g$), tourist ecological fees ($F_p$)

.

In terms of horizontal fund allocation, a linkage mechanism of “theoretical demand—fund constraint—actual distribution” is established. The $C_i$ calculated in Section 6.3 reflects the theoretical compensation demand intensity driven by core regional obstacles, while the total fund pool $F$ represents the actual available funds raised through multiple channels. Considering that fund supply is constrained by fiscal capacity, corporate willingness, market financing conditions and public participation, actual allocated funds often deviate from theoretical demand. Therefore, a horizontal allocation rule is formulated to determine the actual compensation quota $A_i$ for each region:

$$A_i={\displaystyle \frac{C_i}{{\sum }_i{C}_i}}\times F$$

(9)

where A_i denotes the actual compensation funds obtained by region i, C_i refers to the theoretical compensation demand intensity of region i, ∑_iC_i is the sum of the theoretical compensation demand intensity across all regions and F represents the total amount of the compensation fund pool. Under limited fund supply, this rule distributes compensation funds proportionally corresponding to demand intensity toward regions with prominent obstacle factors, heavy resource conservation pressure and strict development restrictions. When F ≥ ∑_iC_i, residual funds after covering basic compensation demands can be put into performance rewards, long-term ecological funds or market-oriented compensation projects.

6.5. Implementation Paths of Marine Compensation

On the basis of fundraising and regional allocation, targeted fund utilization and implementation paths are defined to address core developmental shortcomings. Consistent with obstacle diagnosis results, major constraints of the marine resource system focus on resource exploitation loss, marine spatial occupation, insufficient island resource supply and excessive inshore aquaculture pressure. Accordingly, compensation strategies are designed around five dimensions: resource restoration, spatial governance, aquaculture regulation, island protection and community sharing, transforming general investment into targeted governance tools [25].

First, resource restoration-oriented compensation: Targeting resource depletion caused by excessive marine mineral exploitation (reflected by the persistent high obstacle degree of O₂), this path focuses on ecological restoration. Compensation funds are dedicated to seabed landform remediation, inshore water quality improvement, habitat restoration, coastline ecological rehabilitation and long-term ecological monitoring. Entities causing ecological disturbances through marine mineral exploitation, port construction and marine engineering projects are required to pay compensation fees based on resource utilization intensity and ecological impact, with all revenues incorporated into unified marine ecological restoration project management.

Second, spatial governance-oriented compensation: To alleviate marine spatial scarcity induced by the rising obstacle degree of O₈ sea area utilization rate, this path implements opportunity cost compensation for spatial ecological protection. Compensation is provided to regions undertaking ecological redline protection, natural coastline conservation, low-efficiency sea-use withdrawal, inshore aquaculture reduction and tourism ecological space maintenance. Especially in the high-intensity development areas such as the Bohai Rim, compensation supports coastline renovation, subsidies for inefficient sea-use withdrawal, tourism ecological space replacement and marine spatial control to mitigate spatial competition among ports, industry, aquaculture and tourism sectors.

Third, aquaculture regulation-oriented compensation: Addressing the ecological and spatial pressure from excessive inshore aquaculture (reflected by the rising obstacle degree of O₃ mariculture output), this path promotes the ecological transformation of aquaculture industries. Though categorized as a natural resource indicator, excessive mariculture severely impairs ecological environments and coastal tourism development. Compensation funds are used for traditional aquaculture withdrawal subsidies, ecological facility upgrading, tailwater treatment, aquaculture capacity control and ecological industrial transformation support.

Fourth, island resource protection-oriented compensation: Targeting the persistent constraint of insufficient island resource supply and carrying capacity (reflected by the high obstacle degree of O₁ island quantity), this path focuses on island ecological protection and public service improvement. Funds support island ecological restoration, tourism public service facility construction, transportation connection, garbage and sewage treatment, emergency rescue systems and tourist capacity regulation. For regions with rapid island tourism growth but inadequate public services, island ecological funds, tourist ecological surcharges and tourism revenue feedback mechanisms are adopted to enhance island resource protection and tourism carrying capacity.

Fifth, community sharing-oriented compensation: Marine resource protection and tourism development rely on extensive community participation. A proportion of compensation funds is allocated to ecological management posts, fisherman vocational training, marine cultural interpretation, community homestay operation, recreational fishery transformation and tourism service capacity improvement. This design enables coastal residents to benefit from compensation while participating in resource protection and tourism development. Benefit distribution, employment placement, public service optimization and community co-governance mechanisms effectively resolve the misalignment between protectors and beneficiaries and mobilize grassroots enthusiasm for marine ecological protection.

The above five paths precisely target core problems including resource depletion, spatial occupation, ecological pressure, insufficient island carrying capacity and benefit mismatch, ensuring that compensation funds are accurately invested in empirically identified developmental shortcomings.

6.6. Operational Guarantee Mechanisms for Marine Compensation

To ensure the long-term and effective operation of the marine compensation mechanism, a full-process supervision, evaluation and dynamic adjustment system is established to avoid one-time investment and procedural formalism. Based on the logical framework of “standard quantification—fund allocation—implementation paths”, the operational guarantee system covers five key links: dynamic monitoring and accounting, fund supervision, performance evaluation, information disclosure and real-time feedback, forming a closed-loop governance mechanism from demand identification to effect assessment.

First, dynamic monitoring and accounting mechanism: Integrating multi-department data from natural resources, ecological environment, culture and tourism and marine fisheries sectors, this mechanism continuously monitors marine mineral exploitation, sea area utilization, aquaculture pressure, island resource utilization, marine cultural heritage protection and tourism revenue changes, aligning with the established obstacle indicator system. Annual monitoring updates the ranking of core obstacle factors and dynamically calculates regional compensation demand intensity, providing scientific evidence for compensation standard adjustment and fund optimization.

Second, fund supervision mechanism: Classified management is implemented for all types of compensation funds, clarifying funding sources, scopes of expenditure and responsible entities. Special account management and project-based utilization are enforced for all compensation funds, with priority given to resource restoration, spatial governance, aquaculture regulation, island protection, cultural resource conservation and community sharing. Strict supervision prevents fund embezzlement, inefficient utilization and off-target investment.

Third, performance evaluation mechanism: Fund utilization performance is linked to core improvements in obstacle mitigation, coupling coordination promotion, ecological quality optimization, cultural resource protection and community benefit growth. The evaluation system covers five dimensions: resource restoration performance, spatial governance performance, ecological transformation performance, cultural protection performance and community sharing performance. Specifically, resource restoration performance assesses inshore water quality improvement, habitat recovery and ecological monitoring; spatial governance performance evaluates low-efficiency sea-use withdrawal, coastline protection and tourism ecological space maintenance; cultural protection performance focuses on heritage park operation, cultural space construction and cultural tourism activation; community sharing performance targets resident employment, vocational training, benefit distribution and public service upgrading [21].

Fourth, information disclosure and third-party supervision mechanism: Detailed information on fund sources, regional allocation, investment projects, implementation progress and performance results is regularly disclosed to accept public, community and institutional supervision. Independent evaluations conducted by universities, research institutions, audit departments and professional assessment agencies enhance the transparency, impartiality and credibility of the compensation system.

Fifth, dynamic adjustment mechanism: Compensation standards, fund allocation schemes and implementation paths are dynamically optimized according to changes in obstacle factors, regional development stages and performance outcomes. Regions with continuously rising obstacles in sea area utilization, marine mineral exploitation and mariculture scale receive increased investment in spatial governance, resource restoration and aquaculture ecological transformation. Regions with declining obstacle degrees and improved coupling coordination are granted higher proportions of performance rewards, market-oriented compensation and long-term ecological funds. The dynamic adjustment mechanism transforms the static fund allocation model into a sustainable, iterative governance tool for high-quality marine tourism development.

7. Conclusions and Discussions

This paper selects 11 coastal provincial-level regions of China from 2008 to 2024 as research samples and establishes an evaluation index system for the coupled coordination between the marine resource subsystem and the tourism industry subsystem. By comprehensively adopting the coupling coordination degree model, spatial Markov chain, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model, this study systematically identifies the spatiotemporal differentiation, internal obstacle factors, external driving mechanisms and spatial heterogeneity of the coupled coordination of the two systems and further proposes a marine resource compensation mechanism for tourist destinations. The main conclusions are summarized as follows.

First, from the perspective of temporal evolution, the coupling coordination degree between marine resources and the tourism industry keeps rising steadily during 2008–2024 and can be divided into three phases. The initial development stage spans 2008–2013, in which the tourism industry grows far faster than marine resources and forms a development pattern of “tourism leading and marine resources following up”. Driven by the Maritime Power Strategy, both sectors achieve synchronous growth in the rapid improvement stage (2014–2019), and the coupling coordination degree enters the primary coordination interval. The shock and recovery stage covers 2020–2024; the COVID-19 pandemic triggers a temporary downturn of the tourism sector and a mild drop of coordination degree, which rebounds gradually after 2023 and proves strong systemic resilience.

Second, in terms of spatial pattern, the coupling coordination presents a stable hierarchical layout with “higher coordination in southern coast and lower coordination in northern coast featuring three-tier differentiation”. The Yangtze River Delta and southeast coastal areas stay at high and stable coordination levels, whereas the Bohai Rim remains relatively backward with remarkable interregional gaps. Results of the spatial Markov chain reveal obvious path dependence and club convergence of coordination grades; cross-grade transitions only occur between adjacent tiers. High-level neighboring regions generate significant positive spatial spillover effects, while low-level adjacent areas are prone to development lock-in.

Third, regarding internal restrictive obstacles, the marine resource subsystem is mainly constrained by excessive marine mineral exploitation, over-high sea area utilization and insufficient island reserves. Spatial restrictions become increasingly prominent in the Bohai Rim, and aquaculture expansion constitutes a prominent constraint for the Yangtze River Delta. The core bottleneck of the tourism industry subsystem shifts from insufficient market scale in the early stage to inadequate human capital supply, and the shortage of professional tourism talents turns into a universal restriction nationwide and across the three coastal zones. Deficient supporting infrastructure and underdeveloped marine cultural resources further widen regional disparities.

Fourth, for external driving factors, the proportion of tertiary industry, outstanding corporate loans and digital infrastructure serve as core positive drivers. By contrast, marketization index and opening-up level impose significantly negative constraints overall, implying severe factor mismatch and capital crowding-out effect. Economic aggregate and traditional fixed-asset investment exert no statistically significant influences. GTWR outcomes verify evident spatial heterogeneity across drivers: industrial upgrading and digital empowerment dominate growth in the Yangtze River Delta; the southeast coastal regions benefit from sound financial support and mature marketization; the Bohai Rim features divergent growth momentum and is heavily restricted by distorted market allocation.

Fifth, in terms of institutional design, targeted at the quasi-public-good attributes, ecological externalities and cost-benefit mismatch of marine resources, this paper constructs a tourist-oriented marine resource compensation mechanism covering quantified compensation criteria, fundraising, regional fund allocation, diversified compensation modes and operational safeguards. The mechanism takes obstacle degree estimation as the benchmark for compensation demand calculation and designs differentiated financing channels and regional allocation schemes grounded in fixed-effect and GTWR regression results, providing institutional solutions to resolve the mismatch between marine conservation costs and tourism benefits.

Threefold theoretical contributions are highlighted in this research. Firstly, this paper builds an integrated dual-system analytical framework for marine resource–tourism industry coupling and incorporates characteristic indicators including natural, spatial, cultural and ecological marine resources into the evaluation system, improving the indicator suitability compared with conventional marine economy or tourism research. Secondly, it identifies the phased transition law of restrictive obstacles: the marine resource subsystem is persistently hampered by overexploitation and improper spatial utilization, while the dominant constraint of the tourism industry evolves from market size limitation to insufficient human capital. Thirdly, this study empirically verifies spatially heterogeneous impacts of tertiary industry development, digital infrastructure and financial support on coupled coordination, confirming no universal development pathway for coastal marine tourism integration but regionally differentiated driving patterns.

Corresponding policy implications are put forward based on the above findings. First, the Bohai Rim should prioritize mitigating conflicts in sea space utilization and excessive resource exploitation and consolidate fiscal appropriation, special ecological restoration funds and mandatory contributions from marine-related enterprises to facilitate withdrawal of inefficient sea use, coastline conservation and construction of tourism ecological space. Second, relying on mature service sectors and digital infrastructure, the Yangtze River Delta shall promote tourism revenue feedback, digital supervision and ecological product value realization, alongside strengthened aquaculture regulation and environmental governance to curb inshore breeding pressure. Third, the southeast coastal regions can leverage superior financial resources, marketization and digital facilities to introduce diversified financing approaches including green finance, blue carbon trading, corporate contribution and tourism revenue deduction, with funds prioritized for island conservation, ecological restoration and community benefit sharing. Fourth, in view of multi-stakeholder accountability, governments are responsible for institutional formulation, fiscal underwriting and whole-process supervision and assessment; marine and tourism enterprises bear compensation obligations proportional to resource consumption intensity, ecological damage and operational tourism gains; coastal residents share benefits via ecological management, tourism service provision and marine cultural inheritance; tourists participate in compensation through eco-ticket fees and additional island protection surcharges.

Several limitations remain in this study. Restricted by data availability, the empirical analysis is conducted on provincial panel data, failing to capture fine-grained disparities at smaller scales such as individual islands, coastal cities, scenic spots and local communities. Three directions are proposed for future research: first, integrate multi-source big data including remote sensing monitoring, tourist mobility trajectories, online consumer reviews, enterprise operational statistics and marine ecological monitoring data to refine the accuracy of coupling assessment; second, incorporate carbon emission, blue carbon value and ecosystem service value into the analytical framework to explore coupling evolution under carbon constraints and blue low-carbon transition; third, carry out numerical simulation on compensation standard quantification and policy scenario assessment to test implementation effectiveness of marine resource compensation under alternative funding sources, allocation rules and performance constraints.