Sage Journals: Discover world-class research

Abstract

Objective evaluation of yachting tourism competitiveness is crucial for promoting the sustainable development of China’s yacht industry. This study adopts a sustainable development perspective and focuses on 12 coastal cities with strong yachting tourism development. We construct a comprehensive evaluation system covering five dimensions: resource endowment, natural environment, economic foundation, social support, and industrial development. Using the AHP-entropy method combined with obstacle factor analysis, we quantitatively assess the competitiveness and identify the key barriers of yachting tourism in 2014, 2017, 2019, and 2022. The results indicate that yachting tourism competitiveness in Chinese coastal cities has generally improved, with significant regional variations. Economic development and the advancement of the yacht industry are the primary drivers of competitiveness enhancement, highlighting the need for targeted policies to support sustainable growth and industry development.

Plain Language Summary

Understanding the Growth and Challenges of Yachting Tourism in China’s Coastal Cities

Yachting tourism is becoming increasingly popular in China’s coastal cities. This study looks at 12 cities that are leading in yachting tourism to understand how well they are developing this industry and what obstacles they face. We examine factors such as natural resources, economic conditions, social support, and the availability of yachting facilities. Our findings show that overall, yachting tourism in these cities is growing, especially in economically developed cities like Shanghai, Guangzhou, and Shenzhen. These cities have strong financial resources, good infrastructure, and supportive policies, which make it easier for residents and tourists to enjoy yachting. On the other hand, some cities face challenges such as limited access to yachts, fewer yacht clubs, and transportation difficulties, which can slow down the growth of the industry. By identifying both the strengths and the obstacles, this study provides insights for city planners, policymakers, and businesses to improve yachting tourism. The goal is to help these cities develop their yachting industry in a way that benefits local communities, supports sustainable economic growth, and makes yachting more accessible to a wider range of people. In short, the research highlights where yachting tourism is doing well, where it needs improvement, and how thoughtful planning and policy support can help unlock its full potential for both local residents and visitors.

Keywords

yachting tourism competitiveness evaluation AHP-entropy method obstacle factor analysis Chinese coastal cities sustainable development

Introduction

As a sector characterized by a long industrial chain and strong spillover benefits, yachting tourism contributes meaningfully to service-sector upgrading and marine economic growth. These characteristics position it as a rapidly expanding niche with increasing strategic value. In 2021, the 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of the Vision Goals for 2035 explicitly incorporated policies to improve yachting development, while the 14th Five-Year Plan for Tourism Development called for further popularization of yachting consumption. In response, local governments have facilitated infrastructure construction, expanded marina and berth capacity, and developed route networks to promote coastal recreational mobility. Mainland China now hosts 114 marinas, 12,000 water berths, 2,000 dry storage facilities, and 2,600 yachting-related enterprises (CCYIA, 2022). By 2030, the size of the Chinese yacht market is projected to reach 12 billion US dollars (HIRI, 2024).

With the end of the COVID-19 pandemic, the global yachting market has recovered steadily, and Asian markets such as mainland China, Hong Kong, Singapore, and Thailand continue to demonstrate sustained growth momentum (Dezan Shira & Associates, 2022; NMMA, 2021). Despite this upward trajectory, substantial structural constraints persist in China’s yachting tourism sector. First, yachting infrastructure remains underdeveloped, with insufficient planning and construction of marinas and terminals, and the per-capita berth ratio lags significantly behind countries with mature yachting industries. Public marinas are scarce relative to private facilities. Second, yachting routes and destination planning lag behind demand; entertainment offerings lack diversity, and navigation culture has yet to take root, collectively diminishing perceived value for prospective yacht owners and tourists (ECSIP, 2015). Consequently, consumer willingness to participate remains low (Liu & Jia, 2018; Yao et al., 2022b). Third, policy frameworks are underdeveloped, and institutional innovation often trails industry practice, as reflected in outdated inspection and registration procedures, high taxes and fees, and limited designation of seaworthy waters (Yao & Luan, 2017). These challenges highlight the need for systematic, evidence-based evaluation to clarify development strengths and constraints.

Existing research has primarily examined the economic, social, and environmental effects of yachting tourism (Diakomihalis, 2008; Sariisik et al., 2011; Venturini et al., 2016), the development of industry clusters and service supply chains, and the management of yacht clubs (Dimitrovski et al., 2021; Park, 2016; Paker & Vural, 2016). Studies on yachting consumption behavior underscore the role of demographic factors, income, preferences, leisure time, attitudes, and perceived behavioral control (Liu & Jia, 2018; Yao et al., 2022a). However, systematic evaluations of destination-level competitiveness and its constraints remain scarce. A unified and empirically grounded competitiveness assessment is therefore necessary to support policy formulation and guide strategic decisions by industry stakeholders.

As two conceptual pillars in tourism competitiveness research, the theories of competitive advantage and sustainable development offer complementary perspectives for understanding the sources and mechanisms of destination competitiveness (Porter, 1980; Ritchie & Crouch, 2003). Competitive advantage theory highlights industrial structure, factor conditions, and market positioning—elements closely linked to the economic and industrial attributes of yachting tourism—while sustainable development theory emphasizes the long-term balance among economic vitality, environmental stewardship, and social well-being. Drawing on these theoretical underpinnings and existing destination evaluation frameworks (Butowski, 2018; Ritchie & Crouch, 2003), this study constructs an integrated analytical framework comprising five dimensions essential to yachting tourism competitiveness: resource endowment, natural environment, economic foundation, social support, and industrial development.

To operationalize these dimensions, a single, comprehensive set of 25 indicators was developed through a systematic review of tourism competitiveness models, marine and coastal assessment literature, and research specific to the yachting industry. These indicators reflect both the industrial drivers emphasized in competitive advantage theory and the ecological–social criteria embedded in sustainable development theory, and they align with the United Nations Sustainable Development Goals (SDGs). Using data from 12 Chinese coastal cities, competitiveness was evaluated for the years 2014, 2017, 2019, and 2022 through a combined Analytic Hierarchy Process (AHP) and entropy weighting approach, followed by obstacle degree analysis to identify key constraints on sustainable competitiveness. This integrated approach provides a clearer diagnostic of competitiveness patterns and governance gaps, contributing to both theoretical refinement and practical policy insights.

Literature Review

Yachting Tourism

The International Maritime Organization (IMO) does not incorporate “yacht” in the mandatory management of international maritime conventions. Each member state defines yachting and formulates management rules independently. In China, The China Cruise and Yacht Industry Association (CCYIA) defines yacht as ships and all other forms of navigation equipment utilized for sightseeing, recreation, entertainment, and water sports, also referred to as leisure boats, encompassing inboard or outboard motorboats, fishing boats, catamarans, sailing boats, hovercrafts, jet boats, kayaks, and motorboats. Yachting tourism represents a novel form of tourism that integrates navigation, sports, leisure, entertainment, and social interaction. Broadly speaking, it includes sailing, cruising, rowing, kayaking, water skiing, and sailing activities, and hydrophilic activities such as surfing, fishing, diving, and exploration (Luković, 2013; Mikulić et al., 2015; Yao & Luan, 2017). However, the “Regulations on Yacht Safety Management” issued by the Ministry of Communications of China defines a yacht as a vessel equipped with a mechanical propulsion device and intended for the owner’s sightseeing, leisure, and entertainment activities, hence, the yachting tourism in this study is confined to motor yacht which is in a narrow sense.

Tourism Competitiveness

Given the acceleration of the global economic integration process, competitiveness has become a key factor to measure the development level and market position of economic entities, such as countries, enterprises and industries. Porter (1980) proposed the famous “Five Forces Model,” which had a profound impact on the development of competitiveness theory. Focusing on strategic planning and resource allocation around core competitiveness to achieve sustainable development, Prahalad and Hamel (2000) put forward the theory of core competitiveness. Further, Teece et al. (1999) have put forward the dynamic capability theory, which emphasizes the ability of enterprises to adapt to environmental changes and create competitive advantages by continuously integrating, structuring and reconstructing internal and external resources in a rapidly changing market environment.

The application of the competitiveness theory in different fields has given rise to new branches of research. In general, tourism competitiveness refers to the ability of different regional economic entities to obtain industrial development factors, compete for market share, and obtain profits through various channels in tourism market competition to achieve sustainable tourism industry development (Kozak & Rimmington, 1999). Ritchie and Crouch (2003) constructed a comprehensive model of tourism destination competitiveness, which covers core resources and attraction; it supports factors and resources, destination management, demand conditions, macro environment, and other aspects. The scholars affirm that these factors interact with each other to determine the competitiveness of tourist destinations. In addition, tourism competitiveness includes the overall attractiveness of the tourist destination and the ability to provide products and services, as well as comparative and competitive advantages (Abreu-Novais et al., 2015; Gooroochurn & Sugiyarto, 2005).

Research on tourism competitiveness at home and abroad is mainly based on multi-dimensional and multi-factor comprehensive evaluation, focusing on the evaluation of the overall tourism industry competitiveness and the evaluation of special tourism competitiveness (Kaya & Kozak, 2010; Romeiro & Costa, 2010; Rodríguez-Díaz & Pulido-Fernández, 2020). The previous research concentrates on the competitiveness of tourism destinations, the determinants of tourism competitiveness, and the promotion path of tourism competitiveness, covering objects include rural, sports, red, ecological, and cruise tourism. The evaluation methods included the Delphi method, the AHP entropy method, cluster analysis, the PROMETHEE model, the coupling coordination degree model, and the regression model (Bazargani & Kiliç, 2021; Butowski, 2018; Cibinskiene & Snieskiene, 2015; Croes et al., 2019; Higham et al., 2021; Lopes et al., 2018; Michae et al., 2019; Moradi et al., 2022; Nie & Dong, 2010; Vila et al., 2015).

The theories of competitive advantage and sustainable development represent fundamental cornerstones of tourism competitiveness research (Angelkova et al., 2012; Ogutu et al., 2023). First, Porter’s competitive advantage theory is at the core of obtaining and maintaining the competitiveness of tourism destinations (Porter, 1989). From the perspective of dynamic sustainable development, the theory of competitive advantage analyzes the influence of production factors, demand factors, related industries and auxiliary industries, opportunities, and the government on the competitive situation. It analyzes how to always be in a competitive advantage in the process of dynamic change. The theory of competitive advantage provides a relatively complete knowledge framework for competitive action and is one of the most instructive theories for the evaluation and promotion of tourism competitiveness (Smith et al., 2010; Wright et al., 2016).

Second, sustainable development is of heightened global concern and is also one of China’s strategic guides in economic development, resource protection, and environment protection. The theory of sustainable development centers on balancing the present and future and promoting fair and sustainable progression of society and the environment (Gladwin et al., 1995; Purvis et al., 2019). The theory of sustainable development has a profound impact on tourism competitiveness by influencing resource utilization, innovation ability, reputation image, industrial structure, and policy system. Further, it has provided great contributions to maximizing regional economic, social, and environmental benefits (Esty & Porter, 2005). It is thus necessary to introduce the theory of sustainable development into the study of yachting tourism competitiveness.

Yachting Tourism Competitiveness

With the rapid development of yachting tourism, scholars have increasingly examined the competitiveness of this niche tourism sector (Yao et al., 2023). Yachting tourism competitiveness refers to the ability of coastal cities to attract and efficiently utilize resources, capital, and policy support in the tourism market, thereby achieving both sustainable development and profitability. It encompasses not only the current competitiveness of a destination but also its future growth potential (Butowski, 2018; Pardali & Giantsi, 2018; Ritchie & Crouch, 2003).

Early studies have explored yachting tourism competitiveness in various international contexts. Horak (2006) evaluated Croatia and highlighted that while natural resources and safety constitute key advantages, yachting infrastructure and service standards remain relatively weak. Similar research has been conducted in Turkey, Montenegro, Greece, and the Atlantic region, emphasizing differences in resource endowment, service quality, and institutional support (Kovačević & Mladenović, 2018; Pardali & Giantsi, 2018; Santos et al., 2022; Sariisik et al., 2011). In China, Wang (2007) assessed the international competitiveness of Chinese yachting tourism, recommending improvements in legal frameworks and policy support. More recently, Pan (2017) and Chen (2024) analyzed the competitiveness of Xiamen and Sanya, respectively, suggesting strategies for cultivating public awareness and participation in yachting tourism. Yao et al. (2022a) quantitatively evaluated 84 wharfs across 29 Chinese coastal cities to measure tourism development potential.

Despite these contributions, existing research has limitations (Gladkikh et al., 2021; Spinelli & Benevolo, 2022). Systematic evaluations of yachting tourism competitiveness, including comprehensive indicator frameworks and the identification of obstacle factors, remain scarce. Moreover, few studies explicitly integrate the perspective of sustainable development, which considers the interplay of economic, social, and environmental dimensions in assessing both present competitiveness and future growth potential (Gooroochurn & Sugiyarto, 2005; Zhang et al., 2011). Building on this gap, the present study constructs a comprehensive evaluation index system for yachting tourism competitiveness, encompassing five dimensions: resource endowment, natural environment, economic foundation, social support, and industrial development. The AHP-entropy method is employed to quantitatively evaluate the competitiveness, while obstacle factor analysis identifies the primary barriers constraining yachting tourism development across different urban contexts. This approach not only addresses existing empirical gaps but also provides a framework for sustainable development-oriented policy and industry planning.

Methodology and Data

Construction of Index System

Yachting tourism draws upon marine resources and natural environmental advantages, targets residents with higher disposable income, and integrates yacht manufacturing with tourism services. Guided by Competitive Advantage Theory and Sustainable Development Theory, and informed by existing tourism competitiveness evaluation frameworks (Butowski, 2018; Yao et al., 2022a), this study constructs a five-dimensional evaluation system covering resource endowment, natural environment, economic foundation, social support, and industrial development (25 indicators; Table 1).

Table 1.

Evaluation Index Framework and Weight of Yachting Tourism Competitiveness in Coastal Cities.

Goal layer	Criterion layer	Indicator layer	SDG alignment	AHP weight	Entropy weight (2019)	Comprehensive weight (2019)
A₁ Yachting tourism competitiveness	B₁ Resource endowment	C₁ Coastline length	SDG 14	.0180	.0395	.0200
		C₂ Sea area	SDG 14	.0413	.0348	.0405
		C₃ Number of islands	SDG 14	.0240	.0690	.0466
		C₄ Number of sea baths	SDG 14	.0259	.0112	.0082
		C₅ The abundance of marine tourism resources	SDG 14	.0695	.0241	.0471
	B₂ Natural environment	C₆ Area of marine nature reserve	SDG 14	.0250	.0635	.0448
		C₇ Marine water quality	SDG 14	.0439	.0169	.0209
		C₈ Comfort period for tourism	SDG 3	.0332	.0433	.0405
		C₉ AQI standard days	SDG 3	.0281	.0093	.0073
		C₁₀ Percentage of forest cover	SDG 15	.0261	.0234	.0173
	B₃ Economic foundation	C₁₁ Tourism consumption per person	SDG 8	.0570	.0313	.0502
		C₁₂ Tourism consumption rate	SDG 8	.0292	.0558	.0460
		C₁₃ Total tourism income	SDG 8	.0474	.0460	.0615
		C₁₄ Per capita GDP	SDG 8	.0368	.0372	.0386
	B₄ Social support	C₁₅ Star hotels	SDG 11	.0354	.0334	.0333
		C₁₆ Travel agencies	SDG 11	.0283	.0551	.0440
		C₁₇ Tourism traffic accessibility	SDG 9	.0241	.0810	.0549
		C₁₈ Yacht management policy	SDG 16	.0664	.0323	.0604
		C₁₉ Popularization of yacht culture	SDG 11	.0661	.0185	.0344
	B₅ Industrial development	C₂₀ Per capita number of yachts	SDG 11	.0230	.1111	.0720
		C₂₁ Yachting tourism search index	SDG 9	.0462	.0263	.0342
		C₂₂ Yacht marinas	SDG 9	.0458	.0306	.0395
		C₂₃ Yacht berths	SDG 9	.0403	.0389	.0442
		C₂₄ Yacht clubs	SDG 9	.0379	.0497	.0530
		C₂₅ Yacht industry supporting	SDG 8	.0812	.0178	.0407

Note. SDG alignment refers to the correspondence between each indicator and the relevant United Nations Sustainable Development Goals (SDGs). Specifically, SDG 14 Life Below Water, SDG 3 Good Health and Well-being, SDG 15 Life on Land, SDG 8 Decent Work and Economic Growth, SDG 11 Sustainable Cities and Communities, SDG 9 Industry, Innovation and Infrastructure, and SDG 16 Peace, Justice and Strong Institutions are referenced in this study based on the ecological, economic, social, and governance attributes of each indicator.

Compared with prior frameworks (e.g., Sariisik et al., 2011; Yao et al., 2022a), the present indicator system incorporates several newly added or refined measures that better reflect the characteristics of emerging yachting tourism markets. The “yachting tourism search index” (C21) is newly introduced to capture real-time demand dynamics, and the “per capita number of yachts” (C20) is improved to more accurately represent regional market penetration.

At the structural level, the five dimensions embed key sustainability principles. Resource endowment (B1) and natural environment (B2) reflect ecological integrity and correspond to SDG 14 and SDG 3/15; economic foundation (B3) relates to SDG 8 by capturing economic vitality; social support (B4) aligns with SDG 9, SDG 11, and SDG 16 through infrastructure readiness and governance capacity; and industrial development (B5) links to SDG 8 and SDG 9, highlighting industrial resilience and innovation.

Resource endowment and natural environment constitute the foundational conditions for competitiveness and correspond to the environmental dimension of sustainable development. Indicators such as coastline length, number of islands, and marine water quality capture both the abundance and ecological quality of marine resources, shaping long-term destination attractiveness while mapping to factor conditions in Competitive Advantage Theory (Song & Du, 2019; Yang et al., 2022).

Economic foundation provides the economic driving force for competitiveness and reflects the economic pillar of sustainability. Indicators including per capita GDP, tourism consumption per person, tourism consumption rate, and total tourism income assess purchasing power, market scale, and economic vitality. These factors align with demand conditions in Competitive Advantage Theory and indicate the extent to which coastal cities can transform natural advantages into stable tourism markets (Liu & Jia, 2018).

Social support strengthens the institutional and societal framework required for competitiveness and corresponds to the social pillar of sustainability (Gon et al., 2016; Walker et al., 2023). Star-rated hotels, travel agencies, tourism traffic accessibility, yacht management policy, and yacht culture popularity capture governance effectiveness, infrastructure conditions, service capacity, and social acceptance. Here, “star hotels” refer to China’s official 1 to 5 star rating system, in which a higher number of stars indicates higher service quality and facility standards, and “travel agencies” denote the number of licensed agencies in each city.

Industrial development underpins the continuity and resilience of competitiveness and represents the economic sustainability dimension. Indicators such as the number of marinas, berths, yacht clubs, yachting tourism search index, and industry support mechanisms evaluate whether yachting tourism can develop into a mature and innovation-oriented industry. These indicators correspond to related and supporting industries as well as firm strategy, structure, and rivalry in Competitive Advantage Theory (Yao & Luan, 2018).

Research Areas

Yachting tourism is inherently sea-oriented, although its scope can extend to other types of water bodies by analogy (Spinelli & Benevolo, 2022). In China, the development of yachting tourism shows a strong coastal orientation. Industry reports consistently identify Dalian, Tianjin, Qingdao, Weihai, Ningbo, Zhoushan, Shanghai, Xiamen, Guangzhou, Shenzhen, Haikou, and Sanya as key cities driving the expansion of China’s yachting sector. These cities not only represent the current spatial concentration of the industry but also constitute the main engines supporting its future growth (CCYIA, 2020).

The selection of these 12 cities is grounded in their industrial significance, policy relevance, and the availability of consistent and high-quality data. They are among the earliest and most dynamic coastal regions engaged in yachting tourism development, with relatively comprehensive statistical information and well-documented policy practices that reflect differentiated regional trajectories. Other coastal cities—such as Zhuhai or Tangshan—are not included due to the lack of city-specific industry data in the CCYIA reports. While the sample cannot cover all coastal destinations, it effectively captures the core landscape of China’s yachting tourism development and provides a robust basis for comparative analysis. Accordingly, these 12 cities constitute the research area for this study (see Figure 1).

Figure 1.

The development of yachting tourism in coastal cities of China.

Data Sources

As the development of Chinese yachting tourism is still in its infancy, it is difficult to obtain comprehensive time-series data. This study depends on the 2014/2017/2019 China Yacht Industry Report and China Cruise Yacht & Ship Industry Development Report in 2022 to 2023 (CCYIA, 2014, 2017). We obtained cross-sectional data for 2014, 2017, 2019, and 2022. Coastline length, area of marine nature reserves, and other data were mainly from the marine functional zoning of coastal cities and marine environmental status bulletins. Research on the suitable period for yachting comes from the study of tourism climate comfort in coastal cities (Zhou et al., 2022). Per capita tourism consumption, tourism consumption rate, total tourism income, and star-rated hotels were mainly derived from the statistical bulletins of the national economic and social development of cities over the years, urban statistical yearbooks, the China Tourism Statistical Yearbook, and tourism sampling survey data.

Yacht marinas, berths, other industrial developments, and expert scoring data were derived from the corresponding years of the China Yacht Industry Report and China Cruise Yacht & Ship Industry Development Report. Yacht clubs were retrieved from the database (https://www.qcc.com/).

To improve data reliability, all index data were preprocessed by elimination, consistency checking, and multiparty verification. For individual missing values, the weighted average method was applied. While this approach reduces bias from incomplete datasets, it may smooth over extreme variations between cities. Therefore, the results should be interpreted with awareness of these data constraints, although the cross-validation with multiple sources enhances their robustness.

Research Method

AHP-Entropy Method

The AHP-entropy method is a comprehensive multi-criteria decision-making technique that integrates subjective judgments with objective data (Wang, Li, et al., 2021; Wu et al., 2022). The AHP component was implemented to derive subjective weights through structured expert judgment. To ensure transparency and reproducibility, an expert panel of five professionals with extensive experience in yachting operations, tourism planning, and maritime management was established. The AHP modeling was conducted in four sequential steps: (1) A hierarchical structural model was constructed by decomposing the complex system of institutional indicators into different levels, including the goal, criteria, and sub-criteria, based on their interrelationships and affiliations. (2) Pairwise comparison matrices for the indicators at each level were constructed. Experts used the fundamental 1 to 9 scale to make qualitative judgments on the relative importance between elements. (3) The relative priorities (eigenvectors) of elements within each level were calculated, yielding the local weights. (4) The local weights were synthesized across the entire hierarchy to obtain the global weights for all elements at the lowest level with respect to the overall goal.

The consistency of each pairwise comparison matrix was rigorously checked. A matrix was deemed acceptable only if its Consistency Ratio (CR) was less than 0.1; otherwise, the judgments were revised. The results of the consistency check, which confirmed the validity of all matrices, are summarized in Table 2.

Table 2.

Summary of Consistency Check Results.

Maximum eigenvalue	CI value	RI value	CR value	Consistency check result
5.057	.014	1.12	.013	Pass

Under the guidance of the above steps, the weight of each index tier was obtained. This AHP-based weight reflects the subjective judgments of the expert panel while ensuring consistency and reliability through aggregation and consistency checks.

The entropy method was then applied for objective weighting, and the index weight was adjusted according to the degree of difference between the indices to reduce the influence of subjective biases. The calculation steps for the entropy method are as follows:

The first step is to use the linear dimensionless threshold to process the original data:

x_{ij}^{'} = \frac{x_{ij} - x_{\min}}{x_{\max} - x_{\min}} (i = 1, 2, \dots, n; j = 1, 2, \dots, m)

(1)

In the formula, $x_{ij}$ is the value of each city j under the index i, is divided into the minimum and maximum values of the index i in each city, and $x_{ij}^{'}$ is the non-dimensional treatment and non-negative value of $x_{ij}$ .

The second step is to calculate the proportion of city j in the index and of all cities under the index i:

P_{ij} = \frac{x_{ij}^{'}}{\sum_{j = 1}^{m} x_{ij}^{'}} (i = 1, 2, \dots, n; j = 1, 2, \dots, m)

(2)

The third step is to calculate the information entropy value $e_{i}$ and the information utility value $d_{i}$ of the i index:

e_{i} = \frac{1}{\ln (m)} \sum_{n}^{m} P_{ij} \ln (P_{ij}) (e_{i} > 0)

(3)

d_{i} = 1 - e_{i} (0 \leq d_{i} \leq 1, \sum_{i = 1}^{n} d_{i} = 1)

(4)

In the fourth step, the information utility value $d_{i}$ of the entropy method is used to adjust the weight obtained by the analytic hierarchy process.

w_{i} = \frac{w d_{i}}{\sum_{i = 1}^{n} w d_{i}}

(5)

Finally, the AHP weights were integrated with the entropy weights to derive the revised comprehensive weights. These weights are dynamic, adjusting annually in response to variations in the underlying objective data. The results for 2019 are presented in Table 1 for brevity, while the results for the other years are provided in the Supplemental Appendix.

Obstacle Factor Diagnosis Method

The obstacle degree model is applied to identify the key factors that restrict the improvement of yachting tourism competitiveness across cities. It helps reveal the “bottleneck indicators” that most significantly hinder competitiveness enhancement, thus offering targeted guidance for policy formulation.

The obstacle degree of each indicator is determined by three components:

(1) Factor contribution ( $F_{i}$ ): The relative importance of indicator i, represented by its comprehensive weight obtained from the AHP–entropy method.

(2) Indicator deviation ( $I_{i}$ ): The gap between the actual performance of indicator i and its optimal condition. Consistent with existing studies (e.g., Wang, Fang, et al., 2021), the deviation is computed as:

I_{i} = 1 - r_{ij}

(6)

where $r_{ij}$ is the normalized value obtained through min–max normalization.

In this study, the optimal target value is based on the sample optimum rather than a theoretical maximum. Thus, normalization rescales all indicators to the [0,1] interval, and $1 - r_{ij}$ reflects the distance of each city from the best-performing sample city.

(3) Obstacle degree ( $O_{i}$ ):

O_{i} = \frac{I_{i} \cdot F_{i}}{\sum_{i = 1}^{n} I_{i} \cdot F_{i}} \times 100 %

(7)

The obstacle degree reflects the extent to which each indicator restricts the development of yachting tourism competitiveness.

Following common practice in obstacle analysis, indicators with obstacle degrees exceeding the national sample average are classified as major barriers. This approach ensures a systematic, reproducible identification of key constraints without imposing arbitrary numerical thresholds.

Research Results

The Spatial and Temporal Evolution of Yachting Tourism Competitiveness

Using Equations 1 to 5, we calculated the comprehensive scores of yachting tourism competitiveness in Chinese coastal cities for 2014, 2017, 2019, and 2022 (Table 3).

Table 3.

Comprehensive Scores and Rankings of Yachting Tourism Competitiveness.

City (region)	2014	2017	2019	2022	Average value	Ranking
Dalian	34.999	33.104	33.222	35.194	34.130	7
Tianjin	33.183	30.395	25.477	21.733	27.697	8
Qingdao	49.105	43.578	45.258	45.494	45.859	4
Weihai	19.923	19.119	16.380	15.494	17.729	12
(Bohai Sea Rim)	34.303	31.549	30.085	29.479	31.354
Ningbo	31.932	32.290	35.098	33.341	33.165	9
Zhoushan	22.456	25.841	30.512	30.220	27.257	10
Shanghai	57.835	55.379	55.878	55.202	56.073	1
(Yangtze River Delta)	37.408	37.837	40.496	39.587	38.832
Xiamen	36.046	39.745	39.745	37.969	38.376	6
Guangzhou	46.799	44.757	44.757	52.982	47.324	2
Shenzhen	36.712	51.144	51.144	46.839	46.460	3
Haikou	21.523	17.426	17.426	23.132	19.877	11
Sanya	42.224	38.062	38.062	48.051	41.600	5
(Pan-Pearl River Delta)	36.661	38.227	38.227	41.795	38.727
Average	36.062	35.903	36.080	37.138	36.296

Note. The scores represent relative levels of yachting tourism competitiveness, calculated using the AHP–entropy weighted evaluation method. “Average” refers to the arithmetic mean of the comprehensive scores across all cities for the given year.

Temporal perspective: Overall, yachting tourism competitiveness in China’s coastal areas has increased over time, with annual averages of 36.062, 35.903, 36.080, and 37.138, respectively. Between 2014 and 2017, competitiveness showed a slight decline, with 2017 marking the lowest level. Haikou, Qingdao, Sanya, and Tianjin decreased by 19.03%, 11.26%, 9.56%, and 8.40%, respectively. From 2017 to 2019 and 2019 to 2022, competitiveness gradually increased, by .799 and .435, respectively. Haikou and Sanya exhibited pronounced upward trends of 36.74% and 26.24%. The overall improvement from 2014 to 2022 reflects the combined effects of resource and environmental enhancement, economic growth, implementation of yachting policies, and the construction of yachting infrastructure in coastal cities.

Spatial perspective: Significant disparities exist among cities. Shanghai, Guangzhou, and Shenzhen consistently ranked highest, while Zhoushan, Haikou, and Weihai ranked lowest. Economically developed first-tier cities benefit from high per capita income, strong resident consumption, mature social service systems, and well-established yachting infrastructure, attracting more yachting tourists. In contrast, cities with rich natural resources, such as Zhoushan, Haikou, and Weihai, face limitations in economic, social, and industrial development. Regionally, average competitiveness scores show a descending pattern from the Yangtze River Delta (38.832) to the Pan-Pearl River Delta (38.727) and the Bohai Sea Rim (31.354). The Bohai Sea Rim’s slower economic development, weaker social management, and lower government support explain its relative lag in yachting tourism competitiveness.

Subsystem Analysis of Yachting Tourism Competitiveness

An in-depth analysis of each subsystem is presented in Table 4.

(1) Resource endowment subsystem: The average score was 4.747, showing an initial increase followed by a decline. Zhoushan, Dalian, and Qingdao led with scores above 7, with Zhoushan highest at 10.354 due to its rich archipelagic resources. Haikou scored lowest at .326, 32 times lower than Zhoushan, highlighting strong spatial disparities in marine resource endowment.

(2) Natural environment subsystem: The average score was 5.141 and showed an upward trend. Ningbo, Xiamen ranked highest, with Ningbo at 9.128 owing to its developed inland lakes and sea islands. Tianjin ranked lowest at 1.438, 6 times lower than Ningbo.

(3) Economic foundation subsystem: The average score was 6.646, showing an overall downward trend. Guangzhou, Shenzhen, and Shanghai led, with Guangzhou highest at 16.176. Haikou was lowest at 1.499, illustrating a substantial economic gap that constrains yachting tourism competitiveness.

(4) Social support subsystem: The average score was 10.293, first decreasing and then increasing. Shanghai, Guangzhou, and Shenzhen ranked top three, with Shanghai highest at 18.857, benefiting from comprehensive social services and supportive government policies. Weihai ranked lowest at 3.625, 4 times less than Shanghai, indicating that institutional and community support remains uneven.

(5) Industrial development subsystem: The average score was 9.624, showing consistent growth. Sanya, Shenzhen, Shanghai, Xiamen, and Qingdao ranked top five, with Sanya highest at 19.070 due to the Free Trade Port policy and strengthened yachting infrastructure. Weihai scored lowest at 3.124. As of 2022, the 12 coastal cities averaged 4.92 yacht marinas, 683.9 yacht berths, and 91.33 yacht clubs, with the subsystem showing an annual growth rate of 15.89%, reflecting continuous industrial improvement and rising tourism-related demand.

In summary, economic foundation, resource endowment, and social support subsystems show a slight decline or slower growth, suggesting room for improvement in economic and social environments. Conversely, natural environment and industrial development subsystems exhibit upward trends, with industrial development showing the most pronounced growth, highlighting the critical role of the yacht industry foundation in promoting overall tourism competitiveness. Enhancing economic conditions and accelerating industrial construction remain the primary drivers for future yachting tourism competitiveness.

Table 4.

Yachting Tourism Competitiveness Subsystem Score.

Year	Yachting tourism competitiveness comprehensive score	Resource endowment	Natural environment	Economic foundation	Social support	Industrial development
2014	36.062	4.774	5.261	6.761	10.274	8.992
2017	35.903	4.722	5.149	6.776	9.873	9.384
2019	36.702	4.911	5.216	6.961	9.689	9.926
2022	37.138	4.581	4.939	6.086	11.335	10.196
Mean value	36.451	4.747	5.141	6.646	10.293	9.624

Obstacle Degree Analysis of Yachting Tourism Competitiveness

Equation 6 was used to diagnose the obstacles to yachting tourism competitiveness in coastal cities in China. Among them, the per capita number of yachts, tourism traffic accessibility, number of islands, total tourism income, tourism consumption rate, and yacht clubs were identified as the top six obstacles. The sum of the 6 obstacles exceeded 40.30%. The main obstacle factors showed local consistency across cities, whereas the degree of other obstacles varied considerably.

Since the 2022 data were affected by the COVID-19 pandemic, the year 2019 was taken as a benchmark (Figure 2) to analyze the cities facing the most severe constraints. At the subsystem level, industrial development, social support, and economic foundation emerged as the most critical barriers, with their combined obstacle degree exceeding 68.8%. Specifically, Guangzhou, Zhoushan, and Tianjin were most restricted by the industrial development subsystem, with obstacle degrees exceeding 33.55% against a national average of 28.96%. Ningbo, Zhoushan, and Dalian were constrained primarily by the social support subsystem, exceeding 25% compared to the national average of 20.02%. Qingdao, Haikou, and Xiamen were most restricted by the economic foundation subsystem, with obstacle degrees above 22.89% compared to a national average of 19.86%. Shenzhen, Guangzhou, and Sanya were most constrained by the resource and environment subsystem, exceeding 25.63% against a national average of 18.60%. For the natural environment subsystem, Shenzhen, Tianjin, and Guangzhou experienced the highest constraints, surpassing 14.47% compared to the national average of 12.55%.

Figure 2.

The obstacle factors of yachting tourism competitiveness subsystem in Chinese coastal cities.

As shown in Table 5, the first obstacle in 11 of the 12 cities was C₂₀ (per capita number of yachts). This indicator captures not only the popularity of yachting but also broader consumption patterns, lifestyle preferences, and the level of industrial development in each region. For instance, Shanghai was the most severely restricted by this factor, while Sanya was the least. The difference stems from Sanya having the highest total number of yacht registrations nationwide, whereas Shanghai’s dense population base significantly lowers the per capita ratio. In this sense, the barrier associated with per capita yacht ownership primarily reflects the combined effects of economic affordability, consumer willingness to adopt yachting as a leisure practice, and the limited scale of local yacht-leasing markets.

Table 5.

Analysis of the Obstacle Degree of Yachting Tourism Competitiveness.

City	C ₂₀	C ₃	C ₁₇	C ₁₃	C ₁₂	C ₂₄
Dalian	.1052	.0585	.0689	.0676	.0566	.0573
Tianjin	.0967	.0621	.0470	.0170	.0512	.0665
Qingdao	.1184	.0810	.0658	.0758	.0693	.0475
Weihai	.0830	.0550	.0639	.0681	.0491	.0630
Ningbo	.1087	.0402	.0684	.0569	.0627	.0764
Zhoushan	.0941	.0010	.0731	.0756	.0598	.0714
Shanghai	.1624	.1056	.0716	.0010	.1042	.0479
Xiamen	.1030	.0749	.0749	.0740	.0669	.0492
Guangzhou	.1339	.0865	.0010	.0206	.0010	.0992
Shenzhen	.1406	.0896	.0679	.0912	.0710	.0010
Haikou	.0772	.0572	.0568	.0759	.0508	.0461
Sanya	.0010	.0791	.0960	.1007	.0396	.0406

For the number of islands (C₃), Shanghai faced the greatest restriction, while Zhoushan was the least affected. Zhoushan’s unique natural endowment—an archipelago of nearly 1,390 islands—creates abundant spatial and resource advantages, which Shanghai lacks. This reveals how geographical resource endowment acts as a structural constraint that cannot be easily offset by policy alone.

Regarding tourism traffic accessibility (C₁₇), Sanya was most restricted, while Guangzhou faced the least. Guangzhou benefits from its location in the Pearl River Delta, with a dense transport network and high accessibility index. By contrast, Sanya’s lack of railways and limited highway capacity undermines connectivity, showing that infrastructure development and seasonal transportation bottlenecks are critical determinants of yachting tourism participation.

In terms of total tourism income (C₁₃), Sanya was most affected while Shanghai was least. Shanghai’s diversified and open economy cushions fluctuations, whereas Sanya’s reliance on tourism makes it more sensitive to external shocks. This suggests that the resilience of yachting tourism competitiveness is shaped by underlying economic structures and the degree of dependence on tourism-driven revenue.

For the tourism consumption rate (C₁₂), Guangzhou had the lowest restriction due to its cultural-tourism integration strategies, which boosted tourism-related spending. In contrast, other cities lag behind because of weaker tourism consumption cultures and fewer diversified products.

The number of yacht clubs (C₂₄) was a major obstacle in Guangzhou, while Shenzhen was least constrained. Shenzhen was among the first cities in China to establish yacht clubs, benefiting from early industrial clustering and sustained policy support. In contrast, Guangzhou’s slower growth in yacht club development suggests the presence of institutional and industry-building constraints, rather than merely insufficient market demand.

Additionally, yacht management policies constrained cities like Dalian and Ningbo. Their development was hindered by strict safety regulations, fragmented cross-departmental governance, and limited coordination mechanisms, which increased industry entry barriers and slowed club expansion.

Overall, the obstacle analysis highlights that the barriers to yachting tourism competitiveness are not merely technical or infrastructural, but are shaped by policy regimes (licensing, governance), economic conditions (industrial maturity, income levels), cultural perceptions (yachting as a luxury vs. lifestyle), and structural endowments (island resources, accessibility). This suggests that overcoming these obstacles requires both supply-side reforms (infrastructure, policy support, industry clustering) and demand-side efforts (cultural diffusion, consumption upgrading; Pardali & Giantsi, 2018; Yao et al., 2022b; Yao, Li, & Zhou, 2023).

Discussion and Implications

Theoretical Implications

Currently, there are few systematic assessments of yachting tourism competitiveness. Previous studies have mainly focused on the economic, social, and environmental impacts of tourism (Diakomihalis, 2008; Sariisik et al., 2011; Venturini et al., 2016). This study extends competitiveness research by integrating sustainability principles into the evaluation framework for yachting tourism, a domain where competitiveness analysis is still underdeveloped. Rather than treating economic, environmental, and social factors as separate components, the proposed framework embeds sustainability into the core logic of competitiveness, advancing traditional models grounded in Competitive Advantage Theory. By aligning the five dimensions of the index system with the UN SDGs, the framework connects industry-level performance with broader global development goals. This multidimensional perspective underscores that long-term competitiveness in emerging marine tourism sectors hinges on ecological integrity, institutional capacity, and industrial resilience, not merely short-term economic gains.

The empirical results show that the competitiveness of yachting tourism in China’s coastal cities first declined and then improved, with an overall upward trend and significant regional differences. The relatively stable ranking pattern, with Shanghai, Guangzhou, and Shenzhen consistently leading, suggests that competitiveness is shaped not only by market size but also by structural advantages rooted in economic openness and industrial clustering (Pardali & Giantsi, 2018; Porter, 1998).

Overall, the competitiveness level of China’s coastal cities exhibits a clear upward trajectory throughout the study period, with distinct temporal stages. A decline occurred between 2014 and 2017, followed by a sharp rise from 2017 to 2019 driven by improvements in resource and environmental conditions, rapid economic development, and stronger industrial support, and remained relatively stable from 2019 to 2022 under the constraints of the COVID-19 pandemic. These temporal shifts reflect deeper institutional and market dynamics: the early decline corresponds to limited marina infrastructure, low public awareness, and insufficient coordination of emerging yachting policies; the subsequent rise aligns with expanding disposable incomes, improved coastal-zone governance, and accelerated integration of yachting-related industries; the pandemic-induced stabilization indicates that although mobility was restricted, cities strengthened local service systems, thereby preventing a structural downturn. The relative stability of most cities’ ranking positions, with only minor fluctuations, further suggests strong path dependence shaped by long-term economic structure and local governance capacities (Song & Li, 2008).

Spatially, the competitiveness pattern exhibits a decreasing gradient from the Pan–Pearl River Delta to the Yangtze River Delta and then to the Bohai Rim. The Pan–Pearl River Delta and the Yangtze River Delta show continuous improvement, whereas the Bohai Rim experiences a year-on-year decline. This spatial divergence reflects underlying differences in local policy intensity, yachting-tourism infrastructure, consumer group maturity, and regional economic strength. Southern coastal cities benefit from stronger marine economies, earlier institutional reforms, and more diversified tourism markets, enabling them to attract investment, cultivate consumption, and upgrade industry chains. In contrast, northern cities face structural constraints such as smaller consumer bases, slower service innovation, and weaker industrial linkages, resulting in declining competitiveness despite certain resource advantages (Butowski, 2018; Pardali & Giantsi, 2018).

The obstacle factor analysis further reveals that per capita number of yachts, accessibility of tourism transportation, number of islands, total tourism income, tourism consumption rate, and number of clubs constitute the major barriers restricting competitiveness. These obstacles indicate that both supply-side and demand-side shortcomings—such as insufficient marina and club infrastructures, limited cultural penetration of yachting, and inadequate social service capacity—remain core constraints for many coastal cities. This pattern aligns with previous findings emphasizing the importance of diversified product design, infrastructure development (Diakomihalis, 2008), institutional support (Venturini et al., 2016), and tourism consumption dynamics (Sariisik et al., 2011). Moreover, the co-existence of shared and region-specific obstacles underscores that China’s yachting tourism, as a late-developing sector, still lacks the mature industrial foundation, service systems, and cultural acceptance necessary to fully meet expanding consumer demand.

Furthermore, when compared with cruise tourism and general coastal tourism, yachting tourism emerges as a niche, high-end sector characterized by lower environmental pressures but greater dependence on policy support, infrastructure, and cultural promotion. This comparative perspective underscores the unique sustainability challenges and opportunities of yachting tourism, providing practical insights for urban planners and policymakers. By integrating SDG mapping with empirical findings, the study not only validates the relevance of sustainability theory but also extends its applicability to the evaluation and strategic planning of emerging tourism sectors.

Practical Implications

Considering the main obstacles to the competitiveness of Chinese yachting tourism, several practical implications can be drawn. These recommendations stem directly from the empirical findings of the study. In particular, the identified obstacles such as the low number of yachts per capita, poor tourism accessibility, limited use of island resources, weak tourism consumption capacity and insufficient social service support. As well as the observed regional differences and the dominance of the economic base and industrial development as the main drivers of competitiveness.

First, enhancing cultural awareness and social acceptance. Public communication platforms such as short videos, social media, and news outlets can be more effectively used to popularize yachting tourism. Activities such as popular science lectures, test drives, and community engagement events may help dispel the perception of yachting as an exclusive luxury and encourage broader participation. The suggestion corresponds to the empirical finding that weak cultural diffusion and low public participation constitute important barriers limiting competitiveness. In addition, promoting marine sports (e.g., sailing, diving, surfing) and cultural events (e.g., creative design competitions, sailing festivals) can strengthen cultural identity and improve the attractiveness of yacht-related experiences.

Second, improving accessibility and the tourism experience. To address transportation barriers, seasonal coordination of trains, flights, and other transport links between coastal cities and major tourist source markets should be strengthened. Upgrading passenger terminals—including airports, cruise terminals, and railway stations—can enhance the convenience and quality of the tourist experience. Our advice here is based on the finding that accessibility of tourism traffic is one of the most significant obstacle factors constraining regional competitiveness. Integrating transport with distinctive coastal industries and tourism resources, for example through scenic coastal roads and land–sea combined products, could further diversify the tourism offering and stimulate yachting-related consumption.

Third, strengthening infrastructure and industrial linkages. The development of yachting tourism requires both improved physical infrastructure and better integration of the upstream and downstream sectors of the yacht industry chain. In particular, the standardized management of marinas, upgrading of aging docks, and expansion of public marinas with docking points, ramps, and anchorages are needed to meet the growing demand for mid- and low-end markets. These recommendations stem from empirical findings showing that the per capita number of yachts and the number of clubs are major barriers that limit the expansion of yachting tourism, indicating insufficient infrastructure and weak industrial support. Establishing a second-hand yacht trading market and expanding yacht leasing services can reduce entry costs, improve resource utilization, and promote inclusive growth. At the same time, vocational education and training should be enhanced to cultivate professional talent in yacht design, manufacturing, operations, and services.

Finally, promoting ecological sustainability and regional collaboration. Local governments are encouraged to strengthen ecological protection of coastal zones, islands, and marine protected areas, ensure water quality and safety standards in bathing beaches, and safeguard marine biodiversity. Developing eco-oriented products such as marine sightseeing and science education can enhance public interest while maintaining ecological balance. These actions align with the empirical finding that long-term competitiveness growth depends on sustainable resource use and supportive social institutions. In parallel, leading cities such as Shanghai, Guangzhou, Shenzhen, and Sanya should play a demonstration role by hosting regular forums and industry exchanges, which is consistent with the result that these cities maintain stable leading positions due to their stronger economic foundations and industrial clustering advantages.

Limitations

This study has several limitations. First, given that yachting tourism in China is still in its early stages, it was difficult to obtain complete time-series data for the period 2014 to 2022.

Second, the analysis was limited to 12 major coastal cities, such as Dalian, Qingdao, and Shanghai, which were selected based on industry significance, policy representativeness, and data availability. While these cities are moderately representative, the sample scope may limit the generalizability of the findings to smaller or emerging yachting destinations. This also constrains the spatial granularity, making advanced visualizations—such as spatial–temporal thematic maps—less informative or potentially misleading.

Third, the data cutoff is 2022. Although the COVID-19 pandemic affected both supply and demand in yachting tourism, this study did not explicitly adjust for these impacts through modeling (e.g., control variables or segmented analysis). Future research with longer time-series data or panel models could better capture such phase-specific effects.

Fourth, future research could explore additional theoretical and comparative dimensions. For example, incorporating dynamic capability theory would help explain how digital technologies (e.g., VR-based tourism experiences, smart marina systems) enhance firms’ abilities to sense market trends, seize emerging opportunities, and reconfigure resources, thereby shaping the evolution of yachting tourism competitiveness. Comparative analyses between China and Mediterranean coastal countries could further reveal how different policy frameworks drive variations in industry performance.

As China’s economy and yachting tourism continue to develop in tandem, the availability and quality of relevant data are expected to improve, providing opportunities for longer time-series analyses, larger sample sizes, and richer forms of visualization. These enhancements would allow for a more robust and persuasive evaluation of yachting tourism competitiveness.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440261421716 – Supplemental material for Evaluating Yachting Tourism Competitiveness and Identifying Obstacle Factors in Chinese Coastal Cities: A Sustainable Development Perspective

Supplemental material, sj-docx-1-sgo-10.1177_21582440261421716 for Evaluating Yachting Tourism Competitiveness and Identifying Obstacle Factors in Chinese Coastal Cities: A Sustainable Development Perspective by Yunhao Yao, Na Zang, Chen Li and Merle Parmak in SAGE Open

Footnotes

ORCID iD

Yunhao Yao

Ethical Considerations

The present study was non-interventional in nature, did not involve deception of participants or the participation of vulnerable groups, and therefore did not require institutional review board approval in accordance with national laws. The ethical considerations of survey study were followed throughout the study, the purpose of the study was explained to the participants, the anonymity of the participants was guaranteed and the data was stored securely.

Author Contributions

Yunhao Yao: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. Na Zang: Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Chen Li: Writing – review & editing, Validation, Supervision, Visualization. Merle Parmak: Writing – review & editing, Validation, Supervision.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article was supported by the National Social Science Found of China (No. 25CJY112).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data are available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

References

Abreu-Novais

Ruhanen

Arcodia

(2015). Destination competitiveness: What we know, what we know but shouldn’t, and what we don’t know but should. Current Issues in Tourism, 19(6), 492–512.

Angelkova

Koteski

Jakovlev

(2012). Sustainability and competitiveness of tourism. Procedia - Social and Behavioral Sciences, 44, 221–227.

Bazargani

R. H. Z.

Kiliç

(2021). Tourism competitiveness and tourism sector performance: Empirical insights from new data. Hospitality and Tourism Management, 46, 73–82.

Butowski

(2018). An integrated AHP and PROMETHEE approach to the evaluation of the attractiveness of European maritime areas for sailing tourism. Moravian Geographical Reports, 26(2), 135–148.

Chen

T. L.

(2024). Research on the competitiveness of Sanya yacht tourism [Master’s thesis]. Hainan Tropical Ocean University.

China Cruise and Yacht Industry Association (CCYIA). (2014). China yacht industry report in 2014. China Cruise & Yacht Industry Association.

China Cruise and Yacht Industry Association (CCYIA). (2017). China yacht industry report 2016–2017. China Cruise & Yacht Industry Association.

China Cruise and Yacht Industry Association (CCYIA). (2020). China yacht industry report 2019–2020. China Cruise & Yacht Industry Association.

China Tourism Automobile and Cruise Association (CTACA). (2022). China cruise yacht & ship industry development report 2022–2023. China Tourism Automobile & Cruise Association.

10.

Cibinskiene

Snieskiene

(2015). Evaluation of city tourism competitiveness. Procedia - Social and Behavioral Sciences, 213, 105–110.

11.

Croes

Ridderstaat

Shapoval

(2019). Extending tourism competitiveness to human development. Annals of Tourism Research, 80, Article 102825.

12.

Dezan Shira & Associates. (2022, June 21). China’s yacht market: Opportunities and challenges for foreign players. CorporateLiveWire. https://www.corporatelivewire.com/index.php/www.dezshira.com/images/www.http:/top-story.html/?id=chinas-yacht-marketopportunities-and-challenges-for-foreign-players

13.

Diakomihalis

M. N.

Lagos

D. G.

(2008). Estimation of the economic impacts of yachting in Greece via the tourism satellite account. Tourism Economics, 14(4), 871–887.

14.

Dimitrovski

Lemmetyinen

Nieminen

(2021). Understanding coastal and marine tourism sustainability – A multi-stakeholder analysis. Journal of Destination Marketing & Management, 19, Article 100554.

15.

Esty

D. C.

Porter

M. E.

(2005). National environmental performance: An empirical analysis of policy results and determinants. Environment & Development Economics, 10(4), 391–434.

16.

Gladkikh

Séraphin

Gladkikh

Vo-Thanh

(Eds.) (2021). Introduction: Luxury yachting—A growing but largely unknown industry. In Luxury yachting: Perspectives on tourism, practice and context (pp. 1–8). Springer International Publishing.

17.

Gladwin

T. N.

Kennelly

J. J.

Krause

T. S.

(1995). Shifting paradigms for sustainable development: Implications for management theory and research. Academy of Management Review, 20(4), 874–907.

18.

Gon

Osti

Pechlaner

(2016). Leisure boat tourism: Residents’ attitudes towards nautical tourism development. Tourism Review, 71(3), 180–191.

19.

Gooroochurn

Sugiyarto

(2005). Competitiveness indicators in the travel and tourism industry. Tourism Economics, 11(1), 25–43.

20.

Higham

Font

(2021). Code red for sustainable tourism. Journal of Sustainable Tourism, 30(1), 1–13.

21.

Horak

Marusic

Favro

(2006). Competitiveness of Croatian nautical tourism. Tourism in Marine Environments, 3(2), 145–161.

22.

Huajing Industry Research Institute (HIRI). (2024). 2024–2030 Market research and future development trends forecast report on China’s yacht economy industry. https://www.docin.com/touch_new/preview_new.do?id=4724205672

23.

Kayar

Ç. H.

Kozak

(2010). Measuring destination competitiveness: An application of the travel and tourism competitiveness index (2007). Journal of Hospitality Marketing & Management, 19(3), 203–216.

24.

Kovačević

Mladenović

(2018). Planning and developing human resources as a factor of improving the competitiveness of Montenegrin nautical tourism. Economic Themes, 56(2), 269–281.

25.

Kozak

Rimmington

(1999). Measuring tourist destination competitiveness: Conceptual considerations and empirical findings. International Journal of Hospitality Management, 18(3), 273–283.

26.

Liu

Jia

(2018). Research on the influence mechanism of yacht tourism behavior intention based on TPB extended model. Ocean Bulletin, 37(4), 378–388.

27.

Lopes

A. P. F.

Muñoz

M. M.

Alarcón-Urbistondo

(2018). Regional tourism competitiveness using the PROMETHEE approach. Annals of Tourism Research, 73, 1–13.

28.

Luković

(Ed.). (2013). Tourism and nautical tourism. In Nautical tourism (pp. 5–32). CABI.

29.

Michael

Reisinger

Hayes

J. P.

(2019). The UAE’s tourism competitiveness: A business perspective. Tourism Management Perspectives, 30, 53–64.

30.

Mikulić

Krešić

Kožić

(2015). Critical factors of the maritime yachting tourism experience: An impact-asymmetry analysis of principal components. Journal of Travel & Tourism Marketing, 32(sup1), S30–S41.

31.

Moradi

Ehsani

Saffari

(2022). Developing an integrated model for the competitiveness of sports tourism destinations. Journal of Destination Marketing & Management, 26, Article 100743.

32.

National Marine Manufacturers Association. (2021). 2021 Canadian boating statistical abstract – Retail markets report. NMMA. https://www.nmma.org/statistics/article/24122.

33.

Nie

Dong

G. Z.

(2010). Analysis of port city cruise tourism competitiveness based on entropy weight-TOPSIS method. Tourism Forum, 3(6), 789–794.

34.

Ogutu

Adol

G. F. C.

Bujdosó

(2023). Theoretical nexus of knowledge management and tourism business enterprise competitiveness: An integrated overview. Sustainability, 15, Article 1948.

35.

Paker

Vural

C. A.

(2016). Customer segmentation for marinas: Evaluating marinas as destinations. Tourism Management, 56, 156–171.

36.

Pan

J. H.

(2017). Xiamen yacht tourism competitiveness evaluation research. Fujian Normal University.

37.

Pardali

Giantsi

(2018). The formation of maritime tourism (yachting) cluster as a vehicle for competitiveness improvement: The Greek case. International Journal of Research in Tourism and Hospitality (IJRTH), 4, 64–74.

38.

Park

(2016). Qualitative versus quantitative research methods: Discovery or justification? Journal of Marketing Thought, 3(1), 1–8.

39.

Porter

M. E.

(1980). Competitive strategy: Techniques for analyzing industries and competitors (2nd ed., pp. 86–87). Social Science Electronic Publishing.

40.

Porter

M. E.

(1989). From competitive advantage to corporate strategy. Harvard Business Review.

41.

Porter

M. E.

(1998). Clusters and the new economics of competition (Vol. 76, No. 6, pp. 77–90). Harvard Business Review.

42.

Prahalad

C. K.

Hamel

(2000). The core competence of the corporation. In Cross

Israelit

(Eds.), Strategic learning in a knowledge economy (3rd ed., pp. 3–22). Taylor & Francis.

43.

Purvis

Mao

Robinson

(2019). Three pillars of sustainability: In search of conceptual origins. Sustainability Science, 14, 681–695.

44.

Ritchie

J. R. B.

Crouch

G. I.

(2003). The competitive destination: A sustainable tourism perspective. CABI.

45.

Rodríguez-Díaz

Pulido-Fernández

J. I.

(2020). Sustainability as a key factor in tourism competitiveness: A global analysis. Sustainability, 12, Article 51.

46.

Romeiro

Costa

(2010). The potential of management networks in the innovation and competitiveness of rural tourism: A case study on the Valle del Jerte (Spain). Current Issues in Tourism, 13(1), 75–91.

47.

Santos

Castanho

R. A.

Meyer

(2022). Is investment contributing to competitiveness in nautical tourism in the Atlantic area? Water, 14(19), Article 2964.

48.

Sariisik

Turkay

Akova

(2011). How to manage yacht tourism in Turkey: A SWOT analysis and related strategies. Procedia Social and Behavioral Sciences, 24, 1014–1025.

49.

Smith

A. J.

Dieppe

Vernon

(2010). The competitive advantage of nations: Is Porter’s Diamond Framework a new theory that explains the international competitiveness of countries? Southern African Business Review, 14(1), 1199–1204.

50.

Song

(2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203–220.

51.

Song

(2019). The environmental carrying capacity of marine resources in the offshore areas of the Yangtze River Economic Belt in China. Journal of Coastal Research, 98(sp1), 14–17.

52.

Spinelli

Benevolo

(2022). Towards a new body of marine tourism research: A scoping literature review of nautical tourism. Journal of Outdoor Recreation and Tourism, 40, Article 100569.

53.

Teece

D. J.

Pisano

Shuen

(1999). Dynamic capabilities and strategic management. Knowledge and Strategy, 18(7), 77–115.

54.

The European Competitiveness and Sustainable Industrial Policy Consortium (ECSIP). (2015). Study on the competitiveness of the recreational boating sector. https://single-market-economy.ec.europa.eu/sectors/maritime-industries/recreational-craft-sector/what-commission-doing_fr

55.

Venturini

Massa

Castellano

(2016). Recreational boating in Ligurian Marine Protected Areas (Italy): A quantitative evaluation for a sustainable management. Environmental Management, 57, 163–175.

56.

Vila

T. D.

Darcy

González

E. A.

(2015). Competing for the disability tourism market: A comparative exploration of the factors of accessible tourism competitiveness in Spain and Australia. Tourism Management, 47, 261–272.

57.

Walker

T. B.

Rolle

McLeod

(2023). Yachting tourism’s contribution to the Caribbean’s social economy and environmental stewardship. Worldwide Hospitality and Tourism Themes, 15(4), 422–430.

58.

Wang

(2007). Study on the international competitiveness of Chinese yacht tourism. East China Normal University.

59.

Wang

Yang

Zhang

Gao

Zhou

Zhuo

Wen

Guo

(2021). Evaluating urban ecological civilization and its obstacle factors based on integrated model of PSR-EVW-TOPSIS: A case study of 13 cities in Jiangsu Province, China. Ecological Indicators, 133, Article 108431.

60.

Wang

Fang

Yin

Chen

(2021). Low-carbon development quality of cities in China: Evaluation and obstacle analysis. Sustainable Cities and Society, 64, Article 102553.

61.

Wright

P. M.

McMahan

G. C.

McWilliams

(2016). Human resources and sustained competitive advantage: A resource-based perspective. International Journal of Human Resource Management, 5(2), 301–326.

62.

Chen

(2022). Assessment of long and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake basin. International Journal of Disaster Risk Reduction, 75, Article 102968.

63.

Yang

Ren

Gou

(2022). An empirical study on the environmental carrying capacity of marine resources based on the entropy-weight TOPSIS model. Journal of Coastal Research, 38(5), 1037–1049.

64.

Yao

Y. H.

Z. S.

Zhou

X. X.

(2023). Yachting tourism consumption potential and its influencing factors: Considering 12 coastal cities in China as examples. Sustainability, 15(16), Article 12490.

65.

Yao

Y. H.

Luan

W. X.

(2017). Analysis on the dilemma and countermeasures of China’s yachting tourism system. Journal of Dalian Maritime University (Social Science Edition), 16(1), 12–17.

66.

Yao

Y. H.

Luan

W. X.

(2018). Study on the location characteristics of Chinese yacht clubs. Geographical Science, 38(2), 249–257.

67.

Yao

Zheng

R. Q.

Parmak

(2022a). Using an evaluation index system to examine the developmental potential of yachting tourism. Journal of Coastal Research, 38(6), 1195–1206.

68.

Yao

Zheng

Parmak

(2022b). Factors influencing the willingness to pay in yachting tourism in the context of COVID-19 regular prevention and control: The case of dalian, China. Sustainability, 14, 13132.

69.

Zhang

C. L.

L. W.

Zhang

(2011). The evaluation of tourism destination competitiveness by TOPSIS & information entropy – A case in the Yangtze River Delta of China. Tourism Management, 32(2), 443–451.

70.

Zhou

Zhang

Cao

(2022). Spatio–temporal evolution and factors of climate comfort for urban human settlements in the Guangdong–Hong Kong–Macau Greater Bay Area. Frontiers in Environmental Science, 10, Article 1001064.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB