Comparative Analysis of Coupled Coordination Between Tourism Urbanization and Ecological Environment: Evidence from Tourism and Non-Tourism Coastal Cities in China

Abstract

This study aims to explore the coupled and coordinated development of tourism urbanization and the ecological environment in China’s coastal cities. By constructing an index system for tourism urbanization and the ecological environment, this research evaluates the spatiotemporal characteristics of tourism urbanization and ecological environment changes, as well as their coupling and coordination levels in China’s coastal cities from 2010 to 2019. The results are as follows: (1) From 2010 to 2019, the level of tourism urbanization, ecological environment conditions, and the degree of coupling and coordination in China’s coastal tourism cities were all superior to those in non-tourism cities. (2) During the period from 2010 to 2019, the level of tourism urbanization in coastal cities showed an upward trend, the ecological environment exhibited significant fluctuations, and the overall level of coupling and coordination showed an increasing trend. (3) Overall, there is a clear gradient difference in both the comprehensive index of tourism urbanization and the ecological environment index, which has created a disparity in the levels of coupling and coordination between the tourism urbanization and ecological environment of different cities. Most cities are at a stage where tourism urbanization is lagging, and the coupling and coordination level of some large cities is constrained by the ecological environment. Different cities need to develop differentiated development strategies based on their characteristics to promote the sustainable development of coastal tourism cities.

1. Introduction

Tourism urbanization was first proposed by Mullins [1]. Subsequently, scholars both domestically and internationally have conducted broader and more in-depth research on tourism urbanization. Tourism urbanization is a special type of urbanization driven by the development of the tourism industry, based on pleasure and consumption [2]. It can better exert the tourism functions of cities, meet tourism demands, and bring about changes in local education, transportation, and infrastructure. Relying on locational advantages and policy dividends, China’s coastal areas have shown significant characteristics of tourism urbanization since the beginning of the 21st century. Taking the Yangtze River Delta city cluster as an example, under the guidance of national strategies, its tourism and urbanization processes are ahead of other regions in China and have become a regional economic growth pole [3]. With the advancement of new-type urbanization construction in coastal cities, the driving effect of the tourism industry on regional economy has become increasingly evident [4]. This development trend is beneficial for promoting local economic growth and providing tourism convenience. However, unreasonable urban planning, land use, and infrastructure construction during the process of tourism urbanization can exert pressure on the coastal ecological system. For instance, in Turkey’s coastal areas, unreasonable planning during the process of tourism urbanization has led to the destruction of the ecological balance in the nearshore marine areas, the salinization of coastal aquifers, and the loss of agricultural land [5]. In the coastal areas of Koh Chang, Thailand, although the tourism industry has developed rapidly, the region is more susceptible to environmental impacts and pressures from climate change due to insufficient urban and environmental infrastructure construction [6].

China possesses an extensive continental coastline of 18,400 km and an island coastline of 14,247 km. The coastal regions, with their unique natural landscapes and cultural resources, attract a significant number of domestic and international tourists. Contemporary tourism development in coastal urban areas is undergoing a strategic transition from conventional sightseeing paradigms to multifaceted experiential models, accompanied by progressive cross-sectoral integration with adjacent industries. The Chinese government is also actively promoting the high-end and international development of the tourism industry in coastal cities. However, due to differences in resource endowment and economic development levels, the tourism industry in coastal regions has shown significant regional disparities during its rapid development [7]. For example, the South China Sea coast, leveraging its year-round favorable climate and high-end consumer groups, has formed high-value-added tourism formats. In contrast, the Bohai and Yellow Sea regions, constrained by seasonal limitations and weaker industrial foundations, still primarily focus on traditional sightseeing and rely heavily on local tourist sources.

Compared to other areas in China, the coastal regions have a higher level of tourism and urbanization. Guided by the concept of green development, the importance of the ecological environment is becoming increasingly prominent. High-quality urbanization and tourism development both require a sound ecological environment as support. Therefore, coastal areas should place greater emphasis on the protection of the ecological environment. In the process of tourism urbanization, it is essential to follow the principle of ecological balance to achieve harmonious coexistence between tourism urbanization and the ecological environment.

Based on this, this paper takes the 53 prefecture-level cities along the coast of China as the research subjects, divides the coastal cities of China into tourism cities and non-tourism cities based on the status of urban tourism development, constructs and optimizes the index system for tourism urbanization and ecological environment, and quantitatively analyzes the relationship between tourism urbanization and the ecological environment in each city from 2010 to 2019 through the construction of a coupling coordination degree model, in order to reveal the spatiotemporal differentiation rules of tourism urbanization and the ecological environment in China’s coastal cities, as well as the coordinated development between the process of tourism urbanization and the ecological environment.

2. Literature Review

Early research on tourism urbanization was primarily focused on coastal tourism areas. Mullins [1] posited that tourism urbanization is a model of urbanization aimed at establishing or regenerating areas for the sale and consumption of pleasure, and promoting the development of related tourism industries, with empirical analysis conducted on Australia’s Gold Coast and Sunshine Coast. Currently, research on the coupling relationship between urbanization and the ecological environment in China covers various scales, including townships, cities, provincial regions, and economic belts. For the eastern coastal regions of China, studies often focus on economic belts and city clusters, including the Yangtze River Delta city cluster [3,8], the Pearl River Delta city cluster [9], the Shandong Peninsula city cluster [10], and the Liaoning coastal economic belt [11]. Research on tourism urbanization in China has mainly concentrated on typical tourism cities where the tourism industry is a pillar, such as Guilin [12], Lijiang [13], and Zhangjiajie [14]. Through these studies, scholars have summarized various development models of tourism urbanization. In recent years, the research focus has gradually shifted to the development of new urbanization, tourism economy, and rural tourism.

Different studies have employed various methods and indicators to assess the coupling coordination degree, yet there is a widespread agreement on the interdependent coupling interaction between tourism urbanization and the ecological environment [15,16]. Existing research has demonstrated that elements such as tourism revenue, environmental governance, and population urbanization influence the system’s coordination degree through complex mechanisms [17]. Among these factors, the match between industrial policies and ecological carrying capacity is particularly critical, as their interaction affects sustainable development [18]. The relationship between tourism development and environmental degradation is nonlinear. On one hand, the growth of the tourism industry can boost government revenues and encourage a focus on environmental quality, thereby reducing environmental degradation. On the other hand, the expansion of transportation and the construction of facilities resulting from the agglomeration of factors can exacerbate local environmental loads [19]. The input of energy and the discharge of wastewater during urbanization negatively affect the ecological environment of tourist destinations, and the living standards and resource environment of urban residents indirectly impact the ecological environment of tourist destinations by influencing internal urban economic factors [20]. In general, the impact of tourism urbanization on the ecological environment has both advantages and disadvantages, but its positive impacts are more pronounced. Essentially, the impact of tourism urbanization on the ecological environment depends on the choice of development model. Its advantages of low resource consumption and minimal pollution offer the potential for a sustainable path [21].

Ecological conditions serve as both the basis for the development of tourism urbanization and a factor that can, in turn, exert a restrictive effect. Environments with suitable climates and good resource endowments foster the development of tourism urbanization, while adverse natural conditions and environmental pollution can inhibit urban development to a certain degree. In regions with fragile ecosystems, emphasis should be placed on environmental protection and the adoption of a green tourism urbanization development path [22,23]. China’s coastal cities, acting as pillars of socio-economic development, are also ecologically vulnerable due to their unique geographical position at the junction of land and sea [3]. Existing research has indicated that the overall coupling coordination degree in the eastern coastal regions is superior to that in the central and western regions [24]. However, significant internal gradient differences are evident within these regions [25]. For example, Tian et al. [26] examined the coastal province of Shandong and found that cities with higher economic levels exhibited greater coupling coordination than other cities. Meanwhile, for island areas at the land–sea junction, environmental carrying capacity is identified as a key bottleneck constraining the coordination of the economic–ecology–tourism trinity [27].

At present, the research focusses of scholars both domestically and internationally on tourism-related coupling and coordination studies is primarily centered on the interactive relationships between tourism and urbanization, tourism and the ecological environment, tourism and the economy, and the tourism economy and the ecological environment. There is a comparatively limited amount of research on the mutual influences between tourism urbanization and the ecological environment. In terms of the spatial selection of research areas, existing studies mostly concentrate on the coupling and coordination relationships within individual cities or urban clusters, with a notable absence of extensive comparative analyses both horizontally and vertically among cities that share common characteristics. In addition, existing studies are primarily focused on cities with high levels of tourism urbanization, with less attention given to cities with lower levels of tourism urbanization.

Compared with existing studies, this research makes two primary contributions. First, it establishes a comprehensive evaluation framework for the tourism urbanization and ecological environment subsystems in China’s coastal cities, innovatively classifying these cities into tourism-dominant and non-tourism categories. This classification provides a refined quantitative foundation for analyzing human–environment coupling coordination mechanisms in coastal zones. Second, this study systematically investigates the spatiotemporal dynamics of coupling coordination between tourism urbanization and ecological environments across coastal cities. By doing so, it expands the applicability of tourism–ecology coupling theory to coastal contexts while advancing the extension of sustainable development principles into spatially refined coastal management research.

Guided by the concept of green development, the tourism urbanization process in coastal cities must follow the principle of ecological balance to achieve harmonious coexistence with the ecological environment, thereby building beautiful coastlines and advancing the construction of a beautiful China. The aim of this study is to explore the fundamental patterns of coordinated development between tourism urbanization and the ecological environment in China’s coastal cities, with the intention of providing insights for the future high-quality and healthy development of coastal cities.

3. Materials and Methods

3.1. Study Area

China’s coastline is home to 57 cities, including 53 prefecture-level cities, 2 municipalities directly under the central government, and 2 special administrative regions. At the beginning of the reform and opening-up period, China implemented policies to open up coastal areas first, promoting the development of the eastern coastal regions ahead of others. To date, the economic development and urbanization levels in China’s coastal areas are significantly higher than those in other regions. The coastal cities in China are rich in tourism resources; the vast maritime space has given birth to diverse coastal scenery, as well as rich cultural arts and historical relics. At the same time, the modern urban landscapes and leisure and entertainment facilities brought about by their vibrant economies have also become important tourism resources. Relying on a strong economic foundation and unique tourism resources, the tourism industry in China’s coastal regions started early compared to other areas. It is both an important source of tourists and a significant tourism destination.

As of 2023, China’s coastal cities have a total of 2824 A-level scenic spots (excluding Hong Kong, Macau, and Taiwan), accounting for 19% of the national total. The tourism industry, as one of the important industries in coastal cities, contributes significantly to urban economic growth and plays a certain role in promoting the improvement of urban infrastructure, thereby driving the development of tourism urbanization. However, improper actions during the process of tourism urbanization may lead to environmental damage, and high-quality development of the tourism industry usually sets higher requirements for urban environment and ecological protection. As pioneers with high levels of urbanization and rapid tourism development, China’s coastal regions need to find the best balance between tourism urbanization and ecological environment protection.

Considering the accessibility of data, this study selected 53 cities as the research subjects. By integrating data on the ratio of tourism revenue to regional gross domestic product, the number of tourist visits, and the quantity of A-level scenic spots, this paper categorized China’s coastal cities into tourism cities and non-tourism cities, with 25 tourism cities and 28 non-tourism cities (Figure 1).

3.2. Data Sources

The data sources for this study include statistical yearbooks and bulletins from 53 coastal cities for the years 2010–2020, the Baidu Map Open Platform, and the NASA Earth Data platform (https://www.earthdata.nasa.gov/, accessed on 6 May 2024). Among these, urbanization data related to tourism is derived from the “China City Statistical Yearbook”, “The Yearbook of China Tourism”, various prefecture-level city statistical yearbooks, and statistical bulletins on national economic and social development. Points of Interest (POI) data for scenic spots were sourced from the Baidu Map Open Platform, utilizing Python 3.8 to scrape POI data for scenic spots in 53 cities for the years 2012, 2013, 2014, 2016, 2018, and 2020, totaling 286,834 entries. For some missing values, linear interpolation with Stata SE 16 software was employed to fill in the gaps [15]. Ecological environment data were obtained from the “China Statistic Yearbook on Environment, “China Marine Economic Statistical Yearbook”, “Bulletin of Marine Ecology and Environment Status of China”, various prefecture-level city statistical yearbooks, and environmental condition statistical bulletins. For some missing values, linear interpolation was used to fill in the gaps; the vegetation coverage mentioned in this paper is quantified using the normalized difference vegetation index (NDVI), with data sourced from the NASA Earth Data platform’s annual vegetation NDVI dataset from 2010 to 2020, with a spatial resolution of 1 km.

3.3. Research Methods

3.3.1. The Index System

The present study establishes a dual-coupling system framework, comprising two subsystems: tourism urbanization and the ecological environment (

Table 1. Index system for the tourism urbanization and ecological environment subsystem.

Subsystem	First-Class Index	Second-Class Index	Attributes	Weight
Tourism urbanization	Population	Percentage of urban population (%)	+	0.0223
		Proportion of tertiary industry employees (%)	+	0.0218
		Urban year-end registered unemployment rate (%)	−	0.0099
	Economy	GDP per capita (CNY)	+	0.0420
		Proportion of tertiary industry value in GDP (%)	+	0.0191
		Tourism foreign exchange earnings (104 USD)	+	0.1904
		Urban residents’ disposable income (CNY)	+	0.0305
	Society	Per capita retail sales of consumer goods (CNY)	+	0.0702
		Number of star hotels	+	0.0921
		Number of domestic tourists (104 people)	+	0.1868
		Number of inbound tourists (104 people)	+	0.0470
		Number of scenic spots POIs	+	0.1014
	Space	Population density (people/km2)	+	0.0521
		Built-up area (km2)	+	0.0935
		Urban road area per capita (m2)	+	0.0207
Eco-environment	Natural Environment	Vegetation coverage NDVI	+	0.0801
		Per capita park land area (people/m2)	+	0.1449
		Percentage of Class I and Class II seawater (%)	+	0.3603
	Ecological Hazard	Discharged of industrial waste water (104 t)	−	0.0183
		Discharged of industrial SO2 (t)	−	0.0233
		Average concentration of PM2.5 (μg/m3)	−	0.0468
		Total CO2 emissions (104 t)	−	0.0204
	Ecological Protection	Hazard-free treatment rate of household garbage (%)	+	0.0144
		Urban wastewater treatment rate (%)	+	0.0204
		Percentage of days with air quality meeting or exceeding Grade 2 standards (%)	+	0.0504
		Green coverage in built-up areas (%)	+	0.0362
		Comprehensive utilization rate of industrial solid waste (%)	+	0.0326
		Environmental regulation strengency	+	0.1519

“+” and “−” respectively represent the positive and negative indicators data in the min–max normalization method.

). Utilizing a coupling coordination degree model, a quantitative analysis of the interactions between these two subsystems is carried out. The tourism urbanization subsystem mirrors the impact of regional tourism development on the urbanization process, which includes four dimensions: population, economy, society, and space. The eco-environment subsystem represents the overall condition of regional natural resources and environmental quality, encompassing three dimensions: the natural environment, ecological hazard, and ecological protection. This research framework, grounded in the perspective of coupling coordination, facilitates a more profound comprehension of the intricate interactive mechanisms between urbanization processes propelled by tourism development and the ecological environment at the urban scale.

Previous studies on tourism urbanization have predominantly conducted quantitative research from four dimensions: population, economy, society, and space [16,21]. These aspects collectively reflect the critical elements within the urbanization process and the overall state of urban development, influencing the quality and outcomes of urbanization.

Population structure changes are one of the primary characteristics of the socio-economic transformations resulting from urbanization [14], with the most economically vibrant populations engaged in the tourism service and wholesale and retail sectors [28]. Thus, this study employs the percentage of urban population, the proportion of tertiary industry employees, and the urban year-end registered unemployment rate to denote population urbanization. The urbanization rate captures macro-level rural–urban migration patterns and remains a cardinal measure of conventional urbanization [29,30]. The tertiary employment ratio partially reflects tourism’s structural reshaping of urban labor markets. As a counter-cyclical metric, the registered unemployment rate inversely indicates municipal capacities for employment absorption and economic stabilization, while signaling urbanization quality and sustainability.

Economic urbanization serves as an essential impetus for the development of tourism urbanization. This study selects per capita GDP, the share of tertiary industry value in GDP, tourism foreign exchange earnings, and urban residents’ disposable income as second-class indexes for economic urbanization. GDP per capita and disposable income levels demonstrate positive correlations with tourism urbanization intensity, reflecting regional economic development thresholds [14]. The tertiary sector’s GDP share evolution captures tourism’s catalytic role in economic structural upgrading [31], revealing urban economic centralization patterns. Tourism foreign exchange earnings, as an external economic exposure metric, dynamically measures urban competitiveness within global tourism markets.

Social urbanization is primarily manifested in the influence of the tourism industry on cities [16], encompassing per capita retail sales of consumer goods, the number of star hotels, domestic tourist visits, inbound tourist visits, and the quantity of scenic spots POI (Points of Interest). Tourist-driven consumption demand and its qualitative transformation constitute primary drivers for tourism urbanization’s spatial expansion and functional evolution [32]. Tourist volumes directly index urban tourism market scale, while star-rated hotels and scenic spots serve as proxy measures for tourism infrastructure development [33].

Tourism urbanization has led to a notable expansion of the urban built environment, with swift growth in tourism and urban construction land [34]. Built-up area expansion directly measures land use intensification, while per capita road infrastructure reflects transportation-driven spatial investments. Population density serves as a critical spatial constraint indicator for tourism development capacities. Therefore, population density, urban built-up area, and per capita urban road area are adopted as second-class indicators for spatial urbanization.

China’s coastal cities enjoy a superior geographical location and are rich in tourism resources. However, corresponding to their efficient productivity is the vulnerability of the coastal region’s ecosystem, which can be damaged to some extent when disturbed by natural or human activities. As transitional zones between land and sea, the ecological environment of coastal cities is unique. This paper, based on the research experience of scholars and with a full understanding of the special ecological environment of coastal cities, constructs an ecological environment subsystem from three aspects: the natural environment index, the ecological hazard index, and the ecological protection index [35].

The natural environment index reflects the quality of the ecological environment in the study area, including NDVI, per capita park land area, the percentage of Class I and Class II seawater. Higher NDVI values indicate denser vegetation coverage and more vigorous plant growth in the area; Class I and Class II seawater are evaluated based on the “sea water quality standard” (GB3097-1997) [36] for the quality of seawater in the nearshore areas, with higher proportions indicating better seawater environment in the region. The ecological hazard index measures the potential degree of harm of specific pollutants or environmental factors to the ecosystem, including industrial wastewater discharge, industrial SO₂ emissions, average PM_2.5 concentration, and total CO₂ carbon emissions, reflecting the negative impact of human activities on the ecological environment. The ecological protection index reflects the effectiveness of urban environmental protection efforts, including the hazard-free treatment rate of household garbage, urban wastewater treatment rate, the percentage of days with air quality meeting or exceeding Grade 2 standards, the green coverage rate in the built-up area, the comprehensive utilization rate of industrial solid waste, and environmental regulation stringency (ERS). Specifically, the domestic waste innocuous treatment rate quantifies land-based pollution control capacities through municipal solid waste management performance. Urban wastewater treatment rate directly correlates with marine ecosystem security through pollutant load reduction mechanisms. Air quality compliance days function as a composite atmospheric pollution index integrating multiple contaminants’ synergistic effects. Built-up area green coverage modulates urban microclimates via evapotranspiration processes while maintaining biodiversity through habitat provision. The industrial solid waste utilization rate operationalizes circular economy implementation levels through secondary resource recovery efficiency. ERS measures the effectiveness of ecological protection through policy enforcement.

The indicators are divided into positive indicators and negative indicators, with each type having a corresponding impact on their respective systems. To eliminate the dimensional differences among the indicators, this study adopts the min–max normalization method to transform the raw data into dimensionless values, with a numerical range of [0, 1]. To reduce the influence of subjective factors on the research results, this paper uses the entropy method to calculate the weights of each indicator. The specific calculation steps are as follows:

• Calculate the proportion$P_{ij}$ of the $j$-th indicator for the $i$-th year relative to the total of that indicator, where $X_{ij}$ is the normalized value and $n$ is the total number of years.

  $$P_{ij}={\displaystyle \frac{X_{ij}}{\sum _{i=1}^n{X}_{ij}}}$$

  (1)
• Calculate the entropy value $e_j$ of the $j$-th indicator.

  $$e_j=-k{\sum }_{i=1}^n{P}_{ij}\ln \left(P_{ij}\right),\mathrm{where}k={\displaystyle \frac{1}{\ln n}}>0$$

  (2)
• Calculate the information entropy redundancy degree $d_j$ of the $j$-th indicator.

  $$d_j=1-e_j$$

  (3)
• Calculate the weight of $a_j$ each indicator.

  $$a_j={\displaystyle \frac{d_j}{\sum _{j=1}^m{d}_j}}$$

  (4)

After determining the weights of each indicator, the comprehensive indicator values of the subsystem can be calculated by weighting the normalized second-class indicators, with the formula as follows:

$$U_1=\sum _{i=1}^n{X}_{ij}\times a_j$$

(5)

$$U_2=\sum _{i=1}^n{Y}_{ij}\times b_j$$

(6)

where $U_1$ represents the comprehensive value of the binhai tourism urbanization subsystem, and $U_2$ represents the comprehensive value of the ecological environment subsystem. $X_{ij}$ and $Y_{ij}$ are the normalized values of the tourism urbanization indicators and eco-environment indicators, respectively, while $a_j$ and $b_j$ are the weights of the $j$-th indicator. $U_1$ and $U_2$ reflect the development level of the subsystems, allowing for the analysis of the subsystems’ development trajectories over space and time; the higher the values, the higher the level of development.

3.3.2. Coupling Coordination Degree Model

The coupling coordination degree model is an effective tool for assessing the overall balanced development status of a region or society based on the level of coordination and development. Coupling refers to the phenomenon where two or more systems influence each other through mutual interactions [16]. However, while the degree of coupling can reflect the extent of interaction between systems, it does not characterize whether they are mutually enhancing at a high level or constraining each other at a low level. Therefore, this paper introduces the coupling coordination index to construct a coupling coordination model for the tourism urbanization and ecological environment, with the calculation formula as follows:

$$C=\sqrt{{\displaystyle \frac{U_1{U}_2}{{\left(\frac{U_1+U_2}{2}\right)}^2}}}={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(7)

$$\mathrm{T}=\alpha U_1+\beta U_2$$

(8)

$$D=\sqrt{C\times T}$$

(9)

where $U_1$ and $U_2$ represent the comprehensive values of the two subsystems, respectively. C denotes the coupling degree between tourism urbanization and the ecological environment, with a value range of [0, 1]. Following the median segmentation method used by many scholars in the past [37,38], this paper categorizes the coupling degree C into four stages, which are, from low to high, the low coupling stage, the antagonism stage, the break-in stage, and the high coupling stage (

Table 2. Coupling degree classification.

$\mathit{C}$	Coupling Stage
0.8 < $C$ ≤ 1	High coupling stage
0.5 < $C$ ≤ 0.8	Break-in stage
0.3 < $C$ ≤ 0.5	Antagonistic stage
0 < $C$ ≤ 0.3	Low coupling stage

).

$T$represents the comprehensive coordination index between tourism urbanization and the ecosystem, while $\alpha$ and $\beta$ denote the contribution rates of tourism urbanization and the ecological environment, respectively. Considering the equal importance of both in the coupling coordination relationship and drawing on previous research experience [16,21], this paper sets $\alpha$ = $\beta$ = 0.5. D stands for the level of coordinated development between the two systems, reflecting the overall synergy effect of coastal tourism urbanization and the ecological environment, with a value range of [0, 1]. To better illustrate the coordination relationship between coastal tourism urbanization and the ecological environment, this paper categorizes the coupling coordination degree D into six levels (

Table 3. Coordination degree classification.

D	Coordination Degree
0.8 < D ≤ 1.0	Superior coordination (SC)
0.6 < D ≤ 0.8	Intermediate coordination (IC)
0.5 < D ≤ 0.6	Primary coordination (PC)
0.4 < D ≤ 0.5	Slight incoordination (SI)
0.2 < D ≤ 0.4	Intermediate incoordination (II)
0 < D ≤ 0.2	Extreme incoordination (EI)

).

3.3.3. Relative Development Index

To deeply analyze the coordinated development patterns between tourism urbanization and the ecological environment system, this study constructs a classification standard for coupling coordination types based on the relative development index (E). When E > 1.2, it is classified as the ecological environment lagging type; when E ∈ [0.8, 1.2], it is the synchronous development type; and when E ∈ (0, 0.8), it is the tourism urbanization lagging type. This classification standard refers to the research findings of scholars [16,39] on the assessment of coordinated regional development, and it objectively reflects the dynamic equilibrium relationship between the two subsystems through quantitative indicators.

$$E={\displaystyle \frac{U_1}{U_2}}$$

(10)

where $U_1$ and $U_2$ denote the composite indices of the respective subsystems.

3.4. Technology Roadmap

This study is divided into the following steps (Figure 2). The first step involves establishing the indicator systems for the tourism urbanization and ecological environment subsystems and then conducting dimensionless processing on the collected data from China’s coastal cities for the years 2010–2019. The second step employs the entropy method to objectively assign weights to each indicator, to calculate the composite indices for both the tourism urbanization subsystem and the ecological environment subsystem. The third step utilizes the coupling coordination model to compute the degree of coupling and coordination between tourism urbanization and the ecological environment, based on previous research and calculation outcomes, to categorize the levels of coupling and coordination for further analysis. The final step is to conduct spatiotemporal analysis on the calculation results and to delve into the discussion of the coordination and interactive types between tourism urbanization and the ecological environment.

4. Results

4.1. Spatial-Temporal Analysis of Tourism Urbanization

This study utilizes the Jenks Natural Breaks method within ArcGIS to stratify the indicators of the tourism urbanization subsystem into four levels. On the temporal dimension, the tourism urbanization of both tourism cities and non-tourism cities shows an increasing trend, with the overall comprehensive index of tourism cities being higher than that of non-tourism cities. Spatially, there is a pronounced polarization in the level of coastal tourism urbanization among the various coastal cities, with the Yangtze River Delta and the Pearl River Delta regions exhibiting significantly higher comprehensive indices compared to other areas (Figure 3).

For tourism cities, Shanghai and Guangzhou’s comprehensive indices of tourism urbanization are significantly higher than those of other cities, reaching 0.8165 and 0.6841 in 2019, respectively. The primary reason is that these cities, as international metropolises with high levels of economic development, enjoy a good reputation both domestically and internationally, with their tourism appeal, population size, and economic level far exceeding those of other cities. The comprehensive indices of Tianjin, Qingdao, Xiamen, Hangzhou, Dalian, Wenzhou, Quanzhou, Fuzhou, and Ningbo are markedly higher than those of other tourism cities, exceeding 0.1 in 2010, all surpassing 0.15 by 2013, and all surpassing 0.25 by 2019, with a greater increase in magnitude. Among them, Tianjin, Qingdao, Xiamen, and Hangzhou performed better in terms of tourism urbanization; these cities had an early start in the development of the tourism industry, and years of accumulation have endowed these cities with market appeal and maturity in the tourism industry, with a strong ability to drive economic growth through tourism. The comprehensive indices of the remaining cities ranged from 0.0677 to 0.1237 in 2010 and from 0.0986 to 0.1995 in 2019. This indicates that the development of tourism urbanization in these cities started from a low base and grew slowly; although they possess abundant tourism resources and potential, their economic scale is smaller compared to other tourism cities, which may lead to relatively insufficient tourism infrastructure, thus affecting the overall development of tourism urbanization. After 2014, the development speed of tourism urbanization in tourism cities significantly accelerated, with the growth range between 0.0195 and 0.1512, while the growth range from 2010 to 2014 was between 0.0033 and 0.0740. This phenomenon is likely due to the gradual advancement and implementation of the new-type urbanization strategy and the construction of smart tourism, which has injected new vitality and momentum into the development of the tourism industry in these cities, promoting the development of tourism urbanization.

Non-tourism cities do not consider tourism as their leading industry. Consequently, during their urbanization process, the economic benefits, job opportunities, and impetus for infrastructure development brought by the tourism sector are relatively minor. Consequently, the comprehensive index of tourism urbanization is generally low, with a general trend that higher economic levels correspond to greater comprehensive values. Shenzhen has the highest comprehensive index of tourism urbanization, reaching 0.6408 in 2019, followed by Dongguan and Zhuhai, with indices of 0.3566 and 0.2532, respectively. The cities of Shantou, Nantong, and Zhangzhou, as well as Huizhou and Jiangmen in the Pearl River Delta region, have relatively higher comprehensive indices of tourism urbanization compared to other cities, all exceeding 0.15 in 2019. The comprehensive indices of tourism urbanization in the remaining cities exhibit a low starting point and slow growth trend, with an increase of less than 0.06 over the decade.

4.2. Spatial-Temporal Analysis of Ecological Environment

The comprehensive value of the ecological environment is generally higher than that of tourism urbanization. On a temporal scale, both tourism cities and non-tourism cities show a characteristic of fluctuating changes in the comprehensive value of the ecological environment. On a spatial scale, cities around the Bohai Sea, the Beibu Gulf, and the coastal cities of Fujian Province have good ecological environments, while cities in the Yangtze River Delta show poor ecological performance (Figure 4).

The ecological environment comprehensive value of tourism cities fluctuates less. The average comprehensive indices for the years 2010, 2013, 2016, and 2019 are 0.5508, 0.5744, 0.6009, and 0.5530, respectively. From 2010 to 2019, Shanghai’s ecological environment comprehensive index fluctuated within a lower range of 0.25 to 0.3, being at the lowest level among tourism cities. This is likely due to the constraints on Shanghai’s resource and environmental carrying capacity imposed by rapid urban development and population growth, affecting its overall ecological performance. The long-term low proportion of Class I and Class II seawater areas near the Yangtze River estuary, coupled with poor seawater quality, has significantly negatively impacted the overall ecological environment of the region. Tourism cities in the Yangtze River Delta region, such as Hangzhou and Shaoxing, have shown poor ecological performance, with their ecological environment comprehensive indices not showing significant growth, fluctuating between 0.3 and 0.4. In contrast, Wenzhou, Taizhou, and Zhoushan have relatively better ecological environments, generally showing an increasing trend over the years. Cities around the Bohai Sea, including Dalian, Tangshan, Qinhuangdao, Dandong, Yantai, Weihai, Qingdao, and Rizhao, have higher ecological environment comprehensive values, all above 0.65, indicating good natural environmental protection and quality, with relatively better seawater quality. Additionally, Haikou, Sanya, Zhanjiang, and Beihai have maintained good ecological conditions from 2010 to 2019, demonstrating continuous stability. Guangzhou’s ecological environment comprehensive index, on the other hand, has shown a stable increasing trend, with environmental quality gradually improving from poor to good status.

Overall, the comprehensive ecological environment index of non-tourism cities is lower compared to tourism cities, and it also exhibits greater fluctuations. Within Guangdong Province, cities such as Huizhou, Jiangmen, Jieyang, Maoming, and Shanwei have higher comprehensive ecological environment values from 2010 to 2019, with an average exceeding 0.7. In contrast, the ecological environment comprehensive scores of cities like Zhuhai, Zhongshan, and Dongguan are relatively lower, fluctuating between 0.3 and 0.6. For cities in the Yangtze River Delta region, such as Taizhou, Nantong, and Yancheng, their comprehensive ecological environment indices are generally low and show a trend that is inversely proportional to their geographical distance from the economic center of the delta, meaning the closer a city is to the central area, the poorer its ecological environment quality tends to be. Cities along the coast of the Liaodong Bay, such as Jinzhou, Panjin, and Yingkou, have a significant variability in their comprehensive ecological environment indices, ranging from 0.2 to 0.7 across different cities, with considerable annual variation. Meanwhile, coastal cities in Fujian Province and cities in the Beibu Gulf region demonstrate better ecological conditions and maintain a relatively stable level.

4.3. Coupling Coordination Analysis of Tourism Urbanization and Ecological Environment

4.3.1. Spatio-Temporal Analysis of Coupling Degree

The coupling degree between tourism urbanization and the ecological environment in China’s coastal cities has generally reached the break-in stage or higher, with no low-level coupling phenomena observed. Temporally scale, the coupling degree between tourism urbanization and the ecological environment has been increasing year by year. In 2010, the coupling degree of most cities was at the break-in stage, with only one city in the antagonism stage, and the proportion of high-level coupling cities was 34%. By 2019, the proportion of high-level coupling cities had reached 49%. Spatially, in 2010, cities in the Yangtze River Delta region had basically achieved a high level of coupling. With years of development, the core areas of the Pearl River Delta and cities on the Shandong Peninsula had also mostly reached a high-level coupling state. In addition, cities with more developed urban functions, such as Tianjin, Dalian, and Fuzhou, had also achieved high-level coupling (Figure 5).

For tourism cities, in 2010, a total of 13 cities reached the high coupling stage, and by 2019, 17 cities had reached the high coupling stage. Zhanjiang, Beihai, Rizhao, Tangshan, Qinhuangdao, Dandong, Weihai, Haikou, and Sanya had not yet reached the high-level coupling stage by 2019. Specifically, the tourism urbanization process in these cities is slower and at a lower level compared to other cities, but their ecological environment performance is better than other tourism cities, which causes their coupling degree to lag behind other cities. For non-tourism cities, five cities reached the high coupling stage in 2010, with most cities in the break-in or antagonism stage; by 2019, nine cities had reached the high coupling stage, with the remaining cities basically at the antagonism stage. Tourism cities, due to their specific economic structure and policy support, are more likely to achieve high coupling, while non-tourism cities may lack the corresponding driving force and generally remain in the break-in stage. The average coupling degree of tourism cities is higher than that of non-tourism cities, with a difference ranging from 0.1008 to 0.1249 (Figure 6).

4.3.2. Spatio-Temporal Analysis of Coordination Degree

Overall, the coordination degree between coastal tourism urbanization and the ecological environment in China has generally shown a consistent upward trend, indicating that with the dynamic changes in coastal tourism urbanization and the ecological environment, the two can achieve coupling coordination at a relatively high level. In 2010, the average coordination degree of the system was 0.4872, and it rose to 0.5589 in 2019, with an increase of 14.72%. In 2010, most cities within the coastal range were in a state of SI, with no cities in an EI state or a SC state; by 2019, most cities had reached a PC state, with the remaining cities roughly equally divided between IC and SI. Spatially, the coordination degree of tourism urbanization and the ecological environment in the cities of the Beibu Gulf and the Liaodong Bay was average, while the cities of the Shandong Peninsula and the core cities of the Pearl River Delta performed well (Figure 7).

Tourism cities can be categorized into four levels based on their coordination levels. The first level is Guangzhou, which had the highest average coordination degree between tourism urbanization and the ecological environment from 2010 to 2019, reaching 0.7510, indicating a good synchronous development trend. Guangzhou’s coordination type experienced a development process from IC to SC, suggesting that Guangzhou’s tourism urbanization and ecological environment can promote each other at a high level. The second level includes Qingdao, Shanghai, Dalian, Xiamen, and Hangzhou, with average coordination degrees all exceeding 0.6 from 2010 to 2019. Their coordination types improved from PC to IC. Shanghai maintained a state of IC throughout the period, indicating its relative stability in coordination. The third level comprises Wenzhou, Quanzhou, Fuzhou, and Yantai, which advanced from PC to IC. The remaining cities are in the fourth level, such as Qinhuangdao, Weihai, Beihai, Sanya, etc., which maintain PC or gradually transition from the state of SI to PC.

Non-tourism cities, such as Shenzhen, Dongguan, and Zhuhai, have long maintained the level of IC. Cities like Huludao, Jinzhou, Cangzhou, Panjin, Shanwei, Yangjiang, and Qinzhou have consistently maintained a lower level of coupling coordination, generally showing a slow upward trend, transitioning from a state of EI or II to a state of SI. Other coastal cities are steadily advancing in their tourism urbanization processes while maintaining a relatively stable ecological environment, thus their coordination degree continues to improve, shifting from a state of SI or PC to PC or IC.

4.4. Analysis of the Types of Coordination Between Tourism Urbanization and Ecological Environment

4.4.1. Comprehensive Analysis of Tourism Urbanization and Ecological Environment

The process of tourism urbanization exhibits a non-equilibrium growth trend, with an average annual increase in the tourism urbanization index of 5.19%. The growth rate of tourism cities is 5.23% (Figure 8). Among them, a few tourism cities have experienced phased declines in their indices. The ecological environment indices vary. Cities such as Weihai, Beihai, and Haikou, which rely on high-quality ecological foundations, have ecological environment indices that remain above 0.7. In contrast, some large-scale tourism cities, affected by the intensity of development, have ecological environments that are secondary to those of other tourism cities.

Non-tourism cities exhibit a low-level operation characteristic in their tourism urbanization index (Figure 9), yet the average annual increase is 5.15%, which is not significantly different from that of tourism cities. Some non-tourism cities, influenced by geographical conditions and industrial foundations, face substantial ecological environment pressure and have low ecological environment indices, such as Cangzhou, Jinzhou, and Panjin. In contrast, some coastal non-tourism cities that are dominated by relatively eco-friendly industries or have a better natural environment base maintain relatively higher ecological environment indices, above 0.6, like Fangchenggang, Jiangmen, and Shanwei.

4.4.2. Analysis of Coordination Interaction Types

Based on the relative development index, each type of coordination can be divided into three categories: tourism urbanization lag (TU lag), ecological environment lag (EE lag), and synchronous development (SD). During the period from 2010 to 2019, the coordination level of most cities was mainly influenced by the level of tourism urbanization. As the situation of tourism urbanization lag gradually improved, its coordination level also gradually increased. In 2010, most cities were in the “SI-TU lag” state, and by 2019, most cities were in the “PC-TU lag” state (Figure 10). This indicates that the lag in tourism urbanization is an important reason affecting the coordination level between tourism urbanization and the ecological environment in most coastal cities. With the development of the tourism industry and urban construction, while maintaining a good ecological environment, the tourism urbanization and the ecological environment of coastal cities will further promote each other, and the coordination level will continue to rise. Shanghai has always been in the “IC-EE lag” state, Shenzhen has shifted from “IC-SD” to “IC-EE lag”, and Guangzhou has developed from “IC-EE lag” to “EC-SD”. The three major cities have different trajectories, with the ecological environment playing an important role in each. This indicates that for large, economically developed coastal tourism cities, the ecological environment plays a core role in urban sustainable development, and formulating and effectively implementing environmental policies are crucial for enhancing the coordination level of urban development [18]. In contrast, Tianjin and Hangzhou, although their ecological environment and tourism urbanization are also developing synchronously, only maintain a basic level of coordination. These cities can learn from the experiences of cities like Guangzhou to better promote the coordinated development of tourism urbanization and the ecological environment. The improvement of the coordination level between tourism urbanization and the ecological environment is a complex process that requires cities to consider comprehensively in terms of policy formulation, ecological environment protection, economic development, and other aspects.

5. Discussion

China’s coastal cities, leveraging their unique resource endowments and geographical advantages, have emerged as frontiers of tourism urbanization. However, high-intensity tourism development and urban expansion in these coastal areas exert significant pressure on marine ecosystems. Existing studies predominantly treat coastal cities as homogeneous entities, focusing on unidirectional impact analyses in developed regions such as the Yangtze River Delta [8,40,41]. These studies fail to uncover differences in tourism-driven mechanisms and ecological response patterns across distinct types of coastal cities. This research addresses this gap by categorizing coastal cities into “tourism cities” and “non-tourism cities”, systematically revealing heterogeneous patterns in tourism urbanization processes and eco-environmental coordination mechanisms between the two categories. This approach rectifies the cognitive limitation of prior research that homogenized study regions. By constructing a dual-subsystem coupling model integrating “tourism urbanization” and “ecological environment”, alongside the introduction of a relative development index, the study elucidates the spatiotemporal evolutionary pathways of coupling interactions between tourism urbanization and ecological environments across diverse coastal zones, thereby providing references for regionally differentiated governance strategies.

The composite index of tourism urbanization shows a steady upward trend, while the ecological environment maintains a relatively stable level. Therefore, tourism urbanization and the ecological environment are not inversely proportional but can promote each other and develop together. Overall, the coupling coordination degree of coastal cities shows an upward trend, which validates the positive interactive mechanism of “promoting urban development through tourism and prospering tourism through urban development”.

Tourism revenue in tourism cities accounts for a relatively high proportion of the regional gross product, and the development of their tourism industries significantly drives urbanization. As a result, the composite index of tourism urbanization in tourism cities is significantly higher than that in non-tourism cities. This is consistent with the research findings of Wang et al. [42]. In terms of driving mechanisms, the interactive effect of economic development level and urban capacity influences the spatial pattern of tourism urbanization [43,44]. First-tier cities such as Shanghai, Guangzhou, Shenzhen, and Hangzhou have the highest composite indices of tourism urbanization, largely due to their economic and industrial development levels. Cities like Xiamen and Qingdao have similar levels of fame to Sanya, but the former, as larger economic centers with clearer strategic positioning, have a much higher composite index of tourism urbanization than the latter. For regions with relatively low levels of economic development, the level of tourism urbanization is more influenced by tourism resources compared to regions with higher levels of economic development. Tourism cities such as Qinhuangdao, Weihai, Beihai, and Sanya, although experiencing relatively slow growth in their tourism urbanization levels, still have higher levels than most non-tourism cities. The composite index of tourism urbanization in non-tourism cities is generally low, which is consistent with the research results of Shi [22].

The direct cause of urbanization’s impact is the continuous intensification of human activities. Taking the Yangtze River Delta region, which has a low ecological environment index, as an example, on the one hand, changes in land use are evident, with construction land expanding from urban centers to surrounding areas, encroaching on ecological spaces and causing fragmentation of ecological land, thereby affecting the regional ecological environment [45,46]. On the other hand, urbanization leads to a decrease in the density of river networks and the degradation of tributaries, which affects the pollution absorption capacity of rivers and results in increasingly prominent water environment issues [47]. Of course, the factors influencing the ecological environment of coastal cities are diverse. For instance, pollution from industrial waste transported by rivers from upstream areas may accumulate in coastal regions, thereby affecting the marine ecological environment of coastal cities [48].

Tourism urbanization affects the regional ecological environment through mechanisms such as strengthening tourism development positioning, guiding industrial agglomeration, and adjusting land use types. Relying on advantageous tourism resources, tourism-oriented towns and cities rapidly expand spatially to systematically promote tourism development, restructuring regional land use and landscape patterns. This exerts continuous pressure on ecosystem services and exacerbates environmental problems [49]. Moreover, tourism can exert multifaceted impacts on ecological environments through pathways such as energy consumption, biodiversity alteration, and disease transmission [50]. However, the positive impact of tourism urbanization on the ecological environment should not be overlooked. The development of tourism can provide funding, policy, and structural support for ecological protection, forming a virtuous cycle of “sustaining the environment through tourism.” For example, tourism cities significantly enhance regional ecological governance capabilities through channels such as ticket revenue and ecological compensation funds [51]. The urbanization model dominated by the tertiary sector, especially the tourism industry, optimizes the economic structure by expanding urban consumption functions. Its systemic upgrading characteristics form a synergistic effect with the intensive orientation of new-type urbanization, effectively avoiding the spatial fragmentation issues associated with traditional urbanization [26]. The optimization of the ecological environment can also promote the benign development of tourism urbanization. A high-quality ecological base not only nurtures tourism resources but also continuously optimizes the suitability of tourism urbanization by constructing livable environments [52].

While ecological lag remains a critical challenge in megacities like Shanghai and Shenzhen, the coupling coordination between tourism urbanization and ecological environments across most coastal cities is predominantly constrained by lagging tourism urbanization. The phenomenon of ecological lag fundamentally stems from the tension between “economies of scale” and “ecological carrying capacity thresholds” under high-intensity development models. As industrial structure optimization advances, the direct influence of regional economic strength on system coordination diminishes, whereas the alignment efficiency between industrial configurations and resource endowments emerges as a critical determinant [26]. uncoordinated In coastal cities characterized by low coordination degree and underdeveloped tourism urbanization, limited coordination improvements may derive from dual constraints: inadequate economic foundations to support industrial upgrading and path-dependent development patterns rooted in overreliance on natural endowments. These structural rigidities systematically hinder industrial structure optimization [53].

6. Conclusions

This study employs the coupling coordination degree model and selects 53 coastal cities in China as the research subjects. By constructing an index system for tourism urbanization and the ecological environment, and based on panel data from 2010 to 2019, it quantitatively assesses the coupling coordination relationship between coastal tourism urbanization and the ecological environment at the prefecture-level city level in China. The conclusions are as follows:

(1)

From 2010 to 2019, the level of tourism urbanization, the state of the ecological environment, and the degree of coupling coordination in tourism cities are superior to those in non-tourism cities. Specifically, the high-value areas of the comprehensive index of tourism urbanization are mainly the core cities of the Yangtze River Delta, the core cities of the Pearl River Delta, and the cities of the Shandong Peninsula; the high-value areas of the comprehensive index of the ecological environment are scattered, with significant differences within the regions.

(2)

From 2010 to 2019, the level of tourism urbanization in coastal cities showed an upward trend, while the ecological environment fluctuated significantly, and the overall level of coupling coordination increased. The coupling degree between coastal tourism urbanization and the ecological environment was high, with all cities basically reaching the break-in stage or the high coupling stage. However, the coordination degree was relatively low; in 2010, most cities were in a state of slight incoordination, and by 2019, most cities were in a primary coordination state, with some reaching an intermediate coordination state, and only Guangzhou achieved a high coordination state. Most cities belong to the type where tourism urbanization lags, and only a few large cities are of the ecological environment lag type or synchronous development type.

(3)

Overall, there is a distinct spatiotemporal gradient in the comprehensive indices of both coastal tourism urbanization and the ecological environment, which creates a disparity in the levels of coupling and coordination between tourism urbanization and the ecological environment across different cities. Among them, first-tier cities and some well-known coastal tourism cities stand out in the process of tourism urbanization. In contrast, other cities still need to continuously explore and strengthen their path towards tourism urbanization. This gradient distribution characteristic suggests that local governments need to develop differentiated strategies tailored to the characteristics of different cities to promote the sustainable development of coastal tourism cities. Particularly, the ecological environment plays a crucial role in the development of the tourism industry and the level of urbanization. For cities with significant fluctuations in the ecological environment, governments need to enhance the efficiency of environmental policy implementation to ensure the healthy and sustainable long-term development of the city’s tourism industry, while also advancing the city’s green development strategy. This includes not only focusing on the green economic effects brought about by tourism development but also exploring green development models within the tourism industry itself and actively addressing the potential negative environmental impacts of tourism urbanization.

Human and natural subsystems cannot achieve complete coordination, and the coupling and coordination between tourism urbanization and the ecological environment will inevitably undergo continuous dynamic fluctuations [54]. Analyzing these fluctuations can provide critical insights into the coordination mechanism between tourism urbanization and the ecological environment. Based on research findings and the characteristics of coastal cities, the following recommendations are proposed:

• Implement Differentiated Development Strategies. Given the gradient differences in the coupling and coordination degree of tourism urbanization and the ecological environment in coastal cities, a classified guidance mechanism should be established. For superior coordinated cities and potentially superior coordinated cities such as Guangzhou and Shenzhen, the focus should be on enhancing tourism quality under ecological constraints. For cities with intermediate coordination, efforts should be made to optimize the alignment between industrial structure and resource factors, avoid unchecked expansion and over-reliance on natural resources, and reduce ecological pressure through rational tourism and infrastructure planning. For cities with low coordination and lagging tourism urbanization, priority should be given to improving ecological infrastructure networks to break the path dependency on natural endowments and enhance ecological capacity and environmental quality.
• Establish an Ecological Compensation System. Given the unique ecological environment of coastal cities, it is essential to establish an integrated land–sea ecological compensation mechanism. Specifically, in river basins such as the Yangtze River Basin, a horizontal compensation system can be implemented where tourism revenue is used to support upstream ecological governance, ensuring adequate funding for the ecological protection of upstream areas and safeguarding the water environment of the delta region. At the urban agglomeration level, tourism carbon emission quota trading can be introduced to guide financial flows towards ecological restoration. At the project level, it is mandatory to allocate budgets for ecological restoration in tourism development projects, institutionalizing the “sustaining the environment through tourism “mechanism. Through cross-regional ecological governance and cooperation in the tourism industry, the overall level of coordination between tourism urbanization and the ecological environment can be enhanced.
• Innovative Industry Synergistic Development Model

Addressing the issue of delayed industrial structure optimization, this model promotes the deep integration of tourism with other industries. In ecologically sensitive areas, low-environmental-impact initiatives such as educational tourism and eco-wellness ventures can be developed. In industrial transition cities, pathways for repurposing industrial heritage into tourism assets should be explored. Meanwhile, in service industry-dominated cities, technological innovation can empower tourism activities to reduce their ecological footprint. Through multidimensional expansion of the tourism industry, a distinctive network of tourism-centric cities can emerge, transforming the tourism sector from a singular economic function into a comprehensive urban development engine. This approach fosters synergistic progress in enhancing ecological benefits and optimizing urban spatial structures.

This paper quantitatively analyzed the coupling and coordinated development between tourism urbanization and ecological environment in China’s coastal cities using data from 2010 to 2019, and the above conclusions were drawn. However, this study still has the following shortcomings: (1) The temporal scope of this study, confined to statistical data from 2010 to 2019, restricts its capacity to capture recent dynamics in the coupling coordination trends, further compounded by the exclusion of cumulative ecological pressures from non-tourism stressors. This dual constraint may hinder a holistic understanding of land–sea interaction mechanisms, particularly in rapidly evolving delta regions. Future research should integrate predictive modeling with multi-source monitoring to explore future development trends and multi-driver synergies; (2) The study did not delve into and quantify the specific influencing factors of the coupling and coordinated development between tourism urbanization and eco-environment in China’s coastal cities, focusing mainly on the analysis of macro-level indicators. These factors may include, but are not limited to, policy factors, economic development levels, traffic carrying capacity, and natural geographic factors. Future research could employ methods such as the Gray Relational Analysis (GRA) model and Geographically Weighted Regression (GWR) model for a more in-depth investigation.