An Empirical Study on the Coupling of Wetland Ecotourism and Resource–Environmental Carrying Capacity in Dongting Lake Wetland

Abstract

This study explores the coupling relationship between wetland ecotourism and resource–environmental carrying capacity in the Dongting Lake region. By constructing a comprehensive index system and utilizing a coupling coordination degree model, we analyzed the temporal and spatial evolution characteristics across 24 districts and counties from 2014 to 2022. The results indicate the following: (1) The quality of both ecotourism and environmental carrying capacity has steadily improved, though significant regional disparities remain. (2) The coupling coordination degree exhibits a “high in the center, low in the periphery” spatial pattern, showing a positive correlation between ecotourism levels and environmental capacity. (3) The region comprises three development types: balanced coordination, well-matched, and lagging. These findings provide a scientific basis for optimizing ecotourism pathways and achieving high-quality regional sustainable development.

1. Introduction

As the intrinsic value of wetland ecosystem services gains global recognition, sustainable management has become a non-negotiable priority for international conservation efforts [1]. Ecotourism offers a promising compromise in this context, theoretically reconciling the often-conflicting goals of regional economic growth and ecological preservation [2,3,4,5,6]. Yet, this symbiotic relationship is far from guaranteed; it manifests as a dynamic, nonlinear coupling process where tourism expansion and resource and environmental carrying capacity (RECC) continuously stress and constrain one another [7,8,9,10]. Consequently, moving beyond qualitative descriptions to systematically quantify this complex interplay becomes not just an academic exercise but a prerequisite for achieving high-quality development in sensitive wetland regions [11,12,13,14].

The academic discourse surrounding this balance has evolved significantly to match these challenges. In the realm of management, the focus has shifted from simplistic resource exploitation models to complex adaptive systems (CASs), where stakeholder dynamics—particularly the support of local residents—are now seen as decisive factors for long-term sustainability [9,15,16]. To assess these complex systems, methodological innovation has been equally rapid. Traditional reliance on static ecological footprints [17] is gradually giving way to multidimensional evaluation frameworks that integrate ecosystem services [18,19,20] and big data analytics [21]. This methodological maturity has empowered researchers to explore the coupling coordination between socio-economic activities and ecological environments across various spatial scales, spanning from provincial regions to river basins [22,23,24,25,26,27], laying a robust foundation for quantitative inquiry.

Despite these broader advancements, research specific to the Dongting Lake region—a critical ecological barrier in the Yangtze River basin—remains surprisingly fragmented. Early inquiries were largely confined to qualitative descriptions of ecological degradation or broad development patterns [28,29,30,31], failing to capture the nuance of modern environmental stresses. While theoretical models for ecotourism have been proposed, many are now dated, lacking the support of recent quantitative data to reflect current realities [32]. Even among the few recent empirical studies, such as the work by Xiong et al. (2020), the analytical lens has primarily focused on the interaction between tourism and “urbanization systems” [33]. This leaves a critical blind spot: the direct, quantified coupling mechanism between wetland ecotourism and the specific constraints of resource and environmental carrying capacity (RECC) remains largely unexplored.

Bridging this gap requires a holistic approach that integrates quantitative statistical rigor with a multidimensional evaluation of regional characteristics. This study constructs a comprehensive evaluation index system to empirically analyze the temporal and spatial evolution of 24 districts and counties in the Dongting Lake region from 2014 to 2022. By shifting the focus to RECC, we aim to not only measure the comprehensive development levels of these dual systems but also to decode the spatiotemporal characteristics of their coupling coordination. We operate under the hypothesis that while a positive coupling mechanism generally exists, it is subject to significant spatial heterogeneity driven by disparate local economic conditions. Ultimately, clarifying these mechanisms will provide the scientific evidence needed to classify development stages and tailor optimization strategies, supporting the ecological protection goals of both the Dongting Lake Ecological Economic Zone [34,35] and the broader Yangtze River Economic Belt [10].

2. Theoretical Model Construction

2.1. Coordination Mechanism

The theoretical framework delineated in Figure 1 captures the coupling interaction between the ecotourism system and the resource and environmental carrying capacity (RECC) system. Far from a simple bidirectional exchange of elements, this relationship represents a complex functional coupling anchored in specific theoretical mechanisms [36,37,38].

Acting as an active value-conversion engine, the ecotourism system theoretically facilitates the “valorization of ecological capital.” It transmutes intangible ecosystem services into tangible economic assets. This metamorphosis generates a positive feedback loop: as the landscape’s economic valuation rises, it unlocks the financial capital requisite for environmental protection while simultaneously incentivizing a paradigm shift in local governance toward sustainability. Furthermore, the flourishing of ecotourism catalyzes industrial restructuring, pivoting the local economy away from extractive resource consumption and thereby alleviating direct anthropogenic stress on the wetland ecosystem [39,40].

Conversely, the RECC system functions as the material foundation and rigid boundary for all tourism activities [41]. Governed by the theory of ecological thresholds, the RECC dictates the absolute limits of a destination’s ability to support visitor influxes and infrastructure expansion [42,43]. A robust RECC guarantees the perpetual supply of high-quality ecological products—the very essence of the destination’s allure. Yet, this support is finite. Should tourism expansion breach these carrying capacity thresholds, a “limiting factor” effect is triggered; the resulting ecological degradation erodes the destination’s appeal, effectively placing a hard constraint on the long-term viability of the tourism industry [44]. Specifically, as further detailed in the mechanism pathways of Figure 1, the coordination process is driven by four key integration vectors: urban–rural mobility, industrial upgrading, functional integration, and structural optimization. The diagram illustrates that the RECC system (left side) functions as the supply foundation, providing “livable and business-friendly space” and “infrastructure guarantees.” Conversely, the ecotourism system (right side) acts on the benefit dimension to generate “economic benefits” and facilitate “cultural inheritance.” Under the consistent goal of sustainable health, these two systems interact through a “diagnostic feedback” loop to resolve conflicts. This dynamic integration ultimately facilitates the transition towards “man-earth harmony” and promotes the comprehensive “revitalization of the Lake District.”

2.2. Coupling Coordination Degree Model

To rigorously quantify the complex interaction between wetland ecotourism and the resource and environmental carrying capacity (RECC) in the Dongting Lake region, this study constructs a coupling coordination degree model synthesized from existing theoretical frameworks [38,39,41,42]. The quantitative assessment begins with the calculation of the comprehensive development index ($U$) for each subsystem. By applying the linear weighting method to the standardized indicator values ($x_{ij}^{\prime },y_{ij}^{\prime }$), the aggregate performance of both the ecotourism system ($U_1$) and the RECC system ($U_2$) can be derived:

$$U_1=\sum _{j=1}^m{w}_j\times x_{ij}^{\prime },\hspace{1em}{U}_2=\sum _{j=1}^n{w}_j\times y_{ij}^{\prime }$$

where $w_j$ represents the weight of indicator $j$. Building upon these subsystem evaluations, the focus shifts to their dynamic interplay. The coupling degree ($C$) is employed to measure the intensity of this interaction, reflecting the extent to which the two systems influence each other:

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

However, a high coupling degree alone does not necessarily imply a high level of sustainable development, as it could theoretically result from two systems “resonating” at a very low level of development. To address this limitation and accurately reflect the overall synergy, the Comprehensive Coordination Index ($T$) is introduced. In this study, consistent with the principle that economic development and ecological protection are of equal importance for sustainable wetland management, the contribution coefficients are set as $\alpha =\beta =0.5:$

$$T=\alpha U_1+\beta U_2$$

Finally, by integrating the interaction strength ($C$) with the comprehensive development level ($T$), the coupling coordination degree ($D$) is obtained as the ultimate metric for evaluation:

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

The resulting $D$ value serves as a comprehensive indicator of the system’s status; generally, a value closer to 1 signifies a more optimized and harmonious state of coupling coordination [34,45]. To facilitate a nuanced spatial–temporal analysis, these calculated values are further categorized into six distinct intervals corresponding to three development stages (

Table 1. Classification table of coupling coordination degree grades and development stages.

Interval of D Values for Coupling Coordination	Degree of Coupling Coordination	Developmental Stage
(0.00–0.35]	Moderate imbalance	Dissonance stage
(0.35–0.40]	Mild imbalance
(0.40–0.45]	Primary coordination	Adaptation stage
(0.45–0.50]	Intermediate coordination
(0.50–0.55]	Advanced coordination	Coordination stage
(0.55–1.00)	Quality coordination

).

3. Indicator System and Weight Determination Method

3.1. Selection of Indicator System

The development of ecological tourism resource and environmental carrying capacity involves many factors such as nature, ecology, and society. It is a huge and complex system, including natural quality, ecological stress, economic strength, economic structure, living standards, social development costs, and many other aspects. The establishment of its indicator system should reflect the essence and characteristics of the coordinated development of ecological tourism resource and environmental carrying capacity in lake areas, and it should scientifically and systematically expound the essence of coordinated development through the indicator system. The coordinated development of ecological tourism and resource and environmental carrying capacity in the Dongting Lake area is also a dynamic process. Therefore, the selected indicators should be able to reflect the dynamic changes in the development of ecological tourism and resource and environmental carrying capacity in the Dongting Lake area, and they should also have a certain degree of foresight to reflect the overall trend of the development of ecological tourism and resource and environmental carrying capacity in the new era. In addition, compared with the the Taihu Lake Lake Rim District, which has a large amount of research, a balanced economic level, an early and relatively complete development of ecotourism resources, the economic development level of each county and city in Dongting Lake District and the ecotourism resources in various regions are quite different, so the selection of the scope of the indicator system should be tailored to local conditions to avoid the occurrence of outlier indicators in some counties and cities affecting the fairness of the evaluation.

On the basis of comprehensive consideration of previous research results and the actual situation of the research area [34,35,41,42,45,46,47,48,49,50,51,52,53,54], and based on the principles of indicator selection mentioned above, this article refers to the planning outline of ecological tourism in Dongting Lake area in the “Dongting Lake Ecological Economic Zone Plan (2014)” and “Hunan Province Dongting Lake Protection Regulations (2021)”, and it selects 11 indicators from three dimensions: development degree and benefits, infrastructure and management situation to construct an ecological tourism evaluation index system (

Table 2. Evaluation index system and weights of ecotourism system in Dongting Lake area.

System Level	Standardized Level	Standardized Level Weights	Indicator Level	Indicator Level Weights
Ecotourism system in the Dongting Lake area A	Extent and benefits of ecotourism development A1	0.6782	Spatial density of tourism A11	0.3554
			Tourism resource class A12 Area of excursion zone A13 Ecotourism income A14	0.0475 0.1952 0.0802
	Ecotourism infrastructure A2	0.1796	Density of the road network A21 Economic development capacity A22	0.0573 0.1026
			Accommodation and food accessibility A23	0.0196
	Ecotourism management A3	0.1423	Average education level of tourism workers A31	0.0373
			Environmental protection index of tourist transportation A32	0.0438
			Local people’s perception of wetland ecotourism A33	0.0266
			Ecotourism complaint resolution rate A34	0.0346

). The ecological tourism development degree and benefit units are used to evaluate the development status and tourism revenue of ecological tourism in various counties and cities in the Dongting Lake area. The ecological tourism infrastructure unit mainly reflects the convenience and environmental protection index of tourism in the Dongting Lake area. The ecological tourism management unit can analyze the satisfaction of tourists and local residents with ecological tourism, as well as the future development trend of scenic spots. The carrying capacity of resources and the environment is comprehensively affected by a variety of factors, and the combination of factors at different times and in different regions has obvious differences. In combination with the above requirements and the actual development of ecotourism in Dongting Lake District, with reference to indicators such as water resources per capita, cultivated land per capita, GDP per capita, economic growth rate, wastewater discharge compliance rate, solid waste treatment rate, the development and utilization of water resources, land resource pressure and other indicators included in the Chinese Academy of Sciences Sustainable Development Indicator System, and based on the indicators’ comprehensiveness, pertinence and data availability, we selected 15 indicators from three dimensions—natural resource carrying capacity, ecological resource carrying capacity, and economic resource carrying capacity—to construct a resource and environmental carrying capacity indicator system (

Table 3. Evaluation index system and weight of resource and environmental carrying capacity system in Dongting Lake area.

System Level	Standardized Level	Standardized Level Weights	Indicator Level	Indicator Level Weights
Environmental Carrying Capacity System for Wetland Resources B	Natural resources carrying capacity B1	0.4040	Cultivated land area per capita B11	0.0251
			Annual sunshine hours B12	0.0199
			Water resources per capita B13	0.0758
			Annual precipitation B14	0.0202
			Wetland area per capita B15	0.2629
	Ecological carrying capacity B2	0.1161	The number of days with air quality at or above Level 2B21	0.0023
			Air composite index B22	0.0181
			Sewage treatment rate B23	0.0208
			Utilization rate of solid waste B24	0.0168
			Forest coverage rate B25	0.0582
	Socio-economic support B3	0.4800	Green coverage rate of built-up area B31	0.0350
			Per capita green park area B32	0.0233
			Urbanization rate B33	0.0303
			Per capita GDP B34	0.0799
			Economic density B35	0.3115

). The natural resource carrying capacity unit mainly reflects and evaluates the richness of regional water resources and land stress index. The ecological environment carrying capacity unit is used to evaluate the quality of the regional ecological environment and the efficiency of pollutant treatment. The socio-economic carrying capacity unit can reflect the overall quality and efficiency of local economic growth, as well as the degree of impact of economic development on residents’ environment.

Specifically, regarding the climatic factors in the resource and environmental carrying capacity system (Table 3), the annual sunshine duration (B12) was selected. Scientifically, adequate sunshine is recognized as the fundamental energy source for wetland vegetation photosynthesis, underpinning the ecosystem’s self-purification capacity. Furthermore, it serves as a direct determinant of climatic comfort for outdoor activities, acting as a natural prerequisite for ecotourism development.

3.2. Weight Determination

This paper uses the entropy method to determine the weights of evaluation indicators for ecotourism systems and resource and environmental carrying capacity systems, which, to some extent, overcomes the drawbacks of subjective weighting methods such as subjectivity and strong one-sidedness. Xu (2024), Dai (2024), and Luo (2024) have verified the applicability and scientificity of the comprehensive evaluation model combined with the entropy weight method in the measurement of resource and environmental carrying capacity [55,56,57]. This paper uses the entropy method to calculate the indicator weights and refers to the method of Zhou (2023) for data processing [58].

The entropy method, as an objective assignment method, can objectively reflect the effective value of information entropy and overcome the overlap of information between multiple indicator variables, making it more suitable for comprehensive evaluation of multiple indicators. The entropy method calculates the weights of indicators based on data standardization. Subsequently, the entropy and difference coefficients of each indicator are calculated, and finally the weights of each indicator are calculated (Table 2 and Table 3)

(1)

Calculate the entropy values of various indicators. If there are m evaluation units and n indicators, and the entropy value of the jth indicator is defined as E_j, the calculation formula is:

$$E_j=-k{\sum }_{k=1}^h{\sum }_{i=1}^m{P}_{kij}\ln p_{kij}$$

$$k={\displaystyle \frac{1}{\ln \left(h\times m\right)}}$$

(2)

Calculate the redundancy of entropy values for various indicators:

                D_j = 1 − E_j

(3)

Calculate the weights of various indicators:

$$W_j = {\displaystyle \frac{D_j}{\sum _{j=1}^n{D}_j}}$$

3.3. Overview of the Research Area and Data Sources

3.3.1. Overview of the Research Area

The Dongting Lake area mainly involves Changde City, Yueyang City, and Yiyang City within Hunan Province (Figure 2). The sample size of this study is refined to county-level units, with a total of 24 counties in the three cities (

Table 4. Districts and counties within the Dongting Lake area of Hunan Province.

City	District/County
Yueyang City	Huarong County, Xiangyin County, Miluo City, Yueyang Lou District, Yunxi District, Junshan District, Yueyang County, Linxiang City, Pingjiang County
Changde City	Shimen County, Li County, Jinshi City, Linli County, Taoyuan County, Anxiang County, Hanshou County, Dingcheng District, Wuling District
Yiyang City	Anhua County, Taojiang County, Nan County, Yuanjiang City, Heshan District, Ziyang District

).

In 2022, the total population of the district was 14.02 million. This area is one of the four major economic sectors in Hunan Province and also a “land of fish and rice” in Hunan and even the whole country. There are abundant tourism resources and numerous scenic spots. In 2022, the total tourism revenue was 138.3 billion yuan, accounting for 28.8% of the total value of the tertiary industry. The development of tourism has promoted the economic, social, and cultural progress of the region, but its natural conditions, traditional culture, and other factors have also been damaged to varying degrees due to the bad behavior of tourists, the extensive development of rural tourism in the early stage, and the vicious competition of homogeneous tourism resources. As a key area of the national ecological economic zone and the Yangtze River Economic Belt, the coordination of its ecological tourism development and resource and environmental carrying capacity has received widespread attention. The development strategy of facing green mountains and clear waters is as valuable as mountains of gold and silver. It is urgent to develop an effective path for improving the quality of ecological tourism in the Dongting Lake area, based on its own strengths and weaknesses, to continuously promote sustainable development in the region, as well as to prevent and resolve the contradiction between optimizing the carrying capacity of resources and environment and the development of ecological tourism.

3.3.2. Data Sources

The relevant data involved in constructing the indicator system mainly come from the “China County Statistical Yearbook”, “Hunan Statistical Yearbook”, “Hunan Rural Statistical Yearbook”, and “Hunan Tourism Development Report” from 2014 to 2022, as well as the statistical bulletins on national economic and social development in Changde City, Yueyang City, and Yiyang City from 2014 to 2022. While most socio-economic data were sourced from statistical yearbooks, specific indicators reflecting tourism management and social perception—namely the environmental protection index of tourist transportation (A32), local people’s perception of wetland ecotourism (A33), and ecotourism complaint resolution rate (A34)—were extracted from the Hunan Tourism Development Report (2014–2022) and the annual tourism work summaries of the relevant municipal culture and tourism bureaus. These reports provide aggregated data based on annual sample surveys and administrative records regarding tourist satisfaction and environmental governance. The rural living environment and rural tourism development involve multiple dimensional indicators, among which an increase in positive indicators leads to better evaluation results, while an increase in negative indicators can be detrimental to the evaluation. This article uses the range method to standardize them.

To ensure data reliability and reproducibility, descriptive statistics for the key raw indicators (e.g., A14, B13, B34) and the calculated system indices ($U_1,U_2,D)$ are summarized in

Table 5. Descriptive statistics of key indicators and system evaluation indices.

Category	Variable	Symbol	N	Mean	Std. Dev.	Min	Max
Raw Indicators	Ecotourism Revenue (108 CNY)	A14	216	44.32	37.53	4.12	198.6
Raw Indicators	Per Capita Water Resources (m3)	B13	216	3217.78	2289.39	753	10,936
Raw Indicators	Per Capita GDP (CNY)	B34	216	58,904.88	41,901.38	17,840	242,991
Evaluation Indices	Ecotourism System Index	$U_1$	216	0.201	0.045	0.112	0.356
Evaluation Indices	RECC System Index	$U_2$	216	0.205	0.032	0.15	0.28
Evaluation Indices	Coupling Coordination Degree	$D$	216	0.432	0.085	0.315	0.65

. As indicated in the table, the high standard deviation of ecotourism revenue (SD = 37.53) reflects significant regional heterogeneity in tourism development.

4. Calculation Results and Analysis

4.1. Coupling Degree Calculation

According to the calculation of the coupling degree coordination index of each county and district in Dongting Lake area, the results show (

Table 6. Results of coupling and harmonization of ecotourism and resource and environmental carrying capacity in each county and district and grading of status.

Region	Year
	2014		2016		2018		2020		2022
	D Value	The Coupled Coordination Degree	D Value	The Coupled Coordination Degree	D Value	The Coupled Coordination Degree	D Value	The Coupled Coordination Degree	D Value	The Coupled Coordination Degree
Anhua County	0.40328	Primary coordination	0.44448	Primary coordination	0.48198	Intermediate coordination	0.49631	Intermediate coordination	0.51094	Advanced coordination
Anxiang County	0.30815	Moderate imbalance	0.31666	Moderate imbalance	0.34199	Moderate imbalance	0.36045	Mild imbalance	0.36355	Mild imbalance
Dingcheng District	0.44771	Primary coordination	0.46211	Intermediate coordination	0.49563	Intermediate coordination	0.51082	Advanced coordination	0.51614	Advanced coordination
Hanshou County	0.36980	Mild imbalance	0.38308	Mild imbalance	0.40303	Primary coordination	0.41224	Primary coordination	0.42677	Primary coordination
Heshan District	0.37007	Mild imbalance	0.39896	Mild imbalance	0.42902	Primary coordination	0.48866	Intermediate coordination	0.49989	Intermediate coordination
Huarong County	0.39273	Mild imbalance	0.38528	Mild imbalance	0.41551	Primary coordination	0.44050	Primary coordination	0.46487	Intermediate coordination
Jinshi City	0.29724	Moderate imbalance	0.31318	Moderate imbalance	0.33972	Moderate imbalance	0.38557	Mild imbalance	0.40447	Primary coordination
Junshan District	0.38587	Mild imbalance	0.40189	Primary coordination	0.42621	Primary coordination	0.41759	Primary coordination	0.43685	Primary coordination
Li County	0.38509	Mild imbalance	0.37881	Mild imbalance	0.40948	Primary coordination	0.42331	Primary coordination	0.41425	Primary coordination
Linli County	0.34639	Moderate imbalance	0.38346	Mild imbalance	0.43282	Primary coordination	0.42629	Primary coordination	0.45517	Intermediate coordination
Linxiang City	0.26214	Moderate imbalance	0.33620	Moderate imbalance	0.41572	Primary coordination	0.42781	Primary coordination	0.43967	Primary coordination
Miluo City	0.43315	Primary coordination	0.46708	Intermediate coordination	0.47674	Intermediate coordination	0.49164	Intermediate coordination	0.51600	Advanced coordination
Nan County	0.23251	Moderate imbalance	0.25927	Moderate imbalance	0.35745	Mild imbalance	0.39134	Mild imbalance	0.39824	Mild imbalance
Pingjiang county	0.34883	Moderate imbalance	0.38029	Mild imbalance	0.44324	Primary coordination	0.44266	Primary coordination	0.47107	Intermediate coordination
Shimen County	0.31744	Moderate imbalance	0.34004	Moderate imbalance	0.37731	Mild imbalance	0.41169	Primary coordination	0.42045	Primary coordination
Taojiang County	0.35774	Mild imbalance	0.40979	Primary coordination	0.46912	Intermediate coordination	0.49294	Intermediate coordination	0.51494	Advanced coordination
Taoyuan County	0.45763	Intermediate coordination	0.47167	Intermediate coordination	0.50815	Advanced coordination	0.52060	Advanced coordination	0.51828	Advanced coordination
Wuling District	0.40891	Primary coordination	0.47525	Intermediate coordination	0.53632	Advanced coordination	0.55796	Quality coordination	0.56137	Quality coordination
Xiangyin County	0.38085	Mild imbalance	0.46035	Intermediate coordination	0.46896	Intermediate coordination	0.50084	Advanced coordination	0.50904	Advanced coordination
Yuanjiang City	0.38465	Mild imbalance	0.42279	Primary coordination	0.42662	Primary coordination	0.44026	Primary coordination	0.46269	Intermediate coordination
Yueyanglou District	0.66037	Quality coordination	0.73444	Quality coordination	0.77026	Quality coordination	0.72224	Quality coordination	0.71504	Quality coordination
Yueyang County	0.45173	Intermediate coordination	0.46992	Intermediate coordination	0.49717	Intermediate coordination	0.54556	Advanced coordination	0.55295	Advanced coordination
Yunxi District	0.49540	Intermediate coordination	0.50771	Advanced coordination	0.51939	Advanced coordination	0.52534	Advanced coordination	0.55730	Quality coordination
Ziyang District	0.31291	Moderate imbalance	0.34702	Moderate imbalance	0.38604	Mild imbalance	0.39517	Mild imbalance	0.43644	Primary coordination

) that from 2014 to 2022, the average coupling degree between ecotourism and resource and environmental carrying capacity systems (1) maintained an upward trend, gradually increasing from 0.4092 to 0.4547, and the overall coordination degree changed from mild imbalance to primary coordination, indicating that the connections between the systems have become closer and are in an orderly development stage. (2) There are significant differences in coupling coordination between regions. In 2022, the coupling coordination index of each county and city ranged from 0.39824 to 0.71505, and the degree of coupling coordination spans five levels: mild imbalance, primary coordination, intermediate coordination, advanced coordination, and high-quality coordination. It can be seen that some districts and counties in Dongting Lake area are still in the cultivation stage of ecotourism, and the development mode is still relatively extensive, with huge room for improvement. It still needs time to settle in order to effectively integrate with the carrying capacity of resources and environment, and promote the coordinated development of the two.

4.2. Evaluation of Ecotourism and Resource and Environmental Carrying Capacity in Dongting Lake Area

According to relevant models, the ecological tourism and resource–environmental carrying capacity scores of 24 counties in Dongting Lake area from 2014 to 2022 were analyzed. Based on the processing methods of relevant literature at home and abroad, combined with the natural breakpoint method, the scores were divided into five levels according to the index size: low index area, low index area, general index area, high index area, and high index area [59,60,61,62,63,64,65]. The index results of each region were visualized using ArcGIS 10.8.

4.2.1. Time Dimension

(1)

Evaluation of the Development Level of Ecotourism: From 2014 to 2022, the average quality evaluation index of ecological tourism development in Dongting Lake area increased from 0.1397 to 0.2632. In 2014, there was only one county in the Dongting Lake area that was in a high-index zone, which was Yueyanglou District. There are two counties in the high-index zone, namely Yunxi District and Dingcheng District. There is one general-index area, which is Taoyuan County. There are 11 areas with a lower index. Nine counties are located in low-index areas. The number of low-index areas and low-index counties accounts for 83.33% of the total, and the overall quality of ecotourism urgently needs to be improved(Figure 3). Since 2020, due to the impact of COVID-19, the development of ecotourism has suffered setbacks, so the ecotourism evaluation index has fluctuated, and the growth rate has also decreased compared with previous years. Although affected by the epidemic, the Dongting Lake area, with its good tourism foundation and years of tourism development experience, has adopted forms such as “online tour” and “cloud explanation” to reduce tourism benefits to a certain extent. By 2022, ecological tourism development in the Dongting Lake area will no longer have low-index areas. At the same time, except for Yueyanglou District, a total of seven counties including Yunxi District, Anhua District, Taojiang County, Dingcheng District, Heshan District, and Miluo City have entered the high-index area, with six high-index areas, five general-index areas, and six low-index areas. Additionally, 75% of the counties are already in the general index or above level area, and the overall quality of ecotourism has been significantly improved.

(2)

Evaluation of Resource and Environmental Carrying Capacity Level: From 2014 to 2022, the development trend of resource and environmental carrying capacity quality in the Dongting Lake area was good, and the average evaluation index increased from 0.1835 to 0.2205. In 2014, only Yueyanglou District and Yueyang County in the Dongting Lake area were in the high-index zone. There are four counties in the high-index zone, namely Wuling District, Yunxi District, Xiangyin County, and Taoyuan County. The remaining 18 counties are either general-index areas or low-index areas, accounting for 75% of the total. The overall level of resource and environmental quality in the Dongting Lake area needs to be improved. In 2022, with the continuous improvement of the environment, the resource and environmental carrying capacity score of the Dongting Lake area has significantly increased. Except for Wuling District, Yueyanglou District, and Yueyang County, which have stabilized as high-index areas, the number of counties in high-index areas has risen to nine, and many counties in general-index areas have approached the threshold of high-index areas. By 2022, 50% of counties will be at or above the high index level, and significant achievements will be made in resource and environmental governance (Figure 4).

4.2.2. Spatial Dimensions

From the perspective of spatial distribution pattern, the ecological tourism development index and resource and environmental carrying capacity level of counties in the Dongting Lake area are generally similar in spatial distribution. According to the distance from the lake area, as time goes by, by 2022, a distribution pattern of “high in the center and low in the periphery” will be presented. The ecological tourism index and the resource and environmental carrying capacity index have basically maintained consistency in spatial distribution, and counties with high-index levels are mainly distributed in the central areas of Dongting Lake, such as Yueyanglou District and Yueyang County, which are closer to the water body. The counties with low index levels are mainly distributed in the peripheral areas of the Dongting Lake region, such as Nanxian and Hanshou counties.

4.3. Coupling and Coordination of Ecotourism and Resource and Environmental Carrying Capacity in Dongting Lake Area

4.3.1. Spatiotemporal Patterns of Coupling and Coordination Levels Between Ecotourism and Resource and Environmental Carrying Capacity

Based on the calculation results of the coupling coordination between ecological tourism and resource and environmental carrying capacity in 24 counties in Dongting Lake area from 2014 to 2022, a time variation chart of coupling coordination degree was drawn (Figure 5), which shows that the overall level of coordination between ecological tourism development and resource and environmental carrying capacity in 24 counties in the Dongting Lake area shows a steady upward trend. From the grading results of the coupling coordination level between ecological tourism and resource and environmental carrying capacity in the Dongting Lake area, in 2014, the 24 counties in the Dongting Lake area were mainly concentrated in three levels: moderate imbalance, mild imbalance, and primary coordination, with eight, eight, and four counties, respectively. There was only one county with an advanced coordination level, Yueyanglou District, and no county with high-quality coordination. After 8 years of development, the coupling and coordination level of 24 counties has continuously improved. In 2022, there will be no counties with moderate levels of imbalance among the 24 counties in the Dongting Lake area. The results of the coupling coordination level grading are concentrated at the coordination level, among which six counties, including Taoyuan County, Anhua County, Taojiang County, Dingcheng District, Xiangyin County, and Miluo City, at the advanced coordination level. Yueyang County, Yunxi District, Wuling District, and Yueyanglou District have even reached the high-quality coordination level. From the perspective of changes in coupling and coordination between counties, the overall coupling and coordination degree of counties has increased significantly from 2014 to 2022. Among them, Linli County, Pingjiang County, Taojiang County, and Xiangyin County have a larger span, and the coupling and coordination degree has increased by two levels. Yunxi District, Yueyanglou District, Yueyang County, and Taoyuan County have always maintained a high level of coupling and coordination due to their abundant tourism resources and good infrastructure. However, some counties such as Nanxian, Anxiang, and Shimen do not have abundant tourism resources, or due to relatively backward infrastructure and late development and promotion, the economic indicators brought by tourism are not enough to drive the development of the entire system. Therefore, these counties have been at a low level of coupling and coordination.

Table 7. Comparison of average scores of ecotourism system ($U_1$) and resource and environmental carrying capacity system ($U_2$) from 2014 to 2022.

Year	Ecotourism Index (U)	RECC Index (U2)	Difference (U1 − U2)	Coordination Type Characteristic
2014	0.1344	0.1815	−0.0471	Ecotourism Lagging
2016	0.1723	0.1922	−0.0199	Ecotourism Lagging
2018	0.2195	0.207	0.0125	Transition/RECC Lagging
2020	0.2548	0.2168	0.038	RECC Lagging
2022	0.259	0.2185	0.0405	RECC Lagging

reveals a critical structural shift in the coupling relationship. In the early stage (2014–2016), the resource and environmental carrying capacity (RECC) index ($U_2$) was consistently higher than the ecotourism index ($U_1$), indicating an “Ecotourism Lagging” phase when the region’s environmental foundation was underutilized. However, a turning point occurred around 2018, when $U_1$ surpassed $U_2$ for the first time (0.2195 vs. 0.2070). By 2022, the gap widened further, with the ecotourism index reaching 0.2590 while the RECC index grew modestly to 0.2185. This “crossover” trajectory signifies a transition to a “Resource and Environment Lagging” type, suggesting that the rapid expansion of the tourism industry has begun to outpace the improvement of environmental carrying capacity.

4.3.2. Types of Coupled Coordination Stages Between Ecotourism and Resource and Environmental Carrying Capacity

Based on the current natural and socio-economic conditions of each county in the Dongting Lake area, and based on the calculated D value range and the three stages of coordinated development (

Table 8. Classification of coupling coordination types between ecotourism development and resource and environmental carrying capacity in various counties of Hunan Province in Dongting Lake area.

Coupling Coordination Type	District/County
Coordinated and balanced type	Wuling District, Yueyanglou District, Yunxi District, Yueyang County, Taoyuan County, Anhua County, Taojiang County, Dingcheng District, Xiangyin County, Miluo City
Well-adapted type	Shimen County, Li County, Jin City, Hanshou County, Ziyang District, Junshan District, Linxiang City, Linli County, Huarong County, Yuanjiang City, Heshan District, Pingjiang County
Imbalance lag type	Nan County

), relevant materials [60,65,66,67,68,69,70] were consulted. Combined with the spatial distribution pattern of the coupling coordination degree between the development of ecological tourism and the level of resource and environmental carrying capacity in the Dongting Lake area, counties with a D value above 0.50 that have entered the coordination stage and have achieved certain results in the development of ecological tourism and resource and environmental carrying capacity were classified as coordinated and balanced, with a total of 10 counties belonging to the coordinated and balanced type. The counties with D values between 0.40 and 0.50 in the adjustment stage, where only one of the values is high but both indices have not been fully achieved, are classified as the good adjustment type. A total of 12 counties belong to the good adjustment type. The counties with D values below 0.40 that are in the stage of imbalance and have obvious contradictions are classified as the imbalance lag type, with a total of 1 county belonging to the imbalance lag type.

(1)

Coordinated and balanced type: The coupling coordination degree is in the coordination stage. A D value above 0.50 is classified as a coordinated balance type. This type is located in the central part of Dongting Lake area, mostly adjacent to water bodies, with abundant water resources, good agricultural foundation, excellent air quality, and abundant ecological resources, providing a solid foundation for the development of the resource environment and ecological tourism. The evaluation index of ecological tourism and resource–environment carrying capacity are both high and maintain synchronous growth. At the same time, the ecological tourism resources in this type of area are relatively abundant, with 5A-level tourist attractions such as Yueyang Tower. The tourism development is early and the degree of openness is high. The abundant tourism resources have enabled the area to achieve good overall planning, and various elements have achieved a virtuous cycle. The evaluation index and coupling coordination level are both at the leading level in the 24 counties of Dongting Lake.

(2)

Well-adapted type: The coupling coordination degree is in the adaptation stage, and those with a D value between 0.40 and 0.50 are classified as the well-adapted type. The evaluation index of ecological tourism and resource and environmental carrying capacity in this type shows a state of “one high and one low”. For example, the evaluation index of resource and environmental carrying capacity in counties represented by Linxiang City and Jinshi City is relatively high, while the evaluation index of ecological tourism development is relatively low. The evaluation index of resource and environmental carrying capacity in counties represented by Pingjiang County and Yuanjiang City is relatively low, but the evaluation index of ecological tourism development is relatively high. The low level of economic development in this type of county has to some extent constrained the coordinated development of ecotourism and resource and environmental carrying capacity. For this type of county, it is necessary to strengthen the coordination and interaction with development and promote the high-level development of its coupling and coordination level.

(3)

Imbalance lag type: The coupling coordination degree is in the stage of imbalance, and those with a D value below 0.40 are classified as the imbalance lag type. The evaluation index of ecological tourism and resource and environmental carrying capacity in this type is relatively low, and the development is relatively lagging behind. Due to the late promotion of ecological tourism resource development, lagging infrastructure development, inconvenient transportation conditions, and serious population outflow in this type of county, the driving force for ecological tourism development and resource and environmental remediation is insufficient. It is difficult for various development factors to achieve efficient interaction, and the evaluation index and coupling coordination level are both lagging behind. However, the transportation location advantages of this type of county are obvious, with north–south and east–west transportation arteries passing through. For this type of county, it is necessary to strengthen the complementary advantages with surrounding counties and promote the improvement of evaluation index and coupling coordination level.

5. Discussion

5.1. Interpreting the Spatiotemporal Dynamics

The empirical results reveal a distinct temporal trajectory where the coupling coordination degree between wetland ecotourism and resource and environmental carrying capacity (RECC) has steadily improved from 2014 to 2022. This upward trend aligns with the strategic implementation of the “Yangtze River Protection” initiative, which has effectively mitigated the ecological risks previously identified in the region [25]. Spatially, the emergence of a “high in the center, low in the periphery” pattern offers critical insights into regional disparities [40]. The central districts, such as Yueyanglou and Wuling, possess superior economic foundations and mature infrastructure, allowing them to absorb the environmental stress generated by tourism activities more efficiently. In contrast, peripheral counties like Nanxian, despite rich natural endowments, suffer from a “lagging effect” where insufficient environmental governance infrastructure constrains the potential conversion of ecological capital into tourism benefits. This heterogeneity underscores the role of economic resilience as a critical, yet often overlooked, buffer within the coupling system. It suggests that purely ecological models may underestimate coordination potential in economically robust areas, highlighting the necessity for integrated socio-ecological assessment frameworks [27].

5.2. Dialogue with Existing Literature

Our findings confirm the hypothesis that a benign coupling mechanism exists between ecotourism and RECC, supporting the theoretical proposition by Xu et al. (2020) [3] regarding the potential synergy between recreational services and conservation. However, our study adds a nuanced layer to the existing body of knowledge. Unlike Xiong et al. (2020) [33], who identified urbanization level as the primary driver of coordination in the Dongting Lake region, our RECC-centric coupling analysis reveals that environmental carrying capacity has emerged as a primary bottleneck for development in the post-pandemic era. While previous studies focused on how tourism drives urbanization [35], our results highlight a reverse constraint: without the supporting capacity of the resource environment—specifically water and land stress indices—tourism growth in wetland areas encounters a “glass ceiling” more rapidly than in urban settings, where land and infrastructure can be more readily expanded [50]. This distinction is vital for understanding why some resource-rich counties fail to achieve high-quality coordination. These findings necessitate a move away from uniform regional policies and toward differentiated development strategies, which we delineate below.

Furthermore, the transition from ‘imbalance’ to ‘coordination’ in the Dongting Lake region is corroborated by recent international evidence on wetland social–ecological systems. For instance, Samal and Dash (2024) emphasized that sustainable ecotourism in the Chilika Wetland requires a community-oriented collaborative approach to bridge the gap between ecological conservation and local development, which supports our proposal for a “diagnostic feedback” mechanism [71]. Supporting this, Sethy and Senapati (2023) demonstrated that stakeholders’ willingness to pay for conservation is intrinsically linked to their perception of ecotourism impacts, validating our inclusion of social perception indices as critical drivers of coupling coordination [72]. Most importantly, Shi and Chen (2024) highlighted that in the Chinese context, the cultivation of environmental values is a fundamental determinant of ecotourism intention [73]. These frontier studies collectively suggest that long-term coordination necessitates a holistic strategy that aligns environmental thresholds with the psychological and economic incentives of the local community, ultimately facilitating a sustainable “man-earth harmony” in wetland regions.

5.3. Implications for Differentiated Development

The distinct stratification of the 24 counties into three development stages—coordinated, well-adapted, and lagging—signals a crucial policy reality: the traditional “one-size-fits-all” governance model is no longer tenable for the complex Dongting Lake ecosystem. Instead, an adaptive management strategy tailored to specific lifecycle stages is required to sustain the delicate coupling mechanism.

For the region’s ecological pioneers, such as Yueyanglou and Wuling, the challenge is no longer about “growth” but about “limitations.” These districts, having achieved a high-level equilibrium between tourism revenue and environmental capacity, now face the risk of “over-tourism”—a phenomenon where unchecked visitor flows begin to erode the very “three living” (production, living, and ecological) environments that attract them. Therefore, the strategic priority must shift from quantitative expansion to qualitative precision. By establishing a dynamic monitoring mechanism based on the evaluation index system constructed in this study, these areas can scientifically manage visitor peaks and prevent the overload of the resource and environmental carrying capacity system ($U_2$).

Yet, a different dilemma confronts the “Well-Adapted” counties like Linxiang and Jinshi. Our data reveal a mismatch in these areas: they possess abundant ecological assets (high $U_2$), yet their ecotourism system indices ($U_1$) remain disproportionately low. This suggests that their development is constrained by a low efficiency in converting ecological resources into economic benefits. To break this impasse, policy interventions should focus on “unlocking” these dormant assets. Rather than merely expanding scale, the emphasis should be on improving the quality of tourism supply and enhancing the value-added capabilities of the industry to turn environmental advantages into tangible economic flows.

Conversely, for the “Lagging” cluster represented by Nan County, the constraints are structural and severe. The low coupling coordination here is not merely a management issue but a foundational deficit in infrastructure. Observation of these areas indicates that without raising the baseline threshold of environmental governance—specifically regarding sewage treatment and waste management capacities—any aggressive tourism marketing would likely lead to rapid ecological degradation. Thus, the immediate pathway is not commercial promotion, but a “rehabilitation first” approach, directing fiscal transfers to compensate for historical infrastructure debts before scaling up tourism activities.

5.4. Limitations and Future Directions

While this study offers a quantitative lens into the coupling dynamics of wetland ecotourism, it is, like all empirical research, bounded by the resolution of its data and the assumptions of its methods.

A primary constraint stems from the temporal lag inherent in statistical reliance. Although we attempted to capture the “human dimension” by incorporating qualitative indicators like resident perceptions (A32–A34), the majority of our dataset relies on statistical yearbooks. This introduces an unavoidable delay, meaning our model may not fully capture high-frequency ecological fluctuations—such as the dramatic seasonal water level changes unique to the Dongting Lake wetlands—which can alter carrying capacity literally overnight.

Beyond data availability, the methodological lens itself imposes certain distortions. The entropy weight method, while effective at reducing subjective human bias, operates on the mathematical premise that “higher data dispersion equals higher importance”. In complex ecological systems, however, a critical threshold factor (like water quality) might remain stable for years before collapsing; such a stable but vital indicator would be undervalued by the entropy algorithm due to its low variance. This suggests that purely statistical weighting needs to be cross-validated with expert ecological knowledge in future iterations.

Looking forward, to pierce through these veils of uncertainty, future research must move beyond static yearbooks. The integration of multi-source heterogeneity data—combining remote sensing imagery for environmental monitoring with real-time cellular signaling data for tourist tracking—holds the promise of constructing a dynamic early-warning mechanism. Such a system would evolve the current static assessment into a responsive management tool, capable of guiding the Dongting Lake region toward a truly resilient and high-quality future.

6. Conclusions

This study constructed a RECC-centric coupling coordination model to systematically quantify the interaction between wetland ecotourism and environmental carrying capacity in the Dongting Lake region. By analyzing panel data from 24 counties over the period 2014–2022, we moved beyond simple correlation analysis to evaluate how these coupled systems evolve under the dual constraints of economic development and ecological protection.

The core empirical findings are summarized as follows:

(1)

Temporal Evolution: The region has achieved a definitive transition from “mild imbalance” to “primary coordination.” This trajectory validates the efficacy of the “Yangtze River Protection” strategy, confirming that strict ecological redlining has successfully reversed the degradation trends of the previous decade and initiated a synchronous improvement in both tourism quality and environmental capacity.

(2)

Spatial Heterogeneity: A distinct “center-periphery” disparity has been identified. High-level coordination is heavily concentrated in economically mature central districts, while resource-rich but economically peripheral counties remain trapped in lower coordination levels. This confirms that natural endowment alone is insufficient for sustainable development; economic infrastructure serves as the essential prerequisite for translating ecological assets into coordinated development.

(3)

Typological Differentiation: The region comprises three distinct evolutionary stages—coordinated, well-adapted, and lagging. This classification deconstructs the feasibility of uniform regional policies, establishing that the primary development bottleneck shifts from infrastructure deficits in lagging areas to operational inefficiencies in adapting areas and finally to carrying capacity limits in coordinated areas.

In terms of theoretical and practical contributions, this study shifts the discourse on wetland development from passive resource extraction to active capacity stewardship. It demonstrates that high-quality development in sensitive lake regions relies on engineering the carrying capacity of the underlying socio-ecological system. Consequently, future regional strategies must prioritize the differentiation of governance models, ensuring that policy interventions are precisely matched to the specific lifecycle stage of each county to raise the “glass ceiling” of environmental constraints.