Assessing the Coevolution Between Ecosystem Services and Human Well-Being in Ecotourism-Dominated Counties: A Case Study of Chun’an, Zhejiang Province, China

Abstract

Investigating the coevolution between ecosystem services (ES) and human well-being (HWB) holds significant implications for achieving the sustainable operation of human–environment systems. However, limited research has focused on ES-HWB interactions in ecotourism-dominated counties. To address this gap, this study takes Chun’an County in Zhejiang Province, China, as a case study, with the research objective of exploring the processes, patterns, and mechanisms of the coevolution between ecosystem services (ES) and human well-being (HWB) in ecotourism-dominated counties. By integrating multi-source heterogeneous data, including land use data, the normalized difference vegetation index (NDVI), and statistical records, and employing methods such as the dynamic equivalent factor method, the PLUS model, the coupling coordination degree model, and comprehensive evaluation, we analyzed the synergistic evolution of ES-HWB in Chun’an County from 2000 to 2020. The results indicate that (1) the ecosystem service value (ESV) fluctuated between 30.15 and 36.85 billion CNY, exhibiting a spatial aggregation pattern centered on the Qiandao Lake waterbody, with distance–decay characteristics. The PLUS model confirms ecological conservation policies optimize ES patterns. (2) The HWB index surged from 0.16 to 0.8, driven by tourism-led economic growth, infrastructure investment, and institutional innovation, facilitating a paradigm shift from low to high well-being at the county level. (3) The ES-HWB interaction evolved through three phases—disordered, antagonism, and coordination—revealing tourism as a key mediator driving coupled human–environment system sustainability via a pressure–adaptation–synergy transmission mechanism. This study not only advances the understanding of ES-HWB coevolution in ecotourism-dominated counties, but also provides a transferable methodological framework for sustainable development in similar regions.

1. Introduction

Under the framework of the United Nations’ 2030 Agenda for Sustainable Development (SDG), the synergistic evolution between ecosystem services (ES) and human well-being (HWB) has emerged as a pivotal issue for achieving regional sustainability [1,2]. Within China’s dual strategic context of building a tourism powerhouse and advancing county-level high-quality development, a systematic investigation into the processes, patterns, and mechanisms of ES-HWB coevolution holds significant theoretical and practical value. This research not only contributes to refining the theoretical framework of human–environment interactions in geographical science, but also provides critical scientific support for regional sustainable development policies.

ES and HWB serve as fundamental metrics for assessing coupled human–environment systems, where ES represents the direct and indirect benefits humans derive from ecosystems, and HWB characterizes comprehensive life quality across material, social, psychological, and environmental dimensions [3,4]. Their coevolution has emerged as a frontier in sustainability science. Theoretically, established research confirms that ES provides material foundations for HWB enhancement, while evolving HWB demands reciprocally reshaped ES supply patterns, with their dynamic equilibrium constituting a critical sustainability threshold [1,5]. Contemporary scholarship focuses on quantifying ES-HWB linkages, identifying threshold effects, and deciphering underlying mechanisms, establishing a preliminary theoretical framework [6,7]. Methodologically, analytical approaches have undergone a continuous process of updating and iteration: Early studies employed conventional formulae for static correlation analysis, while recent advances integrate machine learning, system dynamics, and PLUS models, enabling deeper mechanistic understanding [8,9]. This methodological progression has shifted research paradigms from phenomenological description to process-based mechanism analysis. Spatially, ES-HWB research demonstrates clear scale-refinement trends: Initial global/national-scale assessments have progressed to meso-scale (urban agglomerations/watersheds) spatial heterogeneity analysis [10,11], with current breakthroughs in remote sensing and AI facilitating micro-scale (catchments/communities) investigations [12,13,14,15]. Collectively, these advances have established supply–demand feedback theoretical paradigms and achieved methodological transitions from static to dynamic coupling analyses, significantly advancing the field.

While ES-HWB coevolution research provides critical theoretical and practical insights for sustainable development, its frameworks face emerging challenges amid rapid socioeconomic transformations. First, systematic county-scale studies remain notably underdeveloped, despite China’s 1866 counties constituting fundamental administrative units that cover 90% of national territory, harbor 52.5% of the population, and contribute 38.1% of GDP [16]. County-level ES-HWB coordination directly impacts 260 million rural residents and influences national sustainability through spatial spillover effects [9], underscoring urgent research needs. Second, significant gaps persist in current research on the mediating effects between ES and HWB, particularly regarding systematic examination of industrial mediators [17]. As a core driver of regional development, industrial evolution reshapes county-level ES-HWB synergies through complex pathways, with tourism emerging as a pivotal mediator due to its unique spatial attributes [18,19,20]. Defined by the UNWTO as a demand-driven integrative system encompassing transportation, accommodation, catering, and recreation sectors, tourism exhibits three distinctive characteristics: (1) strategic pillar status, (2) well-being orientation, and (3) ecological dependency [21,22]. In ecotourism-dominated counties (e.g., Chun’an, where tourism constitutes 96.43% of GDP), the industry serves as the dominant regulator of human–environment interactions. Tourism directly enhances material well-being through job creation and income generation while funding ecological conservation [12,23]. However, uncontrolled expansion may trigger habitat fragmentation and pollutant emission surges, compromising regional ecological security and health outcomes [24,25,26]. These findings demonstrate tourism’s dual externality effects on county-level human–environment systems, establishing it as a critical industrial mediator in ES-HWB coevolution. Notably, ecotourism—representing tourism’s advanced sustainable form [20,27]—exhibits more profound and complex impacts through its development–conservation synergy mechanisms. Under China’s concurrent ecological civilization and tourism powerhouse strategies, investigating the processes, patterns, and mechanisms of ES-HWB coevolution in ecotourism-dominated counties has become both (1) a pivotal theoretical frontier for advancing sustainability science, and (2) an essential scientific basis for guiding regional sustainable development practices.

In view of the above factors, this study takes Chun’an County in Zhejiang Province, China, as a case study, with the research objective of exploring the processes, patterns, and mechanisms of the coevolution between ES and HWB in ecotourism-dominated counties. This study first quantitatively assesses the spatiotemporal characteristics and development trends of ecosystem services in Chun’an County from 2000 to 2020. Second, it constructs a county-level human well-being evaluation index system and analyzes its evolutionary patterns. Finally, by integrating spatial computation data, socioeconomic statistics, and policy documents, the study systematically reveals the intrinsic mechanisms of the synergistic evolution between ecosystem services and human well-being. This study deciphers the complete ES-HWB coevolution process in ecotourism-dominated counties, offering theoretical and policy implications for human–environment system coordination.

2. Data and Methodology

2.1. Overview of the Research Area

Chun’an County, situated in southwestern Zhejiang Province (118°34′–119°15′ E, 29°22′–29°50′ N), features a unique topography characterized by elevated peripheries and a depressed central basin, with an average elevation of 767.3 m. The central area is dominated by Qiandao Lake, a large artificial reservoir covering 580 km², with a storage capacity of 17.8 billion cubic meters. Renowned for its exceptional water quality, scenic landscapes, and temperate climate, Qiandao Lake serves as a critical ecological water source for eastern China and provides ideal natural conditions for tourism development. Geographically, Chun’an lies within the “1-hour transportation circle” of Hangzhou, leveraging high-speed rail connectivity to efficiently absorb tourist flows from the provincial capital. Its proximity to UNESCO World Heritage Sites—Xidi and Hongcun villages, as well as Mount Huangshan (a mixed cultural and natural heritage site)—further enhances its tourism competitiveness by capitalizing on spatial agglomeration and spillover effects [12]. These natural and locational advantages solidify Chun’an’s status as a pivotal tourism hub within Zhejiang Province and the Yangtze River Delta urban agglomeration. The study area map is shown in Figure 1.

Since Qiandao Lake’s designation as a National AAAA Tourist Attraction in 2000, tourism has become the cornerstone of Chun’an’s socioeconomic and ecological transformation. Through strategic initiatives such as scenic area revitalization, ecological restoration, and livelihood transitions for local fisher communities, the county was recognized as a provincial demonstration zone for all-for-one tourism in Zhejiang. Tourism statistics for 2020 reveal that the study area received 19.2852 million visitors, predominantly domestic tourists from the Yangtze River Delta region, with international tourists accounting for a negligible proportion (only 5339 persons). The total tourism revenue reached CNY 23.204 billion, representing year-on-year growth of 2.3% and 0.1%, respectively. Particularly noteworthy is the rural tourism sector, which hosted 16.7956 million visits and generated CNY 1.686 billion in revenue, demonstrating robust year-on-year growth rates of 15.1% and 17.1%, respectively. Most significantly, the tourism industry’s value-added contribution comprised 96.43% of the county’s total GDP, unequivocally characterizing this as a quintessential ecotourism-dominated region [28]. The development of tourism in Chun’an County has systematically transformed the community’s ecosystem services–human well-being nexus, facilitating optimized human–environment interactions. The county’s high-quality forest–lake ecosystems not only significantly enhance habitat quality but also form the fundamental resource base for ecotourism development, particularly supporting sightseeing and aquatic recreational activities. Concurrently, tourism growth has not only driven rapid economic expansion but also enabled the local government to allocate fiscal revenues through taxation mechanisms, providing critical funding and technical support for ecological conservation and well-being improvement. This “resources-industry-well-being” virtuous cycle has effectively mitigated human-land conflicts characteristic of conventional development paradigms.

2.2. Data Sources and Processing

The data used in this study are categorized into two types: statistical data and spatial data. Statistical data were primarily sourced from statistical yearbooks and bulletins published by Chun’an County, Zhejiang Province, and the National Bureau of Statistics. Land use data were derived from China’s 30 m land cover dataset (1990–2020) produced by a research team at Wuhan University. Population data were obtained from the World Pop Open Population Density Platform. Normalized difference vegetation index (NDVI) data and administrative boundary data were provided by the Resources and Environmental Science Data Center of the Chinese Academy of Sciences (RESDC). Additional spatial data were acquired from specialized geospatial platforms, with detailed sources listed in

Table 1. Overall data status for the study.

Data Type	Name of Data		Resolution	Data Sources
Spatial data	Ecological and environmental spatial data	Elevation	30 m	https://earthexplorer.usgs.gov/ (accessed on 15 May 2020)
		Slope	30 m	https://earthexplorer.usgs.gov/ (accessed on 15 May 2020)
		Normalized vegetation index (NDVI)	30 m	http://www.resdc.cn/ (accessed on 15 May 2020)
		Temperature	1 km	https://lpdaac.usgs.gov/ (accessed on 15 May 2020)
		Precipitation	4 km	https://www.climatologylab.org/ (accessed on 15 May 2020)
		Natural place names	Vector data	http://www.webmap.cn/ (accessed on 15 May 2020)
		Water boundary	Vector data	http://www.webmap.cn/ (accessed on 15 May 2020)
	Economic and infrastructure spatial data	Land use	30 m	https://zenodo.org/records/12779975 (accessed on 15 May 2020)
		GDP	1 km	https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0 (accessed on 15 May 2020)
		Highway routes	Vector data	http://www.webmap.cn/ (accessed on 15 May 2020)
		High-speed rail lines	Vector data	http://www.webmap.cn/ (accessed on 15 May 2020)
	Social and administrative spatial data	Population density	1 km	https://www.worldpop.org/ (accessed on 15 May 2020)
		Human settlements points	Vector data	http://www.webmap.cn/ (accessed on 15 May 2020)
		Administrative boundaries	Vector data	http://www.resdc.cn/ (accessed on 15 May 2020)
Statistical data	Per capita GDP in Chun’an County		—	https://www.stats.gov.cn/ (accessed on 15 May 2020)
	Grain production per unit area		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Scale of tourism industry and consumer market		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Secondary industry output		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Tertiary industry output (including tourism service industry)		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Total tourism revenue		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Tourism passenger transportation		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Urban registered unemployment rate		—	https://tjj.zj.gov.cn/ (accessed on 15 May 2020)
	Number of unemployment insurance participants		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Number of students enrolled in primary and secondary schools		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Investment in education		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Per capita food production		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	General public budget expenditure (including tourism infrastructure development)		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Housing area per capita		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Number of beds in hospitals, health centers		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Number of doctors		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)
	Number of participants in basic medical insurance		—	https://www.qdh.gov.cn/ (accessed on 15 May 2020)

. Spatial data preprocessing was conducted using ArcGIS 10.5, while other datasets were processed with Python 3.13.5 and SPSS Stat 29. Figures and tables were generated using Python 3.13.5, Origin 2021, and Excel.

2.3. Method

Exploring the spatiotemporal dynamics of ES and their coevolution with HWB in tourism-oriented counties holds significant scientific value. This study focuses on Chun’an County (2000–2020), a critical period encompassing key ecological initiatives including the national tourism demonstration zone construction, Qiandao Lake ecological restoration, and fishery industry restructuring. Given the spatiotemporal heterogeneity of ES-HWB relationships, we innovatively develop a continuous time-series assessment model to analyze county-level ES evolution and its coupling coordination with HWB, complemented by predictive simulations. The methodology comprises four phases: First, ES valuation employs an equivalent factor method enhanced with tourism-specific correction factors. Spatiotemporal ES patterns are analyzed and projected using the PLUS model. Second, a multidimensional HWB evaluation system is constructed for tourism-dependent counties, incorporating material, security, health, socio-cultural, and freedom-of-choice dimensions. Third, coupling coordination mechanisms are quantified through coupling degree indices and coordination typologies. Finally, we elucidate ES-HWB interaction mechanisms in tourism-driven counties by synthesizing spatiotemporal differentiation patterns, coupling dynamics, and local tourism development trajectories.

2.3.1. ES Assessment Framework

ES refers to the direct and indirect benefits humans obtain from ecosystems, quantitatively categorized into provisioning, regulating, cultural, and supporting services [3]. However, conventional ES evaluation methods often yield biased results in ecotourism-dominated counties due to ecological heterogeneity, uneven economic development, and imbalanced population distribution, failing to accurately reflect local ecosystem realities [29,30,31]. To address this, we propose a novel ES assessment framework integrating standard models with region-specific correction factors (NDVI, GDP and population density), building upon advancements in ES research [32,33,34,35]. The ES valuation formula is expressed as follows:

$${ESV}_j=Q\times P_j\times S_j\times {\displaystyle \frac{{NDVI}_{county}}{{NDVI}_{national}}}\times {\displaystyle \frac{{GDP}_{county}}{{GDP}_{national}}}\times {\displaystyle \frac{{ln}_{county}}{{ln}_{national}}}$$

(1)

where j denotes land-use types (j = farmland, forest, shrub, grassland, waters, construction land) and ESV_j denotes the ecosystem service value (ESV) attributable to a given land use category. Q denotes the standardized equivalent factor coefficient, P_j represents the equivalent factor for distinct land-use types (Table 2) [3,31], and S_j indicates the actual area of each land-use type. NDVI_{county/national}, GDP_{county/national}, and ln_{county/national} represents the ecological quality, economic development level, and population density index in Chun’an County and the whole country, respectively, and the three constitute the correction factors for ecosystem service assessment (Table 3) (detailed descriptions of the formulas are provided in Supplementary Table S1).

It should be noted that the standard unit equivalent factor coefficient (Q) is defined as one-seventh of the ratio of total grain output value to total planted area in Chun’an County (Q = (total grain output/Cultivated area) × 1/7) [3,31]. By analyzing data from the Zhejiang Statistical Yearbook, Chun’an Statistical Yearbook, and National Compilation of Cost and Revenue Data for Agricultural Products, the coefficient was ultimately calculated as 2232.78 CNY.

Table 2. Equivalent factors for ecosystem service valuation in Chun’an County, China (2000–2020).

Primary Classifications	Secondary Classifications	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	Food production	1.11	0.27	0.19	0.38	0.8	0.01
	Raw Material production	0.25	0.63	0.43	0.56	0.23	0
	Water supply	−1.31	0.33	0.22	0.31	8.29	0
Regulating services	Gas regulation	0.89	2.07	1.41	1.97	0.77	−2.42
	Climate regulation	0.47	6.2	4.23	5.21	2.29	0
	Environmental purification	0.14	1.8	1.28	1.72	5.55	−2.46
	Hydrological regulation	1.5	3.86	3.35	3.82	102.24	−7.51
Supporting services	Soil conservation	0.52	2.52	1.72	2.4	0.93	0.02
	Nutrient cycling maintenance	0.16	0.19	0.13	0.18	0.07	0.02
	Biodiversity maintenance	0.17	2.3	1.57	2.18	2.55	0.34
Cultural services	Aesthetic landscape	0.08	1.01	0.69	0.96	1.89	0.01

Note: All values are dimensionless equivalent factors. Ecosystem service value coefficients based on Xie and Costanza’s equivalent factors [3,31].

Table 2. Equivalent factors for ecosystem service valuation in Chun’an County, China (2000–2020).

Primary Classifications	Secondary Classifications	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	Food production	1.11	0.27	0.19	0.38	0.8	0.01
	Raw Material production	0.25	0.63	0.43	0.56	0.23	0
	Water supply	−1.31	0.33	0.22	0.31	8.29	0
Regulating services	Gas regulation	0.89	2.07	1.41	1.97	0.77	−2.42
	Climate regulation	0.47	6.2	4.23	5.21	2.29	0
	Environmental purification	0.14	1.8	1.28	1.72	5.55	−2.46
	Hydrological regulation	1.5	3.86	3.35	3.82	102.24	−7.51
Supporting services	Soil conservation	0.52	2.52	1.72	2.4	0.93	0.02
	Nutrient cycling maintenance	0.16	0.19	0.13	0.18	0.07	0.02
	Biodiversity maintenance	0.17	2.3	1.57	2.18	2.55	0.34
Cultural services	Aesthetic landscape	0.08	1.01	0.69	0.96	1.89	0.01

Note: All values are dimensionless equivalent factors. Ecosystem service value coefficients based on Xie and Costanza’s equivalent factors [3,31].

Table 3. Correction factors (NDVI, GDP, population density) for ecosystem service assessment in Chun’an County (2000–2020).

Year	Ecological Quality (NDVI)	Economic Development (GDP)	Population Density	Year	Ecological Quality (NDVI)	Economic Development (GDP)	Population Density
2000	1.524	0.858	0.849	2011	1.536	0.848	0.885
2001	1.535	0.874	0.851	2012	1.506	0.869	0.889
2002	1.525	0.886	0.856	2013	1.481	0.851	0.893
2003	1.532	0.905	0.858	2014	1.561	0.861	0.896
2004	1.534	0.893	0.862	2015	1.592	0.871	0.901
2005	1.526	0.900	0.865	2016	1.585	0.876	0.904
2006	1.547	0.879	0.869	2017	1.558	0.837	0.905
2007	1.558	0.841	0.874	2018	1.571	0.766	0.909
2008	1.533	0.839	0.875	2019	1.563	0.797	0.912
2009	1.599	0.845	0.879	2020	1.530	0.734	0.915
2010	1.564	0.830	0.884

Note: The table results were calculated by the author. The normalized difference vegetation index (NDVI) serves not only as a key indicator of vegetation growth status but also as a crucial parameter for characterizing ecosystem dynamics. NDVI effectively quantifies ecological quality by capturing vegetation photosynthetic activity, representing structural–functional relationships in ecosystem services, and enabling the sensitive detection of degradation/recovery trends through temporal analysis [36,37,38,39]. In the assessment of ecosystem service value (ESV), NDVI is often employed as a core correction factor to enhance the accuracy and scientific rigor of calculations [40,41,42,43,44]. Therefore, our study selects NDVI as an indicator to characterize ecological quality.

Table 3. Correction factors (NDVI, GDP, population density) for ecosystem service assessment in Chun’an County (2000–2020).

Year	Ecological Quality (NDVI)	Economic Development (GDP)	Population Density	Year	Ecological Quality (NDVI)	Economic Development (GDP)	Population Density
2000	1.524	0.858	0.849	2011	1.536	0.848	0.885
2001	1.535	0.874	0.851	2012	1.506	0.869	0.889
2002	1.525	0.886	0.856	2013	1.481	0.851	0.893
2003	1.532	0.905	0.858	2014	1.561	0.861	0.896
2004	1.534	0.893	0.862	2015	1.592	0.871	0.901
2005	1.526	0.900	0.865	2016	1.585	0.876	0.904
2006	1.547	0.879	0.869	2017	1.558	0.837	0.905
2007	1.558	0.841	0.874	2018	1.571	0.766	0.909
2008	1.533	0.839	0.875	2019	1.563	0.797	0.912
2009	1.599	0.845	0.879	2020	1.530	0.734	0.915
2010	1.564	0.830	0.884

Note: The table results were calculated by the author. The normalized difference vegetation index (NDVI) serves not only as a key indicator of vegetation growth status but also as a crucial parameter for characterizing ecosystem dynamics. NDVI effectively quantifies ecological quality by capturing vegetation photosynthetic activity, representing structural–functional relationships in ecosystem services, and enabling the sensitive detection of degradation/recovery trends through temporal analysis [36,37,38,39]. In the assessment of ecosystem service value (ESV), NDVI is often employed as a core correction factor to enhance the accuracy and scientific rigor of calculations [40,41,42,43,44]. Therefore, our study selects NDVI as an indicator to characterize ecological quality.

2.3.2. PLUS Model

The PLUS model is an advanced cellular automaton (CA) framework that integrates a land expansion analysis strategy (LEAS) and a multi-level patch seed growth mechanism (CARS) to improve traditional CA simulations [45]. Compared with conventional models like CLUE-S and FLUS, the PLUS model demonstrates superior accuracy in spatial pattern replication, providing enhanced scientific reliability for simulating and predicting ES in ecotourism-dominated counties. The operational workflow of the PLUS model comprises the following key steps:

(1)

LEAS

The LEAS module utilizes the areal extent of emerging land categories and their associated driving factors as inputs [45]. The random forest algorithm subsequently calculates both the transition probabilities between future land categories and the contribution rates of respective driving factors to land-use changes. The computational framework is expressed as follows:

$$P_{i,k}^d\left(X\right)={\displaystyle \frac{\sum _{n=1}^{m}I(h_n\left(x\right)=d)}{M}}$$

(2)

Herein, $P_{i,k}^d\left(X\right)$ represents the probability of land-use type i transitioning to target type k under the influence of driving factors X. d represents the transition type identifier. d takes a value of either 1 or 0, where 1 indicates conversion from other land-use types to type K, and 0 represents other transitions. X denotes a vector composed of multiple driving factors. h_n(x) denotes the output prediction generated by the n-th decision tree for input variable x in the random forest ensemble. I(∙) represents the indicator function of the decision tree ensemble. M indicates the total number of decision trees.

Accurate selection of driving factors for land-use change is critical for the effective operation of the PLUS model. Integrating insights from telecoupling theory and the tourism area life cycle model (TALC), tourism development in dominated counties accelerates the reallocation of land resources. Specifically, tourism-driven economic growth reshapes household livelihood strategies, significantly reducing rural dependence on forested lands [46,47], thereby promoting restorative expansion of natural land cover types such as forests and grasslands [48]. Furthermore, tourism intensity systematically alters agricultural practices, ultimately inducing systemic shifts in land use patterns [49]. Therefore, this study identifies driving factors across three dimensions—natural environment, socioeconomic dynamics, and tourism industry characteristics—to simulate future ecosystem service patterns in Chun’an County under multiple scenarios (

Table 4. Specific details of the driving factors.

Driving Factor	Specific Details
Elevation	Remote sensing products
Slope	Remote sensing products
Temperature	Remote sensing products
Precipitation	Remote sensing products
GDP per capita	Remote sensing products
Population	Remote sensing products
Human settlement points	Euclidean distance from each grid center to the nearest village settlement, tourist distribution center, tourist lodging, tourist resort, and high business hotel
Natural place names	Euclidean distance from each grid center to the nearest natural attraction, infrastructure, and public service facility
Highway routes	Euclidean distance from each grid center to the nearest highway (including internal roads of tourist attractions)
High-speed rail lines	Euclidean distance from the center of each grid to the nearest high-speed rail line
Water boundary	Euclidean distance from each grid center to Qiandao Lake Scenic Area, Xin’an River and other water systems

).

(2)

Land Demand Prediction

This module employs a Markov chain to forecast the demand for each land-use type, with demand parameters adjusted according to scenario settings [45]. The Markov chain calculates transition probabilities based on historical land-use change patterns (2000–2020), ensuring temporal consistency in future projections. To enhance reliability, the demand predictions are further refined using a linear programming optimization algorithm that balances socioeconomic development constraints and ecological protection targets [50]. The formulation is expressed as follows:

$$\left\{\begin{array}{c}{S}_{\left(t+1\right)}=P_{ij}\times S_t\\ P=\left[\begin{array}{c}{P}_{11} P_{12} \dots P_{1n}\\ P_{21} P_{22} \dots P_{2n}\\ ⋮ ⋮ \ddots ⋮ \\ P_{n1} P_{n2} \dots P_{nn}\end{array}\right]\end{array}\right.\left(\begin{array}{c}{P}_{ij}\in \left[\mathrm{0,1}\right)\\ \sum _{i=1,j=1}^n{P}_{ij}=1(I,j=\mathrm{1,2}\dots .n)\end{array}\right)$$

(3)

where P_ij(i, j = 1,2…n) represents the transfer probability of conversion from one land-use type i to another land-use type j; P represents the state transfer probability matrix; n represents the number of land-use types; and S_t, S_t+1 represent the state of the land-use types from moment t to t + 1.

In Chun’an, because it is a county dominated by the tourism industry, the development of the tourism industry reshapes the human–land relationship in the county. Therefore, this study set up natural development, tourism development, farmland conservation, and ecological conservation scenarios to simulate the spatial pattern of ES in Chun’an County, aiming to promote the sustainable development of the county (Table 5).

(3)

CARS

The CARS module employs a randomized approach to generate seeds and dynamically reduces growth thresholds, enabling automated simulation of patch growth for various land-use types while strictly adhering to constraints on land use conversion probabilities [45]. Within this framework, the neighborhood weight parameter quantitatively characterizes the expansion intensity of different land classes, calculated as follows:

$$X_i={\displaystyle \frac{\Delta {TA}_i-\Delta {TA}_{min}}{{\Delta TA}_{max}-\Delta {TA}_{min}}}$$

(4)

where X_i represents the neighborhood weighting parameters for a class of land-use types; ΔTA_i represents the amount of change in TA for a given class of land; and ΔTA_max and ΔTA_min represent the maximum and minimum values of TA change for a particular class of land, respectively.

(4)

Verification of simulation accuracy

In this study, the simulation accuracy is verified using the Kappa coefficient, which measures the agreement between simulated and observed land-use maps [45]. The Kappa coefficient ranges from −1 (complete disagreement) to 1 (perfect agreement), with values >0.75 indicating strong consistency [51]. The formulation is expressed as follows:

$$kappa={\displaystyle \frac{x_0\times x_1-\sum _{i=1}^m(y_0\times y_1)}{{x_0}^2-\sum _{i=1}^m\left(y_0\times y_1\right)}}$$

(5)

where Kappa represents the simulation accuracy; X₀ and X₁ represent the total number of pixels used for evaluation and the total number of all correctly categorized pixels, respectively; and Y₀ and Y₁ represent the total number of pixels of a particular land-use type in the simulation results and the total number of pixels of a particular land-use type in reality, respectively.

This study employed the PLUS model to simulate land-use changes in Chun’an County under a natural development scenario from 2010 to 2030, with model predictions for 2010 and 2020 being compared against actual observed values. Validation results demonstrated high model accuracy, with Kappa coefficients reaching 83.2% and 89.06% for 2010 and 2020 simulations, respectively (Table 6). These robust validation metrics not only confirm the appropriateness of the selected driving factors but also provide a reliable foundation for predicting the county’s ecosystem service patterns in 2030.

Table 5. Specific details of scenario simulation.

Simulation Scenarios	Parameter Settings	References
Natural development	Land-use change in county-level regions is predominantly driven by regional socioeconomic development, with relatively limited influence from direct human intervention factors.	[31,39,45,52,53,54]
Tourism development	Increased intensity of tourism infrastructure construction leads to ecological space compression, so the probability of natural land (farmland/forest/shrubs/grassland) being converted to built-up land increases by 20% and the probability of reverse conversion of built-up land decreases by 30%.
Farmland conservation	Strict agricultural land protection policies are implemented, and the occupation of arable land by tourism development is strictly limited, thus reducing the probability of arable land being transferred by 30%.
Ecological conservation	Strengthening of ecological spatial control, so that the probability of conversion of forest land/shrubs/waters to construction land is reduced by 50%, and the probability of conversion of agricultural land is simultaneously reduced by 30%.

Table 5. Specific details of scenario simulation.

Simulation Scenarios	Parameter Settings	References
Natural development	Land-use change in county-level regions is predominantly driven by regional socioeconomic development, with relatively limited influence from direct human intervention factors.	[31,39,45,52,53,54]
Tourism development	Increased intensity of tourism infrastructure construction leads to ecological space compression, so the probability of natural land (farmland/forest/shrubs/grassland) being converted to built-up land increases by 20% and the probability of reverse conversion of built-up land decreases by 30%.
Farmland conservation	Strict agricultural land protection policies are implemented, and the occupation of arable land by tourism development is strictly limited, thus reducing the probability of arable land being transferred by 30%.
Ecological conservation	Strengthening of ecological spatial control, so that the probability of conversion of forest land/shrubs/waters to construction land is reduced by 50%, and the probability of conversion of agricultural land is simultaneously reduced by 30%.

Table 6. Correspondence between Kappa coefficients and simulation effects.

Kappa	0–0.2	0.2–0.4	0.4–0.6	0.6–0.8	0.8–1
Precision of simulation	Minimum	Low	Medium	High	Maximum

Table 6. Correspondence between Kappa coefficients and simulation effects.

Kappa	0–0.2	0.2–0.4	0.4–0.6	0.6–0.8	0.8–1
Precision of simulation	Minimum	Low	Medium	High	Maximum

2.3.3. HWB Assessment Framework

HWB refers to the comprehensive quality of life for individuals or groups across material, social, psychological, and environmental dimensions. According to the authoritative definition established by the United Nations Millennium Ecosystem Assessment [4], HWB encompasses five key dimensions: material well-being, security, health, social and cultural relations, and freedom of choice and action—a conceptual framework that has gained broad consensus in academia. Tourism development influences ES by driving the flow of county-level production factors, structural coordination, and functional integration, ultimately affecting HWB levels [29]. However, existing research has yet to adequately examine HWB dynamics in ecotourism-dominated counties or their interactive relationships with ES. To address this gap, this study integrates HWB theory with tourism industry research paradigms to develop a novel assessment framework for county-level HWB under ecotourism-dominated development (

Table 7. Indicator system for human well-being assessment in Chun’an County (2000–2020).

Primary Indicator	Secondary Indicator	Weighting	Primary Indicator	Secondary Indicator	Weighting
Material well-being	Per capita GDP in Chun’an County	0.071	Social and cultural well-being	Urban registered unemployment rate	0.010
	Grain production per unit area	0.028		Number of unemployment insurance participants	0.062
	Scale of tourism industry and consumer market	0.065		Number of students enrolled in primary and secondary schools	0.062
Security well-being	Normalized vegetation index (NDVI)	0.034		Investment in education	0.055
	Per capita food production	0.043	Freedom of choice and action well-being	Secondary industry output	0.052
	General public budget expenditure (including tourism infrastructure development)	0.081		Tertiary industry output (including tourism service industry)	0.069
	Housing area per capita	0.057		Total tourism revenue	0.093
Health well-being	Number of beds in hospitals, health centers	0.103		Tourism passenger Transportation	0.022
	Number of doctors	0.065
	Number of participants in basic medical insurance	0.029

Note: The evaluation index system is compiled from [4,11,12,57,58,59,60,61,62,63,64]. The HWB evaluation index system developed for ecotourism-dominated counties in this study integrates Maslow’s hierarchy of needs theory, Darrin McMahon’s comprehensive interpretation of human well-being, and specific indicator recommendations from the UN Millennium Ecosystem Assessment report. It is further refined to account for the specific spatial heterogeneity (tourism dominance, county scale) of the study region. This construction process adheres to the established logic of index system development: “theory-guided → framework-referenced → empirically adjusted. This is the classic and recent literature on human well-being research [4,11,12,57,58,59,60,61]. Meanwhile, NDVI is not merely a vegetation monitoring index, but fundamentally serves as a comprehensive indicator of environmental security status. Grounded in Maslow’s hierarchy of needs theory, NDVI—through its characterization of key ecosystem services (including hazard regulation, climate buffering, and resource provisioning)—provides an indispensable quantitative basis for assessing security well-being during humanity’s progression from basic material needs to higher-level safety, social belonging and self-actualization requirements [4,62,63,64].

) [4,12,55,56]. The computational procedures for HWB assessment are as follows:

$$S_i={\displaystyle \frac{X_i-X_{min}}{X_{max}-X_{min}}}; S_i={\displaystyle \frac{X_{max}-X_i}{X_{max}-X_{min}}}$$

(6)

$$B_i={\displaystyle \frac{S_i}{{\sum }_{i=1}^n{S}_i}}$$

(7)

$$Q_i=-k{\sum }_{i=1}^n(B_i\times ln{B}_i)$$

(8)

$$W_i=(1-Q_i)/{\sum }_{i=1}^n(1-Q_i)$$

(9)

$$HWB={\sum }_{i=1}^n{W}_i\times B_i$$

(10)

where S_i represents the normalized value for each well-being dimension, X_i denotes the original observed value, B_i indicates the indicator contribution coefficient, Q_i corresponds to the information entropy measure, and W_i signifies the indicator weighting factor.

2.3.4. Coupled Coordination Model

The coupling coordination model employs mathematical modeling to quantify the coupling degree and coordination degree, thereby enabling scientific determination of whether system interactions manifest as inefficient conflict or efficient synergy [65]. ES and HWB exhibit nonlinear characteristics and scale-dependent relationships. Global and regional-scale studies demonstrate synchronized growth between provisioning services and HWB [66,67], whereas national and watershed-scale analyses reveal either insignificant spatial correlations or negative associations [68]. Notably, despite counties being fundamental socioeconomic spatial units in China [69], research on ES-HWB relationships at this scale remains limited, particularly in ecotourism-dominated counties. This study applies the coupling coordination model to systematically investigate ES-HWB interactions in tourism-driven county systems. The computational procedures are structured as follows:

(1)

Calculate the degree of coupling

This study employs a coupling degree model to quantify the relationship between ES and HWB. Building on established methodologies [12,29], we apply a median segmentation approach to classify the coupling degree index into six distinct tiers (

Table 8. Classification of coupling degree between ES and HWB.

Coupling Degree (C)	Coupling Stage	Characterization
0	Minimal coupling	No significant linkage; disordered development
(0.0~0.3)	Low-level coupling	High ES but lagging HWB improvement
[0.3~0.5)	Antagonism	Growing HWB begins conflicting with ES
[0.5~0.8)	Running-in	Conflicts mitigate; initial synergistic interactions emerge
[0.8~1)	High-level coupling	Sustained enhancement of ES-HWB synergies
1	Optimal coupling	Healthy, ordered development with fully coordinated ES-HWB

). This method ensures statistically informed categorization that preserves data distribution characteristics while maintaining unambiguous tier boundaries, enabling precise identification of coupling development stages. The model is formulated as follows:

$$C=2\times \sqrt{{(U}_1{\times U}_2)/({U_1{+U}_2)}^2}$$

(11)

where C represents the coupling degree and U₁ and U₂ represent the raw values of ES and HWB, respectively.

(2)

Calculate the coupling coordination degree

While the coupling degree reflects interaction intensity between ES and HWB, it fails to differentiate synergistic quality. To address this limitation, we introduce a coupling coordination degree model. Drawing on established methodologies [70,71], the coupling coordination index is classified into six discrete tiers using a median segmentation approach (

Table 9. Classification of coupling coordination degree between ES and HWB.

Coupling Coordination Degree (D)	Coordination Level	Characterization
0	Uncoordination	Negative evolution trend without coordinated development
(0.0~0.3)	Moderate imbalance	Asynchronous development with one dimension progressing faster than the other
[0.3~0.5)	Mild imbalance	Dominant dimension grows rapidly while the subordinate dimension accelerates
[0.5~0.8)	Primary coordination	Initial synergistic interactions emerge
[0.8~1)	Intermediate coordination	Deepening coordination approaching optimal state
1	Advanced coordination	Optimal mutualism achieved

). The model is formulated as follows:

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

(12)

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

(13)

where T represents the degree of coordination; α and β represent the importance weights of ES and HWB, for which, in this paper, we formulate in such a way that both are of the same importance to the county and so both take the value of 0.5; and D represents the degree of coupled coordination.

(3)

Introduce the relative development coefficient K

While the coupling coordination degree model effectively evaluates the coordination level between ES and HWB, it cannot assess their relative development states. To address this limitation, we introduce the relative development coefficient (K = ES/HWB) to determine their coordinated development types. By quantifying their developmental disparity, this approach systematically reveals coupling coordination characteristics [40,71,72,73] (

Table 10. Classification of coordinated development types between ES and HWB.

Coupling Coordination	K	Development Type	Coupling Coordination	K	Development Type
0	(0–0.8]	Uncoordinated ES lagging	[0.5~0.8)	(0–0.8]	Primary coord. ES lagging
	(0.8–1.2]	Uncoordinated dual-system lagging		(0.8–1.2]	Primary coord. dual-system lagging
	(1.2, +∞)	Uncoordinated HWB lagging		(1.2, +∞)	Primary coord. HWB lagging
[0.0~0.3)	(0–0.8]	Moderate imbalance ES lagging	[0.8~1)	(0–0.8]	Intermediate coord. ES lagging
	(0.8–1.2]	Moderate imbalance dual-system lagging		(0.8–1.2]	Intermediate coord. dual-system lagging
	(1.2, +∞)	Moderate imbalance HWB lagging		(1.2, +∞)	Intermediate coord. HWB lagging
[0.3~0.5)	(0–0.8]	Mild imbalance ES lagging	1	(0–0.8]	Advanced coord. ES lagging
	(0.8–1.2]	Mild imbalance dual-system lagging		(0.8–1.2]	Advanced coord. dual-system lagging
	(1.2, +∞)	Mild imbalance HWB lagging		(1.2, +∞)	Advanced coord. HWB lagging

).

3. Results

3.1. Dynamic Evolution of ES

Figure 2a demonstrates the temporal dynamics of the ecosystem service value (ESV) in Chun’an County during 2000–2020, revealing significant interannual fluctuations ranging from CNY 30.15 to 36.85 billion with a distinct oscillatory pattern. The evolution exhibited three characteristic phases: Phase I (2000–2008) showed an inverted U-shaped trend (CNY 32.71–33.20 billion), reaching a trough in 2000 (CNY 32.71 billion) and peaking in 2005 (CNY 34.93 billion); Phase II (2009–2013) displayed high variability (CNY 33.10–35.10 billion) with a moderate 0.65% annual growth rate (CNY 4.4 billion absolute increase) during 2010–2012; and Phase III (2014–2020) presented a unimodal distribution (CNY 30.15–36.85 billion) featuring the study period’s extremes. The maximum (CNY 36.85 billion, 2016) and minimum (CNY 30.15 billion, 2020) periods recorded during this phase represent the absolute extremes across the entire study period, yielding a remarkable differential of CNY 6.70 billion.

Figure 2b presents the value distribution of different ecosystem service (ES) types in Chun’an County during 2000–2020. Regulating and supporting services dominated ES contributions (>99% combined), while provisioning and cultural services accounted for a minor share (1%). Specifically, regulating and supporting services contributed CNY 27.17–33.20 billion annually, compared with CNY 2.99–3.65 billion from provisioning and cultural services.

Figure 2c illustrates the contributions of different land-use types to ESV in Chun’an County (2000–2020). Forest (CNY 18.1–22.1 billion), waters (CNY 11.9–14.5 billion), and farmland (CNY 0.18–0.25 billion) collectively accounted for >99% of total ESV, while shrub and grassland contributed minimally (1%). Notably, construction land exhibited significant ESV reduction effects, with its area expanding from 22.2 to 47.3 km² due to tourism development and urbanization, while maintaining a constant negative ESV contribution of CNY −0.6 billion.

Figure 3 demonstrates the spatial-temporal dynamics of ESV in Chun’an County (2000–2020). ESV hotspots were predominantly distributed across water bodies, exhibiting a clear distance–decay pattern from lake centers. Significant high-value clustering was observed in southeastern, central, northwestern, southwestern and northeastern sections of Qiandao Lake, particularly in Qiandaohu Town, Jieshou Township, Jinfeng Township, Jiangjia Town and Lishang Township, where open water areas with minimal anthropogenic disturbance showed the most pronounced aggregation effects. Conversely, ESV coldspots were concentrated in urban built-up areas, notably Qiandaohu Town (the county’s administrative and tourism hub), Fenkou Town, Zhongzhou Town and Langchuan Township. Intensive human activities in Qiandaohu Town resulted in markedly reduced ESV values.

Figure 4 and

Table 11. The ESV in Chun’an County under different simulation scenarios (CNY).

Natural Development Scenario	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	378.34	155,949.86	2.90	15.10	132,898.30	21.61
Regulating services	25,096.37	1,767,431.69	35.44	153.64	1,580,662.74	−26,775.52
Support services	7104.33	635,633.01	11.80	57.49	50,621.13	821.21
Cultural services	630.56	127,633.76	2.38	11.60	26,950.41	21.61
Tourism development scenario	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	375.61	155,776.36	2.90	14.66	132745.70	22.64
Regulating services	24,915.23	1,765,465.44	35.42	149.14	1,578,847.71	−28,058.97
Support services	7053.06	634,925.88	11.79	55.81	50,563.01	860.57
Cultural services	626.01	127,491.77	2.38	11.26	26,919.46	22.64
Farmland Conservation Scenario	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	393.60	155,810.54	2.90	13.38	132,897.28	20.83
Regulating services	26,108.59	1,765,852.79	35.47	136.18	1,580,650.57	−25,809.99
Support services	7390.87	635,065.18	11.81	50.96	50,620.74	791.59
Cultural services	655.99	127,519.74	2.38	10.28	26,950.20	20.83
Ecological Conservation Scenario	Farmland	Forest	Shrub	Grassland	Waters	Construction Land
Provisioning services	382.58	156,311.60	2.90	16.42	133,258.83	19.53
Regulating services	25,377.74	1,771,531.43	35.49	167.05	1,584,950.76	−24,197.48
Support services	7183.98	637,107.43	11.82	62.51	50,758.46	742.14
Cultural services	637.63	127,929.82	2.38	12.61	27,023.52	19.53

Note: The table results were calculated by the author.

present the simulated ESV projections (2030) for Chun’an County under different development scenarios. The total ESV exhibited scenario-dependent gradients: The ecological conservation scenario yielded the highest value (CNY 44.99 billion), followed by the natural development and farmland conservation scenarios (CNY 44.85–44.86 billion), with the tourism development scenario showing the lowest ESV (CNY 44.79 billion). Forest and waters constituted the primary ESV contributors (98–99.8% combined), with forest dominating regulating (74.6–74.6%) and supporting services (15.5–15.6%), while waters accounted for >99% of provisioning services. Spatially, ESV hotspots persistently clustered around Qiandao Lake and adjacent forested areas, exhibiting characteristic distance–decay patterns. New construction land and farmland expansions predominantly occupied mountainous gullies, transportation nodes, and lakeside lowlands, causing localized ESV reductions. Notably, human activity intensity showed significant negative correlation with ESV—the tourism development scenario expanded construction land to 18.4 km², reducing ESV by CNY 70 million compared with natural development, whereas the ecological conservation scenario maximized ESV through development restrictions. Among service types, regulating services maintained stable dominance (74.6–74.6%), while cultural services remained minimal (3.5%), confirming hydro meteorological regulation as the ecosystem’s core function.

3.2. Dynamic Evolution of HWB

Figure 5 demonstrates the evolution of HWB in Chun’an County (2000–2020). The composite HWB index exhibited sustained growth from 0.16 to 0.80, marking a transition from low to high well-being status in this ecotourism-dominated county (Figure 5a). Dimensional analysis revealed significant disparities (Figure 5b): Material well-being showed the most dramatic improvement, surging from 1.6 × 10⁻⁵ to 0.13 (compound annual growth rate: 64.2%), peaking at 0.14 in 2016; security well-being grew steadily from 0.04 to 0.14 (CAGR: 6.3%), reaching 0.16 in 2016; health well-being increased consistently from 0.02 to 0.19 (CAGR: 11.6%), with a notable 2011–2012 acceleration (0.05→0.09); social and cultural well-being progressed modestly (0.09→0.13; CAGR: 1.85%); and (5) freedom of choice and action well-being expanded from 0.0089 to 0.2 (CAGR: 17%).

3.3. ES-HWB Coupling Coordination Dynamics

Table 12. Coupling coordination between ES and HWB in Chun’an County (2000–2020).

Year	HWB Index	ESV Index	Coupling Degree	Coordination Degree	K	Development Type	Coupling Stage
2000	0.156	0.0001	0.898	0.140	0.0001	Moderate imbalance (ES lagging)	High coupling
2001	0.145	0.080	0.605	0.132	1.821	Moderate imbalance (HWB lagging)	Running-in
2002	0.161	0.163	0.735	0.220	0.984	Moderate imbalance (dual-system lagging)	Running-in
2003	0.179	0.261	0.776	0.292	0.688	Moderate imbalance (ES lagging)	Running-in
2004	0.197	0.295	0.832	0.338	0.666	Mild imbalance (ES lagging)	High coupling
2005	0.209	0.347	0.841	0.371	0.603	Mild imbalance (ES lagging)	High coupling
2006	0.225	0.380	0.865	0.404	0.591	Mild imbalance (ES lagging)	High coupling
2007	0.278	0.332	0.971	0.479	0.838	Mild imbalance (dual-system lagging)	High coupling
2008	0.309	0.300	0.996	0.513	1.031	Primary coordination (dual-system lagging)	High coupling
2009	0.337	0.516	0.961	0.577	0.653	Primary coordination (ES lagging)	High coupling
2010	0.348	0.472	0.978	0.584	0.737	Primary coordination (ES lagging)	High coupling
2011	0.408	0.499	0.993	0.647	0.817	Primary coordination (dual-system lagging)	High coupling
2012	0.486	0.569	0.999	0.724	0.854	Primary coordination (dual-system lagging)	High coupling
2013	0.514	0.488	0.998	0.733	1.054	Primary coordination (dual-system lagging)	High coupling
2014	0.563	0.754	0.996	0.807	0.746	Intermediate coordination (ES lagging)	High coupling
2015	0.619	0.941	0.991	0.869	0.658	Intermediate coordination (ES lagging)	High coupling
2016	0.711	1.000	0.997	0.936	0.711	Intermediate coordination (ES lagging)	High coupling
2017	0.760	0.788	0.997	0.938	0.964	Intermediate coordination (dual-system lagging)	High coupling
2018	0.773	0.591	0.974	0.910	1.307	Intermediate coordination (HWB lagging)	High coupling
2019	0.811	0.755	0.990	0.961	1.074	Intermediate coordination (dual-system lagging)	High coupling
2020	0.798	0.458	0.934	0.889	1.742	Intermediate coordination (HWB lagging)	High coupling

Note: The table results were calculated by the author.

presents the evolving coupling coordination between ES and HWB in Chun’an County (2000–2020). The tourism-driven development significantly enhanced ES-HWB coordination, transitioning from discordance to harmony. The evolution exhibited three characteristic phases: the initial development phase (2000–2008), where the ES-HWB synergy showed preliminary improvement, with coupling degree maintaining high levels (>0.8), except during 2001–2003 (running-in stage: 0.605–0.776). The peak coupling value reached 0.996 in 2008, indicating significantly enhanced system interactions. Coordination degree increased from 0.140 (moderate imbalance) to 0.513 (primary coordination) at an annual rate of 15.2%, recovering from the study period minimum (0.132 in 2001). The development type transitioned from ES lagging (2000, K = 0.0001) to dual-system lagging (2008, K = 1.031), reflecting progressive equilibrium. Notably, K exceeded 1.0 during 2007–2008, marking accelerated HWB growth relative to ES recovery. Secondly, there is the transitional phase (2009–2013), where ES-HWB coordination improved markedly, with consistently high coupling (0.961–0.999). Coordination degree rose steadily from 0.577 to 0.733 (6.2% annually), maintaining primary coordination status. K values shifted from 0.653 (ES lagging) to 1.054 (HWB lagging), indicating reversed growth precedence after 2013. Finally, there is the advanced development phase (2014–2020), where optimal synchronization was achieved, with coupling remaining high (0.934–0.997) and coordination peaking at 0.961 in 2019 (intermediate coordination), representing a 31.2% improvement over 2009–2013. Dynamic K transitions occurred from ES lagging (0.658–0.746) to dual-system/HWB lagging (0.964–1.742) configurations.

4. Discussion

4.1. Characteristics and Drivers of ES-HWB Coordination in Chun’an County

This study presents the first empirical evidence of nonlinear dynamics in ES-HWB coevolution in ecotourism-dominated counties, demonstrating distinct phase-transition characteristics (Figure 3 and Figure 6; Table 12). This study combines Chunan County’s county records, Chunan County’s statistical yearbook, and field research to obtain specific information on the coordination and interaction of ES-HWB in Chunan County [12,23,28,74]. For the initial development phase (2000–2008), Chun’an County prioritized integrated development and ecological conservation. The Scenic Revolution initiative and infrastructure projects reconfigured human–environment interactions, triggering construction land expansion that caused transient ecosystem degradation. As tourism industry restructuring advanced, its economic contribution reached 34.5% of county GDP (2008), driving synchronous growth in HWB and ES. The development type transitioned from moderate imbalance (ES lagging) to primary coordination (dual-system lagging), accompanied by ESV growth from CNY 32.71 to 33.20 billion and an HWB index increase to 0.3. For the transitional development phase (2009–2013), under dual pressures from the global economic crisis and natural disasters, Chun’an County implemented synergistic developing county strategies with lakes and all-for-one tourism strategies, concurrently advancing ecological remediation (e.g., aquaculture net removal, wastewater treatment) and tourism upgrading (scenic spot renovation, luxury hotel introduction). Those strategies facilitated the ES-HWB system’s progression to primary coordination, yielding tourism revenues of CNY 8.121 billion (2013) and earning national ecological civilization pilot county status. However, the global economic crisis induced short-term ESV fluctuations, with a modest CAGR of 0.65% and absolute growth of a mere CNY 440 million during 2010–2012. These findings demonstrate that, while eco-tourism policies generate long-term synergies, external economic disturbances can induce transient ES-HWB decoupling. For the advanced development phase (2014–2020), Chun’an County implemented tourism cluster development strategies, establishing high-end complexes (e.g., Qiandao Lake International Business Resort) and boutique homestay clusters, significantly upgrading its tourism industry. Empirical data show tourism revenues surged from CNY 8.121 billion (2013) to 23.204 billion (2020), with per capita GDP reaching CNY 73,026. Notably, despite rapid tourism expansion, Qiandao Lake maintained Class I water quality (TN ≤ 0.15 mg/L, TP ≤ 0.01 mg/L) and oligotrophic status (TSI < 30), indicating successful socio-ecological coupling. Concurrently, ESV peaked at CNY 36.85 billion (study period maximum), while the HWB index rose to 0.81. The ES-HWB coordination degree improved from 0.807 (2014) to 0.961 (2019), achieving intermediate coordination. These results demonstrate that an optimized industrial structure coupled with ecologically constrained development enables ecotourism-dominated counties to achieve high-level economic–ecological synergy.

The tourism industry functions as a critical mediator in ES-HWB coordination, with its phased development exerting distinct transmission and modulation effects on their dynamics. Specifically, tourism expansion and structural upgrading induced significant landscape reorganization at the county scale, driving characteristic phase-specific ESV declines. This phenomenon validates Butler’s (1980) destination life cycle theory regarding ecological degradation from overdevelopment during stagnation phases. Notably, the observed ESV recovery during transitional and advanced development phases exhibited strong temporal alignment with all-for-one tourism policy implementation, demonstrating institutional interventions’ regulatory capacity in optimizing human–environment systems for sustainable tourism [75,76,77].

4.2. Tourism Industry Driving Mechanisms

Tourism, as a composite service industry, exhibits three distinctive characteristics that differentiate it from conventional sectors. While traditionally labeled as a “smokeless industry” for its perceived environmental friendliness, this notion oversimplifies its ecological impacts, which are inherently tied to its operational nature [78,79,80]. Compared with other industries, the special characteristics of the tourism industry are mainly reflected in the following three aspects. First, tourism demonstrates unique ecological dependence—pristine environments serve as both its foundational requirement and core attraction, necessitating protective measures that create positive feedback loops between conservation and development [12,13,14,15,16,17,18,19,20,21,22,23]. Second, it possesses exceptional value conversion capacity, transforming ecological assets into economic gains through sustainable practices that harmonize ecological and financial benefits [81]. Third, tourism acts as a regional development catalyst, particularly for geographically disadvantaged counties, overcoming locational constraints to drive high-quality economic growth [82]. The inherent characteristics of tourism necessitate development strategies grounded in scientific assessments of ecosystem carrying capacity, employing innovative models to achieve synergistic advancement of ecological conservation and industrial growth.

This study uncovers a coordination mechanism between ES and HWB in ecotourism-dominated counties (Figure 6). The research demonstrates that tourism industry development directly drives nonlinear ES-HWB coupling dynamics through phased transformations. The initial phase of tourism expansion induced ecological pressure. Large-scale infrastructure development, exemplified by scenic area revitalization projects, triggered periodic ES decline through land occupation and landscape fragmentation, manifesting as a 0.12% annual reduction in ESV. Concurrently, increased economic contribution generated modest HWB gains. This phase exhibited weak, economy-driven coordination characteristics, demonstrating the paradoxical impacts characteristic of tourism resource development [83,84]. The subsequent phase of tourism industry upgrading facilitated systemic adaptation. Coordinated strategies combining ecological remediation and tourism product innovation established parallel pathways for ES recovery and HWB improvement. However, external economic shocks induced coordination fluctuations, revealing the sensitivity threshold of county-level tourism systems to exogenous disturbances. The final optimization phase achieved synergistic transition through dual drivers of industrial clustering and ecologically constrained policies. This generated mutually reinforcing mechanisms between economic upgrading and ecological enhancement, elevating ES-HWB coordination beyond 0.96. This phase empirically validated the sustainable development pathway, combining ecological industrialization and industrial ecologization, with its core mechanism being the positive ES-HWB feedback loop created through industrial upgrading and optimization [12]. The tourism industry modulates ES-HWB coordination through three key dimensions: resource intensity, ecological restoration, and economic efficiency. High-level county-scale ES-HWB coordination can only be achieved by transitioning from traditional scale expansion to multidimensional optimization integrating quality, economic benefits, and ecological sustainability. These findings provide both theoretical and practical solutions for addressing the ecological–economic trade-off in tourism-oriented counties.

4.3. Evaluation Indicator System

A scientific evaluation index system serves as a critical tool for analyzing the coordination between ES and HWB. Building on established research frameworks including [85] regional a sustainable development assessment system and the tourism eco-efficiency indicator systems developed by Neto and Peng [86,87], this study constructs a comprehensive evaluation index system to assess ES-HWB coevolution in ecotourism-dominated counties, with particular emphasis on quantifying tourism industry impacts (Table 3 and Table 7). The ES assessment framework innovatively addresses limitations in the static assessment approach of Xie [31] by dynamically incorporating NDVI and population density indicators, thereby enabling quantitative analysis of tourism development’s dynamic impacts and resolving the tourism disturbance quantification challenges identified by Wang [14]. In the HWB assessment framework, the integration of tourism economic multiplier effects through indicators including tertiary industry output value, total tourism revenue, passenger volume, and tourism infrastructure investment budgets effectively compensates for the insufficient quantification of tourism economic spillover effects in both Chen and Fang’s assessment framework and Smith’s HWB indicator system [85,88]. Compared with existing studies, the proposed indicator system achieves three significant advances: (1) Systematic incorporation of tourism economic externalities into the ES assessment framework, (2) the quantitative establishment of tourism activity–ecological response relationships, and (3) the development of a coordination evaluation model specifically tailored for ecotourism-dominated counties. This indicator system can reveal the interaction characteristics of ES-HWB in tourism-dominated counties and provide scientific basis for sustainable development decision-making.

4.4. Sustainable Development Policies for County-Level Tourism

The study reveals contradictions between Chun’an County’s current tourism development model and sustainable development goals. Multi-scenario simulations demonstrate higher ESV under ecological conservation scenarios (CNY 44.99 billion) compared with tourism development scenarios (CNY 44.79 billion) (Figure 5, Table 11). The results indicate that current tourism development practices may compromise ecosystem functions through construction land expansion and water pollution. These findings highlight a critical gap in the UN Sustainable Development Goals (SDGs 8.9 and 12.b) (UN, 2015), underscoring urgent needs for policy innovation. Building on tourism industry driving mechanisms, we propose an integrated policy framework combining spatial governance, economic instruments, and regional coordination. The details are as follows: (1) Establishes a four-tier management system based on ESV spatial heterogeneity (Figure 4), as follows: ① Development prohibition in core protection zones (water bodies and high-sensitivity areas), ② intensity restrictions in ecological buffers, ③ ecological restoration quotas in tourism development zones, and ④ landscape connectivity optimization in built-up areas; (2) implements a tourism environmental tax–ecological compensation mechanism, as follows: ① Differentiated ecological taxes for peak-season tourists, ② 30% tourism consumption tax allocation for ecological restoration, and ③ cross-regional compensation funds based on ES transactions; (3) forms a tourism alliance, as follows: ① Unified environmental standards and monitoring, ② collaborative ecotourism corridors, and ③ joint emergency response systems. This policy framework addresses traditional sectoral fragmentation by systematically integrating spatial planning with economic tools, offering a replicable paradigm for ecotourism-dominated regions globally. Future research should prioritize cost–benefit analysis of policy implementation and synergistic effects.

5. Conclusions

Investigating the coevolution between ES and HWB holds significant theoretical and practical value for advancing regional high-quality development and sustainable human-land system development. To address this, our study innovatively adopts ecotourism-dominated counties as a research unit and develops an integrated “process-pattern-mechanism” analytical framework. By combining the dynamic equivalent factor method, PLUS model, coupling coordination degree model, and comprehensive evaluation methods, we conduct a multidimensional diagnosis of the coevolution of ES-HWB in Chun’an County (2000–2020). This work provides critical insights for optimizing county-level human–land relationships and for promoting sustainable development in similar regions. The research conclusions are as follows:

(1) To decipher the spatiotemporal patterns of ES in Chun’an County (2000–2020), we conducted quantitative analysis, spatial visualization, and multi-scenario simulations. The results reveal significant temporal fluctuations and spatial heterogeneity in ES, demonstrating that conservation-oriented policies effectively promoted sustainable county-level development. The ES value fluctuated between CNY 30.15–36.85 billion, exhibiting marked interannual variability with alternating peak–trough patterns. Spatially, values demonstrated significant water-centric clustering around Qiandao Lake, with distance–decay effects. Scenario simulations confirmed ecological protection scenarios outperform tourism development scenarios in optimizing ES spatial patterns.

(2) For HWB evolution, a multidimensional evaluation index system was established. Our measurements indicated a marked transition of HWB in Chun’an County from low to high quality during the study period. The HWB index achieved transformative growth from 0.16 (2000) to 0.8 (2020), marking a qualitative transition from low to high well-being stage, driven by tourism-induced economic growth, infrastructure investment, and institutional innovation.

(3) By integrating statistical data and model outputs, we identified the driving mechanism of ES-HWB coordination: The tourism industry functioned as a critical mediator in ecotourism-dominated counties, whose developmental dynamics directly steered the synergistic changes of ES and HWB. ES-HWB coupling coordination progressed through three phases: Discordance (2000–2008), antagonism (2009–2013), and coordination (2014–2020). Tourism functioned as a key mediator via tripartite mechanisms: Ecological pressure induction, systemic adaptation, and synergistic transition. These findings provide empirical support for developing eco-economic synergy pathways in ecotourism-dominated Counties.

(4) As a typical ecotourism-dominated county, Chun’an’s industrial development continuously reshapes the spatial patterns of ecosystem services (ES) and human well-being (HWB), indicating that tourism acts as a key mediator in their coevolution. This aligns with Fedele and Wang’s findings that ES-HWB interactions may involve not only direct linkages but also intermediary variables [89,90]. To quantify ES-HWB coordination, we established an evaluation index system based on existing dimensional classifications, data availability, and indicator validity. This approach mirrors FU’s methodology in the Xin’an River Basin, where MA-defined ES-HWB indicators were refined through surveys and interviews to address watershed-specific contexts [12]. Policies are designed to address developmental uncertainties through targeted interventions. Our proposed policy framework aims to foster synergistic ES-HWB sustainability, consistent with prior evidence that incentive-constraint mechanisms in policies can optimize human–environment relationships toward sustainable transitions [60,91].”

This study has several limitations. First, the study period (2000–2020) was insufficient to fully represent Chun’an County’s tourism development cycle due to data limitations, including missing pre-2000 baseline data and unavailable post-2020 official records. This temporal truncation may partially limit the interpretation of long-term evolutionary patterns. In future work, we will combine post-2020 monitoring data with historically reconstructed records to develop a dynamic framework of tourism-mediated ecosystem service–human well-being interactions at the county scale. Second, reliance on statistical yearbooks and remote sensing data may underestimate informal tourism sectors’ impacts on ES and HWB. Finally, as the study focuses on a representative county near a megacity, the generalizability of findings requires further validation across diverse county types (e.g., agricultural/ecological counties or population-shrinking regions). Nevertheless, this study presents novel insights into the nonlinear evolution of human–land system coordination in ecotourism-dominated counties through the development of an ES-HWB coupling coordination model. The identified stage-specific patterns empirically validate the tourist destination life cycle theory. Furthermore, the implemented pressure-state-response analytical framework offers a replicable research paradigm for investigating sustainable development in comparable county-level regions.

Future research should prioritize three key directions to advance this field. First, integrating multi-source geospatial data, including mobile signaling and point-of-interest datasets, would enable extended spatiotemporal analysis and refined indicator systems for comprehensively understanding ES-HWB coevolution. Second, methodological synergies between geographic simulation systems, like CLUMondo and InVEST, with machine learning algorithms could precisely quantify interaction thresholds across development scenarios. Third, typological comparisons focusing on diverse county categories—including metropolitan-adjacent, specialized-function, agricultural, ecological, and population-declining counties—would elucidate differentiated evolutionary mechanisms and establish a robust theoretical framework integrating universal patterns with context-specific dynamics. These advances will support the development of differentiated tourism-ecological governance strategies.