Evaluation and Enhancement of Landscape Resilience in Mountain–Water Towns from the Perspective of Cultural and Tourism Integration: Case Study of Yinji Town, Wugang City

Abstract

Rural tourism in China is advancing rapidly, with cultural and tourism integration (CTI) becoming a vital pathway for sustainability. Mountain–water towns, given their special geographical conditions, face numerous challenges in CTI development, which need to enhance landscape resilience. This study proposes the theoretical framework of landscape resilience in mountain–water towns from the perspective of CTI. Taking Yinji Town of Wugang City as an example, it constructs a resilience evaluation system including three dimensions: cultural landscape, natural landscape, and social systems. The study uses the AHP–Entropy Weight combined method to determine indicator weights. Indicator scores are obtained through field research and GIS analysis, which are substituted into the preparedness–vulnerability resilience model to calculate resilience level, and the Jenks Natural Breaks method is used for level classification. Finally, the Obstacle Degree Model is applied to identify the primary obstacle factors affecting landscape resilience. The results indicate the following: (1) The average landscape resilience (RI) score of the 19 villages in Yinji Town is 0.84 (RI < 1), indicating a generally low level. Two villages are in the high-level range, while four villages are in the low-level range. (2) Cultural landscape resilience is the primary weakness, with the lowest average score (0.70), while natural landscape resilience is the highest (1.03). (3) Major obstacles include such as the number of cultural inheritors, the degree of susceptibility to natural disasters, and the distance to core mountain–water resources. The study contributes a CTI-based evaluation framework and methodology for assessing landscape resilience, offering enhancement strategies through increased preparedness and reduced vulnerability.

1. Introduction

With the implementation of the rural revitalization strategy, rural tourism in China has entered a stage of rapid development [1]. Rural tourism serves as a crucial driving force for rural revitalization and an important means to consolidate the achievements of poverty alleviation [2]. Culture and tourism integration (CTI) is a new development pathway for China’s tourism industry in recent years. Its core is the mutual integration of cultural and tourism industries, gradually breaking down the original boundaries to form a new cultural-tourism industrial system as a phenomenon and process [3,4]. In China, the merging of the former Ministry of Culture and the former National Tourism Administration to form the Ministry of Culture and Tourism in 2018 marked the formal proposal of the CTI concept and its entry into a new developmental stage [5]. By 2024, CTI was formally incorporated into the overall framework of China’s rural revitalization strategy, becoming a vital pathway for advancing comprehensive rural revitalization. CTI is a product of tourism development in the new era, an inevitable trend for the integration of cultural and tourism industries, and an essential route for rural tourism development [6,7].

Townships are the carriers of rural cultural-tourism construction. Based on different resource endowments and landscape characteristics, townships can be classified into various types [8], and mountain–water town is a unique type [9]. In 1990, Mr. Qian Xuesen proposed the concept of “Shanshui City”, with the core idea of being urban construction harmonizing with the natural environment [10]. Subsequently, Mr. Wu Liangyong further developed this theory, proposing that urban development should follow the principle of combining natural protection with cultural heritage [11]. Under the background of the intersection between new urbanization and the rural revitalization strategy [12], the “Shanshui City” theory gradually extended and evolved towards township areas, forming the concept of “mountain-water towns” with rural characteristics [13]. A mountain–water town is a complex township ecosystem that possesses natural resources of mountains and water [14]. Precious landscape resources endow these towns with inherent advantages for CTI development. However, the specific natural and geographical conditions also impose limitations on their development, such as higher risks of natural disasters, transportation constraints, and difficulties in engineering construction [15]. Concurrently, rural areas face a series of challenges, including lagging infrastructure and low levels of economic development [16]. Consequently, mountain–water towns are more susceptible to impact and disturbances during CTI construction, and it is difficult to develop sustainably. To solve this problem, the resilience theory, emphasizing resistance capacity and sustainability, is applied to the fields of rural landscape and rural tourism [17].

The original meaning of resilience refers to a system’s ability to mobilize available resources, maintain normal functions, and cope with challenges and changes under external pressure and disturbance [18]. In 1973, Canadian ecologist Holling first applied the concept of engineering resilience to ecology, after which it gradually expanded from natural ecology to the social sciences [19]. Since its proposal, resilience theory has evolved from engineering resilience and ecological resilience under static, equilibrium perspectives towards evolutionary resilience under dynamic, adaptive evolutionary perspectives. Evolutionary resilience is the core of current resilience theory, which focuses on the sustainable development ability of systems in dynamic changes [20]. Currently, the research on resilience of rural tourism mostly focuses on the evaluation method of resilience, the analysis of influencing factors of resilience, and the strategies of sustainable development. Shi et al. propose a “sustainable resilience model” to evaluate tourism development projects in rural land (TDPRL). Using the Analytic Network Process (ANP), the model assesses resilience across the dimensions of tractive, driving, and internal dynamic forces. The study provides a novel tool for decision-making in rural tourist project development and land use planning [21]. Liu et al. explore the sustainable development of rural tourism based on the average variable cost (AVC) theory. This study supports the application of AVC theory in rural tourism research and emphasizes the importance of rural landscape quality and economic vitality. It provides scientific evaluation methods and development strategies for the sustainable development of rural tourism [22]. Zhong et al. investigated the contribution of information and communications technologies (ICTs) to rural tourism resilience in China, analyzing data from 20,716 rural tourism operators in Beijing. The research results show that ICTs can be an effective way to promote the development of rural tourism and, therefore, to enhance the resilience of rural tourism [23]. Gocer et al. propose the framework of cultural tourism and rural community resilience and conducted application research. The study investigates rural community resilience strategies adopted by residents to cope with transformation led by cultural tourism and heritage conservation restrictions [24].

Existing studies have laid a theoretical and methodological foundation for the resilience assessment and development planning of rural tourism, but there are still some key research gaps. Firstly, the cross-study of CTI and landscape resilience has not been fully explored, and there is a lack of research on landscape resilience from the perspective of CTI. Secondly, the current research pays less attention to the mountain–water towns, which are sensitive to the environment but rich in tourism resources. Therefore, based on resilience theory and the characteristics of mountain–water towns, this study constructs a landscape resilience evaluation system tailored for the CTI perspective. Using Yinji Town as an empirical case, this study employs field surveys, GIS analysis, and a literature review to assess its landscape resilience level and identify primary obstacle factors. Finally, strategies for enhancing landscape resilience are proposed. This study provides an evaluation method of landscape resilience of mountain–water town from the perspective of CTI, explores the impact mechanism of resilience, and fills the research gap in this direction, providing solutions of Chinese origin for global sustainable tourism and rural landscape planning.

2. Theoretical Foundation

Based on CTI theory and the theory of complex ecosystems, and building upon Kusumastuti’s resilience evaluation model, this study proposes a theoretical framework for landscape resilience applicable to mountain–water towns.

The theory of CTI emphasizes the co-existence of culture and tourism in resources, functions, space, and other aspects [25]. In terms of landscape resources, cultural landscape and natural landscape are the core elements of CTI [26,27], and together constitute important components of the landscape system of mountain–water towns. The cultural landscape reflects the regional cultural characteristics and tourism attraction and is the “soul” in the CTI; the natural landscape is the material carrier and spatial basis of tourism development, providing environmental support for CTI [28]. The theory of complex ecosystems regards townships as a complex system composed of natural, social, and economic subsystems [29]. Among them, the natural subsystem provides ecological background and resource conditions; the social subsystem covers elements such as political system and culture, and undertakes the function of system regulation; and the economic subsystem includes factors such as production, circulation, and consumption, forming a closed-loop material metabolism chain [30]. This theory reveals the composition structure of the township system, emphasizes the interaction and coordination between various subsystems [31], and provides a theoretical reference for understanding the landscape system structure and resilience mechanism of mountain–water towns. Based on the integration of the aforementioned theories, this study has re-deconstructed the landscape system of mountain–water towns. Firstly, the theory of CTI clarifies that culture and nature are fundamental dimensions of the landscape system of mountain–water towns, while the complex ecosystem theory also emphasizes the natural dimension. Secondly, the social subsystem in the complex ecosystem theory, as a key force in system regulation, plays an important supporting and regulating role for the former two dimensions [31]. Furthermore, in the development of culture and tourism integration, economic factors are more reflected in social aspects such as financial support and tourism revenue. Therefore, the economic dimension is integrated into the social system dimension and is no longer listed separately. Consequently, this study defines the landscape system of mountain–water towns from the perspective of CTI as a composite system integrating cultural landscape, natural landscape, and social systems. The cultural landscape system encompasses aspects like cultural resources, cultural benefits, and cultural inheritance; The natural landscape system covers scenic resources, ecological foundations, and environmental experience; The social system includes locational factors, transportation, population structure, policy support, and funding. Cultural and natural landscapes serve as the cultural and material foundations for CTI in the town, while the social system influences and regulates their development.

Since the application of resilience theory to the landscape field, various resilience evaluation models have emerged. At the beginning of the 21st century, Bruneau proposed that resilience could be measured by the functional performance of community infrastructure before and after disasters [32]. Building on Bruneau’s work, Henry suggested that resilience could be analyzed through key parameters such as disruptive events, system recovery capacity, and overall resilience strategies [33]. Referring to the above model, Kusumastuti references the definition of disaster resilience proposed by the United Nations International Agency for Disaster Reduction, which defines resilience level as the ratio of a system’s preparedness to its vulnerability [34,35]. This theoretical model clearly reveals that resilience is the result of the interaction between positive capacities and negative risks, as shown in Equation (1), where RI is the resilience level score. If RI > 1, it indicates that the system’s preparedness can overcome its vulnerability when facing disruptive events, ensuring sustainable and healthy development, meaning the system has a high resilience level. Conversely, if RI < 1, it signifies that the system’s preparedness is insufficient to compensate for its vulnerability, resulting in poor risk resistance and a lack of ability for sustainable development, meaning the system has a low resilience level [36]. This study selects the Kusumastuti model based on the following considerations: firstly, the model has a concise structure, is easy to quantify and evaluate, and can intuitively reflect the level of system resilience; secondly, the binary framework of “preparedness-vulnerability” is highly compatible with the development needs and potential risks faced by the landscape system of mountain–water towns under the background of CTI [37]. Analyzing landscape preparedness helps identify key elements supporting the vitality and sustainable development of CTI. Analyzing landscape vulnerability can reflect the potential risks or obstacles faced by landscape systems during CTI development. Combining both analyses allows for assessing rural landscape resilience from both positive capacities and negative risks, evaluating the sustainability of the landscape system under the CTI development model.

Therefore, based on the “cultural landscape-natural landscape-social system” complex landscape system for mountain–water towns under the CTI perspective described above, additionally combined with resilience theory and Kusumastuti’s resilience assessment model, this study defines the landscape resilience of mountain–water towns as the ratio of preparedness and vulnerability in three dimensions: cultural landscape, natural landscape, and social systems. A landscape resilience theoretical framework is proposed from the perspective of CTI, which integrates three dimensions: cultural landscape resilience, natural landscape resilience, and social system resilience (Figure 1). This study is the first to integrate the CTI concept into landscape resilience assessment, which not only applies and expands existing resilience evaluation models but also provides a new theoretical perspective and analytical method for understanding the sustainable development mechanism of CTI.

$$\mathit{Resilience} \mathit{Index} (\mathit{RI})={\displaystyle \frac{\mathit{Preparedness} \mathit{Index} \left(PI\right)}{\mathit{Vulnerability} \mathit{Index} \left(VI\right)}}$$

(1)

3. Study Area and Methods

3.1. Study Area

Yinji Town, administratively under Wugang City in Henan Province, China, is selected as the empirical case for this study. The overall terrain of Yinji Town is high in the south and low in the north, with an average elevation of 180 m. The town has 19 administrative villages and four ecological reserve areas, with a total area of 101.2 square kilometers. Due to the strong restrictive nature of cultural-tourism development within the ecological Reserves, this study primarily focuses on the 19 administrative villages of Yinji Town (Figure 2).

Yinji Town is built amidst mountains and waters, and has abundant natural mountain and water resources, making it a typical representative of a mountain–water town. Its core natural landscape attractions include the nationally 4A-level Erlang Mountain Scenic Area, Shimantan Reservoir, and Jiutouya Scenic Area. The town’s three core cultural-tourism themes are the Hundred Herbs Culture, the Hundred Fruits Culture, and the Hundred Dwellings Culture. Additional landscape resources and cultural heritage sites include the Yuhuangmiao River, Rendonggou Reservoir, Xugou Iron Smelting Ruins, and Qinglongshan Rock Paintings, among others.

Yinji Town is recognized as a nationally renowned scenic tourism town, actively promoting the development strategy of “ecological town building, industrial town building, and tourism revitalization”. Taking Yinji Town as a research case is very consistent with the CTI research perspective. At the same time, there are significant differences in the quantity of tourism resources, distance to core mountain and water resources, and policy support among the villages in Yinji Town, and the development is extremely uneven. This heterogeneity makes Yinji Town an ideal case study for investigating the landscape resilience of mountain–water towns under CTI, as it involves various development scenarios and challenges. The research methods and strategies can be extended to other mountain–water towns under the development of CTI.

3.2. Construction of the Landscape Resilience Evaluation System for Mountain–Water Towns from the CTI Perspective

3.2.1. Selection of Evaluation Indicators

Based on the aforementioned theoretical framework of landscape resilience for mountain–water towns from the CTI perspective, this study selects evaluation indicators from three dimensions: cultural landscape, natural landscape, and social system. It also constructs two indicator systems: landscape preparedness and landscape vulnerability.

First, the indicators are selected through the Delphi method combined with relevant research [38,39]. The Delphi method is a structured forecasting and decision-making approach that involves anonymously soliciting expert opinions to gradually reach a consensus, and it is widely applied across various fields. In this study, twelve experts from three fields are invited, including four scholars in landscape architecture, four scholars in urban and rural planning, two scholars specializing in tourism, and two local tourism practitioners. Initial evaluation indicators are determined by referring to relevant literature and incorporating expert opinions from the first round [40]. The results are then fed back for the second round to allow experts to revise and refine the indicators, and a final consensus on indicator selection is reached. Second, in accordance with the evaluation structure of the Analytic Hierarchy Process (AHP), this study divides the hierarchical structure of evaluation indicators into three layers: target layer, criterion layer, and indicator layer. Finally, the specific indicators and hierarchical structures for landscape preparedness and landscape vulnerability are determined, respectively. Landscape preparedness indicators consist of 12 indicators across three dimensions: cultural landscape preparedness, natural landscape preparedness, and social system preparedness. Landscape vulnerability indicators include 12 indicators in three dimensions: cultural landscape vulnerability, natural landscape vulnerability, and social system vulnerability. Additionally, the indicator types are classified as qualitative or quantitative (

Table 1. Landscape preparedness indicator system.

Target Layer	Criterion Layer	Indicator Layer	Indicator Description
Landscape Preparedness (PI)	Cultural landscape preparedness (P1)	A1—Number of cultural resources	Quantity of material and intangible cultural resources
		A2—Attractiveness of cultural-tourism IP	Attractiveness of cultural-tourism features to tourists
		A3—Number of cultural-tourism specialties	Reflects the economic conversion capacity of cultural tourism
		A4—Number of cultural inheritors	Reflects cultural inheritance capacity
	Natural landscape preparedness (P2)	B1—Number of natural landscapes	Quantity of natural landscapes (mountains, rivers, lakes, ancient trees, among others)
		B2—Scenic beauty degree	Degree of beauty of natural landscapes
		B3—Vegetation coverage rate	Reflects forest coverage rate and rural greening level
		B4—Environmental comfort level	Reflects environmental quality
	Social system preparedness (P3)	C1—Road density	Road length per unit area, reflecting internal transportation convenience
		C2—Number of hotels and agritainments	Total number of hotels and agritainments, reflecting cultural-tourism reception capacity
		C3—Proportion of villagers engaged in CTI work	Proportion of CTI practitioners in the local population, reflecting the participation of villagers
		C4—Strength of CTI policy support	Level of government support for CTI development

and

Table 2. Landscape vulnerability indicator system.

Target Layer	Criterion Layer	Indicator Layer	Indicator Description
Landscape Vulnerability (VI)	Cultural landscape vulnerability (V1)	D1—Homogenization rate of cultural resources	Proportion of cultural resources that are similar to those in other regions
		D2—Unused rate of cultural resources	Proportion of unused cultural resources, which reflects lack of cultural development
		D3—Damage degree of historical buildings	Damage degree of historical buildings or relics, reflecting difficulty of cultural regeneration
		D4—Destruction degree of traditional rural character	Degree of destruction of traditional rural landscape character by rural construction
	Natural landscape vulnerability (V2)	E1—Distance to core landscape resources	Distance to core landscape resources within the town territory
		E2—Susceptibility to natural disasters	Risk level of natural disasters, reflecting ecological pressure
		E3—Average elevation	Reflects engineering construction pressure and transportation limitations
		E4—Average slope	Reflects engineering construction pressure and transportation limitations
	Social system vulnerability (V3)	F1—Distance to town center	Reflects locational disadvantage; greater distance implies greater disadvantage
		F2—Distance to main roads	Reflects locational accessibility; greater distance implies lower accessibility
		F3—Proportion of out-migrating population	Proportion of out-migrants to registered population; if too high, affects labor force base
		F4—Dependence on CTI revenue	Proportion of cultural-tourism revenue in total village income

).

• Cultural landscape preparedness indicators: These include the number of cultural resources, attractiveness of cultural-tourism IP (intellectual property), number of cultural-tourism specialties, and number of cultural inheritors. These reflect the richness of cultural landscape resources, their attractiveness to tourists, the economic conversion capacity of the cultural-tourism industry, and cultural inheritance capacity.
• Natural landscape preparedness indicators: This includes the number of natural landscapes, scenic beauty degree, vegetation coverage rate, and environmental comfort level. These reflect the richness and beauty of natural landscapes, the abundance of forest resources and greening level, and the quality of the human living environment.
• Social system preparedness indicators: These include the road density, number of hotels and agritainments, proportion of villagers engaged in CTI work, and strength of CTI policy support. These reflect the convenience of rural transportation, cultural-tourism reception capacity, villager participation and collaboration, and the level of government support for cultural-tourism.
• Cultural landscape vulnerability indicators: These include the homogenization rate of cultural resources, unused rate of cultural resources, damage degree of historical buildings, and destruction degree of traditional rural character. These reflect the lack of cultural distinctiveness and utilization, difficulties in cultural resource regeneration, and the degree of damage to traditional rural landscapes.
• Natural landscape vulnerability indicators: These include the distance to core landscape resources, susceptibility to natural disasters, average elevation, and average slope. These reflect resource disadvantages for CTI development, ecological pressure, engineering construction pressure, and transportation limitations.
• Social system vulnerability indicators: These include the distance to the town center, distance to main roads, proportion of out-migrating population, and dependence on CTI revenue. These reflect rural locational conditions, regional accessibility, population structure, and economic pressure.

3.2.2. Determination of Indicator Weights

This study employs a combined subjective–objective approach integrating the AHP and the Entropy Weight method to calculate indicator weights, mitigating the bias inherent in single subjective weighting methods.

• Determining subjective weights using AHP

A total of 12 experts were invited from the three fields mentioned above to compare the criteria layer and indicator layer of the indicator system pairwise, construct judgment matrices, and divide the relative importance of indicators into nine levels, corresponding to scores of 1 to 9. Then the weight vector was calculated, and consistency testing was performed. When the consistency ratio CR < 0.1, the judgment matrix meets the consistency requirements; otherwise, adjustments need to be made. The final subjective weight of each indicator is calculated by taking the geometric mean of the weights obtained by all experts [40]. The calculation equations are as follows:

$$T_i = \sqrt[n]{\prod _{j=1}^n{a}_{\mathit{ij}}}$$

(2)

where $T_i$ is the geometric mean vector of the i-th indicator, $a_{\mathit{ij}}$ is the scale value of the relative importance of the i-th indicator to the j-th indicator, i is the serial number of evaluation indicators, i = 1, 2, …, n, and n is the number of indicators.

$${\alpha }_i={\displaystyle \frac{T_i}{\sum _{i=1}^n{T}_i}}$$

(3)

where ${\alpha }_i$ is the subjective weight of the i-th indicator, and i = 1, 2, …, n, and n is the number of indicators.

2.

Determining objective weights using the Entropy Weight method

To avoid the subjectivity of relying solely on AHP, the Entropy Weight method is used to calculate objective weights. The Entropy Weight method is a common multi-indicator evaluation method that determines weights by calculating the information entropy of each indicator. First, raw data are standardized to eliminate dimensional effects. If any data is less than or equal to 0, non-negative translation is applied. Then, the proportion of samples for each indicator is calculated, followed by the indicator entropy value, and finally the indicator weight [41]. The equations are as follows:

$$E_{i }=-(\mathit{ln}m{)}^{-1}\sum _{k=1}^m{p}_{\mathit{ki}}\mathit{ln}{p}_{\mathit{ki}}$$

(4)

where $E_i$ is the entropy value of the i-th indicator, $-(\mathit{ln}m{)}^{-1}$ is the information entropy coefficient, k is the serial number of samples, k = 1, 2, …, m, m is the number of samples, i is the serial number of evaluation indicators, i = 1, 2, …, n, n is the number of indicators, and $p_{ki}$ is the proportion of the k-th sample on the i-th indicator.

$${\beta }_i={\displaystyle \frac{1\mathit{-}{E}_i}{\sum _{i=1}^n(1-E_i)}}$$

(5)

where ${\beta }_i$ is the objective weight of the i-th indicator, and i = 1, 2, …, n, n is the number of indicators.

3.

Calculation of combined weights for evaluation indicators

After determining the subjective and objective weights, the combined weight for each indicator is calculated using the following equation:

$$W_i = \theta {\alpha }_i + (1-\theta ){\beta }_i$$

(6)

where $W_i$ is the combined weight of the i-th indicator, ${\alpha }_i$ is the subjective weight calculated by AHP, ${\beta }_i$ is the objective weight calculated by the Entropy Weight method, and $\theta$ is the weighting coefficient. To ensure a balance between subjective and objective weights, $\theta$ is set to 0.5 in this study [42]. The combined weight results are shown in

Table 3. Landscape preparedness indicator weights and ranking.

Target Layer	Criterion Layer	Indicator Layer	Subjective Weight	Objective Weight	Combined Weight	Rank
Landscape Preparedness (PI)	Cultural landscape preparedness (P1)	A1—Number of cultural resources	0.2055	0.0663	0.1359	02
		A2—Attractiveness of cultural-tourism IP	0.1297	0.0778	0.1038	03
		A3—Number of cultural-tourism specialties	0.0686	0.0861	0.0774	07
		A4—Number of cultural inheritors	0.0867	0.1910	0.1389	01
	Natural landscape preparedness (P2)	B1—Number of natural landscapes	0.1544	0.0454	0.0999	04
		B2—Scenic beauty degree	0.0552	0.0810	0.0681	08
		B3—Vegetation coverage rate	0.0756	0.0406	0.0581	09
		B4—Environmental comfort level	0.0267	0.0810	0.0539	10
	Social system preparedness (P3)	C1—Road density	0.0538	0.0295	0.0417	12
		C2—Number of hotels and agritainments	0.0182	0.1493	0.0838	06
		C3—Proportion of villagers engaged in CTI work	0.0286	0.0776	0.0531	11
		C4—Strength of CTI policy support	0.0971	0.0742	0.0857	05

and

Table 4. Landscape vulnerability indicator weights and ranking.

Target Layer	Criterion Layer	Indicator Layer	Subjective Weight	Objective Weight	Combined Weight	Rank
Landscape Vulnerability (VI)	Cultural landscape vulnerability (V1)	D1—Homogenization rate of cultural resources	0.2324	0.0359	0.1342	01
		D2—Unused rate of cultural resources	0.0566	0.1313	0.0940	04
		D3—Damage degree of historical buildings	0.0759	0.0533	0.0646	10
		D4—Destruction degree of traditional rural character	0.1255	0.0270	0.0762	07
	Natural landscape vulnerability (V2)	E1—Distance to core landscape resources	0.1300	0.0720	0.1010	03
		E2—Susceptibility to natural disasters	0.0841	0.1523	0.1182	02
		E3—Average elevation	0.0601	0.0836	0.0719	08
		E4—Average slope	0.0377	0.0955	0.0666	09
	Social system vulnerability (V3)	F1—Distance to town center	0.0543	0.0328	0.0435	12
		F2—Distance to main roads	0.0392	0.0782	0.0587	11
		F3—Proportion of out-migrating population	0.0765	0.0833	0.0799	06
		F4—Dependence on CTI revenue	0.0276	0.1548	0.0912	05

.

3.2.3. Indicator Grading Standardization

The indicators of this study are divided into preparedness indicators and vulnerability indicators, both of which include quantitative and qualitative indicators. The scoring standard for each indicator is divided into five levels, corresponding to scores of 1 to 5. A total of 5 points represents the highest level, and 1 point represents the lowest level. For landscape preparation indicators (positive indicators), a higher score (5) means more favorable, while a lower score (1) means worse. On the contrary, for the landscape vulnerability indicator (negative indicator), a higher score (5) indicates higher vulnerability and is less favorable for system resilience. In quantitative indicators (e.g., elevation, slope, and vegetation coverage rate), grading standards are established by referencing relevant national standards such as the “Technical Specification of Ecological and Environment Quality Evaluation” [43], “Rating of Quality Levels of Tourist Attractions” [44], and “Technical Regulation of the third nationwide Land and resources Survey” [45], combined with related research [46,47,48]. For indicators lacking relevant standards or literature references, such as the number of cultural inheritors, distance to core landscape resources, the grading standards are determined based on regional averages or Natural Breaks classification methods. The regional average is obtained by calculating the indicator values of each sample village, and the median is the general level standard. Then, the low-level standard and high-level standard are determined based on low-value samples and high-value samples. The Natural Breaks classification method generates a set of level thresholds based on data from all sample villages, which are then applied to score the indicators of each village. For qualitative indicators, such as the attractiveness of cultural-tourism IP and strength of CTI policy support, are divided into five levels, i.e., very low, low, average, high, and very high, corresponding to 1–5 points, through field surveys and expert evaluation (

Table 5. Indicator grading standards.

Category	Type	Evaluation Indicator	Grading and Scoring
			5	4	3	2	1
Landscape Preparedness (PI)	Quantitative	A1—Number of Cultural Resources	>10	8–10	5–7	2–4	≤1
		A3—Number of cultural-tourism specialties	>10	8–10	5–7	2–4	≤1
		A4—Number of cultural inheritors	≥4	3	2	1	0
		B1—Number of natural landscapes	>10	8–10	5–7	2–4	≤1
		B3—Vegetation coverage rate	>0.8	(0.6, 0.8]	(0.4, 0.6]	(0.2, 0.4]	≤0.2
		C1—Road density (km/km2)	>4.2	(3.3, 4.2]	(2.6, 3.3]	(1.9, 2.6]	≤1.9
		C2—Number of hotels and agritainments	>20	16–20	11–15	6–10	≤5
		C3—Proportion of villagers engaged in CTI work	>0.5	(0.4, 0.5]	(0.3, 0.4]	(0.2, 0.3]	≤0.2
	Qualitative	A2—Attractiveness of cultural-tourism IP	Very high	High	Average	Low	Very low
		B2—Scenic beauty degree	Very high	High	Average	Low	Very low
		B4—Environmental comfort level	Very high	High	Average	Low	Very low
		C4—Strength of CTI policy support	Very high	High	Average	Low	Very low
Landscape Vulnerability (VI)	Quantitative	D1—Homogenization rate of cultural resources	>0.5	(0.4, 0.5]	(0.3, 0.4]	(0.2, 0.3]	≤0.2
		D2—Unused rate of cultural resources	>0.5	(0.4, 0.5]	(0.3, 0.4]	(0.2, 0.3]	≤0.2
		E1—Distance to core landscape resources (km)	>2.7	(1.5, 2.7]	(1.0, 1.5]	(0.6, 1.0]	≤0.6
		E3—Average elevation (m)	>450	(350, 450]	(250, 350]	(150, 250]	≤150
		E4—Average slope (°)	>25°	(15°, 25°]	(6°, 15°]	(2°, 6°]	≤2°
		F1—Distance to town center (km)	>5.1	(4.4, 5.1]	(2.7, 4.4]	(1.3, 2.7]	≤1.3
		F2—Distance to main roads (km)	>2.0	(1.2, 2.0]	(0.5, 1.2]	(0.2, 0.5]	≤0.2
		F3—Proportion of out-migrating population	>0.5	(0.4, 0.5]	(0.3, 0.4]	(0.2, 0.3]	≤0.2
	Qualitative	D3—Damage degree of historical buildings	Very high	High	Average	Low	Very low
		D4—Destruction degree of traditional rural character	Very high	High	Average	Low	Very low
		E2—Susceptibility to natural disasters	Very high	High	Average	Low	Very low
		F4—Dependence on CTI revenue	Very high	High	Average	Low	Very low

).

3.3. Data Sources and Processing

Research data for this study are categorized into spatial data, statistical data, and subjective evaluation data to ensure comprehensiveness and scientific rigor. Data acquisition and processing methods included field surveys, government websites, and geospatial analysis.

3.3.1. Spatial Data

Elevation DEM data, remote sensing image data, and land cover data are sourced from the Geospatial Data Cloud website and Wuhan University’s 30 m annual land cover data. Vector boundaries of the study area, administrative divisions of villages, geological hazard points, and other data are obtained from relevant management departments in Wugang city. Transportation network data are sourced from the OpenStreetMap website. After data acquisition, ArcGIS 10.8 software is used to calculate village-level average elevation, average slope, vegetation coverage rate, susceptibility to natural disasters, and road density, among others.

3.3.2. Statistical Data

Data such as the number of cultural resources, number of natural landscapes, number of cultural inheritors, number of hotels and agritainments, among others, are obtained from field surveys and government websites. The data related to population and economy also comes from field surveys and government websites.

3.3.3. Subjective Evaluation Data

The attractiveness of cultural tourism IP, environmental comfort level, and scenic beauty degree are obtained through field survey and expert evaluation [49]. Data like the strength of CTI policy support and dependence on CTI revenue are acquired from field surveys and government websites.

3.4. Resilience Evaluation Score Calculation

3.4.1. Landscape Preparedness and Vulnerability Score Calculation

Combining the evaluation indicator system described above and the grading standards for each indicator, preliminary scores are obtained. Using a multi-factor weighting equation [50], the preliminary score of each indicator is multiplied by its respective weight to obtain the final score for each indicator. The final scores of all indicators are summed to obtain landscape preparedness and landscape vulnerability scores. The calculation equation is as follows:

$$R_{\mathit{pv}} = \sum _{i=1}^n{W}_i{Z}_i$$

(7)

where $R_{\mathit{pv}}$ is the score for landscape preparedness or vulnerability, i is the evaluation indicator number, n is the total number of indicators, $W_i$ is the weight of the i-th indicator, and $Z_i$ is the preliminary score value of the i-th indicator.

The score range for landscape preparedness and landscape vulnerability is (0, 5]. Using the equal division method, they are divided into four levels: low level (0, 1.25], medium-low level (1.25, 2.5], medium-high level (2.5, 3.75], and high level (3.75, 5].

3.4.2. Landscape Resilience Score Calculation

After calculating landscape preparedness and landscape vulnerability scores, they are substituted into Kusumastuti’s resilience calculation equation (Equation (1)) to derive the cultural landscape resilience, natural landscape resilience, social system resilience, and comprehensive landscape resilience scores for each village. To visually compare the levels and spatial distribution of resilience across different dimensions and comprehensive resilience, this study used the Jenks Natural Breaks classification method in the ArcGIS 10.8 software to divide the resilience scores of different dimensions in each village into four ranges: low level, medium-low level, medium-high level, and high level [51].

3.5. Obstacle Factor Analysis

The Obstacle Degree Model is used to identify the main obstacle factors hindering system development and quantify their impact [52]. In this study, to further clarify the shortcomings of landscape resilience in Yinji Town and propose more targeted enhancement suggestions, the Obstacle Degree Model is applied. Through analysis of factor contribution, indicator deviation, and obstacle degree, the main obstacle factors of landscape resilience are identified and ranked. Since landscape vulnerability is the inverse property of preparedness, the landscape vulnerability obstacle degree calculation requires first normalizing the indicators negatively. The calculation equation of obstacle degree is as follows:

$$O_i = {\displaystyle \frac{w_i(1-Y_i)}{\sum _{i=1}^n{w}_i(1-Y_i)}}$$

(8)

where $O_i$ is the obstacle degree of the evaluation indicator, $w_i$ is the combined weight of the evaluation indicator, $Y_i$ is the standardized value of the evaluation indicator, and n is the number of indicators.

4. Results

4.1. Results of Landscape Preparedness and Vulnerability

4.1.1. Landscape Preparedness

Landscape preparedness (PI) scores ranged from 1.21 to 3.78, with an average of 2.13, indicating an overall medium-low level. Jizhuang village (3.78) recorded the highest PI, classified as high level, followed by Weiziyuan (3.72) and Zhangzhuang (3.34), which fell into the medium-high level along with three other villages (Yinji, Caizhuang, and Daliuzhuang). In contrast, most villages (12 out of 19), such as Kangzhuang and Liangzhuang, are at the medium-low level. Shigang village exhibited the lowest preparedness (1.21), being the sole village in the low level range (Figure 3a). Spatially, villages with higher PI are clustered near the town center or core landscape resources.

4.1.2. Landscape Vulnerability

Landscape vulnerability (VI) scores ranged from 2.07 to 3.16, with a higher average of 2.54, placing the town’s overall vulnerability at a medium-high level. Notably, Jizhuang (3.16) and Weiziyuan (3.14), despite their high preparedness scores, also registered the highest vulnerability scores, residing in the medium-high level alongside seven other villages like Zhuwa (2.97) and Loufangwan. The remaining ten villages, including Xiegudong, which had the lowest VI (2.07), are in the medium-low vulnerability range. No villages are classified in the low-level range (Figure 3b). Spatially, villages with higher VI are often in peripheral areas with complex terrain.

4.2. Results of Resilience Across Different Dimensions

4.2.1. Cultural Landscape Resilience

Cultural landscape resilience is the most critical shortcoming. The average score is 0.70 (RI < 1), and most villages (10 out of 19) are classified as low level. Only three villages, Jizhuang (1.52), Weiziyuan (1.29), and Zhangzhuang (1.21), achieved high resilience levels. Villages like Lizhuang and Shigang (0.36) are at the bottom, indicating widespread challenges in the cultural dimension (Figure 4a). Villages with higher cultural landscape resilience scores are mostly key tourism villages in Yinji Town.

4.2.2. Natural Landscape Resilience

Natural landscape resilience presented a more positive picture, with an average RI of 1.03 (RI > 1). The seat of the town government, Yinji village, scored the highest (1.69), followed by Caizhuang (1.57) and Zhangzhuang (1.42). However, the distribution is polarized, with eight villages, including Gengshang (0.65) and Zhuwa, still trapped in low resilience levels. (Figure 4b). Villages with higher natural landscape resilience scores are mostly located near the town center and possess certain natural landscape resources.

4.2.3. Social System Resilience

Social system resilience averaged at 0.99, indicating a borderline level of sustainability. Yinji village again led with a score of 1.69, with Liangzhuang (1.43) and Zhangzhuang (1.39) also showing high resilience. Conversely, six villages, such as Zhuwa (0.47) and Gengshang, demonstrated low social system resilience. (Figure 4c). Similarly, Villages with higher social system resilience scores are mostly located around the town center.

4.3. Results of Comprehensive Landscape Resilience

The comprehensive landscape resilience score for each village is the ratio of the comprehensive PI score to the comprehensive VI score. Comprehensive landscape resilience had an average score of 0.84 (RI < 1), indicating that the overall landscape resilience of Yinji Town is insufficient to support its CTI sustainable development. According to the classification results of Jenks Natural Breaks, only two villages, Zhangzhuang (1.33) and Yinji (1.28), reach the high-resilience level. Four villages, including Jizhuang (1.20) and Weiziyuan, are in the medium-high level. Alarmingly, six villages, Zhuwa (0.51), Shigang, Gengshang, Lizhuang, Jishan, and Loufangwan, are classified as low resilience, indicating nearly one-third of the townships face significant sustainability risks (Figure 5).

4.4. Classification of Village Resilience Types

To further understand the structural differences in landscape resilience among villages in Yinji Town and guide the formulation of subsequent resilience enhancement strategies, a four-quadrant classification method is used to categorize villages into four types: High Preparedness–High Vulnerability (H-H), Low Preparedness–High Vulnerability (L-H), Low Preparedness–Low Vulnerability (L-L), and High Preparedness–Low Vulnerability (H-L) (Figure 6).

• H-H type (quadrant I): This group included key tourism villages like Jizhuang and Weiziyuan. They represent areas of high development potential coupled with high systemic risk.
• L-H type (quadrant II): This is the most challenged type, including villages such as Zhuwa and Gengshang. These villages suffer from both a weak development foundation and high exposure to disturbances.
• L-L type (quadrant III): Villages like Shigang and Lizhuang fell into this category, characterized by a state of low vitality and low risk, often due to a lack of significant development pressure or resources.
• H-L type (quadrant IV): This is the most sustainable and ideal type, exemplified by Yinji and Caizhuang villages. They possess strong intrinsic capacities while facing relatively lower risks.

4.5. Results of Primary Obstacle

4.5.1. Criterion Layer Obstacle Degree

In the criterion layer obstacle degree analysis for landscape preparedness, cultural landscape preparedness has the highest average obstacle degree (0.4728), making it the primary factor constraining landscape preparedness levels. Next is social system preparedness (0.2765). Natural landscape preparedness has the lowest average obstacle degree (0.2508). The criterion layer obstacle degrees for landscape preparedness are shown in Figure 7a.

In the criterion layer obstacle degree for landscape vulnerability, cultural landscape vulnerability has the highest average obstacle degree (0.5188), indicating it is the most vulnerable dimension in Yinji Town. Next is social system vulnerability (0.2415). Natural landscape vulnerability has the lowest average obstacle degree (0.2397). The criterion layer obstacle degrees for landscape vulnerability are shown in Figure 7b.

4.5.2. Indicator Layer Obstacle Degree

This study takes the top four obstacle factors in the indicator layer obstacle degrees for landscape preparedness and vulnerability in each village (

Table 6. The main obstacle factors in the landscape preparedness indicator layer.

Village	Rank 1		Rank 2		Rank 3		Rank 4
	Indicator	Obstacle Degree	Indicator	Obstacle Degree	Indicator	Obstacle Degree	Indicator	Obstacle Degree
Caizhuang	A4	0.2054	C2	0.1506	A3	0.0916	B3	0.0847
Daliuzhuang	A4	0.2385	B1	0.1373	A3	0.1196	A1	0.1000
Gengshang	A4	0.1549	A1	0.1515	A2	0.1157	C4	0.0955
Jishan	A4	0.1578	A1	0.1323	A2	0.1179	C4	0.0973
Jizhuang	C2	0.4206	B1	0.2133	C1	0.1996	A1	0.1036
Junwang	A4	0.1204	A2	0.1200	A1	0.1122	C4	0.0990
Kangzhuang	A4	0.2245	A1	0.1569	C2	0.1314	A3	0.1126
Lizhuang	A4	0.1455	A1	0.1423	A2	0.1087	B1	0.0942
Lianzhuang	A4	0.1594	A1	0.1337	A2	0.1191	B1	0.1032
Liangzhuang	A4	0.1769	A1	0.1236	C2	0.1067	B1	0.1018
Loufangwan	A4	0.1616	A1	0.1355	A2	0.1208	C4	0.0997
Qingliangsi	A4	0.1683	A1	0.1412	B1	0.1211	C2	0.1015
Shigang	A4	0.1430	A1	0.1399	A2	0.1069	B1	0.0926
Weiziyuan	A4	0.5857	C1	0.1757	A3	0.1632	C3	0.0546
Xiaowangzhuang	A4	0.1528	A1	0.1281	A2	0.1142	C2	0.0921
Xiegudong	A4	0.1604	A1	0.1345	B1	0.1154	C2	0.0967
Yinji	B1	0.1565	A4	0.1554	C2	0.1488	A3	0.1212
Zhangzhuang	A4	0.3944	B1	0.2270	A3	0.1978	C3	0.0735
Zhuwa	A4	0.1601	A1	0.1566	B1	0.1037	A3	0.0892

and

Table 7. The main obstacle factors in the landscape vulnerability indicator layer.

Village	Rank 1		Rank 2		Rank 3		Rank 4
	Indicator	Obstacle Degree	Indicator	Obstacle Degree	Indicator	Obstacle Degree	Indicator	Obstacle Degree
Caizhuang	F4	0.2357	D1	0.2312	D2	0.1069	E1	0.1035
Daliuzhuang	D1	0.2354	F4	0.2000	E2	0.1728	F2	0.0899
Gengshang	D1	0.2424	E1	0.1824	D4	0.1376	E2	0.1067
Jishan	D2	0.2005	D4	0.1626	E1	0.1514	D1	0.1431
Jizhuang	E2	0.1988	F4	0.1534	E1	0.1102	D3	0.1087
Junwang	D1	0.5289	D4	0.2002	E1	0.1183	F1	0.0894
Kangzhuang	D1	0.2741	E2	0.2414	F4	0.0931	F3	0.0795
Lizhuang	D1	0.3781	D4	0.2147	F3	0.1385	F1	0.0710
Lianzhuang	D1	0.2656	D4	0.2011	D2	0.1861	E1	0.1405
Liangzhuang	D2	0.2504	D4	0.2030	E2	0.1574	D1	0.1180
Loufangwan	E1	0.1858	D4	0.1620	D1	0.1427	E2	0.1257
Qingliangsi	D2	0.2768	D3	0.1902	D4	0.1496	F1	0.1170
Shigang	D1	0.3677	D4	0.2088	F3	0.1291	F2	0.0842
Weiziyuan	E2	0.1974	F4	0.1523	F3	0.1232	E3	0.1201
Xiaowangzhuang	D1	0.4264	D4	0.1614	E1	0.1214	E3	0.0914
Xiegudong	D2	0.2965	F2	0.1764	D4	0.1603	D3	0.1359
Yinji	D4	0.2590	D1	0.2281	D2	0.1598	F4	0.1550
Zhangzhuang	F4	0.2382	D1	0.2208	E2	0.1543	F3	0.0909
Zhuwa	D1	0.2279	E2	0.2008	F3	0.1357	D4	0.1294

) as examples to analyze the key obstacle factors for landscape resilience in Yinji Town.

The most frequently occurring obstacle factors in the landscape preparedness indicator layer are A4 number of cultural inheritors (18 times), A1 number of cultural resources (15 times), B1 number of natural landscapes (11 times), and A2 attractiveness of cultural-tourism IP (8 times). Therefore, A4, A1, B1, and A2 are the main obstacle factors hindering the improvement of landscape preparedness in Yinji Town.

The most frequently occurring obstacle factors in the landscape vulnerability indicator layer are D1 homogenization rate of cultural resources (15 times), D4 destruction degree of rural landscape character (13 times), E2 susceptibility to natural disasters (9 times), and E1 distance to core landscape resources (8 times). Therefore, D1, D4, E2, and E1 are the top four indicators with the highest vulnerability, constituting the main factors impeding the enhancement of landscape resilience in Yinji Town.

5. Discussion

This study reveals that the overall landscape resilience of Yinji Town is insufficient to reliably support sustainable CTI development, with an average comprehensive RI score below 1. A significant spatial disparity exists among villages, and the resilience structure is unbalanced across dimensions. The cultural landscape is identified as the primary shortcoming, while the natural landscape provides a relative advantage. Key obstacles include a shortage of cultural inheritors and cultural resources, susceptibility to natural disasters, and locational disadvantages.

This study both corroborates and extends the existing literature. The efficacy of the “preparedness-vulnerability” model [34,35] in quantifying resilience is confirmed. However, by applying it to the CTI context in a mountain–water town, this study provides a novel perspective. The spatial heterogeneity of resilience types underscores that “one-size-fits-all” strategies are inadequate. This resonates with Wang et al. [53] on the study for rural tourism characteristics classification, and the four-quadrant classification in this study offers a practical tool for targeted intervention.

5.1. Discussion on Resilience in Various Dimensions

The low cultural landscape resilience underscores a critical challenge. Key tourism villages like Jizhuang and Zhangzhuang, which possess distinct cultural IPs and inheritors, demonstrate higher resilience. In contrast, many villages suffer from a lack of unique, well-utilized cultural assets. This finding aligns with the broader challenge in rural tourism where cultural commodification often leads to homogeneity, eroding local distinctiveness. The damage to historical buildings and rural landscape character further diminishes the authenticity and regenerative potential of cultural resources, exacerbating vulnerability [54].

The natural landscape resilience, scoring above 1 on average, acts as the foundational strength for Yinji’s CTI. Villages like Yinji and Caizhuang, benefiting from proximity to scenic spots and favorable topography, exhibit high resilience. However, the prevalence of low-level resilience in eight villages highlights that natural advantages are not universal. Constraints such as complex terrain, which increases construction costs and disaster risks, and geographical isolation from core attractions significantly hinder the translation of natural resources into sustainable tourism development. This echoes concerns in mountainous regions where ecological fragility and accessibility issues can impede development [55].

The social system’s resilience is at a critical threshold. Villages in central locations with developed infrastructure show robust resilience. However, peripheral villages face a vicious cycle: poor location and infrastructure lead to population out-migration, which weakens the labor force and community cohesion, further hampering CTI development and increasing economic reliance on a single, potentially volatile tourism income. This phenomenon of “hollowing out” is a common threat to rural social–ecological systems globally, undermining their long-term adaptability [56].

5.2. Strategies for Enhancing Landscape Resilience

5.2.1. Cultural Landscape Resilience Enhancement Strategies

Strategies must focus on deepening cultural excavation, ensuring protection, and fostering inheritance. Firstly, cultural resources should be actively integrated into tourist experiences. Creating CTI experiential nodes, such as immersive workshops in traditional Chinese medicine preparation or iron smelting, can enhance attractiveness and economic value, moving beyond the static display that often leads to homogenization. Secondly, establishing cultural archives and activity centers is crucial for both physical and digital preservation. Cultivating a new generation of cultural inheritors is the most critical action to address the primary obstacle factor, a need strongly emphasized in studies of intangible cultural heritage [57]. Micro-renovations of traditional dwellings should be prioritized to restore rural character, countering the damage caused by indiscriminate modern construction. Finally, securing dedicated funding for cultural projects is essential to translating strategy into action.

5.2.2. Natural Landscape Resilience Enhancement Strategies

Strategies should balance rational development with ecological restoration. Firstly, the development of core scenic areas must be strictly coupled with ecological carrying capacity studies to avoid environmental degradation, a principle central to the “Shanshui City” concept [10]. Secondly, investing in Green Infrastructure (GI) and Blue Infrastructure (BI) is key. Constructing ecological corridors along the Shimantan lakeside and Yuhuangmiao River can enhance ecosystem connectivity and stability, which is a cornerstone of landscape ecological resilience [58]. Introducing sponge facilities (e.g., rain gardens) can improve flood resilience, helping to address the vulnerability from natural disasters. Finally, disaster prevention facilities should be installed in high-risk villages and promotional activities should be carried out to raise the environmental awareness of villagers.

5.2.3. Social System Resilience Enhancement Strategies

Enhancement should center on infrastructure, inclusive governance, and economic diversification. Firstly, improving transportation and telecommunication infrastructure is a fundamental step to reducing locational disadvantages and improving the tourist experience. Secondly, optimizing the industrial structure is vital. Developing organic agriculture and other complementary industries can reduce the over-dependence on CTI revenue, thereby buffering economic shocks and creating more diverse employment opportunities to curb out-migration. Actively promoting a “government-enterprise-resident” co-construction model can enhance local ownership and ensure tourism development retains its local character [59]. Finally, integrating resilience indicators into the top-level designs of territorial spatial planning ensures long-term institutional support.

5.2.4. Resilience Enhancement Strategies for Different Village Types

• H-H type (quadrant I): Focus on risk management and capacity transformation. Specific measures include controlling development intensity, restricting development in ecological protection areas, and reducing adverse environmental impacts from tourists. Implement disaster prevention and ecological restoration, emphasizing soil and water conservation and biodiversity protection. Promote diversification and upgrading of rural industries to reduce reliance on a single tourism sector. The goal is to transform high preparedness into the capacity to cope with high vulnerability, maintaining its role as a sustainable engine for CTI.
• L-H type (quadrant II): The core focus is on basic security and risk mitigation. The primary task is to improve infrastructure (transportation, water, electricity, and disaster prevention facilities) and provide basic public services and social security. Implement ecological protection priority projects to reduce ecological pressure. Explore suitable small-scale, low-impact CTI formats, such as ecological study tours. The goal is to prioritize vulnerability reduction, ensuring basic safety and survival conditions, and gradually enhance intrinsic motivation.
• L-L type (quadrant III): Focus on resource excavation and vitality stimulation. Specific measures include discovering and cultivating characteristic resources (traditional crafts, specialty agricultural products, and natural landscapes). Improve basic development conditions and introduce external support and cooperation. Develop small-scale, high-quality CTI projects, like farming experience or nature healing. The goal is to break the low-level equilibrium, stimulate development vitality, and enhance preparedness to address future potential risks.
• H-L type (quadrant IV): The core focus is on consolidating advantages and leading by example. Emphasis should be on maintaining the current good state through continuous ecological protection and facility maintenance. Promote high-quality development and innovation, such as upgrading cultural-tourism products and implementing smart management. Play a radiating and driving role in assisting with the development of surrounding villages. The goal is to establish these villages as model villages for regional CTI and resilience construction.

6. Conclusions

6.1. Research Overview and Main Findings

This study establishes a landscape resilience evaluation system for mountain–water towns by integrating resilience theory with the cultural and tourism integration (CTI) concept. Using Yinji Town as an empirical case, it systematically assesses the landscape resilience level, identifies key obstacle factors, and proposes differentiated enhancement strategies. The findings indicate that the overall landscape resilience of Yinji Town is insufficient to support sustainable CTI development, with significant spatial disparities and an unbalanced resilience structure across cultural, natural, and social dimensions. Cultural landscape resilience is identified as the primary weakness.

6.2. Theoretical Implications

This study’s primary theoretical contribution lies in its novel integration of the CTI concept into landscape resilience assessment. It innovatively proposes a theoretical framework that deconstructs the landscape system of mountain–water towns into three dimensions: cultural landscape, natural landscape, and social system. By adapting Kusumastuti’s preparedness–vulnerability model to the CTI context, this research provides a new analytical lens and a replicable methodological system for understanding the sustainable development mechanisms of rural tourism. The application of the AHP–Entropy Weight combined method, along with GIS spatial analysis and the Obstacle Degree Model, offers a comprehensive approach for quantifying resilience and diagnosing its constraining factors.

6.3. Practical Implications

The practical implications of this study are manifold. For planners and local governments, the constructed evaluation system serves as a practical tool to diagnose the resilience status of similar mountain–water towns. The spatial visualization of resilience levels and the four-quadrant classification of village types provide a clear basis for formulating spatially targeted and type-specific governance strategies. The identified primary obstacle factors, such as the shortage of cultural inheritors and high susceptibility to natural disasters, offer direct and actionable insights for prioritizing resource allocation and policy interventions. The proposed enhancement strategies across different dimensions and village types provide a concrete roadmap for boosting preparedness and reducing vulnerability, thereby guiding rural CTI from short-term popularity toward long-term sustainability.

6.4. Limitations and Future Research Directions

Despite its contributions, this study has certain limitations that point to avenues for future research. Firstly, the assessment is essentially a static, cross-sectional analysis. Future work should incorporate dynamic simulation to model the temporal evolution of landscape resilience under different development scenarios and policy interventions. Secondly, the study does not deeply explore the compounded impacts of external pressures, such as climate change and tourism seasonality, on the resilience of the CTI system. Investigating these dynamic interactions would yield a more robust understanding. Finally, applying and validating the proposed framework in other types of townships or different geographical contexts would help refine its generalizability and effectiveness.