Balancing Safety and Growth: An Ecological Resilience Framework for Great Wall Tourism Towns

Abstract

The Great Wall’s heritage, as an iconic symbol of traditional Chinese culture, has fostered prosperous development of the surrounding tourism industry. As a representative of linear cultural heritage, the Great Wall traverses mountainous regions and stretches thousands of kilometers. Given challenges such as climate change, complex geological conditions, and excessive tourism development, safeguarding the Great Wall’s heritage for sustainable tourism development is a pressing concern. Therefore, this study aims to evaluate the interaction between ecological resilience and tourism development and to provide a coordinated spatial strategy for the sustainable management of Great Wall towns. From the perspective of ecological safety and resilience, this study focuses on towns located along the Beijing section of the Great Wall. Based on the foundational principles of ecological resilience assessment, an evaluation framework was established to assess the ecological resilience and tourism development levels of these towns during peak tourism periods. Furthermore, we applied a coupling coordination degree model to determine the relationship between ecological safety and resilience subsystems and tourism development. The analysis revealed the ecological safety, ecological resilience, and tourism development levels and their interrelationships in these towns. The assessment identified relatively safer areas such as the Gubeikou Great Wall, Badaling Great Wall, and adjacent tourism towns, revealing the interrelationships among these elements. We not only identified areas with certain ecological risks but also those exhibiting middle ecological resilience capacities, such as the Shuiguan Great Wall, Mutianyu Great Wall, and Jiuyanlou Great Wall, recommending moderate tourism development in these regions. For every town, this study also calculated the average and maximum values of the coupling coordination degree to guide future ecological construction and tourism strategies. Finally, we offer insights into the present state of ecological development in the studied regions and propose strategies to bolster ecological resilience and optimize spatial tourism layouts.

1. Introduction

The Convention on the Protection of World Cultural and Natural Heritage was ratified at the 1972 UNESCO Congress. In 1987, the Great Wall was inscribed as a World Heritage Site, marking the beginning of systematic heritage protection in China under international conventions. The Great Wall stretches over 20,000 km across arid and semi-arid mountain regions, embodying more than a thousand years of history [1]. Although its original military and trade functions have diminished, the Great Wall remains a powerful symbol of the Chinese people’s resilience and ingenuity [2]. The natural and ecological value of the Great Wall has also become increasingly prominent [3,4]. The Great Wall heritage fosters the tourism value along the route, promoting the development of the tourism sector in neighboring regions and revitalizing local communities [5,6,7,8,9,10].

Linear heritage sites span vast areas with uneven socio-economic conditions, posing challenges for coordinated management, particularly under limited funding. The climate transition zone where the Great Wall is located is a delicate ecological environment [11,12]. Its mountainous terrain complicates construction and makes the surrounding environment particularly vulnerable. In recent years, climate change has markedly increased the frequency, intensity, and duration of extreme weather events, such as heavy rain, floods, droughts, and sandstorms. These conditions pose a significant threat to the existing Great Wall. In the area where the Beijing Great Wall is located, the increasingly complex urban and rural spaces, along with the cultural and tourism industry network, are facing serious threats due to extreme weather, geological disasters, and the disorderly spread of industries [13,14,15]. The preservation of the Great Wall heritage site is challenged by threats such as wind and rain erosion, geological disasters, overtourism, residential construction, and limited facility capacity [16,17,18]. As an important cultural heritage site both in China and worldwide, the protection of the Great Wall has always been a central focus of academic research. With the impact of both climate change and human activities, the preservation of the Great Wall is facing unprecedented challenges. Given the diversified cultural significance of the Great Wall’s heritage and the increasing number of tourists, it is vital to evaluate the carrying capacity of the Great Wall from an ecological perspective and develop a safer and more sustainable path for its future. This has gradually become an important topic.

Therefore, this study aims to address the spatial mismatch between ecological resilience and tourism development in Great Wall towns. Focusing on the Beijing section, we construct an integrated evaluation framework combining ecological risk indicators and tourism development levels. By applying the coupling coordination degree model, we seek to reveal eco-resilience spatial patterns and propose region-specific strategies for sustainable tourism and heritage protection.

2. Literature Review

2.1. Cultural Heritage and the Great Wall

Heritage, as a representative of human civilization, is valued for its authenticity, integrity, and multi-dimensional significance [19]. Scholars have applied various methodologies to evaluate heritage value under different regional and disciplinary contexts. For example, Arrage and Chamra (2022) quantified geological heritage through accessibility and protection indicators [20], while Liao and Tung (2024) adopted a value-based method integrating on-site and secondary data to assess urban heritage [21]. From an archeological perspective, Nokandeh et al. (2006) examined the historical significance of the Golgan and Tamish walls during the Persian period [22]. Ancient Chinese engineering provided not only economic functions—such as irrigation and transportation—but also reflected military roles, which demonstrate the integration of engineering technology and strategic planning [3]. In recent research, the focus has also expanded to industrial, regional, and discourse-based heritage. Zhao et al. (2024) proposed a systematic approach for assessing industrial heritage, particularly under conditions of uncertainty [23]. Their method integrates cloud theory and the Dempster–Shafer theory, enabling managers to anticipate potential directions of heritage transformation [23]. Liu et al. (2024) introduced a method aimed at uncovering the inherent cultural value of regional heritage and advocated for its protection through value construction [24]. Zhang and Brown (2023) argued that the heritage discourse surrounding West Lake reveals tensions between traditional culture and internationalization and reflects diverse expressions of social value [25].

Research on the Great Wall’s heritage encompasses the integration of spatial resources, the articulation of cultural value, and the advancement of preservation technologies. The Great Wall goes beyond its physical structure as a city wall, military defense system, and corridor. Therefore, research on the integration and cultural presentation of the Great Wall is inherently interdisciplinary and multi-dimensional. Tang et al. (2016) proposed the spatial framework of the “Beijing Great Wall Cultural Belt” based on the theory of regional collaborative development, emphasizing the integration of the Great Wall with adjacent heritage sites, landscapes, and settlement resources [26]. He et al. (2022) suggested that the natural environment, historical military zoning, and military governance systems help delineate the spatial extent of the Ming Great Wall in Beijing, highlighting its unique spatial attributes [27]. Peng and Shi (2023) emphasized the need for the multi-dimensional interpretation of the cultural value of the Great Wall and proposed strategies for creating cultural spaces and developing the Great Wall Intellectual Property [28]. Bai et al. (2022) examined the development of cultural exhibition spaces by integrating natural landscapes and relics within Hebei Province’s Great Wall cultural heritage display system [29]. Zhou et al. (2023) proposed a methodological framework for crafting an interpretative system for the cultural heritage of the Great Wall, drawing inspiration from the interpretative practices established for Hadrian’s Wall in the UK [30].

The preservation and utilization of the Great Wall have evolved continuously from theoretical concepts to practical strategies. The construction of heritage corridors and the delineation of protected spaces are essential strategies with respect to heritage conservation. Feng and Chen (2019) used the Datong section of the Ming Great Wall as a case study to analyze the spatial configuration of linear cultural heritage, offering theoretical insights for its spatial planning and conservation [31]. They employed various tools and models—including the Minimum Cumulative Resistance (MCR) model and Geographic Information System (GIS) spatial analysis techniques—to evaluate the suitability of heritage corridors and optimize their spatial layout. Su et al. (2022) proposed a heritage corridor construction strategy for the Gubeikou Great Wall and its surroundings, grounded in an ecological suitability assessment to support comprehensive heritage preservation [12]. Sun et al. (2022) introduced a strategic plan for the protection and utilization of cultural heritage landscapes through the Landscape Character Assessment (LCA) method [32]. They considered the Great Wall heritage site, village settlements, and the natural environment as an integrated system, proposing five landscape zoning planning strategies through the delineation of basic landscape units, feature identification, and sensitivity analyses [32]. He et al. (2022) established a framework for identifying the elements of cultural heritage by examining the Great Wall’s historical roles in defense, reclamation, and military operations. They developed various landscape feature types and explored the interconnection between natural landscapes and cultural heritage, along with their formation mechanisms [33]. Tan et al. (2017) addressed the challenge of defining protection boundaries of the Great Wall in complex geographical environments [16]. They proposed a multi-level comprehensive overlay method for delineating protection boundaries based on a GIS spatial analysis platform [16]. Modern digital technology plays an increasingly vital role in the conservation of the Great Wall [34,35,36]. Wang et al. (2017) collected and analyzed the construction data of the Shixia Great Wall in Beijing using digital tools such as 3D laser scanning and close-range photogrammetry, providing a scientific foundation for the protection of architectural heritage [37]. Sang et al. (2021) proposed a theoretical model and technical framework for creating a digital twin of the Great Wall using digital twin technology [38].

In conclusion, the existing research has acknowledged the historical and military significance and the benefits of the cultural integration and presentation of the Great Wall as a linear cultural heritage site. Scholars also explore approaches to heritage protection and tourism development through spatial planning and digital technology. Nevertheless, several important gaps remain in the existing literature. First, insufficient attention has been given to the vulnerability and resilience of the Great Wall ecosystem. Second, systematic analyses of the relationship between ecological risks and the dynamics of tourism development are lacking, especially in the context of global climate change and rapid urbanization. Third, the relationship between cultural heritage protection and sustainable development has not been thoroughly explored. These shortcomings emphasize the importance of further research on the coordinated development of cultural heritage protection, the ecological environment, and tourism.

2.2. Ecological Resilience and Related Assessment Approaches

The concept of “resilience” was first proposed by the ecologist Holling [39]. Originally rooted in ecology [40,41], the concept was later extended to the study of regional systems [42,43]. Resilience encompasses various dimensions, such as ecological, social, and economic resilience, among which ecological resilience has become a central focus in regional system research [44].

Climate change and extreme weather events are major sources of urban and rural risks, and it is necessary to formulate risk management strategies to strengthen regional resilience to meteorological risks [45]. Simulating urban flood risks helps identify vulnerable zones and supports the implementation of comprehensive flood mitigation measures [46]. Some studies have proposed managing risk response capacities as a key policy instrument. Etinay et al. (2018) developed a disaster prevention framework to enhance urban and rural disaster resilience [47]. Mavhura et al. (2021) established assessment dimensions for regional intrinsic resilience, enabling the targeted formulation of disaster risk reduction, relief, and preparedness measures [13]. Nguyen et al. (2022) assessed the resilience of coastal ecosystems and conducted zoning analyses in response to the risks associated with rising sea levels [48]. In recent years, systematic approaches such as big data, machine learning, and deep learning have been increasingly incorporated into resilience assessment methodologies [49,50]. These methods facilitate the simulation of complex recovery systems in the face of global environmental risks, marking a promising direction in resilience research.

Urban agriculture and green infrastructure, along with their integration, play a pivotal role in ensuring urban ecological security, achieving the following goals: climate regulation, stormwater runoff mitigation, the protection of species habitats, the provision of food and recreational spaces, and the safeguarding of human living environments [51,52]. Scholars have applied landscape ecology methodologies, using a range of landscape indices to evaluate landscape networks and formulate strategies for enhancing landscape network resilience [53,54,55].

In response to disasters and pandemics affecting urban areas, several studies have examined methods for incorporating resilience principles into urban and rural planning systems. Ribeiro et al. (2019) developed a framework for urban resilience, emphasizing its relevance across environmental, socio-economic, and political domains [44]. Koutra et al. (2022) emphasized the necessity of incorporating resilience principles into urban and rural planning frameworks [56]. Ravagnan et al. (2022) proposed planning strategies to enhance the resilience of public spaces, with a focus on green and waterfront infrastructure [57]. Marome et al. (2022) applied regional circular and ecological strategies to improve sustainability and disaster risk resilience within local initiatives conducted in Udon Thani, Thailand [58].

In the field of ecological resilience research, although scholars adopt diverse perspectives, they ultimately converge on the importance of building regional ecological resilience to withstand potential ecological risks. Against the backdrop of climate change, measures such as urban and rural planning, green infrastructure, landscape ecological corridors, and disaster emergency systems have been employed to enhance regional ecological resilience. These insights offer valuable implications for subsequent studies, which should identify the specific risks faced by the research area and explore unique pathways to enhance its capacity to resist ecological threats. However, few studies have examined how ecological resilience frameworks can be adapted to the spatial characteristics of large-scale linear cultural heritage sites such as the Great Wall, where heritage protection and tourism development frequently intersect. Therefore, the focus of assessments should vary according to different research contexts.

2.3. Ecological Risks Faced by the Great Wall Heritage Site

Compared to other cultural heritage sites, the Great Wall exhibits a deep-rooted connection to its climatic and environmental context. Its spatial and temporal distribution, construction philosophy, and conservation practices are deeply influenced by these natural factors [11]. The entire defensive system of the Great Wall, including its planning, construction, and historical evolution, is closely linked to the water and waterways of the surrounding valleys, creating an inherent water system, which constitutes a vulnerability within the Great Wall system. The symbiotic relationship between the architecture of the Great Wall and water results in overlaps between hydraulic engineering and heritage preservation, which often results in unintentional damage inflicted on the heritage site during construction efforts. Furthermore, water leakage has resulted in dense vegetation growth surrounding the Great Wall, accelerating its weathering process [59]. In Beijing, the Great Wall crosses mountainous regions in the city’s northern areas, which are frequently affected by severe summer storms—resulting in damage from rainfall, water erosion, and mountain torrents [17]. The Great Wall stands as a physical representation of the farming–pastoral boundary in northern China. It stretches horizontally from the east to the west, starting from the coast of the Bohai Sea to the outside of the Yumen Pass in the western Gobi Desert; it spans thousands of miles and passes through various terrains, such as plains, hills, mountains, geological subsidence zones, and plateaus [16,60]. In the Miyun section of the Great Wall Cultural Belt, ecological degradation trends are especially pronounced. Despite heightened efforts in recent years to protect the Great Wall heritage site, the natural ecological environment remains under pressure from encroachment. Construction and development activities have expanded rapidly, with built environments such as villages, towns, and farmlands gradually replacing natural ecological systems such as grasslands, forests, and water areas. This transformation has resulted in significant ecological decline in areas surrounding the Great Wall heritage site [61]. Moreover, landuse change promotes tourism urbanization, which exerts pressure on the ecological environment [18]. While several studies have noted the ecological risks and spatial vulnerabilities of the Great Wall, few have provided a quantitative framework that simultaneously assesses ecological risk, resilience, and tourism pressure. Therefore, it is necessary to construct an integrated assessment model tailored to the regional features of Great Wall towns.

3. The Tourism Industry of the Great Wall Cultural Belt in Beijing

3.1. The Great Wall Cultural Belt (GWCB) in Beijing

The Great Wall is a linear cultural heritage site that simultaneously serves as a vital cultural corridor. Holistic preservation efforts contribute significantly to enhancing the overall value of the Great Wall. The Beijing section of the Great Wall is a key segment of this World Heritage site, stretching 520.77 km, containing over 2000 cultural relics, and featuring eight publicly accessible scenic areas [26] (Figure 1). In 2015, the Beijing government initially proposed the construction of the “Northern Great Wall Cultural Belt” in its planning report, which was subsequently incorporated into the Beijing Urban Master Plan as part of an overarching development strategy [62]. In 2018, the cultural management authorities formulated a protection plan for the GWCB [63]. The Cultural Belt encompasses 43 townships, covering an area of 6152.27 km² and accounting for about 37.4% of Beijing’s total land area (Figure 2).

3.2. Tourism Industry of the GWCB in Beijing

The Great Wall, renowned for its vast scale and profound historical significance, has long served as a major tourist attraction in China. For instance, the Mutianyu section of the Great Wall, which was restored and opened to the public in 2000, is recognized for having the highest management quality rating in China and attracts the largest proportion of international visitors among all sections of the wall. The Badaling section, known for its steep terrain, is widely regarded as one of the most iconic parts of the Great Wall. It was first opened to tourists in 1958 and underwent significant restoration in 1984. China Great Wall Museum, the largest of its kind in the country, is located adjacent to the Badaling Scenic Area. The Shuiguan section, named after its adjacent water pass, was opened to the public in 1995. Built in 1368, the Juyongguan section is distinguished by the Cloud Terrace (Yuntai), which was among the first group of national key cultural relics to receive state-level protection. The Huanghuacheng Lakeside section is renowned for its scenic landscape, where the imposing Great Wall seamlessly merges with the serenity of the surrounding waters. Relying on the water body, water-related recreational activities such as kayaking, boating, and dragon-boat racing are available. Located at the foot of the Simatai Great Wall, the Gubei Water Town tourism complex is the largest Great-Wall-themed resort in Beijing, integrating heritage with recreational tourism. The Jiuyanlou section is notable for its distinctive nine-window watchtower and its well-preserved natural surroundings. This section provides visitors with an opportunity to enjoy the cultural heritage of the Great Wall while participating in outdoor hiking endeavors. The Xiangshui Lake section lies northwest of Mutianyu, where the Xiangshui Lake Reservoir has been constructed at the foot of the Wall.

The heritage of the Great Wall has fostered the tourism industry in the Great Wall towns. Four villages surrounding the Mutianyu Great Wall collectively form the distinctive tourism brand known as the “Great Wall International Cultural Villages”. The villages of Shixia and Chadao, situated near the Badaling Great Wall, retain the traditional features of ancient villages along the Yan mountains. The Simatai Great Wall, situated around the Miyun Reservoir, has historically been a popular destination for vacation tourism in Beijing. The Gubei Water Town, completed in 2012, along with the Simatai Great Wall, represents a quintessential case of the integration of China’s Great Wall heritage with vacation tourism.

Figure 2. Scenic areas in the study area.

Figure 2. Scenic areas in the study area.

This study evaluates the 43 towns along Beijing GWCB as a cohesive spatial unit (Figure 3). Utilizing an ecological resilience assessment framework, we quantify the coupling coordination degree between ecological resilience and tourism development levels, identify spatial mismatches between ecological and tourism subsystems, and validate the assessment’s applicability through on-site field investigations. The findings aim to inform decision-making and guide spatial policy adjustments that support tourism development driven by the cultural value of the Great Wall.

4. Research Framework

4.1. Assessment of Ecological Resilience

In assessment of ecological resilience, two prevailing frameworks are widely employed: the “Pressure–State–Response” model and the “Resistance–Adaptation–Recovery” model. These models guide the construction framework and selection of evaluation indicators [64,65,66,67]. Pressure and resistance are commonly quantified using indicators of risk and threat levels: pressure from human activities, environmental pollution, and socio-economic development. State and adaptation typically refer to the quality of the ecosystem and are often measured via indicators such as landscape indices, vegetation coverage, and land use status. Response and recovery denote the capacity to cope with risks and threats, such as ecological policies and ecosystem functions. When response and recovery are viewed as capacities inherent in an ecosystem, ecological resilience can be evaluated from two perspectives: the ecological risk level and the baseline quality of the ecosystem [68].

Ecological risk is the environmental impact of stressors and the immediate and long-term damage they may cause in ecosystems [69]. The assessment of ecological risk comprises two dimensions: habitat vulnerability and ecological loss degree [70]. The potential for habitat alterations in response to specific stress factors must be considered for habitat vulnerability assessment [71]. In this study, indicators—including vegetation, slope, flood risk, peak precipitation, and population density—are utilized to represent the ecological risks associated with topography, climate, and population carrying capacity. The degree of ecological loss refers to the extent to which external disturbances render the landscape sensitive and the consequent ecological degradation that occurs [72]. The factors evaluating the degree of ecological loss encompass landscape fragmentation, landscape separation degree, and the landscape fractal index. The formulas for calculating ecological risk-related indicators are presented in

Table 1. Ecological risk indicators.

Indicators	Formula		Instruction
Ecological risk	$LER={\displaystyle \sum _{i=1}^8{w}_i}{f}_i$	(1)	LER is the level of ecological risk; fi is an index of ecological vulnerability indicators and ecological loss levels, including 8 factors in this study; wi is the weight of indicator i.
Normalized differential vegetation index	NDVI = (NIR − Red)/(NIR + Red)	(2)	NDVI is the normalized differential vegetation index; NIR is the near-infrared spectral band in remote sensing images; Red is the visible red light band.
Landscape fragmentation	Ci = Ni/Ai	(3)	Ci is landscape fragmentation; Ni is the number of patches; Ai is the area of patches.
Landscape separation degree	Si = Din	(4)	Si is the landscape separation degree; Din is the distance from patch i to its nearest patch of a similar land use type n.
Landscape fractal index	$F_i={\displaystyle \frac{C_i}{4\sqrt{A_i}}}$	(5)	Fi is the landscape fractal index; Ai is the area of the patch i; Ci is the perimeter of the patch i.

. Based on initial research carried out by the study team, expert consultations, group discussions, and a review of the relevant literature [68,73,74,75,76,77], the coefficients of the indicators are comprehensively determined and listed in

Table 2. Ecological risk evaluation indicators and weights.

Dimension	Indicators	Weight
Habitat vulnerability	NDVI (negative indicator)	0.10
	Slope	0.25
	Altitude (submergence risk, negative indicator)	0.10
	Density of population	0.10
	Peak precipitation	0.20
Ecological loss index	Landscape fragmentation	0.10
	Landscape separation index	0.05
	Landscape fractal index	0.10

.

The term “quality of an ecosystem” refers to the space and resources an ecosystem provides for human survival and development. Habitat quality, which supports human survival and development, is employed to assess the quality of an ecosystem. Utilizing the habitat quality assessment module of the InVEST model (Formula (6)), habitat quality is calculated based on the distribution of habitat threat sources and the sensitivity of different land use patterns to these threat sources. The relevant parameter settings are detailed in

Table 3. Habitat threat sources and related parameters.

Threat Sources	Maximum Impact Distance/km	Weight	Distance Decay Function
Agriculture	2	0.6	Linear
Urbanization	1	0.9	Exponential
Unused land	0.5	0.4	Linear

and

Table 4. Threat source sensitivity.

Land Use	Habitat	Sensitivity
		Cropland	Impervious	Unused Land
Cropland	0.5	0	0.8	0.5
Forest	1	0.8	0.9	0.4
Grassland	1	0.9	0.5	0.3
Shrubland	1	0.7	0.7	0.3
Wet land	1	0.4	0.8	0.1
Water body	1	0.4	0.8	0.2
Impervious	0.1	0	0	0
Unused land	0.3	0.2	0.2	0

.

$$Q_{xj}=H_j\left\{1-{\displaystyle \frac{D_{xj}^z}{D_{xj}^z+k^z}}\right\}$$

(6)

Here, Q_xj represents the habitat’s quality. k is the half-saturation constant, which is typically set to 0.5. z is generally assigned a value of 2.5. H_j denotes the habitat suitability of land use type j.

The data for the assessment of ecological resilience is derived from land use data [78], digital elevation models (NASA and METI, GDEMV3, https://www.gscloud.cn/, accessed on 10 November 2024), the normalized vegetation index (Landsat 8, https://www.gscloud.cn/, accessed on 10 November 2024; after radiometric calibration and atmospheric correction, the calculation is carried out according to Formula (2)), population density [79], and peak precipitation [80] (

Table 5. Data sources.

Data	Source	Year
Land use	Liu and Zhang, 2020 [78]	2020
Remote sensing image	www.gscloud.cn/	2020
DEM	www.gscloud.cn/	2019
Precipitation	Tudaqi et al., 2024 [80]	2023
POI	Bigmap software (v1.6.23)	2021
Data of the Great Wall	www.thegreatwall.com.cn, accessed on 10 November 2024	/

). Peak precipitation data simulates the rainfall experienced during a severe flooding event that occurred in Beijing in 2023. The digital elevation model is based on a 2019 dataset, while the remaining datasets pertain to the year 2020. All indicators are normalized and weighed appropriately to derive the final ecological risk results. The ultimate measure of ecological resilience is the ratio of ecological quality to ecological risk.

4.2. Assessment of Tourism Industry Development

The evaluation of the development of the tourism industry is often multifaceted, encompassing tourism satellite accounts, assessments of potential development, evaluations of sustainable tourism development, and assessments of the level of tourism industry development [81,82,83,84]. While indicators such as tourism revenue or the volume of tourism industry components can effectively provide insights into the industry’s current state, standardizing tourism revenue measurements at the township level presents significant challenges. As a result, many small-scale spatial studies have increasingly adopted point-of-interest (POI) data for their evaluations [85,86].

This study evaluates the development of the tourism industry based on point-of-interest (POI) data procured from the BigMap platform in February 2021. This timeframe effectively mirrors the tourism industry development level corresponding to the ecological resilience assessment at nearly the same temporal period. The dataset comprises major tourism-related entities: scenic areas, accommodations (hotels and homestays), and restaurants. Scenic areas represent the principal attractions, while hotels, homestays, and restaurants function as essential reception service facilities within the tourism industry system. The grading data for scenic areas were sourced from the official website of the Beijing Municipal Bureau of Culture and Tourism.

With respect to the utilization of point-of-interest (POI) data, the majority of studies employ kernel density as an indicator of quantifying industrial development levels. However, kernel density inadequately distinguishes disparities among heterogeneous industrial elements. Therefore, we use the following method: Scenic areas are categorized according to the levels assessed by the industry authorities. Within a 10-grid radius of the tourism industry, AAAAA scenic areas receive the highest score of 5, AAAA scenic areas score 4, AAA scenic areas score 3, AA scenic areas score 2, and A scenic areas score 1. Hotels and homestays score 0.3 within a one-grid radius. Restaurants score 0.08 within a one-grid radius. The tiered scoring system enables the spatial measure of tourism industry development along the GWCB.

4.3. Coupling and Coordination Degree Model

The degree of coupling serves as an indicator of the level of association between subsystems within a given system, while the degree of coupling coordination provides a measure of harmony between these subsystems. In this study, the evaluation result can determine the level of association and harmony between ecological resilience and the development of the tourism industry, providing insights into the future coordinated development of the tourism industry and ecology.

Based on relevant research [87,88], in scenarios featuring two subsystems, the coupling degree C_i for spatial position i can be calculated using the following formula:

$$C_i={\displaystyle \frac{2\sqrt{U_{iA}{U}_{iB}}}{U_{iA}+U_{iB}}}$$

(7)

In Formula (7), U_iA and U_iB represent the value of subsystems A and B.

The formula for T_i is as follows:

$$T_i={\beta }_{iA}{U}_{iA}+{\beta }_{iB}{U}_{iB}$$

(8)

In Formula (8), β represents the weight of the subsystem, which is taken as 0.5 here.

The formula for the coordination degree D_i is as follows:

$$D_i=\sqrt{C_i\ast T_i}$$

(9)

The values of both C_i and D_i range between [0, 1]. A larger C_i value suggests a reduced degree of discreteness between subsystems A and B, thus indicating a higher degree of coupling. A larger D_i value implies a greater degree of coordination between the subsystems. Values in the range of [0, 0.3) indicate an imbalance, and the system faces a risk of decline. Values within [0.3, 0.7] indicate slight imbalances with respect to preliminary coordination, and the system remains relatively stable. Values in the range of (0.7, 1] indicate coordination, and the system will have the potential for development. Figure 4 shows the technical framework of this study.

The standardization formula for positive indicators is as follows:

$$x_i^{\prime }={\displaystyle \frac{x_i-\min x_i}{\max x_i-\min x_i}}$$

(10)

The standardization formula for negative indicators is as follows:

$$x_i^{\prime }={\displaystyle \frac{\max x_i-\min x_i}{\max x_i-\min x_i}}$$

(11)

4.4. The Suitability of the Research Framework

Due to data uniformity constraints, this study is limited to the year 2020, corresponding to a temporal period during the height of the tourism industry’s development prior to COVID-19. This focus comes with certain limitations in terms of understanding future trends. As time progresses and changes occur in industrial space and climate conditions, subsequent research could employ multiple temporal cross-sections for trend prediction.

The parameters and weights of the evaluation model can be fine-tuned based on the specific characteristics of the evaluation object [89]. As relevant research and practice continue to evolve, the model’s applicability can also be refined through a diverse array of case studies. The GWCB in Beijing is classified under small-scale and medium-scale spaces. Against the backdrop of ongoing ecological construction efforts in Beijing, the potential risk of flood disasters is more critical among ecological risks, with higher weights given to indicators such as slope, extreme precipitation, and altitude. However, in some areas where the risk of green space destruction is high, factors such as NDVI and ecological landscape patterns should be assigned greater weight.

5. Results and Discussion

5.1. Ecological Risk

Ecological risk is characterized by two dimensions and eight indicators (Figure 5). As depicted in Figure 6, Zhaitang Town and Yanchi Town in the western section of the GWCB exhibit relatively elevated ecological risks, followed by Qingshui Town, Nankou Area, Changling Town, Bohai Town, Yanqi Town, and Huaibei Town. Areas in the central section of the GWCB exhibit moderate ecological risks. The ecological risks in the Yanqing Basin and the regions east of the Miyun Reservoir are substantially low.

The western section of the GWCB is predominantly affected by extreme precipitation, resulting in inundation risks in valleys and a fragmented landscape. The mountainous terrain experiences severe weathering, and the presence of steep slopes elevates the risks of landslides and collapses. These hazards are further exacerbated by disrupted energy flows caused by irregular and fragmented habitat patches. In the central section of the GWCB, while meteorological risks have diminished, ecological risks remain high due to landscape patch fragmentation, significant elevation variations, and complex habitat patch edges. In other regions, ecological risks are comparatively lower but still influenced by threats such as flooding, excessive population pressure, and patch fragmentation.

5.2. Ecological Resilience

According to the assessment framework of the habitat quality module within the InVEST model, land use significantly impacts habitat quality (Figure 7 and Figure 8). The area containing human settlements, artificially modified agricultural land, and hardened infrastructure causes a decrease in habitat quality. These land use types, due to the high degree of anthropogenic surface alteration, exhibit substantial surface runoff, high population densities, and fragile plant community ecosystems when subjected to ecological disturbances. Consequently, the natural ecosystems’ capacity for self-recovery is significantly diminished. In addition, the disturbed adjacent areas can also be negatively affected by the flow of material and energy and the fragmentation of ecological functions in the landscape [68,90,91].

The level of ecological resilience can be determined based on the evaluation of habitat quality and ecological risk (Figure 9). Within the study area, the Yanqing Basin and the small alluvial plains at the base of the mountains feature flat terrain. Due to ecological risk factors—such as high levels of human interference, low vegetation coverage, fragmented landscape patches, and the separation between natural areas and settlements—ecological resilience in these areas is at a relatively low level. In the valleys stretching from Qingshui Town to Liucun Town, pronounced geological risks are observed—including mudslides and collapses—due to the frequency of extreme precipitation, substantial topographic drops, comparatively low overall terrain, and high levels of rock weathering. Consequently, the Great Wall relics in the surrounding areas of this axis suffer the most severe damage, and ecological resilience is at a medium–low level. The mountainous towns of Gaoling, Gubeikou, and Xinchengzi in the Miyun and Pinggu District exhibit relatively gentle topography. These towns benefit from the water source regulation of the Miyun Reservoir and high vegetation coverage. Meteorological records indicate minimal extreme precipitation, contributing to a higher resilience capacity within the GWCB. In contrast, other regions exhibit moderate to moderately high levels of ecological resilience. Although the central segment of the GWCB, encompassing Yanqing District, Huairou District, and Miyun District, is characterized by a higher level of ecological risk, this area also boasts superior habitat quality, resulting in a moderately high level of resilience. Additionally, the region south of Dachengzi Town in Pinggu District, extending to Jinhai Lake, demonstrates low ecological risk and relatively high habitat quality, resulting in a higher level of ecological resilience.

5.3. Tourism Industry Level

The main development hubs along the GWCB are the Badaling Great Wall, Gubeikou Great Wall, Qinglong Gorge–Yanqi Lake-Mutianyu Great Wall, and Shicheng Town west of Miyun Reservoir. The secondary hubs are the Huanghuacheng Lakeside Great Wall, Zhaitang Town, Jiuxian Town, Kangzhuang Town, Zhangshanying Town, and southern Pinggu District, specifically Xiong’erzhai Town–Huangsongyu Town–Jinhai Lake (Figure 10). The tourism industry within the GWCB is centered on Great Wall tourism. Many homestays, hotels, and entertainment facilities have been built around scenic Great Wall areas, forming an integral component of Beijing’s tourism sector. Given the alignment of the Great Wall with water systems, the structure serves to protect significant water sources. This heritage–waterbody combination model is conducive to resort-style tourism development, resulting in a spatial overlay effect of tourism resources. Based on the assessment, Figure 11 depicts the median values of ecological risk, ecological resilience, and tourism development for each township.

5.4. Coupling Coordination Degree

5.4.1. Coupling Degree

The coupling degree reflects the relationship between two subsystems within a system. When both subsystems A and B exhibit a high value, this indicates a higher level of coupling. Consequently, combinations such as “high–high”, “medium–medium”, and “low–low” represent a high coupling level, whereas the “high–low” combination reflects a low coupling level. Other combinations represent transitional levels of coupling. Based on the coupling degree indicator, the spatial proportion of the coordination space between the ecological risk and tourism industry development subsystems is 8.48%, while the discoordination space accounts for 45.01%. The remaining space is in a transitional state. Within the ecological resilience and tourism industry development subsystems, the proportion of coordination is 22.45%, with discoordination accounting for 39.50%, and the remaining space is in a transitional state (Figure 12 and Figure 13).

When ecological risk (negative indicator) is designated as subsystem A and tourism development is designated as subsystem B, the towns of Huabei, Badaling, Nankou, Yanqing, Xiong’erzhai, and Shicheng exhibit high to moderate levels of coupling. Conversely, when ecological resilience is designated as subsystem A and tourism development is designated as subsystem B, the towns of Badaling, Kangzhuang, Yanqing, Huaibei, and Shicheng demonstrate the highest coupling levels (

Table 6. Coupling coordination degree parameters of every town.

Towns	Max_C_ Risk	Mean_C_Risk	Max_D_ Risk	Mean_D_Risk	Max_C_ Resilience	Mean_C_Resilience	Max_D_ Resilience	Mean_D_ Resilience
Qingshui A1	0.8140	0.3529	0.5126	0.2489	0.9184	0.4090	0.4604	0.2165
Yanchi A3	0.8088	0.2489	0.3882	0.1566	0.9441	0.2720	0.3783	0.1380
Zhaitang A2	0.9395	0.3433	0.4869	0.2025	0.9929	0.3878	0.4250	0.1860
Liucun B1	0.7819	0.1438	0.4993	0.1082	0.9984	0.2006	0.4499	0.0919
Nankou B2	0.9452	0.5682	0.5575	0.3728	0.9622	0.6484	0.4933	0.2998
Changling B3	0.7990	0.4901	0.5002	0.3500	0.9944	0.6046	0.4587	0.3009
Badaling C1	0.9998	0.6920	0.7233	0.4789	0.9999	0.8022	0.6365	0.3981
Dayushu C6	0.8196	0.3911	0.5883	0.3506	0.9992	0.5892	0.5080	0.2598
Dazhuangke C3	0.5738	0.2602	0.4139	0.2093	0.9826	0.3375	0.4337	0.1959
Jingzhuang C2	0.7771	0.2739	0.5644	0.2307	0.9999	0.3637	0.5340	0.1960
Jiuxian C13	0.9123	0.2993	0.6824	0.2702	1.0000	0.3985	0.5507	0.2043
Kangzhuang C7	0.7323	0.5534	0.5881	0.4707	0.9873	0.7838	0.5560	0.3430
Liubinpu C11	0.4073	0.0299	0.3751	0.0313	0.9929	0.0453	0.3552	0.0255
Sihai C4	0.4739	0.1091	0.3801	0.0936	0.7096	0.1467	0.3797	0.0861
Xiangying C12	0.3666	0.0240	0.3798	0.0290	0.9624	0.0468	0.4344	0.0249
Yanqing C8	0.8176	0.4298	0.6080	0.4044	1.0000	0.7459	0.5488	0.2749
Yongning C5	0.7792	0.0748	0.5884	0.0759	0.9983	0.1178	0.5144	0.0542
Zhangshanying C14	0.7868	0.4495	0.5852	0.3787	0.9997	0.5766	0.6302	0.3147
Zhenzhuquan C10	0.4675	0.0256	0.3323	0.0237	0.5564	0.0315	0.3269	0.0202
Shenjiaying C9	0.9991	0.3012	0.5275	0.3121	0.9995	0.5225	0.5490	0.2219
Bohai D2	0.9764	0.4157	0.5821	0.2829	1.0000	0.5049	0.5426	0.2461
Huaibei D4	0.9815	0.7483	0.7435	0.4952	1.0000	0.8446	0.6482	0.4081
Jiuduhe D1	0.9555	0.2862	0.5764	0.2070	0.9983	0.3716	0.4887	0.1847
Liulimiao D5	0.8688	0.1954	0.5382	0.1477	0.9531	0.2331	0.5022	0.1293
Yanqi D3	0.9851	0.5613	0.6534	0.3769	0.9999	0.6175	0.5913	0.3055
Huangsongyu F5	0.7282	0.4994	0.5356	0.4102	0.9590	0.6404	0.5110	0.3557
Jinhaihu F4	0.7258	0.3941	0.5439	0.3454	1.0000	0.5367	0.5447	0.2766
Nandulehe F3	0.6906	0.3227	0.4924	0.2719	0.9989	0.4341	0.4239	0.2272
Shandongzhuang F2	0.6861	0.3433	0.5008	0.2977	0.9998	0.4800	0.4728	0.2410
Xiongerzhai F6	0.6861	0.5215	0.5148	0.4215	0.9339	0.6468	0.4605	0.3652
Zhenluoying F7	0.6922	0.2861	0.5111	0.2554	0.9742	0.3869	0.4617	0.2232
Wangxinzhuang F1	0.6368	0.2006	0.4781	0.2004	1.0000	0.3049	0.4817	0.1601
Beizhuang E10	0.4658	0.1007	0.4165	0.1039	0.9352	0.1360	0.4992	0.0943
Bulaotun E5	0.5305	0.1905	0.4368	0.1778	0.9994	0.2650	0.5079	0.1504
Dachengzi E11	0.4844	0.0433	0.4195	0.0410	0.6570	0.0525	0.3916	0.0352
Fengjiayu E4	0.5221	0.0711	0.3995	0.0619	0.9992	0.0806	0.4885	0.0484
Gaoling E6	0.3743	0.0508	0.3898	0.0595	0.5778	0.0689	0.4120	0.0540
Gubeikou E7	0.9999	0.4833	0.9337	0.4439	1.0000	0.5665	0.8423	0.3795
Shicheng E3	0.9628	0.6222	0.6465	0.4278	0.9999	0.7289	0.5900	0.3806
Taishitun E9	0.9043	0.3458	0.6962	0.3229	0.9996	0.4487	0.6405	0.2795
Xitiangezhuang E1	0.8575	0.3822	0.5335	0.3191	0.9991	0.5700	0.5144	0.2545
Xiwengzhuang E2	0.7834	0.4426	0.5316	0.3861	0.9892	0.6109	0.4888	0.3183
Xinchengzi E8	0.9893	0.3684	0.8259	0.3564	0.9976	0.4556	0.7656	0.3217
	[0, 0.3]		(0.3, 0.5]		(0.5, 0.7]		(0.7, 1]
C Value	Discoordination		Medium discoordination		Medium coordination		Coordination
D Value	Deterioration		Medium deterioration		Medium development		Development

Max_C_risk represents the maximum coupling degree of ecological risk and the tourism industry. Mean_C_risk represents the average value of the coupling degree of ecological risk and the tourism industry. Other parameters can be analogized. The chromatic significations are delineated in the penultimate and ultimate rows of Table 6.

).

5.4.2. Coordination Degree

The coordination degree serves as a metric for forecasting the future developmental trends in a system. A high coordination degree suggests that the system is likely to evolve, whereas a low degree indicates a potential for decline. According to the coordination degree index, the two ecological risk and tourism industry development subsystems occupy a development space ratio of 0.25% and a decline in space ratio of 42.85%, while the remaining space is in a transitional state. In the case of ecological resilience and tourism industry development, the subsystems exhibit a development space ratio of 0.09% and a decline space ratio of 57.37%, with the remaining space also in a transitional state (Figure 14 and Figure 15).

When ecological risk (negative indicator) is designated as subsystem A and tourism development is designated as subsystem B, Badaling, Gubeikou, Huaibei, and Xinchengzi are the four towns with the highest degree of coordination. When ecological resilience is designated as subsystem A and tourism development is designated as subsystem B, Gubeikou Town and Xinchengzi Town perform well and exhibit high and moderate values. Additionally, the towns of Badaling, Zhangshanying, Huaibei, and Yanqing also exhibit high-scoring levels (Table 6).

6. Conclusions and Suggestions

6.1. Conclusions

In this study, we developed an ecological resilience assessment framework and conducted spatial analyses using multi-source data to evaluate the coupling and coordination between ecological resilience and tourism development in the townships along the Beijing GWCB.

(1) Based on natural breaks (0.35 and 0.52), low-, medium-, and high-ecological-risk areas account for 37.40%, 38.32%, and 24.28%, respectively. High-ecological-risk areas are mainly distributed in Mentougou District, Changping District, and Huairou District, while medium-ecological-risk areas are mainly located in Yanqing District, Miyun District, and Pinggu District. Based on natural breaks (0.23 and 0.39), areas with low, medium, and high ecological resilience account for 14.03%, 60.71%, and 25.26%, respectively. Low-ecological-resilience areas are primarily found in Yanqing District, Changping District, Huairou District, Miyun District, and Pinggu District, mainly in river valleys, basins, and foothill plains.

(2) Based on natural breaks (0.04 and 0.20), the regions with low, medium, and high levels of tourism development account for 64.25%, 34.94%, and 0.80%, respectively. The high-level areas include Badaling Town, Gubeikou Town, Yanqi Town, Huaibei Town, and Jiuxian Town. The medium-level areas are mainly distributed in the western part of Mentougou, along the axis from Nankou Town to Zhangshanying Town, from Bohai Town to Shicheng Town, from Gubeikou Town to Xinchengzi Town, and in the Pinggu area.

(3) The coupling degree between ecological risk and tourism development, as indicated via the C value results, comprises 91.52% of the regions, suggesting an uncoordinated or transitional level. Similarly, 77.55% of regions where ecological resilience and tourism development overlap fall into an uncoordinated or transitional phase. The D value results, which signify future development trends, reveal that over 97% of the regions are experiencing a transitional or declining trend for both ecological risk and ecological resilience as subsystem members, with over 42% of these regions projected to decline. Notably, the Badaling Great Wall, Gubeikou Great Wall, and Huaibei Town in Huairou District have emerged as the most advantageous areas in the coupling coordination assessment.

6.2. Suggestions

Our findings suggest that a secure tourism development space for the Great Wall heritage site can be established from two key perspectives: strengthening the construction of ecological resilience capacity and adjusting tourism development strategies. Currently, the tourism development pattern centered around the Great Wall heritage site can be correspondingly adjusted, necessitating the formulation of appropriate spatial planning approaches. Simultaneously, efforts to strengthen ecological resilience should be undertaken. From safety and resilience perspectives, a combination of ecological construction and restoration measures should be implemented to build a safe space for Great Wall heritage tourism. The proposed recommendations can serve as valuable references for sustainable development in other comparable industrial areas.

6.2.1. Strategies for Enhancing Ecological Resilience

We recommend improving the flood-control level of the Great Wall heritage site and its buffer zone. Moreover, the relationship between the Great Wall heritage site and water sources should be analyzed, and multi-level protective dams should be constructed along the heritage site, roads, and surface runoff channels. For important heritage sites, ecological projects—such as rockfall dams and slope protection—should be carried out to improve protection capacity while preserving the landscape. Within the Great Wall scenic area and adjacent tourism communities, ecological projects such as artificial flood channels, permeable pavements, small reservoirs, and wetlands should be developed to effectively intercept rainwater, enhance infiltration, and reduce runoff [17,92,93,94].

We also suggest the connection of ecological corridors and the restoration of natural habitats. Optimizing landscape patterns and developing green infrastructure can enhance the resilience level of the Great Wall heritage site [95]. By establishing ecological corridors, the boundaries between different patches can be overcome, thereby connecting various patch types [68,74]. This facilitates a continuous restoration of habitat connectivity functions and alleviates patch fragmentation. Different ecological restoration strategies should be tailored according to different degrees of ecological loss. For example, in areas experiencing high ecological loss, artificial interference should be eliminated, and connections with the natural matrix should be restored. In regions with low ecological loss, improvements can be made through natural beautification, terrain micro-modification, gully dam ecological engineering, and slope beautification [12,96]. Applying the aforementioned recommendations to the Huanghuacheng Lakeside Great Wall case, the corresponding measures are detailed in Figure 16.

6.2.2. Strategies for Tourism Management

The heritage of the Great Wall and the moderate development of tourism in surrounding communities aid both the sustainable preservation of the heritage site and the rejuvenation of the local economy [6,7]. From the perspectives of safety and resilience, the tourism industry centered around the Great Wall heritage site can implement spatial adjustments and plan unique future development strategies tailored to different regions. Furthermore, establishing a responsive mechanism for tourism in light of the inevitable challenges posed by climate change and geological risks is critical [8,97,98].

Delimiting the Potential Ecological Space for Great Wall Tourism: Zoning and control, along with enhancing ecological resilience, require the comprehensive planning of ecological and development spaces [99,100]. In regions with a relatively good coupling and coordination degree, such as the Gubeikou Great Wall and the Badaling Great Wall, the tourism industry is relatively mature and exhibits a relatively high level of ecological resilience. These areas should be prioritized for tourism optimization and enhancement. The Great Wall passes through the Qinglong Gorge area in Huaibei Town, but the scenic area has yet to be developed. Instead, this area relies on the Huaibei Ski Resort in the surrounding area, allowing tourists to engage in outdoor sports while enjoying views of the Great Wall ruins. It is also relatively close to the Mutianyu Great Wall Scenic Area. Therefore, this site exhibits a good combination of “Great Wall–water body–ecology–outdoor” resources and can also be regarded as another key tourism development zone. Areas such as the Shuiguan Great Wall, Mutianyu Great Wall, Jiuyanlou Great Wall, Jinhai Lake, and Yanqi Lake are facing certain ecological risks. Nevertheless, the relatively well-functioning ecosystem in these areas helps mitigate such risks. Accordingly, moderate tourism development can be implemented based on existing ecological resilience. In contrast, other areas without a tourism foundation should not proceed with new construction. Tourism development is gradually shifting towards in-depth tourism, ecological tourism, and outdoor tourism, reducing the interference of tourism activities on the heritage system.

Establishing an Ecological Early Warning and Risk Mitigation Mechanism for Great Wall Tourism: Smart meteorological stations should be established in Great Wall scenic areas and tourist towns to enable real-time monitoring of various meteorological and geological elements, rapid data dissemination and processing, disaster forecasting and early warning, and the development of a decision-support system [101]. Moreover, an emergency broadcasting system should be set up in scenic areas. Building upon tourist information broadcasting and tourist services, early warnings should be promptly issued, emergency evacuation commands conducted, and psychological support provided. A three-tier emergency response linkage mechanism—including the municipal, town, and scenic area levels—is recommended to enable a flexible and rapid response to potential risks, ensuring efficient execution of disaster response and rescue operations.

6.2.3. Discussion of Strategies for Other Similar Industry Development Areas

In the context of climate change, industries that are closely tied to the natural environment will face heightened challenges. Agriculture, with significant investment in farmland water conservancy facilities and fruit and forest cultivation, is likely to experience fluctuating yields due to climatic and environmental changes. Sustainable multi-year operations will also encounter escalating risks. Service sectors that are reliant on natural landscape resources, climatic comfort, and ecosystem services—such as research and development centers, medical rehabilitation, outdoor sports, and natural education—will face increasing uncertainties [102,103]. To address these challenges, the adaptability assessment period for industrial development should be reduced, prioritizing ecological and climatic factors during project site selection and bolstering infrastructure redundancy to promote a resilience-oriented sustainable ecological industry. Industrial development should be protected through risk hedging and green financial innovation. Moreover, measures such as carbon trading, ecological compensation, and the monetization of ecological product prices should support resilient infrastructure construction, innovate climate-hedging products, and diversify business layouts across various ecological climate zones. Lastly, an emergency response mechanism should be established, fostering collaborative government–corporation–community governance and leveraging ecological and industrial big data to mitigate systemic risks.