Introduction

Since Holling (1973) introduced the concept of “resilience”, its connotation and research scope have expanded across various disciplines, including psychology (Jo et al., 2024), geography (Yabe et al., 2025), economics (Hynes et al., 2022), and sociology (Moro et al., 2021), forming an interdisciplinary research paradigm. Resilience refers to a systematic ability to resist, maintain, recover, adapt, or transform in response to disturbances from uncertain factors, which is essential for ensuring continuous system operation (Chaigneau et al., 2022; Benitez et al., 2023). As the global economy enters an era of intense competition and increasing uncertainty, tourist cities exhibit trends of resource expansion, product diversification, and complex value realization potential (Yang and Fik, 2014; Tsai et al., 2016). This has heightened their vulnerability to future extreme events and disasters, such as heat waves, hurricanes, and heavy rainfall caused by climate change (Dogru et al., 2019), as well as short-term travel disruptions triggered by political events (Schroeder et al., 2018). This real predicament sharply contrasts to the current insufficient research on the resilience of tourist cities in the academic circle, underscoring the urgent need for more comprehensive theoretical exploration in this domain.

Tourist city resilience (TCR) is a critical indicator for assessing a city’s ability to respond to external disturbances and internal changes (Cristiano and Gonella, 2020). Notable advancements have been achieved in the study of city resilience across various application domains, including climate change adaptation (Hill and Kakenmaster, 2018), urban planning governance (Rogatka, 2021), and disaster risk management (Benitez et al., 2023). These studies have yielded substantial outcomes in developing measurement methodologies, analyzing driving mechanisms, and uncovering spatiotemporal evolution patterns (Joerin et al., 2012; Yang et al., 2024). Focusing on the unique context of tourist cities, scholars have primarily investigated the role of resilience in reducing the vulnerability of the tourism industry (Espiner and Becken, 2014), shaping urban policies and planning (Cristiano and Gonella, 2020), improving disaster prevention capabilities (Ley et al., 2024), and supporting sustainable tourism development (Lew, 2013). These studies confirm that enhancing resilience significantly boosts the stability and shock resistance of tourist city systems (Espiner and Becken, 2014). It is worth noting that the construction of TCR exhibits resource-constrained characteristics, which differ significantly from industrial cities developed under the Fordist production system. The systematic management and collaborative development of tourism resources serve not only as a source of competitiveness for tourist cities but also as an essential medium for achieving socio-economic system resilience (Torres-Bagur et al., 2019).

Tourism resource value (TRV), as a critical reference in tourism resource development, has established a multidimensional research system encompassing landscape quality evaluation (Wozniak et al., 2018), economic value assessment (Xiang et al., 2020), and development suitability analysis (Zhang et al., 2022). However, existing studies predominantly focus on single or specific types of tourism resources, neglecting the reality that such resources often exist in clusters (Todorovic, 2015; Zhang et al., 2022) and that a complex interplay of factors, including resource scale, type structure, spatial configuration, and environmental adaptability, influences their value. It is crucial to emphasize that an intrinsic connection exists between TRV conversion effectiveness and TCR: for example, during major crises such as natural disasters (Rossello et al., 2020), the COVID-19 pandemic (Viana and Nello, 2022), and political events (Mair et al., 2021), the extent to which the market value of tourism resources is realized directly determines the risk resistance and recovery elasticity of urban economic systems. Current widespread issues, such as the spatial mismatch between tourism resources and facilities and the imbalance between development and protection timing (Gilliland et al., 2020; Han et al., 2024), stem from a superficial understanding of the connection between TRV and TCR, highlighting the need to coordinate TRV realization and TCR construction.

Although the academic community has conducted separate research on TRV assessment and TCR measurement, there remain three key research gaps in understanding the intrinsic connection between the two: (i) At the theoretical construction level, the systematic coupling relationship between TRV dimensions and TCR elements remains to be fully revealed; (ii) At the content system level, the pathways through which the quantity, quality, and spatial configuration of tourism resources influence TCR formation need further exploration; (iii) At the methodological level, due to limitations in administrative region-based statistical data, most existing studies adopt macro-scale analysis units, making it challenging to capture the micro-resilience mechanisms within tourist cities. These theoretical and methodological limitations significantly constrain the ability of tourist city managers to formulate scientifically precise strategies for enhancing resilience.

Based on the above, this paper selects Penglai, China, a coastal cultural tourist city, and Qimen, China, a mountainous ecological tourist city, as typical cases, focusing on the interactive relationship between TRV and TCR. By constructing a “TRV-TCR” system coupling model, it aims to address the long-standing scientific question that has puzzled the academic community: “How does TRV influence TCR?” This paper innovatively transcends traditional administrative boundaries and introduces the spatial grid analysis method to systematically compensate for the research gaps in the correlation between TRV and TCR within the existing theoretical framework. To achieve the research objectives, this paper: (i) Establishes a multi-dimensional evaluation system for TRV focusing on the value characteristics based on field investigation data, and enables spatially quantitative assessments of TRV; (ii) Constructs a comprehensive TCR evaluation index system encompassing economic, social, cultural, and ecological dimensions based on multi-source heterogeneous data to reveal spatial heterogeneity patterns of TCR; (iii) Employs spatial econometric methods to analyze the spatial correlation mechanisms between TRV and TCR. This paper aims to deepen the understanding of TCR and the elastic function of TRV in enhancing resilience, promote the integration of city resilience theory with tourism geography, and provide a reference basis for spatial governance to optimize the risk prevention systems and resilience governance decision-making of tourist cities.

Theoretical framework

The duality of TRV

The TRV system exhibits an intrinsic-conversion dual structure, and its value creation process adheres to the dual-action mechanism of “resource endowment, market conversion.” TRV can be deconstructed into two dimensions: intrinsic value (rooted in resource endowments) and conversion value (dependent on market mechanisms). The former is grounded in the inherent attributes of resources, such as their ecological base, cultural genes, and economic potential, forming the material foundation for value creation. The latter reflects the capacity to convert latent value into market benefits through the recombination of production factors and institutional arrangements within a specific spatiotemporal context.

The value conversion process exhibits pronounced spatial reconfiguration characteristics and relational network effects. Under the framework of new economic geography, the “core-periphery” theory (Krugman, 1991) demonstrates that the efficiency of spatial element location combinations not only directly shapes the spatial distribution patterns of tourism consumption but also determines the intensity of value realization. For instance, empirical research shows that enhancing the spatial alignment between tourism resources and reception facilities can increase tourist consumption density (Guaita Martinez et al., 2023) - an effect fundamentally achieved by reducing spatiotemporal friction in factor flows and lowering transaction costs. Meanwhile, institutional arrangements, defined as “game rules” (North, 1990), regulate the behavioral boundaries of tourism stakeholders to construct an institutional elasticity space that influences value conversion. Paradoxically, systemic management deficiencies and insufficient regional organization functions that exist in tourist cities (Michalkova et al., 2023) counteract the conversion effect of TRV.

This dual-value structure ultimately shapes and influences TCR through the co-evolution mechanism of complex adaptive systems, which emphasizes non-linear interactions between system components. Hoffmann et al. (2023) confirmed that the conversion rate of TRV exhibits a correlation with the system’s anti-interference capability. This correlation mechanism is specifically manifested: (i) intrinsic value determines the foundational scale and diversity of the tourism industry’s element combinations (Narancic and Bulatovic, 2022); (ii) Conversion value dictates the structure and evolution of the tourism element relationship network, as well as the tourist city’s capacity to withstand pressures arising from tourism industry expansion (Zha et al., 2024). These pressures include resource supply to meet tourism demands, reconfiguration of resource facilities, and adjustments in capital and social interest networks (Zhu et al., 2021), all of which influence the system’s ability to respond to shocks.

The multi-dimension of TCR

As a core manifestation of complex adaptive systems, the multi-dimensional characteristics of TCR stem from the interaction mechanisms within the social-ecological systems framework. Based on the resilience transformation theory (Folke et al., 2010), the adaptive capacity exhibited by tourist cities in response to disturbances is fundamentally a collaborative network formed through the self-organization and cross-scale connectivity of economic, social, cultural, and ecological subsystems. This multi-dimensional and integrative feature adheres to the dynamic principles of adaptive cycles (Holling, 2001) while also embodying emergent properties as understood through complexity science. The non-linear interactions among these dimensions provide the structural foundation for crisis resistance and sustainable development (Calgaro et al., 2013).

In the economic resilience dimension, tourist cities exhibit a distinct “Dutch disease” effect, which renders their economic systems highly sensitive to fluctuations and vulnerable to path dependence risks (Wang et al., 2021). According to the regional resilience model (Martin, 2012), fostering breakthrough innovation capabilities is critical for overcoming lock-in effects. This involves optimizing industrial economic structures (e.g., constructing smart tourism ecosystems), promoting technological innovation (e.g., digital transformation), and enhancing policy frameworks (e.g., guiding industrial integration through strategic policies) (Glyptou and Choi, 2021). Through this creative destruction mechanism (Schumpeter, 1942), the economic system can reacquire city resilience, enabling dynamic value chain reorganization in response to market demand fluctuations.

In the social resilience dimension, tourism-induced social space reconfiguration generates a unique risk of “place identity” dissolution. The strong social bonds formed through place attachment and institutional trust networks collectively constitute the buffering layer of social capital. As a practical application of multi-center governance theory, inclusive policies (Ostrom, 2010) regulate host-guest group conflicts to maintain social system stability (Fan et al., 2020). This social resilience mechanism aligns with the autopoiesis theory of social systems (Luhmann, 1995), achieving dynamic equilibrium in the social structure and enhancing governance efficiency in tourist cities through conflict resolution.

In the cultural resilience dimension, the cultural heritage, as the material carrier of the “place of memory” (Nora, 1989), its protection is essentially to maintain the stability of cultural genes, while innovative practices such as the digitalization of intangible cultural heritage (Ma et al., 2019) are in line with the cultural evolution mechanism. The dialectical unity of cultural inheritance and innovation ensures the spatio-temporal coherence of cultural identity through traditional continuation, and realizes the market conversion of symbolic value by means of innovation diffusion, thus forming a unique cultural adaptive competitive advantage.

In the ecological resilience dimension, tourism activities in tourist cities are deeply reliant on and significantly interfere with natural ecosystems, thereby intensifying resource metabolic pressure (Gonzalez-Perez et al., 2023). The synergy between ecological sustainability and economic development represents a critical challenge for the advancement of tourist cities. By systematically aligning economic growth with ecological conservation, TCR can be comprehensively strengthened (Chertow, 2000).

The coupling mechanism among the four aforementioned dimensions aligns with synergetic principle (Haken, 1983). The diffusion of innovation within the economic system offers technological support for ecological transformation, while the social capital network establishes an institutional framework conducive to cultural inheritance. Additionally, ecological carrying capacity defines the physical constraints of economic development. This cross-dimensional interaction substantiates the nested resilience model (Walker et al., 2004), enabling tourist cities to preserve identity continuity amidst disturbances while facilitating a creative transformation of their development paradigm.

“TRV-TCR” system coupling model

It is evident that TRV, its conversion effects, and TCR collectively constitute the foundational logic of sustainable development for tourist cities (Simarmata et al., 2024). TRV and TCR establish a dynamic inter-constructive relationship within the spatio-temporal dimension. From an ontological perspective, TRV comprises a dual structure of intrinsic value and market conversion value, with intrinsic value serving as the ontological basis of existence and market conversion value representing the pathway to value realization. TCR highlights the dynamic synergy among the four dimensions of economy, society, culture, and ecology, achieving a balance between risk mitigation and sustainable development through system self-organization and adaptive evolution.

There is a dynamic and mutually reinforcing theoretical relationship between TRV and TCR. The realization of TRV provides a material basis for TCR construction. Specifically, the consumption capital attracted by high-value tourism resources can be channeled into investments such as disaster prevention infrastructure and ecological restoration, while the cultural attributes of tourism resources contribute to community cohesion and social adaptability. Conversely, TCR ensures the sustainability of TRV through multiple mechanisms: redundant infrastructure design mitigates natural disaster impacts on landscapes, smart monitoring systems enable dynamic regulation of tourism resource carrying capacity, and economic diversification strategies reduce risks associated with tourism industry fluctuations. Based on system resilience theory, these two factors form a “value creation, risk resistance” coupling system. Rational tourism resource development enhances the buffering and adaptive capabilities of urban subsystems, and improved resilience, in turn, sustains tourism resource sustainability and promotes value growth. This bidirectional interaction mechanism offers a systematic analytical framework for risk governance in tourist cities.

Based on the above, this paper innovatively proposes the “TRV-TCR” system coupling model (Fig. 1), which systematically integrates the multiple attributes of the “resource-resilience” coupling development in tourist cities. In terms of TCR assessment, it transcends the traditional single economic perspective by establishing a comprehensive evaluation framework encompassing four dimensions: economic, social, cultural, and ecological. Regarding TRV, it constructs a three-dimensional assessment framework—“quantity, quality, diversity”—to precisely characterize the baseline features and market conversion potential of tourism resources. Subsequently, through geographic spatial analysis techniques, it thoroughly examines the correlation characteristics and interaction mechanisms between these two systems in regional spatial distribution, revealing the dynamic matching patterns between the accumulation of TRV and the enhancement of TCR.

Fig. 1
figure 1

Framework of the study.

Methodology

Study area

This paper selects two typical types of tourist cities in China as comparative samples: Penglai in Shandong Province represents a coastal cultural tourist city, and Qimen in Anhui Province represents an inland mountainous ecological tourist city. The basic geographical features of the case areas are as follows (Fig. 2): Penglai covers an area of 1014.6 km2 and comprises 11 townships, excluding areas not directly under the jurisdiction of the Penglai Government; Qimen covers an area of 2257 km2 and comprises 11 townships.

Fig. 2: Geographical location of the case study areas.
figure 2

The figures (a) and (b) respectively show the geographical location of Penglai and Qimen.

Penglai exhibits three defining characteristics: (i) Unique natural landscapes, exemplified by the mirage phenomenon; (ii) Cultural heritage landmarks, such as the Penglai Pavilion and the historical site of Qi Jiguang; (iii) Imbalanced coastal zone development, characterized by the fragmentation of ecological corridors due to high-intensity exploitation. Meanwhile, the COVID-19 pandemic has revealed the universal challenges faced by the TCR mechanism in tourist cities: The ecological dimension reflects the inherent contradictions of coastal urban development, the cultural dimension underscores the tension between commercialization and preservation, and the economic dimension’s reliance on ticket revenue exacerbates systemic vulnerability, echoing the global transformational dilemmas of coastal tourism.

Qimen manifests the innovative system: (i) The balanced integration of ecological conservation and tourism development in the Gu’niujiang Reserve; (ii) The “tea tourism and ancient village” model that transforms intangible cultural heritage and historic architecture into sustainable industrial chains, with tourism revenue accounting for 22% of GDP in 2024. Its TCR features highlight the shared traits of mountainous tourist cities: ecological resilience is constrained by soil and water conservation challenges at high altitudes, cultural resilience is deeply rooted in the fusion of merchant heritage and agrarian civilization, and spatial resilience confronts the dispersion of key elements due to fragmented topography.

The two cases presented above, characterized by the spatial heterogeneity of tourism resources (coastal versus mountainous) and differing development models (coastal culture-driven versus ecological resource-driven), collectively form a representative spectrum for TCR research. This dual-case comparative framework not only elucidates the unique challenges faced by specific types of tourist cities but also extracts common principles underlying how tourist cities address systemic risks.

Data sources and preprocessing

POI data

The POI (Point of Interest) data includes three categories: tourism resources, infrastructure, and immovable cultural relics (Table 1).

Table 1 POI data statistics.

Tourism resource data. Our team conducted an on-site investigation and data collection in Qimen and Penglai in July-August 2022 and June-July 2023, respectively. During the data processing, we collaborated with the tourism departments of the local towns to verify and supplement the collected data. This process resulted in a database of 1886 (Penglai) and 2879 (Qimen) tourism resource points with stable spatial locations. According to the Chinese national standard “Classification, Investigation, and Evaluation of Tourism Resources (GB/T18972-2017)”, each resource point was evaluated using an expert scoring method across nine dimensions: scenic value, cultural value, rarity and uniqueness, scale and abundance, preservation integrity, comprehensiveness, transportation and facilities, suitable visiting period, and popularity. Based on these evaluations, the resource points were classified into five levels of comprehensive value, with level 5 representing the highest.

Tourism service facility data. This paper used Python to extract information on the tourism service facilities in the study area from AMap. After screening and organizing the data, 1791 POI data of tourism accommodation facilities, 3132 POI data of tourism catering facilities, and 45 POI data of cultural service venues (such as museums and exhibition halls) in Penglai, as well as 154 POI data of tourism accommodation facilities, 663 POI data of tourism catering facilities, and 24 POI data of cultural service venues in Qimen were obtained.

Immovable cultural relics data. This type of POI data originates from the information on immovable cultural relics disclosed by Chinese governments at all levels in eight batches from 1977 to 2019, encompassing five grades: national-level, provincial-level, municipal-level, county-level, and general cultural relics. There are a total of 326 in Penglai and 185 in Qimen.

Basic geographic spatial data

The basic geographic spatial data utilized are sourced from publicly available datasets commonly used in the scientific community. These datasets include boundary data of the case study areas and their townships, road network data, land use data, and DEM data. Specifically: (i) The boundary data were obtained from the Geospatial Data Cloud (http://www.gscloud.cn). (ii) The road network data were sourced from OpenStreetMap (https://www.openstreetmap.org/). (iii) The land use data were acquired from GlobeLand30 (http://www.globeland30.org). (iv) The 12.5 m resolution DEM data were provided by the European Space Agency (https://dataspace.copernicus.eu/).

Socio-economic Data

The socio-economic data consist of three main components. (i) The 100 m resolution population grid data for Penglai from the seventh national census were sourced from Chen et al. (2024). (ii) The 500 m resolution NPP-VIIRS nighttime light grid data were obtained from Chen et al. (2021). (iii) Socio-economic statistical data were derived from the “Statistical Bulletin of the National Economic and Social Development”: In 2023, Penglai’s GDP was 49.22 billion yuan and it received 13.06 million tourists; while Qimen’s GDP was 9.45 billion yuan and it received 3.18 million tourists.

Methods

Data gridding

To enhance the accuracy of the analysis, this paper introduces grids for analysis. Moreover, it has been indicated that a 0-1 km grid scale is suitable for studying the spatial distribution characteristics of tourism resources(Kauppila and Rusanen, 2009; Li et al., 2022). Thus, this paper conducts repeated experiments with five typical grid scales of 100 m, 200 m, 500 m, 800 m, and 1000 m: (i) Although the 100 m and 200 m grids can reflect micro-spatial differences, they are constrained by the resolution of raster data (such as 500 m resolution NPP-VIIRS nighttime light raster data) and the area of the study region. Upsampling raster data through interpolation algorithms will result in significant errors and generate excessive computational redundancy; (ii) The 800 m and 1000 m grids present certain spatial distortion phenomena, and the clustering characteristics of key features are weakened. Since the 500 m grid has the optimal comprehensive performance, which can both match the data resolution and effectively identify spatial characteristics, it is ultimately determined that the 500 m × 500 m grid is the basic evaluation unit, thereby dividing Penglai into 4403 grids and Qimen into 9303 grids.

Subsequently, the data were assigned to the corresponding grid cells as follows: (i) Point vector data were spatially associated to count the number of points falling within each grid. (ii) Linear and polygon vector data were intersected with the grids to calculate the length or area within each grid. (iii) Raster data were converted into vector format through raster-to-point or polygon conversion and then assigned to the respective grid cells.

After this processing, the study area grids now contain data on the number of tourism resources, accommodation facilities, catering facilities, cultural venues, immovable cultural relics, road lengths, nighttime light intensities, areas of various land types, highest and lowest altitudes, and population density (Table 2).

Table 2 Data sources and grid information.

Furthermore, to assign the values of GDP and tourist numbers to the corresponding grids, we first calculated the GDP per unit area of construction land by dividing the total GDP by the urban construction land area. This value was then multiplied by the construction land area of each 500 m × 500 m grid cell to obtain the GDP raster data suitable for this paper’s resolution. Simultaneously, the tourist numbers were assigned to grids using formula (1):

$$F(P)=P\times \mathop{\sum }\limits_{j=1}^{m}cj/\mathop{\sum }\limits_{i=1}^{n}ci$$
(1)

In the formula, F(P) represents the number of tourists in the target grid, P denotes the total number of tourists, c indicates the grade of each tourism resources, n is the total number of tourism resources in the study area, and m is the number of tourism resources within the target grid.

Spatial clustering of TRV

The quantity, type, and grade of tourism resources significantly influence a city’s tourism development (Todorovic, 2015; Saryan, 2021; Zhang et al., 2022). This paper considers quantity, quality, and diversity as the key value characteristics of tourism resources and conducts a spatial assessment based on these attributes. Specifically: (i) Quantity is measured by the number of tourism resources within each grid cell. (ii) Quality is assessed by each grid cell’s average grade of tourism resources. (iii) Diversity refers to the number of tourism resource categories in each grid cell, encompassing six categories: geomorphic landscapes, water landscapes, biological landscapes, celestial phenomena and climate, buildings and facilities, historical relics.

  1. (1)

    Spatial characteristics

    This paper employs the local spatial autocorrelation method to investigate the spatial differentiation characteristics of TRV in the study area and identify hotspots of TRV. Specifically, the Getis-Ord G* index is utilized to quantify the spatial autocorrelation of elements, capturing the interaction of spatial distributions across different regional units. This index is particularly suitable for analyzing the local spatial distribution patterns of TRV within the study area. The formula is as follows:

    $${\rm{Gi}}\ast =\frac{\mathop{\sum }\limits_{{\rm{i}}=1}^{{\rm{m}}}{w}_{ij}{x}_{{\rm{i}}}}{\mathop{\sum }\limits_{{\rm{i}}=1}^{{\rm{m}}}{x}_{i}}$$
    (2)

    In the formula, m represents the number of research units, xi denotes the attribute value of the i-th research unit, and wij indicates the spatial weight between the i-th and j-th research units.

  2. (2)

    Cluster analysis

Based on the G-index results for the three characteristic dimensions of tourism resources—quantity, quality, and diversity—the K-means clustering algorithm is employed to perform multi-dimensional clustering of the spatial value distribution of tourism resources. This approach aims to analyze the clustering patterns of tourism resources in terms of spatial aggregation. The K-means algorithm initially selects k elements randomly as the seed points for k clusters, then assigns the remaining elements to the nearest cluster based on their distance from the seed points. The mean of each cluster is recalculated iteratively as the new centroid until the cluster membership stabilizes (Shen and Karimi, 2017).

Measurement of TCR

Multiple factors influence city resilience, and tourist cities exhibit greater distinctiveness compared to general cities. Currently, there is no unified measurement system for assessing TCR. Based on various backgrounds such as urban development and disaster response, previous studies have conducted extensive evaluations of city resilience levels (Sharifi and Yamagata, 2016; Buyukozkan et al., 2022), but insufficient attention has been paid to tourist cities as a specialized urban type. Therefore, this paper first establishes an evaluation index system for TCR, then calculates the weights of each index, and finally measures the resilience level of each grid unit.

  1. (1)

    Indicator system

    Based on existing research (Joerin et al., 2012; Sharifi and Yamagata, 2016; Zhong et al., 2024) and the understanding of city resilience, this paper develops a comprehensive measurement system for TCR. The system encompasses four dimensions: economic resilience, social resilience, cultural resilience, and ecological resilience. These dimensions account for the unique characteristics of tourist cities, such as their reliance on the tourism industry, resource dependence, and ecological vulnerability (Dai et al., 2019; Gafter et al., 2022). Specific indicators are detailed in Table 3. The selection of indicators adheres to the following principles: (i) Covering the core elements of resilience; (ii) Highlighting the particularity of tourist cities; (iii) Ensuring the accessibility of data.

    Table 3 Evaluation index system of resilience level.

    Economic Resilience: This dimension measures the ability to rapidly recover and adjust industrial structures in response to economic pressures and shocks. Per capita GDP, as the most fundamental economic indicator, represents a city’s overall economic level and provides a foundation for structural adjustments in tourism-dependent economies (Risso, 2018; Shi et al., 2022). The number of tourists reflects the city’s attractiveness and forms the basis for tourism economic recovery (Khani et al., 2020; Yang et al., 2024). Nighttime light intensity indicates urban vitality and the city’s capacity to cope with economic risks (Zhang et al., 2023). In contrast to other potential economic indicators, such as the proportion of the tertiary industry and the amount of fixed asset investment, the aforementioned indicators are more concentrated on the core elements of the tourism economy, and the data sources are authoritative with high temporal and spatial resolutions.

    Social Resilience: This dimension ensures the recovery and stable development of tourist cities. Population size provides human capital essential for development and risk resistance (Boucekkine et al., 2013; Yang et al., 2024). Transportation density, the number of accommodation facilities, and the number of catering establishments reflect the transportation conditions and reception levels critical for tourism activities (Hacia, 2016; Zhang et al., 2023). Other indicators related to social resilience, such as the density of medical facilities, although belonging to the domain of social welfare, have a relatively weak direct association with the tourism industry.

    Cultural Resilience: Given the high dependence of tourism on culture, this dimension evaluates the stability of tourism development. The number of cultural venues reflects the richness of cultural resources and the emphasis on cultural protection and inheritance (Whiting, 2021). The quantity of immovable cultural relics underscores the historical and cultural heritage of tourist cities (Durrant et al., 2023). In contrast to the related indicators of cultural resilience, such as the number of inheritors of intangible cultural heritage, the indicators selected in this paper possess the characteristic of spatial visualization and are more applicable for spatial assessment.

    Ecological Resilience: This dimension evaluates the self-recovery ability of the ecological environment during urban development, taking into account the dual nature of ecological vulnerability and dependence in tourism cities. Topographic relief (Bumtsend et al., 2020), forest and grass coverage (Reed et al., 2009; Yang et al., 2024), and water and wetland coverage (Zhong et al., 2024) are crucial for maintaining ecosystem diversity and stability. In the screening of ecological indicators, natural geographical indicators with temporal and spatial stability are given priority to avoid data fluctuations caused by short-term climate changes. For example, pollution-related indicators such as the air quality index are excluded because of their time-sensitive characteristics.

  2. (2)

    Evaluation method

The “weight, linear weighting” method evaluates the TCR level, and principal component analysis (PCA) and the entropy weight method are attempted to determine the indicator weights. After the Kaiser-Meyer-Olkin and Bartlett’s sphericity tests, it is confirmed that the data is applicable for PCA (KMO > 0.7, p < 0.05). Four principal components are extracted in Penglai (with a cumulative variance contribution rate of 65.891%), and the first three cover 56.656% of the total variation information. Three principal components are extracted in Qimen, and the first two cover 51.331% of the total variation information. It can be seen that PCA has the problem of ambiguous index interpretation caused by dimension compression, and its applicability in this paper is relatively weak. However, The entropy weight method has a significant advantage in maintaining the independence of the original indicators and can avoid distortion caused by dimension reduction. It includes specific steps such as data normalization, sample proportion calculation, indicator entropy value computation, and indicator weight derivation. The calculation formulas are provided below (Afrin et al., 2024):

$$Rij=\left\{\begin{array}{c}\frac{{x}_{ij}-\,\min \{{x}_{ij}\}}{\max \{{x}_{ij}\}-\,\min \{{x}_{ij}\}},{\rm{positiveindicators}}\\ \frac{\max \{{x}_{ij}\}-{x}_{ij}}{\max \{{x}_{ij}\}-\,\min \{{x}_{ij}\}},{\rm{negativeindicators}}\end{array}\right.$$
(3)
$$Uij=\frac{{R}_{ij}}{\mathop{\sum }\limits_{i=1}^{m}{R}_{ij}}$$
(4)
$$ej=-\frac{1}{{\mathrm{ln}}\,m}\mathop{\sum }\limits_{i=1}^{m}{U}_{ij}\,{\mathrm{ln}}{U}_{ij}$$
(5)
$$Wj=\frac{1-{e}_{j}}{\mathop{\sum }\limits_{i=1}^{n}(1-{e}_{j})}$$
(6)

In the formula: m represents the number of grid cells, and n represents the number of indicators, forming the original data matrix X = (xij)m×n. Rij is the standardized original data. Uij denotes the proportion of the j-th indicator in the i-th grid cell among all j-th indicators. ej is the entropy value of the j-th indicator, and due to the meaningless result when Uij = 0, it is stipulated that ej = 0 when Uij = 0; Wj is the weight of the j-th indicator.

Results

Spatial assessment of TRV

TRV can be converted into economic, social, cultural, and ecological benefits for a city. This conversion process exhibits elasticity, with the intrinsic value of tourism resources serving as the foundation for determining its effectiveness. This paper evaluates TRV based on three dimensions: quantity, quality, and diversity. We then analyzes the spatial characteristics of these values and perform cluster analysis using the results from these three dimensions, ultimately identifying five distinct TRV clusters.

Dimensional value assessment

The Getis-Ord G* analysis was employed to investigate the spatial distribution of TRV hotspots across three dimensions. The results revealed that TRV exhibited distinct patterns of spatial agglomeration in each dimension (Fig. 3).

Fig. 3: Distribution of hotspots of TRV.
figure 3

The figures (a1) and (b1) respectively show the quantity hotspots of Penglai and Qimen; the figures (a2) and (b2) respectively show the quality hotspots of Penglai and Qimen; the figures (a3) and (b3) respectively show the diversity hotspots of Penglai and Qimen.

Quantity dimension. Penglai exhibited “point-axis agglomeration”, with 41% of its tourism resources concentrated within a 5-kilometer northern coastal corridor. Relying on the key attractions such as the Penglai Pavilion, the Eight Immortals Crossing the Sea architectural and cultural landscapes, and the Three Immortal Mountains architectural and cultural landscapes, it has formed hotspots and sub-hotspots agglomeration areas, covering 1.36% of the total area. In contrast, Qimen has developed a “one main and multiple secondary” pattern, with hotspots and sub-hotspots agglomeration areas (1.06% of the total area). Qishan (the county seat) is the primary agglomeration area, relying on the Qihong Cultural Expo Park to form an industrial resource agglomeration.

Quality dimension. In Penglai, part of the quality hotspots and sub-hotspots overlap with quantity-based aggregation areas in the coastal cultural tourism areas (e.g., Penglaige), while another part is located in the ecological industrial zones supported by Aishan National Forest Park and the wine industry chain (e.g., Cunliji, Daliuhang). These hotspots and sub-hotspots cover 11% of the total area. In Qimen, a “quality-quantity” spatial coupling pattern is evident. The hotspots and sub-hotspots aggregation areas exhibit significant spatial overlap with the quantity-based aggregation areas, accounting for ~6% of the total area. This suggests that high-quality resources tend to generate scale economic effects.

Diversity dimension. In Penglai, the area of diversity hotspots and sub-hotspots (accounting for 4.22% of the total) decreased by 62% compared to the quality dimension, reflecting a homogenization trend in resource types. In contrast, Qimen maintained an aggregation scale of 6.43%, demonstrating a more complementary resource combination. These findings indicate that the coastal tourist city’ TRV agglomeration exhibits less significant diversity, predominantly dominated by single-type resources, whereas the mountainous tourist city shows relatively more affluent diversity.

The distribution of tourism resources typically exhibits agglomeration characteristics (Zhang et al., 2023; Han et al., 2024). Across different dimensions, the hotspots and sub-hotspots across all value dimensions have formed distinct aggregation areas, constituting critical zones for converting TRV. With the implementation of supportive policies and market investments, these areas possess significant potential to become the most valuable regions for tourism development. Moreover, the quality agglomeration areas act as the primary carriers for value conversion, with their spatial scale being 8–15 times larger than that of the quantity dimension. This highlights the significant spillover effect of high-quality resources on TRV.

Clusters of TRV

A K-means multivariate cluster analysis was conducted based on the Getis-Ord G* index of TRV’s quantity, quality, and diversity characteristics. In selecting the clustering scheme, this paper combines the Calinski-Harabasz pseudo-F index to validate the clustering validity. The initial global optimal solution is k = 7, but its spatial division shows a fragmented trend, resulting in poor geographical continuity for each category. Therefore, an attempt is made to reduce the number of clusters to achieve a balance between statistical robustness and spatial rationality. Finally, the number of clusters is determined to be 5 (Clusters I–V).The resulting clusters exhibit a gradual increase in TRV from Cluster I to Cluster V, where clusters IV and V represent the advantage clusters, while clusters I-III constitute the general clusters (Fig. 4).

Fig. 4: Spatial clusters of TRV.
figure 4

The figures (a) and (b) respectively show the TRV clusters of Penglai and Qimen.

TRV Advantage Clusters. Cluster V demonstrates the highest TRV advantage, with significant quantity, quality, and diversity advantages. The TRV advantage cluster of Penglai is concentrated in the Penglaige, where each grid unit contains an average of ~24 tourism resources across 3 types (the average grade is 2.35); the TRV advantage cluster of Qimen is distributed along the axis of Qishan-Liko, where each grid unit contains an average of ~8 tourism resources across 2 types (the average grade is 1.94). Cluster IV demonstrates the second-highest TRV advantage, primarily distributed around the areas with the highest TRV. In terms of quantity (7.09 per grid in Penglai and 2.22 per grid in Qimen), quality (average grade of 1.72 in Penglai and 1.75 in Qimen), and diversity (1.65 types per grid in Penglai and 1.25 types per grid in Qimen), they exhibit potential value for complementing and supporting the development of Cluster V.

TRV General Clusters. Clusters III-I exhibit less pronounced tourism resource advantages. These clusters’ tourism resources have a smaller scale (<1.18 per unit), lower quality grades (<1.12), and less diverse type combinations (<0.64 per unit). The tourism resources in these areas lack significant clustering effects and are unlikely to generate substantial economic benefits in future market value conversions. However, the inherent ecological and cultural values of these tourism resources can still contribute to maintaining and enhancing the natural and humanistic environment of the tourist city, mitigating the negative impacts of tourism development, and sustaining TCR.

It is worth noting that Penglai and Qimen show different forms of agglomeration. Qimen exhibits a multi-centered pattern, whereas that in Penglai demonstrates a single-centered characteristic. This is because, as a coastal tourist city, Penglai’s core resources are constrained by the rigid limitations of the coastline, thereby forming an irreplaceable resource advantage. Consequently, this leads to the single-pole concentration of resource elements toward Penglaige. In contrast, as an inland tourist city, Qimen’s infrastructure elements, such as major transportation routes, can disrupt spatial resource agglomeration and induce the formation of multiple TRV centers along the infrastructure corridor.

Tourist attractions result from the development and utilization of tourism resources and the conversion of their value into tangible products. All A-level scenic spots are distributed within clusters IV and V, which verifies the value chain transmission mechanism of “resource endowment, product development” (Erislan, 2017). This indicates that, supported by resources, technology, and policies, the TRV advantage cluster can enhance the tourism economic contribution rate (The total tourism revenue of Penglai in 2024 was nearly five times that of Qimen), strengthen cultural revitalization (Penglai has 113 intangible cultural heritage items at or above the county level, compared to Qimen’s 32), and contribute to the construction of TCR.

Spatial characteristics of TCR

Based on the index evaluation system outlined in Table 3, the TCR level was calculated and subsequently visualized spatially using ArcGIS Pro. The natural breaks method was employed to classify the overall resilience index, economic resilience index, social resilience index, cultural resilience index, and ecological resilience index into five categories: high, medium-high, medium, medium-low, and low levels, as illustrated in Fig. 5.

Fig. 5: Spatial characteristics of dimensional resilience.
figure 5

The figures (a1) and (b1) respectively show the overall resilience of Penglai and Qimen; the figures (a2) and (b2) respectively show the economic resilience of Penglai and Qimen; the figures (a3) and (b3) respectively show the social resilience of Penglai and Qimen; the figures (a4) and (b4) respectively show the cultural resilience of Penglai and Qimen; the figures (a5) and (b5) respectively show the ecological resilience of Penglai and Qimen.

Spatial continuity of overall resilience

By comparing the overall resilience of Penglai and Qimen, it can be observed that both regions exhibit similarities in spatial location and spatial characteristics (Fig. 5a1, b1).

The high-level and medium-high-level areas of overall resilience exhibit a relatively high degree of spatial consistency with the TRV advantage areas. In Penglai, the high-level areas are concentrated in three central townships along the northern coast: Penglaige, Zijingshan, and Dengzhou, covering a total area of 3 km². The medium-high-level areas primarily surround the high-level areas, forming an aggregation area centered on Penglaige along the northern coast. In Qimen, the high-level and medium-high-level areas of overall resilience are relatively small and concentrated within Qishan, where the medium-high-level areas surround the high-level areas to form an aggregation area.

The overall resilience level exhibits a typical spatial continuity, with high-level aggregation areas as the central focus. In Penglai, the resilience level gradually decreases from the northern coastal region toward the south, resulting in a spatial distribution characterized by higher values in the north and lower values in the south. In Qimen, the resilience level diminishes radially outward from Qishan, leading to a spatial distribution marked by higher values in the center and lower values in the surrounding areas. The distribution patterns of TCR in both Penglai and Qimen, along with the “high in the middle, low around” spatial pattern observed for red tourist cities in southeastern China by Zha et al. (2024), collectively reflect the spatial continuity feature of TCR.

However, significant differences exist in the spatial patterns of resilience levels between Penglai and Qimen. Specifically, the resilience level of Qimen exhibits a network-like spatial pattern, whereas that of Penglai demonstrates a surface-like distribution. These spatial pattern differences are shaped by the spatial characteristics of TRV advantage clusters. In Penglai, the high degree of spatial monopoly in tourism resources drives the concentration of resilience-related elements, such as tourism service facilities and socio-economic conditions, toward the core area, forming a surface-like aggregation pattern. Conversely, in Qimen, the multi-centered clustering of TRV advantages promotes the aggregation of resilience elements around each center and enhances the resilience levels of the regions connecting these centers, leading to a network-like distribution structure.

Spatial heterogeneity of dimensional resilience

Economic, social, cultural, and ecological resilience levels exhibit significant spatial heterogeneity.

Economic resilience. While sharing some similarities with overall resilience, the high-level economic resilience areas are more extensive. In Penglai, high-level areas are distributed in a surface-like pattern across four central townships along the northern coastline—Penglaige, Zijingshan, Dengzhou, and Xingang—with a total area of 15.75 km². In Qimen, high-level areas are concentrated in Qishan, covering an area of 2.74 km². The medium-to-high-level areas in both cities predominantly surround the high-level areas, spanning a total area of 50.75 km² in Penglai and 15.5 km² in Qimen (Fig. 5a2, b2).

Social resilience. The high-level and medium-high-level areas exhibit significant clustering, forming small-scale aggregation areas. In Penglai, these areas are concentrated in the three northern central townships with the highest levels of human activity: Penglaige, Zijingshan, and Dengzhou. In Qimen, the high-level and medium-high-level areas are primarily located in Qishan, showing spatial overlap with the high-level areas of overall resilience and economic resilience (Fig. 5a3, b3).

Cultural resilience. The number of high-level and medium-high-level cultural resilience areas is limited, lacking prominent aggregation characteristics. In Penglai, High-level areas are scattered across Penglaige, Liujiagou, and Beigou, covering a total area of 1.5 km². In Qimen, the high-level areas are dispersed in Qishan, Liko, and Xin’an, with a total area of 1 km². Medium-high-level areas are generally adjacent to these high-level areas, covering a total area of 1.5 km² in Penglai and 2.25 km2 in Qimen (Fig. 5a4, b4).

Ecological resilience. The overall level of ecological resilience is relatively high, and its spatial distribution pattern differs significantly from those of other resilience dimensions. In Penglai, High-level and medium-high-level areas are predominantly distributed in a band along the northern coastline and the southern Aigu Mountain range, covering 29.25 km² (High-level) and 106.25 km² (medium-high-level). In Qimen, high-level areas are primarily located along the southwestern and southeastern boundaries, covering a total area of 20.75 km². Medium-high-level areas are predominantly clustered around these high-level areas, extending an additional 82.25 km² (Fig. 5a5, b5).

Spatial correlation between TRV and TCR

Overall correlation

As previously discussed, there is a notable correlation between the advantages of TRV and the effectiveness of its conversion. The TRV clusters I-V exhibit significant value gradient disparities. By statistically analyzing the number of resilience grids at each level within each cluster and calculating the average resilience level for each cluster, the relationship between TRV clusters and TCR can be elucidated (Fig. 6a1, b1). It is observed that an enhancement in TRV corresponds to an elastic response in the overall TCR level.

Regarding individual dimensions, economic resilience exhibits the most substantial response sensitivity. This finding validates the central role of the tourism multiplier effect generated by TRV in promoting urban employment growth and optimizing the industrial structure. Social resilience demonstrates a moderate level of responsiveness, with the conversion of TRV contributing to enhancing social resilience in tourist cities. In contrast, cultural and ecological resilience levels across clusters are generally low, and the two forms of resilience show no significant correlation with TRV clusters. This reflects challenges such as inadequate activation and utilization of intangible cultural heritage and increased environmental governance costs associated with tourism resource development.

Dimensional correlation

By analyzing the proportion of grids with varying resilience levels across different grades of hotspots, the coupling and correlation characteristics between the three elements of TRV and the four dimensions of TCR were deconstructed. We found that the responses of TCR in different dimensions to TRV showed significant differences.

Fig. 6: Spatial correlation between TRV and TCR.
figure 6

The figures (a1) and (b1) respectively show the overall correlation of Penglai and Qimen; the figures (a2), (b2), (a3), (b3), (a4), (b4), (a5), and (b5) respectively show the correlation between the three elements of TRV and the four dimensions of TCR of Penglai and Qimen.

Economic resilience (Fig. 6a2, b2) exhibits a monotonically increasing trend with the enhancement of hotspots in tourism resource quantity, quality, and diversity. This indicates regions with higher TRV exhibit substantial development potential and can enhance economic resilience by developing upstream and downstream tourism-related industries. This finding validates the value-added principle of the tourism economic system: high-value tourism resources create an element network through industrial chain extension, thereby establishing a positive feedback mechanism of “resource endowment, industrial agglomeration, resilience strengthening”.

Social resilience (Fig. 6a3, b3) continuously correlates with the quality and diversity of tourism resources. There are notable differences in the quantity dimension between the two case studies: In Qimen, a significant correlation is observed, whereas in Penglai, an inverted U-shaped relationship is evident. This suggests that while social resilience is strongly influenced by the quality and diversity of tourism resources, its relationship with quantity is more intricate. Specifically, when tourism resource quality surpasses a certain threshold, the spatial monopoly of high-quality tourism resources may result in the polarized distribution of infrastructure. This phenomenon is particularly pronounced in coastal tourist destinations, thereby corroborating theory of tourism spatial deprivation (Smith, 1990).

Cultural resilience (Fig. 6a4, b4) exhibits a continuous positive correlation with the quality and diversity of tourism resources. However, it shows significant polarization with the increase in the grade of hotspots for tourism resource quantity, where both high and low levels exhibit a relatively high proportion of quantity hotspots. This suggests a significant relationship between cultural resilience and the quality and diversity of tourism resources, but not with their quantity. This phenomenon can be attributed to the interaction between cultural and tourism resources. On the one hand, tourism resources derived from cultural heritage tend to have higher quality. On the other hand, cultural venues are often situated in areas with tourism resource advantages, particularly those characterized by high quality and diversity, leading to the continuous enhancement of cultural resilience.

Ecological resilience (Fig. 6a5, b5) exhibits a continuous positive correlation with the diversity of tourism resources, but does not show a significant upward trend with increases in quantity. Differences in the quality dimension are observed between the two case areas: in Penglai, there is no apparent correlation between ecological resilience and the quality of tourism resources, whereas in Qimen, a notable correlation existed. This suggests that the diversity of tourism resources positively influences the enhancement of ecological resilience in cities.

It is evident that the correlation patterns between economic resilience, cultural resilience, and TRV in Penglai and Qimen are consistent. However, compared to Penglai, Qimen exhibits more pronounced correlations between social resilience and the quantity characteristics of TRV, as well as between ecological resilience and the quality characteristics of TRV. Regarding social resilience, the monopolistic advantage of coastal tourism resources in Penglai causes infrastructure agglomeration to depend on resource quality and combination rather than merely on resource quantity. In contrast, the distributed tourism resource nodes in Qimen activate community networks, establishing a strong correlation mechanism between resource quantity and the enhancement of social resilience. Concerning ecological resilience, the high-intensity development of core scenic areas in Penglai, constrained by the linear distribution of high-quality tourism resources along the coast, compresses ecological buffer zones, leading to a weak correlation between improvements in ecological resilience and tourism resource quality. By contrast, in Qimen, tourism resources are distributed along natural corridors formed by water systems and mountain ranges, where the ecological background and the realization of TRV form a spatially nested structure.

Overall, the difference in resilience response between Penglai and Qimen originates from their distinct distribution patterns of tourism resources. The single-core radiation structure in Penglai reinforces a quality-dominant mechanism, whereas the multi-core network layout in Qimen stimulates a quantity-driven value-added effect. This spatial heterogeneity suggests that enhancing TCR requires constructing models tailored to local natural and humanistic environments, rather than merely transplanting universal development paradigms.

Discussion

Spatial extension and elasticity of TRV cluster

The elasticity of TRV is characterized by the co-evolution of spatial expansion capacity and industrial driving benefits. This paper elucidates the intrinsic TRV can convert into market value through market conversion mechanisms. The discrepancy between these two values can be defined as the elastic space. This elastic feature manifests geographically, not only through the spatial extension of boundaries from core regions to marginal regions but also via variations in industrial driving effects between core and peripheral regions (Fig. 7).

Fig. 7
figure 7

Spatial extension and elasticity of TRV cluster.

In terms of the expansion of geographical space, the spatial production of tourism resources follows the movement law of “agglomeration, diffusion, re-agglomeration” (Harvey, 1982). The case study of this paper shows that whether it is the coastal tourist city Penglai or the mountainous tourist city Qimen, the spatial integration and expansion of tourism resources lead to the coexistence of agglomeration and diffusion of TRV. Agglomeration manifests as the formation of clusters with TRV advantage, while the spatial extension of these clusters characterizes diffusion. This phenomenon arises because the landscapes and surrounding areas of characteristic tourist city resources attract increased attention. Through cultural excavation and artificial construction, new tourism resources emerge (Dolores et al., 2015), leading to a concentration of tourism resources within a relatively small area around these characteristic resources. Consequently, clusters develop from core to peripheral areas (Guedes and Jimenez, 2015), exhibiting an extended spatial pattern.

In the extension of industrial space, the spatial extension of clusters introduces heterogeneity in converting TRV within the clusters. Core areas of clusters typically become key development zones during the early stages of tourism development, where TRV has already undergone substantial conversion (Ma and Hassink, 2014). This, in turn, stimulates the development of tourism resources in peripheral areas. However, differences in functional connections, industrial linkages, and structural organization between core and peripheral areas influence the effectiveness of TRV conversion (Ma et al., 2015; Pang et al., 2023). Consequently, the extent of spatial extension and the degree of the core area’s driving effect on peripheral areas determine the elasticity of TRV.

The elasticity of TRV and the enhancement of TCR

City resilience is not static (Xu and Chopra, 2023). Both natural factors, such as ecosystems and climate conditions, and human factors, including policy systems and resource allocation, significantly influence a city’s capacity to cope with challenges (Huang et al., 2021). Through the case study of Penglai and Qimen, it is evident that tourism resources play a crucial role in supporting the development of specialized tourist cities. The value of these resources and their conversion effects exhibit a clear correspondence and demonstrate a strong spatial correlation with TCR level. Our findings confirm that the conversion of TRV impacts the level of TCR (Fig. 8).

Fig. 8
figure 8

The mechanism of the effect of TRV elasticity on TCR.

The development of tourism resources is fundamentally a spatial production process (Lefebvre, 1991) driven by value flow. Once the threshold of development intensity is surpassed, the system activates a correlation-trigger mechanism across multiple dimensions: (i) Economic resilience is strengthened as the tourism multiplier effect enhances the industrial diversity index. Through the three-tier network structure of “core enterprises, associated enterprises, supporting institutions” (Porter, 1998), an industrial system with robust risk-resistance capabilities emerges; (ii) Social resilience undergoes restructuring due to the exertion of TRV attracts the participation of outside capital represented by tourism enterprises and the support of the government, which is conducive to the improvement of the infrastructure level of tourist cities (Ning et al., 2016); (iii) Cultural resilience evolves through the density gradient of cultural display spaces, which effectively activates mechanisms for preserving cultural memory (Assmann, 1995); (iv) Ecological resilience experiences a leap forward, as there exists a Kuznets turning point between ecological tourism development intensity and environmental governance efficiency. When ecological tourism remains within the carrying capacity range, growth in ecological service value contributes to enhancing the overall level of TCR (Huang et al., 2021).

To enhance the system of TCR, it is essential to optimize the coordinated model of space, institutions, and technology. (i) Regarding spatial elasticity control, improving accessibility from peripheral areas to core areas and optimizing tourist flow networks are critical steps. (ii) For institutional flexibility innovation, a dynamic compensation mechanism should be established. When tourism development intensity surpasses a specific ecological carrying capacity threshold, automatic triggers for tax leverage and eco-bond issuance mechanisms will activate. (iii) In terms of technological flexibility empowerment, digital twin technology can be employed to construct a TCR simulation system, effectively reducing response times when TCR reaches low levels.

It can be seen that based on the resilience model of “economy-society-culture-ecology”, through the application of resilience-oriented tourism planning, the establishment of tourism ecological account, resilience monitoring cloud platform, and other methods, the multi-dimensional system of TCR can be managed with multistability.

TCR and “economic-ecological” tension

The differences in development models among various types of cities result in their resilience being constrained by different tensions (Meerow et al., 2016). Through the analysis of the study areas, it is evident that areas with high economic resilience and those with high ecological resilience exhibit significant scale advantages. However, the spatial overlap between these two types of areas is limited, suggesting that it is challenging for a single area to simultaneously possess both high economic resilience and high ecological resilience.

Tourism’s environmental dependency amplifies this tension, as ecological conditions critically influence both tourism experience quality and economic development trajectories (Zhu et al., 2023). While abundant tourism resources, distinctive cultural assets, and preserved natural environments form essential foundations for sustainable tourism (Richards, 2018), counterproductive patterns emerge through uncontrolled tourism expansion, resource overexploitation, and insufficient environmental safeguards - practices that exert substantial ecological pressures (Zeng et al., 2022).

To address this resilience challenge, it is essential to integrate the TRV in terms of quantity, quality, and diversity, thereby constructing an “intensity control, spatial reallocation, institutional innovation” framework for enhancing TCR (Fig. 9): (i) Establish a monitoring and early warning system for tourism resource development and TCR. When the carrying capacity threshold is exceeded, stringent spatial controls should be enforced, with adjustments to construction land indicators used to mitigate excessive agglomeration; (ii) Through tourism functional zoning, achieve the reorganization of elements by differentiating among tourism core zone, tourism comprehensive zone, tourism integration zone, and tourism protection zone, optimizing the spatial allocation of TRV; (iii) Institutional innovation: Develop an ecological credit trading mechanism for tourism development, such as allowing the exchange of tourism resources development quotas through ecological restoration or cultural preservation projects, thereby improving economic-ecological synergy efficiency.

Fig. 9
figure 9

Tourism spatial zoning based on the correlation between TRV and TCR.

Conclusion

This paper innovatively constructs a coupling analysis framework of a three-dimensional evaluation model of TRV and a four-dimensional assessment system of TCR. It conducts a refined-scale analysis based on the grid method, revealing the dynamic response mechanism between the elasticity of TRV and TCR. It fills the theoretical gap in the research of TCR and provides a brand-new perspective for the sustainable management of tourist cities.

Firstly, a “quantity-quality-diversity” TRV analytical framework was developed, with differential TRV clusters identified through spatial clustering algorithms. Findings demonstrate significant spatial extension of TRV, characterized by value spillover range and intensity variations as core TRV clusters extend influence to peripheral regions. This establishes a theoretical basis for sustainable spatial organization in tourist cities.

Secondly, a TCR evaluation framework was proposed based on economic, social, cultural, and ecological dimensions. By integrating overall resilience with dimension-specific resilience, the spatial characteristics of TCR were systematically analyzed. The findings indicate that TCR exhibits significant spatial continuity, with a clear gradient difference radiating outward from high-level areas to surroundings. Furthermore, the paper uncovers TCR’s dimensional paradox through spatial competition between economic and ecological resilience, providing new theoretical perspectives for reconciling development-protection dilemmas.

Thirdly, spatial correlations between TRV and TCR were innovatively identified. It demonstrates the coupling relationship between TRV advantage clusters and high-level TCR. The dimensional correlations exhibit typological variations: Penglai is characterized by quality dominance, whereas Qimen reflects a scale-driven pattern. These findings provide a solid foundation for the spatial optimization and scientific planning of different types of tourist cities.

Accordingly, this paper proposes an elastic regulation strategy and a dynamic resilience optimization pathway. On the one hand, a gradient development strategy is adopted to construct a three-tier spatial system comprising the TRV core area, elastic expansion area, and derivative function area. By establishing an industrial chain innovation network, the level of TCR is enhanced from both spatial and industrial perspectives. On the other hand, a dynamic resilience strategy tailored to different typological areas is implemented. An innovative four-quadrant decision matrix is designed: for “high TRV, high TCR” areas, an added-value conversion strategy is applied; for “high TRV, low TCR” areas, an income feedback mechanism is established; for “low TRV, high TCR” areas, digital activation technology is integrated; and for “low TRV, low TCR” areas, strict ecological intensity control measures are enforced.

Due to the limitations of methodology, this paper acknowledges limitations in spatial resolution granularity and macro-case scale. Future research could construct a three-dimensional TRV assessment framework and extend the analysis to national or regional scales, thereby deepening the exploration of characteristic patterns and underlying rules. Additionally, as the tourism resource data are point-based, the spatial extent attributed to these points was not considered during the gridding process. Future research could be enhanced by incorporating area-based data on tourism resources, thereby enabling a more comprehensive analysis.