Study on the Spatial–Temporal Evolution and Driving Mechanisms of Tourism Ecological Security in the Jianmen Shu Road Heritage Area

Abstract

Heritage is the commonwealth of all humankind. In the context of the rise in global tourism and the continuous deepening of cultural and tourism integration, tourism has emerged as an effective vehicle for the preservation and development of heritage sites. However, it also imposes adverse effects on the local ecological environment and heritage sites, exerting significant pressure on regional sustainable development. In this study, three cities along Jianmen Shu Road were selected as the study area. A comprehensive evaluation index system was developed for tourism ecological security (TES) based on the Driver–Pressure–State–Impact–Response model, and an in-depth analysis of its spatial–temporal evolution characteristics, spatial–temporal migration trends, and influencing factors was performed. The results show that (1) from 2012 to 2022, the average TES in the study area decreased annually, while it increased in Jiange County, Anzhou District, and Santai County. The TES indices were generally higher in areas with a high density of heritage sites or developed economies. Additionally, the districts and counties along the Jianmen Shu Road route never exhibited a deteriorated state. (2) From 2012 to 2022, TES in the study area exhibited an obvious “northeast–southwest” directional pattern, and its center of gravity followed a “V”-shaped trajectory. Overall, the spatial patterns showed minimal variation and exhibited agglomeration characteristics. (3) From 2012 to 2022, the main factors influencing TES included the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of cultural tourism and sports in total expenditure (R3), the Normalized Difference Vegetation Index (NDVI) (S4), and other tourism and environmental factors. Moreover, TES systems are becoming increasingly complex and diverse. Finally, based on the results, a comprehensive conceptual framework of the driving mechanism was developed. Additionally, four targeted and scientifically grounded policy recommendations were formulated for restoring, protecting, and managing the TES in the Jianmen Shu Road Heritage Area. This study provides significant reference value for ecological environment preservation and the high-quality development of cultural tourism integration in heritage areas.

1. Introduction

Shu Road is an ancient road that was artificially excavated to connect China’s Guanzhong and Bashu regions. It is also one of the best-preserved large-scale transportation relics in China, possessing significant military, economic, cultural, and transportation value [1]. Jianmen Shu Road, a critical component of the Shu Road system, is situated within the Jianmen Shu Road Scenic Area, one of China’s first nationally designated scenic areas. Named for its passage through the perilous Jianmen Pass, it is also referred to as “Jiange Road” or “Jiange Post Road”. During the 2019 National Day holiday, the Jianmen Pass 5A Scenic Area along Jianmen Shu Road generated a ticket revenue of approximately CNY 19.892 million, ranking first in Sichuan Province. This significant income has been crucial in promoting local socioeconomic development, tourism, and cultural heritage preservation [2]. In 2021, as outlined in the State Administration of Cultural Heritage’s “14th Five-Year Plan” for the protection and utilization of Major Heritage Sites, Shu Road was explicitly listed as one of the core objects for protection and utilization, with particular emphasis on Jianmen Shu Road due to its unique historical and cultural significance [3]. In this context, research on Jianmen Shu Road has gradually expanded from early literary education, history, and route verification [4] to multiple disciplinary fields, such as tourism development, site protection, landscape features, and spatial patterns [5,6,7,8,9,10]. However, due to the extensive span and complex routes of Jianmen Shu Road, along with the rapid expansion of towns along the road and the impact of natural disasters, some ancient roads have been covered by modern roads, ruins have been destroyed, the quality of the ecological environment along the route has continued to deteriorate, and the historical features and natural landscapes of the surrounding environment have been transformed and damaged. Additionally, Jianmen Shu Road faces challenges such as tourist numbers that exceed the capacity of scenic areas during peak hours, highlighting the urgent need to develop and emphasize its comprehensive value and regional characteristics. Therefore, it is necessary to conduct a thorough and systematic assessment of the ecological environment, tourism resources, and social and economic development of Jianmen Shu Road and perform an in-depth analysis of its influencing factors and risks to provide a solid basis for its effective protection, rational utilization, and scientific management.

In recent years, there has been a growing global interest in the sustainable development of tourism, which has become a core policy objective at international, national, and regional levels [11]. Studies on the sustainable development of tourism have focused on its impacts on the environment in both the short and long term [12]. Additionally, the importance of TES has become increasingly prominent and a key research direction in this field [13]. In China, ecological security has become a critical research topic for the high-quality development of tourism since the 18th National Congress of the Communist Party of China, held in Beijing on November 8, 2012, which incorporated the construction of ecological civilization into the overall layout of the “five-in-one.” China’s tourism industry has continued to flourish recently, with a rising trend in applying for scenic spots on the World Heritage List [14]. This not only enhances the popularity of tourist destinations but also attracts many domestic and foreign tourists, creates considerable employment opportunities, and promotes the optimal allocation and efficient utilization of regional resources. This has made important contributions to the country’s social and economic development [15,16,17]; however, with the rapid growth of tourism, issues such as climate change, ecological damage, and cultural stress have gradually emerged, placing significant pressure on local ecological environments and resources [18,19]. A good ecological environment and abundant resources are key to tourism development and are an important foundation for sustainable development [20]. Therefore, scholars have gradually begun to focus on maintaining regional TES systems to enhance their stability and sustainability.

As an emerging interdisciplinary field at the intersection of tourism ecology and geography, TES originated from the concept of ecological security [21]. The core objective of TES is to maintain a sustainable equilibrium between the ecological environment and tourist destination resources during tourism development. This is accomplished through the rational utilization of tourism resources and effective ecological environment management [22]. Early research predominantly focused on areas such as environmental management, carrying capacity, and sustainable tourism [23,24,25]. In recent years, studies have increasingly emphasized TES assessment, obstacle factor diagnosis, spatial–temporal pattern evolution, early risk warning, trend prediction, and dynamic simulation across diverse contexts [26,27,28,29,30,31]. Scholars have employed a diverse array of indicator models, including the Pressure–State–Response (PSR), Driver–Pressure–State–Exposure–Effect–Action (DPSEEA), Carrying–Supporting–Attractive–Evolutional–Developing (CSAED), and Driving–Pressure–State–Impact–Response (DPSIR) models, to assess the current status of TES across various spatial scales (nationwide, provincial, municipal, county, city cluster, and scenic area levels) [32,33,34,35,36,37]. In comparison with alternative evaluation frameworks, the DPSIR model is superior in terms of its comprehensiveness, logical structure, and systematic approach. This makes it highly applicable for assessing tourism ecological security [38]. Furthermore, the authors of these studies have integrated methods such as the entropy-weighted TOPSIS method, the obstacle degree model, gray relational analysis, spatial autocorrelation, GeoDetector [39], the fuzzy matter-element model, the ecological footprint (EF) method, the ordinary least squares (OLS) model, the autoregressive integrated moving average (ARIMA) model, the criteria importance through intercriteria correlation (CRITIC) method, and the system dynamics evaluation (SDE) model to elucidate the spatial–temporal evolution characteristics, identify obstacle factors, conduct trend predictions, perform dynamic simulations, analyze influencing factors, and explore the driving mechanisms of TES [40,41,42,43]. The main methods used in previous TES studies are shown in Table S1.

In this study, the Jianmen Shu Road Heritage Area was used as the research object. A TES evaluation index system was constructed based on the DPSIR model, and the entropy-weighted TOPSIS method was used to measure the TES of each district and county in the study area between 2012 and 2022. Simultaneously, the SDE model was employed to analyze the spatial–temporal migration trends in TES in this region, and GeoDetector was used to identify the impact factors and interaction modes between the factors. On this basis, a conceptual framework for the driving mechanism was constructed, and policy implications were proposed. This study aimed to enrich the content and methods of TES research in heritage areas and explore the impact of sites on TES to provide decision-making references for ecological environment protection, heritage protection and utilization, and sustainable tourism development in heritage areas. The research framework is shown in Figure 1.

2. Research Area, Data Sources, and Methods

2.1. Research Area

The Jianmen Shu Road Heritage Area encompasses 22 districts and counties in Guangyuan, Mianyang, and Deyang (Figure 2). It is situated in the cross-geological structural belt within China’s hinterlands and on the southern side of the boundary between temperate and subtropical climates. The terrain gradually transitions from northwest to southeast, shifting from the plateau to mountainous regions and ultimately to the Chengdu Plain. Jialing, Fujiang, and other tributaries of the Yangtze River traverse this area, boasting a vibrant diversity of species, geomorphic types, and geological landscapes. As of 2022, the Jianmen Shu Road Heritage Area hosts 106 A-level tourist attractions and enjoys high visibility and a good reputation. Consequently, this heritage area, selected as the research object, offers a distinct regional typicality and significant academic value. Additionally, 101 Jianmen Shu Road heritage sites were categorized based on site characteristics, planning documents, and related studies [44,45]. These heritage sites are classified as cultural sites (e.g., ancient tombs, buildings, and stone carvings), infrastructure sites (e.g., ancient roads, bridges, and paleochannels), natural sites (e.g., natural landscapes, scenic areas, forest parks, and nature reserves), and affiliated sites (e.g., ancient passes, monuments, ferries, pavilions, and posthouses).

2.2. Data Sources and Processing

In 2012, Jianmen Shu Road was included on the Preparatory List of China’s World Cultural Heritage Sites. In 2017, the master plan for the Jianmen Shu Road Scenic Area and the application for World Natural and Cultural Heritage Sites were completed. Since 2022, several local laws and regulations, such as the “Regulations on the Protection of Jianmen Shu Road in Guangyuan City”, have been implemented [46]. Therefore, 2012, 2017, and 2022 were selected as the research periods, and the data used are listed in Table 1.

Data preprocessing included the following steps: (1) According to the DPSIR framework, data for the indicators in 2012, 2017, and 2022 were systematically collected. Ordinary Kriging (OK) interpolation was utilized to address missing data. (2) The entropy-weighted TOPSIS method was employed for data standardization, weight calculation, the determination of positive and negative ideal solutions, and nearness degree computation. (3) ESRI ArcMap version 10.8 software [47] was utilized to visualize the data.

2.3. Methods

In this study, 22 districts and counties in the Jianmen Shu Road Heritage Area were considered basic units, and the DPSIR model, entropy-weighted TOPSIS method, SDE model, and GeoDetector were used to conduct an in-depth analysis of the TES evaluation index system in the study area, the characteristics of TES spatial–temporal evolution, trends in TES spatial–temporal migration, influencing factors, and the interaction modes of these factors.

2.3.1. DPSIR Model

As an extension and optimization of the PSR and the driving–state–response (DSR) models [48], the DPSIR model is a comprehensive evaluation framework with internal circulation. Initially developed by the European Environment Agency (EEA), this model is primarily used to investigate interactions among nature, the economy, society, policies, resources, and the ecological environment, with a particular emphasis on the interplay between tourists, tourism destinations, and the environment [49,50]. It not only evaluates the TES status of tourist destinations more comprehensively and scientifically but also finds extensive application in sustainable development research because of its strong applicability [51,52,53]. The DPSIR model comprises five interconnected elements: driver (D), pressure (P), state (S), impact (I), and response (R) [54,55]. Driver (D) encompasses socioeconomic activities and other underlying factors that induce ecological pressure, such as socioeconomic development and demographic changes [56]; pressure (P) refers to the direct pressures exerted on ecosystems by driving forces [57]; state (S) describes the conditions of an ecosystem resulting from the combined effects of driving forces and pressures [58]; impact (I) denotes the effects of various factors on environmental development [59]; and response (R) involves the actions taken by institutions and governments to prevent, compensate for, mitigate, or adapt to these impacts [60].

2.3.2. Construction of the TES Evaluation Index System

The TES system is essentially a multivariable coupled system that considers the environmental impact and economic development of tourism. Therefore, this study was based on the DPSIR model. We initially screened out TES evaluation indicators by comprehensively considering relevant research conducted by other scholars and the characteristics of the study area [61,62,63,64,65,66]. We then distributed online and offline questionnaires to 10 experts and scholars in the fields of landscape architecture, heritage protection, economy, resources, and the environment, specifically those familiar with Jianmen Shu Road, and asked them to provide suggestions for modifying the evaluation indicators. Ten questionnaires were distributed for this survey, with a recovery rate of 100%, all of which were valid.

On the basis of expert recommendations, the index system was refined and supplemented, leading to the establishment of a TES evaluation index system for the Jianmen Shu Road Heritage Area (Table 2). This system comprises a total of 27 indicators: population urbanization rate (D1), tertiary industry growth rate (D2), Gross Domestic Product (GDP) growth rate (D3), elevation (D4), slope (D5), year-on-year growth rate of tourism revenue (D6), year-on-year growth rate of tourist arrivals (D7), population density (P1), road network density (P2), soil erosion intensity (P3), number of industrial enterprises (P4), average PM_2.5 concentration (P5), frequency of earthquakes with magnitudes of 3 and above (P6), economic density (S1), average annual temperature (S2), average yearly precipitation (S3), NDVI (S4), number of 3A and above scenic areas (S5), density of Jianmen Shu Road heritage sites (S6), nighttime light intensity (I1), proportion of tertiary industry in total expenditure (I2), forest coverage rate (I3), water network density (I4), fertilizer application rate (I5), proportion of energy conservation and environmental protection in total expenditure (R1), sewage treatment rate (R2), and proportion of cultural tourism and sports in total expenditure (R3). This provides a solid foundation for the subsequent data collection, weight assignment, and calculation of nearness degrees for each district and county.

2.3.3. Entropy-Weighted TOPSIS Method

To evaluate and analyze the TES status in the study area more accurately and objectively, the entropy-weighted TOPSIS method was employed to calculate the weights of the 27 evaluation indicators (Table S2) and the nearness degrees of the 22 districts and counties (Table S3) from 2012 to 2022. This method integrates the entropy-weighted method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), enabling the comprehensive utilization of raw data for weight determination. The bias associated with subjective assignment [67,68] was mitigated by the specific steps and formulas of this method, which are outlined in Table S4 (1–10).

2.3.4. TES Classification Standard

Given the current lack of a unified standard for TES evaluation, a methodology based on the relevant literature was employed [69,70] to classify the TES indices of each district and country within the Jianmen Shu Road Heritage Area into seven levels (Table 3). This approach aims to examine the spatial–temporal evolution of TES in this area from 2012 to 2022.

2.3.5. Standard Deviational Ellipse Model

The SDE model accurately described the spatial distribution characteristics and evolutionary trends of the study area. Changes in the center of gravity reflect the spatial–temporal migration trends of the TES center of gravity in the Jianmen Shu Road Heritage Area [71,72]. The main parameters of the SDE model include the center of gravity of the ellipse, the azimuth angle, and the long and short axes. Its calculation formulas are outlined in Table S4 (11–13).

2.3.6. GeoDetector

GeoDetector is a statistical model used to analyze spatial data and is primarily employed to investigate the influencing factors and driving mechanisms leading to geospatial differentiation. It has four functions: factor, interaction, risk, and ecological detection [73,74,75]. Based on the TES evaluation index system established for the Jianmen Shu Road Heritage Area, the natural breakpoint grading method (Jenks) was employed to classify the factor data [76]. The factor detection and interaction detection functions of GeoDetector were utilized to examine the explanatory power of various factors in the spatial–temporal evolution of TES in the Jianmen Shu Road Heritage Area from 2012 to 2022, as well as the interaction patterns among these factors, thereby constructing its driving mechanism. The factor detection formula is as follows:

(1)$\mathrm{q}=1-{\displaystyle \frac{1}{{n\sigma }^2}}\sum _{h=1}^L{n}_h^2{\sigma }_h^2$

In Equation (1), q represents the explanatory power of detection factor X for variable Y, with a range of [0, 1]. The larger the value, the more pronounced the spatial heterogeneity. $n$ is the number of samples in the research area; $L$ is the stratification of variable Y or factor X; and $n_h$ and $n$ are the sample size and total sample size of the $h$ layer, respectively. The variances in TES for hierarchical $h$ and for the Jianmen Shu Road Heritage Area are represented by ${\sigma }_h^2$ and ${\sigma }^2$, respectively.

The objective of interaction detection is to ascertain whether factors X_i and X_j exhibit interactive effects with variable Y, either individually or in conjunction. If an interaction exists, its effect can be amplified or mitigated. Interaction detection can be categorized into five types: nonlinear attenuation, single-factor nonlinear attenuation, double-factor enhancement, independence, and nonlinear enhancement [77], as presented in Table 4.

3. Results

3.1. Spatial–Temporal Evolution Characteristics of TES

Based on the DPSIR model and entropy-weighted TOPSIS method, the TES indices were calculated for each district and country in the Jianmen Shu Road Heritage Area from 2012 to 2022, and the routes and heritage sites of Jianmen Shu Road were visualized. A TES spatial distribution map was constructed to clearly display the evolutionary characteristics of TES in each district and country, as well as in the areas encompassing heritage sites.

From the perspective of districts and counties (Figure 3), in 2012, the proportions of deteriorated, risky, sensitive, and general state areas were 13.64%, 50%, 31.82%, and 4.55%, respectively. In 2017, the proportions had changed to 9.09%, 54.55%, 22.73%, and 13.64% for deteriorated, risky, sensitive, and critical state areas, respectively, which, in 2022, accounted for 45.45%, 27.27%, 18.18%, and 9.09%, respectively. In 2012, Shifang City, Anzhou District, and Yanting County were classified as deteriorating; Mianzhu City, Zhongjiang County, and Luojiang District were in a risky state; and Chaotian District, Zhaohua District, and Jiangyou County were in a sensitive state. Only Lizhou District was in a general state (Figure 3a). In 2017, Anzhou District, Yanting County, Chaotian District, and Fucheng District shifted from a sensitive to a critical state; Mianzhu City moved from a risky to a sensitive state; Lizhou District was downgraded from a general to a critical state; and Jiangyou City was reclassified from a sensitive to a risky state (Figure 3b). In 2022, Jiangyou County and Qingchuan County remained in a critical state, whereas Tiantai District, Zhaohua District, Fucheng District, and Santai County were classified as being in a sensitive state; Lizhou District, Zitong County, and Youxian District were in a risky state; and the other areas were in a deteriorated state (Figure 3c). Additionally, there were no relatively or very safe areas between 2012 and 2022, and a generally safe area existed only in 2012.

As shown in Table S3, from 2012 to 2022, the TES indices of Jiange County, Anzhou District, and Santai County demonstrated a consistent upward trend, whereas those of other districts and counties experienced fluctuating trends. On average, Lizhou District, Fucheng District, and Jiange County had the highest TES indices: 0.4569, 0.4555, and 0.4470, respectively. In contrast, Anzhou District had the lowest index (0.2296). Furthermore, the overall average TES indices for 2012, 2017, and 2022 were 0.3316, 0.3300, and 0.3000, respectively. Thus, the TES situation in the Jianmen Shu Road Heritage Area has progressively deteriorated annually.

According to the statistical analysis of the heritage site types, there are 44 infrastructure sites, which constitute 43.56% of all the sites and are predominantly situated in Jiange County. There are 25 cultural sites that are widely distributed across various regions, accounting for 24.75% of the total. Additionally, there are 23 affiliated sites, primarily concentrated in the Chaotian and Zhaohua Districts and representing 22.77% of the total. Natural sites account for the smallest proportion, with only nine sites located within the administrative boundaries of Qingchuan County, Chaotian District, and Wangcang County, comprising 8.91%.

The distribution of the states of heritage sites in different years was analyzed, and the results show that, in 2012, there were 28 sites in risky areas, 67 sites in sensitive areas, and 6 sites in general areas (Figure 3a); in 2017, there were 30 sites in risky areas, 47 sites in sensitive areas, and 24 sites in critical areas (Figure 3b); and in 2022, there were 11 sites in deteriorated areas, 15 sites in risky areas, 36 sites in sensitive areas, and 39 sites in critical areas (Figure 3c). Heritage sites appeared in deteriorated areas in 2022 only. The number of heritage sites in risky areas initially increased and then sharply decreased; the number of heritage sites in sensitive areas decreased annually, whereas that in critical areas significantly increased. Heritage sites existed in general areas in 2012 only (Table 5). Notably, heritage sites situated in deteriorated areas tend to be located a considerable distance from the Jianmen Shu Road route or in areas with a high concentration of heritage sites.

In 2012, the Jianmen Shu Road route traversed three risky areas, six sensitive areas, and one general area; in 2017, it traversed three risky areas, four sensitive areas, and three critical areas; and in 2022, it traversed six risky areas, three sensitive areas, and two critical areas. These results show that, despite fluctuations in the number and types of areas traversed, the overall condition of the counties along Jianmen Shu Road did not significantly deteriorate. This indicates that the TES conditions of this route were better than those of the broader study area.

3.2. Spatial–Temporal Migration Trends in TES in the Jianmen Shu Road Heritage Area

To further analyze the spatial variation trend in TES in the Jianmen Shu Road Heritage Area, we utilized the SDE model to derive the parameters of SDE and the center-of-gravity migration trajectory from 2012 to 2022. We then plotted the changes in the SDE and the center-of-gravity migration trajectory within the study area.

The spatial distribution of TES in the Jianmen Shu Road Heritage Area from 2012 to 2022 exhibited a “northeast-to-southwest” pattern (Figure 4a). The center of gravity of the SDE in the research area has consistently been located within Zitong County, with its movement trajectory forming a “V” shape (Figure 4b). Between 2012 and 2017, the center of gravity shifted toward the southwest, whereas from 2017 to 2022, it moved toward the northeast, covering a total migration distance of 11.331 km. The annual migration distance of the center of gravity was observed to decrease (Figure 4c).

As shown in Table 6, the SDE in the study area exhibited a decreasing trend from 2012 to 2022 with significant clustering characteristics. The length differences between the long and short axes of the SDE were substantial, with ratios of 2.09, 2.16, and 2.0 in the three years, indicating strong directionality in the spatial distribution of TES in the study area. The long axis gradually decreased from 131.64 km to 127.01 km, while the short axis increased from 63.02 km to 63.60 km, suggesting that, despite some fluctuations in TES within the study area, the overall spatial pattern remained relatively stable. The azimuth angle of the SDE continuously decreased annually from 48.46° to 45.83°, indicating that the TES in the study area continued to shift in a clockwise direction; however, the magnitude of this change was small and had a limited impact on the spatial pattern.

3.3. Influencing Factors of TES in the Jianmen Shu Road Heritage Area

3.3.1. TES Factor Detection

Factor detection can reveal the explanatory power of different factors for TES in the study area. We calculated the explanatory power q-values for each factor (Table S5) and tested the significance of the corresponding p-values. The results indicate significant differences in the explanatory power of various factors for TES, as shown in Table S6.

From the perspective of the criterion layers (Figure 5), from 2012 to 2022, the state (S) layer had the highest average explanatory power at 0.3037, and the pressure (P) layer had the lowest at 0.1741. The driver (D) and state (S) layers showed a decreasing trend from 2012 to 2022, followed by an increasing trend; the pressure (P) and response (R) layers showed a trend of first decreasing and then increasing; and the impact (I) layer increased annually. Notably, the explanatory power of the response (R) layer significantly increased from 0.1542 to 0.4759 between 2017 and 2022.

As shown in Figure 6, in the driver (D) layer, except for elevation (D4) and slope (D5), the q-values of the factors showed fluctuating downward trends. The population urbanization rate (D1), GDP growth rate (D3), and year-on-year growth rate of tourist arrivals (D7) were important driving factors; however, their q-values decreased from 0.3481, 0.4892, and 0.3526 to 0.2379, 0.3595, and 0.231, respectively.

In the pressure (P) layer, population density (P1), road network density (P2), and frequency of earthquakes with magnitudes of 3 and above (P6) are important pressure factors. Population density generally shows a downward trend, while the q-values of road network density and the frequency of earthquakes with magnitudes of 3 and above show fluctuating upward trends. Additionally, the explanatory power of soil erosion intensity (P3) has been increasing yearly, whereas that of average PM_2.5 concentration (P5) has decreased annually.

In the state (S) layer, the density of the Jianmen Shu Road heritage sites (S6) is a vital state factor, and its q-value has increased annually. The q-values of economic density (S1) and NDVI (S4) decreased annually, whereas the average annual temperature (S2) and the number of 3A and above scenic areas (S5) showed fluctuating decreases. However, the average yearly precipitation (S3) increased after 2012.

In the impact (I) layer, nighttime light intensity (I1) and water network density (I4) are important influencing factors, and their q-values have significantly increased since 2012. In contrast, the other factors show a fluctuating downward trend.

In the response (R) layer, the proportion of cultural tourism and sports expenditures in total expenditure (R3) was the main response factor, and all the factors in this layer exhibited fluctuating upward trends.

The average q-values of the factors indicate that, from 2012 to 2022, the density of the Jianmen Shu Road heritage sites (S6) had the most significant impact on TES in the study area, with a value of 0.6337. The next most influential factors were the number of 3A and above scenic areas (S5), the proportion of culture, tourism, and sports in total expenditure (R3), and the NDVI (S4), with values of 0.3502, 0.334, and 0.3207, respectively. Additionally, the soil erosion intensity (P3), economic density (S1), and fertilizer application rate (I5) had the lowest impact, with values of 0.0997, 0.0906, and 0.0176, respectively.

3.3.2. TES Interaction Detection

Interaction detection illustrates the trends of changes in explanatory power when different factors interact. The results of interaction detection showed that the interaction between different factors had a significant impact, with the combined effect being substantially greater than those of the individual factors. This manifests as a nonlinear enhancement and double-factor enhancement, with no instances of independence or weakening. These findings indicate that the development of TES in the Jianmen Shu Road Heritage Area is influenced by multiple interacting factors.

As shown in Figure 7a, in 2012, 310 groups showed nonlinear enhancement, and 40 groups showed double-factor enhancement. Among these, the interactions between the proportion of cultural tourism and sports in the total expenditure (R3) and NDVI (S4), between the sewage treatment rate (R2) and number of industrial enterprises (P4), and between the NDVI (S4) and road network density (P2) exhibited the strongest explanatory power, with values of 1.0, 0.9775, and 0.9493, respectively. In contrast, the interaction between slope (D5) and elevation (D4) had the weakest explanatory power at only 0.1026.

In 2017, 308 groups showed nonlinear enhancement, and 42 groups showed double-factor enhancement (Figure 7b). Among these, the interactions between the proportion of energy conservation and environmental protection in the total expenditure (R1) and density of Jianmen Shu Road heritage sites (S6), between the average annual temperature (S2) and population urbanization rate (D1), and between the forest coverage rate (I3) and number of 3A scenic areas (S5) exhibited the strongest explanatory power, with values of 0.9757, 0.9486, and 0.9345, respectively. The interaction between the average annual temperature (S2) and frequency of earthquakes with magnitudes of 3 or more (P6) had the weakest explanatory power, with a value of 0.1149.

In 2022, there were 272 groups with nonlinear enhancement and 78 groups with double-factor enhancement (Figure 7c). Among these, the interactions between the water network density (I4) and tertiary industry growth rate (D2), between the water network density (I4) and NDVI (S4), and between the proportion of cultural tourism and sports in total expenditure (R3) and the density of Jianmen Shu Road heritage sites (S6) showed the strongest explanatory power, with values of 0.9957, 0.9952, and 0.9927, respectively. The interaction between the forest coverage rate (I3) and NDVI (S4) had the weakest explanatory power, with a value of 0.0933.

According to the analysis of the average q-values of factor interactions, from 2012 to 2022, the interactions between the density of Jianmen Shu Road heritage sites (S6) and the number of 3A and above scenic areas (S5), between the density of Jianmen Shu Road heritage sites (S6) and NDVI (S4), and between the proportion of energy conservation and environmental protection in total expenditure (R1) and the density of Jianmen Shu Road heritage sites (S6) had the most significant explanatory power for TES in the study area, with average q-values of 0.9122, 0.9114, and 0.9076, respectively. In contrast, the interaction between slope (D5) and elevation (D4) had the lowest explanatory power at only 0.1565. Additionally, the number of double-factor enhancement groups increased from 40 in 2012 to 78 in 2022, indicating that the TES system in the study area has become more complex and diverse.

4. Discussion

4.1. Analysis of TES Status in the Jianmen Shu Road Heritage Area

TES has garnered significant attention from both the government and the public, as it directly impacts tourists’ interests and ecological well-being and serves as crucial ecological support for the high-quality development of the tourism industry [12,78]. The TES situation in the Jianmen Shu Road Heritage Area from 2012 to 2022 was not optimal and exhibited a trend of annual deterioration. This result is corroborated by Weng et al. [79] in their study of TES in five provinces along the Silk Road. Despite being included in the “Chinese World Cultural Heritage Preparatory List” in 2012 and qualifying for application as a World Natural and Cultural Heritage Site, the development of tourist attractions in the Jianmen Shu Road area lacked standardization, government support was insufficient, and measures for effective ecological environment and site protection management were inadequate, leading to a suboptimal TES situation. After the “Overall Plan for Jianmen Shu Road Scenic Area (2017–2030)” was formulated in 2017 and the application for World Natural and Cultural Heritage was initiated, relevant regulations were issued, heritage site sorting and protection scope division were conducted, tourism planning was implemented, and tourism infrastructure was improved, resulting in some progress being made in ecological environment and site protection. However, owing to population growth and excessive human intervention in the study area, issues such as environmental degradation and disorder in scenic areas have emerged, leading to a continuous decline in TES. In 2019, the outbreak of COVID-19 [80] resulted in the closure of numerous tourist attractions and public venues in the study area, significantly restricting tourist activity and reducing tourism revenue. Concurrently, fiscal expenditures were redirected to heavily affected regions, leading to a substantial increase in medical waste [81], which also inflicted considerable economic losses in the cultural and tourism industries. This situation contributed to the further deterioration of TES in 2022. However, Chen et al. [15], who studied the Dabie Mountain Geopark in Huanggang, found that TES in the study area exhibited an increasing trend, both overall and regionally, over the years. This can be attributed to the local government’s early emphasis on TES development, substantial investment in professional personnel training, infrastructure improvements, and the establishment of regulations for resource protection, leading to the creation of an effective protection management system that comprehensively enhances the ecological environment and promotes tourism development. It is important to note that data from the post-2019 COVID-19 outbreak period were not analyzed in their study.

From the perspective of various districts and counties, there were significant variations in TES from 2012 to 2022. Specifically, Lizhou District, Fucheng District, and Jiange County exhibited the highest TES indices, and Anzhou District and Santai County demonstrated consistent improvements, while the TES indices of other districts and counties fluctuated. This phenomenon can be attributed to the fact that Lizhou and Fucheng Districts are the central areas of Guangyuan City and Mianyang City, respectively, and benefit from evident economic development advantages. As the primary location of the Jianmen Shu Road heritage sites, Jiange County possesses core tourist attractions, such as Jianmen Pass and the Cuiyun Corridor, and has implemented numerous tourism development initiatives. These results suggest that economic development and tourist attractions significantly influence TES [18,82]. Although Anzhou and Santai Counties lack notable economic or tourism advantages, they have prioritized ecological civilization and green development by vigorously promoting urban and rural greening, implementing comprehensive environmental improvements, and strengthening the protection and development of characteristic cultural tourism resources. Consequently, the TES indices have steadily increased over the years.

Based on the spatial–temporal evolution analysis of TES in areas encompassing the Jianmen Shu Road route and its heritage sites, it is evident that, from 2012 to 2022, apart from a minor increase in heritage sites within deteriorated areas and the disappearance of the general safety area, TES in areas with heritage sites showed significant improvements. Heritage sites in deteriorated areas were isolated from the Jianmen Shu Road route and areas with high heritage site density. Furthermore, the districts and counties along Jianmen Shu Road have never experienced deterioration. This indicates that integrating heritage site protection into regional development strategies [83] can effectively mitigate the decline in TES in certain areas and contribute positively to the sustainable development of the regional ecological environment, heritage sites, and tourism economy [84,85].

4.2. Analysis of the Spatial–Temporal Migration Trends in TES in the Jianmen Shu Road Heritage Area

According to the results of the SDE model, the spatial distribution of TES in the Jianmen Shu Road Heritage Area from 2012 to 2022 exhibits a northeast–southwest directional pattern. The long axis is larger than the short axis, indicating a strong orientation and a relatively stable spatial distribution pattern, which is consistent with the findings of Xiao et al. [35]. The center of gravity of TES has consistently been located in Zitong County, which has the largest ancient cypress forest in China. From 2012 to 2017, the center of gravity moved southwest, and from 2017 to 2022, it shifted northeast. This movement may be attributed to the influence of national policies such as the Great Western Development Strategy (GWDS) at the initial stages of the study [86]. These policies promoted the integrated development of the cultural tourism and sports industries, initially focusing on the southwest area because of improved economic efficiency in tourism. However, with the rapid development of the tourism economy, its negative impact on the ecological environment and heritage sites has become increasingly prominent, leading to a gradual shift in focus to the northeast, which is characterized by high ecological capacity and heritage site density [87]. Meanwhile, due to the release of guidance on promoting the development of comprehensive tourism [88] and the formulation of relevant regulations, such as the “Regulations on the Protection of Jianmen Shu Road in Guangyuan City”, tourism construction projects that affect the landscape and ecological environment of Jianmen Shu Road have been strictly restricted, leading to a gradual reduction in the shift in the center of gravity. Additionally, the COVID-19 pandemic has had a comprehensive negative impact on the tourism industry, not only causing the stagnation of activities, declining income, idle resources, and environmental pressures but also further restricting the normal operation and development of tourism-related enterprises. Consequently, this slowed the shift in the center of gravity toward the northeast.

4.3. Analysis of TES Influencing Factors in the Jianmen Shu Road Heritage Area

From the perspective of the criterion layers, the average explanatory power of the state (S) layer was highest from 2012 to 2022, as verified by Zhao and Guo’s [31] study. This trend initially decreased and then increased, indicating that there were issues with incomplete planning and insufficient attention paid to ecological environmental protection in the research area before 2017. However, these issues have gradually been resolved. Specifically, in 2015, the Ministry of Housing and Urban-Rural Development included the Jianmen Shu Road Scenic Area in the “National Endangered List of Scenic Areas” and issued a serious warning, ordering the rectification of its issues within a specified timeframe and subjecting it to focused supervision [89]. Consequently, from 2017 to 2022, the local government placed significant emphasis on rectification efforts, strictly adhered to the demands from the Ministry of Housing and Urban-Rural Development, implemented corrective measures for each item, established a management organization and planning system for scenic spots, optimized architectural styles and the ecological environment, and achieved notable improvements. The average explanatory power of the pressure (P) layer was the lowest but showed an upward trend after an initial decrease. This suggests that, although the pressure on the study area is relatively low, continuous attention is still required. Notably, during the research period, the explanatory power of the average annual temperature (S2) and average annual precipitation (S3) in the state (S) layer exhibited instability. This instability may contribute to extreme weather events, thereby leading to natural disasters, such as floods and landslides, which can cause significant damage to the ecological environment and heritage sites. The Jianmen Shu Road Heritage Area, located in Sichuan Province, is characterized by complex terrain and a high frequency of floods and waterlogging disasters. According to statistical data, Sichuan Province has the most floods and waterlogging disasters, on average, in the country [90]. Moreover, the research area lies within the northeastern section of the Longmen Mountain seismic belt and the central–southern section of the north–south seismic belt, which contain numerous fault zones, making it an earthquake-prone area. During the study period, 44 earthquakes with magnitudes of 3 or more occurred. The response (R) layer has significantly increased since 2017 and has consistently been higher than the state (S) layer since 2022, indicating that it has become a key factor influencing the development of TES in the study area. This trend can be attributed to the recent emphasis on building a comprehensive natural reserve system and enhancing the level of heritage site protection and development, which are now central to the construction of China’s ecological civilization. Numerous national laws, regulations, local ordinances, and guidelines have been enacted [91]. Among them, the “Regulations on the Protection of Jianmen Shu Road in Guangyuan City” ¹ explicitly prohibit any construction project that may impact the overall landscape or damage the ecological environment within the Jianmen Shu Road construction control zone. Additionally, the implementation of the “forest chief scheme + protection of old and famous trees” management model, which includes regular patrols for each tree and scientifically tailored rescue plans, has been established [46]. Through the establishment of nature reserves, the protection of natural forest resources, wetland conservation and restoration, the reversion of farmland to forest and grassland to wetlands, and other key ecological projects, these measures not only prevent natural resource degradation and ecological damage but also effectively maintain the integrity and authenticity of heritage sites.

From the perspective of the indicator layer, between 2012 and 2022, various tourism and ecological environmental factors significantly contributed to the stable development of TES. These factors include the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of culture, tourism, and sports in total expenditure (R3), and the NDVI (S4), which aligns with the research by Liu et al. [92]. The influence of the Jianmen Shu Road site on TES has become increasingly significant, a trend that can be attributed to the local government’s comprehensive promotion of national A-level tourist attractions since 2017 [93]. The government has achieved significant improvements in both the ecological environment and cultural tourism within the study area by implementing green development concepts, innovating cultural and tourism formats, guiding the green and transformational development of tourist attractions, and enhancing service quality, economic benefits, popularity, and regional greening. During this period, multiple important projects and activities were initiated [94]. In 2022, with the official implementation of local regulations, such as the “Guangyuan Jianmen Shu Road Protection Regulations”, clear boundaries for the protection and development of Jianmen Shu Road were established. This has significantly enhanced the conservation of both the ecological environment and heritage sites. Furthermore, local cultural tourism and sports authorities are actively enhancing the tourism infrastructure, developing diverse tourism products, and promoting the integrated development of culture and tourism along Jianmen Shu Road. However, factors such as soil erosion intensity (P3), economic density (S1), and the fertilizer application rate (I5) had relatively limited effects. Among these, economic density (S1) and the fertilizer application rate (I5) were positive contributors, whereas soil erosion intensity (P3) was a negative factor. This suggests that, although significant progress has been made in the protection and rational utilization of water and soil resources, challenges remain in optimizing the economic structure, addressing increased food demand due to population growth, reducing the arable land area, and improving fertilizer efficiency. These issues may lead to economic and ecological imbalances and the loss of biodiversity, hindering the stable development of TES in the study area. Therefore, actively promoting high-quality economic development, optimizing the industrial structure, and continuously focusing on scientific fertilization and environmental protection are crucial for enhancing TES [95,96].

The interaction results show that factors have mutually reinforcing relationships rather than being independent or additive. TES systems in the study area have become increasingly complex and diverse. This conclusion is supported by the studies conducted by Gan et al. [97] and Peng et al. [98]. Specifically, the explanatory power of interactions between the density of Jianmen Shu Road heritage sites (S6) and the number of 3A and above scenic areas (S5), the NDVI (S4), and the proportion of energy conservation and environmental protection in the total expenditure (R1) is particularly significant. This indicates a substantial interactive influence between the Jianmen Shu Road heritage site and other factors. Therefore, to promote the stable development of TES in the study area, special attention should be paid to coordinating dominant interaction factors such as heritage sites, tourism, and the ecological environment. In contrast, the interaction between the slope (D5) and elevation (D4) had the least explanatory power, possibly because they are critical terrain factors. Within a specific range, higher altitudes and steeper slopes tend to experience less human disturbance, resulting in more diverse habitats and resources for flora and fauna [99], which contributes to higher TES. However, areas with higher TES, including the Jianmen Shu Road route and heritage sites, are generally located far from areas with complex terrain or are difficult to access.

4.4. Conceptual Framework of TES Driving Mechanisms

The detection results of the aforementioned influencing factors and the analysis of the trends of interaction changes, combined with ecological patterns and ecosystem theory, reveal that the various elements of TES are in a state of mutual influence and cyclical operation. Regulating the key factors of the TES system is beneficial for maintaining its stable operation. Therefore, the mean q-values of the various factors from 2012 to 2022 were ranked (Table S5), and the two influencing factors with the highest mean values in each criterion layer were selected, namely, the population urbanization rate (D1), GDP growth rate (D3), population density (P1), road network density (P2), number of 3A and above scenic areas (S5), density of Jianmen Shu Road heritage sites (S6), nighttime light intensity (I1), water network density (I4), sewage treatment rate (R2), and proportion of cultural tourism and sports in total expenditure (R3). A conceptual framework of the driving mechanisms of TES in the Jianmen Shu Road Heritage Area has also been drawn (Figure 8).

The driver (D) is the power source for the evolution of TES in the Jianmen Shu Road Heritage Area. Population urbanization and GDP growth within the study area have provided foundational support and a significant impetus to the tourism industry. However, the rapid expansion of tourism has led to an increase in population and road network densities, which have exerted various pressures (P) on the study area. These pressures impact population dynamics, resource allocation, and environmental conditions, which negatively influence TES. In particular, the surge in tourist numbers and heightened transportation demands at the Jianmen Shu Road heritage sites and other core 3A scenic areas have caused considerable pollution and damage, intensifying pressure on the ecological environment and heritage sites. This disrupts the stable state (S) of the original TES system and has a negative impact (I) on both the level of regional development and the quality of the ecological environment, potentially leading to further deterioration of the TES system through cumulative causal cycles. Consequently, a series of response (R) measures can provide effective positive incentives and solutions for the healthy operation of the TES system in the Jianmen Shu Road Heritage Area, such as improving the level of environmental protection and governance and paying more attention to cultural tourism and sports. Moreover, different response measures have reciprocal effects on various factors, including enhancing the effectiveness of driver forces, mitigating the emergence of pressures, maintaining or regulating state factors, and promoting or alleviating impacts, ultimately achieving a complete closed-loop, virtuous cycle for the TES system.

4.5. A TES Optimization Strategy for the Jianmen Shu Road Heritage Area

From the above results and discussion, it is evident that TES in the Jianmen Shu Road Heritage Area has deteriorated, with a clear direction in the migration of its center of gravity. However, this migration trend has been gradually decelerating over the years. Furthermore, there are notable disparities in the stability of the TES index and the explanatory power of influencing factors. To prevent further deterioration in the study area, mitigate adverse effects on sustainable development, and promote the flourishing of natural and cultural diversity, the following optimization strategies are proposed:

(1) According to the spatial–temporal evolution results of TES, a zoning control method was adopted to implement targeted measures in areas with different TES indices. For districts and counties with superior TES indices (e.g., Lizhou District, Fucheng District, and Jiangye County), the protection and utilization of the ecological environment and heritage sites, as well as tourism development, should continue in accordance with relevant laws and regulations. Additionally, these areas should enhance disaster prevention and control awareness and improve emergency response capabilities. Districts and counties with poor TES indices (e.g., Anzhou District, Shifang City, and Yanting County) should be prioritized for improvement, and more scientific, rigorous, and standardized policies should be formulated, such as increasing financial support, controlling urban expansion, establishing early warning mechanisms, and actively addressing traffic and environmental issues. Simultaneously, diversion measures should be implemented during peak tourist periods to reduce the pressure on tourist attractions [38]. Moreover, exchange and cooperation with high-quality regions should be strengthened. By considering the differences in TES across various districts and counties and combining the regional synergistic effects of tourism cooperation development and environmental protection policies [100], the government should provide appropriate policies to maximize the advantages of regional characteristics and jointly promote the restoration and improvement of TES in the study area.

(2) According to the site distribution of TES, the local government should integrate heritage site protection and utilization into development planning in accordance with the overarching global, national, and regional development strategies [101]. First, by enhancing the comprehensive planning of heritage site protection, key national heritage routes such as the Jianmen Shu Road, Qin Zhi Road, Xiang Gui Ancient Road, and Nan Yue Ancient Post Road can be integrated into a cohesive protection framework. This initiative would constitute an essential component of the Chinese civilization identification system and establish a new paradigm for the protection and utilization of large-scale heritage sites that integrates points, lines, and surfaces. This would contribute to the Jianmen Shu Road World Natural and Cultural Heritage Application. Second, by establishing ecological, cultural, and heritage corridors, remote heritage sites can be effectively integrated to achieve comprehensive spatial and elemental coverage, thereby significantly expanding the reach of cultural tourism. Additionally, by prioritizing the core heritage sites and their advantageous areas, emphasis is placed on promoting premium routes for China’s cultural tourism, mountain experience tourism, and forest health tourism. This will enhance the quality of tourism services, increase the visibility of tourism destinations, establish a model eco-tourism demonstration area in the heritage areas, and promote the integration of cultural and tourism development with TES in the study area.

(3) According to the spatial–temporal migration trends in TES, in the northeast area, which has a high ecological environment capacity and a dense concentration of heritage sites, ecological environmental protection supervision should be further strengthened to ensure steady improvement in ecological environment quality and to build a solid ecological barrier. Simultaneously, it is imperative to rationally plan the surrounding land uses to prevent damage resulting from excessive development and construction. This strategic planning will ensure both the safety and environmental harmony of the heritage sites while enhancing the visitor experience. In the southwest area, where the ecological environment capacity is low and there are few heritage sites, the protection and restoration of the existing ecological environment and heritage sites should be strengthened. The integrity and authenticity of the heritage sites should be ensured through ecological restoration, heritage site reinforcement, and other measures. Cooperation and exchange with important core areas should be enhanced to learn from their successful experiences and jointly promote the improvement of the regional ecological environment and heritage site protection levels, achieving comprehensive development in TES [102].

(4) According to the influencing factors of TES, first, the local government should systematically and periodically undertake specialized surveys in the heritage area to comprehensively and accurately identify all heritage sites. Through meticulous investigation, the precise location, historical significance, and current condition of each heritage site can be ascertained. Subsequently, more precise protection zones can be delineated based on the types and characteristics of the heritage sites, ensuring that every historically and culturally significant site receives appropriate preservation measures. Second, the local government should enhance the ecological compensation policy; implement targeted protection measures, such as returning farmland to forests, grasslands, and wetlands; and actively support the creation of nature reserves, forest parks, wetland parks, and national A-level tourist attractions. In addition, it is necessary to increase investments and supervision in culture, tourism, sports, energy conservation, and environmental protection. By establishing special funds for the integrated development of culture and tourism, energy conservation, and environmental protection and by enhancing the construction of green infrastructure [103], the government can consistently promote scientific education and cultural tourism activities, raise public awareness of environmental protection, promote China’s excellent culture, and establish public participation mechanisms such as resource co-management and collaborative protection. These efforts will collectively help maintain the stability of the ecological environment and cultural inheritance of TES in the study area.

(5) According to the model results of interactions between factors of TES, the local government should prioritize green and low-carbon development by optimizing the industrial structure, accelerating the construction of green factories and parks, and promoting the green transformation of industries [104]. Comprehensive environmental governance and ecological restoration measures should be implemented, including coordinated management of sewage, household waste, and environmental infrastructure [105]. Land management and protection should be strengthened through the enforcement of farmland protection red lines and urban development boundaries to ensure sustainable farmland use and the production of high-quality ecological agricultural products. The government should also attract high-level talent and establish partnerships with universities and research institutions to promote the application of modern technologies such as big data, blockchain, and artificial intelligence. This will enhance the development of smart tourism and introduce innovative tourism methods such as “cloud tourism” and live-streaming tours. Furthermore, support for the innovation of cultural and tourism IP projects and the integration of cultural and tourism products [106] is essential. Efforts should focus on exploring the value of the heritage site while prioritizing its protection to promote the high-quality development of TES in the study area.

4.6. Research Innovations and Future Prospects

4.6.1. Research Innovations

The above research results on TES have laid a solid foundation for this study, but several aspects are worthy of further exploration: (1) In the construction of index systems, previous research has predominantly employed easily obtainable and quantifiable indicators, such as the number of star hotels, travel agencies, air quality, and green coverage rates in built-up areas, while insufficient consideration has been given to indicators related to natural disasters, remote sensing images, and measured data [107,108]. (2) Concerning the research period, many studies have focused on short-term analysis and overlooked long-term impacts, especially after the outbreak of COVID-19 in 2019, which profoundly impacted the tourism economy and ecological environment [109]. However, most studies failed to pay sufficient attention to data from 2019 onwards. Further analysis of post-outbreak data is crucial for understanding changes in TES after the pandemic. (3) In terms of research methods, previous researchers exploring TES spatial–temporal patterns predominantly relied on conventional spatial autocorrelation methods and commonly employed the default threshold distance provided by GIS systems. However, these methods lack universal applicability. Furthermore, most studies exploring TES’s driving factors frequently relied on global regression models, such as the OLS model. However, the OLS model assumes that data are evenly distributed in space, meaning that data points at different locations share the same regression coefficient, which is often not true in practice [110]. (4) In terms of research areas, tourist destinations are characterized by a high volume of visitors, frequent activities, and comprehensive tourism facilities, forming an integrated tourism system. As a result, these destinations have attracted considerable attention from scholars, leading to a relatively abundant body of research. In contrast, studies on heritage areas have been limited. Some scholars have utilized the Analytic Network Process (AHP) and Delphi methods to develop a TES evaluation index system for heritage areas, which includes natural ecological security, landscape visual security, and local cultural ecological security, and proposed corresponding effective measures [111]. Other scholars have constructed a TES evaluation index system for heritage areas based on the PSR model, determined index weights through expert questionnaires, calculated the TES composite index, and conducted relevant analyses [112]. However, previous studies on TES in heritage areas exhibit shortcomings in the construction of the evaluation index system, the exploration of spatial–temporal evolution trends, the analysis of influencing factors, the investigation of driving mechanisms, and the examination of the relationship between heritage sites and TES. These aspects present important opportunities for further research. Heritage areas not only embody rich historical and cultural values but also serve as crucial witnesses to urban development. Their uniqueness and importance cannot be overlooked.

In response to the limitations identified in previous TES studies, this research introduces several innovations: (1) A range of key indicators is incorporated into the evaluation index system for TES to enhance its comprehensiveness. These indicators include natural disasters, remote sensing images, and measurement data (e.g., frequency of earthquakes with magnitudes of 3 and above, average PM_2.5 concentration, NDVI, and nighttime light intensity), as well as readily accessible indicators (e.g., density of Jianmen Shu Road heritage sites, number of industrial enterprises, number of 3A and above scenic areas, and fertilizer application rate). These enhancements not only improve the applicability and accuracy of the evaluation index system but also better address the research and management requirements of different heritage areas. (2) The TES data for the study area were systematically collected and analyzed for 2022, with a focus on changes in TES following the onset of the COVID-19 pandemic. (3) The SDE model and GeoDetector were utilized to more accurately investigate the spatial–temporal evolution characteristics within the study area. (4) The scope of research content and methods concerning TES in heritage sites were expanded, examining the relationship between heritage sites and TES, and a conceptual framework for the driving mechanisms was developed. This provides essential decision-making support for ecological environmental protection, heritage site preservation and utilization, and sustainable tourism development.

4.6.2. Future Prospects

However, there are still the following limitations: (1) Due to the blurred boundaries and numerous intersections within the tourism industry, it is difficult to obtain data on the ecological environmental damage directly caused by tourism, as well as natural disasters that occur in counties and districts [41]. Moreover, most indicators rely on statistical data, which generally have lower accuracy than remote sensing observational data [113]. Therefore, future research should consider a more comprehensive set of indicators, such as Points of Interest (POIs), carbon emissions, the number of star-rated hotels, forest fires, and flood data [33,80]. The proportion of monitoring or remote sensing data indicators in the overall indicator system will be increased as much as possible to develop a universally applicable TES evaluation framework. (2) We engaged 10 experts to assist with the screening indicators. Considering the limited number of experts involved and the restricted participation frequency, future studies will expand the panel of experts and increase their involvement in the determination of indicator weights and the development of strategies. Furthermore, we will conduct a deeper analysis of the differences in concerns between experts and residents. This will enhance the comprehensiveness and scientific rigor of our research. (3) Although a TES driving mechanism framework was constructed using GeoDetector, the framework remains hypothetical, and the transmission channels and mechanisms between the various levels of the TES system have not been thoroughly tested. Future research should explore the transmission pathways between the criterion and indicator layers using methods such as structural equation modeling (SEM) or hierarchical linear modeling (HLM) [18]. Additionally, comparative analyses of different heritage areas can be conducted to reveal deeper insights into the internal mechanisms of TES in heritage areas, providing more practical models and management ideas for the TES system in heritage areas. (4) Although we have utilized the entropy-weighted TOPSIS method to calculate the weights of 27 evaluation indicators and the nearness degrees of 22 districts and counties from 2012 to 2022, the stability of these results has not been rigorously verified. Therefore, in future studies, we will conduct a detailed sensitivity analysis on the input data for each indicator to evaluate changes in the entropy-weighted TOPSIS results and assess the impact of data variations on the results, thereby further verifying the reliability of the TES index.

5. Conclusions

In this study, the DPSIR model, in conjunction with the entropy-weighted TOPSIS method, was used to scientifically evaluate the TES index of the Jianmen Shu Road Heritage Area from 2012 to 2022. Leveraging the SDE model and GeoDetector, we performed an in-depth analysis of the spatial–temporal evolution characteristics, migration trends, influencing factors, and interaction mechanisms of TES within the study area. A conceptual framework of the driving forces was constructed to reveal the interplay between the ecological environment, site protection, and tourism economic development. Additionally, corresponding optimization strategies were proposed, providing a robust theoretical and practical foundation for promoting sustainable development in heritage areas. The key findings are summarized as follows:

(1) From the perspective of spatial–temporal evolution, the TES in the study area exhibited a general deterioration trend from 2012 to 2022. Jiange County, Anzhou District, and Santai County maintained steady improvements in their TES indices throughout this period. In contrast, other districts and counties experienced fluctuating TES indices. Areas characterized by a high concentration and high density of heritage sites or robust economies generally had higher TES indices. Furthermore, the districts and counties along the Jianmen Shu Road route never exhibited a deteriorated state. The heritage sites in areas showing a state of deterioration were far from both the Jianmen Shu Road route and areas with high heritage site density.

(2) From the perspective of spatial–temporal migration trends, TES in the study area between 2012 and 2022 exhibited a clear “northeast–southwest” directional pattern, with its center of gravity consistently located in Zitong County, and followed a “V”-shaped movement trajectory that decelerated year by year. The variation in the spatial patterns was minimal and exhibited agglomeration characteristics.

(3) From the perspective of impact factors, from 2012 to 2022, the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of culture, tourism, and sports in total expenditure (R3), the NDVI (S4), and other tourism- and environment-related factors were significant. In contrast, the soil erosion intensity (P3), economic density (S1), and fertilizer application rate (I5) had the lowest explanatory power. The interaction of all the factors had higher explanatory power than any single factor. Among these, the density of Jianmen Shu Road heritage sites (S6) had the most significant interaction with the number of 3A and above scenic areas (S5), the NDVI (S4), and the proportion of energy conservation and environmental protection in the total expenditure (R1). In contrast, the interaction between the slope (D5) and elevation (D4) had the lowest explanatory power. In addition, the TES systems in the study area have become more complex and diversified.

Author Contributions

Conceptualization, C.J. and Q.C.; methodology, C.J., H.L. and X.D.; validation, C.J., X.D., Y.P. and J.C.; investigation, H.L., Y.P. and X.D.; resources, Q.C. and J.C.; writing—original draft preparation, C.J.; writing—review and editing, C.J., X.D., Y.P. and J.C.; visualization, H.L., Y.P. and X.D.; supervision, Q.C and J.C.; project administration, Q.C and J.C.; funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

We are grateful to the College of Landscape Architecture of Sichuan Agricultural University and the Office of the Leading Group for the Declaration of Natural and Cultural Sites of Shu Road for their support.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Note

1. The Standing Committee of the People’s Congress of Guangyuan City, 2021. Guangyuan Jianmen Shu Road Protection Regulations. https://flk.npc.gov.cn/detail2.html?ZmY4MDgxODE3ZDU1NjdjZTAxN2Q3NTU1YzI2NjFjZDM (accessed on 20 November 2024).

Footnotes

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Figures and Tables

Figure 1. The research framework of the tourism ecological security of the Jianmen Shu Road Heritage Area.

Figure 2. Geographical location of the Jianmen Shu Road Heritage Area.

Figure 3. The spatial–temporal distribution of TES and heritage sites in the Jianmen Shu Road Heritage Area from 2012 to 2022.

Figure 4. The TES standard deviation ellipse and center-of-gravity migration in the Jianmen Shu Road Heritage Area from 2012 to 2022: (a) Spatial distribution pattern of SDE, (b) Migration trajectory of the center of gravity, (c) Details of SDE spatial variation.

Figure 5. The evolutionary trends of the TES criterion layers in the Jianmen Shu Road Heritage Area from 2012 to 2022.

Figure 6. The evolutionary trends of the TES index layers in the Jianmen Shu Road Heritage Area from 2012 to 2022.

Figure 7. Detection results of interactions between factors affecting TES in the Jianmen Shu Road Heritage Area from 2012 to 2022.

Figure 8. TES driving mechanisms of the Jianmen Shu Road Heritage Area.

Table 1

Research data types and sources.

Order	Data Type	Data Sources
1	Jianmen Shu Road route and heritage sites’ location data	The Office of the Leading Group for the Declaration of Natural and Cultural Sites of Shu Road, Sichuan Province
2	Social, economic, environmental, and tourism-related data	China Environmental Statistical Yearbook, China County Statistical Yearbook, China Urban Construction Statistical Yearbook, Sichuan Provincial Statistical Yearbook, Sichuan Culture and Tourism Yearbook, Guangyuan Statistical Yearbook, Mianyang Statistical Yearbook, and Deyang Statistical Yearbook, as well as the national economic and social development statistical bulletins and statistical yearbooks of various districts and counties. (https://navi.cnki.net/knavi/yearbooks/index?uniplatform=NZKPT) (accessed on 20 November 2024)
3	Digital Elevation Model (DEM) data	91 Weitu Assistant (http://www.91weitu.com/) (accessed on 18 November 2024)
4	Earthquake location data	China Earthquake Administration (CEA) (https://www.ceic.ac.cn/history/) (accessed on 21 November 2024)
5	Remote sensing data of nightlight illumination	Harvard Dataverse platform (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GIYGJU) (accessed on 22 November 2024)
6	Soil erosion intensity data	Earth Resources Data Cloud (http://www.gis5g.com/data/qxsj/) (accessed on 19 November 2024)
7	NDVI data	National Ecological Data Center Resource Sharing Service Platform (http://www.nesdc.org.cn/sdo/list/) (accessed on 22 November 2024)
8	Road and water data	OpenStreetMap (https://www.openstreetmap.org/)

Table 2

The evaluation index system for tourism ecological security in the Jianmen Shu Road Heritage Area.

Criterion Layer	Element Layer	Indicator Layer (Unit)	Indicator Meaning	Attribute
Driver(D)	Society	Population urbanization rate D1 (%)	Regional urbanization degree	+
	Economy	Tertiary industry growth rate D2 (%)	Tertiary industry development speed	+
		GDP growth rate D3 (%)	Economic development level	+
	Environment	Elevation D4 (m)	General condition of surface morphology	+
		Slope D5 (°)	General condition of surface morphology	−
	Tourism	Year-on-year growth rate of tourism revenue D6 (%)	Tourism economy development trend	+
		Year-on-year growth rate of tourist arrivals D7 (%)	Increasing trend in number of tourists	−
Pressure(P)	Society	Population density P1(persons/km2)	Situation of occupying the area	−
	Traffic	Road network density P2 (km/km2)	Urban road congestion degree	−
	Environment	Soil erosion intensity P3(t/(km2·a))	Situation of soil erosion	−
		Number of industrial enterprises P4 (individual)	Situation of environmental pollution	−
		Average PM2.5 concentration (ug/m3)	Harm to human body and environment	−
	Disaster	Frequency of earthquakes with magnitudes of 3 and above P6 (frequency)	Number of occurrences of earthquake disasters	−
State(S)	Economy	Economic density S1(ten thousand CNY/km2)	Level of economic development	+
	Environment	Average annual temperature S2 (°C)	Quality of ecological environment	+
		Average annual precipitation S3 (mm)	Quality of ecological environment	+
		NDVI S4	Condition of land cover vegetation	+
	Tourism	Number of 3A and above scenic areas S5 (individual)	Development status of tourism industry	+
		Density of Jianmen Shu Road heritage sites S6 (individual/km2)	Distribution of heritage sites	+
Impact(I)	Society	Nighttime light intensity I1 (nW/cm2/sr)	Level of regional development	+
	Economy	Proportion of tertiary industry in total expenditure I2 (%)	Scale of tertiary industry	+
	Environment	Forest coverage rate I3 (%)	Quality of ecological environment	+
		Water network density I4 (m)	Quality of ecological environment	+
		Fertilizer application rate I5 (t)	Benefits of agricultural production	+
Response(R)	Environment	Proportion of energy conservation and environmental protection in total expenditure R1 (%)	Degree of emphasis on energy conservation and environmental protection	+
		Sewage treatment rate R2 (%)	Governance level of environmental protection	+
	Tourism	Proportion of cultural tourism and sports in total expenditure R3 (%)	Level of emphasis on culture, tourism, and sports	+

Table 3

The classification standard for TES indices of the Jianmen Shu Road Heritage Area.

Range	[0, 0.25]	(0.25, 0.35]	(0.35, 0.45]	(0.45, 0.55]	(0.55, 0.65]	(0.65, 0.75]	(0.75, 1]
Level	I	II	III	IV	V	VI	VII
State	Deteriorated	Risky	Sensitive	Critical	General	Relative	Very

Table 4

Types of interactions.

Interaction Type	Description
Nonlinear attenuation	q (X1∩X2) < Min [q (X1), q (X2)]
Single-factor nonlinear attenuation	Min [q (X1), q (X2)] < q (X1∩X2) < Max [q (X1), q (X2)]
Double-factor enhancement	q (X1∩X2) > Max [q (X1), q (X2)]
Independence	q (X1∩X2) = q (X1) + q (X2)
Nonlinear enhancement	q (X1∩X2) > q (X1) + q (X2)

Table 5

TES state evolution statistics of Jianmen Shu Road heritage sites from 2012 to 2022.

State	Deteriorated	Risky	Sensitive	Critical	General	Relative	Very
2012	0	28	67	0	6	0	0
2017	0	30	47	24	0	0	0
2022	11	15	36	39	0	0	0

Table 6

Changes in the center of gravity and SDE parameters of tourism ecological security in the Jianmen Shu Road Heritage Area.

Year	Mean Center Coordinates	Area/km2	XStdDist/km	YStdDist/km	Rotation/°
2012	105°3′54.41″ E, 31°47′14.25″ N	26,057.72	131.64	63.02	48.46
2017	105°1′35.52″ E, 31°44′48.96″ N	25,256.30	131.93	60.94	47.95
2022	105°3′32.92″ E,31°46′4.35″ N	25,375.50	127.01	63.60	45.83

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14030509/s1, Table S1: The main methods used in previous TES research; Table S2: Weight calculation results of the Jianmen Shu Road Heritage Area the indicators from 2012 to 2022; Table S3: Calculation results of the TES index in the Jianmen Shu Road Heritage Area; Table S4: Methods and formulas used; Table S5: The influence factor q-value of the Jianmen Shu Road Heritage Area; Table S6: The p-value of the influence factor for the Jianmen Shu Road Heritage Area.

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