Spatio-Temporal Evolution Characteristics of Tourism Ecological Resilience in China

Abstract

Tourism ecological resilience (TER) is an important indicator of the healthy and sustainable development of the tourism industry, which provides a new analytical perspective for the anti-fragility and tourism ecological security of the tourism industry. This study takes 31 provinces in China as the research area, constructs a comprehensive evaluation index system of TER based on the theory of evolutionary resilience, and uses a comprehensive evaluation index, GIS spatial analysis technology, kernel density estimation, Dagum–Gini coefficient, and other research methods to analyze the spatial and temporal evolution of China’s TER from 2010 to 2022 and the spatial distribution pattern of three dimensions of DPC-ARC-OIC. The results show that (1) In the process of time evolution, the ecological resilience of tourism in China continues to increase, and from 2010 to 2022, China’s TER first increased and then decreased, with an average annual growth rate of 1.47%, among which Yunnan and Jiangxi provinces increased significantly. (2) In the process of spatial evolution, there is an obvious spatial gap in tourism ecological resilience. From 2010 to 2022, China’s TER generally presents a pattern of “high in the east and low in the west, high in the south and low in the north”, forming a hierarchical spatial structure with Beijing and Shanghai as the “dual cores”, decreasing to the periphery. (3) There are obvious spatial differences in the three dimensions of DPC, ARC, and OIC. The DPC of the economically developed regions is higher than that of the economically less developed regions; the ARC fluctuates greatly due to the environmental vulnerability and economic level of the western region, and the OIC, as a whole, rises and presents a multi-polar distribution. (4) The overall difference in China’s TER fluctuates and increases, and regional differences have always been dominant, so it is necessary to take systematic measures according to local conditions to help improve the resilience of the tourism ecosystem and the sustainable development of regional tourism. This study can enrich the theoretical research of TER, but it mainly uses provincial macro data for analysis. It still needs to be strengthened to depict regional heterogeneity characteristics to provide Chinese practice for studying TER.

1. Introduction

With global climate change, the ecological environment problem is becoming more and more serious, and the construction of ecological civilization has become a hot spot of common concern for the international community. Since 2017, global environmental management has shifted from conceptual and holistic governance to specific and breakthrough rectification. The promotion of numerous international measures reflects the pertinence of global resource conservation and environmental governance, such as the establishment of the ecological civilization system in China, the proposal of carbon neutralization and carbon peak [1], and the United Nations’ call for a “zero-pollution planet” reflect the pertinence of global resource conservation and environmental governance. The quality of the ecological environment is directly related to the success of high-quality development [2]. The ecological environment is the foundation of regional sustainable development, and it is imperative to continuously improve the quality of the ecological environment [3].

The high-quality development of tourism is an important measure to protect the regional ecological environment [3]. As a green industry with low resource consumption, low environmental pollution, and strong industrial drive, tourism is known as a green and pollution-free “smoke-free industry”; the spatial spillover effect of green development of tourism has been theoretically supported [4], which plays a positive role in promoting the construction of ecological civilization. However, the tourism industry shows significant environmental dependence. The contradictory attributes of environmental friendliness and resource consumption determine the symbiotic relationship between the ecological environment and tourism development [5]. The vulnerability index of the tourism ecosystem is high, and the vulnerability of tourism to climate change is 2.3 times that of other industries [6], and the recovery cycle of the tourism ecosystem (average 3.2 years) is significantly longer than that of other industries [7], affecting the high-quality development of regional tourism.

The construction of the TER provides an important direction and path for the high-quality development of tourism. The impact of the new coronavirus epidemic not only exposes the inherent vulnerability of the tourism industry but also provides a rare opportunity to break through innovation [8]. Scholars have combined resilience and anti-fragility when examining social ecosystems [9] and countered vulnerability by enhancing resilience. There is a harmonious contradiction between the vulnerability and resilience of the tourism ecosystem [10]. The theory of evolutionary resilience undoubtedly provides an important research perspective in analyzing the influence process of external shocks on the tourism ecosystem and exploring its recovery and reconstruction mechanism. Therefore, China’s tourism industry is in a critical period of structural transformation and upgrading from high-speed growth to high-quality development; it is necessary to pay attention to and strengthen the resilience construction and sustainable development transformation of the tourism ecosystem, which has become an important direction and path for the high-quality development of the regional tourism industry [11].

“Resilience” is a multidisciplinary concept, and resilience research has attracted attention in many fields with considerable breadth and depth [12]. The related research on resilience has roughly gone through five development stages: ‘engineering resilience–ecological resilience–social resilience–social ecological resilience integration–evolution resilience’. In the early 21st century, scholars such as Walker B., Folke C., and Carpenter R. integrated previous theories and proposed an evolutionary resilience theory system [13,14]. Evolutionary resilience integrates the concepts of ecological resilience, social resilience, and social–ecological resilience. It has the three-dimensional characteristics of persistence, adaptability, and transformability and emphasizes the dynamic process of resilience evolution through ‘change–adaptation–transformation’ [15]. Although some early scholars have introduced the dynamic, non-linear, and uncertain views of evolutionary resilience in the theory and review of foreign resilience, most of the domestic research still regards the concept of resilience as a short-term result-oriented behavior, and the research on the dynamic evolution perspective of resilience with long-term and periodicity is still in its infancy [16].

It was first applied to the discipline of physics and developed to the introduction of the concept of resilience into the study of social systems in the 90s of the 20th century [17], and with the intervention of the system evolution view [12], it gradually turned to evolutionary resilience, which is more suitable for the characteristics of the non-equilibrium continuous dynamic evolution of social systems, and the research perspective has gradually expanded to the fields of urban development and tourism development [4]. With the negative impact of tourism development, such as ecological environment damage and increasingly serious pollution emissions, more and more studies have begun to pay attention to the contents of tourism ecological footprint [18,19], tourism environmental carrying capacity [20,21], tourism ecological efficiency [22,23,24], and tourism ecological security [25,26]. This research method mainly measures and evaluates the regional tourism ecology from a relatively single index. Generally speaking, research on the resilience of the tourism industry started late and has produced few results [12]. However, in recent years, scholars at home and abroad have continued to pay more attention to the research on tourism resilience, and research results such as tourism economic resilience [27,28,29], tourism community resilience [30,31,32], tourism enterprise (organization) resilience [33,34,35], and tourism destination resilience [36,37,38,39] have been emerging. The academic research results on the resilience of the tourism environmental system [3,40,41,42,43] have begun to emerge, but there are few relevant studies on the resilience of tourism ecology, which is still in its infancy.

TER is the steady-state resilience of tourism development in the face of resource stress and environmental pressure and the basis for measuring the sustainable development of tourism [44,45]. However, the current research lacks research on tourism ecological resilience and considers its complexity, complexity, and evolutionary characteristics less [46], and it is difficult to comprehensively characterize the connotation of tourism ecosystem that is superimposed and integrated by society, economy, tourism, and ecology and cannot reflect the vulnerability, resistance, adaptability, and resilience of tourism ecosystem in the face of internal and external shocks and disturbances, as well as the adaptation and recovery mechanism and growth trajectory in the face of different risks and challenges and lacks in-depth research on tourism ecological resilience in the context of the new era.

In view of this, this study takes 31 provinces in China as the research object. Based on the theory of evolutionary resilience and the theory of sustainable development, the theoretical framework of TER and the comprehensive evaluation index system of TER are constructed from three core dimensions: DPC, ARC, and OIC. On this basis, the comprehensive level index, Dagum–Gini coefficient, and kernel density estimation are used to explore the spatial and temporal patterns of China’s TER and its sub-dimensional spatial and temporal evolution characteristics from the provincial and seven geographical division scales and to reveal the TER and its sub-dimensional development level, dynamic evolution characteristics, spatial distribution pattern and regional differences from different scales. This study breaks through the traditional single-dimensional perspective and introduces the concept of ‘evolutionary resilience’ into the study of the tourism ecosystem. The resilience index system shifts from the traditional static, rigid index of the natural environment to the dynamic, comprehensive index, which makes up for the lack of attention to the complexity and dynamic evolution characteristics of the tourism ecosystem in previous studies and provides a new analytical perspective for tourism ecosystem assessment, tourism anti-vulnerability, and tourism ecological security. This research uses GIS spatial analysis technology, kernel density estimation, and the Dagum–Gini coefficient, which are less commonly used in the study of tourism ecosystem resilience.

2. Materials and Methods

2.1. Theoretical Framework

From the perspective of evolutionary resilience, resilience is a dynamic process in which the system constantly resists and adapts to external disturbances. It is a kind of ability to change, adapt, and transform, being stimulated by the system in response to pressure and constraints. It transforms the original short-term ‘result-oriented’ behavior concept into a long-term ‘process-oriented’ dynamic behavior concept. It is believed that resilience should not only be regarded as the ability of the system to return to the initial state but also as a process with time attributes in which the system continuously responds to disturbances through a variety of resilience characteristics. Ability includes stability, adaptability, and transformation ability [15,47]. Resilience can be reflected by observing the change in system identity. The system is composed of a variety of elements. The elements interact with each other to generate temporal and spatial correlations. The dynamic changes in resilience are characterized by the dynamic changes in system elements. A variety of characteristics form a collection of capabilities to cope with the whole process of disturbance [16]. When disturbances occur, individuals, organizations, communities, and natural ecosystems in the system must respond to disturbances and changes and have the ability to adapt, respond, and evolve. They can either respond through a specific resilience feature or a combination of multiple features throughout the entire process of disturbance occurrence, thus forming a dynamic set of resilience capabilities [16]. When a system with low resilience encounters external disturbances, the system characteristics will change or even be lost due to the lack of elements and the fracture of the relationship. On the contrary, systems with high resilience will maintain or update system characteristics through resilience [48,49].

The tourism ecosystem is a composite system composed of society, economy, tourism, and ecology, and, in the process of dynamic operation, each subsystem is symbiotic and synergistic, with the composite evolution of the structure and the vulnerability of the development state and the composite evolution is its most significant feature [50]. Evolutionary resilience is more suitable for the characteristics of non-equilibrium and continuous evolution of tourism ecosystems, and the key to the sustainable development of tourism ecosystems lies in building and strengthening tourism ecological resilience. As a complex system, the tourism ecosystem may be out of a stable state due to the disturbance of uncertain external factors, and its system components will change. In order to avoid the stagnation or collapse of the system operation caused by excessive external disturbance, the tourism ecosystem realizes the collection and transformation of DPC, ARC, and OIC by adjusting and absorbing the self-organization process of elements in the system so that the system can continuously respond to, quickly recover, and adapt to external disturbances. Therefore, this paper draws on the connotation of evolutionary resilience and tries to define TER as follows: tourism ecological resilience refers to the ability of the tourism ecosystem to defend against risks, adapt and recover after shocks, and promote system upgrading and renewal when facing uncertain risks, which can integrate the basic components of tourism, ecology, economy, and society and play their functional roles, effectively respond to uncertain risks, and realize the sustainable development of the tourism ecosystem.

According to the characteristics of the tourism ecosystem and the concept, basic connotation, and existing research of tourism ecological resilience, the defense and protection ability, the ability to adapt to and recover, and the ability to optimize and innovate constitute the cycle mechanism of ‘defense–adaptation–optimization’: DPC provides initial stability for the system; ARC alleviates the impact through its own dynamic adjustment, and OIC promotes the long-term upgrading of the system. The three core dimensions of tourism ecological resilience, and each dimension is interrelated and mutually supportive, which, together, constitute the conceptual connotation of tourism ecological resilience from the perspective of evolutionary resilience, reflecting that the tourism ecosystem can defend and protect, adapt to restore, optimize innovation in the face of various challenges and changes, and realize the improvement and improvement in the system. Therefore, this study integrates the most prominent compound evolution of the tourism ecosystem into the multi-dimensional perspective of system development and constructs a theoretical framework and evaluation index system for TER based on the framework of DPC→ARC→OIC. The causal relationship and cyclic transmission path between the dimensions of tourism ecological resilience are shown in Figure 1.

(1)

DPC is the basis of the TER index system, which reflects the buffer capacity, ‘resistance’, and ‘basic stability’ of the system in the face of external shocks. Socio-economic support and the basic support for the development of the tourism industry itself are the basis for maintaining the operation of the tourism ecosystem and for the overall response of the tourism ecosystem to external pressures, which provide financial support and development momentum for the system to cope with shocks, and are also the guarantee for the tourism industry [51] to continue to carry out tourism activities in the face of external shocks. It can cope with the disturbance caused by insufficient system elements through the accumulation and reserve of elements, such as resource shortage, population loss, economic downturn, and natural disasters. Therefore, this study constructs indicators from three aspects: social and economic support, tourism attraction, and tourism reception capacity (

Table 1. Comprehensive Evaluation Index System for Tourism Ecological Resilience.

Capabilities	Feature Layers	Indicator Layer (Number)		Unit	Metric Attributes
Defensive protection capabilities A	Socio-economic support A1	GDP per	A11	Dollar	+
		GDP growth rate	A12	%	+
		Per capita disposable income of residents	A13	Dollar	+
		Total retail sales of consumer goods	A14	billion dollars	+
		Urbanization rate of the population	A15	%	+
		population density	A16	person/km2	+
	Tourist attraction A2	Tourism resource evaluation index	A21	-	+
		Tourism Spatial Density (Tourism Spatial Index)	A22	person /km2	−
		Tourist Population Density (Tourist Density Index)	A23	people/hundreds	−
		Growth rate of tourist arrivals	A24	%	+
		Tourism revenue growth rate	A25	%	+
		Average daily visitor traffic	A26	10,000 people/day	+
	Tourist reception capacity A3	Density of A-level tourist attractions	A31	pcs/km2	+
		Travel agency density	A32	pcs/km2	+
		Density of star-rated hotels	A33	pcs/km2	+
Adaptive resilience capabilities B	Ecological and environmental pressures B1	Solid waste discharge	B11	t	−
		The amount of household waste generated	B12	t	−
		Domestic sewage discharge	B13	10,000 t	−
		Industrial SO2 emissions	B14	t	−
		Industrial wastewater discharge	B15	10,000 t	−
		Industrial soot emissions	B16	t	−
	Development of the tourism industry B2	Total number of tourists	B21	10,000 people	+
		Comprehensive tourism income	B22	billion dollars	+
		Per capita tourism income of residents	B23	Dollar	+
		Tourists spend per capita	B24	Dollar	+
		Tourism economic density	B25	10,000/km2	+
	Ecological and environmental quality B3	precipitation	B31	mm	+
		Green cover area	B32	hectare	+
		Forest cover	B33	%	+
		The area of green space in parks per capita	B34	m2/person	+
		The number of days when the air quality reaches level 2 or above	B35	days	+
		Environmental Composite Index	B36	-	+
		Area of natural wetlands	B37	Thousand hectares	+
Optimization innovation capabilities C	Innovative R&D C1	Tourism R&D funding	C11	billion dollars	+
		Number of tourism-related patent applications	C12	item	+
		Quantity of high schools in each district	C13	piece	+
		Number of tourism-related papers published	C14	chapter	+
		Number of students majoring in tourism in colleges and universities	C15	person	+
	Funding control C2	Tourism management services expenditure ratio	C21	%	+
		Expenditure on cultural construction	C22	%	+
		Tourism capital productivity	C23	%	+
		Investment in the tertiary industry	C24	%	+
		Environmental Investment (Proportion of GDP in Environmental Protection)	C25	%	+
		Energy conservation and environmental protection expenditure ratio	C26	%	+
	Environmental governance C3	Comprehensive utilization rate of industrial solid waste	C31	%	+
		Harmless treatment rate of domestic waste	C32	%	+
		Municipal sewage treatment rate	C33	%	+
		Industrial water reuse	C34	%	+
		Afforestation area	C35	10,000 hectares	+
		The number of workers engaged in environmental protection	C36	person	+

). The stronger the tourism attraction, the greater the tourism population, indicating that the better the tourism market foundation, the greater the consumption potential, and the tourism industry is more capable of resisting the crisis [12]. The growth rate of tourist arrivals and the average daily tourist flow reflect the scale of tourism development and tourism attraction [52]. The density of A-level tourist attractions, star hotels, and travel agencies reflects the scale and reception capacity of tourism service facilities [53,54];

(2)

ARC is the core of the TER index system, reflecting the ability of ‘dynamic adaptation’ and ‘regulatory feedback’ after the system is disturbed. It is emphasized that when the system is faced with internal and external environmental changes or shocks (such as socio-economic changes, natural disasters, etc.), it absorbs exogenous disturbances through self-regulating mechanisms such as ecological restoration, resource recycling, tourism industry organization, and socio-cultural interaction mode to adapt to changes and dynamic recovery, reflecting the system’s adaptability, repair ability, and self-adjustment feedback mechanism. The potential damage and ecological environment pressure caused by pollutant emission intensity to the ecological environment of tourist destinations are mainly manifested in the types of waste water, waste gas, solid waste, domestic waste pollution, etc., which are non-exclusive and indivisible. The system actively resists and absorbs the disturbance caused by internal vulnerability through the robustness and stability of its own functions, such as industrial self-regulation and green environment restoration, and adapts to changes, dynamic recovery, and timely feedback, reflecting the adaptability, repair ability, and self-adjustment feedback mechanism of the system to cope with pressure. Abundant water resources (e.g., precipitation indicators) support higher biodiversity and vegetation cover, better sustain agriculture, forestry, and other productive activities, and, thus, enhance ecosystem resilience [55]. Therefore, this study constructs indicators from three aspects, ecological environment pressure, industrial self-regulation, and green environmental restoration (Table 1) to improve the carrying capacity of the system to absorb disturbances and dynamically adapt to timely feedback so as to respond to various changes and new situations more flexibly and maintain the stable development of the system;

(3)

OIC is the direction of improving the TER index system, which characterizes the ability of ‘innovation transformation’ and ‘upgrading optimization’ when the system responds to challenges. This dimension reflects the ability of the tourism ecosystem to reorganize and evolve according to actual conditions and is the ability of the system to cope with potential external pressures. The reflection system can not only minimize or avoid the negative effects of similar disturbances by continuous memory and learning to deal with similar disturbances but also deal with possible future disturbances by establishing new elements and operating mechanisms. When the number of tourists increases, the natural environment in the tourism ecosystem may be under certain pressure, but the socio-cultural and economic factors in the system will be adjusted accordingly through industrial investment to obtain financial support, strengthen environmental protection measures, investment, and ecological environment governance optimization, improve the quality of tourism services and technological innovation and other ways to restore the normal development path, break the original path dependence, and promote the system to improve quality and efficiency, industrial transformation, optimization, and upgrading. This paper constructs indicators from three aspects: innovation and R&D, capital regulation, and environmental governance (Table 1), and by strengthening the innovation and optimization of these aspects, we can promote the upgrading and transformation of the tourism ecosystem and realize sustainable development of the tourism industry.

2.2. Indicator System

According to the evolution and cyclic transmission path of tourism ecological resilience, following the principles of systematic, scientific, representative, and index data availability, a comprehensive evaluation index system of tourism ecological resilience was constructed from the three core dimensions of DPC, ARC, and OIC (Table 1).

The indicators show the following: (1) The diversity of resources promotes the stability of the system, has the function of natural capital reserve and buffer capacity to cope with shocks, and is characterized by the abundance of tourism resources. The abundance of tourism resources is calculated by assigning 5, 2.5, 1.75, 0.5, and 0.25 weights to the scenic spots according to the classification of scenic spots [56]; (2) The tourist density index (tourist population density) reflects the relative size and potential of the current tourism consumption market, measures the regional tourism carrying capacity, and is expressed as the number of tourist receptions divided by the total number of permanent residents in the region [57]; (3) Tourism spatial index (tourism spatial density) reflects the degree of concentration of tourists per unit area and the degree of utilization of tourism resources, which is characterized by the ratio of the number of tourist receptions to the total area of the region [58]; (4) Tourism R&D funds are based on the R&D funds of the whole society, combined with the proportion of tourism income in the total national economic output value, and the ratio is calculated [56]. To improve the construction of tourism ecological resilience, in addition to strengthening the investment of funds and policies for environmental protection, it also includes improving the capacity of cultural tourism construction, improving the cultural literacy of residents and tourists, and strengthening the training of tourism service quality management. This study uses cultural construction expenditure ratio, investment in the tertiary industry, tourism management service expenditure ratio, and other indicators to characterize the construction capacity of cultural tourism: tourism management service expenditure ratio is calculated by the proportion of tourism management and service expenditure to GDP; the cultural construction expenditure ratio is calculated by the proportion of cultural business expenses in the national fiscal expenditure; the tertiary industry investment intensity is calculated by the proportion of the tertiary industry fixed asset investment to the total investment [59]; (5) Tourism capital productivity is an important indicator to measure the input and output efficiency of the tourism industry, which reflects the efficiency level and capital regulation ability of the tourism industry in the use of capital resources [60]; (6) Environmental investment power reflects the investment of tourism destination government in alleviating pollution damage and improving ecology, which is calculated by the proportion of investment in environmental pollution control to GDP [25].

2.3. Research Methods and Data Sources

2.3.1. Entropy Weight Method

The entropy weight method is a method to determine the objective weight according to the size of the information entropy. The smaller the information entropy, the greater the degree of variation in the index, the greater the weight [29], and the difference and importance between the indicators can be reflected by calculating the information entropy of each index. In this paper, the comprehensive level index is determined by the entropy method to measure the ecological resilience level of China’s provincial tourism. The specific steps are as follows: In order to ensure the comparability and accuracy of the data, firstly, the definition, calculation method, and statistical caliber of each index are clearly defined, and different units are converted into international standard units or research general units. On this basis, the data of each index are nondimensionalized, and the original index is converted into a non-dimensional index to eliminate the influence of different dimensions and units. For macroeconomic data, the price index is used to eliminate the impact of price fluctuations. The entropy method is used to process the data in depth and determine the weight of each index. Specifically, it includes entropy calculation and difference coefficient calculation and determines the weight of each index in the comprehensive level index, which provides an important basis for subsequent weighted calculation. The evaluation value of the comprehensive level of tourism ecological resilience of 31 provinces in China from 2010 to 2022 is calculated using the weighting method. The specific calculation formula is detailed in the relevant literature [61].

Let x_ij represent the j-th second-level indicator under the i-th first-level indicator, and the data should be standardized first, considering the comparability of the data:

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }=\left\{\begin{array}{c}{\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-\min \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}{\max \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)-\min \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}} \mathrm{Positive} \mathrm{indicators}\\ {\displaystyle \frac{\max \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)-{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\max \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)-\min \left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}} \mathrm{Negative} \mathrm{indicators}\end{array}\right.$$

(1)

where x’_ij represents the standardized value of the j-th second-level indicator under the i-th first-level indicator. Its value range is [0, 1]; max(x_ij) and min(x_ij) represent the maximum and minimum values of the indicator, respectively. Then, the entropy method is used to determine the weight w_ij of the x’_ij index, and then the comprehensive evaluation value of different dimensions is calculated.

2.3.2. Dagum–Gini Coefficient

Compared with the traditional Gini coefficient and Theil index, the Dagum–Gini coefficient not only takes into account the differences between subgroups but also solves the problem of cross-overlap of sample data. The Dagum–Gini coefficient can be decomposed into intra-group differences (G_ω), between-group differences (G_cb), and supervariable density (G_t), which can be calculated in accordance with the research of Xiang et al. [62].

$$\mathrm{G}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{k}}\sum _{\mathrm{m}=1}^{\mathrm{k}}\sum _{\mathrm{j}=1}^{{\mathrm{n}}_{\mathrm{i}}}\sum _{\mathrm{r}=1}^{{\mathrm{n}}_{\mathrm{m}}}\left|{\mathrm{y}}_{\mathrm{i}\mathrm{j}}-{\mathrm{y}}_{\mathrm{m}\mathrm{r}}\right|}{2{\mathrm{n}}^2\mathsf{\mu }}},\mathrm{}{\mathsf{\mu }}_{\mathrm{m}}\le \dots \le {\mathsf{\mu }}_{\mathrm{i}}\le \dots \le {\mathsf{\mu }}_{\mathrm{k}}$$

(2)

$${\mathrm{G}}_{\mathrm{i}\mathrm{i}}={\displaystyle \frac{\sum _{\mathrm{j}=1}^{{\mathrm{n}}_{\mathrm{i}}}\sum _{\mathrm{r}=1}^{{\mathrm{n}}_{\mathrm{i}}}\left|{\mathrm{y}}_{\mathrm{i}\mathrm{j}}-{\mathrm{y}}_{\mathrm{i}\mathrm{r}}\right|}{2{\mathrm{n}}_{\mathrm{i}}^2\mathsf{\mu }}}$$

(3)

$${\mathrm{G}}_{\mathrm{i}\mathrm{m}}={\displaystyle \frac{\sum _{\mathrm{j}=1}^{{\mathrm{n}}_{\mathrm{i}}}\sum _{\mathrm{r}=1}^{{\mathrm{n}}_{\mathrm{i}}}\left|{\mathrm{y}}_{\mathrm{i}\mathrm{j}}-{\mathrm{y}}_{\mathrm{m}\mathrm{r}}\right|}{{\mathrm{n}}_{\mathrm{i}}{\mathrm{n}}_{\mathrm{m}}\left({\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{m}}\right)}}$$

(4)

$$\mathrm{G}={\mathrm{G}}_{\mathsf{\omega }}+{\mathrm{G}}_{\mathrm{c}\mathrm{b}}+{\mathrm{G}}_{\mathrm{t}}$$

(5)

$${\mathrm{G}}_{\mathrm{g}\mathrm{b}}={\mathrm{G}}_{\mathrm{c}\mathrm{b}}+{\mathrm{G}}_{\mathrm{t}}$$

(6)

$${\mathrm{G}}_{\mathsf{\omega }}=\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{G}}_{\mathrm{i}\mathrm{i}}{\mathrm{p}}_{\mathrm{i}}{\mathrm{s}}_{\mathrm{i}}$$

(7)

$${\mathrm{G}}_{\mathrm{c}\mathrm{b}}=\sum _{\mathrm{i}=2}^{{\mathrm{n}}_{\mathrm{i}}}\sum _{\mathrm{m}=1}^{\mathrm{i}-1}{\mathrm{G}}_{\mathrm{i}\mathrm{m}}({\mathrm{p}}_{\mathrm{i}}{\mathrm{s}}_{\mathrm{m}}+{\mathrm{p}}_{\mathrm{m}}{\mathrm{s}}_{\mathrm{i}}){\mathrm{D}}_{\mathrm{i}\mathrm{m}}$$

(8)

$${\mathrm{G}}_{\mathrm{t}}=\sum _{\mathrm{i}=2}^{{\mathrm{n}}_{\mathrm{i}}}\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{G}}_{\mathrm{i}\mathrm{m}}({\mathrm{p}}_{\mathrm{i}}{\mathrm{s}}_{\mathrm{m}}+{\mathrm{p}}_{\mathrm{m}}{\mathrm{s}}_{\mathrm{i}})({1-\mathrm{D}}_{\mathrm{i}\mathrm{m}})$$

(9)

$${\mathrm{D}}_{\mathrm{i}\mathrm{m}}={\displaystyle \frac{{\mathrm{d}}_{\mathrm{i}\mathrm{m}}-{\mathrm{p}}_{\mathrm{i}\mathrm{m}}}{{\mathrm{d}}_{\mathrm{i}\mathrm{m}}+{\mathrm{p}}_{\mathrm{i}\mathrm{m}}}},\mathrm{}\mathrm{}\mathrm{}{\mathrm{d}}_{\mathrm{i}\mathrm{m}}=\underset{0}{\overset{\mathrm{\infty }}{\int }}\mathrm{d}{\mathrm{F}}_{\mathrm{i}}(\mathrm{y})\underset{0}{\overset{\mathrm{y}}{\int }}\left(\mathrm{y}-\mathrm{x}\right)\mathrm{d}{\mathrm{F}}_{\mathrm{m}}\left(\mathrm{x}\right),$$

$${\mathrm{p}}_{\mathrm{i}\mathrm{m}}=\underset{0}{\overset{\mathrm{\infty }}{\int }}\mathrm{d}{\mathrm{F}}_{\mathrm{m}}\left(\mathrm{y}\right)\underset{0}{\overset{\mathrm{y}}{\int }}\left(\mathrm{y}-\mathrm{x}\right)\mathrm{d}{\mathrm{F}}_{\mathrm{i}}\left(\mathrm{y}\right),$$

(10)

$${\mathrm{p}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{n}}_{\mathrm{i}}}{\mathrm{n}}},{\mathrm{s}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{n}}_{\mathrm{i}}{\mathrm{u}}_{\mathrm{i}}}{\mathrm{n}\mathsf{\mu }}},\mathrm{i}=\mathrm{1,2},3,\dots ,\mathrm{k}$$

(11)

2.3.3. Kernel Density Estimation

Kernel density estimation is a non-parametric estimation method, which is mainly used to estimate the density function of unknown random variables, which can describe the distribution morphology of random variables through continuous density curves. The formula for calculating the probability density of random variables can be found in the study of Zan Xin et al. [63].

$$\mathrm{f}\left(\mathrm{x}\right)={\displaystyle \frac{1}{\mathrm{N}\mathrm{h}}}\sum _{\mathrm{i}=1}^{\mathrm{N}}\mathrm{K}\left({\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}}-\mathrm{x}}{\mathrm{h}}}\right)$$

(12)

$$\mathrm{K}\left(\mathrm{x}\right)={\displaystyle \frac{1}{\sqrt{2\mathsf{\pi }}}}\exp \left(-{\displaystyle \frac{{\mathrm{x}}^2}{2}}\right)$$

(13)

2.3.4. Data Sources

This paper takes 31 provinces in China as the research object (due to the lack of statistical data from Hong Kong, Macao, and Taiwan, so it is not included in this study), and the data of various indicators are derived from the national and provincial (autonomous region, municipal) statistical yearbooks, national economic and social development statistical bulletins and official websites from 2011 to 2023, such as China Statistical Yearbook, China Environment Statistical Yearbook, China Urban Construction Statistical Yearbook, and Chinese Cultural Relics and Tourism Statistical Yearbook (China Tourism Statistical Yearbook) and the official websites of the National Bureau of Statistics of the People’s Republic of China, the Ministry of Culture and Tourism of the People’s Republic of China, the General Office of the State Council of the People’s Government of the People’s Republic of China, and the provincial departments of culture and tourism. The data collection of the paper follows the principle of unity of data sources, that is, the same index data are obtained from the same source as far as possible. If there is a lack of individual data, they can be supplemented and improved by other data sources, and the similarity and rationality of the data distribution of each source can be strictly tested to ensure the data quality and the reliability of the research results. For a small amount of missing data, appropriate interpolation techniques (interpolation or fitting values) are used to fill in.

3. Results

3.1. Temporal Evolution Characteristics of Tourism Ecological Resilience

3.1.1. Overall Temporal Evolution Characteristics

From the estimated kernel density curve of tourism ecological resilience (Figure 2a), box plot (Figure 2b), and three-dimensional kernel density distribution (Figure 3a), it can be seen that the distribution dynamics of tourism resilience in China, such as distribution location, distribution shape, number of peaks and distribution ductility, are as follows:

(1)

China’s TER generally shows a steady growth trend, and regional differences are gradually expanding. It can be seen from Figure 2 that the core density center of China’s TER moved to the right as a whole from 2010 to 2022, and the median line in the box plot showed a fluctuating trajectory to the upper right, and the average value of resilience water increased first and then decreased. The average value of resilience first increased and then decreased, from 0.1302 in 2010 to 0.1866 in 2019, an increase of 43.32%, reaching a peak in 2019, and the impact of the COVID-19 pandemic decreased to 0.1552 in 2022. It can be seen that the level of ecological resilience of China’s tourism industry is generally on the rise, with an average annual growth rate of 1.47%, reflecting the continuous optimization of the development momentum, quality efficiency of China’s tourism industry, the improvement in the green effect of the tourism ecosystem, and the ability to resist potential internal and external threats [64]. The peak value of the kernel density curve of China’s tourism ecological resilience decreased, the kurtosis first decreased and then slowly increased, showing a fluctuating downward trend as a whole, and the overall width of the curve expanded as a whole, and the length of the upper and lower limits (interquartile ranges) of the box plot tended to increase, indicating that with the passage of the development cycle and the replacement of development stages, the difference degree of TER in different provinces in China gradually increased, and the absolute difference showed a significant expansion trend (Figure 2 and Figure 3a);

(2)

The polarization of China’s TER has been alleviated, but individual provinces occupy an absolute leading position in the development of tourism ecological resilience. From the perspective of the number of peaks and the ductility of the distribution, the kernel density curve showed a state of “main peak + double peak” from 2010 to 2017, and a “single peak” state from 2018 to 2022, and there was an obvious right tail characteristic, and the distribution ductility broadened significantly, indicating that the polarization of China’s tourism ecological resilience has been alleviated, but there are still individual provinces that occupy an absolute leading position in the development of tourism ecological resilience (Figure 2). The main reasons for this phenomenon are the great differences in resource endowment, industrial structure, policy orientation, tourism development foundation, geographical environment and other factors in different provinces, the different development paths and speeds of tourism ecological resilience between provinces, and the different ability of tourism ecosystems to resist internal and external disturbances and their own recovery, which leads to the gradual expansion of the divergent distribution trend and differences in tourism ecological resilience in different provinces. At the same time, due to the force majeure factors of the new crown epidemic, the graphic trend has changed significantly since 2020, and the overall resilience level of the country has declined, making the ecological resilience of China’s inter-provincial tourism show a convergence trend.

3.1.2. Temporal Evolution Characteristics of Partitions

From the perspective of the seven regions (East China, South China, North China, Central China, Southwest China, Northwest China, and Northeast China), combined with Figure 2 and Figure 3b–h, the center of the kernel density curve of the seven regions shifted to the right to varying degrees from 2010 to 2022, and the tourism ecological resilience increased in different ranges. Specifically, the kurtosis in East China showed a slight decrease and then a sharp increase and evolved from a single peak to a double peak, indicating that the polarization of tourism ecological resilience in East China gradually emerged. The North China region has always presented a ‘main peak + secondary peak’ state, and the distribution curve has an obvious right-trailing phenomenon, indicating that there are obvious differences in tourism ecological resilience in different regions of North China, and the level of TER in individual provinces is higher. The peak fluctuation range in Central China is large, showing an evolution trend of “multi-peak–unimodal–multi-peak”, and the polarization phenomenon obviously exists. The kurtosis in South China decreased first and then increased, but the decrease was greater than the increase, and it evolved from bimodal to unimodal, indicating that the differences between the two obviously different resilience groups represented by the bimodal structure were blurred, and the polarization phenomenon was improved. The variation in curve width in Southwest China is small, and there is a trend of bimodal transformation in 2022, indicating that the dispersion degree of tourism ecological resilience in Southwest China has expanded. The kurtosis decreased significantly in Northwest China, evolving from bimodal to unimodal, and the width of the single peak showed an expanding trend, indicating that the dispersion degree of tourism ecological resilience in Northwest China increased, the absolute difference tended to expand, and the polarization phenomenon was improved.

Specifically, the order of tourism ecological resilience from strong to weak is as follows: East China (0.2053) > North China (0.1741) > South China (0.1696) > Central China (0.1597) > Southwest China (0.1430) > Northeast China (0.1292) > Northwest China (0.1056). East China has a significant advantage in the level of ecological resilience of tourism, forming a peak convex form of “high agglomeration”, with strong anti-risk ability and development resilience. The tourism ecosystem continues to move toward a balanced and steady-state development stage. There are gaps between North China, South China, Central China, Southwest China, Northeast China, and Northwest China compared with East China, as follows (Figure 4):

(1)

Central China, as the main undertaking place for the transfer of high-pollution and high-consumption industrialized industries along the eastern coast, has experienced significant pressure on the environment, and this industrialization process has constituted an obvious coercive effect on the ecological environment, causing the tourism ecosystem to suffer from external shocks and face the dual pressure of environmental construction and ecological protection [4];

(2)

The southwest region is rich in natural landscapes, such as the Guilin landscape and Yunnan stone forest, but due to the remote geographical location and inconvenient transportation, due to the complex terrain in some areas, the protection and governance of the ecological environment are difficult, the development of tourism resources is limited to a certain extent, and the development of the tourism industry is relatively lagging behind;

(3)

Northeast China is an important industrial base. Economic transformation faces certain difficulties, and investment in tourism development is limited. For example, Jilin, Heilongjiang, and other places have low tourism eco-efficiency [65]. In addition, the problem of population aging in Northeast China and Southwest China has a significant impact on the eco-tourism efficiency of both in the long and short term [66];

(4)

The economic development of Northwest China is relatively lagging behind; the industrial structure is relatively single, and the support of the factor scale is weak. The tourism industry in Northwest China, such as Qinghai and Xinjiang, started late, and the tourism eco-efficiency is low [65]. It is subject to the comprehensive influence of factors such as a weak economic foundation, low green development efficiency, poor traffic accessibility, and lagging infrastructure construction, which restricts the resilience of the tourism ecosystem.

From the perspective of inter-provincial development, except for Tibet, Heilongjiang, and Liaoning Province, the TER decreased slightly, and the remaining provinces showed an upward trend. Among them, Yunnan Province and Jiangxi Province increased significantly, increasing by 56.5% and 52.4%, respectively, compared with 2010, followed by Hubei Province, Qinghai Province, and Guizhou Province, which increased by 44.2%, 41.5%, and 40.4%, respectively. Taking the mean value as a reference, the top six resilience levels are Shanghai (0.3163) > Beijing (0.2694) > Guangdong (0.2404) > Jiangsu (0.2315) > Zhejiang (0.2201) > Shandong (0.2137). The top six rankings are Chongqing (0.1167) > Shaanxi (0.1119) > Jilin (0.1081) > Hainan (0.1078) > Xinjiang (0.0998) > Ningxia (0.0725). Except for Jilin and Hainan, all are in the western region.

3.2. Spatial Evolution Characteristics of Tourism Ecological Resilience

3.2.1. Overall Spatial Trend Characteristics

In order to deeply analyze the overall situation of the spatial differentiation of China’s TER, this paper uses ArcGIS10.1 software to simulate and visualize the spatial distribution trend and analyzes the global spatial distribution trend of resilience from different perspectives (Figure 5). It can be seen from Figure 5 that the spatial pattern of tourism ecological resilience in China at the provincial level generally shows a layout trend of “high in the east and low in the west, high in the south and low in the north”, and the east and south are the regions with high resilience of the tourism ecosystem, forming a distribution pattern of high in the southeast and low in the northwest, with significant spatial directionality. Specifically, from 2010 to 2022, China’s tourism ecological resilience gradually increased from north to south and decreased from east to west with the increase in geographical distance, and there were differences in the trend line in different directions, with the trend line transition in the north–south direction being steep and the trend line transition in the east–west direction tending to be flat, indicating that the differentiation characteristics of tourism ecological resilience in the north–south direction were more obvious and strong, and there was significant spatial heterogeneity. It is worth noting that with the passage of time, the height of the peak point of the inverted U-shaped fitting curve generally increased, indicating that the overall level of China’s tourism ecological resilience is on the rise.

3.2.2. Spatial Differentiation Characteristics

In order to explore the spatial and temporal heterogeneity characteristics of China’s provincial TER, based on the relevant research results and the actual measurement of China’s TER, the natural breakpoint classification method is used to divide the resilience classification standard into five grades: Low resilience, Relatively Low resilience, Critical resilience, Relatively Strong resilience, and Strong resilience. ArcGIS is used to draw the spatial differentiation map of China’s TER in 2010, 2015, 2019, and 2022 (Figure 6). From 2010 to 2022, the ecological resilience of China’s provincial tourism showed an upward trend of different ranges, and the upward trend was obvious from 2010 to 2019 and then decreased significantly due to the impact of the new crown epidemic, forming a hierarchical spatial structure with Beijing and Shanghai as the “dual-core” high resilience centers, Jiangsu, Zhejiang, and Guangdong as the higher resilience centers, Hebei, Shanxi, Shandong, Henan, Anhui, Guangxi, and Guizhou as the critical resilience centers, and the central and western regions as the outer edges. Specifically, in 2010, all provinces in China were at the level of critical resilience and below, and the overall level of resilience was low. In 2019, Beijing and Shanghai were promoted to high-toughness areas, Jiangsu, Zhejiang, and Guangdong were higher-toughness areas, and the critical toughness and higher-toughness areas increased significantly. In 2022, the high-toughness area and the critical-toughness area affected by the epidemic will be significantly reduced, while the low-toughness area will increase significantly, but the ‘Beijing–Shanghai’ dual-core high-toughness area will still be formed, showing strong stability. It is worth noting that the provinces in the unsafe state below the critical resilience still account for the vast majority, which indicates that there is a long way to go to improve the resilience of China’s tourism ecosystem.

3.3. Spatial and Temporal Evolution Characteristics of TER in Different Dimensions

3.3.1. Temporal Characteristics

According to the further analysis of the changes in the mean of the indicators in Figure 7, it can be seen that from 2010 to 2022, the dimension of China’s tourism ecological resilience defense and protection capacity showed a significant change trend, with obvious phased characteristics, and its value increased from the lowest value in the evaluation value of each dimension to the highest value, from 0.0946 to 0.1677, with a growth rate of 77.32%, with a large increase, and reached a peak in 2019, decreased significantly in 2020 due to the impact of the pandemic, and then showed a fluctuating growth trend. The adaptive resilience decreased from 0.1639 to 0.1572, although there was some fluctuation in the middle, but the overall fluctuation and decline were maintained, and the optimization and resilience capacity increased from 0.1290 to 0.1363, showing a steady upward trend before 2019, reaching a peak in 2019 and decreasing year by year, with significant phases.

In general, there is significant spatial heterogeneity in China’s tourism ecological resilience, and the actual development of different regions is quite different, and the high-resilience areas are mainly distributed in regions with relatively developed economic conditions, abundant environmental protection investment, and strict environmental regulations. The mean values of tourism ecological resilience in Central China, South China, Southwest China, and Northeast China showed the characteristics of adaptive resilience > optimized innovation ability > defense and protection ability; East China showed the characteristics of defensive protection ability > adaptive recovery ability > and optimized innovation ability; North China showed defensive protection ability > optimized innovation ability > adaptive recovery ability, and Northwest China showed optimized innovation ability > adaptive recovery ability > defense and protection ability. The northwest region has the lowest mean value in the dimension of defense protection ability and adaptive recovery ability, and the northeast region has the lowest mean value in the dimension of optimization and innovation ability.

3.3.2. Spatial Characteristics

The cross-sectional data of 2010, 2015, 2019, and 2022 were selected; the geospatial pattern of tourism ecological resilience was drawn with the help of ArcGIS (Figure 8); the natural breakpoint classification method was used to divide each dimension into five levels, and its spatial pattern showed the following characteristics:

(1)

The overall upward trend of defense and protection capability is obvious. From 2010 to 2022, the overall defense and protection capability showed a pattern of gradual attenuation from dual-core to periphery, and the mean value of DPC in Northwest China is the lowest, which is closely related to the level of regional economic development and related investment [67], reflecting that economic drive is the key variable to regulate the improvement in defense and protection capability. At the end of this study, the provinces with the leading level of defense and protection capabilities still maintained a high level, such as Beijing and Shanghai, which have always been at the leading level in forming a dual-core system, mainly because of the high level of economic development, perfect infrastructure, and strong financial investment capacity, which can provide sufficient financial support for the tourism ecosystem. From 2010 to 2022, the high-level and high-level regions showed a strong increasing tendency in terms of number or scale, reflecting obvious upward dynamics. For example, in 2010, most of them were at the middle and bottom levels with large spatial differences, and by 2022, they were all at the critical level and above except for Qinghai and Heilongjiang, and the change trend was very significant (Figure 8);

(2)

Adaptability and resilience fluctuate greatly. The ability to adapt and recover has increased overall but has decreased significantly after the impact of the epidemic in 2019. The level of adaptation and resilience capacity in the western region fluctuates greatly, and the other regions remain relatively stable, mainly because the fragility of the ecological environment and the relatively low level of economic development in the western region make the tourism ecosystem susceptible to external environmental changes and shocks such as natural and human factors, such as drought, rocky desertification, soil erosion, low efficiency of vegetation net primary productivity, and the contradiction between tourism ecological space and urban space. However, there has been an overall improvement in the middle and bottom areas;

(3)

Optimize and innovate continuously. The optimization and innovation capability showed a significant overall upward trend, forming a multi-polar distribution pattern with “Beijing–Shanghai–Zhejiang–Guangdong–Gansu–Qinghai–Tibet” as the core and gradually decreasing outward. The optimization and innovation ability of the western plateau ethnic areas such as Gansu, Qinghai, and Tibet has always maintained a high level, which shows that the region has received extensive attention and related investment in the process of tourism development due to its unique ethnic, cultural resources, special geographical features, and national policy support, which not only promotes the development of its own tourism economy but also promotes the healthy and sustainable development of the regional tourism ecosystem. Yuhuan Sun et al. conducted an empirical study on the green R&D and achievement transformation efficiency of China’s tourism industry. The results show that the western provinces perform better in green R&D than the eastern provinces, while the performance in green achievement transformation is the opposite [68]. It is worth noting that the average value of the optimization and innovation ability of the northeast region is the lowest, and the green innovation efficiency of the northeast tourism industry from 2000 to 2020 is in a state of stagnation [69].

3.4. Analysis of Regional Differences in Tourism Ecological Resilience

This paper uses MATLAB software (version 2024b) to measure the Gini coefficient of China’s tourism ecological resilience and decomposes it according to the seven regions of East China, South China, North China, Central China, Southwest China, Northwest China, and Northeast China. The evolution trend of regional differences in China’s tourism ecological resilience during the study period is depicted, and the overall and regional differences in China’s tourism ecological resilience are intuitively grasped, as shown in Figure 9.

(1)

Figure 9a reflects the evolution trend of China’s TER and regional Gini coefficient from 2010 to 2022. During the study period, the overall difference in national TER showed a fluctuating upward trend, and the overall Gini coefficient increased from 0.177 in 2010 to 0.183 in 2022, with a slight increase. From the perspective of the seven major regions, the intra-regional differences in East China and Northeast China fluctuated and decreased, while other regions showed a fluctuating upward trend. Among them, the decline in East China was 31.67%, and the decline in Northeast China was 32.58%. The increases in Central China, North China, South China, Northwest China, and Southwest China were 5%, 19.21%, 10.27%, 59.26%, and 2.53%, respectively, indicating that except for the narrowing of the internal differences in East China and Northeast China, the internal differences in other regions showed a significant trend of expansion. The Gini coefficient in the region shows a spatial distribution pattern of ‘South China (0.1735) > North China (0.1505) > East China (0.1500) > Northwest China (0.0957) > Northeast China (0.0942) > Southwest China (0.0742) > Central China (0.0314)’, indicating that the difference in provincial TER within South China is the largest, followed by North China and East China, and Central China is the smallest;

(2)

From 2010 to 2022, the Gini coefficient of China’s TER shows the characteristics of high coincidence and fluctuation balance. The fluctuation range is small (the rise and fall are all below 7%), which reflects the stability, balance, and self-regulation ability of TER. A dynamic balance mechanism has been formed in the development process of each region, which makes the regional differences fluctuate within a small range and can maintain the development within a relatively stable difference range. From the perspective of numerical differences, the average inter-regional Gini coefficients of East China–Northwest China, South China–Northwest China, North China–Northwest China, Northeast China–East China, and Central China-Northwest China are 0.3211,0.2517,0.2472,0.2416, and 0.2028, respectively, ranking the top five. The inter-regional Gini coefficient of East China–Northwest China is the largest, because the level of tourism ecological resilience in East China is much higher than that in Northwest China. With the rapid catch-up of the northwest region, the level of TER has increased rapidly, resulting in a steady decline in the regional Gini coefficient between East China and Northwest China. From the perspective of the change trend, the Gini coefficients of Northeast China–Northwest China, East China–Central China, East China–South China, East China–Southwest China, and Central China–Southwest China regions all showed a fluctuating downward trend, but the decline was less than 5.5%, indicating that the gap in the level of TER between these regions is shrinking and gradually tends to be balanced. The Gini coefficients of other regions showed a fluctuating upward trend, indicating that the differences between regions showed an expanding trend;

(3)

Figure 9b reflects the evolution trend of the sources and contribution rates of regional differences in China’s TER from 2010 to 2022. From the perspective of the dynamic evolution of the sources of differences, the fluctuation of the contribution rate of inter-regional differences is on the rise as a whole, from 66.76% in 2010 to 71.02% in 2022, which is an increase of about 6.38%. The contribution rate of regional differences experienced a fluctuation process of ‘decline–rise–decline–rise–decline’, and the overall downward trend was about 13.32%. The contribution rate of hypervariable density showed a significant downward trend, from 21.157% in 2010 to 18.506% in 2022, a decline of about 12.53%. From the perspective of the contribution rate of the source of difference, the average contribution rates of intra-regional difference, inter-regional difference, and hypervariable density are 11.47%, 68.87%, and 20.66%, respectively. It can be seen that the contribution rate of inter-regional difference is the largest and has been in a dominant position. It is the main source of overall difference, followed by the contribution rate of intra-regional difference, and the contribution rate of hypervariable density is the smallest.

4. Discussion

(1)

From 2010 to 2022, the level of tourism ecological resilience in all provinces in China has improved to varying degrees, mainly due to the fact that China has entered a new era of socialist ecological civilization, deeply practiced the concept of green development and the concept of “lucid waters and lush mountains are invaluable assets”, and continuously promoted the strategic action of “ecological civilization construction”. However, there is significant spatial heterogeneity in China’s tourism ecological resilience, and the actual development of different regions varies greatly, and the high-resilience areas are mainly distributed in regions with relatively developed economic conditions, abundant environmental protection investment, and strict environmental regulations. The higher increase in tourism ecological resilience in Yunnan, Jiangxi, Guizhou, and other places is mainly due to the rapid economic development and relatively high investment intensity in tourism infrastructure and environmental governance [70]. Therefore, each region should take measures according to local conditions, combine the actual differences in the economic foundation, geographical environment, resource endowment, location conditions, and other actual differences in the region, and learn from successful experience on the basis of clarifying the insufficient development of its own tourism ecological resilience, and scientifically formulate tourism ecological resilience improvement strategies;

(2)

According to the spatial characteristics of ‘high in the east and low in the west, high in the south and low in the north’, a hierarchical structure with ‘Beijing–Shanghai’ as the dual core is formed, and the regional difference is the main source of the overall difference. The regional difference between East China and Northwest China is large, and the internal difference between South China and North China is large. This result also fully confirms the conclusion that the level of economic development in North China is quite different; the industrial division of labor and cooperation is not deep enough; the radiation effect of the rapid economic growth of Beijing, Tianjin, and Hebei on the surrounding cities of Hebei is not good, and the overall coordination of the region is poor [71]. Therefore, the improvement in China’s future tourism ecosystem needs to build a ‘tourism life community’, focus on regional differences, implement a regional tourism coordinated development strategy, establish a regional tourism development coordination mechanism, and promote the free flow of diversified resource elements between regions. Through policy guidance and financial support, we should strengthen tourism cooperation and exchanges between the eastern and western regions, the northern and southern regions, and the eastern and northwestern regions. In some provinces in the central and western regions where the economy is underdeveloped but the tourism resources are abundant, we should cultivate new tourism growth poles and promote the coordinated development of inter-regional TER. At the same time, we should not ignore the gap within the region;

(3)

In the future, the resilience of the tourism ecosystem must pay attention to the full play and improvement in defense and protection abilities, adaptation and recovery abilities, and optimization and innovation abilities, deeply understand the connotation of tourism ecological resilience, promote the long board and make up the short board, and adhere to the multi-dimensional value co-creation of tourism ecosystem. In view of the fact that defense and protection capabilities are closely related to the level of regional economic development and related inputs, economic driving is a key variable that regulates the improvement in defense and protection capabilities. Therefore, it is necessary to increase capital investment and policy support for tourism ecological protection in economically underdeveloped areas, establish a diversified tourism economic structure through industrial poverty alleviation and development of characteristic industries, reduce dependence on a single tourism product or market, improve the level of economic development and anti-risk ability in underdeveloped areas, and enhance the defense and protection ability of the national tourism ecosystem. For example, in the western ethnic areas, the development of folk culture tourism, rural tourism, eco-tourism, and other forms of tourism industry improves the local residents’ income level and the stability of the tourism economy;

(4)

Due to the vulnerability of the ecological environment and the relatively low level of economic development in the western region, the tourism ecosystem is vulnerable to external environmental changes and shocks, such as natural and human factors. The efficiency of vegetation net primary productivity is low, and the restoration and adjustment of the tourism ecosystem is difficult. The stability is poor, showing that the vulnerability of the western environment has a greater impact on the ability to adapt to recovery. Therefore, adaptive management strategies (such as ecological compensation mechanism) can reduce system sensitivity, increase ecological restoration and environmental protection in ecologically fragile areas in the western region, implement large-scale afforestation, soil and water conservation, desertification control, and other ecological projects, and improve vegetation coverage and ecosystem stability in the western region. At the same time, combined with the local ecological characteristics, the development of eco-friendly tourism projects, such as ecological tourism, ecological tourism, popular science tourism, etc., so that tourism development and ecological protection can promote each other;

(5)

According to the characteristics of the multi-polar distribution of optimization and innovation capabilities, we will further strengthen the innovation-driven development strategy of the national tourism industry and strengthen the application of digital technology to enhance system resilience. For example, in the core areas of innovation (Beijing, Shanghai, Zhejiang, Guangdong, Gansu, Qinghai, and Tibet), we should increase investment in tourism science and technology innovation, build tourism science and technology research and development center, innovation and entrepreneurship base, attract high-end tourism talents and innovative enterprises, strengthen the cooperation between universities, scientific research institutions and tourism enterprises, and promote the innovation of tourism products, services, and management modes. At the same time, we should establish a cross-regional tourism innovation alliance or cooperation platform, give full play to the role of radiation and collaborative innovation in the core areas of innovation, and promote the innovation and development of tourism in the surrounding areas and the central and western regions through technology transfer, talent exchange, and industrial cooperation, so as to realize resource sharing and complementary advantages. For example, the eastern coastal innovation area can help the central and western regions use new technologies to develop emerging tourism models such as smart tourism and virtual reality tourism, enhance the overall optimization and innovation capabilities of the national tourism ecosystem, and enhance the resilience of the tourism ecosystem.

5. Conclusions

This study measured China’s TER from 2010 to 2022 from three dimensions, DPC, ARC, and OIC, and explored the spatio-temporal characteristics and regional differences in China’s TER. The main conclusions are as follows:

(1)

The overall level of China’s TER is low, but it shows a steady-state growth trend. Although the growth is slow, it has strong stability and sustainability. The development trend of the seven major regions is consistent with the national average trend. It shows that the green effect of the tourism ecosystem in China’s provinces is enhanced, the ability to resist internal and external potential threats is improved, and the sustainable development ability of the tourism industry is enhanced;

(2)

The polarization of China’s TER has been alleviated, but there are still some provinces that occupy an absolute leading position with significant spatial heterogeneity, and the actual development of each region is quite different. The overall characteristics of China’s TER are ‘high in the east and low in the west, high in the south and low in the north’, forming a distribution pattern of high in the southeast and low in the northwest, and the change trends in different directions are different. The differentiation characteristics in the north–south direction are more obvious and strong, and the non-equilibrium differentiation characteristics are obvious. The overall formation of ‘Beijing–Shanghai’ as a dual-core high-toughness center, with the central and western regions as the outer edge of the hierarchical spatial structure characteristics;

(3)

The overall upward trend of DPC is obvious, showing a pattern of gradual attenuation from dual core to periphery. The average value of DPC in Northwest China is the lowest, which is closely related to the level of regional economic development and related investment, reflecting that economic driving is the key variable to regulate the improvement in defense and protection capacity. The mean value of ARC in Northwest China is the lowest; the level of ARC in the western region fluctuates greatly, and other regions remain relatively stable. The OIC shows a significant overall upward trend, forming a multi-polar distribution pattern with ‘Beijing–Shanghai–Zhejiang–Guangdong–Ganqing–Tibet’ as the core and gradually decreasing outward radiation;

(4)

The regional difference in China’s TER is gradually expanding, and the overall difference degree shows a fluctuating upward trend. The regional differences show the characteristics of high coincidence and fluctuation balance, and the fluctuation range is small, which indicates that the TER has strong stability, balance, and self-regulation ability. A dynamic balance mechanism has been formed in the development process of each region, which can maintain the development in a relatively stable difference interval. The difference within the region presents a spatial distribution pattern of ‘South China > North China > East China > Northwest > Northeast > Southwest > Central China’. The difference between East China and Northwest China is the largest, and the contribution rate of regional differences is the largest. It has been in a dominant position and is the main source of overall differences.