Synergistic relationship or not? Understanding the resilience and efficiency of the tourism economy: evidence from Hainan Province, China

Abstract

The COVID-19 pandemic has dealt a serious blow to the global tourism industry, causing a fracturing of and decline in tourism development efficiency and even a stagnation of tourism development in some regions. To solve the contradiction between efficiency and quality, it is necessary to ensure the endogenous power of tourism resilience while pursuing the efficiency of tourism development. This study assumes that Hainan Province follows a tourism development path led by resilience. The improved weighting method, EBM model and Haken model are used to evaluate the level of resilience, the level of efficiency and their co-evolution. The findings indicate that the core tourism cities represented by Sanya and Haikou have a high level in the individual fields of tourism development efficiency and tourism economic resilience but have limited performance in the synergistic relationship between tourism development efficiency and tourism economic resilience. In contrast, the marginal tourism cities represented by Tunchang County and Ledong County have low tourism development efficiency and resilience, but their synergistic development level is high. This result proves that co-evolution plays a dual forward and reverse driving role. Based on the identification of the order parameters, it is concluded that Hainan Province is characterized by a synergistic evolutionary synergy dominated by resilience, which is in line with the trend of social development and the sustainable development of tourism. While reasonably pursuing the tourism economy and development efficiency, we should pay attention to strengthening resilience construction based on multiple aspects, such as tourists, enterprises, organizations, governments and destinations.

Introduction

With the continuous progress in the world's transportation channels, information networks and science and technology, tourism has developed rapidly and is generally regarded as an important industry that benefits the economy and community well-being (Andrades & Dimanche, 2017). However, if we emphasize only the economic attributes of tourism development, the uncontrolled development of mass tourism will lead to the continuous development of tourism resources in a short period of time, and the vulnerability of tourism will be more prominent, which is likely to cause an irreversible decline due to the exhaustion of destination resources and attractions (Boskovic et al., 2020). At the same time, because of the complexity and comprehensive characteristics of tourism, the tourism development process is characterized by being easily affected by the external environment. The impact of COVID-19 on the world's tourism industry effectively proves the vulnerability and sensitivity of tourism development. In 2021, the total number of global tourist trips and total revenue accounted for only 53.7% and 55.9% of the 2019 levels, respectively (World Tourism Organization, 2021). As a developing country, China, especially 40 years ago, was in a state of economic underdevelopment and transformation, and it took the development of tourism as the main means of economic growth and source of foreign exchange (Andrades & Dimanche, 2017). With the progress of the times, tourism has developed rapidly because it meets the national trend and the common demand of the public for experience. However, the rapid development of tourism in China has also brought many problems, such as destruction of the ecological environment, the loss of cultural deposits and regional economic imbalance (Goh et al., 2015). These problems can be classified into two categories: tourism resilience and tourism efficiency. Effectively coordinating the contradiction between tourism resilience and tourism efficiency is an effective way of solving the problems of tourism development in China and ensuring the development efficiency and quality of tourism destinations as much as possible. This study not only provides a theoretical basis for the development of tourism in China but also has reference value for the coordinated development of tourism in developing countries such as India, South Africa and Serbia.

To coordinate the resilience of the tourism economy and the efficiency of tourism development, it is necessary to start from their ontological connotations. From the perspective of tourism economic resilience, the resilience of the tourism economy usually emphasizes the ability of the tourism economic system to resist and recover development when it encounters a crisis (Prayag, 2018). In particular, under the impact of COVID-19, the resilience of the tourism economy is also an important theme of great academic interest, with results related to tourism and regional economic resilience, tourism revitalization, tourism resilience and crisis management (Bellini et al., 2017; Prayag, 2018; Sharma et al., 2021). Tourism development efficiency is a reflection of tourism resource input and its allocation and utilization, and tourism destinations can reasonably guide the intensive utilization of capital based on their input and output effects (Wang et al., 2020). In particular, the potential elements of technical efficiency could be mined, and the efficiency value could also serve as an objective basis for responding to external conflicts, which is internally related to the resilience of the tourism economy (Tsionas & Assaf, 2014). Efficiency often pursues maximum performance with the minimum resource input (capital, technology, manpower, etc.), but it is easy to fall into a short-term development vision, and its internal development speed and problems are also difficult to effectively control (Song, 2022).

Based on the understanding of the concepts of the resilience and efficiency of the tourism economy, resilience emphasizes recovery from and resistance to impacts, dynamic adaptation and innovative transformation, while efficiency emphasizes low input and high output; there is an objective contradiction between the two (Zhu et al., 2021). From the perspective of persistence and evolutionary development, regions with stronger tourism resilience are clearly more capable of dealing with external shocks and reorganizing system elements to stabilize the speed and quality of tourism development (Galaitsi et al., 2021). Despite proposals from the International Human Dimension Programme (IHDP) and the International Risk Governance Centre (IRGC) for implementation in the face of the complex situation brought by COVID-19 to specific projects of the UNEP and the World Bank, such as integrating resilience and integrating the resource efficiency agenda into urban planning (Zhu et al., 2021), for tourism development, research on the interaction between the resilience and efficiency of the tourism economy is still limited (Essuman et al., 2020). The relationship between the two fields is still unclear (Negri et al., 2021). The pursuit of tourism development efficiency alone still leads to environmental pollution, ecological damage and resource deterioration (Ivanov et al.,2014). The views on whether prioritizing development efficiency will damage resilience or whether tourism economic resilience and development efficiency can achieve a dynamic balance need to be further confirmed (Markolf et al.,2022; Karakoc & Konar, 2021). Especially in the complex situation of the extensive spread of the pandemic, regional conflicts and disputes, and economic market instability, there is an urgent need to further explore the internal operation mechanism of the tourism economy to understand the relationship between the resilience and efficiency of the tourism economy.

In view of the leading position of Hainan Province in the international development of tourism in China and the special situation in which tourism accounts for nearly 20% of China’s national economic income (Xie et al., 2019), 17 cities in China’s Hainan Province are selected as case studies. This study comprehensively uses an evaluation index system for tourism economic resilience, the EBM model and the Haken model to explore the relationship between tourism economic resilience and efficiency. This study aims to answer the following questions:

• Q1. How do the resilience and efficiency of Hainan's tourism economy work?

• Q2. What characterizes the co-evolution of the resilience and efficiency of Hainan's tourism economy?

This study makes the following three key contributions to the study of the resilience and efficiency of the tourism economy:

• (I) Research on the synergistic relationship between resilience and efficiency and tourism economic resilience is very limited. Existing research focuses on the resilience construction of tourism communities (Bec et al., 2016; Yang et al., 2021) and tourism and regional economic resilience (Bellini et al., 2017), and the empirical research is seriously insufficient. Based on official data published by the Chinese government, this study comprehensively evaluates the level of resilience and efficiency. Empirical methods such as an evaluation index system, the EBM model and the Haken model are used to quantitatively analyse the relationship between tourism economic resilience and tourism development efficiency, and the synergistic relationship between them is clearly clarified, enriching the results of research on the synergistic relationship between the resilience and efficiency of the tourism economy.

• (II) This study makes new improvements in the measures of the resilience and efficiency of the tourism economy, as well as the measurement methods for the synergistic relationship. An evaluation index system for the resilience of the tourism economy is constructed from the dimensions of stability, diversification, innovation and vitality. The EBM model of tourism efficiency measurement is compatible with the radial ratio of the input frontier value and actual value, as well as the non-radial relaxation of input differentiation, to realize the effective combination of radial and non-radial methods in data envelopment analysis (Tone & Tsutsui, 2010). Moreover, the Haken model is used to evaluate the level of coordination between tourism economic resilience and tourism development efficiency and to identify the ordered parameters of the coordinated development of tourism economic resilience and efficiency by constructing the motion equation between tourism economic resilience and tourism development efficiency in Hainan Province (Zhu et al., 2021).

• (III) In terms of verifying the relationship between tourism economic resilience and tourism development efficiency, this study uses the Haken model in the field of regional economics (Zhu et al., 2021) to demonstrate the role of the two in tourism development in a harmonious and contradictory relationship. The study confirms that the tourism development model led by resilience is more consistent with the social trend of sustainable development. In general, both resilience and efficiency affect the tourism development system, and the pursuit of dynamic balance and coordination is an effective way of promoting the high-quality development of regional tourism (Markolf et al.,2022). As a traditional tourism destination in China, Hainan Province has relatively mature development experience and a relatively mature model in regard to the tourism economy, infrastructure, the policy environment and other aspects. It is a typical representative of maritime island tourism destinations, and it provides a case study contribution to the collaborative perspective certification and theoretical expansion of resilience and efficiency.

Literature review

Resilience of the tourism economy

Resilience, derived from the Latin word "resillo", is used to describe the ability of a system to respond to disturbances and the speed and degree of recovery from failure to the normal state (Bec et al., 2016; Sun & Song, 2021), as exhibited in Fig. 1. In general, research on its connotation has gone through three stages: engineering resilience in the state of system function maintenance, ecological resilience in the state of system continuity and evolutionary resilience in the context of system adaptation. Additionally, the chaos characteristics and adaptive cycle characteristics of resilience have gradually been recognized. It has been pointed out that the chaos model of the adaptive cycle has robustness and timeliness advantages (Yang et al., 2020). Tourism has accepted the concept of community resilience through the concepts of adaptation and vulnerability, and many studies have focussed on the ability of tourism systems to cope with short-term disasters and crises (Bec et al., 2016). The Community Baseline Resilience Indicator has been recognized as a representative indicator of community resilience. It is used to comprehensively consider community dimensions such as community capital and economic and social development (Yang et al., 2021). At the same time, a perception-access transformation dynamic resilience framework has been developed to explain how tourism organizations coordinate resilience elements at each stage of disaster management (Jiang et al., 2021).

Fig. 1.

The performance of the resilience of the destination tourism economy

The tourism system of protected areas based on resilience research has been proposed. It constructs the social ecosystem and adopts resilience assessment principles (Strickland-Munro et al., 2010), and it also adopts an economic resilience evaluation index system. It is used to verify whether tourism will stimulate post-disaster economic recovery (Cheng & Zhang, 2020). However, research on resilience of the tourism economy is insufficient, and there is still room for improvement in resilience measurement methods (Dai et al., 2019). From the perspective of the social ecosystem, tourism economic resilience is dynamic, multi-dimensional and multi-scale (Prayag et al., 2019). Most studies on tourism resilience focus on major disasters and crises but pay insufficient attention to the variables that affect resilience in the long run (Lew, 2014). Research on the dynamic sensitivity of tourism development and the guiding role of science and technology has been largely ignored (Mandal, 2019), and the network resilience construction of the tourism industrial chain, supply chain and innovation chain is not clear. At the same time, tourism resilience construction or recovery development proposals are usually based on the existing tourism dynamics, the economic structure and other relevant ecosystems, which to some extent limits the proposal of innovative paths (Romao, 2020).

Since environmental shocks do not occur every year, directly using panel data to measure the resilience index of the tourism economy in various regions is a reflection of the growth rate of the tourism economy or employment, which does not conform to the connotation of resilience (Peng et al., 2021). The resilience of the tourism economy is evaluated based on the aspects of government trust, network construction, local control, infrastructure, prevention and governance (Holladay & Powell, 2013). Although the evaluation dimension is relatively comprehensive, the inherent reductionism and domain bias hypothesis based on the indicator framework are not suitable for new ideas containing complexity and uncertainty. The current evaluation index system may not adapt to the complex situation of system change (Strickland-Munro et al., 2010). The reason is the lack of understanding of the resilience of the tourism economy and effective theoretical support for the recovery development of tourism in the post-pandemic era, leading to the creation and implementation of tourism construction programmes with no significant effects (Calgaro et al., 2014).

Tourism development efficiency

Tourism development efficiency is defined as a tourism destination taking advantage of its comprehensive capabilities, such as hotels, travel agencies and scenic spots, to maximize the needs of tourists (Niavis & Tsiotas, 2019) and pursue the efficient allocation of tourism resources. It emphasizes the balance between the effective allocation of resources and input‒output (Wang et al., 2020). External factors such as transport accessibility, the regional economic basis, crisis events and trade openness affect the efficiency of tourism development (Assaf & Cvelbar, 2015). Reviewing the efficiency of tourism development from the perspective of productivity, we focus on the key role of material capital, human skills, innovation and the competitive environment and explore the contribution of tourism development efficiency to growth in tourism productivity (Blake et al., 2006). In addition to these factors, we include the scale of the accommodation industry (Pestana et al., 2011), the number of tourists and the total income from tourism (Wang et al., 2020). These key factors are used as input or output variables to evaluate the efficiency of tourism development. The commonly used methods include data envelopment analysis, random boundary analysis (Tsionas & Assaf, 2014), fuzzy set qualitative comparative analysis (Corne & Peypoch, 2020), etc. To some extent, it is inevitable for tourist destinations to develop long-term inefficiencies (Tsionas & Assaf, 2014). Such development is closely related to changes in tourist attractions, technological development and tourist preferences, but the solution to this problem should not be limited to these traditional factors (Nurmatov et al., 2020). Improvements in tourism development efficiency may mainly come from the innovation of tourism products and improvements in service quality rather than cost reduction (Blake et al., 2006).

At present, human skills and technological innovation play an important role in the growth in tourism development efficiency, and the comprehensive benefits of tourism destination development are positively correlated with these two key factors (Blake et al., 2006). Mature tourism destinations are characterized by intensive coordination between the input and output of production technology innovation and simultaneously achieve the maximum social service output under the optimal commercial and environmental goals (Chen et al., 2020). Unfortunately, innovation factors are ignored in many studies on tourism development efficiency, and such studies cannot effectively meet the practical needs of technological practice (Tsionas & Assaf, 2014). At the same time, under the impetus of global warming and China's carbon neutrality strategy, more attention should be paid to the environmental health pressure on destinations exerted by tourism development (Gossling et al., 2005). At the global level, environmental pollution not only places enormous pressure on ecology but also adversely affects the efficiency of tourism development (Mitra, 2020). In general, tourism destinations with better ecological environments tend to have higher operational efficiency and resource-driven efficiency (Parte & Alberca, 2021). Although growth of the tourism economy is usually emphasized by a country, growth in the tourism scale also means an increase in energy consumption (Castilho et al., 2021). Therefore, it is necessary to balance the relationship between tourism economic development and the ecological environment to achieve sustainable tourism development as soon as possible (Wang et al., 2020).

The relationship between tourism economic resilience and efficiency

In an uncertain external environment, both resilience and efficiency provide effective ideas for understanding and coping with risks. Decision-makers need to have a clear understanding of the possible synergies and tensions between the two (Han et al., 2021), as exhibited in Fig. 2. Despite the existence of a correlation between the resilience and efficiency of the tourism economy, empirical knowledge of the interactive relationship between the two is still underdeveloped (Essuman et al., 2020). Existing studies on the synergistic relationship between resilience and efficiency mainly focus on transportation (Ganin et al., 2017), water resources (Li & Yang, 2011), food trade (Karakoc & Konar, 2021), the marine economy (Zhu et al., 2021) and other fields and mainly pursue the transformation from a velocity model growth mode with high input and low output to a high quality and beneficial growth mode with coordinated toughness and efficiency (Sun & Meng, 2020). The collaborative evaluation methods used are typically the synergy degree model (Acton et al., 2019), Wilson coefficient method (Bampatsou & Halkos, 2019), grey GM model, relative index method of industrial synergy agglomeration (Comert et al., 2021) and Haken model. In this study, the Haken model, which is widely used in regional economics, can identify the order parameters of system development and analyse the evolutionary mechanism and situation in the process of system self-organization (Zeng et al., 2021). In judging the dominant relationship between the resilience of the tourism economy and the efficiency of tourism development, analysing the level of collaborative development has the advantage of precision. In terms of the logical relationship of system evolution, strengthening the resilience of the tourism economy may require compromising development efficiency (Jin et al., 2021) because they may be mutually exclusive in some cases and require streamlining and redundancy of the tourism economic system. It may be necessary to harmonize the relationship between resilience, efficiency and interruption (Essuman et al., 2020; Galaitsi et al., 2021).

Fig. 2.

Cognition of the relationship between the resilience and efficiency of the tourism economy

Coordinating the relationship between resilience and efficiency does not always mean treating them in a balanced position. Rather, it may be necessary to emphasize the recovery effect of development efficiency in a short period of time after a crisis. In long-term and stable development, we should pay attention to the resilience construction of the tourism economy (Zhou et al., 2020). Compared with industry and agriculture, tourism is more vulnerable to the impact of emergencies (Li & Bao, 2011), which is closely related to the fluidity and comprehensive characteristics of tourism. Especially in the face of the realistic impact of COVID-19, while thinking about how to restore development, we should pay more attention to the adaptability, response and evolution of the resilience of the tourism economy (Prayag, 2018). Rather than seeking a single adaptive match between the organizational system and environment, resilience and efficiency assume an endogenous and continuous adaptive cycle in which there is no optimal match or equilibrium point (Song, 2022). This is indeed worth further exploration, especially to explore the development and coordination between the resilience and efficiency of the tourism economy under realistic situations with strong uncertainties (Sun & Meng, 2020).

Methodology

Evaluation of the resilience of the tourism economy

The measurement methods of measuring the tourism economic resilience are mainly divided into two categories, one is the resilience measurement model and the other one is the construction of evaluation index system. The resilience measurement model is actually a reflection of the speed of the tourism economy or employment growth in different regions, which does not conform to the connotation of the resilience of the tourism economy (Peng et al., 2021). Compared to the resilience measurement model, the evaluation index system has the advantages of being systematic, transparent and reproducible (Sharma et al., 2021), and it is inspired by previous results (Cheng & Zhang, 2020; Zhu et al., 2021). The existing evaluation dimensions of tourism resilience include stability, sensitivity, adaptability, resistance, restoration, reconstruction, renewal, etc. This evaluation method takes the evolutionary process of external emergencies more into account but ignores the evaluation of the long-term cumulative effect of the tourism destination system. Therefore, this study constructs an evaluation index system for tourism economic resilience based on the dimensions of stability, diversification, innovation and vitality. The impact of external emergencies and the long-term impact of the system will be fully incorporated into the evaluation index system to improve the accuracy of the resilience measurement of the tourism economy, as exhibited in Table 1.

Table 1.

Evaluation index system for the resilience of the tourism economy

Rule layer	Index layer	Weight
Stability	X1 Per capita GDP	0.0351
	X2 Land average GDP	0.0492
	X3 Urbanization rate	0.0336
	X4 Registered urban unemployment rate	0.0213
	X5 Per capita living expenditure of urban residents	0.0378
	X6 Total revenue from tourism	0.0843
Diversification	X7 Dependence on inbound tourism	0.0124
	X8 Proportion of the output value of the tertiary industry in GDP	0.0336
	X9 Proportion of tourists to local residents	0.0596
	X10 Degree of abundance of tourism resources	0.0589
	X11 Index of industrial structure diversification	0.0255
Innovation	X12 Urban fixed asset investment	0.0699
	X13 Investment in tourism scientific research	0.0653
	X14 Proportion of expenditure on science and education in GDP	0.0263
	X15 Number of papers related to tourism	0.1231
	X16 Number of senior high school students in school	0.0261
Vitality	X17 Permanent resident population/registered population	0.0296
	X18 Number of star hotel rooms	0.0731
	X19 Reception of overnight visitors	0.0702
	X20 Per capita disposable income of urban residents	0.0229
	X21 Growth rate of tourism revenue	0.0424

Stability is an important basis for the sustainable development of the urban tourism economy (Yang et al., 2020). Obviously, per capita GDP, land average GDP and the urbanization rate are direct reflections of the urban development level, and there is always a positive relationship between them (Ribeiro et al., 2018). The consumption level of urban residents often plays a key role in urban economic construction. Thus, this index is chosen to describe the stability of urban tourism economic resilience. As the main research object, urban tourism needs to pay attention to the development of the tourism economy and the unemployment situation when encountering external environmental shocks, and the employment rate is usually taken as the key index of the measurement model. However, since the amount of direct unemployment in tourism is difficult to effectively identify, this study uses the urban registered unemployment rate as a substitute (Khanyile & Fatti, 2022).

Diversification is used to describe various dimensions of urban tourism economic development (Dogru et al., 2019). The degree of dependence on inbound tourism mainly considers the proportion of the scale of inbound tourists in the overall scale of tourists, and the lack of a mobility environment has a stronger impact on the development of inbound tourism. The quality of tourism resources as tourism attractions often affects the comprehensive development of tourism destinations. The formula for calculating the abundance of tourism resources is as follows: Number of 5A tourist attractions × 10 + Number of 4A tourist attractions × 7 + Number of 3A tourist attractions × 5 (Ma et al., 2018). From the perspective of urban development, it is necessary to include the status of the industrial structure and the level of the three types of industries in the measurement range. The formula for calculating the index of industrial structure diversification is $\sum I_i\times ln{I}_i$, where $I_i$ is the proportion of the added value of the i_th industry in GDP (Ma et al., 2018). The travel ratio in the diversification measurement dimensions considers the various carrying capacities between urban residents and foreign tourists.

Innovation is used to measure the potential and competitiveness of urban tourism development, and it is the main factor in promoting urban tourism development and transformation (Andrades & Dimanche, 2017). The proportion of urban fixed asset investment, tourism scientific research investment and science and education expenditure in GDP represent a city's social investment capacity, the degree of emphasis on tourism scientific research and the intensity of investment in education and training (Buijtendijk & Eijgelaar, 2020), respectively, which can be used as the input indicators of urban tourism innovation and development. The index of innovation output is the number of relevant papers and the number of high school students. The number of authorized tourism patents and the transaction volume of the technology market can also be used as evaluation indicators, but due to the availability of data, we represent the index by the number of papers and the number of high school students.

Vitality is a direct manifestation of the operational state of a city's tourism economy system. The impact of external environmental impacts on activity intensity is obvious. The number of overnight tourists received and the growth rate of tourism income can directly express the state of tourism development. The number of star hotel rooms and the per capita disposable income of urban residents are used to represent the security ability of tourism accommodations and the inner vitality of a city (Prayag, 2018). The proportion of the permanent resident population to the registered population is used to measure the mobility of the urban population. The stronger the mobility is, the more benefits it usually brings to related industries and metaphorically represents the attractiveness of a city.

Based on the weight method of improvement CRITIC, the evaluation index of urban tourism economic resilience is weighted (Diakoulaki et al., 1995), and the linear weighted model is used to measure the comprehensive level of urban tourism economic resilience. The single standard deviation in the original method is replaced by the coefficient of variation, which significantly reflects the degree of data dispersion. The formula is as follows (Lee & Lee, 2013):

$$C_{\text{k}}=\frac{{\sigma }_{\text{k}}}{u_{\text{k}}}\sum _{i=1}^m(1-\left|r_{\text{ij}}\right|),(k=1,2,···,m)$$ 1

$$W_{\text{k}}=C_{\text{k}}/\sum _{\text{i}}\text{=}{\text{1}}^m{C}_{\text{i}}$$ 2

$$y=\sum _{\text{i}}\text{=}{\text{1}}^m{w}_{\text{k}}{x}_{\text{i}}$$ 3

where $C_k$ is the information of improvement CRITIC of the evaluation indicator $k$, ${\sigma }_k$ is the standard deviation of the evaluation indicator $k$, $u_{\text{k}}$ is the mean value of the evaluation indicator $k$, and $r_{\text{ij}}$ is the correlation coefficient of the indicator $i$ _and $j$. $w_{\text{k}}$ is the index weight and meets $\sum _{i=1}^m{w}_{\text{k}}=1$; $m$ is the index item of the evaluation index system; $x_{\text{i}}$ is the city's $i$ index value; $y$ is the comprehensive level of the resilience of the city's tourism economy.

Evaluation of tourism development efficiency

In the Cobb‒Douglas production function, land, labour and capital are usually defined as the most basic inputs of economic production activities (Munguia et al., 2019). However, as it is difficult to obtain the use of tourism land, this index is not applied in many studies on tourism development efficiency (Wang et al., 2020). The labour and capital indicators are replaced by the number of tertiary industry employees and urban fixed asset investment, respectively. The number of star hotel rooms and the tourism resource endowment are important carriers of urban services for tourists, which are included in the evaluation index (Assaf & Cvelbar, 2015). In the face of technological change and rapid economic and social development, knowledge innovation plays an increasingly prominent role in urban tourism competitiveness (Song, 2022; Wang et al., 2020). The number of tourists and the total revenue from tourism are still stable outputs of tourism development. At the same time, with the scholarly attention to the ecological environment and green development, environmental health pressure is included in the indicators of unexpected outputs (Castilho et al., 2021).

The CCR model and BBC model are traditional efficiency measurement methods, but they both have different degrees of defects. The radial model ignores the role of non-radial relaxation variables, while the non-radial model ignores the proportional relationship between the target value of input or output and the actual value (Tone & Tsutsui, 2010). This study uses the EBM model to measure the efficiency of tourism development. The EBM model can take into account the advantages of the radial and non-radial DEA models simultaneously. Environmental health pressure is included in the evaluation index, and tourism development efficiency is redefined as the "full efficiency of tourism development" (FEOTD). The tourism output value of each city in Hainan Province is Y, the input factors of tourism development are $X=(x_1,x_2,···,x_{\text{m}})$, and $m$ is the number of input indicators (Wang et al., 2017). Based on the definition of Kuosmanen and Kortelainen (2005), the full efficiency formula of tourism development is expressed as follows:

$$\text{FEOTD}=y/\sum _{\text{i}}\text{=}{\text{1}}^m{\omega }_{\text{i}}{x}_{\text{i}}$$ 4

where $\text{FEOTD}$ is the full efficiency of tourism development and ${\omega }_{\text{i}}(i=1,2,···,m)$ is the input weight of each tourism factor, satisfying $\sum {\omega }_{\text{i}}=1$. The linear programming of the full efficiency measurement of tourism development in Hainan cities $o(o=1,2,···,17)$ to be evaluated is expressed as follows:

$$min{\gamma }^{\ast }=\theta -{\epsilon }_{\text{x}}\sum _{\text{i}}\text{=}{\text{1}}^m\frac{{\omega }_{\text{i}}{\overline{s}}_{\text{i}}}{x_{\text{io}}}$$ 5

$$s.t.X\lambda -\theta x_{\mathit{io}}+\overline{s}=0,Y\lambda \ge y_{\text{o}},\lambda \ge 0,\overline{s}\ge 0$$ 6

where $X$, $Y$, $\lambda$ and $\overline{s}$ are input, output, the weight coefficient and the input relaxation variable, respectively; $r^{\ast }$ is the tourism development efficiency of each city to be evaluated; $\theta$ is the radial component of $r^{\ast }$; and ${\epsilon }_x$ is a key parameter, and its value range is [0,1], indicating the importance of the non-radial part in efficiency calculation. When the efficiency is 0, the radial model is set, and when the efficiency is 1, the SBM model is set (Tone & Tsutsui, 2010).

If "$\sum \lambda =1$" is added to Formula (2), the pure technical efficiency of tourism development factors in Hainan cities can be calculated. The ratio of tourism development efficiency to pure technical efficiency is the scale efficiency of tourism development resource utilization in a city, from which the technological and scale efficiency of tourism development resource utilization can be judged (Wang et al., 2017). In addition, the EBM model based on an input orientation and constant returns to scale is abbreviated as EBM-I-C, while the EBM model based on an input orientation and variable returns to scale is abbreviated as EBM-I-V.

Synergistic calculation of the resilience and efficiency of the tourism economy

The Haken model is an important method of measuring the order degree of a system. It evaluates the evolutionary stage of a system by identifying the order parameters (Haken, 1977), which are the parameters that play a dominant role in the system and describe the macroscopic order degree or macroscopic mode of the system (Han et al., 2021). Suppose that in a moving system, order variables $q_1$ and variables dominated by order parameters $q_2$ satisfy the following equation:

$${\overline{q}}_1=-{\gamma }_1{q}_1-a{q}_1{q}_2$$ 7

$${\overline{q}}_2=-{\gamma }_2{q}_2+b{q}_1^2$$ 8

where ${\gamma }_1$ and ${\gamma }_2$ are the damping coefficients of the two subsystems,${\gamma }_2>0$ and $\left|{\gamma }_2\right|>>\left|{\gamma }_1\right|$. To satisfy the "adiabatic approximation hypothesis" of the system, there must be an order of magnitude difference between the two (Zhu et al., 2021). When $q_2$ is quickly deleted, order variable $q_1$ has no time to change. We set ${\overline{q}}_2=0$, and take it into the equation of the evolution of the order parameters to obtain the following system evolution equation:

$${\overline{q}}_1=-{\gamma }_1{q}_1-\frac{\mathit{ab}}{{\gamma }_2}{q}_1^3$$ 9

To reflect the state of the system, the negative integral of ${\overline{q}}_1$ is obtained to obtain the system’s potential function:

$$v=\frac{1}{2}{\gamma }_1{q}_1^2+\frac{\mathit{ab}}{4{\gamma }_2}{q}_1^4$$ 10

Since the model was initially applied in the field of physics, it needs to be optimized for the fields of economics and social science. The variables are discretized, and constant terms are set (Han et al., 2021; Li & Liu, 2014). The equation of the co-evolution of tourism economic resilience and efficiency is obtained as follows:

$$lnRE{S}_{\text{(t)}}=(1+{\gamma }_1)lnRE{S}_{\text{(t}}\text{- 1)}+alnRE{S}_{\text{(t}}\text{- 1)}lnFEOT{D}_{\text{(t}}\text{- 1)}+C_1$$ 11

$$ln{\text{FEOTD}}_{(t)}=(1+{\gamma }_2)ln{\text{FEOTD}}_{(t-1)}+bln{\text{RES}}_{(t-1)}ln{\text{FEOTD}}_{(t-1)}+C_2$$ 12

Data sources

The data used in this study are mainly tourism data, economic and social data and infrastructure data covering 17 cities in Hainan Province from 2001 to 2020. With full consideration of the accessibility and authority of the data, the China Economic and Social Big Data Research platform is used to collect relevant yearbook data, including yearbook data on China, Hainan Province and various cities from the China City Statistical Yearbook, China County Statistical Yearbook, Hainan Statistical Yearbook, Haikou Statistical Yearbook, etc. Some of the data come from national statistical bulletins on the economic and social development of Hainan Province and cities, as well as data released by government statistical agencies and cultural and tourism administration agencies. To ensure data integrity, the SPSS linear interpolation method was used to supplement missing data for individual years. In addition, due to the serious lack of data for Sansha city and Baisha Li Autonomous County, they are not included in the study.

Results analysis

Measurement results of the resilience and efficiency of the tourism economy

Results of the resilience of tourism development

From the perspective of the time series development of tourism economic resilience, the growth trend of tourism economic resilience in Hainan Province is not significant, with a high degree of spatial differentiation. Tourism economic resilience is mainly concentrated in the range of 0–0.4, and the relatively concentrated range is 0.6–0.8, represented by Sanya city and Haikou city, as exhibited in Fig. 3. The resilience level of Sanya and Haikou is 0.71 and 0.70, respectively, far ahead of other cities in Hainan Province, and their resilience level is approximately three times the average of Hainan Province. Sanya and Haikou are famous traditional tourist destinations in China. In particular, Sanya is the only tropical tourist city in China, and the development of tourism has been of great concern to the government and society (Dai et al., 2019). In addition to the economic support brought by large-scale tourists, the infrastructure of Sanya and Haikou plays an important role. Especially after the national International Tourism Island Strategy was issued in 2009, the international tourism reception capacity of Sanya and Haikou approached or partially reached the international level. The second-phase expansion of Meilan International Airport and Phoenix International Airport has been implemented to make it more convenient for the more than 40 million tourists who fly to Hainan every year (Xie et al., 2019).

Fig. 3.

Time series chart of the resilience of the tourism economy in Hainan Province from 2001 to 2020

Qionghai city and Wanning city in eastern Hainan Province also show stronger resilience characteristics, and Danzhou city in the west occasionally shows high resilience. The source of their resilience is very different from that of Sanya and Haikou, which do not have superior economic, policy or transportation conditions. Qionghai city and Wanning city have jointly formed the eastern Hainan health tourism group, making full use of rural and hot spring tourism resources in the region, and they have committed to building an international rural ecological and cultural tourism destination. The rural tourism economic system has strong stability and adaptability. It has a relatively stable buffer zone in the face of external environmental impacts, and its internal ecology, economy and culture continue to operate normally, which directly affects the resilience system of the urban tourism economy. Ledong County, Lingao County, Qiongzhong County and Tunchang County are cities with low levels of tourism economic resilience and poor resilience in the face of specific environmental shocks and disruptions (Jiang et al., 2021). The tourism industry in these cities developed late and is restricted in terms of the scale and technological level of the tourism industry. In addition to Lingao County, the other three cities face traffic inconveniences due to terrain restrictions, limiting inbound tourism development. Moreover, the tourism brand effect of these cities is not significant, and the social sensitivity is poor (Essuman et al., 2020). Local and neighbouring residents have become the main potential tourists, showing a tourism development model with weak sustainability.

Results of tourism development efficiency

From the time series characteristics of tourism development efficiency, the mean value of tourism development efficiency in Hainan Province from 2001 to 2020 is 0.7435, showing an overall upward trend. Additionally, the value of urban tourism development efficiency is concentrated in the range of 0.4–0.8, as exhibited in Fig. 4. In Hainan Province, Sanya has always been the leading city in tourism development efficiency, followed by Haikou, Wenchang and Wanning, with significant differences in regional tourism development efficiency (Zhang et al., 2022). Hainan Province takes marine tourism in the broad sense (coastal tourism, island tourism, marine tourism, etc.) as the general theme of development (Xie et al., 2019). In the early stage of tourism development, Sanya and Haikou were welcomed and recognized by tourists, especially due to their high-quality infrastructure and tourism resources. In the later stage of the study, the tourism development efficiency of Sanya city approaches 1, which indicates high development efficiency. The tourism development efficiency of Wenchang, Wanning and Chengmai Counties is also high, and the tourism scale and environmental pollution degree are kept within a reasonable spatial range. In general, however, the number of tourists, energy consumption, trade openness and other factors will affect the efficiency of tourism development (Castilho et al., 2021). The expansion of the tourism economy will also cause more environmental pressure. Thus, it is necessary to carry out transformation and reform in the later stage of tourism development.

Fig. 4.

Kernel density estimation of tourism economic resilience and efficiency

Among the 17 evaluated cities in Hainan Province, Tunchang County has the lowest average tourism development efficiency, which is only 0.5832. This result is mainly due to the late development of tourism in Tunchang County. Although it has the dream Xiangshan tourism area, the Nankun ethnic customs tourism area and other tourist destinations, the lack of high brand value of the core attraction and tourism infrastructure is still to be improved. This greatly restricts the development of tourism in Tunchang County (Zhang et al., 2022). At the same time, the tourism development efficiency of Dingan County, Ledong County and Changjiang County is low, and to some extent, the advanced industrial development mode is lacking. The insufficient scale of tourism development is the main obstacle. In the process of tourism development in Hainan Province, 2008 and 2020 are important impact nodes. Due to the comprehensive impact of the Wenchuan earthquake, the financial crisis and the International Olympic Games in Beijing, inbound tourism in Hainan suffered a severe impact in 2008 (Xie et al., 2019). In 2020, the impact of COVID-19 on the tourism industry of Hainan Province presented great threats and challenges. Therefore, at these two time nodes, the tourism development efficiency of various cities in Hainan Province declined to different degrees, as exhibited in Fig. 5. The pursuit of tourism development efficiency usually means the pursuit of specialization and the minimization of redundancy, but the unbalanced pursuit of tourism development efficiency will also bring complex practical problems (Song, 2022). The impact of tourism economic development on the regional ecosystem shows a relatively significant environmental Kuznets curve effect (Chen et al., 2020), which is an important practical contradiction that should be addressed in the later stage of tourism development in Hainan Province, especially in Sanya and Haikou.

Fig. 5.

Kernel density analysis of the synergistic effect of tourism economic resilience and efficiency in Hainan Province

The co-evolution of the resilience and efficiency of the tourism economy

Model construction and order parameter recognition

The linear regression method is used to diagnose the collinearity of the resilience and efficiency of the tourism economy. The value of the VIF is less than 5, which does not indicate collinearity. Based on the basic principle of the Haken model, with tourism economic resilience (TER) and tourism development efficiency (TDE) as variables, the model hypothesis is proposed, the model motion equation is constructed, the relevant parameters are solved to judge whether the model hypothesis is valid, and the order parameters are obtained (Han et al., 2021). The model equation was obtained by EViews 10.0, as shown in Table 2.

Table 2.

Measurement results of the variables from 2001 to 2020

Model hypotheses	Motion equations	Parameters	Conclusions
q1 = TER	$q_1(t)=0.853q_{1(t-1)}+0.133q_{1(t-1)}{q}_{2(t-1)}$	${\gamma }_1=0.147$	The motion equation is established; the model hypothesis is satisfied; TER is the order parameter
	(14.934)*** (2.304)**	${\gamma }_2=0.440$
q2 = TDE	$q_2(t)=0.56q_{2(t-1)}+0.312q_{1(t-1)}^2$	$a=-0.133$
	(12.755)*** (5.249)**	$b=0.312$

In the motion equations, the values in parentheses are the t test values; *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively

According to the model results in Table 2, the tourism economic resilience (TER) of Hainan Province from 2001 to 2020 is the order parameter, and each parameter is ${\gamma }_1=0.147$, ${\gamma }_2=0.440$, $a=-0.133$, and $b=0.312$.

The evolutionary equation is:

$$\overline{q_1}=-0.147q_1+0.094q_1^3$$

The potential function is:

$$v=0.074q_1^2+0.024q_1^4$$

Set $\overline{q_1}=0$, and three solutions to the potential function are obtained: $q_1=-1.251$, $q_1=0$, and $q_1=1.251$. Since the values of both tourism economic resilience and tourism development efficiency are positive, the potential function only needs to consider the calculation results and obtain stable point $v$ (1.251,1.175). The distance between any point and the stable point is the state of the cooperative evolution of tourism economic resilience and efficiency, namely the cooperative value of tourism economic resilience and efficiency $D$:

$$D=\sqrt{{(q-1.251)}^2+{(v(q)-1.175)}^2}$$

Synergistic development analysis of tourism economic resilience and development efficiency

From the perspective of synergistic time series changes, the synergistic characteristics of tourism economic resilience and efficiency in Hainan Province from 2001 to 2020 are generally stable. Additionally, the synergistic kernel density fluctuation curve changes from bimodal to multi-modal, with significant differences in curve shape and flattening degree in different years (Han et al., 2021). According to the calculation results of the Haken model, Hainan Province is a co-evolutionary system dominated by the resilience of the tourism economy, emphasizing the matching relationship between the tourism development model and the environment rather than simply emphasizing the organizational variable of efficiency (Song, 2022). In the research stage, the synergistic value of Hainan's tourism economic resilience and development efficiency is mainly concentrated in the range of 0.5–0.8, and a small part is concentrated in the range of 0.2–0.4.

In 2008, the synergistic value of tourism economic resilience and development efficiency in Hainan Province was highly concentrated, and the kernel density value was the highest in the research stage. This result was mainly caused by the impact of the global financial crisis (Han et al., 2021). In a synergistic system dominated by resilience, the impact of the external environment forces the tourism industry to reduce its development efficiency, which makes the resilience of the tourism economy better match tourism development efficiency. This is also consistent with the empirical theory proposed by Jin that efficiency needs to be compromised to build resilience (Jin et al., 2021). After 2011, the synergistic system of the resilience and efficiency of Hainan's tourism economy had a convergence development trend, the crest of the kernel density curve was developing towards diversification, and the synergistic development system dominated by twin peaks was expected to break through. This result is closely related to the supply-side structural reform carried out by China to promote high-quality tourism development, which no longer merely emphasizes the role of economic benefits and pursues sustainable development with multiple dimensions of the economy, society and the environment. In 2020, the outbreak of COVID-19 presented uncertain and powerful crises and challenges to the tourism industry in Hainan Province. The lack of liquidity made it difficult for the tourism industry to develop, and coordinated development was seriously threatened. Therefore, the relationship between the resilience of the tourism economy and development efficiency should be viewed dialectically. The synergistic state of the two does not pursue a single adaptive matching but focuses on the co-evolution of the key points of the synergistic system and the environment (Song, 2022).

Spatial state of the synergy between tourism economic resilience and development efficiency

In terms of the spatial pattern of synergistic development, the synergistic value of tourism economic resilience and development efficiency in Hainan Province is relatively stable, with Lingao County and Ledong County having the highest synergistic value. The synergistic value in the central region shows a trend of gradually increasing development and evolves from the spatial pattern of high in the west and low in the east to the spatial pattern of high in the middle and low on both sides, as exhibited in Fig. 6. In the cooperative evolutionary system dominated by the resilience of the tourism economy, it should be noted that a high synergy value does not mean healthy development; low or vicious competition can also produce a high synergy value (Han et al., 2021). The co-evolution between systems is not always positive; thus, it is necessary to systematically analyse the internal development state of each city and analyse its co-evolution situation in depth.

Fig. 6.

Distribution map of the synergistic effect of tourism economic resilience and efficiency in Hainan Province

The high synergy value of Lingao County and Ledong County is the result of the combined effect of low resilience and low efficiency. The economic foundation of Lingao County and Ledong County is relatively poor, and the tourism industry developed late. However, the internal elements of the system operate in a healthy and coordinated way, and the spatial development, resource allocation and management level have achieved multi-dimensional integration (Sun & Meng, 2020). Therefore, the synergy value of tourism economic resilience and development efficiency is high. Compared with Lingao County and Ledong County, the coordinated development of Haikou city and Sanya city is quite different, although the efficiency and resilience of the two cities are individually better. The synergistic development effect is very limited. This result shows that Haikou and Sanya pay insufficient attention to internal development quality when pursuing development efficiency, and the internal elements of the system have a chaotic flow phenomenon. The system is characterized by conflict and maladaptation in the face of emergencies (Han et al., 2021).

The synergistic development effect of Dingan County, Tunchang County, Qiongzhong County and Baoting County in the central region of Hainan Province gradually increased, and the outstanding effect of the central region increased in the late stage. These cities mainly take rainforest tourism as an attraction to build a national tropical forest park tourist destination. Baoting is included in the greater Sanya economic circle and constantly strengthens the tourism industry with the help of Sanya's radiation and driving role. Qionghai city and Wanning city jointly form the eastern tourism development area of Hainan Province with health resources. Based on the stability of rural and hot spring resources, the coordinated development is not significant. Danzhou city and Changjiang County in the western region are dominated by mountains and sea resources, and the tourism market has limited attraction, making it difficult to attract international tourists (Xie et al., 2019). In addition, because their own development is lagging, the coordinated development of the western region of Hainan is relatively stable.

Discussion and future research agenda

Discussion

Jin (2021) proposed that the establishment of system resilience requires compromising efficiency development. Is there such an internal logic between economic resilience and the tourism industry? On the premise of inheriting Haken’s synergy theory and taking Hainan Province in China as a case, this study measures the resilience of the tourism economy based on an evaluation index system methodologically built on an improved entropy weight. Additionally, it uses the EBM model to measure the efficiency of tourism development (Wang et al., 2017). Finally, the Haken model is selected to verify the order parameters and the level of synergistic development in the system and to explore the difference between the dominant organizations based on efficiency and the dominant organizations based on resilience (Song, 2022).

The difference in the dominant relationship between resilience and efficiency in synergistic systems is worth discussing. First, it should be clear that in the case study of Hainan Province, China, there is a synergistic relationship between tourism economic resilience and development efficiency, and this synergistic relationship is dominated by resilience. The empirical results are consistent with those in other fields, such as fisheries and water resources (Zhu et al., 2021). The compromise between resilience and efficiency is also a manifestation of the synergistic system. In terms of long-term development, the pursuit of synergy between resilience and efficiency is the way to develop sustainable tourism. In terms of the differences in dominant synergistic effects, the resilience-dominated synergistic system applies system thinking, is non-mechanical, non-linear and non-deterministic (Song, 2022) and will not be easily interrupted or collapse due to emergencies (Essuman et al., 2020). Efficiency-based synergistic systems believe that there is a balanced central point whose goal is to minimize transaction costs. Unbalanced efficiency-led development is likely to lead to a decline in the ability to diversify and resist risks, and ultimately, it will lead to the disintegration of the system (Karakoc & Konar, 2021). In the case study of Hainan Province, China, Hainan Province is a development system dominated by resilience, but there is still much room for improvement in its synergistic development, and spatial cooperation and resource complementarity still need to be strengthened.

There is a simple logical reasoning: a higher level of resilience and efficiency leads to a higher level of synergistic development of the system. However, the empirical results of Hainan Province as a case show that the result is not so simple. This also verifies that the non-positive resilience and efficiency evolution proposed by Han ZL (2021) can also achieve a high level of synergy, although there are potential problems within the system (Prayag, 2018). Sanya and Haikou show good leading effects in the individual areas of tourism economic resilience and tourism development efficiency, and they show the direction of long-term healthy development based on the advantages of transportation, resources, policies and funds. However, the coordinated development of the system is at a relatively low level, which is inseparable from the chaotic flow of elements within the system and poses more challenges to the decentralization of tourism development risks and reasonable intervention to promote recovery under the uncertain conditions brought by COVID-19 (Galaitsi et al., 2021).

This research makes the following three innovations. First, based on the Haken model, resilience and efficiency are linked, and the concept of synergy is emphasized to lead development. The well-developed Haken model in the field of resources is introduced into tourism research, which enriches the results of related research on tourism system coupling. Second, this study emphasizes the supporting role of resilience in tourism development, proposes that resilience should be taken as the dominant factor of coordinated development in the long-term development stage and calculates the resilience level of the tourism economy, providing a theoretical basis for the quantitative analysis of tourism resilience. Finally, the EBM model is used to comprehensively measure tourism development efficiency, covering the basic elements of tourism development, the ecological environment, innovative production and other fields. This model has the advantages of both radial data and non-radial data (Wang et al., 2017). It compensates for the shortcomings of the BBC and CCR models of traditional tourism development efficiency.

Future research agenda

COVID-19 has caused unprecedented human casualties in a life-changing environment, and it has posed serious threats and challenges to the sensitive tourism industry (Sharma et al., 2021). Under the scenario of strong uncertainty, attention should be paid to the relationship between tourism resilience and sustainable development, and the synergistic development between tourism economic resilience and tourism development efficiency should be explored (Galaitsi et al., 2021). This section proposes a set of complete and interesting research topics based on the gaps in research and framework system verification.

P1. What is the resilience of the tourism economy? In the post-pandemic era, it is necessary to strengthen research on resilience, especially research on evolutionary resilience. That is, how can tourism adapt to the specific impacted environment by taking advantage of various conditions and realizing the recovery and development of the tourism industry?

P2. What level of resilience and development efficiency of the tourism economy should be reached so that the tourism system can realize healthy and orderly circular development? Is there a contradictory relationship between resilience construction and development efficiency? If this contradictory relationship is established, how should we resolve it?

P3. After basically recognizing the function of tourism economic resilience and clarifying the relationship between tourism economic resilience and development efficiency, we should identify the corresponding construction measures, verify resilience theory with practical results and improve and expand resilience theory to realize the mutual promotion of theory and practice.

These research topics can be explored through case studies, and the evaluation framework of tourism economic resilience, tourism development efficiency and synergistic development can be constantly improved. In addition, we can objectively recognize the resilience of the tourism economy and the efficiency of tourism development by means of concept decomposition, theoretical system construction and a combination of mixed methods. In particular, the cognition of the synergistic relationship between different subjects coordinates academic research and practical interest relationships (Sharma et al., 2021). In future studies, it will be necessary to quantify the relationship between the resilience and efficiency of different types of subjects to obtain theoretical cognition and practical measures to adapt to some common development situations.

Conclusions

In the context of the uncertainties caused by COVID-19, this study takes Hainan Province, China, as a case study and evaluates tourism development efficiency based on the EBM model, considering the role of eco-environmental quality and innovative production factors. An evaluation index system for the resilience of the tourism economy is constructed based on the dimensions of stability, diversification, innovation and vitality, and quantification is carried out by an improved entropy weight method. Finally, the synergistic relationship between tourism economic resilience and development efficiency is explored.

This study holds that the basic view that establishing resilience will damage development efficiency is in line with reality. The empirical results confirm that Haikou and Sanya have a high level value of tourism economic resilience and tourism development efficiency in individual fields, but their synergistic effect is very limited, and they have become the region with the worst synergistic development in Hainan Province. However, Lingao County and Ledong County, which have low tourism economic resilience and tourism development efficiency, have become cities with a high degree of coordination. This result also proves that the coordinated evolution of tourism economic resilience and development efficiency is not always positive and that negative resilience and efficiency can also achieve a higher level of synergy. After identifying the order parameters, we find that Hainan Province is a synergistic evolutionary system dominated by resilience. It pursues not the balance and stability of a single point but the harmony of multiple elements of the system, and such coordination exists more within a spatial range. Moreover, the co-evolutionary system dominated by resilience is in line with the trend of social development and can effectively avoid potential development problems caused by the unbalanced pursuit of efficiency.

Although this study has made some attempts to introduce synergy theory and to optimize the model methods, there are still three limitations. First, this study takes Hainan Province in China as the case study. As an island-type tourism destination, Hainan Province cannot be promoted to reach a broader adaptability research framework. The results should be verified in multiple types of tourist destinations to obtain a more general and objective theoretical analysis logic. Second, when calculating the efficiency of tourism development, the comprehensive effect of transportation and other factors is not considered in this study, which may lead to objective deviation in the calculation results. Finally, quantitative research on tourism economic resilience, especially synergistic research on tourism economic resilience and development efficiency, is still in its infancy, with limited reference to relevant research results, and such research may not be mature in terms of theoretical cognition and the evaluation index system.

Funding

Hebei Cultural and Artistic Science Planning and Tourism Research Project, HB22-ZD002, Pengfei Zhang, the 2nd Comprehensive Scientific Investigation and Research Project of the Qinghai-Tibet Plateau, 2019QZKK0401, Pengfei Zhang, the Special Project for Type-A Strategic and Leading Technologies under the CAS, XDA20020301, Social Science Foundation of Hebei Province, HB22GL017, Pengfei Zhang.

Data availability

Data are available from the authors upon reasonable request.

Declarations

Conflict of interests

None.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.