Coupling Dynamics of Resilience and Efficiency in Sustainable Tourism Economies: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration

Abstract

This study investigates the coupling and coordination between resilience and efficiency in promoting the sustainable development of tourism economies, using the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employs an integrated approach combining the improved CRITIC-Entropy method, super-efficiency SBM model, and coupling coordination degree model to measure the coupling coordination degree between tourism economic resilience and efficiency, examining their spatiotemporal evolution. Further, a PVAR model is used to explore the bidirectional dynamic relationship between resilience and efficiency. The findings indicate that the coupling coordination between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration has evolved from a “coordination transition stage (2011–2012)” to a “coordination development stage (2013–2020)”, showing a trend towards positive coordination. Spatial analysis reveals significant regional differences, with Beijing and Tianjin having higher coupling coordination levels than Hebei Province, demonstrating the radiating effect of core cities, while the overall level within Hebei’s cities still needs improvement. The study confirms a positive interaction between tourism economic resilience and efficiency, with both exhibiting self-enhancing mechanisms. This research highlights the importance of balancing resilience and efficiency for sustainable tourism economic development. It offers valuable insights for policymakers and regional planners to enhance the adaptability and competitiveness of tourism economies in response to external shocks, contributing to the long-term sustainability of the industry.

1. Introduction

Tourism economies ensure the long-term viability of the industry in the face of external challenges like economic crises, environmental changes, and global health emergencies. In recent years, uncertain risks such as public health emergencies, global financial crises, and industrial structural transformations have posed serious threats to the sustainable development of tourism economies [1]. The COVID-19 pandemic, in particular, led to a substantial decline in global tourism revenue in 2020, dealing a severe blow to the tourism industry [2]. As a result, promoting the recovery and sustainable development of tourism economies has become a critical strategy in addressing current global challenges.

In studying the sustainable development of tourism economies, resilience and efficiency are two essential components. Economic resilience refers to a regional economy’s ability to withstand, recover from, adapt to, and adjust its development trajectory in response to external shocks [3,4]. Efficiency, on the other hand, emphasizes optimizing resource allocation to achieve higher output with lower input, thereby fostering economic growth [5]. The interplay between these two dimensions is crucial, as resilience ensures long-term stability and adaptability, while efficiency drives productivity and competitiveness. Studies have shown that resilience enables tourism economies to recover more rapidly from crises such as financial downturns, pandemics, and climate change events [6]. Conversely, efficiency is essential for maintaining profitability and ensuring sustainable growth in competitive tourism markets [7]. Focusing solely on efficiency may undermine a tourism economy’s flexibility in adapting to external changes, while an overemphasis on resilience could lead to resource inefficiency and development stagnation [8,9]. Currently, there is a lack of research analyzing the coordinated development of resilience and efficiency in tourism economies. Thus, developing a tourism economic system that balances resilience and efficiency is crucial for promoting the sustainable development of tourism. Clarifying the relationship between resilience and efficiency will not only enhance the adaptability of tourism economies but also provide important theoretical and practical insights for achieving sustainable tourism growth globally [10].

Resilience theory was first applied in physics, where it described the ability of metals to recover after being subjected to force. In 1973, Canadian biologist Holling brought the concept of resilience into the field of ecology, defining it as the capacity of an ecosystem to resist and recover from external disturbances, thereby broadening the scope of resilience theory [11]. This study, building on the evolutionary resilience theory [12], defines urban tourism economic resilience as the ability of an urban tourism economic system to respond effectively to external disturbances by adjusting its economic structure and development pathways through mechanisms such as risk resistance, functional recovery, and structural reorganization, thus maintaining system equilibrium and transitioning to a more optimal state. Based on the definitions of efficiency and economic efficiency [13], this study defines urban tourism economic efficiency as the capacity to minimize tourism input factors while maximizing tourism economic outputs at given economic costs. These two concepts—resilience and efficiency—are complementary and together drive the sustainable development of urban tourism economies.

Resilience emphasizes the capacity to withstand disturbances, while efficiency focuses on achieving “low input, high output”. Urban tourism economic resilience and efficiency are key complementary variables in the urban tourism economic system. According to synergetics theory, the urban tourism economic system can dynamically transition from a state of disorder and inefficiency to one of order and efficiency. Under stable operating conditions, the system is driven by efficiency, with “low input, high output” promoting urban tourism economic growth, thereby providing a foundational support for resilience and jointly fostering the sustainable development of tourism economies [14]. Conversely, under unstable, complex, and chaotic conditions, resilience becomes the dominant factor. The urban tourism economy, through its strong capabilities in risk resistance, functional recovery, and structural reorganization, can enhance urban tourism efficiency, achieving a balance between resilience and efficiency and advancing the sustainable development of tourism economies [10].

Resilience and efficiency are vital dimensions for driving the sustainable development of tourism economies. Their dynamic balance and synergistic development are effective under both stable and unstable conditions, making it crucial to maintain a dynamic balance between the two (Figure 1).

In this context, this study constructs an index system to assess the resilience and efficiency of the tourism economy. By applying the improved CRITIC-Entropy (Criteria Importance Through Intercriteria Correlation-Entropy) method, the coupling coordination model, and the PVAR (Panel Vector Autoregression) model, the study investigates the coordination effects and dynamic interactions between tourism economic resilience and efficiency within the Beijing–Tianjin–Hebei urban agglomeration from 2011 to 2020. The fundamental aim is to clarify how these two dimensions interact, as understanding this relationship is crucial for ensuring the tourism economy’s long-term viability. This research seeks to enhance the analytical framework for studying the coordinated development of tourism economic resilience and efficiency, offering valuable insights to policymakers and regional planners to promote a more sustainable and adaptable tourism economy in response to external challenges.

2. Literature Review

2.1. Tourism Economic Resilience

Tourism economic resilience has emerged as a vital area of research in response to increasing global economic uncertainties and natural disasters, gaining significant attention in recent years. Resilience refers to the capacity of a tourism economy to absorb, adapt to, and recover from external shocks while maintaining long-term sustainability [3,11].

Early studies on economic resilience primarily focused on conceptualizing the ability of economies to respond to and recover from various shocks. Rose [15] emphasized that economic resilience not only involves recovery capacity after a disaster but also the preparedness level and strategies implemented before such shocks occur. As the research developed, tourism economic resilience came to be understood as a multidimensional concept, incorporating elements such as recovery capacity, adaptability, and the ability to cope with disturbances [16]. In this context, Hall [17] underscored the vulnerability of the tourism industry to unforeseen events, noting that its seasonal nature and demand fluctuations make it particularly susceptible. Therefore, enhancing tourism economic resilience requires effective risk management and crisis response mechanisms.

Biggs et al. [18] extended this understanding by linking tourism economic resilience with social and environmental resilience, suggesting that it not only involves economic recovery but also includes strengthening social capital and improving community adaptability. The importance of this research area has been further highlighted by the global COVID-19 pandemic, which demonstrated that tourism economies with higher resilience were able to recover more swiftly by adapting to the new normal through innovation and strategic shifts [19]. Liao et al. [20] examined how various tourism-dependent economies adapted to the pandemic, emphasizing the role of government policies in enhancing resilience. Studies from different countries and regions indicate that the tourism industry plays a crucial role in economic resilience. Dube and Nhamo further explore the challenges to tourism resilience in developing countries, emphasizing the difficulties faced in post-pandemic recovery [21]. In Turkey, tourism has provided stability during economic shocks, exhibiting an asymmetric impact on economic resilience: stimulating growth in periods of prosperity while mitigating economic downturns during crises [22]. Regional studies in Italy highlight the profound effects of COVID-19 on the tourism economy, showing that regions highly dependent on tourism experienced slower recovery, whereas those with diversified economies demonstrated greater resilience [23]. These findings suggest that resilience is not merely a recovery mechanism but a proactive strategy for ensuring the long-term sustainability of tourism economies.

2.2. Tourism Economic Efficiency

Tourism economic efficiency has become a key metric for assessing how effectively resources are allocated and utilized within the tourism sector, attracting increasing scholarly attention in recent years. The study of tourism economic efficiency originates from the foundational framework of efficiency analysis, where scholars have applied various economic and econometric approaches for multidimensional analysis. Two of the most commonly used methods are stochastic frontier analysis (SFA) and data envelopment analysis (DEA) [24,25]. These methods not only measure the economic efficiency of tourism enterprises or regions but also identify key factors influencing efficiency, providing both theoretical foundations and practical guidance for improving tourism economic efficiency. For instance, the DEA model, first introduced by Charnes et al. [26], has been widely applied in tourism efficiency studies to assess the relative efficiency of different destinations or enterprises. Barros [27] later utilized DEA in tourism research, finding it highly effective in analyzing resource utilization efficiency. Due to its flexibility, DEA has been widely implemented in different resource allocation contexts, including labor, capital, and land resources.

In the context of globalization, research on tourism economic efficiency has increasingly focused on cross-national comparisons and international competitiveness. Assaf and Josiassen [28] explored tourism efficiency across several Asia-Pacific countries, uncovering significant differences in tourism efficiency and international competitiveness. These disparities are largely driven by factors such as policy frameworks, infrastructure, and market conditions. As a result, improving tourism economic efficiency is not only the responsibility of individual enterprises or destination managers but also requires strategic planning and policy support at the national level.

2.3. The Coordinated Relationship Between Resilience and Efficiency

In recent years, academic interest in resilience and efficiency has grown considerably. While extensive research has been conducted on each concept independently, the coordinated investigation of their relationship remains relatively underexplored. Resilience typically refers to a system’s capacity to adapt to and recover from various shocks, while efficiency emphasizes the optimal allocation and utilization of resources. While resilience and efficiency have traditionally been examined as separate constructs, recent studies suggest that their interaction is essential for sustainable tourism development [25]. Destinations that achieve a balance between resilience and efficiency tend to exhibit stronger long-term sustainability.

In other fields, researchers have started examining the coupling and coordination between resilience and efficiency. For example, Bai et al. [29] explored the relationship between urban resilience and land use efficiency, finding that well-planned land use can enhance a city’s ability to recover from natural disasters, thus improving its resilience. Similarly, Sun and Meng [30] studied water resource systems and observed that optimizing water resource management can simultaneously boost water use efficiency and the system’s capacity to withstand risks. Additionally, Han et al. [31] found that the co-evolution of economic resilience and efficiency in marine fisheries is critical for achieving sustainable development, underscoring the importance of balancing short-term efficiency with long-term resilience when resources are scarce.

In the context of tourism, while substantial research exists on both tourism economic resilience and tourism economic efficiency, the coordinated relationship between the two remains largely unexplored. Studies have shown that resilience and efficiency are complementary dimensions within the tourism economic system, and their synergy can significantly enhance the industry’s ability to withstand risks and optimize resource utilization [32]. For instance, optimizing tourism service processes and improving visitor experiences through digital technologies can mitigate losses and accelerate recovery during crises [33]. Meanwhile, industrial agglomeration and regional cooperation can enhance efficiency through economies of scale and resource sharing, while also strengthening the risk resistance of regional economies [34]. Additionally, stakeholder engagement and innovative experiments offer new pathways for achieving efficient operations and enhancing resilience under resource constraints [35].

Despite increasing research on resilience and efficiency in tourism, few studies have examined their interactive mechanisms within a unified framework. Most prior studies have focused on either tourism economic resilience or efficiency from a single-dimension perspective, neglecting the systematic examination of their interaction in dynamic environments. Although these single-dimensional studies have provided valuable insights into how the tourism industry responds to uncertainties, they have not sufficiently addressed the potential synergies between tourism economic resilience and efficiency. Therefore, future research should prioritize the dynamic coupling relationship between these two aspects to better understand their interaction and potential for promoting sustainable tourism development. This study aims to bridge this gap by developing a theoretical framework that analyzes the coupling dynamics of resilience and efficiency in urban tourism economies. By applying a combination of the CRITIC-Entropy method, the Super-Efficiency SBM model, and the Coupling Coordination Degree model, this research provides empirical insights into how tourism economies can achieve sustainable development through an optimal balance of resilience and efficiency.

3. Methodology

3.1. Research Method

(1)

Improved CRITIC-Entropy Weighting Method

The typical objective weighting methods are the entropy method, principal component analysis method, coefficient of variation method, CRITIC method, etc. The entropy method highlights the information content of indicators and ignores the correlation between indicators, while the improved CRITIC method is an objective weighting method dealing with multiple criteria, which makes up for this deficiency and takes into account the correlation and conflict between indicators. The improved CRITIC method and entropy method are comprehensively used to weight the indicators of urban tourism economic resilience, making the indicators more objective and comprehensive. In this study, the CRITIC method determines indicator weights by considering their standard deviation, mean value, and correlation with other indicators. The entropy method, on the other hand, measures information uncertainty by evaluating the distribution of indicator values across different cities. The final comprehensive weight is obtained by averaging the weights from both methods. Based on the research of Zhang Yueqian [36], this paper assumes that the two weighting methods have the same importance, and the specific formula for using the improved CRITIC-Entropy method to synthesize weights is as follows:

$${\omega }_1={\displaystyle \frac{c_j}{{\displaystyle {\sum }_{j=1}^n}{c}_j}}, c_j={\displaystyle \frac{{\sigma }_j}{\overline{x_j}}}{\sum }_{i=1}^m\left(1-\left|r_{ij}\right|\right)$$

(1)

$${\omega }_2={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^n\left(1-e_j\right)}}, e_j=-{\displaystyle \frac{1}{\mathit{ln}m}}{\sum }_{i=1}^m{p}_{ij}\mathit{ln}{p}_{ij}, p_{ij}={\displaystyle \frac{x_{ij}}{\sum _{i=1}^m{x}_{ij}}}$$

(2)

$$u={\sum }_{i=1}^m{\omega }_j\times x_{ij}, {\omega }_j={\displaystyle \frac{1}{2}}\left({\omega }_1+{\omega }_2\right)$$

(3)

In this context, “${\omega }_1$” represents the weight of the indicator determined by the CRITIC method, “${\sigma }_j$” is the standard deviation of indicators, “$\overline{x_j}$” is the average of indicators, “$r_{ij}$” denotes the correlation between the city and the indicator, “${\omega }_2$” stands for the weight of the indicator calculated using the entropy method, and “${\omega }_j$” is the comprehensive weight of the indicator. Additionally, “$x_{ij}$” refers to the standardized data, and “$u$” represents the comprehensive evaluation measure of urban tourism economic resilience.

(2)

Super-Efficiency SBM Model

The DEA framework encompasses various models including CCR, BCC, SBM, EBM, along with Super-Efficiency DEA, Super-Efficiency SBM, and the DEA–Malmquist Index. Tone [37] extended the traditional CCR and BCC models to create the Super-SBM model. By comparing the advantages and characteristics of these models, this study has identified the Super-SBM model as the most suitable method for evaluating urban tourism economic efficiency. The specific formula for the Super-SBM model is presented as follows:

$$\begin{array}{l}\theta ={\displaystyle \frac{1+\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{s_i^{-}}{x_{jm}}}}{1-\frac{1}{q}{\displaystyle \sum _{i=1}^q\frac{s_i^{+}}{y_{jq}}}}}\\ s.t.\left\{\begin{array}{l}{\displaystyle \sum _{j=1,j\ne k}^n{x}_j{\lambda }_j-s_i^{-}\le x_k}\\ {\displaystyle \sum _{j=1,j\ne k}^n{y}_j{\lambda }_j+s_i^{+}\ge y_k}\\ \lambda ,s^{-},s^{+}\ge 0\\ i=1,2,\cdots ,m; r=1,2,\cdots ,q; j=1,2,\cdots ,n(j\ne k)\end{array}\right.\end{array}$$

(4)

In the formula, “$x_{jm}$” and “$y_{jq}$” represent the input and output variables of decision-making unit j, respectively. “${\lambda }_j$” is the weight used to construct the efficiency benchmark. “$s_i^{+}$” and “$s_i^{-}$” are slack variables that indicate input excess and output shortfall. $\theta$ is the efficiency score, where $\theta$ > 1 signifies super-efficiency. Additionally, the constraint “j ≠ k” indicates that when measuring and evaluating the efficiency of a decision-making unit, this unit is only compared to the linear combination of all other decision-making units in the sample, excluding the decision-making unit “j” itself.

(3)

Coupling Coordination Degree Model

The coupling coordination degree serves as a crucial indicator for assessing the coordination relationships between different subsystems, providing a real and effective reflection of the coordination effects and the extent of coordination among systems. In this study, a coupling coordination model is constructed to examine the coupling coordination state and evolutionary trends of tourism economic resilience ($U_1$) and tourism economic efficiency ($U_2$) in the Beijing–Tianjin–Hebei region. The specific details of the model are described as follows:

$$C=2\sqrt{{\displaystyle \frac{\left(U_1{·U}_2\right)}{{\left(U_1{+U}_2\right)}^2}}}$$

(5)

$$D=\sqrt{C·T}; T={\alpha U}_1+\beta U_2$$

(6)

In this context, $U_1$ and $U_2$ denote the composite indices of tourism economic resilience and efficiency, respectively. C denotes the coupling degree between the two systems; an increase in C indicates a more harmonious relationship between tourism economic resilience and efficiency, suggesting that their development trends are becoming more orderly. D represents the coupling coordination degree, while T is the comprehensive evaluation index of both tourism economic resilience and efficiency. The weights of tourism economic resilience and efficiency, denoted as $\alpha$ and $\beta$, are both set to 0.5 based on previous research [38,39]. Building on prior studies, this research categorizes the coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei region into three coordination stages and ten coordination levels, as outlined in

Table 1. Classification Standards for Coupling Coordination Degree.

Coupling Coordination Degree D Interval	Coordination Level	Coordination Stage
[0.0~0.1)	Extreme Imbalance	Coordination Decline Stage
[0.1~0.2)	Severe Imbalance
[0.2~0.3)	Moderate Imbalance
[0.3~0.4)	Mild Imbalance
[0.4~0.5)	On the Verge of Imbalance	Coordination Transition Stage
[0.5~0.6)	Barely Coordinated
[0.6~0.7)	Primary Coordination	Coordination Development Stage
[0.7~0.8)	Intermediate Coordination
[0.8~0.9)	Good Coordination
[0.9~1.0]	Excellent Coordination

.

(4)

PVAR Model

The PVAR model is a statistical technique that combines panel data analysis and vector autoregression (VAR) to examine dynamic relationships among multiple variables over time while accounting for cross-sectional heterogeneity. The estimation methods for the PVAR model include the commonly used least squares and generalized method of moments. The estimation process encompasses key steps such as parameter estimation, model diagnostics, and evaluation. The PVAR model is commonly used in macroeconomics, international economics, and financial economics, primarily to study dynamic interactions among variables and to forecast future trends. Particularly when examining the interaction between tourism economic resilience and efficiency, the key feature of the PVAR model is its approach of considering each endogenous variable as a function of the lagged values of all endogenous variables within the system. This approach helps avoid model specification errors and endogeneity issues. Its mathematical expression is as follows:

$$y_{it}=a_i+{\beta }_0+{\sum }_{j=1}^n{\beta }_j{y}_{i,t-j}+u_i+{\epsilon }_{it}$$

(7)

where $y_{it}$ is the endogenous variable, i represents the region, t represents the year, n is the lag order, ${\beta }_0$ is the intercept, $y_{i,t-j}$ are the lagged terms of the endogenous variable $y_{it}$, $a_i$ and $u_i$ denote the individual-specific and time-specific variations, respectively, and ${\epsilon }_{it}$ is the random error term.

3.2. Indicator System Construction

3.2.1. Measurement Indicator System for Tourism Economic Resilience

As a vital component of the regional economic system, regional economic resilience is typically assessed across four dimensions: resistance capability, recovery capability, reconstruction capability, and regenerative capability [40,41]. These dimensions, respectively, reflect a region’s capacity to withstand external shocks, the speed of its recovery, its ability to adjust structurally, and its potential for innovative development. Building on these dimensions and considering the specific characteristics of the tourism industry, Xie Chaowu [41] proposed a more detailed analytical framework that categorizes urban tourism economic resilience into three levels: crisis risk resistance capability, system function recovery capability, and industry structure reorganization capability. These levels correspond to the tourism industry’s processes of prevention, emergency response, and adjustment when faced with crises. To effectively assess these resilience capabilities, this study has selected 21 indicators to develop a tourism economic resilience indicator system specifically for the Beijing–Tianjin–Hebei urban agglomeration. The detailed indicators are listed in

Table 2. Measurement indicator system for tourism economic resilience in the Beijing–Tianjin–Hebei urban agglomeration.

Target Layer	Rule Layer	Index Layer	Weight
Resilience of urban tourism economy	Resistance	X1 Gross Domestic Product of the region	0.04705
		X2 Per capita GDP	0.03985
		X3 Per capita disposable income of urban residents	0.03185
		X4 Domestic tourism income	0.04635
		X5 Inbound tourism income	0.08400
		X6 Total tourism income	0.04710
		X7 Number of A-grade scenic spots	0.04245
		X8 Abundance of tourism resources	0.03245
	Recovery	X9 GDP growth rate	0.04020
		X10 Fiscal self-sufficiency level	0.04625
		X11 Total retail sales of consumer goods	0.05350
		X12 Total number of tourists received	0.03955
		X13 Per capita tourism consumption level	0.02770
		X14 Number of travel agencies	0.05110
		X15 Number of star-rated hotels	0.05385
	Recombination	X16 The proportion of the tertiary industry to GDP	0.03350
		X17 The proportion of total tourism income to the gross regional product	0.05380
		X18 The proportion of total tourism income to the value-added of the tertiary industry	0.05820
		X19 The proportion of the total number of tourists received by the permanent population	0.04475
		X20 Per capita tourism income	0.04610
		X21 Number of tourism employees	0.08040

.

Resistance capability measures the ability of urban tourism economies to withstand external crises. To accurately reflect this capability, this study assesses it from three perspectives: local economic foundation, the scale of the tourism economy, and the endowment of tourism resources. These indicators collectively reflect economic strength and the fundamental conditions of the tourism industry, both of which are critical for tourism resilience. The robustness of the local economic foundation is the bedrock of a city’s tourism sector’s risk resistance capability. Key indicators such as the Gross Domestic Product (X1), GDP per capita (X2), and per capita disposable income of urban residents (X3) are used to measure the local economic foundation. These economic indicators not only reflect the overall economic strength of the city but also provide financial support for coping with external economic shocks, allowing urban areas to absorb crises effectively [42]. The scale of the tourism economy is another key factor in assessing tourism resilience. Tourism revenue acts as a core indicator in this dimension, encompassing domestic tourism revenue (X4), inbound tourism revenue (X5), and total tourism revenue (X6). These indicators provide insights into the overall size and diversity of the tourism economy, enabling an assessment of its stability in response to market fluctuations, thereby demonstrating the industry’s financial resilience [43,44]. Moreover, the abundance and quality of tourism resources play a crucial role in strengthening an urban area’s ability to resist external risks. This study selects the number of A-grade scenic spots (X7) and the abundance of tourism resources (X8) as key indicators to evaluate the attractiveness and long-term development potential of tourism resources. These resource endowments not only enhance the city’s recovery capacity during crises but also lay the foundation for long-term sustainable development [45].

Recovery capability emphasizes the speed and extent to which an urban tourism economy recovers after experiencing significant shocks. This study measures this dimension through economic vitality and tourism reception capacity. Economic vitality reflects the potential for economic recovery after a shock, with the GDP growth rate (X9) serving as a direct indicator of economic growth speed, offering insight into overall economic recovery. Meanwhile, the fiscal self-sufficiency level (X10) and the total retail sales of consumer goods (X11) as auxiliary indicators can further characterize the performance of economic vitality after the crisis [46]. In addition, the urban tourism reception capacity is particularly critical in the late crisis. The total number of tourists received (X12) and the per capita tourism consumption level (X13) are important indicators to measure the recovery quality of the tourism market, which can effectively reflect the recovery situation of the tourism industry. The number of travel agencies (X14) and the number of star-rated hotels (X15) are important indicators to evaluate the urban reception capacity, which can show the city’s service capacity and facilities’ carrying capacity when external tourists return [45]. Recombination capability reflects an urban tourism economy’s ability to achieve stable growth through industrial transformation and workforce adaptation following external shocks. This study evaluates six key indicators to assess economic diversification, tourism’s role within the service sector, and labor market resilience. The proportion of the tertiary industry in the GDP (X16), total tourism income in GRP (X17), and total tourism income in the tertiary industry (X18) capture the degree of tourism integration within the broader economy, indicating the extent of dependence or diversification [47]. Meanwhile, the proportion of total tourists received by the permanent population (X19), per capita tourism income (X20), and the number of tourism employees (X21) measure tourism intensity, economic benefits per resident, and workforce adaptability, respectively. A well-balanced economic structure, sustainable tourism demand, and a resilient labor force contribute to an urban region’s ability to adjust, recover, and sustain long-term tourism development [48].

In summary, by comprehensively evaluating the above dimensions, we can systematically understand the resilience performance of urban tourism economies under various crisis scenarios. This assessment system not only provides a solid foundation for theoretical research but also offers important practical guidance for policymakers to enhance the risk resistance capability of urban tourism and achieve sustainable development.

3.2.2. Measurement Indicator System for Tourism Economic Efficiency

In researching urban tourism economic efficiency, this study focuses on capital and labor inputs as the primary input factors. Building on previous research [46,49], capital input is represented by the number of star-rated hotels, the number of travel agencies, and the number of A-grade scenic spots. These indicators reflect the infrastructure and service capacity of the city’s tourism sector, serving as key factors in evaluating tourism economic efficiency.

For labor input, given the lack of official statistics on tourism employment, this study uses the total employment figures from the accommodation, catering, and cultural, sports, and entertainment sectors as a proxy for tourism employment [50]. This approach comprehensively considers employment in multiple industries closely associated with tourism, providing a more accurate reflection of the labor input in the urban tourism industry.

In terms of output indicators, this study primarily uses the total tourism revenue and total number of tourists received. These two indicators capture the economic benefits and market size of tourism, respectively, and are critical dimensions for assessing urban tourism economic efficiency. By integrating these input and output indicators, this study aims to systematically evaluate the efficiency of urban tourism economies, offering theoretical support for enhancing resource allocation and output effectiveness in the tourism industry. The detailed indicators are listed in

Table 3. Measurement indicator system for tourism economic efficiency in the Beijing–Tianjin–Hebei urban agglomeration.

Indicator Category	Primary Indicators	Secondary Indicators
Investment	Capital investment	Number of star-rated hotels
		Number of travel agencies
		A-level Scenic Area
	Manpower investment	Number of tourism practitioners
Produce	Expected output	Total tourism revenue
		Total number of tourists received

.

3.3. Data Sources

The study focuses on 13 cities in the Beijing–Tianjin–Hebei urban agglomeration. The data primarily originate from the “China Tourism Statistical Yearbook”, “China City Statistical Yearbook”, “China Regional Economic Statistical Yearbook”, “Beijing Statistical Yearbook”, “Tianjin Statistical Yearbook”, “Hebei Economic Yearbook”, statistical yearbooks of various cities in Hebei Province, the national economic and social development statistical bulletins of each city from 2011 to 2020, and the official websites of local governments. For years with incomplete data, the mean interpolation method was used to supplement the data.

4. Results

4.1. Analysis of Coupled Coordination Development of Tourism Economic Resilience and Efficiency in the Beijing–Tianjin–Hebei Urban Agglomeration

4.1.1. Temporal Evolution Characteristics of Coupling Coordination Degree

By integrating the enhanced CRITIC-Entropy method, the super-efficiency SBM model, and the coupling coordination degree model, we calculated the tourism economic resilience and efficiency values of the Beijing–Tianjin–Hebei urban agglomeration from 2011 to 2020, along with their coupling coordination degree. The findings are summarized in

Table 4. Coupling coordination degree of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration (2011–2020).

Urban	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Average
Beijing	0.736	0.832	0.850	0.818	0.847	0.894	0.929	0.949	0.958	0.657	0.847
Tianjin	0.605	0.648	0.679	0.756	0.780	0.823	0.865	0.903	0.949	0.611	0.762
Shijiazhuang	0.470	0.536	0.587	0.627	0.681	0.727	0.816	0.870	0.993	0.705	0.701
Tangshan	0.466	0.522	0.562	0.585	0.608	0.681	0.768	0.845	0.967	0.688	0.669
Qinhuangdao	0.507	0.536	0.584	0.594	0.631	0.724	0.848	0.862	0.983	0.580	0.685
Handan	0.440	0.501	0.529	0.578	0.639	0.720	0.791	0.864	0.954	0.675	0.669
Xingtai	0.473	0.534	0.578	0.619	0.667	0.814	0.797	0.879	0.970	0.727	0.706
Baoding	0.468	0.510	0.533	0.591	0.657	0.711	0.747	0.817	0.966	0.679	0.668
Zhangjiakou	0.427	0.492	0.542	0.597	0.644	0.853	0.809	0.874	0.984	0.554	0.678
Chengde	0.466	0.518	0.560	0.613	0.647	0.745	0.806	0.869	0.986	0.605	0.682
Cangzhou	0.546	0.824	0.687	0.672	0.696	0.825	0.808	0.855	0.979	0.713	0.761
Langfang	0.483	0.445	0.561	0.585	0.640	0.723	0.874	0.867	0.977	0.653	0.681
Hengshui	0.422	0.504	0.568	0.587	0.630	0.730	0.803	0.848	0.958	0.723	0.677
Average	0.501	0.569	0.602	0.632	0.674	0.767	0.820	0.869	0.971	0.659	0.707

.

From an overall perspective of the Beijing–Tianjin–Hebei region (Table 4), the coupling coordination degree of tourism economic resilience and efficiency steadily increased from 0.501 in 2011 to 0.971 in 2019, demonstrating a significant upward trend. Specifically, the region achieved a barely coordinated level during 2011–2012, progressed to primary coordination between 2013 and 2015, reached intermediate coordination in 2016, moved to good coordination in 2017–2018, and finally attained excellent coordination in 2019. Throughout this period, the coupled coordination between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration progressed from the “coordination transition stage (2011–2012)” to the “coordination development stage (2013–2019)”. However, due to the impact of the COVID-19 pandemic, the coupling coordination degree fell to 0.659 in 2020, but still remained within the coordination development stage. This indicates that the coupling coordination between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration is gradually strengthening, with both aspects increasingly supporting and promoting each other. This positive development trend is likely closely associated with the implementation of regional coordinated development policies and the simultaneous progress of the tourism market.

From the perspective of the three regions within the Beijing–Tianjin–Hebei area, the evolution of the coupling coordination degree between tourism economic resilience and efficiency is shown in Figure 2. While Beijing, Tianjin, and Hebei all exhibit a year-by-year increase in coupling coordination levels, Hebei Province consistently lags behind Beijing and Tianjin in overall performance. Specifically, Beijing’s coupling coordination development level is higher, rising from intermediate coordination in 2011 to excellent coordination after 2017, and consistently ranking first in the overall coordination level. Tianjin’s coordination level also improved gradually from primary coordination, reaching excellent coordination between 2018 and 2019, showing a steady upward trend. In contrast, Hebei Province was on the verge of imbalance in 2011 but continuously developed, achieving excellent coordination by 2019, gradually narrowing the gap with Beijing and Tianjin.

The primary reasons for this disparity can be attributed to several policy and economic factors. First, Beijing and Tianjin benefit from preferential policies under the Beijing–Tianjin–Hebei-coordinated development strategy, such as infrastructure investment, financial support, and talent attraction initiatives, which enhance their tourism economic efficiency. Second, the economic structure of Beijing and Tianjin is more diversified, with strong service industries and high-value tourism services, whereas Hebei relies more on traditional tourism sectors with lower economic resilience. Additionally, the differences in transportation accessibility and tourism service quality contribute to this gap. Beijing and Tianjin, as major international tourism destinations, have well-developed transportation networks and higher-quality tourism services, whereas Hebei’s tourism infrastructure and service capabilities still need improvement. This indicates the significant radiating and driving effect of the core cities, with cities in Hebei making substantial progress under policy support, although further improvements are still needed overall.

From 2011 to 2020, the coupling coordination degree between tourism economic resilience and efficiency in the 13 cities of the Beijing–Tianjin–Hebei urban agglomeration (Figure 3) exhibited an overall upward trend. Beijing and Tianjin consistently remained in the coordinated development stage, peaking in 2019 with a slight decline in 2020. Initially, the coupling coordination level of the 11 cities in Hebei Province was in the transitional stage but developed rapidly. By 2014, Shijiazhuang, Xingtai, Zhangjiakou, and Cangzhou had reached the primary coordination level, and by 2015, all 11 cities in Hebei Province had achieved primary coordination. By 2016, except for Tangshan, all other cities had been upgraded from primary to intermediate coordination. Post-2016, the growth rate of coupling coordination levels across various cities accelerated significantly, gradually narrowing the gap with Tianjin and Beijing. Despite a slight decline in some cities in 2020 due to the pandemic, the overall trend remained positive, indicating the beneficial impact of policy support and infrastructure improvements on the tourism economy of each city.

4.1.2. Spatial Evolution Characteristics of Coupling Coordination Degree

As illustrated in Figure 4, the overall coordination trend between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration is positive. Spatially, the coupling coordination degree follows a diffusion pattern, gradually spreading outward from the core to the periphery. Beijing consistently maintains the highest coupling coordination degree, creating a central radiation effect that significantly stimulates the surrounding areas. The coordination degree of Tianjin and its surrounding cities, such as Langfang and Cangzhou, has been improving annually. High coupling coordination areas gradually expand outward from the center, forming a highly coordinated region centered on Beijing and Tianjin. As regional integration advances, more cities have progressed from low to medium–high coupling coordination degrees, demonstrating dynamic spatiotemporal evolution characteristics and economic agglomeration effects. The coupling coordination degree of cities surrounding Beijing, such as Langfang and Baoding, was relatively low in 2011 and 2014, but significantly improved by 2017 and 2020. This indicates that these cities have enhanced their economic resilience and efficiency coordination through proactive tourism economic development strategies and measures. Other cities in Hebei Province, such as Shijiazhuang, Tangshan, Xingtai, and Handan, overall exhibited a gradual upward trend from 2011 to 2020. Particularly in 2017, several cities reached a high coupling coordination degree, indicating that Hebei Province has achieved significant results in promoting tourism economic development.

Overall, the coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration shows an upward trend, reflecting the trend of regional coordinated development. Particularly in 2017 and 2020, more cities moved into the medium–high coordination degree range, illustrating significant spatial optimization and coordinated development trends. High coupling coordination areas gradually formed economic agglomeration effects, further promoting the coordinated development of the tourism economy within the region, highlighting the closeness of regional collaboration and development. At the same time, despite the overall positive trend, spatial disparities persist, with peripheral areas such as Zhangjiakou and Chengde lagging in coordination degree. These spatial distribution characteristics indicate that the Beijing–Tianjin–Hebei urban agglomeration has made substantial progress in improving the coupling coordination degree between tourism economic resilience and efficiency. However, further efforts are needed to promote regional integration, reduce regional disparities, and achieve more balanced development.

As illustrated in Figure 5 and

Table 5. Parameters of the standard deviation ellipse for the spatial distribution pattern of the coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration.

Year	Centroid Coordinates	Major Axis (km)	Minor Axis (km)	Azimuth (°)	Eccentricity	Area (10,000 km2)
2011	(116°22′29.2″, 39°06′32.9″)	2.376	1.249	46.903	0.474	9.326
2012	(116°21′28.5″, 39°04′14.0″)	2.354	1.259	46.095	0.465	9.311
2013	(116°20′55.8″, 39°05′15.7″)	2.376	1.261	46.372	0.470	9.410
2014	(116°19′42.9″, 39°05′03.0″)	2.389	1.267	46.121	0.470	9.510
2015	(116°18′22.4″, 39°04′14.9″)	2.398	1.268	45.996	0.471	9.548
2016	(116°17′27.3″, 39°04′36.6″)	2.399	1.306	45.390	0.456	9.844
2017	(116°19′49.7″, 39°04′36.9″)	2.425	1.278	46.495	0.473	9.738
2018	(116°18′40.7″, 39°03′53.2″)	2.437	1.283	46.152	0.474	9.817
2019	(116°18′37.1″, 39°03′39.8″)	2.442	1.287	46.529	0.473	9.873
2020	(116°15′46.3″, 38°58′11.5″)	2.434	1.234	46.120	0.493	9.438

, from 2011 to 2020, the shape and orientation of the ellipse have changed slightly but remained generally stable, with an azimuth angle around 46°, reflecting stability in the diffusion direction of the coupling coordination degree between tourism economic resilience and efficiency. The area of the ellipse increased from 93,260 km² in 2011 to 98,730 km² in 2019, indicating an overall upward trend in the coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration. The changes in the lengths of the major and minor axes were minimal, reaching their maximum in 2016, with the major axis at 2.399 km and the minor axis at 1.306 km, suggesting that the diffusion range of the coupling coordination degree was the largest in that year. The eccentricity increased to 0.493 in 2020, indicating that the ellipse became more elongated, possibly due to the impact of the COVID-19 pandemic, which led to a decline in the coupling coordination degree in 2020.

From the centroid movement trajectory (b), it is evident that from 2011 to 2020, the centroid generally moved from the northeast to the southwest. Specifically, the centroid moved southwest from 2011 to 2012, northwest from 2012 to 2013, southwest from 2013 to 2015, northwest from 2015 to 2016, east from 2016 to 2017, and southwest from 2017 to 2020. Throughout these 10 movements, the centroid moved southwest and northwest a total of nine times, with a movement frequency of 90%, while moving east only once, with a movement frequency of 10%, indicating a continuous rise in the coupling coordination degree between tourism economic resilience and efficiency in the western region of the Beijing–Tianjin–Hebei urban agglomeration.

4.1.3. Dynamic Evolution Characteristics of Coupling Coordination Degree

This paper uses the Kernel Density Estimation method to analyze the dynamic evolution characteristics of the coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration, as shown in Figure 6.

During the study period, the overall coupling coordination degree between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration shifted rightward, indicating a significant improvement in the overall coupling coordination degree and a gradual strengthening of the coordinated development trend of tourism economic resilience and efficiency. However, the leftward shift of the curve in 2020 is evident, indicating that the COVID-19 pandemic has impacted the coordinated development of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration. In terms of distribution pattern, the increase in peak height and the reduction in width reveal that the differences in the level of coordinated development within the region are gradually decreasing. In terms of polarization characteristics, the kernel density curve presents a single-peak pattern, indicating a stable upward trend in its coordinated development level, which also demonstrates the necessity for further enhancement and stable development. The convergence of the curve in distribution extensibility suggests a reduction in overall differences within the Beijing–Tianjin–Hebei region. This is primarily due to the implementation of the national strategy for the coordinated development of the Beijing–Tianjin–Hebei region, which has received extensive policy support in economic development, environmental management, and cultural tourism, thereby narrowing the regional disparities.

Throughout the study period, the coupling coordination development of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration evolved from the “coordination transition stage (2011–2012)” to the “coordination development stage (2013–2020)”, showing a gradual trend towards a positive coordination stage. This change reflects that the development between tourism economic resilience and efficiency has achieved effective coordination, with tourism economic resilience and efficiency maintaining a synchronous advancement pattern, and the enhancement of tourism economic resilience in the Beijing–Tianjin–Hebei region corresponding to an improvement in tourism efficiency.

4.2. Analysis of the Interactive Response Between Tourism Economic Resilience and Efficiency in the Beijing–Tianjin–Hebei Urban Agglomeration

4.2.1. Unit Root Test

To eliminate the interference of heteroscedasticity on the research results, this study applied logarithmic transformation to the original data of tourism economic resilience and efficiency, resulting in lnr (log of tourism economic resilience) and lne (log of tourism economic efficiency). The LLC (Levin–Lin–Chu) test, ADF–Fisher (Augmented Dickey–Fuller–Fisher) test, and IPS (Im–Pesaran–Shin) test were used to perform unit root tests on lnr and lne. The relevant results are shown in

Table 6. Unit root test results.

Variable	LLC	ADF-Fisher	lPS	Conclusion
lnr	−8.1069 ***	61.3718 ***	−3.1578 ***	Stationary
lne	−11.3201 ***	55.7774 ***	−3.5435 ***	Stationary

Note: The asterisks (***) indicate statistical significance at the 1% level.

.

As shown in Table 6, the p-values from the unit root tests are all below 0.01, confirming that the tests pass at the 1% significance level and rejecting the null hypothesis of a unit root, thereby indicating that the variables are stationary series. Additionally, the Pedroni cointegration test yields a p-value of 0.0054, which is also below 0.01, signifying that the test passes at the 1% significance level and confirming the existence of a long-term dynamic equilibrium relationship between tourism economic resilience and efficiency.

4.2.2. Optimal Lag Order Selection

Based on the results of the AIC, BIC, and HQIC information criteria, the optimal lag order for the model is determined to be one (1) (

Table 7. Optimal lag order selection.

Lag	AIC	BIC	HQIC
1	−0.46936 *	0.293445 *	−0.160325 *
2	0.158367	1.09649	0.536842
3	0.094417	1.24256	0.554039
4	0.166756	1.57175	0.721114
5	0.590548	2.31665	1.25229

Note: The asterisks (*) indicate statistical significance at the 10% level.

).

4.2.3. GMM Estimation Results

Based on the optimal lag order of one (1) determined in

Table 8. GMM estimation results of the PVAR model.

	h_dlnr	h_dlne
L1.h_dlnr	0.2231188 (0.2125265)	−0.3835178 (0.3967767)
L1.h_dlne	0.2707036 ** (0.115078)	0.9431634 *** (0.2059084)

Note: The asterisks (**) and (***) indicate statistical significance at the 10% and 5% level.

, a PVAR model is constructed using the Generalized Method of Moments (GMMs) estimation to analyze the relationship between tourism economic resilience and efficiency. The GMM estimation results of the model are shown in Table 8. From the values of the impact coefficients, it can be seen that tourism economic efficiency has the strongest driving effect on tourism economic resilience.

When tourism economic resilience (lnr) is the explained variable, the impact of the one-period lagged tourism economic resilience on itself is positive but not significant (p = 0.294), indicating that while there is a positive reinforcement effect of tourism economic resilience, its persistence over time is not strong. The one-period lagged tourism economic efficiency has a positive and significant impact on current tourism economic resilience at the 5% level (p = 0.019), indicating that improving tourism economic efficiency has a significant positive effect on enhancing tourism economic resilience, showing a coordinated trend in the same direction between the two.

When tourism economic efficiency (lne) is the explained variable, the impact of the one-period lagged tourism economic resilience on tourism economic efficiency is negative but not significant (p = 0.334), indicating that tourism economic resilience does not have a significant promoting effect on the improvement of tourism economic efficiency, and the direct relationship between the two is weak. The one-period lagged tourism economic efficiency has a positive and significant impact on itself at the 1% level (p = 0.000), indicating that tourism economic efficiency has a self-reinforcing ability and shows significant persistence over time. Therefore, improving tourism economic efficiency has a significant positive effect on its own sustainable development, indicating that tourism economic efficiency has a significant self-reinforcing effect over time.

4.2.4. Impulse Response Analysis

To further explore the interactive response relationship between tourism economic resilience and efficiency, this study sets the shock lag period to 10 periods and obtains the impulse response graphs of tourism economic resilience and efficiency through 500 Monte Carlo simulations. The results are shown in Figure 7, where the response curves show a converging trend, proving that the model is stable.

From Figure 7, it can be seen that the impact of tourism economic resilience on itself is gradually weakening, with all periods showing positive responses, and the trend declines from the initial impact. In the long term, the initial impact is more significant, and the mid to late periods gradually stabilize, indicating that the positive effect of tourism economic resilience on itself diminishes over time. The impact of tourism economic resilience on efficiency shows a trend of first rising and then falling, with positive responses in all periods. From period 1 to period 2, there is a steady increase, and from period 3, the response effect steadily declines and stabilizes by period 10. In the long term, the changes in impact are relatively balanced, indicating that the improvement of tourism economic resilience has a positive short-term impact on tourism economic efficiency, but the effect is not significant in the long term. The impact of tourism economic efficiency on both tourism economic resilience and itself is gradually weakening, with positive responses in all periods, showing a declining trend from the initial impact. The impact on tourism economic resilience shows a significant declining trend in the early and mid-periods, gradually stabilizing in the later periods, indicating that improving tourism economic efficiency has a short-term promoting effect on enhancing tourism economic resilience. The impact on itself is minimal in period 1, gradually decreasing and stabilizing from period 2, indicating that the self-reinforcing effect of tourism economic efficiency is significant in the short term but limited in duration.

Through impulse response analysis, the results are generally consistent with the GMM estimation results, further confirming the positive interactive development relationship between tourism economic resilience and efficiency.

4.2.5. Variance Decomposition

To reveal the contribution rate of each variable’s impact on the results, variance decomposition is used to analyze the mean square error changes of the two systems over the next 20 periods. As shown in

Table 9. Variance decomposition results of tourism economic resilience and efficiency.

Period	Variance Decomposition of Tourism Economic Resilience		Variance Decomposition of Tourism Economic Efficiency
	lnr	lne	lnr	lne
1.000	1.000	0.000	0.535	0.465
2.000	0.930	0.070	0.461	0.539
3.000	0.860	0.140	0.422	0.578
4.000	0.812	0.188	0.400	0.600
5.000	0.784	0.216	0.389	0.611
6.000	0.767	0.233	0.382	0.618
7.000	0.758	0.242	0.379	0.621
8.000	0.753	0.247	0.377	0.623
9.000	0.750	0.250	0.376	0.624
10.000	0.749	0.251	0.375	0.625

, the tourism economic resilience of the Beijing–Tianjin–Hebei urban agglomeration is mainly influenced by its own impact in the initial periods, but the explanatory power of efficiency gradually increases, rising from 7% in period 2 to 25.1% in period 10. This indicates that over time, the impact of tourism economic efficiency on tourism economic resilience gradually strengthens. Tourism economic efficiency is initially influenced by both resilience and its own impact, with the influence of resilience being slightly higher than its own. However, over time, the explanatory power of its own impact gradually increases, rising from 46.5% in period 1 to 62.5% in period 10. This indicates that in the long term, tourism economic efficiency mainly relies on its own improvement, but tourism economic resilience still maintains a certain explanatory power. This mutual influence relationship suggests that there is a dynamic interaction between resilience and efficiency in the tourism economic development of the Beijing–Tianjin–Hebei urban agglomeration.

The dynamic changes in tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration result from the combined effects of multiple factors. Initial policy and infrastructure investments bring significant short-term effects, but with increasing market competition, resource limitations, and diminishing marginal benefits, the improvement of efficiency gradually slows down. At the same time, regional coordinated development, improved management and service levels, and long-term innovation and resource optimization jointly promote the sustainable development of the tourism economy.

5. Conclusions and Discussion

5.1. Conclusions

This study examines the Beijing–Tianjin–Hebei urban agglomeration by establishing an evaluation index system to assess the coordinated development of tourism economic resilience and efficiency, grounded in an analysis of their coupling mechanism. Utilizing the coupling coordination degree model, we explore the coordinated effects of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration from 2011 to 2020, and verify their long-term dynamic relationship through the PVAR model. The key conclusions are as follows:

The coupling coordination of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration has improved annually: From 2011 to 2019, the coupling coordination degree of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration significantly improved, reaching a high level of coordination in 2019. However, due to the impact of the COVID-19 pandemic in 2020, the coordination degree declined, remaining at the initial coordination level. The development of tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei urban agglomeration evolved from the “coordination transition stage (2011–2012)” to the “coordination development stage (2013–2020)”, showing a gradual trend towards benign coordination.

Significant spatial differences exist in tourism economic resilience and efficiency within the Beijing–Tianjin–Hebei region: The coupling coordination degree of Beijing and Tianjin is significantly higher than that of cities in Hebei Province, with Beijing maintaining a high level of coordination throughout the study period, demonstrating the leading role of core cities in driving surrounding areas. Cities within Hebei Province, such as Shijiazhuang and Zhangjiakou, have also shown significant improvement in recent years, but the overall level still needs further enhancement.

There is a positive interaction between tourism economic resilience and efficiency: Analysis through the PVAR model reveals that the positive impact of tourism economic efficiency on resilience is significant, while the impact of resilience on efficiency is relatively weak. This indicates that improving tourism economic efficiency not only directly promotes economic growth but also enhances the ability to withstand external risks, forming a virtuous cycle.

5.2. Discussion

The study demonstrates that the coupling coordination between tourism economic resilience and efficiency in the Beijing–Tianjin–Hebei region has significantly improved, especially with the support of regional coordinated development policies, where Beijing and Tianjin play pivotal roles. However, Hebei Province still shows considerable potential for growth. To promote the sustainable development of the tourism economy, future efforts should prioritize strengthening regional cooperation, enhancing the influence of core cities on surrounding areas, and improving the overall resilience and efficiency of the region’s tourism economy.

To promote the sustainable development of the tourism economy, future efforts should focus on the following specific strategies: (1) Infrastructure enhancement: prioritizing investment in Hebei’s tourism infrastructure, particularly in transportation and hospitality services, to reduce the development gap between Hebei and the core cities of Beijing and Tianjin. (2) Market diversification: encouraging the development of experience-based and high-end tourism products in Hebei to reduce reliance on traditional nature-based tourism and enhance economic returns. (3) Cross-regional coordination: strengthening policy coordination across the Beijing–Tianjin–Hebei region by establishing joint tourism marketing initiatives and shared governance frameworks to ensure balanced growth. (4) Stakeholder engagement: actively involving local communities, businesses, and policymakers in tourism planning to ensure policies align with local needs and enhance resilience at the grassroots level.

The study also reveals an interactive relationship between resilience and efficiency, suggesting that future policies should take a holistic approach by leveraging their synergistic effects. Enhancing efficiency not only boosts economic output but also strengthens resilience, contributing to the sustainable development of the tourism economy. This research introduces an innovative perspective by examining the coordinated relationship between resilience and efficiency, offering valuable theoretical and practical insights for fostering sustainable tourism development in the Beijing–Tianjin–Hebei urban agglomeration.

5.3. Limitations and Future Proposals

The findings provide useful insights for policymakers and regional planners in the Beijing–Tianjin–Hebei urban agglomeration. Beyond China, similar dynamics can be observed in global tourism economies facing resilience–efficiency trade-offs. For instance, European cities with mature tourism markets, such as Paris and Barcelona, must balance efficiency-driven mass tourism with resilience-building sustainability measures. In emerging tourism destinations like Vietnam and Thailand, rapid tourism growth poses challenges in maintaining long-term resilience. By adapting the resilience–efficiency framework developed in this study, policymakers worldwide can design more sustainable tourism strategies that align with their regional contexts.

This study highlights the importance of balancing resilience and efficiency for sustainable tourism economic development. However, some limitations remain. First, while this study provides a regional perspective, a comparative analysis with other international tourism economies could further validate the findings. Second, the study focuses primarily on economic resilience and efficiency, while social and environmental resilience—such as community adaptability and ecological sustainability—deserve further exploration. Addressing these gaps in future research could provide a more comprehensive understanding of sustainable tourism development.