Research on Tourism Carrying Capacity and the Coupling Coordination Relationships between Its Influencing Factors: A Case Study of China

Abstract

The large increase in the number of tourists brings challenges to the tourist carrying capacity of tourist destinations. By constructing a tourism carrying capacity indicator system and a coupling coordination model, we calculate and compare the development of tourism carrying capacity and the coupling coordination between all first-class indicators of tourism carrying capacity for provinces and cities in China. We find that the tourism carrying capacity and the coupling coordination between all first-class indicators of tourism carrying capacity for provinces and cities in China both showed an upward trend from 2008 to 2017, but the development was not balanced. In coastal provinces, their development level was high although lately showing a downward trend. In the provinces of the northwest China, their development level is low, and their development speed is relatively slow. The provinces and cities in the middle reaches of the Yangtze River and the southwest China recorded the fastest growth rate. In addition, we found that the development of tourism carrying capacity is closely related to coupling coordination between all first-class indicators of tourism carrying capacity.

1. Introduction

Tourism is one of the strategic pillar industries of the national economy. The healthy and sustainable development of tourism is significant to China’s economic recovery and coordinated regional development. Since the implementation of the 13th Five-Year Plan, the building of a moderately prosperous society in all respects has imposed higher requirements on the development of China’s tourism industry. Moreover, new modes, such as “mass tourism”, “all-for-one tourism” [1], and “sojourn life”, have injected continuous vitality into the development of tourism. According to “China’s Domestic Tourism Development Report 2020” [2], China’s tourist reception scale has maintained a steady growth, from 2.641 billion in 2011 to 6.016 billion in 2019. On the one hand, China’s tourism industry has developed rapidly and the number of tourists has surged, which brings huge benefits in many aspects, such as promoting employment, increasing residents’ income in tourist areas, stimulating local economic development [3], and developing cultural heritage [4]. On the other hand, the explosion in the number of tourists has had different levels of influence on the local economy, society, and ecological environment of tourist areas [5]. The extensive and excessive development of tourism has also led to a series of problems, such as overpopulation, traffic congestion, and environmental deterioration [6]. These “over tourism” problems—that is, the rapid development of tourism—lead to a series of negative impacts caused by the number of tourists receiving more than the carrying capacity of the destination. This has presented higher requirements and challenges to the tourism carrying capacity (TCC) of provinces and cities.

TCC refers to the maximum ability (in terms of size and intensity) of the natural human environment, tourism facilities, social economic environment, and tourist area residents to bear tourists and their related activities in a certain period of time and within a certain region under the premise of sustainable development [7]. TCC is an important standard for evaluating and measuring the current situation and future development of tourism regions. It also provides a significant basis on which to measure the coordination between the tourism environment and the development level, and comprehensively reflect the characteristics of a tourism system [8]. Scientific evaluation of TCC provides a strong basis for judging the sustainable development of tourism. In past studies, most scholars estimated and evaluated the overall development level of TCC or the sub-development level of TCC of specific destinations or cities by constructing a relevant evaluation indicator system and bringing dates into the models. For example, a study found that in 2012, the Berlin Mitte District’s tourist social carrying capacity had exceeded its limit, and the policy interventions adopted by the local government could ease tensions by encouraging tourists to flow to other parts of the city that do not meet the tourist social carrying capacity threshold [9]. Wuhu Fantawild Adventure (WFA) tourist volume, tourist experience, and tourist satisfaction affected the TCC of the theme park [10]. However, only a few studies estimated and evaluated the development trend in TCC in various provinces from the perspective of China’s overall situation, and discussed the coordinated development of various influencing factors of TCC, so as to provide theoretical support for solving the problems that limit the promotion and development of local TCC.

What is more, a tourist destination is a dynamic and complex system. Economic development, transportation and service facilities, the social cultural atmosphere, and the ecological environment quality are all factors that will affect TCC to different degrees. These factors also impact each other to different extents. For example, the regional economy plays a vital role in the protection and development of the local ecological environment. For one thing, economic development provides beneficial financial and technical support for ecological environment protection and optimization. For another, as the material basis of economic activity, good ecological resources and environment will attract more tourists and promote the further development of the local economy [11,12]. Therefore, it is vital for us to examine and quantify the degree of coordinated development between the various factors that influence TCC. The major purpose of this study is to evaluate the development level of TCC of 31 provinces and cities in China, and analyze the coupling coordinated development status of TCC (economic facilities carrying capacity ECC, social cultural carrying capacity SCC, and ecological carrying capacity ECC), in order to alleviate the “over tourism” problem, as destination TCC is limited in providing effective theoretical support. At the same time, it provides ideas for the Chinese government and tourism bureau to put forward corresponding tourism guidelines and develop adjustment polices for different provinces and cities, which has important theoretical and practical significance. In view of this, we will construct an evaluation indicator system of the development level of the TCC of 31 provinces and cities from the perspective of China’s overall situation, and will measure the evolution of the development level of the TCC, EFC, SCC, and ECC of 31 provinces and cities from 2008 to 2017 by using the combination weighting method. At the same time, the coupling coordination model is used to calculate the evolution of the coupling coordination relationship of the TCC of 31 provinces and cities in China from 2008 to 2017.

The rest of the paper is arranged as follows. Section 2 reviews the relevant literature. Section 3 describes data and methodology of the paper. Section 4 describes the results of the study. Section 5 contains the discussion of the results. Section 6 contains the conclusions and proposes future work.

2. Literature Review

2.1. The Conception and Definition of TCC

In the 1970s, scholars defined “carrying capacity” as the level of human activity that a region can maintain in order to ensure an acceptable quality of life in the long run [13]. The term “carrying capacity” also has been considered the ability of natural or man-made systems to absorb the impact of population growth without having a significant adverse impact on their own state or attributes [14]. In the tourism industry, research on TCC is very crucial for high-quality and sustainable development of tourism destinations, and a lot of research has been carried out.

The WTO defined TCC as the level of tourists in a tourist destination that met the high-level experience of tourists and had no impact on tourist destination resources [15]. At the beginning of the 21st century, scholars proposed that TCC should be defined as the maximum number of tourists in a destination, so that tourist activities are not unbearable the local residents [16]. In addition, Zelenka et al. [17] believed that TCC not only depends on the number of tourists at the destination, but also involves a series of other factors (infrastructure, behaviors of tourist, tourism distribution, etc.), and evaluation of TCC should focus on different dimensions, such as physical, socio-cultural, economic, etc. De Sousa et al. [18]. also believed that TCC was the threshold value of tourism activity intensity that the natural, economic, and social system of a tourist destination can carry, which was essentially a comprehensive reflection of the composition and structural characteristics of the tourism environment system. It could be seen that many scholars give detailed explanations of TCC, mainly from the perspectives of economy, society, and ecology. Meanwhile, in 2016, the European Commission proposed to measure and monitor the sustainable development performance of tourism destinations based on destination management, social and cultural impact, economic value, and environmental impact, and formulated the European Tourism Indicator System (ETIS) [19]. ETIS offers a methodology to measure the current situation of sustainable development of tourism destinations and provides a theoretical basis and indicators basis for evaluating the TCC of tourism destinations.

TCC has always been an important issue for the sustainable development of tourism. Many scholars have explained TCC from the perspectives of economy, society, and ecology. Firstly, economic carrying capacity is an important component of TCC, which refers to the carrying capacity of a tourist destination’s economic base to tourism and related activities in a certain period of tourism development. The economic infrastructure can meet the needs of tourism activities, which is an embodiment of the good development of TCC in the tourist destination [20]. Secondly, the social environment and cultural atmosphere of the tourist destination constitute the social carrying capacity of tourism, which refers to the maximum number of tourists that could be carried to a tourist destination without interfering with the enjoyment of the destination and activities that were not acceptable to local residents [21]. Social carrying capacity is measured in terms of the perceived impact of tourism on residents’ lives [9]. Finally, TCC is closely affected by the ecological environment and resources of tourist destinations. The ecological level of tourism carrying capacity refers to the ecological resilience, environmental pollution, and environmental governance of the destination. When the self-healing capacity exceeds the level of pollutants, the ecological carrying capacity system is sustainable, and the negative impact on society and environment is the lowest [4]. The eco-environmental quality and ecological resources of a tourist destination can reflect whether its ecosystem has been damaged and whether it is in a benign stage of development.

2.2. The Construction of TCC Indicator System

In the past, different regions or cities had different TCC indicator systems due to the differential influence of location, culture, resources, and economic conditions when constructing the TCC indicator system. For instance, Ye et al. [22], based on natural, economic, and social dimensions, built a TCC indicator system for Chinese island cities. The natural carrying capacity of island cities was reflected by the per capita coastline length and forest coverage rate. Indicators such as water supply and the number of star-rated hotels reflected the city’s service level and tourism facilities construction, which are used to represent the economic carrying capacity. The number of service industry employees, the ratio of tourists to residents, and other indicators to reflect the level of urban social development represent the social carrying capacity. Yang et al. [23] constructed an indicator system from the three dimensions of natural environment, economic environment, and social environment when studying the tourism carrying capacity of Qinhuangdao, a coastal tourism city. The natural environment mainly included the quality of the ecological environment and tourism resources. The economic environment mainly included infrastructure and tourism service facilities. Social environment mainly referred to regional harmony, cultural atmosphere, scientific and technological medical environment, etc. Zhao et al. [24] focused on the four aspects of local resources, ecology, psychology, and environment when studying the tourism carrying capacity of the resource-based city Pingdingshan. The resource dimension mainly included the per capita occupied area of tourists, the visiting time of tourists, and other indicators; ecological dimension selection mainly included water environment carrying capacity, per capita green space area, solid waste carrying capacity, and other indicators; the psychological dimension mainly included the indicators of residents’ psychological ability and tourists’ psychological ability; and the environmental dimension mainly included the maximum tourist capacity and other indicators.

There are significant differences in tourism resource endowments in different tourism destinations, and the main factors affecting their tourism carrying capacity are also different. For example, Diaoshuihu National Forest Park has a unique biological environment, a wide area of scenic spots, and can accommodate a large number of tourists and complete tourism facilities. Therefore, its TCC was mainly affected by the natural environment, resource environment, economic environment, social environment, and other factors. Establishing the TCC evaluation indicator system of the National Forest Park consisted of these four aspects, in which the air quality, waste treatment, and other indicators reflect the natural environment of the scenic spot; regional bearing capacity and road bearing capacity reflect the resources and environment of scenic spots; the supply level of the tourism service facilities reflect the economic environment of the scenic spots; and the psychological carrying capacity of the tourists and local residents reflect the local social environment [25]. When studying the TCC of the Wuhu Fangta theme park, considering the excessive number of daily tourists and long waiting time in the theme park, the indicator system was constructed from five aspects: tourists, management, facilities, economy, and social environment. Tourists’ perspective mainly includes tourists’ perceived experience, tourists’ satisfaction, and other indicators. The management perspective included capacity management, queue management, and other indicators; facility factors included facility capacity, attraction rate, spatial layout, and other indicators; economic factors included operating costs, profits brought by each tourist, and other indicators; and social environmental factors included ecological impact, resident experience, and other indicators [10].

2.3. The Coordinated Development of Tourism

The sustainable development of the tourist destination involves the coordinated development of local tourism, economy, and ecology. Many scholars have studied its coordinated development with tourism from the aspects of economy and ecological environment, and obtained a series of valuable research results.

On one hand, economic development provides relatively perfect infrastructure and service facilities for tourism, and thus promotes the development and upgrading of regional tourism. On the other hand, while driving the development of regional catering, accommodation, and transportation, tourism also increases the resource exchange between the local region and the outside world, and thus promotes the openness of the regional economy [12]. According to research results, in 2008–2019, the coupling and coordination relationship between the tourism industry and the regional economy presented a gradually rising trend. Meanwhile, the coordinated development of tourism and the economy was conducive to boosting the transformation and upgrading of the regional industrial structure [12]. Liao [26] studied the coupling coordination relationship between the tourism system and the economic system in Zhangjiajie, Huangshan, and Sanya using the Granger causality test and coupling coordination models. He found that the economy-tourism coordination degree fluctuated greatly in Zhangjiajie, rose stepwise in Huangshan, and expressed a frequent, tiny, fluctuating trend in Sanya.

Furthermore, a good ecological environment will increase the attraction of tourism resources, and the reasonable development of tourism resources is beneficial to the sustainable development of the ecological environment. The unreasonable development of tourism, however, can cause serious damage to the ecological sustainability of the tourist destination’s environment and further restrict the development of tourism itself [27]. Zhang [28] used the combined empowerment method and the coupling coordination model (CWM-CCDM) to calculate the degree of coordination between tourism and the ecological environment in Chongqing. His research results showed that the degree of coupling coordination between the two systems expressed an improving trend in the period 2000–2017, showing a medium level of coordination. At the provincial level, Lai [29] studied the coordinated development of the tourism system and the ecological environment system in 31 provinces and cities in China. The results showed that the ecological environment in the central and western regions was well protected but economically backward, with a low level of coordinated development. Meanwhile, the coastal provinces had great pressure on their ecological environment, a good tourism economy, and a low degree of coupling. Xie [30] studied the coupling coordination relationship between tourism, urbanization, and the ecological environment in 35 major tourist cities in China, and the spatio-temporal differences in it. The research results showed that the overall coordinated development between the three was still low in those tourist cities. The coupling coordination degree for cities in the eastern, central, and western regions were significantly different, indicating that the government would need to balance the level of development between cities. In addition, Yang [31] predicted that the coupling degree between the tourism industry and the ecological environment in provinces and cities in China would enter the extreme coordination stage in 2023.

To sum up, although previous research into the construction of the TCC indicator system, the measurement of TCC, and the coupling coordination relationship between tourism and other systems have achieved rich results, there are still a series of problems to be reflected on. Firstly, when selecting the relevant indicators of TCC, most previous studies have only selected a few representative indicators. Having only a few indicators will lead to incomplete coverage of the relevant information, which may lead to bias in the measurement of the TCC indicator system. Secondly, few studies have focused on the dynamic coupling relationships among the factors influencing TCC, and their evolution. Thirdly, there is also only scant literature exploring and comparing TCC and coupling coordination for provinces and cities in China from the perspective of spatio-temporal dynamic pattern evolution. In this paper, representative indicators are selected from the three dimensions of economic facilities carrying capacity (EFC), social cultural carrying capacity (SCC), and ecological carrying capacity (ECC) to construct an indicator system that influence the TCC for provinces and cities in China. At the same time, TCC and the coupling coordination relationships between its influencing factors are measured and compared. Their spatio-temporal dynamic pattern evolution for provinces and cities in China are evaluated. This should clarify the problems and bottlenecks that restrict the development of tourism in different provinces and cities in China, and provide a theoretical basis for the Chinese government and tourism administration to put forward corresponding tourism guidelines relevant to the different provinces and cities, and promote the healthy and sustainable development of local tourism industries.

3. Data and Methodology

3.1. Data Sources and Indicator Selection

Previous literature studies have shown that factors such as the economic facilities, social culture, and ecological environment will affect the TCC [22,23]. The TCC of a destination should allow the balance and sustainable development of the economy, social culture, and ecology of the region to be maintained. Referring to previous relevant studies [22,23,25], this study measures the TCC of 30 provinces and cities in China from three aspects: economic facilities, social culture, and ecological environment. Due to data limitations, we could not obtain the relevant data for Hong Kong, Macao, Taiwan, and Tibet, so they were omitted. Based on the existing research results for TCC at home and abroad, we selected three second-class indicators and ten third-class indicators representing EFC, three second-class indicators and nine third-class indicators of SCC, and two second-class indicators and seven third-class indicators of ECC to construct the TCC indicator system.

Table 1. TCC indicator system.

First-Class Indicator	Second-Class Indicator	Third-Class Indicator	IA	${\widehat{\mathit{w}}}_{\mathit{j}}$	References
EFC	Infrastructure status	Length of highways (km)	+	0.17	[4,32,33]
		Total number of travel agencies (unit)	+	0.19	[4,22,23,32]
		Amount of water supply (10,000 m3)	+	0.24	[4,22,23]
		Number of taxis (10,000 units)	+	0.19	[22,32]
		Number of road-operating car ownership (10,000 units)	+	0.21	[32]
	Economic pressure indicator	Water consumption per 10,000 yuan of GDP (m3/10,000 yuan)	-	0.33	[34,35]
		Energy consumption per 10,000 yuan of GDP (tons of SCE)	-	0.67	[35]
	Social economic development	Tourism revenue as a percentage of local GDP (%)	+	0.22	[22]
		Foreign exchange earnings from tourism (USD 10,000 million)	+	0.62	[33]
		Natural population growth rate (%)	-	0.16	[35]
SCC	Harmony	Urbanization level (%)	+	0.20	[23,32]
		Unemployment rate (%)	-	0.17	[35]
		Possession of civil motor vehicles (10,000 units)	+	0.23	[22]
		Passenger-kilometers (100 million passenger-km)	+	0.22	[33]
		Number of hospital beds (unit)	+	0.19	[22,32]
	Residents psychological	Ratio of tourists to residents (%)	-	0.70	[22,23,32,33]
		Resident Engel’s coefficient (%)	-	0.30	[35]
	Social cultural atmosphere	Number of students enrolled in universities (persons)	+	0.52	[20,23]
		Number of cultural and art institutions (unit)	+	0.48	[32]
ECC	Ecological environment quality	Volume of garbage disposal (10,000 tons)	+	0.54	[4,23,36]
		Waste water treatment rate (%)	+	0.46	[4,22,23,33,36]
	State of natural resources	Total amount of water resources (m3)	+	0.24	[23,36]
		Green coverage area (hectare)	+	0.19	[4,33]
		Area of parks and green land (hectare)	+	0.21	[23,32]
		Cultivated land (hectare)	+	0.18	[23]
		Land for construction (hectare)	+	0.18	[4,32]

Note: The data were mainly obtained from the 2009–2018 “China Statistical Yearbook”, “China City Statistical Yearbook”, “China Urban Construction Yearbook”, “China Population and Employment Yearbook”, provincial and municipal yearbooks, and the official websites of provincial and municipal tourism bureaus. IA indicates the indicator attribute; i.e., “+” implies a positive attribute while “-” negative.

provides the details.

The EFC indicators of TCC reflect the infrastructure status, economic pressure, and socio-economic development level of the tourism space in a tourism destination [23]. The infrastructure condition of the tourist destination reflects whether the tourist destination can meet the traffic demand and tourism demand of tourists. The economic pressure on a tourist destination reflects the local energy supply level and also affects the tourist experience. The level of social and economic development reflects the economic phenomenon and development law of the tourism destination, and is an important factor to measure the carrying capacity of the economic facilities of a tourism destination. Research shows that the condition of transportation facilities in an area is an important symbol to measure the level of local infrastructure construction [22]. Referring to previous relevant studies, we selected the length of highways, total number of travel agencies, amount of water supply, number of taxis, and number of road-operating car ownership to evaluate the state of infrastructure construction in a tourist destination. Secondly, the energy supply level (economic pressure) of a region is often reflected in the consumption of energy and drinking water [36]. Therefore, we selected water consumption per 10,000 yuan of GDP and energy consumption per 10,000 yuan of GDP to evaluate the economic pressure on tourist destinations. Finally, the socio-economic development level of a tourist destination is closely related to local tourism income, tourism foreign exchange income, natural population growth rate, and other indicators [23,32]. Therefore, we selected tourism revenue as a percentage of local GDP, foreign exchange earnings from tourism, and the natural population growth rate to evaluate the social and economic development level of the tourism destinations [22,36].

The SCC indicators of the TCC reflect the harmony of the tourism destination, residents’ psychology, and socio-cultural atmosphere [22,23]. The harmony of a tourist destination mainly reflects the degree of harmony between the residents and tourist destination; residents’ psychology reflects the acceptable psychological tolerance of the residents of the tourist destination for the number of tourists; and the cultivation of high-quality tourism facilities reflects the strong cultural atmosphere and local cultural conditions. Referring to previous relevant studies [4,22,23,32], we selected the urbanization level, unemployment rate, possession of civil motor vehicles, passenger kilometers, and number of hospital beds to evaluate the harmony of the urban development. The ratio of tours to residents and resident Engel’s coefficient were selected to reflect the psychological status of the residents. We selected the number of students enlisted in universities and number of cultural and art institutions to reflect the social and cultural atmosphere.

The indicators of ECC reflect the ecological environment and natural resources of a tourism destination [4,22]. Having a better ecological environment is an important factor for tourist destination to attract tourists, and its natural resources can reflect the openness of the tourist destination. These natural resources include water resources, land resources, greening resources, etc. Therefore, we selected the volume of garbage disposal and waste water treatment rate to characterize the eco-environmental quality of a tourism destination [4]. We selected the total amount of water resources, green coverage area, area of parks and green land, cultivated land, and land for construction to characterize local resources. In this paper, the indicator of ECC reflects the ecological environment and natural resources of a tourism destination. Referring to previous literature [23], the indicator land for construction was chosen to characterize the state of the natural resources (i.e., land resource) of a tourism destination.

3.2. Methodology

3.2.1. Indicator Weights Calculation based on Combined Weighting Method

In previous studies, a large number of scholars have used different evaluation methods to determine the weights of the indicators and compared the reliability of the methods [37]. The research results show that the entropy method is relatively reliable if the sample data is complete [38]. The combination of objective weights usually is oriented by the specific problem, and there are different combined methods according to different problems and different perspectives. Thus, as a good default in the absence of information about the priors, the index weight can be calculated by the entropy method through analyzing the relevance and information between indexes, which can to a certain extent avoid the deviation caused by subjective influence. As an objective evaluation method, entropy evaluation can not only avoid the subjective judgments of researchers, but also solve the problem of information overlap among multiple indicators Simultaneously, in order to avoid a situation where the final evaluation results (comprehensive evaluation scores of TCC of various provinces and cities) are too different due to the single method used to calculate the indicator weights, the coefficient of variation method is introduced to make the weighted results more objective and reasonable. The combined weighting method based on the entropy weight method and the coefficient of variation method considers the connection between multiple sample data, which weakens the influence of outliers, avoids the equalization problem of using the entropy weight method alone, and makes the evaluation results more reasonably and accurate [39]. Therefore, this paper adopts the objective combined weighting method to calculate the level of TCC for provinces and cities in China. The specific measurement steps are as follows:

Calculating the weights of the TCC indicators by the entropy value method. As a completely objective evaluation method, information entropy is used to calculate the entropy value of each indicator according to the difference between the information they provide, and finally the weight of each indicator is obtained.

Step 1: Data standardization. For the purpose of eliminating the differences in units and orders of magnitude among different indicators, we firstly standardize the original data. The matrix composed of all the observed values of 26 third-order indicators is denoted as $X=(X_{ij})$ where $i$ represents provinces and cities, $i=1,2,\cdots ,30$, and $j$ represents the third-class indicators, $j=1,2,\cdots ,26$. In addition, the values of the positive indicators and negative indicators have different meanings. In this study, a positive indicator is one whose growth promotes TCC—the higher the value of a positive indicator, the better the TCC is; a negative indicator is one whose growth has a negative impact on TCC—the lower the value of a negative indicator, the better the TCC is. Therefore, different algorithms should be used for the data standardization for positive and negative indicators. The specific measurement method is shown in Formulas (1) and (2) below:

$$\mathbf{Positive} \mathbf{indicator}\mathbf{:} Y_{ij}{}^{\prime }=\frac{X_{ij}-\min X_{ij}}{\max X_{ij}-\min X_{ij}}$$

(1)

$$\mathbf{Negative} \mathbf{indicator}\mathbf{:} Y_{ij}{}^{″}=\frac{\max X_{ij}-X_{ij}}{\max X_{ij}-\min X_{ij}}$$

(2)

where in the standardization of the TCC indicators, $Y_{ij}$ ($Y_{ij}{}^{\prime }$ and $Y_{ij}{}^{″}$) represents the standardized value of the indicator $j$ for the province/city $i$.

Step 2: Calculating the information entropy and weight of each indicator with the entropy value method. We measure the information entropy and the weight $A_j$ of the indicator $j$, as shown in Formula (3):

$$\begin{array}{l}{P}_{ij}=Y_{ij}/{\displaystyle \sum _{i=1}^{30}{Y}_{ij}},e_j=-\frac{1}{ln30}{\displaystyle \sum _{i=1}^{30}{P}_{ij}\times ln{P}_{ij}}\left(0\le e\le 1\right)\\ A_j=(1-e_j)/{\displaystyle \sum _{j=1}^K(1-e_j)}\end{array}$$

(3)

where $P_{ij}$ is the proportion of the value of the indicator $j$ of the province/city $i$ in all provinces and cities. When evaluating the TCC indicators, $K$ represents the number of indicators in each of the three aspects used (for the EFC indicators, $K=10$; for the SCC indicators, $K=9$; and for the ECC indicators, $K=7$).

Calculating the indicator weights by the coefficient of variation method. Firstly, the coefficient of variation (the ratio of the standard deviation to the mean) is calculated, and then the coefficient of variation of each indicator is directly used to calculate the weight of the indicator. Suppose for the dimensionless data matrix $V={(Y_{ij})}_{m\times n}$, after normalization, $m$ represents the number of evaluation samples, namely, the number of provinces and cities, while $n$ represents the number of indicators. The measurement steps are as follows:

Step 1: Calculate the mean value and standard deviation of each column vector:

$$\overline{Y_j}=\frac{1}{m}{\displaystyle \sum _{i=1}^m{Y}_{ij}}$$

$$s_j=\sqrt{\frac{1}{m}{\displaystyle \sum _{i=1}^m{(Y_{ij}-\overline{Y_j})}^2}}$$

Step 2: The coefficient of variation is then calculated from the standard deviation and the mean:

$$v_j=\frac{s_j}{\left|\overline{Y_j}\right|}$$

Step 3: The coefficient of variation is then normalized to calculate the weight:

$$w_j=\frac{v_j}{{\displaystyle \sum _{j=1}^m{v}_j}}$$

(3) Calculating the weight and comprehensive TCC score by the combined weighting method. First of all, the scores for each second-class indicator of the TCC ($F_{i1},F_{i2},F_{i3},S_{i1},S_{i2},S_{i3},E_{i1},E_{i2}$) are calculated using the third-class indicators and the combined weighting method. $F_{i1}$ represents the comprehensive score of the infrastructure status indicator; $F_{i2}$ represents the comprehensive score of the economic pressure indicator; $F_{i3}$ represents the comprehensive score of the social economic development indicator; $S_{i1}$ represents the comprehensive score of the harmony indicator; $S_{i2}$ represents the comprehensive score of the resident psychological indicator; $S_{i3}$ represents the comprehensive score of the social cultural atmosphere indicator; $E_{i1}$ represents the comprehensive score of the ecological environment quality indicator; and $E_{i2}$ represents the comprehensive score of the state of the natural resources indicator. The calculation Formula (4) is as follows:

$$\begin{array}{c}{\widehat{w}}_j=0.5A_j+0.5w_j\\ F_{i1}={\displaystyle \sum _{j=1}^5{\widehat{w}}_j\times }{Y}_{ij},F_{i2}={\displaystyle \sum _{j=6}^7{\widehat{w}}_j\times }{Y}_{ij},F_{i3}={\displaystyle \sum _{j=8}^{10}{\widehat{w}}_j\times }{Y}_{ij};\\ S_{i1}={\displaystyle \sum _{j=11}^{15}{\widehat{w}}_j\times Y_{ij}},S_{i2}={\displaystyle \sum _{j=16}^{17}{\widehat{w}}_j\times Y_{ij}},S_{i3}={\displaystyle \sum _{j=18}^{19}{\widehat{w}}_j\times Y_{ij}}\\ E_{i1}={\displaystyle \sum _{j=20}^{21}{\widehat{w}}_j\times Y_{ij}},E_{i2}={\displaystyle \sum _{j=22}^{26}{\widehat{w}}_j\times Y_{ij}},\end{array}$$

(4)

where ${\widehat{w}}_j$ represents the combined weight of the third-class indicators. $A_j$ and $w_j$ represent the importance of the weight obtained by the entropy value method and the coefficient of variation method, respectively. In this paper, a linear weighting method is used to combine the two kinds of weighting results. Without considering special conditions, the two kinds of weighting methods are generally considered to be equally important [40,41]. Thus, we value both $A_j$ and $w_j$ as 0.5 by default. In other words, we think the two kinds of weights are equally important.

Then, the scores for the second-class indicators of TCC are used to calculate the first-class indicators of the TCC, namely, for EFC ($F_i$), SCC ($S_i$), and ECC ($E_i$), using the combined weighting method as shown in Formula (5):

$$F_i={\displaystyle \sum _{j=1}^3{\tilde{w}}_j\times F_{ij}},S_i={\displaystyle \sum _{j=1}^3{\tilde{w}}_j\times S_{ij}},E_i={\displaystyle \sum _{j=1}^2{\tilde{w}}_j\times E_{ij}}$$

(5)

where ${\tilde{w}}_j$ represents the combined weight of the second-class indicators. Finally, using the first-class indicators of the TCC, the comprehensive TCC score ($TC{C}_i$) is calculated using the combined weighting method as follows:

$$TC{C}_i={\tilde{w}}_F\times F_i+{\tilde{w}}_S\times S_i+{\tilde{w}}_E\times E_i$$

(6)

where ${\tilde{w}}_F,{\tilde{w}}_S,{\tilde{w}}_E$ represent the combined weights of the first-class indicators of the TCC. The larger the TCC, the higher the TCC development level.

3.2.2. Construction of Coupling Coordination Model

The degree of coupling coordination reflects the degree of coordinated development between systems [42]. A high degree indicates more coordinated development. The high coupling coordination degree in terms of TCC can be embodied by the mutual promotion of EFC, SCC, and ECC. A low degree indicates uncoordinated development among the systems, namely, unbalanced development of EFC, SCC, and ECC. Based on the coupling coordination degree model, firstly, we measured the coupling coordination degree among EFC, SCC, and ECC in 30 provinces and cities in China, and clarified the influence of various factors on TCC. Secondly, we measured the coupling coordination degree among each pair of the EFC, SCC, and ECC combinations. We also explored the mechanism of coordinated development among the factors of TCC. The specific measurement steps are as follows:

Step 1: We calculate the coupling degree ($C_i^{F\text{-}S\text{-}E}(i=1,2,\dots 30)$) among the EFC ($F_i$), SCC ($S_i$) and ECC ($E_i$) of the province/city $i$ as follows:

$$C_i{}^{F\text{-}S\text{-}E}={\left.\left\{\frac{F_i\times S_i\times E_i}{{\left[\left(F_i+S_i+E_i\right)/3\right]}^3}\right.\right\}}^{1/3}$$

(7)

and the coupling degrees between each pair out of $F_i$, $S_i$, and $E_i$.

$$\begin{array}{c}{C}_i{}^{F\text{-}S}={\left.\left\{\frac{F_i\times S_i}{{\left[\left(F_i+S_i\right)/2\right]}^2}\right.\right\}}^{1/2},C_i{}^{F\text{-}E}={\left.\left\{\frac{F_i\times E_i}{{\left[\left(F_i+E_i\right)/2\right]}^2}\right.\right\}}^{1/2}\\ C_i{}^{S\text{-}E}={\left.\left\{\frac{S_i\times E_i}{{\left[\left(S_i+E_i\right)/2\right]}^2}\right.\right\}}^{1/2}\end{array}$$

(8)

Step 2: We calculate the degree of coupling coordination between $F_i$, $S_i$, and $E_i$.

$$D_i^{F\text{-}S\text{-}E}=\sqrt{C_i^{F\text{-}S\text{-}E}\times T_i^{F\text{-}S\text{-}E}}$$

(9)

where $T_i$ is the comprehensive evaluation indicator of the three subsystems of TCC in the province/city $i$, and $T_i^{F\text{-}S\text{-}E}=\alpha F_i+\beta S_i+\phi E_i$, where $\alpha$, $\beta$, and $\varphi$ are the undetermined coefficients and satisfy $\alpha +\beta +\varphi =1$. Referring to relevant studies, the undetermined coefficients were found to be $\alpha =0.3$, $\beta =0.3$, and $\varphi =0.4$.

Then, the coupling coordination degrees between each pair of the $F_i$, $S_i$, and $E_i$ combinations were calculated as follows:

$$D_i{}^{F\text{-}S}=\sqrt{C_i{}^{F\text{-}S}\times T_i{}^{F\text{-}S}},D_i{}^{F\text{-}E}=\sqrt{C_i{}^{F\text{-}E}\times T_i{}^{F\text{-}E}},D_i{}^{S\text{-}E}=\sqrt{C_i{}^{S\text{-}E}\times T_i{}^{S\text{-}E}}$$

(10)

where $D_i{}^{F\text{-}S}$ is the coupling coordination degree between EFC and SCC, $T_i{}^{F\text{-}S}=\alpha F_i+\beta S_i$, $\alpha =0.5$, and $\beta =0.5$; $D_i{}^{F\text{-}E}$ is the coupling coordination degree between EFC and ECC, $T_i{}^{F\text{-}E}=\alpha F_i+\phi E_i$, $\alpha =0.4$, and $\varphi =0.6$; and $D_i{}^{S\text{-}E}$ is the coupling coordination degree between SCC and ECC, $T_i{}^{S\text{-}E}=\beta S_i+\varphi E_i$, $\beta =0.4$, and $\varphi =0.6$.

Based on the degree of coupling coordination [43], the classification criteria are as shown in

Table 2. Classification based on the coupling coordination degree.

$\mathit{D}$	Classification
$0.8<D\le 1.0$	Superiorly balanced development
$0.6<D\le 0.8$	Favorably balanced development
$0.5<D\le 0.6$	Barely balanced development
$0.4<D\le 0.5$	Slightly imbalanced development
$0.2<D\le 0.4$	Moderately imbalanced development
$0.0<D\le 0.2$	Seriously imbalanced development

below.

4. Results

4.1. Analysis of Spatio-Temporal Dynamic Evolution of TCC for Provinces and Cities in China

4.1.1. Analysis of the Spatio-Temporal Distribution of TCC

Formulas (1)–(6) were used to calculate the comprehensive evaluation scores of the TCC and the three first-class indicators (EFC, SCC, and ECC) for 30 provinces and cities in China from 2008 to 2017. The results are shown in Figure 1. On the whole, the level of TCC for provinces and cities in China has shown an upward trend from 2008 to 2017. The average annual TCC increased from 0.3496 in 2008 to 0.3941 in 2017, an increase of 12.7%. To some extent, the above results show that the tourism development policies implemented by the state and local governments have promoted the development of tourism, which is reflected in a gradual improvement in infrastructure such as transportation, accommodation, and travel agencies; an increased richness in tourism resources; and the gradual improvement in the TCC. As can be seen from Figure 1, the growth rate of the TCC gradually slowed down from 2012 to 2017, mainly due to the unbalanced development of TCC among regions, unreasonable investment in tourism industry development funds, and insufficient investment in scientific and technological innovation in tourism industry development.

From the scores for the three first-class indicators of TCC, it can be seen that the relationship was ECC > EFC > SCC from 2008 to 2009; ECC > SCC > EFC from 2010 to 2014; SCC > ECC > EFC from 2015 to 2016; and ECC > SCC > EFC in 2017. This shows that the development of a TCC in provinces and cities in China largely depends on ECC. A good ecological environment can not only guarantee a good tourism environment in a region, but also provide rich natural resources for the development of the region’s TCC. SCC and EFC are also important influencing factors in TCC, representing the regional infrastructure conditions, residents’ living standards, and the cultural atmosphere, and their sustainable development is also conducive to improving in TCC.

Looking at the trends in each score, ECC has always been at a high level. For example, from 2008 to 2014, ECC always ranked first. However, with the improvement in EFC and SCC, its position has gradually weakened. Therefore, while developing tourism, China should also pay attention to the protection of the ecological environment and resources. In addition, on the whole, EFC and SCC have both shown an upward trend, with average annual growth rates of 0.72% and 2.542%, respectively. It can be seen from the figure that the growth rate of SCC has been the fastest, which indicates that harmony, residents’ psychological carrying capacity, and the social cultural atmosphere are the key factors in promoting the development of TCC. The growth rate of the EFC was the slowest.

4.1.2. Comprehensive Evaluation of TCC for Provinces and Cities in China

In order to comprehensively reflect the development of the TCC for provinces and cities in China, based on the TCCs for the 30 provinces and cities in China from 2008 to 2017, the average TCCs and average annual growth rates of the TCCs of each province and city were calculated. Based on this, a matrix diagram was constructed, as shown in Figure 2. In addition, Figure 3 also shows the spatio-temporal variation trend of the TCC development level.

From 2008 to 2017, the average TCC of the provinces and cities in China was 0.38. Guangdong, Shandong, Zhejiang, Henan, Liaoning, Sichuan, Hebei, Hubei, Anhui, and Hunan are located in the first quadrant, as their TCCs were higher than the average, and the average annual growth rates in their TCCs were greater than zero. Among these provinces and cities, Hunan had the highest growth rate of TCC, mainly because its EFC, SCC, and ECC all showed an upward trend from 2008 to 2017. Guangdong and Shandong rank high for TCC, but the growth rates of their TCCs are relatively small. The small growth rate in the TCC of Guangdong is mainly related to a decline in EFC, mainly due to a decrease in the number of vehicles in operation on the roads and an increase in the natural population growth rate. The small growth rate in the TCC of Shandong was mainly related to a decline in ECC from 2008 to 2017, mainly due to a decrease in total water resources. Therefore, in order to further improve the level of TCC, Guangdong should stop the decline of EFC and Shandong should stop the decline in ECC.

Shaanxi, Jiangxi, Fujian, Heilongjiang, Yunnan, Guangxi, Chongqing, Shanxi, Inner Mongolia, Jilin, Guizhou, Xinjiang, Gansu, Hainan, Qinghai, and Ningxia are located in the second quadrant, with the average annual growth rates of their TCCs being greater than zero, but their TCCs lower than the average. Among these provinces and cities, Guizhou and Gansu had relatively high average annual growth rates in TCC (18.9% and 15.2%, respectively), while Fujian and Hainan had relatively low annual growth rates. The increase in TCC in Guizhou and Gansu was mainly due to an increase in its SCC, by 22.1% in Guizhou and 37.7% in Guizhou from 2008 to 2017. The TCC of Qinghai, Ningxia, and Hainan are lower than 0.2, and their development levels of EFC, SCC, and ECC are low. Both the TCC and its average annual growth rate were small, mainly because Hainan’s economic investment in tourism was insufficient, the supporting infrastructure imperfect, and the levels of EFC, SCC, and ECC all low. The TCCs of Ningxia and Qinghai occupied the last two places in the ranking throughout the period, and the growth rates in their TCCs were 5.86% and 5.05% respectively. From 2008 to 2017, the EFC and SCC in Ningxia and Qinghai increased, but their ECCs showed a downward trend, indicating that Ningxia and Qinghai should pay more attention to the governance of the ecological environment and the protection of ecological resources.

Tianjin is located in the third quadrant, with its TCC lower than average and the average annual growth rate in its TCC less than zero. This is mainly because Tianjin’s ECC decreased from 2008 to 2017, which was mainly related to a decrease in the harmless disposal rate of domestic garbage and an increase in construction land area. Tianjin can improve its TCC by improving the relevant indicators. Jiangsu, Beijing, and Shanghai are located in the fourth quadrant, with TCCs higher than average but the average annual growth rates in their TCCs less than zero. Jiangsu’s TCC ranks second among the 30 provinces and cities, but its average annual growth rate is less than zero, and its TCC shows a downward trend. From 2008 to 2017, the EFC, SCC, and ECC in Jiangsu all decreased, with ECC decreasing the most (10.04%). Therefore, although the level of TCC in Jiangsu has been high, the development of its TCC does not give cause for optimism. The relevant departments should take measures to prevent the further decline of Jiangsu’s TCC. The TCC of Beijing and Shanghai also show a declining trend, mainly because these cities have huge numbers of tourists and high-density resident populations, putting a heavy burden on the ECC; also, ecological resources have become scarce, restricting the development of TCC in these cities.

Next, we analyzed the spatial distribution of TCC in China from the perspective of eight economic regions (see Appendix A

Table A1. Division of provinces and cities by areas.

Areas	Provinces and Cities
The Eastern coastal areas	Shanghai, Jiangsu, Zhejiang
The Northern coast areas	Beijing, Tianjin, Hebei, Shandong
The Middle reaches of the Yangtze River	Anhui, Jiangxi, Hubei, Hunan
The Southern coastal areas	Fujian, Guangdong, Hainan
The Middle reaches of the Yellow River	Shanxi, Inner Mongolia, Henan, Shaanxi
The Northeast areas	Liaoning, Jilin, Heilongjiang
The Southwest areas	Guangxi, Chongqing, Sichuan, Guizhou, Yunnan
The Northwest areas	Gansu, Qinghai, Ningxia, Xinjiang

and Figure A1 for the division of China’s eight regions). Division is a basic task of regional policy. The regional division used in this paper was proposed by the Development Research Center of The State Council of China in the 11th Five-Year Plan according to the economic development status of the different regions. In fact, it has been widely used in many research studies [44,45]. In conclusion, the spatial distribution of TCC in various regions of China shows obvious differences, which shows that the development level of the coastal areas is higher and that of inland areas is lower. With its natural location advantages, rich tourism resources, and good economic foundation, the eastern coastal areas and the northern coastal areas have a high level of development of TCC. The TCC in the southern coastal areas and the middle reaches of the Yangtze River areas is at the upper middle level; the TCC in the middle reaches of the Yellow River, the northeast areas, and the southwest areas is at a medium level; and the TCC in the northwest areas is relatively low. In terms of growth rate, the TCC in the middle reaches of the Yangtze River areas and the southwest areas grew faster, with an average annual growth rate of 3.23% and 3.3%, respectively. The rapid improvement in TCC in the middle reaches of the Yangtze River areas is mainly due to its rich natural and cultural tourism resources. With the improvement in living standards, travelers are no longer satisfied with the traditional sightseeing tour, but paying more attention to the cultural connotation of the tourist destination, which brings new opportunities to the middle reaches of the Yangtze River areas for tourism development and promotes the development of a TCC. The tourism industry in the southwest areas is relatively developed and has rich tourism development experience. With the support of national tourism-related policies, a large number of manpower and material resources have been invested in tourism development; thus, the TCC has developed rapidly. In contrast, the TCC in the northwest areas has always been at a low level. Inconvenient transportation, a low level of economic development, and imperfect tourism infrastructure have always been important reasons for restricting the development of TCC in the northwest areas.

4.1.3. Existence Test of the Development Trend of TCC, EFC, SCC, and ECC in Provinces and Cities in China

In order to better clarify the development trend in TCC in 30 provinces and cities in China from 2008 to 2017, we used Mann–Kendall and Spearman’s Rho to test the trend in TCC, EFC, SCC, and ECC. In the Mann–Kendall test, a Z > 0 indicates that with the increase of time the sequence of variables has an upward trend. A Z < 0 indicates that with the increase in time the sequence of variables has a downward trend. In the Spearman’s Rho test, Rho > 0 indicates that with the increase in time the sequence of variables has an upward trend. Rho < 0 indicates that with the increase in time the sequence of variables has a downward trend.

Table 3. Statistical results of the trend tests of the TCC, EFC, and SCC from 2008 to 2017.

Provinces (Cities)		TCC		EFC		SCC		ECC
		Z	Rho	Z	Rho	Z	Rho	Z	Rho
Entirety		3.399 **	0.927 **	2.862 **	0.891 **	3.220 **	0.915 **	−0.894	−0.236
Eastern coastal areas	Shanghai	−3.936 **	−1.000 **	−3.757 **	−0.988 **	−2.862 **	−0.855 **	−2.147 *	−0.661 *
	Jiangsu	−2.326 *	−0.794 **	−1.073	−0.588	−1.252	−0.406	−3.041 **	−0.903 **
	Zhejiang	0.358	0.127	−0.179	−0.152	2.504 *	0.745 *	−2.326 *	−0.745 *
Northern coast areas	Beijing	−3.220 **	−0.952 **	−3.757 **	−0.988 **	0.179	0.079	−2.683 **	−0.855 **
	Tianjin	−1.968 *	−0.648 *	1.610	0.576	1.431	0.430	−3.041 **	−0.903 **
	Hebei	3.757 **	0.988 **	3.757 **	0.988 **	2.326 *	0.794 **	0.358	0.055
	Shandong	−0.894	−0.212	0.179	0.067	1.431	0.430	−3.220 **	−0.939 **
Middle reaches of the Yangt -ze River	Anhui	3.399 **	0.927 **	3.220 **	0.939 **	3.220 **	0.915 **	1.610	0.576
	Jiangxi	3.041 **	0.915 **	3.578 **	0.976 **	3.578 **	0.964 **	0.000	0.055
	Hubei	3.399 **	0.952 **	3.578 **	0.964 **	3.041 **	0.903 **	2.862 **	0.879 **
	Hunan	3.399 **	0.952 **	3.220 **	0.939 **	3.220 **	0.939 **	2.504 *	0.794 **
Southern coastal areas	Fujian	1.968 *	0.564	2.504 *	0.818 **	2.683 **	0.830 **	−0.716	−0.273
	Guangdong	3.220 **	0.939 **	−0.537	−0.236	2.683 **	0.867 **	2.147 *	0.770 *
	Hainan	0.000	0.018	0.179	0.042	1.610	0.527	−1.431	−0.564
Middle reaches of the Yellow River	Shanxi	3.041 **	0.867 **	2.683 **	0.806 **	3.220 **	0.879 **	−1.073	−0.321
	Inner Mongolia	2.326 *	0.782 *	2.683 **	0.855**	0.000	−0.030	0.537	0.261
	Henan	2.147 *	0.758 *	0.894	0.273	3.399 **	0.952 **	−1.252	−0.479
	Shaanxi	2.504 *	0.770 *	3.757 **	0.988 **	3.220 **	0.879 **	−0.358	−0.091
Northeast areas	Liaoning	0.179	0.091	−1.968 *	−0.697 *	1.252	0.406	−0.358	0.055
	Jilin	2.683 **	0.733 *	3.220 **	0.927 **	1.968 *	0.661 *	1.252	0.394
	Heilongjiang	1.968 *	0.709 *	1.968 *	0.673 *	−0.179	−0.176	1.789	0.697 *
Southwest areas	Guangxi	3.399 **	0.952 **	3.578 **	0.976 **	3.757 **	0.988 **	0.716	0.248
	Chongqing	2.504 *	0.770 *	3.041 **	0.903 **	3.041 **	0.867 **	−2.862 **	−0.891 **
	Sichuan	3.578 **	0.976 **	3.041 **	0.927 **	3.041 **	0.903 **	1.431	0.576
	Guizhou	3.757 **	0.988 **	3.399 **	0.939 **	3.578 **	0.976 **	0.358	0.224
	Yunnan	3.757 **	0.988 **	3.578 **	0.964 **	3.757 **	0.988 **	−1.789	−0.612
Northwest areas	Gansu	3.578 **	0.964 **	3.757 **	0.988 **	3.041 **	0.891 **	1.610	0.539
	Qinghai	2.147 *	0.697 *	1.789	0.539	3.041 **	0.891 **	−1.431	−0.648 *
	Ningxia	1.789	0.564	3.578 **	0.976 **	1.252	0.333	0.000	−0.055
	Xinjiang	1.431	0.539	1.431	0.467	0.716	0.309	−1.252	−0.479

Note: ** and *, respectively, represent significance levels of 0.05 and 0.01.

shows the trend test results of TCC, EFC, SCC, and ECC of the 30 provinces and cities in China from 2008 to 2017 using the Mann–Kendall and Spearman’s Rho test method.

As can be seen from

Table 4. Statistical results of the trend test of the EFC–SCC–ECC, EFC–SCC, EFC–ECC, and SCC–ECC from 2008 to 2017.

Provinces (Cities)		EFC–SCC–ECC		EFC–SCC		EFC–ECC		SCC–ECC
		Z	Rho	Z	Rho	Z	Rho	Z	Rho
Entirety		2.862 **	0.891 **	3.578 **	0.964 **	0.894	0.418	2.326 *	0.770 *
Eastern coastal areas	Shanghai	−3.578 **	−0.976 **	−3.399 **	−0.952 **	−2.683 **	−0.818 **	−3.220 **	−0.915 **
	Jiangsu	−2.862 **	−0.842 **	−1.073	−0.455	−2.326 *	−0.806 **	−3.220 **	−0.927 **
	Zhejiang	−0.179	−0.103	1.431	0.309	−2.326 *	−0.770 *	−0.358	−0.042
Northern coast areas	Beijing	−3.220 **	−0.939 **	−2.862 **	−0.891 **	−3.041 **	−0.927 **	−2.862 **	−0.915 **
	Tianjin	−2.683 **	−0.830 **	2.326 *	0.697 *	−3.578 **	−0.976 **	−2.683 **	−0.830 **
	Hebei	3.578 **	0.976 **	3.936 **	1.000 **	2.683 **	0.842 **	1.789	0.636
	Shandong	−1.610	−0.382	0.716	0.273	−2.504 *	−0.770 *	−1.431	−0.358
Middle reaches of the Yangt -ze River	Anhui	3.041 **	0.903 **	3.578 **	0.964 **	2.326 *	0.770 *	2.504 *	0.782 *
	Jiangxi	3.041 **	0.903 **	3.578 **	0.964 **	2.147 *	0.733 *	2.326 *	0.782 *
	Hubei	3.578 **	0.976 **	3.220 **	0.915 **	3.399 **	0.952 **	3.399 **	0.964 **
	Hunan	3.220 **	0.939 **	3.399 **	0.964 **	3.220 **	0.939 **	3.041 **	0.903 **
Southern coastal areas	Fujian	1.252	0.394	2.862 **	0.855 **	−0.537	−0.176	0.894	0.236
	Guangdong	3.220 **	0.939 **	2.147 *	0.709 *	2.326 *	0.782 *	3.220 **	0.939 **
	Hainan	−0.179	−0.055	1.968 *	0.612	−1.431	−0.515	−0.358	−0.127
Middle reaches of the Yellow River	Shanxi	1.968 *	0.612	3.578 **	0.976 **	−0.716	−0.188	0.894	0.321
	Inner Mongolia	1.252	0.527	2.147 *	0.733 *	1.431	0.636	0.358	0.164
	Henan	0.894	0.333	3.399 **	0.952 **	−0.358	−0.164	0.894	0.345
	Shaanxi	1.789	0.612	3.399 **	0.927 **	0.179	0.188	0.894	0.418
Northeast areas	Liaoning	0.179	0.152	0.179	0.091	−0.358	0.055	0.000	0.139
	Jilin	2.504 *	0.721 *	2.862 **	0.879 **	2.147*	0.600	1.610	0.467
	Heilongjiang	1.968 *	0.709 *	0.179	−0.030	2.326 *	0.782 *	2.147 *	0.733 *
Southwest areas	Guangxi	3.220 **	0.939 **	3.757 **	0.988 **	2.683 **	0.855 **	3.041 **	0.903 **
	Chongqing	1.789	0.600	3.041 **	0.867 **	−2.504 *	−0.830 **	0.358	0.188
	Sichuan	3.578 **	0.976 **	3.399 **	0.952 **	3.399 **	0.939 **	3.041 **	0.891 **
	Guizhou	3.757 **	0.988 **	3.578 **	0.976 **	2.504 *	0.830 **	3.757 **	0.988 **
	Yunnan	3.936 **	1.000 **	3.936 **	1.000 **	0.179	0.030	3.757 **	0.988 **
Northwest areas	Gansu	3.220 **	0.915 **	3.578 **	0.976 **	2.504 *	0.818 **	3.041 **	0.891 **
	Qinghai	1.610	0.527	3.041 **	0.879 **	−1.431	−0.636	0.000	−0.188
	Ningxia	1.610	0.515	2.683 **	0.842 **	0.716	0.285	1.073	0.358
	Xinjiang	0.000	0.006	1.431	0.491	−0.358	−0.236	−0.358	−0.115

Note: ** and *, respectively, represent significance levels of 0.05 and 0.01.

, generally speaking, the TCC, EFC, and SCC of 30 provinces and cities in China showed a significant upward trend from 2008 to 2017, and all passed the significance test. Although ECC showed a downward trend, it did not pass the significance test. ECC did not show a continuous downward trend until 2014. From the perspective of provinces and cities, except for Beijing, Tianjin, Shanghai, Jiangsu, and Shandong, the TCC of the other 25 provinces and cities showed an upward trend. Among the five provinces and cities with a decreasing trend in TCC, Beijing, Shanghai, Tianjin, and Jiangsu had a significant decreasing trend in TCC, which all passed the significance test. The decreasing trend in TCC in Shandong was not significant and failed to pass the significance test. Among the provinces and cities with an upward trend in TCC, Hebei, Shanxi, Jilin, Anhui, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Sichuan, Guizhou, Yunnan, Gansu, Inner Mongolia, Heilongjiang, Fujian, Henan, Chongqing, Shaanxi, and Qinghai showed a significant upward trend of TCC, which passed the significance test. The increasing trend in TCC in Liaoning, Zhejiang, Hainan, Ningxia, and Xinjiang was not significant, and all of them failed the significance test.

The results of Mann-Kendall and Spearman’s Rho test were basically consistent, and the results of TCC, EFC and SCC development trend test in all provinces and cities were consistent with the previous description and analysis results. To some extent, it can be explained that the overall TCC of the 30 provinces and cities in China showed a significant upward trend from 2008 to 2017, and most provinces and cities achieved significant growth in the level of TCC during the study period. With the rapid development of tourism, TCC in most provinces and cities in China has also been promoted rapidly.

4.2. Analysis of the Spatio-Temporal Dynamic Evolution Characteristics of EFC–SCC–ECC for Provinces and Cities in China

4.2.1. Analysis of the Spatio-Temporal Distribution Characteristics of EFC–SCC–ECC

Formulas (7)–(10) were used to calculate the degree of coupling coordination between all three first-class indicators (EFC–SCC–ECC) and that between each pair of them (EFC–SCC, EFC–ECC, and SCC–ECC) for the 30 provinces and cities in China from 2008 to 2017. Let EFC–SCC–ECC denote the degree of coupling coordination between all three first-class indicators of TCC; EFC–SCC denote that between EFC and SCC; EFC–ECC denote that between EFC and ECC; and SCC–ECC denote that between SCC and ECC. The results are shown in Figure 4. On the whole, the EFC–SCC–ECC has shown an upward trend during the decade from 2008 to 2017 (see red solid line in Figure 4). The annual average increased from 0.5827 in 2008 to 0.6141 in 2017, with an increase of 5.4%. This was a change from the barely balanced development stage to the favorably balanced development stage. This shows a gradual improvement in China’s TCC.

Turning to the pairs of first-class indicators of TCC, the degree of coupling coordination was different for each pair. That of EFC–SCC developed the fastest, with an increase of 8.8%, followed by SCC–ECC, with an increase of 5.5%. Both represented transitions from the barely balanced development stage to the favorably balanced development stage. Combined with Figure 1, it can be seen that the improvement in the EFC–SCC and SCC–ECC was mainly due to the development of SCC, indicating that the improvement of residents’ living standards and the development of cultural and educational undertakings can not only promote the improvement of TCC but also contribute to its coordinated and stable development. Meanwhile, EFC–ECC was in the favorably balanced development stage throughout the period. However, with a decline in ECC and increase in EFC (see Figure 1), the EFC–ECC shows an ECC lag, and it began to show a declining trend in 2014. In 2015–2016, the situation was “EFC–ECC < EFC–SCC < SCC–ECC”. To some extent, this shows that strengthening the protection of the ecological environment and ecological resources and preventing the further decline in ECC is not only conducive to maintaining good tourism resources, but also can provide resource support for the development of EFC and SCC, which plays an important role in the coordinated development of TCC in China.

4.2.2. Comprehensive Evaluation of EFC–SCC–ECC for Provinces and Cities in China

In order to comprehensively evaluate the coupling coordination among the first-class indicators of TCC for the provinces and cities in China, the average value of the EFC–SCC–ECC and its average annual growth rate are calculated for each of the provinces and cities. These are plotted in a matrix diagram shown in Figure 5. In addition, Figure 6 also shows the spatio-temporal variation trend of the EFC–SCC–ECC development level.

From 2008 to 2017, the average EFC–SCC–ECC for provinces and cities in China was 0.605, indicating the favorably balanced development stage. Guangdong, Shandong, Henan, Sichuan, Liaoning, Hebei, Anhui, Hubei, Hunan, and Jiangxi are located in the first quadrant of the matrix, their EFC–SCC–ECC being higher than average, and its average annual growth rate greater than zero. Among them, Hunan ranked first in terms of the average annual growth rate for EFC–SCC–ECC, while its EFC–SCC, EFC–ECC, and SCC–ECC all showed an upward trend. In Figure 2, Jiangxi was in the second quadrant, while in Figure 5, it is in the first quadrant, indicating that, although its TCC score is low at present, its EFC–SCC–ECC is high. The average annual growth rate of Jiangxi’s TCC and the average annual growth rate of its EFC–SCC–ECC are both greater than zero. The coordinated development of EFC, SCC, and ECC in Jiangxi has become higher and higher, and its TCC has developed steadily. Guangdong and Shandong have a higher EFC–SCC–ECC; Guangdong’s EFC–SCC–ECC has been in a superiorly balanced development stage.

Fujian, Yunnan, Guangxi, Shaanxi, Heilongjiang, Chongqing, Inner Mongolia, Shanxi, Guizhou, Jilin, Xinjiang, Gansu, Hainan, Qinghai, and Ningxia are located in the second quadrant, with the average annual growth rates of their EFC–SCC–ECC being greater than zero, while their EFC–SCC–ECC values are lower than average. Among them, the EFC–SCC–ECC of Yunnan, Guangxi, Shaanxi, Heilongjiang, Xinjiang, Gansu, and Guizhou are improved, and its development has been fast; the EFC–SCC–ECC of Yunnan, Guangxi, Shaanxi, and Heilongjiang has transited from the barely balanced development stage to the favorably balanced development stage, while that of Guizhou, Gansu, and Xinjiang has transited from the slightly imbalanced development stage to the favorably balanced development stage. Although the average annual growth rates of the EFC–SCC–ECC in Fujian, Inner Mongolia, Chongqing, Shanxi, Jilin, Hainan, Qinghai, and Ningxia are greater than zero, their classification level has not improved, and the growth rates of their EFC–SCC–ECC are relatively slow. Among them, the EFC–SCC–ECC in Qinghai and Ningxia has been in a moderately imbalanced development stage, and the EFC–SCC–ECC in Hainan has been in a slightly imbalanced development stage.

Tianjin is located in the third quadrant, with an EFC–SCC–ECC lower than average, and an average annual growth rate of EFC–SCC–ECC less than zero. From 2008 to 2017, the EFC–ECC and SCC–ECC in Tianjin showed a decreasing trend, with the fall in ECC the main reason for this. Jiangsu, Zhejiang, Beijing, and Shanghai are located in the fourth quadrant, with the EFC–SCC–ECC higher than average, but average annual growth rate of EFC–SCC–ECC less than zero. Among them, the EFC–SCC–ECC of Shanghai was in the favorably balanced development stage in 2008 and 2011, and in the barely balanced development stage in 2014 and 2017, showing a decrease. The classification of the EFC–SCC–ECC for Jiangsu also decreased. The main reason for this was again the fall in ECC which led to the decline in EFC–ECC and SCC–ECC, and, in turn, the EFC–SCC–ECC.

At the regional level, from the perspective of space, the development degree of EFC–SCC–ECC in different regions is uneven, which is manifested as a relatively high EFC–SCC–ECC in coastal areas and relatively low EFC–SCC–ECC in inland areas. For example, the EFC–SCC–ECC in the eastern coastal areas, the northern coastal areas, and the southern coastal areas are all in a favorably balanced development stage, while the highest EFC–SCC–ECC of the northwest areas is only in a slightly imbalanced development stage, and the coordinated development level among the first-class indicators of the TCC is relatively poor. From the perspective of time, the development level of EFC–SCC–ECC in other regions showed an upward trend, except for the eastern coastal areas and northern coastal areas. In terms of growth rate, EFC–SCC–ECC in the middle reaches of the Yangtze River areas and the southwest areas grew faster, with an annual growth rate of 1.25% and 1.35%, respectively. Combined with the changing trend in TCC, it was found that the provinces and cities with a faster growth in EFC–SCC–ECC also have faster growth in TCC. To some extent, this indicates that the coordinated development among EFC, SCC, and ECC is conducive to the improvement in TCC.

4.2.3. Existence Test of Development Trend of EFC–SCC–ECC, EFC–SCC, EFC–ECC, and SCC–ECC in Provinces and Cities in China

In order to clarify the coordinated development trend of TCC indicators in 30 provinces and cities in China from 2008 to 2017, we used Mann–Kendall and Spearman’s Rho to test the development trend of EFC–SCC–ECC, EFC–SCC, EFC–ECC, and SCC-ECC; the results are shown in Table 4.

As can be seen from Table 4, generally speaking, the EFC–SCC–ECC, EFC–SCC, and SCC–ECC of the 30 provinces and cities in China showed a significant upward trend from 2008 to 2017, and all passed the significance test. Although EFC–ECC also showed an upward trend, it did not pass the significance test. From the perspective of provinces and cities, except for Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Shandong, and Hainan, the EFC–SCC–ECC of the other 23 provinces and cities showed an upward trend. Among the seven provinces and cities with a decreasing trend in EFC–SCC–ECC, Beijing, Tianjin, Shanghai, and Jiangsu had a significant decreasing trend in EFC–SCC–ECC, which all passed the significance test. The decreasing trend in EFC–SCC–ECC in Zhejiang, Shandong, and Hainan was not significant and failed to pass the significance test. Among the provinces and cities with an upward trend in EFC–SCC–ECC, Hebei, Anhui, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Sichuan, Guizhou, Yunnan, Gansu, Shanxi, Jilin, and Heilongjiang showed a significant upward trend in EFC–SCC–ECC, which passed the significance test. The increasing trend in EFC–SCC–ECC in Inner Mongolia, Liaoning, Fujian, Henan, Chongqing, Shaanxi, Qinghai, Ningxia, and Xinjiang was not significant, and all of them failed the significance test.

The results of the Mann–Kendall and Spearman’s Rho tests were basically consistent, and the results of the EFC–SCC–ECC, EFC–SCC, EFC–ECC, and SCC–ECC development trend tests for all provinces and cities were consistent with the previous description and analysis results. To some extent, it can be explained that the overall EFC–SCC–ECC of the 30 provinces and cities in China showed a significant upward trend from 2008 to 2017, but relatively few provinces and cities recorded significant growth in EFC–SCC–ECC during the study period (only 14). For the healthy and sustainable development of TCC, all provinces and cities should attach importance to the promotion effect of coordinated development of TCC indicators on the development of the TCC, fully tap the positive impact potential among EFC, SCC, and ECC, and promote the TCC better, with faster development.

4.2.4. The Relationship between TCC and EFC–SCC–ECC

In order to clarify the relationship between the TCC and EFC–SCC–ECC in 30 provinces and cities in China from 2008 to 2017, we use Pearson, Kendall’s tau-b (K) correlation coefficient, and Spearman’s Rho to test the correlation of development level and growth rate between TCC and EFC–SCC–ECC. The results are shown in

Table 5. The correlation of development level between TCC and EFC–SCC–ECC.

Year	Pearson	Kendall’s Tau-b (K)	Spearman’s Rho
2008	0.931 **	0.770 **	0.921 **
2011	0.931 **	0.793 **	0.919 *
2014	0.933 **	0.747 **	0.888 **
2017	0.938 **	0.789 **	0.913 **

Note: ** represents a significance level of 0.01, * represents a significance level of 0.05.

and

Table 6. The correlation of development growth rate between TCC and EFC–SCC–ECC.

Year	Pearson	Kendall’s Tau-b (K)	Spearman’s Rho
2008–2011	0.971 **	0.830 **	0.950 **
2011–2014	0.967 **	0.880 **	0.973 **
2014–2017	0.989 **	0.931 **	0.988 **

Note: ** represents a significance level of 0.01.

, respectively.

It can be seen from Table 5 and Table 6 that there is a significant positive correlation between TCC and EFC–SCC–ECC in terms of both development level and growth rate. The development of TCC is closely related to the coupling coordination between all first-class indicators of TCC; that is, promoting the coordinated development of first-class indicators is conducive to promoting the development of TCC.

5. Discussion

The coupling and coordination degree of TCC is an effective indicator to evaluate the healthy development of regional tourism. A well coupled and coordinated TCC can not only meet the needs of tourists, but also bring economic, social, and ecological benefits to tourism while maintaining high tourism quality. Based on the research results of the development of TCC and EFC–SCC–ECC in 30 provinces and cities in China from 2008 to 2017, we will put forward relevant strategic suggestions on how to improve the carrying capacity of a regional tourism system and the coupling coordination relationship between indicators, which will help to promote the high-quality and sustainable development of regional tourism.

It is necessary to clarify the short board of TCC development in different regions and improve the bearing capacity at different aspects. Different regions and provinces have different tourism resource endowments, so they will show different characteristics and face different development problems in the development of TCC. For example, thanks to natural location advantages, rich tourism resources, and good economic foundation, provinces and cities located in the eastern coastal areas and the northern coastal areas have always been in a leading position in terms of TCC level and EFC–SCC–ECC level. However, with the improvement in EFC and SCC, the development level of the ECC has lagged behind or even decreased. This not only affects the coordinated development of the first-class indicators of TCC, but also hinders the overall development of the TCC. For example, the TCC in Beijing, Shanghai, Jiangsu, and other provinces showed a significant downward trend. In order to prevent a further decline in TCC in these areas, more attention should be paid to environmental governance and ecological protection in the process of development. In other words, improving ECC in these areas should become the focus of the next development.

Different from the eastern coastal areas and the northern coastal areas, the TCC in the northwest areas has been at a low level. It is mainly subject to the problems of inconvenient transportation, low level of economic development, imperfect tourism infrastructure, and so on. Although the TCC of its provinces and cities increased during the study period, the rising speed was relatively slow. Therefore, on the one hand, the northwest areas need the policy support of the state and the government to help it develop its economy, promote the construction of infrastructure, and improve the accessibility of transportation. On the other hand, these areas should actively seek the optimal path of economic, social, and cultural development, promote the transformation of tourism resources to the economy, and form a characteristic tourism industry in the northwest areas, which is crucial to their tourism development. Tourism in the middle reaches of Yangtze River and the southwest areas has developed vigorously in recent years; the number of tourists is increasing, and the level of TCC is improving rapidly. However, the rapid development of tourism has also caused great pressure on its ecological resources. Therefore, while vigorously developing tourism, these areas should focus on strengthening the protection of ecological resources. While continuously improving the local TCC, they should ensure a coordinated development taking into account economic, social, and ecological aspects. The TCC of the southern coastal areas, the middle reaches of the Yellow River, and the northeast areas showed an upward trend as a whole, but the growth rate was slow, which was mainly related to the unbalanced development of the first-class indicators of TCC in provinces and cities. On the one hand, these regions need to pay attention to the improvement in ECC; on the other hand, they need to formulate corresponding measures according to their own development weaknesses. For example, Inner Mongolia can start from the advantages of tourism resources, increasing the promotion and governance of SCC and ECC, and further improving the quality of the tourism resources and TCC. Based on the current high level of TCC, Liaoning can improve infrastructure construction, improve the economic and ecological environment, and promote the level of TCC.

The balance between TCC first-class indicators in different regions should be improved, and the coupling coordinated development of the TCC should be emphasized. The development of a TCC in a region is closely related to its economic facilities, social culture, and ecological environment. There are interactions among the first-class indicators of TCC, such as need and be needed, push and be pushed, forming an interdependent and complementary interactive relationship. When all indicators develop in an orderly and coordinated way, they can promote each other and depend on each other, which can provide favorable conditions for the healthy development of TCC. When the development of indicators is unbalanced, they will not only restrict each other, but also hinder the development of TCC. For example, ECC is affected by regional natural endowment, and its promotion is relatively difficult and slow. As a result, with the development of EFC and SCC, the EFC–SCC–ECC of regions and provinces with higher ECC has been continuously improved, and the overall TCC level has also been continuously improved, thus enabling the sustainable and healthy development of tourism. However, for provinces and cities with relatively lagging ECC, with the rapid development of EFC and SCC, the burden of ecological environment is becoming more and more serious. This will easily lead to excessive utilization of ecological resources, which is not conducive to the sustainable development of a TCC.

Although the EFC–SCC–ECC in the eastern coastal areas and the northern coastal areas has been in a favorably balanced development stage, the development level of EFC–SCC–ECC is also affected due to the decline in ECC, showing a downward trend. Preventing a further decline of ECC is an important measure to maintain the current EFC–SCC–ECC level in the region. The development level of EFC–SCC–ECC in the northwest areas is poor, which is only in the slightly imbalanced development stage. The level of EFC–SCC–ECC also increased slightly during the study period. Therefore, while developing TCC, the northwest areas should also pay attention to the improvement of the coordinated development relationship between the TCC indicators and promote the improvement of TCC level by means of the mutual promotion between indicators. The EFC–SCC–ECC in the southern coastal areas, the middle reaches of the Yellow River, the middle reaches of Yangtze River, the northeast areas, and the southwest areas are all in a favorably balanced development stage, but the development level of EFC–SCC–ECC still needs to be improved. Among them, the EFC–SCC–ECC in most provinces and cities in the middle reaches of the Yangtze River areas and the southwest areas with the fastest growth of TCC has achieved a significant increase. The coordinated development of TCC indicators is conducive to the promotion of TCC [23]. Therefore, all regions, provinces, and cities should pay attention to the coupling coordination among the first-class indicators of TCC, tap the interactive impact of economic facilities, social culture, and the ecological environment, and promote the coordinated development of economy, society, and ecology in tourist destinations.

6. Conclusions and Future Work

From the perspective of spatio-temporal dynamic evolution, this paper studied TCC and its coupling coordinated development relationships for provinces and cities in China, which is of great significance for promoting the healthy and sustainable development of tourism [46]. By constructing a TCC indicator system and coupling coordination degree model, this paper has calculated and compared the degree of TCC and its coordinated development relationships for various provinces and cities and different regions in China. Then, it discussed the characteristics of the development of TCC in different provinces, cities, and regions, drawing the following series of valuable conclusions.

First of all, on the whole, the level of tourism carrying capacity of all provinces and cities in China is relatively coordinated, and the development level is good. Relying on the high level of ECC and the rapid growth in EFC and SCC, the development level of TCC and the EFC–SCC–ECC for provinces and cities in China showed a significant upward trend during the decade from 2008 to 2017. However, the ECC has shown a downward trend since 2014, which has affected the coordinated development between EFC and ECC. This indicates that strengthening the protection of the ecological environment and ecological resources and improving ECC are important measures in promoting the improvement in EFC–SCC–ECC and the TCC of provinces and cities in China. Secondly, from the perspective of regions and provinces, the development of both the EFC–SCC–ECC and the TCC shows similar spatial and temporal distribution characteristics, but the level of the EFC–SCC–ECC and the TCC varies greatly. Both measures are high in the coastal areas and low in the inland areas. For example, the eastern coastal areas and the northern coastal areas not only have a high level of TCC but also have been in the favorably balanced development stage of EFC–SCC–ECC. The TCC level of the northwest areas is the lowest among the eight regions, and its EFC–SCC–ECC is only in the slightly imbalanced development stage. In the time dimension, since the development level of ECC lags behind EFC and SCC in recent years, TCC and EFC–SCC–ECC in Beijing, Tianjin, Shanghai, and Jiangsu, which are located in the eastern and northern coastal areas, all showed a significant downward trend. Thanks to the development of the first-class indicators of TCC and the improvement in their coordinated development level, TCC and EFC–SCC–ECC have developed rapidly in the middle reaches of Yangtze River and in the southwest areas. These similar temporal and spatial distribution characteristics confirmed that good ecological resources and the coordinated development of EFC, SCC, and ECC are very important for the healthy and sustainable improvement of the TCC. Protecting and improving the ECC and exploring the positive impact mechanism and coupling coordination mechanism between indicators will be conducive to the healthy, coordinated, and sustainable development of tourism.

According to the above analysis, this paper proposes an improvement in the level of TCC of provinces and cities in China in the following ways: (1) The difference in TCC between different regions is mainly due to the unbalanced development of transportation conditions, economic development level, infrastructure, ecological resources, and ecological environment, which restrict the improvement of the overall development level of TCC. Therefore, provinces and cities should implement corresponding measures according to their own shortcomings in order to develop TCC. (2) Provinces and cities should pay attention to achieving coordinated development between the first-class indicators of TCC, ensuring that the first-class indicators of TCC promote each other, analyzing the factors that restrict the coordinated development of TCC, and giving full play to synergistic development of the first-class indicators, as this is of great significance for the stable and sustainable growth of TCC. (3) The improvement of living standards not only stimulates people to travel and brings large numbers of tourists to tourist areas, but also brings many challenges to local tourism development. For example, the level of tourism services will need to be raised, tourism-related facilities enhanced, and environmental governance improved [47]. Dealing with such challenges has become a major problem in the quest to achieve the sustainable and healthy development of a TCC.

There are still some limitations in the research process of this paper, which we hope can be solved in future work. Firstly, we used a unified indicator system to calculate and evaluate the development level of the TCC of 30 provinces and cities in China. However, different provinces and regions have different tourism resource endowments, and their TCC development depends on different resources. For example, coastal cities rely more on coastal resources when developing tourism while inland cities rely more on natural and cultural landscapes. Therefore, in order to more accurately evaluate the development level of TCC in different provinces and cities, future research can consider establishing a differentiated evaluation indicator system based on regional characteristics. Secondly, although this paper analyzes the coordinated development of the first-class indicators of TCC and draws the conclusion that the coordinated development of indicators is crucial to the healthy and sustainable development of TCC, it does not specifically discuss the interaction mechanism between indicator systems. Subsequent research can explore the relationship among the first-class indicators of TCC and its evolution mechanism, so as to standardize the development of regional TCC more scientifically and reasonably.