Study on Coupling and Coordinated Development of Regional Tourism Industry and New-type Urbanization Using Entropy Method: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area

doi:10.21203/rs.3.rs-2233291/v1

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Study on Coupling and Coordinated Development of Regional Tourism Industry and New-type Urbanization Using Entropy Method: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area

https://doi.org/10.21203/rs.3.rs-2233291/v1

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The new development pattern of "dual circulation" raises interesting questions regarding new pathways of coordinated development between the tourism industry and urbanization. The Guangdong-Hong Kong-Macao Greater Bay Area (Greater Bay Area) is a connecting area of internal and external circulation of China’s economy, and accordingly, this paper selects the “9 + 2” urban agglomeration of the Greater Bay Area as the study area. Given the differences in statistical indexes and units among Guangdong, Hong Kong and Macao, this paper selects suitable indexes to establish an evaluation system for development of the tourism industry and new-type urbanization. The paper then calculates the comprehensive development level and coupling coordination degree of the tourism industry and new-type urbanization in the 11 cities of the Greater Bay Area from 2010 to 2019 using the entropy method and coupling coordination degree model. The results show that the comprehensive development levels of and coupling coordination degree between the tourism industry and new-type urbanization in the 11 cities exhibit evident grading. Ranking the high-quality development level from high to low, the 11 cities can be divided into three echelons: Guangzhou is the first echelon, Shenzhen and Hong Kong are the second echelon, and the other cities are the third echelon. All 11 cities exhibited a steady development trend in the period. Accordingly, this paper further puts forward countermeasures and suggestions to cope with the "dual circulation" strategy and continuously enhance the benign interaction between the development of the tourism industry and the process of urbanization.

Dual circulation

Tourism industry

New-type urbanization

Coupling degree

Coordination degree

At the meeting of the Standing Committee of the Political Bureau of the CPC Central Committee on May 14, 2020, it was stated that supply-side structural reforms must be deepened in order to establish a new development pattern characterized by domestic and international circulations reinforcing each other via leveraging the advantages of China’s ultra-large-scale market and domestic demand potential. On May 23, 2020, at the joint meeting of the economic circle of the Third Session of the 13th National Committee of the Chinese People's Political Consultative Conference, it was reiterated that “meeting domestic demand should be taken as the starting point and objective of development... to gradually form a new development pattern with domestic circulation as the mainstay and domestic and international circulations reinforcing each other”. Later, at the meeting of the Political Bureau of the CPC Central Committee on July 30, 2020 and then at a symposium with grassroots representatives on September 17, 2020, the need to accelerate the formation of the new development pattern was emphasized. On October 29th, 2020, in the Proposal of the Central Committee of the Communist Party of China on Formulating the 14th Five-Year Plan (2021–2025) for National Economic and Social Development and Vision 2035 of the People's Republic of China adopted at the Fifth Plenary Session of the 19th Central Committee of the Communist Party of China, the topic of new development pattern was emphasized again, and a roadmap was charted for accomplishing the two major tasks of “smoothing domestic circulation” and “promoting both domestic and international circulations”.

“Smoothing domestic circulation” requires building a complete system for domestic demand, so that China’s economic growth relies on the links among domestic production, distribution, circulation, and consumption within the country, with the proportion of international trade in the national economy being decreased, in order to reduce China's economic dependence on foreign countries and enhance its economic resilience. “Promoting both domestic and international circulation” requires the international circulation to be reshaped by benign domestic circulation; then, the reshaped international circulation should function so as to ultimately facilitate domestic circulation. The development pattern of “dual circulation” is an important economic development strategy for China to address both current and future domestic development situations and international upheaval (Jiao and Zhang 2021). Promoting the formation of this new development pattern will become the overarching theme of China's economy over the next five years.

The new strategy under the new development pattern of “dual circulation” will become an important driving force for development of the tourism industry in the new period. For the tourism industry, the “internal circulation” consists of urban leisure and rural tourism, the “big circulation” is trans-provincial travel and spending holidays at holiday destinations, and the “dual circulation” is the development of both inbound and outbound tourism. At present, the COVID-19 pandemic means it is not a good moment for promoting “dual circulation” in the tourism industry. Nonetheless, with outbound tourism “suspended”, there is no longer an “outflow” of tourists spending money, which has, correspondingly, boosted demand for domestic tourism. Thanks to the backflow of overseas spending, i.e., caused by Chinese tourists spending domestically because they could not spend abroad as well as overseas tourists returning to China, and the recovery in consumption after a depression, substantially high levels of tourism consumption occurred in 2020 (Wei 2021). The development of the tourism industry can promote economic and social development in a multi-functional and comprehensive way. Compared with other industries contributing to the national economy, the tourism industry can encompass many sectors and play a stronger role in stimulating economic growth. In urban-rural circulation, the tourism industry has exhibited a new effect on urbanization. This new economic development pattern has raised new questions and tasks for the new coordinated development of the tourism industry and this new-type urbanization. It is very meaningful to explore how the new coordinated development of the tourism industry and urbanization can cope with the dual circulation strategy.

The Greater Bay Area refers to an urban agglomeration composed of nine cities (Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, Huizhou, Zhongshan, Jiangmen, and Zhaoqing) in the Pearl River Delta of Guangdong Province along with two special administrative regions (Hong Kong and Macao) (“9 + 2” urban agglomeration). As the connecting area between the Chinese economy’s internal and external circulation, the Greater Bay Area is intended to be developed into a world-class urban agglomeration and a model of high-quality development. Promoting the benign interactive development of the tourism industry and new-type urbanization is important to guarantee such high-quality development of the urban agglomeration. The construction of the Greater Bay Area plays an important role in promoting the aforementioned “dual circulation” and serves as a driving force to accelerate the formation of the new development pattern. Therefore, this paper selects the “9 + 2” urban agglomeration of the Greater Bay Area as the study area. Given the differences in caliber of the statistical indexes among Guangdong, Hong Kong, and Macao, this paper selects suitable evaluation indexes to establish an evaluation system for development of the tourism industry and new-type urbanization, and then introduces a coupling coordination degree model to empirically analyze interactions between the tourism industry and new-type urbanization in the Greater Bay Area. The results will provide a reference basis for the coordinated development of the tourism industry and new-type urbanization.

Coupling, which refers to the physical relationship between two or more elements, originally originated in the field of physics (He et al. 2017; Ma et al. 2018; Illingworth 1996). Similarly, in economics, it refers to a phenomenon in which two or more systems affect each other through various interactions (Wang and Xiang 2018; Wu et al. 2001; Liu et al. 2019). Coordination refers to the harmonious functioning of systems or of elements in one system for effective results (Wu et al. 2001; Liu et al. 2019). Coupling degree represents the interaction degree between systems, and a mismatch can exist between development level and coupling degree; in other words, the development level could be high but the coupling degree low or the development level could be low whereas the coupling degree is high. Therefore, it is necessary to measure the coupling coordination degree as well the coordination degree between development level and coupling degree (Illingworth 1996).

In analysis, the coupling degree is mainly used to quantitatively analyze the interaction degree between elements or systems, whereas the coupling coordination degree is used to qualitatively show the coordination degree between elements or systems (Liu and Ma 2017). Coupling theory aims to analyze the coordination degree and quality between systems or between elements in one system, and it mainly measures the coordination degree between different elements or systems by two indexes, i.e., coupling degree and coupling coordination degree, to study the law of coordinated development between the elements or systems (Ma et al. 2018). When the elements or systems function well for harmonious development, it is called a positive coupling. Otherwise, it is called a vicious coupling (Zhou et al. 2016). The coupling relationship between the elements or systems may change from "uncoordinated" to "coordinated" over time (Ma et al. 2018; Liu and Ma 2017).

The tourism industry, urbanization and their interrelationships have long been topics of interdisciplinary research (Zhao and Fang 2019). With the continuous high-quality development of China's economy and acceleration of its urbanization, the tourism industry has become a strategic industry through which China can achieve national development goals (Higgins-Desbiolles 2017). According to current research results, as two complex systems, the tourism industry and urbanization have significant coupling development characteristics (Xie et al. 2021; Zhao and Fang 2019), mainly manifested in the following.

On the one hand, tourism, as an important component of the tertiary industry, has emerged as an approach to promote urbanization (Xie et al. 2021; Zhao and Fang 2019). The concept of "tourism urbanization" was first proposed by Australian scholar Mullins, who believed that tourism development can foster the emergence of urbanization (Mullins 1991). Subsequent studies further proved that tourist activities can promote the shifting to and accumulation of population, materials and capital in tourism-dependent areas (Wang 2003). Likewise, development of the tourism industry can accelerate the process of local urbanization, contributing to comprehensive development of local cities (Zhao and Chen 2020).

Tang conducted an empirical analysis and found that development of tourism industry had important driving effects on urban expansion in the east and south of Lhasa City (Tang et al. 2017), mainly via the following pathways. First, the tourism industry promotes demographic urbanization. The tourism industry is a comprehensive service-oriented industry that acts as a strong driving force for economic development, and its development can create a large number of non-agricultural job opportunities for surplus labor from rural areas (Zhao and Chen 2020; Zhang and Li 2020). Second, the tourism industry promotes economic urbanization. The tourism industry has a wide scope and strong relevance to other industries. Its development can boost the development of related industries such as lodging, catering, transportation, entertainment, and leisure, thereby playing a strong role in stimulating economic growth. At the same time, tourists engage in diversified consumption, whichcan provide economic capital for local urbanization and create a favorable economic environment (Zhang and Li 2020; Liang et al. 2014).

Third, the tourism industry promotes spatial urbanization. The development of rural tourism contributes to improving transportation, communication, and other infrastructure in the rural areas, expanding urban space, and accelerating overall and coordinated development of urban and rural areas (Zhang and Li 2020; Cai and Cai 2018). With the development of the tourism industry, local infrastructure and service facilities can be improved, which in turn promotes the development and upgrading of regional tourism (Liu and Yang 2011). Fourth, the tourism industry promotes social urbanization. Its development helps improve the public service infrastructure used by tourists, thereby laying a foundation for upgrading urban infrastructure, service quality, and residents' lifestyles (Zhao and Chen 2020).

Finally, the tourism industry promotes environmental urbanization. A good ecological environment is one of the basic conditions for its development. In order to develop the tourism industry, it is necessary to make urban cities more beautiful, improve local environmental governance capabilities, and reduce production energy consumption and environmental pollution, resulting in a good local ecological environment (Zhang and Li 2020). However, some scholars hold opposite opinions. They believe that tourism development is a double-edged sword. On one hand, it can promote economic growth; on the other side, without good planning, it may cause environmental pollution and ecological damage (Zhong et al. 2011).

Nonetheless, urbanization is an important underlying source of support for the tourism industry because it can improve the latter’s development quality and promote growth of the tourism economy. Specifically, from the perspective of the backing function, urbanization can gradually improve urban infrastructure, services, education, and other supporting systems, thus helping enrich tourism resources, cultivate talent for tourism services, and improve the service experience and sense of gain among tourists, to form a lasting attraction to tourists (Zhang and Li 2020). Secondly, from the perspective of consumption and benefits, local construction projects for urbanization can facilitate regional opening-up, raise local people’s disposable income and spending power, expand the market size for the tourism industry, and increase the benefits of tourism-related industries such as lodging and catering (Cai and Cai 2018).

In recent years, many studies have been conducted on the coordinated development of tourism industry and new-type urbanization. In particular, research on the coupling coordination degree that can characterize the coordinated development of tourism industry and new-type urbanization has become a hot topic, and such research efforts include those by Zhao and Chen (2020), Xie (2021), and Zhang and Li (2020) focusing on the major tourist destination cities in Yunnan Province, Heilongjiang Province, and overall China, respectively. Similarly, most studies used single cities or provinces as research objects, with few research efforts focused on inter-provincial comparison and large-region coordination analysis conducted on a macroscopic scale (Zhou et al. 2016). From the existing references, it can be seen that there are abundant theoretical discussions and quantitative research on the relationship between the tourism industry and urbanization. However, with the target of establishing a new economic development pattern of “dual circulation”, the relationship between the tourism industry and new-type urbanization has become an area attracting substantial attention. Furthermore, due to a lack of unified statistical indexes among Guangdong, Hong Kong, and Macao, there is no research on the coupling coordination degree between the tourism industry and urbanization in the Greater Bay Area, a gap in the literature that this study aims to address.

3.1 Variable selection

The index evaluation methods for development level include single-index evaluation methods and multi-index comprehensive evaluation methods. Considering that a single index cannot fully reflect the development levels of the tourism industry and urbanization, as well as the internal relationship between the two (Jiao and Zhang 2021), this paper uses a multi-index comprehensive evaluation method for the analysis. After studying the related references, practicable indexes were selected from the perspective of index comparability and data integrity based on the available statistical data for Guangdong, Hong Kong, and Macao in order to establish an evaluation index system for the development of the tourism industry and new-type urbanization in the Greater Bay Area.

The index system is divided into target layers, criterion layers, and index layers. The target layers include tourism industry development and new-type urbanization. Previous research on the target layer of tourism industry development focused on the economic benefit, market scale, and development scale. Economic benefit is the direct embodiment of tourism development level, market scale is the space where the tourism industry is located and determines the limit of the maximum profit obtainable from the market, and development scale is the industry supply for tourism industry development. Therefore, the evaluation index system for tourism industry development established in this paper (see Table 1) includes three criterion layers composed of economic benefit, market scale and development scale, and nine indexes set thereunder. Four indexes reflect the economic income of tourism industry and its relative proportions, namely the indexes of domestic tourism income, foreign exchange earnings from international tourism, proportion of tourism income in the tertiary industry, and proportion of tourism income in GDP, which were mainly selected for economic benefit. Two indexes, i.e., person-times of overnight domestic tourists received, and person-times of overnight inbound tourists received, mainly selected for market scale, reflect the number of consumers in the tourism industry. Limited by availability of data, three indexes, i.e., number of A-level tourist attractions, number of travel agencies, and number of star hotels, mainly selected for development scale, reflect the development level of supply side of tourism industry.

The new-type urbanization is "people-oriented" and implements the "new development philosophy", marking a change from "quantity" to "quality" for urbanization (Jiao and Zhang 2021). Since the concept of new-type urbanization was first proposed, development goals have changed from the traditional approach of expanding urban land and space to improving the working and living conditions in rural areas, constructing infrastructure, optimizing ecological environment, and so on. The concept of development has become a comprehensive perspective that covers demographic attributes, economic model, spatial attributes, and the social lifestyle evolution process (Ren et al. 2019). Existing studies have mainly measured the development quality of urbanization from the perspectives of demographic urbanization (focusing on demographic citizenization), economic urbanization (focusing on productivity improvements), spatial urbanization (focusing on optimization of urban layouts), social urbanization (focusing on urban function improvement), and environmental urbanization (focusing on green and sustainable development). However, this paper does not include environmental urbanization due to the fact that the statistical indexes published by Hong Kong and Macao for environmental protection are very different from those used by the nine cities of Guangdong Province in the Greater Bay Area, and it is not feasible to unify the statistical indexes for environmental urbanization.

Wang and Huang have broken down the urbanization system into four dimensions, namely demographic urbanization, economic urbanization, spatial urbanization, and social urbanization, and argued that urbanization is a comprehensive concept integrating these four dimensions, with people as the core, economy as the driving force, space as the carrier and society as the guarantee (Wang and Huang 2016). Accordingly, this paper establishes an evaluation index system for new-type urbanization (see Table 1) that includes four criterion layers composed of demographic urbanization, economic urbanization, spatial urbanization and social urbanization, and 14 indexes set thereunder.

Table 1

Multi-index Comprehensive Evaluation System for Tourism Industry and Urbanization in the Greater Bay Area
Target layer	Criterion layer	Number	Index layer	Unit	Index attribute
Tourism industry development	Economic benefit	X₁	Domestic tourism income	×10⁸ yuan	+
		X₂	Foreign exchange earnings from international tourism	×10⁸ yuan	+
		X₃	Proportion of tourism income in the tertiary industry	%	+
		X₄	Proportion of tourism income in GDP	%	+
	Market scale	X₅	Person-times of overnight domestic tourists received (person-time)	×10⁴ person-times	+
	Market scale	X₆	Person-times of overnight inbound tourists received (person-time)	×10⁴ person-times	+
	Development scale	X₇	Number of A-level tourist attractions	Tourist attractions	+
		X₈	Number of travel agencies	Travel agencies	+
		X₉	Number of star hotels	Hotels	+
New-type urbanization	Demographic urbanization	Y₁	Natural population growth rate	‰	+
	Demographic urbanization	Proportion of employees for the tertiary industry	%	+
	Economic urbanization	Y₃	GDP growth rate	%	+
		Y₄	GDP per capita	yuan	+
		Y₅	Proportion of output value of secondary and tertiary industries in GDP	%	+
		Y₆	Fixed assets investment of the whole society	×10⁸ yuan	+
	Spatial urbanization	Y₇	Mileage in highway open to traffic	kilometres	+
	Spatial urbanization	Y₈	Urban built-up area	square kilometres	+
	Social urbanization	Y₉	Total retail sales of social consumer goods	×10⁴ yuan	+
		Y₁₀	Private car ownership	×10⁴ cars	+
		Y₁₁	Holdings of public library	×10⁴ books or pieces	+
		Y₁₂	Number of students in regular institutions of higher education	persons	+
		Y₁₃	Number of beds in medical and health institutions	beds	+
		Y₁₄	Number of health technicians	persons	+

Note: "+" means a positive effect, and "-" means a negative effect.

3.2 Research methods

3.2.1 Entropy method

The entropy method is derived from entropy theory to weigh the importance of research objects. It uses an objective weighing method to determine the weight of each index, avoiding errors caused by subjective factors. According to information entropy theory, an index’s variation degree is the only way to measure that index’s importance in an index system. An index’s variation degree is directly proportional to the amount of information it reflects; in other words, the smaller an index’s entropy value, the greater its coefficient of variation and the greater its weight, indicating it has higher importance (Liang et al. 2014). This paper uses the entropy method to determine the weight of each index to analyze the comprehensive development level of the tourism industry and new-type urbanization. The entropy method specifically includes four steps, namely data standardization processing, index weight transformation, information entropy value and weight, and calculation for comprehensive evaluation.

1) Data standardization processing

Due to the differences among the units and magnitudes of the indexes used in this study, dimensionless processing is used for the original data. All indexes included in the evaluation index system established in this paper exert a positive effect. The specific calculation formula is as follows:

$${\text{u}}_{\text{i}\text{j}}=\frac{{\text{x}}_{\text{i}\text{j}}-\text{m}\text{i}\text{n}\left({\text{x}}_{\text{j}}\right)}{\text{m}\text{a}\text{x}\left({\text{x}}_{\text{j}}\right)-\text{m}\text{i}\text{n}\left({\text{x}}_{\text{j}}\right)}\ast 0.99+0.01$$

where ${\text{x}}_{\text{i}\text{j}}$ represents the original value of the i^th sample of the j^th index, $\text{m}\text{i}\text{n}\left({\text{x}}_{\text{j}}\right)$ represents the minimum value in the original data of the j^th index, and $\text{m}\text{a}\text{x}\left({\text{x}}_{\text{j}}\right)$ represents the maximum value in the original data of the j^th index.

2) Index weight transformation

The index weight transformation is carried out according to the following formula:

$${s}_{ij}=\frac{{u}_{ij}}{\sum _{i=1}^{n}{u}_{ij}}$$

3) Calculation of information entropy value and weight

The information entropy value and weight are calculated according to the following formulas:

Entropy value of the j^th index: ${ℎ}_{j}=-\frac{1}{\text{ln}n}\sum _{i=1}^{n}{s}_{ij}\text{ln}{s}_{ij}$ (3)

Variation degree of the j^th index: ${\partial }_{\text{j}}=1-{ℎ}_{j}$ (4)

Weight of the j^th index: ${{\omega }}_{\text{j}}=\frac{{\partial }_{\text{j}}}{\sum _{\text{j}=1}^{\text{p}}{\partial }_{\text{j}}}$(5)

where n is the number of samples, and p is the number of indexes.

4) Calculation for comprehensive evaluation

This paper evaluates the comprehensive development level of the tourism industry and new-type urbanization in 11 cities of the Greater Bay Area using the linear weighting method, and the formula is as follows:

$${u}_{i}=\sum _{j=1}^{p}{{\omega }}_{\text{i}\text{j}}{\text{u}}_{\text{i}\text{j}} \left(6\right)$$

$$\sum _{\text{j}=1}^{\text{p}}{{\omega }}_{\text{i}\text{j}}=1$$

3.2.2 Coupling coordination degree model

After measuring the comprehensive development level of the tourism industry and new-type urbanization in the focus cities, this paper tries to determine the coupling coordination degree between the tourism industry and new-type urbanization. The specific formula is as follows:

$$\text{C}=\frac{2\sqrt{{\text{u}}_{1}+{\text{u}}_{2}}}{{\text{u}}_{1}+{\text{u}}_{2}}$$

where C is the coupling degree, and ${\text{u}}_{1}$ and ${\text{u}}_{2}$ represent the comprehensive development level values of the tourism industry development system and new-type urbanization system, respectively. Because the coupling degree can only clearly reflect the coupling degree between the tourism industry and new-type urbanization numerically but cannot reflect the exact interaction degree between the two, it is necessary to further calculate the coupling coordination degree to measure their coordination degree as follows:

$$\text{D}=\sqrt{\text{C}\ast \text{T}}$$

$$\text{T}={\alpha }{\text{u}}_{1}+{{\beta }\text{u}}_{2}$$

where D is the coupling coordination degree, T represents the comprehensive evaluation score of the tourism industry and urbanization, and ${\alpha }$ and ${\beta }$ are the coefficients to be determined. Because tourism industry development is not the only driving force promoting urbanization, but rather urbanization development is driven by many factors, this paper sets ${\alpha }$ and ${\beta }$ to be 0.4 and 0.6, respectively (Wang and Xiang 2018; Zhang and Li 2020). In other words, the weight of the tourism industry development system is 0.4, and that of the new-type urbanization system is 0.6. The value range of D is [0, 1], and the coupling coordination degree for development is divided into 10 grades, as shown in the following table.

Table 2

Grades of Coupling Coordination Degree
Coupling coordination degree interval	Coordination level (In Roman numeral)	Coupling coordination stage
0.00-0.09	Extreme incoordination (I)	Incoordination exploration stage
0.10–0.19	Serious incoordination (II)
0.20–0.29	Moderate incoordination (III)
0.30–0.39	Mild incoordination (IV)
0.40–0.49	On the verge of incoordination (V)	Transitional development stage
0.50–0.59	Narrow coordination (VI)	Transitional development stage
0.60–0.69	Primary coordination (VII)	Innovation and rise stage
0.70–0.79	Moderate coordination (VIII)
0.80–0.89	Good coordination (IX)
0.90-1.00	High-quality coordination (X)
Source: Zhang and Li 2020

3.3 Data sources

The data in this paper mainly come from the respective statistical yearbooks and Statistical Communiqué on National Economic and Social Development of the nine cities in Guangdong Province from 2011 to 2020, Guangdong Statistical Yearbook (2011–2020), Hong Kong Annual Digest of Statistics (2011–2020), and Macao Yearbook of Statistics (2011–2020). For the few missing values, this paper uses the TREND function to determine them based on the linear interpolation method.

3.4 Calculation process

The first step is to unify the units of the original data on the 11 cities through conversion and convert the currency units in the data of Hong Kong and Macau into yuan based on the exchange rate on July 12, 2021.

To make the values of different indexes comparable, the second step is to carry out dimensionless processing of the 10-year data of the 23 indexes for the 11 cities using above-mentioned formula (1) so that the data are standardized.

The third step is to transform the index weights individually using the above-mentioned formula (2), and then calculate the information entropy value, variation degree, and weight of each index using formulas (3), (4) and (5), as shown in Table 3. Using the results of the third step, the fourth step is to calculate the comprehensive development level of the two systems using formulas (6) and (7), and the results are shown in Tables 4 and 5. The last step is to calculate the coupling coordination degree between the two systems with formulas (8) through (10) and determine the coupling coordination development level between the tourism industry and new-type urbanization of each city according to the grades of coupling coordination degree in Table 2. The results are shown in Table 6.

Table 3

Multi-index Comprehensive Evaluation System for Tourism Industry and Urbanization in the Greater Bay Area
Target layer	Criterion layer	Index layer	Weight
Tourism industry development	Economic benefit 0.4157	X₁ Domestic tourism income	0.1736
		X₂ Foreign exchange earnings from international tourism	0.1052
		X₃ Proportion of tourism income in the tertiary industry	0.0708
		X₄ Proportion of tourism income in GDP	0.0661
	Market scale 0.2121	X₅ Person-times of overnight domestic tourists received	0.1189
	Market scale 0.2121	X₆ Person-times of overnight inbound tourists received	0.0932
	Development scale 0.3722	X₇ Number of A-level tourist attractions	0.0930
		X₈ Number of travel agencies	0.1719
		X₉ Number of star hotels	0.1073
New-type urbanization	Demographic urbanization 0.0998	Y₁ Natural population growth rate	0.0331
	Demographic urbanization 0.0998	Y₂ Proportion of employees of the tertiary industry	0.0667
	Economic urbanization 0.1986	Y₃ GDP growth rate	0.0252
		Y₄ GDP per capita	0.0836
		Y₅ Proportion of output value of secondary and tertiary industries in GDP	0.0178
		Y₆ Fixed assets investment of the whole society	0.0720
	Spatial urbanization 0.1419	Y₇ Mileage in highway open to traffic	0.0551
	Spatial urbanization 0.1419	Y₈ Urban built-up area	0.0869
	Social urbanization 0.5597	Y₉ Total retail sales of social consumer goods	0.0918
		Y₁₀ Private car ownership	0.0666
		Y₁₁ Holdings of public library	0.1229
		Y₁₂ Number of students in regular institutions of higher education	0.1696
		Y₁₃ Number of beds in medical and health institutions	0.0513
		Y₁₄ Number of health technicians	0.0575

4.1 Comprehensive development level of the tourism industry in the Greater Bay Area

In respect of spatial change, the comprehensive development levels of the tourism industry in the 11 cities of the Greater Bay Area exhibit evident grading, with significant development gaps among different cities. In order of high-quality development level from high to low, the 11 cities can be divided into three echelons (please see Fig. 1 and Table 4): Guangzhou is in the first echelon with ${\text{u}}_{1}$≥0.7, Shenzhen and Hong Kong are in the second echelon with 0.4 ≤${\text{u}}_{1}$< 0.7, and other cities, including Jiangmen, Macao, Zhuhai, Huizhou, Zhaoqing, Foshan, Dongguan, and Zhongshan, are in the third echelon with ${\text{u}}_{1}$< 0.4, where the tourism industry’s comprehensive development level is below average.

In respect of temporal change, the tourism industry exhibited a steady development trend in Macao and the nine cities of Guangdong Province, but not in Hong Kong, during the period from 2010 to 2019. Due to the “protests against parallel traders” in 2015 and the “Occupy Central protests” in 2016, the number of tourists visiting Hong Kong decreased significantly, resulting in Hong Kong’s tourism industry exhibiting a descending trend in development level. Moreover, in 2019, the series of violent incidents in the “protests against the extradition bill” and the COVID-19 pandemic had a great negative impact on the development of Hong Kong's tourism industry.

Table 4

Comprehensive Development Level of Tourism Industry in Each City of the Greater Bay Area from 2010 to 2019
City	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Guangzhou	0.7274	0.7372	0.7472	0.7480	0.7503	0.7578	0.7741	0.7740	0.7274	0.7372
Shenzhen	0.4690	0.4605	0.4567	0.4774	0.4862	0.4845	0.4822	0.4591	0.4690	0.4605
Zhuhai	0.2566	0.2445	0.2307	0.2004	0.2065	0.1858	0.1986	0.1929	0.2566	0.2445
Foshan	0.1201	0.1162	0.1335	0.1472	0.1632	0.1373	0.1521	0.1428	0.1201	0.1162
Huizhou	0.1494	0.1352	0.1293	0.1406	0.1621	0.1740	0.1780	0.1877	0.1494	0.1352
Dongguan	0.1363	0.1300	0.1396	0.1489	0.1356	0.1240	0.1309	0.1334	0.1363	0.1300
Zhongshan	0.0612	0.0554	0.0625	0.0666	0.0773	0.0605	0.0739	0.0875	0.0612	0.0554
Jiangmen	0.1194	0.1276	0.1386	0.1543	0.1876	0.1982	0.2141	0.2406	0.1194	0.1276
Zhaoqing	0.1171	0.1495	0.1781	0.1764	0.1516	0.1432	0.1527	0.1488	0.1171	0.1495
Macao	0.2369	0.2306	0.2313	0.2325	0.2236	0.2011	0.2056	0.2100	0.2369	0.2306
Hong Kong	0.6516	0.6590	0.6811	0.7146	0.7133	0.6352	0.5899	0.5720	0.6516	0.6590
Average	0.2768	0.2769	0.2844	0.2915	0.2961	0.2820	0.2866	0.2863	0.2768	0.2769

4.2 Comprehensive development level of new-type urbanization in the Greater Bay Area

In respect of spatial change, the development levels of new-type urbanization in the 11 cities of the Greater Bay Area exhibit evident grading. In order from high to low, the high-quality development level of the 11 studied cities can be divided into three echelons (please see Fig. 2 and Table 5): Guangzhou is in the first echelon with ${\text{u}}_{2}$ ≥ 0.7, Shenzhen and Hong Kong are in the second echelon with 0.4 ≤${\text{u}}_{2}$< 0.7, and other cities, including Dongguan, Foshan, Huizhou, Zhuhai, Macao, Jiangmen, Zhaoqing, and Zhongshan, are in the third echelon with ${\text{u}}_{2}$<0.4, where the development level of new-type urbanization is below average.

In respect of temporal change, the development level of new-type urbanization exhibited a slight ascending trend, albeit with ups and downs for the whole in Macao and the nine cities of Guangdong Province (except Hong Kong, which exhibited a descending trend) during the period from 2010 to 2019. The development levels in some cities in the third echelon, especially Dongguan and Foshan, exhibited a relatively evident rise, bringing them very close to the levels of the two second echelon cities.

Table 5

Comprehensive Development Level of New-type Urbanization in Each City of the Greater Bay Area from 2010 to 2019
City	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Guangzhou	0.7629	0.7645	0.7724	0.7656	0.7523	0.7203	0.7769	0.7828	0.7629	0.7645
Shenzhen	0.5613	0.5628	0.5748	0.5744	0.5820	0.5554	0.6035	0.6191	0.5613	0.5628
Zhuhai	0.1449	0.1379	0.1418	0.1515	0.1652	0.1565	0.1646	0.1902	0.1449	0.1379
Foshan	0.2484	0.2442	0.2391	0.2443	0.2426	0.2969	0.2716	0.3052	0.2484	0.2442
Huizhou	0.1708	0.1671	0.1806	0.1837	0.1861	0.1816	0.2041	0.2128	0.1708	0.1671
Dongguan	0.2657	0.2621	0.2701	0.2840	0.2763	0.2852	0.3002	0.3293	0.2657	0.2621
Zhongshan	0.1090	0.1136	0.1229	0.1246	0.1169	0.1276	0.1225	0.1231	0.1090	0.1136
Jiangmen	0.1380	0.1369	0.1340	0.1459	0.1363	0.1502	0.1577	0.1681	0.1380	0.1369
Zhaoqing	0.1362	0.1256	0.1405	0.1426	0.1446	0.1422	0.1424	0.1444	0.1362	0.1256
Macao	0.2172	0.2185	0.2100	0.2151	0.1897	0.1882	0.1877	0.2090	0.2172	0.2185
Hong Kong	0.5084	0.4999	0.4897	0.4607	0.4587	0.4161	0.4350	0.4303	0.5084	0.4999
Average	0.2966	0.2939	0.2978	0.2993	0.2955	0.2927	0.3060	0.3195	0.2966	0.2939

4.3 Coupling coordination degree between the tourism industry development and new-type urbanization in the Greater Bay Area

During the period from 2010 to 2019, the coupling coordination degree between tourism industry development and new-type urbanization was relatively stable in the 11 cities of the Greater Bay Area. In respect of spatial change, the coupling coordination degree in the 11 Greater Bay Area cities exhibited similar evident grading. In order from high to low, the 11 cities’ coupling coordination degree can be divided into three echelons (please see Fig. 3 and Table 6): Guangzhou is in the first echelon, Shenzhen and Hong Kong are in the second echelon, and the other cities are in the third echelon.

In respect of temporal change, Guangzhou, in the first echelon, maintained a level of “Good coordination (IX)”. Hong Kong, in the second echelon, dropped from “Moderate coordination (VIII)” in 2010 to “Primary coordination (VII)” in 2019, and Shenzhen, in the second echelon, remained at the level of “Moderate coordination (VIII)”. Out of the eight cities in the third echelon, six had unchanged coupling coordination levels. Zhuhai, Foshan, Dongguan, and Macao were in the “Transitional development stage” and remained at the level of “On the verge of incoordination (V)”, Zhaoqing maintained the level of “Mild incoordination (IV)”, and Huizhou dropped from “On the verge of incoordination (V)” in 2010 to “Mild incoordination (IV)” in 2011–2012, and then returned to “On the verge of incoordination (V)” in 2013.

For the remaining two cites, the coupling coordination level exhibited an ascending trend. Jiangmen entered the “Transitional development stage” from the “Incoordination exploration stage”, with the coupling coordination level rising from “Mild incoordination (IV)” in 2010 to “On the verge of incoordination (V)” in 2015. Zhongshan remained in the “Incoordination exploration stage” with the coupling coordination level rising from “Moderate incoordination (III)” in 2010 to “Mild incoordination (IV)” in 2012, which was maintained until 2019.

Table 6

Coupling Coordination Degree between Tourism Industry Development and New-type Urbanization in the Greater Bay Area and Corresponding Grade of the Degree from 2010 to 2019
City	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Guangzhou	0.8652 (IX)	0.8680 (IX)	0.8730 (IX)	0.8709 (IX)	0.8669 (IX)	0.8574 (IX)	0.8808 (IX)	0.8827 (IX)	0.8847 (IX)	0.8875 (IX)
Shenzhen	0.7227 (VIII)	0.7206 (VIII)	0.7239 (VIII)	0.7303 (VIII)	0.7359 (VIII)	0.7251 (VIII)	0.7427 (VIII)	0.7409 (VIII)	0.7238 (VIII)	0.7334 (VIII)
Zhuhai	0.4267 (V)	0.4164 (V)	0.4150 (V)	0.4116 (V)	0.4250 (V)	0.4094 (V)	0.4212 (V)	0.4374 (V)	0.4540 (V)	0.4546 (V)
Foshan	0.4299 (V)	0.4248 (V)	0.4346 (V)	0.4461 (V)	0.4547 (V)	0.4655 (V)	0.4634 (V)	0.4732 (V)	0.4884 (V)	0.4858 (V)
Huizhou	0.4024 (V)	0.3918 (IV)	0.3973 (IV)	0.4062 (V)	0.4196 (V)	0.4225 (V)	0.4396 (V)	0.4499 (V)	0.4604 (V)	0.4669 (V)
Dongguan	0.4501 (V)	0.4439 (V)	0.4545 (V)	0.4674 (V)	0.4547 (V)	0.4504 (V)	0.4624 (V)	0.4768 (V)	0.4890 (V)	0.4890 (V)
Zhongshan	0.2937 (III)	0.2912 (III)	0.3056 (IV)	0.3109 (IV)	0.3146 (IV)	0.3068 (IV)	0.3160 (IV)	0.3276 (IV)	0.3362 (IV)	0.3524 (IV)
Jiangmen	0.3608 (IV)	0.3648 (IV)	0.3685 (IV)	0.3862 (IV)	0.3935 (IV)	0.4096 (V)	0.4221 (V)	0.4404 (V)	0.4785 (V)	0.4772 (V)
Zhaoqing	0.3581 (IV)	0.3669 (IV)	0.3930 (IV)	0.3940 (IV)	0.3839 (IV)	0.3777 (IV)	0.3826 (IV)	0.3822 (IV)	0.3890 (IV)	0.3939 (IV)
Macao	0.4742 (V)	0.4725 (V)	0.4672 (V)	0.4711 (V)	0.4501 (V)	0.4396 (V)	0.4412 (V)	0.4576 (V)	0.4577 (V)	0.4399 (V)
Hong Kong	0.7492 (VIII)	0.7471 (VIII)	0.7474 (VIII)	0.7409 (VIII)	0.7397 (VIII)	0.7019 (VIII)	0.7009 (VIII)	0.6943 (VII)	0.6998 (VII)	0.6625 (VII)

5.1 Conclusions

Close interaction exists between the tourism industry’s development and new-type urbanization, and under the new development pattern of “dual circulation”, it is of great significance to achieve coordinated development between the two and make full use of such interactions. In order to aid such efforts, this paper first untangles the coupling relationships between tourism industry development and new-type urbanization based upon established theories, and then establishes a multi-index comprehensive evaluation system for the tourism industry and urbanization in the Greater Bay Area according to the principles of index comparability, data integrity, and index practicality. The coupling and coordination relationship between the tourism industry and new-type urbanization in the “9 + 2” cities of the Greater Bay Area during the period from 2010 to 2019 were analyzed empirically. The main conclusions are as follows.

First, on the whole, tourism industry development, new urbanization, and their coupling coordination degree in the 11 cities of the Greater Bay Area all could be classified as being in a steady development stage. Second, in respect of spatial change, the development levels of the tourism industry and new-type urbanization and the coupling coordination degrees in the 11 cities of the Greater Bay Area exhibit evident grading. In order from high to low, the high-quality development level and coupling coordination degree of the 11 cities can be divided into three echelons: (1) Guangzhou is in the first echelon, with high comprehensive development levels of the tourism industry and new-type urbanization and a high coupling coordination degree between the two; (2) Shenzhen and Hong Kong are in the second echelon, with intermediate comprehensive development levels of the tourism industry and new-type urbanization and intermediate coupling coordination degrees between the two; and (3) the remaining cities are in the third echelon, with low comprehensive development levels of the tourism industry and new-type urbanization and low coupling coordination degrees between the two.

Third, in respect of temporal change, the tourism industry, urbanization, and coupling coordination degree all exhibited steady development trends in the studied cities, with the exception of Hong Kong, which showed marked declining trends in the development levels of the tourism industry and urbanization, as well as in the coupling coordination degree between the two, due to the successive influences of the “protests against parallel traders”, “Occupy Central protests”, “protests against the extradition bill”, and the “COVID-19 pandemic”. The development gaps among the three echelons remained relatively stable without any sign of narrowing.

5.2 Countermeasures and suggestions

First of all, strategically, it is necessary to enhance the understanding of the philosophy of promoting benign interactions between tourism industry development and new-type urbanization under the new economic development pattern of “dual circulation”. The new economic development pattern of “dual circulation” is a strategic approach of balanced development in all aspects from the industrial chain and supply chain to the demand chain at the same time (Wei 2021). The new-type urbanization is “people-oriented” and marks a change from “quantity” to “quality” for urbanization. From a supply-side perspective, the new-type urbanization results in talent agglomeration and technological innovation that supports economic development. From a demand-side perspective, the new-type urbanization allows more permanent residents to live in cities, where they can pursue a higher quality of life; cities will thus become an important spatial carrier for urban residents to enjoy a better life (Jiao and Zhang 2021).

Due to the requirements of regular pandemic prevention and control policies, outbound tourism has been suspended, the “outflow” of tourist spending has halted, and people are trying to find other opportunities. Therefore, short-distance tours, regular tours, wild tours, family tours, and tailor-made tours are becoming hot in domestic tourism and can basically meet the demand for consumption upgrading by domestic residents. However, great differences in tourism consumption were identified between urban and rural areas. Thus, under the new “dual circulation” economic development pattern, much effort can still be made to realize the continuous benign interaction between tourism industry development and new-type urbanization.

Secondly, during the 10 years from 2010 to 2019, the tourism industry’s development and new-type urbanization were in a steady development stage in the 11 Greater Bay Area cities, with development gaps among the cities remaining unchanged. The new economic development pattern provides an opportunity to more quickly narrow the gap in coupling coordination degrees between the tourism industry and new-type urbanization that exists between the third echelon and first and second echelons. However, in order to improve the development levels of the tourism industry and new-type urbanization, cities in different echelons should take different measures according to their actual conditions. Based on the specific comparison among these cities, Guangzhou, the only first echelon city, should continue to deepen the coordinated development of tourism and urbanization and shore up its weaknesses. The city can consider introducing the philosophy of new-type urbanization construction guided by tourism and leisure to promote the new-type urbanization through the development of pan-tourism industry. This will help optimize urban and rural tourism environments and improve the subjective well-being of both urban residents and tourists.

Shenzhen and Hong Kong, the two cities in the second echelon, should focus even more on the domestic tourism consumer market under the new economic development pattern, rely on themselves to realize the development and upgrading of the tourism industry, and be prepared for the inbound tourism in the future after the pandemic is controlled. All third echelon cities, whether tourism-promoted cities (where tourism is more developed than urbanization, including Macao, Zhuhai, Huizhou, Jiangmen, and Zhaoqing) and urbanization-supported cities (where urbanization is more developed than the tourism industry, including Foshan, Dongguan, and Zhongshan) should seize the opportunity for “internal circulation", actively improve the quality of tourism services, and enrich the supply of tourism products in order to attract tourists from the Greater Bay Area and other cities and release the tourism consumption potential.

Lastly, it is recommended to release the potential and raise the competitiveness of Chinese cities through construction for new-type urbanization and high-quality development of the tourism industry to create favorable conditions for the formation of a new development pattern with domestic circulation as the mainstay and domestic and international circulations reinforcing each other to cope with the greatest changes unseen in a century.

5.3 Study limitations

Due to the great differences in statistical objects, standards, and caliber as well as differing availability of statistical data in Guangdong, Hong Kong, and Macao together with the lack of statistical data for some indexes covering several cities of Guangdong Province, the evaluation index system for tourism industry development and new-type urbanization established in this paper has some limitations, including (1) the whole evaluation system has relatively small number of indexes, (2) the dimension of environmental urbanization was reduced, and (3) there are limited number of indicators in the criterion layer of market scale, demographic urbanization, and spatial urbanization. In addition, the data collected are limited. Therefore, some deviations may occur in actual calculation.

Ethical Approval

This declaration is not applicable.

Competing interests

The authors declare no conflict of interest.

Authors' contributions

Both authors contributed to the study conception, design and discussion. Material preparation, data collection and analysis were performed by Feng Minyan. The first draft of the manuscript was written by Feng Minyan. And Kan Xinglong commented on previous versions of the manuscript and finished the further revision. Both authors read and approved the final manuscript.

Funding

This work was supported by two provincial grants from Department of Education of Guangdong Province: [2021ZDZX3005] “Research on Digital Transformation and Innovative Development of Cultural and Tourism Performing in 5G era”, and [2022GXJK391] “Research on Academic Drift and Correction of Applied Undergraduate Tourism Education: Based on the Perspective of Outcome Based Education Concept”.

Availability of data and materials

The data of this paper were collected from Statistical Communiqué on National Economic and Social Development of the nine cities (including Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, Zhaoqing) in Guangdong Province from 2011 to 2020, Guangdong Statistical Yearbook (2011–2020), Hong Kong Annual Digest of Statistics (2011–2020), and Macao Yearbook of Statistics (2011–2020). The statistic data were open to access and can be freely downloaded from the government's website.

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No competing interests reported.

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Study on Coupling and Coordinated Development of Regional Tourism Industry and New-type Urbanization Using Entropy Method: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area

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Abstract

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1. Introduction

2. Literature Review

3. Study Design

3.1 Variable selection

3.2 Research methods

3.2.1 Entropy method

3.2.2 Coupling coordination degree model

3.3 Data sources

3.4 Calculation process

4. Analysis Of Results

4.1 Comprehensive development level of the tourism industry in the Greater Bay Area

4.2 Comprehensive development level of new-type urbanization in the Greater Bay Area

5. Conclusions

5.1 Conclusions

5.2 Countermeasures and suggestions

5.3 Study limitations

Declarations

References

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