Coupling Coordination between Cultural Heritage Protection and Tourism Development: The Case of China

Abstract

The systems of cultural heritage protection and tourism development are intertwined, so enhancing the coupling coordination status between them is beneficial to stimulate their growth. This study first constructs the theoretical coupling coordination mechanism and explores the assessment structure with detailed indicators to explore their coordinated interactions. Moreover, it selects the provincial regions of China as the case study to compare the temporal and spatial variations of both systems’ growth statuses and their coupling coordination status. Moreover, it provides beneficial insights for different regions to enhance cultural heritage protection and tourism development with coordinated and differentiated approaches based on dynamic predictions. We find that for the coupling coordination status, the temporal fluctuations of the regions were mild and aggregated, and it is rare to find obvious exceptions. Moreover, the spatial distributions exhibit apparent geographical correlations, with distributions being “higher in the coastal, central, and southwestern areas”. This study boasts several novelties. First, we select multiple regions for comparison, which offsets previous research gaps focusing only on individual regions. What is more, we construct the coupling coordination mechanism with an assessment structure and indicators, which theoretically explains their interactive correlations and explores the evaluation approaches. Moreover, we predict the temporal and spatial changes of the coupling coordination status and provide differentiated suggestions, which could contribute to the future coordinated development of different regions.

1. Introduction

The coordination interactions between cultural heritage protection (CHP) and tourism development (TD) are drawing increasing attention worldwide. Both systems are intricately correlated; however, their interactions and their coordination mechanism have not been thoroughly explored, which is critical to achieving their steady growth. Previously, CHP has been proven to affect TD positively and negatively. For example, better CHP provides sufficient resources for tourists. It enhances the attractiveness of local tourism, while some improper CHP activities may keep tourists away and thus hinder TD. Furthermore, it has been proved that TD affects CHP in both positive and negative ways. For instance, the tourism industry provides more funds to protect cultural heritage. At the same time, increasing tourists may damage cultural heritage sites and hinder the effectiveness of CHP. Their interactions are complicated and unclear; thus, it is crucial to explore their coordination mechanism and assess and predict their coordination status. This is essential to pinpointing the specific actions that must be taken to gradually improve CHP and TD.

Selecting the provincial regions of China as the example, this study aims to fill this gap in the literature and first constructs the coordination mechanism between the two systems to discuss their mutually coordinated interactions. Secondly, it assesses the two systems’ development statuses and coupling coordination statuses. Thirdly, it proposes specific actions to enhance their coordinated interactions based on the quantitative predictions of the coupling coordination status.

Cultural heritage protection, in this study, refers to the actions taken to protect cultural heritage so that national values and spirits can be inherited and shared [1]. It includes protecting both tangible cultural heritages (such as relics) and intangible cultural heritages (such as folk songs and skills). Therefore, such protective activities may happen in museums (to protect tangible ones) and in art performance venues (to protect intangible ones). Cultural heritage protection, to some degree, reflects both the benefits and deficiencies of cultural heritage-related and tourism activities. Tourism in this study refers to the activities of travelers who travel, stay, sightsee, and seek entertainment in areas other than their typical abode for a particular time duration [2,3]. TD assesses the local tourism industry’s growth process, which includes tourism agencies, hotels, and tourist attractions. CHP and TD are intertwined because they have similar elements, despite the fact that they are two different systems. For instance, museums are places of cultural heritage protection and tourism activities; highly recognized folk skills are both the protection targets and the tourist attractions. Thus, we need to explore their coordinated interactions to achieve mutual enhancement.

2. Literature Review

2.1. CHP’s Effect on TD

CHP affects TD both positively and negatively. Positively, cultural heritage acts as a helpful tourism resource. Virtual reality and augmented reality tools can better protect cultural heritage, leading to better tourist experiences and tourism industry growth [4,5]. Secondly, the stakeholders of CHP (such as cultural heritage preservers and residents) are also the components of TD. These stakeholders participate in tourism activities, contribute to tourism services and products, enhance the image of the local tourism industry, and thus promote TD [6]. Cases in China have proved that residents’ recognition and attitudes toward CHP positively affect their willingness to visit tourist sites [7]. Thirdly, CHP activities will likely cultivate the creative cultural industry. Turning cultural heritage into a local tourism brand is beneficial, promoting the sustainability of TD and enhancing the artistry of tourism activities [8]. Fourthly, CHP involves various aspects such as leisure or recreational activities, accommodation, catering, public transportation, and event organization. These elements boost both TD and the local economy [9,10]. Cases in Iran have proved that CHP is beneficial to accelerate local tourism and urban regeneration.

On the other hand, CHP also negatively affects TD. Firstly, more CHP activities may increase protection costs and necessitate more severe protection countermeasures. As a result, cultural heritage visiting sites may raise visiting prices and limit the number of visitors, decreasing the tourism experience and the total revenue of the tourism industry [11]. Secondly, sometimes there are conflicts between the protection and commercialization of cultural heritage and between cultural heritage production and pricing, leading to collisions among different interest groups in the tourism industry [12]. Thirdly, CHP strengthens local traditions, residents’ social cohesion, and cultural identity. However, studies prove that authentic local traditions built by local cultural protection activities may hinder attracting tourists (especially international tourists). That, in turn, hinders the development of the tourism industry [13,14].

2.2. TD’s Effect on CHP

TD also affects CHP from both positive and negative perspectives. Positively speaking, TD means increasing tourism production and consumption; some tourism facilities and revenues can be applied to CHP activities, which promotes the re-production and re-protection of cultural heritages [15]. Moreover, TD means increasing the number of tourists, some of whom may actively participate in CHP activities with innovative approaches; this is beneficial to protecting cultural heritages effectively [16]. In addition, tourists’ experiences also affect their attitudes towards CHP and their behaviors towards protecting cultural heritages; better tourist experiences lead to better CHP activities [17]. Moreover, tourism development planning, usually processed by the local authorities, often includes CHP sections; planning often takes the form of blueprints and aerial views, attracting stakeholders to participate in CHPs and creating more value [18].

Negatively, tourism destinations may adopt differentiated development paths to create unique tourism images and attract more tourists. Some destinations with rich natural resources may strengthen the positioning of “marvelous sceneries”, which impedes the development of local culture-related tourism and cultural heritage protection activities [19]. Moreover, TD is usually accompanied by more cultural exhibitions, and the exhibitions in globally famous tourist cities may display the cultural heritage of foreign countries. That may cause the severe problem of cultural heritage in foreign countries being plundered and smuggled, which is harmful to CHP [20]. In addition, tourism activities are accompanied by many tourists who may be of different races and have different values. Cultural disputes and conflicts between tourists and residents may adversely affect the national spirit embodied by CHP [21]. What is more, TD causes tension between CHP and commercial utilization, which, to some degree, decreases the effectiveness and efficiency of CHP [22].

2.3. Interactions between CHP and TD

CHP and TD affect each other positively and negatively, as discussed in previous studies [23]; thus, they have complicated relations and interactions. Coupling coordination can measure such interactive relations; a better coupling coordination status means better system interactions [24]. Previous studies have applied coupling coordination to measure the interactive relations among different systems [25,26,27], especially the interactions between CHP and tourism [28]. However, they either select an individual place as the case or fail to explore such coupling coordination interactions from the perspectives of both temporal and spatial comparisons quantitatively. Thus, the exploration of the coupling coordination mechanism and the measurement of the coupling coordination status between the two systems (CHP and TD) are less comprehensive, dynamic, sufficient, and convincing.

Exploring the coupling coordination status between systems is essential to understanding the evolving interactions between CHP and TD and taking more specific actions to enhance such coordination interactions. However, it is still hard for us to precisely measure their coupling coordination status for the following reasons: (1) there are no scientific mechanisms to explain their interactions theoretically; (2) there are no widely accepted assessment structures with detailed indicators to measure their coupling coordination status. Moreover, as no studies compare the dynamic changes and spatial variations of the coordination status or predict the tendencies among different regions, it is also challenging to propose specific actions to enhance their growth and coordination status. Thus, this study aims to solve these problems and contribute to better CHP and TD.

3. Methods

3.1. Theoretical Coupling Coordination Mechanism

The CHP and TD systems are intertwined and interactive; therefore, both systems can form a coupling coordination mechanism wherein both support and hinder one another and gradually evolve to a benign interactive status. The CHP system follows the “scale–performance–efficiency” structure, while the TD system follows the “scale–performance” structure. These structures are accepted and applied in other studies and have proved influential in evaluating their interactive coordination [29,30]. Figure 1 shows the theoretical coupling coordination mechanism between the two systems.

For the CHP system: (1) a larger scale of CHP provides abundant tourism revenue, which in turn enhances the scale and performance of the tourism industry, and further stimulates TD. On the contrary, increasing the scale of CHP activities occupies the fiscal and land resources of TD and thus hinders the scale and performance of TD. (2) Better performances of CHP enhance the local image and competitiveness and thus promote TD; on the contrary, the performances of CHP reveal the conflicts between local tradition and global commercialization and between residents and external tourists, which impedes TD. (3) More expenditure on CHP brings more qualified tourism resources, further enhancing the tourism industry’s scale and performance. On the contrary, more expenditure on local CHP may reduce the expenditures on the local tourism industry, thus impeding the improvement of the scale and performance of local tourism [31,32,33].

For the TD system: (1) more hotels, travel agencies, and tourism sites contribute to the places and service providers participating in CHP activities, enhancing the scale and performance of CHP activities. On the contrary, with the growth of the tourism industry, some tourism attractions reinforce the positioning and image of “nature” rather than “culture”, which impedes local CHP’s scale, performance, and efficiency. Some tourist sites exhibiting exotic and smuggled cultural relics also hinder CHP. (2) More intelligent tourists participate in CHP activities and use innovative approaches, further enhancing the efficiency of protection activities. On the contrary, some tourists may find value conflicts between their own beliefs and the local traditional values; moreover, the commercialization of cultural heritage accompanied by TD hinders the stakeholders from protecting the tradition more efficiently and effectively [34,35].

3.2. Assessment Structure with Indicators

According to the theoretical coupling coordination mechanism, we select the indicators and construct the assessment structure (

Table 1. Assessment Structure.

System	Aspect	Indicator
CHP	Scale	Number of museums
		Number of cultural institutions
		Number of art troupes
		Number of cultural art performance venues
		Number of cultural relic preservation agencies
	performance	Number of collections in museums
		Number of visitors to museums
		Number of cultural activities
		Number of art troupes’ performances
	Efficiency	Museum expenditure as a percentage of GDP
		Cultural institution expenditure as a percentage of GDP
		Cultural relics preservation expenditure as a percentage of GDP
TD	Scale	Number of star-rated hotels
		Number of travel agencies
		Number of A-class tourist attractions
	Performance	Star-rated hotel revenue
		Number of domestic tourists
		Number of international tourists
		Number of visitors to A-class tourist attractions

). The selection of the indicators obeys the following requirements: (1) the indicators should reflect well the core activities or sections of the systems; (2) the indicators should be easy for readers to understand; (3) they should be widely known, accepted, and applied; (4) the data should be accessible without too many missing values [36]. The final 19 selected indicators are subjected to the significance and correlation tests, proving to be valid. The assessment structure contains three levels: system, aspect, and indicator.

The CHP system includes three aspects: scale, performance, and efficiency. The scale aspect demonstrates the overall number of CHP participants. The five indicators reflect the overall scales of the museums, cultural institutions (such as cultural exhibition halls and cultural activity centers), art performance troupes, cultural art performance venues, and cultural relic preservation agencies. These indicators are accessible in the data and represent the CHP subjects’ complete structure and scale [37,38]. The performance aspect demonstrates how these subjects perform and contribute to CHP; the four indicators cover the number of collections in and visitors to museums, cultural activities, and art performance troupes’ shows [39]. These indicators are selected because they correspond to the indicators in the scale aspect and illustrate whether the overall size has a corresponding compelling performance. The efficiency aspect demonstrates whether the CHP activities are efficient (namely, whether the budgets are used wisely). The expenditures of museums, cultural institutions, and cultural relic preservation agencies as a percentage of GDP are selected to evaluate whether these sections are emphasized from a fiscal perspective [40].

The TD system consists of two aspects: scale and performance. The aspect of scale mainly assesses the input to enhance TD from the perspective of hotels, travel agencies, and tourism sites; in detail, three indicators, the numbers of hotels, travel agencies, and A-class tourism sites, are evaluated. These indicators effectively demonstrate the input scale of tourism [41,42]. The performance aspect mainly assesses local TD’s output or results. We select four indicators: the revenue of star-rated hotels, the numbers of domestic and international tourists, and the number of visitors to A-class tourist attractions. These indicators precisely reflect the output performances of local tourism [43,44].

3.3. Study Area

This study selects 31 of 34 provincial regions of China as the study area (Taiwan, Hong Kong, and Macau are excluded because of data availability issues). We select these 31 regions because of their representativeness. (1) As China is a large country, the CHP activities and TD vary considerably both temporally and spatially among different regions; therefore, comparing their differences will provide valuable insights into places with similar conditions in other countries. (2) Certain regions in China have proposed policies and countermeasures to enhance CHP and TD in recent years. Therefore, evaluating whether those countermeasures have positively enhanced the coordination growth between CHP and TD is beneficial. These will also be significant references to other places to promote coordination growth. Figure 2 shows the study area; specifically, there are seven geographical areas: Central, East, North, Northwest, Northeast, South, and Southwest China.

4. Calculation

4.1. Method and Data Selection

This study uses the information entropy method and the technique for order preference by similarity to an ideal solution approach (TOPSIS) to explore the growth statuses of both systems and assess the coordination status between CHP and TD. There are several reasons to use these two methods jointly. (1) They are quantitative analysis methods and obtain results via calculation; thus, they are more objective than qualitative methods and avoid personal biases, making results more objective and convincing. (2) The joint use of these two approaches facilitates the measurement of the relative importance among different alternatives, making results easier to interpret. (3) Using the two methods together negates the deficiencies of using one method solely; for instance, it negates the need to eliminate specific indicators, which would decrease the accuracy of results. (4) They allow for a convincing analysis of the development status and the coupling coordination status, as their scientificity has been proved in previous studies [45,46,47].

The data are mainly from the China Statistical Yearbook of Cultural Relics and Tourism, China Statistical Yearbook of Cultural Relics, China Tourism Statistical Yearbook, and China Statistical Yearbook. The national authorities collect these data and publish the yearbooks annually; therefore, the data are convincing, consistent, and objective. A few missing data are predicted and calculated.

4.2. Growth Status

The steps for calculating the growth status are as follows.

(1) Data standardization. There are 31 selected provincial regions, and the statistical duration is nine years; therefore, there are 279 alternatives in the original matrix $m$ where $a$ represents the alternative and $b$ represents the index. In Table 1, there are 12 indexes in the CHP system and seven in the TD system. In the new standardized matrix $M$, $a=1, 2, 3, \dots , p; b=1, 2, 3, \dots , q$.

$$M_{ab}=\frac{m_{ab}}{{{\displaystyle \sum }}_{a=1}^q{m}_{ab}}$$

(1)

(2) Obtain the information entropy $E$.

$$E_b=-\left({{\displaystyle \sum }}_{a=1}^p{f}_{ab}\ln f_{ab}\right), \mathrm{where} f_{ab}=\frac{1+M_{ab}}{{{\displaystyle \sum }}_{a=1}^p\left(1+M_{ab}\right)}$$

(2)

(3) Obtain the weight $W$.

$$W_b=\frac{1-E_b}{q-{{\displaystyle \sum }}_{b=1}^q{E}_b}$$

(3)

(4) Use the TOPSIS method to obtain the positive and negative values $PO$ and $NE$. $M^{\prime }$ is the matrix slice of $M$ in a particular year; $a^{\prime }$ and $b^{\prime }$ are the alternative and index in this time slice. In each time slice, there are 31 alternatives (namely provincial regions) in total, which means $a^{\prime }=1, 2, 3, \dots , 30, 31$. Moreover, $b^{\prime }=1, 2, 3, \dots , q$.

$$P{O}_{b^{\prime }}=\left(\underset{1\le b^{\prime }\le q}{max}{M}_{a_1^{’}}^{’},\underset{1\le b^{\prime }\le q}{max}{M}_{a_2^{’}}^{’},\dots ,\underset{1\le b^{\prime }\le q}{max}{M}_{a_{30}^{’}}^{’},\underset{1\le b^{\prime }\le q}{max}{M}_{a_{31}^{’}}^{’}\right)$$

(4)

$$N{E}_{b^{\prime }}=\left(\underset{1\le b^{\prime }\le q}{min}{M}_{a_1^{’}}^{’},\underset{1\le b^{\prime }\le q}{min}{M}_{a_2^{’}}^{’},\dots ,\underset{1\le b^{\prime }\le q}{min}{M}_{a_{30}^{’}}^{’},\underset{1\le b^{\prime }\le q}{min}{M}_{a_{31}^{’}}^{’}\right)$$

(5)

(5) Obtain the growth status $GR$ of the alternative.

$$G{R}_{a^{\prime }}=\frac{\sqrt{{{\displaystyle \sum }}_{a^{\prime }=1;b^{\prime }=1}^q{W}_b{\left(M_{a^{\prime }{b}^{\prime }}^{’}-N{E}_{b^{\prime }}\right)}^2}}{\sqrt{{{\displaystyle \sum }}_{a^{\prime }=1;b^{\prime }=1}^q{W}_b{\left(M_{a^{\prime }{b}^{\prime }}^{’}-P{O}_{b^{\prime }}\right)}^2}+\sqrt{{{\displaystyle \sum }}_{a^{\prime }=1;b^{\prime }=1}^q{W}_b{\left(M_{a^{\prime }{b}^{\prime }}^{’}-N{E}_{b^{\prime }}\right)}^2}}$$

(6)

(6) Grade classification. The interval of $GR$ is from 0 to 1; thus, we segment the breaks equally and classify five grades in

Table 2. Grade Classification.

GR Value	[0, 0.2)	[0.2, 0.4)	[0.4, 0.6)	[0.6, 0.8)	[0.8, 1]
Grade	Poor	Weak	Average	Good	Perfect

. The segmentation with equal intervals is also accepted in previous studies [24].

4.3. Coupling Coordination Status

The steps for measuring the coupling coordination status are as follows.

(1) Obtain the coupling degree $C$. $G{R}_c$ and $G{R}_t$ represent the growth statuses of CHP and TD in a particular year slice, respectively.

$$\mathrm{C}={\left\{\frac{G{R}_c\times G{R}_t}{{\left(\frac{G{R}_c+G{R}_t}{2}\right)}^2}\right\}}^{\frac{1}{2}}$$

(7)

(2) Obtain the coupling coordination status $S$. In this study, CHP and TD enjoy the same importance and interact equally; thus, their coefficients are the same (equal to 0.5) [48].

$$S=\sqrt{C\times \left(0.5G{R}_c+0.5G{R}_t\right)}$$

(8)

(3) Grade classification. The interval of $S$ is from 0 to 1; thus, we segment the breaks equally and classify three ranges and ten grades in

Table 3. Range and Grade of the Coupling Coordination Status.

Range	Interval	Grade
Incoordination	[0, 0.1)	Serious incoordination
	[0.1, 0.2)	High incoordination
	[0.2, 0.3)	Moderate incoordination
	[0.3, 0.4)	Slight incoordination
Transition	[0.4, 0.5)	Transitional incoordination
	[0.5, 0.6)	Transitional coordination
Coordination	[0.6, 0.7)	Slight coordination
	[0.7, 0.8)	Moderate coordination
	[0.8, 0.9)	High coordination
	[0.9, 1]	Extreme coordination

. The segmentation with equal intervals is also accepted in previous studies [24].

4.4. Prediction of Coupling Coordination Status

We use the GM (1,1) model to forecast the changes in the coupling coordination status. The GM (1,1) model has been widely applied to predict the dynamics of systems [49,50]. The steps taken are as follows.

(1) Obtain the new sequence of every region. In this study, the time sequence is nine years; thus, for a specific region, the original time sequence of coupling coordination status $S_0=\left\{S_{0\left(1\right)},S_{0\left(2\right)},\cdots ,S_{0\left(8\right)},S_{0\left(9\right)}\right\}$. Then, we use $S_1\left(g\right)={{\displaystyle \sum }}_{a=1}^g{S}_0\left(a\right), g=1,2,\dots , n$ to obtain the new cumulative time sequence $S_1=\left\{S_{1\left(1\right)},S_{1\left(2\right)},\cdots ,S_{1\left(8\right)},S_{1\left(9\right)}\right\}$.

(2) Define the grey differential equation. $Z_1$ is the immediate mean sequence of $S_1$; $Z_1=\left\{Z_{1\left(1\right)},Z_{1\left(2\right)},\cdots ,Z_{1\left(8\right)},Z_{1\left(9\right)}\right\}$, and $Z_{1\left(\mathrm{g}\right)}=0.5S_{1\left(\mathrm{g}\right)}+0.5S_{1\left(\mathrm{g}-1\right)}$. Then we can define the grey differential equation, where $\mathrm{a}$ and $b$ are the development coefficient and grey action value, respectively.

$$b=S_{0\left(\mathrm{g}\right)}+\mathrm{a}{Z}_{1\left(\mathrm{g}\right)}$$

(9)

(3) Obtain the whitening equation. $\widehat{a}={\left(a,b\right)}^T$ is the parameter vector, and we can obtain $\widehat{a}={\left(B^{T}B\right)}^{-1}{B}^{T}Y$, where $B={\left[-Z_{1\left(2\right)},-Z_{1\left(3\right)},\cdots ,-Z_{1\left(\mathrm{n}\right)},1,1,\cdots 1\right]}^T$, and $Y={\left[S_{0\left(2\right)},\cdots ,S_{0\left(\mathrm{n}\right)}\right]}^T$. Then we can obtain the whitening equation.

$$\frac{d{S}_1}{dt}+\mathsf{\alpha }{S}_1=b$$

(10)

(4) Obtain the prediction value. The solution of Formula (10) is ${\widehat{S}}_{\left(1\right)\left(t\right)}=\left[S_{\left(1\right)\left(0\right)}-\frac{b}{\mathrm{a}}\right]e^{-\mathrm{at}}+\frac{b}{\mathrm{a}}$; the time response sequence of Formula (9) is ${\widehat{S}}_{\left(1\right)\left(g+1\right)}=\left[S_{\left(1\right)\left(0\right)}-\frac{b}{\mathrm{a}}\right]e^{-\mathsf{\alpha }\mathrm{g}}+\frac{b}{\mathrm{a}},\left(g=1,2,\cdots ,n\right)$. We set $S_{\left(1\right)\left(0\right)}=S_{\left(0\right)\left(1\right)}$; then we can obtain ${\widehat{S}}_{\left(g+1\right)}=\left[S_{\left(0\right)\left(1\right)}-\frac{b}{\mathrm{a}}\right]e^{-\mathsf{\alpha }\mathrm{g}}+\frac{b}{\mathrm{a}}$ and thus obtain the prediction value.

$${\widehat{S}}_{\left(0\right)\left(g+1\right)}={\widehat{S}}_{\left(1\right)\left(g+1\right)}-{\widehat{S}}_{\left(1\right)\left(g\right)}$$

(11)

(5) Process the residual error test. The absolute error is ${\mathsf{\epsilon }}_{\left(0\right)\left(\mathrm{a}\right)}=S_{\left(0\right)\left(\mathrm{a}\right)}-{\widehat{S}}_{\left(0\right)\left(a\right)}$; the relative error is ${\mathsf{\omega }}_{\left(0\right)\left(\mathrm{a}\right)}=\left|\frac{S_{\left(0\right)\left(\mathrm{a}\right)}-{\widehat{S}}_{\left(0\right)\left(a\right)}}{S_{\left(0\right)\left(\mathrm{a}\right)}}\right|$. The data are applicable for prediction if $a\le 0.5$, and r and P are larger than 0.6 [51].

5. Results and Discussions

5.1. Temporal Analysis of the Growth Status

The CHP’s growth status statistics can be found in

Table A1. Growth Status of the CHP System *.

Area	Region	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean	Grade
Southwest China	Chongqing	0.215	0.214	0.217	0.242	0.212	0.192	0.248	0.261	0.197	0.222	weak
	Sichuan	0.278	0.318	0.319	0.328	0.307	0.245	0.297	0.297	0.258	0.294	weak
	Guizhou	0.201	0.483	0.192	0.176	0.170	0.164	0.241	0.171	0.147	0.216	weak
	Yunnan	0.225	0.229	0.260	0.258	0.260	0.193	0.246	0.244	0.208	0.236	weak
	Tibet	0.545	0.560	0.556	0.495	0.548	0.588	0.576	0.559	0.643	0.563	average
Northeast China	Liaoning	0.194	0.169	0.173	0.178	0.192	0.136	0.164	0.222	0.138	0.174	poor
	Jilin	0.158	0.128	0.173	0.138	0.138	0.113	0.139	0.157	0.139	0.143	poor
	Heilongjiang	0.150	0.146	0.170	0.169	0.165	0.138	0.163	0.176	0.145	0.158	poor
East China	Shanghai	0.255	0.234	0.245	0.235	0.219	0.175	0.216	0.244	0.183	0.223	weak
	Jiangsu	0.273	0.242	0.241	0.251	0.236	0.197	0.238	0.260	0.205	0.238	weak
	Zhejiang	0.329	0.307	0.322	0.349	0.350	0.296	0.351	0.369	0.279	0.328	weak
	Anhui	0.310	0.300	0.296	0.331	0.323	0.284	0.320	0.297	0.247	0.301	weak
	Fujian	0.230	0.202	0.208	0.196	0.189	0.144	0.183	0.196	0.165	0.190	poor
	Jiangxi	0.190	0.188	0.208	0.193	0.181	0.153	0.206	0.189	0.174	0.187	poor
	Shandong	0.249	0.240	0.288	0.282	0.276	0.229	0.296	0.302	0.268	0.270	weak
North China	Beijing	0.372	0.345	0.349	0.400	0.334	0.303	0.312	0.322	0.279	0.335	weak
	Tianjin	0.123	0.140	0.129	0.139	0.118	0.100	0.142	0.142	0.126	0.129	poor
	Hebei	0.255	0.257	0.259	0.269	0.260	0.199	0.255	0.262	0.219	0.248	weak
	Shanxi	0.263	0.268	0.276	0.268	0.275	0.223	0.278	0.294	0.236	0.265	weak
	INR MN	0.211	0.195	0.190	0.203	0.189	0.164	0.201	0.206	0.184	0.194	poor
Central China	Hubei	0.233	0.210	0.206	0.203	0.197	0.172	0.305	0.219	0.191	0.215	weak
	Hunan	0.217	0.222	0.246	0.244	0.221	0.179	0.211	0.328	0.187	0.228	weak
	Henan	0.270	0.309	0.301	0.345	0.340	0.301	0.313	0.328	0.266	0.308	weak
Northwest China	Shaanxi	0.314	0.309	0.295	0.323	0.307	0.282	0.347	0.332	0.277	0.310	weak
	Gansu	0.288	0.286	0.270	0.268	0.264	0.215	0.248	0.265	0.297	0.267	weak
	Qinghai	0.184	0.137	0.156	0.194	0.192	0.154	0.235	0.216	0.184	0.184	poor
	Ningxia	0.286	0.249	0.387	0.327	0.256	0.242	0.300	0.311	0.248	0.290	weak
	Xinjiang	0.284	0.167	0.199	0.200	0.197	0.154	0.181	0.200	0.149	0.192	poor
South China	Guangdong	0.219	0.196	0.204	0.200	0.191	0.153	0.191	0.215	0.182	0.195	poor
	Guangxi	0.171	0.162	0.175	0.177	0.162	0.125	0.158	0.179	0.145	0.161	poor
	Hainan	0.241	0.151	0.151	0.149	0.132	0.155	0.139	0.184	0.119	0.158	poor

* Original data sources: China Statistical Yearbook of Cultural Relics and Tourism, China Statistical Yearbook of Cultural Relics, China Tourism Statistical Yearbook, and China Statistical Yearbook. The numbers in the table are obtained after calculation with the methods illustrated in this paper.

, with the temporal changes shown in Figure 3. Interestingly, the 31 regions can be generally categorized into two clusters: Tibet and others. (1) Tibet was outstanding in the growth status of the CHP system compared with other regions. It witnessed a slight increase in the growth status from the grade average in 2012 (0.545) to the grade good in 2020 (0.643). Tibet is outstanding in CHP because many of its cultural relic preservation agencies and culture-related activities can protect and enhance its cultural heritage. Moreover, Tibet has continuously devoted much of its budget to museums, cultural institutions, and cultural relic preservation: the expenditure as a percentage of the local GDP was constantly and significantly higher than that of other regions. The discovery that Tibet surpassed other regions in China is novel, as former studies found that Tibet usually lagged in various aspects [52,53]. (2) Other regions remained relatively stable. They mainly fell into the grades Poor and Weak, which proved that the regions had a relatively weak growth status in CHP. For instance, five regions (Tianjin, Liaoning, Jilin, Heilongjiang, and Guangxi) remained in the grade poor (below 0.2) in the observed period, demonstrating their weak status in CHP.

The growth status of the TD system is found in

Table A2. Growth Status of the TD System *.

Area	Region	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean	Grade
Southwest China	Chongqing	0.309	0.333	0.323	0.292	0.283	0.209	0.246	0.307	0.310	0.290	weak
	Sichuan	0.409	0.384	0.333	0.323	0.307	0.225	0.273	0.388	0.417	0.340	weak
	Guizhou	0.129	0.121	0.135	0.150	0.214	0.154	0.198	0.266	0.357	0.192	poor
	Yunnan	0.329	0.351	0.322	0.312	0.299	0.197	0.249	0.339	0.375	0.308	weak
	Tibet	0.147	0.147	0.149	0.140	0.146	0.085	0.095	0.134	0.171	0.135	poor
Northeast China	Liaoning	0.323	0.341	0.340	0.352	0.373	0.262	0.323	0.437	0.328	0.342	weak
	Jilin	0.128	0.185	0.146	0.156	0.139	0.095	0.123	0.172	0.162	0.145	poor
	Heilongjiang	0.236	0.209	0.208	0.201	0.196	0.133	0.147	0.191	0.218	0.193	poor
East China	Shanghai	0.390	0.384	0.376	0.374	0.364	0.264	0.302	0.390	0.392	0.359	weak
	Jiangsu	0.580	0.619	0.620	0.602	0.585	0.575	0.591	0.620	0.570	0.596	average
	Zhejiang	0.484	0.491	0.513	0.496	0.484	0.342	0.414	0.581	0.731	0.504	average
	Anhui	0.299	0.291	0.304	0.313	0.317	0.232	0.272	0.356	0.394	0.309	weak
	Fujian	0.351	0.378	0.395	0.392	0.403	0.303	0.361	0.496	0.387	0.385	weak
	Jiangxi	0.198	0.207	0.207	0.198	0.210	0.157	0.179	0.254	0.333	0.216	weak
	Shandong	0.473	0.492	0.495	0.494	0.483	0.327	0.382	0.477	0.483	0.456	average
North China	Beijing	0.477	0.461	0.416	0.409	0.422	0.283	0.332	0.441	0.398	0.404	average
	Tianjin	0.103	0.091	0.075	0.093	0.115	0.087	0.096	0.100	0.125	0.099	poor
	Hebei	0.231	0.244	0.234	0.235	0.340	0.164	0.192	0.250	0.307	0.244	weak
	Shanxi	0.171	0.174	0.174	0.153	0.159	0.136	0.150	0.187	0.239	0.171	poor
	INR MN	0.178	0.178	0.191	0.168	0.171	0.131	0.146	0.185	0.242	0.177	poor
Central China	Hubei	0.382	0.389	0.430	0.413	0.406	0.294	0.344	0.441	0.455	0.395	weak
	Hunan	0.385	0.379	0.381	0.373	0.388	0.276	0.326	0.429	0.414	0.372	weak
	Henan	0.252	0.246	0.232	0.229	0.255	0.184	0.216	0.280	0.328	0.247	weak
Northwest China	Shaanxi	0.294	0.309	0.318	0.329	0.355	0.264	0.305	0.378	0.341	0.321	weak
	Gansu	0.128	0.136	0.137	0.146	0.165	0.113	0.139	0.189	0.257	0.157	poor
	Qinghai	0.152	0.136	0.133	0.120	0.146	0.083	0.099	0.158	0.147	0.130	poor
	Ningxia	0.146	0.140	0.138	0.131	0.151	0.083	0.100	0.125	0.151	0.130	poor
	Xinjiang	0.164	0.168	0.164	0.156	0.167	0.117	0.142	0.195	0.254	0.170	poor
South China	Guangdong	0.747	0.708	0.709	0.672	0.668	0.531	0.562	0.655	0.540	0.644	good
	Guangxi	0.266	0.260	0.249	0.252	0.264	0.185	0.237	0.320	0.362	0.266	weak
	Hainan	0.215	0.222	0.226	0.224	0.248	0.224	0.247	0.295	0.359	0.251	weak

* Original data sources: China Statistical Yearbook of Cultural Relics and Tourism, China Statistical Yearbook of Cultural Relics, China Tourism Statistical Yearbook, and China Statistical Yearbook. The numbers in the table are obtained after calculation with the methods illustrated in this paper.

, with the temporal changes in Figure 4. Compared with the growth statuses of the CHP system, the statuses of TD were more varied. There are some more detailed findings. (1) There were apparent fluctuations (mostly declines) in 2017, mainly because of the decline of both domestic and international travelers that year. (2) The variations in the TD system among regions were more apparent than in the CHP system. In detail, for the CHP system, regions were more aggregated (with the value 0.1–0.4). In contrast, regions varied in the TD system with values from 0.1 to 0.5. (3) The leading regions of the two systems were different. The leading regions in the TD system were Guangdong and Jiangsu; Tibet, the leading region in the CHP system, was relatively behind in this system (in the grade poor). This finding is consistent with other former studies proposing that the growth statuses of the two different systems of one specific region may not be concurrent [54,55].

5.2. Spatial Analysis of the Growth Status

Figure 5 exhibits the average grades of the growth statuses of the two systems. The spatial distributions of the two systems exhibit distinct differences. (1) For CHP, the spatial distributions exhibit that “the middle areas were higher than those in the north and the south”. More specifically, the Northeast China, Northwest China, and South China regions are relatively lower than other areas in the middle. Such spatial distributions are consistent with the historical development of China: historically, Chinese civilization originated and flourished in Central China, East China, and the surrounding regions; thus, there are usually more cultural heritage sites in these regions than in the northern and southern areas which were relatively less developed in early periods of history. (2) For the TD system, the spatial distributions exhibit that “the eastern regions were higher than the west, and the southern regions were higher than the north”. Specifically, the eastern coastal regions, such as Shandong, Jiangsu, and Zhejiang, and the southern coastal region (Guangdong) were higher in TD than other regions in the west and the north (such as Xinjiang, Qinghai, Ningxia, Gansu, Heilongjiang, Jilin, and Inner Mongolia). This was because the eastern and southern regions usually enjoy better transportation facilities, scenic spots, a higher population and number of potential tourists, and better tourism services. Thus, attracting more tourists and developing local tourism is easier [56,57].

5.3. Temporal Analysis of the Coupling Coordination Status

The temporal dynamics of the coupling coordination status between CHP and TD are in Figure 6, with the detailed statistics in

Table A3. Coupling Coordination Status *.

Area	Region	2012	2013	2014	2015	2016	2017	2018	2019	2020
Southwest China	Chongqing	0.507	0.517	0.514	0.516	0.495	0.447	0.497	0.532	0.497
	Sichuan	0.581	0.591	0.571	0.571	0.554	0.485	0.533	0.583	0.573
	Guizhou	0.401	0.492	0.401	0.403	0.437	0.398	0.468	0.462	0.478
	Yunnan	0.522	0.532	0.538	0.533	0.528	0.441	0.497	0.536	0.529
	Tibet	0.532	0.535	0.537	0.513	0.532	0.473	0.483	0.524	0.576
Northeast China	Liaoning	0.500	0.490	0.492	0.500	0.517	0.435	0.480	0.558	0.461
	Jilin	0.377	0.392	0.399	0.383	0.372	0.322	0.361	0.405	0.388
	Heilongjiang	0.433	0.418	0.434	0.430	0.424	0.368	0.394	0.428	0.422
East China	Shanghai	0.561	0.548	0.551	0.545	0.531	0.464	0.505	0.555	0.518
	Jiangsu	0.631	0.622	0.622	0.623	0.610	0.580	0.612	0.634	0.585
	Zhejiang	0.632	0.623	0.637	0.645	0.642	0.564	0.618	0.680	0.672
	Anhui	0.552	0.544	0.548	0.567	0.566	0.507	0.543	0.570	0.559
	Fujian	0.533	0.526	0.535	0.526	0.525	0.457	0.507	0.558	0.503
	Jiangxi	0.440	0.444	0.455	0.442	0.442	0.394	0.438	0.468	0.490
	Shandong	0.586	0.586	0.614	0.611	0.604	0.523	0.580	0.616	0.600
North China	Beijing	0.649	0.631	0.617	0.636	0.613	0.541	0.567	0.614	0.577
	Tianjin	0.336	0.336	0.314	0.337	0.342	0.305	0.342	0.345	0.354
	Hebei	0.493	0.500	0.496	0.501	0.545	0.425	0.470	0.506	0.509
	Shanxi	0.461	0.464	0.468	0.450	0.457	0.418	0.452	0.484	0.488
	INR MN	0.440	0.431	0.436	0.430	0.424	0.383	0.413	0.442	0.460
Central China	Hubei	0.546	0.535	0.546	0.538	0.532	0.475	0.569	0.557	0.543
	Hunan	0.538	0.539	0.553	0.549	0.541	0.472	0.512	0.613	0.527
	Henan	0.511	0.525	0.514	0.530	0.543	0.485	0.510	0.550	0.543
Northwest China	Shaanxi	0.551	0.556	0.553	0.571	0.575	0.522	0.570	0.595	0.554
	Gansu	0.438	0.445	0.439	0.445	0.457	0.395	0.431	0.474	0.526
	Qinghai	0.409	0.369	0.379	0.391	0.409	0.336	0.391	0.430	0.406
	Ningxia	0.452	0.432	0.481	0.455	0.443	0.377	0.416	0.444	0.440
	Xinjiang	0.465	0.410	0.425	0.420	0.426	0.366	0.400	0.444	0.441
South China	Guangdong	0.636	0.611	0.617	0.605	0.598	0.534	0.572	0.613	0.560
	Guangxi	0.462	0.453	0.457	0.459	0.455	0.390	0.440	0.489	0.479
	Hainan	0.477	0.428	0.430	0.428	0.426	0.432	0.431	0.483	0.455

* Original data sources: China Statistical Yearbook of Cultural Relics and Tourism, China Statistical Yearbook of Cultural Relics, China Tourism Statistical Yearbook, and China Statistical Yearbook. The numbers in the table are obtained after calculation with the methods illustrated in this paper.

. Generally, we can see that the fluctuations in the coupling coordination statuses of the regions were mild and aggregated, and it is rare to find apparent exceptions. There are some detailed findings. (1) Most regions fluctuated mildly in the transition range (transitional coordination and transitional incoordination), proving that these regions kept relatively stable statuses at the average range. This result also indicates that most regions should proactively enhance the coupling coordination statuses between these two systems in China. (2) The coupling coordination statuses of most regions were aggregated within two grades, proving that they face similar situations, though the causes may vary. (3) Certain regions witnessed upgradations or degradations. For instance, Hebei and Gansu upgraded from transitional incoordination to transitional coordination. In contrast, Beijing, Jiangsu, and Guangdong degraded from slight coordination to transitional coordination, and Chongqing degraded from transitional coordination to transitional incoordination. The two regions of upgradation mainly benefited from the increase in the TD system. In contrast, degradation regions faced different situations: the coordination statuses in Jiangsu and Chongqing degraded mainly because of the decline of the CHP system, and Beijing and Guangdong saw the decline of both systems.

5.4. Spatial Analysis of the Coupling Coordination Status

The spatial variations of the coordination status exhibit apparent geographical correlations (Figure 7). Generally, the spatial distributions appear to be “higher in the coastal, central, and southwestern areas”. More specifically: (1) The coastal areas (Jiangsu, Zhejiang, Guangdong, and Shandong) have the relatively highest coupling coordination status (slight coordination), proving their leading roles in the coupling coordination development between CHP and TD. Coastal areas have a higher coupling coordination status because of their economic foundation and population size advantages. They can devote many fiscal resources to infrastructure construction (such as museums, public transportation or facilities, and industry human resource training) and attract more tourists more conveniently. (2) Central China (Henan, Hubei, and Hunan) is also outstanding as a whole (between transitional coordination and slight coordination). However, their advantages vary: Henan is advanced in CHP, especially in the number of art troupes, cultural activities, and art troupes’ performances; Hunan and Hubei are advanced in TD, especially in the number of domestic and international tourists. (3) Southwest China generally has a high coupling coordination status (mainly in the transitional coordination status), and the causes vary for different regions. Specifically, Tibet is advanced in the system of CHP. In contrast, the other regions have relatively balanced development between the two systems. Tibet enjoys outstanding performances in CHP while the tourism system is lagging behind, meaning the system of CHP plays a sufficient leading role in enhancing TD. The apparent differences between systems in the coupling coordination mechanism in Tibet are less well-observed in other studies; previous research found a relatively balanced development among systems in Tibet) [58,59,60]. This new discovery demonstrates that enhancement of the development of specific systems and further, achieving a more benign coupling coordination status among systems is possible.

5.5. Temporal Prediction of the Coupling Coordination Status

The temporal prediction of the coupling coordination status is found in Figure 8, with the exact numbers in

Table A4. Temporal Prediction *.

Area	Region	a	r	p	Year
					2021	2022	2023
Southwest China	Chongqing	0.004	−1.175	0.778	0.494	0.492	0.490
	Sichuan	0.006	1.477	0.556	0.544	0.541	0.538
	Guizhou	−0.010	0.998	0.556	0.463	0.468	0.473
	Yunnan	0.005	−370.854	0.667	0.504	0.502	0.499
	Tibet	−0.002	0.325	0.667	0.525	0.526	0.527
Northeast China	Liaoning	0.000	0.691	0.556	0.491	0.491	0.490
	Jilin	0.004	0.996	0.667	0.371	0.370	0.369
	Heilongjiang	0.005	0.766	0.667	0.406	0.404	0.402
East China	Shanghai	0.009	7.353	0.778	0.507	0.503	0.498
	Jiangsu	0.005	0.749	0.667	0.597	0.594	0.591
	Zhejiang	−0.008	1.093	0.667	0.657	0.662	0.667
	Anhui	−0.002	−0.486	0.556	0.555	0.556	0.557
	Fujian	0.004	0.721	0.778	0.508	0.506	0.504
	Jiangxi	−0.009	0.303	0.667	0.465	0.469	0.474
	Shandong	0.001	2.556	0.667	0.588	0.587	0.586
North China	Beijing	0.014	1.382	0.667	0.564	0.556	0.549
	Tianjin	−0.010	−5.867	0.667	0.349	0.352	0.356
	Hebei	0.002	0.801	0.667	0.489	0.488	0.486
	Shanxi	−0.005	−0.390	0.556	0.472	0.474	0.477
	INR MN	−0.004	1.017	0.667	0.435	0.437	0.438
Central China	Hubei	−0.003	1.541	0.778	0.545	0.547	0.548
	Hunan	−0.001	−0.413	0.667	0.540	0.541	0.541
	Henan	−0.004	0.894	0.556	0.536	0.538	0.540
Northwest China	Shaanxi	−0.003	0.434	0.667	0.570	0.572	0.573
	Gansu	−0.018	−1.220	0.667	0.488	0.497	0.505
	Qinghai	−0.013	0.569	0.667	0.413	0.418	0.424
	Ningxia	0.009	0.583	0.778	0.419	0.416	0.412
	Xinjiang	−0.006	1.357	0.667	0.427	0.430	0.432
South China	Guangdong	0.011	−171.100	0.778	0.560	0.554	0.548
	Guangxi	−0.006	−0.371	0.778	0.465	0.468	0.470
	Hainan	−0.013	1.279	0.778	0.465	0.471	0.477

* Original data sources: China Statistical Yearbook of Cultural Relics and Tourism, China Statistical Yearbook of Cultural Relics, China Tourism Statistical Yearbook, and China Statistical Yearbook. The numbers in the table are obtained after calculation with the methods illustrated in this paper.

. Most regions’ data passed the accuracy test and proved that the prediction results were convincing. Generally, the temporal changes of most regions will remain stable within the same grades. Specifically, 12 regions will undergo a slight decline, whereas 15 regions will see a slight increase within the same grades. This proves that it is possible to maintain the coupling coordination between CHP and TD. However, there are some exceptions. For instance, Shanghai and Yunnan will degrade from the transitional coordination grade to the transitional incoordination grade; Gansu will upgrade from the transitional incoordination grade to the transitional coordination grade. Gansu’s increase proves its continuous devotion to CHP and TD has gradually played a positive role. In contrast, Shanghai and Yunnan’s cases prove that certain regions need further actions to prevent the decline and maintain a satisfying coupling coordination status. This statement is supported by the findings of previous studies [61,62].

5.6. Spatial Prediction of the Coupling Coordination Status

The spatial variation of the future coupling coordination status is found in Figure 9. Generally, we find that the spatial distributions of the coupling coordination status will remain “high in East and Central China”. More specifically, all three regions in Central China and six out of seven regions in East China will be in the transitional and slight coordination grades. This proves their continuous advances in the coupling coordination relations between CHP and TD will be maintained in the coming years. Moreover, Southwest China and the northern part of China (North China, Northeast China, and Northwest China) have lower grades than East and Central China. This demonstrates that the coupling coordination status variation among different parts will gradually be enlarged. The spatial differences between the northern and southern parts, with the northern part being relatively lower, are also observed in other systems, such as in the performance of the education system in China [63,64]. Thus, countermeasures are mandatory to enhance the coupling coordination status in the Southwest and the northern parts of China.

6. Countermeasures

The coupling coordination status shows mild fluctuations and geographical variations. Thus, it is necessary to take differentiated and specific actions to improve the growth and the coupling coordination between the two systems among different regions.

Central and East China, which have high coupling coordination status, should devote more of their resources and budget to achieving better CHP and TD performances. Specifically: (1) explore new cultural heritages, and build more CHP venues equipped with new technologies and convert them into tourism resources; (2) construct new high-quality tourism sites with cultural heritage exhibitions, and encourage more investment into the tourism industry (such as travel agencies, hotels, and transportation), thus enhancing the local tourism business’ attractiveness and convenience; (3) enhance the ratio of fiscal devotion (to CHP and TD) to the local GDP, and achieve a more efficient growth of the two systems.

The regions in Southwest China, where there is variation in the systems lagging in terms of the coupling coordination status, must explore the specific weaknesses and take targeted countermeasures to compensate for the weak systems. Specifically: (1) regions lagging in the TD system should use targeted advertisements and marketing campaigns to attract both domestic and international tourists and enhance the service quality of the local tourism business (especially hotels and restaurants) and thus enhance the tourism revenue; (2) regions lagging in the CHP system should take a targeted and differentiated approach to market the local cultural heritage. A unique, distinctive, and identifiable cultural heritage image is essential to stand out and be remembered.

For the northern part of China (including North, Northeast, and Northwest China), it is necessary to appreciate the importance of the coupling coordination between CHP and TD and achieve a giant leap in both systems. Specifically: (1) initiate cooperation among regions (such as cross-regional exhibitions of cultural heritage, cross-regional cultural activity interactions, and cross-regional joint travel routes) to efficiently increase the performances of both systems; (2) improve the local investment environment and attract more stakeholders to participate in the constructions and operations of the facilities of both systems (such as local museums, art troupes, art performance venues, hotels, agencies, and attractions cites); (3) adopt innovative technologies (such as using digital media and artificial intelligence in CHP, and using visual reality in the tourism industry) to stimulate CHP and TD and to achieve rapid development.

There are also some countermeasures to be taken by the national authorities. It is necessary to fully consider every aspect of the coupling coordination status between the two systems and provide incentive policies. Specifically: (1) identify pilot areas for the enhancement of coupling coordination relations, issue targeted guidance and directional policies, and summarize and promote the excellent replicated experiences from the pilot areas’ practices; (2) issue national programs (such as tax reduction and exemption measures, fiscal subsidies, and capital allowances) to encourage high-quality CHP and TD; (3) strengthen the publicity and news reports surrounding the coupling coordination of the two systems in order to create a positive atmosphere in society that it is honorable to participate in CHP and promote TD.

7. Conclusions

In this study, we constructed the theoretical coupling coordination mechanism and the assessment structure with clear indicators to explore the coordinated interactions between CHP and TD systems theoretically. Moreover, we selected the provincial regions of China to compare the temporal and spatial variations of both systems’ growth statuses and their coupling coordination status. Furthermore, the temporal and spatial predictions of the coupling coordination statuses of the regions provide beneficial insights for different regions to enhance both CHP and TD with coordinated and differentiated approaches.

This study obtains some key conclusions.

(1) Temporally, Tibet was outstanding in the growth status of the CHP system compared with other regions. In contrast, the variations of the TD system were more apparent than those of the CHP system.

(2) Spatially, the distributions of the growth statuses of the two systems show distinct differences. For CHP, the spatial distributions exhibit that “the middle areas were higher than those in the north and the south”. In contrast, for the TD system, the spatial distributions exhibit that “the eastern regions were higher than the west, and the southern regions were higher than the north”.

(3) For the coupling coordination status, the temporal fluctuations of the regions were mild and aggregated, and it is rare to find apparent exceptions. That proves that the regions face similar situations, though the causes may vary.

(4) The spatial distributions of the coupling coordination status exhibit apparent geographical correlations; generally, the spatial distributions show “higher in the coastal, central, and southwestern areas”.

(5) The temporal changes in the coupling coordination status of most regions will remain stable within the same grades. Twelve regions will see a slight decline, whereas 15 regions will undergo a slight increase within the same grades. This proves that it is possible to maintain the coupling coordination between CHP and TD.

(6) The spatial distributions of the coupling coordination status will remain “high in East and Central China”. Countermeasures are mandatory to enhance the coupling coordination status in the Southwest and the northern parts.

This study interconnects with previous studies and results from the following aspects.

(1) The coupling coordination mechanism constructed in this study helps explain the interactions between CHP and TD; cultural heritage protection interacts with tourism development in the dimensions of scale, performance, and efficiency; tourism development interacts with cultural heritage protection in the aspects of scale and performance. This study’s assessment structure measured their interactions precisely. As such, this study resolved the problem presented in the literature review section: “there is no scientific mechanism and indicators to explain their interactions”.

(2) The temporal and spatial analysis of the growth status of CHP and TD systems, the exploration of their coupling coordination status, and the dynamic predictions of the status provide new insights for exploring and understanding the development of CHP and TD in China. These new findings are not found in the previous studies; thus, they provide valuable references for us to evaluate the interactions of the two systems.

(3) Different areas have different coupling coordination statuses; thus, specific actions are needed to enhance the coupling coordination interactions between CHP and TD. Moreover, the national authority should consider every aspect to provide incentive policies. These policies should be based on the study results of this paper so that a more balanced development among areas can be achieved.

This study has presented several novelties.

Firstly, we selected multiple regions to compare temporal and spatial variations of the growth status and the coupling coordination status of CHP and TD. This offsets the weaknesses of previous studies that focused only on individual regions. Moreover, we constructed the coupling coordination mechanism with an assessment structure and indicators, which theoretically explains the interactive correlations between CHP and TD and explores the evaluation approaches. Moreover, we predicted the dynamic changes in the coupling coordination status and provided differentiated suggestions to enhance the interactive coordination performances for regions with different conditions. This contributes to the future coordinated development of cultural heritage protection and the tourism industry.

We also admit to the deficiencies of this study.

Firstly, we did not analyze the influencing factors of both systems due to the word limit. Moreover, we did not compare the coordination differences or variations among more specific regions (such as cities and towns) due to the unavailability of data and the statistical discrepancy. We may promote further studies exploring the main influencing factors and comparing the coordination status of different municipal regions.