Impact of Chinese Heritage, Cultural Protection, and Green Innovation on Tourism Development

Abstract

This study used Chinese data to discover the causal relationship between the cultural and historical preservation and foreign tourism consumption and development. China has increased its cultural and historical protection investments and has made significant efforts in terms of environmental protection after economic growth. Tourism as an industry that develops with local environmental protection while providing economic growth is believed to be highly sustainable and attractive for many provinces to restructure their economic growth in China. This research uses empirical data from 2011 to 2019 and the regression method to show that cultural investment and environmental protection efforts have increased the amount of foreign visitors as well as the destination’s image and reputation. The results show that more cultural tourism resources and larger protection investments lead to greater tourism consumption. The cultural and historical protections have attracted foreign visitors from countries with completely different cultural backgrounds than China, particularly visitors from countries geographically far from China. Furthermore, the local service and hospitality industry grows with the development of tourism, and green innovation policies, which improve the local environment, increase tourism motivation, and develop the local economy by increasing foreign tourism consumption. This study contributes to the literature by connecting regional preservation, tourism development, and green innovation and motivates future policy decisions by demonstrating that the green policy effect stimulates tourism development; such development could alleviate the negative impact of the green innovation process on economic structural changes. Further details of cultural and historical interests from foreign visitors could aid in better understanding the tourism demand and increasing a destination’s reputation.

1. Introduction

Culture shows the history inherited from past generations and civilizations in different regions of the world [1]. Different cultures can reflect regional characteristics, and these characteristics can affect neighboring regions [2]. For example, religion has a great impact and regulates our behaviors. The combination of our philosophies, beliefs, ethics, and values shapes our culture, and it is modified throughout each generation from ancient history to the present. Sometimes, similar cultures in close geographical regions can develop into different branches, and diversification becomes greater over longer periods of time [3]. Different groups of people may also adopt other groups’ habits if they believe that such adoptions improve their utility, and in this way, cultures become convergent. Cultural and historical preservation becomes meaningful since it shows which aspects of the past are similar to the present. A better understanding of the past could help society to further improve in the future. Since the cultures change as time passes by, without appropriate protection and preservation, it becomes difficult to trace back these cultures once history disappears, and the process of civilization development cannot be shown to the later generations [4].

After social development and the increase in wealth, people become curious and want to know about other cultures [5]. It has become human nature to compare and evaluate different cultures. Culture discovery could be a reason for people to make travel decisions, and culture becomes a significant motivation for tourism [6]. Culture could also be included in entertainment and literary creations. For example, a destination could become well known because it was mentioned in famous novels or movies [7]. Visiting such destinations could echo the plots of the novel or movie, which makes the destination a top choice for fans of the novel or movie.

Cultural tourism can increase local cultural recognition and improve local reputation [8,9]. Additionally, understanding culture and cultural recognition could improve cross-regional social collaboration and communication. Mutual understanding and cultural diversity motivate trade and improve across-regional business investments [10]. Cultural recognition could not only improve our living standards but also contribute to social and economic development since culture has externalities. People from different regions can learn from each other, and the combined culture, which retains all the advantages, has greater productivity, which shows social improvement.

In this study, we chose China as our study subject. The major reason we chose China is that it has abundant cultural tourism attractions, including temples, museums, and historical sites [11]. The expenditure to maintain these cultural sites would require a large investment. Second, China is a large country, and geographical distance could lead to greater culture diversification [12]. With different levels of cultural protection and tourism resources, we explore tourism incentives and recent Chinese environmental protection policies have allowed us to study policy effects from the perspective of tourism.

Our study contributes to the literature in the following ways: First, the previous literature focused on tourism, income, and economic development, and we further explore the effects of tourism development on cultural recognition. Further, such cultural recognition is analyzed using different tourism groups, visitors with similar cultural background, and visitors with completely different backgrounds to understand the level of attraction; Second, much of the existing literature has emphasized the environment and tourism relations, and we extend it to the effects of industrial and green policy on tourism development and tourism consumption. Our study results offer policy decisions and industrial development value. The higher preservation investment of local historical sites and culture would increase tourism motivation and further develop the local tourism market.

2. Literature Review and Hypotheses

2.1. Culture and Humanity or Nature and Landscape

When consumers make a travel decision, they must have a destination in mind that attracts them [13]. Most travelers consider travel safety and local attractions at the same time [14]. Some visitors who love nature tend to participate in mountain climbing, skiing in winter, surfing in the sea in summer, laying in the sunshine on the beach, or participating in other activities that are gifted by nature [15]. Some counties are surrounded by many natural views, and they can build resorts around these views, which allow visitors to enjoy special nature activities and provide accommodation and food services [16,17]. These resorts could significantly boost the local economy. Some of these nature-gifted tourism attractions, such as snow-clade slopes for skiing and beaches, could be seasonal [18]. Serving many tourists within a short period could be challenging, and the large number of visitors could cause other problems, such as environmental pollution, and destroy the local ecological systems [19,20]. There are tourists who avoid the peak season, and families with children may choose the nonpeak season [21]. The local government should invest well in environmental protection and ensure that tourism development is sustainable.

Not all tourists aim to enjoy nature; some tourists choose to experience exotic culture, which includes architecture, food, religion, history, and other local customs [22,23]. City tours could be a good approach to understanding what locals do and how they behave differently than other cultures [24,25]. Architecture, food, religion, and history are all closely related. Countries with long histories abound with a large number of historical buildings, different religions, and diverse foods. Countries with larger territories tend to be more attractive since different regions may have different cultures; thus, their histories, buildings, religions, and foods may have unique characteristics worthy of discovering [26]. For example, our study focuses on China which is home to the historical city of Beijing, the famous Forbidden City, the Great Wall, and many other temples that attract millions of visitors every year. China is a nation with many different cultures. Beijing, being the old capital, is where the Han, Muslim, Mongolian, and Manchus cultures mix. One good evidence of this cultural mixing comes from food. Many traditional Beijing restaurants are Halal restaurants offering lamb and beef, since Muslims eat only Halal food, and most Mongolian and Manchus love lamb and beef, which is very close to Muslim food if the food is Zabiha-treated. The food choice is very different if the visitors visit the old southern capital city of Nanjing, which is dominated by Han culture. These differences offer visitors unique experiences and arouse their curiosity to explore further.

Compared to natural attractions, culture-related attractions may require a greater level of protection, especially if the number of visitors is large. The buildings, temples, and museums all need investments to protect them, and relying only on admission ticket income may be insufficient. The government usually owns these sites, and they should plan a special budget or attract commercial investment for historical site protection purposes [27]. Figure 1 and Figure 2 show the increase in cultural and historical site protection investment after the year 2011 and the growing number of historical sites and museums for tourists. Such initiatives could attract many city visitors, and their local tourism consumption could significantly boost the local economy. We propose the first hypothesis on the basis of the above cultural protection and preservation points.

Hypothesis 1a (H1a).

Higher levels of cultural protection expenditures by the local government result in high levels of tourism consumption.

Hypothesis 1b (H1b).

A higher level of cultural tourism attraction leads to greater tourism consumption.

2.2. Asian and Non-Asian Visitors

When local governments consider using tourism as one of their local economic growth strategies, they should consider the marketing target. Visitors can be strongly culturally motivated when they decide on which destinations to visit [28]. Cultural similarities may reduce city visit attractiveness since visitors already know the local culture, so there is nothing that surprises them. Usually, cultural similarities occur between neighboring countries and cultures [29]. Using China as an example again, most East Asian countries have similar cultures. For example, South Korea and Japan both share similar religions and histories, and their food is also similar. Southeast Asia has a large Chinese population; thus, Chinese culture has merged with the local culture and so visitors may find the culture less attractive if they visit China. People from Europe and America have completely different feelings about culture. Geographical distance could be a significant measure of cultural curiosity and cultural motivation, and cities with larger historical sites are preferred by visitors from great distances [30,31]. The travel cost could be another reason that the visitors would want a fuller picture of the local culture since the visitors could better learn and understand the local society, value, and other characteristics through local cultures [32]. For example, local food is well connected with the cultural anthropology [33,34], which could be a pull factor in the push and pull theory [35] and is highly attractive for visitors with completely different cultures. However, due to the similarities between neighboring countries, China becomes less attractive for other Asian countries. Noteworthily, destination management, particularly local government promotions and showing the local tourism features to potential foreign visitors, is important [36,37]. The current development of social media increases the information transmission speed, and greater local cultural protection efforts increase the interest of potential visitors if they have cultural tourism motivations. We propose the following hypothesis concerning cultural motivation for tourism in the Chinese market.

Hypothesis 2 (H2). 

High cultural protection by the local government enhances the tourism motivation of non-Asian visitors if local tourism resources are abundant.

2.3. Tourism and Local Development

The growth of the tourism industry could lead to local economic growth and be accompanied by other related industries [38]. For example, hospitality, transportation, and small manufacturing industries could grow with an increase in the number of local tourism visitors. Transport is associated with tourism growth and is required for travelers to travel to destinations [39]. This could be particularly challenging if tourism is seasonal, which means that there are only a few months available in the year and that there are extra requirements for the vehicles and drivers for the sudden growth of the number of passengers in the peak season. In many emerging societies, roads and transportation could be the key factor of community tourism [40], which is a key economic solution for many poor villages [41]. In the Chinese market, the central and local governments have spent large resources on building provincial- and city-level infrastructure, which benefit the visiting tourists. The infrastructure includes highways, high-speed trains, and city airports, and the level of services could directly affect the destination reputation [42]. The improvements in these infrastructures increase the accessibility of a tourism site far from the provincial capital and increase the tourism travel satisfaction [43]. The hotels and accommodation also need to match the number of tourists in the peak season and ensure that the rooms match the different needs of the visitors [44]. Small manufacturers and shops may have a good chance of developing since visitors always enjoy buying small gifts and products that can represent local cultural elements. Using the number of four- and three-star hotels that target more tourism as indicators of the tourism-related hospitality industry, we propose the following hypothesis.

Hypothesis 3 (H3). 

A greater number of developed tourism sites in a province leads to a greater number of tourism-targeted hotels.

2.4. Policy, Environmental Protection, and Tourism Growth

The environment and tourism policies could be key elements for potential visitors to make designation decisions [45,46]. Environmental policy is always tourism-related since no individual wants to visit a place with high pollution. The cost of environmental protection may be greater for the manufacturing industry, even if it helps to develop and attract more visitors. When the economy considers changing from second-tier manufacturing to third-tier manufacturing and service-focused businesses, the local government would have a high incentive to make policies to change the composition of the local economy. Such a switch from manufacturing to tourism could increase the sustainability of the local economy and improve the local environment and quality of life [47]. Tourism visits also depend on how easily foreign visitors can visit the destination country. Less paperwork and documentary requirements are more tourism friendly and increase the number of visitors [48]. Using China as an example, the Chinese government emphasized green innovation to combat pollution issues, particularly the negative image of northern China’s air pollution issues [49]. The Chinese government started green bonds and finance in 2015, and green bonds show a significant “green premium”, which means that if a firm is green-qualified, its borrowing cost decreases. Such green innovation could significantly help the local environment and increase the incentive of potential travelers, particularly in northern Chinese cities that have more historical cultural sites than southern cities do [50,51]. Government policy guidance and support would be important toward tourism development [52,53,54]. The tourism industry differs from most manufacturing industries, and pollution control could be a big problem if the local government wants to balance the growth of the industry and environment, but the tourism industry develops simultaneously with the improvement in environmental qualities [55]. Environmental sustainability would contribute to tourism development and increase the local income in a longer term [56]. Figure 3 below shows that the significant improvement in environmental protection stimulates economic growth in the Chinese market after implementing the green policy in 2011 and its significant impact after the green finance recognition and policy revisions in 2015.

3. Data and Methodologies

3.1. Data

This study focuses on foreign tourists visiting China and Chinese provincial-level culture-related historical tourism resources. The data are collected from the Chinese Yearly Statistics and the China Stock Market and Accounting Research Database (CMSAR). The sample periods cover 2011 to 2019. The year 2015 is excluded since many provinces did not report their statistics in that year. Since the development of the Chinese tourism market is stable, the smooth growth of tourism is unlikely to have a large impact on our study. The general statistics of the variables are shown in

Table 1. Descriptive statistics.

Statistic	N	Mean	St. Dev.	Min	Max
Foreignexpday	247	199.876	30.176	151.630	309.660
Fiscalw	248	0.447	0.121	0.230	0.860
Ticketr	244	15.754	8.825	1	31
Fivestar	246	24.150	24.300	0	107
Fourstar	248	75.351	42.891	10	188
Threestar	248	161.617	98.161	20	567
Five	248	6.661	4.108	0	24
Four	248	87.907	56.070	6	269
Three	248	120.698	102.847	7	657
Two	248	65.500	64.152	0	396
One	248	3.645	5.867	0	28
Changeair	248	−14.585	43.008	−300.101	71.580
High	248	0.323	0.468	0	1
Post	248	0.500	0.501	0	1
Rental	248	54.759	6.541	39.570	76.850
Tincome	247	120.806	129.097	2.040	708.880
noasia	186	62.481	16.033	27.749	100.000
Asia	185	37.722	15.835	1.314	72.251

.

Table 2. Variable definitions.

Variable	Symbol	Variable Treatment
Average cost of the foreign tourist per day in different provinces	Cost	From database
Cost to maintain the cultural historical site and other related museums	Fiscalw	Cost of maintenance/total local fiscal expenditure
The ranking of ticket income from historical sites	Ticketr	The historical site ticket income, provincial ranking in year. 1 indicates highest rank and 31 indicates lowest rank.
The ranking of the Fiscalw	InvFiscalr	The provincial ranking in year of the cultural historical site and museum preservation costs, but the top is assigned equal to 31 (larger number for high investment provinces) and bottom is assigned 1
Number of five “A” to “A” ranked tourism sites, provincial level information	Sites: Five, Four, Three, Two, One	From database
The most visited tourist attractions	Sight	Sum of five A and four A tourism attractions in the province
Air quality indicator	Air	The second year Sulfur Dioxide—the first year Sulfur Dioxide/total sector two output in the province in Yuan
Number of different hotels in each province	Hotels: Fivestar, Four-star, Three-star	From database
Tourism aimed hotels	tHotel	Sum of four- and three-star hotels
Number of Asian tourists	Asia	From database
Number of non-Asian tourists	noAsia	From database
High culture attraction regions	High	The top 10 provinces ranked by number of historical sites
Post green policy	Post	Dummy variable, if year is after 2015, it equals 1
Direct tourism income	tincome	Tourism attraction ticket income
The hotel rental rate	rental	The hotel rental rate in different provinces

shows the variable definitions and their treatments.

3.2. Methodology

Equations (1) and (2) are used to test Hypothesis H1. In Equations (1) and (2), the foreign tourism cost is regressed on the interest variables, namely, preservation cost as a percentage of the provincial-level fiscal income and the provincial-level income of the historical sites. There are 31 provinces and direct-controlled municipalities excluding Taiwan, Hongkong, and Macau. The coefficients of the interest variables are expected to have positive coefficients that indicate the higher preservation and popularity of the province in terms of culture and history would increase the tourism consumption for that province.

$${Cost}_{i,t}={\beta }_0+{\beta }_1{Fiscalw}_{i,t}+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_4{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(1)

$${Cost}_{i,t}={\beta }_0+{\beta }_1{Ticketr}_{i,t}+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_4{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(2)

The term “Fiscalw” is the weight of the historical site maintenance expenditure among the local provincial fiscal expenditures and is the interest variable. Higher expenses are expected to attract more foreign tourists and increase tourism consumption.

The term “Site” is a set of tourism attractions that includes five variables, from five A tourism attractions to one A tourism attraction, reflecting the Chinese office level of tourism recognition; a higher level indicates greater tourism popularity. The term “Hotel” reflects the collection of three variables, from five-star to three-star hotels, which reflect the hospitality ability of local tourism. Regional control is clustered by using official Chinese classifications, and all provinces are classified into six different regions.

Similarly, the term “Ticketr” is the ticket of the historical site income rank among all provinces. Even if such a ranking considers both tickets paid by Chinese and foreign visitors, a higher number of tickets could still successfully measure the level of popularity of the provinces as tourism destinations or tourism motivation places. Notably, the variable is expected to be negatively associated with the dependent variable since a negative value indicates a smaller reduction in the dependent variable since a value of 1 indicates the highest ticket revenue. The negative coefficient multiplied with a small number (e.g., 1) indicates a small negative number for the tourism consumption, but the negative coefficient multiplied with 31 is larger negative number, which is even lower.

The second set of tests aims to test Hypothesis H2. Equations (3) and (4) reflect what attracts Asian and non-Asian visitors. The expectation is that provinces with more historical sites would attract more non-Asian visitors since the distance would make their cultural tourism motivation stronger. Thus, the coefficient of the preservation cost is expected to be positive for non-Asian visitors in Equation (3), and the coefficient of the preservation cost is expected to be negative or insignificant for Asian visitors in Equation (4) as they could have similar cultural experiences.

$${noAsia}_{i,t}={\beta }_0+{\beta }_1{Fiscalw}_{i,t}+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_2{Air}_{i,t}+{\beta }_3[{Fiscalw}_{i,t}\times {Five}_{i,t}]+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(3)

$${Asia}_{i,t}={\beta }_0+{\beta }_1{Fiscalw}_{i,t}+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_2{Air}_{i,t}+{\beta }_3[{Fiscalw}_{i,t}\times {Five}_{i,t}]+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(4)

The third set of tests aims to test Hypothesis H3 and the associated development of local tourism sites and tourism-related markets. Equations (5) and (6) reflect such associated relationships. Both the sum of five and four A tourism attractions and the variable High, which indicates the top ten historical site provinces, are expected to be associated with greater hotel growth. A greater tourism demand provides opportunities for the hospitality industry. The two interest variables in Equations (5) and (6) are the number of highly attractive tourism sights “Sight” and the high cultural attraction provinces “High”. They are expected to have positive coefficients which indicate that the province with higher tourism resources or highly recognized cultural provinces would have better hotel industry development.

$${tHotel}_{i,t}={\beta }_0+{\beta }_1{Sight}_{i,t}+{\beta }_2{Fivestar}_{i,t}+{{\beta }_3{three}_{i,t}+{\beta }_4{two}_{i,t}+{\beta }_5{one}_{i,t}+\beta }_6{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(5)

$${tHotel}_{i,t}={\beta }_0+{\beta }_1{High}_{i,t}+{\beta }_2{Fivestar}_{i,t}+{{\beta }_3{three}_{i,t}+{\beta }_4{two}_{i,t}+{\beta }_5{one}_{i,t}+\beta }_6{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(6)

The Chinese government has attempted to control pollution and change the industrial structure for prompting green innovation that can lead to the development of the tourism industry. Equations (7) and (8) use the difference-in-differences test to assess the effect of a policy on tourism income. Tourism attraction ticket income is used as an indicator of tourism income. The interesting variables are the treatment and post-green policy interactive terms that indicate the policy effect in difference-in-difference models. It is expected that the policy effect term has positive coefficients in both Equations (7) and (8).

$${rental}_{i,t}={\beta }_0+{\beta }_1{High}_{i,t}+{\beta }_2{post}_{i,t}+{\beta }_3[{High}_{i,t}\times {Post}_{i,t}]+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_4{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(7)

$${tincome}_{i,t}={\beta }_0+{\beta }_1{High}_{i,t}+{\beta }_2{post}_{i,t}+{\beta }_3[{High}_{i,t}\times {Post}_{i,t}]+{\beta }_a{Site}_{i,t}+{{\beta }_b{Hotel}_{i,t}+\beta }_4{Air}_{i,t}+\sum Region+\sum Year+{\epsilon }_{i,t}$$

(8)

4. Results

4.1. Cultural Protection

Table 3. Culture protection and foreign tourism consumption.

	Dependent Variable:
	Cost
	(1)	(2)	(3)
Fiscalw	52.483 ***	40.324 ***	34.465 **
	(14.664)	(14.657)	(13.847)
Fivestar	0.548 ***	0.554 ***	0.527 ***
	(0.113)	(0.111)	(0.102)
Fourstar	0.090	0.084	−0.225 ***
	(0.075)	(0.074)	(0.080)
Threestar	−0.096 ***	−0.064 **	0.025
	(0.024)	(0.025)	(0.026)
Five	0.285	−0.134	0.811
	(0.618)	(0.610)	(0.608)
Four	−0.147 **	−0.170 ***	−0.195 ***
	(0.057)	(0.056)	(0.053)
Three	0.134 ***	0.085 ***	0.099 ***
	(0.025)	(0.027)	(0.026)
Two	0.010	0.056	−0.022
	(0.034)	(0.035)	(0.036)
One	0.493 *	0.423	0.789 **
	(0.268)	(0.262)	(0.319)
Air	0.070 *	0.071	0.049
	(0.036)	(0.046)	(0.042)
Constant	165.452 ***	164.822 ***	161.601 ***
	(7.489)	(7.875)	(8.507)
Region Control	N	N	Y
Year Control	N	Y	Y
Observations	245	245	245
R2	0.441	0.487	0.594
Adjusted R2	0.418	0.449	0.554
Residual Std. Error	23.050 (df = 234)	22.430 (df = 227)	20.173 (df = 222)
F Statistic	18.496 *** (df = 10; 234)	12.674 *** (df = 17; 227)	14.773 *** (df = 22; 222)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

and

Table 4. Cultural attraction resources and foreign tourism consumption.

	Dependent Variable:
	Cost
	(1)	(2)	(3)
Ticketr	−0.298	−0.349 *	−0.479 ***
	(0.185)	(0.179)	(0.172)
Fivestar	0.705 ***	0.660 ***	0.601 ***
	(0.105)	(0.102)	(0.093)
Fourstar	0.077	0.076	−0.238 ***
	(0.077)	(0.074)	(0.080)
Threestar	−0.122 ***	−0.082 ***	0.010
	(0.024)	(0.025)	(0.027)
Five	0.278	−0.192	0.554
	(0.627)	(0.614)	(0.611)
Four	−0.159 ***	−0.185 ***	−0.198 ***
	(0.058)	(0.057)	(0.053)
Three	0.141 ***	0.085 ***	0.090 ***
	(0.025)	(0.027)	(0.026)
Two	−0.009	0.044	−0.033
	(0.035)	(0.035)	(0.036)
One	0.487 *	0.404	0.924 ***
	(0.273)	(0.264)	(0.313)
Air	0.070 *	0.063	0.041
	(0.037)	(0.047)	(0.042)
Constant	197.188 ***	190.568 ***	184.241 ***
	(5.603)	(6.493)	(8.321)
Region Control	N	N	Y
Year Control	N	Y	Y
Observations	241	241	241
R2	0.414	0.471	0.587
Adjusted R2	0.388	0.431	0.545
Residual Std. Error	23.399 (df = 230)	22.560 (df = 223)	20.171 (df = 218)
F Statistic	16.216 *** (df = 10; 230)	11.699 *** (df = 17; 223)	14.078 *** (df = 22; 218)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

show the results of the first set of hypotheses. In Table 3, maintenance expenses have significantly positive coefficients. This finding indicates that greater historical cultural protection at tourism sites could increase tourism consumption by foreign tourists. The travel motivation of foreign tourists would be greater if the local cultural protection made the city famous, with a good reputation. Tourism motivation can increase tourism experience expectations and increase the willingness of tourists to spend more money and time when they travel to a province.

In Table 4, the coefficients are negative, as expected. A higher rank would have a lower negative impact on the cost of foreign tourism. A lower rank toward 31 would lower tourism consumption. Tourists prefer to visit places with more tourist attractions, so they have a greater chance of wholistically understanding the local culture.

4.2. Visitors’ Originality

Table 5. Visitors’ home country and culture.

	Dependent Variable:
	Non-Asian	Asian
	(1)	(2)
Fiscalw	−16.545	16.939
	(22.019)	(21.412)
Fivestar	−2.621 *	2.344 *
	(1.340)	(1.306)
Fourstar	0.099	−0.122
	(0.090)	(0.087)
Threestar	−0.178 **	0.203 ***
	(0.070)	(0.068)
Five	0.015	−0.021
	(0.022)	(0.022)
Four	0.047	−0.015
	(0.054)	(0.054)
Three	−0.061 **	0.068 **
	(0.029)	(0.028)
Two	0.061 *	−0.078 **
	(0.035)	(0.034)
One	0.879 ***	−0.824 ***
	(0.267)	(0.261)
Air	−0.012	0.013
	(0.031)	(0.030)
Fiscalw*Five	4.887 *	−4.995 **
	(2.575)	(2.504)
Constant	50.980 ***	47.297 ***
	(10.554)	(10.277)
Region control	Y	Y
Year control	Y	Y
Observations	186	185
R2	0.274	0.297
Adjusted R2	0.181	0.206
Residual Std. Error	14.508 (df = 164)	14.108 (df = 163)
F Statistic	2.950 *** (df = 21; 164)	3.277 *** (df = 21; 163)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

shows the results of the cultural attraction of Asian and non-Asian visitors. The interest is at the intersection between the historical protection expense and the number of five A tourism attractions. A higher protection level and more abundant resources indicate a higher level of cultural attraction. The results show that the coefficient of non-Asian visitors is significantly positive and that the coefficient of Asian visitors is significantly negative. This heterogeneity confirms our original hypothesis that distance strengthens cultural tourism motivation.

4.3. Related Industries and Their Growth

Table 6. Tourism and related industry.

	Dependent Variable:
	Thotel
	(1)	(2)
Sight	0.816 ***
	(0.167)
High		31.553 **
		(15.638)
Fivestar	2.883 ***	3.143 ***
	(0.258)	(0.265)
Three	0.121	0.313 ***
	(0.098)	(0.092)
Two	0.293 **	0.443 ***
	(0.133)	(0.136)
One	1.670	0.508
	(1.207)	(1.242)
Air	0.063	0.043
	(0.164)	(0.172)
Constant	85.682 ***	112.160 ***
	(27.826)	(29.276)
Region Control	Y	Y
Year Control	Y	Y
Observations	246	246
R2	0.675	0.647
Adjusted R2	0.649	0.619
Residual Std. Error (df = 227)	78.959	82.249
F Statistic (df = 18; 227)	26.144 ***	23.106 ***

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

shows the results of Hypothesis H3. The results show that tourism hotels, which are the sum of four- and three-star hotels, grow significantly if the province has a high number of five A and four A tourism attractions or if the province has a greater number of cultural attractions. These results confirm our hypothesis that the tourism-related industry is growing with the tourism industry. Local economics could largely benefit if tourism consumption and the number of visitors increase. These results also encourage an environmental protection policy since such a policy would lower the growth of second-tier industries, usually manufacturing, which could result in high levels of pollution, and the protection policy would be costly. Environmental protection would result in the growth of the tourism industry and further the economy, and such growth could compensate for costly protection policies.

4.4. Green Innovation Policy Effect

Table 7. Green innovation policy effect on tourism.

	Dependent Variable:
	Trental	Tincome
	(1)	(2)
High	0.941	−0.285
	(1.355)	(23.173)
Post	−4.338 ***	−37.226 **
	(1.061)	(18.190)
High*Post	0.613	78.339 ***
	(1.754)	(30.024)
Fivestar	0.081 ***	−1.541 ***
	(0.025)	(0.428)
Fourstar	−0.010	1.535 ***
	(0.019)	(0.322)
Threestar	0.006	−0.220 **
	(0.006)	(0.108)
Four	−0.002	0.939 ***
	(0.014)	(0.241)
Three	0.012 *	0.177
	(0.007)	(0.114)
Two	−0.026 ***	−0.459 ***
	(0.008)	(0.145)
One	−0.188 ***	−0.263
	(0.066)	(1.135)
Air	0.016 *	0.121
	(0.009)	(0.159)
Constant	55.864 ***	12.305
	(1.023)	(17.720)
Observations	246	245
R2	0.265	0.455
Adjusted R2	0.230	0.430
Residual Std. Error	5.707 (df = 234)	97.608 (df = 233)
F Statistic	7.667 *** (df = 11; 234)	17.708 *** (df = 11; 233)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

shows the results of our last hypothesis: the effect of green innovation policy on tourism. The treatment group includes the top ten most culturally abundant provinces. After green innovation started in 2016, the top ten most culturally abundant provinces had significantly higher tourism incomes. Even though the hotel rental rate does not show significant growth, the coefficient is positive. Since hotel rentals may be affected by businesses and visitors not associated with tourism, this may have affected the results.

4.5. Endogeneity and Robustness

For endogeneity concerns and to show the robustness of the results, the instrumental variables are used in the 2SLS tests.

Table 8. The 2SLS instrumental test of the baseline model.

	Dependent Variable:
	Fiscalw	Foreignexpday
	OLS	Instrumental Variable
	(1)	(2)	(3)	(4)
invfiscalr	0.012 ***
	(0.0003)
fiscalw		35.936 **	32.123 **	29.692 **
		(15.946)	(15.670)	(14.837)
fivestar	0.00004	0.603 ***	0.580 ***	0.542 ***
	(0.0002)	(0.115)	(0.112)	(0.103)
fourstar	0.0002	0.088	0.083	−0.227 ***
	(0.0001)	(0.076)	(0.074)	(0.080)
threestar	−0.0001	−0.102 ***	−0.066 ***	0.024
	(0.00005)	(0.025)	(0.025)	(0.027)
five	−0.001	0.305	−0.135	0.804
	(0.001)	(0.619)	(0.611)	(0.608)
four	0.0001	−0.147 **	−0.170 ***	−0.194 ***
	(0.0001)	(0.057)	(0.056)	(0.053)
three	0.00003	0.135 ***	0.084 ***	0.099 ***
	(0.00004)	(0.025)	(0.027)	(0.026)
two	−0.0001	0.006	0.055	−0.023
	(0.0001)	(0.034)	(0.035)	(0.036)
one	0.001 **	0.516 *	0.432	0.811 **
	(0.001)	(0.269)	(0.262)	(0.320)
changeair	0.0001	0.072 **	0.071	0.049
	(0.0001)	(0.036)	(0.046)	(0.042)
constant	0.187 ***	172.738 ***	168.135 ***	163.091 ***
	(0.013)	(7.985)	(8.189)	(8.670)
region control	Y	N	N	Y
year control	Y	N	Y	Y
observations	246	245	245	245
R2	0.924	0.438	0.486	0.594
adjusted R2	0.916	0.414	0.448	0.554
residual std. error	0.035 (df = 223)	23.113 (df = 234)	22.445 (df = 227)	20.178 (df = 222)
F statistic	123.057 *** (df = 22; 223)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

shows the instrumental results of the baseline model. The ranking of the preservation cost as a percentage of the local provincial fiscal income is used as the instrumental variable. Such a ranking directly reflects the preservation costs, but it is not directly related to tourism consumption; as it is difficult for the tourists to know the preservation investments of different provinces, the preservation investment ranking becomes a qualified instrumental variable. The high ranking would ideally have a smaller number, and low ranking would ideally have a higher number, but we use the inverse way by assigning the largest number to the top-ranking province and smallest number to the bottom-ranking province. Thus, it is expected that a larger ranking number is associated with higher preservation investments. The column (1) in Table 8 shows the qualification of the instrumental variable and the positive relationship between the inversed ranking and the preservation costs. The 2SLS results from columns (2) to (4) show similar results to the baseline model in Table 3, which alleviates the endogeneity concerns. In

Table 9. The 2SLS instrumental test of the visitors’ original country.

	Dependent Variable:
	Fiscalw	Notasia	Asia
	OLS	Instrumental Variable	Instrumental Variable
	(1)	(2)	(3)
invfiscalr	0.012 ***
	(0.0003)
fiscalw		−22.410	21.125
		(25.532)	(24.820)
fivestar	0.00004	0.105	−0.126
	(0.0002)	(0.091)	(0.088)
fourstar	0.0002	−0.181 **	0.205 ***
	(0.0001)	(0.070)	(0.069)
threestar	−0.0001	0.015	−0.021
	(0.00005)	(0.022)	(0.022)
five	−0.001	−2.898 *	2.542 *
	(0.001)	(1.472)	(1.434)
four	0.0001	0.046	−0.014
	(0.0001)	(0.054)	(0.054)
three	0.00003	−0.062 **	0.069 **
	(0.00004)	(0.029)	(0.028)
two	−0.0001	0.062 *	−0.079 **
	(0.0001)	(0.035)	(0.035)
one	0.001 **	0.883 ***	−0.827 ***
	(0.001)	(0.268)	(0.261)
changeair	0.0001	−0.011	0.012
	(0.0001)	(0.031)	(0.030)
Fiscalw*five		5.469 *	−5.411 *
		(2.877)	(2.797)
constant	0.187 ***	53.275 ***	45.658 ***
	(0.013)	(11.704)	(11.392)
region control	Y	Y	Y
year control	Y	Y	Y
observations	246	186	185
R2	0.924	0.274	0.297
adjusted R2	0.916	0.181	0.206
Residual std. error	0.035 (df = 223)	14.511 (df = 164)	14.109 (df = 163)
F statistic	123.057 *** (df = 22; 223)

Note: ***, **, and * denote statistical significance at 1 %, 5 %, and 10 %; standard errors are shown in pa-rentheses.

, the first column is the same as that in Table 8, and columns (2) and (3) show similar results to those in Table 5; more top-ranked preservation provinces with more five A tourism sites would attract more non-Asian tourists, but not Asian visitors, showing that the distance and cultural similarity could be a significant factor for cultural tourism motivation.

4.6. Discussion

The cultural impact on tourism destination motivation is significant for tourists from non-Asian countries, and tourists from Asian countries may visit only specific destinations in China. One reason is that the distance is short, and Asian tourists can discover China on multiple trips. There is no need to focus on visiting all culturally representative historical sites at once, but they can focus on visiting only one region and then visiting another region during the next trip. Visitors from countries far away may want to visit at least the most representative historical sites, and they believe that the more places they can visit, the more worthwhile their trip will be.

A green innovation policy can help local provinces develop their tourism market. The regions with the most pollution, such as northeast China, have high steel production and coal usage. Green innovation can cause large economic structural changes and may increase structural unemployment, and local tourism development could provide the people of these regions a chance to switch to working in tourism and related industries to address the local unemployment problems. Since the tourism industry is more sustainable and associated with the growth of related industries, the local economy could avoid high volatility by adjusting the proportion of high-pollution-causing industries.

The above empirical results demonstrate the higher preservation cost on cultural and historical sites and historical cultural popularity could increase tourism consumption. The cultural and historical site effect is enhanced by the abundance of other high-quality tourism resources to attract non-Asian visitors, indicating that distance traveled by the visitors for seeing cultural and history attractions could increase tourism consumption. A high level of tourism resources could result in the accommodation and hotel industry simultaneously developing with the tourism industry. The green innovation and green policy effect improve the tourism income and motivate the local government to increase the level of green development, which could further benefit the local tourism growth. The findings of this study could significantly contribute to the local policy and motivate further green development. As mentioned earlier, local firms and government may hesitate to make changes. One of the concerns of the government is the creation of structured unemployment during the green innovation, but tourism development could resolve this issue by creating jobs while promoting the green economy.

5. Conclusions

In this study, we have shown that cultural attraction could develop local tourism in the Chinese market. The higher the local cultural protection expense of historical sites, the greater the number of cultural resources that could increase foreign tourism consumption. Culture increases the tourism motivation for non-Asian visitors, since a larger geographical distance traveled by the visitors could increase the value of the cultural experience and the cultural curiosity of the visitors, and a smaller geographical distance lowers the degree of cultural curiosity of Asian visitors. One of the reasons for this is that a short travel distance allows Asian visitors to discover China through multiple visits; thus, they can explore one region during each visit rather than visiting all of China during one visit. Furthermore, industries related to tourism grow with tourism development and help the local economy. The green innovation policy has a positive effect on local tourism growth. From the local government aspect, the development of local tourism by the preservation of local culture and history could further increase their confidence in environmental management and green innovation. Moreover, the risk of structured unemployment becomes lower as employment opportunities are provided by the tourism service sector. The higher level of green innovation could also improve the future economic competitiveness and social sustainability.

The limitation of this study is that we have only conducted a macro-level analysis as obtaining the micro-level data is difficult. A more detailed survey may help to understand the specific interests of foreign visitors pertaining to destination culture and history. Sometimes, it is important to provide tailored services that maintain some of the traditions but are modified in order to meet the expectations of foreign visitors, thus providing them a taste of different cultures and values. Such an understanding of tourists’ demands could significantly increase a destination’s reputation.

This study focuses on culture and tourism, and the Chinese cultural tourism market can be explored further. If a Chinese cook is famous and is attracting more visitors, a systematic study to assess food attraction would not be valid. There are eight major cuisines in China, and each region has its own cuisine. In terms of analyzing the push–pull factors of destination choice, visitors may visit completely different regions with diverse cultures for curiosity; however, if an opposite viewpoint is considered, visitors may choose to visit a familiar region to minimize surprise. This is also applicable to food curiosity as part of social culture and history, and uncertainty about cuisine as a concept is worth exploring further.