Research of the Effect of Tourism Economic Contact on the Efficiency of the Tourism Industry

Round 1

Reviewer 1 Report

The article it is interesting for tourism area  and concentrated on statistical topic, but he needs  improvements:

Abstract: at this stage it is concentrated only on variables,  it is necessary to present also research method and results in a new shape.

Keywords: nothing about tourism where the article was send

3. Research Design

Line 140 :  3.1. Study Areas

Line 147  ’’The tourism resources of the 20 cities in the West Coast of the Strait 148 urban agglomeration are extremely rich and highly complementary, and it is one of the most mature 149 regions for tourism development in China’’.

We know that the minimum target in statistic it is 30,  so maybe will be better for readers to mention why the authors select only 20 or what it is behind that 20 from target group.

Line 186:  2.3.1. Samples and Data

’’This study selected 2008 as the starting point for data collection and established a tourism-187 related database for the West Coast of the Strait urban agglomeration between 2008 and 2017.’’

Why they choose 2008-2017 and establish the model  and  to apply it in recent years  

or  they have the results from that period and it is only a theoretical application ?

Line 198  2.3.2. Definition of Variables

• Explanatory variable: the.....
•  

Would be good  a scheme to structure the theoretical and  see exactley the variables for a better understanding, at this stage there are only an enumeration

Line 239

Control variables: ① Regional economic level (REL): If the level of economic development is 240 higher, the tourism infrastructure will be more perfect and the ability of providing tourism services 241 to the public will be higher, which will have a certain impact on the development of tourism. The 242 level of regional economic development is expressed in terms of per capital GDP. ② Industrial 243 structure (IS):

Line 520

This study explores the nonlinear effect of the strength of tourism economic contact on the 521 efficiency of the tourism industry, but it has the following limitations: ① This study uses the urban 522 clusters on the West Coast of the Strait as the case. Due to the difficulty in obtaining small-scale data, 523 this study uses the city area as the research unit, making the research scale relatively macro. Because 524 the intensity of tourism economic contact has a U-shaped effect on the efficiency of the tourism 525 industry, future research can further explore the influence on a smaller scale. ② The tourism

The authors can use anther type of classification more easy to understand

 

Line 401

5.1. Conclusion and Discussion

Line 408  ’’Between 2008 and 2017, the intensity of the tourism economic contact in the West Coast of the 408 Strait urban agglomeration has a U-shaped effect on the efficiency of the tourism industry’’

Can the authors to give some information  why they choose exactly that years 2008-2017, why they are so important for tourism and how we can apply in  recent years or for future.

Author Response

Reviewer #1

Abstract: at this stage it is concentrated only on variables, it is necessary to present also research method and results in a new shape.

Thank you very much for your comments and suggestions regarding our abstract. It is true that the abstract is still on the elaboration of variables. According to your ssuggestions, we have modified the contents of the abstract, expanded some contents, and tried to reflect relevant contents in a new way.. The specific changes are as follows.

Abstract

Following regional tourism cooperation constantly promotes the balanced of sustainable development and its plays a vital role in the of tourism industry. Using the West Coast of the Strait urban agglomeration as an example, this study using data envelopment analysis (DEA) to analyzes the nonlinear relationship between tourism economic contact intensity and tourism industry efficiency by constructing a mixed effect model. The results show the following: (1) In the early stage of regional tourism cooperation, the efficiency of the tourism industry will decrease with an increase in the intensity of tourism economic contact. As regional cooperation tends to a stable stage, the efficiency of the tourism industry will continue to increase with the strengthening of the intensity of tourism economic contact. (2) The regional economic level has a negative effect on the efficiency of the tourism industry. The urbanization level has a positive effect on the efficiency of the tourism industry. (3) The level of opening up and transportation development in the region will not only bring tourism resources or tourists, but also lead them to flow out. They have no significant impacts on the efficiency of the tourism industry.

Keywords: nothing about tourism where the article was send.

Thanks for your suggestions. After discussed with other authors, we also think that the keywords should reflect something related to tourism, so we made some adjustments to the keywords. The specific changes are as follows.

 

Keywords. tourism contact; tourism efficiency; U-shaped; DEA

Research Design-Line 140 : 3.1. Study Areas: We know that the minimum target in statistic it is 30,  so maybe will be better for readers to mention why the authors select only 20 or what it is behind that 20 from target group.

Thanks for your suggestion. Because the study area chosen by this research is the West Coast of the Strait urban agglomeration, which only includes 20 cities in total. At the same time, the division of urban agglomeration has certain boundary. So we selected these 20 target groups in our study. And your suggestions have given us some inspiration. We have inserted references in the research area of the article,The specific changes are as follows.

 

Research Design

Study Areas

The West Coast of the Strait urban agglomeration, also known as the West Strait Economic Zone, is located in Southeast China. According to the Coordinated Development Plan for the Cities Group on the West Bank of the Straits, the city group is a national-level city cluster with Fuzhou, Xiamen, Quanzhou, Shantou and Wenzhou as the core of the five major central cities, with a total of 20 prefecture-level cities. With a total land area of 270,000 square kilometers and a regional GDP of more than 587.2345 billion yuan in 2018, it is one of the most dynamic areas in China, with a high level of transportation development. The tourism resources of the 20 cities in the West Coast of the Strait urban agglomeration are extremely rich and highly complementary, and it is one of the most mature regions for tourism development in China.

 

New reference:

Ma, Y.; Xue, F.; Sun, W.; et al. Analysis of urban Network Characteristics in the Economic Zone west of the Straits: From the perspective of Functional network and Innovation Network. Geography Research, 2019, 38, 3010-3024.

Zheng, W.; Xu, W. L.; Chen, Y. Dynamic Evolution of Trans-regional Urban Agglomeration Economic Network: Based on Analysis of Urban Agglomeration in Haixi, Yangtze River Delta and Pearl River Delta. Economic Geography, 2019, 39, 58-66,75.

Research Design-5.Line 186 2.3.1. Samples and Data: Why they choose 2008-2017 and establish the model and to apply it in recent years or they have the results from that period and it is only a theoretical application ?

Thanks for your suggestion. In 2008, the great tee between the two sides were officially realized, and have truly entered the one-day life circle. The direct air transportation, sea transportation and mail between the two sides of the strait are officially launched. In the global financial crisis and economic recession, the realization of the "Great tee" is closely linked with the economic development and has brought broad development space for both sides of the strait. It is more conducive to the tourism intercommunication of the Strait City Group, and lays a certain foundation for the further promotion of the tourism development of the West Coast of the Strait urban agglomeration. Therefore, 2008 was selected as the starting point of this study. However, due to the large number of subdivision data designed in this study, some data updates remain at 2017. Therefore, looking at the whole, this study selects the period from 2008 to 2017 for research. The specific modifications are as follows.

 

In 2008, the great tee between the two sides of the strait were officially realized. The official launch of direct air transportation, direct maritime transportation, and direct mail have promoted the development and further development of tourism in strait cities. Therefore, this study selected 2008 as the starting point for data collection and established a tourism-related database for the West Coast of the Strait urban agglomeration between 2008 and 2017. Within the database, the efficiency of the tourism industry is regarded as the explained variable and the intensity of tourism economic contact is regarded as the core explanatory variable. Additionally, the regional economic level, industrial structure, urbanization level, traffic development level and level of opening up to the outside world are selected as control variables in the model. The tourism-related data used in this study come from the “Statistical Almanac of Fujian Province”, “Statistical Almanac of Zhejiang Province”, “Almanac of Xiamen Special Economic Zone”, “The statistical bulletin of national economic and social development of Fuzhou”, “The statistical bulletin of national economic and social development of Yingtan”, the CEIC database and others. The data processing software used in this study is Stata14.0.

Line 198 2.3.2. Definition of Variables+Line 239 Control variables: Would be good a scheme to structure the theoretical and see exactley the variables for a better understanding, at this stage there are only an enumeration.

Thanks for your suggestions. Indeed, we think that would be good a scheme to structure the theoretical and to check the variables for a better understanding. Therefore, we have added a conceptual explanation of the explanatory variable. The changes are as follows.

 

Explanatory variable: the intensity of tourism economic contact (TER). The tourism economic contact is mainly manifested as the mutual flow of tourism elements in space, including the tourists flow, tourism commodities, practitioners, and information, etc., In this study, the gravity correction model is used to measure the intensity of tourism economic contact between cities in the West Coast of the Strait urban agglomeration, and the intensity of tourism economic contact between each city and other cities is summed to obtain the intensity of tourism economic ties in the city group. The amount of tourism economic contact measured based on the gravity model has a clear regional boundary, and the amount of tourism economic contact as a measure index of the explanatory variable is helpful to better explore the influence of the tourism economic contact intensity on the efficiency of the tourism industry.

 

Explained variable: the efficiency of tourism industry (TIE). The efficiency of tourism industry refers to the economic benefits that can obtain after applying certain costs, which reflects the internal relation and ratio relation between the input and output of tourism economic activities. In this study, data envelopment analysis (DEA) was used to measure the tourism industry efficiency of 20 cities in the West Coast of the Strait urban agglomeration between 2008 and 2017. From the perspective of economics, the factors of production mainly include capital factors, labor factors and land. The development of regional tourism is not restricted by land [33], so it is not included in this study. In terms of investment indicators, this study uses urban fixed asset investment and the number of star-rated hotels as capital input variables from the perspective of capital factors. Direct investment in tourism infrastructure and construction of tourist attractions are capital input factors related to regional tourism. However, due to the lack of these data in the domestic statistical almanac, urban fixed asset investment is selected instead. Although urban fixed asset investments are mainly used for the construction of urban infrastructure and the improvement of related main functions and the direct part of investment in regional tourism is a small proportion, to a certain extent, the improvement of urban self-construction is also a very advantageous attraction in tourism. In addition, A-level scenic locations are an important part of tourism resources in tourism destinations and have a certain appeal to tourists. A-level scenic locations are not only an important factor reflecting the tourism reception capacity of a region but are also an important indicator of the region’s investment in tourism capital [34].

 

Control variables: ① Regional economic level (REL): This index reflects the economic development of a region (e.g. development scale and speed) to some extent. If the level of economic development is higher, the tourism infrastructure will be more perfect and the ability of providing tourism services to the public will be higher, which will have a certain impact on the development of tourism. The level of regional economic development is expressed in terms of per capita GDP. ② Industrial structure (IS): Also known as the sectoral structure of the national economy, from the perspective of the three industries, it mainly refers to the internal relationship between the primary industry, the secondary industry, and the tertiary industry. The change of industrial structure has constantly change of the proportion of the primary industry, the secondary industry, and the tertiary industry. An industrial structure with a higher optimization level can play a certain role in promoting the healthy development of the tourism industry, and it will also play a certain role in improving the efficiency of the tourism industry [37]. Therefore, this study represents the industrial structure as the proportion of the output value of the tertiary industry to the total GDP. ③ Urbanization level (UL): It refers to the degree of urbanization reached in a region, reflecting the proportion of the population living in large, medium, and small towns in the total urban and rural population of a region, country, or the world. The urbanization level not only has an impact on the level and pattern of tourism consumption in the region but also has a certain impact on the development of tourism enterprises [38]. In this study, the urbanization level is represented by the proportion of the urban population to the total population of the region at the end of the year. ④ The level of opening up to the outside world (DO): It refers to the degree to which a country or region's economy is open to the outside world under the condition of market economy. It also indicates the degree of contact between a country or region and the outside world. The level of opening to the outside world represents advanced science, technology, management level and concept. Driven by the rapid development of Chinese inbound and outbound tourism, the degree of opening up to the outside world will have a profound impact on the development of the tourism industry [39]. ⑤ Traffic development level (TL): It refers to the development stage or development degree of a region's transportation at a certain time, taking some measurement indicators as the object and according to the corresponding evaluation indicators. Transportation infrastructure is one of the most important foundations and prerequisites for tourism development. Additionally, the degree of transportation convenience has a direct influence on tourism accessibility [40], which is not only conducive to the development of the tourism economy and enhances its potential but also to the emergence of spatial spillover effects that can affect the tourism economic development of the surrounding areas.

 

Line 520: The authors can use anther type of classification more easy to understand.

Thanks for your suggestions. The the textual expression of the study limitations has been revised as recommended. The specific modifications are as follows.

 

Research Limitations and Future Prospects

This study explores the nonlinear effect of the strength of tourism economic contact on the efficiency of the tourism industry, but it has the following limitations: ① This study uses the urban clusters on the West Coast of the Strait as the case. Due to the difficulty in obtaining small-scale data, this study uses the city area as the research unit, making the research scale relatively macro. The intensity of tourism economic contact has a U-shaped effect on the efficiency of the tourism industry, and future research can further explore the change trend of this effect on another type of classification.

 

Line 401-5.1. Conclusion and Discussion: Can the authors to give some information  why they choose exactly that years 2008-2017, why they are so important for tourism and how we can apply in  recent years or for future.

Thanks for your suggestion. The two sides of the Taiwan Straits officially entered the era of "Great tee" from 2008. Since then, the tourism interconnectedness of the city clusters across the strait has been significantly improved, and its tourism economic cooperation and exchanges have gradually developed in an all-round way, and its tourism advantages have been brought into full play. At the same time, there are many subdivision data designed in this study, some of which are updated in 2017. To sum up, exploring the relationship between tourism economic cotact and tourism industry efficiency from 2008 to 2017 can provide more instructive suggestions for tourism development of the West Coast of the Strait urban agglomeration. The specific modifications are as follows.

 

Conclusion and Discussion

Between 2008 and 2017, the intensity of the tourism economic contact in the West Coast of the Strait urban agglomeration has a U-shaped effect on the efficiency of the tourism industry. This result means that before reaching the threshold, the efficiency of the tourism industry will be reduced with an increase in the tourism economic contact intensity; after reaching the threshold, the tourism industry efficiency will increase with an increase in the intensity of tourism economic contact intensity between the cities in the West Coast of the Strait urban agglomeration. The two sides of the strait officially entered the era of "Great tee" from 2008. Since then, the tourism interconnectedness of the city clusters across the strait has been significantly improved, and its tourism economic cooperation and exchanges have gradually developed in an all-round way, and its tourism advantages have been continuously exerted. Considering the heterogeneity of resources, the short distance between cities and the guiding power of policies, the regional urban system is a combination of centripetal and centrifugal force composed of external economies and noneconomies. Additionally, the imbalance of these two forces will lead to the siphon effect of the central city on the surrounding cities. The siphon effect means that the economic developments of the urban clusters on the West Coast of the Strait remain unbalanced, the degree of the regional industry correlation is not high, the linkage is weak and the central city’s radiation capacity is weak [46]. At the beginning of the realization of the "Great tee", regional tourism cooperation is still in its infancy, tourism economic contact among cities is weak and the tourism cooperation model is not perfect. The high-quality tourism elements and resources in the region will appear as counter-current phenomena [47] and will flow to the surrounding areas with better economic development, which will lead to a low balance of flow of tourism elements between cities in the region, weak interactivity, low optimization of tourism resource allocation, and slow development of tourism. These effects will have a negative impact on the development of the tourism industry. However, with the further development of the "Great tee", the economic ties between the cities of on the West Coast of the Strait have gradually become more open, the links between them have gradually strengthened [48]. As regional tourism cooperation has improved, the economic ties between regional cities have reached a stable level, the tourism industry cooperation model has reached maturity, and high-quality tourism resources and factors will return, regional cities will absorb the tourism resources and elements of the low-level economic development area, tourism interactivity will be enhanced and the input and output of tourism elements in the region will be gradually stabilized [49]. The overall allocation of the tourism resource elements will be optimized to promote the rapid and efficient development of the tourism industry in the region.

 

Research Recommendations

Based on the above analysis, this study posits the following suggestions for the development of the tourism industry in the West Coast of the Strait urban agglomeration: ① To give full play to the role of the intensity of tourism economic contact. After the realization of the "Great tee", direct air transportation, direct sea transportation and direct mail across the Straits have strengthened the economic exchanges between the strait city clusters, which has laid a certain foundation for the tourism cooperation and exchange of the strait city group. Taking that as an example, first, we should strengthen the interaction of tourism between cities in the region to form efficient tourism flow routes between regions. Moreover, the roles and responsibilities of each city in tourism cooperation should be clearly defined to promote the optimization of the allocation of tourism resources among regions to improve the efficiency of the regional tourism industry. Clear definitions of the roles and responsibilities of each city in tourism cooperation are essential for the development of urban tourism in the region, especially for cities with a low overall development level. Second, the tourism cooperation mechanism of the city group in the West Coast of the Strait urban agglomeration should be improved, and the cooperation between the city group and surrounding areas should be strengthened. Based on forming a tourism area with unimpeded information, people and resource flows, it should complement and interact with the surrounding areas.

Author Response File: Author Response.docx

Reviewer 2 Report

Dear authors:

It has been a pleasure to read an article with such a clear and interesting approach. Although the proposal is not a novelty, it is true that few studies address the problem posed from the point of view that you have chosen.

In my opinion, the article would be improved with a better review of the art, emphasizing similar or international works that address similar topics,and similar international experiences and problems. (Simmons, 1994; Marrocu and Paci, 2011; Lime and McCole, 2001 ... for example). It would be interesting a review of Dwyer, L., Forsyth, P., & Spurr, R. (2004).

Methodologically the research is impeccable as well as the results, findings and recommendations made.

For a better understanding of the study area I would add a situation map, for those who do not know China in depth it would be very useful, as well as the distances between cities. Sometimes the geographic analysis adds a plus of understanding and interest.

The results and implications of the study are correctly described.

 

Author Response

In my opinion, the article would be improved with a better review of the art, emphasizing similar or international works that address similar topics,and similar international experiences and problems. (Simmons, 1994; Marrocu and Paci, 2011; Lime and McCole, 2001 ... for example). It would be interesting a review of Dwyer, L., Forsyth, P., & Spurr, R. (2004).

Thanks for your suggestions. Although the previous literature review of this paper has sorted out the relevant contents of the study, it is necessary to add some relevant studies. Your suggestions provide information for the review of the literature on ascension. Therefore, we have followed your advice and appropriately added some relevant research discussions. The specific modifications are as follows.

There are few studies of the direct impact of tourism economic contact on the development of tourism, and most existing studies have explored the impact of tourism economic contact on the development of the tourism industry from the outside. Simmons (1994) proposed the relationship between local residents' participation in community tourism development and their tourism development. Dwyer, Forsyth and Spurr(2004) explored the economic benefits of tourism economic industry and proposed CEG model as a potential model to analyze the economic impact of tourism. Marrocu and Paci (2011) explored the relationship between new information, tourism flow and product efficiency in Europe and proposed that tourism could help improve regional efficiency. Jiang (2017) proposed that with the rapid development of the economy and the Internet, regional links will become closer and more efficient, which will promote the rapid expansion of the tourism market [24]. Chen et al. (2009) and Gavilán et al. (2015) discussed the impact of tourism cooperation and the spatial structure of the tourism economy on the development of the tourism industry [25,26]. Ye and Wang (2019) and other scholars believe that the improvement of regional accessibility has a connection with the tourism economy, promotes scientific and rational allocation, integrates and optimizes various elements in a regional tourism system, drives the improvement of the efficiency of tourism spatial cooperation, and ultimately enables the common and high-quality development of a regional tourism economy [10]. Liu, Lu (2015) and other scholars have posited that the development efficiency and growth quality of the tourism industry are the primary conditions for tourism sustainable development and that strengthening the exchange and cooperation of key elements of regional tourism development, such as information, technology and talents, will be beneficial to realize the regional sharing of tourism elements and can effectively improve the overall efficiency of tourism industry development [27].

 

New reference:

David, G. Simmons. Community participation in tourism planning. Tourism Management. 1994, 15, 98-108.

Larry, Dwyer.; Peter, Forsyth.; Ray, Spurr. Evaluating tourism's economic effects: new and old approaches. Tourism Management. 2004, 25, 307-307.

Emanuela, Marrocu.; Raffaele, Paci. They arrive with new information. Tourism flows and production efficiency in the European regions. Tourism Management. 2011, 32, 750-758.

For a better understanding of the study area I would add a situation map, for those who do not know China in depth it would be very useful, as well as the distances between cities. Sometimes the geographic analysis adds a plus of understanding and interest.

Thanks for your suggestions. After discussion with other authors, we feel that your suggestion will help readers better understand the research area and improve the readability and interest of the paper to some extent. Therefore, we add a map to the paper, as shown below.

 

3. Research Design

3.1. Study Areas

This study takes 20 cities in he West Coast of the Strait urban agglomeration, China, as the study site, and Figure 1 indicates the 20 cities it contains. The West Coast of the Strait urban agglomeration, also known as the West Strait Economic Zone, is located in Southeast China. According to the Coordinated Development Plan for the Cities Group on the West Bank of the Straits, the city group is a national-level city cluster with Fuzhou, Xiamen, Quanzhou, Shantou and Wenzhou as the core of the five major central cities, with a total of 20 prefecture-level cities. With a total land area of 270,000 square kilometers and a regional GDP of more than 587.2345 billion yuan in 2018, it is one of the most dynamic areas in China, with a high level of transportation development. The tourism resources of the 20 cities in the West Coast of the Strait urban agglomeration are extremely rich and highly complementary, and it is one of the most mature regions for tourism development in China.

——Insert Figure 1 here——

 

3.2. Research Methods

3.2.1. Gravity Correction Model

Various elements between regions are constantly flowing and exchanging, with the cities as carriers. Therefore, this study uses a gravity correction model to measure the intensity of tourism economic contact in the West Coast of the Strait urban agglomeration [20]. Due to the inequality between the intensity of tourism economic contact in various regions and to show the directivity of the intensity of tourism economic contact, the proportion of tourism resource endowment stakes is the sum of the tourism resource endowments between two associated cities. This study measures the amount of tourism economic contact for a city by summing the amount of tourism economic contact between the city and all other cities in the region [30]. The relevant formulas are as follows……

 

Map source:

Standard Map Service of Ministry of Natural Resources of China. Available online: http://bzdt.ch.mnr.gov.cn/ (accessed on 28 June 2020).

Author Response File: Author Response.docx

Reviewer 3 Report

• What is the UNIT OF P CALCULATED AS A RESULT OF FORMULA 1? rEVENUES AND gdp ARE IN CURRENCY UNITS, POPULATION ARE IN NUMBERS SO HOW CAN YOU CALCULATE GEOMETRIC AVERAGE FROM VALUES IN TWO DIFFERENT UNITS? IF WE ASSUME THAT POPULATION HAS NO UNIT THEN THE FINAL UNIT OF P IS THE THIRD ROOT (??) OF CUURENCY SQUARE PER SQUARE KILOMETER.
• iN fORMULA (2), WHY DO WE HAVE s(I)/(s(I)-s(J)), AND NOT s(j)/(s9I)-s(J)), OR AVERAGE FROM BOTH?
• In formula (4) miu is not very clearly explained
• Significance level is a probability of rejecting null hypothesis which is in fact true. According to kolmogorov definition, probability is a number between 0 and 1. so signifivance level can be 0.05 not 5%, etc.
• in tables 2 anD 3, reporting values of test statistics (like t and f) is not very convenient. As a redaer i do not have statistical tables with me, and i don’t know the number of degrees of freedom. instead p-values should be given
• line 290 – what was the criterion to omit variable in the next step of stepwise regression?
• you should comment very low values of r squared
• line 387 – null hypothesis cannot be accepted, never! You should write that “h0 cannot be rejested”

Author Response

What is the unit of P calculated as a result of formula 1? revenues and gdp are in currency units, population are in numbers so how can you calculate geometric average from values in two different units? if we assume that population has no unit then the final unit of P is the third root (??) of currency square per square kil ometer.

Thanks for your suggestions. First of all, in the calculation of gravity correction model, P represents the attraction of tourism between cities, and also the strength of tourism economic contact, gravity correction model is a relatively mature research methods, according to previous studies (such as Wu Zhicai Zhang Lingyuan Huang Shihui, 2020), the total number of tourists is based on how many people to calculate , tourism revenue and GDP in yuan to calculate, and then the gravitational coefficient P is calculated comprehensively. Moreover, modify content are marked in the paper.

Reference:

Wang, J.; Xu, J. H.; Xia, J. C. Research on Spatial Correlation Structure and Its Effect in China's Regional Tourism Economy-Based on Social Network Analysis. Tourism Tribune, 2017, 32, 15-26.

Morley, C.; Rossello, J.; Santana-Gallego, M. Gravity models for tourism demand: Theory and use. Annals of Tourism Research, 2014, 48, 1-10.

In formula (1),  represents the attraction of tourism between city  and city ; and also represents the strength of tourism economic contact between city  and city ,  and  represent the total number of tourists in cities  and  that year, respectively;  and  represent the total tourism revenue of the two cities in that year;  and  are the total GDP of the two cities that year; and  represents the spatial linear distance between the two cities. In formula (2),  is the gravitational coefficient, which uses the numbers of 4A and 5A scenic locations as a measurement index, and  and  represent the total number of 4A and 5A scenic locations in the two cities that year, respectively. In formula (3),  represents the amount of tourism economic contact of city .

In formula (2), why do we have s(I)/(s(I)-s(J)), and not s(j)/(s9I)-s(J)), or average from both?

Thanks for your suggestions. In the calculation of gravity correction model, it is the gravity coefficient, which is to adjust parameters, according to previous studies (such as Wu Z C, Zhang L Y, Huang S H, 2020), it is primarily considered tourism economic contact between the two cities and purely economic attraction, is used to response the weight of city i to the tourism economic contact of city j,, so the calculation formula is s(i)/(s(i)+s(j).

Reference:

Wu , Z. C.; Zhang, L. Y.; Huang,S. H. Spatial Structure and Collaborative Cooperation Model of Tourism Economic Links between Guangdong, Hong Kong and Macao Greater Bay Area. Journal of Geographical Research, 2020, 39, 1370-1385.

In formula (4) miu is not very clearly explained

Thanks for your suggestions. In this study, min means minimum. min represents a part of the overall BCC model.

 

Reference:

Liu, J.; Lu, J.; Liu, N.; et al. Spatial-temporal Evolution, Influencing Factors and Formation Mechanism of Tourism Industry Efficiency in China's Coastal Areas based on DEA-Malmquist Model. Resources Science. 2015, 37, 2381-2393.

 

Significance level is a probability of rejecting null hypothesis which is in fact true. According to kolmogorov definition, probability is a number between 0 and 1. so signifivance level can be 0.05 not 5%, etc.

Thanks for your suggestions. Your suggestions have further improved the overall rigor and scientific nature of the paper. According to your suggestions, we have modified the expression of significance level, as shown below.

 

Table 2. Basic Model Test.

Variable	TIE
	Model 1	Model 2	Model 2	Model 4	Model 5	Model 6
TER	-0.111** (-2.220)	-0.115** (-2.350)	-0.113** (-2.260)	-0.114** (-2.310)	-0.111** (-2.240)	-0.110** (-2.190)
TER2	0.009* (1.850)	0.011** (2.140)	0.011** (2.070)	0.011** (2.220)	0.011** (2.150)	0.011** (2.100)
REL		-0.015*** (-2.860)	-0.017*** (-2.680)	-0.022*** (-3.140)	-0.020*** (-2.760)	-0.021*** (-2.710)
IS			0.001 (0.470)	0.001 (0.340)	0.001 (0.400)	0.001 (0.440)
UL				0.002* (1.670)	0.002 (1.650)	0.001 (1.220)
DO					-0.010 (-0.700)	-0.009 (-0.550)
TL						-0.011 (-0.330)
Constant	1.032*** (8.600)	1.085*** (9.100)	1.031*** (6.270)	0.982*** (5.900)	1.068*** (5.140)	1.161*** (3.340)
R2	0.024	0.059	0.055	0.063	0.061	0.057
F	3.463	5.126	3.886	3.692	3.149	2.703
N	200	200	200	200	200	200

Note: *** p＜0.01, ** p＜0.05, * p＜0.1. Source: collation of this study

In terms of the control variables, the influence of the regional economic level (REL) on the efficiency of the tourism industry is negative ( p＜0.01), which shows that the efficiency of the tourism industry will continue to decline with the improvement of the regional economic level. The t-value of the urbanization level (UL) on the efficiency of the tourism industry is 1.22 ( p＜0.1).

 

Table 3. System GMM estimation results.

Variable	TIE
TER	-0.120*** (-4.740)
TER2	0.008*** (2.860)
REL	-0.004 (-1.340)
IS	-0.000 (-0.290)
UL	0.001** (2.460)
DO	0.017*** (4.400)
TL	0.007 (1.170)
N	180
AR(1)	0.401
AR(2)	0.132
Hansen	17.220 （0.102）

Note: t statistics are in parentheses, *** p＜0.01, ** p＜0.05, * p＜0.1. AR (1) and AR (2) represent the P values of the Arellano-Bond test statistic. The error term used to test the model is a second-order sequence correlation. The Hansen value is used to test whether the setting of the tool variable is reasonable. Source: Collated by this study

 

In tables 2 anD 3, reporting values of test statistics (like t and f) is not very convenient. As a redaer i do not have statistical tables with me, and i don’t know the number of degrees of freedom. instead p-values should be given.

Thanks for your suggestions. Your suggestion helps us to make the tables more readable. Therefore, we have replaced the t value in brackets with the P value. Specific modifications are as follows, which are marked in red in the paper.

Table 2. Basic Model Test.

Variable	TIE
	Model 1	Model 2	Model 2	Model 4	Model 5	Model 6
TER	-0.111** (0.02)	-0.115** (0.02)	-0.113** (0.03)	-0.114** (0.04)	-0.111** (0.03)	-0.110** (0.04)
TER2	0.009* (0.06)	0.011** (0.04)	0.011** (0.03)	0.011** (0.04)	0.011** (0.02)	0.011** (0.04)
REL		-0.015*** (0.00)	-0.017*** (0.00)	-0.022*** (0.00)	-0.020*** (0.00)	-0.021*** (0.00)
IS			0.001 (0.13)	0.001 (0.24)	0.001 (0.11)	0.001 (0.14)
UL				0.002* (0.08)	0.002 (0.13)	0.001 (0.12)
DO					-0.010 (0.70)	-0.009 (0.55)
TL						-0.011 (0.33)
Constant	1.032*** (0.00)	1.085*** (0.00)	1.031*** (0.00)	0.982*** (0.00)	1.068*** (0.00)	1.161*** (0.00)
R2	0.024	0.059	0.055	0.063	0.061	0.057
F	3.463	5.126	3.886	3.692	3.149	2.703
N	200	200	200	200	200	200

Note: *** p＜0.01, ** p＜0.05, * p＜0.1, P statistics are in parentheses. Source: collation of this study

 

Table 3. System GMM estimation results.

Variable	TIE
TER	-0.120*** (0.00)
TER2	0.008*** (0.00)
REL	-0.004 (0.14)
IS	-0.000 (0.46)
UL	0.001** (0.03)
DO	0.017*** (0.00)
TL	0.007 (0.21)
N	180
AR(1)	0.401
AR(2)	0.132
Hansen	17.220 (0.102)

Note: P statistics are in parentheses, *** p＜0.01, ** p＜0.05, * p＜0.1. AR (1) and AR (2) represent the P values of the Arellano-Bond test statistic. The error term used to test the model is a second-order sequence correlation. The Hansen value is used to test whether the setting of the tool variable is reasonable. Source: Collated by this study

 

Line 290-what was the criterion to omit variable in the next step of stepwise regression? you should comment very low values of r squared.

Thanks for your suggestions. Generally, the first regression is to put only the core variables of concern, and then add the control variables one by one to observe whether there is a big change in the estimated coefficient and statistical significance of the core explanatory variables in the process of gradually adding the control variables. The reason for the stepwise addition of variables is mentioned in the article to ensure the robustness of the estimated results of the core explanatory variables. And there is no relevant elaboration about r-squared in the article, because of the need to pay attention to when doing forecast r square, it will be more accurate, and this study focuses on the impact of economic contact strength on the efficiency of tourism indusry, focusing on causality, so there is no explanation for the extremely low value of R squared in the article.

 

Reference:

Song, W. X.; Liu, C. H. Research on the Differentiation Mechanism of Urban Commodity Housing Prices in the Integrated Yangtze River Delta. Geography Research, 2018, 37, 92-102.

 

Line 387-null hypothesis cannot be accepted, never! You should write that “h0 cannot be rejested”.

Thanks for your suggestions. Your suggestions are of great help to improve the rigorous and scientific expression of this research. Therefore, this research has modified this part of the article according to your Suggestions, and the specific modification contents are as follows.

 

Regions with highly efficient tourism industries in the current period may have closer ties. As a result, there is an endogenous problem in estimating the influence of the intensity of tourism economic contact on the efficiency of the tourism industry. To prevent possible errors in the estimation results caused by the endogenous problem, this study further adopts the dynamic panel estimation method and systematic GMM estimation to further investigate the influence of the intensity of tourism economic connection on the efficiency of the tourism industry. The estimation results are shown in Table 3. The Hansen test is used to examine the instrumental variables for transitional identification problems and to determine whether the instrumental variables set is reasonable. If the P value is greater than 0.1 in the Hansen test results, then the null hypothesis is accepted and the choice of instrumental variables is reasonable [45]. From the results of the Hansen test in Table 3, the P value is greater than 0.1, indicating that the setting of the instrument variables in the model is reasonable. This study uses the Arellano-Bond test statistics to test whether the error term of the model is related to a second-order sequence. Because GMM is valid regardless of whether there is a first-order sequence correlation in the residual term after the difference, the GMM estimation cannot have second-order sequence correlation. In the results of the Arellano-Bond test presented in Table 3, the P values of AR (1) and AR (2) are all greater than 0.2, and the null hypothesis cannot be rejected; thus, there is no first- and second-order autocorrelation. The above tests indicate that the estimation results of SYS-GMM in this section are valid.

According to your valuable suggestions, we have made some major changes to the paper. In the past days, we have tried to answer these questions first in the response. We will then make detailed comments and modification based on specific recommendations and requirements for your suggestions. We believe that you will find that this paper has been greatly improved. Your opinion is very valuable for highlighting the contribution of the paper and improve the quality of the paper. We attach great importance to these issues and try to make this research more perfect.

 

Thank you very much for your help in this process.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Accept in present form