Spatial and Temporal Evolution of Tourism Ecological Security in the Old Revolutionary Region of the Dabie Mountains from 2001 to 2020

Round 1

Reviewer 1 Report

This paper is a very well-structured manuscript. It is written clearly.

Thus, it is possible to follow the problem statement, a very clear literature review which is well connected with the research problem.

It also clearly explains the research methodology and analysis and supports the maps and tables.

The research findings are extensively discussed in relation to the literature, and policy proposals are offered accordingly in the conclusion part. 

 

Author Response

We greatly appreciate the reviewer providing valuable and constructive comments on our manuscript entitled "Spatial and Temporal Evolution of Tourism Ecological Security in the Old Revolutionary Region of the Dabie Mountain from 2001 to 2020". Meanwhile, to highlight the uniqueness and necessity of this study, we have added to the manuscript the expressions of "red tourism", a new content of tourism. This will be more closely related to our research question (Please see Lines 45-47,62-65).

Reviewer 2 Report

I am honored to read this paper and have the following questions:

1. There are massive existing literatures, particular for the analysis of Chinese cases, including those with similar approaches and the authors will need to clarify what are the uniqueness of this particular paper.  The authors start with explanations of rather technical issues and simple feature description , extracted data from applied to the Dabie Mountain What is the added value of the manuscript? what is the implications of this study for other studies? Related researches of ecological tourism is not reviewed. 

2. What is the additional significance of the spatial variance model that mere spatial visualization is sufficient. And the standard deviation ellipse is also used to analyze the spatial parent-child relationship, so why describe the spatial pattern in depth? You should explore the infuencing factors of this patterns. 

3. I don't know if this prediction makes sense, and predicting that long. As with the many uncertainties associated with the outbreak of COVID 19, the increase trend of many regional socioeconomic data in China will tend to slow in future.  

4. In terms of the evaluation system, i think the indicators should aviod the yuan, persons in total, density or %  can better aviod the scale problem.

Author Response

Response 1: Thanks for your evaluation of our manuscript and constrictive comments. So far, there have been a lot of studies on tourism ecological security in China (Li et al., 2017; Xu et al., 2017; Yang et al., 2021). These studies promote the progress of tourism ecological security research and deepen the cognition of tourism ecological security. From the perspective of research scale, most of these studies focus on the provincial scale or a single city, and little is known about the spatiotemporal dynamics among cities and smaller spatial units within the province. From the perspective of research, more studies focus on tourism activities based on natural landscapes, while less attention is paid to tourism activities represented by human landscapes, especially "red tourism" with the theme of remembering revolutionary martyrs. From the perspective of the depth of research, most scholars stay on the changes of tourism ecological security in individual time sections, and there is a lack of research on its long-term continuous changes and future prediction. Based on these previous studies, we selected smaller scale cities, districts and "red tourism" with less focus in Dabie Mountain Old Revolutionary Region for research. In order to highlight the uniqueness of this study, we have added statements about "red tourism" and implications of this study for other studies as suggested by other reviewers. At the same time, in order to make readers more aware of the current status of tourism ecological security, we added this part of the content in the manuscript (Please see Lines 45-47, 62-65, 77-79, 101-104).

Response 2: Many thanks for the careful review and the constructive comments. The reason for using the standard deviation ellipse model is that it is a spatial statistical method that can accurately describe the overall characteristics of the spatial distribution of evaluation objects and the spatial and temporal evolution process (Zheng et al., 2018). It can directly express the main trend direction of the spatial change of the centroid of evaluation factors. At the same time, we decided to add the influencing factors analysis of the variation patterns of tourism ecological security to the manuscript according to the valuable suggestions of the reviewer. We selected the factor detection of Geo-detector to detect the main influencing factors of the spatial distribution of tourism ecological security index in Old Revolutionary Region of the Dabie Mountain. Geo-detector is a group of statistical methods that detect spatial variability and reveal the driving forces behind it. It has been reported in human health (Tao et al., 2016; Wang et al., 2010), Human economy (Li et al., 2020), Physical geography (Pei et al., 2019), Ecology (Liang et al., 2016; Zhang et al., 2020) has been widely used. The main influencing factors of tourism ecological security in Dabie Mountain Old Revolutionary Region are different at different rule levels, but each of the main influencing factors is always in the dynamic change of mutual influence and cyclic operation from the perspective of the whole system (Table 3). The specific description of this part is added to the manuscript (Please see Lines 250-265, 609-659, 688-692).

 

Table 3. Results of impact factor.

Detection rule	Detection Factor	Detection index	q	sig
Driving	Economic factors	D1 Per capita GDP	0.69	0.44
		D2Growth rate of tertiary industry	0.66	0.73
	Social elements	D3Urbanization rate	0.98	0.00
		D4 Natural growth rate of population	0.58	1.00
	Tourism elements	D5 Growth rate of tourism revenue	0.49	1.00
		D6 Growth rate of tourists	0.59	1.00
Pressure	Tourism Transport	P1 Tourism traffic pressure	0.98	0.01
	Tourism Society	P2 Population density	0.97	0.00
		P3Tourism spatial index	0.99	0.00
		P4 Visitor density index	0.99	0.00
	Ecological environment	P5 production of wastewater	0.47	0.52
		P6 SO2 emission	0.53	0.72
		P7 Solid waste output	0.42	0.55
		P8 Domestic waste removal volume	0.71	0.47
	Energy consumption	P9 Energy consumption per 10000-yuan GDP	0.66	0.25
State	Tourism economy	S1 Domestic tourism income	0.90	0.00
		S2 Tourism foreign exchange income	0.47	1.00
		S3 Per capita tourism income	0.97	0.00
		S4 Number of visitors	0.91	0.04
	Tourism facilities	S5 Number of star -hotels	0.72	0.16
		S6 Number of travel agencies	0.61	0.22
	Ecological environment	S7 green region	1.00	0.00
		S8 Per capita Park green region	0.98	0.00
		S9 Green coverage rate of built-up region	0.90	0.02
Impact	Economic impact	I1 Proportion of tertiary industry	0.69	0.19
		I2Proportion of total tourism revenue in GDP	0.95	0.00
	Consumption impact	I3 Per capita consumption of tourists	0.81	0.09
		I4 Stay of length	1.00	0.00
Response	Social response	R1 Number of college students per 10000 people	0.58	0.99
		R2 Number of students in Tourism Colleges	0.53	0.75
	Economic regulation	R3 Proportion of fiscal expenditure in GDP	0.96	0.00
		R4 Proportion of environmental pollution control investment in GDP	0.88	0.02
	Environmental governance	R5 Comprehensive utilization rate of solid waste	0.64	0.24

Response 3: Many thanks for the careful review and the constructive comments. The simplest understanding of forecasting is to use statistical methods to make judgments about the future based on past changes. There are a lot of predictions like this. For example, the best known IPCC scenario projections can be extended to 2100 (Li et al., 2012). Of course, it takes into account the lowest and highest modes. As you said, there will be many uncertain factors that will affect the accuracy of this research prediction in the future, such as the slowdown of regional economic growth, the improvement of science and technology, and the improvement of tourist quality. But these changes are difficult to make a big change in the short term, just like the IPCC fifth and sixth assessment reports, after eight years or so, there is still no big change (Zhou et al., 2021). In this research, Gray Prediction Model is used to predict the change trend of Dabie Mountain Old Revolutionary Region in the next 10 years. The gray prediction model has the dual advantage of being a simple principle and being highly accurate in its predictions. It also can pre-process the original data to obtain better smoothing, which makes the prediction more effective (Xu et al., 2017). As in the IPCC report, this prediction is certainly different from the actual situation, but the overall trend should be consistent.

Response 4: Many thanks for the careful review and the constructive comments. Indeed, yuan, persons in total, density or % makes a difference in scale. However, these differences are a fact and a concrete manifestation of regional differences. In order to avoid this scale problem, researchers engaged in tourism ecological security only research the same scale when designing the research framework. For example, the comparison between the municipal level and the provincial level is not involved in the process of tourism ecological security assessment at the provincial level (Li et al., 2017). On the other hand, the raw input data is normalized and normalized during calculations using the Entropy weight TOPSIS method, which also reduces the large variance caused by the data. Reviewer can put forward forward-looking suggestions outside the existing knowledge system, which is helpful to promote the development of the discipline.

 References: Li LH, Bai L, Yao YN, et al. Projection of Climate Change in Xinjiang under IPCC SRES. Resources Science, 2012, 34(4):602-612.Li XG, Wu Q, Zhou Y. Spatio-Temporal Pattern and Spatial Effect of Chinese Provincial Tourism Eco-Security. Economic Geography,2017, 37(3): 210:217.Li Zaijun, Yin Shanggang, Zhang Xiaoqi et al. The spatial-temporal evolution and driving factors of floating population's rent income ratio in prefectural city of China. Scientia Geographica Sinica,2020,40(1):103-111.Liang P, Yang X. Landscape spatial patterns in the Maowusu (Mu Us) Sandy Land, northern China and their impact factors. Catena, 2016,145: 321-333.Pei ZL, Yang QK, Wang CM, et al. Spatial Distribution of Vegetation Coverage and Its Affecting Factors in the Upper Ｒeaches of the Yellow Ｒiver. Arid Zone Research, 2019, 36(03): 546-555.Tao HY,Pan ZZ, Pan ML, et al. Mixing spatial-temporal transmission patterns of metropolis dengue fever: Acase study of Guangzhou, China. Acta Geographica sinica, 2016,71(09): 1653-1662.Wang JF, Li XH, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 2010, 24: 107-127.Xu M, Liu CL, Li D, et al. Dynamic early warning of tourism ecological security in Zhangjiajie City based on improved TOPSIS-Grey GM (1, 1) model. Journal of Applied Ecology, 2017,28:3731-3739.Yang LJ, Cao KJ. Spatiotemporal pattern and driving mechanism of tourism ecological security in 85counties and cities of Xinjiang. Acta Ecologica Sinica, 2021, 41(23):1-14.Zhang Wenjing, Sun Xiaoyin, Shan Ruifeng, et al. Spatio-temporal quantification of landscape ecological risk changes and its driving forces in the Nansihu Lake basin during 1975-2018[J]. Ecological Science, 2020, 39(3): 172–181.Zheng DF, Hao S, Sun CZ, et al. Analysis of spatial and temporal evolution of eco-efficiency and its trend prediction in mainland China. Geography Research, 2018,37,1034-1046.Zhou TJ, Chen ZM, Chen XL, et al. Interpreting IPCC AR6: future global climate based on projection under scenarios and on near-term information[J]. Climate Change Research, 2021, 17 (6): 652-663.

Author Response File: Author Response.docx

Reviewer 3 Report

The audience of this journal may not know where is the region of Dabie Mountain. More descriptions and pictures of this region are indispensable: its location in China nation, as well as its tourism development condition (number of tourists, stakeholders involved in spatial and temporal evolution, etc). Argumentative writing is needed on why this geographical scope is essential to be analyzed using the tourism ecological security model and concept. The writing in (line 244) “3.1. Definition of the scope of the region under study” is not adequate to enable the reader understand this region.

Refinements of spatial methods and mapping levels are needed, e.g., by updating drivers, pressures, state, impact, and response model of layers to include more recent and detailed layers. This could also involve modeling key driving factors of the phenomena under study, such as Tourism Ecological Security diagrammatic picture(s).

(line 151) Figurw1. DPSIR conceptual model for the evaluation of tourism ecological security. Notice the mistype of e in the figure.

(line 243) Data sources are not in the reference.

The conclusion would be better to include the limitations and weaknesses of the study process, especially the methods chosen.

Further, what are the implications of points (1) (2) (3) (4) in the conclusions?

Author Response

Response 1: Many thanks for the careful review and the constructive comments. We have drawn the location map of Dabie Mountain Old Revolutionary Region and added it to the manuscript according to the valuable suggestions of reviewer (Figure2). Audience can quickly locate our research region from the left picture, and have a clear understanding of the topographic changes of Dabie Mountain Old Revolutionary Region through the elevation map on the right. At the same time, to enhance audience' understanding of the development and current situation of tourism industry in Dabie Mountain Old Revolutionary Region in the past 20 years, we have added relevant expressions in the manuscript (Please see Lines 45-47, 267-281).

In view of the necessity of using the tourism ecological security model and concept to analyze the Dabie Mountain Old Revolutionary Region, there are mainly the following reasons. First, the Dabie Mountain Old Revolutionary Region is a well-known "red tourism" area in China. The evaluation of the tourism ecological security of this area is conducive to the country and people to understand the development status of the old Revolutionary Region. Second, Dabie Mountain Old Revolutionary Region is an economically backward mountainous area, and tourism income is an important source of people's income in this region.To identify the important influencing factors of tourism ecological security in this region and provide guidance for tourism planning, which is conducive to the sustainable development of tourism and the continuous improvement of people's lives. Thirdly, this study can provide reference for the research on tourism ecological security of "red tourism" regions located in mountainous areas like Dabie Mountain Old Revolutionary Region. To enhance the purpose of this study, we have also added this part of the description to the manuscript (Please see Lines 62-65).

Response 2: Many thanks for the careful review and the constructive comments. There is no doubt that covering a longer period of time and including more comprehensive indicators will make the assessment of tourism ecological security more accurate. However, compared with the municipal and provincial scales, it is very difficult to obtain data at the county and district levels. Since our research project is located in the Dabie Mountain Old Revolutionary Region, we applied for relevant data from some county and district statistical bureaus according to the research project, and this research was completed. To make an in-depth analysis of this study and combine the reviewer's suggestions, we added the related content of identification of key drivers of tourism ecological security in our study. The main influencing factors of tourism ecological security in Dabie Mountain Old Revolutionary Region are different at different rule levels, but each of the main influencing factors is always in the dynamic change of mutual influence and cyclic operation from the perspective of the whole system (Table 3). The specific description of this part is added to the manuscript (Please see Lines 250-265, 609-659, 688-692).

Table 3. Results of impact factor.

Detection rule	Detection Factor	Detection index	q	sig
Driving	Economic factors	D1 Per capita GDP	0.69	0.44
		D2Growth rate of tertiary industry	0.66	0.73
	Social elements	D3Urbanization rate	0.98	0.00
		D4 Natural growth rate of population	0.58	1.00
	Tourism elements	D5 Growth rate of tourism revenue	0.49	1.00
		D6 Growth rate of tourists	0.59	1.00
Pressure	Tourism Transport	P1 Tourism traffic pressure	0.98	0.01
	Tourism Society	P2 Population density	0.97	0.00
		P3Tourism spatial index	0.99	0.00
		P4 Visitor density index	0.99	0.00
	Ecological environment	P5 production of wastewater	0.47	0.52
		P6 SO2 emission	0.53	0.72
		P7 Solid waste output	0.42	0.55
		P8 Domestic waste removal volume	0.71	0.47
	Energy consumption	P9 Energy consumption per 10000-yuan GDP	0.66	0.25
State	Tourism economy	S1 Domestic tourism income	0.90	0.00
		S2 Tourism foreign exchange income	0.47	1.00
		S3 Per capita tourism income	0.97	0.00
		S4 Number of visitors	0.91	0.04
	Tourism facilities	S5 Number of star -hotels	0.72	0.16
		S6 Number of travel agencies	0.61	0.22
	Ecological environment	S7 green region	1.00	0.00
		S8 Per capita Park green region	0.98	0.00
		S9 Green coverage rate of built-up region	0.90	0.02
Impact	Economic impact	I1 Proportion of tertiary industry	0.69	0.19
		I2Proportion of total tourism revenue in GDP	0.95	0.00
	Consumption impact	I3 Per capita consumption of tourists	0.81	0.09
		I4 Stay of length	1.00	0.00
Response	Social response	R1 Number of college students per 10000 people	0.58	0.99
		R2 Number of students in Tourism Colleges	0.53	0.75
	Economic regulation	R3 Proportion of fiscal expenditure in GDP	0.96	0.00
		R4 Proportion of environmental pollution control investment in GDP	0.88	0.02
	Environmental governance	R5 Comprehensive utilization rate of solid waste	0.64	0.24

Response 3: We are sorry about the inaccurate expression. We have corrected the mistakes raised by the reviewer and rechecked and corrected the expression of the entire manuscript. (Changes are marked in a different colour).

Response 4: Many thanks for the careful review and the constructive comments. However, I do not have a definitive understanding of this comment, could the reviewer elaborate further? Thank you very much. In addition, we have added new data sources in this section (Please see Lines 291-293).

Response 5: Many thanks for the careful review and the constructive comments. It's true that every research has its flaws, and this one is no exception. According to the reviewer's suggestions and the actual limitations of the study, we elaborated the deficiencies and future expectations of this study from the aspects of research scale, method and index selection, so as to provide reference for subsequent researchers. Related expressions have been added to the manuscript. The four points included in the conclusion of this study were originally summarized according to the four research results in the manuscript, which led to the insufficient clarity of our conclusion. In addition, we have added new content in the conclusion according to the reviewer's suggestion. Therefore, we re-summarize the conclusion from four perspectives: time change, space change, main influencing factors and research limitations of tourism ecological security (Please see Lines 670-703).

Author Response File: Author Response.docx

Reviewer 4 Report

1. Please check the numbering and spelling error. Such as at line number 29, 151

Author Response

Response 1: We greatly appreciate the reviewer providing valuable and constructive comments on our manuscript entitled "Spatial and Temporal Evolution of Tourism Ecological Security in the Old Revolutionary Region of the Dabie Mountain from 2001 to 2020". Meanwhile, we are sorry about the inaccurate expression. We have corrected the mistakes raised by the reviewer and rechecked and corrected the expression of the entire manuscript. (Changes are marked in a different colour).

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Great improvemetns has been done, but i still have some questions:

1. The introduction should add more about the connotation of ecological tourism or ecological tourism security, which can also serve for your evaluation system construction.

2. in part 4.2, i cannot understand why you visualize the value both using the shapefile mode (Figure 5) and raster mode (Figure 6). what is the potential value. 

3. when using the Geo-detector model, subsystem indicators are used to detect the Y, namely ecological security value, which is the composite of subsystem indicators, whether this has repetition information or endogeneity ?

4. If you can add the discussion part, this paper looks more perfect.

 

Author Response

The response can be found in the attached file "Respond to reviewer#2.pdf".

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

thanks for your careful work, great improvements, it can be accepted.