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Journal of Resources and Ecology >

2023 , Vol. 14 >Issue 3: 631 - 643

DOI: https://doi.org/10.5814/j.issn.1674-764x.2023.03.017

Ecotourism

Study on the Evaluation and Optimization Strategy of Tourism Environmental Suitability in China based on the AHP-TOPSIS Algorithm

  • LI Ying , 1, 2 ,
  • WANG Yiran 1 ,
  • ZOU Tongqian , 1, *
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  • 1. China Academy of Culture and Tourism, Beijing International Studies University, Beijing 100024, China
  • 2. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
*ZOU Tongqian, E-mail:

LI Ying, E-mail:

Received date: 2021-12-02

  Accepted date: 2022-08-13

  Online published: 2023-04-21

Supported by

The Beijing Social Science Fund(20GLC064)

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Abstract

The condition of the tourism environment is the key factor that affects the tourism experience. A comprehensive evaluation of the tourism environmental suitability score is of great significance for guiding tourism planning and decision-making, improving the suitability score of the tourism environment in a targeted manner and guaranteeing the sustainable development of tourist cities. By adopting various indicators, such as the Universal Thermal Climate Index, the amount of precipitation, the vegetation index, the concentration of fine particulate matter in the atmosphere, and the ultraviolet radiation intensity index, this study quantifies the comfort level of weather, the weather impacts, the vegetation status, the atmospheric environment, ultraviolet radiation, and other key factors. Based on employing the AHP-TOPSIS algorithm, this study then conducts a comprehensive evaluation of China’s tourism environmental suitability score and a comprehensive comparative analysis of the tourism environmental suitability scores of three typical tourist areas: the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta. The results show that the tourism environmental suitability score in China has obvious characteristics of spatial differentiation: the scores in East and South China are the highest while the score in the northwest inland area is the lowest. Among the three typical tourist areas, the Pearl River Delta region has the highest tourism environmental suitability score, followed by the Yangtze River Delta region, and the Beijing-Tianjin-Tangshan region has the lowest score. The northern regions of the Beijing-Tianjin-Tangshan area, the southern regions of the Yangtze River Delta and the surrounding areas of the Pearl River Delta are more suitable for the sustainable development of the tourism industry. PM2.5 is the main factor limiting the tourism environmental suitability scores in the Beijing-Tianjin-Tangshan area and the Yangtze River Delta, so atmospheric environment management will be an effective way to improve their tourism environmental suitability scores.

Key words： tourism environmental suitability score; AHP; TOPSIS; optimization strategy

Cite this article

LI Ying , WANG Yiran , ZOU Tongqian . Study on the Evaluation and Optimization Strategy of Tourism Environmental Suitability in China based on the AHP-TOPSIS Algorithm[J]. Journal of Resources and Ecology, 2023 , 14(3) : 631 -643 . DOI: 10.5814/j.issn.1674-764x.2023.03.017

1 Introduction

In recent years, the concept of tourism has been undergoing dramatic changes. The consumers’ demand for tourism is changing from sightseeing to leisure experience, and their requirements for comfort and health in the tourism experience are gradually improving. However, under the influence of various factors, such as air pollution, uncomfortable weather, lack of vegetation and others, the tourism environmental suitability of tourist cities is declining precipitously, and this trend has even given birth to a series of new forms of tourism that are designed to avoid uncomfortable experiences, such as “tour of avoiding haze”, “summer vacation tour”, “tour of avoiding cold weather” and so on. Therefore, a timely and accurate assessment of China’s tourism environmental suitability score is of great significance for the sustainable development of tourism cities.
Suitability analysis is widely used in a variety of scientific fields. It can determine the suitability and convenience of carrying out certain activities by using a variety of methods (Steiner, 1983), as well as the degree of suitability in specific regions (Hopkins, 1977), the results of which can be used to guide sustainable development. The assessment of the tourism suitability score is an assessment of whether a certain region is suitable for carrying out related tourism activities (Xue et al., 2014). Tourism environmental suitability is an index for evaluating the suitability of the natural environment for tourism activities. The suitable tourist conditions of cities can produce a sustained attraction of tourists, which is one of the factors driving the healthy development of urban tourism (Li et al., 2012).
Studies on the tourism environmental suitability score at home and abroad are abundant, and the evaluation index system for the tourism environmental suitability score is relatively mature. Tourism is an industry that relies heavily on resource conditions, the ecological environment and climatic conditions (Zhong et al., 2011). Therefore, the evaluation of the tourism environmental suitability score mostly revolves around tourism resource conditions (Qu and Li, 2008), the ecological environment level (Dhami et al., 2017; Ayhan et al., 2020), climate comfort (Yang et al., 2018) and similar issues. Qu and Li (2008) examined the suitability score of coastal tourism from three aspects: natural conditions, economic conditions and traffic conditions. Dhami et al. (2017) conducted research using the sensitivity of the ecotourism environmental suitability score in west Virginia, USA to tourism destination remoteness, slope, vegetation, wildlife, mining, and similar factors. Ayhan et al. (2020) used altitude, soil properties, vegetation types and other elements to evaluate the suitability score of rural tourism in Turkey. Yang et al. (2018) used the Universal Thermal Climate Index to analyze the distribution of the summer climate suitability scores in China's tourist destinations. There are abundant studies on tourism environmental suitability score evaluation at home and abroad, and its evaluation index system is relatively complete and mature.
The methods of tourism environmental suitability score assessment are also abundant, but the quantification process used in many studies relies too much on experience. The assessment of tourism environment suitability is in fact an issue of Multi-Criteria Decision Making (MCDM), which helps decision makers to choose the best option. At the core of this assessment is the prioritization of criteria and the quantification of the criteria (Jankowski, 1995). MCDM is reflected in tourism environmental suitability score assessment, and the key is to determine the weight ratios between different indicators and the quantitative method of samples within the indicators. There are many multi-criteria assessment methods for the tourism environmental suitability score, such as the AHP (Analytical hierarchy process) algorithm (Kiker et al., 2005), MAUT (Multi-Attribute Utility Theory) algorithm (Torrance et al., 1982), PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) algorithm (Brans and Vincke, 1985), and ELECTRE (Elimination Et Choix Traduisant la Realite) algorithm (Roy et al., 1966). Among these methods, the AHP algorithm is one of the most scalable MCDM algorithms, so it is especially suitable for the optimal scheme evaluation under the framework of a multi-level index system (Deng et al., 2012). The AHP algorithm has obvious advantages in hierarchical analysis, but more empirical methods are used for samples within indicators, and different thresholds are formulated for grading the quantification. Such quantification methods are often too rough and greatly influenced by subjective factors, which can easily cause a certain quantitative deviation (Joshi et al., 2011). The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm can solve this problem precisely. It uses the gap between the indicator samples and the optimal samples to quantify the suitability of the indicators individually, so it can generally avoid the deviation caused by the use of threshold quantification as much as possible. Adopting the TOPSIS algorithm to extend the AHP algorithm, resulting in the AHP-TOPSIS algorithm, has been successfully applied in the evaluation of urban tourism competitiveness (Yue et al., 2010) and tourism development suitability (Du et al., 2018).
This study takes tourists' travel activities as the main object, and comprehensive consideration is given to climate comfort, health impacts (including the impacts on the respiratory system and skin), vegetation conditions, whether there is precipitation interference, and other factors. By quantifying temperature comfort, weather impact, vegetation status, the atmospheric environment and ultraviolet radiation, the suitability of the tourism environment in China is comprehensively evaluated. This study adopts the AHP-TOPSIS algorithm to comprehensively evaluate the tourism environmental suitability scores in the China mainland, Hainan Island, Taiwan Island and other regions in China by quantifying the temperature comfort degree, weather influence, vegetation condition, atmospheric environment, ultraviolet radiation, and similar factors. Through a comparative analysis of the annual and seasonal change patterns in the tourism environmental suitability scores of three typical tourist areas (Beijing-Tianjin-Tangshan area, Yangtze River Delta and Pearl River Delta), some strategies for optimizing and improving the tourism environmental suitability are put forward.

2 Research areas and data sources

2.1 Research areas

China is located in the temperate and subtropical zones, it is influenced by the subtropical high pressure, and the monsoon climate is significant. Due to the influences of the land and sea positions, latitude and longitude differences, altitude differences and other factors, the distributions of tourism environmental conditions in the scales of time and space in China are highly variable. In general, the coastal areas of southeast China are rich in precipitation and the vegetation coverage is relatively good; there is less precipitation in the inland area of northwest China, and the vegetation types are mainly grassland and desert; the vegetation in Qinghai-Tibet Plateau is mainly alpine meadow which is affected by the altitude, and the ultraviolet radiation is strong; the climate conditions in north China are relatively good, but the air pollution is serious due to urbanization; and the vegetation coverage conditions in northeast China are better, but the climate suitability is relatively low.
This study takes the Chinese mainland and Taiwan Island as the research areas, which is roughly located within 73°33′E-135°5′E, 18°9′N-53°33′N. To analyze the spatial-temporal differentiation characteristics of the tourism environmental suitability in China, this study uses Beijing-Tianjin-Tangshan area (115°12′E-119°36′E, 38°36′N- 41°12′N), Yangtze River Delta (117°48′E - 122°30′E, 28°01′N-33°42′N), Pearl River Delta (111°02′E-115°48′E, 21°30′N-24°30′N) and other regions as the typical tourist areas to carry out an in-depth and focused analysis. Regarding the spatial distribution of the research areas and typical tourist areas in this study, the three typical tourist areas of Beijing- Tianjin-Tangshan area, Yangtze River Delta and Pearl River Delta have obvious latitudinal differences, so they can represent the overall tourism environment of the popular tourist areas in North, Central and South China. They are representative of those areas, and can better assist in excavating the spatial and temporal differentiation characteristics of the tourism environmental suitability throughout China.

2.2 Data indicators and data sources

Previous studies on tourism suitability have involved the assessment and simulation of environmental factors, mainly from the aspects of climate comfort (Yang et al., 2018) and ecological environment level (Dhami et al., 2017; Ayhan et al., 2020). In addition, tourists are also very concerned about the environmental factors that affect health (Li et al., 2021). On the basis of previous studies, this study comprehensively evaluates the tourism environmental suitability of China from the three aspects of weather conditions, vegetation status, and health effects. It adopts five indicators as the quantitative indicators of China’s tourism environmental suitability: Universal Thermal Climate Index (UTCI, K), Total Precipitation (PREC, m), Vegetation Index (including High Vegetation Leaf Area Index (LAIH, m2 m‒2); Low Vegetation Leaf Area Index (LAIL, m2 m‒2); Particulate matter with a diameter of no more than 2.5 micrometers (PM2.5, μg m‒2); and Downward UV Radiation at the surface (UVB, J m‒2). The explanations of these tourism environmental suitability indicators are shown in Table 1.
Table 1 Tourism environmental suitability index system
Target layer Criterion layer Index layer Index interpretation Sources
Tourism environmental suitability Weather
conditions		 Universal Thermal Climate Index Used to characterize the thermal stress state and comfort degree of the human body under outdoor environmental conditions Ge et al., 2017
Total Precipitation Used to characterize the influence of
adverse weather factors on tourism		 Lise and Tol, 2002; Day et al., 2013
Vegetation
status		 Vegetation Index High Vegetation Leaf Area Index Mainly simulates the growth of shrubs, trees and other vegetation Kamal et al., 2016
Low Vegetation Leaf Area Index Mainly simulates the coverage degree of grassland and similar vegetation Kamal et al., 2016
Health effects Particulate matter with a diameter of no more than 2.5 micrometers Used to characterize the influence and damage degree of ultraviolet radiation on the skin Yu et al., 2014; Li
et al., 2021
Downward UV radiation at the surface Adopted to reflect the air pollution factors in the tourism environment Lim et al., 2012
The Universal Thermal Climate Index combines several climate indicators, including air temperature, wind, radiation, air humidity and others. By examining the intensity of physiological responses (including sweating, trembling, skin moisture, skin blood flow, rectum, skin and face temperature, etc.) under the combinations of these indicators, this Index is used to characterize the thermal stress state and comfort degree of the human body under outdoor environmental conditions. It represents the latest technology of bio-climatology in human comfort data analysis. The Universal Thermal Climate Index is directly related to human feelings and reflections, so it is one of the ideal indexes for describing and characterizing the suitability of the tourism environment. Compared with the use of air temperature, air humidity and other individual indicators for tourism environmental suitability score evaluation, this Index is obviously superior.
The Vegetation Leaf Area Index refers to the proportion of the total vegetation leaf area to the land area, which is the preferred index for characterizing the vegetation environment. In this study, the vegetation environment is characterized by both the high vegetation leaf area index and the low vegetation leaf area. The high vegetation leaf area index mainly simulates the growth of shrubs, trees and other taller vegetation, while the low vegetation leaf area index mainly simulates the coverage degree of grassland and shorter vegetation. The separate application of the leaf area indexes of high and low vegetation in tourism environmental suitability evaluation is helpful for clarifying the effects of high and low vegetation in different tourist areas, which can provide more detailed information support for future optimization decisions.
The amount of precipitation is used to characterize the influence of adverse weather factors on tourism. In light of the fact that the degree of influence of precipitation on tourism is different, this study uses precipitation to quantify the degree of precipitation’s influence on tourism.
If the ultraviolet radiation is too strong, it will damage the skin cells, and existing studies have generally not accounted for the skin damage factors during tourism (Yu et al., 2014). In this study, the intensity of ultraviolet radiation is used to characterize the influence and degree of damage to skin from the ultraviolet radiation.
Atmospheric fine particles can pass through the respiratory tract and enter the blood circulation system of the human body, which is harmful to human health (Lim et al., 2012). The study adopts the PM2.5 concentration of atmospheric fine particulate matter to reflect the air pollution factors in the tourism environment, which also emphasizes that this study pays a great deal of attention to health factors in its tourism environment evaluation.
The data for the Universal Thermal Climate Index, the Total Precipitation, the High Vegetation Leaf Area Index, the Low Vegetation Leaf Area Index, and the Ultraviolet Radiation Intensity come from the reanalysis datasets of the ERA5 (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) in the European Centre for Medium- Range Weather Forecasts (ECMWF). The ERA5 uses advanced models and data assimilation systems to incorporate large amounts of historical observations into global estimates in order to provide reanalysis data on atmospheric, terrestrial and marine climatic variables. The concentration of atmospheric fine particulate matter is derived from the China National Environmental Monitoring Centre (http://www.cnemc.cn/) and the PM2.5 spatial coverage dataset for this study area was obtained by using the Inverse Distance Weight (IDW) spatial interpolation algorithm (Setianto and Triandini, 2013). The data used in this study all represent 2019 full-year data.

3 Methods

This study adopts several indicators, including the Universal Thermal Climate Index (UTCI), the High Vegetation Leaf Area Index (LAIH), the Low Vegetation Leaf Area Index (LAIL), the Total Precipitation (PREC), the Ultraviolet Radiation Intensity (UVB), and the PM2.5 concentration of the atmospheric fine particulate matter, and uses the AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution) coupling algorithm to carry out the comprehensive evaluation of the tourism environmental suitability score in China.

3.1 Weight determination of the tourism environmental suitability score based on the AHP algorithm

Analytic hierarchy process (AHP) is a method for guiding decision-making by organizing perception, judgment and other considerations into a multilevel hierarchy that affects decision-making (Saaty, 1994). Saaty (1994) also suggested that the AHP is used to decompose a problem, and then polymerize the solutions of all the subproblems into a final conclusion. The overall decision goal of this study, the tourism environmental suitability, is at the top level; while the decision factors, UTCI, PREC, LAI (LAIH, LAIL), PM2.5, UVB, etc. are at the middle level, and the decision factor weight W is at the bottom level. The AHP provides a structured framework for tourism environmental suitability evaluation, through a 1-9 scale (Saaty, 1980), as well as pairwise comparisons and quantitative priority through a consistency test, in order to obtain a more consistent pairwise comparison matrix and comprehensively calculate the weight of the role of each factor. The calculation steps are as follows:
Let j be the order of indicators, m be the number of indicators, and w1, w2, $\cdots$, wm be the normalized weight vectors, according to the requirements:
$\underset{j=1}{\overset{m}{\mathop \sum }}\,{{w}_{j}}=1;\ {{w}_{j}}\ge 0;\ j=1,2,\cdots ,m;\ m=5$
Through consulting experts, we obtained the relative importance degree of the tourism environmental suitability score evaluation between indicator parameters P1, P2, $\cdots$, Pm, quantified the relative importance degrees between parameters with a 1-9 scale, and obtained the m×m dimension pair comparison matrix:
$A={{\left( {{a}_{ij}} \right)}_{m\times m}}=\begin{matrix} {{P}_{1}} \\ {{P}_{2}} \\ \vdots \\ {{P}_{m}} \\ \end{matrix}\left[ \begin{matrix} {{a}_{11}} & {{a}_{12}} & \ldots & {{a}_{1m}} \\ {{a}_{21}} & {{a}_{22}} & \ldots & {{a}_{2m}} \\ \vdots & \vdots & \vdots & \vdots \\ {{a}_{m1}} & {{a}_{m2}} & \ldots & {{a}_{mm}} \\ \end{matrix} \right]$
When any i, j =1, 2, …, k, …, m, satisfies aij=aik akj in a pairwise comparison matrix, it is called a fully consistent matrix AW, otherwise it is an incompletely consistent matrix. The i, j, k indicate the order of indicators, and m is the number of indicators. Weight vector W can be obtained by solving the following characteristic equations (Saaty, 1980):
$AW={{\lambda }_{\max }}W$
where λmax is the maximum eigenvalue in the pairwise comparison matrix A, which needs to be within the acceptable consistency range and can be tested by the CR (Consistency Ratio):
$CR=\frac{\left( {{\lambda }_{\text{max}}}-m \right)/\left( m-1 \right)}{RI}$
In this equation, RI is the average random consistency of the same order matrix, which can be obtained by looking it up in a table. If CR≤0.1, then the constructed pairwise comparison matrix consistency is acceptable; otherwise, the pairwise comparison matrix needs to be rebuilt until a more consistent pairwise comparison matrix is obtained. Through the above algorithm, the weight vectors of [0.2636,0.5109,0.0366,0.0366,0.1155,0.0368] were calculated for parameters UTCI, PREC, LAIH, LAIL, PM2.5, and UVB.

3.2 Evaluation of the tourism environmental suitability score based on the TOPSIS method

The main idea of TOPSIS is to sort the parameters by defining the positive and negative rational solutions of the problem by calculating the distances between the parameters and the positive and negative rational solutions, and then to give the corresponding scores for evaluating the merits and demerits of the samples. This study calculates the scores of UTCI, PREC, LAIH, LAIL, PM2.5, UVB and others by the TOPSIS algorithm, then conducts the weighted coupling of the above weight of each parameter (see Section 3.1), and finally obtains the results for the research areas, which are typical tourist areas. The specific steps of the TOPSIS algorithm are as follows:
${{x}^{N}}=\left\{ \begin{align} & ({{x}_{i}}-{{x}_{\text{min}}})/({{x}_{\text{max}}}-{{x}_{\text{min}}}\text{) }\ \ \ \ \ \ \ \ x\in LAIH,LAIL \\ & ({{x}_{\text{max}}}-{{x}_{i}})/({{x}_{\text{max}}}-{{x}_{\text{min}}})\text{ }\ \ \ \ \ \ \ \ x\in \text{P}{{\text{M}}_{2.5}},\text{ }UVB \\ & \left\{ \begin{array}{*{35}{l}} 1,\ \ \ \ \ \ \ \ \ \ \ \ \ \ 9\le {{x}_{i}}\le 26 \\ \left( 9-{{x}_{i}} \right)/\left( 9-{{x}_{\text{min}}} \right)\text{, }{{x}_{i}}<9 \\ \left( {{x}_{i}}-26 \right)/\left( {{x}_{\text{max}}}-26 \right)\text{, }{{x}_{i}}>26 \\\end{array} \right.\text{ }x\in UTCI \\ & \left\{ \begin{array}{*{35}{l}} 0,\ \ \ \ \ \ \ \ \ \ \ \ \ {{x}_{i}}>8 \\ \left( 8-{{x}_{i}} \right)/\left( 8-{{x}_{\text{min}}} \right)\text{, }{{x}_{i}}\le 8 \\\end{array} \right.\text{ }\ x\in PREC \\ \end{align} \right.$
where i is the sample number, xN is the normalized value, x is the parameter, xi is the parameter sample to be normalized, and xmax, xmin are the parameter maximum and minimum, respectively; UTCI is the Universal Thermal Climate Index, LAIH is the High Vegetation Leaf Area Index, LAIL is the Low Vegetation Leaf Area Index, PREC is the Total Precipitation, UVB is the Ultraviolet Radiation Intensity, and PM2.5 is the PM2.5 concentration of the atmospheric fine particulate matter. First, the parameter samples are orthogonalized and normalized. The LAIH and LAIL are positive indicators, that is, the greater the value, the better the performance. PM2.5 and UVB are negative indicators, that is, the smaller the better. UTCI is a stage-type indicator. According to the UTCI definition document, there is no thermal stress in the range of 9-26 oC, but if the temperature exceeds 26 oC or goes below 9 oC, then thermal stress will be felt. PREC is a threshold indicator, so when the precipitation exceeds the threshold range of heavy rain (>8 mm h-1), it will be completely unfavorable to the launch of tourism activities, and its contribution to tourism environmental suitability will be reduced to 0.
Next, the parameter samples are standardized to determine the distribution of each parameter in the sample population. The formula is as follows:
${{x}^{N,STD}}=\left( x_{i}^{N}-{{x}_{\text{mean}}} \right)/{{x}_{std}}$
where xN,STD is the standardized post-sample value, xiN is the aforementioned positive post-sample value, and xmean and xstd are the sample mean and standard deviation, respectively.
Then the positive and negative ideal solutions are calculated as:
$\left\{ \begin{matrix} S_{i}^{+}=\text{max}\left( {{x}^{N,STD}} \right) \\ S_{i}^{-}=\text{min}\left( {{x}^{N,STD}} \right) \\\end{matrix} \right.$
where Si+ and Si-are the positive and negative ideal solutions, respectively.
Then the solution distances between the positive and negative ideal solutions are calculated as:
$\left\{ \begin{matrix} S{{d}^{+}}=\sqrt{\underset{i=1}{\overset{n}{\mathop \sum }}\,{{\left( S_{i}^{+}-x_{i}^{N,STD} \right)}^{2}}} \\ S{{d}^{-}}=\sqrt{\underset{i=1}{\overset{n}{\mathop \sum }}\,{{\left( S_{i}^{-}-x_{i}^{N,STD} \right)}^{2}}} \\\end{matrix} \right.,i=1,2,\ \cdots ,\ n$
where $S{{d}^{+}}$ and $S{{d}^{-}}$ are the positive and negative ideal solution distances, i is the sample number and n is the sample quantity.
Then, the degree of closeness is calculated as:
$\xi =\frac{S{{d}^{-}}}{S{{d}^{+}}+S{{d}^{-}}}$
where ξ is the sample score.
Lastly, the weight vector W of each parameter indicator calculated by the AHP algorithm and the total score of each parameter sample calculated by the TOPSIS algorithm are weighted and coupled to obtain the final total score of the AHP-TOPSIS tourism environmental suitability EBS (Environmental Suitability Score):
$EBS=({{w}_{1}}\times {{\xi }_{1}}+{{w}_{2}}\times {{\xi }_{2}}+\cdots +{{w}_{m}}\times {{\xi }_{m}}),m=1,2,\cdots ,5$
where m is the number of indicators, w1, w2, …, wm are the normalized weight vectors, and ξ1, ξ2,…, ξm are the sample scores of the different indicators. The EBS is the total score of tourism environmental suitability, its value range is [0,1] according to the national optimal and the worst sample ranking calculation, and its values can characterize the relative advantages and disadvantages of tourism environmental suitability throughout the whole country.

4 Results

4.1 Temporal and spatial analysis of tourism environmental suitability scores for China

According to the AHP-TOPSIS algorithm, the annual average tourism environmental suitability score of China in 2019 was calculated, and its spatial distribution is shown in Fig. 1. The tourism environmental suitability score of our country has obvious spatial heterogeneity. The tourism environmental suitability scores of the East and South China regions are the highest, those of North and Northeast China are relatively high, and the score of the northwest inland area is the lowest. In the south and north of the Qinling-Huaihe River, the differences in tourism environmental suitability are significant, and the south of the Qinling-Huaihe River is characterized by better vegetation coverage, longer periods of suitable temperature, weaker ultraviolet radiation and less air pollution. The combination of these positive factors makes the tourism environment more suitable in the south of Qinling-Huaihe River. In North China, the air pollution is relatively heavy and the vegetation cover condition is poor, which affects the potential for improving the tourism environmental suitability. In Northeast China, the vegetation coverage is high, but under the influence of temperature, the tourism environmental suitability in this area is lower than that in South China. To the west and east of the Heihe-Tengchong line, the difference in tourist environment suitability is also remarkable. The influences of altitude, topography, land and sea position, vegetation coverage, temperature suitability and ultraviolet intensity in areas to the east of the Heihe-Tengchong line are superior to those in areas to the west of the Heihe-Tengchong line, thus forming a boundary of tourism environmental suitability.
Fig. 1 Spatial distribution of annual average tourism environmental suitability scores in China in 2019
In order to further understand the temporal distribution characteristics of tourism environmental suitability scores in China, this study compares the scores in different seasons (Fig. 2). Overall, China’s tourism environmental suitability in the summer and autumn seasons are higher, and the lowest in winter. Under the influence of temperature, precipitation, ultraviolet haze and other factors, South China and East China are more suitable for tourism in autumn and winter, and the tourism environment of Northeast China in summer is more suitable. The suitability of the tourism environment is relatively high in Yunnan and Fujian all year round.
Fig. 2 Spatial distribution of seasonal average tourism environmental suitability China in 2019

4.2 Temporal and spatial analysis of tourism environmental suitability scores for typical tourist areas

A comparative analysis of the seasonal and interannual changes in the tourism environmental suitability scores was carried out for the three typical tourist areas of the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta (Figs. 3-5) The suitability of the tourism environment in the Beijing-Tianjin-Tangshan area has obvious seasonal changes. Affected by haze, the suitability scores of the tourism environment in winter and spring are obviously lower than in summer and autumn. There are north-south differences in the suitability of the tourism environment within the region. The tourism environmental suitability scores in the western and northern regions of Beijing are relatively higher than in the urban areas, and they are suitable for launching the development and planning of short-distance leisure and health care projects. The overall tourism environmental suitability of the Tianjin and Tangshan area is relatively low.
Fig. 3 Spatial distribution of seasonal average tourism environmental suitability scores in the Beijing-Tianjin-Tangshan typical tourism area
The seasonal variation of the tourism environmental suitability in the Yangtze River Delta region (Fig. 4) is obviously different from that in the Beijing-Tianjin-Tangshan area. The tourism environmental suitability scores in the Yangtze River Delta region are relatively high in spring and autumn, but low in winter and summer. There are obvious differences between the north and south in the tourism environment of this region, and the tourism environmental suitability is higher in the southern Zhejiang region and lower in the northern Jiangsu region. Therefore, Zhejiang is more suitable for the development of leisure tourism activities. Due to the influences of vegetation coverage, the atmospheric environment and air humidity, the tourism environmental suitability score in the Shanghai area is lower than in the Zhejiang area.
Fig. 4 Spatial distribution of seasonal average tourism environmental suitability scores in the typical tourist area of the Yangtze River Delta in 2019
The tourism environmental suitability score of the Pearl River Delta is relatively high throughout the whole year (Fig. 5), and mainly affected by temperature. The tourism environmental suitability score is the highest in autumn and the lowest in summer. In the Zhongshan, Guangzhou, Dongguan and Shenzhen areas, which are affected by urbanization and air pollution, the tourism environmental suitability scores are significantly lower than they are in the surrounding areas. Therefore, it is more suitable to launch leisure tourism activities in Foshan, Jiangmen, Huizhou and other places in this region.
Fig. 5 Spatial distribution of seasonal mean tourism environmental suitability scores in the typical tourist area of the Pearl River Delta in 2019
By comparing the tourism environmental suitability scores of the Beijing-Tianjin-Tangshan area, the Pearl River Delta and the Yangtze River Delta region (Fig. 6), it is found that the Pearl River Delta region has the highest score among these three regions, followed by the Yangtze River Delta region, and the Beijing-Tianjin-Tangshan region has the lowest score. The tourism environmental suitability scores are low in the concentrated urban areas. Affected by urbanization, the urban area vegetation coverage is low, the air pollution is serious, and the heat island effect is remarkable. These factors together cause the decrease in the tourism environmental suitability scores of urban areas. Therefore, the northern region of the Beijing-Tianjin-Tangshan area, the southern region of the Yangtze River Delta and the surrounding areas of the Pearl River Delta are more suitable for the sustainable development of the tourism industry.
Fig. 6 Spatial distribution of annual average tourist environmental suitability scores in the typical tourist areas of the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta in 2019

4.3 Analysis of key factors for optimizing the tourism environmental suitability

A comparative analysis of the contributions of the factors affecting the year-round and four-season tourist environment suitability in the three typical tourist areas of the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta (Fig. 7) shows that the dominant factor in the Beijing-Tianjin-Tangshan area is grassland coverage, and the main limiting factor is PM2.5. Grassland coverage and temperature are the dominant factors in the Yangtze River Delta region, and PM2.5 is also the limiting factor of tourism environmental suitability in this region. The dominant factor in the Pearl River Delta region is vegetation coverage, and precipitation is the main tourism limiting factor in this region. In addition to the low contribution of precipitation factors, the superiority of other factors such as temperature, vegetation coverage and atmospheric environment in the Pearl River Delta region is stronger than in the Beijing-Tianjin-Tangshan area and the Yangtze River Delta region. In the Yangtze River Delta and the Beijing-Tianjin- Tangshan area, the contribution rates of the other factors to the tourism environmental suitability are roughly consistent, except for the temperature and vegetation coverage factors.
Fig. 7 Analysis of the factors driving tourism environmental suitability for 2019 in the typical tourist areas of the Beijing-Tianjin-Tangshan area, the Yangtze River Delta and the Pearl River Delta

Note: BTT represents the Beijing-Tianjin-Tangshan area; YRD represents the Yangtze River Delta; and PRD represents the Pearl River Delta.

The PM2.5, UTCI, UVB and other factors in the Beijing-Tianjin-Tangshan area change significantly with the seasons, especially the marked difference between PM2.5 in winter and summer. For the Beijing-Tianjin-Tangshan area, the key to improving the tourism environmental suitability is to improve the quality of the atmospheric environment, especially in winter, which will play a key role in optimizing the tourism environmental suitability. The dominant factors affecting the tourism environmental suitability score in the Yangtze River Delta region are LAIL and UVB, the effects of which change considerably with the season. In winter, the contribution rate of UVB is larger, but in summer, the contribution rate of LAIL is larger, and the effects of these two on the tourism environmental suitability score are complementary.
PM2.5 is also a disadvantageous factor affecting the tourism environmental suitability of the Yangtze River Delta, especially in winter. Therefore, the most effective way to improve the tourism environmental suitability of the Yangtze River Delta is to control air pollution and increase the density of green trees, so as to improve the contributions of PM2.5 and LAIH to the tourism environmental suitability. During the spring and summer seasons, the tourism environmental suitability scores of the Pearl River Delta are significantly affected by the precipitation. PM2.5 has a positive effect on the tourism environmental suitability scores in spring and summer, while in autumn and winter, PM2.5 is also the major factor limiting the tourism environmental suitability in the Pearl River Delta. Because the vegetation cover condition of the Pearl River Delta area is relatively complete, the space for further improving its tourism environmental suitability score is relatively small, so PM2.5 management is one key way to improve the tourism environmental suitability score of the Pearl River Delta region.

5 Discussion

This study uses the AHP-TOPSIS algorithm to comprehensively evaluate the tourism environmental suitability of China’s mainland, Hainan Island, Taiwan Island and other regions. It adopts the UTCI, PREC, LAI (LAIH, LAIL), PM2.5, UVB and other indicators to quantify the factors of temperature comfort, weather impact, vegetation status, atmospheric environment, UV radiation, etc., in order to comprehensively evaluate the tourism environmental suitability of China in 2019. Through a comparative analysis of the impacts of various indicators on tourism environmental suitability, the strategies for optimizing the tourism environmental suitability are proposed for each region.
Compared with previous studies, this study has four main innovations.
(1) This study involves targeted research on the tourism environmental suitability in China. Compared to studies that use a single indicator like air temperature or air humidity, it has a strong advantage in describing and characterizing tourism environmental suitability by using the most advanced UTCI in human body comfort analysis (Zou, 2018). In addition, the UVB of ultraviolet radiation intensity is included in the evaluation index system of tourism environmental suitability in order to characterize the influence of solar radiation on the skin. This further reflects the fact that the tourism environmental suitability evaluation system built in this study has taken tourism health care into consideration.
(2) This study carries out a nation-level tourism environmental suitability evaluation. Compared to the previous tourism environmental suitability studies which took only prefecture-level administrative regions as the research units (Gu et al., 2015), the results of this analysis can better reflect the detailed distribution of tourism environmental suitability across heterogeneous space, and so they provide more abundant information for tourism decision-making throughout the country.
(3) This research adopts the AHP-TOPSIS algorithm and the method of combining subjective evaluation with objective evaluation, which not only takes into account the weight differences between different indicators but also gives the tourism environmental suitability scores objectively. This evaluation method is more reasonable and reliable than the objective evaluation methods, such as the entropy method (Shen et al., 2013) and the CRITIC method (Rostamzadeh et al., 2018).
(4) Previous studies tended to focus only on the evaluation of tourism suitability and rarely conducted any further factor analysis on the evaluation results (Dhami et al., 2017; Ayhan et al., 2020), which greatly limits the role of those previous tourism suitability evaluations in guiding real environmental governance. Through a comprehensive analysis of the evaluation results of national tourism environmental suitability, this study compares the contributions of different factors to the improvement of tourism environmental suitability, and puts forward some opinions on ways to improve the tourism environmental suitability in different regions. Therefore, it has an important value for guiding the improvement of tourism environmental quality.

6 Conclusions

The main conclusions of this study are four-fold.
(1) China’s tourism environmental suitability shows obvious spatial differentiation. East China and South China have the highest tourism environmental suitability scores. North China and Northeast China have relatively high tourism environmental suitability, while the northwest inland area has lower tourism environmental suitability, which is consistent with the evaluation of China’s ecotourism environmental suitability by Geng et al. (2019).
(2) Overall, China’s tourism environmental suitability scores are higher in summer and autumn and the lowest in winter. In autumn and winter, South China and East China are more suitable for tourism, and the tourism environmental suitability of Northeast China is higher in summer. The suitability of the tourism environment is high in Yunnan and Fujian all year round.
(3) The Pearl River Delta is the region with the highest tourism environmental suitability score among the three typical tourist regions, followed by the Yangtze River Delta region. The northern region of the Beijing-Tianjin-Tangshan area, the southern region of the Yangtze River Delta and the surrounding areas of the Pearl River Delta are more suitable for the sustainable development of the tourism industry.
(4) To increase LAI, especially LAIH, i.e., to increase the leaf area index of trees, has great potential for improving the tourism environmental suitability of the Beijing-Tianjin- Tangshan area and the Yangtze River Delta. PM2.5 is the limiting factor of tourism environmental suitability in the Beijing-Tianjin-Tangshan area and the Yangtze River Delta, so atmospheric environmental control is an effective way to improve the suitability of the tourism environment.
Based on a balanced consideration of multiple factors, such as tourism comfort, tourism experience and tourism health, this study provides ideas and methods for the comprehensive evaluation of tourism environmental suitability. It carried out a multi-dimensional evaluation and analysis of tourism environmental suitability in China, and provides decision support for the tourism planning locations and the further optimization of the tourism environment. In the next step, further research will include an in-depth prediction analysis and research on the tourism environmental suitability, in order to provide valuable tourism environment predictions and forecast information for tourists, tourism enterprises, the government and other stakeholders.

This article was supported by a grant from the State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Science. We also want to express our sincere gratitude to the anonymous reviews and editors for their efforts in the improvement of the manuscript.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Ayhan Ç K, Taşlı T C, Özkök F, et al. 2020. Land use suitability analysis of rural tourism activities: Yenice, Turkey. Tourism Management, 76: 103949-103951.

[2]
Brans J P, Vincke P. 1985. Note—A preference ranking organisation method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31(6): 647-656.

[3]
Day J, Chin N, Sydnor S, et al. 2013. Weather, climate, and tourism performance: A quantitative analysis. Tourism Management Perspectives, 5: 51-56.

[4]
Deng X, Li J M, Zeng H J, et al. 2012. Analytic hierarchy process weight calculation method analysis and its application research. Mathematics in Practice and Theory, 42(7): 93-100. (in Chinese)

[5]
Dhami I, Deng J Y, Strager M, et al. 2017. Suitability-sensitivity analysis of nature-based tourism using geographic information systems and analytic hierarchy process. Journal of Ecotourism, 16(1): 41-68.

[6]
Du J P, Shao J A, Zhou C R, et al. 2018. Study on reclamation decision of coal mine temporary construction land based on ecological suitability and triangle model. Journal of Natural Resources, 33(11): 1872-1885. (in Chinese)

[7]
Ge Q S, Kong Q Q, Xi J C, et al. 2017. Application of UTCI in China from tourism perspective. Theoretical and Applied Climatology, 128(3-4): 551-561.

[8]
Geng S F, Guo A D, Yang J, et al. 2019. The comprehensive evaluation of suitability of green tourism base in China, Scientia Geographica Sinica, 39(9): 1507-1515. (in Chinese)

[9]
Gu Y Q, Yang J, Feng X L, et al. 2015. Spatial differentiation of human settlement environment suitability in Chinese typical tourist cities. Scientia Geographica Sinica, 35(4): 410-418. (in Chinese)

[10]
Hopkins L D. 1977. Methods for generating land suitability maps: A comparative evaluation. Journal of the American Institute of Planners, 43(4): 386-400.

[11]
Jankowski P. 1995. Integrating geographical information systems and multiple criteria decision-making methods. International Journal of Geographical Information Systems, 9(3): 251-273.

[12]
Joshi R, Banwet D K, Shankar R. 2011. A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain. Expert Systems with Applications, 38(8): 10170-10182.

[13]
Kamal M, Phinn S, Johansen K. 2016. Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176: 242-254.

[14]
Kiker G A, Bridges T S, Varghese A, et al. 2005. Application of multicriteria decision analysis in environmental decision making. Integrated Environmental Assessment and Management, 1(2): 95-96.

[15]
Li J J, Duan K F, Xu Q W, et al. 2021. Efficiency of tourism development in China’s major cities under the constraint of PM2.5. Plos One, 16(8): e0255508. DOI: 10.1371/journal.pone.0255508.

[16]
Li R, Wu D T, Zhu T X, et al. 2012. Models of integrated development of coastal tourism of China based on its connotation. Tropical Geography, 32(5): 527-536. (in Chinese)

[17]
Lim S S, Vos T, Flaxman A D, et al. 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859): 2224-2260.

[18]
Lise W, Tol R S J. 2002. Impact of climate on tourist demand. Climatic Change, 55(4): 429-449.

[19]
Perch-Nielsen S L, Amelung B, Knutti R. 2010. Future climate resources for tourism in Europe based on the daily Tourism Climatic Index. Climatic Change, 103(3-4): 363-381.

[20]
Qu X L, Li Y Z. 2008. Research on the development of coastal leisure tourism in Liaoning Province. Marine Development and Management, 25(2): 117-121. (in Chinese)

[21]
Rostamzadeh R, Ghorabaee M K, Govindan K, et al. 2018. Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach. Journal of Cleaner Production, 175: 651-669.

[22]
Roy B, Benayoun R, Sussman B. 1966. ELECTRE: Methods for guiding selection when multiple perspectives exist. Notes of Travai, 49: 49-50. (in French)

[23]
Saaty T L. 1980. The analytic hierarchy process. New York, USA: McGraw Hill.

[24]
Saaty T L. 1994. How to make a decision: The analytic hierarchy process. Interfaces, 24(6): 19-43.

[25]
Setianto A, Triandini T. 2015. Comparison of kriging and inverse distance weighted (IDW) interpolation methods in lineament extraction and analysis. Journal of Applied Geology, 5(1): 21-29.

[26]
Shen J H, Yu Z W, Zhou B H, et al. 2013. Suitability analysis and countermeasures of regional hot spring tourism development. Journal of Natural Resources, 28(12): 2127-2138. (in Chinese)

[27]
Steiner F. 1983. Resource suitability: Methods for analyses. Environmental Management, 7: 401-420.

[28]
Torrance G W, Boyle M H, Horwood S P. 1982. Application of multi-attribute utility theory to measure social preferences for health states. Operations Research, 30(6): 1043-1069.

[29]
Xue C H, Li L T, Ren J, et al. 2014. Evaluation of desert tourism environmental suitability in Ningxia. Journal of Desert Research, 34(3): 901-910. (in Chinese)

[30]
Yang J, Zhang Z C, Li X M, et al. 2018. Spatial differentiation of China’s summer tourist destinations based on climatic suitability using the Universal Thermal Climate Index. Theoretical and Applied Climatology, 134(3-4): 859-874.

[31]
Yu Z K, Sun G N, Feng Q, et al. 2014. Temporal and spatial dynamic analysis of tourism climate comfort and climate risk in Qinghai-Tibet Plateau. Resources Science, 36(11): 2327-2336. (in Chinese)

[32]
Yue C J, Wu Z W, Zheng T X, et al. 2010. Tourism competitiveness evaluation study of coastal cities. Journal of Natural Resources, 25(5): 795-801. (in Chinese)

[33]
Zhong L S, Tang C C, Cheng S K, et al. 2011. Impacts of global climate change on China’s tourism industry and countermeasures. China Software Science, (2): 34-41. (in Chinese)

[34]
Zou J H. 2018. Spatial and temporal characteristics of tourism environment comfort in China’s important tourist cities. Diss., Changsha, China: Hunan Normal University. (in Chinese)

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