Research Progress on the Evaluation of Tourism Climate Comfort and Its Application in China: A Bibliometrics-Based Review

Abstract

The evaluation of tourism climate comfort is a systematic assessment of the climate conditions of tourist destinations. It is of great significance for improving the tourism experience, promoting the sustainable development of the tourism industry, and protecting the natural environment. In this study, the CiteSpace software was used to conduct a bibliometrics analysis of the study on tourism climate comfort in China, and the conceptual framework of this study was established based on the bibliometrics results. In the conceptual framework, this study firstly summarized eight indicators widely used in the current evaluation of tourism climate comfort. Secondly, four key technical means in the evaluation process were summarized, including geographic information system, analytic hierarchy process, fuzzy comprehensive evaluation, and cluster analysis. And three calculation methods of tourism climate comfort period were summarized, namely number of days with comfortable climate, five-day moving average method, and probability of climate-suitable days. Subsequently, the main application areas of tourism climate comfort evaluation were introduced: (1) exploration of the relationship between climate comfort and tourism activities (i.e., heat/cold-escape tourism, ice-snow tourism, outdoor rafting, coastal tourism, and other types of tourism activities); (2) exploration of the relationship between climate comfort and tourist flow; (3) the response of climate comfort to climate change; and (4) tourism climate regionalization. Finally, the main problems of current research and future development directions were proposed.

1. Introduction

Tourism is closely related to climate conditions and is often influenced by various aspects of climate elements [1]. Climate directly reflects the seasonality of a tourist destination, influences tourists’ choice of activities, and plays a role in landscape creation and shaping [2,3]. Therefore, understanding and assessing the impact of climate on tourism is crucial for decision-making and planning in the tourism sector. Governments, tourism professionals, and tourists all need to consider climate factors to achieve the sustainable development and scientific management of tourism [4,5,6]. The evaluation of tourism climate comfort is an important research area in the field of tourism climatology because it is significant for enhancing the attractiveness of tourist destinations, increasing tourist satisfaction, and promoting local economic development [5,6]. From the perspective of governments or tourism professionals, evaluating tourism climate comfort helps destinations and operators utilize resources effectively [5,6,7]. From the tourists’ perspective, evaluating tourism climate comfort can help them understand and assess the climate conditions of a destination, enabling them to choose destinations and plan trips better, thus maximizing the enjoyment and pleasure of their travel experience [7]. Scenic areas can develop specific tourism projects based on local climate conditions, and they can take appropriate preventive and protective measures according to climate disaster risk assessments, ensuring the safety of tourists [8].

Climate comfort represents the degree of comfort or discomfort that the human body feels under the combined influence of external meteorological conditions [9]. It plays an important role in tourism activities, human health, and architectural design [10,11,12]. For tourism, it directly influences destination choices, travel seasons, and revenue [13,14,15]. As climate change accelerates, shifts in thermal conditions, precipitation regimes, and extreme weather events are altering the spatio-temporal distribution of climate comfort periods worldwide, posing risks to destination competitiveness and operational stability [16,17]. The evaluation of tourism climate comfort is not merely a regional concern but a global imperative for sustainable tourism development [16,17]. Therefore, the evaluation of tourism climate comfort has been widely carried out worldwide [18,19,20,21], which is of great significance for the sustainable development of the global tourism industry, the construction of tourist destinations, and the improvement of tourists’ travel experience [16,22].

Research on climate comfort evaluation began early in Western countries, with its origins tracing back to the early 20th century, making it over a century old [9]. Early studies on climate comfort mainly relied on meteorological indicators such as temperature and humidity, which were directly observed using instruments [7,9]. Later evaluations were often based on various empirical models and mechanistic models proposed by different scholars [22]. Terjung [23] developed the climate comfort index and applied it to tourism studies, marking the beginning of climate comfort in tourism as a research direction for many other scholars. Other representative climate comfort evaluation models included the wind chill index proposed by Siple and Passel [24], and the clothing index proposed by Fischer [25].

In China, research on tourism climate began in 1996, and the evaluation of tourism climate comfort often drew from the research methods of Western scholars [26,27,28]. In order to effectively assess the tourist climate comfort in China, some scholars attempted to establish evaluation models suitable for the native tourism climate [29,30]. Representative models included the comprehensive comfort index proposed by Lu et al. [29] and the weighted comprehensive evaluation model for tourism climate comfort proposed by Ma et al. [30]. In addition, several evaluation models have been optimized based on specific differentiated climate environments [31,32]. The various evaluation indicators and methods of tourism climate comfort mentioned above have continuously developed over the years, and they were widely applied in China [33]. Numerous provinces, cities, and scenic tourist areas have conducted extensive studies on tourism climate comfort evaluation [34,35]. Given the above, tourism climate comfort evaluation is of great significance for the development of tourism and the formulation of future management policies. It has long attracted the widespread attention of scholars all over the world, and research methods and directions are diversifying.

2. Bibliometrics

Given China’s vast geographic diversity and that it is a top international destination, research on tourism climate comfort is exceptionally voluminous and diverse [1,4]. The bibliometric analysis becomes crucial to map the knowledge graph, identify dominant research themes and evolving trends, and uncover potential gaps between Chinese and international research. The synthesis of these findings not only elucidates the extant research landscape but also establishes a critical framework for future studies examining climate adaptation challenges and opportunities in China’s tourism sector.

2.1. Data Sources and Methods

In this study, the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) databases were used as data sources, and the visualization analysis software CiteSpace v6.3.R1 was used to analyze the number of published articles, research hotspots, and development trends of tourism climate comfort evaluation in China. The combination of the following terms was used to search for articles: Topic = “tourism” AND “climate comfort”. A total of 821 articles were retrieved, with 755 articles from CNKI and 66 articles from WOS. These data were used to analyze the annual trend of the number of published articles. It should be noted that due to the limited number of English articles retrieved through WOS, this study only included Chinese literature data from CNKI in the CiteSpace software for keyword co-occurrence, keyword clustering, and keyword burst analysis to ensure sufficient sample size in the bibliometric analysis.

2.2. Publication Dynamics

As shown in Figure 1, the number of published articles on tourism climate comfort in China showed a significant upward trend from 1996 to 2024. Chinese scholars began to publish articles on tourism climate in CNKI in 1996, and they used various climate comfort indicators to evaluate tourism climate resources in several provinces and cities [26,27,28]. The number of publications between 1996 and 2006 was low, ranging from 1 to 8 articles per year. Since 2007, the number of publications showed an upward trend and reached its peak in 2018, with a total of 77 articles published in 2018.

2.3. Keyword Co-Occurrence Analysis

Keywords reflect thematic information in a paper and are an important basis for evaluating the research topic of the article. Keyword co-occurrence analysis can display the associations and evolutionary trends between keywords in the literature, thereby revealing research hotspots and future trends. As shown in Figure 2, the size of the circle node represents the frequency of keyword occurrence, and the larger the frequency, the larger the circle node. Centrality is a key indicator to describe the importance of a keyword. A large value of centrality (pink ring in the figure) indicates the importance and influence of the node in the research. In this study, the keyword co-occurrence map contained 283 keyword nodes and 475 connections, with a network density of 0.0119 (Figure 2). The research diverged in multiple directions with the core keywords of “tourism” (centrality = 0.33), “comfort” (centrality = 0.24), “tourism climate” (centrality = 0.28), “temperature–humidity index” (centrality = 0.16), and “climate change” (centrality = 0.12). The high frequency of the keywords “temperature–humidity index” (frequency = 69), “wind effect index” (frequency = 48), “clothing index” (frequency = 22), and “wind chill index” (frequency = 14) indicated that these four indicators were widely used in the evaluation of tourism climate comfort in China. Climate comfort affects tourists’ travel intention and travel experience, and further affects the passenger flow [2,9]. Therefore, “tourist flow” (frequency = 10) was identified as an important keyword, and the relationship between climate comfort and tourist flow was also a research hotspot. The keywords “rural tourism” (frequency = 6) and “ecotourism” (frequency = 6) were hot topics in Chinese tourism research in recent years, indicating that climate comfort was applied in multiple directions and effectively combined with social development.

2.4. Keyword Clustering Analysis

Cluster analysis is a hierarchical clustering method according to the principle of similarity from large to small, so as to visually show the relationship between the articles, and it is a key method to explore the main research trends and focuses of specific disciplines. In the CiteSpace software, the evaluation of clustering performance depends on two parameters: Modularity Q (Q-value) and Silhouette (S-value) [36]. A Q-value > 0.3 means that the cluster structure is significant; an S-value > 0.5 means that the cluster is reasonable, and an S-value > 0.7 means that the cluster is convincing [36]. In this study, the Q-value was 0.548 and the S-value was 0.774 (Figure 3), indicating that the keyword clustering structure was significant and the clustering was convincing. The result of the cluster analysis showed that the literature topics were divided into eight clusters, namely “#0 tourism”, “#1 climate”, “#2 tourism climate”, “#3 temperature–humidity index”, “#4 climate change”, “#5 applied meteorology”, “#6 heat-escape tourism”, and “#7 tourist flow” (Figure 3).

2.5. Hotspots and Emerging Trends

Keywords with the strongest citation bursts are the words with a high frequency change rate detected from a large number of subject words, which reflects a change of the research focus. The top 22 keywords with the strongest citation bursts in the field of tourism climate research in China from 1996 to 2014 were extracted in this study (Figure 4). Four keywords (“fuzzy evaluation”, “comfort indicator”, “climatic resources”, and “meteorological element”) appeared around 2000, indicating that Chinese scholars began to pay attention to tourism climatic resources during this period. And these studies were mainly devoted to the exploration of evaluation indicators and methods. In fact, a variety of evaluation methods and indicators were continuously applied and improved in many Chinese studies for a long time because the keywords “wind chill index” and “clothing index”, which were related to evaluation indicators, were also identified as keywords with the strongest citation bursts from 2013 to 2019. The keywords “spatial-temporal variation” and “spatial-temporal distribution” had similar meanings and referred to the trend of tourism climate comfort over time or its comparison between different study sites. These trends were important results that were reported in most studies; therefore, the two words appeared in 2006 and became a research hotspot in 2020, receiving the attention of scholars for a long time. The keywords “city”, “Nanjing City”, and “Ningxia Province” showed that many studies on tourism climate comfort in China were carried out by administrative units. In addition, it also indirectly reflected that tourism climate evaluation has been widely carried out throughout China. The keyword “applied meteorology” became a research hotspot in 2015, and “heat-escape tourism”, “negative oxygen ions”, and “health and wellness tourism” became research hotspots from 2020 to 2024, indicating that the practical applicability of this research field was gradually strengthened, and one of the development directions was about the human living environment and public health.

2.6. Bibliometrics-Based Conceptual Framework

Based on the bibliometrics results above, we reviewed the literature and proposed a conceptual framework for this study combined with the evaluation process of tourism climate comfort (Figure 5). For the evaluation objects, tourism climate comfort evaluation was carried out at various scales in China, including the national scale, the provincial scale, the city scale or a single site (such as a park or a tourist attraction). For the evaluation methods, the meteorological data used for the evaluation of tourism climate comfort usually included air temperature, precipitation, solar radiation, wind speed, sunshine duration, and relative humidity. We summarized eight widely used indicators for evaluating tourism climate comfort. In addition, some new evaluation indicators were established and some evaluation models were optimized in many studies. Geographic information system (GIS), analytic hierarchy process (AHP), fuzzy comprehensive evaluation, and cluster analysis were frequently used in the evaluation of tourism climate comfort. Three calculation methods for the comfortable period of tourism climate were summarized. For the evaluation results, tourism climate comfort was quantitatively assessed on different time scales, including interannual variation, monthly variation, seasonal variation, and diurnal variation. Among them, the monthly variation of climate comfort was widely reported, while the diurnal variation received less attention. The comfort period of tourism climate was also reported in some studies. For practical applications, these studies were divided into four aspects: (1) the relationship between climatic comfort and tourism activities; (2) the relationship between climate comfort and tourist flow; (3) the response of climate comfort to climate change; and (4) tourism climate regionalization. Finally, the main limitations and prospects were put forward.

3. Evaluation Methods of Tourism Climate Comfort

3.1. Evaluation Indicators

3.1.1. Widely Used Indicators

Through a summary of the research on the evaluation of tourism climate comfort in China, this study identified eight indicators that were frequently used (

Table 1. Eight widely used evaluation indicators of tourism climate comfort in China.

Indicator	Abbreviation	Calculation Formula	Formula Description	References
Temperature–humidity index	THI	THI = (1.8T + 32) − 0.55 (1 − F) (1.8T − 26)	T, temperature (°C); F, relative humidity (%).	[37]
Wind effect index	WEI	$\mathrm{WEI}=-(10\sqrt{\mathrm{V}}$ + 10.45 − V) × (33 − T) + 8.55S	T, temperature (°C); V, wind speed (m·s−1); S, sunshine duration (h·d−1).	[23]
Wind chill index	WCI	$\mathrm{WCI}=(33-T) \times (9+10.9\sqrt{\mathrm{V}}$ − V)	T, temperature (°C); V, wind speed (m·s−1).	[24,38]
Clothing index	ICL	$\mathrm{ICL}={\displaystyle \frac{33-T}{0.155H}}- {\displaystyle \frac{H+AR\cos \mathsf{\alpha }}{(0.62+19\sqrt{V})H}}$	T, temperature (°C); V, wind speed (m·s−1); H, metabolic rate of the human body under light activity, 87 W/m2; A, absorption of solar radiation by the human body takes a value of 0.06; R, the solar radiation received per unit area of land is 1367 W/m2; α, solar altitude angle.	[25]
Human comfort index	HCI_1	$\mathrm{HCI}\_1=1.8T-0.55 (1.8T-26) (1-F)-3.2\sqrt{\mathrm{V}}$ + 32	T, temperature (°C); F, relative humidity (%); V, wind speed (m·s−1).	[39]
Holiday climate index	HCI_2	HCI_2 = 4TP + 2C + (3P + V)	TP, perceived temperature (°C); C, cloud coverage (%); P, precipitation (mm); V, wind speed (m·s−1).	[40]
Comprehensive comfort index	CCI	CCI = 0.6 (|T − 24|) + 0.07(|F − 70|) + 0.5 (|V − 2|)	T, temperature (°C); F, relative humidity (%); V, wind speed (m·s−1).	[29]
Weighted comprehensive comfort index based on widely used indicators	WCCI	WCCI = a × THI + b × WCI + c × ICL	a, b, and c are the weights of THI, WCI, and ICL, respectively. The sum of a, b, and c is 1.	[30]

). Among these, the first five indicators were particularly widely applied. In addition to these indicators, various other indicators were also used in tourism climate comfort evaluation in China, but their usage frequency was low, therefore they were not listed here.

3.1.2. Development and Improvement of Evaluation Indicators

Many tourism climate comfort evaluation indicators (models) were derived from Western scholars. Based on these Western studies, Chinese scholars developed various evaluation indicators in recent years to specially assess the climate characteristics of tourism in China. For example, Chinese scholars proposed evaluation systems of climate comfort for different types of tourism activities, such as heat-escape tourism [41], cold-escape tourism [42], coastal tourism [43], and ice-snow tourism [44], among others. Overall, the establishment of evaluation indicators of tourism climate comfort is evolving toward diversification.

In addition to creating new evaluation indicators, some scholars have modified or improved existing evaluation indicators to enhance the scientific accuracy of the assessments. For example, Liu et al. [31] improved the calculation of surface solar radiation in the ICL model and validated the new model using meteorological data from 31 provincial capitals in China, and found that it performed better than the original model. Feng et al. [45] improved the classical THI model by using MODIS remote sensing data combined with the geographically weighted regression model, and realized an accurate assessment of the climatic comfort in areas with sparse meteorological stations. Du et al. [46] refined the tourism climate index model proposed by Mieczkowski [47] by incorporating factors such as the proportion of daily rainfall, the proportion of rainy days, and sunshine duration, and developed a tourism climate suitability index. Zhu et al. [48] improved the tourism climate index [47] to more accurately evaluate tourism climate comfort in mainland China.

The development, current application, existing problems, and future prospects of evaluation indicators of tourism climate comfort in China have been systematically reported in detail in several studies [49,50], which were not reiterated in this study.

3.2. Key Technical Methods for Evaluating Tourism Climate Comfort

3.2.1. Geographic Information System (GIS)

GIS has an important application in tourism climate comfort evaluation research. GIS can be used to collect, integrate, and analyze various climate data such as temperature, humidity, and precipitation [51]. These data can then be used to assess the climate conditions of different regions and present the results in the form of climate comfort maps. Specifically, its applications mainly included the following three aspects:

• Using GIS spatial interpolation tools to refine the scale of meteorological data and climate comfort evaluation, enabling the “point-to-area” assessments. This means that data from a few meteorological stations can be used to cover the entire study area [52].
• Modifying the calculation of comfort evaluation indicators to enhance the accuracy of the assessment. For example, Liu et al. [53] used MODIS remote sensing data to retrieve the surface temperature and normalized water vapor index in the area of the Tropic of Cancer in Yunnan Province, and applied the established indicator of human health comfort to assess the tourism climate comfort in the region. Chen et al. [54] used digital elevation models and GIS software to modify and improve the calculation of the THI and WEI in Chongqing City, achieving a scientific assessment of tourism climate suitability in complex terrains.
• It can be used for tourism climate regionalization and visualization. As mentioned above, GIS can be used to obtain tourism climate comfort across the entire study area. On this basis, methods such as cluster analysis or natural break classification can be applied to achieve climate zoning of the study area [55,56].

3.2.2. Analytic Hierarchy Process (AHP)

AHP is a quantitative decision-making method that helps determine the best choice and is suitable for complex decision problems [57]. In tourism climate comfort evaluation, AHP was mainly used to develop an evaluation system for tourism climate comfort. In this process, AHP can quantify the importance of various factors, thereby determining their weights in the evaluation system. Currently, AHP was mainly used to establish the WCCI (Table 1), where the weights of multiple climate indicators were determined through AHP, and the final comprehensive index was obtained by weighted summation [30].

3.2.3. Fuzzy Comprehensive Evaluation

Fuzzy comprehensive evaluation combines fuzzy mathematics theory with expert knowledge to handle the complex, fuzzy, and uncertain information in tourism climate comfort evaluation [58]. This method can combine various meteorological indicator data with expert knowledge to achieve a quantitative evaluation of tourism climate comfort [58]. Peng et al. [59] used this method to assess the drifting climate comfort in the Mengdong River scenic area of Hunan Province, determining the comfort levels for different months of the year. This method has also been widely applied in tourism climate comfort studies in areas such as Taibai Mountain [60], Tongren City [61], and Xi’an City [62]. Additionally, Luo et al. [63] combined this method with AHP, using the fuzzy AHP method to evaluate the climate resources of rural tourism in Meizhou City, Guangdong Province, thus avoiding the impact of subjective judgments and preferences in AHP on the results.

3.2.4. Cluster Analysis

Cluster analysis helps researchers classify and compare the climate conditions of different regions, which can then be used for the scientific division of comfort regions [51]. This helps travelers choose suitable travel times and destinations, enhancing their travel experience and satisfaction. Luo et al. [55] used the Euclidean distance method in Q-type clustering to analyze the monthly average precipitation and sunshine hours in Enshi City, Hubei Province, and divided the climate into five categories combined with topographical features: low mountain terrain, middle mountain terrain, high mountain terrain, riverside valley terrain, and windward slope terrain. Jin and Ren [64] used the fuzzy C-means clustering method to solve for the optimal number of clusters and classified 31 major cities across China based on temperature and relative humidity, providing scientific guidance for urban development and tourist travel. Ma et al. [65] conducted a systematic cluster analysis of 28 major tourist destinations in Sichuan Province based on monthly climate comfort, dividing them into five categories of climate comfort. Zhao and Wang [66] used the WCCI and hierarchical clustering method to classify cities in Henan Province into three categories: “high comfort”, “medium comfort”, and “low comfort”.

3.3. Methods for Determining Tourism Climate Comfort Period

The tourism climate comfort period refers to the period of the year that is suitable for tourism activities, typically including the starting and ending dates [48]. Currently, the methods used in China’s tourism research to determine the climate comfort period included three methods: the number of days with comfortable climate, the five-day moving average method, and the probability of climate-suitable days.

3.3.1. Number of Days with Comfortable Climate

The tourism climate comfort period identified by this method represents the number of days with comfortable climate over a period of time (usually one year) [48]. After completing the calculation of the evaluation indicators for tourism climate comfort, the number of days for each comfort level was obtained based on the classification criteria of evaluation indicators, with the total number of days across all comfort levels adding up to 365 days [48]. The dates within the “comfortable” level obtained are scattered and discontinuous in a year. Therefore, this method cannot provide the start and end dates of the comfort periods with consecutive dates.

3.3.2. Five-Day Moving Average Method

The five-day moving average method can effectively smooth short-term climate fluctuations and accurately reflect seasonal climate changes; therefore, it is used for identifying the comfortable period of tourism climate [67]. First, the average level of climate comfort for five consecutive days from the starting date of the year (January 1st to January 5th) was calculated. Then, the average level of climate comfort of the next five days (January 2nd to January 6th) was calculated. Finally, five-day comfort levels throughout the year were obtained by repeating the calculation process. If the average climate comfort index over five consecutive days reaches the “comfortable” level, the day on which this level is achieved is identified as the start date of the comfort period. If the average over five consecutive days does not reach the “comfortable” level, the day before the failure to meet this level is recognized as the end date. The number of days between the start date and the end date constitutes the tourism climate comfort period [67].

3.3.3. Probability of Climate-Suitable Days

The probability of climate-suitable days (PCSD) refers to the probability that a date is defined as having a comfortable climate. According to the grading criteria of the evaluation indicators, climate comfort levels were obtained for each day across the entire study period. Its application is based on the long-term historical climate data of the study area. PCSD is the ratio of the frequency of a date having a comfort level to the total number of years studied [68]. Therefore, PCSD represents the probability of a certain date being determined as a climate comfort level from the perspective of historical climate. PCSD > 0.0, PCSD ≥ 0.5, and PCSD ≥ 0.8 are used to identify the “relatively comfortable period”, “comfortable period”, and “most comfortable period”, respectively [68].

4. Application of the Evaluation of Tourism Climate Comfort

4.1. Climate Comfort and Tourism Activities

China is vast, with significant differences in natural geography, and its complex terrain and diverse landforms increase the complexity and diversity of its climate [69]. Although many cities in China experience four distinct seasons, the length of each season varies significantly between regions. Since climate impacts many outdoor tourism activities, tourists need to select the appropriate travel timing and activities based on the climate characteristics of their destinations [3]. Based on the bibliometrics results and a review of previous studies, tourism activities related to climate comfort can generally be categorized into five types: heat/cold-escape tourism, ice-snow tourism, rafting tourism, coastal tourism, and various other activities such as flower-viewing, hiking, and sports tourism (Figure 6).

4.1.1. Heat/Cold-Escape Tourism

In the context of global warming and frequent extreme heatwaves and storms in summer, seeking cool and comfortable tourist destinations has become a popular choice for many travelers during summer (Figure 6a). Heat-escape refers to a type of tourism activity where people choose to travel to areas with cool climates to avoid high temperatures in the summer [41]. Therefore, evaluating the tourism climate suitability for summer retreats can provide important references for selecting summer travel destinations and enhancing the quality of the travel experience. Song et al. [41] established a comfort evaluation model of heat-escape tourism based on the climate characteristics of Guizhou Province and used it to analyze the suitability of heat-escape tourism in the region. The results showed that Guizhou was generally very suitable for heat-escape tourism, but the suitability varied spatially. Ding et al. [70] used a thermal index and fuzzy evaluation method to calculate the heat-escape suitability for the Han River Basin, obtaining the number of suitable days at various meteorological stations and analyzed the temporal and spatial variations. Overall, heat-escape tourism studies aimed to reveal the temporal and spatial variations of climate comfort in different regions. Temperature, precipitation, wind speed, and humidity were key factors affecting the suitability of heat-escape tourism [41,70]. Additionally, extreme weather such as high temperatures, heavy rains, strong winds, and thunderstorms can have negative impacts on heat-escape tourism [71].

In contrast to summer, tourists usually choose warm destinations for travel during winter (Figure 6b). Research on the cold-escape tourism comfort was relatively scarce compared to that of heat-escape tourism. In the evaluation system of cold-escape tourism developed by Lin et al. [42], climate comfort accounted for 25.81% of the total weight, making it the most crucial factor affecting the cold-escape tourism. Huang [72] used THI, WEI, and WCCI models to conduct a spatio-temporal analysis and classification of cold-escape tourism cities in the low-latitude regions of southern China. Duan [73] incorporated climate indicators such as January mean temperature, number of snowy days, and suitable wind speed days into the evaluation system for cold-escape tourism and leisure environments.

From the above research, it can be seen that there were significant seasonal differences in the selection of evaluation indicators for heat-escape and cold-escape tourism. Heat-escape tourism focused on the climate conditions from June to August [41,70], while cold-escape tourism focused on the climate conditions from December to February [72,73].

4.1.2. Ice-Snow Tourism

Ice-snow tourism mainly includes sightseeing (glaciers, ice sculptures, ice lanterns, etc.), sports (skiing, ice skating, etc.), and adventure (ice climbing, mountaineering, and polar expeditions), which are important components of winter sports and tourism and offer significant business opportunities (Figure 6c). Ice-snow tourism is highly influenced by climate conditions. For instance, snow is a fundamental condition and a key resource for developing ski resorts, and snow conditions directly affect the profitability of the ski industry [74]. Additionally, temperature fluctuations can affect the duration of the skiing season [74]. As a result, there is growing attention on how climate change impacts ice-snow tourism in winter in recent years. Yu et al. [75] selected temperature, snowfall, wind speed, sunlight, and air quality to assess the climate comfort for ice-snow tourism in the western scenic area of Changbai Mountain. The evaluation results in the above studies were obtained by calculating and analyzing multiple independent indicators separately. Some other studies conducted evaluations by integrating multiple indicators into a comprehensive evaluation model for ice-snow tourism [44,76]. Overall, northern China, including Xinjiang, Inner Mongolia, Heilongjiang, Jilin, and Liaoning provinces, was suitable for ice-snow tourism activities [44,77]. Key climate indicators affecting ice-snow tourism comfort included snow cover (e.g., snow depth, snow period), snowfall (e.g., snowfall amount, number of snowfall days), and temperature (e.g., daily maximum temperature, daily average temperature, winter average temperature) [75,76,77,78]. These climate indicators were often combined with data on population, economy, transportation, and terrain to carry out a more systematic evaluation of the suitability for ice-snow tourism [74,77,78].

4.1.3. Outdoor Rafting

Rafting is an outdoor tourism activity combining adventure and sport, where tourists are usually directly exposed to natural conditions, meaning climate conditions significantly affect its suitability (Figure 6d). Peng et al. [59] selected daily average temperature, relative humidity, and wind speed as indicators and used a fuzzy comprehensive evaluation method to assess the rafting climate comfort in Mengdong River Scenic Area, revealing that climate conditions had a major impact on tourist flow. Shang et al. [79] used the maximum and minimum daily temperature, daily average temperature, daily precipitation, average relative humidity, minimum relative humidity, and wind speed to calculate the rafting suitability level, incorporating tourist flow data to establish an equation for the rafting suitability level. Kong et al. [80] evaluated the rafting tourism suitability in Xianju County, Zhejiang Province, based on CCI model, determining the suitable rafting periods throughout the year and exploring the relationship between tourist flow and climate conditions.

4.1.4. Coastal Tourism

The coastal areas of China are economically developed and serve as an important economic zone (Figure 6e). Coastal tourism is one of the pillars of China’s marine economy, and coastal cities are major tourism reception destinations [43]. Coastal cities, as important holiday destinations, have drawn significant attention regarding their tourism climate comfort [30]. Coastal tourism is highly influenced by climate, as it directly affects the quality of the destination and the length of the tourism season [37]. Ma et al. [30] established a WCCI model to evaluate the climate comfort of 26 hotspot cities along the eastern coastal region of China, classifying them by comfort level and analyzing their latitudinal variations. Gao et al. [43] established a coastal tourism climate index specifically for coastal cities, based on thermal comfort, sunlight, precipitation, wind, and air quality, and applied and verified it in nine coastal regions. Yu et al. [81] used the HCI_2 and a tourism climate index to assess the climate comfort of 14 popular beach destinations in China and found key differences in the results between the two indices. By using micrometeorological measurements and questionnaire surveys, Shang et al. [82] quantitatively analyzed the thermal sensation and thermal acceptability of tourists in Haikou City, Hainan Province, and provided scientific advice for the construction of coastal facilities and the improvement of thermal comfort.

4.1.5. Other Types of Tourism Activities

In addition to the tourism activities mentioned above, other activities such as sports tourism, outdoor adventure tourism, hiking, and flower viewing were also closely related to climate conditions (Figure 6f–h). Zhao and Wang [83] selected altitude, temperature, humidity, and wind speed as climate comfort indicators and established an evaluation system of sports tourism suitability for the Dali region of Yunnan Province, which was significant for the development of local traditional sports and emerging sports projects. Chen [84] used the universal thermal climate index to conduct a detailed evaluation of the climate comfort for hiking tourism activities in spring and autumn in East China. Wang and Wu [85] argued that temperature and wind speed were key climate factors affecting desert tourism, with the most comfortable period for desert tourism in northern China occurring from late summer to early autumn (May–August), and temperature variations during the day and night also significantly affected desert tourism. Climate was also an important factor in evaluating grassland landscape tourism resources [86]. Ren [87] evaluated the climate suitability for adventure tourism in Changbai Mountain Nature Reserve using the WEI model, finding that summer was the best season for adventure tourism in the region. Chen et al. [88] took the example of the citrus flower in Huazhou City, Guangdong Province, and combined meteorological factors for flower-viewing tourism, establishing the method for calculating the flower-viewing tourism meteorological index and its suitability classification standards. Zang et al. [89] designed a phenological viewing index based on flowering and leaf color data for 73 plant species in Beijing City and combined it with WCCI to create a travel suitability index, assessing the suitability period for flower-viewing tourism and simulating its response to future climate scenarios.

4.2. Relationship Between Climate Comfort and Tourist Flow

Studies on the relationship between tourism climate comfort and tourist flow were generally based on long-term climate and tourist flow data from the study area, focusing on the changes in both factors across months in a year and their trends and causes [90,91,92,93,94]. In these studies, climate comfort was generally evaluated using the indicators mentioned in Section 3.1.1, while tourist flow was expressed as a monthly flow index, which represented the ratio of the monthly tourist flow to the total annual tourist flow [90,91,92,93,94]. In such studies, as tourist flow fluctuations were influenced not only by climate and other natural environmental factors but also by numerous cultural and social factors, such as holidays (e.g., summer vacation and Golden Week) and festivals (e.g., Spring Festival), scholars often introduced virtual indices as explanatory variables. These indices were assigned values like “−1, 0, 1” based on their actual impact, and regression models (e.g., ordinary least squares) were fitted to analyze the relationship between tourist flow and climate comfort [90,91,92,93,94]. Using these regression models, the elasticity coefficient of the monthly tourist flow index with respect to climate comfort can also be calculated. This coefficient indicated how much the tourist flow changes (increases or decreases) when the climate comfort index changes by one unit, typically expressed as a percentage [90,91,92,93,94].

Many studies showed that there was a strong relationship between climate comfort and tourist flow [90,91,92,93,94]. Due to the complex territory and diverse climate in China, the tourism climate comfort, tourist flow, and the relationship between them showed great complexity and variability in different regions and scenic spots. For instance, Ma [90] analyzed the climate comfort of 46 typical tourist cities in China and classified them into four types: summer-suitable type, spring and autumn-suitable type, winter-suitable type, and all season-suitable type. The annual variation curve of the overall climate suitability index for these cities showed patterns like “M/U/Inverse U-type”, “M-type”, “M/Wide U-type”, and “Inverse U-type” [90,91,92,93,94]. The annual variation trends of tourist flow also differed significantly by region. For example, the domestic tourist flow in Mount Wuyi, Fujian [91], Beihai City, Guangxi Province [92], and Mount Huang, Anhui Province [92], presents a “3 peaks and 3 valleys” pattern, while the fluctuation curve of tourist flow in Mount Fanjing in Guizhou followed an “M-type” distribution [93]. The tourist flow in Sanya City generally showed a “3 peaks and 2 valleys” pattern [92], while the tourist flow in the Turpan region increased and then decreased, showing a “single peak” pattern [94]. Additionally, some scholars have focused on the inbound tourist flow, whose seasonal variation was also significantly influenced by climate suitability [90,91]. However, its fluctuation curve often differed from that of domestic tourist flow [90,91]. In summary, tourist flow was strongly correlated with climate comfort. Exploring the relationship between the two is of great significance for forecasting tourist flow changes, tourism development, and scenic area management [90,91,92,93,94].

4.3. Response of Climate Comfort to Climate Change

With the intensification of climate change, the climate characteristics of different regions are also changing, which has significant impacts on the tourism industry [5,8]. Climate comfort evaluation helps us to better understand the effects of climate change on tourism and formulate corresponding management strategies to ensure the sustainable development of tourism [22]. The impact of climate change on tourism climate comfort was assessed by analyzing the long-term trends in climate comfort evaluation indicators and the changes in the comfort levels of the study area [95,96,97,98,99,100]. First, the changes in different meteorological elements at the same location may have opposite effects on comfort. Climate change may either extend or shorten the tourism comfort period in a region. For example, in the northwest of Aba Prefecture in Sichuan Province, warming and reduced wind speeds extended the tourism comfort period, but a decrease in sunshine hours would negatively affect tourism activities [95]. Second, the impact of climate change on tourism climate comfort varied among study sites. For example, studies on the tourism comfort periods in Shanxi Province [96] and seven famous mountains in mainland China [97] showed that climate change extended the comfort period. In contrast, Xing et al. [98] found that the frequency of ideal tourism conditions in Hainan Province showed a fluctuating downward trend over the years, indicating that climate warming shortened the climate comfort period. On a national scale, Yu et al. [99] found that most of China’s regions experienced an increase in the tourism climate comfort period from 1981 to 2010, especially in spring and autumn, which were positively impacted by climate change. Moreover, the response of climate comfort to climate change varied in different months of the year [98]. Overall, the impact of climate change on tourism climate comfort was complex. Factors such as the geographical environment of the study area (e.g., latitude, altitude), the time scale of the study (e.g., interannual, monthly), and the uneven spatial distribution of global climate change all influenced how tourism climate comfort responded to climate change [95,96,97,98,99,100]. It should be noted that the climate change mentioned in the studies mentioned referred to historical climate conditions, and few studies explored the response of tourism climate comfort to future climate scenarios.

4.4. Tourism Climate Regionalization

Tourism climate regionalization is based on the climate characteristics and climate change patterns of a region, reflecting the local tourism climate features, and is of reference value for the efficient utilization of natural resources and optimizing resource allocation [101] (Figure 7). Currently, tourism climate zoning research in China has been widely conducted at the provincial, municipal, and county levels [102,103,104,105,106,107]. Wang et al. [102] used multi-year daily data on temperature, precipitation, humidity, and wind speed from Jilin Province to conduct tourism climate regionalization using evaluation indicators and clustering methods (Figure 7a). Zhang et al. [56] established a WCCI based on THI and WCI, and divided Shandong Province into three tourism regions by using the annual comfort index from 2001 to 2010 combined with the natural breakpoint method in ArcGIS (Figure 7b). Luo et al. [55] divided the tourism climate regions of Enshi City in Hubei Province into three types based on THI: spring–autumn type, summer type, and winter type (Figure 7c). Yang et al. [103] analyzed the climate characteristics of Quanzhou City, Zhejiang Province from July to September based on CCI and a high temperature index from 1981 to 2010, and divided the city into four types of areas for heat-escape tourism (Figure 7d). Bai et al. [104] selected six indicators, including temperature, humidity, wind speed, sunshine hours, precipitation, and altitude, to develop an evaluation model of heat-escape tourism for Banan District, and they performed weighted overlay analysis in ArcGIS combined with the present situation of land use to provide a scientific basis for the site selection and layout planning of heat-escape tourism. Li and You [105] calculated the tourism comfort period for different regions in the northern Altai area, eastern Hami area, and southern Xinjiang Uygur Autonomous Region based on THI, WEI, ICL, and HCI_1. Xie et al. [106] followed the standards “Classification for health preservation climate” (T/CMSA 0008-2018) set by the China Meteorological Service Association and analyzed the climatic characteristics of four counties in northern Liangshan Prefecture, Sichuan Province. They found that the entire study area belonged to the moist and nourishing wellness climate, with Yuexi County also classified as having a sunlight therapy wellness climate [106].

5. Conclusions, Limitations, and Prospects

5.1. Conclusions

This study reviewed the evaluation of tourism climate comfort in China through a bibliometric analysis, establishing a comprehensive conceptual framework that integrated study objectives, evaluation indicators, technical methods, and application areas. The above sections summarized eight widely used indicators, four key technical approaches, and three calculation methods for determining comfort periods, which were extensively applied across diverse studies, particularly in analyzing climate suitability for tourism activities, exploring correlations between climate comfort and tourist flow patterns, assessing responses to climate change impacts, and conducting tourism climate regionalization. However, certain limitations and unresolved challenges persist, which are discussed in the following section on limitations and prospects.

5.2. Limitations and Prospects

(1) Establish a more systematic and comprehensive evaluation system for tourism climate comfort. The evaluation index of climate comfort in current research usually only involved meteorological elements such as temperature, relative humidity, and wind speed (Figure 5). However, there were many other factors influencing tourism climate comfort, including air pollution, ultraviolet radiation, atmospheric oxygen content, and vapor pressure [107,108,109]. Therefore, future studies should explore the impact of multiple meteorological factors on climate comfort and the interactions among these factors. In addition, how to verify the accuracy of the evaluation results of climate comfort is also a problem that needs to be solved [49]. At present, most studies focus on the calculation of comfort indicators, while neglecting whether the evaluation results are consistent with the actual situation of the study objects. Therefore, the applicability of the evaluation indicators in different regions should be verified so as to evaluate the tourism climate comfort scientifically and accurately.

(2) Explore the influence of extreme weather or meteorological disaster on tourism climate comfort. Previous studies on the evaluation of tourism climate comfort mainly focused on monthly and inter-annual variations, while ignoring occasional extreme values of meteorological data that are closely related to extreme weather and meteorological disasters. Droughts, heavy rain, sandstorms, thunderstorms, snowfall, typhoons, sudden strong winds, and dense fog may affect tourism climate comfort [110], but such factors are not directly reflected in the currently used evaluation indicators. Therefore, future research needs to consider the impact of these extreme weather events on tourism climate comfort.

(3) Deepen the research on the evaluation of tourism climate comfort in terms of the temporal scale, including two aspects:

• Enhance diurnal-scale evaluation research. One purpose of tourism climate comfort evaluation is to provide references for tourists in selecting travel times. However, most previous studies focused on inter-monthly and inter-annual variations in climate comfort, with comfort calculations usually using mean values of meteorological elements (which included both daytime and nighttime observations). Since tourism activities generally occur during the daytime, tourists’ actual perception of temperature tends to be warmer than the calculated results [100]. Additionally, climate comfort during different times of the day varies, especially in regions with significant temperature differences or large daily variations in other meteorological elements. Therefore, it is crucial to examine the daily variations in climate comfort, particularly in regions with significant diurnal changes.
• Simulating and predicting tourism climate comfort under future climate scenarios. In the context of global warming, changing precipitation patterns, and human activity disruptions, how will tourism climate comfort change in different periods and regions in the future? How can these changes be scientifically predicted, and what targeted management measures should be implemented? These are key issues for future research.

(4) Deepen the research on the evaluation of tourism climate comfort in terms of the spatial scale, including two aspects:

• Considering the impact of microclimate on tourism climate comfort. The evaluation of climate comfort was mainly based on observation data from meteorological stations in the study area or near the study site, so that any location within the study area was considered to have the same climate characteristics. However, in reality, due to factors such as altitude, terrain, and underlying surface types, meteorological station data may not accurately represent the local microclimates [10]. In regions with complex terrain or landscape, particular attention should be paid to the influence of microclimates on climate comfort evaluation [54]. Furthermore, different locations within a large scenic area may also exhibit climate differences. Therefore, the evaluation considering microclimates can provide a reference for the planning and design of scenic spots and the improvement of microclimates in scenic spots from the perspective of climate [12].
• Strengthening the study of tourism climate comfort at the starting location of tourists. Generally, tourists are motivated to travel due to climate differences between the starting location of their trip and the tourist destination. The climate conditions of the tourist source area affect tourists’ travel decisions and destination choices, yet existing research mainly focuses on the destination’s climate, with limited studies on how the climate of the starting location influences tourist motivation and the choice of the tourist destination.

(5) Climate-related landscapes may need to be considered in the evaluation process of tourism climate comfort. A large number of studies evaluated tourism climate comfort from the perspective of human health or physiological perception [1], but overlooked the impact of climate on landscapes [111]. For example, rime ice, sea of clouds, snow scenes, and various vegetation-based tourism landscapes are closely related to climate conditions. These landscapes may be representative of a region and have strong appeal to tourists, yet they were not incorporated into comfort evaluation models of tourism climate. For instance, ice sculptures and snow sculptures are significant tourist attractions in Northeast China during winter, formed and maintained by cold climate conditions. However, when only considering the results calculated by traditional evaluation indicators (Table 1), winter was usually identified as an unsuitable season for tourism. Therefore, climate-related tourism resources and tourism activities, such as outdoor rafting and “3S” (sea, sand, and sun) tourism in summer, should be considered in the evaluation, rather than just focusing on the results calculated by traditional evaluation indicators.

(6) Explore the interaction between tourism development and climate, especially the impact of tourism on climate change. Current studies on tourism climate comfort mainly focused on the impact of climate change on tourism (Section 4.3), such as the comfort period and tourist flow. However, it should be recognized that the development of tourism also impacts the climate. The transportation, hotels, restaurants, and other services relied upon by the tourism industry generate significant carbon emissions, especially from long-haul air travel, which has a noticeable effect on climate [17]. Tourism facilities and services also require a large supply of energy, and the consumption of electricity, fuel, and water in hotels, for example, leads to greenhouse gas emissions, exacerbating climate change [17]. Moreover, the use of water and land resources in tourism development, as well as the management of waste generated by tourism activities, all affect the climate and environment of tourist destinations [107]. Therefore, the impact of tourism development and tourism activities on climate comfort needs to be further explored.

(7) In the context of intensified global climate change, environmental education should be considered in the evaluation process of tourism climate comfort. Environmental education shapes tourists’ awareness, including their perception of climatic conditions and tourism decisions [112], and influences their behaviors [113]. On the one hand, people’s perceptions of the impact of climate change on the ecological environment vary, which to some extent depends on whether they have received environmental education [112]. On the other hand, environmental education influences tourists’ responsible behaviors [113], and these behaviors further affects the environment, including climate environment and climate-related tourism resources [114]. For example, Wang et al. [114] argued that strengthening public environmental education was one of the key measures to enhance the tourism sustainability of alpine glaciers, as climate warming had led to the decline in the tourism quality and attractiveness of alpine glaciers. Under the background of “Ecological Civilization” and “Beautiful China” advocated by the Chinese government, environmental education is gradually gaining attention. Therefore, the relationship between environmental education and tourism climate comfort should be explored in the future.

(8) The tourism meteorological services for the public need to be improved in China. Although numerous studies on tourism climate comfort have been conducted at the provincial, municipal, and regional levels, the accessibility and public awareness of these research findings remain limited. The objectives and significance of these studies have not been practically applied. For example, developing and promoting tourism meteorological service systems could provide convenient access to weather-related information for tourists, tourism operators, and other users, making these research results more practical and accessible to the public.