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Ullah N, Chao L, Khan TU, Sai WL, Yazhuo Z, Khan IA, Hassan MA, Hu Y. Insights into climate change dynamics: A tourism climate index-based evaluation of Gilgit-Baltistan, Pakistan. Heliyon 2024; 10:e35315. [PMID: 39170213 PMCID: PMC11336619 DOI: 10.1016/j.heliyon.2024.e35315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 07/20/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024] Open
Abstract
The tourism industry is a significant contribution to the economy of many countries, including Pakistan. However, its activities often have a negative impact on the environment, particularly related to climate change. Notably, Pakistan ranks fifth among countries most affected by climate change, which requires a targeted analysis of the tourism sector to determine its potential impacts. Despite the critical nature of this issue, there is currently a lack of research that examines how climate change specifically impacts Pakistan's tourism industry. This study aims to address this gap by using the Tourism Climate Index (TCI) to assess the impact of climate change on the suitability of different tourism locations across the country. Our results show that popular tourist destinations such as Gilgit-Baltistan experience their peak season in the warmer third quarter (summer), which corresponds to the highest observed TCI values. This suggests that warmer temperatures could boost tourism activity and spending in these regions. Furthermore, our analysis shows that air temperature plays a crucial role in determining tourist comfort levels and significantly influences tourists' TCI values and sensory experiences. The results of this study show that the TCI methodology can be an effective tool for further research into the geographical impacts of climate change on tourism. By identifying necessary changes due to climatic changes, future studies could provide valuable insights into how the tourism industry can adapt to and reduce its environmental footprint.
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Affiliation(s)
- Nadeem Ullah
- School of Architecture, Tianjin University, Tianjin, 300072, China
| | - Lin Chao
- HUASHI Branch Company of CCCC (China Communications Construction Company Ltd.) Second Highway Engineering Co., Ltd., China
| | - Tauheed Ullah Khan
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, 510260, China
| | - Wu Lun Sai
- School of Architecture, Tianjin University, Tianjin, 300072, China
| | - Zhang Yazhuo
- School of Civil Engineering, Tianjin University, Tianjin, China
| | - Irshad Ahmad Khan
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, School of Landscape Architecture, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Hangzhou, 311300, China
| | - Muhammad Azher Hassan
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
| | - YiKe Hu
- School of Architecture, Tianjin University, Tianjin, 300072, China
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Kloos S, Bigalke C, Neumair M, Menzel A. Weather, weekday, and vacation effects on webcam recorded daily visitor numbers in the alpine winter season. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:305-316. [PMID: 38036707 PMCID: PMC10794479 DOI: 10.1007/s00484-023-02591-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.
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Affiliation(s)
- Simon Kloos
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany.
| | - Carina Bigalke
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany
| | - Matthias Neumair
- TUM School of Life Sciences, Life Science Systems, Technical University of Munich, 85354, Freising, Germany
| | - Annette Menzel
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
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Wang L, Xin L, Zhu Y, Fang Y, Zhu L. Associations between temperature variations and tourist arrivals: analysis based on Baidu Index of hot-spring tourism in 44 cities in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:43641-43653. [PMID: 36670219 PMCID: PMC9857907 DOI: 10.1007/s11356-023-25404-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 01/14/2023] [Indexed: 06/17/2023]
Abstract
Hot-spring tourism refers to entertainment, health preservation, commercial conferences, and other leisure activities at hot-spring locations. This tourism often shows periodic variability, which may be due to seasonal temperature variations. However, studies on the effects of temperature variations on tourist arrivals at hot springs are limited. Therefore, this study aimed to evaluate this relationship in 31 provincial capital cities and 13 s-tier cities in China. Using the Baidu Index, we obtained data for tourist arrivals to hot springs in each city and constructed a generalised additive model to explore the associations between temperature variations and tourist arrivals. We also analysed the statistical significance of the estimated effects during different seasons to explore potential effect modification. A 1 °C increase in temperature was associated with a 1.81% (95% confidence interval (CI): 1.69-1.93) decrease in daily tourist arrivals for hot-spring tourism. Significant positive associations between the abovementioned factors were observed in summer (2.18% change, 95% CI: 1.32-3.04). The effect of temperature on the volume of tourist arrivals may last for approximately 2 months. Robustness analysis confirmed the data reliability. The results indicate that significant relationships exist between temperature variations and hot-spring tourism arrivals, which vary seasonally. This study has significant implications for travel agencies to effectively manage tourist visits to hot spring locations.
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Affiliation(s)
- Li Wang
- Anhui Finance & Trade Vocational College, Hefei, Anhui, China
| | - Ling Xin
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
| | - Yongjian Zhu
- University of Science and Technology of China, Hefei, Anhui, China
| | - Yanyan Fang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Lin Zhu
- Anhui Broadcasting Movie and Television College, Hefei, Anhui, China
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Topic Modelling for Ski Resorts: An Analysis of Experience Attributes and Seasonality. SUSTAINABILITY 2022. [DOI: 10.3390/su14063533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Knowing how to improve skiers’ experiences in ski resorts is vital for developing the ski industry. This study aims to provide a holistic understanding of the key attributes of skiers’ experiences and explore them in the context of seasonality. Based on the user-generated content of 14 ski resorts and the topic modelling and sentiment analysis method, a framework of skiing experience attributes was built. Compared with the seasonal data, the dynamic of skiers’ concerns and perceived performance was revealed. The skiers’ concerns in peak seasons and off seasons manifested different orientations. The results show that the relatively important attributes tend to have relatively low performance in the peak seasons. In off seasons, skiers emphasise non-skiing-oriented attributes. This study showcases that skier’s interests and evaluations of various experience attributes vary with seasons. The findings help to understand the skiers’ peak and supporting experiences, which could be used to build ski resorts management and seasonal hedging strategies.
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Zhang G, Poslad S, Rui X, Yu G, Fan Y, Song X, Li R. Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China. SENSORS (BASEL, SWITZERLAND) 2021; 21:5569. [PMID: 34451012 PMCID: PMC8402293 DOI: 10.3390/s21165569] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/10/2021] [Accepted: 08/16/2021] [Indexed: 11/27/2022]
Abstract
This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people's activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples' density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people's desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens' activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens' activities of daily living.
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Affiliation(s)
- Guangyuan Zhang
- IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK; (G.Z.); (S.P.); (G.Y.); (Y.F.)
| | - Stefan Poslad
- IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK; (G.Z.); (S.P.); (G.Y.); (Y.F.)
| | - Xiaoping Rui
- School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China
| | - Guangxia Yu
- IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK; (G.Z.); (S.P.); (G.Y.); (Y.F.)
| | - Yonglei Fan
- IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK; (G.Z.); (S.P.); (G.Y.); (Y.F.)
| | - Xianfeng Song
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; (X.S.); (R.L.)
| | - Runkui Li
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; (X.S.); (R.L.)
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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An Inter-Comparison of the Holiday Climate Index (HCI:Beach) and the Tourism Climate Index (TCI) to Explain Canadian Tourism Arrivals to the Caribbean. ATMOSPHERE 2020. [DOI: 10.3390/atmos11040412] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Through an empirical investigation of the historical relationship between the destination climate and tourist arrivals in the Caribbean, this study presents the first revealed preference evaluation of a climate index informed by tourists’ stated climatic preferences for coastal-beach tourism (i.e., a sun-sand-surf or 3S travel market). The goal of this multi-organization collaboration was to examine the potential application of a newly designed climate index—the Holiday Climate Index (HCI):Beach—for three Caribbean destinations (Antigua and Barbuda, Barbados, Saint Lucia). This paper provides an overview of the evolution of climate indices, including the development of the (HCI):Beach. To test the validity of climate indices for a beach travel market, daily climate ratings based on outputs from the Tourism Climate Index and the HCI were correlated with monthly arrivals data from Canada (a key source market) at an island destination scale. The results underscore the strength of the new index, with each destination scoring consistently higher using the HCI:Beach, including a stronger relationship (R2) between index scores and tourist arrivals. These findings demonstrate the value of combining stated and revealed preference methodologies to predict tourism demand and highlight opportunities for future research.
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