Academic literature on the topic 'Climate Index for Tourism (TCI)'
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Journal articles on the topic "Climate Index for Tourism (TCI)"
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Haryadi, Adhityo, Eko Kusratmoko, and Asep Karsidi. "Climate Comfort Analysis for Tourism in Samosir District." E3S Web of Conferences 94 (2019): 05001. http://dx.doi.org/10.1051/e3sconf/20199405001.
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Tourism has become one of the sectors which are the mainstay source of foreign exchange in Indonesia. One of the region which has tourism potential is Samosir District at North Sumatra Province. Climatic conditions affect the tourist comfortability while doing the tourism activity. Studies on climate comfort in Toba Lake Region, especially in Samosir District have not been done. Way to determine the level of comfort associated with tourism activities are known to the Tourism Climate Index (TCI). This research aims to determine the level of climate comfort tourist destinations in Samosir District based on the value of TCI and knowing the relation between TCI value with the number of visits a tourist destination.
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Alonso-Pérez, Silvia, Javier López-Solano, Lourdes Rodríguez-Mayor, and José Miguel Márquez-Martinón. "Evaluation of the Tourism Climate Index in the Canary Islands." Sustainability 13, no. 13 (June 23, 2021): 7042. http://dx.doi.org/10.3390/su13137042.
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In this study, we performed a diagnostic and evolutive analysis of the bioclimatology of the Canary Islands, an Atlantic archipelago where the climate itself is a main feature promoting tourism. Among all the tourist-climate indices described in the literature, we evaluated the most widely used, which is the Tourism Climate Index (TCI) proposed by Mieczkowski (1985). Monthly mean TCI time series were calculated using meteorological data from the Spanish State Meteorological Agency database and the European Climate Assessment and Dataset. Our results show TCI values greater than 50 during almost every month in the period 1950–2018, with mean values over the entire time series between 70 and 80. According to the TCI classification scheme, these values correspond to a very good thermal comfort along all of the period. Our results also point to spring as the season with the best TCI, with maximum values around 80 for this index in April—excellent according to the TCI classification. However, we did not find a correlation between inbound arrivals and the TCI index, which might point to a lack of information available to tourists. This opens an opportunity for policymakers and tour operators to better publicize the best seasons for holidays in the islands.
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Hoang Thi Kieu, Oanh. "Assessment climate resource of Con Dao island (Vietnam) by using the tourism climate index." Journal of Science Natural Science 66, no. 1 (March 2021): 188–97. http://dx.doi.org/10.18173/2354-1059.2021-0022.
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This article assesses climatic conditions for tourism by using Tourism Climate Index - TCI, published by Mieczkowsk (1985). This is an experimental synthetic climatic index that evaluates simultaneously the influence of 7 climatic elements as maximum average temperature (oC), minimum average humidity (%), average temperature (oC), average humidity (%), the number of sunny hours, windy speed of Con Dao island. The results of TCI within 12 months in Con Dao island compare to “Classification of advantageous levels of climate for tourism” of TCI (Mieczkowsk, 1985) which shows the advantages of Con Dao for relaxation tourism all year round. The period from December to April is the most favourable time for tourism activities in Con Dao because the TCI index reaches from Good to Very good, while the suitable time is from May to November, due to rainfall and high speed of wind during the rainy season.
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Rutty, Michelle, Daniel Scott, Lindsay Matthews, Ravidya Burrowes, Adrian Trotman, Roché Mahon, and Amanda Charles. "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 11, no. 4 (April 20, 2020): 412. http://dx.doi.org/10.3390/atmos11040412.
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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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Siedlecki, Mariusz. "An evaluation of changes in the bioclimate of Łódź in the light of the tourism climate index." Turyzm/Tourism 25, no. 2 (February 7, 2017): 21–25. http://dx.doi.org/10.1515/tour-2015-0002.
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The paper presents basic information concerning bioclimatic conditions in Łódź based on the Tourism Climate Index (TCI). The index makes it possible to assess in a comprehensive manner, based on specified meteorological parameters, the climatic conditions affecting the development of tourism. The study uses measurements from the weather station, Łódź-Lublinek, taken in the years 1966-2014. The TCI values have a distinct annual pattern with the highest values recorded in summer. The summer season has the highest frequency of days with ‘very good’ or ‘excellent’ conditions for tourism. An assessment of the variability of bioclimatic conditions indicates an increase in the number of days with high TCI values pointing to ‘very good’ or ‘excellent’ conditions for tourism and recreation.
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Németh, Ákos. "Estimation of Tourism Climate in the Lake Balaton Region, Hungary." Journal of Environmental Geography 6, no. 1-2 (April 1, 2013): 49–55. http://dx.doi.org/10.2478/v10326-012-0006-0.
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Abstract Lake Balaton is one of the most important and best-known tourist destinations in Hungary. Although in the last few years, several efforts were implemented to increase the length of the tourist season, the highest visitor turnover occurs in the summer months. We mostly regard the Lake Balaton as a bathing place, despite of the fact that the region offers more and more tourism products. The beach tourism and other lakeside activities are highly dependent on weather and climate. In order to know that a region's climate what extent is suitable to the given tourism activities, the tourism climate potential must be determined. This study aims to illustrate observed changes of the tourism climate potential of Lake Balaton Region during the last half century, by using Tourism Climatic Index (TCI) and Climate-Tourism-Information-Scheme (CTIS). The analysis is based on the long-term measured datasets of Siófok synoptic station. Based on the TCI, the tourism climate potential of the examined region is barely changed over the past 50 years; significant changes can be detected only in February and June. By using the CTIS, smaller changes can also be detected. Such changes are: moderate improvement of the thermal comfort in spring and autumn, slight increase in sunny hours in the tourism season, as well as the sultriness becomes more frequent in the summer months. The results may represent useful background information to the policy decision-makers.
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Croce, Valeria. "Can tourism confidence index improve tourism demand forecasts?" Journal of Tourism Futures 2, no. 1 (March 14, 2016): 6–21. http://dx.doi.org/10.1108/jtf-12-2014-0026.
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Purpose The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts. Design/methodology/approach Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced. Findings Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches. Practical implications Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions. Originality/value The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.
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ALDabbas, Ashraf, Zoltan Gal, and Buchman Attila. "Neural Network Estimation of Tourism Climatic Index (TCI) Based on Temperature-Humidity Index (THI)-Jordan Region Using Sensed Datasets." Carpathian Journal of Electronic and Computer Engineering 11, no. 2 (December 1, 2018): 50–55. http://dx.doi.org/10.2478/cjece-2018-0019.
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Abstract Jordan which is located in the heart of the world contains hundreds of historical and archaeological locations that have a supreme potential in enticing visitors. The impact of clime is important on many aspects of life such as the development of tourism and human health, tourists always wanted to choose the most convenient time and place that have appropriate weather circumstances. The goal of this study is to specify the preferable months (time) for tourism in Jordan regions. Neural network has been utilized to analyze several parameters of meteorologist (raining, temperature, speed of wind, moisture, sun radiation) by analyzing and specify tourism climatic index (TCI) and equiponderate it with THI index. The outcomes of this study shows that the finest time of the year to entice tourists is “ April” which is categorized as to be “extraordinary” for visitors. TCI outcomes indicates that conditions are not convenient for tourism from July to August because of high temperature.
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Roson, Roberto, and Martina Sartori. "Climate change, tourism and water resources in the Mediterranean." International Journal of Climate Change Strategies and Management 6, no. 2 (May 13, 2014): 212–28. http://dx.doi.org/10.1108/ijccsm-01-2013-0001.
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Purpose – This paper aims to present and discuss some quantitative results obtained in assessing the economic impact of variations in tourism flows, induced by climate change, for some Mediterranean countries. Design/methodology/approach – Estimates by a regional climate model are used to build a tourism climate index, which indicates the suitability of climate, in certain locations, for general outdoor activities. As climate change is expected to affect a number of variables like temperature, wind and precipitation, it will have consequences on the degree of attractiveness of touristic destinations. The authors estimate the macroeconomic consequences of changing tourism flows by means of a computable general equilibrium model. Findings – The authors found that more incoming tourists will increase income and welfare, but this phenomenon will also induce a change in the productive structure, with a decline in agriculture and manufacturing, partially compensated by an expansion of service industries. The authors found that, in most countries, the decline in agriculture entails a lower demand for water, counteracting the additional demand for water coming from tourists and bringing about a lower water consumption overall. Research limitations/implications – A great deal of uncertainty affects, in particular: estimates of future climate conditions, especially for variables different from temperature, the relationship between climate and tourist demand, and its interaction with socio-economic variables. This also depends on the reliability of the TCI index as an indicator of climate suitability for tourism, on its application to spatially and temporally aggregated data, on the degree of responsiveness of tourism demand to variations in the TCI. Furthermore, as the authors followed here a single region approach, the authors were not able to consider in the estimates the impact of climate change on the global tourism industry. Nonetheless, the authors believe that a quantitative analysis like the one presented here is not without scope. First, it provides an order of magnitude for the impact of climate change on tourism and the national economy. Second, it allows to assess systemic and second-order effects, which are especially relevant in this context and, moreover, appear to be sufficiently robust to alternative model specifications. In other words, the value added of this study does not lie in the specific figures obtained by numerical computations, but on the broader picture emerging from the overall exercise. Originality/value – To the authors' knowledge, this is the first study in which, by assessing higher tourism attractiveness into a general equilibrium framework, the effect described above is detected and highlighted.
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Sudiar, Nofi Yendri. "KENYAMANAN IKLIM LOKASI WISATA BERBASIS ALAM DI KAWASAN TROPIS." Agromet 33, no. 2 (December 18, 2019): 53–61. http://dx.doi.org/10.29244/j.agromet.33.2.53-61.
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This research reveals visitors perceptions of climate comfort in nature-based tourism areas in Ecopark Ancol, Bogor Botanical Gardens (KRB) and Cibodas Botanical Gardens (KRC). In addition to calculating the comfort score using the TCI and HCI methods and modifying their thermal aspects, a survey was also carried out in all three tourism areas simultaneously. The survey was conducted to collect data on climate comfort perceptions and the role of the weather on these comfort. A total of 793 respondents participated in this study. The majority of visitors stated that the weather affected the comfort of the climate during the tour. But weather conditions do not fully influence decisions in the selection of tourist visits. The level of perceived climate comfort for the three tourism sites namely Ecopark was perceived as neutral (57.3%), KRB was perceived as comfortable (60%) and KRC was perceived as comfortable (78.4%). While based on the score calculation approaching the survey results in Ecopark is TCI index modified in its thermal aspect with PET Tianjin (57.2). KRB is HCI without modification (59) and KRC is HCI modified by its thermal aspect with PET Tianjin (77.6). Statistically there are significant differences between sex, age, education level and topography. By understanding visitor perceptions, strategies and appropriate actions can be developed to increase comfort in the nature-based tourism industry.
Dissertations / Theses on the topic "Climate Index for Tourism (TCI)"
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Tang, Mantao. "Comparing the ‘Tourism Climate Index’ and ‘Holiday Climate Index’ in Major European Urban Destinations." Thesis, 2013. http://hdl.handle.net/10012/7638.
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Tourism is one of the largest economic sectors globally. It is a climate sensitive sector, with climate being one of the most important attributes for a destination. The Tourism Climate Index (TCI), developed by Mieczkowski (1985), is the most widely used index for assessing a destination’s climatic suitability for general tourist activities. Major deficiencies such as the subjectivity of its rating system and component weightings have been identified in the literature, and the need to develop a new index has been identified by researchers for almost a decade. This study aims to fill the research gap by developing a new index, the Holiday Climate Index (HCI), for the purpose of overcoming the deficiencies of the TCI. The HCI was compared with the TCI in rating both current (1961-1990) and future (2010-2039, 2040-2069 and 2070-2099) climatic suitability for tourism of the 15 most visited European city destinations (London, Paris, Istanbul, Rome, Barcelona, Dublin, Amsterdam, Vienna, Madrid, Berlin, Stockholm, Warsaw, Munich, Athens and Venice). The results were also compared with monthly visitation data available for Paris to assess whether the HCI ratings more accurately represent visitation demand than the TCI. The results show that there are key differences between the HCI and TCI in rating the tourism climate suitability of the selected European city destinations, in particular in the winter months of the northern, western and eastern European city destinations where the performance of the TCI had been questioned in the literature. The comparison with leisure tourist visitation data in Paris also revealed that the ratings of the HCI were more reflective of seasonal pattern of tourist arrivals than the TCI ratings. Because the TCI has been widely applied (15 studies), these findings hold important implications for future research in assessing current and future climatic suitability for tourism.
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Yu, Shu-Ting, and 游舒婷. "A Study of the Tourism Climate Index of Xitou Nature Education Area." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27222766809300477854.
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碩士國立臺灣大學
森林環境暨資源學研究所
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Climate is one of the important factors influencing outdoor recreation activities. Climate comfort can also affect the amount of tourists. In this research, two adjustment were made to Mieczkowski’s “Tourism Climate Index” (TCI), which summarizes and combines seven climate variables. First, to prevent overestimate the effect of humidity, the thermal comfort is not measured by the “effective temperature,” but instead by the “Physiologically Equivalent Temperature” (PET). Second, daily data is used to evaluate the climate comfort of Xitou Nature education area from 1990-2012. The results show that calculating by daily data is more appropriate. From 1990 to 2012, the highest TCI’s monthly average is in October and the lowest is in January. The main climate variables affect the TCI value in Xitou are temperature and precipitation. In the comparison between years, winter has greater impact by thermal comfort index, spring and summer are influenced by precipitation index, and autumn is affected by both thermal comfort and precipitation index. In conclusion, the TCI average of each season are above 50（means Acceptable）show that Xitou is quite suitable for tourism activities all year around. TCI values in autumn are significantly higher than in summer, spring and winter. The comfort type of seasonal distribute of Xitou is “autumn peak.” In addition, a preliminary simulation of climate change were made in this study, the average temperature were escalated 0.5℃, 1℃ and 2℃. The results show that in each season, TCI values are on an upward trend, and the comfort type of seasonal distribute are “autumn peak.”
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CHEN, CHIN-HUI, and 陳錦輝. "Research on the Discomfort Index of Elderly Tourism and Residential Climate Environment- A Case Study of Xitou Nature Education Area." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9b7zst.
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碩士南開科技大學
福祉科技與服務管理所
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The aim of this research was to explore whether the climate and environment near the 「Xietou Nature Education Park」were suitable for the elderly tourists to stay for a long time by applying the Discomfort Index （D.I.）. Based on the meteorological observation data from 1961 to 2010 of the NTU Experimental Forest, Xitou agricultural weather station （weather station code: U2H480）, the researcher calculated the Discomfort Index（D.I.） And Comfort Index（C.I.） according to the temperature and relative humidity to analyze the climatic factors of making the comfort of the elderly living environment and furthermore, to explore whether the climate and environment were suitable for the elderly to take a leisure travel. The Discomfort Index is the climate comfort indicators most widely used in the world. The Discomfort Index responses to the comfort of the body. Nobody feels uncomfortable when the value is under70; however, all feel the compressive stress from heat when if the value is above 85. According to the long-term climate data analysis of the "Xitou Nature Education Zone", the average annual Discomfort Index（D.I.） was 61.80, and 68.08 in the hottest summer. They both were below 70, which made body feel comfortable. The average（C.I.）annual value was 16.53, kind of chilly, and 20.02 in the summer, which showed that the stable, comfortable climate and environment were good for elderly tourism. Also, Xitou Nature Education Area will be a healthy aging living environment. Key words︰Climate, Comfort index（C.I.）, Discomfort index （D.I.）, Xitou nature education area
Book chapters on the topic "Climate Index for Tourism (TCI)"
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"Index." In Tourism and Climate Change, 328–29. Multilingual Matters, 2007. http://dx.doi.org/10.21832/9781845410681-016.
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"Index." In Tourism, Recreation and Climate Change, edited by C. Michael Hall and James Higham, 308–9. Bristol, Blue Ridge Summit: Multilingual Matters, 2005. http://dx.doi.org/10.21832/9781845410056-024.
Full textConference papers on the topic "Climate Index for Tourism (TCI)"
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Qi, Dandan. "Analysis on Tourism in Heilongjiang Province Based on Climate Index." In 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/emim-17.2017.156.
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