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After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience. [ABSTRACT FROM AUTHOR]

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis. [ABSTRACT FROM AUTHOR]

This study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises. [ABSTRACT FROM AUTHOR]

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Due to the country-wide lockdown, most activities in the hotel, motel, restaurant, and transportation sectors have been postponed. Consequently, the article investigates four research issues by examining the consequences of global tourism in the private sector before and after COVID-19. As an analytical method, the article suggested qualitative research methodologies to collect information from tourism employees. The opinions of the respondents were gathered through online emails in the questionnaire survey. Further, the article considers people's future desire for specific tourism destinations based on visitor arrivals. Forecasting tourist demand is an essential component of good and efficient tourism management. Consequently, the article proposes an attention-based long short-term memory model for exact demand forecasting. The experimental findings reveal that the model's minimal prediction error accuracy is 0.45%, which indicates that it has a more robust prediction effect, a faster convergence rate, and a greater prediction accuracy. Seasonality has emerged as one of the most distinguishing and defining characteristics of the global tourist business. Accordingly, the article mandated to compare the seasonal and non-seasonal effects of the tourist sector throughout the years 2020–2021. Moreover, Governments must analyse the crises' long-term consequences and, as a result, define the components that constitute government advantages supplied to the tourist sector during the pandemic era. As a result, many governmental policies, especially those about social welfare, may perceive a fresh start during the post-pandemic period, respectively. [ABSTRACT FROM AUTHOR]

This study focuses on quantifying the impacts of climate change on international tourism in Turkey through the estimation of the future number of international tourists for different tourism destinations. For this purpose, 30 tourism destinations were selected from different regions in Turkey offering different kinds of tourism attributes and climatic conditions. Future tourism demand was estimated based on comfort level change, a major determinant of tourist preference, and evaluated through the Tourism Climate Index. Changes in climate comfort levels between a base period (1963–2017), a projected medium term period representing the 2050s (2040–2069), and a projected long term period representing the 2080s (2070–2099) were correlated with the number of international tourists using a regression model developed by Hein et al. (Current Opinion in Environmental Sustainability,1:170–178, 2009). The results of this study project extreme drops in demand, seasonal shifts, and the emergence of new alternative destinations. The study is significant as the first quantitative evaluation of climate change impacts on tourism demand in Turkey through a comparison of the spatial exposures of destinations. The results will help lead the way to a national tourism development roadmap in Turkey through the revelation of regional risks and opportunities and will serve as a benchmarking study for tourism destinations that have similar climate conditions and tourism patterns. [ABSTRACT FROM AUTHOR]

The relationship between corruption and tourism has been sporadically examined over the years. According to the existing theory, there is an inverted U relationship which implies that tourism demand initially increases as corruption increases (greasing the wheels) and after a certain threshold level of corruption, tourism demand decreases (sanding the wheels). Empirical studies so far concentrated on capturing the nonlinear relationship, by applying a simple linear model and by including corruption as a quadratic term. In the current paper, the authors revisit the "greasing and sanding the wheels" hypothesis by applying an advanced econometric technique, the threshold regression model, which deals with a key element of model uncertainty, namely parameter heterogeneity. In particular, using a sample of 83 countries from 2001 to 2018, the authors firstly examine if there is a nonlinear relationship between corruption and tourism, and then, they estimate the threshold value of corruption. According to the results, the null hypothesis of a linear model against the alternative of a threshold model with two regimes is strongly rejected. Furthermore, while the effect of corruption on tourism is positive in the low corruption regime and negative in the high corruption regime, a heterogeneous relationship is also found between other politico-socio-economic variables and tourism demand in the low and high corruption regimes. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of AirBnb on the size of local tourism markets using AirBnb occupancy rates and hotel overnight stays in order to explore the causal relationship in several Spanish cities. A dynamic panel data model is applied at the city level (2014–2017). Our findings show a positive relationship between the increase in the number of properties offered on AirBnb and the implicit volume of tourists received by each city, specifically in two large cities (Madrid and Barcelona), due to higher AirBnb occupancy rate. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the asymmetric associations of environmental degradation and economic growth with Pakistan's tourism demand. To fulfil this purpose, "non-linear autoregressive distributed lag" (NARDL) modelling was performed on the time series data collected from Pakistan for 26 years. The unit root test, co-integration test, long-run estimation and NARDL estimations were applied to the data to generate findings. The present study revealed that there exist the significant asymmetric associations between environmental degradation and tourism demand. It has also indicated that there exist the significant asymmetric associations between economic growth and tourism demand. It is found through the non-linear ARDL estimation results that the increase or decrease in economic growth leads towards the increase of the tourism demand of Pakistan. It means that any change (either positive or negative) in economic growth is followed by the increase of the tourism demand. Results further indicate that the increase in environmental degradation in Pakistan causes its tourism demand to reduce while the negative change in the environmental degradation does not cause any significant effect on tourism demand. However, this relationship becomes significant in the long run as the negative change in environmental degradation caused significant inverse effect on tourism demand in long run. The current study tends to be theoretically significant and practically beneficial for Pakistan's policymakers. It will help them realize the role of economic growth of the country and environmental degradation in shaping Pakistan's tourism demand and thus help them develop and implement better policies for the growth of the tourism sector. [ABSTRACT FROM AUTHOR]

This study was conducted with an aim to assess the status of ecotourism in terms of tourism demand, tourist characteristics, and strength, weakness, opportunity, and threat (SWOT) analysis. For this, the study was conducted in Bhadaure-Tamagi village of the Panchase Protected Forest Area of Gandaki Province, which was planned to be developed as an important tourist destination for tourists visiting Pokhara because of its cultural and natural importance. Mixed-method research approach was implemented in this research for data collection and analysis. One hundred and twenty-two tourism stakeholders (30 hoteliers, 40 homestay owners, and 52 tourists) were surveyed for quantitative data collection and analysis along with seven key informant interviews (KII) for qualitative data collection and analysis. It was observed that the current demand for accommodation facilities in the Bhadaure-Tamagi village was 23,390 bed nights per annum. The tourism demand in terms of tourism revenues estimated through this study amounted to a total of US$10,763.67 per year. The occupancy rate of accommodation facilities at Bhadaure-Tamagi village was a mere 20%, which is well below the national and international average. Despite outstanding ecotourism opportunities and a necessary regulatory framework in place, the SWOT analysis revealed that the tourism sector development is not satisfactory. In the current situation, Coronavirus disease (COVID-19) had adversely affected the ecotourism in the area. So, tourism promotional activities need to be focused by following appropriate health, hygiene, and safety measures. [ABSTRACT FROM AUTHOR]

The accurate forecasting of monthly tourism demand can improve tourism policies and planning. However, the complex nonlinear characteristics of monthly tourism demand complicate forecasting. This study proposes a novel approach named ICPSO-BPNN that combines improved chaotic particle swarm optimization (ICPSO) with backpropagation neural network (BPNN) to forecast monthly tourism demand. ICPSO with chaotic initialization and two search strategies, sigmoid-like inertia weight, and linear acceleration coefficients is utilized to search for the appropriate initial connection weights and thresholds necessary to improve the performance of BPNN. Two comparative real-life examples and one extended example are adopted to verify the superiority of the proposed ICPSO-BPNN. Results show ICPSO-BPNN outperforms that of the basic BPNN, autoregressive integrated moving average model, support vector regression, and other popular existing models. [ABSTRACT FROM AUTHOR]

Recently, empirical studies revealed that democracy is positively associated with environmental quality through the freedom gained by the people to demand environmental protection. In this paper, we explore empirical evidence linking how environmental performance interacts with democracy to influence tourism demand in twenty-seven European countries. To achieve this objective, we use the method of moments quantile regression (MMQR) model by Machado and Silva (J Econ 213: 145-173, 2019) and a balanced panel data covering the period 2002 to 2014. The empirical results suggest that environmental performance interacts heterogenously with democracy at different quantiles of the conditional distribution to stimulate tourism demand. Also, the effect of an increase in income and environmental performance is stronger in countries with lower tourism market shares than in countries with higher tourism market shares. The major implication for this study is that countries with lesser shares of the tourism market should strive for higher environmental performance and economic development as this would grant them more advantage in the tourism sector than their counterparts with higher market shares. [ABSTRACT FROM AUTHOR]

Recent political instabilities in the Middle East have led to strong negative impacts on the tourism sector. The aim of this study is to investigate the extent and the impact of political instability on tourism development in Kurdistan region of Iraq (KRI). The impact of previous wars and incidents including the most recent events by the Islamic State in Iraq and Syria (ISIS) on tourism demand is quantitatively evaluated. Unit roots and structural breaks are detected relating political instabilities with significant tourist declines. A systematic comparison on the effect of terrorist attacks and wars on tourist arrivals is performed by employing a multiple regression model using monthly time series data from January 2003 to September 2018. The analysis revealed stronger negative impact of wars in comparison to terrorist attacks. The novelty of this study lies in its ability to establish that the impacts of war have far reaching consequences that last for at least three consecutive years as compared to short negative effects of terrorist attacks. Our findings imply that political instability, especially wars, leads to large safety concerns in the tourist destinations. [ABSTRACT FROM AUTHOR]

Tourism demand forecasting plays a significant role in devising tourism development policies for countries. Available data on tourism demand usually consist of a nonlinear real-valued sequence. However, the samples are often derived from uncertain assessments that do not satisfy statistical assumptions. Therefore, we use fuzzy regression analysis with neural networks to generate data intervals consisting of upper and lower wrapping sequences to deal with uncertainty. Then, the best non-fuzzy performance values obtained by these data intervals are applied to optimize grey prediction models without statistical assumptions. The forecasting accuracy of the proposed interval grey prediction models was verified using real data on foreign tourists. The results show that the proposed prediction models are comparable to the other interval grey prediction models considered. [ABSTRACT FROM AUTHOR]

Although the main objective of geoparks is to protect and preserve geological heritage, over recent years, touristic use of these parks has become a major tool of economic development. According to UNESCO, geoparks (protected areas containing a highly important geological heritage, rarity, or aesthetic aspect) should have educational and touristic orientations, in addition to their intrinsic protective purpose. In terms of tourism, geoparks have a strong differentiating factor, given their scarcity, rarity, and/or exceptional nature. However, tourism in geoparks demands an extensive knowledge of the demand (geotourists), since this knowledge is vital for the design of development, promotion, and commercialization strategies. Therefore, the objective of this work is to estimate the probability of practicing geotourism in the Villuercas-Ibores-Jara Geopark (Extremadura, Spain) through the use of a binary logistic regression model. For this, sociodemographic explanatory variables (gender, age), travel characteristics (type of travel group, lodging), and destination assessment (lodging, dining, service companies, natural conservation) characteristics are considered. Furthermore, control variables are introduced in the logistic model to determine whether or not this probability is (or is not) conditioned by the tourism season (high season vs low season). [ABSTRACT FROM AUTHOR]

The development of the ICT has deeply transformed the tourism industry. ICT has become a key determinant for competitiveness that deeply impacts on marketing and communication strategies. Online Travel Agency is accumulating a huge mass of valuable information. Web Data (Big Data) can actually represent an up-to-date information, which can be used as a support to improve statistical information, especially for monitoring current phenomena, as arrivals, spent nights, or the average length of stay. In this respect, an interesting issue is the assessment of the contribution of Web data for forecasting tourism flows. Specifically, nowcasting is a special case of forecasting as it deals with the knowledge of the present, immediate past and very near future. The aim of the paper is to assess the effective advantage of Google Trends (GT) data in forecasting tourist arrivals in Italy. The analysis is related to monthly foreign arrivals in tourist accommodations facilities. Google Trends data are used to predict the monthly number of foreign arrivals released by the Italian national statistical office, which is the dependent variable. Specifically, we have assessed the contribution of lagged GT variables in a standard ARIMA model and in a time series regression model with seasonal dummies and autoregressive components. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the impact of geopolitical risks measured by a new index of geopolitical risk (GPR) on inbound tourism. For this purpose, we use the fixed-effects and the least squares dummy variable corrected estimations for panel data of 18 countries for the period from 1995 to 2016. We find that geopolitical risks negatively affect inbound tourism. We also implement various robustness checks, such as introducing control variables, considering the lagged effect of the GPR, and utilizing different econometric techniques. [ABSTRACT FROM AUTHOR]

While climate is an important factor attracting tourists to certain destinations, it can also motivate people residing in a country with a harsh climate to move to another location. By applying X-12 decompositions and a panel data regression analysis, this study analyzes the pull and push effects of climatic seasonal factors between destination (Hainan Island, China) and source countries (Russia and South Korea). The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand, with temperature being the main factor. Furthermore, the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors; countries with a higher numbers of paid vacation days are more sensitive to climatic conditions. Lastly, future global warming may causes the aforementioned pull and push effects to abate, which will have an unavoidable influence on tourism industries. [ABSTRACT FROM AUTHOR]

This study employed a cross-country panel data model to investigate the relative contributions of sociopolitical, natural and cultural characteristics and national tourism policies to international tourism growth. International tourism receipt data were adjusted for country-specific inflation and then analyzed in relation to standardized measures of the World Economic Forum's 'pillars of tourism competitiveness.' Results indicated that international tourism receipts are more responsive to policies and regulations favoring tourism, abundance of natural resources, richness in cultural heritage, and health and hygiene than they are to infrastructure, safety, price competitiveness, and other variables. Findings highlight key factors associated with international tourism receipts and provide a general framework that could inform policies and management strategies designed to promote sustainable international tourism development. [ABSTRACT FROM AUTHOR]

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level. [ABSTRACT FROM AUTHOR]

In general, tourism plays a significant role in the economy of archipelagos and islands. The Autonomous Region of the Azores has a great potential for tourism, offering multiple attractions, both natural and cultural, creating a big challenge for a sustainable tourism policy since little attention has been paid to the archipelagos and their special needs. This work aims to understand the profile and type of ecotourist that visits the Azores. This knowledge is of great importance for a better management and development of nature-based tourism, and products tailored to the needs and expectations of visitors. The data were collected by means of exit surveys conducted at the Airport of São Miguel Island, the larger and most populated island of the archipelago, during the high tourist season-July-September 2009. The analysis of the visitor's profile and preferences is crucial to draw adequate strategies of management for tourism, while it helps to adequate the offer to the demand. Results showed that 41.1 % of the tourists claimed to be attracted to the islands due to their 'natural values' (e.g., landscape, biodiversity, and geodiversity). The most practiced activities were whale-watching (32.4 %) and mountaineering/hiking (31.6 %), followed by diving (7 %) and other sports (5.1 %). The tourists' profile points to a mainstream, soft, and incidental type of ecotourist. This information helps to develop and support a strategic planning and management, both at local and regional levels, for sustainable tourism policies. [ABSTRACT FROM AUTHOR]

The study of tourism demand is attracting more and more attention. Hence, it is important to understand the variables that affect tourism demand and to forecast the demand. Many studies have been conducted to analyze the demands in various countries. Recently, China has been expected to become one of the largest originators of outbound tourists in the world. Hence, it is interesting to explore what the variables are that affect the Mainland Chinese arrivals to Taiwan and to forecast its corresponding tourism demand. This study applies neural networks to select proper models, and then to forecast the demand. [ABSTRACT FROM AUTHOR]

This paper uses the vector autoregressive (VAR) methodology as an alternative to Deaton and Muellbauer’s Almost Ideal Demand System (AIDS), to establish the long-run relationships between I(1) variables: tourism shares, tourism prices and UK tourism budget. With appropriate testing, the deterministic components and sets of exogenous and endogenous variables of the VAR are established, and Johansen’s rank test is used to determine the number of cointegrated vectors in the system. The cointegrated VAR structural form is identified and the long-run structural parameters are estimated. Theoretical restrictions such as homogeneity and symmetry are tested and not rejected by the VAR structure. The fully restricted cointegrated VAR model reveals itself a theoretically consistent and statistically robust means to analyse the long-run demand behaviour of UK tourists, and an accurate multi-step forecaster of the destinations’ shares when compared with unrestricted reduced form and first differenced VARs, or even with the structural AIDS model. [ABSTRACT FROM AUTHOR]