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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.

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This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.

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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.

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This study aims to investigate the moderating effects of various distance measures on the relationship between relative pandemic severity and bilateral tourism demand. After confirming its validity using actual hotel and air demand measures, we leveraged data from Google Destination Insights to understand daily bilateral tourism demand between 148 origin countries and 109 destination countries. Specifically, we estimated a series of fixed-effects panel data gravity models based on the year-over-year change in daily demand. Results show that a 10% increase in seven-day smoothed COVID-19 cases led to a 0.0658% decline in year-over-year demand change. The moderating distance measures include geographic, cultural, economic, social, and political distance. Results show that long-haul tourism demand was less affected by a destination's pandemic severity relative to tourists' place of origin. The moderating effect of national cultural dimensions indulgence versus constraints was also confirmed. Lastly, a discussion and implications for international destination marketing are provided.

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The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

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This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

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An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors' relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

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This paper proposes a new foresight approach to estimate the impact of public health emergencies on hotel demand. The forecasting-based influence evaluation consists of four modules: decomposing hotel demand before an emergency, matching each decomposed component to a forecasting model, combining the predictions as the expected demand after the emergency, and estimating the impact by comparing actual demand against that predicted. The method is applied to analyze the impact of COVID-19 on Macao's hotel industry. The empirical results show that: 1) the new approach accurately estimates COVID-19's impact on hotel demand; 2) the seasonal and industry development components contribute significantly to the estimate of expected demand; 3) COVID-19's impact is heterogeneous across hotel services.

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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. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-021-02193-2.

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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.

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The spread of the coronavirus disease 2019 (COVID-19) has significantly reduced tourism demands worldwide. Employing weekly data on tourist flows between Japanese prefectures, we examine the cost-effectiveness of domestic travel subsidies. Our results provide two implications for the literature. First, we identify the underlying mechanism of tourist flows during the pandemic. In contrast to infectious diseases that have only local effects, the COVID-19 pandemic has decreased tourism demand not only to, but also from, severely affected regions, deteriorating tourism businesses even in areas not severely affected by the disease. Second, we confirm the effectiveness of a price-discount strategy in mitigating economic damage to the accommodation sector caused by the pandemic.

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The COVID-19 crisis is dramatically affecting the world economy and, particularly, the tourism sector. In the context of extreme uncertainty, the use of probabilistic forecasting models is especially suitable. We use Monte Carlo simulations to evaluate the outcomes of four possible tourism demand recovery scenarios in the Balearic Islands, which are further used to measure the risks and vulnerability of Balearic economy to the COVID-19 crisis. Our results show that fear of contagion and loss of income in tourism emitting countries will result in a maximum 89% drop in arrivals in the Balearic Islands in 2020.Given that most tourism-related occupations are not highly skilled and are characterized by lower salaries, there are greater risks of loss of welfare, especially for women, who are a major share of the tourism labour force.The model shows important differences among minimum, average and maximum estimates for tourism sector production in 2021, reflecting considerable uncertainty regarding the speed of the sector's recovery. The results serve as a basis to prepare a range of policies to reduce destination vulnerability under different crisis outcomes.

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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.

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This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.

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In a context in which the tourism industry is jeopardised by the COVID-19 pandemic, and potentially by other pandemics in the future, the capacity to produce accurate forecasts is crucial to stakeholders and policy-makers. This paper attempts to forecast the recovery of tourism demand for 2021 in 20 destinations worldwide. An original scenario-based judgemental forecast based on the definition of a Covid-19 Risk Exposure index is proposed to overcome the limitations of traditional forecasting methods. Three scenarios are proposed, and ex ante forecasts are generated for each destination using a baseline forecast, the developed index and a judgemental approach. The limitations and potential developments of this new forecasting model are then discussed.

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The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic's influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong.

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This study uses a time-varying model that provides new evidence on the changing relationship between domestic overnight stays of selected winter sport destinations and natural snow conditions. A Kalman filter method combined with wavelet-based multiresolution analysis (MRA) is employed to investigate the relationships in intervals between 2 and 4 and up to 16-32 months. The model is applied to domestic overnight stays for selected mountain regions in Sweden (Dalarna and Jämtland), Norway (Buskerud, Hedmark, Hordaland and Oppland) and Austria (Salzburg and Tyrol). Results show that the sensitivity of domestic overnight stays on natural snow conditions varies markedly depending on location, time period and frequency band window used in the estimation. The medium-run relation for Tyrol and Salzburg is significantly declining over time, while in Norway and Sweden, the same relationship is generally volatile and not significant at the end of the sample period. In the short run, none of the regions exhibits a link between domestic overnight stays and snow depth fluctuations.

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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.

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Birdwatching is a tourism activity that relates closely to protected natural spaces and that helps contribute to the balance between economic, social and environmental aspects of sustainability. In some European countries (the United Kingdom, Germany, Holland), this recreational activity has a large number of followers, making it a new segment of tourist demand with great possibilities for growth. The objective of this study is to identify the main characteristics of the demand for birdwatching in one of the European territories having a high resource supply, as is the case with Extremadura (Spain). To do this, a logit modelization has been proposed in order to estimate the probability of going birdwatching in the region, based on a random sample of over 3000 tourists that visited the region in 2017. This characterization of birdwatching demand was carried out using variables such as gender, age, type of travel, type of lodging, and assessment of tourism services. Given that the national and the foreign demand of this tourism modality may present distinct behaviors, and therefore, specific characterizations, a structural change test (Chow test) was also conducted in order to determine to what extent these two segments of demand, based on the source markets, have (or do not have) distinguishing features.