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This study aims to examine the relationship between economic development and environmental degradation based on the Environmental Kuznets Curve (EKC) hypothesis. The level of CO2 emissions is used as the indicator of environmental damage to determine whether or not greater economic growth can lower environmental degradation under the EKC hypothesis. The investigation was performed on eight major international economic communities covering 44 countries across the world. The relationship between economic growth and environmental condition was estimated using the kink regression model, which identifies the turning point of the change in the relationship. The findings indicate that the EKC hypothesis is valid in only three out of the eight international economic communities, namely the European Union (EU), Organization for Economic Co-operation and Development (OECD), and Group of Seven (G7). In addition, interesting results were obtained from the inclusion of four other control variables into the estimation model for groups of countries to explain the impact on environmental quality. Financial development (FIN), the industrial sector (IND), and urbanization (URB) were found to lead to increasing CO2 emissions, while renewable energies (RNE) appeared to reduce the environmental degradation. In addition, when we further investigated the existence of the EKC hypothesis in an individual country, the results showed that the EKC hypothesis is valid in only 9 out of the 44 individual countries.

This study analyzed the nonlinear impacts of education, particularly higher education, on economic growth in the ASEAN-5 countries (i.e., Thailand, Indonesia, Malaysia, Singapore, and the Philippines) over the period 2000–2018. The impacts of education on economic growth are assessed through various education indicators, consisting of public expenditure on tertiary education per student, enrolment rates of primary, secondary, and tertiary levels, educated workforce, and the novel indicator of unemployment rates with advanced education. This study establishes nonlinear regression models—the time-series kink regression and the panel kink regression—to investigate the kink effects of education on the individual country’s economic growth and the ASEAN-5 region, respectively. There are three main findings. Firstly, the nonlinear effects of the government expenditure per tertiary student on economic growth are confirmed for the ASEAN-5 region. However, the impacts do not follow the law of diminishing returns. Secondly, our findings reveal that an increase in unemployment of advanced educated workers can positively or negatively impact economic growth, which requires an appropriate policy to handle the negative impacts. Lastly, secondary and higher education enrollment rates can contribute to the ASEAN-5’s economic growth (both the individual and regional levels). However, the regional analysis reveals that higher education impacts become twice as strong when the enrollment rates are greater than a certain level (a kink point). Therefore, we may conclude that secondary enrollment rates positively affect economic growth; however, higher education is the key to future growth and sustainability.

Purpose This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons. Design/methodology/approach This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models. Findings The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model). Practical implications A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures. Originality/value High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week. Plain Language Summary This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

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