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The SARS-CoV-2 virus that causes the COVID-19 disease is highly infectious and contagious. The long-term consequences for individuals are as yet unknown, while the long-term effects on the international community will be dramatic. COVID-19 has changed the world forever in every imaginable respect and has impacted heavily on the international travel, tourism demand, and hospitality industry, which is one of the world’s largest employers and is highly sensitive to significant shocks like the COVID-19 pandemic. It is essential to investigate how the industry will recover after COVID-19 and how the industry can be made sustainable in a dramatically changed world. This paper presents a charter for tourism, travel, and hospitality after COVID-19 as a contribution to the industry.

Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of the SARS-CoV-2 virus that causes the COVID-19 disease, it is essential to enquire if an outbreak of the epidemic might have been anticipated, given the well-documented history of SARS and MERS, among other infectious diseases. If various issues directly related to health security risks could have been predicted accurately, public health and medical contingency plans might have been prepared and activated in advance of an epidemic such as COVID-19. This paper evaluates an important source of health security, the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly in an effective and timely manner. The GHS index numerical scores are calculated as the arithmetic (AM), geometric (GM), and harmonic (HM) means of six categories, where AM uses equal weights for each category. The GHS Index scores are regressed on the numerical score rankings of the six categories to check if the use of equal weights of 0.167 in the calculation of the GHS Index using AM is justified, with GM and HM providing a check of the robustness of the arithmetic mean. The highest weights are determined to be around 0.244–0.246, while the lowest weights are around 0.186–0.187 for AM. The ordinal GHS Index is regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal Global Health Security (GHS) Index, where the highest weight is 0.368, while the lowest is 0.142, so the estimated results are wider apart than for the numerical score rankings. Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, whereas Risk Environment and Prevention have the smallest effects. The quantitative and qualitative results are different when GM and HM are used.

It is generally accepted in the scientific community that carbon dioxide (CO2) emissions, which lead to global warming, arise from using fossil fuels, namely coal, oil and gas, as energy sources. Consequently, alleviating the effects of global warming and climate change necessitates substantial reductions in the use of fossil fuel energy. This paper uses a financial market-based approach to investigate whether positive stock returns cause changes in CO2 emissions, or vice-versa, based on the Granger causality test to determine cause and effect, or leader and follower. If Granger causality can be determined in any direction, this will enable a clear directional statement regarding temporal predictability between stock returns and CO2 emissions. The empirical data include annual CO2 emissions from fuel combustion of the three main fossil energy sources, namely coal, oil and gas, based on 18 countries with sophisticated financial markets that are in the Morgan Stanley Capital International (MSCI) World Index from 1971 to 2017. The empirical results show clearly that all the statistically significant causality findings are unidirectional from the stock market returns to CO2 emissions from coal, oil and gas, but not the reverse. More importantly, the regression results suggest that when stock returns rise by 1%, CO2 emissions from coal combustion decrease by 9% among the countries that are included in MSCI World Index. Furthermore, when stock returns rise 1%, CO2 emissions from oil combustion increase by 2%, but stock returns have no significant effect on CO2 emissions from gas combustion.

Climate change poses an unprecedented global challenge, which prompts nations to adopt new strategies to mitigate greenhouse gas emissions. The European Union emissions trading system (EU ETS) is a cornerstone of the EU’s efforts towards a cost-effective fight against climate change. This study examines the effectiveness of the EU ETS by analyzing monthly data from December 2008 to December 2021, with the focus on CO2 emission allowance futures prices, renewable energy indices, coal prices, oil prices, and fossil energy indices. The key findings are as follows: The CO2 emission allowance futures prices have averaged EUR 14.83 per ton, ranging from EUR 2.87 to EUR 76.81, which shows a significant upward trend. The renewable energy index also demonstrated strong growth, with a mean 1562.07 and maximum 4571.96. Coal prices have averaged EUR 65.32 per ton, while Brent oil prices averaged EUR 59.85 per barrel. A cointegration analysis revealed a long-run equilibrium relationship between these variables. The Vector Error Correction model (VECM) revealed significant negative responses to long-run equilibrium deviations of the renewable energy index (−0.0155) and oil prices (−0.0236), a significant negative short-run response of CO2 prices to their own lagged values (−0.223), and a significant positive short-run effect of oil prices on the fossil energy index (0.254). These results suggest the EU ETS has created significant linkages between carbon, energy, and financial markets. The study concludes that while the EU ETS has made progress in motivating emissions reductions and promoting renewable energy, the system’s efficacy still needs improvement.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, oligo- or anovulation, and/or polycystic ovary. It frequently presents with dyslipidemia and insulin resistance. Recent studies have shown that the white adipose tissue-derived asprosin is elevated in humans with insulin resistance. Because many PCOS patients have a propensity to develop dyslipidemia and/or insulin resistance, asprosin metabolism could be dysregulated in PCOS patients. Accordingly, we investigated serum levels of asprosin, irisin, GIP, androgens, LH, glucose, insulin, and lipids as well as HOMA-IR, QUICKI and ISI Matsuda in a cohort of 444 PCOS patients and 156 controls. Patients were stratified based on metabolic syndrome risk factors (ATPIII [+] and [−] groups), or BMI (overweight and lean groups). The irisin level was significantly correlated with body weight, SBP, DBP, Ferriman–Gallwey score, and levels of TSH, triglycerides, glucose and insulin in the overall population, and was elevated in ATPIII(+) and overweight PCOS patients compared to corresponding controls. By contrast, asprosin levels in PCOS, ATPIII(+), or overweight patients were similar to those of corresponding controls. This finding indicated that the regulation of irisin, but not asprosin, metabolism is abnormal in PCOS patients, and this metabolic characteristic is distinctly different from that of diabetes patients.

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

The aim of this study is to develop a scoring platform to be used as a reference for both medical preparedness and research resource allocation in the prioritization of zoonoses. Using a case-control design, a comprehensive analysis of 46 zoonoses was conducted to identify factors influencing disease prioritization. This analysis provides a basis for constructing models and calculating prioritization scores for different diseases. The case group (n = 23) includes diseases that require immediate notification to health authorities within 24 hours of diagnosis. The control group (n = 23) includes diseases that do not require such immediate notification. Two different models were developed for primary disease prioritization: one model incorporated the four most commonly used prioritization criteria identified through an extensive literature review. The second model used the results of multiple logistic regression analysis to identify significant factors (with p-value less than 0.1) associated with 24-hour reporting, allowing for objective determination of disease prioritization criteria. These different modeling approaches may result in different weights and positive or negative effects of relevant factors within each model. Our study results highlight the variability of zoonotic disease information across time and geographic regions. It provides an objective platform to rank zoonoses and highlights the critical need for regular updates in the prioritization process to ensure timely preparedness. This study successfully established an objective framework for assessing the importance of zoonotic diseases. From a government perspective, it advocates applying principles that consider disease characteristics and medical resource preparedness in prioritization. The results of this study also emphasize the need for dynamic prioritization to effectively improve preparedness to prevent and control disease.

Climate change and global warming are significantly affected by carbon emissions that arise from the burning of fossil fuels, specifically coal, oil, and gas. Accurate prices are essential for the purposes of measuring, capturing, storing, and trading in carbon emissions at regional, national, and international levels, especially as carbon emissions can be taxed appropriately when the price is known and widely accepted. This paper uses a novel Capital (K), Labor (L), Energy (E) and Materials (M) (or KLEM) production function approach to calculate the latent carbon emission prices, where carbon emission is the output and capital (K), labor (L), energy (E) (or electricity), and materials (M) are the inputs for the production process. The variables K, L, and M are essentially fixed on a daily or monthly basis, whereas E can be changed more frequently, such as daily or monthly, so that changes in carbon emissions depend on changes in E. If prices are assumed to depend on the average cost pricing, the prices of carbon emissions and energy may be approximated by an energy production model with a constant factor of proportionality, so that carbon emission prices are a function of energy prices. Using this novel modeling approach, this paper estimates the carbon emission prices for Japan using seasonally adjusted and unadjusted monthly data on the volumes of carbon emissions and energy, as well as energy prices, from December 2008 to April 2018. The econometric models show that, as sources of electricity, the logarithms of coal and oil, though not Liquefied Natural Gas (LNG,) are statistically significant in explaining the logarithm of carbon emissions, with oil being more significant than coal. The models generally displayed a high power in predicting the latent prices of carbon emissions. The usefulness of the empirical findings suggest that the methodology can also be applied for other countries where carbon emission prices are latent.

Subjective wellbeing maximization is a possible goal of government or public policies, and it is often considered the goal of individual life. This paper proposes an estimation using the Wellbeing Valuation Approach (WVA) to estimate the monetized effect of trust in government. Using a cross-country panel data set for 97 countries in the period from 2011 to 2019, we arrive at three main findings. First, there is a positive relationship between trust in national government and average life satisfaction. Second, trust in the national government has a global median value of Intl$ 5649 per person a year in foregone income. Third, trust affects life satisfaction directly as well as indirectly through per capita GDP. This indirect effect is considered relatively small compared to the direct effect, being approximately six times smaller. This study contributes to the policy evaluation literature by providing an evaluation of trust in government to be used as a proxy to plan future investment or policy assessment.

Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980) causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU), there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE) under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR) test to a quasi-likelihood ratio test (QLR) to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the spot and futures returns and volatility of carbon emissions, crude oil, and coal prices.