Pandemics and Cryptocoins

This study examines the effect of pandemic-induced uncertainty on cryptocoins (Bitcoin, Ethereum and Ripple). It employs Westerlund and Narayan (2012, 2015) predictive model to examine the predictability of pandemic-induced uncertainty and our model's forecast performance. We examine the role of asymmetry in uncertainty and the sensitivity of our results to Salisu and Akanni (2020) recently developed Global Fear Index. Cryptocoins acts as hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry improves predictability and model forecast performance. Our results may be sensitive to the choice of measure of pandemic-induced uncertainty.

Investigating asymmetric impacts of total factor energy efficiency on carbon emissions in India.

As many complex energy relations are not linear and have diminishing returns, assuming a symmetric (linear) effect of energy efficiency (ENEF) on carbon emissions (CAE) has limited our understanding of the emission-ENEF nexus. This research, therefore, initially estimates total factor energy efficiency by applying a stochastic frontier technique using sample panels for India encompassing the period from 2000 to 2014. Further, a nonlinear panel autoregressive distributed lag modelling framework is utilised in order to investigate the asymmetric (nonlinear) long- and short-run impacts of ENEF on CAE. The findings demonstrated that ENEF has asymmetric long- and short-run impacts on CAE in India. Based on the outcomes, numerous crucial implications are discussed with a particular reference to developing economies like India.

Oil price shocks, economic policy uncertainty, and green finance: a case of China

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China's green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China's green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market.

Energy policy simulation in times of crisis: Revisiting the impact of renewable and non-renewable energy production on environmental quality in Germany

In this paper, we examine for the first time in the literature the implications of energy policy alternatives for Germany considering the aftermath of coronavirus as well as Electricity and Gas energy supply shortages. Whilst several policy options are open to the government, the choice of investment in renewable energy generation versus disinvestment in non-renewable energy such as coal energy generation provides divergent impacts in the long term. We utilize data from British Petroleum and the World Bank Development Indicator database for Germany covering 1981 to 2020 to explore a Carbon function by applying a battery of Autoregressive distributed lag model (ARDL), dynamic ARDL and Kernel-Based Regularized Least squares approaches. The particular policy tested is the pledge by Germany to decrease emissions by ∼100% in 2050, and this was integrated through the estimation of dynamic ARDL estimation. The simulation result shows that a ＋61% shock in renewable energy production decreases carbon emissions unlike coal energy production which increases carbon emissions in the beginning but the carbon emissions decrease thereafter. The findings highlight the inevitability of cutting down on coal production, and recommends energy investment alternatives. Hence, Germany's energy policy should contemplate more thoroughly on these factors. © 2023 The Author(s)

Connectedness of green investments and uncertainties: new evidence from emerging markets

Purpose>This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.Design/methodology/approach>This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons;providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.Findings>Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.Originality/value>This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries' green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects;which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Non-Renewable and Renewable Energies, and COVID-19 Pandemic: Do They Matter for China’s Environmental Sustainability?

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China.

This paper explores the nonlinear relationship between poverty and CO2 emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO2 emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO2 emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO2 emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO2 emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO2 Paradox" in China and provides an empirical basis for breaking the "Poverty-CO2 Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO2 emission reduction.

Wagner’s Law vs. Keynesian Hypothesis: Dynamic Impacts

This study analyzes the dynamics between public expenditure and economic growth in Peru for 1980Q1–2021Q4. We used quarterly time series of real GDP, public consumption expenditure, public expenditure, and the share of public expenditure to output. The variables were transformed into natural logarithms, wherein only the logarithm of public expenditure to output ratio is stationary and the others are non-stationary I1. The study of stationary time series assesses whether Wagner’s law, the Keynesian hypothesis, the feedback hypothesis, or the neutrality hypothesis is valid for the Peruvian case according to Granger causality. We found cointegration between real GDP and public expenditure, and public consumption expenditure and real GDP. Estimating error correction and autoregressive distributed lag models, we concluded that Wagner’s law and the Keynesian hypothesis are valid in the Peruvian case, expressed as dynamic processes that allow us to obtain short-run and long-run impacts, permitting the mutual sustainability of economic growth and public expenditure.

Unlocking private investment as a new determinant of green finance for renewable development in China

The COVID-19 pandemic has pushed up the green finance for renewable energy development. Private investment has been recognized as a dominant driver of the renewable energy industry, an essential and critical step in averting greenhouse gas emissions. Nonetheless, despite the increasing pace, private investment in green finance for renewable development is still restricted to several developed nations, where it is crucial. Prior studies have offered some understanding of the complexities and challenges that investment confronts in this industry, which remains underexplored in the case of China. This study employs the ARDL-PMG model used to examine the public listed companies in Shanghai and Shenzhen during China's 2010–2020 period. This research adds to the body of knowledge by rigorously examining the variables on FDI in renewable energy production in China and how these effects differ depending on the source of investment. Some of these factors include the adoption of national renewable energy legislation, the supply of foreign public money, and the broader economic environment. The findings indicate that worldwide financial assistance, legislative support policies, feed-in tariffs, and economic stability are potent drivers of green finance for developing renewable energy investment in China. Further, this research explains that the impacts of private sector investment and entrepreneurial contextual factors on expenditure vary depending on the source of finance, emphasizing the importance of dissecting investment spreads to fully comprehend private investment decisions in green finance for renewable development. © 2022

Is the virus-laden standing water change the transmission intensity of SARS-CoV-2 after precipitation? A framework for empirical studies.

Understanding the relationship between precipitation and SARS-CoV-2 is significant for combating COVID-19 in the wet season. However, the causes for the variation of SARS-CoV-2 transmission intensity after precipitation is unclear. Starting from "the Zhengzhou event," we found that the virus-laden standing water formed after precipitation might trigger some additional routes for SARS-CoV-2 transmission and thus change the transmission intensity of SARS-CoV-2. Then, we developed an interdisciplinary framework to examine whether the health risk related to the virus-laden standing water needs to be a concern. The framework enables the comparison of the instant and lag effects of precipitation on the transmission intensity of SARS-CoV-2 between city clusters with different formation risks of the virus-laden standing water. Based on the city-level data of China between January 01, 2020, and December 31, 2021, we conducted an empirical study. The result showed that in the cities with a high formation risk of the virus-laden standing water, heavy rain increased the instant transmission intensity of SARS-CoV-2 by 6.2% (95%CI: 4.85-10.2%), while in the other cities, precipitation was uninfluential to SARS-CoV-2 transmission, revealing that the health risk of the virus-laden standing water should not be underestimated during the COVID-19 pandemic. To reduce the relevant risk, virus-laden water control and proper disinfection are feasible response strategies.

Determinants of sovereign spreads in Sri Lanka: global factors and country-specific fundamentals

Purpose>The study aims to analyze and compare the influence of country-specific fundamentals and global conditions on sovereign risk of Sri Lanka within the sample period of 2006–2019 while employing Treasury bond rates as proxy for sovereign risk.Design/methodology/approach>The determinant powers of the variables are assessed using the auto regressive distributed lag (ARDL) model to verify both short- and long-run effects on sovereign spreads.Findings>The study finds that Sri Lanka's sovereign spreads are shaped by both country fundamentals and global factors, though local determinants tend to have greater influence when the directions of coefficients are ignored. While the impact of most variables was in line with the researchers' expectations, fiscal deficit was found to have an unconventional negative coefficient which may be explained by investors' optimistic take on Government's involvement in post-war economic development drive during the sample period, enabling Sri Lanka to attract low-cost funding.Research limitations/implications>The study excludes of impact of the ongoing coronavirus disease-2019 ( COVID-19) health crisis which may unduly distort the data. Further, the research does not capture the impact of change in sentiment owing to market information, debt dynamics and policy changes in Sri Lanka.Practical implications>The study reveals that a sound monetary policy directed at preserving both the internal and external value of currency as well as a disciplined fiscal policy are imperative to manage Sri Lanka's sovereign risk, particularly in the face of global uncertainties.Originality/value>The study adds to the literature by investigating the timely importance of a country's internal fundamentals against the global events. Furthermore, the research would complement the scarcity of research regarding that subject focused on the Sri Lankan economy, capturing the rapid variations in the fundamentals that the country has undergone since the end of the civil war while recognizing the growing influence of globalization over the recent years.

Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

La prévision du nombre de cas quotidiens de COVID­19 est cruciale pour la planification à court terme de ressources hospitalières et d'autres ressources publiques. Les données de localisation des téléphones mobiles qui mesurent le temps passé à la maison peuvent constituer un élément d'information important pour prédire les cas de COVID­19. Les modèles de séries chronologiques endémiques­épidémiques sont des modèles auto­régressifs récents où le nombre moyen de cas en cours est modélisé comme une moyenne pondérée du nombre de cas antérieurs multipliée par un taux auto­régressif (reproductif), plus une composante endémique. Les auteurs de ce travail généralisent les modèles endémiques­épidémiques pour y inclure un modèle à décalage distribué, et ce, dans le but de tenir compte du lien entre la mobilité et le nombre de cas de COVID­19 enregistrés. Pour saisir les variations de temps supplémentaires, ils y incorporent une marche hebdomadaire aléatoire d'ordre supérieur. De plus, ils proposent un schéma de pondération binomiale négative décalée pour les dénombrements passés, qui est plus flexible que les schémas de pondération existants. Ils utilisent l'inférence bayésienne afin d'intégrer l'incertitude des paramètres aux prédictions du modèle et ils illustrent les méthodes proposées avec des données provenant de quatre comtés américains.

Electricity Demand and CO Emissions during the COVID-19 Pandemic: The Case of India

The present study examines the impact of electricity demand on CO emissions in the Indian economy using daily real-time data during the Covid-19 period. The subject was hardly addressed explicitly and quantitatively in environmental studies. Our study applied recently developed non-linear (asymmetric) autoregressive distributed lag (ARDL) and the Quantile ARDL techniques for analysis. The empirical findings confirm the existence of an asymmetric long-run relationship between electricity demand and CO emissions during the Covid-19 pandemic. Furthermore, the results reveal that the decrease (increase) in electric demand leads to a reduction (increase) in CO emissions in the long run. Besides, the results show that the increase in electricity demand generates more CO emissions in the short run. Our study will be helpful for policy-makers and regulators associated with energy and climate change amid the ongoing pandemic crisis and provide directions to the expected waves of pandemic scenarios. © 2022, Econjournals. All rights reserved.

Modelling the Dynamic Interaction between Economic Policy Uncertainty and Commodity Prices in India: The Dynamic Autoregressive Distributed Lag Approach

This study examines the dynamic interaction between oil, natural gas, and prices with Indian economic policy uncertainty (EPU). The study finds that gold prices and industrial production are fundamental drivers of Indian economic policy uncertainty in both the short and long runs, using a dynamic autoregressive distributed lag (ARDL) model with monthly data ranging from January 2003 to July 2020. Gold prices are positively related to the Indian EPU, while industrial production is negatively related to it. Thus, investors in the Indian economy should use gold as a hedge for portfolio diversification and as a safe haven during an economic crisis. We also find a significant positive interconnection between gold prices and crude oil prices in both the short run and the long run, while the significant positive impact of natural gas prices on crude oil prices manifests only in the long run. The evidence also indicates that the EPUs of the US and Europe positively affect the Indian EPU, while the EPU of China does not have a significant effect. Higher crude oil prices are associated with higher gas prices, whereas higher gold prices are negatively associated with the natural gas price and vice versa. Furthermore, the evidence shows that the Indian EPU does not have a significant effect on the changes in the prices of goods.

Evaluation of the Relative Risk of Covid-19 Mortality Based on the Number of Hospitalizations in Iran Using a Log-Linear Distributed Lag Model

Background and Objectives: The Covid-19 epidemic began in Wuhan, China in the late 2019 and became a global epidemic in March 2020. In this regard, one of the most important indicators of the healthcare systems is the in-hospital mortality rate, which occurs with a time lag of one to two weeks after hospitalization. The aim of this study was to investigate the relative risk of Covid-19 mortality considering this time lag according to the number of daily hospitalizations. Methods: The data included the number of daily hospitalizations and deaths from Covid-19 from 15 May 2020 to 10 February 2021 in Iran, which was obtained from the Github database. A log-linear distributed lag model was used to evaluate the relationship and lag effect between daily hospitalization and relative risk of death. Results: The mean number of daily hospitalizations and deaths were 1342.2 ± 7 731.5 and 190.6 11±118.6 in the study period, respectively. It was found that an increase in the number of daily hospitalizations had a significant relationship with an increase in the relative risk of death on the same day and in the following days. As the number of hospitalizations exceeded 2000 patients per day, the cumulative relative risk of death increased to more than one. Conclusion: The results showed that the number of hospitalizations exceeding 2000 people per day was an alert for the country's healthcare system. Overall, prevention and observance of health protocols in the first level followed by early diagnosis of the disease, improving the hospitals facilities and preparedness of healthcare staff can reduce the relative risk of death in the possible future peaks. © 2021, Iranian Epidemiological Association. All rights reserved.

Cumulative effects of ambient particulate matter pollution on deaths: A multicity analysis of mortality displacement.

BACKGROUND: Systematic evaluations of the cumulative effects and mortality displacement of ambient particulate matter (PM) pollution on deaths are lacking. We aimed to discern the cumulative effect profile of PM exposure, and investigate the presence of mortality displacement in a large-scale population. METHODS: We conducted a time-series analysis with different exposure-lag models on 13 cities in Jiangsu, China, to estimate the effects of PM pollution on non-accidental, cardiovascular, and respiratory mortality (2015-2019). Over-dispersed Poisson generalized additive models were integrated with distributed lag models to estimate cumulative exposure effects, and assess mortality displacement. RESULTS: Pooled cumulative effect estimates with lags of 0-7 and 0-14 days were substantially larger than those with single-day and 2-day moving average lags. For each 10 µg/m3 increment in PM2.5 concentration with a cumulative lag of 0-7 days, we estimated an increase of 0.50 % (95 % CI: 0.29, 0.72), 0.63 % (95 % CI: 0.38, 0.88), and 0.50 % (95 % CI: 0.01, 1.01) in pooled estimates of non-accidental, cardiovascular, and respiratory mortality, respectively. Both PM10 and PM2.5 were associated with significant increases in non-accidental and cardiovascular mortality with a cumulative lag of 0-14 days. We observed mortality displacement within 30 days for non-accidental, cardiovascular, and respiratory deaths. CONCLUSIONS: Our findings suggest that risk assessment based on single-day or 2-day moving average lag structures may underestimate the adverse effects of PM pollution. The cumulative effects of PM exposure on non-accidental and cardiovascular mortality can last up to 14 days. Evidence of mortality displacement for non-accidental, cardiovascular, and respiratory deaths was found.

The Impact of Mobility Restriction Strategies in the Control of the COVID-19 Pandemic: Modelling the Relation between COVID-19 Health and Community Mobility Data.

BACKGROUND: Non-pharmaceutical interventions (NPIs), particularly mobility restrictions, are mainstay measures for the COVID-19 pandemic worldwide. We evaluated the effects of Oman's mobility restriction strategies to highlight their efficacy in controlling the pandemic. METHODS: Accessible national data of daily admissions and deaths were collected from 1 April 2020 to 22 May 2021. Google Community Mobility Report (CMR) data were downloaded for the same period. Among six CMR categories, three were used and reduced to one index-the community mobility index (CMI). We used a generalised linear model with a negative binomial distribution combined with a non-linear distributed lag model to investigate the short-term effects of CMI on the number of admitted PCR-confirmed COVID-19 cases and deaths, controlling for public holidays, day of the week, and Eid/Ramadan days. RESULTS: We demonstrated the feasibility of using CMRs in the evaluation and monitoring of different NPIs, particularly those related to movement restriction. The best movement restriction strategy was a curfew from 7 p.m. to 5 a.m. (level 3 of CMI = 8), which had a total reduction of 35% (95% confidence interval (CI); 25-44%) in new COVID-19 admissions in the following two weeks, and a fatality reduction in the following four weeks by 52% (95% CI; 11-75%). CONCLUSION: Evening lockdown significantly affected the course of the pandemic in Oman which lines up with similar studies throughout the world.