Effects of cross-border E-commerce customs declaration ceiling increase on export performance under COVID-19 conditions

Purpose: This study examines the impact of raising the ceiling value of Electronic Commerce Custom Declarations (ECCD) on Turkey's export performance processed via ECCD during the COVID-19 period. Design/methodology/approach: This paper examines the impact of the pandemic conditions on Cross-Border Electronic Commerce (ECCD) exports from Turkey to 47 countries over 42 months before and during the pandemic. An empirical analysis using the Pooled Mean Group (PMG) and Mean Group (MG), Panel Autoregressive Distributed Lag (ARDL) approach was conducted to identify the factors affecting export flows. Findings: The findings suggest that raising the ceiling of the ECCD trade is a vital factor in increasing exports. and this result is robust after controlling for pandemic conditions. On the other hand, although the COVID-19 shock mitigates the export volume of ECCD in the short run, it changes by reversal and increases the export level in the long run. Additionally, the number of COVID-19 cases and deaths in Turkey have a significant and negative impact on export flows in the short run, while they have a positive and significant effect in the long run. Practical implications: The results of this study have practical implications for policymakers, emphasizing the potential and significance of Cross-Border E-Commerce (CBEC) trade. Originality/value: The study is a pioneering effort in the literature of CBEC to explore how changes in the upper limit on customs declarations can affect export flows, taking into account the impact of the COVID-19 pandemic. © 2023, Emerald Publishing Limited.

COVID-19 full vaccination and blockchain size: empirical evidence from the cryptocurrency market

PurposeThis paper aims to analyse COVID-19 indices and blockchain features on Bitcoin and Ethereum returns, respectively. The authors focus on the most used and owned cryptocurrencies that cover Europe, the US and Asian countries.Design/methodology/approachAn autoregressive distributed lag panel (pooled mean group and mean group) is utilized, and a robustness check is incorporated by using a Random Effect Model and Generalized Method of Moments (GMM).FindingsFour new findings were discovered, including (1) the vaccine confidence index (VCI) pushes economic recovery and increased demand for the Bitcoin market, but the opposite result was interestingly observed from Ethereum;(2) the blockchain features were revealed to be essential to Bitcoin, while they were irrelevant to Ethereum for short-run country-specific results;(3) the hash rate and network difficulty moved inversely during the pandemic;and (4) the government played a significant role in taking action during uncertain times and regarding cryptocurrency policies.Research limitations/implicationsVCI is constructed by the most used vaccine type in our sample countries (i.e. Pfizer), as the data for a specific classification by each type is still unavailable.Practical implicationsProviding an evenly distributed vaccination program primary vaccination series against COVID-19 to the citizens is an essential duty of the government. Bitcoin policymakers and investors should watch the COVID-19 vaccine distributions closely as it will affect its return. Ethereum is emphasized to keep developing its smart contract which appeared to outplay other blockchain features. Cryptocurrency investors should be wise in their investment decisions by analysing the news thoroughly.Social implicationsThis research emphasizes that the success in the roll-out of COVID-19 vaccination requires citizens' willingness to participate and their trust in the vaccine's efficacy. Such self-awareness and self-discipline in society can ultimately empower individuals and stabilise the economy. Nevertheless, the implementation of health protocols is still highly required to prevent the spread of new variants of COVID-19.Originality/valueThis is the first study that attempts to construct a VCI which denotes the confidence derived from the administration of full-dose COVID-19 vaccines (an initial vaccine and a second vaccine). The authors further find the impact on cryptocurrency returns. Next, blockchain size is utilized as a new determinant of cryptocurrencies.

America's decoupling from China narrative: development, determinants, and policy implications

America's decoupling from China debate started after July 2018, reached its peak in August 2020, and is likely to continue even if it may not be a high priority for the Biden administration. Many studies have examined various aspects of this topic. Unlike previous research, using Google Trends data, this study creatively created a high-frequency weekly dataset to measure the narrative of decoupling from China in the US. Based on this dataset from January 2020 to June 2021, three issues are examined from a novel perspective. First, this study provides a quantitative description of its development. Second, for the first time in the academic literature, this study provides empirical evidence on the determinants of the decoupling narrative, including Chinese trade, Chinese investment, Chinese students, Chinese technology, Chinese companies, and Covid-19. Third, this study also discusses the policy implications of these findings. In particular, if the US government wants to adopt an aggressive strategy of decoupling from China in the future, COVID-19 is one tool that could be used. While this study makes original contributions to policy-makers, it also contributes to academia by presenting a (still) new quantitative approach to international relations. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Influence of the global environment on capital flows in the London Office market

PurposeThe London office market is a major destination of international real estate capital and arguably the epicentre of international real estate investment over the past decade. However, the increase in global uncertainties in recent years due to socio-economic and political trends highlights the need for more insights into the behaviour of international real estate capital flows. The purpose of this study is to evaluate the influence of the global and domestic environment on international real estate investment activities within the London office market over the period 2007–2017.Design/methodology/approachThis study adopts an auto-regressive distributed lag approach using the real capital analytics (RCA) international real estate investment data. The RCA data analyses quarterly cross-border investment transactions within the central London office market for the period 2007–2017.FindingsThe study provides insights on the critical differences in the influence of the domestic and global environment on cross-border investment activities in this office market, specifically highlighting the significance of the influence of the global environment in the long run. In the short run, the influence of factors reflective of both the domestic and international environment are important indicating that international capital flows into the London office market is contextualised by the interaction of different factors.Originality/valueThe authors provide a holistic study of the influence of both the domestic and international environment on cross-border investment activities in the London office market, providing more insights on the behaviour of global real estate capital flows.

Evaluating the trilemma nexus of digital finance, renewable energy consumption, and CO2 emission: evidence from nonlinear ARDL model.

It has been established in 2030 sustainability objectives as per SDGs that highlight the critical importance of access to affordable, renewable energy, robust, long-term industrial progress, and digital financing in CO2 emission. The intent of study is to test the trilemma nexus between digital finance, renewable energy consumption, and carbon emission reduction with nonlinear ARDL tests. The study acquired the data and applied the nonlinear ARDL test, split analysis tests, and vector-error correction model (VECM) tests. The results of the study highlighted that the increase of digital finance positively enhances the renewable energy and negatively reduces the CO2 emissions which we calculate to be 11.4% of the digital finance funding on renewable energy goods. For this, a 39% increase in digital financing is noticed by the research findings during the COVID-19 crisis period. Such robust study findings present the latest insights that digital financing is an eminent and viable source of financing for the trilemma nexus with renewable energy consumption and the CO2 emissions. Following these, multiple research implications are also presented for the key stakeholders.

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)

Study of COVID19 Impact on Moroccan Financial Market Based on ARDL

The aim of this work is to study the impact of the COVID-19 pandemic new cases on the Moroccan financial market using the Autoregressive Distributed Lag (ARDL) approach. The analysis focuses on the relationship between the natural logarithm of the Moroccan All Shares Index (MASI) price and the natural logarithm of new daily cases of COVID-19 in the short term as well as in the long term. A cointegration test is performed on the daily time series for the period from March 3, 2020 to February 11, 2022. A causality test of Toda-Yamamoto is also applied on the variables. The implementation of the forecast with the ARDL method improves the forecast accuracy by 8% to achieve 26.7%. The implementation of the forecast with the ARDL method shows that the addition of the lag of COVID19, the trend and the seasonality makes it possible to achieve a MAPE of 26.7% by improving it by 8% compared to the forecast with the lag of the price only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aquaculture Production in the Midst of GHG Emissions in South Africa

The study ascertained the relationship between aquaculture production and greenhouse gas (GHG) emissions in South Africa. The study used the Autoregressive Distributed Lag—Error Correction Model (ARDL-VECM) with time series data between 1990 and 2020. The results showed that the mean annual aquaculture production, GHG emissions, and Gross Domestic Product (GDP) in the period were 5200 tonnes, 412 tonnes, and US$447 billion, respectively. There was a long-run relationship between GHG emissions and GDP. In the short run, GHG emissions had a positive relationship with GDP and a negative relationship with beef production. Furthermore, there was a bi-directional relationship between aquaculture production and GHG emissions. In addition, beef production and GDP had a bi-directional relationship. Beef production also had a positive relationship with aquaculture production. The study concludes that aquaculture production is affected and tends to affect GHG emissions. Aquaculture legislation should consider GHG emissions in South Africa and promote sustainable production techniques.

Corporate vulnerability in the US and China during COVID-19: A machine learning approach.

The impact of COVID-19 on stock market dynamics and other macroeconomic indicators has been extensively researched. However, the question of how it affects corporate vulnerability has received less attention. This article aims to fill this gap by examining the implications of COVID-19 on corporate vulnerability in the United States (US) and China, using daily data from January 2020 to December 2021. The empirical results of cointegration analysis demonstrate that COVID-19 considerably worsen corporate vulnerabilities in the long-term in the US and in the short-term in China. Additionally, non-linear results demonstrate long-run asymmetries in the US and short-run asymmetries in China, confirming the accuracy of error prediction and suggesting that US corporations are more exposed to COVID-19-induced risks. The channels through which COVID-19 may affect corporate vulnerability include changes in consumer behavior and demand, disruptions in supply chains, financial stress, government policies and regulations, and changes in the competitive landscape. This study sheds light on the effects of the COVID-19 pandemic on corporate vulnerability in the US and China, revealing regulatory implications that may necessitate greater government involvement, managerial implications that emphasize risk management and contingency planning, and social implications that highlight the importance of prioritizing stakeholder welfare and embracing digital transformation.

The Indirect Effect of Coronavirus Disease (COVID-19) Pandemic on Economic Growth in Malaysia: Evidence from The ARDL Approach

This study explores the indirect effect of corona virus (COVID-19) infections on economic growth in Malaysia using the industrial production index (IPI) as a proxy. Since the prevalence of COVID-19 infection, Malaysia's economy has experienced swindles in its growth, just like other countries economy, and the struggle for survival among countries in which Malaysia's economy is not exceptional becomes the current issue. This study incorporates the COVID-19 indirect impacts on economic growth which is conditional to COVID-19 deaths. It also explains a way forward for recuperation among economic sectors for faster economic growth in Malaysia. This paper uses the Auto Regressive Distributed Lag (ARDL) model to explore the indirect effect of COVID-19 infections on economic growth conditional on COVID-19 deaths in Malaysia. As an empirical study, the data used were monthly secondary data and were obtained from reliable sources. The findings from the results of the ARDL model, considering the unconditional model show that COVID-19 infections have a negative relationship with economic growth in Malaysia. The conditional models used to find the indirect impact of COVID-19 on economic growth considering the interaction of the variables at mean, maximum and minimum, prove that COVID-19 has an indirect negative effect on economic growth when COVID-19 deaths are at their mean and maximum. The marginal effect result shows a negative relationship and significance at 1%, indicating that increase in COVID-19 infections leads to decrease in economic growth in Malaysia conditional to COVID-19 deaths © International Journal of Economics and Management

Towards a Multidimensional Model for Evaluating the Sustainable Effect of FDI on the Development of Host Developing Countries: Evidence from Africa

This study aims to comprehensively evaluate the sustainable impact of FDI on the development of host African countries. Previous empirical studies seem to have overestimated the impact of FDI by limiting its effects to one aspect or sub-aspect of sustainable development. This study focuses on the sustainable/net effect of FDI on development in Africa. To achieve this, a multidimensional model that combines two opposing views (mainstream theory of economic development and dependent theory) was tested. Panel data of 35 African countries with the PMG/ARDL approach were used to probe the sustainable effect of FDI from 1990 to 2020. The key findings of this study reveal that the overall estimated sustainable effect of FDI on real GDP per capita is statistically minuscule for the entire sample. Thus, the effect of FDI on the development of host African countries is not inherently more important. The most striking result that emerged from the data is that environmental degradation is the dominant variable that adversely influences overall development in Africa. Another striking finding that emerged from the data is that income inequality, in general, has a significant negative impact on real GDP per capita in the long run. More importantly, the results of this study confirm that CO2, GINI, and GOV play important roles in the relationship between FDI and African development. Estimates of the error correction term for each specific country are negative and statistically significant. The fastest speed of adjustment was observed in Morocco, while the lowest was recorded in South Africa. Furthermore, this study presents different policy implications based on the long-term results.

Analyzing the nexus between pandemic, policy uncertainty, and international tourists' behavior in Taiwan

The tourism and hospitality industry is the hardest-hit industry, owing to the disruptions from COVID-19. The tourism sector witnessed a mounting loss of about 2.86 trillion dollars during the pandemic period. Exploring how inbound international tourists' perception gets affected by uncertainty originating from the pandemics can have important insights to revive tourism during the new normality. Against this backdrop, this paper explores the impact of the pandemics and global economic uncertainty on international inbound tourist arrivals to Taiwan, a major travel destination of the East during the period 1997 to 2020. In particular, we augment the traditional demand model of tourism with economic uncertainty indicators and disaster and pandemic dummies, to explore the impact on visitor arrivals in Taiwan from major countries around Asia, Africa, Oceania, Europe, and America. To this end, the Autoregressive Distributed Lag (ARDL) along with the modified Wald test of Toda Yamamoto (T-Y) was applied. The empirical results depict that apart from the pandemics, the global economic policy uncertainty has adverse implications on international tourism demand. The findings have important policy implications. Recovery of tourism demand should move along: i) new concepts on products;ii) new destination imagery and iii) marketing strategies through collaboration from the state. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The Impact of CO2 Emissions and Energy Consumption on Economic Growth: A Panel Data Analysis

This study aims to examine the dynamic connection among economic growth, CO2 emissions, energy consumption, and foreign direct investments (FDIs). The panel section considers the period of 2000–2020 for 25 EU Member States excluding Malta and Croatia. The annual data are retrieved from the World Bank and Eurostat databases. The empirical analysis used estimation procedures such as first- and second-generation panel unit root tests (CIPS) and panel ARDL based on the three estimators PMG, MG, and DFE. The Hausman test indicated that the PMG estimator is the most efficient. The PMG and DFE estimators suggested that there exist only short-run causalities from CO2 emissions, energy consumption, and FDIs to GDP growth rate, while the MG estimator proved the existence of both short-run and long-run causalities. Three hypotheses on the positive correlation between the three regressors and GDP growth rate were in general confirmed. The identified causalities may represent recommendations for policymakers to stimulate the renewable energy sector to improve sustainable development.

The effect of the global supply chain and oil prices on the inflation rates in advanced economies and emerging markets

This study investigates the effect of global supply chain pressures and crude oil prices on the consumer price index from October 1997 to February 2022 using panel linear and nonlinear autoregressive distributed lags (ARDLs, NARDLs). The results showed that the asymmetric effect of the global supply chain on the inflation rate is stronger when the supply chain increases than when it decreases in the long run for advanced economies and vice versa in emerging markets. A one standard deviation of the supply chain pressures has rebounded the inflation rate by about 1.7% and 0.71% for advanced economies and emerging markets, respectively. The findings establish that a 10 U.S. dollar increase in oil prices leads the inflation rate to rise by 0.1%–0.6% for all countries in the short run. However, the impact of the global supply chain index fits much better with the inflation rate than the oil prices in the short and long run, including the subprime crisis, such as the COVID‐19 outbreak, and the beginning of the Russo–Ukrainian conflict. Thus, the empirical results of the current study provide acumens for policymakers of advanced economies and emerging markets to consume green energy and make use of green technology and environmental innovations for counterbalancing the inflation issues induced by the higher rates of oil prices without halting the economic growth and sustainable development. [ABSTRACT FROM AUTHOR] Copyright of Geological Journal is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Investigation of cointegration and causal linkages on Bitcoin volatility during COVID-19 pandemic

In this study, we focus on a prominent feature in Bitcoin: its volatility. This paper aims to examine the volatility action of Bitcoin's price during the COVID-19 pandemic through various angles: COVID-19 fear sentiments, investor fear sentiments, macro-financial factors, and crypto market factors. The study utilises daily data from 11 March 2020 to 31 May 2021. We implemented an ARDL bound testing approach to find cointegration, and the Toda-Yamamoto approach to further examine any existing causal relationships between the variables. The empirical results show that COVID-19 fear increased Bitcoin volatility and a unidirectional causal relation was found between them. Investor fear sentiments revealed that US dollar volatility moved in the same direction as Bitcoin volatility, while VIX was found to be insignificant. Gold, crude oil, and the stock market did not influence the volatility of Bitcoin. Overall, only crypto market factors were cointegrated with Bitcoin volatility in the long run. Copyright © 2023 Inderscience Enterprises Ltd.

The Impact of Energy Prices on Precious Metals: A Comparison of the SARS-COV2 Period and Prior Period

Commodities are defined as goods that are traded. Thousands of different items are sold on international markets, but strategically important commodities such as gold, silver, and oil are used far more frequently in the real estate industry and financial markets. Oil and these metals are employed in numerous industrial applications throughout the economy, but they also draw substantial investment. The purpose of this study is to investigate the causal connections between the prices of gold and silver, two precious metals, and oil and natural gas, the two most often used energy sources. This was accomplished by comparing the weekly prices of Brent Petroleum (BRENT), Crude Oil (WTI), Natural Gas (NG), and the weekly data of Gold and Silver during and before the SARS-CoV2 epidemic. The study employed the Distributed Delayed Autoregressive Bound Test (ARDL) approach to examine the relationship between the prices of energy (natural gas, Brent oil, and crude oil) and precious metals (silver and gold). The ARDL test showed that the prices of WTI, Brent, and NG had a big effect on the prices of silver and gold during and before the SARS-COV2 pandemic.

The role of income, exports and capital flows on domestic income growth: empirical evidence from Denmark

This article examines the long-term growth path of the Danish economy from 1980 to 2017. Various developments and changes have emerged over the years to determine the robustness and legitimacy of the balance of payments constrained growth model. One of these changes is that capital flows have an important role in the growth process. In this study, relative prices, exchange rate and capital flows are questioned as important determinants. The study uses autoregressive distributed lag (ARDL) methodology to determine the both short- and long-run relationships among the variables. The outcome reveals that all the variables have significant impacts on both the short-run and long-run growth of Denmark. In the light of these findings, considering that the annual real growth of the Danish economy is 1.75%, it can be concluded that this estimation is correct and is proportional to the growth rate of Denmark from 1980 to 2017. It would be a correct assessment to consider income, relative prices and capital flows as important determinants of the growth of the Danish economy on the demand side. Weakening confidence indices and worsening expectations in Danish export markets could quickly affect Danish exports and trade investment. © 2023, Japan Economic Policy Association (JEPA).

Assessing the EKC hypothesis by considering the supply chain disruption and greener energy: findings in the lens of sustainable development goals.

This paper investigates the effect of the supply chain disruption, greener energy consumption, and economic growth on carbon emissions in advanced economies and emerging markets from 1997 to 2021 using panel quantile autoregressive distributed lags (QARDL) and the panel quantile regression (QR). The results of the two models confirm, on the one hand, the validity of the environmental Kuznets curve (EKC) hypothesis and, on the other hand, the role of renewable energy consumption in mitigating carbon emissions in advanced and developing economies. Furthermore, the finding shows that the supply chain disruption for the long run is positive at all quantiles, indicating the evidence of association at the extreme low and high quantiles than at the intermediate quantile. In addition, the effect of the supply chain decreases at the lower quantile. It turns negative at the upper 90th quantile in the short run, indicating that the supply chain disruption reduces the environmental degradation under the bearish market conditions. In the future, the increasing supply chain disruptions due to the Russia-Ukraine conflict and further COVID-19 worldwide can consider sluggish economic growth and play an essential role in promoting renewable energy abundance and reducing CO2 emissions. Practical implications are reported in the lens of carbon neutrality and structural changes.

Services as a Determinant of Botswana's Economic Sustainability.

In 2015, the services sector contributed about 58 percent to the gross domestic product (GDP) in Sub-Saharan Africa (SSA), which was a significant increase from the 47.6 percent observed in 2005, and a shift from the mining, agriculture, and manufacturing sector. This increase calls to support services as the catalyst for sustained economic development as indicated by the structural transformation and modernization theories. The main objective of this paper was to examine the relationship between and the impact of services on the economic development in Botswana and make recommendations on how Botswana can apply well-directed policies to improve its services sector and diversify its impact on other sectors and GDP, making it less reliant on mining which is vulnerable to price volatilities. The paper applied econometric modeling and results of the Autoregressive-Distributed Lag (ARDL) Bounds test for cointegration indicate that services and other industries services, agriculture, industry, mining, and investment impact GDP over the short and long run. These variables impacted GDP and converged to equilibrium at the speed of 46.89 percent, with a percent change in services in the short and long run impacting GDP by 0.328 and 0.241 percentages, respectively, and the outcome of the Wald test indicated causality from services to GDP growth. The services sectors have contributed over 40 percent to the country's GDP from 1995 to the present, though the sectors have not gone without challenges with limitations such as limited infrastructure development; poverty and inequality; unemployment of over 20 percent; disease, which has dampened productivity; and lack of proper governance and accountability, which has created a habitat for an increase in cases of corruption in state and private entities. The findings of the study with the lessons learned from other studies with similar findings recommend that the government of Botswana should formulate suitable policies and strategies for services diversification. This is by expanding the market for the sector in areas such as tourism that were impacted by the COVID-19 pandemic, escalating investments by instituting strategies to attract and grow domestic and foreign investments, and improve on management of institutions and resources.

Does stock market development and COVID-19 pandemic lead to financial crisis: the case of largest Islamic stock exchange market?

This research investigates the short- and long-term effect of COVID-19 pandemic period and the largest Islamic stock market development (SMD) in term of size and market activity (turnover) on financial crises (FCs) indicator and identifies the Granger–causality relationship exist between them. We apply an autoregressive distributed lag technique analysis method in estimating short- and long-run models of this relationship, using 34 monthly observations, from 2018M1 to 2020M10 Islamic stock market of Kingdom of Saudi Arabia (KSA). The short-term result implies that the FC indicator had significant positively affected by the COVID-19 pandemic. However, the long-run model result shows that market size indicator leads to a major FC, while market trade value indicator has negative impact on FC indicator in KSA Tadawul market. In addition, the results show that the only market capital indicator Granger–causes FCs. We concluded that the Saudi policymakers should make new regulations to avoid the negative effect of FC, in order for the growth of its stock market size and trade, for the stability of financial economic in Saudi Arabia. They should add more support to stock market trading to eliminate the potential threat of FC. We believe this is the first empirical study that investigates the short- and long-term effect of SMD, in terms of size and activity development on the FCs volatility in largest Islamic stock exchange market without ignoring the potential effect of COVID-19 pandemic. Also, this research is novel in terms of studying a market that was previously closed to outsiders and has implemented various financial sector reforms in recent years. © 2021 Informa UK Limited, trading as Taylor & Francis Group.