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This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.

This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.

The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.

The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.

Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

Globally, the paucity of conventional energy sources has created an unprecedented increase in demand for green energy. Continuous dependency on conventional energy sources has given rise to several undesirable environmental consequences. In the 20th century, the international forum pondered about the development and uses of green energy, which commenced with the realization of global warming and the signing of the Kyoto Protocol agreement. This study aims to divulge the nexus between green energy, carbon emissions and economic prosperity from a global perspective. The study has been conducted by considering panel data of 35 global economies from 1971 to 2019.

To calibrate the uses of green energy, this study dwells upon the ratio between green energy consumption and total energy use. These instrumental variables have been widely acknowledged and accepted by several empirical analysis done in the past (Lin and Moubarak, 2014; Shahbaz et al., 2015). This research specifically uses the emission of carbon dioxide in a million tons as an instrumental variable of environmental degradation, which has been disregarded by all-preceding researchers from a global perspective. Additionally, this study also considers real gross domestic product value in terms of US$ (2010 constant price) as an indicator of economic prosperity. The same has been contemplated by an ample number of empirical research studies conducted previously. Thus, the authors adopted the panel autoregressive distributed lag (ARDL) technique to achieve this research objectives; and to tackle the issue of contemporaneous correlation, the authors applied cross-sectional augmented autoregressive distributed lag (CSARDL) of common correlated effect pooled mean group (CCEPMG).

The results of panel ARDL analysis reveal that in the long-run, real gross domestic product (GDP) leads to carbon emission, whereas green energy uses do not have a substantial effect on the reduction of carbon emission. However, in the short-run, green energy consumption seems definitely helpful for combating carbon emission, while real GDP instigates carbon emission. This study effectively fortifies the notion of a trade-off between ecological pollution and economic prosperity. The empirical results of the Granger Causality test produce evidence of unidirectional causality from carbon emission to green energy uses and from real GDP to carbon emission in the panel countries

First, decisive corollaries of the conclusions drawn above have been made purely on the basis of a comprehensive investigation of 35 global economies. However, there is the scope for inclusive examination by considering more modern economies simultaneously. Second, this paper studied the potential impact of the uses of green energy and real GDP on carbon emission. Notably, the inference of this study has been grounded on three relevant variables, whereas there are possibilities that such an investigation could possibly be extended by considering other instrumental variables of environmental pollution.

A significant number of studies in the past have investigated the connection between renewable energy consumption (REC) and economic growth. To the best of the authors’ knowledge, none have looked to investigate the nexus between REC, economic prosperity and environmental sustainability simultaneously, specifically from the global perspective. Hence, this study intends to widen the prevailing perception of the emerging context above in two ways; first, by reconnoitering the effect of REC on environmental consequences and economic progress simultaneously, which has not been accomplished in extant literature. Second, the authors also strive to gradually augment the comprehensive analysis by expanding the study from a global perspective and by constructing the panel data of developing and advanced economies.

This study aims to examine the impact of geopolitical uncertainty and inflation on green energy consumption. In addition, it aims to reveal the cause-and-effect relationship among these variables.

The authors exert the panel dynamic ordinary least squares (OLS) approach of Kao and Chiang (2000) and the Granger non-causality method of Dumitrescu and Hurlin (2011). On the other hand, current study relies on the annual time series data of 29 countries from 1985 to 2022.

The panel dynamic OLS results confirm a long-run relationship between geopolitical uncertainty, inflation and green energy consumption. On the other hand, inflation negatively impacts green energy consumption, with high inflation levels potentially halting the transition. Conversely, geopolitical uncertainty shows no significant effect on green energy use, indicating a reliance on traditional energy sources. Moreover, the current investigation reveals the unidirectional causality from green energy consumption to inflation, while no short-run causality exists from inflation as well as geopolitical uncertainty to green energy consumption in the selected countries.

The authors drew conclusions about the potential impact of geopolitical uncertainty and inflation on green energy uses by considering the macro-level data. However, this investigation further be enhanced by looking at such issues through the micro perspective by understating the thought and perspective of public on the implication of geopolitical uncertainty and inflation on green energy uses. Such micro-level, country-specific study would definitely help us to generate more concrete ideas about such themes.

The empirical findings have important implications for policymakers. Results suggest that high inflation negatively impacts green energy use, so policymakers must consider implementing measures to control the inflation while promoting green energy uses for economic prosperity and environmental sustainability. In the short run, green energy use leads to inflation. Therefore, macroeconomic policymakers can implement various subsidy policies for the general public to mitigate the short-term inflationary impact of green energy use.

This investigation is novel in three ways. First, it explores the impact of geopolitical uncertainty and inflation on green energy use, using Caldara and Iacoviello’s (2022) geopolitical risk index as a proxy of geopolitical uncertainty. Second, it uses panel data econometric analysis, a method that most previous studies have not contemplated for this type of investigation. Finally, by including both advanced and emerging economies, it provides valuable insights for policymakers to develop effective strategies related to green energy consumption for achieving sustainable economic development.

The eruption of coronavirus disease 2019 (COVID-19) has pointedly subdued global economic growth and producing significant impact on environment. As a medicine or a treatment is yet available at mass level, social distancing and lockdown is expected the key way to avert it. Some outcome advocates that lockdown strategies considered to reduce air pollution by curtailing the carbon emission. Current investigation strives to affirm the impact of lockdown and social distancing policy due to covid-19 outbreak on environmental pollution in the QUAD nations.

To calibrate the social movement of public, six indicators such residential mobility, transit mobility, workplace mobility, grocery and pharmacy mobility, retail and recreation mobility and park mobility have been deliberated. The data of human mobility have been gathered from the Google mobility database. To achieve the relevant objectives, current pragmatic analysis exerts a panel autoregressive distributed lag model (ARDL)-based framework using the pooled mean-group (PMG) estimator, proposed by Pesaran and Shin (1999), Pesaran and Smith (1995).

The outcome reveals that in the long-run public mobility change significantly impact the pollutants such as PM2.5 and nitrogen dioxide; however, it does not lead to any changes on ozone level. As per as short run outcome is concerned, the consequence unearths country wise heterogeneous impact of different indicators of public mobility on the air pollution.

The ultimate inferences of the above findings have been made merely on the basis of examination of QUAD economies; however, comprehensive studies can be performed by considering modern economies simultaneously. Additionally, finding could be constraint in terms of data; for instance, Google data used may not suitably signify real public mobility changes.

A considerable amount of investigation explores the impact of covid-19 on environmental consequences by taking carbon emission as a relevant indicator of environmental pollution. Hence, the present pragmatic investigation attempts to advance the present discernment of the above subject in two inventive ways. Primarily, by investigating other components of environmental pollution such as nitrogen dioxide, PM2.5 and ozone, to reveal the impact of covid-19 outbreak on environmental pollution, as disregarded by the all preceding studies. Additionally, it makes a methodological contribution before integrating supplementary variables accompanying with ecological air pollution. Finally, the current research article provides an alternative and creative approach of modeling the impact of public mobility on environmental sustainability.

This pragmatic investigation examines the dynamic nexus between crude oil prices and food inflation from South and Southeast Asian perspectives.

This study investigates the asymmetric effects of global crude oil prices on food inflation using a nonlinear autoregressive distributed lag (ARDL) model with monthly data covering the period from May 2012 to April 2022.

The empirical evidence reveals that international crude oil has a substantial impact on food prices in the majority of countries. Additionally, the relevant outcome documents that the asymmetric effect of global crude oil on food inflation applies to Sri Lanka and Vietnam, while in the other countries, it is symmetric.

Considering the optimistic outcomes, this empirical investigation is certain to have important shortcomings. Initially, the conclusions drawn from the above findings were based only on detailed assessments of the aforementioned variables' data over a 10-year period. The current scholarly analysis investigates the existence of an asymmetric impact of crude oil on food inflation, limited to six Asian countries. On the other hand, considering a greater number of Asian economies could enhance the analysis’s robustness and precision.

The current research aims to contribute to the existing literature on food inflation and global oil prices in the following ways: First, this study investigates the nexus between global crude oil and food inflation in a novel way, considering the nonlinear relationship between the variables. To figure out the nonlinear relationship or uneven effect of the global oil shock on food prices, we use the nonlinear ARDL model. Secondly, as food inflation is one of the major issues for the South and Southeast Asian economies, this empirical investigation broadens the analysis by incorporating a perspective from South and Southeast Asia, an area largely overlooked by previous researchers. Finally, we are very optimistic about the phenomenal contribution of current analysis to comprehending the conception of oil and food price dynamics from a broader perspective to achieve the Sustainable Development Goal (SDG), which aims for a sustainable resolution to end hunger in all its forms by 2030 and to accomplish food security, especially in emerging economies.