ABSTRACT
Understanding and examining energy markets correctly is crucial for stakeholders to attain maximum benefit and avoid risks. As a matter of fact, the volatility that occurred in energy markets and recent crises had major impacts on national economies. Dynamic connectedness relationships (DCRs) can make quite powerful predictions for both low-frequency data and limited time-series data. The objective of this study is to explicate the dynamic connectedness relationships among the BIST sustainability index, BIST 100 index, S&P Global Clean Energy index (S&P GCEI), and S&P GSCI carbon emission allowances (EUA). The daily data obtained over the period 11 April 2014–11 November 2022 were used for the research study. The DCRs among the variables used in the study were investigated by employing the time-varying parameter vector autoregressive (TVP-VAR) model. As a result of the study, the volatility from carbon emission allowances was determined to spill over to S&P GCEI, BIST 100, and BIST sustainability indexes. During the COVID-19 pandemic, significant reductions were detected in the volatility spillover (VS) from carbon emission allowances to S&P GCEI, BIST 100, and BIST sustainability indexes. Moreover, it was revealed that a weak VS existed from S&P GCEI to BIST sustainability and BIST 100 indexes. The findings reveal the importance of policymakers taking some incentive measures in EUA prices and also its role in portfolio diversification. © 2023 by the authors.
ABSTRACT
In this chapter, we describe the main molecular features of SARS-CoV-2 that cause COVID-19 disease, as well as a high-efficiency computational prediction called Polarity Index Method®. We also introduce a molecular classification of the RNA virus and DNA virus families and two main classifications: supervised and non-supervised algorithms of the predictions of the predominant function of proteins. Finally, some results obtained by the proposed non-supervised method are given, as well as some particularities found about the linear representation of proteins. © 2022 Scrivener Publishing LLC.
ABSTRACT
The COVID-19 pandemic has had a huge impact on all aspects of the company's life cycle. Some companies are even unable to maintain optimal performance like before during the pandemic. Corporate social responsibility activities that are considered to provide good faith to the good name of the company that contribute to increasing stock returns. However, corporate social responsibility activities are maximized because the costs are chosen for activities around the environment carried out by large-scale companies on the Compass Index 100. This study aims to determine the effect of corporate social responsibility and company size on stock returns through Return on Equity in companies listed on Kompas 100 Index after the COVID-19 Pandemic. The population in this study covers all companies that are members of the Kompas 100 Index and are registered with Indonesia Stock Exchange (IDX). The sample method used is purposive sampling. The data analysis technique used is Structural Equation Modeling (SEM) analysis. The results showed that corporate social responsibility and company size affect Return on Equity and stock returns, and Return on Equity affects stock returns. The demand for shares of companies listed on the Kompas 100 Index is classified as the most consistent because it takes into account the company's sustainability in the future by allocating corporate social responsibility costs to build the company's good name. Corporate social responsibility activities are the example of the company's concern for the surrounding environment which aims to be able to increase the company's Return on Equity. In line with the higher level of Return on Equity, the size of the company as measured by total assets has also increased. © 2023 WITPress. All rights reserved.
ABSTRACT
As a pandemic, COVID 19 spread worldwide in early 2020. Primarily densely populated countries had remained vulnerable due to this biological hazard. Many people were forced to stay home owing to nature of the disease and no respite. A nationwide lockdown was implemented in India for 29 days (March 24th to April 21st) of 2020 during the wake of the COVID-19 pandemic. During the nationwide lockdown, industries, transport, and other commercial activities were suspended, except for necessary services. During the entire pandemic situation, an affirmative impact was observed as the air quality was reported to have improved worldwide. The complete economic lockdown to check COVID-19, brought unforeseen relief from severe condition of air quality. An apparent, reduction in level of PM2.5 and Air Quality Index (AQI) was experienced over Mumbai, Delhi, Kolkata, Hyderabad, and Chennai. Present work explores the various metrics of air pollution in Kolkata, West Bengal, India (imposed as a result of containment measure for COVID-19). The polluting parameters (e.g., PM10, PM2.5, SO2, NO2, CO, O3, and NH3) were chosen for seven monitoring stations (Ballygunge, Fort William, Victoria, Bidhannagar, Jadavpur, Rabindra Bharati, Rabindra Sarabar), which are spread across the metropolitan area of Kolkata. National Air Quality Index (NAQI) has been used to show pre-and during-lockdown air quality spatial patterns. The findings showed major changes in air quality throughout the lockdown period. The highest reduction in pollutants emission was observed for: PM10 (- 60.82%), PM2.5 (-45.05%) and NO2 (-62.27%), followed by NH3 (- 32.12%) and SO2 (-32.00%), CO (-47.46%), O3 (15.10%). During the lockdown, the NAQI value was reduced by 52.93% in the study area. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.
ABSTRACT
We examine the time-frequency co-movements and return and volatility spillovers between the rare earths and six major renewable energy stocks. We employ the wavelet analysis and the spillover index methodology from January 1, 2018 to May 15, 2020. We report that the COVID-19-triggered significant increase in co-movements and spillovers in returns and volatility between the rare earths and renewable energy returns and volatility. The rare earths act as net recipient of both return and volatility spillovers, while the clean energy stocks are net transmitters of return and volatility spillovers before and during the COVID-19 crisis. The solar and wind stocks are net transmitters/receivers of spillovers before/during the pandemic. The remaining markets shift from net spillover receivers to transmitters or vice versa;evidencing the effects of the pandemic. Our results show that cross-market hedge strategies may have their efficiency impaired during the periods of crises implying a necessity of portfolio rebalancing. © 2022 The Authors
ABSTRACT
China's economy and environment urgently require a green recovery as COVID-19's consequences expand over time, and the platform economy is a practical means of pursuing this goal. By employing the Generalized Divisia Index Method (GDIM), this paper aims to analyze the impact of platform economy on carbon emissions in China during the period 2013–2020. Overall, the platform economy has increased carbon emissions, but there was a decrease in carbon emissions in the platform economy between 2014 and 2016. The scale factors of platform economy are the primary contributors to the increase in China's overall emissions and most provincial carbon emissions, while the carbon intensity of platform economy factors contributes most to the decrease in carbon emissions. In particular, the carbon intensity of platform economy factors promoted the most cumulative carbon emissions in Jiangsu, Heilongjiang, Yunnan, Qinghai, and Ningxia between 2013 and 2020, and the energy intensity of platform economy factors reduced most of the cumulative carbon emissions of Heilongjiang, Yunnan, Qinghai, and Xinjiang during the same period. From the perspective of the heterogeneity of platform economic development, the main contributors of carbon mitigation from high-platform economic provinces are intensity effects. However, the platform economic structure of low-platform economic provinces significantly reduces carbon emissions. In addition, we also found significant differences in the factors influencing emissions in different groups under the influence of the pandemic. Finally, we provide some valuable references for China's platform economic development to achieve "carbon neutrality” targets. © 2023 Economic Society of Australia, Queensland
ABSTRACT
Organization of Economic Cooperation and Development (OECD) economies are facing a substantial increase in the information and communication technology (ICT) investments in the context of rapid spread of the Coronavirus Disease-2019 (COVID-2019) pandemic and constraints of emissions reduction. However, the mechanism of the impact of ICT investments on carbon dioxide is still unclear. Therefore, by employing the decoupling-factor model and Generalized Divisia Index Method, we explore the decoupling states of ICT investments and emission intensity, and the driving factors of ICT investments' scale, intensity, structure, and efficiency effects on carbon emissions in 20 OECD economies between 2000 and 2018. The results indicate that the number of economies with an ideal state of strong decoupling rose to nine between 2009 and 2018 compared to no economies between 2000 and 2009. The emission intensity of ICT investments contributes to a significant increase of carbon emissions, and the structure and efficiency of ICT investments always restrain the growth of carbon emissions. Significant emissions changes caused by the driving factors are shown in many economies before and after the crisis, reflecting the differences in the strategic choices of ICT investments and the impact on emissions due to the crisis such as the COVID-2019 pandemic. And policy implications for energy and carbon dioxide mitigation strategies in the post-COVID-2019 era are also provided.
ABSTRACT
China's economy and environment urgently require a green recovery as COVID-19's consequences expand over time, and the platform economy is a practical means of pursuing this goal. By employing the Generalized Divisia Index Method (GDIM), this paper aims to analyze the impact of platform economy on carbon emissions in China during the period 2013–2020. Overall, the platform economy has increased carbon emissions, but there was a decrease in carbon emissions in the platform economy between 2014 and 2016. The scale factors of platform economy are the primary contributors to the increase in China's overall emissions and most provincial carbon emissions, while the carbon intensity of platform economy factors contributes most to the decrease in carbon emissions. In particular, the carbon intensity of platform economy factors promoted the most cumulative carbon emissions in Jiangsu, Heilongjiang, Yunnan, Qinghai, and Ningxia between 2013 and 2020, and the energy intensity of platform economy factors reduced most of the cumulative carbon emissions of Heilongjiang, Yunnan, Qinghai, and Xinjiang during the same period. From the perspective of the heterogeneity of platform economic development, the main contributors of carbon mitigation from high-platform economic provinces are intensity effects. However, the platform economic structure of low-platform economic provinces significantly reduces carbon emissions. In addition, we also found significant differences in the factors influencing emissions in different groups under the influence of the pandemic. Finally, we provide some valuable references for China's platform economic development to achieve "carbon neutrality” targets.
ABSTRACT
Gradual lockdown as a measure was forced into action in India for more than 4 weeks after the beginning of Covid-19 pandemic, as a measure to flatten the epidemic curve. Through our study we are trying to interpret the changes in air quality level during the period of lockdown in Delhi by collecting and evaluating the data of pollutants from 3 major hotspots through updated data of DPCB (Delhi Pollution Control Board). It has noted that after a week and more there was a significant decline in air pollutant level in these areas which lead to improvement in air quality, major decline was noted in PM2.5 (-28.37%,-25.37% and-25.43%), and NO2 a traffic emission related pollutant (-91.29%,-13.29% and-55.26%) respectively whereas slight improvement in ozone has been recorded an association of major pollutant (PM2.5,10, NO2) has shown significant association with impact of lockdown during covid-19 in their decline, whereas further improvement might come in forthcoming days as GOI is going to put more measures to combat the virus spread which came into force from 23rd March 2020. Still there are lots of efforts need to be done to understand impact of lockdown on major polluted level and how a single lockdown could give great result after spending crores of money on project related to same, so it’s our recommendation to GOI to look into this impact and plan measure accordingly in future so that Delhites can breathe a easy air than they used to breathe earlier. © EM International.
ABSTRACT
The COVID-19 epidemic has had a huge impact on human society, providing an opportunity for human beings to reflect on environmental governance. The sediment samples were collected from the Diversion Channel and Baishou Bay in Huizhou to analyze the element speciation distribution and pollution status. By graphite furnace atomic absorption spectrometry, atomic fluorescence spectrophotometry, flame atomic absorption Spectrophotometric methods to determine the content of the bottom sediments. The single factor index method, the Nemero comprehensive index method, the pollution load index method and the coefficient of variation analysis method were used to analyze. This study on the river bottom sediments of Huizhou is significant environmental effects of harmful elements. © 2021 Kriedt Enterprises Ltd. All right reserved.
ABSTRACT
The NPS index is used in the hotel industry to measure customer loyalty and, by extension, customer satisfaction. Many hotel companies set their annual budget based on this index and include it, together with annual economic results, for evaluation when deciding on a potential management bonus. For managers in some companies, achieving a high NPS becomes nearly as important as achieving strong economic results. The purpose of this research is to deepen the study of the NPS index by analysing the existing relationship that the model has with customer satisfaction, focusing on the following main areas of a hotel: reception, cleanliness and room comfort, and gastronomy. To do so, this study uses fuzzy set qualitative comparative analysis (fsQCA). New evidence of value is offered based on the analysis of a sample of six hotels (4 and 5*) located in the Balearic Islands, Spain (Mallorca, Minorca, and Ibiza). In total, 557 surveys were completed in August 2021 and 571 surveys were completed in August 2020, and therefore both sample groups were impacted by a Black Swan (BS) event, the COVID-19 pandemic, in two different stages of its trajectory. The results suggest that in the study sample, the key factor in achieving a high NPS was (1) gastronomy in 2021 (after more than one year of the COVID-19 pandemic), and (2) cleanliness and room comfort in 2020 (at the beginning of the COVID-19 pandemic). These results offer insights for hotel managers, as well as for academics who can develop new lines of research on the subject. © 2022 by the author. Licensee MDPI, Basel, Switzerland.