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.

Machine Learning Model for Identification of Covid-19 Future Forecasting

The global proliferation of COVID-19 has put humanity in jeopardy. The assets of the world’s most powerful economies are at risk due to the disease’s high infectivity and contagiousness. The ability of ML algorithms to forecast the number of future patients COVID-19 has an effect on, which is currently regarded as a potential threat to humanity. In this study, five common guaging models, including LR, LASSO, SVM, ES, and LSTM, were used to assess the COVID-19 underpinning variables. Each model makes three types of predictions: the number of recently contaminated cases, the number of passings, and the number of recoveries. However, it is impossible to predict the exact prognosis for the patients. To combat the problem, a proposed technique based on the long transient memory (LSTM) predicts the number of COVID-19 cases in the following 10 days and the influence of preventive measures such as social seclusion and lockdown on COVID-19 spread. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Risk-Return Trade-off and Volatility Characteristics in the Indian Stock Market

The study examines the volatility characteristics of Indian stock markets and their tradeoff between the risk and return. It finds a positive but insignificant association between the risk and returns during the subsample (the pre-COVID and COVID pandemic outbreak) and whole sample periods. The study also shows that the weak form of Indian stock markets is not sustainable. Consistent with the GARCH literature, persistent and asymmetric effects are evidenced, and the magnitude of the negative shocks has a larger immediate impact than the positive shocks. These results would help measure the volatility in the Indian stock markets and provide investors and regulators with necessary information about the market efficiency, persistency (long-memory process) and asymmetric effects. © 2022 Manickam Tamilselvan et al;published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License