It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission.
TL;DR: The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection and suggested to consider renewable energy transition methods as an opportunity for the society.
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Abstract: During the COVID-19 outbreak, managing energy consumption and CO2 emission remained a serious problem. The previous literature rarely solved this real-time issue, and there is a lack of public research proposing an effective way forward on it. However, the study examines the impact of the COVID-19 outbreak on energy consumption and CO2 emission. The design of the study is quantitative, and the data is acquired from different online databases. The model of the study is inferred by using panel unit root test and ARDL test. The robustness of study findings was checked through panel quantile regression. The findings highlighted that the COVID-19 outbreak is negatively significant with energy consumption and CO2 emission. The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection. The study also contributed in knowledge by suggesting the novel insight into CO2 emission and energy consumption patterns during COVID-19 pandemic and recommended to consider renewable energy transition methods as an opportunity for the society. For a more effective management of energy consumption and environmental pollution, country-specific measures are suggested to be taken, and the national government should support the concerned public departments, ministries and private organizations on it. To the best of our study, this is one of the pioneer studies studying this novel link and suggesting the way forward on recent topicality.
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Citations
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Maximum likelihood estimation and inference on cointegration — with applications to the demand for money
Søren Johansen,Katarina Juselius +1 more
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Maximun likelihood estimation and inference on cointegration - With applications to the demand for money
Søren Johansen,Katarina Juselius +1 more
- 01 Jan 2005
Abstract: This paper gives a systematic application of maximum likelihood inference concerning cointegration vectors in non-stationary vector valued autoregressive time series models with Gaussian errors, where the model includes a constant term and seasonal dummies. The hypothesis of cointegration is given a simple parametric form in terms of cointegration vectors and their weights. The relation between the constant term and a linear trend in the non-stationary part of the process is discussed and related to the weights. Tests for the presence of cointegration vectors, both with and without a linear trend in the non-stationary part of the process are derived. Then estimates and tests under linear restrictions on the cointegration vectors and their weights are given. The methods are illustrated by data from the Danish and the Finnish economy on the demand for money. Copyright 1990 by Blackwell Publishing Ltd
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