Energy markets responds to Covid-19 pandemic

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

A multiscale integrated analysis of the COVID-19 restrictions: The energy metabolism of UK and the related socio-economic changes

The COVID-19 pandemic and the related lockdown restrictions have imposed a wide range of impacts that need to be analysed based on the specific characteristics of countries. By comparing socio-economic and energy data for the four quarters of 2020 to the same period of 2019, the MuSIASEM approach is used, for the first time, to investigate the energy metabolism of UK during a period of economic downturn. Results show that the commercial and the public administration activities have been able to achieve energy efficiency increases, and the residential sector has accounted for energy-related economies of scale. The industrial and the other activity sectors, on the contrary, have raised the energy intensity of production. Comparted to time series data, scenarios, and modelling exercises, the MuSIASEM approach integrates a wide range of intensive and extensive variables across different scales of analysis and investigate how specific socio-economic and energy structures have reacted to the COVID-19 crisis. The methodology can be easily replicated for other case studies and results can support the design of recovery and sustainable transition strategies.