Dynamic correlations of renewable-energy companies: Evidence from a multilayer network model

Against the background of seeking to achieve carbon neutrality, relationships among renewable-energy companies around the world have become multiple and complex. In this work, the Pearson, Kendall, tail, and partial correlation coefficients were applied to 51 global companies - including solar and wind firms, independent power plants, and utilities - to explore the linear, nonlinear, extreme-risk, and direct relations between them. Sample data from 7 August 2015 to 6 August 2021 were considered, and three sub-periods were extracted from these sample data by analysis of the evolution of multiple correlations combined with event analysis. A four-layer correlation network model was then constructed. The main results are as follows. (1) The multiple relations among the selected firms underwent dramatic changes during two external shocks (the China-US trade war and the COVID-19 pandemic). (2) The extreme-risk network layer verified that the trade war mainly affected the relationships among companies in the solar industries of China and the US. (3) During the COVID-19 pandemic period, the linear and direct relationships among wind firms from Canada, Spain, and Germany were significantly increased. In this sub-period, edge-weight distributions of the four different layers were heterogeneous and varied from power-law features to Gaussian distributions. (4) During all the sub-periods, most companies had similar numbers of neighbors, while the numbers of neighbors of a few companies varied greatly in the four different layers. These findings provide a useful reference for stakeholders and may help them understand the connectedness and evolution of global renewable-energy markets. © 2023 Author(s).

COVID-19 vaccine dosages and government factors role on the global variation in COVID-19 mortality: A statistical and regression analysis

The objective of our study was to explore the influence of the current vaccination program and other relevant government factors to explain the variation in COVID-19 mortality in the world. The study involves a cross-sectional survey of COVID-19 related and government factors from 161 countries. We retrieved and processed publically available coronavirus pandemic data (July 17, 2021) from several online databases, excluding countries' data violating correlation and regression analysis assumptions. In addition, partial correlations studies and multivariate analysis were performed to explore the influence current vaccination program and other relevant government factors on the relationship between the explanatory variable and the total deaths due to COVID-19. The partial-correlation studies revealed that controlling for a complete dosage of COVID-19 vaccine per 100 people in the population had a significant (P<0.001) impact on the strength of the relationship between some explanatory variables and the response variable (total COVID-19 mortality). Furthermore, the Stepwise Linear Regression (SLR) model shows that the covariates, namely total_cases, hospital patients per million, hospital beds per thousand, male smokers, and people fully vaccinated per hundred, added significantly (P<0.001) to the prediction of the response variable. Our SLR model validation study revealed that the observed total COVID-19 mortality was highly correlated with the predicted total COVID-19 mortality in various countries (r = 0.977, P<0.001). Our Stepwise Linear Regression model performs significantly better with an R-squared value of 0.958 and adjusted R-squared value of 0.956 than other related regression models built to predict COVID-19 mortality. Based on our current findings, we conclude that governments with better hospital infrastructure and people with complete dosages of the COVID-19 vaccine will have minimal COVID-19 fatalities. © 2022 The Authors.

Air Quality Index (AQI) Using Time Series Modelling During COVID Pandemic

Air quality index is use to identify how polluted the current air is and measures the level of pollution in air. Increasing AQI always been a matter of worry because of rapid increase in traffic, urbanization and pollutants. This paper aims to predict AQI of Delhi region during COVID-19 using time series modelling which is a machine learning algorithm. Time series modelling involves models to fit into collected dataset and use them to predict future values. The research is based on major pollutants like particulate matter, CO, SO, NO, NH3 and ozone. Data of the pollutants are collected from Central Pollution Control Board (CPCB), Government of India. Coefficient of determination of PM 10 is 0.95 and PM 2.5 is 0.82. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The impact of meteorological factors on COVID-19 of California and its lag effect

As of March 30, 2021, COVID-19 has been circulating globally for more than 1?year, posing a huge threat to the safety of human life and property. Understanding the relationship between meteorological factors and the COVID-19 can provide positive help for the prevention and control of the global epidemic. We take California as the research object, use Geodetector to screen out the meteorological factors with the strongest explanatory power for the epidemic, then use partial correlation analysis to study the correlation between the two, and finally construct a distributed lag non-linear model (DLNM) to further explore the relationship between the dominant factor and COVID-19 and its lag effect. It turns out that temperature has a greater impact on COVID-19 and the two have a significant negative correlation. When the temperature is lower than 50°F, it has a significant promotion effect on the epidemic, and the relative risk (RR) increases approximately exponentially as the temperature decreases. The delayed effect of the cold effect on the epidemic can be as long as 15?days. This study has shown that more attention should be paid to epidemic prevention and control when the temperature is low, and the delay effect of temperature on the spread of the epidemic cannot be ignored.

Word Clustering Using Graphical Lasso-Guided PCA for Trend Analysis of COVID-19

This paper proposes a word clustering method using graphical lasso-guided principal component analysis (PCA) for trend analysis of coronavirus disease (COVID-19). We define changes in daily frequencies of words on Twitter as trends, and clustering denotes to find similar trends. There is a problem that trends based on indirect correlations degrade the clustering performance. To address this problem, we newly develop graphical lasso-guided PCA. Specifically, graphical lasso is able to obtain a partial correlation matrix (a graph that represents direct correlations between trends). By calculating loadings of PCA to the partial correlation matrix (authority scores calculated by a hyperlink-induced topic search algorithm), accurate clustering becomes feasible. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to April 30, 2020. The results show that our graphical lasso-guided PCA can distinguish two clusters before and after a state of emergency, unlike comparative method using indirect correlations. © 2021 IEEE.

Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

A global analysis on the effect of temperature, socio-economic and environmental factors on the spread and mortality rate of the COVID-19 pandemic.

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and diurnal temperature variation (difference between daytime maximum and night-time minimum temperature) and other environmental and socio-economic parameters. After controlling the effect of the duration since the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19 at the global scale. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, diurnal temperature variation was significantly and positively correlated with mortality in the low- and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed factor in the model significantly improved model skill predicting mortality in the low- and middle-income countries. Our analysis suggests that warm climate may reduce the mortality rate in high-income economies, but in low- and middle-income countries, high diurnal temperature variation may increase the mortality risk.