This study aims to examine the asymmetric nexus between CO₂ emissions and renewable energy and economic and population growth in seven East African countries (EACs) at the regional level and country levels. Common correlated effect means group (CCEMG), nonlinear autoregressive distributed lagged (NARDL), and causality tests were employed for the panel data from 1980 to 2016. The main findings are as follows: (1) Renewable energy consumption negatively affects CO₂ emissions, while economic and population growth positively affect CO₂ emissions at the regional level. (2) The findings of asymmetric and symmetric linkages between CO₂ emissions and its determinants (economic and population growth and renewable energy) are very volatile across the country levels. (3) The causality hypotheses are different across the country and regional levels. (4) This study shows the renewable energy growth nexus, wherein renewable energy positively affects economic growth at the regional level. Lastly, the study suggests potential policy implications for effectively reducing CO₂ emissions as well as growing the economy at the regional level.

This study focused on exploiting machine learning algorithms for classifying and predicting injury severity of vehicle crashes in Yemen. The primary objective is to assess the contribution of the leading causes of injury severity. The selected machine learning algorithms compared with traditional statistical methods. The filtrated second data collected within two months (August-October 2015) from the two main hospitals included 156 injured patients of vehicle crashes reported from 128 locations. The data classified into three categories of injury severity: Severe, Serious, and Minor. It balanced using a synthetic minority oversampling technique (SMOTE). Multinomial logit model (MNL) compared with five machine learning classifiers: Naïve Bayes (NB), J48 Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results showed that most of machine learning-based algorithms performed well in predicting and classifying the severity of the traffic injury. Out of five classifiers, RF is the best classifier with 94.84% of accuracy. The characteristics of road type, total injured person, crash type, road user, transport way to the emergency department (ED), and accident action were the most critical factors in the severity of the traffic injury. Enhancing strategies for using roadway facilities may improve the safety of road users and regulations.