Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.

With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in depth the substantial role of natural resources abundance in the environmental inequality issue. For this purpose, this study adopted the eXtreme Gradient Boosting (XGBoost), LightGBM, Natural Gradient Boosting (NGBoost), Hybrid hybrid upper confidence bound-long short-term memory-Genetic Algorithm (UCB-LSTM-GA), and the Shapley Additive Explanation (SAE) machine learning algorithms in the context of 21 emerging economies spanning the years 2001 to 2019. The empirical results reveal that natural resource abundance, foreign trade, and foreign direct investment inflows contribute all to higher levels of environmental inequality.

Repercussions the Covid-19 Pandemic on the SDGs Achievement: Is it a New Era for the Development?

Using monthly data, this article examines the influence of Covid-19 on poverty, inequality, well-being, and environmental quality for a sample of 14 African economies from 2018 to 2020. To do so, we employ a GMM approach to look at the influence of the pandemic on achieving the SDGs in Africa. According to our empirical findings, the pandemic significantly impacts poverty and pollution levels.

A non-linear assessment of the urbanization and climate change nexus: the African context.

The climate change issue becomes more challenging with the increasing pace of urbanization in Africa. For this purpose, we attempt to examine the relationship urbanization and CO emissions by applying the panel smooth transition regression model for 47 African countries during the spanning time 1990-2014. Our results reveal that the nexus between urbanization and CO emissions is non-linear.