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

被引：0

作者：

Souissi, Bilel ^{[1
]}

Tiba, Sofien ^{[1
]}

机构：

[1] Faculty of Economics and Management, University of Sfax, Sfax

来源：

Environmental Science and Pollution Research | 2024年 / 31卷 / 40期

关键词：

Climate justice; Environmental inequality; Environmental racism; Machine learning; Natural resource abundance;

D O I：

10.1007/s11356-024-34737-1

中图分类号：

学科分类号：

摘要：

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. However, higher levels of per capita income, gross fixed capital formation, and institutional quality contribute to lower levels of environmental inequality. Addressing climate justice holistically through an integrated supranational vision is significant since every step taken toward eradicating environmental racism matters. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

引用

页码：52841 / 52854

页数：13

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