This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …

Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

The reform of the EU ETS markets in 2017 has induced new carbon price forecasting
challenges. This study proposes a novel decomposition-ensemble paradigm VMD …

In the context of the practice of high-quality social development gradually deepening, the
optimization of energy structure is an important link to promote high-quality economic …

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

The rapid development of big data technologies and the Internet provides a rich mine of
online big data (eg, trend spotting) that can be helpful in predicting oil consumption—an …

Conventional methods are less robust in terms of accurately forecasting non-stationary and
nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based …

Accurate estimations of nuclear energy consumption are an essential process for
formulating appropriate policies and plans in the energy sector and associated companies …

The pursuit of carbon neutrality confronts the twofold challenge of meeting energy demands
and reducing pollution. This review article examines the potential of gasifying plastic waste …

To enhance prediction reliability and accuracy, a hybrid model based on the promising
principle of “decomposition and ensemble” and a recently proposed meta-heuristic called …