Machine Learning Technologies on Energy Economics and Finance: Energy and Sustainable Analytics (1) - Rilegato

Rilegato

ISBN 10: 3031948610 ISBN 13: 9783031948619

Casa editrice: Springer Nature, 2025

Riassunto

This book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector.

It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors—such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance.

This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.

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Informazioni sull'autore

Dr. Mohammad Zoynul Abedin is a Senior Lecturer in FinTech, at the School of Management, Swansea University, UK. He received his B.B.A. and M.B.A. degrees in finance from the University of Chittagong, Bangladesh, and his D.Phil. degree in investment theory from the Dalian University of Technology, China. His work appears on the Annals of Operations Research, International Journal of Production Research, IEEE Transactions on Industrial Informatics, to mention a few. His current research interests include business data analytics, fintech, and computational finance. He is a fellow of the Financial Management Association (FMA), and British Accounting and Finance Association (BAFA).

Dr. Wang Yong is a Professor and the Vice Dean at the School of Statistics, Dongbei University of Finance and Economics (DUFE), China. Dr. Yong was awarded the Outstanding Professor by DUFE, and is also the chief expert of major projects at the National Social Science Foundation of China. His main research interests are energy economics, environmental-economic system analysis, and policy optimization. He has published more than 70 papers in reputable academic journals, including Annals of Operations Research, Technological Forecasting & Social Change, Journal of Environmental Management, Renewable and Sustainable Energy Reviews, and other prestigious journals. 

Dalla quarta di copertina

This book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector.

It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors—such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance.

This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031948610
• ISBN 13: 9783031948619
• Rilegatura: Copertina rigida
• Numero di pagine: 300
• Redattore: Abedin Mohammad Zoynul, Yong Wang
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania