Overview
- Data analytical model for the optimization of the energy system
- Technology-open view on decarbonization
- Less greenhouse gases through data-based processes
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Decarbonization through optimized energy flows. In this book you will learn how a significant reduction in climate changing greenhouse gas emissions can be achieved through systemic optimization of our energy systems. The authors clearly demonstrate how energy-intensive processes can be optimized flexibly by using technology-neutral simulation methods to ensure that significantly fewer greenhouse gases are emitted.
Such field-tested, data-based energy models described in this publication prove that "digital decarbonization" enables an economy that releases significantly fewer climate changing emissions while maintaining its production output. This is a promising message in view of ongoing climate change.
Authors and Affiliations
About the authors
Oliver D. Doleski, Dr. Thomas Kaiser, Dr. Michael Metzger, Professor Dr. Stefan Niessen and Dr. Sebastian Thiem are active in the field of technology development and consulting at Siemens AG.
Bibliographic Information
Book Title: Digital Decarbonization
Book Subtitle: Achieving climate targets with a technology-neutral approach
Authors: Oliver D. Doleski, Thomas Kaiser, Michael Metzger, Stefan Niessen, Sebastian Thiem
DOI: https://doi.org/10.1007/978-3-658-33330-0
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022
Hardcover ISBN: 978-3-658-33329-4Published: 18 January 2022
Softcover ISBN: 978-3-658-33332-4Published: 19 January 2023
eBook ISBN: 978-3-658-33330-0Published: 17 January 2022
Edition Number: 1
Number of Pages: XLIII, 218
Number of Illustrations: 2 b/w illustrations, 82 illustrations in colour
Topics: Sustainable Development, Statistics, general, Environmental Economics, Big Data