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Data Science Applied to Sustainability Analysis - 1st Edition - ISBN: 9780128179765

Data Science Applied to Sustainability Analysis

1st Edition

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Editors: Jennifer Dunn Prasanna Balaprakash
Paperback ISBN: 9780128179765
Imprint: Elsevier
Published Date: 1st November 2020
Page Count: 300
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Description

Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas.

Key Features

  • Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery
  • Includes considerations sustainability analysts must evaluate when applying big data
  • Features case studies illustrating the application of data science in sustainability analyses

Readership

Sustainability analysts, life cycle assessment practitioners, and industrial ecologists, data scientists seeking applications for techniques such as machine learning, computer vision, and other data science tools, policy makers, and graduate students

Table of Contents

1. Overview of Data Science and Sustainability Analysis and State of their Co-Application
2. Government Data Sources for Big Data in Sustainability Analysis
3. Social Media Data Application in Sustainability Analysis
4. Environmental sensor networks as a data source for Sustainability analysis
5. Data Science in the Analysis of Sustainable Agriculture
6. Fusion of Satellite Imagery interpretation and LiDAR data in sustainable forestry management
7. Application of Data Science in Sustainable Supply Chain Management
8. Data Science Applied to Sustainable Building Management
9. Urban water systems management with Data Science
10. Avenues towards Data Science in Life Cycle Analysis
11. Data Science Application in Material Flow Analysis
12. Application of Data Science in Input-Output Frameworks
11. Research and Development for Increased Application of Data Science in Sustainability analysis

Details

No. of pages:
300
Language:
English
Copyright:
© Elsevier 2021
Published:
1st November 2020
Imprint:
Elsevier
Paperback ISBN:
9780128179765

About the Editors

Jennifer Dunn

Jennifer B. Dunn is the Director of Research of the Northwestern-Argonne Institute of Science and a Research Associate Professor at Northwestern University in Chemical and Biological Engineering. She holds a joint appointment in the Energy Systems Division of Argonne National Laboratory, where she led the Biofuels Analysis Team before taking on her current role. In her research, Jennifer investigates life cycle energy consumption and environmental impacts of advanced transportation and fuel technologies, including biofuels and battery-powered electric drive vehicles. She is also interested in carbon capture and utilization (CCU), automotive lithium-ion battery impacts and recycling, and fit-for-purpose water treatment. She holds a PhD in Chemical Engineering from the University of Michigan.

Affiliations and Expertise

Northwestern University, USA

Prasanna Balaprakash

Prasanna Balaprakash is a computer scientist in the Mathematics and Computer Science Division with the joint appointment in the Leadership Computing Facility at Argonne National Laboratory. He is also a Fellow in the Northwestern-Argonne Institute of Science and Engineering of the Northwestern University. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focus is on the automated design and development of scalable algorithms for solving large-scale problems that arise in scientific data analysis and in automating application performance modeling and tuning. He holds a Ph.D. in engineering sciences from CoDE-IRIDIA (AI Lab), Université libre de Bruxelles, Brussels, Belgium, where he was a Marie Curie fellow and later an FNRS Aspirant.

Affiliations and Expertise

Northwestern University, USA

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