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Data Science Applied to Sustainability Analysis

  • 1st Edition - May 11, 2021
  • Latest edition
  • Editors: Jennifer Dunn, Prasanna Balaprakash
  • Language: English

Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessmen… Read more

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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

I. Introduction

1. Overview of Data Science and Sustainability Analysis and State of their Co-Application
Jennifer Dunn

II. Enironmental Health and Sustainability

2. Applying AI for Conservation
Niraj Swami

3. Water balance characterization
Valentijn Pauwels

4. Machine Learning in the Australian Critical Zone
Elisabeth N. Bui

III. Energy and Water

5. A Clustering Analysis of Energy and Water Consumption in U.S. States from 1985 to 2015
Sybil Derrible

6. Energy footprint of big data evaluated with data science
Sarang Supekar

7. Solar PV rooftop disaprities by race and ethnicity in US
Deborah Sunter and Sergio Castellanos Rodriguez

8. Screening materials for solar pv
Jacqueline M. Cole

IV. Sustainable Systems Analysis

9. Machine Learning in life cycle analysis
Amy Landis and Mikaela Algren

10. Industry sustainable supply chain management with data science
Deboleena Chakraborty and Richard K. Helling

V. Society and Policy

11. Machine Learning to Inform Enhance Environmental Enforcement
Daniel E. Ho

12. Sociologically informed use of remote sensing data to predict rural household poverty
Gary R. Watmough

13. Trade-offs Between Environmental and Social Indicators of Sustainability
Esther Sullivan Parish, Keith L. Kline, Virginia Dale, Maggie Davis, Rebecca Efroymson, Michael Hilliard, Henriette Jager and Fei Xie

VI. Conclusion

14. Research and Development for Increased Application of Data Science in Sustainability analysis
Prasanna Balaprakash

Product details

  • Edition: 1
  • Latest edition
  • Published: May 11, 2021
  • Language: English

About the editors

JD

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
Director of Research of the Northwestern-Argonne Institute of Science; Research Associate Professor, Northwestern University, USA

PB

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
Computer Scientist, Mathematics and Computer Science Division, Joint Appointment in the Leadership Computing Facility, Argonne National Laboratory; Fellow, Northwestern University, USA

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