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.
- 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
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.
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.
