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.
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Table of Contents
I. Introduction 1. Overview of Data Science and Sustainability Analysis and State of their Co-Application
II. Enironmental Health and Sustainability 2. Applying AI for Conservation 3. Water balance characterization 4. Machine Learning in the Australian Critical Zone
III. Energy and Water 5. A Clustering Analysis of Energy and Water Consumption in U.S. States from 1985 to 2015 6. Energy footprint of big data evaluated with data science 7. Solar PV rooftop disaprities by race and ethnicity in US 8. Screening materials for solar pv
IV. Sustainable Systems Analysis 9. Machine Learning in life cycle analysis 10. Industry sustainable supply chain management with data science
V. Society and Policy 11. Machine Learning to Inform Enhance Environmental Enforcement 12. Sociologically informed use of remote sensing data to predict rural household poverty 13. Trade-offs Between Environmental and Social Indicators of Sustainability
VI. Conclusion 14. Research and Development for Increased Application of Data Science in Sustainability analysis
