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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contents - p. iii 7. Chapter Opener___Chap num. Contributors - p. iii 8. Chapter 1. Overview of data science and sustainability analysis - p. iii 9. Chapter 2. Applying AI to conservation challenges - p. iii 10. Chapter 3. Water balance estimation in Australia through the merging of satellite observations with models - p. iii 11. Chapter 4. Machine learning in the Australian critical zone - p. iii 12. Chapter 5. A clustering analysis of energy and water consumption in U.S. States from 1985 to 2015 - p. iii 13. CHAPTER 6. Exploring rooftop solar photovoltaics deployment and energy injustice in the US through a data-driven approach - p. iii 14. Chapter 7. Data-driven materials discovery for solar photovoltaics - p. iii 15. Chapter 8. Machine learning in life cycle assessment - p. iii 16. Chapter 9. Industry sustainable supply chain management with data science - p. iii 17. Chapter 10. Deep learning with satellite imagery to enhance environmental enforcement - p. iii 18. Chapter 11. Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals - p. iii 19. Chapter 12. An indicator-based approach to sustainable management of natural resources - p. iii 20. Chapter 13. Research and development for increased application of data science in sustainability analysis - p. iii 21. Index - p. iii
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contents - p. iii 7. Chapter Opener___Chap num. Contributors - p. iii 8. Chapter 1. Overview of data science and sustainability analysis - p. iii 9. Chapter 2. Applying AI to conservation challenges - p. iii 10. Chapter 3. Water balance estimation in Australia through the merging of satellite observations with models - p. iii 11. Chapter 4. Machine learning in the Australian critical zone - p. iii 12. Chapter 5. A clustering analysis of energy and water consumption in U.S. States from 1985 to 2015 - p. iii 13. CHAPTER 6. Exploring rooftop solar photovoltaics deployment and energy injustice in the US through a data-driven approach - p. iii 14. Chapter 7. Data-driven materials discovery for solar photovoltaics - p. iii 15. Chapter 8. Machine learning in life cycle assessment - p. iii 16. Chapter 9. Industry sustainable supply chain management with data science - p. iii 17. Chapter 10. Deep learning with satellite imagery to enhance environmental enforcement - p. iii 18. Chapter 11. Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals - p. iii 19. Chapter 12. An indicator-based approach to sustainable management of natural resources - p. iii 20. Chapter 13. Research and development for increased application of data science in sustainability analysis - p. iii 21. Index - p. iii
