Data Science Applied to Sustainability Analysis
Editat de Jennifer Dunn, Prasanna Balaprakashen Limba Engleză Paperback – 16 mai 2021
- 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
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Specificații
ISBN-13: 9780128179765
ISBN-10: 0128179767
Pagini: 310
Ilustrații: 120 illustrations (60 in full color)
Dimensiuni: 191 x 235 x 22 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128179767
Pagini: 310
Ilustrații: 120 illustrations (60 in full color)
Dimensiuni: 191 x 235 x 22 mm
Greutate: 0.54 kg
Editura: ELSEVIER SCIENCE
Public țintă
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 studentsCuprins
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
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