Research and development for increased application of data science in sustainability analysis

Jennifer B. Dunn, Prasanna Balaprakash

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter summarizes research needs in developing and enhancing data sets, data sources, and data science methods for applying data science to the broad field of sustainability. Overall, while there is a great need to develop environmental science, manufacturing and technology systems, and societal systems data at greater spatial and temporal resolution, there is also a need to consider what sustainability questions can be addressed by creative use of the data sets currently available. Furthermore, advances in data science remain foundational to realizing the potential of many techniques (including machine learning, artificial intelligence) to enhancing environmental and societal sustainability. Advances in explainable AI, edge computing, and applying the advantages of the 5G technology that is on the horizon are all required. Importantly, engagement of multiple disciplines - particularly computer science - along with multiple science and engineering disciplines will be foundational to bringing the power of data science methods to bear on the pressing sustainability challenges.

Original languageEnglish (US)
Title of host publicationData Science Applied to Sustainability Analysis
PublisherElsevier
Pages283-292
Number of pages10
ISBN (Electronic)9780128179765
DOIs
StatePublished - Jan 1 2021

Keywords

  • Data science
  • Energy and water
  • Policy
  • Sustainability
  • Systems analysis

ASJC Scopus subject areas

  • General Environmental Science

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