Overview of data science and sustainability analysis

Prasanna Balaprakash, Jennifer B. Dunn

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Globally, challenges related to sustainability abound, including improving air and water quality, reducing food and water consumption, decreasing waste, enhancing energy efficiency and the share of renewable energy, and conserving ecologically valuable lands. One of the most pressing sustainability-related challenges is reducing greenhouse gas emissions that contribute to climate change while developing environmentally-sound adaptation strategies. Simultaneously, advancing the societal aspect of sustainability is critical, but challenging as large portions of the world’s population live below the International Poverty Line. Data science, including different statistical machine learning techniques, is a tool that will see increasing use in efforts to tackle sustainability challenges. Leveraging the growing volumes of data such as satellite imagery, continuous sensor data from industrial processes, social media data, and data from environmental sensors, requires such techniques. This book provides case studies and examples at the intersection of data science and sustainability in the areas of environmental quality and sustainability, energy and water, sustainable systems analysis, and society and policy.

Original languageEnglish (US)
Title of host publicationData Science Applied to Sustainability Analysis
PublisherElsevier
Pages1-14
Number of pages14
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

Fingerprint

Dive into the research topics of 'Overview of data science and sustainability analysis'. Together they form a unique fingerprint.

Cite this