Overview of data science and sustainability analysis

Abstract

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.