The sessions at the workshop were entitled:
• Session 1: Expanding Science and Engineering with Novel CS/IT Methods: “The Need to Turn Numbers into Knowledge”;
• Session 2: Understanding, Tracking, and Managing Uncertainty Throughout the Science-to-Policy Pipeline;
• Session 3: Creating Institutional and Personal Change with Humans in the Loop;
• Session 4: Overcoming Obstacles to Scientific Discovery and Translating Science to Practice.
The workshop agenda is provided at the end of Appendix A.
SESSION 1: EXPANDING SCIENCE AND ENGINEERING WITH NOVEL CS/IT METHODS: “THE NEED TO TURN NUMBERS INTO KNOWLEDGE”
Discussions during the first session of the workshop focused on the role of computer science in helping solve sustainability challenges. A broad definition of sustainability was employed. Vijay Modi, Columbia University, provided examples of sustainability areas where computer science could help address some challenges; Robert Pfahl, International Electronics Manufacturing Initiative, discussed changes in electronic systems and products to improve sustainability; Neo Martinez, Pacific Ecoinformatics and Computational Ecology Lab, explored the role of computer science in improving ecological sustainability; Adjo Amekudzi, Georgia Institute of Technology, examined planning and management issues around infrastructure; and Thomas Harmon, University of California, Merced, discussed water challenges.
Following are examples given of the ways in which computer science can play a role in addressing sustainability challenges:
• Urban electricity consumption. Gathering fine-grained accurate measurements and statistics on energy usage of individual buildings can be difficult, due in part to the variety and diversity of building types. With better measurements, one could develop a useful model of energy usage over the course of a day and find opportunities, for instance, to store extra energy throughout the day for use at peak times.
• Infrastructure planning. The planning and development of effective infrastructure are very difficult to do at scale for the time span required. Compounding these challenges is a dearth of data on how and where people actually live and what their movements are throughout the day.