Computing Research for Sustainability (2012) / Chapter Skim
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Appendix A: Summary of a Workshop on Innovation in Computing and Information Technology for Sustainability
Appendix A: Summary of a Workshop on Innovation in Computing and Information Technology for Sustainability
Pages 107-148
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The workshop featured invited presentations and discussions that explored research themes and specific research opportunities that could advance sustainability objectives and also could result in advances in computer science (CS)
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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 coinformatics E 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 alifornia, Merced, C 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.
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Workshop participants examined a wide array of sustainability challenges in which specific CS/ information technology (IT) advances could contribute to resolving these challenges.
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Ecological Sustainability Threats to ecological sustainability include loss of biodiversity, species extinction and invasion, and the exploitation of ecosystems.
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For example, paleontological food web analysis has provided a better understanding of the network structures of current food webs.4 Gaining an understanding of food chains on the globe over vast timescales can help provide researchers with a sense of how some kinds of ecosystems evolved. If economic 3The Global Ex-vessel Fish Price Database and its various uses are described in U
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Transportation and Social Sustainability Participants discussed the connections between traditional measures of sustainability, which may typically be functions of space and time, and measures of social sustainability. With social sustainability, as shown in Figure A.1, the sustainability footprint becomes the rate of change of quality of life as a function of one's impact on the environment.
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Gains in positive performance often have less of an impact on satisfaction, whereas reductions in negative performance are often more important to customers. In this case, to build transportation plans that are sustainable both in the traditional sense and socially, customer satisfaction data, both subjective and objective, need to collected and woven into these plans.
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For example, more hydrological data and better models could help scientists to create a virtual watershed that would allow for quick studies of impacts and could potentially enable forecasts of the amount and quality of water available, much like weather forecasts. In addition to creating virtual watersheds for analysis, areas in which improvements in CS and IT are needed in order to add to the understanding of water resources include the following: • Remote sensing.
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More bridges need to be built between computer scientists and other disciplines, between researchers and practitioners, and between the academic and the industrial and the consumer settings. Technology from academic laboratories needs to move more quickly to the industrial and consumer world.
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The goal of the second session of the workshop was to explore some of the computational methods available to address loss of information about uncertainty, to consider what additional methods are needed, and to outline a potential research agenda. Panelists were asked to examine the following questions in relation to sustainability challenges during their talks: • What are the sources of uncertainty that should be explicitly captured?
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• Are there alternatives to explicit uncertainty representations that can improve the robustness of management policies to all of these sources of uncertainty? Chris Forest, Pennsylvania State University, provided information on the sources of uncertainty and the tracking of uncertainty in climate models; Peter Bajcsy, National Institute of Standards and Technology, discussed the development of scientific workflows for tracking uncertainty through the science process; David Brown, Duke University, highlighted new methods for optimization problems under uncertainty; and John Doyle, California Institute of Technology, explored theories for analyzing "robust-yet-fragile" systems.
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However, several of the models were built in the 1960s and 1970s before these tools existed. For example, participants observed that it is not possible to do comparisons between several of the older models, such as that of the United Kingdom's Met Office Hadley Centre and the National Center for BOX A.1 Hierarchy of Climate Models The first climate change assessments were done using the global energy balance model.
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. Atmospheric Research's community climate model, or the Geophysical Fluid Dynamics Laboratory climate models, by swapping in different components from each model; the software is too inflexible.
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(Figure A.2) , a coupling of a human systems model and Earth-system model, illustrates how uncertainty analysis is being applied to climate models.7 The model uses several components: human activity; atmospheric, ocean, land, and ecosystem interactions; and biogeochemical exchanges.
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Cohen, MIT Integrated Global System Model (IGSM) Version 2: Model Description and Baseline Evaluation, Joint Program Report Series (July 2005)
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change allows for better management of water resources, ecosystem changes, and air quality issues. The climate modeling community does not currently have the tools to sample the models for regional uncertainty information.
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Second, the complexity of uncertainty modeling using error propagation is greater than that of the underlying phenomenon itself. Third, and most importantly, scientific workflows are suitable for managing uncertainty modeling; software modules could be reused and could track how collected data are being manipulated.
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Capturing the workflow and managing the computation are particularly useful if all of the calculated information, including uncertainty information, can be delivered to third parties and end users. Workflows can become a communication mechanism for the management of uncertainty.
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Robust optimization provides computational tractability and supports parsimonious modeling demands -- one does not have to worry about the specifics of probability distribution. Robustness is an inherent and essential feature of many important methods across many disciplines, including machine learning and decision theory.
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Robustness in one part of a system may induce fragility in another. Fortunately, evolvability and robustness seem 9John Doyle, "Theory and Methodology of Robust-Yet-Fragile Systems Analysis," pre sentation at the Workshop on Innovation in Computing and Information Technology for Sustainability, Washington, D.C., May 26, 2010.
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Moreover, although there have been many advances in HSI, the literature is replete with failed cognitive models, serving as cautionary tales for HSI in sustainability applications. Panelists were asked to examine the following questions in relation to sustainability challenges during their talks:
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• How do human factors affecting sustainability challenges drive the use and design of technology? How can this interaction be accounted for?
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10Information about this organization is available at http://www.surfrider.org/. 11Bill Tomlinson et al., The EcoRaft project: A multi-device interactive graphical exhibit for learning about restoration ecology, in CHI'06, Extended Abstract on Human Factors in Computing Systems, New York, N.Y.: Association for Computing Machinery (2006)
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However, as cell phones have advanced, a tool similar to GreenScanner has become commercially available through the company Good Guide.13 • Trackulous.14 This tool was designed to help people track their activities and the environmental impact of those activities. People may be aware that they often travel by airplane or car, but they might not understand the cumulative time spent doing these activities in a year or the cumulative impact that such travel can have.
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When the Internet works well, for example, users do not typically know or need to know where the information they are receiving is coming from: e-mails move through various wires, routers, and servers around the globe; web pages are populated by widgets whose data could be sourced from anywhere. One attendee suggested that analogous architectural approaches may be applicable to certain sustainability challenges.
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Residential Energy Measurement and Disaggregated Data Participants discussed how better information can assist consumers in making effective changes in their use of home resources such as power, water, and gas. Current literature suggests that high-granularity or highresolution data -- for example, information about the usage patterns of individual appliances -- are the most useful.
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One goal would be to create a national energy data corpus, which would be very useful to researchers across disciplines as well as in helping meet large-scale energy and sustainability information needs. The research discussed in this session of the workshop focused on the creation of technology that (1)
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This tool has been field-tested with some success by utilities to determine whether consumers are capable of installing it. • Measuring and tracking water usage provides similar challenges.
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alter their behavior and conserve gas, participants observed. Participants described how research that validates the functionality of the devices described earlier has started with in-home trials.
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Interaction designers can help bridge the gap between scientific knowledge and public perception, can build support, and can promote discourse leading toward solutions. Par 21Eli Blevis, Sustainable interaction design: Invention and disposal, renewal, and reuse, in Proceedings of the SIGCHI Conference on Human Factors in Computing, New York, N.Y.: Association for Computing Machinery (2007)
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SESSION 4: OVERCOMING OBSTACLES TO SCIENTIFIC DISCOVERY AND TRANSLATING SCIENCE TO PRACTICE Committee member David Culler, University of California, Berkeley, and David Douglas, National Ecological Observatory Network, led the discussion during the final session, which highlighted some of the impediments to developing and deploying innovative information technologies for sustainability challenges. Guiding questions for this session were as follows: • What are the motivations for and impediments to applying innovative information technologies to sustainability challenges and how do they differ by domain?
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• What is the appropriate mix of empiricism, innovation, and application in order for computer science to have an impact in the area of environmental sustainability? The Energy Challenge Participants suggested considering broad sustainability challenges in the context of the energy challenge.
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Perhaps reductions in several of these "low-hanging fruit" items can contribute significantly in reducing total energy consumption. Impediments to Changing the Energy System Insufficient Scope and Scale of Research and Development Funding to Fuel IT-Enabled Innovation in the Electricity Sector Challenged to consider opportunities for IT and CS research to contribute to sustainability, participants reflected on the history of IT successes and on whether those successes might offer important lessons.
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In 2010, the U.S. Department of Energy provided $130 million and created three different energy hubs in innovation.28 However, a workshop attendee commented that even this amount is much smaller than would be needed if a significant shift were to be made toward sustainable energy sources or if total energy consumption were to be decreased.
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Perhaps the most obvious is that, generally speaking, utility companies have historically charged for usage by the kilowatt-hour, resulting in little economic incentive to reduce the number of kilowatt-hours used. regulation prevents vertical monopolies, but there is often an interest in owning an entire vertical market -- one organization owning or operating both the production and the delivery systems -- and extracting marginal profit mostly by locking customers in to the system.
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Potential Computer Science Contributions In the fourth session of the workshop, participants brainstormed about potential further contributions of computer science to sustainability. Computer science is well positioned to provide technical options that could help address some sustainability challenges.
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Distributed grid management, ecosystems understanding, crisis and disaster response, and resource tracking and optimization can all benefit from open, interoperable information systems. With large amounts of data being collected, privacy and security become an issue, which, again, computer scientists have experience managing.
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Computer scientists can contribute tools that encourage individual participation in addressing sustainability challenges. • Small businesses often require specialized information that can be hard to acquire.
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Another example is ecological resilience and ecosystem function, which is the monitoring and modeling of ecological change and of the interactions related to ecological robustness and requires new tools for temporal and spatial resolution, new methods to explore the dynamics of connectivity in ecological systems, and teasing out the ranges of anthropogenic impacts. Vijay Modi, Columbia University: "Criticality of CS and IT to Sustainability" Robert Pfahl, International Electronics Manufacturing Initiative, Inc.: "Towards a Sustainable World Through Electronic Systems and IT" Neo Martinez, Pacific Ecoinformatics and Computational Ecology Lab: "Numbers: Where They Come from and What to Do with Them to Live More Sustainably on Earth" Adjo Amekudzi, Georgia Institute of Technology: "Using Social Sustainability Measures as Inputs in Planning and Design" Thomas Harmon, University of California, Merced: "Environmental Cyberinfrastructure and Data Acquisition"
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Peter Bajcsy, National Institute of Standards and Technology: "Instruments and Scientific Workflows"
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Bill Tomlinson, University of California, Irvine: "Greening Through IT" Shwetak Patel, University of Washington: "Residential Energy Measurement and Disaggregated Data" Eli Blevis, Indiana University: "Sustainable Interaction Design"
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• What is the appropriate mix of empiricism, innovation, and application for computer science to have an impact in the area of environmental sustainability? David Douglas, National Ecological Observatory Network: "The Role of CS in Open, Sustainability Science" 4:00-5:00 p.m.
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