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Summary of a Workshop on Innovation in Computing and Information Technology for Sustainability

INTRODUCTION

On May 26, 2010, the Committee on Computing Research for Environmental and Societal Sustainability held the Workshop on Innovation in Computing and Information Technology for Sustainability in Washington, D.C. The goal of the workshop was to survey sustainability challenges, current research initiatives, and results from previously held topical workshops and related industry and government development efforts in these areas. 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). Participants were also asked to consider research modalities, with a focus on applicable computational techniques and long-term research that might be supported by the National Science Foundation (NSF), with an emphasis on problem- or user-driven research.

This appendix summarizes the discussion of the workshop panelists and the attendees. The summaries of the four workshop sessions provided in this appendix are a digest both of the presentations and of the subsequent discussion, which included remarks offered by others in attendance. Although this summary was prepared by the committee on the basis of workshop presentations and discussions, it does not, in keeping with the guidelines of the National Research Council on the development of workshop summaries, necessarily reflect a consensus view of the committee.



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A Summary of a Workshop on Innovation in Computing and Information Technology for Sustainability INTRODUCTION On May 26, 2010, the Committee on Computing Research for Envi- ronmental and Societal Sustainability held the Workshop on Innovation in Computing and Information Technology for Sustainability in Washington, D.C. The goal of the workshop was to survey sustainability challenges, current research initiatives, and results from previously held topical work- shops and related industry and government development efforts in these areas. 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). Participants were also asked to consider research modalities, with a focus on applicable computational techniques and long-term research that might be supported by the National Science Foun- dation (NSF), with an emphasis on problem- or user-driven research. This appendix summarizes the discussion of the workshop panel- ists and the attendees. The summaries of the four workshop sessions provided in this appendix are a digest both of the presentations and of the subsequent discussion, which included remarks offered by others in attendance. Although this summary was prepared by the committee on the basis of workshop presentations and discussions, it does not, in keep- ing with the guidelines of the National Research Council on the develop- ment of workshop summaries, necessarily reflect a consensus view of the committee. 107

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108 COMPUTING RESEARCH FOR SUSTAINABILITY 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 Trans- lating 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 Univer- sity, provided examples of sustainability areas where computer science could help address some challenges; Robert Pfahl, International Electron- ics 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 Insti- tute 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. Gathering fine-grained accurate mea- surements 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.

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APPENDIX A 109 This limited knowledge of the movement of people and the limited understanding of where infrastructure needs exist make it difficult to plan infrastructure accordingly. Advances in remote sensing, to improve understanding of the use of current infrastructure, can help cities and utilities to formulate better infrastructure planning. • Clean water. Access to clean water is an ongoing and increasingly challenging problem worldwide. One of the more difficult components of this challenge is detecting water below the surface of Earth. Although detection of water at and just below the surface is well understood, tech- nology for finding water at deeper levels is limited. Better sensing tech- nologies are needed to help differentiate between sand, wet sand, water that is flooding the sand, and so on. The examples above are a just a few of the areas in which computer science has contributions to make to sustainability. Workshop participants examined a wide array of sustainability challenges in which specific CS/ information technology (IT) advances could contribute to resolving these challenges. In many cases, it is a matter of developing new approaches for turning raw data (numbers) into knowledge and, ultimately, prompting action that results in more sustainable outcomes. Research opportunities cited by workshop participants in the areas of ecological sustainability (that is, relating to diverse and productive biological systems), transpor- tation, and water resources are described below, along with associated computer science challenges. The first session concluded with a brief examination of the policy challenges of interdisciplinary work and of turning knowledge into actionable items. Electronic Systems and Products and Sustainability The International Electronics Manufacturing Initiative (iNEMI) is a consortium of electronics manufacturers and affiliates focused on envi- ronmental issues in electronics.1 Every 2 years, iNEMI creates a roadmap that charts future opportunities for and challenges to electronics manufac- turing for reaching sustainability objectives.2 The iNEMI efforts began by focusing on hazardous materials. The early goals of the consortium were aimed at eliminating chlorofluorocarbons from the cleaning of electron- ics, removing lead from electronics, and reducing the use of halogenated flame retardants and polyvinyl chloride (PVC) materials. More recently, the focus has been on the complete energy use of products, as discussed 1A list of iNEMI members is available at http://www.inemi.org/news/council-members. 2The 2011 iNEMI roadmap is available at http://www.inemi.org/2011-inemi-roadmap.

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110 COMPUTING RESEARCH FOR SUSTAINABILITY below. Sound scientific methodologies are needed to take into account total trade-offs among conflicting device requirements and to model long- term reliability and life of these devices. Products that are recyclable, use non-hazardous materials, or mini- mize the use of energy and matter tend to be less harmful for the envi- ronment. Often there are trade-offs among these concerns. For example, using fewer hazardous materials may increase the resources needed to manufacture a certain type of equipment. When considering the size of items, there is often a trade-off of size for function. For example, cel- lular telephones have grown larger in recent years as functionality has increased. This matters especially with regard to calculating potential waste over a product’s entire life cycle, although in the case of cell phones, the increased functionality may mean that other, even larger devices are no longer needed. Digitization is another example in which the function- ality of electronics has decreased the amount of hardware needed. As digital music players have become more ubiquitous, compact disc play- ers—and discs—are becoming less and less necessary. Life-cycle analysis is key to understanding the complete energy use of products, including the energy used in mining raw materials, producing semiconductors and other components, assembly, transportation, and, ultimately, consumer use of the product. Computing research can assist in the tracking and understanding of all of these inputs throughout the life cycle of products. “Green computing”—making computers themselves more environ- mentally friendly—plays a role in the reduction of energy consumption. For example, basic assumptions about computers’ operating environ- ments can be rethought, to yield significant energy savings. The 2011 iNEMI roadmap recommends that server farms and machines be rede- signed so that the temperatures of server rooms can be increased in order to reduce the amount of energy required for cooling. Participants noted that a holistic approach to technology is needed to contribute further to sustainability in electronics. Continued work in the following areas is needed: in digital semiconductor technology, work is needed in order to increase density and reduce cost; in the incorpora- tion of sensor networks, work is needed to provide detailed energy-use data; in electronic packaging technology; and in innovation in CS and IT algorithms and applications. Additionally, participants suggested that standards may play an important role here. Ecological Sustainability Threats to ecological sustainability include loss of biodiversity, spe- cies extinction and invasion, and the exploitation of ecosystems. Each of

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APPENDIX A 111 these threats has consequences for the robustness, resilience, and stability of the respective ecosystems. Computer science can play an important role in enhancing the understanding of the consequences to ecosystems of particular courses of action by assisting in measuring the current impacts of actions and predicting future impacts on these ecosystems. Databases play a crucial role in the understanding of ecosystems. For example, the Global Ex-vessel Fish Price Database of the Fisheries Eco- nomics Research Unit is a valuable, large data set.3 The database provides information on hundreds of types of fish and their market prices over an extended period, thus enabling a better understanding of conditions in the oceans and of the potential effects of fishing. Computing will also play a vital role in helping researchers and deci- sion makers understand collected data, which come from a variety of sources. Hardware and software will be needed to help analyze large sets of heterogeneous data. Advances in modeling and simulation will also contribute to the understanding of the information collected. Ecological networks are complex, high-dimensional, non-linear systems. Therefore, simpler mathematical representations are not adequate. Ecological sys- tems need to be simulated over time. Participants noted that currently, the various time series and relevant data for the simulation of an ecological system can only be summarized. More accessible data including quan- titative information from simulations is needed so that others can use the data and contribute to the work. Some of the challenges created by large, heterogeneous data sets and researchers’ resource limitations have been resolved with remote-computing capabilities (currently referred to as cloud computing). The shared resource of cloud computing can allow for simulations to be run much faster. Additional advances are needed so that data simulations can be stored easier and computing power can be more easily shared. Interdisciplinary research on networks has led to a greater under- standing of food webs and other ecological systems. For example, pale- ontological 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 research- ers 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. Rashid Sumaila, Dale Marsden, Reg Watson, and Daniel Pauly, Global Ex-Vessel Fish Price Database: Construction, Spatial and Temporal Applications, Fisheries Centre Working Paper #2005-01, Vancouver, B.C., Canada: University of British Columbia (2005). 4The Pacific Ecoinformatics and Computational Ecology Lab has done much of the work related to paleontological food web analysis. A list of its publications is available at http:// www.foodwebs.org/.

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112 COMPUTING RESEARCH FOR SUSTAINABILITY information to account for things such as price and biomass can be incor- porated into models based on the understanding of modern food webs, the effect of economic exploitation on ecological systems can be better understood. For example, in a simple three-species food chain (such as large fish eating small fish eating plants), adding economic information to the model also allows for a separation of the effects of exploitation by humans for economic reasons from the effects of human exploitation for the purpose of subsistence. Participants discussed how network-based analyses might be useful in other areas of sustainability. Rules derived about ecological networks, for instance, may also apply to energy and economic networks. Can useful comparison be made between economic and food networks? Does food function like money in any sort of action- able way? Computing-enabled “citizen science” provides ways for volunteers to collect and report information from their own environments and to contribute to the sustainability of those environments. Citizen science programs have existed since the early 1900s, beginning with the Audu- bon Society’s Christmas Bird Count.5 Now, new mobile technologies and social networking tools make collecting and reporting much easier. Volun- teers can easily collect data, for example, on a particular invasive species and send the information to experts to examine. 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 qual- ity of life as a function of one’s impact on the environment. Participants argued that social sustainability can be and needs to be more rigorously accounted for in discussions about other forms of sustainability. Social sustainability can be considered when looking at transportation sustainability, for instance. Definitions of “transportation sustainability” typically focus on moving items (people, goods, and information) in ways that reduce the impact on the environment, economy, and society. Transportation plans have been required in all major metropolitan areas since the 1960s. Although customer satisfaction has been included in transportation planning for some time, assumptions regarding customer needs are often incorrect. Traditionally satisfaction has been seen as a 5Forinformation on and a history of the Audubon Christmas Bird Count, see http://birds. audubon.org/christmas-bird-count.

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APPENDIX A 113 FIGURE A.1  Achieving quality of life within the means of nature. SOURCE: Jamie Montague Fisher and Adjo Amekudzi, Quality of life, sustainable civil in- frastructure, and sustainable development: Strategically expanding choice, Journal of Urban Planning and Development 137(1):39-78 (2011). Reprinted with permission from the American Society of Engineers. linear function of performance: for example, if a road is twice as smooth, customers are supposedly twice as happy. Research in this space has found, however, that this curve does not apply to all performance attri- butes. 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 subjec- tive and objective, need to collected and woven into these plans. Data need to be collected on a wide range of attributes (safety, quality of life, smoothness), on the relative importance of the different factors, and on how customers rate the different attributes. Such information allows plan- ners to distinguish between performance improvements that have posi- tive and negative effects on quality of life and to negotiate the trade-offs between the two.

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114 COMPUTING RESEARCH FOR SUSTAINABILITY Computer science can contribute to such efforts by developing effec- tive systems for collecting data from the public and providing better data- analysis tools to help, for instance, in the assessment of different choices regarding routes and other planning decisions. Real-time data processing and tools for planning and forecasting transportation needs can help urban planners and decision makers balance economic and policy chal- lenges in planning future infrastructure. Sustainable Sources of Water Computer science research can help with the complicated problems of finding, tracking, and monitoring the sources of, need for, and sustain­ ble a use of water. Better sensors for measuring, better models for analyses, and better algorithms for optimization are all areas in which CS research can contribute. 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 understand- ing of water resources include the following: • Remote sensing. Because it is not feasible to have sensors every- where, models will continue to be important. Research is still needed on model-oriented science. Sensors, however, can be used to calibrate and fine-tune these models. A multiscale observation network can combine coarse-grained collection with more densely nested sensors deployed at a smaller scale. • Hyperspectral signal processing. A wealth of information can be garnered from the reflected visible and non-visible energy from plants and water. Although much has already been learned from analyzing this information, more can be learned through a better understanding of the reflective spectrum patterns. • Spatial analyses. Geographic Information Systems (GIS) and spatial analysis could be used for novel recognition and classification techniques and to identify the characteristics of an ecosystem. GIS imagery could also be used to detect shifts in an ecosystem. • Heterogeneous data integration. Data combined from embedded sen- sors in rivers and from satellite images could provide a valuable picture of resources. • Workflows. Tools are needed that are adept at scraping data from a variety of sources and combining them with spatial data repositories. The software tools could create input files and capture the history of simula-

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APPENDIX A 115 tions so that researchers need not start from scratch. See the discussion in the section entitled “Scientific Workflows,” below. • Computation. Non-trivial optimization tools are needed in order to search for solutions to sustainability problems and to manage trade-offs. Powerful computing is needed to facilitate the scaling up of systems and to couple these with other contributing factors (economics, subsistence, and so on). Policy Shifts Although the solving of computing challenges will be one bridge to reaching further sustainability goals, challenges in interdisciplinary part- nerships and in turning research into action and policy will need to be addressed as well. Participants noted that the traditional ways of building models tend to be incredibly time-consuming and isolating. Steps include the following: collecting the data, archiving the data, and selecting an individual (typically a doctoral student) to learn the model and then to deploy the model. A result of such an endeavor tends to be that several years later, only one person knows how to use the model effectively. Some progress might be achieved in this way, but to have a larger impact, computing support involving large data sets and complicated workflows will be needed. But such progress can only take place as far as unique partnerships across disciplines will push it. Participants noted that there tends to be a limited connection between the CS researchers and domain practitioners. More and better communication between the field and the laboratory could inform more useful research. Better partnerships with computing and software experts could move research toward higher-impact results more quickly. As noted throughout the workshop, science and engineering are becoming increasingly depen- dent on software development. Fostering close collaboration between software experts and domain scientists is likely to be more effective than forcing domain scientists to learn advanced software engineering. In addition to the computing research opportunities discussed above, participants urged that shifts be made in how research is translated into policy and action. More bridges need to be built between computer sci- entists 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. This change would require collabo- ration and coordination at the research and development (R&D) level and the intervention of the research supply chain. With fewer and fewer i ­ndustry-managed research labs, participants suggested that there has been a reduction in the integration of research and consumer products

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116 COMPUTING RESEARCH FOR SUSTAINABILITY (of the sort that used to exist at Bell Labs) and that collaboration tends not to happen as smoothly. This lack of collaboration may prevent new technologies that would improve sustainability from reaching consumers. Furthermore, planning and design are frequently done by economists, urban planners, and other decision makers, not by domain scientists or sustainability experts. Participants noted that these domain scientists need to be part of the process in order to provide feedback and more timely data, and they urged that academics more actively engage with policy makers. SESSION 2: UNDERSTANDING, TRACKING, AND MANAGING UNCERTAINTY THROUGHOUT THE SCIENCE-TO-POLICY PIPELINE When scientific information is provided to decision makers by the scientific community, explicit representation of uncertainty is rare. The loss of uncertainty information along the science-to-policy pipeline begins with the initial measurements, which may be recorded into databases just as numbers and without any additional information on how the data were captured or intercepted. From such a data set one might produce a predictive map, and any uncertainty that was captured may then be lost by means of an optimization process. Workshop participants noted that outputs from predictive and simulation models are often treated as exact or overly precise and accurate during policy making. In the end, without careful consideration of uncertainty, policy and decision mechanisms can- not be expected to achieve results. 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? • What methods are suitable for explicitly representing uncertainty? • Is the technological state of the art sufficient to model the many different flavors of uncertainty present in large-scale sustainability prob- lems? If not, what characterizes the types of uncertainty that are insuf- ficiently modeled? • What methods are suitable for assessing uncertainty in each stage of the pipeline? What shortcomings need to be addressed?

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APPENDIX A 117 • Is the state of the art in human factors, interfaces, and computer- supported cooperative work sufficient to support the large-scale systems, models, and data sets that are necessary to tackle large-scale sustainability problems? If not, what needs are unmet? • What are the appropriate techniques for working with uncertain data in data fusion, data assimilation, predictive modeling, simulation modeling, and policy optimization? • How can explicit uncertainty representations be integrated into scientific workflow tools? • 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, dis- cussed 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. Assessing Uncertainty in Climate Models Assessment and understanding of climate change and its impacts are critical to meeting many sustainability challenges. Scientists use a variety of techniques, including a variety of climate models, to assess and understand climate change. The potentially high impact of climate change means that policy makers are faced with hard choices, including but not limited to the reduction of emissions, adaption to climate change, and/ or geoengineering that might help mitigate the effects of climate change. 6 Participants discussed the role of uncertainty in the development and understanding of climate models. Scientists working on the problem of climate prediction must also address uncertainty. This could be done using a workflow plan that captures uncertainty information at each stage of the climate-prediction process. Within each stage, there are data, a model, predictions, assessment of likely impacts, and decision making. At each point there are sources of uncertainty that have to propagate 6National Research Council, America’s Climate Choices, Washington, D.C.: The National Academies Press (2011).

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138 COMPUTING RESEARCH FOR SUSTAINABILITY • How can large-scale science addressing real-world problems be made credible, if reproducibility is not possible? • What lessons can be applied from the transformation of the Inter- net into a critical infrastructure that must avoid ossification? • What is the appropriate mix of empiricism, innovation, and appli- cation 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. The interconnected nature of people’s basic resource needs, such as water, energy, and transportation, and the economic arrangements among these resources create a very complex problem. However, these interactions also mean that the energy challenge can serve as a useful proxy for sustainability challenges related to other limited resources. The primary function of the electric grid is to deliver high-quality, low-cost power to millions of customers who are geographically distrib- uted over thousands of miles. The fact that consumers have been able to make use of the grid without needing much knowledge about their own consumption patterns, or about where the power is coming from, has contributed to rapid economic and industrial growth. People have been able to use a comparatively inexpensive resource—energy created mostly through the burning of fossil fuels—essentially indiscriminately to expand the production of products that spur the economy. Additionally, enabling a usage model in which consumers could remain ignorant of their own consumption patterns meant that the grid has been tasked with delivering a high-quality commodity at extremely low cost. Moreover, the expectation has been that power would be delivered immediately as needed. The power grid is expected to meet these goals with minimal forecasting or anticipation of that need, except at very coarse granularity, and without inventory storage along the energy supply chain. The current energy model is increasingly complex, with numerous sources of energy, a variety of stakeholders and consumers, and a not insignificant fraction lost during transport. A pressing sustainability chal- lenge revolves around these questions: How can energy use be reduced, and can it be done without significant economic hardship? The following question was discussed: Where and how can computer science fit into this picture? Figure A.4 shows the percentage of energy use in the United States by type. Each of these types represents an opportunity for reduction in

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APPENDIX A 139 FIGURE A.4  Energy consumption in the United States, by type of use. SOURCE: Lawrence Berkeley National Laboratory. demand. Commercial light use and residential heating make up the bulk of their respective building types, but several other smaller items make up the rest of the energy usage. 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 con- tribute to sustainability, participants reflected on the history of IT suc- cesses and on whether those successes might offer important lessons. The enormous payoffs from IT R&D investment have been investigated by several studies of the National Research Council’s Computer Science and Telecommunications Board, including Evolving the High Performance Com- puting and Communications Initiative to Support the Nation’s Infrastructure (1995); Funding a Revolution: Government Support for Computing Research (1999); Making IT Better: Expanding Information Technology Research to Meet

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140 COMPUTING RESEARCH FOR SUSTAINABILITY Society’s Needs (2000); and Innovation in Information Technology (2003).25 These reports have shown how research partnerships between the federal government and industry ultimately led to the creation of many well- known multibillion-dollar industries. These results suggest the potential sustainability payoffs from the right investments in IT. Many of the advancements presented in the CSTB reports, such as those in processors or networking, required significant financial invest- ment from both industry and government. The software industry spends approximately 13.5 percent of revenues on R&D, the health care industry spends about the same, and the computer hardware industry spends about half of that.26 By contrast, R&D spending by the electric utility sector is about 0.1 percent of revenues, perhaps due to the fact that the sector has been very stable, with little innovation or push for innovation, a context that seems to be changing rapidly.27 Sustainability is a large, broad-ranging problem, and apportioning limited research dollars to effective ends is a difficult challenge. One con- sequence of this low level of support and the resulting small number of technical researchers at utility companies is that opportunities for partner- ship between academic researchers and utility companies are rare. Government funding is also limited. 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 consump- tion were to be decreased. Misalignment of Incentives for More Sustainable Generation and Use The energy-utility market, as described earlier, has evolved to pro- vide a critical resource, at low price, with supply almost instantaneously 25National Research Council, Evolving the High Performance Computing and Communications Initiative to Support the Nation’s Infrastructure, Washington, D.C.: National Academy Press (1995); National Research Council, Funding a Revolution: Government Support for Computing Research, Washington, D.C.: National Academy Press (1999); National Research Council, Making IT Better: Expanding Information Technology Research to Meet Society’s Needs, Washing- ton, D.C.: National Academy Press (2000); National Research Council, Innovation in Informa- tion Technology, Washington, D.C.: The National Academies Press (2003). 26Jill Jusko, R&D Spending: By the Numbers. Industryweek.com. January 2010. Available at http://www.industryweek.com/articles/rd_spending_by_the_numbers_17988.aspx. 27 Jusko, R&D Spending, 2010, available at http://www.industryweek.com/articles/rd_ spending_by_the_numbers_17988.aspx. 28Department of Energy, “Obama Administration Launches $130 Million Building En- ergy Efficiency Effort,” February 12, 2010, available at http://energy.gov/articles/obama- administration-launches-130-million-building-energy-efficiency-effort.

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APPENDIX A 141 matched to demand. Although historically it required considerable inno- vation and tremendous capital investment to meet these constraints, there are additional market impediments to creating a more sustainable system. 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. Participants observed that horizontal market stratification would help drive efficient markets. This limited-competition system means that the utility industry is not particularly motivated to shift technologies, which may drive up the cost of production in the short term. The question again is where the invest- ment to drive new technologies is going to come from. While the utility companies have little incentive to encourage reduc- tions in energy use, consumers themselves have undervalued energy. As noted earlier, consumers have become accustomed to inexpensive power and also have little understanding of how power is produced and of the resulting environmental damage. Consumers have even less knowledge or easy insight into the energy costs of producing and transporting foods and goods. The energy cost, including the accompanying externalities such as environmental and social damage, is not easily reflected in the price of goods. If these costs were reflected directly in the price, more energy-efficient choices might be made. Infrastructural and Organizational Impediments Impediments to making progress on sustainability in addition to those discussed above include infrastructural and organizational realities. The scale of the sustainability problem is immense, and the infrastructure systems that bear on sustainability—such as energy, water, and food distribution—are just as massive. In addition, diversity of use within the system adds a level of complexity. The use and design of each building site and the water distribution and transportation system of each city have unique characteristics that make a one-size-fits-most solution impractical. Furthermore, the traditional production cycle does not apply; infrastruc- tures are not projects that are developed, improved, and shipped; they are built once. Cities are developed over a span of 100 years or more, with refinements, changes, and “debugging” taking place little by little. Once these systems are rolled out, even if they do not function as well as they could, they become, in effect, stranded assets. The market structure also creates impediments to better technologi- cal change. The market is highly fragmented; energy sources vary, and

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142 COMPUTING RESEARCH FOR SUSTAINABILITY energy use is even more dispersed. Each industry that participates in the energy market has its own unique needs, regulatory requirements, and certification programs. Individual industries and companies cre- ate their own technology standards. Unique industry and corporate technology standards also make one-size-fits-most solutions impracti- cal. Efforts to deploy, say, monitoring and data-collection tools in these sorts of environments are challenged. Equipment used for monitoring the use of each resource system—energy, water, food—within cities becomes difficult to build and deploy. Additionally, these monitoring devices, if built in to the initial infrastructure, need to be able to col- lect a wide variety of data and be sturdy enough to function over long periods of time. Research Impediments The critical nature of the sustainability problem and energy crisis combined with their scale and complexity often means that researchers are dedicating entire careers working to address pieces of the problem. This scale and complexity mean that choosing avenues of investigation is a high-risk proposition. If a path that a researcher follows turns out to be incorrect or a dead end, the mistake can be career ending. Furthermore, these sustainability and energy problems are inherently multidisciplinary, which adds another barrier to academic work often confined to single disciplines. In many subfields of computer science, the ultimate goals can be defined reasonably clearly, even if the description of the goal is as simple as: Make computers faster. Well-defined goals also imply a clear definition of success. While there are some goals to work toward in addressing the sustainability problem, such as decreasing the levels of greenhouse gases in the atmosphere, they tend to be less well defined (should the focus be on lowering energy use or on the use of more sustainable energy sources?) and have less clear benchmarks for success. Potential Computer Science Contributions In the fourth session of the workshop, participants brainstormed about potential further contributions of computer science to sustainabil- ity. Computer science is well positioned to provide technical options that could help address some sustainability challenges. Additionally, the distinctive culture, methodologies, and approaches of computer science may shed new light on methodologies, processes, and concepts that could be useful in sustainability. Speakers discussed several such cultural attri- butes, including the following:

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APPENDIX A 143 • Culture of innovation. Computer scientists are used to developing and deploying new tools almost constantly and to doing these things quickly. Participants argued that this flexible, catch-all approach allows for broader ideas and more creativity. • Large-scale systems approach. Computer scientists have experience building big things, such as massive integrated circuits, which have tens of millions of design points that need to be correct when built, and soft- ware artifacts that today measure in the millions of lines of code. Com- puter scientists also understand system approaches. • Understanding of open-information systems. Computer scientists tend to understand the value of open systems and are often forced to engage with demands for system-level considerations such as compatibility and interoperability. Distributed grid management, ecosystems understand- ing, 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. 29 • Business transformation, often with efficiency as a goal. As new tech- nologies have become available, the computer science industry has trans- formed itself several times. For example, participants noted that data centers are drastically different now than they were just 2 years ago. This change has been driven partly by efficiency concerns. Furthermore, com- puter science has been fundamental in transforming other industries, for example, car ownership, media consumption, and banking, in interest- ing ways. Advances in smartphones, the Global Positioning System, and human-computer interaction have contributed to the success of short- term car-use services, such as Zipcar; advances in telecommunications networks and file compression have made Internet video streaming a viable alternative to the video store; and computer and information secu- rity have encouraged confidence in online banking. • Educating in a dynamic environment. Because sustainability efforts are complex, multidisciplinary problems, universities will need new ways to teach scientists and engineers to resolve these problems. Computer sci- ence has historically adapted to changes in curriculum and changes in the overall technological environment by shifting teaching techniques very 29The Internet is an example in which computer science has incorporated open-informa- tion systems, shared standards, and a complex understanding of intellectual property. Al- though there have been questions and debates about appropriate infrastructure, standards, and intellectual property, especially as more of the Internet has been commercialized, there is still copious knowledge that can be gleaned from the computer science community on building interdisciplinary, complex systems.

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144 COMPUTING RESEARCH FOR SUSTAINABILITY rapidly. These educational tools, developed within the computer science discipline, can help develop the next wave of scientists and engineers. Wrap-Up Discussion This session resulted in a wide-ranging discussion from the partici- pants at the workshop. Several key points raised are outlined below: • Within the information technology industry, significant innovation has been accomplished at small businesses or start-ups, which then are often acquired by large corporations. This suggests that small amounts of money could fund highly innovative projects in sustainability, given the proper organizational structure and incentive. • Although sustainability can be viewed in many ways as a techni- cal problem, it will not be solved through technological solutions alone. Some people conjecture that in addition to major scientific and technical breakthroughs to meet sustainability challenges, large-scale social change will be needed, perhaps even on the scale of the U.S. civil rights move- ment. 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. Computational techniques and technologies can help by providing ways to collect, aggregate, distribute, and analyze data, as well as techniques for communication and coordination as appropriate. 30 • There are trade-offs in discussing solutions. For example, raising temperatures in server rooms may reduce cooling loads but lead to higher failure rates. These trade-offs and failure rates have to be fully understood so that the best trade-offs can be made. • Domain scientists (such as ecologists, transportation specialists, civil and power engineers) need to share information and knowledge with people doing innovation, including computer scientists. The first step for computer science might simply be finding a better way to present these data, which would help policy makers. Decision makers need to under- stand the data more clearly before they can form policy. 30An example was given of a case in which a number of local coffee shops were interested in purchasing biodegradable products. Today, biodegradable cups are more expensive and are only affordable if purchased in very large quantities; IT can link companies willing to purchase and share large shipments. Also, not all biodegradable cups are biodegradable to the same extent, an information gap that could be solved with more usable data. However, computer scientists typically have little knowledge about the chemical makeup of products, and so there is also a need for coordination across multiple disciplines and industries.

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APPENDIX A 145 WORKSHOP AGENDA May 26, 2010 Washington, D.C. 8:30-8:35 a.m. Welcome Deborah L. Estrin, University of California, Los Angeles Chair, Committee on Computing Research for Envi- ronmental and Societal Sustainability 8:35-10:45 a.m.  Expanding Science and Engineering Session 1: with Novel CS/IT Methods: “The Need to Turn Numbers into Knowledge” Committee respondent: Daniel Kammen, Univer- sity of California, Berkeley What are some example areas of efforts in sustainability and related research where the interface of disciplinary and interdisciplinary research with new methods in computer and information science can generate new innovations and knowledge? One example is the smart grid, which pro- vides a physical and information technology medium where new levels of efficient and clean energy and information management are possible, and where new levels of data security are needed. Discussion topics range from grid management to the introduction of smart management and charging systems for low-carbon electric vehicles. 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 sys- tems, 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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146 COMPUTING RESEARCH FOR SUSTAINABILITY 11:00 a.m.-1:00 p.m. Session 2: Understanding, Tracking, and Managing Uncertainty Throughout the Science-to-Policy Pipeline Committee respondent: Thomas Dietterich, Oregon State University Explicit representation of uncertainty is rare in the science-to-policy pipeline. Data products resulting from fusing information from multiple instruments are often treated as exact when input to models. Outputs from predictive and simulation models are often treated as exact when input to policy making. Policy optimization for management (e.g., reserve design, fishing quotas, habitat conservation plans) often is not robust to uncertainty in the problem formulation or the objectives. Uncertainty about future decision making and imperfect implementation of policies injects additional uncertainty into planning for the future. • What are the sources of uncertainty that should be explicitly captured? • What methods are suitable for explicitly representing uncertainty? • Is the technological state of the art sufficient to model the many different flavors of uncertainty present in large-scale sustainability prob- lems? If not, what characterizes the types of uncertainty that are insuf- ficiently modeled? • What methods are suitable for assessing uncertainty in each stage of the pipeline? What shortcomings need to be addressed? • Is the state of the art in human factors, interfaces, and CSCW (com- puter-supported cooperative work) sufficient to support the large-scale systems, models, and data sets that are necessary to tackle large-scale sustainability problems? If not, what needs are unmet? • What are the appropriate techniques for working with uncertain data in data fusion, data assimilation, predictive modeling, simulation modeling, and policy optimization? • Is a pipeline architecture sufficient, or do we need a fully coupled architecture in which policy questions can reach all the way back to guide data collection and data fusion? • How can explicit uncertainty representations be integrated into scientific workflow tools? • Are there alternatives to explicit uncertainty representations that can improve the robustness of management policies to all of these sources of uncertainty? Peter Bajcsy, National Institute of Standards and Technology: “Instruments and Scientific Workflows”

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APPENDIX A 147 Chris Forest, Pennsylvania State University: “Assessing Uncertainty in Climate Models” David Brown, Duke University: “Robust Optimization under Uncertainty” John Doyle, California Institute of Technology: “Theory and Methodology of Robust-yet-Fragile Systems Analysis” 1:30-3:00 p.m. Session 3: Creating Institutional and Personal Change with Humans in the Loop Committee respondent: Alan Borning, University of Washington Achieving sustainability objectives demands behavioral changes at the institutional and individual levels. In designing and developing smarter systems, an important question is how to embed interfaces that work. The human-system interaction literature is replete with counterexamples and numerous failed cognitive models, serving as cautionary tales. Compli- cating matters, human-system interaction issues arise both with regard to individuals in homes and offices and for administrators of larger systems or facilities. Further, interactions occur at different scales—on the one hand in a day-to-day time frame for users and on the other in ways that allow incorporation of feedback from the system either to the system itself or to decision makers thinking about larger-scale resource management considerations, for example. • How can data and information be presented at the appropriate granularity and timescale to be most effective? What system, application, and user factors bear on the human-system interaction design choices? • Describe the potential impacts of the various contexts and trade- off decisions that might need to be made, including the impact of context (e.g., stakeholders, and so on); the impact of large versus small groups versus individuals; the impact of income; the impact of use by or for cities versus businesses versus individuals; the role of middleware, the supply chain, and so on. • How do human factors affecting energy use drive the use and design of technology? How can this be accounted for? When are power, networking, products, and so on really needed? Discuss human choice and its impact on consumption, disposal, reuse, and so on. 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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148 COMPUTING RESEARCH FOR SUSTAINABILITY 3:15-4:00 p.m. Session 4: Overcoming Obstacles to Scientific Discovery and Translating Science to Practice Committee respondent: David Culler, University of California, Berkeley • What are the motivations for and impediments to applying inno- vative information technologies to sustainability challenges, and how do they differ by domain? • How can large-scale science addressing real-world problems be made credible, if reproducibility is not possible? • What lessons can be applied from the transformation of the Inter- net into a critical infrastructure that must avoid ossification? • What is the appropriate mix of empiricism, innovation, and appli- cation for computer science to have an impact in the area of environmen- tal sustainability?   David Douglas, National Ecological Observatory Network: “The Role of CS in Open, Sustainability Science” 4:00-5:00 p.m. Capstone Session and Plenary Discussion Deborah L. Estrin, Committee Chair Randal Bryant, Carnegie Mellon University