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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
