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2
Elements of a Computer Science
Research Agenda for Sustainability
The discussion of sustainability challenges in Chapter 1 shows that
there are numerous opportunities for information technology (IT) to have
an impact on these global challenges. A chief goal of computer science
(CS) in sustainability can be viewed as that of informing, supporting,
facilitating, and sometimes automating decision making—decision mak-
ing which leads to actions that will have significant impacts on achieving
sustainability objectives. The committee uses the term “decision making”
in a broad sense—encompassing individual behaviors, organizational
activities, and policy making. Informed decisions and their associated
actions are at the root of all of these activities.
A key to enabling information-driven decision making is to estab-
lish models and feed them with measurement data. Various algorithmic
approaches, such as optimization or triggers, can be used to support
and automate decisions and to drive action. Sensing—that is, taking
and collecting measurements—is a core component of this approach. In
many cases, models are established on the basis of previous work in the
various natural sciences. However, in many cases such models have yet
to be developed, or existing models are insufficient to support decision
making and need to be refined. To discover models, multiple dimensions
of data need to be analyzed, either for the testing of a hypothesis or the
establishing of a hypothesis through the identification of relationships
among various dimensions of measured data. Data-analysis and data-
mining tools—some existing and some to be developed—can assist with
this task.
51
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52 COMPUTING RESEARCH FOR SUSTAINABILITY
Once a model is established, “what-if” scenarios can be simulated,
evaluated, and used as input for decision making. Modeling and simula-
tion tools vary widely, from spreadsheets to highly sophisticated model-
ing environments. When a model reaches a certain maturity and trust
level, algorithms, such as optimizations or triggers, can be deployed to
automate the decision making if automation is appropriate (for example,
in terms of actuation). Alternatively, information can be distilled and
presented in visual, interactive, or otherwise usable ways so that other
agents—individuals, organizations and businesses, and policy mak-
ers and governments—can deliberate, coordinate, and ultimately make
appropriate, better-optimized choices and, ultimately, actions.
All of the steps described above must be done in an iterative fashion.
Given that most sustainability challenges involve complex, interacting
systems of systems undergoing constant change, all aspects of sens-
ing, modeling, and action need to be refined, revised, or transformed
as new information and deeper understandings are gained. A strong
approach is to deploy technology in the field using reasonably well
understood techniques to explore the space and to map where there are
gaps needing work. Existing data and models then help provide context
for developing qualitatively new techniques and technologies for even
better solutions.
FINDING: Enabling and informing actions and decision making by
both machines and humans are key components of what CS and IT
contribute to sustainability objectives, and they demand advances
in a number of topics related to human-computer interaction. Such
topics include the presentation of complex and uncertain informa-
tion in useful, actionable ways; the improvement of interfaces for
interacting with very complex systems; and ongoing advances in
understanding how such systems interact with individuals, orga-
nizations, and existing practices.
Many aspects of computer science and computer science research
are relevant to these challenges. In this chapter, the committee describes
four broad research areas, listed below, that can be viewed as organizing
themes for research programs and that have the potential for significant
positive impact on sustainability. The list is not prioritized. Efforts in all
of the areas will be needed, often in tandem.
• Measurement and instrumentation;
• Information-intensive systems;
• Analysis, modeling, simulation, and optimization; and
• Human-centered systems.
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 53
For each area, examples of research problems focused on sustain-
ability opportunities are given. The discussions do not provide a com-
prehensive list of problems to be solved, but do provide exemplars of the
type of work that both advances computer science and has the potential
to advance sustainability objectives significantly. In examining oppor-
tunities for research in CS and sustainability, questions that one should
attempt to answer include these: What is the potential impact for sustain-
ability? What is the level of CS innovation needed to make meaningful
progress?
As discussed in Chapter 1, complete solutions to global sustainability
challenges will require deep economic, political, and cultural changes.
With regard to those changes, the potential role for CS and IT research
discussed in this chapter is often indirect, but it is still important. For
example, CS research could focus on innovative ways for citizens to delib-
erate over and to engage with government and with one another about
these issues, with the deliberations closely grounded in data and scientific
theory. For some critical sustainability challenges, such as the anticipated
effects of global population growth, the potential CS research contribution
is almost entirely of this indirect character. For instance, there is potential
for using the results of modeling and visualization research toward the
aim of improved education and better understanding of population and
related issues. In addition, advances in IT in the areas of remote sensing,
network connectivity services, adaptive architectures, and approaches for
enhanced health diagnosis and care delivery—especially in rural areas—
also have a bearing on population concerns. Other contributions from CS
and IT research toward meeting such challenges could be aimed at devel-
oping tools to support thoughtful deliberation, with particular emphasis
on encompassing widely differing views and perspectives.
The research areas described in this chapter correspond well with
the broader topics of measurement, data mining, modeling, control,
and human-computer interaction, which are, of course, well-established
research areas in computer science. This overlap with established research
areas has positive implications—in particular, the fact that research
communities are already established making it unnecessary to develop
entirely new areas of investigation. At the same time, the committee
believes that there is real opportunity in these areas for significant impacts
on global sustainability challenges. Finding a way to achieve such impacts
effectively may require new approaches to these problems and almost
certainly new ways of conducting research.
In terms of a broad research program, an important question is how
to structure a portfolio that spans a range of fundamental questions, pilot
efforts, and deployed technologies while maintaining focus on sustain-
ability objectives. For any given research area in the sustainability space,
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54 COMPUTING RESEARCH FOR SUSTAINABILITY
efforts can have an impact in a spectrum of ways. First, one can explore
the immediate applicability of known techniques: What things do we
know how to do already with computational techniques and tools, and
how can we immediately apply them to a given sustainability challenge?
Second, one can seek opportunities to apply known techniques in inno-
vative ways: Where are the opportunities in which the straightorward f
application of a known technique will not work but where it seems prom-
ising to transform or translate a known technique into the domain of a
particular sustainability challenge? This process tends to transform the
techniques themselves into new forms. Finally, one can search for the
areas in which innovation and the development of fundamentally new
computer science techniques, tools, and methodologies are needed to
meet sustainability challenges. While endorsing approaches across this
spectrum, the committee urges emphasis on solutions that have the poten-
tial for significant impacts and urges the avoidance of simply developing
or improving technology for its own sake.
The advancing of sustainability objectives is central to the research
agenda outlined in this report. As in any solution-oriented research space,
there is a tension between solving a substantive domain problem, perhaps
creating tools, techniques, and methods that are particularly germane
to the domain, and tackling generalized problems, perhaps motivated
by the domain, for which solutions advance the broader field. (Chap-
ter 3 discusses this challenge in more detail and provides the commit-
tee’s recommendations on structuring research programs and developing
research communities in ways that constructively address these issues.)
When focusing on the challenges presented in a particular domain, it is
often essential that the details are right in order for the work to have mean-
ingful impact. For the work to have broader impact, it must be possible
to transcend the details of a particular problem and setting. Much of the
power in computer science derives from the development of appropriate
abstractions that capture essential characteristics, hide unnecessary detail,
and permit solutions to subproblems to be composed into solutions to
larger problems. A focus on getting the abstraction right for large impact,
appropriability, and generalizability is important. Simultaneously, it is
important to characterize aspects of the solution that are not generalizable.
FINDING: Although current technologies can and should be put
to immediate use, CS research and IT innovation will be critical to
meeting sustainability challenges. Effectively realizing the poten-
tial of CS to address sustainability challenges will require sus-
tained and appropriately structured and tailored investments in
CS research.
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 55
PRINCIPLE: A CS research agenda to address sustainability should
incorporate sustained effort in measurement and instrumentation;
information-intensive systems; analysis, modeling, simulation, and
optimization; and human-centered systems.
MEASUREMENT AND INSTRUMENTATION
Historically, sensors, meters, gauges, and instruments have been
deployed and used within the vertically integrated context of a single
task or system. For example, a zone thermostat triggers the inflow of cold
or hot air into specific rooms when the measured air temperature devi-
ates from the target set point by an amount in excess of the guard band;
the manifold pressure sensor in a car dictates the engine ignition timing
adjustment; the household electric meter is the basis for the monthly
utility bill; water temperature, salinity, and turbidity sensors are placed
at particular junctures in a river to determine the effects of mixing and
runoff; and so on. Examples of specific scenarios are innumerable and
incredibly diverse, but they have in common the following: the selection
of the measurement device, its placement and role in the encompassing
system or process, and the interpretation of the readings it produces are
all determined a priori, at design time, and the resulting system is essen-
tially closed—sensor readings are not used outside the system.1
This situation has changed dramatically over the past couple of
decades owing to the following key factors:
• Embedded computing. Until the 1990s, the electronics associated with
the analog-to-digital conversion, the rescaling to engineering units, and
the associated storage and the data processing dwarfed the size and cost
of the transducer used to convert the physical phenomenon to an electri-
cal signal. Consequently, these electronics were shared resources wired to
remote sensors. Over the past 20 years, digital electronics have shrunk to a
small fraction of their former size and cost, have been integrated directly
into the sensor or actuator, and have expanded in function to include
quite general processing, storage, and communication capabilities. The
1In settings in which the transducer is physically and logically distinct from the enclos-
ing system, typified by the Highway Addressable Remote Transducer (HART) for process
control and Building Automation and Control Networks (BACnet) for building automation,
readings are obtained over a standardized protocol, but their interpretation remains entirely
determined by the context, placement, and role of the device in the larger process. The use
of the information produced by the physical measurement, and hence its semantics, are
contained within the enclosing system.
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56 COMPUTING RESEARCH FOR SUSTAINABILITY
configurable, self-contained nature of modern instrumentation reduces
the costs of deployment and enables broader use.
• Information-rich operation. The primary control loop of opera-
tional processes (typically represented in manufacturing as plant-sensor-
controller-actuator-plant) is usually augmented with substantial second-
ary instrumentation to permit optimization. For example, in refinery
process control, such additional instrumentation streams help to tune
controllers to increase yield or reduce harmful by-products. In semicon-
ductor manufacturing, they are employed in conjunction with small-
scale process perturbation and large-scale statistical analysis in order to
shorten the learning curve and reach a final configuration more quickly.
In environmental conditioning for buildings, multiple sensing points are
aggregated into zone controllers. Automotive instruments are fused to
present real-time mileage information to the driver.
• Cross-system integration. Measurements designed for one sys-
tem are increasingly being exploited to improve the quality or perfor-
mance of others. For example, light and motion sensors are installed to
modulate the amount of artificially supplied lighting in many “green
buildings.” But those motion detectors are then also available to serve
as occupancy indicators in sophisticated heating, ventilation, and air
conditioning (HVAC) controls. Rather than simply isolating indoor cli-
mate from external factors, modern design practice may seek to exploit
passive ventilation, heating, and cooling; to do so requires the instru-
mentation of building configuration (such as open and closed window
and door states) and of external and internal environmental proper-
ties (temperature, humidity, wind speed, etc.). All of these sources of
information may also be exploited for longitudinal analysis, to drive
recommissioning, retrofitting, and refining operations. Interval util-
ity meter readings are used not just for time-of-use pricing but also to
guide energy-efficiency measures. Traffic measurements and content-
condition instrumentation are applied to optimize logistics operations.
The factors described above have changed the role of instrumentation
and measurement from a subsidiary element of the system design process
to an integrative, largely independent process of design and provision-
ing of physical information services. For many sustainability challenges,
methodologies are needed that can start with an initial model that is based
on modest amounts of data collected during the design process; those
methodologies would then include the development of an incremental
plan for deploying sensors that progressively improves the model and
exploits the improvements to achieve the goals of the system. In many
sustainability applications, such as climate modeling and building mod-
eling, the most effective approach may involve combining mechanistic
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 57
modeling with data-driven modeling. In these applications, mechanistic
models can capture (approximately) the main behaviors of the system,
which can then be refined by data-driven modeling. Classically, models
may be developed from first principles based on the behavior laws of the
system of interest, given sufficiently complete knowledge of the design
and implementation of the system. Such approaches are reflected not just
in the instrumentation plan, but in simulation tools and analysis tech-
niques. However, for most aspects of sustainability, the system may not be
rigorously defined or carefully engineered to operate under a narrow set
of well-defined behaviors. Examples include watersheds, forests, fisher-
ies, transportation networks, power networks, and cities. New technical
opportunities for addressing the challenges presented by such systems as
well as opportunities in instrumentation and measurement are emerging,
several of which are discussed below.
Coping with Self-Defining Physical Information
Rather than simply drawing its semantics and interpretation from
its embedding in a particular system, each physical information service
could be used for a variety of purposes outside the context of a particu-
lar system and hence should have an unambiguous meaning. The most
basic part of this problem is the conversion from readings to physical
units and the associated calibration coefficients and correction function.2
The much more significant part of the problem is capturing the context of
the observation that determines its meaning.3 For example, in a building
environment, supply air, return air, chilled water supply, chilled water
return, outside air, mixing valve inputs, economizer points, zone set point,
guard band, compressor oil, and refrigerated measurement all have physi-
cal units of temperature, but these measurements all have completely
2These aspects have been examined and partially solved over the years with electronic
data sheets, such as the IEEE [Institute of Electrical and Electronics Engineers] P1451 family,
ISA [Instrumentation Systems and Automation Society] 104 Electronic Device Description
Language, or Open Geospatial Consortium Sensor Model Language (SensorML). However,
many variations exist within distinct industrial segments and scientific disciplines; the stan-
dards tend to be very complex, and adoption is far from universal.
3One example of this problem is a stream-water temperature sensor that is normally sub-
merged but under low-water conditions becomes an air-temperature sensor instead. How
should this contextual change in semantics be captured? One possibility might be a subse-
quent data-cleaning step that determines in what “mode” the sensor-context combination
was (in this case, perhaps by using a stream-flow sensor or by correlating with a nearby air-
temperature sensor). Another example is a soil-moisture sensor whose accuracy can increase
with time when more is known about the soil composition—the parameterized equations
used by the sensors can be tuned to the soil-type details.
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58 COMPUTING RESEARCH FOR SUSTAINABILITY
different meanings. The same applies to the collection of measurements
across many scientific experiments. Typically, contextual factors are cap-
tured on an ad hoc basis in naming conventions for the sense points, the
presentation screens for operations consoles, or the labels in data-analysis
reports.
The straightforward application of known techniques can be employed
to collect the diverse instrumentation sources and deposit readings into a
database for a specific setting or experiment. Similarly, electronic records
can be made of the contextual information to permit an analysis of the
data. The collection, storage, and query-processing infrastructure can be
made to scale arbitrarily; processes can be run to validate data integrity
and to ensure availability; and visualization tools can be introduced to
guide various stakeholders.
To provide these capabilities in general rather than as a result of a
design and engineering process for each specific domain or setting, how-
ever, requires either significant innovation in the techniques deployed
or the development of new techniques. There are, for instance, well-
developed techniques for defining the meaning, context, and interpre-
tation of information directly affected by human actions, where these
aspects are inherently related to the generation process.4 To cope with
many large-scale sustainability challenges, similar capabilities need to be
developed for physical or non-human-generated information.
Closely related to this definitional problem is the family of problems
related to registration, lookup, classification, and taxonomy, much as for
human-generated information, as one moves from physical documents to
interconnected electronic representations. When an application or system
is to be constructed on the basis of a certain body of physical information,
how is the set of information services discovered? How are they named?
If such information is to be stored and retrieved, how should it be classi-
fied? If physical information is to be accessed through means outside such
classifications, how is it to be searched? Keyword search can potentially
apply to the metadata that capture context, type, and role, but what about
features of the data stream itself?
Today one addresses these problems by implicitly relying on the
enclosing system for which the instrumentation is collected. As physi-
cal information is applied more generally, it becomes necessary to rep-
resent the model of the enclosing system explicitly if it is to be used to
4For example, the inventory of products in a retail outlet is quite diverse, but schemas are
in place to capture the taxonomy of possible items, locations in the supply chain or in the
store, prices, suppliers, and other information. Actions of ordering, shipping, stocking, sell-
ing, and so on cause specific changes to be made in the inventory database.
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 59
give meaning to the physical instrumentation. However, general model
description languages and the like are still in their infancy.
The Design and Capacity Planning of Physical Information Services
Once the physical deployment of the instrumentation capability is
decoupled from the design and implementation of the enclosing system,
many new research questions arise. Each consumer of physical informa-
tion may require that information at different timescales and levels of
resolution. Furthermore, the necessary level of resolution can change
dynamically depending on the purpose of the measurements. In prin-
ciple, one could measure everything at the finest possible resolution, but
this is rarely practical because of limitations in power consumption, local
memory, processing capacity, and network bandwidth. What is needed for
many sustainability-related challenges is a distributed system by which
information needs can be routed to relevant sensors—for the purposes of
this discussion, comparatively high bandwidth sensors are meant—and
those sensors can then modulate their sampling rates and resolution as
necessary.5
Recent advances in compressed sensing (to help conserve bandwidth
and power) and network coding (to take advantage of network topolo-
gies for increasing throughput) add to the complexity of such a distrib-
uted system. One can imagine tools that take as input a collection of
information consumers, a set of available sensors, and an understood
network topology and produce as output a set of sensing and routing
procedures that incorporate compressed sensing and network coding.
However, this perspective assumes that the locations of the sensors and
the network topology are already known. In virtually all practical situ-
ations, determining the number, location, and capabilities of individual
sensors is an important design step. To support these design decisions,
algorithms are needed for sensor placement and sizing. These algorithms
require models of the phenomena being measured and of the information
needs of each consumer. How will such models be provided and in what
representation?
As mentioned above, system architecture has traditionally been orga-
nized as a cycle: plant-sensor-controller-actuator-plant. In this model, sen-
sor readings are centralized and aggregated to produce an estimate of the
5Consider, for example, a thermometer in a freshwater stream. For purposes of hydro-
logical analysis, it might suffice to measure only the daily maximum and minimum tem-
peratures and report them once per week. But suppose that one seeks to detect sudden
temperature changes that might indicate the dumping of industrial wastewater. Then the
thermometer may need to measure and report temperatures at 15-second intervals.
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60 COMPUTING RESEARCH FOR SUSTAINABILITY
state of the plant. The controller then determines the appropriate control
decisions, which are transmitted to the actuators. However, as the “plant”
becomes a large, spatially distributed system (e.g., a city, a power grid, an
ecosystem) and the volume of data becomes overwhelming, it is no longer
feasible to integrate centrally the state estimation and decision making.
A recent International Data Corporation study6 suggests that there will
be more than 35 zettabytes of data stored in 2020. Distributed algorithms
are needed that can push as much computation and decision making
as possible out to the sensors and actuators so that a smaller amount of
data needs to be moved and stored. At the same time, these algorithms
will need to avoid losing the advantages of data integration (reduction of
uncertainty and improved forecasting and decision making).
Finally, tools are needed to support the planning and design process.
These tools need to provide the designer with feedback on such things as
the marginal benefit of additional sensing and additional network links,
the robustness of the design to future information needs, and so forth. In
summary, all aspects of capacity planning present in highly engineered
systems, such as data centers and massive Internet services, arise in the
context of the physical information service infrastructure.
Software Stacks for Physical Infrastructures
Potential solutions to the problems delineated above suggest that
sophisticated model-driven predictive control and integrated cross-system
optimization will become commonplace rather than rare. As discussed in
Chapter 1, on the electric grid today, the independent service operator
attempts to predict future demand and to schedule supply and transmis-
sion resources to meet it, with possibly coarse-grained time-of-use rates
or, in rare cases, critical peak notification to influence the demand shape.
In the future, environmental control systems for buildings may be able
to adapt to the availability of non-dispatchable renewable supplies on
the grid, using the thermal storage inherent in a building to “green” the
electricity blend and ease the demand profile. Distributed generation and
storage may become more common in such a cooperative grid. Various
analysis, forecasting, and planning algorithms may operate over the phys-
ical information and human-generated information associated with the
grid, the building, the retail facility, the manufacturing plant. It becomes
important to ask what the execution environment is for such control algo-
rithms and analytical applications. What are the convenient abstractions
6International Data Corporation, “The 2011 Digital Universe Study: Extracting Value from
Chaos” (June 2011), available at http://www.emc.com/collateral/demos/microsites/emc-
digital-universe-2011/index.htm.
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 61
of physical resources that ease the development of such algorithms, and
how is access to shared-resource protected and managed? In effect, what
is the operating system of the building, of the grid, of the plant, of the
fleet, of the watershed? Today these operating systems are rudimentary
and consist of ad hoc ensembles of mostly proprietary enterprise resource
planning systems, building management systems, databases, communica-
tion structures, operations manuals, and manual procedures. An impor-
tant challenge for computer science research is to develop the systems and
design tools that can support effective and flexible management of these
complex systems.
INFORMATION-INTENSIVE SYSTEMS
Sustainability problems raise many research questions for informa-
tion-intensive systems because of the nature of the data sources and
the sheer amount of data that will be generated.7 Computer science has
applied itself broadly to problems related to discrete forms of human-
generated information, including transaction processing, communica-
tions, design simulation, scheduling, logistics tracking and optimization,
document analysis, financial modeling, and social network structure.
Many of these processes result in vast bodies of information, not just from
explicit data entry but through implicit information collection as goods
and services move through various aspects of the supply chain or as a
result of analyses performed on such underlying data. The proliferation
of mobile computing devices adds not just new quantities of data, but
new kinds of data as well. Some data-intensive processes are extremely
high bandwidth event streams, such as clickstreams from millions of web
users. In addition, computer science is widely applied to discretized forms
of continuous processes, including computational science simulation and
modeling, multimedia, and human-computer interfaces. In both regimes,
substantial data mining, inference, and machine learning are employed
to extract specific insights from a vast body of often low-grade, partially
related information.
All of the techniques described above can and must be applied to
problems associated with sustainability. Nonetheless, several aspects of
sustainability, even in addition to the vast quantities of data that will
7Given the vast amounts of data expected to be generated in the near future, traditional ap-
proaches to managing such amounts of data will not themselves be sustainable. Bandwidth
will become a significant barrier, meaning that approaches to computation (such as moving
computational resources to the data, or computing on data in real time and then discarding
them, or other new techniques), different from simply storing the data and computing on
them when necessary, will need to become more widespread.
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 75
ing to some underlying statistical model. Unfortunately, experience with
ecological modeling and environmental policy suggests that there are
many “unknown unknowns”—phenomena that are unknown to the
modelers and decision makers and therefore not accounted for in the
models.19 One possible safeguard is robust optimization.20 Rather than
treating model parameters as known, this approach assumes instead that
the parameters lie within some uncertainty set and optimizes against
the worst-case realization within these sets. The size of these uncertainty
sets can be varied to measure the loss in the objective function that must
be sustained in order to achieve a given degree of robustness. Robust-
optimization approaches can greatly improve the ability to sustain signifi-
cant departures from conditions in the nominal model. Existing robust-
optimization methods generally assume that the decision model can be
expressed as an optimization problem with a convex structure (e.g., linear
or quadratic programs). Robust optimization is sometimes considered
overly conservative. Convex constraints over multiple uncertainty sets
can be introduced to rule out simultaneous extreme events and reduce
the over-conservatism of first-generation robust-optimization methods.21
An open theoretical question is that of determining the best ways to use
data in optimization problems. In some problems in which there are insuf-
ficient data, the question becomes one of how to properly incorporate
subjective opinion about the data and what the best way is to characterize
uncertainty. Another research challenge is to develop robust-optimization
methods that are applicable to the kinds of complex nonlinear models that
arise in sustainability applications.
Optimal Sequential Decision Making
Most sustainability challenges will not be addressed by a decision
made at a single point in time. Instead, decisions must be made iteratively
over a long time horizon since a system is not sustainable unless it can be
operated indefinitely into the future. For example, in problems involving
natural resource management, every year provides a decision-making
opportunity. In fisheries, the annual allowable catch for each species must
be determined. In forests, the location and method for tree harvesting
19D.F. Doak et al., Understanding and predicting ecological dynamics: Are major surprises
inevitable? Ecology 89(4):952-961 (2008).
20A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust Optimization, Princeton, N.J.: Princ-
eton University Press (2009).
21D. Bertsimas and A. Thiele, Robust and data-driven optimization: Modern decision-
making under uncertainty, INFORMS Tutorials in Operations Research: Models, Methods, and
Applications for Innovative Decision Making, pp. 1-39 (2006).
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76 COMPUTING RESEARCH FOR SUSTAINABILITY
must be specified, as well as other actions such as mechanical thinning to
reduce fire risk. In energy generation and distribution, the location of new
generation facilities and transmission lines must be chosen. In managing
global climate change, the amount of required reduction in greenhouse
gas emissions each year must be determined. The state of the art for solv-
ing sequential decision problems is to formalize them as Markov deci-
sion problems and solve them by means of stochastic dynamic program-
ming. However, an exact solution through these methods is only feasible
for processes whose state space is relatively small (tens of thousands of
states). Recently, approximate dynamic programming methods have been
developed in the fields of machine learning and operations research. 22
These methods typically employ linear function approximation methods
to provide a compact representation of the quantities required for stochas-
tic dynamic programming.
An important aspect of sustainability problems is that they often
involve optimization over time and space. For example, consider the
problem of designing biological reserves to protect threatened and endan-
gered species and ecosystems. Many conflicting factors operate in this
problem. Large, contiguous reserves tend to protect many species and
preserve biodiversity. However, such reserves are also more vulnerable
to spatially autocorrelated threats such as fire, disease, invasive species,
and climate change. The optimal design may thus involve a collection of
smaller reserves that lie along environmental gradients (elevation, pre-
cipitation, etc.). The purchase or preservation of land for reserves costs
money, and so a good design should also minimize cost. Another factor
is that reserves typically cannot be designed and purchased in a single
year. Instead, money becomes available (through government budgets
and private donations) and parcels are offered for sale over a period of
many years. Finally, the scientific understanding and the effectiveness of
previous land purchase decisions can be reassessed each year, and that
should be taken into account when making decisions.
The solution of large spatiotemporal sequential decision problems
such as those described above is far beyond the state of the art. Striking
the right balance between complexity and accuracy, especially in the con-
text of complex networked systems, is critical. New research is needed to
develop methods that can capture the spatial structure of the state each
year and the spatial transitions (e.g., fire, disease) that occur. There are
sustainability problems in which all three of these factors—uncertainty,
robustness, and sequential decision making—combine. For example, in
22W. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality (2nd
Ed.), New York, N.Y.: Wiley (2011).
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 77
reserve design, models of suitable habitat for threatened and endan-
gered species are required. These are typically constructed by means
of machine learning methods and hence are inherently uncertain. This
uncertainty needs to be captured and incorporated into the sequential
decision-making process. Finally, existing stochastic dynamic program-
ming methods are designed to maximize expected utility. These methods
need to be extended in order to apply robust-optimization methods. A
research opportunity is to integrate the training of the machine learning
models—which can itself be formulated as a robust-optimization prob-
lem—with the robust optimization of the sequential decision problem.
This integration would allow the machine learning methods to tailor their
predictive accuracy to those regions of time and space that are of greatest
importance to the optimization process and could lead to large improve-
ments in the quality of the resulting decisions.
Formulating problems in terms of sequential decision making can
sometimes make the problems more tractable. For example, Roe and
Baker23 show that structure inherent in the sensitivity of the climate sys-
tem makes it extremely difficult to reduce the uncertainties in the esti-
mates of global warming. However, by formulating the problem as a
sequential decision-making problem, Allen and Frame24 show that it is
possible to control global warming adaptively without ever precisely
determining the level of climate sensitivity.
HUMAN-CENTERED SYSTEMS
It is critical, for real-world applicability, to situate technology inno-
vation and practice within the context-specific needs of the people ben-
efiting from or otherwise affected by that technology. For example, in
the context of introducing intelligence into the electric grid in order to
increase sustainability, the essential measures and relevant information
are very different when considered from the differing perspectives of the
utility, supplier, and customer. The utility may be interested in introduc-
ing payment schedules that influence customer behavior in a manner that
reduces the need to build plants that run for only a tiny fraction of the
time (to serve just the diminishing tail of the demand curve). Avoiding
such construction does reduce overall GHG emissions, but the primary
goal is to avoid capital investment. Trimming the peak does little to reduce
overall energy use, but it reduces the use of the most costly supplies. A
consumer-centric perspective is likely to focus on overall energy savings
23G.H. Roe and M.B. Baker, Why is climate sensitivity so unpredictable? Science 318
(5850):629-632 (2007)
24M.R. Allen and D.J. Frame, Call off the quest,” Science 318(5850):582-583 (2007).
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78 COMPUTING RESEARCH FOR SUSTAINABILITY
and efficiency measures, not just on critical-peak usage. Thus, greater
emphasis may be placed on visualizing usage, understanding demand,
reducing waste, curbing energy consumption and less important usage,
and (if there is dynamic variable pricing) helping to move easily resched-
uled uses (e.g., water heating) to off-peak times. A grid-centric perspec-
tive, by contrast, may focus on the matching of supply and demand, as
well as on the utilization of the key bottlenecks in the transmission and
distribution infrastructure. All of these stakeholders need to be consid-
ered, and ideally involved, to substantially increase the penetration limit
of non-dispatchable renewable supplies, because of the need to match
consumption to supply. And, all stakeholders have substantial needs
for monitoring usage data, determining causal relationships between
activities and usage, and managing those activities to optimize usage. In
addition to the needs and values of these direct stakeholders in the tech-
nology, the indirect stakeholders should also be considered—that is, those
who are affected by the technology but do not use it. In the smart grid
example, the set of indirect stakeholders is broad indeed, since everyone
is (for example) affected by climate change. The ability to understand
such needs and to guide the development of technology on that basis
constitutes a natural application of techniques developed in the area of
human-computer interaction (HCI).
More generally, a human-centered approach can and should be inte-
grated with each of the topics discussed above. Issues such as human-in-
the-loop training of machine learning systems, the interpretability of model
results, and the possible use (or abuse) of large volumes of sensed data
become particularly salient with a human-centered viewpoint. Indeed,
with the vast quantities of data to be generated and used as described
earlier, privacy becomes a first-order concern. The role of computer sci-
ence in sustainability is predicated on the ability to capture and analyze
data at a scale without precedent. The understanding and mitigating of
privacy implications constitute an area in which fundamental CS research
can play a role—in both formalizing the questions in an appropriate way
(and indeed this is research well underway) and potentially in providing
solutions that can help mitigate the loss of privacy that is, to some extent,
inherent in taking full advantage of the power of information-gathering
at a global scale with high resolution. It is essential that a human-centered
approach be integrated with more traditional security approaches: not
only should the techniques for preserving privacy be technically sound,
but they should also be accessible, understandable, and convincing to the
users of these systems.
Historically, much of the research on sustainability in HCI has focused
on individual change. Perhaps one of the best-recognized examples is eco-
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 79
feedback technology, which leverages persuasive interface techniques 25
and focuses primarily on residential settings. Reduced individual use
can socialize people to the issues at hand, and can, at scale, have a direct
if limited effect on overall energy use.26 However, population growth
alone may outstrip the gains realized by such approaches. In response,
the committee notes the importance of significantly increased attention to
social, institutional, governmental, and policy issues in addition to issues
of individual change. A challenging public policy question, for example,
is how to verify compliance with GHG emissions requirements. Reliably
validated carbon reductions, for instance, are important not just to global
progress; they would be also invaluable for guiding sustainability efforts
at a macro level.27
This report emphasizes opportunities for research, in addition to the
data and privacy challenges mentioned earlier, on human-centered sys-
tems both at the individual level and beyond (at the organizational and
societal levels). Examples of such research areas include visualization and
user-interaction design for comprehensibility, transparency, legitimation,
deliberation, and participation; devices and dashboards for individu-
als and institutions; expanding the understanding of human behaviors,
empowering people to measure, argue for, and change what is happening;
and education. Following are brief discussions of each of these.
Supporting Deliberation, Civic Engagement,
Education, and Community Action
As noted in Chapter 1, moving toward a more sustainable society will
require massive cultural, social, political, and economic changes—and
today’s technologies are deeply intertwined with many of these changes.
Technology can help to support an informed and engaged citizenry. Cur-
rently, civic engagement is uneven at best, and thoughtful public delibera-
tion about major issues is often challenging to accomplish. However, the
ease of information access, the existence of community-based knowledge
25For example, see J. Froehlich, L. Findlater, and J. Landay, The design of eco-feedback
technology, in Proceedings of the 28th International Conference on Human Factors in Computing
Systems, New York, N.Y.: Association for Computing Machinery, pp. 1998-2008 (2010).
26For a provocative essay on this issue, see P. Dourish, Print This Paper, Kill a Tree: Environ-
mental Sustainability as a Research Topic for HCI, LUCI-2009-004, Laboratory for Ubiquitous
Computing and Interaction, University of California, Irvine (2009), and a related article: P.
Dourish, HCI and environmental sustainability: The politics of design and the design of
politics, in Proceedings of the 2010 ACM Conference on Designing Interactive Systems, Aarhus,
Denmark, pp. 1-10 (2010).
27See National Research Council, Verifying Greenhouse Gas Emissions: Methods to Support
International Climate Agreements, Washington, D.C.: The National Academies Press (2010).
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80 COMPUTING RESEARCH FOR SUSTAINABILITY
repositories, and the search and social networking capabilities online
are transforming the manner in which humans learn, make decisions,
and interact. These techniques can be adapted for a research program on
designing, deploying, and testing innovative ways for citizens to deliber-
ate and to engage with government and one another, particularly with
those who may hold very different views in the context of sustainability.
These deliberations should be closely coupled to data (gathered both by
professional scientists and citizen-scientists) and simulation results—affor-
dances should be provided to help ground the discussion in the scientific
evidence. Similarly, online curricula for students in kindergarten through
grade 12 and for adults can explore, for instance, ongoing scientific and
policy discussions related to sustainability; and educational initiatives can
contribute to societal changes needed to meet sustainability goals.
In addition to opportunities with respect to tools for engaged citizens
generally, there are also promising areas of research in helping scientists
provide more effective input into these broader discussions and debates
on sustainability and potential initiatives. The intellectual merit of this
research would center on the issues of how to facilitate large-scale online
deliberation about contentious issues; the broader impacts would be in
making the results of scientific inquiry more widely seen and discussed.
As an example, suppose that there was a network supporting online
deliberation among scientists concerned with sustainability for develop-
ing key points, areas of strong consensus, areas of disagreement, and
supporting evidence. Those deliberations would produce a sustainability
action agenda that could be introduced to the public by means of interest-
ing interactive environments designed to appeal to those of all ages. These
sites could feed information by means of different media outlets (both
traditional and emerging) as well as providing interactive scenarios that
people could use to answer questions and debate solutions. One highlight
of this system would be a series of consensus news stories, perhaps on
a weekly basis. These stories could be based on agenda items created by
scientists and rated by public interest.
A core component of such a public education system could be a
forum for discussing scientific data, for voicing views on which stories
to present and when, and for suggesting how to frame them (delibera-
tive forums for the science community for building consensus positions).
A key research issue here is the development of technologies that help
organize the discussion, both for long-standing participants and for
people who are interested in entering into a long-running discussion but
could use help in understanding it and in making useful contributions.
The forum should include affordances that make it easy and natural to
classify suggestions, pro and con arguments, and so on, to keep this
type of exchange from degenerating into just a free-form discussion
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 81
board. Another important kind of affordance would be hooks for giving
sources for assertions (tools to encourage grounding arguments in the
scientific data).
Another project might be a highly visible forum for exchanges between
groups with quite divergent views in a deliberative setting. Again, the
system should include affordances that make it easy and natural to clas-
sify arguments and perspectives and tools that encourage the grounding
of arguments in the scientific data.
Basic research in educational technology is also crucial to increasing
the relevance and effectiveness of tools for a culturally and economi-
cally diverse population. Arguably, better support for deliberation and
engagement will not be enough. Supporting community action is also
essential. In recent years technology has become more and more salient
as an enabler of successful social change.28 In another example, on a local
scale, citizen sensing of environmental indicators (e.g., pollutants) has
influenced the ability of individuals to advocate for change. As the cost
of sophisticated sensors comes down, one can expect to see more and
more of them employed by end users. A citizenry that engages with and
helps to track this information is important to progress on the issues at
stake, and this engagement leads to increased education and engagement
in addition to increasing the amount of information available in crucial
areas. However, this raises fundamental research problems ranging from
the creation of these sensors to our ability to use the data effectively
despite the inherent uncertainties that arise from its production.
Design for Sustainability
Techniques developed to design for manufacturing, design for mass
customization, and user-centric design can expand on the understanding
of what it means to design for sustainability. Techniques such as ENERGY
STAR ratings for appliances and Leadership in Energy and Environmental
Design (LEED) ratings for buildings have had some success in reorienting
industry providers and consumers alike toward more sustainable prac-
tices. These efforts can be substantially informed by the measurement,
information-collection, and model-development techniques described
earlier, but can also use HCI techniques for appropriation, reuse, and end-
to-end design for technology products. This research can be expanded
to shed light on process, distribution, middleware, and other aspects of
the production and distribution of products. Technological advances can
28T. Hirsch and J. Henry, TXTmob: Text messaging for protest swarms, in Extended Ab-
stracts on Human Factors in Computing Systems, New York, N.Y.: Association for Computing
Machinery, pp. 1455-1458 (2005). DOI: http://doi.acm.org/10.1145/1056808.1056940.
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82 COMPUTING RESEARCH FOR SUSTAINABILITY
also contribute to the tracking, monitoring, and analysis of the source
materials, production processes, distribution, and eventual disposal of
products. This information in turn can help to inform purchase decisions,
provide better accounting, and otherwise improve the sustainability of the
consumer economy.
Human Understanding of Sensing, Modeling, and Simulation
As the availability of sophisticated sensors, information collection,
modeling, and dissemination increase, techniques need to be developed
to provide in meaningful forms rich, highly disaggregate information to
households, small groups, and organizations regarding resource usage
(e.g., for electricity or water consumption). In addition to supporting
improved decision making about energy use at the organizational and
individual levels, this information could provide civil and environmen-
tal engineers with a picture at a new level of detail about how and why
these resources are being consumed, allowing their science and practice
to advance. At the same time, this possibility raises challenging research
questions regarding appropriate amounts of information, how to deal
with the inherent uncertainties in the data, techniques for evaluating
such systems, coupling with other systems on the supply side (e.g., the
smart grid), and important value questions regarding fairness, represen-
tativeness, security, and privacy. Better data can also drive modeling and
simulation, which can help with such activities as predicting important
trends, assessing how well proposed policies would meet objectives,
and optimizing resource use. Modeling climate change is an obvious
example, but there are many others, including a simulation of the evolu-
tion of urban areas, freight transport, and natural environments such as
forests or rivers. However, to be effective and relevant to policy making
and decision making, such modeling work must include careful consid-
eration of how it integrates with deliberation and the political process.
This raises issues of design for transparency, legitimation, appropriability,
and participation.
Tools to Help Organizations and
Individuals Engage in More Sustainable Behaviors
Another area for research concerns tools that make it easier and per-
haps even enjoyable for people to engage in more sustainable behaviors.
Some of the many examples in this area are the providing of real-time pub-
lic transit arrival and route information (particularly on mobile devices),
online ride-share matching, geowikis for bicycling, Zipcar, Freecycle, and
the like. Another class of tools provides eco-feedback: targeted informa-
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 83
tion about resource consumption (perhaps in real time), integrated with
suitable visualization techniques and appropriate persuasive technol-
ogy, for example to show progress toward personal or group goals. This
area is also related to the previous opportunity regarding the use of
information from resource-usage sensing. It is important to recognize
the limits of these technologies: better transit information is great if a
good underlying transit system exists, but it is not so useful without
that. Similarly, eco-feedback regarding energy use can be helpful, but it
does not address the more fundamental, underlying energy challenges in
some situations—such as low-income households in which comparatively
expensive upgrades would be a financial hardship, or homes that contain
inefficient appliances or poor insulation. For such challenges, alternative
solutions would be needed.
Many of the techniques described here are relevant to organizations as
well. For example, a large organization might similarly provide targeted
information about resource consumption, in real time, to show progress
toward goals for different branches of the organization.
Mitigation, Adaptation, and Disaster Response
Even under optimistic climate change scenarios, weather disasters
are likely to increase in number and severity, resulting in both the need
for immediate disaster relief and likely the need to assist large numbers
of refugees (e.g., from low-lying regions).29 Also, unfortunately, human
actions are likely to continue to contribute directly to environmental
disasters such as oil spills. There are research challenges with respect to
developing plans that can be revised rapidly under conditions of great
uncertainty, making use of vast numbers of citizen observations (such as
micro-content posted from disaster areas by individuals), coordinating
supply efforts, and others. One challenge for this line of work is recog-
nizing that there are huge uncertainties about the future and thus also
in developing tools and infrastructure that are flexible, adaptable, and
appropriate.
Using Information from Resource-Usage Sensing
Recent work has opened the possibility of providing rich, highly
disaggregate information to households, small groups, and organizations
regarding resource usage. For example, immediate feedback can now be
29NationalResearch Council, Adapting to the Impacts of Climate Change, Washington, D.C.:
The National Academies Press (2010).
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84 COMPUTING RESEARCH FOR SUSTAINABILITY
provided on electrical energy use at the appliance or individual lighting
circuit level. A number of possibilities arise as a result, including detailed
eco-feedback about usage and tighter coupling with smart grid technol-
ogy on the supply side. Similar feedback is possible for other resources
such as water and natural gas.
This possibility does, however, raise a number of challenging research
questions. For example, what is the appropriate amount of information to
provide to households? Clearly there is the possibility of overwhelming
them with information. How are the inherent uncertainties in the data to
be dealt with? How are such systems to be evaluated? The traditional HCI
evaluation techniques of laboratory studies and small-scale deployments
are inadequate, but massive deployments over long periods are slow and
expensive, implying that one can only try a small number of alternatives
(in tension with the need for rapid prototyping and iteration). How can
these systems be coupled with smart grid technology on the supply side?
For example, the grid could signal to the household that the system was
close to capacity and that lowering energy use for the next hour would be
very helpful (or perhaps would result in a lower bill); or, conversely, the
household could be signaled that this would be an opportunity for some
non-time-critical activity. This arrangement would be a combination of
automated actions, with the scripts under the household’s control, and
explicit actions.
Another set of issues concerns fairness and representativeness. For
example, the majority of households in the United States are low-income
and many households rent, although most work in this area focuses on
relatively affluent homeowners. Can systems and policies be designed
that do not unfairly disadvantage some households, particularly ones
that can least afford additional charges? Another set of challenges con-
cerns security and privacy. Such systems offer the potential for reducing
resource consumption and making better use of resources, but there are
clear security and privacy risks if the system is compromised. Related
to that issue are questions of responsibility and power around available
infrastructure that must be addressed. Not everyone owns a home or pays
for energy use, and the relationships between landlords, residents, laws
(incentives, disincentives, and so on), available services (green contrac-
tors), and other factors influence energy use outcomes and may bear on
the design of technology (for example, in terms of authenticating who has
access to what data).
It is difficult to get good information about the fine-grained use of
energy right now. Buildings are not generally instrumented to produce
these data, yet a true understanding of the forces driving energy use is
impossible without better data. Better information about which appliances
are in use and when they are in use can help in developing a more complete
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ELEMENTS OF A COMPUTER SCIENCE RESEARCH AGENDA 85
understanding of human behavior, and perhaps in identifing interven-
tions that can have an impact on energy use. Even a modest advance such
as analysis based on the segmentation of a building’s energy use among
HVAC, lighting, and plug load could yield useful results. Although this
may seem like a pure sensing problem, the process of deploying sensors,
labeling data, and interpreting the results involves people, and computer
science researchers are at the forefront of some of the innovations in this
area.30 Despite these advances, the problem of labeling data and interpret-
ing the results is one that requires more attention.
CONCLUSION
This chapter provides examples of important technical research areas
and outlines a broad research agenda for computer science and sus-
tainability. Although there are numerous opportunities to apply well-
understood technologies and techniques to sustainability, there are also
hard problems—such as mitigating climate change—for which current
methods offer at-best partial solutions, and rapid innovation is essential
in light of the pressing nature of the challenges. The areas highlighted in
this chapter—measurement and instrumentation; information-intensive
systems; analysis, modeling, and simulation; optimization; and human-
centered systems—are counterparts to well-established research areas in
computer science. This overlap has clear positive implications. However,
finding a way to have a significant impact may require new approaches
to these problems and almost certainly new ways of conducting and
managing research. Chapter 3 explores ways of conducting and manag-
ing research so that computer science research can have an even greater
impact on sustainability challenges.
30For example, Patel and others have developed comparatively lightweight methods to
acquire reasonably fine-grained data in homes; see J. Froehlich, E. Larson, S. Gupta, G. Cohn,
M. Reynolds, and S.N. Patel, Disaggregated end-use energy sensing for the smart grid, IEEE
Pervasive Computing, Special Issue on Smart Energy Systems, January-March (2011).
