A broad and growing literature describes the deep and multidisciplinary nature of the sustainability challenges faced by the United States and the world. Despite the profound technical challenges involved, sustainability is not, at its root, a technical problem, nor will merely technical solutions be sufficient. Instead, deep economic, political, and cultural adjustments will ultimately be required, along with a major, long-term commitment in each sphere to deploy the requisite technical solutions at scale. Nevertheless, technological advances and enablers have a clear role in supporting such change, and information technology (IT)1 is a natural bridge between technical and social solutions because it can offer improved communication and transparency for fostering the necessary economic, political, and cultural adjustments. Moreover, IT is at the heart of nearly every large-scale socioeconomic system—including systems for finance, manufacturing, and the generation and distribution of energy—and so sustainability-focused changes in those systems are inextricably linked with advances in IT. In short, innovation in IT will play a vital role if the nation and the world are to achieve a more sustainable future.
Although the greening of IT—for example, the reduction of electronic waste or of the energy consumed by computers—is an important goal of the computing community and the IT industry, the focus of this report is
1“Information technology” is defined broadly here to include both computing and communications capabilities.
“greening through IT,” that is, the application of computing to promote sustainability broadly.
The aim of this report is twofold: to shine a spotlight on areas where IT innovation and computer science (CS)2 research can help, and to urge the computing research community to bring its approaches and methodologies to bear on these pressing global challenges. The focus is on addressing medium- and long-term challenges in a way that would have significant, measurable impact.
The findings and recommended principles of the Committee on Computing Research for Environmental and Societal Sustainability concern four areas: (1) the relevance of IT and CS to sustainability; (2) the value of the CS approach to problem solving, particularly as it pertains to sustainability challenges; (3) key CS research areas; and (4) strategy and pragmatic approaches for CS research on sustainability.
An often-cited definition of “sustainability” comes from Our Common Future, the report of the Brundtland Commission of the United Nations (UN): “[S]ustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”3 The UN expanded this definition at the 2005 World Summit to incorporate three pillars of sustainability: its social, environmental, and economic aspects.4 This report takes a similarly broad view of the term. Although much of the focus in sustainability has been on mitigating climate change, with efforts aimed at managing the carbon dioxide cycle and increasing sustainable energy sources, there are other important sustainability challenges (such as water management, improved urban planning, supporting biodiversity, and food production) that can also be transformed by advances in computing research and are thus considered in this report.
It is natural when viewing sustainability through the lens of computer science to take a systems view. An elaboration on the broad definition of
2“Computer science” is defined broadly here to include computer and information science and engineering.
3United Nations General Assembly, March 20, 1987, Report of the World Commission on Environment and Development: Our Common Future; transmitted to the General Assembly as an Annex to document A/42/427—Development and International Co-operation: Environment; Our Common Future, Chapter 2: Towards Sustainable Development; Paragraph 1, United Nations General Assembly. Available at http://www.un-documents.net/ocf-02.htm.
4United Nations General Assembly, 2005 World Summit Outcome, Resolution A/60/1, adopted by the General Assembly on September 15, 2005.
sustainability above is that a system is not sustainable unless it can operate indefinitely into the future. For a system to do so inevitably requires optimization over time and space—goals that are central to much of computer science.
The report SMART 2020: Enabling the Low Carbon Economy in the Information Age5 usefully groups opportunities for applying IT to sustainability into three broad areas: (1) built infrastructure and systems, (2) ecosystems services and the environment, and (3) sociotechnical systems. The following describes each of these areas and outlines applications of IT and opportunities for computer science research:
• Built infrastructure and systems. This area includes buildings, transportation systems (personal, public, and commercial), and consumed goods (commodities, utilities, and foodstuffs). IT contributes to sustainable solutions in built infrastructure in numerous ways, from improved sensor technologies (e.g., in embedded sensors in smart buildings) and improved system models, to improved control and optimization (e.g., of logistics and smart electric grids), to improved communications and human-computer interfaces (enabling people to make more effective decisions).
• Ecosystems and the environment. This area encompasses assessing, understanding, and positively affecting (or not affecting) the environment and particular ecosystems—these efforts represent crosscutting challenges for many sustainability efforts. The scale and scope of efforts in this area range from local and regional efforts examining species habitats, to watershed management, to understanding the impacts of global climate change. The range of challenges itself poses a problem: how best to assess the relative importance of various sustainability activities with an eye toward significant impact. Additionally, computational techniques will be valuable for developing scientific knowledge and engineering technologies, including improved methods for data-driven science, modeling, and simulation to improve the degree of scientific understanding in ecology.
• Sociotechnical systems. Sociotechnical systems encompass society, organizations, and individuals, and their behavior as well as the technological infrastructure that they use. Large and long-lived impacts on sustainability will require enabling, encouraging, and sustaining changes in behavior—on the part of individuals, organizations, and nation-states over the long term. IT, and in particular real-time information and tools, can better equip individuals and organizations to make daily, ongo-
ing, and significant changes in response to a constantly evolving set of circumstances.
There are, of course, many points of intersection across these areas. For example, eco-feedback devices within the home (a sociotechnical system) interact with the larger, smart grid system (part of the built infrastructure); personal mobile devices (relying on built infrastructure and deployed in a sociotechnical context) provide data that feed into more robust modeling (a crosscutting methodology itself); and so on. In addition, better information about what is happening at an individual or local level can inform broader policy making and decision making.
Smarter energy grids, sustainable agriculture, and resilient infrastructure provide three concrete and important examples of the potential role of IT innovation and CS research in sustainability.
• Moving toward smarter and more sustainable ways of providing electricity will require a rethinking of many aspects of society, including the fundamental electric grid. A forward-looking, sustainable grid scenario presents a fundamentally more cooperative interaction between demand and supply, as well as greater transparency throughout the energy supply chain, with the goal of achieving both deep reductions in peak demand and reductions in overall demand as well as deep penetration of renewables in the supply blend. Information and data management with regard to both time (demand, availability, and so on) and space are essential to making progress toward a smarter, more sustainable electric grid. Computer science research and methodological approaches (in areas including user interfaces and improved modeling and analytical tools) will be needed at all levels to address the broad systems challenges presented by the smart grid.
• With respect to agriculture, there is growing concern regarding whether agricultural productivity can keep pace with human needs. A sustainable food system will be key to ensuring that the world’s population receives necessary nutrition without additional damage being done to the environment and society. As with the electric grid, it is in the systems issues in sustainable agriculture that the opportunities for IT seem most salient. Approaches to a sustainable food system include taking a systems view of the challenge; developing methods for measuring the costs, benefits, and impacts of different agricultural systems; assisting in the use of precision agriculture to minimize needed inputs; making information accessible for informed consumption; and developing social networks for local food sourcing. As with the smart electric grid, information and data management are essential to making progress toward a smarter, more sustainable, global food system. Computer science research
and methodological approaches will be needed to address the broad systems challenges—encompassing the environment and ecosystems, social and economic factors, and personal and organizational behaviors affecting food production, distribution, and consumption.
• The development of sustainable and resilient infrastructures poses crosscutting challenges, especially when a broad view of sustainability is taken that encompasses economic and social issues. Contributing to the challenges of increasing the resilience of societal and physical infrastructures is the growing risk of natural and human-made disasters. Enhancing society’s resilience and ability to cope with inevitable disasters will contribute to sustainability. Even apart from climate change and resource consumption, the sheer magnitude of Earth’s population means that crises, when they happen, will be at scale. Sustainability challenges in this area involve planning and modeling infrastructure, and the anticipation of and response to disasters and the ways in which information technology can assist with developing sustainable and resilient infrastructures.
Sustainability, of course, encompasses much more than the areas and examples outlined above, which are used here to illustrate the breadth of the challenges that need to be faced and the role that computer science and information technology can play.
As the sections below discuss, several key underlying philosophical and methodological approaches of computer science are well matched to key characteristics of sustainability problems.
Sustainability problems often share challenges of scale—sometimes due to the size of the problem space (e.g., geographic or planetary scale), sometimes due to the potential range of impact (e.g., widespread potential health or economic impacts), and often due to both. Sustainability problems are also typically heterogeneous in nature—there is almost never just one variable contributing to the challenge, or one avenue to a solution. Inputs, solutions, and technologies that can be brought to bear on any given problem vary a great deal. Most sustainability challenges emerge in part due to interconnection—multiple interlocking pieces of a system all having effects (some expected, some not) on other pieces of the system. Solutions to sustainability challenges typically involve finding near-
optimal trade-offs among competing goals, typically under high degrees of uncertainty in both the systems and the goals. Hence, methods for finding robust solutions are critical. And finally, human interaction with systems can play a role in both developing solutions and contributing to challenges.6
In addition to systems challenges, many sustainability challenges, particularly those related to infrastructure such as smarter transportation or electric systems, involve architecture. Architecture encompasses not just structural connections among subsystems, but also expectations regarding what a system will do, how it will perform, what behaviors are within bounds, and how subsystems (or external actors) should interact with the system as a whole. A system’s architecture instantiates early design decisions and has a significant effect on the uses, behaviors, and effects of the system over its life cycle long past the time when those decisions were made. As a result, larger-scale systems of necessity merit significant attention and resources devoted to architecture. As computer and information systems have become global in scale, the disciplines of computer science and software engineering have grappled with the challenges of architecture as they pertain to large-scale systems working over large geographic areas with countless inputs and millions of users. Lessons from architecting hardware, software, networks, and information systems thus have broader applicability to the processes of the structuring, designing, maintaining, updating, and evolving of infrastructure in pursuit of sustainability.
FINDING: Although sustainability covers a broad range of domains, most sustainability issues share challenges of architecture, scale, heterogeneity, interconnection, optimization, and human interaction with systems, each of which is also a problem central to CS research.
Given the scope and scale of many of the sustainability challenges faced today, it is very likely that no one solution or approach will suffice, even for those challenges that are comparatively easy to state (such as, “Reduce greenhouse gas emissions”). Thus, multiple approaches from multiple angles will need to be tried. Moreover, the urgency of acting in the face of threats to biodiversity and consequences of global climate change means that the best-known options need to be deployed quickly
6Of course, many other scientific disciplines offer useful methodological approaches to sustainability, some of which overlap with what computer science offers. This report focuses on computer science, as directed in the study committee’s statement of task (see the Preface).
while the adaptive redesign of the deployed system continues to be supported as advances in scientific understanding, changes in technology, and evolution in political and economic systems are incorporated. Thus iteration—adjusting, refining, and learning from ongoing efforts—will be essential, and it will often have to be done at a societal and planetary scale.
Iteration is another core strength of computer science, and learning from iterative approaches to large-scale software systems and applications, and large-scale software engineering and system deployment generally, can help with large-scale sustainability challenges. The approach has been demonstrated in such specific applications as the engineering of the global Internet and the deployment of web search and has been used effectively in a wide array of successful software engineering projects.
Because sustainability challenges involve complex, interacting systems of systems undergoing constant change, a data-driven, iterative approach will be essential to making progress and to making needed adjustments as situations change. One approach is to deploy technology in the field, using reasonably well-understood techniques, at first to explore the space and map gaps that need work. Data and models developed on the basis of this initial foray can then help provide context for developing qualitatively new techniques and technologies for contributing to even better solutions.
FINDING: Fast-moving iterative, incrementally evolving approaches to problem solving in computer science, which were critical to building the Internet and web search engines, will be useful in solving sustainability challenges.
Despite numerous opportunities to apply well-understood technologies and techniques to sustainability, there are also hard problems—for example, the mitigation of climate change—for which current methods offer at best partial solutions and the pressing nature of the challenges motivates rapid innovation. This section describes some salient technical research areas and outlines a broad research agenda for CS and sustainability.
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 potential of CS to address sustainability challenges will require sustained and appropriately structured and tailored investments in CS research.
The committee selected four broad CS/IT research areas meriting attention in order to help meet sustainability challenges—all of which contain elements of sensing, modeling, and action. The following list is not prioritized. Efforts in all of these areas will be needed, often in tandem.
- Measurement and instrumentation;
- Information-intensive systems;
- Modeling, simulation, and optimization; and
- Human-centered systems.
The areas correspond well to measurement, data mining, modeling, control, and human-computer interaction, which are well-established research areas in computer science. This overlap of selected research areas with established research areas has positive implications: research communities are already established, and it will not be necessary to develop entirely new areas of investigation in order to effectively address global sustainability challenges. Nonetheless, finding a way to achieve that impact may require new approaches to these problems and almost certainly new ways of conducting and managing research.
The ultimate goal of much of computer science in sustainability can be viewed as informing, supporting, facilitating, and sometimes automating decision making that leads to actions with 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.
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 information 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, organizations, and existing practices.
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.
For computer science to play an effective part in meeting global sustainability challenges, priority should be given to research that addresses one or more important sustainability challenges and that offers the prospect of tangible impact, either directly or through game-changing contributions that offer leveraging opportunities for other domains. The research areas listed in the section above are the committee’s recommended starting place.
An ongoing challenge is for IT experts and CS researchers to ensure that technologies and approaches represent usable and appropriate solutions, that they are highly effective, and that they take advantage of the deepest and most powerful insights that can be brought to bear.
The committee believes that CS research on sustainability is generally best approached not by striving for universality from the start, but instead by beginning from the bottom up: that is, by developing well-structured solutions to particular, critical problems in sustainability, and later seeking to generalize these solutions. Indeed, this has been a fruitful approach in many other application areas. Progress in many needed advances will require CS research (as described earlier), but those advances may not be immediately evident as universal approaches. Rather, to be judged as a significant contribution at the intersection of CS research and sustainability, the contribution must first have the potential to make a real difference in moving toward a more sustainable future. Embracing the concrete will help researchers hone and filter their approaches, and multiple and adapted applications will emerge. Many potential new applications are developed and find their ultimate universality through bottom-up cycles of change and through the iterative process of design that promotes those cycles of change. Past successful examples of this approach include Internet protocols, machine learning, object-oriented languages, and databases.
A premature focus on universality would be damaging to high-impact sustainability solutions. However, to be considered successful, CS research on sustainability must ultimately contribute to generalizable knowledge about sustainability, and the contribution or proposed solution should, at the same time, require new computational techniques or thinking beyond the current state of the art in computing. Establishing metrics for multidisciplinary work that are both actionable and meaning-
ful across participating disciplines is challenging, and the specific criteria for judging research success should evolve over time, with members of the community proposing and debating what constitutes the most worthy research. The committee emphasizes, however, the criterion of having the potential to make a real difference—that is, to make significant progress on social, economic, and environmental sustainability challenges.
PRINCIPLE: There should be strong incentives at all stages of research for focusing on solving real problems whose solution can make a substantial contribution to sustainability challenges, along with in-depth metrics and evaluative criteria to assess progress.
The solutions for real problems referred to in the principle above should be designed such that they embed the best of CS design and systems learnings—modularity, isolation, simplicity, and so on. Then researchers and practitioners should experiment with, apply, and pilot solutions to specific problems, looking for the successes and reapplying and adapting them to other applications and developing universality, while building the applicability and impact. Such work will need to be done across disciplinary boundaries and involve experts from many fields. Just as specific proposed solutions will need to be assessed in an iterative fashion, so too the research enterprise will need to have informed checkpoints and evaluative criteria in order to ensure that the goal of having a real impact is being met. Thus the committee urges an emphasis on interdisciplinarity, iteration, and high-level information sharing to assess progress.
Programmatically, traditional computer science research funding approaches are unlikely to be adequate to address the need discussed here. The National Science Foundation (NSF) is a primary funder of research in computer science in the United States. The former Information Technology Research programs at NSF and the current Cyber-enabled Discovery and Innovation Program are good examples of multidisciplinary programs, demonstrating that such efforts are feasible. But such programs are still a small minority among funding programs, and in the committee’s view most review panels on most of the programs related to CS research are not generally favorable toward funding domain-specific projects. The committee is encouraged by the establishment of Science, Engineering, and Education for Sustainability (SEES) as an NSF-wide
area of investment. SEES aims for a systems-based approach to “advance science, engineering, and education to inform the societal actions needed for environmental and economic sustainability and sustainable human well-being”7 and places an emphasis on interdisciplinary efforts. It provides a programmatic opportunity to put the recommended principles of this report into practice at NSF. For the field of computer science, efforts such as this can serve as a model for conceptualizing funding structures in order to take the greatest advantage of the depth of IT and CS innovation that the core discipline can offer to the rich and globally important problem space of sustainability.
The type of work described above will have to be done across disciplinary boundaries and to involve experts from many disciplines, as well as individuals who themselves have deep expertise in more than one discipline. Among the several opportunities for enhancing multidisciplinary approaches are scholarships that emphasize the development of expertise in complementary disciplines, and regular, high-level summits involving CS and sustainability experts—practitioners and researchers—to inform shared research design, assess progress, and identify gaps and opportunities.
Research institutions—both universities and funding organizations—could better address the needs of authentic multidisciplinary research, in terms of adjustments to how individuals are evaluated and in terms of publications, funding, criteria for promotion, infrastructure for sustained collaboration, and cross training.
PRINCIPLE: Encourage research at and across disciplinary boundaries, well informed by specifics and well structured to handle scale, data, integration, architecture, simulation, optimization, iteration, and human and systems aspects. CS research in sustainability should be an interdisciplinary effort, with experts in the various fields of sustainability being equal partners in the research.
PRINCIPLE: Refine funding and programmatic options to reinforce and provide incentives for the necessary boundary crossing and integration in CS research to address sustainability challenges. In particular, funding, promotion, and review and assessment (peer
7SEES mission statement. Available at http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504707.
review) models should emphasize in-depth integration with data and deployments from the constituent domains.
A shifting of the culture of CS to embrace sustainability more fully as an important and fruitful application area for research needs to include educating CS students about ways to have an impact with computing, computation, and systems approaches in important areas. Such a shift in culture would encourage students to develop domain expertise and to collaborate directly with domain experts while in graduate school or in preparing for graduate work. Such a shift also requires a culture of experimentation and innovation in the application of computer science.
Adjusting education within the target domains is as important as shifting the culture in CS. Information and data are critical to understanding the challenges, to formulating and deploying solutions, to communicating results, and to facilitating learning and new behaviors based on the results of the work. Thus a significant component of meeting virtually all sustainability challenges is to infuse computational thinking and approaches that are rich in CS and IT into the deploying industry and agencies. This component needs to include cross training students in multiple fields to create “champions” who can bring a CS perspective into other arenas. Sustainability is a challenge that will persist for generations; sustained commitment will be necessary, as well as continuing innovation in support of efforts to meet sustainability challenges.
PRINCIPLE: Undergraduate and graduate education in computer science should provide experience in working across disciplinary boundaries. Graduate training grants and postdoctoral fellowships should support training in multiple disciplines. Undergraduate and graduate programs should include tracks that offer introductory and intermediate course work in such sustainability areas as life-cycle analysis, agriculture, ecology, natural resource management, economics, and urban planning.