Since its formation in 1970, the US Environmental Protection Agency (EPA) has had a leadership role in developing the many fields of environmental science and engineering. From ecology to health sciences, environmental engineering to analytic chemistry, EPA has stimulated and supported academic research, developed environmental education programs, supported regional science initiatives, supported and promoted the development of safer and more cost-effective technologies, and provided a firm scientific basis of regulatory decisions and prepared the agency to address emerging environmental problems. The broad reach of EPA science has also influenced international policies and guided state and local actions. The nation has made great progress in addressing environmental challenges and improving environmental quality in the 40 years since the first Earth Day.
As a regulatory agency, EPA applies many of its resources to implementing complex regulatory statutes, including substantial commitments of scientific and technical resources to environmental monitoring, applied health and environmental science and engineering, risk assessment, benefit-cost analysis, and other activities that form the foundation of regulatory actions. The primary focus on its regulatory mission can engender controversy and place strains on the conduct of EPA’s scientific work in ways that do not affect most other government science agencies (such as the National Institute of Environmental Health Sciences and the National Science Foundation). Amid this inherent tension, research in EPA generally, and in the Office of Research and Development (ORD) in particular, strives to meet the following objectives:
• Support the needs of the agency’s present regulatory mandates and timetables.
• Identify and lay the intellectual foundations that will allow the agency to meet environmental challenges that it faces and will face over the course of the next several decades.
• Determine the main environmental research problems on the US environmental-research landscape.
• Sustain and continually rejuvenate a diverse inhouse scientific research staff—with the necessary laboratories and field capabilities—that can support the agency in its present and future missions and in its active collaboration with other agencies.
• Strike a balance between inhouse and extramural research investment. The latter can often bring new ideas and methods to the agency, stimulate a flow of new people into it, and support the continued health of environmental research in the nation.
Those multiple objectives can lead to conflict. For example, ORD resources that are applied to expanding staff and expediting science reviews and risk assessment in the National Center for Environmental Assessment may divert resources from longer-term program development and research. However, the agency has shown itself capable of maintaining a longer-term perspective in several instances, such as the establishment and maintenance of the Science to Achieve Results (STAR) grant program for extramural research, anticipatory moves to develop capability in computational toxicology, and the development and sustained implementation of multiyear research plans, for example, for research on airborne particulate matter (now the Air Quality, Climate, and Energy multiyear plan). In each of those cases, EPA identified ways both to give longer-term goals higher priority and to identify and commit resources to them. However, the tension between the near-term and longer-term science goals for the agency is likely to increase as more and more contentious rules are brought forward and as continuing budget pressures constrain and reduce science resources overall.
In light of the inherent tension, the emerging environmental issues and challenges identified in Chapter 2, and the emerging science and technologies described in Chapter 3, this chapter attempts to identify key strategies for building science for environmental protection in the 21st century in EPA and beyond. Specifically, the chapter lays out a path for EPA to retain and expand its leadership in science and engineering by establishing a 21st century framework that embraces systems thinking to produce science to inform decisions. That path includes staying at the leading edge by engaging in science that anticipates, innovates, is long term, and is collaborative; using enhanced systems-analysis tools and expertise; and using synthesis research to support decisions. In supporting environmental science and engineering for the 21st century, EPA will need to continue to evolve from an agency that focuses on using science to characterize risks so that it can respond to problems to an agency that applies science to anticipate and characterize both problems and solutions at the earliest point possible. Anticipating and characterizing problems and solutions should optimize social, economic, and environmental factors.
The continued emergence of major new and complex challenges described in Chapter 2—and the need to deal with the inevitable uncertainty that accompanies major environmental, technologic, and health issues—will necessitate a new way to make decisions. As described in Chapter 3, systems thinking has begun to take root in biology and other fields as a means of considering the whole rather than the sum of its parts; this will be essential as increasingly complex problems and the challenges described in Chapter 2 present themselves. The emergence of “wicked problems”, the increasing need to address exposures of humans and the ecosystem to multiple pollutants through multiple pathways (some of which are global), and the continuing challenges for the analysis and characterization of uncertainty throughout science and decision-making combine to make the adoption of systems thinking critical.
The systems-thinking perspective is useful not only for characterizing complex effects but for designing sustainable solutions, whether they are innovative technologies or behavioral changes. Understanding systems is also important for determining where leverage points exist for the prevention of health and environmental effects (Meadows 1999). To successfully inform future environmental protection decisions in an increasingly complex world, systems thinking must, at a minimum, include consideration of cumulative effects of multiple stressors, evaluation of a wide range of alternatives to the activity of concern, analysis of the upstream and downstream life-cycle implications of current and alternative activities, involvement of a broad range of stakeholders in decisions (particularly where uncertainty is significant), and use of interdisciplinary scientific approaches that characterize and communicate uncertainties as clearly as possible. As part of a systems perspective, it will be important for the agency to engage in “systems mapping” to comprehensively understand the way in which interacting stressors (such as environmental, human, technologic, socioeconomic, and political stressors) map to health and environmental impacts and to identify where intervention points can result in primary prevention solutions.
Although EPA has made efforts over the years to attempt to bring systems concepts into its work, most recently in its efforts to reorganize its activities under a sustainability framework (Anastas 2012), these efforts have rarely been integrated throughout the agency, nor sustained from one set of leaders to another. To begin to address the lack of a sustained systems perspective, the committee has developed a 21st century framework for decisions (Figure 4-1) and recommends a set of organizational changes to implement that framework (see Chapter 5). The framework features four elements that will be critical for informing the complex decisions that EPA faces:
• To stay at the leading edge, EPA science will need to
Take the long view.
• EPA will need to continue to evaluate and apply the new tools for data acquisition, modeling, and knowledge development described in Chapter 3.
• EPA will need to continue to develop and apply new systems-level tools and expertise for systematic analysis of the health, environmental, social, and economic implications of individual decisions.
• EPA will need to continue to develop tools and methods for synthesizing science and characterizing uncertainties, and will need to integrate methods for tracking and assessing the outcomes of actions (that is, for being accountable) into its decision process from the outset.
EPA can maintain its global position in environmental protection by staying at the leading edge of science and engineering research. Staying at the edge of science knowledge requires staying at the edge of science practice. In addition to understanding the latest advances in the science and practice of environmental protection, EPA will need to continue to engage actively in the identification of emerging scientific and technologic developments, respond to advances in science and technology, and use its knowledge, capacity, and experience to direct those advances. That is consistent with the two principal goals for science in the agency: to safeguard human health and the environment and to foster the development and use of innovative technologies (EPA 2012).
For EPA to stay at the leading edge, the committee presents a set of overarching principles for research and policy that begins to address the challenges of wicked problems. To be able to predict and adequately address existing challenges and prevent on-the-horizon challenges, EPA’s science will need to
• Anticipate. Be deliberate and systematic in anticipating scientific, technology, and regulatory challenges.
• Innovate. Support innovation in scientific approaches to characterize and prevent problems and to support solutions through more sustainable technologies and practices.
• Take the long view. Track progress in ecosystem quality and human health over the medium term and the long term and identify needs for midcourse corrections.
• Be collaborative. Support interdisciplinary collaboration in and outside the agency, across the United States, and globally.
Those four principles support the flow of science information (from data to knowledge) in EPA to inform environmental decision-making and strategies for inducing desirable environmental behaviors.
FIGURE 4-1 The iterative process of science-informed environmental decision-making and policy. Leading-edge science will produce large amounts of new information about the state of human health and ecologic systems and the likely effects of introducing a variety of pollutants or other perturbations into the systems. In particular, many multifactorial problems require systems thinking that can be readily integrated into other analytic approaches. This framework relies on science that anticipates, innovates, takes the long view, and is collaborative to solve environmental and human health problems. It also supports decision-making and ensures that leading-edge science is developed and applied to inform assessments of the system-wide implications of alternatives for key policy decisions.
Science That Anticipates
Continually striving to more effectively anticipate challenges and emerging environmental issues will help EPA to stay at the leading edge of science. That involves two main sets of activities: anticipating concerns and developing guidance to avoid problems with new or emerging technologies, and establishing key indicators and tracking trends in human health and ecosystem quality to identify and dedicate resources to emerging environmental problems. Furthermore, continuing to anticipate (and direct resources to) targeted science and technology developments will allow EPA to enhance its ability to identify early warnings and prevent effects before they occur. Fulfilling the anticipatory function can be difficult when the day-to-day pressures to respond to regulatory deadlines can take most of, if not all, an EPA leader’s time and attention. Hence, anticipatory activities will need to be pursued in collaboration with other government agencies, the private sector, and academic engineers and scientists.
Anticipating Environmental and Health Effects of New Technologies
One example of EPA’s efforts to identify emerging challenges has been the engagement of its National Advisory Council for Environmental Policy and Technology (NACEPT). NACEPT is an external advisory board established in 1988 to provide independent advice to the agency on a variety of policy, technology, and management issues. The advisory council recently identified several challenges that EPA will need to focus on in the future (EPA NACEPT 2009). The most important challenges identified included climate change, biodiversity losses, and the quality and quantity of water resources. NACEPT also identified corresponding organizational needs for EPA to meet existing and emerging environmental challenges, including improving its ability to use technology more effectively, to transfer technology for commercial uses, and to enhance communication in and outside the agency. The committee concurs with the advisory council’s observations that although EPA has demonstrated the ability to create and implement solutions to new challenges in some cases, emerging challenges need to be approached in a more integrated and multidisciplinary way. The committee also concurs with NACEPT’s recommendation that EPA include “environmental foresight” or “futures analysis” activities as a regular component of its operations.
Some of EPA programs, including its New Chemicals program and Design for the Environment program (see Chapter 3), already demonstrate strategies for anticipating and mitigating future problems (Tickner et al. 2005). In those programs, EPA has used information on what is known about chemical hazards to develop a series of models so that chemical manufacturers and formulators can predict potential hazards and exposures in the design phase of chemicals. The models are updated as new knowledge emerges. The Design for the Environment example demonstrates that EPA will be best able to address
emerging issues through enhanced interdisciplinary collaboration and by using systems thinking and enhanced analysis tools to understand the human health and ecologic implications of important trends. Addressing emerging issues should include consideration of the full life cycle of products, establishment of large-scale surveillance systems to address relevant technologies and indicators, and the analytic ability to detect historical trends rapidly.
Although EPA has engaged NACEPT and its Science Advisory Board (SAB) to help in anticipating trends and has individual programs designed to address concerns about existing and emerging technologies and identify promising new technologies (see, for example, EPA 2011a), the agency does not appear to have a systematic and integrated process for anticipating emerging issues. The example of engineered nanomaterials (discussed below and described in Chapter 3) illustrates some of the problems and pitfalls of current approaches to emerging technologies. A better understanding of such technologies can help to identify and avert ecosystem and health effects and in some cases to avoid unwarranted concern about new technologies that pose little risk.
In principle, early consideration of environmental effects in the design of emerging chemicals, materials, and products offers advantages to businesses, regulatory agencies, and the public, including lower development and compliance costs, opportunities for innovations, and greater protection of public health and the environment. Yet, despite nearly 15 years of investment in engineered nanotechnology and the use of nanomaterials in thousands of products, recognition of potential health and ecosystem effects and design changes that might mitigate the effects have been slow to arrive. Indeed, a December 2011 report by the EPA Office of Inspector General (EPA 2011b) found several limitations in EPA’s evaluation and management of engineered nanomaterials and stated the following:
• “Program offices do not have a formal process to coordinate the dissemination and utilization of the potentially mandated information.
• “EPA is not communicating an overall message to external stakeholders regarding policy changes and the risks of nanomaterials.
• “EPA proposes to regulate nanomaterials as chemicals and its success in managing nanomaterials will be linked to the existing limitations of those applicable statutes.
• “EPA’s management of nanomaterials is limited by lack of risk information and reliance on industry-submitted data.”
The Office of Inspector General concluded that “these issues present significant barriers to effective nanomaterial management when combined with existing resource challenges. If EPA does not improve its internal processes and develop a clear and consistent stakeholder communication process, the Agency will not be able to assure that it is effectively managing nanomaterial risks.”
How EPA arrived at that situation provides important information for the design and evaluation of new and emerging technologies. EPA was actively working with other agencies to make large investments in nanotechnology during implementation of the 21st Century Nanotechnology Research and Development Act. In particular, the agency saw the opportunity to use nanotechnology in remediation and funded this type of research. However, it missed the opportunity to support research that addressed proactively the environmental health and safety of nanomaterials, pollution prevention in the production of nanomate-rials, and the use of nanotechnology to prevent pollution. In early years, the agency focused primarily on the applications of nanomaterials and not on the environmental and health implications. When it did begin to address implications, the agency focused its attention on defining nanomaterials and whether they are subject to new policy structures because of size-specific hazards (an issue that is still discussed) and on cataloging and redirecting existing research and resources toward assessing exposure, hazard, and risk. The private sector has been left looking for signals from the agency about how it should develop ang commercialize nanoscale products.
There were several reasons for the delay in early intervention in the case of nanotechnology. One reason was that materials innovators were focused on discovering new materials and promoting applications of them. Another reason is that materials innovators often have little expertise or formal training in environmental, health, and safety issues. Some of these innovators assumed that nothing about nanomaterials presented new challenges for environmental health and safety and that these were secondary matters to be considered only after commercial products are developed. A third reason was that there was insufficient federal agency leadership, emphasis, and policy regarding proactive rather than reactive approaches to safer design. Even with increasing knowledge about the design of environmentally benign engineered nanomaterials, progress toward incorporating greater consideration of health and safety in nanomaterial design has been limited for a variety of reasons, including the lack of design rules or other guidance for designers in developing safer technologies, the lack of expertise in solutions-oriented research in EPA, and the lack of collaboration between material innovators and toxicologists and environmental scientists.
The case of engineered nanomaterials indicates the need for EPA to establish more coherent technology-assessment structures to identify early warnings of potential problems associated with a wide range of emerging technologies. If EPA is going to play a major role in promoting and guiding early intervention in the design and production of emerging chemicals (through green chemistry), materials, and products, it will need to commit to this effort beyond its regulatory role.
Many new chemicals and technologies hold considerable potential to improve environmental quality, and it may prove useful for EPA to take some specific steps to anticipate and manage new technologies that emerge from the private sector. Some of these specific steps can be done in collaboration with other agencies, industries, and research organizations when possible. They include:
• Develop baseline design guidelines for new chemicals and technologies and fund research that can anticipate potential effects as part of technology development.
• Balance near-term research that is focused on understanding the potential risks posed by chemicals and technologies that are closer to commercialization with substantial development of longer-term predictive, anticipatory approaches for understanding the potential effects of the technologies.
• Establish processes to collaborate with external partners in academe and industry to attain needed expertise in the development of common metrics for evaluation of emerging technologies.
• Establish opportunities that educate and bring together chemical and materials innovators and environmental health and safety experts (and other stakeholders) to collaborate in understanding and intervening in chemical and materials design.
• Support efforts to amass and disseminate data, models, and design guidelines for safer design to guide emerging technologies.
• Embrace imperfect or incomplete information to guide actions. Uncertainty will always exist in the case of emerging technologies, and identifying alternative paths for action would allow EPA to act or provide guidance for development and commercialization in the face of incomplete data.
Anticipating Emerging Challenges, Scientific Tools, and Scientific Approaches
In recent years, EPA has had to make decisions on several headline-grabbing environmental issues with underdeveloped scientific and technical information or short timelines to gather critical new information, for example, during natural disasters. EPA will always need the capacity to respond quickly to surprises, in part by maintaining a strong cadre of technical staff who are firmly grounded in the fundamentals of their disciplines and able to adapt and respond as new situations arise. But the agency also needs to scan the horizon actively and systematically to enhance its preparedness and to avoid being caught by surprise. Anticipating new scientific tools and approaches will allow EPA to fulfill its mission more effectively.
Collaboration is critical for identifying and addressing many of the topics discussed in Chapter 2, such as trends in energy and climate change and “emerging” environmental concerns that are not new but are the result of improvements in detection capabilities. For example, critics have suggested that the agency’s slow response to growing scientific concern about effects of pharmaceutical and personal-care products in surface waters was due in part to its lack of infrastructure or collaboration to address problems that span media and jurisdictions (Daughton 2001). EPA’s efforts to anticipate science needs and emerging tools to meet these needs cannot succeed in a vacuum. As it focuses on organizing and catalyzing its internal efforts better, it will need to continue to look outside
itself—to other agencies, states, other countries, academe, and the private sector—to identify relevant scientific advances and opportunities where collaboration that relies on others’ efforts can be the best (sometimes the only) means of making progress in protecting health and the environment.
Finding: Although EPA has periodically attempted to scan for and anticipate new scientific, technology, and policy developments, these efforts have not been systematic and sustained. The establishment of deliberate and systematic processes for anticipating human health and ecosystem challenges and new scientific and technical opportunities would allow EPA to stay at the leading edge of emerging science.
Recommendation: The committee recommends that EPA engage in a deliberate and systematic “scanning” capability involving staff from ORD, other program offices, and the regions. Such a dedicated and sustained “futures network” (as EPA called groups with a similar function in the past), with time and modest resources, would be able to interact with other federal agencies, academe, and industry to identify emerging issues and bring the newest scientific approaches into EPA.
Science That Innovates
Given EPA’s mission and stature as the leading government environmental science and engineering organization, it is imperative that it innovate and support innovation elsewhere in technologies, scientific methods, approaches, tools, and policy instruments. “Innovation” can be challenging to define for a regulatory agency, but one component involves advancing the ability of the agency to discover and characterize problems at a systems level and to provide decision-makers with solutions that are effective and that balance the multiple objectives relevant to the agency and society. Spinoffs from innovation within the agency and activities to promote innovation outside the agency can help environmental authorities in states and other countries to solve their problems and can encourage the regulated community to discover less expensive, faster, and better ways to meet or exceed mandated compliance. Based on the above perspective and using analogies to the typical business definition of innovation, the section below considers processes by which EPA can incorporate and promote innovation.
Identifying Opportunities and Meeting Desired Customer Outcomes
Innovations typically begin with two processes: the identification of opportunity and the understanding of desired “customer” outcomes. An opportunity is simply a “gap” between the current state and a more desirable situation as envisioned by customers. The gaps can be technologic in nature (for example,
the need for the design of a new sensor to measure something of interest) or related to a process or business (for example, the need for an approach to obtain up-to-date information from stakeholders). Once an opportunity has been identified and analyzed, an understanding of desired customer outcomes is needed to create innovative solutions.
Understanding desired outcomes goes well beyond simply talking to customers; it includes putting oneself in the clients’ shoes to separate what they say they want from what they want. A common mistake in trying to innovate is to substitute desired producer outcomes for desired customer outcomes. While EPA is in a different position from product manufacturers, only by understanding why customers are purchasing products can the agency help promote creative solutions. One example is the development of alternative plasticizers for polyvinyl chloride plastics rather than alternative materials that do not require plasticizers. Another example is the creation of less toxic flame retardants rather than creation of an inherently flame-retardant fabric or even consideration of whether flame retardancy is needed for a particular part or product. Insightful, unbiased determination of desired customer outcomes is crucial for proper support of innovation.
An innovative means of defining desired customer outcomes is ethnography, hypothesis-free observation of customers in their “natural habitats”. The technique, pioneered by such design firms as IDEO (Palo Alto, CA), has produced a number of insights into consumer behavior that have been translated into successful products. For EPA, the analogue of ethnography is the willingness of staff to visit their “customers” (for example, industry, the general public, or even specific EPA regional offices or laboratories) to see technology or science needs, to see where current regulations or prescribed methods cause people to struggle to conform, or to see where regulations create perverse results. An example of the benefits of observing customer needs is the design of the copying machine. In the 1970s, Xerox used anthropology graduate student Lucy Such-man to observe how users interacted with their copying machines. Suchman created a video showing senior computer scientists at Xerox struggling to make double-sided copies with their own machines. Surprising ethnographic results like that have led to a host of innovative alterations in office equipment that render the user experience much more productive (Suchman 1983). While direct observation of this sort may be unusual for a regulatory agency, similar observational activities by EPA might lead to insights regarding how consumer products are actually used (informing exposure models) or whether responses to specific regulations have unintended consequences that could be readily addressed.
In business, innovation is a catalyst for growth. Business innovation involves the development of ideas or inventions and their translation to the commercial sphere. Innovation results in rapid (favorable) change in market size, market share, sales, or profit through the introduction of new products, processes, or services. Those are clear outcomes that are relatively easy to measure. In an agency like EPA, innovation plays a different role but one that is no less important for the success of the agency in achieving its mission, adapting to
changing conditions, and maintaining its authoritative status. Innovation can be thought of as “the conversion of knowledge and ideas into a benefit, which may be for commercial use or for the public good.”1 For the purposes of EPA, the committee is using the term innovation as a new means by which to achieve enhancements to environmental and public health at reduced private-sector and public-sector costs. It is essential for EPA to identify and focus on desired outcomes rather than being tied to established processes, procedures, or routines; a fundamental lesson from research on business innovation is that the process is best served by a focus on outcomes.
The simplest measures of success are advances toward goals like cleaner air or safer drinking water, which are most often guided by legislation. Given the scarcity of resources for environmental protection and given the concern for income and employment, EPA has an interest in the private-sector and public-sector costs of achieving health and environmental goals. For EPA, innovation can be measured in such outcomes as direct benefits to health and the environment or in reductions in private-sector and public-sector costs of achieving these outcomes. Continuing to strive to create and promote new processes, tools, and technologies can advance such outcomes. The agency can be innovative in relation to health and the environment by influencing current business and government practices via technology transfer and education.
US Environmental Protection Agency Supporting Innovation
EPA has done much in the past to support the development of innovative ideas in portions of its activities. One example is the development of ways of evaluating and using rapidly emerging biologic testing, as described in Chapter 3. Another is the recent launch of an internal competition called Pathfinder Innovation Projects, which promotes innovation in the agency (EPA 2011c). The program received 117 proposals from almost 300 scientists after its first call for proposals and, after an external peer-review process, funded 12 initial projects (Preuss 2011). Such programs as Design for the Environment, the agency’s recent efforts to crowdsource some questions through the Innocentive Web site (described in the section “Identifying New Ways to Collaborate” below), and new technologies in hydroinformatics are examples of efforts to identify innovative solutions. In addition, the federal government’s Open Government Initiative and its Challenge.Gov Web site are encouraging innovation in all agencies. Those efforts, however, have not been systematic, and they have not been developed strategically to encourage the much larger potential innovation that could come from the private sector.
When the outcomes are of mutual interest, the agency can help to support and encourage private-sector innovation to serve its desired outcomes in several systematic ways. First, agency scientists generally have a broad view of emerg-
ing toxicologic and human health data—data that may suggest that particular current commercial products are problematic from a health and safety perspective. Highlighting or publicizing such data could provide early hints to manufacturers that replacements may be needed in the future and prompt enterprising companies and entrepreneurs to work to develop alternative means of satisfying desired customer outcomes. The committee does not advise EPA to try to develop solutions, inasmuch as the research environment in the agency is unlikely to be able to duplicate the resources and competitive pressures that drive the commercial product-development market. But providing clear signals of potential future environmental opportunities to the commercial sphere may be enough to prompt the creation of improvements.
Second, EPA can and does provide resources to support private-sector innovation directly. Examples are the EPA Small Business Innovation Research program and enhanced awards programs, such as the Presidential Green Chemistry Awards. Targeting of such programs to address problems that EPA scientists find particularly intractable or to address problems that it does not have the capacity to address can be a valuable means of stimulating the entrepreneurial community to attack problems of direct interest to the agency. To have the resources needed to support private-sector innovation directly at the levels necessary to produce results, the agency would benefit from collaborating and partnering with other agencies that have far greater budgets and resources and similar or complementary innovation challenges, for example, the National Institute of Standards and Technology (NIST), the Department of Energy, and the Department of Defense.
Third, EPA could create an infrastructure that would enable its scientists to serve as a clearinghouse for new technologies, particularly technologies whose effects could cross traditional disciplinary boundaries. The goal of such an infrastructure would be to foster diffusion and adaptation of new technologies, often the slowest step in the innovation process. Steps taken to enhance diffusion could accelerate innovation.
Fourth, technologic innovation relies on willingness (laws and market pressures), capacity, and opportunities for change (Ashford 2000). Capacity becomes a large barrier to innovation adoption, particularly for small and medium-size firms that may not have resources to implement or monitor change or that have legitimate concerns about failed technology adoption. EPA has an important role in addressing capacity and opportunity through science and support that provides information, technical assistance, networking of firms, demonstration activities, and economic incentives and disincentives (Ashford 2000). Many capacity-support mechanisms work most effectively at a state level. Since the passage of the Pollution Prevention Act of 1990, EPA has worked closely with providers of the NIST Manufacturing Extension Partnership and state pollution-prevention technical assistance providers to support innovative adoption of the act. Such models as the Massachusetts Toxics Use Reduction Program provide examples of how an agency like EPA can leverage resources to support innovation. The focus of the program is not on identifying “acceptable” exposure levels
but rather options for reducing toxic-substance use in the first place, with science as a driver of innovation.
Literature that discusses and analyzes incentive prize competitions continues to emerge (Kalil 2006; Stine 2009). The federal government is relatively new to this arena and most agencies are still figuring out how to use prizes to fulfill their missions. As EPA is already discovering, using incentives can be a successful way to drive innovation for mission-related topics.
Leveraging Environmental Protection Agency Actions to Promote Private-Sector Innovation
Both intentional and unintentional actions by EPA can affect the willingness of the private sector to invest in research and development. There have not been formal analyses of the extent of such private investment, but it probably dwarfs the investment made by EPA itself. EPA has the potential to expand the investment in new and innovative science and engineering dramatically if it provides signals that are clear, selects instruments and polices that achieve a specific set of outcomes or performances, and allows the regulated community to benefit from innovations (Jaffe et al. 2002, 2005; Popp et al. 2010).
Throughout EPA’s history, its actions have resulted in substantial investment in new science and engineering by the private sector, at times with beneficial results. Those actions have taken at least three forms:
• Regulations. EPA regulations specify results that need to be achieved and dates by which they need to be achieved. Regulations have, at times, resulted in substantial innovation that might not have been achieved without such clear signals. An example is vehicle carbon monoxide emission standards, which have resulted in substantial investment in developing and continually enhancing the three-way catalyst and dramatically decreasing ambient carbon monoxide despite large increases in travel (NRC 2003).
• Testing Protocols and Risk Assessment. In its pursuit of risk estimates for a wide array of substances, EPA can strongly influence research and development investment by the private sector. For example, recent efforts by EPA to enhance its investment in computational toxicology and high-throughput screening have resulted in substantial private investment as well.
• Public Information. In requiring the public release of information on emissions and discharges, EPA can set strong incentives for private investment in both major process redesign and product substitution to shift to more sustainable production inputs. One cogent example is the Toxics Release Inventory, which collects data on the disposal and release of over 650 toxic chemicals that are submitted by over 20,000 regulated facilities each year; EPA makes the information available through a publicly accessible Web site. It is an example of consumer-driven change that has led to important reductions in some chemical emissions after its initial public release.
However, the process by which EPA provides incentives for private-sector investment and innovation is not without its challenges. Among them are
• Overly Prescriptive Rules. Regulations that use a true performance standard for emissions and discharges can encourage innovation; rules that, in essence, base emission standards on the best current technology (without regular updating) can take away all private incentives for further investment in research and development. For example, the “categorical pretreatment standards” for industrial wastewater discharges locked into place standards based on technologies that were available at the time of promulgation, whereas the “best available control technology” requirements of the Clean Air Act are a “rolling” standard, expressed as performance-based emissions limits that can advance as technology improves. Economic research on innovation and environmental regulation finds that flexible policy instruments that provide rewards for continual environmental improvement and cost reduction tend to promote innovation whereas policies that mandate a specific behavior can deter innovation (Popp et al. 2010).
• Defensive Rule-Making. In the current climate in which nearly every action taken by EPA is challenged, the rules that are issued may be written in a conservative fashion that hews tightly to narrow interpretations of the statutes or to past practice and thus may be less likely to encourage innovation once implemented.
• Reliance Solely on Existing Testing Protocols. To meet toxicity-testing requirements, EPA often specifies testing protocols in detail, generally on the basis of the state of the art. That practice reduces the incentive to innovate in testing and assessment because of the difficulty of getting new approaches and results accepted.
There are several examples where EPA has been successful in leveraging private sector research. One example is in the Technology and Economic Assessment panels of the Montreal Protocol and the various research and development consortiums designed to find substitute chemicals and technologies for ozone depleting substances (EPA 2007, 2010a). Another example is the Green Lights program for energy efficient light bulbs, the Energy Star program for energy efficient appliances, and the Golden Carrot program for energy efficient refrigerators (EPA 1992; Feist et al. 1994; EPA 201 1d; Energy Star 2012). If those examples could be replicated in other situations, EPA would be able to mobilize more industry research and development and implementation to protect the environment.
Finding: EPA has recognized that innovation in environmental science, technology, and regulatory strategies will be essential if it is to continue to perform its mission in a robust and cost-effective manner. However, to date, the agency’s approach has been modest in scale and insufficiently systematic.
Recommendation: The committee recommends that EPA develop a more systematic strategy to support innovation in science, technology, and practice.
In accomplishing the recommendation above, the agency would be well-advised to work on identifying more clearly the “signals” that it is or is not sending and to refine them as needed. Clearly identifying signals could be accomplished by seeking to identify the key desired outcomes of EPA’s regulatory programs and communicate the desired outcomes clearly to the private and public sector. The committee has identified several ways in which EPA could address this recommendation:
• Establish and periodically update an agency-wide innovation strategy that outlines key desired outcomes, processes for supporting innovation, and opportunities for collaboration. Such a strategy would identify incentives, disincentives, and opportunities in program offices to advance innovation. It would highlight collaborative needs, education, and training for staff to support innovation.
• Identify and implement cross-agency efforts to integrate innovative activities in different parts of the agency to achieve more substantial long-term innovation. One immediate example of such integration that is only beginning to occur is bringing the work on green chemistry from the Design for the Environment program together with the innovative work on high-throughput screening from the ToxCast program to improve application of innovative toxicity-testing tools to the design of green chemicals.
• Explicitly examine the effects of new regulatory and nonregulatory programs on innovation while ascertaining environmental and economic effects. Such an “innovation impact assessment” could, in part, inform the economic evaluation as a structure that encourages technologic innovation that may lead to long-term cost reductions. The assessment could also function as a stand-alone activity to evaluate how regulations could encourage or discourage innovation in a number of activities and sectors. It could help to identify what research and technical support and incentives are necessary to encourage innovation that reduces environmental and health effects while stimulating economic benefits.
Science That Takes the Long View
As the committee has emphasized, the nature and scope of environmental challenges are changing rapidly, as are the scientific and technologic tools and concepts for dealing with them. For instance, the importance of nanoparticles was not evident 2 decades ago. The problems that EPA will face 1 or 2 decades from now are certain to include some challenges that we cannot imagine today. But environmental-protection science in EPA has, for the most part, focused on effects over shorter periods, in single media, or over small spatial scales. That is understandable given regulatory demands for science. However, if EPA is to
better understand long-term implications of human effects on ecosystems and health, it will need to develop scientific processes that take the long view—that is, processes that can assess changes, even minor ones, over the long term. To detect trends in environmental and human health conditions and to know whether they fall within the range of recent natural variation, long-term data on the basic functioning of environmental systems and human well-being are needed. For example, the scientific community is aware of recent changes in weather patterns, especially increases in extreme events, only because long-term weather records are available.
A concise set of environmental indicators can provide information about the status of and trends in key components of natural and human systems and provide evidence of changes that should be monitored. A modest number of environmental indicators of fundamental ecologic processes and attributes are in use (Orians and Policansky 2009; see Box 4-1 for a list of principles to guide the development of indicators). In 2002, a committee of EPA’s SAB developed a framework for assessing ecologic conditions (EPA SAB 2002) that is similar to frameworks developed by the H. John Heinz Center (Heinz Center 2002, 2008) and a National Research Council (NRC) committee (NRC 2000). The framework organizes a large number of potential indicators into six categories that represent the key attributes of an ecologic system as a whole. Each attribute can be represented by an individual indicator or by an index created by combining indicators. The six categories can also be used as a checklist for designing environmental management and assessment programs and as a guide for aggregating and organizing information. In its 2008 Report on the Environment, EPA analyzed 85 indicators related to environmental and human health that focused on air, water, land, human exposure and health, and ecologic conditions (EPA 2008). However, it has not been clear that the agency is committed to or has a plan to sustain this effort over the longer term. Furthermore, the NRC Committee on Incorporating Sustainability in the US Environmental Protection Agency (NRC 2011a) found that most indicators chosen by EPA are inadequate for exploring the relationship between economic conditions and ecosystem pressure and did not measure such important elements as environmental justice. The committee called for the development of additional sustainability indicators that could include social and economic conditions and, given the challenges of predicting long-term data, stated that any uncertainties in the understanding of indicators should be clearly communicated.
According to some analyses, it is important that EPA continue to develop and adapt a few indicators that are capable of detecting long-term changes in environmental conditions and human well-being above the inevitable noise of variability (GAO 2004). Such indicators should be designed to provide information on basic processes that are most likely to be useful in dealing with both current and future challenges, many of which are unknown today. Indicators whose
utility can be evaluated retrospectively would add value in constructing long-term datasets, but EPA should not restrict development of indicators to those with historical data. Many agencies will probably be able to use indicator data, so collaboration among federal agencies, including EPA, would support the collection of data and operationalization of the system of indicators in a more cost-effective manner.
The committee endorses the principles in Box 4-1 to guide the development and use of indicators by EPA and other government agencies that can inform long-term trends. Some of these principles are already used by EPA.
• Do not ask an indicator to do too much. Indicators inform us of trends in some entity of interest, but they should not be designed to diagnose the processes that underlie trends.
• Do not design indicators to give grades. Indicators should report objective, scientific information, describe trends, and provide the scientific rationale for interpreting them; value judgments should be kept separate from the scientific and objective aspects of indicators.
• Do not let indicator development be driven by availability of data. Aware of this trap, the Heinz Center (2002) focused on identifying the environmental processes and products that society most needed to know about by including many empty graphs and explanatory text that directed attention to the processes where ignorance most mattered and where increased research and funding would yield social benefits.
• Propose and use only a few indicators. Thousands of environmental indicators have been proposed or are in use. However, an indicator is likely to have the influence it deserves only if it competes for attention with a small number of others.
• Embed indicators in a rigorous archival system. Any dataset will be of little value in the absence of a well-crafted system that monitors data quality, document sampling, and analytic methods; archives data in a secure and recoverable form; and analyzes and reports data in formats that are useful to decision-makers and managers.
• Try to avoid shifting baselines. Because many ecosystems and habitats are poorly understood, and because large fluctuations characterize most natural environments, choosing appropriate baselines is challenging. For some purposes, a shifting baseline is appropriate, but gradual environmental deterioration is likely to be undetected if a shifting baseline is used, because the altered, often degraded, condition becomes accepted as “normal” (Pauly 1995).
• Base indicators on well-established scientific principles and concepts. It is difficult or impossible to interpret the indicator data in the absence of a sound conceptual model of the system to which it is applied.
• Develop indicators that are robust and reliable. A robust indicator is relatively insensitive to expected sources of disturbance and yields reliable and useful numbers in the face of inevitable external perturbations. A robust indicator is based on measurements that can be continued in compatible form when measurement technologies change.
• Understand each indicator’s statistical properties so that changes in its values will have clear and unambiguous meanings. The indicator should be sensitive enough to detect real and important changes but not so sensitive that its signals are masked by natural variability.
• Clarify the spatial and temporal scales over which each indicator is relevant. If indicator data is to be aggregated to yield measures on larger spatial scales, consistency in how the data are gathered in different places is vital.
• Identify the skills needed at all stages of indicator development and use. Acceptance of the indicator requires that potential users have confidence in the skills and integrity of the people that gather, store, and report the data.
• Separate the entities that compute and report status and trends in indicators from management and enforcement agencies. Confidence in the numbers being reported requires the belief that the numbers do not depend on who gathers and reports them. Actual or perceived conflicts of interest are likely to arise if the gatherers and interpreters of data also establish and enforce regulations based on trends in the data.
More detail on the reasoning behind the principles above can be found in NRC (2000) and Orians and Policansky (2009).
Long-Term Data Collection
Once indicators have been established, there is a need to measure them over time. To meet its mission, EPA needs an understanding of long-term changes in the environment and trends in rates at which pollutants enter the environment. In the absence of trend and duration data, it is often hard to know whether any specific pollutant load—particularly the load of a nontoxic pollutant, such as nitrogen—is of concern. Long-term monitoring is essential for tracking changes in ecosystems and populations to identify, at the earliest stage, emerging changes and challenges. Without long-term data, it is difficult to know whether current variations fall within the normal range of variation or are truly unprecedented. It is also essential for knowing whether EPA’s management interventions are having their intended effect. Monitoring is a fundamental component of hypothesis-testing. All management interventions are based on explicit or implicit hypotheses that justify them and explain why they should yield the desired results. The hypothesis may focus on physical and biologic processes or on expected human behavioral responses. If the hypothesis is made explicit
and monitoring is designed specifically to test it, both the value of the monitoring and the details of its design will be clarified and the importance of the monitoring will be evident.
In addition, knowing the pattern of chronic or sporadic exposure of humans and ecosystems to pollutants is essential for understanding their effects. But such an understanding is possible only with the availability of long-term reliable data on pollution loads. Collecting high-quality long-term environmental data on pollutant exposure and ecosystem structure and function is not easy. It might take a decade or more to understand the implications of the trends and the meaning of periodic events. On a practical level, long-term monitoring seldom has general public or political supporters advocating for it, and it is an easy target of budget cutting because it is slow to yield insights.
With the exception of some air and water monitoring programs, there are few long-term monitoring programs, let alone programs that are systematic and rigorous. The paucity of data has made it difficult or impossible to identify key trends related to problems and improvements in environmental quality. That lack of high-quality long-term data is largely the product of four factors:
• Environmental variability across the United States means that what is most useful to monitor differs widely from one place to another.
• It is easy to collect data but much more difficult to collect consistent data, particularly over decades. For example, what is collected may change in response to immediate regulatory needs, thereby reducing its value.
• Over long periods, it is difficult to maintain high-quality data collection systems with solid quality assurance and quality control, well thought-out collection sites, and appropriate collection frequency.
• Monitoring is expensive and often does not produce high-impact information in the short to medium term.
Long-term environmental datasets that have been collected effectively illustrate both the challenges and the rewards of long-term monitoring programs and the importance of collaborations among agencies and organizations. The datasets include those on acid rain, on the Great Lakes ecosystem, and on US Geologic Survey stream-gauging and water-sampling. A key challenge for EPA’s science programs is to determine what environmental characteristics to monitor. The answer is tied to indicators, asking the right questions, and ensuring that long-term funding is available to provide the data necessary to support science-based regulatory decisions.
New technologies enable some environmental characteristics to be measured over time across a large spatial domain, for example, satellite imaging and other remote-sensing technologies (as discussed in Chapter 3). The combination of environmental-monitoring data and medical-history information from electronic medical-records data could help to track environmental exposures of human populations and evaluate health effects and dose-response relationships
between environmental stressors and health outcomes. The benefits of collaboration, discussed in several places in this report, apply to monitoring.
Finding: It is difficult to understand the overall state of the environment unless one knows what it has been in the past and how it is changing over time. Typically this can only be achieved by examining high-quality time series of key indicators of environmental quality and performance. Currently at EPA, there are few long-term monitoring programs, let alone programs that are systematic and rigorous.
Recommendation: The committee recommends that EPA invest substantial effort to generate broader, deeper, and sustained support for long-term monitoring of key indicators of environmental quality and performance.
Science That Is Collaborative
EPA is a world leader in producing and using science for informed environmental protection, but many other public and private parties in the United States and around the world are also making important contributions in environmental sciences and engineering. Many other parties are working outside the conventional environmental science and engineering space but may have technologies, methods, or data streams that could prove valuable for environmental protection. EPA needs to enhance its ability to draw on those other resources, collaborate with others, and offer leadership, especially in issues that are critical for informing its present missions and for providing the understanding that it will need to address future environmental problems.
Collaborating Among Agencies
Over the years, many investigators in the United States and around the world have looked to EPA to provide leadership in identifying important and emerging problems in environmental science and technology. Individual contact by EPA scientists can help to influence and steer the work of others, but more formal strategies are also needed to influence and direct the focus of research conducted outside EPA. The STAR grants and other extramural support have helped to do that in the context of US universities, but these extramural awards are smaller than those for most research activities relevant to EPA’s mission. In some circumstances, EPA may want to consider enhancing its efforts to proactively identify opportunities to collaborate with other federal agencies and national laboratories when practical. In other circumstances, EPA may want to place more focus on clearly articulating the importance of specific research topics to support EPA needs and improve environmental protection more generally.
There has for some time been an established mechanism for coordinating science among agencies of the federal government. It is accomplished through
interagency committees of the Office of Science and Technology Policy, such as the Committee on Environment, Natural Resources, and Sustainability (OSTP 2012). In that setting, agencies that are engaged in science that is relevant to EPA’s needs come together to exchange information, identify priorities, and plan joint efforts to address key science needs. But the mechanism falls short of what is needed to organize and conduct sustained and successful collaborative efforts, especially in the face of increasing budget constraints and emerging environmental and public health challenges. Furthermore, agencies operate under different, sometimes conflicting statutes, and have varied standards of evidence and scientific needs, which can lead to additional barriers to collaboration. EPA participates in a number of collaborative research efforts with other agencies, such as children’s health initiatives with the National Institute of Environmental Health Sciences and the National Nanotechnology Initiative, but future environmental challenges will require much more aggressive efforts to establish and support collaboration. Productive external collaboration should involve a set of proactive steps that include clear mandates from it and other agency leaders and a willingness to understand the regulatory frameworks, strengths, and resource limitations that other agencies face.
Sharing Experiences with Others
EPA maintains world-class laboratories that can serve as a vehicle to induce leading scientists from outside the agency to collaborate with scientists in EPA. EPA also can gain valuable experience and knowledge if its scientists have the ability to work in the research programs and specialized laboratories of other leading research organizations. Such collaborations in either direction can be facilitated through individual arrangements, but it is also important for the agency to continue to support and encourage fellowships that allow outsiders to work with it, university adjunct appointments that allow agency scientists to maintain substantive associations with leading research universities, and a variety of similar programs. That is especially important in addressing problems in which the agency does not have all of the relevant expertise. It will also be important to establish formal mechanisms by which the insights from these collaborations can be shared and infused throughout the agency.
Supporting International Collaboration
As globalization intensifies, domestic action alone will not be enough to address environmental concerns fully, and how other countries protect their environment has an important effect not only domestically but around the world. For example, air pollution, persistent organic pollutants, and mitigating climate change are major challenges that the entire global community faces in the 21st century. They are long-range, transboundary issues; no single nation can solve the problems, and no nation can escape the consequences. Nations have come to
recognize that they can protect their own national interests only when the community of nations is able to protect the commons through sustained international collaboration. EPA has identified a variety of objectives for international collaboration, including building strong science institutions, improving access to clean water, and improving urban air quality. The agency works to achieve those objectives by establishing collaborations and partnership with other nations and international organizations. EPA provides resources, tools, and technologies to support international initiatives. Its involvement in international collaboration is not simply one of supporting developing nations but learning from both developed and developing nations about the most innovative technologies and approaches for environmental protection.
For example, EPA is a leading partner in the Partnership for Clean Indoor Air, to which almost 600 partner organizations from over 120 countries are contributing their expertise and resources to reduce exposure to combustion products of fuels used in household cooking and heating (The Partnership for Clean Indoor Air 2012). Indoor smoke from solid fuels poses one of the top 10 health risks globally, contributes an estimated 3.3% of the global disease burden (WHO 2009), and is a source of effects on global climate through emissions of black carbon (Bond et al. 2004; Venkataraman et al. 2005). Since 2002, the partnership has made profound and broad progress in providing clean, efficient, affordable, and safe cooking technologies through commercial markets; reducing indoor air pollution by adopting improved cooking technologies, fuels, and practices; and monitoring and evaluating the health, economic, and environmental effects of the new energy technologies. The Partnership for Clean Indoor Air has also led to better understanding of indoor air pollution due to smoke from burning solid fuels. The mitigation strategies from the partnership have clearly shown both health and environmental benefits (Smith et al. 2009; Wilkinson et al. 2009).
EPA maintains a leadership role in developing science and technology and in translating scientific results to practice and daily life. Maintaining that leadership role can be accomplished by setting priorities for international collaboration with an emphasis on long-range concerns and long-term partnership; establishing multitier collaborations and partnerships with not only foreign governments but industries, academic institutions, and nongovernment organizations in other countries; and maintaining strong leadership in the dissemination of information, the provision of technical expertise, the implementation of policy, and the ability to receive such information globally and to integrate it into practice. As existing challenges persist and new ones emerge, opportunities and challenges for international collaboration will also evolve for EPA. International collaboration is no longer an option; it is a necessity for global solutions to global concerns. International collaboration should be viewed not as a public service or an aid to developing countries but as a crucial mechanism for improving the domestic environment and to gain critical research and implementation skills. It is about maximizing global resources to protect the environment globally and domestically at the same time.
Identifying New Ways to Collaborate
Collaboration within EPA, between agencies or other domestic institutions, and between countries will be increasingly important for addressing the complex problems of the 21st century, but it can be challenging to implement. Incentive structures need to be appropriately aligned, and there need to be mechanisms to facilitate collaboration among individuals or institutions that have different disciplinary backgrounds, are geographically distributed, and have different goals and objectives. Some collaboration will occur within single disciplines, the primary objective being to share knowledge and best practices. Others will seek to exchange knowledge across multiple disciplines, and this may require substantial sustained work.
Regardless of the goal, one way to achieve collaboration is to create “scientific exchange zones” for promoting interaction between disciplines, between scientists and nonscientists, and between strategic research programs (Gorman 2010). Creating such scientific exchange zones involves
• Allowing learning of the languages of multiple disciplines (for example, social science, physical science, water science, risk science, and decision science), which can be done via fellowships, internships, or short-term deployment from one program to another.
• Defining common science questions and establishing common descriptors.
• Creating new and common research methods.
• Identifying those who have top interactional expertise and training the next generation in interactional expertise.
• Developing and supporting experiential interactive projects.
Advances in information technologies (such as those discussed in Chapter 3 and Appendix D) are increasing opportunities for scientific exchange zones. Physicist Michael Nielson identified two ways in which online tools can advance science—by expanding the array of scientific knowledge that can be shared throughout the world and by changing the processes and scale of creative collaboration (Nielsen 2012). Nielson argues for extreme openness in which “as much information as possible is moved out of people’s heads and labs, onto the network” where it can be effectively used.
The scientific community has been generally slow to embrace that type of sharing of knowledge, in part because of longstanding views about the need to maintain proprietary methods and databases to enhance the reputation of experts within focused content areas, a key criterion for promotion and tenure. However, federally funded projects increasingly require mechanisms for sharing of methods and databases, and universities and other institutions are developing structures to reward collaborative research. EPA science would benefit from adopting best practices of institutions that are trying to reward collaborative and open-
exchange research. There is a growing number of examples of fostering innovation through open communication and collaboration. For example, the Web site InnoCentive is an “open innovation and crowdsourcing pioneer that enables organizations to solve their key problems by connecting them to diverse sources of innovation including employees, customers, [and] partners” (InnoCentive 2012). It uses a “challenge-driven innovation” method that supports innovation programs. Another example of a collaborative-network approach is the National Center for Ecological Analysis and Synthesis, which supports research across disciplines, uses existing data to address ecologic challenges and challenges in allied fields, and encourages the use of science to support management and policy decisions.
Collaboration can also take the form of interaction with members of the general public (which may include people who have scientific expertise). As discussed in Chapter 3, massive online collaboration, also known as crowd-sourcing, involves issuing an open call that allows an undefined large group of people or community (crowd) to address a problem or issue that is traditionally addressed by specific individuals. With a well-designed process, crowdsourcing can help to assemble quickly the data, expertise, and resources required to perform a task or solve a problem by allowing people and organizations to collaborate freely and openly across disciplinary and geographic boundaries.
The idea behind regulatory crowdsourcing is that almost every kind of regulation today, from air and water quality to food safety and financial services, could benefit from having a larger crowd of informed people helping to gather, classify, and analyze shared pools of publicly accessible data—data that can be used to educate the public, enhance science, inform public policy-making, or even spur regulatory enforcement actions. Today, a growing number of regulatory agencies (including EPA, the US Securities and Exchange Commission, and the US Food and Drug Administration) see social media and online collaboration as a means of providing richer, more useful, and more interactive pathways for participation. EPA is no stranger to crowdsourcing. Indeed, for the 2009 Toxic Release Inventory, EPA released preliminary data to the public to utilize crowdsourcing as a means for improving and refining the data. The public right-to-know dimension of TRI provided an early example of using informational approaches to encourage environmental change, and also spurred the development of sites like MapEcos.org and Scorecard.org, which provide visual Web-based interfaces that enable citizens to see toxic emissions data and more in one place.
There are several opportunities for crowdsourcing or citizen science (the involvement of the general public in monitoring or other forms of data collection) to augment or enhance EPA scientific and regulatory capabilities, including crowdsourced data collection, urban sensing, and environmental problem-solving. In some domains, EPA would be poised to launch efforts in the near term on the basis of its experiences and existing infrastructure. In others, there would need to be investment in key technologies or resources to make the efforts practical and informative.
Finding: Research on environmental issues is not confined to EPA. In the United States, it is spread across a number of federal agencies, national laboratories, and universities and other public-sector and private-sector facilities. There are also strong programs of environmental research in the public and private sectors in many other nations.
Recommendation: The committee recommends that EPA improve its ability to track systematically, to influence, and in some cases to engage in collaboration with research being done by others in the United States and internationally.
The committee suggests the following mechanisms for approaching the recommendation above:
• Identify knowledge that can inform and support the agency’s current regulatory agenda.
• Institute strategies to connect that knowledge to those in the agency who most need it to carry out the agency’s mission.
• Inform other federal and nonfederal research programs about the science base that the agency currently needs or believes that it will need to execute its mission.
• Seek early identification of new and emerging environmental problems with which the agency may have to deal.
Crosscutting Example of an Opportunity to Stay at the Leading Edge of Science
As EPA strives to conduct science that anticipates, innovates, takes the long view, and is collaborative, it will be useful for the agency to draw on recent examples to understand in practical terms how it might apply these approaches effectively and in an integrated fashion. The committee describes one such example above in the discussion of the emergence of nanotechnologies and how EPA can better anticipate new technologies. Another broader example, which cuts across all aspects of improving EPA science, is the issue of hydraulic fracturing of shale for natural gas (or hydrofracking). See Box 4-2.
Leading-edge science will produce large amounts of new information about the state of human health and ecologic systems and the likely effects of introducing a variety of pollutants or other perturbations into the systems. In particular, many multifactorial problems require systems thinking that can be
readily integrated into other analytic approaches (which use risk-assessment concepts for components of the analysis but incorporate other information). Over the years, the agency has become more accomplished in addressing cross-media problems and avoiding “solutions” that transfer a problem from one medium to another, for example, changing an air pollutant to a water or solid-waste pollutant. However, future problems will go beyond cross-media situations and will need to consider global climate and local air quality, land-use patterns and environmental degradation, and implications for industry, the public, and the environment.
The set of technologies involved in hydrofracking have implications for many of EPA’s programs. The development and operation of hydrofracking facilities can affect surface and ground water, soil, air quality, and greenhouse gas emissions. More broadly, the availability of growing quantities of economically-competitive natural gas can influence industry choices in response to EPA air quality regulations and other rule makings (for example, utility decisions to replace coal-fired electric generating facilities with combined-cycle natural gas in response to EPA emissions rules). Natural gas availability may also have important impacts on other segments of the economy (for example, transportation would be impacted with the development of natural gas infrastructure).
Over the last several years, EPA has become increasingly involved in investigating hydrofracking, both on its own and in concert with a number of federal agencies. It has responded to local issues raised by the activity (often through regional offices), and it has considered and implemented new regulations on the activity (such as, the recent air quality regulations requiring “green completions” for facilities) (Weinhold 2012). However, getting “ahead” of the activities and implementing studies and other actions has been increasingly controversial. For example, in response to FY2010 appropriations language, the agency launched a study of the potential impacts of hydrofracking on groundwater (EPA 2011e), which has been very closely monitored and criticized by industry (Batelle 2012).
The case of hydrofracking gives EPA an opportunity to consider how its science can anticipate, innovate, take the long view, and collaborate, and how it can better embrace systems thinking. It also gives the agency an opportunity to examine how it did or did not apply the concepts presented in the section “Staying at the Leading Edge of Science” and what it might do differently in the future. Such an examination could try to address the questions posed below, among others.
Anticipate: Hydrofracking emerged in the first decade of this century as a rapidly growing means of natural gas (and some oil) production, first in the western United States and in Texas, and then, beginning in 2007, in the
northeast. Its production has grown from a few wells in the beginning to thousands of wells over the last 5 to 10 years. How well did the agency “see” this rapid development coming? Did it hear from its “ears to the ground” in the regional offices and recognize the issue needed an agency-wide approach? How quickly did it grasp both water and air implications? How quickly did it understand the potential need to revisit both its research and regulatory activities?
Innovate: Innovation can be important in something like hydrofracking in a number of ways. For example, assessing complex hydrogeologic systems to understand potential groundwater contamination requires a set of advanced technical skills and familiarity with the latest technologies. At the same time, understanding the potential biologic and ecologic effects of the large number of chemicals being used in hydrofracking requires relatively rapid action, necessitating a decision on the applicability and utility of tools (potentially including life-cycle assessment, health impact assessment, and high-throughput screening) and techniques to evaluate chemical mixtures. How has EPA met these and other needs for innovation in this case? In addition to their own actions, how well have they brought on board the skills and experience of other agencies and the private sector?
Take the long view: While there has been a primary focus on potential shorter-term effects of hydrofracking, it is likely, as with many cases of potential groundwater contamination, that the full potential for contamination can only be determined with a commitment to long-term monitoring around the facilities. EPA has been part of a government-wide effort to coordinate hy-drofracking activities (for example, working with the US Geological Survey on long term ground water monitoring). But to what extent is the agency looking at any of its relevant permitting and other authorities and considering how to build long-term monitoring and disclosure into all actions? Such an activity would help to build an essential long-term database.
Is collaborative: There has rarely been an issue that touches on so many public agencies at the federal, state, and local level. The US Centers for Disease Control, National Institutes of Health, US Geological Survey, Department of Energy, state and local environment and health agencies, and many others (including the private sector) are engaged in a wide range of testing, research, and other activities necessary to assess potential risk. How well has the agency applied the principles and ideas described above to enhance its collaboration on an issue like hydrofracking? What could it do to improve that collaboration?
Beyond these four important attributes of leading edge science, hydrofracking also raises a number of broader challenges related to systems thinking that are illustrative of the need for EPA to better embrace such thinking in all it does. For example, to what extent should EPA be stepping back from the near-term water-quality and air-quality issues to ask more fundamental systems questions such as: What are the life-cycle implications of natural
gas for greenhouse gas emissions (such as methane emissions) and how do they compare on a life-cycle basis with other alternatives? From a sustainability point of view, are there ways in which consumers could be encouraged to decrease their consumption of energy that comes from natural gas rather than simply increasing the production of natural gas? Questions such as these are of course beyond the sole domain of EPA, but systems thinking can help inform EPA’s scientific research and ultimately its regulatory choices as well.
This case example is not designed to be prescriptive or to suggest that the agency has not been pursuing many of the questions. Rather, a systematic look at the experience with hydrofracking can lend guidance on many fronts for enhancing EPA science’s ability to stay at the leading edge and embrace systems thinking in a variety of important fields.
Many analytic tools and skills can contribute to analyzing and evaluating such complex scenarios. The committee describes below four areas in which the agency’s tools and skills can be enhanced and integrated to support systems thinking better:
• Life-cycle assessment (LCA).
• Cumulative risk assessment.
• Social, economic, behavioral, and decision sciences.
• Synthesis research.
These tools can be used in conjunction with one another and as inputs to methods for synthesis and evaluation for decisions. In each situation, it is important to integrate efforts to characterize both human health and ecosystem effects.
LCA is “a technique to assess the environmental aspects and potential impacts associated with a product, process, or service, by: compiling an inventory of relevant energy and material inputs and environmental releases; evaluating the potential environmental impacts associated with identified inputs and releases; [and] interpreting the results to help [decision-makers] make a more informed decision” (EPA 2006a, p.2). Performing such analysis requires an accounting of where all materials used in an activity originate and end up. It also requires an accounting of all the inputs into the activity (such as energy and transportation) and their associated environmental consequences and of the changes in other behaviors and other activities that the primary activity induces. Box 4-3 discusses an example of the need for and challenges of LCA.
The idea of LCA is appealing, but the technical details of how to do it well are very challenging. Broadly, two approaches are traditionally used. Process-
based LCA is a bottom-up approach that involves itemization of each step in producing a product and consideration of everything from extraction through production and disposal. Although informative and readily interpretable, it systematically underestimates environmental effects by missing key secondary and “ripple” effects (Majeau-Bettez et al. 2011). Data are often inadequate, and strategies to figure out the best way of drawing system boundaries need attention. In addition, although the life-cycle inventory can be constructed in many situations, determining the health or ecologic effects can be challenging given the array of pollutants, the broad scope, and the resulting lack of site specificity of emissions or effects. Researchers have developed approaches to integrating health risk-assessment concepts into process-based LCA, taking account of such factors as pollutant partition coefficients, stack height, and population density to refine the characterization of effects (Humbert et al. 2011), but more work clearly is needed. The second approach involves conducting input—output LCA, in which large matrices of transfers between economic sectors are constructed. That allows consideration of the full ripple effects of actions that are influencing a specific sector (Majeau-Bettez et al. 2011) but with even greater challenges in linking outputs of economic-sector activity with defined health and environmental effects.
EPA has some internal capacity in LCA, has been required to conduct LCA of fuels in the Energy Independence and Security Act of 2007, and has developed tools such as the Tool for Reduction and Assessment of Chemical and Other Environmental Impacts (Bare 2011); but LCA has not been systematically applied to the agency’s mission. LCA tools and inventories have been much further developed and applied in other regions, such as Europe (Finnveden et al. 2009). Nonetheless, even without undertaking a formal quantitative LCA, complex systems-level challenges require that the agency at least apply “life-cycle
The need for and challenges of LCA are seen in the case of biofuels. Some analyses suggest that regulatory requirements regarding the use of such fuels may not reduce carbon dioxide emissions and indeed might even increase them (NRC 2010). Those analyses suggest that such mandates could result in a loss of US crop lands available for food production because of the use of the land to produce fuel. That, in turn, could result in pressures to clear forest land in other parts of the world (which is an example of indirect land-use effects) (Searchinger et al. 2008). In addition, the fertilizer to grow such fuel crops in the midwestern United States may contribute to runoff that exacerbates the anoxic zone in the Gulf of Mexico (Rabalais 2010). Thoughtful analysis and interpretation of the results of LCA for biofuels are necessary because some of its methods and assumptions remain controversial (Khosla 2008; Kline and Dale 2008).
thinking” to characterize where a particular product, action, or decision may shift effects somewhere in the life cycle of a product or activity and how those effects can be minimized or prevented. For example, a simple chemical substitution may result in the use of a new product that may be safer for consumers but may cause effects on workers far upstream in the production process. In addition, LCA is an inherently comparative tool because it considers the life-cycle implications of multiple products or processes that achieve the same end use. This so-called functional unit determination is intended to be broad and to encourage innovation in the development of solutions by focusing on what a consumer needs from a product rather than on the product itself. Box 4-3 outlines the opportunities that LCA or life-cycle thinking can provide to enhance systems thinking about complex problems.
Cumulative Risk Assessment
The advent of new science tools and techniques means that the suite of traditional tools need to be reviewed and enhanced for 21st century challenges and opportunities. Quantitative risk assessment has been central to many aspects of EPA’s mission for decades. The risk-based decision-making framework proposed in Science and Decisions: Advancing Risk Assessment (NRC 2009) offers an opportunity, and detailed recommendations, for the agency to revisit and revamp its current practices. In particular, this would encourage linkages between risk assessment and various solutions-oriented approaches. In addition, as discussed in Chapter 3, a host of rapidly evolving health and ecosystem assessment tools (for example, “-omics” and the exposome) can be applied, with appropriate deliberation, to enhance risk assessment further.
Beyond enhancements in traditional single-chemical risk assessment, many of the trends in both science and risk-assessment practice in recent years involve moving from a single-chemical perspective to a multistressor perspective. EPA has grappled with chemical mixtures for some time, and cumulative risk assessment has come to the forefront of the agency’s thinking over the last decade, although the agency has rarely used it. Multiple recent NRC committees have addressed cumulative risk assessment extensively (NRC 2008, 2009), and the present committee concurs with the prior recommendations. Moreover, the committee supports the growing emphasis in EPA on this topic (which includes both intramural and extramural research), noting that these efforts have increasingly emphasized community-based participatory approaches, applications in disadvantaged communities, and use of epidemiologic insight. Nonetheless, although much of the emphasis of previous NRC reports has been on cumulative risk assessment for human health effects, it is possible that insights and approaches from ecosystem-based cumulative impact analyses (required under the National Environmental Policy Act [NEPA]) could be adapted to cumulative risk assessment for human health effects.
Cumulative risk assessment contains many subcategories of exposure, health, and ecologic risk analyses, and it is important for EPA to examine its research portfolio in this domain carefully to ensure that it is well aligned with the ultimate decision contexts. With the increased use of LCA or life-cycle thinking, identification of combinations of exposures associated with processes or technologies would be increasingly common, and methods to characterize the ecologic and human health implications of combined exposures would be valuable. There are potentially valuable applications of advanced biosciences for evaluating various chemical mixtures rapidly, but they would not capture psychosocial stressors and other prevalent community-scale factors that are of increasing interest to the agency and various stakeholders (Nweke et al. 2011). New epidemiologic methods or application of epidemiologic insights can start to address those factors, but today they are limited in the number of stressors and locations with adequate exposure data and sample size that they can accommodate. Advancing methods along both fronts, ideally in a coordinated and mutually reinforcing manner, would be the most fruitful approach.
As EPA concentrates increasingly on wicked problems and broad mandates related to sustainability, narrowly focused risk assessments that omit complex interactions will be increasingly uninformative and unsupportive of effective preventive decisions. The broad challenge before the agency will involve developing tools and approaches to characterize cumulative effects in complex systems and harnessing insights from multistressor analyses without paralyzing decisions because of analytic complexities or missing data.
Social, Economic, Behavioral, and Decision Sciences
Systems thinking involves acknowledgment, up front, that environmental conditions are substantially determined by the individual and collective interactions that humans have with environmental processes. As discussed in Chapter 2, the human drivers of environmental change include population growth, settlement patterns, land uses, landscape patterns, the structure of the built environment, consumption patterns, the mix and amounts of energy sources, the spatial structure of production, and a host of other relevant variables. Social, economic, behavioral, and decision sciences show that those drivers are not independent of the natural environments in which effects occur, and that there are feedbacks, positive and negative, between human and environmental systems (Diamond 2005; Ostrom 1990; Taylor 2009). Environmental science and engineering also provide technologies for altering the relationships between humans and the environment and tools for predicting environmental change in response to changes in social and economic systems. That knowledge is all essential and useful for informing environmental decisions and policies; however, additional knowledge, skills, and expertise are needed. To make well-informed policies and decisions that are sustainable, it is essential to integrate theories of, evidence on, and tools for understanding how people respond to changes in the environ-
ment, how people respond to interventions that are designed to alter human behavior to achieve desired social and environmental goals, and how specific policies can be implemented within the constraints of legal rights and strongly held, diverse cultural values.
In recognition of that need, it is evident that contributions from the social, economic, behavioral, and decision sciences are crucial for meeting legislative and executive mandates and finding pathways to fulfill EPA’s mission sustainably (that is, cost-effectively and equitably and with the greatest prevention effects). Social, economic, behavioral, and decision scientists have the knowledge and expertise to produce analyses that augment traditional health and ecosystem studies to inform policy-makers and stakeholders of the potential economic and social effects of policy decisions. Such analyses have the potential to elucidate the selection of the best solutions not only for the environment but for society as a whole. Spatially explicit assessments of the effects of policies on wages, employment opportunities, and environmental exposures are crucial for understanding the distribution of the benefits and costs of policies and associated community effects by income class, race, and other characteristics relevant to equity and environmental justice (see, for example, Geoghegan and Gray 2005).
Social, economic, behavioral, and decision scientists can help decision-makers to identify unintended environmental or social consequences of public policies such as through the use of predictive economic modeling integrated with environmental modeling. One example is the identification of adverse effects of economically induced land-use changes that resulted from ethanol and renewable-energy policies on nutrient pollution and greenhouse gas emissions (Searchinger et al. 2008; Hellerstein and Malcolm 2011; Secchi et. al. 2011). The effectiveness of environmental policies can be improved if the heterogeneity of humans, the implications of land use, transportation, and other policies affecting the environment, and general equilibrium feedbacks in economic systems are taken into account (Greenstone and Gayer 2007; Kuminoff et al. 2010; Abbott and Klaiber 2011). Providing such information to decision-makers could avoid unintended environmental or social outcomes of regulations and policies. In addition, social, economic, behavioral, and decision scientists have the knowledge and expertise to analyze consumer and business behavior to find less expensive, more effective, and fairer ways to achieve environmental goals (both in the context of existing legislation and in the context of fundamental policy innovations). For example, research with agent-based simulation models (Roth 2002; Duffy 2006; Tesfatsion and Judd 2006; Zhang and Zhang 2007; Parker and Filatova 2008) and laboratory and field experiments (Roth 2002; Suter et al. 2008) are sources of new economic insights for policy instrument design.
For EPA, social, economic, behavioral, and decision science skills can enhance several types of activities that support decisions, including regulatory impact assessments mandated by Executive Order 12866 and others, estimates of economic and social benefits and costs associated with alternative courses of action, and valuation of health benefits and ecosystem services to inform benefit—cost analysis. EPA has made some strides in improving its efforts in this re-
gard, primarily in its application of economic analysis, but the committee notes three important needs for improvement—the need to better integrate social, economic, behavioral, and decision science in decisions; the need for a renewed research effort to update and enhance health and ecosystem valuation and benefits; and the need for substantially improved staff expertise in this field, especially in the social, behavioral, and decision sciences (see the discussion on this topic in the section “Strengthening Science Capacity” in Chapter 5).
Integrating Social, Economic, Behavioral, and Decision Science Skills
Social, economic, behavioral, and decision sciences can serve many functions that are crucial for meeting legislative and executive mandates and for finding pathways to realize EPA’s mission cost-effectively and equitably. But even if the gaps are addressed, the benefits of using economics, social, behavioral, and decision sciences in EPA cannot be fully realized unless these areas of expertise are genuinely integrated into EPA decision-making and decision support. The gaps identified by the committee are compounded further by the need for tools to address systems-level impacts—which are often highly uncertain in nature (such as indirect but interconnected impacts of a particular decision or activity)—and solutions that address root causes of problems.
The process of developing a total maximum daily load (TMDL) for the Chesapeake Bay is an example in which EPA conducted high-quality environmental science but did not adequately integrate social, economic, behavioral, and decision sciences. The TMDL calls for reductions in nitrogen (by 25%), phosphorus (by 24%), and sediment (by 20%) to restore the bay by 2025 and allocates load reductions in its major tributaries to the bay (EPA 2010b). The TMDL can be viewed as a triumph of EPA-led environmental science. The agency initiated and led research to understand the effects of human activity on the bay’s waters and living resources and to provide a scientific foundation for measures to restore the bay beginning in the 1970s. That research has been crucial for the development of the science that underpins the TMDL, but the TMDL was developed without studies of the benefits and costs. EPA’s National Center for Environmental Economics and its Chesapeake Bay program are only now conducting benefit—cost assessments of the TMDL, which are too late to inform its specification. Furthermore, and perhaps even more problematic, EPA has neither conducted nor sponsored substantial social, economic, behavioral, and decision science research on fundamental policy questions related to inducing the behavioral changes that are essential for achieving the TMDL.
Updating and Enhancing Estimates of Environmental Benefits
Among the social, economic, behavioral, and decision sciences, only economics is generally mandated in EPA. Regulatory impact assessments to determine the benefits and costs of environmental regulation are mandated by various
executive orders. The most important is Executive Order 12866, which requires benefit—cost analyses of proposed and final regulations that qualify as “significant” regulatory actions. The Safe Drinking Water Act, the Toxic Substances Control Act, and the Federal Insecticide, Fungicide, and Rodenticide Act require EPA to weigh benefits and costs in regulatory actions. Some environmental legislation requires benefit and cost evaluations outside the regulatory process. The leading example is Section 812 of the Clean Air Act Amendments of 1990, which requires EPA to develop periodic reports to Congress that estimate the economic benefits and costs of provisions of the act; program offices are responsible for regulatory impact assessments in their fields. EPA’s National Center for Environmental Economics offers a centralized source of technical expertise for economic assessments in the agency.
Evaluations of EPA economic assessments indicate that they can be useful and influential. For example, an early evaluation of economic assessments (EPA 1987) found that “economic analyses improve environmental regulation. EPA’s benefit—cost analyses have resulted in several cases of increased net societal benefits of environmental regulations.” The report also found that “benefit—cost analysis often provides the basis of stricter environmental regulations.…For example, the most dramatic increase in net benefits ($6.7 billion) from EPA’s [regulatory impact assessments] resulted from a recommendation for much stricter standards—to eliminate lead in motor fuels.” The report also noted that, “alternatively, benefit—cost analysis may reveal regulatory alternatives that achieve the desired degree of environmental benefits at a lower cost.”
There are many uncertain and potentially controversial dimensions associated with the use of benefit—cost analysis as conducted for regulatory impact assessments. In principle, such analyses identify, quantify, and monetize the multiple outcomes of an environmental decision or policy into a single indicator of economic efficiency. If multiple alternatives are considered in the analyses, benefit-cost analyses can support a solutions orientation by incorporating economics factors into the risk-based decision-making paradigm described earlier. Apart from procedural details, there is debate about the validity of economic concepts of value for environmental and some other goods (for example, the value of life), the capacity of economics to measure some types of values, the discounting of future costs and benefits, the treatment of uncertainty and irreversibility, and the relevance of economic efficiency, as one among many societal objectives, to environmental decisions (EPA 1987; Ackerman and Heinzerling 2004; Posner 2004; Sunstein 2005). Despite the controversies, the importance of benefit—cost analysis for regulatory impact assessments is recognized almost universally. Harrington et al. (2009) have produced a useful set of recommendations to improve the technical quality, relevance to decision-making, and transparency of regulatory impact assessments and their treatment of new scientific information and balance of efficiency and distributional concerns. If implemented, a number of those recommendations would help integrate benefit—cost analysis with other tools to support systems thinking, including a focus on comparing multiple policy alternatives, making decisions given multi-
ple dimensions of interest, and improving how uncertainties are characterized and communicated. The issues of multidimensional decision-making and addressing uncertainty in complex systems are discussed below. EPA’s economists are cognizant of the controversies and challenges in conducting benefit-cost studies and of the frontiers of economic research in environmental benefit—cost analysis.
Even if benefit—cost analysis were implemented based on the recommendations from Harrington et al. (2009), there are important gaps in the scope of available work on the valuation of benefits, and the literature is becoming dated. For example, a value-of-a-statistical life (VSL) approach is used to assign monetary values to reductions in mortality risk. EPA typically bases its VSL values on a 1992 synthesis of 26 published studies (Viscusi 1992). Although EPA does provide more recent references to frame the discussion, including studies of how VSL may vary as a function of life expectancy or health status, the core quantitative value remains based on old studies that are not necessarily relevant to the people most vulnerable to air-pollution health effects. Inasmuch as analyses have consistently shown that uncertainty in VSL dominates the overall uncertainty in benefit—cost analyses and given that policy choices may hinge on this value, it seems incumbent on EPA to invest in intramural and extramural research specifically on it. Similarly, with respect to morbidity outcomes, the most recent willingness-to-pay study that was incorporated into the analysis of the Clean Air Act Amendments (EPA 2011f) was conducted in 1994. In that benefit-cost analysis, multiple key health outcomes were valued by using only cost-of-illness information.
Valuation of the ecologic and welfare benefits of air-pollution reductions is similarly lacking; the only dimensions monetized are the effects of reductions in agricultural and forest productivity on the price of related goods, the willingness to pay for visibility improvements (based on studies conducted 20–30 years ago), damage to building materials, and effects on recreational fishing and timber in the Adirondacks. A recent workshop on the use of ecologic nonmarket valuation in EPA benefit—cost analysis work concluded that “perhaps the most surprising outcome was the realization of how few nonmarket ecological valuation studies are used by the EPA” (Weber 2010).
Funding for valuation research has been reduced, and disciplinary interest in valuation research, once a major topic in environmental-economics journals, has diminished. Assessing and addressing gaps in the environmental-benefits estimates should have high priority and can be tackled through research designs that produce statistically representative samples for EPA regulatory impact assessments (for the importance of standardization and sampling strategies for water see, for example, Bruins and Heberling 2004; Van Houtven et al. 2007; Weber 2010). The challenges in addressing these gaps are not trivial given budget constraints and logistics barriers to collecting public data.
Two recent EPA documents discuss ecologic-valuation challenges and strategies for the agency (EPA 2006b; EPA SAB 2009). The stated goal of EPA’s Ecological Benefits Assessment Strategic Plan is to “help improve
Agency decision-making by enhancing EPA’s ability to identify, quantify, and value the ecological benefits of existing and proposed policies” (EPA 2006b, p. XV). The agency has devoted resources to enhancing the science of ecologic-service valuation through the STAR grants program and ORD’s ecosystem-services research program. The 2009 report by EPA SAB concluded that a “gap exists between the need to understand and protect ecologic systems and services and EPA’s ability to address this need” (EPA SAB 2009, p.8). The report provides recommendations for enhanced research on “how an integrated and expanded approach to ecologic valuation can help the agency describe and measure the value of protecting ecologic systems and services, thus better meeting its overall mission” (EPA SAB 2009, p.8).
Scientific progress has always depended on synthesis of disparate data, concepts, and theories (Carpenter et al. 2009). The combined forces of increasing research specialization, an explosion of scientific information, and growing demand for solutions to pressing environmental problems have made scientific synthesis more challenging and more urgent than ever before. In recent years, the National Science Foundation and other agencies have invested considerable funds in synthesis research centers. At least 19 such centers have now been established in the United States and abroad. They have demonstrated the power and cost effectiveness of bringing together multidisciplinary collaborative groups to integrate and analyze data to generate new scientific knowledge that has increased generality, parsimony, applicability, and empirical soundness (Hampton and Parker 2011). The impact of well-designed synthesis efforts extends beyond the life of the projects themselves. Projects spin off new and unexpected collaborative research, and researchers tend to expand the multidisciplinary breadth of their research (Hampton and Parker 2011). Several mechanisms that increase the creative productivity of multidisciplinary synthesis research have been identified, notably open, competitive calls for projects; face-to-face interactions at a neutral facility free of distractions; and multiple working group meetings that enable technology and analytic support, institutional diversity, diversity of career stages, inclusion of postdoctoral fellows, and moderately large group size (Hackett et al. 2008; Hampton and Parker 2011).
EPA often produces useful synthesis reports that summarize the state of knowledge on a topic, but this is not a substitute for synthesis research. The agency could make more use of deliberately designed synthesis research activities to promote multidisciplinary collaborations and accelerate progress toward integrated sustainability science. One example is the recent creation by the US Geological Survey of the John Wesley Powell Center for Analysis and Synthesis (The Powell Center 2012). EPA could also pursue opportunities with synthesis centers, such as the National Center for Ecological Analysis and Synthesis (NCEAS 2012) and the newly established Socio-Environmental Synthesis Cen-
ter (SESYNC 2012). Given its corpus of researchers in both environmental and health sciences, the agency is well positioned to pursue synthesis research that brings together environmental science and public-health science data and perspectives.
Systems-level problems are rarely amenable to simple quantitative decision measures. More often than not, complex problems require consideration of multiple types of information (including quantitative and qualitative data), characterization of different types of uncertainty, and consideration of prevention options. The information base might include outputs from tools such as LCA or cumulative risk assessment, integrated with economic and other information in a structured framework to inform decisions. There is a need for the agency to develop consistent approaches for synthesizing a broad array of systems information on hazards, exposures, solutions, and values. Although agencies like EPA regularly “do synthesis” for decision-making, the approaches to synthesis have been varied, often depending on regulatory demands. Most recently, EPA has attempted to realign its existing science decision-making processes in line with the sustainability framework proposed by the NRC Committee on Incorporating Sustainability in the US Environmental Protection Agency (NRC 2011a), although implementation of that realignment is in its early stages. The committee identified several approaches that could provide support to the agency in establishing consistent approaches for more holistic decisions. They include enhanced sustainability analysis (as recommended by NRC 2011a), solutions-oriented approaches (such as alternatives assessment and health impact assessment), and multicriteria decision analysis.
EPA has recently begun to implement tools and approaches to determine how the science that it is developing and decisions made on the basis of it support sustainability (Anastas 2012). The NRC Committee on Incorporating Sustainability in the US Environmental Protection Agency developed a sustainability-analysis framework for EPA (NRC 2011a), starting with the definition of sustainability espoused in Executive Order 13514 (2009). The definition of sustainability provided in that executive order is “to create and maintain conditions, under which man and nature can exist in productive harmony, and fulfill the social, economic, and other requirements of present and future generations of Americans” (42 U.S.C. §§ 4331(a)[NEPA§101]). That committee developed its sustainability framework and the sustainability assessment and management approach (Figure 4-2) to provide guidance to EPA on incorporating sustainability into decision-making. They build on the traditional risk-assessment and risk-management framework of the agency.
The framework and assessment and management approach are built on traditional principles of vision, objectives, goals, and metrics. The goals of sustainability analysis are to expand decision consideration to include multiple sustainability options and their social, environmental, and economic consequences; to include the intergenerational effects of consequences in addition to more immediate ones; and to involve a broad array of stakeholders. Many of these concepts intersect with the solutions-oriented approaches discussed in this section, including the expansive scope and stakeholder involvement that will be discussed in the health impact assessment (HIA) paragraph below, the use of behavioral science and economics to consider an array of impacts, and the use of life-cycle thinking to avoid creating upstream and downstream problems. The framework and approach lay out a series of steps that should be taken in evaluating sustainability implications of a particular decision. The evaluation tools to be used will depend on the nature and needs of the particular decision. Although this framework is new and does not have a particular “toolbox” or analytic technique, it provides a set of steps that can be taken in synthesizing information from varied sources and fields into a coherent sustainability decision.
There has been an increasing emphasis among advisory committees and in EPA on moving away from characterizing problems and toward determining and evaluating solutions. For example, Science and Decisions: Advancing Risk Assessment (NRC 2009) emphasized that risk assessment should be used to discriminate among risk-management options, not as an end in itself, and this suggests a framework within which alternative options are considered upfront. A recent NRC report (NRC 2011b) gave recommendations about HIA as a solutions-oriented policy tool to introduce health considerations into numerous policy decisions that could have direct or indirect health implications. HIA, as defined by the NRC Committee on Health Impact Assessment (2011b), is consistent with the risk-based decision-making framework proposed by Science and Decisions: Advancing Risk Assessment (NRC 2009). Both approaches explicitly emphasize conducting analyses that help discriminate among policy options and that use planning and scoping to devise analyses that are of an appropriate level of sophistication given the decision context. Although it includes approaches beyond risk assessment and has a scope that often extends beyond EPA’s mandate, HIA has many attributes that are well-aligned with the future needs of EPA. For example, HIA incorporates systems thinking and encourages development of broad conceptual models to avoid unanticipated risk tradeoffs, which is a valuable approach to incorporate into numerous analytic tools. HIA also endorses the use of both quantitative and qualitative information to inform decisions, and it explicitly considers equity issues and vulnerable populations that may not be captured within benefit-cost analyses or related tools.
In parallel, alternatives assessment has formed the basis of pollution-prevention planning efforts, the chemical-alternatives assessment processes undertaken by the EPA Design for the Environment program (see Chapter 3), and technology options analysis in chemical safety efforts. Although alternatives assessment is not strictly tied to risk assessment and risk management, it similarly involves the systematic analysis of a wide array of options for a potentially damaging activity that are evaluated on the basis of hazard, performance, social, and economic factors. Beyond HIA and alternatives assessment, there are several other tools for applying systems thinking that are intrinsically solutions-oriented. For example, LCA emphasizes comparing alternative methods for addressing a defined need, and benefit—cost analysis is designed to compare multiple policy options to arrive at an optimal choice.
Regardless of the specific approach and application, those approaches all provide a tool for focusing on solutions and innovation opportunities and drawing attention to what a government agency or proponent of an activity could be doing to solve the problem at hand rather than simply characterizing it in finer detail. They also provide opportunities to evaluate the reduction of multiple risks rather than simply focusing on controlling a single hazard, potentially leveraging the methods and approaches within cumulative risk assessment. Finally, if agencies’ actions promote restriction of a particular activity, there is a responsibility to understand alternatives and support a path that is environmentally sound, technically feasible, and economically viable and that does not create new risks of its own. Box 4-4 gives an example of a solutions-oriented approach for reducing chemical use.
Many of the above solutions-oriented approaches are currently in use in some manner in EPA, but they are not applied comprehensively and systematically across the agency. However, alternatives-assessment approaches are built into numerous laws and international treaties. The process for carrying out an environmental impact statement under NEPA and state programs is one of the most comprehensive examples for the requirement of alternatives assessment at the national level (Tickner and Geiser 2004). When assessments are undertaken under NEPA, agencies and organizations that use public funds and that are carrying out activities that might have substantial effects on the environment need to undergo the process for creating an environmental impact statement. “The goal of NEPA is to foster better decisions and ‘excellent action’ through the identification of reasonable alternatives that will avoid or minimize adverse impacts” (Tickner and Geiser 2004).
NEPA regulations require that the process described above be carried out before the start of any activity that might have environmental effects. An interdisciplinary approach is undertaken to ensure that environmental effects and values are comprehensively identified and examined; to ensure that appropriate and reasonable alternatives are rigorously studied, developed, and described; and to recommend specific courses of action. The first step of assessing effects is a scoping process, during which potential effects are broadly defined and
examined in detail, “including direct and indirect impacts, cumulative effects, effects on historic and cultural resources, impacts of alternatives, and options to mitigate potential impacts” (Tickner and Geiser 2004). The NEPA environmental impact statement approach, supplemented by new approaches to health impact assessment, provides a way of integrating scientific information from multiple sources into decisions that focus on evaluating prevention options.
The solvent trichloroethylene (TCE) has been targeted for substantial reductions in exposure by EPA and numerous states because of its toxicity, particularly its potential carcinogenicity. It is commonly found at Superfund sites and is of particular concern because it can leach into and contaminate groundwater and drinking water supplies. TCE is mainly used to degrease metal parts and it can cause harmful occupational exposures if it is accidentally spilled. Applying traditional end-of-pipe control approaches have, in many cases, resulted in the TCE problem being shifted from air to water to land rather than the problem being eliminated. Reducing human exposures to TCE cannot be solved using a simple solution; a systems-based and solutions-oriented approach must be used.
Under the 1989 Massachusetts Toxics Use Reduction Act, chemical manufacturers that produce large quantities of toxic chemicals, which include TCE, are required to pay a fee and to conduct a form of systems analysis. The analysis includes a materials throughput analysis every year and a facility planning process analysis every 2 years to understand how and why chemicals are being used and to assess potential process and product modifications that would reduce toxic material use and waste. The fee provides funding for the Toxics Use Reduction Institute (TURI) at the University of Massachusetts, Lowell. Most cleaning tasks that use TCE can be performed with alternative organic solvents or with water-based cleaners. In general, water-based cleaners are preferred because they are usually safer for human health and the environment. TURI has been working with manufacturers of metal parts and manufacturers of electronics to help them move from TCE to safer and more cost-effective cleaning solutions. TURI determined that one of the barriers in the adoption of safer alternatives is the concern that productivity and product specifications might suffer if standard metal cleaning procedures are altered. To address this concern, TURI created the Surface Solutions Laboratory to evaluate the effectiveness and safety of TCE alternatives for small-sized and medium-sized companies. By focusing on the “function” that TCE provides, requiring a systems evaluation, and providing support for solutions, industrial TCE use in Massachusetts declined by more than 77% from 1990 to 2005, with greater than 90% reductions in some sectors.
Source: Adapted from Sarewitz et al. 2010 and TURI 2011.
EPA has substantially contributed to the advancement of analytic techniques and tools to detect environmental stressors and characterize health and ecosystem impacts of those stressors. While better characterization of problems is important, it is critical that the agency apply this knowledge to primary pre-vention—that is, the design of safer and more sustainable forms of production and consumption. Like sustainability, a focus on solutions should be more than a simple mission statement. It must be linked to adequate resources, tools, and infrastructure at the highest levels of the agency.
Multiple-Criteria and Multidimensional Decision-Making
The tools of alternatives assessment, HIA, and the sustainability management approach all incorporate an array of information to arrive at a preferred solution, but this becomes increasingly challenging given numerous dimensions that often cannot be compared on the same scale. Benefit—cost analysis is a well-known example in which the multiple outcomes of a decision are monetized (if possible) and aggregated into a single indicator of economic efficiency, but it cannot provide a complete ranking of alternatives if stakeholders and environmental decision-makers are interested in other objectives (such as fairness across income classes, regions, or racial groups; generations in the distribution of burdens and benefits; or norms in the treatment of nonhuman organisms). Benefit— cost analysis is useful and sometimes mandated for regulatory impact assessments, but its value is limited in dealing with complex issues in which economic efficiency is only one of many important objectives for environmental decision-makers and their stakeholders. While deliberative approaches may be warranted in complex situations, especially when both quantitative and qualitative information are being used, analytic approaches to integrate data from multiple sources and types into a single number or range of numbers have tremendous potential.
One approach to solving problems that have multiple incommensurate dimensions is to use tools within the realm of multiple-criteria decision-making (MCDM) (Figueira et al 2005). Within the broad framework of informatics, developing and applying MCDM in conjunction with uncertainty analysis and data-mining (Shi et al. 2002) can provide a set of useful ways for using emerging science and developing evidence-supported policy-making in the agency. Like benefit—cost analysis, MCDM is an approach that creates and assigns a preference index to rank policy options on the basis of the totality of all adopted criteria. However, unlike benefit—cost analysis, MCDM was not designed to rank options based on a consumer’s preference for environmental or other goods. Instead, the method is flexible for selecting weights and it is often designed to use weights assigned by the decision-maker. This flexibility allows for the inclusion of a broader set of objectives, although the selection can be inherently contentious. The preference index value attributable to each criterion reflects the nature and importance of the criterion, for example, cost, benefits,
innovation, or change in ecology or human health. The preference index then leads to a partial ranking of the policy options under consideration and recommendation of an “optimal” set of choices or competitive choices (Brans and Vincke 1985). MCDM has been applied successfully in environmental decision-making (Moffett and Sarkar 2006; Hajkowicz and Collins 2007); however, criterion-specific constituents of the preference index for each policy option are affected by the quality of the science and evidence, scaling, and other factors that can limit validity (Hajkowicz and Collins 2007).
An alternative to single-objective formulations is to provide decision-makers with the Pareto optimal set of nondominated candidate solutions. Essentially, the Pareto optimal set is constructed by identifying decisions that can improve one or more objectives without harming any other. Use of the Pareto optimal set does not determine a single preferred approach but presents decision-makers with a smaller set of options from which to choose. The concept of Pareto optimal sets is not new, but the capacity to apply it in decision-making has been greatly expanded by recent methodologic advances in optimization techniques (most notably multiobjective evolutionary algorithms) and computation of Pareto sets for large complex problems, and this has increased the scope of environmental and other applications (Coello et al. 2007; Nicklow et al. 2010). Rabotyagov et al. (2010) give an example of evolutionary computation for the analysis of tradeoffs between pollution-control costs and nutrient-pollution reductions. Optimal sets of air pollution control measures have been developed that consider aggregate health benefits and inequality in the distribution of those benefits as separate dimensions (Levy et al. 2007). Kasprzyk et al. (2009) demonstrate how multiobjective methods can be used to inform policies for the management of urban water-supply risks that are caused by growing population demands and droughts. Multiobjective optimization in support of environmental-management decisions is especially compelling given the emerging paradigm of managing for multiple ecosystem services and consideration of cumulative risks for human health. Tradeoffs and complementarities can exist between alternative services and between other relevant performance metrics (for example, public and private costs and distribution outcomes by location or income class). Applications of multiobjective optimization methods would promote the explicit specification of preference indices relevant to environmental decision-making and science to quantify outcomes and evaluate tradeoffs; all this would serve to improve the transparency and scientific soundness of decisions.
Addressing Uncertainty in Complex Systems
With any of the solutions-oriented approaches delineated above, regardless of which analytic tools or indicators are used by EPA to support decisions in the future, uncertainty will be an overriding concern. With increasingly complex multifactorial problems and a push for tools that are sufficiently timely and
flexible to inform risk-management decisions (NRC 2009), the importance of uncertainty characterization and analysis will only increase. It should be noted that the increasing importance of uncertainty analysis does not necessarily imply increasing sophistication of computational methods or even increasing necessity of quantitative uncertainty analysis. As discussed in Science and Decisions: Advancing Risk Assessment (NRC 2009), uncertainty analysis is a component to be planned for with the rest of an assessment, and a simple bounding analysis or qualitative elucidation of different types of uncertainties may be adequate if it shows that a given risk-management decision is robust compared with competing options (NRC 2009).
Consistent and holistic approaches are necessary for characterizing and recognizing uncertainty (in particular the various types of uncertainty, including unquantifiable systems-level uncertainties, indeterminacy, and ignorance). Such approaches would allow EPA to articulate the importance of uncertainty in light of pending decisions and not become paralyzed by the need for increasingly complex computational analysis. In addition, applying uncertainty analysis coherently in all EPA’s arenas would ensure that a policy or decision is both tenable and robust (van der Sluijs et al. 2008) and would ensure that uncertainty analysis is a means to an end and is designed with the end use in mind. Similarly, uncertainty analyses that are billed as comprehensive but omit key sources of uncertainty have the potential to be misleading or to lead to inappropriate decisions about research priorities and interventions. Finally, EPA would benefit from communicating uncertainty more effectively. Uncertainty is often mistakenly viewed as a negative form of knowledge, an indicator of poor-quality science (Funtowicz and Ravetz 1992). There is therefore a perception that acknowledging uncertainty can weaken agency authority by creating an image of the agency as unknowledgeable, by threatening the objectivity of “science-based” standards, and by making it more difficult to defend itself in the face of political and court challenges. However, reluctance to acknowledge uncertainty can lead EPA to rely on tools and methods that cannot provide timely answers, can push the agency to use point estimates to defend what are policy decisions (see Brickman et al. 1985), and runs counter to the value of uncertainty analysis in informing research and decision priorities.
The committee has described the important emerging environmental issues and complex challenges in Chapter 2 and the many types of emerging scientific information, tools, techniques, and technologies in Chapter 3 and Appendixes C and D. It is clear that if EPA is to meet those challenges and to make the greatest possible use of the new scientific tools, its problems will need to be approached from a systems perspective. Although improved science is important for EPA’s future, it is not sufficient for fully improving EPA’s capabilities for dealing with health and environmental challenges. Better economic analysis, policy ap-
proaches, stakeholder involvement, communication, policy, and integration for systems thinking are also vital.
In the present chapter, the committee has recommended ways in which the agency can integrate systems thinking techniques into a 21st century framework for science to inform decisions. For EPA to stay at the leading edge, it will need to produce science that is anticipatory, innovative, long-term, and collaborative; to evaluate and apply new tools for data acquisition, modeling, and knowledge development; to continue to develop and apply new systems-level tools and expertise; and to develop tools and methods to synthesize science, characterize uncertainties, and integrate, track, and assess the outcomes of actions. If effectively implemented, such a framework would help to break the silos of the agency and promote collaboration among research related to different media, time scales, and disciplines. In supporting environmental science and engineering for the 21st century, EPA will need to continue to evolve from an agency that focuses on using science to characterize risks so that it can respond to problems to an agency that applies science holistically to characterize both problems and solutions at the earliest point possible.
Finding: Environmental problems are increasingly interconnected. EPA can no longer address just one environmental hazard at a time without considering how that problem interacts with, is influenced by, and influences other aspects of the environment.
Recommendation: The committee recommends that EPA substantially enhance the integration of systems thinking into its work and enhance its capacity to apply systems thinking to all aspects of how it approaches complex decisions.
The following paragraphs provide examples of some of strategies that EPA could use to help it set its own priorities and to enhance its use of systems thinking.
Even if formal quantitative LCA is not feasible, increased use of a life-cycle perspective would help EPA to assess activities, regulatory strategies, and associated environmental consequences. Placing more of a focus on life-cycle thinking would likely include increasing EPA’s investment in the development of LCA tools that reflect the most recent knowledge in LCA and risk assessment (both human health and ecologic). In addition, it may be more cost effective for EPA to provide incentives and resources to increase collaborations between LCA practitioners in the agency and those working on related analytic tools (such as risk assessment, exposure modeling, alternatives assessment, and green chemistry). EPA has some internal capacity for LCA, but could benefit from a more systematic use of such an assessment across the agency’s mission.
Continuing to invest intramural and extramural resources in cumulative risk assessment and the underlying multistressor data, including coordinated bench science and community-based components, would give EPA a broader
and more comprehensive understanding of the complex interactions between chemicals, humans, and the environment. A challenge before the agency is the characterization of cumulative effects using complex, incomplete, or missing data. Even as EPA seeks to improve its understanding of risks, some preventionbased decisions may need to be made in the face of uncertainty.
In EPA’s science programs, environmental decisions will only be effective if they consider the social and behavioral contexts in which they will play out. Such decisions can substantially affect societal interests beyond those that are specifically environmental. Tradeoffs among environmental and other societal outcomes need to be anticipated and made explicit if decision-making is to be fully informed and transparent. Predicting economic and societal responses at various points in the decision-making process is necessary to achieve desirable environmental and societal outcomes. For these reasons, developing mechanisms to integrate social, economic, behavioral, and decision sciences would lead to more comprehensive environmental-management decisions. EPA can engage the social, economic, behavioral, and decision sciences as part of a systems-thinking perspective rather than as consumers and evaluators of others’ science. Human behavior is a major determinant of the state of the environment and, as such, should be an integral part of systems thinking regarding environmental risk and risk mitigation alternatives. In addition, EPA would benefit from a long-term commitment to advancing research in a number of related fields, including valuation of health and ecosystem benefits.
Research centers that focus on synthesis research have demonstrated the power and cost effectiveness of bringing together multidisciplinary collaborative groups to integrate and analyze data to generate new scientific knowledge. Deliberately introducing synthesis research into EPA’s activities would contribute to accelerating its progress in sustainability science. A specific area where knowledge from systems thinking could be applied is in the design of safe chemicals, products, and materials.
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