The definitions provided in this appendix are from the U.S. Environmental Protection Agency report, Sustainability Analytics: Assessment Tools & Approaches (EPA 2013).
Benefit-cost analysis (BCA) (also known as cost-benefit analysis) is a widely used, well-documented tool for assessing the net economic effects of policies. BCA provides a systematic process for calculating, monetizing, and comparing the economic benefits and costs of a particular action, process, regulation, or project by putting benefits and costs in a common metric. The results of a BCA can be used in two key ways: to provide insight into whether a project or policy provides a net economic benefit or cost to a company or society; and, to compare the outcomes of different project or policy alternatives.
BCA is based on economic theory and techniques. Specifically, BCA draws on peer-reviewed economic literature both to identify and define categories of benefits and costs and to help estimate benefits and costs that are not directly bought and sold in markets. BCA has been an important component of regulatory analysis at the EPA for over three decades. Documentation of the EPA’s use of BCA to assess the economic impact of federal policies and programs is extensive. EPA’s 2010 Guidelines for Preparing Economic Analyses provides detailed guidance on the proper use of BCA (and other forms of economic analyses) to assess regulations and policies (EPA 2010a).
The premise behind chemical alternatives assessment (CAA) is that because risk is a function of hazard and exposure, focusing on hazard reduction is an effective way to mitigate risk. By assessing chemicals of potential concern and their functional alternatives with respect to their effects on the environment and human health, CAA enables the substitution of safer chemicals (Lavoie et al. 2010) ] Information gained through CAA can be used by decision-makers in combination with analyses of cost, performance, and other factors to select safer chemical and material alternatives.
CAA compares alternative chemicals within the same functional-use group across a consistent and comprehensive set of hazard endpoints. CAAs may also consider intrinsic properties of chemical substitutes that affect exposure potential, including absorption potential, persistence, and bioaccumulation. This approach to alternatives assessment orients chemical evaluations within a given product type and functionality. Factors related to exposure scenarios, such as physical form and route of exposure are generally constant within a given functional use group and would fall out of the comparison. Thus, the health and environmental profiles in the alternatives assessments become the key variable and source of distinguishing characteristics.
1Page numbers cited immediately after the names of the tools and approaches refer to EPA (2013).
Collaborative problem-solving (CPS) is a tool that allows various stakeholders to work together to address a particular issue or concern. Stakeholders often have to reconcile divergent interests in order to address complex and interrelated environmental, public health, economic, and social problems in local communities. Many of these problems are deeply rooted and difficult to resolve without the concerted effort and active participation of all the stakeholders. When multiple stakeholders work together, they create a collective vision that reflects mutually beneficial goals for all parties. Such collaboration fosters the conditions that enable the parties to mobilize the resources necessary to realize stronger, more enduring solutions.
CPS involves proactive, strategic, and visionary community-based processes that bring together multiple parties from various stakeholder groups (e.g., community groups, all levels of government, industry, and academia) to develop solutions to address local environmental and/or public health issues. Partnerships and negotiations are required to achieve such solutions. Partnerships refer to arrangements through which different stakeholders work together to achieve a common goal. These partnerships can range from informal working relationships to very structured arrangements in which goals, membership, ground rules, and operating principles are clearly defined. Negotiations refer to processes, ranging from informal to formal, through which different stakeholders agree to come together and resolve disagreements.
The National Charrette Institute defines a charrette as “a collaborative design and planning workshop held on-site and inclusive of all affected stakeholders.” (Lennertz et al. 2008). Charrettes enable community organizations, public agencies, developers, and other stakeholders to work together towards solving contentious or complex situations. They are frequently applied in the context of land use planning to support revitalization efforts, including brownfield assessment, cleanup, and reuse. Often facilitated by architects and planners, the goal of design charrettes is to come up with a mutually agreed-upon vision for future development that is both effective and sustainable (EPA 2010b).
Eco-efficiency analysis (EEA) is a tool for quantifying the relationship between economic value creation and environmental impacts, throughout the entire lifecycle of a product or service (Brattebø 2005; Moller and Schaltegger 2005; MBDC 2010; NACFAM 2010; BASF 2011 The term ‘eco-efficiency’ evolved from the work of the World Business Council for Sustainable Development (WBCSD) in response to the first United Nations Earth Summit. The WBCSD defines eco-efficiency as “the delivery of competitively-priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle.” (Lehni and Pepper 2000). In other words, to be eco-efficient is to add more value to a good or service while simultaneously decreasing adverse environmental impacts. EEA evaluates products and services by examining their environmental impact in proportion to their cost-effectiveness. BASF Chemical Corp. was one of the first companies to establish an EEA methodology in the early 1990s with the goal of reducing the environmental impact and costs of its products and processes. BASF’s EEA tool quantifies the sustainability of products or processes throughout their entire life-cycle, beginning with the extraction of raw materials through the end of life disposal or recycling of the product. It compares two or more products analyzed from the end-use perspective to obtain comprehensive data on the total cost of ownership and the impact on the environment (BASF 2012).
EEA differs from benefit-cost analysis (see discussion) in that it does not seek to monetize environmental benefits or costs and compare them to non-environmental benefits or costs (Bohne et al. 2008). Whereas BCA typically seeks to evaluate the net social benefits of a policy or program com-
pared to a baseline without the policy or program, EEA calculates the ratio of the total value of goods and services produced (output) to the sum of the environmental pressures created by the production of those goods and services (input). More sustainable alternatives have a higher output to input ratio, or eco-efficiency ratio (UNESCAP 2010).
EPA defines the term ecosystem as the “dynamic complex of plant, animal, and microorganism communities and their non-living environment.” (EPA 2009) The contributions of ecosystems to human well-being—or ecosystem services—are measured in terms of human values, and can be thought of as the direct and indirect economic, social, and environmental services provided to human populations and reflects the complex interactions between and among living organisms and their natural environment (EPA 2009).
Ecosystem services may be divided into four categories: provisioning services (e.g., food, fibers, drinking water); regulating services (e.g., flood protection, pest control); cultural services (e.g., cultural, spiritual, aesthetic); and, supporting services (e.g., soil formation, primary productivity) (Millennium Ecosystem Assessment 2005). The objective of ecosystem service valuation is to assess the consequences of altering ecosystems or using ecosystem services for human well-being (Millennium Ecosystem Assessment 2005).
For example, one third of our food comes from plants pollinated by birds, bats and insects. The value of these pollination services in the United States is estimated at $6 billion a year. If we destroy populations of pollinators with pesticides, loss of habitat, or other stressors we would be forced to either forgo many fruits, vegetables, and grains we enjoy or replace pollination services with potentially costly alternatives. Thus, pollination is an essential and valuable service provided free in natural functioning ecosystems, and its loss has obvious and direct implications on the economic, social, and environmental systems.
Environmental footprint analysis is an accounting tool that measures human demand on ecosystem services required to support a certain level and type of consumption by an individual, product, or population. Footprint methodologies estimate life-cycle environmental impacts from a narrower viewpoint than traditional life-cycle assessment (see discussion). The environmental footprint methods described below can be classified into two broad categories of analyses: streamlined life-cycle assessments that use a single-unit indicator (e.g., carbon dioxide equivalents) and location-specific analyses (e.g., ecological footprint of a city).
A single-unit indicator does not mean that only one source or one piece of data is used. Typically, many different data are used but are converted to a single common unit, such as carbon or nitrogen. In this manner, single-indicator environmental footprint analyses are similar to economic tools that use currency as their single-unit indicator. Ecological, materials, carbon, nitrogen, and water footprint analyses are common methods available for calculating environmental footprints.
Environmental justice (EJ) is the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies (EPA 2010c). Recognizing that some populations experience higher levels of risk, Executive Order 12898, “Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations,” directs federal agencies to identify and address disproportionately high adverse human health or environmental effects on minority and low-income populations that may result from their programs, policies, and activities (Clinton 1994). The development
of the environmental justice movement has precipitated a great deal of research on the racial and socioeconomic disparities in exposure to environmental health risks (Cole and Foster 2001; Ringquist 2005; Brulle and Pellow 2006). These studies, often referred to as EJ analyses, evaluate risks and may also attempt to address them using other sustainability tools, such as collaborative problem-solving (see discussion), and design charrettes (see discussion), among others (NRC 2011).
Exposure refers to a measurable contact of an agent with a target or receptor for a specific duration of time (IPCS 2000; Zartarian et al. 2005). In the broadest terms, agents can be biological, physical, chemical, social, or psychological, and can produce both adverse and beneficial impacts to the target. EPA has historically focused on minimizing negative impacts, but in a sustainability context assessing exposures that result in positive impacts is also relevant to evaluating tradeoffs. For human exposures, receptors can be individuals, populations, subpopulations, or life-stages of interest. For ecological systems, receptors can be individuals, populations, species communities, or ecosystems that include both wildlife and vegetation. For exposure to occur, the agent and the receptor must intersect in both space and time.
Exposure assessments characterize and predict this intersection by estimating the magnitude, frequency, and duration of exposure (EPA 1992). Exposure assessments also describe the number and characteristics of the population exposed (e.g., vulnerable communities, ecosystems, or endangered species). They describe the sources, routes, pathways, and uncertainty in the assessment. Exposure assessments describe the environment as well as characterize and link the processes that impact the transport and transformation of agents from their source through contact with human or ecological receptors. These assessments are a central component in understanding environmental systems and how they change when intended or unintended perturbations occur.
Futures methods seek to help decision-makers anticipate conditions and events that have not yet fully developed so they can influence or better respond to the ultimate outcome. Futures methods attempt to look “beyond the horizon” to provide insight into future trends that can be used to inform strategic planning. The following four basic techniques are widely used futures methods, each drawing on a different body of knowledge and serving a distinct purpose (EPA 1995, 2007):
• Scanning methods are systematic and broad-based reviews of information gleaned from journal articles, newspapers, websites, books and other sources to identify relevant “weak signals,” early indications of trends that are just beginning to emerge. Scanning methods results typically require further analysis and can provide input for other futures methods.
• Delphi methods use a structured series of interviews to learn from the observations and judgments of experts. Interview questions may explore the probability, timing, and impact of emerging opportunities and challenges.
• Trend analysis methods examine quantitative data for trends and patterns, and use mathematical projections to extrapolate into the future. A complete analysis also requires identifying potential counter trends, exploring possible implications, and identifying options for a response.
• Future scenario analyses construct detailed qualitative or quantitative snapshots of alternative scenarios that serve as plausible images of the future rather than predictions or forecasts and are used to investigate how individual elements might interact under certain conditions (Schoemaker 1995; Swart et al. 2004). This method can provide a context for a diverse group of stakeholders to examine how changes occur in complex systems, and explore how best to achieve positive outcomes given the range of potential changes.
Many variations on these basic techniques have been developed (Glenn, and Gordon 2009).
Green accounting (also known as environmental accounting) seeks to better measure sustainability by expanding gross measures of national welfare (product, investment, etc.) to include non-market values, in particular ones associated with environmental goods and services (Vincent 2000). In addition, green accounting seeks to incorporate costs and benefits of environmental protection and depletion of natural capital – two measurements not typically included in national accounting systems such as gross domestic product (Hecht 1999). While opinions vary on how to perform green accounting, the technique is used worldwide and is well-established in the United States.
Green chemistry is the science and practice of designing chemicals, products and processes in order to reduce or eliminate the generation and use of hazardous substances. Like the related field of green engineering, green chemistry seeks to protect human health and the environment by applying sustainability principles at the design phase of a process or a product, where they can have the greatest impact and be most cost-effective (EPA 2011a).
Green chemistry is a transdisciplinary field encompassing elements of chemistry, engineering, biology, toxicology and environmental science. This nexus across disciplines is essential for focusing on the complex questions associated with sustainability and for providing the tools needed to answer those questions. Green chemistry is guided by a set of principles that encourage the creation of safer, more efficient, and more sustainable designs for chemical products, feedstocks, and processes (EPA 2011b).
Green engineering is the design and use of economically feasible products and processes that: 1) reduce the generation of pollution at the source, and 2) minimize the risks posed to human health and the environment. Green engineering incorporates environmental science along with sound engineering design principles to minimize the overall environmental impact of products and services during manufacture, processing, use, and disposal. Like the related field of green chemistry (described), green engineering operationalizes the philosophy that decisions to protect human health and the environment have the greatest impact and cost effectiveness when applied early in the design phase of a process or product (EPA 2011a).
Health Impact Assessment (HIA) is defined as “a combination of procedures, methods, and tools by which a policy, program, or project may be judged as to its potential effects on the health of a population, and the distribution of those effects within the population.” (WHO 1999) This tool is used to systematically identify how new projects or policies might affect public health. HIAs consider determinants of human health stemming from all of the three pillars of sustainability – social, environmental, or economic (Quigley et al. 2006). For example, HIA takes into consideration factors such as employment, education, and climate change.
The two main objectives of HIA are: (1) to predict the human health impacts of program- or project-related actions, and (2) to provide stakeholders and decision-makers with information to consider when assessing and prioritizing strategies for addressing health risk and preventing adverse health outcomes over the life of a program or project (IFC 2009). HIA is designed to address negative and positive, intended and unintended, and single and cumulative health impacts across entire populations, taking into account the fact that not all subgroups will be affected equally Quigley et al. 2006).
Integrated assessment modeling (IAM) is a tool that integrates knowledge from two or more domains into a single framework. In general, IAM brings a systems-based approach to decision-making that takes into account the three pillars of sustainability. Integration can occur at many different levels: some integrative models are limited to water quality or hydrology while other models integrate two or more environmental components (e.g., soil and water or water and biology). For example, the Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) represents a series of fate and transport models, which are integrated such that the outputs of one model feed seamlessly as inputs into one or more models in the framework. Still other IAMs integrate multiple decision criteria, which can permit stakeholders to consider all economic, social, and environmental criteria they can identify and obtain data for decision analysis. The integration of fate and transport (environmental) models with social and economic models and then the integration of these three components in multi-criteria decision approaches is yet another example of integration. The overarching goal of IAM is to ensure that policy decisions are informed by a thorough understanding of the interdependencies and interactions within a system’s economic, environmental and social spheres. Through IAM, policymakers and stakeholders can gain better insight into the suite of impacts of policy interventions, which is expected to lead to more sustainable outcomes. In a broader context, EPA defines integrated modeling as: (EPA 2008a) “…a systems analysis-based approach to environmental assessment. It includes a set of interdependent science based components (models, data, and assessment methods) that together form the basis for constructing an appropriate modeling system. The constructed modeling system is capable of simulating the environmental stressor-response relationships relevant to a well specified problem statement.”
Life-cycle assessment (LCA) is a systems-based approach to quantifying the human health and environmental impacts associated with a product’s life from “cradle to grave.” A full LCA addresses all stages of the product life-cycle and should take into account alternative uses as well as associated waste streams, raw material extraction, material transport and processing, product manufacturing, distribution and use, repair and maintenance, and wastes or emissions associated with a product, process, or service as well as end-of-life disposal, reuse, or recycling. In some cases, LCA is applied with restricted boundaries, such as “cradle to [loading] gate.” Environmental footprint analysis (see discussion) is a type of bounded LCA (EPA 2006).
LCA typically return two specific types of information:
• A comprehensive life-cycle inventory of relevant energy and material inputs and environmental releases throughout the system (EPA 2006).
• Estimates of the resulting impacts for a wide range of impact categories including global climate change, natural resource depletion, ozone depletion, acidification, eutrophication, human health, and ecotoxicity (Bare et al. 2000).
This information allows an analyst to consider multiple parts of a system and multiple environmental endpoints in developing effective policies.
Resilience analysis investigates the ability of a system (e.g., a human community, a supply chain, or an ecosystem) to continue functioning in the face of disruptions. Generally, resilience can be defined as “the capacity for a system to survive, adapt, and flourish in the face of turbulent change” (Fiksel 2006). Examples of resilience metrics include the magnitude of disruption that is required to move a system out of equilibrium and the cost (or effort) required to restore a system to equilibrium after a disruption has
occurred (Carpenter et al. 2001; Vugrin et al, 2009). Resilience analysis studies the adaptive cycles in a system in order to understand its vulnerabilities and its capacity for resilience (Gunderson and Holling 2002). Once these patterns are understood, a system’s resilience can be enhanced through designs and processes that promote diversity, variation, distributed functions, effective feedback loops, and freedom for innovation and adaptation (Walker and Salt 2006).
The resilience of any system depends on the interconnectedness and functional diversity of multiple subsystems. For example, in decentralized systems, functions are distributed so that a malfunction or disturbance in one area does not necessarily have a critical impact on other system components. More resilient systems are able to absorb larger shocks without changing in fundamental ways (Fiksel 2003). While natural systems tend to be inherently resilient, poorly designed human systems are often brittle and vulnerable to a variety of disruptions.
Risk assessment adds an important contribution to advancing sustainability. In a risk assessment, risk is understood to be the possibility of adverse consequences from an event or activity. A risk assessment, therefore, is a process for evaluating the likelihood and/or magnitude of such consequences. Risk assessment should be viewed as a tool for evaluating the relative merits of various options for managing risk (NRC 2009). This includes carefully posing the risk management questions and evaluating the options available to manage the environmental problems at hand. There are a number of context-specific types of risk assessment that can be useful in understanding aspects of sustainability in complex, real-world situations. Four of these are described below (Bahr 1997; Stewart and Melchers 1997; Landoll 2006; Hiles 2011).
Human health risk assessment (HHRA) is the process used to estimate the nature and probability of adverse health effects for humans who may be exposed to environmental stressors (chemical, non-chemical, or both), now or in the future. HHRA can help inform solutions to a broad range of problems related to human health risk.
Children are a subpopulation that may be more susceptible to harm caused by environmental stressors because of various physiological and behavioral factors (EPA 2008b):
• their bodily systems are still developing;
• they eat more, drink more, and breathe more in proportion to their body size; and,
• their behavior such as crawling on the ground and hand-to-mouth activity can higher exposures to chemicals and organisms.
There are multiple definitions of cumulative risk assessments. The Food Quality Protection Act (FQPA) defines cumulative risk as the risk from the total exposure to multiple stressors (usually chemical) that cause one or more common toxic effects to human health by the same, or similar, sequence of major biochemical events. The EPA’s Cumulative Risk Framework provides a considerably broader definition that includes combined risks from aggregate exposures to multiple agents or stressors, where agents or stressors may be chemical, biological, social, or physical (e.g., noise, nutritional status) (EPA 2003). EPA’s cumulative risk assessment process focuses on populations and consideration of population variability; it has been used in many of the EPA’s programs, including: Research and Development, Super-
fund, Air, Water, and cross-program endeavors like the Community Action for a Renewed Environment program.
An ecological risk assessment (EPA 1998) ] is the process for evaluating how likely it is that the environment may be impacted as a result of exposure to one or more environmental stressors such as chemicals, land change, disease, invasive species and climate change. Ecological risk assessments can be used to predict the likelihood of future effects (prospective) or evaluate the likelihood that effects are caused by past exposure to stressors (retrospective). Information from ecological risk assessment is then used by risk managers for follow-up such as communicating to interested parties and the general public, limiting activities related to the ecological stressor, limiting use of a given chemical, or developing a monitoring plan to determine if risks have been reduced or whether an ecosystem is recovering.
Segmentation analysis (also known as market segmentation or audience segmentation) is a process of dividing a larger population into smaller subpopulations or segments in order to identify psychological and socio-demographic correlates of target behaviors or values. Members of subpopulations are statistically more similar to one another than they are to members of other subpopulations (Grunig 1989). Common segment factors include demographic, psychological, and behavioral variables, such as income, age, attitude, race, sexual orientation, education, consumption, and leisure pursuits. Segmentation analysis combines these data into bundles of closely correlated attributes in order to define specific segments of the population.
Social impact assessment (IA) is a tool used to assess the social impacts—both positive and negative--resulting from planned interventions (such as policies, programs, projects or actions), as well as any social change invoked by those interventions. The goal of social IA is to help decision-makers produce more socially, economically, and environmentally sustainable results. Social IA draws on knowledge gained through collaborative, community-based tools, and is therefore complementary to many other sustainability tools discussed in this document (ICGPSIA 1994; Vanclay 2003).
Social Network Analysis (SNA) refers to a systematic process of analyzing groups (nodes) and relationships among groups (Wellman 1988). The groups and ties comprising the social network can be visually mapped as a scatter plot: interpretation of the social network draws on scientific disciplines focused on understanding interpersonal relations and social structures (e.g., anthropology, psychology, and sociology) (Borgatti et al. 2009). One of the most important uses for SNA is in mapping communications and knowledge flows among groups (Reagans and McEvily 2003). Understanding such knowledge flows has many benefits, including identifying new opportunities for strategic collaboration, identifying communication bottlenecks, streamlining the flow of information across departmental or organizational boundaries, identifying trusted sources of knowledge within the network, and targeting specific stakeholders where key messages will have the greatest impact. Understanding such dynamic network interactions is possible through SNA because the emphasis is not on the attributes of individual groups, but on how the structure of relationships among groups affects how individual groups behave when they are plugged into the network. Thus, the overall shape and connectedness of the social network is an important determinant of what the groups within it do and how effectively the network operates to transmit information or ideas (Granovetter 1973).
Sustainability impact assessment (IA) is a combination of procedures, methods, and tools by which a policy, program, or project may be judged as to its potential impacts on the sustainability of a system and the distribution of those impacts within and among the economic, social, and environmental dimensions.
Sustainability impact assessments are most commonly applied through a multi-criteria decision analytic approach, which helps stakeholders investigate the combined economic, environmental and social impacts of proposed policies. This approach can be used to guide stakeholder and decision-maker engagement and collaboration throughout the entire planning process (OECD 2010). The purpose for conducting a sustainability impact assessment is twofold: inform policy development by explicitly considering impacts within and among the economic, social, and environmental systems; and, assess potential economic, social, and environmental impacts resulting from a proposed policy (OECD 2010). Explicit in a sustainability impact assessment is the integration of all three sustainability pillars; consideration of both spatial and temporal impacts; stakeholder involvement; transparency; accountability; and, match between the level of detail in the assessment and the impacts (OECD 2010).
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