Grantee Research Project Results
Final Report: Socioeconomic and Institutional Research
EPA Grant Number: R828684C004Subproject: this is subproject number 004 , established and managed by the Center Director under grant R828684
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Center for Integrated Multi‐scale Nutrient Pollution Solutions
Center Director: Shortle, James S.
Title: Socioeconomic and Institutional Research
Investigators: Shortle, James S. , Thornton, Kent
Institution: Pennsylvania State University , FTN Associates, Ltd
EPA Project Officer: Packard, Benjamin H
Project Period: March 1, 2001 through February 28, 2005 (Extended to March 15, 2006)
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Water , Aquatic Ecosystems
Objective:
This was one of four projects under the Atlantic Slope Consortium (ASC) Center. The objective of this research project was to provide scientific results that support the choice and communication of suites of environmental indicators that will be meaningful to and relevant for environmental managers and other intended audiences. The specific objectives are to examine: (1) human perceptual and attitudinal dimensions of the types of indicators that different audiences find useful; (2) risk communication methods for presenting indicator information; (3) institutional and jurisdictional obstacles to indicator use; (4) the value of indicators for environmental management; and (5) the relationships of environmental indicators to socioeconomic indicators at multiple scales.
Indicators can provide scientific information for answering questions related to the condition, trends, and causes of problems in aquatic ecosystems. As we move toward answering the questions: (1) “What can we do about it?” (management), (2) “Are we making a difference?” (performance measures), (3) “How do we tell the story?” (communication), we bring in another dimension besides space and time—the human dimension. Effective management, performance measures, and communication require consideration of socioeconomic as well as ecological indicators, information, and insight.
The ASC included the human dimension because we think it is critical in understanding and resolving conflicts between social choices (land uses) and societal choices (designated uses). Ecological, cultural, social, economic, and political factors influence both social and societal choices. Gaining a better understanding of how indicators from each of these sectors interact and affect decisions can contribute to better communication among various stakeholders and better, more informed policy and management decisions.
Useful environmental indicators must have ecological validity and reliability. They also must be meaningful to and relevant for intended audiences. These audiences include decision-makers in environmental and resource management and planning agencies, as well as stakeholders to whom decision-makers must be responsive. Moreover, given limited resources for assessing and protecting ecosystem health, the indicators to which society devotes resources should add significant value to environmental management. Choices about types of indicators, the scales at which they are gathered, and the precision with which they are measured should be guided by the value of the information for management relative to the costs of developing and maintaining the indicators.
The goal of the human dimensions research was to provide scientific results that support the choice and communication of suites of environmental indicators that environmental managers and other audiences will find useful for: (1) characterizing the condition of resources and ecosystems at multiple scales; (2) diagnosing likely causes of degraded conditions; (3) evaluating (when linked with hydrological, ecological process, socioeconomic, and other models) the probable consequences of changes in measurable landscape attributes; and (4) setting management priorities and selecting management strategies. The human dimensions research emphasized: (1) how managers use indicators and their desired indicator characteristics; (2) when factors affect indicator use in public decisions; (3) the effects of comingling socioeconomic and environmental indicators; (4) differences in how scientists and informed citizens perceive ecosystem condition; and (5) how additional information improves decision-making.
Summary/Accomplishments (Outputs/Outcomes):
This is a summary for the NCER Web Site.
Desired Characteristics and Use of Indicators
Managers use indicators to monitor and assess environmental condition and trends, set agency priorities, enforce regulations, measure human and economic consequences of changes in ecological condition, and communicate with stakeholders. In addition, greater emphasis is being placed on government agencies to define desired environmental outcomes and assess the effectiveness of management practices and policies in achieving these desired outcomes.
Personal interviews were conducted with 46 government officials from state and federal agencies and interstate commissions to determine: (1) how environmental indicators were used by managers in assessment and management decisions; and (2) what characteristics were desired in environmental indicators used in decision-making.
Managers preferred suites of indicators with issue-dependent elements rather than a single index or indicator because they were able to construct a more complete picture of environmental condition and the factors contributing to this condition with suites of indicators. Individual indicators were used in assessing attainment of individual water quality standards (WQS) (e.g., dissolved oxygen concentration or fecal coliform bacteria counts). Indicators were considered most useful, however, when they also provided insight into sources and factors responsible for existing conditions, including nonattainment of WQS. Environmental indices that provided a single number (e.g., fish index of biotic integrity) but that did not provide diagnostic information about environmental condition were not considered as useful as suites of indicators.
The attributes that made indicators useful depended on the specific purpose for the indicator. For example:
- For monitoring and assessment, indicators must be sensitive to the relevant spatial and temporal scale and must be adaptable to improving technology.
- For setting priorities, managers considered the ability to measure impairment as the most useful indicator attribute. Indicators that allow agencies to identify impairments influence the distribution of agency resources.
- For regulatory enforcement, managers considered scientific accuracy and consistency in measuring standards as the most important attributes. Indicators must hold up in court. Ambiguity in indicator interpretation was not acceptable.
- For communication, indicators must be adaptable to different audiences and concerns. Officials communicate with a wide variety of stakeholders, ranging from other regulatory agencies to elementary school children.
Each official and agency involved in indicator development had specific goals for the application of indicators. These goals often dictated what kinds of indicator data were collected, where data were collected, and how often data collection occurred. Differing perspectives on indicator development also were apparent between managers and scientists. Managers used indicators as information to contribute to decisions, whereas scientists used indicator information to understand relationships (e.g., cause-effect) in ecosystems. A significant challenge identified by respondents was achieving consistency between the metrics that scientists obtain and the data that managers need.
Although indicator development was important, many managers stated that having tools and approaches for transforming existing raw metrics into useful formats was equally important. There is a wealth of indicator data available for some systems, but these data are in difficult formats or are not readily available, so this information cannot be used readily. If greater access to information were available through indicator clearinghouses or similar vehicles, the data might be applied to a much broader set of problems and in a broader variety of ways than it has been in the past.
Agency officials also stressed the importance of communicating with stakeholders. Indicators must be presented to managers and decision-makers in a language they can understand and a format that they can use. Suites of indicators increased officials’ ability to use a variety of different formats and approaches for communicating with stakeholders. Suggestions for improving the communication and presentation of indicator data included:
- Collect data for commonly used indicators across agencies.
- Use more visuals and graphics.
- Use color in reports and outreach.
- Use more maps and invest in GIS technologies.
- Establish an indicator clearinghouse accessible through the Internet.
- Build communication networks between scientists and managers.
- Engage experts in both the natural sciences and the human dimensions of environmental behavior.
Development and use of indicators has been most successful when an ongoing dialog existed between scientists, managers, and stakeholders and when all parties involved in water resource issues worked to communicate their data and knowledge in creative and audience-specific ways. Suites of indicators that not only describe condition but also help diagnose the underlying factors or stressors contributing to that condition need to be provided to managers. Indicators are particularly useful for management when the information can be communicated clearly and understandably to the public.
Designing Environmental Indicator Systems for Public Decisions
Information is needed not only for better management but also for better public policy decisions. Societal choices have been made about the designated uses desired for aquatic resources. Indicators information can help inform the public on whether these uses are being attained and whether they can be attained.
In addition to interviews with managers on the use of environmental indicators, the Human Dimensions Team also examined factors (including laws, regulations, and policies) that affect the use of environmental indicators in public decisions. Many researchers assume that if environmental indicators are scientifically valid, information from these indicators will be used in making public decisions.
In general, there are three issues that affect indicator usefulness. First, indicators must be relevant for the management purpose. In many instances, it is difficult to link indicators directly to management endpoints and purposes. These endpoints vary from general assessments of environmental condition to evaluating the effectiveness of individual permits. It is critical in designing effective environmental indicator systems to understand who the users are, what endpoints are being considered by management, which legal and jurisdictional constraints are applicable, and the technical sophistication of the user regarding application of indicators.
Second, the indicators must be appropriate for the geographic or spatial scale. For example, watershed-wide indicators might provide little guidance for management decisions related to development on an individual tract of land. Similarly, indicators for small headwater streams might not be appropriate for assessing condition in the Susquehanna River.
Third, it also is important to consider the “delivery” system. How, when, where, and to whom will this information be provided? The delivery system must be capable of providing clearly understood and interpretable information when and where it is needed in the decision-making process.
In designing suites of environmental indicators for public decisions, it is important to consider that:
- Indicators must provide information about specific endpoints used for management and policy decisions.
- Indicators must be appropriate for the geographic or spatial scale of the decision.
- Clear and interpretable indicator information must be able to be delivered to decision-makers when and where they need it.
A New “Frontier” for Analyzing Environmental and Socioeconomic Indicators
Managers indicated that information from both the natural sciences and social sciences (e.g., socioeconomic data and indicators) was important for environmental decision-making, but how do you integrate these different types of data?
A key challenge of the ASC project was to develop methods for integrated assessment of the quality of life in alternative social choice contexts. Scientific assessments of quality of life face two fundamental challenges. One is to identify and measure the economic, social, and environmental factors that contribute to the determination of quality of life. A second is to aggregate across the alternative factors to produce a metric that can be used to assess conditions in different communities. Frontier analysis provides a method for aggregating across alternative factors.
Frontier analyses are approaches that have been used for economic analyses, but have not been used previously in the natural sciences. These techniques can be used to explore the efficiency with which quality of life is “produced” within assessment units, such as counties, communities, or watersheds. Frontier analyses can be used for assessing not only the current quality of life in a community or county but also the extent and direction of changes needed to achieve a feasible future quality of life defined by the performance of other communities or counties.
Quality of life indicators are broadly categorized into three dimensions—social, environmental, and economic. The concept underlying frontier analysis is that ecological, economic, and social constraints associated with a particular community define a maximum achievable quality of life for that community, and that this maximum achievable quality of life or frontier can be approximated by examining the performance of a community in achieving the maximum for various ecological, economic, and social factors. Each community (defined as counties here for a proof-of-concept) has a unique combination of attributes and, therefore, a unique position along the continuum of possible values for these ecological and socioeconomic factors. A set of the counties will therefore form an outer boundary, or frontier, that defines the maximum achievable quality of life based on the combination of factors. The performance of those counties within the frontier then can be measured relative to the performance of those efficient counties that actually comprise the frontier (Figure 1).
Figure 1. Frontier Concept Showing the Distinction Between Rural and Urban Regions
Data Envelopment Analysis (DEA) and Value Efficiency Analysis (VEA) are two statistical methods for integrating environmental, economic, and social indicators, such as those listed in Table 1. DEA makes a weak, but reasonable, assumption that communities prefer to maximize “good” development outcomes (e.g., natural amenities, literacy, affordable living) and to minimize “bad” development outcomes (e.g., poverty, illiteracy, pollution). DEA evaluates the relative efficiency of a community or county in maximizing the good outcomes and minimizing the bad outcomes. DEA provides a measure of that community or counties distance from the efficiency frontier. VEA is similar to DEA, but it permits the decision-maker to designate one community or county (i.e., production unit) as the “most preferred solution.” This most preferred solution, rather than the entire frontier, then becomes the standard against which all other communities or counties are compared. Because the quality of life in rural versus urban communities or counties reflects different social values, urban and rural counties were treated as separate subpopulations in the ASC DEA and VEA analyses.
Environmental Dimension | U.S. Environmental Protection Agency’s cancer risk index (input) % of land area developed (input) |
Social Dimension | Teacher/pupil ratio (input) % of population 25 and older who are high school graduates (input) # of arts, recreation, and entertainment establishments per developed square mile (output) |
Economic Dimension | Median household income (output) % population below poverty level (input) |
Nondiscretionary Amenity Variables | Amenity index (output) |
In this analysis we examined the relationships among undesirable outcomes (e.g., high cancer risk, percent of developed land, miles of impaired streams, and percent of population below the poverty level) and desirable outcomes (e.g., high teacher/pupil ratio, percent of population over the age of 25 that are high school graduates, natural amenity index, miles/acres of wetlands, and miles of streams in good condition) in determining quality of life for the counties in the mid-Atlantic region. We analyzed a subset of such indicators; information for each of these indicators in the ASC analyses were obtained from a number of different sources ranging from 1996 emissions data in the U.S. Environmental Protection Agency’s National Scale Air Toxics Assessment, to 2000 Census data, to the 2000 U.S. Department of Agriculture Amenity Index.
DEA provided estimates of how a county performed with respect to a theoretical maximum frontier in producing quality of life in the mid-Atlantic region. The analyses provided a benchmark for “how far” a county was from the best that could be attained. Counties with high efficiency ratings, suggesting high quality of life, were scattered throughout the mid-Atlantic region, with the areas of lowest efficiency concentrated in West Virginia and Virginia. These counties tended to have low values for a number of indicators, such as poverty level, percent high school graduates, and affordability.
VEA provided an estimate of where a county was with respect to what was considered the most preferred urban or rural county in the region. Based on population migration data and the conjecture that people indicate preference by locational choices, Amelia County, Virginia, was chosen as the reference county for urban counties, and Floyd County, Virginia, was chosen as the reference for rural counties. With VEA, the counties with low values still were scattered throughout West Virginia, but a number of counties with low values also were found in eastern Pennsylvania, Maryland, and central Virginia. VEA yielded a broad range of value efficiencies in the quality of life for mid-Atlantic counties. VEA results, however, are highly sensitive to the reference county selected for comparison. If the VEA county selected as reference was highly unique, the scores of the remaining counties were far below their corresponding DEA scores. Despite this sensitivity, DEA and VEA provide a great deal of information about relative performance of counties in the production of quality of life and where improvements could be obtained.
There were significant differences between rural and urban counties. In general, rural counties, when compared to their efficient frontiers, outperformed similar urban counties on the environmental dimension. Urban counties, when compared to their efficient frontiers, outperformed similar counties on the socioeconomic dimension. Although it might appear that improved socioeconomic dimensions come at the expense of environmental dimensions (i.e., it is possible to have good environmental or good economic condition, but not both), there is evidence that this is only true for areas with very high quality environmental conditions. Outside of very high quality natural environments, urban counties appear to make fewer environmental sacrifices in the achievement of economic development than do rural counties.
Most counties in the mid-Atlantic region were below their potential for maximum achievable quality of life, but improved quality of life could be achieved in most counties. In general, rural counties outperformed urban counties in the environmental dimension, whereas urban counties outperformed rural counties in the socioeconomic dimensions. Although some loss of environmental quality does occur in very high quality natural areas with economic development, it is possible to have both environmental and socioeconomic factors contributing to the quality of life in most mid-Atlantic counties.
Human Perceptions Versus Scientific Assessments; Aquatic Ecosystems and Quality of Life
The frontier analysis used data from secondary sources for integrating social, economic, and environmental factors to examine quality of life for county residents. A more fundamental approach is to ask residents questions about the relationship among environmental, economic, and social factors and human quality of life. A set of questions was formulated and asked through focus groups and sample surveys of residents living within the watersheds studied by ASC scientists. The focus groups and surveys addressed the following issues in the “human dimensions” of aquatic ecosystem indicators:
- What is the relationship between water quality and socioeconomic indicators of quality of life?
- What is the relationship between perceived water quality and perceivedquality of life—does water matter?
- How is water quality perceived by the public?
- What is the relationship between perceived and “actual” water quality (as provided by ecological scientists)?
- What kinds of tradeoffs are people willing to make to obtain higher water quality (what elements, how good, and at what cost?)?
- How are these relationships affected by baseline water quality conditions?
Focus groups were conducted in six different watersheds (Spring and Conodoguinet in Pennsylvania; Gunpowder Falls and Southeast Creek, Maryland; and James and Ware Rivers, Virginia), with 53 participants in all. These focus groups provided insight into the background knowledge of watershed residents: their use of local water quality resources, the importance they place on water, their perceptions of local water quality, as well as factors that threaten water quality. These focus group results helped ensure that the mail surveys reflected local water-related concerns and issues.
A mail survey of residents of 9 watersheds that comprise a portion of watersheds studied in the larger Atlantic Slope project was implemented, and a total of 1,170 useable surveys were received.
While the focus group and resident surveys were being conducted, ASC scientists were assessing the quality of the watersheds they studied. The idea was to compare residents’ “perceived” water quality with scientific assessments of “actual” water quality. Because we were interested in examining both biological quality and recreational quality, scientists were asked to make judgments about both biological and recreational quality.
A “stated choice experiment” was conducted to examine the value of improvements in water quality for recreational uses and biological integrity to watershed residents. For each watershed, average stated willingness to pay (WTP) per month per household was estimated for improvements in water quality that would affected 10 percent of the streams in the watershed.
In general, most respondents rated water quality relatively low, with scores ranging from a high of 3.3 (Spring Creek watershed) to 2.3 (South East Creek watershed), with 1 being poor water quality and 6 being perfect water quality. Out of nine watersheds, respondents in six watersheds indicated that runoff from development was the greatest threat to water quality. Pollution from mining was the greatest perceived threat in Clearfield Creek watershed, whereas pollution from agricultural chemicals was considered the greatest threat in Grindle Creek watershed. Perceived water quality was positively associated with respondents’ satisfaction with water recreation activities across all watersheds. It was the only factor in common with water recreation in all watersheds. A slightly above average quality of life was perceived in all watersheds, with the scores ranging from 3.6 to 4.0, on a six-point scale from 1 = poor to 6 = perfect quality of life.
In the “stated choice experiment,” WTP to improve biological quality was higher than WTP to improve recreation quality in all other watersheds except the Little Contentnea. The WTP values for the Little Contentnea were not significantly different from zero. In general, watersheds with higher WTP values were associated with better quality watersheds.
Preliminary analyses suggest that the opinion of scientists about water quality differed from citizen perceptions. We are exploring these differences. Desired use of aquatic resources, such as recreation, affects public perception of the condition of the resource. Indicators of desired uses, therefore, can be as important as indicators of regulatory designated uses.
There is an apparent relationship between citizens’ willingness to pay to maintain or improve aquatic resources in better quality watersheds. This has implications for watershed, stream, and wetland restoration. The public might be more willing to pay to maintain aquatic resources following restoration or improvement in the quality of these resources.
Valuation of Information Investments
Comprehensive approaches to aquatic ecosystem management require extensive information about existing conditions, threats to these conditions (e.g., development), and how conditions will respond to changes in these threats. Informed choices among alternative management strategies also require information on costs, societal goals, and tradeoffs. Given that information acquisition is costly, decisions are required about the types and amounts of information that should be sought.
A tool for guiding information investments examined in this project was the Expected Value of Information (EVOI). EVOI is a measure of the contribution that additional information makes to the outcome of decisions by reducing uncertainty.
The ASC project developed a procedure for estimating the expected value of information used for water quality management. The tool was demonstrated in case studies for reducing nitrogen loadings from cropland in the Pennsylvania portion of the Susquehanna River Basin.
The case studies revealed that better information on the response of nitrogen loads to changes in farming practices, on the impacts of nitrogen loads on aquatic ecosystem conditions, on the economic benefits of increased ecological services, and on the costs of nutrient management practices all lead to improvements in the design of nutrient management policies as measured by a money metric of expected benefits of increased ecosystem services less the expected costs of nutrient reductions. The greatest gains from additional information come from investments in better understanding of ecosystem responses to changes in nutrient loads and the economic value of increased ecosystem services. The value of information used in aquatic ecosystem management is contingent on policy objectives and the policy instruments used to achieve those policy objectives. For example, the value of information of all types was greater for a policy using quantity controls (e.g., nutrient credit trading) than for a policy using payments to farmers (or their inverse, charges) to induce adoption of nutrient management practices.
Expected value of information tools can contribute to the selection of indicators used in the decision-making process. Value of information approaches may be an approach both for better quantifying the benefits of ecosystem services and communicating the importance of ecosystem services to the public.
Journal Articles on this Report : 4 Displayed | Download in RIS Format
Other subproject views: | All 40 publications | 8 publications in selected types | All 6 journal articles |
---|---|---|---|
Other center views: | All 166 publications | 51 publications in selected types | All 44 journal articles |
Type | Citation | ||
---|---|---|---|
|
Bergstrom JC, Goetz SJ, Shortle JS. Land Use Problems and Conflicts. Routlegde, New York, NY. 2006: |
R828684C004 (Final) |
not available |
|
Borisova T, Shortle JS, Horan RD, Abler DG. The value of information for water quality protection. Water Resources Research 2005;41(6):W06004. |
R828684C004 (2003) R828684C004 (2004) R828684C004 (Final) |
Exit |
|
Marshall E, Shortle J. Using DEA and VEA to evaluate quality of life in the mid-Atlantic states. Agriculture and Resource Economics Review 2005;34(2):185-203. |
R828684C004 (Final) |
not available |
|
McElfish Jr. JM, Varnell LM. Designing environmental indicator systems for public decisions. Columbia Journal of Environmental Law 2006;31(1):45-86. |
R828684C004 (2004) R828684C004 (Final) |
Exit |
Supplemental Keywords:
indicators, integrated assessment, wetland, stream, estuary, watershed, biological integrity, decision-making, ecosystem, environmental exposure and risk, geographic area, ecology, ecosystem indicators, bioindicators, land use, mid-Atlantic, hydrology, estuarine ecosystems,, RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Ecosystem/Assessment/Indicators, Ecosystem Protection, Economics, Ecological Effects - Environmental Exposure & Risk, Ecological Monitoring, decision-making, Ecological Risk Assessment, Ecology and Ecosystems, Social Science, Economics & Decision Making, Ecological Indicators, Risk Assessment, ecosystem valuation, model-based analysis, ecoindicator, policy making, valuation, decision making, environmental decision making, cost of pollution abatement, economic incentives, environmental values, socioeconomics, economic models, environmental benefits assessment, ecological assessment, environmental policy, ecosystem management, environmental decision-making, estuarine ecosystems, environmental protection, public values, cost-effective ecosysem protection, preference survey, economic objectivesProgress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R828684 Center for Integrated Multi‐scale Nutrient Pollution Solutions Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R828684C001 Integrated Assessment of Estuarine Ecosystems
R828684C002 Development of an Optical Indicator of Habitat Suitability for Submersed Aquatic Vegetation
R828684C003 Integrated Assessment of Watersheds
R828684C004 Socioeconomic and Institutional Research
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
6 journal articles for this subproject
Main Center: R828684
166 publications for this center
44 journal articles for this center