Socio-economic Indicators and Integrated Coastal Management

Abstract

The need to better understand the linkages and interdependencies of socio-economic and coastal environmental dynamics has taken on a more deliberate role in the development and assessment of Integrated Coastal Management world-wide. The analysis and establishment of indicator-driven programs to assess change in coastal and watershed systems have increasingly moved to stress socio-economic forcings and impacts. This article serves to review the need for and provide an assessment of important frameworks designed to foster such integration. It argues that the evolution of the Driver–Pressure–State–Impact–Response (DPSIR) framework, now in broad use, provides an essential contribution.

Full text

Ocean & Coastal Management 46 (2003) 299–312
Socio-economic indicators and integrated
coastal management
Robert E. Bowen*, Cory Riley
Environmental, Coastal and Ocean Sciences (ECOS), University of Massachusetts,
Boston, MA 02125, USA
Abstract
The need to better understand the linkages and interdependencies of socio-economic and
coastal environmental dynamics has taken on a more deliberate role in the development and
assessment of Integrated Coastal Management world-wide. The analysis and establishment of
indicator-driven programs to assess change in coastal and watershed systems have increasingly
moved to stress socio-economic forcings and impacts. This article serves to review the need for
and provide an assessment of important frameworks designed to foster such integration. It
argues that the evolution of the Driver–Pressure–State–Impact–Response (DPSIR) frame-
work, now in broad use, provides an essential contribution.
r2003 Elsevier Science Ltd. All rights reserved.
One of the more significant challenges faced by those interested in and responsible
for Integrated Coastal Management is to better refine our understanding of the
linkages between coastal system dynamics and the social benefits associated with
them. While both the coastal management and marine science communities have
developed indicator systems to better assess change, the degree to which such efforts
have been linked has been surprisingly limited. For the most part, the management
community has focused on institutional measures of program performance while the
marine science community has worked to build indicators of the scope and scale of
change in natural systems. The degree of interaction between social systems and
environmental variability has held relatively less focus. That humans and the
environment are linked has been long asserted. Measuring the degree and
importance of those interactions has been less of a core activity.
*Corresponding author. Tel.: +1-617-287-7443.
E-mail address: bob.bowen@umb.edu (R.E. Bowen).
0964-5691/03/$ - see front matter r2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S0964-5691(03)00008-5

However, the past decade has witnessed a more refined effort to expand an
understanding those interdependencies. This has been the result of several related
endeavors. First, pressure has been building by national governments and the
international donor community to better evaluate the success of coastal management
programs. Part of that evaluation pressure has been to link management with the
mitigation coastal environmental degradation. Second, the design of international
monitoring and assessment protocols, such as the Global Ocean Observing System
(GOOS), the Global International Waters Assessment (GIWA), and, more recently,
the Global Terrestrial Observing System (GTOS) and the Millennium Ecosystem
Assessment (MA) has provided an opportunity for more systematic reflection. And,
third, emerging models for indicator development have begun to provide
appropriate frameworks for the articulation effective data strategies. Taken as a
whole these efforts provide an opportunity to engage in more strategic planning for
the design of coastal-based socio-economic indicators. Several nations (or more
appropriately regional programs under national sponsorship) have provided for or
begun efforts to better quantify these relationships. However, these have been
limited in scale, scope and temporal dimensionality. The recent meeting ‘‘The Role
of Indicators in Integrated Coastal Management’’ co-organized by the Department
of Fisheries and Oceans of Canada and the Intergovernmental Oceanographic
Commission established an opportunity for strategic consideration at the interna-
tional level.
For these efforts to achieve their hoped for success they should embrace a
consistent and internationally compatible approach. It should be fully recognized
that local and regional ICM programs must respond to and provide benefit to their
own stakeholders. Integrated Coastal Management is an approach driven by local
conditions. However, a larger context should not be ignored. Our understanding of
social/environmental linkages can only be effectively understood through an
assessment across biomes, social conditions and management approaches. Critical
lessons are established through multiple and cross-cutting case analyses. The choice
of effective and efficient coastal management should draw upon the successes
articulated from both similar and dissimilar regulatory environments. The purpose
of this paper is to describe a set of indicator models that most directly contribute to
this process of strategic planning. It will also report limited and preliminary results
of a set of dialogs (primarily conducted through the recent DFO/IOC meeting and
through various panel meetings of GOOS) establishing a general framework for the
identification of coastal socio-economic indicators.
1. The articulation of indicators
Environmental concerns that have surfaced in the United States and around the
world in the past 30 years have added a new variable into the scientific search for
knowledge of the natural world; human influence. The inclusion of humans into the
natural web of interactions calls for new protocols for studying natural systems and
for solving economic and health problems. Acknowledgment that people are a part
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of biogeochemical cycles and physical processes has necessitated a more integrated
approach to natural resource management and research [1–3]. Economic, political,
and social structures are intertwined with resource use patterns. Changes in the
condition of natural systems have a direct impact on the ecosystemic functions that
humans depend on for health, services, and economic growth. Here, we face a dual
challenge. First, understanding the complexity of those linkages is difficult. Natural
variability, the impact of episodic events (such as major storms) and anthropogenic
forcing all play a substantial role in the flux of natural systems. Isolating the relative
contribution of each is, at best, difficult. Second, the complexities of public health
risk or economic sustainability are difficult to understand and predict. Determining
the role of environmental conditions is even more challenging. These difficulties have
contributed the relative paucity of indicator-based approaches to management. The
recognized conundrum is that without an integrated and sustained indicator-based
system it is unlikely that critical linkages can be established, generally accepted and
acted upon.
The first step to using indicators to advance our knowledge of coastal systems and
the effectiveness of management programs is to establish an appropriate definition
for the term indicator. Measure, variable, parameter, analyte, metric, and index are
all terms that can be found in the literature and in the glossaries of current programs
designed to develop and use indicator frameworks [4–6,24]. Each has been used to
describe (sometimes inappropriately and occasionally to the point of confusion)
efforts to build an empirical approach to understanding coastal system dynamics. In
an effort to mitigate that confusion we suggest that a focus on the function of an
indicator as an appropriate starting point. The OECD [4] has argued that a
successful indicator should:
*Reduce the number of measures which normally would be required for an exact
presentation of a situation; and
*Simplify the process of communication to managers, stakeholders and commu-
nities.
In short, indicators should represent dynamic parts of an overall portrait that is
understandable and compelling to its intended user community. They should be part
of a process to minimize the number of individual variables and data points while
maintaining a sufficient level of critical understanding to those responsible for or
interested in coastal systems.
For this process to succeed purpose and context must initially be established.
What questions serve as motivation for the effort? Some indicator frameworks are
designed to determine program performance, some are created to establish links
between anthropogenic activity and ecosystemic health, and some attempt to track
trends and conditions in ecosystem dynamics or resource use. While this initiating
step appears obvious it is often challenged—particularly, in situations in which
available information takes precedent over appropriate information. The design of
new data-driven programs are often broadly influenced by the availability of existing
data files. While it is essential to fully utilize existing data it is equally essential to
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understand the value and voracity of data when the present context is substantively
and substantially different. That data exists does not de facto mean it should be used.
When the purpose of data collection is established, a blueprint for the design and
use of indicators should be put into place from the inception of the project, to ensure
that the time, effort and money invested are not wasted. Five general steps seem to
summarize the considerations that should be incorporated into the indicator system:
*Articulate an indicator framework driving the selection of specific measures. With
an agreement on a context and question alternative frameworks should be
assessed to determine their applicability in selecting an indicator set of greatest
value. The needs of and value to the user community should sit at the core of these
deliberations.
*Determine an efficient and effective data acquisition strategy. Cost, compatibility
and sustainability of effort should be considered as should the value of existing
data sources.
*Create and maintain a sustained data management system. Making data broadly
and openly available through an established quality assurance/quality control
system is essential.
*Agree to protocols for data analysis. One of the historic difficulties in system
monitoring as been too strong a focus on data acquisition and too little a focus on
data analysis.
*Develop reporting products to ensure information reaches and is understood by the
broader user community. The number and nature of coastal area stakeholders
reaches well beyond the scientific or regulatory communities. Traditional forms of
reporting (i.e., limited runs of printed reports with data tables) are increasingly
limited in terms of their ability to inform those whose interests are at stake. New
graphic display and information management technologies need to be more fully
embraced.
A substantial focus of this paper resides in the first of these steps: that is, the
description of frameworks driving the selection of specific measures. However, it is
important to recognize that the selection of measures and acquisition of data should
be viewed as part of system in which data acquisition, management, analysis and
product production are viewed as part of a synthetic whole which should be
addressed concurrently in the early stages of program initiation.
2. Models for the selection of socio-economic indicators
The indicator models are herein described within a specific context; that is, the
linkages between socio-economic conditions (including management and regulatory
approaches) and changes in coastal environmental dynamics. As such, they serve as
performance measures of success in those aspects of an overall ICM effort for which
those linkages are a part. We acknowledge that these linkages are viewed as an
important part of an ICM framework—but, only a part. The process of developing a
broadly integrated management effort will need to incorporate a richer set of
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performance measures. Institutional evaluation, an understanding community
dynamics, and policy assessment all play a central role. However, the synergies of
social/environmental interaction are also important. Most ICM efforts articulate goals
relating to coastal environmental improvement yet often lack specific performance
measures dedicated to understanding how well those goals are being met.
Ideal performance measures provide a clear indication of how well a program is
achieving its objectives. Thoughtful design, use, and adaptation are critical to their
usefulness as a management tool. Industry, international aid organizations, and
government agencies all have unique processes to implement performance measures
that are compatible with the specific aims of the organization. Most models use
indicators to determine if the performance measures are being met. Considerable
theoretical work has been done discussing the framework and design of evaluation
techniques [4,7–9]. Several themes repeat themselves in the academic and applied
discussions of indicators that may be helpful to keep in mind as we explore the
potential use of performance measures in ICM efforts.
An ideal combination of indicators could be fed into a conceptual or technical model
that efficiently identifies what, where, how, and why change is occurring within the
system. Performance-based management frameworks should organize indicators into
sets that are responsive to and driven by the needs of the user community. The
Organization for Economic Cooperation and Development (OECD) created the
‘‘Pressure–State–Response’’ model in 1993 to help model the cause and effect
relationship between humans and the environment [10,11]. This model has been
expanded since 1993 by the United Nations and the European Commission (among
others) to include the root causes of environmental change and the impacts this change
has on ecosystems and on humans. Input, output, outcome and impact measures are
classified according to the programmatic goals of the management action.
Developing a performance-based evaluation begins with clearly defined strategic
goals and a detailed set of intermediate targets [12]. The intended results of the
program and the specific type of change that is desired need to be understood and
articulated within the context of measurable increments. Differences in the situation,
condition, level of knowledge, or attitudes and behavior of a population need to be
assessed with appropriate units [12]. The more precise the vision of the program, the
easier it will be for the organization to develop measures that yield useful information.
3. Pressure–State–Response model
The Pressure–State–Response (PSR) model, popularized by the OECD [11],isan
example of a common framework for environmental evaluation. Environmental
problems and solutions are simplified into variables that stress the cause and effect
relationships between human activities that exert pressures on the environment, the
condition of the environment, and society’s response to the condition (see Fig. 1).
Water quality is a typical environmental concern that can be used as an example to
display the three types of indicators. Tons of fertilizers used by waterfront property
owners is an indicator that measures the ‘‘pressure’’ that the environment is
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experiencing. ‘‘state’’ variables monitor the condition of the environment. In this
example, the actual nutrient dynamics of the water body would serve as the state
indicator. The ‘‘response’’ indicator measures the actions taken to reduce pressures
or improve the state of the resource in question. The number of workshops held or
amount of protective legislation passed in a certain timeframe to protect water
quality are quantitative examples of response indicators.
The P–S–R approach was a useful addition to the literature in that is made explicit
the need to focus on those factors influencing environmental systems and associated
Fig. 1. Pressure–State–Response model for indicator development. Adapted from: OECD [11] and
LEAD [20].
R.E. Bowen, C. Riley / Ocean & Coastal Management 46 (2003) 299–312304

consequences (both in terms of environmental conditions and regulatory change).
However, its conceptual limitations are significant. It describes a system overly
simple in its view and overly narrow in its scope.
4. Driving force, pressure, state, impact, response model
The original P–S–R descriptions focused on anthropogenic pressures and responses.
One of several problems was that the original definitions did not effectively factor
natural causes into the pressure category. Therefore, natural variability and episodic
events had no real place in the model. While anthropogenic forcing is often an
important, if not dominant, factor in environmental change, efforts that ignore other
influences may lead to the imposition of unwarranted regulatory constraints that hold
little, if any, promise to improve environmental quality.
In part, this challenge led some, most notably the United Nations Commission on
Sustainable Development to describe a Driving Force–State–Response model. A
primary modification here was to expand the concept of ‘‘pressure’’ to incorporate,
social, economic, institutional and natural system pressures [22].However,evenwhen
‘‘driving force’’ replaces ‘‘pressure’’, the model does not explicate a category to account
for the underlying reasons for the pressures. To analyze policy options and resource
allocation in environmental management, it is essential to have a grasp of the root
causes of the problems being addressed [13]. A model that measures pollutants but
gives no information about the social conditions surrounding driving pollutant
introduction (e.g., changes in the organization of watershed agriculture or coastal
industrial production) is not providing the data needed to inspire meaningful change.
Another element missing from the P–S–R model is an examination of human
motivation responding to the state of environmental conditions. While social
stewardship of the environment should be an essential component of environmental
policy, it is not the sole motivation. Social resources are not infinite. Expenditures of
time, energy and effort are prioritized according to a rich and often conflicting suite
of factors. Certainly, one of those factors should be the social costs imposed or
benefits gained through changes in the quality of supporting environments. The
social impact of environmental change is an essential factor in influencing policy. An
indicator system that records the state but not the impact essentially assumes that
every change in the pressure, state, or response should be given the same amount of
attention or resources. Realistically, all ICM efforts are a careful balancing of
priorities. Including indicators that measure impacts to humans and the ecosystem
makes the model a more useful management tool.
Thus, challenges to the initial P–S–R model have contributed to the refined and
expanded approach described as the Driver–Pressure–State–Impact–Response Model
by, among others, the European Commission [9]. Within this model:
*Drivers describe large scale socio-economic conditions and sectoral trends such as
patterns in coastal land use and land cover, and growth and development in
coastal industry sectors,
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*Pressures such as patterns of coastal wetland alteration, the introduction of
industrial POPs/metals and fertilizer use in the coastal watershed hold the ability
to directly affect the quality of coastal environments;
*State indicators describe observable changes in coastal environmental dynamics
and in functions describing sustainable development;
*Impacts are the discrete measured changes in social benefit values linked to
environmental condition such as the cost of marine-vectored disease, loss of
recreational bathing beach value, or losses to commercial fishing value due
contaminant burdens; and,
*Response indicators are described as the institutional response to changes in the
system (primarily driven by changes in state and impact indicators).
Fig. 2 represents the D–P–S–I–R approach and is designed to emphasize the fact
that any indicator framework should focus not only on the articulation of
Fig. 2. Driver–Pressure–State–Impact–Response model for indicator development. Adapted from: IUCN
[21] and European Commission [9].
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Table 1
Illustration of socio-economic indicators within a driver–pressure–state–impact–response framework
Driver/state Pressure Impact
Population dynamics Resident coastal
population
Coastal land-use/land
cover
Coastal zoning patterns
Economic conditions Annual GDP growth
Environmentally
adjusted net domestic
product
Economic value/
employment in coastal
industry
Social conditions and
cultural traditions
% population with
potable water
Change in user conflict
Cultural stability/
integrity
Development pressure/
capital construction
% of altered coastal land Cost of coastal flooding/
hazards and savings
provided by coastal
habitat
% of impermeable
surface in CZ
Dredging costs driven by
sediment contamination
Coastal fill acres/year
Habitat change/
ecological value
Service value of coastal
habitat
Social costs of invasive
species
Value of habitat driven
manufactured products
Service value changes
from habitat alteration
Changes to non-use
values of coastal habitat
Contaminant
introduction
% of population with
wastewater treatment
% of coastal harvesting
areas under
environmental
restrictions
Fertilizer use in coastal
watershed
Industrial inputs of
POPs/metals
Resource extraction
activities
Oil spills from
extraction/transportation
Seafood value changes
from seafood risk/
habitat alteration
Commercial fishery
landings
Seafood consumption
patterns
Human uses/activities Coastal aquaculture Marine-vectored disease
Beach attendance Beach closing costs
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appropriate indicators and on the development of data acquisition systems
(indicators), but should also embrace the need for analysis and capacity
building through the construction of reporting products responsive to user
needs.
As already noted, the present effort is a focus on using conceptual models in the
building of socio-economic indicators viewed to be of greatest value in under-
standing the dynamics of social/environmental integration. Table 1 provides an
illustration of that effort. Here, indicators are classed according to whether they best
meet the description of Driver, State, Pressure or Impact indicators. They are also
classed into the substantive themes of:
*Population dynamics
*Economics conditions
*Social conditions and cultural traditions
*Development pressure/capital construction
*Habitat change/ecological value
*Contaminant introduction
*Resource extraction activities
*Human uses/activities
These substantive themes are used to stress the complexity of the coastal
social system. When developing an indicator system data should be drawn from a
broadest range of human activities influencing, and influenced by, coastal
environments. In any given situation it may be that the range of influence is
more narrow than those characterized in these thematic categories. However, a
systemic review of the possible relationships should be an early part of the design of
any indicator-based effort. Table 1 is provided as an illustration of specific indicators
representing these classes. It draws from a more complete list of indicators developed
for the recent meeting in Ottawa(1). This set can be viewed as a fuller palette
against which the discrete informational needs of individual programs could
be judged. Table 1 also attempts a reflection of more recent discussions on
the application of the model to various questions facing coastal and environmental
managers. In linking the model categories of driver and state the table attempts
to emphasize the value of context in indicator development. In certain instances
indictors may best assess the relationship between drivers and pressures.
In other instances the same indicators are best viewed as contributing toward
an understanding of the influence of Pressures on the State of social sustainability
functions [23]. This illustration is meant to further stress the need for flexibility
in the development and use of indicators. Models hold their greatest value when
they provide insight and inspiration into a broad range of complex questions
and hold less value when viewed as a constraint on creative application.
The D–P–S–I–R approach more effectively represents the complexities of social/
environmental interaction and highlights the need to understand and measure the
nature and scale of that dynamic.
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5. Process and outcome indicators
The indicator approaches described above can, and should, be incorporated into
more traditional program evaluation efforts. They can make important contribu-
tions to emerging and evolving efforts to assess the success of Integrated Coastal
Management. However, again, certain caveats need to be recognized. Integrated
Coastal Management represents a complex set of activities that include, but is by no
means limited to, efforts to improve environmental quality. Assessments of the
success of ICM (particularly at the local level) need to incorporate a broad range of
cultural and institutional measures. However, the degree to which evaluation
measure can expand to more effectively incorporate social/environmental dynamics
the stronger the argument will be that these programs hold broad and general value.
Evaluation efforts can usually be divided into those that measure process, those
that measure outcomes, and those that measure both [14,15]. In the context of
coastal program evaluation, process evaluation measures the policies, the laws
passed, money spent, permits issued/denied, and the management programs
implemented. Outcome indicators document the changes in social or physical
conditions brought about by the activities of the public program [16]. Acres of land
protected, number of public access sites established, an improvement in water
quality, or measures of organizational learning or progress are all considered
outcome indicators. Historically, much of the coastal management evaluations
conducted in the United States, for example, have concentrated on measuring
process indicators [16]. Managing for results has recently become a trend in US
governmental agencies, sparked by the Government Performance and Results Act of
1993. Focusing on outcomes rather than solely on process indicators intends to
shift the government away from overemphasizing inputs and hopes to introduce
accountability for desired agency results [7]. Performance measurement also
promotes communication within an organization about what the exact goals
are, how they are to be achieved, and who is responsible for each aspect of
project implementation [7]. Strengths and weaknesses of the project and of the
organization are more easily identified and addressed early in the process if outcomes
are being measured for each objective [7]. Within coastal management, it is more
difficult to find and collect outcome data than process data. Contributing outcomes
directly to a specific program is also a challenge compounded by the fact that criteria
for success are not often clear from coastal legislation or program plans [17].
Creating an indicator framework that has a place for both process and outcome
indicators can help trace management efforts more directly to environmental and
social conditions.
6. Input, output, outcome, impact indicators
Program assessment indicators can also be theoretically categorized into input,
output, outcome, and impact variables [5]. The World Bank categorizes indicators to
correspond with project components following an implementation scheme that flows
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from project design to implementation of sub-projects interacting toward desired
impacts. Tracking the performance of a project begins with input measures to keep
track of procurement of material of equipment, funds, material and labor. Output
and outcome indicators relate back to the stated goals of sub-projects and impact
indicators measure progress towards the goals stated at the highest level of the
project/organization. The World Bank uses this model to evaluate development
projects of all kinds, but the framework is applicable to coastal program evaluation
as well [18].
To demonstrate some examples of these types of measures, indicators from the
D–P–S–I–R model can be viewed as contributory to each of these categories. Input
variables measure the amount of time, personnel, or resources invested in a project
or task (response indicator). Output indicators measure specific actions taken by the
program, such as a decrease in point pollution (pressure). The outcome indicator
measures larger goals of the program such as improved water quality as measured
through nutrient dynamics (state). Impact indicators take this thinking one step
further, measuring the improved quality of resources or human health (impact).
Any organization attempting to design a performance-measuring system must
recognize that each action could be measured using input, output, outcome,
and impact indicators and design goals with this spectrum of results in mind.
Accepting such an assessment approach provides an effective mechanism to link
specific program performance measures with a broader environmental and social
sustainability perspective. It also more effectively structures the opportunity to link
coastal environmental monitoring programs with local ICM program actions
and goals.
7. Broadening the concept of program performance
As already noted most ICM program efforts incorporate the improvement of local
environmental conditions in either the rationale for the program or in the statement
of program goals. Therefore, indicators of environmental variability and the socio-
economic relationships to them should be built into a comprehensive system of
program performance. And, while it is readily acknowledged that environmental
improvement and social change are not typically reflected within the duration of a
specific ICM program effort this should not preclude their inclusion as indicators of
longer-term program success or as contributors to development of broader-scale
policy development. These broader indicators of change cannot be used alone to
judge program implementation at individual sites, however, this information is
critical to understand local, state, and national trends. Indicators of coastal
environmental change can be used to prioritize projects at the site level and should
feed into state, national, and potentially international efforts to relate coastal and
human health to management efforts. Ecological indicators should be one category
of performance measures crafted from the mission statements of coastal manage-
ment programs to reflect pressures to the environment from natural sources (such as
weather) and the environmental state of coastal systems.
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The pressures imposed by human action are also important in forming
strategic goals program priority actions. Land use patterns may contribute to an
understanding of water quality data, and demographic trends in the surrounding
towns may help managers redesign education programs.
Tracking anthropogenic influences will provide a better long-term picture of
program effectiveness in changing behaviors and attitudes as well as documenting
health and economic consequences (impacts) of environmental services and
degradation. Any practical environmental program must incorporate social realities
into its plan of action. Selecting indicators for socio-economic factors enriches the
body of measures used to improve programming and evaluate progress on a broader
scope than using institutional performance measures alone.
To adapt to the most pressing local issues and assess the long term impacts of
program action monitoring information outside the direct control of the site
management should also be assessed, organized, and used to shape local plans.
Recently, agencies, nations and even small towns have been publishing ‘‘sustain-
ability indicator’’ lists that track economic, ecological, cultural, and social indicators
to alert decision makers to trends [18,19]. Through these data, the state of the
environment can be assessed along with the pressures that result from human social
and economic activity. Models can be drawn, plans made, and actions prioritized
based on monitoring indicators. Program implementation should address the
findings of a monitoring program or data collection that reflects current conditions
and patterns. Achieving the goals of Integrated Coastal Management requires a clear
picture of programmatic progress, environmental conditions and influencing
anthropogenic factors.
This view admits to significant challenges. Attempting to tease out the relative
contributions of natural cycles, episodic events, and anthropogenic influence requires
sophisticated statistical analysis and the occasional heroic assumption. Programs
may contribute to improvements in the state of the natural environment and
relieve certain pressures on the estuary, but it is clearly difficult to measure the
proportion of change attributable to a specific action. The long-term use of socio-
economic and ecological indicators can indicate how well the programmatic
approach contributes to broader goals of Integrated Coastal Management over
the period of years to decades (which, admittedly, is beyond the budgetary cycle of
most local programs). In the short term, these indicators can aid local managers in
moving toward more informed decisions on prioritizing projects and revising
strategic plans.
Socio-economic, ecological, and management indicators all fit into a linked
approach to program performance. Understanding coastal processes, and therefore
evaluating program success in addressing coastal issues, requires a broad and rich set
of integrated indicators. Comprehensive program assessment should incorporate
appropriate components revealed through consideration of a D–P–S–I–R modeling
effort as well as more traditional institutional performance measures. The more
effective integration of social condition, environmental dynamics and institutional
response can only enrich the process of informed decision-making on sustainable
resource use and development practices.
R.E. Bowen, C. Riley / Ocean & Coastal Management 46 (2003) 299–312 311

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