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18METRICS AND INDICES FOR

SUSTAINABLESOCIAL–ECOLOGICAL

LANDSCAPESRonald J. McCormick

Most people think the future is the ends and the present is the means. In fact, thepresent is the ends and the future the means.

Fritz Roethlisberger

This discussion started (Chapter 16) by introducing a question from ecological eco-nomics, “What is the scale of the economy that fits inside the ecology?” We presenteda general deconstruction of neoclassical economics and explored how scale appearsmerely implicit in economic models, whereas explicit definition of scale is required inany model of a complex system. Providing explicit definitions of scale and ecologicaltype sets a clear level of analysis. In Chapter 16, we described how recent advances insystems theory (high-gain/low-gain analysis) allows for the blending of market ther-modynamics and social regulation via the field of economic ecology, which naturallyleads to issues of sustainability. As society moves to a low-gain perspective on man-aging for sustainable social–ecological landscapes, the assumptions and limitationsinherent in available economic valuation techniques become quite apparent.

The primary limitation of most valuation techniques lies in how one sets theboundaries of a system of interest. Benefit–cost analysis has limited temporal rele-vance due to the inherent uncertainty in any financial forecast data. The majority offinancial prognostications put forth in early 2008 proved to be completely and utterly

Environmental Risk and Management from a Landscape Perspective, edited by Kapustka and LandisCopyright © 2010 John Wiley & Sons, Inc.

382 METRICS AND INDICES FOR SUSTAINABLE SOCIAL–ECOLOGICAL LANDSCAPES

useless by the autumn of that year. At the root of the subprime mortgage meltdownwas the idea that one could take securities developed under one (local) level of anal-ysis and (presumed minimal) risk scenario and expand those same risks upscale to aglobal market. Moving upscale changed the initial level of analysis that the systemwas working within, but most analysts, placing total trust in mathematical models,missed that fact. Indeed, even Alan Greenspan, in his testimony to the U.S. Congresson October 23, 2008, said “I found a flaw in the model that I perceived is the criticalfunctioning structure that defines how the world works.” The flaw was that his modelassumed self-correction in the securities market would take place at an individualinstitution level, thus protecting “local” investors. Self-correction did occur, but notat the level in the system where it was needed, it occurred at a global, and nearlycatastrophic, level.

Though the subprime debacle is attributable to no single action, a key indicatorof lender risk, interest rate, was reduced in variance as part of the process of offeringmore loans (Rajan et al. 2008). A person with a poor credit rating received a loan atan interest rate very similar to what someone with a good credit rating was offered.The effect of this was that once a single degree of separation existed between theperson receiving the loan and the person or institution holding the note, there wasno way to compare relative risk between mortgage packages (interest rate providedinsufficient signal). Lack of transparency of actual risk at a low level in the processallowed unequally risky loans to appear as equally risky loans at a different level inthe system.

Human systems, especially socioeconomic systems with designed allowableinteractions, are fraught with these kinds of unrecognized risks. Landscapes supportingecosystem processes and intact communities are self-organizing (no set interactionrules) and open to the flow of energy, materials, and information (total transparency).If a plant or animal signals that “I taste bad” via bright red markings, then mostanimals will heed the warning and steer clear. However, as the communicationof information is open, some animal might see and accept the risk and have asnack. Whether accepting that risk was wise or unwise is answered very quicklyvia feedback from the consumer’s body, not years later via a party not involved inthe original snack decision. Timely and relevant feedback and open communicationmake ecosystems function effectively. Businesses that mimic these characteristicsusually prosper (sensu Pascale et al. 2001). Sustainable social–ecological landscapesneed to adopt these strategies in order to manage risk and flourish in the face ofchange.

Aldo Leopold urged people to “think like a mountain,” a mantra often used bythe deep ecology movement. However, while environmentalists were thinking like amountain, the rest of society built roads, logged off the trees, extracted any valuableores, and harvested the edible wildlife, all while the mountain’s glaciers were meltingand relentlessly eroding it, becoming silt in some valley. Perhaps in this, the 21stcentury, we need to “think like an ecosystem,” to know where the matter and energywe use came from, where it is going, and how effectively we use it while we havecontrol. This is the heart of economic ecology and is the root of sustainability.

ULTIMATE MEANS, ULTIMATE ENDS 383

Ultimate Means

Intermediate Means

Intermediate Ends

Ultimate Ends

Science & Technology

Political Economy

Theology & Ethics

HumanWell-being

Human&

SocialCapital

Built&

HumanCapital

NaturalCapital

HappinessHarmony, identityFulfillment, self-respectSelf-realization, communityTranscendence, enlightenment

Health, wealthLeisure, mobilityKnowledge, communicationConsumer goods

Labor, toolsFactoriesProcessed raw materials

Solar energyThe biosphereEarth materialsBiogeochemical cycles

Figure 18.1. Connections inherent in any social–ecological landscape. [Modified from Mead-

ows (1988).]

ULTIMATE MEANS, ULTIMATE ENDS

Referring to a social–ecological landscape is a short-hand way of referring to a soci-ety’s ultimate ends, the goals and values of that society, and the constraints placedon those goals by the natural capital available on a landscape, the ultimate means ofecological goods and services. Herman Daly, in his 1973 book Toward a Steady-StateEconomy [as quoted in Meadows (1998)], outlined a hierarchy where the economyand daily human endeavors (intermediate means and ends) were placed in contextbetween natural capital and human well-being (Fig. 18.1). This framework remainshighly relevant today, some 35 years later.

Meadows (1998) used Daly’s framework to guide the selection of indicators forsustainable development. Her explanation of what indicators are and why they are sodifficult to develop closes with the notion that (a) we need more than just indicators,(b) we need information systems that are organized and scaled along a hierarchy fromfine-grained to coarse-grained, and (c) information is open to and modifiable at alllevels. This echoes my call to “think like an ecosystem” and reflects a low-gain view ofhow a sustainable world might be organized. Meadows went on to show how systems


thinking is vital to indicator development, and how conceptual modeling and dynamicmodeling are necessary to fully develop and evaluate any multilevel indicator system.

Hierarchy theory (Ahl and Allen 1996) and systems analysis focuses on bound-aries, specifically the porosity and rate of exchange at boundaries. The question “Whatis the scale of the economy?” is a simple model that seeks to know how much theboundary of the economy can infringe on the whole of the ecology. Indicators thatonly track the central tendencies of a system will not provide timely data on a sys-tem’s variance, and a system can fail well before a signal is seen in those indicators.Indicators need to focus on the boundaries of the system, the place where information,matter, and energy are exchanged at a rate. Again, Meadows (1998) reflected thesenotions in her statement “The three most basic aggregate measures of sustainabledevelopment are the sufficiency with which ultimate ends are realized for all people,the efficiency with which ultimate means are translated into ultimate ends, and thesustainability of use of ultimate means.”

Obviously, a thorough reading of Meadows’ work is recommended, because Ihave only presented the key points and there is much more depth to be explored. It isto me one of the most useful and cogent presentations on indicators and informationsystems. My intent in presenting Meadows’ work is to offer not just another genericset of indicators that may not work for your landscape, but instead a conceptualmodel of how to select and structure your own unique and usable indicator set. Toeffectively develop a set of indicators, you will also need a conceptual model of yoursocial–ecological landscape, outlining the hierarchy of interacting elements.

Conceptual Modeling in Ecological Risk Management

Monetized valuation systems are not sufficiently scalable and flexible to help us trackwhat we extract, degrade, and return to a landscape. Indicators are needed at multiplelevels in the system in order to fully assess the rate we extract goods and servicesfrom a landscape, as well as the rate at which we expel waste back into that landscape.There are myriad indicators that could be developed to answer these questions. TheEcological Footprint (Wackernagel and Rees 1996) is one very good example of ascaleable, multilevel indicator. The issue then is to decide which indicators actuallytell us what we need to know when we need to know it. The field of risk assessmentand management offers a process by which key social issues and ecological stressorscan be brought to the surface and analyzed in context. The holistic risk frameworkis presented elsewhere in this book (Chapter 8). Here I focus on the initial step ofproblem formulation, specifically developing a conceptual model of the landscape andecosystem processes of interest.

A conceptual model (CM) depicts the ecological and socioeconomic relationshipsamong components of the landscape and potential stressors from human activities,whether direct or indirect. Stressors can relate to biological, chemical, and physicalinteractions that currently exist as well as those anticipated to occur in the future,including those specifically related to planned human activities. A CM is therefore acharacterization of interactions occurring across spatial and temporal scales relevant todecisions to be made about a project. Temporal and spatial extent, time and space, the


ExistingConditions:

Northern LandsIssues

Wildlife

Caribou

Carnivores

Birds

Birds BehavioralChanges

Human RelatedActivity

Alterations toMigration Corridors

Impact BenefitAgreement

Imported Workers

Influence of ExternalCultural Values

Habital Quality and Quality Changes

CommunityWellness

SocialStabilitySustainability

CommunityCohesion

Affected CommunityDetermination

Emergency andFuture Planning

Widening EconomicDisparity Issues

Controls and Limits onExternal Social Influences

PopulatonTrajectories;Cumulative

Effects

Figure 18.2. Example linkage diagrams. One pathway from the top diagram can be inter-

preted as indicating that ‘‘alterations to migration corridors, related to human activities in the

area, causes behavioral changes in individual birds, which can cumulatively affect population

trajectories in birds, and is a subissue of general wildlife issues that are existing conditions

within Northern Lands.’’ One pathway from the bottom diagram can be interpreted as indicat-

ing that ‘‘imported workers create external social influences that affect community cohesion

and overall social stability, a subissue of the larger sustainability issues related to overall

community wellness.’’

when and where of what we are interested in, are key elements of any level of analysis.Defining a level of analysis is part of the process of creating a conceptual model, thefirst step in developing risk-based management scenarios for complex landscapes.

A conceptual model should be developed as part of any holistic risk assessmentproject plan. It should be designed to be dynamic, yet readily accessible to multiplestakeholders. For this chapter, I developed an example CM for a generic developmentproject in northern Canada. While the CM is generalized for presentation in thisbook, it does reflect actual stakeholder issues publicly expressed during the review andapproval process for projects in the region. The CM (which can be found on the book’sftp site) has 64 pages of linkage diagrams. Details about how to read the diagrams andnavigate through the CM are included in the first few pages. Each linkage diagram(Fig. 18.2) frames a perceived connection from an anticipated activity/source resultingfrom project operations to a specific environmental element (air, water, soil, vegetation,wildlife, humans, etc.). Linkage issues are potentially attributable to sources—that is,community members, elders, project developers, scientists, government officials, andother nongovernmental organization stakeholders.

The sample CM presented here is visually interactive and provides 16 levels ofanalysis covering all phases and timeframes for a project. A project CM should containcross-references to each issue identified by stakeholders as well as those identified bygovernment officials and technical experts. Every issue identified is incorporated into


CM

PlayEnvironment

LocalEconomyWork

Live

LocalValues

Community

People

PeopleActivities

Society

RegionalEconomy

RegionalValues

Fish

Uranium

Oil & Gas

Mining

MineralExploration

Hydro-Power

ClimateChange

GlobalEconomy

ExternalValues

CulturalIdentity

DemographicShifts

Water

WildlifeAir

CaribouHealth

Social – Ecological SettingLandscape Elements

You can move to the next page by pressing Ctrl-Page Down, orby following the arrow link to the right.

Figure 18.3. Nested relationships for residents of a northern social–ecological landscape.

one or several of the linkage diagrams. The CM thus serves as a foundation for orga-nizing a project assessment by various disciplines including human and ecological risk.

Means and Ends on a Specific Landscape

People are closely linked to their ecological setting, their landscape. Page 1 of theexample CM (Fig. 18.3) shows the many ways people interact with place to reflectlocal values and produce a local economy. The key terms used are as those expressedby stakeholders to describe elements of the landscape within which they live, work,and play. This high-level conceptual diagram shows people nested within a local com-munity and its environs. Each community is a self-contained and local unit (indicatedby the dark solid boundary line around the people-community-environment circle),while also belonging to and being nested within a larger, regional landscape.

Regional social, economic, and cultural values emerge from the interactions ofmany local communities and a diverse landscape; these values drive human activ-ities to produce a regional economy. A tacit societal agreement emerges from themany communities and guides management actions within the regional landscape withrespect to caribou, fish, water, human health, and other issues. Even so, the regionalsocial structure is connected to the global landscape with varying degrees of externalinfluences (shown by the lighter-colored shared boundary).


Globally, economic drivers increase the desire for access to local resources. Theresulting infusion of external values and different cultural norms directly affects localpeoples and their landscapes. Along with this interaction of global and local values,human immigration and climate change place added pressures on regional societiesand ecosystems.

The conceptual model identifies and expands on the interconnections betweenglobal drivers and local conditions, with an emphasis on boundaries and interactionrates. For example, as shown on the Social–Ecological Landscape Elements page,local communities are nested within their regional environment. They are tied closelyto this environment and rely on it. However, human activities originating from outsidethe regional landscape weaken or eliminate local–regional bonds, effectively pullingthe local people–community–economy into the global circle by altering the regionallandscape. The CM details these cross-scale interactions for the spatial extent ofnorthern lands affected by the proposed project. The social–ecological relationshipswithin this landscape are illustrated within the CM over time from the present to longafter project closure.

From Conceptual Model to Indicators to Scenarios

Following Meadows’ (1998) list of criteria for what makes a good indicator, onecould develop informative metrics for any and all of the endpoints (far right topic,Fig. 18.2) of each linkage diagram in the conceptual model. Employment statisticsfor the entirety of the far northern territories would be useful for one level of analysispresented in the CM (Existing Conditions: Northern Lands), as would within-territorynumbers (Existing Conditions: Regional), as well as individual community values(Existing Conditions: Local). The criteria used to collect these values would changewith project phase (temporal scale) as well. Similarly, caribou numbers could becollected for all Northern Lands as an aggregate population number, while hours ofhunting effort per caribou harvested might serve to assess the status of local caribounumbers in every affected community.

The key point when developing indicators is to tailor each to each level of analysisand to make sure that the metrics used reflect the desires and values of affectedstakeholders. The trouble with this key point is that desires and values of stakeholderschange with each level of analysis, especially with time. Thus, again as pointed outby Meadows, every indicator should have a sunset date. At some reasonable pointin time, every indicator must be fully reviewed by the relevant stakeholders, and adecision must be made as to whether to keep collecting those data or to start collectingdifferent data to better reflect current needs.

Multilevel conceptual models, dozens of levels of analysis, dozens of dozens ofpossible indicators spread across space and time; suddenly the idea of tracking thesustainable use of a landscape appears exceptionally complicated. Much of the currentdiscourse regarding sustainability metrics appears complicated for just this reason,becoming mired in the details of things that can be measured, but don’t necessarily tellus anything. Essentially they are metrics (e.g., Indicators of Sustainable Community


1998 by Sustainable Seattle) for becoming less unsustainable than ones for trackingour sustainable use of ecosystem goods and services.

Every social–ecological landscape is unique in its complexity. The path to sus-tained use of each landscape will vary widely around a set of common ideas. I havepresented my view of what those common ideas might entail, and I have shown howto integrate theory into practice. The last issue in dealing with each unique complexitylies in how to interpret the signal from your indicator set. Humans are hard-wired toonly be able to value something in comparison to something else (Ariely 2008). Anindicator absent a context lacks value for making decisions about using an ecosystemservice. Most indicators are evaluated with respect to historic values, which can beinformative for short-term (year-to-year) goals. How a given value relates to possiblefuture landscape states is just as, or perhaps even more, important when thinking likean ecosystem.

As far as we know, no one can predict the future. At best, we can only gener-ate possible scenarios using general, plausible trends. The business world does thisquite regularly (e.g., Shell International Ltd. 2005), and they regularly review andupdate their scenarios. Van Der Heijden (2005) and Schwartz (1996) are only two ofmany books written on how to develop scenarios for your particular social–ecologicallandscape. By describing three or four possible future states for the economic, socialecological, and political context that you will be evaluating your landscape indicatorsin, you can assess whether an up or down trend is acceptable.

Referring back to the conceptual model example, at present, using only past andcurrent values, a drop in employment in the mining sector is considered to be pooreconomic and social news. Maintaining the current high-gain system where the entireregion is reliant on a single-source employer will ensure that reduced employment isalways bad news. Re-envisioning an integrated local and regional landscape that has inplace alternative employment and other programs to absorb inevitable downturns in asingle industry is one very plausible scenario, and a scenario where rising single-sectorunemployment is not considered to be poor economic and social news.

Consider two plausible scenarios having the same indicator trend, but with twodifferent evaluations. Social and monetary valuation systems, intermediate means andends, will shift and change with time. Through continuous dialogue and stakeholderinput, a community can decide which possible scenario within the social–ecologicallandscape it desires as an ultimate end, and it can then set about developing conceptualmodels and indicators to address the source of the ultimate means to that end. WhatI have shown here is that by using risk-based decision analysis tools, one can guidestakeholders in a process toward holistic conceptualization of the problems beingaddressed, minimizing the chances of a “solution” becoming the “next and largerproblem.”

REFERENCES

Ahl V, Allen TFH. 1996. Hierarchy Theory: A Vision, Vocabulary and Epistemology . ColumbiaUniversity Press, New York.

REFERENCES 389

Ariely D. 2008. Predictably Irrational: The Hidden Forces That Shape Our Decisions . Harper-Collins, New York.

Meadows D. 1998. Indicators and Information Systems for Sustainable Development . The Sus-tainability Institute, Hartland Four Corners, VT.

Pascale R, Millemann M, Gioja L. 2001. Surfing the Edge of Chaos: The Laws of Nature andthe New Laws of Business . Three Rivers Press, New York.

Rajan U, Seru A, Vig V. 2008. The Failure of Models that Predict Failure: Distance, Incen-tives and Defaults . Available at Social Science Research Network: http://ssrn.com/abstract=1296982. Last accessed November 6, 2008.

Schwartz P. 1996. The Art of the Long View: Planning for the Future in an Uncertain World .Currency Doubleday, New York.

Shell International Limited. 2005. Shell Global Scenarios to 2025 . Royal Dutch/Shell Group.

Van Der Heijden K. 2005. Scenarios: The Art of Strategic Conversation . John Wiley & Sons,West Sussex, England.

Wackernagel M, Rees W. 1996. Our Ecological Footprint: Reducing Human Impact on theEarth . New Society Publishers, Gabriola Island, BC.

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