SYSTEMS MODEL ING AND THE ENV IRONMENT

Dynamic Modeling ofIndustrial EcosystemsMatthias Ruth

The Challenge

Given the complexity of the sus-tainability challenge, dynamicmodels may have an impor-tant role to play in closing theloop that connects informationcapture, knowledge generation,system intervention, observationof outcomes, and generation ofnew knowledge that helps informthe next round of system inter-ventions. Dynamic modelingcapturing the dynamics of sys-tems and making the process ofmodeling itself a dynamic onebecomes then an integral part ofadaptive management.

Managing industrial ecosystems for sustain-ability means, to some extent, having to avoidthe unintended consequences of production andconsumption processes.These consequences arenot easily grasped by thosewho extract, convert, use,and release materials andenergy associated with alltransformations along theentire chain of goods andservices. Some of theseconsequences manifestthemselves as environ-mental insults, humanhealth impacts, and legalliabilities as well as broadereconomic and social costs.And because these conse-quences are typically notfelt instantly, gaining afull understanding of thelags between causes andeffects across space andtime is difficult, if notimpossible.

Themeans to shape the behaviors of industrialecosystemsthe assemblages of producers andconsumers and their interrelationshipsincludechanges in inputs into production and consump-tion processes, alterations of production and con-

Supplementary material is availableon the JIE Web site

c 2009 by Yale UniversityDOI: 10.1111/j.1530-9290.2009.00186.x

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sumption processes themselves through changesin technologies or behaviors, changes in the de-sign of products, and changes in the institutionsthat govern products life cycles from cradle tograve. Again, none of these changes typically

occurs instantly. Substitut-ing inputs or redesigningproducts often results in ex-tended trials, altering pro-cesses requires turning overthe existing capital stockand influencing the behav-iors of large groups of con-sumers, and changing in-stitutions means reshapingthe rules and proceduresthat govern their function-ing and waiting for thenew rules and procedures topenetrate often large, hier-archical organizations thatthemselves exhibit inter-nal inertia and are dis-tributed across the globe.

All this, of course, hap-pens in a world of incom-plete information at eachstep along the way. All too

often, there are fundamental uncertainties aboutthe complexity of interactions among the manymicrodecisions made in the social and economicrealms, the vast number and frequently non-linear nature of environmental processes thatinfluence and are influenced by these deci-sions, and the emergence of novel phenom-ena in each of these realms. New technolo-gies advance the measurement and monitoringof substances that were hitherto not on theradar even of experts. New concerns emergeamong the public. New laws and regulations areinstituted.

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On the one hand, all this change couldgive one pause, if not paralyze decision making.On the other hand, people do make manage-ment decisions and policy interventions to pro-mote sustainability. Assuming that these achievetheir goal presumes some level of knowledgeabout the causeeffect relationships that con-nect behaviors of individual components of theenvironmental, social, and economic sphereswith each other and across system hierarchiesfrom a population to the larger ecosystem,from the household to entire communities, orfrom the machine to the firm and industry, forexample.

Assembling the Pieces of thePuzzle

Of course, all one can ever do is make thebest decisions possible on the basis of the limitedknowledge one has. Modeling has always playeda role in enhancing understanding and in provid-ing a basis onwhich to choose alternative systeminterventions. Historically, much of that mod-eling was mental and verbal in nature, but as thecomplexity of decisions has increased, increasingemphasis has been placed on analytical and quan-titative methods (Pagels 1988). Because efforts tomove industrial ecosystems toward sustainabilitymust be cognizant, to the best degree possible,of unintended consequences in the economy, so-ciety, and the environment, these efforts needto draw on formal modelsparticularly on thosethat connect environmental, social, and eco-nomic processes across system hierarchies, space,and time. And because of the richness of data andrelationships, computers are used to develop andsolve those models.

The study of dynamic systems has evolvedfrom analysis of relatively simple electrome-chanical devices to consideration of interdepen-dent social, economic, political, and environ-mental systems that include highly uncertainhuman perception and behavior. Objectives indynamic modeling include the identification ofsystem conditions associated with degraded per-formance or instabilities, the design of controlsystems to ensure stable performance, character-ization of possible transitions between multiplestable states for a system (i.e., bifurcations or

catastrophes), and the proper use and enhance-ment of monitoring data often plagued bymissingor inaccurate observations. Dynamic modelingmethods are generally lumped or aggregate; theyuse simultaneous nonlinear differential equationsto predict the coevolution of system variables. Inrecent years, however, increases in computationalpower have enabled the development of disag-gregate models, in which individual agents (e.g.,companies or consumers) and their interactionsare modeled explicitly.1

Formal models have many purposes. They canhelp structure what is known about causeeffectrelationships. They help organize that knowl-edge, and, by doing so, they help articulate ques-tions about what is not known. Models thus notonly are repositories of information but can directfurther collection of data and guide research.

If oriented toward support of investment andpolicy decisions, models ideally are amenableto experimentation for and by decision makers.They become the flight simulators by which theimplications of alternative actions are exploredbefore interventions in the more complex set-tings of real-world investment and policy makingare deployed. They can help train the minds andresponses of the pilots who fly entire companies,industriesin some sense, our globe. To be effec-tive, such models capture feedbacks across spaceand time and across the hierarchies of the sys-tems of interest. Many examples of such modelsare found in the various issues of this journal (e.g.,Rejeski 1998) and, for example, in the work byRuth and Davidsdottir (2008, 2009).

What New Can Come FromDynamic Modeling?

Usually, models capture what is known and,on the basis of that knowledge, project into thefuture the behavior of a system. If the systemof interest is large and diverse, experts from dif-ferent disciplines often collaborate to integratetheir data and information into a single model,which allows them to capture what is usually as-sumed to be exogenous from their own narrowerviewpoints. The investment and policy decisionsthen affect parameters in one or more parts of thesystem and thus drive a systems dynamics. Suchdecisions are typically made with an eye toward a

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particular goal, such as waste minimization, profitmaximization, or social welfare maximization.

The tendency for system interventions hereis largely informed by past experiences. For ex-ample, taxes are introduced to raise the cost ofan input, and, on the basis of historical expe-riences of pricequantity relationships, changesin resource use and emissions are observed. Suchinterventions then result in gradual movementaway from the status quo. There is usually littleopportunity for true surprise both about systembehavior and about options for interventions, andthe value of these models largely lies in quantify-ing the outcomes in cases in which the individualpieces of the larger puzzle are fairly well under-stood, whereas the interaction of the pieces witheach other may be less apparent.

Whether interventions that manipulate indi-vidual parameters are sufficient to promote sus-tainability is often at question. Of course, one canalways ratchet up one parameter or another andarrive at more drastic system responses, but onewill largely be tied to the existing structures andthe limited opportunities for thinking and actionthey purport.

There are other ways of using models as guidesfor investment and policy decisions. One ofthem begins with what is known and then ex-plores which parameter choices lead to undesir-able system outcomes. This kind of approachsometimes referred to as anticipatory failure deter-minationawakens the mischievous behavior inpeople, which can free their thinking and pro-mote a different kind of creativity, albeit an ini-tially destructive one. Really exciting surprisesmay be had. A better understanding of the con-stellations of actions that result in system failurecan then be used to put in place safeguards (mon-itoring, controls, policies) that keep parametersoutside the ranges that promote disaster. Thisapproach is particularly useful for risk minimiza-tion in complex settings. Researchers have used asimilar approach to evaluate alternative scenar-ios for future climate change and infrastructureplanning.

Yet another approach begins with a descrip-tion of a desirable end state and then runs themodel back to the present to see which decisionsone needed to make to arrive at the end state.Such backcasting, too, helps modelers and deci-

sionmakers to break out of thinking that is overlyshaped by the status quo and challenges them toenvision and formalize an end state that is typi-cally richer than the one portrayed by traditionaloptimization of a specific target function. Back-casting frees the mind and model of the intellec-tual straightjacket imposed on them by history.Really new knowledge may be generated by thismethod.

Adaptive and AnticipatoryManagement

Although dynamic models can be powerfultools to structure debate, they usually are onlyone of many contributors to decision making. Of-ten, they play no role at all other than perhaps insupporting decisions that would have been maderegardless. Sometimes, the information they gen-erate enters into the decision making processalongside competing pieces of information, and,if done right, models can then help provide astructured basis on which to evaluate and reflecton the information used for decision making.

Given the complexity of the sustainabilitychallenge, dynamic models may have an impor-tant role to play in closing the loop that connectsinformation capture, knowledge generation, sys-tem intervention, observation of outcomes, andgeneration of new knowledge that helps informthe next round of system interventions. Dynamicmodelingcapturing the dynamics of systemsand making the process of modeling itself a dy-namic onebecomes then an integral part ofadaptive management (Hannon and Ruth 2001).Used in combination with such methods as an-ticipatory failure determination or backcasting,it can support anticipation of and preparation forunintended consequences.

Decisions about whether something is indeedan unintended consequence require some formof involvement by those groups that may be af-fected. Similarly, the choice among actions can-notmeaningfully bemade on the basis ofmodeledsystem behavior alone. Value judgments, ethicalcriteria, and norms need to enter the picture. Itis therefore essential to make stakeholders an in-tegral part of the dynamic modeling process.

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What We Do, and What WeDont Do but Should

The scientific and environmental manage-ment literature is replete with descriptions ofdatabases, simulations, and decision supporttools. A growing number of case studies tell ushow to leverage involvement of stakeholders.Theoretical and analytical approaches are be-ing developed to explore behaviors across sys-tem hierarchies, space, and time. But researchershave not fully made the connections across allthese essential elements in part because we lackcommonly agreed-on and followed protocols fordynamic modeling that span system hierarchies,space, and time and because we have not yetdeveloped a sufficiently adequate suite of ap-proaches and tools for model effectiveness assess-ments. As industrial ecologists, we have not yetdefined an equivalent, for example, to the Na-tional Energy Modeling System (NEMS) usedin the United States to monitor and guide en-ergy related developmentsneither at the localnor the national scale. Nor do we have in placeinstitutional structures that enable longer termstakeholder-guided adaptive management of in-dustrial ecosystems. What we do have by now,though, is an impressive arsenal of data, tools,and people to elevate dynamic modeling to thenext level, and with that we can help to drive thesustainability agenda.

Note

1. References on dynamic modeling and related liter-ature can be found in Supplementary Appendix S1on the Web.

References

Hannon, B. and M. Ruth. 2001. Dynamic modeling.Second edition. New York: Springer-Verlag.

Pagels, H. 1988. Dreams of reason. New York: Simonand Schuster.

Rejeski, D. 1998. Learning before doing: Simulationand modeling in industrial ecology. Journal of In-dustrial Ecology 2(4): 2944.

Ruth, M. and B. Davidsdottir, eds. 2008. Changingstocks, flows, and behaviors in industrial ecosystems.Cheltenham, UK: Edward Elgar.

Ruth, M. and B. Davidsdottir, eds. 2009. The dynam-ics of regions and networks in industrial ecosystems.Cheltenham, UK: Edward Elgar.

About the Author

Matthias Ruthholds theRoy F.WestonChairin Natural Economics at the University of Mary-land in College Park, Maryland, where he servesas the director of the Center for Integrative En-vironmental Research.

Address correspondence to:Prof. Matthias RuthCenter for Integrative Environmental ResearchUniversity of MarylandSuite 2202, Van Munching HallCollege Park, MD 20742mruth1@umd.eduwww.publicpolicy.umd.edu/faculty/ruth/

Supplementary Material

Additional Supplementary Material Information may be found in the online version of thisarticle:

Supplement S1.This supplement contains an appendix with references to the research literaturerelated to this columns discussion of dynamic modeling.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supple-mentary materials supplied by the authors. Any queries (other than missing material) should bedirected to the corresponding author for the article.

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