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Chapter 15

SYSTEM DYNAMICS AND SUSTAINABILITY

Andreas Großlera and Jurgen Strohheckerb

aInstitute for Management Research, Radboud University Nijmegen,P.O. Box 9108, 6500 HK Nijmegen, The Netherlands

[email protected]

bFrankfurt School of Finance & Management,Sonnemannstraße 9-11, 60314 Frankfurt am Main, Germany

[email protected]

As the foundation of the field by Forrester in the 1950s, System Dynamics has beenapplied to sustainability issues of various types. While business was the original researchobject of Forrester and his group, due to the systems and control theory perspective atits heart, stretching the scope of System Dynamics to other systems happened as a nat-ural next step. We argue that System Dynamics is particularly suitable for sustainabilityresearch because it provides (1) a structural theory focusing on feedback, accumulation,delays, and non-linearities within dynamic systems and (2) a methodology to formulatecontent theories for a broad range of sustainability issues that can rigorously be testedand be analyzed using computer simulations. This statement is substantiated by a selec-tive review of the literature on System-Dynamics-based sustainability research, whichshows that the history of the sustainable development movement and System Dynamicsis interwoven.

1. Introduction

System Dynamics initially developed from the work of MIT professor JayW. Forrester, who started the System Dynamics Group at the Sloan School in 1956and published the seminal article “Industrial Dynamics: A Major Breakthrough forDecision Makers” in Harvard Business Review (1958). System Dynamics is designedfor studying complex feedback systems and providing decision support for decisionmakers by model-based scenario analysis and policy testing. Originally, as the term“industrial dynamics” implies that it was applied to business systems. It aimed at(1) developing “a better intuitive feel for the time-varying behavior of industrial andeconomic systems,” (2) providing “a background showing how the major aspects ofa company are related to one another,” (3) helping to predict “the future courseof an existing organization,” and (4) improving “the future prospects of a com-pany” (Forrester, 1958). Very soon, however, it became obvious that these goalsand the methodology of industrial dynamics could be extended to all kinds of social

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314 A. Großler and J. Strohhecker

systems; applications to systems such as a city (Forrester, 1969) and the whole world(Forrester, 1971) followed; consequentially, the field was renamed System Dynamics.

During these first heydays of System Dynamics, the term and concept of “sus-tainable development” was not yet commonly used. It was left to the World Com-mission on Environment and Development in 1987 to put the idea of sustainabilityinto the now famous words: sustainable development ensures that “it meets the needof the present without compromising the ability of future generations to meet theirown needs” (World Commission on Environment and Development, 1987). Never-theless, Forrester’s above-stated four goals reveal a relatedness to the sustainabilityconcept. By (1) developing a better intuitive feel for the time-varying behavior ofthe world system, (2) illustrating how the major aspects of the world are related toone another, (3) helping to predict the future course of the world, and (4) improv-ing the future prospects of the world, a sustainable development of the world seemsachievable. Insofar the application of System Dynamics to the issue of providing asustainable future for humankind appears not merely as a chance event, but ratheras corollary.

On the “sustainable development timeline,” which is published and regularlyupdated by the International Institute for Sustainable Development, the probablybest-known System Dynamics study “Limits to Growth” (LTG) ranks 15th outof some 100 entries stretching from 1962 to 2009.1 The non-technical report of a2-years project initiated by the Club of Rome (Meadows et al., 1972), funded bythe Volkswagen Foundation and run by a research group at MIT, was a milestonefor both the young field of Systems Dynamics and the even younger movement ofsustainable development. A broad, hot, and often emotional debate was triggeredoff by the rather “simple” and “obvious” statement in the book that “the earth isfinite and cannot support exponential growth for very long” (Meadows, 2007).

In the Section 2, we present system dynamics as a structural theory and asa methodology to understand, explain, depict, and influence dynamic systems ofany kind. It is discussed that, why System Dynamics is a useful approach whendealing with sustainability issues. In Section 3, we describe and discuss the famousLTG study, a System-Dynamics-based approach to analyze the sustainability ofour current and future economic and social conditions. Section 4 summarizes otherSystem Dynamics studies in the field of sustainability; it discusses in more detailwith examples from climate change, natural resources development, and sustainablebusiness strategies. This chapter closes with a short summary and an outlook onfurther potential work in Section 5.

2. Suitability of System Dynamics for Sustainability Research

2.1. System dynamics as a structural theory and a methodology

System Dynamics is not only a methodology about how to address and solve com-plex dynamic problems using a broad variety of mapping, modeling, simulation,

1http://www.iisd.org/pdf/2009/sd timeline 2009.pdf.


System Dynamics and Sustainability 315

and analysis tools. According to Lane ..(1999), System Dynamics can also be seenas a structural theory of dynamic systems, because a set of statements about thecore characteristics of dynamic systems lies at its heart. It is based on the mainhypotheses that dynamic systems consist of stocks and flows, a feedback struc-ture, accumulation processes, and delays between cause and effect (Forrester, 1961;Forrester, 1968a; Sterman, 2000).

Without engaging in the sprawling philosophical debate on the notion of theo-ries, we pragmatically adopt Popper’s (2002a) characterization of scientific theoriesas universal statements. Seizing on Popper’s (2002b, 2003) “searchlight theory ofknowledge” and Brunswick’s (1952, 1956) “lens model,” we understand theoriesas instruments aiding in the perception of phenomena as well as in their descrip-tion. Theories are essential for explaining, predicting, and designing (Hempel andOppenheim, 1948). In this sense “nothing is so practical as a good theory” as Lewin(1945) famously points out.

Theories can be characterized and categorized along a broad variety of crite-ria. We find the suggestion of Lane (2000) to distinct theories about the contentfrom theories about the structure of systems particularly useful. According to thisconcept, a structural theory makes principal statements about (1) which elementsform a system, (2) how these elements in a system can be configured, and (3) howthey causally relate to each other. In contrast, a content theory typically statescause-and-effect relationships between elements of specified systems; one examplefor such a content theory in the realm of biology would be a set of statements aboutthe impact of increasing water pollution on the fertility of fish.

As a structural theory of dynamic systems, System Dynamics does not offerstatements about specific system elements and processes. It does not hypothesizeabout concrete cause-and-effect relationships. Rather, it states that dynamic sys-tems can be completely described using stock and flow variables. It postulates thatdynamic processes function in feedback loops and that the history of systems accu-mulates over time in state variables. In addition, the accumulated history influ-ences the future development of a system — a process that is often affected by timedelays.

Thus, System Dynamics regards feedback loops, accumulation processes, anddelays as building blocks of all dynamic systems. The concept of feedback loopsemphasizes the fact that actions and results usually are bi-directionally linked:an action leads to an outcome, and the outcome forms the basis for new actions(although the effect of an action may sometimes appear to be remote in time andspace). Accumulation processes highlight the notion that the state of a system fre-quently does not change instantaneously but needs to be gradually developed overtime. The focus on delays is dependent on the former two components and makesclear that in each system time lags exist between decisions and actions, or betweenactions and results: for instance, new information on the state of a system does notimmediately lead to new decisions since it needs time to be perceived, considered,and decided on.



Non-linear behavior of systems results from these three building blocks. Sys-tem Dynamics provides methods for designing formal models and generating theirbehavior over time, i.e., by simulating the models. In order to do so, a graphicalsyntax is used, in which flow (rate) and stock (state or level) variables are distin-guished. These two types of variables are combined to produce so-called stock andflow diagrams (SFD, Forrester, 1968a; Lane, 2000). The graphical representation ofsystems using these diagramming techniques proves to be a valuable tool for under-standing complex issues. Technically, by the quantification of variables and linkagesbetween variables, a system of differential equations is created that is simulatedwith the help of numerical algorithms (Sterman, 2000). The validity of the resultingmodels has to be underpinned by a variety of tests. However, validity can only bejudged relative to the purpose of a model (Barlas and Carpenter, 1990).

Another useful classification scheme for theories uses the degree of generality(Popper, 2002b) of its statements to separate “grand theories” from minor theoriesin the extremes. While a grand theory should be able to cover and explain the com-plete range of phenomena observed in a system, minor theories focus on a narrowselection of phenomena. Although the development of corroborated grand contenttheories would be highly desirable and of huge practical relevance, such an endeavor,is rendered unrealistic by the complexity of natural and social systems. Thus, con-tent theories are usually limited in their scope, not claiming to explain completereal-world domains. Nevertheless, grand structural theories exist; for instance, Sys-tem Dynamics claims to be a theory of the structure of all dynamic systems (Lane,2000).

Figure 1 summarizes our discussion about the role of System Dynamics in scien-tific research (as the classification of theories is qualitative only, we depict reasonableranges only). System Dynamics is a far-reaching theory concerning the structure ofdynamic systems. The products of System Dynamics as a methodology (i.e., themodels produced) are minor or mid-range theories about a real-world domain. Thefollowing section briefly elaborates on this methodological aspect, i.e., on SystemDynamics as a method for investigating real processes in a dynamic world, as theyare ubiquitous in sustainable development.

2.2. The process of modeling and simulation

with System Dynamics

Many authors emphasize the importance of the modeling process (and not just thatof the resulting model) for gaining insights into the problem (Lane, 1995; Sterman,1988a). The reason for this emphasis on the process is that System Dynamics modelsare usually descriptive and not prescriptive, in the sense that they do not providean “optimal” solution, which has two implications. First, insights from the modelcan only indirectly be deduced by formulating its equations and running differentsimulations (in the sense of quantitative scenario analyses). Usually, simulationmodels do not produce the right answer to solve a problem as a direct output.



Fig. 1. System Dynamics as theory and methodology.

Second, while being a modeling endeavor, System Dynamics research is substantiallyempirical: regularly, it does not involve the development of ideal models, but ratherthe depiction of reality as precisely as possible. Based on a valid picture of whatis the case in reality, attempts are made to gain insight into how the real systemcan be improved. As such, System Dynamics models contain all variables that aredeemed important to understand a system, no matter if these variables can beeasily measured. The rationale behind this approach is that even a rough estimateof values and linkages is preferable to “leaving out” important aspects, just becausethey are difficult to measure and/or to operationalize.

The stylized process of System Dynamics modeling and simulation is composedof six steps (Forrester, 1994). Figure 2 depicts this process in graphical form. How-ever, note that the process is not purely sequential as it might occur from the figure:there are various possibilities for jumps back to earlier steps in the process.

Fig. 2. Stylized System Dynamics process (iterations not depicted).



In the first step, the problem to be modeled is defined and key variables are iden-tified. An important question to answer in this phase concerns the system boundary,i.e., it is determined, which factors should be included and which should not. Asa rule of thumb, important feedback loops should not be cut in System Dynamicsmodels, since the assumption is that feedback loops are significantly influencingthe behavior of the system. In the second phase, a level-rate diagram is built thatrepresents the system structure as identified before. This diagram is formalized inthe next (3rd) step, i.e., mathematical functions are defined that determine how val-ues of a variable are calculated based on the values of other variables. Furthermore,values for constants and initial parameters are laid down. Based on this quantifica-tion process, simulation runs are conducted to generate the behavior over time ofthe model. In the following step (4), additional policies and structures are tested,either to try-out other conceivable givens or to find alternatives for improvementof the system. The fifth step acts as a reflection point: insights from the model-ing and simulation activities are discussed and tested for their validity. In the laststep, model-based solutions are implemented in order to improve the real-world sys-tem. Software packages (such as Vensim, Powersim, or Stella/iThink) support thisprocess of conducting a System Dynamics study.

2.3. System Dynamics’ contribution to sustainable development

As we have seen in the previous two sub-sections, System Dynamics can contributeto sustainable development in two ways: first, it offers a structural theory thatis useful to appreciate and understand sustainability issues; second, it provides amethod to analyze and improve those issues. Thus, by applying the “lens” of SystemDynamics as a structural theory, we can use System Dynamics methodology toinvestigate issues in and formulate content theories for sustainable development.

The theoretical contribution to sustainable development must be understoodin a way that System Dynamics offers a mindset to better understand dynamicsystems and their effects. Since most of the issues sustainable development hasto deal with are dynamic by nature, System Dynamics is a framework to addressthese issues. Many issues in sustainable development can be attributed to feedbackrelationships, accumulation of material and immaterial goods, or delays betweenaction and reaction within a system. For instance, the actions of a productioncompany that lead to a polluted environment will eventually — maybe delayedby decades — have an effect back on the company:

• its reputation decreases (leading to lower sales and fewer employees wanting towork for them);

• environmental legislations will become stricter (increasing production costs);• taxes will need to increase to cope with the pollution (again, increasing production

costs);• maybe even the population around the company will move away because of the

polluted area (resulting in lower sales on the home market).



Fig. 3. Exemplary causal-loop diagram.

The theoretically identified characteristics of dynamic systems can be analyzedusing System Dynamics methodology. Since — as we have seen in the previousparagraphs — most sustainable development issues are inherently dynamic, SystemDynamics can be used to study such issues. In the example given above, the relationsthat are mentioned could be mapped in a causal-loop diagram (CLD; Maani andCavana 2007); they could further be quantified in an SFD and simulation experi-ments could be conducted. The simulations can be used to understand the system’sreaction to the company policies and can help to find improvement points for amore sustainable development.

Figure 3 shows a first step toward a formal representation of the productioncompany example in System Dynamics terms. In the CLD, one can see the vari-ables of the system as described above and their interrelation. Note that the linkagesbetween the variables establish feedback loops (numbered as above) and that somelinkages are marked with two short stripes indicating delays. The plus and minussigns indicate effects going into the same and the opposite way, respectively. Forinstance, pollution of environment caused by company has an opposite and delayedeffect on reputation of company: if pollution goes up, with some time lag the repu-tation will go down. While this example is definitely simplified compared with realSystem Dynamics projects, lacks quantification in its current state, and is depictedhere for illustrative purposes only, it should give an idea of how problems are treatedwhen using System Dynamics.

3. The “LTG” Trilogy and Debate

3.1. A note on the history of the Limits to Growth (LTG) study2

The Club of Rome that initiated and received the LTG study as their first reportin 1972 was a rather small group of international professionals from various fields.Founded in 1968 by the Italian industrialist Aurelio Peccei and Scottish scientistAlexander King, the Club of Rome was (and is still) concerned with the definition

2This section is mainly bsed on Meadows (2007) and Meadows et al. (1972, 2004).



and solution of what it called the “world problematique” later described as “thecomplex set of the most crucial problems — political, social, economic, technological,environmental, psychological and cultural — facing humanity” (Visser, 2007). Insearch for order among the list of 66 critical problems that had been compiledby 1970, the Club addressed Forrester, who was known to work on a computer-based approach to solve complex feedback problems. On the return flight from thefirst meeting in Switzerland, Forrester sketched the first world model on a simplenapkin. After some refinements, this model became World1. It started in 1900,showed growth until 1975 and then collapsed.

The negotiations between the Club of Rome and the MIT System Dynam-ics Group were successful. The Club of Rome agreed to start its Project on the“Predicament of Mankind” using System Dynamics. The Volkswagen Foundationprovided the funding, and a team of 17 modelers from seven nations was formed.While Forrester himself continued to improve World1, which lead to the publicationof World2 in his book World Dynamics in 1971, the MIT team worked to developWorld3. Although similar in their basic structure with five sectors — population,industry, agriculture, natural resources, and the environment — and their basicdynamics, the objective for World3 was to advance the sector’s internal structureand strengthen the empirical foundations. Or, as Donella Meadows (2007, p. 192)put it: “Our work made the model more detailed and more consistent with estab-lished concepts in demography, economics, geology, ecology, and agriculture.”

Early in 1972, a popular version of the results achieved by the MIT team wascompiled, handed over to the Club of Rome executive committee, published as a200-pages paperback (Meadows et al., 1972) and spread among selected policymak-ers. The book caused a furor. It received an unexpected intense media attention.Overall, it sold more than 12 million copies in 37 languages. Two years later thedetailed technical report — Dynamics of Growth in a Finite World — was published(Meadows et al., 1974). It detailed the research and provided the documentationfor the World3 model equation by equation.

In 1991, Donella Meadows, Dennis Meadows, and Jørgen Randers conducteda follow-up study based on the World3/91 model. This model introduced onlymarginal changes. The Dynamo mainframe version was converted to a Stella per-sonal computer model. Seven model constants and table functions were changed toreflect updated data; the way in which technology affected the model coefficients wasmodernized. “Beyond the Limits,” published in 1992, found that “many resourceand pollution flows had grown beyond their sustainable limits” (Meadows et al.,1992). In 2004, the authors presented the 30-year update of the LTG study. Thisstudy is based on an again only slightly altered model (World3/03) and containsmore data that support the hypothesis of the 1992 version that the world is in“overshoot mode” (Meadows et al., 2004). The authors show that the options forimplementing a sustainable society have narrowed and actions have to be takenwithout much delay when overshoot and collapse are to be avoided.



In addition, the Club of Rome had another study conducted with the sameresearch question as for the LTG but another methodology employed (Mesarovic andPestel, 1974). This work was based on a far more elaborate and regionalized model.It distinguished 10 world regions and consisted of 200,000 equations (comparedwith 1000 in the LTG model). The basic message of this second report to theClub of Rome was amazingly alike, though. The accelerating growth of industrialproduction had to be drastically reduced in the rich regions to allow the poor regionssome development.

3.2. Key insights of the study

The original World3 model consists of roughly 1000 variables that are located in oneof five sectors: natural resources, population, environmental pollution, industry, andagriculture. The model is openly available and can be used to generate different sce-nario runs (for instance, it comes as World3/03 version with the simulation softwareVensim, www.vensim.com). Figure 4 shows scenario 1, which has been described inthe book “Limits to Growth — The 30-Year Update” (Meadows et al., 2004) toserve as a reference point.3 It assumes a “business as usual” situation, for instance,

1900 1950 2000 2050 2100

Population

Industrialoutput

Resources

Pollution

Food

1900 1950 2000 2050 2100

Human ecological footprint

Human welfare index

Fig. 4. Scenario 1 as the reference point of the LTG study.

3For reasons of simplicity units and scales have been omitted. However, all graphs shown in Figs. 4,5, and 6 have identical scales to allow comparison. The scenarios were created using the Vensimmodel world3 03 scenarios.vmf that ships with Vensim 5.8 or newer.



no major alterations are made to the policies employed concerning economic growth.Population, industrial output, and human welfare increase until peaking around theyears 2020–2025. From then on, bust follows boom due to resource depletion andpollution initiated by high growth in industry and agriculture that — after a fewyears delay — affect population.

The structural explanation for this behavior is a fundamental insight: infinitegrowth cannot exist when resources are limited (Senge, 1990). More and more capitalis necessary to solve problems with resource acquisition and environmental pollu-tion. Finally, industrial production comes to a halt and starts shrinking; simultane-ously, the production of food and services decreases, which in the end also causes adecline in population.

Assuming that initially twice as much non-renewable resources are availablecompared with scenario 1, do not alter the boom, and collapse pattern. Whileindustrial output can grow some 20 years longer, the human ecological footprintpeaks near 2050 at almost double the level shown in Fig. 4. In this so-called scenario2, population reaches a maximum of 8 billion and then declines because of foodshortages and the negative impact of pollution on health (Meadows et al., 2004).

While many public discussions focused on scenarios 1 and 2 condemning themas prophecies of doom, the positive statements and outlooks in the LTG trilogy wereoften perceived to a lesser extent. All three books, even the latest one, emphasizethat transition to a sustainable society would still be possible and collapse couldbe avoided, as Fig. 5 indicates. For this, rigorous policies have to be implemented,

1900 1950 2000 2050 2100

1900 1950 2000 2050 2100

Resources Industrialoutput

Pollution

Food

Human welfare index


Population

Fig. 5. Scenario 9 — A sustainable future.



1900 1950 2000 2050 2100

1900 1950 2000 2050 2100

Human welfare index


Resources

Industrialoutput

PollutionFood

Population

Fig. 6. The difference of 20 years earlier implementation of all scenario 9 policies.

though. Starting in 2002 the desired family size is fixed at two children per couple,perfect birth control is in place and modest limits for industrial production areappointed. Additionally, from 2002 onwards technologies for increasing the efficiencyof resource use, decreasing the pollution per industrial output unit, controlling landerosion, and raising land yields are developed and implemented with an averagetime delay of 20 years at some capital costs.

Although the society in scenario 9 would succeed in avoiding the collapse shownin Fig. 4 and establish a sustainable equilibrium with a population of nearly 8 billionpeople, a moderate food crisis around 2040, causing the human welfare index todrop, cannot be avoided. Growth had gone on for too long to allow a smooth tran-sition. If a smooth behavior had been intended, the policies from scenario 9 had tobe made effective 20 years earlier (Fig. 6).

In addition to the three scenarios shown and described above, the model allowsto explore a plethora of other scenarios. However, even when parameters are changedby quantities, the behavior modes of the world system vary in principal betweens-shaped growth eventually reaching a sustainable equilibrium and boom and bustwith changing peak times. The reason for the similarity of simulation results forvarious parameter settings lies in the dominance of the system structure over theactual values of the parameters. This explanation is supported by the fact that, forinstance, the reference scenario 1 does not change substantially between the threeeditions of the LTG study, although several parameters and table functions havebeen updated concerning the latest information at hand in the newer versions.



3.3. A short synopsis of a hot debate

The effect of LTG on public and scientific discussions can hardly be overestimatedand continuous until today. With some justification can be argued that the devel-opment of societal and political movements to protect the environment and inter-national treaties to do so (e.g., the Kyoto protocol) was initiated or amplified bythe study or the discussions evoked by it.

Nevertheless, the fundamental results of LTG did provoke criticism of variouskinds. Early on such critical accounts are, for instance, Cole et al. (1973), Nordhaus(1973), and Beckerman (1974). However, even today, writers are directly linking toand criticizing the Club-of-Rome study, for example, Popper et al. (2005), Lomborg(2001), and Stott (2000). The criticism refers to various aspects of LTG:

1. System Dynamics as a method and its usage for scenario analyses (including aprincipally critical view on modeling and simulation).

2. Assumptions in the model and its boundary (in particular, what has not beenincluded in the model).

3. The results of the study.4. The consequences and discussions based on these results.5. The context of the study, for instance, the political and ideological mindset of

the modeling group, their assumptions about economic systems.

This chapter argues that System Dynamics is an appropriate tool to modeldynamic systems of any kind; thus, it can be used to model the world in total (thisstatement does not deny the fact that this is of course very complicated). SystemDynamics has proven its suitability and usefulness in a wide area of applications.With regard to the general mistrust in modeling and simulation, it seems appropri-ate to state that modeling and simulation are accepted scientific methods. Therefore,neglecting their principal usefulness is comparable to negating the scientific methodas such.

Related to this point is also a criticism that denies the benefit of using SystemDynamics modeling over plain mathematics. Indeed, from a mathematical point ofview, System Dynamics models are sets of differential equations that are solvednumerically. However, the theoretical perspective of System Dynamics as statedabove (focus on feedback loops, delays, accumulation, and non-linearities) as wellas the mapping techniques provided by System Dynamics (Lane, 2008), make con-structing a global model more efficient and effective.

A skeptical perspective regarding the assumptions used in the model is certainlyworthwhile, as also Meadows et al. (1982) confirm for some of the model’s opera-tionalizations. Certainly, a model of the whole world needs to be highly aggregatedand as such is a compromise between applicability and complexity of the structuresrepresented. It must be discussed that whether and where this abstraction process(which per definition goes along with model building) has validity. System Dynamics



supports this process by making the operationalizations used transparent and openfor scrutiny.

As the results of the simulations are generated directly from model structure,criticizing them as such is not adequate. Usually, System Dynamics models aredeterministic (as is World3); hence, the scenarios result from the model structure —this structure can be criticized (see the 2nd item) but not its outcomes.

Turner (2008) compared three scenarios from the original 1972 study to envi-ronmental data for 1970–2000. He found a surprising match for real data and thebehavior shown in the basis scenario of LTG,” in particular, if the high level ofabstraction within the model and simulation is taken into account. However, moreoptimistic scenarios showed lower congruency with real-world data. Thus, given thefact that LTG predicts major disruptions not before the middle of the 21st century,it seems too premature to disregard the study as empirically wrong.

Most potential consequences of LTG were not presented in the actual bookbut put forward from other parties. In the course of the discussion that followed thepublication, many mis-interpretations and mis-perceptions can be found of what thestudy actually says, how the results must be interpreted, and where the limitation ofsuch a study is. For example, information in the book was interpreted in a way thatoil reserves would run out in 1992, which obviously did not happen. While this isnot really stated in the book, LTG seem to have failed to deliver an understandableand clear message of its results.

This criticism stems from two political camps: left and right. The left campargues that LTG concentrates too much on physical/materialistic limitations andneglects perhaps more important restrictions of social or political nature. The rightcamp argues that market mechanisms and technological innovations are capable ofmitigating negative effects of human endeavor; such structural changes were notrepresented in LTG.

4. Sustainability Research with System Dynamics

After reviewing the still most influential contribution of System Dynamics to thesustainability debate (LTG), we are presenting three more up-to-date works thatuse System Dynamics to study sustainability issues: Climate Change research, eco-system research, and sustainable businesses development. As will be clear in thefollowing sub-sections, the application of System Dynamics in the three case studieshas different objectives: (i) the climate case is about informing laypersons aboutthe effects of CO2 emissions; (ii) the eco-system study aims at consensus buildingbetween stakeholders; (iii) the sustainability in business section reports on usingSystem Dynamics by managerial decision makers in order to design sustainablepolicies for their companies. By concentrating on these newer studies, we do notignore the important work of other authors using System Dynamics in the fieldof sustainability, most notably Ford (1978, 1996, 2009), Bossel (1986, 1998, 1999),Fiddaman (2002, 2007), and many others.



4.1. System-dynamics-based climate change research

Drawing on the tradition of the LTG project at MIT, recently a new program waslaunched by the Sustainability Institute involving a broad range of people com-mitted to using innovative approaches to address climate change. The mission ofthe program Climate Interactive is “to develop, extend, and distribute powerful,open-innovation climate simulations for the world to share” (Sustainability Insti-tute, 2010a). As a first step toward accomplishing this mission, the Climate-RapidOverview and Decision Support Simulator (C-ROADS) was developed advancingand combining previous System Dynamics models and simulators built at MIT(Fiddaman et al., 2009). The purpose of C-ROADS is to “improve public anddecision-maker understanding of the long-term implications of possible greenhousegas emissions futures” (Fiddaman et al., 2009). This purpose shall be achieved byproviding a scientifically rigorous computer simulation of the impacts of greenhousegas emissions and land use on global temperature and sea level rise, which is nev-ertheless fast and user-friendly to allow frequent iterations and rapid learning.

While a strong scientific consensus exists on the causes and consequences ofclimate change, the mental models of the public seem to be widely different, blurred,and frequently erroneous. Although a majority at least in the western world is awareof the phenomenon of global warming and prepared to support actions, scientistscall for implementation of measures at a decidedly faster pace than the public.One reason for this mismatch in the perceived urgency of actions could be seen inthe lack of understanding how the various proposals und pledges affect the climatetrajectory and sea level rise. As the world climate is a non-linear, dynamicallycomplex feedback system, it is hardly surprising that most people are overchargedwhen attempting to foresee the short- and long-term consequences of the differentproposals (Sterman, 1989). The problem is even deeper. Recent research shows thateven highly educated adults have deficits in understanding the behavior of simplesystems consisting of just one stock and one flow, that is, they fail to comprehendthe concept of accumulation (Sweeney and Sterman, 2000; Cronin et al., 2009).Because of these flaws, intuitive predictions of the response of the global climatesystem to emissions’ cuts are often wrong; the degree of emissions’ reductions neededto stabilize atmospheric carbon dioxide levels is underestimated, as is the lag timebetween changes in emissions and changes in global mean temperature.

Computer simulations are considered to accelerate the learning process and todeepen the insight in dynamically complex system by providing fast feedback fromexperimentation with a virtual world (Morecroft and Sterman, 1994; Frensch andFunke, 1995). The existing climate simulations such as MAGICC,4 GCAM,5 AIM6

are based on large, complex, and highly disaggregate models. They are relatively

4http://www.cgd.ucar.edu/cas/wigley/magicc/5http://www.globalchange.umd.edu/models/gcam/6http://www-iam.nies.go.jp/aim/index.htm



Fig. 7. C-ROADS model sector diagram (Fiddaman et al., 2009).

slow to run and difficult to use for non-experts. C-ROADS and especially the3-region, simplified version C-Learn are easy to use and fast to run, allowing totest many what-if questions and explore their short- and long-term consequences. Bythis, they aim at capturing the key insights of larger models and making them avail-able for “rapid policy experimentation” (Fiddaman et al., 2009). The C-ROADSsimulator is not seen as a substitute for the larger and more detailed models.

The basic structure of the C-ROADS simulator is outlined in Fig. 7. Totalfossil fuel carbon dioxide emissions’ scenarios are aggregated from the emissions’scenarios of groups of countries, which can be specified by the user. In addition to,user input is required for land use scenarios implying additional release or uptakeof CO2. Both fossil fuel CO2 emissions and net CO2 emissions from forests formthe input flows to the carbon cycle sector of the model. For other greenhouse gases(CH4, N2O, PFCs, SF6, and HFCs), explicit cycles are also modeled requiring userinput scenarios. The carbon and other greenhouse gas cycles together determine theconcentration of greenhouse gases in the atmosphere, the global temperature, andthe sea level rise.

First tests of the C-ROADS simulators usefulness were conducted within thecourse of the UNFCCC negotiations culminating in the COP-15 conference inCopenhagen, Denmark, in December 2009. Sawin et al. (2009) report how C-ROADSwas used to analyze the expected long-term impacts on the climate of proposals thatwere put forth by national and regional governments. While the Copenhagen Accordcalls for “deep cuts in global emissions ... so as to hold the increase in global temper-ature below 2 degrees Celsius” (UNFCCC, 2009, p. 5), simulations of the C-ROADSmodel indicate that this “requires global greenhouse gas emissions to peak by 2020and then fall 50% below 1990 levels by 2050” (Sustainability Institute, 2010b). Thisamounts to a cut of approximately 60% below current emissions, which is far beyond



the sum of the commitments offered by the individual nations. Confirmed reductionproposals at the time of the Copenhagen conference would raise expected globalmean temperature by 3.9 ◦C — almost double the rise that was agreed on at theconference.

By providing models and simulators like C-ROADS, System Dynamics can helppeople to develop a better understanding of the causes and consequences of climatechange. While more research is needed to increase the scientific knowledge on climatechange and more technical innovations may help to achieve the reduction goals, thisseems to be not sufficient though. Sterman (2008, p. 533) calls for turning “ourattention to the dynamics of social and political change.”

4.2. An example of a System-Dynamics-based study

of a natural system

As an example of a recent System Dynamics project in the area of sustainabilityof a limited eco-system, we take Videira et al. (2009), who report on the usage ofa participatory modeling approach for water supply and river basin planning. Themodeling project was conducted in Portugal, in the region of the Baixo GuadianaRiver area. Modeling work actively involved different stakeholder groups.

The dynamic sustainability issue described in the paper is about water man-agement. More precisely, the fact that “[s]ustainable water management is hinderedby the lack of adequate water institutions, fragmented institutional structures,upstream and downstream conflicting interests regarding rights and access towater, diversion of public resources for private gain and the unpredictability in theapplication of laws, regulations and licensing practices” (Videira et al. 2009, p. 966).As an issue characterized by high complexity and uncertainty, non-linear dynamics,and significant consequences of any policies taken, participatory modeling followingSystem Dynamics was identified as a method to deal with these characteristics.The aims of the project were to construct CLDs of the most important relation-ships, generate a simulation model of the river basin, and to agree on objectives andmeasures for sustainable management of the region in terms of its water resources.

The modeling project was conducted as a sequence of three alternating partici-patory workshops with stakeholders and “behind the scene” works that, for instance,composed of refining and quantifying models, writing scenario plans, and planningfor the implementation of results. With this approach, Videira et al. managed toachieve a satisfactory level of commitment and consensus within the group of stake-holders. In particular, participants remarked that the process of building a modeland running simulations within a group of stakeholders helped to structure discus-sions and allowed for a fair process of finding solutions to the problems. Tangibleoutcomes of the project are, for example, the development of land use plans, theformulation of a policy to reverse population decline, and measures to balance theeconomic and ecologic requirements for the river basin. These results were achieveddespite the fact that the authors report on some problems maintaining the initialgroup composition.



In the light of this paper, it needs to be re-emphasized that Videira et al. (2009)not so much describe a successful sustainable development project based on novelinsights from modeling and simulation. Rather, modeling and simulation provideda framework for discussion between stakeholders having differing and conflictinggoals (Vennix, 1996; van den Belt, 2004). The structure induced to the process bySystem Dynamics modeling was perceived to be useful in providing a just, openand targeted platform for discussing the various topics connected to the sustainablemanagement of a water resource.

4.3. Back to the roots: Sustainable business development

Sustainable business development can at least have a twofold meaning: First, it canbe seen as a request to develop business in an environmentally and ecologically soundway, which may mean, for example, minimizing the use of finite resources, alleviatepollution of all kinds, and behave as eco-sensitive as the business model allows.Second, it can also mean to accept and obey the shareholder’s “performance imper-ative,” which dictates to continuously improve “performance over time” (Warren,2007). Under such a regime not just business performance has to be sustained, theperformance growth rate needs to be maintained — meaning that business perfor-mance grows exponentially.

As in a finite world growth of any real system has at some point to start levelingoff and eventually come to a halt, it would be more realistic for businesses to setthemselves s-shaped growth patterns as objectives — at least in the long run. Bythis, the danger of overexploiting and eroding resources critical for the businessmodel, which results in an ever-present, yet unfavorable boom and bust pattern,could be minimized (Meadows, 2008); exponential growth in the early phases of acompany’s life cycle is hereby not interdicted.

From the very beginning, System Dynamics was applied to a broad range ofbusiness problems. As gaining and sustaining competitive advantage for bringingback or maintaining growth was one of the most frequent issues addressed, thefollowing three references should only be seen as examples. An early case studywas published by Forrester (1968b). In “Market Growth as Influenced by CapitalInvestment” he was pursuing the goal to explain how an “overly cautious capitalinvestment policy” can cause sales stagnation or decline — even in situations wheremarket limits are far afield. The impressive rise and rather sudden fall of PeopleExpress airline provided another exemplary case that was taken up by SystemDynamics research (Sterman, 1988b; Morecroft, 2007). More recently, Arquitt et al.(2005) investigate boom and bust behavior in the shrimp aquaculture industry andsuggest improved policies to shift the system toward sustainability.

For connecting System Dynamics and the resource-based view of strategy(Barney, 2007) in striving for an coherent approach to explain and improve busi-ness performance over time, Warren (2002, 2007) has to be given significant credit.He promotes the need for rigorous causal explanations of performance and suggeststhat these have to be searched for predominantly within an organization. Warren



Fig. 8. Resource system for a fund management business as an example.8

argues that resources drive performance as they are influencing demand and supply,revenue and costs. Therefore, in order to explain why an organizations performancehas shown a specific time path, not only the so-called VRIO7 resources have to beidentified, but all tangible and intangible resources and capabilities that directly orindirectly influence performance. Warren calls for mapping the resource system asa whole to provide an adequate explanation for the observed performance path.

When striving for sustained competitive advantage, managers have to learnhow to build and maintain the quantity of each resource in the resource system. Asresources are represented as stocks in System Dynamics terminology, they changeonly by their rates over time and never instantly. To understand that resourcesaccumulate and deplete is critical for managers to identify the appropriate leversfor gaining and sustaining competitive advantage.

As the resource system in Fig. 8 for a fund management business shows, theresources active brokers or assets under management cannot be changed directly. Forexample, when active brokers should be changed the rates brokers won per monthand brokers lost per month have to be adjusted appropriately.

As the example in Fig. 8 illustrates that the eroding operating profit could betraced back to decreasing assets under management caused by degrading reputationand a peak of active brokers. After having found a valid explanation for the problem,the resource system also helps to identify the effective levers to first stabilize andto improve the performance. In the first instance, the erosion in reputation had

7VRIO stands for valuable, rare, hard to imitate, and supported by other organizational procedures(Barney, 2007).8Figure 8 is reproduced with the permission of the copyright holder Kim Warren.



to be stopped — for the main part by safe investment decisions leading to stableinvestment performance and gradually rebuilding reputation. Only then, intensifyingmarketing efforts for winning new brokers holds promise. As a third step, launchingnew investment funds targeting at a different segment of investors may be used toattract additional brokers and re-activate dormant brokers.

A System Dynamics model covering the resource system shown in Fig. 8 allowsmanagement to test the turn-around strategy outlined above. It can help to exploremodifications and alternate tactical and operational decisions based on this strategy.Furthermore, it would allow the management to explore the sensitivity of importantassumptions. As System Dynamics models aim for covering all major feedback loops,the danger of ignoring undesirable side effects within the strategy evaluation andselection process is greatly reduced. Thus, the likelihood of really achieving sustainedcompetitive advantage is increased.

5. Conclusion

This chapter demonstrates that System Dynamics is valuable to investigate issuesof sustainable development for two reasons. First, as a structural theory SystemDynamics offers a lens to understand dynamic phenomena characterized by feed-back loops, accumulation effects, delays, and non-linearities — as is frequently thecase in sustainable development. Second, System Dynamics as a method providestools to analyze sustainable development issues due to its diagramming and sim-ulation capabilities. Many applications of System Dynamics in the broader areaof sustainability can be identified: starting with the famous and often discussedLTG study to more recent studies of sustainability issues in climate change, naturalreserves conservation and management, and sustainable business development.

Of course, having a better cognitive understanding of sustainable developmentissues — for instance, provided by System Dynamics — does not solve immediatelyor necessarily such issues (Vennix, 1999; Großler, 2007). As has been demonstratedin the water management case (Section 4.2), System Dynamics can also be usedto support the process of consensus finding and commitment. For this purpose,the cognitive, analytical endeavor of modeling and simulation must be augmentedby facilitation techniques and methods to come to sustainable decisions in sociallyconflicting situations.

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