Advancing IntegratedMethodological Framework for

Developing Sustainable andResilient Systems

Anthony Halog, Najet Bichraoui, Yosef Manik, Binod NeupaneResearch Group for Industrial Ecology, LCA and Systems

Sustainability (IELCASS), University of Maine, Orono, ME 04469USA

Anthony.halog@maine.edu

The study aims to analyze holistically and dynamically the adoption of bio-inspired energysystems, technologies and emergence of its supply chains using trans-disciplinary, integrativeand system perspective approaches. An integrated methodological framework has beendeveloped to aid sustainability assessment of emerging systems. This integrated and life cyclecomputational model for sustainability assessment is based upon the science of complexsystems, industrial ecology, systems engineering, ecological economics and biophysicaleconomics. Integrated assessment refers to assessment that crosses issues; spans spatial andtemporal scales; looks forward and backward; and includes stakeholder perspectives. Theintegrated framework is currently being applied to forest based biofuels supply chain.

1 IntroductionAs the rate of global climate change increases while the stocks of fossil

resources continue to deplete over time, the resurgence in importance of renewableenergy is inevitable. Similar to petrochemical industry development in the lastcentury, a biorefinery involves various production pathways where combinations ofbiomass feedstocks and other inputs are processed using either established oremerging conversion technologies to produce desirable bio-products andenvironmentally undesirable emissions and solid wastes. In the past, evaluation ofenvironmental impacts of a technological system was isolated from its broader andlong-term environmental consequences. However, without evaluating the life cycleenvironmental footprints of a technology in the context of its interaction with coupledhuman and natural systems, a new set of environmental problems may potentiallyarise [1]. We need to consider any evolving energy systems as part of a bigger pictureor in holistic perspective, similar to what is advocated by the transformative andinterdisciplinary concept of industrial ecology (IE).

The main research objective of this project is to understand the nature anddynamic interactions of different sub-systems in emerging bioenergy supply systemssuch that we can improve our ability to design resilient systems that are able torespond and adapt to human and environmental changes. We want to seek and

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develop the fundamental basis of complex bioenergy supply chain (BSC) that willresult in formal methods for design and management of engineered energy systems.We argue here that a BSC is a complex system –a representation of coupled humanand natural system. The project will draw on elements of systems analysis &engineering, industrial ecology, chemical engineering, ecological economics, networkanalysis and complexity science. The integral scale of theoretical scientific researchcombined with fieldwork and experimental data gathering to reveal theoretic andpractical knowledge from BSC sustainability and driving forces, to testing variousbiofuel production scenarios are unique in this endeavor.

Nevertheless, developing a sophisticated life cycle analysis model and toolfor sustainability, which is pursued in the European Union, does not complementrecent developments in the U.S. renewable energy sector. The researchers inCoordination Action for Innovation in Life Cycle Analysis for Sustainability(CALCAS) Project [2-4] reported the need of deepening and broadening existing lifecycle analysis (LCA) tool to assess and compare the environmental, social andeconomic implications of various bioenergy production routes and to aid in policydevelopment. CALCAS has highlighted various weaknesses of typical ISO-basedLCA that result in conflicting claims in LCA of corn-based ethanol and unansweredpolicy questions. The researchers at the Energy Biosciences Institute of University ofChicago have pointed out these variations and inconsistencies among published LCAresults of biofuel crops [5]. There is definitely a need for life cycle sustainabilityanalysis (LCSA) tool that is supported by advanced computing systems (e.g. agentbased modeling, dynamic system modeling) and can mimic the interactions amongeconomic, environmental and social variables. With the increasing interest inproduction of bioenergy worldwide, the US should pro-actively pursue a similarendeavor while advancing lignocellulosic, algae based bioenergy and other renewableenergy systems.

2. Current State of Methodological Development andPresent Outlook

2.1 Complex SystemsThe advent of the industrial society has been associated with a large number

of technological changes involving complex systems. Many of these changes inhuman-natural systems are beneficial, and many are not. What they have in commonthat none of these undesirable changes were foreseen at the beginning [3].

Complex systems are systems that can respond and adapt to the changes intheir environment - a property that emerges from the myriad interactions of its sub-systems [6-7]. This research project has been motivated by the observation that manynatural, social, and engineered systems have been recognized to be complex systems,in which the traditional reductionist approach to science and engineering fails topredict and explain the patterns and behaviors that emerge from the functioning ofthese systems. Many emerging engineered systems fall into this category; andunexpected failures and other consequences have been observed as these systemsfunction near the edge of their expected performance capacity, for example in

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chemical industrial systems, manufacturing and service enterprises, and energysystems. Although these unexpected behaviors can be undesirable, complex systemscan display emergent behaviors, which can be preliminarily evaluated to make themresilient and robust - features that are desirable in complex systems such as bioenergysupply chain. The hallmarks of complex systems are adaptation, self-organization, oremergence [6-7].

The advantage of complex systems research is that universal principleslearned from one area (i.e. genomics) may lead to exciting breakthroughs inseemingly unrelated disciplines (i.e. energy network analysis). Unanswered andchallenging questions arise when we extend existing theories such as the theories ofsystem dynamics, multi-agent modeling, network science, and stochastic processes toaddress the core questions about complex systems. What are the fundamental aspectsof a complex bioenergy supply chain? How can complex systems be analyzed,optimized, and synthesized, beyond ad-hoc methods based simulations? How can wetake advantage of the fundamental theories of complex systems to design sustainablepower systems? We must expose the underlying relationships within the systems,the mathematical/statistical features of the essential aspects of complex systems, anduse these features in developing tools. Although it is recognized that implementationof these tools will be through computational methods and algorithms, the expectedoutcome will be advances in the theory of complex systems and application of theseadvances in energy supply systems [6-7], i.e. bioenergy supply chains.

The 2008 National Biofuels Action Plan (NBAP) and the 2010 NationalAcademy of Science Report on Expanding Biofuel Production have categorizedbiofuel supply chains in five segments – feedstock production, feedstock logistics,conversion technology, transportation and end use [8-10]. Figure 1 shows a bioenergysupply chain described here as a complex system. It displays patterns of structure orbehavior of the system at one level that are diagnostic of interactions among parts ofthe bioenergy supply system at other levels. The emergent behaviors or structures arenot evident from considering only the system's separate components (biomassproduction, biomass transport, biomass conversion, fuel distribution, vehicle fuel use)[11-12].

Figure 1 Components of Biofuels Supply Chain System [8-12]

2.1.1 Agent-based ModelingAgent-based Modeling (ABM) is a method for studying complex systems

exhibiting two properties: (1) the system is composed of interacting agents; and (2)the system exhibits emergent properties arising from the interactions of the agents not

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just a simple aggregation of the properties of each agent [13 -15]. BSC exhibits thesetwo properties. ABM builds on proven, highly successful techniques such as discreteevent simulation and object-oriented programming. ABM emphasizes thedevelopment of models that reproduce critical features of complex systems usingcomponent-level rules. Showing the connections between system components allowsusers to investigate possible interactions and test potential interventions. The value ofagent modeling is tying together micro-, meso- and macro-level impacts ofimplementing any emerging system. The behavior of agents can vary fromcompletely reactive, i.e., agents only perform actions when triggered to do so bysome external stimulus (e.g. actions of another agent), to goal-oriented (e.g. throughseeking a particular goal) [13-15].

2.2 Life Cycle Assessment (LCA)Information about the environmental effects of production systems is

becoming increasingly important to consumers and decision-makers in industry,society, and governments worldwide. The scientific approach to comprehensiveenvironmental impact analysis is life cycle assessment (LCA) [16-18] with NationalRenewable Energy Laboratory (NREL)’s Life Cycle Inventory (LCI) as one possiblesource of inventory data in the U.S. LCA is intended to provide a systematicinventory and impact assessment of the full environmental impacts of a productacross its life-cycle stages: materials extraction and production, manufacturing, use,and ultimate fate of the product. Based on LCA results, improvements can be made inthe products or in its unit processes to minimize their environmental footprints.Today, LCI studies typically list dozens of resource inputs and environmentaloutputs. Impact assessment has traditionally included global warming potential,ozone depletion potential, eutrophication potential, and other environmental impactcategories. Most current LCA methods are based on process models that identify andquantify resource inputs and environmental outputs at each life cycle stage based onmass and energy balance calculations [16-20].

Many LCAs of renewable fuels, for instance, produced at a specific plant,are used to represent an entire technology. This can be problematic for emergingtechnologies, where production processes are far from fixed, and data at best comefrom pilot plants using a specific process technology [21-22]. In addition, theevolving technologies interact in different groupings both concurrently and insequence. For strategic purposes, it is questionable whether there is any point indiscussing smaller process differences, for example, instead of considering themcollectively as a single developing technology (or ‘bundle of value chains’). The timeperspective and studied scale of adoption are crucial for the choice of data inassessing the environmental impacts of emerging technologies [3; 23]. This can beexplained by the fact that technology performance changes over time due to generaltechnological development.

The Argonne’s LCA-based GREET (Greenhouse Gases, RegulatedEmissions, and Energy Use in Transportation) model [24] serves the purpose ofassessing the environmental impacts of biofuels in emerging vehicle technologies.However, when we view the evolution of technologies in the context of industrialecology perspective where industries participate in a dynamic exchange of materials,

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resources and wastes and are parts of interconnected complex system, the currentGREET model is inadequate. Additionally, existing LCA models and tools are besetwith controversial issues such as the case of various and competing results in theLCA of corn-based ethanol [4-5]. GREET model and its derivatives are limited anddo not consider the equally important economic and social dimensions ofsustainability. To avoid these controversies in assessing the life cycle sustainabilityimpacts of cellulose- and algae based biofuels, we should develop and use asophisticated approach to assess the environmental, social and economic impacts ofthese emerging bioenergy technologies [2-4, 9, 11-12]. Furthermore, many existingtools abstract the world through linear and static techniques, while the world isinherently nonlinear and dynamic. We need to find a methodological framework tointegrate tools, combined with relevant data, in such a way that it helps us understandthe complex phenomena that underlie sustainability of bioenergy systems over time[11-12]. The proposed research project aims to understand the development of forestbioenergy supply chain in holistic, transdisciplinary and industrial ecologyperspectives.

2.2.1 Integrating LCA into ABMDavis [25] has researched how to integrate ABM and LCA frameworks. He

reported that we can integrate these two methods by understanding their networkedstructures. ABM conceptualizes the network in terms of a graph, consisting of nodesconnected by edges. An LCA conceptualizes the network as a matrix where non-zerovalues represent connections between nodes, whose identities are indicated by therow and column locations. Graphs are very useful for keeping track of complexevolving structures, and graph traversal algorithms can be written to navigate andretrieve relevant information from these structures. Matrices are absolutely essentialfor conducting the sophisticated mathematical calculations required by LCA. Thenetwork data structure of ABM (Fig. 2) is equivalent to the matrix data structure ofLCA (Fig. 3), allowing the modeled system to be represented in both formats. Basedon this insight, ABM will be used to generate a dynamic complex system, and anLCA will be used to analyze it at discrete intervals.

2.3 Dynamic System ModelingDynamic system modeling (DSM) tools can be used to analyze integrated

complex systems that are susceptible to geographical and resource constraints [13].Use of system modeling and simulation to understand the changes and behavior of asystem over time has been successfully demonstrated with the advent of moresophisticated and advanced computing available today. Urban Dynamics [26a], theWorld Dynamics [26b], and Limits to Growth [27] all have used systems dynamicsapproaches to predict a system’s behavior over time. System dynamics uses a set ofdefined causalities/ functions and computes the value of stocks in a defined time step.Stocks (e.g. biomass, biofuels) can accumulate or deplete over time. The changes thatoccur at the end of one time step can be captured in the next simulation.Understanding the changes in the system’s behavior when certainvariables/parameters change either through exhaustion or deliberate perturbation iscritical to understand sustainability of the subject under study [22, 28]. Cruz et al.

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[29] have demonstrated the use of system dynamics to build an equilibrium state foremerging energy supply chains. Likewise, built in the platform of systems dynamicsare numerous models of water supply systems [30], waste water treatment plant [31],assessment of technology penetration in oil sands industry [22] and supply chainmanagement [9,32]. The concept of accumulation and dissemination of stocks overtime is a common feature of all the above research.

A general framework/template for dynamic systems modeling forsustainability analysis in the Canadian oil sands industry is available at the NationalResearch Council of Canada [22]. This methodological framework can be adapted tocapture and represent changes in bioenergy systems using computer modeling andsimulation. This modeling approach allows us to project the trends of criticalsustainability metrics/indicators over time and thus make scenario analysis possible.System modeling can be carried out leading to useful syntheses and guidelines forplanning of emerging energy systems. The elements of modeling include statevariables (i.e. stocks), control variables (i.e. flows) and feedback processes. Thegraphical outputs of this modeling work can provide insights about the systems beinginvestigated, which can be used to support energy policy decisions.

Figure 2 Structure of ABM [23]

2.4 Environmental Sustainability AssessmentCurrent bioenergy sustainability assessment systems such as the GREET

LCA model and its derivatives –ERG Biofuels Analysis Meta-Model (EBAMM) [33]and GREET-BESS Analysis Meta-Model (GBAMM) [34] are overwhelminglyenvironmentally orientated. They all have weakness in describing the social andeconomic aspects of bioenergy sustainability, and have limited ability to consider thelifecycle implications of bioenergy impacts ("cradle-to-cradle"). Existing models andtools have failed to describe and mimic the dynamic interactions between relevantenvironmental, economic and social variables that will eventually affect bioenergydevelopment [3]. This project is developing an integrated decision-supportframework supported by advanced computing for sustainable management of

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biomass feedstocks associated with the provision and use of bioenergy. It will alsodevelop a new "sustainability value" system (possible expressions of "value"including carbon reduction potential and quality of life returns for eco-footprintinvested) to measure bioenergy sustainability throughout the life cycle of biofuels. Itwill widen the bioenergy sustainability performance criteria to encompass social,economic and environmental attributes in line with novel articulations “People,Planet, Prosperity”[3].

Figure 3 Structure of LCA [25]

2.5 Bioenergy Supply Chain (BSC)Recent innovations in systems biology, bioinformatics, biotechnology,

genetics and chemical engineering have contributed to the renewed interest inconverting lignocellulosic biomass to valuable fuels and other bio-products. USDOEand USDA are currently supporting Projects in Genomics to make biofuels andbioproducts economically, socially and environmentally sustainable and viable. GTLBioenergy Research Centers [1] aim to accelerate genomics-based biological systemsresearch to achieve the transformational breakthroughs in basic science needed forthe development of cost-effective technologies to make production of next-generationbiofuels from lignocellulose commercially viable on a national scale. Producingvalue-added products from the biorefinery, by completely using biomass throughintegrated processing for eco-efficiency are gaining momentum [35]. Technologies(e.g. ‘near-neutral hemicellulose extraction) have been demonstrated as one candidatefor sustainable pathway for bio-refinery development [36-38]. Second-generationbiofuels hold great promise for supplementing the energy supply, but the ecologicaland environmental consequences of increasing its use cannot be fully understoodwithout a transparent and robust life cycle, integrative transdisciplinary approach toassess the sustainability of lignocellulosic biofuels.

U.S. forests are projected to provide at least a third of the billion-tonbiomass feedstock needed for the emerging bioenergy and bioproducts industry [39].The Northeast region is expected to contribute 2% of at least 21 billion gallons of

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advanced renewable transportation biofuels (mostly coming from woody biomass)per year by 2022. These fuels should be made from cellulosic feedstocks that reducegreenhouse gas emissions by at least 60% relative to gasoline. The region couldproduce 423.7 million gallons of advanced biofuels from 639,150 acres of dedicatedbioenergy crops (perennial grasses) and 1.7 million acres of harvested loggingresidues in a year.

In summary, we will depict the emerging bioenergy supply chain (BSC) as acomplex system whereby it needs to satisfy the three pillars of sustainability overtime.

3. Preliminary Research ResultsBSC sustainability requires optimization at two levels – unit (agent) level

and systems level. Each unit of the supply chain (as shown in Figure 1) should havepositive gains and the overall net benefits of BSC as a whole system should also bepositive. The conceptual methodology presented in Figure 5 below can evaluatedefined sustainability performance metrics of individual agents and also of the BSCsystem simultaneously. The computational model can evaluate metrics such asgrowth in the economy, emissions, production rate, flow of goods and services, andsystems productive and absorptive capacities over time. The model uses micro levelinteractions of agents between society, the environment and economy to simulate themacro level systemic behavior of the biofuels supply chain.

The different agents of the supply chain have different objectives and aresubjected to different constraints [32]. Though these units are autonomous indecision-making, they are however dependent upon their upstream and downstreamprocesses. Resiliency of individual unit to absorb certain amount of shock imposedby environmental forces (natural disaster, resource limitation etc), social forces(acceptance, desirability etc) and economic forces (demand, price etc) is anotherfactor that affects smooth performance of the BSC system. The industrial network ofbiofuels consists of groups of autonomous units that are connected through materialand energy flows. For example, biomass production/procurement affects biofuelsproduction and demand of biofuels is affected by users’behaviors. It will be difficultto strengthen the overall performance of such supply chains if units are optimized inisolation. To capture the effects of changes in one unit over other units, a holisticsustainability analysis is required.

To apply the system dynamics approach, the supply chain needs to bedefined as a system with inflows and outflows. Biomass and other resources are theinflows; biofuels, co-products and environmental emissions to air, water and land arethe outflows. These inflows and outflows have environmental, social and economicconsequences. The cumulative effects of these consequences over time determineBSC sustainability. The complex interactions occurring between these three aspectstriggered by biofuels production is challenging to calculate as they exhibit inertialbehaviors. The use of system dynamics helps capture cost and benefit transfersamong different agents of the supply chain. For example, extensive management canincrease biomass production rate, which in turn increases feedstock availability andultimately biofuels production quantity. If individual unit assessment is made, then allthe units will show desirable economic outcomes as a result of increased biomass

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production. If these units are assembled in a supply chain and a systems evaluation isdone, then the result might be otherwise. Some amount of biofuels might actually beutilized by the upstream processes of the supply chain to increase productivity, thusreducing the overall economic gain of the system. These kinds of tradeoffs can bemeasured with the application of dynamic system modeling [11-12].

3.1 Integrated and Distributed Architecture for SustainabilityAnalysis of the Biofuels Supply Chain

The proposed sustainability assessment methodology in Figure 5encapsulates the four well-known sustainability frameworks. Aggregatedmeasurement of environmental, social and economic impacts satisfies the TripleBottom Line concept; the flows of biomass, capital and emission determines theresource, economic and environmental footprints of biofuel life cycle; spatialvariation of socio-economic gains and losses pertains to the Natural Step concept;and mapping the impacts of biofuels life cycle in a specified time frame justifies theGraedel and Klee’s Sustainable Emissions and Resource Usage paradigm.

3.1.1 Systemic Approach to the Biofuel Supply ChainThough there are numerous local, regional and global initiations taken to

ensure sustainable forest management, there are no agreements on applicability ofthese for bioenergy production. At present, there are no comprehensive andintegrative models available for sustainability assessment of biofuels. Ifbiofuel/bioenergy development continues to be based on ad-hoc policies, then thismay entail various risks. When financial benefit from biomass cultivation for energyproduction surpasses conventional agriculture benefits, ecosystems, societies and(agrarian) economies will be significantly affected. The unintended consequences ofbiomass production can damage the image of biomass as a sustainable energy source[11-12]. To understand such cross cutting impacts of bioenergy, a holisticsustainability assessment is warranted. Table 1 shows the initial list of most importantcriteria for sustainability assessment of bioenergy using multi-criteria decisionanalysis.

Table 1 Critical Sustainability Criteria for Bioenergy Systems

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In dynamic system modeling, the life cycle sustainability impacts of biofuelscan be broadly classified into three categories represented by three sub-systems -Social Sub-system (SOs), Environmental Sub-system (ENs) and Economic Sub-system (ECs). Each agent of the BSC is expected to have one or more impactspertaining to these three sub-systems. Let us consider biomass production unit of thesupply chain. The biomass production unit is optimized to produce certain amount ofbiomass (B) that generates revenue (R), Emission (E) and Participation (P) belongingto ECs, ENs and SOs respectively. Revenue is the amount generated through biomassproduction; Emission is the amount of carbon dioxide or other non-green house gasemitted; and Participation is inclusion or exclusion of community members inproduction process [11-12]. A production change will have corresponding impactson R, E and P. Next, consider the Feedstock Logistics unit of the same supply chainoptimized to process the biomass B produced by upstream process. Note that thedownstream process (feedstock logistics) is constrained by the upstream process(biomass production). In other words, the impacts that Feedstock Logistic cangenerate is dependent upon the biomass that it processes, which in turn depends uponthe biomass produced by the Production unit. Thus, even if downstream processescan create relatively more social, economic and environmental benefits, they areconstrained by the decision made in production unit. If decision-making authoritiesrely on individual (agent) assessment, then the potential of total impact is likely to beundermined. The whole supply chain needs to be dynamically optimized to maximizethe desirable condition and minimize the adverse effects [11-12]. In this example, Pmight have undesirable condition due to exclusion of small-scale farmers in theproduction unit; but might have a positive impact in downstream processes becauseof job creation along feedstock logistics, conversion technology and transportationunits.

To develop a sustainability assessment methodology for the biofuels supplychain that will include as much as possible all the impacts of biofuel life cycle,several key steps need to be followed. First, ecosystem goods and services that arelikely to witness large and direct impacts need to be identified and be representedthrough variable selections. Secondly, criteria that represent vulnerable ecosystemgoods and services along all subsystems consistently need to be developed. Thesecriteria however, can have different quantitative indicators or metrics for differentsub-system. Finally, these variables impacts need to be measured in an integratedframework [11-12].

Even when biomass use for biofuel production becomes more lucrative, ittakes some time for the industry to respond to such impulses. System dynamics cancapture these kinds of impacts that occur only after some time interval. It will alsohelp understand the interrelationships among different components of a supply chainand thus provides insights on systems synergy. A sustainability assessment like thisalso keeps the principles of industrial ecology (IE) aloft. A simple causal loopdiagram of bio-fuels supply chain shows how the different sectors add up or resolvethe intricacies associated in the supply chain (Figure 4). Biomass contributespositively to biofuels production. Production factor (technological maturity) enhances

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the production process

Figure 4 Causal Loop Diagram of Biofuels Supply Chain [11-12]

generating more revenue. At the same time, emissions caused by the productionprocess accumulate. Policies often intervene to cut-off emission levels. Economicallocation determines the revenue share for biomass production, emission control andreserve (liquid assets holding). A higher level of emissions in the cycle contributes toenvironmental degradation. In the case of the bio-industry, biodiversity loss andresource degradation potentially increases. The environmental degradation wouldhave social, economic, institutional and obviously environmental implications.Ultimately, the biomass from where the process began feels the pressure. The causalloop diagram shows the direct and indirect relationships among different sectors. Thedirect relationships and their impacts are largely understood and are addressedthrough policies. As indicated by Figure 4, some units of the biofuel supply chainsystem take a long time to react to the changes in other units when they are notdirectly linked to each other. Many of the processes involved are extremelycomplicated and it may take several decades to witness significant changes in theirbehaviors. This is particularly true in context of ecosystem functioning as the causalrelationships are non-linear and have inertial characteristics.

To summarize, the relationships between different sectors or agents in BSCare subjected to social, economic and environmental limitations. The limitationsimposed in terms of biomass production, revenue generated by companies engaged inproduction and the environmental emissions associated with production and uses willall change when one of the sub-systems equilibrium is disturbed.

3.1.2 Modeling Architecture

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The proposed modeling architecture is built on a System Dynamics (SD)platform (Fig 5). A system dynamics approach could uncover policy insights onbioenergy supply chain development. The architecture consists of LCA, Input OutputAnalysis (IOA) and ABM to measure environmental, economic and social impactsassociated with biofuels life cycle. A cradle-to-grave LCA of biofuels will captureresource consumptions and associated emissions during the life cycle of biofuelsproduction. IOA is proposed to measure economic impacts of different processesinvolved in biofuels production to end of life. Use of IOA in conjunction with LCAwill help to identify cost and benefits associated with each process. ABM is proposedto evaluate social impacts associated with different units of biofuel supply chain.Geographical Information System (GIS) can be added to see impacts distribution on aspatial scale, particularly in the case of land use cover and change (LUCC) as well asbiodiversity impacts.

As shown in Figure 5, the sustainability analysis of the biofuels supply chainconsists of three levels. At the Data Level, the system is distributed along the supplychain. Criteria and

Figure 5: Integrated and Distributed Architecture for Sustainability Assessment ofBiofuels [9-10]

indicators applicable to each unit of the supply chain are collected and fed into thesystem. The Process Level is divided into two levels. On Level I, data collected alongthe three subsystems of the supply chain will be analyzed using the methodsmentioned. LCA and GIS will serve as tools for environmental performanceassessment. IOA will be used for economic evaluation and ABM would serve togenerate social priorities. Process level II will use the outputs generated by ProcessLevel I for modeling the system behavior. On Process Level II, the SD modeling willbe used to simulate the system behavior. The output will be potential scenarios that

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could occur as a result of some perturbations in supply chain units. Use of SDmodeling would contribute to decision-making on the macro level (policy making)and meso/micro level (supply chain management) through enhanced understanding ofthe processes involved. Creation of different scenarios would aid in identification ofcritical variables at the data level that could create significant distortions to thesystem. From a managerial perspective, such an analysis would help to foreseepotential obstructions that could arise to prevent uninterrupted flow of biofuelswhereas from scholars’pursuit, it would shed light on the level and magnitude ofrelationships among different sectors involved (11-12).

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