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Journal of Cleaner Production 59 (2013) 210e225
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Journal of Cleaner Production
journal homepage: www.elsevier .com/locate/ jc lepro
The impact of carbon pricing on a closed-loop supply chain:an Australian case study
Behnam Fahimnia a,*, Joseph Sarkis b,1, Farzad Dehghanian c,2, Nahid Banihashemi d,3,Shams Rahman e,4
aUniversity of Technology Sydney, UTS Business School, Management Discipline Group, Haymarket, NSW 2000, AustraliabClark University, Graduate School of Management, Worcester, MA 01610-1477, USAc Ferdowsi University of Mashhad, Department of Industrial Engineering, Faculty of Engineering, Mashhad, IrandUniversity of South Australia, School of Mathematics and Statistics, Centre for Industrial and Applied Mathematics, Mawson Lakes, SA 5095, AustraliaeRMIT University, School of Business IT & Logistics, Melbourne, VIC 3000, Australia
a r t i c l e i n f o
Article history:Received 19 October 2012Received in revised form7 May 2013Accepted 28 June 2013Available online 18 July 2013
Keywords:Closed-loop supply chainGreen supply chainEnvironmental sustainabilityCarbon pricingRegulatory policyAustralia
* Corresponding author. Tel.: þ61 2 95143612; fax:E-mail addresses: [email protected] (B.
(J. Sarkis), [email protected] (F. Dehghanian), nah(N. Banihashemi), [email protected] (S. Rahm
1 Tel.: þ1 508 7937659.2 Tel.: þ98 511 8615100.3 Tel.: þ61 8 8302 5296.4 Tel.: þ61 3 9925 5530.
0959-6526/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.jclepro.2013.06.056
a b s t r a c t
Concerns about industrial and supply chain implications on our natural environment have existed fordecades. Climate change and greenhouse gas emissions have caused countries to implement variousinstruments ranging from taxes, permits and voluntary incentives to required regulatory policies. Giventhis environment, we develop a unified optimization model for a closed-loop supply chain in which thecarbon emission is expressed in terms of dollar carbon cost. This study is one of the first to evaluate theforward and reverse supply chain influences on the carbon footprint. A comparative analysis iscompleted with a decomposition of cost and environmental influences across supply chain functions. Weutilize data from a company located in Australia, where the government is currently introducing a carbonpricing scheme. We find that variations in cost and environmental impacts occur over ranges of carbonpricing. Characteristics and patterns of the numerical results over these ranges provide insights forcorporate key strategies and potential additional government policies. These results and implications areanalyzed along with limitations and directions for future research.
� 2013 Elsevier Ltd. All rights reserved.
1. Introduction
As green supply chain management (GSCM) research continuesto evolve and progress, dynamic regulatory, social, environmentaland industrial concerns provide this research field with many op-portunities for further investigation. Regulatory pressures, newindustrial competitive requirements and standards, and commu-nity expectations all play a role in how industry and its supplychains (SCs) are to be developed (Sarkis et al., 2011; Zhu and Sarkis,2007). Fledgling investigations in this research field have beenrelatively descriptive trying to make sense of the practice of GSCM.
þ61 2 95143602.Fahimnia), [email protected]@unisa.edu.auan).
All rights reserved.
There have also been nascent efforts on optimization and formalmodeling for GSCM. Of over 300 papers focusing on GSCM, just over10% have focused the use of analytical and optimization modeling(Seuring, 2013). We seek to add to this dimension of researchliterature, but in a very practically targeted manner.
The importance of GSCM has been duly noted in efforts by na-tional and international organizations to decouple environmentalburdens from economic growth as is evidenced by the recentRIOþ20 United Nations Conference (Vazquez-Brust and Sarkis,2012). Part of this green growth effort includes development ofclosed-loop systems for resource consumption efficiencies inaddition to transportation optimization, dual and sometimescomplementary goals that industry can utilize. Optimization andefficiencies of GSCM are important contributors to this decoupling.Companies have realized the importance of this decoupling ofeconomic growth and environmental degradation from variousexternal, and sometimes internal, forces (Sarkis, 2012a; Seuringand Müller, 2008; Zhu et al., 2007).
Within this environmental and economically competitivecontext, the research presented in this paper was motivated by aGSCM planning problem faced by an Australian organization.
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 211
Utilizing this organizational requirement for a textile manufacturer,we develop a closed-loop SC (CLSC) model that explicitly considersthe reduction of environmental pollution (carbon-equivalentemission) in both forward and reverse SC directions. The proposedmodel uses ‘overall SC cost’ and ‘carbon emissions’ as the primaryperformance measures. The two measures are unified to form asingle objective optimization model by expressing the carbonemissions in terms of dollar carbon cost. The model is generalizableenough to be applied in a number of settings. The implicationsgathered from some parametric analysis provide practical insightswith a possibility for generalization and clearly identified futureresearch work. A winewin goal of improving both economic andenvironmental performance, the core objective of green growthdecoupling, is the explicit objective of the CLSC model presented inthis paper.
We build on and advance work in optimization of CLSC andGSCM. The balance of economic and environmental perspectivesbrings themodeling efforts in these two fields together. After a briefintroduction into the policy and regulatory issues facing corpora-tions in Australia, this research is placed within the previousliterature describing how the work addresses gaps and advancesknowledge in this field. We then introduce the model and the casestudy. The numerical results of executing the bi-criteria model arethen presented along with the careful analysis of the results. Theimplications of the results are the primary focus of the analysis.Further developments and directions for research, as well as limi-tations are discussed in the Conclusion section.
This paper contributes to the GSCM and CLSC planning literatureby focusing on a tactical-operational planning model for carbonfixed price regulatory environments. This study provides a foun-dation for future research to regulatory and organizational SC set-tings where carbon taxation (pricing) and carbon market tradingare used for industrial carbon emissions management. Thiscontribution objective is met through the development and testingof a mixed integer-linear programming (MILP) formulation of anactual case company in Australia.
2. Background and literature review
2.1. Carbon pricing in Australia and organizational implications
We begin this section by providing a brief historical perspectiveof Australia and its efforts to manage national carbon emissions.Having the world’s highest per capita carbon dioxide emissions (Loand Spash, 2012), Australia has debated the merits of introducing acarbon or greenhouse gas emissions pricing and trading scheme forover two decades (Nelson et al., 2012). Internally to Australia anumber of efforts on emissions trading discussions occurred withvarious schemes presented and evaluated. Internationally, addi-tional discussions occurred because of the United Nations Frame-work Convention on Climate Change (UNFCCC), the Kyoto Protocol.Although not signatories to the Kyoto Protocol, the AustralianProductivity Commission urged the consideration of marketmechanisms, including the use of tradable emission permits, tohelp the country achieve emissions reduction goals. The Produc-tivity Commission’s reports included a debate of whether a deter-mined tax rate on carbon emissions is more effective than freemarket trading. In 2002 a review considered in significant detail theform and nature of a national emissions trading system (ETS) inAustralia with a recommendation of a country wide emissionsscheme (Council of Australian Governments, 2002; Nelson et al.,2012).
Various additional Australian studies on the economic implica-tions of carbon pricing, ETS, and economic implications werecompleted throughout the rest of the 2000’s decade. Eventually,
due to the Australian government’s pledge at the Copenhagenaccord, a politically bipartisan supported adoption of ETS policywas agreed upon for commencement in 2012 (Fahimnia et al., 2013;Meng et al., 2013). In 2011 the Commonwealth Government passedthe Clean Energy Future package of legislation. An ETS was thenestablished in Australia to take effect from 1 July 2012. The schemecommences with a three year fixed price period with the price in2012/13 being $23 per tonne of carbon dioxide equivalent. Thisprice will be payable by any corporation operating a facility withannual aggregate emissions more than 25,000 tonnes of carbonequivalent.
The carbon pricing scheme is essentially a carbon tax, apowerful policy mechanism that can help set the market prices forproducts and energy and to internalize organizational costs of themany external climate change costs (Baranzini et al., 2000). Thecarbon tax in Australia, according to some simulation results, cancut emissions across the economy effectively, but will cause a mildeconomic contraction (Meng et al., 2013). Not all economic sectorswill necessarily be influenced equally and some sensitive sectorssuch as soft coal industry are small enough not to have great in-fluence on economic contraction (Fahimnia et al., 2013; Meng,2012).
The policy theory behind reducing emissions through market-based mechanisms (carbon taxes or emission-trading systems) isbased on internalizing externalities, causing a direct influence onorganizational financial and budgeting management (Chen andTseng, 2011). The theory is that ever-rising implied carbon pricesunder market-based mechanisms will induce innovation in low- orno-carbon technologies. With these additional long-term coststhere is an assumed strategic commitment by organizationsfurthering support for technological and organizational in-novations. But, in practice there is still a question on whether asmall economy can provide the marginal benefits of investing incarbon abatement through these mechanisms (Ergas, 2012). Inaddition, the two major mechanisms of fixed price versus tradablepermits may have very different managerial decision outcomes dueto variations in risk and uncertainty associated with each mecha-nism (Chen and Tseng, 2011). Nevertheless, in some trading situa-tions where the cap on the number tradable permits is very tight (alow cap) even market trading mechanisms may act as a tax aftercertain prices are set (Sarkis and Tamarkin, 2005).
The organizational implications of these government regulatorypolicies have been studied from a number of perspectives includingthe development of carbon accounting within organizations(Stechemesser and Guenther, 2012), determining when to invest intechnology (Sarkis and Tamarkin, 2005), and the types of productsand processes that organizations should integrate (Schaltegger andCsutora, 2012). In this study the focus is on how a carbon tax policymechanism may influence SC decisions in a closed-loop system,causing organizations to rethink operational and network designconsiderations.
2.2. Green supply chain management and closed-loop supplychains
Additional field-focused contextual background is providedthrough an overview of GSCM and CLSC management research thatrelates to modeling environmental and carbon (greenhouse gas)emissions that informs some of our research. GSCM is by definitiona way to improve the environmental performance and reduce theenvironmental burdens of SCs (Sarkis, 2003; Zhu and Sarkis, 2004).CLSC can benefit the environment but also cause additional envi-ronmental burdens. CLSC practices and activities can be environ-mentally beneficial from a product stewardship and extension ofproduct/materials life perspective that help in mitigating resource
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225212
depletion and environmental footprints related to disposal of ma-terial and products (Meade et al., 2007). The activities and out-comes in CLSC5 that are environmentally beneficial includereduction, reuse, reclamation of material, result in the reduction ofwastes and resource depletion (El korchi and Millet, 2011).Although these resource depletion concerns can be met throughCLSC operations, the current investigation turns to the environ-mental implications of these CLSC operations activities from acarbon emissions management perspective. That is, the additionalCLSC (reverse logistics) activities for an organization can causegreater overall carbon emissions. The focus of our work and thereview of the models below do not necessarily consider theseenvironmental tradeoffs and burden shift. Yet, the model intro-duced in this study should be part of a systemic perspective inactual implementation to manage the concerns with environ-mental burden shifting (Jensen, 2012; Laurent et al., 2012). Whenlinking life cycle analysis (LCA) to GSCM and CLSC, these types ofshifting environmental burdens should be considered moreexplicitly.
Most of the past GSCM and CLSC studies have either beendescriptive frameworks or conceptual models (Flapper et al., 2005).A recent review on GSCM literature shows that out of 308 paperspublished between 1990 and 2010 only 36 use quantitative, formalmodels (Seuring, 2013). Most of the published models with anexplicit focus on broader GSCM address sustainability from theforward SC perspective using one of the five primary categories ofmodeling approach including analytical hierarchy process (AHP),game theoretic equilibrium, information theoretic approaches, LCAand multi-criteria decision making (MCDM). For example, Hsu andHu (2009) apply the analytical hierarchy process for supplier se-lection in the context of hazardous substancemanagement; Bai andSarkis (2010) adopted rough set theory to develop green suppliers;and Sheu et al. (2005) show how sustainable practices cancontribute to increased profit.
A CLSC incorporates a reverse logistics system designed tomanage the flow of products or parts destined for reuse, recycle,remanufacturing or disposal (Bai and Sarkis, 2013; Carter andEllram, 1998). The review of Fleischmann et al. (2000) shows thatmost of the analytical models published prior to 2000 addressreuse and recycle aspects of reverse SC employing open-loopnetwork systems. Fleischmann et al. (2000) identified only twostudies that modeled CLSC systems including Jayaraman et al.(1999) and Krikke et al. (1999). After Fleischmann et al. (2000), atleast two other reviews have been published investigating thecharacteristics of the reverse SC models (Pokharel and Mutha,2009; Rubio et al., 2007). Both reviews conclude that the use ofquantitative models for the analysis of CLSCs has increased sub-stantially with the primary focus placed on the production plan-ning and inventory management dimensions. Research on theanalytical modeling and optimization of broader CLSCs is stilllimited. There is evidence suggesting that the optimization of CLSCscan be both environmentally sound and economically profitable(Guide et al., 2000; Jayaraman et al., 1999). Primary emphasis ofthese published models has been on business decisions, profit-ability, or cost minimization rather than the environmental aspectsof remanufacturing and CLSCs. This research further exemplifiesthe secondary, but important, role of environmental issues as a sidebenefit of CLSCs.
5 CLSC is heavily dependent on having a series of reverse logistics operations inaddition to standard forward SC activities such as procurement, operations,manufacturing, and distribution. The major reverse logistics functional phases caninclude collection, separation, disassembly, compaction and outbound logistics (Elkorchi and Millet, 2011).
Economically focused analytical models for CLSCs have been therule, with few explicitly considering environmental aspects. Forexample, Inderfurth (2005) developed a model of a CLSC thatevaluates the optimal recovery and manufacturing policy in anuncertain environment. Teunter et al. (2008) investigated a situa-tion where companies use shared and specialized resources formanufacturing and remanufacturing. Zuidwijk and Krikke (2008)studied the strategic questions of how much a company shouldinvest in product design and production processes to process theirreturned products.
More recent models have started to more explicitly incorporateenvironmental factors. Various emergent measures such as Ecoin-dicator 99 and LCA have been integrated into bi-objective MILPmodels that consider the tradeoff between investment costs andenvironmental impact (Bojarski et al., 2009; Dehghanian andMansour, 2009; Hugo and Pistikopoulos, 2005). These modelsconsider strategic investment decisions such as technology and rawmaterials selection, but not necessarily production planning im-plications. Other MILPs have extended research in this area byconsidering the economic and environmental impact of decisionsregarding the planning and design of an SC such as linking partners,supplier selection, determining facility capacities in each timeperiod, and assigning manufacturing and distribution tasks to thenetwork nodes (Chaabane et al., 2012; Diabat et al., 2013). Con-straints include the demand and production amounts, and theenvironmental impact associated with each node using variousgeneral environmental indicators (Bojarski et al., 2009; Jotzo andBetz, 2009). The issue of facility location, a strategic level analysis,taking into consideration carbon emissions in a CLSC further em-phasizes the practical and research importance of carbon sensi-tivity in the SC (Chaabane et al., 2012; Diabat et al., 2013). Whilethese modeling efforts have focused on strategicetactical levelanalysis, in this study we place our emphasis on the tacticaleoperational level analysis in a CLSC and develop an optimizationmodel that is used to investigate cost and environmental influencesof the a carbon pricing scheme on the tacticaleoperational plan-ning contingencies.
3. Model development
The CLSC problem under investigation is shown in Fig. 1. In theforward network, multiple product types (i) are produced indifferent manufacturing plants (m) by travelling through a set ofmachine centers (g). Machine center g has its own production costand carbon emission rate for processing one unit of product i.Finished products are then shipped to the end-users (e) eitherdirectly or via a set of warehouses (w). Product shipments mayoccur through different transport modes (k) with different pricesand fuel efficiency rates (based on the type of vehicles used). In thereverse network, the end-of-life (EOL) products are gathered by thecollection centers (c) where they are inspected and separated intorecyclable and non-recyclable components. The non-recyclablecomponents are destroyed at disposal centers (d). The recyclablecomponents are sent to recycling centers (r) to be processed on aset of recycling machine centers (g0). Recycled raw materials arethen shipped to the manufacturing plants for reuse in productionline.
In terms of the modeling approach, a mathematical optimiza-tion technique is chosen over a simulation modeling technique forone primary reason. The problem under investigation can beformulated using MILP, in which both objective functions andmodeling constraints are linear in form and all decision variable areeither continuous or binary (0e1). A relatively large MILP model issolvable (for exact solution, not heuristically) using standard linearprogramming solvers such as CPLEX (Fahimnia, 2011; Fahimnia
Fig. 1. Decision variables in the CLSC under investigation
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 213
et al., 2012). The proposed MILP model aims to determine thetacticaleoperational SC planning decisions, including productionand distribution allocation strategies for the next planning horizonT (comprising t time periods) in such a way that the sum ofmanufacturing, transport and emission costs is minimized.
From the above problem statement the following indices/nota-tions will be used for the purpose of mathematical modeling:
Product index i Set of products IManufacturing plant index m Set of manufacturing plants MMachine center index g Set of machine centers GWarehouse index w Set of warehouses WTransport mode index k Set of transport modes KEnd-user index e Set of end-users ETime period index t Planning horizon TCollection center index c Set of collection centers CRecycling center index r Set of recycling centers RRecycling machine center index g0 Set of recycling machine centers G0
Disposal center index d Set of disposal centers D
Assumptions in the proposed CLSCmodel include the following:
� Variety of products (i) to be produced is known.� Number, location and capacity of plants (m) and warehouses(w) are known.
� Number and location of end-users (e) are known (i.e. customerzones or retailers).
� Number, location and capacity of collection centers (c), recy-cling centers (r) and disposal centers (d) are known.
� Demand for all product types is assumed to be known (deter-ministic) at all end-users for all periods of the next planninghorizon.
� The forecasted demand for each product has to be satisfied,sooner or later, during the planning horizon. A penalty costmay be incurred if the demand for a certain product at oneperiod is backordered. The backordered demand is to besatisfied in the subsequent periods before the end of theplanning horizon.
� The quantity of EOL products returned to end-users at eachperiod is known.
� A penalty/holding cost is incurred for uncollected EOL productsin end-users.
� A product type can be supplied from more than onemanufacturing plant, but the shipment of products and com-ponents between two manufacturing plants is not allowed.
� Production, distribution, recycling and distribution capacitylimitations are known.
� Raw material and finished product inventory levels are knownat the start and end of the planning horizon.
� End-users are the locations where products are delivered to thefinal customers with no holding capacity to store the products.
� Manufacturing and distribution air emissions are known forthe processing of products and shipment of finished productsfrom plants to end-users. The emission rates are determinedaccording to the manufacturing technology adopted inmanufacturing and transportation mode used in transport (e.g.older machines/trucks produce more emission).
� Cost of emission for holding/storing products (in plants,warehouses, collection centers, recycling centers and disposalcenters) is assumed to be negligible when compared to theoverall SC emission.
The input parameters and decision variables used in the pro-posed model are presented in Appendix 1. Using these notations,the MILP formulation of the proposed CLSC planning model witha unified objective function is presented in this section. Formodeling purpose, we divide the proposed unified objectivefunction into two dependent functions: cost function and emis-sion function.
3.1. Formulation of cost function
The cost function is comprised of 14 elements (Eqs. (1)e(14)).Eq. (1) formulates the cost of opening and operating manufacturingplants (Term 1), warehouses (Term 2), collection centers (Term 3),recycling centers (Term 4), and disposal centers (Term 5).
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225214
Z1 ¼t m
omt Gmt þt w
o0wtG0wt þ
t cocct G
cct
XX XX XXþXt
Xr
orrt Grrt þ
Xt
Xd
oddt Gddt
(1)
Eq. (2) formulates the second component of the cost functionincluding manufacturing cost using virgin material in regular-time(Term 1) and (Term 2) overtime.
Z2 ¼Xi
Xm
Xt
"Qimt
Xg
pigmtligmt þ uimt þ aimt
!#
þXi
Xm
Xt
"Q 0imt
Xg
pigmtl0igmt þ uimt þ bimt
!# (2)
Manufacturing cost using recycled material in regular-time(Term 1) and overtime (Term 2) is presented in Eq. (3).
Z3 ¼Xi
Xm
Xt
"Qrimt
Xg
pigmtligmt þ aimt
!#þXi
Xm
�Xt
"Q 0rimt
Xg
pigmtl0igmt þ bimt
!#(3)
Cost of holding finished products in manufacturing plants (Term1) and warehouses (Term 2) is given in Eq. (4).
Z4 ¼Xi
Xm
Xt
Ximthimt þXi
Xw
Xt
Yiwth0iwt (4)
Eq. (5) formulates the cost of holding virgin raw material andrecycled raw material in manufacturing plants.
Z5 ¼Xi
Xm
Xt
�hrmimtX
rmimt þ hrrmimt X
rrmimt
�(5)
Eq. (6) expresses the forward transportation cost for the ship-ment of finished products from plants to warehouses (Term 1),from warehouses to end-users (Term 2), and from plants to end-users (Term 3).
Z6 ¼Xi
Xm
Xw
Xk
Xt
Fimwktsimwkt þXi
Xw
Xe
Xk
�Xt
F 0iwekts0iwekt þ
Xi
Xm
Xe
Xk
Xt
F 00imekts00imekt (6)
Cost of recycling in (1) regular-time and (2) overtime is pre-sented in Eq. (7).
Z7 ¼Xi
Xr
Xt
"Qrrirt
Xg
prig0rt lrig0rt þ a0irt
!#
þXi
Xr
Xt
"Q 0rrirt
Xg
prig0rt l0rig0rt þ b0irt
!# (7)
Cost of collection/inspection/separation of EOL products atcollection centers is calculated by Eq. (8).
Z8 ¼Xi
Xe
Xc
Xk
Xt
Feiecktpinsic (8)
Cost of destroying the non-recyclable components at thedisposal centers is presented in Eq. (9).
Z9 ¼Xi
Xc
Xd
Xk
Xt
F 0cicdktpdisid (9)
Eq. (10) presents the cost of holding recyclable and disposingmaterial in collection centers.
Z10 ¼i c t
hcrictXcrict þ hcdictX
cdict (10)
XXX� �
Cost of holding recyclable and recycled material in recyclingcenters is given in Eq. (11).
Z11 ¼Xi
Xr
Xt
�hrirtX
rirt þ hrrirtX
rrirt
�(11)
Eq. (12) formulates the cost of purchasing EOL products (Term 1)as well as the cost of transporting EOL products from end-users tocollection centers (Term 1), transporting recyclable products fromcollection centers to recycling centers (Term 2), transportingdisposing material from collection centers to disposal centers(Term 3), and transporting recycled material from recycling centersto manufacturing plants (Term 4).
Z12 ¼Xi
Xe
Xc
Xk
Xt
Feieckt�seieckt þ u0
iet
�þXi
Xc
Xr
Xk
Xt
Fcicrktscicrkt
þXi
Xc
Xd
Xk
Xt
F 0cicdkts0cicdkt
þXi
Xr
Xm
Xk
Xt
F 00rirmktsrirmkt
(12)
Penalty cost of uncollected EOL products at the end-users ispresented in Eq. (13).
Z13 ¼Xi
Xe
Xt
Srietpciet (13)
Eq. (14) presents backordering cost of unsatisfied demand atend-users.
Z14 ¼Xi
Xe
Xt
Sietsciet (14)
The cost function, presented in Eq. (15), is the sum of the 14linear cost components presented in Eqs. (1)e(14).
Z ¼ Z1 þ Z2 þ Z3 þ Z4 þ Z5 þ Z6 þ Z7 þ Z8 þ Z9þ Z10 þ Z11 þ Z12 þ Z13 þ Z14
(15)
3.2. Formulation of emission function
The emission function has four elements presented in Eqs. (16)e(19). The generated manufacturing carbon emission is presented inEq. (16).
Z01 ¼Xi
Xg
Xm
Xt
�Qimt þ Q 0
imt þQrimt þQ 0r
imt
�pigmtcigmt (16)
Carbon emission at recycling centers is presented in Eq. (17).
Z02 ¼Xi
Xg0
Xr
Xt
�Qrrirt þ Q 0rr
irt
�prig0rt c
rig0rt (17)
Forward transport pollution, expressed in Eq. (18), comprises ofthe generated emission for the shipment of finished products fromplants to warehouses (Term 1), from warehouses to end-users(Term 2), and from plants directly to end-users (Term 3):
Z03 ¼Xi
Xm
Xw
Xk
Xt
Fimwktaimwkt
þXi
Xw
Xe
Xk
Xt
F 0iwekta0iwekt
þXi
Xm
Xe
Xk
Xt
F 00imekta00imekt
(18)
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 215
Reverse transport pollution (Eq. (19)) comprises of carbonemission generated for the shipment of EOL products from end-users to collection centers (Term 1), recyclable products fromcollection centers to recycling centers (Term 2), disposing materialfrom collection centers to disposal centers (Term 3), and recycledmaterial from recycling centers to manufacturing plants (Term 4).
Z04 ¼Xi
Xe
Xc
Xk
Xt
Feiecktaeieckt
þXi
Xc
Xr
Xk
Xt
Fcicrktacicrkt
þXi
Xc
Xd
Xk
Xt
F 0cicdkta0cicdkt
þXi
Xr
Xm
Xk
Xt
F 00rirmktarirmkt
(19)
The emission function in Eq. (20) is formed by the summation ofthe four linear cost components presented in Eqs. (16)e(19):
Z0 ¼ Z01 þ Z02 þ Z03 þ Z04 (20)
The unified goal of the proposed optimization model is tominimize the value of Z in Eq. (21) inwhich the emission function ispresented in terms of dollar carbon cost.
Z ¼ Z þ k Z0 (21)
3.3. Constraints
The proposed CLSCmodel is subject to the following set of linearconstraints.
3.3.1. Constraints at manufacturing plantsInventory balance of raw material in manufacturing plants:
Qimt þ Q 0imt þ Xrm
imt � Xrmimðt�1Þ � gimt c i;m; t (22)
Xrmim0 ¼ hrim & Xrm
imT ¼ h0rim c i;m (23)
Inventory balance of recycled raw material in manufacturingplants:
Xrrmimt � Xrrm
imðt�1Þ ¼Xr
Xk
F 00rirmkt ��Qrimt þ Q 0r
imt
�mrpi c i;m; t
(24)
Xrrmim0 ¼ hrrim & Xrrm
imT ¼ h0rrim c i;m (25)
Production capacity constraint (machine center capacity limi-tation) for regular-time and overtime production in manufacturingplants:�Qimt þ Qr
imt
�pigmt � ligmt &�
Q 0imt þ Q 0r
imt
�pigmt � l0igmt c i; g;m; t (26)
Holding capacity restriction at manufacturing plants:
Ximt � hcimt c i;m; t (27)
Inventory balance of products at plants:
Qimt þ Q 0imt þ Qr
imt þ Q 0rimt � Ximt þ Ximðt�1Þ
¼Xw
Xk
Fimwkt þXe
Xk
F 00imekt c i;m; t (28)
Xim0 ¼ him & XimT ¼ h0im c i;m (29)
3.3.2. Constraints at collection centersInventory balance of recyclable material of EOL product i in
collection centers:
Xcrict � Xcr
icðt�1Þ ¼ wEOLi mrmax
i
Xe
Xk
Feieckt
�Xr
Xk
Fcicrkt c i; c; t(30)
Xcric0 ¼ 4c
ic & XcricT ¼ 4ct
ic c i; c (31)
Inventory balance of disposed material of EOL product i incollection centers:
Xcdict � Xcd
icðt�1Þ ¼ wEOLi
�1� mrmax
i
�Xe
Xk
Feieckt
�Xd
Xk
F 0cicdkt c i; c; t(32)
Xcdic0 ¼ 40c
ic & XcdicT ¼ 40ct
ic c i; c (33)
Collection centers capacity restriction:
Xe
Xk
Feieckt � scict & Xcrict þ Xcd
ict � hccict c i; c; t (34)
3.3.3. Constraints at recycling centersInventory balance of recyclable material in recycling centers:
Xrirt � Xr
irðt�1Þ ¼Xc
Xk
Fcicrkt ��Qrrirtþ Q 0rr
irt
�mrri c i;r;t (35)
Xrir0 ¼ 4r
ir & XrirT ¼ 40r
ir c i; r (36)
Inventory balance of recycled raw material in recycling centers:
Xrrirt � Xrr
irðt�1Þ ¼ Qrrirt þ Q 0rr
irt �Xm
Xk
F 00rirmkt c i; r; t (37)
Xrrir0 ¼ 4rr
ir & XrrirT ¼ 40rr
ir c i; r (38)
Production capacity constraint (machine center capacity limi-tation) for regular-time and overtime production in recyclingcenters:
Qrrirtp
rig0rt � lrig0rt & Q 0rr
irt prig0rt � l0rig0rt c i; g0; r; t (39)
Holding capacity constraint at recycling centers:
Xrirt � hcrirt & Xrr
irt � hcrrirt c i; r; t (40)
3.3.4. Constraints at disposal centersDisposing capacity restriction:
Xc
Xk
Xt
F 0cicdkt � sdid c i;d (41)
3.3.5. Constraints at warehousesWarehouse capacity restriction:
Yiwt � hc0iwt c i;w; t (42)
Inventory balance at warehouses:
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225216
Yiwt � Yiwðt�1Þ ¼m k
Fimwkt �e k
F 0iwekt c i;w;t (43)
XX XXYim0 ¼ 4iw & YiwT ¼ 40iw c i;w (44)
3.3.6. Distribution constraintsDistribution capacity limits at plants:
Fimwkt � εmaximwkt c i;m;w; k; t (45)
F 00imekt � dmaximekt c i;m; e; k; t (46)
Distribution capacity constraint at warehouses:
F 0iwekt � qmaxiwekt c i;w; e; k; t (47)
Distribution capacity constraint at customer zones:
Feieckt � εemaxieckt c i; e; c; k; t (48)
Distribution capacity constraint at collection centers:
Xi
Xm
Xw
Xk
Fimwktaimwkt þXi
Xw
Xe
Xk
F 0iwekta0iwekt þ
Xi
Xm
Xe
Xk
F 00imekta00imekt þ
Xi
Xe
Xc
Xk
Feieckt aeieckt
þXi
Xc
Xr
Xk
Fcicrkt acicrkt þ
Xi
Xc
Xd
Xk
F 0cicdkta0cicdkt þ
Xi
Xr
Xm
Xk
F 00rirmktarirmkt � amax
t1000
c j; t
(59)
Fcicrkt � εrmaxicrkt c i; c; r; k; t (49)
F 0cicdkt � εdmaxicdkt c i; c;d; k; t (50)
Distribution capacity constraint at recycling centers:
F 00rirmkt � qrmaxirmkt c i; r;m; k; t (51)
3.3.7. Demand satisfaction constraintsDemand satisfaction constraint: The total amount of production
for every product at all plants must meet the forecasted demand forthat product at the end of planning horizon unless there is in-ventory in manufacturing plants and warehouses at t ¼ 0 and t ¼ T.
Xm
Xt
�Qimt þQ 0
imt þQrimt þ Q 0r
imt
� ¼Xe
Xtdiet þ
Xm
h0im
�Xm
him þXw
40iw
�Xw
4iw c i
(52)
Maximum allowed shortage at end-users:
Siet � smaxiet c i; e; t (53)
Maximum allowed for uncollected EOL products:
sriet � srmaxiet c i; e; t (54)
Inventory balance at end-users:
w k
F 0iwekt þm k
F 00imekt ¼ diet � Siet þ Sieðt�1Þ c i; e; t
XX XX(55)
Inventory balance at end-users for EOL products:
Xk
Xc
Feieckt ¼ d0iet � Sriet þ Srieðt�1Þ c i; e; t (56)
3.3.8. Carbon emission constraintsCarbon emission constraint in manufacturing plants:X
i
Xg
pigmtcigmt�Qimt þ Q 0
imt þ Qrimt þ Q 0r
imt
� � cmaxmt
1000cm; t
(57)
Carbon emission constraint in recycling centers:
Xi
Xg0
prig0rtcrig0rt
�Qrrirt þ Q 0rr
irt
� � crmaxrt1000
c r; t (58)
Carbon emission constraint in transport:
3.3.9. Restrictions on decision variables
0 � Qimt � GmtL & 0 � Q 0imt � GmtL c i;m; t (60)
0 � Qrimt � GmtL & 0 � Q 0r
imt � GmtL c i;m; t (61)
0 � Qrrirt � Gr
rtL & 0 � Q 0rrirt � Gr
rtL c i; r; t (62)
0� Fimwkt �GmtL & 0� Fimwkt �G0wtL c i;m;w;k;t (63)
0 � F 0iwekt � G0wtL c i;w; e; k; t (64)
0 � F 00imekt � GmtL c i;m; e; k; t (65)
0 � Feieckt � GcctL c i; e; c; k; t (66)
0 � Fcicrkt � GcctL & 0 � Fcicrkt � Gr
rtL c i; c; r; k; t (67)
0 � F 0cicdkt � GcctL & 0 � F 0cicdkt � Gd
dtL c i; c; d; k; t (68)
0 � F 00rirmkt � GrrtL & 0 � F 00rirmkt � GmtL c i; r;m; k; t (69)
0 � Ximt & 0 � Xrmimt & 0 � Xrrm
imt c i;m; t (70)
0 � Yiwt c i;w; t (71)
0 � Xcrict & 0 � Xcd
ict c i; c; t (72)
0 � Xrirt & 0 � Xrr
irt c i; r; t (73)
0 � Siet & 0 � Sriet c i; e; t (74)
Table 1Numerical results for Scenario 1 (FS).
Carbonprice($/ton)
Overall SC cost($) from Eq. (21)
Cost function (Z) Emission function (Z’) * k
Forw SC costs ($) Forw emiss costs ($)
Prod Forw dist Prod Forw dist
0 8,622,713 6,055,013 2,567,700 0 020 8,727,515 6,061,017 2,568,118 73,810 24,57040 8,822,990 6,064,231 2,575,057 138,314 45,38860 8,914,450 6,066,557 2,591,613 195,500 60,780
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 217
4. Model implementation
TexF is involved in the provision of fibrous material used in carseats, carriers, sofas, dining chairs, filling material, and seat covers.The wide range of textile products manufactured at TexF can meetthe needs of a wide spectrum of industries including automotiveand furniture. For about a decade, the high volume of damaged andreturned goods at TexF resulted in large amount of product waste.In 2011, the company decided to convert to a CLSC by recycling itsdamaged and EOL products returning them to the production lineas some sort of recycled raw material. Such activities and processesas color sorting, fiber blending, cleaning, weaving, knitting, com-pressing, and shredding are a part of the reverse network at thecollection and recycling centers. Through developing a CLSC notonly could TexF gain some cost reduction benefits, but it has alsobeen a way to build a long-term reputation in the sector. TexF isnow pondering how the manufacturing and distribution strategiesof its CLSC are affected with the recently introduced carbon pricingscheme in Australia. This became the primary motivation for con-ducting this research and developing a unified CLSC model thatexplicitly considers the reduction of carbon pollution in both for-ward and reverse courses.
TexF has two manufacturers in South Australia and Queensland(M ¼ 2). Plant 1 with older machinery is cheaper to run but gen-erates more carbon emission. Plant 2 generates less emission, but iscostlier to produce items. Each plant is equipped with six machinecenters (G ¼ 6) involved in the production of more than 10 producttypes. Here, we target the SC planning of four main product familiesat TexF including fiber car seats, baby carriers, sofas, and diningchairs (I ¼ 4). Each product may or may not need to travel throughall six machine centers. Finished products satisfy the market de-mand at five customer locations (E ¼ 5) through three warehouses(W ¼ 3). The reverse network has five collection centers (C ¼ 5),two disposal centers (D ¼ 2), and three recycling centers (R ¼ 3)each equipped with four machine centers (G0¼4). Three transportmodes are available in all the routes (K¼ 3). Transport mode 1 is thecheapest among the three while produces more emission per unittransportation. Transport mode 2 has an intermediate position andtransport mode 3 is the most expensive but greenest option. Theplanning horizon is defined to be one year consisting of 12 one-month time periods (T ¼ 12).
The authors were initially approached by the case companyrepresentatives seeking advice on the impact of carbon pricing onthe TexF’s CLSC. The data collection for this case study was a longmulti-step process. Forecasted demand data, backordering allow-ances and costs, as well as the historical product return data at thecollection centers were obtained from the sales and marketingdepartment for the financial year 2010e2011. For the same plan-ning period, the SC management unit at TexF provided themanufacturing, recycling and transportation capacities and costs aswell as the inventory costs and holding capacities in plants, ware-houses, collection centers and recycling centers. Carbon emissiondata was available for every machine in manufacturing plants andrecycling centers. Transportation emission rates were estimatedfrom the average values obtained from two third-party logisticsproviders.
The proposed model in Section 3 was coded using AMPLmodeling language with CPLEX as the core optimization solver. Thenumerical results are converted into Excel spreadsheets for analysispurposes. Using the real data collected from TexF’s CLSC operations,the model was run for four hypothetical carbon prices ($0, $20, $30and $40 per ton of emission) in two scenarios: forward SC scenario(FS) and CLSC scenario (CLS). These prices represent a realistic rangegiven market-based policy instrument schemes in other nationsand regions of the world (Chapple et al., 2013; Sarkis and Tamarkin,
2005). FS disregards the reverse SC operations and therefore usesvirgin materials only to produce the products. CLS represents thestatus quo in TexF inwhich a reverse SC is adopted to reduce the useof virgin materials in production lines. Tables 1 and 2 present thenumerical results for FS and CLS respectively. The numerical resultsinclude the model outputs for the four different carbon prices andinclude the overall SC cost for each carbon price as well as thevalues of the disaggregated objective function components (Eqs.(15) and (20)). It should be noted that the cost function in Eq. (15)represents the SC cost excluding the associated emission costs.Emission costs are derived from multiplying emission function bythe associated carbon cost (k).
In these tables, ‘Prod cost’ is the cost of production and inventoryholding in manufacturing plants. ‘Forw Dist’ shows the cost of for-ward transport from plants to end-users as well as the cost of in-ventory holding in warehouses. For the reverse SC, ‘Coll/Rec/Disp’ isthe cost of collection, inspection, separation, recycling and disposalas well as the inventory holding costs in collection centers, recy-cling centers and disposal centers. ‘Trans’ shows the accumulativecost for the shipment of EOL products from end-users back to themanufacturing plants. Analogues abbreviations are used for thecomponents of the emission function presenting the amount ofemission generated for corresponding activities in the costfunction.
5. Discussion
Various insights can be gained from the variations in carbonpricing. Carbon emissions pricing may be dependent on a variety offorces in the market place or through regulatory policies.Depending on the policies set forth by the Australian governmentor strategies set forth by industries and even individual organiza-tions, the pricing of carbon emission can change quite drastically.We vary the prices over a small and realistic range given the historyof these types of market-based policy instrument schemes in othernations and regions of the world (Chapple et al., 2013; Sarkis andTamarkin, 2005). The resulting reactions to these pricing changesin terms of optimal costs and emissions based on two types of SCscenarios and decisions set the stage for the optimization analysis.we design and model two situations, one with a reverse network(CLS) and one with only a forward SC (FS).
Intuitively, as carbon prices rise, the operational costs increasewhile emission rates decrease. This situation assumes similar ratesin demand and manufacturing of products. In a closed-loop envi-ronment complexity in the analysis increases due to potential costsavings and/or cost increases from additional steps in the process.Thus, our investigation at this time is focused on what costs andemissions results occur from an SC planning model with andwithout closing the loop. It is difficult to make initial conjecturesabout the emissions and economic results for both of these cases.The only conjecture that can be made is that we can expect de-creases in optimal design carbon emissions when carbon costs(prices) increase.
Table 2Numerical results for Scenario 2 (CLS).
Carbonprice ($/ton)
Overall SC cost($) from Eq. (21)
Cost function (Z) Emission function (Z’) * k
Forw SC costs ($) Rev SC costs ($) Forw emiss costs ($) Rev emiss costs ($)
Prod Forw dist Coll/Rec/Disp Trans Prod Forw dist Coll/Rec/Disp Trans
0 8,381,213 4,700,960 2,568,330 826,668 285,255 0 0 0 020 8,514,598 4,706,097 2,566,176 831,693 285,629 73,435 24,904 20,941 572340 8,637,980 4,709,235 2,571,482 837,201 287,830 137,122 46,210 38,358 10,54260 8,752,325 4,710,015 2,586,355 841,504 290,351 194,820 62,580 52,580 14,120
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225218
The organization will be interested in knowing the optimalcarbon emissions, especially if carbon prices can be assigned to allactivities managed by the organization. If a larger decrease in car-bon emissions occurs with the CLSC, organizations like TexFmay bewilling to incorporate the CLSC to help gain larger credit amountswhen available. The ultimate decision facing the organization iswhat CLSC operations to adopt in presence of a carbon pricingscheme. It noteworthy that even though there may be greateroverall carbon emissions from additional reverse SC operations,CLSC practices can be environmentally beneficial from a productstewardship and extension of products/materials life perspectivethat help in mitigating resource depletion and environmentalfootprints related to disposal of material and products. Here, weonly focus our CLSC analysis on the implications from a carbonemissions management perspective leaving the resource depletiondiscussion and shift of environmental burden (Finkbeiner, 2009;Jensen, 2012) out of the area of our research focus.
We begin by varying the carbon prices in an exclusively forwardSC scenario (FS) and a CLSC scenario (CLS) with ranges from 0 to 60dollars per ton of carbon emissions. Fig. 2 shows the optimal carbonemissions trend for FS at different carbon prices and Fig. 3 illus-trates the abatement potential achieved when CLSC is in place. Theanalysis for the FS considers the emissions from the production andforward distribution stages of TexF’s operations. Here, transportemissions represent less than 25% of the overall carbon emissionand the remaining 75% belongs to the manufacturing emissions.This result may not be the case for many other types of
Fig. 2. Generated carbon emission at vari
organizations, but it is the case for TexF. The general breakdown ofoperational emissions sources can provide valuable information fororganizations that can target where reductions are more feasibleand where the largest ‘bang-for-the-buck’ exists.
Comparing the differentials of optimal solutions for the FS inFig. 2, as carbon price increases from $0 to $60 per ton, the totaloptimal production emissions decreases by 18.6%. The percentage(and absolute value) decreases at a slower marginal rate as theprice increases. For example, the decrease in emissions is 7.8%, or315 tons, when the carbon price increases from $0 to $20 per ton,and only 6.3% (233 tons) for the carbon price increasing from $20 to$40 per ton.
The total carbon emission in CLS is primarily generated at theproduction stage and only to a minor percentage by the underlyingtransport flows. In the reverse network, the recycling processesgenerate considerably higher emissions than the related transportoperations. For CLSC, the contribution of manufacturing emission isless than 60% in the overall carbon pollution while reverse opera-tions contribute up to 22%.
Interestingly, for the distribution activities of the FS, theresulting optimal emissions reduction rate over a varying pricerange shows an increasing marginal decrease in emissions as priceincreases. Although the total optimal distribution emission de-creases by 18.2%, a very similar rate to the production stage of theFS, the optimal emission reductions are smaller at the lower priceincreases. That is, the rate of change of carbon emissions for the FSdistribution stage is only 0.6%, down by only 8 tons, when the price
ous carbon prices for Scenario 1 (FS).
Fig. 3. Generated carbon emission at various carbon prices for Scenario 2 (CLS).
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 219
changes from $0 to $20 per ton. The carbon emission decreases by10.7% if price increases from $40 to $60 per ton. This increasingmarginal emission reduction results is opposite of the productionstage’s trend.
The initial implication from these numerical results is nowevident. If an organization is seeking to invest in technologies orpractices to help decrease their carbon emissions, it would be moreworthwhile, from an absolute and percentage reduction perspec-tive to focus on production processes and technology at lower levelcarbon pricing. If the carbon emission market pricing gets increasesto a larger value of over $40/ton, the shift in investigating carbonemission savings through new technologies and practices shouldoccur in the transportation and distribution functions.
Operating a CLSC can positively affect the ‘green image’ of TexFthrough its resource consumption reduction efforts. Alternatively,managing the direct operations of a CLSC can also generateconsiderable additional carbon emissions that appear on the or-ganization’s operational books. That is, what would have beenconsidered a Scope 3 emission (indirect SC emission), in the use ofrecycled material, would now become a Scope 1 emission (directoperations) that needs to be managed by TexF in a CLSC model (seeHoffmann and Busch (2008) for a discussion of Scope for CarbonAuditing). It has been shown that the embedded carbon emissionsof products and materials from recycling operations tend to bemore carbon efficient than virgin material procurement (Edwardset al., 2010) where significant emission is generated in raw mate-rial extraction and the associated processing activities.
CLSC environmental advantages are primarily related toreducing resource consumption. While a more systemic, biggerpicture, analysis shows that the global emissions can be reducedthrough recycling, for TexF, running a CLS means incurring about20% more carbon pollution when compared to FS. The globalemission abatement that TexF’s CLS offers need to be investigatedwith the inclusion of virgin material carbon emission values.Overall, this situation with FS performing better from a carbonemissions perspective can be discouraging for establishing CLSCswhen a carbon pricing scheme is in place. Focusing only on oneenvironmental metric (e.g. carbon emissions) can cause environ-mental burden shift that organizations need to be concerned about,
and thus more complete life cycle analyses with multiple di-mensions is recommended to arrive at a more accurate environ-mental burden portfolio with and without CLSC activities(Finkbeiner, 2009; Jensen, 2012).
Once the scheme is converted into a carbon trading mechanismin the near future, mandatory caps on emissions will limit theamount of carbon pollution unless extra permits are available topurchase. One policy implication that can be drawn from thesefindings is that carbon costs incurred via reverse SC operations mayneed to be subsidized to a large extent by the governments in car-bon pricing regulations. Alternatively, additional carbon costs andpricing for an FS system that only uses virgin materials with greatercarbon footprints may be a policy to even the playing field.
For the closing the loop scenario (CLS), Figs. 4 and 5 show theoverall SC cost increase (Fig. 4) and emission improvement (Fig. 5)for three carbon prices ($20, $40, $60 per ton) compared to thebaseline of $0 carbon price. A cost analysis of the optimal results forvariations in carbon prices shows for the CLS, in Fig. 4, the optimalsolution costs are generally increasing as carbon prices increase.This result is not surprising and is expected due to the additionalcarbon costs incurred. Interestingly, at the $20/ton carbon price, thedistribution stage of the SC optimal cost solution decreases slightly(Fig. 4) along with the corresponding slight emissions generationincrease (Fig. 5).
Our analysis, here, is focused on discovering the relationshipbetween the rate at which costs are increased and the corre-sponding emission improvement rate. Fig. 5 indicates with carbonprice rising from $0 to $20 per ton of emission, transport practicesshow only minor adaptation, while forward manufacturing andreverse operations show more significant emission improvements.However, the emission improvement in reverse operations (Fig. 5)is supported by the significant cost increase in these facilities(Fig. 4). This observation indicates that for smaller carbon prices,investing on forward manufacturing can be the most economicalway of emission saving. It should however be noted that the ab-solutemanufacturing cost is about 5.7 times greater than the cost ofthe reverse operations (Coll/Rec/Disp), while the emission gener-ation is only 3.5 times as big. This implies the manufacturing pro-cesses are inherently greener than reverse operations.
Fig. 4. Changes in SC cost components for CLS (compared to the baseline of $0 carbon price).
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225220
While there is a very consistent transport cost increase whencarbon price rises from $0 to $60 (Fig. 4), for carbon prices greaterthan $20 significant improvements can be observed in transportemissions (Fig. 5). A clear managerial implication from this obser-vation is that for larger carbon prices the primary focus in a CLSCshould be given to the use of greener transport modes and tech-nologies, while smaller carbon prices may require the adaptation ofmore carbon-efficient production and recycling systems.
A direct comparative analysis of the overall costs and emissionimprovement trends for FS and CLS are shown in Figs. 6 and 7respectively. Fig. 6 shows that the absolute cost values for FS are
Fig. 5. Changes in carbon savings for CLS (com
greater than those of CLS over the range of carbon prices. This isobviously due to the savings in the decreased use of virgin materialat CLS and hence the lower cost of rawmaterial procurement. Fig. 7,on the other hand, shows that the absolute carbon emission valuesare greater in CLS because of the carbon emissions generated viathe reverse SC activities. In fact, Fig. 7 fails to illustrate the emissionsavings that could be obtained through the reduced extraction,production, and transportation of virgin materials. In other words,part of the overall emissions that is now being generated by TexF asa Scope 1 emission (direct emission) was traditionally a Scope 3emission managed by the raw material suppliers.
pared to the baseline of $0 carbon price).
Fig. 6. Trends in SC cost increase for FS and CLS.
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 221
Another observation is that the overall cost of CLS seems to beincreasing at a faster rate compared to FS (evidenced by the clearconvergence of the two lines in Fig. 6); while the emissions gen-erations are improved at a closer rates (the slighter convergencerate of the two lines Fig. 7). These two latter observations indicatehow the establishment of a CLSC can be discouraging when thecarbon price gets to be larger. Overall, the need to subsidize thecarbon costs incurred via the reverse network becomes morevisible at larger carbon prices.
Fig. 8 compares the trend in the reduction of carbon emissionsfor FS and CLS with the trend in overall cost increase in FS and CLSrespectively. The values shown are incremental that compare the
Fig. 7. Trends in emission ge
increased costs and emission reductions to those at the precedingcarbon price. While the incremental increase in the optimal SCcosts taper off as the carbon price increases from $20 to $60, theincremental change in carbon reduction benefits initially rise thenlevel off for the same carbon price range, especially for the CLS. Thetradeoff is evident, whereby organizations have greater emissionsreductions for a given carbon price increases. At the larger carbonprices (closer to $60 per ton), the incremental emission reductionsin the CLS are similar to those of the FS. In fact, the progressionseems to show the CLS incremental emissions reductions will beless than FS incremental emissions reductions. But, both systemsstill have substantial savings in emissions.
neration for FS and CLS.
Fig. 8. Incremental changes in SC cost and emission generation for FS and CLS.
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225222
The trends in SC cost increase and emission reduction showsthat higher carbon prices can lead to less incremental SC cost in-creases but greater reduction in emission generation. This findingsupports the basic reasoning by having carbon prices increase (ifpolicy makers are to tax at these rates) and further support in-vestments in emissions reduction technology.
From Fig. 8, the most significant incremental improvement inemissions generation reduction happens to be in the CLS and in thecarbon price range of $20e$40. From a policy perspective,assuming all companies fall within the same characteristics para-metric pricing, taxing at this rate or managing the market price toremain in this range will give the largest incremental benefit forCLS. If the FS is used, the incremental reductions seem to becontinuously improving as the carbon prices increase. From anorganizational perspective, companies can gain competitive ad-vantages by focusing on FS at higher rates (outsourcing reverseoperations) if they wish to show the higher emissions reductions.
6. Conclusions
The decoupling of environmental degradation from economicgrowth requires that institutions consider the joint improvement ofboth dimensions when planning their SCs. This balance decision-making has been a concern at the national and regional levels. Thispaper takes this decoupling and ‘winewin’ proposition to the SClevel. We developed an optimizationmodel that seeks to balance anSC carbon footprint (emission reduction goal) and tactical eco-nomic goals. The impacts of carbon pricing on both a standardforward SC and a CLSC were evaluated. Parametric analysis wasconducted over various carbon prices utilizing real case settings.This study is practically valuable since Australia, where the casecompany is located, is currently implementing a carbon pricingmarket. The development of such optimization models is particu-larly valuable for organizations, researchers and policy makers.
We arrive at some important findings from our initial analyses.First, corporations need to evaluate the impact of carbon pricing ontheir SC performance with various implications related to whichoperations and level of ‘scope’ to consider when designing and
determining their carbon footprints. This scoping is importantespecially when such impacts are significant and companies mayneed to establish comprehensive strategic approaches in responseto the introduced carbon pricing schemes.
To promote SC decarbonization, governments may need toprovide subsidies on carbon costs incurred via reverse SC opera-tions. As could be seen in our results, the optimal designs mayinvolve various improvement or deterioration in some SC functions.Identifying these areas is critical for planning purposes becausegovernment subsidization and industry focus may be placed on SCfunctions where more improvements can be gained. One issue isthat expanding the scope of control to include CLSC activities mayactually be detrimental to an organization’s carbon footprint, butother environmental benefits should not be ignored. Subsidizingsome of the reverse SC processes would make the CLSC operationsmore economically viable especially at larger carbon prices. Incor-porating additional carbon costs for upstream virgin materialemissions can be included in future models to illustrate that CLSLsare not necessarily worse.
What we have described are only one set of implications basedon some initial analysis from the model development and execu-tion. Further research could investigate the influence of significantfactors that may have impacts on the SC performance such as therate at which products are returned, product recyclability and thecapacity of reverse operations. Also, the cost of virgin material mayvary with changes in the emission prices (our model disregardedthe carbon pollution generated through the extraction and deliveryof raw material at the supplier side). A more complete analysis of aCLSC needs to include all three Scopes of emission generation. Webelieve that the proposed model in this paper is flexible enough toinclude such information, but such actual data may not be easilyavailable to all companies, as in our case study. These are possibleextensions, but also identify some of the limitations in ourtechnique.
What we have studied is an important and critical issuefacing industry, government, communities and other stake-holders. The study builds the foundation for optimization ofcarbon in CLSC environment. The utilization of optimization
continued)
bimt Variable overhead cost of overtime production of product iin m at t ($/unit)
birt0
Variable overhead cost of overtime recycling for a unit of
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225 223
techniques to investigate environmental issues is a step in thedirection for development of more compassionate operations(Sarkis, 2012b) that include social and environmental sustain-ability issues.
recycled raw material i in r at t ($/kg)sciet Unit backordering (shortage) cost for product i in e at t ($/unit)sietmax Maximum backordering permitted for product i in e
at t ($/unit)pciet Penalty/inventory cost for uncollected EOL product i in e
at t ($/unit)sietrmax Maximum allowed capacity for uncollected EOL product
i in e at t (units)
Acknowledgements
The authors are grateful to the anonymous referees for theirconstructive comments and suggestions to improve the presenta-tion of this paper.
Appendix 1. Model parameters and decision variables
Parametersdiet Forecasted demand for product i in e at t (units)diet
0Quantity of returned EOL product i in e at t (units)
wiEOL Weight of EOL product i (kg)
mirmax Maximum recyclable fraction of EOL product i (%)
mirr Consumption fraction of recyclable material for producing
a unit of recycled raw material for product i (%)mirp Unit consumption of recycled material for producing a
unit of product i (kg/unit)omt Fixed costs of opening and operating m at t ($)owt
0Fixed costs of opening and operating w at t ($)
octc Fixed costs of opening and operating c at t ($)
ortr Fixed costs of opening and operating r at t ($)
odtd Fixed costs of opening and operating d at t ($)
himt Unit holding cost for product i in m at t ($/unit)hiwt
0Unit holding cost for product i in w at t ($/unit)
himtrm Unit holding cost for virgin raw material i in m at t ($/unit)
himtrrm Unit holding cost for recycled raw material i in m at t ($/kg)
hictcr Unit holding cost for recyclable material of EOL product i
in c at t ($/kg)hictcd Unit holding cost for disposed material of EOL product i
in c at t ($/kg)hirtr Unit holding cost for recyclable material of EOL product i
in r at t ($/kg)hirtrr Unit holding cost for recycled raw material i in r at t ($/kg)
hcimt Maximum holding capacity for product i in m at t (units)hciwt
0Maximum holding capacity for product i in w at t (units)
hcictc Maximum holding capacity for EOL product i (both recyclable
and disposed material) in c at t (units)hcirt
r Maximum holding capacity in r for recyclable material ofEOL product i at t (kg)
hcirtrr Maximum holding capacity in r for recycled raw material
of EOL product i at t (kg)sictc Maximum inspection/separation capacity for EOL product
i in c at t (units)sidd Maximum disposing capacity for disposed material of EOL
product i in d (kg)piddis Cost of destroying a unit of disposed material of EOL
product i in d ($/kg)picins Cost of inspection/separation of a unit of EOL product i in
c ($/unit)pigmt Processing time to produce a unit of product i on g in m
at t (h/unit)prig0rt Processing time to recycle a unit of recyclable material of
EOL product i on g0in r at t (h/kg)
ligmt Labour/hour cost for regular-time production of product ion g in m at t ($/h)
ligmt0
Labour/hour cost for overtime production of product i ong in m at t ($/h)
lrig0rt Labour/hour cost for regular-time production of recycledraw material i on g0 in r at t ($/h)
l0rig0rt Labour/hour cost for overtime production of recycledraw material i on g0 in r at t ($/h)
uimt Cost of raw material required for producing a unit of i inm at t ($/unit)
uiet0
Cost of purchasing a returned EOL product i in e at t ($/unit)aimt Variable overhead cost of regular-time production of i in
m at t ($/unit)airt
0Variable overhead cost of regular-time recycling for a unitof recycled raw material i in r at t ($/kg)
ligmt Capacity hours for regular-time production of product i ong in m at t (h)
lrig0rt Capacity hours for regular-time recycling of EOL product i ong0 in r at t (h)
ligmt0
Capacity hours for overtime production of product i on g inm at t (h)
l0rig0rt Capacity hours for overtime recycling of EOL product i on g0
in r at t (h)gimt Capacity units of raw material supply for product i in m
at t (units)simwkt Unit transportation cost of product i from m to w through
k at t ($/unit)siwekt0
Unit transportation cost of product i from w to e throughk at t ($/unit)
simekt00
Unit transportation cost of product i from m to e throughk at t ($/unit)
sieckte Unit transportation cost of EOL product i from e to c throughk at t ($/unit)
sicrktc Unit transportation cost of recyclable material of EOL producti from c to r through k at t ($/kg)
sicdkt0c Unit transportation cost of disposed material of EOL product
i from c to d through k at t ($/kg)sirmktr Unit transportation cost of recycled raw material of EOL product
i from r to m through k at t ($/kg)εimwktmax Maximum allowed distribution of product i from m to w
through k at t (units)qiwektmax Maximum allowed distribution of product i from w to e
through k at t (units)dimektmax Maximum allowed distribution of product i from m to e
through k at t (units)εiecktemax Maximum allowed distribution of EOL product i from e to c
through k at t (units)εicrktrmax Maximum allowed distribution of recyclable material of EOL
product i from c to r through k at t (kg)εicdktdmax Maximum allowed distribution of disposing material of EOL
product i from c to d through k at t (kg)qirmktrmax Maximum allowed distribution of recycled raw material of EOL
product i from r to m through k at t (kg)him Inventory level of product i in m at the start of planning
horizon (units)him
0Inventory level of product i in m at the end of planninghorizon (units)
himr Inventory level of raw material i in m at the start of the planning
horizon (kg)him
0r Inventory level of raw material i in m at the end of the planninghorizon (kg)
himrr Inventory level of recycled raw material i in m at the start of
planning horizon (kg)him
0rr Inventory level of recycled raw material i in m at the end ofplanning horizon (kg)
4iw Inventory level of product i in w at the start of planninghorizon (units)
4iw0
Inventory level of product i in w at the end of planninghorizon (units)
4icc Inventory level of recyclable material of EOL product i in c at
the start of planning horizon (kg)4icct Inventory level of recyclable material of EOL product i in c at
the end of planning horizon (kg)4ic
0c Inventory level of disposed material of EOL product i in c atthe start of planning horizon (kg)
4ic0ct Inventory level of disposed material of EOL product i in c at
the end of planning horizon (kg)4irr Inventory level of recyclable material of EOL product i in r at
the start of planning horizon (kg)4ir
0r Inventory level of recyclable material of EOL product i in r atthe end of planning horizon (kg)
(
(continued on next page)
(continued)
4irrr Inventory level of recycled raw material of EOL product i in r at
the start of planning horizon (kg)4ir
0rr Inventory level of recycled raw material of EOL product i in r atthe end of planning horizon (kg)
cigmt Estimated carbon emission to produce a unit of product i on g inm at t (kg/h)
cmtmax Maximum allowed carbon emission in m at t (ton)crig0rt Estimated carbon emission to produce a unit of recycled material
from EOL product i on g0 in r at t (kg/h)crtrmax Maximum allowed carbon emission in r at t (ton)aimwkt Estimated carbon emission for the shipment of product i from
m to w through k at t (kg/unit)aiwekt
0Estimated carbon emission for the shipment of product i fromw to e through k at t (kg/unit)
aimekt00
Estimated carbon emission for the shipment of product i fromm to e through k at t (kg/unit)
aieckte Estimated carbon emission for the shipment of EOL product i from
e to c through k at t (kg/unit)aicrktc Estimated carbon emission for the shipment of recyclable material
of EOL product i from c to r through k at t (kg/kg)aicdkt
0c Estimated carbon emission for the shipment of disposed materialof EOL product i from c to d through k at t (kg/kg)
airmktr Estimated carbon emission for the shipment of recycled raw
material of EOL product i from r to m through k at t (kg/kg)atmax Maximum allowed transport emission at t (ton)
k Carbon cost ($/kg)L ‘Big M’ standing for a large number
Binary variables:
Gmt ¼�1; If m operates in t0; therwise
G0wt ¼
�1; If w is open in t0; Otherwise
Gcct ¼�1; If c is open in t0; Otherwise
Grrt ¼�1; If r operates in t0; Otherwise
Gddt ¼�1; If d is open in t0; Otherwise
B. Fahimnia et al. / Journal of Cleaner Production 59 (2013) 210e225224
Decision variables
Continuous variables:
Qimt Quantity of i produced in regular-time in m at t (units)Qimt
0Quantity of i produced in overtime in m at t (units)
Qimtr Quantity of i produced from recycled raw material in
regular-time in m at t (units)Qimt
0r Quantity of i produced from recycled raw material inovertime in m at t (units)
Qirtrr Quantity of recycled raw material i produced in
regular-time in r at t (kg)Qirt
0rr Quantity of recycled raw material i produced inovertime in r at t (kg)
Fimwkt Quantity of i shipped from m to w through k duringt (units)
Fiwekt0
Quantity of i shipped from w to e through k duringt (units)
F 00imekt Quantity of i shipped directly from m to e throughk during t (units)
Fieckte Quantity of EOL product i shipped from e to c through
k during t (units)Ficrktc Quantity of recyclable material of EOL product i shipped
from c to r through k during t (kg)Ficdkt0c Quantity of disposed material i shipped from c to d
through k during t (kg)F 00rirmkt Quantity of recycled raw material i shipped from r to m
through k during t (kg)Ximt Inventory amount of i in m at the end of t (units)Ximtrm Inventory amount of raw material i in m at the end
of t (kg)Ximtrrm Inventory amount of recycled raw material i in m at the
end of t (kg)Yiwt Inventory amount of i in w at the end of t (units)Xictcr Inventory amount of recyclable material of EOL product
i in c at the end of t (kg)Xictcd Inventory amount of disposing material of EOL product
i in c at the end of t (kg)Xirtr Inventory amount of recyclable material i in r at the
end of t (kg)Xirtrr Inventory amount of recycled raw material i in r at
the end of t (kg)Sietr Quantity of uncollected EOL product i in e at the end
of t (units)Siet Quantity of i backordered in e at the end of t (units)
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