Conceptual framework for measuring carbon footprint ...

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Conceptual framework for measuring carbon footprint in supply chains Jairo R. Montoya-Torres, Edgar Gutierrez-Franco & Edgar E. Blanco To cite this article: Jairo R. Montoya-Torres, Edgar Gutierrez-Franco & Edgar E. Blanco (2015) Conceptual framework for measuring carbon footprint in supply chains, Production Planning & Control, 26:4, 265-279 To link to this article: http://dx.doi.org/10.1080/09537287.2014.894215

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Date: 16 April 2016, At: 11:16

Production Planning & Control, 2015 Vol. 26, No. 4, 265–279, http://dx.doi.org/10.1080/09537287.2014.894215

Conceptual framework for measuring carbon footprint in supply chains Jairo R. Montoya-Torresa*, Edgar Gutierrez-Francoa,b and Edgar E. Blancoc a

Escuela Internacional de Ciencias Economicas y Administrativas, Universidad de La Sabana, Bogota, Colombia; bFacultad de Ingenieria, Center for Latin-American Logistics Innovation, Bogota, Colombia; cCenter for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA, USA

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(Received 15 April 2012; accepted 10 February 2014) Over the last few years, the fight against climate change has become one of the main topics of international debate. Hence, consumer behaviour has begun to change as they have started to assess the environmental impacts of the products and services they buy. Although various methods exist for measuring environmental (e.g. carbon) impacts, there is no international consensus about the most appropriate one. In addition, calculations can also be affected by limited data availability and uncertainty surrounding the value of key variables. This paper proposes a conceptual framework for measuring and analysing the carbon footprint in supply chains. This research contributes to the knowledge and practice of green supply chain management, at the corporate level, by providing robustness. This aids the decision-making process by identifying strategies in order to reach the efficiency that can be achieved by reducing CO2 emission over the supply network. The framework is validated using real data from a supply chain belonging to the agro-industrial sector. Finally, these results and experience is generalised in order to show the difficulties and challenges in the measuring task. Keywords: framework; carbon footprint; GHG protocol; supply chain; agro-industry

1. Introduction Over the last few years, the fight against climate change has become one of the main topics of international debate and is now identified as one of the greatest challenges humanity has ever faced (Scipioni et al. 2010). The main cause of climate change has been identified as the emission of Greenhouse Gases (GHG) connected to human activities (Solomon et al. 2007). The international community has started several initiatives leading to the drafting of the Kyoto Protocol (Scipioni et al. 2010) that involves both industrialised and developing countries with market economies and aims to reduce GHG emissions on a global scale. Together with the international community’s increased attention on this matter, consumer behaviour has begun to change: consumers have started to assess the environmental impacts of the products and services they buy (Roberts 1999; Nyborg, Howarth, and Brekke 2006; Scipioni et al. 2010). As a matter of fact, enterprises now play a key role in the achievement of GHG reduction goals. Hence, an important way of progress for industrial organisations is to adopt green supply networks. Adopting a policy for the reduction of emissions, when introduced within the wider management context of the environmental aspects of an enterprise can lead, in addition to substantial benefits for the environment, to a reduction of production costs and an increase in market

share and technological leadership (Porter and Van der Linde 1995). From the academic side, a number of papers published in recent years illustrate the interest in environmental-friendly or green supply chain management (Seuring and Müller 2008). In the light of these considerations, it is important for an enterprise to consider introducing management and monitoring of GHG emissions into its definition of corporate strategy (Slater and Tirado Angel 2000; Claver et al. 2007). Among the different GHGs, CO2 has been the one that attract the most the attention from researchers and practitioners. Although there are some carbon footprint calculations methods already available, there is no internationally agreed standard calculation method (Jensen 2012). An International Organisation for Standardisation (ISO) standard (14067) for product carbon footprint exists in draft, but it will not be finalised for publication by 2014 (FEI 2012). Note that this standard focuses on ‘product carbon footprint’, while the literature has recognised the importance of supply chain carbon footprint (see further sections in this paper). This latter is the focus of this paper. In addition, the calculations can also be affected by limited data availability and uncertainty surrounding the value of key variables. The aim of this paper is to propose a conceptual framework for measuring and analysing the carbon

*Corresponding author. Email: [email protected] This paper has been selected from the proceedings of the 4th International Conference on Industrial Engineering and Systems Management IESM 2011, edited by Lyes Benyoucef, Damien Trentesaux, Abdelhakim Artiba and Nidhal Rezg. © 2014 Taylor & Francis


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footprint in supply chains that is able to take into account the uncertainty of collected data. The basics of the Green House Gas (GHG) protocol are used in the framework together with adopted subjective weights that depends on data sources reliability and certainty. This research also contributes to the knowledge and practice of green supply chain management, at the corporate level, by providing robustness. This aids the decisionmaking process by identifying strategies in order to reach the efficiency that can be achieved by reducing CO2 emission over the supply network. Indeed, it has been observed by the academic literature that ‘there is an increasing realisation by managers that their company’s social and environmental accountabilities do not fall solely under the control of any individual organisation, but multiple entities across the supply chain must be involved’ (Winter and Knemeyer 2013). The remaining of this paper is organised as follows. Section 2 is devoted to briefly review relevant literature related to modelling supply chains under green conditions and computation of CO2 emissions. Section 3 presents the methodology followed in this research. The proposed framework is presented in detail in Section 4 and then validated in Section 5 by considering a real company from the agro-industrial sector. Section 6 presents a discussion of the results of the implementation and some managerial implications. This paper ends in Section 7 by presenting some concluding remarks and directions for further research. 2. Literature review In a world increasingly concerned with the optimal use of ever-scarcer natural resources, supply chains need to be re-aligned to adjust to this trend. In the past, enterprises have thrived by providing an optimal service level at the lowest possible price, paying little attention to how supply chain decisions and actions affect other aspects of human life, such as the sustainability of natural resources (Piplani et al. 2008). According to a recent literature review by Gupta and Palsule-Desai (2011), early research efforts in sustainable supply chain management were largely devoted to understanding the technical and operational considerations inherent in collecting, testing, sorting and remanufacturing of returned products. Reviews of scientific literature covering this domain (e.g. Greenberg 1995; Fleischmann et al. 1997; Sbihi and Eglese 2007; Hernández-Hernández and Montoya-Torres 2011) point out that most of the work on sustainable supply chains has focused on environmental sustainability. This finding is echoed by Seuring and Müller (2008) who reviewed 191 papers on sustainable supply chains published between 1994 and 2007. Their results show that apart from considering the traditional economic dimension, 73% papers focused on environ-

mental issues, while the other papers considered some kind of social issues. Other literature reviews (Carter and Rogers 2008; Carter and Easton 2011) confirm these outputs. The fight against climate change has become one of the main topics of international debate, and climate change is now identified as one of the greatest challenges humanity has ever faced (Scipioni et al. 2010). With the international community’s increased attention to this matter, consumer behaviour has begun to change and consumers have started to assess the environmental impact of the products and services they buy (Nyborg et al. 2006; Scipioni et al. 2010). In response, organisations now play a key role in achieving GHG reduction goals. Manufacturing organisations are increasingly taking note that products and processes interact with the environment and generate knock-on effects on environmental pollution (Madu et al. 2002). Consumers and legislation have pushed organisations to redesign their logistic networks to mitigate their negative environmental impact. As presented by Scipioni et al. (2010) and explained by Gutiérrez-Franco et al. (2011) and Hernández-Hernández and Montoya-Torres (2011), over recent years a variety of international standards have been published containing methodologies for monitoring GHG emissions. These include standards published by the Intergovernmental Panel on Climate Change (Springer 2003; CE 2004; Craig, Blanco, and Sheffi 2009), the European Union after the ratification of the Kyoto Protocol (CE 2002, 2003, 2004), the World Business Council for Sustainable Development (WBCSD), known as the GHG Protocol (WBCSD 2004), and the ISO, known as ISO14064-1 (ISO 2006). These standards specify valid methods for rigorous GHG monitoring. The work of Springer (2003) gives an overview of the methods used to quantify the costs of reaching the commitments of the Kyoto Protocol, the results achieved and the size of the market for tradable GHG emission permits. Pandey, Agrawal, and Pandey (2011) present a review of carbon footprint methods available up to date, but no framework of there is presented by these authors. In the scientific literature, some works have very recently been published, most of them focused on measuring, modelling, optimising and simulation of supply chains based on green practices (Sheu, Chou, and Hu 2005; Kainuma and Tawara 2006; Ferretti et al. 2007; Sundarakani et al. 2010; Abdallah, Diabat, and Simchi-Levi 2012; Halabi et al. 2013; Alfonso-Lizarazo, Montoya-Torres, Gutiérrez-Franco 2013). Neto et al. (2008) review the main activities affecting environmental performance and cost efficiency in logistic networks and show the advantages of using multi-objective programming to design more sustainable networks. Logistics and supply chain managers have to balance efforts to reduce


Production Planning & Control costs and innovate while maintaining good environmental and ecological performance (Pagell et al. 2004). Within green supply chain management practices, recoverable product environments and the design of these products and materials have become an increasingly important strategy in industries moving towards environmentally conscious manufacturing and logistics (Jayaraman, Guide, and Srivastava 1999). Optimisation and simulation models dealing with operational issues for green supply chain management have also been proposed (Ravi et al. 2005; Sheu, Chou, and Hu 2005; Cruz-Rivera and Ertel 2009; Jaegler and Burlat 2013). Chaabane, Ramudhin, and Paquet, (2011) presented a multi-objective methodology based on mixed integer linear programming to address supply chain design problems in which carbon emissions and total logistics costs are considered simultaneously. Jaegler and Burlat (2012) proposed a simulation model for computing carbon emission in supply chains under collaborative scenarios. On the other hand, despite the modelling efforts in the area of green SCM, the supply chain and operations management literature is scarce with respect to modelling carbon emissions. Indeed, some scientific works are those presented by Sundarakani et al. (2008), Wu et al. (2006), Cholette and Venkat (2009), and Sundarakani et al. (2010), while surveys of academic literature (e.g. Corbett and Klassen 2006; Seuring and Müller 2008) remarkably show that very few papers focus explicitly on GHG emissions calculations (Plambeck 2012). In summary, companies around the world are showing increased interest in both environmentally-friendly manufacturing and service provision (Gunasekaran and Spalanzani 2012). Several studies in the scientific literature have been interested on the modelling and optimisation of the supply chain design and operation. Also, a variety of international standards are available in order for monitoring GHG emissions. Despite those efforts of the international community, further research is needed in order to assess the actual impact of carbon emissions on supply chain performance. A general conceptual framework is hence required. The aim of this paper is to propose such a framework, in order to contribute to the knowledge and practice of measuring and controlling the carbon footprint across supply chains. 3. Research methodology The field of Supply Chain Management (SCM) has seen rapid advances in recent years thanks to the application and development of a variety of techniques for conducting research into supply chain operations and practice (Seuring et al. 2005). However, the scientific literature also shows that new approaches to empirical research are needed to explore the full scope of supply chain management. The methodologies mostly used in SCM research

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are the following: substantive justification for theory building, surveys, case study research, action research and quantitative modelling (Seuring et al. 2005). In this paper, we build a conceptual framework (i.e. theory building) and then propose how this framework can be used with appropriate methodologies to carry out research. The research study in this paper also follows an inductive approach based on a single-case embedded unit research design (Yin 2009). The mapping of the selected single case study will provide in-depth insights and knowledge about the emerging (and somehow poorly understood) phenomenon of carbon footprint measurement in complex supply chains. According to the literature, research questions focused on process mapping and the identification of relationships between these variables must be resolved with research structures based on case analysis (Stuart et al. 2002) and the use of cases analyses for developing theories and for refining existing theories, among others (Voss et al. 2002). Indeed, several authors (e.g. Seuring 2005; Boyer and Swink 2008) have highlighted the benefits of case study research as a theory-building approach. Case studies have been effectively employed in a large variety of situations, and excellent guides for conducting such research exist in both the broader business (Eisenhardt 1989; Yin 2009) and in the operations management literature (Meredith 1998). Indeed, case study research provides ‘firsthand observation of phenomena in a natural setting’ (Boyer and Swink 2008) and it often yields unintended insights. We are aware of the limitations of case-study research (i.e. cost and time, inability to generalise and prescribe and potential for bias in the perceptions of the researchers). However, these limitations can be substantially mitigated with the use of proper techniques, as with other research techniques. As described later in the paper, the case company belongs to the agro-industrial sector with over 50 years of experience producing and distributing several types of products, including diet drinks, juice, sauces and cocktails. The selection of the case is based on theoretical interestingness (on-going program to drive through an impact of carbon footprinting on decision-making at strategic, tactical and operational levels) and access (Yin 2009). The interest of carbon footprint measurement in agro-industries has been recently pointed out in the scientific literature: see for example the works of Botto et al. (2011), Flysjö (2011), Yuttitham et al. (2011) or Qi and Chang (2012). However, none of these works present a conceptual framework for implementing a formal methodology for carbon footprint calculation. The research approach favoured in this paper is also model-based quantitative analysis (Bertrand and Fransoo 2002). As remarked by Meredith et al. (1989), quantitative model-based research assumes that researchers can build objective models that explain (wholly or partly) the

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behaviour of real-life operational processes or that can capture (wholly or partly) the decision-making problems that are faced by managers in real-life operational processes. Our research approach is empirical (Meredith et al. 1989) since our primarily concern is to obtain a solution for the defined supply chain. We intend to produce knowledge about the behaviour of the environmental performance of the SC measured using its carbon footprint.


4. Conceptual framework In supply chain networks, a number of actors will influence environmental impact: suppliers, manufacturers, consumers, logistic operators, third parties operating in testing, refurbishing, recycling and energy production for the end-of-life products (Quariguasi Frota Neto et al. 2008). These actors perform majority of the activities impacting both business and the environment. In general terms, the activities performed in a supply chain network are related to manufacturing, transportation, usage and end-of-life products’ destination. As explained by Quariguasi Frota Neto et al. 2008, the decisions regarding these activities will therefore determine the supply chain configuration, as well as the costs and the environmental impact. These decisions are strategic (e.g. location of manufacturing plants or warehouses), tactical (e.g. product flows through the chain, choice of suppliers) and operational (e.g. vehicle routing, day-to-day production scheduling). Table 1, which is taken from Quariguasi Frota Neto et al. 2008, presents in board terms, the main activities contributing to the environmental impact and costs in a supply chain. As previously described in Section 2, there is little research work in scientific literature that simultaneously integrates both visions of a supply chain performance: economic (e.g. cost minimisation or profit maximisation) and environmental (i.e. carbon footprint). Hence, most of

literature has been divided in two approaches: minimising costs and minimising environmental impact. As explained by Quariguasi Frota Neto et al. 2008, the drawback of such perspectives is straightforward: they do not capture the trade-off between the supply chain cost and its respective environmental footprint. It hence becomes important to define a conceptual framework that will help enterprises to efficiently calculate the environmental impact, in addition to their well-known methodologies to calculate supply chain costs. Figure 1 presents the proposed framework for calculating carbon footprint in supply chains. It is to note that this framework presents in fact the three dimensions of sustainable development that include economic, environmental and social issues (Elkington 1997). It is important to positioning the current paper against the framework of sustainable supply chain management as it is part of an increasing research subject worldwide. Sustainable development is formally defined as the ‘development which meets the needs of the present without compromising the ability of future generations to meet their own needs’ (United Nations 1983). From the perspective of a firm, this definition suggests not only a focus on economic metrics of businesses, but also a need to equally focus on environmental and social issues. Hence, firm management (including logistics and supply chain management) under sustainability constraints can be understood as a decision-making process in which social, environmental and economic goals have to be achieved simultaneously. In recent years, the academic literature has witnessed the publication of research surveys outlining the relationships between sustainability and supply chain management (Srivastava 2007; Seuring and Müller 2008; Abbasi and Nilsson 2012; Dekker et al. 2012; Gimenez et al. 2012; Gunasekaran and Spalanzani 2012; Winter and Knemeyer 2013; Seuring 2013). Majority of such works, however, has only addressed the environmental and economic dimensions.

Table 1. Main activities influencing cost and environmental impact in supply chains (Quariguasi Frota Neto et al. 2008). Type of factor

Variables

Transportation

Transport from suppliers to manufacturer and vice versa Transport from suppliers to consumer and vice versa Transport from suppliers to end-of-life facility and vice versa Transport from manufacturer to consumer and vice versa Transport from manufacturer to end-of-life facility and vice versa Transport from consumer to end-of-life facility and vice versa Manufacturing at supplier Manufacturing at manufactures Product use by consumers Testing Re-use Refurbishing Recycling Energy production

Manufacturing Product use Testing End-of-life alternatives

Production Planning & Control

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Reasons for measuring carbon footprint Focus Internal Performance of the supply chain (dimensions of sustainability)

This paper

Economic dimension Cost, lead time, service level, resource utilization, etc. Environmental dimension Carbon footprint, ecoefficiency, NOx emissions, waste ratio, material recovery rate, etc.


Social dimension Occupational health and safety, working conditions, workers’ rights, etc.

Measuring carbon footprint

Focus: Internal versus external

External

Driver Corporate

Metrics

Government and/or investor pressure

Supplier selection Supply chain Sourcing decisions Brand labeling or product Supply chain design

Driver: Corporate versus Product or supply chain

Selecting a methodology for carbon footprinting Focus Internal Methodology: GHG protocol, Life-cycle assessment, ETS / CDP, PAS 2050

External

Driver Robustness (GHG Protocol) Supply chain Precision or product (LCA) Corporate

Regulatory standards (ETS / CDP) Industry standards (PAS 2050)

Figure 1. Proposed conceptual framework for studying environmental impact in supply chains.

Some very few papers have addressed the social dimension of sustainability (e.g. Hassini et al. 2012; Hutchins and Sutherland 2012). By presenting all the three dimensions for evaluating sustainable supply chains, we intend to set in context the importance of carbon footprint measurement within a global and integrated evaluation of supply chain performance. The reader must also note that the environmental impact of supply chains can be measured by evaluating different metrics such as carbon footprint, eco-efficiency, NOx emissions, waste ratio, material recovery rate, etc. (Čuček et al. 2012; Montoya-Torres et al. 2012). However, the term carbon footprint has come into wide use among academics and practitioners in the last few years. A systematic definition of carbon footprint is offered by Wiedmann and Minx (2008): ‘carbon footprint is a measure of the total amount of CO2 emissions that is directly and indirectly caused by an activity or is accumulated over the life stages of a product’. They asserted the importance of all direct (on-site, internal) and indirect (off-site, external, upstream, downstream) emissions for measuring carbon footprint in business operations. We do recognise that the other metrics presented in Figure 1 are important as well for measuring environmental impact of supply chains, however, because of simplifications, for now on in this paper, we will only consider carbon footprint. With the enactment of regulations and legislations on low-carbon development, enterprises worldwide have to incorporate carbon footprint management into their business decisions (Hua et al. 2011). In the framework of Figure 1, corporate carbon footprint refers to calculating the direct and indirect emissions of all activities of an organisation, including energy and gas consumption in industrial processes, fuel consumption in vehicles of the

organisation, among others. Product or supply chain carbon footprint refers to calculating the emissions that are along the supply chain of a given product, and only that product. The main two reasons for why organisations are called upon to calculate their carbon footprint are: (1) To reduce and control emissions, because through their carbon footprint can identify the main sources of emissions (2) Make reports with the objective for the company of releasing its commitment to the environment through the responsible implementation of their activities. On the other hand, the product-oriented carbon footprint calculation are motivated for effective emission reduction, cost reduction initiatives, product differentiation, brand recognition, supply chain redesign, to establish collaborative relationships with suppliers, development of better management practices, in general, and for more competition in the marketplace thanks to the publication of emissions. Different methodologies are currently available for measuring carbon footprint (Pandey, Agrawal, and Pandey 2011; Benjaafar, Li, and Daskin, 2013). There is no international consensus about which one is the best, or about which one must be used at an international dimension (FEI 2012). The use of different methodologies results in different estimations for the carbon footprint metric (Blanco and Craig 2009; Dias and Arroja 2012). Depending on the driver (corporative or product/supply chain) and on the focus (internal or external), the methodology employed to measure carbon footprint is different. The company has to first define those two issues in order to select the corresponding more appropriate measuring methodology (as shown in Figure 1).

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When selecting the methodology, decision-makers must take into account the following dimensions (Blanco et al. 2009): Depth: Choosing a depth of the system creates trade-offs between cost, completeness and complexity. As the system is expanded upstream or downstream in the supply chain (e.g. tier 1, 2 or 3 suppliers, as well as customer consumption), its scale can quickly expand. This expansion may greatly increase the effort and cost required to collect the measurements. Further, although the closest trading partners in the supply chain may be clearly visible, the level of visibility may diminish as the measurement system moves further up and down the supply chain. Such induced myopia may decrease the ability to gather the necessary measurements or trace the inputs any further. Breadth: A viable carbon labelling system requires that the GHG generated by each product-related process be measured. The appropriate units of measurement have to be determined. For example, which processes to include in the measurement phase is an obvious consideration. At the broadest level, this may encompass every associated activity, including business travel and even employee commuting. A narrower definition takes in only the materials and energy directly consumed, but even this can be complicated, since capital goods and indirect emissions are difficult to monitor and assign to specific products. These issues must be weighed against the completeness of the information. Precision: The precision dimension determines how the actual measurements should be made. This includes the level of data aggregation, how the measurements can be allocated to products, the appropriate use of data estimates, and how often the measurements must be made. Scope: the scope is defined as breadth + depth. There are three main scopes of reporting that entail different boundaries at which companies can measure their emissions: (1) Scope 1: represents emissions generated from sources that are owned or controlled by the company (e.g. combustion of fossil fuels in boilers, furnaces, vehicles) (2) Scope 2: includes indirect emissions from the electricity purchased from the grid. (3) Scope 3: consists of other indirect emissions from the business’s value chain, like production of purchased materials, waste disposal and employee travel. The methods developed for measuring emissions in the Kyoto Protocol have been adopted by other organisations for use in measuring the GHG emissions of corpo-

Preliminary / preparation work

Methodology selection

Define scope

Hands-on / field work

Reporting

Identification of sourceemissions

Reporting

Collection of data emissions

Identify of reduction opportunities

Measuring carbon footprint

Reporting, implementing

Figure 2. Steps for measuring and reporting CO2 footprint.

rations. The general framework proposed in this paper is inspired from the analysis of the different existing methodologies. The steps that have to be followed (e.g. defining scopes of the calculation, collecting data, identifying sources of emission, measuring carbon footprint, and identifying opportunities for reduction) are based on the GHG Protocol developed by the World Resources Institute (WRI), the WBCSD and the Corporate Accounting and Reporting Standard (see Figure 2). This framework provides guidance for companies to set organisational boundaries, identifying and calculating emissions, tracking emissions over time, and reporting emissions as well as constantly identifying and tracking opportunities for CO2 reduction. It is to note that framework in Figure 1 serves as the basis for carrying out these steps.

5. A real case study from the agro-industrial sector 5.1. Presentation of the case study The case study considered in this paper is taken from a real-life company (whose name is kept confidential) belonging to the agro-industrial sector. It has over 50 years of experience producing and distributing several types of products, including diet drinks, juice, sauces and cocktails. The company’s overall supply chain was considered for study, and boundary was selected in accordance with the framework proposed previously. Some subjective weights in accordance to the reliability of data sources were also included (see Subsection 5.4). These weights are estimated as: high quality for data obtained from the relevant party; low quality for significant level of data and/or emission estimates; and medium quality for partial data and estimation combined. This classification of data may sound too subjective but it is in fact intuitive. In addition, we considered managers’ expertise to obtain the most possible accurate classification of data to avoid excess of subjectivity. The period of March 2008–February 2009 was chosen as the base year for the analysis, coinciding with the ‘crop-year’ business calendar of the Company. Figure 3 shows the overall supply chain of the Company as well as the selected boundary for carbon footprint calculations.

Production Planning & Control The boundary considered for the calculation of carbon footprint in the global supply chain of the Company includes:


Electricity and natural gas usage at the facilities, including receiving stations, freezers, manufacturing plants, distribution centers, warehouses, domestic and international co-packers and offices. Product deliveries controlled, whether performed by the Company private fleet or dedicated third party carriers between facilities. Emissions from employee business travel due to air flights and rental car usage. Waste disposal at the facilities. Other fuel usage. Emissions that were to be included in the analysis, but for which data were not yet available were: Fugitive emissions from refrigerants at facilities Energy consumption by international co-packers. An estimate of these emissions was added to the overall footprint (see below). Some notes on the carbon footprint estimation are depicted next: Transportation emissions estimated by freight transport emission expressed on kilograms of CO2 per ton-mile, which assumes a non-dedicated fleet throughout the distribution network.

Figure 3. Carbon footprint boundary.

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Customer pickup data included in footprint, even though this is generally considered out of scope. Share of emissions in domestic co-packer emissions (e.g. electricity and gas) were allocated by manufacturing volume. As explained previously, the level of quality to all carbon footprint estimations as follows: High quality: if data were obtained from the relevant party, and proved to be consistent with overall business process. No system or physical audits were performed. Medium quality: partial data and estimation combined. Low quality: significant level of data and/or emission estimates. 5.2. Application of the framework and data sources In this paper, the calculations for this analysis were done in accordance with the specifications of the GHG Protocol published in 2003 by the WRI/WBCSD. Note that although a more recent version of the tools is available, analysis from our previous works (see Gutiérrez-Franco et al. 2011) has shown some inconsistencies in the available calculation tools. A number of tools have been published to allow companies to calculate their carbon footprint and were employed in this analysis. In our work, in total, 80 facilities were classified into eight categories as follows (numbers in parenthesis


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represents the number of facilities in each category): receiving stations, freezers, beverage plants, conversion plants, other manufacturing plants, offices, domestic copackers and warehouses and distribution centres. Emissions from purchased electricity were calculated using the tool for ‘Indirect CO2 emissions from purchased electricity’ of the GHG Protocol. This tool requires the electricity consumption in kilowatt-hours (kWh) and the location where the electricity was purchased. The location is then used to determine the right emissions factor, in CO2 per kWh. Data for electricity and gas consumption in beverage and conversion plants were obtained through Summit Energy (2010) database which keeps track of utility usage for the Company. To calculate carbon emissions, the tool ‘GHG Emissions from Fuel Use in Facilities’ of the GHG Protocol with default emission factors was used. Table 2 shows the amount of electricity and gas consumed at conversion and beverage plants. Whenever available, data for electricity and gas consumption was provided directly by the relevant party. Regression analysis was used to estimate missing information if possible; otherwise, the location was ignored for total footprint calculations if estimated contribution was less than 1%. Emissions from deliveries were calculated using freight transportation emission factors (expressed in kg CO2 per ton-mile) based methodologies specified in the GHG calculator ‘CO2 Emissions from Mobile Sources’ of the GHG Protocol combined with total miles and weight for all deliveries in the selected year. All deliveries by the Company’s private fleet and dedicated third party carriers were assumed to use diesel fuel. For reporting purposes, deliveries are classified as inbound (arriving to manufacturing plants), outbound (manufacturing plants to customers) and intraplant (between manufacturing plants). Deliveries from receiving stations to freezers were calculated using data provided from the relevant party. Some distances within the Company’s network were not readily available. We estimated the distance between freezers and manufacturing plants by dividing the country in five zones: northwest, southwest, southeast and two zones in north-east, with the assumption that each manufacturing plant is supplied from the nearest freezers. Since multiple data sources were combined to obtain the overall shipments in the distribution network, we constructed a ‘flow validation’ table to summarise total

volume from/to each facility type. Analysis of this table showed various gaps and inconsistencies on inbound and outbound flows. Thus, after discussions with Company’s representatives, supplemental data were provided to guarantee that an initial estimation of the transportation footprint could be constructed. Emissions for employee rental car were calculated using a fuel usage–based methodology. These cars are used for pickup people from airports: information on total days, number of cars rented and city where each person was picked up was provided. With this information, we estimated the approximate fuel usage using a national fuel efficiency average and the distance to facilities. Emissions from air travel were computed using the distance-based methodology from the tool ‘CO2 Emissions from Business Travel’ of the GHG Protocol. This protocol specifies an emissions factor in kilogram of CO2 per mile of air travel for flights of three different lengths: short (1600 km). Using travel records provided by the company, the number of flights of each length were calculated and the appropriate emissions factor applied to determine the total carbon emissions. Emissions from refrigerants were intended to be included in the analysis but were omitted due to a lack of data. 5.3. Results This section presents a summary of the calculation results. Because of confidentially reasons, some Share Carbon Emissions 200.000,00 150.000,00

Electricity Gas

100.000,00

Transportation Refrigerants

50.000,00

Other emmissions

0,00

Inbound Intraplan Outbound Others

(a) CO

2

emissions by type of activity

% Ton CO2 Emission 2% 14% Inbound to Plant & Trans. Plants, co-pac & Trans. Intraplant Outbound Plants & Trans. Others

27% 57%

Table 2. Amount of electrical and gas consumption in conversion and beverage plants.

Beverage plant Conversion plant

Usage (KWh)

Usage (Therm)

86,300,226 45,744,587

5282,856 5365,558

(b) Percentage of CO

2

emissions (tons)

Figure 4. Summary of CO2 emissions for inbound, intra-plant and outbound plants.


Production Planning & Control numerical results cannot be presented here, but an accurate analysis will instead be presented. Based on the defined boundary and the data collected so far, the total carbon footprint for the company is 267,957 metric tons. Electricity consumption in facilities, distribution centres and co-packers accounted for nearly 47% of total emissions, and the remaining emissions are due to product deliveries (27%), natural gas usage in facilities (25%) and other sources of emissions (1%). Figure 4(a) summarises emissions for inbound, interplant and outbound divided by electricity, gas, transportation, refrigerants and other emissions for each category. Absolute values for emissions for each category can be displayed, but cannot be presented here because of confidentiality. Instead, a summary of this amount of ton-CO2 emissions can be shown in percentage as in Figure 4(b) divided into inbound to plant, interplant and outbound from plant with the emissions for transportation in each category. As electricity represents the higher contribution to CO2 consumption, we took a close look at this issue. The percentage of energy consumption for each facility group is as follows: receiving stations (1.57%), beverage plants (39.28%), fruit processing plant (0.50%), conversion plant (27.77%), fruit manufacturing process (0.38%), freezer (21.97%), offices (1.70%), co-packers (1.90%) and distribution centres (4.93%). We also looked closer at the emissions from transportation. These emissions were broken down between inbound to plant, intraplant, outbound from plants and international transport. Calculations were

Figure 5. Carbon footprint for the global supply network.

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obtained for CO2 emitted by transportation in train, vehicles, watercraft and airplanes for the delivery of products between facilities. Emissions from outbound from plants represent 81% of the total, international transportation 8%, inbound to plants 9%, and intraplant 2%. 5.4. Scopes and uncertainty level As explained before in Section 4, the methodology employed in this paper is based on the GHG Protocol, which categorises direct and indirect emissions into three broad scopes. The Scope 1 corresponds to all direct operational GHG emissions; Scope 2 refers to indirect GHG emissions from consumption of purchased electricity, gas, heat or steam; and finally, Scope 3 concerns other indirect emissions, such as the extraction and production of purchased materials and fuels, transport-related activities in vehicles not owned or controlled by the reporting entity, waste disposal, etc. The relative percentage of emissions from Scope 2 was 66% of the total, while 34% corresponds to Scope 3 operations. Finally, as explained before, the basic GHG protocol does not include features to introduce uncertain data. One of the contributions of this paper is that the methodology proposed here is able to introduce uncertainty in data acquisition. The uncertainty level was divided into high, medium and low quality of data, according to the level of estimation and accurate data from the sources. This uncertainty is given by decision-makers or is

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6. Managerial implications and lessons learned 6.1. Benchmarking In addition to computing the carbon footprint for the case study supply chain, form the managerial perspective, a comparison with other companies of the same industrial sector was carried out. This type of comparisons are often difficult due to the limited amount of information available and differences in calculation methodologies employed by other beverage, food or agricultural companies concerning information regarding their carbon footprint. In fact, this phenomenon is very common in most of industrial sectors because there is no international agreement about which technique should be the best for computing carbon emissions in supply chains (Jensen 2012) Data presented in Figure 6 are based on information published by Carbon Disclosure Project (CDP) in the report 2008 Global 500. We can observe that the results obtained for the company under study here, the value of carbon footprint is the lowest of the set of companies studied in the report. Besides, a benchmark comparison between CO2 emissions for final product versus raw material could be of interest for managers (see Table 3), as well as could be a comparison between two different products versus the global Company’s amount of CO2 emissions (see Table 4). Benchmarking with Companies in CDP

000s metric tons


induced by the different sources of data needed to compute the carbon footprint. Emissions with low uncertainty measure represent 81% of the total and hence the estimation presented here is quite accurate. Emissions with high level of uncertainty are 3%, while those with medium level represents 16%. Figure 5 presents the final measure for each facility and deliveries in the supply chain carbon footprint. Letter E means the amount of CO2 emissions by electricity consumption, while letter G represents the amount of CO2 emissions by Natural Gas consumption. The square in each type of facility represents the total amount of CO2, and colors are used to identified the uncertainty (green is low, orange is medium and red is high).

2500 2000 1500 1000 500 0 This case study

C1

Scope 1

C2

C3

C4

Scope 2

Figure 6. Benchmarking with companies in CDP report 2008.

Table 3. Comparison of CO2 emissions of raw material versus final products. Type of

CO2 emissions

Raw material Final products

1.66 Ton CO2 per Ton product 0.29 Ton CO2 per Ton product

6.2. Lessons learned At the end of the research project, the company under study adopted the framework for measuring the environmental performance of the supply chain. For analysis purposes, it has been revealed that the carbon footprint measurement framework is technically very useful and that the different scopes are also relatively easy to identify, measure and report. Initially, the Company had minimal knowledge or measurement tools or methodologies of carbon footprint. Hence, the Company realises the urgent importance of both corporate-level carbon footprint (as presented in this paper) and product/supply chain-level carbon footprint measurements due to global regulatory and market pressures. The same conclusion has been obtained from other industrial sectors and is reported in literature (see, for example, Blanco and Craig 2009; Plassmann et al. 2010; Lee 2011; Dias and Arroja 2012). There is an old and well-known adage in the management literature stating that ‘no measurement leads to no improvement or innovation’. With the proposed framework and the implemented methodology, the company under study took a rigorous approach to identify and measure its carbon footprint. The main outcomes of the study include the following: A systematic approach, based on a general framework, for measuring carbon footprint A methodology company-focused corporate carbon footprint measurement A carbon footprint calculator, based on spreadsheets, according to the scope defined by decisionmakers. Besides the actual carbon footprint measurement, there is some interesting learning through the process: The objectiveness of the measuring process allows for proper and clear communication with customers regarding our product carbon footprint Enables new tools and processes to support the company’s current sustainability program Defines an objective baseline to measure in preparation of future government regulations Validates defined areas of opportunity where the company has direct control to reduce emissions. Another important implication of this research was the importance of well-defining an upper bound for networks with multiple agents. As explained by Quariguasi

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Table 4. Comparison of CO2 emissions for two different products versus Company’s. Life cycle carbon of company’s products

Company’s

gr CO2 per gr portion of dry product gr CO2 per gr portion of juice


Frota Neto et al. 2008, the importance of defining and efficient frontier for carbon footprint measurement is threefold: (1) Evaluation of the current situation in terms of the system’s efficiency relative to environmental impacts and costs. (2) Determination of the trade-offs between the resulting environmental impact and costs in a logistic network. (3) Evaluation of the necessity of policies and efficiency assessment of different legislations.

7. Concluding remarks and further research Carbon footprint measurement provide a good estimate of the total amount of GHGs emitted during the life cycle of goods and services, from the extraction of raw materials, production, transportation, storage and use to waste disposal (Plassmann et al. 2010). They are calculated by companies, governments and other stakeholders in order to understand the emissions of GHGs from consumer products (Bolwig and Gibbon 2009). As explained in detail by Plassmann et al.(2010), companies can use the results of a carbon footprint to achieve emissions reductions throughout their own operations, to influence different elements of their supply chains, and/or to communicate their carbon footprint to their customers, sometimes via carbon labels on products (Bolwig and Gibbon 2009; Sinden 2009). In this paper, we have undertaken an inductive case study research approach to build a conceptual framework and to show how it can be used to calculate GHG emissions in supply chain management at corporate level. Although the framework was built based on a particular case from the agro-industrial sector, it can be generally applied for every organisation interested in assessing its particular situation regarding carbon footprint calculation. This paper hence contributed to the management of supply chains under sustainability constraints. As presented previously in this paper, in the post Kyoto Protocol, flexibility and cost effectiveness are very important with respect to carbon mitigation efforts (Lee 2011). Reducing carbon emissions for the entire supply chain (Scope 3) may be more cost-effective for companies than reducing direct or purchased electricity-related emissions (Scope 1 and 2). In the current climate risk constrained world, enterprises need a clear picture of

1.97 1.51

Product 1

Product 2

2.14 1.20

traceable direct and indirect carbon emissions in order to make strategic decision regarding (green) supply chain management. Although this paper addressed an inductive case study as a useful approach to explore this ‘new topic’ and to contribute to the management of sustainable supply chains by describing the steps of carbon emissions identification and measurement, some lines for future research are still open. Firstly, because different methodologies exist for carbon footprint measurement, it is necessary to define internationally agreed rules in order to allow robust comparisons between different products or similar products with distinct origins or manufacturing technologies. The type of GHG included, the boundaries of the system, the quality of input data, among other aspects, should therefore be similar of even the same when determining the carbon footprint. These rules have to be technically useful to identify, measure and report. In general terms, with complicated methodologies it would not be realistic for companies to adopt footprint measurement practices. Another line for research is the development of accurate methodologies to treat with the lack of data from outsourced logistics activities. This is one of the challenges faced by corporations when measuring the carbon footprint of logistics operations. The most common approach to overcome this information gap is to use both a simplified representation of the logistics network and average emission factors of outsourced operations to develop an initial baseline, or initial screening (Blanco and Craig 2009). The final measure of carbon footprint can be affected by the approach employed to obtain or to estimate this information. Further research is urgently required to overcome this, possibly using a quantitative research methodology. Acknowledgements The work presented in this paper was carried out while first and second authors were visiting scholars within the MIT Center for Transportation and Logistics, Cambridge, USA, supported by research funds from Universidad de La Sabana, Colombia. Part of the work of Jairo R. Montoya-Torres was also supported by a Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme (project ‘DISRUPT’, Grant No. ESR-299255). We express our deepest acknowledgements to the anonymous reviewers and the Editor for their comments that allowed us to improve this manuscript.


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Notes on contributors

References

Jairo R. Montoya-Torres is Full Professor in the School of Economics and Management Sciences at Universidad de La Sabana, Chia, Colombia. He is currently Visiting Scholar at the Leeds University Business School at University of Leeds, UK, under a Marie Curie International Incoming Fellowship. He obtained the Postdoctoral Degree for Research Direction (Dr.-Hab.) from Institut National des Sciences Appliquees (INSA) de Lyon and Universite Claude Bernard, Lyon France in 2011, and a doctoral degree from Ecole National Superiere des Mines de Saint-Etienne and Universite Jean Monnet, SaintEtienne, France, in 2005. His research interests lie broadly in supply chain management under collaborative and sustainable environments, simulation and optimisation of logistics and production systems, production scheduling and vehicle routing. He has been a member of several academic societies, including ACM / SIGSIM and EUROSIS, and has served as Guest Editor or within the Editorial Board of various international academic journals. He can be contacted by email at [email protected]. His personal web page is http://jrmontoya.word press.com.

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Edgar Gutierrez-Franco obtained an Industrial Engineer degree from Universidad de La Sabana, Chia, Colombia and a MSc in Industrial Engineering (Operations Research and Statistics stream) from Universidad de los Andes, Bogotá, Colombia. He has experience related with supply chain management, logistics, project management, network optimisation, demand planning, sales and operations planning, and trade marketing plans. His research interests include the analysis and improvement of manufacturing processes, including the application of statistical methods, decision making models, software solutions, and Operations Research techniques Edgar E. Blanco is a Principal Research Director at the MIT Center for Transportation and Logistics and is the Executive Director of the MIT SCALE Network in Latin America. His current research focus is the design of environmentally efficient supply chains. He also leads research initiatives on supply chain innovations in emerging markets, disruptive mobile technologies in value chains and optimisation of humanitarian operations. Dr Blanco has over thirteen years of experience in designing and improving logistics and supply chain systems, including the application of operations research techniques, statistical methods, GIS technologies and software solutions to deliver significant savings in business operations. Prior to joining MIT, he was leading the Inventory Optimisation practice at Retek (now Oracle Retail). He received his PhD from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. His educational background includes a BS and MS in Industrial Engineering from Universidad de los Andes (Bogotá, Colombia) and a MS in Operations Research from the Georgia Institute of Technology.


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