Supervisor: Michael Browne Master Degree Project No. 2016:74 Graduate School

Master Degree Project in Logistics and Transport Management

Emission factors and its influence on CO2 calculation A case study of Volvo Group

Josef Larsson and Natalie Goldschmidt

Abstract Carbon dioxide emissions from freight transport are a great contribution to global warming. The increased awareness of its harmful impact leads to an increase of activities among companies. One part of it is the reporting of CO2 emission values. Several guidelines have be development by organisations on how to approach the reporting and the carbon footprint. However, no literature could be detected what challenges companies face when they try set an emission factor for such calculation and how it influences the accuracy and uncertainty of the output. This research tries to address these issues for road emission factors for international operating companies by conducting a case study of Volvo Group’s current calculation setup. Moreover, calculation was conducted in order to evaluate the potential influence of the emission factor on the calculated CO2 output. In addition, a company comparison with SKF Group and Tetra Pak was held to enhance understanding of challenges and ways of determining emission factors. During the research several challenges related to the CO2 calculation and its emission factor determination were detected. The findings emphasize on the need of companies to set their road emission factor with great care, since it has a direct impact on the calculation output, not only in numerical terms, but also on the accuracy and uncertainty of the value. In order to provide guidance to companies how to choose an emission factor, which adds accuracy without increasing the uncertainty three questions are suggested based on the findings of the research: What is the driver for the calculation? What quality of data is available? Which level of accuracy and certainty in regards of emission factor is wanted and how much effort is prepared to put into the task? Depending on the self-evaluation of each company a one-fits-all emission factor, a region or specific emission factor is suggested. Keywords: Emission factor, road emission factor, carbon footprint calculation, challenges in measuring CO2, accuracy and uncertainty in CO2 calculation

Acknowledgement To start with, the authors would like to express their gratefulness to AB Volvo, and more specifically Volvo Group Logistic Services and its employees for making this thesis possible by giving us the opportunity to conduct a case study at their premises. We would like to thank everyone for the patience and helpfulness by answering all our questions and providing us with all necessary material. A special thanks goes to our external supervisor Susanna Hambeson for continuous support and guidance along the project. Furthermore the authors would like to thank the companies participating and cooperating in the benchmarking, Zaher Ashiq at SKF Group and Per Nilsson at Tetra Pak. The information provided has been very helpful and a large asset in the research process. Lastly, the authors want to thank our supervisor Michael Browne at the School of Business, Economics and Law at the University of Gothenburg. Michael´s patiently advices and feedback together with his expertise within this field of research has been essential for the final outcome of this thesis. Gothenburg, June 2, 2016 _____________________ _____________________ Josef Larsson Natalie Goldschmidt

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Table of content List of figures ................................................................................................................. iii  List of tables ................................................................................................................... iv  List of abbreviations ....................................................................................................... v   1   Introduction ............................................................................................................... 1  

1.1   Volvo Group Logistics Services ....................................................................... 2  1.1.1   Transport modes, regions and business processes ................................ 3  1.1.2   Environmental commitment ....................................................................... 4  

1.2   Research question and aim ............................................................................... 4  1.3   Delimitation ......................................................................................................... 5  

2   Methodology .............................................................................................................. 7  2.1   Research process .............................................................................................. 7  2.2   Paradigm ............................................................................................................. 8  2.3   Primary and secondary data collection ........................................................... 8  2.4   Data collection .................................................................................................... 8  

2.4.1   Case study .................................................................................................... 9  2.4.2   Interviews ..................................................................................................... 9  2.4.3   Internal and organisational documents ................................................... 10  

2.5   Searching and reviewing the literature .......................................................... 10  2.6   Data analysis .................................................................................................... 12  2.7   Reliability, validity and generalizability .......................................................... 14  

3   Literature review and theoretical framework ....................................................... 15  3.1   Drivers for emission reporting ........................................................................ 15  3.2   Reporting standards ........................................................................................ 18  

3.2.1   The Greenhouse Gas Protocol ................................................................. 18  3.2.2   Scopes of the Greenhouse Gas Protocol ................................................ 19  3.2.3   Identify and calculate emission ................................................................ 21  3.2.4   Targets related to emissions .................................................................... 23  

3.3   Emission factors .............................................................................................. 24  3.3.1   Vehicle ........................................................................................................ 26  3.3.2   Environmental ............................................................................................ 27  3.3.3   Traffic .......................................................................................................... 27  3.3.4   Driver .......................................................................................................... 27  3.3.5   Operations .................................................................................................. 27  3.3.6   Overview ..................................................................................................... 28  

3.4   Carbon footprint calculation tools .................................................................. 29  3.5   Challenges and risks ....................................................................................... 29  

4   Case Study AB Volvo ............................................................................................. 33  4.1   Drivers of AB Volvo .......................................................................................... 33  4.2   CO2 footprint calculation ................................................................................ 34  

4.2.1   Input ............................................................................................................ 35  4.2.2   Calculation ................................................................................................. 36  4.2.3   Reported result .......................................................................................... 38  4.2.4   Scopes and boundaries ............................................................................ 38  

4.3   Challenges ........................................................................................................ 38  

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5   Road emission factor calculation of AB Volvo .................................................... 41  5.1   NTM-based default emission factor ................................................................ 41  

5.1.1   Input ............................................................................................................ 41  5.1.2   Assumptions .............................................................................................. 43  5.1.3   Calculation ................................................................................................. 43  5.1.4   Output ......................................................................................................... 44  

5.2   Carrier specific emission factor ...................................................................... 44  5.2.1   Input ............................................................................................................ 44  5.2.2   Assumptions .............................................................................................. 45  5.2.3   Calculation ................................................................................................. 46  5.2.4   Output ......................................................................................................... 48  

6   Findings and analysis of VGLS CO2 emission calculation ................................ 49  6.1   CO2 emission calculation ................................................................................ 49  6.2   How are the emission factors in the road emission calculation used? ...... 51  6.3   What variables influence the emission factors? ........................................... 52  6.4   Accuracy and uncertainty of the input ........................................................... 56  

7   Comparison of Tetra Pak and SKF ........................................................................ 59  7.1   Tetra Pak ........................................................................................................... 59  7.2   SKF .................................................................................................................... 60  

8   Discussion ............................................................................................................... 63  8.1   Drivers ............................................................................................................... 64  8.2   Methodologies .................................................................................................. 65  8.3   Challenges ........................................................................................................ 66  

9   Conclusion .............................................................................................................. 69  9.1   Theoretical contributions ................................................................................ 69  9.2   Practical contributions .................................................................................... 70  9.3   Further research ............................................................................................... 71  

Table of references ....................................................................................................... 73  Appendixes ................................................................................................................... 81  

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List of figures Figure 1: The geographical locations of VGLS ................................................................. 2  Figure 2: Transport processes of VGLS ........................................................................... 3  Figure 3: Examples of Volvo packaging ........................................................................... 3  Figure 4: Research process of the report. ........................................................................ 7  Figure 5: Illustration of literature review process ............................................................ 12  Figure 6: Illustration of framework used for data analysis. ............................................. 13  Figure 7: Companies motivations to measure and report climate change related

information. .............................................................................................................. 16  Figure 8: Composition of supply chain mitigation opportunities ...................................... 17  Figure 9: Fuel life cycle analysis ..................................................................................... 19  Figure 10: An illustration of the three scopes, provided to define direct and indirect

emissions. ................................................................................................................ 20  Figure 11: Five steps of how to calculate GHG emissions ............................................. 21  Figure 12: A description of the difference between centralized and decentralized

approach of gathering GHG-emission data ............................................................. 23  Figure 13: Hypothetical relationship between model accuracy, input accuracy and level

modelling detail ........................................................................................................ 25  Figure 14: Visualisation of the interrelation between various variables .......................... 26  Figure 15: Possible influences on road EFs ................................................................... 28  Figure 16: Overview CO2 emission calculation VGLS. .................................................. 34  Figure 17: An overview of transport statistic sources for regions ................................... 35  Figure 18: Map of VGLS’s road CO2 emission calculation ............................................. 37  Figure 19: Transport modes of distribute products. ........................................................ 39  Figure 20: Changes of CO2 footprint with varying EFs in percent for year 2016 ........... 49  Figure 21: Analysis usage of default EF in calculation in percent for year 2016. ........... 51  

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List of tables Table 1: List of interviewees. .......................................................................................... 10  Table 2: Keywords and search terms used .................................................................... 11  Table 3: Vehicle group .................................................................................................... 42  Table 4: Fuel Consumption [l/km] of VGLS class 4, per 0% and 100% filling degree .... 42  Table 5: The output of the NTM-based default EF (40t) ................................................. 44  Table 6: Example of result of carrier survey ................................................................... 45  Table 7: Default values for carrier specific emission factor calculation. ......................... 45  Table 8: Fuel consumption (l/km) depending on loading factor, vehicle classification and

Euro class. ............................................................................................................... 47  Table 9: NTM-based default emission factors in g/tonkm with different max. loading

capacities ................................................................................................................. 52  Table 10: Differences of input values into EF calculation. .............................................. 55  Table 11: Calculated default EFs in g/tonkm for formula (4) and (5) based on region. .. 56  Table 12: Comparison of AB Volvo, Tetra Pak and SKF ............................................... 60

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List of abbreviations Abbreviation Full name AB Volvo Volvo Group CDP Carbon Disclosure Project CSR Corporate social responsibility EF Emission factor GHG Protocol Greenhouse Gas Protocol ISO International Standard Organisations MNC Multinational companies NTM Network for transport measures QV Qlikview TRL Transport Research Laboratory TTW Tank to Wheel UNEP United Nations Environmental Program VGLS Volvo Group Logistics Services WTW Well to Wheel

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1 Introduction Freight transport is a big contributor to CO2 emission, with a growing trend for the future. Around 15% of the world's greenhouse gas emissions occur from freight transportation, whereby CO2 is a major contributor with 23%. From 1990 until 2007 carbon dioxide (CO2) emissions from the freight sector has increased by 45% and is expected to grow by another 40% from 2007 to 2030 (ITF 2010). Research shows a connection between the increasing CO2 levels in environment and the rise of temperature and water levels. The influence of human behaviour is evident and results in severe impacts on the ecosystem, humans and animals life. Droughts, floods, food shortage and extinctions of species are some of the discussed effects (WWF 2016). Increased awareness of the harmful impact and governmental initiatives to cut emissions leads to increase of activities among companies. Many companies realize the growing importance and report beside financial matters in bigger shares also about sustainability or corporate responsibility nowadays (Ditlev-Simonsen 2010). Thereby many companies have adopted the triple bottom line in order to evaluate their performance, not only in regards of financial, but also social and ecological matters (Slaper and Hall 2011). A big stake of the ecological performance is the accounting of emissions. Different initiatives and tools on how to report have been developed over the years. Even though well structured guidelines exist, one challenge for companies is to create a calculation with high accuracy together with low uncertainty. A part of the accuracy and certainty of the output is linked to emission factors. Emission factors (EF) are used to link an activity, such as transporting freight one kilometre far, to the average emitted emission from it. Picking the right EFs are considered as one of the most challenging tasks when calculating emissions (McKinnon and Piecyk 2010). Literature discusses how to calculate and/or generate EFs, but on a practical level how to apply them in real world scenario is not widely explained. (TRL 1999; GHG Protocol 2012; McKinnon and Piecyk 2010; Williams et al. 2012; Odette 2013; Demir et al. 2014). Thereby it can be discussed if the EF´s provided by an organization, like Network for transport measures (NTM), are comparable to the EFs collected from the actual transport carriers and if those differ in accuracy. In order to evaluate the influence of the decision of the EFs on the CO2 emission a case study is conducted at Volvo Group Logistic Services (VGLS). Thereby the question of accuracy1 in regards of usage of different EFs is conducted. The current practice is that VGLS mixes own collected EF with and external EFs based on NTM values and own assumptions. These EFs and the used combination of them are analysed to determine if they are affecting the accuracy of the output. The thesis will continue with a short background of Volvo Group Logistic Services from where the research questions derive. This is followed by a presentation on the methodology of how the research questions will be addressed. Next, the literature review and theoretical framework will present relevant findings from previous studies. 1Accuracy is defined as “the condition or quality of being true, correct, or exact; freedom from error or defect; precision or exactness; correctness” (Dictionary.com 2016).

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Afterwards the case of the VGLS´s CO2 calculation is conducted, followed by an a detailed description of the EF calculation. Subsequently an analysis of such comes. Moreover, two more companies’ way of calculating and setting EFs is presented, which is succeeded by a discussion of the findings. Last, a conclusion of the research is provided.

1.1 Volvo Group Logistics Services The Volvo Group (AB Volvo) is an international operating manufacturing company for commercial vehicles which origins and headquarters lies in Gothenburg, Sweden (for more information see appendix 1). Volvo Group Logistic Services (VGLS) is part of the Group Trucks Operations, which belongs to Volvo AB. VGLS is responsible for the logistic of packaging, inbound, outbound and distribution of the products and parts of AB Volvo (AB Volvo 2016b). Approximately 5000 employees in 55 locations, “design, handle and optimize the supply chain” as shown in figure 1 below (AB Volvo 2016c).

Figure 1: The geographical locations of VGLS. Source: VGLS (2016b).

VGLS strives to reduce costs together with disruption and decrease of environmental impact. Its responsibilities are considered to ensure the supply of products, design and provide packaging with a maximal utilization and least environmental impact to distribute the products to the dealers. Furthermore, assurance of high availability of aftermarket parts and customs handlings is offered by VGLS. In addition, close co-operations should be present to assure efficient work between AB Volvo’s units. Last, risk management from the logistics perspective for AB Volvo is part of VGLS´s responsibilities (AB Volvo 2016b).

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1.1.1 Transport modes, regions and business processes VGLS provides transport solutions using all four types of transport modes: air, rail, sea and road. In order to perform the transportation VGLS outsources all physical transport activities to service providers. Therefore VGLS does not own any assets, such as trucks or trains by itself (AB Volvo 2016b). VGLS covers all three AB Volvo’s business regions named: EMEA, APAC and Americas. EMEA stands for Europe, Middle East and Africa, APAC the Asian-Pacific region and Americas includes North and South America (VGLS 2016c). When dealing with logistics, VGLS identifies four different business processes, presented in figure 2.

Figure 2: Transport processes of VGLS. Source: VGLS (2016c).

● Transport material: the inbound logistics from material suppliers to the plants and

to the aftermarket distribution centres. ● Distribute products: the delivery of the finished products from plants to dealers. ● Refill and distribute parts: covers the aftermarket logistics, for example delivery of

spare parts to dealers and workshops. ● Transport packaging: AB Volvo uses own packaging, which needs to be moved

between the plants, warehouses and material suppliers, as seen in figure 3.

Figure 3: Examples of Volvo packaging. Source: Volvo Group (2016).

For the processes different IT systems are used. For example distribute products uses a program called A4D, which covers the global distribution of products. Therefore required information can be retrieved from a single source. However, the other three processes

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and regions use various programs, resulting in no single access point for transport statistics information (VGLS 2016c).

1.1.2 Environmental commitment AB Volvo assigns high value to environmental matters (AB Volvo 2016a). Thereby AB Volvo is the only automotive company, which joined the Climate Savers commitment from WWF until today. Besides the reduction in CO2 emission in production and in sold products, logistics also plays a major role in the plan. This includes a reduction of CO2 freight transport related emission by 20% from the baseline in 2013 until 2020 (AB Volvo 2016d). VGLS aims to reduce the transportation need, use as CO2 efficient transport modes as possible for every flow, improve the filling degree in vehicles and containers, utilize smarter logistics planning, increase the load capacity by using larger, longer and wider vehicles where it is possible and encourage the usage of more efficient transport fuels and a fuel efficient driving behaviour (VGLS 2016d). In order to achieve the goals and follow up on possible changes through the activities, CO2 emission needs to be measured and documented. The rise of awareness and commitment comes along with a promotion of freight related CO2 emission from a yearly reported figure to a monthly key performance indicator (KPI) reported to top management (VGLS 2016d).

1.2 Research question and aim For an international company such as AB Volvo, calculating the CO2 footprint can be particularly challenging. Many processes, IT programs and businesses in various countries covering all transport modes create complex supply chains. At the same time, various factors need to be considered and accuracy of the calculation is a concern. This thesis will analyse how international companies proceed with the challenges of determine the emission factor and the issues related to it. Based on the introduction the following research questions arose:

RQ1 What are the challenges international companies faces when calculating CO2 emissions with a special focus on adopting road emission factor?

RQ2 How does the emission factor influence the result as well as the uncertainty and accuracy of the carbon footprint of road freight movement?

RQ3 What issues should international companies consider when determining the road emission factor?

In the case of AB Volvo, the thesis aims to evaluate the impact of road EFs and provide recommendations in order to reduce the uncertainties and increase the accuracy of the current methodology. The authors will conduct a comparison among companies to reflect on the methods found in the case of AB Volvo, in order to give suggestions of what factors companies should consider when choosing road emission factors. Thereby the thesis aims to fill the recognized gap in literature and make the findings relevant for other companies.

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1.3 Delimitation If not stated otherwise, the description and discussion in the thesis focuses on the main transport mode road. Thereby CO2 emissions are the main focus and it should be noted that if the general term ‘emission’ is used it refers to CO2 emission. In addition, greenhouse gas emission (GHG)2 includes carbon dioxide and hence is used equivalently. Moreover, the analysis and discussion will focus on the EF and its influence on AB Volvo´s calculation. This does not question if the underlying calculation method of the CO2 footprint is correct or not. For an in depth discussion of other alternative ways to measure an organisation's environmental performance, please see the previous master thesis3. In addition, the reason for only considering default EF based on NTM values and no other organisation or model is because VGLS uses this methodology today together with the fact that NTM, according to research, is considered as well recognized tool to measure environmental performance. In the chapters of theoretical framework the Greenhouse Gas Protocol (GHG-protocol) will be discussed. The reason to focus on this scheme is that GHG-Protocol is a well established and commonly accepted methodology used when dealing with GHG emissions. The GHG-protocol is a framework among several other methodologies, such as the ISO 14064. However, most of the schemes are founded upon the GHG-protocol. A detailed discussion and comparison to other organisations and schemes would not add on to answer the research question.

2GHG are namely: Carbondioxide (CO2), Methane (CH4), Nitrousoxide (N2O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs) and Sulphurhexafluoride (SF6) (GHG Protocol 2012). 3“A method for calculating the carbon footprint at Volvo Logistics Corporation” by Sofie Strömberg Jonzon and André Trönnberg Lundin (2012).

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2 Methodology The following chapter describes the methodologies used in the conducted research. It starts with an overview of the research process, followed by the used paradigm. Next, what data and how it will be collected and analysed is described. Last, a discussion in regards of reliability, validity and generalizability of this thesis is done.

2.1 Research process This section will present the methodology of the research process of the conducted study. To get an early overview of the project, a process map is designed by the authors. The process map, figure 4, is divided into three sections depending on the three earlier presented research questions. Each step includes what data is needed to answer the research question, what methodology to use, how the data should be analysed together with the expected result. How the data is collected and analysed is described in detail in chapter 2.4 and 2.6.

Figure 4: Research process of the report. Source: Own creation.

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2.2 Paradigm When designing research projects, philosophical assumptions need to be considered. Depending on what assumptions are taken this may change the progress of the research. The assumptions of the two main paradigms are positivism and interpretivism. Even though they use different approaches they still share similarities such as using a research question to drive research, use various methods to collect and summarize data as well as draw conclusions (Collis and Hussey 2014). In the following thesis, the authors have decided to rely upon the positivism approach. The positivism tends to use larger samples, produce precise and objective data and allow results to be generalised from a sample to a population. This is considered as a scientific approach using methods that are measurable, organized and involves objective evidence (Collis and Hussey 2014).

2.3 Primary and secondary data collection Primary data is data that origins from the original source, such as interviews and experiments conducted by the authors themselves. Secondary data on the other hand is data that are collected from already existing sources such as databases, publications and internal registers (Collis and Hussey 2014). One drawback of using secondary data is that one cannot be sure for what purpose the data were collected from in the beginning. According to Saunders et al. (2009), this leads to a lack of information or control about the quality of the collected data. The report will contain both primary and secondary data. To increase the reliability of collected data, main parts of theory are to be collected from scientific articles. This can be considered as more reliable sources, than newspaper articles, as data has been peer-reviewed and thereby monitored by experts in relevant fields (Saunders et al. 2009). In the conducted research a combination of primary and secondary data will be used. Primary data is interviews and own conducted calculations. Secondary data will consist of scientific articles, textbooks and information gathered internally within VGLS, such presentation and previous calculations and surveys.

2.4 Data collection Distinctions between studies can be made in terms of quantitative and qualitative data. Quantitative data is often precise, can be measurable and used to uncover patterns and formulate facts. Qualitative data on the other hand are more used when there is a need for a greater understanding of underlying reasons and motivations and helps to develop ideas for possible future quantitative research (Collis and Hussey 2014). In a positivist study the purpose is to ensure that all main variables are identified and it is essential that the collected data is precise and specific (Collis and Hussey 2014). Thereby to be able to answer the research questions of the challenges connected to CO2 calculation together with recognizing the possible influence of the EF, a blend of quantitative and qualitative data will be collected and processed. The quantitative data will consist of calculations and EF comparison, and the qualitative data will derive from

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academic literature, internal documents and interviews. The qualitative and quantitative data will consist of primary data as well as secondary data.

2.4.1 Case study The thesis is conducted as a case study of VGLS. Case study is a methodology used to investigate a single phenomenon within its natural environment. Thereby different methodologies can be used in order to examine such (Collis and Hussey 2014). Within the case study the authors use descriptive, experimental and exploratory case study in order to answer the research question. Descriptive case studies are used in order to describe a current situation, experimental case studies focus on challenges of a process and discuss advantages of it. Explanatory case studies combine research knowledge with the present situation in order to recognize and explain patterns (Ryan et al. 2002). In the case of AB Volvo the CO2 calculation is described in detail as well as the EF determination. Moreover, different EFs are used to explore and overcome the challenge of determining an EF. Flyvbjerg (2006) argues that case studies are an underestimated method in scientific research and cannot only be used to create a hypothesis, but also testing it. Thereby the selection of the right cases help to create a generalizable result (Flyvbjerg 2006). As the gap in literature has shown, this thesis will provide valuable insights into the challenges companies face when calculating their CO2 footprint and the usage of EFs. By performing a case study, it will be possible to discover contextual challenges within a company. AB Volvo is a company with high efforts towards environmental care as shown through the WWF engagement. Through selective comparison a more general conclusion will be achievable, which will provide the possibility to generalize the findings.

2.4.2 Interviews Interviews are used in order to collect primary data whereby interviewees are asked questions in order to learn more about opinions, actions or feelings (Collis and Hussey 2014). For this research, unstructured and semi-structured interviews with open questions will be used. These methods have the advantage to discover several dimensions of a topic as well as the possibility to create an in-depth discussion (Collis and Hussey 2014). In the beginning unstructured interviews will be used to discover relevant information and to let VGLS explain their practices and the processes. Later semi-structured interviews will be used to emphasize on certain areas. For the company comparison, which is conducted through qualitative data collection, semi structured interviews will be used. This is done in order to compare the companies with each other on how they calculate the CO2 emission and determine the EF, but also to give space to elaborate further on possible variations and challenges of the used methodology. Interviews are a good method to gather detailed information but potential bias can occur, which influences validity and reliability. It could be that the interviewee has more than one role and thereby has incentives to not always say the truth or reveal all relevant information. It is also possible that the interviewee wants to meet certain expectations or give the ‘correct’ answer (Collis and Hussey 2014).

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Interview bias will be tried to avoid as much as possible by asking neutral questions and interview people from different backgrounds, such as employees of companies. The questionnaire templates used for the interviews can be seen in appendix 2 and 3. Interviews will be done by internet-call or face-to-face and recorded in addition to the taken notes. Below follows table 1 with the list of interviewees.

Table 1: List of interviewees. Source: Own creation.

2.4.3 Internal and organisational documents To get an insight and understanding of the on-going work of CO2 calculations of AB Volvo, the authors will use internal documentation as a source of data. Bryman and Bell (2011) describes several kinds of internal and organisational documents, such as annual reports, mission statements, internal and external correspondence and manuals. The internal documents used within this report will consist of emission data collected from units and regions, material used for public presentations, internal documentation of processes and manuals. This internal documentation, together with other sources of data, will provide information about the current methodology that is used by VGLS. In the process of examining these documents, the authors need to take into consideration the trustworthiness of the information. Bryman and Bell (2011) state that organizational documents can be inaccurate due to that various actors perceive the situations differently. At VGLS a lot of documentation has been conducted with many different sources. In these various sources some information can be contradicting. This can be an issue to know what information to use and why. To overcome this problem the authors continuously validate the collected information by cross-referencing the documents. By doing this the authors will achieve accurate data throughout the report also for internal documents.

2.5 Searching and reviewing the literature Collis and Hussey (2014) describe a literature search as a systematic procedure with a goal of locating a clear body of knowledge within a certain area or topic. The purpose of the literature search is to locate and gather appropriate literature to read and analyse. By doing this, the authors are given the opportunity to locate possible gaps of literature within the actual topic of this research. The literature is collected from sources of secondary data. This data derives from academic journal databases, reports and papers as well as commercial and governmental created industry data and statistics. The first step of the literature search is to define the scope and in what context to look for

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information. This step can be considered partly as a limitation of the future conducted research (Collis and Hussey 2014). The defined scope included time, geography and industry. For example when looking into the time perspective it was prioritized to look into the most recent sources since the CO2 calculations, equally to technology, is a topic of fast changes. Furthermore, looking into CO2 calculations methodologies origin from different regions in combination with different industries would possibly increase the generalizability of the research outcome. Articles that by the authors were considered as too old or not fit into the research topic were excluded from the literature review. The authors carefully chose the keywords used in the search. The aims of the keywords were to reflect the purpose of the report and to be associated with the research topic. The keywords and its connected search term are presented in table 2 below:

Table 2: Keywords and search terms used. Source: Own creation.

Once all relevant literature is collected the literature review will be held. This is described as critical assessment of already existing knowledge within a certain field of study (Collis and Hussey 2014). The literature review conducted by the authors will aim to collect and present the most appropriate theories within the topic of choice, and in sections present the core content showing possible trends or themes. The process of the literature review is presented in figure 5.

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Figure 5: Illustration of literature review process. Source: Adapted from Collis and Hussey (2014).

2.6 Data analysis The following section will present the process of analysing gathered data. The collection and the analysis of data, qualitative and quantitative, will be performed in parallel continuously during the research. According to Bryman and Bell (2011) this is a common approach to use when analysing data. Analysing large volumes of data is a challenging task. One of several challenges is that there are no universally set of methodologies to analyse collected data (Collis and Hussey 2014). One method that is widely used when analysing qualitative data is the method “General analytical procedure”. This procedure is useful since it is not connected to a specific data collection method. The general analytical procedure includes three flows of activities, which are conducted simultaneous:

● Reduce data ● Display data ● Drawing conclusions and verify the validity of conclusions

Quantitative data is data that is presented in numerical form, which can be put into rank order or into categories (Collis and Hussey 2014). This data can thereby be used to build graphs or presented in tables. The quantitative data used in this research will derive from already existing data, such as emission statistics originating from different regions and processes. For this research, the objective of the quantitative data collection is to visualise current status and envision future development as a result of possible changes of the calculation methodology.

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The authors will analyse the quantitative data by creating statistics, which is define as numbers describing a sample, and from the presented result draw conclusions. Statistics will be used to explain for example averages and means of samples, which will help the authors to define a population and estimate unknown variables (Collis and Hussey 2014). Together with the above presented analytical procedure, the authors have formulated a framework used for the data analysis, see figure 6. This framework is based on the research question and keywords of the literature search. The presented model will be used as a red thread throughout the report, represented in the literature review, the case study, as well as in the comparison with other companies and finally in the discussion chapter.

Figure 6: Illustration of framework used for data analysis. Source: Own creation.

Furthermore, triangulation is used in the thesis. This is part of research when more than one method, multiple sources of data and more than one researcher is involved in the research (Collis and Hussey 2014). Advantages of this methodology are the reduction of possible bias in the sources of data and methods (Jick 1979). Bias could be a problem with this thesis, because a case study is performed and working within the analysed company could influence researches. Therefore, for this case a qualitative, as well as quantitative approach will be conducted to evaluate the research questions. Interviews of different people in various functions and positions within and outside of AB Volvo will be accomplished, as well as examination of internal data and documents of VGLS and a literature review will be done. In addition, to be able to assess the sensitivity of the EF, calculations in VGLS’s CO2 footprint calculation tool is conducted, comprising different combinations of EFs. Moreover, company comparisons will be used to acquire further knowledge of common challenges and business procedures in regards the usage of EF and confirm/questions the findings gathered within AB Volvo.

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2.7 Reliability, validity and generalizability Reliability is a term of repeatability and refers to if the data collection and analysis procedures are generating consistent findings, thereby the same result from repeated observations by other researchers will be achieved (Saunders et al. 2009). Threats connected to reliability in qualitative study, are according to Saunders et al. (2009) related to both observer or participant error and bias. In the conducted research the risks of observer error and bias are avoided by following structured templates and performing interviews with both authors together and afterwards discuss the outcome to make sure all data is interpreted equally. In regards of the interpretations of the written reports and journals, still there is a risk that it might be perceived differently. To avoid misinterpretations the authors continuously discuss findings and results of ongoing research as well as apply triangulation, which according to Saunders et al. (2009) is a way to increase the reliability of a research. Furthermore the authors will describe the own conducted calculations, together with the formulas and the tool used. Moreover, all used EFs are attached in the appendix. In this way the authors ensure high repeatability. Collis and Hussey (2014) describe validity as whether a test is measuring what the researchers want to measure. Reasons that can undermine the validity are research errors such as poor samples, misleading or inaccurate measurements. To increase the validity and make sure that what is measured actually captures what it is suppose to measure, the authors will set clear and defined objectives and continuously follow up targets. For example, predefined keywords will be used to ensure that the data is collected and analysed in connection to the research questions. Furthermore, by comparing conducted measures of EF calculations with other companies and calculating the EF with data from different sources, this will increase the validity of the report. Thereby the focus will be on calculating the EF and the CO2, which are in line with the topic that the authors want to investigate to answer the research questions. Furthermore actions to guarantee the validity of the interviews is to follow pre structured templates that will ensure that the desired questions are asked to the right persons. Generalizability is to what extent the findings of a research can be extended to other populations or settings (Collis and Hussey 2014). This thesis conducts the research following the positivistic approach, and thereby a sample has been chosen and it is of interest of how this sample would be generalizable to further populations. Research argues for that it is possible to generalize from a small population or even a single case. But for this to apply the analysis must capture the main characteristics and interactions of the studied phenomena (Collis and Hussey 2014). By conducting a case study in combination with a comparison among other companies from different industries, the authors aim to see whether concepts, patterns and theories can be applied in further environments. To be capable to do this, Collis and Hussey (2014) state that the authors need to have a deep and comprehensive understanding of behaviour and activities. Due to this the authors will conduct the company comparison in the end of the research, at a stage where the authors have gained the level of understanding. Thereby, the authors believe that the result of this thesis is generalizable.

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3 Literature review and theoretical framework This chapter presents relevant literature in regards of why companies report their CO2 emission and how CO2 emission can be reported. The chapter starts with the driver for emission reporting, followed by the reporting standard of the greenhouse gas protocol. Next, a discussion of possible influences on EFs is presented, succeeded by a chapter about calculation tool for emission factors with emphasize on the Network for Transport Measures (NTM), an organisation providing default values in connection to EFs to companies and organisation. The chapter ends with a description of possible challenges when companies report/ why companies choose not to report CO2 emissions.

3.1 Drivers for emission reporting Recently there has been a trend of significant increase in companies’ awareness of Corporate Social Responsibility (CSR) as well as an increase of the willingness to report on their CSR performance to the public (Lueg et al. 2016). The increased awareness of the impacts caused by climate change connected to company’s activities are leading companies to address and assess opportunities and threats in a new manner. Large part of the corporations nowadays measure the emissions of GHG that are generated by their activity and use this information to weigh their exposure to climate changes, market conditions together with consumer preferences. According to Kauffmann et al. (2012) this evolvement has become an important component of company’s strategy and risk management. Publications present a range of motivations and drivers for firms to conduct environmental reporting (Berthelot et al. 2003). Observation is that firms tend to balance external and internal pressures from a diversity of stakeholders to whom the information can be useful in the decision-making processes (Kolk 2010). Investors, governments and other stakeholders request a higher corporate transparency in regards of corporations’ environmental footprint. The demand from governments often generates schemes that encourage corporations to report their GHG emissions (Kauffmann et al. 2012). Depending on the geographical location of the company, different countries and regions have different requirements of how to report their CSR-performance (UNCTAD 2010). In certain nations the legislations of reporting are introduced by governments (comprising Indonesia, France and South Africa) and in other countries the guidelines are introduced by stock exchanges (for example in Malaysia, Brazil and Singapore). In countries for instance, Denmark, France and South Africa, the requirements cover a wide range of environmental, social and governance areas. Furthermore, several countries follow the approach of the UK to assign specific targets of GHG-emissions. Another example of how government can influence is India where government legislate social responsibility (KPMG 2015). Those schemes provided by governments, drive companies to report and also serve as guidance for how to report and to disclose the information (Kauffmann et al. 2012).

In addition, several aspects emerged from a study of multinational companies (MNC) conducted by the Sustainability and United Nations Environmental Program (UNEP). Except internal and company specific reasons, reputation and credibility was found to be important factors (Kauffmann et al. 2012). Furthermore, Kolk (2005) discuss the ability

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for companies to express the corporate vision internally and externally as well as communicate the efforts achieved. Additionally, the enhanced ability to monitor progress and facilitate an environmental strategy is a reason of reporting and creating a greater awareness of environmental issues within the organisation. Besides regulations and demand from government, other reasons for reporting environmental performance exist. Dias-Sardinha and Reijnders (2001) state that reporting is done to comply with regulations in order to reduce the costs of future compliance. Furthermore reasons may according to Odette (2013) be able to measure changes in carbon emission over time. According to Kauffmann et al (2012) companies address their motivations in relation to their size, sector and location. To maintain or increase a company’s competitiveness in a changing market, figure 7 presents three dimensions of how to achieve this in relation to GHG emissions:

● Identification of potential cost savings ● Identification of potential risk factors and ● New business opportunities

Figure 7: Companies motivations to measure and report climate change related information. Source: Kauffmann et al. (2012).

Identification of potential cost savings The measure and reporting of energy use in terms of CO2 emissions is often a starting point for corporations to reduce emissions (Kauffmann et al. 2012). Depending on what industry the companies are active in, different approaches can be taken. In upstream, savings can be identified in for example raw material, packaging, transport and manufacturing. In downstream the improvements can likely be spotted in distribution,

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retail, consumer use and waste/disposal. The mitigation opportunities of the upstream input are located in optimising transports, change transport mode and use of alternative fuels. In the downstream input, mitigation opportunities are discovered in efficient logistics together with recycling and reuse of packaging (OECD 2010). See figure 8 for a visualisation of mitigation opportunities.

Figure 8: Composition of supply chain mitigation opportunities. Source: OECD (2010).

Identification of potential risk factors A survey performed by OECD in 2010, 59 of 63 respondents assessed increased concern of climate change in relation to their company’s businesses. The primary risks are in connection to operational risks, such as impacts of raising energy and transport prices as well as demand changes. Further mentioned in the report are regulatory risks such as compression of international and national regulations, reputational risks related to consumer perception and the competitive risks from loss of advantage (Kauffmann et al. 2012). Agrawala et al. (2011) emphasizes the importance of developing an internal strategy to cope and protect the business activity from these risks.

Seeking new business opportunities Climate change-related laws and regulations are by some businesses considered as opportunities instead of constraints and can be used to acquire new market shares and create incentives to realize changes of companies structure (Kauffmann et al. 2012). According to CDP (2010), due to customer demands it is more and more important for companies to provide a reliable, secure and less climate damaging supply chain.

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3.2 Reporting standards The content of GHG emissions are in general looked upon from two main aspects; organisational emission and product life-cycle emissions.4

Organisational emissions are generated directly and indirectly by the operations deriving and controlled by an organisation. Product life-cycle emissions are a result of direct and indirect emissions from the manufacturing and the use of a product and service, from an end to end approach including raw material, production, transport and waste management (Odette 2013). The recent 15-years, a number of governmental schemes and methodologies have been developed in different OECD countries. Australia, Japan, the UK, Canada, France, the US and Israel are examples of countries that have or are developing standards. Even though different countries have been using different methodologies, joint standards such as GHG Protocol and ISO 14064 have evolved over time. One common part of practice and language is the use of scope 1, 2, 3 to classify emissions as defined by the GHG Protocol (Kauffmann et al. 2012).

To enable international comparability there have been efforts made to create a joint standard of measuring and reporting of emissions. So far no single standard has been agreed upon but there are two main standards5 that are used in a great extent internationally (Odette 2013; McKinnon and Piecyk 2010). The Greenhouse Gas Protocol together with the second largely used standard; International Standard Organisations (ISO) 14064, that is based on the Greenhouse Gas Protocol (GHG Protocol 2012). Both standards are in general alike (McKinnon and Piecyk 2010; Kauffmann et al. 2012).

3.2.1 The Greenhouse Gas Protocol The GHG-Protocol is a multi stakeholder collaboration of governments, non-governmental organizations, businesses and other actors organized by the World Resources Institute (WRI). The mission of this collaboration is to develop an accounting system for GHG that are internationally accepted and applicable (GHG Protocol 2012). By year 2014, 86% of the Fortune 500 companies reporting to CDP (Carbon Disclosure Project) used the GHG Protocol, direct or indirect through initiatives based upon GHG Protocol (GHG Protocol 2012).

Basically the GHG-Protocol aims to provide standards and supports in preparation of creating a GHG emission inventory. A fair and true account of emissions is to be withheld by standardized principles and approaches. The full use of the protocol covers the reporting and accounting of the six major GHG Kyoto protocol Carbon dioxide (CO2), Methane (CH4), Nitrous oxide (N2O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), Sulphur hexafluoride (SF6) (GHG Protocol 2012).

4This report focuses on emissions connected to logistics and transports; therefore product emissions will not be reviewed. 5Besides the two general standards for GHG reporting, for freight GHG reporting known standard are as well: CEN Standard, French Decree and the UK DEFRA Guidance (Source: Odette 2013).

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The GHG-Protocol is according to Odette (2013) an instrument for defining high-level system boundaries for corporations external reporting. Today the protocol is used by actors such as governments, institutions and business leaders to manage, quantify and understand greenhouse gas emission (Odette 2013; GHG Protocol 2012) and is a considered as a well recognized verification standard (Lee 2011; Kauffmann et al. 2012; McKinnon and Piecyk 2012; UK Government 2013).

3.2.2 Scopes of the Greenhouse Gas Protocol The GHG Protocol offers a framework of how to keep apart and report emissions that are produced by companies’ operations and activities (Odette 2013).

The major part of emissions derive from the fuel combustion, thus it is of importance to have a methodology to quantify these emissions. The general approach is to measure or evaluate how much CO2´s produced by each consumed litre used by a vehicle, this approach is known as tank to wheel (TTW). TTW does not include the environmental impact caused by the production of the fuel. Except the operating phase, emissions can be linked to the upstream-phase, for example the extraction and the transport of the fuel. When considering this phase as well, the total emissions are known as well to wheel (WTW). See figure 9 for an illustration of what is included in WWT (Odette 2013).

Figure 9: Fuel life cycle analysis. Source: Odette (2013).

According to Lee (2011) a distinct scope with clear boundaries of what emissions to include is critical for identification and measuring of direct and indirect emissions within the supply chain. Three scopes were developed to create a clear range of how to define direct and indirect emissions, improve the transparency and simplify the categorisation of emissions (GHG Protocol 2012). In order to avoid counting emissions more than once, companies need to cover scope 1 and 2 in their calculations (GHG Protocol 2012). Avoidance of double counting emissions and should be considered as a top priority (Williams et al. 2012). In figure 10, an overview of the scopes and the emissions emitted within a value chain is presented.

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Figure 10: An illustration of the three scopes, provided to define direct and indirect emissions. Source: GHG Protocol (2012).

Scope 1 – Direct emissions Scope 1 covers the emissions deriving from the processes and activities that are owned or controlled by the company that conduct the reporting. These emissions are described as direct emissions, for example companies own transports (Odette 2013; GHG Protocol 2012).

Scope 2 – Indirect emissions Scope 2 covers the emissions classified as indirect, which results from the production of energy used by the direct and controlled processes and activities of an organisation, for example power stations or purchased electricity (Odette 2013; GHG Protocol 2012).

Scope 3 – Other indirect emissions Scope 3 covers indirect emissions that derive from processes and activities that are contracted by an organisation but controlled directly by others (Odette 2013). The Greenhouse Gas Protocol (2012) describes these emissions as a consequence of the corporation, but arise from processes not owned by the company itself. Therefore, one company’s direct emissions (scope 1) may be a part of another’s company’s indirect emissions (scope 3), for example contracted transports (Odette 2013). A detailed description of the definitions of each scope together with example of emission sources is shown in appendix 4.

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3.2.3 Identify and calculate emission GHG Protocol (2012) suggests the use of the following five steps to calculate GHG emissions, see figure 11. Each of these steps is explained below.

Figure 11: Five steps of how to calculate GHG emissions. Source: GHG Protocol (2012).

1. Identify GHG emissions sources The first step is to identify and categorize the sources of the emission. According to GHG Protocol (2012) emissions in general derive from stationary combustion, mobile combustion, process emissions and fugitive emissions. As a start a company should identify its direct emissions (Scope 1) in each of these four categories and secondly identify the emissions that derive from indirect sources (Scope 2), such as purchased electricity for production. The third step involves the location of other indirect emissions (Scope 3). These indirect emissions might occur in a company’s up and downstream activities or in contracted manufacturing or leases that are not included in scope 1 or scope 2. The last part, Scope 3, are according to GHG Protocol (2012) considered as an optional step, but the possible inclusion of this step expands the boundary of the emission and allows the business to identify all relevant emissions within the whole value chain.

2. Select a GHG emissions calculation approach As of today, constant and immediate measurement of emission by observing intensity and flow rate is not a usual procedure. The regular process of measuring emissions is through documented EFs. The EFs do relate to measures of activity from the source of the emission (GHG Protocol 2012). Read more about EFs in chapter 3.3. Thereby “companies should use the most accurate calculation approach available to them and that is appropriate for their reporting context.” (GHG Protocol 2012, p. 44).

3. Collect activity data and choose EFs In larger corporations, scope 1 GHG emissions are to be calculated based upon quantity of purchased fuel together with the use of EFs. In scope 2 emissions are suggested to be calculated from measured consumption of electricity with published, supplier specific

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or local grid EFs. The scope 3 GHG emissions are to be calculated from data classified as activity data; such passenger miles, fuel use, third party and published EFs. In this scope, if source specific EFs are obtainable these should be preferred over general EFs. If the measuring company is active in any specific industry, one should seek more detailed and sector specific, provided information on the website of the GHG-Protocol (GHG Protocol 2012).

4. Apply calculation tools There are various calculation tools available (Odette 2013). The calculation tools provided by GHG Protocol is divided into two categories: Cross sector tools that are applicable to different sectors and sector specific tools that are meant to assess emissions in specific sectors. Regardless of what tool chosen, the guidance for each method follows the same pattern and includes the following sections:

● Overview: Overview of purpose and content, what method used together with a process description.

● Choice of activity data and EFs: Sector specific practice guidance for default EFs.

● Calculation methods: Description of different calculation methodologies depending on the access to EFs and site specific activity data.

● Quality control: Practical guidance. ● Internal reporting and documentation: Providing guidelines of internal

documentation supporting emission calculations.

To cover all GHG emission sources, most companies are required to use more than one tool of calculation, for example one methodology to calculate the emission of production and another methodology for the mobile combustions occurring from transports.

5. Roll-up GHG emissions data to corporate level To be able to report the total GHG-emission of a company it is often needed to gather data from multiple sources, possibly from different divisions in different regions. By planning this process carefully it will reduce the burden of the reporting process, ensure that collection of data follows consistency and decrease the risk of errors of wrong compiled data (GHG Protocol 2012). Furthermore, in an ideal procedure the corporation will incorporate the GHG reporting together with already existing tools and processes for reporting. By merging the data collection in such way, one can reap benefits if division, regulatory, corporate offices or other stakeholders have already collected data of relevance. For internal reporting it is recommended to standardize the reporting format. This ensures that the information collected from units within the corporation are standardised, comparable and decrease significantly the risks of misreporting (GHG Protocol 2012).

In the data gathering of GHG emission from corporations’ facilities two main approaches exist: centralized or decentralized, see figure 12. In the centralized approach the facilities report from local level to corporate level, where the total GHG emissions are calculated. In the decentralized approach facilities conduct the collection and calculation of the individual level, according to beforehand approved methods, and report the final outcome to corporate level (GHG Protocol 2012).

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Figure 12: A description of the difference between centralized and decentralized approach of gathering GHG-emission data. Source: GHG Protocol (2012).

3.2.4 Targets related to emissions According to KPMG (2013), companies should use key performance indicators and targets to measure progress in relation to set objectives. The targets should be meaningful, e.g. measurable and time bound.

McKinnon and Piecyk (2012) state that the target setting is a primary part of the strategy development process. By establishing clear goals this will define the future direction as well as providing a benchmark that is used to determine the success of a strategy. Targeting is widely used in application of environmental policies.

Absolute and intensity targets McKinnon and Piecyk (2012) state that there is an important distinction between absolute and intensity targets. Reaching an absolute target would reduce the total amount of CO2 produced by a unit or process without regards to its possible changes in activity, which generates the opportunity of lowering the total amount of CO2 emissions regardless of possible business growth.

Intensity targets of CO2 emissions need to be defined to a predetermined variable. The variable, known as the normalizer, can either be a general metric such as tonnes of cargo transported or a logistics related metric such as tonnes/kms. This approach offers the opportunity to express targets as a reduction of the CO2 emissions in relations to a level of logistical or business activity. McKinnon and Piecyk (2012) states that logistic connected normalizers are preferred by companies since they are not vulnerable of misrepresentation by structural business changes within the timeframe of the target period. These logistic connected normalizers are classified in three different groups; Capacity related, freight quantity related and freight movement related (McKinnon and Piecyk 2012).

Capacity related normalizers are expressing the CO2 emission in relation to the amount of capacity available to handle, store or move freight. For example, a CO2 reduction target in relation to the number of freight that the system could accommodate is presented in tonne-kms (McKinnon and Piecyk 2012).

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Freight quantity related normalizers do account for the amount of freight that are distributed and measured in pallets, parcels or tonnes without any regards to the distance the goods are transported. For example decrease the amount of CO2 emissions per pallet delivered (McKinnon and Piecyk 2012).

Freight movement related normalizers do for example couple CO2 emissions to the quantity of freight moved together with the distance travelled such as tonne-kms (McKinnon and Piecyk 2012).

3.3 Emission factors Emissions from transport activities are closely related to the efficiency of energy used, where the energy efficiency can be measured in relation to litres of fuel consumed. Two main measurement approaches consider either the distance travelled (e.g. litres per 100km) or freight moved over a distance (e.g. tonne-kms per litre) (McKinnon et al. 2015). The carbon content of fuel is closely related to the CO2 emission. Therefore, if the fuel consumption is known a relatively accurate calculation of CO2 emission can be accomplished (Fowlie et al. 2013). However, parameter-based EFs are used when total fuel consumption is unknown (Odette 2013). The UN defines EF “[...] as the average emission rate of a given GHG for a given source, relative to units of activity“ (UN 2014). Transport modes, assumptions and boundaries are a few variables, which influence EFs (Odette 2013). This thesis focuses on the EFs for road, hence the following will solely focus on road EFs.

McKinnon and Piecyk (2010) consider the decision of the EFs as the most challenging task in the CO2 footprint calculation. This is due to a high degree of variability and uncertainty in the data. EFs can be retrieved from different databases, such as governmental organizations. Nevertheless, boundaries, assumptions and collection methodologies may differ. Therefore Odette (2013) recommends not using EFs from various databases. This can be challenging, as the available databases are not complete, for example all regions, all vehicle classes or modes of transport are not included. In general, EFs are used in connection with specific traffic activities. For example the EF can be provided for a specific vehicle, using a particular fuel type and a selected engine. The EF can be provided as gram CO2 per kilometre. In order to calculate the emission the EF is multiplied with the distance in km travelled. The Transport Research Laboratory (TRL) (1999, p. 28) provides a general formula:

𝐸! = 𝑒!×𝑎 (1) Where: 𝐸! =  𝑐𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛  𝑡𝑜  𝑡𝑜𝑡𝑎𝑙  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑒! =  𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦  𝑟𝑒𝑙𝑎𝑡𝑒𝑑  𝐸𝐹 𝑎 =  𝑎𝑚𝑜𝑢𝑛𝑡  𝑜𝑓  𝑡𝑟𝑎𝑓𝑓𝑖𝑐  𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦  𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡  𝑡𝑜  𝑡ℎ𝑖𝑠  𝑡𝑦𝑝𝑒  𝑜𝑓𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

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When collecting data to generate EFs, various sources of variability exist, for example traffic conditions, the loading factor of the vehicle and vehicle temperature6. Further, the consumption of a specific engine fluctuates as well as the each driver’s behaviour, which might be unknown (Smit et al. 2010). Additionally the manner how the emission is collected influences the EF. Franco et al. (2013) state that EFs should be derived from a combination of laboratory and “real-world” measurements.7 In general Smit et al. (2010) state that on one hand a model with more parameters provides a more accurate result but on the other hand does the complexity increase (as it can be seen in figure 13). Thereby the chances rise that not all input data is available which results in the requirement of assumptions, and/or data is available but data quality is questionable. The study comes to the conclusion that, “[...] this larger appetite for input data (and their associated errors) offsets the potential accuracy gains of more complex models.” (Smit et al. 2010, pp. 2950-2951).

Figure 13: Hypothetical relationship between model accuracy, input accuracy and level modelling detail. Source: Smit et al. (2010).

Many variables can influence the emissions of a truck (Pandian et al. 2009; McKinnon and Piecyk 2010). Demir et al. (2014) state that those can be classified into five categories: Vehicle, environmental, traffic, driver and operations related. Thereby Demir et al. (2014) argues that common models only cover the vehicle, traffic and environment, however, the driver as well as operational factors influence the fuel consumption, and by that the EF as well. It should be noted, that the variables could be interrelated as it can be seen in figure 14. For example, congestion leads to an increase in acceleration and use of break.

6 For detailed description of vehicle temperature see appendix 5. 7 For an in depth discussion about the different data collection approaches in laboratory and “real-world” measurements see Franco et al. (2013).

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Figure 14: Visualisation of the interrelation between various variables. Source: Adapted from Pandian et al. (2009).

3.3.1 Vehicle For the vehicle many factors influence the CO2 emission. The size and design of the truck, e.g. light or heavy-duty truck8, but also the type of vehicle, such as hybrid or electric trucks, has an influence on the EF. Design factors such as height and aerodynamics impacts emissions (Freight Transport Association 2014). In freight transport the weight of the vehicle is important. An influence of the weight can have the material a truck is built of, for example aluminium or carbon fibre (Greszler 2009). Moreover, the age of the vehicle and the type of the engine has an impact on emission, because a more powerful engine is higher geared than an engine with less power, what results in higher fuel usage at a lower speed in comparison (McKinnon et al. 2015). In addition, the maintenance done, for example low-pressure tyres increase rolling resistance, and the distance run of the vehicle have an influence on the CO2 emission (Vreeswijk and Blokpoel 2013; McKinnon et al. 2015). Additionally, the starting temperature of the engine changes CO2 emission levels, when the engine starts below optimal running temperature it uses more fuel than on operating temperature (TRL 1999). Moreover, the fuel used by the engine matters as well as if any after treatment technology is used. For example an active regeneration diesel particle

8Light duty trucks have a weight less than 3.5t, while heavy duty trucks or heavy goods trucks weigh more than 3.5t (Odette 2013).

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filter leads to an increase of CO2 emission by 20% (Graham et al. 2008). In regards of fuel type carbon content is important as well as how efficiently the fuel can be used (Ericsson 2000). So found the International Energy Agency (2009) that regional differences occur in the usage of fuel in order to transport one tonne of freight one kilometre.

3.3.2 Environmental Which type of roads is used influences the CO2 emission of a vehicle, as the speed varies by road type. The speed is on average higher on a highway than on city roads (Ericsson 2000). Moreover, the design with more lanes tends to reduce the likelihood of congestion. Speed humps, traffic junctions and traffic lights lead to an increase in brakes and acceleration, which is linked to an increase in fuel consumption leading to an increase in CO2 emission. Another influencing factor is the gradient of the road. When a truck runs on a slope more fuel is consumed resulting in an increase in emission (TRL 1999; Pandian et al. 2009). Moreover, if a truck runs a long distance on a high altitude it should be considered, as studies have shown a correlation between altitude and emissions (TRL 1999). Moreover, outside temperature can influence the fuel consumption (Vreeswijk and Blokpoel 2013). Ambient temperatures should be considered when setting EFs for work done in environments with different temperatures. One example is Europe, in which the ambient temperature differs in the regions over the year (TRL 1999).

3.3.3 Traffic Ericsson (2000) states a relationship between average speed and hours of the day. The study finds that during rush hour the average speed is lower than during off-peak hours. Under normal conditions a lower speed creates lower emissions. However, congestions lead to an increase in brakes and acceleration resulting in a lower average speed, increasing the CO2 emission (TRL 1999).

3.3.4 Driver Another factor influencing fuel consumption and CO2 emission is the behaviour of the driver. Shifting gears, acceleration, driving speed and using the brakes have an influence on the fuel consumption. Thereby it matters how well the driver can anticipate the traffic and react accordingly (Van Mierlo et al. 2004). Moreover, Ericsson (2000) states that the experience of the driver has an influence on fuel consumption, the gender as well as the attitude towards environmental matters. A male driver is associated with higher CO2 emission than female. Tools like ecodrive training or on-board support systems can influence the driving behaviour of the driver and result in changes of CO2 emission (McKinnon et al. 2015). Thereafter the use of air conditioning is not to underestimate as studies have shown that CO2 emission rises by approximately 20% when in use (TRL 1999).

3.3.5 Operations When a truck runs empty, ergo with no loading, it emits less CO2 than loaded (TRL 1999). Furthermore, a study by McKinnon and Piecyk (2010) shows that an EF can vary depending on the load in tonnes and percentage of truck runs empty in kilometres. In the example conducted a truck with 10t loading and 50% empty truck running produces

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emissions of 151,1 gCO2/km in comparison to a truck with 0% kilometres empty running and 29t loading weight with 39,7 gCO2/km (see appendix 6) (McKinnon and Piecyk 2010). In addition, when generating an EF for a company or fleet, the fleet size and mix influences the EF. So does a smaller vehicle consumes less fuel, but at the same time can carry less freight, which might lead to less emission when a big vehicle is used instead of two small ones (Demir et al. 2014).

3.3.6 Overview Taking all the named factors together, a model of influences on the EF can be created, see below figure 15.

Figure 15: Possible influences on road EFs. Source: Adapted from Demir et al. (2014).

When analysing the various influences on EFs, it should be considered that some have a higher impact on fuel consumption than others. So considers Van Woensel et al. (2001) that speed has the biggest impact on fuel consumption. In addition, it should be mentioned that in literature it is argued, that accuracy of EFs is difficult to determine, since emission values vary as described prior. In order to create the highest possible accuracy a constant measurement of all vehicles’ emissions would be needed (Smit et al. 2010).

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3.4 Carbon footprint calculation tools McKinnon and Piecyk (2010) describes that there is a range of theories illustrating numerous approaches of calculating the carbon footprint. Even though, there is no agreed standard or guidance of how to assign carbon emissions from activities such as road transports. In order to handle this issue and the complexity of the calculation, many companies refer to calculation tools (McKinnon and Piecyk 2010). These calculation tools can be used to either calculate or develop national and/or regional emission factors. Thereby companies can obtain average emissions instead of capturing real world data (Demir et al. 2014).A good example of this is the Network For Transport Measures (NTM) which is the calculation tool used by VGLS today. NTM is a non-profit organisation providing tools to calculate “emissions, use of natural resources and other external effects” from freight and passenger transport for all transport modes (NTM 2016a). NTM acts as a source for both default average values to generate emission factors as well providing EF´s. The aim of the 1993 founded organisation is to generate a common understanding and way on how to calculate environmental impact from the movement of people and goods (NTM 2016a). Over the years “[...] NTM has gained a reputation as an authoritative source of transport emission values” (McKinnon and Piecyk 2010, p.13). Besides providing guidelines and tools, NTM wants to be a place for knowledge exchange and sharing of experiences (NTM 2016a).

3.5 Challenges and risks When creating an environmental or sustainability report companies encounter several challenges and risks. The challenge starts with how and which data to collect, how to allocate the data and what methodology to chose. Calculating the carbon footprint of a company can be a challenging task due to its high complexity and range (Eitelwein and Goretzki 2010). The decision of what calculation method to use is intricate since no consistent standard of how to calculate CO2 emission is set (Eitelwein and Goretzki 2010). Therefore companies face challenges when trying to benchmark themselves against other companies (KPMG 2015). Moreover, multinational companies might need to report to several governments with different requirements for scope and calculation methods (UNCTAD, 2010). This can be resource constraining, as companies need to align the different regions when presenting a company-wide emission figure or collect additional data (GHG Protocol 2012). At the same time companies most likely do not evaluate if the CO2 reporting provides more benefits than costs. This is due to the complexity to assign monetary values to the costs and benefits. This results in CO2 emission reporting being highly dependent on management interest (Kauffmann et al. 2012). Kauffmann et al. (2012) found in their study that investors often have no interest in the content of the report rather than checking its presence. This can lead to the challenge for those responsible for CO2 calculation and reduction within the organization to attract and maintain high management interest in that topic.

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Moreover, companies are confronted with the challenge that the current IT and organisational infrastructures do not support the data collection process (PWC and CDP 2010; Kauffmann et al. 2012). Needed input data is not measured or must be collected from several different departments. This can result in an extended manual process, handling Excel-files (Eitelwein and Goretzki 2010), which is not only time consuming but also a great risk for human errors. Thereby, notably small and medium sized companies have neither the resources nor the knowledge to adequate calculate the CO2 emission (PWC and CDP 2010). Adams and Frost (2008) found in their study that especially international companies face the challenge to assure consistent data collection over all regions and functions. Thereby several factors can influence the process such as differences in the culture, values as well as rank of importance of the task. Furthermore, the risk of double counting of CO2 emission is given if no clear scope is provided (Williams et al. 2012). But even with a clear defined scope companies face hurdles. With an increasing of CO2 emission coverage companies try to calculate a more realistic number of their actual emission and fulfil request for more and better information regarding emissions. However, this goes hand in hand with an increase in data collection and process. The rise in the demand of resources can challenge companies (Daub 2007). Another challenge of CO2 measurement is the balance between a high degree of accuracy and reasonable effort and money put in the collection and calculation of such. The gathering of actual data through surveys can be too costly and model calculations are therefore used instead (Robertson et al. 2015). Furthermore, the decision of the correct EF is a challenge due to many influence factors on the emissions of each transport mode (McKinnon and Piecyk 2010). After successfully collected the data and calculated emission figures, it occurs that companies struggle to interpret and analysis the gathered data and link it to the overall company strategy (PWC and CDP 2010). Closely connected to that is the challenge for companies to determine their stakeholders and which information are relevant for them (Kauffmann et al. 2012). In addition to that, companies face difficulties setting targets for CO2 emission reduction. When deciding on a target through a top-down approach, a challenge for top management is to be able to set an achievable value for the company, because an analysis from the different departments of potential savings is not considered. In addition, the various company functions differ in potential savings, which is not considered when the same target is set for the whole company. Moreover, when the top-down approach is used, variations in potential savings across different industries are neglected, as well as different stages of already implemented emission saving projects and previous baseline conditions. But also bottom-up targets create challenges. When different business units report potential savings as a range, summing them up can result in vague targets (McKinnon and Piecyk 2012).

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However, not only how to set a target is a challenge, but also what kind of target. On one hand the intensity based target is more practical for a growing company, since a per unit reduction can be reported, but conceals the fact that the total CO2 emission might increase due to the growth of business activities. On the other hand companies are concerned about absolute CO2 targets because those might hinder growth due to reporting increasing CO2 emission figures (McKinnon and Piecyk 2012). Furthermore, companies are challenged to set a reasonable timeframe for their targets. Thereby, longer periods to reach a certain goal come with greater timeframes to accomplish such, but mean also an increase in uncertainty in regards of business development, economic environment, innovations and legal policies (McKinnon and Piecyk 2012). Nonetheless risks come along with carbon reporting. A survey among the G2509 companies by KPMG in 2013 detected that over half of them consider “reputational risk” as the major risk when releasing a sustainability report. But also “social” and “environmental” risks are noted. However, some companies have detected reasons for not reporting at all. These companies fear that instead of creating a competitive advantage through environmental reporting disadvantages can occur. This can be the disclosure of internal information such as the reasons for “particularly satisfied customers or employees” (Daub 2007, p.84). Moreover, companies worry about their reputation which could be negatively affected through emission reporting or that it would “wake up sleeping dogs”, for example non-governmental organisations (Kolk 2010, p.368). Beyond that Kolk (2010) discovered, that companies do not report in order to avoid legal consequences due to their reporting activities. 3.6 Summary of theoretical framework To summarize the above reviewed literature and theoretical background companies have several independent reasons to report on its emissions. Namely governmental and other stakeholders’ request, reduce costs for future compliance, the increased awareness of the corporate social responsibility, communicate goals, monitor changes and/ or progress. Furthermore possible reasons are the identification of new business opportunities, potential cost savings and potential risk factors. The Greenhouse Gas Protocol provides companies and organisations with a basic framework on how to set scopes and boundaries, how to calculate and report their emissions. The boundaries are divided into tank to wheel or well to wheel and three scopes are suggested: direct emissions, indirect emissions and other indirect emissions. For the calculation it is suggested to follow fives steps: identify the sources, select a calculation approach, collect data and choose an emission factor, apply calculation tools and last roll-up the data to the corporate level. Moreover, the concept of setting targets is introduced. Companies can choose to report either absolute or intensity based targets and should evaluate if targets should be set top down or bottom up.

9The top 250 companies identified by the Fortune Global 500 ranking.

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In addition, a detailed description on influencing factors of the emission factor is presented. Thereby those factors were divided into five categories: vehicle, environmental, traffic, driver and operations related. It is discussed that the more input variables a model has the more accurate the result gets, however this can lead to an increase in assumptions or reduction of data quality, which is likely to offset the gain in accuracy of the model. Furthermore, the possible option using calculation tool instead of collecting real world data is presented, which can function as a source for default values to calculate emission factors as well as a possible source of such. NTM is presented as one of those sources. The chapter ends with a short presentation of possible challenges companies face when calculating and reporting, and provides reasons for companies not to report. High complexity of the calculation, lack of consistent standards and as a consequence thereof lack of comparability between companies are some of named the challenges. Moreover, assigning costs and benefits of calculating and reporting is difficult, which can result in lack of top management interest. Other challenges are the lack of IT infrastructure to support the calculation, the time needed, as well as lack of resources and/ or knowledge within the responsible department. Assuring consistent data collection, avoiding double counting and provide high coverage is not easy. Moreover, analysing and interpreting the results of the calculation, reporting the relevant information, setting targets and its timeframe are additional challenges. Thereby reputational, social and environmental risks are reported when communicating emission results. The fear of reputational risks, as well as disclosing confidential information and/or attracting legal consequences is reasons for some companies not to report.

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4 Case Study AB Volvo AB Volvo has been reporting CO2 emissions for the last 15 years. However the calculation has changed and developed over the years. The latest major revision was held in year 2012, when a new methodology of calculation was introduced. In connection to the changes, the focus was moved from averages to absolute numbers. Furthermore from 2016 on, the former Excel calculation is replaced by a program to run the calculations, as well as the CO2 emissions will be reported on a monthly basis and presented as one of the 17 KPI´s for the top management. AB Volvo is regularly audited and the published figures reviewed by external parties (Hambeson 2016). In the following chapter the reasons for AB Volvo to report is presented. Afterwards the calculation made by VGLS is described, including the sources of input, how the calculation is conducted followed by the output, scopes and boundaries. The chapter ends with VGLS’s challenges related to the topic.

4.1 Drivers of AB Volvo AB Volvo, including VGLS, expresses different reasons for reporting on their CO2 emissions. One of the main drivers is the scientific knowledge gathered related to global warming and the impact of emissions towards it. AB Volvo acknowledges its impact on the environment and through reporting sees a chance to be transparent about its own effects (Hambeson 2016). Thereby calculating CO2 emissions paves the way to reduction activities and provides the opportunity to detect changes over the years (Hambeson 2016). By pursuing stability in the calculation, this is a way to follow trends and spot changes (Larsson 2016). At the same time AB Volvo describes that the accuracy should be equally weighted and the reporting is an important work to actively work with continuous improvements (Norinder 2016). Further incentives for AB Volvo to report are to attract young talents. Environmental logistics are more coveted by young students nowadays (Larsson 2016). One way to be perceived as a responsible company is the commitment towards the WWF climate savers program (Larsson 2016). Thereby the goal of AB Volvo is to be a leader within its business environment (Hambeson 2016) and from a future perspective, be a part of developing new reporting standards (Larsson 2016). The society and customers are further drivers for AB Volvo. To be perceived as an environmentally conscious company is important (Larsson 2016). Thereby sustainability is a part of AB Volvo core values (Norinder 2016; Larsson 2016). However, customers do not directly demand reporting, but if companies don’t follow industry praxis, customers will recognize. AB Volvo believes a lot of exposure is put onto companies that do not report. Therefore AB Volvo wants to be as proactive as everyone else but with slightly more effort since this subject is a part of the company’s brand image (Norinder 2016).

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The legal requirements of today are not considered as a driver, rather AB Volvo expects future legislative interference and wants to be ahead of it. AB Volvo believes that not reporting emissions is a risk, because legal restriction will likely be introduced and the companies not working with environmental aspects will face big challenges in the future (Hambeson 2016). Other risks can be connected to actual reporting of the CO2 footprint as well. One example is external exposure that is put towards the society, who might question if the figures are correct. The important part in this sense is to have trustworthy methodologies of calculating (Norinder 2016). In addition, the CO2 emission figures might be used as a comparison against other companies that perhaps present lower figures due to different calculation standards. Furthermore, the internal risks of reporting are about the comprehension of the figures. If the presented figures are too difficult to understand due to inconsistency, they will lose its impact of the decision makers (Larsson 2016) Moreover, AB Volvo is not afraid of critical external feedback, but in order to improve appreciates to discuss its calculation and is confident in presenting already taken activities. In addition, the demand of society for environmental related information is a driver for AB Volvo, which pushes top management interest for this topic (Hambeson 2016).

4.2 CO2 footprint calculation To get an understanding of how VGLS calculates the CO2-footprint, figure 16 provides the basic steps in a simplified mean (for a holistic view see appendix 7).

Figure 16: Overview CO2 emission calculation VGLS. Source: VGLS (2016c).

When calculating the CO2 footprint the emission factor is multiplied by the distance and weight of the transported freight. Various sources are needed in order to conduct the calculation, depending on the process, region and transport mode (VGLS 2016e).

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4.2.1 Input Each region uploads its transport statistics for each month in a common access point for transport material10, transport parts and transport packaging. Distribute products is coming from a global source, which can be seen in figure 17. For some regions and processes the uploading is done automatically, other have to be uploaded manually. The uploaded files have to be in a specific, predefined format in order to be processed by the calculation tool, called Qlikview (QV, further described in chapter 6.1) (VGLS 2016e).

Figure 17: An overview of transport statistic sources for regions.11 Source: VGLS (2016c).

The transport statistics files contain information about the contract number, the weight of the freight, the start and ending ZIP codes and the transport mode (VGLS (2016e). In addition, VGLS provides input in regards of emission factors and a distance database, which was created by VGLS.

10The inbound logistics from material suppliers to the plants and to the aftermarket distribution centers. 11 RLO and A4D are systems for optimising and organising transports.

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Before starting the actual calculation VGLS checks if the reported files contain sufficient information to match the shipments with the distance database. For each file a so called “overall hitrate” is calculated on which it depends if that particular file is included in the calculation or not (for details how the overall hitrate is calculated please see appendix 8. The overall hitrate can be considered as a gate which shows VGLS if the input data is accurate enough to calculate the emission for that specific file and if, based on the matches of the distances, the CO2 emission calculation can be upscaled, which is further described in chapter 4.2.2).

4.2.2 Calculation In a ‘best-case’ scenario the weights can be used directly from the transport statistics for the calculation. Qlikview retrieves the distances from the distance database, where the start and ending ZIP code is used from the transport statistics and matched with them in the distance database. For the road EF, the given supplier number in the transport statistics is transferred into a contract number. The supplier number is a specific number for each carrier. The contract number is matched with the carrier-specific emission factor that is calculated individually for each carrier based on the carrier survey (for further details on the carrier survey and for how the EFs are calculated, see chapter 5). This will not only take place when the transport is done solely by road, but also for trips with the main leg sea or air. This is because the freight is transported by road to and from the port.

What is done if one of the necessary input values is not given or unknown? If the weight is not provided in the transport statistics for the shipment, this specific row in the file is not considered in the calculation.

If the carrier has filled out the carrier survey and an EF could be calculated, Qlikview will use it for the emission calculation. If an error on the way occurs, either no EF is calculated, the contract and supplier number does not match or no supplier numbers is provided in the transport statistics, a Network for transport measures (NTM) -based default value is used. How high the default EF is depends on the country of shipment for a transport only using road. Important to consider is that not only pure road transportation is part of the calculation, but also when a transport has sea or air as the main leg, the first and third leg is considered be done by road. Thereby the default value for the first leg is retrieved based on the sending country and the EF value for the third leg depending on the receiving country.

Moreover, the information ‘region’ or ‘main transport mode’ could not be retrieved from the transport statistics. As this happens very rarely they were not included in the figure below for simplification reasons as well as the calculation tool handles them differently. First, it could happen that the region cannot be found and second, that Qlikview cannot translate the given information into a transport mode. In both cases the calculation tool will give an extra line in the summary of the calculation stating that neither the region nor the transport mode could be found. To be able to evaluate if this has a big impact on the total CO2 emission the total weight, distance and CO2 emission is stated. In some cases following to investigation those values can be assigned manually to the reporting region or transport mode.

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If the ZIP codes were missing in the transport statistics or if the start or ending point/ ZIP code could not be matched within the distance database (reasons could be that no distance exists in the database for that region/ combination or that the ZIP codes are not reported in the right way), the transport will be upscaled based on the calculated CO2 emission for the weight of the freight with distances.

A summary of the previously described process can be seen in the following figure 18:

Figure 18: Map of VGLS’s road CO2 emission calculation. Source: Own creation.

Upscaling In order to consider transports with missing distances, the previously described methodology is used to calculate the emission for all shipments with a weight, distance and EF. Before shipments with missing distance are upscaled, all shipments are assigned to the accountable region. Thereby VGLS assigns emissions for the process transport material to the receiving region. For the other three processes distribute products, transport parts and transport packaging the sending regions accounts for the emissions (VGLS 2016e). If a distance could not be allocated, Qlikview compares the summed up “weight with distances” with the “total transported weight” allocated to that region and process. The calculated (unscaled) CO2 emission for the weights with a distance is multiplied by the rule of three so that the total transported weight is represented in the CO2 emission value for that file. This process is by VGLS called upscaling and the result is called the “upscaled CO2 emission”. This process might be easier to understand when someone imagines that for each missing distance the average (known) distance of that particular

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region and process is used as a default value. Though the calculation is done different by VGLS, the result and underlying idea is the same.

4.2.3 Reported result Qlikview will run through all the reported transport statistics, calculate for each shipment the specific CO2 emission. Thereafter the emissions are assigned to the accountable region, the missing distances are upscaled and finally the CO2 emissions are summed up. For analysis purpose the total CO2 emission in kg is reported as well as separated by region, process and transport mode (VGLS 2016e). The data is reported on a monthly basis as a KPI to top management, together with a baseline from last year and a target line, showing the wanted reduction of emission.

When the overall hitrate for a specific transport statistics file indicates a low value, this specific file will not be included in the calculations and a follow up will be done to detect possible reasons this. If the hitrate can be increased to a satisfying level through the follow up, this transport statistic is part of the total reported emission. Otherwise a note is stated with the reason for the excluded data.

4.2.4 Scopes and boundaries To fully understand the total reported CO2 emission it is necessary to describe the scopes and boundaries for the calculation. The question thereby is what is included and what not. When calculating VGLS includes the direct emission of the actual transport, but does not spread the scope to the provision of the transport network, the production and recycle of the transport vehicle, packaging and so forth. In addition, depending if the carrier specific EF is used or if the NTM-based default value is used, well to wheel or tank to wheel is used. Moreover, VGLS does not include business travels from employees and emission from company own cars in the KPI. In addition some files (processes of specific regions) are excluded beforehand from the calculation due to lack of accurate data. Those files mainly include transport parts from APAC and EMEA as well as transport material from APAC (VGLS 2016k).

4.3 Challenges In the calculation of the CO2 footprint, AB Volvo Group face different challenges. A major challenge is to be able to get all transport statistics. Regions or processes might not be report at all or relevant information is missing so the files are not usable for the calculation. The same challenges exist for data in regards of transport mode, especially missing weights for air transportation is a problem (Hambeson 2016).

VGLS encounters another challenge with the distance data; especially the distance data connected to sea and road transports. VGLS expresses that there is a risk that data is missing in the reporting and that the data do not cover all transports within the defined area as well as if the accurate distance is reported (Hambeson 2016).

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Furthermore, challenges concern the upscaling process. As earlier described in chapter 4.2.2, VGLS upscales conducted transports based on the sum of CO2 emissions using the given weight and distance. Although upscaling is used frequently there is no general rule of when to scale up or leave out certain transport statistics because of too low data certainty. Today this depends solely on the reason for the possible low hit rate. Since there is no fixed level of a “too low” value, each up-scaling decision becomes an individual assessment (Hambeson 2016).

Additional challenges are connected to the standardization of reporting. As of today regions do not report transport mode directly but instead state a code or a designation, which is thereby translated from QV. Any changes or misinterpretations will lead to that QV is not able translate the information and the script or the file including the transports has to be manually adapted. The reported data is often described using various terms and different regions often report it differently, which creates problems and more manual work (Hambeson 2016).

Further areas of developments concern the allocation of emissions deriving from transporting distribute products. A4D12 reports transports of finished goods on chassis level. A4D do not state how the goods are transported, which can be performed by jockey, truck on truck or decking, described in figure 19. Thereby data regarding more detailed emission data is lacking.

Figure 19: Transport modes of distribute products. Source: VGLS (2016c).

As of today, regions and distribution centres report using different systems, which in itself is considered as a challenge, but there is also a risk of double-reporting the emissions from projects and flows. This applies for both planning and execution, which is done in different processes (VGLS 2016e).

Furthermore, challenges are regarding how transport statistics are assigned to transport mode. For example, for transportation logged as road only means that the contract was established for road. Contract and reality may differ because a transport is contracted and thereby logged for a certain mode, yet this is not a guarantee that this mode is used (VGLS 2016h). For road this might not be as important as for air transports.

Incentives for developments are another challenge that VGLS faces, especially when it comes to actively improving the filling degree of trucks. Since average filling degrees are used, incentives for distributors to increase the filling degree are missing. For example, the calculation do not consider if one truck runs 200 km with 20 tonnes or if instead one truck with 40 tonnes runs the same distance of 200 km.

12 IT systems used for global reporting of distribute products.

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5 Road emission factor calculation of AB Volvo As visualized in process map figure 18 in chapter 4, VGLS uses two different emission factors for their CO2 footprint calculation. In order to understand the influence of the EFs on the CO2 emission calculation, it first needs to be understood how they are calculated and what is included in the calculation of the EF. In following chapter the two different calculations including their inputs, assumptions, calculation formulas and the results will be presented. The first subchapter describes the NTM-based default EF, and the second subchapter describes the carrier specific EF. Furthermore, a third way of calculation is presented, which VGLS conducts, but not uses in the calculation.

5.1 NTM-based default emission factor As earlier mentioned, Network for transport measures (NTM) is a non-profit organisation providing tools to calculate emissions from freight and passenger transports (NTM 2016). NTM offers methods to perform calculations together with providing relevant default data (NTM 2016). The basis of what the NTM default figures rely upon and how VGLS uses these numbers to calculate the NTM-based default EF will be presented in the following chapter. First the inputs (vehicle types, fuel consumption and emission data) are described, followed by the assumptions in regards of filling degree, max. loading capacity and road mix. Next the calculation is presented. Last the output used for the CO2 emission calculation is given depending on the euro class.

5.1.1 Input Road vehicle types NTM classifies the vehicles into ten different categories, while VGLS uses four. In order to generate the fuel consumption that fits VGLS’s vehicle classification, the NTM vehicle types were merged with the VGLS classification. Based on the fuel consumption connected to the NTM vehicle categories average fuel consumptions were calculating matching VGLS’s classification. The NTM vehicle category is shown in appendix 9 and the combined vehicle category result is shown in table 3. In the NTM-based default EF, VGLS only uses VGLS group 4.

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Table 3: Vehicle group. Source: VGLS (2016f) and NTM Road (2008).

Fuel consumption The fuel consumption used within NTM is extracted from ARTEMIS (NTM Road 2008). The ARTEMIS project involved over 40 European laboratories of research and the project aimed to establish scales to enable comparison and evaluations of methodologies in assessing emissions deriving from road transport. With real world test procedures incorporating several types of vehicles, with filling degree of 0% and 100%, the project resulted in statistics with representative assumptions of traffic characteristics in regards of speed, traffic flow such as freeflow, saturated and stop and go, together with slopes (Andre et al. 2009).

The distribution of slopes of 0%, +/-2%, +/-4% and +/-6% was collected from the Handbook of Emission Factors for Road Transport (HBEFA), which was developed by environmental protection agencies of several European countries (HBEFA 2016).

The ARTEMIS output of the fuel consumption, considering the traffic flow, average speed as well as various slopes provided by HBEFA, is processed by NTM and generates the fuel consumption in litre/kilometre, depending on the 10 vehicle categories of NTM, Euro Classes and road type. When transferred to the VGLS vehicle classification the output can be seen in table 4.

Table 4: Fuel Consumption [l/km] of VGLS class 4, per 0% and 100% filling degree. Source: VGLS (2016h).

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Emission data Besides the fuel consumption of a vehicle, VGLS uses the emission data related to the Euro classes. The emission data provided by NTM, which covers the principle of well to wheel emissions (NTM 2016b), derives from the same data as the fuel consumption, the ARTEMIS project together with HBEFA. The emission factor is considering the emissions of a vehicle running on diesel fuel. The measured CO2 emission is for all Euro classes 2621 g/l. It should be noted that the emission in g/l is not influenced by the vehicle cross weight (loading capacity as well as loading factor) (NTM Road 2008).

5.1.2 Assumptions Besides the acquired data from NTM, VGLS takes several assumptions when conducting the NTM-based default EF calculation. The assumptions concern the filling degree and the loading capacity are set at 70% respectively 40 tonnes by VGLS.

Furthermore VGLS applies a road mix distribution, which is provided by NTM and adds upon the FC numbers. The road mix is based on the road mix of Sweden, which derives from “Statens väg och transportforskningsinstitut”. The considered values are 41% highway, 47% motorway and 12% rural and are applied to all regions.

5.1.3 Calculation In order to calculate the NTM-based default EF, VGLS uses the following formula:

𝐸𝐹!"#$%&' = 𝑓𝑐!"# ∗ 𝑒𝑚 ÷𝑚𝑙𝑐 ∗ 𝑙𝑓 (2) Where: 𝐸𝐹 =  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑓𝑎𝑐𝑡𝑜𝑟  𝑜𝑓  𝐶𝑂2  𝑖𝑛  𝑔/𝑡𝑜𝑛𝑘𝑚 𝑓𝑐!"#  =  𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛  𝑖𝑛  𝑙/𝑘𝑚  𝑏𝑎𝑠𝑒𝑑  𝑜𝑛  𝑁𝑇𝑀  𝑖𝑛𝑝𝑢𝑡  𝑣𝑎𝑙𝑢𝑒𝑠 𝑒𝑚 =  𝐶𝑂2  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑖𝑛  𝑔/𝑙 𝑚𝑙𝑐 =  𝑚𝑎𝑥. 𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦  𝑖𝑛  𝑡𝑜𝑛 𝑙𝑓 =  𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑓𝑎𝑐𝑡𝑜𝑟 To generate the fuel consumption depending on the Euro Class, the following formula is applied to the fuel consumption numbers provided in table 4 (Demir et al. 2014).

𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛!"#$%&'(  (𝑙/𝑘𝑚) =  𝐹(𝑒𝑚𝑝𝑡𝑦)  + (  𝐹(𝑓𝑢𝑙𝑙)  − 𝐹(𝑒𝑚𝑝𝑡𝑦))  ×𝑙𝑓 (3)

Where: 𝐹 𝑒𝑚𝑝𝑡𝑦 = 𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛  𝑜𝑓  𝑡ℎ𝑒  𝑒𝑚𝑝𝑡𝑦  𝑣𝑒ℎ𝑖𝑐𝑙𝑒  𝑖𝑛  𝑙/𝑘𝑚 𝐹 𝑓𝑢𝑙𝑙 =  𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛  𝑜𝑓  𝑡ℎ𝑒  𝑓𝑢𝑙𝑙𝑦  𝑙𝑜𝑎𝑑𝑒𝑑  𝑣𝑒ℎ𝑖𝑐𝑙𝑒  𝑖𝑛  𝑙/𝑘𝑚 𝑙𝑓   =  𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑓𝑎𝑐𝑡𝑜𝑟 After calculating the fuel consumption per road type and Euro Class, the values are multiplied with each specific road type multiplier (41% highway, 47% motorway and 12% rural) and summed up. The result is the input into 𝑓𝑐!"#.

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5.1.4 Output The output of the calculation for 40t max.loading capacity is as shown below in table 5. These values are applied in case no carrier specific EF exists or could not be correctly matched with the shipment.

Table 5: The output of the NTM-based default EF (40t). Source: VGLS (2016h).

To cope with possible regional differences, VGLS assign a certain Euro class to each country active in. For more details, see appendix 10, which shows the EF for different Euro classes depending on the country. When the default EF is used, the calculation tool uses the sending region of that particular shipment to get the EF.

5.2 Carrier specific emission factor The carrier specific emission factor is generated by VGLS and described as follows: It starts with the input used for the calculation, followed by the assumptions for missing inputs, the calculation itself, a follow-up calculation and a short description of the output. Since the carrier-specific EFs used today by VGLS are based on the survey from 2012 the following description is based on the calculations done in that year.

5.2.1 Input In order to gather data from the carrier, VGLS uses a survey sent to the carriers. Within the survey the carriers are asked to provide information in regards of their fleet used to transport Volvo’s freight (VGLS 2016i):

● Vehicle size (smaller or bigger than 3,5 tons) ● Engine type (Euro class) ● Number of vehicles for that engine type ● Curb weight (total weight of fully laden vehicle in tons) ● Max. loading capacity in tons ● Average filling degree/ loading factor ● Average fuel consumption in litres per 10 km ● Type of fuel

To take in account that carriers might have a fleet mix as exemplified in table 6 for trucks bigger than 3,5t, a multiplier is created to later calculate one EF per carrier.

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Table 6: Example of result of carrier survey. Source: Own creation.

In addition, general data for CO2 emission in kg/litre is used for the calculation depending upon the Diesel type. The values VGLS uses provided by NTM are 2,66 kg/litre, except for all vehicles using Swedish diesel, which is 2,54 kg/litre (VGLS 2016i).

5.2.2 Assumptions In cases where carriers are not able to provide sufficient information, default values are used in each missing category. In situations where the engine type is not provided, for that region a predefined engine class is used. Euro Class 3 is assigned for all trucks in EMEA. For South America Euro Class 2 and for trucks in North America US98 is set as engine default type. APAC has a default engine type of Euro Class 1. The default fuel type is Diesel, with an average default fuel consumption value of 4 litres per 10 km for trucks heavier than 3,5 tons and 1,5 litres for trucks smaller than 3,5 tons. The default filling degree is set to 70% and curb weight for big trucks to 40t and small trucks to 3,5t, whereby the max. loading capacity is 25t or 1,5t VGLS (2016j). The assumptions are shown below in table 7.

Table 7: Default values for carrier specific emission factor calculation. Source: Own creation.

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5.2.3 Calculation In order to calculate the EF, the litre per 10 km is converted into litre per km and the engine class is converted to its specific CO2 emission in g/l. The following formula is used to calculate the EF per carrier and truck specification: 𝐸𝐹!"#$ = 𝑓𝑐!" ∗ 𝑒𝑚 ÷𝑚𝑙𝑐 ∗ 𝑙𝑓 (4) Where:

𝐸𝐹 =  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑓𝑎𝑐𝑡𝑜𝑟  𝑜𝑓  𝐶𝑂2  𝑖𝑛  𝑔/𝑡𝑜𝑛𝑘𝑚 𝑓𝑐!"  =  𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛  𝑖𝑛  𝑙/𝑘𝑚  𝑏𝑎𝑠𝑒𝑑  𝑜𝑛  𝑡ℎ𝑒  𝑐𝑎𝑟𝑟𝑖𝑒𝑟  𝑠𝑢𝑟𝑣𝑒𝑦 𝑒𝑚 =  𝐶𝑂2  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑖𝑛  𝑔/𝑙 𝑚𝑙𝑐 =  𝑚𝑎𝑥. 𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦  𝑖𝑛  𝑡𝑜𝑛 𝑙𝑓 =  𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑓𝑎𝑐𝑡𝑜𝑟 In order to account for that carriers have a fleet mix and to achieve one EF per carrier, the EF per carrier and specification is taken times the carrier multiplier (see 6.2.1) and summed up (for a detailed list see appendix 11). The described EF calculation will be referred to as Idle EF in the following. Second approach of EF calculation VGLS considers that the Idle EF calculation does not take the influences of the road mix sufficiently into consideration and a second calculation is done to provide another EF per carrier. Therefore, VGLS takes the reported engine class and size of truck and translates them into categories based on the Euro classes (Diesel, 80-ties and Euro class 1-5) and four truck sizes based on the maximum loading weight (< 16t, 16t - 26t, 27t - 40t, 40t -60t) as described in 5.1.1. Based on the fuel consumption reported by NTM for each category and loading factor of 0% and 100%, for each carrier one fuel consumption is calculated (NTM Road 2008). The different inputs are shown in table 8.

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Table 8: Fuel consumption (l/km) depending on loading factor, vehicle classification and Euro class. Source: VGLS (2016h).

To gather information about the fuel consumption the reported engine class and transport weight (=curb weight - max.loading capacity + (max.loading capacity * loading factor)) is translated into a VGLS vehicle category and Euro Class. The fuel consumption for 0% and 100% loading factor is added up and multiplied by the loading factor reported by the carrier. To get one value of fuel consumption per l/km, for each road type the fuel consumption is multiplied by the road mix factor of Sweden (see 5.1.3) and added up. The max. loading capacity and loading factor is taken from the carrier survey. The formula for the EF per carrier and specification used by VGLS is as following: 𝐸𝐹!"#$%&%$ = 𝑓𝑐!"# ∗ 𝑒𝑚 ÷𝑚𝑙𝑐 ∗ 𝑙𝑓 (5) Where:

𝐸𝐹 =  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑓𝑎𝑐𝑡𝑜𝑟  𝑜𝑓  𝐶𝑂2  𝑖𝑛  𝑔/𝑡𝑜𝑛𝑘𝑚 𝑓𝑐!"  =  𝑓𝑢𝑒𝑙  𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛  𝑖𝑛  𝑙/𝑘𝑚  𝑏𝑎𝑠𝑒𝑑  𝑜𝑛  𝑁𝑇𝑀  𝑣𝑎𝑙𝑢𝑒𝑠 𝑒𝑚 =  𝐶𝑂2  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑖𝑛  𝑔/𝑙 𝑚𝑙𝑐 =  𝑚𝑎𝑥. 𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦  𝑖𝑛  𝑡𝑜𝑛 𝑙𝑓 =  𝑙𝑜𝑎𝑑𝑖𝑛𝑔  𝑓𝑎𝑐𝑡𝑜𝑟 For this calculation the CO2 emission is set at 2621g/l assuming that all vehicles are diesel trucks (NTM Road 2008). As in the calculation above, the multiplier is used to generate one EF per carrier (see appendix 11). This EF calculation is further on called Specific EF.

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5.2.4 Output Each formula generates a different emission factors for the same carrier (appendix 11). The idle emission factors calculated with the formula (4) ranges from 14,2 g/tonkm up to 1396,7 g/tonkm. The range of the specific emission factors of formula (5) is from 45,2 g/tonkm to 2848,7 g/tonkm. Thereby VGLS uses solely the output of the formula (5) in combination of the default value described in chapter 5.1.

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6 Findings and analysis of VGLS CO2 emission calculation This chapter presents findings conducted by CO2 footprint calculations and analyses how the different EFs influence the calculation result. Therefore the chapter starts with a description of the findings on how VGLS’s CO2 footprint would change by using different EFs. Next, an analysis of how often the carrier specific EF and the NTM-based default EF is used in order to explain the result from the conducted calculation. This is followed by the analysis of which variables influence the emission factors. This chapter ends with an analysis of the uncertainty and accuracy of the inputs values into the EF.

6.1 CO2 emission calculation Qlikview (QV) is a business intelligence program, which can be used to analyse data from various sources and not a special program to calculate emissions. How and what QV calculates and how the output is presented depends on the scripts written within that program (QlikTech 2015). VGLS uses this tool to calculate its CO2 emissions by combining the various Excel-files of the distance database, the emission factors and the transport statistics. The results are presented in Excel-files, one audit file per transport statistic with a high degree of detailed information and a summary file per transport statistics with the total values for each file. Last a total summary for each month can be generated. In the following different calculation outputs for the total road CO2 emission per month is presented. Thereby QV is used to calculate the CO2 emission for various road EFs, using the same transport statistics. This is done in order to see how big the influence of EFs is on the output. In addition, this chapter includes a short explanation why those emission factors are chosen.

Figure 20: Changes of CO2 footprint with varying EFs in percent for year 2016. Source: Own creation.

0%  

20%  

40%  

60%  

80%  

100%  

120%  

140%  

160%  

180%  

February   March  

EF:  Speci8ic  +    NTM-­‐based  default  40t  

EF:  NTM-­‐based  default  40t  

EF:  NTM-­‐based  default  25t  

EF:  Speci8ic  

EF:  Idle  

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In figure 20 (see for total values appendix 12), various calculation outputs are shown in percent. Thereby the CO2 emission of road as calculated today is set at 100%. In order to detect if changes in the data occur two months from 2016 are taken. The first bar presents the today’s calculated figure of AB Volvo with the specific EF based on formula (5), and the alternative default value based on NTM data (formula (2)). The red bar shows the CO2 emission values AB Volvo would get if they would base their calculation solely on the EF based on formula (2), the NTM-based default value (see appendix 10). This approach was used in order to see how big the influence of the carrier survey is on the output of the emission calculation. Based on that approach it could be argued that a maximum loading capacity of 40t, as used in the formula for the NTM-based default value, is too high according to the findings of the carrier survey. Therefore the average default value for the max. loading capacity of the carrier survey of 25t is taken and put into the formula (2) of the NTM-based default value. Those EFs are assigned to the countries according to the Euro classes based on the list of VGLS (appendix 13). The result is the green bar. Next, the road emission based solely on the output of formula (4) is calculated (purple bar). Thereby the carrier specific EF is taken for matching contract numbers and to generate a default value based on the four regions VGLS is reporting of (EMEA, APAC and North and South America). In order to do so, the top ten carriers from each region are taken based on the turnover from 2012. For those carriers, which answered the carrier survey the calculated EF, are taken on average and weighted according to the turnovers of those carriers. The result is a default EF per region (see table 11 in chapter 6.4). This is done in order to test if a sample of the carrier survey could be sufficient to provide region specific emission factors. The same approach is used for the light blue bar. However, instead of using the EF calculated according to formula (4), the EF based on formula (5) is used to show the influence of the calculation taking different approaches and assumptions. From the figure 20 it can be seen that by using only the NTM-based default value as it is calculated at the moment by VGLS lowers the CO2 emissions by 14-15%, while the same calculation but with 25t maximum loading capacity in comparison to 40t max. loading capacity leads to an increase of 37-39% in comparison to the used numbers. If the default value would be based on the calculation of formula (4) an increase of 55-57% is observed and if the EFs are based on formula (5) the CO2 emission calculation output for road would decrease by 7-8%. It can be seen, that the EF can influence the carbon footprint significantly, especially when the max. loading capacity is changed.

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6.2 How are the emission factors in the road emission calculation used? From figure 20, which visualises the differences using various EFs, the first bar (dark blue) represents VGLS current calculation, while the second (red) bar shows the result that VGLS would get if the carrier survey is left out and all shipments are calculated with the NTM-based default EF. It is noticeable that whether the carrier survey is used or not has not a big impact on the calculation since the values between the first two bars do not differ to a great extent. This is interesting since the EFs based on the carrier survey is on average 85% higher than the default values. To be able to make further remarks on that, a short analysis of the usage of the specific EF is made of which the output can be see in figure 21 below (for the total values see appendix 14).

Figure 21: Analysis usage of default EF in calculation in percent for year 2016. Source: Own creation.

Again two months transport statistics from 2016 were taken to see if considerable differences in the numbers could be found. The blue bar shows the percentage of counts of road shipments for which the default emission value is used based on the total shipments. The orange bar shows the weight transported for which the default emission factor is used in comparison to the total transported weight via road. This is done as the count of shipments alone can only show how often the default EF is used, but not if those shipments represent the major work done in the road transportation. Looking at figure 20 and 21 it can be noted that between the two calculated month's output does not differ significantly, which indicates that the collected data is reliable. Figure 21 shows that for all road transports done in APAC and North America the default EF is used, while for South America over 80% of the transport is assigned to the default EF. For the transport statistics of EMEA and the global source for distribute products and transport packaging around 60-75% of the transports the default EF is used. This shows that the EF calculated based on the carrier survey is rarely used and therefore cannot have a big impact on the calculated CO2 emission. Rather the default EF determines the road CO2 emissions reported by VGLS at the moment. Possible reasons for the low rate are missing supplier numbers, that the supplier number cannot be translated into a contract number or that the contract number does not have a matching EF. Where exactly the highest rate of errors occurs is not possible to examine, because the transport statistics only provide the supplier number, but not the name of

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the carrier. While analysing the carrier survey it could be noted that since 2012 contract numbers have changed and that inconsistencies within the various files exist, which result that in some cases an EF exist for the carrier handling the shipment, but is not used in the CO2 footprint calculation. This is supported by the fact that, besides the cases where the default EF is used 100%, more shipments with the default EF are counted than shipments with a default contract number (see appendix 14).

6.3 What variables influence the emission factors? By looking at the output of the default EF based on NTM, it can be seen that the decreasing of the max. loading capacity by 37,5% from 40t to 25t leads to an increase of 60% of the EF described in table 9 below. The result is a direct increase in the CO2 footprint of 60%. However, the change of the max.loading capacity can be seen more easily by taking the reciprocal of the percentage change calculation (40/25-1= 60%), because the max. loading capacity is in the denominator of the emission factor formula. In addition, it is worth noting that a change of the filling rate can not be determined that quickly, because the filling rate influences the fuel consumption in l/km as well as the fuel consumption in l/tonkm.

Table 9: NTM-based default emission factors in g/tonkm with different max. loading capacities. Source: Own creation.

However, this does not explain why the different EF calculations generate numbers that differ to such great extent, which results in CO2 footprints with an increase of 80% from one EF based output to another (NTM 40t to Specific EF). When looking at the output from the two VGLS EF calculations per carrier (formula (4) and (5)) a difference from -62% up to an increase by 1481% for certain carriers’ EFs can be noted with an average increase of 85% from formula (4) to formula (5) (see appendix 11). In the following the inputs into the three different EF calculations and the underlying formulas are compared and analysed to detect possible reasons for the divergent values. It can be noted that all three EF calculations are based on the same formula, which takes the fuel consumption times the emission value in l/km and divide it by the max. loading capacity times the

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filling degree in order to generate a EF in g/tonkm. Hence each part of the formula is separately discussed. Max. loading capacity For the NTM-default EF the max.loading capacity is set to 40t, while for the Idle and Specific EF calculation the carrier survey is used as the basis, where the capacity ranges between 0,21 - 124,9 tonnes (VGLS 2016h). However, it is worth noting that the default max. loading capacity for the Idle and Specific EF is 25t, based on the average carriers reported, while for the NTM-based default EF it is 40t. As previously shown the max. loading capacity has a significant influence on the EF calculation output. In addition, the max. loading capacity influences the fuel consumption used in the EF calculation. Filling degree The NTM-based default value considers a filling degree of 70%. Idle and Specific EF calculation takes the filling degree provided by the carriers and use as default value 70% as well. This is the only time all three calculations have set the same value as basis for the default calculation. Fuel consumption Specific EF and the NTM-based default EF calculations are based on the same fuel consumption values provided by NTM. In addition, both calculations use the same road mix based on Swedish statistics. The major differences between those two formulas in consideration of the fuel consumption are that the default EF assumes a filling degree of 70%, while Specific EF uses the filling degree provided in the carrier survey. In addition, the NTM-based default EF always assumes a max. loading capacity of the truck and uses the fuel consumption accordingly. The Specific EF calculation at the other hand considers the Euro class and the transport weight reported by the carrier resulting in a more accurate fuel consumption figure. Moreover, the default EF generates a fuel consumption value per Euro class considering the fuel consumption of the empty truck plus the fuel consumption added based on how high the filling degree is. The Specific EF calculation at the other side uses the fuel consumption for the empty truck and adds on the fuel consumption of the full truck times the loading capacity, which leads to an overestimation of the fuel consumption, since the fuel consumption of the empty truck is counted twice. The Idle EF takes the fuel consumption straight from the carrier survey or uses the default value of 1,5l for small and 4l for large trucks. This can be argued to be the most accurate approach, since the fuel consumption depends on various local factors, such as road mix, slopes etc. that is more likely to be reflected by this EF, than by the Specific EF and the NTM-based default value. At the moment the Specific EF and the default EF solely are based on the Swedish road mix and fuel consumption values provided by NTM with limited inputs, leaving no room for regional difference.

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Emission value Emission values are directly linked with the fuel type. Since the Specific and NTM-based default EF only consider Diesel as fuel type the emission value is set at 2,621 kg/l based on NTM values for WTW. For the Specific EF calculation between two motor classes is differentiating both using Diesel and TTW, though with slightly different content resulting in values either of 2,66 kg/litre or 2,54 kg/litre. Though the numbers are really close, they still result in a small increase or decrease depending, which one is used. Multiplier Lastly, the multiplier used to generate one EF per carrier is used differently for the Idle and Specific EF. In the Idle EF calculation for all carriers reported on small and large trucks two EFs were created, one for small vehicles and one for large vehicles. Nevertheless the Specific EF calculation creates one EF per carrier independently of small or large vehicle. Problematic thereby is that the calculation uses the multipliers calculated for the Idle EF calculation and thereby sums up to two. The result is that the EF per carrier is not the (weighted) average of the EF for small and large trucks, but the sum, resulting in a significant higher EF for carriers, which reported in both categories than for carriers only reporting for large trucks. In following table 10, all three EF calculations conducted by VGLS are compared based on the set categories, which influence the EF. Namely those are vehicle, environmental, traffic, driver and operations related. In addition one category is added, which represented variables which are either a result of the other categories variables, namely fuel consumption, or are depending on a company's decision such as the boundaries of the calculation. It should be noted that not all influencing factors, which were presented figure 13, are mentioned here, because those factors are not considered directly in the three calculation approaches.

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Table 10: Differences of input values into EF calculation. Source: Own creation.

From the analysis of the calculation inputs and how it is conducted it can be seen, that different factors influence the EFs variously depending on the way of calculating:

● The calculation is extremely sensitive to the input values, since all inputs are weighted equally. Consequently the further away the carrier survey answers are from the set values for the NTM-based calculation, and the fuel consumption used in the Specific EF calculation, the more differs the EF. However, since the max. loading capacity and the filling degree play a major role in the calculation and can be in contrary directions the output is not clear.

● In the Specific EF calculation the fuel consumption is overestimated, which results in a higher EF, in case the reported fuel consumption of that carrier is below that value.

● For the EF in the Specific EF calculation small and large trucks EF are added instead of taking the average, which leads to an overrating of the EF. However, this is only the case if a carrier has reported for both, small and large trucks.

● The Default EF considers 40t max. loading capacity which seems based on the carrier survey too high, which results in a lower default EF.

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● The EFs use different scopes, WTW and TTW. However the CO2 emission values used in the calculations do not mirror this, though a small part of the variance of the calculation result derives from the different CO2 emission values of the fuel type. Reasons for the relatively small difference between the WTW and TTW values is according to Odette (2013), that NTM considers Swedish refineries, which have a high level of energy efficiency.

● Interestingly it can be seen that the NTM-based default EF leads to a similar result as the Idle EF calculated EF, while the Specific EF stands out. Two factors are that the Specific EF calculation overestimates the fuel consumption and the sums up the EFs for small and large trucks per carrier. However, it is not that easy, since the Specific EF CO2 footprint is rather close to the output of the CO2 calculation using the default value with 25t max. loading capacity instead of 40t.

In general it can be concluded that higher average fuel consumption increases the EF, while a higher max. loading capacity and loading factor decreases the emission factor. However, in the three calculations all possible variables between the carriers are continuously changing which makes it impossible to account proportions of the influence, but rather an analysis for each carrier would be needed. Therefore a general analysis of the question which calculation is closest to reality and provides an accurate EF will follow.

6.4 Accuracy and uncertainty of the input Not only the amount of considered variables are important, but also the quality of the input values and how accurate they are. Depending on which values provided by the carrier survey, the emission factors calculated can differ substantially. Interestingly, as shown in table 11, the default EF based on the top 10 carriers per region doesn’t follow a clear rationale.

Table 11: Calculated default EFs in g/tonkm for formula (4) and (5) based on region. Source: Own calculation.

Worth noticing is for example that for the default EF in the Idle EF calculation for South America is lower than for North America. Moreover, in the Specific EF calculation the default EF for APAC is below the value for North and South America. This raises the question if the carrier survey inputs (fuel consumption, max. loading capacity and filling degree) are a good source to rely upon for the calculation or if the assumptions taken by VGLS and the fuel consumption provided by NTM are better. Thereby two main factors are important to consider: the accuracy and the uncertainty. Main driver for the carrier survey is to have an as accurate EF as possible, which provides the possibility to account for regional differences, as well as differences in carriers’ fleet performance. However, the carrier survey can be also seen as a source of uncertainty, because for the conducted carrier surveys no audits are done. Carriers may have a lack of incentives of

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reporting as accurate as possible, and without any audits there is a risk that information reported is incomplete or inaccurate. But does that mean that an EF based on assumptions and values from organizations are better? One challenge is to account for regional differences due to a lack of a single source providing all relevant information. In addition, assumptions are difficult to set and might differ too much from reality, leading to an inaccurate result. On the other hand, set inputs reduce the risk of uncertainty in a way that if a reliable and good source backs it up it simplifies the calculation without “leaving out too much”. The EFs that VGLS use at the moment, is at the one hand the NTM-based default EF, which is fully based on assumptions and NTM values and at the other side the Specific EF calculation is a mix of the carrier survey and NTM values. The third EF calculation, Idle EF, which is not in use, uses solely carrier survey values if possible. Which of those calculations reflects reality the most dependence on the confidence in the data, especially of the carrier survey. As described in the theoretical framework, a high degree of complexity in the model can lead to a reduction of the accuracy of the input values, because of lack of data or questionable quality as such. From chapter 6.2 it could be noted that the complex model to generate a carrier specific EF is not used to a great extent within the CO2 footprint calculation. This leads to the result that even if the carrier specific EF would be highly accurate with low uncertainty, the CO2 result VGLS is communicating would not mirror that, because this EF is rarely used. In addition, as seen in chapter 6.3, all three ways of calculating uses a variety of inputs from various sources and different assumptions, which results in if using more than one of the calculations, “apple and pears” are used as a basis. One could argue for that this outcome is a value with rather low accuracy and certainty, as a result of that, low reliability. This leads to the question if the time and effort spent to generate the EFs leads to the wanted result or if there is a more suitable way of ensure the quality of the measurement. In order to answer this question a company comparison is conducted, since scientific research and standards do not provide companies with a sufficient guideline. The company comparison is a way to increase the understanding of the challenges companies face with the CO2 calculation, how companies pick the EFs and how it is affecting the accuracy and uncertainty.

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7 Comparison of Tetra Pak and SKF In the previous chapters an illustration of how VGLS conduct their CO2 footprint reporting was given. This following chapter presents a brief overview of how other companies conduct their CO2 footprint calculations. Why and how they report together with possible challenges connected to the calculations will be described. Companies presented are Tetra Pak and SKF Group. The reasons for choosing these companies are that all of them are global companies, active in numerous countries and represent different kinds of industries within production. They are all committed to targets in terms of reducing the CO2 footprint and thus the calculation is an important tool to measure baseline and continuous development.

7.1 Tetra Pak Tetra Pak, founded in 1951, is a company for food packaging, present with its products in over 170 markets. Since year 1999, Tetra Pak measures and reports its greenhouse gas emissions according to the GHG-Protocol, which is described in 3.2.1. Until 2010 only direct GHG emission from own operations (Scope 1) were summarized and presented. In 2010 a revision was held and from that year on the emissions of the whole value chain are reported, from the raw material to the end of the products lifecycle (Scope 3). In 2013 external auditors have verified Tetra Pak’s published emission data. The reporting is presented on a yearly basis, January – December (Tetra Pak 2016a&b). Emissions deriving from transports estimates to be 4-5% of the total emission figures, while the major part originates from production, suppliers and raw materials. Since transport constitutes to a rather small proportion of the total carbon footprint, this can be a reason why resources may be allocated towards other activities than the calculation (Nilsson 2016). The main driver for Tetra Pak to conduct the reporting of the CO2 footprint is the demand from customers. Sustainability is a strategic priority and one action connected to this is the decision to cut emissions and keep the total figure stable until 2020 with the baseline in 2010 and not increased (Nilsson 2016). This goal applies for all operations within Tetra Pak, regardless of business growth (Tetra Pak 2016b). To calculate the CO2 footprint of road transports including business travels, Tetra Pak uses the formula: EF x distance x weight. The weight and distances is provided by a “control tower” that handles the transport statistics. The value for the EF comes from NTM. The EF used by Tetra Pak contains assumptions in regards of two loading capacities: tractor and semi-trailer with max. loading weight of 26t, and for Sweden and Finland trucks with trailers of max. loading capacity of 40 tonnes. The filling degree is set to 70%. This results in two EFs of 57 g CO2/tonkm and 53 g CO2/tonkm. Furthermore empty truck running is included in the EF and the EFs scope is well to wheel. At the moment Tetra Pak assumes one EF globally without any adjustments to regions or countries, except for Sweden and Finland. Region and supplier specific EF values is something that Tetra Pak has been considering to introduce, but due to time constraints this works has not yet commenced. The collected data is summarized and calculated using excel. In addition to the calculated CO2 emissions, Tetra Pak adds total emission

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numbers of express deliveries provided by the carriers. Example of suppliers is DHL and TNT, which are connected to NTM and use the same standards as Tetra Pak. Tetra Pak describes different challenges when it comes to the calculation of the CO2 footprint. The data collection is improving, but still one challenge is the collection of the increased data deriving from multiple sources and supply chains. This causes larger files, which demand more resources. Since a lot of the work currently is done manually, this process becomes very time consuming. Furthermore Tetra Pak expresses its challenges connected to internal and external audits. It is an important task to make sure that the presented data cover all transports. As Nilsson (2016) noted, one year a region was not included because the carrier forgot to report this data.

7.2 SKF SKF is a technology provider of products and services of bearings and seals. Founded 1907 in Gothenburg, Sweden, SKF is active in 140 countries nowadays. The sustainability reporting started 1994 and by 2002 it became a part of the annual report (SKF 2016). SKF has expressed that CO2 emissions are the most substantial GHG produced due to SKF’s business activities. The SKF Group monitors and reports CO2 emissions by the framework of the GHG protocol and characterize its GHG into the three scopes (SKF 2016). As a part of the sustainability work, SKF joined the WWF climate savers 2012. The main driver for SKF to report its CO2 carbon footprint is a mix between customer and top management demand (Ashiq 2016). Thereby, reporting is part of a strategy to increase carbon-efficiency and a chance to gain competitive advantage in an environment with presumably increasing energy prices (SKF 2015). SKF has set a target of reducing the CO2 emissions per tonne/km for all transports by SKF Logistics Services by 30% of the level 2011, to the end of 2016 (SKF 2015). The reporting is presented on a monthly basis (Ashiq 2016). To calculate the CO2 footprint of the road transports, SKF uses the formula: EF x distance x weight. SKF clusters its business into 13 units, which provides SKF with the information to calculate the EFs. This information is vehicle type, fill rate, fuel type and road mix. Together with NTM values for the fuel consumption and CO2 emissions tank to wheel, an EF for each unit is calculated. The 13 different units are spread out globally, which enables regional differences to be accounted for. Thereby a range of 74,6 to 194,8 g CO2/ tonkm is used within the calculation depending on the unit. Furthermore, SKF’s 13 units collect and consolidate the data in regards of weight and distances from the regional facilities. Those transport statistics are based on input from the carriers. The central logistics department calculates the CO2 emission for each unit and merges them to present the total figure on a monthly basis. This is to a large extent an automated process handled by special designed software tool that imposes a low degree of manual handling. SKF considers that to a high degree the data covers all shipment. Trends are easy to follow and as a sum, SKF overall feels confident in the data (Ashiq 2016).

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SKF expresses challenges connected to gathering information of newer fuels and trucks. Since data is not yet available for newer trucks and fuels it is hard to assess possible changes or improvements if changing to newer technologies such as using newer trucks (Ashiq 2016).

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8 Discussion This chapter contains the discussion in which a connection between the literature, the case of AB Volvo and its analysed calculation is drawn together with the two compared companies. The discussion will be held with the structure of the headings: driver, methodologies and challenges. An overview of the comparison can be seen in table 12 below, which will be further used in the discussion.

Characteristic AB Volvo Tetra Pak SKF

Driver - Brand image - Be leader - Detect changes and guide reduction activities

- Customer demand - Strategic priority

- Mix of customer and top management demand - Gain competitive advantage

Methodology Reporting standard

GHG Protocol GHG Protocol GHG Protocol

Calculation weight*distance*EF Calculate each shipment

weight*distance*EF Use total weight and distance

weight*distance*EF Use total weight and distance

EFs in use Carrier specific EFs + NTM-based default EF

Two EF: One for Sweden + Finland and one for the rest of world

Unit-based EF

EF range in g CO2/ tonkm

41,9- 2848,7 53- 57 74,6- 194,8

Scopes and boundary

Well to wheel Scope 3

Well to wheel Scope 3

Tank to wheel Scope 3

Data source Carrier survey and NTM

NTM NTM and units

Data collection

Centralized Centralized Decentralized

Filling degree

From carrier survey or 70%

70% Reported from units

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Max. loading capacity

default EF: 40t carrier specific EF: reported in carrier survey or 25t

Sweden & Finland: 40t Rest of world: 26t

Reported from units

Challenges - Data collection from multiple sources and processes - Distances - Upscaling

- Data collection from multiple sources and processes - Resources - Assure that all transport are covered

- Get information about newer fuels and trucks

Table 12: Comparison of AB Volvo, Tetra Pak and SKF. Source: Own creation.

8.1 Drivers As previously mentioned, according to literature there is an ongoing trend of increased awareness in CSR. This evolvement has become an important component of a company’s strategy and stakeholders request a higher transparency when it comes to the environmental footprint. After conducted company comparison the authors can confirm this evolvement of increased awareness of environmental issues. All three companies have an on going commitment of measuring and reporting its CO2 footprint. One example of a company that has clearly implemented this into the corporate strategy is AB Volvo who has sustainability as one of their main core values. Having a strong brand image related to environmental concern together with being an industry leader drives AB Volvo’s calculation and reporting efforts. There is a large collection of drivers for companies to conduct environmental reporting. This statement is confirmed by the conducted company comparison. All three companies show a range of drivers of why reporting and why this is important for each company. One common driver among the companies is to follow customer demand and furthermore meet set targets of reducing the CO2 emissions, such as Tetra Pak´s decision to keep the CO2 footprint figure stable from 2010 until 2020. Literature brings up companies’ ability to communicate the efforts achieved and to monitor progress. However, this puts pressure on the methodologies of calculating and measuring the CO2 footprint, because without a stable reliable calculation this is not achievable. This issue concerns the mitigation opportunities presented by Kauffmann et al. (2012) as well. Reporting is as a starting point for reducing emissions and that different approaches can be taken to locate savings and improvements that implies that a reliable calculation should be in place first. All three companies looked into during the case study confirm the mentioned drivers for calculating. Drivers such as regulations and governmental demand presented in literature seem to be considered as secondary driver and AB Volvo do not consider legislations and regulations as a driver, but rather expect it and aim to be ahead of it.

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As the company comparison has shown, companies seems to have similar drivers, however, those are ranked differently. Tetra Pak considers its reporting activities first as a result of customer demand and then of strategic interest, while SKF states equally that customer demand and top management interest drives its reporting activities. Furthermore, it can be noted that the two companies with a more detailed EF, AB Volvo and SKF, are committed to an external organization, in these cases the WWF climate saver. Thereby it seems that the driver influences the way how the EF is set, so companies which want to start calculating should ask themselves: “What are the main drivers for my calculation?” Cautions should be put on to agree on those, since the case of AB Volvo shows that if many reasons for reporting exists and differ between the persons involved, a calculation is set which tries to fulfil all requirements at the same time, being really precise and accurate to detect changes, and having a solid number to communicate.

8.2 Methodologies Different methodologies can be used for defining scopes or to use for the calculation of the CO2 footprint. The companies described all use the GHG-protocol and report based on scope 3, described in chapter 3.2.1 and 3.2.2. Even though companies seem to follow the methodologies of this international standard, still uncertainties and differences of the output occur. All three companies use the same formula to calculate CO2 emission13, but do all use different EFs and way of setting them and what is included. For example AB Volvo and Tetra Pak include well to wheel in their emission factor calculation, while SKF set the boundary at tank to wheel. The result is a range of emission factors with huge differences between the companies. Tetra Pak’s emission factors are either 53 or 57 g CO2/tonkm, while SKF starts by 74,6 g CO2/tonkm and AB Volvo’s EFs range from 41,9 to 2848,7 g CO2/tonkm. The guidelines provided by the GHG-protocol regarding the EF are rather broad and all three companies use different degrees of complexity when deciding upon the EF. GHG-protocol suggests that source specific EF are to be preferred over general EF, but do not provide guidelines of how to generate them. These results in difficulties dealt with differently by the companies. For example Tetra Pak solely relies upon emission factors generated by NTM. This results in an EF, which has the least uncertainty, but does not contribute to the aspect of accuracy of the calculation. Moreover, those general EFs tend to lack the possibility to reflect changes in the transportation, such as moving from Diesel fuel to alternative fuels. Especially for international companies this can be criticised since regional differences are not taken into account. In the example of SKF regional differences are considered by combining default values from NTM with information from their regional units. Another step further goes VGLS with its carrier specific emission factors. However the problem of mixing two emission factors, which are on the basis of its calculation and assumptions, arose. In the case of VGLS it means that only by using the supplier survey it does not automatically suggest that the CO2-footprint creates a higher accuracy of the end result, but instead it could be the opposite and act as a source of uncertainty.

13 EF*distance*weight = carbon footprint

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Furthermore, GHG-protocol facilitates two approaches of gathering emission data, centralized or decentralized. From the comparison both approaches are complied with. In regards of the determination of the EF Tetra Pak uses a centralized approach by setting two EFs retrieved from NTM, whilst SKF uses a decentralized approach by collecting data from its entities in order to calculate region specific EFs. In the case of AB Volvo a mix of decentralized and centralized data collection through the carrier survey and centrally taken decisions such as the assumptions can be detected. AB Volvo is considered as centralized, because carrier survey not really used and hence the calculation is primarily based on centralized collected default values in addition to the set assumptions. Comparing the NTM-based default EF set per region by VGLS in comparison to how SKF determines its regional emission factor a difference in uncertainty can be detected. SKF collects its data decentralized within its units, while VGLS assigns centralized Euro classes to each country based on policies. Thereby it can be argued, that in order to create a regional EF a decentralized approach can be helpful, because the relevant real world information are down in the regions. Whereby a generic EF a centralized data collection is more reasonable, since input values and assumptions should be coherent. Nevertheless, the major question thereby is if the data, which is used to generate the emission factor, is of high quality and if highly specific EFs can be utilised. In the case of Tetra Pak the safest options is considered, since NTM is perceived as a reliable source for those values. In the case of AB Volvo and SKF, where NTM values are mixed with additional data, a higher risk occurs to incorporate uncertainty without increasing accuracy. The case of AB Volvo and the conducted carrier survey rose the question of how reliable the external reported data is (see chapter 6.4) and how well the highly specific emission factor is used (chapter 6.2). Those two challenges of AB Volvo seems to be overcome by the case of SKF by collecting data internally and instead of calculating shipment by shipment a total per region is calculated based on total distances and weights. Out of these examples it can be concluded that companies should question themselves how good their data quality is in order to take decisions on how detailed and specific an emission factor should be, which leads to the decision of a centralized or decentralized calculation setting.

8.3 Challenges When calculating the CO2 footprint companies face different challenges. Challenges connected to reporting are many and further discussed below, but worth keep in mind is that although a company decides to not report, other challenge will rise as well. In the literature different reasons are described for not reporting, such as disclosure of certain information, but on the other hand, if a company nowadays decides to not report, they will stand out and a reputational risk is substantial. AB Volvo state that a lot of exposure is put onto companies that don’t report, and that AB Volvo, to be able to be perceived as a environmentally conscious company thus want to follow like everyone else. At the same time when companies report, public will question the actions and numbers provided and mistakes will lead to discredit of the reputation of the company. Therefore companies are in a situation where they should consider to only report data in which they are confident to publish.

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Furthermore, challenges described in literature are how and which data to collect as well as what methodology to use for the calculation, this can result in extensive manual work and time-consuming processes. These issues are recognized within the three compared companies as well. All studied companies admit challenges in terms of data collection and data quality, those were mostly focused on the distances for the calculation as well as to assure that all shipments were collected and used within the calculation. Since international companies often use several different systems and process across their entities no single point of access to all relevant data exist. In addition, the companies described in the comparison are all working in various degrees of handling regional differences, which is mirrored in the theory that international companies face challenges of assuring consistent data from different regions (Adams and Frost 2008). This is in particular a challenge for companies operating internationally, because they face the question how accurate they want to mirror regional differences in their calculation. Tetra Pak barely considers regional differences by using two EF for all shipments, one for Sweden and Finland and one for all other countries, while SKF has several region specific EFs depending on where the shipment took place. VGLS in theory goes a step beyond that by having carrier specific EFs. However, as the analysis has shown this is not used widely, but the region specific default EF is most commonly used. Thereby it is worth noting that SKF retrieves it region specific EF out of a combination of NTM values with information gathered from their regions, while VGLS creates the default EF based on NTM values and centrally taken assumptions. These challenges arise out of the fact that until today not one organisation provides EFs for different regions, so if a company wants to account for such one has to take actions itself. This leads to another challenge companies face if they want to compare themselves with other companies. Since no consistent standard of how to calculate exists, the reporting values by the companies may differ due to the set scope and boundaries. An influencing factor also has the EF, which can be seen from the values Tetra Pak and SKF use in comparison to VGLS. Even though if all three companies would exactly ship the same amount over the same distance they would not report the same CO2 emission due to different EFs applied within the calculation. As shown in chapter 6 the EF has a noticeable influence on the output, not only in regards of the value as such, but also on its accuracy and certainty. The question on how accurate and certain a reported result should be is closely linked to another challenge companies face. An increase in accuracy of the calculation goes hand in hand with an extension of required input data. However, as stated in the literature (Daub 2007), the increased data collection is a challenge through rise of the demand of resources. This is especially visible when the calculation has a high content of manual work, which further bears the risk of mistakes. Therefore how much emphasize should be put on to increase accuracy depends on how much resources top management is willing to grant. This is especially a question for international operating companies, since they face challenges of a more complex data collection process and assurance of consistent data throughout the regions.

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This is not an easy decision, since as stated in the literature, it is complex to assign monetary values to reporting (Kauffmann et al. 2012). Therefore how much effort is assigned to the calculation depends on top management interest, which is often reflected in a company’s strategy and core values. All companies of the conducted benchmarking express different needs in terms of increasing quality of the data collection and that more resources would be helpful. In the case of Tetra Pak, the CO2 emissions deriving from transports represent a rather small part of the total CO2 emissions, and therefore the calculation for transports gain a rather small attention from the top management. But also for AB Volvo the calculation of the CO2 emission from logistic is just one small part of their work and the wish to spend less time on the actual calculation than creating strategies of reducing emissions were expressed. It is shown that companies face the challenge of a trade-off where the driver for the calculation results in the need of the most accurate calculation possible, while the challenges in regards of resources and data quality tends towards a more simple and robust calculation. Furthermore, the lack of standards requires initiative to make values comparable and provide companies with sufficient guidelines on how to approach this. Nevertheless, the question remains how much effort companies would like to put into setting up, conducting and follow up on the calculation itself. Carbon footprint is a challenging task, especially for international companies, due to its extensive and often difficult supply chain spanning over several countries and involve various databases. So more accurate and detailed the calculation should be so more time and effort needs to be put in, not only for to set up the calculation and gather data, but also constantly follow up on developments within the supply chain. 8.4 Summary To summarize above discussion, the three issues arose in the discussion in order to set an emission factor:

● What is the driver for the calculation? ● What quality of data is available? ● Which level of accuracy and certainty in regards of EF is wanted and how much

effort is prepared to put into the task? These three questions guide how to choose the EF. For example if the main driver is customer demand, according to the findings, this suggests a lower needed level of detailed EF, whilst if top management communicates the core values of a company, then this indicates that a more detailed EF is demanded. In addition, if a company wants a highly accurate EF, it should be evaluated if the data quality is supporting it. Since a highly specific EF goes together with resources, it should be asked if the company is willing to provide the resources to set up and also update such calculation. Thereby the questions should not be considered separately, but rather the holistic view of the three questions together. Depending on how a company answers those questions a one-fits-all EF, a region or a source specific EF can and should be applied.

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9 Conclusion After conducted research, case study and company comparison, the authors conclude that challenges connected to the road emission factor when calculating CO2 footprint for freight movement for international companies involves multiple challenges. Most challenges discovered are connected to: accounting for regional differences, data quality and assigning resources. Furthermore, after conducted own calculations it is concluded that influence of the EF impact the final output of CO2 footprint substantially. The most influencing factors found were filling degree and maximum loading capacity, which should be set carefully. If a carrier or region specific EF is actually increasing the accuracy and certainty of the CO2 calculation depends upon if the data quality and resources are sufficient. Thereby the driver of the CO2 calculation influences such. 9.1 Theoretical contributions Numerous challenges connected to the CO2 calculation were located by the authors. Namely high complexity of the calculation, lack of common IT infrastructure to support the calculation, such as missing distance databases, lack of resources and knowledge within the responsible department. Moreover, lack of comparability between companies, assigning benefits and costs of the calculating and the reporting, which could lead to lack interest from top management, are areas of concern. Furthermore challenges are connected to assuring consistent and full coverage data collection and setting targets. These challenges were detected in theory as well as in the companies. Contradicting to the theories AB Volvo described regulations and laws not as a driver, but more as an future expectation which should be counteracted upon today. Furthermore, the EF calculation incorporated challenges. Theses challenges concerns the data collection, where companies should get reliable information from and how to use own data with external provided default data. Moreover, international companies face challenges in how to cope with regional differences, as well as account for new truck and fuel types. After conducted research, the authors conclude that there is a lack of knowledge of the influence of the EF on the CO2 footprint output. Theories map factors influencing the EF into five categories. The more factors included in an EF one could argue for that the result would be more accurate and closer to real world conditions. On the other hand, from the findings made by the authors, including more factors may also be a source of more assumptions. Depending on the data quality, these assumptions may instead lower the accuracy of the EF due to increased uncertainties. One way to overcome this issue is to use factors with high data quality and complement with default data provided by calculation tools. Furthermore, the calculations conducted by the authors found that the EF has a substantial impact on the numerical output. Thereby the choice of which EF´s to use is an important decision. The most influencing factors are maximum loading capacity and the filling degree, which should be determined by companies with great care to not erode

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the accuracy of the calculation. In order to decide upon what EF to use, the authors present a guideline consisting of three issues companies should consider: What is the driver for the calculation? What quality of data is available? Which level of accuracy and certainty in regards of EF is wanted and how much effort is prepared to put into the task? Depending on the extent that companies can address these issues, a source specific, region specific or a one-fits-all EF should be used. 9.2 Practical contributions The research has shown that in the case of AB Volvo the used determination of EFs follows the recommendation of the GHG Protocol (2012) by trying to being as case specific as achievable. The goal is to provide the most accurate carbon footprint measurement possible. However, the analysis has shown that there three areas to consider in the recent calculation setup. First, the emission factor for each carrier can not be utilized in a satisfying manner, because the process of translating the transport statistics information into carrier specific emission factors can often not be accomplished. In addition, it could not be validated that shipments calculated with a carrier specific EF are matched with the correct carrier emission factor. This is connected to low data quality due to several access points of information and resource constraints for regularly updating the data basis. Second, the current default and carrier specific emission factors are based on different assumptions and calculation formulas, which does not make them suitable to be used together in one calculation. Significant differences in the values lead to the situation that depending how often each type of EF is used the carbon footprint could fluctuate immense. Third, the carrier specific emission factor is based on the carrier surveys, which are not validated externally. During the analysis findings were made questioning the reliability of the information from the surveys and if those decrease the certainty instead of increase the accuracy of the calculation. The authors recommend AB Volvo to reduce the complexity in their calculation by eliminating the carrier specific EF, because of the resources and data quality given. The current determined emission factors neither increase the accuracy nor decrease uncertainty. A simplified setup for the emission factor can contribute to detect trends in the CO2 calculation with known scopes, boundaries and clear assumptions. AB Volvo should in particular emphasize on the max. loading capacity and filling degree, since the EF calculation is particular sensitive to those two values. This will help to communicate the results in an understandable manner. Thereby the authors recommend using region specific emission factors to keep a sufficient level of accuracy in the calculation.

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9.3 Further research The topic of CO2 carbon footprint calculations is according to the authors an ongoing topic and a subject of continuous improvements. It is suggested to conduct further research to provide guidelines of how companies can move from generic EFs to region or carrier specific EFs, and how to calculate them. In addition, clear guidelines and standards should be set so that companies are able to compare themselves with others on the same basis. Moreover, the authors would suggest a broader study the comparison with more companies to test the suggested “question scheme” in order to support companies choosing a fitting emission factor in their current state of operation.

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81

Appendixes Page Appendix 1 Background of Volvo Group a Appendix 2 Interview questionnaire 1 c Appendix 3 Interview questionnaire 2 d Appendix 4 Example of scopes of emission sources e Appendix 5 Influence of engine temperature on emission factor f Appendix 6 Influence of loading weight on emission factor g Appendix 7 Process map of CO2 calculation of VGLS h Appendix 8 Calculation of overall hitrate i Appendix 9 Vehicle classes of NTM j Appendix 10 NTM-based default EFs (40t) k Appendix 11 Carrier’s emission factors l Appendix 12 Total CO2 emission values 2016 n Appendix 13 NTM-based default EFs (25t) o Appendix 14 Total values of usage of EFs p

a

Appendix 1(p.1/2) The Volvo Group (AB Volvo) is an international operating manufacturing company for commercial vehicles which origin and headquarter is located in Gothenburg, Sweden. The main products are trucks, buses and coaches, construction equipment, marine and industrial engines. Moreover, financial services, industrial IT and logistics solutions are offered. AB Volvo has around 100.000 employees, production facilities in 19 countries and over 190 sales markets around the world (Volvo Group 2015). Founded 1927 by Assar Gabrielsson and Gustaf Larson, Volvo’s history began with the production of a passenger car. One year later trucks were added to the assortment and over the years the business further extended to other industries such as marine and aircraft. In 1999 Volvo cars were sold off and a period of acquisitions of truck and construction equipment companies started for AB Volvo. Additionally, joint ventures with various companies were set up (Volvo Group 2015). The organization of AB Volvo Group today is presented in the figure below.

Volvo Group organization. Source: VGLS (2016a).

AB Volvo identifies three core values for itself, namely quality, safety and environmental care. According to AB Volvo quality is achieved if the customer is satisfied. This not only includes the product, but also the service offered to the customers, dealers as well as intra-organizational. Thereby AB Volvo expects from all employees to strive to exceed customer expectations. Moreover, safety drives the actions of AB Volvo. The company works to reduce the impact of accidents and works on intelligent safety systems, with … the goal of zero accidents. But not alone the reduction or elimination of the impact on humans due to AB Volvo products, also the influence on environment is part of the values. Thereby AB Volvo realizes the need of transportation and the impact of their own products, but also admit its own responsibility to reduce such. This should be reached

b

together with increasing competitiveness for AB Volvo, but also for their customers (AB Volvo 2016a). All this is captured in AB Volvo’s vision “[...] to become the world leader in sustainable transport solutions [...]” (AB Volvo 2016a). Emphasis is put on “pioneering” with its products and services in regards of sustainable transport solutions and creating customer value. Until 2020, AB Volvo want to be the most profitable company within their industry, be widely known for its energy-efficient products as well as make use of profitable growth opportunities (AB Volvo 2016a). Background of Volvo Group. Source: Own creation.

c

Appendix 2 Interview questionnaire 1: Used during the company comparison. Interviews held with: Per Nilsson, Tetra Pak Susanna Hambeson, AB Volvo, VGLS Zaher Ashiq, SKF Group CO2 calculations How is the calculation done and which inputs are required? What are the scopes and boundaries? Are any assumptions made within the calculations? Are any reporting schemes or frameworks used, such as GRI/GHG- Protocol/ISO- standards? Do you use a centralized or a decentralized method of measuring? What is included in the carbon footprint? Such as freight transport/travels energy use etc. Emission factor Where is the data collected from and how is it used? What are the boundaries? Are assumptions used, and in which way? Are regional differences considered? Carrier specific emission factors, what questions are asked and what data is collected? What is the value of the EF? And what is the source?

d

Appendix 3 Interview questionnaire 2: Interviews held with: Magnus Larsson, AB Volvo, VGLS Anna Norinder, AB Volvo, VGLS

Can you describe the drivers of AB Volvo to report their CO2 emissions? What are the main incentives for AB Volvo to report their CO2 emissions? What are the long-term goals with the calculation? What is more important, to increase accuracy or decrease uncertainty? What is the biggest concern related to VGLS´s CO2 calculation and reporting? When reporting emissions, what is the role of Volvo in comparison to other companies?

e

Appendix 4

Example of scopes of emission sources. Source: GHG Protocol (2012).

f

Appendix 5 For calculating the emitted emissions different formulas can be used depending on the considered inputs. Regardless of what inputs are considered, EFs calculation can be split into three segments: hot emissions, extra/ excess emission from cold starts and evaporative emissions (TRL 1999; Ericsson 2000). When an engine has reached its operating temperature, less emission is ejected than during cooler engine temperatures. Therefore EFs can be split into cold EF, when engine temperature is below working temperature and hot EF, after the engine reached normal operating temperature. The corrected EF for hot emissions can be for example stated as following (TRL 1999, p. 90):

𝑒!!" = 𝑓(𝑣)  ×  𝐺𝐶  ×  𝐿𝐶×  𝑀𝐶  ×  𝑇𝐶 (2) Where: 𝑒!!" =  𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑  ℎ𝑜𝑡  𝐸𝐹 𝑓(𝑣)  = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒  𝑠𝑝𝑒𝑒𝑑  (𝑣)  𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  𝑟𝑎𝑡𝑒  𝑓𝑜𝑟  𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑  𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠 𝐺𝐶, 𝐿𝐶,𝑀𝐶  𝑎𝑛𝑑  𝑇𝐶   =  𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛  𝑓𝑎𝑐𝑡𝑜𝑟𝑠  𝑓𝑜𝑟  𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡, 𝑙𝑜𝑎𝑑,𝑚𝑖𝑙𝑒𝑎𝑔𝑒  𝑎𝑛𝑑  𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 For cold emissions, also called excess emissions, the following formula can be used (TRL 1999, p. 91):

𝑒𝑥𝑐𝑒𝑠𝑠  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛   =  𝑤  ×   𝑓(𝑉)  +  𝑔(𝑇)  − 1 ×ℎ(𝑑) (3) Where: 𝑒𝑥𝑐𝑒𝑠𝑠  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛      𝑓𝑜𝑟  𝑎  𝑡𝑟𝑖𝑝  𝑖𝑠  𝑒𝑥𝑝𝑟𝑒𝑠𝑠𝑒𝑑  𝑖𝑛  𝑔 𝑉 =  𝑚𝑒𝑎𝑛  𝑠𝑝𝑒𝑒𝑑  𝑖𝑛  𝑘𝑚/ℎ  𝑑𝑢𝑟𝑖𝑛𝑔  𝑡ℎ𝑒  𝑐𝑜𝑙𝑑  𝑝𝑒𝑟𝑖𝑜𝑑 𝑇 =  𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒  𝑖𝑛  𝑔𝑟𝑎𝑑  𝐶𝑒𝑙𝑠𝑖𝑢𝑠  (𝑎𝑚𝑏𝑖𝑒𝑛𝑡  𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒  𝑓𝑜𝑟  𝑐𝑜𝑙𝑑  𝑠𝑡𝑎𝑟𝑡, 𝑒𝑛𝑔𝑖𝑛𝑒  𝑠𝑡𝑎𝑟𝑡  𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒  𝑓𝑜𝑟  𝑠𝑡𝑎𝑟𝑡𝑠  𝑎𝑡  𝑎𝑛  𝑖𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑡𝑒  𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒) 𝑑   =  𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒  𝑡𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑 𝑤   =  𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒  𝑒𝑥𝑐𝑒𝑠𝑠  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛  (𝑎𝑡  20  𝑔𝑟𝑎𝑑  𝐶𝑒𝑙𝑠𝑖𝑢𝑠  𝑎𝑛𝑑  20  𝑘𝑚/ℎ) Influence of engine temperature on emission factor. Source: Own creation.

g

Appendix 6

Influence of loading weight and percentage of empty running on EF. Source: McKinnon and Piecyk (2010).

h

Appendix 7

Process map of CO2 calculation of VGLS. Source: VGLS (2016l).

i

Appendix 8

Calculation of overall hitrate. Source: Own creation.

j

Appendix 9

Vehicle classes of NTM. Source: NTM Road (2008).

k

Appendix 10 Areas Country Year Euro

Class Emission for

Class

Europe EU 5 42,58146805 SE 5 42,58146805

RU 4 41,87281518

BE 5 42,58146805

NO 5 42,58146805

GB 5 42,58146805

UK 5 42,58146805

DE 5 42,58146805

FR 5 42,58146805

IT 5 42,58146805

NL 5 42,58146805

PL 5 42,58146805

CH 5 42,58146805

FI 5 42,58146805

HU 5 42,58146805

LT 5 42,58146805

DK 5 42,58146805

CZ 5 42,58146805

SK 5 42,58146805

TR 5 42,58146805

EE 5 42,58146805

RO 5 42,58146805

LV 5 42,58146805

TW 5 42,58146805

PT 5 42,58146805

SI 5 42,58146805

LU 5 42,58146805

IE 5 42,58146805

ES 5 42,58146805

AD 5 42,58146805

AL 5 42,58146805

AM 5 42,58146805

BA 5 42,58146805

BG 5 42,58146805

BY 5 42,58146805

CY 5 42,58146805

GE 5 42,58146805

GR 5 42,58146805

HR 5 42,58146805

IS 5 42,58146805

KZ 5 42,58146805

LI 5 42,58146805

MC 5 42,58146805

MD 5 42,58146805

ME 5 42,58146805

MK 5 42,58146805

MT 5 42,58146805

RS 5 42,58146805

SM 5 42,58146805

North America US 6 42,58146805 CA 6 42,58146805 MX 4 41,87281518

South America BR 4 41,87281518 AR 4 41,87281518

Asia and the Pacific

CN 4 41,87281518 KR 5 42,58146805 IN 3 45,1349773 JP 3 45,1349773 AU 5 42,58146805 TH 3 45,1349773 SG 4 41,87281518 AE 4 41,87281518 ID 4 41,87281518

Africa ZA 3 45,1349773 NG 3 45,1349773

NTM-based default EFs (40t) based on regional assigned euro classes. Source: VGLS (2016h).

l

Appendix 11 (p.1/2)

m

Appendix 11 (p.2/2)

Carrier’s emission factors based on Idle and Specific calculation in g CO2/tonkm. Source: VGLS (2016h), VGLS (2016i) and own calculation.

n

Appendix 12

Total CO2 emission values 2016 based on region and process. Source: Own calculation.

o

Appendix 13 Areas Country Year Euro Class Emission for Class

Europe EU 5 68,13034889 SE 5 68,13034889

RU 4 66,99650429

BE 5 68,13034889

NO 5 68,13034889

GB 5 68,13034889

UK 5 68,13034889

DE 5 68,13034889

FR 5 68,13034889

IT 5 68,13034889

NL 5 68,13034889

PL 5 68,13034889

CH 5 68,13034889

FI 5 68,13034889

HU 5 68,13034889

LT 5 68,13034889

DK 5 68,13034889

CZ 5 68,13034889

SK 5 68,13034889

EE 5 68,13034889

RO 5 68,13034889

LV 5 68,13034889

TW 5 68,13034889

PT 5 68,13034889

SI 5 68,13034889

LU 5 68,13034889

ES 5 68,13034889

AD 5 68,13034889

AL 5 68,13034889

AZ 5 68,13034889

BA 5 68,13034889

BG 5 68,13034889

BY 5 68,13034889

CY 5 68,13034889

GE 5 68,13034889

GR 5 68,13034889

HR 5 68,13034889

IS 5 68,13034889

LI 5 68,13034889

MC 5 68,13034889

MD 5 68,13034889

ME 5 68,13034889

MK 5 68,13034889

MT 5 68,13034889

RS 5 68,13034889

SM 5 68,13034889

North America US 6 68,13034889 CA 6 68,13034889 MX 4 66,99650429

South America BR 4 66,99650429 AR 4 66,99650429

Asia and the Pacific

CN 4 66,99650429 KR 5 68,13034889 IN 3 72,21596369 JP 3 72,21596369 AU 5 68,13034889 TH 3 72,21596369 SG 4 66,99650429 AE 4 66,99650429 ID 4 66,99650429

Africa ZA 3 72,21596369 NG 3 72,21596369

NTM-based default EFs (25t) based on regional assigned euro classes. Source: Own calculation.

p

Appendix 14

Total values of usage of EFs. Source: Own calculation.