Exploring the Visibility of
Emission Data in Transport Chains
Challenges within emissions traceability for companies and
their stakeholders
Master’s thesis in Quality and Operations Management

JOHANNA HJORTSBERG
IDA WÅGESSON

DEPARTMENT OF TECHNOLOGY MANAGEMENT AND ECONOMICS
DIVISION OF SUPPLY AND OPERATIONS MANAGEMENT

CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden 2025
www.chalmers.se

www.chalmers.se




Exploring the Visibility of
Environmental Data in Transport Chains

Challenges within emissions traceability for companies and their
stakeholders

JOHANNA HJORTSBERG
IDA WÅGESSON

Department of Technology management and economics
Division of Supply and Operations Management

CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden 2025



Exploring the Visibility of Environmental Data in Transport Chains
Challenges within emissions traceability for companies and their stakeholders
JOHANNA HJORTSBERG
IDA WÅGESSON

© JOHANNA HJORTSBERG, 2025.
© IDA WÅGESSON, 2025.

Department of Technology Management and Economics
Chalmers University of Technology
SE-412 96 Gothenburg
Sweden
Telephone + 46 (0)31-772 1000

Cover:
Visualisation of data sharing in transport chains created in DALL-E showing a
world map with different locations being connected by different transport modes
and linkages.

Gothenburg, Sweden 2025

iv



Exploring the Visibility of Environmental Data in Transport Chains
Challenges and opportunities within emissions traceability for companies and their
stakeholders

JOHANNA HJORTSBERG
IDA WÅGESSON

Department of Technology Management and Economics
Chalmers University of Technology

SUMMARY
In response to the increasing urgency of climate change and the introduction of reg-
ulatory frameworks such as the EU’s Corporate Sustainability Reporting Directive
(CSRD), companies are under mounting pressure to quantify and report greenhouse
gas (GHG) emissions throughout their value chains. The transport sector, which ac-
counts for a significant share of global emissions, presents particular challenges due
to its fragmented structure, complex stakeholder networks, and widespread reliance
on subcontracted services. Despite recent standardisation efforts, including ISO
14083 and the GLEC Framework, companies continue to face difficulties in access-
ing high-quality emission data, ensuring methodological consistency, and facilitating
reliable data exchange.
This study explores the barriers and enablers related to the collection and sharing of
transport emissions data between transport service providers and transport buyers.
A qualitative case study was conducted, based on 19 semi-structured interviews with
companies operating across various sectors and supply chain roles. The collected
data was analysed through a thematic analysis, which allowed for the identification of
recurring patterns and underlying challenges across stakeholder groups. To structure
the findings and enhance clarity, a framework was developed that organises the key
themes and illustrates how they interact within the broader context of transport
emissions reporting.
The results of the study show that the main barriers to effective emissions reporting
lie not in the absence of regulatory ambition, but in the limited practical capacity
to meet increasing data demands. A key gap exists between top-down policy re-
quirements and the day-to-day realities of data collection and exchange, especially
within subcontracted and SME-dominated logistics chains. The findings highlight
the urgent need for interoperable digital systems, harmonised templates, and support
mechanisms that enable more efficient, accurate, and scalable reporting. Further-
more, the study underscores the importance of sector-wide collaboration and shared
responsibility among transport buyers, providers, and policymakers.

Keywords: Emission Data, Transport Emission Calculation, Transport Emission
Reporting, Logistic Emission Calculation, ISO 14083, CSRD.

v





Acknowledgements
We would like to begin by expressing our sincere gratitude to Centiro for welcoming
two students with a somewhat scattered idea of writing a thesis related to CSRD.
The journey has been both engaging and rewarding, with many valuable discussions
and fruitful exchanges throughout the spring. It has truly been a pleasure collabo-
rating with you. We also want to thank our supervisor, Tarun, for his continuous
support, insightful ideas, and guidance in structuring our work. Your input has
been greatly appreciated. Finally, we would like to extend our thanks to all the
companies that generously participated in our interviews. We are truly grateful for
the time you took to support our data collection, and we found the conversations
both highly enjoyable and incredibly insightful.

Ida Wågesson & Johanna Hjortsberg, Gothenburg, June 2025

vii





List of Acronyms

Below is the list of acronyms that have been used throughout this thesis listed in
alphabetical order:

CSRD Corporate Sustainability Reporting Directive
EFRAG European Financial Reporting Advisory Group
ERP Enterprise Resource planning
ESRS European Sustainability Reporting Standards
FMS Fleet Management System
GHG Greenhouse gas
GLEC Global Logistics Emissions Council
ISO International Organization for Standardization
OEM Original Equipment Manufacturer
SME Small and medium-sized enterprises
TMS Transport Management System
TTW Tank-to-Wheel
WTT Well-to-Tank
WTW Well-to-Wheel

ix





Contents

List of Acronyms ix

List of Figures & List of Tables xiii

1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Structure of the Report . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theory 5
2.1 Standardising Emissions Reporting . . . . . . . . . . . . . . . . . . . 5

2.1.1 CSRD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 EN 16258 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 GLEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.4 ISO 14083:2023 . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.5 CountEmissionsEU . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Drivers for Transport Emission Reporting . . . . . . . . . . . . . . . 9
2.3 Challenges in Transport Emission Reporting . . . . . . . . . . . . . . 11

2.3.1 Organisational and Methodological Barriers . . . . . . . . . . 11
2.3.2 Data Quality and Availability . . . . . . . . . . . . . . . . . . 13
2.3.3 Data Sharing and Collection . . . . . . . . . . . . . . . . . . . 17

2.4 The Need for Accurate Data . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.1 Data Categories . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.2 GHG Emission Factors . . . . . . . . . . . . . . . . . . . . . . 20
2.4.3 Impact of Input Parameters on Emissions . . . . . . . . . . . 21

3 Methods 23
3.1 Empirical research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1.1 Secondary Data . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.2 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2 Company Selection and Study Implications . . . . . . . . . . . . . . . 26
3.2.1 Transport Buyers . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.2 Transport Service Providers . . . . . . . . . . . . . . . . . . . 28

3.3 Centiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4 Result 33

xi



Contents

4.1 Barriers and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1.1 Organisational and Methodological Barriers . . . . . . . . . . 34
4.1.2 Data Quality and Availability . . . . . . . . . . . . . . . . . . 38
4.1.3 Data Sharing and Collection . . . . . . . . . . . . . . . . . . . 50

4.2 Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.1 External Drivers . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.2 Internal Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.3 Enablers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.3.1 Technological Solutions and Standardisation . . . . . . . . . . 61
4.3.2 Collaborative practices . . . . . . . . . . . . . . . . . . . . . . 65

5 Analysis 69
5.1 Framework for Emission Data Visibility . . . . . . . . . . . . . . . . . 69
5.2 Research Question 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3 Research Question 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

6 Conclusion 81

Bibliography 83

A Appendix 1 I

B Appendix 2 III

xii



List of Figures

2.1 Visualisation of the WTT + TTW = WTW calculation . . . . . . . . 21

3.1 Conceptualisation of transport structure and relations . . . . . . . . . 29

5.1 Framework for emission data visibility . . . . . . . . . . . . . . . . . 70

xiii



List of Figures

xiv



List of Tables

2.1 Primary methods for GHG emissions calculations in the transport
sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Drivers for Transport Emission Reporting from Literature . . . . . . 10
2.3 Organisational and Methodological Barriers from Literature . . . . . 11
2.4 Data Quality and Availability from Litareture . . . . . . . . . . . . . 13
2.5 Data Sharing and Collection Challenges from Literature . . . . . . . 17
2.6 List of key parameters for determining GHG emissions . . . . . . . . 22

3.1 Interviewed Transport Buyers . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Interviewed Transport Service Providers . . . . . . . . . . . . . . . . 30

4.1 Overview of Themes and Associated Challenges . . . . . . . . . . . . 34
4.2 Organisational and Methodological Barriers . . . . . . . . . . . . . . 34
4.3 Data Quality and Availability Challenges . . . . . . . . . . . . . . . . 39
4.4 Transport Assets and Use of Subcontractors Among Interviewees . . . 48
4.5 Data Sharing and Collection Challenges . . . . . . . . . . . . . . . . 50
4.6 List of external Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.7 List of internal Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.8 Enabling technological Solutions and Standardisations . . . . . . . . . 62
4.9 Enabling collaborative practices . . . . . . . . . . . . . . . . . . . . . 65

5.1 Primary challenges and barriers in the transport chain . . . . . . . . 71
5.2 Effects of Current Emissions Data Practices . . . . . . . . . . . . . . 76

xv



List of Tables

xvi



1
Introduction

Transport plays a vital role in modern society, enabling the movement of goods in
business operations and facilitating mobility through land, air and sea. The seam-
less integration of different modes of transport has become routine, often overlooking
the scale and environmental impact of these operations (World Economic Forum,
2022b; Stockholm Environment Institute, 2023).

With increasing urgency to address climate change, frameworks and regulatory ini-
tiatives have emerged to improve transparency in emissions accounting and support
more informed decision making by companies and consumers. Notable among these
efforts are the International Organization for Standardization (2023) standard and
proposed EU regulations EUR-Lex (2020), which establish guidelines for reporting
greenhouse gas (GHG) emissions across complex transport chains. These initiatives
aim to increase accountability, encourage innovation, drive behavioural change, and
ensure emissions reductions aligned with international goals. However, the frag-
mented and global nature of transport chains, characterised by numerous small ac-
tors, poses significant challenges to comprehensive emissions accounting (EUR-Lex,
2020).

1.1 Background
The transport sector is essential for the global economy, but it remains a major con-
tributor to GHG emissions. In 2020, transport accounted for approximately 26%
of the GHG emissions in the EU, with road transport alone responsible for around
20%. (European Commission, 2022).

To address climate change, the European green deal (European Commission, 2019)
and the European climate law (European Commission, 2021b) have set the goal of
achieving climate neutrality by 2050, aligning with the Paris Agreement (United
Nations, 2024). Within this framework, the EU aims to reduce transport-related
emissions by 90% by 2050 (European Commission, 2019). However, projections
suggest that global transport demand could double by 2050, potentially more than
double the emissions in the absence of mitigation efforts (Smart Freight Centre,
2024).

The growing pressure for more sustainable transport options, combined with reg-
ulatory emission reporting requirements and voluntary climate commitments, has

1



1. Introduction

increased the focus on quantifying GHG emissions in the sector (CLECAT, 2024).
This has been reinforced by the EU Corporate Sustainability Reporting Directive
(CSRD), which since January 2023 requires larger companies to report their emis-
sions, including emissions across their value chains (CLECAT, 2024).

Over time, various stakeholders have developed calculation methods and frameworks
for GHG emissions in transport. However, the absence of a universally adopted
methodology and globally harmonised reporting requirements has led to a frag-
mented landscape. As a result, actors often apply different models, yielding incon-
sistent and incomparable data that reduce transparency, traceability, and accuracy.
Consequently, researchers and industry organisations have advocated for a common
framework (EUR-Lex, 2020; Smart Freight Centre, 2021; CLECAT, 2022).

Several initiatives have responded to these challenges. In 2012, the European Com-
mittee for Standardisation introduced EN 16258, the first transport-specific standard
for calculating and reporting energy consumption and GHG emissions (EUR-Lex,
2020). In 2016, the Global Logistics Emissions Council (GLEC), led by the Smart
Freight Centre, developed a framework to further standardise the accounting of
transport emissions (Smart Freight Centre, 2024). This framework was instrumen-
tal in the creation of ISO 14083, an international standard released in March 2023,
which offers a comprehensive methodology for quantifying and reporting emissions
between transport modes and logistics hubs (International Organization for Stan-
dardization, 2023). Other initiatives, such as the French Transport Code, the GHG
Protocol, and SmartWay, have also established reporting standards, the French Code
specifying additional national requirements (EUR-Lex, 2020).

A central challenge in emissions reporting is the availability, quality, and exchange
of accurate data across complex supply chains. The logistics industry comprises
many small and medium sized enterprises (SMEs) that often lack the resources
to implement robust emission measurement systems (European Parliament, 2023).
A survey of 800 European road transport SMEs found that 43% did not measure
emissions at all, 32% did so only for internal operations, and just 25% extended
the measurement to customer levels (Finger and Serafimova, 2021). Outsourcing is
common in the industry, further complicating data access and reporting. Effective
data exchange between stakeholders is therefore essential, but not without barriers.
Businesses may be reluctant to share emissions data due to concerns about com-
petition, ownership, and confidentiality (European Parliament, 2023). As emissions
calculations depend on inputs such as fuel consumption, vehicle type, and route
efficiency, companies relying on subcontractors often lack direct access to primary
data. Consequently, many use activity-based approaches with default or estimated
values instead of energy-based calculations grounded in operational data, introduc-
ing uncertainties and inaccuracies (CLECAT, 2024; European Parliament, 2023). In
the absence of reliable primary data, companies often rely on default or modelled
datasets. Although default data reflect industry averages, they may not represent
actual operations and can significantly distort results (Auvinen et al., 2014).

2



1. Introduction

Moreover, as EUR-Lex (2020) notes, emission factors used in these calculations
vary widely between databases, with differences in national scope, specification, and
underlying models, resulting in divergent results even when the same method is ap-
plied. Finger and Serafimova (2021) emphasise that without high quality data, it is
impossible to evaluate decarbonisation options, track emission reduction progress,
or set science-based targets. The lack of standardised data formats further hinders
interoperability and emission tracking between systems and stakeholders (EUR-Lex,
2020).

Although standards such as ISO 14083 and the GLEC framework provide struc-
tured methodologies, their implementation varies between industries and countries,
limiting their global effectiveness (CLECAT, 2024). To fully realise the potential
of harmonised reporting standards, it is critical to ensure both the collection and
creation of accurate emissions data. As surveys indicate that most companies do not
measure their GHG emissions themselves but rely on data from transport providers,
carriers must expand their measurement capabilities.

1.2 Purpose
The purpose of this study is to explore the challenges and barriers related to col-
lecting and sharing transport emission data among transport service providers and
transport service buyers in the transport chain. By identifying obstacles in data
availability and exploring enablers for improved information sharing, this study
seeks to contribute to improving the transparency, comparability, and reliability
of emissions data.

1.3 Research Questions
Q1. What are the primary challenges and barriers faced by transport service
providers and transport service buyers in collecting transport emission data?

Q2. How do the existing practices and technologies for sharing transport emission
data among stakeholders in the transport chain impact the efficiency and accuracy
of data exchange?

1.4 Structure of the Report
This report is structured into six main chapters, each designed to build an under-
standing of the challenges and opportunities related to the visibility of environmental
data in transport chains.

Chapter 2 provides the theoretical foundation for the study. It introduces the key
frameworks relevant for transport emissions reporting, such as CSRD, EN 16258,
GLEC, and ISO 14083. The chapter also explores the main drivers behind emissions

3



1. Introduction

reporting, the associated challenges, and the importance of accurate data. Chap-
ter 3 outlines the methodology used in the study. It explains how the empirical
material was collected through interviews and secondary data, the selection process
of the participating companies, and the role of Centiro in this study. Chapter 4
presents the empirical findings of the interviews. The results are structured around
barriers and challenges, drivers and enables improving for emissions reporting. The
perspectives of both transport buyers and transport service providers are included
to capture different points of view throughout the value chain.

Chapter 5 provides an analysis of the findings by comparing it with the theory
presented in Chapter 2. Initially, a framework is presented that was developed to
provide an understanding of the results. This is followed by the research questions
that have guided the study. These are thoughtfully addressed to be able to answer
the purpose of the study. Chapter 6 presents the main conclusions of the study
by identifying the main challenges and barriers in collecting and sharing transport
emission data among transport service providers and transport service buyers in the
transport chain. The report concludes with references and appendices that contain
the questionnaires used.

4



2
Theory

This chapter presents the theoretical framework developed to support the study. It
is based on a review of the existing literature on emissions reporting methodologies
and the challenges associated with calculating emissions in transport systems. The
review also includes research on the sharing of emissions data across supply chains
and between stakeholders. Additional efforts were made to identify the literature
that addresses the availability and use of different data types by transport buying
companies. However, most of the existing literature approaches this topic from a
general perspective, with limited qualitative studies or empirical findings specific to
transport buyers.

The chapter begins by outlining the need for emissions reporting, highlighting key
frameworks and regulations that influence reporting practices. Then it examines the
main drivers and challenges associated with transport emissions reporting. Finally,
the chapter discusses the importance of accurate data, describing the different data
types used in emissions calculations and the most influential factors affecting data
quality and reliability.

2.1 Standardising Emissions Reporting
Sustainability reporting is gaining prominence, particularly following the introduc-
tion of the CSRD in 2023. Given the breadth of reporting requirements that span
most aspects of business operations, several voluntary frameworks have been devel-
oped to support emissions reporting. These initiatives aim to harmonise the calcu-
lation and reporting of GHG emissions from transport services, though they vary
in scope and complexity. Before 2012, there was no unified standard for report-
ing transport emissions, resulting in inconsistencies and output that was difficult
to compare between organisations (CLECAT, 2024). As illustrated in Table 2.1,
a wide range of methods has since emerged, varying in terms of transport modes
and segments covered. Initial efforts included the GHG Protocol (2001) and ISO
14064-1 (2006), which provided corporate-level reporting guidelines, but lacked spe-
cific methods for transport chains (International Organization for Standardization,
2006; World Resources Institute and World Business Council for Sustainable Devel-
opment, 2004). Other frameworks, such as ISO 14067 (focussing on product carbon
footprints) and national guidelines such as DEFRA (Department for Environment,
Food and Rural Affairs) (UK) or EU Emissions Trading System (EU ETS) for avi-
ation, introduced sector-specific approaches (International Organization for Stan-

5



2. Theory

dardization, 2018; Ehrler and Seidel, 2014). Calculation tools such as EcoTransIT
and SEMBA, and various emission factor databases also emerged. However, without
common regulation, these tools produced divergent results (Ehrler and Seidel, 2014).

Table 2.1: Primary methods for GHG emissions calculations in the transport sector

Standard/methodology Modes Segments
GHG protocol All modes Passengers & freight
EN 16258 All modes Passengers & freight
ISO 14083 All modes Passengers & freight
PEF All modes Passengers & freight
French transport code (Article L. 1431-3) All modes Passengers & freight
Parcel Delivery Environmental Footprint All modes Parcel
GLEC All modes Freight
SmartWay All modes Freight
Topsector All modes Freight
Clean Cargo Working Group Maritime Freight
EU MRV Maritime Freight
IMO DCS Maritime Freight
CORSIA Aviation Passengers & freight
ICAO/IATA RP1678 Aviation Freight
IATA Aviation Passengers
EU ETS aviation Aviation Passengers & freight

Source: (Schroten et al., 2024)

Although CSRD is not included in Table 2.1, this is because CSRD is not a methodol-
ogy for calculating emissions. Rather, it is a directive that mandates sustainability
reporting and refers companies to the ESRS (European Sustainability Reporting
Standards) for guidance on what must be reported. The ESRS outlines the required
disclosures but does not specify how to calculate emissions. Therefore, the frame-
works introduced in more detail in the following sections, EN 16258, GLEC, ISO
14083, and CountEmissionsEU, are specifically chosen because they focus on how
to calculate transport-related GHG emissions. These methodologies represent the
main approaches referenced in policy and practice today, and they form the basis for
the practical calculation work conducted by companies. They are also the methods
most frequently mentioned in interviews and secondary sources in this study.

6



2. Theory

2.1.1 CSRD
The CSRD, introduced in January 2023, is part of the EU’s Green Deal and aims to
improve consistency, comparability, and accessibility of sustainability information
(European Commission, 2021a). Given the significant transition required for busi-
nesses, it is being gradually rolled out based on company size, starting in 2024 with
companies previously covered by the Non-Financial Reporting Directive (NFRD)
(European Commission, 2025). Over time, additional companies will be included in
the scope of the directive (European Council, 2024). By allowing access to compara-
ble non-financial information from investment entities between the EU, the directive
is expected to facilitate investor assessments of sustainability risks and the broader
implications of their investments (European Commission, 2021a).

The European Commission (2023b) believes that an EU directive is essential to
establish uniform regulations throughout the EU, thus mitigating the risk of incon-
sistent reporting requirements between member states. Such discrepancies could
lead to increased costs for companies operating across borders (European Commis-
sion, 2023b). At the global level, an initiative of this nature is also expected to
positively influence the development of policies on sustainability reporting (Euro-
pean Commission, 2021a).

To enable companies to meet the requirements of the CSRD in practice, the Euro-
pean Financial Reporting Advisory Group (EFRAG) has developed the reporting
standards known as the European Sustainability Reporting Standards (ESRS) (Eu-
ropean Commission, 2025). The ESRS also takes into account existing voluntary
sustainability reporting frameworks, to prevent double reporting by companies (Eu-
ropean Commission, 2023a). The CSRD follows the principle of double materiality,
which means that companies must report both their impact on the environment,
such as emissions and pollution, and how environmental factors, such as climate
change, affect them (European Commission, 2023b). Emissions are classified into
three scopes to provide a comprehensive view of a company’s climate impact (ESRS
E1 Climate Change, 2022).

Scope 1: Direct emissions from a company’s own operations, for example, emissions
from facilities and vehicles. (ESRS E1 Climate Change, 2022).

Scope 2: Indirect emissions from energy consumption, particularly purchase of elec-
tricity, district heating, or cooling from external providers. Although these emissions
are not generated directly by the company, they are a consequence of its activities.
(ESRS E1 Climate Change, 2022).

Scope 3: Indirect emissions that occur outside of the company’s direct operations
but within its value chain. These can originate from suppliers, transportation, prod-
uct use, and waste management (ESRS E1 Climate Change, 2022). Scope 3 can be
divided into upstream (indirect emissions related to purchased goods and services)
and downstream (indirect emissions related to sold goods and services) (WBCSD
and WRI, 2011).

7



2. Theory

For a complete climate impact assessment, total GHG emissions must include all
three scopes (ESRS E1 Climate Change, 2022).

In February 2025 the European Commission adopted a simplification package, called
the Omnibus package, which entails several changes to the CSRD. The package ex-
empts about 80% of companies that were previously covered by the CSRD and
introduces some changes in how reporting should be conducted. Reporting require-
ments for companies currently covered by the CSRD will be postponed by two years.
These changes aim to make sustainability reporting more efficient and less burden-
some, while still holding the largest companies with the greatest environmental and
climate impact accountable. (Commission, 2025).

2.1.2 EN 16258
Published in 2012 by the European Committee for Standardisation, EN 16258 was
the first European standard for quantifying GHG emissions in transport services
(CLECAT, 2024). It applies to all modes of transport and uses the Well-to-Wheel
(WTW) approach, which incorporates both upstream (Well-to-Tank, WTT) and
operational (Tank-to-Wheel, TTW) emissions. The development of EN 16258 was
driven by the need for a standardised method to compare transport emissions. While
EN 16258 provided a foundational methodology, differences in interpretation led to
inconsistent results across companies, prompting the need for further harmonisation
(Ehrler and Seidel, 2014).

2.1.3 GLEC
Developed by the Global Logistics Emissions Council (GLEC) and launched in 2016,
the GLEC Framework provides a globally harmonised approach to calculating emis-
sions in multimodal freight chains (Smart Freight Centre, 2024; Fancello et al.,
2023). The framework is updated continuously to align with developments in the
climate and logistics sectors. Widely used by carriers, freight forwarders, and logis-
tics providers, it enables transparent comparisons across transport options (Fancello
et al., 2023; Smart Freight Centre, 2024). The framework aligns with several other
standards, including the GHG Protocol, the UN’s Global Green Freight Action
Plan, and CDP Reporting. It has also significantly influenced the development of
ISO 14083 (Smart Freight Centre, 2024).

2.1.4 ISO 14083:2023
ISO 14083:2023, published in 2023, replaced EN 16258 as a standard for quantifying
and reporting GHG emissions from transport chains (International Organization for
Standardization, 2023). It includes more detailed guidelines to account for both
direct and indirect emissions throughout the entire transport chain (International
Organization for Standardization, 2023; CLECAT, 2024). Developed by ISO in col-
laboration with Smart Freight Centre and the German Institute for Certification, it
builds on the GLEC Framework (Smart Freight Centre, 2023). The purpose of ISO

8



2. Theory

14083 is to create an international standard that can be used by both small and
large companies within global transport chain operations. It includes all transport
modes and extends coverage to operational emissions from logistics hubs (Interna-
tional Organization for Standardization, 2023).

The standard outlines four data types for emissions calculations:

• Option A: Primary data
• Option B: Model-Based Calculation
• Option C: Default values from a database
• Option D: Values from contracted operator using Option A or B.

Primary data is preferred, with clear guidance to prioritise modelled data over de-
fault values when primary data is unavailable. The selected method must accurately
reflect actual emissions, not just minimise reported figures (International Organi-
zation for Standardization, 2023). Further explanations on the different data types
can be found in Section 2.4.1.

2.1.5 CountEmissionsEU
CountEmissionsEU is a proposed EU initiative to introduce a mandatory standard
methodology for emissions reporting in transport, aiming to improve data compara-
bility and reliability across Europe. The proposal, tabled in July 2023, is currently
under legislative review. If adopted, companies reporting emissions from transport
chains would be required to follow ISO 14083, though reporting itself would remain
voluntary. The proposal also includes certification of external calculation tools to
ensure compliance with the methodology. As in ISO 14083, primary data is pri-
oritised, with a clear data hierarchy for consistent application of the methodology
(European Parliament, 2023).

2.2 Drivers for Transport Emission Reporting
Since most frameworks for transport emissions reporting have historically been vol-
untary, it is important to understand the underlying drivers that motivate organisa-
tions to engage in such efforts. Understanding these drivers also provides insight into
some of the challenges associated with implementing emissions reporting in practice.

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Table 2.2: Drivers for Transport Emission Reporting from Literature

Drivers Description
Internal • KPI for internal sustainability performance

• Adapting to regulations and prepare for
future standards

• Operational efficiency improvements and
cost savings

External • Commercial pressure
• Competitive advantage
• Emissions tracking is often a low

organisational priority

Anticipating Regulatory
Developments • Preparing for legal requirements

• Regulatory readiness

Dobers et al. (2019) categorises the drivers into three different categories. The
first relates to internal performance and strategic alignment. Tracking emissions is
necessary to monitor sustainability goals and internal improvements. Calculating
emissions and related key performance indicators (KPIs) enables organisations to
evaluate their progress, support strategic decision-making, and report transparently
to external stakeholders such as customers and shareholders (Dobers et al., 2019).
Additionally, Finger and Serafimova (2021) emphasise the growing business case for
carbon accounting. Emissions tracking is increasingly viewed as a means to improve
operational efficiency, reduce costs, and lower emissions. The ability to demonstrate
environmental progress can also provide competitive advantages, particularly when
customers are willing to pay a premium for more sustainable transport solutions.

The second driver is external commercial pressure. As noted by Dobers et al. (2019),
organisations are often required to report emissions to fulfil customer or contractual
demands. This is further supported by Kühne Logistics University (2024a), whose
survey shows that customer pressure is the second most important motivation for
SMEs to invest in decarbonisation. While emissions reporting is not explicitly men-
tioned, it can reasonably be assumed as a prerequisite for demonstrating progress,
as measurement is essential for validation.

The third driver involves anticipating regulatory developments. According to Dobers
et al. (2019), many companies adopt emissions reporting to prepare for future legal
requirements, positioning themselves as proactive and adaptable. Engaging in GHG
reporting prior to it becoming mandatory demonstrates not only regulatory readi-

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2. Theory

ness but also a commitment to environmental responsibility. As noted by (World
Economic Forum, 2022a), this proactive approach is increasingly seen as necessary
for all companies aiming to remain competitive and compliant in a rapidly evolving
regulatory landscape.

2.3 Challenges in Transport Emission Reporting
Emissions calculations are influenced by several factors, including the quality, data
completeness, calculation tools, system boundaries, modelling assumptions, and
emission factors. This section synthesises key challenges from the literature re-
garding sustainability reporting within transport emissions.

2.3.1 Organisational and Methodological Barriers
Table 2.3 presents challenges identified in the literature, categorised here as organi-
sational and methodological barriers. The remainder of this section provides a more
detailed description of these challenges and their implications.

Table 2.3: Organisational and Methodological Barriers from Literature

Challenge Description

Fragmented reporting
standards • Different actors use varied methods to

collect and report emissions
• Inconsistent terminologies and practices

complicate comparison
• Lack of harmonised metrics
• Feasibility for companies competing with

granularity from regulations

Regulatory ambiguity • Weak enforcement limits compliance
incentives

• Need for alignment with existing
regulations to avoid extra administration

• Potentially complicates international
operations and data collection

• Fragmented reporting standard
To effectively monitor, benchmark, and improve environmental performance, a clear
understanding of current and historical levels is needed (European Parliament, 2023;
Finger and Serafimova, 2021). However, the absence of a universally accepted
methodology for emission data collection, calculation and reporting poses a signifi-
cant challenge (EUR-Lex, 2020). In the current landscape, companies must choose

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2. Theory

among a wide range of frameworks (see Section 2.1), tools, and emission factors
databases (see Section 2.4), each differing in scope, allocation logic, methodology,
default values, objectives, and perspectives (European Parliament, 2023). Conse-
quently, methodological inconsistencies, large variances, and fragmented reporting
practices emerge, undermining the accuracy, comparability, relevance, and reliability
of emission data and reporting (Dobers et al., 2019; Finger and Serafimova, 2021;
European Parliament, 2023).

A key issue is the lack of a holistic framework that encompasses all transport modes,
regions, and operational contexts. This gap results in significant discrepancies not
only between companies but also across various transport modes (Dobers et al.,
2019; Finger and Serafimova, 2021). In addition, emissions scopes and organisa-
tional boundaries are often defined differently. For instance, emissions may be in-
consistently classified as direct (Scope 1) or indirect (Scope 3), and many reports
exclude emissions from upstream activities or administrative operations (Finger and
Serafimova, 2021). A review of 121 corporate sustainability reports found that only
a small fraction included emissions from energy supply or administrative processes,
resulting in substantial underestimations of overall carbon footprints (Dehdari et al.,
2023).

Frameworks also differ in granularity, with some applying TTW and others WTW
perspectives. Additional complexity arises in shared transport operations, where
emission allocation across multiple users is often unclear and non-standardised.
Emissions reporting also varies across hierarchical levels, ranging from product-level
calculations to corporate-level aggregates, making it difficult to integrate or com-
pare data across different levels of the supply chain Finger and Serafimova (2021).
These inconsistencies are illustrated by Stevens (2018) who described how Heineken
voluntary explored the possibility of broadening their emission report to include
air pollutants, illustrating the evolving and often ambiguous nature of emissions
scoping. Because reporting entities often determine their own reporting boundaries
without providing clear justifications, the comparability of reported emissions data
across companies is compromised (Stevens, 2018).

This fragmentation reflects a broader tension between methodological rigour and
operational feasibility. While academia often promotes granular data collection,
companies must consider constraints of time, budget, and personnel. As noted
by Stevens (2018), some companies question whether increased accuracy justifies
the cost. This highlights a broader divergence where academic approaches prioritise
rigour, while companies value simplicity, practicality and comparability. To improve
consistency, Dobers et al. (2019) recommend adopting an ISO standard. However,
stakeholder feedback in Finger and Serafimova (2021) highlight industry support for
the GLEC Framework, which is favoured for its flexibility, adaptability, and ongoing
updates.

• Regulatory ambiguity
To promote wider adoption of harmonised frameworks, some stakeholders advocate

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for mandatory emissions reporting. The European Commission’s CountEmission-
sEU initiative aims to standardise reporting methodologies across the transport
sector. Yet, it also recognises that in the absence of mandatory obligations, many
companies continue to rely on diverse and inconsistent methodologies (European
Parliament, 2023). Kühne Logistics University (2024a) emphasises the pivotal role
of national governments and the EU in decarbonising European road freight. Their
findings suggest that stakeholders view regulatory intervention as essential for estab-
lishing unified methodologies and clearer guidance. Furthermore, the effectiveness
of any new framework depends on its widespread adoption among cargo operators,
without broad industry uptake, its impact remains limited (Finger and Serafimova,
2021). Similarly, Stevens (2018) highlight that the effectiveness of voluntary frame-
works like GLEC depends heavily on collective adoption across the industry. How-
ever, their study also underscores a key challenge: while academia tends to focus on
the lack of standardisation, companies more often cite data access as the primary
barrier.

Notably, research suggests that developing a rigid, all-inclusive framework is not
sufficient on its own. Alignment with existing regulations, such as those within
the EU, is crucial to avoid creating additional administrative burdens or duplicating
reporting obligations (Finger and Serafimova, 2021). For example, European Parlia-
ment (2023) note that the proposed CountEmissionsEU regulation could complicate
reporting for international operators working across different jurisdictions. Varia-
tions in national reporting rules further compound this challenge, reducing data
comparability and increasing compliance costs for multinational firms (Finger and
Serafimova, 2021).

2.3.2 Data Quality and Availability
Table 2.4 presents challenges identified in the literature, categorised here as chal-
lenges with data quality and availability. The remainder of this section provides a
more detailed description of these challenges and their implications.

Table 2.4: Data Quality and Availability from Litareture

Challenge Description

Limited data access • Emissions data from subcontractors is often
unavailable

• Different degree of data depending on mode
and structure of transport chain

(Continued on next page)

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(Continued from previous page)

Challenge Description
Inconsistent practices • Varying databases used for emission factors

• Different calculation methods and model
assumptions

• Data quality varies and causes reduced trust

Technological limitations • IT systems between actors are not
interoperable

• Many systems lack real-time tracking
capability

• Aggregating and integrating data from
multiple sources is complex

• Limited data access
Improving the collection and accessibility of operational data and current emissions
levels is essential for accurate emissions reporting and for advancing the transport
sector toward more environmentally sustainable practices and more informed de-
cisions regarding transport mode selection (Finger and Serafimova, 2021). While
much of the existing literature emphasises harmonising of reporting frameworks,
challenges related to data collection are also addressed. Stevens (2018) report that,
according to one interviewed expert, the lack of comparability in emission results
stems less from methodological differences and more from data related issues. In
line with this, the GHG Protocol recommends prioritising robust data collection
systems, noting that the availability of high-quality data is often the primary obsta-
cle in developing GHG inventories (World Resources Institute and World Business
Council for Sustainable Development, 2004). Dobers et al. (2019) identify data col-
lection, data sharing between different stakeholders in the supply chain, and data
quality as the main barrier to emission calculation, with primary data often being
non-existent. Even when clear allocation methods are applied, such as those aligned
with EN16258, implementation remains difficult due to missing or inaccessible data
(Ehrler et al., 2016). Finger and Serafimova (2021) argue that more disaggregated
and activity-based primary data is needed to enable eco-labelling of products, sup-
port consumer transport choices, offer adequate recycling of products, and track
progress on emission reduction targets.

One structural issue is the limited control organisations have over the full extent
of transport chains. Multimodal and subcontracted arrangements introduce admin-
istrative complexity and hinder data collection and transparency, as the ability to
collect emissions data from transport providers depend on the extent of control
exerted over the transport. This leads firms to rely on internal data to maintain
traceability (Stevens, 2018). Moreover, data availability varies by transport mode.
Road transport, for instance, has seen increased adoption of granular tracking meth-

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2. Theory

ods, while inland waterway and rail sectors often lag behind (Finger and Serafimova,
2021).

Another dimension is trust. Data uncertainties can arise from inconsistencies in fuel
consumption records (Finger and Serafimova, 2021), vehicle specifications (Kiout-
sioukis et al., 2004), or trip distances (EUR-Lex, 2020), all of which are sensitive
to the data collection methods used and to real-world operating conditions (Euro-
pean Parliament, 2023). European Parliament (2023) report that transport buyers
have limited confidence in the data provided by carriers. As a result, users may
not demand emissions data, which in turn reduces carriers’ motivation to invest in
accurate calculations.

• Inconsistent practices
The absence of a standardised framework has led to a fragmented practices across
the sector. A wide range of tools, databases, and modelling methods are employed,
often based on varying assumptions and data inputs. Due to significant data gaps,
the use of default or modelled values is common. Consequently, there is substantial
variation in data quality, input data types, as well as data collection efforts across
firms (European Parliament, 2023; Ehrler et al., 2016).

Default values are based on general assumptions, such as average vehicle efficiency or
driver behaviour, that may not reflect actual operations (Kioutsioukis et al., 2004).
Methodological differences also persist in terms of granularity, system boundaries,
and geographical scope, resulting in incompatible outputs. As Finger and Serafi-
mova (2021) observe, the sector suffers from an “over-reliance on industry-default
values of carbon intensity and a failure to regularly recalibrate emission factors in line
with technology advancements and improvements in business practice”. To address
these shortcomings, several authors have advocate for default values to be scientifi-
cally validated and independently verified (European Parliament, 2023; Ehrler et al.,
2016). According to Ehrler et al. (2016), a centralised and widely accepted data-
bank, ideally established at the EU level, could significantly enhance the reliability
and consistency of emissions data.

Similarly, emission factors, used to estimate emissions per unit of fuel or activity, fur-
ther contribute to inconsistencies, varying widely in their source, transparency, and
relevance (International Organization for Standardization, 2023; Plassmann et al.,
2010; Finger and Serafimova, 2021). Selecting appropriate factors is challenging,
as assumptions are often undocumented or poorly explained (Stevens, 2018). To
improve consistency across transport modes and contexts, Finger and Serafimova
(2021) emphasise the need for regular updates and greater transparency.

The selective use to apply default values, particularly when these yield lower re-
ported emissions, undermines trust and credibility. Although this may be beneficial
for the reporting entity, it undermines the overall reliability of emissions reporting.
European Parliament (2023) and Stevens (2018) note that a harmonised verification
system would strengthen data reliability, but may simultaneously reduce the will-

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ingness to share data due to added costs and administrative burden.

Even when using the same tools and datasets, subjective judgment in areas like
boundaries setting and data allocation methods introduce subjectivity to the pro-
cess (Kioutsioukis et al., 2004). Dobers et al. (2019) describe this challenge as a
trade-off between granularity and feasibility, while aggregated default values are
easier to apply, they may not reflect actual operations, conversely, detailed data de-
mand more resources and specialised knowledge. These issues are especially evident
in decentralised organisational and complex supply chains, where multiple actors
may apply inconsistent assumptions. This diminishes transparency and traceability,
complicating verification and stakeholder trust (Stevens, 2018).

These issues result in considerable uncertainty in reported emissions, sometimes
yielding counter-intuitive outcomes, such as more precise data leading to higher
reported emissions. Inconsistent practices weaken comparability, as organisations
operating under similar conditions may report vastly different figures. This un-
dermines benchmarking and the credibility of environmental claims (Stevens, 2018;
Ehrler et al., 2016).

• Technological limitations
Reliable transport emissions calculation depends on coordinated data sharing across
the supply chain, but integration remains limited. As no single actor has all the
necessary information, collaboration is essential to generate reliable estimates on
shipment level (Dobers et al., 2019).

A major barrier lies in the lack of interoperability between systems. Despite the
widespread use of digital tools such as telematics, fuel receipts, and third-party
platforms, data is often stored in incompatible formats (Freitas and Gervásio, 2024;
Plassmann et al., 2010). Within organisations, disconnected operational and ad-
ministrative systems further fragment data flow, while between companies, digital
ecosystems lack standardisation (Kühne Logistics University, 2024b).

Finger and Serafimova (2021) highlight that data exchange is frequently constrained
at both the intra- and inter-organisational levels. They emphasise the need for
stronger collaboration mechanisms, citing promising examples, such as partnerships
between vehicle manufacturers and operators to access real-time data. Workshop
participants echoed this view, suggesting regulatory measures to require original
equipment manufacturers (OEMs) to share relevant vehicle data. A harmonised
framework for secure and standardised data exchange was identified as a key enabler.
However, implementing comprehensive data systems is resource intensive. Data
retrieval, validation, and integration require multiple iterations and quality checks.
These efforts require significant investments in time, personnel, and technology,
making it challenging for companies, particularly SMEs, to adopt robust tracking
solutions (Dobers et al., 2019).

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2.3.3 Data Sharing and Collection
Table 2.5 presents challenges identified in the literature, categorised here as chal-
lenges with data sharing and collection. The remainder of this section provides a
more detailed description of these challenges and their implications.

Table 2.5: Data Sharing and Collection Challenges from Literature

Challenge Description

Lack of trust and
collaboration • Emissions data is seen as commercially

sensitive and confidential
• Fear of negative exposure discourages

transparency
• Low collaboration across supply chain

actors

Limited data collection
capabilities • Many small carriers lack capacity to collect

and report emissions data
• Emissions accounting is seen as complex

and costly
• Limited demand and few financial

incentives discourage investments

• Lack of trust and collaboration
A major barrier to the availability of emissions data is the lack of trust among supply
chain actors, particularly in the sharing of operational and emissions-related data.
Although primary data, such as fuel or energy consumption, is considered the most
accurate input for emissions calculation, access to this information often requires
insight into operational processes that transport service providers are reluctant to
share (European Parliament, 2023). This reluctance is mentioned to stem from con-
cerns over data confidentiality. Ehrler et al. (2016) and European Parliament (2023)
emphasise that sharing activity-based data can conflict with company confidential-
ity policies, as it can reveal sensitive information on fuel usage, cost structures,
and operational performance. Kühne Logistics University (2024b) and Ehrler et al.
(2016) also report that emission data is widely viewed as commercially sensitive,
preventing providers from sharing it with regulators, industry groups, or reporting
platforms.

Importantly, this challenge is not exclusive to smaller companies. Although large
companies typically struggle with issues of data completeness and system integra-
tion, SMEs face more fundamental challenges, such as limited capabilities and in-
creased concerns about competitive exposure. Due to their smaller size and weaker
market positions, SMEs perceive a greater risk of sharing detailed operational data

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(European Parliament, 2023).

Despite these concerns, several authors argue that confidentiality should not out-
weigh the need for transparency. Dobers et al. (2019) advocate prioritising emissions
data exchange, noting that withholding such information undermines sector-wide
efforts to improve data quality and comparability. Without trust and collabora-
tion, companies often fall back on default values, perpetuating low data availability,
quality and limited emissions insight (Finger and Serafimova, 2021; European Par-
liament, 2023).

• Limited data collection capabilities
A core barrier to emissions data collection and reporting lies in the structure of
the land-based transport sector, which is dominated by a large number of SMEs.
This fragmentation creates a competitive environment with low margins, limiting
the resources available for investments in emissions tracking systems (Finger and
Serafimova, 2021).

Several studies highlight the capacity gap between smaller operators. Finger and
Serafimova (2021) report that 43% of 800 SMEs surveyed lacked any emission mea-
surement capabilities, while only 25% could measure emissions at the customer
level. The remainder reported only company-level measurements. Kühne Logistics
University (2024a) similarly found a strong correlation between fleet size and data
capability: 60% of carriers with fewer than 10 vehicles were unable to measure emis-
sions, compared to only 20% among those with fleets consisting of more than 100
vehicles. These figures underscore the difficulty of mandating detailed emissions
reporting in the sector (Finger and Serafimova, 2021). However, involving these
actors in the development of future reporting regulations is crucial to achieving EU
climate targets, given their numerical dominance and their pivotal role in transport
chains (Kühne Logistics University, 2024a).

The perception that emissions accounting is complex and costly further discour-
ages adoption, particularly among SMEs. Data calculation often requires combin-
ing information from multiple systems, databases, and calculation models. Smaller
companies typically lack the personnel, expertise, or digital infrastructure needed
to manage these tasks (European Parliament, 2023). Financial incentives also play
a role in shaping company priorities. EUR-Lex (2020) note that in the absence
of market-based rewards, such as tax incentives or sustainability-linked financing,
many companies deprioritise emission tracking, especially when not legally required.
Without a clear return on investment, emissions reporting is often seen as a cost
rather than a value-adding activity. In addition, complex structures further increase
resource demands. Larger transport service providers may rely on multiple subcon-
tractors, each with their own data practices, formats, and assumptions. Internal
organisational structures and the complexity of global operations further compli-
cate data sharing between departments and subsidiaries, with regional disparities in
data maturity (Stevens, 2018). In contrast, a small carrier that works with multiple
carriers may face conflicting data demands, resulting in a variety of method and for-

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mat requirements from their multitude of customers (European Parliament, 2023).

Another barrier to data collection and data sharing is the limited demand for emis-
sion data from customers. Finger and Serafimova (2021) report that many transport
service providers do not collect or share emission data simply because it is not re-
quested. In one study by Kühne Logistics University (2024a), 76% of the carriers
stated that fewer than 10% of their customers requested GHG-related data. Since
customer demand is a key driver of emissions reporting (see Section 2.2), this lim-
ited interest contributes to a low engagement among providers. Although integrating
emissions criteria into procurement processes could help incentivise data sharing, en-
vironmental performance still ranks below cost in most purchasing decisions (Finger
and Serafimova, 2021). Without clear market signals or commercial advantages re-
lated to emissions transparency, providers deprioritise such efforts (Kühne Logistics
University, 2024a).

2.4 The Need for Accurate Data
This section outlines why accurate data are essential for reliable emissions report-
ing in the transport sector. It begins by explaining the different data categories
defined in ISO 14083, primary, modelled, and default data, and highlights the im-
portance of using primary data whenever possible. The section then describes how
GHG emission factors are used in calculations, including the challenges posed by
variation between countries and methodologies. Finally, it presents key operational
parameters that significantly influence emission estimates, such as load factor, ve-
hicle type, and transport distance, and discusses how assumptions about these can
lead to major deviations in reported emissions.

2.4.1 Data Categories
Emission calculations in the transport sector rely on two primary data categories,
primary and secondary data (International Organization for Standardization, 2023).
Secondary data includes both modelled and default data and do not meet the criteria
required for primary data. International Organization for Standardization (2023)
emphasises that primary data is the preferred input, as it is derived from direct
measurements and is therefore expected to give more accurate results. This data
should be possible to obtain from the transport operator or carrier.

International Organization for Standardization (2023) defines the different data
types as;

• Primary data: “quantified value of a process [...] or an activity obtained
from a direct measurement or a calculation based on direct measurements”,

• Modelled data: “data established by use of a model that takes into account
primary data [...] and/or greenhouse gas (GHG) emission-relevant parameters

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of a transport operation [...] or hub operation [...]”,

• Default data: “secondary data [...] value drawn from a published source”,

• Secondary data: “data which do not fulfil the requirements for primary data
[...]”. (International Organization for Standardization, 2023).

Secondary data include modelled or default data and therefore do not meet the re-
quirements for primary data (CLECAT, 2024). Modelled data are processed, struc-
tured, or created based on a model. Meaning it is a representation of reality based
on assumptions, calculations, or simulations (SAP SE, 2024). For transparency and
verifiability, models and data sources should be thoroughly documented (CLECAT,
2024). Default data are typically used when neither primary nor modelled data is
available. It is a predefined estimate or standard value and is retrieved from ex-
isting databases, industry standards, or standard/preset values. Although default
data simplifies the reporting process, especially for activities with low importance
of emission, they vary in quality. Some default values are broad approximations,
while others are based on robust empirical data. The closer these values are to ac-
tual operational conditions, the more reliable they become for emission estimation
(CLECAT, 2024).

Although primary data are generally considered the most precise, their accuracy still
depends on how they are collected, processed, and verified. Likewise, default emis-
sion intensities, despite being standardised, may fail to reflect real-world scenarios.
For this reason, transparency regarding data sourcing, modelling assumptions, and
verification processes is essential. Clearly revealing how emissions are calculated not
only improves trust in the results but also improves the comparability and credibility
of sustainability disclosures (International Organization for Standardization, 2023).

2.4.2 GHG Emission Factors
GHG emission factors are coefficients used to convert energy consumption and re-
frigerant leakage into quantifiable GHG emissions (International Organization for
Standardization, 2023; CLECAT, 2024; Smart Freight Centre, 2024). These factors
form the basis of emissions calculations, enabling organisations to translate opera-
tional data into measurable climate impacts. Emission factors for energy carriers
incorporate both upstream and operational emissions, covering the entire energy
lifecycle, whereas refrigerant factors typically capture emissions only from the oper-
ational phase. To ensure accurate results, each transport activity must be linked to a
specific emission factor that corresponds to the relevant energy carrier or refrigerant.

The variability in emission factors can be attributed to several contextual variables,
such as the energy source’s origin, production method, and the characteristics of the
local consumption environment. National emission factors are often tailored to local
conditions, including average temperatures, the regional electricity grid mix, typi-
cal vehicle technologies, and fuel types, to improve comparability within national

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2. Theory

borders. However, this localisation may hinder global reporting efforts. For multi-
national logistics operators, differences in national methodologies and calculation
assumptions create significant challenges for producing consistent and comparable
emissions data. Discrepancies also persist due to the varied approaches taken by
different stakeholders when establishing emission factors. Some emission factors
conform to ISO 14083 by accounting for WTT emissions, while others capture only
TTW emissions. This distinction affects the comprehensiveness of the emissions
inventory. Given these variations, several authors underscore the need for trans-
parent, reliable, and standardised emission factors (International Organization for
Standardization, 2023; CLECAT, 2024; Kioutsioukis et al., 2004; EUR-Lex, 2020).

2.4.3 Impact of Input Parameters on Emissions
Greenhouse gas (GHG) emissions from transport are typically calculated using a
well-to-wheel (WTW) approach, as defined by International Organization for Stan-
dardization (2023) and Smart Freight Centre (2024). This methodology encompasses
both direct and indirect emissions and is divided into two components: well-to-tank
(WTT), which includes emissions from energy production and distribution up to the
fuelling point, and tank-to-wheel (TTW), which includes emissions from the actual
operation of the vehicle or vessel. See figure 2.1.

Figure 2.1: Visualisation of the WTT + TTW = WTW calculation

As emissions are calculated based on the energy consumed throughout the entire
transport chain, several operational and contextual parameters become critical for
accurate results. Both ISO 14083 and the GLEC Framework recommend including
emissions from all legs of the transport chain, including return or empty trips, and
calculating them using a tonne-kilometre unit, which requires data on cargo mass
and distance transported.

International Organization for Standardization (2023) and EcoTransIT World (2024)
identify the most influential factors that affect GHG emission, as shown in Table
2.6:

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Table 2.6: List of key parameters for determining GHG emissions

Category Parameter Details

Vehicle/Vessel Type Ship type, freight/passenger aircraft
Size and weight Payload capacity, motor concept, en-

ergy, transmission

Operational Capacity utilisation Load factor, empty trips
Cargo specification Mass-limited, volume-limited, general

cargo, pallets, container

Driving Conditions Conditions Number of stops, speed, acceleration,
air/water resistance

Traffic Route Route Road category, rail/waterway class,
curves, gradient, flight distance

Freight Details Weight Total weight of freight

Distance Transport distance

While these parameters are essential for accurate emission calculations, they are of-
ten difficult to obtain. As discussed in the section on data types (see Section 2.4.1),
many companies lack access to detailed primary data and instead rely on secondary
sources. This introduces uncertainty because assumptions must be made about key
variables, which might have a substantial impact on emission outcomes. An impact-
ing parameter is the load factor, the ratio of cargo transported to available transport
capacity. Calculating the load factor is complex and varies between stakeholders.
It may be defined based on pallets, volume, weight, volumetric weight or shipping
weight, each producing different results (Wang et al., 2022). These variations can
have significant consequences. For example, calculating the load factor is vastly
complicated and the methods differ between different stakeholders. (Wang et al.,
2022). These differences give large effects. For example, reducing the assumed load
factor from 100% to 20% can result in an increase in emissions of more than 260%
per transported unit (Smart Freight Centre, 2024).

Therefore, although the calculation methodology is standardised, the quality of the
input data plays a decisive role. Inaccurate assumptions can lead to an overesti-
mation or underestimation of emissions, reducing the credibility and comparability
of sustainability reports. Thus, it is crucial for companies to ensure that secondary
data are as representative of actual transport conditions as possible and to document
all assumptions transparently.

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3
Methods

This study aim to explore the challenges and barriers associated with the collec-
tion and sharing of transport emission data among transport service providers and
buyers within the transport chain. To address this, a multiple case study design
was employed. The study relied on primary and secondary data. Primary data were
collected through semi-structured interviews, while secondary data, drawn from sus-
tainability reports, company information, and academic literature, served to con-
textualise and complement empirical findings.

3.1 Empirical research
The research was carried out in collaboration with Centiro, a company operating
in the logistics sector. The authors initially approached Centiro with a proposed
research topic, which was refined through a series of exploratory interviews and
workshops. The final topic was mutually agreed on, aligning with Centiro’s opera-
tional expertise and offering a valuable context for accessing professional knowledge
relevant to the study. More details about the company are provided in section 3.3.

A qualitative, inductive approach guided the concept development, with the study
primarily built on empirical data. 19 semi-structured interviews were conducted with
selected companies to investigate the current practices and challenges connected to
collection and exchange of emissions data. These challenges were analysed from the
perspectives of different stakeholder roles in the value chain, taking into account
differences based on stakeholder position and organisational characteristics.

3.1.1 Secondary Data
The study began with a review of secondary data related to existing and emerging
regulations and frameworks that govern the reporting of transport emissions. This
provided an overview of the regulatory landscape and the expectations placed on
companies. The review focused on official sources such as the European Commis-
sion, European Parliament, and EFRAG (European Financial Reporting Advisory
Group), as well as industry organisations such as the Smart Freight Centre and
international standardisation bodies such as ISO and SIS (Swedish Standards Insti-
tute). These sources were deemed reliable given their relevance and wide application
in the sector.

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3. Methods

Google was primarily used to locate regulatory content, as such materials are more
commonly published on institutional websites than in academic journals. Scopus
and Google Scholar were used to identify academic studies that address the impli-
cations of these regulations and methodologies for the calculation of emission.

The most used search terms:

• ISO 14083
• ISO 14064
• CSRD
• Emission Data
• Transport Emission Calculation
• Transport Emission Reporting
• Logistic Emission Calculation
• Emission Calculation Methodology

Additional search terms were used to ensure the inclusion of relevant literature sup-
porting the research objectives. Since the CSRD directive was introduced in 2021
and finalised in 2022, sources published from 2021 onward were prioritised to capture
contemporary insights and early implementation experiences. As recently published
sources as possible were preferred, as they are likely to include more insights and
empirical experiences from the market, rather than merely forecasts or potential
challenges. Regarding the calculation of transport emissions, the publication date
was deemed to not impact the reliability. Although more current research were
important, to understand how current regulations and frameworks recommend the
calculation of emissions, the underlying principles and definitions used often build
on previous research within the field. Therefore, older sources were also considered
valuable, given the continuity in underlying theoretical frameworks, and provided
that they were of sufficient quality.

Prior to each interview, sustainability reports and general company information
were reviewed to tailor questions to each participant’s organisational context. This
preparatory step enabled more targeted and relevant discussions, strengthening the
overall quality of the interviews.

3.1.2 Interviews
To address the purpose of this study, semi-structured interviews were conducted
with stakeholders in the transport chain. This method offered both structure and
flexibility, enabling consistency across interviews while allowing interviewees to elab-
orate and introduce new perspectives. Each interview lasted approximately 45–60
minutes, which provided enough time to explore the subject in depth, while main-
taining a clear structure aligned with our research questions.

The interviewees received an advance copy of the interview guide, a background sum-
mary, and the study’s objectives. This preparation allowed participants to nominate

24



3. Methods

the most suitable respondent within their organisation. Typically, a representative
participated per company, though their roles varied, ranging from sustainability and
procurement to data analysis. In some cases, the roles were even more diverse, de-
pending on how the responsibilities were distributed within the organisation. These
variations influenced the responses to some questions, as the participants had dif-
ferent levels of insight into specific areas. When participants were unable to answer
certain questions due to role limitations, this was taken into account in the anal-
ysis and interpretation of the results. In some interviews, multiple representatives
with different roles participated. The diversity of roles enriched the discussion and
provided more comprehensive answers. In addition, obtaining varying perspectives
increased the likelihood that all questions could be answered more thoroughly, since
participants were able to complement each other’s knowledge and provide more in-
depth information.

Semi-structured interviews are widely used in qualitative research because they are
based on open-ended pre-defined questions, enabling the researcher to guide the
conversation while still allowing the respondents to elaborate on their thoughts and
introduce new perspectives (DiCicco-Bloom and Crabtree, 2006). This format is
particularly useful in this type of study, where practices and challenges may vary
across actors, and where the goal is to explore both shared and divergent expe-
riences. The use of a semi-structured interview guide ensured that all interviews
addressed the key research themes systematically, while allowing space for further
follow-up questions (DiCicco-Bloom and Crabtree, 2006). The interview guides used
for transport buyers and transport service providers are included in Appendix 1 and
Appendix 2.

Additionally, semi-structured interviews offer a practical advantage in that they can
be conducted in a single session, making them well-suited for professionals with
limited availability (DiCicco-Bloom and Crabtree, 2006). This was particularly im-
portant in our study, as several participants held time-constrained roles and were
unable to commit to more extensive participation. The majority of interviews were
conducted remotely, with only two held in person. This choice improved efficiency
given the wide geographical distribution of participants, spanning across Sweden
and Denmark, and the time limitations of the project. No noticeable difference in
quality or depth was observed between remote and in-person interviews, and there-
fore no separation of the results based on interview format was made.

Alternative formats of interviews were considered, but ultimately excluded. Unstruc-
tured interviews, for example, are better suited for long-term research, resembling
informal conversations, lacking the consistency needed to address specific research
questions in a focused way, making it harder to compare responses between partic-
ipants (Corbin and Morse, 2003; Gray, 2009). Non-directive or informal conversa-
tional interviews, which involve spontaneously generated questions, were also ruled
out, as they risk drifting too far from the core themes and reducing the compara-
bility between interviews (Gray, 2009).

25



3. Methods

In contrast, the semi-structured format provided a strong balance between depth
and comparability, facilitating the collection of rich empirical data while maintaining
coherence between interviews (DiCicco-Bloom and Crabtree, 2006). This method
proved effective for identifying the range of experiences and perceptions related to
emission data collection and sharing in transport chains.

3.2 Company Selection and Study Implications
This section presents the selection of the companies included in the study and out-
lines the rationale for their inclusion. A brief description of the operations of each
company is provided to give context to their role within the transport and logistics
sector. Each section also discusses how the selection of the specific companies may
have influenced the study and its results.

In this study, we have chosen to anonymise all interviewed companies, even though
all but one, gave their consent to be named in the report. This decision was made to
ensure equal treatment between all participants and create a safe space that encour-
ages openness and honesty in descriptions. By treating all interviews confidentially,
we minimise the risk that sensitive or critical information could be linked to a spe-
cific organisation. The companies are therefore referred to using code names (e.g.,
GlobalFreight, LocalHaulier and BioIndustry) to preserve anonymity while retaining
relevant contextual information.

3.2.1 Transport Buyers
In selecting interview participants from the category of transport buyers, the main
objective was to include large companies subject to the CSRD. These companies
were expected to have experience with the collection, management and report-
ing of transport-related emissions data, making them particularly relevant for the
study’s focus on practical challenges and barriers. To ensure broader representation,
the sample also included companies not currently covered by CSRD requirements,
such as TechDistributor, which does not meet the size thresholds, and Forrester, a
member-owned company not legally obliged to report under the directive. These
organisations were included to explore how smaller or differently structured compa-
nies handle emission data, particularly when indirectly pressured by customers or
market expectations. It was assumed that such companies may still face growing
demands for emissions data, especially from clients with their own reporting obli-
gations. Their inclusion offered valuable insights into challenges experienced under
different conditions, such as reduced bargaining power with suppliers or fewer inter-
nal resources for sustainability reporting.

The selection of companies also deliberately aimed to include a range of industries.
The final sample included product manufacturers, trading firms, and service-oriented
companies. This diversity was intended to avoid sector bias and better reflect the
variation in industry norms, customer demands, and regulatory pressures that in-
fluence how companies approach sustainability. By engaging organisations across

26



3. Methods

multiple sectors, the study sought to identify both common patterns and industry-
specific challenges, thereby enhancing the generalisability of the findings.

Participants were identified through multiple channels. For transport buyers, a
majority of contacts were made through Chalmers’ career fair, where the authors
actively sought out representatives in relevant roles. Additional interviewees were
found through network contacts. Some contact information was also provided by
Centiro, helping to establish a connection with a key stakeholder. This combination
of outreach methods was essential in securing a diverse set of interviewees under the
time constraints of the study.

While this approach enabled access to a broad range of perspectives, it also had
limitations. The heterogeneity of the companies, in terms of size, industry, owner-
ship, and regulatory exposure, can limit the comparability of results. Furthermore,
companies with more advanced sustainability initiatives may be overrepresented,
as they were more likely to respond to outreach and have staff capable of address-
ing emissions-related questions in detail. Nonetheless, this variety is believed to
have provided a more comprehensive view of market conditions and to have helped
identify both shared and context-specific challenges. The resulting insights offer a
balanced understanding of the reporting landscape for transport emissions across
different types of organisations.

A thematic analysis was conducted to structure the results by identifying recurring
patterns and themes in the collected data. This method was chosen as it allows for a
systematic approach to analyse qualitative material and provide deeper insight into
complex issues (Naeem et al., 2023).

Table 3.1: Interviewed Transport Buyers

Company Role of Interviewee Transport
Modes

Size Geographical
Range

MedTech Customer Relationship
Manager • Air

• Maritime
• Rail
• Road

Large Global

TechDistributor Logistics Specialist,
Sustainability
Coordinator

• Air
• Maritime
• Road

Medium Global

AeroManufacturer Environmental
Sustainability Lead • Air

• Maritime
• Rail
• Road

Large Global

(Continued on next page)

27



3. Methods

(Continued from previous page)
Company Role of Interviewee Transport

Modes
Size Geographical

range

TruckOEM Supply Chain
Sustainability Controller • Air

• Maritime
• Rail
• Road

Large Global

Recycler Logistics Developer • Maritime
• Rail
• Road

Medium Nordics

Forrester Quality and Compliance
Manager • Maritime

• Rail
• Road

Medium Nordics

AutoOEM Sustainability Leader,
Purchasing Analyst • Air

• Maritime
• Rail
• Road

Large Global

WholesaleDistributor Transport Manager • Air
• Maritime
• Rail
• Road

Medium Nordics

3.2.2 Transport Service Providers
In selecting transport service providers for this study, we initially prioritised larger
companies, based on the assumption that these organisations would possess more es-
tablished processes for emissions data management and allocate greater resources to
sustainability reporting. However, early interviews with transport buyers revealed
the crucial role played by smaller hauliers, often operating fewer than ten vehicles,
within many transport chains. These smaller actors are frequently subcontracted by
larger transport providers, freight forwarders, third-party logistics providers (3PLs),
and end customers. Their central role adds complexity to both data flows and the
overall structure of the value chain. Figure 3.1 presents our conceptualisation of the
hierarchy typically found in land-based transport supply chains. The figure illus-
trates the layered subcontracting relationships, with smaller carriers positioned at
the base, operating under larger logistics providers and intermediaries. Blue arrows
indicate one-way contractual relationships, while red arrows represent two-fold rela-
tionships, where an actor may both contract others and be contracted themselves.
To reflect this structure and gain a more comprehensive understanding of the sec-
tor, we sought to include the perspectives of smaller transport companies. Engaging
with smaller hauliers proved challenging, as these companies often lacked the time
or capacity to participate in interviews. In several instances, our outreach efforts
received no response. To address this, we interviewed representatives from trans-

28



3. Methods

port cooperatives and industry associations such as LocalHaulier2, LocalHaulier3,
LocalHaulier1, and IndustryOrg. These organisations, which support their member
hauliers in operational matters but do not operate fleets themselves, offered valuable
insights into the conditions and data-related challenges faced by smaller carriers, in-
sights that would otherwise have been difficult to obtain.

Figure 3.1: Conceptualisation of transport structure and relations

Participants were identified through a combination of outreach strategies. The ma-
jority of transport providers were contacted via LinkedIn, where we targeted indi-
viduals in relevant roles. Additional contacts were established through Centiro, the
supporting company in this study, and one interview was arranged through direct
email. This varied approach was instrumental in securing a diverse set of partici-
pants within the time constraints of the research.

Consistent with the study’s purpose, no specific transport mode was prioritised.
Instead, the sample included companies operating across multiple modes to reflect
the diversity of the sector. For example, GlobalShipping and PublicTransporter
contributed insights related to ocean and public transport, respectively. Several
companies, including ParcelCarrier and GlobalFreight1, operated multimodal net-
works that combined road, rail, ocean, and air freight. While this provided a more
comprehensive understanding of emissions data challenges across transport modes,
it is important to note that none of the companies interviewed directly operated rail
or air transport services. Rather, these services were procured from third parties.
As such, the findings related to rail and air modes are based on the perspective of

29



3. Methods

transport buyers, rather than operators, which may limit the depth of insight into
the specific challenges of these segments.

The included providers also represented a range of operational contexts. A few fo-
cused on parcel delivery and last-mile logistics, while others were involved in large-
scale freight transport for construction or industrial sectors. This diversity enabled
the study to explore how emissions data management practices vary depending on
the type of cargo, transport distance, and customer demands. While the breadth of
operations covered contributes to a more holistic understanding, it also limits the
granularity of insights within specific transport segments, such as last-mile delivery.

In summary, the selection of transport service providers facilitated a broad and rep-
resentative understanding of the challenges associated with emissions data collection
and reporting. Although the inclusion of larger companies and industry organisa-
tions strengthened the study’s empirical base, the limited coverage of rail and air
operators and the broad scope of transport operations constitute limitations that
should be considered when interpreting the findings.

Table 3.2: Interviewed Transport Service Providers

Company Role of Interviewee Transport
Modes

Size Geographical
Range

ParcelCarrier Sustainability Manager • Air
• Maritime
• Rail
• Road

Medium Europe

GlobalFreight1 Sustainability Specialist • Air
• Maritime
• Rail
• Road

Large Global

GlobalFreight2 Sustainability Director • Air
• Maritime
• Rail
• Road

Large Global

LocalHaulier1 Vice CEO & Head of
Distribution and Crane • Road Small Sweden

LocalHaulier2 Sustainability Manager,
Head of Business
Development for Thermo
and Distribution

• Road Small Sweden

GlobalShipping Senior GHG Emissions
Specialist • Air

• Maritime
• Rail
• Road

Large Global

(Continued on next page)

30



3. Methods

(Continued from previous page)
Company Role of Interviewee Transport

Modes
Size Geographical

range

LocalHaulier3 CEO • Road Small Sweden

Nordic3PL Sustainability Manager • Air
• Maritime
• Rail
• Road

Medium Nordics

IndustryOrg Technology Specialist,
Sustainability Manager,
Industry Representative

• Road N/A Sweden

Speditor Business Developer • Air
• Maritime
• Rail
• Road

Medium Global

PublicTransporter Sustainability Manager • Road
• Maritime

Medium Sweden

3.3 Centiro
Centiro is a Swedish technology company headquartered in Borås, founded in 1998.
The company specialises in developing cloud-based solutions that support the man-
agement of goods flows in e-commerce, logistics, and industrial operations. Through
its software platforms, Centiro helps businesses connect and optimise their delivery
and service networks. These solutions enable more efficient transport operations,
improved customer experiences, and real-time handling of complex logistics flows.

In this study, Centiro has been both a collaboration partner and a knowledge re-
source. At the initial stage of the project, representatives from Centiro participated
in exploratory discussions with the authors to define the overall research direction
and formulate the study’s focus. Leveraging their industry expertise and familiarity
with emerging challenges in the logistics sector, Centiro played an active role in
refining the study’s purpose and shaping the final research questions. Centiro also
contributed to the theoretical groundwork by helping to identify relevant regulatory
frameworks and current developments in transport emissions reporting. In addition,
the company provided valuable support in the data collection process by facilitating
contact with potential interview participants. This enabled the inclusion of diverse
perspectives from both transport buyers and transport service providers. Through-
out the course of the study, preliminary findings and analytical reflections were
continuously shared with Centiro. Their feedback not only strengthened the practi-
cal relevance of the study but also ensured that the research remained grounded in
the operational realities of the industry.

31



3. Methods

32



4
Result

This chapter presents a structured synthesis of the empirical findings derived from
interviews with both transport service buyers and transport service providers. The
results are illustrated with selected quotations from the interview transcripts to sup-
port interpretation and authenticity. To address the study’s objective of identifying
challenges in collecting, and sharing transport emission data, a thematic analysis
was conducted. As summarised in Table 4.1, the chapter is organised into four main
sections. It begins by outlining key barriers and challenges, followed by an overview
of current sustainability reporting practices and transport chain structures to pro-
vide the necessary context. The subsequent sections explore the identified drivers
that incentivise emissions reporting and conclude with a presentation of enablers
that could facilitate improved data transparency and reporting accuracy.

Three overarching categories of challenges were identified: organisational and method-
ological barriers, issues related to data quality and availability, and difficulties sur-
rounding data sharing and collection. In parallel, both internal and external drivers
for emissions reporting were recognised, ranging from regulatory compliance to com-
mercial incentives. Finally, the study identified several enablers, including digital-
isation, standardisation, and collaboration mechanisms, that have the potential to
support enhanced emission visibility.

In this framework, barriers and challenges refer to factors that obstruct the flow and
visibility of emission data across supply chains. Drivers represent motivating forces
that motivate organisations to engage in emissions reporting and data exchange.
Enablers, while not prerequisites for reporting, are elements that facilitate better
alignment with standards such as ISO 14083. Their absence does not necessarily
prevent emissions reporting, but often leads to reliance on less accurate forms of
data, such as secondary sources rather than primary sources.

This structure aims to clarify the nature of existing obstacles, the motivations to
address them, and the mechanisms through which more transparent and standard-
ised reporting practices could be achieved.

33



4. Result

Table 4.1: Overview of Themes and Associated Challenges

Theme Challenges
Barriers and Challenges • 4.1.1 Organisational and Methodological

Barriers
• 4.1.2 Data Quality and Availability
• 4.1.3 Data Sharing and Collection

Drivers • 4.2.1 External drivers
• 4.2.2 Internal drivers

Enablers • 4.3.1 Technological Solutions and
Standardisations

• 4.3.2 Collaborative practices

4.1 Barriers and Challenges
This section presents the principal barriers and challenges identified through the in-
terviews, structured around three thematic areas: organisational and methodological
barriers, data quality and availability, and data sharing and collection. Each theme
is further divided into specific challenges, which are elaborated through narrative
analysis and supported by illustrative quotations from respondents.

4.1.1 Organisational and Methodological Barriers
Table 4.2 summarises the organisational and methodological barriers reported by
interview participants. These challenges reflect limitations in internal coordination,
strategic prioritisation, and understanding of emissions calculation methodologies
across both transport buyers and service providers.

Table 4.2: Organisational and Methodological Barriers

Challenge Description

Fragmented organisational
structures and communication • Decentralised procurement complicates

emission tracking
• Lack of internal coordination between

sustainability and operations teams

(Continued on next page)

34



4. Result

(Continued from previous page)

Challenge Description

Low strategic prioritisation of
transport emissions • Cost is prioritised over sustainability

• Limited resources for emissions data
management internally

Lack of understanding of
methodologies • Confusion regarding required data types

and input parameters
• Uncertainty about allocation methods
• Inconsistent calculation approaches across

companies
• Limited internal knowledge of emissions

modelling

• Fragmented organisational structures and communication
As shown in Tables 3.1 and 3.2, the interviewees held a diverse set of roles, including
sustainability managers, logistics specialists, procurement professionals, and quality
coordinators. A key observation made by the researchers was the noticeable dis-
connect between those responsible for emissions reporting and those tasked with
operational data collection. While sustainability teams frequently perceived data
availability as adequate, individuals working closer to day-to-day operations de-
scribed the process as challenging, often citing difficulties in accessing accurate,
complete, and consistent data. This disconnect was also identified and described by
LocalHaulier2: "We should also note that often those of us in logistics talk to logistics
people. (...) You can see that CSRD, it is something that the quality departments
are doing. The people I talk to do not know what it means.".

Several organisations noted that complex, decentralised structures, particularly in
global operations, hindered emissions data visibility and coherence. At AeroManu-
facturer, fragmented procurement processes and organisational complexity presented
a substantial challenge: "Since we are such a big company with so many different
locations with such complex business structures, even for us to just know who do we
work with where and what constellations (...). To organise all of that and to have a
structured approach to collecting and calculating data is still a major challenge.".

Similarly, MedTech described the difficulty of aligning data systems across global
and local teams: "There has been demands for much, much more detailed data and
in such an extremely large organisation like MedTech, some data may not be shared
as much. So, the global functions may be sitting on certain data that may be com-
plete, but then we in Sweden may not get all the data or do not even know that it
exists. Currently, we are synchronising data usage so that everyone uses the same
data.".

35



4. Result

In summary, organisational fragmentation, both vertically between strategic and
operational levels and horizontally across departments and geographic locations,
emerged as a key internal barrier to reliable emissions reporting. Addressing these
structural and communication gaps is a prerequisite for enabling consistent and ver-
ifiable emission data management.

• Low strategic prioritisation of transport emissions
The interviews revealed substantial variation in how companies organise and pri-
oritise their sustainability work, particularly regarding emissions reporting. Not all
participating companies report according to regulatory frameworks, often due to
exemptions related to size or ownership structure. Nevertheless, some companies
chose to voluntarily align with such standards as a demonstration of environmental
responsibility. For example, Forrester will begin to report in accordance with the
CSRD, despite not being formally obliged to do so. An observation in the interviews
was the relationship between regulatory exposure and the maturity of sustainability
practices. Companies subject to mandatory reporting obligations typically demon-
strated a more developed understanding of emission scopes, data categorisation, and
pursued a higher degree of granularity in their data collection. Additionally, the
duration of a company’s engagement in sustainability reporting appeared to influ-
ence the institutionalisation of routines. Organisations with a longer history in this
domain generally had more formalised processes and internal structures, whereas
those newer to emissions reporting described difficulties in establishing consistent
data flows.

In most companies, the main strategic focus was on reducing emissions rather than
improving access to emissions data. This was especially true for transport buy-
ers, whose emissions are mostly caused by other parts of their operations, not by
transport. In contrast, transport service providers emphasised that limited customer
interest constrained their ability to prioritise and invest in both emissions reduction,
and data collection, despite interest in more sustainable solutions. A central theme
emerging from the interviews was the prevailing cost pressure in the logistics sector,
particularly in transactions between transport buyers and service providers. Many
respondents noted that sustainability considerations were frequently deprioritised
in favour of cost-efficiency and delivery performance. This was clearly articulated
by LocalHaulier2: "But it is about saving money, shipping costs, delivery quality,
delivery precision. That is what customers want." and "Many in an organisation see
logistics as a necessary evil. It does not generate money, it only costs money.".

Despite this, some transport service providers expressed a proactive approach, aim-
ing to position themselves competitively through environmental branding. As Lo-
calHaulier1 noted: "We want to have a strong environmental profile with our vehicles
all the time. Because we believe that it will get us more jobs.". However, for smaller
hauliers operating under tight financial margins, such efforts were often infeasible
without clear commercial benefits or customer demands. This was underscored by
IndustryOrg: "There must be a logic to what problems to solve. (...) I think it has to

36



4. Result

become part of the business more than anything else. Then I think that competence
will come with it.".

Similarly, Speditor explained that transportation is often perceived as a background
function that is expected to operate seamlessly, which contributes to the low prioriti-
zation of emission data among their customers: "For customers transport is probably
a question of price and lead time. It easily becomes a function that should just work,
and you may not put much thought into how to do it or who to do it with. (...) The
customers might not spend as much time on the environmental aspects.".

A lack of strategic integration was also noted on the buyer side, particularly in
translating emissions data into actionable business intelligence. AeroManufacturer
articulated this challenge: "Another challenge is how to use the information [the
transport data] – to formulate a strategy around it, and embed it in the business
strategy, to get the support from top management. Because we are talking absolute
emission reductions while the company wants to grow. So how do you do that? To
use the emissions data and translate it to something that business people understand
and take decisions on, which usually is financial data.".

These findings suggest that low strategic prioritisation of transport emissions, both
in terms of data collection and internal integration, significantly limits the availabil-
ity and quality of emissions data. For service providers, the absence of customer-
driven incentives reduces the return on investment in sustainable practices. For
buyers, the lack of internal prioritisation hinders the potential for data to inform
improvement initiatives and support long-term emission reduction strategies.

A recurring barrier identified across both transport buyers and service providers
was the lack of clarity surrounding emission calculation methodologies. While most
interviewees expressed confidence in their chosen approaches, often viewing their
results as valid within their operational context, closer examination revealed diver-
gent interpretations of core methodological concepts. Specifically, inconsistencies
were observed in how companies applie