Transformative Drivers of Environmental Sustainability in Contemporary Organizations [2017-2020]

By
Aida Sajid
B.Sc. Lancaster University, U.K. (2020)

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Environmental Science

Kamloops, British Columbia
May 2023

Thesis Supervisor:
Panagiotis Tsigaris (Ph.D.), Supervisor and Professor, Department of Economics, Bob
Gaglardi School of Business and Economics, Thompson Rivers University

Thesis Advisory Committee:
Salvador Barragan (Ph.D.), Committee Member and Associate Professor, Department of
Human Enterprise and Innovation, Bob Gaglardi School of Business and Economics,
Thompson Rivers University
Terry Kading (Ph.D.), Committee Member and Associate Professor, Department of History,
Philosophy and Political Studies, Faculty of Arts, Thompson Rivers University

Thesis Examiner:
Khaled Soufani (Ph.D.), External Examiner and Professor, Department of Financial
Economics and Policy, Judge Business School, University of Cambridge

© Aida Sajid, 2023

ACKNOWLEDGMENTS

My earnest gratitude to my thesis supervisor, Dr. Peter Tsigaris, for his guidance, support, and
encouragement throughout the research process. Peter, your insights, expertise, and feedback
have been invaluable in shaping the direction and scope of this thesis. I am in awe of your
mind, your knowledge, and the fantastic work we have created together. It has been a privilege
to be your student.
I would like to express my deepest appreciation to my parents, who have been my most ardent
supporters throughout all my milestones in life. Thank you for instilling in me a penchant for
growth and intellectual curiosity from an early age. Your encouragement and belief in my
abilities has always been a constant source of motivation and inspiration. I have learned so
much from you.
I am grateful for my thesis advisory committee, Dr. Salvador Barragan and Dr. Terry Kading,
who provided valuable feedback throughout various stages of my degree. Your input helped
me refine my research goals and data analysis, and I am sincerely appreciative of your insights.
I am lucky to have had such a supportive committee.
I would like to thank Dr. Khaled Soufani, for serving as the external examiner of my thesis. I
am grateful for his involvement in the final stages of my research journey, and for his thoughtprovoking questions during the defense. His perceptive feedback and analysis have enhanced
the outcome of my research.
I would like to acknowledge my classmates for the engaging and thought-provoking
discussions we have had throughout our graduate studies. Their diverse scientific backgrounds
helped me broaden my perspectives and deepen my understanding of several topics. I will
cherish the friendships and connections that I have made during this journey.
I appreciate my professors Dr. Lauchlan Fraser, Dr. Louis Gosselin, and Dr. Laura Ploughe for
fostering a collaborative learning environment that inculcated in us the belief that no global
challenge is insurmountable, as long as we are working on solving it together.

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DEDICATION
To Mom and Dad, without whom none of this would be possible. Everything I am, and all
that I’ve done, is because of you. All of my accomplishments will always be dedicated to
you.
To my grandfather, Dada, whose pride and keen interest in my academic and professional
journey has meant the world to me. You have cheered me on for as long as I can remember.
Your intellect is something I have always looked up to, and I am grateful for your influence
in my life.

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ABSTRACT
Seventy-one percent of global emissions are attributed to just 100 companies, which are all
from the fossil fuel sector. The world’s current trajectory of exponential population growth is
yielding widespread environmental degradation, highlighting the need for transformation in the
usual business models and substantial action towards environmentally responsible initiatives.
For global collaboration on combatting climate change, the United Nations introduced
international climate treaties, the most recent and notable example being the Paris Agreement,
which aims to keep the global temperature rise below 2℃, and ideally within 1.5℃ relative to
pre-industrial levels.
Using a bottom-up approach, this research explores the emissions reduction efforts of for-profit
companies. Towards this end, it draws upon extensive datasets for the 2017-2020 period from
the Carbon Disclosure Project, on over 100 large corporations across eleven industries in the
U.S. and Europe. It aims to address whether the Kyoto Protocol and Paris Agreement may have
influenced corporate strategy, which in turn would affect changes in corporate governance, risk
management, and targets and performance, thereby translating to lowered emissions and/or
energy intensity reductions. Additionally, a modified version of the IPAT model is applied at
the corporate level to assess drivers of environmental impact, which helps identify areas
needing change. The IPAT also helps track changes in emissions, revenue, and use of
renewable versus non-renewable energy in these 100+ corporations, both before and after the
COVID-19 pandemic came into play.
The thesis found that over the four-year period of 2017-2020, these 102 European and U.S.
companies represented 5.5% of the 2021 global CO2 emissions. For both regions, the highestemitting industries are in exactly the same order: Power Generation, Airlines, and Fossil Fuels.
In the three-year period of 2017-2019, prior to COVID-19, emissions dropped in both regions
and most sectors due to a joint improvement in energy and carbon efficiency. Moreover, the
pandemic’s impact in 2020 was just as significant as that of technology’s impact over three
years.
A notable finding was that COVID-19 lockdowns reduced emissions significantly more in the
U.S. than policies and business strategies could before the pandemic year of 2020.
Interestingly, the research also saw that emissions can be reduced with increasing revenue and
possible profitability, depending on cost increases.
This thesis demonstrates that corporations in advanced industrialized nations have the potential
to play an instrumental role in reducing emissions through improving both energy and carbon
efficiency, and by making robust efforts. These corporations can set an example for their
counterparts in developing nations, proving that it is possible to reduce emissions while
increasing revenue. Ultimately, this can create a pathway toward stabilizing climate change.

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Highlights:


IPAT model applied to organizations to assess drivers of environmental impact



Role of corporations in anthropogenic climate change and how they can mitigate it



Impact of Kyoto Protocol and Paris Agreement on a select group of companies in
Europe and the U.S.



CDP data showing influence on business strategy from Kyoto and Paris treaties

Keywords:
Carbon Disclosure Project; IPAT; Change Management; Climate Change; Corporate Strategy;
Corporate Social Responsibility; GHG Emissions; Kyoto Protocol; Paris Agreement; COVID19; Net-Zero; Sustainability.

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Acronyms:
BY: Base Year
CAI: Carbon Accountability Institute
CDP: Carbon Disclosure Project
CH4: Methane
CO2: Carbon Dioxide
COP: Conference of the Parties
CSR: Corporate Social Responsibility
EKC: Environmental Kuznets Curve
ETS: Emissions Trading System
GDP: Gross Domestic Product
GHG: Greenhouse Gas Emissions
HFCs: Hydrofluorocarbons
ICP: Internal Carbon Pricing
IPCC: Intergovernmental Panel on Climate Change
N2O: Nitrous Oxide
NDCs: Nationally Determined Contributions
NF3: Nitrogen Trifluoride
PHCs: Perfluorocarbons
SBTi Science-Based Targets Initiative
SEC: Securities and Exchange Commission
SF6: Sulfur Hexafluoride
TY: Target Year
UN PRI: United Nations Principles for Responsible Investment
UN: United Nations
UNCCS: United Nations Climate Change Secretariat
UNFCCC: United Nations Framework Convention on Climate Change

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TABLE OF CONTENTS
Acknowledgments.......................................................................................................... 2
Dedication ...................................................................................................................... 3
Abstract .......................................................................................................................... 4
Highlights: .................................................................................................................. 5
Keywords:................................................................................................................... 5
Acronyms: .................................................................................................................. 6
List of Figures ................................................................................................................. 9
List of Tables .................................................................................................................. 9
Chapter 1: Introduction ............................................................................................... 10
Research Inquiry and Questions .............................................................................. 12
Chapter 2: Kyoto Protocol............................................................................................ 14
Progress and Results of the Kyoto Protocol at the National Level .......................... 15
Progress and Results of the Kyoto Protocol at the Corporate Level ....................... 17
Chapter 3: Paris Agreement ......................................................................................... 21
Progress and Results of the Paris Agreement at the National Level ....................... 23
Progress and Results of the Paris Agreement at the Corporate Level .................... 26
Chapter 4: The IPAT Framework .................................................................................. 28
IPAT Framework at the National Level .................................................................... 28
IPAT Framework at the Corporate Level.................................................................. 33
Chapter 5: Methodology .............................................................................................. 35
Carbon Disclosure Project [CDP] .............................................................................. 37
Data for Analysis ...................................................................................................... 38
Chapter 6: IPACT Results and Discussion ..................................................................... 40
Initial Descriptive Data Exploration ......................................................................... 40
Results from the Simple Corporate IPAT Framework: Pre-COVID 2017-19 period . 44
IPACT Results: Concluding Remarks......................................................................... 52
Chapter 7: The STIRPAT Framework ............................................................................ 53
The STIRPAT Framework at the National Level ....................................................... 53
The STIRPAT Framework at the Corporate Level ..................................................... 55
Chapter 8: STIRPAT Results and Discussion ................................................................. 56
Results from the STIRPAT Estimations ..................................................................... 56
STIRPAT Results: Concluding Remarks ..................................................................... 62
Chapter 9: Targets and Emission Reductions .............................................................. 63
Introduction: Targets at the Corporate Level .......................................................... 63
Case Studies: IPAT Framework to Hit Targets at the Corporate Level .................... 66
I. Apple Inc. ........................................................................................................... 67
II.

Nokia Group ................................................................................................... 69
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III.

Ørsted ............................................................................................................. 71

IV.

Nike Inc. .......................................................................................................... 72

Introduction: Targets at the National Level ............................................................. 74
Case Study: IPAT Framework to Hit Targets at the National Level.......................... 74
Global Emissions ...................................................................................................... 78
Targets: Concluding Remarks................................................................................... 79
Chapter 10: Conclusion ................................................................................................ 81
Discussion................................................................................................................. 81
Key Findings ............................................................................................................. 82
Limitations................................................................................................................ 84
Future Research ....................................................................................................... 85
Final Concluding Remarks ........................................................................................ 86
References ................................................................................................................... 87
Appendix: Complete List of 102 Corporations in this Thesis Sample .......................... 98
I. Europe: 52 Companies [Graph]............................................................................. 98
Europe: 52 Companies [Sorted Alphabetically by Industry] .................................... 99
II. United States of America: 50 Companies [Sorted Alphabetically by Industry] . 102

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LIST OF FIGURES
Figure 1: Distribution of CO2 emissions across the world: 1750 - 2020 ................................. 16
Figure 2: Global CO2 emissions .............................................................................................. 17
Figure 3: Pathways to a lower increase in temperature anomaly............................................. 22
Figure 4: Sweden's decoupling of CO2 emissions with the economy...................................... 23
Figure 5: Potential impact of the agreements .......................................................................... 38
Figure 6: Emissions distribution industry-wise, Europe VS U.S............................................. 42
Figure 7: Europe’s distribution of Renewable versus Non-Renewable sources of Energy ..... 43
Figure 8: U.S. distribution of Renewable versus Non-Renewable sources of Energy ............ 43
Figure 9: Europe VS U.S. Pre-COVID (2017-2019) Emissions distribution, industry-wise .. 47
Figure 10: Europe VS U.S. COVID (2020) Emissions distribution, industry-wise ................ 48
Figure 11: Europe Revenue distribution, industry-wise, Pre-COVID (2017-2019) VS COVID
(2020) ....................................................................................................................................... 51
Figure 12: U.S. Revenue distribution, industry-wise, Pre-COVID (2017-2019) VS COVID
(2020) ....................................................................................................................................... 51
Figure 13: CO2 per capita and GDP per capita ........................................................................ 54
Figure 14: Effects of Revenue on CO2 emissions in 2019. Data points plotted for the 99
companies in Table 10. ............................................................................................................ 57
Figure 15: Historical global carbon intensity and projections, from 2000 to 2100. ................ 78
Figure 16: Net-Zero Carbon in 2050 ....................................................................................... 81
Figure 17: Distribution of Firms in Europe by Country & Industry ........................................ 98

LIST OF TABLES
Table 1: Symbols for environmental impacts at the firm level ................................................ 34
Table 2: Emissions & Energy Data for 52 European Companies, sorted industry-wise for the
period of 2017-2020. ................................................................................................................ 40
Table 3: Emissions & Energy Data for 50 U.S. Companies, sorted industry-wise for the
period of 2017-2020. ................................................................................................................ 41
Table 4: Europe IPAT calculations, industry-wise, Pre-COVID (2017-2019) ....................... 45
Table 5: U.S. IPAT calculations, industry-wise, Pre-COVID (2017-2019) ............................ 46
Table 6: Europe IPAT calculations, industry-wise, COVID (2020) ........................................ 49
Table 7: U.S. IPAT calculations, industry-wise, COVID (2020) ............................................ 50
Table 8: Effects of Revenue and Energy Efficiency on 2019 CO2 emissions of 99 companies*
in Europe and the United States. .............................................................................................. 56
Table 9: Effects of Revenue on CO2 emissions of 99 companies in 2019. For convenience,
the 11 industries were sorted into the five groups* listed below. ............................................ 58
Table 10: Technology Multiplier for 99 companies’ CO2 emissions*, for both regions of
Europe and the United States, sorted by industry, for the pre-COVID year of 2019. ............. 59
Table 11: Pre-COVID growth rate of CO2 and type of control for 65 companies across
Europe and the U.S. ................................................................................................................. 65
Table 12: Pre-COVID growth rate of CO2 and type of control for 37 companies in Europe. . 65
Table 13: Pre-COVID growth rate of CO2 and type of control for 28 companies in the U.S. 66
Table 14: Target details of companies Nokia Group, Apple Inc., Ørsted, & Nike Inc. .......... 66
Table 15: IPAT summary of targets scenarios in Canada........................................................ 77
Table 16: IPAT summary of targets performance in Canada after international agreements . 78

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CHAPTER 1: INTRODUCTION
With the advent of the catastrophic implications of climate change (Ripple et al., 2019), the
instrumental role that corporations should play in taking stewardship of the environment is
becoming increasingly clear (Alibašić, 2018). Recent evidence finds that 71% of global
emissions are from just 100 companies, which are all in the fossil fuel sector (Riley, 2017;
Griffin, 2017). Climate experts assert that without formidable efforts by businesses to reduce
greenhouse gas [GHG] emissions, the planet is moving towards an irreversible and calamitous
global temperature rise of 3-6℃ by the end of the century, which would result in risks such as
the extinction of numerous critical species, global food scarcity, and 3.5 billion people living
in scorching temperatures outside the human climate niche (IPCC, 2021; Riley, 2017; Xu et
al., 2020).
The world’s current trajectory of exponential population growth is yielding widespread
environmental degradation (Ehrlich, and Ehrlich, 2009; Gans & Jost, 2005; Leridon, 2020;
Ripple et al., 2020; Bocken et al., 2014), highlighting the need for transformation in the usual
business models and profound action towards environmentally responsible initiatives (Henry
et al., 2019; Ceesay et al. 2021).
The mounting pressure on corporations has resulted in them finding different strategies to
measure their environmental impact. One example is the Triple Bottom Line concept, a threedimensional framework that prompts companies to consider not only their standard bottom line
of financial performance but also the results of their social and sustainable undertakings (Liute
& de Giacomo, 2021; Slaper & Hall, 2011).
Managing the planet’s climate crisis is mainly contingent on the ability of corporations to
decarbonize within the coming decades, following commitments such as the UNFCCC’s Race
to Zero campaign, which requires companies to halve their carbon emissions by 2030
(UNFCCC, 2021a; Rockström et al., 2017; Wade & Rekker, 2020). By playing an influential
role as non-state actors, organizations can fortify national efforts to limit emissions and curtail
the emissions gap between actual progress and the action needed to meet climate targets, thus
moving on track towards a pathway that would mitigate global warming to 1.5℃ above preindustrial levels by 2100 (Hale, 2018). Today, over 4,500 non-state actors from 92 countries
worldwide have pledged to take transformative, rapid, and substantive action to meet the targets
underlined in the Paris Agreement and are continually becoming the engine of mitigation and
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adaptation to climate action (UNFCCC, 2021a; Kinley, 2016). In addition, the Yearbook of
Global Climate Action report from the United Nations shows that by 2018, 70% of international
initiatives had rendered a high or medium-high output performance, indicating that these nonstate actors were poised to catch up with the climate targets they should be aiming for (UNCCS,
2018).
Firms' economic viability and prosperity are contingent on their ability to balance the
interdependent forces of people, planet, and profit, to fulfill an obligation to become more
sustainable (Liute & de Giacomo, 2021; Bhat, 1998). This balancing act also leverages the
competitive advantage of sustainable companies (Hussain et al., 2018; da Silva et al., 2019).
One avenue that an estimated 1,200 organizations are already implementing or plan to
implement is internal carbon pricing [ICP]. This voluntary measure helps companies assign a
monetary value to their GHG emissions to assess and mitigate the scale and impact of their
long-term future operations and business decisions. It also acts as a means of risk management,
lest they must report and manage their emissions to meet government-imposed regulations
(Bento & Gianfrante, 2020; WBCSD, 2015, CDP, 2021b). Almost half of the largest 500
companies in the world are pricing carbon or working towards it as of today, spanning eleven
different industries, including manufacturing, power and fossil fuel, and financial services
(CDP, 2021b).
Over the years, organizational change management has swiftly become a pivotal concept in the
business world. Former CEO of General Electric, Jack Welch, famously said, “When the rate
of change on the outside exceeds the rate of change on the inside, the end is near” (Daft, 2007,
p.454). Companies are constantly subject to improving their operations to remain competitive
both technologically and economically, for which they need to be innovative and open to
change. However, the kind of radical, organization-wide transformations needed for businesses
to adapt to the threat of climate change consistently can pose an arduous challenge. Thus, one
viable solution is to utilize the wealth of knowledge offered by researchers and leaders who
can better guide and inform companies through change management coaching. Organizational
change experts can direct businesses on rapid divestment and clean investment decisions at a
low cost. Further input should be welcome from strategic management and finance scholars,
whose strengths would be employable in the decarbonization pathway. Solid research put
forward by academic experts could ensure buy-in from the company’s executives, and
academics can also facilitate effective internal and external communication throughout the
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company to initiate tactical avenues for transformation (Schaffer, 2017; DuBois & DuBois,
2012; Wade & Rekker, 2020).
Academic research is conducive to guiding companies in developing their commitment
parameters and decarbonization pathways. Academia today is presented with imperative
opportunities to conduct advanced and expansive research on both theoretical and practical
fronts for leading organizations towards finding and adopting suitable methodologies that help
them translate climate targets into action. One such approach is to see how frameworks
implemented at the national level could be reapplied to organizations (Wade & Rekker, 2020;
Robiou Du Pont et al., 2017), such as the IPAT model formulated by Commoner, Ehrlich, and
Holdren in the 1970s (Waggoner & Ausubel, 2002).
Research Inquiry and Questions
This thesis will mainly draw upon the in-depth reports, data, and surveys provided by the
Carbon Disclosure Project [CDP], along with the Carbon Majors Database by Richard Heede,
to analyze data on 102 high-emitting companies worldwide and their current and future
sustainability strategies, as expanded on in the Methodology section. It aims to address the
following research question:
“Have international climate treaties like the Kyoto Protocol
and Paris Agreement yielded any measurable impact, which
has trickled down to large, high-emitting corporations,
causing them to transform their business strategy,
governance, and energy use towards lowering GHG
emissions
and
becoming more
environmentally
responsible?”

To answer the research question, we created a theoretical framework by using existing
IPAT/IPACT/STIRPAT frameworks from the literature and applying them to the corporations.
This resulted in a new theoretical framework that helps identify the main factors responsible
for changes in emissions over time at a micro-level. The key drivers of emissions in this new
framework are revenue growth, energy efficiency, and carbon intensity.
The orginal IPAT model evaluates a nation’s environmental impact based on drivers such as
population, affluence, and technology (Chertow, 2000). Despite certain limitations to the IPAT
model, adopting it at the company level, as done by da Silva et al. (2019), can provide deeper
insights regarding the main drivers of environmental impact beyond intensity and facilitate
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decision-making to achieve future targets. Therefore, establishing a proper theoretical
framework of the IPAT model at the company level with more drivers than da Silva et al.
(2019) is a necessary first step of the thesis. For example, what is the best substitute for
population, P, at the company level? What is affluence, A, at the company level? Can these be
operationalized? Why are these drivers important? After establishing the new theoretical
framework, the application follows.
The sample will consist of 102 large public and private companies across eleven industries in
the United States and Europe, responsible for substantial carbon emissions, comparing the preCOVID-19 period of 2017 to 2019 with the pandemic year of 2020. One of the objectives is to
determine a change in the drivers towards sustainability. Another objective is to assess and
compare the impacts of international climate agreements against those induced by the COVID19 pandemic during the 2017-2020 time period.
The upcoming chapters will provide an overview of the Kyoto Protocol and Paris Agreement,
discussing their objectives and accomplishments both on a national and corporate scale. The
IPAT framework will also be introduced and explored, first at a national level, then at a
corporate level, with each component defined in detail.
The methodology of the study will be presented in Chapter 5, which will also offer additional
information on the 102 companies and 11 industries examined. Chapter 6 will present the
results and findings of the IPACT model.
Following this, the next two chapters will analyze the STIRPAT model, including its
definitions and scope, as well as the outcomes derived from its application.
Finally, we will investigate the targets set by the companies and conduct four case studies to
assess their progress in meeting those targets by 2030, using their pre-COVID performance as
a benchmark. Similarly, we apply the aforementioned approach to a country to evaluate its
progress towards achieving its set target.

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CHAPTER 2: KYOTO PROTOCOL
After years of global negotiations, the Kyoto Protocol was signed in Kyoto, Japan, on the 11th
of December, 1997 at the 3rd Conference of the Parties [COP3], and it was a landmark
environmental treaty that represented the first time that nations agreed to legally-mandated,
country-specific emissions reduction targets (UNFCCC, 2021f). The primary mission of the
Kyoto Protocol was to limit and stabilize atmospheric concentrations of the seven GHGs,
namely carbon dioxide [CO2], methane [CH4], nitrous oxide [N2O], hydrofluorocarbons
[HFCs], perfluorocarbons [PHCs], sulfur hexafluoride [SF6], and nitrogen trifluoride [NF3], to
“a level that would prevent dangerous anthropogenic interference with the climate system”
(UNFCCC, 1998, “Article 2” section). Unfortunately, due to a complex ratification process,
the Kyoto Protocol only came into force on the 16th of February 2005, after Russia finally
signed up, thus satisfying the clause of having ‘55% of 1990 CO2 emissions of the Parties
included in Annex I’ on-board (Freedman & Jaggi, 2005; King, 2015; UNFCCC, 2021f).
By founding the first mechanisms for reducing GHG emissions, the Kyoto Protocol saw that
each country was allocated a particular allowance for emissions depending on their historical
responsibility for the build-up of GHGs in the atmosphere. Deemed as Annex I parties, the
reduction targets were set mainly for the industrialized or developed nations, since at the time,
they were the major emitters rather than developing nations, which were given no restrictions
requirements as per the Protocol (UNFCCC, 2021f; Madrigal, 2009). Thus, although 192
parties had ratified, only a total of 37 industrialized countries, including the U.K., U.S.,
Australia, New Zealand, Canada, and most EU nations, were essentially expected to make
emissions reductions of up to 5% compared to 1990 levels by the first commitment period of
2008-2012 (UNFCCC, 2021f, Maamoun, 2019).
However, the U.S. Senate refused to ratify due to former president George W. Bush thinking
it would hurt the U.S. economy, Canada later invoked its legal right to withdraw in 2011, for
not achieving its targets by the end of the first commitment period, and also to not hurt its
booming oil industry, with Environment Minister Peter Kent stating that “Kyoto was not the
way forward for Canada or the world.” A common grievance was also that the largest emitter,
China, was not doing enough, and thus the protocol would be futile without its efforts (The
Guardian, 2011; Beggin, 2017; King, 2015; Madrigal, 2009).
Eventually, compliance to the first commitment period was by 36 countries, out of which only
nine countries, including Austria, Iceland, Denmark, Japan, Lichtenstein, Luxembourg,
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Norway, Switzerland, and Spain emitted higher levels of GHG emissions than they had
committed under the Kyoto Protocol. Moreover, six of these nine countries had minimally
higher emissions, whereas Austria, Spain, and Japan were 13%, 15%, and 44% over their Kyoto
target limit, respectively (Shishlov et al., 2016).
Developing nations like India, China, Brazil, and South Africa had no emissions restrictions,
but they were encouraged to adopt green policies in order to be more environmentally
responsible and sustainable. To evidence their solidarity and accelerate transfers of technology,
developed nations, which were the Annex I parties, were expected to help developing countries,
in return for which they would receive a Certified Emission Reduction Credit. This initiative
was called the Clean Development Mechanism (UNFCCC, 2021d; Britannica, 2020; King,
2015; Madrigal, 2009).
As of June 2013, the Kyoto Protocol had a total of 192 parties, excluding the United States and
Canada. With the first commitment period of 2008-2012 expiring, the Doha Amendment was
adopted in Qatar on 8th December 2012, extending a second commitment period from 20132020, which included new commitments for Annex I parties, and had been accepted by 147
parties by October 2020 (UNFCCC, 2021f).
Progress and Results of the Kyoto Protocol at the National Level
According to the UNFCCC, some success in the Kyoto Protocol’s 1st commitment period was
that the progress was better than expected; GHG emissions were reduced by about 22.6%
instead of the original 5% target, perhaps because the 2008 economic crisis also really helped
reduce emissions in developed nations (UNFCCC, 2015; King, 2015; Shishlov et al., 2016). It
also inspired other green mechanisms such as carbon taxes and carbon pricing in many
countries (UNFCCC, 2018). Other data suggests that countries which were legally bound by
the Protocol were actually able to reduce their carbon emissions by 7%, compared to their
anticipated business-as-usual levels (Maamoun, 2019). Moreover, even though the United
States was not part of the Protocol, it has still markedly lowered emissions in the past few
decades (le Quéré et al., 2019).
Another study by Kim et al. (2020) highlights that participating in the Protocol as an Annex I
party yielded substantial positive results on cutting down carbon emissions, although it is to be
noted that the parties’ Gross Domestic Product [GDP] was also negatively altered in the long
run. The study’s findings showed that without following the Protocol’s imperative to lower

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carbon emissions, Annex I parties were on track to produce 14% more CO2 as per the 2005
base-year model; however, 7% of GDP growth amounting to USD 2.3 trillion would also have
been earned in the event of their non-participation. Moreover, the marginal benefits of
emissions reductions from the Protocol covered only a fraction of the GDP loss – if we were
to use the mean benefits value of USD 279 billion, it would account for only 12% of the total
GDP loss. Kim et al.’s (2020) research proposes the need for future international climate
frameworks to be able to strike a balance between economic and environmental performance
by prioritizing sustainable development, as well as safeguarding the interests of developing
countries which cannot afford GDP losses while also investing in emissions reductions and
climate policy adaptations.
Some experts consider the progress to be not too significant since the two highest emitters in
the world were not bound by the Protocol, because the U.S. did not ratify the treaty, and China
was exempt from it as a developing nation (Britannica, 2020). However, the world has changed
– developing nations are now rapidly surpassing higher emissions than developed ones, thus
prompting the question of whether their emissions should really be left unrestricted (Figure 1).

Figure 1: Distribution of CO2 emissions across the world: 1750 - 2020

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Today, the world’s three largest emitters, namely India, China, and the United States, are
causing 50% of global emissions, along with the fact that nineteen of the world’s top thirty
emitters are developing countries (Martínez-Zarzoso & Maruotti, 2011; Mott et al., 2021).
While GDP per capita has nearly tripled since 1960, carbon emissions have also quadrupled,
and two-thirds of these impacts have come forth in the last three decades alone (Figure 2).

Figure 2: Global CO2 emissions

Progress and Results of the Kyoto Protocol at the Corporate Level
The EU Emissions Trading System [ETS], a carbon allowance system developed by European
countries mainly for meeting the Kyoto Protocol’s targets, enables nations to utilize it as a
market mechanism for overall reductions in their carbon emissions. It currently operates in all
EU countries, as well as Iceland, Norway, and Liechtenstein. It assigns specific allowances to
all companies based on their historical performance. If a company ends up with higher
emissions than its allowances, it can buy allowances in the ETS market from other companies
with higher allowances than their actual emissions. This mechanism allows buying companies
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to offset their additional emissions against the allowances. Thus, ETS promotes nations to
monitor and manage their overall emissions, and companies also have an incentive to decrease
their emissions. So far, ETS restricts emissions from around 10,000 installations in the
manufacturing industry and the power sector, along with airlines operating in those countries,
thereby covering approximately 40% of the EU’s GHG emissions (EU Commission, 2022;
Freedman & Jaggi, 2011).
The “evolving technology and economies of the climate issue form an important backdrop to
business engagement with international negotiations” (Begg et al., 2014, p.24). The Protocol’s
idea of emissions limits was initially met with opposition from trade associations such as the
Global Climate Association, which debated that climate mitigation efforts would impose
critical economic damage. However, these claims were unsubstantiated, since it was uncovered
that the costs of climate action had been overstated for several years (Begg et al., 2014). Big
Oil companies during the 1990s to the 2010s published misleading models of inflated predicted
costs, in order to undermine the potential results of climate action and sway public opinion
against the effectiveness of international climate treaties (Franta, 2021; TEDx, 2021).
Additionally, with the accumulating evidence showing the costs of inaction as well as the
proven success of market-based instruments such as the EU’s ETS, such claims regarding
economic concerns were rendered less viable (Begg et al., 2014, European Commission, 2022).
With this cited among other reasons, companies such as BP, General Motors, DuPont,
DaimlerChrysler, and Ford withdrew from the Global Climate Association between 1997 to
2000 (Begg et al., 2014).
In light of the developing technological and economic outlook, and moreover, the momentum
in the policy process that created new opportunities to influence implementation rules, the
establishment of business groups that were more productively engaged with international
negotiations began pushing forward. For example, the Business Environmental Leadership
Council of the Pew Center on Global Climate Change was formed in support of the Kyoto
Protocol, consisting of 37 companies including BP, Boeing, Hewlett Packard, Shell, IBM, Intel,
Whirlpool, and United Technologies as a first step in the global collaboration towards
addressing climate change. Their mission states that businesses in the U.S. and around the
world can and should take concrete steps for setting and meeting emissions reduction targets
by investing in more new and efficient technologies, products, and practices. Other such
business groups in favor of the Protocol are namely the U.S. and European Business Councils
for Sustainable Energy, the EU-Japan Business Dialogue, the U.S.-based Social Venture
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Network, and a coalition of 150 European and Japanese companies called E-mission 55, which
is joined by Deutsche Telekom and insurance firm Gerling Group (Begg et al., 2014).
Insurance giant Swiss Re has warned that climate change could cut the world economy by USD
23 trillion by the year 2050, through weather impacts and hits to agriculture and industry
(Flavelle, 2021). The economic risk has become apparent for many companies, and insurance
and reinsurance firms face massive liabilities from increasing weather-related claims. In
addition, it is estimated by the World Resources Institute that shareholders in leading oil and
gas corporations might lose up to 6% or more of their investment value due to sustainabilityrelated regulations imposed on the industry. Since quantifying these risks, the financial sector’s
perceived self-interest in negotiations has grown (Begg et al., 2014).
Early research by Freedman and Jaggi (2005) showed a notable positive association between
high emissions disclosure rates and companies based in ratifying countries, also stating that
these companies were more forthcoming about sharing their comprehensive plans on tackling
the climate emergency. In contrast, firms from non-ratifying home countries were not
disclosing their emissions data or plans, despite having operations in ratifying countries – and
although such firms were still required to comply with the Protocol, they had found a loophole
to avoid or diminish voluntary disclosure about their emissions and pollution performance.
Their findings also indicated a direct correlation between firm size and disclosure
thoroughness, meaning larger companies provided more meticulous disclosures than smaller
ones. They claim that there were “no significant differences” in disclosures between the
selected firms based on the industry they belonged to, ranging from companies earning
revenues of $6 billion or more from the “chemicals, oil and gas, energy, motor vehicles, and
casualty insurers industries,” which were industries most likely to be impacted by climate
change (Freedman & Jaggi, 2005, p.221).
Freedman and Jaggi’s (2005) research also had an interesting finding about their sampled
Japanese firms, wherein some had disclosed current costs of curbing their carbon emissions,
but none had disclosed any forecasted future costs. The authors argue that the absence of such
critical cost information would impede even the most experienced financial statements users
from gaining clarity on the firm’s impact on climate change. The authors suggest that regulators
consider making detailed emissions disclosures mandatory to combat the lack of voluntarism
from corporations for facilitating investment decisions.

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In further studies conducted closer to the end of the first commitment period, Freedman and
Jaggi (2011) additionally addressed the differences in disclosure rates within ratifying
countries, since developing nations like India and China had ratified the Protocol but had not
set any emissions restrictions because they were not required to; only developed nations had to
create reductions targets. By sampling 510 firms headquartered in India, Japan, Canada, the
EU, and the United States from the Forbes 2000 list of the largest companies worldwide, the
authors utilized disclosure data from the companies’ websites and their annual statements,
ESG, and sustainability reports, as well as CDP questionnaire data. Satisfying their conjectures,
corporations from a non-ratifying country like the United States inclined toward lower
emissions disclosure rates compared to those from all the ratifying countries, with the exception
of Indian firms, whose disclosure rates were even lower than those of U.S.-based firms. In a
surprising finding from the same study, Canadian and Japanese corporations had been making
substantially greater disclosure contributions than firms from the EU, despite the EU having
been a frontrunner both in terms of setting emissions reduction targets as well as disclosure
rates. Although Japanese and Canadian companies were not required to start making emissions
cuts until 2008, it was determined that firms from the two countries took the issue of GHG
pollution with great seriousness, hence such results.
Freedman and Jaggi’s (2011) findings were consistent with their own beliefs (Freedman &
Jaggi, 2005) and the case made by the New York Attorney General and environmental groups,
stating that firms would not provide sufficient disclosures of their own accord; they should be
obligated to disclose information more thoroughly since voluntary disclosures were not
motivating them enough to improve their sustainability performance.
In encouraging news as of 21st March 2022, the Securities and Exchange Commission [SEC]
in the U.S. has approved a landmark proposal mandating all publicly-traded companies to
disclose emissions information as well as the climate risks they are subject to. Although several
U.S. companies already share emissions data in their annual sustainability reports, there are
wide discrepancies between businesses and competitors. This initiative would be the first time
that all companies have to report emissions data in a standardized manner to the SEC, where
they must detail how climate risks distress their business and strategy. While all companies
would have to share information about emissions generated at their own facilities, large firms
are additionally required to trace emissions from their suppliers and customers if this
information is deemed material to investors or incorporated in the firm’s targets. All large
companies would also need to have their reported numbers scrutinized by an independent audit
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firm. The SEC will also obligate corporations that have declared public pledges about cutting
their carbon footprint to outline how they aim to reach their goal and provide pertinent data. In
addition, any company that has set an ICP must list details about the price and explain how it
is implemented. Lastly, companies will be required to disclose how much they intend to rely
on carbon offsetting to meet these goals. Similar plans are underway in the U.K. and Japan,
which will mandate disclosures from certain large corporations beginning in April 2022, while
the EU is also implementing a climate disclosure rule for all large companies listed on the
European Stock Exchange starting in 2024 (Joselow & Macmillan, 2022).

CHAPTER 3: PARIS AGREEMENT
In order to combat climate change and mitigate its negative impacts, world leaders at the 21 st
UN Climate Change Conference of the Parties [COP21] in Paris adopted the breakthrough
Paris Agreement in December 2015. It focused on reframing and replacing the Kyoto Protocol
to better suit the world’s shifting economic and political developments, like the growth of
developing countries and the increasing existential peril of climate change (Thakur, 2021;
Zhou, 2020). It came into force in November 2016, built upon the foundations laid by the 2009
Copenhagen Accord, with 192 parties currently on board. The primary mission of the Paris
Agreement is to keep the global temperature rise well below 2℃, and ideally within 1.5℃,
compared to pre-industrial levels (Thakur, 2021; UNFCCC, 2021e; United Nations, 2021).
Keeping the temperature rise below 2℃ requires global emissions in 2030 to be 25% less than
2018 levels (Le Quéré et al., 2019). Figure 3 illustrates different paths towards a lower increase
in the temperature anomaly by 2100. To achieve the 2℃ mission, worldwide GHG emissions
must peak and swiftly decline. The more stringent the temperature anomaly target, the faster
the reduction rate would need to be. Also, the longer the delay, the faster emissions will need
to fall to meet the 2℃ target. Current policies are insufficient and will lead to a 2.7 – 3.1℃
increase in the temperature anomaly.
Pathways towards a 2℃ trajectory transpired in the nations of the European Union, where
emissions had already peaked and begun to fall in the mid-1970s. There was an especially
noticeable continuity in decline in 2005-2015. On the one hand, many European countries hit
their peaks and subsequently bring emissions down by switching their energy sources from
coal to oil, gas, and nuclear power. However, rapid industrialization in China caused worldwide
emissions to spike again beginning in 2000 (Le Quéré et al., 2019).
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Figure 3: Pathways to a lower increase in temperature anomaly

A study by Le Quéré et al. (2019) showcases the story of a group of nations in the 2005-2015
period comprising 18 developed countries, which represents 28% of worldwide emissions, and
how they were able to take the ‘peak-and-decline’ route with between –2.9% and –1.4%
emissions reductions per year. These 18 countries were the United Kingdom, Sweden,
Romania, France, Ireland, Spain, Bulgaria, the Netherlands, Italy, Germany, Denmark,
Portugal, Austria, Hungary, Belgium, Finland, Croatia, and the United States. For example,
Sweden increased its GDP while at the same time reducing consumption and production
emissions, as seen in Figure 4.
By lowering the fossil share of final energy, the amount of available energy supplied to
consumers for end consumption, the peak-and-decline group made emissions reductions of
47% and lowered energy use by about 36%. This policy initiative was the most potent method
they employed for reductions, followed by the implementation of different climate and energy
policies. By 2015, each country had, on average, 35 policies in place to promote energy
efficiency and 23 policies furthering the need for renewable energy. The study highlights a
negative correlation of –54% between the number of policies advocating for reduced energy
use and, subsequently, the marked declines in energy use. Similarly, decreases in fossil share
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of energy were negatively correlated by –75% with policies on renewable energy. Finally,
decreases in total emissions were also negatively correlated with the number of climate policy
frameworks in place. Thus, these notable negative correlations indicated how emissions were
reduced at a larger scale by implementing a higher number of climate policies. From the results
of this study, the authors concluded that future emissions reductions are contingent on
persistent decreases in fossil shares in energy and structural decreases in energy use altogether,
for which policy support will need to be bolstered (le Quéré et al., 2019). The framework of
their analysis is discussed in the Methodology section under ‘The IPAT Framework at the
Corporate Level’.

Figure 4: Sweden's decoupling of CO2 emissions with the economy

Progress and Results of the Paris Agreement at the National Level
As of 22 September 2021, Turkey announced it is preparing to ratify the Paris Agreement
before the year’s Conference of the Parties [COP26] in November 2021 in Glasgow (Hacaoglu,
2021). The United States also re-joined after coming under the Biden administration since its
withdrawal from the Agreement when Donald Trump was U.S. President (Denchak, 2019).
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Like the Kyoto Protocol, the Paris Agreement provides pathways for developed nations to
assist developing countries financially and technologically, making for collaboration towards
controlling and alleviating the impacts of climate change. However, unlike the Kyoto Protocol,
the Paris Agreement urges all parties – rich or poor, developed or undeveloped – to do their
part and slash GHG emissions (UNFCCC 2021e, United Nations, 2021). A noteworthy positive
outcome of this policy has been on China, which previously had no restrictions under Kyoto.
Although the country generates a quarter of the world’s GHG emissions on its own today and
has been the biggest global CO2 producer since 2006, China is now leading the switch to green
energy in an effort to align with the Paris Agreement’s 2℃ goal. As of 2020, China’s solar
power generation is 3.3 times more than that of the United States and the highest in the world,
as are its wind power installations being triple that of any other country (Brown, 2021).
NDCs are Nationally Determined Contributions that each party is obligated to report, where
they must outline their climate actions such as targets, policies, and government measures and
their progress in meeting them. The Agreement requires all parties to review and increasingly
strengthen their commitments every 5 years (UNFCCC, 2021b; UNFCCC 2021c; UNFCCC
2021e, United Nations, 2021). Notably, unlike its predecessor, the Kyoto Protocol, the Paris
Agreement does not legally bind parties to meeting their emissions reduction targets. While the
legal obligation did prompt action in Annex I participants and added to the effective
implementation of the Kyoto Protocol, it also proved to have negative economic impacts on
their GDPs. Although, since the Paris Agreement monitors developing countries and holds
them equally responsible for lowering emissions, it is expected that in this climate treaty and
the next ones, these lower-income countries will be given adequate financial and technological
support from industrialized nations, which would also protect them from incurring economic
losses when adjusting to new climate regimes (Kim et al., 2020).
Other factors to consider in the efficiency of the Paris Agreement are that developing nations
are not solely responsible for the damages they deal to the environment; a lot of this damage is
due to industrialized nations' involvement and consumption demands. For example,
deforestation is the second-largest source of anthropogenic GHG emissions and costs the world
between USD 2 to 5 trillion annually in forest loss (Pendrill et al., 2019; Kindermann et al.,
2008; Black, 2008). According to a CDP (2020) report, 80% of deforestation is brought about
by the agriculture and forestry industries, driven by demands for cattle, palm oil, soy, and
timber. Here is the issue of ‘outsourcing’ deforestation: recent research by Hoang and
Kanemoto (2021) indicates that five of the G7 countries, namely Germany, France, Japan, Italy,
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and the United Kingdom, have 91-99% of their deforestation footprints in nations abroad,
attributing 46-57% of this to tropical deforestation in 2015. With the exception of Norway and
Sweden, tropical deforestation is driven by developed nations' demands and consumption
patterns, which sharply amplified their GDP per capita from 2001 to 2015. Their dependency
on tropical deforestation has also grown since not much change has occurred in their
international trade patterns. While industrialized nations pocket economic gains, the tropical
deforestation footprint is charged to net exporters of its commodities, meaning developing
countries such as Brazil and Indonesia (Hoang & Kanemoto, 2021). Kindermann et al. (2008)
promote avoided deforestation as a more cost-effective addition to the existing forest
management methods that climate policy considers, encouraging that carbon markets could
create more opportunities for this solution. Reforestation techniques could move us closer to
reaching the Paris Agreement’s goals (The Economist, 2015).
The recent NDC Synthesis Report by the UNFCCC in 2021 regrettably shows that while the
majority of countries have elevated their ambitions, they are still not on track to meet the
conditions of the Paris Agreement, and developing countries remain in critical need of support
in order to take climate action (UNFCCC, 2021g). The global energy system continues to be
dominated by fossil fuels, which account for 81% of primary energy demand (Welsby et al.,
2021). After having seen decades of growth, reductions in their rate of production and
utilization are imperative for meeting the internationally pledged targets for climate action.
Research suggests that 33% of oil, 50% of gas, and 80% of coal reserves in the world must stay
untapped to keep temperatures below the 2℃ limit. To gain at least a 50% chance of achieving
the more aspirational objective of the Paris Agreement and keeping warming below 1.5℃, 58%
of oil reserves, 59% of gas reserves, and 89% of coal reserves must remain in the ground
(Welsby et al., 2021; Spash, 2016; Nogrady, 2021).
Spash (2016) attempts to promulgate the deficiencies of the Paris Agreement, insinuating that
it will never reach even the more relaxed target of keeping the temperature rise below 2℃, far
be it 1.5℃. Spash (2016, p.929) attributes this “failure to be anywhere near on target” to
strategically ambiguous wording that does not highlight the sources of anthropogenic GHG
emissions or the structures that generate them, and no mention of oil, natural gas, coal, or
fracking. The author emphasizes that in fact, the Paris Agreement encourages further
industrialization, economic growth, and energy use under one of its resolutions in the opening
statements, and given the outlined actions and current trajectory of progress, we are past

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meeting the 2℃ target and will likely reach 3℃ of global warming (The Economist, 2015;
Spash, 2016).
Progress and Results of the Paris Agreement at the Corporate Level
A study by Dietz et al. (2018) analyzed 138 of the largest companies globally, operating in
sectors with high carbon footprints, comprising the oil and gas, coal mining, cement,
automotive, electricity, steel, and paper industries, for the period of 2013-2030, including
present and past performance as well as projections for the future. Their research about the
current scenario pinpointed the following findings: 85% of the 138 companies had made a
public pledge or published policy regarding the issue of climate change and that they intended
to reduce emissions or improve their energy efficiency towards that end. But only 40%
expressed support for the role of public policy in mitigating climate change, while 67% stated
that climate change was material to their business. Another finding showed that although 77%
of the companies disclosed their operational emissions, only 47% used an international
assurance standard or had the data verified by an independent third party. Notably, only 38%
of these 138 firms had set a long-term, time-specific quantitative target for increasing energy
efficiency or lowering emissions, meaning up to at least five years from the date of their pledge.
In addition, 49% explicitly designated a board member of the company responsible for
overseeing their climate change policy, and 47% incorporated environmental, social, and
governance [ESG] concerns into executive compensation.
Dietz et al. (2018) also found sector, size, and region to be associated with the implementation
of carbon management practices: the automotive manufacturers had implemented the most,
while steel and coal mining businesses were lagging the farthest behind; corporations with a
large market capitalization were implementing more than medium- and small-capitalization
companies; and those headquartered in Western Europe and the Asia-Pacific region were more
active implementers than those in emerging markets and North America. To see improvement
in the performance of mining firms, Mebratu-Tsegaye et al. (2021) recommend that
governments should mandate the inclusion of climate risk assessments into mining contracts,
restrict deforestation by holding mining companies accountable for all direct, indirect, and
induced impacts on forests during operations, regulate water use efficiency with penalties for
overuse or release of non-treated waste water, and integrate renewable energy sources into
mining projects. Since the steel sector generates 8% of the world’s total carbon emissions,
Bhatnagar (2021) proposes the need for steelmakers to adopt newer technologies like carbon
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capture that can recycle up to 90% of exhaust gas, use green hydrogen or alternative smelting
reduction procedures, capitalize on increased sustainable steel production, and focus on
enhancing their ESG performance not just for the sake of meeting regulations but also to better
manage stakeholder expectations.
Dietz et al. (2018) also set out to assess how many companies were commensurate with the
Paris Agreement’s primary goal of limiting the temperature rise to below 2℃ using NDCs, and
they discovered that in the 41 out of 138 companies that even had targets, only 59% were
aligned with NDCs and the 2℃ benchmark. Naturally, the aligned companies had implemented
more robust carbon management practices, albeit this excluded the majority of firms in the
study since they had either not created or aligned targets. Thus, Dietz et al. (2018) revealed that
while many companies had taken the first steps towards addressing the need for climate action,
only a handful had progressed to more advanced stages of setting long-term time-specific
targets or involving board members in executive decisions for policy-making, especially
towards meeting the goals of the Paris Agreement.
Another high-emitting business sector explored in this thesis is retail, placing among the top
ten most carbon-intensive industries. Due to its extensive hold on the consumer market, the
retail industry holds the key to influencing consumer behavior, trends, and preferences. Retail
can also affect changes in the construction industry by influencing the supply stream of
materials and technology used in construction (Ferreira et al., 2019). In a study conducted on
the top 27 retailers in the world, Ferreira et al. (2019) share largely positive findings, stating
that 85% of them publish sustainability and Corporate Social Responsibility [CSR] reports
every year, with 63% of retailers also publicly supporting and aligning their targets with those
of the Paris Agreement. In addition, standardized reporting has seen an upward trend, with 89%
of retailers adhering to reputable sustainability frameworks like the GHG Emissions Protocol,
81% following the CDP, and 78% adhering to the Global Reporting Initiative. With both
investor and consumer perceptions in mind, retailers are forming a proclivity for better
transparency and accountability, even more so since the Paris Agreement. In most retailers’
CSR reports, absolute GHG emissions reductions are emphasized, with long-term
commitments of up to 30 years listed as well, while some retailers such as Carrefour and Target
have also released short-term climate targets. Retailers have also been found to either follow
upon or improve their nations’ NDCs (Ferreira et al., 2019).

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CHAPTER 4: THE IPAT FRAMEWORK
IPAT Framework at the National Level
As discussed in the introduction, the IPAT, formulated by Commoner, Ehrlich, and Holdren in
the 1970s, argues that the driving forces of environmental impact are population, affluence and
technology (Chertow, 2000). The IPAT, also known as the Kaya formulation, is a simple but
powerful identity for establishing targets and the requirements for technology (i.e., the carbon
intensity of production) to offset economic growth while at the same time reducing emissions
to hit the set targets. Although a powerful tool to guide policymakers, its limitation is that one
cannot derive causal relationships amongst its factors. Therefore, the IPAT at the national level
can be stated as:
𝐼 =𝑃∗𝐴∗𝑇
Where I represents environmental impact, calculated through population P, affluence A and
technology T. Operationalizing the above in terms of 𝐶𝑂2 emissions, the framework becomes:
𝐶𝑂2,𝑡 = 𝑃𝑡 ∗

𝐺𝐷𝑃 𝐶𝑂2
∗
𝑃 𝑡 𝐺𝐷𝑃𝑡

where the impact I is measured by the nations’ 𝐶𝑂2 emissions at time t, while the drivers are
population at time t, and affluence A, which is calculated using GDP per capita as a crude
measure of the standard of living of a nation, and technology is measured by emissions per $
of GDP (i.e., carbon intensity). The key for emissions reductions is reducing carbon intensity
rather than population and affluence, which would have ethical implications as well as
acceptability concerns for the public. The difficulty with achieving 2030 targets through
emissions reductions is that it will be done under an expanding population and affluence, which
are forces that add to GHG emissions, thus making it more challenging to achieve the targets
set by the respective nation. Chapter 9 illustrates what it would take for Canada to hit its
ambitious 2030 target (i.e., 40‑45 percent below 2005 levels by 2030) (Environment and
Climate Change Canada, 2021). The Kaya identity can also be expressed in growth rates as
follows:
𝑔𝑒,𝑡 = 𝑔𝑝,𝑡 + 𝑔𝐴,𝑡 + 𝑔𝑇,𝑡
where 𝑔𝑒,𝑡 represents the growth rate of emissions at time t relative to past years, 𝑔𝑝,𝑡 represents
the growth rate of population at the same time period t, 𝑔𝐴,𝑡 measures the growth rate of
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affluence (i.e., measured as the rate of growth of GDP per capita), and finally 𝑔𝑇,𝑡 measures
the growth rate of carbon intensity. For example, if the population is rising at 2% and affluence
is rising at 6%, then carbon intensity has to decrease more than 8% for emissions to start falling.
Note that carbon intensity could decline over time; however, this does not mean that emissions
are decreasing. A declining carbon intensity can be caused by GDP rising faster than emissions
are increasing. China has carbon intensity targets allowing it to increase emissions, but at a rate
that is slower than its fast-growing economy, since it currently pledges to reduce CO2
emissions per unit of GDP by over 65% from 2005 levels, by the target year of 2030 in
alignment with its Paris Agreement goals. As of 2020, China had cut its CO2 intensity by 48.4%
below 2005 levels, thereby surpassing its “40-45%” pledge announced towards the
Copenhagen Accord at the 2009 UN Climate Summit (Liu & You, 2021). However, after an
economic rebound from the COVID-19 pandemic during the first quarter of 2021, China’s
emissions grew by 14.5%, the most rapid pace in a decade, primarily due to ramped up fossil
fuel and cement production (Myllyvirta, 2021).
A reformulation of the IPAT is the ImPACT by Waggoner and Ausubel (2002), which attempts
to associate the driving forces of impact I to population P and affluence A just as the earlier
version, measured using GDP per capita. However, it breaks the technology part from carbon
intensity (i.e., CO2 per $ of GDP) with the dematerialization or intensity of energy use C,
measured by energy used per $ of GDP, and efficiency T, which is the carbon intensity of
energy used. Expressed as:
𝐼 =𝑃∗𝐴∗𝐶∗𝑇
The ImPACT, like the IPAT, has been applied at the aggregate level rather than at the
organizational level. Different combinations of these four factors yield results pertaining to the
sustainability challenges of an increasing PA using two sustainability levers CT to regulate the
economic expansion. ImPACT shows a need to slow population growth P and raise A,
encourage successful conservation efforts in C, and regulate pollution in T. In terms of CO2, it
would be expressed as follows:
𝐶𝑂2,𝑡 = 𝑃𝑡

𝐺𝐷𝑃 𝐹𝐸 𝐶𝑂2
𝑃 𝑡 𝐺𝐷𝑃𝑡 𝐹𝐸 𝑡

where, for example, energy use is expressed in terms of British thermal unit (Btu) used in a
given period. Waggoner and Ausubel (2002) examined GHG emissions for the world during
the 1990-1997 period, which was prior to the Kyoto Protocol, and compared it to the 1950Page 29 of 103

1990 period examined by Hoffert et al. (1998) using the IPACT. During 1950-1990, the latter
authors found that world energy intensity of producing a $ of world GDP declined by 0.3% per
year on average, and carbon intensity of energy improved by 0.4% per year, which in a fastgrowing world economy at the rate of 3.7% moderated the increase in emissions to about 3%
per year. For the 1990s, Waggoner and Ausubel (2002) estimated the slowdown of income
growth, with the continued reduction in the intensity of use to produce a $ of GDP as well as
faster efficiency. These changes kept emissions to 0.5% per year from the 3% per year during
1950-1990.
Waggoner and Ausubel (2002) speculated that an ImPACT analysis could relay some foresight
about the Kyoto Protocol’s targets for the first commitment period because the ImPACT can
gauge the practicability of targets and timetables identifying how forces must change to achieve
a goal. Since Kyoto endeavored for industrialized nations to reduce their emissions by 5%
compared to 1990 levels in the first commitment period of 2008-2012 (UNFCCC, 2021f),
Waggoner and Ausubel (2002) started their analysis by comparing the aspirations of two
countries: France and the United States, firstly during the 1980-1990 period, to understand the
emissions situation Kyoto negotiators would have assessed for that decade. While population
P grew slowly in both nations, income A rose rapidly. Moreover, both nations had also lowered
their energy intensity use C, meaning per capita use was left almost unaltered, implying an
income elasticity near zero. As a result, efficiency T of carbon emission per energy had mildly
improved in the U.S., but France saw remarkable improvement, as the latter moved to nuclear
energy power. This driving force resulted in national emissions in France falling by 2.8% per
year with expanding affluence, while emissions were on a slow rise of 0.5% in the U.S. As for
what was required by Kyoto, the U.S. needed a national emissions decline of 1.4% to reach its
target of 93% of 1990 emissions for the 1997-2010 period. For the same period, France also
needed a 0.8% decline to meet its 92% target of 1990 emissions levels. To calculate how these
declines would follow through in light of the four forces of the ImPACT model, the authors
found that the U.S. would have had to match France’s efficiency T of carbon emissions per
energy from its 1980s levels, while France would simply have had to continue pursuing its
trend of drastic improvement.
In reality, the 1990-2000 period saw the following actual changes: Income A grew mainly in
the U.S. while both nations experienced a moderate growth in population P. Intensity of energy
use C declined in the U.S., thus implying income elasticity of 0.3, but C did not decline in
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France, implying a full elasticity of 1. While efficiency T, measured as carbon emissions per
energy, was seeing improvements in both countries, it was slower compared to France’s 1980s
performance and even slower still for both nations to meet their Kyoto targets (Waggoner &
Ausubel, 2002).
After the Kyoto agreements reached in Bonn and Marrakech in 2002, targets were relaxed.
From this point on, Waggoner and Ausubel (2002, p.7864) were using the ImPACT for
predicting the changes in performances needed by France and the U.S., wherein they
anticipated that reaching either actual or relaxed Kyoto targets demanded “unlikely behavior”
from both countries. In terms of meeting even the relaxed targets, they proposed that the U.S.
would have to apply its sustainability levers to bring down C + T from the annual declines of
1.8% in the 1990s to 4.3%. Meanwhile, France would also need to push its 0.9% decline of the
1990s to 3.2%. Waggoner and Ausubel (2002) showed that therefore, ImPACT is a swift and
transparent tool for evaluating the changes required for accomplishing environmental goals and
testing their practicability.
As discussed previously, le Quere et al. (2019) explored the drivers of declining CO2 emissions
in 18 developed economies by focusing on the decomposition of technology into four
compartments. First, total final energy use, FE. Second, the fossil fuel share of final energy
𝐹𝐸

𝑃𝐸

use, 𝐹𝐸𝑓𝑓 . Third, the fossil fuel utilization rate, 𝐹𝐸𝑓𝑓 , where primary fossil energy is 𝑃𝐸𝑓𝑓 and
𝑓𝑓

fossil final energy use is 𝐹𝐸𝑓𝑓 , meaning the energy used and lost from extracting fossils and
converting them into fuels for providing heat or electricity for final consumption. Finally, the
𝐶0

fossil fuel intensity is represented by 𝑃𝐸 2 , which indicates the amount of CO2 from a unit of
𝑓𝑓

primary energy. Their formulation is as follows:
𝐹𝐸𝑓𝑓
𝑃𝐸𝑓𝑓
𝐹𝐸
𝐶𝑂2
𝐶𝑂2,𝑡 = 𝐺𝐷𝑃𝑡 (
) (
) (
) (
)
𝐺𝐷𝑃 𝑡 𝐹𝐸 𝑡 𝐹𝐸𝑓𝑓
𝑃𝐸𝑓𝑓
𝑡

𝑡

With the corresponding growth rates, their evidence indicates that the largest contributor to the
reduction in CO2 emissions is energy use FE, followed by fossil share, while the fossil
utilization rate and fossil CO2 intensity have an insignificant role in reducing emissions. Le
Quéré et al. (2019) highlight that between the 2005-2015 period, carbon emissions sourced
from fossils fuels and industries worldwide saw an annual increase of 2.2% on average. Among
the group of eighteen industrialized countries analyzed by le Quéré et al. (2019),
decarbonization and falling emissions are explained by displacing fossil fuels through
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renewable energy. However, the decrease in energy use can partly be attributed to slower GDP
growth. While renewable energy policies seem to bolster emissions reductions and fossil fuel
displacement, such results were only found in these 18 countries and not anywhere else. Still,
policies that support energy efficiency seem to be dropping energy use worldwide, not just in
these 18 countries. Results from le Quéré et al.’s (2019) paper are expanded on in the Paris
Agreement section of this thesis.
Recent research advocates adding another driver to the IPAT to account for factor input
substitution by reformulating the IPAT to an ‘IPAST’ model (Bretschger, 2021).
𝐼 =𝑃∗𝐴∗𝑆∗𝑇
where for impact I, Bretschger (2021) uses fossil fuels FF instead of GHG emissions, arguing
that combustion of these natural resources is the source of emissions. Hence reducing the use
of fossil fuels will reduce emissions. Instead of population, Bretschger (2021) uses the labor
force L, highlighting the role of the workforce instead of the population in producing fossil
fuels. This reformulation of the population force is essential for applying the IPAT to
corporations. In terms of affluence, it is replaced with labor productivity. This change is also
an important element for the application of IPAT to corporations. Finally, technology is
represented by the intensity of inputs FF/GDP, equating to how much fossil fuel usage per $
of GDP, which is the inverse of the productivity of fossil fuels, namely how much GDP a unit
of fossil fuel (i.e., barrel of oil) produces. The IPAT is then:
𝐹𝐹𝑡 = 𝐿𝑡

𝐺𝐷𝑃 𝐹𝐹
𝐿 𝑡 𝐺𝐷𝑃𝑡

And in growth rates expressed as follows:
𝑔𝐹𝐹,𝑡 = 𝑔𝐿,𝑡 + 𝑔𝐴,𝑡 + 𝑔𝑇,𝑡
To arrive at the PAST, Bretschger (2021) added a substitution force as a “driver” of resource
use and extending the IPAT identity by an additional term K, representing production inputs
other than fossil fuels, such as a “broad (real) capital” (e.g., human capital, equipment,
buildings, dams, and windmills to produce renewable energy). The substitution force S is
measured by fossil fuels used per capital unit. For example, a reduction in FF can be achieved
by increasing human capital and reducing fossil fuels to generate GDP. Technology in this
model is measured by the amount of the broad capital excluding fossil fuels per $ of GDP
produced. This factor could be falling because GDP increases slower than K increases. The
Page 32 of 103

IPAT then is:
𝐹𝐹𝑡 = 𝐿𝑡

𝐺𝐷𝑃 𝐹𝐹 𝐾
𝐿 𝑡 𝐾 𝑡 𝐺𝐷𝑃𝑡

With the equivalent expression in growth rates.
IPAT Framework at the Corporate Level
The IPAT model reformulated and adopted at the company level can provide information on
corporate environmental impact in driving forces and facilitate decision-making to achieve
future targets with a growing demand for the corporations’ products and services. However,
can these formulations be applied at the organizational level? What would the sustainability
challenges be at the organizational level, and what levers would be needed to control the
challenges?
As the determinants of ICP, technological progress and innovation in the IPAT offer critical
insights into the valuation of carbon and its reduction. As per the da Silva et al. (2019) study,
‘technology’ was computed by focusing on emissions, water consumption, energy
consumption, generation of effluents, and waste production relative to the company's
production levels. Apart from technology, they used ‘production’ as the second factor,
replacing GDP (i.e., PA). Thus, their IPAT-e rendition of the IPAT model replaced certain
original factors more relevant to business calculations and formulated it as Impact = Production
x Technology, which essentially measures the carbon intensity of firms over time. When
applied to contemporary organizations, their IPAT-e model was additionally able to analyze
which particular area(s) of the technology factor they could improve their performance in, such
as Vale Company’s inefficiencies in atmospheric emissions, water consumption, and solid
waste, or Fibria’s weakness in energy consumption.
In this thesis, the simple IPAT is formulated as follows:
𝐸
𝐶𝑂2
𝐶𝑂2𝑒,𝑡 = 𝑅𝑡 ( ) (
)
𝑅 𝑡 𝐸 𝑡
At the corporate level, the forces of emissions include firstly the size of the firm, as determined
by revenue per year to capture the equivalent of the size of the economy as per GDP, and
secondly technology, captured from the ImPACT – a reformulation of the original IPAT by
Waggoner and Ausubel (2002). Technology is broken down to energy per unit of revenue,
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which measures energy efficiency and emissions per unit of energy, thus measuring
technological changes.
In Table 1, “I” stands for environmental impact, which is what we aim to assess. “P” is for
population and “A” is for affluence, which both represent the size of the firm. “C” denotes
intensity of energy use, and is levered by the organizations or producers themselves, since the
extent of their energy consumption is in their hands. “T” represents technology and sheds light
on the sources of energy that firms or producers rely on for their operations. For example, the
carbon intensity of energy would matter here as it is the organization’s choice to choose either
renewable sources of energy or fossil-based ones. See Column 2.
Table 1: Symbols for environmental impacts at the firm level

Category

IPAT
Symbol

Corporate
Symbol

Actors

Dimension

Impact

I

𝐶𝑂2𝑒,𝑡

All

Emissions

Revenue

P*A

𝑅𝑡

Consumers

Size of the firm

Energy intensity
of revenue

C

𝐸
( )
𝑅 𝑡

Consumers and
Firms

Energy efficiency

Carbon intensity
of energy

T

𝐶𝑂2
(
)
𝐸 𝑡

Firms

Sources of energy

Consumer and
producers
challenge

R*T

𝐶𝑂2
𝑅𝑡 (
)
𝐸 𝑡

All

Size and technology

Technology
challenge

R*C

𝐸
𝑅𝑡 ( )
𝑅 𝑡

Firms/Producers

Energy

Sustainability
challenge

P*A

𝑅𝑡

All

Revenue

Sustainability
levers

C*T

𝐸
𝐶𝑂2
( ) (
)
𝑅 𝑡 𝐸 𝑡

Firms/Producers

Emissions per unit
of revenue

The actors drive these forces of change, suggested Waggoner and Ausubel (2002), who
designated four actors. Namely, parents being responsible for population (P), workers for
affluence (A), consumers for intensity of use (C), and producers for efficiency (T). At the firm
level, there are only two actors. Consumers determine the revenue of the firm and the intensity
of revenue, while firms control the intensity of energy and carbon (C and T). See Column 4.
Thus, we can understand the three challenges; firstly of the consumers and producers, then of
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technology, and finally sustainability. These challenges can be controlled using the
sustainability levers (C*T). The challenges that consumer and producers jointly face is revenue
and technological challenges. The sustainability challenge is to keep revenue growing using
the levers to meet target reductions. The technology challenge is in terms of energy use.
Consumers ultimately can exercise their power over corporations by influencing demand for
sustainable products and services from companies that show less environmental responsibility,
while favoring those that are achieving or directing resources towards sustainable goals.
Companies have to embrace innovation, accept responsibility for the environmental impact
they cause, and adopt greener practices and technologies, without bearing losses. In the next
chapters, the thesis proves how accomplishing this is a real, lucrative possibility for
organizations.

CHAPTER 5: METHODOLOGY
You cannot manage what you do not measure. Since carbon emissions account for 81% of
overall GHG emissions (US EPA, 2022b), businesses need to start monitoring their carbon
emissions if they want to reduce or even eliminate them. The GHG Protocol’s corporate
standard classifies companies’ GHG emissions into three scopes, out of which Scopes 1 and 2
are mandatory to report (Bernoville, 2022). Under Scope 1 are direct GHG emissions, caused
by the company’s controlled or owned resources, such as fuel combustion occurring in boilers,
furnaces, and vehicles, or also emissions released through industrial processes and
manufacturing. Scope 2 includes indirect emissions that are the result of energy purchased from
a utility provider, such as electricity, cooling, heating, or steam (US EPA, 2022c; Bernoville,
2022). Scope 3 emissions are also indirect emissions, which are generated by resources not
owned or controlled by the reporting company, but are still associated with the company’s
operations and indirectly impact its value chain (US EPA, 2022a). Although these are often the
biggest source of emissions for companies, Scope 3 emissions are notoriously hard to monitor
and also only voluntary to report. There are several high-emitting activities that companies
commit regularly which count as Scope 3 emissions, such as waste generation, transport and
distribution, leased assets, franchises, capital goods like buildings and machinery, and usage of
sold products over time by consumers (Bernoville, 2022; US EPA, 2022a).
For analyzing company scope 1 and 2 emissions data in this thesis, 102 large corporations have
been chosen from the United States and Europe, of which 50 are in the U.S. and 52 are spread
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across the European countries of Denmark, Finland, France, Germany, Norway, Sweden, and
the United Kingdom. Although many of our chosen companies are well-known brands that
have worldwide operations, we have selected only one country where each operates (e.g. CocaCola’s operations in the U.S., Adidas’ in Germany, Carrefour’s in France, etc.). A complete
list of these 102 corporations is provided in Appendix 1. The period investigated was from
2017-2020 with 2017-2019 the post-Paris Agreement and pre-COVID period, while the 2020
year was the COVID period. Data came from the Carbon Disclosure [CDP] database, which is
discussed in the next section.
We aimed to choose five companies per industry from each region; i.e. five from Europe and
five from the United States, to include a maximum of ten companies per industry. The final
102 corporations are distributed among the eleven industries listed below, including one
example from Europe and the U.S. respectively, in brackets:
1. Apparel [Burberry, Nike];
2. Biotech & Big Pharma [GlaxoSmithKline, Pfizer];
3. Food, Beverage and Agriculture [Danone, Dr. Pepper];
4. Fossil Fuels [Equinor, ConocoPhillips];
5. Manufacturing (Automobiles) [BMW, Ford];
6. Manufacturing (Electronics) [Nokia, Apple];
7. Materials (Chemicals, Personal Care, & Household Products) [Unilever, Proctor &
Gamble];
8. Power Generation [Ørsted, Duke Energy];
9. Retail (Hypermarkets and Department Stores) [Marks & Spencer Group, Walmart];
10. Services (Banks) [HSBC, Morgan Stanley];
11. Transportation Services (Airlines) [Lufthansa, American Airlines].
Energy is the highest-emitting industry globally as it includes pollution from electricity, heat,
and transport, thus, it alone accounts for 73.2% of GHG emissions. The next biggest offenders
are agriculture, forestry, and land use, accounting for 18.4%, then industrial processes
involving chemicals and cement, which contribute 5.2%, and also waste, which generates 3.2%
of worldwide GHG emissions (Richie & Roser, 2020). As mentioned earlier, retail is also
among the top ten most carbon intensive industries (Ferreira et al., 2019). Therefore, this thesis
analyzes Scope 1 and 2 emissions in companies from some of the highest-emitting industries,

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such as power generation, transport (aviation and automobiles), fossil fuels, chemicals, food
and agriculture, retail, apparel, financial services, and manufacturing.
Carbon Disclosure Project [CDP]
Founded in 2000, the CDP is a not-for-profit charity that operates a worldwide disclosure
system aiming to create sustainable economies by helping companies, investors, cities, nations,
states, and regions spanning over 90 countries to measure and manage their environmental
impacts. The CDP is heralded for being a rich source of environmental reporting, with its
extensive datasets about the climate action and planning that cities, countries, and corporations
are undertaking (CDP, 2021a). As of 2020, the CDP is receiving carbon pricing data reported
by 5,900 companies globally (CDP, 2021b). Companies that disseminate GHG emissions data
voluntarily through the CDP are inclined to perform better on the environmental and CSR front
(Ferreira et al., 2019).
The CDP also works in collaboration with the Carbon Accountability Institute [CAI], an
organization dedicated towards researching anthropogenic climate change, hazardous
interferences with nature’s systems, and the involvement of fossil fuel producers in generating
carbon to atmospheric carbon dioxide content (CAI, 2019). While large-scale data on GHG
emissions is usually gathered at the country-level, the Carbon Majors Database, established by
researcher Richard Heede from the CAI, traces emissions to a smaller set of commercial
decision-makers (CDP, 2017). Heede’s (2013) work finds that the brunt of anthropogenic
climate change, which is about two-thirds of the carbon dioxide released since the year 1750,
can be attributed to the world’s 90 largest cement and fossil fuel producers, the majority of
which are still in operation as of today (CAI, 2020). Additionally, over 50% of worldwide
industrial GHG emissions since 1988 are attributable to only 25 corporate and state producers.
Heede’s (2013) research also identified the top 20 companies on the list, including Saudi
Aramco, Chevron, Exxon, and Shell, to be responsible for 35% of the world’s total carbon
emissions, equaling more than 529 billion tons of CO2 since 1965 (Taylor & Watts, 2019).
Most of these companies have since accepted responsibility and commented on the efforts and
technological innovations they will be investing in to reduce their future impact on
environmental degradation (Taylor, 2019).
The Kyoto Protocol and Paris Agreement may lead to the implementation of national policies
and regulations, which will affect corporations and the countries in which they are based. This
regulatory action by nations may create new risks, challenges, and opportunities for companies
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that could eventually influence their governance. Risks and governance are what formulate the
company’s strategy, both of which influence corporate actions towards environmental
sustainability, through initiatives such as more robust engagement with their suppliers and
consumer base regarding their sustainability strategy, setting stricter targets, and techniques
like ICP – provided there is no national pricing of GHGs (CDP, 2021b). Finally, the results of
these initiatives will show emissions levels and energy use of the companies. This thesis will
use the CDP database to examine corporate strategies that lead to emissions and energy use
reductions, as depicted in Figure 5.

Kyoto Protocol & Paris
Agreement

Risks & Opportunities

Corporate
Governance

Corporate Strategy

Engagement

Carbon Pricing

Emissions and Energy
use

Targets &
Performance

Figure 5: Potential impact of the agreements

Data for Analysis
The data can be found in sheets C6.10 of the CDP Excel workbook, in which the firms describe
their gross global combined Scope 1 and 2 emissions for the reporting year in metric tons CO2e
per unit currency total revenue and provide any additional intensity metrics that are appropriate
to your business operations. The emissions intensity per unit of revenue is reported in C1
(column L) and was crossed checked with the following two variables. A few inconsistencies
were found and were corrected by cross checking the sustainability reports of the companies.

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𝐶𝑂2𝑒,𝑡 is located in C2 (column M) reported in gross global combined Scope 1 and 2 emissions
in metric tons of CO2e. Sometimes reporting values were in megatonnes.
Total revenue in local currency, 𝑅𝑡 is located in C4 (column N). Sometimes reporting values
were in millions of the currency unit (i.e., $s in millions)
C6 (column Q) in sheet C6.10 also reported gross global combined Scope 1 and 2 emissions as
a % change from the previous year and C7 (column R) reports the direction of change of
emissions.
Energy usage was reported in sheet C8.2a in megawatt hours (MWh). The questionnaire states:
“Report your organization’s energy consumption totals (excluding feedstocks) in MWh”, with
C2 (column M) from renewable sources, C3 (column N) from non-renewable sources, and C4
(column O) reporting total (renewable and non-renewable) MWh. Once the above variables
𝐸

were retrieved for the 2017-2020 period, energy per unit of revenue, (𝑅) , which represents
𝑡

𝐶02

energy efficiency, and emissions per unit of energy, ( 𝐸 ) , representing technology, were
𝑡

computed. The rate of change (growth rates) of emissions and the three forces (i.e., size of the
firm, energy efficiency, and technology) as shown next:
𝑔𝑖,𝑝𝑟𝑒−𝐶𝑜𝑣𝑖𝑑 =

𝑔𝑖,𝐶𝑜𝑣𝑖𝑑 =

𝑌2019 − 𝑌2017
∗ 100
𝑌2017

𝑌2020 − 𝑌2019
∗ 100
𝑌2019

These were averaged across each sector as well as in total. These values were reported terms
of growth rates to satisfy the IPAT in growth rates and to determine which of the three forces
played the most significant effect on emissions.
𝑔𝑒,𝑡 = 𝑔𝑅,𝑡 + 𝑔𝐸/𝑅,𝑡 + 𝑔𝑇,𝑡

Page 39 of 103

CHAPTER 6: IPACT RESULTS AND DISCUSSION
Initial Descriptive Data Exploration
Table 2 and Table 3 show industry-wide emissions and energy data for companies in their
respective regions of Europe and the U.S. during the 2017-2020 period. In total, these 102
companies for the period 2017-2020 emitted approximately 1.98 gigatonnes (giga=billions) of
scope 1 and 2 GHG emissions. To put this figure into perspective, global CO2 emissions from
energy combustion and industrial processes hit 36.3 gigatonnes in 2021, rebounding from the
2020 lockdowns and worldwide economic meltdown arising from the COVID-19 pandemic
(IEA, 2022). Thus, over the four-year period of 2017-2020, these 102 European and U.S.
companies represented 5.5% of the 2021 global CO2 emissions.
At a glance, we can see that the U.S. companies together generate almost double the amount
of carbon emissions than Europe’s in the same time period, despite also having two fewer
companies than Europe in this study. Moreover, U.S. corporations are using roughly only half
the amount of renewable energy, as well as almost twice the amount of non-renewable energy
that the European corporations use.

Table 2: Emissions & Energy Data for 52 European Companies, sorted industry-wise for the period of 20172020.
Industry
Apparel
Biotech & Big
Pharma
Food, Beverage,
& Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power
Generation
Retail
Financial Services
(Banks)
Transportation
Services (Airlines)
Total

(MWh)

NonRenewable
Energy (MWh)

5

1,344,441

2,399,869

3,242,731

5,642,600

268,888

4

10,493,086

6,038,780

36,406,367

42,445,146

2,623,272

5

11,629,925

12,787,236

43,265,939

56,055,176

2,325,985

5

80,588,078

25,691,779

313,979,299

339,734,076

16,117,616

4

39,911,755

34,384,671

130,347,139

164,731,812

9,977,939

5

5,277,164

2,815,199

12,316,749

15,131,946

1,055,433

5

6,675,662

12,624,239

24,221,445

36,845,684

1,335,132

5

264,018,815

235,671,635

1,130,717,737

1,366,389,372

52,803,763

5

20,203,719

9,418,882

51,656,552

61,063,004

4,040,744

5

3,705,620

4,048,975

7,723,298

11,772,525

741,124

4

228,087,949

637,625

721,965,839

726,756,938

57,021,987

52

671,936,214

346,518,889

2,475,843,095

2,826,568,279

12,921,850

Number of
firms

CO2e
Emissions

Renewable
Energy

Page 40 of 103

Total
Energy
(MWh)

Emissions
per firm

For both regions, the three highest-emitting industries are in exactly the same order: first is
Power Generation, with 264 million metric tons in Europe and 623.1 million metric tons in the
U.S., which means that only five American utility providers are responsible for 1.7% of 2021’s
global GHG emissions, and along with the five European companies, this becomes almost 2.4%
of the global figure. In second place is Transportation Services, which is the airlines industry,
accounting for 228 million metric tons in Europe and 357.7 million metric tons in the U.S.; do
note that we are looking at only three American airlines versus four airlines in Europe. Still,
these 7 airlines together made 1.61% of global 2021 emissions. The third highest emitter, which
should probably come as no surprise, is the Fossil Fuel industry, generating 80.5 million metric
tons in Europe and 146.5 million metric tons in the States; again, in this study there were four
Fossil Fuel companies in the U.S., versus five in Europe, and altogether they generated 0.62%
of 2021’s worldwide emissions.
Table 3: Emissions & Energy Data for 50 U.S. Companies, sorted industry-wise for the period of 2017-2020.
Industry
Apparel
Biotech & Big
Pharma
Food, Beverage,
& Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power
Generation
Retail
Services (Banks)
Transportation
Services (Airlines)
Total

Total
Energy

(MWh)

NonRenewable
Energy (MWh)

3,284,115

9,645,925

12,930,040

493,891

17,934,981

5,002,543

67,799,994

72,802,546

3,586,996

4

11,209,298

1,581,540

31,352,571

32,934,111

2,802,324

4

146,582,662

2,274,944

483,370,801

485,645,746

36,645,666

4

40,790,878

8,881,107

130,615,237

139,492,340

10,197,720

5

1,218,886

9,927,417

3,020,257

12,947,943

243,777

5

19,397,726

10,794,301

72,182,544

82,688,132

3,879,545

5

623,157,420

126,966,974

2,401,662,399

2,511,077,505

124,631,484

5
5

84,240,666

14,467,225

195,442,937

209,910,158

16,848,133

7,331,253

10,930,851

9,722,874

20,653,767

1,466,251

3

357,754,411

391,900

1,398,226,543

1,225,486,729

119,251,470

50

1,312,087,635

194,502,917

4,803,042,082

4,806,569,017

2,624,175

Number of
firms

CO2e
Emissions

5

2,469,454

5

Renewable
Energy

(MWh)

Emissions
per firm

The highest energy consumption was also by the same three industries, in exactly that order,
for both regions. The three lowest emissions- and energy-intensive industries are also the same
in both regions, although not in the same exact order: Apparel, Financial Services (Banks), and
Manufacturing (Electronics). The ten apparel companies from Europe and America thus caused
0.01% of 2021’s global emissions, 10 banks caused 0.03%, and 10 electronics manufacturers
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caused 0.017%. In terms of both emissions and total energy, the overall rankings from highest
to lowest look very similar for both the Europe and U.S. tables.
Some examples of renewable sources of energy that companies reported using are biogas,
biodiesel, landfill gas, wood waste, ethanol, etc. In Europe, the three industries using renewable
sources of energy are Power Generation, Manufacturing, and Fossil Fuels in absolute terms
(Table 2). In the U.S., Power Generation is also at the top, along with Retail and Services
(Banks) in absolute terms (Table 3).

Transportation Services
Retail
Power Generation
Materials
Manufacturing (Electronics)
Manufacturing (Automobiles)
Fossil Fuels
Food, Beverage and Agriculture
Financial Services (Banks)
Biotech & Big Pharma
Apparel
0

200,000,000

Emissions [US]

400,000,000

600,000,000

Emissions [EU]

Figure 6: Emissions distribution industry-wise, Europe VS U.S.

Interestingly, in relative to total energy, the U.S. Fossil Fuel industry is among the three least
renewable energy-friendly industries in the region, with Food, Beverage, and Agriculture in
2nd place and Transportation Services (Airlines) being the least inclined to using renewable
sources (Figure 8). The three U.S. airline companies, namely Delta, United, and American
Airlines, used only 391,900 units of renewable energy compared to 1.39 billion MWh units of
non-renewable energy. European airlines, namely Finnair, Air France, Lufthansa, and SAS,
were also the worst in that regard, using 637,625 units of renewable versus 721.9 million units
of non-renewable energy. According to a study by Klöwer et al. (2021), the aviation industry

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makes up 4% of human-induced global warming, and if allowed to grow at pre-pandemic rates,
aviation is expected to trigger almost 0.1℃ (or 0.2℉) of warming by the year 2050.

Apparel
Financial Services (Banks)
Manufacturing (Electronics)
Materials
Biotech & Big Pharma
Food, Beverage and Agriculture
Retail
Manufacturing (Automobiles)
Fossil Fuels
Transportation Services
Power Generation
0%

10%

20%

Renewable [EU]

30%

40%

50%

60%

70%

80%

90% 100%

Non-Renewable [EU]

Figure 7: Europe’s distribution of Renewable versus Non-Renewable sources of Energy

Manufacturing (Electronics)
Apparel
Financial Services (Banks)
Food, Beverage and Agriculture
Biotech & Big Pharma
Materials
Manufacturing (Automobiles)
Retail
Fossil Fuels
Transportation Services
Power Generation
0%

10% 20%

Renewable [US]

30% 40%

50% 60%

70%

80% 90% 100%

Non-Renewable [US]

Figure 8: U.S. distribution of Renewable versus Non-Renewable sources of Energy

Page 43 of 103

Although Apparel is in 2nd place in Europe for using the least amount of renewable sources in
absolute terms, it is worth noting that 42% of all their energy used is renewable, thus showing
a much better ratio of energy sources when compared to other low-performing industries in
both regions (Figure 7). The European apparel companies are Burberry, Puma, Adidas,
LVMH, and Kering. In 3rd place in Europe is the Manufacturing (Electronics) industry, which
includes brands based in Nordic countries like Ericsson, Nokia, and Assa Abloy.
Results from the Simple Corporate IPAT Framework: Pre-COVID 2017-19 period
The results below show the growth rate of emissions, revenue, energy per unit of revenue, and
GHGs per unit of energy used over the pre-COVID (2017-2019) period for Europe and the
USA, industry-wise. These values are reported in terms of growth rates to satisfy the IPAT and
to determine which of the three forces played the most significant effect on emissions. The
association is as follows:
𝑔𝑒,𝑡 = 𝑔𝑅,𝑡 + 𝑔𝐸/𝑅,𝑡 + 𝑔𝑇,𝑡
Observe Table 4 below. On average, emissions in pre-COVID Europe fell by 16.9%. This was
driven by energy efficiency that dropped by 6.1%, but mostly by technology away from fossil
fuels by 13.9% and these emission reductions were done with revenue rising at the rate of 3.1%.
Total energy in Europe fell by almost 3% over this period. Industry-wise emissions in preCOVID Europe fell in all 11 sectors except transportation (airlines), which rose by 5.8%. The
highest reductions were in Apparel at the rate of 41%, followed by Materials at 29%, while the
lowest drops were that of fossil fuel companies at a 4.2% reduction, along with Biotech and
Big Pharma at a 6.8% reduction.
Interestingly, we observe a significant reduction in Power Generation emissions at the rate of
24.2%, due to the strong force of revenue reduction by 12.1%, which thus enabled a 7.2%
reduction in total energy use. As a result, energy per revenue increased, making the sector less
efficient, due to revenue falling at a faster rate than the energy was reduced. Emissions per unit
of energy fell by 17% because emissions fell more than energy dropped.
Revenue increased in all sectors except for Power Generation and Retail, with comparatively
the lowest losses in Materials, indicating that in most industries, emissions could fall without
hurting the companies’ revenue streams. Apparel’s revenue increased by 11.7% and emissions

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dropped by 41%; in comparison, Fossil Fuel revenue increased by a similar rate but emissions
fell only by 4.2%.

Table 4: Europe IPAT calculations, industry-wise, Pre-COVID (2017-2019)

Industry

Apparel
Biotech & Big
Pharma
Food, Beverage, &
Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power Generation
Retail
Services (Banks)
Transportation
Services (Airlines)
Total

CO2e
Emissions
-41.02%

Emissions per
Energy
-34.41%

Total Energy

11.70%

Energy per
Revenue
-18.31%

-6.81%

8.15%

-13.53%

-1.43%

-5.37%

-10.26%

4.11%

3.65%

-18.02%

7.76%

-4.23%

11.55%

-12.25%

-3.54%

-0.70%

-9.55%

3.09%

-3.90%

-8.74%

-0.81%

-10.35%

9.19%

-13.52%

-6.02%

-4.33%

-29.22%
-24.20%
-13.77%

-1.55%
-12.07%
-3.94%

-4.09%
4.86%
5.45%

-23.58%
-16.99%
-15.28%

-5.64%
-7.21%
1.51%

-25.19%

0.29%

-12.11%

-13.38%

-11.81%

5.83%

7.37%

-2.03%

0.49%

5.34%

-16.90%

3.12%

-6.11%

-13.92%

-2.98%

Revenue

(MWh)

-6.61%

The most efficient sector in terms of energy per unit of revenue is Apparel, where energy per
$ of revenue fell by 18.3%, and the least efficient sectors are Retail, Power Generation, and
Food, Beverage, & Agriculture, which actually increased their energy per unit of revenue by
5.45%, 4.9%, and 3.65% respectively. In the cases of both Retail and Food, Beverage, &
Agriculture, the industries’ increase was caused by energy use increasing faster than revenue
increasing, while in Power Generation it was driven by revenue falling faster than energy
falling.
Additionally, the most technologically-efficient industries in pre-COVID Europe include
Apparel, with a 34.4% fall in emissions per unit of energy, as well as Materials showing a
23.5% decrease, followed by Food, Beverage, & Agriculture with an 18% decrease. These
findings are quite consistent with what we have seen in Figure 7, indicating that more
technologically-advanced companies are using more renewable sources along with greener
technology. The least efficient companies hail from the Airlines, Big Pharma, and Fossil Fuels
sectors, showing an actual 0.4% increase, and only 1.4% and 3.5% decrease respectively.
Meanwhile, pre-COVID emissions in the U.S. fell by only 9.53% on average compared to
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Europe’s 16.9% (Table 5). Revenue increased by 9.9%, while Europe’s did so by only 3.1%.
Total energy use saw only a 0.75% reduction in the U.S., compared to a 2.9% reduction in
Europe. Similar to Europe’s, industry-wise emissions fell in every U.S. industry except for
airlines as well, which saw a 4% increase – but somehow, the American Food, Beverage, and
Agriculture sector also had an 8% increase in emissions, with a 20.8% rise in revenue and a
whopping 16.52% increase in total energy used. While three industries lost revenue in preCOVID Europe, only the fossil fuels industry suffered so in the U.S., with a 10.5% reduction
in revenue resulting in a 9.9% reduction in emissions. Less revenue reduced total energy use
by 20.3%, which resulted in energy per revenue to drop 9.81%, offset by an increase in
emissions per energy, which in turn was due to a 10.4% increase from inefficiencies in
technology. The increase in the growth rate of emissions per energy was caused by emissions
falling at a much slower rate than energy use fell.
Table 5: U.S. IPAT calculations, industry-wise, Pre-COVID (2017-2019)
Industry

Apparel
Biotech & Big
Pharma
Food, Beverage, &
Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power Generation
Retail
Services (Banks)
Transportation
Services (Airlines)
Total

CO2e
Emissions
-15.94%

Emissions per
Energy
-23.05%

Total Energy

6.09%

Energy per
Revenue
1.02%

-12.59%

13.26%

-16.80%

-9.05%

-3.54%

8.03%

20.81%

-4.28%

-8.49%

16.52%

-9.92%

-10.55%

-9.81%

10.45%

-20.36%

-4.39%

16.09%

-21.95%

1.47%

-5.86%

-20.14%

22.09%

-11.82%

-30.41%

10.27%

-8.02%
-2.01%
-14.43%
-20.19%

8.47%
9.14%
5.08%
9.42%

-9.80%
-4.38%
-9.28%
-11.30%

-6.69%
-6.76%
-10.23%
-18.31%

-1.33%
4.76%
-4.20%
-1.88%

4.03%

7.15%

0.10%

-3.23%

7.26%

-9.53%

9.92%

-9.17%

-10.28%

0.75%

Revenue

(MWh)

7.11%

Speaking of technology, the most efficient industries in the U.S. are Electronics Manufacturing,
with a 30.4% decrease in emissions per unit of energy, followed by the Apparel sector with a
23% decrease, and Banks with an 18.3% decrease, thus meaning that these three industries are
using more renewable sources and/or greener technology than the others, which is also
observable in Figure 8. Thus, the Apparel industry in both Europe and the U.S. is among the
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top two most technologically-efficient industries. The least efficient U.S. industries include
Fossil Fuels, as we noted earlier, with a ghastly 10.4% increase in emissions per unit of revenue,
Automobiles Manufacturing that also has a 1.4% increase, and Airlines with only a 3.2%
decrease. This is quite similar to the case of Europe, where we also noted that the fossil fuel
and airlines industries were the worst at using renewable or greener sources.
In terms of energy efficiency per $ of revenue in the U.S., Apparel and Airlines are the worst
performers with a 1% and 0.1% increase, unlike in Europe, where Apparel was the best
performer. Apparel and Airlines in the U.S. both also show a 7% increase in total energy use.
The most energy-efficient industries in the U.S. are instead Automobiles Manufacturing and
Big Pharma with decreases of almost 22% and 16.8% respectively. Surprisingly, Electronics
Manufacturing also had a 10.2% increase in total energy use, but still is the third most energyefficient and first most technologically-efficient industry in this group. Food, Beverage, and
Agriculture enjoyed the highest total energy use with a 16.5% increase as well as a 20.8% boost
in revenue, thus making it the most profitable but also the third least energy-efficient.

Transportation Services (Airlines)
Services (Banks)
Retail
Power Generation
Materials
Manufacturing (Electronics)
Manufacturing (Automobiles)
Fossil Fuels
Food, Beverage, & Agriculture
Biotech, Healthcare, & Pharma
Apparel
-50.00%

-40.00%

-30.00%

-20.00%

Emissions [US, 2017-2019]

-10.00%

0.00%

10.00%

20.00%

Emissions [EU, 2017-2019]

Figure 9: Europe VS U.S. Pre-COVID (2017-2019) Emissions distribution, industry-wise

Looking at Figure 9 and Figure 10, we are further able to compare emissions distribution
across the same eleven industries in the different regions of Europe and the U.S., with Figure
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9 highlighting the pre-COVID era, and Figure 10 offering a glimpse in the post-COVID year
of 2020. Next, we are analyzing energy and technology efficiency in the two regions, COVID,
through Table 6 and Table 7.

Transportation Services (Airlines)
Services (Banks)
Retail
Power Generation
Materials
Manufacturing (Electronics)
Manufacturing (Automobiles)
Fossil Fuels
Food, Beverage, & Agriculture
Biotech, Healthcare, & Pharma
Apparel
-70.00%

-60.00%

-50.00%

-40.00%

-30.00%

-20.00%

Emissions [US, 2020]

-10.00%

0.00%

10.00%

20.00%

30.00%

Emissions [EU, 2020]

Figure 10: Europe VS U.S. COVID (2020) Emissions distribution, industry-wise

Notice in Figure 10 how the European Fossil Fuels and Power Generation industries showed
stark emissions increases of 6% and 22.3% respectively, despite being in the COVID era, while
every other industry in each region had emissions falling. We investigated why Power
Generation had precisely a 22.3% rise (Table 6) in emissions, and one big reason for this was
the Finnish company Fortum Oyj being part of our five selected corporations in this industry
and region. In March 2020, Fortum became the majority stakeholder of German energy
company Uniper, thus making Uniper its subsidiary (Ulfves, 2020), and rendering Fortum the
second-largest producer of nuclear power, third-largest producer of carbon-free electricity, and
one of the largest gas companies in Europe (Fortum, 2022).
Unsurprisingly, Europe’s sharpest drop in emissions, which was a 60% decrease (Table 6),
occurred in the transportation services (airlines) sector, naturally due to pandemic-induced
worldwide lockdowns. The industry also had a 53.3% cut in emissions in the U.S. (Table 7),
along with a 63.2% loss in revenue and 53% lower total energy use. The European airline
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companies also utilized about 61% lower total energy, and suffered a 62.8% loss in revenue.
Biotech and Big Pharma had the second largest drop in emissions in both regions, with a 31%
reduction in Europe and 41.2% reduction in the U.S., but also gained revenues of 3.47% and
16.9% in both Europe and the U.S. respectively. This may be due to Big Pharma becoming the
most technologically efficient industry in both regions, as it shows 29.6% lower emissions per
unit of energy in Europe, and 39.1% lower in the U.S. This, once again, highlights a relationship
between technology efficiency and revenue, that being more environmentally sustainable also
reaps higher profits.
Table 6: Europe IPAT calculations, industry-wise, COVID (2020)
Industry

Apparel
Biotech & Big Pharma
Food, Beverage, &
Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power Generation
Retail
Services (Banks)
Transportation
Services (Airlines)
Total

CO2e
Emissions
-24.75%
-31.15%

Emissions per
Energy
-21.78%
-29.69%

Total Energy

-13.25%
3.47%

Energy per
Revenue
10.28%
-4.93%

-6.97%

-7.29%

3.87%

-3.55%

-3.42%

6.20%

-19.26%

15.79%

9.66%

-3.47%

-19.15%

-12.29%

0.14%

-7.00%

-12.15%

-18.44%

-1.45%

-11.65%

-5.34%

-13.09%

-24.75%
22.37%

-6.71%
-8.57%

-2.35%
22.48%

-15.69%
8.46%

-9.06%
13.91%

-11.11%
-18.66%

-5.68%
-11.40%

5.98%
-1.36%

-11.41%
-5.90%

0.30%
-12.77%

-60.14%

-62.81%

1.82%

0.86%

-60.99%

-15.81%

-12.59%

3.91%

-7.14%

-8.68%

Revenue

(MWh)

-2.97%
-1.46%

Unlike in the pre-COVID years, many industries in both Europe and the U.S. seem to be
performing poorly at energy per $ of revenue, with Europe’s Power Generation and America’s
Food, Beverage, & Agriculture leading the way through roughly a 22% increase in both cases
respectively, also seconded by Fossil Fuels in both regions showing about a 15% increase.
Overall for the year 2020, the average of emissions across all industries in Europe was roughly
the same as the three years before COVID, with the 2020 figure being a 15.8% decrease and
the pre-COVID figure being a 16.8% decrease. Seemingly, emissions were slightly lower preCOVID in Europe; this figure may be due to the Apparel sector almost doubling its emissions,
since they had a 41% decrease until 2019, and only a 24% decrease in 2020. Strong surges in
online shopping appear to have led Apparel companies to increase production and international
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shipping, while also sidelining sustainability practices (Vladimirova et al., 2022). In the U.S.
however, Apparel’s emissions remained the same both before and during the pandemic. More
notably, overall emissions in the U.S. decreased nearly two-fold, going from a 9.5% fall in the
pre-COVID era to a 17.5% drop in 2020.
While America’s technology efficiency remains the same at 10% lower emissions per MWh of
energy, Europe’s has roughly halved, rising from a 13.9% low to only 7.1%. On the bright side,
both the U.S. and Europe observed an overall decrease in total energy use, with Europe shifting
from a 2.9% to 8.6% decrease, and the U.S. showing even better progress with a 7% decrease
in energy use compared to a 0.7% increase in the pre-COVID era. However, energy efficiency
also seems to have taken a backseat in both Europe and America during the pandemic year,
since the figures for energy being utilized per $ of revenue have gone up from -6.1% and -9.1%
respectively to a 3.9% and 0.8% high. Revenue additionally suffered heavy blows in both
regions, with Europe’s diminishing from an overall 3.1% to -12.5%, and the U.S,’ plummeting
from 9.9% to -7.9%. See Figure 11 and Figure 12 for observing Revenue pre-2020 and during
2020.
Table 7: U.S. IPAT calculations, industry-wise, COVID (2020)
Industry

Apparel
Biotech & Big Pharma
Food, Beverage, &
Agriculture
Fossil Fuels
Manufacturing
(Automobiles)
Manufacturing
(Electronics)
Materials
Power Generation
Retail
Services (Banks)
Transportation
Services (Airlines)
Total

CO2e
Emissions
-14.22%
-41.70%

Emissions per
Energy
-10.74%
-39.10%

Total Energy

-14.82%
16.90%

Energy per
Revenue
11.34%
-19.50%

-1.08%

1.90%

21.48%

-24.46%

23.38%

-18.02%

-24.68%

14.94%

-8.27%

-9.74%

-20.17%

-24.77%

6.58%

-1.98%

-18.19%

-12.27%

-0.32%

-1.93%

-10.02%

-2.25%

-8.85%
-6.32%
-11.88%
-14.57%

9.34%
0.83%
-10.48%
-2.85%

-9.30%
-6.89%
1.55%
-8.94%

-8.89%
-0.26%
-2.94%
-2.78%

0.04%
-6.06%
-8.93%
-11.79%

-53.31%

-63.42%

10.33%

-0.22%

-53.09%

-17.54%

-7.92%

0.85%

-10.47%

-7.08%

Revenue

Page 50 of 103

(MWh)

-3.48%
-2.61%

Transportation Services (Airlines)
Services (Banks)
Retail
Power Generation
Materials
Manufacturing (Electronics)
Manufacturing (Automobiles)
Fossil Fuels
Food, Beverage, & Agriculture
Biotech, Healthcare, & Pharma
Apparel
-70.00%

-60.00%

-50.00%

-40.00%

-30.00%

Revenue [EU, 2020]

-20.00%

-10.00%

0.00%

10.00%

20.00%

Revenue [EU, 2017-2019]

Figure 11: Europe Revenue distribution, industry-wise, Pre-COVID (2017-2019) VS COVID (2020)

Transportation Services (Airlines)
Services (Banks)
Retail
Power Generation
Materials
Manufacturing (Electronics)
Manufacturing (Automobiles)
Fossil Fuels
Food, Beverage, & Agriculture
Biotech, Healthcare, & Pharma
Apparel
-70.00%

-60.00%

-50.00%

-40.00%

-30.00%

Revenue [US, 2020]

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

Revenue [US, 2017-2019]

Figure 12: U.S. Revenue distribution, industry-wise, Pre-COVID (2017-2019) VS COVID (2020)

Page 51 of 103

IPACT Results: Concluding Remarks
Over the four-year period of 2017-2020, these 102 European and U.S. companies represented
5.5% of the 2021 global CO2 emissions. The 50 U.S. companies together generated almost
double the amount of carbon emissions than Europe’s 52, in the same 4-year time period.
American corporations are using only half the amount of renewable energy, and also almost
twice the non-renewable energy that the European corporations use. For both regions, the
highest-emitting industry is Power Generation. At 264 million metric tons in Europe and 623.1
million metric tons in the U.S., the ten utility companies in this study are responsible for almost
2.4% of 2021’s global GHG emissions.
In the pre-COVID period, total energy use fell in Europe, while it increased in the U.S. In 2020,
both regions observed an overall decrease in total energy use. However, unlike in the preCOVID years, energy efficiency seems to have taken a backseat in both Europe and America
during 2020. Revenue additionally suffered heavy blows in both regions from the pandemic.
In the four-year period, emissions dropped in both regions due to a joint improvement in energy
and carbon efficiency. Interestingly, emissions in both regions fell more than revenue did in
2020. Europe’s emission reductions were achieved more through renewable energy, than
through energy efficiency. Overall emissions in the U.S. decreased nearly two-fold, going from
a 9.5% fall in the pre-COVID era to a 17.5% drop in 2020. While America’s technology
efficiency remains the same at 10% lower emissions per MWh of energy, Europe’s has roughly
halved, rising from a 13.9% low to only 7.1%.
Emissions’ growth rate across all industries in Europe was roughly the same as the three years
before COVID (went from -16.9% to -15.8% in 2020). Seemingly, emissions were slightly
lower during the pre-COVID era in Europe. This may be due to the Apparel industry doubling
production and transportation from a surge in online shopping during the pandemic.
A key finding was that COVID-19 lockdowns reduced emissions significantly more in the U.S.
than policies and business strategies could before the pandemic year of 2020. In Europe,
emissions during COVID-19 fell at a similar rate.
In the next chapter, we set out to estimate the impact the forces would have on emissions if we
were not assuming the IPACT framework, and instead, a different, stochastic model.

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CHAPTER 7: THE STIRPAT FRAMEWORK
The STIRPAT Framework at the National Level
Dietz and Rosa (1994, 1997) recognized the limitations in the IPAT model, the first being that
it is an identity and thus would not allow hypothesis testing, and the second about how the
model assumes proportionality in the relationship between factors, meaning that a doubling of
population P would automatically double the impact I, with everything else held constant (i.e.,
a unitary elasticity of emissions with respect to population). They argued that socio-ecological
theory necessitates hypotheses regarding the relationships between anthropogenic factors and
impacts be falsifiable with empirical evidence, in lieu of simple assumptions. Endeavoring to
counter these limitations, Dietz and Rosa (1997) formulated the model STIRPAT, standing for
“Stochastic Impacts by Regression on Population, Affluence, and Technology” to disaggregate
P, A, and T, thus being able to use regression methods for empirical hypothesis testing and
estimation (Chertow, 2000; York et al., 2003). Dietz and Rosa (1997) estimated the following
model for 𝐶𝑂2 emissions across 111 nations for the year 1989.
𝛽

𝛽

𝐶𝑂2,𝑖 = 𝛽0 𝑃𝑖 1 𝐴𝑖 2 𝜀𝑖
where 𝛽𝑖 are parameters estimated using statistical techniques. The 𝛽1 parameter represents
the percentage change in the 𝐶𝑂2 emissions for a 1% change in population (i.e., the elasticity
of emissions concerning population), while 𝛽2 represents the elasticity of emissions to
affluence A. The parameter 𝛽0 (a scaling factor) and the residual 𝜀𝑖 attempt to capture
technology. According to Dietz and Rosa (1997), technology is considered broader than a black
box labeled “carbon intensity”, and includes institutions, culture, policies, and other variables
that affect the emissions.
Dietz and Rosa (1997, p. 177) found the peak income level, after which emissions start falling
with affluence, was at $10,000. The result is very pessimistic, and they stated the following:
“Seventy-five percent of the 111 nations in our sample have GDPs below $5000. Thus, our
results suggest that, for the overwhelming majority of nations, economic growth that can be
anticipated for the next quarter century or so will produce increasing, rather than declining,
𝐶𝑂2 emissions.”
The sample was drawn before the Kyoto Protocol in 1989. If this non-linear association still
holds today, the decline would occur at $32,000, assuming a 3.5% growth in affluence per year
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on average. Given that the current world income per capita is $14,000 (World Bank in PPP
Int$) this suggests that the world has yet to reach a turning point, and time is of the essence to
hit the ambitious 2030 targets set by nations.

Figure 13: CO2 per capita and GDP per capita

For the elasticity of emissions concerning population, Dietz and Rosa (1997) found it to
increase with population at an increasing rate when China and India were included in the
sample. Otherwise, the population had a unitary elasticity. Either result indicates that
population growth plays a significant role in the environmental degradation we observe today
and into the future.
York et al. (2003) expanded the above model to incorporate technology explicitly into the
STIRPAT with additional factors including percentage of the population that is non-dependent,
percentage of GDP that is the industrial sector, degree of urbanization, whether or not the nation
is in the tropics, and quadratic terms to capture non-linearities. As a result, the estimated
relationship (ignoring the quadratic terms) and generalizing can be expressed as:
𝛽

𝛽

𝛽

𝐼𝑖 = 𝛽0 𝑃𝑖 1 𝐴𝑖 2 𝑇𝑖 3 𝜀𝑖

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Where environmental impact 𝐼𝑖 was measured using the national 𝐶𝑂2 emissions of 146 nations
for the year 1996, and the national energy footprint of 138 nations for 1999. York et al. (2003)
found a 𝐶𝑂2 turning point for affluence, but for the national energy footprint, the elasticity was
increasing with the footprint indicating a sobering result of no turning point yet. As for 𝐶𝑂2,
the turning point was at $61,000. York et al. (2003, p.362) state: “This result, in light of the
highest observed value for affluence, $27,765, for the US in 1996 provides a sober and
challenging warning: if there is an Environmental Kuznets Curve [EKC] for CO2 emissions,
the turning point is clearly unreachable by any nation in the near future.”
The STIRPAT Framework at the Corporate Level
In addition to the simple corporate framework discussed previously, a simple corporate
STIRPAT framework will be estimated. The IPAT model sets the elasticities of each force
equal to unity, but this may not be the case. In order to estimate the elasticities of emissions
with respect to revenue and with respect to energy efficiency, the model is similar to Dietz and
Rosa’s (1997) as well as Yale et al.’s (2003), but applied to firms rather than to nations:
𝛽 𝐸
𝐼𝑖 = 𝛽0 𝑅𝑖 1 ( )𝛽2 𝜀𝑖
𝑅𝑖

Where 𝛽1 is the elasticity of emissions with respect to revenue, and 𝛽2the elasticity of emissions
with respect to energy intensity. The error terms capture technology and other institutional
factors that have been omitted.
As stated previously, in the original IPAT, the elasticities are assumed to equal one, implying
that a 1% change in population leads to a 1% change in emissions and the same with respect to
affluence and technology. In the above expanded model, the elasticity is estimated with the
sample drawn instead of assuming it is unitary. Dietz and Rosa (1997) also included a quadratic
term for affluence and population so that the elasticity varies with affluence and population.
They found that the elasticity of emissions falls as affluence increases and thus is neither
unitary nor constant. This result implies an income level after which emissions will start falling
with income per capita rising. This inverted U shape result is the outcome that is expected from
the literature on the EKC (Grossman and Krueger, 1995; Stern, 2017; Halkos and Managi,
2017). Although observed with other local pollutants, it has not yet occurred with GHGs
worldwide (Figure 13).

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In this thesis, revenue might have a quadratic term impacting emissions. In order for the EKC
model to operate at the firm level, revenue should be increasing at a decreasing rate and
eventually falling with revenue increases. This implies that elasticity of emissions with respect
to revenue is greater than unity for small firms, and as firms increase in size the elasticity falls,
peaks, and then becomes negative, making emissions a normal good from inferior good.
Two effects explain why emissions follow an inverted U shape pattern with revenue; rising
initially, reaching a peak, and then falling with emissions. First, emissions should be falling
with revenue of an affluent organization. The effects are based on the interactions of production
and technology. On the production side, there are the scale effects. Emissions initially increase
with production as revenue expands. The technology depends on input substitution, the energy
intensity of production, and carbon energy intensity. Reduction in emissions with expanding
revenue may result from a nation's environmental regulations or innovation policies than
revenue itself. Although the EKC has been studied extensively at the national level, it has not
been applied at the firm level yet (Stern, 2017; Halkos and Managi, 2017).

CHAPTER 8: STIRPAT RESULTS AND DISCUSSION
Results from the STIRPAT Estimations
Table 8: Effects of Revenue and Energy Efficiency on 2019 CO2 emissions of 99 companies* in Europe and the
United States.

Revenue
Revenue2
Energy
Efficiency
Intercept
R-Squared

Log-polynomial model
Log-linear model
Standard 95% Confidence
Standard 95% Confidence
Coefficient
Coefficient
Error
Interval
Error
Interval
6.663
1.399
3.884
9.441
1.182
0.163
0.858
1.507
-0.119

0.030

-0.179

-0.060

1.019

0.034

0.950

1.088

1.007

0.048

0.910

1.103

16.300 -100.321 -35.599
0.9348

-5.767

4.238 -14.180
0.8805

2.646

-67.960

*99 companies instead of the 102, because three companies (ADVA Optical, Dr. Pepper, and Church & Dwight) did not have
data for 2019; they only did for 2017, 2018, and 2020.

For the log-linear model, the elasticity of emissions with respect to revenue is 1.2 with a 95%
confidence interval [0.86, 1.51] and the elasticity of emissions with respect to energy efficiency
is 1.0 with a 95% confidence interval [0.91, 2.65]. This supports the IPACT, because in that
model, the elasticities are unitary. The implication is that emissions are relatively sensitive to
the two forces with the remaining force indicated by the residual. For example, a firm can
Page 56 of 103

reduce emissions by 10% if its energy efficiency increases by 10%, holding revenue constant.
Revenue can increase by 10% with emissions falling, if energy efficiency improves by 20%.
For the log-polynomial model, the energy efficiency elasticity remains similar to the log-linear
model around the unitary elasticity, but the revenue force surprisingly has an elasticity that
varies with size of the firm, as determined by revenue. Firms with high revenue have a lower
elasticity than firms with a low revenue, since the revenue elasticity is not constant and
decreases with revenue increasing. This has implications that very large firms can increase their
revenue while reducing emissions, holding energy efficiency constant; most likely through
utilizing better technology from the technology multiplier. As highlighted in the previous
chapter, this scenario of falling emissions with rising revenue was strongly observable in the
Big Pharma industry throughout the 2017-2020 period, in both Europe and the U.S. (Table 5,
Table 6, Table 7).

Figure 14: Effects of Revenue on CO2 emissions in 2019. Data points plotted for the 99 companies in Table 10.

In Figure 14, the American company All Access Apparel is an influential variable with a
PLnCO2 Rev value of 8.43.

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Table 9: Effects of Revenue on CO2 emissions of 99 companies in 2019. For convenience, the 11 industries
were sorted into the five groups* listed below.

Technology Multiplier
Energy
Services
Manufacturing
Personal Care
Consumer Goods

Coefficient
1.228
1.045
1.539
1.019
1.166

Standard Error
0.169
0.133
0.266
0.117
0.186

95% Confidence Interval
0.891
1.565
0.780
1.310
1.010
2.069
0.786
1.253
0.796
1.537

*“Energy” includes Fossil Fuels and Power Generation, “Services” includes Banks and Airlines, “Manufacturing” includes both
the Electronics and Automobile manufacturing industries, “Personal Care” comprises Materials and Big Pharma, and
“Consumer Goods” consists of Retail, Apparel, and Food, Beverage, & Agriculture.

In both the original IPAT formula and its stochastic reformulation, the STIRPAT, recall that
“T” stands for “technology”, which is modeled as a residual term in the formulation.
“Technology” here embodies not only technology in terms of non-renewable versus renewable
sources of energy but also institutions, culture, governance and power, and other internal and
external factors. Similar to the IPAT, the STIRPAT integrates the multiple factors that
contribute to “technology” into a single multiplier (Dietz & Rosa, 1997). The technology
multiplier is the antilog of the residual from the log–polynomial regression reported in Table
8 earlier.
Technology multipliers corresponding to the CO2 emissions of 99 companies in the pre-COVID
year of 2019 can be observed in Table 10 below. Table 9 provides sectorial technology
multipliers by averaging the individual multipliers within each sector. Note that the higher the
multiplier, the more responsive is the force of technology in influencing either emissions
reductions, or increases in the force of CO2 per unit of energy. For example, if the multiplier is
greater than 1, such as 1.2, it would indicate a 1% increase in CO2 per unit of energy, resulting
in a more than 1% increase in emissions, i.e. 1.2%.
In Table 10, we can observe that Walmart, a large American retailer, has a very high
technology multiplier of 5.443, which can lead to big reductions in emissions from reducing
the carbon intensity of energy. A 1% reduction in carbon intensity of energy, i.e. employing
natural gas trucks instead of diesel or gasoline, will reduce emissions by 5.4%. Also, in the
U.S., electronics manufacturer ADTRAN has a multiplier above 5 as well.
However, Apple Inc., one of the most prominent American corporations in the world that is
also in the electronics manufacturing industry, has the lowest multiplier of all 99 companies
listed in the table, at 0.15. This is most likely because Apple has already achieved major
Page 58 of 103

reductions, thus making it more difficult to use T to reduce emissions further. From 2017 to
2019, Apple’s T was reduced by almost 70%. Other companies with technology multipliers
below 0.5 are L’Oreal, Nordea and Danske Banks, Ørsted, and Kering in Europe, along with
American Express in the U.S.
While most Power Generation companies in Europe have a multiplier below 1, two of
America’s have a multiplier of 1.6 and 2.8. The Apparel industry in both regions seems to have
emissions less responsive to T than others, with the exception of Burberry Group in Europe
and Nike Inc. in the U.S., both having technology multipliers above 1. All European electronics
manufacturers have multipliers above 1, and the same is true for all automobile manufacturers
in the U.S.
Examining Table 9, in which firms have been placed in their respective sectors, the technology
multipliers of these sectors hover around the unitary elasticity, consistent with the IPAT
formulation. ‘Manufacturing’ has a higher multiplier than the other sectors.

Table 10: Technology Multiplier for 99 companies’ CO2 emissions*, for both regions of Europe and the United
States, sorted by industry, for the pre-COVID year of 2019.

ORGANIZATION

COUNTRY

INDUSTRY

ADIDAS AG
KERING
LVMH
PUMA SE
BURBERRY GROUP
CARREFOUR
KESKO CORPORATION
J SAINSBURY PLC
CASINO GUICHARDPERRACHON
MARKS AND SPENCER
GROUP PLC
DANONE

Germany
France
France
Germany
United Kingdom
France
Finland
United Kingdom
France

Apparel
Apparel
Apparel
Apparel
Apparel
Retail
Retail
Retail
Retail

TECHNOLOGY
MULTIPLIER
0.548
0.435
0.956
0.934
2.044
1.584
0.380
0.544
1.869

United Kingdom

Retail

1.456

France

0.823

BRITVIC

United Kingdom

DIAGEO PLC

United Kingdom

PERNOD RICARD

France

CARLSBERG BREWERIES A/S

Denmark

Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture

Page 59 of 103

0.791
0.509
0.709
0.812

ORGANIZATION

COUNTRY

INDUSTRY

EQUINOR
TULLOW OIL
LUNDIN ENERGY AB
NESTE OYJ
AKER BP ASA
ERICSSON

Norway
United Kingdom
Sweden
Finland
Norway
Sweden

ADVA OPTICAL
NETWORKING SE
NOKIA GROUP

Germany

ASSA ABLOY

Sweden

APTIV

United Kingdom

FINNAIR
AIR FRANCE – KLM
DEUTSCHE LUFTHANSA AG
SAS
GLAXOSMITHKLINE

Finland
France
Germany
Sweden
United Kingdom

NOVO NORDISK A/S

Denmark

SANOFI

France

ASTRAZENECA

United Kingdom

HENKEL AG & CO. KGAA
L’ORÉAL
ORIFLAME HOLDING LTD
PZ CUSSONS
UNILEVER PLC
RENAULT GROUP

Germany
France
Sweden
United Kingdom
United Kingdom
France

BMW AG

Germany

VOLKSWAGEN AG

Germany

DAIMLER AG

Germany

ØRSTED
SSE
FORTUM OYJ
EDF
CENTRICA
BNP PARIBAS
HSBC HOLDINGS PLC
DANSKE BANK A/S

Denmark
United Kingdom
Finland
France
United Kingdom
France
United Kingdom
Denmark

Fossil Fuels
Fossil Fuels
Fossil Fuels
Fossil Fuels
Fossil Fuels
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Transportation services
Transportation services
Transportation services
Transportation services
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Materials
Materials
Materials
Materials
Materials
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Power generation
Power generation
Power generation
Power generation
Power generation
Financial Services
Financial Services
Financial Services

Finland

Page 60 of 103

TECHNOLOGY
MULTIPLIER
0.908
2.812
1.252
0.764
1.055
1.590

1.105
2.040
1.423
1.227
0.856
0.870
0.878
1.143
1.073
0.793
0.833
0.916
0.318
1.017
2.797
0.579
0.849
0.736
2.171
1.403
0.440
1.102
0.512
0.857
0.753
0.939
1.603
0.198

TECHNOLOGY
MULTIPLIER
0.249
2.134
1.093
0.917
0.868
0.700
0.214
5.443
2.108

ORGANIZATION

COUNTRY

INDUSTRY

NORDEA BANK ABP
NATWEST GROUP PLC
NIKE INC.
PVH CORP
HANESBRANDS INC.
LEVI STRAUSS & CO.
ALL ACCESS APPAREL, INC.
WALMART, INC.
WALGREENS BOOTS
ALLIANCE
BEST BUY CO., INC.
MACY’S, INC.
NORDSTROM, INC.
THE COCA-COLA COMPANY

Sweden
United Kingdom
United States of America
United States of America
United States of America
United States of America
United States of America
United States of America
United States of America

Financial Services
Financial Services
Apparel
Apparel
Apparel
Apparel
Apparel
Retail
Retail

United States of America
United States of America
United States of America
United States of America

0.933
1.205
0.893
1.981

KEURIG DR PEPPER

United States of America

CONSTELLATION BRANDS,
INC.
MOLSON COORS BREWING
COMPANY
DEVON ENERGY
CORPORATION
CONOCOPHILLIPS
WILLIAMS COMPANIES, INC.
HESS CORPORATION
APPLE INC.

United States of America

Retail
Retail
Retail
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Fossil Fuels

1.042
0.907
2.377
0.150

COMMSCOPE, INC.

United States of America

ARISTA NETWORKS

United States of America

JUNIPER NETWORKS, INC.

United States of America

ADTRAN INC

United States of America

AMERICAN AIRLINES GROUP
INC
DELTA AIR LINES
UNITED AIRLINES HOLDINGS
PFIZER INC.

United States of America

Fossil Fuels
Fossil Fuels
Fossil Fuels
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Manufacturing
(Electronics)
Transportation services

0.879
0.905
1.114

ABBVIE INC

United States of America

BRISTOL-MYERS SQUIBB

United States of America

JOHNSON & JOHNSON

United States of America

Transportation services
Transportation services
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma

United States of America
United States of America
United States of America
United States of America
United States of America
United States of America

United States of America
United States of America
United States of America

Page 61 of 103

0.891
1.033
1.256

1.796
1.733
0.907
5.500
0.954

0.891
0.778
1.054

TECHNOLOGY
MULTIPLIER
0.787

ORGANIZATION

COUNTRY

INDUSTRY

MERCK & CO., INC.

United States of America

PROCTER & GAMBLE
COMPANY
COLGATE PALMOLIVE
COMPANY
ESTEE LAUDER COMPANIES
INC.
CLOROX COMPANY
CHURCH & DWIGHT CO., INC
FORD MOTOR COMPANY

United States of America

Biotech, health care &
pharma
Materials

United States of America

Materials

0.901

United States of America

Materials

1.019

United States of America
United States of America
United States of America

1.387

GENERAL MOTORS
COMPANY
NAVISTAR INTERNATIONAL
CORPORATION
PACCAR INC

United States of America

EXELON CORPORATION
CMS ENERGY CORPORATION
DOMINION ENERGY
PINNACLE WEST CAPITAL
CORPORATION
DUKE ENERGY
CORPORATION
AMERICAN EXPRESS
MORGAN STANLEY
U.S. BANCORP
WELLS FARGO & COMPANY
CITIGROUP INC.

United States of America
United States of America
United States of America
United States of America

Materials
Materials
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Manufacturing
(Automobiles)
Power generation
Power generation
Power generation
Power generation

United States of America

Power generation

0.751

United States of America
United States of America
United States of America
United States of America
United States of America

Financial Services
Financial Services
Financial Services
Financial Services
Financial Services

0.286
1.142
0.979
1.793
1.884

United States of America
United States of America

0.954

1.830
1.842
1.131
1.068
0.730
2.847
0.587
1.606

*99 companies instead of the 102, because 3 companies (ADVA Optical, Dr. Pepper, and Church & Dwight) did not have data
for 2019; they only did for 2017, 2018, and 2020.

STIRPAT Results: Concluding Remarks
In summary, the findings from the STIRPAT estimations are consistent with those of the
IPACT model. Table 8’s log-linear calculations show unitary elasticities of emissions just like
in the IPACT model, signifying that emissions are relatively sensitive to revenue and energy
efficiency. The log-polynomial exhibits a difference in the revenue force, which has an
elasticity that varies with size of the firm, as determined by revenue. In this case, revenue
elasticity is not constant and decreases with revenue increasing, violating the IPACT
formulation that has a constant unitary elasticity. This implies that very large firms can increase
their revenue while reducing emissions, holding energy efficiency constant; most likely
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through utilizing better technology from the technology multiplier. Smaller firms have more
difficulty and may be focusing more on energy efficiency than switching to renewable energy
sources.
Furthermore, similar to the IPACT, the STIRPAT integrates the multiple factors that contribute
to “technology” into a single multiplier, which is the antilog of the residual from the log–
polynomial regression reported in Table 8. The higher the technology multiplier, the more
responsive is the force of technology in influencing either emissions reductions or increases.
Table 9 shows that the technology multipliers of the sectors hover around the unitary elasticity,
consistent with the IPACT formulation.

CHAPTER 9: TARGETS AND EMISSION REDUCTIONS
Introduction: Targets at the Corporate Level
According to the United Nations Principles for Responsible Investment [UN PRI], setting
carbon neutrality and net-zero GHG commitments for attaining specified future emissions
levels by a target year is starting to become the norm for both businesses and governments.
The question today is not so much about whether companies will ever set targets, but rather
how they plan to meet them (Geck, 2021).
Although the term '"carbon-neutral" is sometimes used interchangeably with "net-zero," the
two are not the same. When a company claims to be carbon-neutral, it typically offsets its
carbon emissions by paying carbon tax or buying carbon credits, but essentially does not reduce
emissions by an amount that reaches net-zero at the global or sector level. This may mask the
need for further emissions cuts, consistent with science calling for limiting global warming to
1.5°C. Also, the carbon-neutral claim does not necessarily cover any GHGs other than CO2
(Tarrant, 2021).
Net-zero, on the other hand, involves overall GHG reductions throughout the organization’s
entire supply chain. Net-zero means emissions are offset by the removal of an equivalent
amount of CO2 from the atmosphere. It can be implemented through investment in renewable
energy, planting trees, or processes like carbon capture and storage. In order to limit global
warming to 1.5 °C and prevent the worst effects of climate change, anthropogenic emissions

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must be net-zero by the year of 2050 (ISO, 2022; Tarrant, 2021).
As per the 2021 Corporate Sustainability Assessment conducted by S&P Global, almost 60%
of European companies had declared plans to reduce their direct emissions as well as those
generated from their purchased energy. Meanwhile, only 35% companies in North America,
31% in South America, and 29% in the Asia-Pacific region had announced Scope 1 and/or
Scope 2 emissions targets (Whieldon & Almtoft, 2022).
A recent report by Accenture highlights that 93% of companies will fail to meet their net-zero
commitments if they do not double their rate of emissions reductions by 2030. In spite of the
fact that more businesses in every region are setting clear, publicly visible decarbonization
goals, with a record number of corporate targets validated by the Science-Based Targets
Initiative [SBTi] this year alone, and despite 84% of businesses planning to increase
investments in their sustainability initiatives before the end of 2022, the study finds that rising
energy price inflation and supply insecurity are pushing commitments out of reach (Aizenberg
& Luu, 2022).
Despite the pledges made, just 7% of businesses are on track to meet their net-zero goals for
Scope 1 and 2 emissions at the observed rates of change by 2030, and the remaining most likely
will move their targets to 2050. Assuming that corporations accelerated emissions reductions
to twice the existing levels in the years leading up to 2030, and then three times after that, 59%
would still fail by 2050, the timeframe deemed necessary to avert the most catastrophic and
long-lasting effects of climate change (Aizenberg & Luu, 2022).
The Accenture report recommends that companies will need to create several 'carbon
intelligence' capabilities at the same time to operate at the pace and scale demanded by science
and economics. This entails integrating carbon data and insights that are ready for decisionmaking across their systems and processes, enabling efficient use of financial and non-financial
resources, as well as sound risk management when deploying the digital, biological, and
industrial technologies that will be required to achieve net-zero (Aizenberg & Luu, 2022).
The next sections in this chapter will compare the nature of targets set by the 102 companies
in this thesis. Additionally, using past performance as an indicator, case studies will be
observed on two companies from each region to assess whether they are on track to meet their
targets. Finally, we will also conduct the IPAT case study at the national level on one country.

Page 64 of 103

Targets from European and American Firms
Absolute targets refer to reductions in CO2 emissions while intensity targets refer to carbon
emissions in relation to a benchmark like total energy, size of the operation, the firm’s revenues,
etc. With intensity targets, emissions can increase; but as long as they increase at a slower rate
than the benchmark, their target can be achieved. For example, emissions currently may be
60% of energy. The target could be to reduce intensity to 30% of energy. This target can be hit
even if emissions increase as long as they increase at a slower rate than energy rises.
Table 11: Pre-COVID growth rate of CO2 and type of control for 65 companies across Europe and the U.S.

Type of control
Absolute
Intensity**
Both
Total

Target:
All
46
17
39
102

Target:
Coefficient
2030*
22
-0.139
17
-0.082
26
-0.148
65

Standard
Error
0.045
0.051
0.041

95% Confidence
Interval
-0.229
-0.049
-0.184
0.019
-0.231
-0.065

*“Target: 2030” tells the number of companies that have or include an emissions target for the year of 2030. However, other
than ConocoPhillips, none of the 14 companies with intensity targets had provided any detail about their target year, base
year, progress, etc. But for the sake of comparison, we have still included those 13 companies in the intensity row.
**For convenience, the “Intensity” row includes the 3 companies which had not set a target at all. 14 companies had set an
actual intensity target.

Table 11 assesses the 65 companies across both the U.S. and Europe that have emissions
targets for the year of 2030, since it is a common target for companies, plus not as soon as 2025
and not as far away as 2040 or 2050. Out of the 65 companies, 33.8% have absolute targets.
The “Intensity” target row includes the three companies which had not set a target at all, and
represents 26.2% of the total. The remaining 40% had both absolute and intensity targets. In
the Pre-COVID era, companies with absolute targets reduced emissions on average by 13.9%,
with a 95% confidence interval [-22.9%, -4.9%]. Those with intensity targets reduced the least
by 8.2%, but is not statistically significant and thus inconclusive if they reduced or not. Those
with both control types reduced by 14.8% with a 95% confidence interval [-23.1%, -6.5%],
Table 12: Pre-COVID growth rate of CO2 and type of control for 37 companies in Europe.

Type of Control [EU]
Absolute
Intensity
Both
Total

Target: 2030

Coefficient

11
6
20
37

-0.187
-0.089
-0.201

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Standard
Error
0.070
0.021
0.053

95% Confidence
Interval
-0.331
-0.043
-0.134
-0.045
-0.309
-0.092

Table 13: Pre-COVID growth rate of CO2 and type of control for 28 companies in the U.S.

Type of Control [US]

Target: 2030

Absolute
Intensity*
Both
Total

Coefficient

11
11
6
28

-0.090
-0.078
0.026

Standard
Error
0.044
0.075
0.022

95% Confidence
Interval
-0.183
0.001
-0.233
0.076
-0.020
0.073

*For convenience, the “Intensity” row includes the 3 companies which had not set a target at all. Only 8 U.S. companies
had set an actual intensity target.

Table 12 and Table 13 convey Table 11’s statistics separately by regions, i.e. Europe and the
U.S. respectively, but only taking the target year of 2030 into account. This shows that there
are significant differences between the two regions. European companies with absolute targets
reduced emissions by 18.7% relative to U.S. companies that reduced emissions only by 9%. In
Europe, companies with intensity targets reduced emissions by 8.9%, but in the U.S., emissions
fell by 7.8%. However, this is not statistically significant and hence inconclusive to show if
intensity targets achieve reductions on average. For European companies with both targets,
emissions fell by 20.1%, while in the U.S. emissions increased by 2.6%, although not
significant and hence inconclusive to make any claims.
Case Studies: IPAT Framework to Hit Targets at the Corporate Level
Table 14: Target details of companies Nokia Group, Apple Inc., Ørsted, & Nike Inc.

Organization
Country
Base Year [BY]
Target year
BY Emissions
Targeted reduction from BY (%)
Emissions in TY
Emissions in 2019
Emissions growth (-decline)
Revenue growth
Energy efficiency
Technology
Energy growth

Nokia Group
Finland
2014
2030
710,000
41.00
418,900
452,238
-23.4%
0.7%
-10.3%
-13.8%
-9.6%

Apple Inc.
USA
2019
2030
25,100,000
61.70
9,613,300
25,100,000
-38.1%
13.5%
16.2%
-67.8%
29.7%

Ørsted
Denmark
2018
2032
29,200,000
50.0
14,600,000
25,333,000
-54.3%
13.6%
-45.2%
-22.7%
-31.6%

NIKE Inc.
USA
2015
2030
264,394
65.00
92,538
255,779
1.99%
13.83%
11.66%
-23.50%
25.49%

Note: Growth rates are pre-COVID-19, so that the pandemic does not influence endogenous decisions.

Can a firm meet its target given past performances and future expected revenue growth? The
following application of IPAT illustrates the effort of meeting the targets for reducing CO2
emissions with respect to three companies that had absolute targets – namely Nokia Group,

Page 66 of 103

Apple Inc., Ørsted, and Nike Inc. Details about these companies can be observed in Table 14.
I.

Apple Inc.

Founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne, Apple Inc. is a
multinational technology company based in Cupertino, California. It is considered one of the
Big Four technology companies, alongside Amazon, Google, and Microsoft. Apple reached $1
trillion in value in 2018, ranking 3rd worldwide in Fortune 500 companies (Fortune, 2019).
After going public in 1980, Apple Inc. is now considered to be one of the largest companies in
the world, and has tremendous influence within the U.S. and global economies. According to
data from the World Bank, Apple Inc. is worth more than Canada’s $1.7 trillion economy,
leaving only seven countries in the world with a GDP higher than Apple’s market value. As of
March 15, 2021, Apple's market cap increased to $2.08 trillion, making it the first publicly
traded U.S. company to surpass $2 trillion, and the first company in the world to reach a market
capitalization of $1 trillion (Kolakowski, 2021).
Now, let us conduct the IPAT experiment to see whether Apple will meet its target, which is
for its GHG emissions to be 61.7% below 2019 levels by 2030. From 2017 to 2019, Apple had
the following growth rates – revenue grew at an average rate of 4.5% per year during this
period, while energy use grew at the rate of 9.9% per year (derived from the three-year figures
in Table 14). Thus, energy per unit of revenue grew at the rate of 5.4% per year (i.e., the
difference in the growth rate of energy and revenue). In order to hit the target, assuming revenue
and energy efficiency continue to grow at the same rates, carbon intensity has to fall to a certain
level in 2030 relative to 2019 levels. Using the IPAT equations:
𝐸
𝐶𝑂2
𝐶𝑂2 2030 = (𝑅2030 )(
)(
)
𝑅 2030 𝐸 2030
𝐸
𝐶𝑂2
𝐶𝑂2 2019 = (𝑅2019 )(
)(
)
𝑅 2019 𝐸 2019
And taking the ratio of these two relationships yields:
𝐸
𝐶𝑂2
𝐶𝑂2 2030 (𝑅2030 )(𝑅 2030 ) ( 𝐸 2030 )
=
𝐸
𝐶𝑂
𝐶𝑂2 2019 (𝑅
) ( 𝐸2
)
2019 ) (𝑅
2019
2019

On average, if the above rates of growth are maintained, then in 2030, revenue will be 1.62
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times higher at the average growth rate of 4.5% per year, and energy per unit of revenue will
be 1.78 times higher than energy in 2019 at the average annual growth rate of 5.4%. This results
in:
𝐶𝑂
( 𝐸2
)
𝐶𝑂2 2030
2030
= 1.62 ∗ 1.78
𝐶𝑂
𝐶𝑂2 2019
( 𝐸2
)
2019
Since the target for CO2 is to be 61.7% below 2019 levels:
𝐶𝑂2 2030 = 𝐶𝑂2 2019 -0.617𝐶𝑂2 2019 = 0.383𝐶𝑂2 2019.
Substituting these values into the ratio of the two equations and solving for carbon intensity,
we get approximately:
𝐶𝑂2
0.383
𝐶𝑂2
𝐶𝑂2
(
)=
(
) = 0.133 (
)
𝐸 2030
1.62 ∗ 1.78 𝐸 2019
𝐸 2019
Thus, carbon intensity in 2030 has to be 13.3% of that in 2019, in order to hit the target of
61.7% below 2019 levels by 2030. The reason it has to be way below 61.7% of the 2019 level,
i.e. 13.3% of 2019 level, is because revenue and energy used per unit of revenue are expected
to rise in the future if the same trend continues until 2030.
How fast do carbon emissions have to fall in the future to hit the target requires a growth rate
of carbon intensity that will bring it in 2030 to 0.133, its 2019 level. Carbon intensity should
be falling by g percent per year as follows:
𝑇2030 = 𝑇2019 (1 + 𝑔)11
𝐶𝑂

Where for simplicity, 𝑇2030 = 𝐸 2

𝐶𝑂

2030

is carbon intensity in 2030, and 𝑇2019 = 𝐸 2

2019

is

carbon intensity in 2019. But we also want 𝑇2030 = 0.133𝑇2019 to hit the target. Equating the
last two equations, we get: 0.133 = (1 + 𝑔)11 . Solving for g yields:
𝑔 = 0.1331/11 − 1 = −0.167
Meaning 16.7% reduction per year in carbon intensity. Since energy grew at 9.9% per year,
and if it continues at this rate in the future, carbon emissions will need to fall by 6.8% per year
over the next 11 years. The company can compare the past growth rate of emissions with 6.8%
reduction per year to see if it is on target or not. Apple Inc.’s emissions fell by 11.4% per year
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in the 2017-2019 period, which shows that Apple is on the right track to hit its 2030 target.
Even in the case of revenue and energy efficiency not changing over the next 11 years,
emissions have to fall at a rate of 8.4% per year to hit the target of 9,613,300 in 2030 from
25,100,000 in 2019, as per Table 14 above.
II.

Nokia Group

Nokia is a telecommunication and IT firm based in Finland. Nearly 88,000 people work for the
corporation, which was founded in 1865, and has its headquarters in Espoo. Nokia has ventured
into different markets throughout the years. The company dominated the mobile market as a
global leader in the early days of the cell phone and smartphone era. However, beginning in
2011, Nokia's mobile business started to lose money as a result of its slow adoption of advances
in smartphone technology, which allowed rivals like Samsung and Apple to gradually overtake
it as the market leader. Microsoft purchased Nokia's phone division in 2013 (Alsop, 2022).
Since then, Nokia has concentrated on data networking services and telecommunications
equipment through its subsidiary, Nokia Networks. As of 2021, this subsidiary has become its
highest-earning business segment. Additionally, the technology company has branched out to
providing solutions for cloud computing and mapping applications. The most significant
markets for Nokia globally are those in North America and Europe, where it has emerged
among the leading telecom infrastructure industry. The Nokia brand today is worth roughly US
$28 billion (Alsop, 2022; Financial Times, 2023).
Now, we perform the IPAT experiment on Nokia Group, to see if the company is on track to
meet its target of reducing GHG emissions to be 41% below 2014 levels by 2030. From 2017
to 2019, Nokia had the following growth rates – revenue grew at a rate of 0.23% per year during
this period, while energy use fell at the rate of -3.2% per year. Thus, energy per unit of revenue
fell at the rate of -3.0% per year. Assuming these rates continue into the future and were present
in the past three years (i.e., 2014-2016) on average, carbon intensity has to fall to a certain level
in 2030 relative to 2014 levels. Using the IPAT equations:
𝐸
𝐶𝑂2
𝐶𝑂2 2030 = (𝑅2030 )(
)(
)
𝑅 2030 𝐸 2030

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𝐸
𝐶𝑂2
𝐶𝑂2 2014 = (𝑅2014 )(
)(
)
𝑅 2014 𝐸 2014
On average, if the above rates of change are maintained, then revenue in 2030 will be 1.04
times higher than in 2014, at the assumed average growth rate of 0.23% per year. Energy per
unit of revenue in 2030 will decrease to 0.614 compared to the 2014 figure, at the average
annual decline rate of -3%. Since the target for CO2 is to be 41% below 2014 levels:
𝐶𝑂2 2030 = 𝐶𝑂2 2014 -0.41𝐶𝑂2 2014 = 0.59𝐶𝑂2 2014.
Substituting these values into the ratio of the two equations and solving for carbon intensity,
we get approximately:
𝐶𝑂2
0.59
𝐶𝑂2
𝐶𝑂2
(
)=
(
) = 0.92 (
)
𝐸 2030
1.04 ∗ 0.614 𝐸 2014
𝐸 2014
Thus, carbon intensity in 2030 has to be 92% of that in 2014, in order to hit the target of 41%
below 2014 levels by 2030. The reason carbon intensity does not need to drop by a lot is
because revenue is unchanged but energy per unit of revenue is falling at a very fast rate and
is assumed to continue to drop reaching 61.4% by 2019 relative to 2014. How fast do carbon
emissions have to fall in the future to hit the target requires a growth rate of carbon intensity
that will bring it in 2030 to 0.92, its 2014 level. Carbon intensity should be falling by g percent
per year as follows:
𝑇2030 = 𝑇2014 (1 + 𝑔)16
𝐶𝑂

Where for simplicity, 𝑇2030 = 𝐸 2

𝐶𝑂

2030

is carbon intensity in 2030, and 𝑇2014 = 𝐸 2

2014

is

carbon intensity in 2014. But we also want 𝑇2030 = 0.92𝑇2014 to hit the target. Equating the
last two equations, we get 0.92 = (1 + 𝑔)16. Solving for g yields:
𝑔 = 0.921/16 − 1 = −0.0052
Meaning 0.5% reduction per year in carbon intensity. Since energy fell at 3% per year during
the past on average, carbon emissions will have to fall by 3.5% per year over the 16 years. The
company can compare the past growth rate of emissions with 3.5% reduction per year to see if
it is on target or not. Nokia’s emissions fell by 7.3% per year over the 2017-2019 period, which
shows that Nokia is on the right track to hit its 2030 target. In fact, since Nokia’s emissions fell
much faster than 3.5% per year from 2017-2019, its emissions need to fall only by 0.7% over
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the next 11 years, even if revenue and energy intensity remain unchanged. This is because
emissions in 2030 have to be 418,900 tonnes, and in 2019 were 452,238 tonnes (see Table 14).
III.

Ørsted

To mitigate climate change, companies in every industry must swiftly reduce their GHG
emissions. Although it is not an easy undertaking, a few companies exemplify that it can be
done. Ørsted, an energy company based in Denmark, leads the way in doing so. When it was
still known as DONG Energy twelve years ago, the company generated the majority of its
income from the sale of heat and power, 85% of which was derived from coal (Tryggestad &
Rosenfield, 2020).
In 2009, the corporation then declared a significant strategy shift, aiming to instead produce
85% of its heat and electricity from renewable sources by 2040. Eventually, Ørsted phased out
coal and invested aggressively in offshore wind. By 2019, it had risen to become as the world’s
biggest generator of offshore wind energy. The corporation increased its share of renewable
generation to 86 percent, exceeding its target 21 years earlier than expected. As of today, Ørsted
employs 8,000 people and has a market cap of roughly US$37 billion (Tryggestad &
Rosenfield, 2020; The Wall Street Journal, 2023).
Moving on to conducting the IPAT experiment on Ørsted, which has a target of reducing GHG
emissions to be 50% below 2018 levels by the target year of 2032. From 2017 to 2019, Ørsted
had the following growth rates – revenue grew at a rate of 4.5% per year during this period,
while energy use fell at the rate of -10.5% per year. Thus, energy per unit of revenue fell at the
rate of -15.1% per year. In order to hit the target, assuming revenue and energy efficiency
continue to grow at the same rates, carbon intensity has to fall to a certain level in 2032 relative
to 2018 levels. Using the IPAT equations:
𝐸
𝐶𝑂2
𝐶𝑂2 2032 = (𝑅2032 )(
)(
)
𝑅 2032 𝐸 2032
𝐸
𝐶𝑂2
𝐶𝑂2 2018 = (𝑅2018 )(
)(
)
𝑅 2018 𝐸 2018
On average, if the above rates of change are maintained, then in 2032, revenue will be 1.75
times higher at the average growth rate of 4.53% per year, and energy per unit of revenue will
be 0.135 times lower than energy in 2018 at the average annual decline rate of -15.06%. Since
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the target for CO2 is to be 50% below 2018 levels:
𝐶𝑂2 2032 = 𝐶𝑂2 2018 -0.5𝐶𝑂2 2018 = 0.5𝐶𝑂2 2018.
Substituting these values into the ratio of the two equations and solving for carbon intensity,
we get approximately:
𝐶𝑂2
0.50
𝐶𝑂2
𝐶𝑂2
(
)=
(
) = 2.1 (
)
𝐸 2032
1.75 ∗ 0.135 𝐸 2018
𝐸 2018
Thus, carbon intensity in 2032 can be 210% of that in 2018, in order to hit the target of 50%
below 2018 levels by 2032. This is because of the assumption that energy efficiency continues
to drop significantly, reaching 13.5% of 2018 in 2032, even with revenue rising. Carbon
intensity should be g percent per year:
𝑇2032 = 𝑇2018 (1 + 𝑔)14
But we also want carbon intensity to be 2.1 times the 2018 level by 2032, namely, 𝑇2032 =
2.1𝑇2018 to hit the target. Equating the last two equations, we get 2.1 = (1 + 𝑔)14. Solving for
g yields:
𝑔 = 2.11/14 − 1 = 0.054
Meaning 5.4% increase per year in carbon intensity. Since energy fell at 10.5% per year, carbon
emissions will need to fall by 5.1% per year over the next 14 years. Ørsted’s emissions fell by
23% per year which shows that Ørsted will hit its 2032 target. If revenue and energy efficiency
remain unchanged during this period, emissions can fall at the rate of 3.8% per year and Ørsted
will still hit the target (see Table 14).
IV.

Nike Inc.

Nike Inc. is a multinational company from the United States that specializes in making and
selling athletic shoes, clothing, and sports equipment. It is the largest producer and supplier in
the industry, and competes with other major players like Adidas, Puma, and Under Armour.
Nike owns several brands, including Converse and Jordan, and its headquarters are located in
Beaverton, Oregon (Tighe, 2022).
As of 2022, Nike Inc. has 80,000 employees around the world, with a significant number
located in North America. Despite significant growth in non-U.S. markets, the United States
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remains Nike's primary market, as it generates approximately 40 percent of the company's total
global revenue. Additionally, Nike provides sponsorship to numerous well-known athletes
worldwide, including Cristiano Ronaldo, Rory McIlroy, LeBron James, and Rafael Nadal. The
company also sponsors sports teams such as Barcelona, RB Leipzig, and Paris Saint-Germain.
In 2022, Nike’s brand value was estimated to be over US$33 billion, which represents a growth
of almost $3 billion compared to the previous year (Tighe, 2022).
Being the final company in this case study¸ we now perform the IPAT experiment on Nike,
which has a target of reducing GHG emissions to be 65% below 2015 levels by the target year
of 2030. From 2017 to 2019, Nike had the following growth rates – revenue grew at a rate of
4.61% per year during this period, while energy use grew at the rate of 8.5% per year. Thus,
energy per unit of revenue grew at the rate of 3.88% per year. In order to hit the target, assuming
revenue and energy efficiency continue to grow at the same rates, carbon intensity has to fall
to a certain level in 2030 relative to 2015 levels. Using the IPAT equations:
𝐸
𝐶𝑂2
𝐶𝑂2 2030 = (𝑅2030 )(
)(
)
𝑅 2030 𝐸 2030
𝐸
𝐶𝑂2
𝐶𝑂2 2015 = (𝑅2015 )(
)(
)
𝑅 2015 𝐸 2015
On average, if the above rates of change are maintained, then in 2030, revenue will be 1.76
times higher at the average growth rate of 4.61% per year, and energy per unit of revenue will
be 0.63 times the 2015 figure, at the average annual growth rate of 3.88%. Since the target for
CO2 is to be 65% below 2015 levels:
𝐶𝑂2 2030 = 𝐶𝑂2 2015 -0.65𝐶𝑂2 2015 = 0.35𝐶𝑂2 2015.
Substituting these values into the ratio of the two equations and solving for carbon intensity,
we get approximately:
𝐶𝑂2
0.35
𝐶𝑂2
𝐶𝑂2
(
)=
(
) = 0.315 (
)
𝐸 2030
1.76 ∗ 0.63 𝐸 2015
𝐸 2015
Thus, carbon intensity in 2030 has to be 31.5% of that in 2015, in order to hit the target of 65%
below 2015 levels by 2030. Carbon intensity should be falling by g percent per year as follows:
𝑇2030 = 𝑇2015 (1 + 𝑔)15
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But we also want 𝑇2030 = 0.315𝑇2015 to hit the target. Equating the last two equations, we
get 0.315 = (1 + 𝑔)15 . Solving for g yields:
𝑔 = 0.3151/15 − 1 = −0.071
Meaning 7.1% reduction per year in carbon intensity. Since energy grew at 8.5% per year,
carbon emissions will need to fall by 1.4% per year over the next 15 years. Nike’s emissions
fell by 0.66% per year, which shows that with double the effort, Nike would be on the right
track to hit its 2030 target. Assuming no growth in revenue and no improvements in energy
efficiency, emissions have to fall even faster at the rate of 3.9% per year, since emissions in
2030 have to be 92,538 tonnes. In 2019, they were at 255,779 tonnes (see Table 14).
Introduction: Targets at the National Level
Using a fully statistically based probabilistic framework, Liu and Raftery (2021) determine that
the chances of the top emitting countries achieving their NDCs are low, e.g. 2% for the U.S.
and 16% for China. On the basis of present trends, the likelihood of keeping global warming
below 2°C is only 5%; however, if all nations meet their NDCs and keep reducing emissions
at the same rate until 2030, the likelihood increases to 26%. If the U.S. alone fails to reach its
NDC, it drops to 18%. Therefore, for a fair chance at maintaining the temperature rise below
2°C, the average rate of decline in emissions would need to go up from the yearly 1% required
for meeting the NDCs, to 1.8%.
The expansion of net-zero emission goals illustrates a substantial surge in ambition after the
Paris Agreement. But net-zero is perhaps still in its early stages as a consolidating principle for
climate action. In order to achieve climate goals, these targets must be increased not only
in quantity, but also in quality, and ultimately in execution. All entities must seek to make their
long-term targets clearer, binding, comprehensive, and largely resilient, combined with nearterm targets and initiatives that follow a credible pathway to long-term goals, given that NDCs
have been developed to get progressively more ambitious over time. Thus, further work is
needed to operationalize this concept effectively in policy and practice for all entities (Hale et
al., 2021).
Case Study: IPAT Framework to Hit Targets at the National Level
Canada’s target is for GHGs to be 40-45% below 2005 levels by 2030. Can Canada meet its
target given past performances and future expected economic growth? The following
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application of IPAT illustrates the difficulty of meeting the targets for reducing CO2 emissions.
Past population, affluence, and technology growth rates from 1997 to 2021 including COVID19 were as follows: the population of Canada grew at a rate of 1.02% per year, GDP grew at
2.12%, GDP per person grew at the rate of 1.1% per year on average, CO2 emissions (from
burning fossil fuels) grew at 0.56% per year, and carbon intensity fell at the rate of 1.55% per
year on average. Note that carbon intensity in this case is CO2 emissions per $ of GDP.
Note that carbon intensity fell not because emissions fell, but because the GDP grew faster than
CO2 emissions. CO2 emissions are increasing on average at about the same rate as the
population. Hence CO2 emissions per person have remained relatively constant over the period,
even though GDP per person has been increasing at 1.1% per year.
In order to hit the target, assuming population and GDP per person continue to grow at the
same rates, carbon intensity has to fall to some level in 2030 relative to 2005 levels. Using the
IPAT equations:
𝐶𝑂2 2030 = (𝑃2030 )(

𝐺𝐷𝑃
𝐶𝑂2
)(
)
𝑃 2030 𝐺𝐷𝑃2030

𝐶𝑂2 2005 = (𝑃2005 )(

𝐺𝐷𝑃
𝐶𝑂2
)(
)
𝑃 2005 𝐺𝐷𝑃2005

Since the population in 2030 will be 1.29 times higher at the historical average growth rate of
1% per year over this decade and GDP per capita in 2030 will be 1.30 times higher than GDP
per capita in 2005 at the average annual growth rate of 1.1%. While the target is for CO2 is to
be 40% below 2005 levels:
𝐶𝑂2 2030 = 𝐶𝑂2 2005 -0.40𝐶𝑂2 2005 = 0.60𝐶𝑂2 2005.
Substituting these values into the ratio of the two equations and solving for carbon intensity,
we get approximately
(

𝐶𝑂2
0.60
𝐶𝑂2
𝐶𝑂2
)=
(
) = 0.355 (
)
𝐺𝐷𝑃2030
1.29 ∗ 1.30 𝐺𝐷𝑃2005
𝐺𝐷𝑃2005

Carbon intensity in 2030 has to be 35.5% of that of 2005 to hit the target of 40% below 2005
levels by 2030. Carbon intensity would be 60% of that of 2005 levels in 2030 if population and

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affluence remained unchanged. Now it has to be much lower to accommodate a higher
population and affluence level in the future until 2030.
Next, we need to find the growth rate of carbon intensity that will bring it in 2030 to 0.355 its
2005 level. The growth rate is g (negative number to fall) and carbon intensity should be falling
𝐶0

by 𝑇2030 = 𝑇2005 𝑒 𝑔25, where for simplicity, let 𝑇2030 = 𝐺𝐷𝑃2
𝐶0

and let 𝑇2005 = 𝐺𝐷𝑃2

2005

2030

be carbon intensity in 2030,

be carbon intensity in 2005. But we also want 𝑇2030 = 0.355𝑇2005 to

hit the target. Equating the last two equations we get 0.355 = 𝑒 𝑔25. Solving for g by taking the
natural log on both sides and using the logarithmic exponent rule we get:
𝑔=

ln(0.355)
= −0.041
25

Meaning 4.1% per year reduction in carbon intensity. Carbon intensity has to decline at a
growth rate that is more than double the 40-year period decline of 1.87% per year on average.
In reality, from 2005 to 2021, carbon intensity has been falling at 1.55% per year, similar to
the historical average. Hence at the beginning of 2022 carbon intensity is 𝑇2021 =
𝑇2005 𝑒 −0.0155∗15, or alternatively 𝑇2021 = 0.775𝑇2005 .
In 2021, carbon intensity is only 0.78 of the 2005 level, and we need to get to 0.355 of the 2005
levels by 2030. We need to get to 35.5% of the 2005 level, and we are only at 78% in 2021
relative to the 2005 level. Hence, we need to reduce carbon intensity faster than 4.1% to catch
up with the slower pace in the past. We need carbon intensity over the following decade to be
growing at the rate of 𝑇2030 = 𝑇2021 𝑒 𝑔∗10 , but we know that 𝑇2021 = 0.775 𝑇2005 . Substituting
this into the above equation, we have 2030 in terms of 2005 as follows:
𝑇2030 = 0.78 𝑇2005 𝑒 𝑔∗9
In order to arrive at the solution, we need to keep in mind that 𝑇2030 = 0.355𝑇2005 to hit the
target. Combining the two equations above yields:
0.355 𝑇2005 = 0.78 𝑇2005 𝑒 𝑔∗9
Solving for g:

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𝑔=

ln(0.355/0.78)
= −0.087
9

Meaning carbon intensity has to fall by 8.7% per year over the next 9 years. If the target was
for emissions to be 45% below 2005 levels by 2030, then carbon intensity would have to fall
even more than 8.7% per year. The probability that this will happen is extremely low, indicating
a sobering result from an affluent society like Canada.
To summarize, if carbon intensity should have fallen by 4.1% per year for 25 years, it fell only
by 1.55% per year in the first 15 years. Now it has to fall at 8.7% for the remaining years to
make up the difference. Figure 15 shows what pathways the world would have to take to hit
2℃ (Hope, 2015). Canada should hit 300 million tonnes, and currently emissions are just below
700. Planned policies would bring emissions to roughly 500 million tonnes. Current policies
and actions have emissions slightly above 600 million tonnes. This analysis corresponds to the
previous analysis, which would require unprecedented reductions in emissions to hit the 2030
target. These projections are also available in Canada’s 4th Biennial report on Climate Change,
submitted to the UNFCCC in 2019. Table 15 below shows a summary of the above analysis
for Canada.
Table 15: IPAT summary of targets scenarios in Canada

Trends
Historical
Target for 2030
Observed within
target period
To hit target in 2030
from 2022

Period
1997-2021
2005-2030

i
0.56
-2.04

a
1.09
1.09

p
1.02
1.02

t
-1.55
-4.15

2005 - 2021

0.10

0.54

1.06

-1.50

2022-2030

-6.56

1.09

1.06

-8.71

Note: a and p are assumed to grow at their historical averages.

Table 16 below illustrates Canada's performance in reducing emissions after signing the Kyoto
Protocol in 1997 and the 2015 Paris Agreement until 2021, with two scenarios: including the
pandemic, and assuming there was no pandemic. The last row of the table suggests that Canada
faces an insurmountable challenge relative to the previous period with agreements in place.
The objective of analyzing Canada's performance is to demonstrate that a top-down approach
may not be capable of achieving targets, due to political and socio-economic factors. Instead,
a bottom-up approach, as seen earlier in this chapter with the four large companies, may

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instigate a change in behavior.
Table 16: IPAT summary of targets performance in Canada after international agreements

Agreements
Post-Kyoto

Period
1998-2014

CO2
Population Affluence Technology
0.96
1.00
1.49
-1.53

Post-Paris Agreement, with
pandemic

2015-2021

-0.41

1.09

0.10

-1.60

Post Paris Agreement, assuming
no pandemic

2015-2021

1.00

1.08

1.20

-1.28

What is needed to hit the target

2022 -2030

-6.56

1.09

1.06

-8.71

Figure 15: Historical global carbon intensity and projections, from 2000 to 2100.

Global Emissions
In 2020, global CO2 emissions were 34.8 billion tonnes. CO2 emissions falling by 30% relative
to 2020 would lead to 24.4 billion tonnes per year by 2030. This reduction would require
emissions to fall by 3.5% per year over the next decade, but this does not include that
population and affluence will be higher. Adding population growth of 1% per year and GDP
per capita by 2.5%, carbon intensity would need to fall by 7.5% per year. With intergenerational
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equity issues of climate change, the developed world would have to decrease carbon intensity
by more than 7.5% per year while the rest would by less than 7.5% per year, so the path to 2℃
is highly improbable to occur. This is in line with Figure 15 above, from PwC’s 6th Low Carbon
Economy Index Report (Hope, 2015).
Targets: Concluding Remarks
An Accenture report suggested that 93% of companies will fail to meet their net-zero
commitments if they do not double their rate of emissions reductions by 2030. Assuming that
corporations accelerated emissions reductions to twice the existing levels in the years leading
up to 2030, and then three times after that, 59% would still fail by 2050, the timeframe deemed
necessary to avert the most catastrophic and long-lasting effects of climate change.
In the pre-COVID era, out of the 65 companies from our study with a 2030 target, the firms
with absolute targets reduced emissions on average by 13.9%. Those with intensity targets
reduced the least by 8.2%, but is not statistically significant and thus inconclusive if they
reduced or not. Those with both absolute and intensity targets reduced by 14.8%. See Table
11.
European companies with absolute targets reduced emissions by 18.7% relative to U.S.
companies that reduced emissions only by 9%. In Europe, companies with intensity targets
reduced emissions by 8.9%, but in the U.S., emissions fell by 7.8%. However, this is not
statistically significant and hence inconclusive to show if intensity targets achieve reductions
on average. For European companies with both targets, emissions fell by 20.1%, while in the
U.S. emissions increased by 2.6%, although not significant and hence inconclusive to make
any claims. See Table 12 and Table 13.
Next, by applying the IPAT formula and using past performance as an indicator, we conducted
case studies on Apple Inc., Nokia, Ørsted, and Nike Inc. to assess whether they were on track
to meet their targets by 2030, and 2032 in the case of Ørsted. While Apple, Nokia, and Ørsted
are showing positive signs of reaching their goals, Nike needs to double its efforts in order to
achieve its target of reducing GHGs to 65% below 2015 levels by 2030.
Finally, we also conducted the IPAT case study at the national level, on Canada, and we found
that unfortunately the nation is not on track to meet its target, which is to reduce GHGs to 40-

Page 79 of 103

45% below 2005 levels by 2030.
For both organizations and nations to meet climate goals, it is necessary to enhance the quality,
quantity, and execution of targets. All parties need to make their long-term objectives more
explicit, binding, comprehensive, and resilient. In addition to this, they need to set up nearterm targets and initiatives that lead towards the long-term goals. Therefore, further work is
required to operationalize this concept effectively into practice and policy for all entities.

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CHAPTER 10: CONCLUSION
Discussion
Being the world’s most powerful economic force as well as a key agent in causing
anthropogenic climate change, businesses play a pivotal role in addressing the issues of climate
risk and keeping the global temperature rise below 1.5°C. Corporations are tasked with the
urgent need for instigating transformative action by reducing their GHG emissions and using
renewable energy sources. By empowering global collaboration on combatting the threat of
climate change, international climate treaties such as the landmark Kyoto Protocol and more
recent Paris Agreement may have affected and executed considerable change in the businessas-usual scenarios. Many industrialized and developing nations are preparing to meet their
GHG emissions reductions targets. However, as Figure 16 shows, only a few nations have
committed to being net-zero carbon by 2050 in law, China and India do not yet have such a
target in law, and Russia and the USA have only pledged to be net-zero by 2050. Only a handful
of small nations have achieved net-zero carbon emissions.

Figure 16: Net-Zero Carbon in 2050

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Using data from environmental reporting organizations such as the CDP, in addition to
dynamic frameworks like the STIRPAT, this thesis aimed to recognize the influence of climate
treaties like the Kyoto Protocol and Paris Agreement towards the drivers of environmental
impact in high-emitting corporations, and how changes to corporate strategy and governance
can result in improved risk management, enhanced engagement with suppliers and consumers
towards green initiatives, and optimized targets and performance for lowered GHG emissions
and intensity reductions.
Naturally, the policies set by these nations due to the signing of international agreements could
be absorbed and incorporated by a large portion of the organizations operating in these
countries, driving the world closer to meeting the goals set by these climate treaties and paving
a path towards a more sustainable future. Nevertheless, many discrepancies and gaps still exist,
between companies and even more so between nations and different regions of the world, thus
impeding progress.
Key Findings
Our research question aimed to assess whether the Kyoto Protocol and Paris Agreement have
yielded any measurable impact on corporations, leading them to transform their business
strategy, governance, and energy use towards lowering GHG emissions and becoming more
environmentally responsible.
We applied the IPACT, a modified version of the IPAT model, at the corporate level to assess
drivers of environmental impact and identify areas needing change. The IPACT helped track
changes in emissions, revenue, and use of renewable versus non-renewable energy in 100+
corporations spanning Europe and the USA, both before and after the COVID-19 pandemic
came into play. The 102 companies operate in some of the highest-emitting industries, such as
power generation, transport (aviation and automobiles), fossil fuels, chemicals, food and
agriculture, retail, apparel, financial services, and manufacturing. Over the four-year period of
2017-2020, these 102 European and U.S. companies represented 5.5% of the 2021 global CO2
emissions.
At a glance, we found that the U.S. companies together generate almost double the amount of
carbon emissions than Europe’s in the same time period, despite also having two fewer
companies than Europe in this study. Moreover, U.S. corporations are using roughly only half
the amount of renewable energy, as well as almost twice the amount of non-renewable energy
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that the European corporations use. Stricter regulations in Europe relative to those in the U.S.
can explain this observation.
For both regions, the highest-emitting industries are in exactly the same order: first is Power
Generation, with 264 million metric tons in Europe and 623.1 million metric tons in the U.S.,
which means that only five American utility providers are responsible for 1.7% of 2021’s
global GHG emissions, and along with the five European companies, this becomes almost 2.4%
of the global figure. In second place is Transportation Services, which is the airlines industry,
and these 7 airlines together made 1.61% of global 2021 emissions. The third highest emitter,
which should probably come as no surprise, is the Fossil Fuel industry. In this study there were
four Fossil Fuel companies in the U.S., versus five in Europe, and altogether they generated
0.62% of 2021’s worldwide emissions.
In the pre-COVID period, total energy use fell in Europe, while it increased in the U.S. In 2020,
both regions observed an overall decrease in total energy use. However, unlike in the preCOVID years, energy efficiency seems to have deteriorated in both Europe and America during
2020. Revenue additionally suffered heavy blows in both regions from the pandemic.
In the four-year period of 2017-2020, emissions dropped in both regions due to a joint
improvement in energy and carbon efficiency. Moreover, the pandemic year’s impact was just
as significant as that of technology’s impact over three years. Interestingly, emissions in both
regions fell more than revenue did in 2020. Europe’s emission reductions were achieved more
through renewable energy, than through energy efficiency. Overall emissions in the U.S.
decreased nearly two-fold, going from a 9.5% fall in the pre-COVID era to a 17.5% drop in
2020. While America’s technology efficiency remains the same at 10% lower emissions per
MWh of energy, Europe’s has roughly halved, rising from a 13.9% low to only 7.1%.
A notable finding was that COVID-19 lockdowns reduced emissions significantly more in the
U.S. than policies and business strategies could before the pandemic year of 2020. In Europe,
emissions during COVID-19 fell at a similar rate.
Interestingly, the research also saw that emissions can be reduced with increasing revenue and
possible profitability, depending on cost increases.
Next, we applied a stochastic version of the IPAT model, called the STIRPAT. The findings
from the STIRPAT estimations were consistent with those of the IPACT model in the logPage 83 of 103

linear model, which we discussed above. The STIRPAT’s log-linear assessment showed
unitary elasticities of emissions just like in the IPACT, signifying that emissions are relatively
sensitive to revenue and energy efficiency. The log-polynomial exhibited a difference in the
revenue force, which has an elasticity that varies with size of the firm, as determined by
revenue. In this case, revenue elasticity is not constant and decreases with revenue increasing.
This implied that large firms are capable of increasing their revenue while reducing emissions,
holding energy efficiency constant; most likely through utilizing better technology from the
technology multiplier.
In the final chapter, we used the IPAT formula to analyze the past performance of four
companies to determine whether they were on track to achieve their targets by. While three of
them are making good progress towards their goals, one needs to double its efforts to achieve
its target of reducing GHGs to 65% below 2015 levels by 2030. Additionally, we conducted an
IPAT case study on Canada and found that the nation is not on track to meet its goal of reducing
GHGs to 40-45% below 2005 levels by 2030.
To achieve climate objectives, it is necessary to improve the quality, quantity, and execution
of targets for both organizations and nations. They need to make their long-term objectives
more explicit, binding, comprehensive, and resilient, and establish near-term targets and
initiatives that are aligned with long-term goals. Therefore, more work is needed to effectively
implement this concept into practice and policy for all entities.
Limitations
Following are some limitations to the study. First, data may not be completely error-free as
statistical institutions have not verified them. Second, there is a gap in research at the microlevel to evaluate the progress of the Paris Agreement. This gap is also true regarding the
progress on the Doha Amendment, the second commitment period of the Kyoto Protocol,
implemented for 2013-2020. Also, information prior to 2008 cannot be used as a control period
to examine the impact of agreements. Lastly, due to confounding factors, it is difficult to
pinpoint with precision the contribution of the agreements towards change in corporate
strategic behavior. Effects such as the COVID-19 pandemic from 2020 to 2022 must be
controlled.

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Future Research
Since the themes in this study contribute to a new and developing area of climate research,
there are a number of ideas that can be incorporated into future work.
First, it is possible to explore the effects of additional factors that influence emissions in the
STIRPAT framework, such as institutional elements comprising the culture of an organization,
its governance, composition of board of directors, profitability, regulations, and equality,
diversity, and inclusivity. Additionally, financial and industry data from FactSet at
www.factset.com can be used for supplementing CDP data. Hence, future research can attempt
to incorporate revisions into the IPAT framework such as the improved stochastic nature,
STIRPAT, to account for more determinants such as the industry, company size, energy usage,
input substitution, gender and composition of board members, and the relative performance of
private versus public ownership in reducing their impact.
Second, other IPAT formulations can be explored at the corporate level such as the le Quere et
al. (2019) drivers of declining CO2 emissions focusing on the decomposition of technology
into four compartments. Namely, total final energy use, the fossil fuel share of final energy use,
the fossil fuel utilization rate, and the fossil fuel intensity. Additionally, to account for factor
input substitution, the IPAT model can be reformulated as an 'IPAST' model, as suggested by
Bretschger (2021). In this model, fossil fuels (FF) are used instead of GHG emissions to
represent impact (I), labor force (L) for population, and labor productivity for affluence. Plus,
a substitution force (S) is introduced as a "driver" of resource use that measures fossil fuels
used per capital unit. This substitution force represents production inputs other than fossil fuels,
such as "broad (real) capital". For instance, firms may reduce FF by increasing human capital
to generate revenue, and technology can be measured by the amount of the broad capital,
excluding fossil fuels, per dollar of revenue generated.
Third, future research could also investigate the potential impact of the circular economy
concept on corporate emissions reduction and environmental sustainability. This could involve
exploring how circular economy strategies, such as closed-loop production systems and
product design for reuse and recycling (Tse et al., 2016), can be incorporated into the IPAT
framework to assess their effectiveness in reducing emissions. Additionally, case studies of
companies that have successfully implemented circular economy practices or have started to
incorporate it, such as Michelin, Dell, and Nike (Atasu, 2021; Tse et al., 2016), could provide

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valuable insights and best practices for other organizations to follow.
Fourth, to circumvent the limitations of our research, future studies may also utilize data
verified from the SEC in the U.S., or equivalent regulatory bodies subject to respective regions.
Towards that end, it would also be fruitful to analyze different regions and countries around
the world that were not included in this study, such as developing countries globally, the AsiaPacific region, the remaining countries in Europe and North America, and so on.
Finally, access to data after COVID can show the impact of COVID before and after, possibly
in the form of an event study. Future research could investigate whether COVID played a role
in the future trajectory of emissions for particular firms, or if they went back to old-school
methods.
Final Concluding Remarks
The findings of this study prove that corporations have the capabilities to incorporate
environmental responsibility into their modus operandi – in fact, they have additional incentive
to do so. Through embracing innovation, adopting newer, greener technologies, and using more
renewable sources of energy, corporations can earn higher revenues while simultaneously
decreasing emissions.
As observed in the case of many European countries participating in the Emissions Trading
System, organizations globally should be encouraged to reap the rewards of being sustainable.
Governments should continue to enforce more stringent environmental laws and regulations,
like the landmark proposal launched by the SEC in the U.S. in March 2022 for mandating
public companies to report all emissions and climate risk data.
Businesses around the world can and should take concrete steps for setting and meeting
emissions reduction targets by investing in more novel and efficient technologies, products,
and practices.
In the words of Sir David Attenborough (2020), “We humans have come this far because we
are the smartest creatures to have ever lived. But to continue, we require more than intelligence.
We require wisdom.”

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REFERENCES

Aizenberg, A., & Luu, F. (2022, November 1). Nearly All Companies Will Miss Net-Zero
Goals without At Least Doubling Rate of Carbon Emissions Reductions by. Accenture.
Retrieved April 11, 2023, from https://newsroom.accenture.com/news/nearly-allcompanies-will-miss-net-zero-goals-without-at-least-doubling-rate-of-carbon-emissionsreductions-by-2030-accenture-report-finds.htm
Alibašić, H. (2018). Role of Corporations in Addressing Climate Change. Global
Encyclopedia of Public Administration, Public Policy, and Governance, 1–5.
https://doi.org/10.1007/978-3-319-31816-5_3429-1
Alsop, T. (2022, August 31). Nokia - statistics & facts. Statista. Retrieved April 14, 2023,
from https://www.statista.com/topics/1183/nokia/#topicOverview
Atasu, A. (2021, June 15). The Circular Business Model. Harvard Business Review.
https://hbr.org/2021/07/the-circular-business-model
Attenborough, D. F. (2020, September 28). David Attenborough: Life on Our Planet
(Documentary). Netflix.
Begg, K., van der Woerd, F., & Levy, D. (2014). The Business of Climate Change:
Corporate Responses to Kyoto. Greenleaf Publishing (UK).
Beggin, R. (2017, June 1). The Last Time a US President Dumped a Global Climate Deal.
ABC News. https://abcnews.go.com/Politics/time-us-president-dumped-global-climatedeal/story?id=47771005
Bento, N., & Gianfrate, G. (2020). Determinants of internal carbon pricing. Energy
Policy, 143, 1–31. https://doi.org/10.1016/j.enpol.2020.111499
Bernoville, T. (2022, June 12). What are Scopes 1, 2 and 3 of Carbon Emissions? Plan a
Earth. Retrieved October 7, 2022, from https://plana.earth/academy/what-are-scope-1-23-emissions
Bhat, V. N. (1998). Does Environmental Compliance Pay? Ecotoxicology, 7(4), 221–225.
https://doi.org/10.1023/a:1008938811813
Bhatnagar, S. (2021, July 20). Five actions to improve the sustainability of steel. EY.
Retrieved March 21, 2022, from https://www.ey.com/en_id/mining-metals/five-actionsto-improve-the-sustainability-of-steel
Black, R. (2008, October 10). Nature loss “dwarfs bank crisis.” BBC.
http://news.bbc.co.uk/1/hi/sci/tech/7662565.stm
Bocken, N., Short, S., Rana, P., & Evans, S. (2014). A literature and practice review to
develop sustainable business model archetypes. Journal of Cleaner Production, 65, 42–
56. https://doi.org/10.1016/j.jclepro.2013.11.039
Page 87 of 103

Bretschger, L. (2021). Getting the Costs of Environmental Protection Right: Why Climate
Policy Is Inexpensive in the End. Ecological Economics, 188, 107116.
https://doi.org/10.1016/j.ecolecon.2021.107116
Britannica. (2020, May 26). Kyoto Protocol. Encyclopedia Britannica. Retrieved
September 26, 2021, from https://www.britannica.com/event/Kyoto-Protocol
Brown, B. D. (2021, October 29). Why China’s climate policy matters to us all. BBC
News. Retrieved March 22, 2022, from https://www.bbc.com/news/world-asia-china57483492
Carbon Accountability Institute. (2019). Carbon Accountability Institute: Home.
Retrieved December 25, 2021, from https://climateaccountability.org/
Carbon Accountability Institute. (2020). Carbon Majors Database. Retrieved December
25, 2021, from https://climateaccountability.org/carbonmajors.html
Carbon Disclosure Project. (2017). The Carbon Majors Database CDP Carbon Majors
Report 2017: 100 Fossil Fuel Producers and Nearly 1 Trillion Tonnes of Greenhouse
Gas Emissions. http://www.truevaluemetrics.org/DBpdfs/Carbon/CDP/CDP-CarbonMajors-Report-2017.pdf
Carbon Disclosure Project (2020, October). Zeroing-in on Deforestation: Which
agricultural commodities companies are addressing deforestation issues?
https://6fefcbb86e61af1b2fc4c70d8ead6ced550b4d987d7c03fcdd1d.ssl.cf3.rackcdn.com/cms/reports/documents/000/0
05/430/original/CDP_Agriculture_2020_Exec_sum.pdf?1604570315
Carbon Disclosure Project [CDP]. (2021a). About CDP. Retrieved December 25, 2021,
from https://www.cdp.net/en/info/about-us
Carbon Disclosure Project [CDP]. (2021b, April). Putting a Price on Carbon: The State
of Internal Carbon Pricing by Corporates Globally. https://6fefcbb86e61af1b2fc4c70d8ead6ced550b4d987d7c03fcdd1d.ssl.cf3.rackcdn.com/cms/reports/documents/000/0
05/651/original/CDP_Global_Carbon_Price_report_2021.pdf?1618938446
Ceesay, N., Shubita, M., & Robertson, F. (2021). Sustainability Reporting Practices in
FTSE 100 Companies. The Sustainability Debate, 77–100. https://doi.org/10.1108/s2043905920210000015005
Chertow, M. R. (2000). The IPAT Equation and Its Variants. Journal of Industrial
Ecology, 4(4), 13–29. https://doi.org/10.1162/10881980052541927
da Silva, B. A., Constantino, M., de Oliveira, O. S., dos Santos, S. A. L., Tabak, B. M., &
da Costa, R. B. (2019). New indicator for measuring the environmental sustainability of
publicly traded companies: An innovation for the IPAT approach. Journal of Cleaner
Production, 215, 354–363. https://doi.org/10.1016/j.jclepro.2019.01.039
Daft, R. L. (2017). The Leadership Experience. Nashville: South-Western College Pub.

Page 88 of 103

Denchak, M. (2019, February 19). Paris Climate Agreement: Everything You Need to
Know. NRDC. Retrieved September 26, 2021, from https://www.nrdc.org/stories/parisclimate-agreement-everything-you-need-know
Dietz, S., Fruitiere, C., Garcia-Manas, C., Irwin, W., Rauis, B., & Sullivan, R. (2018). An
assessment of climate action by high-carbon global corporations. Nature Climate Change,
8(12), 1072–1075. https://doi.org/10.1038/s41558-018-0343-2
Dietz, T., & Rosa, E. A. (1994). Rethinking the environmental impacts of population,
affluence and technology. Human Ecology Review, 1, 277–300.
https://www.jstor.org/stable/24706840
Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions.
Proceedings of the National Academy of Sciences, 94(1), 175–179.
https://doi.org/10.1073/pnas.94.1.175
DuBois, C. L. Z., & Dubois, D. A. (2012). Strategic HRM as social design for
environmental sustainability in organization. Human Resource Management, 51(6), 799–
826. https://doi.org/10.1002/hrm.21504
Ehrlich, P. R., & Ehrlich, A. H. (2009). The population bomb revisited. The Electronic
Journal of Sustainable Development, 1(3), 63-71. https://www.populationmedia.org/wpcontent/uploads/2009/07/Population-Bomb-Revisited-Paul-Ehrlich-20096.pdf
Environment and Climate Change Canada. (2021, July 13). Government of Canada
confirms ambitious new greenhouse gas emissions reduction target. Canada.Ca. Retrieved
March 22, 2022, from https://www.canada.ca/en/environment-climatechange/news/2021/07/government-of-canada-confirms-ambitious-new-greenhouse-gasemissions-reduction-target.html
European Commission. (2022). EU Emissions Trading System (EU ETS). Climate
Action. Retrieved March 14, 2022, from https://ec.europa.eu/clima/eu-action/euemissions-trading-system-eu-ets_en
Ferreira, A., Pinheiro, M. D., de Brito, J., & Mateus, R. (2019). Decarbonizing strategies
of the retail sector following the Paris Agreement. Energy Policy, 135, 110999.
https://doi.org/10.1016/j.enpol.2019.110999
Flavelle, C. (2021, November 4). Climate Change Could Cut World Economy by $23
Trillion in 2050. The New York Times.
https://www.nytimes.com/2021/04/22/climate/climate-change-economy.html
Fortum. (2022). Why Invest in Fortum: Markets. Fortum. https://www.fortum.com/aboutus/investors/why-invest-fortum/markets
Fortune. (2020, May 18). Apple. Fortune. Retrieved April 14, 2023, from
https://fortune.com/ranking/fortune500/2019/apple/

Page 89 of 103

Franta, B. (2021). Weaponizing economics: Big Oil, economic consultants, and climate
policy delay. Environmental Politics, 1–21.
https://doi.org/10.1080/09644016.2021.1947636
Freedman, M., & Jaggi, B. (2005). Global warming, commitment to the Kyoto protocol,
and accounting disclosures by the largest global public firms from polluting industries.
The International Journal of Accounting, 40(3), 215–232.
https://doi.org/10.1016/j.intacc.2005.06.004
Freedman, M., & Jaggi, B. (2010). Global Warming Disclosures: Impact of Kyoto
Protocol Across Countries. Journal of International Financial Management & Accounting,
22(1), 46–90. https://doi.org/10.1111/j.1467-646x.2010.01045.x
Gans, O., & Jost, F. (2005). Decomposing the Impact of Population Growth on
Environmental Deterioration. (Discussion Paper Series No 422). University of Heidelberg
Department of Economics. http://www.uniheidelberg.de/md/awi/forschung/dp422.pdf
Geck, M. (2021, December 20). Seven major companies that committed to net-zero
emissions in 2021. UN PRI. Retrieved April 11, 2023, from https://www.unpri.org/priblog/seven-major-companies-that-committed-to-net-zero-emissions-in-2021/9197.article
Grossman, G. M., & Krueger, A. B. (1995). Economic Growth and the Environment. The
Quarterly Journal of Economics, 110(2), 353–377. https://doi.org/10.2307/2118443
Griffin P. (2017). CDP Carbon Majors Report, Climate Accountability Institute.
https://climateaccountability.org/carbonmajors.html
Hacaoglu, S. (2021, September 21). Turkey to Become Last G-20 Country to Ratify Paris
Agreement. Bloomberg. Retrieved September 26, 2021, from
https://www.bloomberg.com/news/articles/2021-09-22/turkey-to-become-last-g-20country-to-ratify-paris-agreement
Hale, T. (2018). The Role of Sub-state and Nonstate Actors in International Climate
Processes. Chatham House, the Royal Institute of International Affairs.
https://www.chathamhouse.org/sites/default/files/publications/research/2018-11-28-nonstate-sctors-climate-synthesis-hale-final.pdf
Hale, T., Smith, S. M., Black, R., Cullen, K., Fay, B., Lang, J. D., & Mahmood, S.
(2021). Assessing the rapidly-emerging landscape of net-zero targets. Climate Policy,
22(1), 18–29. https://doi.org/10.1080/14693062.2021.2013155
Halkos, G., & Managi, S. (2017). Recent advances in empirical analysis on growth and
environment: introduction. Environment and Development Economics, 22(6), 649–657.
https://doi.org/10.1017/s1355770x17000286
Heede, R. (2013). Tracing anthropogenic carbon dioxide and methane emissions to fossil
fuel and cement producers, 1854–2010. Climatic Change, 122(1–2), 229–241.
https://doi.org/10.1007/s10584-013-0986-y

Page 90 of 103

Henry, L. A., Buyl, T., & Jansen, R. J. (2018). Leading corporate sustainability: The role
of top management team composition for triple bottom line performance. Business
Strategy and the Environment, 28(1), 173–184. https://doi.org/10.1002/bse.2247
Hoang, N. T., & Kanemoto, K. (2021). Mapping the deforestation footprint of nations
reveals growing threat to tropical forests. Nature Ecology & Evolution, 5, 845–853.
https://doi.org/10.1038/s41559-021-01417-z
Hoffert, M. I., Caldeira, K., Jain, A. K., Haites, E. F., Harvey, L. D. D., Potter, S. D.,
Schlesinger, M. E., Schneider, S. H., Watts, R. G., Wigley, T. M. L., & Wuebbles, D. J.
(1998). Energy implications of future stabilization of atmospheric CO2 content. Nature,
395(6705), 881–884. https://doi.org/10.1038/27638
Hope, M. (2015). Global carbon intensity is falling – but not quickly enough to avoid
worst impacts of climate change. Carbon Brief. Retrieved April 21, 2023, from
https://www.carbonbrief.org/global-carbon-intensity-is-falling-but-not-quickly-enoughto-avoid-worst-impacts-of-climate-change/
Hussain, N., Rigoni, U., & Orij, R. P. (2016). Corporate Governance and Sustainability
Performance: Analysis of Triple Bottom Line Performance. Journal of Business Ethics,
149(2), 411–432. https://doi.org/10.1007/s10551-016-3099-5
IEA. (2022). Global Energy Review: CO2 Emissions in 2021. In Global Energy Review.
Paris. Retrieved October 14, 2022, from https://www.iea.org/reports/global-energyreview-co2-emissions-in-2021-2
Intergovernmental Panel on Climate Change [IPCC]. (2021, August). Summary for
Policymakers. In: Climate Change 2021: the Physical Science Basis, the Working Group
I contribution to the Sixth Assessment Report.
https://www.unep.org/resources/report/climate-change-2021-physical-science-basisworking-group-i-contribution-sixth
International Organization for Standardization [ISO]. (2022, June 3). Defining net zero.
ISO. Retrieved April 13, 2023, from
https://www.iso.org/contents/news/2022/06/defining-net-zero.html
Joselow, M., & MacMillan, D. (2022, March 21). The SEC proposed a landmark climate
disclosure rule. Here’s what to know. Washington Post.
https://www.washingtonpost.com/business/2022/03/21/sec-climate-change-rule/
Kim, Y., Tanaka, K., & Matsuoka, S. (2020). Environmental and economic effectiveness
of the Kyoto Protocol. PLOS ONE, 15(7), e0236299.
https://doi.org/10.1371/journal.pone.0236299
Kindermann, G., Obersteiner, M., Sohngen, B., Sathaye, J., Andrasko, K., Rametsteiner,
E., Schlamadinger, B., Wunder, S., & Beach, R. (2008). Global cost estimates of reducing
carbon emissions through avoided deforestation. Proceedings of the National Academy of
Sciences, 105(30), 10302–10307. https://doi.org/10.1073/pnas.0710616105

Page 91 of 103

King, E. (2015, February 16). Kyoto Protocol: 10 Years of the World’s First Climate
Change Treaty. Climate Change News. Retrieved September 26, 2021, from
https://www.climatechangenews.com/2015/02/16/kyoto-protocol-10-years-of-the-worldsfirst-climate-change-treaty/
Kinley, R. (2016). Climate change after Paris: from turning point to transformation.
Climate Policy, 17(1), 9–15. https://doi.org/10.1080/14693062.2016.1191009
Klöwer, M., Allen, M. R., Lee, D. S., Proud, S. R., Gallagher, L., & Skowron, A. (2021,
October 1). Quantifying aviation’s contribution to global warming. Environmental
Research Letters, 16(10), 104027. https://doi.org/10.1088/1748-9326/ac286e
Kolakowski, M. (2021). At $2.08 Trillion, Apple Is Bigger Than These Things.
Investopedia. https://www.investopedia.com/news/apple-now-bigger-these-5-things/
le Quéré, C., Korsbakken, J. I., Wilson, C., Tosun, J., Andrew, R., Andres, R. J.,
Canadell, J. G., Jordan, A., Peters, G. P., & van Vuuren, D. P. (2019). Drivers of
declining CO2 emissions in 18 developed economies. Nature Climate Change, 9(3), 213–
217. https://doi.org/10.1038/s41558-019-0419-7
Leridon, H. (2020). World Population Outlook: Explosion or Implosion? Population
Societies, 573(1), 1–4. https://doi.org/10.3917/popsoc.573.0001
Liu, H., & You, X. (2021, December 23). Q&A: What does China’s new Paris Agreement
pledge mean for climate change? Carbon Brief. Retrieved March 22, 2022, from
https://www.carbonbrief.org/qa-what-does-chinas-new-paris-agreement-pledge-mean-forclimate-change
Liu, P. R., & Raftery, A. E. (2021). Country-based rate of emissions reductions should
increase by 80% beyond nationally determined contributions to meet the 2 °C target.
Communications Earth & Environment, 2(1). https://doi.org/10.1038/s43247-021-000978
Liute, A., & de Giacomo, M. R. (2021). The environmental performance of UK‐based B
Corp companies: An analysis based on the triple bottom line approach. Business Strategy
and the Environment, 31(3), 810–827. https://doi.org/10.1002/bse.2919
Maamoun, N. (2019). The Kyoto protocol: Empirical evidence of a hidden success.
Journal of Environmental Economics and Management, 95, 227–256.
https://doi.org/10.1016/j.jeem.2019.04.001
Madrigal, A. (2009, December 11). Dec. 11, 1997: World Signs Onto Kyoto Protocol.
Wired. Retrieved September 26, 2021, from https://www.wired.com/2009/12/1211kyotoclimate-accord/
Martínez-Zarzoso, I., & Maruotti, A. (2011). The impact of urbanization on CO2
emissions: Evidence from developing countries. Ecological Economics, 70(7), 1344–
1353. https://doi.org/10.1016/j.ecolecon.2011.02.009

Page 92 of 103

Mebratu-Tsegaye, T., Toledano, P., Dietrich Brauch, M., & Greenberg, M. (2021). Five
Years After the Adoption of the Paris Agreement, Are Climate Change Considerations
Reflected in Mining Contracts? SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.3894127
Mott, G., Razo, C., & Hamwey, R. (2021, June 2). Carbon emissions anywhere threaten
development everywhere. United Nations Conference on Trade and Development
[UNCTAD]. Retrieved November 29, 2021, from https://unctad.org/news/carbonemissions-anywhere-threaten-development-everywhere
Myllyvirta, L. (2021, November 24). Analysis: China’s carbon emissions grow at fastest
rate for more than a decade. Carbon Brief. Retrieved March 22, 2022, from
https://www.carbonbrief.org/analysis-chinas-carbon-emissions-grow-at-fastest-rate-formore-than-a-decade
Nogrady, B. (2021). Most fossil-fuel reserves must remain untapped to hit 1.5 °C
warming goal. Nature, 597(7876), 316–317. https://doi.org/10.1038/d41586-021-02444-3
Pendrill, F., Persson, U. M., Godar, J., Kastner, T., Moran, D., Schmidt, S., & Wood, R.
(2019). Agricultural and forestry trade drives large share of tropical deforestation
emissions. Global Environmental Change, 56, 1–10.
https://doi.org/10.1016/j.gloenvcha.2019.03.002
Polman, P., & Winston, A. (2021, September). The Net Positive Manifesto: Is the World
Better Off Because Your Company is in it? Harvard Business Review, SeptemberOctober. https://hbr.org/2021/09/the-net-positive-manifesto
Riley, T. (2017, July 10). Just 100 Companies Responsible for 71% of Global Emissions,
Study Says. The Guardian. https://www.theguardian.com/sustainablebusiness/2017/jul/10/100-fossil-fuel-companies-investors-responsible-71-globalemissions-cdp-study-climate-change
Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P., & Moomaw, W. R. (2019). World
Scientists’ Warning of a Climate Emergency. BioScience.
https://doi.org/10.1093/biosci/biz088
Ritchie, H., & Roser, M. (2020, May 11). Emissions by sector. Our World in Data.
Retrieved October 7, 2022, from https://ourworldindata.org/emissions-by-sector
Robiou Du Pont, Y., Jeffery, M. L., Gütschow, J., Rogelj, J., Christoff, P., &
Meinshausen, M. (2016). Equitable mitigation to achieve the Paris Agreement goals.
Nature Climate Change, 7(1), 38–43. https://doi.org/10.1038/nclimate3186
Rockström, J., Gaffney, O., Rogelj, J., Meinshausen, M., Nakicenovic, N., &
Schellnhuber, H. J. (2017). A roadmap for rapid decarbonization. Science, 355(6331),
1269–1271. https://doi.org/10.1126/science.aah3443
Schaffer, R. H. (2017, October 26). All Management Is Change Management. Harvard
Business Review. https://hbr.org/2017/10/all-management-is-change-management

Page 93 of 103

Shishlov, I., Morel, R., & Bellassen, V. (2016). Compliance of the Parties to the Kyoto
Protocol in the first commitment period. Climate Policy, 16(6), 768–782.
https://doi.org/10.1080/14693062.2016.1164658
Slaper, T. F., & Hall, T. J. (2011). The Triple Bottom Line: What Is It and How Does It
Work? Indiana Business Review, 86(1).
https://www.ibrc.indiana.edu/ibr/2011/spring/article2.html
Spash, C. L. (2016). This Changes Nothing: The Paris Agreement to Ignore Reality.
Globalizations, 13(6), 928–933. https://doi.org/10.1080/14747731.2016.1161119
Stern, D. I. (2017). The environmental Kuznets curve after 25 years. Journal of
Bioeconomics, 19(1), 7–28. https://doi.org/10.1007/s10818-017-9243-1
Tarrant, P. (2021, November 24). Net-Zero Jargon Buster - a guide to common terms.
Science Based Targets. Retrieved April 13, 2023, from
https://sciencebasedtargets.org/blog/net-zero-jargon-buster-a-guide-to-commonterms#:~:text=Although%20often%20used%20interchangeably%20with,the%20global%
20or%20sector%20level.
Taylor, M. (2019, October 10). Climate emergency: what the oil, coal and gas giants say.
The Guardian. https://www.theguardian.com/environment/2019/oct/09/climateemergency-what-oil-gas-giants-say
TEDx [TEDx Talks]. (2021, July 20). Global Warming: From Scientific Warning to
Corporate Casualty | Ben Franta | TEDxStanford [Video]. YouTube.
https://www.youtube.com/watch?v=Mp1JGqp7YMI&t=1s
Thakur, S. (2021). From Kyoto to Paris and Beyond: The Emerging Politics of Climate
Change. India Quarterly: A Journal of International Affairs, 77(3), 366–383.
https://doi.org/10.1177/09749284211027252
The Economist. (2015, December 16). Green light: What to expect after a deal that
exceeded expectations. The Economist. Retrieved March 21, 2022, from
https://www.economist.com/international/2015/12/19/green-light
The Guardian. (2011, December 13). Canada Pulls Out of Kyoto Protocol. The Guardian.
https://www.theguardian.com/environment/2011/dec/13/canada-pulls-out-kyoto-protocol
The Wall Street Journal. (2023, April 14). ORSTED | Oersted A/S Stock Price & News WSJ. The Wall Street Jorunal. Retrieved April 14, 2023, from
https://www.wsj.com/market-data/quotes/dk/ORSTED
Tighe, D. (2022, August 17). Nike - Statistics & Facts. Statista. Retrieved April 15, 2023,
from https://www.statista.com/topics/1243/nike/#topicOverview
Tryggestad, C., & Rosenfield, J. (2020, July 10). Ørsted’s renewable-energy
transformation. McKinsey & Company. Retrieved April 14, 2023, from
https://www.mckinsey.com/capabilities/sustainability/our-insights/orsteds-renewableenergy-transformation
Page 94 of 103

Tse, T., Esposito, M., & Soufani, K. (2016, February 1). How Businesses Can Support a
Circular Economy. Harvard Business Review. Retrieved April 17, 2023, from
https://hbr.org/2016/02/how-businesses-can-support-a-circular-economy
Ulfves, I. (2020, March 26). Fortum closes transaction to become majority owner in
Uniper. Fortum. https://www.fortum.com/media/2020/03/fortum-closes-transactionbecome-majority-owner-uniper
United Nations. (2021). The Paris Agreement. Retrieved September 26, 2021, from
https://www.un.org/en/climatechange/paris-agreement
United Nations Climate Change Secretariat [UNCCS]. (2018). Yearbook of Global
Climate Action 2018.
https://unfccc.int/sites/default/files/resource/GCA_Yearbook2018.pdf
United Nations Framework Convention on Climate Change [UNFCCC]. (1998,
December). Kyoto Protocol to the United Nations Framework Convention on Climate
Change. Kyoto Protocol to the United Nations Framework Convention on Climate
Change. 3rd Conference of the Parties [COP3], Kyoto, Japan.
https://unfccc.int/resource/docs/convkp/kpeng.pdf
United Nations Framework Convention on Climate Change [UNFCCC]. (2018, August).
Achievements of the Clean Development Mechanism: Harnessing Incentive for Climate
Action 2001–2018.
https://unfccc.int/sites/default/files/resource/UNFCCC_CDM_report_2018.pdf
United Nations Framework Convention on Climate Change [UNFCCC]. (2021a). Halving
Emissions by 2030 is New Normal - Race To Zero Anniversary. United Nations
Framework Convention on Climate Change. Retrieved December 25, 2021, from
https://unfccc.int/news/halving-emissions-by-2030-is-new-normal-race-to-zeroanniversary#:~:text=UN%20Climate%20Change%20News%2C%2015,backed%20Race
%20to%20Zero%20campaign.
United Nations Framework Convention on Climate Change [UNFCCC]. (2021b). NDC
Spotlight. Retrieved September 26, 2021, from https://unfccc.int/process/the-parisagreement/nationally-determined-contributions/ndc-spotlight
United Nations Framework Convention on Climate Change [UNFCCC]. (2021c). Paris
Agreement - Status of Ratification. Retrieved September 26, 2021, from
https://unfccc.int/process/the-paris-agreement/status-of-ratification
United Nations Framework Convention on Climate Change [UNFCCC]. (2021d). The
Clean Development Mechanism. Retrieved September 26, 2021, from
https://unfccc.int/process-and-meetings/the-kyoto-protocol/mechanisms-under-the-kyotoprotocol/the-clean-development-mechanism
United Nations Framework Convention on Climate Change [UNFCCC]. (2021e). The
Paris Agreement. Retrieved September 26, 2021, from https://unfccc.int/process-andmeetings/the-paris-agreement/the-paris-agreement

Page 95 of 103

United Nations Framework Convention on Climate Change [UNFCCC]. (2021f). What is
the Kyoto Protocol? Retrieved September 26, 2021, from
https://unfccc.int/kyoto_protocol
United Nations Framework Convention on Climate Change [UNFCCC]. (2021g,
February 26). “Climate Commitments Not on Track to Meet Paris Agreement Goals” as
NDC Synthesis Report is Published. United Nations Framework Convention on Climate
Change. Retrieved October 5, 2021, from https://unfccc.int/news/climate-commitmentsnot-on-track-to-meet-paris-agreement-goals-as-ndc-synthesis-report-is-published
United Nations Framework Convention on Climate Change [UNFCCC] [UN Climate
Change]. (2015, February 16). Did you know? Industrialized Kyoto Protocol countries
reduced emissions by 22.6% [Tweet]. Twitter.
https://twitter.com/UNFCCC/status/567313128867004417
US EPA. (2022a, May 12). Scope 3 Inventory Guidance. US EPA. Retrieved October 7,
2022, from https://www.epa.gov/climateleadership/scope-3-inventory-guidance
US EPA. (2022b, May 16). Overview of Greenhouse Gases. US EPA. Retrieved October
7, 2022, from https://www.epa.gov/ghgemissions/overview-greenhouse-gases
US EPA. (2022c, September 9). Scope 1 and Scope 2 Inventory Guidance. US EPA.
Retrieved October 7, 2022, from https://www.epa.gov/climateleadership/scope-1-andscope-2-inventory-guidance
Vladimirova, K., Henninger, C. E., Joyner-Martinez, C., Iran, S., Diddi, S., Durrani, M.,
Iyer, K., Jestratijevic, I., McCormick, H., Niinimäki, K., Thangavelu, P., Sauerwein, M.,
Singh, R., Simek, P., & Wallaschkowski, S. (2022). Fashion consumption during
COVID-19: Comparative analysis of changing acquisition practices across nine countries
and implications for sustainability. Cleaner and Responsible Consumption, 5, 100056.
https://doi.org/10.1016/j.clrc.2022.100056
Wade, B., & Rekker, S. (2020). Research can (and should) support corporate
decarbonization. Nature Climate Change, 10(12), 1064–1065.
https://doi.org/10.1038/s41558-020-00936-0
Waggoner, P. E., & Ausubel, J. H. (2002). A framework for sustainability science: A
renovated IPAT identity. Proceedings of the National Academy of Sciences, 99(12),
7860–7865. https://doi.org/10.1073/pnas.122235999
Welsby, D., Price, J., Pye, S., & Ekins, P. (2021). Unextractable fossil fuels in a 1.5 °C
world. Nature, 597(7875), 230–234. https://doi.org/10.1038/s41586-021-03821-8
Whieldon, E., & Almtoft, A. (2022, February 1). Most companies aren’t setting basic
climate targets, putting net-zero out of reach. S&P Global. Retrieved April 11, 2023, from
https://www.spglobal.com/esg/csa/yearbook/articles/most-companies-arent-setting-basicclimate-targets-putting-net-zero-out-of-reach
World Business Council for Sustainable Development [WBCSD]. (2015). Emerging
Practices in Internal Carbon Pricing: A Practical Guide.
Page 96 of 103

https://docs.wbcsd.org/2015/12/Leadership-2015Emerging_Practices_in_Internal_Carbon_Pricing.pdf
Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J. C., & Scheffer, M. (2020). Future of
the human climate niche. Proceedings of the National Academy of Sciences, 117(21),
11350–11355. https://doi.org/10.1073/pnas.1910114117
York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools
for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3),
351–365. https://doi.org/10.1016/s0921-8009(03)00188-5
Zhou, S. W. W. (2020). Carbon Management for a Sustainable Environment (1st ed.
2020 ed.). Springer. https://doi.org/10.1007/978-3-030-35062-8_1

Page 97 of 103

APPENDIX: COMPLETE LIST OF 102 CORPORATIONS IN THIS THESIS SAMPLE
I. Europe: 52 Companies [Graph]
16

14

2

12

2

1
1

10

2

2
8

6

1

4

2

0

1

1

1

1

1

1

1

1

Denmark

Finland

1

1

1

1

1

1

2
1
1
1

2

3

1

1
2

2

2

France

Germany

Norway

2

2

2

1

1

Sweden

U.K.

Apparel

Biotech, health care & pharma

Food, beverage & agriculture

Fossil Fuels

Manufacturing (Automobiles)

Manufacturing (Electronics)

Materials

Power Generation

Retail

Financial Services

Transportation Services (Airlines)

Figure 17: Distribution of Firms in Europe by Country & Industry

Page 98 of 103

1

Europe: 52 Companies [Sorted Alphabetically by Industry]
COUNTRY

PRIMARY INDUSTRY

PRIMARY SECTOR

KERING

France

Apparel

Apparel design

LVMH

France

Apparel

ADIDAS AG

Germany

Apparel

PUMA SE

Germany
United
Kingdom

Apparel

Apparel design
Textiles & fabric
goods
Textiles & fabric
goods
Textiles & fabric
goods

ORGANIZATION

BURBERRY GROUP
NOVO NORDISK
A/S

DIAGEO PLC

France
United
Kingdom
United
Kingdom

Apparel
Biotech, health care
& pharma
Biotech, health care
& pharma
Biotech, health care
& pharma
Biotech, health care
& pharma
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture

NESTE OYJ

Finland

Fossil Fuels

EQUINOR

Norway

Fossil Fuels

AKER BP ASA
LUNDIN ENERGY
AB

Norway

Fossil Fuels

Sweden
United
Kingdom

Fossil Fuels

France

Manufacturing

BMW AG

Germany

Manufacturing

VOLKSWAGEN AG

Germany

Manufacturing

DAIMLER AG

Germany

Manufacturing

SANOFI
GLAXOSMITHKLINE
ASTRAZENECA
CARLSBERG
BREWERIES A/S
DANONE
PERNOD RICARD
BRITVIC

TULLOW OIL
RENAULT GROUP

Denmark
France
United
Kingdom
United
Kingdom
Denmark
France

Fossil Fuels

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PRIMARY ACTIVITY
Apparel & footwear
design
Apparel & footwear
design
Apparel & footwear
Apparel & footwear
Apparel & footwear

Biotech & pharma

Pharmaceuticals

Biotech & pharma

Pharmaceuticals

Biotech & pharma

Pharmaceuticals

Biotech & pharma
Food & beverage
processing
Food & beverage
processing
Food & beverage
processing
Food & beverage
processing
Food & beverage
processing
Oil & gas
processing
Oil & gas
processing
Oil & gas extraction
& production
Oil & gas extraction
& production
Oil & gas extraction
& production
Transportation
equipment
Transportation
equipment
Transportation
equipment
Transportation
equipment

Pharmaceuticals
Alcoholic beverages
Dairy & egg
products
Alcoholic beverages
Non-alcoholic
beverages
Alcoholic beverages
Oil & gas refining
Oil & gas refining
Oil & gas extraction
Oil & gas extraction
Oil & gas extraction
Automobiles
Automobiles
Automobiles
Automobiles

ASSA ABLOY

Sweden

Manufacturing

APTIV

United
Kingdom

Manufacturing

PRIMARY SECTOR
Electrical &
electronic
equipment
Electrical &
electronic
equipment
Electrical &
electronic
equipment
Electrical &
electronic
equipment
Electrical &
electronic
equipment

France

Materials

Chemicals

Germany

Materials

Chemicals

Materials

Chemicals

Materials

Chemicals

UNILEVER PLC

Sweden
United
Kingdom
United
Kingdom

Materials

ØRSTED

Denmark

Power generation

FORTUM OYJ

Finland

Power generation

EDF

France
United
Kingdom
United
Kingdom

Power generation

Power generation

Chemicals
Energy utility
networks
Thermal power
generation
Nuclear power
generation
Renewable power
generation
Thermal power
generation

Finland

Retail

Convenience retail

France

Retail

Convenience retail

France
United
Kingdom

Retail

Convenience retail

Retail

Convenience retail

Supermarkets, food
& drugstores
Supermarkets, food
& drugstores

Convenience retail

Supermarkets, food
& drugstores

ORGANIZATION

ERICSSON
ADVA OPTICAL
NETWORKING SE

NOKIA GROUP

L'ORÉAL
HENKEL AG & CO.
KGAA
ORIFLAME
HOLDING LTD
PZ CUSSONS

SSE
CENTRICA
KESKO
CORPORATION
CARREFOUR
CASINO
GUICHARDPERRACHON
J SAINSBURY PLC
MARKS AND
SPENCER GROUP
PLC

COUNTRY

Sweden

Germany

Finland

United
Kingdom

PRIMARY INDUSTRY

Manufacturing

Manufacturing

Manufacturing

Power generation

Retail

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PRIMARY ACTIVITY
Communications
equipment
Communications
equipment
Communications
equipment
Electrical
equipment
Electrical
equipment
Personal care &
household products
Personal care &
household products
Personal care &
household products
Personal care &
household products
Personal care &
household products
Electricity networks
CCGT generation
Nuclear generation
Other renewable
generation
CCGT generation
Supermarkets, food
& drugstores
Supermarkets, food
& drugstores

Services
Services

PRIMARY SECTOR
Financial services
Financial services

PRIMARY ACTIVITY
Banks
Banks

Services

Financial services

Banks

Services

Financial services

Banks

Services
Transportation
services
Transportation
services
Transportation
services
Transportation
services

Financial services

Banks

Air transport

Passenger airlines

Air transport

Passenger airlines

Air transport

Passenger airlines

Air transport

Passenger airlines

ORGANIZATION

COUNTRY

PRIMARY INDUSTRY

DANSKE BANK A/S
BNP PARIBAS
NORDEA BANK
ABP
HSBC HOLDINGS
PLC
NATWEST GROUP
PLC

Denmark
France
Sweden
United
Kingdom
United
Kingdom

FINNAIR

Finland

AIR FRANCE - KLM
DEUTSCHE
LUFTHANSA AG

France
Germany

SAS

Sweden

Page 101 of 103

II. United States of America: 50 Companies [Sorted Alphabetically by Industry]
ORGANIZATION
NIKE INC.

PRIMARY INDUSTRY
Apparel

PRIMARY SECTOR
Textiles & fabric goods

PVH CORP
HANESBRANDS INC.

Apparel
Apparel

Apparel design
Textiles & fabric goods

LEVI STRAUSS & CO.
ALL ACCESS APPAREL,
INC.

Apparel

Apparel design

Apparel
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Biotech, health care &
pharma
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture
Food, beverage &
agriculture

Apparel design

PRIMARY ACTIVITY
Apparel & footwear
Apparel & footwear
design
Apparel & footwear
Apparel & footwear
design
Apparel & footwear
design

Biotech & pharma

Pharmaceuticals

Biotech & pharma

Pharmaceuticals

Biotech & pharma

Pharmaceuticals

Biotech & pharma

Pharmaceuticals
Pharmaceuticals
Non-alcoholic
beverages

PFIZER INC.
ABBVIE INC
BRISTOL-MYERS
SQUIBB
JOHNSON & JOHNSON
MERCK & CO., INC.
THE COCA-COLA
COMPANY
CONSTELLATION
BRANDS, INC.

APPLE INC.

Manufacturing

COMMSCOPE, INC.

Manufacturing

ARISTA NETWORKS
JUNIPER NETWORKS,
INC.

Manufacturing

ADTRAN INC

Manufacturing

Biotech & pharma
Food & beverage
processing
Food & beverage
processing
Food & beverage
processing
Food & beverage
processing
Oil & gas extraction &
production
Oil & gas extraction &
production
Oil & gas storage &
transportation
Oil & gas extraction &
production
Electrical & electronic
equipment
Electrical & electronic
equipment
Electrical & electronic
equipment
Electrical & electronic
equipment
Electrical & electronic
equipment

GENERAL MOTORS
COMPANY

Manufacturing

Transportation
equipment

KEURIG DR PEPPER
MOLSON COORS
BREWING COMPANY
DEVON ENERGY
CORPORATION

Fossil Fuels

CONOCOPHILLIPS
WILLIAMS
COMPANIES, INC.

Fossil Fuels

HESS CORPORATION

Fossil Fuels

Fossil Fuels

Manufacturing

Page 102 of 103

Alcoholic beverages
Non-alcoholic
beverages
Alcoholic beverages
Oil & gas extraction
Oil & gas extraction
Oil & gas pipelines &
storage
Oil & gas extraction
Communications
equipment
Communications
equipment
Communications
equipment
Communications
equipment
Communications
equipment

Automobiles

ORGANIZATION
FORD MOTOR
COMPANY
NAVISTAR
INTERNATIONAL
CORPORATION

PRIMARY INDUSTRY
Manufacturing

PRIMARY SECTOR
Transportation
equipment

PACCAR INC
PROCTER & GAMBLE
COMPANY
COLGATE PALMOLIVE
COMPANY
ESTEE LAUDER
COMPANIES INC.

Manufacturing

Transportation
equipment
Transportation
equipment

Materials

Chemicals

Materials

Chemicals

Materials

Chemicals

CLOROX COMPANY
CHURCH & DWIGHT
CO., INC
EXELON
CORPORATION
CMS ENERGY
CORPORATION

Materials

Chemicals

Materials

Chemicals
Nuclear power
generation
Thermal power
generation
Thermal power
generation

Manufacturing

Power generation
Power generation

PRIMARY ACTIVITY
Automobiles

Automobiles
Automobiles
Personal care &
household products
Personal care &
household products
Personal care &
household products
Personal care &
household products
Personal care &
household products
Nuclear generation
CCGT generation

DOMINION ENERGY
PINNACLE WEST
CAPITAL
CORPORATION
DUKE ENERGY
CORPORATION

Power generation

Power generation

Thermal power
generation
Thermal power
generation

WALMART, INC.
WALGREENS BOOTS
ALLIANCE
BEST BUY CO., INC.
MACY'S, INC.
NORDSTROM, INC.
AMERICAN EXPRESS
MORGAN STANLEY
U.S. BANCORP
WELLS FARGO &
COMPANY
CITIGROUP INC.
AMERICAN AIRLINES
GROUP INC
DELTA AIR LINES
UNITED AIRLINES
HOLDINGS

Retail

Convenience retail

Retail
Retail
Retail
Retail
Services
Services
Services

Convenience retail
Discretionary retail
Discretionary retail
Discretionary retail
Financial services
Financial services
Financial services

CCGT generation
Hypermarkets &
superstores
Supermarkets, food &
drugstores
Specialist retail
Department stores
Department stores
Banks
Banks
Banks

Services
Services

Financial services
Financial services

Banks
Banks

Transportation services
Transportation services

Air transport
Air transport

Passenger airlines
Passenger airlines

Transportation services

Air transport

Passenger airlines

Power generation

Page 103 of 103

CCGT generation

CCGT generation