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Firm level responses to environmental regulation, public
pressure, and changing environmental factors
Research proposal for fulfilment of the requirements for Doctor of Philosophy in Finance
School of Economics and Finance
Massey University
Grace Maddox
January 2020
Supervisors
Prof Martin Young
Prof Hamish Anderson
Table of Contents
Table of figures:.......................................................................................................................................3
List of tables:...........................................................................................................................................3
Abstract:...................................................................................................................................................5
1. Introduction:.....................................................................................................................................6
2. Literature Review:...........................................................................................................................8
2.1 Corporate social responsibility overview:.....................................................................................8
2.2.1 Corporate social responsibility and the firm:..........................................................................9
2.2 Environmental responsibility.......................................................................................................10
2.2.2 Environmental responsibility and firm cost of capital:.........................................................10
2.2.3 Environmental responsibility and firm performance:...........................................................12
2.2.4 Environmental responsibility and firm risk:.........................................................................12
2.2.5 Environmental disclosure and firm value:............................................................................13
2.2.6 CSR and environmental responsibility conclusion:..............................................................14
2.3 Climate risk..................................................................................................................................14
2.3.1 Regulatory risk:.....................................................................................................................15
2.3.2 Carbon emissions risk:..........................................................................................................16
2.3.3 Climate risk conclusion:.......................................................................................................17
2.4 Summary of literature review:.....................................................................................................18
3. United States setting:.....................................................................................................................19
3.1 Why the United States?............................................................................................................19
3.2 United States regulation:..........................................................................................................19
1
3.3 State by state regulation and The Climate Alliance:................................................................21
3.4 The Paris Accord:....................................................................................................................22
4. Essay One..........................................................................................................................................24
4.1 Introduction:...........................................................................................................................24
4.2 Literature Review and hypothesis development....................................................................26
4.2.1 Literature Review:.........................................................................................................26
4.2.2 Hypothesis Development:..............................................................................................27
4.3 Research design and methodology:.......................................................................................29
4.3.1 Sample:..........................................................................................................................29
4.3.2 Model:............................................................................................................................32
4.4 Preliminary results:................................................................................................................38
4.4.1 Performance model results....................................................................................................38
4.4.2 Risk model results.................................................................................................................42
4.5 Sub-sample results:................................................................................................................46
4.7 Essay 1 preliminary conclusion:..................................................................................................53
4.8 Further tasks for completion of Essay 1:.....................................................................................53
5. Essay Two......................................................................................................................................54
5.1 Introduction:...........................................................................................................................54
5.2 Literature review and hypothesis development:....................................................................56
5.2.1 Literature review:...........................................................................................................56
5.2.2 Hypothesis Development:..............................................................................................57
5.3 Research design and methodology:.......................................................................................58
5.3.1 Sample:..........................................................................................................................58
2
5.3.2 Model:............................................................................................................................59
5.3 Essay 2 conclusion:................................................................................................................64
6. Essay 3:..........................................................................................................................................65
6.1 Background information and hypothesis development...........................................................66
6.2 Environmental responsibility measures...................................................................................67
7. Proposed timeline for the completion of Dissertation...................................................................68
Appendix 1:............................................................................................................................................69
Appendix 1a. Variable descriptions...................................................................................................69
Appendix 1b. Variables included in Asset4’s Environmental score.................................................73
Appendix 1c. Distribution of sample across characteristics.............................................................79
Distribution across SIC industries.................................................................................................79
Distribution across SIC industry divisions:...................................................................................80
Distribution across years:...............................................................................................................81
Distribution across regions of the United States:...........................................................................81
Distribution across states:..............................................................................................................82
Appendix 1d. Multicollinearity checks:.............................................................................................83
Variance Inflation Factor checks:..................................................................................................84
References:.............................................................................................................................................85
3
Table of figures:
Figure 1: States with carbon pricing schemes as at November 2019....................................................23
Figure 2: Member states of the Climate Alliance as at November 2019...............................................24
Figure 3: Sample distribution across states............................................................................................32
Figure 4: Signatories of the UN Principles of Responsible Investment................................................66
Figure 5: SRI investing in the US 2018.................................................................................................67
Figure 6: ESG Incorporation by Institutional Investors.........................................................................68
List of tables:
Table 1: Sample distribution across SIC industry divisions..................................................................31
Table 2: Sample distribution across state characteristics.......................................................................32
Table 3: Ex-ante cost of equity variables...............................................................................................35
Table 4: Performance model variables..................................................................................................36
Table 5: Risk model variables...............................................................................................................38
Table 6: Summary statistics for performance variables.........................................................................39
Table 7: T-tests for performance variables............................................................................................40
Table 8: Results from firm performance models with emissions intensity...........................................42
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Table 9: Summary statistics for risk variables.......................................................................................43
Table 10: T-tests for risk variables........................................................................................................44
Table 11: Results for firm risk model with emissions intensity............................................................46
Table 12: Additional variables for examining state and year differences in the sample.......................47
Table 13: Performance results with dummy for Climate Alliance member states................................48
Table 14: Performance results with dummy for states with carbon pricing policies.............................49
Table 15: Performance results with Trump administration term...........................................................50
Table 16: Risk results with dummy for Climate Alliance member states.............................................51
Table 17: Risk results with dummy for states with carbon pricing schemes.........................................52
Table 18: Risk results with Trump administration term........................................................................53
Table 19: Asset4 environmental score components..............................................................................60
Table 20: Ex-ante cost of equity formula variables...............................................................................62
Table 21: Performance model variables................................................................................................63
Table 22: Risk model variables.............................................................................................................65
5
Abstract:
This thesis examines the impact of corporate environmental responsibility on firm value in United
States firms. It explores the impacts of emissions intensity, environmental scores, and other measures
of environmental responsibility on firm performance, risk and cost of equity. The lead-lag relationship
between institutional investment and firm environmental profile is also examined. The United States
is chosen as the focus of this study due to the lack of stringent federal climate regulation. In the
absence of such regulation, the evaluation and potential pricing of environmental issues is left to the
market. Examining whether environmental responsibility is priced is the main objective of this thesis.
The United States additionally provides a unique setting in that climate regulation differs on a state-
by-state basis; this study seeks to explore those state differences.
6
1. Introduction:
Climate change is increasingly being recognised as a material risk for companies operating in today’s
market. According to Goldstein, Turner, Gladstone, and Hole (2018) financial markets are already
reflecting environmental risks in cost of capital. Socially responsible investing (SRI) has experienced
huge growth over the past decade with support for initiatives such as the Global Investor Coalition on
Climate Change (GICCC), which uses investors’ collective power to weigh in on political issues
related to climate change. Support has more than doubled from the GICCC’s inception in 2009 to
2018 (GICCC, n.d.).
Numerous other initiatives have also grown rapidly including the Principles for Responsible Investing
(PRI) and the Climate Alliance of States in the United States (US) who have committed to the Paris
Agreement in spite of the federal decision to withdraw the US from the agreement (Eccles &
Klimenko, 2019; Meyer, 2018). Additionally, investor resolutions on the topic of climate change have
grown from a third of all resolutions in 2006-2010 to over half of resolutions in 2017 (Eccles &
Klimenko, 2019).
The rise in SRI suggests disinvestment in socially irresponsible firms and the pricing of climate risk,
however, the empirical literature does not form a consensus on the impact. Further, the literature
focusing on risk from emitting carbon is very sparse. Aldy and Gianfrate (2019) do not believe carbon
risk has been fully taken into account by firms while Matsumura, Prakash, and Vera-Munoz (2013)
find every additional thousand metric tons of carbon emitted reduces firm value.
Climate regulation across the globe is placing direct costs on firm operations which contribute to
climate change, such regulation has grown significantly over the last decade. In 2009, carbon pricing
initiatives covered 16 jurisdictions and 4.27% of global greenhouse emissions, by 2015 this number
had grown to 38 jurisdictions and 12% of global emissions (The World Bank, n.d.) At the time of
writing (2019) carbon pricing initiatives cover 57 jurisdictions and 20% of global greenhouse gas
emissions (The World Bank, n.d.). In terms of emissions trading schemes, only 5% of global
7
emissions were covered in 2005; with the launch of China’s scheme in 2020, 14% will be covered by
emissions trading schemes (International Carbon Action Partnership, 2019).
In 1991 the US was the only industrialised country without a policy on carbon dioxide (Porter, 1991).
In 2019 the US still does not regulate carbon emissions at a federal level. This provides an interesting
setting to examine whether, in the absence of legislation, the market penalises high emitting firms.
Such a penalty could be explained by an expectation that with global agreement about climate
regulation, legislation in the US is inevitable and hence the pricing of firms today should consider
future costs to meet regulatory requirements. Of additional interest in the US setting is the differing
state regulation where several states operate emissions trading schemes and emissions targets.
In the finance literature corporate Social Responsibility (CSR) has been discussed for more than sixty
years; today this concept is separated into environmental, social and governance issues (ESG). This
separation allows for more detailed analysis of risk factors for today’s organisations. This thesis
examines the environmental responsibility component in three ways. Essay 1 examines whether the
market prices carbon emissions in US firms in lieu of any federal regulation. The effect of carbon
emissions intensity is examined on cost of equity, firm performance and firm risk. Also examined is
whether with state-by-state regulation, location of a firm’s headquarters matters. Essay 2 examines the
environmental scores of firms and seeks to identify the specific environmental policies and activities
priced by the market through a similar methodology as the first essay. Despite the lack of federal
regulation on carbon emissions in the US, firms have reduced their emissions over the period 2002-
2018. Essay 3 investigates the relationship between institutional investment and environmental
responsibility to examine the drivers of improvements. It specifically examines the lead-lag
relationship between institutional investment and firm environmental profile.
8
2. Literature Review:
The well-established CSR literature provides the overall setting for this research and provides a clear
consensus of being beneficial to organisations. Within this topic is the element of corporate
environmental responsibility, which encompasses the responses by today’s organisations to the
increased scrutiny on their operations. This element of CSR is the focus on this research.
Environmental responsibility is explored in the frame of climate risk; risks facing firms as a result of
climate change.
As such, this literature review is divided into three sections, the first explores the overall CSR area, the
second examines the literature relating to environmental responsibility of firms, while the third section
focuses on the climate risk aspect of environmental responsibility.
2.1 Corporate social responsibility overview:
This section explores the literature on the established topic of CSR. The concept of CSR originates
from the early twentieth century in the United States of America (US) (Xu, Liu, & Huang, 2015).
Defined differently in almost every piece of literature discussing it, Dahlsrud (2008) finds 37 differing
definitions. CSR can broadly be thought of as the ethical responsibility of firms to the wider world in
which they operate and has been a topic of academic discussion for at least sixty-five years (Dahlsrud,
2008; Moura-Leite & Padgett, 2011). For the purposes of this study, the World Bank definition is
used:
“[CSR is the] commitment of businesses to behave ethically and to contribute to
sustainable economic development by working with all relevant stakeholders to improve
their lives in ways that are good for business, the sustainable development agenda, and
society at large” (Breuer, Müller, Rosenbach, & Salzmann, 2018, p. 34).
With sixty-five years of research, the CSR literature is only briefly reviewed here before focusing
primarily on the environmental responsibility component.
9
2.2.1 Corporate social responsibility and the firm:
The literature reviewed in this section pertains to the potential effects of CSR on firms. Such literature
is too abundant to comprehensively review here, as such, only a portion is discussed.
Examining the relationship between CSR and cost of debt for US firms, Goss and Roberts (2011) find
banks discriminate based on CSR. Firms with CSR concerns are found to pay 7 – 18 basis points
more than more responsible firms (Goss & Roberts, 2011). While a modest penalty, this suggests
banks consider CSR concerns a risk factor. However, the relationship does not hold the other way,
banks are not found to reward superior CSR firms (Goss & Roberts, 2011).
El Ghoul, Guedhami, Kwok, and Mishra (2011) find a significant negative relationship between CSR
and the ex-ante cost of equity of US firms. Additionally, the authors separate the components of CSR
into six categories, one being environmental performance (El Ghoul et al., 2011). Three are found to
be significantly negatively related to ex-ante cost of equity; employee relations, product
characteristics and environmental performance. This means firms with better environmental
performance have lower ex-ante cost of equity. Community relations, diversity and human rights were
not significantly related to cost of equity. This provides evidence that the market rewards good
environmental performers and that not all CSR components are material (El Ghoul et al., 2011).
Similarly seeking to disaggregate the effect of CSR on firm value, Gregory, Tharyan, and Whittaker
(2014) find environmental concerns are significantly related to firm value whereas environmental
strengths are not. The social and governance elements of CSR are found to be primarily attributable to
industry effects, whereas the environmental dimension is not (Gregory et al., 2014).
Albuquerque, Koskinen, and Zhang (2018) investigate the effect of CSR on firm value in the US. The
authors find a significant positive relationship between CSR and Tobin’s q; when CSR increases one
standard deviation, Tobin’s q increases by 5% relative to the average (Albuquerque et al., 2018). The
authors also examine the impact on firm risk and find a significant negative effect on firm beta. The
economic magnitude being a one standard deviation increase in CSR reduces beta by 0.014; equating
10
to a 1% decrease on the average firm beta of 1.228 (Albuquerque et al., 2018). The authors conclude
that CSR decreases firm systematic risk, however, a reduction in beta of 0.014 is economically
insignificant. Additionally, this study highlights a major impediment to this area of research; poor
data availability means samples used may not necessarily be representative of the market, as shown
by the average beta being above one.
Additionally examining CSR and firm risk, Jo and Na (2012) investigate whether CSR reduces risk in
controversial industries such as tobacco, alcohol and gambling. Using a sample of US firms from
1991 to 2010, the authors find a significant negative relationship between CSR and both daily stock
volatility and firm beta (Jo & Na, 2012). Firms in controversial industries with higher levels of CSR
are therefore found to have lower risk.
2.2 Environmental responsibility
In recent years, CSR has been disaggregated into three distinct components: environment
responsibility, social responsibility, and governance, or ESG. This new breed of CSR literature
referring to ESG factors allows researchers to isolate the effect of each component on the firm. Due to
the relative immaturity of environmental responsibility research there is significantly less literature on
this element of CSR. With environmental responsibility being the focus of this study, the existing
literature is reviewed in this section.
2.2.2 Environmental responsibility and firm cost of capital:
The literature reviewed in this section pertains to the potential effect of environmental responsibility
on firm cost of capital.
One of the most widely cited papers on this topic is that of Chava (2014); who finds both lenders and
investors demand a higher rate of return to firms with environmental concerns. The concerns
considered are toxic chemical emissions, hazardous waste, and climate change. Chava (2014) finds
investors require a 0.7% higher return from firms with these concerns and lenders charge almost 25
11
basis points more to environmentally concerning firms compared to control firms. Ng and Rezaee
(2015) similarly examine the relationship between US firms environmental performance and cost of
capital. The authors find a significant negative relationship between environmental performance and
implied cost of equity (Ng & Rezaee, 2015).
Taking a slightly different view, Sharfman and Fernando (2008) investigate the effect of US firm’s
environmental risk management on their cost of capital. The environmental risk management
coefficient has a significant negative relationship with the various measures of weighted average cost
of capital used by the authors (Sharfman & Fernando, 2008). Interestingly, the relationship direction
found for debt and equity are opposite; the effect on cost of debt is positive while the effect on cost of
equity is negative. However, the combined effect is a reduction in cost of capital from higher
environmental risk management. This result suggests firm policies and strategies around
environmental issues are rewarded by the shareholders. Unfortunately, the measure of environmental
risk management used by Sharfman and Fernando (2008) was created by the authors from two
different databases and as such, the study is not easily replicable.
Another way of investigating whether environmental responsibility is rewarded by the market is by
examining environmental costs; where reduced costs indicate improved efficiency. This is the
measure used by El Ghoul, Guedhami, Kim, and Park (2018) who investigate the effect of
environmental costs on implied cost of equity across 30 countries. The authors find a significant
positive relationship between environmental costs and cost of equity; that is, firms with poor
environmental performance are found to have higher costs of equity (El Ghoul et al., 2018).
Interestingly, the results are significant prior to 2007 and after 2008; the relationship is insignificant
during the global financial crisis (GFC) (El Ghoul et al., 2018).
Likewise finding a change in market perception around the GFC, Lopatta and Kaspereit (2014)
examine the effect of environmental sustainability on firm market value across 26 countries. The
authors find a significant negative relationship between sustainability and market value prior to the
GFC. Post-2008 the relationship changes to positive, where an improvement in sustainability
positively effects the market value of a firm (Lopatta & Kaspereit, 2014). Environmental
12
sustainability is also found to be a significant determinant of implied cost of equity by Gupta (2018).
Across a sample of 43 countries, Gupta (2018) finds firms could reduce their cost of capital by 0.77%
if they moved from the bottom quartile of firm sustainability to the top quartile. As with Lopatta and
Kaspereit (2014), and El Ghoul et al., (2018), Gupta (2018) uses a sample covering pre-GFC, GFC,
and post-GFC, however, Gupta (2018) does not examine sub-periods.
2.2.3 Environmental responsibility and firm performance:
US firms that adopted voluntary environmental sustainability standards in 1993 are found to have
significantly outperformed control firms for the next 18 years (Eccles, Ioannou, & Serafeim, 2014).
The sustainable firms are found to outperform their counterparts both in terms of stock market and
accounting performance (Eccles et al., 2014). Taking a slightly different approach, De Jong, Paulraj,
and Blome (2014) examine the effects of obtaining certification in ISO 14001, an environmental
management standard which requires firms consider environmental impacts in all decision-making.
The authors find a significant improvement in firm’s top and bottom line in the long-term following
certification (De Jong et al., 2014).
The timing of financial performance may affect the relationship. Horváthová (2012) finds
environmental performance has a negative impact on financial performance when lagged by one year,
and a positive impact when lagged by two years. Similarly finding a different relationship depending
on the time period Russo and Pogutz (2009) find a positive impact on ROA, ROS and ROE of firms
implementing pollution prevention strategies in the short-term. However, in the medium and long-
term Russo and Pogutz (2009) find no sustained effect.
2.2.4 Environmental responsibility and firm risk:
The environmental element of European firms’ CSR profiles is found to have a significant negative
effect on idiosyncratic risk by Sassen, Hinze, and Hardeck (2016). Examining the relationship
between US firms environmental concerns and risk, Oikonomou, Brooks, and Pavelin (2012) find a
significant positive relationship between beta and environmental concerns. This suggests an increase
in environmental concerns increases firm sensitivity to market fluctuations. In their discussion,
13
Oikonomou et al., argue that CSR, including environmental responsibility, is a wealth-protective
measure rather than a wealth-enhancing one.
Cai, Cui, and Jo (2016) also find environmental responsibility of US firms to decrease firm risk. A
one standard deviation increase in environmental responsibility score is found to result in a decrease
in CAPM beta of 1.08% of its unconditional mean (Cai et al., 2016). This risk reduction is found to be
concentrated in certain industries including food, chemical products and industrial machinery.
2.2.5 Environmental disclosure and firm value:
A key component in environmental responsibility of a firm is disclosure. The disclosure or non-
disclosure of environmental performance and concerns may be considered by investors in their
evaluation a firm. A heavy polluting non-disclosing firm may be punished more severely by the
market than an equally heavy polluting firm that discloses its environmental impact; investors may
consider the non-disclosing firm to be a heavier polluter simply due to a lack of information and
concern that the firm is hiding its environmental impact. This idea is referred to as signalling theory;
the activities of the firm (or lack thereof) signals investors as to whether the company is trying to
build social capital by being environmentally responsible (Petitjean, 2019). The following literature
empirically investigates the impact of disclosure.
Matsumura, Prakash, and Vera-Munoz (2013) find US firms who voluntarily disclose their carbon
emissions have a median value US$2.3 billion higher than comparable non-disclosing firms. The
quality of these disclosures may also have an impact. With a sample of US firms across five
industries, Plumlee, Brown, Hayes, and Marshall (2015) find the quality of environmental disclosures
to effect firm value through both future cash flows and the cost of equity. Conversely, in Europe’s
most polluting industries voluntary disclosures are found to have no relevance to firm value
(Clarkson, Li, Pinnuck, & Richardson, 2014).
Environmental disclosures are found to reduce firm idiosyncratic risk in the United Kingdom
(Benlemlih, Shaukat, Qiu, & Trojanowski, 2018). Benlemlih et al., (2018) suggest such disclosure
reduces information asymmetry and therefore hypothesise a resulting decrease in firm systematic risk.
14
This relationship is, however, found to be insignificant. Rather, the authors find a marginally
significant negative relationship between environmental disclosures and idiosyncratic risk (Benlemlih
et al., 2018).
2.2.6 CSR and environmental responsibility conclusion:
CSR is well-documented as being beneficial to firms and is practiced widely by firms today. In
comparison, the question of which elements of CSR drive the benefits, is relatively new. With regard
to the environmental component extant literature shows environmental responsibility to enhance firm
value; both lower cost of capital and lower risk are found for more responsible firms. Results are
somewhat conflicting as to whether environmental responsibility improves firm performance.
However, responsibility through way of transparency is found to be beneficial.
The environmental responsibility literature is not without limitations, many of the studies reviewed
use reasonably old data (over ten years old at the time of writing). The climate situation has changed
since many of these studies were conducted; regulations have changed and there has been a shift in
institutional investors’ attitudes. The literature is also relatively sparse which limits a strong
consensus being formed. However, overall, better environmental performers are found to have higher
expected cash flows and be less risky (Plumlee et al., 2015).
2.3 Climate risk
Climate risk is increasingly becoming a material risk factor for today’s organisations. Consequently,
academic literature is investigating the potential ways in which this risk factor impacts firms.
Climate risk does not have a universally accepted definition. However, most authors use climate risk
as an umbrella term for all risk factors relating to the changing climate; including regulatory change,
disruptive weather events, and increased costs resulting from climate issues. The term ‘carbon risk’ is
used to specify risks relating to the emission of carbon and other greenhouse gases, however, most
authors use them interchangeably. Kim, An, and Kim (2015) define carbon risk as any future losses or
current debts that result from increasing greenhouse gas regulation around the world; Hoffmann and
15
Busch (2008) define carbon risk as a type of climate risk arising from climate change or the use of
fossil fuels.
The Intergovernmental Panel on Climate Change (IPCC) outlines the different forms of carbon risk
as: physical risk, litigation risk, competition risk, production risk, and reputation risk (Kim et al.,
2015). This study focuses on the non-physical risks of climate change, it excludes droughts, flash-
floods and other weather-events. Non-physical risks to a firm’s cashflows may include legal and
clean-up costs from polluting local environments, losses in competitive position due to poor
environmental profile, increased operational costs from a change in regulation, or reputational costs
for firms who’s environmentally damaging operations appear in the media.
2.3.1 Regulatory risk:
Regulatory risk is increasingly becoming a concern for organisations globally as governments
consider ways to curb climate change. Balachandran and Nguyen (2018) investigate the impact of
Australia ratifying the Kyoto Protocol in December 2017 which may affect firms through increased
uncertainty around future carbon regulation. The authors hypothesize such regulation to adversely
affect carbon-emitters’ cash flows and therefore the likelihood of paying dividends (Balachandran &
Nguyen, 2018). Confirming the hypothesis, the authors find both the probably of paying dividends
and the dividend pay-out ratio to be smaller after the ratification for high-emitting industries when
compared to low-emitting industries (Balachandran & Nguyen, 2018). The authors suggest the
explanation as increased cash-flow uncertainty; however, they do not discuss whether the cash is
instead invested or held to allow for potential carbon costs.
Similarly investigating Australian regulation, Ramiah, Martin, and Moosa (2013) examine nineteen
announcements of green regulation over the period 2005 through 2011. As expected, the impact and
magnitude of announcements is found to vary between sectors. Announcement day abnormal returns
range from -9.50% to 0% to 14.69%; evidencing the varying effects of climate regulation (Ramiah et
al., 2013). Additionally, an overall change in long-term systematic risk is found in eleven of the
twenty-nine industries investigated following introduction of climate regulation (Ramiah et al., 2013).
16
Andersson, Bolton, and Samama (2016) point out that governments do not actually need to implement
carbon policies to change firm and investor behaviour; just the expectation that they will introduce
such regulation will affect heavy-emitters stock prices. Jeremy Leggett of the Carbon Tracker
Initiative concurs, saying that policymakers do not need to do anything, there just needs to be
recognition that they might (Van Renssen, 2014).
2.3.2 Carbon emissions risk:
Empirical research shows a negative impact on firm value from increased emissions. While scrutiny of
a firm’s emissions has increased in recent years, the negative relationship with firm value is not a new
finding.
Konar and Cohen (2001) use data primarily from the year 1989 and find a 10% reduction in toxic
chemical emissions by S&P500 firms results in an average increase in market value per firm of $34
million. Additionally, the average firm sampled is found to have an intangible liability resulting from
environmental concerns of some US$380 million (Konar & Cohen, 2001). With data from 2006 –
2008 Matsumura, Prakash, and Vera-Munoz (2013) also examine the effect of emissions intensity for
S&P500 firms. The authors find for every additional thousand metric tons of carbon emitted firm
value reduces by an average of US$212,000 (Matsumura et al., 2013). Studying the effect of carbon
risk on Korean firms’ cost of equity in the period 2007 to 2011, Kim, An, and Kim (2015) find a 10%
increase in carbon intensity (total emissions divided by total sales revenue) increases firm cost of
equity by between 0.08% and 0.79%. Due to the age and lack of data in these studies, the results need
to be repeated with a larger and more recent sample to find if they still hold.
In a working paper with data across 43 countries covering nine years (2008 – 2016), Trinks, Ibikunle,
Mulder, and Scholtens (2017) investigate the impact of firm’s emissions intensity on cost of equity.
The authors find a 25% reduction in carbon emissions leads to a decrease in cost of equity of 0.4 basis
points (Trinks et al., 2017). Trinks et al., (2017) also find a positive carbon beta, suggesting higher
carbon emitting firms have increased sensitivity to the market. The authors do not, however, divide
the sample into sub-periods. The impact of carbon risk on firms may vary over time, for example,
17
Ziegler, Busch, and Hoffmann (2011), in their study of disclosed corporate responses to climate
change in Europe, find risk-adjusted abnormal returns in the 2004 to 2006 period, whereas in the
years 2001 to 2003 no such abnormal return is found.
In terms of firm performance, King and Lenox (2002) investigate the effect of pollution reduction on
US firm performance using data from 1991 to 1996. The authors find lower total emissions is
significantly related to higher firm return on assets (ROA) and Tobin’s q. Examining the different
measures of pollution reduction King and Lenox (2002) find that it is preventing waste which drives
this result as opposed to waste treatment or transfer. This result suggests it is efficient technology and
innovation preventing waste in the first place that leads to higher financial performance. In their
working paper Delmas and Nairn-Birch (2011) examine the effect of increases in total emissions on
accounting measures of firm performance and on Tobin’s q. The authors find an increase in total
emissions is positively related to financial performance including return on assets, conversely a
negative relationship is found between emissions and Tobin’s q; suggesting increasing emissions
improves firm performance but worsens firm market value.
Hart and Ahuja (1996) find a reduction in firm emissions leads to a significant improvement in return
on sales (ROS) and return on assets (ROA) in the year following the reduction. Return on equity
(ROE) is found to improve two years after the reduction in emissions (Hart & Ahuja, 1996).
2.3.3 Climate risk conclusion:
While literature on the impact of climate risk from a finance perspective is relatively sparse, it is
growing. The literature reviewed here suggests emissions levels of an organisation have a relationship
with firm cost of equity and performance, however, there are conflicting studies, and many were
undertaken under different market conditions to today.
18
2.4 Summary of literature review:
CSR has long been touted as beneficial to firms and the extensive literature supports this; higher CSR
is significantly related to lower cost of capital, lower risk, and higher firm value. In recent years CSR
has been separated into three components, environmental, social, and governance (ESG), to
investigate more closely the drivers of the well-documented benefits of CSR. Focusing on the
environmental element, literature finds environmental responsibility to increase firm value through
lower cost of capital and lower risk. No clear consensus is apparent as to whether environmental
responsibility impacts firm performance, with timing and the chosen measure of performance playing
a large part in the significance of this relationship. Consistent with signalling theory, transparency of
environmental impacts is found to positively impact firm value.
Climate risk focuses on risks to organisations from the changing climate and the related changes to
the business environment including changing regulation and investor preferences. The literature
shows a change in climate regulation to impact firm dividend policy, stock price and long-term
systematic risk. In terms of carbon emissions, the limited research finds lower corporate emissions are
related to higher market value, and lower cost of equity. As with the CSR literature, research on the
relationship with firm performance reveals mixed results. Across the environmental responsibility and
climate risk literature many studies are based on older data from periods where investor attitudes and
regulation differ to the business environment of our sample.
19
3. United States setting:
This section discusses the unique setting for studying climate change risk in the US.
3.1 Why the United States?
There is a distinct lack of stringent climate regulation in the US; as discussed in the following section
there are two regulations which seek to limit emissions and protect the environment. There is,
however, no country-wide legislation regulating the emission of carbon.
The Organisation for Economic Co-operation and Development (OECD) has ranked 27 of the OECD
countries by environmental policy stringency from 1990 when the US ranked 18 th (OECD, 2019). In
2000, the US ranked 14th, 2006 saw the US drop to 19th and in 2012 the US was 11th (OECD, 2019).
The year 2012 being the last year of complete data. While the US is certainly not last in
environmental policy stringency among OECD countries, the position of the US is poor when
considering for each of these years, the US was number one in amount of greenhouse gases emitted
(OECD, 2019).
3.2 United States regulation:
The two pieces of relevant regulation to greenhouse gas emissions in the US are the Clean Air Act
and Title 40: Protection of Environment.
The federal law regulating air emissions in the US is the Clean Air Act which covers both stationary
and mobile sources of emissions (Environmental Protection Agency, n.d.-c). It is within this act that
the Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards
(NAAQS) designed to regulate hazardous emissions and protect the health and welfare of the public.
The Clean Air Act seeks to set NAAQS in each state and directs the individual states to create
implementation plans to achieve the standards. The pollutants included in NAAQS have not been
amended since 1990, they are: (1) Carbon Monoxide, (2) Lead, (3) Nitrogen Dioxide, (4) Ozone, (5)
Particle Pollution, (6) Sulfur Dioxide (Environmental Protection Agency, n.d). Of the six pollutants,
none of the five main gases that cause climate change are included. The National Aeronautics and
20
Space Administration (NASA) lists the five gases in no particular order as: (1) Water vapour, (2)
Nitrous Oxide (not to be confused with Nitrogen Dioxide), (3) Methane, (4) Carbon Dioxide (not to
be confused with Carbon Monoxide), (5) Chlorofluorocarbons (NASA, n.d.) The Intergovernmental
Panel on Climate Change concludes the gases primarily responsible for global warming are carbon
dioxide, methane and nitrous oxide; none of which are regulated under the Clean Air Act
(Environmental Protection Agency, n.d; National Geographic, 2019).
The EPA, under the Clean Air Act, requires stationary sources of air pollution to install equipment
controlling pollution and to operate within emissions limits (Environmental Protection Agency, n.d.-
a). The sources receiving the most attention from the EPA are: coal-fired power plants, acid
manufacturers, glass manufacturers, cement manufactures, and petroleum refineries. Unfortunately,
even with the increased attention to air polluters, as above, the main sources of climate change are not
included within the emissions monitored. The EPA establishes emission standards for ‘major sources’
requiring the maximum degree of reduction. Major sources of air pollution are defined as emitting or
having the potential to emit 10 tons or more per year of one hazardous pollutant, or 25 tons or more of
combined air pollutants (Environmental Protection Agency, n.d.-c).
Within the Code of Federal Regulations is Title 40: Protection of Environment which outlines the
Environmental Protection Agency’s (EPA) mission (Environmental Protection Agency, n.d.-b). Title
40 is made up of 37 volumes, within which the ambient air quality standards are set, and mandatory
greenhouse gas reporting is explained (The Federal Government of the United States, 2019). While
the scope of provision for mandatory greenhouse gas reporting is laid out, the purpose is not clear. No
mention is made in section 98.1 ‘Purpose and scope’ as to the reason for mandatory reporting nor the
goal from collecting such information. Section 98.2 outlines who is required to report their emissions;
the requirements only apply to facilities located in the US or facilities that are under or attached to the
Outer Continental Shelf of the US (The Federal Government of the United States, 2019).
Facilities or suppliers, that meet the requirements outlined in section 98.2 subsection A must report
their greenhouse gas emissions each year. There are several ways in which a facility or supplier may
be subject to mandatory reporting; if the facility contains a listed source category, if the facility
21
contains a listed source category that emits 25,000 metric tons of carbon dioxide or equivalent each
year, if a facility does not contain a listed source category but has a heat input capacity over a certain
level, or if the supplier is a listed supplier (The Federal Government of the United States, 2019). A
source category is a stationary fuel combustion source, defined as a device which combusts fuel (The
Federal Government of the United States, 2019). Some source categories subject to mandatory
reporting are electricity production, aluminium production, petrochemical production, and soda ash
production. Activities which are for research and development are not subject to mandatory reporting.
While the legislation goes some way in protecting air quality, it does not, as discussed, target carbon
emissions. Hence the onus to regulate carbon emissions lies with the market. 2Legislation covering
other pollutants includes the Clean Water Act which provides minimal restriction on pollution by
making it illegal to discharge pollutants into navigable waters without a permit (Environmental
Protection Agency, n.d.-d). Additional regulations include the Oil Pollution Act which enables the
EPA to prevent and respond to oil spills, and the Pollution Prevention Act which focuses attention on
reducing pollution at the source (Environmental Protection Agency, n.d.-e, n.d.-f).
3.3 State by state regulation and The Climate Alliance:
As per the Clean Air Act, states are directed to implement their own air quality standards.
Consequently, state by state regulation on emissions differ widely, with some states simply following
the NAAQS and others implementing stricter restrictions and even having standards for carbon
emissions. For example, Minnesota bought into effect the Next Generation Energy Act in 2007 which
requires the state to reduce greenhouse gas emissions by 80% between 2005 and 2050 (Minnesota
Pollution Control Agency, n.d.). Figure 1 highlights the states with carbon pricing schemes.
In June 2017, an alliance of 17 governors from both the Republican and Democrat parties, announced
new policies for battling climate change (United States Climate Alliance, n.d.-a). The group,
representing half of the US GDP and 40% of the population, have vowed to abide by the Paris
Accord; they are joined by Canada, Mexico and Puerto Rico in their commitments (Meyer, 2018). By
22
March 2019 this number reached 22 states (United States Climate Alliance, n.d.-b). Member states are
highlighted in Figure 2.
Figure 1: States with carbon pricing schemes as at November 2019
This figure presents a map of the US where states with carbon pricing schemes are highlighted in purple. This map is reproduced from the Center for Climate and Energy Solutions (C2ES, n.d.)
3.4 The Paris Accord:
In September 2016, under the Obama administration, the US signed the Paris Accord (Somanader,
2016). Only eight months later in June 2017, President Trump announced his intention to withdraw
the US from the agreement as soon as legally possible (BBC, 2019). The withdrawal process is now
underway with the US issuing its formal withdrawal on the first day possible, the fourth November
2019. The actual withdrawal process will not be completed for another twelve months due to the four
year exit process meaning the US will be officially withdrawn from the agreement on the fourth of
November 2020 (Pompeo, 2019; Rafferty, n.d.).
23
Figure 2: Member states of the Climate Alliance as at November 2019
This figure presents a map of the US with member states of the Climate Alliance highlighted in green. This map is created from the United States Climate Alliance list of states (United States Climate Alliance, n.d.-b)
24
4. Essay One
4.1 Introduction:
Investors are increasingly concerned about organisations’ environmental responsibility. Evidencing
this is the growing support for the Global Investor Coalition (GIC) Global Investor Statement on
Climate Change which seeks to use the collective power of investors to engage with policymakers
worldwide (Global Investor Coalition on Climate Change, n.d.). In 2009 the first statement was
published calling world leaders to sign a global agreement on climate change with the support of 181
investors representing US$13 trillion in assets under management (GICCC, 2009). For the 2011
statement, support reached 285 investors representing more than US$20 trillion and by 2018
signatories numbered 415 representing over US$32 trillion in assets (GICCC, 2011, 2018). The 2018
statement expresses the need for companies to report reliable climate related information so that
investors can price opportunities and risks effectively.
Aldy and Gianfrate (2019) believe many firms have not fully taken in carbon risk and suggest that as
climate change worsens, firms can expect stricter regulation to extract a growing price for carbon
emissions. This, the authors claim, could catch out the unprepared. Extant literature has shown a
relationship between firm emissions and their cost of equity, risk, and financial performance.
However, a comprehensive investigation into the existence of risk arising from firm emissions in the
US has not been undertaken to the best of our knowledge.
The US provides an ideal setting for this analysis as the emission of carbon and carbon equivalents is
not regulated at a federal level. There is no direct cost to emit carbon as there is in Europe where an
emissions trading scheme prices carbon emissions. This weak regulatory setting allows for an
examination of whether the market regulates heavy polluters in the absence of a legislated price on
carbon. Additionally, there are several states with their own carbon regulation, including the
California cap-and-trade system. This provides a setting to compare firms headquartered in states with
25
varying degrees of regulation as well as across time as regulations are introduced and withdrawn. The
US setting was discussed further in section 3.
Emissions intensity is our chosen measure of carbon risk, it is referred to interchangeably as carbon
intensity in some literature. Hoffmann and Busch explore different corporate carbon performance
measures and define carbon intensity as relating to “a company’s physical carbon performance and
describes the extent to which its business activities are based on carbon usage” (Hoffmann & Busch,
2008, p. 509). We choose to use the term emissions intensity as the emission data used is carbon and
carbon equivalent greenhouse gases. An intensity measure, which is scaled by a business metric such
as sales, allows comparisons between companies and more a transparent view of reduction potential
than a gross measure of total emissions (Hoffmann & Busch, 2008).
While minimal, emissions intensity has been examined empirically, this study contributes to the
literature by examining emissions intensity’s impact on firm value using a larger, more homogenous
sample than prior research. It also examines whether there is a shift in risk perception of carbon
emissions over time and across states. The motivation arises from the changes in regulation around
carbon emissions and indeed the failed changes in regulation in the last 15 years. In recent years there
has also been a fundamental shift in federal policy on climate change as well as an apparent increase
in concern from institutional investors. This study is the first, to the best of our knowledge, to
examine carbon risk in the US with a focus on regulatory setting.
This essay investigates whether investors price emissions. It investigates these questions by examining
the effect of emissions intensity on firm cost of equity, risk and performance over differing regulatory
settings and time.
26
4.2 Literature Review and hypothesis development
4.2.1 Literature Review:
Empirical research shows a positive impact on firm value from reducing emissions. Konar and Cohen
(2001) find a 10% reduction in toxic chemical emissions results in an average increase in market
value per firm of $34 million. Similarly, Matsumura et al., (2013) find every additional thousand
metric tonnes of carbon emitted reduces firm value by $212,000 on average. For cost of equity capital,
Kim et al., (2015) find a 10% increase in carbon emissions intensity increases firm cost of equity by
between 0.08% and 0.79%.
Emissions are also found to have a relationship with firm performance, however, the literature does
not provide a strong consensus with differing measures of financial performance and time periods
over which the effect is observed. King and Lenox (2002) find reducing emissions significantly
relates to higher firm ROA and Tobin’s q. Lewandowski (2017) finds lower emissions positively
impact firm performance, through ROS, while worsening Tobin’s q. Hart and Ahuja (1996) find a
reduction in firm emissions leads to a significant improvement in ROS and ROA in the year following
the reduction. ROE is found to improve two years after the reduction in emissions (Hart & Ahuja,
1996).
Horváthová (2012) finds a negative impact of environmental performance on financial performance
with a one-year lag, and a positive impact on financial performance with a two-year lag. Russo and
Pogutz (2009) find pollution prevention policies positively impact ROA, ROS and ROE in the short-
term, however, in the medium to long term Russo and Pogutz (2009) find no sustained positive effect.
In a more recent study, Petitjean (2019) finds no relationship between the level of total greenhouse
gas emissions and financial performance, however, a sample of only 58 companies is used.
Literature on the relationship between firm environmental performance and firm risk is sparse.
Oikonomou et al., (2012) find an increase in environmental concerns relates to an increase in beta;
suggesting an increased sensitively to the market. Cai et al., (2016) find an improvement in US firm’s
environmental scores relates to a decrease in beta. Environmental disclosure is found to be
27
significantly and negatively associated with firm total and idiosyncratic risk by Benlemlih et al.,
(2018).
With very few studies examining the impact of emissions levels on firms, the effect remains
ambiguous. The studies on firm value are relatively old with regulation and market sentiment on
environmental issues having changed since the period examined. Those investigating firm
performance generally find a positive effect from reducing emissions; while no studies are found on
the relationship between emissions levels and firm risk.
4.2.2 Hypothesis Development:
As discussed in the introduction, SRI has experienced growth since many of the studies discussed in
the literature review were undertaken. Additionally, public perceptions have changed, with the
percentage of the American public who believe global warming is real, increasing from an average
under Obama of 65%, to an average of 71% under Trump (Leiserowitz et al., 2019). While this may
not appear a large change; the 2019 survey is the first since 2008 to show more than 60% of
respondents believe global warming is mostly caused by humans (Revkin, 2019).
During the sample periods used by prior studies, the US administrations’ overall perspective on
climate change was stable. With President Obama in office there was a general consensus that more
needed to be done to protect the environment; during this administration billions of dollars were
provided to green initiatives, greenhouse gas standards were tightened, and the US joined
international climate agreements (Farber, 2016). At the same time some states were introducing and
strengthening their own climate change regulation. For example, in 2013 California launched their
cap-and-trade program. In the time since the reviewed studies were undertaken, there has been a
major shift in the perspectives of US regulators. Following the election of the Trump administration
there has been a significant effort to roll-back environmental regulation. By June 2019, 49
environmental rules have been withdrawn and an additional 34 are in the process of being rolled back
(Popovich, Albeck-Ripka, & Pierre-Louis, 2019).
28
With a record high number of Americans believing global warming is personally important, the
increasing focus on climate change risk by institutional investors and the continuing withdrawal of
climate regulation by the Trump Administration, this study asks: are investors regulating US firms in
the absence of legislation by pricing carbon emissions?
The differing regulation between US states also provides an ideal setting for analysis. Due to the well
documented home bias, investors have a tendency to invest in firms in their home state (Coval &
Moskowitz, 1999; Lewis, 1999; Pool, Stoffman, & Yonker, 2012). As such, there is potential for
regulation of the state in which a firm is headquartered to influence how investors perceive emissions.
Cost of equity:
The limited literature examining firms’ emissions find the higher the emissions the higher the cost of
capital or lower the firm value (Kim et al., 2015; Konar & Cohen, 2001; Matsumura et al., 2013).
However, as discussed, shifts in regulation and sentiment have occurred since the time of prior studies
and the effect of emissions intensity may have changed. This hypothesis is examined through the
effect on ex-ante cost of equity.
H1: Higher emissions intensity is significantly related to higher ex-ante cost of equity
Firm performance:
We follow the extant literature in hypothesising that an improvement in environmental responsibility
is positively related with firm performance (Albuquerque et al., 2018; Hart & Ahuja, 1996;
Horváthová, 2012; King & Lenox, 2002; Russo & Pogutz, 2009).
H2: Higher emissions intensity is significantly related to lower firm performance
Firm risk:
While no studies are found directly examining the relationship between emissions levels and firm
risk, several studies examine the effect of general environmental performance on firm risk.
Oikonomou et al., (2012) find a decrease in environmental concerns is related to a decrease in beta
while, Cai, Cui and Jo (2016) find environmental responsibility negatively affects firm risk (total and
29
beta). In terms of firm-specific risk, Sassen et al., (2016) find general CSR to negatively affect firm
idiosyncratic risk.
As such, the effect of emissions intensity on total risk, beta, and idiosyncratic risk are examined. An
additional measure of risk used, operating risk, is an accounting-based measure. It is the standard
deviation of earnings before interest and tax (EBIT) divided by sales for the 5 years prior (Doukas &
Pantzalis, 2003). This measure is used to ensure each hypothesis tests both the market and accounting-
based relationship.
H3: Higher emissions intensity is significantly related to higher firm risk
4.3 Research design and methodology:
4.3.1 Sample:
Data is drawn from publicly traded US firms for the period 2007 – 2018; the start date is determined
by the availability of emissions data.
Data on emissions is gathered from Datastream’s Asset4 database. This database comes from publicly
available information including annual reports, firm websites, stock exchange filings, CSR reports,
and news sources (Refinitiv, n.d.-a). Covering over 7000 companies worldwide, the sample of firms
in the US is around 2800. Due to availability of data on emissions, the number of firms in the sample
differs by year; with a maximum of 2595 firms. Emissions in this database are carbon and carbon
equivalents measured in tonnes. Emissions data is only available on a yearly basis; consequently, all
data is annual.
The self-reported nature of emissions data is a limitation of this study. However, as the US doesn’t
legally require firms to report, self-reported data is standard in such environmental responsibility data.
Refinitiv (n.d.-b) explains that where a carbon emissions figure is not reported by a firm, Refinitiv
uses a sophisticated model to estimate emissions; the details for which are available from Refinitiv.
As such, some of the emissions measures may be estimated from prior self-reported emissions levels.
30
Firm characteristics and accountancy data are taken from Compustat while stock prices are taken from
CRSP. Sub-samples by state are investigated due to differing regulation with state data being gathered
from Datastream. The state variable is the address of the firm’s head office. If a firm is multinational,
the state is the location of its US head office. Further information on state differences and the
rationale behind dividing by state is provided in section 3.
Konar and Cohen (2001) remove financial institutions from the sample due to their non-polluting
nature; Petitjean (2019) follows this decision. We follow this method and remove banking and finance
firms from our sample; the remaining sample contains 1972 firms.
Distribution of the sample across industry divisions is presented in Table 1. Manufacturing provides
the largest number of emissions observations. A table of this distribution is provided in the appendix.
Sample distribution across states is presented in Figure 1 where the size of the circle represents the
portion of firms from the state. This information is presented in a table in the appendix. California is
the largest sample, this is not unexpected as California has the strongest emissions regulation and we
are inherently working with a sample of self-reporting firms.
A small portion of firms have no identifiable state in which they are head quartered. These firms are
included in the main empirical examination, while being excluded from any state-by-state
comparisons.
Table 1: Sample distribution across SIC industry divisions
This table presents the distribution of 1972 firms across ten SIC industry divisions.
31
SIC divisions FirmsAgriculture, Forestry and Fishing 5Construction 35Real Estate 14Manufacturing 952Mining 105Public Administration 5Retail Trade 138Services 426Transportation and Public Utilities 222Wholesale Trade 70Grand Total 1972
Figure 3: Sample distribution across states
This figure shows the distribution of the sample across states of the US. The number of firms in each state is represented by the size of the circle with larger circles representing a larger percentage of the sample being headquartered in that state.
Dummy variables are created based on the characteristics of the state they are located in. Table 2
shows the distribution of the data. For Climate Alliance membership, firms are given a 1 if located in
a member state and 0 otherwise. For state level regulation, firms are given are 1 if located in a state
with a carbon pricing scheme, and 0 otherwise. Due to the obvious correlation of these measures, they
are investigated individually.
State characteristics FirmsClimate alliance:Member states 1232Non-member states 740Carbon pricing schemesStates with carbon pricing schemes 798States with no scheme 1174
Table 2: Sample distribution across state characteristics
32
This table presents the distribution of firms across states with regard to state regulation. The number of firms headquartered in states with two types of regulation are presented along with the number of firms in the sample which are headquartered in states without such regulation.
4.3.2 Model:
Emissions Intensity measure:
In line with prior literature, total emissions are converted into an emissions intensity measure by using
the following formulae. Sales are used to scale emissions to represent the value of all upstream
activities plus the firm’s step in the value chain, a cradle-to-gate perspective (Hoffmann & Busch,
2008). This is also the intensity measure used by Kim et al., (2015).
ei i , t=COi , t / Salesi , t Equation 1
ei is emissions intensity for the firm i, for the year t
CO is carbon output which is equal to gross total tonnes of carbon or carbon equivalents emitted by firm i in
year t
Sales is the total sales revenue for firm i, in year t
Emissions intensity gives the ratio of emissions in tonnes to $1000 of sales and allows a comparison
across firms of different sizes. It provides a kind of efficiency measure when comparing firms in the
same industry, if one can produce the same dollar amount in sales with fewer emissions, there is an
indication of efficiency.
The effect of emissions intensity on firm value is examined in three ways. First, firm ex-ante cost of
equity capital is examined to find whether emissions intensity is a significant determinant. Second, the
relationship between emissions intensity and firm financial performance is investigated; both
accounting and market-based measures of performance are considered. Lastly, firm risk is
investigated to determine if emissions intensity has an impact on the measures of risk. These measures
being total, systematic, idiosyncratic and operating risk. The following sections explain the methods
by which these questions are examined.
33
Cost of equity:
Following Kim, et al., (2015), El Ghoul et al., (2018), and El Ghoul et al., (2011) ex-ante cost of
equity is used. There are several advantages to using ex-ante cost of equity, including avoiding the use
of noisy realised returns and the ability to consider growth rates and expected future cash flows (El
Ghoul et al., 2011).
Hail and Leuz (2006), El Ghoul et al., (2018), and El Ghoul et al., (2011), calculate ex-ante cost of
equity by averaging the results of four models due to the uncertainty around relative performance. The
models used are Claus and Thomas (2001), Gebhardt, Lee, and Swaminathan (2001), Ohlson and
Juettner-Nauroth (2005), and Easton (2004). Following Dhaliwal et al., (2006) and El Ghoul et al.,
(2011), the 10-year US Treasury Bond yield is subtracted from the cost of equity estimates found with
each model to account for the risk-free rate of return. The cost of equity estimates are then averaged.
The average ex-ante cost of equity is used as the dependent variable to examine the effect of
emissions intensity. Control variables used follow the method of El Ghoul et al., (2011) market beta
is used to control for systematic risk and is expected to have a positive sign (Fama & French, 1993;
Gordon & Gordon, 1997). Book-to-market value and leverage control for additional risk (Fama &
French, 1995; Miller & Modigliani, 1958). Beta is calculated using the Russell 2000 as the market
index due to the sample size used. The size disparity of firms is also controlled for. El Ghoul et al.,
(2011) add to these controls forecast dispersion and consensus long term growth forecast to control
for analyst forecasts and expect both to be positively related to the cost of equity capital. Industry and
year effects are additionally controlled for.
Variable: Description: Direction of relationship
34
expected:
KAvg Average of implied cost of
equity from four models
beta Systematic risk, a measure of a
firm’s sensitivity to market
fluctuations
+
btm Book-to-market value: Ratio of
book value of equity to market
value of equity
+
size Natural logarithm of total assets -
lev Leverage: Book value of debt
divided by total assets
+
disp Dispersion: Variation in 1-year
ahead EPS forecasts
+
ltg Long-term growth forecast:
Average long-term growth
forecast consensus
+
Equation: following JBF paper (El Ghoul et al., 2011)
K Avg=B 0+B1 ei+B 2beta+B 3btm+B 4¿5lev +B 6 disp+B 7 ltg+ year∧industry effects+ε
Equation 2
Firm performance:
The measures of financial performance used are return on assets (ROA) and Tobin’s q following King
and Lenox (2002). Return on sales (ROS) and return on equity (ROE) are also investigated following
Lewandowski (2017).
Control variables follow the methodology of King and Lenox (2002), Lewandowski (2017), and Hart
and Ahuja (1996). Firm size, firm leverage, research and development (R&D) intensity, capital
35Table 3: Ex-ante cost of equity variables
This table presents the dependent and independent variables for the ex-ante cost of equity model. It provides a description of the variables and indicates the expected direction of the relationship with ex-ante cost of equity. Full variable definitions are provided in the appendix
intensity, sales growth, and cash flow are controlled for. Advertising expenditure intensity is not
controlled for due to a lack of sufficient data. The description and predicted direction of these controls
are presented in the following table:
Variable: Description: Direction of relationship
expected:
FP Financial performance as measured by
one of: return on assets (ROA), return on
equity (ROE), return on sales (ROS), or
Tobin’s q
size Natural logarithm of total assets -
lev Leverage: Book value of debt divided by
total assets
+/-
rd R&D expenditure intensity: R&D
expenditure divided by total assets
+/-
capex Capital expenditure intensity: Capital
expenditure expense divided by total sales
-
sg Sales growth rate from t -1 to t +
cf Operating cash flow divided by total
assets
+
Table 4: Performance model variables
This table presents the dependent and independent variables for the performance model. It provides a description of the variables and indicates the expected direction of the relationship with firm performance. Full variable definitions are provided in the appendix.
Following King and Lenox (2002) we use a fixed effects models with financial performance as the
dependent variable and emissions as the independent variable of interest.
FP = B0 + B1 ei + B2 size + B3 lev + B4 rd + B5 capex + B6 sg + B7 cf + year
and industry effects + ε Equation 3
36
Firm Risk:
As with the financial performance measures, firm risk is calculated using both accounting and market
measures. Following Jo and Na (2012) and Benlemlih et al., (2018) total risk is used to measure firm
risk based on market variables. Total risk being composed of systematic market risk and firm-specific
risk. Firm risk is also examined using the operating risk measure of Doukas and Pantzalis (2003).
Total risk is measured as standard deviation of daily stock returns while systematic risk is measured
by CAPM beta (Benlemlih et al., 2018; Jo & Na, 2012). Beta is calculated using the Russell 2000
index as the market index due to the size of the sample. Benlemlih et al., (2018) adds a measure of
idiosyncratic risk as measured by the standard deviation of CAPM daily stock return residuals.
Operating risk is measured by the standard deviation of earnings before interest and tax (EBIT)
divided by sales for the 5 years prior to the year of interest (Doukas & Pantzalis, 2003).
Control variables used follow the methodology of Benlemlih et al., (2018) and Jo and Na (2012).
Variable: Description: Direction of relationship
expected:
Firm risk Measured by one of: total risk
(standard deviation of daily stock
returns), beta (systematic risk),
operating risk, or idiosyncratic risk
size Natural logarithm of total assets -
mtb Market-to-book: Market value of
assets divided by book value of assets
-
lev Leverage: Book value of debt divided
by total assets
+
rd R&D expenditure intensity: R&D
expenditure divided by total assets
-
roa Return on assets: Operating income
before depreciation divided by total
assets
-
capexp Capital expenditure intensity: Capital
expenditure expense divided by total
+
37
sales
opcf Operating cash flow divided by total
assets
-
sg Sales growth rate from t -1 to t +/-
ag Asset growth rate: Total assets in year
t divided by total assets in year t-1
+/-
Table 5: Risk model variables
This table presents the dependent and independent variables for the risk model. It provides a description of the variables and indicates the expected direction of the relationship with firm risk. Full variable definitions are provided in the appendix.
Equation: following JBE paper (Jo & Na, 2012)
Firmrisk = B0 + B1 ei + B2 size + B3 mtb + B4 lev + B5 rd + B6 roa + B7 capexp
+ B8 opcf + B9 sg + B10 ag + year and industry effects + ε Equation 4
4.4 Preliminary results:
4.4.1 Performance model results
Summary statistics and correlation:
Summary statistics for performance variables are presented in Table 6. On average the firms in the
sample have a return on assets of 10.7%, return on equity of 28.9%, return on sales of -0.4% and a
Tobin’s q of 1.95. The Tobin’s q measure represents the ratio of market value of assets to their
replacement value. All variable descriptions and methods of calculation are provided in the appendix.
38
Table 6: Summary statistics for performance variables
This table presents the summary statistics for the variables used in the performance model. The sample is unbalanced with 11128 firm-year observations for the variable of interest: emissions intensity. The variable statistics represent the sample period of 2007-2018. Full variable definitions are provided in the appendix.
The variables are checked for multicollinearity with correlation coefficients and variance inflation
factor (VIF) results presented in the appendix. High positive correlation is found between ROA and
cash flow (0.8179), consequently the model of ROA is compared with and without cash flow and in
the final model cash flow is excluded.
VIF for the models are less than 2 and therefore do not suggest any multicollinearity problems.
Variable N Mean Min Median Max Std.devei 11128 0.255 0.000 0.030 4.634 0.743roa 30705 0.107 -0.695 0.125 0.480 0.172roe 30132 0.289 -2.539 0.278 3.486 0.663ros 29800 -0.004 -7.461 0.144 0.736 1.015tq 30147 1.950 0.014 1.471 8.481 1.513size 30147 7.395 3.080 7.397 12.306 1.775lev 30147 0.218 0.000 0.186 0.936 0.210rd 30147 0.043 0.000 0.000 0.496 0.090capex 30147 0.051 0.000 0.035 0.300 0.054sg 38136 0.111 -0.414 0.031 1.882 0.297cf 30147 0.079 -0.520 0.091 0.344 0.127
Univariate tests:
T-tests are conducted to initially investigate the relationship between emissions intensity and
performance measures. The t-tests are conducted by splitting the emissions intensity sample into low
and high emissions intensity based on the median; the results are presented in Table 71.
The results of univariate testing show that for ROE and Tobin’s q performance measures there is a
statistically significant difference in mean performance between firms in the low emissions intensity
group and in the high emissions intensity group. As shown in Table 7, the ROE test shows high
emissions intensity firms have an average ROE of 36.22% whereas low emissions intensity firms
1 T-tests are also conducted using one, two, and three year lags of emissions intensity. Consistent relationships
are found for one lag. When emissions intensity is lagged two years, average ROS between the two groups is
also significantly different. With three lags average ROA, ROE, and Tobin’s q are significantly different
between the two groups. Hence, the choice of lag appears important to the results.
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have an average of only 32.73%. Low emissions intensity firms have an average Tobin’s q of 2.254
where high emissions intensity firms have an average Tobin’s q of 1.785.
ei Means (1) (2) (1) - (2)
Low emissions intensity
High emissions intensity
Difference t-statistic
roa 0.1278 0.1254 1.019roe 0.3273 0.3622 -2.633***ros 0.1117 0.0957 1.1429tq 2.2543 1.7847 17.175***size 8.0481 8.6768 -23.048***lev 0.2296 0.2638 -8.893***rd 0.0426 0.0238 14.398***capex 0.0381 0.0604 -23.835***sg 0.1013 0.0838 3.503***cf 0.1022 0.0956 3.657***
Table 7: T-tests for performance variables
This table presents results from difference-in-means tests for the sample. Low emissions intensity firms are those with emissions intensity below the median. High emissions intensity firms are those with emissions intensity above the median level. The superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
In addition to firm characteristics, emissions intensity is expected to be time varying. The sample used
covers 12 years, over which the business and political environment have changed significantly.
Environmental regulation, as discussed in the hypothesis development has been introduced and
subsequently rolled back. In addition, there has been a shift in public perceptions around climate
change. A further variable of interest to emissions intensity is the industry in which a company
operates. It is reasonable to expect certain industries such as manufacturing to have higher emissions
intensity on average than such industries as retail trade. The following fixed effects regression model
addresses these additional variables in a multivariate setting.
Fixed effects regression:
Following prior literature in this area including Jo and Na (2012), El Ghoul, et al., (2011), and
Petitjean (2019), we use industry and year fixed effects in our models. Table 8 presents the results for
the four measures of firm performance used. Emissions intensity is significantly negatively related to
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both ROA and ROS at the 1% significance level. For Tobin’s q, emissions intensity is significantly
positively related at the 1% level. No significant relationship is found between emissions intensity and
ROE2.
Discussion of results:
The main results presented in Table 8 show a significant negative relationship between emissions
intensity and ROA and ROS. The results suggest higher emissions intensity is related to lower firm
performance, supporting our hypothesis. For our market-based measure of performance, Tobin’s q,
the result is the opposite. The results suggest higher emission intensity is related to higher Tobin’s q.
(1) (2) (3) (4) roa roe ros tq ei -0.015*** 0.004 -0.077*** 0.053*** (-5.677) (0.280) (-4.182) (2.661)size 0.007*** 0.032*** 0.047*** -0.231*** (5.363) (5.173) (9.105) (-18.777)lev -0.057*** 0.287*** 0.295*** 0.085 (-5.909) (3.693) (5.275) (1.011)rd -0.971*** -0.179 -2.504*** 9.232*** (-20.812) (-0.845) (-7.700) (21.864)capex 0.313*** -0.626*** -1.011*** -0.010 (7.405) (-3.168) (-5.004) (-0.026)sg 0.024*** 0.041 0.151*** 0.691*** (3.040) (1.441) (2.776) (9.155)cf 1.371*** 3.580*** 5.334*** (10.543) (19.664) (19.481)Intercept 0.120*** -0.039 -0.608*** 3.141*** (9.903) (-0.675) (-9.506) (23.302) Observations 10,676 10,672 10,668 10,676Adjusted R-squared 0.392 0.118 0.499 0.510Industry Yes Yes Yes Yes
2 As with univariate testing, the fixed effects regression models are run using lagged emissions intensity. One
year’s lag on emissions intensity provides consistent results for ROA and ROS. When lagged one year,
emissions intensity is significantly negatively related to ROE. However, emissions intensity is no longer
significant to Tobin’s q. For lags two and three, emissions intensity is only significantly related to ROA and
ROS.
41
Year Yes Yes Yes Yes
Table 8: Results from firm performance models with emissions intensity
This table presents the results of fixed effects regression model of firm performance. Models 1-4 have dependent variables of return on assets (roa), return on equity (roe), return on sales (ros), and Tobin’s q (tq) respectively. All four models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
Overall the results suggest higher emissions intensity reduces ROA and ROS, two accounting-based
measures of performance. While for the Tobin’s q, the market measure, an increase in emissions
intensity improves this measure of performance. This is in contrast to the findings of Delmas and
Nairn-Birch (2011), who examine the relationship between total greenhouse gas emissions and both
ROA and Tobin’s q in their working paper. The authors find a positive relationship with ROA and a
negative relationship with Tobin’s q. As noted, Delmas and Nairn-Birch (2011) use total emissions
rather than emissions intensity so the results are not directly comparable.
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Table 9: Summary statistics for risk variables
This table presents the summary statistics for the variables used in the risk model. The sample is unbalanced with 11128 firm-year observations for the variable of interest: emissions intensity. The variable statistics represent the sample period of 2007-2018. Full variable definitions are provided in the appendix.
4.4.2 Risk model results
Summary statistics and correlation:
Summary statistics for the risk variables are presented in Table 9 and show average total risk as 2.7%
and beta as 0.866. The beta reflects the data availability issue in this area of research, due to the
limited data, a sample cannot be formed to track the market perfectly; this sample has lower volatility
than the overall market. Average operating risk is 40.6% and represents the standard deviation of
earnings before interest and tax divided by sales for the preceding 5 years.
Variable N Mean Min Median Max Std.devei 11128 0.255 0.000 0.030 4.634 0.743oprisk 30073 0.406 0.002 0.025 17.296 2.154trisk 27009 0.027 0.008 0.023 0.094 0.015beta 27052 0.866 -0.038 0.849 2.129 0.421size 30147 7.395 3.080 7.397 12.306 1.775lev 30147 0.218 0.000 0.186 0.936 0.210rd 30147 0.043 0.000 0.000 0.496 0.090mtb 30147 1.950 0.014 1.471 8.481 1.513roa 30147 0.103 -0.637 0.121 0.418 0.156capexp 29800 0.109 0.000 0.038 1.656 0.240opcf 30147 0.079 -0.520 0.091 0.344 0.127sg 38136 0.111 -0.414 0.031 1.882 0.297ag 29879 1.146 0.000 1.060 3.723 0.461
Variables are checked for multicollinearity using correlation coefficients and VIF tests. As mentioned
ROA and operating cash flow are highly correlated. The models of risk are therefore tested with one,
not both, of these controls and it is found that ROA is the stronger control. Operating cash flow is
therefore excluded from the final models of risk. VIF for the models are less than 2 and therefore do
not suggest any multicollinearity problems. The results of these tests are presented in the appendix.
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Univariate tests:
T-tests are conducted to initially investigate the relationship between emissions intensity and risk
measures. The t-tests are conducted by splitting the emissions intensity sample into low and high
emissions intensity based on the median; the results are presented in Table 103.
The results of univariate testing show that for all three measures of risk there is a statistically
significant difference in mean risk between firms in the low emissions intensity group and in the high
emissions intensity group. As shown in Table 10, the average operating risk for low emissions
intensity firms is 19.01% whereas for high emissions intensity firms it is 28.56%. For total risk and
beta, the relationship is in the opposite direction, with low emissions intensity firms have higher
average risk than high emissions intensity firms.
ei Means (1) (2) (1) - (2)
Low emissions intensity
High emissions intensity
Difference t-statistic
oprisk 0.1901 0.2856 -3.133***trisk 0.0233 0.0226 3.413***beta 0.8866 0.8723 2.035**roa 0.1249 0.1225 1.037size 8.0481 8.6768 -23.048***mtb 2.2543 1.7847 17.175***lev 0.2296 0.2638 -8.893***rd 0.0426 0.0238 14.398***
capexp 0.0723 0.1433 -16.497***opcf 0.1022 0.0956 3.657***
sg 0.1013 0.0838 3.503***ag 1.1071 1.0807 3.991***
Table 10: T-tests for risk variables
This table presents results from difference-in-means tests for the sample. Low emissions intensity firms are those with emissions intensity below the median. High emissions intensity firms are those with emissions intensity above the median level. The superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
3 Consistent with models of performance, univariate tests are repeated with lagged emissions intensity. With one
and two lags on emissions intensity the results are consistent except for the significance for the difference in
operating risk dropping to 10%. When emissions intensity is lagged by three years, the difference in total risk is
significant at 10% and the difference in beta remains significant at 1%.
44
As with the firm performance model, we can expect there to be time-varying and industry effects on
emissions intensity; a multivariate model follows.
Fixed effects regression:
Again, following prior literature in this area including Jo and Na (2012), El Ghoul, et al., (2011), and
Petitjean (2019), we use industry and year fixed effects in our models. Table 15 shows that despite the
highly significant t-tests, for the full sample with relevant controls, emissions intensity only has a
statistically significant relationship with operating risk4.
Discussion of results:
The results of our firm risk models suggest that higher emissions intensity is significantly related to
higher operating risk. For total risk and beta, no statistically significant relationship is found with
emissions intensity.
To the best of our knowledge, this is the first study to examine the relationship between emissions
intensity and firm risk. There are however several studies which more broadly examine environmental
responsibility and firm risk. Oikonomou et al., (2012) find a positive significant relationship between
environmental concerns and firm beta in the period 1992 - 2009. Cai et al., (2016) find for US firms in
the period 1991 – 2012 corporate environmental responsibility has a significant negative relationship
with firm beta and total risk. The results are not perfectly comparable since prior literature examines a
broader measure of firm environmental responsibility. Nevertheless, we contrastingly, find an
insignificant negative relationship between emissions intensity and firm beta and total risk.
A significant positive relationship is found between emissions intensity and operating risk. Operating
risk is an accounting measure of risk representing the expected costs of bankruptcy (Doukas &
4 The fixed effects regression models of firm risk are repeated with emissions intensity lagged by one, two, and
three years. With one lag, the results are consistent with Table 10 with the additional of emissions intensity
being significantly negatively related to beta at the 10% level. When lagged by two years, emissions intensity
becomes significantly related to total risk at the 1% level, as well as consistently significant to operating risk.
Finally, when lagged by three years, emissions intensity is only significantly related to total risk.
45
Pantzalis, 2003). The positive relationship suggests higher emissions intensity is related to higher
variability in the ratio of EBIT to sales.
(1) (2) (3) oprisk trisk beta ei 0.195*** -0.000 -0.010 (4.294) (-0.965) (-1.601)roa -4.307*** -0.025*** -0.669*** (-12.458) (-17.603) (-15.627)size -0.035*** -0.002*** -0.063*** (-2.997) (-28.530) (-24.772)mtb 0.094*** -0.000*** -0.007** (4.021) (-3.101) (-1.964)lev 0.024 0.006*** 0.170*** (0.172) (9.786) (8.340)rd 1.401* -0.001 0.055 (1.691) (-0.526) (0.621)capexp 2.388*** 0.001* 0.066*** (8.377) (1.685) (2.636)sg 0.891*** 0.002*** 0.056*** (4.871) (3.851) (3.381)ag -0.436*** 0.000 0.001 (-4.484) (0.505) (0.082)Intercept 1.007*** 0.043*** 1.378*** (5.718) (43.322) (43.649) Observations 10,668 9,986 10,003Adjusted R-squared 0.451 0.487 0.460Industry Yes Yes YesYear Yes Yes Yes
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Table 11: Results for firm risk model with emissions intensity
This table presents the results of fixed effects regression model of firm risk. Models 1-3 have dependent variables of operating risk (oprisk), total risk (trisk), and systematic risk (beta) respectively. All three models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
4.5 Sub-sample results:
To investigate the significant of emissions intensity for companies headquartered in states with
varying degrees of climate regulation we use a dummy as explained in Table 12. We also run an
interaction term between a Trump administration dummy and emissions intensity to examine whether
the relationship of emissions intensity with performance and risk changed under the Trump
administration. As previously discussed, environmental regulation and the federal stance on climate
change in the US has drastically changed under the Trump administration. Table 12 outlines the
additional variables used.
Variable: Description:
climateall Climate Alliance dummy, 1 if firm headquartered in member state, 0
otherwise.
carbonprice Carbon pricing regulation dummy, 1 if firm headquartered in state with
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carbon pricing regulation, 0 otherwise.
Trump Trump administration dummy which is 1 if year is during Trump
administration (2017 & 2018), and 0 otherwise
Table 12: Additional variables for examining state and year differences in the sample
This table presents additional independent variables used to test the relationship of emissions intensity with performance and risk in different regulatory settings.
The performance results presented in Table 13, show the interaction between the Climate Alliance
dummy and emissions intensity. The interaction term is significant negatively related to both ROA
and ROS. Consistent with the main results in Table 8 higher emissions intensity is significantly
related to lower ROA and ROS for firms headquartered in member states of the Climate Alliance. For
Tobin’s q the relationship in no longer significant, suggesting the positive relationship found in the
main sample does not hold for firms located in member states. Table 14 presents the results for firms
headquartered in states with carbon pricing policies. The interaction term with emissions intensity is
also significantly negatively related to ROA and ROS, consistent with the Climate Alliance results.
These results are consistent with the expectation that regulation comes with costs which decrease firm
performance.
For firms headquartered in states with carbon pricing policies, the relationship between emissions
intensity and Tobin’s q is consistent with the full sample. The results suggest higher emissions
intensity is related to higher market performance. Being located in a state which prices carbon does
not change the direction of this relationship.
Performance:
(5) (6) (7) (8)roa roe ros tq
ei -0.011*** 0.003 -0.047*** 0.041** (-5.226) (0.318) (-3.652) (2.450)climallei -0.009*** 0.001 -0.068*** 0.027 (-3.159) (0.079) (-3.240) (1.191)
48
size 0.007*** 0.032*** 0.048*** -0.231*** (5.396) (5.168) (9.155) (-18.783)lev -0.057*** 0.287*** 0.294*** 0.085 (-5.923) (3.693) (5.268) (1.017)rd -0.969*** -0.179 -2.492*** 9.226*** (-20.792) (-0.848) (-7.705) (21.827)capex 0.314*** -0.626*** -0.994*** -0.017 (7.470) (-3.165) (-4.946) (-0.044)sg 0.024*** 0.041 0.153*** 0.690*** (3.100) (1.434) (2.830) (9.156)cf 1.371*** 3.568*** 5.339*** (10.552) (19.631) (19.508)Intercept 0.120*** -0.039 -0.611*** 3.142*** (9.863) (-0.674) (-9.549) (23.305) Observations 10,676 10,672 10,668 10,676Adjusted R-squared 0.392 0.118 0.500 0.510Industry Yes Yes Yes YesYear Yes Yes Yes Yes
Table 13: Performance results with dummy for Climate Alliance member states
This table presents the results of modelling firm performance. Models 5-8 have dependent variables of ROA, ROE, ROS and Tobin’s q respectively. Relevant controls variables are included as well as emissions intensity variable and a dummy for firms headquartered in Climate Alliance states. All four models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
Table 15 presents the results for firm performance with the Trump administration term. The results
show that emissions intensity is significantly negatively related to ROE, ROS, and Tobin’s q in the
years under the Trump administration. This is the opposite result for the market measure of
performance, Tobin’s q, as in the full sample presented in Table 8. In 2017 and 2018, under the
Trump administration, results suggest higher emissions intensity is related to lower Tobin’s q. While
not statistically significant, the Trump administration variable coefficient for ROA is also negative.
These results suggest that in the time under this administration the impact of emissions intensity on
market performance has reversed and the impact on overall performance is negative.
49
Table 14: Performance results with dummy for states with carbon pricing policies
This table presents the results of modelling firm performance. Models 9-12 have dependent variables of ROA, ROE, ROS and Tobin’s q respectively. Relevant controls variables are included as well as emissions intensity variable and a dummy for firms headquartered in states with carbon pricing regulation. All four models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
(13) (14) (15) (16) roa roe ros tq ei -0.014*** 0.016 -0.062*** 0.073*** (-5.242) (1.157) (-3.785) (3.904)trump*ei -0.005 -0.060** -0.075** -0.097** (-1.124) (-2.415) (-1.972) (-2.372)size 0.007*** 0.032*** 0.048*** -0.230*** (5.367) (5.188) (9.121) (-18.773)lev -0.057*** 0.286*** 0.293*** 0.083
50
(9) (10) (11) (12)roa roe ros tq
ei -0.011*** 0.003 -0.042*** 0.033** (-5.832) (0.287) (-3.778) (2.189)carbonei -0.021*** 0.004 -0.194*** 0.112** (-3.493) (0.117) (-3.981) (2.297)size 0.007*** 0.032*** 0.048*** -0.231*** (5.423) (5.159) (9.259) (-18.802)lev -0.057*** 0.287*** 0.290*** 0.087 (-5.950) (3.693) (5.272) (1.042)rd -0.963*** -0.180 -2.465*** 9.205*** (-20.787) (-0.855) (-7.739) (21.765)capex 0.315*** -0.626*** -0.968*** -0.035 (7.535) (-3.161) (-4.920) (-0.092)sg 0.024*** 0.041 0.158*** 0.687*** (3.208) (1.423) (2.967) (9.157)cf 1.372*** 3.534*** 5.362*** (10.552) (19.577) (19.639)Intercept 0.119*** -0.039 -0.613*** 3.144*** (9.832) (-0.672) (-9.617) (23.316) Observations 10,676 10,672 10,668 10,676Adjusted R-squared 0.394 0.118 0.505 0.511Industry Yes Yes Yes YesYear Yes Yes Yes Yes
(-5.916) (3.682) (5.252) (0.987)rd -0.971*** -0.177 -2.504*** 9.235*** (-20.822) (-0.839) (-7.726) (21.864)capex 0.313*** -0.625*** -1.010*** -0.009 (7.398) (-3.161) (-5.002) (-0.022)sg 0.024*** 0.045 0.155*** 0.697*** (3.084) (1.580) (2.879) (9.231)cf 1.365*** 3.573*** 5.325*** (10.495) (19.680) (19.458)Intercept 0.120*** -0.039 -0.609*** 3.140*** (9.891) (-0.686) (-9.513) (23.304) Observations 10,676 10,672 10,668 10,676Adjusted R-squared 0.392 0.118 0.500 0.511Industry Yes Yes Yes YesYear Yes Yes Yes Yes
Table 15: Performance results with Trump administration term
This table presents the results of modelling firm performance. Models 13-16 have dependent variables of ROA, ROE, ROS and Tobin’s q respectively. Relevant controls variables are included as well as emissions intensity variable and an interaction variable between emissions intensity and dummy for years under the Trump administration. All four models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
Risk:
The following tables present the results for the risk models with state and year variables.
(4) (5) (6)oprisk trisk beta
ei 0.116*** -0.000 -0.014** (3.791) (-1.556) (-2.168)climallei 0.182*** 0.000 0.009 (3.513) (1.087) (1.217)
51
roa -4.283*** -0.024*** -0.668*** (-12.511) (-17.591) (-15.605)size -0.036*** -0.002*** -0.064*** (-3.068) (-28.578) (-24.797)mtb 0.093*** -0.000*** -0.007** (4.009) (-3.105) (-1.969)lev 0.027 0.006*** 0.170*** (0.194) (9.790) (8.342)rd 1.378* -0.001 0.053 (1.671) (-0.544) (0.602)capexp 2.383*** 0.001* 0.066*** (8.375) (1.691) (2.641)sg 0.883*** 0.002*** 0.056*** (4.857) (3.831) (3.360)ag -0.433*** 0.000 0.001 (-4.475) (0.511) (0.090)Intercept 1.012*** 0.043*** 1.378*** (5.751) (43.348) (43.652) Observations 10,668 9,986 10,003Adjusted R-squared 0.453 0.487 0.460Industry Yes Yes YesYear Yes Yes Yes
Table 16: Risk results with dummy for Climate Alliance member states
This table presents the results of modelling firm risk. Models 4-6 have dependent variables of operating risk, total risk and beta respectively. Relevant controls variables are included as well as emissions intensity variable and a dummy for firms headquartered in Climate Alliance member states. All three models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
The results for the state sub-samples are presented in Tables 16 and 17. As shown in Table 16, the
interaction term between the Climate Alliance dummy and emissions intensity has a positive
significant relationship with operating risk. Table 17 presents the results for firms headquartered in
states with carbon pricing regulation. The relationship with operating risk is consistent, however, for
market risk the relationship becomes significant. The results suggest higher emissions intensity is
related to higher total and systematic risk for firms located in states with carbon pricing regulation.
(4) (5) (6)oprisk trisk beta
ei 0.114*** -0.000* -0.014** (3.470) (-1.672) (-2.331)carbonei 0.451*** 0.001** 0.023* (4.082) (2.078) (1.750)
52
roa -4.217*** -0.024*** -0.665*** (-12.517) (-17.503) (-15.539)size -0.038*** -0.002*** -0.064*** (-3.211) (-28.539) (-24.806)mtb 0.089*** -0.000*** -0.007** (3.908) (-3.143) (-2.008)lev 0.032 0.006*** 0.170*** (0.233) (9.792) (8.343)rd 1.359* -0.002 0.051 (1.673) (-0.577) (0.574)capexp 2.378*** 0.001* 0.066*** (8.417) (1.678) (2.622)sg 0.866*** 0.002*** 0.055*** (4.814) (3.774) (3.309)ag -0.424*** 0.000 0.001 (-4.409) (0.532) (0.110)Intercept 1.014*** 0.043*** 1.378*** (5.795) (43.281) (43.639) Observations 10,668 9,986 10,003Adjusted R-squared 0.458 0.488 0.461Industry Yes Yes YesYear Yes Yes Yes
Table 17: Risk results with dummy for states with carbon pricing schemes
This table presents the results of modelling firm risk. Models 7-9 have dependent variables of operating risk, total risk and beta respectively. Relevant controls variables are included as well as emissions intensity variable and a dummy for firms headquartered in states with carbon pricing regulation. All three models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
(10) (11) (12) oprisk trisk beta ei 0.118*** -0.000 0.004 (3.282) (-0.385) (0.606)trump*ei 0.387*** -0.000* -0.064*** (3.772) (-1.708) (-6.250)roa -4.317*** -0.024*** -0.668*** (-12.616) (-17.629) (-15.722)size -0.036*** -0.002*** -0.063*** (-3.028) (-28.516) (-24.770)mtb 0.097*** -0.000*** -0.007**
53
(4.191) (-3.140) (-2.120)lev 0.032 0.006*** 0.168*** (0.235) (9.767) (8.280)rd 1.368* -0.001 0.058 (1.668) (-0.519) (0.657)capexp 2.352*** 0.001* 0.073*** (8.300) (1.750) (2.916)sg 0.855*** 0.002*** 0.063*** (4.720) (3.948) (3.851)ag -0.416*** 0.000 -0.002 (-4.314) (0.440) (-0.192)Intercept 0.990*** 0.043*** 1.381*** (5.645) (43.402) (43.759) Observations 10,668 9,986 10,003Adjusted R-squared 0.456 0.487 0.463Industry Yes Yes YesYear Yes Yes Yes
Table 18: Risk results with Trump administration term
This table presents the results of modelling firm risk. Models 10-12 have dependent variables of operating risk, total risk and beta respectively. Relevant controls variables are included as well as emissions intensity variable and an interaction variable between emissions intensity and dummy for years under the Trump administration. All three models control for industry and year fixed effects. Robust t-statistics are presented in brackets with the superscripts *, ** and *** demonstrating significance at the 90%, 95%, and 99% confidence levels, respectively.
The Trump administration’s effect on the relationship between emissions intensity and firm risk is
investigated in Table 18. For all three measures of risk, the direction of the relationship between
emissions intensity and risk is consistent in the full sample and the years under the Trump
administration. However, emissions intensity is significantly negatively related to total risk and beta
under the Trump administration where the relationship was not significant for the full sample. The
results suggest emissions intensity has an impact on market risk during this time where no significant
impact was found for the full sample period.
4.7 Essay 1 preliminary conclusion:
Preliminary results of this study suggest higher emissions intensity worsens firm accounting
performance, while improving market performance. In terms of firm risk, preliminary results suggest
higher emissions intensity is related to higher operating risk, that is, greater variability in the ratio of
54
earnings (EBIT) to revenue (sales). No significant relationship is found between market risk and
emissions intensity in the full sample.
The preliminary results of our sub-sampling suggest for firms located in Climate Alliance states,
higher emissions intensity is significantly related to lower accounting performance and higher
operating risk. While for firms headquartered in states with carbon pricing schemes, higher emissions
intensity is significantly related to lower accounting and market performance, as well as higher
operating, total and systematic risk. The results are largely consistent with expectations; such
regulatory environments imply current and future costs to firms, lowering their performance and
increasing their market risk.
The final sub-sample separated 2017 and 2018 into the period under the Trump administration and
results suggest higher emissions intensity lowers firm performance during this period. Higher
emissions intensity is suggested to increase firm operating risk while lowering firm market risk in this
time. With the extensive withdrawal of environmental legislation under this administration, the
lowering of market risk is not unexpected.
Findings on the relationship between emissions intensity and firm cost of equity will assist in
completing this story.
4.8 Further tasks for completion of Essay 1:
Completion of Essay 1 will involve investigation of the relationship between emissions intensity and
ex-ante cost of equity and idiosyncratic risk. The issue of reverse causality will be addressed. Control
variables will be standardised between models for consistency. The categorising of firms into states
based on their headquarters will be examined for robustness.
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5. Essay Two
5.1 Introduction:
After decades of academic research on corporate social responsibility (CSR) there is a clear consensus
on the positive relationship between corporate responsibility and firm value. Literature finds
environmental responsibility, a pillar of CSR, to also have a positive impact on firm value. In practice,
however, firms today are faced with a myriad of ways in which they can be more environmentally
responsible and seen to be doing so. The problem being uncertainty as to which policies and activities
are rewarded by investors. From reducing fleet fuel consumption to attaining an International
Organisation for Standardisation (ISO) 14001 environmental management system certification; each
effort towards environmental responsibility has costs for the firm while not necessarily being valued
equally by investors. This essay seeks to disaggregate firm’s environmental responsibility rating to
determine which activities are material to their cost of equity, risk and financial performance.
With institutional investors leading the way with their support, the increasing expectations of
investors for corporate environmental, social and governance (ESG) performance show no sign of
slowing down. In a 2013 global survey, 81% of asset owners and 68% of asset managers considered
climate change a material risk to their entire investment portfolio, the remaining responses largely saw
climate change as material to a portion of their portfolio rather than to the entirety (Global Investor
Coalition on Climate Change, 2013). A 2015 EY survey of institutional investors finds over a third
(36%) of respondents had divested assets in the prior year due to ESG factors and a further 27% said
they plan to monitor this risk more closely in future (EY, 2016). Additionally illustrative of the
growing ESG activism by investors is the growth in shareholder resolutions on the topic; in the
period 2006 – 2010 around 33% of resolutions were focused on environmental and social issues; by
2017 the proportion was just over 50% (Eccles & Klimenko, 2019).
Further evidencing the growing trend towards ESG investing is the growing support for the Principles
for Responsible Investment (PRI). Launched in 2006 with the support of the UN, PRI created six
principles of responsible investing which emphasise the importance of ESG factors. In its first year
56
PRI gathered 63 signatories representing $6.5 trillion in assets under management (AUM); by 2019,
2372 signatories representing over $31 trillion in AUM committed to the principles of responsible
investing. BlackRock, the world’s largest asset manager, is one of these signatories and the full
integration of ESG into their investment strategies illustrates just how important ESG is becoming in
financial analysis (Eccles & Klimenko, 2019).
ESG covers three main areas and a multitude of factors from employee training and business ethics,
through to carbon emissions. Extant literature predominately examines the ESG score of firms
without separating out which ESG factors are material. With the increasing emphasis on how these
issues may impact firms long-term and investors making explicit stock selection decisions on ESG
factors, it is important to empirically examine the effect of these factors. This study examines the
materiality of the environmental (E) score in today’s business and regulatory environment. Further, it
seeks to understand which environmental efforts made by firms are value-creating or risk minimising.
The setting chosen for this analysis is the US, which provides an ideal setting due to the minimal
federal regulation on corporate environmental responsibility. The lack of robust climate regulation
and intention to withdraw from the Paris Accord in the US provides an ideal setting to examine
whether investors penalise and reward firms for their voluntary climate efforts. The regulatory
situation in the US was discussed in detail in section 3.
Lewandowski (2017) explains that the important question in the literature today is not ‘does it pay to
be green?’ but rather ‘when and under which conditions does it pay to be green?’. We examine this
question by asking which environmental performance management strategies pay under the US
conditions in the period 2007 to 2018. Previous literature has examined whether overall
environmental scores are relevant to firm value. Environmental scores can be based on a multitude of
factors and significance of the overall score does not imply that all efforts made by firms will be
rewarded by the market. This study contributes to the literature by being the first to examine specific
corporate environmental responsibility efforts, which make up a firm’s environmental score, to
determine their significance to firm value.
57
5.2 Literature review and hypothesis development:
5.2.1 Literature review:
A lack of disaggregation of the efforts encompassed by CSR and ESG means uncertainty as to which
socially and environmentally responsible efforts are worthwhile investments. Overall, extant literature
finds CSR has a positive relationship with firm value (Albuquerque et al., 2018; Gregory et al., 2014),
and a negative relationship with cost of capital (Attig, El Ghoul, Guedhami, & Suh, 2013; Bae, El
Ghoul, Guedhami, Kwok, & Zheng, 2018; El Ghoul et al., 2011; Goss & Roberts, 2011; Wang, Feng,
& Huang, 2013). Focusing on just the environmental responsibility and performance element of CSR,
the same effect is found (Chava, 2014; El Ghoul et al., 2018; Gupta, 2018; Lopatta & Kaspereit, 2014;
Ng & Rezaee, 2015; Sharfman & Fernando, 2008; Xu et al., 2015).
No strong consensus exists as to the relationship between CSR and firm risk. While a significant
negative relationship is found between environmental performance factors and idiosyncratic risk by
Sassen, Hinze, and Hardeck (2016), no effect is found on total or systematic risk. Where
Albuquerque, Koskinen, and Zhang (2018) find CSR has a negative impact on systematic risk; Cai,
Cui, and Jo (2016) find environmental responsibility negatively affects systematic risk. Conversely,
Benlemlih, Shaukat, Qiu, and Trojanowski (2018) find environmental disclosures reduce idiosyncratic
risk. Due to the wide variation in CSR and environmental responsibility measures and the disparity in
findings, no clear consensus is found in the literature.
Further, there is a lack of empirical evidence as to which environmental efforts are responsible for the
effect on firm value, as well as a general lack of literature on the effect of environmental
responsibility on firm risk. Additionally, existing studies use measures of environmental
responsibility (or irresponsibility) which are difficult to replicate empirically and in practice. Of the
24 articles found to investigate CSR and firm value and firm risk, only four use data from within the
last five years (post - 2013). One study, Gupta (2018) separates a firm’s environmental score into its
three components of emissions, resource use and innovation, however, the author designs their own
method of scoring which is not easily replicable and uses data ending in 2012.
58
Public and investor perspectives of CSR and environmental issues are constantly changing, and the
scrutiny currently placed on firms regarding their environmental impact appears stronger than when
many of these studies took place.
5.2.2 Hypothesis Development:
The setting chosen for this study is the US due to the comparatively poor environmental policy
stringency. The Organisation for Economic Co-operation and Development (OECD) has ranked 27 of
the OECD countries by environmental policy stringency from 1990 (OECD, 2019). In 2012 (the most
recent year with complete data) the US was 11th (OECD, 2019). While the US is certainly not last in
environmental policy stringency among OECD countries, the position of the US is poor when
considering that in the same year the US ranked number one in greenhouse gas emissions (OECD,
2019). In general, the US government does little to encourage firms to improve their environmental
performance with the Clean Air Act and Clean Water Act presenting a minimum standard for
organisations to abide by. In addition, the US has begun the process of withdrawing from the
international Paris Agreement.
Environmental regulation in the US is discussed more thoroughly in section 3. With relatively low
environmental policy stringency, the onus of encouraging environmental responsibility may be placed
on the market. The hypotheses formed in this study investigate whether the market prices
environmental responsibility efforts of US firms.
Following the prior literature on CSR and environmental performance discussed in the literature
review, the following hypotheses are made:
H1a: Higher environmental scores are significantly related to lower ex-ante cost of equity
H2: Higher environmental scores are significantly related to higher firm performance
H3: Higher environmental scores are significantly related to lower firm risk
59
The E score of an organisation considers a firm’s ability to use resources efficiently, reduce
emissions, and to innovate (Thomson Reuters, 2019). Following the examination of total E score, this
score will be separated into its three components and each will be tested to establish materiality. The
components being resource use score, emissions score, and innovation score as explained in Table 19.
Following which, specific strategies and efforts made by firms will be investigated for materiality.
With 40 discrete variables and 21 continuous variables making up a firm’s E score, there are a
multitude of ways a firm might demonstrate environmental responsibility. The 61 variables are
described in the appendix. The variables valued by investors will be investigated with the aim of this
study being to identify when and where US firms are being rewarded or punished by the market for
their environmental responsibility or lack thereof.
5.3 Research design and methodology:
5.3.1 Sample:
Data on environmental responsibility of US firms is from Datastream’s Asset4 database. This
database comes from publicly available information including annual reports, firm websites, stock
exchange filings, CSR reports, and news sources (Refinitiv, n.d.-a). Covering over 7000 companies
worldwide, the sample of firms in the US is around 2800.
An overall environmental score can be obtained for each firm in the sample. This score is made up of
three components: an emissions score, resource use score, and environmental innovation score. Each
component is weighted by Asset4 as 35.3%, 32.3% and 32.3% respectively, to form an overall
environmental score out of 100. The definitions of these scores are presented in the table below:
Score Definition
60
Emissions score Measures a company’s commitment and effectiveness towards reducing
environmental emissions in the production and operational processes.
Resource use score Reflects a company’s performance and capacity to reduce the use of
materials, energy, or water, and to find more eco-efficient solutions by
improving supply chain management.
Innovation score Reflects a company’s capacity to reduce the environmental costs and
burdens for its customers, thereby creating new market opportunities
through new environmental technologies and processes or eco-designed
opportunities.
An advantage of using Datastream’s Asset4 database is that the scores are calculated in such a way
that industries can be compared. Each company’s score is calculated by comparing the firm with
industry peers. As such, results can be found for the complete sample and not just within industry sub-
samples.
Data for firm characteristics and accountancy data is from Compustat. Share prices are from CRSP.
Sub-samples by state are investigated due to differing state-level regulation. State data comes from
Datastream and is the state in which the firm’s head office is located. If an international firm, the state
is where the firm’s US head office is located.
Konar and Cohen (2001) remove financial institutions from the sample due to their non-polluting
nature; Petitjean (2019) follows this decision. We follow this method and remove banking and finance
firms from our sample; the remaining sample contains 1972 firms.
5.3.2 Model:
Ex-ante Cost of Equity:
Model of ex-ante cost of equity using appropriate control variables from extant literature and
environmental factors as dependent variables. Ex-ante cost of equity is used following Kim, et al.,
61
Table 19: Asset4 environmental score components
This table explains the three components of Asset4’s E score and is reproduced from Thomson Reuters (2019)
(2015), El Ghoul et al., (2018), and El Ghoul et al., (2011); this method avoids noisy realised returns
and considers both growth rates and expected future cash flows (El Ghoul et al., 2011).
Ex-ante cost of equity is typically calculated as an average of four models: Claus and Thomas (2001),
Gebhardt, Lee, and Swaminathan (2001), Ohlson and Juettner-Nauroth (2005), and Easton (2004).
We follow El Ghoul et al., (2011) by using an excess return; before averaging the cost of equity
estimates, the risk-free rate is subtracted. The 10-year US Treasury Bond yield is used (Dhaliwal et
al., 2006; El Ghoul et al., 2011).
The average ex-ante cost of equity is used as the dependent variable to examine the effect of
emissions intensity. Control variables used follow the method of El Ghoul et al., (2011) and are
described in detail in the appendix and briefly in the table below.
Beta, book-to-market value, leverage, and size are all controlled for following standard finance
literature (Fama & French, 1993, 1995; Gordon & Gordon, 1997; Miller & Modigliani, 1958). Beta is
calculated using the Russell 2000 as the market index owing to the size of the sample used. Following
El Ghoul et al., (2011), forecast dispersion and consensus long term growth forecast are added to
control for analyst forecasts. Industry and year effects are also controlled for.
Variable: Description: Direction of relationship
expected:
KAvg Average of implied cost of
equity from four models
E Variable of interest, will differ in
models between a score and
dummy for environmental effort
or strategy
+/-
beta Systematic risk, a measure of a
firm’s sensitivity to market
fluctuations
+
btm Book-to-market value: Ratio of
book value of equity to market
value of equity
+
size Natural logarithm of total assets -
62
lev Leverage: Book value of debt
divided by total assets
+
disp Dispersion: Variation in 1-year
ahead EPS forecasts
+
ltg Long-term growth forecast:
Average long-term growth
forecast consensus
+
This table presents the dependent and independent variables for the ex-ante cost of equity model. It provides a description of the variables and indicates the expected direction of the relationship with firm performance. Full variable descriptions are provided in the appendix.
Equation: following JBF paper (El Ghoul et al., 2011)
K Avg=B 0+B1 E+B 2beta+B 3btm+B 4¿5 lev+B 6 disp+B 7 ltg+ year∧industry effects+εEquation 5
Firm performance:
The measures of financial performance used are return on assets (ROA) and Tobin’s q following King
and Lenox (2002). Return on sales (ROS) and return on equity (ROE) are also investigated following
Lewandowski (2017).
Control variables follow the methodology of King and Lenox (2002) and Hart and Ahuja (1996) for
both measures of financial performance. Firm size, firm leverage, research and development (R&D)
intensity, capital intensity, sales growth, and cash flow are controlled for. Advertising expenditure
intensity is not controlled for due to a lack of sufficient data. The description and predicted direction
of these controls are presented in the following table:
Variable: Description: Direction of relationship
expected:
63
Table 20: Ex-ante cost of equity formula variables
FP Financial performance as measured by
one of: return on assets (ROA), return on
equity (ROE), return on sales (ROS), or
Tobin’s q
E Variable of interest, will differ in models
between a score and dummy for
environmental effort or strategy
+/-
size Natural logarithm of total assets -
lev Leverage: Book value of debt divided by
total assets
+/-
rd R&D expenditure intensity: R&D
expenditure divided by total assets
+/-
capex Capital expenditure intensity: Capital
expenditure expense divided by total sales
-
sg Sales growth rate from t -1 to t +
cf Operating cash flow divided by total
assets
+
Table 21: Performance model variables
This table presents the dependent and independent variables for the performance model. It provides a description of the variables and indicates the expected direction of the relationship with firm performance. Full variable descriptions are provided in the appendix.
Following King and Lenox (2002) we use panel regression with financial performance as the
dependent variable and emissions as the independent variable of interest.
FP = B0 + B1 E + B2 size + B3 lev + B4 rd + B5 capex + B6 sg + B7 cf + year
and industry effects + ε Equation 6
Firm Risk:
Following extant literature, we use beta, standard deviation and idiosyncratic risk to examine the
effect on risk. Total risk is measured as standard deviation of daily stock returns while systematic risk
64
is measured CAPM beta (Benlemlih et al., 2018; Jo & Na, 2012). Beta is calculated using the Russell
2000 index as a market proxy due to the size of the sample. Benlemlih et al., (2018) adds a measure of
idiosyncratic risk as measured by the standard deviation of CAPM daily stock return residuals. Firm
risk is additionally examined using the operating risk measure of Doukas and Pantzalis (2003).
Control variables used follow the methodology of Benlemlih et al., (2018) and Jo and Na (2012).
Variable: Description: Direction of relationship
expected:
Firm risk Measured by one of: total risk
(standard deviation of daily stock
returns), beta (systematic risk),
operating risk, or idiosyncratic risk
E Variable of interest, will differ in
models between a score and dummy
for environmental effort or strategy
+/-
size Natural logarithm of total assets -
mtb Market-to-book: Market value of
assets divided by book value of assets
-
lev Leverage: Book value of debt divided
by total assets
+
rd R&D expenditure intensity: R&D
expenditure divided by total assets
-
roa Return on assets: Operating income
before depreciation divided by total
assets
-
capexp Capital expenditure intensity: Capital
expenditure expense divided by total
sales
+
opcf Operating cash flow divided by total
assets
-
sg Sales growth rate from t -1 to t +/-
ag Asset growth rate: Total assets in year
t divided by total assets in year t-1
+/-
65
Table 22: Risk model variables
This table presents the dependent and independent variables for the risk model. It provides a description of the variables and indicates the expected direction of the relationship with firm performance. Full variable descriptions are provided in the appendix.
Equation: following JBE paper (Jo & Na, 2012)
Firmrisk=B 0+B1 E+B 2¿3mtb+B 4 lev+B 5 rd+B 6 roa+B 7 capexp+B 8 opcf +B 9 sg+B 10 ag+ year∧industry effects+ε
Equation 7
5.3 Essay 2 conclusion:
Essay 2 updates extant literature on how a firm’s environmental score impacts firm value in today’s
business climate. It provides a contribution by being the first to comprehensively examine how
environmental strategies and efforts are priced by the market. The impacts of such efforts on firm cost
of equity, performance, and risk are investigated with a 12-year sample of US firms.
6. Essay 3:
There has undoubtedly been an increase in socially responsible investing (SRI) over the last decade.
This trend appears to be driven by institutional investors and their commitments to various investment
principles such as the Principles for Responsible Investing (PRI) which has been signed by 432 asset
managers representing $86.3 trillion in assets under management as at November 2019 (Principles for
66
Figure 4: Signatories of the UN Principles of Responsible Investment
This figure shows the growth in signatories of the UN’s Principles for Responsible Investment from 2006 through 2019. The figure is sourced from the Principles for Responsible Investment (2019).
Responsible Investment, n.d.-a). This trend is demonstrated in Figure 4. The Forum for Sustainable
and Responsible Investing (USSIF) reports that in the US alone, sustainable investing assets have
grown 38% between 2016 and 2018 (USSIF, 2018b). The 38% increase is representative of an extra
$3.3 trillion (Global Sustainable Investment Alliance, 2018). Figure 5 demonstrates just how big SRI
investing is; at the end of 2017, 26% of all professionally managed assets in the US were classed as
SRI assets (USSIF, 2018b).
Essay 3 seeks to investigate whether the growth in SRI has influenced firms to improve their
environmental performance; it does so by asking: do institutional investors lead or follow
environmental responsibility? This question is examined using environmental scores and carbon
emission data of US firms.
6.1 Background information and hypothesis development
With over 80% of the US market being owned by institutions, the changing attitudes of institutional
investors towards SRI can be expected to have some effect on the how organisations manage their
social and environmental responsibility (Atkins, 2019). The 506 PRI signatories in the US have
committed to incorporating environmental, social and governance (ESG) issues into their investment
67
Figure 5: SRI investing in the US 2018
This figure reveals the percentage of professionally managed assets in the US that are classed as SRI assets in 2017. It is sourced from USSIF (2018b).
analysis and decision making and seeking disclosure on ESG issues from the firms they invest in
(Principles for Responsible Investment, n.d.-b). BlackRock, the world’s largest asset manager, is a
signatory of the PRI and has committed to full integration of ESG factors in their investment
strategies (Eccles & Klimenko, 2019). BlackRock’s commitment demonstrates how pervasive SRI is;
it is not a fringe phenomenon specific to ‘green’ or ‘ethical’ funds.
If ESG factors are considered in mainstream investment strategies and decisions, it appears
institutional investors would follow good historical environmental responsibility. However, there is a
lack of empirical research to support this hypothesis. The alternative being that institutional investors
are increasingly demanding better environmental responsibility from their investments and as such,
are leading environmental responsibility.
Figure 6 further demonstrates the rise in SRI, in particular the growth in incorporation of ESG factors
by institutional investors.
68
Figure 6: ESG Incorporation by Institutional Investors
This figure presents the trend in ESG incorporation of institutional investors in the US from 2005 to 2018. It is sourced from USSIF (2018a).
6.2 Environmental responsibility measures
Environmental responsibility will be measured both through a firm’s E score, and more directly
through their annual carbon emissions.
7. Proposed timeline for the completion of Dissertation
Schedule Task
January 2020 Ph.D. confirmation
February 2020 – June 2020 Complete essay one
69
July 2020 – December 2020 Complete essay two
January 2021 – October 2021 Complete essay three
October 2021 Submit draft copy of the dissertation
January 2022 Submit final bound copy of dissertation
Appendix 1:
Appendix 1a. Variable descriptions
Essay one variables of interest
Variable Definition SourceEmissions intensity Total emissions divided by total sales for the year of
interestAuthors' calculations based on Datastream data
Table 23: Essay 1 variable of interest
70
This table describes the variable of interest for essay 1.
Essay two variables of interest
Variable Definition SourceEnvironmental score The Environmental or E score measures a firm’s
impact on the natural environment and reflects how well the firm uses best practice to avoid environmental risks. It also considers how a company utilizes environmental opportunities to generate long-term value.
Datastream
Resource use score The Resource Use Score measures a firm’s performance regarding use of natural resources, as well as its’ capacity to reduce resource use and find more efficient solutions.
As above
Emissions score The Emission Reduction Score commitment and effectiveness in reducing a firm’s emissions.
As above
Innovation score The Innovation Score measures a firm’s ability to reduce its environmental impact and burden on customers by creating new opportunities, technologies and processes.
As above
Table 24: Essay 2 variables of interest
This table describes the variables of interest for essay 2.
Cost of equity Dependent variable Definition SourceCost of equity Ex-ante cost of equity calculated as the average of the
following methods: Claus and Thomas (2001), Gebhardt, Lee, and Swaminathan (2001), Ohlson and Juettner-Nauroth (2005), and Easton (2004)
Authors calculations based on Compustat data
Control variables
71
Beta Market beta calculated using the market model of monthly share returns and monthly returns on market index (Russell 2000). Based on at least 23 monthly return observations and up to 40.
Authors calculations based on Compustat data
Book-to-market value Book value of equity divided by market value of equity. Book value of equity is the value of common stock, capital surplus and retained earnings on the balance sheet. It does not include preference shares. Market value of equity is the common share price multiplied by the number of common shares outstanding.
As above
Size The natural logarithm of total assets (millions) As above
Leverage Following El Ghoul et al., (2011) leverage is equal to the book value of total debt divided by market value of equity
As above
Dispersion Following El Ghoul et al., (2011) dispersion is defined as the coefficient of variation of 1-year ahead EPS forecasts
IBES data from Datastream
Long-term growth forecast
Following El Ghoul et al., (2011) defined as the mean long-term growth consensus
As above
Table 25: Ex-ante cost of equity variables
This table describes the variables for calculating ex-ante cost of equity. It includes he definition and source of the data.
Firm performanceDependent variables: Definition SourceROA Return on assets, calculated as earnings before interest
and tax divided by average total firm assets between t and t-1
Authors' calculations based on Compustat data
72
ROE Return on equity, calculated as earnings before interest and tax divided by book value of equity
As above
ROS Return on sales, calculated as earnings before interest and tax divided by sales
As above
Tobin's q Calculated as market value of assets (market capitalisation + book value of long-term debt + current liabilities) divided by book value of assets (total assets)
As above
Control variables Size The natural logarithm of total assets (millions) Authors'
calculations based on Compustat data
Leverage Following King and Lenox (2002) leverage is equal to the book value of total debt divided by total assets
As above
R&D expenditure intensity
Calculated as R&D expenditure divided by total assets As above
Capital expenditure intensity
Calculated as capital expenditure divided by total assets As above
Sales growth Sales growth rate from t-1 to t As above
Cash flow Operating cash flow divided by total assets As above
Table 26: Firm performance variables
This table describes the variables for calculating firm performance. It includes he definition and source of the data.
Firm risk
Dependent variables Definition SourceTotal risk Standard deviation of daily stock returns in the
current yearAuthors' calculations based on Compustat data
Systematic risk CAPM beta calculated using the market model of As above
73
monthly share returns and monthly returns on market index (Russell 2000)
Idiosyncratic risk Standard deviation of CAPM daily stock return residuals for current year
As above
Operating risk Standard deviation of EBIT/Sales for 5 years prior As above
Control variables Firm size Natural logarithm of total assets Authors'
calculations based on Compustat data
Market to book ratio Market value of assets (market value of equity + book value of total debt) / book value of assets
As above
Leverage Book value of debt divided by total assets following Jo and Na (2012)
As above
R&D expenditure intensity R&D expenditure divided by total assets As above
Return on assets Operating income before depreciation and amortization divided by total assets
As above
Capital expenditure intensity
Capital expenditure expense divided by total sales As above
Operating cash flow Operating cash flow divided by total assets As above
Sales growth Sales growth rate from t -1 to t As above
Asset growth Total assets in year t divided by total assets in year t-1
As above
Table 27: Firm risk variables
This table describes the variables for calculating firm risk. It includes he definition and source of the data.
Appendix 1b. Variables included in Asset4’s Environmental score
Variable Description
Emissions Score
74
Emission Reduction Processes/Policy
Emissions Reduction
Does the company have a policy to improve
emissions reduction?
Emission Reduction Objectives/Targets
Emissions Reduction
Has the company set targets or objectives to be
achieved on emissions reduction?
Biodiversity Impact Reduction Does the company report on its impact or on
activities to reduce its impact on biodiversity?
Flaring of Natural Gas Total direct flaring or venting of natural gas
emissions
Cement CO2 Equivalents Emission Total CO2 and CO2 equivalents emission in tonnes
per tonne of cement produced.
Ozone-Depleting Substances Total amount of ozone depleting (CFC-11
equivalents) substances emitted
NOx and SOx Emissions Reduction
Initiatives
Does the company report on initiatives to reduce,
reuse, recycle, substitute, or phase out SOx (sulfur
oxides) or NOx (nitrogen oxides) emissions?
e-Waste Reduction Initiatives Does the company report on initiatives to recycle,
reduce, reuse, substitute, treat or phase out e-
waste?
Emissions Trading Does the company participate in any emissions
trading initiative, as reported by the company?
Environmental Partnerships Does the company report on partnerships or
initiatives with specialized NGOs, industry
organizations, governmental or supra-
governmental organizations, which are focused on
improving environmental issues?
ISO 14000 or EMS Certified Percent The percentage of company sites or subsidiaries
that are certified with any environmental
management system.
Environmental Restoration Initiatives Does the company report or provide information
on sizable company-generated initiatives to restore
the environment?
Staff Transport Impact Reduction Initiatives Does the company report on initiatives to reduce
the environmental impact of transportation used for
its staff?
Climate Change Risks/Opportunities Is the company aware that climate change can
represent commercial risks and/or opportunities?
75
Self-Reported Environmental Fines Environmental fines as reported by the company
Estimated CO2 Equivalents Emission Total The estimated total CO2 and CO2 equivalents
emission in tonnes.
Value - Emission Reduction/VOC Emissions
Reduction
Does the company report on initiatives to reduce,
substitute, or phase out volatile organic compounds
(VOC) or particulate matter less than ten microns
in diameter (PM10)?
Value - Emission Reduction/Waste Total amount of waste produced in tonnes divided
by net sales or revenue in US dollars.
Value - Emission Reduction/Waste
Recycling Ratio
Total recycled and reused waste produced in tonnes
divided by total waste produced in tonnes.
Value - Emission Reduction/Hazardous
Waste
Total amount of hazardous waste produced in
tonnes divided by net sales or revenue in US
dollars.
Value - Emission Reduction/Discharge into
Water System
Total weight of water pollutant emissions in tonnes
divided by net sales or revenue in US dollars.
Value - Emission Reduction/Environmental
Expenditures
Does the company report on its environmental
expenditures or does the company report to make
proactive environmental investments to reduce
future risks or increase future opportunities?
Table 28: Emissions score variables
This table presents the variables making up a firm's emissions score.
Resource Use Score
Environment Management Team Does the company have an environmental
management team?
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Resource Efficiency Processes/Policy Water
Efficiency
Does the company have a policy to improve its
water efficiency?
Resource Efficiency Processes/Policy Energy
Efficiency
Does the company have a policy to improve its
energy efficiency?
Resource Efficiency Processes/Policy
Sustainable Packaging
Does the company have a policy to improve its use
of sustainable packaging?
Resource Efficiency Processes/Policy
Environmental Supply Chain
Does the company have a policy to include its
supply chain in the company's efforts to lessen its
overall environmental impact?
Resource Efficiency Objectives/Targets
Water Efficiency
Has the company set targets or objectives to be
achieved on water efficiency?
Resource Efficiency Objectives/Targets
Energy Efficiency
Has the company set targets or objectives to be
achieved on energy efficiency?
Materials Sourcing Environmental Criteria Does the company claim to use environmental
criteria to source materials?
Toxic Substances Reduction Initiatives Does the company report on initiatives to reduce,
reuse, substitute or phase out toxic chemicals or
substances?
Cement Energy Use Total energy use in gigajoules per tonne of clinker
produced.
Green Buildings Does the company report about environmentally
friendly or green sites or offices?
Water Recycled Amount of water recycled or reused
Environmental Supply Chain Selection
Management
Does the company use environmental or
sustainable criteria in the selection process of its
suppliers or sourcing partners?
Environmental Supply Chain Partnership
Termination
Does the company report or show to be ready to
end a partnership with a sourcing partner, in the
case of severe environmental negligence and
failure to comply with environmental management
standards?
Land Environmental Impact Reduction Does the company report on initiatives to reduce
the environmental impact on land owned, leased or
managed for production activities or extractive
use?
Environmental Supply Chain Monitoring Does the company conduct surveys of the
77
environmental performance of its suppliers?
Value - Resource Reduction/Energy Use Total direct and indirect energy consumption in
gigajoules divided by net sales or revenue in US
dollars.
Value - Resource Reduction/Renewable
Energy Use
Total energy generated from primary renewable
energy sources divided by total energy.
Value - Resource Reduction/Water Use Total water withdrawal in cubic meters divided by
net sales or revenue in US dollars.
Table 29: Resource use score variables
This table presents the variables making up a firm's resource use score.
Environmental Innovation Score
Environmental Products Does the company report on at least one product
78
line or service that is designed to have positive
effect on the environment, or which is
environmentally labeled and marketed?
Noise Reduction Does the company develop new products that are
marketed as reducing noise emissions?
Fleet Fuel Consumption Total fleet's average fuel consumption in l/100km.
Hybrid Technology Is the company developing hybrid technology?
Fleet CO2 Emissions Total fleet's average CO2 and CO2 equivalent
emissions in g/km.
ESG Screened Asset Under Management Does the company report on ESG screeened Assets
Under Management?
Nuclear Production Percentage of total energy production from nuclear
energy.
Labeled Wood Percentage The percentage of labeled wood or forest products
from total wood or forest products.
Organic Products Initiatives Does the company report or show initiatives to
produce or promote organic food or other
products?
GMO Products Does the company produce or distribute genetically
modified organisms (GMO)?
Agrochemical Products Does the company produce or distribute
agrochemicals like pesticides, fungicides or
herbicides?
Animal Testing Is the company involved in animal testing?
Clean Technology Is the company developing clean technology
(wind, solar, hydro and geo-thermal and biomass
power)?
Water Technology Does the company develop products or
technologies that are used for water treatment,
purification or that improve water use efficiency?
Sustainable Building Products Does the company develop products and services
that improve the energy efficiency of buildings?
Real Estate Sustainability Certification Does the company claim to lease, rent or market
buildings that are certified by BREEAM, LEED or
any other nationally recognized real estate
certification?
79
Value - Product Innovation/Environmental
R&D Expenditures
Total amount of environmental R&D costs
(without clean up and remediation costs) divided
by net sales or revenue.
Value - Product Innovation/Environmental
Project Financing
Is the company a signatory of the Equator
Principles (commitment to manage environmental
issues in project financing)? OR Does the company
claim to evaluate projects on the basis of
environmental or biodiversity risks as well?
Value - Product Innovation/Renewable
Energy Supply
Total energy distributed or produced from
renewable energy sources divided by the total
energy distributed or produced.
Value - Product Innovation/Product Impact
Minimization
Does the company reports about take-back
procedures and recycling programs to reduce the
potential risks of products entering the
environment? OR Does the company report about
product features and applications or services that
will promote responsible, efficient, cost-effective
and environmentally preferable use?
Table 30: Environmental innovation score variables
This table presents the variables making up a firm's resource use score.
Appendix 1c. Distribution of sample across characteristics
80
Distribution across SIC industries
SIC industries FirmsAgricultural production - crops 3Agricultural production - livestock 1Agricultural services 1Amusement and recreation services 24Apparel and accessory stores 19Apparel and other finished products made from fabrics and similar materials 14Automotive dealers and gasoline service stations 14Automotive repair, services and parking 6Bituminous coal and lignite mining 6Building construction general contractors and operative builders 19Building materials, hardware, garden supply and mobile home dealers 5Business services 280Chemicals and allied products 263Communications 61Construction special trade contractors 4Eating and drinking places 33Educational services 13Electric, gas, and sanitary services 95Electronic and other electrical equipment and components, except computer equipment 131Engineering, accounting, research, management, and related services 40Fabricated metal products except machinery and transportation equipment 29Food and kindred products 58Food stores 9Furniture and fixtures 16General merchandise stores 15Health services 36Heavy construction other than building construction contractors 12Home furniture, furnishings, and equipment stores 9Hotels, rooming houses, camps and other lodging places 10Industrial and commercial machinery and computer equipment 116Leather and leather products 7Local and suburban transit and interuban highway passenger transportation 1Lumber and wood except furniture 15Measuring, analysing and controlling instruments 110Metal mining 8Mining and quarrying of nonmetallic minerals except fuels 7Miscellaneous manufacturing industries 11Miscellaneous retail 34Motion pictures 9Motor freight transportation and warehousing 17Nonclassifiable establishments 5Oil and gas explorations 84
81
Paper and allied products 19Personal services 6Petroleum refining and related industries 18Pipelines, except natural gas 1Primary metal industries 26Printing, Publishing and allied industries 21Railroad transportation 5Real estate 14Rubber and miscellaneous plastics products 17Social services 2Stone, clay, glass and concrete products 12Textile mill products 5Tobacco products 3Transportation by air 16Transportation equipment 61Transportation services 10Water transportation 16Wholesale trade - durable goods 43Wholesale trade - nondurable goods 27Grand Total 1972
Table 31: SIC industry distribution
Distribution across SIC industry divisions:SIC division FirmsAgriculture, Forestry and Fishing 5Construction 35Real Estate 14Manufacturing 952Mining 105Public Administration 5Retail Trade 138Services 426Transportation and Public Utilities 222Wholesale Trade 70Grand Total 1972
Table 32: SIC division distribution
Distribution across years:
Year
Emissions intensity
observations2007 503
82
2008 6292009 7192010 7532011 7572012 7522013 7572014 7612015 12152016 16852017 16302018 814Grand Total 10975
Table 33: Distribution of emissions intensity data across years
Distribution across regions of the United States:Region FirmsMW 379NE 429SE 347SW 249W 466Blank 102Grand Total 1972
Table 34: Sample distribution across regions of the US
Distribution across states:State Firms State FirmsAL 7 MS 2AR 8 NC 48AZ 37 ND 1
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ei roa roe ros tq size lev rd capex sg cf
ei 1roa -0.1084 1roe -0.0036 0.2626 1ros -0.0262 0.64 0.1639 1tq -0.149 0.1002 -0.0424 -0.1695 1size 0.1834 0.1581 0.133 0.2638 -0.3775 1lev -0.1011 -0.01 0.0602 0.0394 -0.0868 0.1052 1rd -0.0842 -0.4491 -0.1537 -0.4772 0.4395 -0.3391 -0.1342 1capex 0.1835 0.0713 0.0094 0.0636 -0.0658 0.0497 0.0004 -0.1511 1sg 0.0101 -0.0448 -0.0436 -0.0774 0.2379 -0.1353 -0.0359 0.1892 0.0739 1cf -0.1043 0.8179 0.1897 0.5822 0.152 0.124 -0.0867 -0.3649 0.1868 -0.0895 1
CA 323 NE 8CO 50 NH 7CT 41 NJ 60DC 8 NM 1DE 3 NV 22FL 71 NY 120GA 50 OH 70HI 3 OK 21IA 8 OR 11ID 6 PA 77IL 104 RI 4IN 22 SC 10KS 8 SD 4KY 13 TN 30LA 8 TX 190MA 118 UT 16MD 29 VA 60ME 2 WA 35MI 39 WI 37MN 45 Blank 102MO 33 Grand Total 1972
Table 35: Sample distribution across states of the US
Appendix 1d. Multicollinearity checks:
84
ei roa roe ros tq size lev rd capex sg cf
ei 1roa -0.1084 1roe -0.0036 0.2626 1ros -0.0262 0.64 0.1639 1tq -0.149 0.1002 -0.0424 -0.1695 1size 0.1834 0.1581 0.133 0.2638 -0.3775 1lev -0.1011 -0.01 0.0602 0.0394 -0.0868 0.1052 1rd -0.0842 -0.4491 -0.1537 -0.4772 0.4395 -0.3391 -0.1342 1capex 0.1835 0.0713 0.0094 0.0636 -0.0658 0.0497 0.0004 -0.1511 1sg 0.0101 -0.0448 -0.0436 -0.0774 0.2379 -0.1353 -0.0359 0.1892 0.0739 1cf -0.1043 0.8179 0.1897 0.5822 0.152 0.124 -0.0867 -0.3649 0.1868 -0.0895 1
ei oprisk trisk beta size lev rd mtb roa capexp opcf sg ag
ei 1oprisk 0.0681 1trisk -0.0506 0.2098 1beta -0.1141 0.1868 0.5822 1size 0.1875 -0.2006 -0.2702 -0.3298 1lev -0.1075 -0.0069 0.0694 0.1049 0.1122 1rd -0.0874 0.4073 0.1951 0.2042 -0.3387 -0.1367 1mtb -0.1549 0.1748 -0.026 -0.0051 -0.3809 -0.0816 0.4543 1roa -0.1069 -0.4841 -0.2997 -0.3065 0.1642 0.0109 -0.4586 0.0794 1capexp 0.2288 0.3113 0.1442 0.1354 0.0481 0.0337 0.0104 -0.0893 -0.2424 1opcf -0.1016 -0.4723 -0.2249 -0.2483 0.1235 -0.0777 -0.3652 0.1439 0.8174 -0.1276 1sg -0.0024 0.2338 0.0792 0.0834 -0.1344 -0.0339 0.1981 0.2381 -0.0823 0.1012 -0.0927 1ag -0.0347 0.0529 0.0237 0.0268 -0.0576 -0.0086 0.0718 0.1653 -0.0442 0.062 -0.0387 0.4746 1
Table 37: Risk variables correlation coefficients
85
Variance Inflation Factor checks:
roa roe ros tobin's qVariable VIF VIF VIF VIF
rd 1.37 1.37 1.36 1.37cf 1.26 1.26 1.25 1.26size 1.18 1.18 1.18 1.18ei 1.13 1.13 1.13 1.13capex 1.1 1.1 1.1 1.1lev 1.07 1.07 1.07 1.07sg 1.06 1.06 1.06 1.06 Mean VIF 1.17 1.17 1.17 1.17
Table 38: Performance variables VIF test results
This table presents the results of variable inflation factor tests on the firm performance model variables.
oprisk trisk betaVariable VIF VIF VIF
roa 3.62 3.66 3.65opcf 3.24 3.27 3.26mtb 1.88 1.92 1.92rd 1.68 1.7 1.71sg 1.39 1.38 1.39ag 1.32 1.31 1.32size 1.29 1.29 1.29capexp 1.17 1.17 1.16ei 1.14 1.14 1.14lev 1.08 1.08 1.08 Mean VIF 1.78 1.79 1.79
Table 39s: Risk variables VIF test results
This table presents the results of variable inflation factor tests on the firm risk model variables.
86
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