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

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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

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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

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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

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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.

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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

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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.

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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.

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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

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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

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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

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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,

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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.

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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

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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.

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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.

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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.

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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%.

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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.

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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.

55

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?

76

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

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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

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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?

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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

83

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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