Value enhancement by being green

Value enhancement by being green

by Borgert de Jong*

Abstract

This paper analyzes the wealth effects of implemented proactive environmental strategies (PES) by U.S. listed firms over the period 2003-2017 and aims to extent traditional finance theory by explaining value enhancement due to PES. We test whether environmental efforts are associated with a higher value of equity. The analysis is performed over time on aggregate level, industry level, and using different estimation methods. We have found a robust significant positive relationship between PES and firm value. These findings are in line with the literature that states that firms who in engage socially responsible strategies have higher valuations.

JEL classification: C23, G32, Q51

Key words: Proactive Environmental Strategies, Value Enhancement, U.S. firms, Panel Data

* _{Student number: 2757818. Supervisor: dr. A. Dalò. This is a thesis written for the MSc Finance at the University of}

ii 1. Introduction

In 2016 the Paris agreement was negotiated and signed by all 196 members of the UNFCCC (United Framework Convention on Climate Change). This agreement was signed in order to limit global temperature increases well below 2 degrees Celsius above pre-industrial levels.1 Though, some argue that due to a lack of priority in the past 20 years the 2 degrees increase is extremely hard to achieve. As some countries have to cut their emissions by approximately 80 per cent. Hence, the 2009 UNFCCC Copenhagen Accord concluded that with current commitments only a 63 per cent cut will be realized (Mark New et al, 2011). As a result, they see virtually no chance to reach the 2 per cent goal with current commitments, and state that a 3 to 4 degrees temperature increase is more realistic. The associated consequences are more severe for a temperature increase which is greater than 2 degrees; the sea level will rise even more resulting in necessary relocating of major cities, dry areas will experience more drought which make agriculture hard to even impossible, and the higher temperature requires larger annual investments in order to adapt to a changing environment (Stafford Smith et al, 2011). In order to reach the agreed aspirations of the Paris agreement, today’s living standards should be radically changed.

Businesses worldwide should adopt new strategies and production methods so as to limit greenhouse gasses emissions. Since greenhouse gasses are the driver of climate change (IPCC, 2015). The associated contribution per sector is as follows: electricity and heat production (25%), industry (21%), agriculture and other land use (24%), transportation (14%), buildings (6%), and other energy (10%). In order to minimize emissions firms are adopting environmental strategies. In such a strategy emission reduction and waste minimization are central (Gladwin et al., 1995). Hence extensive implementation of such strategies is crucial in the war against climate change. The necessary adaptions go hand in hand with additional investments for firms. Consequently, today’s investment climate is skewed towards fossil fuels instead of renewable energy.2 _{This raises} the question whether green investments are profitable or not. Current academic literature has addressed the effect of Corporate Social Responsibility (CSR) on firm value and deal premia widely. Moreover, it has addressed the relation between Corporate Environmental Performance (CEP) and financial performance. However, the literature lacks attention on sustainable business strategies’ effect on firm value.

Outcomes of studies on whether implementing a CSR strategy enhances firm value are ambiguous. Multiple studies find a positive relation between CSR and firm value (Klassen &

1_{https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement, (consulted on 3-8-2018)} 2

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McLaughin, 1996, Dowell et al., 2000, El Ghoul et al., 2011). Contrary, Sheikh (2018) finds that in low product fluidity markets CSR has no effect on firm value.3 Erragragui (2018) states that CSR practices are related with an increase of the cost of debt due to increased uncertainties. Endrikat et al. (2014) performed a meta-analysis of the relationship between CEP and financial performance and found a positive relationship. Conversely, Jacobs et al. (2010) found a negative stock market reaction as a result of voluntary emission cutting of production metrics.

Despite a lack of a holistic belief that CSR and environmental strategies are value enhancing, one can argue that for the sake of society these initiatives are important objectives to pursue. As mentioned earlier, academic literature has devoted little attention to sustainable strategies in a purely finance context. Therefore studying the wealth effects of implementing an environmental strategy is highly relevant with rising global temperatures.

This thesis is structured as follows. Firstly, the written literature is reviewed and hypotheses are formulated in section 2. Secondly, applied methodology and both dependent and independent variables will be discussed in section 3. Thirdly, data gathering and descriptive statistics regarding the data set will be presented in section 4. Lastly, in section 5 results and robustness checks will be discussed and lastly section 6 will provide conclusions and limitations of our empirical research.

2. Literature review and hypothesis formulation 2.1 Environmental strategy definition

Classic definition of business models have not incorporated social and environmental aspects since the primary goal of businesses is to maximize shareholder value (Hovenkamp, 2009). Environmental efforts are subordinate to creating economic value in neoclassical economic theory (Stubbs & Cocklin, 2008). As a result, this view on business models is imperfect to address social and environmental deterioration (Shrivastava, 1995). Thus, a more comprehensive view of business models should be adopted in order to cope with nowadays’ societal challenges. Stubbs and Cocklin (2008) developed a sustainable business model that has implemented both social and environmental aspects to deal with all stakeholders. Their analysis showed that an organization should implement internal structural and cultural potential to realize firm-level sustainability and collaborate with stakeholders to realize sustainability for its supply chain. Lafferty and Hovden (2003) state that an integrated environmental strategy should incorporate sustainable goals into all stages of policy making, supplemented by an aggregate evaluation of the adopted policy. Furthermore, Klassen and McLaughin (1996) define environmental management as a strategy in

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which product and operational technologies are designed and utilized to minimize their environmental impact. This entails prevention of spills and reduced material and energy consumption. Likewise, Buysse and Verbeke (2003) state that firms should adopt strategies that include five resource domains where firms can engage to become greener. They state that the following investments should be incorporated; investments in conventional green competences related to manufacturing, in employee skills, organizational competences, in formal management systems and procedures at the input process and output sides, and efforts to reconfigure the strategic planning process.

Thus an environmental strategy is defined as a strategy in which the whole organization, its activities, and investments are focused on minimizing pollution and efficient use of input materials and energy.

2.2 Motivation to pursue such strategy

First, one should make a distinction between reactive and proactive drivers to implement an environmental strategy. With a reactive strategy a firm engages in actions that comply with set standards by the law. A reactive firm will not take actions that exceed these standards (Russo & Fouts, 1997). A firm’s management provides no support for such practices and there is no environmental reporting (Henriques & Sadorsky, 1999) Consequently, laws imposed by the government act as one of the most important factors affecting a firm’s incentive to pursue an environmental strategy (Henriques & Sadorsky, 1996). Thus laws can be classified as an exogenous factor affecting a firm’s decision making. In addition, market actors may also affect a firm’s strategy. Zhu et al. (2005) found that market pressure has become a dominant exogenous factor influencing green practices. Moreover, Liu et al. (2010) state that market factors and proximate institutions and individuals are important to tackle environmental issues.

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making, and expertise skills. Contrary, Cortazar et al. (1998) found, by using a real options approach, that firms are reluctant to invest in environmental technologies due to uncertain future price levels. Further, firms could have financial incentives to adopt PES. Clarkson et al. (2011) performed a study on whether it pays to be green. They found that improvements in a firm’s environmental performance are related to positive changes in financial resources, measured by a change in Return on Assets (ROA). Likewise, Dowell et al. (2000) found a positive relationship between market value, measured by Tobin’s q, and compliance with global environmental standards. They compared market values of firms who had adopted these strategies with firms that did not comply with the standards. Aragon-Correa et al. (2008) found a significant relationship between proactive environmental strategies and financial performance in the automotive sector.

The literature has devoted attention on the motives for implementing PES, however it lacks focus on value effects due to PES. Therefore, this thesis aims to explain value changes due to implementing PES. The research question that is central in this thesis is as follows:

- To what extent is firm value affected by proactive environmental strategies?

It adds to the literature as current literature has performed either CSR as an explanatory variable for changes in firm value or it has used different metrics to measure financial consequences.

2.3 Value creation

The most basic definition of value creation is the added value a firm creates by manipulating input materials using labor and production metrics. Created value is the difference between inputs and outputs (Dobb & Dobb, 1975). The monetary benefits of the created value are solely experienced by the firm and consequently by its shareholders which is in line with neoclassical economic theory of firms as described in Hovenkamp (2009). In addition, Koller et al. (2015) state that value is a function of Invested capital (IC), Return On Invested Capital (ROIC), Weighted Average Cost of Capital (WACC), and the growth rate (g). 4 Thus, a company can create value by affecting one or multiple parameters of the value function. In addition, Rappaport (1986) presented a graphical representation of valuation creation which can be found in figure 1.

Management can create or destroy value by a threefold of decisions as shown in figure 1. These decisions affect the value drivers and subsequently shareholder value. This metric of valuation yields the fundamental value of a firm. Another metric widely used in the literature is market value which is the market price of total outstanding shares (Berk et al., 2014).

4 _{Koller et al. (2015) value formula: 𝐹𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙 𝑣𝑎𝑙𝑢𝑒}

0= 𝐼𝐶0+

vi Figure 1. Value drivers of shareholder value (Schaltegger & Fegge, 2000).

Consequently, optimistic investors can cause substantial increases of stock prices, and vice versa, leading to significant deviations from fundamental values (Ofek & Richardson, 2003).

2.4 Value creation due to environmental strategies

A sustainable strategy can affect multiple parameters of the value formula. First, PES can impact the WACC. As mentioned earlier, Erragragui (2018) found that CSR practices are related to a higher Cost of Debt (CoD). Conversely, El Ghoul et al. (2011) found that engaging in CSR leads to lower Cost of Equity (CoE). Both CoD and CoE affect the WACC and subsequently firm value. Furthermore, PES are associated with more efficient use of inputs and cost savings (Sharma & Sharma, 2011). The increased efficiency and cost saving cause a margin widening. The margin widening positively affects ROIC and subsequently fundamental value. On the other hand, a sustainable strategy is likely to affect market value as some investors exclude heavily polluting firms from their portfolios which results in downward pressure on stock prices (Cox et al., 2004). Moreover, Deng et al. (2013) found that high CSR acquiring companies attain higher merger announcement returns and larger post-merger long-term operating performance. Wagner and Schaltegger (2004) observed that investors positively value PES due to operational and competitive advantages. Contrary, Galema et al. (2008) found negative alpha concerning Social Responsible Investing (SRI) . Some studies found neither a significant positive nor negative sign for alpha concerning environmental performance (Wagner & Schaltegger, 2001, Hamilton et al., 1993). Thus, the literature does not provide a clear answer whether sustainable business strategies are value enhancing.

This thesis will analyze whether value, measured by both market value and book value, is affected by environmental performance. This yields the following hypotheses:

Objective

Value components

Value drivers

Management decisions

Creating shareholder value

Cash flow from operations

Discount rate Debt

- Value growth duration - Sales growth - Operating profit margin - Investments in both working capital and fixed assets

- Cost of capital

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𝐻₁ = 𝑝𝑟𝑜𝑎𝑐𝑡𝑖𝑣𝑒 𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑒𝑠 𝑑𝑜 𝑛𝑜𝑡 𝑎𝑓𝑓𝑒𝑐𝑡 𝑚𝑎𝑟𝑘𝑒𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑒𝑞𝑢𝑖𝑡𝑦 𝐻₂ = 𝑝𝑟𝑜𝑎𝑐𝑡𝑖𝑣𝑒 𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑒𝑠 𝑑𝑜 𝑛𝑜𝑡 𝑎𝑓𝑓𝑒𝑐𝑡 𝑏𝑜𝑜𝑘 𝑣𝑎𝑙𝑢𝑒 of equity

Both book and market value are used in order to sketch a complete picture and since the values can deviate from fundamentals due to investor sentiment (Ofek & Richardson, 2003).

As both book- and market value effects will be analyzed this paper will provide a comprehensive picture of value creation due to environmental efforts.

2.5 Control variables

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Table 1. Definition of control variables and expected signs

Variable Description Expected sign

Profitability (ROA) Profits made that year divided by average book value

of total assets. +

Interest coverage EBIT divided by financial expenses +

Leverage Debt divided by enterprise value +

Size Total reported capital +

This table summarizes the description of control variables and their expected sign.The left column the control variables are denoted. The middle column provides a description of the control variables. The right column provides the expected sign of the control variables consistent with current literature. The expected sign applies to both ME and BE.

3. Methodology

The method that will be used in order to analyze our data will be similar to the method used in El Ghoul et al. (2011). In their research they performed both comparative and multivariate regressions to describe the change in CoE by CSR. With their comparative analysis they compared whether above median CSR scores are related with lower CoE. Furthermore, they performed multivariate regression analysis with control variables in order to isolate the effects of CSR practices.

3.1 Explanatory variables

This thesis will use the environmental pillar of Thomson Reuters’ ESG scores. The ESG scores assess firms on Environmental, Social, and Governmental performance. The environmental performance of a firm is a proxy of PES in this thesis. The environmental pillar is based on three scores; resource use, environmental innovation, and emissions score. Therefore the environmental pillar measures a company’s impact on living non-living natural systems. 5 _{Hence it reflects how} well the firm has implemented strategies which account for minimizing environmental risks and exploit environmental opportunities in order to maximize shareholder wealth in the long term.

3.2 Dependent variables

In this thesis market value of equity (ME), also known as market capitalization, and book value of equity (BE) will be measured by the following equations:

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𝑀𝐸_𝑡 = 𝑃_𝑡∗ 𝑆_𝑡 (1)

𝐵𝐸𝑡= 𝑇𝐴𝑡 − 𝐷𝑡 (2)

where 𝑃_𝑡 is the current share price, 𝑆_𝑡 is the current number of shares outstanding, 𝑇𝐴_𝑡 denotes the current book value of total assets, and 𝐷_𝑡 denotes the current book value of debt. Book value of equity is used as a proxy for fundamental value as the inputs of the value formula of Koller et al. (2015) are not widely available and are sensitive to inconsistencies due to possible differences in accounting measures.

3.3 Regression analysis

Inference in this thesis will be based on both comparative analysis and multivariate regression analysis, using panel data, as it enhances robustness. For the comparative analysis the same distinction as in El Ghoul et al. (2011) on performance will be made; above median scores will be compared to below median scores on both ME and BE. Then, the difference will be computed and tested for significance. The multivariate regression will be formulated as follows:

𝑉𝑖,𝑡 = 𝛽0+ 𝛽1𝐸𝑁𝑉𝑖,𝑡−1+ 𝛽2𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝜀𝑖,𝑡 (3)

where i is an index for a firm, t is an index for time, 𝑉𝑖,𝑡 is a proxy for value and will be both expressed in market value of equity (ME) and book value of equity (BE), 𝛽₀is the intercept of the regression function, 𝐸𝑁𝑉_{𝑖,𝑡−1} is a proxy for the environmental performance, 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠_{𝑖,𝑡−1} are the

control variables discussed earlier, and 𝜀 denotes the residuals of the regression.

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therefore analysis is based on an unbalanced dataset. Pooled OLS will be applied in order to run the regressions.

The pooled OLS method assumes that average value of observations do not change over time and cross-sectional. Since multiple unique firms are used over a period of fifteen years this assumption may not be realistic, due to trends or heterogeneity. Therefore both firm effects and time effects will be included as well. The formulation of both firm and time fixed effect can be found below. Before running either the firm or period fixed effect the Hausman test will be performed in order to assess whether random or fixed effects is appropriate.

The regression equation regarding the firm fixed and random effects will be computed as follows: 𝑉_𝑖,𝑡 = 𝛽₀+ 𝛽₁𝐸𝑁𝑉_{𝑖,𝑡−1}+ 𝛽₂𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠_{𝑖,𝑡−1}+ 𝜇_𝑖 + 𝜀_𝑖,𝑡 𝑉𝑖,𝑡 = 𝛽0+ 𝛽1𝐸𝑁𝑉𝑖,𝑡−1+ 𝛽2𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝜀𝑖,𝑡, 𝑤ℎ𝑒𝑟𝑒 𝜀𝑖,𝑡 = 𝜇𝑖 + 𝜐𝑖,𝑡 (4) (5)

where i is an index for a firm, t is an index for time, 𝑉𝑖,𝑡 is a proxy for value and will be both expressed in market value of equity (ME) and book value of equity (BE), 𝛽₀is the intercept of the regression function, 𝐸𝑁𝑉_{𝑖,𝑡−1}is a proxy for the three environmental performance, 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠_{𝑖,𝑡−1} are the control variables discussed earlier, 𝜇_𝑖 denotes the firm fixed effect, and 𝜀_𝑖,𝑡 denotes the residuals of the regression. Equation 4 refers to firm fixed effects. Equation 5 refers to firm random effects. As one can see with random fixed effects; the firm effect is part of the error term. Likewise, the time fixed is formulated as follows:

𝑉𝑖,𝑡 = 𝛽0+ 𝛽1𝐸𝑁𝑉𝑖,𝑡−1+ 𝛽2𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝜆𝑡+ 𝜀𝑖,𝑡 (6)

xi 4. Data and descriptive statistics

Firm specific data necessary for the computation of the regressions above will be retrieved from Thompson Reuters’ EIKON. The companies used for this analysis are currently and previously listed firms on the Standard and Poor’s (S&P) 500 index over the last fifteen years. The S&P 500 is designed to widely measure performance of the entire US economy.6 Since the index provides information of the entire US economy it is assumed to be a suitable sample. The sample contains firms listed on the S&P 500 over a period of fifteen years. The total sample consists of 623 firms which results in 9.345 firm-year observations. Moreover, a sample of the Financial Times Stock Exchange (FTSE) 100 will be used as a data comparison in order to enhance robustness. This sample consist 142 firms which yield 2.131 firm-year observations.

4.1 Descriptive statistics

Table 2 provides insight of the scores per sector both in median and mean. One can see that, except for environmental scores, differences between mean and median scores are large. This is caused since median scores correct for outliers. Especially interest coverage ratios are strongly affected by differences in observations. Based on reported mean and median scores telecommunication services seem to score best on environmental practices, though this industry is by far the smallest. In addition, both ME and BE are substantially larger than other industries. This is caused since the number of firms in this sector is notably smaller than other sectors and is therefore more sensitive to large observations. Note that there are only ten Global Industry Classification Standard (GICS) industries used. We aggregated utilities and energies for brevity, since similar firm observations and activities were found, and both accounted for only five per cent of the total sample.

Table 3 shows descriptive statistics per variable used in the regression. One can see that some data points are missing since the number of observations are not equal for the used variables. The observations for some variables differ substantially if one considers minimum and maximum scores which can also be seen in standard deviation. One can also note that no firm has obtained a score of 100 on the used environmental proxy. In addition, table 3 shows that book value of equity differs greatly; the standard deviation of observations is larger than the median of observations. This might be caused by differences in accounting measures applied by firms. Furthermore, table 3 shows that all regression variables are skewed to the right. The positive sign is caused since the majority of the regression observations are positive. Moreover, one can observe that the magnitude of skewness increases when the mean and median differ more in terms of value.

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Table 2. Scores per industry given by median and mean of S&P 500 firms, 2003-2017

Panel A.

Contribution ME BE ENV TA ROA LEV COV

Industrials 13,17% 20.521 6.461 54,03 15.696 7,95 22,36 106,34 Health Care 11,79% 26.759 8.555 53,55 12.546 7,31 16,53 46,60 Information Technology 14,24% 38.520 9.066 55,55 12.721 10,50 16,41 142,09 Consumer Discretionary 15,93% 18.902 5.738 51,12 11.790 8,66 21,89 101,16 Utilities 11,94% 25.181 12.862 56,14 20.780 4,68 32,79 150,43 Financials 12,40% 30.388 42.649 49,90 43.057 3,07 40,27 41,33 Materials 7,35% 11.853 6.503 57,19 7.276 6,96 24,77 12,30 Real Estate 5,21% 11.915 7.457 54,18 9.038 3,01 33,56 10,84 Consumer Staples 7,04% 34.700 8.393 60,40 15.890 9,21 20,72 16,73 Telecommunication Services 0,92% 72.671 30.302 61,97 55.628 7,29 30,55 39,87 Panel B.

Contribution ME BE ENV TA ROA LEV COV

Industrials 13,17% 10.206 2.687 53,07 6.239 7,13 16,60 9,10 Health Care 11,79% 10.762 2.649 54,01 4.930 7,69 13,40 11,08 Information Technology 14,24% 10.557 2.416 53,22 4.206 10,20 10,19 12,08 Consumer Discretionary 15,93% 9.017 2.287 46,76 4.451 7,64 17,72 8,19 Utilities 11,94% 12.189 5.431 57,37 13.597 3,38 34,09 3,92 Financials 12,40% 13.618 7.640 42,82 11.051 1,43 31,36 7,10 Materials 7,35% 7.681 2.348 57,34 4.620 6,27 20,71 6,27 Real Estate 5,21% 6.974 3.112 49,39 7.069 2,80 33,28 1,91 Consumer Staples 7,04% 13.529 3.043 64,43 7.234 8,58 18,15 8,88 Telecommunication Services 0,92% 36.050 13.217 66,77 6.429 4,76 28,22 6,59

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Table 3. Descriptive statistics per variable of S&P firms, 2003-2017

N Mean Median Skewness St. Dev. Min Max

ME 8.594 25.667 10.382 5,789 50.984 0,363 868.880 BE 8.516 12.926 3.339 26,632 75.673 -51.129 3.754.894 ENV 7.540 54,224 52,890 0,098 26,980 0,139 99,816 TA 8.648 16.686 6.332 8,458 42.419 -39.138 637.676 ROA 7.443 7,114 6,290 22,829 9,210 -502 170 LEV 8.426 25,139 19,614 16,789 28,052 0,000 1.381 COV 7.946 79,676 7,475 11,740 1.561 -677 124.817

This table shows the descriptive statistics variables used in the analysis. ME denotes market value of equity. BE denotes book value of equity. USE denotes resource usage. INN denotes environmental innovation. EMS denotes emissions score. TA denotes total capital. ROA denotes return on assets. LEV denotes leverage. COV denotes interest coverage. ME, BE, and TA are presented in billions. All variables are retrieved from Thomson Reuters’ EIKON.

Table 4 shows the full sample correlation matrix for the S&P 500 firms over the fifteen year sample period. ME and TA are moderately positively correlated. This is probably caused by the fact that both variables are balance sheet items and a proxy for value. Therefore it is likely that they tend to move in the same direction. The remaining low correlations imply that the variables will likely not influence each other. Likewise, size and the environmental proxy are moderately correlated with each other as well. Which can imply that firms with higher total assets are more likely to implement sustainable strategies. Based on the correlation matrix multicollinearity should not be a problem in our multivariate regression analysis.

Table 4. Correlation matrix variables S&P 500 firms, 2003-2017

ME BE ENV TA ROA LEV COV

ME 1 BE 0,322 1 ENV 0,321 0,115 1 TA 0,613 0,378 0,277 1 ROA 0,119 -0,025 0,038 -0,055 1 LEV -0,163 0,051 0,005 0,219 -0,334 1 COV -0,002 -0,002 -0,017 -0,009 0,040 -0,048 1

xiv 5. Empirical results

5.1 Comparative analysis

The comparative analysis results are presented in table 5 below. Our comparative analysis compares the mean and median of both market and book value of equity based on whether they score above or below median. Panel A compares the mean of both ME and BE for firms with high and low scores on environmental efforts. Panel B shows a similar comparison however the median of ME and BE is used. Panel A shows that firms score above median on environmental efforts have higher market and book value of equity. Likewise, panel B compares the median of ME and BE for firms who have implemented PES. Both panels show both a difference in value yet the magnitude is smaller than mean scores. The median filters for large deviations from the mean and therefore causes the observations be lie more close together.

Table 5. Comparative analysis

Panel A. ME BE ENV_S ≥ median (1) 39.786 19.906 ENV_S < median (2) 14.154 7.621 Difference (1)-(2) 25.632 12.286 T-Stat 21,988 *** 10,539 *** Panel B. ME BE ENV_S ≥ median (1) 17.231 5.778 ENV_S < median (2) 6.818 2.210 Difference (1)-(2) 10.413 3.568 T-Stat 8,933 *** 1,941 **

This table shows the results of the comparativeanalysis for both market value of equity and book value of equity. ME denotes market value of equity. BE denotes book value of equity. Both ME and BE are presented in billions. Panel A shows the mean of market and book values of both firms who score above and below mean of environmental practices. Panel B shows the median of market and book values of both firms who score above and below median of environmental practices. ***,** denotes statistical significance at a 1% and 5% level respectively. All variables are retrieved from Thomson Reuters’ EIKON.

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relationship between PES and firm value a multivariate regression analysis will be performed in the next section.

5.2 Multivariate regression analysis

The results of the multivariate regression analysis can be found in table 6 below. The regression is estimated using pooled OLS based on an unbalanced dataset. As shown in the table we find a significant positive relationship between environmental efforts and market value of equity. The results of the regression using the book value of equity are similar yet differ somewhat regarding significance and magnitude. These findings suggests that PES are a driver of value enhancement, and thus contradicts both our hypotheses.7 _{Firms who perform better on} environmental measure are thus associated with a larger value of equity. Contrary we found evidence of endogeneity by using the 2SLS method which can be found in table 9 in appendix 1.8

As discussed earlier, a possible explanation of the increase in equity is a margin widening due to a more efficient use of input materials. In order to isolate the effect of PES on firm value we have included firm specific control variables as discussed earlier. We observe a significant positive relationship between both TA and ROA and firm value. Which is in line with expectations. These results are in line with common reasoning as equity is part of total assets and the correlation between the two variables is moderately positive. Moreover, higher returns lead to higher free cash flows.9 Therefore higher values of both TA and ROA will result in larger values of equity. 10 In addition, we observe a significant negative relationship between leverage and firm which contradicts our earlier expectations. This finding might be caused due to increased agency costs and risk of losing control of the firm (Yazdanfar & Öhman, 2015). Furthermore, we observe a negative relationship between interest coverage for ME. Yet, this result is insignificant. Moreover, adjusted R-squared differ greatly between the two regressions. Since R-squared and the significance level of ME is higher than BE it is better able to explain the relationship between environmental efforts and value enhancement.

7 _{We ran the regression with White standard errors as well and observed no substantial differences. Therefore}

heteroscedasticity is assumed not to be a problem. Regression output is not included for brevity.

8 _{The used instruments, blue state dummy and religiosity, in the 2SLS regression have very low correlation with the}

endogenous variable ENV contrary to Deng et al. (2013). Therefore, inference is less reliable than OLS (Hill et al., 2011).

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Table 6. Multivariate analysis

ME BE Constant 7.335 *** 4.019 *** (1.829) (1.368) Independent variable ENV 412 *** 47 ** (27) (20) Controls TA 0,828 *** 0,359 *** (0,016) (0,012) ROA 288 *** -58 (63) (47) LEV -782 *** -107 *** (33) (24) COV -0,213 0,002 (0,306) (0,223) N 5.068 5.056 Adjusted R-squared 0,441 0,172 F-stat 802 *** 210 *** Durbin-Watson stat 0,171 1,534

This table shows the results of the multivariate analysis for market value of equity The explanatory variables are lagged one year in order to prevent for endogenous effects. ***, ** denotes statistical significance at a 1% and 5% level respectively. The sample period is 14 years. ME, BE, and TA are presented in millions. Results larger than 10 are rounded for brevity.

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This table shows the results of the multivariate analysis for market value of equity. Panel A. shows the regression output of firm fixed effects. Panel B shows regression output of period fixed effects. The explanatory variables are lagged one year in order to prevent for endogenous effects. ***, ** denotes statistical significance at a 1%, 5% level respectively. The sample period is 14 years. ME, BE, and TA are presented in millions. Results larger than 10 are rounded for brevity.

Table 7. Firm and time fixed effects

Panel A. Panel B.

ME BE ME BE

Constant 10.494 *** 2.948 Constant 10.118 *** 3.715 ***

(1.409) (1.989) (1.870) (1.410)

Independent variable Independent variable

ENV 206 *** -19 ENV 373 *** 52 ** (21) (30) (27) (20) Controls Controls TA 0,678 *** 0,543 *** TA 0,829 *** 0,359 *** (0,025) (0,036) (0,016) (0,012) ROA 23 -5,379 ROA 277 *** -59 (36) (50) (62) (47) LEV -231 *** -55 LEV -805 *** -107 *** (30) (42) (33) (25) COV -0,133 0,019 COV -0,157 0,000 (0,155) (0,219) (0,304) (0,229) N 5.068 5.056 N 5.068 5.056

Adjusted R-squared 0,872 0,323 Adjusted R-squared 0,451 0,1713

F-stat 66 *** 5,555 *** F-stat 232 *** 59 ***

xix 5.3 Industry specific effects

The obtained dataset of the S&P 500 index over the last fifteen years is split per industry using GICS codes similar as in El Ghoul et al (2011). As mentioned earlier our split yields ten unique industries. As shown in the descriptive statistics not every industry is equally weighted in the data. After the split both comparative and multivariate analysis are performed per industry. Comparative analysis per industry yields similar results than the aggregated analysis. Though, the difference in median for book value of equity for real estate, financials, and consumer staples is insignificant. Despite these deviations market value of equity is significant for all industries, for brevity we did not include the tables in the appendix. The split dataset is used for multivariate analysis as well, which can be found in table 8. We performed regressions on each different industry. The results per industry are somewhat different than the aggregated regression regarding significance. For utilities, telecommunications services, financials, information technology, and consumer staples we do not find a significant relationship between PES and firm value. The insignificant result for financials is not surprising since this industry is not polluting since the activities are mainly providing intangible services. We find a significant negative relationship for real estate. In addition, we find a significant negative relationship between control variables, leverage and interest coverage, and firm value. The negative relation can be a result of the expectation that sustainable investments will not materialize since tenants are not willing to pay extra (Galuppo & Tu, 2010). These findings contradict our earlier sign expectation regarding control variables.

Though, these industries have relatively few observations compared to the other industries. Common reasoning would suggest that the relationship between firm value and PES is positive for all industries as environmental efforts can increase efficiency, in a real estate example this can imply less usage of fossil fuels for heating purposes (Miller et al., 2008). Contrary, the findings for industrials, health care, consumer discretionary, materials, and telecommunication services are still positive and significant. Moreover, the majority of observations are in these industries. Thus, these findings support earlier found results.

5.4 Financial crisis effects

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Table 8. Sample split per industry

[1] [2] [3] [4] [5] [6] Constant 154 1.133 5.752 16.036 *** 4.03 16.611 *** (3.623) (3.004) (5.254) (4.047) (3.481) (3.398) Independent variable ENV 387 *** 238 *** -141 ** 281 *** -41 74 (38) (50) (68) (51) (41) (64) Controls TA 0,381 *** 1,761 3,394 *** 0,932 *** 1,589 *** 0,263 *** (0,014) (0,046) (0,065) (0,058) (0,031) (0,022) ROA -20 442 *** 965 *** -279 1.110 *** 0,089 (224) (113) (273) (212) (235) (47) LEV -206 *** -636 *** -812 *** -773 *** -488 *** 317 *** (56) (66) (97) (75) (53) (56) COV -1.088 1.837 0,361 -0,956 -0,169 -1,964 (2.288) (8.388) (1,707) (1,326) (0,154) (3,042) N 660 618 691 826 670 382 Adjusted R-squared 0,627 0,769 0,836 0,345 0,841 0,307 F-stat 223 *** 411 *** 703 *** 88 *** 708 *** 35 *** Durbin-Watson stat 0,339 0,499 0,402 0,258 0,515 0,092

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Table 8. Sample split per industry cont'd

[7] [8] [9] [10] Constant 228 11.252 ** -22.728 *** -8.289 (1.424) (1.477) (5.480) (25.625) Independent variable ENV 86 *** -61 *** 68 -208 (16) (16) (57) (281) Controls TA 0,920 *** 1,284 *** 1,945 *** 1,137 *** (0,049) (0,054) (0,041) (0,090) ROA 550 *** 131 3.146 *** 3.081 (82) (124) (262) (2.179) LEV -153 *** -252 -148 -19 (25) (29) (97) (392) COV -19 ** 8,104 -50 * 104 7,896 (2,834) (26) (122) N 411 317 439 54 Adjusted R-squared 0,604 0,699 0,864 0,875 F-stat 126 *** 148 *** 557 *** 76 *** Durbin-Watson stat 1,115 0,715 0,415 0,712

xxii

When we run regressions for the different time intervals we observe no changes concerning significance of ME, results are presented in table 10 in appendix 2. The influence of PES on BE are insignificant before and during the financial crisis.

The insignificance might be caused due to the turbulent business environment as a result of the global financial crisis and there are fewer observations on BE pre crisis. Since the majority of observations are post-crisis and the magnitude and sign of the variable are similar we expect this deviation to be negligible. Hence these results are in line with previous findings.

5.5 Robustness

In order to enhance inference we made multiple alterations to the data and analysis as so to enhance robustness of our results. We use a different proxy for value and a different dataset in order to see whether our results hold under different circumstances.

5.5.1 Different value measurement

In order to perform our analysis we used ME and BE as proxies for value. Still, one can use multiple ways to measure value. Besides market capitalization one could use total equity (TE). It incorporates equity value of preferred shareholders, general and limited partners, and common shareholders and is therefore similar to market capitalization.11 Due to the similarity to earlier used variables, TE will be used in order enhance robustness. Besides pooled OLS both firm and period fixed effects are used as well.

Results of multivariate regression analysis can be found in table 11 in appendix 3. For the proxy under all three estimation methods we find that PES are significant and positive at a 1 per cent level. These findings are thus in line with results found earlier.

5.5.2 Data comparison FTSE 100 index

To enhance inference we perform both comparative and multivariate analysis based on a different sample. This sample is the FTSE 100 index over the same time interval. We choose the FTSE 100 index since it resembles one of the largest European stock markets and consists of the 100 largest UK firms. As a result, it is similar to the S&P 500 index and is therefore suitable to validate our results.

Results of our data comparison using comparative analysis can be found in appendix 4 in table 12. The results indicate that PES are value enhancing. Moreover, we performed multivariate

xxiii

regression analysis over the FTSE 100 sample, which can be found in table 13 in appendix 5. The findings are in line with previous findings. We found significant positive relationships between PES and both ME and BE. In addition, the magnitude of results is similar. However, we found a significant negative relationship between ROA this might be caused due to a smaller dataset compared to the S&P 500 index. In a smaller dataset results are more sensitive to outliers.

The results of the FTSE 100 index are comparable and therefore enhance robustness of earlier outcomes.

6. Conclusion

Environmental strategies are becoming increasingly important for firms to incorporate. At first, firms need to implement these strategies in order to comply with imposed laws. These laws have become more strict over the last years and are likely to become more strict due to our changing climate. Moreover, businesses worldwide are rated yearly on their environmental performance. This rating creates pressure from society so as to implement environmental strategies. Besides these exogenous pressures, endogenous motivations have arisen. Besides altruistic incentives, PES are associated with a more efficient use of input materials and energy and therefore may lead to lower production costs and subsequently, ceteris paribus, higher shareholder wealth due to increased free cash flows. The question that is central in this thesis addresses the relationship between PES and shareholder wealth. More specifically, this thesis studies the relationship between PES and firms value. This thesis is unique as it provides insight into whether environmental strategies are value enhancing. Our main findings rejects both our hypotheses and therefore presents evidence that environmental strategies are value enhancing. However, the results of book value of equity are not as robust as the results from market value of equity. Especially when the estimation method is changed or we made a sample split. On the other hand, the effect on market value of equity was under the majority estimation methods highly significant. Another proxy for value yielded highly significant results as well. Therefore, the majority of our results are robust and significant. Hence we can conclude that proactive environmental strategies are value enhancing.

xxiv

The S&P 500 is constructed as to reflect the entire US economy as well as possible. The index covers all industries of the economy and is therefore a suitable reflection. However, some industries are more present than others, as a result some industries are not well covered in our dataset. Therefore the results of these industries are less reliable. Moreover, in order to be included in the S&P 500 a company should have a market capitalization larger than 6,1 billion USD.12 Thus only the largest US listed firms are included in our sample. The FTSE 100 is constructed in a similar manner. As a result, our findings may not hold for small and medium-sized enterprises (SMEs). Furthermore, PES are solely measured by the environmental pillar of Thomson Reuters’ ESG scores. This makes the results of our research dependent on the estimation methods of Thomson Reuters. Inference could be enhanced by incorporating additional measures for PES. There is also presence of endogeneity in our model. Future research could incorporate additional control variables in order to isolate the effect of PES.

The addressed limitations are a good starting point for future research. Firstly, the scope of the research should be widened in order to state that environmental strategies lead to more firm value in general. Future research can focus on wealth effects in SMEs. Secondly, one could use a dataset with equally weighted industries included so as to draw inference on industry level. Thirdly, one could use additional proxies for environmental strategies in order to enhance robustness of the outcomes. Lastly, one could go more in detail by studying the impact of PES on value drivers. Because firm value is affected by PES some value drivers are affected as well since they are the fundamentals of firm value.

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xxix Appendices

Appendix 1

Table 9. 2SLS regression analysis

ME BE Constant 20.257 -1.077 (38.539) (30.482) Independent variable ENV 156 188 (720) (570) Controls TA 0,899 *** 0,326 *** (0,130) (0,103) ROA 648 *** -379 * (287) (225) LEV -912 *** -132 *** (64) (50) COV 7,717 5,152 (8,482) (6,578) N 4.392 4.385 Adjusted R-squared 0,339 0,057 F-stat 753 *** 163 *** Durbin-Watson stat 0,433 1,523

xxx

Appendix 2

Table 10. Sample split financial crisis

[1] [2] [3] [4] [5] [6] Constant 602 -105 -1.359 1696 -13.231 *** 5.74 *** (2.788) (9.792) (2.552) (3.266) (2.633) (829) Independent variable ENV 253 *** -34 372 *** 11 412 *** 66 *** (43) (152) (37) (47) (37) (12) Controls TA 1,427 *** 0,464 *** 0,484 *** 0,538 *** 0,899 *** 0,320 *** (0,046) (0,162) (0,023) (0,029) (0,020) (0,006) ROA 576 *** -157 497 *** -42 251 *** -50 ** (144) (505) (138) (177) (75) (0,155) LEV -515 *** -198 -358 *** -37 -1.019 -194 *** (50) (174) (44) (56) (46) (15) COV -0,409 0,298 -0,817 0,127 -0,194 -0,002 (0,811) (2,865) (1,08) (1,413) (0,356) (0,112) N 666 664 1.048 1.043 3.354 3.349 Adjusted R-squared 0,669 0,015 0,430 0,254 0,451 0,467 F-stat 270 *** 2,976 *** 159 *** 72 *** 552 *** 586 *** Durbin-Watson stat 0,260 1,742 0,392 0,826 0,159 0,128

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Appendix 3

Table 11. Multivariate regression analysis using total equity

[1] [2] [3] Constant 3.314 *** 3.242 *** 3.576 *** (478) (317) (492) Independent variable ENV 80 *** 38 *** 78 *** (7) (4,830) (7,139) Controls TA 0,358 *** 0,305 *** 0,359 *** (0,004) (0,058) (0,004) ROA -50 *** -5,960 -53 *** (16) (8,036) (16) LEV -176 *** -48 *** -182 *** (8,453) (6,634) (8,595) COV -0,004 ** 0,014 -0,004 0,004 (0,035) (0,081) N 5.109 5.109 5.109 Adjusted R-squared 0.631 0,937 0.632 F-stat 1.751 *** 144 *** 489 *** Durbin-Watson stat 0,134 0.339 0.133

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Appendix 4

Table 12. Comparative analysis FTSE 100 sample

Panel A. TE BE ENV_S ≥ median (1) 15.551 21.404 ENV_S < median (2) 3.018 3.242 Difference (1)-(2) 12.533 18.162 T-Stat 11,440 *** 7,413 *** Panel B. TE BE ENV_S ≥ median (1) 4.060 4.451 ENV_S < median (2) 1.272 1.274 Difference (1)-(2) 2.788 3.177 T-Stat 2,544 *** 1,297 **

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Appendix 5

Table 13. Multivariate analysis FTSE 100

ME BE Constant -21.783 *** -13.879 ** (3.397) (5.308) Independent variable ENV 522 *** 479 *** (52) (69) Controls TA 0,873 *** 0,884 *** (0,022) (0,011) ROA -825 *** -817 *** (155) (200) LEV -240 -232 (1.349) (1.721) COV -3.281 3.392 (3.098) (3.954) N 1.309 1.258 Adjusted R-squared 0.078 0.056 F-stat 29 *** 19 *** Durbin-Watson stat 0.037 0.278