Can environmental sustainability be used to manage energy price risk?

Energy Economics 32 (2010) 1131–1138

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Energy Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e n e c o

Can environmental sustainability be used to manage energy price risk? Irene Henriques, Perry Sadorsky ⁎ Schulich School of Business, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3

a r t i c l e

i n f o

Article history: Received 14 May 2009 Received in revised form 11 January 2010 Accepted 14 January 2010 Available online 22 January 2010 Keywords: Environmental sustainability Energy price risk Stock prices Oil prices

a b s t r a c t Energy security issues and climate change are two of the most pressing problems facing society and both of these problems are likely to increase energy price variability in the coming years. This paper develops and estimates a model of a company's energy price exposure and presents evidence showing that increases in a company's environmental sustainability lowers its energy price exposure. This result is robust across two different measures of energy prices. These results should be useful to companies seeking new ways of addressing energy price risk as well as governments concerned about the impact that energy price risk can have on economic growth and prosperity. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Rising energy prices represent an additional cost of doing business for most non-energy producing companies. Much of the published research investigating the relationship between energy prices and financial performance has focused on the relationship between oil prices and stock prices (see for example, Kaneko and Lee, 1995; Ferson and Harvey, 1994, 1995; Jones and Kaul, 1996; Huang et al., 1996, 2005; Sadorsky, 1999, 2001, 2008; Faff and Brailsford, 1999; Papapetrou, 2001; Hammoudeh and Aleisa, 2004; Hammoudeh et al., 2004; Hammoudeh and Huimin, 2005; El-Sharif et al., 2005; Basher and Sadorsky, 2006; Boyer and Filion, 2007; Henriques and Sadorsky, 2008; Nandha and Faff, 2008; Park and Ratti, 2008). To date, most of this research has focused on the relationship between oil price movements and stock prices at the country level, but some research looks into this relationship at the industry sector level and firm level (Sadorsky, 2008). The consensus from most of this research is that rising oil prices depress stock prices. While the impact that rising oil prices have on stock prices is an important topic to be studying, it is a topic that is likely to grow in importance in the coming years. Energy security issues and climate change are two of the most pressing problems facing society and both of these problems are likely to lead to increases in energy price risk in the coming years. At a 2005 conference titled “The Top Ten Financial Risks to the Global Economy: A Dialogue of Critical Perspectives” sponsored by Goldman Sacks, for example, the world oil supply in general and rising oil prices in particular, topped the list of concerns by participants (Goldman Sachs, 2005). Effectively managing energy ⁎ Corresponding author. E-mail addresses: [email protected] (I. Henriques), [email protected] (P. Sadorsky). 0140-9883/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2010.01.006

price risk, of which oil price risk is a major contributor, can lead to competitive advantage through lower operating costs and less business risk (which translates into lower borrowing costs) and environmental sustainability might be one way to manage energy price risk. To date, a number of authors have advocated the use of environmental sustainability to manage energy price risk (Hart, 1995; Hart and Milstein, 2003; Esty and Winston, 2006; Busch and Hoffmann, 2007; Enkvist et al., 2008). Theoretically, since an environmental sustainable approach to business focuses on reducing energy consumption and increasing energy efficiency, companies that undertake environmental sustainable initiatives like promoting environmentally beneficial products and services, pollution prevention, recycling, clean energy, new property plant and equipment, and management environmental systems should be better prepared to deal with unexpected changes in energy prices. According to the resource based view of the firm, companies that develop the resources and capabilities necessary to create sustainable value should be better at managing energy price risk because an environmental sustainable approach to business forces companies to think about how to reduce their energy usage and increase energy efficiency (Hart, 1995; Hart and Milstein, 2003). In a similar vein, Sharfman and Fernando (2008), for example, find that companies with better environmental risk management have a lower cost of capital. The purpose of this paper is to determine whether or not environmental sustainability can be used to manage energy price risk at the company level. While a number of authors have put forward theoretical or ad hoc reasons as to why and how environmental sustainability can be used to manage energy price risk, there is, to our knowledge, no published research that empirically models the relationship between environmental sustainability, energy prices and stock prices. This paper develops and estimates a

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model of a company's energy price exposure and presents evidence showing that increases in a company's environmental sustainability lowers its energy price exposure. The results are robust across two measures of energy prices (a producer price energy index and oil prices). The following section of the paper presents the model development, after which sections are presented for the empirical methodology, data, empirical results, policy implications, and conclusions.

2. Model development Energy, along with capital, labour, and raw materials, is an essential input into the production of most goods and services. In the United States, approximately 40% of the energy the country uses comes from oil, making oil the single largest source of energy (Economic Report of the President, 2006).While most companies do not consume crude oil, they do consume petroleum products (gasoline, diesel fuel, heating oil, jet fuel, etc.) derived from crude oil and changes in crude oil prices send price signals to the petroleum products that companies do consume. In the United States, the cost of crude oil accounts for 53% of the retail price of gasoline (Economic Report of the President, 2006, p.239) and movements in the price of crude oil is the primary driver behind gasoline price movements. While rising energy prices may be good for the financial performance of energy companies that sell energy or energy related products, rising energy prices are, in general, not good for most other companies (Huang et al., 1996; Sadorsky, 2008). A company's share price is equal to the present value of discounted expected cash flows and anything that affects future cash flows can affect stock prices. Energy price movements can affect stock prices either by affecting future cash flows or by affecting the discount rate used in the equity pricing formula. Rising energy prices increase the costs of producing goods and services and without an offsetting increase in either the price of final goods or services or a substitution between the factors of production, the effect of rising energy prices are likely to be reduced profits. Rising energy prices can also impact stock prices through the discount rate used in the equity pricing formula because rising energy prices are often seen as inflationary and central banks respond to inflationary pressures by raising interest rates (Ferderer, 1996; Sadorsky, 1999). To date, most research investigating the relationship between energy price movements and stock prices has focused on oil prices and a number of authors have found a negative relationship between changes in oil prices and changes in stock prices (e.g., Kaneko and Lee, 1995; Ferson and Harvey, 1994, 1995; Jones and Kaul, 1996; Huang et al., 1996, 2005; Sadorsky, 1999, 2001, 2008; Faff and Brailsford, 1999; Papapetrou, 2001; Hammoudeh and Aleisa, 2004; Hammoudeh et al., 2004; Hammoudeh and Huimin, 2005; El-Sharif et al., 2005; Basher and Sadorsky, 2006; Boyer and Filion, 2007; Henriques and Sadorsky, 2008; Nandha and Faff, 2008; Park and Ratti, 2008). While managing energy price risk is clearly of importance today, in the future, managing energy price risk is likely to become even more important as energy security issues and climate change grow in importance. Energy security issues arise from a combination of factors including the increased reliance of most developed and many developing countries on imported oil from politically sensitive regions of the world and peak oil theories which predict that the world's supply of oil is starting to run out. Currently, four countries, Saudi Arabia, Iran, Iraq, and Kuwait account for 21.3%, 11.2%, 9.3% and 8.2% respectively of the world's 1.238 trillion barrels of proven oil reserves (British Petroleum, 2008).1 The world's largest consumer of oil at 21 million barrels per day (24% of the world total), the United States, imports 66% of its oil and oil imports are only going to increase in the 1

This does not include 152 billion barrels of oil from the Alberta tar sands.

future. Rapidly developing economies like China and India are dependent on imported oil to fuel their economic growth. Oil supply disruptions (like monopoly pricing power or terrorist attacks) in countries which hold large amounts of the world's proven oil reserves will lead to increased oil price variability. At current global consumption rates, there is 41.6 years of oil remaining in the world. While the world will never completely run out of oil, the cost of extraction is expected to increase across time as oil becomes scarcer. As energy security issues gain in importance, oil price risk is likely to grow in importance in the coming years. The ability of companies to manage oil price risk can be viewed as a source of competitive advantage. Environmentally sustainable companies will be looking at ways of reducing their consumption of fossil fuel based energy inputs through increased energy efficiency and a substitution towards renewable energy sources both of which should help to reduce a company's exposure to oil price fluctuations. While economic wealth and prosperity have, for many countries, increased dramatically over the past 100 years, so too have environmental damages. Wealth creation has, as a by-product of the production of goods and services, created environmental problems (such as land, air and water pollution). Moreover, environmental problems like air and water pollution, which were once thought of as localized pollution problems, have now increased in size and magnitude to become global problems. Climate change, for example, is now one of the biggest threats to society (Stern, 2006, 2009). Climate change (like rising temperatures, rising sea levels, altered rain fall patterns, floods, droughts, and greater variability and unpredictability in weather patterns), if left unchecked, has the potential to seriously disrupt business activity. Adequately addressing climate change will likely require putting a price on carbon (either through taxes on carbon intensive products or a cap and trade system) with the result that carbon based energy products like oil will increase in price. Pricing carbon will raise the price of carbon intensive energy products to companies and this may or may not impact energy price risk in a predictable way. Addressing carbon issues presents an environmental sustainability opportunity to manage energy price risk in several ways. A company can reduce its carbon emissions by reducing its usage of carbon intensive energy products. A reduction in energy usage is accompanied by a reduction in energy costs and energy price risk. While many companies are concerned with reducing energy price risk, companies pursuing environmentally sustainable initiatives are the ones most likely to be better positioned to manage energy price risk for several reasons. First there are direct effects. Environmentally sustainable companies focus directly on reducing consumption of fossil fuel energy either by increasing energy efficiency or by using more renewable energy. Both of these initiatives help to manage energy price risk by reducing the impact that energy price movements can have on company financial performance. There are also indirect effects. A company might want to go beyond normal environmental compliance and pursue sustainable value creation in order to create internal efficiency and external legitimacy (Hart and Milstein, 2003). Minimizing waste and pollution from operations can increase a firm's internal efficiency resulting in lower operating costs and risks of being fined. Environmental health and safety initiatives can contribute to a healthier and safer work place for employees which increase productivity. On the revenue side, environmentally sustainable companies can create a competitive advantage by offering green products that consumers are willing to buy. Environmentally sustainable companies can create a business payoff in the form of increased reputation and legitimacy. Society grants businesses a license to operate and this license can be taken away if a company becomes an undesirable member of society. The resources and capabilities necessary to achieve internal efficiency and external legitimacy require a strong commitment to sustainable value creation and these initiatives are likely to be useful for managing energy price risk.

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This present paper follows in the footsteps of financial risk management papers and uses multivariable regression models to investigate the relationship between environmental sustainability, energy price risk and stock price performance. A priori, it is expected that companies that score higher on environmental sustainability should be better at managing energy price risk. 3. Empirical methodology The impact of energy price risk on company stock prices can be estimated using a multi-factor market model. A multi-factor model is an extension of the capital asset pricing model (CAPM) to include the impact of several different risk factors on asset returns (Brealey and Myers, 2000; Bodie et al., 1996). While the CAPM relates stock price returns to just one factor, the market returns, a multi-factor model extends the CAPM to allow for multiple factors. A multi-factor market model has been used in the energy economics and finance literature by a number of authors (see for example, Hilliard and Danielsen, 1984; Al-Mudhaf and Goodwin, 1993; Faff and Brailsford, 1999; Rajgopal, 1999; Sadorsky, 2001, 2008; El-Sharif et al., 2005; Boyer and Filion, 2007; Jin and Jorion, 2006). In this paper a multi-factor model is used which allows for factors on the market return and energy prices. Rit = αi + βiet Ret + βim Rmt + εit

ð1Þ

In Eq. (1), firm stock returns are Rit (i denotes the firm and t denotes the time period) Rmt are stock market returns and Ret are energy price returns. The random error term εit is assumed to be distributed with a zero mean and constant variance. The parameters, βe and βm are the energy beta and market beta respectively. Eq. (1) is the baseline model. In order to investigate the relationship between environmental sustainability and energy price risk, it is necessary to specify an equation for energy price exposure (Rajgopal, 1999; Jin and Jorion, 2006). It is most likely that energy price exposure is time varying and related to the size of the firm (Size), energy price volatility (OilVol), and environmental sustainability (ES). Increases in environmental sustainability through greater energy efficiency and usage of renewable energy, the focus of this paper, should lead to increases in the ability to manage energy price risk and a reduction in energy price exposure (Hart, 1995; Hart and Milstein, 2003; Esty and Winston, 2006; Busch and Hoffmann, 2007; Enkvist et al., 2008). After all, companies that use energy more efficiently and use more renewable energy should be less adversely affected by movements in the price of fossil fuel based energy products. Environmental sustainability can be viewed as a form of hedging against energy price increases and become part of a company's larger risk management strategy. In order to deal with possible endogeneity issues, the environmental sustainability variable is lagged one period. In general, larger firms are more likely to have more extensive resources, capabilities and experience in deploying their resources, realize economies of scale and have higher productivity (Caves and Barton, 1990) or profitability (Bradburd and Ross, 1989) and these factors should be useful when dealing with how to respond to changes in the external business environment of which oil or energy price changes are a specific example. Thus increases in firm size should reduce energy price exposure. Modern portfolio theory posits a positive relationship between risk and return and investors need to be compensated with higher returns for holding more risky assets. Increases in oil price volatility increases the variability of future cash flows and the risk of holding shares in a company (Huang et al., 1996; Sadorsky, 2003). This increase in uncertainty in the oil markets should increase energy price exposure (Sadorsky, 2008). In this paper, energy price volatility is measured using oil price volatility (OilVol) because oil prices are observed on a daily basis (which provides a large number of

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observations from which to calculate annual volatility) while many energy price indicators are usually observed monthly. Eq. (2) specifies this relationship for time varying energy beta. βiet = δi + γis Sizeit + γiv OilVolt + γies ESit−1 + υit

ð2Þ

Eq. (2) cannot be directly estimated because the energy beta is not known. The model can be estimated by allowing the energy beta in Eq. (1) to be time varying and substituting Eq. (2) into Eq. (1) for the energy price risk factor. This approach has been previously used by Rajgopal (1999) and Jin and Jorion (2006). The estimated equation is2 Rit = α + δ Ret + γs Sizeit Ret + γv OilVolt Ret

ð3Þ

+ γes ESit−1 Ret + βm Rmt + εit The relationship between energy price risk and stock prices is investigated using a firm-year level of analysis. A panel data set consisting of a cross-section of 302 firms was followed over the time period 1996 to 2006 and estimated using panel regression techniques that pool cross-sectional and time-series data (Greene, 2007). Following the recommendations of Beck and Katz (1995), the panel data set is estimated using ordinary least squares and panel corrected standard errors (PCSE), which are robust to heteroskedasticity and serial correlation, are computed. More specifically, the standard errors used to compute the t statistics are robust to arbitrary serial correlation and time varying variances in the disturbances (the so called White period estimator). Robustness is checked by estimating the models using two different measures of energy prices (a producer price energy index (PPI) and oil prices) and by re-estimating the models excluding oil and oil related companies. 4. Data For this study, data is required on firm stock prices, firm size, firm level environmental sustainability, market returns, and energy prices. Two data sets (COMPUSTAT and KLD) were merged together to form a panel data set consisting of a cross-section of 302 firms which is followed over the time period 1996 to 2006. Continuously compounded annual company stock returns (adjusted for stock splits and including dividends) are the dependent variable in each regression model. Firm size is measured by the natural logarithm of annual sales in millions of dollars. Annual sales as a measurement of firm size are consistent with much of the economics literature (e.g. Schumpeter, 1942; Scherer, 1980; Capon et al., 1990) that uses sales volume to classify the size of firms. Company stock market return data and size data is available from COMPUSTAT. Market returns are measured by the returns on a value weighted U.S. stock market index (Chen et al., 1986; Chen, 1991). This data is available from CRSP. All raw data are annual and cover the period 1996 to 2006. Two measures of energy prices are used in this paper. The first is the producer price index (PPI) for finished energy products. This variable (PPIFEG, 1982 = 100) provides a reasonable approximation for what businesses pay for energy products. This data is available from the Federal Reserve Bank of St. Louis.3 Energy price returns are measured as the log difference in this index. The second measure of energy prices uses oil prices. Oil price returns are measured as the log difference of the yearly return on the West Texas Intermediate (WTI) nearest crude oil futures contract which trades on the New York Mercantile Exchange (NYMEX). The WTI futures contract is the most widely traded oil futures contract in the world and is used as a 2 Notice that it is not possible in practice to estimate six firm specific coefficients for each of the firms in the sample. This would use up so many degrees of freedom that it would defeat the efficiency gains realized in using panel regression techniques. 3 http://www.stlouisfed.org/default.cfm.

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benchmark to set other oil product related prices. Moreover, The NYMEX oil futures contract is the most heavily traded futures contract on a physical commodity in the world and therefore represents an efficient flow of information between buyers and sellers.4 Following Sadorsky (2006, 2008), annual oil price volatility is measured by calculating the square root of the sum of squared daily returns for each calendar year to arrive at an annual measure of oil price volatility. The oil price futures series is available from Datastream. Oil prices and oil price volatility have both varied considerably over the estimation period (Fig. 1). Notice how closely together the producer price index (PPI) for finished energy products and oil prices move. Environmental sustainability is measured using the environmental screens from Kinder, Lydenburg, and Domini (KLD) Research and Analytics.5 KLD is an “independent investment research firm providing management tools to professionals integrating environmental, social and governance factors (ESG) into their investment decisions.”.6 KLD is a company that researches the social, environmental, and governance performance of corporations. KLD collects data from a number of different sources (like, company annual and quarterly reports, company proxy reports, government, non-governmental, and media) and uses this data in combination with a proprietary screening process to evaluate the social, environmental, and governance performance of corporations. KLD assigns positive (strengths) and negative (concerns) indicators (binary 0/1 variables) across a wide variety of stakeholder and social issues. Companies are evaluated on their strengths or concerns in areas like community, corporate governance, diversity, employee relations, environment, human rights and product. Companies are also evaluated with respect to their involvement in businesses related to alcohol, firearms, gambling, military, nuclear power and the tobacco. KLD has covered the S&P 500 companies since 1991 and since 2002 has covered the largest, by market capitalization, 3000 U.S. companies. The KLD data base is widely used in research (see, for example, the recent paper by Becchetti, Di Giacomo, and Pinnacchio (2008) and the references cited within their paper). This paper uses data from KLD's environmental screen. The environmental screen includes sub-categories for strengths in pollution prevention, recycling, clean energy, new property plant and equipment, management environmental systems, and weaknesses in hazardous waste, regulation problems, ozone depleting chemical, substantial emissions, agriculture chemicals, and climate change issues (see Appendix A). KLD uses a proprietary methodology to assign a 0 (neutral) or 1 (for strength or concern) to each of these sub-categories. An index of environmental sustainability is computed as the net value of the total number of environmental strengths less the total number of environmental concerns.7 Summary statistics and correlations are reported in Tables 1 and 2 respectively. Following Aiken and West (1991) the interaction variables, firm size, environmental sustainability, energy prices, oil prices and oil price volatility are standardized around their mean values to avoid possible problems with multicollinearity. Notice that the average annual market returns have a higher mean value and lower standard deviation than do individual firm returns, illustrating the benefits of portfolio diversification (Table 1). Notice also that market returns and environmental sustainability correlate positively with company stock returns while the other explanatory variables

4

www.nymex.com. http://www.kld.com/research/methodology.html. 6 http://www.kld.com. 7 The data set used in our paper is complex to construct because it involves merging two different databases (KLD and COMPUSTAT). In designing the empirical approach for our paper our objective was to follow as many firms as possible over a reasonably long (ten-year) estimation period. Starting in 1996, we found 302 companies that were continuously listed for each of the years 1996 to 2006 in the KLD database with no missing values and for which there were no missing values in the COMPUSTAT database. After allowing for lags and the calculation of returns, there is a ten-year estimation sample (1997–2006). 5

Fig. 1. Crude oil price and volatility (NYMEX near-month futures prices) and PPI for finished energy products.

correlate negatively with company stock returns (Table 2). The reported variance inflation factors (VIFs) are each less than 10 which indicates that multicollinearity among the explanatory variables is not a problem (Kennedy, 2003). 5. Empirical results and discussion This section reports empirical results from estimating energy price exposure. Energy price exposure is calculated from pooled crosssectional time-series regression of stock returns on the market changes and oil price changes with coefficients adjusted for the effect of firm size, oil price volatility and environmental sustainability. Empirical results are reported for regression models estimated using two different measures of energy prices. The first set of results is from models that use the producer price index of finished energy products as the energy price variable. The second set of results is from models that use oil prices as the energy price variable. 5.1. Empirical results — energy prices measured using PPI for finished energy products This section reports empirical results when energy prices are measured using the producer price index for finished energy products. The empirical results are reported under the second, third, and fourth columns of Table 3. The model labeled “Basic” is the benchmark model and relates company stock price returns to market returns and energy price returns. The model labeled “All COs” reports coefficient estimates from estimating Eq. (3) using all companies in the data set. The model labeled “Ex-oil COs” is similar to the “All COs” but omits oil and oil related companies from the estimation. The “Exoil COs” model is included in Table 3 in order to provide robustness checks to see how sensitive the empirical results are to the inclusion of oil and oil related companies. The estimated coefficient on the market return variable is positive and statistically significant at the 1% level in each model that uses the PPI indicating that the average firm in the sample has a market beta of approximately 0.4. The estimated coefficient on the energy price returns variable is negative and statistically significant at the 1% level in each model that uses the PPI and reasonably similar in value across the models (it ranges in value from −1.4 to −2.7 indicating that a one percent increase in energy prices (standardized) lowers stock prices between 1.4% and 2.7%) providing strong evidence that increases in energy prices reduces company stock returns. This is consistent with a growing literature that finds increases in oil prices leads to a reduction in stock prices (see for example the discussion in Sadorsky (2008) and the references cited within).

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Table 1 Summary statistics.

Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera Probability Observations

Returns

Market

Size

Energy

Oil

Oil vol

ES(− 1)

8.274 10.203 156.977 − 210.163 32.574 − 0.624 6.707 1925.5 0.000 3020

9.816 13.977 29.553 − 21.363 16.931 − 0.557 1.910 305.8 0.000 3020

0.000 0.054 3.116 − 3.175 1.000 − 0.089 2.962 4.2 0.124 3020

0.000 0.442 1.230 − 1.799 1.000 − 0.529 1.825 314.4 0.000 3020

0.000 − 0.117 1.862 − 1.312 1.000 0.234 2.066 137.4 0.000 3020

0.000 − 0.138 1.634 − 1.505 1.000 0.072 1.860 166.2 0.000 3020

0.000 0.224 3.226 − 4.780 1.000 − 1.333 6.198 2181.4 0.000 3020

Stock returns and oil price returns are continuously compounded at annual rates over the period 1997 to 2006. Size, energy price returns, oil price returns, oil price volatility and environmental sustainability are standardized around zero. There are 3020 observations.

5.2. Empirical results — energy prices measured using oil prices

Table 2 Correlations.

Returns Market Size Energy Oil Oil vol ES(− 1) VIF

Returns

Market

Size

Energy

Oil

Oil vol

ES(− 1)

1.000 0.302 − 0.010 − 0.043 − 0.143 − 0.067 0.036

0.302 1.000 − 0.027 − 0.085 − 0.161 − 0.216 0.049 1.118

− 0.010 − 0.027 1.000 0.046 0.042 − 0.046 − 0.276 1.086

− 0.043 − 0.085 0.046 1.000 0.820 − 0.241 − 0.030 3.080

− 0.143 − 0.161 0.042 0.820 1.000 − 0.269 − 0.021 3.233

− 0.067 − 0.216 − 0.046 − 0.241 − 0.269 1.000 − 0.008 1.167

0.036 0.049 − 0.276 − 0.030 − 0.021 − 0.008 1.000 1.085

There are 3020 observations. Correlation coefficients above 0.047 (0.036) in absolute value are statistically significant at the 1% (5%) level of significance. VIF are the variance inflation factors.

The model labeled “All COs” reports coefficient estimates from estimating Eq. (3) when all of the companies are included in the data set.8 The estimated coefficient on the energy price returns × environmental sustainability variable is negative and statistically significant at the 1% level indicating that increases in environmental sustainability reduce energy price risk. This result provides strong statistical evidence that increases in environmental sustainability reduce energy price risk. The estimated coefficient on the energy price returns × oil price volatility variable is positive and statistically significant at the 1% level indicating that increases in oil price volatility increases energy price risk. The estimated coefficient on the energy price returns × size variable is negative and statistically significant at the 10% level indicating that increases in firm size reduce energy price risk. A plot of the time varying energy beta, calculated using the estimated coefficients from this model, shows how the energy beta changed across time (Fig. 2). Notice how the energy beta tends to be high when oil price volatility, shown in Fig. 1, is high. As a robustness check on the results, the “All COs” model was reestimated omitting oil and oil related companies from the sample. It is important to do this because, while rising energy prices depress stock prices for most companies, rising energy prices are beneficial to companies engaged in the oil industry. Omitting the oil companies leaves 294 companies in the estimation sample. The empirical results from the “Ex-oil COs” model are very similar to the estimated results from the “All COs” model in terms of the signs magnitudes, and significance of estimated coefficients. Omitting the oil and oil related companies from the sample, does not affect the estimated coefficients in any significant way (possibly due to the fact that there are so few oil companies in the sample to begin with).

8 The regression results reported in Table 3 are estimated without fixed effects. This is because an F test for redundant cross-section effects turns up a value of 0.613 (p value = 1.00) indicating that fixed effects can be removed from the model. Moreover, models estimated with fixed effects produced estimated coefficients that were remarkably similar in sign, significance, and magnitude to those reported in Table 3.

This section reports results from estimating a time varying energy price beta when energy prices are measured using oil prices. The results are reported in the three most right hand columns of Table 3. Qualitatively, the results from estimating energy price exposure using oil prices are very similar to the results obtained from estimating energy price exposure using the PPI for finished energy products.9 The estimated coefficients on the market return variable are positive and statistically significant at the 1% level while the estimated coefficients on the oil price variable are negative and statistically significant at the 1% level. Increases in environmental sustainability reduce energy price risk while increases in oil price volatility increase energy price risk. The firm size variable has a stronger impact on energy price risk when energy prices are measured using oil prices. The results from Table 3 show that increases in environmental sustainability reduce energy price risk while increases in oil price volatility increase energy price risk. This result is robust across two different measures of energy prices.10 Moreover, the time varying energy price beta estimates using the PPI for finished energy products are very similar to the time varying oil price beta estimates from oil prices. A plot of the time varying oil beta (Fig. 3), calculated from the estimated regression coefficients shown in column six of Table 3, shows a time-series pattern very similar to the time varying energy price (PPI) beta shown in Fig. 2. 6. Policy implications Table 4 uses the regression results from the All COs model for the PPI in Table 3 to provide estimates of the energy price exposure Eq. (2) under different assumptions about environmental sustainability and oil price volatility. In the scenarios, shown in Table 4, the standardized size of the firm is set at its sample mean value of zero. This translates into a firm with average annual sales of 5.3 billion dollars. The benchmark energy price exposure value of − 2.34 is determined by setting standardized sales, oil price volatility, and environmental sustainability at their sample average values of zero. Recall, that firm sales, oil price volatility and environmental sustainability are each standardized around zero. Setting standardized oil price volatility equal to zero translates into an annualized volatility of 37.06%. Setting

9 With reference to the All COs model, an F test for redundant cross-section effects turns up a value of 0.625 (p value = 1.00) indicating that fixed effects can be removed from the model. 10 Following the suggestion of a reviewer, the all COs Models were re-estimated using a GARCH measure of oil price volatility. For comparison purposes, a second annual volatility measure is calculated from a GARCH(1.1) model with a generalized error distribution. The correlation coefficient between the two oil price volatility measures is 0.988. The two different measures of oil price volatility produce remarkably similar regression results in terms of the sign, magnitude and significance of their respective estimated regression coefficients.

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Table 3 Energy prices, environmental sustainability and stock returns. Energy price is measured by PPI

Market returns PPI returns

Energy price is measured by oil prices

Basic

All COs

Ex-oil COs

Basic

All COs

Ex-oil COs

0.441*** (15.907) − 1.361*** (− 3.390)

0.446*** (12.153) − 2.343*** (− 4.080) − 1.095* (− 1.785) 3.662*** (6.776) − 1.538*** (− 2.997)

0.455*** (12.085) − 2.742*** (− 4.849) − 0.911 (− 1.542) 3.530*** (6.387) − 1.460*** (− 2.683)

0.452*** (12.664)

0.405*** (10.394)

0.415*** (10.420)

− 3.642*** (− 6.297)

− 3.908*** (− 6.845) − 1.385** (− 2.267) 3.740*** (6.471) − 1.385** (− 2.489) 6.038*** (9.307) 0.114 77.335 0.000

− 4.203*** (− 7.370) − 1.193** (− 2.003) 3.419*** (5.895) − 1.428** (− 2.478) 5.701*** (8.689) 0.118 78.403 0.000

PPI returns × Size PPI returns × Oil vol PPI returns × ES(− 1) Oil price returns Oil price returns × Size Oil price returns × Oil vol Oil price returns × ES(− 1) Constant Adjusted R-squared F-statistic Prob (F-statistic)

4.823*** (11.423) 0.081 184.799 0.000

5.498*** (8.987) 0.100 67.187 0.000

5.236*** (8.407) 0.103 67.489 0.000

4.556*** (8.204) 0.097 160.827 0.000

Basic is the benchmark model and relates company stock price returns to market returns and energy price returns. All COs reports coefficient estimates from estimating Eq. (3) using all companies in the data set. Ex-oil COs is similar to the All COs but omits oil companies from the estimation. Heteroskedasticity and autocorrelation consistent t statistics, in parentheses, reported below estimated coefficients. Panel estimation technique is ordinary least squares. F-statistic is a joint test for all slope variable coefficients equal to zero. ***p < 0.01, **p < 0.05, *p < 0.10.

standardized environmental sustainability equal to zero translates into an environmental sustainability value of − 0.21. This means that for a company with $5.3 billion in sales and an environmental sustainability index of −0.21, and oil price volatility at 37.06%, a one unit increase in standardized PPI returns (which corresponds to a 18.17% increase in actual PPI returns) decreases stock price returns by 2.34%. A similar calculation can be done for oil prices, estimated using the estimated coefficients from column six of Table 3, showing that for a company with $5.3 billion in sales and an environmental sustainability index of −0.21, and oil price volatility at 37.06%, a one unit increase in standardized oil price returns (which corresponds to a 46.38% increase in actual oil price returns) decreases stock price returns by 3.91%. Combinations of oil price volatility and environmental sustainability that yield lower energy price exposure than the benchmark value of −2.34 are shown in bold. Table 4 shows that when oil price volatility is low, say less than or equal to − 1.10, small increases in environmental

sustainability can easily reduce energy price sensitivity below the baseline value even if environmental sustainability is not a positive number. Recall that environmental sustainability is measured as the standardized value of the net value of the total number of environmental strengths less the total number of environmental concerns. As oil price volatility increases, however, environmental sustainability needs to increase in order to mitigate the impact of higher oil price volatility. Moreover, for very high values of oil price volatility, environmental sustainability needs to be a large positive value. The good news is that with almost any value of oil price volatility, a large enough value of environmental sustainability can offset the impact of oil price volatility and reduce a company's energy price exposure and thereby reduce the energy price risk a company faces from rising energy prices. Table 5 reports on scenario analysis for energy price risk when energy prices are measured using oil prices. The scenarios in Table 5 are constructed using the estimated regression coefficients from the

Fig. 2. Time varying energy (PPI) beta.

Fig. 3. Time varying energy (oil prices) beta.

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Table 4 Scenario results of changes in oil price volatility and environmental sustainability on PPI energy beta sensitivity.

− 1.70 − 1.50 − 1.30 − 1.10 − 0.90 − 0.70 − 0.50 − 0.30 − 0.10 0.10 0.30 0.50 0.70 0.90 1.10 1.30 1.50 1.70

− 5.00

− 4.00

− 3.00

− 2.00

− 1.00

0.00

1.00

2.00

3.00

4.00

5.00

− 0.88 − 0.14 0.59 1.32 2.05 2.79 3.52 4.25 4.98 5.72 6.45 7.18 7.91 8.64 9.38 10.11 10.84 11.57

− 2.41 − 1.68 − 0.95 − 0.22 0.51 1.25 1.98 2.71 3.44 4.18 4.91 5.64 6.37 7.11 7.84 8.57 9.30 10.04

− 3.95 − 3.22 − 2.49 − 1.76 − 1.02 − 0.29 0.44 1.17 1.91 2.64 3.37 4.10 4.84 5.57 6.30 7.03 7.76 8.50

− 5.49 − 4.76 − 4.03 − 3.29 − 2.56 − 1.83 − 1.10 − 0.36 0.37 1.10 1.83 2.56 3.30 4.03 4.76 5.49 6.23 6.96

− 7.03 − 6.30 − 5.57 − 4.83 − 4.10 − 3.37 − 2.64 − 1.90 − 1.17 − 0.44 0.29 1.03 1.76 2.49 3.22 3.96 4.69 5.42

− 8.57 − 7.84 − 7.10 − 6.37 − 5.64 − 4.91 − 4.17 − 3.44 − 2.71 − 1.98 − 1.24 − 0.51 0.22 0.95 1.68 2.42 3.15 3.88

− 10.11 − 9.37 − 8.64 − 7.91 − 7.18 − 6.45 − 5.71 − 4.98 − 4.25 −3.52 − 2.78 − 2.05 − 1.32 − 0.59 0.15 0.88 1.61 2.34

− 11.65 − 10.91 − 10.18 − 9.45 − 8.72 − 7.98 − 7.25 − 6.52 − 5.79 − 5.05 − 4.32 − 3.59 − 2.86 − 2.12 − 1.39 − 0.66 0.07 0.81

− 13.18 − 12.45 − 11.72 − 10.99 − 10.25 − 9.52 − 8.79 − 8.06 − 7.32 − 6.59 − 5.86 − 5.13 − 4.40 − 3.66 − 2.93 − 2.20 − 1.47 − 0.73

− 14.72 − 13.99 − 13.26 −12.53 − 11.79 − 11.06 − 10.33 − 9.60 − 8.86 − 8.13 − 7.40 − 6.67 − 5.93 − 5.20 − 4.47 − 3.74 − 3.00 − 2.27

− 16.26 − 15.53 − 14.80 − 14.06 − 13.33 − 12.60 − 11.87 − 11.13 − 10.40 − 9.67 − 8.94 − 8.20 − 7.47 − 6.74 − 6.01 − 5.27 − 4.54 − 3.81

The table is drawn with standardized environmental sustainability on the horizontal axis (− 5 to 5) and standardized oil price volatility on the vertical axis (− 1.70 to 1.70). Standardized firm size is set at the sample average of zero. The values in bold are less than the energy price exposure baseline value of − 2.34.

All COs model using oil prices in Table 3. The benchmark energy price exposure value of −3.91 is determined by setting sales, oil price volatility, and environmental sustainability at their standardized sample average values of zero. While the numerical values of the entries in Table 5 differ from those in Table 4, the main results from Table 5 are also found in Table 4. Both tables show that for relatively small values of oil price volatility, small increases in environmental sustainability can easily reduce energy price risk below the baseline value. As oil price volatility increases in value, energy price risk can be reduced below the baseline value only for relatively high values of environmental sustainability. The scenario results reported in Tables 4 and 5 supports each other in showing how useful improvements in environmental sustainability can be in managing energy price risk. Firms which promote beneficial products and services, pollution prevention, recycling, clean energy, new property plant and equipment, and management environmental systems are going to have greater environmental strengths than companies that do not promote these initiatives. Firms which reduce hazardous waste, regulation problems, ozone depleting chemical, substantial emissions, agriculture chemicals, and climate change issues will also have a stronger environmental sustainability record compared to firms that do not address these concerns. Government programs that encourage the

building of environmental strengths (possibly through subsidies and government support programs which encourage the building of environmental sustainable resources and capabilities) and discourage the accumulation of environmental concerns (possibly through taxes or quotas on pollution generating activities) would be beneficial in helping companies reduce their exposure to energy price risk. This makes particular sense for companies located in countries that import a lot of oil. Since oil is a globally traded commodity and its price subject to the global economics of supply and demand, institutional factors (like OPEC), geopolitical issues, and speculation, price movements in oil are exogenous to the operations of any one business. Managing oil price risk through environmental sustainability at the company level provides one way for companies to deal with changes in oil prices. After all, companies may not be able to control the price of oil or energy related products, but they can control the way they use energy, how much energy they use, the energy mix in their operations, and the level of environmental sustainability. 7. Conclusions The impact that rising energy prices have on stock prices is an important and interesting topic to study and one that is likely to grow

Table 5 Scenario results of changes in oil price volatility and environmental sustainability on oil beta sensitivity.

− 1.70 − 1.50 − 1.30 − 1.10 − 0.90 − 0.70 − 0.50 − 0.30 − 0.10 0.10 0.30 0.50 0.70 0.90 1.10 1.30 1.50 1.70

− 5.00

− 4.00

− 3.00

− 2.00

− 1.00

0.00

1.00

2.00

3.00

4.00

5.00

− 3.34 − 2.59 − 1.84 − 1.10 − 0.35 0.40 1.15 1.90 2.64 3.39 4.14 4.89 5.64 6.38 7.13 7.88 8.63 9.38

− 4.73 − 3.98 − 3.23 − 2.48 − 1.73 − 0.99 − 0.24 0.51 1.26 2.01 2.75 3.50 4.25 5.00 5.75 6.49 7.24 7.99

− 6.11 − 5.36 − 4.61 − 3.87 − 3.12 − 2.37 − 1.62 − 0.87 − 0.13 0.62 1.37 2.12 2.87 3.61 4.36 5.11 5.86 6.61

− 7.50 − 6.75 − 6.00 − 5.25 − 4.50 − 3.76 − 3.01 − 2.26 − 1.51 − 0.76 − 0.02 0.73 1.48 2.23 2.98 3.72 4.47 5.22

− 8.88 − 8.13 − 7.38 − 6.64 − 5.89 − 5.14 − 4.39 − 3.64 − 2.90 − 2.15 − 1.40 − 0.65 0.10 0.84 1.59 2.34 3.09 3.84

− 10.27 − 9.52 − 8.77 − 8.02 − 7.27 − 6.53 − 5.78 − 5.03 − 4.28 − 3.53 − 2.79 − 2.04 − 1.29 − 0.54 0.21 0.95 1.70 2.45

− 11.65 − 10.90 − 10.16 − 9.41 − 8.66 − 7.91 − 7.16 − 6.42 − 5.67 − 4.92 − 4.17 − 3.42 − 2.68 − 1.93 − 1.18 − 0.43 0.32 1.06

− 13.04 − 12.29 − 11.54 −10.79 − 10.04 − 9.30 − 8.55 − 7.80 − 7.05 − 6.30 − 5.56 − 4.81 − 4.06 − 3.31 − 2.56 − 1.82 − 1.07 − 0.32

− 14.42 − 13.67 − 12.93 − 12.18 − 11.43 − 10.68 − 9.93 − 9.19 − 8.44 − 7.69 − 6.94 − 6.19 − 5.45 − 4.70 − 3.95 − 3.20 − 2.45 − 1.71

− 15.81 − 15.06 − 14.31 − 13.56 − 12.81 − 12.07 − 11.32 − 10.57 − 9.82 − 9.07 − 8.33 − 7.58 − 6.83 − 6.08 − 5.34 − 4.59 − 3.84 − 3.09

− 17.19 − 16.44 − 15.70 − 14.95 − 14.20 − 13.45 − 12.70 − 11.96 − 11.21 − 10.46 − 9.71 − 8.96 − 8.22 − 7.47 − 6.72 − 5.97 − 5.22 − 4.48

The table is drawn with standardized environmental sustainability on the horizontal axis (− 5 to 5) and standardized oil price volatility on the vertical axis (− 1.70 to 1.70). Standardized firm size is set at the sample average of zero. The values in bold are less than the oil price exposure baseline value of − 3.91.

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in greater importance in the coming years as energy security issues and climate change become more pronounced. Energy security issues and climate change are two of the most pressing problems facing society and both of these problems are likely to lead to increases in energy price risk in the coming years. Effectively managing energy price risk can lead to competitive advantage through lower operating costs and less business risk and environmental sustainability might be one way to manage energy price risk. In this paper, a model is postulated and estimated showing that increases in environmental sustainability can lead to reductions in the exposure of firms to energy price risk. The main finding in this paper is that companies that score higher on environmental sustainability are better positioned to manage energy price risk. This result is robust across two different measures of energy prices. These results should be useful to companies seeking new ways of addressing energy price risk as well as governments worried about the impact that energy price risk can have on economic growth and prosperity. Moreover, environmental sustainability also offers a way of addressing energy security issues and climate change since both of these issues contribute to energy price risk. Acknowledgements We thank the Social Sciences and Humanities Research Council of Canada (Grant #410-2005-2188) for partial funding of this research and two anonymous reviewers for helpful comments. Appendix A

KLD environment screens Beneficial products and services Pollution prevention Recycling Clean energy Property, plant, equipment (through 1995) Management systems strength (from 2006) Other strengths Total number of environment strengths (max 7) Hazardous waste Regulatory problems Ozone depleting chemicals Substantial emissions Agriculture chemicals Climate change (from 1999) Other concerns Total number of environment concerns (max 7)

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