Sustainable energy systems and company performance: Does the implementation of sustainable energy systems improve companies’ financial performance?

Accepted Manuscript Sustainable energy systems and company performance: Does the implementation of sustainable energy systems improve companies’ financial performance?

Carmen-Pilar Martí-Ballester PII:

S0959-6526(16)32061-3

DOI:

10.1016/j.jclepro.2016.12.015

Reference:

JCLP 8590

To appear in:

Journal of Cleaner Production

Received Date:

02 August 2015

Revised Date:

28 November 2016

Accepted Date:

03 December 2016

Please cite this article as: Carmen-Pilar Martí-Ballester, Sustainable energy systems and company performance: Does the implementation of sustainable energy systems improve companies’ financial performance?, Journal of Cleaner Production (2016), doi: 10.1016/j.jclepro.2016.12.015

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ACCEPTED MANUSCRIPT Theme 5: Sustainable energy innovations, green products and services Sustainable energy systems and company performance: Does the implementation of sustainable energy systems improve companies’ financial performance?* Carmen-Pilar Martí-Ballester Business Department Centre for Studies and Research in Humanities Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain. Phone: +34935814425 E-mail: [email protected]/[email protected] Highlights We propose a model that analyses the nexus between sustainable energy systems and CFP. The adoption of sustainable energy management systems reduces CFP. Improving Energy Efficiency does not affect CFP. The use of renewable energy sources does not influence CFP. Abstract The main aim of this paper is to analyse whether the adoption of sustainable energy systems improves corporate financial performance. To this end, we obtained data from a sample of 574 multinational companies from 36 countries in the 2008-2013 period. On this data we implement a dynamic system panel data method. Our findings show that the adoption of sustainable energy systems allows firms to improve their short-term corporate financial performance while not leading them to reduce their corporate financial performance in the long term. Specifically, our results indicate that an increase in energy efficiency and the use of renewable energy sources do not significantly affect corporate financial performance. Neither does the integration of energy efficiency systems and renewable energy sources have any significant influence on corporate financial performance, while the level of implementation of sustainable energy management systems has a significant effect on short-term, but not long-term, corporate financial performance. Furthermore, other control variables, such as research and development expenditure and year, are also relevant in explaining firms’ financial performance. The adoption of sustainable energy systems helps to improve corporate financial performance in the short-term but has no affect in the long-term. This might be because the monitoring of energy efficiency using key performance indicators allows firms to detect and correct failures in the production process and hence improve shortThe author is grateful to the journal’s Editorial Board (Prof. Rodrigo Lozano, Prof. Yang Liu, Prof. Jussi Kantola and Prof. Pekka Peura) and two anonymous referees for their helpful comments. *

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ACCEPTED MANUSCRIPT term financial performance. However, this measure does not generate competitive advantages for firms and therefore has no effect on long-term corporate financial performance. From a scientific perspective of energy, this paper contributes to the literature by providing a detailed explanation of how the adoption of sustainable energy systems proposed by Peura (2013) influences corporate financial performance on the basis of new empirical evidence. These explanations provide a strong rationale for improving the efficient use of energy and harvesting low hanging fruits in the short term from a managerial perspective. Policymakers should encourage firms to align sustainable energy systems with their core business strategy by developing green technologies that allow them to acquire rare and valuable capabilities and abilities, which generate competitive advantage, and therefore allow firms to increase their longterm corporate financial performance. Keywords: renewable energy, rational use of energy, corporate financial performance, companies, dynamic panel data model Abbreviations NGOs: Non-governmental organizations CPF: Corporate financial performance CO2: Carbon Dioxide RUE: Rational Use of Energy RES: Renewable Energy Sources VIF: Variance Inflation Factors TRBC Industry: Thomson Reuters Business Classification Industry GMM: generalised method of moments estimator

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ACCEPTED MANUSCRIPT 1. Introduction Energy is an important issue to be considered in the achievement of sustainable development1 as its use produces greenhouse gases, mostly CO2 resulting from the oxidation of carbon in fuels during combustion (International Energy Agency, 2015), which affects all aspects of environmental, social and economic development (Houston et al., 2014). Specifically, 81%-82% of the world’s total primary energy supply was based on fossil fuels in the 2008-2013 period (International Energy Agency, 2015). This type of energy source is finite and polluting (Peura, 2013), its combustion generating between 60%-70% of global CO2 emissions from 2008 to 2013 (International Energy Agency, 2015). An important percentage of worldwide CO2 emissions are related to business activities. Specifically, energy industries and construction and manufacturing industries produced an average of 66.72% of global CO2 emissions from 2008 to 2013 (International Energy Agency, 2015). Meanwhile, CO2 emissions from the transport sector -which includes all transport activity- represented about 22%-23% of global emissions between 2008 and 2013, as shown in table 1. These CO2 emissions generated by business activity come from (1) the use of fossil fuels, which are burnt for energy during the production process and transportation of products (Abdelaziz et al., 2011; Geller et al., 2004), and (2) energy demand. Companies are the main consumers of electricity, and heat generated by the energy sector contributed to about 37.61%, on average, of the CO2 emissions produced by the energy sector and 66.72%, on average, of global CO2 emissions from 2008 to 2013 (International Energy Agency, 2015).

[Table 1]

These emissions produce an environmental impact associated with air, water and soil pollution, which have serious implications for climate, health, water and food safety (Abulfotuh, 2007). Stakeholders concerned about this situation are pressuring companies into implementing sustainable energy systems in their business activities (Hepbasli, 2008). Taking the definition of sustainable energy provided by Peura (2013), sustainable energy systems should include: (1) the Rational Use of Energy (RUE), (2) Renewable Energy Sources (RES), (3) Integration of RUE and RES, and (4) sustainability management. The Rational Use of Energy (RUE) consists of a set of actions aimed at increasing the efficient use and saving of energy (Peura, 2013; Schaumann, 2007) in order to balance the energy availability/consumption ratio with environmental status (Dias et al., 2004). 1Bolis

et al. (2014) define sustainable development as “the kind of development aimed at satisfying the human needs of society as a whole (including future generations) beyond a minimum level, which is enabled by an axiological perspective in decision-making, considering environmental limits”.

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ACCEPTED MANUSCRIPT For companies, high energy efficiency in industrial production processes to enable them to maximise energy productivity and reduce energy input and costs is of utmost importance in order to gain a competitive edge through financial savings, the mitigation of energy dependence (Geller et al., 2004; Marques and Fuinhas, 2011) and the attainment of sustainable development (Schaumann, 2007). To increase energy efficiency, companies should implement RUE technologies (Abulfotuh, 2007; Peura, 2013), cogeneration technologies based on heat-and-power and heat-pump systems being especially important (Schaumann, 2007) as they allow companies to reduce the fuel input (primary energy/exergy) for a given useful energy application, which could mean a reduction in energy costs (Geller et al., 2004). In complement to these efficient energy technologies, renewable technologies may improve the reliability and efficiency of the companies that implement them (Abulfotuh, 2007). Such renewable technologies include modern wind turbine technology, solar thermal technology, photovoltaic technology and so on (Saidi and Fnaiech, 2014). These clean technologies harness the Renewable Energy Sources (RES), such as biomass, solar, hydro and wind power (Peura, 2013), that are available in each region to produce wind, geothermal, biomass/biogas, hydropower and solar energy (Abulfotuh, 2007; Houston et al., 2014). The advantages of renewable energy resources include, among others, their widespread availability, the security and stability of energy supply and their contribution to energy independence (Del Rio, 2010; Geller et al., 2004). With different combinations of Rational Use of Energy (RUE) and Renewable Energy Sources (RES) in the composition of their energy resource portfolio, companies could achieve different degrees of energy self-sufficiency (Marques and Fuinhas, 2011; Peura, 2013). According to Houston et al. (2014) and Peura (2013), the starting point is the implementation of energy efficient measures to reduce overall consumption. Once energy efficient measures have been applied, energy consumption should ideally use clean energy sources with low-to-zero carbon emissions (Brecha et al., 2011; Depoorter et al., 2015; Peura, 2013). In this case, companies should decide between (1) generating their own renewable energy on-site and/or off-site or (2) buying it from other companies through different legal instruments (Depoorter et al., 2015). To implement these sustainable energy strategies, companies should establish management systems (Peura, 2013) that allow them to (1) establish a policy to improve their energy efficiency, (2) set targets for energy efficiency and/or renewable energy, (3) use key performance indicators to monitor energy efficiency and/or renewable energy sources used and (4) describe and adopt procedures in place to improve their energy efficiency. The degree of strategy implementation may have an impact on the level of energy efficiency achieved and on renewable energy sources employed. This could affect companies’ cost savings and consequently their financial performance. Therefore, the main aim of this paper is to analyse how the components of sustainable energy strategies influence large firms’ financial performance. Large companies are likely to respond more openly to the environmental demands of stakeholders than smaller 4

ACCEPTED MANUSCRIPT companies due to the former (1) being under greater pressure from civilian activists to adopt environmental strategies because they are more visible and thus easier to hold accountable for the environmental impact of their business activities on society than smaller companies (Serafeim, 2013) and (2) having more financial resources than smaller companies to make the high investments required to implement sustainable energy systems. We are not aware of any other studies that have examined the relationship between sustainable energy systems and large companies’ financial performance. Utilizing Peura’s (2013) framework as a reference, we argue in this research that firms that implement sustainable energy systems achieve cost savings and increased profits from a stakeholder theory and resource-based perspective. It is therefore important to examine how the components of sustainable energy systems, i.e. the Rational Use of Energy (RUE), Renewable Energy Sources (RES), the Integration of RUE and RES, and sustainability management influence corporate financial performance. Using a sample of 574 large firms, over which we adopt a dynamic system panel data estimator (Blundell and Bond, 1998; Arellano and Bover, 1995), we find that the adoption of sustainable energy systems helps to improve short-term corporate financial performance (ROA) due to the level of implementation of sustainable energy management systems, while they are not detrimental to long-term corporate financial performance (Tobin’s Q). By introducing sustainable energy systems, firms help to reduce environmental impact while their corporate financial performance is not reduced. Therefore, the implementation of sustainable energy systems contributes to sustainable development.

2. Literature review and development hypotheses Companies with environmental concerns implement sustainable energy strategies that are designed to increase energy efficiency and adopt renewable energy sources in their business activities. This has attracted the attention of researchers, who have (1) analysed the environmental policies and programmes that promote the use of energy efficiency and/or renewable energy, (2) designed new technologies and processes to reduce the use of fossil fuels, and (3) examined the relationship between the implementation of energy efficient systems and the use of renewable energy sources. However, there has been very little analysis of the relationship between the adoption of energy efficient/renewable energy systems and corporate financial performance, and studies have been limited to the industry rather than company level. The implementation of energy efficient and/or renewable technologies requires a high initial investment cost (Abulfotuh, 2007; Cooremans, 2012; Houston et al., 2014) that decreases companies’ marginal net benefits (Van Soest and Bulte, 2001) and hence leads to a reduction in competitiveness (Hull and Rothenberg, 2008). This contravenes any company’s main objective, which is to maximise its profit, and therefore shareholders’ return, from neoclassical economic theory (Friedman, 1970; King and Lenox, 2002; Levitt, 1958). 5

ACCEPTED MANUSCRIPT On the other hand, from the stakeholders’ theoretical perspective (Freeman, 1984; Donaldson and Preston, 1995), the integration of sustainable energy strategies in their core business strategy allows companies to obtain better financial performance (Lopez et al., 2009) in the long term (Porter and Kramer, 2006). Companies that adopt energy efficient and/or renewable technologies reduce their demand for conventional energy sources while, at the same time, decreasing greenhouse gas emissions, which could (1) increase their profitability in the long term by mitigating energy operating costs (Schaumann, 2007) and consequently (2) offsetting the initial high capital costs in the long-term. Additionally, the alignment of a corporate environmental strategy with the environmental preferences of stakeholders (consumers/customers, suppliers, investors, governments, regulators, financial institutions, NGOs) could provide differentiationbased competitive advantages for the companies that implement one (Galdeano-Gómez et al., 2008; Rivera, 2002). Citizens that demand rational energy use might perceive products that are made using renewable technologies or those designed to optimise environmental performance during their use to be more valuable (Russo and Fouts, 1997; Spangenberg et al., 2010). This might increase their consumption and therefore business profits in the short and long term (Hart and Ahuja, 1996). Moreover, these companies may gain a better reputation because of greater social approval (Miles and Covin, 2000), which may also increase their financial performance. Therefore, we hypothesise that: H1: Companies that implement energy efficient systems outperform those that adopt conventional energy systems. H2: Companies that use renewable energy sources outperform those that employ conventional energy sources. H3: Companies that simultaneously implement energy efficient systems and use renewable energy sources achieve better financial performance than those that adopt conventional energy systems and energy sources. The adoption of energy efficient technologies and renewable energy sources requires the implementation of management systems (Peura, 2013), which lead to changes in routines and operations, i.e., in organisational practices (Aragon-Correa et al., 2008; Crowe and Brennan, 2007; Wong, 2013). This allows companies to develop resources and capabilities based on (1) effective coordination of human and technical skills (Wong, 2013), (2) promotion of the learning process in energy efficient and renewable energy issues (Lopez et al., 2009), (3) cooperation with their stakeholders (Christmann, 2000) and (4) development of environmental information exchange channels (Hart, 1995) to adapt to changing market needs (Sirmon et al., 2007). These organisational resources and capabilities controlled by companies are valuable, rare, imperfectly imitable and non-substitutable (Crowe and Brennan, 2007) and therefore able to generate competitive advantages that help to improve corporate 6

ACCEPTED MANUSCRIPT financial performance (Leonidou et al., 2014; Lopez et al., 2009) from a resource-based view perspective. Companies with a high level of implementation of environmentfriendly practices based on energy efficiency and renewable energy could develop superior company resources and capabilities that would enable them to gain more competitive advantages and consequently improve their corporate financial performance in comparison with companies with a low level of implementation of sustainable energy management systems. We can therefore hypothesise that: H4: Companies with a high level of implementation of sustainable energy management systems outperform those that have a low level. The adoption of sustainable energy management systems could benefit from a company’s experience (Leonidou et al., 2014) because older companies may have concluded the first phase of implementation of sustainable energy systems based on adopting energy efficiency measures (Houston et al., 2014 and Peura, 2013) which allows them to initiate the second phase of implementation of sustainable energy systems focused on increasing the use of renewable energy sources (Brecha et al., 2011, Peura, 2013) while recently created firms are still at the first phase of implementation of sustainable energy systems. This would give older firms first mover advantage, leading them to become leaders in environmental practices in their sector (Lopez et al., 2009) and hence improve their financial performance. On the other hand, older firms that did not adopt sustainable energy strategies could gain an advantage with respect to recently created firms when they do begin to do so, because the former would possess more of the infrastructure required to manage environmental issues than recently created firms, which would allow them to reduce costs (Villiers et al., 2011). Older firms might have developed superior resources and capabilities related to management systems over time due to the learning effect (Paton, 2001). If these capabilities are company specific and therefore non-transferable, firms could use them to implement sustainable energy strategies, facilitating first mover advantage and improving their financial performance. We can therefore hypothesise that: H5: Older companies outperform younger companies. On the contrary, the implementation of sustainable energy management systems could be affected by companies’ lack of financial resources (Bunse et al., 2011; Rohdin et al., 2007). Managers could consider energy efficiency investments to be non-strategic and relegate them to lower priority status (Henriques and Sadorsky, 1996) and decide to invest in strategic activities as suggested by Cooremans (2012). Therefore, companies that lack financial resources to meet the requirements of implementing sustainable energy management systems would not be able to reduce their energy consumption and mitigate their environmental impacts effectively in relation to competitors that may mitigate their competitive advantages and destroy their credibility and reputation (Hart and Ahuja, 1996). 7

ACCEPTED MANUSCRIPT On the other hand, managers that have a greater stock of excess resources at their disposal during a given planning cycle may view the implementation of sustainable energy strategies as opportunities (Aguilera-Caracuel et al., 2012; Bansal, 2005) and adopt them, thus achieving competitive advantages and consequently improving financial performance. There could be an increase in the spare financial resources that are required to engage in sustainable energy systems due to better past corporate financial performance (Etzion, 2007). Therefore, we can hypothesise that: H6: Companies with more spare financial resources outperform those with a lack of financial resources. H7: Companies with better previous corporate financial performance achieve better financial performance than those with poorer previous corporate financial performance. Small companies deal with the lack of financial resources by means of specific organisational capabilities based on an entrepreneurial orientation (Aragon-Correa et al., 2008), and tend to avoid introducing environmental practices to their organisation (Leonidou et al., 2014), probably because they face less environmental risk (Chen et al., 2006) than larger companies. Larger companies are more visible to the public (Lopez et al., 2009) and present a greater environmental risk (Chen et al., 2006), which is why the adoption of sustainable energy strategies allows them to reduce their environmental impact and improve their reputation and, consequently, their market value (Cooremans, 2012) by attracting investors, customers and better employees, among others. Furthermore, large companies could benefit from the existence of economies of scale in their production processes and achieve better financial performance than smaller companies (Roberts and Dowling, 2002). We can therefore hypothesise that: H8: Large companies outperform small companies Large companies whose managers control spare financial resources might be willing to invest these excess resources in innovative technologies, new products and/or improved processes. Research and development investments associated with environmental innovation capabilities are costly for competitors to copy while environmental innovation could become a source of competitive advantage (Russo and Fouts, 1997). Thus the adoption of energy efficient and renewable energy technologies in the production process and/or in the design of new green products that optimise energy efficiency allows companies to reduce their production costs by reducing energy consumption and improving the quality and attractiveness of their products, which improves product differentiation (Hart, 1995; McWilliams and Siegel, 2000) and provides cost-based competitive advantages (King and Lennox, 2002), hence increasing their corporate financial performance (Konar and Cohen, 2001). We can therefore hypothesise that: H9: Companies that make research and development investments outperform those that do not.

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ACCEPTED MANUSCRIPT Research and development investment could be considered a form of investment in technical capital (McWilliams and Siegel, 2000) that has an influence on the relationship between sustainable energy systems and corporate financial performance, for example, when it improves or helps to develop energy efficiency and/or clean technologies used in firms’ production process (Hart, 1995; Russo and Fouts, 1997). This would increase firms’ long-term financial performance (Endrikat et al., 2014) by innovating in lower production costs due to a reduction in energy consumption. Thus, capital-intensive firms need long-term planning horizons to adapt their technologies to environment-friendly machinery due to the high investments that this involves, which has a negative effect on their cost structure and consequently on short-term corporate financial performance (Berman et al., 1999, Endrikat et al., 2014). Given that our sample includes data from 2008 to 2013, we hypothesize that: H10: Companies that make capital investments outperform those that do not. Firms might need to increase their level of debt in order to finance their environmentfriendly infrastructure and/or environmental research and development activities as suggested by Waddock and Graves (1997). This increases firms’ financial leverage, which could have a negative influence on their financial performance from neoclassical economic theory, due to the market possibly perceiving that high leveraged firms will be unable to meet their financial obligations (Ross et al., 2008) and therefore be too risky in the short-term (Brealey and Myers, 2003). However, from stakeholder theory, firms can take advantage of increased debt (Lucas and Wilson, 2008) when this financial resource is employed to acquire environmental technology and/or perform environmental research and development activities that allow them to reduce the ecological footprint of their business activities and therefore decrease the future costs of improving their production processes and avoid environmental fines in the long-term, respectively (Waddock and Graves, 1997; Porter and van der Linde, 1995). This helps to improve corporate financial performance in the long-term. We can therefore hypothesise that: H11: Companies with lower leverage outperform (underperform) those with higher leverage in the short-term (long-term). Capital and research and development investments could increase due to environmental policies and regulations (Crowe and Brennan, 2007). The requirements of environmental regulations are basically technological (López et al., 2010), so they could encourage companies to invest their spare financial resources in environmental innovations (Porter and van der Linde, 1995). Companies affected by these regulations could research and develop energy efficient technologies and introduce their results to their production processes by using new environment-friendly machinery. This would enable them to reduce their energy consumption and therefore their ecological footprint while improving their ability to face costs resulting from current and future environmental regulations (Bunse et al., 2011). Therefore, environmental regulations that encourage companies to invest in strategic energy efficient technologies could help 9

ACCEPTED MANUSCRIPT to improve the competitive advantages of companies and consequently their financial performance (Porter and Kramer, 2006). On the contrary, Jaffe et al. (2002) and Palmer et al. (1995) state that environmental regulations increase a company’s production costs because the expense of increased environmental compliance impairs a company’s competitiveness and productivity, and consequently its financial performance. This could be due to environmental regulations encouraging companies to adopt environmentally responsible activities (Porter and Kramer, 2006) that are not integrated in their core business strategy. Government policymakers could intervene in the reduction of environmental impacts by promoting different environmental programmes and instruments that encourage companies in their countries (Abdelaziz et al., 2011; Cooremans, 2012; Martínez de Alegría Mancisidor et al., 2009) and/or that belong to a specific industry (Abdelaziz et al., 2011) to (1) innovate in end-of-pipe technologies and/or in cleaner production technologies, so called integrated technologies, (Rennings et al., 2006) and (2) adopt responsible or strategic environmental innovation strategies that could influence corporate financial performance. We can hypothesize that: H12: Companies located in different countries could achieve different financial performance. H13: Companies belonging to different industries could achieve different financial performance. Investments related to environmental issues, such as the implementation of sustainable energy strategies, could be affected by the economic situation that occurred during the period analysed (Muhammad et al., 2015). According to Gallego-Alvarez (2012), in times of economic crisis, corporate financial performance usually diminishes, which could lead managers to behave conservatively and defensively, and avoid projects that they consider too risky (Cheney and McMillan, 1990). Given that environmental projects require initially large investments that only obtain benefits in the long-term (Abulfotuh, 2007), large firms might reduce their investments in sustainable projects (Gallego-Alvarez et al., 2014), including those that decrease their energy consumption and CO2 emissions, which fails to align their business strategy with stakeholder expectations (Rodriguez, 2013). This reduces corporate financial performance from stakeholder theory (Freeman, 1984). Therefore, we hypothesize that: H14: Financial crisis negatively influences corporate financial performance. These hypotheses are shown in Figure 1. [Figure 1] 3. Methods

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ACCEPTED MANUSCRIPT To test our hypotheses we propose the following research method that describes the sample used and the variables introduced to our proposed model. This model controls the effects of several factors as indicated by the literature. Sample and data collection In order to carry out this study, we collected data from two main sources managed by Datastream (Thomson Reuters): (1) ASSET4 and (2) Worldscope. The sample data for energy efficiency and renewable energy measurements uses the ASSET4 database, which was founded in 2003 and whose 130 analysts annually collect and compile publicly available information on firms’ CSR strategies using 750 individual data points that are combined into over 280 key performance indicators from over 4,612 large firms (Ionnou and Serafeim, 2012) from all around the world and several industries. The ASSET4 database is recognized as one of the most complete ratings of social responsibility including environmental, corporate governance, economics and social pillars (Ortas et al., 2014) providing objective, comparable and systematic extrafinancial information that is assessed by independent external social audits (Orlitzky et al., 2003). Specifically, from ASSET4 we collected information about the energy consumption, level of implementation of energy efficiency management system and renewable energy consumption2 of 612 firms (these data are scarce). For each firm, we compile the return on assets, market to book value, the research and development expenses3, capital expenditures, sales, inception date, current ratio, total assets, total debt, country of domicile and TRBC industry group code yearly data from the Worldscope database from 31 December 2008 to 31 December 2013. The Worldscope database contains historical data obtained from the annual reports of publicly traded companies around the world from 1980. It currently provides information on over 58,000 firms, more than 37,000 of which are active, and which are included in this database if they meet at least one of these criteria: (1) its market capitalisation must be greater than US$100 million, (2) it must be a member of one or more global or local indexes, (3) it must be quoted in many stock markets, and (4) it must have high visibility. After merging environmental data from the ASSET4 database with accounting and financial data from the Worldscope database, the resulting sample is an unbalanced panel of 574 large firms4 and 1,647 firm-year observations with complete data as shown in table 5. This allows us to overcome the organizational survivorship bias (Martí, 2015) that is present when the sample selection procedure only takes into account firms with all available data from 2008 to 2013 (Acquaah, 2003), and which leads to an examination of only the most successful firms (Dahlmann and Brammer, 2011). 2

We assume that missing values of renewable energy consumption correspond to firms that do not demand renewable energy and therefore we consider a value of zero. 3 We consider that R&D expenses with missing values in Worldscope database are zero as in Lev et al. (2010) and Wang and Choi (2013). 4 We exclude financial firms because they use different accounting standards as mentioned by Zhang et al. (2015).

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ACCEPTED MANUSCRIPT Additionally, we have data on CO2 emissions from fossil-fuel use and cement production by country from 2010 to 2013 obtained from the PBL Netherlands Environmental Assessment Agency and the classification of industries according to the low, medium and high environmental impact of its business activities provided by the FTSE Group.

Dependent variable Our dependent variable is corporate financial performance. Following previous literature by Gallego et al (2014), Griffin and Mahon (1997) and Chiu and Sharfman (2011), we use an accounting-based measure of profitability and a measure of a company’s stock market performance that allows us to examine two dimensions of corporate financial performance: short-term profitability and market evaluation of future profitability (Inoue and Lee, 2011; Martí et al., 2015). As an accounting-based performance measure, we adopt the return on assets ratio (ROA) (Choi et al., 2011; Gallego et al., 2011) defined as the net income and total asset ratio. This measure focuses on how a company’s earnings respond to different managerial policies and to the relative efficiency of asset utilisation (Cochran and Wood, 1984; Lee et al., 2009). As a market-based performance measure, we employ Tobin’s Q, which we define as the ratio of equity market value to equity book value at the end of each year. This measure represents the company’s long-term profitability (Inoue and Lee, 2011), referring to investors’ evaluations and expectations of company performance (Scholtens, 2008). Independent variables As independent variables we take three components of sustainable energy strategy (Peura, 2013): renewable energy sources, energy efficiency and energy efficiency management variables, representing two different concepts. Energy efficiency management variables are dummy variables that show how developed the energy efficiency management system is. Thus, the Level1MS variable takes the value of 1 if the company has a policy to improve its energy efficiency and 0 otherwise; The Level2MS variable takes the value of 1 if the company sets targets or objectives to be achieved in energy efficiency and 0 otherwise; The Level3MS variable has the value of 1 if the company claims to use Key Performance Indicators (KPIs) or a balanced scorecard to monitor energy efficiency and 0 otherwise; The Level4MS variable takes the value of 1 if the company describes, claims to have or mentions processes in place to improve its energy efficiency and 0 otherwise. As stated by Lopez et al. (2009) environmental management systems encompass technical and organisational activities that enable companies to reduce their ecological footprint and to minimise their effects on society and the environment. Renewable energy and energy efficiency performance is the output of this sustainable energy management system. The renewable energy sources (RES) variable represents the 12

ACCEPTED MANUSCRIPT consumption of renewable energy (Bunse et al., 2011; Park and Behera, 2014; Marques and Fuinhas, 2011), being measured as the total energy consumed by primary renewable energy sources divided by total energy. The energy efficiency (EE) variable is an indicator created using equation [1] as follows: 𝐸𝐶𝑓,𝑡

𝐸𝐸𝑓,𝑡 =

𝐸𝐶𝑓,𝑡 ‒ 1

𝑆𝑎𝑙𝑒𝑠𝑓,𝑡

‒ 𝑆𝑎𝑙𝑒𝑠

𝐸𝐶𝑓,𝑡 ‒ 1

𝑓,𝑡 ‒ 1

[1]

𝑆𝑎𝑙𝑒𝑠𝑓,𝑡 ‒ 1

Where EC represents total energy consumption in gigajoules divided by net sales (Sales) for company f at time t or at time t-1. Thus, the variable EEf,t is defined as the reduction or growth in energy consumption of a given company f at the end of a given year t, adjusted by sales. Therefore, a company that improves its energy efficiency achieves a negative value for this indicator. The interaction term between energy efficiency (EE) multiplied by minus one and renewable energy sources (RES) is a proxy for the level of integration of Energy Efficiency systems (EE) and Renewable Energy Sources (RES). A company that reduces its total energy consumption (adjusted by sales) while increasing renewable energy consumption achieves a higher value for this interaction term than a company that does not use renewable energy sources in its business activities and therefore obtains a value of zero for this interaction term.

Control variables Previous authors, Clarkson et al. (2011), Fujii et al. (2013) and Crowe and Brennan (2007), have shown which company and industry characteristics could affect the relationship between corporate financial performance and corporate social/environmental performance. We therefore include, as control variables, company size, company age, industry, country, capital intensity, research and development intensity, company risk, spare financial resources and year. Following previous literature (Elsayed and Paton, 2009; Hart and Ahuja, 1996), we measure research and development intensity (RESEARCH variable) by dividing research and development expenses by total sales revenue at fiscal year-end expressed in percentage terms. This allows us to capture the company’s technological knowledge level (Fujii et al., 2013). Given that managers that have more spare financial resources are more likely to divert resources toward environmental issues (Aguilera et al., 2012), we control for spare financial resources by introducing the current ratio (CR) variable to our study, which is directly obtained from the Worldscope database. We also control for the level of risk of the company’s operations using the LEVERAGE variable as a proxy that is measured as the ratio of total debt to total assets (Graves and Waddock, 1994; King and Lenox, 2001). We capture industry variation by incorporating dummy variables that take into account whether a firm belongs to an 13

ACCEPTED MANUSCRIPT industrial sector with low (LINDUSTRY), medium (MINDUSTRY) or high (HINDUSTRY) environmental impact according to the FTSE Group’s categorization (Hart and Ahuja, 1996; Martí et al., 2015). The learning effect is measured by introducing the LAGE variable as a proxy that represents the Napierian logarithm of number of years passed from the date of foundation. Furthermore, we control for the existence of scale economies by introducing the LSIZE variable as a proxy that is defined as the logarithm of total assets (Crowe and Brennan, 2007; Elsayed and Paton, 2009; Walls et al., 2011). We also introduce the dichotomous country variable (COUNTRY), which takes the value of 1 if company f is located in the indicated country and 0 otherwise (Aerts et al., 2008) and the dummy year variable (YEAR) to control for year effects in the crisis period. Table 2 summarizes these variables.

[Table 2]

Table 3 presents the descriptive statistics of the aforementioned independent and control variables including their mean and standard deviation. Table 4 provides the correlation coefficients between the independent and control variables, and shows that there are no multicollinearity problems in our sample given that their values are lower than 0.8 (Sharma and James, 1981). The variance inflation factor values for each independent and control variable included in table 4 support this finding (Qi et al., 2014).

[Table 3] [Table 4]

Modelling the effect of the integration of the rational use of energy and renewable energy sources on corporate financial performance In order to test the relationship between the implementation of sustainable environmental strategies and the company’s corporate financial performance as described in our hypothesis, we propose the following dynamic panel data model [2]: CFPf,t= α+ β1EEf,t-1+ β2RESf,t-1+ β3(-EE*RESf,t-1) + β4Level1MSf,t-1+ β5Level2MSf,t-1+ β6Level3MSf,t-1+ β7Level4MSf,t-1+ β8LAGEf,t-1+ β9CRf,t-1+ β10CFPf,t-1+ β11LSIZEf,t-1+ β12RESEARCHf,t-1+ β13CAPITALf,t-1+ β14LEVERAGEf,t-1+ ∑β15COUNTRYf+ β16HINDUSTRYf+ β17MINDUSTRYf+ β18YEAR2011f+ β19YEAR2012f+ β20YEAR2013f+ Ɛf,t [2] Where CFPf,t-1 denotes the lagged corporate financial performance for company f at time t-1 being ROA measure (model 1) and Tobin’s Q (model 2); EEf, t-1 represents the 14

ACCEPTED MANUSCRIPT reduction in energy consumption for company f at time t-1; RESf,t-1 indicates total energy consumed by primary renewable energy sources in relation to total energy usage; Level1MSf,t-1 represents the earlier stage of management systems for company f at time t-1; Level2MSf,t-1 indicates whether company f set targets to be achieved regarding energy at time t-1; Level3MSf,t-1 denotes whether company f had indicators to monitor energy efficiency at time t-1; Level4MSf,t-1 indicates whether company f described processes to improve its energy efficiency at time t-1; LAGEf,t-1 is the Napierian logarithm of number of years passed from the date of foundation for company f at time t-1; CRf,t-1 denotes the spare financial resources of company f at time t-1; LSIZEf,t-1 is the logarithm of total assets for company f at time t-1; RESEARCHf,t-1 represents the research and development expenses in relation to total sales revenue at time t-1 charged to company f; β10CAPITALf,t-1 denotes the ratio of capital expenditure charged to company f in relation to total sales revenue at time t-1; COUNTRYf represents a dummy variable that takes the value of 1 if company f is located in the country indicated and 0 otherwise; HINDUSTRYf is a dummy variable that takes the value of 1 if company f belongs to an industrial sector with high environmental impact and 0 otherwise; MINDUSTRYf is a dummy variable that takes the value of 1 if company f belongs to an industrial sector with medium environmental impact and 0 otherwise; LEVERAGEf,t-1 indicates the level of risk of the company’s operation at time t-1; YEAR2011f, YEAR2012f, YEAR2013f are the yearly dummy variables. We estimate this regression model by adopting the dynamic generalised method of moments (GMM) estimator5 (Blundell and Bond, 1998; Arellano and Bover, 1995) with two-step robust standard errors, proposed by Windmejier (2005), which is asymptotically more efficient than a one-step estimator. This method allows us to control for the potential endogeneity of the independent and control variables derived from the existence of unobservable heterogeneity, reciprocal causality and the presence of persistent series. The unobservable individual heterogeneity problem arises due to the existence of differences in unobserved managerial concerns and perceptions of environmental risks (for example, those derived from CO2 emissions) and market opportunities (Fraj-Andrés et al., 2009) that influence managers’ investment behavior and therefore corporate financial performance and the implementation of sustainable energy systems (Bakker et al., 2002). The reciprocal causality (so-called dynamic endogeneity) problem occurs when assuming that the adoption of sustainable energy systems and other explanatory variables (such as size, research and development expenditures, among others) are predetermined, and therefore influenced by past values of corporate financial performance, i.e. they are correlated as demonstrated by Qi et al. (2014), Perkins and Neumayer (2009) and Wintoki et al. (2012). In turn, corporate financial performance is affected by past values of the adoption of sustainable energy systems and other explanatory variables, forming what Hart and Ahuja (1996) call the virtuous circle. The adoption of sustainable energy systems may allow firms to detect and repair leaks, 5

We use the Stata program provided by Roodman (2009).

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ACCEPTED MANUSCRIPT preventing environmental liability and improving corporate financial performance (Endrikat et al., 2014). The presence of persistent series describes the dynamic pattern of corporate financial performance, i.e. a firm’s current financial performance substantially depends to some extent on its past corporate financial performance as noted by Cavaco and Crifo (2014) and Elsayed and Paton (2009). To overcome these problems, we introduce to our model unobservable firm specific characteristics (i) that are constant over time (Belu and Manescu, 2013). This fixedeffects term (i) is correlated with lagged corporate financial performance (Belu and Manescu, 2013), which makes Ordinary Least Square (OLS), random and fixed effects estimators inconsistent and biased (Belu and Manescu, 2013; Yamaguchi and van Kooten, 2008). To remove the correlation between lagged corporate financial performance and the individual fixed effect (i), Arellano and Bond (1991) transform all regressors by first differencing using the “difference GMM” estimator that enables them to eliminate fixed effects (Belu and Manescu, 2013). Arellano and Bover (1995) and Blundell and Bond (1998) improve this estimator by adding equations in levels to the equations in differences in order to avoid endogeneity problems (Yamaguchi and van Kooten, 2008; Roodman, 2009). However, to obtain consistent regression estimators (Arellano and Bover, 1995) this method requires the absence of second-order correlation in errors. We verify this using the Arellano-Bond test for AR(2) in second differences, whose results are shown in table 5, suggesting no second-order serial correlation in errors in our model. Additionally, we test the instrument’s validity using the Hansen test of moment conditions whose results, presented in table 5, do not indicate overidentification problems.

4. Results Table 5 shows the dynamic panel model estimation results with corporate financial performance (ROA or Tobin’s Q) as the dependent variable. Model 1 reports the direct effects of independent and control variables with a 1-year time lag (Fujii et al., 2013) on short-term profitability (ROA) while Model 2 analyses these effects on long-term profitability (Tobin’s Q).

[Table 5]

Hypothesis 1 contends that firms that improve their energy efficiency achieve better financial performance than those that increase their energy consumption ceteris paribus. As presented in table 5, the coefficients associated with our energy efficiency (EE) variable are negative, as expected, but not significant, in Model 1 (β1=-0.0002, p16

ACCEPTED MANUSCRIPT value>0.10) and Model 2 (β1=-0.0001, p-value>0.10). This indicates that a reduction in energy consumption does not significantly influence firms’ financial performance in the short and long-term, which is incongruent with neoclassical economic theory postulates (Friedman, 1970) and stakeholder theory postulates (Freeman, 1984). The RES coefficient variables are negative and non-significant in model 1 (β2=-0.0212; p-value>0.10) and Model 2 (β2=-0.0167, p-value>0.10), which implies that the integration of renewable energy sources in the energy resource portfolio does not significantly influence companies’ financial performance in the short and long-term. Therefore our hypothesis H2 is not supported. This also contradicts neoclassical economic theory postulates (Friedman, 1970) and stakeholder theory postulates (Freeman, 1984). The coefficient associated with the interaction term (-EE*RES) shows a negative sign but is not statistically significant in model 1 (β3=-0.2097, p-value>0.10) or in model 2 (β3=-0.0550, p-value>0.10), so the related hypothesis H3 is not supported given that those firms that have reduced their energy consumption (adjusted by sales) while integrating renewable energy sources in their energy resource portfolio perform similarly to those that do not improve their energy efficiency systems and/or do not employ renewable energy sources. According to Hypothesis H4, the implementation of sustainable energy strategies enhances a company’s financial performance. As presented in table 5, the coefficient associated with the sustainable energy management variable in Model 1 (Level3MS: β6=1.3171, p-value<0.01) is significantly positive. This finding only partly supports Hypothesis H4. The result of Model 1 indicates that companies that monitored their energy efficiency systems using Key Performance Indicators (KPIs) or a balanced scorecard in the previous years in order to control energy consumption obtain higher short-term profitability than those that do not. This might be because the monitoring of energy efficiency systems allows firms to detect and decrease energy losses through supervision (Thant and Charmondusit, 2010) and/or the introduction of improvements that require moderate managerial, organizational or economic efforts thus enabling firms to capture so-called ‘low-hanging fruits’ in the short-term (Hart and Ahuja, 1996; Oh et al., 2014). However, the systems that supervise energy leakages could be standardized in the long-term and thus become uniform improvements for all firms, which is why its effect on long-term corporate financial performance is non-significant in model 2 (Level3MS: β6=0.1532, p-value>0.10). Thus, once the initial improvements have been introduced to the production processes and the ‘low-hanging fruits’ have been harvested in the short-term, energy losses decrease and it is not enough to monitor the consumption of energy in order to reduce it and improve long-term corporate financial performance, for instead firms should increase their investments in environmental innovation in order to use renewable energy sources and reduce the use of fossil-fuelled and nuclear energy (Hart and Ahuja, 1996; Oh et al., 2014).

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ACCEPTED MANUSCRIPT Other coefficients associated with sustainable energy management variables are not significant. The Level1MS coefficient variables are positive in model 1 (Level1MS: β4=0.4212, p-value>0.10) and in model 2 (Level1MS: β4=0.3371, p-value>0.10) but not significant in either. Firms that have an energy efficiency policy have slightly improved corporate financial performance in the short-term but this improvement does not lead to significant differences with regard to the financial performance of firms that do not establish an environmental policy. Additionally, the result of Model 2 indicates that some stakeholders and investors perceive that the establishment of an environmental policy could save costs in the future. This would slightly increase the company’s share price and consequently the company’s Tobin’s Q. However, this increase is not significant. This finding is incongruent with Freeman (1984), Porter and Kramer (2006) and Schaumann (2007) from stakeholder theory. The Level2MS coefficient variables are also positive in model 1 (Level2MS: β5=0.3007, p-value>0.10) and in model 2 (Level2MS: β5=0.0628, p-value>0.10) but not significant in either, which implies that the objectives on energy efficiency set by firms allow them to perform slightly better than other firms that do not set environmental objectives. However, this improvement is not significant. The Level4MS coefficient variables are negative and not significant in model 1 (Level4MS: β7=-0.4693, p-value>0.10) but positive and not significant in model 2 (Level4MS: β7=1.1318, p-value>0.10). These findings show that firms that describe their processes in place to improve their energy efficiency achieve similar financial performance to those that do not claim to have processes to improve their energy efficiency in the short and long-term. Our hypothesis H5 that older companies outperform younger companies is not supported by the regression results. The coefficient associated with the control variable LAGE shows a non-significant and negative effect in Model 1 (β8=-0.1197, pvalue>0.10) and a non-significant and positive effect in Model 2 (β8= 0.0504, pvalue>0.10). This might mean that (1) older firms possess capabilities that are inadequate to implement sustainable energy challenges (Dangelico, 2015) that allow them to gain a significant advantage, (2) younger companies are hiring employees that were working for established companies and take advantage of their know-how to manage company resources and capabilities, allowing them to achieve similar financial performance to older companies; and/or (3) younger companies are providing good training to their personnel by investing in human and knowledge capabilities (Crowe and Brennan, 2007; Wong, 2013; Sarkis et al., 2010). Our findings also show that the Current Ratio (CR) variable has a non-significant and negative effect with the dependent variable in Model 1 (β9=-0.6171, p-value>0.10) and Model 2 (β9=-0.0570, p-value>0.10). Therefore, we cannot accept our Hypothesis H6 that spare financial resources positively affect a company’s financial performance. This might indicate that companies with more financial resources are not allocating their excess resources efficiently, probably because the financial crisis has (1) reduced the opportunities for investment or (2) led managers to adopt conservative financial strategies (Martí et al., 2015). 18

ACCEPTED MANUSCRIPT As predicted by Hypothesis H7, the relationship between the CFP variable (ROA and Tobin’s Q) and its lagged value is significant and positive in Model 1 (β10=0.4294, pvalue<0.01) and Model 2 (β10=0.4098, p-value<0.01). These results indicate that firms that reached their peak financial performance in the previous year outperform in the present those that were the poorest performers in the past, thus confirming the persistence effect found by Cavaco and Crifo (2014), Elsayed and Paton (2009) and Etzion (2007). This could be because firms with high financial performance generate resources that could be used for research and development activities and thus offer new products that are attractive to their customers, hence increasing their sales and therefore their corporate financial performance. On the contrary, we find a non-significant and positive relationship between corporate financial performance and the LSIZE variable in Model 1 (β11=0.5276, p-value>0.10). These results do not support our hypothesis H8. This might indicate that (1) large companies are able to benefit from the existence of small scale economies whose effect on financial performance is not significant and/or (2) the positive effects on financial performance of the greater financial resources of large companies are similar to the positive effects produced by the more flexible and less formalised organisational structure of small companies (Leonidou et al., 2014). For model 2, we find a nonsignificant and negative relationship between corporate financial performance and the LSIZE variable (β11= -0.2671, p-value>0.10). This finding might indicate that a nonsignificant part of the stock market perceives that large companies could (1) generate diseconomies of scale during the period analysed or (2) find it harder to make changes that allow them to adapt to industry shocks generated by the financial crisis. As shown in table 5, Hypothesis H9 is supported in the regression results. The coefficient associated with the RESEARCH variable has a positive and significant effect with the dependent variable in Model 1 (β12= 0.3864, p-value<0.01) and in Model 2 (β12= 0.1702, p-value<0.10). This indicates that the costs derived from research and development activities are lower than the benefits that these activities generate. These research and development investments could focus on (1) developing new (green) products that are attractive for potential (environmentally-friendly) customers, thus enabling firms to increase their sales, and therefore their corporate financial performance from stakeholder theory and/or (2) introducing improvements to the production processes to enable firms to generate competitive advantages by saving costs, which would decrease their expenses, and increase their profits and consequently their financial performance from natural resource theory as suggested by Russo and Fouts (1997). On the other hand, we find a non-significant relationship between the capital expenditure (CAPITAL) variable and corporate financial performance in Model 1 (β13= 0.0483, p-value>0.10) and Model 2 (β13= 0.0251, p-value>0.10), therefore our hypothesis H10 that high capital-intensive firms outperform low capital-intensive firms is rejected. This result indicates that high capital-intensive firms buy the technology produced by other firms rather than investing in research and development activities in 19

ACCEPTED MANUSCRIPT order to create the environmental technologies that would enable them to gain a competitive edge in their production processes. Therefore, high capital-intensive firms might invest in research and development activities that are focused on developing new products rather than improving processes or developing innovative technologies. Hypothesis H11 contends that the level of leverage affects the company’s financial performance. As presented in table 5, the coefficient associated with the LEVERAGE variable in Model 1 (β14= -0.0579, p-value>0.10) is negative and non-significant while that associated with the LEVERAGE variable in Model 2 is positive and non-significant (β14= 0.0045, p-value>0.10). Therefore, companies with less leverage might invest more financial resources in projects that slightly improve their short-term financial performance. Some stakeholders would prefer to do business in the long-term with high leveraged companies because high debt levels could be related with large investments in environmental capital and/or research and development activities, which positively (although not significantly) influences its reputation and therefore its financial performance (Tobin’s Q) in the long-term (Lucas and Wilson, 2008; Waddock and Graves, 1997; Porter and van der Linde, 1995). On the contrary, some stakeholders with short-term perspectives could perceive that high leveraged companies are taking too many risks, which negatively (but not significantly) influences corporate financial performance in the short-term (ROA). Our Hypothesis H12 that firms located in different countries might achieve different financial performance is not supported by the regression results in Model 1 and in Model 2. Therefore, the country in which the company is located does not influence its financial performance. This could be because the environmental actions adopted by a country could be implemented in other countries due to the globalisation and convergence processes between regions, which tend to mitigate differences in corporate financial performance (Martínez de Alegría Mancisidor et al., 2009). We also find a non-significant relationship between the HINDUSTRY variable and the CFP variable in Model 1 (β15= -0.2353, p-value>0.10) and in Model 2 (β15= 0.0544, pvalue>0.10) and between the MINDUSTRY variable and the CFP variable in Model 1 (β16= 0.1254, p-value>0.10) and in Model 2 (β16= 0.1535, p-value>0.10). Therefore, our hypothesis H13 is not supported given that there are no significant differences between the financial performance achieved by companies belonging to different industries, which could be due to the financial crisis that has affected all sectors around the world (Gallego et al. 2014). This finding is congruent with those obtained by Martí et al. (2015). The control variables, YEAR2010, YEAR2011, YEAR2012 and YEAR2013, show a statistically significant effect on corporate financial performance (ROA and Tobin’s Q) as suggested in our hypothesis H14. Short-term financial performance (ROA) is more negatively affected in 2012, as the results of model 1 (β18= -2.0436, p-value<0.01) show while long-term financial performance (Tobin’s Q) is significantly and positively affected in 2013 in comparison with other years as shown in model 2 (β19= 0.3571, p20

ACCEPTED MANUSCRIPT value<0.05). These findings could indicate that when the financial crisis began, firms started to have significantly lower sales and short-term corporate financial performance (ROA) and this continued until 2012. Since then, short-term corporate financial performance has improved slightly, generating expectations of economic recovery among stakeholders, which are supported by the International Monetary Fund’s projections (Gallego, 2012). Higher stakeholder expectations encourage investment in stock markets, which increases firms’ stock prices, stock returns in relation to book value being significantly higher in 2013, as shown in table 5.

5. Conclusions Firms obtain energy in order to conduct their business activities by (1) demanding over 40% of the electricity produced by the energy industry around the world and (2) burning fossil fuels. Both options produced an average of 66.72% of global CO2 emissions from 2008 to 2013, thus generating environmental and social damage. The implementation of sustainable energy systems should reduce their ecological footprint, and hence influence large firms’ financial performance. For this reason, the main aim of this study was to analyse the relationship between sustainable energy systems and corporate financial performance in consideration of the sustainable energy concept proposed by Peura (2013), which includes: (1) the Rational Use of Energy (RUE), (2) Renewable Energy Sources (RES), (3) Integration of RUE and RES, and (4) sustainability management. To this end, we obtained sample data on 574 multinational companies from 36 countries in the 2008-2013 period. After applying a dynamic panel data method, our findings showed that improvements in energy efficiency and the use of renewable energy sources do not significantly affect corporate financial performance. This might indicate that (1) the costs of adopting energy efficiency and renewable technologies compensate for the savings derived from them and that (2) the use of energy efficiency and renewable technologies may be affordable for all firms but does not generate competitive advantages, so their use is not perceived or assessed by investors in the long term. In times of financial crisis, firms might prefer to use standard energy technologies rather than investing in research and development projects focused on achieving competitive advantages from sustainable energy systems, given that environmental research and development activities require a large amount of financial resources and are risky because they offer no guarantee of success (Gallego-Alvarez et al., 2014; Cheney and McMillan, 1990). Neither does the joint implementation of energy efficiency systems and renewable energy sources have a significant influence on corporate financial performance, which might indicate that a combination of Rational Use of Energy (RUE) and Renewable Energy Sources (RES) in the composition of firms’ energy resource portfolios does not allow them to benefit from the existence of scope economies in the management of both energy measures. The adoption of energy efficiency measures requires the implementation of sustainable energy management systems whose effect on short-term 21

ACCEPTED MANUSCRIPT corporate financial performance (ROA) is significantly positive but not significant in the long-term (Tobin’s Q). This may be because firms that control their energy efficiency using key performance indicators are able to detect leaks and failures in the production process and correct them. This allows them to (1) have a more efficient production process than those that do not monitor and, (2) initially benefit from harvesting ‘low-hanging fruits’ (Hart and Ahuja, 1996), thus improving their short-term corporate financial performance due to these improvements decreasing energy usage and leading to lower costs. However, the procedure implemented by firms to control energy efficiency does not lead them to develop rare and inimitable capabilities or resources that generate competitive advantages from the natural resources based view (Hart and Ahuja, 1996), which can easily be adopted by other firms in the long term. In this case, investors do not take the matter into account when bidding for stocks as this has no effect on long-term corporate financial performance, as suggested by Porter and Kramer (2006) from stakeholder theory. These findings are congruent with those obtained for the research and development variable. Although firms make research and development investments that significantly increase their corporate financial performance, these activities may be designed to introduce improvements or create new (green) products rather than improving or developing sustainable energy technologies in order to gain competitive advantages. Therefore, firms that invest in research and development focused on (green) products are able to generate rare and inimitable resources that allow them to differentiate their products (Hart and Ahuja, 1996) and attract potential green customers, thereby improving both their short-term and long-term financial performance (Russo and Fouts, 1997). This product-oriented research and development investment could be due to the financial crisis decreasing short-term corporate financial performance (ROA) during the period analysed and managers becoming more conservative and preferring to create new green products in order to attract potential clients over improving or developing new cleaner technologies that require more financial resources and the assumption of greater risk. This is congruent with the (non)findings concerning capital expenditures. Firms could finance their research and development expenditures by using internally generated resources. The firms that performed better in the previous year may have more internally generated financial resources and be more willing to reinvest these in the firm in the form of research and development activities focused on new (green) products, which could be sold more easily than those of competitors with poorer past financial performance, due to past financial performance being seen by key stakeholders (customers, investors and managers, among others) as an objective and reliable indicator of firm quality (Rinallo and Basuroy, 2009) that improves its reputation by helping to increase corporate financial performance in the short and long term. However, customers’ purchase intentions also depend on their purchasing power, which could be lower in times of economic crisis and thus affect a firm’s sales revenue. Thus, at the beginning of crisis, firms’ sales could have decreased, thereby reducing corporate financial performance until 2012. Since then, the economic situation has improved 22

ACCEPTED MANUSCRIPT slightly, which has helped to increase short-term corporate financial performance. This improvement generates expectations of economic recovery (Gallego, 2012), which encourages investors to buy shares in the firm, thus increasing its stock prices and consequently improving long-term corporate financial performance. We also find no relationship between a company’s industry or country and corporate financial performance. This might indicate that the financial crisis affected all industries and all countries during the period analysed due to the globalization effect. Other control variables that do not influence corporate financial performance, such as firm size, firm age, firm risk and current ratio, might be excessively broad proxies that are influenced by other effects, such as organizational culture or corporate governance, which we have not taken into account in order to examine their relation with corporate financial performance. These findings could be of interest to environmental and management scientists, shareholders, managers and policy-makers. For environmental and management researchers, this is the first empirical study to have analysed the relationship between sustainable energy systems and corporate financial performance, thereby extending on the energy research line initiated by Peura (2013). For shareholders and managers, this study shows that the implementation of sustainable energy strategies does not reduce corporate financial performance, but in order for it to be significantly increased, firms should align such environmental strategies with their core business strategy in order to generate competitive advantages. For policy-makers and governments, our findings show that more actions aimed at (1) improving the rational use of energy and replacing traditional energy sources with renewable energy sources and (2) educating civil society about the importance of reducing energy from fossil fuels should be promoted. To this end, governments could (1) subsidise environmental actions to offset the costs of these changes and (2) encourage firms to collaborate with other companies and universities in the development of innovative green/cleaner technologies by providing grants or economic aid. As a complement to these actions, governments should establish educational programmes to (1) provide basic knowledge about the rational use of energy and (2) to change individual attitudes and encourage society to make rational use of energy and renewable energy sources by revealing its social, economic and environmental consequences, for example by using energy efficient labels on the products offered by companies. This paper has provided new empirical evidence by (1) proposing an empirical model based on Peura (2013) that takes into account the use of renewable energy sources (RES), the reduction of used energy (RUE) and the level of implementation of sustainable energy management systems, and (2) using a sample that includes large companies belonging to different industries and located around the world while previous studies have (1) examined the effect of energy efficiency and/or the use of renewable energy sources on environmental profits in Germany (Schaumann, 2007) and the US (Brecha et al., 2011), (2) analysed the relationship between energy efficiency and renewable energy systems in different simulated scenarios (Del Rio, 23

ACCEPTED MANUSCRIPT 2010) and European countries (Marques and Fuinhas, 2011), and (3) made a theoretical contribution (Abulfotuh, 2007; Geller et al., 2004; Peura, 2013). Our analysis has several limitations that pose interesting challenges for further research. First, this research employs data from 2008 to 2013 to analyse environmental strategies whose results could be reproduced in the long-term. Future research should extend this period, which would allow us to (1) study the behaviour of companies in the long term and (2) the moderator effect of the crisis period on the relationship between sustainable energy systems and corporate financial performance, theorizing that firms are less likely to implement sustainable energy management systems in order to gain long-term benefits by improving their energy efficiency in times of economic crisis. Second, different corporate structures and legal systems could affect financial performance, which is something for future analysis to consider. Third, our sample only includes data on large companies. Future research should extend the sample to include small and medium firms, thus enabling us to compare the sustainable energy strategies of companies of different sizes. Finally, the measure of financial performance used in this research does not provide explicit information about how the implementation of sustainable energy systems influences a firm’s sales growth or operational performance. Future research should extend our conceptual model by integrating firms’ actions aimed at producing green or clean products with their effects on sales growth.

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31

Table 1 CO2 emissions on average from fuel combustion by sector during 2008-2013 period*

Year 2008 2009 2010 2011 2012 2013

Total CO2 emissions 29,381.4 28,999.4 30,276.1 31,342.3 31,734.3 32,189.7

Electricity and heat production Total 11,987.9 11,827.1 12,480.6 13,066.8 13,346.4 13,655.6

of which: industrial 4,799.6 4,590.5 4,980.3 5,693.9 6,299.7 5,794.2

of which: residential 3,310.4 3,356.4 3,495.9 3,394.7 3,389.6 3,540.9

Other energy industry own use

Manufacturing industries and construction

Transport

Other sectors

Residential

1,491.9 1,464.1 1,570.8 1,542.9 1,557.6 1,674.0

5,943.6 5,870.9 6,186.4 6,508.7 6,456.8 6,114.8

6,604.7 6,543.8 6,755.8 7,001.1 7,187.0 7,384.9

1,448.3 1,418.4 1,402.2 1,371.3 1,367.4 1,491.8

1,905.1 1,875.0 1,880.4 1,851.6 1,819.2 1,868.7

* Million tonnes of CO2 Source: International Energy Agency (2009, 2010, 2011, 2012, 2013, 2014)

32

Figure 1 The conceptual model for sustainable energy systems and corporate financial performance link H1 (+)

SUSTAINABLE ENERGY SYSTEMS

RATIONAL USE OF ENERGY (EE)

H2 (+)

RENEWABLE ENERGY SOURCES (RES)

CORPORATE FINANCIAL PERFORMANCE (CFP)

H3 (+)

INTEGRATION OF RUE AND RES (-EE*RES) SUSTAINABILITY MANAGEMENT

H4 (+)

POLICY (LEVEL1MS) TARGETS (LEVEL2MS) MONITOR (LEVEL3MS)

AGE

CURRENT RATIO

CFPt-1

R&D EXPENSES CONTROL VARIABLES SIZE

33

CAPITAL EXPENSES

LEVERAGE

H14 (+)

H13 (+)

H12 (+)

H11 (-)

H10 (+)

H9 (+)

H8 (+)

(+)

H7 (+)

H5

H6 (+)

PROCESSES (LEVEL4MS)

COUNTRY

INDUSTRY YEAR

ACCEPTED MANUSCRIPT

CFPt EEf,t-1 RESf,t-1 Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1 Level4MSf,t-1 LAGEf,t-1 CRf,t-1 CFPf,t-1 LSIZEf,t-1 RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 HINDUSTRYf MINDUSTRYf YEAR2011t YEAR2012t YEAR2013t Countryf

Table 2 Definition of variables Return on Assets in model 1 and Tobin’s Q in model 2 at time t. Reduction of energy consumption at time t-1. Consumption of renewable energy sources at time t-1. Dummy variable=1 if company has a policy to improve its energy efficiency; 0 otherwise Dummy variable=1 if firm set targets to be achieved in energy at time t-1; 0 otherwise Dummy variable=1 if company f had indicators to monitor energy efficiency at time t-1; 0 otherwise Dummy variable=1 if company f described processes to improve its energy efficiency at time t-1; 0 otherwise Natural log of number of years since firms set up at time t-1 Spare financial resources of company at time t-1. Return on Assets in model 1 and Tobin’s Q in model 2 at time t-1. Natural log of assets of each firm at time t-1. Research and development expenses in relation to total sales revenue at time t-1 charged to company Capital expenditure charged to company in relation to total sales revenue at time t-1. Level of risk of the company’s operation at time t-1 Dummy variable=1 if company belongs to an industrial sector with high environmental impact; 0 otherwise Dummy variable=1 if company belongs to an industrial sector with medium environmental impact; 0 otherwise Dummy variable=1 if data correspond to year indicated; 0 otherwise Dummy variable=1 if data correspond to year indicated; 0 otherwise Dummy variable=1 if data correspond to year indicated; 0 otherwise Dummy variable=1 if company is located in the country indicated; 0 otherwise

34

ACCEPTED MANUSCRIPT

Table 3 Description statistics of variables Variables

Number observations

Standard Deviations

Mean

Minimum

Maximum

2010-2013 CFP(ROA)f,t CFP(TOBINSQ)f,t EEf,t-1 RESf,t-1 (-EE)*RES Level1MSf,t-1

1647 1647 1647 1647 1647 1647

5.20 2.02 29.81 2.80 -0.01 0.97

6.16 6.25 335.21 9.95 0.12 0.16

-22.39 -101.26 -99.98 0.00 -4.58 0.00

78.41 77.73 9,368.09 97.00 1.39 1.00

Level2MSf,t-1 Level3MSf,t-1 Level4MSf,t-1 AGEf,t-1 CRf,t-1 CFP(ROA)f,t-1 CFP(TOBINSQ)f,t-1 SIZEf,t-1* RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 COUNTRYf HINDUSTRYf MINDUSTRYf LINDUSTRYf YEAR2010t YEAR2011t YEAR2012t YEAR2013t

1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647 1647

0.85 0.58 0.96 77.98 1.61 4.85 1.92 20.30 2.95 7.75 27.06 0.60 0.55 0.33 0.12 0.24 0.25 0.25 0.27

0.36 0.49 0.20 48.85 1.41 6.31 6.58 31.90 4.84 8.44 15.77 0.49 0.50 0.47 0.32 0.43 0.43 0.43 0.44

0.00 0.00 0.00 1.00 0.18 -46.80 -101.26 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 297.00 40.73 43.60 103.07 302.00 43.24 85.73 80.51 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

395 395 395 395 395 395 395 395 395 395 395 395 395

5.51 2.01 33.52 2.04 -0.00 0.97 0.83 0.46 0.96 79.65 1.73 2.95 2.26

6.26 8.23 233.33 8.12 0.03 0.17 0.38 0.50 0.20 49.08 2.34 7.49 6.45

-14.32 -101.26 -87.64 0.00 -0.56 0.00 0.00 0.00 0.00 4.00 0.37 -46.80 -33.28

42.13 50.50 3,828.57 76.00 0.07 1.00 1.00 1.00 1.00 294.00 40.73 43.60 103.07

2010 CFP(ROA)f,t CFP(TOBINSQ)f,t EEf,t-1 RESf,t-1 (-EE)*RES Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1 Level4MSf,t-1 AGEf,t-1 CRf,t-1 CFP(ROA)f,t-1 CFP(TOBINSQ)f,t-1

35

ACCEPTED MANUSCRIPT

SIZEf,t-1* RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 COUNTRYf HINDUSTRYf MINDUSTRYf LINDUSTRYf YEAR2010t YEAR2011t YEAR2012t YEAR2013t

395 395 395 395 395 395 395 395 395 395 395 395

18.90 3.53 8.43 28.39 0.67 0.54 0.34 0.12 1.00 0.00 0.00 0.00

29.40 5.87 8.72 16.50 0.47 0.50 0.47 0.32 0.00 0.00 0.00 0.00

0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00

239.00 43.24 66.65 75.58 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00

CFP(ROA)f,t CFP(TOBINSQ)f,t EEf,t-1 RESf,t-1

405 405 405 405

5.50 1.53 36.30 2.53

5.33 4.55 292.27 9.29

-16.97 -65.03 -99.84 0.00

23.96 20.93 3,746.15 79.00

(-EE)*RES Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1 Level4MSf,t-1 AGEf,t-1 CRf,t-1 CFP(ROA)f,t-1 CFP(TOBINSQ)f,t-1 SIZEf,t-1* RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 COUNTRYf HINDUSTRYf MINDUSTRYf LINDUSTRYf YEAR2010t YEAR2011t YEAR2012t YEAR2013t

405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405 405

-0.01 0.96 0.81 0.53 0.95 79.00 1.61 5.66 1.60 20.50 2.98 7.10 26.59 0.64 0.56 0.33 0.11 0.00 1.00 0.00 0.00

0.23 0.20 0.39 0.50 0.22 49.60 1.10 5.84 9.01 31.20 4.75 6.93 15.63 0.48 0.50 0.47 0.32 0.00 0.00 0.00 0.00

-4.58 0.00 0.00 0.00 0.00 3.00 0.29 -14.32 -101.26 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00

0.14 1.00 1.00 1.00 1.00 295.00 12.41 41.56 50.50 278.00 36.27 44.56 73.85 1.00 1.00 1.00 1.00 0.00 1.00 0.00 0.00

409 409 409 409 409

4.85 2.10 20.78 2.88 -0.00

7.04 5.85 311.43 9.98 0.03

-20.23 -65.92 -99.98 0.00 -0.52

78.41 65.43 5,426.32 95.00 0.09

2011

2012 CFP(ROA)f,t CFP(TOBINSQ)f,t EEf,t-1 RESf,t-1 (-EE)*RES

36

ACCEPTED MANUSCRIPT

Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1 Level4MSf,t-1 AGEf,t-1 CRf,t-1 CFP(ROA)f,t-1 CFP(TOBINSQ)f,t-1 SIZEf,t-1* RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 COUNTRYf HINDUSTRYf MINDUSTRYf LINDUSTRYf YEAR2010t YEAR2011t YEAR2012t YEAR2013t 2013 CFP(ROA)f,t CFP(TOBINSQ)f,t EEf,t-1 RESf,t-1 (-EE)*RES Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1

409 409 409 409 409 409 409 409 409 409 409 409 409 409 409 409 409 409 409 409

0.98 0.87 0.65 0.96 76.33 1.55 5.78 1.67 22.00 2.69 7.65 26.32 0.54 0.55 0.33 0.11 0.00 0.00 1.00 0.00

0.15 0.34 0.48 0.20 48.12 0.93 5.36 4.35 34.70 4.39 8.78 15.71 0.50 0.50 0.47 0.32 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 4.00 0.18 -12.26 -65.03 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

1.00 1.00 1.00 1.00 296.00 9.33 27.06 20.93 297.00 37.16 73.27 73.09 1.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00

438 438 438 438 438 438 438 438

4.94 2.41 28.88 3.65 -0.00 0.98 0.88 0.69

5.87 5.88 452.20 11.81 0.03 0.14 0.33 0.46

-22.39 -53.12 -99.55 0.00 -0.44 0.00 0.00 0.00

36.02 77.73 9,368.09 97.00 0.08 1.00 1.00 1.00

Level4MSf,t-1 AGEf,t-1 CRf,t-1 CFP(ROA)f,t-1 CFP(TOBINSQ)f,t-1 SIZEf,t-1*

438 438 438 438 438 438

0.97 77.07 1.56 4.95 2.16 19.90

0.16 48.74 0.81 6.02 5.71 32.10

0.00 1.00 0.22 -20.23 -65.92 0.17

1.00 297.00 6.03 28.72 65.43 302.00

RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 COUNTRYf HINDUSTRYf MINDUSTRYf LINDUSTRYf YEAR2010t YEAR2011t

438 438 438 438 438 438 438 438 438

2.64 7.82 26.97 0.53 0.55 0.33 0.12 0.00 0.00

4.22 9.09 15.25 0.50 0.50 0.47 0.33 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

29.23 85.73 80.51 1.00 1.00 1.00 1.00 0.00 0.00

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ACCEPTED MANUSCRIPT

YEAR2012t YEAR2013t

438 438

0.00 1.00

*The size variable is measured in milliards of euros.

38

0.00 0.00

0.00 1.00

0.00 1.00

Table 4 Correlation matrix for regression variables VARIABLES 1.EE 2.RES 3.(-EE)*RES 4.Level1MS 5.Level2MS 6.Level3MS 7.Level4MS 8.Lage 9.CR 10.ROA 11. Tobin’s Q 12.Lsize 13.Research 14. Capital 15.Country 16.Hindus 17.Mindus 18.Leverage 19.Year2011 20.Year2012 21.Year2013 VARIABLES 11. Tobin’s Q 12.Lsize 13.Research 14. Capital 15.Country 16.Hindus 17.Mindus 18.Leverage 19.Year2011 20.Year2012 21.Year2013

VIF (ROA) 1.05 1.23 1.06 1.13 1.12 1.37 1.20 1.26 1.27 1.37 ---1.27 1.30 1.16 1.09 3.02 3.02 1.44 1.60 1.64 1.68 11 1.000 -0.051 0.056 -0.012 -0.012 -0.018 0.026 -0.057 -0.028 -0.022 0.022

VIF (TOBIN'S Q) 1.05 1.23 1.06 1.13 1.12 1.36 1.21 1.26 1.27 ---1.04 1.38 1.27 1.30 1.06 3.00 3.02 1.39 1.56 1.61 1.66

1

2

3

4

5

6

7

8

9

10

1.000 -0.010 -0.165 -0.036 -0.037 -0.046 -0.018 0.017 -0.012 -0.026 -0.003 -0.003 -0.009 0.008 0.026 0.003 0.013 0.035 0.011 -0.016 -0.002

1.000 -0.112 0.034 0.011 0.106 0.015 -0.084 -0.042 0.089 -0.017 0.056 -0.055 0.096 -0.094 -0.042 -0.095 0.055 -0.016 0.005 0.052

1.000 -0.010 0.003 -0.024 -0.004 -0.009 0.008 0.018 0.007 -0.024 0.010 -0.013 -0.010 -0.024 0.017 -0.023 -0.041 0.020 0.012

1.000 0.071 0.104 0.116 -0.053 -0.040 0.084 -0.018 0.155 -0.149 0.055 0.003 0.047 -0.060 0.003 -0.040 0.021 0.027

1.000 0.145 0.119 0.017 -0.064 0.046 0.008 0.085 -0.063 -0.014 -0.020 0.077 -0.033 -0.009 -0.061 0.031 0.055

1.000 0.091 -0.017 -0.072 0.210 0.021 0.060 -0.124 0.053 -0.239 0.069 -0.041 0.012 -0.067 0.074 0.132

1.000 0.001 -0.086 -0.032 -0.071 0.147 0.021 0.004 0.040 -0.042 0.013 0.011 -0.027 -0.004 0.039

1.000 0.020 0.009 0.033 0.014 -0.009 -0.222 0.098 0.095 0.007 -0.033 0.008 -0.014 -0.009

1.000 0.046 0.010 -0.224 0.223 -0.048 0.081 -0.041 0.101 -0.319 0.001 -0.025 -0.024

1.000 0.094 -0.012 -0.076 0.027 -0.100 0.062 -0.106 -0.194 0.074 0.084 0.010

12

13

14

15

16

17

18

19

20

21

1.000 -0.065 0.132 0.010 0.183 -0.231 0.143 0.015 0.029 -0.008

1.000 -0.125 0.151 -0.134 0.130 -0.212 0.004 -0.031 -0.038

1.000 -0.117 0.072 -0.097 0.234 -0.044 -0.007 0.005

1.000 -0.038 0.077 -0.059 0.058 -0.064 -0.075

1.000 -0.782 0.146 0.012 0.000 -0.003

1.000 -0.141 -0.009 0.002 -0.003

1.000 -0.017 -0.027 -0.003

1.000 -0.328 -0.344

1.000 -0.346

1.000

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ACCEPTED MANUSCRIPT Table 5 Results of regression analysis

Variables EEf,t-1 RESf,t-1 (-EE)*RES Level1MSf,t-1 Level2MSf,t-1 Level3MSf,t-1

Model 1 (ROA) Standard Coefficients Errors

Model 2 (Tobin’s Q) Standard Coefficients Errors

Level4MSf,t-1

-0.0002 -0.0212 -0.2097 0.4212 0.3007 1.3171 *** -0.4693

0.0005 0.0275 0.5063 0.6476 0.6151 0.3767 1.9028

-0.0001 -0.0167 -0.0550 0.3371 0.0628 0.1532 1.1318

0.0002 0.0153 0.2010 0.5682 0.3402 0.2469 1.3708

LAGEf,t-1 CRf,t-1 CFPf,t-1 LSIZEf,t-1 RESEARCHf,t-1 CAPITALf,t-1 LEVERAGEf,t-1 HINDUSTRYf MINDUSTRYf YEAR2011t YEAR2012t YEAR2013t Constant

-0.1197 -0.6171 0.4294 0.5276 0.3864 0.0483 -0.0579 -0.2353 0.1254 -1.2155 -2.0436 -1.5823 7.3129

0.4523 0.4429 0.0750 0.5642 0.1460 0.0511 0.0498 0.6441 0.5864 0.3179 0.3150 0.3027 14.792 Yes -4.92 (0.000) -0.12 (0.902) 86.73 (0.148) 574 1,647

0.0504 -0.0570 0.4098 *** -0.2671 0.1702 * 0.0251 0.0045 0.0544 0.1535 -0.1360 0.1643 0.3571 ** 5.2710

0.2911 0.3034 0.0205 0.4348 0.1017 0.0327 0.0352 0.4415 0.4017 0.1182 0.1509 0.1611 10.873 Yes -0.25 (0.801) -0.94 (0.347) 45.18 (0.971) 574 1,647

Country effectsa AB test for AR(1)b (p-value) AB test for AR(2)b (p-value) Hansen test χ2 (p-value) N. firms Observations

*** ***

*** *** ***

aCountry

dummies are included in all models (not reported to save space, available on request). test for AR(1) in first differences and AR(2) in second differences. The p-values are provided in parenthesis.*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level. bArellano–Bond

40