Dutch Corporate Greenhouse Gas Disclosure Quality: Stakeholder Pressure and the Role of Management

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Dutch Corporate Greenhouse Gas Disclosure Quality:

Stakeholder Pressure and the Role of Management

Master Thesis

Fleur van Veen S2931036

Supervisor: Dr. D.A. de Waard Co-assessor: Dr. T.A. Marra

University of Groningen Faculty of Economics and Business

MSc Accountancy & Controlling Track: Accountancy

21 June 2020 Word count: 10,294

Abstract

This paper explores the effect of internal (management) and external (stakeholder) pressure on Dutch corporate greenhouse gas (GHG) disclosure quality. To test this, I analyzed 130 companies originating from the “Transparantie Benchmark” (2019), utilizing the Ordinary Least Square and the Industry Fixed Effects method. Additionally, a benchmark based on quantitative measures has been developed to measure the quality of GHG disclosure. The research findings provide strong evidence that government pressure, investor pressure, competitor pressure, and management pressure are positively related to the quality of GHG disclosure. However, no evidence of a similar relationship has been found for customer and supplier pressure and NGO pressure. These findings contribute to the growing empirical literature regarding environmental disclosures, yet the quantitative approach to measure environmental disclosure quality provides a new perspective.

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3.2.5 Competitor Pressure ... 16 3.2.6 NGO Pressure ... 17 3.2.7 Management Pressure... 17 3.2.8 Control variables ... 18 3.3 ECONOMETRIC MODELLING ... 19 4 | RESULTS ... 21 4.1 DESCRIPTIVE STATISTICS ... 21 ... 23 4.2 MULTICOLLINEARITY ... 23 4.3 LINEAR REGRESSION ... 24 4.3.1 Control Variables ... 25 4.3.2 Government Pressure ... 25 4.3.3 Investor Pressure ... 26

4.3.4 Customer and Supplier Pressure... 26

4.3.5 Competitor Pressure ... 27 4.3.6 NGO Pressure ... 27 4.3.7 Management Pressure... 27 4.4 ROBUSTNESS CHECKS ... 29 5 | CONCLUSIONS ... 31 6 | REFERENCES ... 33 7 | APPENDIX ... 46

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Introduction

Climate change is one of the most pervasive and threatening issues of our time, with far-reaching consequences for the twenty-first century (UN Environment, 2018). Due to the negative environmental, social, and economic effects of global warming (Karl & Trenberth, 2003), climate change mitigation represents one of the greatest challenges facing us today (Ramanathan & Feng, 2008). The corresponding devastating effects are already visible. From 1910 to 2010 greenhouse gas emissions have increased by 60% causing a 19 centimeters rise in mean sea level (Capece et al. 2017). These, and similar scenarios that map the (probable) consequences of global warming, are brought up daily and request an urgent response.

As a result of the social fear towards the risks of climate change, companies, as primary producers of greenhouse gases (hereafter: GHG), are held responsible for their destructive influence on our natural environment (Solomon et al., 2011). Since GHG is elected as the major cause of climate change, the path to climate change mitigation is a significant reduction in total GHG emissions (Meinshausen et al., 2009). This means that the amount of GHGs emitted by firms must be reduced (Bradford & Fraser, 2008). Hence, to reduce GHG emissions and thus prevent global warming, it is necessary to understand a firm’s behavior.

As the climate change issue has gained traction in society, firms have adapted and changed their strategies and routines (Backman et al. 2017). The term “environmental management” refers to a firm’s operational methods for the management of activities, in a manner that protects the environment and reduces impacts (Lash & Wellington, 2007). The aim of supposing environmental management is to predict the environmental effects of a firm's activities and to voluntarily define the purposes for continuous improvement (Capece et al. 2017). The inclusion of environmental issues into corporate strategy beyond what is required by government regulation could be seen as a means to improve a firm’s alignment with the growing environmental concerns and expectations of its stakeholders (Garrod, 1997; Gladwin, 1993; Steadman et al., 1995).

Various stakeholder groups worldwide are calling for action and putting forward proposals to combat climate change (Prado-Lorenzo et al. 2009), which led to initiatives such as the Kyoto Protocol (1997) and the subsequent Paris Agreement (2015). As a result, socially responsible investing is becoming more popular, with $30.7 trillion worldwide invested according to social or ethical criteria at the start of 2018. This is considered a 34 percent increase in two years (GSIA, 2018). Additionally, the members of the Carbon Disclosure Project (CDP),

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information about their impact on the environment. “Our vision is for a thriving economy that works for both people and planet” (CDP, 2020).

Sustainability disclosure represents a response to many calls for greater corporate accountability towards environmental impacts and has experienced rapid growth over the last 30 years (Solomon et al., 2011). Although all sustainability-related topics are of great priority, the issue that has generated the most disclosure expectations recently is climate change, or more concretely: GHG emission (Prado-Lorenzo et al., 2009). According to the KPMG (2017) Survey “Corporate Responsibility Reporting”, academic research into non-financial disclosure has grown exponentially since the 1980s. However, previous research has mainly focused on sustainability disclosure in its entirety, and only a handful of papers have been dedicated to the disclosure of GHG emission specific.

The purpose of this study is to fulfill this research gap and to find out to what extend internal (manager) and external (stakeholder) pressure influences the quality of GHG disclosure, to paint a picture of which factors do trigger firms in this respect, and which factors do not. This leads to the following research question:

Does an increase in stakeholder and management pressure result in a higher quality of greenhouse gas disclosure among Dutch firms?

Also, prior literature has primarily focused on qualitative measures or used a combined qualitative and quantitative index (e.g. Tauringana & Chithambo, 2015). However, previous studies have found evidence of greenwashing in qualitative climate change disclosure (Hrasky, 2012). This means that organizational disclosures of qualitative nature are awash with positive environmental initiatives or characteristics whereas negative ones are hidden (Chithambo et al., 2020). Therefore, this study seeks to respond to the call for a specified quantitative measure of GHG disclosure quality. Moreover, my focus is on Dutch companies since The Netherlands has the longest established tradition of environmental concern compared to other European countries (Barrow, 1999).

The remaining of this paper proceeds as follows: I will first discuss the theoretical background on the issues described above followed by an overview of the developed hypotheses. Then, I will describe the sample of observed firms and the methods of data collection. In the fourth section, I will elaborate on the results, and finally, these results will be discussed in section five.

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Theory

2.1. External Pressure

2.1.1 Stakeholder Theory

According to Freeman (1984, p. 46), a stakeholder is “any group or individual who can affect or is affected by the achievement of the organization’s objectives”. The stakeholder theory expects an organization’s management to undertake activities deemed important by its stakeholders and to report those activities back to the stakeholders (Guthrie et al., 2006).

For this study, the stakeholder theory seems particularly relevant since it is thought to be one of the most valuable conceptual frameworks in the area of non-financial accounting (Gray et al., 1997), and it provides a convincing basis for environmental disclosures (Cormier et al., 2004). Various interest groups are concerned about global warming and exert pressure on organizations to disclose their GHG emissions (Depoers et al., 2014). The stakeholder theory explains GHG disclosure as regards to a firm's responsiveness towards global warming, its strategic positioning towards ecological responsibility, and the trade-off between economic and environmental objectives (Macve & Chen, 2010).

Stakeholders exert more and more pressure on firms to release information concerning their GHG emissions to evaluate the appropriateness of the firm’s climate policy (Gray et al., 1995). Companies, in turn, take actions to fulfill the expectations of stakeholders, and particularly those who have the power to influence their performance (Deegan, 2009). As stakeholders are more focused on strategies, devotion, and stance of a company towards environmental issues, they appreciate economic profit or performance which does not contribute to global warming (Huang & Kung, 2010). Corporate disclosure is considered as part of the relation between the firm and its stakeholders (Roberts, 1992). Firms have incentives to disclose particular information to important stakeholders in order to demonstrate to them that they are fulfilling their expectations (Cotter & Najah, 2012). It is therefore extremely important for companies to disclose information regarding its GHG strategy and especially that strategy’s outcomes (Liao et al., 2015).

2.1.2 Stakeholder Pressure

KPMG’s (2005; 2008) research states that stakeholder pressure has been identified as one of the main drivers for increased corporate sustainability reporting. In terms of GHG reporting,

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reports (Prado-Lorenzo et al., 2009). Companies who consider their stakeholder requirements perform better than those who do not (Maltby, 1997; Ullmann, 1985). Moreover, Rakhmawati et al. (2017) argue that to be successful in the long run, firms have to pay attention to the interests of stakeholders in managing their business. Therefore, it seems logical to conclude that stakeholder pressure leads to increased GHG disclosure quality.

Nevertheless, “stakeholders are any group or individual who can affect or is affected by the achievement of the organization’s objectives” (Freeman, 1984, p. 46), which means that this is a very wide and divergent gathering. For most companies applies: the more important the stakeholder to the company, the more the company considers managing and dealing with this stakeholder (Gray et al., 1996).

Therefore, I will make a stakeholder specification. Stakeholders can generally be separated into primary and secondary stakeholders (Freeman, 1984), where primary stakeholders are critical for the organization’s survival and secondary stakeholders are not directly involved in the organization’s activities (Clarkson, 1995). DiMaggio and Powell (1983) classify stakeholder pressure into mimetic, coercive and normative, whereas mimetic forces push firms to copy other firms’ actions, coercive forces are regulatory pressures pushing firms towards prescribed preferred systems, and normative forces are pressures from professional standards or communities within the firm’s network. Henriques and Sadorsky (1999) categorize stakeholders into regulatory, organizational, and community. Comparably, Buysse and Verbeke (2003) distinguish between regulatory stakeholders, external primary and internal primary stakeholders, and secondary stakeholders. However, following Busch and Hoffmann (2007), and Kolk and Pinkse (2007), I will differentiate between governments, investors, customers, suppliers, competitors and nongovernmental organizations (NGOs), since those are considered to be the most relevant (external) stakeholders for companies in the specific context of climate change. This division is illustrated in figure 1.

Prior literature states that government pressure is coercive and thus considered as the most influential (Berrone et al., 2013). Government pressures are followed by pressures from investors, competitors, and customers and suppliers, who are thought to be market actors. Market actors do not have the coercive power that governmental parties do. Nevertheless, they have a great impact on polluting companies (Cadez et al., 2019). This is due to their engagement with polluting firms through transactions, in which choices made by these market actors can trigger the deficit of economic rents in polluting companies (Delmas & Toffel, 2008). Finally, the NGO pressures are considered as indirect pressure, and therefore, less effective than the others (Henrique & Sadorsky, 1999).

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Figure 1: Stakeholder Specification

2.1.3 Governments

In contrast to other stakeholders, the distinctive characteristic of governments is the power to force conformity through environment‐related expectations (Berrone et al., 2013; Okereke & Russel, 2010). Due to government pressure, companies may increase their environmental disclosure as a way to improve their communication with governmental organizations to achieve legitimacy (Patten, 1991). Peters and Romi (2009) found that the level of disclosure in a firm is related to the environmental regulatory stringency of the government. Additionally, regulatory interference created by government pressure which has the purpose to reduce GHG emissions is rapidly growing worldwide (Hepburn, 2006). Figures show that the average number of rules per country has increased by approximately 50% since 2013 (KPMG, 2017). Harper (2007) argues that governments can, for example, use energy taxes as a tool to motivate corporate actions related to climate change reduction, and she notes that the Netherlands, among others, specifically implemented those energy taxes.

GHG emissions are ubiquitous and tenacious, thus the climate-change legislation affects a company either directly or indirectly, and either favorably or unfavorably (Liao et al., 2015). The most commonly known example of government pressure to take environmental action is the Kyoto Protocol (1997) (Sprengel & Busch, 2011). Previous literature discovered that firms that are headquartered in countries that have endorsed the Kyoto Protocol disclose on GHG emissions more extensively compared to firms which headquarters are located in non-endorsing countries (Freedman & Jaggi, 2005; Prado-Lorenzo et al., 2009). Moreover, empirical evidence implies that every time a reporting regulation or important governmental milestone related to

GHG-related External Stakeholders Governments Investors Customers Suppliers Competitors NGOs

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global warming is reached, firms expose their reaction through increased GHG disclosure (Tauringana & Chithambo, 2015). This leads to the following hypothesis:

Hypothesis 1:

Government pressure is positively associated with the quality of GHG disclosure

2.1.4 Investors

Weber et al. (2010) found that investors increasingly exert pressure on firms by requesting information on their environmental practices and related reduction strategies. Active ownership through corporate engagement activities signifies a convincing power in representing investors’ interests in firms (Hebb, 2006). Especially large equity-holding investors are vulnerable to market performance as a whole, and therefore, they have an incentive to reduce potential risks like GHG pollutions (Cotter & Najah, 2012). Besides, all matters that could negatively influence a company might stop a company from meeting its unresolved duties to its investors. Consequently, investors request transparent disclosure from companies to stay informed about the company state with regard to environmental pollution (Chithambo et al., 2020). Failure to report important details might push investors to remove their investments from the company (Huang & Kung, 2010).

Thus, transparent GHG disclosure diminishes environmental information asymmetry between a firm and its investors. As a result, investors show a greater willingness to trade, leading to higher firm liquidity (Verrecchia, 2001; Amihud & Mendelson, 1986), which is a solid reason for companies to invest in their GHG disclosure. Besides, a high level of transparency in GHG disclosure reduces investor’s monitoring costs, and therefore, they require a lower rate of return for holding stocks, which is highly beneficial for companies (Lombardo & Pagano, 1999).

Likewise, this development is reflected in the increasing support of the Carbon Disclosure Project (CDP). The CDP is a non-profit organization that pushes firms to manage and disclose their GHG emissions in a structured and detailed way (CDP, 2020). The asset value of investors supporting this project has increased from USD 5.4 trillion in 2003 to USD 96 trillion in 2019 (CDP, 2020). Thus, a growing number of investors push firms towards a strategic consideration of increasing the quality of their GHG disclosure. This leads to the following hypothesis:

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Hypothesis 2:

Investor pressure is positively associated with the quality of GHG disclosure

2.1.5 Customers and Suppliers

There is a growing trend toward green consumerism. Especially in developed countries, like The Netherlands, customers are more and more concerned with the environmental performance of manufacturers as well as the GHG emissions generated in the production of the products they buy (Sprengel & Busch, 2011). Moreover, research of GFK, which is one of the four largest research institutes worldwide, has shown that in 2018, 36% of Dutch consumers were willing to pay more for environmentally responsive products. Since customers are often a firm's most direct provider of income, firms take their customers' concerns and demands very seriously (Montiel & Husted, 2009). Failure to meet customers’ expectation will make it difficult for companies to achieve their goals since the disappointment of customers will ultimately reduce companies’ revenues (Rokhmawati et al., 2017). Therefore, firms with stronger customer relationships are expected to be more actively and transparently disclosing their corporate contributions to environmental protection (Park, 1999), to demonstrate their willingness to cooperate with green consumerism.

Similarly, Freedman and Jaggi (2011) state that suppliers exert pressure on companies to release more transparent information regarding the company's contribution to global warming, to avoid their reputation to be linked to low environmental performance. Gathering information from buyers regarding their climate change vulnerabilities and GHG emissions enables suppliers to identify cost- and risk-reduction opportunities (Jira & Toffel, 2013). Moreover, firms are acknowledging the significance of climate change since the issue directly influences both consumers’ and suppliers’ trust, and therefore, their decisions. Consequently, firms need to implement proper climate change strategies with additional clarifying disclosure that will help them to narrow this “trust gap” (Bonini et al., 2008). In addition, under the Greenhouse Gas Protocol, scope 3 emissions are defined as “all indirect emissions that occur in the value chain of the reporting company”. Thus, effectively integrating sustainability-based activities into firm disclosures often requires co-ordination beyond individual organizational boundaries (Wagner, 2011). This potentially has a key role in companies meeting and disclosing their GHG emission targets (Gualandris et al., 2014).

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Since suppliers and customers are often referred to as “value chain stakeholders” (Huang & Kung, 2010; Chithambo et al., 2020) and therefore taken together as one variable, I will adopt this interpretation into my research. This leads to the following hypothesis:

Hypothesis 3:

Customer and supplier pressure is positively associated with the quality of GHG disclosure

2.1.6 Competitors

In a (highly) competitive market, firms need a means to distinguish themselves from other market players in order to gain a competitive advantage. Hence, the reduction of GHG emissions has found to be a very effective tool to strengthen a market position (Lash & Wellington, 2007). Companies who perform a reactive environmental strategy may face an overall loss of competitive advantage if proactive environmental management becomes a common practice (Garrod, 1997). However, benefits related to a proactive strategy can only be achieved if the corresponding practices can actually be noticed by the public, for example through environmental disclosure (Dienes et al., 2016). This means that more competition indirectly pushes companies to disclose more environmental information to stay ahead of the competition.

Nevertheless, competitors can also directly influence other firms to increase their disclosure quality. Competitive pressures compel companies to respond to each other's moves (Kolk & Levy, 2004; Levy, 2005). When companies mimic practices that turned out to be successful for other firms, it is called “isomorphism” (Delmas & Toffel, 2010). In the context of GHG disclosures, when multiple firms are competing and one of those firms gains a competitive advantage through high-quality GHG disclosure, institutional legitimacy appears. For those who already implemented superior GHG disclosure, it will become a routine which creates stability, whereas, for those whose GHG disclosure is considered weak, a pressure is created to adopt similar behaviors, thus to increase their GHG disclosure quality (Tuttle & Dillard, 2007). Therefore, the total GHG disclosure quality in the market will increase. This leads to the following hypothesis:

Hypothesis 4:

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2.1.7 Nongovernmental Organizations

Other relevant stakeholders, including NGOs, are not directly influential. However, they can exert indirect pressure on polluting companies through their communication of information (Henriques & Sadorsky, 1999). NGOs are key players in the area of CSR (Arenas et al., 2009), they play a major role in the climate change debate (Gough & Shackley, 2001), and they are taking many actions to affect corporate environmental behavior (Carpenter, 2001). Many NGOs are operating throughout the world supporting and opposing a variety of causes. Some of them are small and very locally oriented, whereas others, such as Greenpeace, Friends of the Earth, and the World Wide Fund for Nature (WWF), are global in nature. This paper focuses on NGOs which exert pressure on companies that exhibit negative polluting behavior to attain a more efficient and eco-friendly production process and to disclose information concerning their products’ impact on the environment (Huang & Kung, 2010).

One of the ways NGOs exert pressure is by publicly criticizing companies on climate change-related issues (Rodrigue, 2014). Failure to act by those public statements is perceived to have negative implications for the ongoing operations and existence of the company (Deegan & Blomquist, 2006). For example, evidence from Deegan and Blomquist (2006) demonstrates that initiatives of the WWF that pushed Australia’s mineral industry to meet the society’s information demands ultimately influenced revisions to the reporting behavior of the individual mineral companies in a positive way. Moreover, NGOs are strong opponents of greenwashing. Often, NGOs embarrass greenwashing firms in the media and encourage customers to boycott them (Lyon & Maxwell, 2011). Since greenwashing firms falsely portray themselves as “environmentally responsible”, the environmental disclosure quality decreases. Therefore, NGO actions against greenwashing will result in higher quality GHG disclosures. As a result, Marquis et al. (2016) found that companies that are headquartered in countries with a higher density of NGO members reported their environmental pollutions more completely. This leads to the following hypothesis:

Hypothesis 5:

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2.2 Internal Pressure

2.2.1 Signaling Theory

According to An et al. (2011), the signaling theory is concerned with how to address problems arising from information asymmetry in any social setting. This theory suggests that information asymmetry reduces if the party possessing more information can send signals to other related parties. A signal can be an observable action, or an observable structure, which is used to indicate hidden characteristics (or quality) of the signaler. There are several means for companies to signal information about themselves. Among these, voluntary disclosure of positive accounting information is considered to be one of the most effective (Ross, 1979; Watson et al., 2002; Xiao et al., 2004).

The signaling theory appears relevant in this context since, due to asymmetric information, interest groups do not know to what extent a firm's management is focused on climate change, and therefore, cannot differentiate between various firms. Consequently, the firm with an above-average focus incurs an opportunity loss since its superior quality is not perceived by its stakeholders, while the firm with a low environmental focus obtains an opportunity gain. Under these circumstances, the highly environmentally-focused firm has an incentive to highlight its superior quality to attract more investors and satisfy its stakeholders. Thus, companies with an above-average focus on the environment resulting in superior environmental performance, tend to signal this by disclosing relevant information to stakeholders to gain a competitive advantage (Braam et al., 2016). To distinguish themselves from poor performers, good environmental performers, thus companies with an environmentally dedicated management team, employ more objective and verifiable GHG disclosure to ensure credibility and accuracy for their stakeholders (Clarkson et al., 2008).

2.2.2 Management Pressure

Environmental management is defined as “The organization-wide process of applying innovation to achieve sustainability, waste reduction, social responsibility, and competitive advantage via continuous learning and development, and by embracing environmental goals and strategies that are fully integrated with the goals and strategies of the organization" (Haden et al., 2009, p. 1052). At its simplest, environmental management must do three things: (1) identify goals; (2) establish whether these can be met; (3) develop and implement the means to do what it deems possible (Barrow, 1999). Companies are demanded by society to actively pursue ways to minimize their exposure to risk and take a proactive approach to environmental

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management (Phan & Baird, 2015). There are numerous different initiatives considering a company’s management focus on environmental aspects, such as the Environmental Management System (EMS), the Principles for Responsible Investment (PRI), the Equator Principles, the VN Global Impact, and the Global Environmental Management Initiatives (GEMI).

Environmental management and voluntarily proposing objectives for continuous improvement will ensure the responsibility of a management team for its operational activities related to the environment (Rondinelli & Vastag, 2000). Consequently, studies have shown that GHG emissions diminish as managers are environmentally focused, through which they ensure the adoption of appropriate environmental policies (Capece et al., 2017). Although the implementation of environmental management practices is non-compulsory, it is seen as a significant strategic choice since it reveals the accountability of a company towards its stakeholders for its environmental impacts (King & Lenox, 2002), such as GHG emissions.

Brammer and Pavelin (2006) emphasize that managers intend to disclose the successful results obtained from their environmental focus, especially if the management considered the prerequisites that are certified by international standards such as the ISO 14000 series or the Eco-Management and Audit European Scheme (EMAS). Additionally, Peglau (2005) confirms that companies whose managers are focused on global warming, disclose a higher level of environmental information. This is later confirmed by Mitchell and Hill (2009) who state that the existence of environmental management facilitates an improved environmental disclosure quality. In terms of GHG disclosure, Wahyuni et al. (2009) found that firms that have an environmentally focused management team are more inclined to voluntarily report information concerning their GHG emissions than firms without environmentally-focused managers.

Managers who incorporate an environmental focus into their business activities can more easily obtain emission-related information to measure, manage, and disclose their GHG emissions in an improved way (Montiel & Husted, 2009). Therefore, those managers are more likely to generally communicate their efforts regarding the reduction of their GHG emissions to influential stakeholders and the public (Anandale et al., 2004). Hence, companies with an environmentally focused management are more likely to voluntarily disclose information about their GHG emissions. Moreover, the disclosed GHG information will most probably be more credible than the information presented by firms without environmentally-focused managers (Ranking et al., 2011). This leads to the following hypothesis:

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Hypothesis 6:

Management pressure is positively associated with the quality of GHG disclosure

Figure 2: Theoretical Model

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Methodology

3.1 Sample Selection

My sample consists of the top 130 Dutch listed companies originating from the “Transparantie Benchmark” (2019). I choose this sample because the included companies are considered the top 130 most transparent Dutch companies as regards to CSR reporting. The data were taken from three sources: (1) the integrated annual and the sustainability reports of the sampled firms, (2) the Thomson Reuters Eikon database, and (3) Company.Info. Firstly, the sustainable and annual reports were used to measure the GHG disclosure quality. Secondly, from the Thomson Reuters Eikon database, I used the Asset4 ESG dataset, which contains social corporate responsibility data. This was used for the measurement of the variables with environmental proxies1; NGO pressure and Management pressure. Thirdly, Company.Info contains Dutch company data originating from the Dutch Chamber of Commerce, which I used for the

1 Chapter 3.2.6 and 3.2.7 (page 17)

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remaining variables. In addition, my sample period includes data for the year 2018, since this is the most recently available data, making my research as representative for the contemporary world as possible. Besides, there is barely any data on environmental scores available before this period. To arrive at my sample no firms were excluded, which means that my final sample consists of 130 firms.

However, a limitation to my sample is the number of observations for management pressure. This is due to the lack of available ESG data for Dutch companies. Therefore, I executed my analyses over two separate panels:

Table 1: Panel Division

Panel A: External Pressure Panel B: External & Internal Pressure

GHG disclosure quality GHG disclosure quality

Government pressure Government pressure

Investor pressure Investor pressure

Customer & supplier pressure Customer & supplier pressure

Competitor pressure Competitor pressure

NGO pressure NGO pressure

Controls Management pressure

Controls

N = 130 N = 48

The data analysis will be exposed through a quantitative method. If needed, variables were winsorized at +/- 3 times the standard deviation to control for possible effects of outliers.

3.2 Measurement of the Variables

3.2.1 GHG Disclosure Quality

In this study, the quality of GHG disclosure is used as a dependent variable. This variable has been measured based on a benchmark including several self-developed criteria. These criteria are based on the Carbon Emission Disclosure Index of Tauringana and Chithambo (2015), which are, in turn, drawn upon several GHG reporting frameworks including the GHG Protocol (2004), Global Reporting Initiative (2006), DEFRA (2009), ISO 14064-1 (2006), Global Framework for Climate Risk Disclosure (2006) and the Climate Disclosure Standard Board (2012).

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The final benchmark consists of 30 disclosure items2 (i). A content analysis technique is used to quantify the GHG disclosures. According to Jaggi et al. (2018), the quantification of disclosures can be completed using a weighted disclosure index or through an unweighted disclosure index. This paper has assumed an unweighted disclosure index, which treats the individual items as dichotomous variables (Hossain, 2002). Thus, the only consideration is whether or not a firm discloses an item of information in its sustainability report. A firm is awarded a "1" if an item is disclosed and a "0" if not. Subsequently, the total disclosure score is captured for each firm as a ratio of the total disclosure score divided by the maximum score per firm. DS =∑ di 30 i=1 30 Where, DS = Disclosure Score di = 1 if the item i is disclosed di = 0 if the item i is not disclosed

3.2.2 Government Pressure

The first independent variable in this study is government pressure. This variable has been proxied as firm size since Haque and Ntim (2018) state that larger companies are more vulnerable to public scrutiny, and thus to governmental interference. Similarly, larger companies attract greater attention from regulators and are therefore under greater pressure to act in a manner consistent with governmental protocols than smaller companies (Watts & Zimmerman, 1986; Freedman & Jaggi, 2005). Firm size is measured as the natural log of the total assets.

3.2.3 Investor Pressure

The second independent variable is investor pressure, which is (inversely) proxied as ownership

concentration. When ownership is more dispersed across many investors, there is an increased

investor pressure (Ullmann, 1985), and thus a large ownership concentration equals low investor pressure (Brammer & Pavelin, 2008). Ownership concentration has been measured as the Herfindahl-Hirschman Index (HHI) of the percentages of shares owned by the largest five

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investors. The HHI is one of the best-know measures of concentration in the existing economic research. It is commonly used in research on wealth distribution and market concentration, yet appropriately applicable to ownership concentration analysis as well (Herfindahl, 1950). The HHI is calculated as the sum of the squared ownership percentages. However, the index is mostly calculated based on only the largest owners since including more shareholders has barely any effect on the values of the index. Therefore, I calculated the HHI for the five largest investors (i). H = ∑ O_i 5 i=1 ² Where,

H = HHI (market concentration)

Oi = Ownership percentage of investor i

3.2.4 Customer and Supplier Pressure

The third independent variable is a combination of customer and supplier pressure, often called: value chain pressure. This variable is (inversely) proxied as market share since the more a company controls the market (i.g. greater market share), the more it shapes its value chain relationships (Porter, 2008). Hence, companies with a lower market share experience greater customer and supplier pressure on their GHG disclosure decisions. This paper measured market share as the total turnover expressed as a proportion of the total turnover of the largest Dutch companies in the sample drawn from the same industry.

3.2.5 Competitor Pressure

The following independent variable is competitor pressure. In this case, market concentration is used to proxy the extent of competitive pressure companies face, since market concentration measures the degree of competition in a specific market (Were & Wambua, 2014). Therefore, a higher market concentration means higher competitor pressure. Again, I used the HHI to measure market concentration. However, in this case, it is defined as the sum of the square of the market shares of the largest Dutch companies in the concerned market. This is not a fixed number since each market has a different number of "largest" players.

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H = ∑ s_i 2 n

i=1

Where,

H = HHI (market concentration) Si = Market share of firm i

n = Number of large firms that participate in the market

3.2.6 NGO Pressure

The subsequent independent variable is NGO pressure, which has been proxied as

environmentally sensitive industries. Companies in environmentally sensitive industries are

generally subject to greater pressure from NGOs than other companies (Cho & Patten, 2007; Cowen et al., 1987; Patten, 1991). These highly sensitive industries include energy and utilities, chemicals, and pharmacy (Cho & Patten, 2007). I used a dummy variable to designate companies from these industries, which is 1 if a company belongs to one of the environmentally sensitive industries, and 0 otherwise. The classification has been based on the Thomas Reuters Business Classification (TRBC).

The next independent variable is management pressure, which is measured using the Thomson Reuters ESG scores. The ESG scores provide data regarding the three pillars of CSR; Environment, Social, and Corporate Governance, of which I will focus on the environmental pillar. The Environmental Pillar Score is, in turn, based on 61 indicators of environmental performance and it consists of three underlying categories; Resource Use, Emissions, and Innovation. To measure management pressure, I used the Emissions Score, since it represents a company's management commitment and effectiveness towards reducing environmental emission in the production and operational processes. Moreover, this score reflects the management’s capacity to reduce air emissions (e.g. GHGs), waste, hazardous waste, water discharges, spills, or its impacts on biodiversity and to partner with environmental organizations to reduce the environmental impact of the company in the local or broader community.

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3.2.8 Control variables

The first control variable included is firm age, since older firms are deemed well enough established to own resources to manage and disclose climate change issues compared to younger ones which might have other pressing issues (Chithambo & Tauringana, 2014). Firm age is measured as the natural log of the number of years since a firm has been founded.

As a second control variable, I assumed the effect of leverage. According to Dwyer et al. (2009), managers increase their levels of disclosures in a highly leveraged company as a way of minimizing agency costs and any possible conflicts of interest between owners and creditors. Moreover, Clarkson et al. (2008) found a positive relationship by studying the effect of leverage on environmental information exclusively. Leverage is measured as the total debt divided by total shareholders’ equity.

The third control variable is liquidity. According to the signaling theory, a company with a high liquidity ratio is expected to disclose more information to distinguish itself from other companies with less favorable liquidity positions (Aly et al., 2010; Oyeler et al., 2003). Thereby, environmental-related activities including GHG disclosures need adequate liquid resources (Chithambo & Tauringana, 2014). Current Ratio is used as a proxy for liquidity and it is measured as current assets divided by current liabilities.

The last attribute that I want to control for is profitability. Prior research found that good environmental performers disclose more pollution-related environmental information than poor performers do (Al-Tuwaijri et al., 2004). Besides, Brammer & Pavelin (2008) discuss that profitability provides managers with a pool of resources that can be used to absorb the costs of environmental disclosures. In this study, Return on Assets is used as a proxy for profitability and it has been measured as profit after tax, divided by total assets.

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Table 2: Variable Description

Variables Proxy Measurement

Dependent

GHG Disclosure Quality + GHG disclosure index Disclosure score expressed as a ratio of

the total possible score (30) Independents

Government pressure + Company size Total assets expressed as the natural log

Investor pressure - Ownership concentration HHI index: squaring the percentage of

shares owned by the largest 5 investors and summing the resulting numbers Customer & supplier

pressure

- Market share Total turnover expressed as the

proportion of total turnover of firms drawn from the same industry

Competitor pressure + Market concentration HHI index: squaring the market share of

each firm competing in a market and then summing the resulting numbers

NGO pressure + Environmentally sensitive

industry

Dummy variable to designate firms from sensitive industries

Management pressure + Emission reduction

commitment

A company's management commitment and effectiveness towards reducing environmental emission in the production and operational processes Controls

Firm age + Firm age Natural log of the firm’s age expressed

in years

Leverage + Debt-to-Equity ratio Total debt divided by total shareholders’

equity

Liquidity + Current Ratio Current assets divided by current

liabilities

Profitability + Return on Assets Profit after tax, divided by total assets

3.3 Econometric Modelling

The hypotheses are tested using the ordinary least square method (OLS), which measures the effect of the independent and control variables on the dependent variable. The models representing respectively panel a and panel b are represented below:

GHGDQ = β₀+ β₁∗ Gov + β₂∗ Inv + β₃∗ CustSup + β₄∗ Comp + β₅∗ NGO + β₆∗

Fage + β₇∗ Lev + β₈∗ Liq + β₉∗ Prof + ε (1)

GHGDQ = β₀+ β₁∗ Gov + β₂∗ Inv + β₃∗ CustSup + β₄∗ Comp + β₅∗ NGO + β₆∗ Mng + β7∗ Fage + β8∗ Lev + β9∗ Liq + β10∗ Prof + ε (2)

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Where,

GHGDQ = GHG disclosure quality Gov = Government pressure Inv = Investor pressure

CustSup = Customer and supplier pressure Comp = Competitor pressure

NGO = NGO pressure

Mng = Management pressure Fage = Firm age

Lev = Leverage Liq = Liquidity Prof = Profitability β₀ = Constant β₁₋₁₀ = Coefficients ε = Residual

To prevent my model from errors and to check whether it meets the OLS assumptions, I ran several tests which are all demonstrated in the Appendix. Firstly, I analyzed unusual and influential data. As aforementioned, I winsorized my data to prevent it from outliers. Besides, I plotted a scatter graph to test if there were still some remaining outliers and removed them.

Secondly, I checked for normality by using density plots, normal probability plots, and the Shapiro-Wilk W test3. Since only government pressure and firm age are normally distributed (> 0.05), I fail to reject the null hypothesis of normality. Hence, I conclude that my data does not meet the requirements of the normal distribution. This is most likely due to the size of my dataset since I already used logarithms.

Thirdly, I examined for heteroskedasticity by using the Breusch-Pagan test4. The test provides a p-value which is greater than the 10% significance level, thus I cannot reject the hypothesis of constant variance. This means that my data is free of heteroskedasticity.

Furthermore, I regressed a post-estimation test to describe the model specification. The Link Test5 examines for a specification error, also known as the “link error”. This test adds a squared independent variable to the model and tests for the significance of the squared model

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versus the non-squared model. Since the squared independent variable is insignificant, I can conclude that my model passes the link test, which means that the independent variables are correctly linked and related to the dependent variable.

Additionally, I executed the RESET test by Ramsey6 to test for omitted variables. Based on the test outcomes, I adopt the null hypothesis which states that the model has no omitted variables for panel a. This means that the independent and control variables do add additional explanatory power to the model. However, the null hypothesis is rejected for panel b. Therefore, I will run an Industry Fixed Effects regression to control for omitted variable biases.

Overall, I conclude that the OLS method is indeed the best suitable regression method for my model. However, to control for possible biases due to industry effects, I will additionally conduct an Industry Fixed Effects regression. This technique enables me to take unobserved variables into account and to control for unobserved heterogeneity.

4 |

Results

4.1 Descriptive Statistics

Table 3 provides the descriptive statistics of the non-transformed dependent variable, independent variables, and control variables of the sample. The mean of the GHG disclosure quality is about 32% (0.316), whereas the lowest and highest disclosure levels were 0% and 87% (0.867), respectively. Figure 3 shows the degree to which this ratio is distributed across the participating firms. Merely 30 firms score above 50% which demonstrates that GHG disclosure is a very new concept that still needs a lot of development and improvement.

Additionally, I added a descriptive overview of the quantitative disclosure index and the subsequent statistics in the Appendix7. The most frequently reported quantitative item was “Comparative data on total GHG emissions in CO2 metric tonnes”, which was reported by 72% of the companies. Whereas scope 1 and 2 emissions were both reported by 55% of the companies, information regarding scope 3 emissions has only been reported by 37% of the companies. However, evidence especially indicates low levels of descriptive information per scope. For example, “Future estimates” and “Exclusions” score very low for all scopes. Overall, there was a lack of information related to the “Change of base year”.

Besides, figure 4 shows the GHG disclosure quality ratio per industry, based on the Standard Industry Classification (SIC). Whereas both agriculture, foresting & fishing, and

6 Appendix, table 11 (page 47) 7 Appendix, table 7 (page 46)

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services score relatively high (+/- 45%), construction scores rather low (12,6%). This already demonstrates that highly sensitive industries8 (NGO pressure) will almost certainly not relate to GHG disclosure quality.

Moreover, table 3 indicates that my sampled firms range from €62.8 million to €887,000 million with a mean of €35,100 million in size, indicating that overall, the sample had relatively large firms. In addition, with a mean of 69 years, my sampled firms are quite experienced. Besides, my sample generally consists of firms that, on average, possess more debt than equity (mean: 1.91), and own enough assets to meet their financial obligations (mean: 1.32). However, with an average of 0.04, the sampled firms are not very profitable.

In the Appendix I added a table9 consisting of the listwise descriptive statistics for panel b with 48 observations for all variables.

Table 3: Descriptive Statistics

Variables N Minimum Maximum Mean Std. Dev.

GHGDQ (%) 130 0 .867 .316 .208 Gov (€million) 130 62.8 887,000 35,100 113,000 Inv (%) 130 0 1 .397 .438 CustSup (%) 130 .000 1 .274 .289 Comp (%) 130 .036 1 .337 .261 NGO (dummy) 130 0 1 .085 .279 Mng (%) 48 .009 .986 .659 .255 Fage (years) 130 3 406 69.438 65.158 Lev (ratio) 130 -12.392 43.077 1.908 5.219 Liq (ratio) 130 -3.093 87.496 1.324 7.879 Prof (ratio) 130 -.625 .401 .036 .087

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4.2 Multicollinearity

Tables 4a and 4b demonstrate the Pearson correlation between the explanatory variables for panels a and b, respectively. From these tables can be deduced that the multicollinearity falls within the acceptable limit. However, according to Myers (1990), a certain degree of multicollinearity can still exist even when none of the correlation coefficients is substantial.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Figure 3:

GHG Disclosure Quality ratio

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Figure 4:

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Therefore, I also used the variance inflation factors (VIFs)10 to further test for multicollinearity. These results show that all VIFs are significantly lower than 10 with mean 1.39 for panel a and 1.56 for panel b, which confirms that multicollinearity is not a concern in my sample.

Table 4a:

Pearson Correlation - Panel a

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) GHGDQ 1 Gov .377*** 1 Inv -.240*** -.213** 1 CustSup .139 .271*** .118 1 Comp .055 .154* .149* .648*** 1 NGO .114 .131 -.091 -.001 .143 1 Fage .051 .023 .057 .105 .103 .129 1 Lev .016 .457*** .034 -.003 .016 -.052 -.069 1 Liq -.208** -.238*** .161* .192** .177** -.011 -.037 -.108 1 Prof .171* .083 -.027 .072 -.035 .106 .069 -.107 -.058 1 N = 130 *** p<0.01, ** p<0.05, * p<0.1 Table 4b:

Pearson Correlation - Panel b

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) GHGDQ 1 Gov .182 1 Inv -.135 -.251 1 CusSup .115 .352 -.166 1 Comp -.095 .039 -.014 .598 1 NGO .055 .017 .101 -.005 .240 1 Mng .645*** .520*** -.197 .284* .102 -.009 1 Fage -.014 -.253 .200 .087 .134 .105 -.237 1 Lev -.049 .313 -.126 .003 -.134 -.085 .105 -.200 1 Liq -.310 -.237 -.092 .019 .133 .134 -.197 .010 -.097 1 Prof -.060 -.144 .028 .254 .226 -.069 .036 .142 -.158 .531 1 N = 48 *** p<0.01, ** p<0.05, * p<0.1

4.3 Linear Regression

The main results are presented in tables 5 and 6. Since my model does not contain heteroskedasticity11 and has no multicollinearity that falls above the critical boundary12, I used

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OLS regression with normal standard errors. Table 5 presents several regression models to measure the effects of both the external and internal pressures on GHG disclosure quality. Table 6 shows an industry-fixed effects regression to capture the possible variation across different industries. The classification for table 6 has been based on the Standard Industry Codes (SIC).

4.3.1 Control Variables

The first column in both tables includes only the control variables. Two of them – liquidity and profitability – are significant at the 5% level. Thus, they are important predictors of GHG disclosure quality. However, as opposed to my prior prediction, the coefficient of liquidity seems negative, indicating that lower liquidity will result in a higher quality of GHG disclosure. This is in line with results from Chithambo and Tauringana (2014), and Aly et al. (2010). Nevertheless, profitability perfectly meets my expectations, since the coefficient of 0.419 shows that higher profitability results in a higher quality of GHG disclosure.

The other two control variables – firm age and leverage – are insignificant, meaning that the age and leverage of a firm do not influence the quality of GHG disclosure. This result is consistent with prior studies (Aerts & Cormier, 2009; Cho et al., 2012; Cormier & Gordon, 2001; Ho & Taylor, 2007; Smith et al., 2007).

After including industry-fixed effects, all relations stayed unchanged, except for the relationship between liquidity and GHG disclosure, which became weaker. However, it is still negatively significant.

4.3.2 Government Pressure

The second column in both table 5 and 6 includes the regression for hypothesis 1, that assumes a positive effect of government pressure on GHG disclosure quality. In table 5, the coefficient of government pressure is 0.091, statistically significant at the 1% level. Since government pressure is measured as a logarithm of a firm’s total assets, an increase of 1% of government pressure results in an increase of 0.00095 in GHG disclosure quality. In addition, a 10% increase in government pressure will lead to 0.0950 * log(1.10) = 0.004 increase of GHG disclosure quality. There are no substantial differences discovered after including industry-fixed effects. Thus, hypothesis 1 is confirmed.

This is consistent with the notion that firms expose their reaction to regulatory interference created by government pressure through increased GHG disclosure (Hepburn, 2006; Chithambo & Tauringana, 2015). Thereby, large firms are generally resource-rich

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causing more flexibility in managers’ disclosure decisions. Consequently, my results agree with prior research (Berthelot & Robert, 2012; Chithambo et al., 2020; Freedman & Jaggi, 2005; Prado-Lorenzo et al., 2009; Rankin et al., 2011).

4.3.3 Investor Pressure

The third column in table 5 and 6 provides the regression for hypothesis 2, indicating that investor pressure positively affects GHG disclosure quality. In table 5, the parameter of investor’s pressure is -0.100, statistically significant at the 5% level. Since investor pressure is measured by the HHI, which is an inversed measure of investor pressure, an increase of 1 unit of investor pressure results in an increase of 0.108 in GHG disclosure quality. After including industry-fixed effects, the relationship between investor pressure and GHG disclosure became stronger, significant at the 1% level. Therefore, hypothesis 2 can be accepted.

This corresponds to the belief that investors show a greater willingness to buy shares from a company that discloses its GHGs more transparently (Verrecchia, 2001; Amihud & Mendelson, 1986). The result both agrees and contradicts with prior empirical evidence. Tauringana & Chithambo (2015) indicate that ownership concentration has a significant negative association with GHG disclosure. On the other hand, Liesen et al. (2015) found that concentrated, instead of dispersed, ownership of institutional investors is positively related to the disclosure of GHG emissions.

4.3.4 Customer and Supplier Pressure.

The fourth column in both tables regresses hypothesis 3, which states that customer and supplier pressure has a positive effect on GHG disclosure quality. In table 5, the coefficient of customer and supplier pressure is 0.125, and it is significant at the 10% level. After including industry-fixed effects, the relationship is positively significant at the 5% level. However, since this variable has been measured by market share, which is an inversed measure of customer and supplier pressure, I expected a negatively significant relationship. Thus, my results indicate that an increase of 1 unit of customer and supplier pressure results in a decrease of 0.125 in GHG disclosure quality. Therefore, hypothesis 3 cannot be accepted.

A possible explanation for the positive effect of market share on GHG disclosure quality could be that the market leaders recognize that their visibility makes them vulnerable to future environmental regulations. Consequently, they use their disclosure as a method to obstruct

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regulatory intervention. Similarly, part of their tactic to remain market share could be to respond to their customers’ and suppliers’ demands by increasing their GHG disclosure.

4.3.5 Competitor Pressure

In both tables, column 5 provides no evidence for a relationship between competitor pressure and GHG disclosure quality. This contradicts my theoretical framework explanation, which suggests more GHG disclosures by firms in a highly competitive market (Dienes et al., 2016; Kolk & Levy, 2004). However, Gale (1972) discusses that firms that possess a higher market share may exert greater influence in controlling the market, suggesting that high market share equals high competitor pressure. Therefore, Huang & Kung (2010) considered market share as a proxy for competitive pressure. Since tables 5 and 6 show a significant positive relationship between market share and GHG disclosure quality at respectively the 10% and 5% level, I cannot completely reject hypothesis 4. Therefore, hypothesis 4 has been rejected as measured by market concentration but accepted when measured by market share.

4.3.6 NGO Pressure

Column 6 of both tables demonstrates that NGO pressure had no significant effect on GHG disclosure quality. This is in contrast to my literature review which implies that NGOs push companies to release information regarding the impact their products are having on the environment (Deegan & Blomquist, 2006). However, it is consistent with the findings of Huang and Kung (2010), who also did not find a significant relationship between environmentally sensitive industries and GHG disclosure quality. Moreover, Friedman and Miles (2002) discuss that the relationship between NGOs and companies is mostly noncontractual, and often, they do not need each other to survive. Therefore, companies frequently ignore the requests of NGOs, and thus do not respond to NGO pressure. Hence, I reject hypothesis 5.

The seventh column of tables 5 and 6 includes the regression for hypothesis 6. The OLS regression demonstrates a coefficient of 0.398, statistically significant at the 1% level, which indicates a positive relationship between management pressure and the quality of GHG disclosure. Thus, if management pressure increases with one unit, GHG disclosure increases with 0.398/0.611. After including industry effects, the relationship remains significant at the 1% level. Therefore, hypothesis 7 is accepted.

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This result is consistent with prior studies which argue that managers who incorporate an environmental focus into their business activities provide higher quality GHG disclosure (Montiel & Husted, 2009; Rankin et al., 2011). Nevertheless, it should be noted that this regression includes a low number of observations, which means that the outcome is not extremely statistically relevant. Yet, the significance level of 1% indicates that this relationship has the potential to be further investigated in the future when more observations will become available.

Table 5:

Ordinary Least Square Regression

Variables (1) Contr (2) Gov (3) Inv (4) CustSup (5) Comp (6) NGO (7) Mng (8) Panel a (9) Panel b Ext. pressure Gov .091*** (.021) .074** (.023) -.054* (.028) Inv -.100** (.041) -.069* (.041) -.121 (.132) CustSup .125* (.063) .063 (.082) .084 (.088) Comp .079 (.070) -.008 (.087) -.195* (.101) NGO .079 (.064) .034 (.063) .066 (.049) Int. pressure Mng .398*** (.073) .453*** (.081) Controls Fage .015 (.043) .008 (.040) .022 (.042) .005 (.043) .009 (.070) .011 (.043) .051 (.049) .008 (.040) .044 (.049) Lev .001 (.005) -.009* (.005) .001 (.005) .000 (.005) .009 (.043) .001 (.005) -.004 (.004) -.007 (.005) -.003 (.004) Liq -.011** (.005) -.007 (.004) -.009** (.005) -.013*** (.005) -.012** (.005) -.011** (.005) -.025 (.019) -.007 (.005) -.033 (.020) Prof .419** (.207) .287 (.195) .404** (.203) .393* (.205) .429** (.207) .413** (.206) -.112 (.495) .285 (.196) .017 (.513) Constant .293*** (.075) -.545*** (.202) .317*** (.074) .279*** (.074) .278*** (.076) .293*** (.075) .095 (.111) -.372 (.226) .639** (.291) Observations 130 130 130 130 130 130 48 130 48 Adj. R2 .046 .170 .083 .068 .048 .050 .419 .167 .455

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 6:

Industry Fixed Effects Regression

Variables (1) Contr (2) Gov (3) Inv (4) CustSup (5) Comp (6) NGO (7) Mng (8) Panel a (9) Panel b Ext. pressure Gov .087*** (.024) .054* (.028) -.053 (.101) Inv -.128*** (.049) -.107** (.049) -.142 (.272) CustSup .161** (.072) .141 (.095) .115 (.253) Comp .091 (.079) -.048 (.098) -.226 (.203) NGO .108 (.071) .072 (.069) -.003 (.077) Int. pressure Mng .611*** (.119) .620*** (.161) Controls Fage -.009 (-.052) -.003 (.049) .003 (.051) -.016 (.051) -.013 (.052) -.010 (.052) .157 (.099) .001 (.049) .099 (.126) Lev -.003 (.006) -.012** (.006) -.004 (.005) -.002 (.005) -.003 (.006) -.003 (.006) -.006 (.007) -.009 (.006) -.010 (.009) Liq -.009* (.005) -.006 (.005) -.006 (.005) -.012** (.005) -.011** (.005) -.009* (.005) -.002 (.022) -.006 (.005) -.016 (.038) Prof .571** (.233) .401* (.225) .541** (.227) .494** (.231) .582** (.233) .563** (.232) -.991 (.665) .360 (.225) .646 (1.027) Constant .331*** (.091) -.486** (.244) .359*** (.089) .305*** (.090) .310*** (.093) .324*** (.090) .211 (.192) -.176 (.271) .472 (1.087) Observations 130 130 130 130 130 130 48 130 48 Adj. R2 .093 .205 .157 .141 .106 .116 .699 .267 .745

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

4.4 Robustness checks

A potential problem in my model might be endogeneity since literature suggests that GHG disclosure quality may affect competitor pressure. Olson (2010), among others, argues that a firm’s carbon footprint and the effectiveness of GHG reduction programs to improve the footprint, as evidenced in its integrated report, can cause a competitive advantage, which reduces competitor pressure. This implies that causality might appear in a reversed direction, causing biased results. Therefore, I used the two-stage least square (2SLS) method to address

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this potential endogeneity. The results of the 2SLS regression are demonstrated in table 14 and 15 in the Appendix13.

Competitor pressure is instrumented with R&D investments and advertisement expenses since those affect competitor pressure, yet are not related to GHG disclosure quality. The f-value in the first stage of 2SLS is very close to 10, which implies that the instruments are jointly valid, and therefore I can conclude that I do not have weak instruments. The Sargan and Bassman tests for overidentifying restrictions do not allow me to reject the null-hypothesis that my instruments are valid. Therefore, my IV’s satisfy the relevance and validity conditions. Next, I performed the Durbin and Wu-Hausman tests for endogeneity, which provide strong evidence that the variables are exogenous (p-values of 0.84 and 0.85). Therefore, my model does not suffer from endogeneity, meaning that my OLS results are not biased.

In addition, government pressure has been proxied by firm size which was found to be highly significantly related to GHG disclosure quality. However, since firm size is quite comprehensive, more reasons can be designated to this relation than only government pressure. Therefore, I reran my model using a different proxy for government pressure to check whether the relationship remains significant. Patten (2002), Cho and Patten (2007), and Alrazi et al. (2016) state that firms with poor environmental performance can expect greater regulatory scrutiny. Therefore, I used a firm’s total CO2 emissions as the substituted proxy. In the revised relationship, CO2 emission is expected to be positively related to GHG disclosure quality. However, the findings14 do not indicate a significant relationship which implies that robustness can be questionable in my model for governance pressure.

Moreover, prior literature has proxied liquidity and profitability with varying measures. Therefore, I collected different measures for liquidity and profitability and reran my model. For liquidity, I used quick ratio (instead of current ratio), which is measured as cash and cash equivalents plus short-time investments plus accounts receivables, divided by current liabilities. For profitability, I used return on equity (instead of return on assets), measured as profit after tax divided by total equity. The subsequent findings15 are almost completely consistent with the results of the original model, indicating that my model is robust.

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5 |

Conclusions

The purpose of this study was to find out to what extend internal and external pressures influence the quality of GHG disclosure, in order to paint a picture of which factors trigger firms, and which factors do not. To achieve this, I examined the relationship between the pressure of different stakeholders and management, and GHG disclosure quality of 130 Dutch companies, through the lenses of the Stakeholder Theory and the Signaling Theory.

The findings indicate that government pressure, investor pressure, customer and supplier pressure, and management pressure are positively significantly related to GHG disclosure quality, whereas competitor pressure and NGO pressure were found to have no significant influence on GHG disclosure quality. However, referring to customer and supplier pressure, the predicted sign was negative which means that I cannot accept hypothesis 3. As regards to competitor pressure, although the original relationship was found to be insignificant, a proxy change confirmed by literature resulted in a positively significant relationship.

Thus, does an increase in stakeholder and management pressure result in a higher quality of GHG disclosure among Dutch firms? The answer is: partly. Hypothesis 1, 2, 4, and 6 have been accepted, meaning that an increase in government pressure, investor pressure, competitor pressure, and management pressure increases GHG disclosure quality. However, there is no evidence for an increase in GHG disclosure caused by customer and supplier, and NGO pressure. Hence, these are logical outcomes since research suggests that government pressures are the most influential, followed by pressures from investors, competitors, and customers and suppliers, and lastly, NGO pressures.

These results should be interpreted in the light of the following limitations. First, my focus on the “Transparantie Benchmark” top 130 firms means that my sample is rather small, which is most likely the reason for my low adjusted R-squares, and could affect the relevance of my research. Moreover, as mentioned in the methodology section16, the data for management pressure contains only 48 observations due to a lack of ESG data for Dutch companies. Thereby, I utilized archival data in my empirical analysis which does not allow me to capture the dynamics in stakeholder and manager pressures. Another limitation is related to the proxies I used in this research. Proxies limit the validity and can have more than one meaning. For example, market share could be used as a proxy for customer and supplier pressure, and for competitor pressure. Moreover, governance pressure was found to be significant for only one of the two proxies which were used as a measurement. This may cause biased results.

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Despite these limitations, my results contribute to the existing literature in the following ways. Firstly, my study contributes to the growing literature which is investigating GHG disclosures. However, the quantitative approach of GHG disclosure quality which has been used in this study provides a new perspective, and has potentially limited biases that could have arisen from a qualitative approach through greenwashing. Secondly, my findings designate which factors do trigger Dutch firms to disclosure more environmentally-related information, and which factors do not. Furthermore, my research declares which items are included in most companies’ disclosures, and which criteria have yet to be discovered. This could be useful information for further research on Dutch firms’ GHG disclosures, but also for company managers and for auditors.

Finally, my results suggest some recommendations for future research. At first, I would advise other researchers to focus on creating a larger sample. The Netherlands is an interesting country for my research, however firms from countries like Sweden and Denmark are on the same page as Dutch firms when it comes to environmental awareness. Thus, adding these countries to the sample might be beneficial. Moreover, I would recommend other researchers to exchange the proxies for alternative ways of measurement. Preferably manners that improve the validity of the model. Lastly, I would suggest to explore the relationship between management pressure and GHG disclosure quality, with the (significant) stakeholder pressures included as moderators. The direct relation between management pressure and GHG disclosure quality might be too obvious, yet it is both interesting and useful to investigate this relation in a different light.

Overall, I would like to encourage others to conduct more research into the topics that have come up in this paper. Climate change is a huge part of our society and I believe that every research into this topic can be considered as a possibility to create more environmental awareness and can bring us closer to finding a solution.

Dutch Corporate Greenhouse Gas Disclosure Quality: Stakeholder Pressure and the Role of Management