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Korean J Financ Stud > Volume 52(4); 2023 > Article
기관투자자 지분율과 기업의 온실가스 배출

Abstract

This study examines whether institutional investors promote corporate environmental performance. Specifically, we investigate how institutional investors affect a firm’s greenhouse gas (GHG) emissions. Using data on US firms’ GHG emissions over the 2002-2019 period, we find that institutional ownership is negatively related to the firm’s GHG emissions. This negative relationship persists after adjusting for endogeneity. Our results suggest that institutional investors tend to have incentives to monitor managers to reduce the firm’s GHG emissions. Overall, we contribute to the literature on corporate finance by identifying institutional investors’ role in enhancing the corporate environmental performance.

요약

본 연구는 기관투자자의 지분율과 기업의 온실가스 배출 사이의 관계에 대해 분석하였다. 2002년부터 2019년까지 미국 기업에 대한 온실가스 배출 데이터와 기관투자자 지분율 데이터를 이용하여 분석한 결과, 기관투자자의 소유지분과 기업의 온실가스 배출 사이의 유의한 음(-)의 관계가 발견되었다. 또한, 두 변수간의 음의 관계는 내생성 문제를 완화하기 위한 추가 분석에서도 일관되게 나타났다. 이러한 결과는 기관투자자가 투자기업의 경영진에게 기업의 환경적 책임을 강조함으로서 온실가스를 감축하게 하는 효과가 있음을 시사한다. 본 연구는 기업의 환경적 성과를 개선하는데 있어 기관투자자의 중요성을 보였다는 점에서 관련 문헌에 공헌한다.

1. Introduction

In recent decades, business practitioners and academic researchers have paid a great deal of attention to climate change concerns. Notably, alongside the surging greenhouse gas (GHG) emissions, many countries have attempted to control GHG emissions since the 2005 adoption of the Kyoto Protocol. Moreover, consumers are becoming increasingly concerned about rapid climate changes. In response to institutional and social demands for GHG reduction, firms have devoted considerable resources to corporate environmental responsibility (CER) in order to comply with relevant regulations and satisfy customer expectations (Hoffman, 2005). Additionally, existing studies investigate the effects of CER on firm value (e.g., Reinhardt, 1999; Sen et al., 2015). However, relatively less focus has been placed on the factors that influence CER activities.
Institutional investors have acted as firms’ major shareholders by gradually increasing their ownership (Barber et al., 2008; Bolton and Kacperczyk, 2020; Dyck et al., 2019; Ilhan et al., 2021; Mehrani et al., 2017). Chen et al. (2007) show that institutional ownership of the US stock market exceeded 50% in 2004. In addition to their substantial ownership, they have had a significant impact on corporate policies and decisions (Aghion et al., 2013; Neubaum and Zahra, 2006). Particularly, institutional investors are tend to have incentives to pay more attention to CER activities because these activities can lead to an increase in the long-term value of firms (e.g., Deng et al., 2022; Dyck et al., 2019; Holm and Rikhardsson, 2011; Krueger et al., 2019; Miles and Covin, 2000).
This study examines the impact of institutional investors on CER activities. Among various dimensions of CER, we focus on a firm’s GHG emissions because they have a direct impact on climate change and are widely recognized as the determining factor for CER activities. CER can enhance a firm’s long-term value through two channels. First, CER can enhance a firm’s reputation by fulfilling stakeholders’ demands and boosting its value (Dangelico and Pujari, 2010; Hill and Jones, 1992). Second, CER can reduce a firm’s risks, including those associated with uncertain explicit claims, litigation, and sanctions (Agle et al., 1999; Waddock and Graves, 1997). Institutional investors have strong incentives to encourage managers to invest in more environmental activities because these channels are likely to induce higher firm valuation. In line with this discussion, we hypothesize that an increase in institutional ownership will reduce a firm’s GHG emissions.
To test this hypothesis, we collect data from various sources, including Trucost, Compustat, the Center for Research in Security Prices (CRSP), and the CDA/ Spectrum 13F database of Thomson Reuters. Particularly, we obtain the GHG emission data from Trucost and measure a firm’s GHG emissions. By merging this measure with data on institutional ownership and accounting and financial information, we eventually have 8,268 US firm-year observations over the 2002-2019 period. Using this sample, we conduct multivariate regressions to examine the relationship between institutional ownership and a firm’s GHG emission rate while controlling for various factors that affect GHG emissions. We find that institutional ownership is negatively related to a firm’s direct and indirect GHG emissions. This finding indicates that institutional investors are more likely to monitor their firm’s CER. To alleviate endogeneity concerns, such as reverse causality and omitted variable bias, we conduct additional tests, including two-stage least squares (2SLS) regressions, change regressions, and regressions with firm fixed effects. Even when we address endogeneity issues, our primary findings still hold.
Our paper contributes to the literature on determining a firm’s GHG emissions. Earlier studies identify a few determinants of GHG emissions including institutional pressures and firm characteristics (Comyns, 2016; Faisal et al., 2018). We complement these studies by investigating the impact of institutional investors on a firm’s GHG emissions. We also contribute to the literature on the positive role of institutional investors in a firm’s long-term investments (Aghion et al., 2013; Bushee, 1998; Eng and Shackell, 2001) because a firm’s environmental performance is highly associated with its long-term value. Furthermore, we contribute to the extant literature regarding the association between institutional investors and climate risks. Recent studies (e.g., Krueger et al., 2020; Bolton and Kacperczyk, 2021) have explored the relationship between institutional investors and climate risks in the context of asset pricing and investment. However, we examine this relationship from the perspective of corporate finance.
Moreover, using data on the actual amount of a firm’s GHG emissions, this study adds to the literature by providing direct evidence of corporate environmental performance. For example, Krueger et al. (2020) use survey data on climate risk perceptions. In addition, Ilhan et al. (2021) construct an indicator variable that measures whether a firm discloses the quantity of its carbon emissions. On the contrary, we employ the firm’s actual quantity of direct and indirect GHG emissions, allowing us to precisely investigate corporate environmental performance.
The rest of this paper is structured as follows: Section 2 reviews relevant literature and fully develops the hypothesis. Section 3 outlines the data and describes the primary variables that were constructed. Section 4 presents our empirical findings, while Section 5 concludes the paper.

2. Literature Review and Hypothesis Development

In recent decades, environment protection has become a global concern; hence, studies on a firm’s CER activities have become more critical. In particular, there are two prominent strands of research on CER. The first one focuses on how CER affects a firm’s performance (Elsayed and Paton, 2005; Miles and Covin, 2000). Some studies argue that CER activities may be regarded as unnecessary costs that have negative short-term effects on a firm’s profitability and shareholder wealth (Waddock and Graves, 1997). However, from a long-term perspective, CER can have a positive effect on the firm’s value (Jo et al., 2015; McGuire et al., 1988). This positive relationship between CER and a firm’s long-term value can be supported by two main views on CER: reputation-building and risk mitigation.
First, a firm that engages in more CER activities by reducing GHG emissions can increase its long-term value by building positive reputation (Hill and Jones, 1992; Hoffman, 2005). The firm with more CER activities is likely to attract greater attention from a diverse range of stakeholders, such as shareholders who place a high premium on environmental performance and customers who prefer environmentally friendly products. Liedong et al. (2017) show that pursuing CER activities strengthens a firm’s relationship with the local community. Second, CER enables a firm to mitigate risks, such as sanctions and litigation (Agle et al., 1999; Hoffman, 2005; Waddock and Graves, 1997). For instance, if firms exceed the international limit for GHG emissions under mandatory emissions mitigation schemes, they may be subject to sanctions, such as substantial penalties and suspension of business. On the contrary, firms with better environmental policies can avoid unnecessary costs associated with environmentally irresponsible activities (Sharfman and Fernando, 2008).
The second strand of literature explores the determinants of firms’ CER activities. Cowen et al. (1987) show that firm size and industrial characteristics are positively associated with the disclosure of a firm’s social activities. Freedman and Jaggi (2005) place greater emphasis on a firm’s environmental performance and find that firm size relates positively to GHG pollution disclosures. Meanwhile, given that the government regulates GHG emissions, previous literature has examined the relationship between national legislation changes and a firm’s GHG emissions. Kumar and Sharma (2014) suggest that various governance regulations for environmental protection induce firms to implement environmentally friendly policies, including the construction of new facilities and the introduction of innovative technologies, resulting in a reduction in GHG emissions.
Meanwhile, because managers implement corporate decisions and policies, managerial risk-taking behavior can significantly affect a firm’s environmental policy. Although there is a growing demand from society for environmentally responsible activities, managers’ decisions may not reflect this demand. Thus, it is essential to investigate the impact of corporate governance on a firm’s environmental performance because corporate governance designs managerial incentives to implement corporate policies (Sommer, 2019). Existing studies show that institutional investors have become major shareholders and have played critical roles in establishing corporate governance (Barber et al., 2008; Cao et al., 2020; Chen et al., 2007). In line with these studies, institutional ownership can be the primary determinant of a firm’s CER, particularly GHG emissions, by influencing managerial risk-taking incentives.
Institutional investors comprise various types of investors, such as investment companies, pension funds, investment advisors, bank trusts, and insurance companies (Bushee, 1998; Carey et al., 1998). Compared with retail investors, institutional investors are more motivated to participate in the corporate decision-making process because they tend to hold substantial ownership and remain in the firm as shareholders over longer periods (Aghion et al., 2013; Gaspar et al., 2005). Aghion et al. (2013) document that improved monitoring by institutional shareholders can precisely capture the ability of managers and, thereby preventing managers from being dismissed because of stochastic reasons. Consequently, the presence of institutional investors as better monitors shields managers from the reputational risk of poor performance resulting from stochastic circumstances, such as industry shocks and regime or regulation changes, mitigating managers’ career concerns. Thus, when institutional investors monitor managers more closely, these managers are likely to pursue corporate policies that enhance the firm’s long-term value. Consistent with this reasoning, Aghion et al. (2013) show that institutional investors induce firms to invest in long-term intangible assets by increasing their innovative activities. Similarly, Kim et al. (2019b) find that long-term institutional investors positively affect corporate innovation.
As we have discussed, CER can increase a firm’s long-term value by fostering a favorable reputation and mitigating risk. Therefore, managers in firms with greater institutional ownership are more likely to engage in CER to enhance the firm’s long-term valuation. In particular, among the various dimensions of CER, a firm’s GHG emissions have a significant impact on climate change and are widely regarded as the critical component that measures the firm’s environmental performance (Hatakeda et al., 2012). Thus, focusing on corporate GHG emissions, we propose the testable hypothesis that an increase in presence of institutional investors will reduce a firm’s GHG emission rate.

3. Data and Research Design

3.1 Data

We collect US firms’ GHG emission data from the Trucost database, which provides information on firms’ environmental costs and performance. We merge GHG emission data with institutional ownership data from the Thomson Reuters CDA/Spectrum database (13F). Then, these data are intersected with firm-level financial data from Compustat and the CRSP. Observations with missing data are eliminated. Financial and utility industries (SIC codes from 6000 to 6999 and from 4900 to 4949) and service industries (SIC codes from 7000 to 8744) are also excluded because financial and utility firms are heavily regulated by the government and also service firms are less related to GHG emissions. Our final sample contains 8,268 US firm-year observations for 1,428 firms from 2002 through 2019.

3.2 Variable Construction

3.2.1 Firm’s GHG Emission

We construct a firm’s GHG emissions in order to measure its environmental performance. The GHG comprises carbon dioxide, methane, nitrous oxide, sulfur hexafluoride, perfluorocarbons, and hydrofluorocarbons (Lee, 2012). Based on three scopes, the Trucost database provides information on firms’ GHG emissions. The three scopes of GHG emissions are defined based on the extensive literature (e.g., Braam et al., 2016; Lee, 2012; Li et al., 2017). Scope 1 is defined as the direct GHG emissions generated by a firm’s operations, such as emissions from factories’ fossil fuel combustion. Scope 2 encompasses indirect GHG emissions from off-site energy production, such as purchased electricity or steam. Scope 3 represents “upstream” GHG emissions embedded in a firm’s non-electricity supply chain, such as emissions generated by products that the firm purchases or processes. However, Scope 3 GHG emissions cannot be controlled directly by the firm because they include emissions along supply chains. To measure a firm’s GHG emissions, we thereby only consider Scopes 1 and 2.1) The emission rate is calculated by dividing GHG emissions by total sales (Jung et al., 2018). Finally, GHG emission rates measured based on Scopes 1 and 2 are denoted as Scope 1 GHG Emission and Scope 2 GHG Emission, respectively.

3.2.2 Institutional Ownership

The level of firm ownership held by institutional investors is an additional primary variable in our study. Following extensive literature (e.g., Bushee, 1998; Graves and Waddock, 1994), we measure institutional ownership by dividing institutional investors’ share of a company by the firm’s outstanding shares.

3.2.3 Control Variables

We control for other variables identified in previous literature as determinants affecting a firm’s GHG emissions. First, we control for firm growth by including the sales growth ratio (denoted Firm Growth) in regressions. Firms with higher growth rates are likely to emit more GHG because they manufacture more products. Moreover, we control for intangible assets as measured by selling and general administration expenses (Eisfeldt and Papanikolaou, 2013). Investment in intangible assets, such as information and organizational capital, is crucial to increase firms’ survival and growth (e.g., Lev et al., 2009; Li et al., 2018; Francis et al., 2021, Kim et al., 2021). Therefore, firms with more intangible assets are less likely to generate GHS emissions because they are better able to focus on CER activities to enhance their long-term value. The intangible assets of a firm are measured as selling, general, and administrative expenses divided by total assets (denoted as Intangibility).
Further, we also include cash and leverage as control variables in regressions, given that firms with greater capital can engage more in environmental management. The level of cash holding (denoted as Cash) is computed as cash and short-term investments divided by total assets, whereas leverage (denoted as Leverage) is calculated as the sum of short- and long-term debt divided by total assets. In addition, larger firms are more likely to effectively manage their GHG emissions due to economies of scale. Thus, we control for firm size (denoted as Firm Size), which is measured as the natural log of the total assets plus 1. To minimize the outlier effect, the top and bottom 1% of all continuous variables are winsorized. All variable definitions are provided in the Appendix.

3.2.4 Summary Statistics and Correlations

<Table 1> presents the summary statistics of all variables used in this study and the correlations among the variables. Panel A of <Table 1> reports the descriptive statistics, including the variables’ 25th percentile, median, mean, 75th percentile, and standard deviation. The mean (median) Scope 1 GHG emission and Scope 2 GHG emission are 2,493,776 (112,310.7) and 647,234.6 (102,088.5), respectively. These GHG emission variables have mean values that are greater than their median values, indicating that they are right-skewed. In regression analyses, we therefore use their logarithmically transformed values to account for the positive skewness.
Meanwhile, institutional investors own 73.5% of firms on average, and the average growth rate of firms is 8.9%. The mean intangible assets of firms are 20.7%, whereas the average cash-to-assets ratio is 14.3%. In addition, the mean leverage is 74.3%, and the mean value of total assets is 18,892.22 million dollars. Furthermore, Panel B of <Table 1> displays the correlations among all variables. All correlations among independent variables are lower than 0.7, suggesting that multicollinearity among those variables may not be a significant concern in our regressions (Wooldridge, 2016).
<Table 1>
Summary Statistics and Correlations
This table provides descriptive statistics for this study’s variables and correlations among variables. The sample includes 8,268 firm-year observations from 2002 to 2019. All variable definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Summary statistics
Variable 25% Median Mean 75% SD N
Amount of Scope 1 Emission (tonne) 22,172.24 112,310.7 2,493,776 537,243.7 10,919,003 8,268
Amount of Scope 2 Emission (tonne) 24,527.4 102,088.5 647,234.6 453,970.7 1,812,026 8,268
Scope 1 GHG Emission (tonne/mil $) 11.973 23.881 190.480 93.124 4871.269 8,268
Scope 2 GHG Emission (tonne/mil $) 15.015 26.687 50.713 49.973 77.805 8,268
Institutional Ownership 0.635 0.797 0.735 0.908 0.227 8,268
Firm Growth -0.015 0.060 0.089 0.150 0.235 8,268
Intangibility 0.071 0.154 0.207 0.283 0.189 8,268
Cash 0.039 0.091 0.143 0.194 0.150 8,268
Leverage 0.171 0.495 0.743 0.978 2.246 8,268
Total Assets (mil $) 1,266.738 4,356.091 18,892.22 15,842.630 44,535.87 8,268

Panel B: Correlation matrix

Variable (1) (2) (3) (4) (5) (6) (7) (8)
Scope 1 GHG Emission (1) 1.000
Scope 2 GHG Emission (2) 0.460*** 1.000
Institutional Ownership (3) -0.149*** -0.077*** 1.000
Firm Growth (4) 0.010 -0.056*** -0.062*** 1.000
Intangibility (5) -0.360*** -0.187*** -0.025** 0.033** 1.000
Cash (6) -0.238*** -0.158*** -0.102*** 0.120*** 0.332*** 1.000
Leverage (7) 0.029*** -0.002 -0.016 -0.014 -0.085*** -0.112*** 1.000
Firm Size (8) 0.036*** 0.023** 0.161*** -0.103*** -0.412*** -0.342*** 0.098*** 1.000

4. Empirical Findings

4.1 Univariate Tests

Through the univariate analysis, we aim to gain preliminary insight into the association between institutional ownership and firms’ GHG emissions. Based on institutional ownership, we split the sample into two groups: one group and the other group with institutional ownership above and below the sample firms’ median value, respectively. Subsequently, we test whether significant differences exist among these two subsamples.
<Table 2> displays the results of univariate tests. For firms with higher institutional ownership, the mean (median) of Scope 1 GHG Emission (GHG emissions / total sales) is 137.207 (22.638). In contrast, this mean (median) value of this variable for those with lower institutional ownership is 243.754 (27.637). The mean and median differences among these two groups are statistically significant at the 1% level. In addition, the same analyses performed for Scope 2 GHG Emission yield the identical results. Overall, our results show that firms with a higher proportion of institutional ownership tend to generate fewer GHG emissions than those with a lower proportion of institutional ownership.
<Table 2>
Univariate Tests
This table presents the two subsamples’ mean and median difference tests of GHG emission measures and control variables. The sample is divided into two subsamples based on the sample’s median value. t-tests and Wilcoxon-Mann-Whitney tests are conducted for the tests of the mean and median differences, respectively. t- or z-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The definitions of variables are provided in the Appendix.
Variable Sample firms with low institutional ownership (A) Sample firms with high institutional ownership (B) Mean diff. (A-B) Median diff. (A-B)



Mean Median Mean Median t-statistics z-statistics
Scope 1 GHG Emission 243.754 27.637 137.207 22.638 106.547 *** (10.127) 4.999 *** (8.934)
Scope 2 GHG Emission 55.556 28.854 45.870 25.192 9.686 *** (5.671) 3.662 *** (6.302)
Firm Growth 0.096 0.056 0.082 0.064 0.014 *** (2.723) -0.008 (-1.476)
Intangibility 0.213 0.147 0.201 0.159 0.011 ** (2.712) -0.012 *** (-3.605)
Cash 0.153 0.095 0.133 0.089 0.020 *** (6.058) 0.006 *** (2.999)
Leverage 0.789 0.469 0.697 0.517 0.093 * (1.875) -0.048 *** (-2.483)
Firm Size 8.232 8.267 8.444 8.457 -0.212 *** (-5.178) -0.190 *** (-4.168)

4.2 Baseline Regression Analysis

In this section, we conduct ordinary least squares (OLS) regressions to investigate whether institutional investors’ ownership positively affects firms’ GHG emissions when control variables, which are the determinants of GHG emissions identified in prior studies, are included. Our regression model is as follows:
(1)
ln(1+GHGEmissioni,t)=α0+α1InstitutionalOwnershipi,t1+X'i,t1β+εi,t
where GHG Emissioni,t is the GHG emission rate of firm i in year t. Institutional Ownershipi,t-1 is the percentage ownership of firm i held by institutional investors in year t-1. X is a set of control variables, including Firm Growth, Intangibility, Cash, Leverage, and Firm Size. Detailed definitions of the variables are provided in the Appendix. The regressions include year and industry fixed effects, which are defined based on two-digit SIC codes, to control for the specific effects of year and industry. All independent variables are 1-year lagged to mitigate concerns about reverse causality. Standard errors are robust to heteroscedasticity and clustered at the firm level.
We employ Scope 1 GHG Emission, which measures the direct GHG emissions generated by a firm’s operations, as our main dependent variable. In addition, we employ Scope 2 GHG Emission, which measures the firm’s indirect GHG emissions, as our complemental variable. Results on the relationship between institutional ownership and a firm’s GHG emissions are presented in <Table 3>.2) The dependent variables in Columns (1) and (2) are Scope 1 GHG Emission and Scope 2 GHG Emission, respectively. In both columns, the coefficients on institutional ownership (i.e., -0.519 and -0.295) are significantly negative at the 1% level , thus indicating that there is a negative association between institutional ownership and a firm’s direct and indirect GHG Emissions.3) Overall, the results suggest that institutional investors induce firms to decrease their GHG emissions, leading to an increase in CER activities.
<Table 3>
Institutional Ownership and a Firm’s GHG Emissions
This table presents the regression results to examine the relationship between institutional ownership and a firm’s GHG emissions. The definitions of variables are provided in the Appendix. All independent variables are 1-year lagged. Regressions include year and industry fixed effects. Standard errors are robust to heteroscedasticity and clustered at the firm level. t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Variable Dependent variable

ln(1+Scope 1 GHG Emission) (1) ln(1+Scope 2 GHG Emission) (2)
Institutional Ownership -0.519 *** -0.295 ***
(-3.246) (-2.592)
Firm Growth -0.201 ** -0.208 ***
(-2.509) (-3.349)
Intangibility -1.490 *** -1.125 ***
(-6.157) (-6.630)
Cash -1.243 *** -0.845 ***
(-5.148) (-5.213)
Leverage 0.018 ** 0.004
(2.150) (0.656)
Firm Size -0.110 *** -0.057 ***
(-4.433) (-3.364)
Observations 6,362 6,362
Adjusted R2 0.632 0.401
Year fixed effects Yes Yes
Industry fixed effects Yes Yes

4.3 Endogeneity Issue

Although we have documented a negative relationship between institutional ownership and a firm’s GHG emission, our results may be attributed to endogeneity concerns. While our regression model includes control variables identified in previous literature, omitted variables can bias the relationship between institutional ownership and a firm’s GHG emissions. In addition, we have argued a causal relationship between institutional ownership and GHG emissions. However, some of the observed relationships could be the result of institutional investors selecting firms with lower GHG emissions. These endogeneity issues can make our baseline regressions biased and inconsistent. Thus, we conduct additional tests to alleviate endogeneity concerns.

4.3.1 Two-stage Least Squares Regressions

We first perform a two-stage least squares (2SLS) estimation with an instrumental variable to address endogeneity issues. The instrument variable should be related to institutional ownership but exogenous to firms’ GHG emissions. Based on previous literature, we use the stock illiquidity variable as the instrument variable (Yan and Zhang, 2009; Kim et al., 2019a). Stock illiquidity is measured as the average ratio of absolute daily stock returns to daily trading volumes (Amihud, 2002). Then, we construct an indicator variable of stock illiquidity (denoted as Illiquidity), which is equal to one if a firm’s stock illiquidity is above the industry median; otherwise, it is zero. Institutional investors prefer more liquid stocks because illiquid stocks may convey to the market a bad signal about the firms’ performance, facing demand pressure (Amihud et al., 2006).
Consequently, illiquid stocks are unlikely to attract institutional investors’ attention. Meanwhile, stock illiquidity is unlikely to influence firms’ GHG emissions. Thus, the variation in institutional ownership caused by stock illiquidity can be regarded as exogenous variations to alleviate concerns about reverse causality and omitted variables. Our 2SLS model of regression is as follows.
First-stage regression:
(2)
InstitutionalOwnershipi,t=μ+δIlliquidityi,t1+γCi,t1+κIi+λYt+ηi,t
Second-stage regression:
(3)
ln(1+GHGEmissioni,t)=α+βInstrumentedInstitutionalOwnershipi,t1                                                                               +ςCi,t1+ηIi+θYt+εi,t
where Institutional Ownershipi,t is the percentage ownership of firm i held by institutional investors in year t. Illiquidityi,t-1 is equal to one if stock illiquidity for firm i in year t-1 is above the industry median value; otherwise, it is zero. GHG Emissioni,t is the GHG emission rate of firm i in year t. ∑Ci,t-1 is a set of control variables for firm i in year t. ∑Ii and ∑ηIi are industry dummies, while ∑Yt and ∑θYt are year dummies. Instrumented Institutional Ownership is the fitted value obtained from the first stage regression.
The 2SLS estimation results are presented in <Table 4>. In column (2) of <Table 4>, the Wu-Hausman test rejects the null hypothesis that institutional ownership is exogenous to firms’ GHG emissions, suggesting that the endogeneity issue regarding institutional ownership should be addressed. When institutional ownership is the dependent variable, the results of the first-stage regression results are reported in Column (1). As predicted, Illiquidity is negatively associated with Institutional Ownership at the 1% level. The F-statistics for the weak instrumental test at the first-stage regression is 28.038, which is greater than the acceptance value of 10 suggested by Stock and Yogo (2005). This result shows that the null hypothesis of weak instrumental variables is rejected by the test.
<Table 4>
Institutional Ownership and a Firm’s GHG Emissions: 2SLS Regressions
This table presents the 2SLS regression results to investigate the effects of institutional investors on a firm’s GHG emissions. Illiquidity is used as an instrument variable. Instrumented Institutional Ownership is the fitted value obtained from the first stage regression. The definitions of variables are provided in the Appendix. All independent variables are 1-year lagged. Regressions include year and industry fixed effects. Standard errors are robust to heteroscedasticity and clustered at the firm level. t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Variable First stage Second stage First stage Second stage

Institutional Ownership (1) ln(1+Scope 1 GHG Emission) (2) Institutional Ownership (3) ln(1+Scope 2 GHG Emission) (4)
Instrumented Institutional Ownership -3.603 *** -1.495 **
(-3.20) (-2.01)
Illiquidity -0.076 *** -0.076 ***
(-5.29) (-5.29)
Firm Growth -0.020 -0.251 ** -0.020 -0.228 ***
(-1.21) (-2.58) (-1.21) (-3.46)
Intangibility -0.112 *** -1.783 *** -0.112 *** -1.239 ***
(-2.64) (-5.89) (-2.64) (-6.59)
Cash -0.136 *** -1.561 *** -0.136 *** -0.969 ***
(-3.27) (-5.30) (-3.27) (-5.19)
Leverage -0.004 ** 0.005 -0.004 ** -0.001
(-2.18) (0.51) (-2.18) (-0.16)
Firm Size 0.006 -0.050 0.006 -0.034
(1.05) (-1.45) (1.05) (-1.44)
First-stage F-statistics 28.038 N/A 28.038 N/A
Wu-Hausman F-statistics N/A 9.182 N/A 2.864
Observations 6,362 6,362 6,362 6,362
Adjusted R2 0.188 0.500 0.188 0.342
Year fixed effects Yes Yes Yes Yes
Industry fixed effects Yes Yes Yes Yes
Column (2) presents the second-stage regression results, including Scope 1 GHG Emission as a dependent variable. The coefficient on Instrumented Institutional Ownership is significantly negative at the 1% level. Further, we conduct the 2SLS regressions, including Scope 2 GHG Emission. The coefficient on Instrumented Institutional Ownership in column (4) is significantly negative. The results suggest that institutional investors tend to induce firms to reduce their GHG emissions, confirming that our baseline findings are unlikely to be driven by endogeneity issues.

4.3.2 Change Regressions

In this section, we perform change regressions to address endogeneity concerns further. These regressions enable us to examine the incremental effects of institutional ownership on a firm’s GHG emissions. Specifically, our change regressions are based on the first differences of dependent and independent variables, which are computed as year-by-year changes in the main variables.
The results of change regressions are reported in <Table 5>. Changes in the firm’s GHG emission from year t-1 to t are denoted as ΔScope 1 GHG Emission (or ΔScope 2 GHG Emission). Moreover, changes in institutional ownership from year t-2 to t-1 are denoted as ΔInstitutional Ownership. <Table 5>’s Columns (1) and (2) show that the coefficients for ΔInstitutional Ownership are significantly negative. These findings indicate that an increase in institutional ownership will likely induce firms to reduce their GHG emissions.
<Table 5>
Institutional Ownership and a Firm’s GHG Emissions: Change Regressions
This table presents the regression results of changes in a firm’s GHG emissions from year t-2 to t-1 on changes in institutional ownership from year t-1 to t. All independent variables are lagged by one period (i.e., from year t-2 to t-1). The definitions of variables are provided in the Appendix. All independent variables are 1-year lagged. Regressions include year and industry fixed effects. Standard errors are robust to heteroscedasticity and clustered at the firm level. t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Variable ln(1+Scope 1 GHG Emission) ln(1+Scope2 GHG Emission)

(1) (2)
Institutional Ownership -0.661*** -0.399**
(-2.708) (-2.421)
Firm Growth -0.203* -0.219*
(-1.751) (-1.812)
Intangibility 8.318*** 5.031***
(17.072) (15.305)
Cash -1.541*** -1.275***
(-4.278) (-5.298)
Leverage 0.018** 0.001
(2.054) (0.099)
Firm Size -0.128*** -0.082***
(-3.800) (-3.744)
Observations 3,913 3,913
Adjusted R2 0.658 0.409
Year fixed effects Yes Yes
Industry fixed effects Yes Yes

4.3.3 Regressions with Firm Fixed Effects

In addition to 2SLS estimations and change regressions, we conduct regressions with firm fixed effects to mitigate any omitted variable bias driven by unobserved time-invariant firm attributes.
In <Table 6>, we present the results of regressions with firm fixed effects as well as industry-year fixed effects. The regressions include Scope 1 GHG Emission and Scope 2 GHG Emission as dependent variables in columns (1) and (2), respectively. We find that the negative association between institutional ownership and a firm’s direct GHG emissions is statistically significant. Meanwhile, the relationship between institutional ownership and the firm’s indirect GHG emissions is insignificant. These results suggest that earlier findings on the firm’s direct GHG emissions still persist after addressing an omitted variable issue.
<Table 6>
Institutional Ownership and a Firm’s GHG Emissions: Firm Fixed Effects
This table presents the regression results to examine the relationship between institutional ownership and a firm’s GHG emissions. The definitions of variables are provided in the Appendix. All independent variables are 1-year lagged. Regressions include industry-year and firm fixed effects. Standard errors are robust to heteroscedasticity and clustered at the firm level. t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Variable Dependent variable

ln(1+Scope 1 GHG Emission) (1) ln(1+Scope 2 GHG Emission) (2)
Institutional Ownership -0.326 ** 0.045
(-2.001) (0.296)
Firm Growth -0.046 -0.068
(-0.721) (-1.278)
Intangibility -0.125 -0.158
(-0.444) (-0.665)
Cash 0.264 -0.011
(1.24) (-0.057)
Leverage 0.000 -0.001
(0.131) (-0.428)
Firm Size -0.054 0.032
(-0.783) (0.605)
Observations 6,362 6,362
Adjusted R2 0.432 0.299
Industry-Year fixed effects Yes Yes
Firm fixed effects Yes Yes

5. Conclusion

Research on corporate environment activities has grown along with an emphasis on the environment in the business area. In particular, among various dimensions of CER, a firm’s GHG emissions attract much attention from researchers because they have a direct impact on climate change and are widely recognized as the key factor in determining CER activities. Meanwhile, institutional investors have increased their ownership and become US firms’ largest shareholders, significantly affecting corporate decision-making. Because they place considerable emphasis on the long-term value creation of their firms, institutional investors are likely to exert pressure on managers to invest in more CER activities, especially to reduce GHG emissions. Thus, this study examines how institutional ownership influences a firm’s GHG emissions.
We find that institutional ownership is negatively related to firms’ direct and indirect GHG emissions. Moreover, we conduct additional tests to address endogeneity concerns. The results of 2SLS regressions, change regressions, and regressions with firm fixed effects indicate that our main findings remain consistent when endogeneity is controlled for. Overall, our results suggest that institutional ownership encourages firms to enhance their CER performance by reducing GHG emissions.
This study contributes to the literature on determining a firm’s GHG emissions. Existing studies have found a few factors of GHG emissions, such as institutional pressures and firm characteristics (Comyns, 2016; Faisal et al., 2018). We complement these studies by providing empirical evidence that the presence of institutional investors affects a firm’s GHG emissions through enhanced monitoring. In addition, our study adds to the literature on the positive role of institutional investors in a firm’s long-term investments because the reduction in GHG emissions induced by the presence of institutional investors can increase the firm’s long-term value. Further, we contribute to the existing literature on the relationship between institutional investors and climate risks. Recent studies (e.g., Krueger et al., 2020; Bolton and Kacperczyk, 2021) have examined the relationship between institutional investors and climate risks in the context of asset pricing and investment. In contrast, we examine this relationship from the perspective of corporate finance.

Notes

1) We thank an anonymous reviewer for this suggestion.

2) We check the variance inflation factors (VIFs) to identify the presence of multicollinearity. All VIFs are below 10 (maximum value: 2.02), showing no multicollinearity issue among independent variables in our regression model.

3) In untabulated results, our main findings broadly hold when we employ alternative measures of a firm’s GHG emissions, which include GHG emissions, ln(GHG emissions), GHG emissions/sales, and GHG emissions/assets.

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Appendices

<Appendix>
Variable Definitions
Variable Definition Data Source
Scope 1 GHG Emission The ratio of GHG emissions based on Scope 1 to total sales. Scope 1 is defined as direct GHG emissions generated by firm operations. Trucost
Scope 2 GHG Emission The ratio of GHG emissions based on Scope 2 to total sales. Scope 2 includes indirect GHG emissions generated by off-site energy production such as purchased electricity or steam. Trucost
Institutional Ownership The number of shares held by institutional investors divided by the number of firm’s outstanding shares 13F and CRSP
Firm Growth The change in firm’s sales growth rate over a given year from the previous year Compustat
Intangibility The selling, general, and administrative expenditures divided by total assets Compustat
Cash Cash and short-term investments divided by total assets Compustat
Leverage The sum of current liabilities and long-term debt divided by total assets Compustat
Firm Size The natural logarithm of 1 plus total assets Compustat
Illiquidity An indicator variable that is equal to one if a firm’s stock illiquidity is above the industry median and otherwise zero CRSP
ΔScope 1 GHG Emission Changes in the firm’s scope 1 GHG emission from the previous year to the given year Trucost
ΔScope 2 GHG Emission Changes in the firm’s scope 2 GHG emission from the previous year to the given year Trucost
ΔInstitutional Ownership One-year lagged changes in institutional ownership (i.e., from year t-2 to t-1) 13F and CRSP
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