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Voluntary Engagement in Environmental Projects: Evidence from Environmental Violators

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Abstract

An important question in the business ethics literature concerns organizational response in the aftermath of an unethical business practice. This study examines factors affecting firms’ decision to take reparative action in the aftermath of an environmental violation. Specifically, we investigate environmental violators’ decision to undertake a Supplemental Environmental Project (SEP), which is an initiative that promotes restorative justice. To settle an environmental violation, the United States’ environmental regulator allows offenders the option of either paying the full penalty or a reduced sum while spending additional effort and engaging in an environmental project. As predicted, we find that firms with poorer past environmental performance and greater shareholder environmental activism are more likely to engage and invest in a SEP. Additionally, there is a stronger association between shareholder activism and SEP investment when firms have poorer past environmental performance. Our findings inform regulators, stakeholders, and business ethics researchers on the factors that lead firms to undertake reparative action following unethical business practices.

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Notes

  1. For example, see the public outcry that ensued after General Electric’s pollution of the Hudson River (https://www.nysenate.gov/newsroom/press-releases/brad-hoylman/25-new-york-senators-call-general-electric-commit-full-cleanup), Volkswagen’s emissions scandal (https://www.thetimes.co.uk/article/volkswagen-the-car-maker-that-sees-no-evil-loses-its-moral-compass-z99wjv663), and BP’s Deepwater Horizon oil spill (https://edition.cnn.com/2012/04/20/opinion/brazile-earth-day-bp/index.html).

  2. Restorative justice relates to the manner in which to deal with the aftermath of an offense and its implications for the future (Braithwaite 1999; Goodstein and Butterfield 2010).

  3. The ECHO database provides information on environmental regulatory compliance and enforcement, with the US EPA stating on its website that such information is both reliable and of high quality (see: https://echo.epa.gov/resources/general-info/echo-faq). The first trial run of the ECHO database from 2002 to 2003 was well received by the public, thus, leading to a beta version being released in late 2013. Updates on the ECHO database can be viewed on the official website: https://echo.epa.gov/resources/general-info/release-notes.

  4. Other policy revisions since then include the May 1995 Interim Revised Supplemental Environmental Projects Policy, and the May 1998 EPA Supplemental Environmental Projects Policy. The US EPA (2015) recently issued an update to the SEP policy in March 2015 to encourage and expand the use of SEPs in settlements(US EPA 2015).

  5. A list of acceptable SEPs is available at the US EPA’s official website: http://www2.epa.gov/enforcement/supplemental-environmental-projects-seps.

  6. The penalty mitigation credit for profitable projects is further limited to 60 percent of the estimated cost of a SEP. Only in exceptional circumstances, in which a project is deemed to be of “outstanding quality,” does the US EPA US EPA (2015) allow 100 percent of the assessed penalty to be mitigated.

  7. This satisfaction may be caused by a halo effect. Vo et al. (2018) argue that firms with better corporate social responsibility performance casts a halo effect on stakeholders such that they are biased (consciously or unconsciously) toward forming a favorable perception of the firm.

  8. Our results (not tabulated) are robust when using the total number of environment proposals as an alternative measure of shareholder environmental activism.

  9. Chatterji et al. (2016) demonstrated low convergence among different environmental rating systems and suggested that environmental rating systems should be validated with objective outcome measures. In this regard, the KLD “concern” ratings have been found in prior studies (e.g., Chatterji et al. 2009; Delmas and Blass 2010; Semenova and Hassel 2015) to be significantly associated with negative environmental outcomes. In addition, Semenova and Hassel (2015) found a much stronger correlation between emissions (an environmental outcome measure) and KLD ratings (Spearman correlation of 0.70), as compared to the environmental pillar score from the Asset4 database (Spearman correlation of 0.27). Thus, KLD concern ratings are used in this study as a primary measure. In an additional analysis described in “Alternative Measures of Past Environmental Performance” section, a robustness test is conducted using the environmental pillar score from Asset4.

  10. Clarkson et al. (2011) also included research and development intensity in their model, as measured by research and development expenses over the beginning of period total assets. We exclude this variable from our primary model, as missing observations drastically reduce the sample size.

  11. Consistent with prior literature (e.g., Adams et al. 1998; Fernandez-Feijoo et al. 2014; Gamerschlag et al. 2011; Hackston and Milne 1996; Roberts 1992), we classify automobile industry as environmentally sensitive. The classification of automotive as an environmentally sensitive industry is, however, contentious. Specifically, Dierkes and Preston (1977) highlighted that while the automotive industry has enormous, pervasive and multidimensional influence on the environment, it is the use of the products it instead generates, rather than the industry’s productive activity itself, which leads to its pervasive and significant effect on the environment.

  12. The results are not sensitive to using a logistic specification when estimating the model with SEP_DUM as the dependent variable.

  13. We estimate the following model: ACCt/ATt−1 = β0 + β1(1/ATt−1) + β2(CHGSALE − CHGREC)/ATt−1 + β3PPE/ATt−1 + β4ROAt + εt, where ACC is income before extraordinary items minus cash flow from operating activities, AT is total assets, CHGSALE is the change in net operating revenue, CHGREC is the change in net receivables, PPE is gross property, plant and equipment, and ROA is return on asset. Discretionary accruals are estimated by industry-year, and we require at least 10 firms per industry-year to estimate this variable (Dechow et al. 1995).

  14. Given the contentious nature of classifying the automobile industry as an environmentally sensitive industry, we classify the automobile industry as an environmentally non-sensitive industry. Upon rerunning this analysis, the results remained robust.

  15. As a robustness test, an alternative measure for environmental disclosure—the environmental reporting score from the Pacific Sustainability Index (PSI), published by the Roberts Environmental Center—was used. While the Roberts Environmental Center no longer performs the PSI since 2013—in light of the increasing trend that most large companies do a good job on environmental reporting (Maltzman and Shirley 2015)—the PSI has been considered one of the more integrated and comprehensive rating systems available (e.g., Bullock and Wilder 2016; Morhardt 2010; Skouloudis et al. 2009). Indeed, these scores have also been used in prior research to capture a firm’s environmental disclosure (e.g., Delmas and Blass 2010; Morhardt 2010). Our inferences are similarly consistent upon using the PSI score.

  16. Asset4 refits its environmental performance score whenever new data emerges. Our analysis was performed with data downloaded from Asset4 in August 2018. We thank an anonymous reviewer for alerting us to this issue.

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Authors and Affiliations

  1. The University of Melbourne, Melbourne, VIC, Australia

    Gladys Lee

  2. Monash University, Melbourne, VIC, Australia

    Xinning Xiao

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Correspondence to Xinning Xiao.

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Appendices

Appendix 1: Distribution of Environmental Violation Settlement

See Figs. 1, 2 and 3.

Fig. 1
figure 1

Distribution of LNPENALTY between SEP and non-SEP observations

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Fig. 2
figure 2

Distribution of LNCCOST between SEP and non-SEP observations

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Fig. 3
figure 3

Distribution of LNPENSEP between SEP and non-SEP observations

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Appendix 2: Variable definitions

Variable

Variable definition

Data source

SEP

The dollar value amount a firm invests in a supplemental environment project divided by the sum of the penalty amount and cost of SEP paid by the firm to settle the environmental violation

US EPA’s ECHO database

SEP_DUM

Indicator variable coded ‘1’ if the firm chose to perform a SEP, and ‘0’ otherwise

US EPA’s ECHO database

PASTPER

Environmental performance concerns index calculated as number of environmental concerns divided by the maximum possible number of environmental concerns, in the year prior to the settlement of the environmental violation. A higher score indicates poorer environmental performance

MSCI ESG KLD database

ACTIVISM

Number of environmental related shareholder proposals divided by total number of shareholder proposals, in the year prior to the settlement of the environmental violation

Form DEF 14A from SEC’s EDGAR database

ACTIVISM × PASTPER

Interaction term between ACTIVISM and PASTPER.

 

SIZE

Natural logarithm of total assets

Compustat

OPCF

Net cash flow from operations divided by beginning of period total assets

Compustat

CAPEX

capital expenditures divided by beginning of period total assets

Compustat

ROA

Operating income divided by beginning of period total assets

Compustat

NEW

Net property, plant, and equipment divided by gross property, plant, and equipment

Compustat

GROWTH

Change in sales

Compustat

EV

Market capitalization plus debt and preferred shares, divided by beginning of period total assets

Compustat

LEV

Total debt divided by total shareholders’ equity

Compustat

ENVSTR

Environmental performance strengths index calculated as number of environmental strengths divided by the maximum possible number of environmental strengths concerns in the year. A higher score indicates higher environmental performance

MSCI ESG KLD database

ENVREP

Environmental reporting scores from Bloomberg ESG Disclosure Scores

Bloomberg ESG

ENVSENIND

Environmentally sensitive industries. Indicator variable coded ‘1’ for firms in the energy (GICS code: 10), materials (GICS code: 15), industrials (GICS code: 20), automobiles and components (GICS code: 2510) and utilities (GICS code: 55) industry groups. Firms in non-environmentally sensitive are coded ‘0’ and consists of firms in the consumer discretionary (GICS code: 2520 through 2550), consumer staples (GICS code: 30), health care (GICS code: 35), financials (GICS code: 40), information technology (GICS code: 45), and telecommunication services (GICS code: 50) industry groups

Compustat

LNPENALTY

Natural logarithm of (1 + the penalty a firm paid to settle the environmental violation)

US EPA’s ECHO database

LNCCOST

Natural logarithm of (1 + the compliance cost a firm paid to settle the environmental violation)

US EPA’s ECHO database

LNPENSEP

Natural logarithm of (1 + the penalty and SEP costs a firm paid to settle the environmental violation)

US EPA’s ECHO database

TAXINC

Indicator variable coded ‘1’ for years prior to 2009, and ‘0’ for years 2009 onwards

 

INDDIR

Proportion of independent directors

BoardEx

DUALITY

Indicator variable coded ‘1’ if CEO is also the chairperson, and ‘0’ otherwise

BoardEx

ECOM

Indicator variable coded ‘1’ if a firm has an environmental committee, and ‘0’ otherwise

BoardEx

ABSDA

Absolute discretionary accruals computed using the modified Jones model: ACCt/ATt-1= β0+ β1(1/ATt-1) + β2(CHGSALE - CHGREC)/ATt-1+ β3PPE/ATt-1+ β4ROAt+ εt where ACC is income before extraordinary items minus cash flow from operating activities; AT is total assets; CHGSALE is the change in net operating revenue; CHGREC is the change in net receivables, PPE is gross property, plant and equipment; and ROA is return on asset. We estimate discretionary accruals by industry year and require at least 10 firms per industry year to estimate this variable (Dechow et al. 1995)

Compustat

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Lee, G., Xiao, X. Voluntary Engagement in Environmental Projects: Evidence from Environmental Violators. J Bus Ethics 164, 325–348 (2020). https://doi.org/10.1007/s10551-018-4074-0

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