J Bus Ethics (2015) 128:547–574
DOI 10.1007/s10551-014-2122-y
How the Market Values Greenwashing? Evidence from China
Xingqiang Du
Received: 8 September 2013 / Accepted: 22 February 2014 / Published online: 8 April 2014
Ó Springer Science+Business Media Dordrecht 2014
Abstract In China, many firms advertise that they follow
environmentally friendly practices to cover their true
activities, a practice called greenwashing, which can cause
the public to doubt the sincerity of greenization messages.
In this study, I investigate how the market values greenwashing and further examine whether corporate environmental performance can explain different and asymmetric
market reactions to environmentally friendly and
unfriendly firms. Using a sample from the Chinese stock
market, I provide strong evidence to show that greenwashing is significantly negatively associated with cumulative abnormal returns (CAR) around the exposure of
greenwashing. In addition, corporate environmental performance is significantly positively associated with CAR
around the exposure of greenwashing. Furthermore, my
findings suggest that corporate environmental performance
has two distinct effects on CAR around the exposure of
greenwashing: the competitive effect for environmentally
friendly firms and the contagious effect for potential
environmental wrongdoers, respectively. The results are
robust to various sensitivity tests.
Keywords Greenwashing Corporate environmental
performance Cumulative abnormal returns (CAR) Media
coverage The Global Reporting Initiative (GRI) The
competitive effect The contagious effect Environmental
wrongdoer China
X. Du (&)
Center for Accounting Studies and Accounting Department,
School of Management, Xiamen University, No. 422, Siming
South Road, Xiamen 361005, Fujian, China
e-mail: xqdu@xmu.edu.cn
Introduction
Today’s intense business competition compels firms to
continuously differentiate themselves from their rivals. As
a result, firms use greenization as an important and effective way to achieve differentiation and to answer environmental concerns. In addition, firms are also increasingly
recognizing that they carry out environmental responsibilities because consumers and other stakeholders care about
corporate environmental performance. Furthermore, some
firms frequently construct advertising messages to include
environmentally friendly buzzwords and phrases (Chen
and Chang 2013) such as ‘‘ECO, environmentally friendly,
taintless, earth-friendly, sustainability, uncontaminated,
green, and greenization.’’
From 2006 to 2009, green advertising in developed
countries grew about 300 % (TerraChoice Environmental
Marketing 2009). By 2015, sales revenues related to green
products and services are expected to reach $845 billion
(Tolliver-Nigro 2009). More than 75 % of S&P 500 firms
use their websites to regularly disclose information about
their environmental policies and performance (Alves
2009). In 2010, 60 major global firms used social media to
convey sustainability dialogs with stakeholders; by 2012
that number grew to 176 (Lyon and Montgomery 2013;
Yeomans 2013).
Corporate green claims mushroom, but the public is
getting increasingly skeptical about their authenticity. In
addition, the press expresses concerns about causes and
consequences of greenwashing, that is, corporate efforts to
cloak environmental misconducts with claims of being
environmentally friendly. As a result, scholars cast doubt
on the factuality and credibility of advertising messages in
which firms declare their greenization through environmentally friendly behavior, products, processes, and
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services (Chen and Chang 2013; Horiuchi and Schuchard
2009; Laufer 2003; Parguel et al. 2011; Ramus and Montiel
2005; TerraChoice 2010). In truth, however, about 98 % of
products with environmental claims misled consumers by
committing one or more aspects of the ‘‘seven sins of
greenwashing’’ (Fieseler et al. 2010; TerraChoice Environmental Marketing 2009).
Greenwashing, or green sheen, refers to a misleading
action in which green public relations or green marketing
are used deceptively to promote the perception that a firm’s
products, aims, and/or policies are environmentally
friendly (Greenpeace USA 2013; Parguel et al. 2011; Wikipedia 2013).1 According to Concise Oxford English
Dictionary (10th edition), greenwashing is defined as
‘‘disinformation disseminated by an organization so as to
present an environmentally responsible public image’’. In
fact, a relatively broader definition of greenwashing signifies a firm’s deviation from its commitment to positive
environmental policies (Ramus and Montiel 2005). Specifically, relevant to environmental conservation, greenwashing means ‘‘selective disclosure of positive
information about a company’s environmental or social
performance, while withholding negative information on
these dimensions’’ (Lyon and Maxwell 2011, p. 5). Overall,
greenwashing means that a firm uses environmentally
friendly appearance to cover its environmentally
unfriendly substance. Greenwashing includes changing
names or labels of harmful products to evoke the natural
environment, advertising green characteristics of firms and
their productions, and using multimillion dollar advertising
to portray polluting companies as eco-friendly (Burdick
2009; Joshua 2001).
Investors and the public rely heavily on advertisements,
but greenwashing betrays their trust. Some nongovernmental media and press assume the roles of market monitors or ‘‘watchdogs’’ (Miller 2006), investigating firms and
publishing lists of firms with greenwashing. Media coverage assures that greenwashing is exposed, and thus the
public loses faith in those firms, their advertisements, and
their products. Without trust, the market’s signal effects
collapse, so that the public no longer knows where to place
their trust and which goods or services they should buy. As
a result, greenwashing endangers investors’ confidence and
causes negative market reactions.
Nevertheless, surprisingly, few studies address the
concerns about whether and how the market values and
1
‘‘Green public relation (PR) is a sub-field of PRs that communicates
an organization’s corporate social responsibility or environmentally
friendly practices to the public. The goal is to produce increased
brand awareness and improve the organization’s reputation. Tactics
include placing news articles, winning awards, communicating with
environmental groups and distributing publications’’ (available at:
http://en.wikipedia.org/wiki/Green_PR).
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reacts to corporate greenwashing. One important reason is
that researchers cannot obtain persuasive or convincing
lists of firms that use greenwashing from nongovernmental
organizations (NGOs) or authoritative institutions. To
investigate market reactions to greenwashing, researchers
should indentify a benchmark in which some firms show
greenwashing behavior. Fortunately, the Chinese stock
market provides a natural experimental setting because the
South Weekend, the most influential Chinese newspaper
hosted by an unofficial organization, publishes a yearly list
of firms with greenwashing.
My study distinguishes itself from the existing literature
on greenwashing (e.g., Chen 2008a, b, 2010; Chen and
Chang 2013; Chen et al. 2006; Lyon and Maxwell 2011;
Parguel et al. 2011; Polonsky et al. 2010; Ramus and
Montiel 2005; Self et al. 2010) by documenting empirical
evidence on market reactions (cumulative abnormal
returns, CAR; similarly hereinafter) to the exposure of
greenwashing. Moreover, I also explore the role of corporate environmental performance in explaining CAR
tendencies around the exposure of greenwashing. More
specifically, I focus on (1) whether CAR is significantly
negatively associated with greenwashing, and (2) whether
CAR is significantly positively associated with corporate
environmental performance.
For empirical tests, I hand-collect data on greenwashing
and corporate environmental performance and then construct a sample from the Chinese stock market during the
period of 2011–2012. My study focuses on environmental
greenwashing in which firms spend a great deal of money
to advertize themselves as environmentally friendly (Burdick 2009; Joshua 2001) and then examines how the
market values greenwashing. In brief, my findings show
that greenwashing is significantly negatively associated
with CAR around the exposure of environmental wrongdoings, suggesting that the market negatively values
greenwashing. Moreover, corporate environmental performance is significantly positively associated with the tendencies on CAR around the exposure of greenwashing,
implying that corporate environmental performance plays
an important role in explaining the market reaction to
greenwashing.
This study contributes to the existing literature in several
ways. First, to my knowledge and literature in hand, this
study is the first to examine market reactions to greenwashing. Most previous literature has investigated the
determinants and economic consequence of greenwashing
(e.g., Chen 2008a, b, 2010; Chen and Chang 2013; Chen
et al. 2006; Parguel et al. 2011; Ramus and Montiel 2005),
but these studies have provided little evidence to inform the
public about the impacts of greenwashing on investors’
behavior and market reactions. In this study, I fill that gap
by investigating market reactions to press reports of
Market Values Greenwashing
greenwashing. My results echo findings in extant studies
that media coverage plays an important monitoring role in
affecting investors’ behavior and market reactions (Bushee
et al. 2010; Dyck et al. 2008; Fang and Peress 2009; Joe
et al. 2009; Miller 2006).
Second, this study is the first to link corporate environmental performance scores and market reactions (CAR) by
investigating whether and how corporate environmental performance scores can distinguish environmentally friendly
firms from environmentally unfriendly firms for companies
excluded from the yearly greenwashing lists. Specifically, my
findings show two distinct effects of corporate environmental
performance on CAR around the exposure of greenwashing:
the upward competitive effects for environmentally friendly
firms and the downward contagious effects for environmentally unfriendly firms, respectively.
Third, my study is one of very few studies to use corporate
environmental performance scores based on the Global
Reporting Initiative (2006; GRI, similarly hereinafter) sustainability reporting guidelines. A stream of early studies
uses the number of pages (Gray et al. 1995; Guthrie and
Parker 1989; Patten 1992), sentences (Ingram and Frazier
1980), and words (Deegan and Gordon 1996; Zeghal and
Ahmed 1990) to measure environmental disclosure in the
annual or standalone report. Another stream of extant literature adopts a disclosure-scoring measure derived from
content analysis (e.g., Al-Tuwaijri et al. 2004; Blacconiere
and Northcut 1997; Cormier and Magnan 1999; Konar and
Cohen 2001; Wiseman 1982; Please refer to a relatively
comprehensive review in Du et al. 2013). However, previous
measures generate endless controversies about the relation
between corporate environmental performance and corporate environmental disclosure because they use different data
sources and data coding criteria (Clarkson et al. 2008). In
comparison, corporate environmental performance scores
based on the GRI sustainability reporting guidelines are
relatively persuasive and less controversial.
Finally, I focus on China, the world’s largest developing
country and second largest economy, to make additional
contributions to previous conclusions using developed
markets where firms are under environmental responsibilities and other CSR pressure (Sharfman and Fernando 2008;
Ye and Zhang 2011). In China, however, laws and regulations are less effective and business ethics are still being
formed, so many firms lack CSR consciousness (Du 2012;
Sharfman and Fernando 2008). As a result, China is rife with
environmental destruction. Therefore, the Chinese context
supplements conclusions based on developed markets that
may fit poorly with the Chinese context.
In the next section, I introduce the institutional background
and develop research hypotheses. Then I illustrate empirical
models and key variables, followed by the sample construction, descriptive statistics, univariate tests, and correlation
549
analysis. I then report empirical analysis results and conduct a
variety of robustness checks. Finally, I summarize conclusions and ethical implication of my findings.
Institutional Background and Hypotheses Development
Institutional Background
Although greenwashing is not new,2 for the past decade
firms have begun to use it more frequently in response to
public demands for environmental responsibility. In China,
the largest emerging market and second largest economy,
environmental protection regulations are under construction and environmental laws and regulations are poorly
implemented, so many firms excessively abuse greenwashing to appear that they are environmentally responsible. As a result, greenwashing is pervasive in China, and
thus China provides a distinguished setting for observing
and examining whether and how the market reacts to
greenwashing.
Many Chinese listed firms spend substantial advertising
money to shape their green and environmentally friendly
images while actually using numerous additives and toxic
materials in production. For example, in 2008, in the notorious
melamine incident, several milk enterprises (e.g., Mengniu
Milk Co., Ltd. and Yili Co., Ltd., etc.) were revealed to have
been using tripolycyanamide as an additive, seriously damaging the health of infants and young children. Ironically,
before the melamine incident, these firms had been hailed as
environmentally friendly pacesetters and philanthropic compasses. In other words, they had used greenwashing to shape
positive and environmentally friendly images. Moreover,
firms in controversial or heavily polluting industries such as
tobacco, metal smelting, petrochemicals, and food service
industries, regularly advertise their green images while recklessly depleting resources and polluting the environment. The
public, wary of the discrepancies between image and reality,
have begun to doubt greenization claims.
Indeed, China has some environmental laws, regulations, and rules to restrain firms from environmentally
unfriendly behaviors.3 However, China lacks an
2
Jay Westervelt, a New York environmentalist, first coined the term
greenwashing in his 1986 essay regarding the hotel industry’s practice
of placing placards in each room asking guests to reuse their towels to
‘‘save the environment’’. He argued that hotels were actually using
their green campaigns to reduce costs (Hayward 2009; ‘‘Usage’’ in
Wikipedia 2013).
3
‘‘Since 2007, the Ministry of Environmental Protection of China
has enacted measures regarding corporate environmental reporting’’
(Du et al. 2013, p. 3). ‘‘The Regulation on Environmental Information
Disclosure, effective May 1, 2008, compels environmental agencies
and heavy-polluting companies to publically disclose certain environmental information. Moreover, the government has enacted stricter
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independent and efficient judicial system, so the existing
laws, regulations, and rules related to environmental conservation are performed poorly (Du 2012; Du et al. 2013).
Without strong enforcement, environmental conservation is
only on paper (Du et al. 2013). In addition, codes of ethics
in Chinese listed firms are still being formed and play a
limited role in motivating environmentally friendly
behavior. Therefore, one cannot assume that environmental
laws, regulations, and rules can restrain Chinese enterprises
from environmentally unfriendly activities.
Hypotheses Development
Corporate environmental policies provide a framework of
environmental objectives and targets, suggesting company
intentions and principles in relation to overall environmental performance (Ramus and Montiel 2005). Ideally, in
committing to environmental conservation, firms seriously
intend to implement positive and appropriate measures and
policies to obtain sustainable development. Some companies appear to be managing their environmental impacts
responsibly and sustainably (Holliday et al. 2002), so that
self-regulation may ensure that regulatory intervention is
rarely needed.
However, self-regulation may be inadequate (Howard
et al. 1999; King and Lenox 2000). For example, firms in
the same industries, such as oil and gas, may show significantly different levels of environmental conservation
(Logsdon 1985; Sharma et al. 1999). On the other hand,
industry sectors may not have greatly varying commitments to specific environmental policies, but only large,
leading-edge corporations in industry sectors implement
policies proactively (Ramus and Montiel 2005). Commitment to environmental conservation and implementation of
environmental policies are distinct constructs (Winn and
Angell 2000) and thus the public cannot arbitrarily assume
ex ante that the commitment necessarily translates to
Footnote 3 continued
regulations requiring Chinese listed firms to take environmental
responsibility. Shanghai and Shenzhen stock exchanges issued several
regulations and required a subset of listed firms to issue CSR reports
from 2008 considering social, economic, and governmental sustainability and recognizing that environmental protection is one of the
most important aspects of CSR. These regulations include: (1) notice
of supervising the listed firms in Shanghai Stock Exchange to disclose
the annual report of year 2008 (SHSE); (2) notice of supervising the
listed firms in Shenzhen Stock Exchange to disclose the annual report
of year 2008 (SZSE); and (3) guide to environmental information
disclosure for listed firms in Shanghai Stock Exchange of year 2008
(SHSE) (Du et al. 2013, Footnote #3)’’.
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X. Du
factual green activities. When companies fail to translate
policy into actual implementation, they are likely to resort
to greenwashing.
Even worse, greenwashing has had an even more negative influence in the Chinese stock market because China
has very lax law enforcement. In fact, critics argue that
ineffective regulation motivates companies to tout greenization while actually using greenwashing.4 As a result,
external forces that should drive companies toward greener
solutions are diminished (Dahl 2010). Unfortunately, the
Chinese stock market does show that companies are
increasingly using greenwashing under ineffective regulation and weak business ethics.
Environmental wrongdoers may spend a substantial
proportion of their advertising budgets to shape environmentally friendly images while unscrupulously destroying
the environment. Therefore, the public cannot help questioning the real motivations underlying greenization. In
other words, the public has come to doubt whether many
firms advertize their greenization just to cover their environmental misconducts. Certainly the market may not be
deceived permanently. Some nongovernment-owned press
or media outlets can serve as ‘‘watchdogs’’ by conducting
independent investigations to expose firms that use greenwashing. Next, I discuss market reactions to greenwashing
from two aspects: (1) media coverage and (2) the backfire
effect.
Extant studies have provided strong evidence that a free
media or pressure created by media coverage can play an
important role in the market and corporate governance
(Djankov et al. 2001; Dyck and Zingales 2002; Miller
2006). The nongovernment-owned press or media per se
are special market participants with many interests aligned
with the market and investors (Miller 2006). Thus nongovernment-owned presses are more likely to ‘‘cause
concern regarding the market or face scrutiny themselves’’
(Herman 2002; Miller 2006).
Media coverage always serves as a monitor or ‘‘watchdog’’, the most important functions of the press (Djankov
et al. 2001; Miller 2006), for the public by early identification of corporate misconduct. The watchdog process
often includes ‘‘combining public and non-public information with an analysis that highlights potential problems’’
(Miller 2006, p. 1006). To uncover corporate misconduct,
reporters must collect supporting data and synthesize the
information (Miller 2006). The investigative press and its
4
For example, greenwashing has less influence in developed
countries because of enforcement by regulatory agencies such as
the Federal Trade Commission in the United States, the Competition
Bureau in Canada, and the Committee of Advertising Practice and the
Broadcast Committee of Advertising Practice in the United Kingdom
(Wikipedia 2013).
Market Values Greenwashing
reporter, as the first or original analysts and information
providers, serve as the best monitors.
The existing literature increasingly shows strong evidence that the market reacts to environmental news, and
that the media plays an important role in corporate governance and the market (Bushee et al. 2010; Dyck et al.
2008; Fang and Peress 2009; Joe et al. 2009; Miller 2006;
Morris and Shin 2002). For example, ‘‘The press can serve
as an information intermediary and thus potentially shape
firms’ information environments by packaging and disseminating information, as well as by creating new information through journalism activities’’ (Bushee et al. 2010,
p. 1). Specifically, Bushee et al. (2010) find that greater
press coverage mitigates information asymmetry around
earnings announcements. Using the Russia context, Dyck
et al. (2008) find that media coverage in the AngloAmerican press increases the probability that a corporate
governance violation is reversed. Media can alleviate
informational frictions and affect security pricing even
without reporting genuine news, i.e., the positive association between media coverage and expected stock returns
(Fang and Peress 2009). Joe et al. (2009) find that media
exposure of board ineffectiveness regarding corporate
governance, investor trading behavior, and security prices
forces the targeted agents to take corrective actions and
enhance shareholder wealth. Also, the press can fulfill the
monitoring role by rebroadcasting information from other
information intermediaries such as analysts and auditors,
from information about lawsuits, and from original investigations and analyses (Miller 2006).
Overall, previous studies support the role played by
media coverage as an important information intermediary
in creating new information through journalism activities
and disclosing and rebroadcasting information, and thus
affecting investor trading behavior and security prices.
Also, these findings can be applied to both developed and
emerging markets (Dasgupta et al. 2001; Gupta and Goldar
2005; Khanna et al. 1998). In this regard, media coverage
should matter to CAR after the media discloses negative
events. Relevant to (environmental) greenwashing in my
study, I can further predict that the market negatively reacts
to the exposure of greenwashing.
In addition to the monitoring and governance role of
media coverage in affecting investors’ behavior and the
market reaction to negative environment information in the
press, we must also consider that investors may punish
environmental wrongdoers. In fact, after media disclosures
of hypocritical greenization or greenwashing, companies’
reputations may be perpetually damaged through what is
called ‘‘the backfire effect’’ (Brown and Dacin 1997; Yoon
et al. 2003).
‘‘Backfire effects’’ occur when individuals encounter
evidence contradictory to their beliefs (Nyhan and Reifler
551
2010). Individuals tend to reject the evidence and instead
more firmly adhere to their initial beliefs. Specifically,
investors exhibit the phenomenon when they see that a firm
advertizes greenization while actually polluting the environment. In such a case, investors form intuitive and initial
impressions that the company is greenwashing. Once the
press discloses greenwashing, investors more firmly adhere
to their initial negative judgment that the firm is making
greenization claims simply to hide its environmental
transgressions. The backfire effect is particularly relevant
to my study questioning whether investors will punish
environmental wrongdoers that use greenwashing.
In sum, in terms of media coverage and the backfire
effect, the market is likely to react negatively to environmental greenwashing exposures. Thus, I formulate the
following Hypothesis 1:
Hypothesis 1 Ceteris paribus, greenwashing is negatively
associated with cumulative abnormal returns (CAR) around
the exposure of environmental wrongdoers.
Hypothesis 1 predicts a negative association between
greenwashing and CAR. However, Hypothesis 1 only differentiates firms with greenwashing from other firms. As a
result, one cannot rule out ex ante that some firms may
escape to be included in the list of greenwashing because of
media limitations. For example, the press often covers only
firms and frauds that interest a broad set of readers or that
are less costly to identify and investigate (Miller. 2006). In
fact, some firms do manage to hide their environmentally
unfriendly activities. Therefore, I further discuss the role of
corporate environmental performance in distinguishing
environmentally friendly firms from environmental
wrongdoers for firms outside the greenwashing lists.
Corporate environmental performance measures how
successful a firm, relative to industry average or peer
group, is in reducing and minimizing its impact on the
environment (Investor Responsibility Research Center
1992; Klassen and McLaughlin 1996). Nevertheless, firms’
operations may result in negative externalities such as
environmental pollution (Bragdon and Marlin 1972; Coase
1960). Therefore, corporate top managers often make
tradeoffs between environmental performance and
accounting profits (Walley and Whitehead 1994).
In developing countries such as China, firms are less
likely to invest in pollution control because environmental
laws and regulations are implemented less effectively (Du
2012) and compliance costs may exceed expected benefits
(Dasgupta et al. 2001). Aside from lax government regulation, however, the market’s invisible hand can play an
important and alternative role in motivating firms to fulfill
their environmental responsibilities. Specifically, the
market reacts negatively to announcements of adverse,
passive, or negative environmental incidents such as
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pollution spills and investors’ complaints, and the market
reacts positively to announcements of better corporate
environmental performance (Dasgupta et al. 2001; Walley
and Whitehead 1994). As a result, firms may significantly
underestimate expected costs associated with poor environmental performance if they fail to consider penalties
from the market and investors. For example, abnormal
returns caused an average loss of $4.1 million in stock
value for firms reporting Toxics Release Inventory (TRI)
pollution on the day the pollution figures were first
released (Hamilton 1995). Therefore, compared with the
traditional channel of fines and penalties from regulators,
the market can give firms, especially firms in developing
countries, substantive incentives for better environmental
performance through communities and investors (Dasgupta et al. 2001).
Extant studies document systematic evidence to show
that corporate environmental performance matters to
investor behavior, market reactions, and CAR (Dasgupta
et al. 2001; Gupta and Goldar 2005; Hamilton 1995;
Khanna et al. 1998; Klassen and McLaughlin 1996; Lanoie
et al. 1998; Laplante and Lanoie 1994). For example,
stockholders in firms reporting TRI pollution experienced
negative, statistically significant abnormal returns on the
first release of negative information (Hamilton 1995).
Klassen and McLaughlin (1996) find significantly positive
(negative) associations between CAR and strong (weak)
environmental management, indicating that better environmental performance improves future stock market performance (CAR). Laplante and Lanoie (1994) find the
negative impact of environmental incident announcements
on equity value for Canadian-owned firms (significantly
declining stock value on the day of the announcement).
Moreover, Lanoie et al. (1998) find that large polluters in
America and Canada were more affected than smaller
polluters.
In developing markets such as Argentina, Chile, Mexico,
and the Philippines, capital markets also react to announcements of environmental events (Dasgupta et al. 2001). A
positive correlation was found between abnormal stock
returns and environmental performance; specifically, the
market penalized environmentally unfriendly firms with
negative abnormal returns of as much as 30 % (Gupta and
Goldar 2005). Repeated provisions of Toxics Release
Inventories caused statistically significant negative stock
market returns during the one-day period following disclosure (Khanna et al. 1998).
Overall, most of the prior studies provide strong evidence that the market does react to the disclosure of
5
EntreMed’s stock price rose from 12.063 at the Friday close. On
Monday, it opened at 85 and closed near 52 (Huberman and Regev
2001).
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X. Du
environmental information, and especially reacts asymmetrically to better and worse environmental performance.
For example, after the New York Times reported that EntreMed Co. had developed potentially cancer-curing drugs,
enthusiasm for EntreMed’s stock price spilled over to other
biotechnology stocks5 in a positive contagious effect
among companies in the biotechnology industry (Huberman and Regev 2001).
Furthermore, the existing literature is increasingly
addressing whether and how emerging markets react to
corporate environmental performance (Dasgupta et al.
2001; Gupta and Goldar 2005). ‘‘Public disclosure mechanisms in developing countries may be a useful model to
consider given limited government enforcement resources’’
(Dasgupta et al. 2001, p. 310). Capital markets could play
the most important role in environmental management,
particularly in developing countries where environmental
monitoring and enforcement are weak (Gupta and Goldar
2005). Therefore, I can rationally expect that the market
will react distinctly to firms with different corporate environmental performance.
Company commitment to environmental conservation
does not necessarily mean that the company will fulfill
environmentally friendly policies (Ramus and Montiel 2005;
Winn and Angell 2000). In fact, greenization slogans or
claims are not equivalent to actual environmentally friendly
activities. Therefore, the major problem is to evaluate factual
environmental performance ex post rather than be deceived
by ex ante claims, because greenwashing firms are less likely
to fulfill their environmental policies and are more likely to
have relatively poor environmental performance.
Compared with companies with greenwashing, firms
outside the greenwashing list fall into two categories based
on their corporate environmental performance: (1) firms
with worse environmental performance; and (2) firms with
better environmental performance. Logically, firms with
worse environmental performance are more likely to be
identified as unexposed greenwashing wrongdoers. In
addition, firms with better environmental performance are
more likely to be environmentally friendly. Therefore, CAR
around the exposure of greenwashing should be positively
related with corporate environmental performance. Furthermore, CAR will show two isolated tendencies6: the
competitive effect for environmental friendly firms and the
6
Greenwashing lists provide investors with information about
environmentally unfriendly activities. Investors can infer that firms
with worse environmental conservation will be more likely to be
environmentally unfriendly, although they might be not on the
greenwashing list. In addition, greenwashing lists convey information
about environmentally unfriendly firms in relation to others in an
industry. As a result, around the exposure of greenwashing, I predict
that those with worse environmental conservation will display
contagious effects and those with better conservation will display
competitive effects.
Market Values Greenwashing
553
contagious effect for environmental wrongdoers with
unexposed greenwashing. Based on the aforementioned
discussions, I formulate Hypothesis 2 as below:
Hypothesis 2 Ceteris paribus, corporate environmental
performance is positively associated with cumulative
abnormal returns (CAR) around the exposure of
greenwashing.
Empirical Models Specification and Variables
Multivariate Test Model for Hypothesis 1
To test Hypothesis 1, I estimate Eq. (1) including greenwashing and other determinations:
CAR½1; t ¼ a0 þ a1 GREENWASH þ a2 FIRST þ a3 DUAL
þ a4 INDR þ a5 LNBOARD þ a6 SIZE þ a7 LEV
þ a8 ROA þ a9 MTB þ a10 CROSS þ a11 ST
þ a12 GEB þ a13 SMEB þ a14 EXCHANGE
þ a15 LISTAGE þ a16 STATE
þ Industry Dummies þ Year Dummies þ e:
ð1Þ
In Eq. (1), the dependent variable is CAR [-1, t],
measured as CAR from day -1 to day t (t = 0, 1, 2, 3, 4,
5) using an industry-adjusted model (Kolari and Pynnönen
2010; Lewellen and Metrick 2010). In addition, in Eq. (1),
GREENWASH is the main independent variable, equaling
1 if a firm is on the list of greenwashing published in the
South Weekend, one of China’s most influential and
widely circulated newspapers, and 0 otherwise. In Eq. (1),
if the coefficient on GREENWASH (i.e., a1) is significantly negative, Hypothesis 1 is supported by empirical
evidence.
To isolate the influence of greenwashing on CAR, I
refer to extant studies (Clarkson et al. 2008; Du et al.
2013) to include a set of control variables in Eq. (1). (1) I
incorporate four variables, FIRST, DUAL, INDR, and
LNBOARD, into Eq. (1) to control the influence of various
corporate governance mechanisms on CAR. FIRST is the
percentage of common shares owned by the controlling
shareholder. DUAL is an indicator variable, equaling 1 if
the same person serves as the CEO and the chairman of
the board of directors and 0 otherwise. INDR equals the
number of independent directors to the number of directors in the boardroom. LNBOARD is the natural log of the
number of directors in the boardroom. (2) SIZE, LEV,
ROA, and MTB are included in Eq. (1) to control the
influence of a firm’s financial characteristics on CAR.
SIZE denotes firm size, measured by the natural log of
total assets. LEV is financial leverage, measured as the
ratio of total liabilities to total assets. ROA is return on
total assets, measured as net operating income deflated by
total assets. MTB is market-to-book ratio, calculated as the
market value of a firm to its book value. Also, book value
is calculated by looking at the firm’s historical cost or
accounting value, and market value is determined in the
stock market through its market capitalization. (3) I also
include six variables displaying a firm’s listing characteristics in the Chinese stock market into Eq. (1). CROSS
is a dummy variable of listing locations, equaling 1 when
a firm’s stock lists in two or more markets and 0 otherwise. ST is a dummy variable of listing status, equaling 1
when a firm’s stock is denoted as special treatment (ST) or
special treatment with star (*ST) and 0 otherwise. Growth
Enterprise Board (GEB) (Small and Median Enterprise
Board, SMEB) is a dummy variable of listing boards,
equaling 1 when a firm lists in GEB (SMEB) and 0
otherwise. EXCHANGE is a dummy variable of listing
markets, equaling 1 when a firm lists in Shanghai Security
Exchange and 0 otherwise. LISTAGE is the number of
years since a firm’s IPO. (4) STATE is introduced in
Eq. (1) to control the impact of the nature of the ultimate
owner on CAR. STATE is a dummy variable, equaling 1
when the controlling shareholder of a firm is a central or
local government agency or government-controlled stateowned enterprises and 0 otherwise. (5) Finally, I include
industry and year dummies into Eq. (1) to control the
industry and year fixed effects, respectively. Table 7 in
Appendix details variable definitions.
Multivariate Test Model for Hypothesis 2
To test Hypothesis 2, I estimate the following Eq. (2)
including environmental performance in last year (ENV)
and other determinations:
CAR½1; t ¼ b0 þ b1 ENV þ b2 FIRST þ b3 DUAL
þ b4 INDR þ b5 LNBOARD þ b6 SIZE
þ b7 LEV þ b8 ROA þ b9 MTB þ b10 CROSS
þ b11 ST þ b12 GEB þ b13 SMEB þ b14 EXCHANGE
þ b15 LISTAGE þ b16 STATE
þ Industry Dummies þ Year Dummies þ d:
ð2Þ
In Eq. (2), CAR [-1, t] (t = 0, 1, 2, 3, 4, 5) is the
dependent variable and ENV is the main independent variable, respectively. ENV is corporate environment performance score in last year (Table 8 in Appendix provides
details). In Eq. (2), if the coefficient on ENV (i.e., b1) is
significantly positive, Hypothesis 2 is supported by
empirical evidence. Moreover, control variables in Eq. (2)
are the same as those in Eq. (1). I provide variable definitions in detail in Table 7 of Appendix.
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554
The Market Reactions to Greenwashing
In this study, I follow Kolari and Pynnönen (2010) and
Lewellen and Metrick (2010) to calculate CAR around the
exposure of the greenwashing list. Specifically, I use CAR
[-1, t] (t = 0, 1, 2, 3, 4, 5) as the labels of CAR from day
-1 to day t and calculate CAR [-1, t] based on the following Eq. (3):
Xt
CAR½1; t ¼
ARj ðt ¼ 0; 1; 2; 3; 4; 5Þ:
ð3Þ
j¼1
In Eq. (3), AR is the label of industry-mean-adjusted
abnormal returns around the exposure of greenwashing
lists, measured as day trading returns minus the average
day trading returns of all firms in the same industry (Kolari
and Pynnönen 2010; Lewellen and Metrick 2010).
For robustness checks, I also adopt the market model
(Baker et al. 2010) and the market adjusted model (Brown
and Warner 1985) to calculate CAR around the exposure of
the greenwashing list, labeled as CARM [-1, t] and CARA
[-1, t] (t = 0, 1, 2, 3, 4, 5), respectively.
The Measure of Greenwashing
I follow the list of greenwashing published in the South
Weekend to measure GREENWASH. Specifically, GREENWASH equals 1 if a firm is on the greenwashing list published
in the South Weekend and 0 otherwise. Therefore, it is crucial
to ask whether the greenwashing lists published in the South
Weekend are persuasive. Business-oriented or nongovernmental press outlets are more likely to undertake original
analyses (Miller 2006). In this regard, greenwashing lists
published in the South Weekend are relatively persuasive
because the news outlet belongs to the largest nongovernment and business-oriented press in China.
According to South Weekend’s disclosure policy, they
first appoint a Greenwashing Identification Committee
comprising industry experts and exclusive members from
academic, NGOs, and advisory institutions (Peng 2012).
Second, the Greenwashing Committee judges each case
to determinate whether firms are guilty of open fraudulence, intentional concealment, double standards, empty
promises, policy interferences, impression management,
and negative externalities. The South Weekend (http://
www.infzm.com) explains those characteristics as below:
(1) open fraudulence means that the corporation is entirely
contrary to eco-friendliness or sustainable development by
mendaciously and deliberately labeling its products as
being environmentally friendly. (2) Intentional concealment means that the firm declares and shapes an ecofriendly or sustainable development image while intentionally concealing environmentally unfriendly behavior or
products. (3) Double standard indicates that a multinational
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X. Du
company declares that it is environmentally friendly in its
home country while being environmentally unfriendly in
other countries, or vice versa. (4) Empty promises imply
that firms issue apologies but fail to change their operations, even after their violations cause significantly hazardous consequences. (5) Policy interference suggests that
firms having industry monopolies and strong lobbying
power interfere with or impede laws and regulations on
environmental protection, sustainable development, or the
launch of eco-friendly products. (6) Impression management means that firms word their advertisements and
annual reports vaguely to mislead consumers. (7) Negative
externality indicates that a firm’s products and practices
have severely negative environmental impacts.
Third, using that evaluation system, the Greenwashing
Identification Committee drafts a list of candidates potentially guilty of greenwashing, along with punishment
information, authoritative media coverage, and exclusive
survey data from domestic and international NGOs.
Finally, after the South Weekend deals with challenges
and responses, they publish the list of greenwashing.
Considering their extensive procedures and principles, their
final yearly list of greenwashing is fair, open, transparent,
and authoritative.
The Measure of Corporate Environmental Performance
To calculate corporate environmental disclosure scores, I
adopt the GRI sustainability reporting guidelines (2006).
An under-researched issue is the association between the
level of environmental disclosures and environmental
performance (Al-Tuwaijri et al. 2004; Hooks and van
Staden 2011; Huang and Kung 2010; Hughes et al. 2001;
Patten 1992), so environmental performance scores may be
questioned because the companies themselves disclose the
information. However, corporate environmental performance scores based on GRI sustainability reporting
guidelines are relatively persuasive because corporate
environmental responsibility and environmental information disclosure have a positive association (Clarkson et al.
2008).
Performance-based metrics of corporate environmental
responsibility are important because they enable cross-firm
comparisons and provide stakeholders with more reliable,
consistent, and accurate information (Du et al. 2013; Ilinitch et al. 1998). Using proprietary databases (e.g., KLD),
some studies analyze the patterns of publicly available
voluntary environmental disclosures to assess environmental performance. Extant literature quantifies the number of pages, sentences, and words in the annual report or
standalone report to measure the level of environmental
disclosure or uses a disclosure-scoring measure derived
Market Values Greenwashing
from content analysis (e.g., Al-Tuwaijri et al. 2004;
Clarkson et al. 2008; Cormier and Magnan 1999; Deegan
and Gordon 1996; Gray et al. 1995; Guthrie and Parker
1989; Ingram and Frazier 1990; Patten 1992; Wiseman
1982; Zeghal and Ahmed 1990). However, different data
coding criteria generate countervailing arguments on the
relation between environmental performance and environmental disclosure.
The existing literature has been extended by focusing on
purely discretionary environmental disclosures and developing a content analysis index based on GRI sustainability
reporting guidelines (Clarkson et al. 2008). Specifically,
corporate environmental performance scores include seven
components: governance structure and management systems, credibility, environmental performance indicators
(EPI), environmental spending, vision and strategy claims,
environmental profile, and environmental initiatives. The
seven components can be further divided into forty-five
subcomponents (Clarkson et al. 2008; Du et al. 2013;
Rahman and Post 2012).
Rahman and Post (2012, p. 308) emphasize that Clarkson et al.’s (2008) method is better than previously used
indices to capture disclosures related to environmental
protection commitment because of its breadth, transparency, and validity. In other words, corporate environmental
disclosure score based on GRI properly reflects corporate
environmental performance. ‘‘The content analysis index
based on GRI can assess the level of discretionary environmental disclosures in environmental and social
responsibility reports provided on the firm’s web site or
annual reports’’ (Clarkson et al. 2008, p. 2). Because
environmental performance score based on GRI focuses on
a firm’s disclosure related to its commitment to protect the
environment, it differs from the Wiseman (1982) index
which is generally used in extant studies (Clarkson et al.
2008). Specifically, ‘‘environmental performance score
based on GRI allows all environmental stakeholder groups
(investors, regulators, etc.) to infer environmental performance from the disclosure score. Thus, it is valuable to
users who seek to assess firms’ true environmental commitment and environmental exposures’’ (Clarkson et al.
2008, p. 3).
Also, following Clarkson et al. (2008) and Rahman and
Post (2012), a branch of growing literature is increasingly
adopting GRI sustainability reporting guidelines to calculate corporate environmental performance scores (e.g., Cho
et al. 2012; Clarkson et al. 2011a, b; De Villiers and Van
Staden 2010; Dixon-Fowler et al. 2013; Du et al. 2013;
Lyon and Maxwell 2011; Rahman and Post 2012; Zeng
et al. 2012). Therefore, based on Rahman and Post (2012)’s
discussion and the existing literature, corporate environmental performance scores based on GRI are less
controversial.
555
In this study, based on GRI sustainability reporting
guidelines, I follow Clarkson et al. (2008) and Du et al.
(2013) to calculate corporate environmental performance
scores first by extracting environmental information from
firms’ annual reports, CSR reports, and other disclosures.
Second, I use content analysis and conduct the scoring
procedure based on GRI, which feature guidance on what
should be reported in disclosures on management approach
and performance indicators, such as economic, environment, labor, human rights, society, and product responsibility. Third, based on the raw score of forty-five subcomponents, I calculate and merge them to obtain seven
aggregate scores. Finally, according to scores of seven
components, I calculate the total score of corporate environmental performance. Table 8 in Appendix provides the
procedures for computing corporate environmental performance score and the descriptive statistics for seven
components and forty-five subcomponents of corporate
environmental performance.
Overall, corporate environmental disclosure scores
based on GRI sustainability reporting guidelines are relatively persuasive. Moreover, the descriptive statistics of
corporate environmental performance scores are similar to
those in Du et al. (2013) who also use the Chinese context.
Sample, Data, and Descriptive Statistics
Identification of Sample
On February 15, 2012 and January 31, 2013, the South
Weekend, one of China’s most influential newspapers,
published the 2011 and 2012 lists of greenwashing,
respectively. The South Weekend is famous for focusing on
current social problems, acute criticism and commentary
on politics, and telling it like it was. Miller (2006)
emphasizes that business-oriented press is more likely to
undertake original analysis, and thus the South Weekend, as
the biggest business-oriented and non-governmental press
in China, undertakes a number of original investigations.
As a result, one can conclude that the yearly list of
greenwashing published in the South Weekend should be
relatively persuasive.
In this study, I select my sample according to the following procedures: First, I identify 14 Chinese listed firms in
the lists of greenwashing and corresponding industries. The
2011 list of greenwashing includes seven Chinese listed
firms (excluding one firm from finance industry): (1) Shuanghui Investment & Development Co., Ltd (000895.SZ); (2)
Supor Co., Ltd. (002032.SZ); (3) Humon Smelting Co., Ltd.
(002237.SZ); (4) China Sinopec (600028.SH); (5) Shenghua
Biok Biology Co., Ltd. (600226.SH); (6) Hisun Pharmaceutical Co., Ltd. (600267.SH); and (7) Harbin
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556
Pharmaceutical Group Holding Co., Ltd. (600664. SH). The
2012 list of greenwashing consists of seven Chinese listed
firms: (1) Gree Electric Appliances Inc (000651.SZ); (2)
Beijing Shougang Co., Ltd. (000959.SZ); (3) Metersbonwe
Fashion & Accessories Co., Ltd. (002269.SZ); (4) Baotou
Dongbao Bio-Tech Co., Ltd. (300239.SZ); (5) China Sinopec (600028.SH); (6) China Shenhua Energy Company
Limited (601088.SH); and (7) China Coal Energy Company
Limited (601898.SH). Second, I include all firm-years in the
same industries to constitute the initial sample (629 observations). Third, I exclude firm-year observations whose net
assets or shareholders equity are below zero (16 observations); Fourth, I delete firm-year observations whose data
required to measure CAR are not available (17 observations).
Fifth, I discard firm-year observations whose data required to
measure firm-specific control variables are not available (35
observations). Finally, I obtain 561 observations to constitute the research sample. Also, I winsorize the top and bottom
1 % of each variable’s distribution to alleviate the influence
of extreme observations.7
Data Source
Data sources in this study are reported as below. (1) I handcollect data on GREENWASH from the South Weekend,
one of China’s most widely circulated newspapers. (2)
Using February 15, 2012 and January 31, 2013, two dates
related with the published 2011 and 2012 lists of greenwashing in the South Weekend, as the event days, respectively, I compute CAR [-1, t] (t = 0, 1, 2, 3, 4, 5) based
original data from China Stock Market and Accounting
Research (CSMAR), which is frequently used database in
extant China studies. (3) I compute corporate environmental performance score (ENV), including seven components and forty-five subcomponents in light of
procedures and principles in Table 8 of Appendix. (4)
Other data except for CAR [-1, t] (t = 0, 1, 2, 3, 4, 5),
GREENWASH, and ENV are collected from CSMAR.
Please see Table 7 in Appendix for data sources in detail.
Descriptive Statistics and Univariate Tests
Panel A of Table 1 reports descriptive statistics results of
variables used in this study. As shown in Panel A, the mean
values of CAR [-1, 0], CAR [-1, 1], CAR [-1, 2], CAR
[-1, 3], CAR [-1, 4], and CAR [-1, 5] are 0.0133, 0.0180,
0.0251, 0.0168, 0.0239, and 0.0177, respectively, revealing
the basic characteristics of CAR around the exposure of
greenwashing.
7
The results are not qualitatively changed by deleting the top and the
bottom 1 % of the sample, by no deletion, or by no winsorization.
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X. Du
GREENWASH has a mean value of 0.0250, suggesting
that 2.50 % of firms are indentified as environmental
wrongdoers with greenwashing. The mean value of ENV is
3.7745, indicating that the average corporate environmental performance score is 3.7745 for the sample in this study.
Obviously, 3.7745 is a very low score, compared with the
full marks (95) in Table 8 in Appendix.
Descriptive statistics results of four corporate governance variables show that the average percentage of shares
owned by controlling shareholders (FIRST) is 37.71 %, the
same person serves as the CEO and the chairman (DUAL)
for about 26.20 % of firms, the average ratio of independent directors (INDR) is 36.63 %, and the number of
directors in the boardroom (LNBOARD) is nine on average.
Descriptive statistics results of financial characteristics
variables reveal that the average size for the sample (SIZE)
is about 3.55 billion Chinese Yuan, the average financial
leverage (LEV) is about 40.65 %, the average returns on
total assets (ROA) is about 7.19 %, and the average marketto-book ratio is 2.6365 with a relatively big standard
deviation of 2.2270. Descriptive statistics results of six
variables related with a firm’s listing characteristics show
that 6.60 % of firms list in two and more stock markets
(CROSS), 1.43 % of firms’ stocks are marked with ST or
*ST, 7.84 % of firms list in growth enterprises board
(GEB), 30.12 % of firms list in SMEB, 41.18 % of firms list
in the Shanghai Securities Exchange (EXCHANGE), and
the average firm age is about 9.8824 (LISTAGE). In addition, the ultimate owners of about 60.61 % of firms are
(central or local) government agencies or governmentcontrolled state-owned enterprises (STATE).
Panel B presents results of t tests for differences in the
mean values of CAR between the greenwashing subsample (n = 14) and the non-greenwashing subsample
(n = 547). As shown in Panel B, compared with the nongreenwashing subsample, the greenwashing subsample has
significantly lower CAR [-1, 0], CAR [-1, 1], CAR [-1,
2], CAR [-1, 3], CAR [-1, 4], and CAR [-1, 5],
respectively. These results provide preliminary support to
Hypothesis 1.
Using the average environmental performance score of
firms on the yearly list of greenwashing as the benchmark,
Panel C reports results of t tests for differences in the
mean values of CAR among the greenwashing subsample
(n = 14), the high-ENV subsample (n = 161), and the
low-ENV subsample (n = 386). Results in Panel C reveal
the followings: (1) CAR [-1, 0], CAR [-1, 1], CAR [-1,
2], CAR [-1, 3], CAR [-1, 4], and CAR [-1, 5] are
significantly higher for the high-ENV subsample than
for the GREENWASH subsample, respectively. (2) The
low-ENV subsample has significantly lower CAR [-1, 0],
CAR [-1, 1], CAR [-1, 2], CAR [-1, 3], CAR [-1, 4],
and CAR [-1, 5] than the GREENWASH subsample has,
Market Values Greenwashing
557
Table 1 Descriptive statistics and univariate tests
Panel A: descriptive statistics
Variables
N
Mean
SD
Min
Q1
Median
Q3
Max
CAR [-1, 0]
561
0.0133
0.0269
-0.0783
-0.0013
0.0110
0.0240
0.1825
CAR [-1, 1]
561
0.0180
0.0340
-0.1182
-0.0016
0.0138
0.0336
0.2087
CAR [-1, 2]
561
0.0251
0.0402
-0.1228
0.0016
0.0196
0.0452
0.2135
CAR [-1, 3]
561
0.0168
0.0432
-0.1511
-0.0087
0.0075
0.0386
0.2585
CAR [-1, 4]
561
0.0239
0.0474
-0.1287
-0.0065
0.0160
0.0483
0.2793
CAR [-1, 5]
561
0.0177
0.0489
-0.1186
-0.0123
0.0101
0.0411
0.2755
GREENWASH
561
0.0250
0.1561
0
0
0
0
1
ENV
561
3.7745
6.0402
0
0
1
5
40
FIRST
561
0.3771
0.1581
0.0852
0.2459
0.3694
0.4817
0.7578
DUAL
561
0.2620
0.4401
0
0
0
1
1
INDR
561
0.3663
0.0499
0.3077
0.3333
0.3333
0.3750
0.5714
LNBOARD
561
2.1899
0.1936
1.6094
2.1972
2.1972
2.1972
2.7081
SIZE
561
21.9895
1.3112
18.9972
21.0728
21.7371
22.7252
26.7963
LEV
561
0.4065
0.2228
0.0303
0.2184
0.4118
0.5775
0.9839
ROA
561
0.0719
0.0858
-0.2006
0.0217
0.0617
0.1038
0.6646
MTB
561
2.6365
2.2270
0.5535
1.3905
1.9320
3.0766
18.4443
CROSS
561
0.0660
0.2484
0
0
0
0
1
ST
561
0.0143
0.1187
0
0
0
0
1
GEB
561
0.0784
0.2691
0
0
0
0
1
SMEB
561
0.3012
0.4592
0
0
0
1
1
EXCHANGE
561
0.4118
0.4926
0
0
0
1
1
LISTAGE
561
9.8824
5.8901
1
4
10
15
23
STATE
561
0.6061
0.4891
0
0
1
1
1
Panel B: t tests for differences in the mean values of CAR between the greenwashing subsample and the non-greenwashing subsample
Variables
The greenwashing subsample
N
Mean
t tests
The non-greenwashing subsample
SD
N
Mean
SD
CAR [-1, 0]
14
-0.0005
0.0014
547
0.0136
0.0271
-11.54***
CAR [-1, 1]
14
-0.0016
0.0043
547
0.0185
0.0342
-10.85***
-10.04***
CAR [-1, 2]
14
-0.0026
0.0084
547
0.0259
0.0404
CAR [-1, 3]
14
-0.0048
0.0162
547
0.0174
0.0436
-4.70***
CAR [-1, 4]
14
-0.0048
0.0167
547
0.0247
0.0477
-5.99***
CAR [-1, 5]
14
-0.0034
0.0117
547
0.0183
0.0494
-5.76***
Panel C: t tests for differences in the mean values of CAR among greenwashing subsample, high-ENV subsample and Low-ENV subsample
Variables
(1)
(2)
(3)
The high-ENV subsample
The greenwashing subsample
The low-ENV subsample
N
N
N
Mean
SD
Mean
SD
t tests
Mean
SD
(1)VS (2)
(2)VS (3)
CAR [-1, 0]
161
0.0581
0.0259
14
-0.0005
0.0014
386
-0.0086
0.0275
8.37***
-6.02***
CAR [-1, 1]
161
0.0950
0.0327
14
-0.0016
0.0043
386
-0.0150
0.0344
10.84***
-7.68***
CAR [-1, 2]
161
0.1450
0.0409
14
-0.0026
0.0084
386
-0.0233
0.0398
13.22***
-8.88***
CAR [-1, 3]
161
0.1552
0.0421
14
-0.0048
0.0162
386
-0.0275
0.0428
13.71***
-7.10***
CAR [-1, 4]
161
0.1775
0.0475
14
-0.0048
0.0167
386
-0.0302
0.0447
13.91***
-7.54***
CAR [-1, 5]
161
0.1763
0.0481
14
-0.0034
0.0117
386
-0.0334
0.0471
13.67***
-10.07***
***, **, and * represent the 1, 5, and 10 % levels of significance, respectively, for two-tailed tests
123
558
respectively. These results provide preliminary support to
Hypothesis 2.
Table 8 in Appendix provides the scores of seven
components and forty-five subcomponents of corporate
environmental performance. As shown in Table 8 of
Appendix, the maximum value of corporate environmental
performance score is 40, a relatively low score compared
with the full marks of 95 (see Table 8 in Appendix in
detail). In addition, for corporate environmental performance in the sample, the major defect rests with the
component of ‘‘Environmental performance indicators
(EPI)’’ because the maximum value of 19 for this sample is
far below the full marks of 60.
Pearson Correlation Analysis
Table 2 reports Pearson correlation analysis of variables
used in this study, and the p value is in parentheses below
the coefficients. As shown in Table 2, the following findings are noteworthy: (1) there are significantly negative
correlations between GREENWASH and CAR [-1, 0], CAR
[-1, 1], CAR [-1, 2], CAR [-1, 3], and CAR [-1, 4],
repsectively. Moreover, CAR [-1, 5] displays a marginally
significantly negative correlation (p value = 0.1040) with
GREENWASH. These results, taken together, suggest that
greenwashing is significantly negatively associated with
CAR around the exposure of environmental wrongdoers
(i.e., the yearly list of greenwashing) on the whole, preliminarily support Hypothesis 1. (2) The correlation coefficients between ENV and CAR [-1, 0], CAR [-1, 1], CAR
[-1, 2], CAR [-1, 3], CAR [-1, 4], and CAR [-1, 5] are
all significantly positive, suggesting that higher corporate
environmental performance score can lead to higher CAR
around the exposure of greenwashing. These results provide preliminary support to Hypothesis 2.
As for Pearson correlation between CAR and control
variables, CAR is significantly negatively (positively)
related with FIRST, SIZE, LEV, and STATE (MTB).
Moreover, as expected, the coefficients of pair-wise correlation among control variables are generally low, suggesting no serious multicollinearity problem exists when
these variables are included in the regression
simultaneously.
Empirical Results
Multivariate Test of Hypothesis 1
Hypothesis 1 predicts the negative association between
greenwashing and CAR. Table 3 presents the feasible
generalized least squares (FGLS) regression results of CAR
on GREENWASH and other determinants.8
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X. Du
As shown in Columns (1)–(6) of Table 3, the coefficients
on GREENWASH are negative and significant across all
cases (-0.0075 with z = -3.37, -0.0137 with z = -13.10,
-0.0215 with z = -8.72, -0.0213 with z = -17.47,
-0.0293 with z = -6.44, and -0.0164 with z = -7.69,
respectively), suggesting the significantly negative association between greenwashing and CAR and thus providing
strong and consistent support to Hypothesis 1. In addition,
these coefficient estimates on GREENWASH suggest that
CAR [-1, 0], CAR [-1, 1], CAR [-1, 2], CAR [-1, 3], CAR
[-1, 4], and CAR [-1, 5] for firms with exposed greenwashing are 0.75, 1.37, 2.15, 2.13, 2.93, and 1.64 % lower
than their counterparts on average, respectively. In this
regard, these coefficient estimates are economically significant, in addition to their statistical significances.
As for the signs and significances of control variables in
Table 3, it is noteworthy the following aspects. (1) The
coefficients on FIRST are significantly positive in Columns
(1) and (3), significantly negative in Columns (5) and (6), and
insignificant in Columns (2) and (4), respectively. These
results mean that the influence of the percentage of shares
owned by the controlling shareholder on CAR is inconsistent
during different time windows, especially, positive influence
on CAR [-1, 0] and CAR [-1, 2], negative impacts on CAR
[-1, 4] and CAR [-1, 5], and insignificant impacts on CAR
[-1, 1] and CAR [-1, 3]. (2) DUAL has significantly negative coefficients in Columns (1)–(4), indicating that CAR
[-1, t] (t = 0, 1, 2, 3) are lower for firms with the same
person serves as the CEO and the chairman simultaneously
than for their counterparts. (3) The coefficients on INDR are
negative and significant across all columns, indicating that
firms with higher ratio of independent directors experience
lower CAR [-1, t] (t = 0, 1, 2, 3, 4, 5). (4) In Columns (1)–
(4), the coefficients on LNBOARD are significantly negative,
but LNBOARD has significantly positive coefficient in
Column (6), suggesting the asymmetric influence of board
size on CARs during different time windows. (5) SIZE has
significantly negative coefficients in Columns (1)–(4) and
significantly positive coefficient in Column (6), implying
that firm size’s negative impacts on CAR [-1, t] (t = 0, 1, 2,
3) and positive influence on CAR [-1, 5]. (6) The coefficients
on LEV are negative and significant in Columns (2)–(6),
implying the negative impacts of higher financial leverage on
CAR [-1, t] (t = 1, 2, 3, 4, 5). (7) ROA has significantly
8
I acknowledge my great thanks to the referee for his/her insightful
suggestion. The data in my study are quasi-panel-data type, so the
OLS regression procedure is not very suitable. As a result, I conduct
Hausman tests to determine whether fixed effects or random effects
are appropriate for quasi-panel data in my study. Non-tabulated
results of Hausman tests show that all null hypotheses are not rejected
because all v2 values are insignificant. Therefore, according to Greene
(2012), random effects regression using the feasible generalized least
squares (FGLS) approach is more appropriate than fixed effects and
the LSDV (the least squares dummy variable estimation) approach.
-0.0059 (0.8898)
(23)
STATE
(14)
SIZE
LEV
(17)
(13)
LNBOARD
CROSS
(12)
INDR
(16)
(11)
DUAL
MTB
(10)
FIRST
(15)
(9)
ENV
ROA
(7)
(8)
GREENWASH
Variables
(21)
(22)
EXCHANGE
LISTAGE
(19)
(20)
GEB
SMEB
-0.0599 (0.1563)
(17)
(18)
1
0.1417 (0.0008)
0.0005 (0.9901)
0.0044 (0.9176)
0.0441 (0.2969)
0.2207 (\0.0001)
0.0120 (0.7759)
0.0520 (0.2190)
0.0346 (0.4134)
0.1480 (0.0004)
0.0311 (0.4622)
(7)
-0.0885 (0.0361)
0.0319 (0.4509)
-0.0494 (0.2424)
0.0047 (0.9111)
-0.0206 (0.6271)
0.0238 (0.5735)
0.1212 (0.0040)
-0.0960 (0.0229)
-0.1571 (0.0002)
-0.0449 (0.2884)
-0.0545 (0.1970)
-0.0035 (0.9339)
CROSS
(11)
INDR
ST
(10)
DUAL
0.1193 (0.0047)
-0.0900 (0.0331)
(15)
(16)
(9)
FIRST
ROA
MTB
(8)
ENV
-0.0827 (0.0502)
(14)
(7)
GREENWASH
0.5780 (\0.0001)
0.6241 (\0.0001)
LEV
(6)
CAR [-1, 5]
(12)
(5)
CAR [-1, 4]
0.6337 (\0.0001)
(13)
(4)
CAR [-1, 3]
0.7528 (\0.0001)
LNBOARD
(3)
CAR [-1, 2]
1
0.8511 (\0.0001)
(1)
SIZE
(1)
(2)
CAR [-1, 0]
CAR [-1, 1]
Variables
Table 2 Pearson Correlation Matrix
1
1
0.0226 (0.5933)
0.0112 (0.7906)
-0.0150 (0.7222)
0.0852 (0.0436)
0.1478 (0.0004)
-0.0251 (0.5525)
0.0174 (0.6801)
-0.0145 (0.7316)
0.0371 (0.3803)
(8)
-0.0565 (0.1814)
0.0487 (0.2498)
-0.0315 (0.4569)
-0.0061 (0.8856)
-0.0208 (0.6227)
-0.0625 (0.1395)
-0.0298 (0.4806)
-0.0068 (0.8723)
0.1164 (0.0058)
-0.0628 (0.1373)
-0.1748 (\0.0001)
-0.0364 (0.3899)
-0.0613 (0.1470)
0.0072 (0.8650)
-0.1175 (0.0053)
0.1703 (0.0001)
-0.0938 (0.0263)
0.6851 (\0.0001)
0.7257 (\0.0001)
0.7542 (\0.0001)
0.8568 (\0.0001)
(2)
1
1
0.0678 (0.1087)
-0.1579 (0.0002)
0.0180 (0.6705)
0.0029 (0.9445)
0.3111 (\0.0001)
0.1218 (0.0039)
0.0249 (0.5554)
-0.1276 (0.0025)
(9)
0.0198 (0.6407)
0.0795 (0.0597)
0.0183 (0.6660)
-0.0321 (0.4486)
-0.0448 (0.2898)
-0.1135 (0.0071)
-0.0125 (0.7684)
0.0039 (0.9261)
0.0734 (0.0824)
-0.0337 (0.4261)
-0.0875 (0.0383)
-0.0074 (0.8612)
-0.0339 (0.4230)
0.0145 (0.7326)
-0.0764 (0.0707)
0.1624 (0.0001)
-0.1075 (0.0108)
0.7768 (\0.0001)
0.8526 (\0.0001)
0.8963 (\0.0001)
(3)
1
1
-0.0767 (0.0695)
0.0151 (0.7206)
-0.0109 (0.7975)
-0.1207 (0.0042)
-0.1818 (\0.0001)
-0.1075 (0.0109)
0.0090 (0.8322)
(10)
0.0533 (0.2077)
0.0717 (0.0896)
0.0247 (0.5592)
-0.0377 (0.3728)
-0.0789 (0.0618)
-0.0665 (0.1156)
0.0183 (0.6655)
-0.0531 (0.2091)
-0.0012 (0.9769)
0.0542 (0.1999)
0.0313 (0.4593)
0.0151 (0.7219)
-0.0162 (0.7016)
0.0003 (0.9940)
-0.0309 (0.4647)
0.0998 (0.0181)
-0.0786 (0.0628)
0.8715 (\0.0001)
0.9246 (\0.0001)
(4)
1
1
0.0453 (0.2843)
-0.0270 (0.5237)
-0.0844 (0.0456)
-0.0094 (0.8234)
0.0690 (0.1027)
-0.2666 (\0.0001)
(11)
0.0938 (0.0262)
0.0567 (0.1802)
0.0099 (0.8146)
-0.0263 (0.5340)
-0.0389 (0.3574)
-0.0767 (0.0697)
0.0162 (0.7018)
-0.0270 (0.5227)
0.0139 (0.7417)
0.0091 (0.8301)
0.0032 (0.9393)
0.0200 (0.6362)
-0.0256 (0.5453)
0.0017 (0.9689)
-0.0397 (0.3478)
0.1384 (0.0010)
-0.0918 (0.0297)
0.9494 (\0.0001)
(5)
1
1
0.0021 (0.9611)
-0.0967 (0.0219)
-0.0100 (0.8128)
0.1671 (0.0 001)
0.2703 (\0.0001)
(12)
0.0742 (0.0789)
0.0237 (0.5750)
0.0119 (0.7791)
-0.0326 (0.4410)
0.0026 (0.9516)
-0.0528 (0.2120)
0.0033 (0.9382)
-0.0259 (0.5401)
0.0090 (0.8307)
0.0157 (0.7113)
0.0200 (0.6367)
0.0276 (0.5136)
-0.0252 (0.5522)
0.0223 (0.5977)
-0.0566 (0.1809)
0.1591 (0.0002)
-0.0687 (0.1040)
(6)
Market Values Greenwashing
559
123
123
(22)
(23)
LISTAGE
STATE
(22)
(23)
LISTAGE
STATE
(20)
(21)
(22)
(23)
SMEB
EXCHANGE
LISTAGE
STATE
1
0.2909 (\0.0001)
0.4732 (\0.0001)
0.3363 (\0.0001)
-0.3485 (\0.0001)
-0.3157 (\0.0001)
0.1945 (\0.0001)
0.1323 (0.0017)
-0.1447 (0.0006)
-0.3716 (\0.0001)
-0.2441 (\0.0001)
-0.1915 (\0.0001)
(19)
0.2717 (\0.0001)
0.2523 (\0.0001)
0.2923 (\0.0001)
-0.3059 (\0.0001)
0.3099 (\0.0001)
1
-0.3754 (\0.0001)
0.1857 (\0.0001)
(14)
0.0898 (0.0335)
0.0215 (0.6106)
-0.0038 (0.9290)
0.0567 (0.1796)
-0.0200 (0.6370)
-0.0292 (0.4905)
(8)
1
-0.3214 (\0.0001)
-0.6557 (\0.0001)
-0.5494 (\0.0001)
(20)
-0.1938 (\0.0001)
-0.0893 (0.0344)
-0.0788 (0.0620)
0.0786 (0.0630)
0.0738 (0.0806)
-0.2125 (\0.0001)
-0.0534 (0.2065)
1
0.0870 (0.0394)
(15)
0.1224 (0.0037)
-0.2112 (\0.0001)
0.1152 (0.0063)
0.0407 (0.3359)
-0.0577 (0.1720)
-0.0247 (0.5592)
(9)
p value is presented in parentheses. All the variables are defined in Table 7 in Appendix
(19)
GEB
Variables
(21)
EXCHANGE
-0.2151 (\0.0001)
(19)
(20)
GEB
(18)
ST
SMEB
0.0505 (0.2327)
(17)
CROSS
0.4508 (\0.0001)
-0.0568 (0.1795)
-0.2826 (\0.0001)
(14)
(15)
(16)
1
LEV
(13)
0.0354 (0.4022)
0.0071 (0.8671)
-0.0303 (0.4735)
0.0287 (0.4978)
-0.0042 (0.9216)
-0.0192 (0.6493)
(7)
ROA
MTB
SIZE
(13)
(20)
(21)
SMEB
EXCHANGE
Variables
(18)
(19)
ST
GEB
Variables
Table 2 continued
1
1
0.2520 (\0.0001)
0.4051 (\0.0001)
(21)
0.0121 (0.7752)
0.2400 (\0.0001)
0.1116 (0.0082)
-0.1201 (0.0044)
-0.1018 (0.0158)
0.2266 (\0.0001)
-0.0054 (0.8988)
(16)
-0.1501 (0.0004)
-0.1541 (0.0002)
0.1565 (0.0002)
-0.1444 (0.0006)
0.1277 (0.0024)
-0.0033 (0.9380)
(10)
1
1
0.3000 (\0.0001)
(22)
0.1113 (0.0083)
0.2128 (\0.0001)
0.0695 (0.0999)
-0.1745 (\0.0001)
-0.0775 (0.0665)
0.0286 (0.4988)
(17)
0.0418 (0.3227)
-0.0235 (0.5780)
0.0356 (0.4004)
-0.0408 (0.3348)
-0.0057 (0.8920)
0.0051 (0.9045)
(11)
1
1
(23)
0.0662 (0.1173)
0.0561 (0.1849)
-0.0090 (0.8318)
-0.0790 (0.0616)
-0.0351 (0.4068)
(18)
0.1504 (0.0003)
0.0533 (0.2078)
-0.2032 (\0.0001)
0.2109 (\0.0001)
0.0540 (0.2015)
0.0324 (0.4438)
(12)
560
X. Du
Market Values Greenwashing
561
Table 3 Regression results of CAR on greenwashing and other determinants
Variable
(1)
CAR [-1, 0]
Coefficient
(z value)
(2)
CAR [-1, 1]
Coefficient
(z value)
(3)
CAR [-1, 2]
Coefficient
(z value)
(4)
CAR [-1, 3]
Coefficient
(z value)
(5)
CAR [-1, 4]
Coefficient
(z value)
(6)
CAR [-1, 5]
Coefficient
(z value)
GREENWASH
-0.0075***
-0.0137***
-0.0215***
-0.0213***
-0.0293***
-0.0164***
(-3.37)
(-13.10)
(-8.72)
(-17.47)
(-6.44)
(-7.69)
FIRST
0.0044***
-0.0002
0.0074***
0.003
-0.0179***
-0.0211***
(4.33)
(-0.16)
(3.37)
(1.47)
(-7.84)
(-8.82)
DUAL
-0.0025***
-0.0026***
-0.0034***
-0.0019**
0.0003
-0.0005
(-6.51)
(-4.94)
(-4.62)
(-2.50)
(0.44)
(-0.53)
INDR
LNBOARD
-0.0253***
-0.0338***
-0.0334***
-0.0372***
-0.0588***
-0.0371***
(-4.77)
(-7.54)
(-6.30)
(-6.60)
(-9.37)
(-4.82)
-0.0067***
-0.0032**
-0.0038***
-0.0041**
-0.0017
0.0095***
(-8.35)
(-2.49)
(-2.63)
(-2.27)
(-0.78)
(4.45)
SIZE
-0.0012***
-0.0007**
-0.0015***
-0.0013***
0.0007
0.0019***
(-5.12)
(-2.50)
(-3.94)
(-3.04)
(1.63)
(4.11)
LEV
-0.0001
-0.0037**
-0.0064***
-0.0130***
-0.0141***
-0.0209***
ROA
(-0.10)
-0.0053***
(-2.28)
-0.0406***
(-4.38)
-0.0253***
(-5.07)
-0.0238***
(-5.35)
-0.0125**
(-8.33)
0.0116
(-2.70)
(-15.94)
(-7.34)
(-6.87)
(-2.25)
(1.51)
MTB
-0.0003**
0.0001
0.0008***
0.0016***
0.0012***
0.0013***
(-2.25)
(0.60)
(4.84)
(7.55)
(7.01)
(3.58)
CROSS
0.0025***
0.0017
0.0004
0.0025
0.0055**
0.0017
ST
GEB
SMEB
(2.62)
(1.10)
(0.15)
(1.14)
(2.16)
(0.63)
-0.0057**
-0.0235***
-0.0407***
-0.0329***
-0.0390***
-0.0388***
(-2.06)
(-13.31)
(-33.27)
(-12.52)
(-25.64)
(-23.69)
-0.0097***
-0.0181***
-0.0231***
-0.0187***
-0.0235***
-0.0086***
(-7.69)
(-11.42)
(-8.00)
(-8.44)
(-9.27)
(-3.56)
-0.0007
-0.0100***
-0.0117***
-0.0139***
-0.0184***
-0.0121***
(-0.63)
(-7.89)
(-5.77)
(-7.65)
(-9.99)
(-5.84)
EXCHANGE
-0.0002
-0.0039***
-0.0013
-0.0040***
-0.0059***
0.001
(-0.49)
(-8.22)
(-1.19)
(-4.55)
(-5.82)
(0.94)
LISTAGE
0.0001
(1.06)
-0.0002***
(-3.19)
-0.0006***
(-4.50)
-0.0007***
(-6.58)
-0.0011***
(-8.46)
-0.0010***
(-7.55)
STATE
-0.0018***
-0.0042***
-0.0038***
-0.0031***
-0.0014*
0.0007
(-5.10)
(-7.25)
(-5.55)
(-4.54)
(-1.75)
(0.73)
Constant
0.0377***
0.0260***
0.0557***
0.0734***
0.0233**
-0.0472***
(5.72)
(3.38)
(5.94)
(6.96)
(2.10)
(-4.04)
INDUSTRY
YES
YES
YES
YES
YES
YES
YEAR
YES
YES
YES
YES
YES
YES
Number of Obs.
561
561
561
561
561
561
v2 value (p value)
9908.97***
12309.73***
12248.65***
33283.65***
38468.94***
28878.10***
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
***, **, and * represent the 1, 5, and 10 % levels of significance, respectively, for two-tailed tests. All the variables are defined in Table 7 in
Appendix
negative coefficients in Columns (1)–(5), suggesting that
better accounting performance negatively influences CAR
[-1, t] (t = 0, 1, 2, 3, 4). (8) The coefficients on MTB are
significantly negative in Column (1) and significantly positive in Columns (3)–(6), implying the positive and negative
impacts of market-to-book ratio on CAR [-1, 0] and CAR
[-1, t] (t = 2, 3, 4, 5), respectively. (9) CROSS has significantly positive coefficient in Columns (1) and (5), suggesting
that firms listed in two or more stock markets simultaneously
have higher CAR [-1, 0] and CAR [-1, 4]. (10) The
123
562
X. Du
CAR
Non-greenwashing
Greenwashing
0.0300
0.0250
0.0200
0.0150
0.0100
0.0050
0.0000
-0.0050
-0.0100
-1
0
1
2
3
4
5
t
Fig. 1 The tendencies on CAR [-1, t] for the greenwashing
subsample and the non-greenwashing subsample
coefficients on ST are all significantly negative in Columns
(1)–(6), revealing that firms with listing status of ST or *ST
have significantly lower CAR around the exposure of
greenwashing. (11) The coefficients on GEB are negative
and significant in Columns (1)–(6), meaning that CAR [-1, t]
(t = 0, 1, 2, 3, 4, 5) is lower for firms listed in GEB than for
their counterparts. (12) SMEB has significantly negative
coefficients in Columns (2)–(6), suggesting that firms listed
in SMEB experience lower CAR [-1, t] (t = 1, 2, 3, 4, 5).
(13) EXCHANGE has significantly negative coefficients in
Columns (2), (4), and (5), implying that firms listed in
Shanghai Security Exchange have significantly lower CAR
[-1, t] (t = 1, 3, 4). (14) LISTAGE displays negative and
significant coefficients in Columns (2)–(6), suggesting that
elder firms experience lower CAR [-1, t] (t = 1, 2, 3, 4, 5)
than do younger firms. (15) The coefficients on STATE are
significantly negative in Columns (1)–(5), meaning that CAR
[-1, t] (t = 0, 1, 2, 3, 4) are lower for state-owned enterprises than for non-state-owned enterprises.
To better understand results in Table 3, I visually plot
Fig. 1. In Fig. 1, blue line and red line denote the average
tendencies of CAR [-1, t] (t = 0, 1, 2, 3, 4, 5) for the
greenwashing subsample and the non-greenwashing subsample, respectively. As shown in Fig. 1, there are obvious
tendencies that the average of CAR [-1, t] for the greenwashing subsample is significantly lower than CAR [-1, t]
for the non-greenwashing subsample, providing visual and
straightforward evidence for Hypothesis 1.
performance can explain CAR around the exposure of
greenwashing. In addition, these coefficient estimates on
ENV indicate that 1 SD increases in environmental performance score can increase CAR [-1, 0], CAR [-1, 1],
CAR [-1, 2], CAR [-1, 3], CAR [-1, 4], and CAR [-1, 5]
by 9.08, 20.13, 16.85, 25.17, 20.22, and 17.06 % of the
mean value of CAR[-1, t] for the sample, respectively.
Without doubt, these coefficient estimates are economically significant.
As for the signs and significances of control variables in
Table 4, they are qualitatively similar to those in Table 3.
Similarly, to better represent results in Table 4, I also visually
plot Fig. 2. For better graphics effects, I use CAR_adj, measured as CAR [-1, t] minus the average CAR [-1, t] for firms
with greenwashing by the same year and the same industry.
In Fig. 2, blue line, red line, and pink line denote the
average tendencies of CAR [-1, t] (t = 0, 1, 2, 3, 4, 5)
for the greenwashing subsample, the high-ENV subsample,
and the low-ENV subsample, respectively. As shown in
Fig. 2, there are very obvious tendencies on CAR as below:
(1) the average CAR [-1, t] is significantly higher for the
high-ENV subsample than for the greenwashing subsample. (2) The average CAR [-1, t] is significantly lower
for the low-ENV subsample than for the greenwashing
subsample. (3) The average CAR [-1, t] is significantly
higher for the high-ENV subsample than for the low-ENV
subsample. Therefore, visual and straightforward evidence
in Fig. 2 shows that corporate environmental performance
score significantly positively affects CAR [-1, t], providing additional support to Hypothesis 2.
In addition, compared with CAR [-1, t] (t = 0, 1, 2, 3, 4,
5) for greenwashing subsample, Fig. 2 also suggests two
isolated and distinct effects of corporate environmental
performance score on CAR [-1, t] around the exposure of
greenwashing9: (1) the competitive effects on CAR [-1, t]
for the high-ENV subsample; and (2) the contagious effects
on CAR [-1, t] for the low-ENV subsample. These results
visually show asymmetric economics consequence of better
and worse environmental performance on CAR [-1, t].
Multivariate Test of Hypothesis 2
Robustness Checks
Hypothesis 2 predicts the positive association between corporate environmental performance score and CAR. Table 4
presents the Feasible Generalized Least Squares (FGLS)
regression results of CAR on ENV and other determinants.
As shown in Table 4, the coefficients on ENV are
positive and significant in Columns (1)–(6) (0.0002 with
z = 8.92, 0.0006 with z = 18.53, 0.0007 with z = 13.28,
0.0007 with z = 13.55, 0.0008 with z = 12.86, and 0.0005
with z = 7.10, respectively), providing strong support to
Hypothesis 2 and suggesting that corporate environmental
Robustness Checks of Hypotheses 1 and 2 Using
Different Dependent Variables
123
In Tables 3 and 4, I use CAR based on industry-adjusted
model (Kolari and Pynnönen 2010; Lewellen and Metrick
9
To better and more visually illustrate the competitive effects and the
contagious effects, I plot Figure 2 using adjusted CAR, measured as a
firm’ s CAR minus average CAR of firms with exposed greenwashing.
Also, the tendencies on CAR remain qualitatively similar to Figure 2
using the raw (original) CAR.
Market Values Greenwashing
563
Table 4 Regression results of CAR on corporate environmental performance and other determinants
Variables
(1)
CAR [-1, 0]
Coefficient
(z value)
(2)
CAR [-1, 1]
Coefficient
(z value)
(3)
CAR [-1, 2]
Coefficient
(z value)
(4)
CAR [-1, 3]
Coefficient
(z value)
(5)
CAR [-1, 4]
Coefficient
(z value)
(6)
CAR [-1, 5]
Coefficient
(z value)
ENV
0.0002***
0.0006***
0.0007***
0.0007***
0.0008***
0.0005***
(8.92)
(18.53)
(13.28)
(13.55)
(12.86)
(7.10)
0.0053***
0.0004
0.0023
-0.0013
-0.0181***
-0.0232***
(8.10)
(0.34)
(1.51)
(-0.94)
(-12.07)
(-14.59)
-0.0020***
-0.0027***
-0.0032***
-0.0024***
-0.0007
-0.0009
(-6.76)
(-7.23)
(-5.33)
(-3.51)
(-1.19)
(-1.09)
-0.0209***
-0.0174***
-0.0172***
-0.0369***
-0.0371***
-0.0202***
(-4.98)
(-5.38)
(-4.55)
(-11.67)
(-8.22)
(-2.98)
FIRST
DUAL
INDR
LNBOARD
SIZE
-0.0047***
-0.0005
-0.0006
-0.0034**
0.0036***
0.0117***
(-7.14)
(-0.37)
(-0.59)
(-2.09)
(3.52)
(8.51)
-0.0017***
-0.0030***
-0.0037***
-0.0032***
-0.0016***
-0.0008
(-9.79)
(-13.90)
(-9.41)
(-8.83)
(-4.26)
(-1.50)
LEV
-0.0011
-0.0059***
-0.0025*
-0.0092***
-0.0141***
-0.0168***
ROA
(-1.27)
-0.0158***
(-3.69)
-0.0400***
(-1.91)
-0.0259***
(-4.76)
-0.0089***
(-5.94)
-0.0340***
(-7.38)
0.0243***
(-10.12)
(-12.89)
(-12.40)
(-2.97)
(-7.09)
(3.78)
MTB
-0.0004***
0.0002
0.0004***
0.0010***
0.0011***
0.0005
(-3.75)
(1.48)
(3.04)
(6.1)
(5.26)
(1.35)
0.0035***
0.0005
0.0009
0.0017
0.0034
0.0017
(5.21)
(0.55)
(0.80)
(0.85)
(1.11)
(0.62)
-0.0012
-0.0226***
-0.0365***
-0.0316***
-0.0421***
-0.0357***
(-0.24)
(-16.11)
(-33.71)
(-28.76)
(-26.52)
(-17.83)
-0.0099***
-0.0256***
-0.0287***
-0.0287***
-0.0270***
-0.0096***
(-8.41)
(-18.06)
(-12.87)
(-16.69)
(-10.66)
(-4.30)
-0.0011
-0.0161***
-0.0178***
-0.0212***
-0.0212***
-0.0148***
(-1.34)
(-14.61)
(-12.11)
(-14.22)
(-13.22)
(-7.20)
EXCHANGE
0.0007*
-0.0057***
-0.0062***
-0.0064***
-0.0080***
-0.0009
(1.83)
(-11.37)
(-8.60)
(-12.13)
(-9.76)
(-0.96)
LISTAGE
0.0001
(0.35)
-0.0006***
(-8.40)
-0.0008***
(-7.79)
-0.0013***
(-16.16)
-0.0014***
(-14.02)
-0.0011***
(-8.76)
STATE
-0.0024***
-0.0044***
-0.0057***
-0.0047***
-0.0021***
0.0009
(-6.36)
(-12.71)
(-11.58)
(-7.89)
(-5.36)
-0.98
Constant
0.0481***
0.0924***
0.1325***
0.1546***
0.0977***
0.0235*
(8.68)
(13.09)
(11.71)
(10.26)
(10.75)
(1.70)
INDUSTRY
YES
YES
YES
YES
YES
YES
CROSS
ST
GEB
SMEB
YEAR
YES
YES
YES
YES
YES
YES
Number of Obs.
561
561
561
561
561
561
v2 value (p value)
9,036.28***
95,118.52***
7,673.52***
105,241.17***
264,250.90***
36,550.11***
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
(\0.0001)
***, **, and * represent the 1, 5, and 10 % levels of significance, respectively, for two-tailed tests. All the variables are defined in Table 7 in
Appendix
2010). To examine whether my findings in Tables 3 and 4 are
robust to other dependent variables, I adopt the market
adjusted model (Brown and Warner 1985) and the market
model (Baker et al. 2010) to compute CARA [-1, t] and
CARM [-1, t] (t = 0, 1, 2, 3, 4, 5) and re-estimate Eq. (1) and
Eq. (2) using the FGLS regression procedure, respectively.
Regression results are reported in Table 5. In Panels A
and B of Table 5, CARA [-1, t] and CARM [-1, t] (t = 0,
123
564
X. Du
Greenwashing
High-ENV subsample
CAR_adj
0.20
Conclusions
Low-ENV subsample
0.15
0.10
0.05
0.00
-0.05
-1
0
1
2
3
4
5
t
Fig. 2 The tendencies on CAR [-1, t] for the greenwashing
subsample, the high-ENV subsample, and the low-ENV subsample.
Note To better and more visually illustrate the competitive effects and
the contagious effects, I plot Fig. 2 using adjusted CAR, measured as
a firm’ s CAR minus average CAR of firms with exposed greenwashing. Also, the tendencies on CAR remain qualitatively similar to Fig. 2
using the raw (original) CAR
1, 2, 3, 4, 5), the dependent variables, are CAR based on the
market adjusted model (Brown and Warner, 1985) and the
market model (Baker et al. 2010), respectively.
As shown in Columns (1)–(6) of Panel A, the coefficients on GREENWASH are all negative and significant,
providing additional support to Hypothesis 1. In addition,
ENV in Columns (7)–(12) of Panel A have significantly
positive coefficients except for CAR [-1, 0], consistent
with Hypothesis 2 on the whole.
In Panel B, GREENWASH in Columns (1)–(6) have significantly negative coefficients, providing additional support to
Hypothesis 1 and echoing findings in Table 3 and Panel A of
Table 5. As shown in Columns (7)–(12) of Panel B, the coefficients on ENV are all positive and significant except CAR [-1,
0], providing additional support to Hypothesis 2 on the whole.
Overall, using CARA [-1, t] and CARM [-1, t] (t = 0, 1,
2, 3, 4, 5) as the dependent variables, results in Table 5 are
indistinguishable compared with those in Tables 3 and 4.
Robustness Checks of Hypothesis 2 Using the Rank
of Corporate Environment Disclosure Score
In Table 4, I use the raw score of corporate environmental
performance. To address the concern about whether results
in Table 4 are robust, I adopt ENV_RANK, measured as the
ordered variable according to the rank of corporate environment disclosure score in last year, to re-estimate Eq. (2).
As shown in Table 6, the coefficients on ENV_RANK
are positive and significant in all columns, providing strong
and additional support to Hypothesis 2 and echoing findings in Table 4. These results reveal that higher rank
of corporate environmental performance score is significantly positive associated with CAR around the exposure of
greenwashing. These results suggest that my main conclusions are not qualitatively changed using ENV_RANK as
the independent variable.
123
In this study, I investigate whether and how the market
values greenwashing and further examine whether corporate environmental performance score can explain market
reactions to greenwashing. My findings show that greenwashing and CAR have a significantly negative association.
I also provide systematic evidence to show that corporate
environmental performance is significantly positively
associated with CAR around the exposure of greenwashing.
Using the Chinese context, I first investigate market reactions to greenwashing and explore the role of corporate
environmental performance in explaining two isolated
effects on CAR: the competitive effect for environmentally
friendly firms and the contagious effect for environmental
wrongdoers with unexposed greenwashing.
This study has several practical implications for literature on corporate social responsibility and business ethics.
First, my findings reveal that the market disfavors greenwashing by showing significantly negative CAR, echoing
‘‘backfire effects’’ (Nyhan and Reifler 2010). That is, after
a company is exposed for using greenwashing, investors
adhere more firmly to their initial impression that the
company is environmentally unfriendly, and that its greenization claims are dishonest. As a result, investors negatively value the company.
Second, my findings indicate that the market reacts negatively to the exposure of greenwashing, implying that media
coverage plays an important governance role in affecting
investor behavior and market reactions. This result echoes
studies emphasizing that media coverage serves as an
important intermediary in creating new information and
rebroadcasting information (Bushee et al. 2010; Dyck et al.
2008; Fang and Peress 2009; Joe et al. 2009; Lyon and
Montgomery 2013; Miller 2006). Especially, in emerging
markets like China where standard corporate governance
mechanisms are less effective and business ethics are being
formed, media coverage can serve as an alternative monitor.
Third, I provide strong evidence to show that corporate
environmental performance score is significantly positively
related to CAR around the exposure of greenwashing. This
result can inspire regulators and the public to link corporate
environmental performance to factual environmental greenization, rather than to accept green semblance or environmental greenwashing in advertising messages. In
addition, this result lends support to arguments that public
disclosure of environmental performance in emerging
markets are useful for environmental management, particularly in developing countries where environmental monitoring and enforcement are weak (Dasgupta et al. 2001;
Gupta and Goldar 2005).
Fourth, I provide systematic evidence to show two isolated influences of corporate environmental performance
(4)
CAR [-1, 3]
Coefficient
(z value)
(5)
CAR [-1, 4]
Coefficient
(z value)
(6)
CAR [-1, 5]
Coefficient
(z value)
STATE
LISTAGE
EXCHANGE
SMEB
GEB
ST
CROSS
MTB
ROA
LEV
SIZE
LNBOARD
INDR
DUAL
FIRST
ENV
GREENWASH
-0.0039***
(-5.29)
(-9.84)
(-1.75)
-0.0029***
(0.87)
-0.0001*
(-8.26)
(-1.06)
0.0001
-0.0044***
(-6.66)
(-2.30)
-0.0004
-0.0090***
(-7.86)
(-1.37)
-0.0016**
-0.0100***
(-14.12)
-0.0012
-0.0240***
(-2.57)
(2.14)
(6.11)
-0.0038**
0.0040**
0.0042***
0.0003**
(2.26)
-0.0004***
(-11.27)
(-7.84)
(-4.59)
-0.0403***
(-6.09)
(-2.87)
-0.0130***
-0.0084***
(-4.34)
(-9.88)
-0.0024***
-0.0008***
(-2.16)
(-6.05)
-0.0014***
-0.0031**
(-9.78)
(-7.06)
-0.0047***
-0.0399***
(-4.09)
(-7.03)
-0.0276***
-0.0026***
-0.0022***
0.0008
(0.43)
(4.83)
(-9.73)
(-4.20)
0.0044***
-0.0106***
-0.0027***
(-9.47)
-0.0045***
(-22.54)
-0.0016***
(-13.14)
-0.0088***
(-25.72)
-0.0247***
(-15.57)
-0.0268***
(-36.41)
-0.0427***
(1.16)
0.0018
(0.68)
0.0001
(-6.12)
-0.0095***
(4.46)
0.0053***
(-13.20)
-0.0038***
(-5.42)
-0.0062***
(-4.76)
-0.0261***
(-3.25)
-0.0025***
(4.64)
0.0065***
(-17.96)
-0.0223***
(-4.51)
-0.0031***
(-10.58)
-0.0010***
(-10.47)
-0.0060***
(-11.49)
-0.0149***
(-8.04)
-0.0152***
(-13.06)
-0.0359***
(1.52)
0.002
(4.56)
0.0008***
(-3.23)
-0.0126***
(-1.98)
-0.0042**
(-5.60)
-0.0023***
(-2.08)
-0.0033**
(-13.32)
-0.0508***
(-3.72)
-0.0025***
(1.00)
0.0021
(-18.94)
-0.0198***
(-0.99)
-0.0004
(-16.34)
-0.0016***
(-8.83)
-0.0086***
(-17.08)
-0.0234***
(-15.37)
-0.0268***
(-27.23)
-0.0411***
(0.87)
0.0024
(1.33)
0.0003
(-2.19)
-0.0039**
(-5.54)
-0.0096***
(-1.77)
-0.0007*
(-0.95)
-0.0013
(-9.55)
-0.0582***
(-0.92)
-0.0006
(-9.92)
-0.0207***
(-11.57)
-0.0208***
(1.74)
0.0016*
(-7.98)
-0.0010***
(-1.81)
-0.0020*
(-9.08)
-0.0164***
(-9.38)
-0.0212***
(-12.94)
-0.0343***
(-1.80)
-0.0050*
(1.13)
0.0003
(4.09)
0.0235***
(-7.05)
-0.0177***
(4.85)
0.0015***
(1.30)
0.0029
(-6.14)
-0.0373***
(1.37)
0.001
(-12.88)
-0.0243***
(-9.52)
-0.0141***
(-9.68)
-0.0022***
(1.72)
0.0001*
(-3.45)
-0.0009***
(-4.36)
-0.0023***
(-1.40)
-0.0013
(-1.61)
-0.0044
(4.96)
0.0026***
(-4.95)
-0.0004***
(-8.28)
-0.0127***
(-3.64)
-0.0027***
(-11.11)
-0.0014***
(-11.36)
-0.0045***
(-4.08)
-0.0129***
(-8.33)
-0.0020***
(12.63)
(-10.09)
-0.0046***
(-5.96)
-0.0005***
(-11.64)
-0.0067***
(-11.24)
-0.0153***
(-9.39)
-0.0163***
(-16.84)
-0.0220***
(3.12)
0.0028***
-2.09
0.0002**
(-9.40)
-0.0300***
(-3.17)
-0.0050***
(-9.91)
-0.0029***
(-0.62)
-0.0007
(-7.39)
-0.0232***
(-4.62)
-0.0026***
(0.68)
0.0011
(-0.61)
0.0064***
0.0004***
(13.05)
-0.0001
(8)
CAR [-1, 1]
Coefficient
(z value)
(-7.50)
-0.0044***
(-8.65)
-0.0008***
(-12.86)
-0.0097***
(-17.06)
-0.0186***
(-9.93)
-0.0178***
(-18.63)
-0.0353***
(2.00)
0.0033**
-0.71
0.0001
(-4.74)
-0.0207***
(-0.46)
-0.0011
(-12.23)
-0.0047***
(-0.10)
-0.0001
(-4.49)
-0.0156***
(-4.36)
-0.0028***
(3.81)
0.0056***
(10.31)
0.0006***
(9)
CAR [-1, 2]
Coefficient
(z value)
(7)
CAR [-1, 0]
Coefficient
(z value)
(3)
CAR [-1, 2]
Coefficient
(z value)
(1)
CAR [-1, 0]
Coefficient
(z value)
(2)
CAR [-1, 1]
Coefficient
(z value)
Section B: Robustness checks of Hypothesis 2
Section A: Robustness checks of Hypothesis 1
Panel A: Robustness checks of Hypotheses 1 and 2 using CAR based on the market adjusted models
Variables
Table 5 Robustness checks of Hypotheses 1 and 2 using different dependent variables
(-6.34)
-0.0041***
(-10.79)
-0.0011***
(-8.13)
-0.0069***
(-11.06)
-0.0180***
(-8.11)
-0.0184***
(-11.40)
-0.0280***
(3.06)
0.0049***
(2.19)
0.0006**
(-0.65)
-0.0023
(-1.27)
-0.0028
(-7.61)
-0.0036***
(-3.14)
-0.0065***
(-9.45)
-0.0459***
(-2.89)
-0.0026***
(0.08)
0.0002
(6.34)
0.0004***
(10)
CAR [-1, 3]
Coefficient
(z value)
(-1.47)
-0.0009
(-15.38)
-0.0015***
(-8.20)
-0.0088***
(-12.00)
-0.0209***
(-12.25)
-0.0269***
(-30.64)
-0.0430***
(1.33)
0.0038
(1.99)
0.0005**
(-4.30)
-0.0197***
(-4.18)
-0.0078***
(-6.31)
-0.0031***
(0.94)
0.0019
(-9.03)
-0.0516***
(-2.31)
-0.0019**
(-6.25)
-0.0159***
(8.41)
0.0007***
(11)
CAR [-1, 4]
Coefficient
(z value)
(-1.45)
-0.001
(-10.32)
-0.0013***
(-2.43)
-0.0025**
(-9.33)
-0.0194***
(-10.03)
-0.0223***
(-22.24)
-0.0334***
(0.44)
0.0008
(-1.30)
-0.0004
(1.97)
0.0118**
(-5.60)
-0.0114***
(-2.73)
-0.0014***
(3.00)
0.0057***
(-6.81)
-0.0404***
(-0.43)
-0.0003
(-10.24)
-0.0223***
(8.49)
0.0006***
(12)
CAR [-1, 5]
Coefficient
(z value)
Market Values Greenwashing
565
123
123
(4)
CAR [-1, 3]
Coefficient
(z value)
(5)
CAR [-1, 4]
Coefficient
(z value)
(\0.0001)
(\0.0001)
v2 value
(p value)
0.1519***
(\0.0001)
74,634.61***
561
YES
(23.56)
0.1219***
(\0.0001)
21,038.14***
561
YES
(10.58)
0.0833***
(\0.0001)
(\0.0001)
GEB
ST
CROSS
MTB
ROA
LEV
SIZE
LNBOARD
INDR
DUAL
FIRST
ENV
-0.0164***
(-9.68)
(-2.42)
(-9.15)
-0.0030**
-0.0205***
(-4.89)
(-0.78)
(6.07)
-0.0117***
-0.001
(-4.60)
(-6.59)
0.0072***
-0.0007***
(-6.82)
(-4.38)
-0.0008***
-0.0350***
-0.0078***
(-7.40)
(-2.66)
(2.88)
-0.0114***
(-4.80)
-0.0028***
0.0009***
(-4.84)
(-9.06)
-0.0011**
-0.0073***
(-6.28)
(-2.53)
-0.0066***
-0.0318***
(-3.94)
(-3.49)
-0.0110**
-0.0027***
-0.0013***
-0.0046**
(-2.21)
0.0027**
(-7.30)
(-12.58)
(2.49)
-0.0080***
-0.0063***
(-9.74)
-0.0202***
(-13.96)
-0.0401***
(0.21)
0.0005
(1.35)
0.0003
(-6.52)
-0.0273***
(-1.26)
-0.0039
(-3.38)
-0.0015***
(-1.34)
-0.0015
(-5.90)
-0.0294***
(-3.08)
-0.0029***
(1.88)
0.0040*
(-9.74)
-0.0202***
(-10.20)
-0.0242***
(-16.86)
-0.0462***
(0.98)
0.0042**
(7.14)
0.0014***
(-11.20)
-0.0345***
(-7.43)
-0.0192***
(0.71)
0.0004
(-2.69)
-0.0057***
(-10.16)
-0.0503***
(0.80)
0.0006
(0.20)
0.0005
(-6.07)
-0.0262***
(-10.93)
-0.0294***
(-18.79)
-0.0412***
(2.70)
0.0048***
(0.53)
0.0001
(-7.30)
-0.0358***
(-8.66)
-0.0199***
(1.28)
0.0005
(-2.12)
-0.0044**
(-14.12)
-0.0638***
(-1.71)
-0.0018*
(-3.38)
-0.0098***
(-13.53)
-0.0221***
(-11.08)
-0.0270***
(-36.36)
-0.0406***
(-0.95)
-0.0026
(0.18)
0.0001
(-0.22)
-0.0011
(-6.52)
-0.0181***
(4.96)
0.0026***
(-1.20)
-0.0024
(-13.62)
-0.0735***
(3.35)
0.0029***
(-7.12)
-0.0208***
(-4.90)
-0.0220***
8,779.79***
561
YES
(-1.08)
-0.0097
97,050.10***
561
YES
(9.39)
Panel B: Robustness checks of Hypotheses 1 and 2 using CAR based on the market models
561
55,625.56***
561
39,376.13***
Observations
YES
0.0447***
(6.80)
0.0524***
(13.69)
YES
GREENWASH
(6)
CAR [-1, 5]
Coefficient
(z value)
(-3.75)
-0.0047***
(-3.89)
-0.0120***
(3.44)
0.0033***
(-4.76)
-0.0007***
(-3.32)
-0.0053***
(-2.74)
-0.0030***
(-3.22)
-0.0010***
(-7.85)
-0.0062***
(-4.32)
-0.0151***
(-4.53)
-0.0020***
(3.19)
0.0039***
(-9.07)
-0.0188***
(-11.30)
-0.0217***
(-4.88)
-0.0040***
(-5.16)
-0.0007***
(-9.04)
-0.0462***
(-8.00)
-0.0132***
(-1.63)
-0.0006
(-3.78)
-0.0066***
(-3.10)
-0.0171***
(-4.79)
-0.0028***
(-1.54)
-0.0033
0.0002***
(4.14)
(-0.89)
(\0.0001)
40,283.42***
561
YES
(13.48)
0.0979***
-0.0001
(\0.0001)
358,637.62***
561
YES
(13.50)
0.0479***
(8)
CAR [-1, 1]
Coefficient
(z value)
(-10.73)
-0.0230***
(-10.32)
-0.0367***
(-0.65)
-0.0018
(1.16)
0.0003
(-8.17)
-0.0295***
(-3.20)
-0.0093***
(-5.60)
-0.0026***
(-2.85)
-0.0036***
(-2.60)
-0.0167***
(-4.37)
-0.0034***
(1.00)
0.0024
(5.50)
0.0004***
(\0.0001)
22,288.32***
561
YES
(17.87)
0.1709***
(9)
CAR [-1, 2]
Coefficient
(z value)
(7)
CAR [-1, 0]
Coefficient
(z value)
(3)
CAR [-1, 2]
Coefficient
(z value)
(1)
CAR [-1, 0]
Coefficient
(z value)
(2)
CAR [-1, 1]
Coefficient
(z value)
Section B: Robustness checks of Hypothesis 2
Section A: Robustness checks of Hypothesis 1
INDUSTRY/
YEAR
Constant
Variables
Table 5 continued
(-9.53)
-0.0234***
(-8.24)
-0.0333***
(0.75)
0.0019
(1.27)
0.0004
(-6.68)
-0.0342***
(-5.01)
-0.0151***
(-2.50)
-0.0012**
(-2.86)
-0.0078***
(-7.21)
-0.0510***
(-1.98)
-0.0015**
(-2.20)
-0.0061**
(7.29)
0.0005***
(\0.0001)
28,870.86***
561
YES
(13.25)
0.1788***
(10)
CAR [-1, 3]
Coefficient
(z value)
(-14.03)
-0.0344***
(-12.66)
-0.0406***
(5.43)
0.0090***
(-0.92)
-0.0002
(-7.23)
-0.0330***
(-4.30)
-0.0111***
(-2.36)
-0.0011**
(-1.47)
-0.0031
(-12.26)
-0.0593***
(-0.05)
-0.0001
(-5.26)
-0.0122***
(8.63)
0.0006***
(\0.0001)
68,548.47***
561
YES
(12.78)
0.1512***
(11)
CAR [-1, 4]
Coefficient
(z value)
(-10.03)
-0.0283***
(-23.76)
-0.0427***
(0.81)
0.002
(-1.18)
-0.0002
(-2.59)
-0.0113***
(-8.53)
-0.0178***
(2.49)
0.0014**
(-0.42)
-0.0011
(-11.25)
-0.0501***
(3.19)
0.0027***
(-13.10)
-0.0305***
(7.65)
0.0006***
(\0.0001)
19,440.93***
561
YES
(5.90)
0.0756***
(12)
CAR [-1, 5]
Coefficient
(z value)
566
X. Du
(4)
CAR [-1, 3]
Coefficient
(z value)
(5)
CAR [-1, 4]
Coefficient
(z value)
(6)
CAR [-1, 5]
Coefficient
(z value)
(\0.0001)
(\0.0001)
v2 value
(p value)
(\0.0001)
7,025.52***
561
YES
(7.31)
0.0803***
(-13.47)
-0.0080***
(-5.83)
-0.0007***
(-11.18)
-0.0085***
(-8.74)
-0.0161***
(\0.0001)
16,339.02***
561
YES
(4.55)
0.0643***
(-8.53)
-0.0071***
(-12.76)
-0.0013***
(-7.01)
-0.0083***
(-14.80)
-0.0232***
(\0.0001)
8,215.09***
561
YES
(3.21)
0.0344***
(-4.14)
-0.0034***
(-11.36)
-0.0012***
(-9.17)
-0.0110***
(-10.32)
-0.0209***
(\0.0001)
42,352.07***
561
YES
(-3.61)
-0.0410***
(-3.40)
-0.0031***
(-13.79)
-0.0015***
(-4.27)
-0.0054***
(-14.88)
-0.0215***
(\0.0001)
110,412.79***
561
YES
(5.29)
0.0407***
(-5.28)
-0.0024***
(-1.19)
-0.0001
(-3.82)
-0.0016***
(-4.81)
-0.0038***
(\0.0001)
22,323.95***
561
YES
(5.41)
0.0584***
(-7.83)
-0.0043***
(-4.59)
-0.0004***
(-10.49)
-0.0092***
(-9.18)
-0.0158***
(\0.0001)
4,949.01***
561
YES
(9.41)
0.1201***
(-11.49)
-0.0067***
(-7.22)
-0.0010***
(-8.23)
-0.0090***
(-10.52)
-0.0207***
(9)
CAR [-1, 2]
Coefficient
(z value)
***, **, and * represent the 1, 5, and 10 % levels of significance, respectively, for two-tailed tests. All the variables are defined in Table 7 in Appendix
561
57,250.86***
561
96,654.47***
Observations
YES
(2.52)
(8.38)
YES
0.0231**
(-7.65)
(-5.02)
0.0482***
-0.0030***
(-7.15)
(-1.44)
-0.0018***
-0.0004***
(-10.58)
(-2.37)
-0.0001
-0.0080***
(-8.97)
(-3.45)
-0.0011**
-0.0134***
-0.0027***
(8)
CAR [-1, 1]
Coefficient
(z value)
(7)
CAR [-1, 0]
Coefficient
(z value)
(3)
CAR [-1, 2]
Coefficient
(z value)
(1)
CAR [-1, 0]
Coefficient
(z value)
(2)
CAR [-1, 1]
Coefficient
(z value)
Section B: Robustness checks of Hypothesis 2
Section A: Robustness checks of Hypothesis 1
INDUSTRY/
YEAR
Constant
STATE
LISTAGE
EXCHANGE
SMEB
Variables
Table 5 continued
(\0.0001)
16,002.04***
561
YES
(8.94)
0.1293***
(-4.30)
-0.0033***
(-11.09)
-0.0013***
(-8.49)
-0.0115***
(-14.08)
-0.0224***
(10)
CAR [-1, 3]
Coefficient
(z value)
(\0.0001)
17,896.66***
561
YES
(8.03)
0.0899***
(-6.84)
-0.0061***
(-15.52)
-0.0016***
(-9.55)
-0.0129***
(-12.02)
-0.0250***
(11)
CAR [-1, 4]
Coefficient
(z value)
(\0.0001)
71,204.65***
561
YES
(0.24)
0.0036
(-0.08)
-0.0001
(-15.08)
-0.0017***
(-7.44)
-0.0084***
(-12.18)
-0.0233***
(12)
CAR [-1, 5]
Coefficient
(z value)
Market Values Greenwashing
567
123
123
-0.0234*** (-10.50)
-0.0001 (-1.07)
0.0033*** (5.02)
-0.0050** (-2.28)
-0.0134*** (-13.21)
-0.0041*** (-6.68)
-0.0011*** (-3.91)
-0.0001*** (-2.75)
-0.0032*** (-14.08)
0.0560*** (11.65)
ROA
MTB
CROSS
ST
GEB
SMEB
EXCHANGE
LISTAGE
STATE
Constant
108,152.53*** (\0.0001)
561
YES
YES
0.1014*** (11.07)
-0.0035*** (-6.23)
-0.0005*** (-8.13)
-0.0055*** (-8.92)
-0.0135*** (-14.03)
-0.0230*** (-17.04)
0.0037*** (3.88)
-0.0162*** (-13.28)
-0.0001 (-0.34)
-0.0241*** (-7.61)
-0.0049*** (-2.89)
-0.0039*** (-11.36)
-0.0008 (-0.57)
-0.0207*** (-4.49)
-0.0021*** (-3.82)
-0.0002 (-0.12)
0.0001*** (20.81)
(2)
CAR [-1, 1]
coefficient (z value)
187,886.15*** (\0.0001)
561
YES
YES
0.1238*** (11.85)
-0.0056** (-9.62)
-0.0008*** (-10.83)
-0.0055*** (-7.50)
-0.0170*** (-17.45)
-0.0280*** (-13.59)
0.0012 (0.84)
-0.0356*** (-21.90)
0.0005*** (2.82)
-0.0205*** (-3.59)
-0.0001 (-0.04)
-0.0045*** (-11.88)
0.0018 (1.04)
-0.0059 (-1.56)
-0.0025*** (-3.35)
0.0028* (1.95)
0.0001*** (19.11)
(3)
CAR [-1, 2]
Coefficient (z value)
323,983.50*** (\0.0001)
561
YES
YES
0.1129*** (13.03)
-0.0047*** (-7.46)
-0.0010*** (-9.80)
-0.0071*** (-12.84)
-0.0178*** (-10.98)
-0.0267*** (-13.33)
0.0011 (0.50)
-0.0325*** (-21.19)
0.0009*** (6.82)
-0.0114*** (-4.89)
-0.0087*** (-7.05)
-0.0026*** (-7.93)
-0.0029*** (-2.90)
-0.0202*** (-4.85)
-0.0014** (-2.32)
-0.0058*** (-3.44)
0.0001*** (26.69)
(4)
CAR [-1, 3]
Coefficient (z value)
64,577.40*** (\0.0001)
561
YES
YES
0.0995*** (11.77)
-0.0030*** (-6.86)
-0.0013*** (-14.82)
-0.0046*** (-5.91)
-0.0188*** (-12.61)
-0.0248*** (-9.77)
0.0066*** (4.93)
-0.0375*** (-26.16)
0.0005* (1.82)
-0.0194*** (-3.21)
-0.0084*** (-4.04)
-0.0028*** (-7.27)
0.0047*** (2.86)
-0.0377*** (-8.23)
-0.0025*** (-3.86)
-0.0171*** (-8.48)
0.0001*** (25.94)
(5)
CAR [-1, 4]
Coefficient (z value)
***, **, and * represent the 1, 5, and 10 % levels of significance, respectively, for two-tailed tests. All the variables are defined in Table 7 in Appendix
561
-0.0058*** (-11.15)
LEV
75,931.86*** (\0.0001)
-0.0019*** (-9.88)
SIZE
v2 value (p value)
-0.0053*** (-10.98)
LNBOARD
Number of Obs.
-0.0140*** (-3.41)
INDR
YES
-0.0021*** (-7.58)
DUAL
YES
0.0023*** (3.48)
FIRST
YEAR
0.0001*** (26.85)
ENV_RANK
INDUSTRY
(1)
CAR [-1, 0]
Coefficient (z value)
Variable
Table 6 Robustness checks of Hypothesis 2 using the rank of corporate environmental performance score
68,733.02*** (\0.0001)
561
YES
YES
0.0234** (2.28)
0.0002 (0.32)
-0.0012*** (-10.32)
-0.0033*** (-3.89)
-0.0147*** (-8.27)
-0.0118*** (-5.42)
0.0057*** (5.10)
-0.0345*** (-18.45)
0.0004 (1.45)
0.0094 (1.36)
-0.0140*** (-6.47)
-0.0012*** (-2.63)
0.0133*** (6.80)
-0.0119** (-2.11)
0.0002 (0.28)
-0.0261*** (-15.71)
0.0001*** (20.62)
(6)
CAR [-1, 5]
Coefficient (z value)
568
X. Du
Market Values Greenwashing
on CAR around the exposure of greenwashing: the competitive effect for environmentally friendly firms with
better environmental performance and the contagious
effect for environmental wrongdoers with potential greenwashing. These findings suggest that the market can identify environmental wrongdoers through corporate
environmental performance scores and thus distinguish
environmental wrongdoers from environmentally friendly
firms. Therefore, a firm should fulfill its environmental
responsibility in substance rather than in false claims. If
not, the market will punish the firm severely.
Finally, my findings can apply to other emerging markets in addition to the context of China. In emerging
markets, many firms greedily grab profits at the expense of
reckless environmental destruction. Moreover, the commitment to environmental conservation does not necessarily translate to factual greening activities, and thus many
firms claim greenization in appearance rather than in substance. Therefore, pure self-regulation is inadequate and
effective government regulation is essential. Otherwise,
commitments to environmental conservation are just cheap,
irresponsible, unauthentic, and empty promises. As a result,
regulators, stakeholders, and the public should pay close
attention to the phenomena of pseudo-greenization.
My study explores the association between greenwashing, environmental performance, and market reactions (i.e.,
CAR), it has two limitations that future research may
address. First, I focus mainly on environmental greenwashing. Because of data limitations, I do not investigate
569
whether and how the market reacts to other dimensions of
greenwashing such as production with pseudo-greenization
and vaunted advertising regarding green productions.
Second, I conducted this study in the Chinese context, and
thus my findings may fail to fit in with other markets.
Future research should extend to other markets and
examine their reactions to different greenwashing
dimensions.
Acknowledgments I am especially grateful to the editor (Prof.
Professor Gary S. Monroe) and two anonymous reviewers for their
many insightful and constructive suggestions. I appreciate constructive comments from Quan Zeng, Yingjie Du, Wentao Feng, Dongchang Ke, Wei Jian, Hongmei Pei, Feng Liu, Jinhui Luo, Yingying
Chang, Shaojuan Lai, Jun Lu, Hao Xiong, Xue Tan, and participants
of my presentations at Xiamen University, Anhui University, Ocean
University of China, Shandong university, and Shanghai University. I
also thank Quan Zeng and Yingjie Du for excellent research assistance. I acknowledge the National Natural Science Foundation of
China (Approval Number: 71072053), the Key Project of Key
Research Institute of Humanities and Social Science in Ministry of
Education (Approval Number: 13JJD790027), the Specialized
Research Fund for the Doctoral Program of Higher Education of
China (Approval Number: 20120121110007) and Xiamen University’s Prosperity Plan Project of Philosophy and Social Sciences (Subproject for School of Management) for financial support.
Appendix
See Tables 7 and 8.
Table 7 Variables definition
Variables
Definition
Data source
Variable for main tests
CAR [-1, t]
CAR from day -1 to day t (t = 0, 1, 2, 3, 4, 5) using industry-adjusted model (Kolari
and Pynnönen 2010; Lewellen and Metrick 2010);
Author’s calculation based on the
original data form CSMAR
GREENWASH
A dummy variable, equaling 1 if a firm was exposed to be an environmental wrongdoer
with greenwashing by the South Weekend and 0 otherwise;
Author’s hand-collected data from
the South Weekend
ENV
Corporate environment disclosure score in last year (please refer to Table 8 in
Appendix in detail);
Author’s hand-collected data
FIRST
The percentage of shares owned by the controlling shareholder;
CSMAR
DUAL
An indicator variable, equaling 1 if the same person serves as the CEO and the
chairman of the board of directors and 0 otherwise;
CSMAR
INDR
The ratio of the number of independent directors to the number of directors in the
boardroom;
CSMAR
LNBOARD
The natural log of the number of directors in the boardroom;
CSMAR
SIZE
Firm size, measured as the natural log of total assets;
CSMAR
LEV
Financial leverage, measured as the ratio of total liabilities to total assets;
CSMAR
ROA
Returns on total assets, measured as net operating income deflated by total assets;
CSMAR
MTB
Market-to-book ratio, measured as the market value of a firm to its book value (book
value is calculated by looking at the firm’s historical cost or accounting value, and
market value is determined in the stock market through its market capitalization);
CSMAR
CROSS
A dummy variable of listing locations, equaling 1 when a firm’s stock has listed in two
or more markets and 0 otherwise;
CSMAR
123
570
X. Du
Table 7 continued
Variables
Definition
Data source
ST
A dummy variable of listing status, equaling 1 when a firm’s stock is denoted as
special treatment (i.e., ST) or special treatment with star (i.e., *ST) and 0 otherwise;
CSMAR
GEB
A dummy variable, equaling 1 when a firm lists in growth enterprise board (GEB) and
0 otherwise;
CSMAR
SMEB
A dummy variable, equaling 1 when a firm lists in small and median enterprise board
(SMEB) and 0 otherwise;
CSMAR
EXCHANGE
A dummy variable of listing markets, equaling 1 when a firm lists in Shanghai Security
Exchange and 0 otherwise;
CSMAR
LISTAGE
The number of years since a firm’s IPO;
CSMAR
STATE
A dummy variable, equaling 1 when the ultimate controlling shareholder of a listed
firm is a (central or local) government agency or government-controlled state-owned
enterprises and 0 otherwise;
CSMAR
CARM[-1, t]
CAR from day -1 to day t (t = 0, 1, 2, 3, 4, 5) using market model (Baker et al. 2010);
Author’s calculation
CARA[-1, t]
CAR from day -1 to day t (t = 0, 1, 2, 3, 4, 5) using market adjusted model (Brown
and Warner 1985);
Author’s calculation
ENV_RANK
The ordered variable according to the rank of corporate environment disclosure score
in last year.
Author’s hand-collected data
Table 8 The procedures for computing corporate environmental performance score and the descriptive statistics for seven components and
forty-five subcomponents of corporate environmental performance
Item
Mean
SD
Min
Q1
Median
Q3
I: Governance structure and management systems (max score is 6)
0.4519
0.8042
0
0
0
1
4
0.1658
0.3674
0
0
0
0
1
1. Existence of a Department for pollution control and/or management positions for environment
management (0–1)
Max
2. Existence of an environmental and/or a public issues committee in the board (0–1)
0.0036
0.0597
0
0
0
0
1
3. Existence of terms and conditions applicable to suppliers and/or customers regarding
environment practices (0–1)
0.0214
0.1385
0
0
0
0
1
4. Stakeholder involvement in setting corporate environmental policies (0–1)
0.0089
0.0892
0
0
0
0
1
5. Implementation of ISO14001 at the plant and/or firm level (0–1)
0.2077
0.4021
0
0
0
0
1
6. Executive compensation is linked to environmental performance (0–1)
II: Credibility (max score is 10)
0.0446
0.2022
0
0
0
0
1
0.4804
0.9499
0
0
0
1
8
1. Adoption of GRI sustainability reporting guidelines or provision of a CERES report (0–1)
0.2014
0.3992
0
0
0
0
1
2. Independent verification/assurance about environmental information disclosed in the EP report/
web (0–1)
0.0267
0.1558
0
0
0
0
1
3. Periodic independent verifications/audits on environmental performance and/or systems (0–1)
0.0446
0.1977
0
0
0
0
1
4. Certification of environmental programs by independent agencies (0–1)
0.0330
0.1724
0
0
0
0
1
5. Product Certification with respect to environmental impact (0–1)
0.0410
0.1962
0
0
0
0
1
6. External environmental performance awards and/or inclusion in a sustainability index (0–1)
0.0838
0.2607
0
0
0
0
1
7. Stakeholder involvement in the environmental disclosure process (0–1)
0.0071
0.0842
0
0
0
0
1
8. Participation in voluntary environmental initiatives endorsed by Ministry of Environmental
Protection of China (0–1)
0.0169
0.1238
0
0
0
0
1
9. Participation in industry specific associations/initiatives to improve environmental practices
(0–1)
0.0027
0.0472
0
0
0
0
1
10. Participation in other environmental organizations/associations to improve environmental
practices (if not awarded under 8 or 9)
0.0232
0.1476
0
0
0
0
1
III: Environmental performance indicators (EPI) (max score is 60) *
1.1658
2.9546
0
0
0
0
19
1. EPI on energy use and/or energy efficiency (0–6)
0.3039
0.7535
0
0
0
0
4
2. EPI on water use and/or water use efficiency (0–6)
0.1622
0.5307
0
0
0
0
3
3. EPI on green house gas emissions (0–6)
0.0989
0.4376
0
0
0
0
3
4. EPI on other air emissions (0–6)
0.1720
0.5609
0
0
0
0
3
123
Market Values Greenwashing
571
Table 8 continued
Item
Mean
SD
Min
Q1
Median
Q3
Max
5. EPI on TRI (land, water, air) (0–6)
0.0517
0.3176
0
0
0
0
6. EPI on other discharges, releases and/or spills (not TRI) (0–6)
0.0838
0.3793
0
0
0
0
3
7. EPI on waste generation and/or management (recycling, re–use, reducing, treatment and
disposal) (0–6)
0.2050
0.6137
0
0
0
0
4
8. EPI on land and resources use, biodiversity and conservation (0–6)
0.0740
0.3748
0
0
0
0
3
9. EPI on environmental impacts of products and services (0–6)
0.0116
0.1534
0
0
0
0
3
3
10. EPI on compliance performance (0–6)
0.0027
0.0472
0
0
0
0
1
IV: Environmental spending (max score is 3)
0.2148
0.4138
0
0
0
0
2
1. Summary of dollar savings arising from environment initiatives to the company (0–1)
0.0107
0.0985
0
0
0
0
1
2. Amount spent on technologies, R& D and/or innovations to enhance environ. Performance and/
or efficiency (0–1)
0.2023
0.3903
0
0
0
0
1
3. Amount spent on fines related to environmental issues (0–1)
0.0018
0.0422
0
0
0
0
1
0.9100
1.1735
0
0
0
2
6
1. CEO statement on environmental performance in letter to shareholders and/or stakeholders
(0–1)
0.1622
0.3641
0
0
0
0
1
2. A statement of corporate environmental policy, values and principles, environ. codes of conduct
(0–1)
0.3538
0.4706
0
0
0
1
1
3. A statement about formal management systems regarding environmental risk and performance
(0–1)
0.0749
0.2530
0
0
0
0
1
4. A statement that the firm undertakes periodic reviews and evaluations of its environment
performance (0–1)
0.0321
0.1660
0
0
0
0
1
5. A statement of measurable goals in terms of future environmental performance (if not awarded
under A3) (0–1)
0.0187
0.1306
0
0
0
0
1
6. A statement about specific environmental innovations and/or new technologies (0–1)
0.2683
0.4369
0
0
0
1
1
V: Vision and strategy claims (max score is 6)
VI: Environmental profile (max score is 4)
1. A statement about the firm’s compliance (or lack thereof) with specific environmental standards
(0–1)
0.2433
0.5445
0
0
0
0
4
0.0820
0.2713
0
0
0
0
1
2. An overview of environmental impact of the industry (0–1)
0.0544
0.2200
0
0
0
0
1
3. An overview of how the business operations and/or products and services impact the
environment. (0–1)
0.0891
0.2756
0
0
0
0
1
4. An overview of corporate environmental performance relative to industry peers (0–1)
0.0178
0.1255
0
0
0
0
1
VII: Environmental initiatives (max score is 6)
0.2647
0.6310
0
0
0
0
4
0.1275
0.3277
0
0
0
0
1
1. A substantive description of employee training in environmental management and operations
(0–1)
2. Existence of response plans in case of environmental accidents (0–1)
0.0499
0.2096
0
0
0
0
1
3. Internal environmental awards (0–1)
0.0125
0.1028
0
0
0
0
1
4. Internal environmental audits (0–1)
0.0134
0.1090
0
0
0
0
1
5. Internal certification of environmental programs (0–1)
0.0169
0.1238
0
0
0
0
1
6. Community involvement and/or donations related to environ. (if not awarded under A1.4 or
A2.7) (0–1)
0.0446
0.1954
0
0
0
0
1
3.7745
6.0402
0
0
1
5
40
Total
* In Section III in Table 8, the scoring scale of environmental performance data is from 0 to 6. A point is awarded for each of the following items: (1)
Performance data is presented; (2) Performance data is presented relative to peers/rivals or industry; (3) Performance data is presented relative to previous
periods (trend analysis); (4) Performance data is presented relative to targets; (5) Performance data is presented both in absolute and normalized form; (6)
Performance data is presented at disaggregate level (i.e., plant, business unit, geographic segment)
123
572
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