Working Paper
Issue Date: December 29, 2015
Corporate Environmental Responsibility and Firm Performance Beyond
the Boundary of the Firm
Jongjin Sohn
Korea Advanced Institute of Science and Technology
Abstract
This study explores whether corporate environmental responsibility plays an important role in
improving financial performance. I examines the impact of CER on a firm’s short-term and
long-term financial performance. By employing a unique dataset about the carbon footprint
covering 19 industry sectors in North America for 2003–2010, I found the evidence that firms
benefit not only from CER in internal operation but also from CER in their supply chain.
Further, I found that regulatory stringency weakens the positive CER effects on short-term
financial performance, but it strengthens the positive CER effects on long-term financial
performance. This finding is consistent with the prediction of a natural resource-based view
and stakeholder theory. By investigating the carbon footprint issue, this study discusses the
importance of CER not only for our society to be green, but also for a focal firm to be
financially sustainable.
* Supervisor: Professor Bae Zong Tae
Copyright by Graduate School of Green Growth, College of Business, KAIST. All Rights Reserved. All Pages
cannot be copied without permission
1 / 29
Contents
1. INTRODUCTION………………………………………………………………………..3
2. THEORY AND HYPOTHESES…………………………………………………………5
2.1 Background …………………………………………………………………………...5
2.2 A natural resource-based view and short-term financial performance………………....9
2.3 Stakeholder theory and long-term financial performance……………………………12
3. RESEARCH METHODS …………………………………………………………….. 14
3.1 Data and Measurement……………………………………………………………….14
3.1.1 Dependent variables ……………………………………………………………..14
3.1.2 Independent variables ………………………………………………….………..15
3.1.3 Moderating variable…………………………………………………….………..15
3.1.4 Control variables…………………………………………………….……….…..16
3.2 Data Analysis………………………………………………………………….……..17
4. RESULTS…………………………………………………………………………....…..18
5. Discussion………………………………………………………………………….…….20
6. Reference ………………………………………………………………………….…… 22
7. Tables and Figures ……………………………………………………………………24
8. Appendix ………………………………………………………………………….…… 29
2 / 29
1.
INTRODUCTION
Climate change is one of the greatest challenges facing humanity and is already
manifest in the 21st century. Recently, climate change has received considerable attention from
strategy scholars. Many of these scholars have detailed efforts to take steps to mitigate climate
change and addressed the importance of new approaches to governance systems and to
environmental imperatives. As climate impacts become more apparent, corporate
environmental responsibility (CER) is also becoming a major issue and playing an important
role in the corporate landscape. For example, as climate change can reshape value chain,
including supply network, production arrangement, and the provision of energy and water [1],
it requires companies to become far more efficient in use of energy such as transportation of
raw materials, components, and products.
The increasing attention of CER has already led some companies to rethink their
supply chain. For example, Walmart ambitiously launched sustainability program in October
2005 with three plans: (1) be supplied 100 percent by renewable energy, (2) create zero waste,
and (3) sell products that sustain people and environment [46]. In addition, the UK retailer
Sainsbury’s has also made “20×20 Sustainability Plan” which covers reducing environmental
impact of supply chain’s carbon footprint. As companies put more efforts on improving their
energy efficiency and reducing carbon footprint, large-scale changes are likely to arise in supply
chain as well as the companies’ internal operations [1].
The increasing important of CER has also received huge attention from academic
research. Previous studies have discussed from the reasons why companies go green [2] to why
companies engage in CER and how CER relates to corporate financial performance (CFP) [3,
4]. However, while researchers have long explored the relationship between CER and CFP, the
evidence shows mixed results and is inconclusive [5, 6]. More importantly, little is known about
3 / 29
the relationship between CER and firm performance when taking into account the
environmental responsibility in the supply chain.
In this study, I attempt to extend two existing theories to develop hypotheses on how
CER influence firms’ short-term and long-term financial performance. To address the
importance of expanding the CER practice beyond a traditional firm boundary, I consider CER
in two distinct scopes: a focal firm’s internal operation and its supply chain. While previous
research has addressed CER with narrow scope of CER practice (e.g., firms’ internal toxic
chemical uses or carbon emissions), this study encompass the CER practice performance by a
focal firm’s suppliers. Furthermore, I investigate effects of regulatory stringency, which
influences the relationship between CER practices and a firm’s financial performance. In sum,
this study investigates a conventional domain of CER research by investigating CER effects on
firms’ financial performance, but highlights a wider range of factors, which are critical to
sustainability research.
The conceptual framework in this study primarily depends on two existing theories:
the natural resource-based view of the firm [6, 7] and stakeholder theory [8]. First, from the
natural resource-based view, I assumed that CER generates competitive resources for firms,
which in turn enhance profitability as exemplified by Porter [9]. With efforts to increase
environmental responsibility, the companies have greater chance to discover inefficient process
in the companies’ internal operation as well as in their supply chain. In support of these
arguments, I find that firms with high CER–measured by firm-level carbon footprint from
Trucost database–in the internal operation and supply chain gain short-term financial return.
Second, from the stakeholder theory, I assumed that as the norm institutionalizes
environmental responsibility, the companies with higher CER in their internal operation as well
as supply chain accrue more profit than those with lower CER. Similarly, I argue that the
4 / 29
companies with lower carbon footprint benefit from the market, while companies with higher
carbon footprint experience higher penalty from the market. In keeping with the above
arguments, I find that the lower carbon footprint is positively associated with a firm’s market
value.
Overall, the findings support the idea that environmental responsibility, not only from
the companies’ own operation but also from their supply chain, enhance their economic value.
This study also explores how the impact of environmental responsibility in the supply chain on
financial performance depends on the regulatory stringency. In the following sections, I review
related literatures, advance my theoretical arguments in detail. Then I present the data and
methodology, analyze the empirical results, and conclude with discussion and implications of
my findings.
2.
THEORY AND HYPOTHESES
Background
In the business strategy literature, the “pay to be green” debate has discussed whether
firms profit from improving their environmental impact on our natural environment and society
as a whole [5, 10, 11]. Conventional thought adopted a perspective seeing the issues as
inevitable trade-offs between social benefit and private costs to companies [43]. For example,
a government pushes for stringent standards while companies try to beat the standards back.
The classic Friedman’s [43] view holds that environmental responsibility will offset economic
benefits because of entailing substantial costs. Against the conventional thought, Porter and
Van der Linde [12] criticized that the conventional notion only reflects the static view, in which
production process, technology, and customer demands are all fixed. They addressed
5 / 29
competitive advantage rests on the capacity for innovation and improvement which shift the
constraints, rather than on static efficiency or on optimizing within fixed constraints. In this
regard, environmental issues may impose constraints but also offer opportunities, thereby
shifting the competitive landscape in many industries [13].
Scholars have conceptualized the “pay to be green” debate and provided “win-win”
hypothesis that firms can profit from providing the social benefit by improving environmental
performance [3, 4, 14]. For example, strategic decision based on the firm’s relationship to the
ecological environment, such as carbon footprint, can create competitive advantage by
reducing the environmental concerns [7]. King and Lenox [15] found that only by preventing
pollution, firms can recognize inefficient process, reduce unnecessary costs and increase profit
from pollution reduction. The line of literature above is consistent with a natural resourcebased view, which addressing that embracing the environmental challenge into a firm’s
resource develops competitive advantage [7].
However, this recent works have been limited in three important ways. First, although
a natural resource-based view [7] suggest three sources of strategic capabilities – pollution
prevention, product stewardship, and sustainable development – as competitive advantages,
previous research have focused narrowly on pollution prevention issues, which emphasize on
environmental management in a firm’s internal operation such as total quality environmental
management. While pollution prevention focuses on building new capabilities in internal
production and operation, product stewardship illustrates stakeholders’ perspective toward
environmental impact, integrating activities at every set of supply chain from raw material
access to disposition of used products [7].
Last two decades have shown a clear trend towards focusing on a few activities in
internal operation, and outsourcing the rest of activities such as procuring raw materials and
6 / 29
components, transporting materials, and designing products as relying on supply chain.
Stakeholders have increasingly focused their attention not only on firms’ internal operations but
also on supply chain. As firms have become more accountable for their suppliers’ environmental
performance, firms face greater risk of managing their reputation when handling environmental
problems poorly [16]. However, measuring environmental performance of supply chain is not
easy in that capturing environmental performance within a firm’s supply chain requires an
assessment extending beyond the boundary of the firm.
Recently, growing environmental concerns about carbon footprints, air pollutants,
general waste, and land and water pollutants, have attracted media attention and those have been
measured far beyond the boundary of a focal firm (i.e., its supply chain). In particular, the carbon
footprint captures the greenhouse gas (GHG) emissions generated by first-tier and further
suppliers. As stakeholders have shown growing attention to the environmental issues such as
climate change, stakeholders’ demand for reporting CER also has been increased. For example,
Apple’s environmental responsibility reports inform that iPhone 6 has a carbon footprint of
95kg of carbon dioxide-equivalent, of which 89 percent comes from the supply chain, including
production (85%), transport (3%) and recycling (1%) [40]. As this example illustrates, CER in
supply chain can have fruitful implications for practitioners with more severe magnitude.
Although researchers and managers have developed the notion that companies and
their supply chain partners should appraise both CER and CFP [17], only few studies
investigated relationship between the environmental performance and firm performance in
supply chain [e.g., 18] and large part of green supply chain literatures exist far from main stream
of “pay to be green” debate.
Recently, OECD guidelines recommends companies to take fully into account of
established policies in which they operate and consider the view of stakeholders. More
7 / 29
importantly, the guidelines urge to consider an impact not only from enterprises’ own internal
operations, but also from their supply chain partners. According to its guidelines, “enterprises
should seek to prevent or mitigate an adverse impact where they have not contributed to that
impact, when the impact is nevertheless directly linked to their operations, products or services
by a business relationship” [42]. Responding to this environmental concerns extending toward
beyond the boundary of firm, the growing number of companies participate in Carbon
Disclosure Project (CDP) voluntarily [19]. The participating companies disclose information
about carbon footprint both from companies’ internal operation and their supply chain. Thus it
is a timely issue to explore whether the “win-win” hypothesis is applicable only for a focal firm
or it is also applicable when extending it beyond the boundary of the firm; its supply chain.
Second, the literature studying the relationship between CEP and CFP more rely on
the process-based measurement than outcome-based measurement. While process-based
measurement considers the companies’ internal efforts to deal with environmental issues,
outcome-based measurement focuses on direct measure of environmental performance such as
pollution reduction and carbon emissions [20]. Although the process-based measurement may
reflect commitments for improvement in environmental performance, still there is no guarantee
that firms’ commitments will reduce their ‘true environmental cost’ and subsequently influence
their performance [21, 22].
Scholars have examined CER on outcome-based measure and studied the relationship
between CER and CFP [e.g., 3, 15]. For example, Hart and Ahuja [3] investigated the
relationship between pollution reductions and firm performance. They found pollution
reductions (i.e. outcome-based measurement) profit firms by increasing efficiency, giving cost
advantage, and saving money. The line of research based on outcome-based measure however,
has limited in that it only examined a focal firm’s CER. Since outsourcing can be an effective
8 / 29
means by which firms can shift environmental responsibility to supply chain, they reduce
emissions by outsourcing environmental concerns to supply chain partners [23]. Consequently,
it has been difficult to draw a conclusion whether firms profit by taking environmental
responsibility themselves or by just shifting their responsibility to partners. Therefore, it is still
unclear whether an effort to reduce the environmental cost indeed satisfies the “win-win”
hypothesis when encompassing the firms’ supply chain.
Third, since previous studies have focused on heavily polluting industries such as
chemical, mining, and utility industry, the studies could not generalize implications to the other
industries. However, environmental impacts from GHG emissions is notably influenced by
other sector such as information and communication technology (ICT) sector because the GHG
emission from purchased electricity is significantly large and still growing. For example, data
centers in ICT sector take almost 2 ~ 2.5% of global GHG emissions [24]. Thus it is necessary
to consider wider range of industries, including seemingly to nonpolluting sectors, because the
true cost of environmental damage from them is unexpectedly large.
In sum, management scholars have long examined the relationship between CER and
CFP however, they found inconclusive results. In particular, it is indeed still in a nascent stage
to discuss the “pay to be green” issue with encompassing a wider range of the boundary of the
firm. To fill the research gap mentioned above, this paper considers two empirical questions.
First, this research focuses more on carbon footprint from supply chain and how it influences
CFP. Second, by taking account regulatory stringency in the empirical model, this study
investigates whether firms within stringent regulation face greater tradeoff between social
benefit and private cost. To answer these questions, I use on unique dataset which measures
firm-level information of GHG emissions from a focal firm’s internal operation and from its
supply chain.
9 / 29
2.1. A natural resource-based view and short-term financial performance
In the natural environment context, firms can acquire cost advantages by reducing
environmental damages [12]. For example, significant cost saving can be achieved through
more efficient use of energy and materials, reduction of waste, and addressing life cycle
assessment (LCA). In particular, LCA is one popular approach to quantify the whole
environmental impacts of a product’s use of material and energy throughout its extended supply
chain [23]. Thus, firms with LCA have more potential to increase resource productivity because
they are more likely to find another source (i.e., supply chain) of cost-saving.
Hart [7] suggested a natural resource-based view of a firm and addressed the
importance of environmental management principles expanded to supply chain. It is known as
product stewardship, including activities such as remanufacturing, reverse logistics, and
product recovery. By implementing such activities, firms are more likely to recognize
inefficiencies not only in their own process, but also in the supply chain network. For example,
Samsung Electronics joined the ‘Energy mentorship program for Small and Medium
Enterprises’ on April 2012 to collaborate with small and medium size suppliers to improve the
energy efficiency by transferring expertise [41]. Samsung Electronics’ commitment to
sustainable development with supply chain increase frequency of sharing resource, which helps
the company know more about their supply chain network. As the example illustrates,
incorporating ecological concerns into business strategy may enhance firms’ recognition of
inefficiency in the supply chain as well as a focal firm’s internal operations.
Accordingly, the natural resource-based view regards organizational capabilities to
dealing with environmental problems as a source of competitive advantage [7]. In addition,
CER can profit firms through preempting competitors by setting new standards or accessing
preferred locations, production capacity, and customers [7]. More importantly, such capabilities
10 / 29
are either tacit or socially complex [25], therefore it is difficult for competitors to replicate. In
this regard, keeping low levels of carbon footprint represents that the firm’s operations are
relatively efficient, and in turn it may increase return on asset (ROA) which is assumed to
reflect the efficiency of assets in generating income [20, 26]. Therefore, I argue that not only
CER in firms’ internal operation, but also CER in their supply chain enhances short-term
financial performance (i.e., ROA). Hence, I have the following hypotheses.
H1a: Corporate environmental responsibility in their internal operations is positively
associated with short-term financial performance (ROA).
H1b: Corporate environmental responsibility in their supply chain is positively associated
with short-term financial performance (ROA).
However, the CER positive effects on firms’ short-term performance may diminish if
the firms belongs to an industry sector that has regulatory stringency. For example, for the firms
within relatively more stringent regulation, incorporating ecological concerns into business
activity may hinder firms’ core activities (e.g., cost, quality, and faster time-to-market) to
maximize efficiencies and acquire competitive advantage in a short period of time [27]. In
particular, since GHG emissions are tightly coupled with energy consumption, addressing
environmental issues may contradict to the firms’ production which in turn reduce firms’ short
term performance.
In addition, the raising the environmental issues to the firms’ suppliers are likely to
compromise a central part of firms’ advantage over competitors at the supply chain. Even
though firms’ commitment to reduce GHG emissions in supply chain might increase future
value of a focal firm as well as its supply chain network by sharing resource and increasing
synergy effects, there is little evidence to believe that this investment results in enhanced shortterm profits (Hart, 1995).
11 / 29
In sum, although firms’ efforts to minimize GHG emissions enhance their ability to
recognize inefficient process in their own business as well as their supply chain, the endeavor
has no guarantee to gain financial benefit in short period of time if regulation is stringent [7,
10]. Therefore, if firms get too much pressure from regulation, ensuing cost to reduce GHG
emissions is difficult to offset by the few benefit in short period of time [28]. Thus, I
hypothesize as following.
H2: Regulatory stringency weakens the relationship between corporate environmental
responsibility in supply chain and short-term financial performance.
2.2. Stakeholder theory and long-term financial performance
Recently firms face a great deal of scrutiny not only from a government but also from
many other stakeholders including non-profit organizations and non-governmental
organizations. As a result, the scrutiny increasingly demands firms to introduce more
environmental friendly practices [29]. In particular, one important criteria of firms’
environmental friendly practices is to deal with environmental issues not only for their internal
operation, but also for their supply chain partners. Therefore, supply chain management has
been in increasing demands to consider a complex array of elements that include the product
and the process of the supply chain.
Freeman’s [44] stakeholder theory suggested that firms can benefit when considering
the interests of a broad group of stakeholders. Instrumental stakeholder theory, as an important
extension of stakeholder theory, has proposed that CER efforts can be seen as instrumental in
acquiring necessary resource or stakeholder support [8]. For example, Walmart’s introduction
of a sustainability program improved the company’s reputation and appealed to stakeholders
who are concerned about the environment. According to New York Times [47], “while the
initiative may be good for the environment, it may also be good for Wal-Mart. Driving costs out of
12 / 29
the supply chain could result in savings for Wal-Mart that can be passed along to consumers—
enabling the company to uphold its reputation as a destination for rock-bottom prices.”
As the example illustrates, companies can generate profit through attaining reputation
from stakeholders when managing environmental responsibility effectively. Following the
previous literature [e.g., 14, 30], I consider Tobin’s q as effective indicator of intangible value
and assume Tobin’s q reflects the firms’ long-term financial performance. Hence, I propose
following hypotheses.
H3a: Corporate environmental responsibility in their internal operations is positively
associated with long-term financial performance (Tobin’s q).
H3b: Corporate environmental responsibility in their supply chain is positively associated
with long-term financial performance (Tobin’s q).
I do not expect firms to have cost uniformly to increase CER. The costs of maintaining
the higher CER may vary, in part, due to difference across the regulatory stringency that each
firm faces. In addition, the cost of complying with government regulations vary widely across
industries. As discussed earlier, the trade-offs between social benefits and private costs is likely
to become more visible under stringent regulation [31]. Therefore, firms within stringent
regulation are exposed greatly to the public policy process [32], and potential risk is also higher
due to the high visibility to media coverage and surrounding communities.
However, although regulatory stringency may harm the positive effects of CER on
firms’ short-term financial performance, it may benefit firms by increasing the positive CER
effects on long-term financial performance. Only when core concerns (e.g., cost, quality, and
faster time-to market) are resolved, commitment to environmental issues with suppliers could
bring benefit [29].
13 / 29
As stakeholder theory suggests, companies can profit through acquiring reputation
from stakeholders when dealing with environmental responsibility effectively. Stakeholder
coalitions have increasingly formed for better transparent practices with respect to GHG
emissions [33]. This concern for transparency is conspicuous especially in the capital market,
where there is an increasing attention to the climate change and environmental responsibility
[27]. As firms’ supply chain management have a significant impact on value creation, a
growing demand for transparency in supply chain is not uncommon [34, 35]. Furthermore, the
increasing stakeholders’ demands for CER are not only limited to the level of carbon footprint
from firms’ own operation, but also that from their supply chain. Accordingly, I expect that
positive CER effects on long-term financial performance are more visible for firms under
regulatory stringency. Hence, I suggest the following hypothesis.
H4: Regulatory stringency strengthens the relationship between corporate environmental
responsibility in supply chain and long-term financial performance.
3.
RESEARCH METHODS
3.1. Data
I used three data set – Trucost, Compustat, and KLD Research Analytics (KLD) – and
merged based on the list of North American companies in the Trucost database from 2003 to
2010 because almost all information of firms in Trucost was available in Compustat. Figure 1
shows the number of observation by industry Classification Benchmark (ICB) supersectors
based on data from Trucost.
3.1.1. Dependent variables
Return on assets & Tobin’s q
14 / 29
I use the Compustat database to create two financial performance variables: return on
assets (ROA) and Tobin’s q. Following the previous literature [4], I calculate ROA by dividing
earnings before interest by total sales. Tobin’s q, defined as the ratio of a firm’s market value
to the replacement cost of its assets, following the method developed by Chung and Pruitt [36].
Tobin’s q is adequate measure for intangible value [14, 30], especially when predicting effects
of CER as it reflects firms’ reputation, and investor’s trust towards them [20].
3.1.2. Independent variables
Carbon footprint
I use carbon footprint as a proxy for corporate environmental responsibility. Carbon
footprint in the regression model require special attention in interpretation, as high carbon
footprint would imply low CER. In other words, a low level of carbon footprint corresponds to
high CER. I create two main independent variables using Trucost database to define a firm’s
carbon footprint: internal carbon footprint and supply chain carbon footprint. GHG emissions
is measured in carbon dioxide equivalents (CO2e) based on the Greenhouse Gas Protocal,
which is the most widely accepted standard as an accounting tool [37]. Internal carbon
footprint is measured by normalizing GHG emissions from internal operation by sales. Supply
chain carbon footprint is calculated by normalizing GHG emissions from first-tier supplier by
sales. I capture only first-tier indirect GHG emissions as supply chain GHG emissions mainly
because these are the emissions over which the company has control. Figure 2 shows the
average GHG emissions by each sector, indicating that internal carbon footprint and supply
chain carbon footprint are all different by each industry.
3.1.3. Moderating variable
Regulatory stringency
15 / 29
Following the prior studies [e.g., 32, 38], I identified firms based on a standard
industrial classification (SIC), operating in a stringent regulation in terms of environmental
issues. A dichotomous variable, regulatory stringency, was coded ‘1’ for companies with a
primary two-digit SIC code of mining (10), oil exploration (13), paper (26), chemical and allied
products (28), petroleum refining (29), metals (33), or utilities (49), and ‘0’ for otherwise.
3.1.4. Control variables
Environmental impacts
To control environmental issues other than GHG emissions, I put five additional
indicators of environmental impacts: water abstraction, general waste, land and water
pollutant, air pollutant, and natural resource use. Trucost database offers a unique subset of
GHG emissions. To resolve multicollinearity concerns raised by relatively high pair-wise
correlations between some of environmental control variables, I dropped two variables: water
abstraction and air pollutant. I checked multicollinearity by using variance inflation factors.
As presented in Appendix A, all factors were below 10, suggesting that there is no
multicollinearity issue [39]. For robustness check, I included the two variables and run same
regression but it does not change the results.
KLD environmental performance
To control any effect of process-based environmental performance on financial
performance, I obtained relevant data from KLD, which is the most widely adopted proxy for
environmental performance. As shown in the Appendix B, KLD has been issuing
environmental ratings for all members of the S&P 500 Index and Domini Social 400 Index
since 1991. I aggregate environmental performance scores by two groups. KLD environmental
concerns indicates a sum of all environmental concerns. KLD environmental strengths
16 / 29
indicates a sum of all environmental strengths.
Disclosure
To control any effect of voluntary behavior of disclosing environmental information
on financial performance, I include a binary variable to take into account a variation across
firms. I put ‘1’ if the environmental data was provided by the firm so that publicly available,
and ‘0’ if the data was imputed by third parties using input-output (IO) economic model [45].
This variable allows to control for any potential bias based on a firm’s voluntarily disclosing
behavior.
Financial variables
Following the previous studies of relationship between CER and CFP [14, 15], I
includes several financial variables to control for firm-level heterogeneity. I obtained financial
information from Compustat database. Total assets are included to account for variation in firm
size. The ratio of total debt to total assets are also included to control firms’ leverage. To control
effect of production variation on firm performance, I put growth variable, defined as the annual
change in sales divided by total sales. Finally, I control capital intensity impact on firm
performance by dividing capital expenditures by total sales. Each financial control variables is
transformed using the natural logarithm to correct for skewed distributions.
Industry dummy
I put a series of dummies, indicating each firm’s primary industry using the 19 Industry
Classification Benchmark (ICB) supersectors based on data from Trucost.
3.2. Data Analysis
I used the random effect regression model, which achieves greater efficiency. However,
17 / 29
the random effect model imposes a strong assumption that an unobserved effect can be
randomly distributed to each firm. To minimize this concern, I attempted to compare with fixed
effect regression model. Unfortunately however, it was inappropriate to use fixed effect model
in this study since the variable–regulatory stringency–was time-invariant. More importantly,
the goal of this study is to make generalizable results to a wide range of industry. Therefore, I
used the random effects model that favors to make general inferences about the relationship
between carbon footprint and financial performance.
4.
RESULTS
-----------------------------------------Insert Table 1 about here
----------------------------------------------------------------------------------Insert Table 2 about here
-----------------------------------------Table 1 displays the descriptive statistics and Table 2 shows the estimated random
effects regression model using ROA as the dependent variable. In models (1)–(6) of Table 2,
we test our hypotheses with and its interactions with regulatory stringency. In model (2), the
coefficient for internal carbon footprint is significantly negative, supporting Hypothesis 1a,
which suggests that CER in their internal operations are positively associated with ROA. In
model (4), the coefficient of supply chain carbon footprint is also significantly negative,
supporting Hypothesis 1b, predicting that CER in supply chain is positively associated with
ROA.
Model (5) in Table 2 adds the interaction term between supply chain carbon footprint
and regulatory stringency variables in order to test Hypothesis 2. The interaction term is
significantly positive, suggesting that the relationship between the level of carbon footprint in
18 / 29
supply chain and regulatory stringency is weakened for those firms within stringent regulation,
thereby supporting Hypothesis 2. The main effect of supply chain carbon footprint, which is
negatively associated with ROA in model (4), stay significantly negative in Model (5),
suggesting its effect on ROA is not conditional on regulatory stringency. In model (3), I include
the interaction term between internal carbon footprint and regulatory stringency variables to
see whether the effect of internal carbon footprint on ROA is moderated by regulatory
stringency. However, the coefficient of the interaction term does not have statistical
significance.
Among the control variables, the estimates for firm size and leverage are negatively
associated with ROA and two of environmental impact control variables–general waste and
natural resource use–show statistical significance across all models. Disclosure does not show
an effect on ROA in Model (1)–(3) however, it becomes significantly positive in Model (4)–
(6). Perhaps, voluntary behavior of disclosing carbon emissions is more relevant when
considering supply chain carbon footprint.
-----------------------------------------Insert Table 3 about here
-----------------------------------------Table 3 shows the estimated random effects regression model using Tobin’s q as the
dependent variable. In model (2), the coefficient for internal carbon footprint is significantly
negative, supporting Hypothesis 3a, which suggests that the CER in their internal operations
are positively with Tobin’s q. In model (4), the coefficient of supply chain carbon footprint is
also significantly negative, supporting Hypothesis 3b, suggesting that CER in supply chain is
positively associated with Tobin’s q.
Model (5) in Table 3 adds the interaction term between supply chain carbon footprint
and regulatory stringency variables in order to test Hypothesis 4. The interaction term is
19 / 29
significantly negative, suggesting that the relationship between carbon footprint in supply chain
and regulatory stringency is strengthened by stringent regulation, thereby supporting
Hypothesis 4. The main effect of supply chain carbon footprint, which is negatively associated
with Tobin’s q in model (4), turns insignificant in Model (5), suggesting its effect on Tobin’s q
is conditional on regulatory stringency.
Among the control variables, the estimated KLD environmental concerns shows a
positive effect on Tobin’s q. When looking at the financial variables, the estimates for firm size
and leverage are negatively associated with Tobin’s q and estimates for growth and capital
intensity are positively related to Tobin’s q across all models. Only one environmental impact
control variable–natural resource pollutant–shows statistical significance. Regulatory
stringency shows a positive effect on Tobin’s q.
5.
Discussion
Using Trucost data from 2003 to 2010, I demonstrate that higher levels of CER not
only increase their short-term financial performance but also enhance their long-term financial
performance. I further demonstrate that the companies’ CER in their supply chain also increase
firms’ short-term and long-term financial performance. By providing a theoretical account for
this relationship, I highlight the important role of improving CER especially in their supply
chain as well as their internal operation.
This study contributes to a natural resource-based view by extending “pay to be green”
debate beyond the boundary of a firm. This study provides the evidence how important for
firms to find greener suppliers with low carbon footprint [7]. As illustrated earlier, both
Samsung and Wal-Mart examples present the firms’ efforts to reduce carbon emissions increase
20 / 29
sharing expertise, which in turn builds more fruitful knowledge about the supply chain network.
I argue that outcome-based lower levels of carbon footprint implies that firms have higher
capabilities to dealing with environmental responsibility. More importantly, this capabilities
not only contribute to greener ecological environment but also enhance the firms’ competitive
advantage.
This research also contributes to stakeholder theory by exploring the relation between
CER and market value (i.e., Tobin’s q) in terms of stakeholders’ perspective. Whereas it has
been already discovered about stakeholders’ pressure on firms’ CER in their internal operation,
I examine that stakeholders’ influence reaches far beyond the boundary of a firm, previously
unexplored in the literature. In addition, I propose that regulatory stringency may hinder the
positive CER effects on firms’ short-term financial gain but it improves long-term market value.
This improves our understanding that regulatory stringency seems to be an obstacle to increase
profitability however, in fact, regulatory stringency appears to be an another source of
competitive advantage if well-managed.
The present research has a number of limitations that may offer opportunities for
further research. First, this study employs two-digit SIC code as a criterion to distinguish firms
by regulatory stringency, following the [32, 38]. Future research could employ firm-level data
to examine the regulatory pressures on individual firms for more rigorous measure of
regulatory stringency. Second, as a proxy for CER, I use a level of carbon footprint, calculated
by dividing firm-level GHG emissions by the firm’s revenue. For this calculation method, it is
difficult in precise interpretation if lower levels of carbon footprint represent lower GHG
emissions or higher levels of sales. Future studies can use more rigorous measure to avoid the
potential bias. Nevertheless, I believe that the carbon footprint measure is a conservative test
for my hypotheses. Finally, since this study aim to examine outcome-based CER, we do not
21 / 29
examine the effects of process-based CER effects. Future research could conduct comparative
analysis by comparing firms with only process-based measure and outcome-based measure.
This study suggests several implications for practitioners. First, policy makers need to
consider both a focal firm and its supply chain. In particular, if a firm can influence over supply
chain partners, then it might be reasonable for the firm to mitigate environmental concerns by
transferring the burden of environmental responsibility to its supply chain, rather than by truly
mitigating the environmental damages. Therefore, policy makers can make better decisions
with a holistic approach and give proper incentives for a focal firm to mitigate environmental
concerns by itself or collaborate with its supply chain partners.
Second, managers should recognize the importance of understanding their supply
chain network and collaborate with their partners to reduce carbon emissions. The overall
empirical results indicate that reducing the carbon footprint is beneficial for firms’ own good
as well as for the ecological environment. As more firms are connected and as more
stakeholders show concern about the environment, managers need to pay more attention to
environmental impacts generated from the firm’s whole business processes. As managers
expand the boundary of decision-making factors, they could have higher capabilities to
understand the complicated interdependency, to deal with stakeholders’ demand, and to place
their firm to the center of a sustainable business network.
The world faces the greatest unprecedented challenge that requires a paradigm shift to
the new industrial revolution. In the new paradigm, the new form of governance demands a
substantial change to reflect the interdependence of various entities. Understanding such
interdependence may help firms to create a hybrid type of network for sustainable development.
As firms take the lead in the network for sustainable development, firms could not only provide
innovative mechanisms in the new governance systems, but also increase their private benefits.
22 / 29
6.
References
Journal Papers and Books
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
Howard-Grenville, J., et al., Climate change and management. Academy of Management
Journal, 2014. 57(3): p. 615-623.
Bansal, P. and K. Roth, Why companies go green: a model of ecological responsiveness.
Academy of management journal, 2000. 43(4): p. 717-736.
Hart, S.L. and G. Ahuja, Does it pay to be green? An empirical examination of the relationship
between emission reduction and firm performance. Business strategy and the Environment,
1996. 5(1): p. 30-37.
King, A.A. and M.J. Lenox, Does it really pay to be green? An empirical study of firm
environmental and financial performance: An empirical study of firm environmental and
financial performance. Journal of Industrial Ecology, 2001. 5(1): p. 105-116.
Margolis, J.D. and J.P. Walsh, Misery loves companies: Rethinking social initiatives by business.
Administrative science quarterly, 2003. 48(2): p. 268-305.
Russo, M.V. and P.A. Fouts, A resource-based perspective on corporate environmental
performance and profitability. Academy of management Journal, 1997. 40(3): p. 534-559.
Hart, S.L., A natural-resource-based view of the firm. Academy of management review, 1995.
20(4): p. 986-1014.
Jones, T.M., Instrumental stakeholder theory: A synthesis of ethics and economics. Academy
of management review, 1995. 20(2): p. 404-437.
Porter, M.E., America’s Green Strategy. Scientific American, 1991. 264(4): p. 168.
Ambec, S., et al., The Porter hypothesis at 20: can environmental regulation enhance
innovation and competitiveness? 2011: Dicussion Paper 11-01. Washington, DC: Resources for
the Future.
Orlitzky, M., F.L. Schmidt, and S.L. Rynes, Corporate social and financial performance: A
meta-analysis. Organization studies, 2003. 24(3): p. 403-441.
Porter, M.E. and C. Van der Linde, Toward a new conception of the environmentcompetitiveness relationship. The journal of economic perspectives, 1995: p. 97-118.
Shrivastava, P., Environmental technologies and competitive advantage. Strategic management
journal, 1995. 16(S1): p. 183-200.
Dowell, G., S. Hart, and B. Yeung, Do corporate global environmental standards create or
destroy market value? Management Science, 2000. 46(8): p. 1059-1074.
King, A. and M. Lenox, Exploring the locus of profitable pollution reduction. Management
Science, 2002. 48(2): p. 289-299.
Miles, M.P. and J.G. Covin, Environmental marketing: a source of reputational, competitive,
and financial advantage. Journal of business ethics, 2000. 23(3): p. 299-311.
Carter, C.R. and D.S. Rogers, A framework of sustainable supply chain management: moving
toward new theory. International journal of physical distribution & logistics management, 2008.
38(5): p. 360-387.
Delmas, M.A. and N.S. Nairn-Birch, Is the tail wagging the dog? An empirical analysis of
corporate carbon footprints and financial performance. Institute of the Environment and
Sustainability, 2011.
Stanny, E., Voluntary disclosures of emissions by US firms. Business Strategy and the
Environment, 2013. 22(3): p. 145-158.
Busch, T. and V.H. Hoffmann, How hot is your bottom line? Linking carbon and financial
performance. Business & Society, 2011. 50(2): p. 233-265.
Delmas, M., D. Etzion, and N. Nairn-Birch, Triangulating Environmental Performance: What
Do Corporate Social Responsibility Ratings Really Capture? The Academy of Management
Perspectives, 2013: p. amp. 2012.0123.
Schneider, A. and E. Meins, Two dimensions of corporate sustainability assessment: Towards
a comprehensive framework. Business Strategy and the Environment, 2012. 21(4): p. 211-222.
Klassen, R.D. and S. Vachon, Greener supply chain management. chapter, 2012. 15: p. 26923 / 29
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
289.
Kumar, R. and L. Mieritz, Conceptualizing ‘Green’IT and data center power and cooling issues.
Stamford: Gartner, Inc, 2007.
Winter, S., Knowledge and competence as strategic assets. The strategic management of
intellectual capital, 1998: p. 165-187.
Cohen, M.A., S. Fenn, and J.S. Naimon, Environmental and financial performance: are they
related? 1995: Investor Responsibility Research Center, Environmental Information Service.
Delmas, M.A. and N.S. Nairn-Birch, Is the tail wagging the dog? An empirical analysis of
corporate carbon footprints and financial performance, in Institute of the Environment and
Sustainability. 2011.
Walley, N. and B. Whitehead, It's not easy being green. The Earthscan reader in business and
the environment, 1994: p. 36-44.
Vachon, S. and R.D. Klassen, Extending green practices across the supply chain: the impact of
upstream and downstream integration. International Journal of Operations & Production
Management, 2006. 26(7): p. 795-821.
Konar, S. and M.A. Cohen, Does the market value environmental performance? Review of
economics and statistics, 2001. 83(2): p. 281-289.
Leone, R.A., Government regulation of business: Developing the managerial perspective. 1981:
Division of Research, Harvard Business School.
Cho, C.H. and D.M. Patten, The role of environmental disclosures as tools of legitimacy: A
research note. Accounting, Organizations and Society, 2007. 32(7): p. 639-647.
Kolk, A., D. Levy, and J. Pinkse, Corporate responses in an emerging climate regime: the
institutionalization and commensuration of carbon disclosure. European Accounting Review,
2008. 17(4): p. 719-745.
Christopher, M. and L. Ryals, Supply chain strategy: its impact on shareholder value.
International Journal of Logistics Management, The, 1999. 10(1): p. 1-10.
Hendricks, K.B. and V.R. Singhal, The effect of supply chain glitches on shareholder wealth.
Journal of Operations Management, 2003. 21(5): p. 501-522.
Chung, K.H. and S.W. Pruitt, A simple approximation of Tobin's q. Financial management, 1994:
p. 70-74.
Ranganathan, J., et al., The greenhouse gas protocol: a corporate accounting and reporting
standard (revised edition). Washington, DC: World Resources Institute and World Business
Council for Sustainable Development, 2004.
Chatterji, A.K. and M.W. Toffel, How firms respond to being rated. Strategic Management
Journal, 2010. 31(9): p. 917-945.
Kutner, M.H., C. Nachtsheim, and J. Neter, Applied linear regression models. 2004: McGrawHill/Irwin.
Techincial Reports
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
Apple
2014.
Environmental
Responsibility.
Apple
Inc.
http://www.apple.com/environment/reports.
Samsung
2014,
Sustainability
Reports.
Samsung
Electronics
Co.,
Ltd.
http://www.samsung.com/us/aboutsamsung/sustainability/sustainabilityreports.
OECD 2011. OECD Guidelines for Multinational Enterprises. OECD Publishing.
http://dx.doi.org/10.1787/9789264115415-en.
Friedman, M. 1970. The social responsibility of business is to increase its profits. The New
York times Magazine, September 13.
Freeman, R. E. 1984. Strategic management: A stakeholder approach. Boston: Pitman.
Trucost Plc. 2008. Trucost methodology overview: Measuring company environmental impacts.
London: Trucost Plc.
Walmart. 2014. Walmart 2014 global responsibility report. Bentonville, AR: Walmart.
New York Times. 2010. Wal-Mart unveils plan to make supply chain greener. February 25
24 / 29
Table 1. Descriptive statistics
Variables
Mean S.D.
Min
Max
(1)
(1) ROA
0.13 0.11 -0.64
0.95
1
(2) Tobin's q
2.10 9.81
(3) Internal Carbon Footprint
3.19 2.22 -4.06
0.00
774.90 0.08
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13) (14) (15)
1
9.65
0.07 0.00
1
(4) Supply Chain Carbon Footprint 4.15 1.26
1.24
7.80
0.16 0.00 0.77
(5) Regulatory Stringency
0.23 0.42
0.00
1.00
0.03 -0.02 0.62 0.43
(6) KLD Environmental Concerns
0.47 0.97
0.00
5.00
-0.01 -0.18 0.51 0.43 0.49
(7) KLD Environmental Strengths
0.42 0.87
0.00
5.00
0.04 -0.07 0.12 0.21 0.16 0.32
(8) General Waste
0.82 1.85 -9.03
7.33
0.11 -0.02 0.41 0.50 0.29 0.54 0.35
(9) Land and Water Pollutants
0.94 2.06 -8.67
8.72
0.14 -0.01 0.42 0.56 0.30 0.51 0.36 0.90
(10) Natural Resource Use
1.00 2.13 -8.40
8.81
0.06 -0.02 0.56 0.60 0.35 0.58 0.26 0.88 0.85
(11) Firm Size
8.76 1.60 -0.72
14.63 -0.26 -0.05 -0.10 -0.15 0.05 0.32 0.25 0.51 0.44 0.44
(12) Leverage
-1.70 1.20 -11.20
2.91
-0.11 -0.03 0.25 0.26 0.14 0.13 0.07 0.13 0.14 0.16 0.09
(13) Growth
-2.18 1.16 -11.60
8.22
0.03 0.05 0.02 0.00 0.07 -0.03 -0.10 -0.17 -0.17 -0.12 -0.16 -0.07
(14) Capital Intensity
-3.15 1.23 -8.24
8.55
0.00 0.02 0.44 0.28 0.40 0.19 0.03 -0.01 0.00 0.11 -0.04 0.17 0.15
(15) Disclosure
0.16 0.37
1.00
0.00 -0.03 0.18 0.21 0.26 0.35 0.45 0.39 0.37 0.35 0.30 0.05 -0.09 0.08
0.00
25 / 29
1
1
1
1
1
1
1
1
1
1
1
1
Table 2. The random effects regression model of return on assets
KLD Environmental
Concerns (t-1)
KLD Environmental
Strengths (t-1)
General Waste
Land and Water Pollutant
Natural Resource Use
Firm Size
Leverage
Growth
Capital Intensity
Disclosure
Regulatory Stringency
Industry Dummy
Year Dummy
Internal Carbon Footprint
Internal Carbon Footprint
* Regulatory Stringency
Supply Chain Carbon
Footprint
Supply Chain Carbon
Footprint * Regulatory
Stringency
Constant
Model 1
-0.003
(0.00)
-0.002
(0.00)
0.026***
(0.00)
0.019***
(0.00)
0.001
(0.00)
-0.058***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.004†
(0.00)
0.004
(0.00)
-0.014
(0.01)
Included
Included
Model 2
-0.003
(0.00)
-0.002
(0.00)
0.026***
(0.00)
0.019***
(0.00)
0.002
(0.00)
-0.059***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.003
(0.00)
0.004
(0.00)
-0.010
(0.01)
Included
Included
-0.003*
(0.00)
Model 3
-0.003
(0.00)
-0.002
(0.00)
0.026***
(0.00)
0.019***
(0.00)
0.002
(0.00)
-0.059***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.003
(0.00)
0.003
(0.00)
-0.027
(0.02)
Included
Included
-0.005*
(0.00)
0.004
(0.00)
Model 4
-0.003
(0.00)
-0.001
(0.00)
0.027***
(0.00)
0.022***
(0.00)
0.003
(0.00)
-0.063***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.003
(0.00)
0.007*
(0.00)
-0.009
(0.01)
Included
Included
Model 5
-0.003
(0.00)
-0.001
(0.00)
0.026***
(0.00)
0.023***
(0.00)
0.003
(0.00)
-0.063***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.002
(0.00)
0.007*
(0.00)
-0.066*
(0.03)
Included
Included
-0.018***
(0.00)
-0.022***
(0.00)
0.012*
(0.01)
0.509***
0.525***
0.530***
0.632***
0.651***
(0.03)
(0.03)
(0.03)
(0.04)
(0.04)
Observations
3330
3328
3328
3330
3330
R-squared
0.2489
0.2521
0.2503
0.2558
0.2558
Wald Chi-squared
727.15*** 732.15*** 733.79*** 764.80*** 769.66***
Standard errors appear in parentheses.
†, *, **, *** indicates statistical significance at the 10%, 5%, 1%, and 0.1% level, respectively.
26 / 29
Model 6
-0.003
(0.00)
-0.001
(0.00)
0.026***
(0.00)
0.022***
(0.00)
0.004
(0.00)
-0.063***
(0.00)
-0.006***
(0.00)
0.001
(0.00)
-0.002
(0.00)
0.006*
(0.00)
-0.066*
(0.03)
Included
Included
-0.002
(0.00)
0.001
(0.00)
-0.021***
(0.00)
0.011†
(0.01)
0.652***
(0.04)
3328
0.2559
770.03***
Table 3. The random effects regression model of Tobin’s Q
KLD Environmental
Concerns (t-1)
KLD Environmental
Strengths (t-1)
General Waste
Land and Water Pollutant
Natural Resource Use
Firm Size
Leverage
Growth
Capital Intensity
Disclosure
Regulatory Stringency
Industry Dummy
Year Dummy
Internal Carbon Footprint
Internal Carbon Footprint
* Regulatory Stringency
Supply Chain Carbon
Footprint
Supply Chain Carbon
Footprint * Regulatory
Stringency
Constant
Model 1
0.116***
(0.03)
-0.044
(0.03)
0.047
(0.05)
0.020
(0.05)
0.098*
(0.04)
-0.723***
(0.04)
-0.139***
(0.02)
0.113***
(0.01)
0.188***
(0.03)
0.013
(0.04)
0.437**
(0.16)
Included
Included
Model 2
0.122***
(0.03)
-0.048
(0.03)
0.051
(0.05)
0.014
(0.05)
0.118**
(0.04)
-0.738***
(0.04)
-0.137***
(0.02)
0.114***
(0.01)
0.198***
(0.03)
0.001
(0.04)
0.507**
(0.16)
Included
Included
-0.063*
(0.02)
Model 3
0.123***
(0.03)
-0.047
(0.03)
0.058
(0.05)
0.007
(0.05)
0.118**
(0.04)
-0.738***
(0.04)
-0.138***
(0.02)
0.113***
(0.01)
0.197***
(0.03)
0.007
(0.04)
0.806**
(0.26)
Included
Included
-0.037
(0.03)
-0.071
(0.05)
Model 4
0.116***
(0.03)
-0.040
(0.03)
0.051
(0.05)
0.037
(0.05)
0.116**
(0.04)
-0.754***
(0.04)
-0.136***
(0.02)
0.113***
(0.01)
0.195***
(0.03)
0.028
(0.04)
0.461**
(0.16)
Included
Included
Model 5
0.120***
(0.03)
-0.038
(0.03)
0.060
(0.05)
0.024
(0.05)
0.117**
(0.04)
-0.751***
(0.04)
-0.137***
(0.02)
0.114***
(0.01)
0.188***
(0.03)
0.031
(0.04)
1.421**
(0.44)
Included
Included
-0.107*
(0.05)
-0.049
(0.05)
-0.198*
(0.08)
7.918***
8.223***
8.144***
8.658***
8.326***
(0.46)
(0.48)
(0.48)
(0.57)
(0.58)
Observations
3338
3336
3336
3338
3338
R-squared
0.3799
0.3848
0.3872
0.3827
0.3859
Wald Chi-squared
1290.32*** 1301.21*** 1305.53*** 1296.93*** 1306.72***
Standard errors appear in parentheses.
†, *, **, *** indicates statistical significance at the 10%, 5%, 1%, and 0.1% level, respectively.
27 / 29
Model 6
0.125***
(0.03)
-0.043
(0.03)
0.065
(0.05)
0.013
(0.05)
0.130**
(0.04)
-0.757***
(0.04)
-0.136***
(0.02)
0.114***
(0.01)
0.195***
(0.03)
0.021
(0.05)
1.500***
(0.44)
Included
Included
-0.038
(0.03)
-0.037
(0.05)
-0.034
(0.06)
-0.172†
(0.09)
8.423***
(0.58)
3336
0.3899
1314.53***
Figure 1. Sample composition
The number of observations (firm-years) by sector
Utilities
Travel & Leisure
Telecommunications
Technology
Retail
Real Estate
Personal & Household Goods
Oil & Gas
Media
Insurance
Industrial Goods & Services
Healthcare
Food & Beverage
Financial Services
Construction & Materials
Chemicals
Basic Resources
Banks
Automobiles & Parts
0
200
400
600
800
1000
1200
1400
Frequency
Figure 2. Sample composition
Greenhouse gas emissions (Average)
Utilities
Travel & Leisure
Telecommunications
Technology
Retail
Real Estate
Personal & Household Goods
Oil & Gas
Media
Insurance
Industrial Goods & Services
Healthcare
Food & Beverage
Financial Services
Construction & Materials
Chemicals
Basic Resources
Banks
Automobiles & Parts
0
5
10
15
20
25
30
CO2-equivalent (million tons)
Internal Carbon Footprint
Supply Chain Carbon Footprint
28 / 29
APPENDIX A. Variance inflation factors–Multicollinearity test
Variable
General Waste
Natural Resource Use
Land and Water Pollutant
Supply Chain Carbon Footprint
Internal Carbon Footprint
Firm Size
KLD Environmental Concerns (t-1)
Regulatory Stringency
Capital Intensity
Disclosure
KLD Environmental Strengths (t-1)
Leverage
Growth
Mean VIF
VIF
7.09
6.35
5.52
4.35
4.08
2.94
1.98
1.94
1.58
1.35
1.28
1.13
1.08
3.13
APPENDIX B. List of KLD environmental performance
Environmental Concerns
Hazardous Waste
Regulatory Problems
Ozone Depleting Chemicals
Substantial Emissions
Agriculture Chemicals
Climate Change (from 1999)
Negative Impact of Products and Services
Land Use & Biodiversity
Non Carbon Releases
Supply Chain Management
Water Management
Environment Other Concerns
Environment - Number of Concerns
Environmental Strengths
Beneficial Products and Services
Pollution Prevention
Recycling
Clean Energy
Property, Plant, Equipment (through 1995)
Management Systems Strength
Water Stress
Biodiversity & Land Use
Raw Material Sourcing
Environment Other Strength
Environment - Number of Strengths
29 / 29