Value drivers of corporate eco-efficiency

Value drivers of corporate eco-efficiency

A replication study

University of Groningen

MScBA Organisational and Management Control

Master Thesis

Name:

Barbara Maria Sabine Ruppel

Address:

Stationsweg 33, 9781 CG Bedum

Student Number:

s2346028

Telephone Number: +31 6 24442187

Email-address:

barbara.ruppel@gmail.com

Supervisor:

Dr. Hilco van Elten

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1 C

ONTENT

1. Introduction ... 2

1.1. Description of the underlying study ... 2

1.2. Purpose of the present study ... 3

1.3. Structure of the thesis ... 3

2. Literature ... 4 2.1. General Overview ... 4 2.2. Environmental Accounting ... 5 2.3. Eco-Efficiency ... 7 2.4. Formulation of Hypotheses ... 8 3. Method... 10 3.1. Replication study ... 10

3.2. Research design of the original research ... 10

3.3. Research design of this research ... 10

3.3.1. Variables ... 10

3.3.2. Datastream and Asset 4 ... 10

3.3.3. Data analysis ... 11 3.4. Research Criteria ... 11 3.4.1. Controllability ... 11 3.4.2. Reliability ... 11 3.4.3. Validity ... 12 4. Results ... 13

4.1. Sample as in Figge and Hahn (2010) ... 13

4.2. CO2-efficiency of Sectors ... 14

4.3. CO2-Efficiency of Countries ... 15

5. Discussion and Conclusion... 16

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1. I

NTRODUCTION

“We believe a business cannot continue to exist without the trust and respect of society for its environ-mental performance.” Shinroku Morohashi, President Mitsubishi Corporation (in Changing Course, Schmidheiny, 1992)

Throughout the past couple of decades Sustainability Accounting, Environmental Accounting, Social Accounting and Corporate Social Responsibility (CSR) became more and more important and have been subject of various research. Companies became aware of the fact that responsible and sustainable man-agement in matters of environment and society are required by their stakeholders but can also help in improving their business. While these subjects were the focus of much research most of the terms are still used interchangeably. This is particularly the case because there is no clear definition of the terms. While Sustainability Accounting seems to combine the terms Social and Environmental Accounting, Corporate Social Responsibility is more a comprehensive term that includes Economic Environmental and Social Responsibility (van Marrewijk, 2003). The special issue of Management Accounting Re-search in 2013 shows the importance of Sustainable Development, management and accounting. Topics range from internal issues such as the use of management control to manage CSR strategy to the influ-ence of stakeholders on environmental strategy and social and environmental accounting. One of the articles is Figge and Hahn (2013) about eco-efficiency which is part of Environmental accounting. Ac-cording to Schaltegger and Burrit (2000) Environmental Accounting contains effectiveness, efficiency and equity issues. While effectiveness is about the framework for sustainable development and equity is about accountability, Eco-efficiency “expresses the efficiency with which ecological resources are used to meet established economic goals.” (Schaltegger and Burrit, 2000) The concept of eco-efficiency was introduced by the World Business Council for Sustainable Development in 1992 in the run-up to the Earth Summit in Rio de Janeiro in 1992 (Schmidheiny, 1992). Eco-efficiency is not just achieved through technological change, but through also adjusting goals and assumptions that drive corporate activities and change daily routines and tools to reach them. (Schmidheiny, 1992)

While there are many theories about eco-efficiency and how to integrate the calculation into financial accounting there are yet not many studies that actually try to calculate the eco-efficiency and give a tool for everyday business. One important piece of research in that area is the above mentioned article by Figge and Hahn (2013).

This thesis will be in the form of a replication with extension (Hubbard and Vetter, 1996). Burman et al. (2010) “call for replication studies”, as the replicability of research is a basic integrity requirement. Although replication studies are neither as much in demand nor as rewarded it is important to conduct them. Whether results can be reproduced and generalized is a basic academic assumption.

1.1. D

The basis of this research is the 2013 article “Value drivers of corporate eco-efficiency: Management accounting information for the efficient use of environmental resources” from Frank Figge and Tobias Hahn. The main purpose is to introduce a management tool that helps with everyday decision making. A value-based approach is used to find value drivers of corporate efficiency. Basis for the eco-efficiency formula is the capital eco-efficiency formula. The return on equity ratio is divided into three com-ponents. This application is also used for the Eco-efficiency ratio return on environmental capital and splits the formula into sales margin, capital turnover and sustainability leverage (an in-depth explanation of the formula is provided in chapter 2).

3 company is outperforming its sector in regard of ecological or in environmental terms. Figge and Hahn perform a practical application of the formula by calculating a CO2-efficiency of car manufacturers. The practical application shows whether outperformance in CO2-efficiency is due to strong environ-mental performance or due to strong economic performance.

Their contribution is methodological as well as conceptual. The identified components help to better understand the relationship between different forms of resources. They identify the value drivers for sustainable value: sales margin (pos. influence), sales growth (pos.), investment of economic capital (pos. /neg.), cost of economic capital (neg.), use of environmental resources (neg.) and cost of environ-mental resources (neg.).

1.2. P

This study aims to replicate the research of Figge and Hahn. As a direct replication is almost impossible (Hubbard and Vetter, 1996) it is done in the form of a replication with extension. While Figge and Hahn calculate the CO2-efficiency of the main car manufacturers from all over the world, this study extends the time period and changes the population. Therefore the original sample is chosen with data from another year, namely 2011 instead of 2007 (as in the original study). To extend it further two other industries are chosen, one supposed with less CO2-emissions, the banking sector and one with assumed higher CO2-emissions, the chemicals sector. For these two samples also the year 2011 is chosen, as that makes it comparable to the original study. This thesis will contribute to the existing literature as it tests whether the value drivers of eco-efficiency/CO2-effiiciency are the same in other industries as in the automobile industry. Furthermore it examines whether value drivers shift over time. As replicability is one of the research criteria of good research a possible replication will contribute to the literature as it shows that the underlying study is of good quality. The main research question is:

“Is it possible to replicate the study of Frank Figge and Tobias Hahn (2013)?”

The results show that a replication is in general possible. While the replicated results are rather surpris-ing, the extended results are very interesting. The replicated results show a change of value drivers for all companies. The extended results show that there are indeed differences between sectors and coun-tries.

1.3. S

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2. L

ITERATURE

“Sustainable development is the central public policy goal of our times. It is the only ‘big idea’ that provides the moral basis for grappling with the twin challenges of achieving ecological and social sus-tainability.” (Unerman et al., 2007)

As mentioned above eco-efficiency is part of environmental accounting. In the following part existing literature about environmental accounting and eco-efficiency will be presented and relevant hypotheses will be developed.

2.1. G

O

The quotation above from the foreword of Unerman et al. (2007) gives a good idea about sustainable development. Sustainable Development became more and more important throughout the last couple of decades, although, as Buhr states in Unerman et al. (2007) environmental problems were already known centuries ago. The most common definition of Sustainability Development is from the United Nations: “Sustainable Development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. […] Thus the goals of economic and social development must be defined in terms of sustainability in all countries (UN, 1980).”

As demanded by the United Nations, Sustainability Development is also an objective of the European Union: “to promote economic and social progress and a high level of employment and to achieve bal-anced and sustainable development.” Furthermore the European Union wants to “promote a prosperous, innovative, knowledge-rich, competitive and eco-efficient economy.” Similar to the United Nations the World Business Council for Sustainable Development demand that by 2050 companies should be meas-ured by their “True Value”. Environmental, Social and governance (ESG) indicators have to be inte-grated into corporate performance. (WBCSD, 2013)

As pointed out above Sustainable Development is important all over the world. One tool for a sustainable development is Sustainability Accounting. Sustainability Accounting is based on the Financial Account-ing which is extended by the sustainability component. The term Sustainability AccountAccount-ing is used two-fold. On the one hand it refers to the reporting part, to inform external stakeholders and on the other hand it refers to a managerial part to inform internal entities. Schaltegger et al. (2006) distinguish these two and divide them into sustainability accounting and sustainability reporting. Although many govern-mental and non-governgovern-mental organizations are working on official reporting standards, there are as yet no general binding reporting guidelines. The Global Reporting Initiative (GRI), the Sustainability Ac-counting Standards Board and others are trying to provide frameworks for Sustainability Reports, but depending on the stakeholders each company is still providing their “own design”.

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2.2. E

A

Environmental Accounting covers “all areas of accounting that may be affected by the business response to environmental issues” (Gray et al., 1993).

According to Mathews (1997) research into social and environmental accounting can be divided into three periods. Before 1980, 1981 to 1990 and 1991 to 1995. The first period is shaped by the lack of a theoretical base. Environmental concerns were not major issues and therefore research was mainly fo-cused on social accounting. In the second period social disclosures became less important and were replaced by environmental disclosures and regulations. First attempts were made to separate environ-mental from social accounting and a philosophical discussion evoked which role accountants should play in the social and environmental accounting areas. One became aware of the environmental damages and saw opportunities for the accounting discipline to adopt a different perspective. The third period, ending around 1995 was dominated by environmental over social accounting. Major accounting journals issued specials which gave researchers the opportunity to present their research in that field. The legal efforts towards sustainability were mapped. While critical researchers warned for institutionalising so-cial and environmental aspects, other literature shows attempts to develop environmental management systems for control and strategic planning.

In 1997 Mathews still wonders why social and environmental accounting was yet not accepted by ac-counting academia.

About a decade after Mathews, Burrit and Saka (2005) observe the development of a comprehensive framework of environmental management accounting. Environmental management accounting made managers curious as there were more tools available and eco-efficiency improvements were adopted by more and more businesses. One of the benefits of Environmental Management Accounting is improved environmental performance as well as opportunities for cost savings and improved product mix. Parker (2011) evaluates the Social and Environmental Accounting Literature from 1988 to 2008. The research confirms the findings of Mathews that in the late 1980’s and early 1990’s environmental ac-counting research prevailed. This sustained until 2003 when social acac-counting got more important again. The last research period between 2004 and 2008 showed equal levels of research for environmental and social accounting.

The study of Christ and Burrit (2013) for the year 2011 shows that in Australia the perceived level of Environmental Management Accounting is still low. Eco-control is part of environmental management accounting and it is the application of financial and strategic control methods to environmental manage-ment (Henri and Journeault, 2010). Henri and Journeault (2010) say that eco-control consists of perfor-mance measures, budgeting and incentives. As will be explained in the next section, eco-efficiency is therefore a part of eco-control as it is a performance measurement tool.

As Burrit, Hahn and Schaltegger (2002) point out, environmental accounting is broader and the basis for environmental management accounting. Environmental accounting includes both internal and exter-nal accounting while environmental management accounting is just about interexter-nal accounting. Environ-mental management accounting can be further broken down into monetary environEnviron-mental management accounting (MEMA) and physical environmental management accounting (PEMA). Difference is that the former is based on conventional Management Accounting only adapted for environmental aspects and it is used as a decision tool. The latter serves as an information tool for decision making and focuses on the impact on the natural environment (Burrit et al, 2002).

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Triple Bottom Line

One very well-known concept that includes environmental accounting is Triple Bottom Line (TBL) accounting as described by Elkington (1997). Future success is dependent on the successful combination of social, environmental/ecological and financial abilities. Until then these three parts were seen as in-dividuals and Elkington (1997) was able to map these as a whole interacting and influencing system. Likewise the expression three P “People-Planet-Profit” was formed.

Above a schematic overview of the idea of TBL or 3P is presented. The three areas need to be equally considered to assured organisational performance.

Although the construct of Elkington was and still is extoled, there are also critics which expect that companies are and have been aware of their stakeholders and that specific social and environmental reporting would be dispensable. Furthermore they argue companies might hide behind TBL and might take less effort in social and environmental areas (Norman and MacDonald, 2004).

Sustainability Balanced Scorecard

To embed sustainability into the corporate routine it is often tried to fit it into existing theories and concepts by expanding them. One very famous but also much disputed concept is that of the balanced scorecard (BSC) introduced by Kaplan and Norton (1996). They proposed four perspectives –financial-customer-learning and growth-internal business processes. Hubbard (2009) proposes the sustainable balances scorecard (SBSC) to measure organizational sustainability. In that TBL and the ‘classic’ bal-anced scorecard by Kaplan and Norton (1996) are integrated. Through adding non-market environmen-tal and social elements to the BSC the SBSC covers a broad range of possible measurements. These new measurements need entirely new data (Möller and Schaltegger, 2005). Möller and Schaltegger (2005) suggest an efficiency ratio as it is not bound to the financial or technological dimension. It is able to combine an economic and an environmental dimension. Eco-efficiency is an instrument for estimating and controlling the key performance indicators for these two dimensions.

Life cycle costing (LCC)

One concept that is trying to integrate environmental costs into business context dates back to the 1970’s. Life cycle costing was promoted by as well the US as the UK government. It is an analysis method where all costs of a product from inception to its disposal are included (Sherif and Kolarik, 1981). There are many variants of LCC tools among which full cost accounting, total cost assessment, total cost accounting, full cost pricing and others (Gluch and Baumann, 2004). Different authors advocate the use of environmental LCC (Swarr et. al, 2011; Krozer, 2008; Gluch and Baumann, 2004). Although Gluch and Baumann (2004) are more sceptical about a proper usability as there are many difficulties such as underrating future costs, biases and poor availability and reliability of data.

7 In figure one the relations between Environmental Accounting, Environmental Management Account-ing, Eco-control and the different concepts are presented. Environmental Accounting is the broadest term including EMA. EMA on the other hand is concerned with internal accounting and therefore prac-tical methods are found within. Eco-efficiency, SBSC and LCC are concepts of EMA trying to support decision making.

In the following section the core concept of this study, eco-efficiency, is presented in more detail.

2.3. E

-E

Eco-efficiency is part of environmental accounting. It is a returning element in concepts that try to map environmental accounting. As it is also the underlying concept of this study it is explained in detail. The term of efficiency is widespread, but also widely defined. In general it is about creating eco-nomic value while decreasing environmental impact. In this combination it fits perfectly in the frame-work of Sustainability Development. The OECD (2005) gives a definition for eco-efficiency which combines the major views: “Eco-efficiency profiles combine economic contribution and environmental burden by industry. The economic contribution is represented, for example, by the percent each industry contributes to GDP or employment. The environmental burden is represented by the percentage each industry contributes to the emission of various residuals, or the use of materials and energy.”

Huppes and Ishikawa (2005) propose to make eco-efficiency an overarching concept with four basic variants, depending on the type of the nominator and denominator.

 Environmental productivity

 Environmental intensity of production  Environmental improvement cost  Environmental cost-effectiveness

Product primary Environmental improvement primary

Economy divided by environ-ment

Environmental productivity Environmental improvement cost

Environment divided by econ-omy

Environmental intensity of pro-duction

Environmental cost-effective-ness

Environmental Accounting

Environmental Management Accounting

SBSC

LCC

Eco-Control

8 Additionally there are the following related concepts: energy productivity, resource productivity, capital productivity and labor productivity. (Huppes and Ishikawa, 2005)

These definitions show that eco-efficiency does not have to be a ratio by definition. But as eco-efficiency is meant to be a management tool and data is easier to process and analyse, it enables management to operate with sound and reliable figures. The only difficulty with a ratio is, as Figge and Hahn (2013) describe, the ratio has to be benchmarked as it is otherwise not clear what that ratio specifies.

In this thesis the definition used by of Figge and Hahn (2013) is adopted. They use a Dupont formula that is also used for Capital efficiency and translate it into the Eco-efficiency ratio.

𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =𝑅𝑒𝑡𝑢𝑟𝑛 𝐸𝑞𝑢𝑖𝑡𝑦 = 𝑅𝑒𝑡𝑢𝑟𝑛 𝑆𝑎𝑙𝑒𝑠 𝑥 𝑆𝑎𝑙𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑥 𝑇𝑜𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 𝐸𝑐𝑜 − 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = 𝑅𝑒𝑡𝑢𝑟𝑛 𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 =𝑅𝑒𝑡𝑢𝑟𝑛 𝑆𝑎𝑙𝑒𝑠 𝑥 𝑆𝑎𝑙𝑒𝑠 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑥 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠

The separation of the eco-efficiency ratio into three components: sales margin, capital turnover and sustainability leverage allows determination of what drives eco-efficiency. Sales margin and capital turnover are the return on economic capital. Sustainability leverage is analogue to financial leverage. The lower the sustainability leverage the more environmental resources a company requires relative to economic capital (Figge and Hahn, 2013). Through a good use of environmental resources sustainable value is created, this is also in analogy to shareholder value.

The term eco-efficiency fits quite well into the business sector as the pursuit of efficiency is well known (Hahn et al., 2010).

A more practical use is the CO2-efficiency as climate change is mostly identified with the output of CO2

-emissions. CO2-emissions are one of the most important pollutants and is especially not limited to boarders. For this purpose Figge and Hahn (2013) narrow down the eco-efficiency ratio to a CO2

-effi-ciency: 𝐶𝑂2 − 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑝𝑟𝑜𝑓𝑖𝑡 𝐶𝑂2 − 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 = 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑝𝑟𝑜𝑓𝑖𝑡 𝑆𝑎𝑙𝑒𝑠 𝑥 𝑆𝑎𝑙𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠𝑥 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 𝐶𝑂2 − 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠

CO2-efficiency is measured in Euro per tonne CO2- emission. If the ratio is greater than 1 then Sustain-able Value is created. Otherwise it has to be assumed that the respective company was acting on the expense of the environment.

2.4. F

H

“Is it possible to replicate the study by Frank Figge and Tobias Hahn (2013)?” is the main question of this study and to answer this research question and to be able to extend the research a couple of hypoth-eses (H1 to H3b) have to be set up:

 Relationship between the original study and the replication,  Differences between sectors and

9 Camarero et al. (2013) found an upward trend in eco-efficiency for companies between 1980 and 2008. Especially the CO2-efficiency level was clearly better than in 1980. The findings of Tyteca (1996) are comparable. He found that while during the late 1970’s environmental pollution was not punished by stakeholders, in the late 1980’s there were first indications of a reward for less pollution. Furthermore the study of Sinkin et al. (2008) discovered that eco-efficient firms are more highly valued by the market than firms that are not eco-efficient. With this in mind and the ongoing development in corporate sus-tainability the first hypothesis is:

H1: Based on the original sample we will see improvements in eco-efficiency in 2011.

Tahara et al (2005) show the importance of each company developing its own eco-efficiency index, but that it is also necessary to develop a standardized indicator for an industry sector. They found significant differences between service sector and manufacturing sector. Also within the manufacturing sector there are differences between companies using primary materials and assembly sectors. Guenster et al. (2011) found evidence that a strong eco-efficiency policy can be significant from a financial perspective. From this it is suspected that the banking sector has a higher average eco-efficiency than the chemicals or industrials sector. To verify this the second hypothesis is formulated:

H2: The banking sector will be more eco-efficient on an average than the chemicals sector.

In addition to the above results Camarero et al. (2013) found that the United States had low eco-effi-ciency. More in detail the CO2-efficiency was below average. Germany however was slightly better and performs around the average. Callens and Tyteca (1999) emphasize that geographical and socio-demo-graphic environment in which a plant or company is located are important. E.g. in the middle of an uninhabited area or in an urban area where pollution is of far greater interest as it directly influences a large number of people. Research on Japanese firms (Burrit and Saka, 2006) found that these have al-ready developed and implemented eco-efficiency indicators and that because of this development the government started to promote Environmental Management Accounting. Therefore the last hypothesis is:

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3. M

ETHOD

This section describes the research methods and explains how the original study was replicated, how the data was collected and how it was analysed. Lastly important research criteria are addressed.

3.1. R

As mentioned in the introduction this study is a replication of the research performed by Figge and Hahn (2013) “A replication is a duplication of a previously published empirical study whose purpose is to determine whether the findings of that study are repeatable. “ (Hubbard and Vetter, 1996) There are two kinds of replication studies: direct or exact replication and replication with extension. A direct replica-tion uses another sample from the same populareplica-tion as the original study. A replicareplica-tion with extension tries to generalize the earlier results. Certain aspects of the earlier study are modified: e.g. population, geographical areas, time periods or a combination of them.

Sekaran and Bougie (2013) define replicability of a study as the possibility of a re-study. Therefore it is important that a detailed description of the study design is provided.

Hubbard and Vetter (1996) found that there were no exact replications, while there were 266 replications with extension, about 6.2 % of the published articles. The lack of replications restricts the empirical generalizability.

3.2. R

The original research by Figge and Hahn developed an Eco-efficiency formula that is split up into three components. They tested their formula practically for the car manufacturing sector. They used a sample of 16 companies and determined the Sales margin, the Capital Turnover, the CO2-leverage and the associated multiples. There is no further information about the underlying data and the way of calcula-tion (“Own calculacalcula-tions based on company reports”). By means of the ROC multiple and the CO2-leverage multiple the use of economic and environmental capital is shown in a diagram.

3.3. R

As the original data was unavailable, data had to be collected in a comparable way Figge and Hahn conducted their study. First the necessary variables had to be identified, then the according benchmark and finally a decision for a data source had to be made. In the next section it is explained which variables and which database were used and why.

3.3.1. Variables

As explained in the literature section, the CO2-efficiency ratio includes four variables: Operating profit, Sales, Total Assets and Co2-emissions. Therefore the provided data Operating Income, Net Sales, Total Assets and “CO2-equivalents emission total” are chosen. It was decided that three samples will be needed. A replication of the original research, a sample for the German, US and Japanese banking sector and a sample for the German, US and Japanese chemicals sector.

3.3.2. Datastream and Asset 4

Datastream is a Thomson Reuters Database that contains more than 3 million global financial instru-ments, securities and indicators across multiple asset classes. It includes international macroeconomics, equities, indices and estimates. (Datastream Factsheet)

11 calculated. With their help the performance in the 4 fields, Economic, Environmental, Social and Cor-porate Governance are shown (Thomson Reuters, 2010). Data included is strictly publicly available and combined in Asset 4.

3.3.3. Data analysis

All necessary variables are selected and found through the Datastream Navigator. The data is summa-rized in an Excel document and needs to be edited. First all companies that do not provide all four variables are removed. For the remaining data the Sales margin, the Capital Turnover, the CO2-leverage are calculated. Then the data is transferred to SPSS. For the comparison in time a paired samples t-test is performed (Pallant, 2010, p.243). For the sector and country comparison each an independent samples t-test was performed.

3.4. R

C

To ensure a high quality research in this study it needs to be free of bias. Quality criteria are controlla-bility, reliability and validity:

3.4.1. Controllability

Controllability is essential for reliability and validity; description of data collection and analysis methods facilitates to control or even replicate the research in the future. For this study the following questions as proposed by van Aken et al. (2012) are important to answer:

 How was data collected?  How was data analysed?

 What conclusions were drawn / how were conclusions drawn? These questions are answered within this methodological section.

3.4.2. Reliability

If research results can be replicated at any other moment and the results that are measured are consistent the research and the instruments are called reliable. (Sekaran and Bougie, 2013; van Aken et al., 2012) A measurement is more reliable when it is bias free. (Sekaran and Bougie, 2013)

Reliability can be increased by precisely documenting data collection methods and data analyses. There are four potential sources of bias:

Researchers and Reliability

Research results must be independent from the researcher conducting the study (van Aken et al., 2012) As this research does not consist of interviews which has a higher possibility of being biased by the researcher this is just relevant for the interpretation of the results. By linking the results to the existing literature this bias is reduced as much as possible.

Instruments and Reliability

Research instruments should provide the same results as any other instrument. Through using multiple instruments at once the research gets more reliable (van Aken et al., 2012). As this study is a replication the choice for more than one instrument is not made.

Respondents and Reliability

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Circumstances and Reliability

Place or time of an interview may affect the answers of an interviewee. Through changing these, results get more reliable (van Aken et al., 2012). Once again this is not applicable as there were no interviews conducted.

3.4.3. Validity

Validity is concerned with whether the measurement is suitable for the concept. Sekaran and Bougie (2013) define three types of validity:

Content validity requires that an adequate and representative set of items is included in the measure; Criterion-related validity, the measure differentiates individuals on a criterion it is expected to predict, Construct validity, the use of the measure fits the theories around the test.

Van Aken et al. (2012) on the other hand define the following three validities: construct, internal and external validity. As there is not much difference in the content of the different validities in the following the description by van Aken et al. (2012) is explained.

Construct Validity

Is the measurement instrument suitable to measure what is intended to? The whole concept should be covered and there should be no parts that do not fit the concept. It is supposed that construct validity is given as we try to test whether the original study found valid results.

Internal Validity

Internal validity can be achieved if conclusions about relationships can be justified and are complete. Through good explanation of the developed hypotheses and detailed description of the results and inter-pretation this study will be internally valid.

External Validity

If research is generalizable and transferable it is externally valid. This is of concern in two ways for this study. First it is tried to generalize another research and second the findings should be generalizable. If the earlier study is generalizable so should this study. It would therefore be external valid.

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4. R

ESULTS

A résumé of the original results is the starting point. A comparison of the original study from the year 2007 with the same sample from the automobile industry in the year 2011 is the replication of the study of Figge and Hahn. For the extension of the original study the other samples originate from the banking sector and from the chemicals sector. Three countries, the United States, Germany and Japan are chosen. This should show major differences between Europe, the US and the Asian region. For sector compari-son the same year - 2011 - is selected. Two reacompari-sons lead to this decision: it is a more or less post crisis year, while 2007 is a pre-financial crisis year and because of data selection reasons: it is one of the years where the most data is available for all sectors.

4.1. S

F

H

(2010)

2007

In 2007 Honda had the highest CO2-efficiency and Ford the lowest CO2-efficiency. Besides Ford four other companies have a CO2-efficiency less than 1 (Fiat: 0.3553; Ford: 0.1070; GM: 0.1503; Isuzu: 0.9506; VW: 0.9474). Besides Honda (4.76), BMW (3.35), Daimler (2.02) and Renault (2.010) are the most eco-efficient. As you can see in table 1 the causes are either economic (higher ROC) or the higher sustainability performance (higher CO2-leverage). E.g. the high eco-efficiency of Daimler is owed to the high CO2-leverage, while the ROC is with 1.95 % rather low. The high eco-efficiency of Honda is a combination of good economic and sustainability performance. (ROC=11.77%; CO2-leverage= 40.42) The poor performance of Fiat is caused by both a low ROC (4.42 %) and a low CO2-leverage (8.03). Fiat was for a rather long time in a permanent crisis which would partially explain those results. Table 1: Sample as in Figge and Hahn (2010) for 2007

Sales margin (I) Capital Turnover (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) BMW 7.51% 62.32% 4.68% 71.79 3.36 Daimler 7.71% 76.26% 5.88% 34.47 2.03 Fiat 3.01% 146.78% 4.42% 8.03 0.36 Ford 0.49% 61.50% 0.30% 35.25 0.11 GM 1.09% 121.36% 1.32% 11.40 0.15 Honda 8.35% 141.04% 11.77% 40.43 4.76 Hyundai 4.10% 82.82% 3.40% 34.47 1.17 Isuzu 5.09% 128.74% 6.55% 14.51 0.95 Mitsubishi 4.00% 171.38% 6.86% 19.44 1.33 Nissan 7.31% 90.20% 6.59% 21.25 1.40 PSA 2.91% 87.74% 2.56% 71.79 1.84 Renault 3.31% 59.04% 1.95% 102.89 2.01 Suzuki 3.85% 199.26% 7.68% 16.07 1.23 Tata 9.48% 113.98% 10.81% 9.59 1.04 Toyota 8.65% 80.36% 6.95% 23.58 1.64 Volkswagen 5.63% 74.62% 4.20% 22.55 0.95 2011

14 Table 2: Sample as in Figge and Hahn (2010) for 2011

Sales margin (I) Capital Turnover (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) BMW 11.42% 56.64% 6.47% 86.36 5.59 Daimler 7.03% 73.29% 5.15% 42.31 2.18 Fiat 4.02% 76.03% 3.06% 18.67 0.57 Ford 8.60% 82.58% 7.10% 32.36 2.30 GM 5.42% 103.92% 5.63% 18.93 1.07 Honda 6.89% 78.32% 5.39% 2,614.09 141.01 Hyundai 9.90% 71.36% 7.06% 47,737.07 3,371.92 Isuzu 6.24% 128.35% 8.01% 6,850.36 548.50 Nissan 6.13% 82.25% 5.04% 3,720.40 187.46 PSA 2.09% 88.68% 1.85% 48.04 0.89 Renault 2.10% 58.90% 1.24% 60.76 0.75 Suzuki 4.10% 121.70% 4.99% 2,642.57 131.85 Tata 8.46% 120.11% 10.16% 1,497.38 152.14 Toyota 2.09% 63.95% 1.33% 4,111.20 54.84 Volkswagen 5.94% 64.43% 3.83% 27.32 1.05

To compare the two samples for the two points in time a paired-samples t-test is chosen. It is just one group of companies and the data is collected at two different occasions. Companies’ efforts to improve numbers affecting the calculations can be seen as some kind of intervention resulting in different results at the later time. For each company the variables are added in SPSS for the years 2007 and 2011. Then the variables are compared to each other.

The paired-samples t-test shows no significant differences for sales margin, ROC, CO2-leverage and the CO2-efficiency as the p-values are greater than 0.05. Only the capital turnover shows a significant difference between 2007 and 2011 (p=0.04 (2-tailed)). Focus of this study is on the CO2-efficiency. Although the mean CO2-efficiency increased between 2007 and 2011 from 1.53 to 306.80 and the first impression also was an increase in CO2-efficiency the test statistics show that there is no significant difference between 2007 and 2011. The correlation shows the same results with even a negative effect for CO2-efficiency but with a positive effect for capital turnover (0.68). The test statistics for the paired samples t-test can be found in Appendix I. As a result of those figures hypotheses 1 has to be declined.

4.2. CO2-

S

The first extension is a comparison of the banking sector with the chemicals sector. After the data was edited there remained 21 banks and 50 companies from the chemicals sector in the sample. As this compares two different groups of participants/companies, an independent samples t-test is performed. The independent variable is the sector-chemic or bank- and the dependent variables are the sales margin, the capital turnover, the ROC, the CO2-leverage and the CO2-efficiency. It tests whether there is a statistically significant difference between the mean values.

Before checking whether the differences between the sectors are significant it has to be examined whether the variances of the two groups are assumed to be the same. If the Sig. value for the Levene’s test is larger than 0.05 equal variances are assumed. As the Sig. value is 0.00 for all variables, equal variances are not assumed. The t-test gives the following results:

15 With all p-values less than 0.05 all variables are significant different. The mean CO2-efficiency of the banking sector is 1,389.77 and 29.82 for the chemicals sector. It can be summarized that the banking sector is more CO2-efficient than the chemicals sector, this could be because of an economic or envi-ronmental outperformance or an outperformance of both value drivers. In this case the banking sector has a rather low mean ROC but a huge CO2-leverage of 235,105.84. What stands out is the difference in the ROC with a mean of the banking sector of 1% and 9% for the chemicals sector with a standard deviation of 4% for the chemicals sector. The test statistics for the independent samples t-test can be found in Appendix II and the calculations for the companies in Appendix V.

The independent samples test showed a statistically significant difference in CO2-efficiency between the banking and the chemicals sector and with a higher CO2-efficiency of the banking sector, the second hypotheses can be accepted.

4.3. CO2-E

C

After the data was edited there remained 32 Japanese firms, 10 German firms and 29 US firms in the sample. Two independent samples t-tests are performed to compare the German and the US firms and the German and the Japanese firms. The procedure is the same as for the sector analysis, described above.

Germany and US

The independent samples t-test for the comparison between Germany and the US shows that equal var-iances are assumed (Levene’s Sig. value =0.143) for CO2-efficiency. The same applies for capital turn-over and ROC. For CO2-leverage and sales margin equal variances are not assumed. The t-test gives the following results:

 Sales margin: t(28.83)=-3.04; p=0.03 (two-tailed)  Capital turnover: t(37)=0.11; p=0.92 (two-tailed)  ROC: t(37)=-0.16; p=0.88 (two-tailed)  CO2-leverage: t(9.45)=1.16; p=0.27 (two-tailed)  CO2-efficiency: t(37)=-1.02; p=0.32 (two-tailed)

The mean CO2-efficiency of the US firms is 7.6 and the mean of the German firms is 2.34. The most CO2-efficient German firm is the Deutsche Bank, the most CO2-efficient US firm is Credicorp Ltd. The least CO2-efficient are Linde (Germany) and Cabot Corp (USA). The 2-tailed significance value (0.32) shows that there is no significant difference between the two countries with regard to CO2-efficiency. Further investigation shows that just CO2-leverage and sales margin have different variances, but for all variables besides sales margin, the differences are not significant. The Sales margin between German and US’ firms are significantly different. Hypotheses 3a, stated that German companies would be more eco-efficient. That was not confirmed and therefore hypotheses 3a has to be rejected. The detailed test statistics can be found in Appendix III and the calculations for the companies in Appendix V.

Germany and Japan

The independent samples t-test for comparison between Germany and Japan shows that equal variances are not assumed (Levene’s Sig. value = 0.005) for CO2-efficiciency. The same applies for CO2-leverage and ROC. For the sales margin and the capital turnover equal variances are assumed. The t-test gives the following results:

16 As mentioned above the mean CO2-efficiency of Germany is 2.34, for the Japanese firms the mean CO2-efficiency is 951.02. The Japanese company with the highest CO2-efficiency is Mitsubishi Ufj Financial (8,314.89). The independent samples t-test must indicate whether the differences between Germany and Japan are significant. The differences of CO2-leverage and CO2-efficiency are signifi-cantly different. Sales margin, capital turnover and ROC are not signifisignifi-cantly different. The mean num-bers show that Japanese companies are more CO2-efficient than German firms. The detailed test statis-tics and the calculations for the companies can be found in Appendix IV and V.

As Hypotheses 3b stated that Japanese firms might be more eco-efficient, Hypotheses 3b can be ac-cepted.

5. D

ISCUSSION AND

C

ONCLUSION

It was strived to reproduce the study of Frank Figge and Tobias Hahn from 2013. As the original data was not available, it was performed as a replication with extension. Noticeable is the clear outperfor-mance of Japanese firms through all sectors. The reason can be found in the energy structure of Japan. They need to import 84 % of their energy requirements and 30 % of the country’s electricity stem from nuclear power which is not very CO2 polluting. Germany in comparison had just 18 % from nuclear energy but got almost half of its electricity delivered from coal, which is emitting high levels of CO2 (World Nuclear Association). The American Banks performed rather well but the chemical companies are not very sustainable. The reason for that may be the denial of climate change in the US and the associated reluctance to cut down CO2-emissions.

This study was able to replicate the study from Figge and Hahn (2013) but had some rather surprising outcomes. One of the goals of Figge and Hahn was to provide a meaningful tool to support decision making. The use of the formula is easy, but it must be clearly defined which variables have to be chosen. It was expected that the Eco-efficiency of the original sample would have increased by 2011. This trend was not observed. Instead of a clear direction most companies had a change in either economic or envi-ronmental performance. There are several explanations for that. One possibility is that Eco-efficiency has not a linear direction over time. Value drivers of Economic value and Sustainable Value as found by Figge and Hahn (2013) are Sales margin, Sales growth, Investment of economic capital, cost of economic capital, use of environmental resources and cost of environmental resources. These six make it on the one hand an instrument that covers a broad range but on the other hand it is also prone to high volatility. Another possibility are difficulties with the calculations and therefore difficulties with the replication. It is noticeable that there are relatively high differences in the absolute numbers of the CO2-efficiencies. E.g. some Japanese firms have very high CO2-leverages and therefore high CO2-efficien-cies. Whether this is accurate or it is a calculation mistake is not possible to verify, as the data and calculation of Figge and Hahn are not available. When acting on the assumption that the calculation is correct it is still a huge variance. A third possibility is that the calculations are correct but that there are difficulties with the different ways of data collection. While Figge and Hahn (2013) analysed data from corporate reports, this study relies completely on an independent database. While this has advantages as data is collected very fast, it is difficult to verify whether the data is correct. E.g. it could be compromised through problems with the exchange rate. While the choice of a good database like Datastream in this study reduces this bias, it cannot be excluded. The t-test also showed that there is no significant differ-ence and therefore Hypothesis 1 must be declined.

17 the chemicals sector. High CO2-emissions can result from business travels, buildings and air condition-ing. The chemicals sector uses a lot of primary materials and is energy consuming and therefore pro-ducing high amounts of CO2-emissions.

Last but not least was the analysis of the country differences. It was supposed that Germany would perform better than the US and Japan better than Germany. With Eco-efficiencies of 2.097 (Japan), 3,595 (Germany) and 9, 81 (US) it can be determined that Hypothesis 3a must be declined and Hypoth-esis 3b can be accepted, as the US perform better than Germany. That is a rather surprising result, espe-cially because the sustainability development is not that far in the US as in Germany. Suspected reason is a bias in the sample size. There are just ten German firms in the sample while there are 29 US firms in the sample. Of course that is not a compulsory explanation, but that has to be verified in future re-search. With the eco-efficiency formula of Figge and Hahn this study was able to prove a widespread assumption, Japan is already outperforming the Western Industries, but has the disadvantage that it is primarily using nuclear energy. Another possible bias might be the sample size and the company size that was not taken into account in this study. That this could be a problem show Burrit and Saka (2005); they found that there will be no or less Environmental Management Accounting in smaller organisations, organisations in industries not subject to high levels of environmental exposure and those that have not incorporated environment into their strategy. For this case this could also be an indicator for the bad numbers of US firms. Environment is not as much a concern as in Germany and therefore public pressure is lower.

The initial research question was “Is it possible to replicate the study of Figge and Hahn (2013)?” After having conducted the research this question can be answered positively. It was not possible to directly replicate the results, but it was possible to show the importance of the calculations of eco-efficiency. The replication showed that the value drivers are the same, but that over time and across industry borders these drivers shift and that there are big differences between industry sectors. Although some results were surprising others were reassuring. In line with this results are the appreciation of fellow authors: E.g. “Figge and Hahn (2013) demonstrate how a widely used management account tool, DuPont analy-sis, can be adapted to provide an integrated assessment of corporate environmental and economic per-formance to support decision making for more efficient use of environmental resources.” (Bebbington and Thomson, 2013)

But maybe eco-efficiency must also be seen more critical. Are companies influencing their CO2 effi-ciency through the usage CO2 certificates? This was not topic of the study but may crucial for future research. Schmidheiny (1992) says hereby that eco-efficiency is not achieved by technological change alone. It is achieved only by profound changes in the goals and assumptions that drive corporate activi-ties and change in the daily practices and tools used to reach them. Burrit and Saka (2006) also criticise that there might be problems with focusing on efficiency as it is not the only thing conventional or environmental management accounting are concerned off.

18

6. A

PPENDIX

A

I

Paired Samples Statistics

Mean N Std. Deviation Std. Error Mean

Pair 1 Salesmargin2007 0.05 15 0.03 0.01 Salesmargin2011 0.06 15 0.03 0.01 Pair 2 Capitalturnover2011 1.02 15 0.40 0.10 Capitalturnover2011 0.85 15 0.23 0.06 Pair 3 ROC2007 0.05 15 0.03 0.01 ROC2011 0.05 15 0.03 0.01 Pair 4 CO2leverage2007 34.54 15 27.42 7.08 CO2leverage2011 4,633.85 15 12,102.20 3,124.78 Pair 5 CO2efficiency2007 1.53 15 1.22 0.31 CO2efficiency2011 306.81 15 860.12 222.08

Paired Samples Correlations

Pair 1 Salesmargin2007 & Salesmargin2011 15 0.22 0.44

Pair 2 Capitalturnover2007 & Capitalturnover2011 15 0.68 0.01

Pair 3 ROC2007 & ROC2011 15 0.30 0.27

Pair 4 CO2leverage2007 & CO2leverage2011 15 -0.06 0.82

19 Paired Samples Test

Paired Differences t df Sig. (2-tailed) Mean Std. Devi-ation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Salesmargin2007 – Salesmargin2011 -0.01 0.04 0.01 -0.03 0.01 -0.87 14 0.40 Pair 2 Capitalturno-ver2007 – Capital-turnover2011 0.17 0.29 0.08 0.01 0.33 2.25 14 0.04 Pair 3 ROC2007 – ROC2011 0.00 0.03 0.01 -0.02 0.02 0.21 14 0.84 Pair 4 CO2leverage2007 - CO2leverage2011 -4,599.32 12,103.99 3,125.24 -11,302.28 2,103.65 -1.47 14 0.16 Pair 5 CO2efficiency2007 - CO2effi-ciency2011 -305.28 860.22 222.11 -781.65 171.10 -1.37 14 0.19

A

II

sector N Mean Std. Deviation Std. Error Mean

20

Independent Samples Test

Levene's Test for Equality of Vari-ances

t-test for Equality of Means

21

A

III

Country N Mean Std. Deviation Std. Error Mean

Salesmargin Germany 10 0.10 0.05 0.02 US 29 0.18 0.09 0.02 Capitalturnover Germany 10 0.62 0.47 0.15 US 29 0.60 0.52 0.10 ROC Germany 10 0.08 0.06 0.02 US 29 0.08 0.07 0.01 CO2leverage Germany 10 1,410.52 2,587.02 818.09 US 29 447.16 696.84 129.40 CO2efficiency Germany 10 2.35 3.87 1.23 US 29 7.60 16.05 2.98

Independent Samples Test

Levene's Test for Equality of Vari-ances

t-test for Equality of Means

F Sig. t Df Sig. (2-tailed) Mean Differ-ence Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Salesmar-gin Equal vari-ances as-sumed 7.83 0.01 -2.32 37.00 0.03 -0.07 0.03 -0.14 -0.01 Equal vari-ances not as-sumed -3.04 28.83 0.01 -0.07 0.02 -0.12 -0.02 Capital-turnover Equal vari-ances as-sumed 0.31 0.58 0.11 37.00 0.92 0.02 0.19 -0.36 0.40 Equal vari-ances not as-sumed 0.11 17.25 0.91 0.02 0.18 -0.35 0.39 ROC Equal vari-ances as-sumed 0.65 0.43 -0.16 37.00 0.88 0.00 0.02 -0.05 0.04 Equal vari-ances not as-sumed -0.17 18.10 0.87 0.00 0.02 -0.05 0.04 CO2lever-age Equal vari-ances as-sumed 21.06 0.00 1.86 37.00 0.07 963.36 518.02 -86.26 2,012.97 Equal vari-ances not as-sumed 1.16 9.45 0.27 963.36 828.26 -896.67 2,823.38 CO2effi-ciency Equal vari-ances as-sumed 2.23 0.14 -1.02 37.00 0.32 -5.25 5.17 -15.72 5.22 Equal vari-ances not as-sumed

22

A

IV

Country N Mean Std. Devia-tion Std. Error Mean Salesmargin Japan 32 .12 .10 .018 Germany 10 .10 .05 .016 Capitalturnover Japan 32 .73 .40 .071 Germany 10 .62 .46 .14 ROC Japan 32 .05 .03 .006 Germany 10 .07 .05 .017 CO2leverage Japan 32 153,996.63 302,408.53 53,458.78 Germany 10 1,410.52 2,587.02 818.08 CO2efficiency Japan 32 951.02 2,190.79 387.28 Germany 10 2.34 3.87 1.22

Independent Samples Test

Levene's Test for Equality of

Vari-ances t-test for Equality of Means

F Sig. t Df Sig. (2-tailed) Mean Dif-ference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Salesmar-gin Equal vari-ances as-sumed 2.41 .12 .47 40 .64 .01 .034 -.05 .08 Equal vari-ances not as-sumed .65 31.16 .51 .01 .02 -.03 .06 Capital-turnover Equal vari-ances as-sumed .40 .52 .72 40 .47 .10 .15 -.19 .41 Equal vari-ances not as-sumed

.66 13.46 .51 .10 .16 -.24 .46

ROC Equal

23 Equal

vari-ances not as-sumed 2.85 31.01 .008 152,586.11 53,465.04 43,545.51 261,626.71 CO2effi-ciency Equal vari-ances as-sumed 8.96 .005 1.35 40 .18 948.67 698.71 -463.48 2,360.83 Equal vari-ances not as-sumed

24

A

V

Sales margin (I) Capital Turn-over (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) Deutsche Postbank 1.10% 4.50% 0.05% 2,587.11 1.28 Commerzbank 2.63% 3.73% 0.10% 7,674.46 7.54 Deutsche Bank 11.91% 2.50% 0.30% 3,813.02 11.35

Table 3: Japanese banks

Sales margin (I) Capital Turnover (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) Mitsubishi Ufj Fin 47.80% 2.16% 1.03% 806,109.86 8,314.89 Mizuho Finan-cial Grp 31.07% 1.67% 0.52% 807,396.62 4,185.92 Shinsei Bank Ltd -2.65% 4.37% -0.12% 689,597.91 -799.53 Sumitomo Mit-sui 24.60% 2.44% 0.60% 668,565.63 4,019.79 Sumi Mitsui Fin

Grp 29.56% 2.78% 0.82% 925,276.50 7,610.96 Resona Holdings Inc 24.56% 2.01% 0.49% 507,634.30 2,510.60 Hachijuni Bank, Ltd 24.44% 2.52% 0.62% 505,719.31 3,113.53 Table 4: US banks Sales margin (I) Capital Turno-ver (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) Bank of America 1.27% 5.11% 0.06% 1,235.60 0.80 Citigroup Inc 16.34% 5.68% 0.93% 1,721.65 15.98 Comercia Inc 23.93% 4.26% 1.02% 760.36 7.75 Credicorp Ltd 27.70% 10.81% 3.00% 2,779.94 83.27 Fifth Third Bancorp 30.88% 5.30% 1.64% 867.82 14.22 Huntigton Bancshr 24.42% 5.48% 1.34% 610.02 8.16 JP Morgan Chase &Co 28.60% 4.90% 1.40% 1,711.85 23.97 Keycorp 28.70% 5.27% 1.51% 919.93 13.91 PNC Finl Svcs Group 26.69% 5.89% 1.57% 631.62 9.93 U.S. Bancorp 30.94% 6.04% 1.87% 788.44 14.73 Wells Fargo &Co 29.52% 6.72% 1.98% 820.63 16.28

Table 5: German chemicals

25 Table 6: Japanese chemicals

Sales margin (I) Capital Turno-ver (II) ROC (I)x(II) CO2-leverage (III) CO2-efficiency (I)x(II)x(III) Mitsubishi Chem 7.15% 99.45% 7.11% 366.02 26.03 Nitto Denko 13.36% 99.09% 13.24% 934.44 123.73 Kaneka 4.73% 100.86% 4.77% 347.47 16.59 Asahi Kasei 7.69% 113.86% 8.76% 266.90 23.37 Teijin 5.95% 107.70% 6.41% 294.68 18.89 Toray 6.50% 100.34% 6.53% 311.27 20.31 Kurary 14.62% 72.26% 10.56% 290.69 30.71 Showa Denko 5.54% 93.48% 5.18% 340.32 17.64 Sumitomo Chemical 4.44% 84.49% 3.75% 247.77 9.29 Tosoh Corp 4.90% 95.33% 4.67% 91.34 4.27 Tokuyama 6.95% 61.78% 4.29% 73.64 3.16 Denki Kagaku Kogyo 6.88% 89.26% 6.14% 162.33 9.97 Shin-Etsu Chemical 14.10% 60.06% 8.47% 464.96 39.38 Nippon Shokubai Co 10.34% 88.50% 9.16% 300.62 27.52 Mitsubishi Chem Gas 5.18% 80.33% 4.16% 377.59 15.71 Mitsui Chemicals 2.91% 107.70% 3.14% 227.34 7.13 JSR Corp 11.48% 88.38% 10.14% 546.96 55.48 Daicel Corp 9.25% 86.23% 7.97% 248.00 19.78 Ube Industries 7.23% 94.48% 6.83% 59.49 4.06 Hitachi Chemical 8.74% 117.31% 10.25% 1,031.77 105.77 Kansai Paint Co 8.90% 87.65% 7.80% 5,890.50 459.72 Dic Corp 4.77% 117.11% 5.59% 965.38 53.92 Zeon Corp 13.07% 93.74% 12.25% 420.47 51.51 Nippon Kayaku Co 14.09% 72.70% 10.25% 2,989.50 306.29 Air Water Inc 6.63% 116.58% 7.73% 342.69 26.48

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