An empirical analysis of the relationship between innovation activity

An empirical analysis of the relationship between innovation activity and
environmental management toward climate change
Emiko Inoue
PhD candidate; JSPS Research Fellow
Graduate School of Economics, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501 JAPAN
E-mail: [email protected]
Abstract No. 0201
ABSTRACT
Climate change is one of the serious problems which we are facing right now. Many
simulations show that a great amount of emission reduction is necessary, but it seems
to be impracticable as long as we deal with in the existing framework. In this context,
technological innovation is now expected to become one of the essential factors to
change the existing framework and overcome this difficult situation. In order to
examine what may induce innovation, previous researches (e.g. Milliman and Prince,
1989; Jaffe and Palmer, 1997) analysed the relationship between environmental policy
and innovation, but there are few researches which focused on the influence of
environmental voluntary approaches on innovation activity. This study scrutinises how
the voluntary approaches toward climate change influence innovation activity of
corporations. Using firm-level panel data of European corporations (2000-08), which
was constructed based on the data of corporate responses toward “Carbon Disclosure
Project”, “EU industrial R&D Investment” data, and the corporations’ CSR reports, I
estimate two dynamic panel models by system GMM estimator. Innovation activity is
measured by R&D investment of the corporations. The results show that one of the
voluntary approaches influences on the R&D investment, and prove that corporations
which disclose the information verified in whole or in part by external entities are
likely to invest on R&D. This study reveals an interesting implication that it is
important to implement a policy which stimulates the corporations’ incentives for
disclosing the verified information by external entities in order to enhance innovation
activity.
Key words: Innovation, Voluntary approach, Climate change
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1. Introduction
Climate change is one of the serious problems which we are facing right now.
Although the fact that a great amount of emission reduction is inevitable is suggested
by many simulations such as IPCC Fourth Assessment Report (2007) and Stern
Review (2006), this is not an easy task and may sometimes be impracticable without
sacrificing further economic growth of both developed and less developed countries.
Technological innovation is now expected to play a key role to overcome this
difficult situation. This is because people begin to recognise the limits of dealing with
the difficulty based on existing technology and social systems. In order to tackle the
problem, it is necessary to handle it from new dimensions or frameworks which may
be realised through technological innovations and innovations of social systems.
Therefore, it is important to scrutinise which policies and/or factors may induce
innovation. Moreover, focusing on the activities of corporations which are one of the
significant actors in the economy is essential to examine effective policies and/or
factors to induce innovations. When considering the influence of “eco-friendly” trend,
it is also interesting to seek the relationship between corporations’ environmental
management and technological innovation.
This study focuses on the corporates’ voluntary approaches (henceforth VAs)
toward climate change issue, and examines the influence of those environmental VAs
on technological innovation which derives from R&D activity. In addition, the
influence of EU emission trading scheme (henceforth EU ETS) on innovation activity
is examined. Launched in 2005, EU ETS has been playing an important role in climate
change policy. By adopting VAs and interaction terms regarding EU ETS, I would like
to focus on the relationship between EU ETS and corporations’ innovation activities.
For this research, I focus on EU corporations. Since they are facing various kinds
of environmental regulations, such as carbon taxes and emission trading scheme, it is
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worth examining how those corporations react toward environmental regulations. In
addition, EU has unique data regarding environmental issues, especially climate
change. As a measure of technological innovation, R&D investment, the input for
innovation, is used. In order to deal with the influence of past R&D investments,
dynamic panel model is assumed in this analysis. I also include the financial status and
sectors which are the potential factors having impacts on R&D activity.
This study shows that one of the VA positively influence the R&D investment.
Corporations which disclose the information verified in whole or in part by external
entities are likely to invest on R&D. Whether EU ETS positively influence on R&D
investment or not is ambiguous, however, the result shows that some indirect impacts
are exist.
This study will contribute to the literatures in many points. It is unique in terms of
using data of Carbon Disclosure Project (henceforth CDP) which has not been used so
much for academic purposes. CDP enables us to examine first-hand information of
corporations’ responses regarding climate change issue, which may extract intriguing
factors. As far as I recognise, this is the first literature which focuses on how VAs
regarding climate change influence corporates’ R&D investment by using CDP survey
data. Moreover, this research may give interesting implications for environmental
policy to be effective on encouraging corporations’ innovation activity.
This paper is structured as follows: Section 2 provides the background literature,
and Section 3 mentions an overview of VAs and R&D. In Section 4, econometric
models and methodology used for the analysis are presented, and in Section 5, data
description is shown. The results are examined in Section 6, and finally a conclusion
and an implication of the study are discussed in Section 7.
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2. Literature review
The role of technological innovation has received great attentions in the field of
environmental economics (Jaffe et al., 2003). When facing on the long term
environmental problems, such as climate change, they remind us how technological
innovation is vital for society. Since the benefits of innovations tend to diffuse into
society, market mechanism alone has been little power to stimulate R&D incentive. On
the other hand, environmental regulation or public funding of R&D has often provided
opportunities of R&D activity (Popp et al., 2010). Therefore, technological innovation
has often been scrutinising in the context of environmental policy.
At the beginning, in 1970s, an interest was focused on which policy
measurement may induce innovations (e.g. Magat, 1979; Milliman, and Prince, 1989).
In 1990s, Porter Hypothesis brought a sensational argument to the relationship between
environmental policy and technological innovation; because the argument was unlike a
traditional view which economists had that time. Many economists, at that time,
believed that strengthened environmental regulation may impose additional costs to
corporations in order to comply with. However, in this hypothesis, Porter mentioned
that when corporations face with strengthened environmental regulations, they are
inclined to look for potential technological innovation (Porter, 1991). Through the
process of seeking for unnoticed seeds of innovation, and positive involvement in
R&D activity, corporations can encourage innovations which may boost the
competitiveness in the international market (Porter and Linde, 1995).
Many studies on the Porter Hypothesis were done thereafter. Lanjouw and
Mody (1996) examined the relationship between patents and pollution abatement cost,
a proxy for the stringency of environmental regulation, based on a data set of Japan,
U.S., and Germany. Their results showed that pollution abatement cost successfully
affected the number of patents, but they did not control for other factors which may
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influence technical innovation. Jaffe and Palmer (1997) analysed how the stringency of
environmental regulations may affect R&D expenditure and patents. Pollution
abatement cost was also used as a proxy for the severity level of regulations in this
study. They concluded that pollution abatement costs positively affected only R&D
expenditures.
In order to scrutinise the Porter Hypothesis, Jaffe and Palmer (1997) also
categorised it into three types: (i) narrow version, (ii) weak version, and (iii) strong
version. The first version mentions that flexible environmental policies may stimulate
corporations’ incentives to innovate more than prescriptive ones. Also it says that
environmental regulations should focus on outcomes rather than processes. The second
version states that “certain kinds” of environmental innovations may be stimulated by
environmental regulation. And the last type asserts that when environmental
regulations are properly designed, cost-saving innovations may be induced. This is
most attractive version of hypothesis, which would be related with competitiveness.
Hamamoto (1997) also examined whether the Porter hypothesis can be
observed in Japan by utilising the similar model as seen in Jaffe and Palmer (1997).
The result of this study which used Japanese industry-level data showed that pollution
prevention investment and low-energy consuming investment increased R&D
expenditures. Both investments were used as a proxy for the stringency of
environmental regulations. Brunnermeier and Cohen (2003) empirically analysed the
relationship between regulation and the environmental related technological innovation.
As a proxy for the stringency of regulation, they used pollution abatement costs and
the number of inspections committed by regulatory institutions. Although the pollution
abatement costs increased the environmental related patents, inspections did not affect
technological innovation.
Arimura et al. (2007) analysed what may induce environmental R&D, and
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tested the three variations of the Porter Hypothesis proposed by Jaffe and Palmer
(1997). For this analysis, data of manufacturing facilities in seven OECD countries was
used. The results showed that public policy could induce environmental R&D
investment. The adoption of an environmental accounting system also induced
environmental R&D investment, which was promoted by flexible policy instruments.
The direct relationship between flexible policy instruments and environmental R&D
investment was not proved. “Strong” version of the Porter Hypothesis was indirectly
supported in their study. Lanoie et al. (2011) also tested the Porter Hypothesis by using
same data as Arimura et al. (2007) did. In this study, four main elements:
environmental policy, R&D, environmental performance, and commercial performance
were examined. “Weak” version was strongly supported, “narrow” version was
conditionally observed, and “strong” version was not supported in their study.
In terms of the VAs, various studies analysed the determinants of
Environmental Management System (henceforth EMS) certificate adoption and the
effectiveness of implementing EMS on environmental performance. Nakamura et al.
(2001) found that several factors, such as firm size, the average age of employees and
export ratio affected the ISO 14001 adoption by using data of Japanese manufacturing
corporations. Nishitani (2009) mentioned that the determinants differed depending on
the year of adoption, and the relationship between economic performance and initial
ISO 14001 adoption was positively proved.
Regarding the effectiveness of certified EMS on environmental performance,
Potoski and Prakash (2005) found that ISO 14001 show a significant influence on
reducing the environmental impacts of US corporations. However, in Germany case,
Ziegler and Rennings (2004) could only find a significantly weak positive effect on
environmental innovation and abatement behaviour. Using Japanese facility-level data,
Arimura et al. (2008) examined the effectiveness of VAs (implementing ISO 14001
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and publishing environmental reports) on the environmental performance. The results
showed that both VAs contributed to reduce all the impacts, and environmental
regulations did not exert a negative effect on ISO 14001. They also found that ISO
adoption was encouraged by assistance programs of authorities which is one of the
VAs as well.
Few studies investigated the effects of VAs besides EMS on environmental
innovation. Demirel and Kesidou (2011) examined how external policy instruments
and firms’ VAs influence three different types of eco-innovations: end-of-pipe
technologies, cleaner production technologies, and environmental R&D. They found
that first and second types of innovation are influenced by VAs which improves
efficiency of machinery and equipment. The results also showed that environmental
regulations play an effective role to stimulate the first and last types of innovation.
3. Overview of VAs and R&D investment
3.1. What is the VA?
In the theory of environmental economics, the VAs are considered as a supplemental
measurement to traditional economic policies which use market mechanisms (Pearce
and Turner, 1990). However, if VAs are judged in more empirical context, they have
started to recognise as being more flexible, effective, and sometimes cost less than
those traditional economic policies (Alberini and Segerson, 2002; Arimura et al., 2008).
Moreover, VAs are considered to be one of the most important policy instruments to
reach environmental targets (Vogel, 2005). Therefore, it is worth scrutinising the VAs.
The term “VA” has a broad meaning which includes several kinds of
approaches, such as “self-regulation”, “voluntary agreements”, and “voluntary
initiatives”, to name just a few. Many studies (EEA, 1997; Carrao and Leveque, 1999;
Segerson and Li, 1999; Alberni and Segerson, 2002; Lyon and Maxwell, 2002; Brau
7
and Carraro, 2004) have agreed that VAs can be classified into three categories. The
first category is a unilateral action by a single polluter or a group of polluters, which
develops without any regulatory involvement. These unilateral commitments consist of
environmental improvement programmes set up by corporations. The second type is a
bilateral agreement or negotiated agreement between public authorities and a polluter
or group of polluters. Under this bilateral agreement, the terms of agreement are
determined through negotiation process between authorities and polluters. The last case
is voluntary government programmes or public voluntary schemes such as
Eco-management and Auditing Scheme (EMAS) initiated by EU. In this case,
authorities unilaterally determine both rewards for and obligations towards
participation, and criteria for participation.
In this study, I focus on the first category. This type is a basic form of VAs,
which occurs voluntary responding to the surrounding circumstances. Facing on the
world-wide trend towards increased environmental responsibility, there is no doubt that
corporations have been influenced by this trend and have been trying to become
eco-friendly. Not only the pressure from outside, this type of VAs is also strongly
connected with corporations’ characteristics and internal decision making, thus it is
interesting to examine.
3.2. Impacts of VAs on R&D investment
Based on the argument that innovation is connected with competitiveness by
Porter and Linde (1995), R&D investment, an input for innovation, is the source of
competitiveness. Since R&D investment is vital for corporations to success in the
market, they strategically consider the optimal conditions for investment (e.g. the
amount of investment, timing to invest), which strongly affected not only by the factors
like size, economic performance and human resources, but also the internal decision
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making process.
In addition, in this global economy, corporations are influenced by pressures
from outsides, such as stakeholders, media and global issues. Climate change issue is
one of serious global problems we have to face on. VAs regarding climate change is
one of the measurements which corporations have taken to deal with this issue. These
VAs are basically implemented by corporations without any regulatory involvement.
Therefore, VAs would become an indicator which shows how the corporations think
about the issue, and what they do to deal with. In this context, VAs may become one of
the ways to improve the appropriateness of its actions within the regulations, brand
images, and legitimacy. Not only stimulated by incentives to improve their values,
corporations also aim to find seeds to attain potential opportunities for further growth.
Some of the corporations are keen to implement VAs because through the
implementing process, they might seize the fact and circumstance precisely, which
would help them to integrate the strategy toward climate change to their business. The
corporations may also gain an important insight and information regarding R&D
investment through VAs. Therefore, my hypothesis is that VAs regarding climate
change are more likely to encourage R&D investment.
4. Model
In order to examine the relationship between environmental VAs and innovation, I
estimate two types of models. In Model 1, R&D investment divided by net sales is
used as the dependent variable. In Model 2, the dependent variable is R&D investment
divided by number of employees.
I assume that corporation’s environmental R&D investment is expressed as:
,
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(1)
where i denotes corporations and t years. Lagged dependent variable
,
adopted as one of the explanatory variables, as well as other control factors (
is
and
). Eq. (1) is a dynamic model which shows that R&D investment is determined by
its own past realisations. According to Baumol (2002), corporations which invest great
on R&D activity are likely to encourage R&D in the future as well. Many reasons for
this tendency can be explained by the corporate actions, for example, if a company
developed a new product in the process of R&D activity, a company may invest on
R&D further in order to improve the product or create superior substitution next.
Dependent variable in this study may also be influenced by past realisations of itself,
thus it is essential to include its lagged variable in a set of explanatory variables.
is dummy variables of corporations’ voluntary approaches.
of control variables. ,
and
individual corporation-specific effect,
is a set
are parameters to be estimated.
is a time specific effect, and finally
is an
is
the error term. To control for time-dependent determinants of R&D investments such
as changes in environmental policies which affect corporations’ overall R&D
incentives, time specific effects ( ) are included. Corporation-specific effects ( )
captures the response of each corporation to external factors such as regulatory shocks.
Several econometric problems may arise from estimating the dynamic model
equation above. Firstly, VA is assumed to be endogenous variables which may correlate
with the error term due to two-way causality between VA and R&D investment. If we
fail to take this relationship into account, a simultaneity bias may occur. The fact that
endogenous variables are included in this model makes it difficult to comply with the
“strict exogeneity” condition. “Strict exogeneity” means that correlation between the
error term and explanatory variables across all time periods may occur (Wooldridge,
2010). If variables which are not strictly exogenous are included, OLS estimator may
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be inconsistent.
Secondly, the lagged dependent variable is endogenous to the individual
effects
, which may give rise to a dynamic panel bias. When “small T, large N"
panels (few time periods and many individuals) are used for estimation, this bias may
become significant (Roodman, 2006). Since the lagged dependent variable would be
correlated with the error term, OLS estimator is inconsistent.
Based on these econometric problems, I must therefore focus on estimation
methods that can be used for explanatory variables or instruments which are not
strictly exogenous. In this study, generalized method of moments (henceforth GMM) is
used for the dynamic models of panel data. These models adopt lags of the dependent
variables as covariates, and include unobserved individual effects. The difference
GMM, a consistent GMM estimator of dynamic panel models, is developed by
Arellano and Bond (1991). In order to eliminate the individual effects, the difference
GMM uses Eq. (2) by first-differencing Eq. (1), and adopts previous observations of
the endogenous variables and lagged dependent variable as instruments. It is based on
the assumption that the error term is not serially correlated and the explanatory
variables are not correlated with future realisations of the error term (Roodman, 2006).
∆
∆
,
∆
∆
∆
(2)
However, this difference GMM has limitations that the lagged levels may
become weak instruments for the first-differences when the explanatory variables are
persistent over time, and this may lead to a large finite sample bias (Blundell and Bond,
1998). In my study, some of the explanatory variables are persistent over time, and
there is some possibility that it may lead to the bias and imprecision.
Therefore, to deal with this problem and to generate consistent and efficient
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estimates, I selected the System GMM estimator, which is proposed by Blundell and
Bond (1998) as an extended version of the differenced GMM estimator. Blundell and
Bond (1998) mention that it is possible to overcome the bias and poor precision by
using two equations. One is the difference Eq. (2) adopting suitably lagged levels as
instruments, and second equation is the Eq. (1), the equation in levels, which uses
suitably lagged differences of the explanatory variables as instruments. One-step
estimate is implemented to obtain the results.
A misspecification
test
for
second-order serial correlation in the
first-differenced error term is done. If we cannot reject the null hypothesis that there is
no second-order serial correlation in the differenced residual, the error term
(in
levels) is not serially correlated at order 1. In this case, the estimated model is
supported. I also perform a Sargan test of overidentifying restrictions (OIR), which
tests for the validity of the instruments used in the model. The null hypothesis is that
overidentifying restrictions are valid. If this null hypothesis is not rejected, the
instruments can be considered valid.
5. Data description
5.1. CDP survey data and R&D investment data
To scrutinise the relationship between VAs regarding climate change issue and R&D
investment, the firm level dataset (fiscal year 2000-08) of EU corporations (301
corporations) is structured based on data of “CDP”, “EU industrial R&D Investment”,
and corporations’ CSR reports.
The Carbon Disclosure Project, CDP, is an independent non-profit
organisation working on how to actualise greenhouse gas emissions reduction and
sustainable water use. Working with the world’s largest investors, businesses and
governments, CDP engages in the actions to realise a more sustainable economy, and
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provides measurement and information for thousands of companies and cities to
improve their management toward environmental risk. As said in an old management
adage “You can't manage what you don't measure”, it is difficult to manage for
improvement if we do not measure to see what is going on. CDP also thinks that
evidence and insight is vital for real change, and gives an incentive to corporations and
cities to measure and disclose their greenhouse gas emissions, potential risks and
opportunities related to climate change, and strategies for managing those risks and
opportunities by leveraging market forces including shareholders, governments and
rival corporations (CDP, 2012).
Figure 1: Number of institutional investors and the amount of funds under management
(Trillion USD)
Source: CDP website (2012)
On behalf of institutional investors, CDP requests information on greenhouse
gas emissions, energy use and the risks and opportunities from climate change from
thousands of the world’s largest companies. This programme started in 2003, and the
questionnaire has being sent to companies each year. The quantity and quality of data
which disclosed by companies has advanced significantly, and the number of
responding companies has been growing since the first CDP report in 2003 (see
figure.2). In 2009, backed by 475 institutional investors with $55 trillion in assets (see
Figure 1), the questionnaires were sent to more than 3,700 of world’s largest
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companies, and about 60% of companies responded. If you focus on Global 500
companies (500 largest companies in the FTSE Global Equity Index Series), the
overall response rate for CDP2009 was 82% (409 companies) (CDP, 2012)
Figure 1 and 2 show that CDP has been gaining its influence on business
arena and financial market. Those globally collected climate change data is now
utilised for the evidence of investment and policy decision making. CDP data is
increasingly becoming more integrated into financial analysis, and indices such as the
Carbon Disclosure Leadership Index (CDLI) and the Carbon Performance Leadership
Index (CPLI) are focused on. CDLI is an index which indicates high disclosure quality
of companies’ responses, and CPLI index is qualified to companies which are taking
positive measures on climate change mitigation. The responses and indices are
disclosed to public.
CDP data is very unique and valuable because it is possible for us to examine
the first-hand response from corporations, which is not always possible in other
surveys. However, there are few academic papers using this CDP data, and the data is
not fully scrutinised. This study is original in terms of using new dataset constructed
mainly based on this CDP data.
Figure 2: Number of responding companies to CDP
Year
Source: CDP website (2012)
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Regarding R&D investment, data of “EU industrial R&D Investment” and
corporations’ CSR reports are used. The EU Industrial R&D Investment Scoreboard
was first issued in 2004 and has been providing firm level data of R&D investment
since then. This data has been practically used to monitor the situation on R&D in the
EU. CSR reports of each company are also used in this research to supplement CDP
data and R&D investment data.
5.2. Variables
A set of variables, besides the lagged dependent variable, is summarised in Table 1. As
for characteristics of corporations, the following variables: “lnPROFIT” and
“lnMRKTCPTL” are focused on. Corporations’ economic performance is explained by
the variable “lnPROFIT” which indicates operating profit. Since corporations with
better economic performance are more likely to pursue environmental goals
(Nakamura et al., 2001), the coefficient for “lnPROFIT” is expected to be positive.
Market capitalisation (lnMRKTCPTL) shows not only the economic performance but
also explains overall evaluations of corporations in the market. Stakeholders’ decision
is one of the most important external factors which influence corporate actions. It is
plausible that larger corporations may experience more pressure to be green from
stakeholders (Nishitani, 2009). Corporations with high value of market capitalisation
also tend to be affected by those pressures. This external pressures may encourage
corporations to be green and may increase R&D investment. Considering this
possibility, I predict that the expected sign for this variable becomes positive.
Regarding corporations’ VAs toward climate change, the following seven
variables
(“RES”,
“PLAN_GHG”,
“TRG”,
“EMISSION”,
“ENERGYCOST”,
“INFO_VERIF”, and “EUETSSTR”) are noticed. All variable take the value 1 if the
facility has implemented or had following VAs: whether a Board Committee or other
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executive body have overall responsibility for climate change issue (RES); whether the
corporation implements GHG emission reduction plans (PLAN_GHG); whether the
corporation has emissions reduction targets with time frames to achieve (TRG);
whether the corporation discloses GHG emissions (EMISSION); whether the
corporation discloses the total cost of its energy consumption e.g. from fossil fuels and
electric power (ENERGYCOST); whether any of the information disclosed has been
externally verified/assured in whole or in part (INFO_VERIF); whether the corporation
has a strategy for EU ETS (EUETSSTR). “PLAN_GHG”, “TRG” may act on
corporations to integrate a measure toward climate change issue to their business with
clear timeline. By doing “RES”, they may also consider the issue more specifically.
Disclosure is one of the important factors which encourage corporations to grasp the
situation of the issue precisely. Therefore, level of disclosing “EMISSION”,
“ENEGYCOST” may become an indicator to show how seriously the corporations deal
with the climate change issue. Considering the fact that whether the reported
information has externally verified or not is one of the vital factors for corporations to
receive high CDLI and CPLI score, “INFO_VERIF” is essential not only for their
environmental management but also for success in financial market. These VAs may
play positive roles to tackle the climate change issue, and drive corporations to be
greener. As a result, corporations may encourage their environmental R&D investment
in order to become more cost effective and energy efficient by innovating new
technology, products and production process, for instance. In fact, according to some
corporations’ responses to CDP survey, the corporations which consider the climate
change issue seriously are inclined to encourage innovation activity. Some
corporations might even think that this risk will give a potential business opportunity
to them, and increase their R&D investment in order to reinforce their status as leading
corporations and gain competitiveness as a result of innovations. Therefore, I assume
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that these variables are influential to R&D investment. And finally, in order to consider
the impact of EU ETS, a variable “EUETSSTR” is added in the model. Corporations
might invest on R&D as one of the internal strategy toward EU ETS. This is because it
is necessary for them to control CO2 emissions in order to comply with EU ETS, and
changing and innovating processes and/or products may be one of means to achieve
emission abatement. Therefore, this variable may encourage R&D investment. Since
corporation specific factors, such as corporations’ internal decision making system and
managers’ attitude toward climate change, are likely to be correlated with these VA
variables, they are assumed to be potentially endogenous variables.
Sector dummy variables are created by referring to NACE code system and
Industry Classification Benchmark (ICB). The former is the European standard for
industry classifications and the latter one is an industry classification developed by
Dow Jones and FTSE. In addition, year dummy are created. These variables are treated
to be strictly exogenous in the model.
Lastly, interaction terms of “EUETS” and sector dummy are included in the
models. “EUETS” is a dummy variable which takes the value “1” if the corporations
have involved in EUETS. Interaction terms of “EUETS” and “EUETSSTR”. These
interaction terms are included in order to examine the influence of EUETS on
innovation activity.
6. Results and Discussion
In this study, two types of models were estimated; the model using the ratio of R&D
investment to net sales as a dependent variable (Model 1) and the other using the
amount of R&D investment per employee as a dependent variable (Model 2). The
estimation results are shown in Table 2.
The results of the Arellano–Bond test show that in both Model 1 and Model 2,
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the null hypothesis was not rejected. In other words, there is no evidence of serial
correlation and does not imply model misspecification. Both models also pass the
Sargan test for overidentifying restrictions which confirms that the instruments can be
considered valid. In both models,
In Model 1, the estimated coefficient of the lagged dependent variables is
positive and statistically significant at the 1% level. Regarding VAs, it is interesting to
notice that “INFO_VERIF” is positively significant at the 10% level. This indicates
that corporations whose disclosed information reported in CDP responses has been
externally verified in whole or in part are likely to encourage R&D investment and to
increase the ratio of R&D investment to net sales. This implies that VA such as
disclosing verified information is important for corporations, which enhance the
accuracy of information, and give a chance to examine the relationship between
corporate actions and climate change issue deeply. This process may lead corporations
to notice the necessity of R&D investment. Other VAs, however, did not show
significant influences on R&D investment.
Some of the interaction terms of EUETS dummy and sector dummy are
negative and statistically significant (EUETS*DS1: 5% level; EUETS*DS2: 5% level;
EUETS*DS6: 10% level). These results show that corporations in the sector 1 (electric
utilities, gas and water supply), sector 2 (oil, metals, mining, paper and forest products)
or sector 6 (automobiles and auto parts) which are involved EU ETS are not likely to
invest on R&D. Sector 1, 2 and 6 are energy-intensive industries and the important
industries which are controlled by various environmental policies. The results are
unexpected, however, they may indicate some possibilities that these industries have
been proactively dealt with environmental policies and have already invested great
amount budgets on R&D activities. Since EU ETS has started, those corporations
might have changed their policies by cutting down the R&D investment, and have
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started to spend on other areas besides R&D activities, such as increasing the accuracy
of monitoring the impact of their environmental actions. Other variables, including
sector dummies and time dummies, are not significant.
In Model 2, similar to the result of Model 1, “INFO_VERIF” is positive and
statistically significant at the 10% level. The results imply that if any of the
information reported has been externally verified, corporations are inclined to boost
their R&D investment per employees. Year dummy of 2006 has a positive coefficient,
which is significant at the 5% level. Since EU ETS has started in 2005, this result
might indicate some positive impacts of EU ETS.
7. Conclusion
Using the data set of EU corporations which constructed based on “CDP”, “EU
industrial R&D Investment data” and CSR report, the effects of VAs on R&D
investment are scrutinised. System GMM was employed in order to estimate dynamic
panel models. The findings of this study show that a VA positively influenced on R&D
investment. A tendency that corporations which disclose the information verified in
whole or in part by external entities are likely to invest on R&D was observed in both
Model 1 and 2. This robust result may remind an important message I previously
mentioned: “You can't manage what you don't measure”. In order to disclose the
verified information, corporations may try to measure the environmental impacts with
high accuracy, and this process will provide some chances to closely examine the
relationship between corporate actions and the climate change issue. Corporations may
be able to aware the importance of innovation activity through this commitment and
tend to encourage R&D investment.
While interaction terms of “EUETS” and sector dummy (sector 1, 2 and 6)
have negative coefficients, year dummy 2006 has positive coefficients and apt to
19
encourage R&D investment. The results of interaction terms show that EU ETS may
give negative impact on R&D investment, however, according to the result of year
dummy 2006, it is difficult to assert the negative influence of EU ETS on innovation
activity.
This is a unique study which shed a light to the relationship between VAs and
R&D investment, but estimation methods may be able to improve further. Also
increasing the number of years and/or corporations’ data makes this study more
persuasive. In addition, since this study could not prove the direct impacts of EU ETS
toward innovation, I would like to scrutinise the direct influences of EU ETS in next
study by utilising different estimation methods.
Although some limitations might exist, this study still presents an intriguing
implication that it is important to implement a policy which stimulates the corporations’
incentives for disclosing the verified information by external entities in order to
enhance innovation activity.
20
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23
Table 1 Summary statistics
Number of
observations
Variables
Mean
Std. Dev.
Min
Max
RDSALES
R&D investment/net sales
1098
0.041
0.181
0
5.149
lnRDEMPLO
R&D investment/number of employees (log)
1097
1.676
1.058
0
5.519
lnPROFIT
Operating profit (log)
772
6.845
1.798
-0.036
15.156
lnMRKTCPTL
Market Capitalisation (log)
1040
8.922
1.594
3.555
12.169
RES
A Board Committee or other executive body have overall
responsibility for climate change issue
626
0.891
0.311
0
1
PLAN_GHG
Implements GHG emission reduction plans
605
0.853
0.355
0
1
TRG
Has emissions reduction targets with time frames to achieve
607
0.743
0.437
0
1
EMISSION
Discloses GHG emissions
Discloses the total cost of its energy consumption e.g. from
fossil fuels and electric power
Any of the disclosed information has been externally
verified/assured in whole or in part
735
0.875
0.331
0
1
684
0.482
0.5
0
1
533
0.775
0.418
0
1
ENERGYCOST
INFO_VERIF
EUETSSTR
Has a strategy for the EU ETS
431
0.826
0.38
0
1
DS1
Electric Utlities, Gas and water supply
1690
0.092
0.289
0
1
DS2
Oil, Metals, Mining, Paper and Forest products
1690
0.103
0.304
0
1
DS3
Chemicals and Pharmaceuticals
1690
0.125
0.331
0
1
DS4
Food, beverage, tobacco
1690
0.047
0.212
0
1
DS5
Industrial machinery, high-tech
1690
0.206
0.404
0
1
DS6
Automobiles and auto parts
1690
0.046
0.21
0
1
DS7
Other manufacturing goods
1690
0.11
0.313
0
1
DS8
Bank and diversified financials, other services
1690
0.27
0.444
0
1
DY2000
Dummy variable for year 2000
1690
0.041
0.198
0
1
DY2001
Dummy variable for year 2001
1690
0.043
0.202
0
1
DY2002
Dummy variable for year 2002
1690
0.066
0.248
0
1
DY2003
Dummy variable for year 2003
1690
0.117
0.321
0
1
DY2004
Dummy variable for year 2004
1690
0.148
0.355
0
1
DY2005
Dummy variable for year 2005
1690
0.166
0.372
0
1
DY2006
Dummy variable for year 2006
1690
0.161
0.368
0
1
DY2007
Dummy variable for year 2007
1690
0.147
0.355
0
1
DY2008
Dummy variable for year 2008
1690
0.112
0.315
0
1
EUETS*EUETSSTR
Interaction term of EUETS dummy and EUETSSTR
431
0.513
0.5
0
1
EUETS*DS1
Interaction term of EUETS dummy and DS1
1690
0.045
0.207
0
1
EUETS*DS2
Interaction term of EUETS dummy and DS2
1690
0.04
0.195
0
1
EUETS*DS3
Interaction term of EUETS dummy and DS3
1690
0.037
0.188
0
1
EUETS*DS4
Interaction term of EUETS dummy and DS4
1690
0.021
0.142
0
1
EUETS*DS5
Interaction term of EUETS dummy and DS5
1690
0.023
0.15
0
1
EUETS*DS6
Interaction term of EUETS dummy and DS6
1690
0.015
0.123
0
1
EUETS*DS7
Interaction term of EUETS dummy and DS7
1690
0.017
0.128
0
1
EUETS*DS8
Interaction term of EUETS dummy and DS8
1690
0.009
0.094
0
1
24
Table 2 Estimation results
Variables
Model 1
Model 2
R&D investment/sales
R&D investment/employees
Coefficient (Robust Std. Err.)
Coefficient (Robust Std. Err.)
0.914 (0.059)***
-
L. lnRDEMPLO
-
0.894 (0.058)***
lnPROFIT
0.002 (0.003)
0.008 (0.081)
L. RDSALES
lnMRKTCPTL
0.001 (0.003)
0.016 (0.079)
RES
-0.001 (0.003)
-0.183 (0.145)
PLAN_GHG
-0.010 (0.009)
-0.146 (0.229)
TRG
0.007 (0.007)
0.052 (0.145)
EMISSION
-0.004 (0.010)
0.024 (0.175)
ENERGYCOST
0.001 (0.003)
-0.045 (0.084)
INFO_VERIF
0.010 (0.006)*
0.313 (0.163)*
EUETSSTR
-0.015 (0.011)
-0.107 (0.203)
DS1
0.013 (0.009)
0.035 (0.373)
DS2
0.006 (0.006)
-0.101 (0.162)
DS3
0.018 (0.012)
0.262 (0.174)
DS4
0.006 (0.007)
0.006 (0.151)
DS5
0.003 (0.009)
0.145 (0.191)
DS6
0.011 (0.007)
0.171 (0.181)
DS7
-0.001 (0.007)
0.004 (0.152)
DY2001
-
-
DY2002
-
-
DY2003
-
-
DY2004
-0.007 (0.008)
0.036 (0.137)
DY2005
-0.000 (0.007)
0.152 (0.117)
DY2006
-0.001 (0.002)
0.136 (0.066)**
DY2007
0.001 (0.002)
0.086 (0.052)
DY2008
-
-
EUETS*EUETSSTR
0.008 (0.007)
0.119 (0.147)
EUETS*DS1
-0.021 (0.010)**
-0.243 (0.383)
EUETS*DS2
-0.016 (0.008)**
-0.010 (0.196)
EUETS*DS3
-0.016 (0.011)
-0.223 (0.163)
EUETS*DS4
-0.011 (0.008)
-0.130 (0.193)
EUETS*DS5
-0.009 (0.011)
-0.234 (0.184)
EUETS*DS6
-0.016 (0.008)*
-0.194 (0.191)
EUETS*DS7
-0.007 (0.007)
-0.200 (0.204)
Number of observations
230
230
Arellano-Bond test for AR(2)
in first differences Pr > z
0.694
0.273
Sargan test of OIR
0.535
0.385
Note: System GMM, Robust one-step. Standard errors are shown in parentheses. *, ** and *** indicate the significance at the 10%,
5% and 1% levels, respectively. Constant is included in the model, though its coefficient is not reported here.
25