by lennart d. osses · 2020. 11. 21. · submitted by lennart d. osses student id um: 6095088...
TRANSCRIPT
THE EUROPEAN UNION EMISSIONS TRADING
SYSTEM AND ITS IMPACT ON ECO-INNOVATION
MASTER THESIS
BY LENNART D. OSSES
The European Union Emissions Trading System and its
Impact on Eco-Innovation
Empirical evidence for the weak Porter Hypothesis from the
world’s most extensive cap-and-trade system
Master Thesis at
Maastricht University School of Business and Economics
& NOVA School of Business and Economics
Submitted by Lennart D. Osses
Student ID UM: 6095088
Student ID NOVA: 38749
Thesis supervised by Michael Grätz and Leid Zejnilovic
Second read by Martin Carree and Jolien Huybrechts
December 2019
I
ABSTRACT
The European Union Emissions Trading System (EU ETS) is the world’s most extensive
emissions cap-and-trade system. The Porter Hypothesis states that a policy like the EU ETS
can induce eco-innovation and ultimately alter the financial performance of regulated firms.
Nevertheless, previous research has not yet comprehensively confirmed the validity of the
Porter Hypothesis in cap-and-trade systems in general, and in the EU ETS specifically.
Exploiting installation-level inclusion criteria, the paper assesses the causal impact of the EU
ETS on firm’s eco-patent output and return on assets by following a matched difference-in-
difference approach. In line with the weak Porter Hypothesis, it is found that the second phase
of EU ETS altered the eco-patent output of regulated firms in the sample by 1.8%, mainly
inducing innovations in German companies. Reasons for the rather low policy effect entail the
innovation-impeding impact of the Financial Crisis, an oversupply of allowances, and the
subsidized deployment of renewable energy sources to electricity, which resulted in low permit
prices. Contrary to the strong Porter Hypothesis, the policy did not enhance financial
performance of regulated companies, which can be further expounded by the time lag
associated with the profitability of eco-innovations. Deriving from the findings, policy makers
should enact EU ETS reforms focused on decreasing the emission cap, introducing means to
stabilize the allowance price, carefully assessing if the scheme can be extended to other sectors,
and launching other eco-innovation enhancing instruments.
Keywords:
cap-and-trade system, difference-in-difference estimation, eco-innovation, EU ETS, induced
innovation, patent analysis, policy analysis, Porter Hypothesis, propensity score matching
II
TABLE OF CONTENTS
ABSTRACT .................................................................................................................................. I
TABLE OF CONTENTS ................................................................................................................. II
LIST OF TABLES AND FIGURES .................................................................................................. IV
LIST OF ABBREVIATIONS ............................................................................................................ V
1. INTRODUCTION........................................................................................................................ 1
2. THE EUROPEAN UNION EMISSIONS TRADING SYSTEM ............................................................ 4
2.1. Design: market-based pollution control via a cap-and-trade system .............................. 4
2.2. Implementation: increasing the stringency of the EU ETS via four phases .................... 7
3. THEORETICAL FRAMEWORK .................................................................................................... 9
3.1. Theoretical foundation: from Induced Innovation Hypothesis to Porter Hypothesis ..... 9
3.2. Literature review and hypotheses: the EU ETS and eco-innovation ............................ 10
3.2.1. The EU ETS and the weak Porter Hypothesis ..................................................... 11
3.2.2. The role of increasing stringency and certainty in the EU ETS .......................... 14
3.2.3. The EU ETS and the strong Porter Hypothesis ................................................... 15
4. METHODOLOGY .................................................................................................................... 19
4.1. Data description............................................................................................................. 19
4.1.1. Identification of regulated companies in the EU ETS ......................................... 19 4.1.2. Identification of comparable non-regulated companies and matching via PSM . 20 4.1.3. Composition and description of the final dataset ................................................ 23
4.2. Empirical specification .................................................................................................. 24
4.2.1. Regression models ............................................................................................... 24
4.2.2. Dependent variables ............................................................................................ 27
4.2.3. Independent and control variables ....................................................................... 28 4.2.4. Estimation approach ............................................................................................ 29
5. RESULTS ............................................................................................................................... 31
5.1. Empirical results ............................................................................................................ 31
5.1.1. Model one: the EU ETS - eco-innovation relationship ....................................... 32 5.1.2. Model two: the EU ETS - financial performance relationship ............................ 33
5.2. Robustness checks ......................................................................................................... 35
6. DISCUSSION .......................................................................................................................... 38
6.1. The Porter Hypothesis in the EU ETS .......................................................................... 38
6.1.1. The EU ETS and the weak Porter Hypothesis ..................................................... 38
6.1.2. The EU ETS and the strong Porter Hypothesis ................................................... 42
III
6.2. Contributions and implications ..................................................................................... 44
6.2.1. Theoretical contributions ..................................................................................... 44 6.2.2. Policy implications .............................................................................................. 45
6.3. Limitations and future research ..................................................................................... 46
6.3.1. Sample construction and composition ................................................................. 47 6.3.2. Methodological approach .................................................................................... 47 6.3.3. Further research opportunities ............................................................................. 49
7. CONCLUSION ......................................................................................................................... 50
8. LIST OF REFERENCES ............................................................................................................. V
9. APPENDICES ..................................................................................................................... XVII
Appendix A – Firm demographics .................................................................................. XVII
Appendix B – Dataset descriptive statistics ................................................................... XVIII
Appendix C – Hypotheses and results............................................................................... XIX
Appendix D – Robustness checks ...................................................................................... XX
10. DECLARATION OF ORIGINALITY .................................................................................... XXIII
IV
LIST OF TABLES AND FIGURES
Table 1. Implementation phases of the EU ETS …………………………………………….. 6
Table 2. Adjusted descriptive statistics ……………………………………………………... 24
Table 3. Estimation results for model one ………..………………………………………… 33
Table 4. Estimation results for model two …………………………………………………... 34
Figure 1. Price development of EUAs between 2004 and 2015 ............................................ 14
Figure 2. Identification strategy to obtain sample .................................................................. 19
Figure 3. Sample propensity score distribution ..................................................................... 22
Figure 4. Conceptual mediation model .................................................................................. 26
Figure 5. Mean eco-patent output from 2000-2014 ................................................................ 31
Figure 6. Frequency diagram of eco-patent counts ................................................................ 37
Figure 7. Summary of interactions between EU ETS and RES-E policies ............................. 42
Figure 8. Investigation of parallel trends assumption in a comparable study ........................ 49
V
LIST OF ABBREVIATIONS
ATT Average treatment effect on the treated
CER Certified Emission Reduction
CPC Cooperative patent classification
DID Difference-in-difference
EPO European Patent Office
ERU Emission Reduction Unit
EU European Union
EUA European Union Allowance
EUC European University Center
EU ETS European Union Emissions Trading System
GLS General least squares regression
GHG Greenhouse gas
LM Lagrangian Multiplier
NACE Classification of Economic Activities in the European Community
NAP National Allocation Plan
NIM National Implementation Measure
NIS National Innovation System
OECD Organization for Economic Co-operation and Development
PATSTAT Worldwide Statistical Patent Database (EPO)
POLS Pooled Ordinal Least Squares regression
PSM Propensity score matching
R&D Research and development
ROA Return on assets
RES-E Renewable energy sources to electricity
VIF Variance Inflation Factor
1
1. INTRODUCTION
One of the most salient issues of today’s global society is anthropogenic climate change
(United Nations, 2019). The first supranational attempts to mitigate the impact of humankind
on global warming are manifested in the Kyoto Protocol, which stipulates target values for
greenhouse gas (GHG) emissions in industrialized countries (United Nations, 1998). In 2005,
the European Union (EU) established the EU Emissions Trading System (EU ETS) to achieve
its Kyoto Protocol target of 20% GHG emissions reduction, compared to 1990 levels, by 2020
(European Commission, 2004). The EU ETS is the world’s most extensive cap-and-trade
program for carbon dioxide (CO2), nitrous oxide, and perfluorocarbons, covering
approximately 48% of GHG emissions in the EU (Dechezleprêtre, Nachtigall, & Venmans,
2018). Divided into three implementation phases, the EU ETS is primarily focusing on
reducing emissions and has been successful in doing so (Venmans, 2012; European
Commission, 2015; Borghesi & Montini, 2016; Dechezleprêtre et al., 2018).
However, the EU ETS also impacts firm-level determinants. In particular, the ability of
the EU ETS to incentivize eco-innovation is crucial to accomplishing the EU’s long-term GHG
emission targets (Calel & Dechezleprêtre, 2016; Dechezleprêtre et al., 2018; Joltreau &
Sommerfeld, 2019). The EU ETS administers carbon pricing through the issuance of EU
Allowances (EUAs), which are permits to emit one equivalent ton of CO2 (tCO2). Thereby,
emissions become valued, and firms’ emission costs increase (Greenstone, List, & Syverson,
2012; Joltreau & Sommerfeld, 2019). If relative prices of input factors change, Hick's (1932)
Induced Innovation Hypothesis suggests that companies will deploy investments that target the
cost reduction of these input factors. Porter (1991) is credited with making this principle
accessible in the context of environmental policy. He argues that strict environmental
regulations can induce innovation, which ultimately improves firms’ competitiveness by
increasing efficiency. Under the Porter Hypothesis, firms can be expected to deploy capital
targeting the development and commercialization of technologies that reduce emissions and
costs associated with it (Porter & Van der Linde, 1995). The Organization for Economic Co-
operation and Development (2009b, p. 8), OECD in short, labels this type of innovation “eco-
innovation”, which covers process and product innovations (Rennings, 2000).
A higher level of eco-innovation is a central assumption of the EU ETS, but ex-post research
in the field has not yet provided comprehensive insights on whether the policy has induced eco-
2
innovation. This is because the use of unrepresentative samples and the confinement on the
first phase of the regulation (2005-2007) restrict existing insights.
Considering the first limitation, a skein of scientists studies the impact of the EU ETS
on innovation in a specific country or industry context and thus fails to provide EU-wide
insights. For instance, Anderson, Convery, and Di Maria (2011), survey Irish EU ETS firms1
and find that 48% of organizations employed new, less carbon-intensive machinery and
equipment, and 78% made process changes. Borghesi et al. (2012) use the Community
Innovation Survey to study Italian manufacturing firms and find that the effect of the policy on
environmental innovation is weak expect for energy efficiency innovations. Löfgren, Wrake,
Hagberg, and Roth (2013) assess the technology adoption of EU ETS and non-EU ETS firms2
in Sweden. They find no significant effect of the EU ETS on large or small investments into
green technologies. Although these studies do not shed light on the EU-wide impact of the
policy, it appears that there is limited evidence for an eco-innovation stimulating effect of the
EU ETS in specific countries or industries. To date, only one study is known that presents
robust and representative evidence (Marcantonini, Teixido-Figueras, Verde, & Labandeira-
Villot, 2017; Joltreau & Sommerfeld, 2019). Calel and Dechezleprêtre (2016) investigate the
direct impact of the EU ETS by comparing patent applications for low-carbon eco-innovations
across regulated and non-regulated firms. In their sample, the authors find that the EU ETS
was responsible for a 36.2% increase in low-carbon patent applications.
However, Calel & Dechezleprêtre (2016) primarily analyze data from the first phase of
the policy, which constitutes the second major limitation. In the initial setup of the regulation,
the EU devised the first phase as the pilot phase, focusing principally on developing the
regulatory infrastructure (European Commission, 2004, 2015). From phase two (2008-2012)
onwards, the stringency of the policy has been increasing. Under the Porter Hypothesis, a
stricter policy leads to a higher level of innovation outputs because emission costs increase.
Furthermore, firms intend to increase their level of efficiency, thereby cutting costs and
maintaining competitiveness. In addition to that, clarity about the regulatory framework makes
investments in the development of innovations more likely (Jalonen, 2012). Hence, assessing
more recent data is imperative to provide insights into whether the second phase of the EU ETS
had a (different) impact on the eco-innovativeness of regulated firms.
1 In the EU ETS, installations, not firms, are regulated. The thesis considers firms that possess at least one
regulated installation as EU ETS firms. 2 Non-EU ETS firms are similar but unregulated companies. See section 2.2. for a detailed explanation
3
Consequently, the central purpose of the thesis is to give forth new insights into the effect of
the EU ETS on eco-innovation by assessing firm-level eco-patent output in the first two
implementation phases of the policy. To do so, firstly, the thesis intends to create generalizable
new insights that close the research gap outlined above. The study uses a quasi-experimental
study design by constructing a sample of regulated and comparable non-regulated firms via
propensity score matching (PSM). Subsequently, applying a difference-in-difference (DID)
approach, the study compares the eco-patent output of the two company groups. Patent data is
considered a reliable proxy for measuring eco-innovation because it is quantitative, output-
oriented, and can be disaggregated (Acs & Audretsch, 1989; Johnstone, Haščič, & Popp, 2010;
Haščič & Migotto, 2015). Secondly, the study extends the existing body of research on the
Porter Hypothesis. An extensive collection of literature has assessed the impact of
environmental policies on innovation (Goulder & Schneider, 1999; Van Der Zwaan, Gerlagh,
Klaassen, & Schrattenholzer, 2002; Gerlagh, 2008; Acemoglu, Aghion, Bursztyn, & Hemous,
2012). However, evidence for the validity of the Porter Hypothesis in cap-and-trade systems is
limited and contradictory (Tietenberg, 2010; Ambec, Cohen, Elgie, & Lanoie, 2013). As the
EU ETS is the first supranational cap-and-trade system, comprehension of its impact on eco-
innovation can provide essential novel insights. Thirdly, the thesis gives a practical rationale
for the possible extension of the policy to currently exempted sectors (e.g., road logistics3) and
development of other emission trading markets (e.g., in China4).
The remainder of the thesis is structured as follows. In section two, the study provides a more
detailed introduction to the EU ETS. The theoretical foundation for the paper is articulated in
section three by introducing the Induced Innovation and Porter Hypotheses. Based on this, the
paper develops a conceptual framework to infer relevant research hypotheses. In section four,
the sample and regression models used to analyze the hypotheses are delineated. Section five
comprises the description of the results accompanied by robustness checks. In section six, the
results are thoroughly discussed to deduce relevant theoretical and political implications as
well as limitations and future research opportunities. Finally, section seven concludes the thesis
with a comprised summary of the advancements found.
3 The road transport sector is among the highest emitting sectors not regulated by the EU ETS, which has sparked
discussion on whether it should be regulated by the policy as well (Nader & Reichert, 2015). 4 China, the country with highest CO2 emissions worldwide, is currently testing a regional ETS in the Shenzhen
region, indicating that national measures might follow soon (Ye, Jiang, Miao, Li, & Peng, 2015).
4
2. THE EUROPEAN UNION EMISSIONS TRADING SYSTEM
This section introduces the EU ETS and two of its mechanisms, namely the cap-and-trade
design and the sequential implementation structure. The EU ETS is a sophisticated EU
directive, and providing a detailed description of the policy extends the scope of the thesis5.
Nevertheless, the two mechanisms outlined above are of particular importance for the research
hypotheses developed in section three. Thus, this section firstly explains the underlying
economic theory of the EU ETS by unveiling the mechanisms of cap-and-trade systems.
Secondly, the four phases of policy implementation are briefly summarized.
2.1. Design: market-based pollution control via a cap-and-trade system
The EU ETS is designed as a cap-and-trade system to “effectively reduce GHG emissions while
producing the least overall costs to participants and the economy as a whole” (European
Commission, 2015, p. 5). The theoretical foundation of cap-and-trade systems was introduced
by Coase (1960), who argued that by making property rights explicit and transferable, the
market instead of the government can ensure that these rights are valued adequately and
brought to their best use. Crocker (1968) was the first to apply the Coase Theorem in the field
of air pollution control. Here, the government sets a cap on the overall level of emissions
allowed for a specific timeframe. Then, permits, which are allowances to emit a certain amount
of pollutant, are allocated based on historical emissions, a procedure called “grandfathering”
(Clò, 2010), or are auctioned off to participants.
One specific advantage of a cap-and-trade system when compared to a command-and-
control system is that, in theory, it stimulates eco-innovation. In a command-and-control
system, which regulates pollution, e.g., via taxation, introducing eco-innovations that yield
emissions below the required level does not offer any additional benefit to the innovator. That
is because there is no reward for exceeding the regulated pollution standard (Popp, 2003). In
contrast, in a cap-and-trade system, innovations that lead to lower emissions reduce the
financial burden of the polluter because fewer pollution permits must be purchased (Tietenberg,
2010). The innovator will continue to innovate until (marginal) abatement costs equal the
(marginal) benefit of pollution (Coase, 1960; Crocker, 1968). Because (marginal) abatement
5 See Ellerman, Convery, and de Perthuis (2010) for a comprehensive assessment of the design and
implementation of the EU ETS.
5
costs differ between firms, permits are traded until an efficient equilibrium is reached (Crocker,
1968; Montgomery, 1972). On a firm-level, this provides participants with a degree of
flexibility not given under command-and-control systems because they can either innovate to
reduce the number of permits required or purchase additional allowances. The decision depends
on the individual participant’s innovation capabilities and its (marginal) abatement costs.
Consequently, the price of permits, which is a function of its demand and supply, plays a vital
role in this bilateral decision.
The supply of permits is, amongst others6, influenced by the use of offsets and the initial
allocation of allowances. Offsets are permits from other sources than that specific cap-and-
trade system. In the EU ETS, Emission Reduction Units (ERU) from Joint Implementation
Projects and Certified Emission Reductions (CER) from Clean Development Mechanisms, as
defined in the Kyoto Protocol (United Nations, 1998), can be used as offsets (European
Commission, 2015). In theory, the use of offsets allows emission mitigation to be done at least
cost because firms that operate in different geographical locations can decide where to abate
(Tietenberg, 2010). However, offsets also yield to an influx of permit supply (Perman, Ma,
McGilvray, & Common, 2008), which lowers its price. A low permit price provides fewer
incentives for eco-innovation because the option to purchase allowances becomes financially
more attractive. Furthermore, opposing the assumptions of the Coase Theorem, the initial
allocation of allowances must be considered (Abrell, Zachmann, & Ndoye, 2011). One reason
why free allocations matter is carbon leakage. Carbon leakage describes the situation that
occurs if businesses transfer production to other countries with less stringent emission
constraints (Babiker, 2005; European Commission, 2015). The potential for carbon leakage is
considered as one of the main threats to the investment stimulus that is induced by carbon
pricing (European Commission, 2004). To counteract carbon leakage, the EU exempts certain
sectors from the system or allocates free permits to firms (European Commission, 2015).
Nevertheless, freely awarded permits reduce the (marginal) abatement cost of firms because
neither do they have to purchase allowances, nor are they incentivized to abate (Hahn &
Stavins, 2011). Thus, free allocation in a cap-and-trade system bears the danger of creating
windfall profits (Sijm, Neuhoff, & Chen, 2006). The use of offsets and the initial allocation of
allowances had a particular influence on the design and adjustments in the different phases of
the EU ETS (Ellerman, Convery, & De Perthuis, 2010), which are introduced below.
6 See Joltreau and Sommerfeld (2019) for a summary of factors that impact the supply and demand of allowances
and the overall emission reduction effectiveness of the EU ETS.
6
Table 1. Implementation phases of the EU ETS
Phase 1
(2005-2007)
Phase 2
(2008-2012)
Phase 3
(2013-2020)
Phase 4
(2021-2030)
Geography EU27 Same as phase 1
and:
Norway
Iceland
Lichtenstein
Same as phase 2
and:
Croatia
Same as phase 3
Regulated
installation
types
Power stations
and combustion
plants with
thermal input of
≥ 20 MW p.a.
Oil refineries
Coke ovens
Iron and steel
plants with
production
capacity of
> 2.5 t per hour
Cement clinker
Glass with melt-
ing capacity of
> 20 t per day
Lime
Bricks
Pulp
Paper and board
Same as phase 1
and:
Aviation
(from 2012)
Same as phase 2
and:
Aluminum
Petrochemicals
Ammonia
Nitric, adipic,
and glyoxylic
acid production
CO2 capture,
transport in
pipelines, and
geological
storage of CO2
Same as phase 3
GHGs CO2 CO2, N2O
(via opt-in)
CO2, N2O, PFC
(perfluorocarbon)
Same as phase 3
Cap 2,058 mtCO2 1,859 mtCO2 2,084 mtCO2
in 2013,
decreasing
linearly by
1.74% per year
1,858 mtCO2
in 2018,
decreasing
linearly by
2.20% per year
Trading
units
EUAs EUAs, CERs,
ERUs
All units must be
converted into
EUAs first
Same as phase 3
Non-com-
pliance fine
€40 per tCO2 €100 per tCO2 Same as phase 2 Same as phase 3
Note. Adapted from “EU ETS Handbook”, by European Commission, 2015.
7
2.2. Implementation: increasing the stringency of the EU ETS via four phases
The EU ETS is divided into four implementation phases to leave sufficient room for policy
adjustments and gradually increase the regulatory stringency (European Commission, 2004,
2015). Table 1 summarizes the regulation criteria for each phase. One must note that regulation
happens at the installation, not the firm level. This detail of the EU ETS allows to compare
firms with similar characteristics but different regulatory status with each other. For example,
one can consider the hypothetical case in which two firms (firm A and firm B) each emit a
similar amount of CO2 and require 50 MW thermal input per annum to operate their power
stations. However, firm A possesses only one large power station, while Firm B possesses five
power stations. Firm A qualifies for the EU ETS because its installation with a thermal input
of 50 MW exceeds the regulatory limit of 20 MW per annum. In contrast, firm B is not
regulated because each of the five power stations requires a thermal input of 10 MW, which is
below the threshold. In the example, firm A, the account holder of a regulated installation, must
submit an annual emission report validating that it holds enough permits for its emission output.
To present a balance, firm A can reduce emissions or acquire additional permits from other
firms directly or from the auction market. In the following, the regulatory specifics of the four
implementation phases are summarized based on the elaboration provided by the European
Commission (2015).
The first phase of the EU ETS lasted from 2005-2007 and was established as a pilot
phase to test price formation and develop the required regulatory framework for the carbon
market. In 2005, more than 12,000 power stations and other industrial installations were
categorized according to their primary activity, such as cement, paper and pulp, or coke ovens
and received a regulatory status. The account balances of all individual account holders in a
country were documented in National Allocation Plans (NAPs). These were used to set the
overall emission cap to 2,058 million tCO2 per year in the first phase. Account-holders who
failed to provide a balance for emissions and permits were obliged to pay a penalty of €40 per
tCO2. As elaborated on above, EUAs were allocated freely in phase one to prevent carbon
leakage. Nevertheless, offset units could not yet be used. The first phase further clarified
unanswered regulatory questions. For example, at the end of the phase, it was communicated
that banking of permits from phase one was not allowed and that a progressively decreasing
cap would be introduced in phase three.
The second phase of the EU ETS lasted from 2008-2012, which was the first
commitment period of the Kyoto Protocol. Here, the cap decreased to 1,859 million tCO2 per
8
year. Still, 90% of EUAs were allocated freely, but CERs and ERUs could be submitted in
addition to EUAs. Moreover, Norway, Iceland, and Lichtenstein joined the EU ETS, and since
2012 also aviation operators must present allowance balances. Besides, the penalty for
surrendering emissions without permits was increased to €100 per tCO2. Furthermore, at the
end of phase two, it was decided to move from NAPs to National Implementation Measures
(NIPs) to harmonize the administrative processes of permit surrendering among member states.
The third phase began in 2013 and will last until 2020. One significant adjustment
comprised the reduction of free allocations, shifting the core of the system towards permit
auctioning. In phase three, auctions are expected to make up 50% of allowances allocated
(Wråke, Burtraw, Löfgren, & Zetterberg, 2012). Furthermore, a decreasing cap of -1.74%
annual linear reduction (equivalent to 38 megatons of CO2) entered force. In addition to that,
Croatia joined the EU ETS, and policymakers introduced installation caps for N2O as well as
perfluorocarbon from aluminum production. The EU also launched a best-practice fund, called
NER3000, to showcase environmentally safe carbon capture and innovative renewable energy
technologies. In 2018, the EU agreed upon a fourth implementation phase, lasting from 2021
until 2028 (European Parliament & European Council, 2018). The primary modification will
entail a further reduction of the emission cap. The rate of linear decline is set to change from
-1.74% to -2.20%. Besides, the EU will explore options to link the EU ETS to other carbon
trading systems (e.g., Switzerland and Australia).
9
3. THEORETICAL FRAMEWORK
Based on the brief introduction to the EU ETS, this section lays the theoretical foundation for
the thesis and derives research hypotheses. Firstly, the Induced Innovation Hypothesis and the
Porter Hypothesis are introduced. Secondly, empirical evidence for the hypotheses in the
context of cap-and-trade systems and the EU ETS, specifically, is assessed to develop relevant
research hypotheses.
3.1. Theoretical foundation: from Induced Innovation Hypothesis to Porter Hypothesis
The thesis draws from two economic hypotheses, the Induced Innovation Hypothesis and the
Porter Hypothesis. In the early 20th century, John Hicks (1932) proposed an economic theory,
labeled the Induced Invention (Innovation) Hypothesis. According to Hicks (1932), increasing
real wages induce innovations that cut labor costs and thereby foster operational cost-
effectiveness. If, ceteris paribus, the price of a factor of production increases sharply relative
to the price of others, firms will innovate by developing technologies that economize on that
factor of production. Induced innovation, akin to the role of innovation in general, is a critical
determinant of technological change, which in turn is a decisive factor for economic growth
considering the Schumpeterian school of thought (Ruttan, 1959, 1997; Scherer, 1986). Even
though widely accepted, the Induced Innovation Hypothesis was criticized for relying on
exogenous changes in the economic environment. It has thus been replaced by theories that
focus on endogenous factors, e.g., path dependency, as the primary driver for innovation and
technological change (Ruttan, 1997).
Nevertheless, Porter (1991) and Porter and Van der Linde (1995), pick up Hicks’ (1932)
focus on exogenous factors and argue that environmental regulation can spur (eco-)innovation.
In what is today known as the Porter Hypothesis, the authors state that well-designed, strict
environmental regulations induce innovation, which in turn can result in a better competitive
performance of firms. Following Porter and Van der Linde (1995), pollution is considered a
waste of resources, and a reduction in emissions can lead to an improvement in productivity.
Hence, regulation can “trigger innovation that may partially or more than fully offset the costs
of complying with them” (Porter & Van der Linde, 1995, p. 98). The Porter Hypothesis can be
classified into three forms (Jaffe & Palmer, 1997). Firstly, the “weak” Porter Hypothesis
assumes a positive impact of environmental regulation on innovation. Secondly, the “strong”
Porter Hypothesis expands the weak Porter Hypothesis by adding a positive influence of
10
policy-induced innovation on competitive performance. Thirdly, the “narrow” Porter
Hypothesis considers that flexible regulations give firms more significant incentives than
prescriptive forms of regulation. Akin to the Induced Innovation Hypothesis, the Porter
Hypothesis has received some criticism. The major neo-classical argument against the Porter
Hypothesis is that it is incompatible with the assumption that firms aim at maximizing profits.
Palmer, Oates, and Portney (1995) state that the cases brought forward are exemptions and
offsets are minuscule relative to control costs. Nevertheless, empirical assessment of the Porter
Hypothesis from past decades has partly rescinded this argument, as an examination of the
existing evidence on the different specifications of the Porter Hypothesis elucidates.
3.2. Literature review and hypotheses: the EU ETS and eco-innovation
In general, empirical evidence supports the Porter Hypothesis. In an environmental context,
Goulder and Schneider (1999), Van Der Zwaan et al. (2002), Popp (2004), Gerlagh (2008), and
Acemoglu et al. (2012) find evidence for an increased level of innovation induced by the
increase of relative price factors. Explicitly investigating the weak Porter Hypothesis, Lanjouw
and Mody (1996), Brunnermeier and Cohen (2003), Popp (2006), Johnstone, Haščič, and Popp
(2010), Lanoie et al. (2011), and Lee, Veloso, and Hounshell, (2011), find that environmental
policies positively influence innovation. In contrast, evidence for the strong Porter Hypothesis
is incongruent. Cohen and Tubb (2018) review 103 studies on the impact of environmental
regulation on business performance and find that approximately half of the studies report a
significant positive effect, while the other half fails to detect significant results. The authors
further specify that most studies focus on the national or industry-level as the subject of interest,
not the firm-level. Considering the narrow Porter Hypothesis, existing evidence is also
inconsistent (Ambec et al., 2013). One difficulty acknowledged in the research field on the
narrow Porter Hypothesis is the requirement to either develop complex counterfactual
scenarios or compare different policies with each other (Ambec et al., 2013). That limits both
supporting (e.g., Burtraw, 2000) and opposing findings (e.g., Driesen, 2005; Lanoie et al.,
2011) on the narrow Porter Hypothesis (Lanoie et al., 2011; Ambec et al., 2013). As a
consequence, the thesis considers only the weak and strong Porter Hypothesis.
Albeit the evidence above suggests that the (weak) Porter Hypothesis holds in general,
previous research has not yet comprehensively validated the Porter Hypothesis in the context
of cap-and-trade systems (Tietenberg, 2010). The next sections summarize, for both weak and
strong Porter Hypotheses, the empirical evidence from existing cap-and-trade systems, the
11
theoretical arguments for the validity of the hypothesis in the EU ETS, and empirical evidence
from the first implementation phase to develop four research hypotheses.
3.2.1. The EU ETS and the weak Porter Hypothesis
Evidence for the weak Porter Hypothesis in existing cap-and-trade systems, other than the EU
ETS, is limited. The most scrutinized cap-and-trade systems are the US endeavors to reduce
sulfur dioxide emissions, which were introduced with the Clean Air Act in 1990 and continued
with the Acid Rain Program in 1995 (Calel & Dechezleprêtre, 2016). However, evidence for
an innovation-inducing effect of these environmental regulations is low. Popp (2003)
investigates whether a command-and-control policy, as it was in place before 1990, or a cap-
and-trade system, which was introduced in 1990, spurred more patent applications. The author
finds that, contrary to the Porter Hypothesis, patent counts in the command-and-control period
were higher than under the more flexible cap-and-trade system. However, Popp (2003) shows
that the introduction of the cap-and-trade system led to more patent applications that regard
environmental innovations compared to the command-and-control period. Nevertheless, the
phenomenon remained to be a one-time event rather than a consistent pattern induced by the
regulation (Lange & Bellas, 2005; Taylor, Rubin, & Hounshell, 2005).
One explanation for the lack of evidence from the US is the increased use of fuel
switching strategies (Schmalensee, Joskow, Ellerman, Montero, & Bailey, 1998; Gagelmann
& Frondel, 2005; Calel & Dechezleprêtre, 2016). Fuel switching considers the strategic
decision to bring less polluting gas-fired plants online before coal-fired plants when power
demand increases, which changes the fuel mix in favor of less carbon-intensive natural gases
(Gagelmann & Frondel, 2005; Calel & Dechezleprêtre, 2016). This strategy does neither
require research and development (R&D) nor capital investment to reach emission targets and
therefore provides a cheap alternative to innovation. In the US case, fuel switching is
considered to be responsible for nearly half the emission reductions achieved (Schmalensee et
al., 1998). The US policy design allowed participants to apply fuel switching strategies and
thus reduced incentives to innovate, leading to the conclusion that the weak Porter Hypothesis
did not hold in in the US case.
Comparing the design of the US policy with the EU ETS, economic theory suggests that the
EU ETS can induce eco-innovation, which gives reason to believe that the weak Porter
Hypothesis holds. From a theoretical standpoint, the EU ETS can be rated as a strict but flexible
policy, which is a critical underlying assumption of the Porter Hypothesis. The EU ETS fulfills
12
the three principles proposed by Porter and Van der Linde (1995): providing flexibility,
fostering continuous improvement, and providing regulatory certainty. Regarding the first two
principles, the EU ETS offers room for innovation and fosters continuous improvement through
its cap-and-trade design. As outlined above, a cap-and-trade system allows for market-based
pollution control and provides incentives to innovate further than under command-and-control
policies. That is, as long as marginal abatement costs differ between firms, participants in the
system can economically decide whether to abate emissions or purchase permits (Coase, 1960;
Crocker, 1968; Montgomery, 1972). Since the cap is set and decreasing in the EU ETS, the
latter option will become more costly, incentivizing firms to innovate (Porter & Van der Linde,
1995; Mohr, 2002; Ambec et al., 2013). Furthermore, the cap-and-trade system leaves the
technology decision to the participants in the EU ETS, thus fostering continuous improvement
rather than locking into one specific technology (Tietenberg, 2010).
Regarding the third principle, the EU ETS provides participants with security about the
implementation scope and timing, which is the main differentiator compared to the US systems.
Since the introduction of the policy, the EU has communicated that the ETS is and will be its
main instrument to tackle climate change, ultimately regulating installations in all member
states and adjacent countries willing to opt-in (European Commission, 2015). That gives
participants security in two ways. Firstly, it ensures that there is only one market for emission
trading, in which regulated firms from all over Europe can trade. That is important, because in
the US case, the trading systems remained regional initiatives, providing little incentives for
(inter-) national players to adjust the entirety of their business operations to the policies
(Borghesi & Montini, 2016). Secondly, the EU communicated that the level of stringency
increases over time, which further reduces uncertainty and makes short-term strategies such as
fuel switching not viable. Early evidence from the EU ETS implies that a majority of the
emission reductions in the period from 2005-2007 stem from fuel switching strategies (Delarue,
Ellerman, & D’haeseleer, 2010; Koch, Fuss, Grosjean, & Edenhofer, 2014). However, contrary
to the US case, fuel switching strategies in the EU ETS are not sustainable in the long-term.
Delarue, Voorspools, and D’haeseleer (2008) estimate that fuel switching can achieve a
maximum reduction of 300 million tCO2 annually. That is approximately 10% of the cuts
required to achieve the EU 2050 goals of 80%-95% emission reduction compared to 1990
(European Commission, 2018). Since companies can anticipate that fuel switching strategies
are not economically sustainable, they are expected to engage in activities that ensure the long-
term reduction of emission costs such as eco-innovation. Although certainty in the EU ETS is
higher arguably than it was in the US case, the EU ETS pilot phase was subject to regulatory
13
adjustments (European Commission, 2015). That could, in theory, delay the policy impact (see
section 3.2.2.).
Empirical evidence from the first phase of the EU ETS further indicates that the policy has
induced eco-innovation. Qualitative studies show that managers in the EU expect the
stringency of the EU ETS to increase and intend to make adequate investments (Martin, Muûls,
& Wagner, 2012); Irish EU ETS firms pursue operational innovations like investing into the
development of new machinery (Anderson et al., 2011); and German experts in the power
sector expect the direction of technological change to shift towards more sustainable methods
of power generation (Rogge, Schneider, & Hoffmann, 2011). Although the evidence is only
indicative, and quantitative country-specific studies fail to report a significant impact of the EU
ETS on eco-innovation (Löfgren et al., 2013; Borghesi, Cainelli, & Mazzanti, 2015), Calel and
Dechezleprêtre (2016) find that the EU ETS had a substantial impact on the patenting behavior
of regulated firms. They show that EU ETS was responsible for a 36.2% increase in low-carbon
patent applications when comparing the patent output of EU ETS firms and non-EU ETS firms.
In contrast to other studies, Calel and Dechezleprêtre (2016) follow a robust methodological
approach, which is essential for three reasons. Firstly, the study does not fall short to the
limitation of an unrepresentative sample size. The authors include firms from all over Europe,
and thus, their sample is more representative than that of country or sector studies (Joltreau &
Sommerfeld, 2019). Secondly, they estimate the impact of the EU ETS on regulated firms by
matching each EU ETS firm to a similar non-regulated firm based on observable company
characteristics. In doing so, the authors elude the impact of sector-level shocks on innovation
output that was unrelated to the EU ETS. Thirdly, Calel and Dechezleprêtre (2016) use patent
counts as a measure for innovativeness. Patents are considered the most robust measure of
innovation activity because they are quantitative, output-oriented, and can be disaggregated
(Acs & Audretsch, 1989; Johnstone, Haščič, & Kalamova, 2010; Haščič & Migotto, 2015).
Consequently, their study is considered the most comprehensive in the literature on innovation
in the EU ETS (Marcantonini et al., 2017; Joltreau & Sommerfeld, 2019).
Considering the more stringent and far-reaching policy design of the EU ETS, the underlying
innovation-inducing mechanism of a cap-and-trade system and the aforementioned empirical
findings from the first phase of the EU ETS, the following hypothesis is postulated:
H1: The EU ETS increased the eco-innovativeness of regulated firms.
14
3.2.2. The role of increasing stringency and certainty in the EU ETS
The Porter Hypothesis states that the degree of stringency in the outline of the policy is
influencing participants’ incentivizes to innovate. Porter and Van der Linde (1995) suggest that
an effective policy is stringent enough to affect the cost of emitting significantly. However,
assessing the stringency level of policies provides a methodological challenge because there is
little agreement on objective comparison criteria (Johnstone, Haščič, & Kalamova, 2010; Botta
& Koźluk, 2014). Yet, in the case of the EU ETS, Joltreau and Sommerfeld (2019) suggest that
the emission reduction target and the penalty fees for non-compliance are appropriate
stringency estimates. Firstly, considering the emission target, the second phase of the EU ETS
introduced a more stringent emission cap compared to phase one. That makes intuitive sense
because the first phase was intended to function as a pilot phase. Studies that do not find an
innovation-inducing effect of the EU ETS in the first implementation phase consider the rather
low emission cap as one possible explanation (Anger & Oberndorfer, 2008; Rogge et al., 2011;
Joltreau & Sommerfeld, 2019). For phase two, the overall cap was reduced by approximately
10% (see Table 1), which makes it more stringent than phase one. Secondly, the fines for non-
compliance were increased significantly in phase two. The penalty charge for each tCO2 rose
from €40 to €100.
Figure 1. Price development of EUAs between 2004 and 2015. Reprinted from “The best (and
worst) of GHG Emission Trading Schemes: Comparing the EU ETS with its followers” by S.
Borghesi and M. Montini, 2016, Frontiers in Energy Research, 4(83), 27.
Furthermore, the price of EUAs indicates that phase one entailed a high level of
uncertainty regarding the future administration of the policy. Figure 1 depicts the daily rates of
15
EUAs from 2005-2015. One can see that the prices of the first-phase EUAs were highly
volatile. Marin et al. (2018) suggest that a major factor of influence was uncertainty about
whether allowances from the first phase could be banked for the second phase. The latter was
not the case, which is why the EUA price dropped to €0 in 2007. Also, the decreasing cap for
the third phase was only communicated at the start of the second phase (European Commission,
2015). The enhanced certainty could have motivated regulated companies to reduce their
emissions with a long-term focus, which ultimately encourages eco-innovation.
With a more stringent design and more certainty, one can expect that the second phase of the
EU ETS increased the eco-innovativeness of regulated firms more than the first phase. Thus,
the study postulates hypothesis two as follows:
H2: The second phase of the EU ETS increased the eco-innovativeness of regulated firms
more than the first phase of the EU ETS.
3.2.3. The EU ETS and the strong Porter Hypothesis
There is little evidence on firm-level performance indicators from the US cap-and-trade
systems. One reason is that the specification of the different Porter Hypothesis forms followed
only after the programs’ enacting. While the weak Porter Hypothesis has received considerable
coverage in academic literature, the debate on the strong Porter Hypothesis has only recently
shifted towards assessing cap-and-trade systems in detail (Ellerman et al., 2010). However,
the few existing ex-post analyses of the US cap-and-trade systems do not support the strong
Porter Hypothesis. For instance, Popp (2003) finds that the introduction of the Clean Air Act
of 1990 increased the costs for firms without affecting revenues. Although there appears to be
no evidence for the strong Porter Hypothesis, it must be considered that the outline of the EU
policy differs significantly from the US policy design.
Comparing the EU ETS to the US policy, the more far-reaching implementation scope suggests
that the strong Porter Hypothesis holds in the EU ETS. Porter and Van der Linde (1995) argue
that eco-innovation leads to enhanced firm performance via two mechanisms. Firstly, eco-
innovation can offset the costs of regulation by efficiency gains. That is, if firms use new
equipment that requires fewer resources, they can produce the same output with lower input
costs. However, the authors admit that the productivity gains “cannot always completely offset
16
the cost of compliance, especially in the short term before learning can reduce the cost of
innovation-based solutions” (Porter & Van der Linde, 1995, p. 100).
Secondly, firms can alter their financial performance by either selling eco-innovations
to other players in the market or passing through the costs of investing in new eco-innovations
to customers, resulting in windfall profits. The US policy was limited in terms of geographical
implementation scope, possibly reducing the revenue potential from selling newly developed
technologies directly (Popp, 2003; Ellerman et al., 2010). In contrast, companies in the EU
ETS have access to a bigger market, providing them with a stronger business case since eco-
innovations can become a new source of revenue (Bocken, Farracho, Bosworth, & Kemp,
2014). Furthermore, EU ETS firms might pass through additional regulatory costs to customers
by raising prices. Cost pass-through occurs if firms change the prices of their products due to
an increase in the expenses that arise during the production process. Most of the regulated
sectors in the EU ETS consider the manufacture of mineral products and the production of
utilities (European Commission, 2015). In these sectors, price elasticity has historically been
low, which makes cost pass-through to customers likely (Joltreau & Sommerfeld, 2019). In
combination with the free allocation of EUAs, this presents a profit opportunity for firms (Sijm
et al., 2006). For example, one can consider the case in which a company, which operates
differently sized power stations, becomes regulated, and receives a certain number of EUAs
via grandfathering. The increase in marginal costs is added to its energy bids since it is
considered an opportunity cost for the company (Sijm et al., 2006). If energy prices are high
and demand decreases, the company can switch from expensive to cheaper power stations,
decreasing their real marginal costs. However, price bids do not fall, offering the potential for
windfall profits. Sijm et al. (2006) estimate that, at a CO2 price of €20 per tCO2, policy-induced
profits range from €3-5 per MWh. Thus, considering potential new revenue streams as well as
potential windfall profits, regulated companies can be expected to perform better financially.
Empirical research indicates that the introduction of the EU ETS enhanced financial
performance. Multiple researchers find no effect of the EU ETS on firm performance. Anger
and Oberndorfer (2008) investigate a German sample of regulated firms and discover no policy
impact on neither revenues nor employment. The results are confirmed by Petrick and Wagner
(2014), who find no statistically significant effect of the EU ETS on employment, turnover,
profits, or exports in Germany. One issue with the findings above is that other stringent national
environmental policies have been introduced even before the EU ETS. In Germany, the
Renewable Energy Act entered into force in 2000 (Bundesministerium für Wirtschaft und
17
Energie, 2019), which set the target of doubling the share of renewable energies in electricity
consumption in Germany by 2010. Nevertheless, also in Lithuania, the EU ETS has not
impacted the profitability of regulated firms (Jaraite & Di Maria, 2016).
In contrast, other country studies find a significant positive impact of the EU ETS on
business performance indicators. Examining a Norwegian sample of regulated plants,
Klemetsen, Rosendahl, and Jakobsen (2016) find that the policy had positive effects on value-
added and productivity. According to the authors, one reason were the massive amounts of free
allowances and that firms could pass on marginal costs to customers. Furthermore, Calligaris,
Arcangelo, and Pavan (2019) find a positive policy impact on total factor productivity.
However, the findings above fail to provide comprehensive insights into the overall EU impact
of the EU ETS on economic performance.
Research that focuses on firms from multiple EU countries finds evidence for a positive
effect of the EU ETS on firm performance indicators. Dechezleprêtre et al. (2018) investigate
a sample of 1,728 ETS firms and find a positive impact on fixed assets and revenues. The
former increased by 17%, while the latter increased by 8%, indicating that the EU ETS firms
reacted to the policy by EU ETS by adopting costly emissions-reduction technologies while
they were further able to increase their revenues. Also, Chan, Li, and Zhang (2013) empirically
investigate the impact of the EU ETS on material costs, employment, and revenues in the
European power, cement, iron, and steel sectors. Their results reveal positive effects on
material costs and revenues in the power sector. They demonstrate that the impact on material
costs stems from additional costs to comply with emission constraints while the effect on
revenue bears upon cost pass-through to consumers. Furthermore, Marin, Marino, and
Pellegrin (2018) confirm a positive influence of the EU ETS on multiple measures of economic
performance at the firm level. They report that the EU ETS increased employment among
regulated firms by 8%, investment by 26%, turnover by 15%, and value-added by 6%. Their
insights illustrate that the EU ETS, while driving up sales, increased material and other variable
costs, indicating that cost pass-through happened, which led to windfall profits. Summarizing,
the evidence suggests a positive impact of the EU ETS on business performance indicators.
Conjointly, theory and empirical findings suggest that the EU ETS had a positive impact on a
firm’s performance. In particular, the financial performance of regulated firms can be expected
to increase. Thus, the thesis postulates hypothesis three as follows:
H3: The EU ETS increased the financial performance of regulated firms.
18
The underlying argument of the strong Porter Hypothesis is that innovation mediates the
relationship between environmental policy and increased firm performance. Mohr (2002)
elaborates on this causal chain. He suggests that environmental regulations can induce
companies to become a first-mover in developing and investing in new environmental
technology, which then increases business performance. That occurs since the productivity of
a firm in using a specific type of technology depends on the market participants’ use of it. Even
if a certain technology used can be considered as “old”, it can still be reasonably productive
due to collective learning. Thus, companies become hesitant to invest in research and
development of new technologies because they want to avoid incurring the learning costs for
other players. That implies that it is profitable for a firm to wait until other firms innovate and
then benefit from their experience. Nevertheless, environmental regulation can overcome this
market failure (Porter & Van der Linde, 1995). Reducing the burden of becoming a first-mover
regarding both development and investment in new technologies, creates an incentive to
innovate (Mohr, 2002). As first-movers, companies can sell their innovations to other firms,
enhancing their financial performance (Bocken et al., 2014).
Following this line of argumentation, one can expect that eco-innovation mediates the potential
positive impact of the EU ETS on financial performance. Thus, the paper postulates hypothesis
four as follows:
H4: The eco-innovativeness of regulated firms mediates the relationship between the
introduction of the EU ETS and enhanced financial performance.
In summary, a gap in the literature on the Porter Hypothesis exists. Its validity in cap-and-trade
systems has not yet been comprehensively assessed since evidence from both US and EU ETS
is contradicting. The design of the EU ETS, however, suggests that one can expect innovation
and financial performance of regulated firms to increase. By incentivizing participants to eco-
innovate, they can increase efficiency and derive new streams of revenue. Especially
implementation phase two is expected to spur eco-innovation due to an enhanced level of
regulatory stringency and certainty.
19
4. METHODOLOGY
This section aims at delivering insight into the empirical design chosen to answer the
previously developed hypotheses. The section firstly describes the construction of the sample.
Since the thesis applies a quasi-experimental study design, it explains the matching procedure
in detail. Secondly, the paper elaborates on the empirical specification by describing regression
models, variables, and the estimation approach.
4.1. Data description
In the EU ETS, regulation occurs at the installation level, which allows comparing account
holders of regulated installations with account holders of non-regulated installations based on
similar firm characteristics. Nevertheless, identifying regulated companies and appropriate
non-regulated counterparts provides a challenge due to the lack of harmonization and data
availability in the first phases of the EU ETS (Jaraité, Jong, Kažukauskas, Zaklan, &
Zeitlberger, 2014). The following paragraphs elaborate on the identification strategy applied,
which is also depicted in Figure 2.
Figure 2. Identification strategy to obtain sample
4.1.1. Identification of regulated companies in the EU ETS
The European Commission (2012) publishes the NAPs and, since 2013, NIPs for each year and
each participating country in the EU ETS. That permits identifying account holders of regulated
installations. The entries of the NAPs are, however, not harmonized, which makes matching
firms to account holders difficult (Jaraité et al., 2014). For example, names of account holders
are reported in national languages, and the ownership structure of internationally acting
companies is not considered. Nevertheless, researchers who investigate the EU ETS have made
20
matched datasets available. The data set of the European University Center (EUC) developed
by Jaraité et al. (2014) forms the base for the thesis. The authors attempt to match the entries
of 8,466 account holders at the end of the second implementation phase with firm data from
Bureau van Dijk’s Orbis database and identify 3,689 regulated companies. The study only
considers companies that were regulated in both phases one and two of the EU ETS to
investigate the differential policy effect of the implementation phases. Thus, companies from
countries that entered the EU ETS in the first phase but after 2005 (i.e. Bulgaria, Romania,
Croatia) and non-EU countries that joined in the second phase are excluded (i.e. Norway,
Liechtenstein, Iceland), reducing the sample to 2,095 firms.
Since the thesis makes use of PSM (see section 4.2.2.), restrictions regarding the
availability of firm data further reduce the sample size. Matching in PSM should occur based
on variables before treatment (Caliendo & Kopeinig, 2008). The EU ETS became active in
2005, which is why the thesis only considers regulated companies with firm data entries that
go back until 2004. This reduces the sample size of regulated firms to 412 companies. Of the
412 firms, approximately half provide sufficient information concerning the matching criteria.
Thus, 210 regulated companies enter the sample used for the following PSM. For these
companies, Orbis firm and financial data, as well as patent data from PATSTAT, the European
Patent Office’s (EPO) worldwide patent database, is retrieved.
4.1.2. Identification of comparable non-regulated companies and matching via PSM
PSM is a statistical matching technique that is frequently used in observational studies to
control for treatment selection bias and thus mimic randomized allocation (Rosenbaum &
Rubin, 1983; Caliendo & Kopeinig, 2008). In PSM, the probability of assignment to treatment
(regulation), conditional on pre-intervention variables, is condensed to a single score: the
propensity score (Rosenbaum & Rubin, 1983). The PSM algorithm allocates one non-treated
individual (non-regulated firm) to each treated individual (regulated firm) based on the
similarity of their propensity scores, forming a statistically similar pair. In theory, this allows
estimating the average treatment effect on the treated (ATT), which is the difference between
expected outcome values for treated compared to non-treated individuals. Assessing the ATT
implies that all factors that influence the probability of an individual to be treated are
observable (Caliendo & Kopeinig, 2008). That is, however, problematic in the case of the EU
ETS because the level of regulation and the level of analysis differ (Calel & Dechezleprêtre,
2016; Jaraite & Di Maria, 2016; Marin et al., 2018). As described in section 2.2., for an
individual installation of a particular size (in terms of capacity), any other plant of the same
21
size should be regulated as well. Nevertheless, if the unit of analysis is the firm, then there
might be the case in which one firm owns regulated installations, and a firm of comparable size
does not own regulated installations. To account for unobservable but temporally invariant
differences in outcomes, the study combines PSM with a DID estimation, as proposed by
Abadie (2005). That is, the sample is constructed using PSM and, subsequently, assessed
through a DID estimation. To build the sample, companies are matched on a set of covariates
following the guideline of Caliendo and Kopeinig (2008).
The selection of appropriate covariates is case-specific, and little consensus regarding
the applicable threshold for a minimum number of covariates exists (Caliendo & Kopeinig,
2008). Therefore, the thesis follows approaches taken by comparable matching efforts in the
setting of the EU ETS (e.g., Calel & Dechezleprêtre, 2016; Jaraite & Di Maria, 2016;
Dechezleprêtre et al., 2018; Marin et al., 2018). The propensity scores are determined based on
fixed assets and turnover in the pre-treatment year 2004. Fixed asset size is the most used
matching determinant in EU ETS research since it proxies the installation capacity, although
at an aggregated level (Joltreau & Sommerfeld, 2019). Turnover is further used to assess firms
with a comparable level of gross income. Thereby, the study compares companies that are in a
similar financial situation and have comparable resources available to innovate. To account for
unobserved shocks that had a differential impact on countries and sectors, the matching
procedure applied requires that each EU ETS firm is matched to a firm in the same country and
the same industry. The sector matching criterium is the two-digit NACE Rev. 2 code as defined
by the European Commission (2013).
Using the Stata package “psmatch2”, propensity scores for the 210 EU ETS and more
than 50,000 firms from the same countries and industries are computed. Caliper matching is
used to impose a maximum tolerance level on the propensity score distance. Caliper matching
enforces the common support condition of PSM and thus provides better quality matches than
other matching algorithms (Caliendo & Kopeinig, 2008). The caliper is set to 0.2, which is
considered an optimal level for PSM in observational studies (Austin, 2011). Although caliper
matching increases the quality of matches, it reduces the number of matches and thus increases
the variance of the covariates (King & Nielsen, 2019). This becomes evident when applying
the matching algorithm. The algorithm finds an appropriate match for only 146 of the 210
identified EU ETS companies. Investigating the excluded companies, the assessment shows
that predominantly large companies are excluded. For instance, there is no other German
company in the size of Siemens that operates in the same sector and is not regulated.
22
To assess the quality of the matches, Caliendo and Kopeinig (2008) suggest visual
inspection of the propensity score distribution. Figure 3 displays the propensity score
distribution of the 146 EU ETS and 146 non-EU ETS firms. The dispersion graph suggests that
a sufficient level of overlap and common support are achieved. However, there are a few
outliers on the right-hand side of the graph. To decide whether one can include the outliers,
Caliendo and Kopeinig (2008) propose assessing the difference in means of the covariates
between treated and non-treated individuals. The outcomes of a t-test suggest that there is no
statistically significant difference between the means of fixed assets (t = .710, p = .476) and
turnover (t = .610, p = .539) among the two groups. Furthermore, investigation of the
standardized percentage bias shows that the sample is balanced. The standardized bias
percentage is the percentage difference of the sample means in the treated and non-treated sub-
samples, expressed as a percentage of the square root of the average of the sample variances in
the treated and non-treated group bias (Rosenbaum & Rubin, 1985). For both fixed assets
(8.3%) and turnover (7.2%), it is below the commonly used threshold of 10% (Zhang, Kim,
Lonjon, & Zhu, 2019).
Consequently, 292 firms, 146 regulated and 146 unregulated firms, make up the dataset
for the study. This represents a significant reduction from the 3,689 firms identified by Jaraité
et al. (2014). Nevertheless, Dehejia and Wahba (1999) show that what is lost in sample size in
PSM is regained in robustness and accuracy. Furthermore, researchers applying PSM in the
setting of the EU ETS report sample size reduction of similar magnitude (see, e.g., Calel &
Dechezleprêtre, 2016; Dechezleprêtre et al., 2018; Marin et al., 2018).
Figure 3. Sample propensity score distribution
23
4.1.3. Composition and description of the final dataset
For the 292 companies, financial and patent data is retrieved for the time frame from 2000-
2014. Patent data is retrieved from PATSTAT. The European Patent Office (2013) categorizes
patents according to cooperative patent classification (CPC) codes, which classify the
application area of the patent. The CPC entails a sub-category (Y02) of inventions that patent
technologies or applications for the mitigation or adaptation against climate change. These are
considered as a proxy for eco-innovations (Martin et al., 2012; Calel & Dechezleprêtre, 2016).
The study contemplates only EPO-granted patents with an application date between 2000-
2014. That is because patents take on average four years from application date to publication
date in databases (OECD, 2009a), which makes 2014 the last reliable selection year.
Furthermore, financial data for each firm is obtained. In addition to the covariates used for
matching (i.e., fixed assets and turnover), data on firms’ R&D expenditure and return on assets
(ROA) is retrieved from Orbis (see section 4.2.2. for reasoning). One limitation of combining
the databases is the shortage of unique firm identifiers. Although Orbis offers a link to
PATSTAT, it only provides entries for 169 of the 292 firms. For the remaining companies,
string matching on company name and country is used to identify the number of eco-patents
filed. In this effort, the entries reported in the Orbis patent database are cross-validated.
The final panel dataset comprises 4,380 observations, one for each year and each of the
292 firms. Countries from 19 of the EU27 countries are represented in the sample. Most
companies in the dataset are from Spain (35%), followed by Poland (14%), and Germany
(12%). For Austria, Greece, Hungary, Luxembourg, and Slovakia, respectively, only two
companies are included in the sample. Regarding industries, the majority of companies are
active in the sectors of manufacture of non-metallic mineral products (33%) and electricity,
gas, and air conditioning supply (29%). Overall, the sample is representative of the actual
installation allocation in the EU ETS considering country and industry dispersion (see, e.g.,
Calel & Dechezleprêtre, 2016). Only Spanish companies are overrepresented due to dataset
construction. Appendix A reports the firm demographics of the sample. In the sample, only
companies from Belgium, Germany, France, and Spain filed eco-patents in the period from
2000-2014. Although patent data is available for each year from 2000-2014, financial data
entries are limited. Table 2 provides an overview of the number of observations for the eco-
patent counts, fixed assets, turnover, R&D expenditures, and ROA in the panel. In addition to
that, measures of central tendency (means), dispersion (standard deviations) as well as
minimum and maximum values are displayed for the overall dataset, EU ETS firms, and non-
EU ETS firms, respectively. The variables are presented in the form of natural logarithms duet
24
to skewness of raw data entries (see Appendix B). One must note that the number of
observations varies for the variables. For fixed assets, turnover, and return on assets,
approximately 60% of the panels have entries, while R&D expenditures data is available for
only 6% of the panels.
Table 2. Adjusted descriptive statistics
Variables Mean Std. Dev. Min. Max. N
Overall sample
lnEcopat 0.026 0.228 0 3.584 4,380
lnFixedAssets 17.309 2.289 6.261 23.715 2,783
lnTurnover 17.218 2.743 0.693 24.417 2,705
lnRDexpense 6.279 9.982 0 25.904 266
ROA 0.019 0.116 -0.982 0.901 2,762
EU ETS firms
lnEcopat 0.032 0.273 0 3.584 2,190
lnFixedAssets 17.461 2.288 6.261 23.715 1,475
lnTurnover 17.262 2.796 3.135 24.417 1,439
lnRDexpense 6.073 9.973 0 25.774 163
ROA 0.019 0.112 -0.976 0.901 1,481
Non-EU ETS firms
lnEcopat 0.012 0.171 0 2.890 2,190
lnFixedAssets 17.139 2.279 7.652 23.159 1,308
lnTurnover 17.169 2.683 0.693 23.202 1,266
lnRDexpense 6.604 10.038 0 25.904 103
ROA 0.018 0.121 -0.982 0.852 1,281
Note. Natural logarithm is computed by lnX = ln(x+1) to account for zeros.
4.2. Empirical specification
The thesis formulates two general models to analyze the dataset regarding the weak and strong
Porter Hypothesis. In the following, regression models are constructed, variables are described,
and the approach followed to estimate the two models is laid out.
4.2.1. Regression models
The first general model assesses the weak Porter Hypothesis in the EU ETS. To determine the
causal impact of the EU ETS on eco-innovation, the thesis makes use of a DID estimation. DID
25
assessments have become widely spread since the seminal work of Ashenfelter and Card
(1985). They are frequently used in policy analysis because they provide unbiased effect
estimates of a policy intervention using a non-experimental design (Stuart et al., 2014). To
estimate a policy effect, DID estimations compare changes over time in a group unaffected by
the policy to changes in a group affected by the policy, and attributes the DID to the effect of
the policy (Ashenfelter & Card, 1985; Stuart et al., 2014). The selection of the pre-treatment
and treatment period is vital for the interpretation of the treatment effect. De Chaisemartin &
D’haultfoeuille (2019), as well as Wing, Simon, and Bello-Gomez (2018) show that DID with
multiple periods can be estimated by comparing different linear models. The applicability of a
linear model to assess panel data and patents, in particular, is debated in academia due to the
data’s tendency to suffer from overdispersion7 (Hausman, Hall, & Griliches, 1984; Blundell,
Griffith, & Van Reenen, 1995; Allison & Waterman, 2002). However, the ease of interpretation
justifies the use of linear models (Williams, 2008), and various studies have assessed patent
data in this way (Jaffe & Palmer, 1997; Sanyal & Vancauteren, 2013; Yoshikane, Suzuki,
Arakawa, Ikeuchi, & Tsuji, 2013).
Firstly, the thesis formulates model (1a)8 to analyze the impact of the EU ETS on a
firm’s eco-innovativeness comparing the entire post-EU ETS period to the pre-EU ETS period:
𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖𝑡 = 𝛽0 + 𝛽1𝐸𝑇𝑆𝑖 + 𝛽2𝑝𝑜𝑠𝑡 + 𝛽3𝐸𝑇𝑆𝑖 ∗ 𝑝𝑜𝑠𝑡 + 𝛽4𝑠𝑖𝑧𝑒𝑖,𝑡−2 + 𝛿𝑗 + 𝜇𝑐
+𝜃𝑡 + 휀𝑖𝑡, (1a)
where 𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖𝑡 is the natural logarithm of the number of granted patents for firm i with an
application date at time t. 𝐸𝑇𝑆𝑖 is a dummy variable equal to one if a firm i became regulated
by the EU ETS in 2005; 𝑝𝑜𝑠𝑡 is a dummy variable taking on value one for the time period from
2005-2012; 𝐸𝑇𝑆𝑖 ∗ 𝑝𝑜𝑠𝑡 is the interaction between the these variables. In a DID study, the
estimator of the interaction effect is of interest, in this case 𝛽3, which es expected to carry a
positive sign. 𝑆𝑖𝑧𝑒𝑖𝑡 is a control variable for the fixed assets size (as natural logarithm) of each
firm i in year t-2. The lag to eco-patent output is considered because Hall, Griliches, and
Hausman (1986) suggest that the average time from R&D to patent application is two years,
which is also an appropriate estimate for eco-innovations (Hall & Helmers, 2013). 𝛿𝑗 is a set
7 In section 5.2. a non-binomial model is assessed in addition to the linear models posed. 8 The thesis uses the terms “model one” and “model two” when referring to the general models, while using the
specific model names when considering specific models, e.g., “model (1a)”.
26
of sector dummies; 𝜇𝑐 is a set of country dummies; 𝜃𝑡 is a set of year dummies. The constant
is denounced by 𝛽0, the error term by 휀𝑖𝑡.
Secondly, model (1b) compares EU ETS phases one and two, respectively, with the
pre-EU ETS period from 2000-2004. The model examines three instead of two periods, which
allows assessing the differential impact of the first two implementation phases:
𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖𝑡 = 𝛽0 + 𝛽1𝐸𝑇𝑆𝑖 + 𝛽2𝑝ℎ𝑎𝑠𝑒 + 𝛽3𝐸𝑇𝑆𝑖 ∗ 𝑝ℎ𝑎𝑠𝑒 + 𝛽4𝑠𝑖𝑧𝑒𝑖,𝑡−2 + 𝛿𝑗 + 𝜇𝑐
+𝜃𝑡 + 휀𝑖𝑡, (1b)
where 𝑝ℎ𝑎𝑠𝑒 is a categorical variable taking on value zero for the period from 2000-2004,
value one for the period from 2005-2007, and value two for the period from 2008-2012.
Figure 4. Conceptual mediation model. Adapted from “The Moderator-Mediator Variable
Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical
Considerations” by R. Baron and D. Kenny, 1986, Journal of Personality and Social
Psychology, 51(6), 1173-1182.
The second general model analyses the strong Porter Hypothesis in the EU ETS. The thesis
follows the steps suggested by Baron and Kenny (1986) for mediation analysis (Figure 4). This
is, firstly, the effect of the policy introduction on financial performance is estimated with model
(2a), which assesses path “c” in Figure 4:
𝑅𝑂𝐴𝑖𝑡 = 𝛽0 + 𝛽1𝐸𝑇𝑆𝑖 + 𝛽2𝑝𝑜𝑠𝑡 + 𝛽3𝐸𝑇𝑆𝑖 ∗ 𝑝𝑜𝑠𝑡 + 𝛽4𝑠𝑖𝑧𝑒𝑖,𝑡−3 + 𝛿𝑗 + 𝜇𝑐
+𝜃𝑡 + 휀𝑖𝑡, (2a)
where 𝑅𝑂𝐴𝑖𝑡 is the return on assets, computed as net income divided by total assets, for a
company i in year t. ROA is considered with a three-year forward to account for the time from
R&D to patenting of two years (Hall et al., 1986; Hall & Helmers, 2013) and an additional year
to commercialize a patented invention, as proposed by Hart, Ahuja, and Arbor (1996). If the
relationship proves to be significant, secondly, the effect of the policy on eco-innovation is
analyzed, which represents path “a” in Figure 4. Note that model (1a) already describes this
27
relationship. Thirdly, if the impact of the policy introduction on eco-innovation is significant,
model (2b) is applied, which assesses the effect of eco-innovation on financial performance
(path “b” in Figure 4):
𝑅𝑂𝐴𝑖𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖,𝑡−1 + 𝛽2𝑠𝑖𝑧𝑒𝑖,𝑡−3 + 𝛿𝑗 + 𝜇𝑐 + 𝜃𝑡 + 휀𝑖𝑡. (2b)
If this tie also proves to be significant, model (2a) and (2b) are combined to model (2c), which
represents path “c’” in Figure 4:
𝑅𝑂𝐴𝑖𝑡 = 𝛽0 + 𝛽1𝐸𝑇𝑆𝑖 + 𝛽2𝑝𝑜𝑠𝑡 + 𝛽3𝐸𝑇𝑆𝑖 ∗ 𝑝𝑜𝑠𝑡 + 𝛽4𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖,𝑡−1
+ 𝛽5𝑠𝑖𝑧𝑒𝑖,𝑡−3 + 𝛿𝑗 + 𝜇𝑐 + 𝜃𝑡 + 휀𝑖𝑡, (2c)
where 𝑙𝑛𝐸𝑐𝑜𝑝𝑎𝑡𝑖,𝑡−1 is a mediator if the estimated value of 𝛽3 is significantly lower (ideally
0) in model (2c) than in model (2a) (Baron & Kenny, 1986; Hayes, 2017), which can be
assessed with the Sobel (1982) test.
4.2.2. Dependent variables
In model one, the dependent variable is the number of eco-patents filed by a firm in a given
year. A firm’s patent output is widely accepted as a measure of innovativeness because it
provides three distinct advantages compared to other measurements. Firstly, patents are a
quantitative output measure of innovation activities. Compared to input-oriented measures
(e.g., R&D expenditure), patents consider inventions that are intended to be commercialized
(OECD, 2009a). Secondly, patents are commensurable. Only granted patents filed at the EPO
are analyzed, which provides a degree of standardization regarding quality and novelty of
innovations that is not obtained when considering patents from different national patent offices
(Squicciarini, Dernis, & Criscuolo, 2013). Thirdly, patents can be disaggregated, which allows
one to specifically consider eco-innovations (Johnstone, Haščič, & Kalamova, 2010; Veefkind,
Hurtado-Albir, Angelucci, Karachalios, & Thumm, 2012; Hall & Helmers, 2013). As
elaborated on in section 4.1.3., the study utilizes this feature of patent data to assess the CPC
sub-class Y02 as a proxy for eco-innovation.
Nonetheless, patent data is not free of limitations. Because only patents filed at the EPO
are considered, high-value innovations might be overrepresented. Harhoff, Scherer, and Vopel
(2003) show that due to the high costs associated with filing patents at the EPO, firms instead
turn to national patent offices to protect their intellectual property. Nevertheless, Calel and
Dechezleprêtre (2016) suggest that Y02-class innovations are high-tech and of high value, and
thus, the decision to file a patent at the EPO is less affected by patenting costs. A more
28
impactful limitation of patent data is that most companies do not file patents at all, which zero-
inflates patent counts and limits potential findings from the analysis (Archibugi & Pianta,
1996).
Model two considers ROA as the dependent variable to measure financial performance. ROA,
here defined as net income divided by total assets, is one of the most widely used accounting
measures of financial performance and provides various advantages over other metrics (Barton,
Hansen, & Pownall, 2010). Three advantages are of particular importance for the thesis. Firstly,
although being an accounting measure, ROA has a high predictive power of actual market
performance. Particularly in industries that rely on fixed assets such as manufacturing or utility
production (Aliabadi, Dorestani, & Balsara, 2013), ROA is considered as a relevant proxy.
Therefore, ROA is an appropriate measure, since the sample mainly consists of companies
from these two particular sectors (see Appendix A). Secondly, research has shown that ROA
is particularly relevant when assessing eco-innovations. Since most eco-innovations occur at
the process level, (fixed) assets are directly affected and should be accounted for (Peloza, 2009;
Przychodzen & Przychodzen, 2015). Thirdly, ROA is proven to be a stable measure of
corporate performance. Different from return on equity or return on investment, ROA is less
likely to be affected by short-term financial manipulation (Aliabadi et al., 2013).
4.2.3. Independent and control variables
The study controls for year, country, and industry dummies as well as firm size. These variables
are also used in other EU ETS studies (see, e.g., Calel & Dechezleprêtre, 2016; Dechezleprêtre
et al., 2018; Marin et al., 2018). Firstly, year dummies account for time-specific shocks. It is
necessary to capture the impact of macro-economic events such as the recession following the
Financial Crisis in 2008 because it had a negative effect both on the innovation and financial
performance of European firms (Archibugi, Filippetti, & Frenz, 2013). Secondly, country
dummies capture possible country-specific effects that influence eco-innovation or financial
performance. This is grounded in the National Innovation System (NIS) perspective, which
states that innovation is a combined effort of several parties, including private and public
institutions in a country (Carlsson, 2006). Thirdly, industry dummies are deployed to account
for the fact that the composition of industries has proven to affect innovativeness (Hunt, 2004)
and financial performance (Porter, 1980). Fourthly, the study controls for firm size measured
by fixed assets. One the one hand, Acs and Audretsch (1987) show that larger firms have an
innovation advantage that results in enhanced financial performance. On the other hand, the
29
EU ETS regulates based on installation specific criteria, which at an aggregated level are
approximated by fixed asset size.
In theory, the models should also account for R&D expenditure since it is known to be
highly correlated with patent output (Hall et al., 1986). However, the dataset structure shows
that this is not possible. Only 6% of observations entail entries on R&D expenditures.
Furthermore, in the sample, there is a statistically significant difference in eco-patent output
between companies that have listings for R&D expenditures and companies that do not
(t = -13.872, p = .001). Confining the analysis on companies with entries for R&D expenditure
would thus significantly reduce representativeness.
Apart from the dummy variables for regulated companies and specific periods, the only
independent variable considered is the number of eco-patents in model two. As outlined in
section 4.2.2., the eco-patent output of a firm is an adequate measure for eco-innovation.
4.2.4. Estimation approach
Before estimating the models, diagnostic tests are conducted to assure that estimates and
standard errors are not biased. The thesis follows the sequential diagnostic approach used by
Baltagi (2005) to test the panel on homoskedasticity, autocorrelation, and cross-sectional
independence. Firstly, a modified Wald test statistic is computed, which tests for group-wise
homoskedasticity in the error terms (Baum, 2001). Rejecting the null hypothesis of
homoskedasticity for both models one and two, evidence suggests that the error terms are
heterogeneously distributed, which can cause bias in the standard errors. Secondly,
Wooldridge's (1990) F-statistic as is computed to check for serial correlation in the error terms.
Akin to heteroskedasticity, autocorrelation can cause an understatement of standard errors. The
F-statistics for both models one and two do not support the alternate hypothesis that first-order
autocorrelation is present. Thirdly, because the panel is characterized by a large number of
firms compared to year observations, an additional test for weak cross-sectional dependence of
residuals is conducted. Following Pesaran (2012), a test statistic is computed and tested against
the null hypothesis that errors are weakly cross-sectionally dependent. For both models one
and two, the test statistics suggest to reject the null hypothesis, indicating that cross-sectional
independence can be assumed. Results for the three tests in models one and two are reported
in Tables 2 and 3, respectively. Although diagnostics show that autocorrelation and cross-
sectional dependence pose no issue for the estimation of models one and two, robust standard
errors must be computed to account for the presence of heteroskedasticity (Greene, 2000).
30
Thus, the thesis uses standard errors calculated with White's (1980) heteroscedasticity-
consistent estimator.
In multiple regression models, Variance Inflation Factors (VIFs) should be assessed to
validate that multicollinearity is not affecting results (Baltagi, 2005). Due to the nature of
models one and two, certain control variables are expected to be omitted from analysis due to
multicollinearity. The models use phase dummies and year dummies in one regression formula.
Consequently, there is a high likelihood of collinearity between the phase dummies and year
dummies that fall into the phases. These year dummies are omitted from the analysis. Apart
from this, there is no indication of multicollinearity as further assessment of the VIFs shows.
For each model, VIFs of the variables is below the conventional threshold of ten (Baltagi, 2005)
and are thus not reported. The results are consistent with the setup of the models considering
that apart from dummies for phase and regulatory status of firms, only eco-innovation and firm
size are used as independent variables.
Models one and two are estimated using a general least squares (GLS) regression with random
effects specification. The choice for the appropriate regressions model follows a systematic
execution of a set of preliminary tests, as proposed by Baltagi (2005). To assess whether data
can be pooled in an ordinal least squares model (POLS) or a GLS is appropriate, the Breusch
and Pagan (1980) Lagrangian Multiplier (LM) test for random effects is conducted. While a
POLS assumes that there are no individual-specific effects that influence the outcome
variables, a GLS accounts for individual-specific effects. The LM tests against the null
hypothesis that there are no individual-specific effects in the model. The results for model one
and two are suggestive of rejecting the null hypothesis and indicate that significant random
effects are present. Thus, a GLS is preferred over a POLS regression as it allows for superior
treatment of heterogeneity. Following this, it must be examined whether the individual-specific
effects occur randomly or systematically. The Sargan-Hansen test is applied9, which tests
against the null hypothesis that no correlation between the unique errors and the explanatory
variables exists (Hansen, 1982). Results for model one and two fail to reject the null hypothesis,
indicating that a GLS with random effects specification is appropriate for estimating the
models.
9 Typically, the Hausman (1978) test is applied, however, it does not allow for robust standard errors. For
coherency, the results of diagnostics were also considered without accounting for robust standard errors.
Nevertheless, the implications do not change.
31
5. RESULTS
This section delineates the main empirical results and robustness checks applied. Firstly,
descriptive statistics are briefly presented, and the estimation results of models (1a) and (1b)
as well as model (2a), (2b), and (2c) are described. Secondly, the results are tested in different
settings to validate their robustness.
5.1. Empirical results
As outlined above, the study aims to examine both the weak and the strong Porter Hypothesis
in the context of the EU ETS. Assessment of the descriptive statistics depicted in Table 2 and
correlations (see Appendix B) does not provide indicative insights on the impact of the policy.
Also, the explanatory graph of ROA for regulated and non-regulated companies (see Appendix
B) does not suggest structural changes between the two groups in either of the period under
investigation. This is, however, different for eco-patent output. Figure 5 shows that the eco-
patent output follows similar trends for regulated and non-regulated firms from 2000-2010.
From 2011 onwards, the eco-patent output of EU ETS firms increases, while it decreases for
non-regulated firms, suggesting that the policy might have had an impact. Nevertheless, to
derive statistically relevant insights, the estimation results of models one and two must be taken
into consideration, which is done in the following.
Figure 5. Mean eco-patent output from 2000-2014
32
5.1.1. Model one: the EU ETS - eco-innovation relationship
The results for the estimation of models (1a) and (1b) are displayed in Table 2. The
coefficients show that the postulated direction and significance of the relation between the
policy introduction and eco-innovation are substantiated to some extent. In general, both
models (1a) and (1b) explain 23.8% of the overall variation in eco-patent output10.
Hypothesis one states that the EU ETS increased the eco-innovativeness of regulated
firms, considering the first two implementation phases of the policy. Analyzing model (1a), no
evidence is found that the introduction of EU ETS significantly increased eco-patent output
(𝑏3 = .007, p = .316). Furthermore, EU ETS firms in the overall period did not have a
significantly higher eco-innovation output (𝑏1 = .004, p = .812) than non-EU ETS firms. Also,
firms in the past-2005 period, in general, did not have a significantly higher eco-innovation
output (𝑏2 = .006, p = .512) than in the anteceding period. Results for model (1b) provide more
detailed insights. The disaggregation into phase one and two reveals that the effect of the policy
introduction in phase one did not significantly alter the eco-patent output of regulated firms in
the sample (𝑏3 = -.006, p = .326). Also, regulated firms in general (𝑏1 = .004, p = .832) as well
as firms in the first (𝑏2 = .010, p = .441) and second implementation phase (𝑏2 = -.001,
p = .993) did not have a significantly higher eco-patent output compared to respective
counterparts. However, the introduction of the second implementation increased eco-patent
output of regulated firms in the sample (𝑏3 = .018, p = .088), considering a 10% significance
level. The evidence suggests that, while the first phase did not significantly alter the eco-
innovation output of regulated firms, the introduction of the second phase increased the patent
output of regulated firms by 1.8% compared to the pre-ETS period. Thus, hypothesis one is
partially supported.
Hypothesis two states that the second phase increased eco-innovation output more than
the first phase of the EU ETS. As the estimation results of model (1b) suggest, the introduction
of the second phase of the EU ETS significantly increased the eco-innovation output of
regulated firms by 1.8%, while phase one had no impact significantly different from zero. The
outcome suggests that hypothesis two holds. For coherency, the linear combinations of
coefficients in model (1b) are tested. A t-test is applied to assess whether the estimated
coefficient of Phase2 is significantly larger than the estimated coefficient of Phase1. The
results indicate that the difference between the two interaction coefficients is significant at the
5% level (t = 2.140, p = .032). Consequently, hypothesis two is supported.
10 The R2 is only equal due to rounding. Considering four decimals, R2 in (1a) is 0.2383; R2 in (1b) is 0.2377.
33
Table 3. Estimation results for model one
Independent variables Model (1a) Model (1b)
Dependent variable lnEcopat lnEcopat
ETS 0.004
(0.017)
0.004
(0.017)
Post 0.006
(0.009)
-
ETS*post 0.007
(0.007)
-
Phase1 -
0.010
(0.013)
ETS*phase1 -
-0.006
(0.006)
Phase2 -
-0.001
(0.008)
ETS*phase2 -
0.018*
(0.010)
Size 0.007
(0.004)
0.007
(0.005)
Constant -0.202
(0.135)
-0.194
(0.134)
Year fixed effects
Country fixed effects
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
N (observations) 2,558 2,558
N (groups) 292 292
Wald χ2 p-value 0.001 0.001
R2 (overall) 0.238 0.238
Modified Wald statistic
(H0: homoskedasticity)
21,000.0***
50,000.0***
Wooldridge F-statistic:
(H0: no serial correlation)
0.216 0.212
Pesaran test statistic
(H0: cross-sect. dependence)
143.664*** 85.199***
Breusch Pagan LM
(H0: no ind.-spec. effects)
58.200*** 58.350***
Sargan-Hansen test statistic
(H0: random effects)
3.727 4.004
Note. Random effects GLS estimation of models (1a) and (2a). A panel covering the period from 2000-2014.
Robust standard errors in parentheses. (Two-tailed) significance levels: * p<0.1; ** p<0.05; *** p<0.01.
5.1.2. Model two: the EU ETS - financial performance relationship
The results for models (2a), (2b), and (2c) are reported in Table 4 and indicate that the policy
introduction did not affect the financial performance of regulated firms. Furthermore, there is
no evidence for a mediating role of eco-innovation. The specifications of model two explain
13.7%, in model (2a), and 13.9%, in model (2b) and (2c), of the variation in ROA.
34
Table 4. Estimation results for model two
Independent variables Model (2a) Model (2b) Model (2c)
Dependent variable ROA ROA ROA
ETS 0.013
(0.009)
- 0.013
(0.010)
Post -0.020
(0.024)
- -0.021
(0.018)
ETS*post -0.014
(0.012)
- -0.013
(0.011)
lnEcopat - 0.030
(0.019)
0.029
(0.019)
Size 0.006***
(0.002)
0.006***
(0.002)
0.006***
(0.002)
Constant 0.068
(0.073)
0.080
(0.081)
0.077
(0.081)
Year fixed effects
Country fixed effects
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
N (observations) 1,779 1,779 1,779
N (groups) 286 286 286
Wald χ2 p-value 0.001 0.001 0.001
R2 (overall) 0.137 0.139 0.139
Modified Wald statistic
(H0: homoskedasticity)
24,000.0*** 39,000.0*** 34,000.0***
Wooldridge F-statistic:
(H0: no serial correlation)
0.322 0.100 0.312
Pesaran test statistic
(H0: cross-sect. dependence)
5.435*** 5.453*** 5.192***
Breusch Pagan LM
(H0: no ind.-spec. effects)
3.120*** 3.100*** 3.100***
Sargan-Hansen test statistic
(H0: random effects)
2.438 2.474 2.583
Note. Random effects GLS estimation of models (2a), (2b), (2c). A panel covering the period from 2000-2014.
Robust standard errors in parentheses. (Two-tailed) significance levels: * p<0.1; ** p<0.05; *** p<0.01.
Hypothesis three postulates that the introduction of the EU ETS altered the financial
performance of regulated firms. In general, regulated firms had higher, yet not significantly
different from zero, ROA (𝑏1 = .013, p = .178). Firms in the post-introduction period had lower,
but not significantly different, ROA (𝑏2 = -.020, p = .256). The impact of the EU ETS
introduction on ROA was negative but not significantly different from zero (𝑏3 = -.014,
p = .223). As a consequence, hypothesis three is not supported at a 5% significance level.
In hypothesis four, it is stated that the positive effect of the EU ETS introduction on
financial performance is mediated by eco-innovation. Following Baron and Kenny (1986), a
35
mediation effect is present, firstly, if the effect of the policy introduction on ROA is significant.
As outlined above, the estimation results of model (2a) show that the according hypothesis is
rejected. Baron and Kenny (1986) propose not to continue the investigation of a mediation
effect if the predictor does not affect the mediator. The results of Model (1a), (2b) and (2c)
show why. Considering model (1a), the policy introduction had no significant impact on eco-
innovation output (𝑏3 = .007, p = .316). The estimates of model (2b) show that eco-patent
output did not significantly influence ROA (𝑏4 = .030, p = .117). Also, model (2c) does not
describe a statistically significant relationship. Neither the introduction of the policy
(𝑏3 = -.013, p = .238) nor eco-patent output (𝑏4 = .029, p = .127) had a significant impact on
ROA, considering generally applied significance levels. In line with Baron and Kenny (1986),
the Sobel test proves to be insignificant as well (t = .844, p = .456), which indicates that eco-
patent output cannot be considered a mediator. Consequently, hypothesis four is rejected at a
5% significance level. Appendix C provides an overview about all formulated hypotheses
including expected signs and respective findings.
5.2. Robustness checks
The results suggest that there is some indication of a policy effect on eco-innovation. The study
performs four robustness checks to verify the structural validity of the coefficient estimates.
The results are displayed in Appendix D.
Firstly, the differential policy effect from phases one and two is reassessed by
modifying model (1a). Model (1b) analyzes the differential effect of the policy in comparison
to the pre-policy time frame (2000-2004), not accounting for potential changes in the first
implementation phase. Although the assessment of the estimate difference has shown to be
significant, further analysis adds robustness to the model. Model (1a.dif) compares phase one
and phase two in a DID estimation, treating phase one as the pre-treatment period to phase two.
In doing so, the model considers any potential changes in patent output that were induced in
the first implementation phase as a random occurrence. Thus, it is more sensitive to changes
induced by the introduction of the second phase of the policy. The results confirm that the
second implementation had a significant impact on eco-patent output (𝑏3 = .023, p = .047).
Secondly, model (1b) is assessed by excluding companies from Belgium, Germany,
Spain, and France, respectively. Only companies from the four countries had eco-patent records
and, in addition to that, Spanish companies are overrepresented in the sample. Consequently,
changes in the significance of the reduced models can provide insights into the policy effect in
36
certain national economies. Models (1b.-be), (1b.-de), (1b.-es), and (1b.-fr) omit firms from
Belgium, Germany, Spain, and France, respectively. Akin to model (1b), the impact of the first
phase is not significant in either of the models. Also for the second implementation phase
precluding Belgian (𝑏3 = .019, p = .087), French (𝑏3 = .017, p = .010), and Spanish firms (𝑏3
= .024, p = .090), respectively, does not change the positive and significant impact of the policy.
However, when German companies (𝑏3 = .003, p = .674) are excluded, the policy effect is not
significant anymore, considering generally accepted significance levels. This suggests that the
policy had a differential impact on national economies. In particular, it appears that eco-patent
output increased in German, but decreased in Spanish firms.
Thirdly, model (1b) is assessed with different time forwards. There is an ongoing debate
on how much time should be accounted for the process from R&D to patent. Although Hall et
al. (1986) suggest a time-to-patent of two years, other researchers suggest that certain
innovations might be patented faster, particularly under high regulatory pressure (Danguy, De
Rassenfosse, & Van Pottelsberghe de la Potterie, 2009). Yet others propose that it takes
significantly longer than two years from idea to patent, which is especially relevant for
manufacturing firms (Gurmu & Pérez-Sebastián, 2008). To account for these different lines of
argumentation, models (1b.0f), (1b.1f), and (1b.5f) assess the policy impact with a zero-year,
one-year, and five-year forward, respectively. The impact of the first implementation phase is
not significant in all three models,. With respect to the second phase, the impact of the EU ETS
further is not significant when considering no forward (𝑏3 = -.005, p = .752) or a one-year
forward (𝑏3 = .011, p = .390). In the five-year forward model the interaction coefficient for the
second phase remains positive and significant at a 10% level (𝑏3 = .023, p = .053). This suggests
that the policy effect is also present when a longer, but not a shorter, time-to-patent horizon
than two years is considered. Nevertheless, in model (1b.5f), approximately 25% of
observations are not taken into account due to the time shift, which reduces the explanatory
power.
Fourthly, model (1b) is estimated by a (non-linear) negative binomial regression. The
variable of interest, eco-patent output, is a count variable, which has unique characteristics
(Hausman et al., 1984). Figure 6 shows that the data is inflated by counts of zero. Moreover,
the variance of eco-patent counts is significantly larger than the mean (χ2 = 153.72, p = .001),
indicating overdispersion. Thus, eco-patent counts follow a negative binomial distribution
(Allison & Waterman, 2002) and should be assessed with a negative binomial regression
model. In line with Hausman et al. (1984), model (1b) is adapted to model (1b.nbd), which
37
does not consider the natural logarithm, but the actual number of eco-patents as the dependent
variable. The interpretation of the results requires some explanation because the model follows
a non-linear distribution. If the variable of interest were continuous, a marginal effect analysis
would be conducted to interpret its coefficients (Williams, 2012). However, this is not possible
for an interaction term because it is, by definition, one of the factors used for calculating the
marginal effect of its constituent terms and thus itself does not have a marginal effect
(Williams, 2012). The results of the negative binomial regression do not allow to reject the null
hypothesis at convenient significance levels (𝑏3 = .757, p = .145). Still, when considered in
combination with the linear model, the results of the negative binomial model support a slight
trend towards significance. Furthermore, it must be considered that model (1b.nbd) can, from
a technical standpoint, only be estimated without controlling for sector effects, which might
explain the insignificance of the results at conventional levels. Furthermore, standard statistical
software is criticized for computing significance levels incorrectly (Ai & Norton, 2003), which
further can explain a less significant model (Buis, 2010).
Figure 6. Frequency diagram of eco-patent counts
Although the results for model two were not significant, two robustness checks are conducted.
Firstly, the model is validated with a four-year forward to account for the fact that eco-
innovation might take longer to impact ROA. Nevertheless, the results do not provide different
insights. Secondly, the model is examined using model (1b), which accounts for the two
implementation phases. Also, here, no different conclusions can be drawn11.
11 Since the assessment does not generate further insights, the study does not report the estimation results.
38
6. DISCUSSION
This section discusses the results stated above in the context of the EU ETS. Firstly, the study
elucidates the outcomes focusing on the weak and the strong Porter Hypothesis, respectively.
Secondly, theoretical contributions and policy implications are derived from the findings.
Thirdly, the thesis lays out limitations and suggestions for future research.
6.1. The Porter Hypothesis in the EU ETS
The EU ETS is the world's most extensive cap-and-trade system, designed to reduce GHG
emissions and incentivize eco-innovation. Nevertheless, the present state of research on the
impact of the EU ETS on eco-innovativeness is still in its infancy and has not yet provided
comprehensive insights into whether the policy has induced eco-innovation. Existing studies
make use of non-representative samples and confine analyses on the first implementation phase
of the system. Hence, this study examines the policy effect with more representative and recent
data. Precisely, the paper compares the eco-patent output of 292 EU ETS and non-EU ETS
firms from 2000-2014 by means of a DID estimation. Furthermore, the thesis provides a more
holistic perspective on the impact of environmental regulations on firm performance
characteristics, compared to previous research endeavors, by assessing the Porter Hypothesis
both in its weak and strong form.
6.1.1. The EU ETS and the weak Porter Hypothesis
Generally, the study reveals that the EU ETS induced eco-innovation to some extent. The
results show that the policy did not significantly increase eco-patent output when the first two
implementation phases are considered in conjunction. However, investigation of the
implementation phases in isolation suggests that, while phase one did not have a significant
impact, phase two increased the eco-patent output of regulated firms by 1.8%. The results are
significant considering a lag between patent application and development of two years.
Robustness checks show that the results also hold when a five-year delay is applied or the time
frame of analysis is restricted to the first and second implementation phase. Also when Belgian,
Spanish, or French firms are excluded from the sample, respectively, or the model is estimated
with a negative binomial regression (only suggestive statistical trend) the implications do not
change. The Porter Hypothesis provides two possible explanations for the outcomes, namely
39
the increasing stringency of the EU ETS in the second phase and a high level of uncertainty in
the first phase.
Firstly, it appears that, contrary to phase one, regulations in the second phase were
sufficiently stringent to induce innovation. Porter and Van der Linde (1995) state that a policy
must be strict enough to affect the cost of emitting significantly and induce innovation. The
overall emission cap, which is one proxy for the stringency of the policy (Joltreau &
Sommerfeld, 2019), was reduced by 10% with the beginning of the second phase, decreasing
the amount of EUAs available in the market (European Commission, 2015). In contrast, the
first phase was characterized by over-allocation of EUAs (Anderson & Di Maria, 2011;
Venmans, 2012; Joltreau & Sommerfeld, 2019), which were mostly distributed for free. This
is seen as one of the main reasons why phase one did not positively influence eco-innovations
(Anger & Oberndorfer, 2008; Rogge et al., 2011; Joltreau & Sommerfeld, 2019). Furthermore,
the price for non-compliance increased from €40 to €100 per tCO2, which is another proxy of
increased stringency (Joltreau & Sommerfeld, 2019). The penalty increase might have made
regulated firms more aware of the importance of reducing emissions effectively by, e.g., the
means of eco-innovation. With the beginning of the second phase, it was also communicated
that phase three would entail a decreasing emission cap, which further increases the policy’s
stringency. This might have given firms additional incentives to invest in the development of
low-carbon technologies. The results also suggest that the time to patent these eco-innovations
is at least two years, as proposed by Hall et al. (1986). Conjointly, these changes provide a
possible explanation of why the second phase induced eco-innovation.
Secondly, the high degree of uncertainty was reduced with the beginning of phase two,
providing a stronger incentive to eco-innovate. Porter and Van der Linde (1995) argue that
certainty is imperative for innovation since investments will only be made when their value
can be assessed beforehand. The first phase of the EU ETS, also labeled the pilot phase, was
intended to clarify regulatory questions, e.g., whether banking of permits from phase one is
possible (European Commission, 2015). As both organizational and technical issues had to be
refined and communicated (Ellerman et al., 2010), EUA prices fluctuated tremendously in the
first phase, indicating a high level of uncertainty. From phase two onwards, firms had more
clarity about the overall emission targets and the policy mechanisms deployed, which can
incentivize eco-innovations. The role of uncertainty is exemplified by the use of fuel switching
strategies. In the first phase, most companies used fuel switching to reduce emissions (Delarue
et al., 2010; Koch et al., 2014). With the communication of the emission reduction goals for
phase two and three, however, companies can be expected to turn towards eco-innovation for
40
emission reduction since fuel switching can only reduce as much as 10% of the reduction goals
in the EU ETS (Delarue et al., 2008; European Commission, 2018).
Although the results of the study align with the weak Porter Hypothesis, a closer assessment
reveals that they stand in contrast to the findings of other studies. In particular, Calel and
Dechezleprêtre (2016) estimate that the EU ETS induced a spike of 36.2% in the eco-patent
output of regulated firms, which is a significantly stronger policy effect than found in this study.
Furthermore, and contrary to the results reported in this paper, Calel and Dechezleprêtre (2016)
find the effect to be present already in phase one. Nevertheless, the methodological approach
of Calel and Dechezleprêtre (2016) differs. The authors assess only two of the 14 Y02 patent
sub-classes. Calel and Dechezleprêtre (2016) state that someone studying the impact of the EU
ETS at a more aggregated level could find a policy impact of lower magnitude. The EU ETS
might only induce eco-innovations that specifically treat the emission and capture of GHG.
Nevertheless, empirical evidence shows that environmental policy also produces innovations
that are related to innovations emission treatment and capturing innovations (Popp, 2003, 2006;
Johnstone, Haščič, & Popp, 2010). Furthermore, Calel and Dechezleprêtre (2016) match their
sample in the year 2005, which is the first year of the pilot phase. Matching firms on covariates
in the treatment period, rather than before, can overestimate results since not the entire
treatment period is considered (Rosenbaum & Rubin, 1985; Ho et al., 2007; Caliendo &
Kopeinig, 2008). Nevertheless, the rather small effect size in the second phase can also be
explained by considering macroeconomic events and the low EUA price in phase two of the
policy.
Firstly, the Financial Crisis and the subsequent recession that hit European markets in
2008 can expound the limited policy effect. Archibugi et al. (2013) show that innovation
activities decreased overall after the crisis. However, the authors explain that the recession had
a differential impact on economies, with companies in less affected countries reporting a lower
willingness to reduce R&D expenditures (Archibugi et al., 2013). This also becomes evident
in the assessment of the present sample. Once German companies are excluded from the
sample, the innovation-inducing policy effect is not significant anymore, indicating that the
policy was less effective in other countries. The NIS perspective sheds light on this
phenomenon (Carlsson, 2006). German firms are considered as particularly strong eco-
innovators because of advanced national environmental policies (Horbach, Oltra, & Belin,
2013). Together with strong normative pressures in the country, the regulatory framework
entails multiple complementary direct regulations, which have incentivized eco-innovations
41
(Ekins, 2010). Further investigation of the other country-reduced models suggests that when
Spanish companies are excluded, the policy effect is higher. The results imply that Spanish
companies were more affected by the Financial Crisis, reducing eco-patent output. The findings
of Madrid-Guijarro, García-Pérez-de-Lema, and Van Auken (2013) show that, indeed, Spanish
firms were less willing to engage in process innovations, which encompass the majority of eco-
innovations are (Anderson et al., 2011), in the crisis period due to worse economic conditions.
Since the overall sample entails mainly Spanish firms, this can explain the limited policy effect.
Secondly, the price of EUAs might have been too low to induce eco-innovation more
effectively. The price for emitting one tCO2 fell from €30 to €5 in 2013 (Koch et al., 2014). A
low permit price provides fewer incentives for eco-innovation because the option to purchase
permits becomes financially more attractive (Popp, 2002). A large body of literature has
investigated sources for the price drop and its impact on the effectiveness of EU ETS (see, e.g.,
Martin et al., 2011; Koch et al., 2014; Marin et al., 2018; Hintermann & Gronwald, 2019;
Joltreau & Sommerfeld, 2019). Koch et al. (2014) find that most of the variability in the EUA
prices remains unexplained. Yet, the authors show that three factors significantly influenced
the price drop, namely the economic crisis, an oversupply of EUAs, and the introduction of
national policies that support the deployment of renewable energy sources to electricity (RES-
E). The Financial Crisis might have directly affected innovation activity of regulated firms, as
elaborated on above, or indirectly via the EUA price. Furthermore, the use of offsets and the
issuance of free allowances increased permit supply, which in turn decreased the price of
EUAs. As described in section 2.1., allowances were allocated freely to thwart carbon leakage
(Ellerman et al., 2010; Weishaar, 2016). The third factor, the role of RES-E policies, requires
more explanation. The wholesale electricity market is impacted by both EU ETS and RES-E
policies, which are inherently interconnected (Del Río González, 2007; Van den Bergh,
Delarue, & D’haeseleer, 2013; Koch et al., 2014). Figure 7 depicts the relationships between
the EU ETS, the electricity market, and the deployment of RES-E. The introduction of the EU
ETS increases wholesale electricity prices since electricity generators and distributors tend to
pass the extra costs of GHG control to electricity prices (Sijm et al., 2006). In contrast,
wholesale electricity prices decrease with increasing deployment of RES-E policies since
RES-E displace and reduce the demand for fossil-fuel-based electricity (Moreno, López, &
García-álvarez, 2012). The interaction between the EU ETS and the promotion of RES-E
deployment is more complicated. The EU ETS makes RES-E deployment more competitive
because it alters the costs of conventional electricity. In turn, RES-E deployment reduces the
demand for EUAs in the EU ETS, and, thus, their price (Del Río González, 2007). Although
42
this poses no problem in the electricity market, the EU ETS also covers other economic sectors,
which are indirectly influenced by (nationally subsidized) RES-E deployment. That is, EUAs
decrease in value due to the changes in the electricity market, and emitters from other sectors
in the EU ETS can buy EUAs at a discounted price, reducing the incentive to eco-innovate.
During the second phase of the EU ETS, a lot of European countries enacted RES-E supportive
laws (Fouquet & Johansson, 2008; Moreno et al., 2012), which might have caused a price drop
and lowered innovation incentives of regulated firms.
Figure 7. Summary of interactions between EU ETS and RES-E policies. Adapted from
“Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything?
- New evidence” by N. Koch et al., 2014, Energy Policy, 73, 676-685
In summary, the results of the study support the three principles of the (weak) Porter
Hypothesis. That is, a policy must be flexible, stringent, and there must be certainty about the
policy outline to induce eco-innovation. Nonetheless, the magnitude of the policy effect is
lower than estimated by previous research. Besides methodological discrepancies, the impact
of the Financial Crisis, the oversupply of EUAs, and the introduction of RES-E policies might
explain the rather small policy effect.
6.1.2. The EU ETS and the strong Porter Hypothesis
The evidence presented in the study suggests that the EU ETS has neither negatively nor
positively affected ROA of regulated firms and that eco-innovation cannot be considered a
mediator in this relationship. All of the paths investigated in the model of Baron and Kenny
(1986) prove to be insignificant. Also, different time forwards considered do not change the
results. Although the strong Porter Hypothesis is not supported in the study, Porter and Van
der Linde’s (1995) arguments can explain why no significant effect on ROA is found.
Porter and Van der Linde (1995, p. 100) argue that “innovations cannot always
completely offset the cost of compliance, especially in the short term before learning can reduce
43
the cost of innovation-based solutions.” The thesis analyzes the impact of the first and second
phase of the EU ETS from 2005-2012. Considering the time lags associated with the
development and diffusion of eco-innovations (Hall & Helmers, 2013), the time frame might
be too short to find a significant effect on ROA. Although the thesis applied different time
forwards, any ROA forward of more than four years does not yield in a significant model. The
outcomes are line with McWilliams and Siegel (2000), who state that the association between
sustainable practices and altered financial performance might only hold in the long-term.
Considering the sample used for analysis reinforces this aspect. The majority of firms stem
from Spain, a country that was significantly impacted by the Financial Crisis. Spanish firms in
the manufacturing sector were particularly affected by increased unemployment (Éltető, 2011),
and Spanish firms, in general, had severe difficulties in borrowing money for their operations
(Casey & O’Toole, 2014). Madrid-Guijarro et al. (2013) show that these factors impaired the
innovation and financial performance of Spanish SMEs. Investigation of the mean ROA for
firms in the sample (Appendix B) shows that it decreased for both groups in the crisis period,
which suggests that even if the EU ETS had affected ROA, other, crisis-related, factors would
have most likely eradicated this effect.
Furthermore, it appears that windfall profits did not affect ROA in the sample. Joltreau
and Sommerfeld (2019) suggest that windfall profits in the EU ETS can only be earned under
specific market structures. That is, firms must have sufficient market power to pass costs
through onto customers. One example is the electricity sector. Electricity distribution is based
on national grid structures, preventing most international companies from competing, and
hence resulting in historical monopolies (Clò, 2010). Joltreau and Sommerfeld (2019) show
that cost pass-through is possible in the electricity, but not the manufacturing sector, due to
differing market structures. This might have affected the results in this study. The sample
consists of companies from the two sectors, and since it is assessed at an aggregated level, it
could be that a positive policy impact in the electricity sector is neutralized by no effect in the
manufacturing sector12. Furthermore, windfall profits might not be found since assumptions
used in the estimation do not hold in reality. The initial estimations of Sijm et al. (2006)
assumed a permit price of €20, which was not reached in the crisis period. Together, this can
explain why no overall impact on ROA is found. In general, the findings are in line with
12 An ex-post analysis, reducing it to the “35” NACE Rev. 2 code, finds a positive impact (𝑏3 = .036, p = .010) of
the second implementation phase on ROA considering a three-year forward and 10% significance level and
controlling for time fixed effects.
44
previous studies that fail to report a positive effect of the EU ETS on a firm’s financial
performance (Petrick & Wagner, 2014; Jaraite & Di Maria, 2016).
Although the strong Porter Hypothesis is not supported by the findings of the study, the
evidence implies that the EU ETS had no adverse effect on ROA of regulated companies. This
is important since a body of ex-ante assessments of the policy raised concerns that the increase
in emission costs would negatively impact profitability of firms and entire industries (Boemare,
Quirion, & Sorrell, 2003; Smale, Hartley, Hepburn, Ward, & Grubb, 2006; Hertin et al., 2009).
In summary, the results of the study fail to provide support for the strong Porter Hypothesis.
One explanation encompasses the time required for eco-innovations to affect the bottom line
of companies, which is suggested to take longer in times of economic downturn. Furthermore,
windfall profits most likely did not affect ROA since the sample includes sectors in which
competitive market structures impede cost pass-through. Still, the outcomes imply that the EU
ETS had no negative impact on the financial performance of firms as apprehended by ex-ante
studies on the effectiveness of the EU ETS.
6.2. Contributions and implications
The results from assessing more representative and recent data provide insights into the
requirements for effective cap-and-trade policy in general and rationale for policy implications
to ensure the future effectiveness of the EU ETS.
6.2.1. Theoretical contributions
Considering the general applicability of the Porter Hypothesis in cap-and-trade systems, the
outcomes of the study suggest that the weak but not the strong Porter Hypothesis holds. Porter
and Van der Linde (1995) argue that a policy must be stringent but flexible to induce eco-
innovation. The nature of a cap-and-trade system like the EU ETS attempts to achieve that. In
contrast to studies from the US trading systems that fail to provide evidence for the weak Porter
Hypothesis (Popp, 2002, 2004; Lange & Bellas, 2005; Taylor et al., 2005), the EU ETS appears
to provide incentives to innovate. The more stringent, more far-fetching, and more certain
policy design explains why an innovation-inducing effect seems to be evident in the EU ETS.
Nevertheless, there is no evidence for the strong Porter Hypothesis. Although it might relate to
the lack of time for innovations to become profitable, the concept of the strong Porter
Hypothesis remains disputed (Cohen & Tubb, 2018). Altogether, the study advances both the
45
literature on environmental policy and on the validity of the Porter Hypothesis in cap-and-trade
systems. However, it is worth highlighting that cap-and-trade systems are highly idiosyncratic,
and mechanisms of the EU ETS might not work in other ETS like the proposed Chinese
endeavors (Borghesi & Montini, 2016). Consequently, detailed policy implications derived in
the following focus on the EU ETS.
6.2.2. Policy implications
Before turning to the implications, it must be recalled that the primary goal of the EU ETS is
and will be the reduction of GHG emissions, and the EU ETS has so far been successful in
doing so13. Consequently, the effectiveness in terms of GHG reduction will remain the
superordinate goal of the policy. It is expected that GHG emissions will further decrease
considering the third implementation period (Hu, Crijns-Graus, Lam, & Gilbert, 2015) and the
recently passed reforms (European Parliament & European Council, 2018). The changes
enacted in 2018, however, will most likely also impact innovation activities of regulated firms.
Thus, they are in line with the political implications that derive from the limited innovation-
inducing effect found in the study.
That is, firstly, incentives to eco-innovate should be altered by decreasing the emission
cap further. The study suggests that only the second phase of the EU ETS was stringent enough
to induce eco-innovation. An even lower emission cap signals that short-term abatement
strategies like fuel switching are not sustainable (Delarue et al., 2010) and thus incentivizes
eco-innovation further. Nevertheless, the risk of carbon leakage in specific sectors should be
taken into consideration. Although no evidence of significant carbon leakage has been found
yet (Martin, Muûls, De Preux, & Wagner, 2014; Naegele & Zaklan, 2017), the issue should
remain on the agenda of policymakers (Clò, Battles, & Zoppoli, 2013).
Secondly, measures to stabilize the price of EUAs should be implemented. Eco-
innovations are more likely to be developed when emissions costs are sufficiently high (Porter
& Van der Linde, 1995). Part of the somewhat limited policy effect reported in this study can
be accredited to the low EUA price and reflected uncertainty. Shifting towards more auctioning
and reducing the supply of EUAs can help to increase the price again. Nevertheless, these
measures do not guarantee lower price volatility. Thus, the EU could consider establishing
pricing instruments like a price floor, as proposed by Wood and Jotzo (2011). A price floor sets
13 It must be noted that there are controversies about how effective the EU ETS has reduced emissions. Borghesi
and Montini (2016) provide a comprehensive summary on the findings from the first and second phase.
46
an auction reserve price. Thereby, an auction is only released when the auction price is beyond
a pre-defined minimum price. Consequently, price volatility and uncertainty would decrease.
Furthermore, the regulation of the EUA price could eventually be imposed on an independent
authority to ensure that the short-term EUA price is in line with the long-term target. The
advantages of such an institution would be a smoother decision-process and locating decisions
on reforms outside of the political sphere (Clò et al., 2013; De Perthuis & Trotignon, 2014).
Nevertheless, the specific mandate of this institution and the market instruments to be used are
difficult to define (Grosjean, Acworth, Flachsland, & Marschinski, 2014).
Thirdly, the extension to other sectors, like the possible extension to the road logistics
sector described in the introduction, should be carefully considered. Although the study shows
a positive innovation impact of the policy introduction, the case of nationally sponsored RES-
E deployment exemplifies that the interaction of European and national policies can influence
the functioning of the EU ETS (Del Río González, 2007; Venmans, 2012; Koch et al., 2014).
When differently structured markets, both in terms of emission and competition, are linked
within one ETS, national policies and market dynamics might collide (Koch et al., 2014).
Considering specifically the road logistics sector, the main argument against the inclusion into
the EU ETS is that, even though EUA prices would likely increase (CE Delft, 2007), emission
costs for companies in the transport sector would be lower than they are under fuel taxes today
(European Commission, 2013b). Thus, firms would not be incentivized to engage in eco-
innovation (Zhang & Wei, 2010). Nevertheless, when carefully considered, an extension to
other sectors increases the efficiency of the EU ETS (European Commission, 2018).
Fourthly, the EU should more actively deploy and use other complementary innovation-
incentivizing instruments. The EU has already set up an innovation fund and a modernization
fund for lower-income member states (European Commission, 2018). The funds will become
active from 2021 onwards and are meant to create more financial incentives for regulated firms
to innovate (European Commission, 2019). Funding is provided via the auctioning of EUAs.
Dorsch, Flachsland, and Kornek (2019) argue that initiatives like the innovation fund can help
strengthening the inherent incentives of the EU ETS further, particularly when financed by
auctioning revenues.
6.3. Limitations and future research
Albeit essential insights can be drawn from the results of the study, it is stressed that the
research conducted is not exempt from restrictions. One the one hand, the limitations may
47
affect the generalizability of results, which is why they should be treated with caution. On the
other hand, future research opportunities arise from the restrictions. In general, two veins of
limitations exist in the study, covering the sample construction, and the methodological
approach followed.
6.3.1. Sample construction and composition
The sample construction and composition comprise certain limitations that must be taken into
account. Starting with the use of PSM, researchers have criticized the method to create
imbalance, inefficiency, model dependence, and bias, which can be avoided by applying more
advanced matching techniques (King & Nielsen, 2019). Secondly, the PSM approach in the
study concerns only turnover and fixed assets as covariates. Ideally, matches would have been
computed with either pre-2005 patent records or another proxy of innovation activity (Caliendo
& Kopeinig, 2008). However, data unavailability disallowed to include any of the measures as
covariates in PSM. Thirdly, and related, the lack of available financial data also affected the
initial consideration of which ETS companies to include in the sample and the final analysis.
Since a lot of EU ETS companies are private firms, they do not share any financial data (Jaraité
et al., 2014; Weishaar, 2016) and thus could not be included in the sample. Even for the
accessible firms, observations are only available for approximately 60% of the panels.
Altogether these factors result in a final sample that is not accurate regarding country and sector
coverage of the policy, particularly in absolute terms.
Thus, future studies should take measures to not fall short on these limitations. To
ensure data availability, e.g., national databases could be combined (Kalemli-Ozcan, Sorensen,
Villegas-Sanchez, Volosovych, & Yesiltas, 2015) or SME databases could be consulted
(Ayyagari, Demirgüç-Kunt, & Beck, 2007). Furthermore, advanced imputation methods could
be applied to address the issue of missing values in the independent variables (Van Buuren,
2018). A larger and more representative sample would also allow for applying more advanced
matching techniques (King & Nielsen, 2019). Future endeavors could also add companies to
the sample that have joined the EU ETS in particular phases, which would enable researchers
to assess the differential impact of the increase in policy stringency in more detail
(Dechezleprêtre et al., 2018; Marin et al., 2018).
6.3.2. Methodological approach
In addition, the methodological approach encompasses certain inherent limitations. Firstly, the
use of patent data as a proxy of innovation activity might omit a range of inventions that are
48
not patented. In keeping with past authors (Acs & Audretsch, 1989; Archibugi & Pianta, 1996;
Jaffe & Palmer, 1997; Haščič & Migotto, 2015), the thesis acknowledges the criticism of
Griliches (1990, p. 1669) who succinctly emphasizes that “not all inventions are patentable,
not all inventions are patented, and the inventions that are patented differ greatly in quality,
and in the magnitude of inventive output associated with them.” Furthermore, as outlined in
section 6.1.1., assessing eco-patents at the Y02-level might limit the interpretative power of
the study. Calel and Dechezleprêtre (2016) state that if eco-patents are considered at an
aggregated level, the targeted policy effect of the EU ETS might be overlooked. Secondly, the
use of a DID estimation is based on assumptions that, in reality, might not hold. The DID
method allocates all of the differences in outcome variables to the policy (Ashenfelter & Card,
1985). However, Bertrand, Duflo, and Mullainathan (2002) suggest that convenient tests fail
to detect underlying serial correlation, particularly in studies with relatively few periods, as is
the case in this study. The study applied PSM before the analysis, controlled for certain
variables, and checked for serial correlation to make the estimation more accurate (Stuart et al.,
2014). Still, the parallel trends assumption is not fully supported in the entire period assessed,
which might reduce the interpretative power of the study. Bertrand et al. (2002) claim that only
those DID studies are reliable, in which the outcome variable follows a similar trend for both
treatment and control groups before treatment. Figure 5 suggests that patterns were not always
parallel in the period from 2000-2004. Nevertheless, the findings of other DID studies in the
EU ETS are also limited by non-observance of the assumption in certain years. For example,
Figure 8 shows the intervals, in which the parallel trends assumption is not fully supported in
the study of Calel & Dechezleprêtre (2016).
Consequently, future studies should take measures to mitigate the limitations
mentioned above. For instance, researchers could assess eco-innovation at a more
disaggregated level, which would allow for investigating the direction of technological change
in more detail (Gerlagh, 2008; Calel & Dechezleprêtre, 2016). Of interest could be the size and
types of investments and innovations taken (Marcantonini et al., 2017). Furthermore, new
insights could be generated by comparing output-oriented measures of innovativeness, like
patents, with input-oriented proxies, like R&D spending (Schiederig, Tietze, & Herstatt, 2012;
Sanyal & Vancauteren, 2013). In addition to that, if DID estimations are used, techniques to
overcome the issue of underlying serial correlation, like bootstrapping (Bertrand et al., 2002),
should be applied. Also, it should be ensured that the parallel trends assumption holds.
49
Figure 8. Investigation of parallel trends assumption in a comparable study. Adapted from
“Environmental policy and directed technological change: evidence from the European carbon
market” by R. Calel and A. Dechezleprêtre, The Review of Economics and Statistics, 98(1),
173-191
6.3.3. Further research opportunities
The design of the EU ETS allows for additional research opportunities. Firstly, with the third
implementation phase ending in 2020, a first overall evaluation of the EU ETS impact could
yield promising insights into the functioning of the policy. Since the third phase of the EU ETS
introduced the progressively decreasing emission cap (European Commission, 2015), it should,
in theory, have further induced eco-innovation. Secondly, the performance of the newly
introduced innovation funds, in conjunction with and isolation of the EU ETS, could be a future
research endeavor (Marcantonini et al., 2017). Thirdly, the interaction between national
policies and the EU ETS could provide insights into which policy designs counteract or
complement the EU ETS (International Emissions Trading Association, 2015). Fourthly, it
might be interesting to assess the share of eco-patents rather than eco-patents in general. This
was done in the US systems (Popp, 2002, 2003) and could also bear further insights into the
direction of technological change in the EU ETS (Calel & Dechezleprêtre, 2016). Fifthly, the
EU ETS could be compared to the Californian ETS that was introduced in 2013. California
used the policy design of the EU ETS as a blueprint for its system (Borghesi & Montini, 2016).
However, as a reaction to the low allowance price in the EU ETS, a price floor was introduced
(Narassimhan, Gallagher, Koester, & Alejo, 2018). Thus, it could be interesting to investigate
the price development in the Californian ETS and compare it to the EU ETS.
50
7. CONCLUSION
The EU ETS is the world’s most extensive emission cap-and-trade system. A crucial
underlying assumption of the policy is its ability to induce eco-innovation. The innovation-
inducing effect of environmental policies can be explained by the Porter Hypothesis, which in
its strong form, even suggests that regulated firms can financially benefit from such policies.
Existing evidence from US ETS does not provide comprehensive insights if the Porter
Hypothesis holds in cap-and-trade systems. Although CO2 reduction targets will not be met
unless firms develop and invest in eco-innovations, empirical research on the innovation-
inducing effect of the EU ETS is still in its infancy. Previous studies could only provide
suggestive evidence for an increased level of innovation, as the use of unrepresentative samples
and the focus on the pilot phase of the regulation limit existing insights.
Thus, this study draws on more representative and recent data from the EU ETS to
investigate whether the first two phases of the policy induced innovation. Installation-level
inclusion criteria are exploited that allow constructing a sample of regulated and non-regulated,
but statistically similar, firms. Comparing the eco-patent output of the two groups with the
means of a DID estimation, it is found that the EU ETS only induced innovation in its second
implementation phase. In line with the weak Porter Hypothesis, possible explanations entail a
higher degree of regulatory stringency and an increased level of certainty compared to phase
one. Still, the policy effect was relatively low, increasing the eco-patent output of regulated
firms by 1.8% compared to the pre-policy period. Mainly German companies were affected by
the positive policy impact. Reasons for the limited effect encompass the Financial Crisis and
low permit prices. The latter was affected by, amongst others, national RES-E policies and an
oversupply of allowances. Contrary to the strong Porter Hypothesis, eco-innovation did not
enhance financial performance in the EU ETS. Return on assets was neither positively nor
negatively impacted by the introduction of the policy. A possible explanation for the lack of
evidence bears upon the time lag considered with the profitability of innovations.
Although the analysis is not exempt from limitations, generally, findings have relevant
implications for policy-making. To ensure that innovation is further induced, policymakers
should make changes in future phases of the policy. The adjustments should encompass
decreasing the emission cap, introducing means to stabilize the EUA price, carefully assessing
if the scheme can be extended to other sectors, and launching other eco-innovation enhancing
instruments.
V
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9. APPENDICES
Appendix A – Firm demographics
Countries
Code Name N
AT Austria 2 (1%)
BE Belgium 16 (5%)
CZ Czech Republic 6 (2%)
DE Germany 34 (12%)
DK Denmark 4 (1%)
ES Spain 110 (38%)
FI Finland 4 (1%)
FR France 22 (8%)
GB Great Britain 10 (3%)
GR Greece 2 (1%)
HU Hungary 2 (1%)
LT Lithuania 4 (1%)
LU Luxembourg 2 (1%)
LV Latvia 4 (1%)
NL The Netherlands 14 (5%)
PL Poland 40 (14%)
PT Portugal 4 (1%)
SE Sweden 10 (3%)
SK Slovakia 2 (1%)
Sectors
Code Sector description N
06 Extraction of crude petroleum and natural gas 8 (3%)
08 Other mining and quarrying 2 (1%)
09 Mining support service activities 2 (1%)
10 Manufacture of food products 26 (9%)
11 Manufacture of beverages 2 (1%)
13 Manufacture of textiles 6 (2%)
16 Manufacture of wood and products of wood and cork 2 (1%)
17 Manufacture of paper and paper products 24 (8%)
19 Manufacture of coke and refined petroleum products 4 (1%)
20 Manufacture of chemicals and chemical products 14 (5%)
21 Manufacture of basic pharmaceuticals and
pharmaceutical preparations
2 (1%)
22 Manufacture of rubber and plastic products 2 (1%)
23 Manufacture of other non-metallic mineral products 96 (33%)
24 Manufacture of basic materials 10 (3%)
27 Manufacture of electrical equipment 2 (1%)
35 Electricity, gas, steam and air conditioning supply 86 (29%)
46 Wholesale trade of machinery and equipment 2 (1%)
52 Warehousing and support activities for transportation 2 (1%) Note. Code based on two-digit NACE Rev. 2 classification according to European Commission (2013)
XVIII
Appendix B – Dataset descriptive statistics
Unadjusted descriptive statistics
Variables Unit Mean Std. Dev. Min. Max. N
Overall sample
Eco-patents # 0.104 1.329 0 35 4,380
FixedAssets m€ 385.723 1,539.443 0.001 19,926.460 2,783
Turnover m€ 498.983 2,373.885 0.001 40,208.410 2,705
RDexpense m€ 9,716.487 32,545.750 0.000 177,846.400 266
ROA % 1.856 11.606 -98.160 90.120 2,762
EU ETS firms
Eco-patents # 0.157 1.786 0 35 2,190
FixedAssets m€ 502.708 1,954.351 0.001 19,926.460 1,475
Turnover m€ 670.988 3,104.713 0.001 40,208.410 1,439
RDexpense m€ 9,437.269 31,635.030 0 156,200.000 163
ROA % 1.876 11.151 -97.620 90.120 1,481
Non-EU ETS firms
Eco-patents # 0.051 0.582 0 17 2,190
FixedAssets m€ 253.802 839.054 0.002 11,435.390 1,308
Turnover m€ 303.474 1,008.161 0.001 11,925.430 1,266
RDexpense m€ 10,158.350 34,089.91 0 177,846.400 103
ROA % 1.833 12.117 -98.160 85.230 1,281
Pairwise Spearman correlations
Variables 1 2 3 4 5
1 lnEcopat 1.000
2 ROA 0.057 1.000
3 ETS -0.010 -0.006 1.000
4 Post -0.107 0.028 0.110 1.000
5 Size 0.161 0.092 0.078 0.136 1.000
Mean ROA for EU ETS and non-EU ETS firms
XIX
Appendix C – Hypotheses and results
Results at a glance
Label Hypotheses Exp. sign Variables of interest Supported
H1 The EU ETS increased the
eco-innovativeness of
regulated firms.
+ lnEcopat &
ETS*post Partially
H2 The second phase of the EU
ETS enhanced the eco-
innovativeness of regulated
firms more than the first
phase of the EU ETS.
+ lnEcopat &
ETS*phase Yes
H3 The EU ETS increased the
financial performance of
regulated firms.
+ ROA &
ETS*post No
H4
Eco-innovativeness mediates
the positive impact of the
EU ETS on the financial
performance of regulated
firms.
+
ROA &
ETS*post &
lnEcopat
No
XX
Appendix D – Robustness checks
Results of alternative regression models (1/3)
Independent variables Model (1a.dif) Model (1b.nbd)
Dependent variable lnEcopat Ecopat
ETS -0.003
(0.200)
1.009
(1.586)
Post -.004
(0.010)
-
ETS*post 0.023**
(0.011)
-
Phase1 - 1.278
(0.799)
ETS*phase1 - -0.437
(0.437)
Phase2 - 0.456
(0.697)
ETS*phase2 - 0.757
(0.517)
Size 0.011
(0.008)
0.215
(0.252)
Constant -.0297
(0.208)
-29.244
(149141.8)
Year fixed effects
Country fixed effects
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
No
N (observations) 1,643 2,558
N (groups) 289 292
Wald χ2 p-value 0.001 n.a.
R2 (overall) 0.243 n.a.
Modified Wald statistic
(H0: homoskedasticity)
150,000.0*** not applicable
Wooldridge F-statistic:
(H0: no serial correlation)
2.016 not applicable
Pesaran test statistic
(H0: cross-sect. dependence)
34.719*** not applicable
Breusch Pagan LM
(H0: no ind.-spec. effects)
37.470*** 153.720***
Sargan-Hansen test statistic
(H0: random effects)
3.785 27.030
Note. Random effects GLS estimation of models (1a.dif), (1b.nbd). A panel covering the period from 2000-
2014. Robust standard errors in parentheses. (Two-tailed) significance levels: * p<0.1; ** p<0.05; ***
p<0.01.
XXI
Results of alternative regression models (2/3)
Independent variables Model
(1b.-be)
Model
(1b.-de)
Model
(1b.-es)
Model
(1b.-fr)
Dependent variable lnEcopat lnEcopat lnEcopat lnEcopat
ETS 0.004
(0.0182)
-0.006
(0.008)
0.001
(0.027)
0.012
(0.017)
Phase1 0.011
(0.014)
0.008
(0.010)
0.007
(0.017)
0.012
(0.014)
ETS*phase1 -0.007
(0.007)
-0.006
(0.005)
-0.001
(0.005)
-0.007
(0.007)
Phase2 -0.001
(0.009)
-0.009
(0.005)
0.005a
(0.013)
0.004
(0.008)
ETS*phase2 0.019*
(0.011)
0.003
(0.007)
0.024*
(0.014)
0.017*
(0.010)
Size 0.006
(0.005)
0.004*
(0.002)
0.008
(0.007)
0.003
(0.004)
Constant -0.203
(0.140)
-0.098*
(0.057)
-0.276
(0.205)
-0.134
(0.134)
Year fixed effects
Country fixed effects
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
N (observations) 2,399 2,266 1,602 2,380
N (groups) 276 258 182 270
Wald χ2 p-value 0.001 0.001 0.001 0.001
R2 (overall) 0.238 0.066 0.250 0.253
Modified Wald statistic
(H0: homoskedasticity)
150,000.0*** 110,000.0*** 19,000.0*** 480,000.0***
Wooldridge F-statistic:
(H0: no serial correlation)
0.212 17.040*** 0.002 0.984
Pesaran test statistic
(H0: cross-sect. dependence)
78.436*** 164.523*** 63.090*** 95.590***
Breusch Pagan LM
(H0: no ind.-spec. effects)
57.730*** 10.710*** 74.520*** 69.350***
Sargan-Hansen test statistic
(H0: random effects)
3.725 4.052 3.311 4.036
Note. Random effects GLS estimation of models (1b.-be), (1b.-de), (1b.-es), (1b.-fr). A panel covering the
period from 2000-2014. Robust standard errors in parentheses. (Two-tailed) significance levels: * p<0.1; **
p<0.05; *** p<0.01.
XXII
Results of alternative regression models (3/3)
Independent variables Model
(1b.0f)
Model
(1b.1f)
Model
(1b.5f)
Dependent variable lnEcopat lnEcopat lnEcopat
ETS 0.010
(0.021)
0.004
(0.225)
0.002
(0.024)
Phase1 0.000
(0.014)
-0.005
(0.124)
-0.010
(0.012)
ETS*phase1 0.008
(0.010)
0.005
(0.009)
0.012
(0.009)
Phase2 0.019
(0.013)
-0.002
(0.013)
-0.013
(0.013)
ETS*phase2 -0.005
(0.009)
0.011
(0.009)
0.024**
(0.011)
Size 0.007***
(0.003)
0.007**
(0.003)
0.006**
(0.003)
Constant -0.214
(0.020)
0.194
(0.211)
-0.197
(0.233)
Year fixed effects
Country fixed effects
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
N (observations) 2,558 2,558 2,009
N (groups) 292 292 292
Wald χ2 p-value 0.001 0.001 0.001
R2 (overall) 0.209 0.231 0.207
Modified Wald statistic
(H0: homoskedasticity)
19,000.0*** 7,000.0*** 6,000,000.0***
Wooldridge F-statistic:
(H0: no serial correlation)
0.748 2.182 8.653***
Pesaran test statistic
(H0: cross-sect. dependence)
193.214*** 217.958*** 47.817***
Breusch Pagan LM
(H0: no ind.-spec. effects)
45.360*** 56.520*** 54.560***
Sargan-Hansen test statistic
(H0: random effects)
3.562 3.584 3.883
Note. Random effects GLS estimation of models (1b.0f), (1b.1f), (1b.5f). A panel covering the period from
2000-2014. Robust standard errors in parentheses. (Two-tailed) significance levels: * p<0.1; ** p<0.05; ***
p<0.01.
XXIII
10. DECLARATION OF ORIGINALITY
I hereby certify that I am the sole author of the thesis written to obtain the Master of Science
in International Business - Strategy and Innovation from Maastricht University and the
International Master of Science in Management from NOVA School of Business and
Economics. The views and arguments provided in the thesis reflect my own opinion and not
the views of Maastricht University or NOVA University Lisbon.
I certify that, to the best of my knowledge, my thesis does not infringe upon anyone’s copyright
nor violate any proprietary rights. Any ideas, techniques, quotations, or any other material from
the work of other people included in my thesis, published or otherwise, are fully acknowledged
following standard referencing practices.
_______________________________
Lennart David Osses
Maastricht, December 16th, 2019