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Citation: Bordignon, M.; Gamannossi
degl’Innocenti, D. Third Time’s a
Charm? Assessing the Impact of the
Third Phase of the EU ETS on CO2
Emissions and Performance.
Sustainability 2023,15, 6394. https://
doi.org/10.3390/su15086394
Academic Editor: Wen-Hsien Tsai
Received: 27 February 2023
Revised: 30 March 2023
Accepted: 31 March 2023
Published: 8 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Third Time’s a Charm? Assessing the Impact of the Third Phase
of the EU ETS on CO2Emissions and Performance
Massimo Bordignon and Duccio Gamannossi degl’Innocenti *
Department of Economics and Finance, Università Cattolica del Sacro Cuore, Via Necchi 5, 20123 Milan, Italy
*Correspondence: duccio.gamannossi@unicatt.it
Abstract:
The EU Emissions Trading System (ETS) is the largest cap-and-trade scheme for CO
2
emissions globally. This study evaluates the impact on CO
2
-equivalents emission of the increased
stringency of Phase 3, which marked a significant shift from the previous phases of the EU ETS and
significantly reduced the number of emissions permits (EU Allowances—EUA) freely allocated. Our
analysis reveals that the increase of purchased EUA had a statistically significant, substantial impact
on emissions reduction from Phase 2 to Phase 3, with a decrease in emissions of approximately half
a ton for each additional allowance bought. Our (conservative) estimate of the total reduction in
emission is 422 MtCO
2
-eq, 22.5% of the average yearly EU ETS emissions or 4.3–3.0% of emissions in
Phases 2 and 3, respectively. Under Article 10c of the ETS directive, lower-income Member States
have been allowed to continue the free allocation of EUA to electricity-generating installations during
Phase 3 to provide more time and resources for modernization. We show that such derogation had a
sizeable and significant detrimental impact on the achievement of emission reduction targets, leading
to an increase in emissions of about half a ton for each additional allowance bought; a result that
highlights the need for increased efforts on support measures (e.g., the Modernization Fund). We
also investigate the impact of the EU ETS on output, capital productivity, and labour productivity.
Our analysis indicates that the performances were not negatively impacted by the tightening in
regulation that occurred between Phases 2 and 3. We also find no evidence that the derogation
status impacted performances, which further ameliorates the concerns about the potential intra-EU
competitive distortions induced by the regulation. Our results cast a favourable light on the reduction
of the free allocation of EUA and the tightening of the regulation implemented in Phase 4 of the
EU ETS.
Keywords:
EU Emissions Trading System (ETS); carbon emissions; greenhouse gases (GHG);
climate policy
JEL Classification: H23; Q48; Q58; D24
1. Introduction
European citizens recognize climate change as a pressing issue and support the EU’s
actions to tackle it [
1
]. Addressing climate change allows the pursuit of multiple positive
objectives, including economic growth, job creation, improved competitiveness, and tech-
nological advancements. For instance, reaching the target of 2
°
C global warming and 20%
renewable energy by 2020 could prevent economic losses of EUR 160 billion and create
400,000 jobs, as reported by the EPRS [
2
]. The EU’s coordinated approach to tackling
climate change has the potential to yield significant welfare benefits by addressing negative
externalities of greenhouse gas (GHG) emissions that would otherwise lead to sub-optimal
outcomes without coordination among member states.
The 1997 Kyoto Protocol saw 37 nations pledge to specific, enforceable GHG reduction
targets for 2008–2012. The EU responded with the EU Emissions Trading System via
the 2003 EU ETS directive, the main tool for meeting the commitment. The EU ETS is
Sustainability 2023,15, 6394. https://doi.org/10.3390/su15086394 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 6394 2 of 21
a pioneering, multi-country and multi-sector GHG emissions trading program. It is the
largest program of its kind globally, covering upwards of 11,000 energy-using installations
and the aviation industry across 30 countries, representing approximately 45% of total
EU GHG emissions. The EU ETS has undergone various phases of development, starting
with a pilot phase (Phase 1, 2005–2007) to establish the monitoring, reporting, verification,
and market infrastructure. In Phase 2 (2008–2012) the system became effectively operational
to comply with the start of the first commitment period of the Kyoto Protocol. Over time,
increasingly stringent and refined regulations on emissions have been implemented with
Phase 3 (2013–2020) and Phase 4 (2021–2030). We discuss at length the details of these
phases in Section 2.
The EU ETS sets a limit on the overall quantity of GHGs that regulated businesses
are allowed to release. This cap is divided into European Union Emission Allowances that
grant the owner the right to release GHGs equal to one ton of CO
2
-eq. Under the EU ETS,
installations and air operators are required to annually surrender allowances to compensate
for their emissions. The EU Registry, which maintains records of EU Allowance holders,
effectively enforces the regulation through the identification and imposition of penalties on
non-compliant entities.
Allowances for emissions reduction can be obtained through three methods: free
allocation, auctions, and purchases on the secondary market (any unused allowances
by a company can be saved for future use or sold in the market). The value of these
allowances, i.e., the carbon price, is determined by the balance between the demand from
companies and the supply, as set by the cap. A liquid EUA market helps in the efficient
identification of the carbon price, promoting efforts to decrease emissions at the lowest cost
and incentivizing investment in low-carbon technology. The EU ETS requires a healthy
EUA market for effective operation, but since 2009, it has faced a supply surplus of 2 billion
caused by the 2008 financial crisis, high imports of foreign carbon credits, and increasing
use of renewable energy sources. This surplus resulted in low carbon prices that might have
lessened incentives to cut emissions. Free permit allocation and over-allocation throughout
Phases 1–2 and the first part of Phase 3 hindered the market’s effectiveness [
3
–
5
]. As a short-
term solution, the EU Commission delayed several permission auctions (“backloading”)
and, on a longer horizon, implemented the Market Stability Reserve (MSR) in 2019, which
adjusts the supply of allowances based on pre-defined rules. As a result, over-allocation
declined between 2013 and 2016 [6].
The energy sector is crucial in achieving emission reduction goals as it accounts for
over 35% of total CO
2
emissions and
≈
62% of emissions in the EU ETS. An integrated
energy market could bring potential benefits of EUR 231 billion annually, according to the
EPRS [
2
]. In 2009, the EU established the 2020 package, a series of binding legislation aimed
at creating a sustainable, secure, and competitive energy system. The package includes
three key goals: 20% GHG reduction vs. 1990 levels, 20% of EU energy from renewable
sources, and a 20% increase in energy efficiency over baseline projections. The EU’s energy
system has undergone significant changes as a result of the efforts to increase the use of
renewable energy, which rose from 9.6% in 2004 to 22.1% in 2020, with most member states
meeting their 2020 renewable energy targets. In line with the Paris Agreement of 2015,
the EU has established a new 2030 Climate and Energy Policy with three key targets:
1. Reduce GHG emissions by at least 40% compared to 1990 levels;
2. Have at least 32% of energy produced from renewable sources;
3. Improve energy efficiency by at least 32.5%.
The European Green Deal aims to achieve a climate-neutral EU by 2050, and the
reduction of GHG emissions represents a key step in this direction. Part of this effort
includes proposals to raise GHG reduction targets to 50% and renewable energy targets
to 40%.
Sustainability 2023,15, 6394 3 of 21
Literature Review and Article’s Contribution
The amendments to the EU ETS system enacted in 2013 (the start of Phase 3), increased
the EUA purchased by companies through stricter enforcement of the regulation and by
decreasing the free allocation of permits. In this study, we evaluate the consequences of the
tightening in regulation in terms of CO
2
-eq emissions and performances. Our investigation
shows that the policy change significantly reduced emissions and had an insignificant
impact on performance. Lower-income Member States have been granted a derogation
(under Article 10c of the EU ETS Directive) that allowed for the free allocation of EUA
to electricity-generating installations during Phase 3. While this exemption increases
the resources available for emissions reduction, it also weakens the incentives for its
realization, so its impact is theoretically ambiguous. The evidence we provide shows that
such derogation had a sizeable and significant detrimental impact on the achievement of
emission reduction targets.
Our contribution connects to the literature investigating the impact of the EU ETS
on reducing GHG emissions in the first two EU ETS Phases. A small 3% reduction was
identified in Phase 1 [
7
,
8
]. However, the growth rate of emissions was found to have
increased by 3.6% in 2005/06 compared to 2007/08 [
9
]. Country-level data show modest
reductions of 1% and 5% from 2006 to 2009 [
10
]. In Germany, manufacturing plants under
the ETS lowered their emissions by 18% more than unregulated ones in Phase 1 and by
20% in the following phase [
11
]. The emissions of French manufacturing installations
decreased by 8–12% in Phase 2, but not in the previous phase [
12
]. Lithuanian firms saw
little change in CO
2
emissions from 2003 to 2010 [
13
], with a slight decrease in emission
intensity in 2007. A study of firms in France, Netherlands, Norway and the UK found a
6% (but not statistically significant) reduction of emissions in Phase 1 and a significant
15% reduction in Phase 2 [
14
]. A recent study on Norway’s installations reports a 30%
statistically significant cut in emissions during Phase 2, but no significant effect during
Phase 1 [
5
]. Notably, the reduction in emissions during the first two phases was attributed
mainly to the economic crisis by some scholars [
15
]. A comprehensive investigation of
Phase 3 of the EU ETS has not yet been carried out in the literature (with the exception
of [
5
] considering 2013). This paper aims to fill this gap by providing, to the best of our
knowledge, the first assessment of the impact on CO
2
emissions of the tightening of the
regulation that occurred between Phase 2 and Phase 3. As detailed in Section 2.1, there are
several reasons to believe that Phase 3 was more effective in reducing emissions, partly
due to a stricter cap, but also as a result of other aspects of the new regulation, such as
the sizeable reduction in freely allocated EUA, stricter enforcement, and the adoption of a
benchmark-based quantification mechanism to allocate free EUA instead of grandfathering.
The chief objective of the EU ETS is to curb emissions, but economic theory suggests
that environmental regulations could harm firm productivity [
16
] by distorting invest-
ment [
17
] or reducing operating flexibility [
18
]. The EU ETS also raises concerns about
emissions-intensive companies relocating to jurisdictions with weaker carbon regulations.
However, the Porter hypothesis [
19
] posits that strict environmental regulations can boost
productivity by promoting innovation and efficiency, and it is supported by several stud-
ies [
20
–
22
]. To investigate whether the tightening of EU ETS regulation between Phases
2 and 3 has had a negative impact on the performances of the regulated installations, we
analyze its impact on output and the productivity of capital and labour. In the literature,
the impact of EU ETS on firms’ value added, profit margin and employment between 2005
and 2008 was slightly negative but not statistically significant [
9
]. However, a study of
the power sector in 24 European countries from 1996 to 2007 showed a positive impact on
technological change during Phase 1 [
23
]. Firm-level data have been used to investigate
the impact of the ETS on the three heaviest polluting sectors (power, cement, and iron and
steel), identifying an increase in revenues and costs for the power sector during the period
from 2005 to 2009 [
24
]. Moreover, firms under the EU ETS outperform those not regulated,
with higher turnover, markup, investment intensity and labour productivity in Phases 1
and 2, as reported by [
25
]. Additionally, no significant impact on profitability indicators of
Sustainability 2023,15, 6394 4 of 21
ETS firms was observed in Phase 3 [
6
]. An analysis of all transactions in the first two phases
showed a positive correlation between emission abatement and trading profits, indicating
a positive impact on performance [
26
]. Overall, the empirical literature suggests that EU
ETS has not had a significant negative effect on performance and profitability in Phases 1
and 2. However, as Phase 3 had a more stringent regulation compared to previous phases,
it is important to examine its impact on performance. Our analysis shows that the impact
on the output produced and on the productivity of capital and labour of regulated firms
has been small and statistically not significant.
The paper is organized as follows: in Section 2we provide more details on the EU ETS
system and describe the data and methods used in the analysis; in Section 3we study the
impact of the EU ETS on emissions and performance. Results discussion and interpretation,
as well as future research directions, are offered in Section 4.
2. Materials and Methods
2.1. Institutional Setting
The EU ETS is divided into 4 main phases:
Phase 1 (2005–2007).
In Phase 1, carbon-intensive industries across the EU-27 member
states were regulated with a hard cap of 2.058 Gton of CO
2
-eq. The distribution of
EU Allowances was mainly through free allocation, accounting for approximately
98% of the total. The hard cap was established as a constraint to reduce overall
emissions. The regulated industries included power stations and other combustion
plants (
≥
20 MW), oil refineries, coke ovens, iron and steel plants, cement clinker,
glass, lime, bricks, ceramics, pulp, paper, and board.
Phase 2 (2008–2012).
In Phase 2, the system was extended to include three more countries
(Norway, Iceland, and Liechtenstein) and the aviation sector (The cap on aviation
emissions is separate from the other sectors and in Phase 3 has been set at a constant
level equivalent to 95% of the historical aviation emissions). From 2021 onward,
the linear reduction factor of 2.2% that applies to stationary installations will also
apply to the aviation cap.) The cap for CO
2
-eq emissions was lowered to 1.859
Gton, and member states could include certain emissions of N
2
O and PFC. Free
allocation remained the primary method of EUA distribution, accounting for 96%
of the total. A company that receives EUA for free faces no financial burden for
compliance, but may still have the incentive to reduce emissions to sell the EUA at
market price ( The Coase theorem [
27
] states that, theoretically, the initial allocation
of permits should not impact incentives even if it has distributional effects. However,
its assumptions are rarely satisfied, such as in the presence of taxes [
28
]). However,
free allocation reduces the cost of compliance with EU ETS requirements, potentially
reducing the urgency for businesses to reduce emissions, especially if regulations
are uncertain. The free allocation of allowances may also lead to windfall gains for
companies that can pass the cost of allowances to their clients, especially in markets
with limited competition. Evidence from Phases 1 and 2 suggests that this occurred
among companies in the energy industry [4,29].
Phase 3 (2013–2020).
During Phase 3, Croatia joined the EU ETS and the scope of the sys-
tem grew to include more industrial sectors ( aluminium, petrochemicals, ammonia,
nitric, adipic and glyoxylic acid production, CO
2
capture, transport in pipelines, and
geological storage of CO
2
). The cap on CO
2
-eq emissions was set at 2084 Gton for
2013, with an annual reduction of 1.74% until the start of Phase 4 when it will reduce
by 2.2% yearly (the legislation also covered PFC emissions from aluminium manufac-
turing as well as N
2
O emissions from nitric, adipic, and glyoxylic acid production). To
overcome some of the pitfalls of previous phases’ regulation, the proportion of freely
allocated EUA underwent a stark reduction (from 96% to 43%) by implementing 100%
auctioning for power generation installations and increasing the target of auctioning
for industrial installations from 20% in 2013 to 70% in 2020, with a target of 100% for
Sustainability 2023,15, 6394 5 of 21
2030. While all electricity producers have been mandated to acquire EUA in Phase 3,
as per Article 10c of the ETS directive certain EU Member States (Bulgaria, Czechia,
Croatia, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, and Slovakia) are
granted a derogation from the general rule. These lower-income Member States
may provide free EUA to electricity-generating installations to support investments
that contribute to the diversification of the energy mix, restructure, and upgrade of
energy infrastructure, implement clean technologies, and for modernization of the
energy production and transmission sectors. Free allocation in Phase 3 was also used
to prevent emission-intensive, internationally competing industries from relocating
to countries with weaker environmental regulations, and avoid job losses, market
share decline, and the offsetting of EU emissions reductions through carbon leakage.
The targeting of free allocation in this phase had the double objective of achieving
emission reduction targets while fostering investment in emissions reduction and
energy-efficient technology. As such, the allocation of free EUA was revised, shifting
from “grandfathering” (based on historical emissions) to benchmarks based on the
lowest GHG emitters in each production process. Hence, the least polluting com-
panies were fully covered by free allocation, while the others had to purchase EUA
for their excess emissions, encouraging them to search for the most efficient way to
improve their environmental performance.
Phase 4 (2021–2030).
Recently the European Commission set the following 2030 targets: (i)
at least 55% reduction in GHG emissions from 1990 levels; (ii) a minimum renewable
energy share of 32% (with a clause for a possible upwards revision by 2023); (iii) at
least 32.5% improvement in energy efficiency compared to projections of the expected
energy use. To meet the EU’s 2030 GHG emissions reduction target, the EU ETS
installations must reduce their emissions by 43% compared to 2005. The annual
decline rate of emission allowances will increase to 2.2% starting in 2021 to step up
the pace of emissions cuts. To improve the resilience of EU ETS to future market
shocks (A marked oversupply of EUA arose during the period 2009–2013 due to: (i)
the 2008 economic crisis; (ii) unexpectedly high imports of international carbon credits;
(iii) the significant increase in the use of renewable sources. This resulted in low prices
of EUA between 2012 and 2017 that altered the functioning of the carbon market [
3
–
5
]
and reduced the ability of the ETS system to curb emissions.), the Market Stability
Reserve will be substantially reinforced. The revised EU ETS Directive includes
new rules to address the risk of carbon leakage with the free allocation system
being extended for another decade, with a focus on sectors at high risk of relocating
outside the EU. These sectors are entitled to 100% free allocation of allowances, while
those at lower risk of relocation will phase out after 2026. From 2020, a part of
the EUA has been devoted to the Innovation Fund (EUR 38 billion in 2020–2030,
assuming a EUA price of EUR 75) which is allocated by the European Investment
Bank to support highly innovative projects on low-carbon technologies and to bring
to the market novel decarbonization processes. To foster the adoption of emissions
reduction technologies in lower-income Members States, 2% of the allowances have
been earmarked to financing the Modernization Fund, which supports transitioning
to a low-carbon economy of energy-intensive sectors by: “funding the demonstration
of innovative technologies, increasing energy security, expanding the use of renewable
energy sources and promoting the exchange of best practices”. While the optional
transitional free allocation provided by Article 10c remains available for lower-income
Member States, only Bulgaria, Hungary, and Romania are taking advantage of this
opportunity in Phase 4. Czechia, Croatia, Lithuania, Romania, and Slovakia have
chosen to transfer some or all of their Article 10c allocations to the Modernization
Fund, which will increase their respective volumes and share of spending under
the Fund. Finally, Estonia, Latvia, and Poland, which also fall under Article 10c
derogation, chose to auction their allowances instead.
Sustainability 2023,15, 6394 6 of 21
2.2. Datasets and Methodology
In this study, we exploit a panel dataset of yearly sector-by-country observations for
the period 2008–2020 (Phases 2 and 3). Specifically, we use the Nace classification to analyze
the two sectors accounting for the vast majority of CO
2
-eq emissions (
≥
98%) under the EU
ETS: sector C (
≈
63%) “Manufacturing” and sector D (
≈
35%) “Electricity, gas, steam and
air conditioning supply” (energy from now on).
Our primary data source is the European Union Transaction Log (EUTL). We selected
the following variables: verified CO
2
-eq emissions (VE), surrendered allowances (SA),
and freely allocated allowances (AA). To align the installation-level data from the Union
Registry with the sector of activity used in the analysis, we used an imputation table
(available at https://euets.info (accessed on 26 February 2023)) based on the proceedings
of the stakeholder meeting on the preliminary carbon leakage list for Phase 4 of the EU
ETS [30].
A careful imputation of the missing values was performed before the analysis. Indeed,
we find that the observations reporting both VE and SA, but missing AA, were of negligible
weight until 2012, representing less than 0.3% of the sample. However, this proportion
surged to
≈
16% with the onset of Phase 3 ( further details on the data imputation process
are provided in Appendix A). On the abrupt change in behaviour of missing observations,
see in particular Figure A1 and the related discussion. The affected installations represent a
sizeable share of both VE and SA (>20%) and an overwhelming majority (>88%) are in the
Nace category “Energy: Electricity generation” ( see Figure A2). We argue that the most
likely explanation for the surge in the number and weight of observations reporting both
VE and SA, but lacking AA, is that they stopped reporting AA (or that their AA was not
recorded) because they were no longer entitled to free allocation in Phase 3. Our conjecture
is supported by the evidence that a stark majority of affected installations are missing AA
for the entire Phase 3 and belong to countries where derogation 10c does not apply (where
free allocation to the energy sector is not allowed). Along this line of thought, we presume
that in several instances the affected observation should be imputed a zero AA and apply
the following imputation strategy:
1. Identify observations reporting VE and SA but not AA for the entire third phase.
2.
Identify the observations of Step 1 that are in the Nace category “Energy: Electricity
generation”.
3.
Identify the observations of Step 2 that are not in a country granted derogation by
Article 10c of the ETS directive.
4.
Impute 0 to the AA variable in place of a missing value to the observations identified
in Step 3.
The total number of installations imputed is 366, with a decrease in the weight of
the affected installations from
≈
16% to
≈
5% and a reduction of one-third of the average
emissions of the observations affected (see Figure A3).
We argue that this imputation procedure brings the data closer to the real AA dy-
namic. Indeed, the installations subject to our imputation strategy are likely to be among
the most affected by the policy change (high-emitters losing entirely the free allocation)
and excluding them from the analysis would significantly—and mistakenly—reduce the
estimated effect of the change in regulation. In support of this interpretation, we find
that removing these observations from the dataset reduces the estimated impact of the
treatment on emissions by
≈
20%, although significance is not affected (The whole set of
results without imputation is available on request.)
We use the same policy evaluation techniques to investigate the impact of the EU ETS
Phase 3 on emissions and performance. Here we outline these methods for the analysis
regarding emissions, the extension to performance is straightforward.
To assess the impact of the EU ETS on emissions, it is crucial to determine if the
emission dynamic was driven by regulation or other confounding factors. The ideal
approach would be to compute the difference between observed emissions of installations
Sustainability 2023,15, 6394 7 of 21
under the new regulation against the emissions of the same installations under no change
in regulations. Although this latter scenario is unobservable, econometric techniques
can be used to approximate it. Our first method is an event study (ES) design, which
enables the evaluation of the effect of a policy change on the variable under study over
time. In the case of emissions, VE is the dependent variable while the policy change is
represented by the implementation of Phase 3 in 2013. To determine the impact of the policy
change on emissions, we compare the intensity of EU ETS regulations across installations,
under the assumption that installations more heavily affected by regulations will have
larger responses. Exploiting the observed differences in responses, we aim to estimate the
regulation’s effect on emissions.
Using data from the EUTL, we define the variable Purchased EU Allowances (PEUA)
as the difference between SA and AA. PEUA are a measure of the strength of the monetary
incentives imposed by the EU ETS regulation on an installation ( the exploitation of the
variation between emissions and freely allocated EUA has been explored already in the
literature [
6
,
9
,
31
]). In certain instances, particularly during Phase 2, there has been an
excess of freely allocated emissions relative to verified emissions. This circumstance led to
negative PEUA values. Consequently, in these cases installations receive a financial subsidy
for emitting CO
2
, creating an incentive for increased emissions. In any case, economic
rationality predicts that an increase of PEUA by an installation should induce a reduction
of VE.
Due to the potential endogeneity arising from regulators’ decisions to treat emitters
based on past behaviour, we set the treatment variable for each year at the median in
Phase 2 (using the 2008 value does not alter significantly the results). Given the decrease in
allowances allocated for free between Phase 2 and Phase 3, our treatment variable likely
underestimates the impact and our findings should be considered a conservative lower
bound of the true effect. Notice, however, that the adoption of the median value of PEUA
in Phase 2 as a treatment implicitly assumes proportional treatment across years.
Equation (1) describes the event study specification [
32
] for sector-by-country observa-
tion
i
where the treatment year is identified by a dummy variable measuring the time to
Phase 3 implementation 1
{t−t∗=y}
with
t∗=
2013 (i.e., “event time”), and the intensity
of treatment is measured by the (continuous) value of median PEUA in 2008–2012
PEU Ai
:
VEit =PEUAi"−2
∑
y=−5
ρy1{t−t∗=y}+
7
∑
y=0
λy1{t−t∗=y}#+γXi,2008 +θt+ηi+eit (1)
The event study methodology is a panel (2008–2020) regression of sector-by-country
verified emissions (
VEit
) (the dependent variable) over a measure of the treatment (
PEU Ai
)
interacted with year dummies, while controlling for other covariates that might affect
emissions (
Xi,2008
). We are especially interested in the coefficients
ρy
and
λy
that provide an
estimate of the (covariate-adjusted) linear dependence of emissions from
PEU Ai
in Phase 2
and Phase 3. Estimates are normalized to the year before the shift from Phase 2 to Phase
3 (
y=−
1, the year 2012). The resulting estimated set of coefficients, one for each year,
measures the impact of the treatment on emissions. A negative coefficient implies that
in that year, the emissions decrease induced by the treatment has been bigger than in the
base year (and vice-versa). We cluster the standard errors at the country level to account for
potential correlation within sectors of the same country. The obvious concern with event
studies is the violation of the common trend assumption. To investigate the reliability of
this assumption,
ρy
can be used as a falsification test. Indeed, a lack of significance and
a constant dynamic of the
ρy
coefficients suggests that observations subject to different
treatment intensities share a similar trend during the pre-treatment period. The
λy
estimate
intention-to-treat (ITT) effects and they measure, for each year of Phase 3, the impact on
emissions of an additional median PEUA in Phase 2.
Our empirical strategy relies only on the parallel trends assumption, thereby obviating
the need for balancing the levels of characteristics across observations subjected to varying
degrees of treatment (as in methods relying on randomization). Notwithstanding, to control
Sustainability 2023,15, 6394 8 of 21
for differential treatment effects that might arise based on baseline country-sector char-
acteristics, we incorporate a battery of baseline sector-by-country and country covariates
interacted with year dummies Xi,2008.
The variables used to this end are related to (i) energy consumption—energy use
(Eurostat, see Appendix Afor details on the imputation of the Nace sector to energy data),
cooling and heating degree days (Agri4Cast); (ii) regulation—environmental taxes net of
ETS revenues as a per cent of GDP (Eurostat) (https://ec.europa.eu/eurostat/statistics-
explained/index.php?title=Tax_revenue_statistics, accessed on 30 March 2023), taxes on
corporations (Eurostat), government expenditure as a per cent of GDP (Eurostat); (iii)
development–labour hourly compensation (Eurostat), capital (Eurostat); (iv) innovation–
intramural R&D expenditure as a per cent of GDP (Eurostat), (GERD and BERD, Eurostat),
total R&D personnel and researchers (Eurostat).
Furthermore, the fixed-effects panel approach used in the analysis also includes a set
of year dummies θtthat absorbs temporal shocks and a set of sector-by-country dummies
(the fixed effects
ηi
) that absorbs any time-constant characteristic at the observation level,
such as regulation and social capital.
Equation (2) describes our difference-in-difference (DD) specification including an
indicator variable that is equal to one during Phase 3, and the same treatment variable used
in the ES specification, PEUAi. The model used in DD is as follows:
VEit =β11{t−t∗>0}+β2PEUAi+β3PEUAi×1{t−t∗>0}+β4tXi,2008 +θt+ηi+ei t (2)
The variable 1
{t−t∗>0}
indicates if the year belongs to Phase 3 of the EU ETS
and its coefficient,
β1
, tells us whether the years in Phase 3 saw a change in the outcome
analyzed; the variable
PEU Ai
is our treatment variable and the associated coefficient
β2
captures differences in levels of the outcome
VEit
between observations subject to different
intensity of treatment. Our parameter of interest is
β3
, the coefficient of the interaction term
PEU Ai×
1
{t−t∗>0}
, that estimates the mean additional reduction in CO
2
-eq emissions
for the period 2013–2020 that is induced by an additional median PEUA in Phase 2 when
passing from Phase 2 to Phase 3 of the EU ETS.
Through Article 10c of the EU ETS Directive, lower-income Member States had the
opportunity to grant free allocation to electricity-generating installations during Phase 3 to
support modernization investments. To assess the impact of this derogation on the effec-
tiveness of the EU ETS, we implement a triple difference (DDD) design. This heterogeneity
analysis is performed by including a binary variable 1
{Derog}
, that indicates whether the
country-sector falls under the derogation offered by article 10c, and its interactions with the
variables 1
{t−t∗>0}
,
PEU Ai
and
PEU Ai×
1
{t−t∗>0}
. The coefficient of interest of
this specification, the one of the triple interaction term 1
{Derog}×PEU Ai×
1
{t−t∗>0}
,
measures the differential impact on VE emissions in 2013–2020 induced by an additional
median PEUA in Phase 2 for observations granted/not-granted by the derogation of Article
10c. A positive value of this coefficient would indicate a lower efficacy of the EU ETS for
countries under derogation relative to those that were not granted the derogation.
Our analysis of performance aims to evaluate the impact of the tighter regulation of
Phase 3 on the output produced and on the productivity of the inputs: capital and labour.
We measure the output as the gross domestic product (GDP) of a sector-country at PPP.
Capital productivity is measured as GDP per euro of capital in net fixed assets (at current
replacement cost) while labour productivity is measured as GDP per euro spent on labour.
Eurostat national accounts do not explicitly report GDP at the sectoral level. We
computed sectoral GDP by first identifying taxes and subsidies and then subtracting them
from the sectoral gross value added. Capital productivity is obtained by dividing sectoral
GDP by sectoral net fixed assets as reported by Eurostat. The total cost of labour by sector
is computed as the number of hours worked times the nominal per-hour compensation
of labour (adjusted for inflation and purchasing power). Labour productivity was then
computed as the ratio of GDP over the cost of labour.
Sustainability 2023,15, 6394 9 of 21
The analysis of performance is carried out using these three measures as dependent
variables in the policy evaluation setting outlined above for the case of emissions.
3. Results
The EU ETS reform from Phase 2 to Phase 3 exposed different sector-by-country to
a differential treatment intensity that we proxy using purchased EUA. In this section, we
estimate the impact of the reform on CO
2
-eq emissions and performance measuring the
differences in outcomes stemming from the difference in treatment intensity.
3.1. Descriptive Evidence
Before presenting the policy evaluation study, we report some descriptive evidence on
our variables of interest and on our treatment. Figure 1displays the evolution of the key
variables of the European Union Registry from the cleaned sample (Our sample is
≈
25%
smaller relative to the EU ETS registry due to data cleaning. However, the dynamics of the
variables in the two datasets are substantially the same, see Figure A4). The solid cyan line
represents the evolution of total verified emissions, exhibiting a moderate downward trend
that becomes more pronounced starting in 2018. Total surrendered allowances (dashed
blue line) are consistently below VE during Phase 2, with a growing discrepancy over
time. However, starting with the first year of Phase 3, SA closely matches VE, suggesting
a tightening of the enforcement of the EU ETS regulations. The evolution of total freely
allocated allowances (dotted pink line) mostly tracks the verified emissions in Phase 2,
albeit with a sizeable over-allocation at the end of the phase. At the beginning of Phase 3,
AA plummets (due to the shifting towards auction allocation) and then sets on a moderately
decreasing trend. As a consequence, purchased EU Allowances experienced a significant
increase from Phase 2 to Phase 3, due to both the stricter enforcement (rise in SA) and the
reduction in free allocation (decline in AA). As illustrated by the final row in the table
presented in Figure 1, the average PEUA was
−
150 during Phase 2, but it increased to 654
during Phase 3.
Figure 1.
Dynamic of total verified emissions, surrendered allowances, (freely) allocated allowances,
and purchased EUA during Phases 2 and 3 of the EU ETS.
Information on the distribution of our treatment (
PEU Ai
) is reported in Figure 2,
where it is apparent that sector D is characterized by a higher intensity relative to sector
Sustainability 2023,15, 6394 10 of 21
C where lower, and generally negative values, are observed. From the figure, we can also
see that the treatment is quite concentrated at zero, albeit with significant outliers in both
directions. In general, more developed countries tend to occupy the extreme on the right
with their D sector and the extreme on the left with their C sector, suggesting a widespread
over-allocation of EUA in the latter sector during Phase 2. Notably, the only quintile with
positive treatment (both average and median) is the top one.
Figure 2.
Distribution and descriptive statistics of median Purchased EUA during Phase 2 of the
EU ETS.
In Figure 3, we illustrate the dynamic of the outcomes of interest, emissions and
performance, by quintiles of the treatment. Interestingly, panel A shows that the evolution
of verified emissions for all quintiles is constant or slightly decreasing during 2008–2012,
casting a favourable outlook on the assumption of parallel trends before treatment, crucial
to our policy evaluation exercise (event study–ES, difference-in-difference–DD, and triple
difference—DDD). As the panel shows, there is a heterogeneous dynamic across treatment
quintiles, with the top one, and in a lesser measure the third, displaying a more marked
reduction in emissions at the onset of Phase 3. Conversely, the other quintiles show no
discernible change in trend after 2013. Based on this descriptive evidence, it appears that
buying EUA may have had a significant impact on reducing emissions, while the role of
overallocation seems limited. Panels B, C, and D show the evolution of the measures of
performance: output, capital productivity, and labour productivity. The panels display
more variable dynamics and also a more marked heterogeneity across quintiles relative to
panel A during Phase 2. While panel B shows a very low heterogeneity across quintiles
during the whole period, panels C and D display more variability, but no significant change
seems to occur between Phases 2 and 3.
We use standard causal inference methods to investigate this descriptive evidence
rigorously. Our empirical strategy exploits the panel nature of the data and does not
require a balance in levels between observations under different treatment intensities.
Nevertheless, observations with different baseline characteristics might undergo differential
Sustainability 2023,15, 6394 11 of 21
treatment effects, so we explore the distribution of variables that might be correlated with
heterogeneity in treatment effects. In Table 1, we report the average value of each covariate
by quantile and test for the presence of differences in mean. We can see that energy and
capital are negatively correlated with the treatment quintile and that there are significant
differences across quintiles. Furthermore, the top quintile is different from the others on
several dimensions: labour hourly compensation, GERD, BERD, and R&D employees.
The remaining covariates show no significant differences in means across quintiles.
Figure 3.
Dynamic of verified emissions (
A
), output (
B
), capital productivity (
C
), and labour produc-
tivity (D) by quintiles of purchased EUA in Phases 2 and 3 of the EU ETS.
Table 1.
Covariates mean by treatment quintile. The significance refers to a t-test for the difference
between the mean µkq of covariate kfor quintile qagainst the mean of other quintiles µk−q.
<20% >20% and <40% >40% and <60% >60% and <80% >80%
Energy 23,923 ** 8206 1237 *** 668 *** 737 ***
Capital 271,730 ** 78,865 55,601 24,075 *** 54,593
Labour HC 21.29 18.92 22.49 22.35 37.56 **
GERD 0.016 0.017 0.014 0.016 0.023 **
BERD 0.009 0.011 0.008 0.01 0.015 **
R&D Employees 1.16 1.22 1.13 1.24 1.59 **
Cold Hot DD 2900 3416 2826 3375 3555
Env. Tax (Net) 0.014 0.016 0.012 0.014 0.014
Corp. Tax 0.026 0.025 0.021 0.022 0.027
Gov. Exp. 0.423 0.441 0.406 0.431 0.454
** p< 0.05, *** p< 0.01.
3.2. Policy Evaluation
Figure 4illustrates in black the event study design, which showcases the progression
of the coefficients
ρy
and
λy
for our four dependent variables of interest: verified emissions
(panel A), output (panel B), capital productivity (panel C), and labour productivity (panel
D). In Table A1 of Appendix Bwe report the key coefficients of the event study for all
the dependent variables using the same panel names. For verified emissions, results
during the pre-reform period are largely insignificant, apart from a small but statistically
significant coefficient observed in 2011. Conversely, during Phase 3, all but two coefficients
are highly significant at the 1% level, with large, negative, and diminishing values over
time. The treatment coefficients for VE steadily decrease from zero in 2013 to
−
1.4 in
2020. The assumption of parallel trends during Phase 2 also holds for all the performance
Sustainability 2023,15, 6394 12 of 21
variables (see panels B, C, and D, of Figure 4and Table A1), albeit with some evidence
of higher output for more intensely treated observations during the years 2009 and 2010.
All Phase 3 coefficients on panels B, C, and D are small and insignificant. For output,
our analysis implies that an increase in
PEU Ai
by one unit led to a decrease in output
of around one hundred euros each year, which is comparable to the current cost of an
allowance. The coefficients estimated for capital and labour productivity (panels B and D)
are negligible for all practical purposes.
Figure 4.
Event study (in black) and difference-in-difference (in blue) coefficients estimating the
causal impact of purchased EUA on: verified emissions (
A
), output (
B
), capital productivity (
C
), and
labour productivity (D).
Figure 4also illustrates in blue the treatment effect for the whole third phase, which
we estimate using our DD specification. The 99% confidence interval for the VE coefficient
is [
−
0.0896,
−
0.867] with a point estimate of
−
0.479 (see panel A, row DD of Table 2). This
result implies that a unitary increase in median purchased allowances during Phase 2 led
to a decrease of almost half a ton of CO
2
-eq emissions during Phase 3. The DD coefficients
of the productivity variables, reported in panels B, C, and D of Table 2, are small and
not significant.
Rows DDD of Table 2present the results of our heterogeneity analysis that investigates
the impact of the derogation to the EU ETS granted under article 10c of the ETS directive.
Panel (A) shows the coefficient of Post
×
Treatment to be largely unaffected while the coef-
ficient of the triple interaction term is large, positive, and highly significant (at the 1% level).
The possibility granted to the less-developed Member States to freely allocate allowances to
the installations of the energy sector in Phase 3 resulted in a large and significant increase
in verified emissions. For the countries under Derogation 10c, an increase by one unit of
PEU Ai
led to an increase of more than half a ton of CO
2
-eq. The triple interaction term for
labour productivity (Panel D) is negative and significant at the 10% level but its magnitude
is negligible. The DDD coefficients of the remaining performance variables are small and
not significant. Although countries that were granted Derogation 10c took advantage of
the relaxed emission constraint, this did not lead to an improvement in their performance;
a finding suggesting that derogation 10c induced limited competitive distortions.
To further corroborate the validity of our finding on verified emissions, we first run a
placebo event study whose results are reported in Figures A6–A8 of Appendix B. These
placebo tests switch the dependent variable and the covariates one at a time. As covariates
should not be impacted by
PEU Ai
, we do not expect to observe any discontinuity in
Sustainability 2023,15, 6394 13 of 21
the estimated coefficients at the cutoff. Reassuringly, the estimated effects are small and
statistically insignificant.
Furthermore, in Appendix B, Figure A5, we show that the ES results are robust to the
inclusion of different sets of covariates. We report in Table A2 the analogous analysis on
the DDD estimator. The DD table is omitted for brevity.
Table 2.
Rows DD report the difference-in-difference coefficients of the causal impact of purchased
EUA on verified emissions (A), output (B), capital productivity (C), and labour productivity (D).
The DDD rows investigate the heterogeneous impact of the treatment on observations under Deroga-
tion 10c using the triple difference estimator.
Post ×Treatment Post ×Derog. 10c Post ×Treatment ×Derog. 10c
Panel A: Verified Emissions
DD −0.479 ***
[−0.867, −0.0896]
DDD −0.458 *** 1.42 ×107** 1.054 ***
[−0.7335, −0.183] [−2.81 ×106, 3.12 ×107] [ 0.0827, 2.026]
Panel B: Output
DD −250
[−1036, 536]
DDD −77 1.77 ×1010 −1662
[−1037, 883] [−1.47 ×1010, 5.01 ×1010] [−4.64 ×103, 1317]
Panel C: Capital Productivity
DD 2.31 ×10−9
[−2.18 ×10−9, 6.81 ×10−9]
DDD 2.82 ×10−90.031 −6.88 ×10−9
[−2.10 ×10−9, 7.73 ×10−9] [−0.393, 0.456] [−4.46 ×10−8, 3.08 ×10−8]
Panel D: Labour Productivity
DD −4.76 ×10−9
[−7.16 ×10−8, 6.21 ×10−8]
DDD −6.49 ×10−9−3.56 ** −3.32 ×10−7*
[−7.97 ×10−8, 6.67 ×10−8] [−7.264, 0.150] [−8.32 ×10−7, 1.67 ×10−7]
Controls—All Panels:
Xi,2008, Contry-Nace FE, Year FE, Std.Errors: by Country, Num.Obs.: 559
Confidence intervals at the 99 per cent. * p< 0.1, ** p< 0.05, *** p< 0.01.
4. Discussion
The EU Emissions Trading System serves as a cornerstone in the achievement of the
GHG emission reduction targets established in the 2020 climate and energy package and in
the 2030 Climate and Energy framework. This study investigates the impact on CO
2
-eq
emissions and on the economic performance of the tightening of the EU ETS regulation
between Phases 2 and 3.
The extant empirical literature demonstrates the limited emission reduction efficacy of
the EU Emissions Trading System in Phase 1 and the more marked, albeit limited, effects in
Phase 2. In the first examination of Phase 3 in the literature, we show that tighter regulation
of Phase 3, by increasing the purchased EUA, led to a statistically significant and sizeable
reduction of emissions relative to Phase 2. An additional median purchased allowance
in Phase 2 (
PEU A
) reduced verified emissions in Phase 3 by half a ton. This implies that
incentives within the EU ETS are significantly enhanced when companies are required to
purchase allowances, rather than having them allocated for free.
We can use our estimates to quantify the total amount of CO
2
-eq reduction achieved
during Phase 3 as a result of the increase in purchased EUA induced by the tightening of the
EU ETS regulation. To carry out this exercise, we multiply the difference between median
allowances allocated for free during the pre-treatment (Phase 2) and the post-treatment
(Phase 3) by the estimated coefficient
ˆ
β3
of Equation (2). Using a 5% confidence interval
on
ˆ
β3
, the reduction in CO
2
-eq emissions ranges between
−
675 and
−
170 Mt, with a point
estimate of
−
422 Mt. The reduction is substantial, around 4.3% of Phase 2 emissions (21.6%
Sustainability 2023,15, 6394 14 of 21
of the average year) and 3.0% of Phase 3 emissions (24.1% of the average year) in our
sample. Notably, this is a conservative appraisal of the impact of the EU ETS since it is just
accounting for the differential impact of PEU A (excluding the baseline).
The scientific literature presents very limited evidence of a detrimental effect of the
EU ETS on performance during Phases 1 and 2, potentially due to positive innovation
developments. Despite the increase in strictness in Phase 3, our results also show no sign
of an adverse impact on performance.
Our analysis reveals heterogeneity in the EU ETS effect across countries, with those
allowed to provide a free allocation of allowances to energy installations (under the deroga-
tion provided by Article 10c of the ETS Directive) experiencing an increase in emissions of
half a ton for each additional
PEU A
. However, we find no evidence of a relative improve-
ment in performances for countries under Derogation 10c. These findings warn against the
adverse impact of derogations to the ETS system on emissions while softening the concerns
regarding the competitive distortions induced by the regulation. Given the reduction in
emissions and the lack of a detrimental impact on performance, our investigation sheds a
favourable light on the additional reduction of the free allocation of allowances entailed by
Phase 4.
The identification strategy proposed in this study compares European country-sectors
that have been induced to purchase different amounts of EUA and that were/were not
eligible for Derogation 10c. Hence, our estimations do not entail a comparison of the
performance against non-European counterparts. Based on the findings here presented,
it appears unlikely that the change in the regulation of the EU ETS led to a performance
decline compared to non-European competitors. However, further research to delve deeper
into this issue is recommended. Our study leaves open several other key areas of inquiry,
particularly on the technological and organizational changes that allowed a reduction of
emissions without losses in performance and on the impact of EUA prices in inducing
emission reductions. On this latter point, in the current analysis we used quantities of EUA
to measure economic incentives induced by the tighter regulation of Phase 3. An even
better measure would have been the cost of the purchased EUA, but endogeneity concerns
prevented its utilization. Several aspects of the EU ETS regulation implemented in Phase 4
(and currently under discussion) will be affecting companies’ decisions by raising the price
of EUA (e.g., the Market Stability Reserve). Hence, we strongly believe that further research
focused on evaluating the impact of EUA prices using sources of exogenous variation
would provide key insights for future scientific and policy debate.
Author Contributions:
M.B. and D.G.d. contributed equally to the manuscript. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data on emissions and EUA have been scraped from the EUTL registry
using the code in this repository https://github.com/dgdi/EUTL_OHA_data (accessed on 30 March
2023 . The rest of the data is freely available on the EUROSTAT database website https://ec.europa.
eu/eurostat/data/database (accessed on 30 March 2023) and on the Agri4Cast database https:
//agri4cast.jrc.ec.europa.eu/dataportal/ (accessed on 30 March 2023) .
Acknowledgments:
This paper builds on a preliminary analysis that we developed in the context
of a CIFREL study for the EU Parliament [
33
]. The authors would like to extend their sincerest
gratitude to M. Buso, R. Caruso, L. Gerotto, R. Levaggi, L. Rizzo, R. Secomandi, and G. Turati for
their valuable comments and discussions on an earlier version of this draft. The authors would also
like to acknowledge the valuable insight provided by D. Cipullo, T. Colussi, and L. Salvadori on the
policy evaluation design.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2023,15, 6394 15 of 21
Abbreviations
The following abbreviations are used in this manuscript:
AA Freely allocated allowances
DD Difference-in-difference
DDD Triple difference
ES Event study
EU ETS European Union Emissions Trading System
EUTL European Union Transaction Log
EUA European Union Allowances
PEUA Purchased EUA
SA Surrendered allowances
VE Verified emissions
Appendix A. Data
Panel A of Figure A1 shows the sudden and dramatic increase in the weight of
observations reporting VE and SA but missing AA at the onset of Phase 3. The dynamic
of this increase can be decomposed into three parts: (i) a significant rise in the number of
installations no longer reporting AA (panel B), from about 50 in the 2008–2012 period to
around 800 in the 2013–2020 window; (ii) a pronounced increase (more than three-fold) in
the average values of VE and SA of the observations (panel C), pointing to a shift towards
high-emitting installations; (iii) a decreasing trend in both VE and SA levels, as illustrated
in panel D.
Figure A1.
Observations with available VE and SA but missing AA during Phases 2 and 3—before im-
putation. (
A
) Affected observations as a share of VE and SA; (
B
) Number of affected installations; (
C
)
Average size of affected observations; (D) Dynamic of total VE and SA.
The dynamic of VE and SA of the affected installations, those that in at least one year
report both VE and SA but lack AA, is plotted in panels A and B of Figure A2. In panels C
and D of the same figure we illustrate the dynamic of VE and SA of installations subject
to imputation.
Sustainability 2023,15, 6394 16 of 21
Figure A2.
Observations with available VE and SA but missing AA by Nace category during Phases
2 and 3. (
A
) Affected observations as a share of VE by Nace category; (
B
) Affected observations as
a share of SA by Nace category; (
C
) Affected observations as a share of VE by Nace category and
imputation status; (
D
) Affected observations as a share of SA by Nace category and imputation status.
Figure A3 replicates Figure A1 after the imputation.
An additional imputation procedure for the EU registry data was implemented to
exploit the information contained in the “Compliance_Code” variable. When the value
of “Compliance_Code” is “A” the installation is compliant in that year, meaning that
the SA covers the VE. For any observation where either SA or VE is missing and “Com-
pliance_Code” is “A”, the missing value is imputed based on the available value. As a
robustness check, we also performed the analysis without this imputation step, the impact
on results is negligible.
Figure A3.
Observations with available VE and SA but missing AA during Phases 2 and 3—after im-
putation. (
A
) Affected observations as a share of VE and SA; (
B
) Number of affected installations; (
C
)
Average size of affected observations; (D) Dynamic of total VE and SA.
The energy data (from the energy balances Eurostat dataset) are imputed to Nace sec-
tors following the Energy Balance Guide [
34
], the Manual for Air Emissions Accounts [
35
],
and the Validation rules for Air Emissions Accounts [
36
]. To validate our findings, we also
devised an alternative, less-conservative specification of the energy variable by following
the International Recommendations for Energy Statistics [
37
]. Specifically, we employed
a residual imputation technique to address any gap in our energy data. Our results are
robust to the imputation method (imputation tables and the associated results are available
upon request).
Sustainability 2023,15, 6394 17 of 21
Below we compare the dynamic of VE, SA and AA during Phases 2 and 3 for our
cleaned sample (panel A, the plot is the same as the one in Figure 1) and the whole
Union Registry dataset (panel B). While the whole dataset is more comprehensive, cov-
ering approximately 25% more emissions/units, the qualitative dynamics of the two are
remarkably similar.
Figure A4.
Dynamic of total verified emissions, surrendered allowances, (freely) allocated allowances
and purchased EUA during Phases 2 and 3 of the EU ETS for the cleaned dataset used in our analysis
(panel A) and the whole EU ETS dataset (panel B).
Appendix B. Policy Evaluation
Here we report the table with ES coefficients for all the dependent variables considered.
Table A1.
Event study coefficients—verified emissions (A), output (B), capital productivity (C), and
labour productivity (D).
Verified Emissions Output Capital Productivity Labour Productivity
(A) (B) (C) (D)
Treatment ×2008 0.0216 −19.75 −1.61 ×10−9** −3.39 ×10−8
(0.0832) (231.3) (7.56 ×10−10 ) (3.03 ×10−8)
Treatment ×2009 −0.1777 * 629.5 ** −1.75 ×10−9−9.25 ×10−9
(0.0889) (262.2) (1.16 ×10−9) (1.35 ×10−8)
Treatment ×2010 −0.0326 526.9 *** −3.14 ×10−10 1.04 ×10−8
(0.0454) (124.9) (7.72 ×10−10 ) (1.02 ×10−8)
Treatment ×2011 −0.0766 *** −8.188 −1.69 ×10−10 6.83 ×10−10
(0.0112) (51.27) (4.32 ×10−10 ) (9.52 ×10−9)
Treatment ×2013 0.0557 −95.26 ** −3.49 ×10−11 −1.57 ×10−8
(0.0448) (38.14) (2.03 ×10−10 ) (1.53 ×10−8)
Treatment ×2014 −0.1587 *** −164.9 ** −5.07 ×10−11 −1.32 ×10−8
(0.0466) (77.74) (3.5 ×10−10 ) (9.87 ×10−9)
Treatment ×2015 −0.2915 *** −83.95 1.17 ×10−9−3.48 ×10−8*
(0.0839) (142.3) (1.56 ×10−9) (1.93 ×10−8)
Treatment ×2016 −0.3439 ** −28.27 1.41 ×10−9−2.66 ×10−8
(0.1606) (181.5) (1.9 ×10−9) (2.1 ×10−8)
Treatment ×2017 −0.5021 *** −47.65 2.32 ×10−9−1.11 ×10−8
(0.1474) (236.9) (2.31 ×10−9) (3 ×10−8)
Treatment ×2018 −0.5758 *** −79.70 2.75 ×10−92.83 ×10−9
(0.1608) (273.9) (2.37 ×10−9) (2.76 ×10−8)
Treatment ×2019 −1.069 *** 15.05 2.49 ×10−92.46 ×10−9
(0.1359) (348.3) (2.61 ×10−9) (2.89 ×10−8)
Treatment ×2020 −1.367 *** 289.0 2.28 ×10−96.77 ×10−9
(0.1553) (311.8) (1.94 ×10−9) (3.24 ×10−8)
Xi,2008 X X X X
Country-Nace FE X X X X
Year FE X X X X
Observations 559 559 559 559
*p< 0.1, ** p< 0.05, *** p< 0.01.
Sustainability 2023,15, 6394 18 of 21
Figure A5 illustrates the ES analysis with specifications including a progressively larger
set of controls. Notably, all models show evidence in line with our preferred specification—
model (5), presented in the main text. Model (2) includes the variables energy and cooling
and heating degree days, model (3) adds environmental taxes net of ETS revenues, taxes on
corporations and government expenditures, model (4) adds labour hourly compensation
and capital and model (5) (the one presented in the main text) also includes intramural
R&D expenditure (GERD and BERD) and total R&D personnel and researchers.
Figure A5.
Event study—specifications with different sets of covariates. Estimated causal impact
of purchased EUA on: verified emissions (
A
), output (
B
), capital productivity (
C
), and labour
productivity (D).
Additionally, the results of the DDD analysis show limited sensitivity to the inclusion
of controls:
Table A2.
Triple difference—specifications with different sets of covariates. Estimated causal impact
of purchased EUA and derogation status on Verified Emissions.
Verified Emissions
(1) (2) (3) (4) (5)
Post ×Derog. 10c 7,006,021.1 *** 4,331,965.5 ** 9,866,287.5 ** 10,956,577.5 ** 14,199,090.8 **
(1,973,301.1) (1,909,609.4) (4,490,879.2) (4,566,324.7) (6,007,911.5)
Post ×Treatment −0.4450 *** −0.5622 *** −0.5702 *** −0.4599 *** −0.4581 ***
(0.0844) (0.1322) (0.1214) (0.1070) (0.0973)
Post ×Treatment ×Derog. 10c 1.353 *** 1.250 *** 1.335 *** 0.9509 *** 1.054 ***
(0.2528) (0.3080) (0.3648) (0.3246) (0.3432)
Xi,2008 X X X X X
Country-Nace FE X X X X X
Year FE X X X X X
Observations 598 598 572 559 559
** p< 0.05, *** p< 0.01.
As a final robustness check, we report in Figures A6–A8 the placebo tests for the event
study where the dependent variable is verified emissions. In particular, we use the event
study specification including all covariates and switch the VE with the covariates one at a
time. Note that when used as the dependent variable, the covariates vary in time (do not
interact with the dummy for the year 2008). As expected, the figures show that
PEU Ai
has
no impact on control variables.
Sustainability 2023,15, 6394 19 of 21
Figure A6. Placebo event study—panel 1.
Figure A7. Placebo event study—panel 2.
Figure A8. Placebo event study—panel 3.
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