Low Carbon Economy, 2011, 2, 71-90
doi:10.4236/lce.2011.22011 Published Online June 2011 (http://www.SciRP.org/journal/lce)
The Impact of European Union Emissions Trading
Scheme (EU ETS) National Allocation Plans (NAP)
on Carbon Markets
Andrew Lepone*, Rizwan T. Rahman, Jin-Young Yang
Finance Discipline, University of Sydney Business School, Sydney, Australia.
Received January 14th, 2011; revised February 11th, 2011; accepted March 9th, 2011.
ABSTRACT
This paper empirically examines the extent to which participants in the carbon market perceive EU ETS NAP and Veri-
fications announcements to possess informational value. The study directs its attention to carbon returns and volatility
movements around official EU ETS PHASE II announcements. Following Mansanet-Bataller and Pardo (2007), we
adapt an event study methodology which caters for the peculiarities of our data, using a Regression and Truncated
Mean Model approach. Further, we source the earliest date a certain announcement is publicly released from both offi-
cial and news sources, and examine both Phase I & II front futures and sole Phase II prices. We find that Phase II an-
nouncements have an influence on both Phase I & II front futures and sole Phase II futures carbon returns. In addition,
we find that the announcements have no significant impact on volatility. Together, the findings suggest a systematic
leakage of information across all types of announcements, consistent with Mansanet-Bataller & Pardo (2007).
Keywords: Emissions Trading, Carbon Futures, Information Asymmetry
1. Introduction
The main rationale for an emissions trading scheme (ETS)
is to achieve cost-effective and economic reductions in
green house gas emissions. Additionally, it provides a
market efficient price for emission units that can be util-
ized by companies for future investment or business
planning purposes. Launched in 2005, the EU ETS now
operates in 30 countries (the 27 EU Member States plus
Iceland, Liechtenstein and Norway)1. It covers CO2 emi-
ssions from installations such as power stations, combus-
tion plants, oil refineries and iron and steel works, as
well as factories making cement, glass, lime, bricks, ce-
ramics, pulp, paper and board. The first two phases have
established the free trading of emission allowances
across the EU, put in place the necessary infrastructure
and developed a dynamic carbon market. Therefore it is
imperative to review the market integrity of the EU ETS
as it is central to the pricing and the cost-effective, eco-
nomic abatement of emissions.
The market for European Union Allowances (EU ETS
carbon credits) is characteristically unique in several
ways, which introduces a high degree of information
asymmetry and uncertainty within the system. First, the
asset itself is a product of legislation, where individual
governments under the supervision of the European
Commission, are responsible for setting emissions caps
and allocating EUAs to firms.2 Therefore the National
Allocation Plans that we examine essentially set the sup-
ply of EUAs, and the Verifications report the demand
during the preceding period and the remaining supply.
Further, because supply and demand in carbon markets
operates within constraints set by the ruling government,
it creates a level of political risk not present in other
markets. Second, a select group of government employees
and firm level auditors are apt to information regarding
caps and yearly net positions in advance of the market,
thereby increasing the likelihood of information leakage
and insider trading.
*This research was funded by the Sydney Futures Exchange unde
r
Corporations Regulation 7.5.88(2). This paper was improved by the
comments of participants of the EFMA 2010 annual meetings.
1Emissions Trading System: Policy, European Commission Climate
Action, 15 Nov 2010,
htt
p
://ec.euro
p
a.eu/clima
/
p
olicies/ets/index
_
en.ht
m
The two major sources of information asymmetry and
2A European Union Allowance (EUA) gives the holder the right to emit
one tonne of carbon dioxide. Each futures contract represents 1,000
EUAs.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 72
uncertainty are derived from the process of setting future
emissions caps based on projected figures and past emis-
sions (the supply constraint), and the yearly verification
of emission through audits. A National Allocation Plan
(NAP), which determines both the total quantity of CO2
allowances available in the Member State and the alloca-
tion made to each installation covered by the Scheme,
has to be submitted 18 months before the start of a Phase;
the European Commission has 3 months to decide upon
approval or rejection. Prior to the National allocation
plan being delivered to the European Commission, the
Member States must each publish a draft for public con-
sultation. It is compulsory that the European Commission
approves the NAP of each country. If it is not the case,
the NAP will be modified until the European Commis-
sion approves it. The procedure makes it difficult to
know in advance the exact date of publication of new
information.3 Figure 1 depicts a timeline of the major
announcements between June 2006 and December 2007,
prior to Phase II.
Currently, the EU ETS covers more than 11,5004 in-
stallations across Europe, that are obliged to hold an
emission permit for their operations, as well as to sur-
render EUAs corresponding to the installation’s CO2
emissions after every year of operations. Each EUA is
equal to one tonne of CO2 and can be freely traded be-
tween the installations covered by the ETS. The installa-
tions are required to surrender emission allowances cor-
responding to their emissions in the previous year before
30 April. For every tonne of emissions that is not covered
by an allowance, a company will have to pay a penalty of
€40 in the first phase and €100 thereafter. Additionally,
around 15 May, the Members States must submit a report
of the verified emission to the European Commission
including all the companies in the country covered by the
European Directive.
Inconsistencies in emissions data from the different
reporting agencies also creates a level of information
asymmetry and uncertainty among market analysts and
diminishes their ability to make accurate assessments of
the market.5 Emissions data published by the European
Environment Agency and the EU transaction log differ
substantially. They are collected according to different
procedures and sector definitions and sometimes by dif-
ferent government bodies. In addition, the allocation and
reporting process for the national allocation plans in
Phase I and Phase II lacked transparency and hence led
to further uncertainty.
Mansanet-Bataller and Pardo (2007) study the effect of
Phase I and Phase II information releases on Phase I
prices during the period October 2004 through May 2007.
They document that returns are significantly higher on
days when the European Commission released additional
information and approved Phase I National Allocation
Plans. Their results also reveal significantly higher re-
turns after the 2005 verifications and significantly lower
returns following 2006 emissions announcements. The
study suggests that differences in the EU ETS being short
Figure 1. Phase II National Allocation Plan Announcements. This Figure shows major Phase II NAP announcements between
2006 and 2008. Two years before the compliance period, NAPs have to be submitted before 30 June to the European Com-
mission. They have to be approved before 31 December of the same year. The Figure highlights that very few nations abided
by the set schedule. In late 2006, the European Commission started infringement proceedings against Austria, Czech Repub-
lic, Denmark, Hungary, Italy and Spain, for failur e to subm it their proposed National Allocation Plans on time.
3Mansanet-Bataller and Pardo (2007).
4Mansanet-Bataller, Tornero, and Mico (2006).
5Joost L.M. Kanen, CARBON TRADING AND PRICES, Market inefficiencies: regulatory effects (Chapter 4).
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets73
or long during the trading period affected the opposite
returns to the verifications data. These results provide
evidence that information regarding Phase I NAPs and
verifications have a material effect on Phase I carbon
prices.
Further, they also examine returns and volatility sur-
rounding the announcement days. The study documents
significant returns preceding Phase I National Allocation
Plan notification, Phase I NAP additional information,
Phase II National Allocation Plan notification, and 2005
verifications announcements. In concert with their find-
ing that volatility is not significantly different following
announcements, their study reveals a systematic leakage
of information preceding EU ETS announcements.
Following Mansanet-Bataller and Pardo (2007), Mi-
clăuş, Lupu, Dumitrescu, and Bobircă (2008) also exam-
ine the effect of EU ETS Phase I & II National Alloca-
tion Plans and Verifications announcements on both spot
and futures prices by testing the AR(1)-GARCH(1,1)
model. The AR-GARCH model in their case presents the
markets’ expectations, and is used to provide forecast
returns in the period around the event. Their methodol-
ogy analyses both the daily differences in the realised
and expected returns as well as the cumulated differences
for the period around the event. Consistent with Man-
sanet-Bataller and Pardo (2007), trends in the cumulated
abnormal returns in their study preceding the event sug-
gest that the information about the event is known by
some part of the market in advance. They also find that
verifications announcements have a greater effect on
market dynamics than NAP announcements.
Similarly, Rotfuß, Conrad, and Rittler (2009) investi-
gate price formation around Phase II EU ETS National
Allocation Plan Approvals by the European Commission.
They develop a model of expectation formation where
agents anticipate the EC’s decision on Phase II NAPs to
account for unexpected information. The paper explicitly
employs market expectations and high-frequency data.
For each member state, they model the conditional mar-
ket expectation with respect to the EC’s decision on the
total number of EUAs in the second NAP. They then
regress the surprise element, the difference between the
expected and the approved number of EUAs in the sec-
ond NAP of the member state, on EUA price differences
in ten-minute intervals following the announcement.
They find that EUA prices react immediately after the
publication of the EC’s decision on second NAPs. In
particular, unexpected cuts lead to price increases and
unexpected over-allocation to price decreases. However,
they report that the adjustment is not instantaneous, but
takes up to six hours after the decision announcement
and conclude that the EU ETS is not fully information-
ally efficient regarding the determination of the size of
the overall cap.
Likewise, Chevailler, Ieplo, and Mercier (2008) ex-
amine the impact of the 2006 emissions verification an-
nouncement on changes in investors’ risk aversion on the
European Carbon Market using options and futures mar-
ket data. They test the hypothesis that strong reversals in
investors’ anticipations occur during the 2006 compli-
ance event, and in addition, that the level of volatility
decreases after the diffusion of information by the EC
which tends to dissipate previously misleading trading
information on this new market. The study empirically
recovers risk aversion adjustments on the period 2006 -
2007 by first estimating the risk-neutral distribution from
option prices, and then the actual distribution from fu-
tures on the European Climate Exchange. Their study
uncovers a shift in the level of risk aversion on the EU
ETS following the publication of the 2006 verified emis-
sions data by the EC on April 30, 2007. Further, they
observe lower levels of volatility for contracts of matur-
ity December 2008 and December 2009 during the time
period after the 2006 compliance event. This latter result
suggests that Phase I verification information has a
strong market effect.
This study analyzes the impact of Phase II National
Allocation Plans announcements on carbon returns dur-
ing the period February 2006 through December 2008,
during which time more than 170 announcements were
released. Following Mansanet-Bataller & Pardo (2007),
two event study approaches are used. The first consists of
estimating the abnormal returns as coefficients of the
dummy variables that correspond to event days in a re-
gression (see Lusk and Schroeder (2002) and Simpson
and Ramchander (2004), among others). The second ap-
proach is the Constant Mean Return model that measures
the abnormal returns from a benchmark period (see
Mann and Dowen (1997) and Tse and Hackard (2006),
among others). In this study, we follow these two ap-
proaches when applying statistical event study method-
ology using daily carbon futures returns. However, in
line with Mansanet-Bataller and Pardo (2007), the un-
scheduled, sporadic and numerous nature of the an-
nouncements affecting a sole price series requires the
need to minimize large surprises during the prediction
period when applying the Constant Mean Return model.
Therefore, the Truncated Mean model is used which is a
modification of the Constant Mean Return model in
which the abnormal returns in the estimation period are
obtained using a truncated mean.
This study differs to that of Mansanet-Bataller and
Pardo (2007) in that we focus mainly on EU ETS Phase
II announcements (National Allocation Plans) and Phase
I verifications on both the front futures (which include
both Phase I & II prices) and the sole Phase II futures

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 74
prices (December 2008 expiry). The study of Phase II
prices and announcements is of greater importance be-
cause under the EU ETS, it is the first Kyoto Protocol
compliant phase of emissions trading. The EU Phase I
emissions trading scheme was initiated as a trial phase to
prepare for Phase II in which real abatement was to occur.
Subsequently, Phase I EUAs were found to be over allo-
cated. Phase II allocations are more restrictive and are
likely to lead to a real reduction and abatement in emis-
sions. As reported on April 1, 2009 by the European
Commission after the release of 2008 verified emissions
data, the second phase ETS was short in 2008 despite the
economic downturn.6
Further, since mid-2006, the majority of EU ETS
trading occurs in the Phase II December 2008 expiry
carbon contract.7 Therefore the study of Phase II announ-
cements and its impact on both the front futures and
Phase II futures returns is likely to yield more robust
conclusions regarding the impact of carbon announce-
ments on carbon returns and volatility. This will provide
further insights into the operation of the EU ETS into the
future, and may highlight regulatory factors which can be
improved upon.
An advancement of this study is that we source the
earliest date on which an official announcement becomes
public by searching through both official and carbon
specific news databases. This is an attempt to address a
limitation in the Mansanet-Bataller and Pardo (2007)
study which does not account for information that be-
comes public before the official announcement date. In-
formation leakage occurred most notably in Phase I when
several member states released their 2005 emissions data
ahead of the European Commission’s official release
date.8
The remainder of the paper is organized as follows.
The next section describes the data, in particular the car-
bon futures price series and announcements used in the
study. This is followed by an analysis of the impact of
the different types of announcements on both returns and
volatility in the next two sections. Finally, the last section
summarizes the findings and concludes the paper.
2. Data
Trading of emission allowance futures contracts is pri-
marily performed through the European Climate Ex-
change (ECX) in the Netherlands. Since the ECX does
not allow spot EUA trading, it uses Powernext spot
prices as a reference for the futures contracts. From 1
February, 2006 to the end of the sample (31 December,
2008), we use the European Climate Exchange (ECX)
nearest Carbon Futures Instrument (CFI) contract for the
front futures analysis and the ECX CFI with December
2008 expiry for sole Phase II price analysis.
Table 1 summarises the price and announcement data
used in previous studies. Although no-arbitrage argu-
ments stipulate that there should not be significant price
differences for EUA futures prices with the same matur-
ity among the different exchanges, the ECX futures price
emerges as the predominantly used price data in previous
academic literature. This is because the ECX accounts
for approximately 87% of the total exchange-based fu-
tures contract transactions in Europe and has the greatest
volume among all carbon markets.9,10 Consistent with
prior studies, we use the ECX futures prices to analyse
the impact of NAP related announcements on carbon
prices as it is the most representative series of EUA
prices. The Carbon Index EEX and ECX Futures Price
plot in Panel C, Figure III of Mansanet-Bataller and
Pardo (2007) appears volatile in nature as a result of the
time period under investigation and the futures contracts
examined. After a price ‘collapse’ in April 2006 due to
the publication of the 2005 verified emissions data by the
EC, the December 2007 futures price asymptotically de-
creased towards zero because of the impossibility to
transfer allowances to the next period. Figure 2 shows
our sample features a more stable price series relative to
that of Mansanet-Bataller and Pardo (2007). This allows
our study to isolate the impact of EU ETS announce-
ments on carbon prices more effectively.
We source the ECX futures contracts data from the
Reuters DataScope Tick History (RDTH) Database pro-
vided by SIRCA, which includes every bid and ask price
submitted each day (together with accurate time stamps).
The underlying asset of the futures contract is 1,000 spot
EUAs, with the most liquid contracts being those with
annual (December) maturities. We use all futures con-
tracts that expire in December of each year between 2006
and 2008. The data correspond to the daily average mid-
point of intraday quotes calculated from every quote up-
date within a day.
Finally, given that carbon prices are not stationary,
they are converted into stationary returns by taking first
logarithm differences. That is, we use continuously com-
pounded returns constructed as

,,,1ctct ct, where
Pc,t is the carbon price at time t. The financial economics
literature provides evidence that most asset prices are
non-stationary. That is, asset prices feature a unit root,
which can cause numerous problems in statistical infe-
lnrPP
6The World Bank, May 2009, State and Trends of the Carbon Market
2009.
7Frino, Kruk, and Lepone (2010) report that the European Climate
Exchange Carbon Financial Instrument (ECX CFI) futures represent
approximately 80 per cent of exchange traded volume.
8Frino, Kruk, and Lepone, 2008, The effects of EUA supply disruptions
on market quality in the European carbon market, Australian Securities
Exchange Market Insights, Edition 26.
9Frino et al., 2010.
10Mansanet-Bataller & Pardo (2007).
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 75
Table 1. Summary of Prior Literature.
Time Period
Exchange Instrument Expiry Announcements Data
Start Date End Date
European Energy Exchange (EEX)Forward 25-Oct-2004 30-Nov-2005
European Climate Exchange (ECX)Futures Nearest Phase I NAPs & Verifications 1-Dec-2005 18-May-2007
European Climate Exchange (ECX)Futures 2007 ExpiriesPhase I &II NAPs, Phase I Verifications 22-Apr-2005 17-Dec-2007
European Climate Exchange (ECX)Futures Dec 08 Phase II NAPs 29-Nov-2006 26-Oct-2007
European Climate Exchange (ECX)Options Dec 08, Dec 091-Oct-2006 23-Nov-2007
European Climate Exchange (ECX)Futures Dec 08, Dec 10
Phase I, 2006 Verifications
1-Oct-2006 23-Nov-2007
This table summarises the price and announcement data used in previous literature. In addition it also shows the time period and instruments examined. The
Exchange column is the exchange on which the instrument of interest is traded. The Instrument column highlights the financial instrument price series exam-
ined while the Expiry column illustrates the contract expiry date. Announcement Data summarises the types of announcements analysed and the Time Period
specifies the sample time period under review.
Figure 2. ECX Futures Prices over Sample Period. Figure 2 shows the ECX CFI futures price progression (Euros/tCO2) be-
tween February 2006 and December 2008 (our sample period).
rence unless an appropriate adjustment is made. The
most commonly used method is to use the returns or
logarithm of asset prices since these variables measure
‘changes’. The literature also shows that most asset price
changes are indeed stationary. Nevertheless, this study
examines the presence of a unit root in the carbon price
series using the Dickey-Fuller method to test for station-
arity of carbon returns and prices during our sample pe-
riod. The null hypothesis for the Dickey-Fuller test is that
the variable of interest has a unit root. Panel A in Table 2
shows that the p-values for carbon prices for both con-
tracts (Phase I/II front futures and Phase II (December
2008 futures)) are greater than 0.01, indicating the pres-
ence of a unit root at the 1% level. In contrast, the
p-values for carbon returns are less than 0.0001, which
implies that carbon returns are stationary. Additionally,
we calculate various statistics of carbon returns in Panel
B of Table 2. Although the Shapiro-Wilk test indicates
that carbon returns are not normally distributed, its im-
pact on our statistical inference is limited since: (1) our
sample size is large and (2) the test statistics used in this
study have properties that ensure asymptotic normality.
Mansanet-Bataller and Pardo (2007) and Chevallier,
Ielpo, and Mercier (2008) both study the impact of
strictly Phase I announcements. Although, Miclăuş, Lupu,
Dumitrescu, and Bobircă (2008) and Rotfuß, Conrad, and
Rittler (2009) also analyse Phase II announcements, our
examination of abnormal returns and volatility on strictly
Phase II futures differentiate our paper from these previ-
ous studies. Additionally, our study extends to the end of
2008, once Phase II was already in progress.
The announcement data is gathered from a combina-
tion of the European Commission official website, the
Community Independent Transaction Log (CITL) web-

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 76
Table 2. Dickey fuller test and statistics of carbon returns.
Panel A:
Augmented Dickey - Fuller Test Statistics for Carbon Prices and Returns
Phase I & II Front Futures
ADF Statistic (Tau) Pr < Tau
Carbon Prices –2.63 0.0871
Carbon Returns –22.71 <0.0001
Phase II (Dec, 2008) Futures
ADF Statistic (Tau) Pr < Tau
Carbon Prices –3 0.036
Carbon Returns –17.13 <0.0001
Panel B:
Descriptive Statistics of Carbon Returns
Phase I & II Front Futures
rc
Mean –0.000775
Median –0.002370
Standard Deviation 0.293817
Skewness 6.450795
Kurtosis 161.5743
Shapiro-Wilk 0.227434
Phase II (Dec, 2008) Futures
rc
Mean –0.000823
Median –0.000819
Standard Deviation 0.027997
Skewness –1.050538
Kurtosis 8.864452
Shapiro-Wilk 0.917092
Panel A of this table shows the results of the Dickey-Fuller test for the carbon prices and returns. The critical values for the rejection of the null hypothesis of
the existence of a unit root are –3.4336, –2.8621 and –2.5671 for 1%, 5%, and 10% significance levels respectively (MacKinnon, 1991). In Panel B the descrip-
tive statistics for carbon returns are shown.
site, and the Point Carbon news archives. From the three
sources we are able to determine the earliest date that
each announcement is made public. This is a clear im-
provement on announcement data from the previous lit-
erature summarized in Table 1, and an attempt to ac-
count for the numerous NAP and verifications informa-
tion leakages prior to official release dates throughout
Phase I and II. There are in total 179 separate announce-
ments on Phase II NAPs and 17 announcements on Phase
I verifications.
The various types of announcements are divided into
two categories: news strictly related to National Alloca-
tion Plans (NAPs) and news related to the Verification of
Emissions (VER). In the first group we have 11 sub-
categories of events: the First Draft of the NAP, Second
Draft of the NAP, Initial Notification of the NAP to the
European Commission, Second Notification of the NAP
to the European Commission, Notification of Additional
NAP Information related to the NAP to the European
Commission, NAP Approval by the European Commis-
sion, NAP Conditional Approval, NAP Amendment,
NAP Amendment Additional Information, NAP Amend-
ment Approval, and Other announcements that relate to
the EU ETS such as administrative changes. In the sec-
ond group, the Verification of Emissions, there are 3
subcategories: verified emissions for the year 2005, veri-
fied emissions for the year 2006, and verified emissions
for the year 2007. All dates on which more than one dif-
ferent type of announcement occurred are eliminated
from the sample for robustness.11
3. Influence of Announcements on Carbon
Returns
If security prices reflect all currently available informa-
tion, then price changes must reflect new information.
Therefore, it is possible to measure the importance of an
11An analysis which includes all the announcements does not produce
results that are qualitatively different. These are available upon request
from the authors.
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets77
event of interest by examining price changes during the
period in which the event occurs. We apply event study
methodology to the return series constructed to examine
carbon return behavior around NAP and Verification
related events. Following Mansanet-Bataller and Pardo
(2007), we use two approaches; a regression method, and
the Constant Mean Adjusted Return model. This study
examines issues similar to those in Mansanet-Bataller
and Pardo (2007) using a different sample period. Hence,
to directly compare our results (for a more recent period)
to those of Mansanet-Bataller and Pardo (2007), the same
method is utilised. In addition, this is a standard event
study method in the finance literature.
3.1. Regression Method
The regression approach involves modelling daily ab-
normal returns as coefficients of dummy variables for the
event period and the returns before and after. The
dummy variables are used to parameterize the effects of
each particular event. An advantage of this approach is
that it takes into account distributional aspects such as
volatility clustering, leptokurtosis or the presence of
ARCH effects. Following the methodology of Mansanet-
Bataller and Pardo (2007), we regress the carbon returns
on non-event related explanatory variables and dummy
variables representing each of the events considered.
Each event variable is equal to one on the announcement
day, zero otherwise.
The non-event related variables include the energy
commodities variables which are used as explanatory
variables of carbon prices. Following Mansanet-Bataller
and Pardo (2007), we select the most representative
prices of oil and natural gas in Europe. To account for
the series of energy variables that better fits the front
futures contract of carbon, we also construct the front
contract for the energy variables. That is, we select the
contract for the energy variables with the closest maturity
to the maturity of the carbon contract considered. All
series data are obtained from the Reuters Database. The
futures contract on WTI Crude Oil is quoted in USD per
barrel, the futures contract on Natural Gas is quoted in
GBP per therm. Both values are converted into Euros
using the daily exchange rate data available from the
European Central Bank.12 As with carbon prices, energy
prices also present a unit root, and are thus converted into
stationary returns by taking first logarithm differences.
The dummy variables are analysed in two ways. In the
first model, we consider the effect of one dummy vari-
able for each type of event described (NAPs and Verifi-
cations). In the second model, we separate the two vari-
ables into 14 dummy regressors (explained in the Release
of information in the EU ETS section) and re-estimate
the regressions. For each type of event, the dummy vari-
ables are constructed with ones on the days of an-
nouncements of its type, and zero otherwise. The regres-
sions are estimated for both the front futures prices and
the sole Phase II prices (December 2008 expiry). All
regressions are estimated by applying the Newey-West
covariance matrix estimator that is consistent with the
presence of heteroskedasticity and autocorrelation. The
results of the regressions are presented in Tables 2 and 3.
Examining the estimated regressions for the Phase I &
II front futures in Table 3, only in the regression with the
dummy variables considered separately are any event
coefficients statistically different from zero (see Model 2
in Panel A). The significant variables include WTI Crude
Oil returns, Notification of Additional NAP Information,
NAP Conditional Approval, and NAP Amendment Ap-
proval. These findings suggest that news related to Phase
II of the EU ETS affects the front futures contracts which
mainly consist of prices from Phase I of the scheme. In
addition, all the significant announcements have negative
coefficients. This may imply that the EUA market de-
duced that Phase I EUAs were over allocated by observ-
ing the restrictive nature of the NAPs for Phase II. These
results are in contrast with that of Mansanet-Bataller and
Pardo (2007) who find that Phase II announcements had
no significant impact on front futures prices during the
period October 2004 through May 2007. A possible ex-
planation is that NAP announcements related to the con-
ditional approval of NAPs and amendments are signifi-
cant, and these announcements usually arise later in the
NAP setting process and are not captured in the sample
period examined by Mansanet-Bataller and Pardo (2007).
Additionally, the coefficients associated with verifica-
tions of emissions for 2006 are marginally significant at
the 10% level, and are negative. This is explained by the
fact that verified emissions were long in 2006. However,
the results differ from that of Mansanet-Bataller and
Pardo (2007) who find that 2005 verifications also have a
significant negative impact on the front futures. On fur-
ther inspection, it is revealed that the initial primary 2005
verifications announcements were eliminated from the
sample because of other confounding announcements on
the same days. Those that remain were late verifications
data from individual smaller countries.
Assessment of the Phase I and II regression results in
Table 3 reveal that the coefficients of determination (R2)
are extremely low, and fail to explain more than 1.2% of
the variation in carbon returns. Regressions estimated on
Phase II (December 2008) returns in Table 4, however,
yield superior coefficients of determination at 9.7% and
10.3%, respectively. Panel A of Table 4 reveals that both
Natural Gas returns and WTI Crude Oil returns are
highly significant at the 1% level, with both having a 12See http://www.ecb.int.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 78
Table 3. Regression model results.
Panel A: Estimates of Model 1 and Model 2 for the Phase I & II Front Futures
Model 1 Model 2
Variable Coefficient t-statistic Coefficient t-statistic
α –0.0108 –1.4001
0.0034 0.3627
rg,t (Natural Gas returns) –0.2559 –1.0226 –0.2593 –0.9969
rc,t (WTI Crude returns) 0.7062 2.4669 0.8024 2.4806
ALL NAPs 0.0629 1.2747
ALL Verifications 0.0083 0.2235
First Draft of the NAP –0.0027 –0.1990
Second Draft of the NAP 0.0054 0.5369
Initial Notification of the NAP –0.0015 –0.1237
Second Notification of the NAP 0.0548 1.2014
Notification of Additional NAP Information –0.0300 –2.1984
NAP Approval –0.0018 –0.0270
NAP Conditional Approval –0.0510 –2.4132
NAP Amendment 0.0076 0.5672
NAP Amendment Additional Information –0.0484 –1.0303
NAP Amendment Approval –0.2091 –2.2038
Verification 2005 0.0050 0.0611
Verification 2006 –0.1054 –1.9440
Verification 2007 –0.0034 –0.2407
Other –0.1356 –1.8906
Panel B: Goodness of Fit Measures
Model 1 Model 2
R2 squared 0.011658
0.008686
R2 - Adjusted 0.006337 –0.013012
Akaike criterion 0.330704 0.365793
Schwarz criterion 0.361569 0.470734
Panel A presents the estimates of Model (1) and Model (2). In Model (1) the repression of CO2 returns has been calculated on energy variables and dummy
variables considered grouped. In Model (2) the regression of CO2 returns has been calculated on energy variables and dummy variables considered separately.
Panel B reports the R2, the Adjusted R2, the Akaike Information Criteria (AIC) and the Schwarz Criteria (SC).
positive effect on carbon returns. Panel B reveals similar
results for the energy variables. A possible reason that
Gas and Oil returns are not significant in explaining car-
bon returns in Phase I and II front futures, but significant
in explaining carbon returns variation in Phase II prices,
is that the trial phase EUAs were over-allocated. For a
fuel switching price to arise, which would make energy
commodities viable explanatory variables of carbon re-
turns, there would have to be a lower supply than de-
mand for EUAs. This provides further support for our
motivation in examining Phase II prices.
Although the in the regressions are very low, it is
only of importance when the predictive power of ex-
planatory variables is of interest. In contrast, the objec-
tive of this study is to examine whether the relations be-
tween the dependent variable and explanatory variables
are significant. In this study, therefore, is not a rele-
vant statistic. Furthermore, low is are common in
2
R
2
R
2
R

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets79
Table 4. Regression Model Results.
Panel A: Estimates of Model 1 and Model 2 for the Phase II (December, 2008) Futures
Model 1 Model 2
Variable Coefficient t-statistic Coefficient t-statistic
α –0.0009 –0.9114 –0.0006 –0.6422
rg,t (Natural Gas returns) 0.0629 2.6924 0.0627 2.8153
rc,t (WTI Crude returns) 0.3581 7.5568 0.3571 7.7499
ALL NAPs 0.0006 0.2464
ALL Verifications –0.0018 –0.0765
First Draft of the NAP 0.0001 0.0216
Second Draft of the NAP –0.0008 –0.5330
Initial Notification of the NAP –0.0045 –1.1531
Second Notification of the NAP –0.0014 –0.2688
Notification of Additional NAP Information –0.0023 –0.5008
NAP Approval 0.0115 0.5126
NAP Conditional Approval 0.0125 2.5178
NAP Amendment 0.0091 3.6979
NAP Amendment Additional Information –0.0005 –0.0425
NAP Amendment Approval –0.0136 –11.4151
Verification 2005 –0.0093 –0.1837
Verification 2006 –0.0138 –1.0521
Verification 2007 0.0056 1.0235
Other 0.0000 0.0010
Panel B: Goodness of Fit Measures
Model 1 Model 2
R2 squared 0.0973 0.1037
R2 - Adjusted 0.0923 0.0838
Akaike criterion –4.4812 –4.4558
Schwarz criterion –4.4500 –4.3498
Panel A presents the estimates of Model (1) and Model (2). In Model (1) the repression of CO2 returns has been calculated on energy variables and dummy
variables considered grouped. In Model (2) the regression of CO2 returns has been calculated on energy variables and dummy variables considered seperately.
Panel B reports the R2, the Adjusted R2, the Akaike Information Criteria (AIC) and the Schwarz Criteria (SC).
the empirical finance literature.
Similar to Table 3, only in the regression with the
dummy variables considered separately are any of the
announcement dummy variable coefficients statistically
different from zero (see Model 2 in Panel A). Both NAP
Conditional Approval and NAP Amendment have a sig-
nificant positive effect on carbon returns, while NAP
Amendment Approval has a highly significant (at the 1%
level) negative effect on carbon returns. This may sug-
gest that on conditional approval by the European Com-
mission or the request for amendments to the submitted
NAP, the market overreacts on average. The subsequent
price reduction on news of the amendment approval cor-
responds to a correction of the market. The results from
Table 4, together with the results from Table 3, suggest
that news concerning NAPs following their NAP condi-
tional approval or requests for amendments to the NAP
by the European Commission are the most significant

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 80
announcements concerning NAPs in Phase II. Examining
the sample announcements data, it is quickly apparent
that a very small minority of NAPs are approved initially,
with most progressing to conditional approvals and re-
quests for amendments. This may explain the findings,
and also suggest that in Phase II, the European Commis-
sion took a more hardline approach to the approval of
NAPs.
Concerning verifications announcements, all the veri-
fications dummy variables are insignificant in explaining
any of the variation in Phase II carbon returns. This result
is expected as the verifications announcements all relate
to Phase I of the EU ETS. In addition, because there is no
inter-phase banking of EUAs between Phase I and Phase
II, these announcements have no bearing on the Phase II
EUA supply or prices.
Overall, the results suggest that carbon returns do react
to Phase II announcements, although their impact is
greater in Phase II futures. However, because of the un-
certain and volatile nature of the market, and inefficien-
cies in its administration, we require an assessment of the
days surrounding an announcement to adequately inter-
pret the results. Furthermore, following McKenzie et al.
(2004), the use of all available data could lead to spuri-
ous inferences when carbon returns do not present a
normal return constant over time.13 Additionally, when
examining regulatory events on the carbon market, the
formal date or the day the information becomes public
may not coincide with the date when the new information
reaches the market. This is due to the high level of in-
formation asymmetry present in the infant stage EU ETS,
as discussed earlier. In this case, the use of the regression
approach may have little power to reject the null hy-
pothesis of no effect on the carbon price. Based on this,
we extend the analysis to include the Truncated Mean
model analysis that allows a broader range of days to be
analyzed.
3.2. Truncated Mean Model
Following Mansanet-Bataller and Pardo (2007), we adopt
the truncated mean model approach, which is a truncated
version of the Constant Mean Return Model (Brown and
Warner, 1985). The abnormal returns are measured as the
difference of the returns in t minus a mean return from
some benchmark of the estimation period. However, the
benchmark return is a truncated average of the estimation
period. That is, to calculate the truncated mean return, we
exclude the largest and smallest 10% of returns during
the estimation period. As we are examining a sole com-
modity (carbon prices) which is affected by a large quan-
tity of closed and sporadic announcements, the objective
is to minimize the effect of large surprises in the estima-
tion period.
Following Mansanet-Bataller and Pardo (2007), we
consider three different scenarios. Panel A of Tables 5
and 6 present results when considering all the an-
nouncements released in the sample period. The results
are grouped in NAPs and Verifications and Table 5 il-
lustrates the results when examining the Phase I & II
front futures, while Table 6 examines Phase II (Decem-
ber 2008) futures. The second scenario considers only the
announcements that do not have another announcement
in the three previous days. These results are presented in
Panel B of Tables 5 and 6. The third scenario is limited
to the announcements where no other announcements are
released in the six days surrounding it. We consider all
three scenarios, each more restrictive than the previous,
to assess whether our results are robust given that sur-
rounding announcements could be leading to confound-
ing findings. For example, suppose that two separate
national allocation plans are announced on consecutive
days, in this case we would not be able to decipher
whether abnormal returns or volatility observed in the
EUAs prior to the second national allocation plan are
because of a leakage of information or whether they were
caused by the first national allocation plan announcement.
For this reason we have considered the three different
scenarios to assess the impact of NAP and verification
announcements prior to and following the announce-
ments. These results are presented in Panel C of Tables 5
and 6. Additionally, we undertake the same analysis by
substituting the returns series by the residual series of the
regression of carbon returns, taking as independent vari-
ables the energy variables from the previous section.14
The results are presented in Panels A, B and C of Table
5 and 6.15
Tables 5 and 6 document that there are many events
with statistically significant differences before the an-
nouncement date. This occurs when we consider the
complete sample (Panel A), and when we consider the
other two scenarios (Panels B and C). Additionally, most
of the announcement days present statistical significance,
suggesting that the new information has an effect on the
price series when it becomes public.
For a more in-depth examination of which type of an-
nouncement is relevant to the market, we undertake the
analysis with the events considered separately. The re-
sults for the most restrictive scenario, the one considering
only the announcements without any other announce-
ment in the six days surrounding it, are presented in Ta-
14The specification of the regression is .
,1,ctgt t
rr   
15We only present the results with the returns (residuals) standardized
with the truncated mean and variance of the estimation period of 10
days. The results of the standardized returns with the truncated mean
and variance of the estimation period of 20 and 30 days are qualita-
tively similar.
13Mansanet-Bataller and Pardo (2007).
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 81
Table 5. Truncated mean model results.
Panel A: All announcements considered.
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 –0.4491 0.0000 –2.3484 0.0000 0.0275 0.7595 –1.2435 0.0000
–2 0.4393 0.0000 –1.4315 0.0000 0.8326 0.0000 –1.5929 0.0000
–1 –0.5132 0.0000 –1.9966 0.0000 –0.5135 0.0000 –2.4266 0.0000
0 1.0002 0.0000 –2.5557 0.0000 0.8751 0.0000 –3.8448 0.0000
1 –2.6907 0.0000 –4.8835 0.0000 –2.4941 0.0000 –6.9974 0.0000
2 0.4405 0.0000 –6.2177 0.0000 0.6439 0.0000 –8.1976 0.0000
3 –0.3941 0.0000 –7.6080 0.0000 –1.3347 0.0000 –9.5946 0.0000
Number 124 11 124 11
Panel B: Announcements without any other announcement 3 days before.
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p–value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 0.2660 0.0967 –0.8824 0.0776 2.0553 0.0000 0.0829 0.8683
–2 –0.0403 0.8011 –2.1632 0.0000 1.7087 0.0000 –3.0198 0.0000
–1 0.1327 0.4071 –2.6841 0.0000 0.0047 0.9766 –2.3527 0.0000
0 1.4412 0.0000 1.8645 0.0002 2.2176 0.0000 0.9269 0.0638
1 –2.3473 0.0000 –17.1697 0.0000 –1.8431 0.0000 –19.5371 0.0000
2 –3.9144 0.0000 –5.5336 0.0000 –4.9059 0.0000 –6.3544 0.0000
3 0.3975 0.0130 0.4278 0.3922 1.0239 0.0000 1.8585 0.0002
Number 39 4 39 4
Panel C: Announcements without any other announcement 3 days on either side.
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 0.7089 0.0060 –0.6654 0.3467 4.8996 0.0000 0.1418 0.8410
–2 0.3144 0.2234 –2.2495 0.0015 3.7597 0.0000 –1.4847 0.0358
–1 –0.4632 0.0728 –3.4980 0.0000 –1.1300 0.0000 –4.1473 0.0000
0 –1.0108 0.0001 0.3189 0.6520 –1.0699 0.0000 0.4499 0.5246
1 –1.4787 0.0000 –2.3920 0.0007 –1.0767 0.0000 –2.2469 0.0015
2 0.0609 0.8135 0.3247 0.6461 –1.0043 0.0001 2.0581 0.0036
3 2.9829 0.0000 –1.3717 0.0524 4.0501 0.0000 0.6343 0.3697
Number 15 2 15 2
In this Table we present the results for portfolio excess returns around NAP and verifications announcements on ECX front futures prices. We provide results
for the day of the announcement, the 3 previous days and the 3 next days. In Panel A we present the results with the complete sample. In Panel B we consider
the announcements days where there has not been an announcement within the 3 previous days. Finally in Panel C we consider the announcements days where
there has not been an announcement within the 6 days round the announcement. The first column in the Table presents the days (“0” is the announcement day).
The next four columns refer to the standardized returns and the last 4 columns to the standardized residuals of the model 1 in the previous Table regression. The
ZRt mean column shows the mean of the portfolio of the standardized returns (residuals) for each of the event groups (NAPs and Verification), and the p-value
column shows the p-value of the test. Number refers to the number of times an announcement of each kind event has been produced.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 82
Table 6. Truncated mean model results.
Panel A: All announcements considered.
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 –0.1838 0.0407 –1.18630.00010.06830.4467 –1.4209 0.0000
–2 –0.5076 0.0000 –1.51640.0000–0.36170.0001 –1.4948 0.0000
–1 –0.0308 0.7320 –1.71130.00000.17780.0478 –2.1793 0.0000
0 –0.5196 0.0000 –1.82740.0000–0.39230.0000 –1.1827 0.0001
1 0.0162 0.8571 –3.33070.0000–0.18420.0403 –2.5033 0.0000
2 0.3954 0.0000 –3.41390.00000.41830.0000 –2.1346 0.0000
3 –0.2579 0.0041 –4.04020.0000–0.32600.0003 –2.6905 0.0000
Number 124 11 124 11
Panel B: Announcements without any other announcement 3 days before.
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 0.6277 0.0001 –0.2550 0.6101 0.7736 0.0000 0.3380 0.4991
–2 –1.2986 0.0000 –2.6091 0.0000 –1.3230 0.0000 –2.9444 0.0000
–1 0.7475 0.0000 –1.6009 0.0014 0.8514 0.0000 –1.8199 0.0003
0 –0.6489 0.0001 1.0215 0.0411 –0.5685 0.0004 0.7575 0.1297
1 1.1793 0.0000 –6.7492 0.0000 0.1853 0.2472 –7.0955 0.0000
2 1.4828 0.0000 –0.0168 0.9732 1.2651 0.0000 0.5334 0.2860
3 0.1567 0.3279 0.5628 0.2603 0.1103 0.4908 1.2869 0.0101
Number 39 4 39 4
Returns Residuals
ALL NAPs ALL Verifications ALL NAPs ALL Verifications
Days ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 0.3820 0.1390 –0.6654 0.3467 0.2439 0.3449 0.1657 0.8148
–2 –2.9883 0.0000 –2.2495 0.0015 –2.8924 0.0000 –2.3902 0.0007
–1 0.5349 0.0383 –3.4980 0.0000 0.5376 0.0373 –4.5007 0.0000
0 –2.0054 0.0000 0.3189 0.6520 –1.4588 0.0000 0.8007 0.2575
1 3.7518 0.0000 –2.3920 0.0007 1.5065 0.0000 –2.5011 0.0004
2 2.4106 0.0000 0.3247 0.6461 1.7142 0.0000 1.8891 0.0075
3 1.5976 0.0000 –1.3717 0.0524 0.6414 0.0130 –0.2258 0.7495
Number 15 2 15 2
In this Table we present the results for portfolio excess returns around NAP and verifications announcements on ECX December 2008 expiry futures prices. We
provide results for the day of the announcement, the 3 previous days and the 3 next days. In Panel A we present the results with the complete sample. In Panel B
we consider the announcements days where there has not been an announcement within the 3 previous days. Finally in Panel C we consider the announcements
days where there has not been an announcement within the 6 days round the announcement. The first column in the Table presents the days (“0” is the an-
nouncement day). The next four columns refer to the standardized returns and the last 4 columns to the standardized residuals of the model 1 in the previous
Table regression. The ZRt mean column shows the mean of the portfolio of the standardized returns (residuals) for each of the event groups (NAPs and Verifi-
cation), and the p-value column shows the p-value of the test. Number refers to the number of times an announcement of each kind event has been produced.
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 83
bles 7 and 8. Examining Panel A of both tables, it is ap-
parent that within the NAP announcements category,
only on the days of the Initial NAP Notification are there
significant positive returns across both the Phase I & II
front futures and the sole Phase II futures. In contrast, the
remaining types of announcements in the NAP category,
such as Additional NAP info, NAP Approval, NAP Con-
ditional Approval, NAP Amendment Additional Info,
and Amendment Approval all exhibit a significant nega-
tive reaction. For Phase II futures, it may reflect that the
market tends to price in a restrictive cap when member
states initially notify the EC of their NAP. Therefore, on
subsequent amendments and conditional approvals, the
market reduces its perceived expectation of a very re-
strictive cap and hence the negative reactions. In addition,
although the Phase II NAPs are more restrictive and will
result in an average cut of nearly 7% below the 2005
emission levels, the inclusion of offsets undermines this
claim. This may be another reason for the negative reac-
tions to the majority of Phase II NAP announcements.
Reviewing the reactions on the days surrounding Veri-
fications announcements (2005 and 2007), we observe
that they fail to cause a significant reaction on the day of
the announcement.16 However, there are significant price
movements leading up to the announcement day. This
confounding discovery may suggest that there is consid-
erable leakage of verifications data before the informa-
tion becomes public, and that the information is already
impounded into prices. These findings lend further cre-
dence to the allegations of a high degree of information
asymmetry and possible insider trading concerning EU
ETS official announcements. The leakage of information
is further pronounced when considering the most restric-
tive scenario in which there are no other announcements
in the 6 days surrounding the announcement of interest.
In many cases, the significant price reaction leading up to
an announcement is also in the same direction. This
again suggests the existence of information leakage.
Panel B of Table s 7 and 8 present the results when the
residual series are considered, and confirm the finding
that the market reacts before (or on) the day of the offi-
cial announcement. Several other announcements such as
Initial NAP Notification, Additional NAP Info and NAP
Amendment Additional Info also lead to significant reac-
tions beyond t = 0 in Phase I and II front futures, while
Additional NAP Info, NAP Amendment Additional Info,
and NAP Amendment Approval all cause significant
reactions beyond t = 0 in the sole Phase II futures. This
may suggest that there is uncertainty following informa-
tion releases in the EUA market, and that it requires sev-
eral days to resolve the uncertainty and accurately price
in the information.
4. Influence of Announcements on Carbon
Volatility
This section reviews the impact of Phase II NAPs and
Phase I Verifications announcements on carbon return
volatility. This allows an examination of whether there is
a systematic leakage of information. As the announce-
ments are mainly unscheduled and sporadic, it is ex-
pected that upon becoming public, there will be a higher
degree of volatility as the news is priced in. However, if
there is no change in volatility, it may suggest a system-
atic leakage of information before it becomes public.
To test the difference in volatility before and after the
event, we undertake two tests – the Brown-Forsythe test
and the sign test. Consistent with the previous section,
we use both the return series and the residual series of the
regression.
4.1. Brown and Forsythe Test
The Brown-Forsythe test allows testing for seasonality in
the unconditional variance. Following Mansanet-Bataller
and Pardo (2007), applying this test to the peculiarities of
the sample is coherent with the idea of minimising the
effects of large surprises in the estimation period. Spe-
cifically, we consider a prediction period of 10 days and
have separated it into two sub-periods, both of 5 days.
The first sub-period consists of the 5 days preceding the
announcement and the second sub-period includes the
announcement day and the following 4 days. Therefore,
the division of the prediction period is the announcement
day.
We present the results of the Brown-Forsythe test ap-
plied to the announcement days without any other an-
nouncement on the 6 days around it in Panels A and B of
Tables 9 and 10. This sample is chosen for two reasons.
First, following this criteria we are consistent with the
more restrictive analysis of the impact of the announce-
ments on carbon returns presented in the previous section.
Second, if we apply the test only to announcement days
without any other announcement during the 10-day pre-
diction period, the sample will be drastically reduced.
Additionally, the Brown-Forsythe test uses the mean
absolute deviation from the median, and thus the possible
extreme values provoked by an announcement in the
prediction period will not distort the results.
Focusing on Panel A of Tables 9 and 10, the results
for the Brown-Forsythe test for both the return series and
the residual series are similar. If we consider the vari-
ables grouped in NAPs and Verifications (Panel A), in
both cases the null hypothesis is never rejected. Further-
more, NAP announcements lead to a higher variance
16Verifications for 2006 are not in the analysis because they were
eliminated from the sample as they had other announcements in the 6
days surrounding it.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 84
Table 7. Truncated mean model results: Events separated.
Panel A: Results with the Returns series
First Draft NAP Initial NAP
Notification
Additional
NAP Info NAP Approval
NAP
Conditional
Approval
NAP
Ammendment
Additional Info
Ammendment
Approval
Verification
2005
Verification
2007
Days ZRt
mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean
p
-value
–3 –1.3099 0.0233 –4.6476 0.00005.4844 0.0000 –0.41780.5546–9.92730.00000.14940.8813 0.29110.7709 –3.9905 0.0001 2.65980.0078
–2 –5.5094 0.0000 –7.6383 0.00006.3348 0.0000 0.31300.65802.69930.0069–5.2550 0.0000–1.59200.1114 –1.8214 0.0685 –2.67760.0074
–1 –1.2728 0.0275 –1.1904 0.2339–0.9661 0.0308 1.04380.13993.12700.0018–6.34640.00003.70690.0002 –3.2650 0.0011 –3.73100.0002
0 1.9077 0.0010 6.3023 0.0000–1.5619 0.0005 –2.60320.0002–0.44010.6599–12.7321 0.0000 –2.99050.0028 0.4065 0.6844 0.23130.8171
1 –0.5729 0.3211 –2.8623 0.0042–3.7676 0.0000 0.31300.65800.15250.8788–0.39730.6911 –2.35830.0184 –5.0077 0.0000 0.22370.8230
2 0.7078 0.2202 –0.1149 0.90850.7910 0.0770 0.31300.65800.24600.8057–10.34490.0000 4.83860.0000 –3.0929 0.0020 3.74220.0002
3 0.0617 0.9149 0.4521 0.65123.4524 0.0000 0.31300.6580–0.66450.506420.0081 0.0000 4.25530.0000 –2.6136 0.0090 –0.12970.8968
Num-
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Panel B: Results with the Residuals series
First Draft
NAP
Initial NAP
Notification
Additional
NAP Info NAP Approval
NAP
Conditional
Approval
NAP
Ammendment
Additional Info
Ammendment
Approval
Verification
2005
Verification
2007
Days ZRt
mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean
p
-value
–3 –0.3488 0.5457 –2.7948 0.005217.5665 0.0000 –0.9478 0.1801–9.5762 0.00000.17400.86190.47150.6373 –5.2720 0.0000 5.52640.0000
–2 –2.9767 0.0000 –7.6125 0.000015.7594 0.0000 –0.18320.7955 2.53930.0111 –4.88080.0000–1.14990.2502 1.5998 0.1097 –4.53000.0000
–1 1.3331 0.0209 –4.8699 0.0000–3.3573 0.0000 0.79650.26002.76960.0056–5.41640.00003.56150.0004 –3.8622 0.0001 –4.28880.0000
0 –0.3092 0.5923 7.9124 0.0000–1.4139 0.0016 –2.11120.0028–0.39340.6940–10.73610.0000–2.37920.0174 0.3535 0.7237 0.51070.6096
1 0.4587 0.4269 –3.6964 0.0002–3.4158 0.0000 0.19680.78080.21870.8269–0.45960.6458–1.77210.0764 –2.4007 0.0164 –2.04930.0404
2 –1.2732 0.0274 4.7718 0.0000–2.3690 0.0000 0.22890.74610.04090.9674–8.16470.00004.34360.0000 –1.0331 0.3016 5.01090.0000
3 –0.1923 0.7391 3.6554 0.00035.0136 0.0000 –0.29140.6803–0.61520.5384 16.00830.00003.67360.0002 1.5194 0.1286 –0.39190.6952
Num-
ber 315211111
In this Table we present the results of the test in which the null hypothesis is that the portfolio excess return is equal to zero, for the scenario most restrictive
(considering the announcement day without any other announcement on the six days surrounding it). In our case we perform this test for the day of the an-
nouncement, the 3 previous days and the next 3 days. Panel A (B) present the results for the returns (residuals of the regression of Model 1 in Table II & III)
taking into account exclusively the announcements without any other announcement 3 days before and after it. In all cases the ZR mean column shows the mean
of the portfolio of the standardized returns for each of the events considered, and the p-value column shows the p-value of the test. Number refers to the number
of times an announcement of each type has been produced.
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets85
Table 8. Truncated mean model results: Events separated.
Panel A: Results with the Returns series
First Draft NAPInitial NAP
Notification
Additional
NAP Info NAP ApprovalNAP Condi- tional Approval
NAP Am-
mendment
Additional Info
Ammendment
Approval
Verification
2005
Verification
2007
Days ZRt
mean
p
-valu
e
ZRt
mean p-valueZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean
p
-value
–3 –3.5919 0.0000 –5.5658 0.00001.4548 0.0011 7.37460.0000–5.01730.00002.44920.01434.58320.0000 –3.9905 0.0001 2.65980.0078
–2 –19.9123 0.0000 –10.1226 0.0000 1.6944 0.0002 6.75540.00000.89450.37102.9014 0.0037–0.98040.3269 –1.8214 0.0685 –2.67760.0074
–1 –0.7119 0.2176 5.1078 0.00000.2491 0.5776 –0.28190.69026.28840.0000–3.03890.0024–2.81240.0049 –3.2650 0.0011 –3.73100.0002
0 –0.9286 0.1078 5.6758 0.0000–2.3993 0.0000 –6.60010.0000–2.76450.0057–4.31560.0000–1.24850.2119 0.4065 0.6844 0.23130.8171
1 17.7824 0.0000 1.7209 0.08530.5567 0.2132 0.72290.30662.85900.0043–3.78860.0002–1.41000.1585 –5.0077 0.0000 0.22370.8230
2 2.9102 0.0000 –2.0113 0.04433.2430 0.0000 8.66740.00002.43790.01481.71910.08560.79760.4251 –3.0929 0.0020 3.74220.0002
3 –0.2835 0.6234 3.5712 0.00042.7995 0.0000 5.21100.00001.96570.04932.11780.03420.89210.3723 –2.6136 0.0090 –0.12970.8968
Num-
ber 315 2111 11
  
Panel B: Results with the Residuals series
First Draft NAP Initial NAP
Notification
Additional
NAP Info NAP Approval
NAP
Conditional
Approval
NAP
Ammendment
Additional Info
Ammendment
Approval
Verification
2005
Verification
2007
Days ZRt
mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value ZRt mean p-value
–3 –1.5074 0.0090 –4.9478 0.00000.3022 0.4992 5.16630.0000–3.40370.00072.56970.01024.6536 0.0000 –5.5056 0.0000 5.85450.0000
–2 –16.146
1 0.0000 –11.9966 0.00001.0389 0.0202 4.22340.00000.74780.45462.25380.0242–2.47260.0134 –0.3796 0.7042 –4.40880.0000
–1 1.2073 0.0365 2.7830 0.00540.3840 0.3905 –1.08300.12563.98670.0001–2.80400.0050–2.23930.0251 –4.0018 0.0001 –4.99860.0000
0 –1.8183 0.0016 7.4953 0.0000–1.2541 0.0050 –2.60790.0002–1.56150.1184 –4.10120.0000 –3.3857 0.0007 0.7385 0.4602 0.86250.3884
1 5.5842 0.0000 1.4477 0.1477 1.5212 0.0007 0.46980.50642.9453 0.0032–3.24450.0012–2.62650.0086 –4.3270 0.0000 –0.67060.5025
2 1.3295 0.0213 1.5843 0.11312.5341 0.0000 8.14440.00001.52850.1264 1.98000.0477 0.01000.9920 –2.6081 0.0091 6.39220.0000
3 –0.6653 0.2492 7.1540 0.00003.3550 0.0000 2.93510.00002.00520.04492.01990.0434–2.26870.0233 –0.6396 0.5224 0.17010.8650
Nu-
mber 3152111 11
In this Table we present the results of the test in which the null hypothesis is that the portfolio excess return is equal to zero, for the scenario most restrictive
(considering the announcement day without any other announcement on the six days surrounding it). In our case we perform this test for the day of the an-
nouncement, the 3 previous days and the next 3 days. Panel A (B) present the results for the returns (residuals of the regression of Model 1 in Table II & III)
taking into account exclusively the announcements without any other announcement 3 days before and after it. In all cases the ZR mean column shows the mean
of the portfolio of the standardized returns for each of the events considered, and the p-value column shows the p-value of the test. Number refers to the number
of times an announcement of each type has been produced.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 86
Table 9. Equality test results.
Panel A: Brown-Forsythe test for events considered grouped
Returns Residuals
Null Hypothesis Alternative
Hypothesis NAPs VER NAPs VER
σ0 = σ1 σ0 ≠ σ1 0% 0% 0% 0%
σ0 = σ1 σ0 < σ1 0% 0% 0% 0%
σ0 = σ1 σ0 > σ1 0% 0% 0% 0%
Number of announcements = 15 2 15 2
Panel B: Brown-Forsythe test for events considered separated
Returns
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval
Condi-
tional
Approval
Ammendment
Additional Info
Am-
mendment
Approval
VER
2005
VER
2007
σ0 = σ1 σ0 ≠ σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 < σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 > σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
Number of announcements = 3 1 5 2 1 1 1 1 1
Residuals
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval
Condi-
tional
Approval
Ammendment
Additional Info
Am-
mendment
Approval
VER
2005
VER
2007
σ0 = σ1 σ0 ≠ σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 < σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 > σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
Number of announcements = 3 1 5 2 1 1 1 1 1
Panel C: Sign test for the events considered grouped
Returns Residuals
Null Hypothesis Alternative Hypothesis NAPs VER NAPs VER
σ0 = σ1 σ0 > σ1 0.3036 0.2500 0.6964 0.2500
σ0 = σ1 σ0 < σ1 0.8491 1.0000 0.5000 1.0000
Number of announcements = 15 2 15 2
Panel D: Sign test for the events considered separated
Returns
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval
VER
2005
VER
2007
σ0 = σ1 σ0 > σ1 0.5000 0.5000 0.5000 0.75000.5000 1.0000 0.5000 0.50000.5000
σ0 = σ1 σ0 < σ1 0.8750 1.0000 0.8125 0.75001.0000 0.5000 1.0000 1.00001.0000
Number of announcements = 3 1 5 2 1 1 1 1 1
Residuals
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval
VER
2005
VER
2007
σ0 = σ1 σ0 > σ1 0.5000 1.0000 0.8125 0.7500 0.5000 1.0000 0.5000 0.50000.5000
σ0 = σ1 σ0 < σ1 0.8750 0.5000 0.5000 0.7500 1.0000 0.5000 1.0000 1.00001.0000
Number of announcements = 3 1 5 2 1 1 1 1 1
This Table presents the results of two equality tests. Panel A (B) shows the results of the Brown-Forsythe test for the carbon returns and the residuals series
considered grouped (separated). Panel C (D) shows the p-value for the standardized returns and residual series sign test for the variables considered grouped
(separated). In all cases, the null hypothesis is that the variance during the 5 days preceding the announcement day is equal to the variance in the period made up
of the announcement day and the next 4 days. In Panel A and B, the times the null hypothesis is rejected expressed in percentage. The different rows present the
results for the possible alternative hypothesis. The last row shows the total of announcements of each type of event. In order to be consistent with the previous
analysis, the announcement days considered are those without any announcement on the 6 days around it. For both Panel C and D, the series are standardized
with the truncated mean and variance of a period of 10 days.
C
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The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets87
Table 10. Equality test results.
Panel A: Brown-Forsythe test for events considered grouped
ReturnsResiduals
Null HypothesisAlternative HypothesisNAPsVERNAPsVER
σ0 = σ1 σ0 ≠ σ1 0%0%0%0%
σ0 = σ1 σ0 < σ1 7%0%7%0%
σ0 = σ1 σ0 > σ1 0%0%0%0%
Number of announcements =152152
Panel B: Brown-Forsythe test for events considered separated
Returns
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval VER 2005VER 2007
σ0 = σ1 σ0 ≠ σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 < σ1 0% 0% 20% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 > σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
Number of announcements = 3 1 5 2 1 1 1 1 1
Residuals
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval VER 2005VER 2007
σ0 = σ1 σ0 ≠ σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 < σ1 0% 0% 20% 0% 0% 0% 0% 0% 0%
σ0 = σ1 σ0 > σ1 0% 0% 0% 0% 0% 0% 0% 0% 0%
Number of announcements = 3 1 5 2 1 1 1 1 1
Panel C: Sign test for the events considered grouped
ReturnsResiduals
Null HypothesisAlternative HypothesisNAPsVERNAPsVER
σ0 = σ1 σ0 > σ1 0.50000.25000.69640.2500
σ0 = σ1 σ0 < σ1 0.69641.00000.50001.0000
Number of announcements =152152
Panel D: Sign test for the events considered separated
Returns
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval VER 2005VER 2007
σ0 = σ1 σ0 > σ1 0.5000 0.5000 0.5000 1.0000 0.5000 1.0000 0.5000 0.5000 0.5000
σ0 = σ1 σ0 < σ1 0.8750 1.0000 0.8125 0.2500 1.0000 0.5000 1.0000 1.0000 1.0000
Number of announcements = 3 1 5 2 1 1 1 1 1
Residuals
Null
Hypothesis
Alternative
Hypothesis
First
Draft
Initial
Notfication
Additional
Info Approval Conditional Approval
Ammendment
Additional Info
Ammendment
Approval VER 2005VER 2007
σ0 = σ1 σ0 > σ1 0.5000 0.5000 0.9688 0.7500 0.5000 1.0000 0.5000 0.5000 0.5000
σ0 = σ1 σ0 < σ1 0.8750 1.0000 0.1875 0.7500 1.0000 0.5000 1.0000 1.0000 1.0000
Number of announcements = 3 1 5 2 1 1 1 1 1
This Table presents the results of two equality tests. Panel A (B) shows the results of the Brown-Forsythe test for the carbon returns and the residuals series
considered grouped (separated). Panel C (D) shows the p-value for the standardized returns and residual series sign test for the variables considered grouped
(separated). In all cases, the null hypothesis is that the variance during the 5 days preceding the announcement day is equal to the variance in the period made up
of the announcement day and the next 4 days. In Panel A and B, the times the null hypothesis is rejected expressed in percentage. The different rows present the
results for the possible alternative hypothesis. The last row shows the total of announcements of each type of event. In order to be consistent with the previous
analysis, the announcement days considered are those without any announcement on the 6 days around it. For both Panel C and D, the series are standardized
with the truncated mean and variance of a period of 10 days.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets 88
after the announcement only in 7% of the cases when
examining Phase II returns.
Panel B of Tables 9 and 10 present results where the
events are considered separately for both Phase I and II
front futures. None of the announcements provoke any
change in carbon variance, when examining both returns
and residuals. For Phase II futures, only Additional NAP
information announcements cause an increase in carbon
variance following the announcement in 20% of the
cases, both for returns and residuals. Finally, in no case is
the null hypothesis rejected when considering the an-
nouncements related to verification of emissions.
Overwhelmingly, the results illustrate that the majority
of announcements cause no statistical difference in vari-
ance following the announcement, which is in concert
with the findings of Mansanet-Bataller and Pardo (2007).
In the isolated case where the variance before and after
the announcement is statistically different, an increase of
the variance is detected after the announcement. This
latter result is in contrast to Chevailler, Ieplo, and
Mercier (2008) who report lower levels of volatility fol-
lowing announcements. Overall, our findings are consis-
tent with the notion that NAP and verification related
announcements do not have a significant effect on carbon
volatility.
4.2. Sign Test of Carbon Variance
Following Mansanet-Bataller and Pardo (2007) and
Milonas (1987), we undertake the equality test of the
variance of the standardized excess returns to completely
assess the equality of the variance before and after an-
nouncements. Consistent with the previous analysis, we
also apply this test to the residual series. Specifically, we
separate the period which comprises of the 5 previous
days to the announcement from the period comprised of
the day of the announcement and the next 4 consecutive
days. We then test the equality of the variances of the
standardized returns explained in the Truncated Mean
Model section with l = 5 between the two sub-periods.
As in the case of the Brown-Forsythe test, and for the
same reasons, we consider the sample period of the an-
nouncements without any other announcements during
the 6 days surrounding it.
The results of the one-sided tests for the events con-
sidered grouped are shown in Panel C of Tables 9 and 10,
and the results of the test for the events considered sepa-
rately are in Panel D.17 In both cases, the p-value is pre-
sented for the two possible alternative hypotheses. As
shown in Panels C and D of Table 9, for all of the events,
the carbon returns present the same variances before and
after the event unanimously (all p-values are larger than
α = 0.05). In the case of the residuals series, it is not pos-
sible to reject the null hypothesis and consequently we
cannot reject the equality of variances of the residual
series before and after the announcement. The results of
the tests are the same for all types of events, independent
of whether we consider the variables grouped together or
separately. These results are consistent with the results
obtained with the previous test, and indicate a statisti-
cally insignificant effect of Phase II EU ETS NAP an-
nouncements on carbon volatility.
5. Conclusions
The EU emissions trading scheme highlights various
design issues related to the efficiency and equity of such
market-based mechanisms. An efficient system would
lead to the equalization of marginal abatement costs
among participants, yielding a unique market price that
acts as a medium-term signal for investors to make cost
estimates of delivering different levels of energy effi-
ciency and the size of emissions abatement. During its
Pilot Phase, the EU ETS failed to provide appropriate
incentives. This investigation sheds light on the effec-
tiveness of central regulatory agencies such as the EC,
national governments and reporting authorities in pro-
viding a fair and equitable platform for the goal of eco-
nomically reducing emissions.
This study is based on the notion that commodity mar-
kets are information driven mechanisms which determine
equilibrium prices. If markets are active, the information
is quickly disseminated among market participants who,
upon trading, determine a fair price. Prices can also re-
flect information which is not publicly announced by a
governmental agency but yet successfully forecasted by
private agents or leaked by insiders as illustrated by our
study.
We find that Phase II NAP announcements have an
effect on both Phase I and II front futures and the sole
Phase II futures contracts. That is, Phase II NAP an-
nouncements act as new information for the Phase I EU
ETS. Phase I verifications announcements, however,
only affect the Phase I and II front futures, which is con-
sistent with the information inherent in Phase I verifica-
tions, and the no banking of allowances between phases
restriction. We also detect significant returns on days
leading up to both NAP and Verifications information
becoming public. Further, we find no significant differ-
ences in the volatility of carbon returns before and after
NAP and Verifications announcements. Consistent with
the findings of Mansanet-Bataller and Pardo (2007) re-
garding Phase I NAP announcements, we find that there
are significant abnormal returns up to 3 days prior to
several Phase II NAP-related events, and that there is an
absence of volatility effects when the information be-
17In this case, the returns and residuals are standardized with the trun-
cated mean and variance of a period of 10 days.

The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on Carbon Markets89
comes public. Together, these findings suggest a system-
atic leakage of information across all types of an-
nouncements.
An equitable system would involve allocating allow-
ances based on uniform criteria that is mostly perceived
as fair and agreed upon by the various stakeholders. In
contrast, the first two trading periods consisted of widely
different national methods for allocating allowances to
installations threatening fair competition in the internal
market and creating a level of political risk not present in
other markets. Our findings support the proposed changes
to Phase III of the scheme by the European Commission
announced in January 200818 , where the allocation of
EUAs is centralized to an EU authority (no more national
allocation plans). Further, this will lead to a greater har-
monization, clarification and refinement of processes and
information dissemination within the system. This will in
effect directly address the constant leakage of emissions
cap data and reduce the high level of information asym-
metry and uncertainty previously inherent in the EUA
markets. These changes are a constructive improvement
for all stakeholders considering the previous request
made by the European Federation of Energy Traders
(EFET, 2006) to the European Commission for carbon
price sensitive information that was “accurate, final and
published in such a way as to be available to all market
participants at the same time”.
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APPENDIX
Augmented Dickey - Fuller Test Statistics
for Energy Variables.
This Table shows the results of the Augmented Dickey -
Fuller test for all the energy series taken into account in
the regression approach (Natural Gas and WTI Crude Oil)
in all cases for both prices and returns. The critical values
for the rejection of the null hypothesis of the existence of
a unit root in the series are –3.4336, –2.8621 and –2.5671
for 1%, 5% and 10% significance levels (MacKinnon,
1991).
Table 1. Results of the Augmented Dickey - Fuller test.
ADF Statistic (Tau) Pr < Tau
Natural Gas Prices –1.05 0.7356
Natural Gas Returns –19.31 <0.0001
WTI Crude Prices –0.92 0.7814
WTI Crude Returns –17.93 <0.0001
Table 2. ECX CFI Contract Specifications’ Regards Andrew.
Contract ECX CFI Futures
Unit of trading 1 lot = 1,000 CO2 EU Allowances (EUAs)
1 EUA = entitlement to emit 1 tonne of CO2 or equivalent
Minimum trade size 1 lot
Quotation Euro (€) and Euro cent (c) per metric tonne
Tick size €0.01 per tonne (€10 per lot)*
Max. price fluctuation No limit
Contract months Monthly - September 2006 to March 2008 (Phase I)
Yearly - December expired 2008 to 2012 (Phase II)
Expiry day Last Monday of contract month
Trading hours 07:00 - 17:00 UK local time
Settlement price
Trade-weighted average during the daily closing period (17:00 - 17:15) with Quoted
Settlement Prices if liquidity is low.
Settlement and delivery
Physically settled. Transfer of EUAs in a national registry three days after last trading
dat (LTD + 3 delivery)
Margin All open contracts marked-to-market daily