an analysis of the eu emissions trading system – …

JOHANNES KEPLER UNIVERSITÄT LINZ Altenberger Straße 69 4040 Linz, Österreich jku.at

Eingereicht von Philipp Mahringer Angefertigt am Institut für Volkswirtschaftslehre Beurteiler / Beurteilerin a.Univ.-Prof. Dr. Franz Hackl März 2021

AN ANALYSIS OF THE EU EMISSIONS TRADING SYSTEM – INSIGHTS FROM THE EUROPEAN UNION TRANSACTION LOG

Diplomarbeit

zur Erlangung des akademischen Grades

Magister der Sozial- und Wirtschaftswissenschaften

im Diplomstudium

Wirtschaftswissenschaften (180)

2

EIDESSTATTLICHE ERKLÄRUNG

Ich erkläre an Eides statt, dass ich die vorliegende Diplomarbeit selbstständig und ohne fremde Hilfe verfasst, andere als die angegebenen Quellen und Hilfsmittel nicht benutzt bzw. die wörtlich oder sinngemäß entnommenen Stellen als solche kenntlich gemacht habe. Die vorliegende Diplomarbeit ist mit dem elektronisch übermittelten Textdokument identisch. Linz, 15.03.2021

Contents

1 Introduction 1

2 The Economics of Emissions Trading 4

2.1 The Economics of TPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 The EU ETS 9

3.1 The History of the EU ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 The Evolution of the ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Core Components of the EU ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3.1 The Allocation of Allowances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3.2 Carbon Leakage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3.3 The Auctioning of Allowances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3.4 The Union Registry and the EUTL . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3.5 Monitoring, Reporting, Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3.6 The NER and the NER 300 Programme . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.7 The Market Stabilty Reserve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 The Future of the EU ETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4 Data 27

4.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1.1 The EUTL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1.2 Aggregated Data compiled by the European Environment Association (EEA) . . . 30

4.1.3 Auxiliary Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.2 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2.1 Coping with the Limitations of the EUTL . . . . . . . . . . . . . . . . . . . . . . . 33

4.2.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5 Discussion and Results 36

5.1 Emissions & Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.1.1 The Impact of Free Allocation on Industry Sectors . . . . . . . . . . . . . . . . . . 41

5.2 The Emissions Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3 Insights from Transaction Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.3.1 Average Daily Transaction Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.3.2 The Monthly Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.3.3 Time and Weekday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.4 Auctioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.5 Austrian Accounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.5.1 An Anatomy of the Austrian Emissions Market . . . . . . . . . . . . . . . . . . . . 72

5.5.2 The Sectoral Distribution of Market Activity . . . . . . . . . . . . . . . . . . . . . 75

5.5.3 National and International Transactions . . . . . . . . . . . . . . . . . . . . . . . . 78

6 Conclusion 83

List of Figures 97

Abstract

With the ratification of the Kyoto Protocol in 1997, the European Union committed itself to an

ambitious environmental policy with the aim of reducing greenhouse gas emissions by 8% compared to

1990 until 2012. In order to meet this goal, new policy options were considered, so that by the beginning

of 2005, the world’s first transnational emissions trading system for CO2 and other greenhouse gases was

initiated. Looking back on a 15-year history, the European Emissions Trading System has undergone three

evolutionary stages, which can be traced using transaction data from the European Union Transaction

Log (EUTL). The core aim of my thesis is to analyze this development on the basis of system-internal and

-external data while evaluating the usability of the EUTL as a data source for empirical research. Since

this transaction-level perspective has received little attention in literature so far, my thesis enables new

perspectives on central aspects of the European Emissions Trading System. Using descriptive statistics,

I relate the results of my own research to recent publications, covering a wide range of aspects from

the development of greenhouse gas emissions, allowance allocation and trading activity since 2005 to an

evaluation of allowance auctions while addressing systemic weaknesses of the ETS and their implications.

Zusammenfassung

Mit der Ratifizierung des 1997 beschlossenen Kyoto-Protokolls hat sich die Europäische Union zu

weitreichenden Maßnahmen mit dem Ziel einer Reduktion der Treibhausgas-Emissionen von 8% in Rela-

tion zum Stand von 1990 bis 2012 verpflichtet. Um diesem Anspruch gerecht zu werden, beschritt man

neue Wege und realisierte mit Beginn des Jahres 2005 das weltweit erste transnationale Emissionshan-

delssystem für CO2 und weitere Treibhausgase. Mittlerweile hat das europäische Emissionshandelssystem

drei Entwicklungsstufen durchlaufen und blickt auf eine 15-jährige Geschichte zurück, die anhand von

Transaktionsdaten des European Union Transaction Log (EUTL) nachvollzogen werden kann. Ziel der

vorliegenden Arbeit ist es, diese Entwicklung auf Basis systeminterner sowie -externer Datensäze nachzu-

vollziehen und gleichzeitig die Nutzbarkeit des EUTL als Grundlage empirischer Forschung zu evaluieren.

Da die Analyse von Transaktions- und Kontendaten in der Literatur bislang wenig Beachtung erfahren

hat, bietet meine Arbeit neue Perspektiven auf zentrale Aspekte des europäischen Emissionshandelssys-

tems. Hierfür setze ich mittels deskriptiver Statistik die Ergebnisse eigener Analysen in Bezug zum

aktuellen Stand der Forschung und spanne einen Bogen von der Entwicklung der Treibhausgasemissio-

nen in Relation zu Allokation und Handelsaktivität unterschiedlicher Branchen über eine Bewertung

des als Alternative zur kostenlosen Zuteilung von Zertifikaten etablierten Auktionssystems, bis hin zu

systemischen Schwächen des Emissionshandels und deren Folgen.

Chapter 1

Introduction

By ratifying the Kyoto protocol in 1997, the EU agreed on reducing its greenhouse gas emissions by 8%

in relation to 1990 levels until 2012, with further, more ambitious goals lying ahead. Announcing what

is referred to as the European Green Deal, the EU committed itself to far-reaching measures intended to

"modernise and transform the economy with the aim of climate neutrality" (European Commission, 2019f,

p.4). These objectives call for an effective environmental policy taking into account the heterogeneity of

the European market while allowing to impose binding limits on emission levels. After a failed attempt

at introducting a carbon tax, the European Union finally agreed on the implementation of the world’s

first large scale, transnational emissions trading sytem, which was set into operation on 1 January of

2005. During the first 15 years of its existence, the EU ETS has evolved both in terms of organization,

size, scope and effectiveness. Having undergone three evolutionary phases from 2005-2007, 2008-2012 and

2013-2020, the system is currently on the verge of entering the fourth development stage from 2021 to

2030.

My thesis aims at tracing this development based on empirical data, focusing primarily on the publicly

available EUTL database, which records and publicizes all transactions issued within the ETS from 2005

onwards with a 3-year delay. Hence, the EUTL or European Union Transaction Log, which complements

the ETS registry responsible for managing emissions trading, grants access to both transaction and

account data, offering new perspectives on the emissions market. However, despite its potential and, most

likely, due to the undeniable shortfalls of the EUTL, researchers have been reluctant towards utilizing

this data source in recent years. Hence, my thesis may contribute to research on emissions trading in the

European Union by presenting new data on several aspects of the ETS. This refers particularly to areas

requiring extensive data manipulation, such as the auctioning of allowances or the sectoral analysis of

transaction volumes and transaction numbers.

CHAPTER 1. INTRODUCTION

Nevertheless, since my thesis is intended to cover a broader range of topics related to the EU ETS,

I employ a number of additional data sources for my analyses. This includes both aggregated data on

annual emissions and allowance allocation compiled by the European Environment Agency, GDP data

from EUROSTAT, allowance price data compiled by the Ember Foundation or auction data obtained

from ICE London and EEX Leipzig. After discussing the economic theory of GHG abatement policies

in general and tradable pollution permits or emissions trading in particular, I desctribe the political

process behind the implementation of the EU ETS, tracing its origins back to the 1990s. Subsequently,

I address the development of emissions trading from 2005 onwards, detailing on the complex set of

measures developed to manage the ETS. This extends to core instruments of the system such as allowance

allocation, the surrendering of allowances, market stability measures and the monitoring, reporting and

verification system. Based primarily on legal documents issued by the European Commission, I establish

the theoretical foundation necessary to gain a thorough understanding of the processes analyzed in the

empirical part. Finally, I discuss the data sources involved in my research, expanding on the process of

gathering and preparing EUTL data while addressing its limitations.

Starting my analysis on the basis of aggregated data by the European Environment Agency, I in-

vestigate the development of verified emissions and allowance allocation from 2005 to 2019 both on an

aggregated and on an industry-level. Proceeding to the issue of overallocation, I employ allowance price

data to investigate the consequences of an accumulating allowance surplus from 2009 onwards. Next, I

discuss general data on the size and development of the European emissions market both in terms of

emissions, account numbers and industry sector, utilizing GDP data to adjust the results for economic

performance. Using EUTL transaction data, I compare the annual transaction volumes and transaction

numbers registered from 2013 to 2016, differentiating by transaction type in order to separate market

transactions from administrative transfers. I extend this analysis to a monthly and daily perspective in

order to identify irregularities or spikes in the dataset. Furthermore, I investigate the distribution of

transaction numbers across the week as well as across the day in order to identify patterns in transaction

data.

Proceeding to the auctioning of EU allowances, I investigate both general metrics such as auction price,

auction volumes and bid-to-cover ratio for both market places appointed by the European Commission.

In order to gain insight into the process behind allowance auctions, I perform a transaction-level analysis

tracing the transfer of allowances from the official EU auctioning account through the market places to

the bidders.

2

CHAPTER 1. INTRODUCTION

My thesis is finalized by an in-depth analysis of certain aspects of emissions trading requiring extensive

data manipulation on a limited subset of the EUTL database. By restricting my focus to transactions

involving Austrian companies, I am able to establish a link between transaction and account data, mak-

ing it possible to distinguish between account types and analyze market activity based on the NACE

classification.

3

Chapter 2

The Economics of Emissions Trading

Faced with the challenge to implement effective measures aimed at reducing greenhouse gas emissions,

policy makers have several options at their disposal. Fundamentally, the instruments available can be

divided into two groups based on the degree of coercion or enforcement involved. On the one hand,

there are voluntary policies aimed at raising awareness, educating the public or motivating firms to

commit to GHG-abatement measures. On the other hand, reaching ambitious climate goals requires less

subtle instruments with a binding character, which, in turn, can be further separated into two categories.

Whereas regulatory instruments or, as they are often referred to, command and control schemes, rely

on imposing mandatory standards such as emission limits or energy efficiency requirements, economic

instruments aim at assigning greenhouse gas emissions a monetary value, holding polluters responsible

for the negative externalities of their actions. This category includes both emissions trading or tradable

permit schemes as well as environmental taxes and subsidy reform. In terms of performance, policy

options can be ranked by both effectiveness and efficiency. While non-binding instruments are typically

ineffective due to their voluntary nature, they prove comparatively resource efficient. Command and

control schemes, on the other hand, offer high effectiveness at low costs, whereas economic instruments

tend to require a higher administrative effort to be comparably effective. In practice, however, the choice

of instruments may rely on a broader range of criteria, taking into account existing policies and regulations

(Görlach et al., 2015a).

Since my thesis is centered entirely around emissions trading as one of the key components of the

European Union’s environmental policy, it is necessary to analyze how this particular instrument com-

pares to the aforementioned alternatives. Since voluntary policies are both uncertain in outcome while

comparatively ineffective, this refers solely to economic as well as to regulatory instruments. One of the

main advantages of emissions trading over a carbon tax is the certainty of outcome guaranteed by the

predefined cap on emissions, which can be flexibly adjusted to meet GHG abatement goals. In addition,

CHAPTER 2. THE ECONOMICS OF EMISSIONS TRADING

ECONOMIC INSTRUMENTS

REGULATORYINSTRUMENTS

(COMMAND & CONTROL)

SUASIVE INSTRUMENTS

VOLUNTARY AGREEMENTS

Tradable Pollution Permit Schemes

Carbon Tax

Emissions Limits

E�ciency Standards

TechnologyStandards

Subsidy Reform

InformationCampaigns

Education

Social recognition for correct behavior

Established at a sectoral level

Often used to avoid strict regulation

No Enforcement

BINDING NON-BINDING

Figure 2.1: Climate policy instruments. Adapted from Görlach, B., Mehling, M., Zelljadt, E., Gillenwater,

M., & Barata, P. (2015a). ETS E-Learning Online Course: Unit 1 - Instrument Choice in Climate Policy:

Theory and Practice (Ecologic Institute, Berlin, Ed.). Retrieved, from https://ec.europa.eu/clima/

policies/ets/ets-summer-university/content/ets-e-learning-online-course.

allowance allocation serves as an instrument to adapt the ETS to the specifics of different industry sec-

tors. Also, unlike a tax, an ETS can be integrated with corresponding international systems. On the

other hand, however, a carbon tax requires less administrative effort and is cheaper to implement, since

it affects all market participants indiscriminately. Also, whereas the emissions cap inherent to an ETS

ensures control over quantitiy, its effect on the allowance price is less significant. In fact, the price, which,

in theory, should reflect the social cost of carbon, is dependent on both the development of the economy

and on the discrepancy between the cap and the actual emissions. Reacting in an anticyclical fashion,

it is expected to rise in periods of economic growth, fueled by increasing emissions. During periods of

recession, however, stagnating demand on the emissions market leads to a decrease in price. This effect

has already been observed in the aftermath of the 2007/2008 financial crisis. Compared to a command

and control scheme, tradable pollution permits offer greater flexibility, enabling emitters to reduce emis-

sions and trade allowances based on their indivdual marginal abatement cost curves. Its market character

also tackles one of the main drawbacks of regulative instruments, mitigating the impact of information

asymmetry between policy makers and market participants (Görlach et al., 2015b).

Whereas these theoretical considerations imply that emissions trading is installed as the only policy

instrument for GHG abatement in the covered sectors, real-world scenarios are commonly more complex.

5

https://ec.europa.eu/clima/policies/ets/ets-summer-university/content/ets-e-learning-online-course


CHAPTER 2. THE ECONOMICS OF EMISSIONS TRADING 2.1. THE ECONOMICS OF TPP

This is especially true for transnational ETS such as the European Union’s approach, which interact

with a multitude of existing national policies. Whereas such overlaps are not necessarily detrimental to

overall GHG abatement, studies on the EU ETS reveal that under certain circumstances, a combination

of complementary policies may lead to undesired effects. In particular, national measures subsidizing

the reduction of GHG emissions in certain industry sectors grant companies a surplus of allowances,

resulting in a lowered allowance price while leading to increasing emission levels in other areas. Hence,

the overall abatement efficiency is compromised in comparison to an isolated perspective treating both

the ETS and complementary policies as independent. This phenomenon, which is commonly referred to

as the waterbed effect, can be attributed to the static nature of the emissions cap and constitutes one of

the most widely discussed aspects of emissions trading in recent publications. From a theoretical point

of view, said detrimental effects can at least be mitigated by adapting the ETS to the policy mix both

on a national and on an EU-wide level (Görlach, 2013; Rosendahl, 2019).

2.1 The Economics of Tradable Pollution Permits

QaggQ3Q2Q1

P*=MACagg

P

MAC 1

MAC 2

MAC 3

MAC agg.

emissionreduction

Figure 2.2: The abatement-based model as an adaption of a general oligopoly.

As fig. 2.2 illustrates, the ideal representation of an emissions market is very similar to a general

oligopoly, in which each market participant has an individual marginal abatement cost or MAC curve.

Following this simplified economic model, the MAC curve across all market participants, which is rep-

resented by the dashed line in the diagram, is obtained by horizontally aggregating the individual MAC

6


functions. We assume that a regulator sets a certain amount of necessary emission reductions Qagg based

on cost benefit considerations. The price for one emissions reduction is then determined by the given level

of Qagg and the aggregated MACagg function. As the sample calculation presented in equation 2.1 indi-

cates, the individual quantity Qi is dependent on MACi, meaning that each market participant reduces

emissions based on their individual MAC curve. Accordingly, firms with high MACs are likely to buy

allowances to fulfill their compliance obligation, whereas firms with low MACs are expected to reduce

emissions, resulting in lower average costs compared to a command and control scheme with identical

emissions abatement.

Assume MAC1 = 9Q1, MAC2 = 6Q2, MAC3 = 3Q3

Then the aggregated MAC curve is Q = P

9 + P

6 + P

3 = 11P

18 or P = 18Q

11If we assume Qagg then P ∗ = 18Qagg

11and, therefore, Q1 = P ∗

9 , Q2 = P ∗

6 , Q3 = P ∗

3

(2.1)

Hanley et al. (2008, pp.130), in turn, propose a different, damage or emission-based perspective to

illustrate this mechanism, the fundamental principle of which is identical to the aforementioned model.

Introducing a cap on emissions, the authors set the supply of allowances equal to MACagg, so that the

equilibrium price P ∗ can be calculated at the intersection of the aggregated marginal benefit function

of emissions and the emission supply function E. Note, that marginal benefits from emissions (MAB)

and marginal cost of emissions reductions can be traced back to the same economic fact – the amount

of income which can be earned with one unit of emissions. As fig.2.3 indicates, the emissions abatement

Qagg is defined as the difference between Ef , which equals P = 0 in fig. 2.2, and the cap on emissions

(E).

7


P*

E Ef

MAC

Σi MABi = MBagg

emissions

NUMBER OF PERMITSEMISSION TARGET

Qagg

permits

Figure 2.3: The emission-based model. Adapted from Hanley, N., Shogren, J. F., & White, B. (2008).

Environmental economics in theory and practice (2nd edition). Basingstoke, Palgrave Macmillan.

8

Chapter 3

The European Union’s Emissions

Trading System

3.1 The History of the EU ETS

Designed as a means of meeting the GHG reduction targets agreed upon in the Kyoto protocol in 1997,

the EU Emissions Trading System or ETS was introduced in 2005 on the basis of a directive of the

European Parliament issued in 2003. Both its theoretical foundation and its implementation were first

discussed in a research paper published by the European Commission in 2000, which details on several

aspects that are crucial to the understanding of the EU ETS: Elaborating on the scope of a potential

European ETS, its participants, the sectors to cover, the level of centralization necessary, or potential

means of allocating allowances, the European Commission’s Green Paper on greenhouse gas emissions

trading within the European Union (European Commission, 2000) provides a theoretical framework for

the political process eventually leading to the implementation of the ETS in 2005. Both Ellerman et al.

(2010) and Skjærseth et al. (2016) give a detailed account of the events and developments that preceded

its creation. Starting their observation in the the early 1990’s, Ellerman et al. (2010, ch.2) state that

the European Commission’s original intent had been to install an EU-wide carbon tax. Nevertheless,

this attempt failed in 1992 due to the EC’s inability to reach a unanimous decision supported by all

member states. While during the Kyoto conference in 1997, the EC had still largely been opposed to the

implementation of a transnational ETS, they embraced the concept only six months later, positioning the

European Union as a global leader in environmental politics. This leadership role was further strengthened

when in 2001, US president George W. Bush decided not to ratify the Kyoto protocol.

CHAPTER 3. THE EU ETS 3.1. THE HISTORY OF THE EU ETS

As Skjærseth et al. (2016, pp.35) point out, the European Commission was still expecting an inter-

national agreement on emissions trading to be reached at the United Nations Framework Convention on

Climate Change (UNFCCC) in Buenos Aires in 1998, when it published its first communication on the

subject titled Climate Change – Towards an EU post-Kyoto strategy (European Commission, 1998). As

agreed at Kyoto, the EU was obliged to cut its greenhouse gas emissions by 8% compared to 1990 until

the end of the first commitment period from 2008 to 2012. In addition, the Kyoto agreement required

the EU to "make demonstrable progress in achieving its commitment by 2005" (Skjærseth & Wettestad,

2016, p.4), highlighting the necessity to develop an effective, community-wide GHG abatement strategy.

Based upon the results of the Vienna European Council in 1998, the European Commission compiled a

second and more elaborate communication titled Preparing for the Implementation of the Kyoto Proto-

col (European Commission, 1999), which was eventually published in 1999. However, both publications

give only a vague indication of a potential, European ETS, which is why, according to Skjærseth et al.

(2016, p.37), the aforementioned Green Paper is to be considered the actual starting point of the emission

trading system’s development phase.

In fact, the Green Paper proposes a cap-and-trade system imposing a centrally defined cap on the

annual emissions of the industry sectors covered rather than a baseline-and-credit system operating on

an installation level. Originally, six industry sectors covering about 45% of CO2 emissions were supposed

to be included in the proposed ETS – electricity& heat production, iron&steel, refining, chemicals, glass,

pottery&building materials as well as paper&printing. With regard to the organizational structure of the

proposed ETS, the Green Paper avoids dogmatism by suggesting several alternative strategies: Both low

and high levels of community harmonization, which translate into grades of member state autonomy, are

considered as viable options. Concerning allowance allocation, however, the paper highlights the technical

superiority of auctioning over the grandfathering approach which has eventually been implemented and

used during phase I&II of the actual EU ETS. Furthermore, the paper considers the possibility of a

voluntary or opt-out system covering only those member states which are willing to participate (European

Commission, 2000).

According to Skjærseth et al. (2016, p.40-46), the next major step towards a European ETS was taken

in 2002 with the publication of the Directive establishing a scheme for greenhouse gas emission allowance

trading within the community and amending council directive 96/61/EC (European Commission, 2002),

or, in short, ETS Directive, which, although based largely on the guidelines formulated in theGreen Paper,

differed from its predecessor in several aspects. For instance, the scope of industry sectors covered was

narrowed down to four activities – energy, production&processing of ferrous metals, mineral industry and

other activities – omitting the chemicals sector. Apart from that, a decentralized approach was favored

with regard to the issuance of allowances – the proposal drafts the implementation of National Action

Plans or NAPs which were used in the actual ETS until 2012. As to allowance allocation, grandfathering

10

CHAPTER 3. THE EU ETS 3.1. THE HISTORY OF THE EU ETS

was determined as the method of choice for phase I of the ETS, in which no legally binding emission caps

would apply.

As Skjærseth et al. (2016, p.103-111) argue, the change of direction reflected in the 2002 proposal

is best explained by the discussion process ensuing the publication of the Green Paper in 2000. In fact,

this period was marked by strong dissent among the EU’s member states concerning several key areas

of the proposed ETS. Whereas there was widespread support for the general concept of implementing

a harmonized system relying on a common allocation method as well as on community-wide monitor-

ing, reporting and verification standards, there was no consensus on both the mandatory nature of the

system and on the allocation strategy. For instance, Germany and the UK as the two most influential

member states, whereas for diferent reasons, favored a voluntary ETS for at least the initial trading

phase. In Germany, the discussion process was influenced by industrial organizations such as the BDI

(Bundesverband Deutscher Industrie) or the VCI (Verband der Chemischen Industrie), which vehemently

opposed the country’s participation in a centralized European ETS. Accordingly, Germany’s negotiating

position, which can be interpreted as a compromise between the BMU (Bundesministerium für Umwelt,

Naturschutz und nukleare Sicherheit) arguing in favor of the proposed ETS Directive and the BMWA

(Bundesministerium für Wirtschaft und Arbeit) taking the opposite stance, was aimed at promoting a

voluntary structure including opt-out options on a national, sectoral or installation level. However, after

extended negotiations lasting until December 2002, Germany was ready to accept the European Union’s

terms and give in to a mandatory ETS. Among the concessions made by the EU in the process, the most

notable is certainly the pooling provision which entered the ETS Directive as Article 28 and enabled

"member states" to "allow operators of installations...to form a pool of installations from the same activ-

ity for...the first five-year period" (European Commission, 2003). The UK, in turn, originally opposed

a centralized European ETS in favor of their own, domestic system, which was initiated as planned in

2002, comprising 34 industrial companies on a voluntary basis. Intended to run for five years until 2007,

the UK ETS also differed from its European counterpart in the inclusion of six GHGs instead of one as

well as in the sectors covered. Eventually, the UK, which had aimed at modelling the EU ETS to its own

approach during the negotiations, had to give in and reconsider their position – however, not without

the EU conceding an opt-out clause for installations on a national level (Skjærseth & Wettestad, 2016,

p.111).

Apart from the aformentioned concessions to Germany and the UK, three other propositions by

member states were accepted by the Environment Council which assembled in December 2002:

• "The possibility of unilateral additions of certain activities and gases from 2008;

• Free of charge allocation of allowances for the first phase and at least 90% free of charge allocation

in the second phase, thereby making the use of auctioning possible for member states who choose

11

CHAPTER 3. THE EU ETS 3.2. THE EVOLUTION OF THE ETS

to do so;

• Penalties to operators of 40 Euros in the first phase and 100 Euros in the second phase for each

excess tonne of carbon dioxide (CO2) emitted and not covered by sufficient allowances"

(Council of the European Union, 2002, pp.6).

On 13 October 2003, the first version of the ETS Directive (European Commission, 2003) was put into

effect, forming the legal basis of the European emissions trading system to be initiated by the beginning

of 2005. It has been amended several times since its publication – to be specific, in 2004, 2008, 2009,

2013, 2014, 2015, 2017 and 2018. The Linking Directive (European Commission, 2004), in turn, was

published in October 2004 and aimed at establishing a "link between the EU Emissions Trading Scheme

and the other two flexible mechanisms born out of the Kyoto Protocol – the JI and the CDM1" (Skjærseth

& Wettestad, 2016, p.45). As Skjærseth et al. (2016, p.45-47) point out, the months before its release

were marked by protests led by both industry and environmental NGOs (ENGOs). Whereas the former

demanded unrestricted transferablility of CERs, the latter feared negative effects on third world countries

as well as a dilution of GHG emission targets. Hence, several of the restrictions proposed by the European

Commission are not reflected in the final version of the ETS Directive: First, the EU-wide cap on CDM

credits was omitted in favor of limits imposed on a national level. Second, the use of CERs became

independent from the start of the Kyoto protocol’s first commitment period launched in 2008, meaning

that external allowances could be employed from 2005 onwards and third, the once permanent exclusion

of nuclear projects was reduced to a temporary ban.

3.2 The Evolution of Emissions Trading in Europe

Eventually, on 1 January 2005, the European Union Emission trading scheme was officially initiated,

covering about 11.500 installations in 25 member countries (Ellerman et al., 2010, ch.1). According

to the ETS handbook (European Commission, 2015b, p.7), the first two years following the system’s

implementation in 2005 were intended as a "pilot phase", the primary objective of which was to create

the infrastructure required for its regular operation. While in its early stages, the ETS had been limited

to carbon dioxide emissions originating from the 25 member states of the EU, its scope was extended over

the years: According to the European Environment Agency (Cludius et al., 2019, pp.25), installations

from Bulgaria and Romania have been covered since the beginning of 2007, while Liechtenstein and1Both Joint Implementation and Clean Development Mechanism are instruments under the Kyoto protocol, which

enable participating countries to substitute domestic GHG abatement with investments in equivalent international projects.

Whereas JI is limited to countries with binding emission limits, CDM aims exclusively at projects in developing countries.

Both mechanisms issue credits – emission reduction units or ERUs for JI projects and certified emission reduction units or

CERs for CDM projects – which can be used in the EU ETS.

12


PHASE I2005-2007

PHASE II2008-2012

PHASE III2013-2020

PHASE IV2021-2030

Assist.-Prof.in Mag.a Dr.in Christine Blanka

GRANDFATHERING BENCHMARKING

NATIONAL ALLOCATION PLANS NATIONAL IMPLEMENTATION MEASURES

CARBON LEAKAGE POLICY

INDEPENDENT NATIONAL REGISTRIES COMMON UNION REGISTRY

EUTL

MRV - MONITORING, REPORTING, VERIFICATION

EUAA - AVIATION ALLOWANCES

MSR - MARKET STABILITY RESERVE

AUCTIONING

Figure 3.1: Development of the EU ETS from 2005 to 2030

Norway joined the ETS in 2008. Iceland and Croatia, in turn, were introduced by the beginning of phase

III in 2013. With regard to the greenhouse gases covered, N2O emissions, which had been included via an

opt-in process by several member states (AT, NL, NO, IT, UK) during phase II, were introduced EU-wide

in 2013. This implied the inclusion of several new activities, among them "the production of nitric and

apidic acid, glyoxal and glyoxilic acid" (2019, pp.25) as well as of perfluorcarbons or PFCs stemming

from the production of aluminium. Extending the scope of the ETS, the European Commission agreed

on including emissions from the aviation sector by the beginning of 2012 (European Commission, 2008).

Designed as a cap-and-trade system, the EU ETS imposes an upper limit on the total emissions

released by all installations. Whereas during phase I&II, said cap was fixed, the European Commission

agreed on a linear decrease of 1.74% p.a. from 2013 onwards. Starting with 2.35 billion tons of CO2 p.a.

in 2005, the cap was lowered to 2.1 billion tons in 2008. For 2013, a base value of 2.084 billion tons was

determined, yielding an annual reduction of 38.3 million tons of CO2. The aviation cap, in turn, has

been fixed to 210 million allowances p.a. during phase III. Whereas throughout phase I&II, the cap on

emissions had been set on a national level, the EU has been in control of its level since 2013 (European

Commission, 2015b, pp.22).

13


Acknowledging that the allocation and surrendering of allowances are among the core activities of a

cap-and-trade system, it is necessary to establish clear guidelines as to how and when these processes are

scheduled. For this purpose, the European Commission has agreed on a set of dates and deadlines which

are mandatory for installation holders participating in emissions trading and national registries alike:

1. Until February 28, allowances for the current trading period are allocated to both stationary in-

stallations and aircraft operators

2. Until 31 March, account holders are obligated to submit the verified emissions for the preceding

period to the national authorities for approval

3. Starting with 1 April, sanctions for accounts and account holders failing to submit their verified

emissions apply. With regard to the punishment of non-compliance, Art. 16 of the ETS Directive

(European Commission, 2020e) grants member states a high degree of autonomy. However, a

penalty of 100 EUR adjusted for inflation from 2013 onwards has been set for each ton of carbon

dioxide equivalent which is incorrectly declared. Discrepancies between the emissions reported for

each installation and the emissions verified by the national authorities are to be accounted for in

the following year.

4. Until 30 April, operators are obliged to surrender the number of allowances corresponding to the

verfied emissions of the last period

5. Starting with 1 May, data on verified emissions as well as on surrendered allowances and compliance

for the previous year is published via the EUTL website.

(Deutsche Emissionshandelsstelle (DEHSt) im Umweltbundesamt, 2017, p.8)

Since the allocation of allowances takes place two months before the deadline for surrendering, it

is possible to use these newly acquired allowances to fulfill the compliance obligation of the previous

period. However, this practice referred to as borrowinng is only possible within the limits of each trading

phase, meaning that allowances acquired in one phase of the ETS cannot be surrendered in the previous.

In a similar fashion, positive account balances may be transferred between trading periods. Since the

transition from phase I to phase II, allowances no longer 7expire, so that market participants may use

allowances from previous trading periods for surrendering in the current period. According to data

published by the EC in 2020 (European Commission, 2020d), the number of allowances banked from

phase II amounts to 1.75 billion. So far, there is no indication that the European Commission is going

to change its position concerning the banking of allowances in phase IV (European Commission, 2015b,

p.133).

14

CHAPTER 3. THE EU ETS 3.3. CORE COMPONENTS OF THE EU ETS

In addition to the specifics of the system already discussed in this section, a broad range of measures

have been implemented, adapted and improved during the first 15 years, the understanding of which is

crucial for the empirical part of my thesis. Fig. 3.1 gives an overview of the milestones in the evolution

of the EU ETS from 2005 to 2020 while providing an outlook on key changes to the system during phase

IV. In the following section, I give a detailed account of the development and the current state of several

key components of the ETS, ranging from allowance allocation and the central registry to the market

stability reserve and the monitoring, reporting and verification system.

3.3 Core Components of the EU ETS

3.3.1 The Allocation of Allowances

The allocation of allowances is one of the core elements of the European Union’s ETS. While throughout

the first years of its existence, free allocation, which is often referred to as grandfathering, had been

the primary means of issuing allowances, auctioning has become the standard allocation method since

the beginning of phase III and is going to gain further importance during the next phase starting in

2021. This development can be traced along the evolution of the ETS Directive, which has been updated

several times since its initial publication in 2003. Accordingly, the allocation policy employed in phase

I&II is based upon Art. 10 of the initial version of the EU ETS Directive (European Commission, 2003),

which states that "Member States shall allocate at least 95% of the allowances free of charge" during a

period from 2005 to 2007 and at least 90% during phase II from 2008 to 2012. According to the ETS

handbook (European Commission, 2015b, p.28), the remaining 5% in phase I and 10% in phase II were

available for auctioning. Nevertheless, this right was scarcely exercised, leading to only 4% of all auctions

being auctioned during phase II. In order to determine the number of allowances allocated to individual

installations, member states were required to submit National Allocation Plans or NAPs detailing both on

the quantity of allowances and on the allocation method employed during a certain period. In accordance

with Art. 9 of the ETS Directive (European Commission, 2003), the national NAPs, which were to be

established at least 18 months before the period they were applied in, were subsequently assessed by the

European Commission on the basis of a set of criteria listed in ANNEX III.

By the beginning of phase III, substantial changes were implemented, the main aim of which was to

reduce the amount of free allocation in favor of auctioning as the primary allocation method. According

to Art. 10a of the 2009 version of the ETS Directive (European Commission, 2009c), the share of free

allocation for installations in the power generation sector has been cut to 0% by 2013, with the exception

of modernization measures meeting the criteria listed in Art. 10c. These are targeted at member states,

which, according to Art. 10c(1), were either

15


• "not directly or indirectly connected to the network interconnected system operated by the Union

for the Coordination of Transmission of Electricity (UCTE)" by 2007,

• connected only "through a single line with a capacity of less than 400 MW" or in which

• "more than 30% of electricity was produced from a single fossil fuel, and the GDP per capita at

market price did not exceed 50% of the average GDP per capita at market price of the Community"

in 2006.

With regard to all remaining industries which are not at risk of carbon leakage2, Art. 10a of the

ETS Directive (European Commission, 2009c) sets a benchmark for free allocation decreasing from 80%

in 2013 to 30% in 2020, which is also referred to as the carbon leakage factor, or CLEF. In fact, the

most radical change implemented by the beginning of phase III is the introduction of a benchmarking

approach to replace the previous allocation method based on grandfathering. According to the ETS

handbook (European Commission, 2015b, p.40), said strategy relies on product-related GHG emission

benchmarks based on the average CO2-efficiency of the top 10% of all installations of each sector rather

than using historical emissions data on an individual level. The authors conclude that unlike grandfather-

ing, benchmarking "allocates allowances based on their production performance instead of their historical

emissions", ensuring that efficient installations are granted a comparative advantage while creating an

incentive for inefficient installations to modernize their production process. Accordingly, a comprehensive

list of 52 products (European Commission, 2011a) covering about 75% of all industrial emissions subject

to the EU ETS has been published by the European Commission for phase III. As the ETS handbook

(European Commission, 2015b, p.103) states, a single installation may produce more than one of the

listed products, which requires the creation of sub-installations in order to calculate the total quantity

of allowances allocated to an applicant. In case a sub-installation is not covered by any of the product

benchmarks listed, three fall-back options have been defined, the first of which is a benchmark for measur-

able heat production using a transfer medium such as water or steam, which is determined as the relation

between emission intensity and net calorific value of natural gas and assumes 90% conversion efficiency in

heat production. The second benchmark, which is based on fuel consumption, also relies on the emission

efficiency of natural gas, whereas the third benchmark is targeted at so-called process emissions, which,

according to the guidelines of the European Commission (European Commission, 2011c, p.8), includes

both "non-CO2 greenhouse gas emissions" and "emissions from the combustion of incompletely oxidised

carbon". In total, the European Commission has published 8 reference guides related to free allocation,

a detailed assessment of which, however, exceeds the scope of this thesis.2Carbon leakage is the presumed tendency of European firms affected by the ETS to shift production to countries with

more lenient environmental standards. For further information, see section 3.3.2.

16


Returning to the standard case, the following formula is used to calculate the amount of free allocation

for an individual installation:

Allocation = Product Benchmark × Historical activity level × CarbonLeakage Exposure Factor

×(Cross-Sectoral Correction Factor OR Linear Reduction Factor)(3.1)

(European Commission, 2015b, p.44)

The second of the eight guidance documents (European Commission, 2011c) contains detailed infor-

mation on all factors of the equation, including the HAL (historical activity level), the CLEF (carbon

leakage exposure factor), the CSCF (cross-sectoral correction factor) and the LRF (linear reduction fac-

tor). The HAL or historical activity level is defined as the median of an installation’s or sub-installation’s

annual activity levels during a baseline period either form 2005 to 2008 or from 2009 to 2010, whereas

the period with the highest activity has to be selected. According to guidance document no.9 (European

Commission, 2011d), which includes an exhaustive list of product types with specific information on

the applicability of free allocation, said activity levels are usually reported in metric tons of production

according to a set of criteria which is not consistent across industries. While installations deemed at

risk of carbon leakage are granted free allowance allocation of up to 100% of the product benchmark,

all sectors which are not included in the current list compiled by the European Commission (European

Commission, 2014a, (13)), receive allowances on the basis of the CLEF. The cross-sectoral correction

factor or CSCF, which is intended to prevent the number of allowances allocated for free from exceeding

the maximum value defined by Art. 10a(5) of the 2009 ETS Directive, applies to all installations, which

are "not identified as ’electricity generator’" (European Commission, 2011b, pp.18). Installations used

for the generation of electricity as well as new entrants, in turn, are subject to the linear reduction factor

or LRF. Both the CSCF and the LRF are compiled annualy by the European Commission on the basis

of the national implementation measures.

3.3.2 Carbon Leakage

The term carbon leakage refers to a firm’s tendency to shift its production to countries outside the EU

in reaction to GHG reduction measures, particularly the EU ETS. According to the ETS handbook

(European Commission, 2015b, p.60-62), these policies result in a competitive disadvantage for firms

in the European Union, which particularly affects energy-intensive industries. While the authors argue

that there is currently no empirical evidence supporting the existence of this phenomenon, the European

Union has been implementing various various measures to tackle carbon leakage and is determined to

sustain these in future evolutions of the ETS. This assessment of carbon leakage is in line with research

by Naegele&Zaklan (2019), who analyze the impact of the ETS on international trade flows to and from

European manufacturing sectors, finding no evidence to support the existence of this effect.

17


Taking a closer look at the evolution of the ETS Directive starting from 2003, it becomes apparent

that the issue of carbon leakage has not been addressed until mid 2013 (European Commission, 2013b).

This is not surprising, considering the fact that the measures proposed to prevent this phenomenon

consist primarily of the prolongation of free allocation for certain industry sectors, which had already

been the preferred allocation method during the first two phases. In order to determine, which sectors

are eligible for these subsidiaries, a two-fold assessment strategy consisting of both a quantitative and

a qualitative analysis has been created. This system, which is going to be replaced by the beginning of

phase four, has been employed to create two lists of industries prone to carbon leakage, with the first one

being applied from 2013 to 2014 and the second one from 2015 to 2020. Currently, the list contains a

total of 245 industry sectors based on the NACE scheme as well as an additional 24 subsectors based on

the CPA- or PRODCOM-classification, which, according to Eurostat’s NACE handbook (Eurostat, 2008,

p.42), serve as extensions to the 4-digit NACE-code (European Commission, 2014a, (13)). Following the

set of criteria defined by the quantitative method, an industry sector is at risk of carbon leakage if:

• "direct and indirect costs induced by the implementation of the directive would increase production

cost, calculated as a proportion of the gross value added, by at least 5% and

• the sector’s trade intensity with non-EU countries (imports and exports) is above 10%" (European

Commission, 2020c).

• In addition, a sector or sub-sector is considered at risk if "the sum of direct and indirect additional

costs is at least 30%" or if

• the non-EU trade intensity is above 30% (European Commission, 2020c).

To complement the quantitative method, the EU has established a framework to assess the eligibility of

industries that do not meet the above requirements. According to Art. 10a(17) of the ETS Directive issued

in 2009 (European Commission, 2009c), the qualitative assessment is based on the following criteria:

• "The extent to which it is possible for installations in the sector to reduce their GHG emissions or

electricity consumption through additional investment

• The current and projected market characteristics of the sector, such as the market concentration,

homogeneity of the product, competitive position relative to non-EU producers and bargaining

power of the sector in the value chain

• Profit margins of the sector as an indicator for the ability to absorb costs and long-run investment

or relocation decisions."

18


In order to complement the aforementioned provisions for carbon leakage and address an issue referred

to as indirect emission costs, Art. 10a(6) of the 2009 ETS Directive (European Commission, 2009c) defines

that member states are allowed to financially compensate electricity-intensive installations for increased

energy prices attributable to the EU ETS. As stated in the Commission’s Official guidelines on state aid

measures in the context of the ETS (European Commission, 2012b, Art. 3.1), these subsidiaries may be

granted directly via national state aid schemes to a limited number of industries. All sectors eligible for

financial compensation are mentioned in an exhaustive list, which has been compiled based on criteria

similar to those applied for measures concerning direct emission costs (European Commission, 2012b,

ANNEX II). In total, this includes 15 industry sectors ranging from "Aluminium Production" to "Mining

of Iron Ores".

Among other changes addressed in the previous chapters, the latest version of the ETS Directive

(European Commission, 2018c) includes a reformed assessment strategy for identifying industries prone

to carbon leakage, which is going to replace the procedures listed in Art. 10a of previous versions in

phase IV. The most notable alteration refers to the quantitative analysis, which is now based on a single

benchmark calculated by multiplying an industry’s "intensity of trade with third countries...by their

emission intensity measured in kg CO2, divided by their Gross Value Added", whereas the trade intensity

is "defined as the ratio between the total value of exports...plus the total value of imports from third

countries and the total market size of the European Economic Area", which, in turn, equals the "annual

turnover plus total imports from third countries" (European Commission, 2018c, Art. 10b(1)).

Risk of Carbon Leakage = Intensity of Trade × Emissions Intensity (kg CO2)Gross Value Added

Intensity of Trade = Total Value of Exports + Total Value of ImportsTotal Market Size of EEA (Annual Turnover+Imports)

In case the result of this calculation exceeds a threshold of 0.2, an industry is eligible for up to 100% free

allocation until 2030. According to Art. 10b(2), industries failing to meet this criterion while yielding

a value of at least 0.15 may qualify for the same subsidiaries based on a qualitative assessment. The

same goes for industries, for which the ratio between emission intensity and gross value added exceeds

1.5. In 2017, the European Commission has initiated a process to reevaluate, which industries are at risk

of carbon leakage (European Commission, 2019e). So far, a preliminary list has been compiled, which is

still pending for adoption (European Commission, 2019a) .

19


3.3.3 The Auctioning of Allowances

While during the first two trading periods of the EU ETS, grandfathering had been the primary method of

allocating allowances, auctioning has been gaining importance since the beginnning of phase III. Starting

with a legal limit of 5% of alllowances to be auctioned in 2005, the cap was raised to 10% in 2008.

However, according to the ETS handbook (European Commission, 2015b, p.28), this option was hardly

ever used in phase I, whereas only 4% of allowances were auctioned between 2008-2012. As stated in

Art. 10 of the 2009 ETS Directive (European Commission, 2009c), "Member states shall auction all

allowances which are not allocated free of charge" from the beginning of phase III onwards in line with

a new Auctioning Regulation (European Commission, 2010), which was last amended in 2019 (European

Commission, 2019c) to reflect changes affecting the fourth trading period of the EU ETS. For the aviation

sector, in turn, a fixed upper limit of 15% of the total allocated volume has been in effect since 2012. For

the fourth trading period to be initiated in 2021, Art. 10(1) of the latest version of the ETS Directive

(European Commission, 2020e) sets a target of 57% of all allowances to be auctioned, whereas a further

2% are intended for the creation of a so-called modernisation fund supporting investment projects in

member states with a per-capita GDP amounting to less than 60% of the EU average in 2013. As to

the distribution of allowances intended for auctioning, Art. 10(2) specifies, that 90% of allowances for

auctioning are allocated to member states in accordance with the share of verified emissions said state

has reported either in 2005 or from 2005-2007. The remaining 10% are reserved for member states with

a comparatively low per-capita GDP, which are listed in ANNEX IIa of the ETS Directive.

However, for the current trading period, slightly different regulations referred to in earlier iterations

of the ETS Directive apply: From 2013 to 2020, 88% instead of 90% of allowances auctioned have been

distributed according to each member state’s share of verified emissions in either 2005 or from 2005 to

2007, whichever yields the highest value. In congruence with the revised directive, 10% are assigned to a

list of member states defined in ANNEX IIa. Further 2% are allocated to member states which reported

an emission reduction of at least 20% from their respective base period specified in the Kyoto protocol

and 2005, which are listed in ANNEX IIb of the 2013 ETS Directive (European Commission, 2013b).

The European Commission’s 2019 Report on the functioning of the European carbon market (2019g,

pp.22) provides an insight into the revenues generated by auctioning during phase III: From 2012 to 2019,

these amounted to EUR 42 billion, 14 billion of which were generated in 2018 alone. Furthermore, about

80% of these revenues have been used for "specified climate and energy related purposes" from 2013 to

2018 in accordance with Art. 10(3) of the ETS Directive, which demands a minimum share of 50% to be

employed for climate-related projects.

20


According to Art. 17 of the Auctioning Regulation (European Commission, 2010), "auctions should be

carried out by means of a single-round, sealed-bid and uniform-price format". This implies that during a

bidding window of at least two hours, registered bidders are able to submit an unlimited number of bids on

lots of 500 allowances. Upon closure, the clearing price at which all allowances in an auction are allocated

is determined as the "price of the bid at which the sum of the volumes bid matches or exceeds the volume

of allowances auctioned". In case the clearing price is lower than the auction reserve price or if the volume

of bids is lower than the number of allowances or lots auctioned, the auction is automatically canceled

(European Commission, 2019c, Art. 7). According to the ETS handbook (European Commission, 2015b,

p.32), both the EEX and the ICE deliver auctioned allowances within one business day as either two-day

spot or five-day futures (European Commission, 2019c, Art. 4). According to Art. 18, access to auctions

is restricted to the following entities: "companies in possession of an operator holding account or aircraft

operating holder account, authorised investment firms, authorised credit institutions and public bodies or

state-owned entities in possession of an OHA or AOHA".

With regard to the practical implementation of the auction process, a common auctioning platform is

nominated for a period of up to five years based upon the guidelines specified in the Joint Procurement

Agreement (European Commission, 2011e). Currently, the European Energy Exchange (EEX) in Leipzig

fills this role for 28 states, with the remaining three – Germany, Poland and the UK – either appointing

the EEX as their opt-out platform, or in case of the UK, using the London-based ICE Futures Europe

instead (European Commission, 2020b). For all states covered by the joint procurement agreement,

weekly auctions are scheduled on Mondays, Tuesdays and Thursdays, whereas German auctions take

place on Fridays. Poland, in turn, holds auctions on a monthly basis, whereas the UK uses a two-week

interval with auctions on Wednesdays (European Commission, 2015b, p.33).

3.3.4 The Union Registry and the EUTL

In the course of a centralization process initiated by the 2009 ETS Directive (European Commission,

2009c), the national registries maintaining the operation of the EU ETS during the first two trading

periods were merged into one common registry under the responsibility of the European Commission.

The Union Registry was created as a centralized system aimed at managing all accounts held by both

natural persons, companies and member states within the ETS while recording transactions with both

european emissions allowances and international CERs and ERUs. In addition, it monitors the allocation

of allowances projected by the national allocation tables as well as the verified emissions both on an

installation and on a national level. As a second line of defense to ensure the integrity of the system,

the EUTL or European Union Transaction Log constantly checks and validates registry data in what is

referred to as the reconciliation process by Art. 103 of the Registry Regulation released in 2013 (European

21


Commission, 2013a), which constitutes the legal basis of both the registry and the EUTL. In addition,

the EUTL serves as a public frontend to the registry. ANNEX XIV of the Registry Regulation details

on the legal requirements for reporting via the EUTL website by providing a comprehensive list of all

information to be made available to the public:

• First, all account data indicated as "displayed on the EUTL public website" in Table I-III of ANNEX

III, including account type, commitment period, account holder name, account holder address,

company registration number, account opening date and account closing date. However, specific

information such as account IDs or account identifiers are only publicised for operator holding

accounts in accordance with table VI-I of ANNEX VI.

• Second, all completed transactions registered by the EUTL with a delay of three years, updated on

a yearly basis on 1 May. This entails information on the transferring and on the acquiring account

involved in a given transaction, their national registries, time and date as well as an identification

code. Information on transactions involving Kyoto units, in turn, is limited.

• Third, data aggregated on a national and EU-wide level, for instance, the national allocation tables

as well as the international credit entitlement tables of all member states or the total number of EU

allowances, ERUs and CERs within the ETS. This also comprises transactions issued in compliance

with the Effort Sharing Decision3.

With the exception of the transaction log and certain other sources, all data is to be updated on a daily

basis.

3.3.5 Monitoring, Reporting, Verification

In order to ensure that operators meet their compliance obligation, the European Commission has created

a system which is referred to as MRV or Monitoring, Reporting and Verification. Based on experience

gained from phase I&II of the ETS, the EU Monitoring and Reporting Regulation or MRR (European

Commission, 2019d) establishes comprehensive guidelines for the verification of emissions. The MRR,

which entered into force by the beginning of phase III in 2013, requires both aircraft and installation op-

erators to submit to an annual compliance cycle involving three national authorities: First, the competent

authority – an agency appointed by each member state, which is not only responsible for the approval

of monitoring plans specifying the monitoring responsibilities of each operator and the issuance of GHG

permits, but also for the inspection and enforcement of the MRV process. In Austria, this function is3The Effort Sharing Decision or ESD (European Commission, 2009b), (European Commission, 2018e) establishes binding

standards for GHG abatement in each member state, including both industry sectors covered by and independent from the

EU ETS.

22


assigned to the Ministry of Sustainability and Tourism. The national accreditation body, in turn, is part

of the Ministry for Digital and Economic Affairs and appoints the verifiers, which, in turn, are responsi-

ble for monitoring the annual emission reports (AER) submitted by operators until March 31 each year.

Currently, three institutions – TÜV SÜD Landesgesellschaft Österreich GmbH, TÜV Austria Services

GmbH and Lloyd’s Register EMEA – are accredited for this purpose in Austria (Bundesministerium für

Digitaliserung und Wirtschaftsstandort, 2019).

In order to participate in the EU ETS, an operator’s first step is to develop a monitoring plan estab-

lishing reproducible and transparent procedures to monitor an installation’s GHG emissions. According

to Chapter 3.3 of the EC’s Guidance document on the Monitoring and Reporting Regulation (European

Commission, 2017), this comprises the following aspects: data collection – whether emissions are calcu-

lated or recorded directly via a CEMS4, measuring procedures, including laboratory analyses, sampling

of materials and fuels as well as calibration of measuring equipment, control procedures, data storage and

constant evaluation of the procedures used. With regard to monitoring, standards differ by an installa-

tion’s average annual emissions. Art. 19(2) of the MRR lists three categories with rising requirements for

data quality: A for installations ≤ 50.000 metric tons of annual carbon dioxide emissions, B for values of

up to 500.000 tons and C for emitters with a total exceeding 500.000 tons. For operators, this classifica-

tion translates into a tier-based system, which defines standards for accuracy, precision and uncertainty.

According to Art. 47 of the MRR, a simplified approach applies for installations with an average emis-

sion of less than 25.000 metric tons per year: member states may supply these smaller emitters with

standardized monitoring plans in order to reduce the administrative burden. Also, lowered standards for

data collection, uncertainty assessment and verification apply.

As soon as the monitoring plan is accepted by the competent authority, a GHG permit is issued,

upon which the operator is obliged to request an Operator Holding Account within 20 working days

(European Commission, 2019d, Art. 17(1)). After the OHA has been set up, the monitoring cycle

starts in accordance with the MRR: By March 31 each year, operators are obliged to submit an annual

emission report (AER) for the preceding year to the competent authority. Prior to submission, the AERs

are verified by one of the aformentioned institutions appointed by the national accreditation body in

accordance with the Accreditation and Verification Regulation 2012/600 (European Commission, 2012a)

which was replaced by Regulation 2018/2067 (European Commission, 2018a) in 2018. In addition to

the annual emission reports, operators are be required to submit so-called improvement reports (IR) if

certain conditions detailed in Art. 69 of the MRR apply. Also, changes to the capacity, activity level and

operation of an installation are to be reported to the competent authority by December 31 in accordance

with Art. 24(1) of Commission Decision 2011/278 (European Commission, 2011a).4continuous emission measurement system

23


3.3.6 The NER and the NER 300 Programme

According to Art. 10a(7) of the 2014 version of the ETS Directive (European Commission, 2009c), five

percent of the EU-wide cap on emissions from 2013 to 2020 has been reserved for free allocation to new

entrants – a term which refers to installations which have either "obtained a greenhouse gas emissions

permit for the first time after 30 June 2011" or to installation which have "had a significant extension after

30 June 2011, only in so far as this extension is concerned" (European Commission, 2009c, Art. 3(h)).

For the period from 2013 to 2020, the European Commission reports a total of 145.8 million allowances

allocated to 996 installations, 568 of which were already in operation before 2011. Another 23.9 million

allowances were still awaiting allocation as of 15 January 2020. These two values combined represent

about 35% of the total volume of 480.2 million reserved for allocation to new entrants. The remaining

65% or 310.5 million allowances will be made available through the Market Stability Reserve in phase IV

(European Commission, 2020a).

In accordance with Art. 10a(8) of the ETS Directive (European Commission, 2009c), another 300

million allowances were directed to a fund referred to as the NER 300 programme, the original objective

of which was "to help stimulate the construction and operation of up to 12 commercial demonstration

projects that aim at the environmentally safe capture and geological storage (CCS) of CO2 as well as

demonstration projects of innovative renewable energy technologies" until 31 December 2015. However,

as the final progress report on the implementation of the NER 300 funding programme (European Com-

mission, 2020f) states, both the programme’s deadline and its limitations were extended, so that in total,

2.1 billion € in funding was awarded to 39 project in 20 member states, 19 of which were still active in

December 2019. Of these 19, one had already been completed, further 9 were in operation and finally, 9

projects were not yet operational.

Funding was organised in two trenches, with the first one in 2012 covering EUR 1.1 billion or 200

million allowances, and the second one initiated in 2014 covering EUR 1 billion or 100 million allowances

plus all allowances not used in the first round (European Commission, 2020i). In order to qualify for

the NER 300 programme, applicants were required to submit to an assessment process administered by

the member states based on eligibility criteria stated in ch.5.1 of the Call for Proposals published by

the European Commission in 2013 (European Commission, 2013b). For instance, the paper presents a

comprehensive list of technology categories eligible for subsidies. Furthermore, different requirements for

renewable energy and CCS projects apply with regard to the innovative nature of the technology used

and the implementation of the project. Also, the NER 300 programme imposes capacity thresholds as

well as deadlines for a project’s entry into operation: Not only were applicants required to obtain permits

in advance, but also they were expected to commence commercial operation by 30 June 2018.

24

CHAPTER 3. THE EU ETS 3.4. THE FUTURE OF THE EU ETS

3.3.7 The Market Stabilty Reserve

In reaction to a surplus of allowances accumulating in connection to the economic crisis since 2009, the

European Commission has implemented measures to counter the steady decline of the allowance price

reaching its all-time low in 2013. By postponing the auctioning of 900 million allowances from 2014-2016

until the end of phase III, the EC managed to reduce the surplus from an initial 2 billion allowances

in 2012, followed by an even higher 2.1 billion in 2013, to about 1.78 billion in 2015, which equals a

reduction of 30% compared to the projected value without intervention (European Commission, 2020g).

The practice of back-loading allowances, which was legitimized in 2014 by an amendment to the Auctioning

Regulation (European Commission, 2014b), has eventually been replaced by a mechanism referred to as

the Market Stability Reserve in January 2019. The MSR, which was established on the basis of a decision

issued in 2015 (European Commission, 2015a), serves two main purposes – to manage and distribute

the existing surplus of allowances on the one hand and to increase the stability of ETS in the event of

economic crises by controlling the supply of allowances on the other hand.

The system operates as follows: If the number of allowances in circulation is higher than 833 million,

the surplus is added to the reserve. Whenever said number is lower than the threshold of 400 million,

allowances from the reserve are distributed. Accordingly, both the allowances withheld from 2014-2016

and all unallocated allowances from 2019 onwards are going to be transferred to the MSR. From 1

September 2020 to 31 August 2021, the total transfer volume will amount to 332,519,000 allowances

(European Commission, 2020d, p.5).

On the basis of Directive 2018/410 (European Commission, 2018b), the mechanism of the MSR for

phase IV was designed to extend this principle: Until 2023, "the percentage of the total number of

allowances in circulation determining the number of allowances put in the reserve if the threshold of 833

million allowances is exceeded is temporarily doubled from 12% to 24%", meaning that over a period of

12 months from 1 September 2020 onwards, at least 200 million allowances are going to be withheld from

auctions. From 2023 onwards, any number of allowances exceeding the previous year’s auction volume

will be invalidated (European Commission, 2020d, p.2).

3.4 The Future of the EU ETS

In order to meet the ambitious goals for mitigating climate change agreed upon in the Paris Agreement

in 2015, the European Union has adopted a long-term strategy to become climate-neutral by 2050. Part

of this strategy is the 2030 climate target plan, the outlines if which were defined in a communication

released in September 2020 (European Commission, 2020d): In relation to earlier attempts envisioning

a 40% cut in GHG emissions compared to 1990 levels by 2030, the EU has raised its ambitions to a

reduction of 55%. As an integral part of this strategy to lower emission levels by promoting renewable

25

CHAPTER 3. THE EU ETS 3.4. THE FUTURE OF THE EU ETS

energy and energy efficiency, the EU ETS is subject to major changes during the fourth trading period

from 2021 to 2030. In the following passage, I summarize the most significant adaptations to the ETS,

some of which have already been discussed in other chapters, in a comprehensive manner:

1. The annual reduction rate of the cap on emissions is going to be increased from the current value

of 1.74% to 2.2% starting with 2021.

2. Free allocation is going to be phased out for certain sectors from an initial 30% 2026 to 0% in 2030.

However, the current carbon leakage policy aimed at protecting vulnerable industries is going to be

maintained throughout phase IV, however under updated guidelines.

3. The market stability reserve, which was put into effect in 2019, is going to undergo further devel-

opment.

• From 2019 to 2023, the number of allowances withheld in the MSR will be doubled to 24% of

the allowances in circulation.

• Starting with 2023, the number of allowances contained in the MSR will be restricted to the

number of allowances auctioned during the previous period, whereas any surplus is automati-

cally invalidated.

• 200 million allowances from the MSR will be reserved for new entrants.

4. Two funds providing subsidies for innovation and modernisation in the power sector and other

energy-intensive industries will be created:

• The modernisation fund will aim at investments in energy efficiency by companies in the power

sector, with a special focus on low GDP member states.

• The innovation fund will succeed the existing NER 300 programme funding renewable energy

and CCS (Carbon Capture and Storage) technology. During phase IV, it is going to provide

the market value of 450 million allowances, which equals a 50% increase compared to its

predecessor (European Commission, 2020h).

26

Chapter 4

Data

4.1 Data Sources

The empirical part of this thesis relies on several independent data sources, not all of which are centered

around the EUTL. Whereas its primary goal is to gain new insight from transaction and account data,

it is nevertheless necessary to make use of auxiliary databases in order to draw a holistic image of the

EU ETS and establish a link between the inside and the outside perspective. In the course of this

section, I discuss the data sources involved in my analyses, detailing on their challenges, potential, and

downsides. Starting with the EUTL database as the most integral component of my research, I discuss

the specifics of aggregated data compiled by the European Environment Agency and end by expanding

on three complementary data sources which are vital in exploring core aspects of the ETS.

4.1.1 The EUTL

According to the ETS handbook (European Commission, 2015b, pp.76), the EUTL or European Union

Transaction Log is a system which "automatically checks, records, and authorises all transactions that take

place between accounts in the Union registry". Established as a successor to the Community Independent

Transaction Log or CIL, which had been in use during phase I and II, it was installed in 2013 and

contains data for all transactions since 2005. Via the publicly available EUTL website, a plethora of

datasets related to the ETS can be accessed, only a fraction of which bears relevance for the research

questions addressed in this thesis. In addition, it provides information on the emission targets specified

by the Effort Sharing Decision as well as on the compliance status and annual balance of each member

state from 2013 to 2020. In connection to the actual ETS, data on the following aspects can be accessed:

• Allowance allocation for phase I, II and III ranging from 2005 to 2020 on a national level including

CHAPTER 4. DATA 4.1. DATA SOURCES

both stationary installations and aircraft operators.

• Allowance allocation, verified emissions, surrendered units and compliance status as well as infor-

mation on the activity type and the holder of each installation covered by the ETS, however limited

to installations active in phase III.

• Data on international credit entitlement for CERs and ERUs originating from CDM or JI projects

on an installation level.

• All transactions within the EU ETS from 2005 to 2017, published with a delay of three years.

In the context of this thesis, I employ four of the datasets available providing information on an installation

level in analogy to research performed by Cludius (2016b)&(2016a):

1. The operator holding accounts or OHA dataset, which is a registry of all installations in the EU

ETS, containing data on operators’ sector, allocated and surrendered allowances, verified emissions

and compliance status. This dataset currently comprises about 16,972 accounts from 31 countries

and is limited to phase III of the EU ETS. Intallations from national registries established since

the beginning of phase I, in turn, are listed in a separate dataset. However, detailed information

on the latter is not publicly available on the EUTL website, so that the OHA dataset remains the

only useful data source.

2. The person holding accounts (PHA) as well as the trading accounts dataset containing information

on all registered accounts which are not related to a physical installation. These are held not

only by banks, brokers or energy trading companies, but also by installation operators using a

separate account for their trading activities. Whereas there are many similarities between the two

account types, they differ in terms of flexibility. Like OHAs, person holding accounts are limited to

interactions with so-called trusted accounts, whereas trading accounts offer unlimited access to the

market. This means that OHAs and PHas are required to submit a list of potential trading partners

to the national registry prior to issuing transactions. However, these distinctions are soon going

to be obsolete, for in accordance with Art. 84 of Regulation 2019/1122 (European Commission,

2019b), all PHAs are to be converted to trading accounts in 2021.

3. The transfer or transaction dataset, which records relevant data of all physical transactions per-

formed within the EU ETS, including information on the parties involved, the transaction date

and time, the transaction volume as well as on the transaction type. This includes international

certificates issued in accordance with the Kyoto protocol such as CERs and ERUs. With regard to

transaction types, the EUTL distinguishes between 9 different categories, each of which is assigned

a numeric identifier:

28


• 1: Issuance or the initial creation of a unit

• 2: Conversion or the transformation of a unit to create an ERU

• 3: External transfer of a unit between national registries, including non-EU countries

• 4: Cancellation or the internal transfer to a cancellation account

• 5: Retirement

• 6: Replacement

• 7: Carry-Over

• 8: Expiry date change

• 10: Internal transfer of a unit between operators

However, three of said transaction types — 6,7 and 8 — have not been employed to a relevant extent

since the creation of the EU ETS and may thus be considered as irrelevant for our analysis. The issuance

(1), the internal transfer (10) and the external transfer (3) of allowances, however, represent the most

commonly used transactions in the EU ETS.

In order to distinguish between account types, the transfer dataset uses three-digit codes referring

to different groups of account holders: According to the the ETS Registry system user guide (European

Commission, 2018d, pp.31), both operator holding accounts, aircraft operator holding accounts, person

holding accounts, trading accounts or accounts from external trading platforms are assigned 100, whereas

121 is reserved for PHAs in national registries which are limited to CERs or ERUs stemming from projects

under the Kyoto protocol. A statistical analysis of the transaction dataset reveals that, for transactions

completed after the 31st of December 2012, 94.4% of all transferring accounts and 95.8% of all acquiring

accounts belonged to the category 100, whereas only 1.4% and 2.5% are attributed to 121. Whereas on

the transferring side, only 4 different types – 100, 110, 120,121 – can be identified, the list of acquiring

accounts contains several types not mentioned in the manual – 210, 230, 250 and 300 – which represent

allowance deletion accounts of minor relevance. Accounts which have been transferred from national

registries until the beginning of phase III, follow a different naming scheme – while PHAs from this

period are denominated as 121, operator holding accounts carry the identification 120. Administrative

accounts, in turn, were already labeled 100 in phase I&II.

In addition to the OHA dataset, the EUTL website provides a plethora of account lists, the majority

of which provide only limited insight: In total, 51 datasets are available in the Accounts menu, ranging

from trading accounts to credit exchange accounts. However, in most cases, said datasets lack crucial

information such as account identifiers, which renders them useless in the context of data manipulation.

For instance, both the person holding accounts as well as the trading accounts list would prove excep-

tionally useful for categorizing individual transactions from the transaction log, if they followed the same

29


structure as the OHA dataset. Since the information available is limited to the account holders, however,

meaning that the only insight to be gained is whether or not a specific company owns PHAs or trading

accounts, I see no use in including these additional datasets in my analysis. Neither the EUTL website

nor literature give any indication as to why this crucial information has been omitted or if there are legal

concerns which might have prevented its publication.

4.1.2 Aggregated Data compiled by the European Environment Association

(EEA)

Second, I rely on aggregated data provided by the European Environment Agency to assess the devel-

opment of free allocation and reported emissions from 2005 to 2019. Available as a comprehensive CSV

file which is freely available online, the EEA dataset contains industry-level data for each member state,

meaning that both the actual emissions and the number of freely allocated allowances for each of the

activities defined by the ETS Directive can be monitored for all trading periods. Hence, data can be

aggregated by both industry, nation and year, enabling comprehensive analyses of allowance allocation

and emission levels over time. In terms of scope, the EEA dataset covers 29 industry sectors or activity

types, including two additional categories offering total values with and without Combustion of Fuels. In

absolute numbers, it contains 57,744 lines of data on all 31 participating countries. Given the complexity

of EUTL database and its issues with data quality, I employ the EEA dataset for areas which do not

require an installation- or transaction-level analysis. In addition, assuming that all data published by

the EEA has undergone a thorough verification process, it serves as a benchmark to gauge the integrity

of the EUTL database.

4.1.3 Auxiliary Datasets

The broad spectrum of subjects covered by my thesis makes it necessary to include additional datasets

which are independent from the EUTL. This involves indicators of economic performance, allowance price

data as well as data on allowance auctions and NACE codes, which I obtain from four independent sources:

First, I employ gross and per-capita GDP data from EUROSTAT for all nations involved with emissions

trading from 2005 to 2019. Second, I use historic spot price data for EU allowances obtained from Ember

Foundation (2020), which is available from 2008 to 2020 in daily intervals. Third, both market places

managing the auctioning of EU allowances – ICE London and EEX Leipzig – offer extensive data on

auction volumes, auction prices and the number of bids for auctions held during phase III. Finally, a

dataset by Jaraite et al. (2013) enables me to link physical installations to corresponding NACE codes.

30

CHAPTER 4. DATA 4.2. DATA PREPARATION

Figure 4.1: The EUTL Web Interface.

4.2 Data Preparation

Whereas both aggregated data from EEA and the complementary datasets require minimum effort for

preparation, the EUTL is less accessible, necessitating a more complex and time consuming process to

extract data. Given the fact that the EUTL web portal does not provide a viable means of exporting

large amounts of data — currently, a restriction of 3,000 lines per download applies — I have used

the commercial software Octoparse to extract HTML tables directly from all search results pages for a

specific query. This process, which is commonly referred to as Webscraping, allowed me to download

and export both the entire transaction log and the OHA dataset at a rate of approximately 400 lines

of data per minute, resulting in a set of CSV files which I subsequently merged and consolidated using

Microsoft Excel. While the extraction of the transaction dataset containing about 990.000 entries from

2005 to 2017 turned out as relatively straightforward, the OHA dataset required me to link separate

tables by programming the software to automatically perform several simulated clicks for each line of

data. Once the CSV files had been compiled, I imported both datasets to SPSS in order to prepare

them for the following statistical analysis. The main objective of this process being to establish a link

between the account identifiers found in the transaction log and their respective counterparts in the OHA

list, I initially attempted to automatically import the variable Main Activity Type into the transaction

dataset using the MERGE command. However, in accordance with (Cludius, 2016a, pp.9), I found that

only a perceived 70-80% of all transactions involving physical installations can be automatically linked

31


to a corresponding activity type, meaning that it is necessary to manually check and correct the output

in order to yield accurate results. Common inconsistencies, which prevent the software from matching

identical account identifiers, include both differences in name as well as mere spelling mistakes.

Taking into account these complications, which necessitate extensive manual adjustments to the

dataset, establishing a link between account and transaction data for all 994,280 entries is a prohibitively

time-consuming task. Hence, limiting the scope to a reduced subset seems a viable option, which is why

I select all transactions involving accounts in the Austrian registry either on the acquiring or on the

transferring side, reducing the size of the dataset to about 21,600 entries, which equals 2.2% of the initial

volume. This extends to account data as well, limiting the number of installations to be linked to 296.

In addition to facilitating the aforementioned process, narrowing the focus enables me to perform a more

in-depth analysis of the sectoral distribution of installations.

Considering that an account’s activity type refers solely to the process by which greenhouse gases

are emitted and often fails to give an indication of the respective installation’s actual industry sector, I

deem it necessary to introduce a different classification scheme. Hence, in order to translate the activity

types specified in ANNEX 1 to the ETS Directive (European Commission, 2009a) to the more universal

NACE v.2 structure, I employ a dataset compiled by Jaraite et al. (2013) with the intention of linking

individual accounts with their parent companies. This dataset, the scope of which is limited to phase I

of the ETS, is publicly available as a XLS file and contains not only the account information from the

OHA list to which it can be matched, but also NACE v.2 codes and information on the current and

past global ultimate owner or GUO – a term, which is used by Bureau van Dijk’s company database

ORBIS to identify subsidiaries of multinational corporations. By means of the MERGE command, I was

able to automatically add NACE data to the OHA dataset using Installation Name as the key variable.

Given that the dataset compiled by Jaraite et al. is based on historical data which does not perfectly

correspond to the most recent version of the OHA list, several manual adjustments were required to

assign each installation the correct industry sector. In order to fill the blanks, I employed the German

website www.firmenwissen.com, which provides NACE codes apart from general company information.

Subsequently, I manually compiled a SPSS dataset matching NACE v.2 codes to written descriptions

based on the EUROSTAT NACE guide (Eurostat, 2008), which I used to assign each installation a label

in plain text. Finally, I automatically matched the NACE v.2 codes to all transactions involving Austian

installations using both the acquiring account identifier and the transferring account identifier as key

variables.

32


4.2.1 Coping with the Limitations of the EUTL

Despite the unmistakable value of the EUTL as a repository of all transactions within the EU ETS,

its publicly available database suffers from several flaws and limitations which impair the quality and

validity of the data contained. One of the first steps in writing this thesis being several case studies

on individual installations, the purpose of which has been to gain a thorough understanding of the

underlying mechanisms of emissions trading, I was able to identify a variety of shortfalls which limit the

EUTL dataset’s usability as a research subject. This section intends to discuss these issues in detail.

Starting with transaction data, the core problem I had to face lies in its incompleteness – in total,

101,858 or 10,2% of 994,280 transactions recorded from January 2005 to April 2017, all of which were

performed between accounts of the same registry, lack information on at least one of the parties involved.

Fig. 4.2 gives an impression of the magnitude of this effect. In absolute numbers, 15.5% of transactions

completed during phase I&II exhibit missing values, which translates to 35.5% of the total transaction

volume. Of all insufficiently labeled transactions, 34.0% alone were issued by UK accounts, whereas

another 18.9% originated from Italy. The regional distribution of the remaining transactions, however, is

more in line with the average transaction volumes of each member state during phase I&II. For another

4,568 transactions, information on both the acquiring and the transferring accounts is missing. Interest-

ingly, the majority of these – 62.1% and 26.8% – originate from Austrian and Greek accounts. Further

3.5% were transferred from EU accounts, whereas the remaining national registries play only a minor role.

However, it is worth mentioning that all of said gaps in the dataset are limited to dates ranging from 2005

to 2012. The causes of these irregularities are subject to speculation – neither literature nor the EUTL

website give a clear indication as to why such a large proportion of the dataset is incomplete. Hence,

it remains unclear wether the loss of data has occurred during the transition process from a national

administrative structure to the current EUTL or if the data collected by the national agencies had been

incomplete in the first place. Apart from these corrupted entries, there are another 35,400 transactions

involving accounts outside the EU ETS as well as CDM accounts, which also lack information on one of

the parties involved. However, this does not constitute an irregularity, since the EUTL keeps no records

of market participants not registered by the system.

A detailed investigation of the individual accounts of several Austrian Companies – Calcit GmbH,

Energie AG, Stölzle Oberglas GmbH, Voestalpine AG and Wienerberger AG – gives further proof of the

necessity to reduce the scope of my investigation to phase III of the EU ETS: Not only is the naming

scheme of individual accounts inconsistent across datasets, but there are also instances in which accounts

are transferred to new owners while maintaining their balance, making it virtually impossible to monitor

individual accounts over an extended period of time. With the support of Dr. Bettina Dallinger and

Wolfgang Strasser from Energie AG, I was able to resolve complications arising from the transition

33


2017201620152014201320122011201020092008200720062005

billi

on a

llow

ance

s tr

aded

30

20

10

0

number of transactions

30,000

20,000

10,000

0

Figure 4.2: For phase I&II, the EUTL dataset contains a high number of transactions with missing values.

Evidently, this issue was resolved by January 2013, since no such cases can be identified in phase III.

process to a centralized EU registry in the course of phase II – evidently, certain transactions transferring

allowances from old CER accounts (Type 120) to newly created EUA accounts exist as duplicates which,

in turn affect the results of my calculations. I encountered similar issues when investigating Stölzle

Oberglas, Voestalpine and Wienerberger accounts. However, several consultations with the Emissions

Trading Department of the Austrian Umweltbundesamt revealed that these complications may not be

resolved without insider information, let alone by employing an algorithm.

4.2.2 Validation

Considering the aforementioned limitations applying especially to transaction data, I conclude that it

is necessary to establish a validation routine in order to ensure that data and results are consistent

across sources. Whereas it is unreasonably complex or, in many cases, technically impossible to check

whether each account yields an even or positive balance based on its transaction history, a comparison

between free allocation and surrendered allowances in both the transaction log and the OHA dataset

proves more practicable. However, since these administrative transactions are not explicitly labeled, I

conducted my calculations manually on an account level, finding that for all aforementioned companies,

both variables correspond perfectly across datasets. The second stage of the validation process, the

results of which are reflected in fig. 4.3, involved comparing free allocation and emission data from the

OHA dataset aggregated by Activity Type with the official statistics provided by the EEA. Evidently,

the deviation of the calculated values derived from the OHA and transaction dataset increases with

34


time, ultimately reaching values of >10% for periods in phase I. This behaviour may most likely be

attributed to changes in the OHA dataset such as account deletions, changes of ownership or transfers

from national registries, which do not manifest in the latest version of the OHA dataset. Even though

the EUTL website provides information on discontinued accounts from national registries, the Former

Operator Holding Account dataset does not contain account identifiers or any other variables necessary

to identify individual accounts. However, as fig. 4.3 indicates, the annual deviations in phase III or the

period between 2013 and 2019 do not exceed 0.2%, with two perfect matches in 2017 and 2018. Hence,

it is safe to state that OHA data is sufficiently accurate within this time range.

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

devi

atio

n fr

om E

EA d

ata

in %

0.00

-5.00

-10.00

Verified EmissionsFree Allocation

Figure 4.3: Deviation of free allocation and verified emissions derived from the OHA and transaction

dataset (EUTL) compared to official EEA Data.

35

Chapter 5

Discussion and Results

In the course of this section, I intend to analyze and discuss the development of the EU ETS since its

launch in 2005 both on the basis of data obtained from the abovementioned sources and by employing

secondary literature. In order to give a comprehensive account of the underlying mechanisms that have

been driving the European attempt at emissions trading during the first 15 years of its existence, I perform

my analysis from different perspectives. First, I present general data on emission levels, free allocation

and the distribution of industry sectors derived from the EEA dataset. Subsequently, I identify the key

determinants of the allowance price on the basis of spot price data from 2008 to 2020, while highlighting

the importance of market stability measures for mitigating the impact of oversupply on the carbon

market. Presenting general metrics of the EU ETS, I investigate the distribution of emissions and

installations across countries and activity types, using GDP data to adjust for economic discrepances and

differences in polupation size. In the following section, I employ a combined dataset of both transaction

and account data to provide insight on the development of transaction numbers and volumes for different

transaction types from an annual, a monthly and a daily perspective, assessing both periodic patterns and

singular events. Relying on both official data and a transaction-level analysis, I investigate the process

of auctioning allowances with a special focus on market structure and market participants. Finally, I use

transaction and account data limited to Austrian accounts to present further insights on the ETS which

require a more thorough analysis of the transaction dataset.

5.1 Emission Levels & Allowance Allocation

By providing aggregated data on an industry level, the EEA dataset constitutes an excellent basis for the

assessment of the EU ETS from a general perspective. Unlike the EUTL, which is best used to perform

research on a transaction level, it was published by an official EU organisation and is thus not limited

CHAPTER 5. DISCUSSION AND RESULTS 5.1. EMISSIONS & ALLOCATION

201920182017201620152014201320122011201020092008200720062005

2.50E9

2.00E9

1.50E9

1.00E9

5.00E8

0.00E0 2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2.50

2.00

1.50

1.00

0.50

0.00

Verified EmissionsFree AllocationEmissions Cap

Total Allocation

bill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

Figure 5.1: Total Free Allocation and Verified Emissions in relation to the cap on emissions imposed by

the ETS Directive from 2005-2019.

by incorrect data or missing values. Hence, I employ the EEA dataset as a reference point against which

EUTL data can be tested. Furthermore, the use of aggregated data simplifies the task of investigating

general parameters of the ETS, namely emission levels and allowance allocation.

In an idealized model, both verified emissions and total allocation can be expected to match or closely

follow the EU-wide emissions cap. Whereas two of these variables – allowance allocation, including both

free allocation and auctioning as well as the cap on emissions, are determined by the European Union

on the basis of National Implementation Measures or, during phase I&II, National Allocation Plans, the

actual emission levels are dependent on the market. In order for the ETS to operate effectively, the overall

balance of allowances, expressed by the difference between verified emissions and total allocation, has to

be even. This ensures that, regardless of temporary flucuations, neither a shortage nor an oversupply

of allowances occurs, which both may impair the effectiveness of the ETS. Whereas from a theoretical

perspective, designing optimal scenarios is relatively straightforward, their practical implementation poses

a more substantial challenge to policymakers.

Fig. 5.1 illustrates the development of allowance allocation in relation to the annual emission levels

and the EU-wide cap on emissions. Evidently, the values displayed fail to exactly match the idealized

model: In all periods observed except for 2008, there has been a considerable gap between the EU-wide

cap on emissions and the actual emission values. This, however, does not constitute a substantial issue, as

37


Date

8/03/20

2/24/20

9/23/19

4/08/19

10/22/18

5/14/18

11/13/17

6/19/17

1/09/17

8/08/16

3/07/16

10/19/15

6/01/15

12/29/14

8/11/14

3/17/14

10/28/13

6/10/13

1/21/13

9/03/12

4/16/12

11/07/11

6/20/11

12/20/10

8/02/10

3/15/10

10/26/09

6/08/09

1/12/09

8/25/08

4/07/08

EUA

Spo

t in

EUR

40

30

20

10

0

over

supp

ly in

bill

ion

allo

wan

ces

2.00

1.50

1.00

0.50

0.00

Figure 5.2: The historical spot price for EU allowances based on ICE data (Ember Foundation, 2020) is

inversely correlated to the oversupply of allowances which has been accumulating since the beginning of

phase III. The effects of this trend were mitigated by both the backloading of 900 million allowances and

by reductions in free allocation since 2013, so that the price for EU allowances is currently approaching

its all-time peak.

long as the number of allowances surrendered is lower than the cap. On the other hand, the gap between

actual and projected emissions, the scale of which varies from 8.4% and 18.5%, may be regarded as a lack

of ambition in terms of emissions abatement on the part of the European Commission, especially when

taking into consideration the EU’s long-term climate strategy. Observing the development of allowance

allocation, in turn, reveals an imbalance from 2009 to 2013, where the number of allowances allocated

exceeds the number of allowances surrendered by a considerable margin. In fact, this overallocation

resulted in the accumulation of a substantial allowance surplus, the magnitude of which is illustrated in

fig. 5.2.

As one of the key determinants of the performance of the EU ETS, the allowance price gvies an

indication of the challenges that the system has been faced with since its initiation in 2005. Whereas

phase I allowances were still designed to expire by the end of 2007, banking of allowances has been

possible since 2008, so that EU allowances (EUA) and EU aviation allowances (EUAA) issued in a given

phase may be traded and surrendered in subsequent phases. As fig. 5.2 indicates, the spot price for

EU allowances has decreased substantially after exceeding 25€ per ton during phase I. This downwards

trend persisted until 2017, with values temporarily falling below the 5€ per ton mark. Using EEA data,

I am able to trace the accumulation of a considerable allowance surplus since 2009 by subtracting the

38


annual number of allowances allocated from the number of surrendered allowances for a given year. This

implies both freely allocated allowances and those emitted through auctions and sales. Reaching a peak of

roughly 200 million by the end of 2013, the number of allowances in circulation is inversely correlated to

the allowance price. However, since the dataset employed is limited to physical installations, allowances

acquried by PHAs or trading accounts are not represented in the graph. An accurate reproduction of the

number of allowances allocated to all account types can only be obtained by analyzing the transaction

dataset. Considering the limitations of the EUTL, however, this would entail identifiying the allocation

and surrendering of allowances to and from all national registries, rendering this task prohibitively time-

comsuming.

According to a report published by Vivid Economics in February 2020 (Vivid Economics Ltd., 2020),

the oversupply of allowances forming from 2009 onwards may be attributed mainly to the reduced eco-

nomic activity in the aftermath of the 2008 financial crisis as well as to the extensive use of CDM credits

during phase II. Subsequently, the European Commission’s attempt at backloading a total of 900 million

allowances intended for auctioning between 2014 and 2016 – 400 million in 2014, 300 million in 2015 and

200 million in 2016 – resulted in an upwards trend, with allowance prices rising above the 20 € per ton

mark (Vivid Economics Ltd., 2020, p.16).

However, not all fluctuations of the allowance price can be explained by factors inherent to the ETS.

During the last few years, several studies have been published, which identify price determinants of EU

allowances, developing predictive models independent from allowance supply. A recent publication by

Chung et al. (2018) analyzes the relationship between the allowance price and climate variables, energy

prices as well as three economic indicators such as the European Industrial Production Index based on

phase III data. Jiménez-Rodríguez (2019), in turn, investigates the impact of stock market indices on

the price of EU allowances using data from 2005 to 2015. Both studies employ a Granger causality test

to identify causal relationships between the variables observed and the EUA price. Chung et al. find

a strong, one-sided causal effect of the spot price of allowances on the electricity and the gas price. In

terms of correlations, the authors conclude that all variables observed with the exception of the minimum

temperature are positively correlated with the EUA price. According to Jiménez-Rodríguez, there is a

statistically significant causality from stock market indices to the price for EU allowances, especially for

phase I and phase III.

Returning to EEA data, fig. 5.1 indicates that free allocation has dropped significantly from 2012

to 2013, leaving a gap to the total number of allowances allocated. This gap, which is formed by the

growing number of allowances distributed via auctioning, has been widening constantly during phase I

and phase II. Since the beginning of phase III, when auctioning was formally established as the primary

allocation method, free allocation has dropped significantly, subsequently following a downwards trend

which is collinear to the emissions cap. The auction volume, in turn, has been more volatile since 2013,

39


2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2.00

1.50

1.00

5.00

0.00

Verified Emissions: Combustion of Fuels

Verified Emissions: All Installations excl. Combustion

Free Allocation: Combustion of Fuels

Free Allocation: All Installations excl. Combustion

bill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

Figure 5.3: Whereas in total, the number of freely allocated allowances has been declining since the

beginning of phase III, this trend mainly affects activities related to the combustion of fuels. Other

activity types, primarily those protected by the carbon leakage policy, still receive a considerable amount

of free allocation.

which may be attributed to the European Union’s attempts to excercise control over the allowance price.

This not only entails the previously mentioned backloading of allowances, but also extends to the Market

Stability Reserve put into effect in January 2019. Since 2018, the European Commission (European

Commission, 2020d) has been publishing reports on the total number of allowances in circulation or

TNAC as a determinant of the allowance surplus on an annual basis. The TNAC is calculated by

subtracting the demand for allowances, which comprises the verified emissions of all installations covered

by the ETS during phase III, from the supply, which, in turn, is obtained by adding the number of

allowances allocated for free or via auctioning, the number of banked allowances from phase II, the

number of international credit entitlements exercised during phase III, the number of allowances held

back from auctions and the volume allocated to the NER 300 programme. As of December 2019, the

total supply of 14.9 billion corresponds with a demand of 12.2 billion allowances, resulting in a balance

of 2.7 billion allowances, from which the 1.30 billion allowances already in the Market Stability Reserve

(MSR) are subtracted to obtain a TNAC of 1.39 billion allowances. Based upon this indicator, the EC

has withheld a total of 397 million allowances from auctioning during 2019 and 2020, leading to a notable

decline in the total allocation volume compared to 2018. For the following period from 1 September 2020

40


2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

250

200

150

100

50

0

Verified Emissions: Production of cement clinker

Free Allocation: Production of cement clinker

Verified Emissions: Production of pig iron or steel

Free Allocation: Production of pig iron or steel

Verified Emissions: Refining of Mineral Oil

Free Allocation: Refining of mineral oil

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

Figure 5.4: Since Combustion of Fuels as the predominant activity type cannot be translated to a single

NACE code, it is necessary to exclude this category in order to study the distribution of industries active

in the EU ETS.

to 31 August 2021, this value will be raised to 332.5 million allowances. According to Vivid Economics

(2020, p.18), the practise of limiting the supply of allowances through the MSR is going to be sustained

until 2022. Transfers from the MSR to the market, in turn, are to be expected no sooner than 2026.

5.1.1 The Impact of Free Allocation on Industry Sectors

Whereas the overall reduction in free allocation from 2012 to 2013 was substantial, it is nevertheless

necessary to differentiate by activity type in order to identify which industry sectors are affected by this

change and which are protected by the carbon leakage policy. Fig. 5.3 not only gives an impression of the

extent to which certain activity types contribute to the declining number of allowances allocated free of

charge, but also shows the dominance of combustion of fuels as the most common activity. The categories

defined by the EU, however, prove to be misleading in this context, considering that a company active

in one particular industry sector can operate multiple installations and sub-installations associated with

different activity types. The Austrian Voestalpine AG provides a concrete example for this dilemma:

Whereas the company operates several installations categorized as production of pig iron or steel, others

are designated as combustion of fuels, production of coke, production or processing of ferrous metals, com-

bustion installations with a rated thermal input exceeding 20 MW or as production of lime or calcination

41

CHAPTER 5. DISCUSSION AND RESULTS 5.2. THE EMISSIONS MARKET

of dolomite/magnesite. Evidently, as fig. 5.3 indicates, several of these activity types have been subject

to overallocation during phase I&II, meaning that the volume of free allocation exceeded the number of

surrendered allowances by a considerable margin. This imbalance was mitigated by the benchmarking

approach introduced in 2013, which features a more performance-oriented assessment strategy than its

predecessor. However, it still took several trading periods before the demand for allowances finally ex-

ceeded the level of free allocation in 2017. With regard to combustion of fuels as the predominant activity,

the reduction in free allocation from 2012 to 2013 was much more drastic than for any other category.

Furthermore, the emission levels, which have been collinear with the development of the EU-wide cap

since 2008, were always higher than the volume of freely allocated allowances, meaning that this activity

type did not contribute to the allowance surplus accumulating since 2009.

By excluding combustion of fuels from the graph, the distribution of industry sectors becomes appar-

ent. According to fig. 5.4, refining of mineral oil, production of pig iron or steel and production of cement

clinker are the most significant activity types in terms of GHG emissions. Whereas the verified emissions

of these activities have remained on a comparable level during all three phases, with the steel industry

reacting strongest to the 2008 financial crisis, the values for free allocation largely differ. Furthermore,

the amount of free allocation to cement and steel production has been exceeding the number of surren-

dered allowances, meaning that despite all efforts to avert overallocation, companies in certain industry

sectors still realise windfall profits thanks to the Carbon Leakage Policy. According to a 2016 report by

Carbon Market Watch (2016), these windfall profits stemming from a surplus of allowances amounted

to €8.1 billion from 2008 to 2014, with iron and steel producers (€1,044 million) as well as the cement

(€2,649 million), the refineries (€170 million) and the petrochemicals sector (€780 million) generating

the most unearned profit. It is evident that overallocation contradicts the fundamental principles of the

EU ETS, since it constitutes a government subsidy to private companies, which, as fig. 5.4 shows, is

not evenly distributed across industries. This not only increases the overall supply of allowances, thus

lowering allowance prices, but also deters companies from investing in energy efficient technologies.

5.2 The Emissions Market – Structure and Participants

Fig. 5.5 and fig. 5.6 give an indication of the annual emission levels of several member countries. Whereas

in absolute numbers, Germany as the EU’s most performant national economy outweighs other nations by

a considerable margin, the adjusted chart shows that, relative to gross GDP, Eastern European countries

yield emission values which are considerably higher than those of their wealthier Western European

counterparts. This observation is corroborated by fig. 5.8, which indicates a statistically significant

correlation between a country’s per capita GDP and its adjusted emissions, leading to the conclusion

that less performant national economies are affected by the burden of the ETS to a higher degree.

42


2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

500

400

300

200

100

0

EU AverageNLFRESITPLGBDE

Figure 5.5: In absolute numbers, large national economies in Western Europe dominate the EU ETS with

regard to annual emissions.

Yielding a correlation coefficient of -0.524, along with a R2 of 0.275, the result is statistically significant

at a 1% level. As a direct comparison between fig. 5.6 and fig. 5.5 reveals, the same rule applies to the

number of installations of a given country.

Another important insight, which can be derived from fig. 5.6, is the substantial downwards trend

in terms of GDP-weighted emission values during all periods since 2005. On average, the reduction in

tons of CO2 from 2005 to 2019 amounts to 57.4%. Comparatively, Malta exhibits an even higher value

of 85.6%, followed by Estonia with 72.9%. On the other end of the spectrum, the Netherlands yield

an only minor reduction of 29.1%, which, however, still exceeds the growth in real GDP across the EU

27 amounting to 16.3% from 2005 to 2019 by a considerable margin. Judging by the graph, it appears

that low-GDP countries exhibit higher reductions in terms of weighted emissions than their wealthy

counterparts, potentially due to higher economic growth rates. In fact, there is a moderate correlation

featuring a correlation coefficient of 0.435 at a significance level of 5% between the average per-capita

GDP and the reduction in weighted emissions during a period from 2008 and 2019. After removing a

single country from the dataset, however, the results change drastically: Without Liechtenstein, which

not only exhibits the highest per-capita GDP of all countries in the ETS, but also an unrealistically high

reduction rate of >99%, no significant correlation can be identified1.1correlation coefficient = -0.028, significance level = 0.56

43


2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

tonn

es o

f CO

2 equ

ival

ent e

mitt

ed p

er m

illio

n EU

R G

DP

1,200

1,000

800

600

400

200

0

EU AverageGRROSKCZPLEEBG

Figure 5.6: When adjusting the emission values for GDP, we find that Eastern European countries are

affected by the EU ETS to a higher degree than their more developed counterparts.

Fig. 5.7, in turn, compares seven countries with the highest per-capita emissions from 2005 to 2019

to the EU average. Given the fragmented nature of the EU and the vast size differences between member

states, this aids in comparing countries on an objective basis. Accordingly, I investigate whether there is

a correlation between per-capita emissions and per-capita GDP, finding that these variables are uncor-

related. A visual analysis of the graph confirms this result, since both high- and low-GDP countries are

among the top seven polluters. However, it is worth mentioning that the per-capita emissions across all

participating countries exhibit a downwards trend, declining by 33.8% from 2005 to 2019. As a conse-

quence of the low population growth during the last 15 years, the extent of this decline is almost identical

to the overall reduction in verified emissions.

Another insight which can be derived from the EUTL’s OHA dataset, is depicted in fig. 5.9. With

regard to the number of installations registered during phase III, the most performant national economies

such as Germany, the UK and France outweigh their smaller or poorer counterparts by a considerable

margin. Hence, unsurprisingly, the four first-ranked countries in terms of installation numbers share the

same positions when ranked by gross GDP. The same applies to Poland, which, despite being on the

27th of 31 ranks with regard to per-capita GDP, is among the top ten in terms of installations due to

its size. However, several other countries do not fit into this scheme, making it necessary to adjust the

actual installation numbers for gross GDP in order to give a more accurate account of the installation

44


2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

per-

capi

ta e

mis

sion

s in

tonn

es o

f CO

2 eq

uiva

lent

12.00

10.00

8.00

6.00

4.00

2.00

0.00

PLNLFIEEDECZCYEU Average

Figure 5.7: Per-capita emissions including the EU average from 2005 to 2019.

density. Accordingly, the ranking of nations in fig. 5.10 takes into account both economic performance

and population, yielding a distribution which is at the same time more even and more ambiguous than

the previous. As the graph suggests, there is no relationship between a country’s per-capita GDP and

its installation density. Whereas it appears that on the high as well as on the low end of the spectrum,

low-GDP and, respectively, high-GDP countries are concentrated, a correlation analysis proves otherwise,

yielding a correlation coefficient of only 0.148 at a significance level of 42.5%.

With regard to the distribution of activity types, fig. 5.11 indicates a moderate concentration (HHI2

2,249.7) with one dominant category – combustion of fuels – which comprises 43.0% of installations across

all member states. 84.6% of installations, in turn, belong to one of the 8 largest categories. On the other

side of the spectrum, 23 of all 38 activity types do not exceed the 1% mark. These results are derived

from the OHA dataset, which lists all physical installations registered in phase III. Hence, in order to

perform a more in-depth analysis encompassing transaction volumes and numbers for each activity type,

it would be necessary to establish a link between datasets. Due to the previously discussed limitations of

EUTL, however, this is only possible for a limited part of the dataset, on which I detail in section 5.5.

Finally, by counting the absolute number of account identifiers in the transaction dataset, I am able

to put the theoretical number of registered installations derived from the OHA register into perspective.

In 2020, 26,079 accounts were registered by the EUTL – 16,972 OHAs, 7,154 PHAs and 1,953 trading2Hirschmann-Herfindahl Index

45


per-capita GPD

150,000100,00050,0000

tonn

es o

f CO

2 eq

uiva

lent

per

mill

ion

EUR

GD

P1200

1000

800

600

400

200

0

R2 Linear = 0.275

Figure 5.8: A country’s per-capita GDP is negatively correlated to its verified emissions, meaning that

poorer countries located primarily in Eastern Europe exhibit higher emission values in relation to their

GDP. Yielding a correlation coefficient of -0.524, the result is statistically significant at a 1% level.

accounts. In relation to the annual number of actively participating accounts, which ranges from 12,700

and 10,800 throughout phase III, this equals between 161% and 192%. Due to the limitations of EUTL

data, I am unable to differentiate by account type, making it impossible to gauge to what extent OHAs,

PHAs and trading accounts are represented in the transaction dataset. Accordingly, fig. 5.12 displays

the annual number of all accounts which have issued or received at least one transaction in a given year

regardless of type. Another potential source of error is the occurrence of missing values in the transaction

dataset before phase III, which may impact the number of accounts from 2005 to 2012. The magnitude

of this effect, however, is hard to predict, further limiting the accuracy of my observations. Nevertheless,

despite said shortfalls, both the overall number of accounts participating in emissions trading and its

development can be estimated based on the data available. In concrete terms, account numbers have

increased by 69.9% over a period of 12 years. Whereas a considerable degree of uncertainty is involved in

this statement, the numbers for phase III are unquestionably more accurate: After a peak in 2013, which

equals a 99.7% increase compared to 2005 values, the number of active accounts has been in decrease,

yielding an overall decline of 14.9% during phase III.

Expanding the perspective, fig. 5.13 focuses on the devolpment of account numbers in the seven

most performant countries of the ETS. Unsurprisingly, the distribution of nations in the graph is almost

46

CHAPTER 5. DISCUSSION AND RESULTS 5.2. THE EMISSIONS MARKET Liechtenstein

Malta

Cyprus

Luxembourg

Iceland

Croatia

Estonia

Slovenia

Latvia

Lithuania

Bulgaria

Norw

ay

Greece

Slovakia

Ireland

Austria

Hungary

Rom

ania

Portugal

Denm

ark

Czech R

epublic

Belgium

Netherlands

Finland

Sweden

Poland

Spain

Italy

France

United Kingdom

Germ

any

3,000

2,000

1,000

0

Figure 5.9: According to the OHA dataset, large national economies in Western Europe exhibit the

highest numbers of installations.

identical to fig. 5.9, suggesting that the relation between OHAs and other account types as well as

between registered and actively participating accounts is relatively constant across the EU. With regard

to the development of account numbers, Germany as Europe’s largest national economy outperforms its

competitors by a considerable margin, operating 228% more accounts than the EU average. In relation to

the overall increase in account numbers, though, Germany exhibits a moderate growth rate of only 10.9%

from 2005 to 2017. 10 of 31 countries, in turn, experienced a decline in account numbers, among them

Slovenia (-37%), Hungary (-23%) and Denmark (-10%). On the other side of the spectrum, Austria, Italy

and Greece yield growth rates exceeding 1000% – an observation, which, however, carries a substantial

level of uncertainty due to the unequal distribution of missing data from phase I and phase II across

national registries. Accordingly, further research is needed to investigate the development of account

numbers on a more sophisticated basis, addressing the uncertainty involved on a transaction level. This

is also true for the EU average, which, due to the high level of inequality in terms of account numbers,

has been stagnating since 2005, eventually returning to 98.3% of its initial value after a peak of 116% in

2013. With more accuracy, though, I am able to judge the development of accout numbers during phase

III: Only 2 countries – Italy (+8%) and Luxembourg (±0%) – do not conform to the downwards trend

indicated in fig. 5.12. Whereas in absolute numbers, this also applies to Croatia, the system’s newest

member country exhibits a pattern which is typical of countries joining the ETS. While in 2012 and 2013,

47


Liechtenstein

Norw

ay

Luxembourg

France

United Kingdom

Austria

Germ

any

Netherlands

Ireland

Italy

Belgium

Greece

Croatia

Spain

Cyprus

Denm

ark

Malta

Rom

ania

Portugal

Sweden

Poland

Slovenia

Iceland

Hungary

Czech R

epublic

Slovakia

Estonia

Lithuania

Finland

Bulgaria

Latvia

inst

alla

tions

per

bill

ion

EUR

GD

P 5.00

4.00

3.00

2.00

1.00

0.00

Figure 5.10: However, Eastern European countries tend to exhibit a higher installation density in relation

to their gross GDP.

no more than 4 different account identifiers were registered by the Croatian authorities, this number

leaped to 58 in 2014, only to follow the common, EU-wide downwards trend in the following years. On

the negative side, several member states were subject to substantial losses during phase III, among them

Liechtenstein (-83%), Iceland (-57%) and Ireland (-48%). Among the big players in the system, the UK

exhibits the most significant decline of 45%, followed by the Netherlands (-26%), Denmark (-25%) and

Spain (-22%).

48

CHAPTER 5. DISCUSSION AND RESULTS 5.2. THE EMISSIONS MARKET Production of glyoxal and glyoxylic acid

Transport of greenhouse gases under Directive 2009/31/EC

Capture of greenhouse gases under D

irective 2009/31/EC

Coke ovens

Metal ore (including sulphide ore) roasting or sintering

installations Production of adipic acid

Metal ore roasting or sintering

Production of soda ash and sodium bicarbonate

Production of carbon black

Production of coke

Production of amm

onia

Mineral oil refineries

Production of primary alum

inium

Production of nitric acid

Production of secondary aluminium

Production of hydrogen and synthesis gas

Production or processing of gypsum or plasterboard

Installations for the production of pig iron or steel (primary or

secondary fusion) including continuous casting M

anufacture of mineral w

ool

Production or processing of non-ferrous metals

Installations for the manufacture of glass including glass fibre

Installations for the production of cement clinker in rotary kilns

or lime in rotary kilns or in other furnaces

Refining of m

ineral oil

Production of pulp

Production of pig iron or steel

Production or processing of ferrous metals

Production of cement clinker

Production of lime, or calcination of dolom

ite/magnesite

Industrial plants for the production of (a) pulp from tim

ber or other fibrous m

aterials (b) paper and board M

anufacture of glass

Production of bulk chemicals

Other activity opted-in pursuant to Article 24 of D

irective 2003/87/EC

Installations for the m

anufacture of ceramic products

Production of paper or cardboard

Manufacture of ceram

ics

Aircraft operator activities

Com

bustion installations with a rated therm

al input exceeding 20 M

W

Com

bustion of fuels

0

100.00

80.00

60.00

40.00

20.00

0.00

Figure 5.11: Distribution of registered installations across activity types according to the OHA dataset.

2017201620152014201320122011201020092008200720062005

25,000.00

20,000.00

15,000.00

10,000.00

5,000.00

0.00

2017201620152014201320122011201020092008200720062005

num

ber o

f act

ive

acco

unts

15,000

10,000

5,000

0

Figure 5.12: Number of active accounts (OHAs, PHAs and transaction accounts) from 2005 to 2017 based

on EUTL transaction data. An account is identified as active if at least one administrative or market

transaction is registered in a given year.

49


2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

Num

ber o

f act

ive

acco

unts

by

coun

try

4,000

3,000

2,000

1,000

0

EU Average Poland Finland Sweden Spain United Kingdom France Germany

2017201620152014201320122011201020092008200720062005

num

ber o

f act

ive

acco

unts

2,500

2,000

1,500

1,000

500

0

EU Average Poland Finland Sweden United Kingdom Spain France Germany

Figure 5.13: Number of active accounts by country from 2005 to 2017.

50

CHAPTER 5. DISCUSSION AND RESULTS 5.3. INSIGHTS FROM TRANSACTION DATA

5.3 Insights from the Transaction Dataset

201620152014201320122011201020092008200720062005

billi

on a

llow

ance

s pe

r yea

r

60

50

40

30

20

10

0

number of transactions per year

200,000150,000

100,00000

0,05

0

201620152014201320122011201020092008200720062005

billi

on a

llow

ance

s (to

nnes

of C

O2 e

quiv

alen

t) pe

r yea

r

60

50

40

30

20

10

0

number of transactions

200,000150,000

100,00000

0,05

0

Figure 5.14: Total annual transaction volume in relation to the number of transactions per year from

2005 to 2016.

In contrast to the EEA dataset providing information in an aggregated form, EUTL data serves as

a basis for more thorough analyses on a transaction level. This not only extends to the number of

transactions or allowances traded per year for each member state, but also includes variables such as

time, date, transaction type, and, in some cases, the activity types of the installations involved. For

the purpose of distinguishing between different types of transactions relevant to the ETS, I establish a

scheme of three categories based on the transferring and acquiring account holders of each transaction:

1. First, market transactions, which are defined as transactions between different account holders in

which no national registry or EU accounts are involved. Transfers between operators within the

framework of multinational corporations also fall into this category, since the transaction dataset

provides no information on an account holder’s parent company. While a dataset establishing this

link has been published in 2013, the data provided by Jaraite et al. (2013) proves too outdated for

automated processing.

2. Second, administrative transactions between an EU account and an OHA, PHA or transaction

account.

3. Third, intra-company transfers between installations or accounts with identical holders. Ideally,

these should be analyzed speparately from market transactions, since transfers within a company

or legal entity do not have an impact on the emissions market.

51


Year

2016201520142013

billi

on a

llow

ance

s (to

nnes

of C

O2 e

quiv

alen

t) 30.00

20.00

10.00

0.00

Administrative TransactionsMarket TransactionsAll TransactionsIntra-Company Transfers

Figure 5.15: Total transaction volume in relation to intra-company transactions from 2013 to 2016.

Transactions are identified as intra-company if the transferring account holder and the acquiring account

holder are identical.

Year

2016201520142013

num

ber o

f tra

nsac

tions

100,000

80,000

60,000

40,000

20,000

0


Figure 5.16: Total number of transactions per year in relation to intra company transfers from 2005 to

2016.

52


2016201520142013

allo

wan

ces

per t

rans

actio

n

600,000

500,000

400,000

300,000

200,000

100,000

0


Figure 5.17: Average number of allowances per transaction from 2013-2016.

Due to the limitations of EUTL data, this more thorough analysis differentiating by transaction type

is only possible for tranasactions issued during phase III. Nevertheless, I am able to extract the annual

transaction volumes as well as transaction numbers across all categories from 2005 onwards in order

to provide an overview of the development of emissions trading in Europe. As fig. 5.14 indicates, the

annual transaction volumes have, with the exception of three spikes in 2008, 2013 and 2015, increased

constantly during phase I&II, entering a downwards slope during phase III. In absolute numbers, the

average trading volume for phase I, which amounts to 9.6 billion allowances per year, has risen by 264%

to 25.4 billion allowances in phase II. Disregarding the peaks in 2013 and 2015, which can be explained

by administrative transactions irrelevant to the market, phase III sees a substantial decline in trading

volumes. Accordingly, the number of transactions per year indicates a downwards trend for phase III,

following a peak in 2009, which is not reflected in the annual transaction volumes. In fact, these numbers

are in line with the development of the ETS during its first three evolutionary phases: Whereas in 2005,

the ETS extended to only 25 member states, this number had increased to 30 by the beginning of phase

II, resulting in an elevated transaction volume between 2008 and 2012. A substantial cut in free allocation

along with a lowered cap on emissions, however, prompted the decline in transaction volumes observed

from 2013 onwards. Evidently, the effects of the revised allocation policy were not entirely mitigated by

the extension of the ETS, the scope of which has been enlarged not only geographically, but also in terms

of greenhouse gases and industry sectors covered.

Observations based on phase I and phase II data, however, are difficult to interpret due to the

considerable number of missing values in the transaction dataset. This is especially relevant for explaining

unusually large transactions or spikes in the dataset. For instance, the exceptionally high trading volume

53


of 2008 is attributed to a relatively low number of administrative transactions between government

accounts within national registries. 13 of these exceed a volume of 1 billion allowances, amounting to

51.0% of all transactions completed during this year. Since the account holders on the transferring as

well as on the receiving side are identical, no actual transfer of allowances takes place. However, given the

fact that 7 of these transactions alone exhibit missing information on at least one of the parties involved,

it is difficult to study the characteristics and magnitude of this effect. Hence, it is necessary to limit the

scope of my analysis of the EUTL transaction dataset to phase III data in order to yield more accurate

results:

Between January 2013 and April 2017, a total of 337,725 transactions involving 15,506 individual ac-

counts have been issued, 301,544 of which until December 2016. Whereas the average transaction volume

amounts to 267,000 allowances, a low median value of 14,800, in combination with a standard deviation

of 6,7 million, indicates an asymmetric distribution due to a considerable number of large administrative

transations. On the other hand, the dataset contains 3,197 "symbolic" transactions with a volume of

only 1 allowance. The number of market transactions, in turn, which are defined as transfers between

OHAs, PHAs and trading accounts, in which no national body is involved, amounts to 48.3%, which

translates to 43.5% of all allowances traded between 2013 to 2016. With a mean of 240,200 allowances

per transaction, a median value of 21,600 and a standard deviation of 1.8 million, the distribution of

transaction volumes across market transactions is less asymmetric than that of the remaining categories.

Contrary to my initial assumption based on transaction-level analyses of several Austrian companies, the

percentage of intra-company transfers – transactions between two account identifiers operated by only

one account holder – amounts to only 7.9% of all transactions or 9.3% of all allowances traded between

2013 and 2016, with a mean of 312,500, a median of 9,050 allowances per transaction and a standard

deviation of 1.7 million.

The share of administrative transactions, however, which are defined as transactions in which either

the European Union or one of 31 national registries are inolved, amounts to 43.7% with regard to the

number of transactions and 47.2% with regard to trading volume, with a mean of 288,200 allowances per

transaction, a median value of 10,600 and a standard deviation of 9.9 million. Using the same strategy

employed to identify intra-company transfers, this category can be further divided in two segments: First,

administrative transactions between different member states, the EU, OHAs and PHAs, a substantial part

of which are linked to the allocation and surrendering of allowances. Second, internal transfers between

individual accounts within the same national registry. The latter category is especially interesting, since

it is responsible for the largest transactions in the system: Accordingly, whereas the number of these

internal administrative transfers amounts to only 0.65% of all administrative transactions, their share

of the total volume – 45.4% – is remarkably high, which is also reflected in other statistical indicators:

Reaching a mean of 20.155 million allowances, along with a median value of only 73,900, this category

54


exhibits a highly uneven distribution of transaction volumes. This observation is confirmed by a standard

deviation of 119.3 million, which equals 592% of the mean. However, the net impact of these internal

transfers is limited, since they merely represent transactions within the bookkeeping of national registries.

Nevertheless, their exceptional volume distorts the overall image represented by fig. 5.15 to fig. 5.17,

which illustrate the annual number of allowances traded, the annual number of transactions as well as

the average volume per transaction from 2013 to 2016. Following this line of reasoning, both the total

transaction volume and the volume of administrative transactions exhibit a volatile behavior, which is

attributed to said internal transfers, whereas the transaction volume between OHAs and PHAs has been

stagnating from 2013 to 2016. The annual transaction numbers, in turn, which are illustrated in fig. 5.16,

give proof of a downwards trend affecting all categories observed. According to fig. 5.15, this development

is contrasted by rising transaction sizes, especially for market transactions and intra-comapny transfers.

In absolute numbers, the volume of market transactions has declined from 11.15 billion to 8.04 billion,

which equals a reduction of 27.8%. Accordingly, the volume of intra-company transfers has dropped by

22.3% from 2.19 to 1.70 billion allowances. With regard to administrative transactions, annual volumes

have dropped from 7.07 billion to 3.57 billion allowances, which translates to a reduction of 49.5%. To put

this substantial downwards trend into perspective, the volumes of allowances allocated and surrendered,

which represent the core activity of the ETS, have decreased by only 18.1% during the same period of

time, from 4.03 billion to 3.30 billion allowances. Accordingly, the overhead of administrative transactions

not directly linked to the allocation and surrendering of allowances has decreased substantially from 75%

in 2013 to 8% in 2016.

5.3.1 The average daily transaction volume as an indicator of periodictiy and

of singular events

By calculating the average transaction volume, number of transactions and volume per transaction for

each day of the year across a period from 2013 to 2016, I am able to investigate periodic processes on

the emissions market. In fact, the graphs displayed in fig. 5.18 to fig. 5.24 exhibit patterns which

can be explained by both administrative procedures inherent to the ETS and by the secondary market

for EU allowances. While in February, for instance, an increased level of administrative transactions is

registered due to the allocation of allowances, a second high in all three categories is triggered by the

deadline for the surrendering of allowances on April 30. Another peak in December, which is caused

mainly by market transactions, is related to the delivery of forwards and futures. Whereas the previously

mentioned patterns conform with data from period I presented by Cludius (2016a, Fig. 5), there are

several anomalies or spikes of increased activity which distort the overall image and are thus worth

investigating. For this purpose, I perform a transaction-level analysis for all dates in question, enabling

55


3603303002702402101801501209060300

Num

ber o

f Allo

wan

ces

5.00E8

4.00E8

3.00E8

2.00E8

1.00E8

0.00E0

Total TransactionsIntra-Company Transfers

3603303002702402101801501209060300

Allo

wan

ces

per D

ay

4.00E8

3.00E8

2.00E8

1.00E8

0.00E0


3603303002702402101801501209060300

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

per d

ay

400

300

200

100

0

All TransactionsIntra-Company Transfers

allo

catio

n of

allo

wan

ces

surr

ende

ring

of a

llow

ance

s

Feb 28 Apr 30

deliv

ery

of fo

rwar

ds &

futu

res

allo

wan

ce d

elet

ion

in 2

013

reitr

emen

t of k

yoto

uni

ts 2

015

day of the year

Figure 5.18: The average number of allowances traded per day is impacted by both administrative

processes inherent to the ETS and the market for emission allowances. Annual peaks are attributed to

the allocation and surrendering of allowances as well as to the delivey of futures and forwards in December.

Transactions are identified as intra-company if the transferring account holder and the acquiring account

holder are identical. Compiled using transaction data from 2013-2016.

me to provide a viable explanation for each spike.

• From day 291 to 300, a substantial number of high volume transactions, which cannot be explained

by market events or regular administrative processes, were issued between administrative accounts

held by national registries. Whereas in 2013, 2014 and 2016, an average of 137.6 million allowances

were traded during this period, this number rises to 9.42 billion in 2015, together with a drastic

increase in transaction volumes (8,36 million versus 181.000 allowances per transaction). Both

fig. 5.18 and fig. 5.19 give a clear account of this phenomenon. A thorough analysis of EUTL

data reveals a considerable number of transactions related to Art. 11 of Regulation 525/2013

(European Commission, 2018f), which demands kyoto credits such as CERs or ERUs from the first

commitment period ending in 2012 to be retired in order to prevent an oversupply of emission

allowances. Evidence from Austrian accounts suggests that the number of transactions issued or

received by national accounts drastically decreases throughout phase III. Of 3,237 transactions

involving the Bundesministerium für Nachhaltigkeit und Tourismus from 2005 to 2017, only 217 or

6.7% were completed in phase III, 180 of which in 2013 alone. In both 2016 and 2017 this number

decreases to 1, indicating that national registries have lost relevance also in terms of managing

kyoto credits.

• A second spike, this time more concentrated, was registered on day 183 of 2013. Within 24 hours,

56


360.00330.00300.00270.00240.00210.00180.00150.00120.0090.0060.0030.00.00

Num

ber o

f Allo

wan

ces

4.00E8

3.00E8

2.00E8

1.00E8

0.00E0

Administrative TransactionsMarket Transactions

3603303002702402101801501209060300

4.00E8

3.00E8

2.00E8

1.00E8

0.00E0


3603303002702402101801501209060300

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 eq

uiva

lent

) per

day

400

300

200

100

0

day of the year

allo

catio

n of

allo

wan

ces

surr

ende

ring

of a

llow

ance

s

Feb 28 Apr 30

deliv

ery

of fo

rwar

ds &

futu

res

allo

wan

ce d

elet

ion

in 2

013

reitr

emen

t of k

yoto

uni

ts 2

015

Figure 5.19: The average number of allowances traded per day is impacted by both administrative

processes inherent to the ETS and the market for emission allowances. Annual peaks are attributed

to the allocation and surrendering of allowances as well as to the delivery of futures and forwards in

December. A transaction is identified as administrative if at least one of the parties involved is the

European Union or a national body. Market transactions, in turn, are defined as instances in which both

parties are either a PHA or an OHA. Compiled using transaction data from 2013-2016.

9096 transactions with a total volume of 9.62 billion allowances were completed, 95.7% of which

in connection with the deletion of allowances. Whereas the European Union uses a single account

named EU Allowance Deletion for both the surrendering of allowances and their deletion, a distinc-

tion can be made on the basis of transaction types. Since the centralization of the ETS, transactions

related to the surrendering of allowances are designated 10-2, whereas all similar transactions com-

pleted on July 2 of 2013 exhibit the transaction type 10-34 for regular transferring accounts and

10-33 for aviation accounts. Fig. 5.20 gives an indication of the magnitude of this effect. Of all

53,766 transactions directed towards the EU Allowance Deletion account, 16.1% were completed

within a single day. While neither the ETS Directive nor other official documents offer an expla-

nation for these exceptionally high tranasaction volumes, further insight can be gained through a

company-level analysis: Transaction data for several accounts held by Energie AG Austria confirms

that the aforementioned transactions were issued in connection with the transition process from

the second to the third trading period, in the course of which a high number of accounts was trans-

ferred from type 120/121 to type 100. Evidently, there are cases in which the same transaction is

registered twice, leading to an incorrect account balance at the end of the trading period. In the

Energie AG dataset, which encompasses 13 individual accounts, 6 of which were either cancelled

or replaced over time, this anomaly occured once in connection with the establishment of a new

57


355

335

314

289

268

243

215

195

170

146

123

103

836343232

ave

rage

tran

sact

ions

per

day

800.00

600.00

400.00

200.00

0.00

20132014-2016

Figure 5.20: Whereas the daily number of transactions to the EU Allowance Deletion account exhibits

a peak in April for all periods from 2013-2016, the spike on the 2nd of July 2013 can be explained by

allowance deletions.

trading account, which was operated in parallel with its predecessor until 2015. Another 5 OHAs

exhibit type 10-34 transactions as well, however with correct account balances. In fact, further

research is needed to identify whether the findings of a single case study can be extrapolated to the

whole EUTL transaction dataset.

• Third, two spikes of minor relevance manifesting primarily in an elevated volume of administrative

transactions (fig. 5.24) can be identified around day 100, the most significant of which is caused by

3 large transfers with a total volume of 1.1 billion allowances issued in 2015 within the UK registry.

With regard to the spike on day 112&113, in turn, no irregularity can be identified: The largest

transactions issued on these two days, which are in the 20-30 million range, are attributed to the

German electricity company RWE Power Aktiengesellschaft. Amounting to 354 million allowances

or 16.2% of the total volume recorded within this short period of time between 2013 and 2016, said

transactions are predominantly related to the surrendering of allowances.

• From day 349 to day 356, exceptionally high trading volumes were registered for market trans-

actions, which is apparent in fig. 5.18 and fig. 5.19. Whereas I concede that more sophisticated

analyses are required to identify patterns in a large number of transactions, I am still able to provide

58


an explanation for this peak based on the account holders involved: Both in terms of transaction

volumes and with regard to transaction numbers, several large banks and the London-based inter-

continental exchange (ICE) dominate the market during this limited time period of time. Both on

the transferring and on the acquring side, the ICE is even ranked second in terms of transaction

numbers, which corroberates my assumption that the delivery of futures and forwards is causal

to increased market activity at the end of the year. This relative dominance is also evident when

investigating transaction volumes: Of the 50 largest transactions issued between day 349 and 356,

which are responsible for 30.8% of the total transaction volume during this period of time, 34 or

68% involve the ICE, translating to a share of 42.1% in terms of allowances traded.

• Finally, the graph for administrative transfers in fig. 5.24 exhibits several spikes caused by high-

volume transactions in 2013 and 2015 which are contrasted by average daily transaction numbers

as low as 38. For instance, the spike on day 313 can be traced back to a single transaction of 144.8

million allowances within the Croatian registry in 2015. On day 278 of 2015, a single transaction

of 378 million allowances was completed within the Belgian registry. Another spike on day 242, in

turn, is attributed to 125.8 million allowances being transferred within the Polish registry in 2013.

Next, the spike on day 220 is stems from 3 transactions amounting to 145.5 million allowances

within the German registry in 2013. On day 172 of 2013, in turn, 8 transactions from national

registries to the EU Clearing Account with a total sum of 336.3 million allowances were completed.

Finally, 2 transactions between UK registry accounts amounting to 150 and 500 million allowances

in 2015 were causal to the spike on day 139.

3603303002702402101801501209060300

Num

ber o

f Tra

nsac

toin

s pe

r Day

1,500.00

1,000.00

500.00

0.00


3603303002702402101801501209060300

aver

age

num

ber o

f tra

nsac

tions

per

day

1,500

1,000

500

0


allo

catio

n of

allo

wan

ces

surr

ende

ring

of a

llow

ance

s

Feb 28 Apr 30

deliv

ery

of fo

rwar

ds &

futu

res

allo

wan

ce d

elet

ion

in 2

013

day of the year

Figure 5.21: Average number of transactions per day from 2013-2016 including intra-company transfers.

59


3603303002702402101801501209060300

Num

ber o

f Tra

nsac

toin

s pe

r Day

1,500.00

1,000.00

500.00

0.00


3603303002702402101801501209060300

aver

age

num

ber o

f tra

nsac

tions

per

day

1,500

1,000

500

0


allo

catio

n of

allo

wan

ces

surr

ende

ring

of a

llow

ance

s

Feb 28 Apr 30

deliv

ery

of fo

rwar

ds &

futu

res

allo

wan

ce d

elet

ion

in 2

013

day of the year

Figure 5.22: Average number of administrative and market transactions per day from 2013-2016.

3603303002702402101801501209060300

mill

ion

allo

wan

ces

per t

rans

actio

n

20.00

15.00

10.00

5.00

0.00

Intra-company transfersAll transactions

day of the year

Figure 5.23: Average number of allowances per transaction from 2013-2016 including intra-company

transfers.

60


3603303002702402101801501209060300

mill

ion

allo

wan

ces

per t

rans

actio

n

20.00

15.00

10.00

5.00

0.00

Market transactionsAdministrative transactions

day of the year

Figure 5.24: Average number of allowances per transaction from 2013-2016 for administrative and market

transactions.

61


5.3.2 The Monthly Perspective

Both the periodicity of transaction volumes and the distorting effect of large administrative transfers are

also apparent when aggregating transaction volumes and numbers by month. In fact, several singular

events referred to in the last section have an impact on the graphs of fig. 5.25, fig. 5.26 and fig. 5.27, with

the average volume per transaction providing the most illustrative representation of these phenomena:

Since the average transaction volume across all categories amounts to only 267,000 allowances, a handful

of large administrative transfers on a single day suffice to significantly raise the monthly average. This

effect is especially relevant during periods of reduced activity. Accordingly, fig. 5.27 corroborates my

conclusions drawn from a transaction-level analysis, proving that all irregularities identified are limited

to two trading periods – 2013 and 2015. The spike in transaction numbers on day 183 of 2013, in turn,

is also apparent in fig. 5.26, creating a peak in administrative transactions which contrasts the periodic

pattern present throughout all trading periods. Finally, the retirement of considerable numbers of Kyoto

credits, which occurred around day 300 of 2015, manifests in a substantial spike in fig. 5.25.

Whereas the detection of periodicities using econometric methods exceeds the focus of my thesis, it

is nevertheless possible to identify patterns on the basis of a visual analysis. These are evident in both

monthly transaction volumes and transaction numbers represented by fig. 5.25 and fig. 5.26. However,

further research is needed to test the hypothesis of periodicity in a more sophisticated way. For this

purpose, my transaction-level analysis of spikes in EUTL data may prove useful, since these singular

events should be eliminated in order to yield relevant results.

2017/3

2017/1

2016/11

2016/9

2016/7

2016/5

2016/3

2016/1

2015/11

2015/9

2015/7

2015/5

2015/3

2015/1

2014/11

2014/9

2014/7

2014/5

2014/3

2014/1

2013/11

2013/9

2013/7

2013/5

2013/3

2013/1

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

per m

onth

10,000

8,000

6,000

4,000

2,000

0


Figure 5.25: Total monthly transaction volume in relation to market transactions, administrative trans-

actions and intra-company transfers from 2005 to 2016.

62


2017/3

2017/1

2016/11

2016/9

2016/7

2016/5

2016/3

2016/1

2015/11

2015/9

2015/7

2015/5

2015/3

2015/1

2014/11

2014/9

2014/7

2014/5

2014/3

2014/1

2013/11

2013/9

2013/7

2013/5

2013/3

2013/1

num

ber o

f tra

nsac

tions

per

mon

th

30,000

20,000

10,000

0


Figure 5.26: Total number of transactions per month in relation to market transactions, administrative

transactions and intra-company transfers from 2005 to 2016.

2017/3

2017/1

2016/11

2016/9

2016/7

2016/5

2016/3

2016/1

2015/11

2015/9

2015/7

2015/5

2015/3

2015/1

2014/11

2014/9

2014/7

2014/5

2014/3

2014/1

2013/11

2013/9

2013/7

2013/5

2013/3

2013/1

num

ber o

f allo

wan

ces

per t

rnas

actio

n 10,000,000

8,000,000

6,000,000

4,000,000

2,000,000

0


Figure 5.27: Average number of allowances per transaction from 2013-2016 aggregated by month and

account type.

63


5.3.3 Time and Weekday

Hour23222120191817161514131211109876543210

50,000

40,000

30,000

20,000

10,000

0

Administrative transactionsMarket transactionsIntra-company transfersAll transactions

Num

ber o

f Tra

nsac

tions

hour23222120191817161514131211109876543210

num

ber o

f tra

nsac

tions

50,000

40,000

30,000

20,000

10,000

0

Figure 5.28: The total number of market transactions registered per hour depends largely on a rigid

time frame imposed by the European Commission. This, however, does not apply to administrative

transactions.

Finally, I examine the distribution of transactions by weekday and by hour. As both the German

DEHSt (2017, p.25) and the Austrian Umweltbundesamt (2019, pp.37) report, strict rules apply with

regard to issuing transactions between operator holding accounts and person holding accounts related to

both physical installations and the aviation sector: Transactions are registered only between 10 am and

4 pm from Monday to Friday and are transferred to the registry with a 26-hour delay. After an ensuing

authorization process, which can take up to 24 hours, the transaction is eventually completed. In case a

transaction is issued outside this timeframe, said process is initiated on the following workday at 10 am.

Whereas fig. 5.28 seems to disprove both manuals, suggesting that there are unpublished exceptions to the

offical guidelines, a transaction-level analysis reveals that 96.7% of market transactions issued outside the

regular time frame originate either from CDM projects (57.7%) or from non-EU countries like Switzerland

(23.6%) or Japan (9.2%). As to the remaining 3.3%, however, I am unable to identify an underlying

structure. The same goes for 79 intra-company transfers issued outside working hours, which can neither

be narrowed down to a specific date nor to a single member country or type of transaction. Literature

also fails to provide an explanation as to why a certain number of transactions were completed outside the

legal timeframe. Administrative transactions, in turn, are not affected by restrictions regarding time and

weekday, which is why significant volumes are registered even outside common office hours. With regard

to the spike in the early morning hours, I find that 99.5% of transactions issued between 12 pm and 3

64


am are related to the allocation of allowances, indicating that this process is performed automatically

without human intervention.

Weekday7.006.005.004.003.002.001.00

Num

ber o

f Tra

nsac

tions

60,000

40,000

20,000

0

Al TransactionsIntra-company transfersAdministrative transactionsMarket transactions

Sat.Fri.Thu.Wed.Tue.Mon.Sun.

num

ber o

f tra

nsac

tions

80,000

60,000

40,000

20,000

0

Figure 5.29: Total number of transactions registered per weekday, aggregated by transaction type.

Fig. 5.29, in turn, displays the total number of transactions for each weekday, aggregated by type.

Analyzing the distribution of transactions between OHAs and PHAs across weekdays, it is evident that

activity levels are fairly constant. This observation is corroborated by statistics, with market transactions

reaching a standard deviation of only 1,207 (3.9%) in relation to a mean of 31,200 per weekday. Evi-

dently, intra-company transfers are also distributed evenly, yielding a standard deviation of 234 (4.4%)

in relation to a mean of 5,265. Expectedly, administrative transaction do not respect the rigid timeframe

imposed by the registry, which is why a considerable number of transactions were issued during weekends.

These, however, are almost exclusively linked to the automated process of allowance allocation, which

is performed on fixed dates regardless of weekdays. Accordingly, standard deviation for administrative

transactions is significantly higher than for the remaining categories, amounting to 12,940 or 58.3% of

the mean (22,200).

65

CHAPTER 5. DISCUSSION AND RESULTS 5.4. AUCTIONING

PHASE I2005-2007

PHASE II2008-2012

PHASE III2013-2020

PHASE IV2021-2030

GRANDFATHERING BENCHMARKING

NATIONAL ALLOCATION PLANS NATIONAL IMPLEMENTATION MEASURES

CARBON LEAKAGE POLICY

INDEPENDENT NATIONAL REGISTRIES COMMON UNION REGISTRY

EUTL

MRV - MONITORING, REPORTING, VERIFICATION

EUAA - AVIATION ALLOWANCES

MSR - MARKET STABILITY RESERVE

t0-1d t0

EU AUCTION ACCOUNT

ICE Clear Europe Ltd.ICE London

European Commodity Clearing AGEEX Leipzig

64 Banks, Brokers & Trading Companies

12%

88%

56%

44%

88%

12%

Acting as Brokers Acting as Sellers

Up to 10,891 other installation operators

58 participatingInstallation Operators

Figure 5.30: The process of auctioning allowances is managed by two clearing agencies receiving allowances

from an EU account. Evidently, the number of market participants is one order of magnitude lower

than the actual number of companies active in phase III. Further research is necessary to determine,

whether banks and trading companies act as brokers or sell auctioned allowances to the remaining 10,900

installation holders not participating in the system.

5.4 Auctioning

With the beginning of phase III, auctioning has been established as the primary allocation method

for EU allowances, replacing free allocation in several industry sectors. Accordingly, both the theoretical

model behind the auctioning of allowances and its practical implementation have been discussed in several

studies since 2013. However, none of these rely on EUTL data, which, unlike external sources, allows for an

analysis of allowance auctions on a transaction level. Whereas both EEX Leipzig and ICE London provide

extensive information on past auctions, including prices, the number of bids submitted versus the number

of successful bids as well as auction volumes, no data to identify market participants is available. Hence,

insight on which account holders usually participate in auctions and to what extent auctioning is used by

OHAs can only be gained on the basis of transaction data extracted from the EUTL. Since the process of

auctioning is organised by two clearing agencies rather than by the ETS Registry, the data necerssary to

accomplish this task cannot be obtained straightforwardly. In fact, the stream of transactions does not

go directly from an EU account to the successful bidders. Rather, the allowances intended for auctioning

66


are transferred one day in advance from the EU AUCTION ACCOUNT to the clearing agency. Whereas

ICE Clear Europe Ltd. manages auctions at ICE London, the European Commodity Clearing AG is

responsible for auctions at EEX Leipzig. Provided that the auction is successful, the lots are either

delivered directly to the buyers’ accounts (ICE) or transferred via an intermediary account (EEX). In

case the auction is automatically canceled, which occurs if the projected volume is not met or if the price

level is too low, the auctioned volume is returned to the EU account within two working days. In order

to maintain a constant level of allocation, these allowances are distributed across the next four scheduled

auctions. However, this situation rarely arises, with only three documented cases at ICE and none at

EEX from 2013 to 2020.

2019201820172016201520142013

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent) 1000

800

600

400

200

0

EEX ICE

Figure 5.31: The annual volume of allowances auctioned is determined by the European Commission

based on the National Implementation Measures.

In absolute numbers, 1,628 auctions have been held at EEX from January 2013 to October 2020, an-

other 162 at ICE. On average, 653,775,500 (std.20.5%) allowances have been auctioned at EEX, 89,837,000

(std.19.4%) at ICE. This translates to an average of 234,400 (std.41.1%) allowances per bidder in a single

auction at EEX and 370,600 (std.42.9%) allowances per bidder at ICE. The average auction volume,

in turn, amounts to 3,212,700 (std.31.8%) allowances at EEX and 3,856,300 (std.29.0%) allowances at

ICE. Both markets yield average bid-to-cover ratios higher than 2 (EEX: 2,96; ICE: 2,17), indicating

a considerably strong demand for EU allowances during phase III. Whereas at ICE, an average of 14.5

bidders (std. 2.11) take part in an auction, 10.6 (std. 2.76) of which are successful, auctions at EEX

67


usually attract more potential buyers with an average of 20.24 (4.60) participants and 14.4 (4.22) win-

ning bidders. However, as fig. 5.34 indicates, the bid-to-cover ratio has constantly been in decline on

both markets since 2015 after reaching a peak in 2014. Interestingly, whereas this development can be

explained by the increase in allowances auctioned from 2014 to 2017, the bid-to-cover ratio has not been

reacting accordingly to the reduced volume from 2018 onwards (fig. 5.31). As to the monetary value of

the auctioned volume, fig. 5.32 displays a steep upwards trend from 2017 to 2019, which can be explained

by the rising spot price for EU allowances. Evidently, this development contrasts the stagnating auction

volumes displayed in fig. 5.31.

2019201820172016201520142013

billi

on E

UR

15.00

10.00

5.00

0.00

EEX ICE

Figure 5.32: Due to the rising allowance price, the monetary value of allowances auctioned has increased

drastically from 2017 onwards.

With regard to participation, the most notable insight to be derived from the EUTL transaction

log is the low number of account holders taking part in allowance auctions: At ICE, only 15 different

account holders have been registered during phase III, not more than one of which is associated with an

installation operator, the remaining accounts belonging to either banks, brokers or trading companies.

At EEX, this number is considerably higher, amounting to 116 individual account holders, about 50% of

which represent either banks, brokers or trading companies. Whereas at ICE, apart from Deutsche Bank,

only UK accounts have participated in auctions, transaction data draws a more diverse image for EEX,

where bidders from 18 member states were recorded. However, accounts from the UK were also dominant

in this context, amounting to 46.2% of bidders from 2013 to 2020. Expectedly, the prevalence of banks and

68


brokers also affects transaction volumes: From 2013 to 2016, 55.7% of allowances in 67.6% of transactions

involving the EEX Auction Delivery Account were directed towards these bidders. However, the average

transaction volume of 347,500 allowances was considerably lower than that of installation holders, which

reached 575,500 allowances. With regard to the average volume per account holder, in turn, banks and

brokers are ahead by a considerable margin, reaching 434,000 versus 368,700 allowances. Considering

these results, two hypotheses can be formulated as to the role banks play in the auctioning of allowances.

First, it is probable that banks predominantly act as brokers or intermediaries by placing bids on behalf

of installation operators lacking the skill or infrastructure necessary to participate in allowance auctions.

This would explain the exceptionally low number of installation operators receiving transactions directly

from ICE or EEX. Second, banks other market participants not directly involved in the ETS may be

acting on their own account, acquiring allowances at auctions with the purpose of trading. According

to Art. 18 of the Auctioning Regulation (European Commission, 2010), both alternatives are legally

viable. However, a transaction-level analysis of accounts operated by two major players – Deutsche Bank

and Citigroup Global Markets – fails to provide useful insight on this issue. In concrete terms, this

involved searching for transactions with identical volumes in temporal proximity to the transfers issued

by either ICE or EEX. Unfortunately, I was unable to identify conclusive patterns using this relatively

straightforward approach. Hence, further research using a more sophisticated, algorithm based method is

needed to shed light on the business practice of banks or financial institutions involved in the auctioning

of allowances.

Finally, fig. 5.35 displays the average auction volume per calendar week in relation to the average

demand for EU allowances with the objective of identifying seasonal fluctuations. Both EEX and ICE

exhibit relatively volatile auction volumes, reaching a peak in march, followed by a temporary low in

August. During Christmas season, demand also drops on both markets, however to a lesser extent at ICE.

This observation is corroborated by descriptive statistics: Data for EEX yields a mean of 13,355,000 with

a standard deviation of 21.2%, the corresponding values for ICE are 1,540,000 and 19.6%. A correlation

analysis reveals that the average weekly demand is tightly linked to the auctioned volume. For EEX,

the correlation coefficient reaches 0.911 at a significance level of 0.01, whereas for ICE, this value is

considerably lower, amounting to 0.491 at a sgnificance level of 0.01.

69


20202019201820172016201520142013mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

per a

uctio

n6.00

5.00

4.00

3.00

2.00

1.00

0.00

EEX ICE

Figure 5.33: The average auction volumes at ICE and EEX, which had been collinear until 2017, have

been diverging since.

20202019201820172016201520142013

bid-

to-c

over

ratio

6.00

5.00

4.00

3.00

2.00

1.00

EEX ICE

Figure 5.34: The bid-to-cover ratio, which, in this case, is an indicator of the demand for EU allowances,

has been in constant decline on both markets since 2015.

70


week

5048464442403836343230282624222018161412108642mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

per w

eek

50.00

40.00

30.00

20.00

10.00

0.00

EEX total bidsEEX auctionedICE total bidsICE auctioned

Figure 5.35: Both EEX and ICE exhibit a strong correlation between the demand for allowances and the

number of allowances auctioned per week.

71

CHAPTER 5. DISCUSSION AND RESULTS 5.5. AUSTRIAN ACCOUNTS

5.5 Narrowing the Focus: Observations based on Transactions

to and from Austrian Accounts

5.5.1 An Anatomy of the Austrian Emissions Market

Given the imperfections of the EUTL, linking account and transaction data is a time consuming and often

unrewarding task which cannot be easily automatized. Nevertheless, said link provides some valuable

insights on certain aspects of the ETS which are impossible to deduce from transaction data alone.

Hence, I perform a more detailed analysis of the EUTL with a limited scope, focusing on transactions

originating from as well as directed to Austrian accounts. This enables me to shed light on aspects of

emissions trading left out or only partially explored in previous sections. For instance, I am able to expand

on the distribution of account types using transaction data, complementing the information provided in

section 5.2. Furthermore, narrowing the focus enables me to translate an account’s activity type to the

NACE code of the account holder, making it possible to identify the distribution of industry sectors in

the ETS as well as the development of trading volumes for each industry sector. Finally, I determine

the share of transactions to and from foreign accounts, investigating which countries Austrian firms have

been interacting with during phase III.

Year

20172016201520142013

num

ber o

f acc

ount

s ac

tive

per y

ear

300

200

100

0

119

296

OthersOHA

Figure 5.36: The number of both physical installations, PHAs and trading accounts, which have been

active at least once in a given year, is substantially lower than the theoretical maximum derived from

account data.

72


Since the EU’s member states are extremely heterogeneous in terms of population size and economic

performance, I first establish, how the Austrian market is structured in relation to the EU average.

In terms of registered accounts, the OHA dataset currently lists 296 physical installations in Austria,

only part of which have been constantly active during phase III. In comparison to the EU average, the

Austrian registry holds a significantly lower number of PHAs and trading accounts: Whereas 65.1% of

26,079 accounts registered across all 31 countries participating in the ETS are OHAs, the share of physical

installations rises to 71.3% when limiting the scope to Austria. As to the remaining account types, the

equivalent numbers are 27.4% for PHAs and 7.5% for trading accounts in the European perspective. Of

415 account identifiers listed by the Austrian registry, in turn, only 20.2% are identified as PHAs and

further 8.4% as trading accounts. In terms of installation density, Austria scores below average, reaching

only 33.4 installations per million inhabitants as opposed to the EU average of 59.9.

Andere

Industrial plants for the production of (a) pulp from

timber or other fibrous m

aterials (b) paper and board

Installations for the production of cement

clinker in rotary kilns or lime in rotary kilns or in

other furnaces

Production of pulp




Other activity opted-in pursuant to Article 24

of Directive 2003/87/EC

Manufacture of glass


Installations for the manufacture of ceram

ic products by firing, in particular roofing tiles, bricks, refractory bricks, tiles, stonew

are or porcelain

Production of lime, or calcination of

dolomite/m

agnesite



ics


Com



W

Com

bustion of fuels

40.00

30.00

20.00

10.00

0.00

ATEU

sha

re in

%

Figure 5.37: Distribution of registered installations in Austria across activity types compared to the EU.

Backing the theoretical numbers with EUTL data, fig. 5.36 illustrates the results of a transaction-

level analysis monitoring the number of PHAs and OHAs which were involved in at least one transaction

in a given year. The considerable discrepancy observed between the number of registered accounts and

the empirical results from transaction data indicates that not all installations have been entitled to

free allocation or surrendered emission allowances in every period. Whereas I am unable to provide an

explanation for this phenomenon based on empirical data, it is probable that a considerable number of

installations have been put into operation, sold, modernized or closed down during phase III, resulting

73


in periods of inactivity. Due to the absence of compliance obligations, this behavior seems even more

plausible for PHAs or trading accounts. Evidently, both account types exhibit a significant downwards

trend during phase III. In concrete terms, the number of active installations has decreased from 220, which

equals 74% of the theoretical maximum, to 188 or 63.5% from 2013 to 2017. PHAs and trading accounts,

on the other hand, exhibit even lower numbers, ranging from 38 accounts or 32% of the theoretical

maximum in 2013 to a peak of 46 or 38.7% in 2014 and the all-time low of 27 or 22.7% in 2017.

Production of amm

onia

Production of secondary aluminium

Production of nitric acid

Production of coke

Refining of m

ineral oil

Production or processing of gypsum or

plasterboard

Production or processing of non-ferrous metals

Industrial plants for the production of pulp from

timber or other fibrous m

aterials / paper

Installations for the manufacture of glass including

glass fibre

Mineral oil refineries

Other activity opted-in pursuant to Article 24 of

Directive 2003/87/EC


Installations for the manufacture of ceram

ic products by firing, in particular roofing tiles, bricks,


Installations for the production of cement clinker or

lime in rotary kilns

Manufacture of glass

Production of pulp



Production of lime, or calcination of

dolomite/m

agnesite



ics


Com



W

Com

bustion of fuels

0

100.00

80.00

60.00

40.00

20.00

0.00

Figure 5.38: Distribution of registered installations in Austria across activity types.

Fig. 5.37 and fig. 5.38 give an indication of the distribution of activity types and industry sectors

across Austrian installations. Due to the limited size of the market, only 23 of 38 possible categories

are actually represented in the Austrian registry. In comparison with EU data, Austria exhibits a lower

concentration of activity types, with combustion of fuels amounting to only 33.8% versus 43.0% of all

installations. Furthermore, 81.8% as opposed to 84.6% of installations belong to one of the eight largest

categories, whereas 30.4% versus 60.5% of activity types do not exceed the 1% mark. With regard to

the Hirschmann-Herfindahl index, Austria scores 1,599 compared to 2,249.7, despite the lower number of

categories in use.

In order to provide an alternative to the category scheme used by the EUTL, which differentiates

installations or sub-installations based on the way GHGs are emitted rather than by focusing on the

installation operators’ industry sectors, I establish a link between each individual account and the parent

company’s NACE code. However, contrary to my initial assumption, this process, which requires extensive

74


manual adjustments and can thus not be extended to the whole transaction dataset, fails to create a more

even distribution. As fig. 5.39 indicates, the largest category – electricity, gas, steam and air conditioning

supply – comprises a similar share of Austrian installations as combustion of fuels (29.7% vs. 33.8%). In

addition, the HHI rises from 1,599 to 1,729, which, however, is still below the EU average. On the other

end of the spectrum, 8 or 38% of 21 NACE codes in the dataset score below the 1% mark, whereas the

8 largest categories make up 91.6% of all installations. However, the dominance of certain sectors can

be explained by both the limited size of the Austrian market and the country’s low supply of natural

resources, challenging the significance of direct comparisons with other countries or the EU as a whole.

NACE_Branche_l1

Herstellung von D

atenverarbeitungsgeräten, elektronischen und optischen Erzeugnissen

Samm

lung, Behandlung und Beseitigung von Abfällen; R

ückgewinnung

Herstellung von M

etallerzeugnissen

Sonstiger Fahrzeugbau

Getränkeherstellung

Gew

innung von Steinen und Erden, sonstiger Bergbau

Herstellung von G

umm

i-und Kunststoffwaren

Herstellung von pharm

azeutischen Erzeugnissen

Gew

innung von Erdöl und Erdgas

Herstellung von Kraftw

agen und Kraftwagenteilen

Herstellung von Textilien

Kokerei und Mineralölverarbeitung

Herstellung von N

ahrungs-und Futtermitteln

Herstellung von H

olz-, Flecht-, Korb-und Korkw

aren (ohne Möbel)

Herstellung von chem

ischen Erzeugnissen

Metallerzeugung und -bearbeitung

Herstellung von Papier, Pappe und W

aren daraus

Luftfahrt

Herstellung von G

las und Glasw

aren, Keramik,

Verarbeitung von Steinen und Erden

Energieversorgung

0

100.00

80.00

60.00

40.00

20.00

0.00

Figure 5.39: Distribution of registered installations in Austria across NACE codes.

5.5.2 The Sectoral Distribution of Market Activity

Whereas valuable insight on the distribution of activity types or industry sectors can be derived from

account data, transaction data is required to investigate the development of both transaction volumes,

transaction numbers and transaction sizes during phase III. As fig. 5.40 reveals, the distribution of

industry sectors in the OHA dataset does not match the actual transaction volumes derived from EUTL

data. Whereas a mere 7.1% of installations are related to the manufacture of basic metals, their share

of allowances traded amounts to 26.3%. On the other hand, electricity and gas supply, which, as the

dominant sector, covers 29.7% of installations, is responsible for only 20.9% of the overall transaction

volume. The same applies to manufacture of non-metallic, mineral products, which is associated with

75


only 10.5% of allowances traded while covering 23.0% of installations. The annual number of transactions

represented by fig. 5.41, in turn, is more in line with account data. Of all transactions issued to and

from Austrian accounts between 2013 and 2016, 35.2% can be attributed to electricity and gas supply,

further 20.8% to manufacture of non-metallic, mineral products, and finally, 8.7% to manufacture of

basic metals. Said results are also apparent in fig. 5.42, which illustrates the average transaction sizes

aggregated by NACE code. From this perspective, manufacture of coke and refined petroleum products as

well as manufacture of basic metals exhibit exceptionally high values, suggesting that company sizes in

these industries are comparably large. In the case of Voestalpine AG and its subsidiaries, this statement

can undoubtedly be confirmed.

The significance of these observations, however, is impaired by both the low sample size of only

296 installations operated by 171 legal entities and the heterogeneous nature of the Austrian market.

As the OHA dataset reveals, the Austrian registry is characterized by a considerable number of SMEs

with relatively low annual turnover and trading activity, which is in stark contrast to global players like

Voestalpine, OMV or Wienerberger. Whereas this may explain why certain industry sectors featuring

low account numbers are overrepresented in terms of transaction volume, it still remains unclear whether

variables such as annual turnover or the number of employees have a causal effect on a company’s trading

activity. A thorough analysis of this relationship, however, would require the use of additional data

sources exceeding the scope of my thesis.

2016201520142013

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

40.00

30.00

20.00

10.00

0.00

Air TransportManufacture of Paper and Paper ProductsManufacture of Chemicals and Chemical ProductsAverageManufacture of Coke and Refined Petroleum ProductsManufacture of Non-Metallic, Mineral ProductsElectricity and Gas SupplyManufacture of Basic Metals

Figure 5.40: Total volume of transactions involving Austrian accounts by NACE code.

76


2016201520142013

num

ber o

f tra

nsac

tions

600

500

400

300

200

100

0

Manufacture of Food ProductsManufacture of Chemicals and Chemical ProductsAir TransportAverageManufacture of Paper and Paper ProductsManufacture of Basic MetalsManufacture of Non-Metallic Mineral ProductsElectricity and Gas Supply

Figure 5.41: Total number of transactions involving Austrian accounts by NACE code.

2016201520142013

allo

wan

ces

per t

rans

actio

n

1,000,000

800,000

600,000

400,000

200,000

0

Manufacture of Paper and Paper ProductsOther Mining and Quarrying Manufacture of Non-Metallic Mineral ProductsElectricity and gas supplyManufacture of Chemicals and Chemical ProductsAverageManufacture of Basic MetalsManufacture of Coke and Refined Petroleum Products

Figure 5.42: Average number of allowances per transaction involving Austrian accounts by NACE code.

77


5.5.3 National and International Transactions

2016201520142013

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent) 50.00

40.00

30.00

20.00

10.00

0.00

NationalInternational

Figure 5.43: Volume of national versus international transactions involving Austrian accounts. A trans-

action is identified as international if the transferring registry and the acquiring registry are not identical,

provided that the transaction is not administrative.

Apart from generating data on the distribution of industry sectors on the basis of NACE codes,

the focus on Austrian accounts provides valuable insight on another relevant aspect: By analyzing the

acquiring as well as the transferring registries of transactions in the Austrian dataset, I am able to

compare the share of transfers between domestic companies to those originating from or directed to

foreign accounts. In this context, I restrict my analysis to market transactions, meaning that both

administrative transactions involving the national registry accounts as well as intra-company transfers are

ignored. Relative to the total number of 8,007 transactions involving Austrian accounts recorded by the

EUTL from 2013 to 2016, the former category makes up 51%, which is on par with the European average

of 48.3%. In terms of transaction volumes, however, only 24.1% as opposed to 56.3% are attributed to

market transactions, which can be explained by a considerable number of internal transfers issued by the

Austrian Emissionshandelsstelle. After eliminating 15 of said transactions with volumes ranging from

1.25 to 41 million allowances, the share of market transactions rises to a more plausible 38.0%. As fig.

5.44 indicates, the numbers of both national and international transactions have been in decline since

2013. Whereas national transactions, have lost 76.1% during phase III, this value amounts to 56.8% for

78


transactions involving foreign accounts.

As to the phase III average, the number of international transactions exceeds that of national transac-

tions by 93.1%. This substantial gap is also evident from fig. 5.44, amounting to an even greater 110.5%.

Concerning the development of transaction volumes, however, the downwards trend observed from 2013

to 2016 is less significant than in terms of transaction numbers, amounting to –21.9% for international

and –30.9% for national transactions. In accordance with these numbers, fig. 5.45 indicates a steep up-

wards movement in terms of transaction sizes, which amounts to +80.9% for international and +189.4%

for national transactions. The phase III average, in turn, is nearly identical for both variables, deviating

by only 3.9%.

2016201520142013

num

ber o

f tra

nsac

tions

600

400

200

0


Figure 5.44: Number of national versus international transactions involving Austrian accounts.

As a final step, I differentiate transactions by their transferring and acquiring registries, enabling me

to determine, which countries Austrian companies are predominantly involved with in terms of emissions

trading. For this purpose, I investigate both transaction volumes and numbers for transactions originating

from and directed to Austrian accounts. In absolute terms, 1,704 international transactions were issued

between 2013 and 2016, 1,079 or 63.3% of which exhibit Austrian recipients. As to the national registries

involved, the transaction dataset lists 22 countries on the transferring as well as 19 on the receiving end.

However, only a small number of countries exhibit substantial volumes in all four trading periods observed:

Among transactions originating from Austrian accounts, the UK yields the highest average annual volume

with 4.34 million allowances, closely followed by Germany with 3.94 million allowances. These two

79


2016201520142013

allo

wan

ces

per t

rans

actio

n150,000

100,000

50,000

0


Figure 5.45: Number of allowances per transaction involving Austrian accounts.

outperform other nations such as Romania (1.07 million), France (0.5 million) and the Netherlands (0.33

million) by a considerable margin. On the receiving end, Germany reaches 8.14 million allowances p.a.,

followed by the UK with 2.96 million, France with 1.83 million, Romania with 1.46 million and the

Netherlands with 0.93 million. Regarding the dominance of Germany and the UK, the EUTL reveals

that a substantial share of transactions originating from these countries involve banks or brokers. This

may also entail the acquisition of allowances through auctions, in which no Austrian installation operators

have partaken so far. However, further research is needed to back this observation, which is based on an

exemplary analysis of a small sample, with concrete data.

The same reasoning applies to the development of transaction numbers and transaction volumes

illustrated in fig. 5.46 to fig. 5.46. Whereas the sharp decline in transactions involving CDM or Kyoto

credits at the beginning of phase III can be explained by legal restrictions put into effect in 2013, it is

difficult to formulate a coherent hypothesis as to the volatility of transaction volumes to and from foreign

accounts. Given the low number of international transactions in combination with the limited share of

Austrian accounts interacting with foreign trading partners, however, an in-depth analysis of this subject

should not yield significant results.

80


2016201520142013

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent)

10.00

8.00

6.00

4.00

2.00

0.00

Netherlands France Romania Germany United Kingdom

Figure 5.46: Annual volume of transnational transactions originating from Austrian accounts, aggregated

by nation.

2016201520142013

num

ber o

f tra

nsac

tions

80

60

40

20

0

Netherlands France United Kingdom Romania Germany

Figure 5.47: Annual number of transnational transactions originating from Austrian accounts, aggregated

by nation.

81


201620152014201320122011201020092008

mill

ion

allo

wan

ces

(tonn

es o

f CO

2 equ

ival

ent) 12.00

10.00

8.00

6.00

4.00

2.00

0.00

Netherlands Romania France CDM United Kingdom Germany

Figure 5.48: Annual volume of transnational transactions received by Austrian accounts, aggregated by

nation.

201620152014201320122011201020092008

num

ber o

f tra

nsac

tions

200

150

100

50

0

Romania Netherlands France CDM United Kingdom Germany

Figure 5.49: Annual number of transnational transactions received by Austrian accounts, aggregated by

nation.

82

Chapter 6

Conclusion

The European Union’s emissions trading system has been in operation since 15 years and is on the verge

of entering into its fourth evolutionary phase. Over time, it has undergone substantial changes aimed

at adapting the system to the development of the market. This not only entails the centralization effort

resulting in the creation of a unified registry by the beginning of phase III, but also affects the way

allowances are allocated. In my thesis, I aim at shedding light on the underlying mechanisms of this

development by making use of a data source which has rarely been used by researchers to this date.

In the course of the establishment of the ETS registry, the EU has created a publicly available database

encompassing both account and transaction data for all trading periods from 2005 onwards with a delay

of three years. Whereas in theory, this comprehensive source of data grants a tremendous opportunity to

perform research on various aspects of the EU ETS and its participants on a transaction level, my personal

experience proves that there are still challenges to be overcome in order to realize the European Union

Transaction Log’s full potential. This not only entails questions of usability, but also encompasses certain

issues with data quality, which have not yet been alleviated. To begin with, I criticize the limitations of

the EUTL website, which, in addition to its unstructured design, lacks a useful export feature for both

account and transaction data. The existing implementation, in turn, restricts the number of data points

per download, necessitating the use of web scraping software in order to compile large datasets. Whereas

said shortfall constitutes only a minor hindrance to the ambitious researcher, deficiencies concerning

data quality are harder to overcome. As my research indicates, a considerable proportion of transactions

issued prior to 2013 exhibit missing account identifiers either on the transferring, the acquiring or on

both sides. The fact that these incompletely labeled transactions amount to 15.5% of the total number

and 35.5% of the total trading volume from 2005 to 2012, renders certain analyses based on transaction

types impracticable. Provided that these inconsistencies stem from the transition to the centralized

registry at the end of phase II, it is doubtful whether substantial improvements are to be expected in the

CHAPTER 6. CONCLUSION

future. Another flaw in the transaction dataset relates to the conversion of emission allowances under

the Kyoto protocol to EU allowances, which resulted in a considerable number of duplicate transactions

in the dataset. These, however, cumulate on a certain date and can thus be eliminated if necessary.

Overall, said insufficiencies of the database have not occurred since 2014, suggesting that data quality

has already seen major improvements during phase III and is going to improve further during phase

IV. Hence, I am positive that in future, researchers working with the EUTL database will be able to

focus more on its content rather than on coping with its deficiencies. Finally, one of the most significant

drawbacks of the EUTL, which should definitely be addressed by the European Commission, is the missing

integration of account and transaction data. Currently, neither the account identifiers nor the account

holders found in the transaction dataset are identical to those in the OHA dataset. Both the PHA and

the trading accounts dataset, in turn, don’t even name account identifiers, making it virtually impossible

to distinguish between these types on a transaction level. By eradicating this shortfall, the European

Commission would enable researchers to conduct a broad range of analyses without having to manually

link transaction and account data. This involves distinguishing between OHAs, PHAs and Trading

Accounts as well as matching installations with corresponding activity types or NACE codes. Ideally,

each individual account should be assigned a unique alphanumeric identifier containing information on

national registry, type and account holder in order to facilitate the preparation and processing of data.

Compared to the emissions trading system as a whole, however, evaluating the EUTL and its challenges

is an infinitely less complex subject. In fact, the development of the European Union’s ETS during the

first 15 years of its existence can be analyzed from a multitude of perspectives. For this purpose, I

employ several independent sources of data, ranging from an official emissions dataset compiled by the

European Environment Agency to GDP statistics by EUROSTAT. The main objective of my thesis being

to provide new insight into emissions trading using EUTL data, I predominantly base my conclusions on

the European Union Transaction Log’s publicy available database, striving to explore aspects of the ETS

which have not yet been covered in literature.

Starting with general data on the scope and size of the European Emissions Trading System, the

EEA’s aggregated dataset makes it possible to trace the development of emission levels and allowance

allocation over time. As my analysis reveals, the supply of allowances has been exceeding the number of

surrendered units from 2009 to 2013, leading to the accumulation of a substantial surplus lowering the

allowance price by more than 80% compared to its initial value. In an attempt to alleviate this systemic

flaw, the European Commission cut the supply of allowances to be auctioned during phase III, eventually

implementing an instrument called the Market Stability Reserve. The MSR, which manages allocation

based on the number of allowances in circulation, will have to prove its effectiveness in stabilizing the

allowance price during phase IV. In recent publications, this subject has been discussed controversially,

with debate centered mainly around an aspect which is referred to as the waterbed effect – a situation, in

84


which complementary environmental policies mitigate the overall GHG reduction achieved by the ETS.

Hence, government subsidies targeting CO2-efficiency in a certain sector are expected to grant beneficiaries

of such policies a surplus of allowances, leading to a decrease in prices, which, in turn, disincentivices

emissions abatement (Mulder, 2021). Following this reasoning, I conclude that the overallocation of

allowances to certain industry sectors, which has remained a major factor throughout phase III, may

result in a similar effect, impairing overall abatement effciency while shifting allowances from subsidized

to non-subsidized industry sectors. As a side effect, this leads to companies from emission-intensive

industries such as steel or cement production realizing substantial windfall profits – a practice, which,

although justified by the European Commission as a means of preventing carbon leakage, contradicts the

fundamental principles of a cap-and-trade system. This is especially relevant since recent studies find no

evidence for the alleged exodus of companies due to the burden of the EU ETS.

According to Perino (2018), the cancellation of allowances via the MSR is going to temporarily reverse

the waterbed effect, whereas the impact of this reversal decreases the later emission abatement takes place.

Eventually, as soon as the number of banked allowances falls below the threshold, which may occur as soon

as 2023, the waterbed effect is expected to return. Rosendahl (2019), in turn, takes a more pessimistic

stance, arguing that even the prospect of future reduction policies complementary to the ETS may impair

emission abatement, since account holders anticipating these changes tend to bank less allowance, leading

to a lower number of cancelled allowances. Flachsland et al. (2020) make a case for the installation of a

price floor for EU allowances, which may not only serve as a complementary measure, but even replace

the existing MSR. In fact, an auction reserve price already exists, albeit with minor practical impact. In

the current auctioning regulation, the price floor is tied to the secondary market price for EU allowances,

so that in reality, the European Commission is unable to exert control over the market. In addition,

bid-to-cover ratios have been stable during phase III, so that only two auctions have been canceled for

this reason so far.

Continuing with the auctioning of allowances, a transaction-level analysis reveals that, despite the

relevance that this allocation method has gained by the beginning of phase III, only 122 different account

holders have received transactions from one of the two market places involved so far. Given the fact that 64

of these are either banks, brokers or trading companies, it is evident that installation operators, which, in

theory, represent the target audience of allowance auctions, hardly ever partake in the system. However,

despite being able to trace transaction flows from from the EU to the bidders’ accounts, exemplary

analyses of Citigroup and Deutsche Bank fail to give a clear indication as to whether PHAs involved in

auctioning have been acting as brokers on behalf of smaller installation operators unwilling or unable to

take part in allowance auctions. In order to explore this question and identify patterns in the dataset,

which go beyond identical transaction volumes appearing in different transactions, further research is

needed. This, however, requires the use of sophisticated algorithms exceeding the scope of this thesis.

85


Comparing emission values between member countries, major national economies such as Germany or

the UK are dominant in absolute numbers. When adjusting for GDP, however, the ranking is inversed,

suggesting that less performant countries predominantly in Eastern Europe are affected by the ETS to

a higher degree than their economically advanced counterparts in Western Europe. In fact, I identify a

statistically significant correlation between a country’s per capita GDP and its adjusted emission levels,

which further corroborates this hypothesis. The same trend can be observed in terms of installation

density, meaning that poorer countries exhibit higher installation numbers per billion EUR of GDP.

This unequal distribution of the burden that the ETS imposes on national economies, has already been

addressed by granting exemptions and subsidies to the countries affected. Given the exceptional GDP

growth rates in countries like Bulgaria, however, I expect the vast economic disparities which still prevails

among the EU’s member countries to narrow in the long run.

With regard to the state and development of the emissions market, several significant insights can be

derived from both transaction and account data offered by the EUTL. In terms of annual transaction

volumes and transaction numbers, both variables have been in decline during phase III after reaching a

peak in 2008/2009, suggesting that the constant reduction of the cap on emissions has a negative effect

on trading activity. Differentiating by three transaction types – market transactions, intra-company

transfers and administrative transactions – I establish a category scheme which aids in understanding

the dynamics of the EU ETS. Relative to the total volume, market transactions make up about 43.5%,

compared to 47.2% for administrative transactions, indicating that administrative processes such as the

allocation and surrendering of allowances or transfers between national registries constitute a slightly

larger volume than actual emissions trading between accounts. Intra-company transfers or transactions

between accounts operated by the same company, in turn, are substantially less relevant both in terms

of transaction volume and transaction numbers, amounting to 9.3% of the total volume. However, my

analysis disregards the affiliation of registered account holders to large corporations, which may constitute

a source of error leading to unrealistically low numbers of intra-company transfers. This flaw in my model

may indeed pose a problem when evaluating market activity, since it is hard to judge whether transactions

between legal entities within a corporation serve the original purpose of emissions trading. Referring to a

strategy used by Jaraite et al. (2013) on phase I data, I recommend linking account holders to a company

database in order to resolve this issue.

Taking an in-depth look at administrative transactions, it becomes apparent that a subset of this

category responsible for only 0.65% of administrative transactions makes up 21.5% of the total transaction

volume between 2013 and 2016. Their low number being contrasted by exceptional transaction sizes, this

category can be identified as the main cause of spikes in the dataset, which, due to their sheer dimension,

have a distorting effect on the overall market activity. In order to identify and analyze these irregularities,

I shift perspective, calculating the average transaction volumes, transaction numbers and transaction sizes

86


for each day of the year. On the one hand, this allows me to examine periodic events such as allowance

allocation and surrendering, while on the other hand, it enables me to isolate spikes both in terms of

transaction volumes and transaction numbers, laying the foundation for a more thorough analysis. Indeed,

several cases of abnormally high transaction volumes or numbers limited to short periods of time can be

identified in the dataset, all of which are attributed to registry-internal processes without relevance for

emissions trading. As a transaction-level analysis reveals, these irregularities can be attributed to singular

events such as the obligation to retire Kyoto credits from previous phases in 2015 or the cancellation of

expired EU allowances in 2013. With regard to recurring patterns in transaction data, I identify three

periods of increased activity related to the allocation of allowances until February 28 – the surrendering of

allowances until April 30 as well as to the delivery of EUA forwards and futures in December. Both these

periodic events and the spikes in transaction data can also be observed when aggregating transaction

volumes and numbers by month.

Aggregating transaction numbers by hour and weekday, in turn, adds another insightful perspective

to my analysis of emissions trading, revealing that transactions issued by regular market participants are

restricted to a narrow timeframe. Whereas market transactions are accepted by the system only within

office hours and require a minimum 26 hours until completion, administrative transactions such as the

allocation of allowances are automatically issued and thus independent of time or weekday. Accordingly,

while the former category is evenly distributed across the permitted timeframe, the latter exhibits a peak

around 1 am.

Finally, by analyzing transaction data limited to Austrian accounts, I investigate certain aspects of

the ETS which require a more thorough analysis of EUTL data, necessitating manual adjustments which

could hardly be upscaled to the whole ETS in a reasonable amount of time. This entails linking account

data with the transaction dataset in order to determine the corresponding account types and industry

sectors for each party involved in a transaction directed to or originating from Austrian accounts. Starting

with a comparison between the number of registered accounts and the number of active accounts, the

limited perspective enables a more thorough analysis compared to using the whole dataset. Across all

participating countries, the number of active accounts has decreased by 14.9% during phase III, ending an

upwards trend which culminated in a peak of 216% of the 2005 value in 2013. This negative development

prevails among most nations, with only Italy and Luxembourg exhibiting neutral to slightly positive

growth rates. However, my analysis based on the entire ETS treats both OHAs, PHAs and trading

accounts indisccriminately, meaning that changes in installation numbers are not addressed individually.

Using data from Austrian accounts enables me to overcome this limitation on the basis of OHA data.

During phase III, the number of Austrian installations participating in emissions trading has decreased

by 14.5% from its initial value of 220, which equals 74% of all registered OHAs. This downwards trend

has been more drastic with regard to PHAs and trading accounts, which exhibit a decrease of 28.9%

87


between 2013 and 2017, from a mere 32% of the theoretical maximum by the beginning of phase III to

only 22.7% within 4 years. Unsurprisingly, participation is considerably higher with physical installations

than with other account types, which can be explained by the formal requirements involved in opening

an Operator Holding Account.

Given the limitations of the activity types used to identify industry sectors, I attempt an analysis based

on the more common NACE scheme by manually assigning each installation operator a corresponding

NACE code, revealing that certain categories such as steel production exhibit disproportionally high

market activity. However, I concede that the conclusions drawn from this observation are questionable

due to the limited sample size of the Austrian market. Taking into account this shortfall, extending the

scope to a larger fraction of the dataset would be advisable. In addition, linking transaction data with a

company database would make it possible to account for differences in annual turnover while analyzing

the impact of company size on market activity.

At last, I analyze the distribution of transactions across Austrian and foreign accounts in order to gain

insight both on the frequency of what I refer to as international transactions and on the countries involved.

Evidently, the share of transactions involving foreign accounts exceeds that of national transactions by

a considerable margin, amounting to 65.9% compared to 34.1% of the total number between 2013 and

2016. Whereas both categories exhibit declining transaction numbers during phase III, international

transactions are affected by this downwards movement to a greater extent. In terms of transaction

sizes, however, both variables are nearly on par, differing by only 3.9% on average. Proceeding to the

distribtion of countries involved in trading with Austrian accounts, Germany and the UK are dominant

both on the transferring and on the acquiring side, followed by Romania, France and the Netherlands. In

total, Austrian companies have interacted with trading partners from 22 countries. Whereas transactions

with smaller countries tend to involve account holders associated with Austrian companies, my analysis

reveals an exceptional share of banks and energy trading companies among German and UK accounts.

Considering this observation, I assume that at least part of said transactions are related to allowance

auctions. However, further research is needed to corroborate this hypothesis based on emipirical data.

88

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List of Figures

2.1 Climate policy instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 The abatement-based model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 The emission-based model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1 Development of the EU ETS from 2005 to 2030 . . . . . . . . . . . . . . . . . . . . . . . . 13

4.1 The EUTL Web Interface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2 Occurrence of missing values from phase I to phase III. . . . . . . . . . . . . . . . . . . . . 34

4.3 Deviation of free allocation and verified emissions derived from the OHA and transaction

dataset (EUTL) compared to official EEA Data. . . . . . . . . . . . . . . . . . . . . . . . 35

5.1 Free allocation and GHG emissions in relation to the EU-wide cap from 2005-2019. . . . . 37

5.2 Development of the allowance price in relation to the accumulated surplus from 2008-2020. 38

5.3 Comparing GHG emissions to free allocation for Combustion of Fuels and other sectors. . 40

5.4 Comparing GHG emissions to free allocation in 4 representative industry sectors. . . . . . 41

5.5 Annual GHG emissions aggregated by country. . . . . . . . . . . . . . . . . . . . . . . . . 43

5.6 Annual GHG emissions aggregated by country and adjusted for GDP . . . . . . . . . . . . 44

5.7 Per-capita emissions including the EU average from 2005 to 2019. . . . . . . . . . . . . . . 45

5.8 Correlation between per-capita GDP and GHG emissions. . . . . . . . . . . . . . . . . . . 46

5.9 Number of registered installations by country. . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.10 Number of registered installations by country adjusted for GDP. . . . . . . . . . . . . . . 48

5.11 Distribution of registered installations across activity types. . . . . . . . . . . . . . . . . . 49

5.12 Number of active accounts (OHAs, PHAs and transaction accounts) from 2005 to 2017. . 49

5.13 Number of active accounts by country from 2005 to 2017. . . . . . . . . . . . . . . . . . . 50

5.14 Total annual transaction volume in relation to the number of transactions per year from

2005 to 2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.15 Total transaction volume in relation to intra-company transactions from 2013 to 2016. . . 52

LIST OF FIGURES LIST OF FIGURES

5.16 Total number of transactions per year in relation to intra company transfers from 2005 to

2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.17 Average number of allowances per transaction from 2013-2016. . . . . . . . . . . . . . . . 53

5.18 Average number of allowances traded per day from 2013 to 2016 including intra-company

transfers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.19 Average number of allowances traded per day from 2013 to 2016 for administrative and

market transactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.20 A spike in transaction numbers occuring around day 183 of 2013. . . . . . . . . . . . . . . 58

5.21 Average number of transactions per day from 2013-2016 including intra-company transfers. 59

5.22 Average number of administrative and market transactions per day from 2013-2016. . . . 60

5.23 Average number of allowances per transaction from 2013-2016 including intra-company

transfers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.24 Average number of allowances per transaction from 2013-2016 for administrative and mar-

ket transactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.25 Total monthly transaction volume in relation to market transactions, administrative trans-

actions and intra-company transfers from 2005 to 2016. . . . . . . . . . . . . . . . . . . . 62

5.26 Total number of transactions per month in relation to market transactions, administrative

transactions and intra-company transfers from 2005 to 2016. . . . . . . . . . . . . . . . . . 63

5.27 Average number of allowances per transaction from 2013-2016 aggregated by month and

account type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.28 Number of transactions registered per hour, aggregated by transaction type. . . . . . . . . 64

5.29 Total number of transactions registered per weekday, aggregated by transaction type. . . 65

5.30 The process of auctioning EU allowances. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.31 Annual volume of allowances auctioned at ICE and EEX from 2013 to 2020. . . . . . . . . 67

5.32 Monetary value of allowances auctioned per year from 2013 to 2020 in EUR. . . . . . . . . 68

5.33 Comparison between the annual auction volumes at ICE and EEX from 2013 to 2020. . . 70

5.34 Development of bid-to-cover-ratios at ICE and EEX from 2013 to 2020. . . . . . . . . . . 70

5.35 Average demand for allowances per week at EUA auctions from 2013 to 2020. . . . . . . . 71

5.36 Number of active accounts per year from 2013 to 2016. . . . . . . . . . . . . . . . . . . . . 72

5.37 Sectoral distribution of registered installations in Austria and the EU. . . . . . . . . . . . 73

5.38 Distribution of registered installations in Austria across activity types. . . . . . . . . . . . 74

5.39 Distribution of registered installations in Austria across NACE codes. . . . . . . . . . . . 75

5.40 Total volume of transactions involving Austrian accounts by NACE code. . . . . . . . . . 76

5.41 Total number of transactions involving Austrian accounts by NACE code. . . . . . . . . . 77

5.42 Average number of allowances per transaction involving Austrian accounts by NACE code. 77

98

5.43 Volume of national versus international transactions involving Austrian accounts. . . . . . 78

5.44 Number of national versus international transactions involving Austrian accounts. . . . . 79

5.45 Number of allowances per transaction involving Austrian accounts. . . . . . . . . . . . . . 80

5.46 Annual volume of transnational transactions originating from Austrian accounts, aggre-

gated by nation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.47 Annual number of transnational transactions originating from Austrian accounts, aggre-

gated by nation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.48 Annual volume of transnational transactions received by Austrian accounts, aggregated by

nation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.49 Annual number of transnational transactions received by Austrian accounts, aggregated

by nation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

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