winners and losers of eu emissions tradingceem.unsw.edu.au/.../jcludius_ets_winners_losers.pdf ·...
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Winners and Losers of EU Emissions Trading
during its first trading period (2005 - 2007)
4th IAEE Asian Conference
19-21 September 2014, Beijing
Johanna Cludius, CEEM, UNSW
Ways to become a ‘winner’
2
Sell overallocation
Engage in EUA-CER swaps
Speculate on carbon market
Offer (costly) brokerage and other services to
liable firms
Pass-through carbon cost to consumers over and
above actual costs incurred
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Data
3
Datasets available on the EU Transaction Log (EUTL) / CITL http://ec.europa.eu/environment/ets/
Operator Holding Accounts
Person Holding Accounts
Transfer Dataset
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
-
500
1,000
1,500
2,000
2,500
Ja
n 2
00
5
Fe
b 2
00
5
Ma
r 2
00
5
Apr
20
05
Ma
y 2
00
5
Ju
n 2
00
5
Ju
l 20
05
Aug
20
05
Sep
20
05
Oct 2
005
No
v 2
00
5
De
c 2
00
5
Ja
n 2
00
6
Fe
b 2
00
6
Ma
r 2
00
6
Apr
20
06
Ma
y 2
00
6
Ju
n 2
00
6
Ju
l 20
06
Aug
20
06
Sep
20
06
Oct 2
006
No
v 2
00
6
De
c 2
00
6
Ja
n 2
00
7
Fe
b 2
00
7
Ma
r 2
00
7
Apr
20
07
Ma
y 2
00
7
Ju
n 2
00
7
Ju
l 20
07
Aug
20
07
Sep
20
07
Oct 2
007
No
v 2
00
7
De
c 2
00
7
Ja
n 2
00
8
Fe
b 2
00
8
Ma
r 2
00
8
Apr
20
08
Ma
y 2
00
8
Mill
ion
EU
A
Allocation
Surrender
Market Transfers
Source: EUTL
Enhancing the dataset
4
Linking datasets
Aggregation from installation to parent company level (Jaraite et
al. 2013) http://fsr.eui.eu/CPRU/EUTLTransactionData.aspx
Defining transfer categories
Admin vs. market transfers (cf. Martino and Trotignon 2013)
Intra- vs. inter-company transfers
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Source: EUTL
Market
Volume (Mt)
inter-
company
intra-
company
inter-
company
1,750 1,603 6,224 9,576 1,750 6,051 9,403
Total
Period I - Transferred
Market
Admin AdminTotal
Period I - Acquired
Adding prices
5
EUTL dataset contains no information on
Time of trade (only physical delivery)
Price employed
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
-5
0
5
10
15
20
25
30
35
Jan 2
005
Feb 2
005
Mar
2005
Apr
2005
May 2
005
Jun 2
005
Jul 2005
Aug 2
005
Sep 2
005
Oct 2005
Nov 2
005
Dec 2
005
Jan 2
006
Feb 2
006
Mar
2006
Apr
2006
May 2
006
Jun 2
006
Jul 2006
Aug 2
006
Sep 2
006
Oct 2006
Nov 2
006
Dec 2
006
Jan 2
007
Feb 2
007
Mar
2007
Apr
2007
May 2
007
Jun 2
007
Jul 2007
Aug 2
007
Sep 2
007
Oct 2007
Nov 2
007
Dec 2
007
Jan 2
008
Feb 2
008
Mar
2008
Apr
2008
€/E
UA
Spot
Average price 2005
Average price 2006
Average price 2007
Average price 2005-2007
Source: Point Carbon
Spot, forwards and futures
6
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Source: EUTL, Point Carbon
0
10
20
30
40
50
60
70
80
90
100
Ja
n 2
00
5
Feb 2
005
Ma
r 2
00
5
Ap
r 2
00
5
Ma
y 2
00
5
Jun 2
005
Ju
l 2
00
5
Au
g 2
00
5
Se
p 2
00
5
Oct 2
00
5
No
v 2
00
5
De
c 2
00
5
Ja
n 2
00
6
Feb
200
6
Ma
r 2
00
6
Ap
r 2
00
6
Ma
y 2
00
6
Ju
n 2
00
6
Ju
l 2
00
6
Au
g 2
00
6
Se
p 2
00
6
Oct 2
00
6
No
v 2
00
6
De
c 2
00
6
Ja
n 2
00
7
Feb
200
7
Ma
r 2
00
7
Ap
r 2
00
7
Ma
y 2
00
7
Ju
n 2
00
7
Ju
l 2
00
7
Au
g 2
00
7
Se
p 2
00
7
Oct 2
00
7
No
v 2
00
7
De
c 2
00
7
Ja
n 2
00
8
Feb
200
8
Ma
r 2
00
8
Ap
r 2
00
8
Mill
ion
EU
A
EUTL Period I market inter- and intra-company transfers
EUTL Period I market L3 inter-company transfers
Point Carbon historical OTC
Point Carbon historical exchange
Shifting between accounts
after allocation and before
compliance date
Delivery of forwards
and futures
Most active entities on clearing days
7
Source: EUTL
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
The
company's
total trading
volume in
Period I
Total trading
volume on
forward /
futures days
The
company's
total trading
volume in
Period I
Total trading
volume on
forward /
futures days
LCH Clearnet 204 91% 17% ELECTRICITE DE FRANCE 40 67% 3%
NASDAQ OMX (Nordpool) 9 11% 1% RWE AG 28 42% 2%
CDC 6 5% 1% E.ON SE 25 35% 2%
SSE PLC 22 60% 2%
UBS AG* 119 82% 10% ENEL SPA 18 27% 2%
Calyon Financial 71 89% 6% ENBW AG 18 57% 1%
BARCLAYS PLC* 68 43% 6% GDF 16 19% 1%
AGEAS SA/NV* 34 38% 3% ESSENT N.V. 16 59% 1%
BNP PARIBAS* 33 72% 3% ALLIANDER N.V. 15 41% 1%
MORGAN STANLEY* 25 58% 2% IBERDROLA SA 14 67% 1%
GOLDMAN SACHS GROUP* 25 78% 2% CENTRICA PLC 13 35% 1%
SOCIETE GENERALE 18 48% 1% DRAX GROUP PLC 12 56% 1%
ROYAL BANK OF SCOTLAND 13 49% 1% CEZ A.S. 12 67% 1%
COMMERZBANK AG 13 37% 1% VATTENFALL AB 12 35% 1%
SAL. OPPENHEIM JR. & CIE. * 9 53% 1% Deeside Power Limited 8 25% 1%
NUCLEAR LIABILITIES FUND 9 74% 1% VEOLIA ENVIRONNEMENT 7 33% 1%
PCE Investors 8 67% 1% Sempra Energy Europe Ltd. 7 44% 1%
MERRILL LYNCH & CO.* 8 34% 1%
DEUTSCHE BANK AG* 6 33% 1% ROYAL DUTCH SHELL 24 41% 2%
BP PLC 18 41% 1%
SAINT GOBAIN SA 19 39% 2% BHP BILLITON LIMITED 9 76% 1%
RHODIA SA 10 43% 1% TOTAL S.A. 8 56% 1%
Representing a share of Representing a share of
Volume on
forward /
futures
days (Mt)
Company Company
Volume on
forward /
futures days
(Mt)
Clearing house, exchange
Financial actors
Energy
Utilitlies
Industry
Biggest winners and losers
8
Source: EUTL
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
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-60
-40
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20
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Ove
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ca
tio
n (
Mt)
Ga
in / L
oss (
M€
)
Gain / Loss (M€)
Overallocation (Mt)
Sensitvity to price assumptions
9
Source: EUTL
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
-800
-600
-400
-200
0
200
400
600E
NE
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Ga
in / L
oss (
M€
)
Tier 1: Spot prices (M€)
Tier 2a: Average yearly prices (M€)
Tier 2b: Forward / futures prices (M€)
Tier 3: Average period prices (M€)
Regression analysis
10 Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
-800
-600
-400
-200
0
200
400
600
-80 -60 -40 -20 0 20 40 60
Gain / loss (M€)
Overallocation (Mt)
Tier 1: Spot prices (M€)
Tier 2a: Average yearly prices (M€)
Tier 2b: Forward / futures prices (M€)
Tier 3: Average period prices (M€)
Two-step model (cf. Zaklan 2013)
Number of accounts used as exclusion restriction
Summary statistics
11
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Sources: EUTL, Point Carbon
Obs. Mean Min P5 P10 P25 Median P75 P90 P95 Max
Trade 4,559 0.65
Number of accounts 4,559 2 1 1 1 1 1 2 4 7 216
Has PHA 4,559 0.04
Short 4,559 0.26
Position (Mt) 4,559 0.03 -60.96 -0.03 -0.01 -0.0002 0.01 0.03 0.15 0.33 41.90
Small 4,559 0.58
Medium 4,559 0.24
Large 4,559 0.13
Very large 4,559 0.05
Electricity 4,559 0.10
Gains
Tier 1: Spot prices (M€) 2,955 0.25 -512.89 -0.20 -0.05 -0.0005 0.02 0.33 1.61 3.86 231.44
Tier 2a: Avg. yearly prices (M€) 2,955 0.19 -518.28 -0.29 -0.08 -0.001 0.03 0.31 1.48 3.66 224.35
Tier 2b: Forw. / fut. prices (M€) 2,955 0.14 -705.71 -0.23 -0.06 -0.0005 0.02 0.36 1.81 4.53 240.43
Tier 3: Avg. period prices (M€) 2,955 0.16 -589.41 -0.51 -0.15 -0.03 0.05 0.34 1.60 4.23 387.63
First 2,955 0.40
Second 2,955 0.36
Third 2,955 0.24
Number of trades 2,955 13 1 1 1 1 2 5 13 27 2,986
Via intermediary 2,955 0.60
Regression results
12
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Tier 1: Spot pricesTier 2a: Average
yearly prices
Tier 2b: Forward /
futures prices
Tier 3: Average period
prices
Short 0.30 0.41*** 0.51** 0.53***
(0.21) (0.10) (0.25) (0.13)
Position -0.08 0.19 0.33 7.62***
(0.38) (0.26) (0.36) (1.01)
PosXFirst 8.05*** 8.46*** 7.32*** 0.45
(1.41) (0.75) (1.38) (1.20)
PosXSecond 2.55*** 3.15*** 2.52*** -0.72
(0.62) (0.48) (0.63) (1.22)
First 0.23*** 0.11* 0.01 -0.06
(0.09) (0.06) (0.08) (0.06)
Second 0.05 0.06** -0.02 -0.004
(0.03) (0.02) (0.03) (0.03)
Medium 0.21 0.21** 0.47** 0.19
(0.17) (0.09) (0.22) (0.12)
Large 0.94** 0.88*** 0.74* 0.57**
(0.39) (0.22) (0.43) (0.23)
Electricity 0.37** 0.34*** -0.01 0.13*
(0.15) (0.12) (0.26) (0.07)
Number trades -0.02*** -0.02*** 0.03*** -0.003
(0.005) (0.003) (0.01) (0.005)
Has PHA -0.95*** -0.68** -0.53 -0.29
(0.37) (0.32) (0.60) (0.32)
Via intermediary 0.06 0.05 -0.01 0.06**
(0.05) (0.04) (0.05) (0.03)
Constant -0.69 -0.80*** -1.16** -0.94***
(0.43) (0.19) (0.51) (0.25)
Country dummies X X X X
Regression results: Selection equation
13
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Tier 1: Spot pricesTier 2a: Average
yearly prices
Tier 2b: Forward /
futures prices
Tier 3: Average period
prices
Number accounts 0.14*** 0.14*** 0.14*** 0.18***
(0.03) (0.03) (0.03) (0.05)
Short 0.87*** 0.82*** 0.83*** 0.82***
(0.10) (0.09) (0.10) (0.09)
Medium 0.31*** 0.30*** 0.30*** 0.29***
(0.05) (0.05) (0.05) (0.05)
Large 0.95*** 1.04*** 1.01*** 0.94***
(0.19) (0.14) (0.11) (0.13)
Electricity 0.11 0.13 0.14* 0.09
(0.09) (0.08) (0.08) (0.08)
Constant -0.09* -0.12 -0.12 -0.19**
(0.08) (0.08) (0.08) (0.09)
Country dummies X X X X
ρ 0.42 0.62** 0.59* 0.67***
(0.38) (0.18) (0.23) (0.14)
Observations
Uncensored (Total)
*** Significant at the 99% confidence level, ** at the 95% level, * at the 90% level
Selection equation
2751 (4343)
Sources: EUTL, Point Carbon
Note: Newey-West standard errors in parentheses
Estimates of windfall profits due to cost
pass-through
14
Electricity sector
€ 5.3 - € 7.7 billion annually for generators in Belgium, France, Germany and
the Netherlands at carbon prices of 20 €/tCO2; free allocation of 90 % (Sijm
et al. 2006)
€ 1.2 – 2.2 billion annually for each of the large four utilities in Germany at
carbon prices of 25 €/tCO2; free allocation of at least 90 % (Matthes 2008)
€ 19 billion annually for generators during 1st period (Keppler and Cruciani
2010)
Industry sector
€ 6.7 billion expected during third period (Martin et al. 2012)
€ 14 billion in 2005 - 2008 for refineries and iron and steel sector (Bruyn et al.
2010)
Less consensus (cf. Demailly and Quirion 2008; Ponssard and Walker 2008)
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Policy implications
15
Significant wealth transfers during the first period of the EU ETS
Overallocation as important determinant for gains calculated from EUTL data
Larger companies more likely to trade (and make a gain) than small
companies
Biggest ‘losers’ on ETS market (electricity generators) likely to
have received windfall profits due to cost pass-through over and
above any costs incurred on the market for EUAs
Who were the ‘real losers’?
Majority of costs likely borne by households
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
16
Low-income households most likely to be affected
Spend a large fraction of their income on energy
Firm profits most likely passed-through to higher income households
High level of free allocation means less revenue for the government with
which unwanted effects could be alleviated
Therefore, free allocation determines winners and losers within the
scheme, but also between scheme participants and households
Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
Outlook
2nd trading period
Majority of allowances still allocated for free
More mature market
3rd trading period
50 % free allocation
Electricity sector has to buy most allowances, but large amount of free
allocation to industry continues
Thank you very much for your attention
Questions?
17 Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014
19
Many of our publications are available at:
www.ceem.unsw.edu.au Johanna Cludius | 4th IAEE Asian Conference | 19-21 September 2014