Feasibility check on correction
factor and benchmark updates
in EU ETS phase IV
Feasibility check on correction factor and
benchmark updates in EU ETS phase IV
By: Long Lam, Sam Nierop, Oskar Krabbe and Bram Borkent
Date: 17 March 2016
Final version
Project number: CSPNL16346
Reviewer: Maarten Neelis
© Ecofys 2016 by order of: Dutch Ministry of Infrastructure and the Environment
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Executive summary
For the fourth phase of the EU Emissions Trading Scheme (EU ETS Phase IV) that will run from 2021
to 2030, the European Commission and other stakeholders are discussing a revised methodology for
the free allocation of emission allowances. The proposal put forward by the Commission includes
updated lower values for the benchmarks that are used to determine the free allocation, an adapted
carbon leakage list, and the continued use of a cross-sectoral correction factor (CSCF) in case the
bottom-up calculated allocation exceeds the available number of allowances for free allocation. In the
EC impact assessment a carbon leakage list with four, rather than the current two tiers, is also
studied which may be another option to design the leakage list.
The Dutch Energy Agreement for Sustainable Growth aims at 100% free allocation compared to a
realistic benchmark for best performing installations in the EU ETS. The choices made with respect to
the proposed benchmark updates, as well as the various options for the carbon leakage list, will
determine whether the resulting allocation is compatible with the Dutch Energy Agreement. Against
this background this report investigates the likelihood that the CSCF is needed for a variety of
scenarios and provides a feasibility check on the proposed default benchmark updates for two
important sectors in the Dutch industry: the steel and refinery sectors.
A scenario where the CSCF is not needed is only possible if the free allocation to industries does not
exceed the amount available for free allocation. In essence, this implies a balancing act between the
stringency of benchmark levels on the one hand and the design of the carbon leakage list in terms of
coverage and compensation levels on the other hand. In this context, it is important to note that this
study does not address options to increase the total amount available for free allocation, via e.g. a
different design of the market stability reserve or changes in the share of free allocation versus
auctioning.
Under the default EC proposal, with benchmarks updated according to the assumptions in the EC
impact assessment, the CSCF would be needed from 2029 onwards. The benchmark update has the
most prominent impact on the need for the CSCF: if the annual benchmark updates are 0.5% less
stringent per year, the CSCF would be needed from 2024 onwards. Hence, a lower benchmark
stringency combined with the proposed almost all-inclusive carbon leakage framework will likely
trigger the CSCF early in the next trading phase. This result is robust for different assumptions on
future production increases, which have only limited impact on the CSCF.
Under a tiered leakage approach with four leakage categories based on the EC’s impact assessment,
the correction factor would very likely not be needed, unless the benchmark values are not updated
at all. The reason is that a tiered carbon leakage list introduces differentiated compensation levels,
adapted to the different risks of carbon leakage. This implies that 100% free allocation is only
possible for those sectors in the highest tier. As a result, the bottom-up calculated allocation will less
likely exceed the available number of allowances for free allocation.
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Another option to avoid the CSCF is to reduce the coverage of the carbon leakage list in another way,
e.g. by increasing the threshold values (calculated by multiplying trade intensity with carbon
intensity) from 0.2 to a range of 0.5-1.0, based on the argument that free allocation to emissions
intensive upstream basic materials could be sufficient to keep the full supply chains in Europe. A
threshold value of 0.5 would still entitle 91% of industrial allocations to be on the carbon leakage list,
compared to 94% under a threshold value of 0.2. A threshold value of 1.0 would put around 14
sectors on the list where the key basic primary materials are produced out of their natural state of
occurrence. Other options to reduce the carbon leakage list exist (e.g. adjusting the compensation
levels, taking account of international carbon pricing developments), but are not further discussed in
this study.
The second design element studied in this report is the appropriateness of the proposed update of the
benchmark levels, in view of historical and future developments. The default update proposed by the
Commission is a flat rate reduction rate of 1% per year. This translates into 15% lower benchmark
values for the first half of Phase IV and 20% lower benchmark value for the second half of Phase IV.
In case the improvement rate is less than 0.5% per year or more than 1.5% per year, as evident by
verified data on the actual efficiency improvements, the flat rate reduction will be fixed to 0.5% or
1.5% per year, respectively.
Determining realistic benchmark updates requires detailed data at the level of process units (subinstallations) of installations. This data is not available in the public domain and, therefore, it is hard
– if not impossible - to assess the appropriateness of the benchmark updates proposed by the
Commission. Therefore, the option to update benchmarks beyond the proposed range (e.g by. 0% or
2%) may be needed in view of realistic benchmark updates.
We can, however, look at proxies for historical efficiency improvements based on aggregated
indicators and/or public data. This can be regarded at best to be reasonable indications of historical
efficiency improvements, but it is insufficiently precise to be used for a realistic benchmark update. In
the first place because the proxies based on aggregate average indicators will show the evolution of
the average sector performance, which may not be representative for the plants determining the
benchmark. Secondly, cross-boundary flows (e.g. electricity, input materials, heat) can impact the
emissions without affecting the final aggregated output, which – if not accounted for - can lead to
erroneous efficiency improvements. These flows should be taken into account if one wants to
calculate the emission intensity improvements in line with the system boundaries under which the
benchmarks were developed.
For the steel sector, we find that the average emission intensity of integrated iron and steel
production via the blast furnace/basic oxygen furnace routes has reduced by 0.1% per year in the
period 2008 - 2014 (the unrepresentative crisis year 2009 is not taken into account). This proxy
cannot be applied one-on-one to the benchmark levels of the various product benchmarks for the iron
and steel sector that are used in the EU ETS, because of the aforementioned caveats.
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A more detailed analysis of one integrated steel plant in Europe indicates that improvements in CO2
performance expressed as CO2 intensity per unit of crude steel produced, have been achieved in the
past (1.3% improvement per year) and may also be possible for the future. However, the observed
improvement is, again, a proxy because the scope of the aggregated indicator is not identical to that
of the individual product benchmarks. Also it is not sure: 1) whether this plant is determining the
steel benchmark values and 2) if cross-boundary streams of intermediate products have had a
decisive impact. This can only be solved by data collection at sub-installation level.
That an analysis using aggregated production or throughput indicator may even give wrong results is
shown by the refinery sector: a process unit weighted approach – in line with the EU benchmarking
methodology - indicates a process efficiency improvement (as can be expected) while the simple
proxy of using emission intensity per unit of throughput shows an increasing intensity.
The observed proxies do show that improvement levels expressed as annual % improvement rates
are rather consistent over time. Therefore, it is not regarded unrealistic to extrapolate historical
trends observed over 2008 - 2015 towards the start of Phase IV, although improvements from the
past are strictly speaking of course no guarantee for improvements in the future.
In short, investment in the data collection at sub-installation level, together with the sectors, is thus
vital to arrive at reliable benchmark updates. This data collection needs to be carefully designed and
may involve more data than just emissions and activity levels at sub-installation level, in order to
account properly for cross-boundary effects. We recommend to start this process sooner rather than
later.
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Table of contents
1
2
3
EU ETS phase IV preparations
1
1.1
EU ETS phase IV – the EC proposal
1
1.2
The EC proposal in the Dutch context
2
1.3
Aim and structure of this report
3
The level of the cross-sectoral correction factor
4
2.1
Introduction
4
2.2
Background on carbon leakage provisions
5
2.3
Approach
6
2.4
Results
7
2.5
Options to reduce the likelihood of the correction factor
11
Realistic benchmarks
14
3.1
Introduction
14
3.2
Complexities in updating benchmark values
16
3.3
Iron and steel
18
3.3.1
The theoretical approach
20
3.3.2
Existing information on development intensity of the sector
22
3.3.3
Direct calculation of historical carbon intensities
24
3.3.4
The TATA case study: more in depth analysis of an efficient plant
26
3.3.5
Conclusion
29
3.4
Refineries
30
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4
3.4.1
The theoretical approach towards the benchmark update
31
3.4.2
Existing information on development intensity of the sector
33
3.4.3
Own calculations on intensity developments based on crude oil throughput figures
36
3.4.4
Conclusion
37
Conclusions
38
References
40
Appendix I
43
Overview of sectors with high carbon leakage indicators
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43
1 EU ETS phase IV preparations
1.1 EU ETS phase IV – the EC proposal
On 24 October 2014, the European Council reached an agreement on the EU 2030 climate and
energy policy framework, including the outline of EU ETS rules for Phase IV (2021 - 2030) (European
Council, 2014). The European Council concluded that emissions reductions in the ETS sector will
reach 43% by 2030 compared to 2005, and that the linear reduction factor will be increased from
1.74% to 2.2%. The share of auctioned allowances should not be reduced, while free allocations is to
be continued nonetheless in order to prevent the risk of carbon leakage. The European Council
specifies that “the most efficient installations […] should not face undue carbon costs leading to
carbon leakage.” This means that the amount of free allowances will be limited and decreasing but
should still ensure (full) cost compensation for the most efficient installations.
On 15 July 2015, the European Commission (EC) released a proposal for a revised EU ETS Directive
that implements the European Council’s decisions (European Commission, 2015a). The proposed
structure would be similar to that of the current rules and has the following main features:

There is a cap on free allocation and thus the need for a cross-sectoral correction factor in
case the bottom-up free allocation exceeds this cap;

The benchmarking framework seems to remain the same, but benchmark levels will be
reduced significantly, by default -1%/a;

The Carbon Leakage compensation follows a two-tier approach: 100% for sectors exposed to
a significant risk of carbon leakage, 30% for non-exposed sectors;

The Carbon Leakage criteria are revised to become slightly more stringent, reducing the
number of eligible sectors, but over 90% of industrial emissions would remain on the Carbon
Leakage list post-2020;

Allocation of free allowances is to be updated every five years, using historical production
levels. In between, allocation will be updated for significant production changes (no longer
capacity changes) with a threshold that needs to be determined.
The EC proposal will now undergo the normal legislative procedure: it will be discussed in parallel in
the EU Council (representatives of the Member States) and the European Parliament. The European
Parliament has appointed a rapporteur on the dossier, Mr Ian Duncan MEP. Each of both institutions
will define their position by amending the EC text, before negotiating to find a common agreement.
The text will then be adopted. This procedure might take up to two years, depending on complexity
of reaching a political agreement. As a consequence, it can be forecasted that the revised EU ETS
Directive for Phase IV will be adopted at the earliest in the beginning of 2017. It is important that the
debate, at the Council and at the European Parliament, builds on fact-based figures and realistic
estimates of potential impacts and costs. This report should contribute to feeding the discussion with
concrete, bottom-up input.
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1.2 The EC proposal in the Dutch context
The Dutch Energy Agreement for Sustainable Growth aims at 100% free allocation compared to a
realistic benchmark for best performing installations in the EU ETS (SER, 2013). This aim was
confirmed when the Dutch House of Representatives asked the Dutch government to strive for
cancellation of the correction factor in the period after 2020, linked to continuous incentives for more
energy efficiency in sectors exposed to carbon leakage risks (Tweede Kamer, 2015).
The principle of 100% free allocation at realistic benchmark levels as defined above is undermined in
case:
1)
Allocation would be based on historical activity levels not being in line with actual activity
levels.
2)
The benchmark values become unrealistically stringent, e.g. below what can be achieved
by the best performing installations.
3)
A cross-sectoral correction factor (CSCF) becomes applicable, which slashes the level of
free allocation to all sectors equally.
4)
Wrong or too stringent carbon leakage criteria are applied.
In relation to the first point, the EC, based on the Council conclusions, aims to better align allocation
levels to actual production levels by proposing two allocation periods of each five years, and by
proposing annual adjustments to the allocation in case of significant production level changes. The
production level threshold will obviously determine the level of alignment between allocation and
actual production, and is, according to the current proposal, to be determined at a later stage via
implementation decisions, although some argue that the threshold is too important to be left out of
the co-decided Directive.
In relation to the second point, the EC proposal includes a provision that the benchmark values are to
be updated by -1%/a by default. Some industrial sectors claim this would lead to unrealistic
reductions of the benchmark values. Even under current benchmark values, some best performing
companies claim that they do not receive 100% compensation due to the combination of benchmarks
and the correction factor.
In relation to the third point, the EC proposal still includes the application of a correction factor in
case the bottom-up free allocation (determined by the level of the benchmarks, the activity levels
and the Carbon Leakage list) would exceed the top-down cap. Hence, a broad definition of the Carbon
Leakage list, as proposed by the EU Commission, or unambitious benchmark updates have the risk
that a correction factor may continue to be needed after 2020. In addition, the option of a more
focused Carbon Leakage list can impact the correction factor which needs to be understood.
In relation to the fourth point, the EC proposes a carbon leakage eligibility that is based on a single
criterion, namely the trade intensity multiplied by the emission intensity. If this product is higher
than 0.2, a sector is exposed to Carbon Leakage risks and receives 100% free allocation, otherwise a
sector receives 30%.
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In the Impact Assessment, the EC presents also alternatives, so-called tiered carbon leakage
approaches that are in more detail described below. The Dutch Energy Agreement does not contain
explicit statements on eligibility criteria for carbon leakage and as such cannot be used to determine
whether or not the EC proposal for carbon leakage eligibility is too stringent or wrong.
1.3 Aim and structure of this report
To contribute to the debate on the EU ETS phase IV reform in view of the specific Dutch context, this
study aims to:

Investigate the likelihood that the cross-sectoral correction factor is needed for a variety of
scenarios (Chapter 2).

Provide a feasibility check on the proposed default benchmark updates for two important
sectors in the Dutch industry: the steel and refinery sectors, and discusses the difficulties in
determining such benchmark updates without having access to detailed bottom-up
installation data (Chapter 3).
In relation to the first aim, it is important to note that this study does not address options to increase
the total amount available for free allocation, via e.g. a different design of the market stability
reserve or changes in the share of free allocation versus auctioning.
Conclusions and recommendations are given in Chapter 4. This report presents fact-based,
independent findings and aims to contribute to the discussions on the future of the EU ETS.
Obviously, the findings do not necessarily reflect the position of the client nor the Dutch government.
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2 The level of the cross-sectoral correction factor
2.1 Introduction
In the current and proposed EU ETS design, a cross-sectoral correction factor (CSCF) is needed if the
bottom-up level of free allocation in a given year exceeds a predetermined cap on free allocation, as
illustrated in Figure 1.
allowances
EU ETS cap
available for auctioning
CSCF
Bottom-up free allocation
free allocation cap
available for free allocation
time
Bottom up free
allocation
Figure 1: Illustration of the need for a cross-sectoral correction factor
(CSCF)
Top down
allocation cap
In Phase IV, the bottom-up free allocation is determined
by the benchmark levels used, the historical production
levels of industry, and the approach towards Carbon
Leakage risks (i.e. which CL compensation factor is
used)1, as shown by Figure 2 and the formula below:
Bottom-up free allocation =
∑i (Benchmark x Historical activity level x CL factor)
1
Figure 2: In case the bottom-up free
allocation (left) is larger than the
allocation cap (right), a correction factor is
needed
In Phase III, the bottom-up free allocation was determined without taking the CL factor into account. However, in Phase IV, the CL factor
will be included before calculating the cross-sectoral factor.
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From a policy design perspective, the benchmark levels and the CL approach are of particular
interest, as they can be designed in such a way that the chances of needing a correction factor post2020 are minimized. While the next Chapter zooms in specifically on the question of the benchmark
updates, the central question in this Chapter is:
How is the need for the cross-sectoral correction factor in the period after 2020 influenced
by several allocation options and under which options, in particular but not limited to a
more focused Carbon Leakage approach, would the need for the correction factor be
eliminated?
The level of the allocation cap is not part of this analysis. The allocation options will focus on the
approach towards carbon leakage compensation and the level of the benchmarks, as further
explained in Section 2.2. In addition, the economic growth of sectors will impact the level of the
allocation2.
2.2 Background on carbon leakage provisions
In this section we provide background on the approaches towards carbon leakage provisions.
Background on the benchmark levels and the possible updates is provided in Chapter 3.1.
In the proposal for a revised ETS post-2020 and the Impact Assessment, the European Commission
presented several options for the carbon leakage approach:

EC proposal: In its official proposal, carbon leakage eligibility is based on a single criterion,
namely the trade intensity multiplied by the emission intensity. If this product is higher than 0.2,
a sector is exposed to Carbon Leakage risks and receives 100% free allocation, otherwise a
sector receives 30%, as shown in Figure 3, left panel.

Tiered carbon leakage approach: In the Impact Assessment, the European Commission
presents two alternatives, so-called tiered carbon leakage approaches. Here four leakage risk
categories are defined (very high, high, medium, and low) with corresponding compensation
levels (100%, 80%, 60% and 30%, respectively), so that free allocation can be adapted more
precisely to the leakage risks of the sector3.
o
“Targeted”: In the “targeted” approach, carbon leakage eligibility is based on a
combination of the emissions intensity and trade intensity, similar to the EC Proposal but
then with more steps, as shown in Figure 3, middle panel.
The allocation for the years 2021 - 2025 is based on production data in the years 2013 - 2017; allocation for the years 2026 - 2030 uses
production data in 2018 - 2022. Annual production adjustments are not taken into account in the model, as these allocations come from or
go to the New Entrants Reserve and therefore do not influence the bottom-up free allocation which is needed for the determination of the
CSCF.
3
More details for these approaches are available on p.148, p.149 and p.172 of the EC Impact Assessment.
2
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o
“Limited Changes”: In “limited changes” approach, carbon leakage eligibility is based
separately on emissions intensity and trade intensity, which should both be above a
certain threshold, as shown in Figure 3, right panel.
Figure 3: Carbon leakage thresholds in the current proposal, and the tiered approaches presented in
the “targeted” package and the “limited changes” package
2.3 Approach
Model used
Answering the question at hand requires a comprehensive model of the level of free allocations to
sectors under the EU ETS for the period 2021 - 2030. The model should include sectoral activity
levels for the period up to 2022, updated benchmark levels, and different options for the future
composition of the Carbon Leakage list, as these are all needed to calculate the level of future
allocations, and hence, the cross-sectoral correction factor.
For this purpose, Ecofys made use of its proprietary E3C3 model (Ecofys EU ETS Carbon Cost
Calculator). The model contains detailed data for the four largest industrial sectors in the EU ETS i.e.
steel, cement, refineries and the basic chemical sector (including fertilizers). Other sectors are
treated as one “rest sector” with one uniform approach. Full details of the model inputs have been
presented recently in a study on the carbon cost impact of the EU ETS on the steel sector (Ecofys,
2015). The model has been extended slightly by including more variations in the growth rates of
sectors and including an option to not update the benchmark values.
Scenarios
The level of the CSCF is analysed for the three CL approaches displayed in Figure 3. Each CL
approach is tested for three scenarios. Scenario 1 and 2 have the same benchmark update but
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different assumptions for industrial growth. Scenario 1 and 3 have the same production growth, but a
different benchmark update. The values corresponding to these scenarios are shown in Table 1.
Table 1: Overview of assumptions used for benchmark updates and annual growth projections
Parameter
Scenario 1: EC-
Scenario 2: EC-proposal
Scenario 3: No
proposal (1)
(2)
benchmark updates
CL approach
•
Current EC proposal
•
“Targeted” approach
•
“Limited changes” approach
Steel
-1.0%
a
0.0% (assumption)
Cement
-0.5%
a
0.0% (assumption)
Benchmark
Refineries
-1.0%
a
0.0% (assumption)
update
Chemicals
-1.5%
a
0.0% (assumption)
-1.0%
a
-1.0% (assumption)
Other
sectors
Steel
0.64%
b
1.15%
c
0.64%
b
Cement
1.28%
b
0.82%
d
1.28%
b
Industrial
Refineries
-0.70%
production
Chemicals
Other
sectors
-0.44%
b
1.42%
b
1.10%
b
e
-0.70%
b
1.25%
f
1.42%
b
1.10%
b
1.10%
b
a (European Commission, 2015b); b (European Commission, 2013); c (Ecofys, 2015); d (IEA, 2015) ; e
(CONCAWE, 2013); f (Cefic, 2013)
The results have been subjected to several sensitivity checks to test which input parameters influence
the outcome the most:

In all scenario’s industrial growth rates have been adjusted by +/- 0.25%/a;

For scenario 1, more limited benchmark updates have been applied, by adding +0.5%/a to
each benchmark update (i.e. -1.0%/a becomes -0.5%/a);

For scenario 3, the “other sectors” benchmark update has been set to 0%.
2.4 Results
Scenario 1
In scenario 1, default values for the benchmark updates are used and the PRIMES scenario is used
for the growth projection (see Figure 4). We see that the CSCF is 1.0 for both tiered CL approaches
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across the entire period, and kicks in from 2029 onwards for the EC proposal4. A difference in
production growth, depicted by the bandwidth of ±0.25% annual production growth (dashed lines),
does not have a big impact on the CSCF. The lines show a non-linear behaviour due to the provision
that allowances in years in which the CSCF is not triggered can be used in later years in which the
CSCF is needed. This would lead to a later start of the CSCF and/or a smaller impact of the CSCF in
its first year.
1.00
0.90
0.80
0.70
Tiered CL (Limited changes)
Tiered CL (Targeted package)
0.60
EC proposal
0.50
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Figure 4: CSCF in scenario 1. Dashed lines show the impact of +/-0.25%/a change in annual
production growth figures.
In contrast to the production growth, a less stringent updates of the benchmark by 0.5% (for each
benchmark) has a relatively large impact on the value and start date of the CSCF, as the CSCF will
then start already from 2024 onwards, compared to starting in 2029 in the default scenario, and will
drop below 0.80 in 2030 (Figure 5). The CSCF is still 1.0 during the whole trading period for both
tiered CL approaches.
4
According to the legal definition, the CSCF can vary between 0.0 and 1.0. A value of 1.0 means that no correction to the amount of free
allocation is made. A value of 0.9 means that allocation is reduced by 10%, etc.
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1.00
0.90
0.80
0.70
Tiered CL (Limited changes)
Tiered CL (Targeted package)
0.60
EC proposal
0.50
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Figure 5: CSCF in scenario 1. Dashed lines show the impact of a less stringent benchmark update by
+0.5% per benchmark, e.g. from -1.0% to -0.5%.
Scenario 2
In scenario 2, production growth figures from industrial sources are used where possible. The results
are shown in Figure 6 and look almost identical to Scenario 1. The reason is that some of the other
sources have higher production growths (e.g. steel goes from 0.64% to 1.15% per year), while
others are lower (cement goes from 1.28% to 0.82% per year). It again underlines that different
assumptions or sources used for production growth figures do not have a significant impact on the
level of the CSCF. The new production growth figures are tested for a deviation by +/- 0.25%/a
(dashed lines) which shows a limited impact on the level and start date of the CSCF.
1.00
0.90
0.80
0.70
Tiered CL (Limited changes)
Tiered CL (Targeted package)
0.60
EC proposal
0.50
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Figure 6: CSCF in scenario 2. Dashed lines show the impact of +/-0.25%/a change in annual
production growth figures.
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Scenario 3
In scenario 3, almost all benchmark updates are set to zero (only other industries is set at -1.0%).
This has a significant impact on the CSCF. For the EC proposal, the CSCF is already necessary at the
start of Phase IV, while even for the tiered CL approaches the CSCF is needed in 2030 (Figure 7).
This result is even more pronounced when the benchmark update for other industries is also set to
0% (Figure 8). In this case, both tiered CL approaches need a CSCF starting well before the end of
Phase IV.
1.00
0.90
0.80
Tiered CL (Limited changes)
0.70
Tiered CL (Targeted package)
0.60
EC proposal
0.50
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Figure 7: CSCF in scenario 3. Dashed lines show the impact of +/-0.25%/a change in annual
production growth figures.
1.00
0.90
0.80
0.70
Tiered CL (Limited changes)
Tiered CL (Targeted package)
0.60
EC proposal
0.50
2021
2022
2023
2024
2025
2026
2027
2028
2029
Figure 8: CSCF in scenario 3 when all benchmark updates are set to 0%.
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2030
General conclusions
We can draw three general conclusions from the analysis of the three scenarios:
1. The benchmark flat rate update has a significant impact on the level and start date
of the CSCF. In scenario 1, if all the benchmark updates are made less stringent by 0.5%
(so e.g. from -1%/a to -0.5%/a) the CSCF already starts in 2024, compared to starting in
2029 in the default scenario. The CSCF is also large in Scenario 3 where there are virtually no
benchmark updates.
2. Different estimations for future industrial production levels only have a limited
impact on the CSCF. Even when production growth rates are set artificially high (e.g. all
sectors at 1.5%), the CSCF will only kick in around 2027. The explanation for this limited
effect is that the production levels are based on actual data until 2014 and forecasted from
2015 onwards. The allocation in Phase 4 is based on 2013 - 2017 activity levels and 2018 2022 activity levels, hence, different growth rates have just limited impact5.
3. Tiered carbon leakage approaches almost completely eliminate the need for a CSCF.
In Scenario 1, the CSCF is 1.0 for both tiered leakage approaches and for all sensitivity
checks. Only in scenario 3 where the benchmark updates are (almost all) zero, the CSCF
would come into play from 2028 onwards.
2.5 Options to reduce the likelihood of the correction factor
The proposed EU ETS Phase IV revision can be adjusted to avoid the likelihood of the cross-sectoral
correction factor from kicking in in the 2021 - 2030 period. This section deals with an alternative
option that would with high certainty exclude the need for the cross-sectoral correction factor in the
EU ETS phase IV: altering the carbon leakage indicator threshold.
Other options that may limit the need for a correction factor, but not discussed in this section, are:

Lowering the compensation level for non-exposed sectors from 30% to 0% (no risk = no free
allocation).

Taking account of international carbon pricing developments in the calculation of the trade
intensity.

Different methods for defining the carbon leakage list, e.g. based on individual products
rather than complete sectors.

Different shares of auctioning versus free allocation or a different design of the market
stability reserve.
5
Annual production adjustments beyond 2022 come from or go to the New Entrants Reserve and therefore do not influence the bottom-up
free allocation which is needed for the determination of the CSCF.
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Altering the carbon leakage indicator threshold
In the EC proposal for the post-2020 ETS, the carbon leakage risk for sectors is assessed by
combining two indicators: trade intensity and emissions intensity. The two are multiplied in order to
calculate the carbon leakage indicator. If this indicator is above the proposed threshold value of 0.2,
the sector is considered to be at risk of carbon leakage. This new approach differs from the
assessment in Phase III, and therefore results in different outcomes for sectors.
Using the values from the most recent carbon leakage assessment from the EC we calculated the
carbon leakage indicator for each NACE sector (European Commission, 2014)6. By combining this
data with EUTL data on the free allocation per sector in 2014, we calculated that with the proposed
carbon leakage indicator threshold of 0.2, 53 out of 236 NACE sectors are above the threshold
(compared to 152 sectors for the 2015 - 2019 CL list). These 53 CL-sectors account for roughly 94%
of the current free allocation, (in comparison, the current 152 CL-exposed sectors are responsible for
96% of free allocation). Hence, in terms of sectors, the new CL list is much shorter, comprising onethird of the currently exposed sectors. In terms of allocation, however, the coverage is reduced by a
relatively small amount.
Following the same approach as above, we estimated the impact of different thresholds on the
amount of free allocation, with the results displayed in Figure 9, using the E3C3 model. The vertical
lines indicate the percentage of free allocation for the sectors at a particular carbon leakage indicator
value. The horizontal sections in the curve indicate that there is a range in carbon leakage indicator
in which no sector is found. The three sectors with the highest carbon leakage indicator values (up to
17.5) are not in the scope of this chart.
The increasing amount of long vertical segments in Figure 9 indicate that generally sectors with a low
carbon leakage indicator have a low amount of free allocation. The large vertical segments around
thresholds of 1.3, 2 and 2.5 are explained by the three largest allocation-receiving sectors cement,
refineries and steel, respectively.
Rationale for a higher carbon leakage threshold in the range of 0.5 – 1.0
The long vertical sections of the curve depicted in Figure 9 indicate that a few sectors account for the
majority of allocations. Our analysis has pointed out that the 14 sectors with the highest carbon
leakage indicator account for over 80% of free allocations. This list of 14 includes the sectors where
the key basic primary materials are produced out of their natural state of occurrence (i.e. primary
metals out of ores, basic chemicals out of petrochemical feedstock etc.). Materials that are often
emissions intensive to produce, but that have a low added value, i.e. materials upstream the
industrial supply chains where the value is added more downstream. Arguably, free allocation to
6
Note that the CL indicator for each sector is subject to changes in emission intensities and trade flows.
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those emissions intensive upstream basic materials could be sufficient to keep the full supply chains
in Europe. This would correspond to a carbon leakage threshold of about 1.0.
The threshold value at which the cross-sectoral correction factor is no longer affecting the EU ETS in
the 2021 - 2030 period under the proposed allocation cap is 0.5. With this threshold, 27 sectors
remain above the threshold, together accounting for 91% of current allocations. The list with sectors
in the range 0.2-0.5 and beyond 0.5 is provided in Appendix I. The benchmark updates assumed
correspond to Scenario 1 and 2 (other benchmark updates will lead to different results). Industrial
production growth is following (Ecofys, 2015).
Figure 9: Share of 2014 free allocation above different carbon leakage indicator thresholds.
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3 Realistic benchmarks
3.1 Introduction
Since Phase III of the EU ETS, the level of free allocation to each installation is determined based on
product-related GHG emission intensity benchmarks instead of historical emissions. For installations
where these product benchmark is not applicable, fall-back approaches are used. The product
benchmarks are set at the GHG emission performance of the 10% most efficient installation
producing that product. The purpose of using benchmarks to determine the level of free allocation is
to incentivise emitters to reduce their emissions and reward early movers: highly efficient
installations with a low CO2 intensity receive all or almost all of its required allowances for free,
whereas inefficient installations need to purchase additional emission allowances to fulfil their
compliance obligations under the EU ETS.
The benchmarks were established in 2011 based on an extensive data collection exercise that started
in 2009 among the major emitting sectors in the EU ETS. In total 52 product benchmark were
established for the Phase III allocation methodology. These benchmarks are in principle based on the
2007 - 2008 CO2 performance of the 10% best performing installations.
In July 2015 the EC published a proposal for the revision of the EU ETS for the period after 2020. One
of the most significant changes is foreseen for the benchmark values. Since the benchmarks used in
Phase III of the EU ETS is based on 2007 - 2008 values, by the start of Phase IV the benchmark
values will be outdated by over a decade. The EC therefore proposes to update the benchmark values
twice in Phase IV, once for the allocation period 2021 - 2025 and once for the period 2026 - 2030.
The amount by which each benchmark will be reduced, is determined using an annual flat rate as
shown in Table 2.
Table 2: Reduction of the 2007 - 2008 benchmark values as proposed by the EU Commission
Verified efficiency
Flat rate
improvement p.a.
reduction p.a.
Compared to the current
Compared to the
benchmark value, the
current benchmark
benchmark value for
value, the benchmark
2021 - 2025 will be
value for 2026 - 2030
reduced by…
will be reduced by…
<-0.5%
-0.5%
-7.5%
-10%
>-0.5% & <-1.5%
-1.0%
-15%
-20%
>-1.5%
-1.5%
-22.5%
-30%
The default flat rate reduction rate for the benchmarks is 1% per year. In case the verified annual
efficiency improvement, based on 2013 - 2017 data, is less than 0.5% per year or more than 1.5%
per year, the flat rate reduction will be fixed to 0.5% or 1.5% per year, respectively. Each benchmark
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will be reduced by a flat rate multiplied by each year between 2008 and the middle of the allocation
period it applies to, i.e. 2023 and 2028 for 2021 - 2025 and 2026 - 2030 respectively. This implies
that the Commission intends to extrapolate the historical efficiency improvement towards the future.
The proposed flat rate reduction would apply to the 52 product benchmarks, as well as to the fallback benchmarks.
The flat rate reduction rate will be based on verified efficiency improvements, based on 2013 - 2017
and 2018 - 2022 data for the two allocation periods (European Commission, 2015a)7. This is
illustrated in Figure 10. If for example the verified efficiency improvement of the top 10% best
performers for a particular product benchmark falls within the green area in 2013 - 2017, the product
benchmark value reduces in line with the green benchmark update line for the first allocation period
2021 - 2025, i.e. by -15%.
Change from current
benchmark values [%]
0%
Benchmark
update
2021-2025
-5%
1
Benchmark 2
update
2026-2030
Benchmark
update EC July
2015 propoposal
Update at
0.5% flat rate
-10%
Update at
1% flat rate
-0.5%
line
-15%
Update at
1.5% flat rate
-20%
-25%
-30%
Verified 1
efficiency
performance
2013-2017
Verified 2
efficiency
performance
2018-2022
-1%
line
-1.5%
line
-35%
2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030
The colour of
the verified
efficiency
performance
corresponds to
the benchmark
update colour.
Figure 10: The benchmark update rate and associated verified efficiency performance
The question that we like to address in this Chapter is:
To what extent is the proposed -0.5 to -1.5% range to update the benchmark values in line
with the historical carbon efficiency improvements and future abatement potential for key
sectors in the EU ETS?
7
Our understanding is based on Article 10a, third subparagraph, and Article 11, of the draft ETS proposal. In particular the requirement that
installations need to report activity levels and emissions per sub-installation, indicates that this data will be used for updating the benchmark
levels. This view is confirmed by the explanatory memorandum preceding the legislative proposal.
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3.2 Complexities in updating benchmark values
There are various complexities that need to be considered in calculating the verified efficiency
improvements for each benchmark.
The impact assessment accompanying the July 2015 proposal for the EU ETS revision states that the
carbon efficiency improvements have been in the order of 2% per year over 1990 – 2010 with a
range of 0.5–3.6% annually (European Commission, 2015b). According to the impact assessment,
the steel, refinery and cement sector had an emission intensity improvement in the order of 1% per
year, and the chemical sector reduced their emissions even further. It is unclear whether these
reflect average emission intensity improvements or intensity improvements of the top 10%
most efficient installations that set the benchmark, which can be significantly different from one
another. Figure 11 shows benchmark curves in two theoretical situations, where on the left the
average emission intensity improved, while the benchmark did not, and on the right the reverse
situation.
CO2 per
tonne product
CO2 per
tonne product
Improved average CO2 intensity
No change in benchmark
No change average CO2 intensity
Improved benchmark levels
Average CO2 intensity, old
Average CO2 intensity
Average CO2 intensity, new
Top 10% CO2 intensity, old
Top 10% CO2 intensity
Top 10% CO2 intensity, new
Installation ranked
by CO2 intensity
Installation ranked
by CO2 intensity
Figure 11: Benchmark curves showing the improvement of a sector‘s average emission performance
without benchmark improvement (left) and vice versa (right)
Figure 11 shows that in theory the average emission intensity improvement can be significantly
different than the development of the benchmark. The situation on the left would for example occur if
the technology options to further improve well performing plants are very limited. Practice shows that
the reality lies in between the two extreme situations sketched in Figure 11. Plants performing
around the average level will move towards the performance of the most efficient installations due to
learning from existing best practices, periodic upgrades of equipment and the closure of some of the
least efficient and less competitive plants. This translates in a gradual improvement of the average
emission intensity. The most efficient plants may also continue to improve their CO2 intensity due to
technological innovation and economies of scale. However, the average emission intensity
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improvement is by no means a direct reflection of the efficiency improvement of the best performing
plants that set the benchmark, but a rough proxy at best.
For sectors that produce only one product, the average emission intensity for that product can be
estimated by dividing all emissions of the sector by the total production. This is in the EU ETS more
or less true only in the case of the cement sector, where almost all installations only produce cement
clinker. However, even in this sector, the sector average is only a proxy for the product emission
intensity. Some installations produce grey and others white cement clinkers and some also process
the clinker into cement, although in the latter case only a limited amount of emissions are produced.
This is more difficult for sectors where multiple benchmarks apply, which is fairly common. For
example, in the steel sector the product benchmarks coke, sintered ore, hot metal, EAF carbon steel
and EAF high alloy steel are relevant, as well as the fall-back benchmarks for heat and fuel use. The
actual CO2 intensity improvement of each benchmark will be different. The sector average CO2
intensity will then be an average of multiple benchmarks, making it increasingly more difficult to use
the sector average CO2 intensity improvement to draw conclusions on the improvement rate of each
benchmark.
On installation level multiple benchmarks are common as well. Installations are divided into
sub-installations based on the boundaries of each relevant benchmark. This means that the actual
CO2 emissions have to be allocated to the relevant sub-installations to determine their CO2 intensity
compared to the benchmark. While for some installations this can be straightforward, for complex
installations CO2 emissions are generally not measured per sub-installation. In this case the method
how the CO2 emissions are assigned to each sub-installation could make a significant difference in the
verified efficiency improvement rate. This could become an issue if the emission allocation method
leads to a CO2 intensity significantly different from simpler installations with the same subinstallation.
Finally, the EC’s proposal for the benchmark updates does not consider whether the new benchmark
levels can be achieved in practice, but is an extrapolation of the historical annual improvement. As
shown in Figure 10, the benchmark value for 2021 - 2025 is based on performance of the 2013 2017 verified performance, extrapolated to 2023. The actual improvement potential might be less
than the benchmark update would imply, for example if there have not been any new technology
developments to further reduce the emissions after 2017. In that case, the product benchmark will
still face a benchmark flat rate update of at least -0.5%. This could lead to carbon costs for the most
efficient installation of the benchmark and go against the principle of the best performer not facing
undue carbon costs. A potential remedy could be to link the benchmark flat rate updates to the
emission intensities in the EC’s Best Available Technology Reference (BREF) documents available for
each sector or other studies, although these documents as such do not contain information on the
performance of the best 10% of installations.
To illustrate the complexities associated with determining the verified efficiency improvements, we
have investigated the past and future CO2 emission intensity improvement in the iron and steel and
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refinery sector. We have calculated the average CO2 intensity of these sectors over 2008 – 2014
based on publicly available data using production metrics that can be derived for the sector as a
whole (i.e. crude oil throughput for refineries rather than production/throughput at the level of
individual process units and crude steel production rather than production of the individual
benchmarks distinguished in the iron and steel sector). We did not calculate the CO2 intensity before
2008, because the scope of the EU ETS and emission data were different.
3.3 Iron and steel
The steel industry value chain includes all the processes required to transform raw materials (mainly
coal, iron ore, electricity and scrap) into finished steel products. Generally, the following
infrastructures are required to produce steel:

Coke ovens

Sinter and pellet plants

Blast furnaces

Steel furnaces

Rolling and finishing mills
Based on the degree of vertical integration, steel making plants can be classified in three different
groups:
Integrated plants: both fully integrated plants, where all the production stages are performed
(from coke making to product finishing), and partially integrated plants, where coke ovens are not
installed and coke making is outsourced. Integrated plants use Blast Furnaces (BFs) and Basic
Oxygen Furnaces (BOFs) to transform iron ore and coke into steel, also referred to as primary
steelmaking. Steel scrap is usually also added.
Electric arc furnaces: plants comprising only steel furnaces and rolling and finishing facilities. These
installations utilize Electric Arc Furnaces (EAFs) to produce steel, and mainly rely on scrap, and only
partially on raw iron, which is usually purchased as processed input, also referred to as secondary
steel-making. In general, EAFs have much lower production capacities than BOFs. EAFs can be
combined with the direct-reduced iron process (DRI), which uses gas rather than coal to make iron
from ore.
According to the World Steel Association, BOFs account for 61% of EU crude steel production whereas
EAFs account for 39% (World Steel Association, 2015).
The steel-making industry’s value chain can be separated into four major production stages: cokemaking, iron-making, steel-making as well as rolling and finishing (Egenhofer, et al., 2013).
Figure 12 shows these major production stages.
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Figure 12: How steel is made: main production routes (World Coal Institute, 2009).
The World Steel Association states that energy constitutes a significant portion of the costs of steel
production, ranging from 20% in some countries to 40% in other (World Steel Association, 2014).
Thus, the industry is continuously seeking for energy efficiency improvements to reduce its energy
costs and thereby improve its competitiveness. Over the last decades, due to technical progress and
energy efficiency improvement measurements, energy consumption per tonne of crude steel reduced
significantly. As demonstrated in Figure 13, from 1960 to 2013, the indexed global energy
consumption per tonne of crude steel reduced by almost 60%.
Figure 13: Indexed global energy consumption per tonne of crude steel production (World Steel
Association, 2014).
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Energy consumption is highly dependent on the degree of vertical integration, production
technologies and plant capacity. BOF integrated plants and EAF minimills therefore have different
energy consumption profiles. EAFs, for example, are much more reliant on electricity per output than
BOFs.
3.3.1 The theoretical approach
As explained above, iron and steel is produced via a number of subsequent process steps resulting
for every iron and steel plant in a range of different intermediate and final products. In consultation
with the iron and steel sector, an allocation methodology was developed for the sector in 2008/2009
consisting of the following product benchmarks:

Coke

Sintered ore

Hot metal

EAF carbon steel

EAF high allow steel
For the production processes not covered by these product benchmarks, the fall back approaches
(fuel and heat benchmark) are applied, i.e. for the downstream production processes like rolling and
finishing. It should be noted that for the coke and hot metal benchmarks, the benchmark values have
been determined based on data in the Best available technology reference documents for the iron
and steel sector (BREF). These data have been used as a proxy for the performance of the best 10%
of installations and to date, there are discussions on whether the calculations have been done
correctly.
Because different integrated iron and steel plants produce different types of steel products and in
some case also sell and buy some of the intermediate products (coke, sintered ore), the CO 2
emissions for integrated iron and steel plants do not necessarily show a clear explainable correlation
with the total amount of (crude) steel produced. The development of such an aggregated indicator in
the best case can only be regarded as a proxy for the actual development of the emissions intensity
of the individual products and then mainly for the hot metal benchmark given that this benchmark is
responsible for the majority of the emissions of integrated iron and steel plants.
For electric arc furnace plants (not the focus of this report), the situation is somewhat better, but also
there the allocation is often a mix of product benchmark and fall-back based allocation and
aggregated intensity developments (based on total site emissions expressed per unit of crude steel)
is only a proxy for the actual intensity development of the product benchmarks. Methodologically, it
should further be noted that as a result of defining benchmarks at the level of intermediate product
and as result of certain benchmark definitions certain emission improvements do not result in a lower
intensity at the level of intermediate products. An example is an increased use of coal rather than
cokes in the blast furnace. This does positively influence the overall emissions intensity of steel
making, but not the emissions intensity of coke making (you just need to produce less coke) and not
necessarily the emissions intensity of the blast furnace itself.
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Each integrated iron and steel plant under the EU ETS has submitted data to the Competent
Authorities (CAs) in the Member States as part of the free allocation process for Phase III and report
data as part of their annual reporting obligations. Since the European Commission (EC) collates all
data from the CAs as part of the process in the EU ETS, the EC has access to the following data for
the years on which the allocation is based:

Production data for each of the product benchmarks;

Total emissions for each of the benchmark types (i.e. not sub-installations), for example, the
emissions that fall under the product benchmarks (the quality of this data is not necessarily
very good, because these data did not play a role in the actual allocation calculation and was
therefore usually not third-party verified);

Energy imported to and exported from the installation;

Total emissions of the EU ETS installation.
The data installations need to report to the CAs annually vary per Member State. For example, in the
Netherlands installations only have to report their annual total emissions, whereas in Germany
installations have to report detailed data on a sub-installation level. In the end, Member States only
have to annually communicate the total emissions of each EU ETS installation to the EC. Whether
other data is communicated, is unclear. This means that the EC would most likely only have subinstallation and activity level that was collected for the determination of free allocation, i.e. data for
2005 - 2008 or 2009 - 2010, and at most more recent data for a few Member States.
To accurately evaluate the development of benchmark levels, i.e. the performance of the top 10%
installations over the years, annual CO2 emission and production data per sub-installation are
required (i.e. for each of the production benchmarks and for the fall-back approaches). The fuels
imported and exported should also be taken into consideration. Various complexities would need to
be taken into account when doing so:

The production data is currently not collected as part of the annual reporting obligations
under the EU ETS in all Member States and this data is not publicly available, although
companies as well as some competent authorities do have scattered information on
production data, e.g. to determine whether an installation is impacted by the partial closure
rules under the EU ETS.

Iron and steel plants produce electricity for their own consumption and for export and do
partly within the system boundaries of their installation or in separate ETS installations
(based on ownership and environmental permit rules). When evaluating the benchmark
improvement of the various benchmarks, the fuel and electricity flows need to be correctly
taken into account to avoid mistakes.
In other words, the iron and steel sector should go through a process of intense data collection (i.e.
comparable to the 2009 data collection exercise) to arrive at updated benchmark values based on
real, verified improvements.
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3.3.2 Existing information on development intensity of the sector
Historical carbon intensity developments in the EU iron and steel sector
Between 1990 and 2010 the emissions from the iron and steel sector declined from 298 to 223
MtCO2. This reduction of 25% was partially the result of a 12% reduction in steel production. The
other part was caused by a reduction of the carbon intensity of steel. According to the European steel
roadmap (developed by the Boston Consulting Group and the Steel Institute VDEh), the average
carbon intensity of crude steel decreased by 14% over this time frame. This reduction was stronger
in the EAF production route than in the BF-BOF production route. The intensity of EAF steel fell from
667 kg CO2/t crude steel in 1990 to 455 kg CO2/t crude steel in 2010, a reduction of 32%. The
intensity of BF-BOF steel fell from 1,968 kg CO2/t crude steel in 1990 to 1,888 kg CO2/t crude steel in
2010, a reduction of 6% (BCG & VDEh, 2013).
Future carbon intensity developments in the EU iron and steel sector
A significant amount of literature is available that depicts future scenarios for the European iron and
steel sector. Numerous studies exist that provide figures on future steel intensity, but most of them
have a different scope (either geographic scope, production route scope, or scope of the research not
including CO2 emissions) (Milford, Pauliuk, Allwood, & Müller, 2013) (Morrow, Hasanbeigi, Sathaye, &
Xu, 2013) (Napp, Gambhir, Hills, Florin, & Fennell, 2014) (Wen, Meng, & Chen, 2014).
Two roadmaps have been developed for the sector that focus on the EU, on all production routes, and
on carbon intensity reductions. The first roadmap is called Prospective Scenarios on Energy Efficiency
and CO2 Emissions in the EU Iron & Steel Industry, and is developed by the EU’s Joint Research
Centre (JRC) in 2012 (Pardo & Moya, 2013). The second roadmap (called Steel’s Contribution to a
Low-Carbon Europe 2050) is developed by the Boston Consulting Group in cooperation with the Steel
Institute VDEh, commissioned by Eurofer (BCG & VDEh, 2013).
The carbon intensity developments that are expected for the iron and steel industry according to
these roadmaps are depicted in Figure 14. Also, the historical intensity developments according to
the BCG/VDEh study is shown, as well as the best BF-BOF and best EAF performer in 2010 according
to Worldsteel (World Steel Association, 2010). The figure shows the clear distinction in carbon
intensity between the production routes: BF-BOF in the 1.5 to 2 tCO2/t crude steel range, DRI-EAF in
the 1 to 1.2 tCO2/t crude steel range and EAF in the 0.3 to 0.7 tCO2/t crude steel range. These
figures are in line with abatement potential found in other studies such as (Milford, Pauliuk, Allwood,
& Müller, 2013) and (IEA, 2015). The pathways from applying the 1% flat rate are also depicted in
the chart.
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Carbon intensity
[tCO2/t crude steel]
2.2
2
BF-BOF
BCG&VDEh (2013) BF-BOF historic
BF-BOF
Worldsteel (2010) Best BF-BOF performer
BCG&VDEh (2013) Upper boundary
1.8
BCG&VDEh (2013) Lower boundary
Pardo & Moya (2013) BS
1.6
Pardo & Moya (2013) 100
Pardo & Moya (2013) 2x fuel
1.4
Pardo & Moya (2013) 200
DRI-EAF
1.2
Pardo & Moya (2013) 5x fuel
1% flat rate
1
DRI-EAF
BCG&VDEh (2013) Lower boundary
0.8
EAF
EAF
BCG&VDEh (2013) EAF historic
0.6
Worldsteel (2010) Best EAF performer
0.4
Pardo & Moya (2013) All scenarios
BCG&VDEh (2013) Upper boundary
0.2
0
1990
BCG&VDEh (2013) Lower boundary
1% flat rate
2010
2030
2050
Figure 14: Carbon intensity pathways of steel production by production route in the EU in different
scenarios.
From Figure 144, it is clear that there is a lot more uncertainty regarding the future intensity of the
BF-BOF route than regarding that of the EAF route. That is because there are more abatement
options in the BF-BOF production route than in the EAF route. Furthermore, the chart shows that the
upper and lower boundary scenarios from the BCG/VDEh roadmap are substantially above those of
Pardo & Moya. This can be explained by the fact that in the BCG/VDEh scenarios, the bulk of emission
reductions is achieved by a structural shift from BF-BOF to DRI-EAF. As a result, the investments in
the BF-BOF production route are not effective.
Figure 15 displays the aggregated development of carbon intensity of EU steel according to the
Eurofer roadmap, which is based on the BCG/VDEh and JRC work. This is the weighted average of the
intensities of the different production routes. This means that it does not only include technological
development, but also a shift between technologies. Four scenarios are depicted:
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1. Implementation of best-practice sharing and increasing scrap availability
scenario
This scenario assumes that the share of EAF is increased on the basis of scrap availability and
best-practice sharing. This scenario can be considered an unrealistic pathway, because it
assumes that the various actors in the iron and steel industry do not optimize economically.
2. Economic scenario
This scenario only implements measures that are cost-effective for the iron and steel
industry. Continued decarbonisation of the power sector, increased scrap availability, bestpractice sharing and implementation of cost-effective incremental technologies are the drivers
for intensity reductions. It represents a pathway that is likely to happen without market
interference.
3. Maximum theoretical abatement without CCS
This scenario assumes production shares of 44% for scrap-EAF, 45% for DRI-EAF, and 12%
for BF-BOF. Furthermore, it includes increased improvements in intensity, especially in BFBOF.
4. Maximum theoretical abatement with CCS
This scenario assumes production shares of 44% for scrap-EAF, and 56% for BF-BOF with top
gas recycling, DRI-EAF or SR-BOF. Furthermore, it assumes adoption of CCS after 2030.
Carbon intensity
[tCO2/t crude steel]
1.6
1. Implementation of bestpractice sharing and increasing
scrap availability scenario
1.4
1.2
2. Economic scenario
1
0.8
3. Maximum theoretical
abatement without CCS
0.6
4. Maximum theoretical
abatement with CCS
0.4
0.2
1% flat rate
0
1990
2010
2030
2050
Figure 15: Carbon intensity pathways for aggregated steel production in the EU (BCG & VDEh, 2013).
3.3.3 Direct calculation of historical carbon intensities
In order to assess realistic benchmarks, one could investigate the historical carbon intensities directly
based on public databases: the EUTL database for the emissions and data from World Steel for the
production. However, there are a few complexities using this data:
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
The scope of the steel sector in the EUTL database and in World Steel are not easy
to match. The EUTL database has emissions on an installation level, but further groupings on
NACE code level or activity codes do not correspond well with the steel production data.
o
In this analysis, we use the scope of the steel sector based on our own work for the
steel sector (Ecofys, 2015) and additional analyses.

The data in the EUTL database do not contain information on the type of steel plant
(EAF or BF/BOF). As the emission intensities of different production routes differ and the
benchmarks are split up for different production routes, we would like to investigate how the
emission intensity develops for each route, especially for the BF/BOF emission intensity.
Because we do know the steel production from World Steel separately for EAF and BF/BOF,
we have to split the EUTL data to calculate a BF/BOF emission intensity.
o
In this analysis, we focus mainly on the development of the BF/BOF emission
intensity. We estimate the EAF intensity based on the emissions for countries with
100% EAF production: the average EAF intensity for 2008 - 2014 is then 0.24 t CO2
per tonne EAF steel. We can use this to calculate the BF/BOF emissions as follows:
BF/BOF emissions = total steel emissions – EAF production * EAF intensity.
Based on these assumptions, we obtain the development of the BF/BOF intensity for the entire EEA
(Figure 16). The BF/BOF intensity shows an almost constant trend, decreasing by -0.5% per year,
based on a best linear fit approach. Without the data point for crisis year 2009 the annual decrease is
-0.1% per year. The temporary increase in intensity for 2009 is likely due to the crisis: production
facilities were not used optimally as the demand decreased rapidly, which lead to worse energy and
carbon performance. Therefore, it is recommended to exclude this year from any trend analysis.
BF/BOF emission intensity
[tonne CO2/
tonne BF-BOF steel]
2.50
2.00
1.92
2.00
1.90
1.88
1.89
1.93
1.89
2010
2011
2012
2013
2014
1.50
1.00
0.50
0.00
2008
2009
Figure 16: BF/BOF emission intensity for the entire EEA for 2008 – 2014.
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Realistic benchmark reductions therefore need to consider that there has been relatively little
improvement in BF/BOF emission intensities in the last six years. However, one should be careful
drawing conclusions from for multiple reasons:

The average emission intensity is not the same as the benchmark emission intensity. In
theory, the emission intensities of the best performing plants (benchmark plants) could
decrease while the average stays the same, or vice versa (see Figure 11).

There are multiple ways in the steel sector to externalize emissions, for instance by
importing pellets from outside the European Union, by externalizing the combustion of waste
gases, or by using more scrap. Some of these effects can be captured by carefully selecting
the right scope of the steel sector (including waste gas installations), while other effects
(scrap use, pellet imports) are not captured in the current benchmark methodology and
should be carefully considered in a benchmarking approach.
3.3.4 The TATA case study: more in depth analysis of an efficient plant
The CO2 emission intensity performance of one of the most efficient plants in Europe can give an
indication of how top 10% best performers have developed in the recent years. We have therefore
selected Tata Steel IJmuiden in the Netherlands as the object of our desk-based case study.
Tata Steel IJmuiden claims to be in the top quartile worldwide in terms of energy and CO2
performance (Tata Steel, 2015a)8. The plant is an integrated steel plant with a production line from
preparing raw materials to the coating of finished products. The plant consist of two coke plants, a
sinter and a pellet plant, two blast furnaces, one basic oxygen steel plant, one direct sheet plant and
one hot rolling mill. Further processing is done in two cold rolling mills and various metallic and
organic coating lines (Hekkens et al., 2015).
The integrated steel plant is connected to two power plants owned by Vattenfall/Nuon that utilises
the waste gases from the steel production process to produce electricity (Nuon, 2016). The first
power plant consists of the two units: Velsen 25, the baseload unit, and Velsen 24, the back-up unit
when additional capacity is needed. Generally natural gas is mixed with the steel waste gases to
produce electricity. The second power plant IJmond is a combined heat and power (CHP) plant that
only burns waste gases to generate electricity and steam for Tata Steel IJmuiden.
Since the CO2 emissions from the power plant are primarily caused by burning the waste gases from
the steel production process, these emissions should be included when determining the total CO2
emissions associated with the steel production of Tata Steel IJmuiden. The Velsen units also have
additional CO2 emissions from the natural gas combustion for electricity production, which cannot be
separated from the CO2 emissions related to waste gases based on available statistics from the EUTL.
8
Note that this benchmark includes direct, indirect and value chain emissions.
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As electricity consumption and related emissions are out of the relevant steel benchmarks, we make
an error by including the emissions from natural gas combustion in the Velsen units, but we assume
this error to be small.
We determined the CO2 intensity of Tata Steel IJmuiden by taking the total CO2 emissions of the
integrated steel plant and of the two power plants. For the crude steel production, we used the total
crude steel production from oxygen-blown converters in the Netherlands. Tata Steel IJmuiden is the
only BF/BOF steel producer in the Netherlands, with the only other Dutch crude steel producer
Nedstaal being an EAF steel producer. The following sources were used to calculate the CO 2 emission
intensity of Tata Steel IJmuiden:


EUTL emissions from the following installations and their corresponding installation ID:
o
NL144 Tata Steel IJmuiden bv BKG 1;
o
NL204781 Tata Steel IJmuiden bv BKG 2;
o
NL185 Nuon Power IJmond;
o
NL188 Nuon Power Velsen;
Total crude steel production from oxygen-blown converters in the Netherlands from the World
Steel Statistics Review 2015.
Figure 17 shows the CO2 emission intensity of Tata Steel IJmuiden over the period 2008 - 2014. The
average emission intensity of Tata Steel IJmuiden was 1.87 tCO2/tonne crude steel, which shows that
the plant is more efficient than the EU average of 1.92 tCO2/tonne crude steel. The emission intensity
shows a peak in the crisis year 2009, and has since then continued to improve annually. Over the
period of 2008 - 2014, the emission intensity has dropped by 1.6% per year, based on a best linear
fit. Without the data point for crisis year 2009 (not representative), the annual decrease is 1.3% per
year.
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CO2 emission intensity
[tCO2/tonne crude steel]
2.50
2.00
1.90
2.01
1.90
1.87
1.83
1.80
1.76
1.50
1.00
Tata Steel IJmuiden
0.50
0.00
2008
2009
2010
2011
2012
2013
2014
Figure 17: CO2 emission intensity of Tata Steel IJmuiden including waste gas installations.
The continuous improvement since 2010 can largely be attributed to the large-scale energy efficiency
programme Tata Steel IJmuiden started from 2011 to 2015 (Hekkens et al., 2015), which include:

The natural gas consumption decreased by 9% through increased use of process gases and
reduction of natural gas demand;

The specific energy consumption per tonne of crude steel was largely reduced by reducing
temperature losses and improving the BOF gas recovery, resulting in a lower hot metal/scrap
ratio;

Electricity consumption was also reduced by aligning the operation of the cooling system with
the actual cooling demand;

Losses of high quality energy carriers were decreased: flaring of process gases reduced,
blow-off of steam reduced, venting of oxygen reduced;

Losses of low quality energy carriers (waste heat) were reduced.
It should be noted that as a result of the benchmark methodology and definitions, not all these
improvements would result in a lower emissions intensity at the level of the individually benchmarked
products if the benchmark definitions used for the allocation are followed. Electricity consumption is
for example not being considered in (some of) the benchmarks and the benchmarks as defined by the
EC correct for waste gas versus natural gas use. As such, the relation between these improvements
and the benchmark updates is not an easy one to make.
Even with the improvements listed above, bringing it to the level of one of the most efficient plants in
Europe, Tata Steel IJmuiden sees room for further improvement in replacing ageing installation parts
such as in the coke plants. Furthermore, the ageing power plants that produce electricity from waste
gas also show potential for improvement. Tata Steel IJmuiden has also embarked on a project to
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install 22 MW of solar capacity on its site to generate electricity for the steel production process,
which will lower their indirect emissions from electricity consumption (Tata Steel, 2015b). Tata Steel
IJmuiden is further running a pilot project with the new steelmaking technology HIsarna, which can
reduce the CO2 emissions from steelmaking by 20%. Industrial commercialisation is expected to be
10-15 year away (Tata Steel, 2015c).
What does this detailed case study tell us about a benchmark update for the steel sector? First, we
need to provide two important disclaimers: firstly, we do not know whether the cokes, sinter and hot
metal production at Tata Steel IJmuiden is amongst the top 10% best in Europe, or in other words:
whether this plant is determining the benchmark levels for steel production. Secondly, some of the
historical emission reductions could have been achieved as a result of increasing the import of
intermediate products such as coke and pellets instead of producing it onsite, or from increasing the
export of half-finished steel products. These cross-boundary flows could – in theory - lead to the
same amount of crude steel production with less onsite emissions, and could therefore result in an
apparent intensity reduction. Also, not all forms of system integration (such as an increased used of
coal rather than cokes in the blast furnace) do result in an improved emissions intensity at the level
of individual intermediate products. To determine the emissions intensity in line with the definitions of
the benchmarks that are used for the allocation, detailed data from Tata Steel IJmuiden would be
needed.
On the other hand, we do know that Tata Steel IJmuiden is actively implementing several energy
efficiency and renewable projects in support of the observed decrease in carbon intensity. In
conclusion, we have found indications that a steel plant in Europe can improve its emission intensity
expressed as t CO2 per unit of crude steel. We do not know, however, whether Tata Steel IJmuiden is
representative of the top 10% best performers that determine the benchmark levels in the steel
sector. Also we do not know how this aggregated emissions intensity improvements translates to
improvements in emissions intensity at the level of individual products following the benchmark
methodology used for the allocation.
3.3.5 Conclusion
An analysis of existing sources shows that the BF-BOF production route reduced its emissions by 6%
over the period 1990 - 2010. This limited emission intensity improvement is reflected in our analysis:
based on public sector data – which covers a different scope than the steel benchmarks used in the
EU ETS – we find a consistent reduction of the average emission intensity for the BF-BOF route of
0.1% per year in the period 2008 - 2014 (excluding the crisis year 2009). This can be regarded at
best to be a reasonable proxy but cannot be applied one on one to the benchmark levels in the EU
ETS, because of its insufficient precision.
A detailed analysis of Tata Steel IJmuiden showed that there are indications that steel plants can
improve in emission intensity consistently, but it is not sure 1) whether Tata Steel IJmuiden is
determining the steel benchmark values and 2) if cross-boundary streams and process integration
effects that are not captured by the benchmark methodology in use have had a decisive impact on
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the observed intensity improvements that are based on a more aggregated crude steel indicator. To
gain this insight, both at installation and at sectoral level, the collection of detailed data on process
unit (sub-installation) level for each iron and steel plant as well as plants that produce semi-products
such as coke and sintered ore under the EU ETS is required.
Looking forward, existing studies show a lot more uncertainty regarding the future intensity of the
BF-BOF route than the EAF route. The future emission intensity of the steel sector will depend not
only on the improvement technology, but also on the shift of steel production between the two
routes. In the latter, the availability of scrap is a dominating factor. Should a larger shift to EAF route
take place, this would hamper investments in the BF-BOF route, with a lower intensity improvement
as a result. Since different benchmarks are associated with each production route, this will also affect
how the emission intensity associated with each benchmark will develop over time.
3.4 Refineries
The European refinery sector covers with 133 MtCO2e in 2014 approximately 7% of the total
emissions under the EU ETS, and receives about 13% of the free allocation in Phase III. Refineries
convert crude oil into various mineral oil products that are used as fuel or input material in other
manufacturing processes. Refineries also produce heat and electricity as a by-product. Figure 18
shows a schematic overview of typical mineral oil refinery units, although each refinery is configured
differently according to the composition of the crude oil going in and the products going out.
Figure 18: Schematic overview of typical refinery units (Source: (UKPIA, 2015)).
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Refineries indirectly uses the crude oil input as its energy source to generate heat required for the
refining process via waste gases produced in a number of the process steps (i.e. refinery gas). In
addition, other fuels such as natural gas are used. The energy consumption of a refinery is largely
depend on the composition of its product mix and required quality standards the products, and to a
lesser degree the crude oil composition (MathPro, 2013). To make cleaner fuels, more energy per
tonne of output is required, e.g. due to the need for deeper desulphurisation of the products. This is
also the case if the crude oil going in the refinery is of relative lesser quality, i.e. crude oil containing
more heavy fractions. Furthermore, refineries can produce the same products through different
processing routes. This means that the GHG emissions of each refinery can vary significantly
depending on the crude inputs, the type of products and the quality of these products.
3.4.1 The theoretical approach towards the benchmark update
The energy consumption and CO2 emissions of each refinery depends on the type of processing units,
the different final product mix and production routes utilised. Some simple refineries are not able to
fully process all fractions of the crude oil and/or produce all end products, and ship intermediate
products to more complex refineries for the further processing. The result is that the CO2 emissions
do not show a clear explainable correlation as such with either the amount of crude throughput or the
final product mix.
To take these complexities in the refining sector into account, the free allocation to the refinery
sector is determined through the CO2 weighted tonne (CWT) approach developed by HSB Solomon
Associates LLC (Solomon). This is a benchmarking approach where the average emissions intensity
performance of each processing unit that is part of a refinery is used to weigh the relative importance
of each unit in the overall emissions intensity of the refinery. Each processing unit is assigned a
predefined weighting factor (i.e. CWT factor) relative to the crude distillation, which are
representative of the CO2 emission intensity of each unit at an average level of energy efficiency
(Ecofys et al., 2009).
The CWT activity level associated with each processing unit is then determined using the associated
CWT factor and the throughput for each of the units. The throughput is the amount of feedstock
going into the processing unit. The performance of each refinery has subsequently been determined
by dividing the total CO2 emissions by the sum of the CWT of each processing unit based on 2007 2008 performance data collected in 2009 (CONCAWE, 2012a).The benchmark level has subsequently
been developed by taking the top 10% of the best-performing refineries under the CWT approach in
terms of CO2 emissions as shown in Figure 19. The final benchmark level was 0.0295 tCO2/CWT
compared to a sector average of 0.0370 tCO2/CWT (CONCAWE, 2012b).
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Figure 19: The EU refinery benchmark curve based on 2007 - 2008 average levels with the top 10%
performers (Source: (CONCAWE, 2012b)).
Each refinery under the EU ETS has submitted data to the Competent Authorities (CAs) in the
Member States as part of the free allocation process for Phase III and their annual reporting
obligations. Since the European Commission (EC) collates all data from the CAs as part of the process
in the EU ETS, the EC has access to the following data for the baseline years 2005-2008 or 20092010:

Throughput data for each refinery processing unit;

The total CWT of the refinery sub-installation;

Total emissions of the refinery sub-installation, i.e. the total emissions that fall under the CWT
approach;

Total crude oil input data for each refinery;

Energy imported to and exported from the installation;

Total emissions of the EU ETS installation.
The data installations need to report to the CAs annually vary per Member State9. This means that
the EC would most likely only have sub-installation and activity level that was collected for the
determination of free allocation, i.e. data for 2005 - 2008 or 2009 - 2010, and at most more recent
data for a few Member States.
9
For example, in the Netherlands installations only have to report their annual total emissions, whereas in Germany installations have to
report detailed data on a sub-installation level. In the end, Member States only have to annually communicate the total emissions of each
EU ETS installation to the EC. Whether other data is communicated, is unclear.
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To accurately evaluate the development of benchmark levels, i.e. the performance of the top 10%
refineries, over the years, annual CO2 emission and CWT data per refinery sub-installation are
required. The energy (both fuels and electricity) imported and exported should also be taken into
consideration. Various complexities would need to be taken into account when doing so:

The CWT data is currently not collected as part of the annual reporting obligations under the
EU ETS and this data is not publicly available.

Refineries produce electricity for their own consumption and export. When evaluating the
benchmark improvement of the refinery benchmark, CO2 emissions from electricity export
should be excluded and those related to the import of electricity should be included.

The CWT factors are determined using the CO2 emission intensity of each processing unit
under “standard conditions” from 2006, i.e. a standard energy performance and standard fuel
consumption mix (CONCAWE, 2012b), relative to the crude distillation unit. The CO2
performance of each processing unit will change over time at different rates, meaning that
the CWT factors may not be representative of the standard performance anymore. The latter
point is a fundamental drawback of the CWT methodology. Updating these averages could
only be done using Solomon proprietary data and approaches.
In other words, the refinery sector should go through a process of intense data collection (i.e.
comparable to the 2009 data collection exercise) to arrive at updated benchmark values.
3.4.2 Existing information on development intensity of the sector
The benchmark for the refinery sector in the EU ETS covers all processing units in a refinery, with
some exceptions for refineries with special product mixes. This means that the evaluation of the CO2
intensity development in the refinery sector should be done at a full installation level, but the number
of relevant studies have been very limited up to date.
The CWT approach is specially developed for the free allocation methodology under the EU ETS for
European refineries. To be able to determine the CO2 emission intensity improvement according to
CWT, the same parameters used for the refinery benchmark studies, detailed data per refinery
processing unit, is required. Due to the confidential nature of this data, no studies to date on the
development of the historical and future development of the CO2 intensity under CWT have been
found.
Studies looking at parameters that can be related to the CO2 performance of refineries are very
limited. Furthermore, these studies only look at the average performance development and not the
best performers or top 10%, on which the benchmarks in the EU ETS are based. The impact
assessment accompanying the EC’s proposal for the revision of the EU ETS post-2020 states that
improvements for refineries have been in the order of 1% per year though (European Commission,
2015b) but the basis for this figure is unclear. On the other hand, Solomon has indexed the energy
intensity of European refineries and found that, on average, EU refineries have become more energy
efficient by roughly 11% over 1992 - 2012, or 0.6% per year, as shown in Figure 20.
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Figure 20: Energy performance of EU refineries between 1992 - 2012 (Source: (FuelsEurope, 2015)
originally from Solomon).
Comparing the findings in Figure 20 with the calculations from the EC’s impact assessment, this
means that the top 10% best performers have improved their emission performance at a similar or a
higher rate than the energy efficiency improvement of EU average refineries. Another possible
explanation for the higher emission improvement rate compared to energy efficiency is that the
process emissions were reduced or fuels with a lower carbon intensity were used for producing heat.
Based on publicly available information, it is therefore not possible to determine to what degree the
top 10% best performing refineries have improved their emission intensity over the years.
Figure 20 also shows the energy consumption per tonne input, which has increased. The reason for
the increase in specific energy consumption despite a higher energy efficiency, is due to pressure to
produce cleaner fuels and change in products to meet the shift in market demand (FuelsEurope,
2015). The production of these products of higher quality requires more energy. This figure alone
gives clear evidence that intensity developments based on crude throughput do not provide insights
into the intensity developments based on a process unit level. Given that the CWT approach is based
on such a process unit level, it is a first clear indication that a crude throughput based method cannot
as such be used for the CWT based benchmark updates.
Despite this and due to the limited availability of any other data, the CO2 emissions per tonne of
crude oil throughput is also often used as a proxy for the CO2 performance of refineries. A mapping of
the CO2 per tonne of throughput for EU refineries showed that the average tCO2 emissions per tonne
throughput was 0.22 in 2008, with 90% of the plants between 0.11-0.40 (European Commission,
2015c). The higher values are generally associated with the most complex sites, although there is no
consistent pattern. Projected towards the future, CONCAWE, a research organisation on
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environmental issues for the European refining industry, expect that the average tCO 2 per tonne
throughput will increase, as shown in Figure 21.
Figure 21: Projection of the CO2 emissions per tonne throughput and change in product mix (Source:
(CONCAWE, 2013)).
The increase in CO2 emissions per tonne throughput results from the ratio between middle distillates
and residue production increasing faster than the ratio between middle distillates and gasoline
production. The higher production of middle distillates is primarily driven by the switch from residual
to distillate fuel in the marine sector as a result of higher fuel quality standards by the International
Maritime Organization (CONCAWE, 2013). This requires a higher hydrogen and energy input to break
down the residual fractions into lighter products and reduce the sulphur content. The demand for a
higher hydrogen production leads to a higher energy consumption as well. These factors lead to a
higher CO2 emissions per tonne throughput.
A recent study for the UK government on decarbonisation pathways for the UK refinery industry
shows that under a business-as-usual pathway, the UK refinery could potentially reduce its CO2
emission intensity by 12% in 2030 compared to 2012 levels (WSP PB & DNV GL, 2015). The study
identified various measures by which the UK refinery sector could reduce its emissions at a high level.
The study, however, does not consider the emission impact of changes in product mixes. The Best
Available Technology Reference (BREF) documents also identify future emission improvement
options, but these are detailed per refinery processing unit (European Commission, 2015c). Since
each refinery can have different configurations, processing units and product mixes, no conclusion on
the actual emission intensity improvement potential can be drawn.
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From these studies we can conclude that while the refining industry has improved its energy and CO 2
emission performance over the years, and there may still be more potential, there are many
complexities to consider. In particular, shifts in demand to cleaner and higher quality fuels may
actually lead to an increase in emission intensity of the refining sector expressed as intensity per unit
of crude oil input.
3.4.3 Own calculations on intensity developments based on crude oil throughput figures
Following the same logic as above, we estimate the emission intensity development in the refinery
sector by comparing the total emissions with the crude oil throughput. The following data has been
used:

EUTL emissions from installations with the following parameters:
o
All installations with the NACE code 19.20 “Manufacture of refined petroleum
products”;

o
All installations with activity code 2 “Mineral oil refineries”;
o
All installations with activity code 21 “Refining of mineral oil”;
EU crude oil throughput from the BP Statistical Review of World Energy 2015.
Figure 22 shows the resulting EU average CO2 emission intensity based on refinery throughput. The
average emission intensity remains fairly stable over the years and over 2008 - 2014 a total increase
of 2.2% can be observed. The values found are close to the emission intensity of 0.22 tCO 2 per tonne
throughput from the refining sector BREF documents (European Commission, 2015c).
CO2 emission intensity
[tCO2/tonne crude oil throughput]
0.300
0.250
0.232
0.232
0.229
0.235
0.224
0.241
0.237
0.200
0.150
0.100
Refineries,
EU average
0.050
0.000
2008
2009
2010
2011
2012
2013
2014
Figure 22: Average CO2 emissions per tonne throughput of the EU refining sector (does not include
Norway).
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Figure 22 does not include EU ETS emissions from Norway as the throughput data was only available
at an EU28 level. The increase in emission intensity based on the refinery crude oil throughput is
broadly consistent with the expectations of CONCAWE (CONCAWE, 2013), but not nearly as steep.
3.4.4 Conclusion
Calculating the improvement rate of the CWT based refinery benchmark requires the collection of
detailed data on process unit level for each refinery under the EU ETS.
Our own analysis and an analysis of existing sources shows that, should a simplified approach with
the emission intensity per unit of crude oil throughput be used, this would give erroneous and
misleading results. Due to shifts in product mixes and product standards, there are indications that
the emission intensity per unit of crude oil processed is increasing. This is confirmed by CONCAWE
and Solomon analyses, as well as the analysis in this study: the average emission intensity over 2008
- 2014 has increased by a total of 2.2%. On the other hand, the only public source on process unit
energy efficiency improvements shows an average improvement rate of 0.6% per year on refinery
level, whereas the European Commission uses a rate of 1% per year in its impact assessment. In
other words, while a process unit weighted approach indicates a process efficiency improvement (as
can be expected), the simple proxy of using intensity per unit of throughput shows an increasing
intensity.
The conclusion, therefore, is that only a detailed data collection at the level of individual process units
(i.e. a data collection similar to the one in 2009) is needed to arrive at a reliable benchmark update.
In doing so, it remains a question whether the weighing factors per individual process unit used in
the CWT approach should also be updated, which would require a new cooperation with Solomon who
owns the underlying database.
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4 Conclusions
This report addresses two design elements related to the level of free allocation in the EU ETS beyond
2020: the likelihood that the cross-sectoral correction factor (CSCF) will be needed, and the level of
the benchmark updates.
The main questions and their conclusions and recommendations are provided below.

How is the need for the cross-sectoral correction factor in the period after 2020 influenced by
several allocation options and under which options, in particular but not limited to a more
focused Carbon Leakage approach, would the need for the correction factor be eliminated?
Under the default EC proposal, with benchmarks updated according to the assumptions in the EC
impact assessment, the CSCF would be needed from 2029 onwards. The benchmark update has the
most prominent impact on the need for the CSCF: if the annual benchmark updates are 0.5% less
stringent for all sectors, the correction factor would be needed from 2024 onwards. Hence, a lower
benchmark stringency combined with the proposed almost all-inclusive carbon leakage framework will
likely trigger the CSCF early in the next trading phase.
This result is robust for different assumptions on future production increases, which have only limited
impact on the CSCF. This is due to the fact that only production up to 2022 determines the level of
free allocations up to 2030, with allocation for production increases after 2022 coming from the New
Entrants Reserve.
Scenarios under which a CSCF is not needed include: using a carbon leakage list with four leakage
categories based on the EC’s impact assessment. In this scenario, benchmark should still be updated,
but the stringency is less crucial. Another option is to increase the proposed threshold (calculated by
multiplying trade intensity with carbon intensity) from 0.2 to a value in the 0.5 - 1.0 range, based on
the argument that free allocation to emissions intensive upstream basic materials could be sufficient
to keep the full supply chains in Europe. Other options to reduce the carbon leakage list exist (e.g.
adjusting the compensation levels, taking account of international carbon pricing developments), but
are not further detailed in this study.

To what extent is the proposed -0.5 to -1.5% range to update the benchmark values in line
with the historical carbon efficiency improvements and future abatement potential for key
sectors in the EU ETS?
Determining realistic benchmark updates requires detailed data at the sub-installation level. This data
is not available in the public domain, and therefore it is hard – if not impossible - to assess whether
the range proposed by the Commission is in line with historical improvement rates. Therefore, the
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option to update benchmarks beyond the proposed range (e.g by. 0% or 2%) may be needed in view
of realistic benchmark updates.
Proxies for historical efficiency improvements based on aggregated indicators and/or public data can
be regarded to be reasonable indications at best, but are insufficiently precise to be used for a
realistic benchmark update. Firstly, proxies based on aggregate average indicators will show the
evolution of the average sector performance, which may not be representative for the plants
determining the benchmark. Secondly, cross-boundary flows (e.g. electricity, input materials, heat)
can impact the emissions without affecting the output, which – if not accounted for - can lead to
erroneous efficiency improvements. These flows should be taken into account if one wants to
calculate the emission intensity improvements in line with the system boundaries under which the
benchmarks were developed.
For the steel sector, we find that the average emission intensity of integrated iron and steel
production via the blast furnace/basic oxygen furnace routes has reduced by 0.1% per year in the
period 2008 - 2014 (the unrepresentative crisis year 2009 is not taken into account). This proxy
cannot be applied one-on-one to the benchmark levels of the various product benchmarks for the iron
and steel sector that are used in the EU ETS, because of the aforementioned caveats.
A more detailed analysis of one integrated steel plant in Europe indicates that improvements in CO 2
performance expressed as CO2 intensity per unit of crude steel produced have been achieved in the
past (1.3% improvement per year) and may also be possible for the future. However, the observed
improvement is, again, a proxy because the scope of the aggregated indicator is not identical to that
of the individual product benchmarks. Also it is not sure: 1) whether this plant is determining the
steel benchmark values and 2) if cross-boundary streams of intermediate products have had a
decisive impact. This can only be solved by data collection at sub-installation level.
That an analysis using aggregated production or throughput indicator may even give wrong results is
shown by the refinery sector: a process unit weighted approach – in line with the EU benchmarking
methodology - indicates a process efficiency improvement (as can be expected), while the simple
proxy of using intensity per unit of throughput shows an increasing intensity.
The observed proxies do show that improvement levels can be rather consistent over time.
Therefore, it is not regarded unrealistic to extrapolate historical trends observed over 2008 - 2015
towards the start of Phase IV.
In short, investment in the data collection at sub-installation level, together with the sectors, is thus
vital to arrive at reliable benchmark updates. This data collection needs to be carefully designed and
may involve more data than just emissions and activity levels at sub-installation level, in order to
account properly for cross-boundary effects. We recommend to start this process sooner rather than
later.
CSPNL16346
39
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Appendix I
Overview of sectors with high carbon leakage indicators
Category
NACE4
Description
On the list in the
proposed
system, but off
the list with a
CSCF-free
system
20.59
24.31
23.49
13.20
32.99
26.80
Manufacture of other chemical products n.e.c.
Cold drawing of bars
Manufacture of other ceramic products
Weaving of textiles
Other manufacturing n.e.c.
Manufacture of magnetic and optical media
Manufacture of non-wovens and articles made from
non-wovens, except apparel
Manufacture of ceramic sanitary fixtures
Manufacture of batteries and accumulators
Preparation and spinning of textile fibres
Manufacture of ceramic insulators and insulating
fittings
Manufacture of tubes, pipes, hollow profiles and
related fittings, of steel
Mining of hard coal
Extraction of salt
Manufacture of veneer sheets and wood-based panels
Manufacture and processing of other glass, including
technical glassware
Manufacture of malt
Manufacture of basic pharmaceutical products
Manufacture of glass fibres
Manufacture of dyes and pigments
Manufacture of ceramic household and ornamental
articles
Manufacture of plastics in primary forms
Manufacture of starches and starch products
Manufacture of oils and fats
Precious metals production
Copper production
Manufacture of sugar
Extraction of crude petroleum
Lead, zinc and tin production
Manufacture of man-made fibres
Processing of nuclear fuel
Mining of chemical and fertiliser minerals
Other non-ferrous metal production
Manufacture of hollow glass
Manufacture of synthetic rubber in primary forms
Manufacture of refractory products
Mining of other non-ferrous metal ores
Manufacture of lime and plaster
13.95
23.42
27.20
13.10
23.43
24.20
05.10
08.93
16.21
23.19
11.06
21.10
23.14
20.12
23.41
On the list in the
proposed
system, and still
on the list with a
CSCF-free
system
CSPNL16346
20.16
10.62
10.41
24.41
24.44
10.81
06.10
24.43
20.60
24.46
08.91
24.45
23.13
20.17
23.20
07.29
23.52
43
Emission
intensity
Trade
intensity
Carbon
leakage
indicator
0.37
0.60
0.50
0.41
0.33
0.23
55%
35%
42%
55%
68%
100%
0.20
0.21
0.21
0.22
0.23
0.23
0.68
35%
0.24
0.72
0.43
0.64
35%
58%
42%
0.25
0.25
0.27
0.61
45%
0.28
0.59
49%
0.29
0.51
2.07
1.44
59%
15%
22%
0.30
0.31
0.31
0.76
43%
0.32
1.17
0.46
1.54
0.87
29%
77%
24%
47%
0.34
0.35
0.37
0.41
0.63
65%
0.41
1.23
2.71
1.08
0.40
1.36
2.69
1.07
2.52
1.67
2.16
1.03
0.96
3.30
1.63
2.02
1.07
21.56
34%
16%
40%
114%
35%
19%
50%
28%
43%
35%
72%
80%
24%
50%
43%
83%
5%
0.42
0.43
0.43
0.45
0.48
0.52
0.53
0.71
0.72
0.74
0.74
0.76
0.78
0.82
0.87
0.88
0.97
Category
NACE4
Description
23.11
17.12
14.11
23.51
Manufacture of flat glass
Manufacture of paper and paperboard
Manufacture of leather clothes
Manufacture of cement
Manufacture of other non-distilled fermented
beverages
Manufacture of pulp
Manufacture of refined petroleum products
Manufacture of ceramic tiles and flags
Manufacture of other organic basic chemicals
Manufacture of other inorganic basic chemicals
Manufacture of basic iron and steel and of ferro-alloys
Aluminium production
Other mining and quarrying n.e.c.
Manufacture of fertilisers and nitrogen compounds
Manufacture of coke oven products
11.04
17.11
19.20
23.31
20.14
20.13
24.10
24.42
08.99
20.15
19.10
CSPNL16346
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Emission
intensity
Trade
intensity
Carbon
leakage
indicator
4.24
4.31
1.77
20.23
24%
27%
72%
6%
1.02
1.17
1.27
1.27
8.41
18%
1.47
3.58
7.82
6.00
4.39
4.00
10.00
9.00
3.09
36.00
15.00
47%
25%
33%
47%
58%
25%
31%
173%
30%
116%
1.68
1.98
1.99
2.08
2.32
2.51
2.76
5.34
10.62
17.45
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