CARBON PRICE FLOOR FOR
ELECTRICITY GENERATION
AND INDUSTRY
A study for the Dutch Ministry of Finance
The customer tax, customs, and international tax
directorate
05 JUNE 2023

WWW.FRONTIER-ECONOMICS.COM

CONTENTS
1

2

Introduction and objective

10

1.1

11

Approach and methodology

12

2.1

Carbon price floor scenarios

13

2.2

Modelling of uncertainty in future EU ETS prices

14

2.2.1

Input for the analysis

14

2.2.2

Employed model for the analysis

15

2.3

2.4

3

4

Structure of the report

Electricity market modelling

16

2.3.1

Modelling assumptions

17

2.3.2

Policy dimensions and key indicators

23

Industry sector modelling: Scenarios and data sources

25

2.4.1

Approach

25

2.4.2

Scenario and data sources

27

2.4.3

Definition of quantitative indicators to analyse impact

27

Probabilistic analysis of future carbon prices

29

3.1

Interim results: Stochastic representation of future carbon price developments

29

3.2

Output from probabilistic analysis of future carbon prices

32

Impact of carbon price floors on the electricity system

34

4.1

Reference scenario: Trends in the Dutch power market (without CPF)

34

4.1.1

Power generation capacities

35

4.1.2

Electricity generation / power plant dispatch

36

4.1.3

Emissions in the Netherlands

37

4.1.4

Wholesale electricity prices

38

4.1.5

Reserve margins

39

4.1.6

Power plant profits

40

4.2

Central EU ETS price case (with non-binding CPFs)

41

4.2.1

Power generation capacities

42

4.2.2

Electricity generation and results for other indicators

45

4.3

4.4

5

6

Low EU ETS price case (with binding CPFs)

45

4.3.1

Impact of CPFs on carbon costs

48

4.3.2

Power generation capacities

51

4.3.3

Electricity generation / power plant dispatch

53

4.3.4

Carbon emissions

54

4.3.5

Wholesale electricity prices

55

4.3.6

Adequacy reserve margins

56

4.3.7

Power plant profits

57

Evaluation of the impact of CPFs on the key policy indicators

60

4.4.1

Investment security for renewable energies

60

4.4.2

International competitiveness

60

4.4.3

Sustainability of the electricity sector, i.e. impact on carbon emissions

61

4.4.4

Affordability of electricity

61

4.4.5

Electricity security of supply (i.e. impact on reserve margin)

62

Impact of carbon price floors on Industry (sensibility check)

63

5.1

Overarching results

63

5.2

International competitiveness / leakage

65

5.3

Investment

71

5.4

Employment

71

5.5

Macro-economic Indicators

72

Summary and conclusion

Annex A – Methodological background on the probabilistic analysis of carbon prices

74
76

A.1

Model specification

76

A.2

Results of an estimation using monthly (averaged) prices from 2018 to 2022

76

A.3

Sensitivity analysis

77

A.4

How to deal with increased uncertainty in the long term

78

Annex B – Probability of future carbon price scenarios until 2040

79

Annex C – Combined Investment and Dispatch model

80

C.1

Model description

80

C.2

Input assumptions

82

Annex D – Reference scenarios against which CPF impact is analysed
D.1

83

Low EU ETS price case

83

Annex E – World Scan model description

87

E.1

Model description

87

E.2

Baseline scenario assumptions and characteristics for reference EU ETS price path

90

E.3

Regions and Sectors in WorldScan

91

E.4

Excursus: Carbon leakage through Fit For 55

92

CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY

EXECUTIVE SUMMARY
In 2022 the Netherlands introduced a carbon price floor (CPF) as a complement to the European
Union Emissions Trading System (EU ETS). The CPF is intended to provide additional economical
certainty for investments in sustainable technologies. The definition of the CPF was made at a time
when the EU ETS price was significantly lower than today. Currently, an update of the CPF level is
considered.
We, Frontier Economics and CE Delft, were commissioned by the Ministry of Finance to analyse
potential CPF levels with regards to their impact on the electricity, discussing potential benefits and
downsides.1 The impact of a potential CPF in the industry sector will be analysed in a sensibility
analysis. The focus of the analyses is on the period until 2030, including an outlook until 2040.
To determine the potential effects of an updated CPF on the electricity and industry sectors, we
undertake three analytical steps:
■

First, we develop a probabilistic analysis of future carbon prices: We employ a stochastic mean
reversion model to estimate future carbon price developments. The model is calibrated using
historical carbon price movements for the price volatility and the trend towards the long-term
mean. Additionally, we use a reference price for future EU ETS development based on the KEV
2022 main scenario. The results serve as an input for the following steps (Frontier Economics).

■

Second, we analyse the impact of different CPF options on the electricity sector by comparing
market outcomes with and without a CPF. We use a stochastic version of Frontier’s electricity
market model which is an integrated investment and dispatch model for the European power
sector (Frontier Economics).

■

Third, we conduct a sensibility analysis on the impact of different CPFs on the industry sector
using the energy version of the WorldScan model which covers emissions of all Greenhouse
gases. This version has a sectoral coverage that includes the agricultural sector, the fossil fuel
markets, oil, gas, and coal, as well as the electricity market, including all major renewable
technologies and carbon Capture and Storage (CCS) (CE Delft).

The development of the Dutch electricity market can be characterised by a number of important
trends. Next to a large increase in renewable generation capacities it is likely that additional storage
capacity will be built to accommodate the intermittent electricity production. Coal plants will be phased
out by 2030 so that renewable generation is in the medium to long term complemented by largely gas
and storage capacities. However, the profitability of gas fired power plants remains weak even without
a CPF. The changes in the power plant park will lead to a strong decline in domestic emissions on
the path towards climate neutrality. The modelling results indicate that these changes in the power
1

In line with the predecessor study (Frontier Economics (2018): “Research on the effects of the minimum CO 2 price”) and as
instructed, our assumptions are based on the report ‘Klimaat en Energieverkenning’ as published by the Netherlands Environmental
Assessment Agency (PBL), including for fossil fuel and carbon prices. Assumptions are complemented by additional sources were
necessary and do not necessarily reflect Frontier’s best guess.

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plant park could lead to a stable reserve margin until 2030, which could decline in the period until
2040. The profits for renewable plants are likely going to decrease throughout the modelled period,
with increasing renewable capacities and falling electricity prices.

CPFs constructed as a lower bound for EU ETS prices
The principal benefit of a CPF as constructed in this study is to provide a lower bound for carbon
emission prices provided by the EU ETS: If EU ETS prices develop as expected (based on current
assumptions regarding the future economic environment and current market indicators), the CPF
should not be binding. Thus, it should not impose additional emissions costs on domestic market
participants.
If the EU ETS price falls below a certain threshold, the CPF results in additional emission costs, so
that a pre-defined minimum carbon emission price is maintained. This could increase the investment
security for low-carbon technologies.
Our analysis shows that, contrary to the time of the first introduction of a CPF in the Netherlands,
renewable technologies in the electricity sector, in particular utility scale solar PV and onshore as well
as offshore wind, are significantly more competitive towards fossil fuel-based technologies than they
used to be. Already at, compared to today, low carbon price levels, renewable energies appear
profitable. This reduces the need for additional support in form of a CPF close to the current EU ETS
price level.

CPFs likely to have no impact if EU ETS prices remain above the floor price
A CPF could impact the electricity and industry sectors even if it does not become binding. Actors in
both sectors take into account potential future price and profitability developments when making
decisions regarding for example new investments or the retirement of generation capacity. However,
our results from the electricity market analysis show no significant impact of the analysed CPF
scenarios for the case that EU ETS prices develop according to the reference scenario and CPFs do
not become binding.
Also for the industry sector we believe that a non-binding CPF will have an insignificant impact on
investments. This is because investments will anyway bias towards higher long-term Emission
Trading Scheme (ETS) prices that align with the near-zero emission targets set for 2040. Moreover,
subsidies in place up to 2030, as part of the Dutch Industrial Policy Package (see also KEV 2022), to
finance industrial emission reductions further support this trend.

CPFs could have an impact on the power sector if EU ETS prices develop
substantially lower than expected
In case of a substantially lower than expected EU ETS price, a CPF results in higher generation cost
for fossil fuel plants. However, our analysis shows that this only partially supports the profitability of
renewables. The impact of a binding domestic CPF is significantly diluted by the strong integration of
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the Netherlands into the European market. Higher domestic generation costs of fossil fuel plants
translate only to a small extent into higher wholesale electricity prices - and therefore into only a
slightly improved economic outlook for renewables. The effect on carbon emissions is likely to be very
small on the European level as domestic fossil generation is replaced to a large extent by carbon
intensive plants in other European countries.
On the other hand, a national CPF comes along with potential negative side effects. The magnitude
of the effects depends heavily on the chosen CPF: While a lower CPF (e.g. at 40% below the expected
EU ETS price) has a rather small impact on the Dutch electricity sector and the Dutch industry, an
ambitious CPF (e.g. 10% below the expected EU ETS price) can have a considerable impact on the
electricity sector and the industry if the EU ETS price decreases significantly. These include
(compared with a situation without a CPF) reduced domestic production of electricity and goods from
the industry, reduced electricity generation capacity and therefore potential adverse effect on security
of supply, and carbon leakage.
The following table summarises the main results of the report:

Table 1

Summary of potential impacts of a Carbon price floor

Policy dimension

Key findings

Investment security
for sustainable
technologies

■

Investments in (utility scale) solar PV, onshore and offshore wind
appear to be already profitable and competitive with fossil fuel-based
electricity generation for the analysed fuel prices and a wide range of
possible EU ETS price scenarios.

■

The overall benefits of a CPF for investment security in renewables
are limited as expected profits (across potential carbon emission price
paths) of new investments are only marginally higher with a CPF
compared to the case without a CPF.

■

A CPF could worsen the competitiveness of domestic fossil fuel plants
in case it becomes binding. However, our results indicate that the
presence of a CPF does not result in capacity retirements if it does
not become binding.

■

A binding CPF could result in declining fossil fuel plant capacity and
domestic generation being replaced by imports.

■

A binding CPF would result in a decline of investment, production,
jobs in the industrial sector, and GDP. However, these are largely
offset by additional investments in lower-carbon production processes
and have no impact on total labour supply as labour shifts in favour of
non-industrial carbon-extensive sectors.

■

A non-binding CPF is unlikely to have an effect on carbon emissions.

International
competitiveness

Sustainability

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Policy dimension

Key findings

Affordability

Security of supply

Source:

■

A binding CPF could reduce emissions in the Netherlands, but the net
effect in Europe is likely to be small.

■

If a CPF becomes binding, leakage of industrial emissions will be for
2030 around 20%. Leakage occurs to countries that have no binding
emission ceilings or relatively low carbon intensity targets.

■

A non-binding CPF is unlikely to impact electricity prices.

■

The impact of higher domestic carbon prices in case of a binding CPF
only partially translates into higher electricity prices.

■

A non-binding CPF does not seem to impact security of supply.

■

A binding CPF could result in lower security of supply, particularly in
the short to medium term.

Frontier Economics

The following table summarises the key figures of our electricity market analysis for the impacts of
the different CPFs in the scenario with a low EU ETS price:

Table 2

Summary: Impact of CPFs on the power system in the low EU ETS price case

Indicator

Year

CPF -10%

CPF -25%

CPF -40%

Generation
capacities

2027

-3.6 GW

-2.3 GW

0.0 GW

2030

-1.4 GW

-1.2 GW

-0.6 GW

Domestic
dispatch

2027

-22.0 TWh

-12.1 TWh

0.0 TWh

2030

-4.2 TWh

-3.2 TWh

-1.5 TWh

Emissions in NL
(modelled region)

2027

-46% (1.5%)

-23% (-0.3%)

0% (0.0%)

2030

-12% (-0.1%)

-10% (-0.1%)

-5% (0.0%)

Electricity price

2027

+3.2%

+1.7%

0.0%

2030

+0.4%

+0.4%

+0.1%

Security of supply
- Adequacy
Reserve Margin
(ARM)

2027

ARM turns negative

ARM declines to close to
zero

ARM remains almost
unchanged

2030

ARM declines slightly but
remains positive

ARM declines slightly but
remains positive

ARM remains almost
unchanged

CCGT
profits

2027

moderate negative impact

low negative impact

minimal impact

2030

high negative impact

moderate negative impact

minimal impact

Source:

Frontier Economics

Note:

Generation capacities indicate total installed and active capacities. The ARM (Adequacy Reserve Margin) is calculated as the difference between
a), the de-rated generation and import capacity and b), the peak demand.

The following table summarises the key figures of our industry sector analysis for the impacts of the
different CPFs in the scenario with a low EU ETS price:

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Table 3

Summary: Impact of CPFs on industry in the low EU ETS price case in 2030

Indicator

CPF
-10%

CPF
-25%

CPF
-40%

Notes

Carbon
leakage rate*

20%

20%

12%

Emission increases in developing countries not only restricted to
highly carbon-intensive industrial goods, but also indirect leakage of
demand for fossil-based electricity and gasoline-based transport
services.

Carbon
emission
reduction**

28%
(0.4%)

17%
(0.2%)

2%
(0.0%)

Additional carbon cost from CPF -10%, CPF -25%, CPF -40% is
equal to 33, 19, and 2 EUR/tCO2eq, respectively. Abatement curve
(marginal cost of emissions reductions vs abatement) is strongly
convex.

0.0%

GDP losses from costly emission reductions, production losses are
partly compensated by gains in service sectors and a terms-of-trade
gain.

GDP loss

0.2%

0.1%

Production
loss

1.3%

0.8%

0.0%

Energy bill is only part of production processes, and costs from
carbon pricing can be significantly avoided by emission reduction
(abatement curve is convex as they include CCS as low-merit order
options).

Employment
loss (‘000)

1.9

1.1

0.0

At the macro-level, there is zero impact on labour supply – non-labour
supply policies have never proven to have an impact on labour
supply.

0%

Investment by industry drops more than production since production
drops especially in highly capital-intensive sectors (chemicals, rubber
and plastics, metal). Investment increases in non-industry sectors,
and at the macro-level the increase is more than the losses in
industry.

Investment
loss

8%

4%

Source:

CE Delft

Note:

* Carbon leakage rate is the % emission increase outside NL divided by emission reduction inside NL.
** Emission reduction is reported for the Netherlands and the EU (between brackets).

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1

Introduction and objective

On the 4th of April 2022, the ‘Wet minimum CO2-prijs voor elektriciteitsopwekking’ (carbon price floor
for electricity generation act) has come into force. The aim of this legislation is to provide an incentive
for environmentally sustainable investments by giving certainty about the minimum carbon price
(carbon price floor, CPF). The CPF was set at 14.90 EUR/tCO2eq in 2022 and 31.90 EUR/tCO2eq in
2030. From 2030 onward the CPF was set to continue at a price of 31.90 EUR/tCO2eq. This price
path was chosen in a period when the EU ETS price was between 10 and 30 EUR/tCO2eq. Since
then the EU ETS price has risen to between 90 and 100 EUR/tCO2eq in early 2023.
The strong increase of the EU ETS price has decreased the relevance of the current CPF, so that an
update of the floor price is considered. In Dutch parliament the evaluation term of the measure was
shortened from five to three years through an amendment on the legislative proposal, and it was
agreed that there will be a supplemental earlier evaluation of the price path. The updated price path
should be put into force through the yearly legislative tax proposal (Belastingplan 2024).
In addition to the CPF for electricity generation, the coalition agreement contains the introduction of a
CPF for industrial companies. The CPF for industry should be same as the one for electricity
generation.
As a result of these political developments, Frontier and CE Delft were commissioned by the Ministry
of Finance to analyse the potential impact of a revised CPF on the electricity sector and the impact of
the introduction of a similar CPF in the industrial sector. The objective of the study is to estimate the
effects of different CPF options in the period between 2024 and 2030, with an outlook on the period
until 2040. While the principal analyses focus on the impact on the electricity sector, a sensibility
analysis for the impact on the industrial sector is also conducted.
The effects of different potential CPFs will be studied with regards to the following policy dimensions:
■

■

For the electricity sector:
□

Investment security;

□

International competitiveness;

□

Sustainability;

□

Affordability;

□

Security of supply;

For the industrial sector:
□

International competitiveness and carbon leakage;

□

Investments;

□

Employment effects;

□

Macro-economic indicators.

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1.1

Structure of the report

The report is structured as follows:
■

Description of the approach and definition of the analysed scenarios (section 2);

■

Probabilistic analysis of future carbon prices (section 3)

■

Analysis of the impact of CPFs on the electricity sector (section 4);

■

Sensibility analysis of the impact of CPFs on the industrial sector (section 5); and

■

Annexes with detailed assumptions and supplementary results.

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2

Approach and methodology

To determine the potential effects of an updated CPF on the electricity and industry sectors, we
undertake the analyses in three tasks (see Figure 1):
■

First, we perform a probability analysis of potential developments of future carbon prices in the
EU ETS system (Task 1). This analysis is based on statistical analyses and a Monte Carlo
simulation.

■

The results from Task 1 feed into Task 2, in which we analyse the impact of different CPF options
on the electricity sector by comparing market outcomes without a CPF with the outcomes if the
different CPF options apply. We use Frontier’s stochastic electricity market model for the analysis
which takes probabilistic carbon price scenarios into account.

■

In Task 3, we analyse the impact of different CPFs on the industry sector. Based on the results
from Task 1 and 2, which will be used as inputs for the industry model, we calculate deterministic
scenarios for the impact of the various CPFs on the industry.

Figure 1

Source:

Overview of the analytical approach

Frontier Economics

We evaluate the modelling results from Tasks 2 and 3 with respect to a set of key indicators. These
indicators will then be used to draw conclusions with regards to a set of policy dimensions, including
investment security, sustainability, international competitiveness, security of supply and affordability.

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2.1

Carbon price floor scenarios

This study analyses the impact of different options for future CPF paths. The CPF path options are
defined relative to an “expected EU ETS price.” The expected EU ETS price is based on the price of
traded EU ETS futures (EU CO2 allowances) and the ETS price outlook provided in the report ‘Klimaat
en Energieverkenning’ (2022) published by the Netherlands Environmental Assessment Agency
(PBL).
The following CPF paths are analysed (see Figure 2):
■

CPF at 10% below the expected price;

■

CPF at 25% below the expected price; and

■

CPF at 40% below the expected price.

All CPF paths are assumed to remain constant from 2030 to 2040, which is in line with current
legislation.2

Figure 2

Analysed CPF options and “expected EU ETS price”

200
180

€2021/tCO2eq

160
140
120
100
80
60
40
20
0
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

Source:

2

Reference carbon price

CPF 10% below reference

CPF 25% below reference

CPF 40% below reference

Frontier Economics based on Ministry of Finance (2022), PBL (2022)

Within this study all prices are shown in real 2021 terms (which is also the case for the KEV 2022 which was used as a basis for
fuel prices, carbon prices and other exogenous inputs). Within the text we use EUR/tCO2eq for simplicity to describe carbon prices
in EUR2021/tCO2eq. The depiction in real 2021 terms applies to modelling assumptions (e.g. EU ETS prices and CPF options) and
to modelling outputs (e.g. electricity market price). The model outcomes are independent from the future development of inflation.
As all prices are equally affected by inflation, the relative costs remain equal and thus also the assessment regarding the policy
indicators (e.g. security of supply) remains unchanged. To obtain values in real terms, the prices shown in the report have to be
adjusted by applying the respective inflation between the base and target year.

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Note:

Depicted prices for 2024 could be below current (nominal) EU ETS prices since they are shown in real 2021 terms and are based on market prices
from January 2023, when assumptions were fixed.

The analysed options for the CPF paths remain below the expected EU ETS price. This is in line with
the political objective of the measures to stimulate environmentally friendly investments by providing
a lower bound for greenhouse gas (GHG) emission costs and therefore investment security for “green
investments.” The instrument is not intended to induce additional carbon costs in case the EU ETS
price develops as currently expected.

2.2

Modelling of uncertainty in future EU ETS prices

EU ETS allowances (EUA) are traded on wholesale markets, their price reflects the balance between
demand and supply. Both the demand and the supply side of EUA are subject to uncertainties that
can impact the future price of allowances. For example, a reduction in newly auctioned certificates
will increase the price, lower than expected economic activity as predicted will dampen it. Actors in
the electricity market take these uncertainties into account when making investment decisions in new
generation capacity, batteries, or flexibility measures. In order to do so, investors have to form an
opinion on possible future carbon price levels and their likelihood.
EUA futures deliver reliable market expectations of carbon prices in the short and medium term. We
will therefore rely on EUA Futures to estimate carbon prices in 2023 and 2024 in our analysis.3 To
form an expectation about future prices and their likelihood from the year 2025 onwards, we employ
a stochastic mean reversion model to estimate future carbon price developments – a method routinely
used to model commodity and carbon prices.4
The objective of this task is thus to describe the uncertainty around the carbon price development by
a stochastic model, which can be used to assess the potential impacts of different CPFs. The specific
output from Task 1 are carbon price probability distributions for each year under consideration, which
are used to inform the following analyses for the electricity (Task 1) and industry sector (Task 2).

2.2.1 Input for the analysis
The employed model simulates potential future carbon prices based on a historical price development
and a forward-looking element. The historical data is used to deduct uncertainties in the carbon price
development, while the forward-looking element includes expectations regarding the development of
carbon prices. Specifically, our model estimates future carbon prices and their likelihood based on:
■

Outlook on basic carbon price development: To capture expected changes in the price level
of the EU ETS price, we take the price development as estimated in KEV 20225 main scenario

3

Futures retrieved on 30.01.2023.

4

For a similar approach see e.g. Graham, Reedman, Lo & Zhu (2009): “A scenario-based integrated approach for modelling carbon
price risk”, Dannenberg & Ehrenfeld (2011): “A Model for the Valuation of carbon Price Risk”, Chang, Wang & Peng (2013): “Mean
reversion of stochastic convenience yields for carbon emissions allowances: Empirical evidence from the EU ETS”

5

PBL (2022). Unless otherwise noted we also refer to the KEV main scenario.

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CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY
into account. This price path describes a general trajectory for carbon prices, without considering
short-term uncertainty (volatility).
■

Historical market data on price volatility and reversion speed: We analyse historical carbon
price movements with regards to price volatility6 and the time frame to revert to the observed
long-term mean (“reversion speed”). These parameters are derived from historical data and are
then used to model future volatility. The use of historical data thereby implicitly assumes that the
structural price formation in terms of volatility and reversion speed remains similar in the future.7

The inputs from historical market data (volatility, reversion speed) depend on the chosen time period
(i.e., the years incorporated in the analysis) and temporal granularity of price signals (“time interval,”
for example daily, weekly or monthly averages). The parameters are chosen to be suitable to the
purpose of this exercise and the goals of the subsequent electricity market and industry analyses. We
therefore use:
■

Monthly (average) prices as a time interval, given the long-term nature of the modelling; and

■

Prices from 2018 to 2022, as this time period includes a broad range of price levels that
incorporate the impacts of severe external shocks (e.g., the Covid-19 pandemic and the Russian
invasion of Ukraine) as well as a period of steady price increase (in 2021).

We conducted a range of sensitivity analyses by testing different time periods and different time
intervals to identify a robust set of assumptions. The results for the sensitivity analyses can be found
in Annex A .

2.2.2 Employed model for the analysis
To derive a large number of potential price paths for the EU ETS and consequently a distribution of
potential future carbon prices (carbon price probability distribution), we use two statistical techniques:
■

We employ a standard discrete time model to describe the uncertain future development of
carbon prices. The time model includes a mean reverting element in order to reflect that specific
expectations with regards to the carbon price development exist. Simply speaking, the
development of carbon prices within the model depends on the current price, the expected price,
and an element of uncertainty. The inputs described above (volatility, reversion speed) are used
to inform the parameters of this model.

■

In order to form a reliable expectation on the likelihood of a certain future price path to be realised,
a multitude of runs of the discrete time model is required. We therefore conduct Monte Carlo

6

Price volatility in terms of the relative standard deviation of carbon prices in time.

7

We decrease the reversion speed from 2030 onwards to reflect fundamental uncertainties in long-term forecasts and represent the
possibilities of alternative carbon price levels emerging over extended periods of time. See Annex A for more details.

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simulations to estimate a large number of potential future price paths (10,000). This allows us
to understand the impact of uncertainty and risk through repeated random sampling.8
The resulting price paths are then used to derive probability distributions for future prices. We describe
the results of this analysis in section 3. Further details on the methodology, including model
specifications and sensitivity analyses, can be found in Annex A .

2.3

Electricity market modelling

In this assignment, we use a stochastic version of Frontier’s electricity market model already applied
in previous studies undertaken on behalf of the Ministry of Economic Affairs and Climate Policy.9 The
model is an integrated investment and dispatch model for the European power sector. The main
characteristics of the model can be summarised as follows:10
■

Cost optimisation model – The model is an integrated investment- and dispatch model for the
European power sector. The model is set up as an optimisation problem minimising the system
costs for serving power demand across the modelled regions. The model optimises the hourly
dispatch of the power plants as well as the development of installed capacity based on
representative hours and selected snapshot-years (investments, divestments, mothballing and
reactivation).

■

Geographical scope – Our model focusses on Central-Western Europe as core-region,
including the Netherlands. Other neighbouring countries are included as non-core regions. This
differentiation allows for modelling of the power plant park in the core-region on a very detailed
(unit-based) basis. Power exchange with regions modelled with lower granularity and level of
detail are at the same time included.

■

Temporal resolution – For this study the model was configured to optimise eight representative
days11 in each year with an hourly resolution. The time horizon for our analysis is from 2024 until
2040. We have modelled the photo years 2024, 2027, 2030 and 2040.

8

Monte Carlo simulations are often used to simulate probabilistic price movements. See for example Graham, Reedman, Lo & Zhu
(2009): “A scenario-based integrated approach for modelling carbon price risk”.

9

Frontier Economics (2015): “Scenarios for the Dutch electricity supply system”; Frontier Economics (2016): “Research of scenarios for
coal-fired power plants in the Netherlands”; Frontier Economics (2018): “Research on the effects of the minimum CO2 price”.

10

For a detailed model description see Annex C .

11

The representative days were constructed based on historical data and optimised to closely replicate full years.

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Figure 3

Frontier Investment and Dispatch Model
Combined Investment and
Dispatch Model (CID)

Input - Dispatch
Hourly load

Output - Dispatch
System cost

Fuel costs / CO2

Hourly electricity prices
Core region
(investment and dispatch)
Non-core region (dispatch)

Power plant capacity and DSR

Hourly availability of RES

Satellite region

Interconnectors

Hourly dispatch / DSR
Emissions
Profitability

Fixed O&M + variable costs
RES-In-feed

Output - Investment

Input - Investment

Security of Supply

Potentials

Investments

Investment costs
Mothballing costs

Market framework

Mothballing
▪ Snap-shot years: 2024, 2027, 2030 and 2040
▪ High granular dispatch p.a.
▪ Differentiated between highly detailed core-region
and less-detailed non-core/satellite regions

Policy measures

Source:

Closure

DSR = demand side response
O&M = Operating & Maintenance

Frontier Economics

A central research task of the project is to analyse the effects of introducing a CPF below the expected
carbon price path. In other words: Does a CPF have an effect even if it might not be binding, and EU
ETS prices remain above the CPF?
To analyse this question, a stochastic representation of future carbon price developments in the
electricity market model is used. The model optimises investment decisions in the power sector under
the uncertainty of the different price paths, thus taking into account possible carbon price paths that
might not materialise. We perform model runs for a reference case (without a CPF) and for each of
the potential CPFs. By analysing the different outcomes of the model runs, we can investigate the
potential impact of the CPF.

2.3.1 Modelling assumptions
In order to analyse the impact of different CPF assumptions, we compare scenarios without a CPF
with the CPF scenarios (each CPF modelled as an individual scenario). All assumptions, other than
the existence and level of the CPF in the Netherlands, remain the same between all scenarios, so
that the impact of the existence of a CPF can directly be analysed.
All scenarios are based on the current and intended policy framework in the Netherlands and in NorthWestern Europe. They represent a framework which is built upon a combination of current market

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expectations, e.g. regarding fuel prices and carbon prices, and political targets for example for the
development of renewable electricity generation.12
In the following, we summarise the main assumptions for the reference case and the CPF scenarios.

Figure 4

Summary of main assumptions

CO2 prices

 Probabilistic representation of future carbon price developments based on output from Task 1
 For the CPF scenarios: CPFs in NL at 10%, 25% and 40% below the “reference EU ETS price”

Fuel costs

 Calculation of the scenarios on the basis of the futures prices until 2025 (as of 30.01.2023) and
interpolation to the KEV 2022 expectations for 2040

Demand

 NL: Medium-term and long-term: Based on main KEV expectations (net demand)
 For other countries we follow TYNDP 2022 National Trends, except where specific plans/legislation
exists (e.g. in Germany based on Easter package (EEG 2023) and network development plan (NEP))

Generation
capacities

 NL: We reflect latest plans, e.g. phase-out of coal-firing until 2030 and potential new nuclear capacity
 NL renewables: We reflect the capacities stated in the KEV 2022
 For other countries we follow TYNDP 2022 National Trends scenario, except where specific
plans/legislation exists (e.g. coal phase-out in Germany)

Interconnector
capacity
Source:

 Until 2030: For NL based on expectations by TenneT, other countries based on TYNDP 2022
 Until 2040: Based on TYNDP 2022

Frontier Economics

Fuel prices
The fuel price projections are based two sources, depending on the timeframe (Figure 5):
■

The short-term price projection for coal and gas prices is derived from current forward prices
(until 2025).

■

The long-term trend (after 2025) is based on the price development of the KEV 2022.

The price paths for gas and coal both show a return to lower price levels after the strong increase
during 2021 and 2022. In the long run, assumed fuel prices show a rather steady path, although
prices, in particular for natural gas, remain above long-term projections issued before the energy
crisis.

12

In line with the predecessor study (Frontier Economics (2018): “Research on the effects of the minimum CO2 price”) and as
instructed, our assumptions are based on the report ‘Klimaat en Energieverkenning’ as published by the Netherlands Environmental
Assessment Agency (PBL), including for fossil fuel and carbon prices. Assumptions are complemented by additional sources were
necessary and do not necessarily reflect Frontier’s best guess.

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Figure 5

Fuel price development

70

€2021/MWhth

60
50
40
30
20
10
0

Hard coal (CIF ARA)
Source:

Gas (LHV - TTF)

Frontier Economics based on Energate Messenger and PBL 2022

Electricity Demand
Our assumption for the development of power demand is based on the demand projection in the KEV
2022. The report assumes a continuous growth of power consumption until 2040 (Figure 6). Including
network losses, net electricity consumption is assumed to increase from 112 TWh in 2022 to 131 TWh
in 2030. Demand is expected to increase to 172 TWh by 2040.

TWh

Figure 6

Development of electricity demand in the Netherlands

200
180
160
140
120
100
80
60
40
20
0

Historical

Projection

Source:

Frontier Economics based on CBS Statline and PBL 2022

Note:

Net power demand incl. network loses, excl. own consumption of power plants.

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Thermal generation capacities
The capacity development of thermal power plants in the Netherlands and neighbouring countries is
an outcome of the model optimisation. Nevertheless, known investment and divestment decisions as
well as mandatory phase-out schemes (nuclear or coal) are taken into account. These include:
■

Coal phase-out in the Netherlands: We assume that the coal phase-out is completed at the latest
by 2030.

■

Nuclear capacities in the Netherlands: We understand that there is the political intention to
maintain nuclear capacities within the Dutch power plant park. We thus assume that some
nuclear capacity (ca. 0.5 GW) is in operation throughout the entire modelling period until 2040.

■

CHP generation in the Netherlands: Next to their electricity generation, CHP capacities have a
commitment for heat generation. Our modelling thus follows the projected path of CHP generation
as depicted in the KEV 2022.

■

Beyond the developments in the Netherlands, we also reflect other decisions in the modelling
regions, for example the reduction of nuclear electricity capacities in Belgium, the nuclear lifetime
extensions in France and the German coal phase out.

Renewable generation capacities
The development of renewable energies in electricity supply is to a large extent driven by policies,
permits and potentially limited by network capacities (e.g. for Dutch offshore wind). Notably, due to
relatively high fuel prices for competing power generation technologies (in particular natural gas),
RES-E13 investments yield a positive rate of return in our model independent of the existence of a
CPF or the level of a CPF. This means the extension of renewable power generation capacity is not
limited by economic viability in the model, but rather by practical considerations (permits, land
availability, grid constraints, etc.). Therefore, we restrict renewable growth to account for these
constraints. The assumptions for renewable capacity extensions in the Netherlands are based on the
KEV 2022 (Figure 7).

13

Renewable sources of electricity.

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Figure 7

Assumed renewable generation capacity development in the Netherlands

45
40
35

GW

30
25
20
15
10
5
0
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
PV
Source:

Wind offshore

Wind onshore

Frontier Economics based on KEV 2022

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Background: Levelized costs of electricity generation
Fossil fuel prices, in particular natural gas but to a lesser extent also coal, increased significantly in
the second half of 2021, a few months before the Russian invasion of Ukraine. This caused the
beginning of a global energy crisis. Prices peaked at more than ten times (natural gas) and five
times (coal) the average price between 2015 and 2020 in the summer of 2022. Although prices have
declined towards spring 2023 (when the study at hand was prepared), the general expectation is
that fossil fuel prices will remain elevated compared to pre-crisis levels (and above previous
expectations regarding future developments) for many years to come.
At the same time, the costs for renewables energies have declined significantly over the past two
decades. From 2010 to 2020 alone, the global average costs of generating electricity with solar PV
declined by more than 85%, onshore wind costs declined by more than 60% and offshore wind more
than 50%.
Both developments together result in a situation in which renewables have lower total average
generation costs (including investment costs) than fossil fuel-based power plants (see Figure 8).
For Europe, this is even the case for relatively low EU ETS prices (60 €/tCO2eq).

Figure 8

Fossil fuel generation costs vs renewable levelized costs of electricity
b) Generation costs (100 €/tCO2eq)

c) Renewable LCOE

180

180

160

160

160

140

140

140

120

120

120

100
80

100
80

100
80

60

60

60

40

40

40

20

20

20

0

0

0

Gas

Source:

€2021/MWh

180

€2021/MWh

€2021/MWh

a) Generation costs (60 €/tCO2eq)

Coal

PV

Wind offshore

Wind onshore

Frontier Economics, renewables data based on: International Energy Agency (2022), Global Energy and Climate Model Documentation 2022.

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Interconnector capacities
The Netherlands has high interconnection capacity to its neighbouring countries, notably Germany
and Belgium. Additional interconnections are in place to Great Britain and Norway. Cross-border
capacity will increase further in the next years.
The assumptions regarding the development of interconnection capacity in the Netherlands until 2030
are based on TenneT’s Monitoring Leveringszekerheid 2022. The expansion of Dutch interconnector
capacities until 2040 as well as the interconnector capacities for all other countries are based on
ENTSO-E's Ten-Year Network Development Plan (TYNDP) 2022.

Figure 9

Assumed Dutch interconnector capacity development
b) Import capacity

16

16

14

14

12

12

10

10

GW

GW

a) Export capacity

8
6

6

4

4

2

2

0

0
2020
DE

Source:

8

2025
BE

UK

2030
NO

2040

DK

2020
DE

2025
BE

UK

2030
NO

2040

DK

Frontier Economics based on TenneT’s Monitoring Leveringszekerheid 2022 and ENTSO-E's Ten-Year Network Development Plan 2022.

2.3.2 Policy dimensions and key indicators
To analyse the effects of an updated CPF in the Netherlands, we will analyse three CPF options as
defined in section 2.1. The evaluation of the impacts of the individual CPF options will be mainly based
on a comparison between the reference case and the individual CPF options.
Based on the outlined approach, in our evaluation we will focus, inter alia, on the areas and related
indicators outlined in Table 4.

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Table 4

Policy dimensions of the impact analysis for CPFs for the electricity sector

Policy dimension

Description and key indicators

Investment security for
sustainable technologies

This dimension refers to the profitability risk associated with
investments in sustainable technologies. In particular, we are
analysing the expected profits and profit range for investments
depending on the CPF and EU ETS price scenarios.

Security of supply

Security of supply refers to the ability of the system to serve
demand at all times. We evaluate this dimension with two
indicators: First, we analyse the installed capacity and
Adequacy Reserve Margin (ARM), calculated as the difference
between a), the de-rated generation and import capacity and b),
the peak demand.
Second, we analyse the short-run profitability of conventional
and nuclear power plants to evaluate their economic well-being.

International
competitiveness

A CPF in the Netherlands potentially results in higher domestic
electricity prices relative to a scenario without a floor price. This
could impact the competitiveness of domestic electricity
generators. Consequently, domestic electricity generation might
be replaced by imports.
We measure the international competitiveness of generation in
the Netherlands based on the generation costs delta as well as
import and export volumes.

Sustainability

To examine the impact of different CPFs on sustainability, we will
compare the CO2-emissions between the different model runs.
Apart from the domestic CO2-emissions in the Netherlands, we
will also consider the net-reduction of carbon emissions in the EU
as lower emissions in the Netherlands potentially come along with
higher emissions in other countries.

Affordability

To assess the impact of a CPF on the affordability of electricity,
we analyse how wholesale power prices in the Netherlands are
affected by a CPF.

Source:

Frontier Economics

Additionally, we identify and evaluate interdependencies and trade-offs between the policy
dimensions.

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2.4

Industry sector modelling: Scenarios and data sources

Based on the results of the probabilistic modelling of the EU ETS prices, we calculate deterministic
carbon price scenarios, which will be used as inputs for the industry model. The scenarios include:
■

a reference scenario in which carbon prices move along the central EU ETS price path; and

■

a low price case that results in EU ETS prices (at least temporarily) falling below the CPF.

By comparing the results with and without the CPF in these scenarios, the impacts on the industry
can be analysed.

2.4.1 Approach
We analyse the effects of a CPF in the Netherlands for industrial sectors as a sensibility check. We
use the WorldScan model simulation outcomes for analysis and restrict the numerical analysis to the
year 2030.14 The impacts that we will present will be based on comparing simulation results with a
binding CPF and without a CPF.
The WorldScan model has been developed as a comprehensive tool to construct long-term scenarios
for the global economy and to facilitate policy analyses in the field of international economics.15 The
model is a global Computable General Equilibrium (CGE) model in which the aggregation of regions
and sectors can be flexibly adjusted. The level of detail is limited only by the detail in the GTAP 9
database, which currently consists of 57 sectors, 140 countries, for example, all EU countries can be
distinguished within the world economy. WorldScan offers a modelling framework for addressing
policy issues in international economics. All types of taxes/subsidies on inputs of production can be
introduced (or removed).
The energy version of the WorldScan model used for simulations presented in this report covers
emissions of all GHGs. This version has a sectoral coverage that includes the agricultural sector, the
fossil fuel markets, oil, gas, and coal, as well as the electricity market, including all major renewable
technologies and carbon Capture and Storage (CCS). In addition, the model covers all major sinks
for CO2, i.e. emissions and removals related to LULUCF, including forestry and agriculture. Thus,
WorldScan allows the possibility to assess interactions between climate polices and other policies, in
particular policies related to industrial climate policies, renewable policies; energy efficiency
objectives; air pollution; land use emissions; energy and resource efficiency; ETS policies and polices
leading to increased use of bio-fuels and biomass, and carbon absorption capacities of lands and

14

Recent WorldScan simulations of climate policy are reported in Hoogendoorn, S., Trinks, A., and Bollen (2021), carbon pricing and
relocation: Evidence from Dutch industry, see voxeu.org. The model is extensively described on its’ general properties in Lejour, A.,
Veenendaal, P. , Verweij, G., and van Leeuwen, N. (2006), WorldScan: a Model for International Economic Policy Analysis, CPB
Document No. 111, CPB. The Energy version of WorldScan is described in Bollen (2015), The value of air pollution co-benefits of
climate policies: Analysis with a global sector-trade CGE model called WorldScan, Technological Forecasting and Social Change,
Volume 90, Part A, 2015, Pages 178-191.

15

This section is based on Lejour et al, (2006).

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forests. In this report, we focus on industrial climate policies, and in particular the introduction of a
CPF in the industry.
The sectoral breakdown of the industry in WorldScan is:
■

Petroleum and coal products;

■

Metals;

■

Chemical rubber & plastic products;

■

Paper products, publishing;

■

Non-metallic minerals (for construction); and

■

Processed food.

The Dutch industrial sector is modelled as part of the global market, taking into account the trade
linkages between countries and the resulting interdependencies between markets. The regional
breakdown of the WorldScan model to simulate the global economy is:
■

The Netherlands (NLD);

■

Other 26 EU countries (REU);

■

USA;

■

Rest-of-the OECD (ROE);

■

China, incl. Hong Kong (CHI); and

■

Rest-Of-the World (ROW).

The primary goal of the analysis here is to better understand the impact of a CPF on the outlook for
the industrial sector and carbon emissions (including potential carbon leakages). A CPF may lead in
specific sectors to cost increases of inputs to production. This in turn has consequences on
competitiveness and supply prices, which in the long run will also lead to structural changes from a
reallocation of labour and investments across sectors. The WorldScan model estimates the
magnitude of direct major industrial sectors shifting to costly carbon-extensive ways of production.
This simulated response simultaneously solves for any production changes resulting from changes in
domestic demand or changes in competitiveness in a trade-dependent world (model covers the entire
world in predefined regions or countries).
Although the WorldScan model simulates at a time-step of one year until any chosen end-year, we
chose here to focus on simulation outcomes of the most policy relevant year of 2030.16 A more general
description of the model can be found in Annex E .
A central research task of the project is to analyse the effects of introducing a CPF below the expected
carbon price path. To analyse this question for the industrial sector, we use the EU ETS and CPF
paths of Task 1. As stochastic price developments cannot be integrated in the WorldScan approach,
we will focus on a predefined low ETS price development with a set of binding CPFs.

16

This year 2030 is also used as the end-year to minimize on the complexity of the analysis.

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2.4.2 Scenario and data sources17
The setup of the baseline simulations in WorldScan aligns with the KEV 2022. This comprises
recalibrating model parameters (e.g. total factor productivity, energy demand technologies) to be able
to simulate macro and meso-economic growth, energy demand by industrial sectors and GHG
emissions, and prices of oil, gas and the EU ETS price as reported in the KEV 2022.
A CPF may change the production processes, thus yielding a switch away from fossil energy to
hydrogen and electricity.18 The model options include different abatement options in terms of
abatement potential (in tCO2eq.) and marginal costs (in EUR/tCO2eq.) as reported in the Dutch draft
climate agreement ("Ontwerp klimaatakkoord”).19
The report shows that there is hardly any electrification or hydrogen to be expected beyond a carbon
price of 55 EUR/tCO2eq.20 As based on our probabilistic analysis even low EU ETS price scenarios
show a price higher than 60 EUR/tCO2eq and therefore the vast majority of electrification and
hydrogen options will be economical in any case, a CPF is unlikely to drive additional electricity
demand even with a low EU ETS price. Thus we do not expect a CPF to result in additional electricity
demand and consequently the interaction of the electricity market with the industry does not have to
be explicitly modelled.21
The KEV 2022 does not include all intended policy ambitions of the coalition, because some policy
initiatives are not yet included in the government’s budget or not translated into concrete policy
actions. Hence the intention of the coalition is to achieve more emission reductions than reported in
the KEV 2022.22

2.4.3 Definition of quantitative indicators to analyse impact
We will use the following set of indicators to analyse the effects of a CPF on the industrial sector:
■

International competitive position (carbon leakage): The impact of a CPF on the
competitiveness of the industry will be evaluate using carbon leakage (simulated with WorldScan)
as the central indicator. With a binding CPF this means that activities will be reallocated to other
countries, which leads to carbon leakage. Part of the leakage will occur to countries outside EU.
The leakage will be decomposed to sectors and regions.

17

More details on the scenario, both with respect to the assumptions and general trends is presented in Annex D2.

18

These abatement functions are based on old data and will be updated to the most recent data in the midden database, see van
Dam, D., Gamboa Palacios, S., and Wetzels, W. (2021), MANUFACTURING INDUSTRY DECARBONISATION DATA EXCHANGE
NETWORK – THE DATABASE, PBL report, The Hague.

19

See https://www.klimaatakkoord.nl/documenten/publicaties/2019/06/28/klimaatakkoord.

20

See page 87 of the Dutch draft climate agreement.

21

Later, we will show that electricity price changes in 2030 from a binding CPF are very limited, so we disregard these as well.

22

See https://www.rijksoverheid.nl/ministeries/ministerie-van-economische-zaken-enklimaat/documenten/publicaties/2022/11/01/klimaatnota-2022.

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CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY
■

Investment security: We will scrutinize sectoral investments and decarbonisation efforts by the
different industrial sectors.

■

Sustainability: We will discuss the impact on GHG-emissions in the Netherlands, the rest of
Europe, and the rest of the World.

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3

Probabilistic analysis of future carbon prices

In the following, we describe the results for the probabilistic analysis of future carbon prices. To
facilitate the understanding of the analyses, we first provide some interim results (section 3.1).
Thereafter, we provide the final output of the probabilistic analysis of future carbon prices (section 0),
which is used as inputs for the electricity market modelling (results in section 4) and industrial
sensibility analysis (results in section 5).

3.1

Interim results: Stochastic representation of future carbon price
developments

As described in Section 2.2, we apply a Monte Carlo simulation to our mean reversion model, which
describes potential carbon price developments. Using the Monte Carlo simulation we obtain 10,000
possible price developments.23 Due to the large number of possible price developments for each year
between 2024 and 2040, we focus on a subset of the years in the following to describe and show the
methodology and (interim) results. The presented approach however remains the same for all of the
modelled years.
In Figure 10 we show for illustrative purposes a depiction of 20 randomly selected future price path
simulations until the end of 2027. The plot of these interim results shows the following aspects
inherent to the analytical approach:
■

The depicted potential price paths have (on average) an upward trend. This reflects the
underlying assumption that in the long run we expect the EU ETS price to increase, as reflected
in the KEV main scenario.

■

The spectrum of potential prices widens over time, for example shown by the distance
between the highest and lowest potential price paths. This reflects the fact that further in the
future there is more uncertainty about the level of the EU ETS price.

■

There is an increasing (absolute) variation of the prices, reflecting in "larger price swings”
further in the future.24 This again reflects a larger uncertainty about the prices further in the future.

23

On a month-to-month basis.

24

Price volatility remains the same in relative terms, leading to an increase in absolute terms as the level of the EU ETS price rises.

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Potential carbon price dvelopment: Random sample of 20 simulated future
price paths

€2021/tCO2eq

Figure 10

Source:

Frontier Economics

Note:

Random sample of 20 (out of 10,000) simulated price paths through Monte Carlo simulations on a month-to-month basis from 2025 until the end of
2027, based on our Monte Carlo simulations using a mean reversion model.

Based on the 10,000 price paths taken from the Monte Carlo simulation we deduct probability
distribution for the carbon prices in each of the modelled year. Figure 11 shows such a probability
distribution for the year 2027, illustrating the likelihood of different carbon price levels. The probability
distribution allows for the following observations:
■

The distribution shows that there is fluctuation around an expected carbon price (weighted
average) of ca. 93 EUR/tCO2eq. The expected price level reflects the reference EU ETS price
(as outlined in section 2.2)

■

The lower and upper tails of the distribution depict uncertainty about the potential price level
in 2027, with the highest likelihoods for price levels around the reference EU ETS price.

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Figure 11

Probability distribution of carbon prices in 2027

€2021/tCO2eq
Source:

Frontier Economics

Note:

The distribution shows the probability of (yearly averaged) prices in 2027 (x-axis is cut off at 40 EUR and 200 EUR respectively for better visibility).
The weighted average of (yearly averaged) prices of the entire distribution is 93.2 EUR and therefore in line with a linear interpolation between
December 2024 EUA Futures and the long-term expectation of future carbon prices for 2030 in the KEV 2022 main scenario.

In order to analyse the effects of a CPF using the stochastic electricity market model (see section
2.3), the results of the probabilistic analysis of future carbon prices needs to be condensed to a limited
set of data points so that it can serve as input to the model. The information displayed as a probability
distribution of future carbon prices in Figure 11 is therefore expressed as a probability tree. Thereby
the probabilities of different carbon prices are reduced into three possible cases – a central price, a
low price and a high price.
In order to reduce the probability distribution to three scenarios, the distribution is “cut” at two different
thresholds (into three areas). These reflect the probability of the price being below or above the
respective threshold. For each of the three areas the weighted average price of the partial distribution
below, above, or in-between thresholds is determined25 (see Figure 12).

25

Reference EU ETS prices (yearly average) for the year 2027 are derived as a linear interpolation between December 2024 EUA
Futures recorded January 30th, 2023, and the long-term expectation of future carbon prices for 2030 in KEV 2022 main scenario.
The expected carbon prices in 2030 (109.82 EUR) and 2040 (178.94 EUR) resemble the estimates in the 2022 KEV main scenario.
The thresholds are set at 30 % below and above the reference EU ETS, defining a low and high price scenario. The respective
thresholds are thus set at 76.87 EUR and 142.77 EUR in 2030 as well as 125.26 EUR and 232.62 EUR in 2040.

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Figure 12

Data condensation into a scenario tree (three nodes)

Probability: .99

Weighted average
price for distribution
30 below the
expected long term
mean: 61.2 EUR

Probability: 9. 6

Probability: 8 .

Weighted average price for
distribution around the expected
long term mean: 90.1 EUR

Weighted average price for
distribution 30 above the
expected long term mean:
138.0 EUR

€2021/tCO2eq
Source:

Frontier Economics

Note:

The data is “cut” at the defied thresholds of 65.28 EUR (low price case) and 121.23 (high price case) – 30% below and above the expected longterm carbon price.

The outputs from the probabilistic analysis in Task 1 imply that there is a probability of ca. 5% that the
carbon price falls below the threshold of 65 EUR/tCO2eq in 2027. The weighted average price of all
price paths that fall within this “low price development” (below the threshold) is ca. 61 EUR/tCO2eq.
We will place a particular focus on this potential “low price development” when assessing the potential
impact of different CPFs in the electricity market modelling (section 4) and industrial sensibility
analysis (section 5).

3.2

Output from probabilistic analysis of future carbon prices

As explained in the section above, the carbon price probabilities are expressed in a three-node
scenario tree. Figure 13 shows the condensed information in a “low price development” case, a
“medium price development”-scenario and a “high price development”-scenario, showing the chance
of the carbon price falling within that scenario and the expected weighted average price of all price
paths within that scenario.

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Figure 13

carbon price probabilities represented in a three-node scenario tree

Source:

Frontier Economics

Note:

All prices in €2021/tCO2eq. See Annex B for a full depiction of the scenario tree including 2040.

The scenario tree illustrates a number of characteristics, which are in line with previous observations
and expectations:
■

There is an expectation that carbon prices follow an upward trend.

■

However, there is uncertainty around the carbon price development as shown in the different
levels of prices and probabilities. The spectrum of potential prices widens over time.

■

The modelling incorporates a price path dependency: For example, price developments up to
and throughout the reference year 2027 have a significant impact on the likelihood of prices in
the following years. If carbon prices are rather low in 2027 (reflected through the low price case)
they have a much higher probability of being low in 2030 (the following modelling year in our
analysis), in comparison to scenarios in which the carbon price is on a higher level in 2027.

The probabilities and corresponding prices represented in a three-node scenario tree serve as input
for the stochastic electricity market analysis presented in the following section 4.

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4

Impact of carbon price floors on the electricity system

In this chapter, we summarise the results of the analysis of the impact of the different CPF scenarios
on the electricity sector. We present the results in the following steps:
■

Reference scenario (central carbon price path without CPF): Based on our stochastic power
market modelling, we summarise major future trends in the Dutch power market for a case without
a CPF (section 4.1). For this we discuss results for a scenario where the carbon price path
develops in line with the central expectations (based on current futures and the KEV 2022 main
scenario).

■

Central carbon price path with a non-binding CPF: The potential CPFs of this study are
designed with the intention to remain below the expected price path for CO2 certificates in the EU
ETS (defined as a price path 10% / 25% / 40% below the expected carbon price path). However,
since there is a certain probability that future carbon prices may fall below a Dutch CPF, market
participants may change their behaviour even if this development does not materialise. The
outcome of the stochastic model, which takes these probabilities into account, indicates if a
change in behaviour of market participants can be expected due to these uncertainties (section
4.2).

■

Low carbon price path with a binding CPF (compared with low carbon price without CPF):
We summarise the stochastic modelling results for a path in which the potential CPFs of this
study become binding. This means that future prices for CO2 certificates in the EU ETS fall below
the respective CPFs in the different CPF scenarios (again 10%, 25%, 40% below the expected
EU ETS price path, section 4.3). For the evaluation, we compare the scenarios with a CPF against
the scenario without a CPF while focussing on the same carbon price path with low ETS prices.

■

Evaluation: Finally, we summarise the results based on the key indicators and draw principal
conclusions from the model results. The key policy indicators are presented in greater detail in
section 4.4.

4.1

Reference scenario: Trends in the Dutch power market (without CPF)

In this section, we summarise the main results of the power market modelling without a CPF.

Table 5

Summary of key results of the reference scenario (without CPF)

Key indicators

Development

Generation capacities

■

Large increase in renewable capacities

■

Storage capacity of ~6 GW built by 2030

■

Installed gas generation capacity of ~15 GW between 2024 and
2030 (incl. some temporary mothballing)

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Key indicators

Development

Domestic dispatch

■

Large increase in generation from renewables, which offset coal
generation and cover increasing electricity demand

■

In 2030 and 2040 gas (incl. CHP) and storage complement
renewable generation

Emissions

■

Significant decline from ~36 MtCO2eq in 2024 to ~17 MtCO2eq in
2030, in particular due to coal phase out, and further decline until
2040

Electricity price

■

Declining electricity prices from ~130 EUR/MWh in 2024 to ~100
EUR/MWh in 2030

■

Further reduction to ~91 EUR/MWh by 2040

■

Increasing peak load and coal phase-out is met by contribution
from largely storages, renewables and imports

■

Towards 2040 decreasing reserve margins

■

CCGT (mostly CHP) profits largely increasing between 2024 and
2027, but from 2030 onwards decreasing

■

Renewables: decreasing profits throughout the modelled period

Reserve Margin

Power plant profits

Source:

Frontier Economics

Note:

The adequacy reserve margin is calculated as the difference between the de-rated available capacity (incl. a share of reliable import capacity) and
peak load. Profits are measured as the short-run profits per installed capacity, i.e. wholesale market revenues minus variable production costs.

4.1.1 Power generation capacities
Figure 14 summarises the main modelling results of the expected future development of power
generation capacities in the Netherlands:

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Figure 14

Reference scenario: Installed generation capacity

100
90
PV

80

GW

Offshore wind
70

Onshore wind

60

Other storage
Bio

50

Hydro
Other non-res

40

Gas

30

Coal
20

Nuclear

10

Peak load

0
2024
Source:

2027

2030

2040

Frontier Economics

■

Renewable generation capacities are likely to increase substantially until 2040. The main
capacity additions are offshore wind and PV. Additional wind onshore is expected to be limited,
e.g. due to limits in the grid capacity.26

■

The coal-fired power generation is phased out by 2030 as determined by the Dutch climate
policy.

■

There is an installed gas generation capacity of about 15 GW between 2024 and 2030. Some
of the capacity is endogenously mothballed temporarily and reactivated in 2027.

■

Additional storage capacity of about 6 GW is expected to be built by 2030. Alongside mostly gas
generation capacities storages will complement the largely intermittent renewable generation.

4.1.2 Electricity generation / power plant dispatch
Figure 15 summarises the main modelling results of the expected future development of power
generation in the Netherlands:

26

PBL (2022), Klimaat- en Energieverkenning 2022, p. 108.

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Figure 15

Reference scenario: Annual dispatch and net imports

200
Net import
150

PV
Offshore wind
Onshore wind

100

TWh

Other storage
Bio
Hydro

50

Other non-res
Gas
Coal

0

Nuclear
Net demand
-50
2024
Source:

2027

2030

2040

Frontier Economics

■

The generation from renewable energy is increasing and will likely constitute the largest source
of domestic electricity generation by 2030. It will likely provide more than 75% of supply by 2040.

■

The relevance of generation from coal plant declines significantly from ca. 28 TWh generation
in 2024 to less than 10 TWh generation by 2027. By 2030 there will be no generation from coal
plants anymore.

■

The share of gas-fired generation to total generation increases until 2027, propelled by a
domestically declining coal generation. The gas generation declines steadily thereafter, but
maintains a generation share of around 15% by 2040 – mostly provided by CHP plants to ensure
security of supply of heat.

■

Domestic generation grows faster than demand until 2030, resulting in increasing net exports of
electricity. Demand catches up with domestic generation by 2040, resulting in an almost balanced
trade balance.

4.1.3 Emissions in the Netherlands
Figure 18 summarises the main modelling results of the expected future development of carbon
emissions and emissions intensity in the Netherlands:

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Reference scenario: Emissions and emission intensity

40

350

35

300

MtCO2eq

30

250

25

200

20
150

15

100

10

50

5
0

0
2024

2026

2028

2030
Emissions

Source:

gCO2eq/kWh

Figure 16

2032

2034

2036

2038

2040

Emission intensity

Frontier Economics

■

The emissions in the Netherlands are expected to decline significantly until 2030. The
development is largely driven by the reduction in coal-fired generation, which is replaced by
generation from renewable sources and gas plants.

■

The Emissions continue to decline with a slower pace after 2030, when gas-fired generation is
gradually replaced by renewable generation. Potential emissions from the generation of gas-fired
plants in 2040, which reflect the need for dispatchable capacities in 2040, can be abated with for
example the use of climate neutral gases such as renewable hydrogen.

■

The emissions intensity of electricity generation declines along a similar path as the absolute
emissions.

4.1.4 Wholesale electricity prices
Figure 17 summarises the main modelling results of the expected future development of average
wholesale electricity prices in the Netherlands:

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Figure 17

Reference scenario: Average wholesale electricity prices

140

120

€2021/MWh

100
80
60
40
20
0
2024
Source:

2026

2028

2030

2032

2034

2036

2038

2040

Frontier Economics

■

The wholesale electricity prices decline from about 130 EUR/MWh in 2024 to about 105
EUR/MWh in 2027. Thereafter there will likely be a slower decline in electricity prices beyond
2030.

■

The wholesale price reduction reflects the expected decline of fossil fuel prices until 2027. In
the long term the cost advantages of increasingly available renewables capacity will come into
effect, together with rather steady fuel costs and increasing carbon prices.

4.1.5 Reserve margins
Figure 14 summarises the main modelling results of the expected future development of the adequacy
reserve margin in the Netherlands:

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Figure 18

Reference scenario: Adequacy reserve margins

30
Import capacity

25

PV
Offshore wind

GW

20

Onshore wind
Other storage

15

Bio
Hydro

10

Other non-res
Gas

5

Coal
Nuclear
Peak load

0
No CPF

No CPF

No CPF

No CPF

2024

2027

2030

2040

Source:

Frontier Economics

Note:

The adequacy reserve margin informs about the level of reliable capacity compared to peak load. It is calculated as the difference between the derated available capacity (incl. a share of reliable import capacity) and peak load. We de-rate import capacity with 60%, which corresponds to the
lowest availability of import capacity observed in the modelled years. Deriving exact values for de-rating would need extensive probabilistic analyses
of availability of foreign generation capacities and the interconnectors which is not subject of this study.

■

The adequacy reserve margin increases until 2030. In 2027, additional gas-fired capacity
overcompensates the decline in coal capacity. In 2030, mainly new storage capacity provides
additional secure capacity, more than compensating declining gas capacity.

■

The adequacy reserve margin declines after 2030. Increasing contributions from new import
capacity and renewables is insufficient to make up for the further decline in gas.

4.1.6 Power plant profits
Figure 19 summarises the main modelling results of the expected future development of power plant
profits in the Netherlands:

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Figure 19

Reference scenario: Power plant profits

Source:

Frontier Economics

Note:

Profits are measured as the short-run profits per installed capacity, i.e. wholesale market revenues minus variable production costs.

■

Gas-fired CHP CCGT plants rely to a large extent on income from heat generation as electricity
sales alone do not cover costs. CCGTs without cogeneration have operational profits close to
zero, indicating a challenging market environment. While CCGT profits improve by 2027 due to
declining coal generation, they degrade again thereafter with increasing generation costs and
competition from renewables.

■

Profits of renewables decline constantly over time, driven by declining wholesale prices and
increasing market saturation of renewables. The high simultaneity of renewables results in lower
market prices during hours with high generation.

■

Nuclear profits follow (declining) wholesale price movements closely, owing to their typical
operation as base-load plants.

4.2

Central EU ETS price case (with non-binding CPFs)

The analysed CPF scenarios set the floor price for carbon emissions in the Netherlands below the
expected carbon price path (CPF defined as prices path at -10%, -25% and -40% below the expected
carbon price path, which is based on prices of traded futures and KEV 2022 main scenario).
The relevant carbon price path from the scenario tree is indicated in Figure 20 and represents the
most probable future carbon price path (“central EU ETS price case”) taken from the analyses in
section 3. It has to be noticed that decisions of market participants at any point in time (e.g. in year

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2027) take possible outcomes of all potential price CO2 paths developments (i.e. in model years 2030
and 2040) into account, not only the most probable development (as a deterministic model would).

Figure 20

Central EU ETS price case

Source:

Frontier Economics

Note:

All prices in €2021/tCO2eq.

Due to our stochastic modelling approach, the model takes into account the probability that from one
representative year to the other the EU ETS price could fall below the CPF. In the following, we
analyse to what extent there is a change in behaviour of market participants from the uncertainties
regarding the future carbon price. Thus, although the presented results in this section show the
outcomes for the EU ETS price following the central price path, they still allow for a deduction of
potential changes in behaviour induced by the possibility of a CPF becoming binding.
In the following, we outline the major results for the different CPF scenarios (-10%, -25%, -40%) by
using the following key indicators:
■

Power generation capacities; and

■

Electricity generation and results for other indicators

4.2.1 Power generation capacities
The introduction of a CPF has a small impact on the expected profits for power plants: While
renewables could benefit from a slightly improved profitability expectation resulting from a CPF (see
the background box at the end of this section), the contrary holds for fossil fuel plants. Potentially

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higher emissions costs induced by a CPF and consequently higher variable generation costs in the
Netherlands could result in a reduced competitiveness of domestic gas- and coal-fired power plants.
The sheer existence of a CPF (without it being binding) could already have an impact on generation
capacity: Compared to a scenario without a CPF, a CPF influences the profitability of power plants in
possible future carbon price scenarios (if the EU ETS falls below the price floor). For example, a plant
operator might decide to retire a plant because he would make less profits in the future if the EU ETS
price falls below the CPF compared to the case without a CPF, influencing his expected profits today.
However, our results for the reference EU ETS price scenario indicate that for the analysed CPF
options the impact is not large enough to trigger any changes in the installed capacity of fossil fuel
plants (Figure 21).

Figure 21

Source:

Central EU ETS price case: Impact on power plant capacities

Frontier Economics

Due to a low probability that the analysed CPFs become binding and a relatively low impact on
wholesale electricity prices if they do (see section 4.3.5), the expected profits of renewables are hardly
affected by the CPFs. Thus, an impact on low carbon technology investments would be small.
However, wind and (utility) solar PV are already profitable and competitive with fossil fuel-based
electricity generation without a CPF for the analysed fuel prices and a wide range of possible EU ETS
price scenarios. As a result the power plant capacities in the CPF scenarios do not differ from the
reference scenario without a CPF.

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Background: Impacts of carbon price floors on expected profits for RES-E
For an investment decision in renewable energies, the expected average profit per unit of generation
is essential to evaluate whether a project is economical or not. The expected profit can be compared
with the Levelized Costs of Electricity (LCOE): If the expected profit is higher than the LCOE, the
investment could generate an expected profit.
Figure 22 illustrates the expected average discounted profits of investments in renewables in 2024
over their lifetime for different CPFs. The values represent the expected profits over all EU ETS
price scenarios, weighted according to each price path’s probability as indicated in the EU ETS price
scenario tree (see Figure 13).

Figure 22

Expected discounted profits and levelized costs of electricity (LCOE) of
renewable investments in 2024

90
80
80.56

80.54

80.52

80.52

€2021/MWh

70

75.62

75.61

75.60

75.60

73.30

73.27

73.26

73.26

60
50
40
30
20
10
0
Offshore wind
CPF -10%

Onshore wind
CPF -25%

CPF -40%

PV
No CPF

Source:

Frontier Economics

Note:

Assuming lifetimes of 25 years and 2% discount rate. Prices assumed constant after 2040.

LCOE

The discounted profits of renewable investments in 2024 show that the expected profits are hardly
affected by the CPFs. A CPF thus does not seem to benefit investment decisions. However, the
expected profits are higher than the levelized costs of electricity generation, indicating that
renewable investments, under our scenario assumption, are expected to be economical already
without additional support.

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4.2.2 Electricity generation and results for other indicators
Given that the model results do not indicate a change in installed capacity of fossil fuel plants and that
the CPFs are not binding in the reference EU ETS price path (and thus do not impact generation
costs), also the electricity generation remains almost unchanged.

Figure 23

Source:

Central EU ETS price case: Impact on power generation

Frontier Economics

Consequently also all other indicators remain unaffected. This indicates that although a CPF slightly
impacts the expected and lower range profits of all generators, the low probability of low EU ETS price
scenarios prevents CPFs to pre-emptively result in changing market outcomes.

4.3

Low EU ETS price case (with binding CPFs)

In the following we present the results for low EU ETS price case. This scenario is designed to explore
the potential impacts of a CPF in case the EU ETS price deviates significantly downwards from the
reference price development. This would case the CPF to impose an additional carbon tax in the
Netherlands.
Based on our analysis for the probability distribution of future EU ETS prices, the likelihood associated
with this low price case for 2027, representing an EU ETS price of ca. 61 EUR/tCO2eq, is about 5%.
For 2030, our probability analysis suggests a 67% probability of continuing on a low price trajectory
given that the price in 2027 was already low.

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Figure 24

Low EU ETS price case (with binding CPFs)

Source:

Frontier Economics

Note:

All prices in €2021/tCO2eq.

In the following, we summarise the main results of the low EU ETS price case for the different CPFs
(defined as price paths at 10%, 25% and 40% below the expected EU ETS price projection, i.e.
“central EU ETS price case”) again by using the following key indicators:
■

Impact of low EU ETS price case (with binding CPF) on carbon costs;

■

Power generation capacities;

■

Domestic dispatch / power generation (and power imports/esports);

■

Carbon emissions;

■

Electricity prices;

■

Reserve Margins;

■

Power plant profits.

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Table 6

Summary: Impact of CPFs in the low EU ETS price case

Indicator

Year

CPF -10%

CPF -25%

CPF -40%

Generation
capacities

2027

-3.6 GW

-2.3 GW

0.0 GW

2030

-1.4 GW

-1.2 GW

-0.6 GW

Domestic
dispatch

2027

-22.0 TWh

-12.1 TWh

0.0 TWh

2030

-4.2 TWh

-3.2 TWh

-1.5 TWh

Emissions

2027

-46%

-23%

0%

2030:

-12%

-10%

-5%

2027

+3.2%

+1.7%

0.0%

2030

+0.4%

+0.4%

+0.1%

Security of supply Adequacy Reserve
Margin (ARM)

2027

ARM turns negative

ARM declines to close to
zero

ARM remains almost
unchanged

2030

ARM declines slightly but
remains positive

ARM declines slightly
but remains positive

ARM remains almost
unchanged

CCGT
profits

2027

moderate negative impact

low negative impact

minimal impact

2030

high negative impact

moderate negative
impact

minimal impact

Electricity price

Source:

Frontier Economics

Note:

Generation capacities indicate total installed and active capacities

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Reference (without CPF) for low EU ETS price case
It has to be noted that in a scenario with a low carbon price in the EU ETS and without a CPF, the
Dutch power system develops slightly different than in the reference scenario with a central EU ETS
price assumption (which is shown in section 4.1). For example, due to the lower carbon price,
■

Not all gas-fired power plants are reactivated in 2027; and

■

Coal-fired power plants are closed later.

Figure 25

Reference for low EU ETS price case: Installed generation capacity

100
90
PV

GW

80

Offshore wind

70

Onshore wind

60

Other storage
Bio

50

Hydro
Other non-res

40

Gas

30

Coal
20

Nuclear

10

Peak load

0
2024
Source:

2027

2030

2040

Frontier Economics

The complete results for the reference scenario (without CPF) for the “low EU ETS price case” can
be found in Annex D.1.
In the following, the variations in the results shown relate to the differences in the results of the
electricity market model runs with and without the CPFs – in all cases assuming a low EU ETS price
development.

4.3.1 Impact of CPFs on carbon costs
The level of the additional emissions costs (costs per tonne CO2eq emitted in addition to the EU ETS
price) is illustrated in Figure 26 for the different CPF scenarios. As the CPFs only influence the

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emissions costs in the Netherlands, the depicted costs also represent the difference between
emissions costs in the Netherlands and neighbouring markets.

Figure 26

Low EU ETS price case: Carbon costs induced by the analysed CPFs

40
35

€2021/tCO2eq

30
25
20
15
10

5
0
2024

2027
CPF -10%

Source:

2030
CPF -25%

CPF -40%

2040
No CPF

Frontier Economics

The higher carbon costs in the Netherlands raise generation costs of fossil fuel plants according to
their fuel efficiency and the carbon content of the used fuel. In the low EU ETS price scenario, the
CPF of 10% below the reference EU ETS price results in between 11 to 17 EUR/MWh higher variable
generation costs for a typical gas-fired power plant in the different analysed years (23-36 EUR/MWh
for coal), whereas the lowest analysed CPF of 40% below the reference EU ETS price only results in
about 1 EUR/MWh (gas) and 3 EUR/MWh (coal) additional variable costs (see Figure 27).

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Figure 27

Low EU ETS price case: Generation cost delta between the Netherlands and
neighbouring markets

40
35

€2021/MWh

30
25
20
15
10
5
0
Coal

Gas

Coal

2024

Gas

Coal

2027
No CPF

CPF -10%

Gas
2030

CPF -25%

Coal

Gas
2040

CPF -40%

Source:

Frontier Economics

Note:

Assuming a fuel efficiency of 50% for gas and 40% for coal. Coal generation costs for 2040 only shown for informative purposes as coal is phased
out by 2030 within the Netherlands.

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Background: Impact of carbon price on variable generation costs
A CPF results in additional costs for carbon emissions when the EU ETS price falls below the CPF.
As the analysed CPF only applies in the Netherlands, this results in higher electricity generation
costs domestically than in neighbouring markets for comparable power plants.
The contribution of carbon costs to total short-term generation costs depends on the fuel efficiency
of a plant and the carbon content of the used fuel. With fuel and carbon prices as of early 2023, the
variable costs of a gas-fired plant consist of about two-third of fuel costs and one-third of carbon
emission costs. For coal, the relation is roughly vice versa.
Figure 28 illustrates for reference coal and gas power plants how additional carbon costs translate
into additional short-run generation costs. For typical gas fired CCGT power plants, every 1
EUR/tCO2eq translates roughly in additional generation costs of 0.5 EUR/MWh. For typical coalfired plants (due to their lower fuel efficiency and higher carbon content of the fuel), generation costs
increase by about 1 EUR/MWh for every 1 EUR/tCO2eq.

Additional costs [€/MWh]

Figure 28

Impact of carbon price on coal- and gas-fired power plant generation costs

45
40
35
30
25
20
15
10
5
0
0

5

10

15

20

25

30

35

40

Carbon price [€/tCO2eq]
Gas

Coal

Source:

Frontier Economics

Note:

Assuming a fuel efficiency of 40% (coal) and 50% (gas) of electricity generation.

4.3.2 Power generation capacities
The low EU ETS price path assumed in this scenario results in higher generation costs for domestic
fossil fuel-based power plants. This weakens the competitive position of these plants compared to
power plants in neighbouring markets. This again could result in lower domestic capacity, triggered
by domestically lower new construction of power plants, earlier retirement, mothballing or nonreactivation of previously mothballed capacity.

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Figure 29

Source:

Low EU ETS price case: Impact of different CPF levels on installed capacity in
the Netherlands

Frontier Economics

Figure 29 summarises the impact of the introduction of different CPF levels on capacity compared to
the reference case without a national CPF:
■

A CPF at 10% below the reference EU ETS price could result in significant fossil fuel plant
retirements in the short-term, while the CPF -40% is likely to have no effect. The additional
carbon costs in the CPF -10% scenario result in a significant reduction of coal (down 68%/2.3
GW) and gas (down 12%/1.3 GW) capacity in 2027 compared to the reference case without CPF.
Coal-fired plants would have to retire in any case by 2030 due to the national coal phase-out.
With a CPF at 10% below the reference EU ETS price, capacity leaves the market earlier as it is
not able to recover the increased generation costs. The CPF -40% scenario does not result in
additional emissions costs in 2027 as it is still below the low EU ETS price scenario. Consequently
the results do not indicate a change in capacity.

■

The different CPF scenarios result in smaller capacity differences in 2030 than in other
years. In the analysed CPF scenarios, the total capacity reduction compared to the base case
without CPF varies between 1.1 GW (CPF -10%) and 0.6 GW (CPF -40%) in 2030 (with no
capacity increase of individual technologies, e.g. storage). As coal-fired plants are phased-out by
2030 in the Netherlands, the vast majority of the decline comes from lower gas-fired capacity.
The relatively small difference between the scenarios is due to the significant share of gas plants
combining heat and power generation and thus being needed for heat generation. For this study,
we assume that those plants are able to compensate their worsened competitive position induced
by higher domestic carbon costs with their heat production capabilities.

■

CPFs could support battery storages to integrate renewable energies. By 2040, in the base
case without CPF, previously mothballed gas capacity in the Netherlands is reactivated to
account for capacity retirements in neighbouring markets improving revenues for domestic
generators. With a CPF -10%, this reactivation does not take place. Instead, about 1 GW of

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battery storage capacity is additionally built, supporting the integration of renewable energies into
the electricity system.27

4.3.3 Electricity generation / power plant dispatch
Higher costs associated with carbon emissions in the Netherlands, induced by a binding CPF, put
domestic electricity generation of fossil fuel-based plants at a disadvantage compared to those in
neighbouring markets. This is already reflected in declining generation capacity. Furthermore, the
impact can also reflect in the changes regarding domestic electricity generation as well as imports
and exports.

Figure 30

Source:

Low EU ETS price case: Impact of different CPF levels on electricity
generation in the Netherlands

Frontier Economics

The key observations can be summarised as follows:
■

A high CPF could result in a significant replacement of domestic coal- and gas-fired
generation by imports in 2027. In the case a CPF in the Netherlands results in additional carbon
costs for fossil fuel-based electricity generation, domestic generation could be replaced by
imports. In our scenario with the highest CPF, more than 20 TWh of domestic generation (~16%
of demand) is replaced by imports in 2027, mainly from Germany. The majority of reduced
domestic generation would stem from coal-fired power plants (down 17.2 TWh / 78.8%) which
experience a higher increase in generation costs than gas-fired plants due to the higher
emissions intensity of electricity generation. Gas-based generation declines by 5.7 TWh / 15.6%,
with a majority of the remaining generation coming from CHP plants.

■

For 2030 and 2040, heat demand could limit the decline in domestic gas generation caused
by a binding CPF. Following the national regulation, coal power plants will be phased-out by
2030 in the Netherlands. While a significant share of the overall impact of the CPFs in 2027

27

However, due to their different technical characteristics (e.g. strong focus on short-term flexibility of batteries), the capacity of
storages and thermal plants cannot be directly compared (in the sense of “replacing” each other).

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results from changes in coal-fired generation, this is naturally no longer the case in 2030 due to
the phase-out. Additionally, CHP plants constitute a large share of the domestic gas-fired plant
fleet particularly in 2030. CHP plants have to run to satisfy heat demand and are therefore less
impacted by higher generation costs. Consequently, in 2030 and 2040, in the CPF -10% and CPF
-25% case, domestic electricity generation declines less than in 2027. For the CPF -40%
scenario, the expected impact of is relatively small, in line with the lower additional emissions
costs than with the higher CPFs.

4.3.4 Carbon emissions
As previously discussed, a CPF in the Netherlands, if becoming binding, results in higher emissions
costs in the Netherlands and consequently in reduced electricity generation from fossil fuel-based
plants. Domestic emissions decline. However, this does not necessarily result in an overall decline of
emissions in the whole system. The net effect depends on how the reduced fossil fuel-based
generation in the Netherlands is compensated.

Figure 31

Source:

Low EU ETS price case: Impact of different CPF levels on carbon emission in
the Netherlands and sourrounding markets

Frontier Economics

The key observations can be summarised as follows:
■

A CPF at 40% below the expected EU ETS price is unlikely to have an effect on carbon
emissions. As the low CPF scenario would only slightly elevate the domestic carbon costs even
assuming a low EU ETS price scenario, and therefore capacity and generation remain mostly
unchanged, also carbon emissions remain stable.

■

The largest domestic emission reductions could potentially occur before 2030. A CPF, if
resulting in additional emissions costs for domestic electricity generation, could have the largest
impact on carbon emissions by impacting coal-fired electricity generation as they emit about twice
as much per generated unit of electricity than gas-fired power plants. This is confirmed by the
model results showing the largest impact in 2027 when active CPFs reduce coal-fired generation.
From 2030 on however, when coal power plants are phased out, the maximum annual emission

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reduction in the Netherlands over all CPF scenarios in the modelled years is ~2.2 MtCO2eq or
17.8% of the total domestic electricity sector emissions in that year.
■

Reduced emissions caused by higher domestic carbon costs are largely compensated by
increasing emissions in neighbouring markets. Adding to the limited impact of a CPF on
carbon emissions in later years is the leaking of emissions into neighbouring markets. While
fossil-fuel based generation would decrease in the Netherlands with additional carbon costs, this
is compensated by imports – and thus the net effect on emissions depends on the emissions
intensity of the imported electricity. Our results show that the imported electricity is produced by
a mix of gas, coal, and bio plants, resulting in a net negative effect of overall emissions in the
electricity sector in 2027 in the CPF -10% case and to a lesser extent in the CPF -25% case. For
later years however, for which domestic gas-generation and no longer coal has to be replaced in
the Netherlands, the net effect is close to zero.

4.3.5 Wholesale electricity prices
A CPF can impact electricity prices due to its impact on generation capacity and on short-run
generation costs of fossil fuel fired power plants. However, the magnitude of the impact depends on
the overall system including the interaction with neighbouring markets and is therefore a priori unclear.

Figure 32

Source:

Low EU ETS price case: Impact of different CPF levels on wholesale prices in
the Netherlands

Frontier Economics

■

While a low CPF would only marginally impact wholesale prices, higher CPFs could have
a noticeable impact. In the CPF -40% scenario, wholesale electricity prices are hardly affected
even in our low EU ETS price scenario and for all reference years. In the CPF -10% case, the
impact on wholesale electricity prices is most pronounced. In 2027, prices in the low EU ETS
price scenario increase by close to 3 EUR/MWh (3.2%). The effect of the CPF -25% ranges
between the other scenarios.

■

While the wholesale price effect of CPFs declines until 2030, it increases again in the
longer term. After the relatively strong wholesale price impact in 2027, all CPF scenarios show

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CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY
only a limited impact in 2030. This is because coal-fired plants are phased out by then and
generations costs of gas-fired plants are less impacted by emissions costs. While the potential
wholesale price effect declines by 2030 due to the phase-out of coal power plants, it is higher
again in 2040, since a larger share of gas plants in 2040 passes through higher costs to the
electricity price.

4.3.6 Adequacy reserve margins
The different levels of available electricity generation capacity have implications for security of supply.
To assess the impact of the different CPF scenarios, we calculate the Adequacy Reserve Margins
(ARM), defined as the difference between reliable generation and (derated) import capacity minus
peak load (illustrated in Figure 33).

Figure 33

Low EU ETS price case: Impact of different CPF levels on peak demand and
de-rated generation and import capacity

35
30

Import capacity
25

PV
Offshore wind

20

GW

Onshore wind
Other storage

15

Bio

Hydro

10

Other non-res
Gas

5

Coal
Nuclear

2027

2030

No CPF

CPF -40%

CPF -25%

CPF -10%

No CPF

CPF -40%

CPF -25%

CPF -10%

No CPF

CPF -40%

CPF -25%

CPF -10%

0

Peak load

2040

Source:

Frontier Economics

Note:

The adequacy reserve margin informs about the level of reliable capacity compared to peak load. It is calculated as the difference of the de-rated
available capacity (incl. a share of reliable import capacity) and peak load. We de-rate import capacity with 60%, which corresponds to the lowest
availability of import capacity observed in the modelled years. Deriving exact values for de-rating would need extensive probabilistic analyses of
availability of foreign generation capacities and the interconnectors which is not subject of this study.

Although the ARM is a simplified measure that, for example, does not take into account the
probabilistic (and time-varying) nature of generation from variable renewables and the time-varying
availability of import opportunities, nor intertemporal dependencies (in particular relevant for the ability
of storage to contribute to security of supply), it provides valuable indications on how different

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scenarios compare to each other in terms of security of supply. For more detailed absolute statements
regarding security of supply however, a more detailed (probabilistic) analysis is required.
■

A CPF close to the reference EU ETS price for 2027 could result in a further decline of
already potentially tight supply capacity. In the scenario with the highest CPF -10%, the sum
of the derated generation and import capacity in 2027 falls short of the assumed peak demand
by about 940 MW (5% below peak demand), indicating a potential supply shortage. This in mainly
due to the early retirement of coal capacity. In the CPF -25% scenario, the gap closes between
peak demand and derated supply, still indicating a rather tight level of secure capacity. For the
CPF -40% case, the ARM is the same as in the scenario without a floor price, with derated supply
being 2.3 GW above peak demand.

■

Small scenario differences for the Adequacy Reserve Margin in 2030, assuming that gasfired CHP plants remain available with higher CPFs. For 2030, all CPF scenarios show a
similar level of secure supply capacity above peak demand, with a significant share of the
capacity is provided by storages, interconnectors and, to a lesser extent, offshore wind. Although
in particular the CPFs at 10% and 25% below the reference EU ETS result in significantly
increased costs for gas plants, their capacity is almost equal across all scenarios. This follows
from the assumption that combined heat and power plants, which constitute the majority of
capacity in the market, are supported by income from heat generation and thus are less
susceptible to the increase in generation costs.

■

For the long term, the relative contribution of gas-fired plants to secure capacity declines
but is still significant. With an expected increasing electrification of energy consumption (for
example due to increasing numbers of electric cars and heat pumps), also peak electricity
demand is likely to increase. While renewables and storages are expected to provide a larger
share of secure capacity, our results indicate that gas-fired plants (potentially operated with
defossilised gases) could still play a significant role in 2040. Although the secure capacity remains
in a CPF scenarios above the assumed peak demand, the CPF -10% and CPF -25% scenarios
show a lower reserve margin than the CPF -40% scenario.

4.3.7 Power plant profits
While additional costs for carbon emissions induced by a CPF result in higher average wholesale
prices, the total impact on profits varies between different types of electricity generation. Generation
costs of renewables and nuclear are not affected by increasing carbon costs. Also they have low
variable generation costs such that they produce close to the technical maximum. They thus generally
benefit from the change in wholesale prices.
Fossil fuel-based power plants have to bear the higher carbon costs and are typically marginal
producers. Thus, they adjust their generation level to the respective wholesale price and competitive
position towards other generators (domestically and internationally). However, this can be different
for CHP plants for which the generation level can depend on the heat demand.

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Figure 34

Low EU ETS price case: Impact of different CPF levels on power plant profits

Source:

Frontier Economics

Note:

Profits are measured as the short-run profits per installed capacity, i.e. wholesale market revenues minus variable production costs.

■

A low CPF is unlikely to have a strong effect on generators’ profits. In the CPF -40%
scenario, profits for all technologies remain almost unchanged. This is consistent with the impact
on the other key indicators: As carbon costs are only slightly raised (if at all) the impact on all
aspects of the electricity system remains small.

■

Gas-fired power plants could see an additional decline in profits in an already difficult
competitive environment. Although annual operating hours for gas -fired plants are only slightly
affected by CPFs (lower generation volumes are served by reduced capacity), annual short-run
profits decline as generation costs are increasing more than wholesale electricity prices. The
effect increases the higher the added carbon costs of a CPF are.

■

Renewable energies could benefit slightly from a binding CPF. Renewable energies could
slightly benefit from a higher wholesale price induced by a binding CPF as their production
volumes remain unchanged.

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Background: Impact of CPFs on minimum profits for RES-E (the low CO2 scenario)
A CPF affects the possible profits of renewables. In particular with low EU ETS prices, wholesale
prices would be low (as price setting fossil fuel plants have lower costs) and thus renewables profits
would be low. A CPF would prevent carbon prices in the Netherlands to fall a certain threshold and
thus support wholesale prices and profits of renewables. In Figure 35, the minimum discounted
profits of selected RES-E technology investments in 2024 over their lifetime are shown for the
different CPF scenarios. Additionally, the Levelized Costs of Electricity (LCOE) are displayed for
comparison.

Figure 35

Discounted profits of renewable investments in the low CO2 scenario in
2024

90
80

€2021/MWh

70

60

68.46

68.17

67.80

67.80

62.94

62.79

62.54

62.54

62.77

62.42

62.09

62.09

50
40
30
20
10
0

Offshore wind
CPF -10%

Onshore wind
CPF -25%

CPF -40%

PV
No CPF

Source:

Frontier Economics

Note:

Assuming lifetimes of 25 years and 2% discount rate. Prices assumed constant after 2040.

LCOE

In our scenarios, the minimum discounted profits for all considered renewables are above their
Levelized Costs of Electricity. This means that investments appear, given the scenario assumptions,
to be profitable. This is already the case without a CPF.
A CPF could, depending on its level, further increase the minimum expected profitability of
renewables investments. Compared to the scenario without a CPF, the highest analysed CPF of
10% below the reference EU ETS price would increase the minimum profits by around 1%.

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4.4

Evaluation of the impact of CPFs on the key policy indicators

In the following, we summarise the finding of the electricity market scenario analysis with respect to
the key policy dimensions. We do this by highlighting the impact of the CPF on the key indicators for
the electricity sector.

4.4.1 Investment security for renewable energies
A CPF results in a lower limit of electricity generation costs for fossil fuel-based power plants and thus
guarantees a certain level of competitiveness of renewable energies in the Netherlands. However,
the benefits appear limited.
■

Investments in (utility scale) solar PV, onshore and offshore wind appear to be profitable
and competitive with fossil fuel-based electricity generation for analysed wide range of
possible EU ETS price scenarios. This follows from our analysis of the Levelized Costs of
Electricity (LCOE) and the expected captured prices by renewables, given prevalent fossil fuel
price expectations. Thus the profitability of renewables does not appear to be a major barrier for
investments in most case. Higher capacity additions than assumed in the modelling exercise are
thus rather contingent on non-financial constraints like the availability of transmission
infrastructure and land, the speed of permitting, sufficient labour, the overcoming of supply chain
constraints and other non-economical constraints than the profitability outlook.

■

The overall benefits for investments in renewables are limited. Additional to renewable
energies being likely to be profitable without a CPF, our analysis shows that the impact of a
binding domestic CPF in the Netherlands is significantly diluted by the strong integration of the
country into the European market (reflected in the large increase of imports and relatively small
impact on wholesale prices). A CPF that raises generation costs of fossil fuel plants in the
Netherlands does only to a small extent translate into higher wholesale electricity prices and
therefore an improved economic outlook for renewables.

4.4.2 International competitiveness
For the electricity sector we evaluate the international competitiveness of the Netherlands based on
the impact of a CPF on installed generation capacity as well as imports and exports. Our analysis
shows that although a non-binding CPF does not appear to have an effect, the competitiveness of
domestic fossil fuel-based generation could be noticeably reduced by domestically higher emission
costs.
■

Although a CPF could worsen the competitiveness of domestic fossil fuel plants, this does
not seem to pre-emptively result in capacity retirements. Our results of the central EU ETS
price case do not show a difference in installed electricity generation capacity between the
different analysed CPF scenarios including the scenario without a CPF. This indicates that the
sheer existence of a floor price (that does not become active and thus not result in higher
domestic carbon costs), and therefore only the possibility of it becoming active, does not have an
impact on the development of capacity and therefore the competitiveness of domestic generation.

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■

An active CPF could result in declining fossil fuel plant capacity and generation. If a CPF
becomes active, it puts fossil fuel fired power plants at a disadvantage compared with their
counterparts in other markets. This results in domestic generation being replaced by imports. The
worsened competitiveness not only result in a reduced dispatch of domestic carbon intensive
plants but also in a lower installed capacity compared to a scenario without a floor price. The
magnitude if the impact depends on the additional cost level imposed on domestic generators
and the year (and therefore the cost difference towards foreign capacity). In 2027, the impact
could be the largest given that coal-fired plants are particularly affected by higher carbon costs.
The magnitude of the capacity effects declines in the medium term, as coal-fired plants are
phased out by 2030 in any case.

4.4.3 Sustainability of the electricity sector, i.e. impact on carbon emissions
Although an active CPF could result in lower carbon emissions in the Netherlands, at least some of
the decline is compensated by increased emissions in other EU ETS countries.
■

An inactive CPF is unlikely to have an effect on carbon emissions. Following the absence
of a capacity and generation change in the reference EU ETS price scenario (in which the
analysed CPFs do not induce additional carbon costs), also emissions in the electricity sector
remain unchanged. This is because the impact on expected technology profits is too low to trigger
capacity changes. Additionally, renewable investments appear already competitive without a CPF
and are thus rather restricted by exogenous capacity constraints than their economics.

■

An active CPF could reduce emissions in the Netherlands, but the net effect in Europe is
likely to be small. For the cases in which the EU ETS price falls below the floor price, the reduced
fossil fuel-based electricity generation in the Netherlands results in lower domestic emissions.
However, this is at least partly compensated by additional emissions in neighbouring countries.
Only in the short term, when more carbon intensive electricity from coal power plants is
substituted, the model results show a net reduction in total electricity system emissions (including
those in other countries). For later years, when coal plants are already phased out, no meaningful
net effect can be observed.

4.4.4 Affordability of electricity
An increase of emissions costs induced by a CPF only partially translates into higher wholesale
electricity prices. The impact on final consumption would be even lower.
■

An inactive CPF is unlikely to impact electricity prices. In the analysed EU ETS and CPF
scenarios, electricity prices do not change as long as EU ETS prices remain above the CPF.

■

The impact of potentially higher domestic carbon prices only partially translates into
higher electricity prices. Although generation costs of fossil fuel fired power plants could be
significantly elevated by a CPF if the EU ETS price falls below the floor price (compared to a
scenario without a floor price), the effects on the wholesale price are smaller. Even with the
highest analysed CPF and in the low EU ETS price scenario (resulting in an increase of carbon

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costs for power generators between 20 and 40 EUR/tCO2eq), the maximum increase of the
average wholesale price across all CPF scenarios and years is about 3 EUR/MWh (about 3%
compared to no CPF). The price increase is dampened by imports of electricity and, particularly
in later years, the increasing share of renewables.

4.4.5 Electricity security of supply (i.e. impact on reserve margin)
A CPF can negatively impact security of supply if resulting in decreasing dispatchable capacity. Our
results show that this is particularly the case if the CPF becomes binding and adds significant extra
costs to domestic carbon emissions.
■

A non-binding CPF does not seem to impact security of supply. The impact of a CPF on
security of supply is closely connected to its impact on installed capacity. As long as a CPF does
not result in additional carbon costs (because the EU ETS price is above the floor price), our
scenario analysis shows no impact on installed capacity. Consequently, the level of security of
supply remains unchanged.

■

A binding CPF could result in lower security of supply, particularly in the short term. For
the coming years, fossil fuel fired power plants provide the majority of secure capacity. At the
same time, the model suggests that a CPF, if binding, could have the largest impact on fossil fuel
capacity in 2027 when in the base case there is still significant coal capacity in the Netherlands.
As a consequence, our analysis of the Adequacy Reserve Margin (ARM) indicates that a high
CPF could potentially cause a capacity shortage. Also, this could exacerbate security of supply
concerns for years towards the end of this decade as identified by the national transmission
system operator in a recent security of supply report28. For later years, when storages and
renewables provide more secure capacity, the impact of a CPF is reduced.

28 TenneT: Monitoring Leveringszekerheid 2022.

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5

Impact of carbon price floors on Industry (sensibility check)

In the following, we summarise the analyses (sensibility check) on potential impacts of different CPFs
on the Dutch industry. The analyses are undertaken for the low EU ETS price path as defined in
chapter 3 of this study (probability of ca. 5% in the stochastic electricity market analysis in section 4).
We apply CE Delft’s WorldScan model, a computational general equilibrium (CGE) model, which
estimates the macroeconomic effects of economic developments and policy decisions e.g. regarding
GDP, employment, investments, carbon leakage (detailed description in Annex D ).
For the analyses, we focus on the following industries:
□

Petroleum and coal products;

□

Metals;

□

Chemical rubber & plastic products;

□

Paper products, publishing;

□

Non-metallic minerals (for construction);

□

Processed food.

The modelled geographical regions comprise of
□

The Netherlands;

□

Rest EU;

□

USA;

□

Rest of the OECD;

□

China;

□

Rest of the World (incl. India).

In the following, we summarise the main results of the analyses in the following steps:
■

Summary of overarching results (section 5.1);

■

Impact of the different CPFs on international competitiveness and carbon leakage (section 5.2);

■

Impact on investments (section 5.3);

■

Impact on employment (section 5.4); and

■

Impact on macroeconomic indicators (section 5.5).

5.1

Overarching results

The introduction of a CPF in the Dutch industry market aims to provide more certainty regarding
emission reductions. In KEV 2022, it is assumed that the ETS price will rise to ca. 110 EUR/tCO2eq
in 2030 for the main scenario. However, uncertainty around the actual development of the ETS price
remains, which we reflect in a probability distribution for the development of the future ETS price. As
an input to the stochastic modelling of the electricity sector there is a chance of ca. 5% that the ETS
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price will be around 63 EUR/tCO2eq in 2030 (low EU ETS price case). In the sensibility analysis of
the industrial sector we use this low EU ETS price case as an input to the deterministic modelling to
identify potential risks and benefits from the CPF options. The following results are thus based on an
assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs
are binding.
If the CPF -10% is chosen, which means a floor price of 10% below the central assumption of KEV
(96 EUR/tCO2eq), the CPF will induce a 28% emission reduction by the industry. However, the cost
price difference between Dutch production and similar sectors in countries where less climate policy
is pursued would increase, which would put the Dutch industry at a competitive disadvantage. This
will lead to a loss on investment (8%), production (1.3%), and 1900 jobs in the industrial sector, and
GDP (0.2%). It will have no impact on total labour supply, but it will lead to labour shifts in favour of
non-industrial carbon-extensive sectors. It is important to realize that most of the emission reduction
will occur by CO2 Capture, transport, and Storage (CCS) in the chemical, and oil refinery sectors.
Hence any labour losses in these sectors are more than compensated by the extra labour demand
involved in CCS. Note that transitional costs of labour shifts (“werk-to-werk”) are not taken into
account.

Table 7

Summary “impact on industry” in the low EU ETS price scenario in 2030
CPF
-10%

CPF
-25%

CPF Notes
-40%

Carbon
leakage rate*

20%

20%

12% Emissions increase in developing countries not just restrict to highly
carbon-intensive industrial goods, but also indirect leakage of demand
for fossil-based electricity and gasoline-based transport services.

Carbon
emission
reduction**

28%
(0.4%)

17%
2% Binding impact of CPF -10%, CPF -25%, CPF -40% is equal to33, 19,
(0.2%) (0.0%) and 2 EUR/tCO2eq, respectively. Abatement curve (marginal cost of
emissions reductions vs abatement) is strongly convex.

GDP-loss

0.2%

0.1%

0.0% GDP losses from costly emission reductions, losses in production are
partly compensated by production gains in service sectors and a termsof-trade gain

Production
loss industry

1.3%

0.8%

0.0% Energy bill is only part of production processes, and costs from carbon
pricing can be significantly avoided by emission reduction (abatement
curve is convex as they include CCS as low-merit order options)

Employment
loss Industry
(‘000)

1.9

1.1

0.0 At macro-level there is zero impact on labour supply. Reason: nonlabour supply policies never have proven to have an impact on labour
supply

Investment
loss Industry

8%

4%

0% Investment by industry drops more than production. The reason is that
production drops especially in highly capital-intensive sectors (chemical,
rubber and plastics and the metal sector). Investment increases in nonindustry sectors, and at the macro-level the increase is more than the
losses in industry.

Source:

CE Delft

Note:

*

Carbon leakage rate is the % emission increase outside NL divided by emission reduction inside NL.

**

Emission reduction is reported for the Netherlands and the EU (between brackets).

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The limited production losses will also lead to limited investment losses, but these are largely offset
by additional investments in lower-carbon production processes. The production advantage for Dutch
competitors in China and countries in the rest of the world leads to extra carbon emissions in these
regions, also because the extra energy-intensive production goes hand in hand with extra demand
for electricity (including fossil) and transport services (gasoline). The carbon leakage rate is a relative
measure, which is equal to the emission increase in the rest of the world divided by the emission
reduction of the Netherlands. The leakage rate is approximately 20% in the CPF -10% case. Please
note that in this analysis, just like in KEV 2022, the CBAM has not yet been included. However, that
means that this Dutch leakage will be additional to the European leakage rate of 45% of the EU
achieving its climate goals of the EU’s Fit for
package (see Annex E.4). Bear in mind that the EU
plans are significantly more stringent than those in the rest of the world.
If CPF -25% is chosen, i.e. the CPF is 25% below the central assumption of the ETS price of the KEV,
this will lead to fewer emission reductions (17%) by the industry than in CPF -10% (285), resulting in
a reduction of 19% emission reduction by the industry. This also means that the incremental costs of
the CPF on production will be smaller than in CPF -10%, and production losses drop to 0,8%, and the
GDP loss will be only 0.1%. The investment loss drops to 4%. There will be no impact on total labour
supply, leading also to smaller shifts of labour in favour of more carbon-extensive sectors. Once again,
the more limited production disadvantages will also limit investment in carbon intensive sectors, while
these are largely offset by additional investment in lower-carbon production processes. The
production advantage for Dutch competitors in China and the rest of the world leads to extra carbon
emissions, although in absolute terms, it is significantly less than in CPF -10%. However, the relative
carbon leakage of CPF -25% remains around 20%.
If CPF -40% is chosen, i.e. the CPF is 40% below the central assumption of the ETS price of the KEV,
this will lead to even fewer emission reductions (only 2%) by the industry. This also means that the
decline of production will be smaller than in CPF -10%, falling to 0.0%, and the GDP loss will be 0%.
There will be no impact on labour supply leading to smaller shifts of labour in favour of more carbonextensive sectors. Once again, the more limited production disadvantages will also limit investment,
while these are largely offset by industry through additional investment in lower-carbon production
processes. The production advantage for Dutch competitors in China and the rest of the world leads
to extra carbon emissions, although in absolute terms, it is significantly less than in CPF -10% and
CPF -25%. However, the relative carbon leakage of CPF -25% remains around 12%.

5.2

International competitiveness / leakage

In the analysed assumption set, if the ETS price drops below 63 EUR/tCO2eq, then the CPF -10% is
binding, i.e. the CPF effectively imposes a CO2 levy of 33 EUR/tCO2eq emissions.29 It is important to

29

Next to the EU-ETS, there are also national climate policy instruments at work, i.e. CO2 levy of 109 EUR/tCO2eq on excess
emissions over the EU-benchmark, combined with trade in emission allowances between and by Dutch companies, and an
emission reduction subsidy to ensure that the CO2 levy does not have to be imposed. In the baseline at this low 63 EUR/tCO2eq
reference ETS price, the emission reduction by the industry is expected to increase up to 12 MtCO2eq reduction, which is in line
with PBL (2022). The 550 million euros subsidizes the non-profitable part of the industrial’ emission reductions. If the ETS price
drops, then this subsidy becomes less effective.

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CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY
realize the potential impacts that we are analysing of a binding CPF are conditional to this price level
occurring (see section 3).
Having said that, a binding CPF leads to a cost price increase at a sectoral level, as a result of which
inputs and/or outputs are substituted in production. This substitution takes place through the following
channels by input or output substitution. Input substitution and/or extra expenditure on CO2-reducing
techniques (adjustments to the production process via so-called retrofit investments or CCS). Output
substitution to industrial sectors or products for which the CO2 intensity of production processes is
lower or to non-industrial products. The former reduces the industry's average CO2 intensity.30 The
latter reduces the CO2 intensity of the whole economy. Such substitutions are costly (imperfect
substitution) and lead to a fall in domestic production in favour of countries with less stringent climate
policies. The tax revenue from the flat CPF is assumed to be channelled back to households in a lump
sum way.31
Implementing a CPF -10% for industries would result in a market price increase of 0.2% for the
industry as a whole (see Figure 36).32 Roughly speaking, with a binding impact of a CPF -10% in 2030
at 33 EUR/tCO2eq against residual emissions around 30 MtCO2eq, the tax revenue can be estimated
at approximately 1 bn EUR. The production value of the industry will grow in the baseline to
approximately 300 bn EUR.33 So the tax revenue amounts to approximately 0.3% of production value.
Since industrial companies cannot fully pass on their extra costs to customers, a market price increase
of almost 0.25%.34 If a CPF -25% or CPF -40% is implemented, then the tax revenues will be lower.
Hence, the market price for the industry will increase by only 0.1% or 0.0%, respectively.
The market price changes are sector-specific, depending on the tax payments (the residual emissions
times 33 EUR/tCO2eq) compared to the sectoral production value. We can see the highest market
price increases for the metals sector (0.5%), followed by the chemical rubber and plastics sector
(0.4%), non-metallic mineral sector (0.4%), and petroleum and oil products (0.25%). Note that a
significant amount of emission reductions relies on CO2 capture, transport, and storage under the sea
especially for petroleum and oil products and the chemical sectors.

30

In the metal sector, one way to reduce carbon emissions is by producing thinner, but more expensive cans that have a lower CO2
intensity. Another option is for a manufacturer of inexpensive cans with a CO2-intensive production process to lose market share to
a competitor who produces more expensive cans with a lower CO2 intensity.

31

Alternatively, the tax revenue could be returned as a targeted subsidy for industries in the Netherlands to reduce carbon emissions.
This approach is expected to result in lower cost price increases and less production loss than implementing a flat CO2 levy for
industries, as the additional costs of the levy will be partially compensated through the subsidy.

32

The market price is equal to the producer price, including all taxes (such as a flat CO2 tax) and subsidies.

33

Value Added (VA) restricts to the value of labour and capital (excluding other payments to intermediate consumption by industry)
and will be 1/3 of the production value, i.e. around 100 bn€.

34

This is in line with CPB and PBL (2019).

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Figure 36

Market price impacts in 2030 at different CPFs
0.5

0.5

0.4

0.4

%

0.3

0.4

0.3

0.3

0.2

0.2

0.2

0.2

0.2

0.1
0.1

0.1
0.0

0.0

0.0

0.0

0.0
0.0

0.0

0.0
0.0 0.0 0.0
-0.1

Petroleum and
coal products.

Metals

Chemical rubber Paper products, Non-metallic Processed food. Energy-intensive
& plastic
publishing.
minerals (for
products.
construction).
CPF -10%

CPF -25%

CPF -40%

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

The expected 0.25% for the industry increase in the market price may cause a slight deterioration in
the industry's competitive position, leading to a decline in exports. The extent to which exports react
to the price change depends on the Armington elasticity, which is a measure of price substitution
elasticity in this market. In the trade literature, the Armington elasticity for the industry is estimated to
be around 6. Due to the relative increase in market price, the Dutch industry is experiencing a decline
in market share at international markets, resulting in a 1.5% decrease in exports (6 times 0.25%).
This, in turn, leads to a production loss of 1.3%.35
The “chemicals, rubber, and plastics” sector and “basic metals” sector are more significantly affected,
with the percentage production losses roughly twice as high as the whole industry sector. This is due
to the fact that these sectors are more CO2 intensive and prone to international competition. However,
with CPF -25% and CPF -40%, the overall production loss will be reduced, with a decrease in
industrial production of 0.8% and 0.1%, respectively. Nevertheless, the chemical and base metal
sectors are still expected to experience higher production losses than the industry as a whole.

35

Here some back-of-the-envelope calculus may be helpful. In 2030, production (305 billion euros) is equal to domestic demand (190
billion euros) plus exports (290 billion euros) minus imports (170 billion euros). Exports fell by 1.5% or 4 bn€. Production would then
have to fall from 305 to 301 bn€, or 1.4%. The simulated outcome is close but 0,1% point lower, i.e. 1.3%.

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Figure 37

Production impacts in 2030 at different CPFs

0.0
-0.5
-1.0

%

-1.5
-2.0

-2.5
-3.0
-3.5
Petroleum and
coal products.

Metals

Chemical rubber Paper products, Non-metallic Processed food. Energy-intensive
& plastic
publishing.
minerals (for
products.
construction).

CPF -10%

CPF -25%

CPF -40%

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

If the Dutch industry is faced with a binding CPF, then their direct carbon emission reduction efforts
will be partially offset by an increase of emissions in other countries. Even in the CPF -10% scenario,
carbon emissions will "leak" to more CO2-intensive companies primarily located outside Europe. This
happens despite the Netherlands' limited production losses.
When the cost price rate is relatively high compared to the production losses, carbon emissions
leakage can be significant because of trade diversification in industrial activities and the
corresponding demand for fossil energy. Here's how it works: as a result of the flat CO2 levy, the
Netherlands' export of products from CO2-intensive production processes to Europe will decline. This
will lead other European countries to produce these products themselves, thereby increasing their
own emissions. It's important to note that only 10% of the leakage occurs within Europe.
The decline in exports from European countries to countries outside Europe is leading to an expansion
of production activities in these non-European countries. To maintain their consumption levels, nonEuropean countries are increasing their production of industrial products, resulting in additional
carbon emissions. On average, the CO2 intensity of industrial activities outside Europe is 2.5 times
higher than in Europe. This relocation of production activities could lead to a fivefold increase in
carbon emissions because the increased demand for industrial products will also drive up electricity
consumption. Figure breaks down the leakage by regions/countries. While countries like China and
the rest of the world are logical candidates for leakage, as they don't have binding CO2 ceilings, our

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simulations show some leakage to the USA as well.36,37 There's therefore a significant risk of a
substantial increase in carbon emissions in countries outside Europe, which would negate the carbon
emission reduction achieved by the Dutch industry by 20%. However, as emphasised before, the
Dutch industry would only experience limited or moderate production losses.
In the CPF -25% case the relative leakage remains at the same level, although the absolute leakage
will be much lower as the emission reduction by the Netherlands will be much lower in the CPF -25%
and especially the CPF -40% scenarios. In the CPF -40% case the relative leakage declines to 10%,
because the small increase in cost prices can more easily be passed on to foreign consumption.

Figure 38

Decomposition of carbon leakage to regions in 2030 at different CPFs

25%

20%

15%

10%

5%

0%
CPF -10%
REU

CPF -25%
USA

Rest-of-the-OECD

China

CPF -40%
Rest-of-the-world

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

Note also that this Dutch carbon leakage is on top of the European carbon leakage already occurring
in the baseline simulation. This European leakage is measured as the emissions increase outside
Europe divided by the emission reductions of the EU when intensifying EU’s climate policy from the
Energy Package to emissions of the Fit-for-55 package.38 The estimated leakage rate is 45%. For
36

There is a non-binding intensity target in China (CO2 / GDP reduction by 2030 of 65% compared to 2005), the Rest-of-the-World
(dominated by e.g. India with a CO2 / GDP reduction by 2030 of 45%), and no NDC in the USA as reported in WEO2021 (see IEA,
2021), while there is a binding pledge in the rest-of-the-OECD (Japan, Australia, and some OECD-South American countries).

37

Biden announced in mid-2021 their NDC with target emissions by 2030 – incl. LULUCF- to decline with 50%–52% below 2005
levels, which is actually a binding ceiling. Unfortunately, this is not included in this analysis. Nevertheless, this will have little impact
on the simulated leakage results. If a binding USA target were assumed in the baseline, then there would be more leakage to the
non-OECD region in the baseline simulation as production expands in the non-OECD. And while a relatively carbon-intensive nonOECD expands, then substitution at constant elasticities will yield a higher absolute leakage in those regions from a Dutch CPF.

38

The CO2eq emissions of the FF55 package are simulated by means of an efficient uniform carbon price over all sectors and yields
a leakage rate of 45%. If the carbon budget is matched by more carbon pricing to the exposed industrial sectors, then leakage rate

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more details, we refer to Appendix D4.39 As a response, the EU has very recently also included a
carbon Border Adjustment Mechanism (CBAM) as part of their FF55 package40 to restore potential
competitiveness losses and reduce leakage.41
Overall, extra production in non-OECD countries will also increase their GDP, leading to additional
transport and demand for other services (also based on fossil fuels). Figure 5 decomposes the
leakage by sectors. We can see that about half of the leakage will likely occur by trade-diversification,
which comprises the most important emission-intensive sectors of the industry: the “chemical, rubber,
and plastics” and the “basic metals” sector. But the other half of the leakage occurs indirectly. As
production of energy intensive goods expand outside Europe, this production gain will be matched by
extra demand for electricity (extra gas/coal) and oil (for transport purposes).

Figure 39

Decomposition of carbon leakage by sectors in 2030 at different CPFs

25%

20%

15%

10%

5%

0%
CPF -10%

CPF -25%
from changes in trade

from transport

CPF -40%
from electricity

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

Concluding, if the CPF ends up as a binding constraint on the Dutch carbon price, then leakage will
be most likely around 20% due to production expanses in countries that have no binding ceilings on
emissions or relatively low carbon intensity targets for the year 2030. If the CPF is less stringent. then
the leakage rate remains at roughly 20%. Nevertheless, it is important to realize that in the CPF -25%
case, the absolute leakage will be lower as the Dutch emission reduction declines compared to the
will directly increase. If the carbon budget is matched by more carbon pricing outside industrial sectors, then the leakage by the
industrial trade channel will decline, but it will increase by the energy price channel. In that case, the EU’s demand for gas for
heating and oil for transport will decline, which will lower the international fossil energy prices for non-EU, see Boeters, S., and
Bollen, J. (2012), Fossil fuel supply, leakage, and the effectiveness of border measures in climate policy, Energy Economics,
Volume 34, Issue6, Available online 30 August 2012.
39

There is no precedent in the literature analysing the leakage associated with EU’s ambitious FF
non-binding pledges by some developing countries for the year 2030.

40

https://climate.ec.europa.eu/eu-action/european-green-deal/delivering-european-green-deal_en

41

CBAM is planned as an import tariff-wall covering some EU ETS sectors. If it successfully helps to improve the EU’s industrial
competitiveness position and avoid leakage, it could stimulate industrial production in the EU.

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package, i.e. compared to the

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CPF 10% case. Only for the CPF -40% scenario the relative leakage will also be lower, and decline
to about 10%, which will hardly generate any absolute leakages as the reduction effort also declines.

5.3

Investment42

Here we present the effects of a binding CPF on investments. It is good to realize at forehand that
investments in abatement only constitute a modest share of the overall investments, and hence the
change in investment follows the changes in production rather than the extra investments from carbon
abatement. Next to that, investment is one of the high-productive inputs to production next to all the
other intermediate inputs producers need for production. So, the percentage changes on investment
may be larger than the percentage changes on production as sectors seek to minimize on potential
production losses. We can see in Figure 40 that the economy-wide investments even increase, so
investments do not just reallocate, but expand as well (real interest rate slightly reduces).

Figure 40

Changes in investment in 2030 with different CPFs

4%
2%
0%
-2%
-4%
-6%
-8%
-10%
CPF -10%

CPF -25%
Industry

Non-Industry

CPF -40%
Total

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

5.4

Employment

Next, we turn to the changes of employment from a binding CPF. The WorldScan model can assume
two opposing strands in the literature, i.e. it allows for zero labour supply assumption versus
endogenous labour supply responding to participation and the hours of work. As the Dutch labour
market historically responded rather flexible with labour supply mostly insensitive to non-labour
policies, we connect to the argument that long run labour supply is not likely to be affected by green
42 As the model setup of WorldScan is deterministic, we cannot quantify the investments when the CPF is not binding. However, we

believe that a non-binding CPF will have an insignificant impact on investments. This is because investments will anyway bias
towards higher long-term Emission Trading Scheme (ETS) prices that align with the near-zero emission targets set for 2040.
Moreover, subsidies in place up to 2030, as part of the Dutch Industrial Policy Package (see also KEV 2022), to finance industrial
emission reductions further support this trend.

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taxes (see also CPB/PBL, 2019). Also, it is important to realize that in this analysis only long-term
changes in sectoral employment are simulated, so we refrain from potentially costly medium-term
adjustments from employment shifts (for example on budgets to assist on so-called “werk-naar-werk”).
Figure 41 shows the impacts on the labour market in 2030, i.e. it illustrates the long-term changes in
2030 on employment at the sectoral level. We report here absolute changes in sectoral employment
of a binding CPF. The general picture that emerges is a likely shift away from carbon intensive
activities to carbon extensive sectors following the production patterns. So the industry employment
will lower with approximately 2000 compared to the baseline without the CPF being offset by more
employment outside the industry. Similarly to production losses, at CPF -25% and CPF -40% the
losses in industrial employment (and gains in non-industrial employment) reduce considerably. We
can see that the industrial employment losses from a CPF decline to roughly 1200 in the CPF -25%
case, and almost to zero in the CPF -40% case.
Also at the subsector industrial level, Figure 41 presents changes in employment that follow the
production pattern losses and yield a shift away from the most carbon intensive sectors. However,
there is striking change not following this general pattern, i.e. the employment changes in the sectors
“petroleum and coal products” and “chemical, rubber, and plastic products.” Whereas production
drops in these highly energy-intensive sectors, we can observe actually employment increases. The
reason is that in these sectors employment losses are more than compensated with the employment
increases for the capture, transport, and storage of CO2.

Figure 41

Changes in employment in 2030 at different CPFs

1.0
0.5

thousands

0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
Petroleum and
coal products.

Metals

Chemical rubber Paper products, Non-metallic Processed food. Energy-intensive
& plastic
publishing.
minerals (for
products.
construction).
CPF -10%

CPF -25%

CPF -40%

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

5.5

Macro-economic Indicators

The average export price deviates slightly from the industry's market price because the industry is
just a part of the Dutch economy, and there are slight adjustments in its structure. It can be observed
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that in the CPF -10% case, the export price increases by almost 0.1%. However, for the CPF -25%
and CPF -40% cases, the export price increase drops back compared to the CPF -10% case, which
follows a similar pattern as the market price increase. Additionally, the export volume now includes
all sectors as compared to just the industry, meaning that the total export responds to a direct loss as
described before. Here, a composite effect from structural shifts may also play a role. Imports adjust
as well, but to a much lesser content as imports by the Netherlands are not directly taxed by a CPF
but from adjustments of global trade flows. Moreover, the Dutch economy has less money to spend
on imports while diversifying its portfolio of imports to more service-oriented activities, which are
activities that are less subject to trade. A similar reasoning applies to the volume of exports and
imports when switching to a less binding CPF, where the changes become smaller and almost reach
zero in the CPF-45% case. It is also interesting to examine the average macro terms-of-trade
changes, which are measured as the average export price divided by the average import price and
resemble the purchasing power of the Dutch economy. In this context, higher prices are passed on
to foreign consumers. We can observe that at the macro-level, there is a terms-of-trade gain of 0,1
implicating that mitigates the GDP losses by 0.05%. Moreover, the argument that a more relaxed CPF
dampens these gains also applies here.
If we were to relax the zero labour supply assumption, a CPF could lead the Dutch government to
increase expenses on social security and increase taxes to maintain budget neutrality. This, in turn,
would result in higher GDP losses from green policies. Therefore, the long-term GDP estimates
reported here serve as a lower-bound estimate. The GDP losses amount to 0.2%, 0.1%, and 0.0% in
the different CPF cases, respectively.

Figure 42

Macro impacts in 2030 at different CPFs

0.2
0.1

0
-0.1
%

-0.2
-0.3
-0.4
-0.5
-0.6

-0.7
GDP

real export price

export volume

CPF -10%

CPF -25%

import volume

terms of trade

CPF -40%

Source:

CE Delft

Note:

The results are based on an assumption set, where the carbon prices are below the expected price level in 2030, so that the CPFs are binding.

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6

Summary and conclusion

The Netherlands introduced a carbon price floor in 2022, during a time when the EU ETS price was
significantly lower than today, to complement the EU-wide emission reduction mechanisms and
provide additional economic certainty for investments in sustainable technologies.
Throughout 2022 and 2023, with EU ETS prices much higher than when the CPF was introduced, the
floor price lost relevance. Currently, an update of the CPF level is considered. We were tasked to
analyse at what level an updated CPF could provide benefits for sustainable technologies and what
potential downsides might have to be taken into account.
To determine the potential effects of an updated CPF on the electricity and industry sectors, we
undertook three analytical steps. First, we developed a probabilistic analysis of future carbon prices,
which served as an input for the following steps. Second, we analysed the impact of different CPF
options on the electricity sector by comparing market outcomes without and without a CPF. Third, we
conducted a sensibility analysis on the impact of different CPFs on the industry sector. In the following
we will summarise the main findings of the study.
The development of the Dutch electricity market can be characterised by a number of important
trends. Next to a large increase in renewable generation capacities it is likely that additional storage
capacity will be built to accommodate the intermittent electricity production. Coal plants will be phased
out by 2030 so that renewable generation is in the medium to long term complemented by largely gas
and storage capacities. The changes in the power plant park will lead to a strong decline in domestic
emissions on the path towards climate neutrality. The modelling results indicate that these changes
in the power plant park could lead to a stable reserve margin until 2030, which could decline in the
period until 2040. The profits for renewable plants are likely going to decrease throughout the
modelled period, with increasing renewable capacities and falling electricity prices.

CPFs constructed as a lower bound to EU ETS prices
The principle benefit of a CPF as constructed in this study is to provide a lower bound for carbon
emission prices provided by the EU ETS: If EU ETS prices develop as expected (based on current
assumptions regarding the future economic environment and current market indicators), the CPF
should not be binding. Thus, it should not impose additional emissions costs on domestic market
participants.
If the EU ETS price falls below a certain threshold, the CPF results in additional emissions costs which
could potentially benefit the profitability of sustainable investments.
Our analysis shows that, contrary to the time of the first introduction of a CPF in the Netherlands,
renewable technologies in the electricity sector, in particular utility scale solar PV and onshore as well
as offshore wind, are significantly more competitive towards fossil fuel-based technologies than they
used to be. Already at, compared to today, low carbon price levels, renewable energies appear

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profitable. This reduces the need for additional support in form of a CPF close to the current EU ETS
price level.

CPFs likely to have no impact if EU ETS prices remain above the floor price
A CPF could impact the electricity and industry sectors even if it does not become binding. Actors in
both sectors take into account potential future price and profitability developments when making
decisions regarding for example new investments or the retirement of generation capacity. However,
our results from the electricity market analysis show no significant impact of the analysed CPF
scenarios for the case that EU ETS prices develop according to the reference scenario and CPFs do
not become binding.
Also for the industry sector we believe that a non-binding CPF will have an insignificant impact on
investments. This is because investments will anyway bias towards higher long-term Emission
Trading Scheme (ETS) prices that align with the near-zero emission targets set for 2040. Moreover,
subsidies in place up to 2030, as part of the Dutch Industrial Policy Package (see also KEV 2022), to
finance industrial emission reductions further support this trend.

CPFs could have an impact on the power sector if EU ETS prices develop
substantially lower than expected
In case of a substantially lower than expected EU ETS price, a CPF results in higher generation cost
for fossil fuel plants. However, our analysis shows that this only partially supports the profitability of
renewables. The impact of a binding domestic CPF is significantly diluted by the strong integration of
the Netherlands into the European market. Higher domestic generation costs of fossil fuel plants
translate only to a small extent into higher wholesale electricity prices - and therefore into only a
slightly improved economic outlook for renewables. The effect on carbon emissions is likely to be very
small on the European level as domestic fossil generation is replaced to a large extent by carbon
intensive plants in other European countries.
On the other hand, a national CPF comes along with significant potential negative side effects. The
magnitude of the effects depend heavily on the chosen CPF: While a lower CPF (e.g. at 40% below
the expected EU ETS price) has a rather small impact on the Dutch electricity sector and the Dutch
industry, an ambitious CPF (e.g. 10% below the expected EU ETS price) can have a considerable
impact on the electricity sector and the industry if the EU ETS price decreases significantly. These
include (compared with a situation without a CPF) reduced domestic production of electricity and
goods from the industry, reduced electricity generation capacity and therefore potential adverse effect
on security of supply, and carbon leakage.

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Annex A – Methodological background on the probabilistic
analysis of carbon prices
A.1

Model specification

A standard discrete time model of a mean reverting process can be expressed as

𝑃𝑡+1 = 𝑃𝑡 + 𝛿 + 𝑟(𝑃0 + 𝛿𝑡 − 𝑃𝑡 ) + 𝜀𝑡+1

(1)

where 𝑃𝑡 is the current carbon price (at date t), 𝛿 is the drift parameter along the long-term
development of the price (between t and t+1), 𝑟 is the reversion speed of the process and 𝜀𝑡+1 the
error term, a normally distributed random shock with mean zero and a relative standard deviation 𝑟𝜎.
The sum of 𝑃𝑡 + 𝛿 represents the expected price at time t, along a linear trajectory towards the
expected future price. The term 𝑟(𝑃0 + 𝛿𝑡 − 𝑃𝑡 ) is the “correcting factor” towards the expected future
price at the rate r, acting as a reverting factor when the actual value of 𝑃𝑡 deviates from the expected
value (𝑃0 + 𝛿𝑡).
While the expected future drift is determined by the price expectations formulated in the KEV 2022
main scenario (110 EUR/tCO2eq in 2030 and 179 EUR/tCO2eq in 2040), we are estimating the
reversion speed 𝑟 and the relative standard deviation 𝑟𝜎 by running a regression analysis using
historical data. Adapting (1) yields

𝑃𝑡+1 = 𝑟𝑃0 + 𝛿 − 𝑟𝛿 + (1 − 𝑟)𝑃𝑡 + 𝑟𝛿 𝑡 + 𝜀𝑡+1

(2)

with dependent variable 𝑃𝑡+1 , independent variables 𝑃𝑡 with slope (1 − 𝑟) and t with slope 𝑟𝛿 and
intercept 𝑟𝑃0 + 𝛿 − 𝑟𝛿.
The expected future drift, inferred from long-term price expectations in the KEV 2022 main scenario,
deviates from the one estimated on the basis of historical data. While acknowledging potential
limitations of transferring the historic relative standard deviation and reversion speed into the future,
we assume that the estimated relative price volatility and reversion speed will remain a fair
approximation of future values. Measuring volatility in relative terms allows to account for differing
price levels within the historical market data analysed, volatility of estimated future prices might
therefore vary in absolute terms, according to price levels.

A.2

Results of an estimation using monthly (averaged) prices from 2018 to 2022

The choice of the time period (as in which past years to incorporate in the analysis) and time interval
(as in whether to use daily changes in carbon prices or weekly/monthly averages) of the historical
market data used to analyse volatility and reversion speed impacts the result. The chosen parameters
should thus be suitable to the purpose of this exercise and goals of the subsequent electricity market
analysis. We therefore suggest using

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■

Monthly (averaged) prices as a time interval, given the long-term nature of the modelling, and

■

A time period from 2018 to 2022, as this time period allows to utilize a broad data basis across
different price levels that incorporates the impacts of severe external shocks (e.g., Covid
pandemic, war in Ukraine) as well as a period of steady price increase (in 2021).

We further conduct sensitivity analyses, testing different time periods and averaging of daily prices to
contrast the choices and underlying assumptions made against possible alternatives.
Using 2018 to 2022 weekly (averaged) prices to estimate parameters through regression analysis
yields a relative standard deviation of 0.118 or 11.8 % and reversion speed of 0.100. The reversion
speed is obtained as 1 - slope of price at t (see equation 2), while the reversion speed translates to a
time frame to revert (1/reversion speed) of around 10 month (see Table 8).

Table 8

Regression analysis results

Main specification
Dependent variable

Price at t+1

Price at t

0.900***
(0.059)

t

0.0154*
(0.008)

Constant

0.815
(1.232)

Adjusted R²

0.964

Observations

59

RMSE

4.768

Mean of price

40.408

Relative standard deviation

0.118

Source:

Frontier Economics based on Energate Messenger

Note:

The significance levels of the coefficients are indicated as follows: *** denotes a result that is significant at the 1% level, ** denotes a result that is
significant at the 5% level, and * denotes a result that is significant at the 10% level. The relative standard deviation is obtained by dividing RMSE
by the mean of price. The reversion speed is obtained as 1- slope of price (see equation 2).

A.3

Sensitivity analysis

In order to contrast our parameter choices against possible alternatives we conducted several
sensitivity analyses, using
■

Alternative time intervals: Daily prices and weekly (average) prices instead of monthly (average
prices) as a time interval, as well as

■

Alternative time periods: Shortening, lengthening, and diverging the time period.

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Table 9
Time period

Overview of reference and sensitivities
Time interval

Relative
Standard
Deviation

Reversion
Speed

Time to revert
(1/reversion speed)

Monthly (avg.)

0.118

0.100

~ 10.0 months

2017-2022

Monthly (avg.)

0.125

0.090

~ 11.0 months

2019-2022

Monthly (avg.)

0.112

0.152

~ 6.5 months

2018-2022

Weekly (avg.)

0.066

0.030

~ 7.5 months

2018-2022

Daily

0.037

0.009

~ 3.5 months

Reference
2018-2022
Sensitivities

Source:

Frontier Economics, based on Energate Messenger.

Note:

The Relative Standard Deviation as well as the reversion speed need to be interpreted considering the time interval chosen, therefore the time to
revert (1/reversion speed) yields a more intuitive measure.

The values in Table 9 need to be interpreted while considering the impacts of the choice of time
interval on the relative standard deviation and reversion speed. A much lower relative standard
deviation is to be expected when choosing a daily time interval, in comparison to aggregating on a
weekly/monthly basis. Furthermore, the results should also be interpreted against the implications of
the choice of the time period and the interwoven implicit choices in terms of incorporating or leaving
out the impacts of certain external shocks and other developments have on the results.

A.4

How to deal with increased uncertainty in the long term

An increasing uncertainty about future developments over time poses a demanding challenge when
conducting long-term simulations. In order to adequately reflect those fundamental uncertainties in
long-term projections:
■

We decrease the reversion speed to the equivalent of a two-year time frame to revert to the longterm mean (reversion speed of 0.0096) from 2030 onwards, to allow for different pricedevelopments to evolve lover a longer period of time.

■

We cap the carbon price in our simulation at a maximum of 300 EUR, which reflects a potential
upper bound of medium- to long-term CO2 abatement cost through Direct Air Capture.43

43

S&P Global Commodity Insights: “Cost of capturing CO2 from air to drop to $250-$300/mtCO2e end-decade: Climeworks”, available
at https://www.spglobal.com/commodityinsights/en/market-insights/latest-news/energy-transition/042222-cost-of-capturing-CO2from-air-to-drop-to-250-300mtCO2e-end-decade-climeworks

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Annex B – Probability of future carbon price scenarios until 2040
Figure 43

Price path probability tree

Source:

Frontier Economics

Note:

All prices in €2021/tCO2eq, probabilities based on 10,000 Monte Carlo iterations

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Annex C – Combined Investment and Dispatch model
C.1 Model description
The Combined Investment and Dispatch model is used to evaluate long-term trends in the power
market. It projects the development of generating capacities as well as their optimised dispatch. For
this study, the power market model would be complemented by a stochastic component as described
in section 2.3.

Figure 44

Frontier Investment and Dispatch Model

Input - Dispatch

Combined Investment and
Dispatch Model (CID)

Output - Dispatch

Hourly load

System cost

Fuel costs / CO2

Hourly electricity prices

Power plant capacity and DSR

Hourly availability of RES

Core region
(investment and dispatch)
Non-core region (dispatch)
Satellite region

Interconnectors

Hourly dispatch / DSR
Emissions
Profitability

Fixed O&M + variable costs
RES-In-feed

Output - Investment

Input - Investment

Security of Supply

Potentials

Investments

Investment costs
Mothballing costs

Market framework

Mothballing
▪ Snap-shot years: 2024, 2027, 2030 and 2040
▪ High granular dispatch p.a.
▪ Differentiated between highly detailed core-region
and less-detailed non-core/satellite regions

Policy measures

Source:

Closure

DSR = demand side response
O&M = Operating & Maintenance

Frontier Economics

The main characteristics of the model can be summarised as follows:
■

Cost optimisation model – The model is an integrated investment and dispatch model for the
European power sector. It is set up as an optimisation problem minimising the system costs for
serving power demand across the modelled regions. The model optimises the hourly dispatch of
the power plants as well as the development of installed capacity based on representative hours
and selected snapshot-years (investments, divestments, mothballing and reactivation).
□

Energy supply and consumption must be balanced in every hour in every region;

□

Power exchange between modelled regions is limited by interconnection capacity;

□

Technical and economic constraints for power plants, storages, demand-side management
(DSM), as well as renewable energy sources.

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Figure 45

Source:

■

■

Key input parameters

Frontier Economics

Investment options – In order to meet future demand at the least cost, the model optimises the
power plant park in the so-called “core regions” of our model through either:
□

Investing in new capacities subject to technical and economic parameters and availability of
different technological options;

□

Closing of existing power plants in the case of overcapacity; or

□

Mothballing plants and reactivation at a later point in time in order to save fixed operation
and maintenance costs.

Geographical scope – Our model focusses on Central-Western Europe as core region.
Depending on the focus of the analysis, we differentiate between
□

Core regions: The power plant park is modelled on a very detailed (unit-based) level, the
dispatch of power plants and demand-side management (DSM), as well as investment or
divestment, are model outcomes.

□

Non-core regions: The power plant park is modelled as aggregated blocks. Capacity is set
exogenously, i.e. investment and divestment decisions are not optimised.

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□

■

Satellite regions: Other adjacent regions are modelled as satellite regions. Power can be
traded with those regions based on typical prices representing the marginal costs of
generation in those countries/regions.

Temporal resolution – The model optimises representative days with an hourly resolution.

C.2 Input assumptions
Table 10

Summary of main power market model assumptions

Parameter

Source

Unit

2024

2027

2030

2040

Net electricity demand
The Netherlands

KEV 2022 (main)

TWh

116.3

123.4

130.6

171.7

Modelled region

Frontier Economics

TWh

2,259

2,373

2,487

3,049

Frontier Economics

GW

18.1

19.0

20.0

25.3

Peak load
The Netherlands

Fuel and carbon prices
Natural gas

Futures 30.01.2023,
KEV 2022 (main)

€/MWhth

59.38

34.03

33.45

33.45

Hard coal (CIF ARA)

Futures 30.01.2023,
KEV 2022 (main)

€/MWhth

16.90

14.21

11.56

12.31

CO2 (EU ETS)

Futures 30.01.2023,
KEV 2022 (main)

€/tCO2eq

80.0

93.3

109.8

178.9

-10% scenario

-

€/tCO2eq

72.0

83.9

98.8

98.8

-25% scenario

-

€/tCO2eq

60.0

69.9

82.4

82.4

-40% scenario

-

€/tCO2eq

48.0

56.0

65.9

65.9

Carbon price floor

Renewable capacity limits (NL)
Wind offshore

KEV 2022 (main)

GW

4.63

5.96

15.76

21.18

Wind onshore

KEV 2022 (main)

GW

6.20

7.38

7.43

7.18

Solar PV

KEV 2022 (main)

GW

19.70

22.94

25.00

41.99

Note:

Net electricity demand includes network losses, excludes own-consumption, storage demand, and DSM. The modelled region constitutes of AT,
BE, CH, CZ, DE, DK, FR, IT, NL, PL, UK. Fuel prices calculated in terms of lower heating value and all prices are stated in real 2021 terms (as in
the KEV 2022).

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Annex D – Reference scenarios against which CPF impact is
analysed
D.1 Low EU ETS price case
Table 11

Summary of key results of the low price case without a CPF

Key indicators

Development

Generation capacities

■

Large increase in renewable capacities

■

Storage capacity of ~6 GW built by 2030

■

Installed gas gen. capacity of ~10 GW between 2024 and 2040

■

Large increase in generation from renewables, which offset coal
generation and cover increasing electricity demand

■

Fossil-based generation mostly by CHP plants

■

In 2030/40 gas and storage complement renewable generation

Emissions

■

Significant decline from ~36 MtCO2eq in 2024 to ~14 MtCO2eq in
2030, in particular due to coal phase out

Electricity price

■

Declining electricity prices from ~130 €/MWh in 202
€/MWh in 2030

■

Further reduction to ~7 €/MWh by 20 0

■

Increasing reserve margin until 2030, decreasing until 2040

■

Increasing peak load and coal phase-out is met by contribution
from largely storages, wind offshore and imports

■

CCGT (mostly CHP) profits largely increasing between 2024 and
2027, but from 2030 onwards decreasing

■

Renewables: decreasing profits throughout the modelled period

Domestic dispatch

Reserve Margin

Power plant profits

to ~8

Source: Frontier Economics

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Figure 46

Reference for low EU ETS price case: Installed generation capacity

100
90
80

PV
Offshore wind

70

GW

Onshore wind
60

Other storage

50

Bio
Hydro

40

Other non-res

30

Gas
Coal

20

Nuclear

10
0
2024
Source:

2027

2030

2040

Frontier Economics

Figure 18

Reference for low EU ETS price case: Annual dispatch and net imports

200
175

Net import
PV

150

Offshore wind

TWh

125

Onshore wind
Other storage

100

Bio
75

Hydro
Other non-res

50

Gas
Coal

25

Nuclear
0

Net demand

-25
2024
Source:

2027

2030

2040

Frontier Economics

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Reference for low EU ETS price case: Emissions and emission intensity

40

400

35

350

30

300

25

250

20

200

15

150

10

100

5

50

0
2024

2026

2028

2030
Emissions

Source:

2032

2034

2036

gCO2eq/kWh

MtCO2eq

Figure 47

0
2040

2038

Emission intensity

Frontier Economics

Figure 48

Reference for low EU ETS price case: Average wholesale electricity prices

140
120

€2021/MWh

100
80
60
40
20
0
2024
Source:

2026

2028

2030

2032

2034

2036

2038

2040

Frontier Economics

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Figure 49

Reference for low EU ETS price case: Adequacy reserve margins

30
Import capacity

25

PV
Offshore wind
20

Onshore wind

GW

Other storage
15

Bio
Hydro
Other non-res

10

Gas
Coal

5

Nuclear
Peak load

0
No CPF
2024
Source:

No CPF
2030

No CPF
2040

Frontier Economics

Figure 50

Source:

No CPF
2027

Reference for low EU ETS price case: Power plant profits

Frontier Economics

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Annex E – World Scan model description
E.1

Model description

Overview
A computational general equilibrium (CGE) model like WorldScan consists of three main elements.
The underlying general equilibrium economic model, the multi-regional input-output data, and a set
of exogenous parameters (being the most import the elasticities). The combination of these three
elements yields a general equilibrium (calibrated) baseline in which all the accounting and market
clearing conditions are met. Policy experiments consist of a shock to one or several exogenous
variables (e.g. tariffs) that generate changes in the price and quantities of the endogenous variables
such that a new general equilibrium is reached: the counterfactual scenario. The behavioural
equations in the economic model determine how the endogenous variables react, while the underlying
baseline data and the exogenous parameters (i.e. the various elasticities in the model) determine the
size and scope of the adjustments.44

Economic model
General equilibrium models describe supply and demand relations in markets. In these models, prices
and quantities of goods and factor inputs (i.e. labour and capital) adjust, such that demand and supply
become equal at an equilibrium price and quantity level. These models also describe the interactions
between several markets. For instance, firms must determine the factor inputs necessary to produce
a final good, given the price and demand of that good. Firms’ supply decisions, therefore, depend on
the equilibrium product price and in turn they determine the demand for the necessary intermediate
and factor inputs required. Consumers preferences and budget constrains will determine the demand
for final goods and the supply of factor inputs (mainly labour). The interaction of the optimisation
decisions by firms and consumers will ultimately determine the equilibrium prices and quantities of
goods and factor inputs. Therefore, the core elements of all CGE models are the micro-economic
founded neo-classical conditions: consumer and producer optimisation under budgetary constraints.
Hence, economic behaviour drives the adjustment of quantities and prices given that consumers
maximise utility given the price of goods and the consumers’ budget constraints, while producers
minimise costs, given input prices, the level of output and the production technology.45
These optimisation conditions are linked with market clearing conditions in the products markets (i.e.
equating demand and supply for each production sector). The number of product markets is defined
by the number of economic sectors in the database. For instance, the GTAP database identifies 57
sectors. In addition, there are market-clearing conditions for the factor markets. Following the example
above, the supply of low- and high-skill labour by households must equal the demand of these factor
44

For a broader view of the CGE modelling framework, see Dixon, P. B. and D. W. Jorgenson, eds. (2012). Handbook of Computable
General Equilibrium Modeling, vol. 1A, Amsterdam: Elsevier.

45

A summary of the general equilibrium equations of WorldScan is provided in Appendix A in Lejour et al. (2006) and in the
Supplementary Material from Rojas-Romagosa (2017).

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inputs by firms. There are five different factor types in the GTAP database: unskilled and skilled
labour, capital, land, and natural resources.46 For instance, the demand of labour (determined from
the profit maximisation conditions of firms) must equal the labour supply by households (which in turn
is a function of economically active population and labour participation rates.) Consumption is
modelled as non-homothetic demand system using the linear expenditure system (LES). All partial
elasticities of substitution for composite commodities as well as price and income elasticities drive
demand responses to economic shocks. Production is modelled as a nested structure of constant
elasticities of substitution (CES) functions. The values of the substitution parameters reflect the
substitution possibilities between intermediate inputs and production factors.

Monopolistic competition
WorldScan has the option of using an imperfect competition setting (described below) or a perfect
competition setting with constant returns to scale where each sector has one representative pricetaking firm that produces one variety (following the description of the supply and private demand
equations above). Our main simulations, however, employ the WorldScan version with monopolistic
competition and increasing returns to scale (see de Bruijn, 2006, for a detailed description). This
version of the model is based on a Dixit-Stiglitz-Armington demand specification. In particular, it uses
the love-of-variety –i.e. Dixit-Stiglitz (DS) – preferences for intermediate and final goods for nonagricultural sectors. Within a representative firm, individual varieties are symmetrical in terms of
selling at the same price and quantity. However, increases in the number of varieties yield economic
benefits because they are perceived to be different by intermediate and final demand agents. This
DS approach is then nested within a basic CES demand system that includes both Armington- and
DS-type demand systems for individual sectors using Ethier and Krugman-type monopolistic
competition models –i.e. differentiated intermediate and differentiated consumer goods. 47
This imperfect competition version of the model slightly modifies the demand and supply equations
from the perfect competition setting, by including the number of varieties by sector and region in the
demand and supply equations following Dixit-Stiglitz preferences. Economies of scales are introduced
in the supply side as a technical scaling factor in combination with imposing a fixed set-up cost for in
the production process. The specific modelling equations can be found in Section 2 in de Bruijn
(2006).

International trade
Finally, the model provides an explicit and detailed treatment of international trade, international
transport margins and other trade costs (e.g. tariffs, NTBs, export subsidies). Bilateral trade is handled
via CES (constant elasticity of substitution) preferences for intermediate and final goods, using the
so-called Armington assumption, where the substitution of domestics and imports –as well as product
differentiation– is driven by the region of origin (i.e. by import source). This assumption is generic to
46

The GTAP-9 version identifies five different labour types, but these can be aggregated to the common two labour types used in
most CGE models.

47

This can be done because one can reduce Ethier-Krugman-models algebraically to Armington type demand systems with external
scale economies linked to a variety of effects (Francois and Roland-Holst, 1997; Francois and Nelson, 2002).

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most CGE models as it is a simple device to account for “cross-hauling” of trade (i.e. the empirical
observation that countries often simultaneously import and export goods in the same product
category).

Representation of energy technologies and abatement GHG
Emissions are introduced at different places of the nested production structure in the model.
Emissions from energy use are calculated using a fixed emission coefficient (i.e. a fixed amount of
emissions per unit of coal, oil, natural gas, or biomass used). The use of chemical fertilizer in
agriculture is a significant source of emissions of CH4. Emissions related to the use of chemical
fertilizer are calculated using the intermediary input from the energy-intensive (chemical) sector to the
agricultural sector as a proxy for the amount of fertilizer used. Process emissions are linked to the
sectoral output, the top of the production function nest. The production structure for the agricultural
sector is similar for other sectors. The main difference with other sectors is that these sectors don’t
have fertiliser as an input.
For CO2, the emission factors link energy use differs per fuel type to emissions but is independent of
the sector and region. Emission factors for other substances are sector and region specific. Emission
factors up to 2030 for non-CO2 GHGs are calculated such that emission levels by region, sector, and
activity in the BAU reproduce the corresponding emission levels of WEO-2021 scenario.48

Applications
The WorldScan model is used in numerous papers to analyse the role of the industry in the EU ETS
as part of climate policy and interactions between climate and air pollution policies in Europe. Further
it is also used in other topics, such as estimating the EU’s structural changes and economic impacts
of policies, for example on Türkiye’s entry to the EU, on Brexit, on trade conflicts, and on protective
CBAM policies.49

48

The calibration of emission factors in WorldScan can be found in Bollen J. (2014), The Value of Air Pollution Co-benefits of Climate
Policies: Analysis with a Global Sector-Trade CGE model called WorldScan, in Technological Forecasting and Social Change
Volume 90, Part A, January 2015, Pages 178-191).

49

See Boeters, S., and Bollen, J. (2012), Fossil fuel supply, leakage, and the effectiveness of border measures in climate policy,
Energy Economics, Volume 34, Issue6, Available online 30 August 2012. Bollen (2015), The value of air pollution co-benefits of
climate policies: Analysis with a global sector-trade CGE model called WorldScan, Technological Forecasting and Social Change,
Volume 90, Part A, 2015, Pages 178-191. Bollen J., and Brink C.J. (2014), Air Pollution Policy in Europe: Quantifying the Interaction
with Greenhouse Gases and Climate Change Policies, CPB Discussion Paper 220, CPB, the Hague, the Netherlands. Lejour A.,
and De Mooij, RA (2004), Turkish Delight – Does Turkey’s accession to the EU bring economic benefits? CESifo Working Paper
Series 1183, CESifo. Bollen, J., G. Meijerink, and H. Rojas-Romagosa (2016). “Brexit Costs for the Netherlands Arise from
Reduced Trade,” CPB Policy Brief 2016/07, CPB Netherlands Bureau for Economic Policy Analysis. Bollen, J., and RojasRomagosa, H. (2018), Trade Wars: Economic impacts of US tariff increases and retaliations, an international perspective, CPB
Background Document, 6 July 2018. Bollen, J., Koutstaal, P., Veenendaal, P. (2011), Trade and Climate Change, EU Commission,
DGtrade, Brussels, Belgium.

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E.2 Baseline scenario assumptions and characteristics for reference EU ETS price
path
Assumed trends
In this report, the baseline scenarios are based on the 2021 World Energy Outlook (WEO-2021, IEA,
2021), and for energy prices we rely on the values reported in the KEV 2022.The BAU calibration
employs trends for population and GDP by region, energy use by region and energy carrier, and world
fossil fuel prices by energy carrier. Population is exogenous, but the other time series are reproduced
by adjusting the model parameters. GDP is targeted by Total Factor Productivity (TFP, differentiated
by sector), energy quantities are targeted by energy efficiency, and fuel prices are targeted by the
amount of natural resources available as input to fossil fuel production.
In policy variants, TFP, energy efficiency, and natural sources are fixed, and GDP, energy use and
prices are endogenous.

Assumed Policies
NDC’s follow the assumptions of the STEPS scenario of IEA’s 2021 outlook. In this report uniform
carbon prices over sectors are simulated for each region to match the carbon budgets of the NDC’s
of the different regions. For the ROW, we assume no binding targets as this region includes too many
countries without a binding carbon budget or intensity target (for example, India), or a non-binding
intensity target (for example, China has a non-binding intensity target, i.e. CO2 / GDP reduction of
65% by 2030 compared to 2005).
For the EU28, we assume the GHG emissions of the fit-for-55 package to hold. In this report uniform
carbon prices over sectors are simulated for the EU28 to match the 55% emission reduction by 2030.
As to align the simulated carbon prices with the KEV, we ad-hoc lowered the marginal carbon
abatement functions of the industry with a uniform factor (13%) in the REU region to simulate the
reference path of the carbon price of the KEV.
For the Netherlands, we implemented also a direct subsidy of 550 mn Euro to carbon abatement by
the industry with the assumption that government finances with this budget the emission reductions
of the “non-profitable” part (“onrendabele top”) at the ETS price of reference path of the KEV.50

Parameters
All other parameters of WorldScan are the same as used in Bollen et al. (2020).51

50

This means that the subsidy becomes less effective if the ETS price drops compared to the

51

Bollen J, Delen, A., Hoogendoorn, S, and Trinks, A. (2020), CO2-heffing en verplaatsing, CPB Achtergronddocument November
2020, CPB, Den Haag.

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E.3

Regions and Sectors in WorldScan

Table 12

Regions

Code

Country / region

NLD

Netherlands

REU

EU-28

USA

USA

ROE

Rest of the OECD

ROW

Rest of the World

Source:

CE Delft

Table 13

Sectors

Code

Sector

GTAP Sector

HOR

Horticulture

Vegetables, fruit, nuts; Other crops;

OAG

Other Agricultural
Activities

Paddy rice; Wheat; Other cereal grains; Oil seeds; Sugar cane;
Plant-based fibres; Bovine cattle; Other animal products; Raw milk;
Wool; Forestry; Fishing

OIL

Winning of Oil

oil

COL

Winning of Coal

Coal

GAD

Winning and
distribution of Gas

Gas; Gas manufacture and distribution

MIN

Other Minerals

Other mining

P_C

Oil Refineries

Petroleum and coal products

BME

Basic Metals

Ferrous metals; Non-ferrous metals

CRP

Chemical, Rubber,
and Plastics

Chemical, rubber, plastic products

PPP

Paper prod.&
publish.

Paper products, publishing

NMM

Other mineral prod.

Non-metallic minerals

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Code

Sector

GTAP Sector

COF

Processed foods

Bovine meat products; Other meat; Vegetable oils and fats; Dairy
products; Processed rice; Sugar; Other food; Beverages and
tobacco

CPI

Capital Goods

Metal products; Motor vehicles and parts; Other transport
equipment; Electronic equipment; Other machinery and equipment

CON

Consumer Goods

Textiles; Wearing apparel; Leather products; Wood products; Other
manufacturing

ELY

Electricity
Production

Electricity

OTP

Transport over land Other transport

TRR

Other Transport

Water transport; Air transport

OSE

Other Services

Water, Construction; Trade; Communication; Other financial
services; Insurance; Other business services; Recreational and
other services; Public administration, defence, education, health;
wellings

Source:

CE Delft

Note:

Aguiar, Narayanan en McDougall, 2016, an Overview of GTAP9 Data Base, Journal of Global Economic Analysis, vol, 1(1): 181-208.

E.4

Excursus: Carbon leakage through Fit For 55

Here we compare the Fit for 55 scenario (55% emission reduction) with the Energy Package scenario
(40% emission reduction), however without reflecting the latest plans for an introduction of a carbon
Border Adjustment Mechanism (CBAM). For 2030, this means that Europe’s GHG emissions will be
reduced with another 6%. We assumed for the Energy Package scenario a reduction of carbon prices
with almost 55 EUR/tCO2eq in all sectors compared to the Fit-for-55 scenario, which simulates a 40%
emission reduction of GHGs by 2030.
Figure 51 breaks down the leakage by regions/countries by comparing the FF55 scenario with the
Energy Package scenario. While countries like China and the rest of the world are logical candidates
for leakage, as they don't have binding CO2 ceilings, our simulations show some leakage to the USA
as well.52 There is therefore a significant risk of a substantial increase in carbon emissions in countries
outside Europe, which would negate the carbon emission reduction achieved by the EU by almost
45% (carbon leakage). However, as emphasised before, the Dutch industry would only experience
limited or moderate production losses.

52

The updated NDC of the USA aiming to reduce GHG emissions by 50‐52% by 2030 is not included in this analysis. Nevertheless,
this will have little impact on the simulated leakage results. If the updated NDC of the USA was assumed in the baseline, then there
would be leakage from the USA to the non-OECD region. So EU’s carbon policies would no longer yield leakage to the USA, but as
carbon-intensive non-OECD expands from USA implanting policies to meet their NDC, then substitution at constant elasticities will
yield also more absolute leakage from EU’s carbon policies to those regions.

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CARBON PRICE FLOOR FOR ELECTRICITY GENERATION AND INDUSTRY

Figure 51

Decomposition of carbon leakage to regions of Fit-for-55 Package (2030)
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
USA

Source:

Rest-of-the-OECD

China

Rest-of-the-world

CE Delft

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