Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 9 | June 2024 | 691–702
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nature energy
https://doi.org/10.1038/s41560-024-01505-x
Article
EU carbon prices signal high policy
credibility and farsighted actors
Joanna Sitarz
  1,2
, Michael Pahle
  1, Sebastian Osorio
  1, Gunnar Luderer1,2 &
Robert Pietzcker
  1
Carbon prices in the EU emissions trading system are a key instrument
driving Europe’s decarbonization. Between 2017 and 2021, they surged
tenfold, exceeding €80 tCO2
−1 and reshaping investment decisions across
the electricity and industry sectors. What has driven this increase is an
open question. While it coincided with two significant reforms tightening
the cap (‘MSR reform’ and ‘Fit for 55’), we argue that a reduced supply of
allowances alone cannot fully explain the price rise. A further crucial aspect
is that actors must have become more farsighted as the reform signalled
policymakers’ credible long-term commitment to climate targets. This
is consistent with model results that show historic prices can be better
explained with myopic actors, whereas explaining prices after the reforms
requires actors to be farsighted. To underline the role of credibility, we test
what would happen if a crisis undermines policy credibility such that actors
become myopic again, demonstrating that carbon prices could plummet
and endanger the energy transition.
The EU emissions trading system (EU ETS) is a central pillar of the Euro-
pean Union’s decarbonization strategy. It covers the electricity sector,
large-scale industrial installations, aviation and maritime transport and
hence controls above 40% of the European Union’s total greenhouse gas
emissions1. Over a period with two major reforms of the ETS and notably
a substantial tightening of the cap, the carbon market underwent a
remarkable transition: carbon prices increased tenfold within four years,
with a first rise in 2018 from below €10 tCO2
−1 to a plateau at €20–30
tCO2
−1 in 2019–2020 and then a second, even sharper, rise during which
prices repeatedly reached almost €100 tCO2
−1 in 2021 and 20222. The
question of why prices have risen so steeply is still unanswered, though,
and a subject of debate among the scientific and policy community.
The literature so far identifies various factors as playing a potential
role in carbon price developments in general: (1) regulatory changes
(such as the introduction of the Market Stability Reserve (MSR) or
changes in the linear reduction factor)3–5, (2) actors’ behaviour (fore-
sight horizon, hedging or participation in trade)6,7 and (3) speculation
and external financial investors8–10. However, most work focuses on one
of those aspects, provides only qualitative assessments and covers only
the period before the recent reforms and price increases.
With a view on understanding what has driven prices in the recent
period, the following puzzle arises. It is economically straightforward
that a tightening of the long-term cap should increase current and
expected prices. However, past research suggests that market partici-
pants in the ETS are myopic7,11. Whereas myopia can always have an impact
on energy sector investments, it is especially relevant when the power
sector is covered by an intertemporal emissions trading system with a
cap that strongly tightens over time, so that future certificate scarcities
can influence current investments. If most market actors were myopic, a
long-term tightening of the cap should thus only have modest effects on
current prices, much lower than the observed increase after the reforms.
In light of that, we hypothesize that the reform could have had
another important effect on actors: making them more farsighted.
The reason is that through the reform EU policymakers substantially
firmed up the credibility of their commitment to the ETS overall. They
did this both explicitly, by emphasizing that the ‘ETS is front and centre
to all our efforts’12, and implicitly, by investing a lot of political capital
in the political negotiation. More broadly, a recent empirical study
also shows that the European Union has currently the world’s highest
climate policy credibility13.
Received: 31 March 2023
Accepted: 13 March 2024
Published online: 30 May 2024
 Check for updates
1Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany. 2Global Energy Systems Analysis, Technische
Universität Berlin, Berlin, Germany.
 e-mail: joanna.sitarz@pik-potsdam.de; robert.pietzcker@pik-potsdam.de

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Different publications furthermore suggest that the limited fore-
sight of compliance actors contributed to low carbon prices4,6,7,18. To
understand the role of foresight, one needs to consider that the EU ETS
allows for almost unlimited forward bankability: any certificate not
used today can be used in the future. Hence, expected future prices
may have a strong influence on today’s prices. In contrast, in a market
without bankability, a surplus of certificates over emissions would
mean the certificate price in that year is zero, as the unused certificates
become worthless at the end of the year.
Now, many firms might not consider the long-term future (inher-
ently, or due to regulatory uncertainty and lack of policy credibility) but
rely on short-term planning horizons of for example, five to ten years19.
If allowances scarcity occurs outside their planning horizon, they will
not anticipate it and hence don’t have incentives to bank certificates
for the future nor decrease emissions in the short term. Consequently,
the carbon price will stay lower and decarbonization will be slower than
if actors were farsighted (Fig. 2).
Thus, for many years the EU ETS failed in establishing a carbon
price that would drive deep decarbonization. In period (1), actors pre-
sumably acted myopically, a behaviour leading to low carbon prices.
However, just a few years later, EU ETS prices are stronger than ever2.
What happened since 2017? Which mechanisms drove the rise in carbon
prices observed in periods (2) and (3)? A plausible explanation would
be that prices simply increased because reforms tightened the cap20.
Here we present a more comprehensive explanation: the reforms had
the side effect that market actors also became less myopic, which drove
prices up. Therefore, we first give an overview of the most relevant
reforms from the past years and then present our modelling results.
The past years were marked by numerous reforms and rapid EU
climate policy developments21–27. While it is challenging to pinpoint
one specific regulation with the highest impact on carbon prices, we
can, generally, speak about an intensive period in climate policy since
2015 with two crucial ETS reform periods: the ‘MSR reform’ and the ‘Fit
for 55’ package, as summarized in Table 1.
Our modelling findings are divided into two segments. We first
present results supporting our hypothesis that actors have extended
their foresight, which strongly impacted historical carbon prices.
Hereafter, we turn to the role of external financial investors, who have
been gaining attention throughout literature and media8–10,28–30, to
delimit their possible impact on the carbon price surge.
Figure 3 shows our modelling results on the impact of reforms and
actors’ foresight on carbon prices (see also Extended Data Figs. 1–6).
First of all, one can see between period (1) and period (2), when the
MSR reform was negotiated and implemented, actors presumably
started to look further into the future. When turning to period (1)
before 2018, one notices that observed ETS prices are closer to the
modelled prices for myopic actors than to the modelled prices for
farsighted actors. It seems therefore plausible to assume that market
actors behaved at least partially myopically, which is in line with ear-
lier assessments7. For periods (2) and (3), one observes the opposite:
both, the 2019–2020 observed ETS prices of €20–30 tCO2
−1 and the
2021–2022 ones of €70–90 tCO2
−1, are consistent with the modelled
prices for farsighted actors (that is, perfect foresight trajectories for
old ‘MSR reform’ targets, and new ‘Fit for 55’ targets, respectively). We
also calculate the Mean Average Percentage Error (MAPE) between
the modelled and historical prices (Extended Data Tables 1–4), which
confirms the visual conclusions drawn from Fig. 3.
Hence, regarding the first rise at the beginning of period (2), a
hypothesis following our results is that prices increased due to a grad-
ual switch from actors’ short- to long-term foresight, which might have
been triggered, among other things, by the MSR reform tightening
the cap and strengthening the MSR. Whereas our results indicate that
the direct effect of the reform—the tighter emissions budget—cannot
explain the substantial increase in prices under the assumption of
continued myopia, the reform might have had a strong indirect impact:
Such instilled credible commitment is essential to shape firms’
expectations about the durability of long-term policies such as the
ETS14, and indeed studies suggest that low policy credibility can be
associated with decreased green investments15, and that policy cred-
ibility can enhance actors’ farsightedness16. The main reason is that
low credibility creates high regulatory uncertainty regarding a future
softening of the cap or interventions to dampen high carbon prices—
a major reason for myopia. Correspondingly, increasing credibility
implies that actors become more farsighted.
Research is still outstanding on whether myopia remains a prevalent
influence within the current EU ETS. Equally, there has been no investiga-
tion into whether any shifts in the foresight horizon have occurred and
their potential impact on the recent surge in carbon prices.
We fill this gap by providing a model-based analysis of the EU ETS
with a specific emphasis on the influence of actors’ foresight horizon.
The contribution of our work is threefold. We first analyse the past:
bringing together the impact of political reforms, the foresight of
compliance actors and the role of external investors, we show which
mix of those mechanisms could explain the observed strong rise in
carbon prices. We discuss the present: by computing marginal carbon
prices necessary to drive the decarbonization of the electricity and
industry sectors in line with the new EU’s 2030 goals as set in the ‘Fit
for 55’ package, we assess that current ETS prices correspond to the
optimal market-efficient carbon price trajectory. We turn to the future:
having understood the mechanisms that could plausibly have led to the
observed increase in carbon prices, we explore in how far this positive
development is vulnerable and potentially could be reversed. We close
with policy recommendations on how to secure the energy transition
in light of our results.
From past to present
When analysing past carbon prices (Fig. 1), one can broadly break down
the timeline into three periods with distinct price regimes: (1) the period
of 2008–2017, in which prices first dropped and then stabilized at a
low level below €10 tCO2
−1, (2) the period of 2018–2020, the first rise
up to a plateau of €20–30 tCO2
−1 and (3) the period since late 2020, the
second rise, in which prices increased strongly and are now stabilizing
around €70–90 tCO2
−1. What might have been the main mechanisms
driving these three regimes, and, in particular, what role could actors’
foresight have played?
Regarding the first period (1), the common understanding is that
prices dropped because of a high surplus of allowances that accumu-
lated since 2008. The financial crisis reduced emissions more than
anticipated, leaving compliance actors with a high number of unused
allowances, hence limiting incentives to decarbonize17.
(1)
(2)
(3)
0
25
50
75
100
2008
2010
2012
2014
2016
2018
2020
2022
2024
Year
Carbon price (€ tCO2
−1)
Fig. 1 | Evolution of carbon prices on the EU ETS from 2008 to 2023. Prices
correspond to historical EU ETS allowances (EUA) prices on the EEX spot
market2,68. The year tick marks the beginning of a year. We categorize the price
evolution into three periods: (1) initial decline and stabilization, (2) first rise and
(3) second rise.

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the negotiations and ultimate implementation of the reform over 2017
and 2018 (Table 1) emphasized the will of EU policymakers to ‘repair’
the ETS (showing ‘the doctor has not given up on the patient’31), which
strongly increased its long-term credibility, inspiring market actors
to show longer foresight. These findings align with previous assess-
ments, which, on the one hand, demonstrate that the MSR can lead to
increased carbon prices32,33, whereas, on the other hand, argue that the
effect of the MSR reform on the emissions budget alone is unlikely to
fully explain the surge in carbon prices6,34.
Secondly, Fig. 3 shows that the ‘Fit for 55’ package sharply increases
the stringency of the EU ETS. Optimal carbon prices (that is, obtained
under the assumption of perfect foresight) to reach the new targets
are substantially higher than those that were necessary for achieving
previous goals. In fact, modelled prices for the ‘Fit for 55’ targets for
2020–2023 are in the order of €70–90 tCO2
−1, corresponding well to
observed 2021–2023 prices on the EU ETS, thus supporting the hypothe-
sis that actors have transitioned towards a more farsighted perspective.
Figure 4 discusses the final point of this section: could an influx
of long-term investors explain the strong rise of carbon prices if other
actors had remained myopic? Here we assume external investors tem-
porarily block a part of the allowances on the market, which then cannot
be used by compliance actors to cover their emissions during the period
(Methods). This influences the price trajectory: when external investors
buy, prices go up; when they sell, prices can go down.
In reality, it is estimated that external investors currently hold
only around 5–10% of allowances futures9, consistent with the scenario
in which 5% of auctioned allowances are bought by external financial
investors. This scenario shows only a small price increase of less than
€10 tCO2
−1 in 2025 compared with the pure myopic scenario (Fig. 4).
Thus, following our results, a major contribution of external investors
to the price rise seems unlikely. What is on the other hand possible, is
that they acted as a catalyser, speeding up the process of compliance
actors switching to longer foresight and anticipating the consequences
of the ‘Fit for 55’ package.
To summarize, we provide a possible explanation of the past: we
show that the two price rises (first to €20–30 tCO2
−1 and more recently
to €70–90 tCO2
−1) are consistent with a first regulatory reform that had
limited impact on the cumulative certificate budget but contributed to
a switch of actors’ behaviour from myopic to farsighted and a second
reform that substantially tightened the emissions cap. Whereas exter-
nal investors may have accelerated the transition, it seems improbable
that prices are artificially high solely due to their activity.
Furthermore, our results provide insights about the present state
of the EU ETS. Our modelling indicates that observed 2022 and 2023
prices of around €80 tCO2
−1 put the ETS sectors on track to achieving
their reduction targets set by the Climate Law, a result in line with
earlier findings35.
Our findings suggest that actors became farsighted, which is
consistent with the initially formulated hypothesis that the ETS reform
heightened policy credibility. Overall, there are thus reasons for careful
optimism: trust in the EU ETS revived, policy credibility seems high,
actors are therefore farsighted and current prices are in line with EU’s
a
Perfect foresight
Time
Myopic foresight
Banked allowances
Allowances cap
Planned emissions (perfect foresight)
Planned emissions (myopic foresight)
Considered time horizon/budget in respective foresight mode
Horizon 1
Considered
budget
Cumulative emissions (tCO2)
Cumulative emissions (tCO2)
Carbon price (€ t−1 CO2)
Carbon price (€ t−1 CO2)
Considered
budget
Years into the future
Years into the future
5
10
15
20
25
30
35
40
(tCO2)
(tCO2)
b
c
5
10
15
20
25
30
35
40
Time
Horizon 1
Horizon 2
Time
Perfect foresight (40 years)
Myopic foresight (ten years, revision after five years)
Final (realized) values
Projected values in each horizon
Final (realized) values accounting for revision
Time
Fig. 2 | Stylized emissions and carbon price trajectory with short-term,
myopic, and long-term, perfect, foresight. Simple cap and trade system
without the Market Stability Reserve, for illustrative purposes. a, Example of a
planning horizon at the beginning of the transformation. With a myopic foresight
of ten years, there is no (or very weak) incentive to reduce planned emissions.
With a perfect foresight, future scarcity is anticipated and planned emissions
get reduced already in the near-term. b, Cumulative emissions over the whole
transformation period. Myopic foresight leads to delayed decarbonization. c,
Carbon prices over the whole transformation period. Myopic foresight leads to
very low carbon prices in the near term. Short lines correspond to the specific
horizons: every five years a new foresight horizon of ten years starts.

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goals. However, the question arises: are these changes principally
reversible? In particular, could credibility plummet again, implying
that actors return to myopic behaviour? If yes—why? And what would
it mean for carbon prices and the energy transition?
A look into the future
The previous section has shown that the recent rise in ETS prices does
not result from an acute scarcity of allowances—as their surplus is
still vast—but can rather be explained by market actors having turned
more farsighted. A plausible interpretation is that this was due to the
long-term cap becoming considerably more credible. This may suggest
that from this point on, ETS prices would only increase. However, what
would happen if policy credibility gets shaken again due to a crisis or
political backlash? How much would prices plummet and what would it
imply for the energy transition? This is what we analyse in the following
with a scenario considering a shock—for illustrative purposes—in 2025.
Before turning to numerical results, to underpin our motivation
for analysing a shock scenario, we develop a conceptual model on how
policy credibility, actors’ foresight and carbon prices influence each
other (Fig. 5). The left side schematically represents the events from
2017 to 2021. It starts from a state with low policy credibility due to the
huge certificate surplus, myopic market actors and ensuing low carbon
prices. Then, the MSR reform and the higher ambition in the ‘Fit for
55’ package substantially strengthened the policy credibility and set
in motion a reinforcing cycle: actors extend their foresight horizon,
which in turn increases carbon prices, which (1) may increase the policy
credibility and (2) attracts non-compliance actors to the market with
at least partially more long-term investment strategies.
The right side shows a path, how the current situation could
unravel again: a price shock or a crisis and the ensuing political reac-
tions could potentially reduce policy credibility and trigger a relapse
into myopic behaviour and hence lower prices. The recent energy crisis
serves as an illustrative example: the tenfold increase36 of European
gas prices in 2022 put pressure on the EU ETS from several directions.
First of all, rising energy prices created strong liquidity prob-
lems for many firms37. Under liquidity problems, firms might sell
assets not required in the short-term—such as banked CO2 certificates,
which could decrease prices and scare away external investors. Sec-
ondly, the rising energy prices directly created pressure to weaken
climate policies. As an example, Poland repeatedly proposed to freeze
Table 1 | An intensive period in climate policy between 2015 and 2022. Developments and reforms relevant for the EU ETS
Date
Event
Impact on climate policy/the EU ETS
Dec. 2015
Adoption of the Paris Agreement21
Global climate policy: ‘goal to limit global warming to well below 2, preferably to 1.5 degrees
Celsius, compared to pre-industrial levels’.
Oct. 2015
Decision on the establishment of an MSR22
EU ETS design: new mechanism with pre-defined rules addressing the high surplus of
allowances. Depending on the total number of allowances in circulation (TNAC), allowances get
placed in the reserve or released from the reserve. However, as all certificates are to be released
in the long term, this reform implies NO tightening of the intertemporal emission cap and thus
had little impact on market prices.
Oct. 2016
Ratification of the Paris Agreement23
EU climate policy: all parties (including the EU) having adopted the Paris Agreement are
required to submit an NDC till 2020, outlining their post-2020 climate actions.
Feb. 2017
‘MSR reform’
Proposal of ETS/MSR reform for trading period
IV (2021–2030)—strengthening the MSR and
tightening ETS targets24
EU ETS design: Parliament and Council formulate their ETS/MSR reform proposals. Council
proposes automatic cancellation of certificates in the MSR above a threshold.
Nov. 2017
‘MSR reform’
Final agreement on ETS/MSR reform for trading
period IV (2021–2030)25,26
EU ETS design: after six trilogues, Commission, Parliament and Council reach an agreement on
the ETS/MSR reform. Tightening of the cap: linear reduction factor of allowances (in percentage
points of 2005 cap) increases from 1.74 to 2.2. Strengthening of the MSR: higher intake and
certificate cancellations from 2024 on.
March 2018
‘MSR reform’
ETS/MSR reform for trading period IV (2021–
2030) officially adopted27
EU ETS design: directive with the ETS/MSR reform officially published.
Dec. 2019
Presentation of the European Green Deal62
EU climate policy: EU Commission presents EU’s new climate action strategy including the goal
of climate neutrality in 2050 and an emissions reduction of 50–55% until 2030, compared with
1990 levels.
March 2020
Proposal for a European Climate Law63
EU climate policy: EU Commission presents legislative proposal of a law setting the objective
for the EU to become climate neutral by 2050.
Sept. 2020
Proposal to set an EU-wide 55% emissions
reduction target for 203064
EU climate policy: EU Commission amends the Climate Law proposal by introducing the
updated 2030 climate target of a net reduction of at least 55% of EU’s greenhouse gas
emissions compared with 1990 levels.
Dec. 2020
The Council agrees on the 55% reduction target
for 203065
EU climate policy: The Council of the EU reaches an agreement on an approach on the climate
law proposal, including an agreement to the updated 2030 climate target of a net reduction of
at least 55% of EU’s greenhouse gas emissions compared with 1990 levels.
April 2021
Final agreement on Climate Law66
EU climate policy: Parliament, Council and Commission agree in trilogue negatiations on the
−55% 2030 reduction target, enabling the formal adoption of the Climate Law in June 2021.
July 2021
‘Fit for 55’ package54,67
EU ETS design: EU Commission publishes package of legislative proposals to meet the
2030 emissions reduction target of 55%. For the EU ETS it includes: steeper annual emission
reductions, strengthening of the MSR, gradual removal of free allowances for the aviation
sector and the inclusion of the maritime sector into the current EU ETS.
Dec. 2022
Agreement on EU ETS ‘Fit for 55’ proposal39
EU ETS design: Parliament, Council and Commission reach final agreement during trilogue
negotiations on the EU ETS ‘Fit for 55’ proposal. Ambitions are kept high: all main elements from
the initial proposal remain; the emissions cap gets slightly more tightened compared to the
initial proposal.
Highlighted in bold are events directly affecting the EU ETS design.

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carbon prices at €30 tCO2
−1 (ref. 29) or even temporarily suspend the
EU ETS38. If the EU were to give in to such proposals, it would decrease
its long-term policy credibility, and hence, following our hypothesis,
compliance actors’ foresight. Given the trilogue results in late 202239,
it appears the European Union managed to overcome this critical
situation without weakening the ETS and undermining its long-term
policy credibility.
Nevertheless, the future remains uncertain, with the energy crisis
serving as just one recent illustration of potential risks. Political crisis
can happen anytime and history has repeatedly shown that all policy
reforms face the threat of being undone or weakened over time40.
This emphasizes the importance of exploring the risks of undermined
policy credibility and actors returning to myopia. More specifically, to
safeguard against such a turn of events, it is important to quantify what
would be lost in terms of price degradation, and how this would slow
down the energy transition.
Figure 6 shows the price trajectory of such a ‘reversal to myopia’
scenario. It presumes actors were myopic in the past, became farsighted
around 2020 and turn fully myopic again in 2025. Prices could then start
falling, reaching a level below €30 tCO2
−1 in 2025. There is currently
no mechanism ensuring prices stay high in the next years. Assuming
such a relapse into myopia really happens and prices fall in the near
future below €30 tCO2
−1, what would it mean for the energy transition?
The general impacts of myopic foresight in the energy sector have
been studied in previous literature41–46. Nerini et al.47 show using the
cross-sectoral capacity expansion model UK Times that myopia might
result in delayed climate action and higher total transformation costs,
compared with the pathway set by a perfect foresight model. On the
one hand, emissions abatement gets delayed. On the other hand, the
solutions chosen are focused on the near-term, creating lock-ins and
not the most efficient ones from a long-term perspective.
To illustrate the delayed action, we focus on the electricity sec-
tor. The major problem we identify under the relapse to myopia is
that, as seen in Fig. 7, delayed investments into wind capacity in turn
delay the phase-out of coal power generation. As illustrated in Fig. 7a,
our modelling shows that myopia could massively slow down wind
capacity expansion in the next ten years, with yearly investments
reduced by a factor of three, compared with the cost-optimal (that
is, perfect foresight) trajectory. The missing wind power in combi-
nation with low carbon prices would strongly delay the phase-out
of coal (Fig. 7b).
These risks are examples of what can happen in the electricity sec-
tor. Any delay poses the risk of climate targets becoming out of reach
or being reachable only at very high costs, as feasible roll-out rates can
be limited, for example, due to the availability of skilled workers or
production capacities48. Likewise, the required steeper carbon price
in the long-term might increase the likelihood of a political backlash
that dismantles the policy49. Hence, it is crucial to be aware that carbon
prices could principally fall again in the near future, with strong conse-
quences for the energy transition. Exploring potentials of a price floor
in the ETS, proposed in the past to address the problem of myopia18 and
designing complementary policy instruments to shore up the energy
transition thus remains critical—despite currently high carbon prices.
Conclusions
This work proposes a quantitative explanation behind the observed rise
in carbon prices on the EU ETS since 2017. We use a numerical model
(see Methods) to simulate different foresight horizons of compliance
actors and to depict the role of external investors. We show that the
combination of stricter EU ETS policies and changed behaviour of com-
pliance actors from myopic to farsighted can explain the rapid increase
in carbon prices over the past years, underpinning with a quantitative
analysis earlier scholarly work emphasizing the role of myopia7,19. Our
results indicate that external investors probably only played a minor
role by, for example, accelerating the price rise.
We discuss the hypothesis that policy credibility impacts actors’
foresight and hence carbon prices. Consequently, a glimpse into the
2013
0
25
Perfect foresight
Myopic foresight
50
75
100
(1)
(2)
MSR reform
(3)
Fit for 55
2014
2015
2016
2017
2018
2019
2020
2021
2023
2022
Year
Model results
EU ETS
targets
Pre-reforms
Foresight
Perfect
Myopic
Real developments
Actual EU ETS
prices
MSR reform
Fit for 55
Carbon price (€ tCO2
−1)
Fig. 3 | Impact of reforms and actors’ foresight on carbon prices. Historical
carbon prices on the EU ETS and modelled carbon price trajectories with the
assumption of either perfect or myopic foresight over the three periods (1)–(3)
as defined in Fig. 1. For each period, we show the carbon price trajectories
required to reach the target that was valid during that period. Thus, jumps in
same-coloured trajectories between one period and the next show the effect that
the change in the ETS targets and MSR parameters has under unchanged actor
foresight. Myopic foresight corresponds to a rolling foresight horizon of ten
years (Methods provide underlying model assumptions). Historical prices are
historical allowances (EUA) prices on the EEX spot market2,68. The mean average
percentage error between modelled and historical prices is available in Extended
Data Table 1. The interaction between the foresight horizon and the MSR is shown
in Extended Data Fig. 1. Prices are nominal until 2023 and real EUR2023 from 2023
on (Methods ‘Carbon prices’).
2015
2020
2025
2030
2035
2040
Year
0
150
50
200
250
100
Carbon price (€ tCO2
−1)
Perfect foresight
Myopic foresight
Investors buy
5%
20%
Fig. 4 | Impact of external financial investors on carbon prices. Carbon price
trajectories assuming perfect foresight, myopic foresight and myopic foresight
with external investors buying 5% or 20% of yearly auctioned allowances and
reselling them once carbon prices reach the theoretical value from the perfect
foresight path. If external investors had started buying 5% or 20% of auctioned
allowances from 2018 onwards, they would own, respectively, around 10% or 40%
of today’s (as of 2022) total number of allowances in circulation. All trajectories
correspond to newest targets from the ‘Fit for 55’ proposal with an active MSR.
Exact assumptions on the number and timing of allowances bought and sold by
external investors is available in Extended Data Fig. 2.

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future shows that carbon prices could fall again if actors become
myopic again (for example, due to a price shock and reduced pol-
icy credibility). A fall back into myopia and low prices can threaten
short-term decarbonization efforts. To prevent such a development,
additional policy instruments seem advisable to stabilize expectations
of agents. As an example, a price floor would limit the drop of carbon
prices in the short term when long-term policy credibility is temporarily
reduced and could even keep prices higher without being binding50.
Overall, we find that observed 2022 and 2023 prices of around €80
tCO2
−1 are in line with EU Climate Law reduction targets and should not
be artificially lowered. Compliance actors seem to trust the political
commitment and act farsighted—a good sign for the reachability of EU’s
2030 climate targets of the ETS sectors, if the current energy crisis and
related policy reactions do not undermine this mindset.
Methods
The model LIMES-EU
All quantitative results in this work are obtained using the model
LIMES-EU (Long-term Investment Model for the Electricity Sector),
version 2.38. LIMES-EU is a linear optimization modelling framework
that simultaneously determines cost-minimizing investment and
dispatch decisions for generation, storage and transmission technolo-
gies in the European electricity sector. Although its clear focus is the
electricity sector, the energy-intensive industry and district heating
are also represented through marginal abatement cost curves. Com-
pared with simple emissions trading models with static exogenous cost
abatement curves, using an energy system model such as LIMES-EU
allows to assess not only market developments (for example, prices or
allowances in circulation) but also the investment dynamics and path
dependencies within the electricity sector.
LIMES-EU allows to fully simulate the EU ETS including the Market
Stability Reserve (MSR)51. Hence, one can analyse figures such as the
number of allowances in circulation, the intake by the MSR and result-
ing carbon prices. By varying the cap and MSR parameters, one can
reproduce the state of the EU ETS between different political reforms.
A comprehensive description of the LIMES-EU model, including
parameters, equations and assumptions, is provided in the documenta-
tion available from the model’s website52.
Past
Low carbon prices
Low carbon prices
2013–2017
Policy
credibility
From past to present
Actors
extend foresight
Potentially
reinforcing cycle
Energy prices
increase
Actors experience
liquidity problems
Actors become
more myopic
Potentially
reinforcing cycle
Example: energy crisis
Example: MSR reform
MSR demonstrates
more credible policy
commitment
Higher climate
ambition
Political or
economical crisis
High carbon prices
Higher policy
credibility
Risks for the future
Present
2021–2023
Potential future?
Lower policy
credibility
Fig. 5 | Role of actors’ foresight and policy credibility in carbon price
formation. A distortion from a high or low prices level can enchain a potentially
reinforcing loop leading to a fall, or rise, in prices, respectively. Policy credibility
plays a major role in how actors react to a distortion. The theoretical hypothesis
is complemented by two examples: the introduction of the MSR as an example
of increased commitment to climate targets and the current energy crisis, as an
example of a potential shock. Following studies highlighting the importance
of climate policy credibility for an acting private sector13,69, we assume actors’
foresight depends on policy credibility.
2015
2020
2025
2030
2035
2040
Year
0
150
50
200
250
100
Carbon price (€ tCO2
−1)
Perfect foresight
Reversal to myopia
High policy
credibility
Low policy
credibility
Fig. 6 | Risk of falling EU ETS prices due to undermined policy credibility.
Carbon price trajectories assuming perfect foresight and reversal to myopia (that
is, actors are first fully myopic, become farsighted around 2020 and then fall
back fully into myopia until 2025). Both trajectories correspond to newest targets
from the ‘Fit for 55’ proposal with an active MSR. The carbon price trajectories
are complemented by the hypothesis that actors’ foresight strongly depends on
policy credibility.

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All changes to LIMES version 2.38 made for the purposes of this
study are described below.
A myopic version of LIMES-EU
Rolling horizon as operationalization of myopia. Originally, LIMES-EU
was formulated as a perfect foresight model running in five-year steps
from 2010 until 2070. For the purpose of this study, to simulate the
effect of myopic behaviour of decisionmakers, we extend the model
with the option to use rolling time horizons instead of full intertem-
poral foresight. Mathematically this means that instead of solving
one optimization problem over the whole time period from 2010 until
2070, we solve multiple (consecutive) optimization problems, covering
shorter time periods.
In our choice to implement a rolling horizon, we follow several
other publications from our field: the rolling horizon approach (that
is, short foresight with overlapping time steps) has already been used
extensively as a way to represent myopia in the context of energy sys-
tems modelling41,43,44,47. Although principally other approaches would be
possible (for example, by varying the discount rate), we are not aware of
any publication in our field representing myopia in a different manner.
Foresight length. All myopic foresight results in this work assume
ten-year horizons with an overlap of five years between the horizons.
Practically it means, actors have foresight of ten years but can revise
their decisions every five years. As LIMES-EU runs in five-year time
steps, one optimization horizon comprises always two time steps (for
example, (2020, 2025), covering years 2018–2027).
The literature provides different estimations on planning horizons
of manufacturing companies, ranging between three and 12 years6.
Bocklet and Hintermayer6 and Quemin and Trotignon7 show that a
horizon of around ten years can best replicate EU ETS developments
(these analyses were conducted around the time of the MSR reform).
Hence, we also chose a foresight horizon of ten years. As our model runs
in five-year time steps, ten years is also the shortest foresight horizon
we can meaningfully implement (that is, which allows for an overlap)
in LIMES-EU. Varying the length of the foresight horizon impacts the
results but not the general trends: the shorter the foresight, the lower
the near-term carbon prices and higher the delays in decarbonization47.
When running in myopic foresight, the model solves consecutively
several individual optimization problems. Still, some variable values
computed in one optimization horizon need to be transferred into the
next optimization horizon. It concerns all previous capacity additions
and decommissioning (needed to correctly compute current capaci-
ties) and emissions and banked certificates (needed for the ETS/MSR
simulation). For instance, for the optimization horizon (2020, 2025)
capacities will be fixed for 2020 and all time steps before 2020. We
assume that dispatch decisions can still get revised every time step
(five years), so for example, for the optimization horizon (2020, 2025),
emissions and banked certificates values get fixed only for all time steps
before 2020 but not 2020 itself.
What do actors neglect and what do they still consider. In our study,
we use rolling horizons as a tool to represent actors’ myopia due to low
trust in the long-term stability of the EU ETS. Hence, our main aim is to
depict actors that are myopic with regards to the ETS. Our modelling
approach implies that actors don’t consider any information outside
of their ten years foresight horizon (that is, the future ETS cap and the
future demand for certificates).
Nonetheless, as ETS actors are mostly large power system or manu-
facturing companies and salvage values (‘book values’) are traditionally
part of companies balance sheets, we still assume that they consider the
future value of capacities also beyond the foresight horizon. Therefore,
a salvage value for the capacity stock remaining at the end the optimiza-
tion horizon is subtracted from the cost function. In the myopic version,
the salvage value is considered in each time horizon. This means that
when we run a diagnostic scenario where we turn off the ETS and keep
technology prices constant over time, the results of the myopic mode
exactly reproduce the results of the perfect foresight mode.
MSR simulation. The MSR, which is originally implemented iteratively
as a loop around the main optimization problem51, runs in the myopic
model version around each time horizon.
Specific modelling aspects
Carbon prices. Reported carbon prices (in € tCO2
−1) represent the mar-
ginal abatement costs in a given year, which are equal to the dual value
(shadow price) associated with the banking constraint in LIMES-EU.
Transaction costs are neglected. Reported historic carbon prices are
nominal, so given in € of the year in which they occurred. LIMES runs
in real €2010, but all reported prices from LIMES until 2023 in this paper
2020
200
Wind onshore capacity (GW)
300
400
500
600
Perfect foresight
Myopic foresight
Perfect foresight
Myopic foresight
200
Coal in electricity mix (TWh yr−1)
0
400
600
a
b
2025
2030
2035
2040
Year
2020
2025
2030
2035
2040
Year
Fig. 7 | Delays in decarbonization due to myopia. a, Expansion of wind onshore
capacity in the European Union under perfect and myopic foresight. b, Yearly
electricity generation in the European Union from black and brown coal under
perfect and myopic foresight. Trajectories in both a and b correspond to newest
targets from the ‘Fit for 55’ proposal with an active MSR. Note that in both a and b,
the 2020 year is fixed to match real 2020 values. Additional data (solar capacity
expansion and total electricity mix) on perfect and myopic foresight scenarios
can be found in Extended Data Fig. 3.

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were converted to nominal prices until 2023, adjusted for inflation using
inflation rates provided by the Organization for Economic Co-operation
and Development53. Computed prices after 2023 are in real €2023.
External investors. To depict external investors in our model, we
assume that the impact on carbon prices of buying/holding/selling
EUA futures can be approximated by the assumption, external investors
buy/hold/sell physical allowances. As we are interested in long-term
price developments, we focus on external investors holding long open
position on EUA futures.
To model the impact of external investors, we implement a one-step
iteration approach. Hence, we implicitly assume that both compliance
actors and external investors can’t react the other group’s action.
(1) In a first instance, a LIMES-EU run with full myopic foresight
without external investors is conducted.
(2) The resulting carbon price trajectory pprice,CO2(ty) serves as input
to the optimization problem from the external investors’
perspective:
max
vbought,vsold
∑
ty∈T
(vsold(ty)pprice,CO2(ty)
−vbought(ty)pprice,CO2(ty)) × e−i(ty−ty0)
(1)
s.t.vbought (ty) ≤αpauction (ty)
(2)
ty
∑
0
vsold(ty) ≤
ty−1
∑
0
vbought(ty)
(3)
vsold (ty) ≤γ ∑
ty∈T
vsold(ty)
(4)
Equation (1) is the profit function: external investors want to maxi-
mize their profit by buying allowances and selling them at a later time
step ty. Herein, ty ∈[2018, … , 2040] are yearly time steps. T is the set
containing all yearly steps part of the optimization. Further, vbought(ty)
and vsold(ty) stand for the number of allowances bought and sold in
time step ty. The profit gets discounted by discount rate i. We assume
i = 5%, same as in the core model assumptions of LIMES-EU. Finally,
pprice,CO2 (ty) corresponds to the carbon price from a myopic run, which
grows at a higher rate than the discount rate of 5%.
Equation (2) sets a limit on the number of allowances external
investors can maximally buy. Herein, α is the share of auctioned allow-
ances pauction(ty). We assume pauction to be the final number of allowances
auctioned, after subtraction of allowances transferred into the MSR.
In our work, α is varied between 5 and 20%. Equation (3) ensures the
number of allowances sold is below the number of allowances external
investors bought prior to time step ty.
Finally, equation (4) limits the number of allowances that can be
sold in a given time step ty, to prevent all of them being sold in a single
year. Results assume an γ of 0.2, meaning allowances need to be sold
over minimum five years.
(3) Having solved the optimization problem from the perspective
of external investors, one can now conduct a new LIMES-EU run with
full myopic foresight and additional input on the number of allowances
blocked by external investors.
pinvestors (t) = vbought (t) −vsold(t)
(5)
vtnac (t) −vtnac (t −1) = pcap (t) −pinvestors (t) −vemi (t)
(6)
Here pinvestors is the absolute number of allowances bought or sold
by external investors. These influence the level of allowances, as shown
in equation (7). Here vtnac(t) is the total number of allowances in circula-
tion (TNAC) at the end of time step t, pcap(t) the total number of allow-
ances auctioned and freely allocated and vemi(t) the total emissions in
time step t. Here t ∈[2010, 2015, … , 2040] are five-year time steps.
To capture the unpredictability of external investors on the price
formation, we assume compliance actors can’t see the realization of
pinvestors (t) before time step t. Hence, even though they have a foresight
of ten years regarding all other model inputs, they only have a foresight
of one LIMES-EU time step (five years) when it comes to pinvestors (t).
It is important to note that the way our approach is implemented,
external investors behave as farsighted actors and have incentives to
enter the market, only if compliance actors are myopic (carbon prices
initially lower than under the perfect foresight scenario). Hence, all
results showing the impact of external investors presume myopic
foresight from compliance actors.
As we conduct only one iteration, we implicitly assume that exter-
nal investors plan all their future behaviour only once and base it on
myopic carbon prices. In the real world, there is a constant feedback
between prices and investors’ buying/selling strategy. Hence, our
methodology does not aim to provide realistic predictions regarding
possible behaviour of external investors. It is, however, suitable to
show the order of magnitude of the increase in carbon prices, assuming
external investors block a certain number of certificates.
Future. In the ‘Reversal to myopia’ scenario from Fig. 5b, similar to
the full myopic version, several consecutive optimization problems
with ten years foresight horizons are solved, with the exception that
the horizon [2020, 2025] gets replaced by [2020,…, 2070] to simulate
perfect foresight in time step 2020. Afterwards, from time step 2025
on, actors have again only myopic foresight.
MACC curves representing industry and heating sectors. As
described in the LIMES-EU Documentation52, the industry and heat-
ing sectors are not modelled explicitly in LIMES-EU, but the cost of
emissions abatement is approximated by marginal abatement cost
curves (MACCs). Originally, as they have been designed for runs start-
ing in 2020, both MACCs assumed a minimum cost of €8 tCO2
−1, being a
well-suited assumption for benchmark modelling, in which modelled
carbon prices always exceed €8 tCO2
−1 for relevant ETS scenarios. As in
this work certain counterfactual scenarios yield prices below €8 tCO2
−1
,
we extrapolate the MACC curves to also cover the price regime of €0–8
tCO2
−1 by analysing the change in industry and heating emissions upon
implementation of the ETS. We thus estimate two additional emissions
steps of 45 Mt CO2 in industry and 15 Mt CO2 in heating that would be
emitted additionally compared to historic industry/heating emissions
when ETS prices remain below €5 tCO2
−1 and again when they remain
below €3 tCO2
−1.
Scenarios
Modelling assumptions regarding calibration, policy targets and
technology costs. The key assumptions behind our study’s main
scenario types are summarized in Extended Data Table 3. In Fig. 3, we
align scenarios with historical conditions as closely as possible, adjust-
ing variables such as ETS modelling start year and technology cost
assumptions. Due to the five-year time steps in our model, complete
historical replication and path dependency coverage may be limited
(for example, ‘Fit for 55’ scenario starts in 2018).
For Figs. 4, 6 and 7, we exclusively use the ‘Fit for 55’ scenario,
representing the current EU ETS state. This simplification serves the
purpose of preventing information overload, aligning with the figures’
primary objective. In Fig. 6, we extrapolate our results to 2015.
‘Fit for 55’ Commission’s proposal vs final agreement. Extended
Data Table 4 summarizes the relevant parameters used in this study
defining the emissions cap and MSR functionality for the ETS state
between different reforms.

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All results in this study related to ETS targets from the ‘Fit for 55’
package assume parameters from the Commission’s proposal pub-
lished in July 202154. As this study takes into account real ETS prices
until December 2022, it is plausible to assume that until then market
actors were basing their decisions on the Commission’s proposal, not
being aware yet of the upcoming changes in the final negotiations.
For completeness reasons, we provide a comparison of modelled
carbon prices according to the emissions cap from the Commission’s
proposal (used in this study) and according to the emissions cap from
the final ETS ‘Fit for 55’ agreement39,55 in Extended Data Fig. 4. The
emissions cap corresponding to the final agreement can be found in
Extended Data Table 2.
Model validation
General modelling choices, for example, the clustering approach and
the representative days choice, are described in the LIMES-EU model
documentation. Here we present additional validation points for the
scenarios presented in this study. First, we show that our model can
approximate historical developments in 2015 and 2020. Then, we
provide references demonstrating that our future estimates for the
EU ETS align with other literature.
Reproducing historical developments in time step 2015. The
capacity spin up of LIMES-EU is fixed so that it matches the 2015 his-
torical mix of installed generation capacities in EU ETS countries.
Extended Data Fig. 5 illustrates that based on this standing capacity,
the model-calculated dispatch then reasonable matches the historic
power generation dispatch in EU ETS countries. The total modelled
emissions from electricity generation in the year 2015 for EU ETS coun-
tries covered by LIMES-EU amount to 981 Mt CO2, closely aligning with
the historical emissions of 967 Mt CO2 reported by Mantsos et al.56
Because emissions from industry, heating and aviation are also cali-
brated to match their historical 2015 levels (as described in LIMES-EU
documentation52), this calibration ensures that our model generates
meaningful values for total emissions in the 2015 time step. Also, the
model-endogenous investments in 2015 lead to standing capacities in
2020 that match historic wind and solar capacities in 2020. To this aim,
we additionally assume subsidies for electricity generated from solar
or wind sources (€0.04 kWh−1 for solar and €0.015 kWh−1 for wind) to
represent the various renewable subsidies that were in place in most
EU member states. Our model, however, underestimates the capacity
additions of offshore wind until 2020, which took place mostly in the
United Kingdom.
Reproducing historical developments in time step 2020. To validate
the 2020 model results, we first fix capacity spin up so that our model
matches the installed generation capacities for both 2015 and 2020 in
EU ETS countries. In Extended Data Fig. 6, we show that this calibration
enables our model to approximate EU-wide dispatch and total emis-
sions from the electricity sector in 2020. It’s important to note that our
model operates in five-year steps, with time step 2020 representing the
actual years 2018–2022. However, due to the exceptional circumstances
of the COVID-19 pandemic, the year 2020 deviates from the typical
trends of 2018–2022. Hence, to validate time step 2020, we provide
real values for the years 2019 and 2020.
With respect to electricity dispatch, our model estimates lower
generation from biomass compared with the International Energy
Agency (IEA) historical data. This discrepancy may be attributed to
several factors, including our reliance on the European Network of
Transmission System Operators for Electricity (ENTSO-E) dataset for
total capacities, whereas using the IEA dataset for generation values
(as ENTSO-E lacks a Statistical Factsheet covering generation for the
years 2019 and 2020). Differences in values from different sources can
often be substantial. Regarding biomass, variations may be due to, for
example, the way biomass co-firing in coal power plants is accounted
for. Nevertheless, despite minor deviations in our 2020 electricity
dispatch from historical data, our model still provides a meaningful
estimate of emissions. This aspect is critical for validating EU ETS mod-
els, as it directly impacts CO2 prices, the total number of allowances in
circulation and the functioning of the MSR.
Estimating future developments. While validating future projections
is inherently impossible, we observe that LIMES-EU generally aligns
with findings in the literature and does not produce results that are
far outliers compared with other models. Osorio et al. discuss that
LIMES-EU’s estimates of MSR cancellations are consistent with other
studies51. Furthermore, a recent model comparison study led by Henke
et al. revealed that LIMES-EU’s projections for various EU electricity
sector variables from 2020 to 2050, such as final energy demand and
the share of renewable energy sources in electricity generation, are in
line with the range provided by ten other energy systems and integrated
assessment models57. In another model comparison study assessing
EUA prices until 2030, LIMES-EU’s estimate of €140 tCO2
−1 falls within
the range of €80 to €160 tCO2
−1 produced by six different models58.
Methodological contribution
Whereas the primary focus of this work lies in providing insights for
the ongoing debates surrounding the EU ETS, we also make a notable
methodological contribution. There have been other studies using
EU ETS models that explicitly simulate the electricity sector33,59,60,
and there have been energy systems analyses using myopic energy
system models41,43,44,47. Also Nerini et al.47 pioneered the idea to compare
myopic and perfect foresight modes of a capacity expansion model to
formulate more robust policies. Our study extends their approach and
employs both types of foresight to evaluate ex post a concrete policy
reform to test whether the change in the observable variable—in our
case, the EU ETS price—can better be reproduced in the myopic or
perfect foresight mode.
Data availability
Data for core model assumptions (investment costs, fuel costs and
so on) are provided in the LIMES-EU documentation (Methods). The
dataset containing all results displayed in this paper is publicly avail-
able via Zenodo at https://doi.org/10.5281/zenodo.10363561 (ref. 61).
Code availability
The LIMES-EU model code is available upon request from the authors.
Moreover, a process has been started to make the model available
under an open-source license. When this process will be completed,
the code will be available for download from the PIK webpage at http://
pik-potsdam.de/limes.
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Acknowledgements
We gratefully acknowledge funding from the Kopernikus-Ariadne
project (FKZ 03SFK5A) by the German Federal Ministry of Education
and Research, funding from the European Union’s Horizon Europe
research and innovation programme under grant agreement number
101056891 (CAPABLE) and the European Union’s Horizon 2020
research and innovation programme under grant agreement numbers
101022622 (ECEMF) and 101003815 (CAMPAIGNers).
Author contributions
R.P. and G.L. suggested the research question. J.S. and R.P. jointly
conceived and designed the study in consultation with S.O. and
G.L. J.S. extended the model, conducted scenario runs and created
visualizations. J.S., R.P., M.P., G.L. and S.O. interpreted the results.
 J.S. wrote the manuscript with contributions from R.P., M.P., S.O.
and G.L.

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Funding
Open access funding provided by Potsdam-Institut für
Klimafolgenforschung (PIK) e.V.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/
s41560-024-01505-x.
Correspondence and requests for materials should be addressed to
Joanna Sitarz or Robert Pietzcker.
Peer review information Nature Energy thanks Arega Getaneh Abate
and the other, anonymous, reviewer(s) for their contribution to the
peer review of this work.
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© The Author(s) 2024

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Extended Data Fig. 1 | Interactions between foresight horizon and MSR. a,
Total number of allowances in circulation (TNAC), theoretical cap (allowances to
be freely allocated and auctioned before accounting for MSR intake or outtake),
total emissions, allowances taken in by the MSR for both perfect and myopic
foresight. b, Cumulative emissions over transformation period for perfect
and myopic foresight. The difference between cumulative theoretical cap and
cumulative emissions corresponds to total number of allowances cancelled by
the MSR. All results in this figure complement Fig. 3 and correspond to runs with
newest targets from the ‘Fit for 55’ proposal with an active MSR.

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Extended Data Fig. 2 | Optimization problem from external financial
investor’s perspective. Investors optimize their profit till 2040 by buying up
to 5% or 20% of yearly’s auctioned allowances and reselling them later. Obtained
buying and selling strategy corresponds to assumption on external investors
from Fig. 4. Before entered to the LIMES-EU model, all values are transformed to
5-year time steps. Cap trajectory corresponds to allowances auctioned, assuming
an MSR intake deducted from cap in years 2019–2023, as seen in Extended Data
Fig. 1 (myopic foresight).

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Extended Data Fig. 3 | Delays in decarbonization due to myopia. All results in this figure complement Fig. 7 and correspond to runs with targets from the ‘Fit for
55’ proposal with an active MSR. a, Expansion of solar capacity in the EU under perfect and myopic foresight. b, Total electricity mix. P: perfect foresight, M: myopic
foresight.

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Extended Data Fig. 4 | ‘Fit for 55’ Commission proposal vs. Final agreement. Carbon prices corresponding to targets from the Commission’s proposal (used
throughout the whole study) and carbon prices corresponding to targets from the final agreement after trilogues. All scenarios include the MSR.

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Extended Data Fig. 5 | Comparison of 2015 model results with historical
data. Scenario: myopic foresight, EU ETS pre-MSR reform. a, Emissions from
electricity generation in year 2015. Real emissions from the Joint Research
Center (JRC) Dataset IDEES56. b, Electricity dispatch in 2015. Real dispatch from
ENTSO-E Power Statistics70. c, Planned capacities for year 2020. In myopic
mode, the model takes this investment decision in time step 2015, hence
the 2020 generation capacities serve to validate decisions in time step 2015.
Real capacities from ENTSO-E Transparency Platform71. All results for EU ETS
countries (EU28 and Norway).

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Extended Data Fig. 6 | Validation of historical time step 2020. Scenario:
perfect foresight, EU ETS post-MSR reform. a, Emissions from electricity
generation in year 2020 and 2019. As we are not aware of a data source providing
2019 and 2020 emissions for the electricity sector, we estimate real electricity
sector emissions by taking EEA ETS emissions data for ‘combustion of fuels’ and
assuming that electricity generation accounted for 79% of this (in 2015, emissions
from electricity generation reported by the JRC constituted 79% of emissions
from ‘combustion of fuels’ reported by the EEA). b, Electricity dispatch in 2020.
Real dispatch from IEA dataset72,73. All results for EU ETS countries (EU28 and
Norway).

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Extended Data Table 1 | Mean absolute percentage error (MAPE) of modeled CO2 prices assuming myopic or perfect
foresight
This table supplements Fig.3 by providing values of the MAPE error between modeled CO2 prices and real historical EUA prices. Highlighted in bold are the runs with the lower MAPE, hence
lower error compared to real historical EUA prices. MAPE is calculated as the average absolute percent difference between two numeric vectors73.

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Extended Data Table 2 | Differences in EU ETS caps and MSR parameters between ‘Fit for 55’ initial proposal and final
agreement

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Extended Data Table 3 | Modeling assumptions across different scenarios

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Extended Data Table 4 | Parameters used in this study representing the state of the EU ETS between different reforms

    Policy Problem:

Answer: The EU ETS puts a cap (and thereby a price) on emissions from power plants and large industry plants, with the cap expected to reach zero around 2040. If actors anticipate very high abatement costs in the future, they can reduce emissions today and bank the certificates. This connects current prices to future scarcities, and should lead to dynamic cost‑effectiveness: emissions are reduced when it is cheapest. Thus, farsightedness helps to align actors’ near‑term investments with long‑term climate targets, prevent stranded assets, and reduce price volatility. However, over the last five years and two policy reforms that included changes to the cap, carbon prices have increased tenfold. For policymakers and investors, it is crucial to understand what drives prices, and to what extent future scarcities are already accounted for in today’s prices. This knowledge is especially relevant to prepare the review of the EU ETS market stability reserve (MSR) in 2026.