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1.5 °C scenarios reported by the Intergovernmental Panel on Climate Change (IPCC) rely on combinations of controversial negative emissions and unprecedented technological change, while assuming continued growth in gross domestic product (GDP). Thus far, the integrated assessment modelling community and the IPCC have neglected to consider degrowth scenarios, where economic output declines due to stringent climate mitigation. Hence, their potential to avoid reliance on negative emissions and speculative rates of technological change remains unexplored. As a first step to address this gap, this paper compares 1.5 °C degrowth scenarios with IPCC archetype scenarios, using a simplified quantitative representation of the fuel-energy-emissions nexus. Here we find that the degrowth scenarios minimize many key risks for feasibility and sustainability compared to technology-driven pathways, such as the reliance on high energy-GDP decoupling, large-scale carbon dioxide removal and large-scale and high-speed renewable energy transformation. However, substantial challenges remain regarding political feasibility. Nevertheless, degrowth pathways should be thoroughly considered.
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ARTICLE
1.5 °C degrowth scenarios suggest the need for
new mitigation pathways
Lorenz T. Keyßer 1,2 & Manfred Lenzen 1
1.5 °C scenarios reported by the Intergovernmental Panel on Climate Change (IPCC) rely on
combinations of controversial negative emissions and unprecedented technological change,
while assuming continued growth in gross domestic product (GDP). Thus far, the integrated
assessment modelling community and the IPCC have neglected to consider degrowth sce-
narios, where economic output declines due to stringent climate mitigation. Hence, their
potential to avoid reliance on negative emissions and speculative rates of technological
change remains unexplored. As a rst step to address this gap, this paper compares 1.5 °C
degrowth scenarios with IPCC archetype scenarios, using a simplied quantitative repre-
sentation of the fuel-energy-emissions nexus. Here we nd that the degrowth scenarios
minimize many key risks for feasibility and sustainability compared to technology-driven
pathways, such as the reliance on high energy-GDP decoupling, large-scale carbon dioxide
removal and large-scale and high-speed renewable energy transformation. However, sub-
stantial challenges remain regarding political feasibility. Nevertheless, degrowth pathways
should be thoroughly considered.
https://doi.org/10.1038/s41467-021-22884-9 OPEN
1ISA, School of Physics A28, The University of Sydney, Sydney, NSW, Australia. 2Department of Environmental Systems Science, Institute for Environmental
Decisions, ETH Zürich, Zürich, Switzerland. email: lkeysser@student.ethz.ch
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Five years after the Paris Agreement, CO
2
emissions are still
rising1, and mitigation timelines for the 1.5 °C and 2 °C
climate target become ever more stringent2. Meanwhile,
integrated assessment model (IAM) mitigation scenarios reported
by the Intergovernmental Panel on Climate Change (IPCC)
Special Report on 1.5 °C (SR1.5) rely on controversial amounts of
carbon dioxide removal and/or on unprecedented technological
changes2,3. Simultaneously, all of them assume continued growth
in gross domestic product (GDP), among other reasons because
this is deemed necessary to support societal wellbeing4. However,
continued GDP growth is widely associated with increasing
mitigation challenges, e.g., with increasing energy and material
consumption58. In contrast, alternative mitigation pathways as
examined by the expanding degrowth literature9, are almost
completely neglected by the IAM community and the IPCC2,4.
Thus, their potential to avoid negative emissions and technolo-
gical change remains unexplored. In this paper, we present an in-
depth comparison of IPCC IAM and degrowth mitigation sce-
narios by applying a simplied quantitative model of the fuel-
energy-emissions nexus.
IAMs are widely used for examining interlinkages between
social and biophysical systems and their scenarios are promi-
nently applied within climate change mitigation research2. These
scenarios are based on different sets of assumptions about future
population and economic growth, income distribution, as well as
behavioural and technological change. The ve shared socio-
economic pathways are an inuential set of such scenarios2.
Moreover, the IPCC SR1.52includes other scenarios such as the
low energy demand (LED) scenario by Grubler et al.3, which
minimises the need for carbon dioxide removal by substantially
increased energy and material efciency, thus strongly decoupling
energy and material use from GDP growth. In order to meet the
1.5 °C target, unprecedented transformations of energy, land,
infrastructure and industrial systems are necessary2. The tech-
nological transformation is especially extraordinary for negative
emission technologies (NETs), as all scenarios assessed in the
IPCC SR1.5 assume carbon dioxide removal of between 100 and
1000 billion tonnes of CO
2
(GtCO
2
) until 2100, mostly through
bioenergy to carbon capture and storage (BECCS), and to a lesser
extent through afforestation and reforestation (AR). However, the
large-scale NETs deployment of several hundred GtCO
2
faces
substantial uncertainty as well as sustainability and feasibility
concerns1013.
None of the 222 scenarios in the IPCC SR1.5 and none of the
shared socioeconomic pathways projects a declining GDP
trajectory2,4, as is examined by the expanding degrowth
literature9. Interestingly, empirical evidence58,14 corroborates
the degrowth hypothesis9that there is a stronger than commonly
recognised relationship between the growth in GDP and energy,
material and fossil fuel use. Consequently, measures to drastically
reduce the latter would also reduce GDP growth57,9,14,15. A GDP
reduction is thus not an end in itself, but embraced as a likely
outcome of the necessary ecological and social measures.
Degrowth is hence dened as (p. 7) equitable downscaling of
throughput [that is the energy and resource ows through an
economy, strongly coupled to GDP], with a concomitant securing
of wellbeing9. On wellbeing, research16,17 shows that high-
income countries could scale back their biophysical impact (and
GDP), while maintaining or even increasing9,18 social perfor-
mance and achieving higher equity among countries. Thus, intra-
and intergenerational equity aspects can be taken into
account9,17,19, e.g., by making the world economy structurally
fairer and redistributing from global North to South17,18. Further,
bottomup studies show that high living standards can be
maintained with substantially less per capita energy use than
currently consumed in afuent countries20. However, to ensure
that such reductions do not lead to the socially harmful and
inequitable effects of a recession requires deep socioeconomic
changes and policy reforms, such as universal basic services,
maximum incomes, working time reductions, and democratic
rm ownership9,17,19,21. Degrowth scenarios have been explored
for single countries22 and only recently globally with complex
IAMs23.
As a rst step to address the lack of IAM-based climate change
mitigation scenarios describing degrowth and to encourage further
research in this area, this article assesses how degrowth scenarios
perform compared with IPCC SR1.5 IAM scenario archetypes
regarding key relative risk indicators for feasibility
and sustainability. We dene feasibility, following the IPCC2
(p. 52), as the capacity of a system as a whole to achieve a
specic outcome, in our case, a scenario. We additionally distin-
guish between socio-technical feasibility, broadly following Loftus
et al.24 (i.e., energy-GDP decoupling, speed and scale of the
renewable energy transition and NETs deployment), as well as
socio-political feasibility, which includes economic feasibility,
broadly following Jewell & Cherp25. The latter denes an out-
come as politically feasible (p. 2) if there is an agent or group of
agents who have the capacity to carry out a set of actions which
will lead to that outcome in a given context.To conduct this
analysis, we apply a simplied quantitative model of the global
fuel-energy-emissions nexus, arriving at several climate change
mitigation scenarios and their indicator values. This modelling
approach is chosen to complement complex IAMs by enhancing
transparency and understanding26 as well as avoiding common
limitations of IAMs, especially regarding degrowth modelling
(see Methods and Discussion). A full version of our model can
be accessed in Supplementary Data 1. We then assess the relative
performance of our scenarios concerning the modelled risk
indicators for socio-technical feasibility and sustainability, equity
as well as socio-political feasibility. Our results indicate that
degrowth scenarios minimise many key risks for feasibility and
sustainability, but substantial challenges remain regarding poli-
tical feasibility. At last, we discuss limitations of our indicators
and modelling approach as well as the implications for future
research and modelling of the IAM and climate mitigation
communities. Here, we conclude that future modelling research
should thoroughly consider degrowth scenarios.
Results
In this section, we rstly describe the scenarios modelled with our
simplied representation. Then, we summarise the scenario
results. At last, we show how our scenarios perform relative to
our indicators, reviewing literature on the signicance and
interpretation of energy-GDP decoupling, speed and scale of the
renewable energy transition, NETs, equity as well as socio-
political feasibility.
Scenario overview. We investigate the following:
four pathways with low energy-GDP decoupling (the
consumption-driven degrowth pathways: Degrowth,
Degrowth-FullNETs,Degrowth-NoNNEand DLE
(Decent Living Energy20)),
ten scenarios with medium energy-GDP decoupling (the
technology-driven scenarios: Moderate,Moderate-FullNETs,
Strong,Extreme,Utopian,IPCC,IPCC-FullNETs,IPCC-
NoNNE,ClimateAnalyticsand Dec-Moderate),
as well as four technology-driven pathways with high energy-
GDP decoupling (called Dec-Strong,Dec-Extreme,Dec-
Extreme-FullNETs,Dec-Extreme-NoNNE; see Table 1and
Fig. 1).
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In the rst group, GDP follows approximately the nal energy
demand curve (low relative energy-GDP decoupling; see Table 1).
In the latter two groups, it is assumed that GDP continues to
grow at current growth rates (between 2.3 and 3.5% p.a. as in the
SSPs), with slower growth (relative energy-GDP decoupling) or
falling nal energy demand (absolute energy-GDP decoupling).
NoNNEstands for no net negative emissions, where only
residual emissions from cement and aring are removed by
NETs, whereas FullNETsstands for high NETs deployment and
slower renewable energy growth. Decstands for higher energy-
GDP decoupling. The respective primary and nal energy
demands mirror selected archetypes from IAM scenarios in the
IPCC SR1.5 (see Fig. 1and Table 1for a juxtaposition), which
mostly show energy-GDP decoupling2. For instance, our IPCC
scenarios closely follow the median primary energy demand
trajectory of the IPCC SR1.5 scenarios, whereas our Degrowth
and Dec-Extremescenarios follow the energy trajectory of the
Table 1 Description of our scenarios and comparison with the IPCC SR1.5.
Scenario Comparable primary energy
scenario in IPCC SR1.5
Key characteristics regarding RE
and NETs
Trajectory of global GDP
20202040
Low decoupling Degrowth LED Strong RE growth, low NETs, no CCS Shrinks ~0.5% p.a.
Degrowth-FullNETs SSP1-1.9/LED Moderate RE growth, high
NETs, no CCS
Shrinks ~0.2% p.a.
Degrowth-NoNNE LED Utopian RE growth, very low
NETs, no CCS
Shrinks ~0.5% p.a.
DLE None Below moderate RE growth, very low
NETs, no CCS
Shrinks ~4% p.a.
Medium
decoupling
Moderate SSP5-1.9 Moderate RE growth, high NETs Grows, ~3.5% p.a.
Moderate-FullNETs SSP5-1.9 Below moderate RE growth, very
high NETs
Grows, ~3.5% p.a.
Strong SSP5-1.9 Strong RE growth, medium NETs Grows, ~3.5% p.a.
Extreme SSP5-1.9 Extreme RE growth, medium NETs Grows, ~3.5% p.a.
Utopian SSP5-1.9 Utopian RE growth, low NETs Grows, ~3.5% p.a.
IPCC IPCC SR1.5 median/SSP2-1.9 Utopian RE growth, low NETs Grows, ~2.3% p.a.
IPCC-FullNETs IPCC SR1.5 median/SSP2-1.9 Moderate RE growth, high NETs Grows, ~2.3% p.a.
IPCC-NoNNE IPCC SR1.5 median/SSP2-1.9 Above utopian RE growth, very
low NETs
Grows, ~2.3% p.a.
ClimateAnalytics None/SSP2-1.9 Utopian RE growth, medium NETs Grows, ~2.3% p.a.
Dec-Moderate SSP2-1.9 Moderate RE growth, high NETs Grows, ~2.3% p.a.
High decoupling Dec-Strong LED Strong RE growth, low NETs Grows, ~2.4% p.a.
Dec-Extreme LED Extreme RE growth, low NETs Grows, ~2.4% p.a.
Dec-Extreme-FullNETs LED Moderate RE growth, high NETs Grows, ~2.4% p.a.
Dec-Extreme-NoNNE LED Above utopian RE growth, very
low NETs
Grows, ~2.4% p.a.
For the low energy-GDP decoupling group, GDP growth rates (market exchange rate, MER, constant 2010 US$) result from our modelled nal energy pathways, combined with average historical
(19692019) energy-GDP decoupling. Historical GDP data are taken from the World Bank. For the scenarios in the other two groups, GDP growth rates are assumed to equal the rates of the comparable
IPCC SR1.5 archetypes linked above in Table 1. Here, we take GDP growth rates in purchasing power parity (PPP, 2010 US$) from the IAMC 1.5 °C Scenario Explorer hosted by IIASA and transform them,
following Brockway et al.8, into MER growth rates using a conversion factor of 0.78, in order to match our historical GDP growth rates in MER. Dec decoupling, RE renewable energy, CCS carbon capture
and storage applied to coal and gas, SSP shared socioeconomic pathway, DLE: decent living energy.
SSP1−1.9 (SR1.5)
SSP2−1.9 (SR1.5)
LED scenario (SR1.5)
SSP5−1.9 (SR1.5)
Moderate
Moderate−FullNETs
Strong
Extreme
Utopian
Dec−Moderate
Dec−Strong
Dec−Extreme
Dec−Extreme−FullNETs
Dec−Extreme−NoNNE
IPCC
IPCC−FullNETs
IPCC−NoNNE
Degrowth
Degrowth−FullNETs
Degrowth−NoNNE
ClimateAnalytics
DLE
100
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700
800
900
1000
1100
1200
1300
1400
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Yea r s
Primary energy consumption (EJ/yr)
a) Primary energy pathways of this study and the SR1.5 1.5°C scenarios
SSP1−1.9 (SR1.5)
SSP2−1.9 (SR1.5)
LED scenario (SR1.5)
SSP5−1.9 (SR1.5)
Moderate
Moderate−FullNETs
Strong
Extreme
Utopian
Dec−Moderate
Dec−Strong
Dec−Extreme
Dec−Extreme−FullNETs
Dec−Extreme−NoNNE
IPCC
IPCC−FullNETs
IPCC−NoNNE
Degrowth
Degrowth−FullNETs
Degrowth−NoNNE
ClimateAnalytics
DLE
−25
−20
−15
−10
−5
0
5
10
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25
30
35
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45
50
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Years
Net CO2 emissions (Gt/yr)
b) CO2 pathways of this study and the SR1.5 1.5°C scenarios
Scenario groups:
High energy demand &
medium decoupling
IPCC SR1.5 archetypes
Low energy demand &
high decoupling
Low energy demand &
low decoupling
Medium energy demand &
medium decoupling
Other SR1.5 1.5°C scenarios
Comparing our scenarios to the IPCC SR1.5
Fig. 1 Comparison of scenarios in this study to the IPCC SR1.5. Comparison of the primary energy aand net CO
2
bpathways of the IPCC SR1.5 1.5 °C
scenarios and our scenarios. Data for IPCC pathways are taken from the IAMC 1.5 °C Scenario Explorer hosted by IIASA.
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LED scenario3. Among the reported IPCC IAM scenarios, the
LED scenario is the closest analogue to a degrowth pathway, as it
features a strong reduction in material and energy use18. The
crucial difference here is that our Degrowthscenarios do not rely
on technological efciency measures leading to substantial
energy- and material-use-GDP decoupling. At last, the scenarios
differ in the speed and scale of renewable energy replacing fossil
fuels (from levels below Moderateto above Utopian) as well as
carbon capture and storage (CCS) and NETs deployment (see
Table 1and Methods).
Scenario results. In this simplied representation, all scenarios
are designed to stay within the carbon budget for a 50% prob-
ability of limiting global temperature rise to below 1.5 °C by 2100
(580 GtCO22). However, Table 2and Figs. 14show that they
achieve this under substantially different primary and nal
energy consumption, GDP and CO
2
emission pathways. Path-
ways not included in these gures can be found in Supplemen-
tary Figs. 13. In Fig. 5, we position all our scenarios as well as
the IPCC SR1.5 LED, SSP1, SSP2 and SSP5 archetypes on a
scenario map along three dimensions: the degree of energy-GDP
decoupling, the speed of renewable energy expansion (the
20202040 annual average growth in solar, wind and other
renewables in EJ/yr) and the level of cumulative NETs and CCS
deployment. In Supplementary Fig. 4 (see also Supplementary
Tables 14) we show a conceptually equivalent gure for a car-
bon budget of 1170 GtCO
2
(>66% chance for 2 °C in 21002)to
make the analysis also broadly applicable to reaching the 2 °C
target. In the following, we shortly state the main results, fol-
lowing the three groups of energy-GDP decoupling.
First, the low energy-GDP decoupling group (<1.45%, Fig. 5)is
the only group showing rates of energy-GDP decoupling that lie
within the range of historically experienced values of the rolling
10-year averages of the past 30 years. Regarding the speed of
renewable energy expansion there is a wider range, depending on
the scale of NETs deployment: with 432 GtCO
2
NETs renewables
increase at 1.1 EJ/yr (18-fold by 2050, Degrowth-FullNETs),
whereas without any net negative emissions they increase at 3.7
EJ/yr (27-fold by 2050, Degrowth-NoNNE). However, the DLE
scenario with very low energy demand manages without any
net negative emissions, whilst being closest to historical data
(0.9 EJ/yr, 11-fold by 2050 and 32 GtCO
2
NETs).
The medium energy-GDP decoupling group (1.453%, Fig. 5)
shows higher longer-term energy-GDP decoupling than histori-
cally experienced as per Fig. 5, as has also been found by
others8,24,27. For the same level of renewable energy increase, the
medium-energy-demand group shows lower NETs deployment
than the high energy-demand group (e.g., IPCC: 144 GtCO
2
vs.
Utopian: 222 GtCO
2
). It generally holds that the more NETs are
deployed the slower renewables are allowed to expand to reach
1.5 °C (Moderate-FullNETs: 1350 GtCO
2
, 10-fold and 1.5 EJ/yr
vs. IPCC-NoNNE: 4 GtCO
2
, 52-fold and 6.7 EJ/yr).
The high energy-GDP decoupling group (>3%, Fig. 5) shows
that the higher the energy-GDP decoupling and the lower the
nal energy consumption, the slower renewables need to expand
and the fewer NETs need to be deployed to reach 1.5 °C
(Moderate: 539 GtCO
2
, 43-fold and 3.1 EJ/yr vs. Dec-Extreme-
FullNETs: 346 GtCO
2
, 13-fold and 0.4 EJ/yr). Next, we assess
what these results imply with respect to relative risks for
feasibility and sustainability.
Scenario assessment: interpretation of relative risk indicators.
In choosing our indicators, we broadly follow Loftus et al.24, e.g.,
in assessing energy intensity (here energy-GDP decoupling)
and the magnitude of renewable energy additions (here
relative increase 20192050 and absolute average growth
rate 20202040). We choose absolute indicators instead of relative
ones (e.g., normalising absolute renewable energy growth by
GDP), because the latter hide important aspects of socio-technical
feasibility (e.g., the magnitude of coordination, material extrac-
tion, land use and infrastructure expansion). We further include
NETs deployment and equity and qualitatively assess socio-
political feasibility, broadly following Jewell & Cherp25. From this
perspective, we conceptualise Fig. 5as a relative risk map indi-
cating higher risks for socio-technical feasibility and sustainability
with increasing energy-GDP decoupling, speed and scale of fossil
Table 2 Summary of the main results of the different scenarios.
Scenario Net CO
2
20182100
(GtCO
2
)
Max. OS
(GtCO
2
)
RE scale
increase 2050/
2019
CDR: start date, max. rate
(GtCO
2
/a), date of max. rate,
cumulative CDR (GtCO
2
)
Cumulative CCS
(GtCO
2
)
Low
decoupling
Degrowth 580 134 25-fold 2051, 3.6, 2071, 143 0
Degrowth-FullNETs 580 336 18-fold 2041, 11, 2082, 432 0
Degrowth-NoNNE 580 0 27-fold 2041, 0.45, 2043, 9 0
DLE 577 3 11-fold 2041, 0.91, 2044, 32 0
Medium
decoupling
Moderate 579 385 43-fold 2041, 14, 2093, 481 58
Moderate-FullNETs 580 470 10-fold 2030, 24, 2074, 1186 164
Strong 580 286 52-fold 2046, 9, 2089, 299 50
Extreme 579 226 53-fold 2051, 8, 2094, 229 46
Utopian 579 180 53-fold 2053, 6, 2088, 183 40
IPCC 579 116 52-fold 2060, 6, >2100, 117 27
IPCC-FullNETs 580 305 40-fold 2044, 12, 2094, 384 68
IPCC-NoNNE 579 0 52-fold 2039, 0.29, 2040, 4 0
ClimateAnalytics 351 185 41-fold 2041, 13, 2089, 473 0
Dec-Moderate 576 322 26-fold 2042, 11, 2085, 412 48
High
decoupling
Dec-Strong 580 179 30-fold 2054, 7, 2095, 186 26
Dec-Extreme 577 94 24-fold 2061, 3.6, 2087, 98 17
Dec-Extreme-
FullNETs
578 291 13-fold 2044, 10, 2088, 346 59
Dec-Extreme-NoNNE 580 0 24-fold 2060, 0.06, 2061, 1 0
Dec decoupling, OS overshoot of the carbon budget, RE renewable energy, CDR cumulative carbon dioxide removal until 2100, CCS carbon capture and storage applied to coal and gas, DLE decent living
energy.
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fuel replacement by renewables as well as NETs and CCS
deployment. In the following, we review and discuss literature on
the importance and interpretation of each risk indicator and
assess how our scenarios perform.
Energy-GDP decoupling. In the LED scenario, technological
efciency measures such as widespread digitalisation and elec-
trication lead to a 53% reduction in nal energy demand in the
global North and 32% in the global South (40% globally) by 2050.
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GDP (MER, trillion 2010 US$/yr)
Filled area: Other renewables
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Dashed lines: GDP
(right y−axis)
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(left y−axis)
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c Degrowth−NoNNE scenario
Selection of results for scenarios with low energy−GDP decoupling
Fig. 2 Selection of 1.5°C scenarios with low energy-GDP decoupling. Selection of 1.5 °C scenarios with low energy-GDP decoupling (ac). On the left, nal
energy consumption (in EJ, left axis), aggregate primary energy consumption (in EJ, red dashed line, left axis) and GDP (MER, in trillion 2010 US$, blue
dashed line, right axis). On the right, carbon emissions (in GtCO
2
/yr). The full collection of scenarios can be found in Supplementary Fig. 1.
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c IPCC−NoNNE scenario
Selection of results for scenarios with medium energy−GDP decoupling
Fig. 3 Selection of 1.5°C scenarios with medium energy-GDP decoupling. Selection of 1.5 °C scenarios with medium energy-GDP decoupling (ac). On the
left, nal energy consumption (in EJ, left axis), aggregate primary energy consumption (in EJ, red dashed line, left axis) and GDP (MER, in trillion 2010 US$,
blue dashed line, right axis). On the right, carbon emissions (in GtCO
2
/yr). The full collection of scenarios can be found in Supplementary Fig. 2.
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0
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(right y−axis)
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(left y−axis)
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c Dec−Extreme−NoNNE scenario
Selection of results for scenarios with high energy−GDP decoupling
Fig. 4 Selection of 1.5°C scenarios with high energy-GDP decoupling. Selection of 1.5 °C scenarios with high energy-GDP decoupling (ac). On the left,
nal energy consumption (in EJ, left axis), aggregate primary energy consumption (in EJ, red dashed line, left axis) and GDP (MER, in trillion 2010 US$, blue
dashed line, right axis). On the right, carbon emissions (in GtCO2./yr). The full collection of scenarios can be found in Supplementary Fig. 3.
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However, Grubler et al.3,5state that they do not explicitly con-
sider the effect of their energy pathway on GDP growth. In
contrast, Hickel18 calls this scenario a degrowth scenario, owing
to the likelihood that such energy-GDP decoupling is impossible.
There are several reasons to justify this likelihood, as recently
summarised by a number of studies5,8,14,15,2729, which are not
considered by the IPCC IAMs8,27. Firstly, Ayres & Warr30, Keen
et al.28 and others7,14,27 show that total factor productivity
(other production factors inuencing economic growth besides
capital and labour) is strongly connected to total energy use and
its conversion efciency into useful energy (energy use after
accounting for production and conversion losses), contrary to
neoclassical economic theory. Secondly, Sakai et al.14,nd that
for industrialised countries such as the UK (p. 1) gains in ther-
modynamic efciency are a key engine of economic growthdue
to economy-wide rebound mechanisms. Thus, (p. 11) [t]he tight
coupling between global energy use and GDP [...] can be
explained because ofnot in spite ofdecades of global energy
efciency investment.This is in line with recent results by Heun
& Brockway31, who state (p. 1): Absolute decoupling of energy
from [GDP] appears mission impossible, again owing to feed-
backs of efciency gains. As a recent review8concludes, such
economy-wide rebound effects undermine more than half of the
potential energy efciency savings. It is further corroborated by
recent evidence showing that until now digitalisation has likely
led to a net increase in energy consumption by driving energy
efciency and thus economic growth19,29. At last, Ward et al.15
show that in the longer term (p. 1) GDP ultimately cannot
plausibly be decoupled from growth in material and energy use.
This is also the case in service-based economies, since services
embody materials and energy29,32 and energy intensive goods are
usually outsourced31. Increasing tertiarization in industrialised
countries has not led to decreases, but rather increases in energy
use and CO
2
emissions32. Biophysical efciency and scale of an
economy appear to be structurally connected5,6,8,14,27,29,31. These
reasons justify considering the reliance upon high energy-GDP
decoupling a substantial risk for feasibility.
From this perspective, the scenarios with the lowest risk for
feasibility are those in the low energy-GDP decouplinggroup,
which comprises our Degrowthscenarios. All other scenarios
show, in part substantially, higher energy-GDP decoupling than
historically experienced as per Fig. 5(e.g., the LED and our Dec-
Extreme scenarios are over three times higher on average between
2020 and 2040).
Speed and scale of renewable energy replacing fossil fuels.
Firstly, all else unchanged, the higher the necessary speed of
increasing renewable energy, the higher is the feasibility
challenge57,33. Second, considering that energy use is strongly
coupled to GDP growth, an important measure for the
Moderate
Strong
Extreme
Utopian
Dec-Moderate
Dec-Strong
Dec-Extreme
IPCC
Degrowth-FullNETs
Degrowth
IPCC-NoNNE
Degrowth-NoNNE
ClimateAnalytics
Moderate-FullNETs
IPCC-FullNETs
Dec-Extreme-FullNETs
Dec-Extreme-NoNNE
DLE
LED (SR1.5)
SSP1-1.9 (SR1.5)
SSP2-1.9 (SR1.5)
SSP5-1.9 (SR1.5)
1995
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2010 2015
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2019
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2
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Energy-GDP
decoupling
(%)
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growth
rate
(MER,
constant
2010 US$)
&
final
energy
growth
rate
>1.45%: higher
than
maximum
in
1995-2019
period;
Scenarios:
average
of
2020-2040 period
Mean
annual
increase
of
each
RE
source
(EJ/yr)
Speed
of RE
transition;
Scenarios:
average
of
2020-2040 period
Scenario groups
High energy demand &
medium decoupling
Medium energy demand &
medium decoupling
Low energy demand &
high decoupling
Low energy demand &
low decoupling
IPCC SR1.5 archetypes
Historical estimates
(Rolling average of past ten years,
World Bank GDP (MER, constant 2010 US$)
& IEA energy data)
Cumulative CO2 removal
until 2100 (incl. CCS, Gt CO2)
100
200
400
600
1200
1.5°C
scenario
map
under
different
levels
of
energy-GDP decoupling,
RE
speed
and
NETs
Increasing relative
risks for feasibility
and sustainability
Fig. 5 1.5 °C scenario map under different levels of energy-GDP decoupling, RE speed and NETs. The dimensions are speed of renewable energy
transition(for the scenarios the 20202040 annual average growth in solar, wind and other renewables, in EJ/yr), energy-GDP decoupling(for the
scenarios the 20202040 average difference between GDP growth rate and nal energy growth rate, in %) and cumulative CO
2
removal until 2100,
including CCS (GtCO
2
). Historical data points are the rolling averages of the past ten years (e.g., for the 1995 point the period 19861995) of the respective
indicators. This averaging was chosen (1) because GDP and nal energy data are noisy and (2) to emphasise longer-term trends. While historically four
years were above a decoupling of 2% since 1986, these are outlieres around a lower, almost constant trend8. Historical GDP data (MER, constant 2010 US
$) is taken from the World Bank. The conceptually equivalent graph for 2 °C can be found in Supplementary Fig. 4.
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performance of the energy-economic system is the energy return
on energy invested (EROI)34. The energy systems EROI is likely
to shrink substantially during the transition to a renewable energy
system34 and to remain lower than the EROI of current fossil
energy systems afterwards35. This is likely to have a limiting effect
on GDP growth23,34. However, there is a wide range of reported
EROI values for individual renewable energy technologies, varying
with geographic location and applied methodologies35. Moreover,
in contrast to fossil fuels, the EROI of renewables is projected to
increase over time36, but there also are counteracting effects,
among others by diminishing returns to EROI at higher grid
penetration27,34,35. Such energy constraints for economic activity
are not taken into account by the IAMs reviewed by the IPCC27.
However, a recent IAM modelling study using the MEDEAS IAM
framework nds that such energy constraints are likely to reduce
GDP growth23. In addition, Floyd et al.37 summarise 10 points
implying deep uncertainties in renewable energys ability to meet a
high and rising energy demand, stating that the lower the energy
demand, the higher the likelihood to meet it. Thus, this review
points towards a 100% renewable energy economy likely being a
smaller one, in GDP and nal energy terms.
Third, there is no empirical evidence for the possibility of an
absolute decoupling between GDP and aggregate material use57.
The reduction of the latter is central for climate mitigation38, the
reduction of environmental impacts5and prevention of biodi-
versity loss39. Large-scale renewable energy deployment is
unlikely to contribute to material use reduction57,34, as renew-
ables have a considerably higher material footprint than fossil
fuels6,34. This may also raise critical risks of metal supply
shortages34. Further, material extraction drives conicts with
local communities around the world, especially in the global
South34,40. In order to be more sustainable, the global material
footprint would need to be signicantly scaled down, to ~50
billion tonnes per year (recognising the limits of aggregate
indicators5), which is highly unlikely to be compatible with
growing GDP57,39. These three points justify considering the
reliance upon high speed and scale of the renewable energy
transition a substantial risk for feasibility and sustainability.
As shown by our results, the transition speed depends heavily
on the accepted NETs deployment as well as the energy demand.
This can well be seen from our three FullNETs-X-NoNNE
scenario combinations in Fig. 5. Similarly, regarding the scale, the
FullNETsscenarios show the lowest levels. If less NETs are
accepted, the scale follows the energy demand level: the two
Degrowthscenarios (25-fold and 27-fold) perform similarly to
the Dec-Extremescenarios (24-fold and 24-fold), followed by
the Dec-Strongscenario and after that by the medium and high
energy demand scenarios.
Negative emission technologies. Large-scale NETs deployment
faces numerous and substantial risks for sustainability and
feasibility2. Only two NETs, AR and soil carbon sequestration, are
currently available at scale13. However, IAMs most prominently
include BECCS12. In doing so, modellers make numerous
assumptions of substantial uncertainty11,41. The EROI of BECCS
may be extremely low27. BECCS is associated with major land-use
change and its potentially negative side-effects2,10,12, e.g., the
further transgression of several planetary boundaries42, especially
biodiversity43. CCS, either as part of BECCS, or applied to coal
and gas, faces similar barriers and uncertainties2,44. More risks of
reliance on large-scale NETs remain2,10,12, e.g., direct air capture
technologies strongly increasing energy and water use45. Even
large-scale AR as a NET is not unproblematic, being vulnerable to
carbon loss and having potentially negative side-effects on land
use change, albedo, biodiversity, and food security12,13,41.
Anderson & Peters46 thus conclude that (p. 183) the mitigation
agenda should proceed on the premise that [NETs] will not work
at scale. The implications of failing to do otherwise are a moral
hazard par excellence.Therefore, it is justied to consider the
reliance upon large-scale (e.g., medium (200400 GtCO
2
) and
high (>400 GtCO
2
)) NETs deployment a substantial risk for
feasibility and sustainability.
The scenarios minimising NETs (<200 GtCO
2
) either show very
high renewable growth and medium energy-GDP decoupling
(IPCCand IPCC-NoNNE), low energy-GDP decoupling and
high renewable growth (Degrowthand Degrowth-NoNNE)or
high energy-GDP decoupling and high renewable growth (Dec-
Extremeand Dec-Extreme-NoNNE). Compared with these
scenarios, degrowth scenarios are relying on the lowest speed
and scale of renewable energy expansion as well as the lowest
energy-GDP decoupling for any shared level of NETs deployment,
thus showing the lowest risks for feasibility and sustainability.
Equity. Equity is vital for sustainability, as increasing the income
of the poorest population segments to above 2.97$ is projected to
use 66% of the carbon budget for 2 °C47, whereas only a global,
afuent minority is currently and historically responsible for most
carbon emissions19,48. This implies later (earlier) peak dates and
lower (higher) mitigation rates for low- (high-) income
countries33. When including equity, as stated throughout the
Paris Agreement, the short-term mitigation agenda for high-
income countries becomes substantially more challenging than is
deemed feasible by IAMs, assuming continued GDP growth, thus
implying the need for degrowth in the global North5,33. Gen-
erally, all our scenarios do not consider the global distribution of
energy consumption. However, taking into account the above
environmental justice perspective is especially important for the
equitable downscaling of throughput in the Degrowth
scenarios9. Thus, and to obtain a rst impression of potential
distributional consequences, we present a scenario for the energy
use distribution between global South and North for two of our
scenarios in Fig. 6. Here, we assume an equal per capita dis-
tribution of global energy use in 2050 among 10 billion people, as
is modelled by Millward-Hopkins et al.20 to be approximately
1520%) the case with respect to global variations in energy
use for basic human needs satisfaction. However, aggregate
growth scenarios, as in the case of the Moderatescenario, are
subject to the limitations and risks of the renewable energy
transition, material extraction, the wider ecological crisis and
NETs discussed above, thus again neglecting equity34,40,46.
Therefore, taking equity into account further justies reconsi-
dering the reliance on high NETs deployment as well as the speed
and scale of the renewable energy transition, as the risks for
feasibility and sustainability increase. This lends further support
to the nding above that Degrowthpathways minimise risks for
feasibility and sustainability.
Political and economic feasibility. Compared with technology-
driven pathways, it is clear that a degrowth transition faces
tremendous political barriers9,49. As Kallis et al.9state, currently (p.
18) [a]bandoning economic growth seems politically impossible,
as it implies signicant changes to current capitalist socioeconomic
systems in order to overcome its growth imperatives9,19,49.
Degrowth, moreover, challenges deeply embedded cultures, values,
mind-sets21 and power structures9,19. However, as Jewell & Cherp
state, political feasibility is softer than socio-technical feasibility25,
with high actor motivation potentially compensating for low action
capacity and social change being complex, non-linear and essen-
tially unpredictable50. Political feasibility further depends to a large
extent on social movements formulating and pushing for the
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implementation of political programs, changing values, practices
and cultures and building alternative institutions49,51 as well
as scientists pointing the way to alternative paradigms49.
Consequently, degrowth implies modications to the strategies
for change, with a stronger focus on bottomup social
movements9,19,49. As many research questions on degrowth
remain open9,19 and the state of political feasibility can change
with better knowledge about and awareness of alternative para-
digms, strengthened social movements and a clearer understanding
about transition processes4951, it is even more crucial to investi-
gate degrowth pathways.
Economic feasibilityusually refers to the monetary costs of a
mitigation pathway, reported as share of GDP4,24. Here, many
IAMs follow a cost-minimisation approach in order to maximise
economic welfare4,41, measured in GDP, by progressively
implementing only the mitigation measures with the lowest
marginal abatement costs. From this perspective, degrowth is
often considered economically inefcient, as the GDP loss is
considered a cost and when weighted with the avoided CO
2
appears to be overly expensive compared to technological
measures52. However, this reasoning presupposes a ctitious
optimalGDP growth path, any negative deviation from which is
a priori dened as a cost41. Importantly, GDP is not a neutral
construct9. Thus, one needs to ask to whom costs occur, who
prots, whose contributions are in- or excluded and nally who
should decide this9. So, even if this GDP loss is accepted as a
cost, this reasoning compares two categories that have very
different, and partly incommensurable, welfare implications. For
instance, the costs of replacing a coal plant with wind turbines
(a technological measure: creating jobs and reducing CO
2
, but
using land and materials) are not directly monetarily comparable
to the costs of producing, consuming as well as working less (a
GDP loss: polluting less, while using less resources and potentially
leading to further positive social consequences, if well managed).
To have a more valid comparison between the two categories, one
would need to monetise the whole variety of ecological and social
impacts on different groups of people and ecosystems, which is
impossible at least without strong value judgements4,9. A more
suitable perspective when dealing with climate justice issues in a
wellbeing context is a human needs provisioning approach16,20,53.
The crucial question then becomes how, if GDP were to shrink as
a result of the required reductions in material and energy use and
CO
2
(the degrowth hypothesis), e.g., through stringent eco-taxes
and/or caps9, this GDP decrease could be made socially
sustainable, i.e. safeguarding human needs and social function9,21.
Here, research shows that in principle it is possible to achieve a
high quality of life with substantially lower energy use and
GDP9,16,17,20. As noted in the introduction, however, substantial
socioeconomic changes would be necessary to avoid the effects of
a recession. Moreover, the reductions and limits would need to be
democratically negotiated9,21,49 and consider potential suf-
ciency rebound effects54 (reduced consumption by some being
compensated through increases by others), e.g., by international
coordination.
To summarise, as indicated by Fig. 5, the 1.5 °C degrowth
scenarios have the lowest relative risk levels for socio-technical
feasibility and sustainability, as they are the only scenarios relying
in combination on low energy-GDP decoupling, comparably low
0
20
40
60
80
100
120
2017 2050 Degrowth 2050 Moderate
Scenario
Average per capita final energy consumption (GJ/year)
Bars
Global North
Global South
Lines
DLE−Higher Demand &
Less Advanced Technology
DLE−Less Advanced Technology
DLE−Higher Demand
DLE (Decent Living Energy)
Distribution scenarios
Fig. 6 A nal energy distribution scenario for our 1.5 °C degrowthand moderatescenario, assuming an equal per capita distribution among 10
billion people in 2050. We additionally include historical data for 2017 and all Decent Living Energy (DLE) scenarios modelled by Millward-Hopkins
et al.20, which give an approximation of the energy use for basic human needs satisfaction under varying assumptions. The global North here comprises the
OECD, non-OECD Europe and Eurasia, whereas the South comprises all other regions from the IEA63. Note that we use territorial and not consumption-
based data, which is likely to understate the differences in 201748.
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speed and scale of renewable energy replacing fossil fuels as well
as comparably low NETs and CCS deployment. When excluding
any NETs and CCS deployment, the degrowth scenarios still
show the lowest levels of energy-GDP decoupling as well as speed
and scale of renewable energy replacing fossil fuels, compared to
the IPCCand Dec-Extremepathways. As a drawback,
degrowth scenarios currently have comparably low socio-
political feasibility and require radical social change. This
conclusion holds as well for the 2 °C scenarios, albeit with less
extreme differences. Here, the Degrowth-NoNNEscenario, with
~0% p.a. global GDP growth, is almost aligned with historical
data, in stark contrast to the technology-driven scenarios without
net negative emissions (see Supplementary Fig. 4).
Discussion
The results indicate that degrowth pathways exhibit the lowest
relative risks for feasibility and sustainability when compared
with established IPCC SR1.5 pathways using our socio-technical
risk indicators. In comparison, the higher the technological reli-
ance of the assessed mitigation pathways, the higher the risks for
socio-technical feasibility and sustainability. The reverse is likely
the case for socio-political feasibility, which, however, is softer
than socio-technical feasibility. This result contrasts strongly with
the absolute primacy of technology-driven IAM scenarios in the
IPCC SR1.5. In what follows, we discuss limitations of our
modelling approach and risk indicators, implications for the IAM
community and further research.
Our results face several limitations. Note that we use the car-
bon budget for a 50% chance to stay below 1.5 °C2, which can be
argued to be too low based on the precautionary principle,
especially when considering that such scenarios still include a
10% chance of reaching catastrophic warming of 3 °C55. Already
increasing the chance for 1.5 °C to 66% lowers the available
carbon budget by 160 GtCO
2
, while including earth system
feedbacks lowers it by an additional ~100 GtCO
2
2. In addition,
note that we do not consider CH
4
and N
2
O emissions, for which
technological mitigation is more problematic than for CO
2
.
Including all these factors would substantially increase the miti-
gation challenges. Any such increase further strengthens the case
for considering degrowth scenarios, since it becomes even more
risky to solely rely on technology to accomplish the higher
mitigation rates. Thus, complementing technology by far-
reaching demand reductions through social change becomes
even more necessary for 1.5 °C to remain feasible. This is espe-
cially the case when considering the softer nature of social fea-
sibility compared with socio-technical feasibility. We nevertheless
stress that feasibility is a highly complex concept that can be
interpreted differently and, in the case of individual scenarios,
remains at least in part subjective24. Therefore, a larger variety of
indicators than ours is certainly necessary to arrive at a more
complete picture of feasibility. However, we maintain that such
research should explicitly consider degrowth scenarios, e.g., along
the lines of the Societal Transformation Scenarioby Kuhnhenn
et al.56 or the SSP0scenario proposed by Otero et al.39. Espe-
cially in view of socio-political feasibility, we argue that not
exploring them actually leads to a self-fullling prophecy: with
research subjectively judging such scenarios as infeasible from the
start, they remain marginalised in public discourse, thus inhi-
biting social change, thus letting them appear as even more
infeasible to the scientist and so on. As McCollum et al.57 and
Pye et al.58 argue, modellers have a collective responsibility to
evaluate the full spectrum of future possibilities, including
scenarios commonly deemed politically unlikely.
A further limitation of this study is our simplied quantitative
model, which only addresses the fuel-energy-emissions nexus
topdown. This enhances transparency and understanding and is
suited for the purpose of this study by allowing to assess relative
feasibility. Moreover, it enables modelling pathways currently
excluded by the IPCC IAMs, avoiding the difculties and com-
plexities with modelling degrowth (see below and Methods).
Nevertheless, our model neglects the monetary sector22, con-
nections between energy and material availability and economic
growth23,34 as well as the bottomup energy and material
requirements for decent living standards20. This potentially ren-
ders some scenarios infeasible, despite our efforts to qualitatively
include these factors in our above treatment of feasibility.
Therefore, our simplied modelling approach can only be a very
rst step to exploring degrowth scenarios and needs to be com-
plemented by more complex modelling.
To our knowledge, no in-depth study examining the reasons
for the omission of degrowth scenarios in mainstream IAM
modelling exists (but see4). Such modelling is highly challenging,
partly because a degrowth society would function differently
compared to the current society. Thus, model parameters and
structures based on past data could no longer be valid59. Fur-
thermore, it would need to recognise that GDP is an inadequate
indicator for societal wellbeing, at least in afuent countries.
Instead, the focus needs to be oriented directly at multi-
dimensional human needs satisfaction9,18,53. This is especially
important given that many degrowth proposals include a
strengthening of non-monetary work, such as care work and
community engagement, as well as decommodication of eco-
nomic activity towards sharing, gifting and commons9,59. This
also implies revisiting the widespread, neoclassical economic
optimisation approach in IAMs4,23,59. More plural economic
perspectives would need to be taken into account to gain a fuller
picture of socioeconomic reality22,59,60, e.g., post-Keynesian,
ecological and Marxian economics. Such modelling would also
need to broaden the considered portfolio of demand-side mea-
sures and behavioural changes4,61,62. At last, it is clear that the
biophysical foundation of economic activity and energy efciency
rebound effects need to be considered in much greater
detail8,23,27. The necessary detailed discussion of how exactly
IAMs would need to change to incorporate some of these features
is beyond the scope of this paper, but such discussions are already
under way in the literature8,27,58,61,62 and could be further
inspired by current efforts in ecological macroeconomic
modelling59. Promising developments in these directions are put
forward by the MEDEAS IAM modelling framework, which
connects biophysical economic insights, system dynamics and
inputoutput analysis23,34. Another recent example is the
EUROGREEN model, combining post-Keynesian and ecological
economics in a system dynamics stock-ow consistent framework
to assess socio-ecological consequences of national degrowth and
green growth scenarios22.
In light of the optimism of IAM mitigation scenarios regarding
technological change3,8,27, NETs2,46 as well as the neglect of the
wider ecological crisis57,39 and equity issues33,40,46, it should be a
priority to explore alternative scenarios. Clearly, degrowth would
not be an easy solution, but, as indicated by our results, it would
substantially minimise many key risks for feasibility and sus-
tainability compared with established, technology-driven path-
ways. Therefore, it should be as widely and thoroughly considered
and debated as are comparably risky technology-driven pathways.
Methods
In this work, we project global CO
2
as the sum of a number of components: (i) CO
2
from fuel combustion, (ii) CO
2
from cement manufacturing and aring and (iii)
CO
2
from forestry and land use change. Our approach is simplied in that it only
addresses the fuel-energy-emissions nexus, and in that it uses heuristic rather than
theory-driven parametrisations. However, this simplied approach enhances
transparency and understanding, whereas it sufces for the purpose of this article:
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to compare degrowth with IPCC SR1.5 scenarios using different risk indicators.
With this approach, we react to the summary of criticisms of complex IAMs by
Gambhir et al.26 and follow their proposed supplementstrategy, complementing
complex IAMs with a simpler, but still t-for-purposemodel. Moreover, with this
approach, we are able to model degrowth and other pathways which are not
included in the IPCC SR1.5. However, we simultaneously avoid the issues and
complexities of modelling degrowth in greater detail, e.g., monetary or biophysical
aspects, which are subject to further research23,59. We recognise the many lim-
itations of this approach (see Discussion), but maintain that it is suited for the
purpose of this study and in calling attention to the need for more research on
degrowth pathways. Our study is a rst step in this direction. A full version of our
model can be accessed in Supplementary Data 1.
Final and primary energy demand and CO
2
emissions from fuel combustion.
Future CO
2
emissions from fuel combustion are derived from future global pri-
mary energy demand, which is in turn derived from future global nal energy
demand e
c
(t)ofc=1,,10 energy carriers (1: natural gas, 2: coal, 3: crude oil, 4:
nuclear energy, 5: traditional biofuels, 6: hydro-electricity, 7: solar PV, 8: wind
energy, and 9: other renewable energy (concentrating solar, wave, geothermal etc),
10: total nal energy. The nal energy demands are converted to primary energy
using constant 2017 conversion efciencies from IEA data63. Throughout this
paper, we will call fuels 15conventionaland 69renewable) for the period
t=2019,,2050. We recursively model future nal energy demand as
ect
ðÞ¼1þγct
ðÞ

ect1
ðÞ
with change rates γct
ðÞ¼γct1
ðÞ
þδcð1Þ
Here, γctðÞare carrier- and time-dependent annual rates of change, and δcare
carrier-dependent annual change rate increments. Given that carrier 10 is the sum
over individual carriers c=1,,9, the annual energy balance of this recursive
scheme is
9
c¼1ectðÞ¼e10 tðÞ:ð2Þ
With recursively determined γcand δc, Eq. 2does in general not hold. To
ensure the balance holds, we adjust the demand of certain fuels at each annual
iteration, giving rise to two cases: (1) If the sum of individual carrier demands
9
c¼1ectðÞexceeds the prescribed total e10 tðÞ, we downscale the combined demand
9
c¼7ectðÞfor solar, wind and other renewables so that it equals the residual of total
nal energy demand minus conventional fuels e10 t
ðÞ6
c¼1ect
ðÞ
. Condition (1)
can occur towards the end of the projection period where the rapid growth of
renewables cannot be absorbed by demand, and their output needs to be curtailed.
(2) If the prescribed total e10 tðÞexceeds the sum of individual carrier demands
9
c¼1ectðÞ, we upscale the demand e1tðÞfor natural gas so that it picks up the slack
e10 tðÞ9
c¼2ectðÞ. Condition (2) can occur towards the beginning of the projection
period where the rapid decommissioning of coal and oil outpaces the growth of
renewables, leaving gaps in supply that need to be lled with natural gas. The
recursive scheme in Eq. 1is seeded with demand values ect¼2019ðÞand rates of
change γct¼2019ðÞ.
CO
2
emissions from fuel combustion.CO
2
emissions from fuel combustion
fctðÞwere derived from primary energy demand through fuel-specic emissions
coefcients φc:
fctðÞ¼ectðÞφcð3Þ
CO
2
process emissions. There is a range of emissions sources that are unrelated to
energy demand. We model CO
2
emissions g(t) from cement manufacturing and
aring through constant change rates βas
gtðÞ¼gt¼2019ðÞ1þβ

t2019
;ð4Þ
as improved technology for processes is assumed to be able to mitigate an
increasing amount of emissions. Finally, we model CO
2
emissions l(t) from forestry
and land use through constant annual increments λas
ltðÞ¼max 24Gt;lt¼2019ðÞþλt2019ðÞ
½
;ð5Þ
which reects constant ongoing efforts of re- and afforestation and the possibility
for negative emissions.
Data sources. Historical records of primary energy demand e
c
(t) by energy carrier
were taken from the IEA64 and used for deriving seed rates of change γct¼2019ðÞ.
Historical CO
2
emissions ftðÞfrom fuel combustion were taken from CDIAC65.
Historical emissions gtðÞand ltðÞfor CO
2
from cement manufacturing, aring, land
use and forestry are from FAOSTAT66.
Scenarios. The scenarios are designed as archetypes, broadly covering the range of
primary energy demand, renewable energy growth, energy-GDP decoupling and
negative emissions of the IPCC SR1.5 (see Fig. 1, Fig. 5and Table 1for a com-
parison). This is done to ensure that our results are broadly applicable to estab-
lished IAM scenarios reviewed in the IPCC SR1.5.
We investigate four pathways with lowenergy-GDP decoupling (the consumption-
driven degrowth pathways: Degrowth,Degrowth- FullNETs,Degrowth-NoNNE
and DLE), 10 scenarios with medium energy-GDP decoupling (the technology-driven
scenarios: Moderate,Moderate-FullNETs,Strong,Extreme,Utopian,IPCC,
IPCC-FullNETs,IPCC-NoNNE,ClimateAnalyticsand Dec-Moderate), as well as
four technology-driven pathways with high energy-GDP decoupling (called Dec-
Strong,Dec-Extreme,Dec-Extreme-FullNETs,Dec-Extreme-NoNNE; see Table 1
and Fig. 1). In the technology-driven pathways Moderate,Moderate-FullNETs,
Strong,Extremeand Utopian,totalnal energy demand is seeded with a 2019
growth rate of γct¼2019ðÞ¼1%(based on the New Policies Scenario of the IEA67:
The 2017 IEA World Energy Outlook states that in the New Policies Scenario, global
energy needs rise more slowly than in the past but still expand by 30% between today
and 2040.This trajectory is equivalent to a constant (δc¼0) annual growth of 1%
over 23 years. In order to increase the variety of energy pathways, we further annually
increase this growth rate by 0.004% (Moderate), 0.003% (Strong), 0.002% (Extreme)
and 0.001% (Utopian). In all the later pathways (including Moderate-FullNETs)we
assume a GDP growth rate (MER, constant 2010 US$) equal to the one of SSP5-1.9.
Here, we take GDP growth rates in purchasing power parity (PPP, 2010 US$) from the
IAMC 1.5 °C Scenario Explorer hosted by IIASA and transform them, following
Brockway et al.8, into MER growth rates using a conversion factor of 0.78, in order to
match our historical GDP growth rates in MER. All respective scenario parameters can
be found in Tables 1,36.
In the two technology-driven pathways Moderateand Dec-Moderate, coal
initially declines at a rate of 2% per year, and this rate is declining further at 0.3%
per year. Similarly, by decreasing their rates of change by 0.3% per year, oil and
gas turn from moderate growth in 2018 to a decline by 2028. Change rates for
traditional biofuels are ramped down as well at 1% per year. We keep hydro-
electricity constant, because its global potential is deemed to be exhausted
considering competing uses, anticipated increases in drought frequency, and
concerns surrounding ecological health6870. As of 2015 and 2016, mature
renewable technologies were growing rapidly at an average annual 29% (solar PV)
and 16% (wind), which we use as start values in 2019. To avoid sudden demand
gaps, we decreased their rates of growth by 0.75% and 0.3% per year, respectively.
Moreover, this is in line with current knowledge about the logistic growth patterns
of renewables71 as well as with increasing issues concerning intermittency, EROI
and resources connected to increasing scale34,37. According to the International
Energy Agency72, other renewable technologies such as concentrating solar power,
geothermal and ocean technologies are currently not on track with their SDS
targets. We, therefore, increase their growth rate by 0.2% per year. The reduction
of emissions from cement manufacturing and aring is technology-driven and
occurs at 2% per year. Reductions of annual emissions from forestry and land-use
change occur at constant increment of 269 Mt and 254 Mt CO
2
, respectively,
until a cap of 3.6 GtCO
2
/yr is reached (the maximum potential for AR as a NET
according to the IPCC2). Thereafter, negative emissions occur in the form of
BECCS until the total maximum for these scenarios is reached (14 and 11
GtCO
2
/yr, respectively). CCS applied to gas and coal starts in 2029 at 3.33% and
increases from 2031 linearly at 1.33% until a maximum of 30% of coal and natural
gas usage is reached (within the approximate range for 2050 in the IPCC SR.152).
The (Dec-)Strong,(Dec-)Extreme,Utopianand IPCCtechnology-driven
pathways work similarly, but with coal, oil, gas, nuclear and traditional biofuels
being phased out successively more rapidly, growth rates for solar PV and wind
being brought down less quickly, or even stabilised until 2050, andoptimistically
other renewables being introduced more rapidly. In addition, industrial
emissions are brought down more rapidly. Caps on NETs as well as the speed of
emission reductions from forestry and land use differ in order to minimise
overshoot in 2100 or during the whole 21st century (the NoNNEscenarios).
Generally, AR is scaled up rst, followed by BECCS. The latter technology
produces primary energy at 18.75 EJ/GtCO
2
xyr, which is within the range
reported by Smith et al.12 and Fajardy & Dowell73. The maximum CCS usage as
well as its increase rate increases in accordance with the technological level of the
scenario. In the scenarios without net negative emissions, other parameters were
adjusted as well to eliminate overshoot during the 21st century. The FullNETs
scenarios were run with the Moderatelevels of renewable energy replacing fossil
fuels, whereas renewable energy expands even slower in the Moderate-FullNETs
scenario.
The consumption-driven degrowthpathwayswithlowenergy-GDP
decoupling are radically different from the technology-driven pathways with
medium and high energy-GDP decoupling (see Table 1). Their main feature is
that nal energy demand turns from an initial growth at 1% per year to an
immediate stabilisation the year after, and then into a slow decline. This is
achieved by less consumption and production, thus global GDP following
the nal energy pathway using the average level of decoupling between 1995 and
2019. In order to model a societal soft landing, we treat the annual change rate
increment as time-dependent,
δFE tðÞ¼min 0%;δFE t¼2019ðÞþεt2020ðÞ

;
with
ε¼0:9%þ0:11%t2020ðÞ:ð6Þ
This means that initially, the nal energy growth rate changes from +1% in
2019 to +0.1% in 2020, to 0.69% in 2021 etc, but then the decline slows down
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until it stops in 2035 from where nal energy use is constant. In our results we
show how this change prole harmonises with the replacement of fossil fuels by
renewables (Fig. 2). In the degrowth pathways, fossil phase-outs and non-
combustion emission reductions are assumed to range between moderate
(Degrowth-FullNETs), strong (Degrowth) and between extreme and utopian
(Degrowth-NoNNE).
At last, the technology-driven pathways with high and medium energy-GDP
decoupling, which reduce nal energy consumption work similarly to the degrowth
scenarios. The key difference to the degrowth scenarios is that here it is assumed
that GDP continues to grow at current growth rates, equal to the GDP (MER)
growth rates of the LED scenario or SSP2-1.9 respectively (see Table 1), despite
falling or stagnating nal energy demand (strong relative or absolute decoupling),
Table 3 Parameters for pathways with low energy-GDP decoupling.
Low energy-GDP decoupling
Annual change rate increments δc(%) Seed value (%)
Carrier Degrowth Degrowth-FullNETs Degrowth-NoNNE DLE γct¼2019ðÞ
Coal 0.6 0.3 1.0 0.9 2.0
Crude oil 0.6 0.3 1.0 0.9 1.8
Natural gas 0.6 0.3 1.0 0.9 2.8
Nuclear 1.0 0.5 1.0 0.5 1.5
Traditional biofuels 2.0 1.0 2.0 1.0 0.5
Hydro-electricity 0.0 0.0 0.0 0.0 0.0
Solar PV 0.65 0.75 0.35 1.0 28.8
Wind 0.25 0.35 0.05 0.4 15.9
Other renewables 0.3 0.2 0.6 0.2 0.5
Total FE demand 0.9 0.9 0.9 1.3 1.0
Constant change rates β(%)
Cement & aring 5.0 2.0 5.0 2.0
Constant annual increments λ(Mt CO
2
/a)
Forestry & land use 179 265 260 265
Maximum rate of negative emissions (GtCO
2
/a)
Forestry & land use 3.6 11.0 0.0 0.0
Maximum CCS share of energy from coal and natural gas (%)
Coal & gas 0.0 0.0 0.0 0.0 0.0
Linear increase rate of CCS (%/a)
Coal & gas 0.0 0.0 0.0 0.0 0.0
PV photovoltaic, FE nal energy. Carrier-dependent annual change rate increments δcand seed values γct¼2019ðÞfor annual rates of change as in Eq. 1; constant change rates βas in Eq 4; constant
annual increments λas in Eq. 5.
Table 4 Parameters for pathways with medium energy-GDP decoupling (1).
Medium energy-GDP decoupling (1)
Annual change rate increments δc(%) Seed value (%)
Carrier Moderate Moderate-FullNETs Strong Extreme Utopian γct¼2019ðÞ
Coal 0.3 0.1 0.6 0.9 1.2 2.0
Crude oil 0.3 0.1 0.6 0.9 1.2 1.8
Natural gas 0.3 0.1 0.6 0.9 1.2 2.8
Nuclear 0.5 0.5 1.0 1.5 2.0 1.5
Traditional biofuels 1.0 1.0 2.0 3.0 5.0 0.5
Hydro-electricity 0.0 0.0 0.0 0.0 0.0 0.0
Solar PV 0.75 0.75 0.65 0.55 0.4 28.8
Wind 0.3 0.35 0.25 0.15 0.05 15.9
Other renewables 0.2 0.2 0.3 0.4 0.5 0.5
Total FE demand 0.004 0.0 0.003 0.002 0.001 1.0
Constant change rates β(%)
Cement & aring 2.0 2.0 5.0 7.5 7.5
Constant annual increments λ(Mt CO
2
/a)
Forestry & land use 269 551 211 183 170
Maximum rate of negative emissions in GtCO
2
/a
Forestry & land use 14.0 24.0 9.0 8.0 6.0
Maximum CCS share of energy from coal and natural gas (%)
Coal & gas 30.0 30.0 35.0 40.0 45.0 0.0
Linear increase rate of CCS (%/a)
Coal & gas 1.33 1.33 1.58 1.83 2.08 0.0
PV photovoltaic, FE nal energy, CCS carbon capture and storage. Carrier-dependent annual change rate increments δcand seed values γct¼2019ðÞfor annual rates of change as in Eq. 1; constant change
rates βas in Eq 4; constant annual increments λas in Eq. 5.
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achieved by technological efciency improvements. We model the societal soft
landingsimilarly to Eq. 6, but instead of 0, 0.9 and 0.11% we use different
parameters: 1.5, 0.15 and 0.008% (Dec-Moderate), 1.5, 0.3 and 0.02% (Dec-
Strong) and 1.5, 0.7/0.5 and 0.035% (Dec-Extreme,Dec-Extreme-FullNETs
and Dec-Extreme-NoNNE). Moreover, the IPCCscenario group as well as the
ClimateAnalyticsscenario are special cases where the factors are empirical
parameters, with, in the former case, Eq. 6becoming
δFE tðÞ¼min 2:2%4:5εt2020ðÞ
2
;
min ð2:5%;δFE t¼2019ðÞþεt2020ðÞÞ
;
Table 5 Parameters for pathways with medium energy-GDP decoupling (2).
Medium energy-GDP decoupling (2)
Annual change rate increments δc(%) Seed value (%)
Carrier IPCC IPCC-FullNETs IPCC-NoNNE Climate Analytics Dec-Moderate γct¼2019ðÞ
Coal 1.2 0.3 1.7 1.2 0.3 2.0
Crude oil 1.2 0.3 1.7 1.2 0.3 1.8
Natural gas 1.2 0.3 1.7 1.2 0.3 2.8
Nuclear 2.0 0.5 2.0 1.0 0.5 1.5
Traditional biofuels 5.0 1.0 5.0 2.0 1.0 0.5
Hydro-electricity 0.0 0.0 0.0 0.0 0.0 0.0
Solar PV 0.40 0.73 0.00 0.40 0.75 28.8
Wind 0.05 0.34 0.35 0.05 0.30 15.9
Other renewables 0.5 0.2 0.9 0.6 0.2 0.5
Total FE demand 0.7 0.7 0.7 0.05 0.15 1.0
Constant change rates β(%)
Cement & aring 7.5 2.0 7.5 1.0 2.0
Constant annual increments λ(Mt CO
2
/a)
Forestry & land use 140 236 285 270 254
Maximum rate of negative emissions in GtCO
2
/a
Forestry & land use 6.0 12.0 0.0 13.0 11.0
Maximum CCS share of energy from coal and natural gas (%)
Coal & gas 45.0 45.0 0.0 0.0 30.0 0.0
Linear increase rate of CCS (%/a)
Coal & gas 2.08 2.08 0.0 0.0 1.33 0.0
PV photovoltaic, FE nal energy, CCS carbon capture and storage. Carrier-dependent annual change rate increments δcand seed values γct¼2019ðÞfor annual rates of change as in Eq. 1; constant change
rates βas in Eq 4; constant annual increments λas in Eq. 5.
Table 6 Parameters for pathways with high energy-GDP decoupling.
High energy-GDP decoupling
Annual change rate increments δc(%) Seed value (%)
Carrier Dec-Strong Dec-Extreme Dec-Extreme-FullNETs Dec-Extreme-NoNNE γct¼2019ðÞ
Coal 0.6 0.9 0.3 2.4 2.0
Crude oil 0.6 0.9 0.3 2.4 1.8
Natural gas 0.6 0.9 0.3 2.4 2.8
Nuclear 1.0 1.5 1.5 2.0 1.5
Traditional biofuels 2.0 3.0 3.0 5.0 0.5
Hydro-electricity 0.0 0.0 0.0 0.0 0.0
Solar PV 0.65 0.55 0.75 0.25 28.8
Wind 0.25 0.15 0.35 0.65 15.9
Other renewables 0.3 0.4 0.2 1.1 0.5
Total PE demand 0.3 0.5 0.5 0.5 1.0
Constant change rates β(%)
Cement & aring 5.0 7.5 7.5 7.5
Constant annual increments λ(Mt CO
2
/a)
Forestry & land use 167 138 228 141
Maximum rate of negative emissions in GtCO
2
/a
Forestry & land use 7.0 3.6 10.0 0.0
Maximum CCS share of energy from coal and natural gas (%)
Coal & gas 35.0 40.0 40.0 0.0 0.0
Linear increase rate of CCS (%/a)
Coal & gas 1.58 1.83 1.83 0.0 0.0
PV photovoltaic, FE nal energy, CCS carbon capture and storage. Carrier-dependent annual change rate increments δcand seed values γct¼2019ðÞfor annual rates of change as in Eq. 1; constant change
rates βas in Eq 4; constant annual increments λas in Eq. 5.
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with
ε¼0:7%þ0:12%t2020ðÞ:ð7Þ
in order to make it as similar as possible to the IPCC SR1.5 median primary
energy pathway. In the latter case, Eq. 6becomes
δFE tðÞ¼max 0;min 1:5%;δFE t¼2019ðÞþεt2020ðÞ

;
with
ε¼0:05%þ0:001%t2020ðÞ:ð8Þ
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
All relevant data underpinning our modelling are cited throughout the study and
Methods. We further deposit a full version of our model, as described in the Methods, in
Supplementary Data 1.
Code availability
The R code used to create the gures is available upon request from the corresponding
author.
Received: 3 September 2020; Accepted: 29 March 2021;
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Acknowledgements
This work was supported by the Australian Research Council through its Discovery Projects
DP0985522 and DP130101293, and Linkage Infrastructure, Equipment and Facilities Grant
LE160100066, and the National eResearch Collaboration Tools and Resources project
(NeCTAR) through its Industrial Ecology Virtual Laboratory VL201. Sebastian Juraszek
expertly managed our advanced computation requirements, and Charlotte Jarabak from
SciTec Library collected data. Lastly, we gratefully acknowledge comments on this manu-
script by Samuel Alexander (University of Melbourne), Stefan Pauliuk (University of
Freiburg), Ursula Fuentes (Climate Analytics), Viktoria Cologna and Giulia Fontana (ETH
Zurich) as well as two anonymous reviewers. Remaining errors are our own.
Author contributions
L.K. crafted the qualitative discussion, M.L. developed the quantitative model, M.L and
L.K. designed the scenarios, analysed the data and wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
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Peer review information Nature Communications thanks Hans Walnum and the other,
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