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

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 10 | March 2025 | 380–394
380
nature energy
Article
https://doi.org/10.1038/s41560-025-01703-1
Demand-side strategies enable rapid
and deep cuts in buildings and transport
emissions to 2050

Rik van Heerden
  1
, Oreane Y. Edelenbosch
  1,2
, Vassilis Daioglou
  1,2,
Thomas Le Gallic
  3,4, Luiz Bernardo Baptista
  5, Alice Di Bella
  6,7,8,
Francesco Pietro Colelli
  6,7,9, Johannes Emmerling
  6,7, Panagiotis Fragkos
  10,
Robin Hasse
  11,12, Johanna Hoppe
  11,12, Paul Kishimoto13, Florian Leblanc
  14,
Julien Lefèvre
  15, Gunnar Luderer
  11,12, Giacomo Marangoni6,7,16,
Alessio Mastrucci
  13, Hazel Pettifor17, Robert Pietzcker
  11, Pedro Rochedo
  18,
Bas van Ruijven
  13, Roberto Schaeffer
  5, Charlie Wilson
  13,17, Sonia Yeh
  19,
Eleftheria Zisarou
  10 & Detlef van Vuuren
  1,2
Decarbonization of energy-using sectors is essential for tackling climate
change. We use an ensemble of global integrated assessment models to
assess CO2 emissions reduction potentials in buildings and transport,
accounting for system interactions. We focus on three intervention
strategies with distinct emphases: reducing or changing activity, improving
technological efficiency and electrifying energy end use. We find that
these strategies can reduce emissions by 51–85% in buildings and 37–91%
in transport by 2050 relative to a current policies scenario (ranges indicate
model variability). Electrification has the largest potential for direct
emissions reductions in both sectors. Interactions between the policies and
measures that comprise the three strategies have a modest overall effect on
mitigation potentials. However, combining different strategies is strongly
beneficial from an energy system perspective as lower electricity demand
reduces the need for costly supply-side investments and infrastructure.
Demand-side mitigation forms a critical part of strategies to meet the
Paris climate goals1–5, involving both consumer technology choices
related to energy efficiency and energy sources, as well as lifestyle
changes. Lower energy demand reduces emissions and also allows
for greater flexibility in technology choices within supply sectors by
lowering the overall energy production and associated investment
requirements2. Two critical demand-side sectors include buildings
(encompassing residential and service-sector buildings) and trans-
port (encompassing aviation, navigation and land transport) that
each represented 29% of global final energy consumption in 20196
and, respectively, 19% and 7% of direct energy-related greenhouse
gas emissions7. In the Sixth Assessment (AR6) Working Group (WG)
III report the Intergovernmental Panel on Climate Change (IPCC) for
the first time included a chapter on demand-side reductions (Ch. 5).
It estimates that demand-side options in buildings and land transport
could potentially lead to greenhouse gas emissions reductions of 66%
and 67% by 2050, respectively2. Complementing this, a similar assess-
ment by Creutzig et al. also reports high potentials of 78% and 62% for
the same sectors3.
These figures represent median estimates derived from a review
of ‘bottom-up’ assessments of individual demand-side mitiga-
tion options found in existing literature. However, the reliance on
bottom-up approaches faces limitations due to potentially inconsist-
ent assumptions regarding the effects of individual measures, varying
Received: 22 December 2023
Accepted: 19 December 2024
Published online: 5 February 2025
 Check for updates
A full list of affiliations appears at the end of the paper.
 e-mail: rik.vanheerden@pbl.nl; o.y.edelenbosch@uu.nl

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Analysis of modelled reduction strategies
We identified a comprehensive list of mitigation measures from rel-
evant literature and developed scenarios to analyse their implications
for mitigation pathways. To ensure that the scenarios are credible and
policy-relevant, while accounting for all key factors, we involved poli-
cymakers and demand-side experts. Through a survey, they evaluated
the feasibility of the narratives and measures and their potential for
emissions reduction (Methods). The various emissions abatement
measures are grouped into behavioural changes impacting activity,
technical efficiency improvements and electrification of energy end
uses. To analyse the impact of these groups and their interactions, we
translated them into the following scenarios, each representing distinct
intervention strategies (Table 1).
The activity-focused strategy (ACT) involves redesigning
service-provisioning systems to either reduce or shift consumption
of energy and transport services. In buildings, this strategy includes
reduction of average dwelling size, working in shared buildings with
flexible use, adjusting thermostat settings to lower (heating) or higher
(cooling) set points. In transport, it includes promotion of active modes
(walking, biking), public and shared mobility options. Air travel is dis-
couraged, whereas advancements in freight logistics and speed restric-
tions in maritime transport enable more efficient movement of goods.
The technology-optimizing strategy (TEC) focuses on improve-
ments in the efficiency of existing technologies. Higher levels of energy
efficiency are achieved in both new constructions and existing build-
ings through increased renovation rates, improved thermal insulation
and more efficient heating, ventilation and air conditioning (HVAC)
systems. Efficiency standards for road vehicles, aircraft and ships are
implemented. Environmental certification of airplanes and ships is
required for using airports and ports.
The electrification-focused strategy (ELE) focuses on switching
to electricity and alternative fuels. Heat pumps and electricity-based
heating systems are widely adopted in buildings. Fossil fuels are
phased out and new natural gas connections are banned in the global
north. Passenger vehicles, light-duty trucks and ports transition to full
electrification. Diesel engines are phased out of heavy-duty vehicles,
and biofuels and electrofuels are increasingly used in aviation and
shipping.
Finally, a combined approach, referred to as all interventions
(ALL), integrates all the interventions from ACT, TEC and ELE.
These scenarios build upon the ASI (Avoid-Shift-Improve) frame-
work developed by Creutzig et al.3, which categorizes demand-side
actions into the three components avoid, shift and improve. However,
our approach provides a direct and consistent mapping to concrete
intervention strategies and their impacts on the energy system and
emissions (Methods). For instance, ASI categorizes installing heat
pumps as a ‘shift’ intervention, whereas adopting electric cars as
‘improve’. In our framework, both interventions are part of the electrifi-
cation strategy, as they do not change the quantity or quality of energy
service provided and have very similar energy system implications.
As a starting point for the analysis, a ‘middle-of-the-road’
socio-economic pathway (SSP2) is used together with current national
policies implementation (NPi)37,38. This includes the most important
policies per country adopted by national parliaments (Supplementary
Information 3). The scenarios use different gross domestic product
(GDP) and population projections for each country to capture inter-
regional heterogeneity. Subsequently, the additional policies and
measures affecting demand-side sectors are explicitly modelled in
scenarios. Although many measures are likely to impact industrial
emissions, this impact is outside the scope of this study. To explore the
interactions with more stringent climate ambitions, we also consider
scenarios aligned with 1.5 °C global warming, implemented through
a carbon tax that limits the cumulative CO2 budget to 400 Gt (peak)
and 650 Gt (by 2100) in the period 2020–2100. An overview of these
scenarios is provided in Table 2.
baselines and the challenge of capturing interactions between multiple
strategies. System interactions that need to be considered include
the complementarity or overlap between options8 (for example, the
electrification of the vehicle fleet limits the potential for emissions
reductions through improving combustion engine efficiency), the
interactions between energy-supply decarbonization and end-use
transformations9–11 and possible ‘rebound effects’ (when efficiency
measures lead to unintended increase of the demand for energy ser-
vices that can partly counterbalance the potential of activity-oriented
mitigation options)12,13. These interactions and system impacts can
either improve or reduce the effectiveness of individual strategies
and have yet to be assessed in transformation pathways that require
adequate modelling tools for doing so.
Whereas recent literature has advanced the understanding of
demand-side mitigation, most scenario studies rely on single-model
approaches, focusing on long-term global pathways3,14–17 and national
trajectories18–21 and critical factors such as material efficiency22,23. This
reliance on single models limits the robustness of findings, as they do
not account for the structural uncertainties inherent in these models.
Integrated assessment models (IAMs) are specifically designed
to capture interactions among the energy system, economy and envi-
ronment, making them valuable tools for assessing the potential of
demand-side intervention strategies in the buildings and transport
sectors. These models not only facilitate the systematic assessment
of the impacts of such strategies through scenario analysis but also
incorporate the system-wide impacts that arise from their implemen-
tation. Integrated modelling frameworks allow to build consolidated
trajectories whereas ensuring consistency not only between sectors
and intervention strategies but also over time.
Historically, global IAMs have predominantly emphasized supply-
side measures in global mitigation scenarios24,25, which has limited their
capacity to address demand-side pathways effectively. This has been
complicated by the complexity of consumer groups and behaviour,
diverse sectors, services and technologies that depend on local cir-
cumstances, climate and socio-economic conditions, infrastructures
and technological development. However, in recent years, IAMs have
improved their representation of energy-demand sectors, particularly
in the buildings and transport sectors. These advancements are driven
by rapid technological advancements, such as electric mobility and
heat pumps, necessitating ongoing model updates. The improvements
encompass various aspects, including alternative electrification path-
ways in transport26,27, more diverse building types and expanded options
for renovations in buildings28–30 and improvements in the representa-
tion of international transport31–34. Moreover, gradual advancements
have been made in modelling behavioural transitions within IAMs35,36.
Our study leverages the modelling improvements of multiple
IAMs (COFFEE, IMACLIM-R, IMAGE, MESSAGEix-Buildings, PRO-
METHEUS, REMIND and WITCH) to analyse the relative importance
of key demand-side mitigation strategies, examining specifically the
areas of buildings and transport, including personal mobility and
freight transport. The main features of each model are provided in Sup-
plementary Information 1. The seven models capture the interactions
between measures and system effects to various degrees. Although
a complete disentangling of the contributions of these interactions
within each model is beyond the scope of this study, our work aims to
provide a robust assessment of demand-side measures potentials and
interactions across the structural uncertainty of the models included.
We analyse interactions between strategies and apply decomposition
analysis to evaluate the scenario results, identifying key drivers of emis-
sions reductions and revealing some interaction effects. Our findings
highlight the key role of electrification, yet underscore that a combined
approach further reduces emissions and alleviates pressure on the
energy supply side. Our results also show that there is a considerable
spread in the results across models, indicating substantial structural
uncertainty about the complex dynamics drive the results.

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Mitigation potential and interactions
In the default current policy scenario (REF), direct CO2 emissions
from buildings increase by −1% to 36% in 2030 and −8% to 31% in 2050
compared with 2015 levels, whereas direct emissions from transport
increase by 5% to 32% in 2030 and −10% to 49% in 2050 (Fig. 1). This
increase is mainly caused by increasing final energy demand after
2015. However, not all models also indicate a correspondingly strong
increase in emissions; in fact, some models even project a decrease
due to a more pronounced shift to less carbon-intense fuels. The wide
variation in projections across the models is closely related to how
efficiency and changes in service demand (for example, elasticities or
relationships with economic activity) are modelled. In some models,
increasing activities result in increasing emissions, whereas in others,
activity growth is partially offset by efficiency improvements. Nonethe-
less, in both sectors, final energy demand continues its upward trend
in the majority of models.
The intervention strategies mitigate the increase in energy
demand and reduce the growth in direct emissions in both buildings
and transport—in a similar way across models. Emissions reductions
from current levels are robust across models for both ELE and ALL,
particularly in buildings, but with more inter-model variation for the
activity-focused and technology-optimizing strategies. Emissions
reduction potentials in 2030, with respect to the reference scenario
(REF), are 3–16% (ACT), 3–19% (TEC) and 10–31% (ELE) of direct build-
ings emissions and 4–15% (ACT), 2–10% (TEC) and 3–17% (ELE) of direct
transport emissions (Fig. 2). The potentials become more substantial in
2050 and reach 6–23% (ACT), 11–33% (TEC) and 45–77% (ELE) for build-
ings and 17–28% (ACT), 2–67% (TEC) and 22–86% (ELE) for transport.
We also estimated the impact of all measures combined as a
product of the impact of each individual strategy, shown as the
no-interaction estimate in Fig. 1. This approach assumes that the meas-
ures interact independently. Comparison with ALL shows that interac-
tion between measures from different strategies actually play a role,
although their impact on the overall mitigation potential is limited. In
the buildings sector, the models project an effective potential in 2050
that is lower than the no-interaction estimate. However, considering
Table 1 | Overview of the demand-side strategies and references supporting the underlying assumptions with further details
and motivation for the assumptions provided in Methods
Scenario
Description and assumptions
Supporting references
Activity reduction and
shifts (ACT)
Buildings
Policies limiting floorspace in new constructions, along with flexible use of buildings and shared building
spaces (such as co-housing and working), reduce per capita floorspace in both the residential and
commercial sectors. Shifts in household preferences and policies that limit new construction of single-family
houses increase the share of multi-family houses. Stimulated by information campaigns and restrictive
policies, the set-point temperatures in buildings shift to 20 °C for heating and 25 °C for cooling.
16,70–73
Transport
Measures such as congestion charges and growing prevalence of remote working reduce demand for
driving private vehicles within cities, and more bike lanes and pedestrian zones increase the adoption of
active modes (bicycles, e-scooters and walking), whereas improvements in public transport infrastructure,
last-mile services and free/lower public transport fares increase the adoption of public transport. Also
car-sharing/pooling is actively promoted. In the aviation sector, passenger transport is reduced by fuel taxes
(by abolishing tax exemptions), movement taxes such as a frequent flyer levy and development of increased
virtual connectivity. Also freight transport is affected by fuel taxes and movement taxes. Policies encourage
development of local manufacturing and storage, and improved road freight logistics reduce road freight
activity. Worldwide speed restrictions are introduced in maritime transport (slow steaming shipping) and
short-haul air travel is phased out by 2030.
14,32,73–79
Technological
improvements (TEC)
Buildings
Building codes and standards, energy performance certification and subsidies are implemented to reduce
energy intensity by improving insulation levels and enhancing overall energy efficiency per surface area.
This results in more efficient heating, ventilation and air conditioning. Additionally, the current retrofit rate
doubles to 2% per year in the global north.
16,30,73,80,82
Transport
Efficiency standards result in efficiency improvements across all new passenger vehicles, trucks, airplanes
and ships. In addition, environmental certification for using airports and ports further drives efficiency
improvements across the entire aviation and shipping fleets.
83,84
Electrification and fuel
shift (ELE)
Buildings
Fuel mandates accelerate electrification and heating fuel switching, with heat pumps in all new buildings
by 2030. By 2050, we assume that 70% of space and water heating is electricity based. Building regulations
and neighbourhood-based approaches also stimulate deployment of on-site, and building-integrated
renewables and renewable energy (photovoltaics and thermal solar) meet 50% of the demand for cooling
and heating in the global north by 2050. New natural gas connections for heating are banned in the global
north by 2030, and non-clean heating fuels are phased out by 2050.
71,73,85
Transport
Fuel/technology mandates ensure full electrification of passenger vehicles and light-duty trucks by 2040,
using battery or fuel cell electric vehicles. In the fleet of heavy-duty vehicles, diesel engines are phased
out by 2040. By 2030, ports are fully electrified, reducing ships’ reliance on auxiliary engines, and vessels
meet zero-emissions berth standards by 2040. Fuel standards/mandates, infrastructure development and
removing blending restrictions increase the use of alternative fuels (biofuels/electrofuels) in international
transport. After 2050, electric short-haul planes become available.
73,76,86–91
Table 2 | Overview of the scenarios with the implementation
of the scenarios detailed in Methods
Additional policies affecting
demand-side sectors
Climate ambitions
Current national policies (NPi)
1.5 °C scenario
None (REF)
NPi-REF
1.5C-REF
Activity-focused (ACT)
NPi-ACT
1.5C-ACT
Technology-optimizing (TEC)
NPi-TEC
1.5C-TEC
Electrification-focused (ELE)
NPi-ELE
1.5C-ELE
All interventions (ALL)
NPi-ALL
1.5C-ALL

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only the interactions represented in the models, the accumulation
of the various measures still remains largely effective. In the trans-
port sector, remarkably, four models simulated an effective potential
slightly greater than the no-interaction estimate (IMAGE, IMACLIM-R,
PROMETHEUS, REMIND).
Examples of counteracting interactions include the diminished
impact of improved insulation on emissions reductions when inef-
ficient boilers are replaced with electric heat pumps. Likewise, lower-
ing set-point temperatures reduces the potential emissions savings
that could otherwise be achieved through enhanced insulation or
the adoption of heat pumps. In transport, the additional impact of
efficiency standards is limited as the vehicle fleet becomes predomi-
nantly electric, given the much higher efficiency of electric motors.
Conversely, certain measures can amplify the effectiveness of others,
which is evident in the transport results for some models. For instance,
as electrification policies increase the market share of electric vehicles
(EVs), declining costs from learning effects enhance their competitive-
ness. At the same time, car-sharing services also reduce the effective
cost of higher-priced EVs, and together these factors can accelerate
the transition.
Under a 1.5 °C climate ambition, the implementation of demand-
side measures generally results in emissions reductions greater than
those in 1.5C-REF, with WITCH being an exception and some mod-
els showing only marginal differences. The COFFEE results show a
short-term rebound effect in oil consumption for TEC, ELE and ALL.
With perfect foresight, COFFEE anticipates a decrease in long-term
fossil fuel demand, leading to price reductions and an increase in
short-term fossil fuel consumption.
Interestingly, there is no consensus among the models about the
most effective strategy under 1.5 °C climate ambition. This could be
attributed to variations in models’ responses to carbon tax, leading
to utilization of different mitigation options. Conventional mitiga-
tion scenarios such as 1.5C-REF include measures that are assumed to
be cost effective, thereby leaving part of the demand-side mitigation
potential unexploited. Such scenarios are often implemented through
a (globally uniform) carbon tax, whereas demand-side measures—such
as accelerating technology adoption and modal shifting—can be cost
insensitive.
The integration of all intervention strategies (ALL) can reverse the
trend of rising emissions in buildings and transport. Without additional
climate policies, the buildings sector could reduce CO2 emissions, on
average across models, by 63% (51–85%) and the transport sector by
70% (37–91%) in 2050. The combination of strategies achieves sectoral
emissions reductions compatible with a 1.5 °C pathway (Fig. 2).
Interestingly, these findings are comparable to—but independ-
ent from—reported potentials in the IPCC’s AR6 WG III report that
use bottom-up studies to estimate emissions reduction potentials in
2050 relative to the IEA WEO (International Energy Agency's World
Energy Outlook) stated-policy scenario of 66% (40–91%) for buildings
and 67% (44–88%) for land transport2. Ranges are not explicitly men-
tioned in the report, but we derived them by multiplying the lowest
and highest reported potentials for the considered mitigation options,
consistent with the computation of median estimates. However, in
contrast to this study, the IPCC figures relate to total emissions includ-
ing indirect emissions from electricity generation. Whereas IAMs can
quantify these indirect emissions, assessing them within the context
+60
+40
+20
0
–20
–40
Relative to 2015 (%)
Final energy
CO2 emissions
–60
–80
+40
+20
0
–20
–40
–60
–80
–100
2030
Buildings
Transport
REF
ACT
TEC
ELE
All
No-interaction estimate
Scenario (NPi)
a
b
c
d
COFFEE
IMACLIM-R
IMAGE
PROMETHEUS
REMIND
WITCH
MESSAGEix-Buildings
Model
2030
2050
Year
2050
Fig. 1 | Change in global final energy use and direct CO2 emissions from fuel
combustion in the buildings and transport sectors. a,b, Final energy use
in buildings (a) and transport (b). c,d, CO2 emissions from buildings (c) and
transport (d). Results are presented relative to 2015 levels to reduce model
differences resulting from calibration against different historical datasets.
All scenarios have current NPi. Markers indicate individual model results and
bars depict the model ranges. The grey hatches and markers represent the
no-interaction estimates, which approximate the combined impact of each
individual strategy relative to NPi-REF by multiplying their respective effects.
MESSAGEix-Buildings results are shown, but for cross-sectoral consistency are
not factored into the averages and ranges (Methods). Projections for activity
patterns in the reference scenario (floorspace, passenger-kilometres and freight
tonne-kilometres) are provided in Supplementary Information 5. Subsectoral
projections for the residential, commercial, passenger and freight sectors are
available in Supplementary Information 8.

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of demand-side potentials poses challenges because this relies on
the degree of decarbonization in the upstream electricity generation
sector that varies vastly across scenarios and models (Supplementary
Information 10). When indirect emissions are considered, our study
shows considerably lower emissions reduction potentials, particularly
in buildings. This difference may stem from the representation of
rooftop solar systems, which are generally not explicitly accounted for
within IAMs, along with the limited representation of the commercial
sector. Also, nuanced disparities arise from differing scopes. Whereas
our study covers reduced international transport, improved logistics
and fuel shifts for aviation and shipping, the IPCC synthesis includes
additional measures for shipping (for example, weather routing) and
buildings (for example, shorter showers, smarter energy use and nature-
based solutions).
The wide range of projections shown in Figs. 1 and 2 arise from
inherent uncertainties in model dynamics and parameterizations that
underscore the importance of multi-model studies. Further uncertain-
ties lie in underlying socio-economic projections. For example, higher
economic or population growth is likely to lead to increased energy
use and emissions39–41 (although in developed countries, this link is
less evident42). Other socio-economic factors, such as urbanization
and household size, affect energy demand43–46. Higher energy demand
could ultimately limit the potential of the ELE scenario, if the energy
supply fails to meet the additional demand for cleaner fuels. Con-
versely, lower growth could alleviate pressure on the supply of cleaner
fuels. Moreover, energy intensity is usually lower in richer countries47,
and higher economic growth could thus result in lower baseline energy
intensities. This, in turn, would reduce the effectiveness of additional
policy measures in the ELE and TEC scenarios. Lastly, the effectiveness
of the ACT scenario depends on the adoption of new behaviours, and
this process is inherently tied to various socio-economic factors.
Reducing pressure on the electricity system
Electrification is widely recognized as a crucial strategy for emissions
reduction48. According to most IAMs in this study, ELE yields greater
emissions reductions by 2050 compared to the other strategies under
current policies (Fig. 2 and Supplementary Information 6). With the
exception of MESSAGEix-Buildings and WITCH, the models project
over 15% more emissions reductions with ELE than with the other strate-
gies. Only MESSAGEix-Buildings demonstrates larger emissions reduc-
tions in TEC, primarily due to the installation of heat pumps, which are
most effective when combined with improved insulation. In the WITCH
model, emissions reductions in land transport are more pronounced
in ELE, consistent with the other models, but bunker emissions remain
considerable (Supplementary Fig. 15), probably due to the low techno-
logical granularity of alternative shipping technologies49.
Electrification also poses substantial challenges, particularly
regarding the surge in electricity demand (Fig. 3). By 2050, global
electricity demand increases by 8–16 EJ per year for buildings and
4–25 EJ per year for transport in NPi-ELE compared to NPi-REF. The
larger potential increase in transport reflects its currently low elec-
tricity share. Although growth rates from 2015 to 2050 are broadly
–20
Buildings
a
b
c
d
Current policies
Transport
Buildings
Strategy
REF
ACT
TEC
ALL
ELE
2030
2050
Year
2050
2030
Limit to 1.5 °C
Transport
–40
–60
–80
–100
–66% (IPCC Ch. 9: Buildings)
CO2 emissions
relative to NPi reference (%)
0
–20
–40
–60
–80
–100
0
Fig. 2 | Deviation in global direct CO2 emissions for buildings and transport
for different intervention strategies relative to the NPi reference scenario.
a,b, Emissions in scenarios with current NPi from buildings (a) and transport
(b). c,d, Emissions in the 1.5 °C climate ambition scenarios from buildings (c)
and transport (d). Boxes represent the interquartile range, with the centre line
indicating the median and whiskers extending to the minimum and maximum
values. The individual data points are shown as dots, with the boxplots based
on data from five models for buildings and six models for transport. A dashed
line provides a comparison with estimated mitigation potentials from Ch. 9
(Buildings) of the IPCC’s AR6 WG III report. A comparable analysis for transport is
not available.

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consistent with those observed in previous decades (Supplementary
Information 9), suggesting infrastructural challenges can be addressed,
more detailed models such as power dispatch models should be used
to further explore this. In addition to requiring resilient energy infra-
structures, enhanced storage capacities and increased flexibility of the
power system, increasing electricity demand may shift emissions from
the demand side to the supply side. This is mostly relevant for a weak
climate policy regime (NPi), whereas under 1.5 °C policies, electricity
supply is projected to be already largely decarbonized by 2040. Moreo-
ver, under 1.5 °C climate ambition, some models show lower electricity
demand growth due to increased availability of alternative fuels such
as modern biomass and/or electrofuels.
In current climate policy scenarios (NPi), emissions reductions in
the ELE and ALL scenarios by 2050 are less pronounced when account-
ing for indirect emissions based on average emissions intensities
(Fig. 4). Particularly for buildings, indirect emissions remain high in
the near term due to limited supply-side decarbonization and the
sector’s heavy reliance on electricity. In contrast, the transport sector
has a considerably larger share of fossil fuel use, which offers greater
potential for emissions reductions through electrification, as there is
more opportunity to replace fossil fuels with cleaner alternatives. This
is also reflected in the greater final energy reductions for transport than
for buildings, as shown in Fig. 1.
In light of increasing electricity demand, it is critical to expand the
capacity of the energy supply sector while simultaneously decarbon-
izing it, as previous research has already suggested50–52. Our scenarios
show that integrating electrification with other demand-side strategies
(ALL) can facilitate necessary transitions, potentially decreasing global
electricity demand by 10 to 39 EJ per year, even under a 1.5 °C climate
target. Depending on the model and climate policy, this reduction
represents 8 to 33% of the electricity demand for transport and build-
ings. Furthermore, the combined approach leads to over 15% more
direct emissions reduction compared to ELE alone in the ALL scenario.
This demonstrates that a comprehensive strategy combining electri-
fication with energy efficiency and activity-focused measures sub-
stantially reduces the need for supply-side investments in low-carbon
generation technologies, large-scale electricity storage and electricity
infrastructure and grids.
Decomposing emissions reductions
To understand the key factors that drive emissions reductions, we
applied decomposition analysis to passenger transport and residential
buildings emissions in 2050. We compared REF and ALL across five
models (IMACLIM-R, IMAGE, REMIND, MESSAGEix-Buildings, WITCH)
(Fig. 5). Efficiency gains stand out as important contributors to emis-
sions reductions for all models. Improved efficiencies are partly a result
of policies that promote higher energy efficiencies (TEC), such as effi-
ciency standards and building codes, but electrification (ELE) also plays
a key role and results in similar or even higher improvements: e-mobility
is much more efficient than internal combustion engines in vehicles,
and so too are heat pumps in comparison to boilers (Supplementary
Information 7 provides a decomposition of the individual strategies).
In addition, shared services further support efficiency gains (ACT).
It is important to note that the contributing elements in the
decomposition are interdependent. For instance, a decrease in car
travel will reduce more emissions in scenarios and models in which
cars have lower fuel efficiency.
The contribution of electrification, defined as the ratio of electric-
ity to final energy demand, varies across models. Whereas some models
show decreasing emissions from electrification (WITCH, REMIND and
IMACLIM-R in passenger transport), others show an increase (IMAGE,
MESSAGEix-Buildings and IMACLIM-R in buildings). This discrepancy
is due to different decarbonization rates of the power supply under
current policies and highlights that additional mitigation potential
could be achieved by expanding on renewable energy (Fig. 3). Likewise,
models also vary in the contribution of carbon intensity reductions.
100
80
60
40
20
0
40
35
30
25
20
15
10
5
0
2030
Current policies
Limit to 1.5 °C
2030
2050
Year
2050
2015
2015
Electricity demand (EJ yr−1)
Buildings
Transport
a
b
c
d
REF
ACT
TEC
ELE
All
Strategy
COFFEE
IMACLIM-R
IMAGE
PROMETHEUS
REMIND
WITCH
MESSAGEix-Buildings
Model
Fig. 3 | Global electricity demand from buildings and transport under two
climate ambitions. a,b, Buildings electricity demand for scenarios with current
national policies implemented (a) and for the 1.5 °C climate ambition scenarios
(b). c,d, Transport electricity demand for scenarios with current national policies
implemented (c) and in the 1.5 °C climate ambition scenarios (d). Markers
indicate individual model results and bars depict the model ranges. A dashed line
shows the 2015 levels. Non-electric energy demand is shown and discussed in
Supplementary Information 11.

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Changes in service demands, such as reduced travel and mode
shifts lead to emissions reductions across all models. Previous research
indicated that IAMs tend to underestimate the potential for mode
shifting when not explicitly prescribed53 due to the complexities of
simulating context-dependent mode choices54. Our analysis shows that
while mode shifts can be effective and useful to lower energy demand,
their contribution to emissions reductions might be limited. Scenario
implications for the buildings sector are further explored in ref. 55.
Regional effects
Differences observed in the scenario results are largely attributed to
regional variations in the reference scenario, as depicted in Fig. 6 for
nine regions. For the reference scenario, all models project an increas-
ing energy demand per capita for all regions except the OECD (Organi-
sation for Economic Co-operation and Development: European Union,
United States and Other OECD) for both sectors (buildings and trans-
port) and the Africa and Middle East, other Asian countries and former
Soviet Union countries for buildings. Figure 6 also shows that part of
the model spread at the global level can be explained by the regional
disparities, especially in the buildings sector or in transports for India.
However, a broad consensus can be found across models for the most
advanced economies of the OECD for transport.
Regional disparities are driven by opposing dynamics in service
demand and energy efficiency. As lower- and middle-income coun-
tries get wealthier, they increasingly demand more energy services
for appliances, heating and cooling to support their improved living
standards. At the same time, the use of traditional fuels, which are
still commonly used for cooking in certain areas (predominantly in
Africa), decreases substantially until 2050. Energy demand reduc-
tions also occur in OECD countries, where use of natural gas for space
heating is steadily decreasing due to improved insulation levels under
current policies. In transport, per capita energy demand reduces in
some regions, despite rising demand for mobility, due to increased
use of electric vehicles with much higher efficiency56 than internal
combustion engine vehicles. In contrast to the global results, some
models signal that emissions from buildings could be higher in ACT
and TEC (Supplementary Fig. 22), probably due to rebound effects.
This is particularly noticeable in India, other Asian and Africa and
Middle East. An increase is also observed in transport emissions by
COFFEE and WITCH for the United States (ACT) and European Union
(TEC), respectively. Carefully adapted rebound mitigation policies,
for example, through carbon pricing or providing consumption
information, could in such cases further increase the effectiveness
of demand-side measures57,58.
The combination of all interventions allows for a relative decrease
in energy demand in 2050 for all regions and within the transport
sector an absolute decrease compared to 2015 levels in all models,
with exceptions for India (all models), Other Asia (IMACLIM-R) and
China (IMAGE). The strongest disparities of demand-side strategies
for developing countries compared to more advanced economies
reflect the strong correlation between energy demand and economic
development59, which is likely to be a challenge for an early adoption of
demand-side policies in the upcoming years. On the other hand, some
interventions, such as reducing the floor space per capita, increasing
the energy efficiency in buildings and using low-carbon transport
modes, strongly depend on the way emerging cities will be shaped.
Through intelligent planning and design, such cities can lead the way
in advancing sustainable transformations60.
Discussion and conclusions
We used seven IAMs to analyse the potential of demand-side mitigation
strategies. The reduction potentials of direct CO2 emissions identified
in our study align with trends found in the synthesis of bottom-up
studies in the demand chapter of the IPCC’s AR6 WG III report and fur-
ther highlight uncertainty and system-wide effects. Unlike the IPCC’s
estimations, our analysis builds upon a consistent set of harmonized
scenario assumptions and includes interactions between measures
and sectors, such as the reduced impact of lowering set-point tempera-
tures in conjunction with enhanced insulation. Our analysis reveals
that interactions among different measures do play a role, yet their
effect on overall mitigation potential is relatively small. Additionally,
we explicitly assessed the importance of decarbonizing electricity
generation to disentangle it from demand-side mitigation potentials.
By using an ensemble of models, and presenting a decomposi-
tion of the effects across models, we explored inherent uncertainty
and diversity of results and the underlying dynamics. The wide
variation in projected mitigation potentials across models (51–85%
for buildings and 37–91% for transport) reveals the high level of
2030
Year
Year
Year
2030
2030
2050
2050
2050
–80
–60
–40
–20
0
+20
+40
+60
a
Buildings
b
Transport
CO2 emissions
relative to 2015 (%)
0
20
40
60
80
100
120
140
Mt CO2 EJ−1
c
Power sector
Model
COFFEE
IMACLIM-R
IMAGE
PROMETHEUS
REMIND
WITCH
MESSAGEix-Buildings
Scenario (NPi)
REF
ACT
TEC
ELE
ALL
Fig. 4 | Global CO2 emissions from buildings, transport and the power
sector relative to 2015 levels under current NPi. a,b, Changes in global CO2
emissions in the buildings (a) and transport (b) sectors including emissions
from electricity, heat and hydrogen generation. The darker shaded ranges
represent the total emissions, combining both direct and indirect sources, with
the latter calculated using model-average emissions intensities. In contrast,
the lighter shaded ranges represent only direct emissions, matching the results
shown in the lower panels of Fig. 1. Markers indicate individual model results
and bars depict the model ranges. c, Global CO2 emissions from the power
sector. The same figure for scenarios with 1.5 °C climate ambition is available in
Supplementary Information 10.

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uncertainty that arises from the complexity of modelling future
energy-demand development.
The impact of the selected set of demand-side interventions is
high: on average, across the models, 65% reduction of direct CO2 emis-
sions in buildings and nearly 70% in transport by 2050, compared
to a current policies scenario. Activity-focused measures result in
reductions by 2050 of 6–23% for buildings and 17–28% for transport,
technology-optimizing measures to 11–33% and 2–67%, respectively,
and electrification-focused measures to 45–77% and 22–86%. However,
the success of these strategies hinges critically on the emergence
of social innovations and implementation of policies to overcome
crucial barriers.
By disaggregating demand-side strategies, our analysis provides
clearer insights into the distinct contributions of different policies.
Whereas the importance of electrification and fuel shifts in reducing
demand-side CO2 emissions is well recognized, our results demonstrate
that across most models, an electrification-focused strategy yields the
greatest reductions in direct CO2 emissions from buildings and trans-
port by 2050, even lowering emissions below 2015 levels. However, this
approach also more than doubles global electricity demand by 2050
compared to 2015. Focusing on increased electrification alone only
reduces emissions if this is supplemented by a sustained effort to decar-
bonize electricity supply. Shifts in activity patterns can also contribute
to emissions reduction but have a greater potential in high-income
regions, as meeting basic energy services is a higher priority in lower-
and middle-income regions.
Integrated approaches combining different strategies not only
lead to the greatest reduction in emissions but also help alleviate
stresses on the upstream energy supply sector that may arise from
individual demand-side strategies, such as an increase in electricity
demand, storage and grids due to electrification. Furthermore, decom-
position analysis shows that efficiency improvements, and to a lesser
extent activity shifts, can contribute to further emissions reductions.
Following the advancements in IAMs, there are several opportu-
nities for improving the assessment of demand-side scenarios. First,
lifestyle changes should be better integrated into scenarios61–66. Reduc-
ing or shifting energy services demand requires widespread changes
in social norms to induce more sustainable lifestyles, as discussed and
modelled in Supplementary Information 13. These can only partially
be achieved by policies and strongly depend on available organiza-
tions and infrastructures67,68. Second, further developments should
enhance the linkage between demand sectors and industrial and
REF: 2050
Floorspace
Eficiency
Electrification
Carbon intensity
ALL: 2050
0
2
4
6
CO2 emissions (GtCO2)
a
IMACLIM-R
REF: 2050
Floorspace
Eficiency
Electrification
Carbon intensity
ALL: 2050
Direct
Electricity generation
b
IMAGE
REF: 2050
Floorspace
Eficiency
Electrification
Carbon intensity
ALL: 2050
c
MESSAGEix-Buildings
REF: 2050
Floorspace
Eficiency
Electrification
Carbon intensity
ALL: 2050
Residential buildings
d
WITCH
CO2 emissions (GtCO2)
REF: 2050
Activity
Modal shift
Eficiency
Electrification
Carbon intensity
ALL: 2050
0
2
4
6
e
IMACLIM-R
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
REF: 2050
Activity
Modal shift
Eficiency
Electrification
Carbon intensity
ALL: 2050
Aviation
Bus
LDV
Rail
f
IMAGE
REF: 2050
Activity
Modal shift
Eficiency
Electrification
Carbon intensity
ALL: 2050
g
REMIND
REF: 2050
Activity
Modal shift
Eficiency
Electrification
Carbon intensity
ALL: 2050
Passenger transport
h
WITCH
Fig. 5 | Decomposition of global CO2 emissions for the current policy
scenarios in 2050 from the residential sector and passenger transport.
a–d, Decomposition for residential buildings in IMACLIM-R (a), IMAGE (b),
MESSAGEix-Buildings (c) and WITCH (d). e–h, Decomposition for passenger
transport in IMACLIM-R (e), IMAGE (f), REMIND (g) and WITCH (h). Figures
include emissions from electricity generation (hatched). Changes in emissions
are attributed to the factors activity/floorspace, modal shifts (transport only),
efficiency, electrification and carbon intensity. Carbon intensity is the average
amount of CO2 emissions per unit energy of fuel combusted or, for emissions
from electricity generation, the average emissions intensity of electricity
generation. For passenger transport, four modal categories are considered:
aviation, bus, light-duty-vehicle (LDV) and rail. Floorspace use serves as a proxy
for activity changes in buildings because most models lack the capability to
generate detailed output for service demand. This implies that all activity
changes not directly related to floorspace use, such as thermostat adjustments,
are classified as efficiency improvements. Methods provide definitions of all
factors. Note that REMIND and MESSAGEix-Buildings are exclusively included
for passenger transport and buildings, respectively, due to a lack of the required
level of granularity.

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material demands22. Strengthening this connection would enable a
more complete evaluation of emissions across the supply chain. Third,
IAMs do not adequately consider local infrastructural challenges,
particularly those related to the power grid. Additional assessments
using more detailed models, such as power dispatch models, can help
to identify risks, such as increased likelihood of blackouts69, that could
impede consumer adoption.
The success of policies depends on their broader support in
society. Measures aimed at behavioural and lifestyle changes, but
also interventions such as taxing air travel and restricting low-cost
carbon-intensive technologies that directly influence affordability
might face resistance. Our study, which assumes that policies are fully
effective, does not account for the potential resistance and partial
implementation that could affect the outcomes. Therefore, future
research should also delve into the impact of these measures on mac-
roeconomics, ensuring that policies are not only effective but also
sustainable in their broader socio-economic context.
Methods
Identification of key measures
In the initial phase of this study, an extensive list of demand-side meas-
ures was identified, based on relevant literature and Ch. 5 of IPCC’s
AR6 WG III report2 (references in Table 1). To improve the credibility
and policy relevance of the scenarios, and to ensure that no factors
were overlooked, we collected input from experts in relevant areas
related to climate change mitigation. This involved conducting an
online stakeholder survey in 2021. Experts were asked by means of a
questionnaire to evaluate the feasibility and effectiveness of different
ways to reduce emissions in the domains of buildings, mobility and
international transport (Supplementary Table 7). Details about the
stakeholder survey are provided in Supplementary Information 2,
and the responses are summarized in Supplementary Tables 7 and 8.
This was used as input for designing the three intervention strategy
scenarios. The full process of designing, simulating and analysing the
scenarios is summarized in Supplementary Fig. 1.
Defining policy scenarios and model assumptions
On the basis of the literature evaluations and feedback from the stake-
holder survey, we compiled a comprehensive list of measures that
were considered to have substantial impact on reducing demand-side
emissions, while also being regarded feasible. We classified these
measures into three different intervention strategies, based on a
slightly adapted version of the ASI (Avoid–Shift–Improve) frame-
work3. Categorizing policy measures in the ASI framework can be
ambiguous in some cases. For example, stimulating adoption of heat
pumps both improves the building technical systems and induces a
shift from higher-carbon fuels to electrification for space heating.
Because we aimed to explore which type of policy interventions are
most effective, we needed a cleaner separation between the miti-
gation options and we use a more distinct separation between the
demand-side measures. Table 1 summarizes the key assumptions for
the three intervention strategies.
This section presents a detailed description of the modelling
protocol used to assess the scenarios. We elaborate on the specific
measures for each sector that the scenarios consider, as summarized
earlier in Table 1. We explain the quantification of the measures and
Africa and
Middle
East
European
Union
Other
OECD
United
States
Former
Soviet
Union
China
Region
India
Latin and
South
America
Other Asia
Model
COFFEE
IMACLIM-R
IMAGE
REMIND
WITCH
MESSAGEix-Buildings
Scenario (NPi)
REF
ACT
TEC
ELE
ALL
–50
0
+50
+100
+150
+200
+250
–50
–25
0
+25
+50
+75
+100
+125
Final energy per capita in 2050
relative to 2015 (%)
Buildings
Transport
a
b
Fig. 6 | Change in final energy per capita in 2050 relative to 2015 under current
NPi for nine regions. a, Buildings final energy use. b, Transport final energy
use. Results are shown for the United States, the European Union, other OECD
countries, countries from the Reforming Economies of the Former Soviet Union,
China, India, Latin and South American countries, other Asian countries and
countries of Africa and the Middle East. Markers indicate individual model results
and bars depict the model ranges.

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outline the literature and scenarios that form the basis of the underly-
ing assumptions.
Activity reduction and activity shifts. Policies that limit floorspace
in new building constructions, along with flexible use of buildings
and shared spaces (such as co-housing and co-working), reduce per
capita floorspace in both the residential and commercial sectors. By
2050, we assume a regional cap of 40 m2 per capita for residential
floorspace, based on the assumptions in the LED (low energy demand)
and SSP1 scenarios from Fishman et al.70 and Mastrucci et al.16. For
commercial floorspace we assume a regional cap of 25 m2 per capita
by 205071.
Changes in household preferences, along with policies that limit
new construction of single-family houses will lead to a higher share
of multi-family housing. The proportion of the population living in
multi-family houses will increase by 10% across the entire housing stock
in 2050, compared to the reference scenario. This is broadly consistent
with two other studies72,73.
Stimulated by policies limiting set-point temperatures and infor-
mation campaigns, the set-point temperatures in buildings shift to
20 °C (heating) and 25 °C (cooling) by 205071,73.
Demand for private vehicles driving within cities decreases, driven
by measures such as congestion charges and growing prevalence of
remote working. The combination of these measures leads to a 20%
reduction in passenger kilometres (pkm) for private cars by 2050
with respect to the reference scenario. This is loosely based on the
High Shift Scenario74, which assumes a 50% reduction in urban vehicle
travel compared to their baseline in 2050 in all regions. The High Shift
Scenario focuses on urban transport and ‘considers what could be if the
policies and investments currently in place in the nations with the most
efficient urban transport, were replicated throughout the world’74.
Because by 2050, still a third of the world population is expected to
be rural (according to UN projections), and distances travelled are
generally longer in rural areas, we assume a lower overall reduction in
passenger kilometres than the High Shift Scenario.
Additionally, ride and car-sharing associated with on-demand AV
(autonomous vehicles), incentivized carpooling by private operators,
Park + Ride and more HOV (high-occupancy vehicle) lanes will increase
the number of passengers per vehicle. We assume that the occupancy
rate will gradually increase until 2050 by around 40% with respect to
the reference scenario. This is in line with results from Akimoto et al.75,
estimating the impact of ride and car-sharing associated with fully
autonomous cars.
More bike lanes and pedestrian zones increase the adoption of
active modes (bicycles, e-scooters and walking). Improvements in
public transport infrastructure, last-mile services and free/lower public
transport fares increase the adoption of public transport. We assume
that the modal shares, as a percentage of total passenger kilometres
(pkm), for the active mode and public transport converge by 2050 to
the levels given in Supplementary Table 9, depending on the region.
The shares are based on projections from the High Shift Scenario74,
where gradual shifts to public transit and active modes are assumed.
Improvements in road freight logistics reduce road freight trans-
port, measured in tonne kilometres (tkm), by 13.5% in 2050 with respect
to the reference scenario, following the Modern Trucking Scenario76.
The Modern Trucking Scenario is an ambitious road freight scenario
laying out a modernization strategy aiming at increased energy security
and prevention of climate change with ‘rapid adoption of the techno-
logical and system-wide measures’.
In the aviation sector, passenger transport is reduced through
the introduction of fuel taxes (by abolishing tax exemptions), the
implementation of movement taxes such as a frequent flyer levy and
development of increased virtual connectivity. This results in higher
fuel prices and reduced RPK (revenue passenger kilometres). Following
the Green Push Scenario14, the scenario strives for approximately 30%
(international) and 40% (domestic) reductions of RPK on the global
level by 2050 with respect to the reference scenario.
Similarly, freight transport is also affected by fuel taxes and move-
ment taxes. In combination with policies that encourage development
of local manufacturing and storage, these measures result in a global
reduction in revenue tonne kilometres (RTK) of 10% with respect to the
reference scenario. This assumption is based on the findings of three
studies. One study, based on the Beyond 2 °C Scenario, which is in line
with countries’ more ambitious aspirations, projects 8% lower freight
demand by 2060 with respect to their baseline scenario, primarily
driven by diminishing trade requirements in fossil fuels77. A second
study by Müller–Casseres et al. shows that in a well-below 2 °C SSP2
scenario a substantial portion of fossil energy trade can be avoided
(20% by 2050 and 25% by 2100)32. A third study by Walsh et al. projects
a decrease of over 15% in imported and domestic trade for the United
Kingdom, based on their analysis of low carbon futures for shipping
from a UK perspective78.
We assume that speed restrictions in maritime transport (slow
steaming shipping) lead to 15% reduction in energy consumption on
fleet basis with respect to the reference scenario, based on compre-
hensive scenarios from CE Delft assuming a 20–25% speed reduction79.
The assumed speed reduction is also in line with scenario assumptions
from Walsh et al.78.
Further, we assume phase out of short-haul air travel by 2030 by
closing the price gap between rail and aviation and policies limiting
short-haul air travel73.
Technology-optimizing strategy. As a result of building codes and
standards, energy performance certification, subsidies and incentives,
the useful energy intensity per area increases and (average) U-values
decrease. The nearly zero-energy buildings (nZEB) level for insulation
in new construction, representing the average for the building enve-
lope, will be 0.3 W m−2 K−1 on average by 203080. Best practice examples
of current nZEB values already reach such efficiencies today81. Energy
savings for renovation are at least 40% by 2030.
Also the ratio between final and useful energy improves by
improvements in HVAC (heating, ventilation, and air conditioning)73.
Conversion efficiency coefficients for air conditioning and heat pumps
reach 6.0 by 210016,30.
An increased renovation rate, stimulated by subsidies and incen-
tives, leads to a doubling of the current retrofit rate to 2% per year in
the global north82.
We assume that efficiency standards for vehicles and trucks lead
to vehicle efficiency improvements (autonomous) of 1.5% per year
until 2050. For trucks we assume even higher improvements of 2% per
year until 2050.
Efficiency standards result in annual autonomous efficiency
improvements of 1.3% per year for new aircrafts and 1.5% per year for
new ships until 205083,84. The fleet efficiency improves autonomously
as a result of environmental certification for using airports and ports.
We assume annual fleet efficiency improvements for new and existing
aircrafts and vessels of 0.7% per year for aviation and 1.1% per year for
shipping until 205083,84.
Electrification-focused strategy. Fuel mandates accelerate electrifi-
cation in buildings and switching to cleaner heating fuels73. By 2030, all
new buildings adopt heat pumps, and by 2050, we assume that 70% of
space and water heating is electricity based71,85. Non-clean heating fuels
are phased out by 2050, and new natural gas connections for heating
will be banned in the global north by 2030.
Building regulations, along with neighbourhood-based
approaches, will promote the deployment of on-site and building-
integrated renewable energy systems73. By 2050, renewable energy
sources (photovoltaics and thermal solar) meet 50% of the heating and
cooling demands in the global north.

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Fuel/technology mandates ensure full electrification of passen-
ger vehicles and light-duty trucks by 2040. We include both battery
electric vehicles and fuel cell electric vehicles in the electrification
targets. Because there are much larger hurdles for full electrification of
heavy-duty vehicles76,86, we only assume a phase-out of diesel engines
in the fleet of heavy-duty vehicles by 2040.
We assume that electric short-haul planes become available after
2050 (broadly consistent two other studies73,87). Further, we assume
full electrification of ports (and a reduction of auxiliary engines
needed in ships) by 2030. In alignment with this, vessels are adapted
to zero-emission berth standards by 2040. This timeline for port elec-
trification is loosely based on a scenario study by Gillingham et al.88 and
the Global EV Outlook89. Assuming that ships spend approximately
15% of the time at berth and that 15% of their total fuel consumption
is related to the auxiliary engine, we assume that 2.3% of the total fuel
consumption can be saved by cold ironing (connecting ships to the
onshore power supply). For reference, Bouman et al. report potential
CO2 reductions of 3–10% (ref. 90).
Fuel standards/mandates, infrastructure development and remov-
ing blending restrictions increase the use of alternative fuels (biofuels/
electrofuels)73. Following the Sustainable Development Scenario91 the
share of hydrogen in final energy demand grows to 40% in the aviation
sector and to 50% in the shipping sector by 2070. The share of biofuels
increases to 15% for both the aviation and shipping sectors.
Model implementation
The scenario implications of the scenarios listed in Table 2 are ana-
lysed by seven global IAMs (COFFEE, IMACLIM-R, IMAGE, MESSAGEix-
Building, PROMETHEUS, REMIND and WITCH), integrating the
high-resolution mobility, transport and buildings modules. A descrip-
tion of the model representation of the transport sector and the build-
ings sector is shown in Supplementary Tables 1–3 of the Supplementary
Materials.
Two models encountered limitations in producing output for
both sectors. COFFEE generated exclusively transport-related output
variables, whereas MESSAGEix-Buildings was utilized to implement the
scenarios for the buildings sector only. To avoid imbalance across sec-
toral and total results, the results of MESSAGEix-Buildings are left out of
calculated averages and ranges. MESSAGEix-Buildings results are visu-
ally presented in figures, wherever possible. Moreover, due to limited
representation of other sectors, the particular MESSAGEix-Buildings
version used here was only able to achieve emissions consistent with
2 °C scenarios.
The implementation of measures largely aligns with existing lit-
erature and scenarios previously developed to analyse demand-side
mitigation, such as the High Shift Scenario74, the LED scenario82 and
the lifestyle scenario explored by Van Vuuren et al.92. An overview of
the implementation of the scenario measures by model is shown in
Supplementary Table 4 (activity-focused strategy), Supplementary
Table 5 (technology-optimizing strategy) and Supplementary Table 6
(electrification-focused strategy). We have made efforts to harmo-
nize the model implementation of demand-side measures as much
as possible across the models; however, some difference remains
inevitable. One key distinction in the implementation is that some
models use exogenous projections to model changes in energy service
(for example, floorspace), whereas other models employ endogenous
representations of energy service and change levers, such as preference
factors, to attain the desired scenario changes. Next to this, differences
in implementations also arise from the fact that some models lack the
capability to explicitly represent specific measures.
The scenarios with only current policies implemented consider
the current climate- energy- and land-use policies and account for
only those that are secured in legislative decisions, executive orders or
equivalent93,94. No additional measures or plans, for example, regarding
Nationally Determined Contributions are considered.
In addition to this baseline, we consider scenarios in which global
warming is limited to 1.5 °C with a low overshoot based on estimated
carbon budgets from the IPCC’s AR6 WG I report95. This is implemented
in all models through the application of a globally uniform carbon
tax, determined by an optimization process for each model and sce-
nario. This tax ensures that cumulative emissions from 2020 onward
never exceed 650 Gt CO2 (peak budget) and that cumulative emissions
between 2020 and 2100 are limited to 400 Gt CO2 (end-of-century
budget). Non-CO2 gases, such as N2O, CH4 and F-gases, are priced
equivalently to CO2 using GWP100 (global warming potential over
100 years). Models that cannot achieve the end-of-century budgets
aim for the lowest budgets feasible. Also, carbon dioxide removal
(CDR) deployment and net-negative emissions are limited; models aim
for no more than 250 Gt CO2 net-negative emissions undercutting the
end-of-century budget and aim to not have cumulated CDR (including
AFOLU CDR) until 2100 exceed 500 Gt CO2.
No-interaction estimate
The no-interaction estimate for combined emissions reductions is
constructed from the NPi-ACT, NPi-TEC, NPi-ELE and NPi-REF scenarios.
This estimate is derived by computing the ratio of CO2 emissions in each
scenario for the year 2050 to the emissions in the reference scenario
for the same year:
CO2 emissionsscenario (2050)
CO2 emissionsNPi-REF (2050)
(1)
The product of these ratios approximates the total relative reduc-
tion in CO2 emissions. This relative reduction is then multiplied by the
absolute emission levels of NPi-REF in 2050 to calculate the correspond-
ing absolute emissions when all measures are combined under the
assumption of no interaction among the strategies.
Decomposition method
In this study we used the Shapley/Sun decomposition method based
on the Laspeyres index to quantify the driving components between
the scenarios. This method has the advantage that it is based on the
familiar concept of percentage change, making it easier to interpret.
Moreover, it can be applied to obtain a perfect additive decomposition,
that is, the total difference is allocated to different components and no
unexplained residual term appears96,97.
For the transport sector, the activity levels are expressed in pas-
senger kilometres (pkm), which yields the following equation for direct
emissions:
CO2 emissionsdirect
= pkm
⏟
activity
×
∑
n∈modes
⎛
⎜⎜⎜
⎝
pkmn
pkm
⏟⎵⏟⎵⏟
modal shift
×
En
pkmn
⏟
efficiency
×
En −Eelectricity,n
En
⏟⎵⎵⎵⎵⏟⎵⎵⎵⎵⏟
electrification
×
CO2 emissionsdirect,n
En −Eelectricity,n
⏟⎵⎵⎵⎵⎵⎵⏟⎵⎵⎵⎵⎵⎵⏟
carbon intensity
⎞
⎟⎟⎟
⎠
(2)
where pkm denotes the number of passenger kilometres travelled in
a year, E the final energy demand in transport and Eelectricity the electric-
ity demand in transport. Electrification is treated as a distinct factor,
following an approach similar to Edelenbosch et al.98, measuring the
share of electricity in final energy. For simplification, alongside direct
emissions, we include only emissions from electricity generation in the
decomposition. This yields:
CO2 emissionselectricity
= pkm
⏟
activity
×
∑
n∈modes
⎛
⎜⎜⎜
⎝
pkmn
pkm
⏟⎵⏟⎵⏟
modal shift
×
En
pkmn
⏟⎵⏟⎵⏟
efficiency
×
Eelectricity,n
En
⏟⎵⎵⏟⎵⎵⏟
electrification
×
CO2 emissionselectricity,n
Eelectricity,n
⏟⎵⎵⎵⎵⎵⎵⎵⏟⎵⎵⎵⎵⎵⎵⎵⏟
carbon intensity
⎞
⎟⎟⎟
⎠
(3)

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The factors electrification and carbon intensity are defined dif-
ferently from the former equation, but they share a similar meaning.
Because the level of detail in the buildings sector varies strongly across
the IAMs, a detailed decomposition in different end uses is not possible
and we use the amount of floorspace as a proxy for activity instead. For
the residential sector we use the following equation:
CO2 emissionsdirect
= floorspace
⏟⎵⎵⏟⎵⎵⏟
activity
×
E
floorspace
⏟⎵⎵⏟⎵⎵⏟
efficiency
×
E −Eelectricity
E
⏟⎵⎵⎵⏟⎵⎵⎵⏟
electrification
× CO2 emissionsdirect
E −Eelectricity
⏟⎵⎵⎵⎵⎵⏟⎵⎵⎵⎵⎵⏟
carbon intensity
(4)
where floorspace denotes the average floorspace used per capita, E the
final energy demand in the residential sector and Eelectricity the electricity
demand in the residential sector. Similarly, this yields for emissions
from electricity generation for:
CO2 emissionselectricity
= floorspace
⏟⎵⎵⏟⎵⎵⏟
activity
×
E
floorspace
⏟⎵⎵⏟⎵⎵⏟
efficiency
×
Eelectricity
E
⏟⎵⎵⏟⎵⎵⏟
electrification
×
CO2 emissionselectricity
Eelectricity
⏟⎵⎵⎵⎵⎵⎵⏟⎵⎵⎵⎵⎵⎵⏟
carbon intensity
(5)
The components are not independent from each other. For exam-
ple, subsidies for electric vehicles can lead to modal shifting, more elec-
trification, higher efficiency and potentially higher activity. Caution is
needed when interpreting results from two separate decompositions.
Similar changes in specific components (for example, activity levels
drop by 10%) may not necessarily result in identical magnitudes for
those components. This is because interactions with other components
(such as efficiency) can lead to varying magnitudes for the attributed
change within different decomposition contexts.
Data availability
Scenario output data are available directly from the IIASA NAVIGATE
database at https://data.ece.iiasa.ac.at/navigate.
Code availability
Extensive documentation of integrated assessment models is available
at https://www.iamcdocumentation.eu/. The source code for some
models is available directly from their developers. Custom code for
plotting (including post-processing steps implemented for decom-
position analysis) can be provided upon request.
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Acknowledgements
This project received funding from the European Union’s
Horizon Europe research and innovation programmes, grant
agreement number 821124 (NAVIGATE; to R.v.H., O.Y.E., V.D.,
T.L.G., L.B.B., A.D.B., F.P.C., J.E., P.F., R.H., J.H., P.K., F.L., J.L.,
G.L., G.M., A.M., H.P., R.P., P.R., B.v.R., R.S., C.W., S.Y., E.Z., D.v.V.)
and number 101081604 (PRISMA; to R.v.H., V.D., T.L.G., A.D.B.,
J.E., P.F., F.L., J.L., G.L., A.M., H.P., R.P., B.v.R., C.W., D.v.V.). Parts
of this work, based on preliminary results, have been previously
published in NAVIGATE project reports and presentations55. The PIK
team acknowledges funding from the German Federal Ministry of
Education and Research under grant agreement number 03SFK5A-2
(Ariadne; to J.H., R.H., G.L., R.P.). C.W. acknowledges funding
from the European Research Council under grant agreement
101003083 (iDODDLE).
Author contributions
R.v.H., O.Y.E., G.L., A.M., B.v.R., R.S., S.Y. and D.v.V. conceived and
designed the experiments. R.v.H., V.D., T.L.G., L.B.B., A.D.B., F.P.C., J.E.,
P.F., R.H., J.H., P.K., F.L., J.L., G.M., A.M., R.P., P.R. and E.Z. performed
the experiments. R.v.H., O.Y.E., V.D., T.L.G., L.B.B., A.D.B., F.P.C., J.E., P.F.,
R.H., J.H., P.K., F.L., J.L., G.M., A.M., R.P., P.R. and E.Z., analysed the data.
R.v.H., H.P. and C.W. contributed materials/analysis tools. All authors
wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version
contains supplementary material available at
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Rik van Heerden or Oreane Y. Edelenbosch.
Peer review information Nature Energy thanks Rui Jing, Érika Mata and
the other, anonymous reviewer(s) for their contribution to the peer
review of this work.
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© The Author(s) 2025
1PBL Netherlands Environmental Assessment Agency, The Hague, The Netherlands. 2Copernicus Institute for Sustainable Development, Utrecht
University, Utrecht, The Netherlands. 3Ecole des Ponts Paris Tech, Centre International de Recherche sur l’Environnement et le Développement (CIRED),
Nogent-sur-Marne, France. 4SMASH – CIRED, Nogent-sur-Marne, France. 5Centre for Energy and Environmental Economics (Cenergia), Energy Planning
Program (PPE), Coppe, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. 6CMCC Foundation – Euro-Mediterranean Center on Climate
Change, Lecce, Italy. 7RFF-CMCC European Institute on Economics and the Environment, Milan, Italy. 8Department of Electronics, Information and
Bioengineering, Politecnico di Milano, Milan, Italy. 9Department of Economics, Ca’ Foscari University, Venice, Italy. 10E3M-Modelling, Athens, Greece.
11Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany. 12Global Energy Systems Analysis, Technische Universität Berlin, Berlin,
Germany. 13Energy, Climate, and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. 14CNRS, CIRED,
Nogent-sur-Marne, France. 15AgroParisTech, CIRED, Nogent-sur-Marne, France. 16Faculty of Technology, Policy and Management, Delft University of
Technology, Delft, The Netherlands. 17University of Oxford, Environmental Change Institute, Oxford Centre for the Environment, Oxford, UK. 18Research
and Innovation Center on CO2 and Hydrogen (RICH Center) and Management Science and Engineering Department, Khalifa University, Abu Dhabi,
United Arab Emirates. 19Chalmers University of Technology, Gothenburg, Sweden.
 e-mail: rik.vanheerden@pbl.nl; o.y.edelenbosch@uu.nl

    Scientific Research Study Methods:

Answer: Our study uses quantitative scenarios generated with global integrated assessment models (IAMs) that are specifically designed to capture the interactions within and between the energy system, the economy, and the environment. We developed three scenarios that each capture the impact of a specific strategy targeting energy use. In the first scenario end users reduce or change energy‑using activities, in the second they adopt more efficient technologies, and in the third they switch to electrification or alternative energy carriers as substitutes to fossil fuels. Using a structured questionnaire, we also gathered inputs from policymakers and experts in demand sectors to refine the scenarios. We quantitatively interpreted and quantified the scenarios through 2050 using seven IAMs. Finally, we compared the resulting emission reductions per strategy across the set of models and scenarios and analysed the results to better understand the main factors driving these reductions.