ArticlePDF Available

Abstract and Figures

The electrification of passenger road transport and household heating features prominently in current and planned policy frameworks to achieve greenhouse gas emissions reduction targets. However, since electricity generation involves using fossil fuels, it is not established where and when the replacement of fossil-fuel-based technologies by electric cars and heat pumps can effectively reduce overall emissions. Could electrification policies backfire by promoting their diffusion before electricity is decarbonized? Here we analyse current and future emissions trade-offs in 59 world regions with heterogeneous households, by combining forward-looking integrated assessment model simulations with bottom-up life-cycle assessments. We show that already under current carbon intensities of electricity generation, electric cars and heat pumps are less emission intensive than fossil-fuel-based alternatives in 53 world regions, representing 95% of the global transport and heating demand. Even if future end-use electrification is not matched by rapid power-sector decarbonization, it will probably reduce emissions in almost all world regions.
This content is subject to copyright. Terms and conditions apply.
Articles
https://doi.org/10.1038/s41893-020-0488-7
1Department of Environmental Science, Faculty of Science, Radboud University, Nijmegen, The Netherlands. 2Cambridge Centre for Environment, Energy
and Natural Resource Governance (C-EENRG), University of Cambridge, Cambridge, UK. 3Department of Economics, Faculty of Social Sciences, University
of Macao, Taipa, Macau. 4Cambridge Econometrics Ltd, Cambridge, UK. 5University of Cambridge Institute for Sustainability Leadership, Cambridge, UK.
6Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK. e-mail: f.knobloch@science.ru.nl
Policymakers widely consider electrification a key measure
for decarbonizing road transport and household heating.
Combined, these sectors generate 24% of global fuel-com-
bustion emissions and are the two major sources of direct carbon
emissions by households15. For passenger road transport, plug-in
battery electric vehicles (EVs) are expected to gradually replace
petrol and diesel vehicles (petrol cars). For heating, heat pumps
(HPs) are an alternative to gas, oil and coal heating systems (fossil
boilers). Recent policy examples aimed at such end-use electrifica-
tion include announced bans of petrol car sales, financial incen-
tives for EV and HP purchases, planned phase-outs of gas heating
and the inclusion of HPs into the European Union’s renewable
heating targets1,2,68.
The use of EVs and HPs eliminates fossil fuel use and tail-pipe/on-
site greenhouse gas (GHG) emissions (hereafter referred to as emis-
sions), but causes emissions from electricity generation. Emission
intensities in the power sector differ widely across the globe and will
change over time3. Additionally, producing and recycling EVs and
HPs involve higher emissions than producing petrol cars and fossil
boilers, owing to battery production for EVs and refrigerant liquid
use for HPs9,10. The question thus arises as to where and when the
electrification of energy end-use could, under a failure to decarbon-
ize electricity generation, increase overall emissions11,12.
Multisectoral mitigation scenarios (such as those reviewed by
the Intergovernmental Panel on Climate Change (IPCC)) have
identified electrification as a robust policy strategy, but they typi-
cally focus on a context of rapid power-sector decarbonization3,5.
However, sector-specific policies and self-reinforcing social and
industrial dynamics could also lead to real-world trajectories in
which end-use electrification and power-sector decarbonization
take place at completely different rates13. In such a context, could
end-use electrification turn into a counterproductive policy strategy
for reducing emissions?
The answer requires a comprehensive and dynamic life-cycle
assessment of all relevant production and use-phase emissions in
different world regions, of current technology in its full heterogene-
ity, now and in the future. Time- and location-specific differences
stem not only from the power-sector fuel mix but also from individ-
ual preferences and decision-making by millions of people. Which
types of fossil fuel technology are likely to be replaced by which types
of EV or HP? This requires a comparison not only of generic (rep-
resentative) technology types but also of technology ranges (market
segments), on the basis of empirically observed sales in each region.
This is different from existing life-cycle studies of EVs and HPs,
which are limited to the present situation and mostly focus on a
few regions or global averages (see refs. 1422 for studies on EVs and
refs. 10,23,24 on HPs). For the case of EVs, only two studies extend the
analysis into the future9,25. However, they do not consider regional
differences around the globe, heterogeneous technology choices by
consumers or the electrification of heating, and thus cannot ade-
quately and comprehensively inform policymaking processes at the
national level.
Our study consistently investigates the full life-cycle emission
trade-offs from EVs and HPs over time in a regionally highly disag-
gregated way, on the basis of forward-looking simulations of hetero-
geneous consumer choices, while explicitly investigating possible
temporal mismatches between end-use electrification and power-
sector decarbonization.
Scenarios of technology diffusion
We simulate future technology diffusion and the resulting emis-
sions in power generation, passenger road transport and household
heating for 59 regions covering the world (Supplementary Table 1),
using the integrated assessment model E3ME-FTT-GENIE26,27.
This model’s representation of technology uptake in transport and
heating is strongly empirical, on the basis of detailed regional
Net emission reductions from electric cars and
heat pumps in 59 world regions over time
Florian Knobloch 1,2 ✉ , Steef V. Hanssen 1, Aileen Lam2,3, Hector Pollitt 2,4, Pablo Salas 2,5,
Unnada Chewpreecha4, Mark A. J. Huijbregts 1 and Jean-Francois Mercure 1,2,4,6
The electrification of passenger road transport and household heating features prominently in current and planned policy
frameworks to achieve greenhouse gas emissions reduction targets. However, since electricity generation involves using fossil
fuels, it is not established where and when the replacement of fossil-fuel-based technologies by electric cars and heat pumps
can effectively reduce overall emissions. Could electrification policies backfire by promoting their diffusion before electricity
is decarbonized? Here we analyse current and future emissions trade-offs in 59 world regions with heterogeneous households,
by combining forward-looking integrated assessment model simulations with bottom-up life-cycle assessments. We show that
already under current carbon intensities of electricity generation, electric cars and heat pumps are less emission intensive than
fossil-fuel-based alternatives in 53 world regions, representing 95% of the global transport and heating demand. Even if future
end-use electrification is not matched by rapid power-sector decarbonization, it will probably reduce emissions in almost all
world regions.
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles NaTurE SuSTaiNabiliTy
datasets on consumer markets, and simulates technology diffusion
profiles consistent with historical observations (Methods)2830. We
combine scenario projections with bottom-up estimates of life-cycle
emissions from producing different technologies and their fuels9,10,
to analyse emissions trade-offs and net changes from end-use elec-
trification under three scenarios:
(1) A scenario projecting existing observed technological trajec-
tories into the future (current technological trajectory)
(2) A scenario of detailed se ctoral climate policies with a 75% prob-
ability of achieving the 2 °C climate target (2 °C policy scenario)
(3) A scenario of mismatched policies, in which climate policies
are applied only to transport and heating (end-use without
power policies)
Figure 1 shows the simulated future diffusion of electricity-gen-
eration technologies in the power sector, passenger cars in the road
transport sector and heating technologies in the household sector,
building on previous detailed modelling studies26,27,2931.
Under the current technological trajectory, the future technol-
ogy uptake is assumed to follow current technological diffusion
trajectories in each sector, as can be observed in the market data
(such as the diffusion of renewables, a shift towards more efficient
petrol cars and an increasing uptake of EVs and HPs). We model
the underlying decision-making by investors and consumers until
2050, using a simulation-based algorithm (Methods). The scenario
includes existing policies (such as the European Union Emissions
Trading System (EU ETS)), but excludes policies that are not yet
implemented (such as announced phase-outs of petrol cars). The
model does not optimize the technological configuration, and
therefore does not prevent end-use electrification where it would
lead to emission increases or higher overall system costs.
In the 2 °C policy scenario, we impose bundles of additional
policies on all three sectors from 2020 onwards26,27,2931 (Methods).
The policies were chosen on the basis of what has already been
implemented in at least some countries, and could therefore also
be politically feasible in other countries. These policies include
carbon pricing and feed-in tariffs for power generation, along with
fuel taxes and technology-specific subsidies for transport and heat-
ing. The policy mixes induce demand reductions and a more rapid
uptake of low-carbon technologies compared with the current
Current technological trajectory
0
20
40
Power generation (PWh)
2 °C policy scenario End-use without power policies
0
20
40
0
10
20
30
Passenger cars (Tpkm)
2010 2020 2030
Year Year Year
2040 2050
0
5
10
Household heating (PWh
th
)
2010 2020 2030 2040 2050 2010 2020 2030 2040 2050
0
5
10
0
10
20
30
Renewables Renewables
Coal
Oil Adv oil
Gas Adv gas
Biomass
District
Nuclear
Oil
Coal
Coal + CCS
Gas
Gas + CCS
Biomass
Biomass + CCS
Hydro
Wind
Solar and other
Petrol Adv petrol
Diesel Adv diesel
Hybrid CNG
Electric
Electricity generation Passenger cars Household heating
Electric
Solar
Renewables
EVs
EVs EVs
HPs HPs HPs
HP
a b c
d e f
g h i
Fig. 1 | Projections of global future technology diffusion in power generation, passenger road transport and household heating. ac, Global technology
mix in power generation. df, Global technology mix in road transport by passenger cars (Tpkm, trillion person-kilometres). gi, Global technology mix in
residential space and water heating (PWhth, PWh thermal). Projections under the current technological trajectory (a, d and g), the 2 °C policy scenario
(b, e and h) and the end-use without power policies scenario (c, f and i) are shown. Dashed lines show the total demand in the current technological
trajectory (a, d and g) for comparison. Relative to this trajectory, global electricity demand in 2050 is around 3% larger in c. CCS, carbon capture and
storage; CNG, compressed natural gas; Adv, advanced.
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles
NaTurE SuSTaiNabiliTy
technological trajectory—not only of EVs and HPs but also of
higher-efficiency petrol cars and heating systems.
In the end-use without power policies scenario, we apply the full
set of climate policies from the 2 °C policy scenario to transport and
heating, but not to the power and other sectors, which are assumed
to follow the current technological trajectory scenario. While such a
combination of policies is perhaps unlikely in reality, the scenario’s
purpose is a worst-case analysis: what impact would an increased
uptake of EVs and HPs have on overall emissions, if the carbon inten-
sity of electricity generation worldwide followed its current trajectory?
Under the current technological trajectory, the global mean
emission intensity of electricity generation (direct plus indirect
emissions per kWh) is projected to decrease 10% by 2030 and
16% by 2050 (relative to a 2015 average of 740 g CO2-equivalent
(CO2e) per kWh), with considerable variation between countries
(Supplementary Table 2). EVs are projected in the current trajec-
tory to account for 19% of global passenger road transport in 2050
(1% in 2030), and HPs for 16% of the global residential heat demand
(7% in 2030)27, also with considerable variation between regions
(Supplementary Tables 3 and 4). In the 2 °C policy scenario, the
power sector’s carbon intensity decreases 44% by 2030, and 74% by
2050 (relative to 2015). The policies will take some time to change
the technology mix in transport and heating, but they eventually
increase the market share of EVs to 50% by 2050 (1% in 2030), and
of HPs to 35% by 2050 (12% in 2030).
Current emission intensities in transport and heating
Figure 2 presents the global conditions under which life-cycle emis-
sion intensities from driving EVs and heating with HPs are lower
than those from new petrol cars and fossil boilers. Figures 3 and 4
illustrate this comparison in more detail for the ten countries
with the largest passenger road transport and residential heating
demand, for all three scenarios, both under current conditions and
in the future. Figure 5 gives a global overview of where and when
electrification would reduce emissions. All estimates include pro-
duction and end-of-life emissions (of cars, batteries and heating
systems), upstream emissions from the extraction and processing
of fossil fuels, and the equivalent indirect emissions from electricity
generation (Methods).
For EVs, the range of emission intensities reflects higher and
lower energy use of different EV models and sizes that are currently
available in the market. The central estimates in different regions
refer to an average efficiency model with an energy use of 19 kWh
per 100 vehicle-kilometres in 2015, subject to future improvements
(17 kWh per 100 km in 2030 and 14 kWh per 100 km in 2050)9
(Methods). For petrol cars, the distribution of intensities refers to
empirically measured and projected sales of all petrol and diesel cars
(including non-plug-in hybrids) in the respective year and coun-
try, according to market data and projections by E3ME-FTT28,29
(Methods). For HPs, the range of emission intensities reflects higher
and lower conversion efficiencies (ratio of heat output to electricity
input) of different HP models and under different operating condi-
tions. The central estimates in each region correspond to an average
efficiency system with a realized conversion efficiency of 300% in
2015 (390% in 2030 and 420% in 2050)32. For fossil boilers, distribu-
tions indicate the intensities of newly sold heating systems in a given
year and region (oil, gas and coal), also on the basis of empirical
data and model projections30.
From a global perspective, given current conversion efficiencies
and production processes, we find that in 2015 driving an average
10 15 20 25 30
0
250
500
750
1,000
1,250
10 15 20 25 30
Electricity demand of driving an EV (kWh per 100 km)
Grid GHG emission intensity (gCO
2
e kWh
–1
)
200300400600
0
250
500
750
1,000
1,250
0.1 0.2 0.3 0.4 0.5 0.6
Conversion efficiency of HP (kWh
th
kWh
–1
) (%)
Electricity demand of heating with HP (kWh kWh
th
–1
)
Grid GHG emission intensity (gCO
2
e kWh
–1
)
Lower than 90% Lower than 75% Lower than 50% Higher than 50% Higher than 75% Higher than 90%
Resulting life-cycle GHG emission intensity of EVs/HPs is
of that of newly sold petrol cars/fossil boilers in 2015 (distribution of average global sales)
90% range (2015)
90% range (2015)
EV efficiency range (2015) HP efficiency range (2015)
Mean (2015) Mean (2015)
a b
Fig. 2 | Boundary conditions for the use of EVs and HPs. a,b, Conditions under which the life-cycle GHG emission intensities from driving EVs (a) and
heating with HPs (b) are currently lower than those from new petrol cars and fossil boilers being sold in the market, given different combinations of
use-phase electricity demand and the electricity grid’s GHG emission intensity. Horizontal dashed lines indicate the average emission intensity of global
electricity generation (in 2015); vertical dashed lines indicate the estimates of average EV and HP use-phase efficiencies (in 2015). Boxes indicate the 90%
range of EV use-phase efficiencies and the range of HP use-phase efficiencies (in 2015). (See Supplementary Figs. 2 and 3 for boundary conditions in 2030
and 2050 under different scenarios.)
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles NaTurE SuSTaiNabiliTy
EV had a lower life-cycle emission intensity than driving an average
new petrol car if the electricity grid’s emission intensity was below
1,100 gCO2e kWh1 (weighted by regional service demand) (Fig. 2a).
For heating, average HPs had a lower life-cycle emission intensity
than average new fossil boilers if the grid’s emission intensity did
not exceed 1,000 gCO2e kWh1 (Fig. 2b). This roughly corresponds
to the emission intensity of older coal power plants33 and is higher
than the estimated life-cycle emission intensity of more than 90% of
the global electricity generation in 2015 (Supplementary Table 2).
On global average, even very inefficient EVs and HPs would be
less emission intensive than very efficient new petrol cars and fossil
boilers if the grid’s emission intensity was below 700 gCO2e kWh1
(in the case of EVs) and 500 gCO2e kWh1 (in the case of HPs),
respectively (Fig. 2). These thresholds roughly correspond to the
emission intensity of gas power plants33 and are lower than the aver-
age emission intensity of the global electricity generation in 2015
(around 740 gC O2e kWh1; see Supplementary Table 2). The gen-
eral finding that EVs and HPs have lower life-cycle emissions than
most petrol cars and fossil boilers is robust against variations in
uncertain production emissions, such as uncertain embodied emis-
sions from producing batteries of EVs9,34 and higher-than-expected
leakage of refrigerant liquids during all life-cycle phases of HPs10
(Supplementary Figs. 5 and 6).
Importantly for policymaking on the national level, region-
specific threshold emission intensities can be lower or higher than
the global averages, depending on the region-specific mix of new
petrol cars and fossil boilers that would be replaced. For road trans-
port, the current thresholds below which average-efficiency EVs
would result in lower net emissions than average new petrol cars are
between 700 gCO2e kWh1 (in Brazil) and 1,500 gCO2e kWh1 (in
the United States and Canada) (Fig. 3), depending on the region-
specific mix of new petrol cars. Very inefficient EVs would still be
less emission intensive than very efficient new petrol cars (‘green
cases), if the electricity grid’s emission intensity was below between
300 gCO2e kWh1 (in Japan) and 1,000 gCO2e kWh1 (in Canada).
For heating, the current threshold emission intensity for average
France
Brazil
Italy
UK
Germany
USA
Japan
Russia
India
0
100
200
300
400
Emission intensity (gCO2e per vehicle-km)
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
FR
BR
IT
UK
DE
US
JP
RU
CN
IN
20 40 60 20 40 60
20 40 60
Current technological
trajectory (gCO2e)
2 °C policy scenario (gCO2e)
End-use without power
policies (gCO2e)
100
200
300
400
0
100
200
300
400
0
100
200
300
400
0
2030 20502015
Share of global demand (%) Share of global demand (%) Share of global demand (%)
EVs: emissions
from electricity use
EVs: emissions
from battery production
EVs: emissions
from car production
EVs: 90% range of
emission intensity per region
Petrol cars: total emission
intensities of sales per region
(mean, 50% range, 90% range)
Upper GHG intensity of
EVs lower than
90% of all petrol cars
Partial overlap of
EV range with 90%
range of petrol cars
Average EV
more GHG intensive
than most petrol cars
a b c
d e
f g
0
Fig. 3 | GHG emission intensities of passenger cars. ag, Current (a) and projected (bg) GHG emission intensities from driving EVs, for the ten countries
with the highest passenger car transport demand in 2015 (the shares in global demand are equivalent to the widths of the bars). Projections under the current
technological trajectory (b,c), the 2 °C policy scenario (d,e) and the end-use without power policies scenario (f,g) are shown. The heights of the vertical bars
show an average EV’s estimated GHG emission intensity, given the power sector’s emission intensity in each country (results from this study). The range of
the GHG emission intensities reflects higher and lower use-phase energy requirements of different available EV models and sizes. For comparison, the grey
box plots show the distributions of GHG emission intensities of newly sold fossil fuel cars in each country (mean, 50% and 90% ranges)28,29.
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles
NaTurE SuSTaiNabiliTy
HPs is between 800 gCO2e kWh1 (in Sweden and the Netherlands)
and 1,400 gCO2e kWh1 (in Poland and South Africa), depending
on the region-specific mix of fossil boilers that HPs could replace
(Fig. 4). Very inefficient HPs would still have lower emission inten-
sities than very efficient fossil boilers when the grid’s carbon inten-
sity was below around 450 gCO2e kWh1.
Accordingly, we find that current models of EVs and HPs have
lower life-cycle emission intensities than current new petrol cars
and fossil boilers in 53 of 59 world regions, accounting for 95%
of the global road transport demand and 96% of the global heat
demand in 2015 (Supplementary Fig. 1). Relative differences range
from EVs being around 70% less emission intensive per vehicle-
kilometre (in largely renewable- and nuclear-powered Iceland,
Switzerland and Sweden) to being 40% more emission intensive (in
oil-shale-dependent Estonia) (Supplementary Table 6). For HPs,
relative differences in life-cycle emissions per kWh of useful heat
are between 88% (Switzerland) and +120% (Estonia). On global
average in 2015, EVs resulted in 31% lower emissions per vehicle-
kilometre than petrol cars (each region weighted by its transport
demand), and the emission intensity of HPs was on average 35%
lower than that of fossil boilers (regions weighted by their heat
demand) (Supplementary Table 6).
While EVs and HPs generally cause less emissions than
fossil-fuel-based technologies in most of the world, this may not
always be true when comparing specific pairs of technologies.
Markets are highly diverse, owing to varying preferences, incomes,
household characteristics and attraction to energy-intense luxury
items28. In many regions, this empirical diversity results in substan-
tial overlap between the observed emission-intensity distributions
of petrol cars and fossil boilers on one side, and the likely emission-
intensity ranges of available EVs and HPs on the other side. Efficient
new petrol cars can cause less emissions than average EVs, and
efficient new gas boilers can outperform average HPs (indicated in
yellow in Figs. 35). In 2015, this happens in regions accounting for
43% of the global demand in road transport (23 regions) and 80% of
the global demand in household heating (28 regions).
Region-wide emission increases are likely only where the aver-
age emission intensity of EVs or HPs is higher than for the majority
France
Canada
Italy
Germany
USA
Japan
Ukraine
Russia
China
0
100
200
300
400
500
Emission intensity (gCO
2
e kWh
–1
)
FR
CA
IT
UK
DE
US
JP
UA
RU
CN
FR
CA
IT
UK
DE
US
JP
UA
RU
CN
FR
CA
IT
UK
DE
US
JP
UA
RU
CN
FR
CA
IT
UK
DE
US
JP
UA
RU
CN
FR
CA
IT
UK
DE
US
JP
UA
RU
CN
100
200
300
400
0
500
100
200
300
400
0
500
100
200
300
400
0
500
20 40 60 20 40 60020 40 60
HPs: emissions
from electricity use
HPs: emissions
from production
HPs: range of
emission intensity per region
Fossil boilers: total emission
intensities of sales per region
(mean, 50% range, 90% range)
Upper GHG intensity of
HPs lower than
90% of all fossil boilers
Partial overlap of
HP range with 90%
range of fossil boilers
Average HP
more GHG intensive
than most fossil boilers
Share of global demand (%) Share of global demand (%) Share of global demand (%)
2030 20502015
b c
d e
f g
a
Current technological
trajectory (
gCO
2
e)
2 °C policy scenario (
gCO
2
e)
End-use without power
policies (
gCO
2
e)
FRCA IT
UK
DE
US
JPUA
RU
CN
UK
Fig. 4 | GHG emission intensities in household heating. af, Current (a) and projected (bg) GHG emission intensities from heating with HPs, for the ten
countries with the highest residential heat demand in 2015 (the shares in global demand are equivalent to the widths of the bars). Projections under the
current technological trajectory (b,c), the 2 °C policy scenario (d,e) and the end-use without power policies scenario (f,g) are shown. The heights of the
vertical bars show an average HP’s estimated GHG emission intensity, given the power sector’s emission intensity in each country. The range of the GHG
emission intensities reflects higher and lower conversion efficiencies of different HP models and operating conditions. For comparison, the grey box plots
show the distributions of GHG emission intensities of newly sold fossil-fuel-based heating systems in each country (mean, 50% and 90% ranges).
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles NaTurE SuSTaiNabiliTy
of new petrol cars or fossil boilers (indicated in red in Figs. 35).
As of 2015, this applies to 5% of the global road transport demand
(five regions) and 4% of the global heating demand (six regions)
(Fig. 5). In the most favourable case (indicated in green), even very
inefficient electrification (equivalent to the upper ends of their
ranges) is less emission intensive than using the most efficient new
petrol cars or fossil boilers instead (equivalent to the lower bounds
of their respective distributions). EVs or HPs can thus reduce
net emissions in almost all situations. This is the case in regions
accounting for 52% of the global demand for passenger road trans-
port (31 regions) and in regions with 16% of the global demand for
household heating (25 regions).
Future emission intensities in transport and heating
Since technology continuously evolves in any policy regime, the
emissions trade-offs change over time (Supplementary Figs. 2 and
3). Under the current technological trajectory, in many regions an
ongoing reduction in the power sector’s emission intensity gradu-
ally decreases the indirect emission intensities of using EVs and
HPs (also the electricity-related emissions from producing them).
In addition, technological progress gradually improves their energy
efficiency (Methods). Owing to a combination of both effects, mean
emission intensities of EVs are projected to be around 20% lower
in 2030 (relative to 2015) and 30% lower in 2050 (weighted by
transport demand in 2015). Mean intensities of HPs are projected
to decrease 30% below their 2015 value by 2030 and 40% by 2050
(weighted by heat demand in 2015).
Meanwhile, in most regions more efficient variants of fossil-fuel-
based technologies will increase their market shares, such as hybrid
cars or condensing gas boilers, reducing the emission abatement
potential from electrification (Supplementary Tables 4 and 5).
Averaged over all regions, new petrol cars in 2050 will emit 20%
less emissions per vehicle-kilometre than in 2015, and new fossil
boilers will be 15% less emission intensive (weighted by service
demand in 2015), with large variations between regions. The larg-
est changes are projected for countries where petrol cars or boilers
are currently still relatively inefficient. For example, on the basis of
current trends, we project that the 2050 emission intensities of new
petrol cars in the United States and new fossil boilers in China will
be around 30% below their 2015 levels.
In 2030, under the current technological trajectory and the end-
use without power policies scenario, the resulting average emission
intensities of EVs and HPs do not exceed those of fossil-fuel-based
alternatives in any of the ten countries with the highest transport and
heating demands, even without additional decarbonization policies
in the power sector (Figs. 3 and 4). The only exception is road trans-
port in Japan: owing to the unique combination of very efficient petrol
cars (with a growing share of hybrids) and a power sector that is not
highly decarbonized, EVs could lead to marginally higher emissions
(Supplementary Table 6). By 2045 and 2035, respectively, EVs and
2030 2050 2030 2050
Passenger cars
a
c
b
d
Household heating
Reduce emissions
on average
Increase emissions
on average
Almost always
reduce emissions
EVs/
HPs would:
Current trajectory2 °C scenarioEnd-use only
Current trajectory2 °C scenarioEnd-use only
2015 2015
Fig. 5 | Relative GHG emission intensities of EVs and HPs around the world. a,b, World regions in which EVs (a) and HPs (b) have lower projected
life-cycle GHG emissions than new petrol cars/fossil boilers in almost all cases (green) or on average (yellow), or are more GHG emission intensive on
average (red). c,d, Projections for 2030 and 2050 for EVs (c) and HPs (d) under the current technological trajectory (current trajectory), the 2 °C policy
scenario (2 °C scenario) and the end-use without power policies scenario (end-use only).
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles
NaTurE SuSTaiNabiliTy
HPs in the current trajectory are on average less emission intensive
than fossil alternatives in all world regions (Supplementary Fig. 1).
This means that electrification will reduce region-wide emissions as
a whole, which is most relevant for policymaking. Note, however,
that the diversity of technology choices implies that in some regions
(indicated in yellow in Figs. 35), some consumers may still buy EVs
or HPs that cause higher emissions than efficient new petrol cars
or gas boilers. Meanwhile, in the green regions, electrification will
reduce emissions in almost any conceivable case.
Possible overlaps between technology categories are much rarer
in the 2 °C policy scenario, with its much faster power sector decar-
bonization. In all world regions, EVs and HPs are on average less
emission intensive than fossil fuel alternatives from around 2025
onwards (Fig. 5c,d). This is despite increased average efficiencies
of new petrol cars and fossil boilers, relative to the current techno-
logical trajectory (Supplementary Table 7). By 2030, even inefficient
EVs or HPs have lower emission intensities than very efficient new
fossil-fuel-based alternatives in regions accounting for around 90%
of the global transport and heat demands. This implies that in the
medium term, in almost all cases the more effective policy strategy
for reducing transport and heating emissions is to push EVs and
HPs, instead of supporting the uptake of more efficient fossil-fuel-
based technologies.
In the end-use without power policies scenario, future intensi-
ties follow the 2 °C policy scenario trend for petrol cars and fossil
boilers, but remain identical to the current technological trajec-
tory for EVs and HPs (Supplementary Table 8). Between 2020 and
2050, there is thus a relatively larger share of the global demand
for which future emission intensities will partially overlap in both
transport and heating (yellow regions), compared with the cur-
rent technological trajectory. Although this reduces the potential
magnitude of net emission reductions from electrification rela-
tive to the 2 °C policy scenario, the risk of region-wide emission
increases (red regions) remains limited. The share of the transport
and heat demands for which EVs and HPs would increase average
emissions compared with the use of their fossil fuel counterparts
never exceeds 6%.
Net changes in total emissions
Finally, we project how EVs and HPs could change future levels
of economy-wide emissions over time, compared with fossil-fuel-
based technologies. For each region, we estimate the emissions
from using and producing EVs and HPs in each year, and subtract
avoided emissions from the alternative use and production of new
petrol cars and fossil boilers (Methods). We find that both EVs
and HPs reduce global emissions in all scenarios and at all times
(Fig. 6): EVs by up to 1.5 GtCO2 yr1 (29% of the total passenger
road transport emissions without the use of EVs), and HPs by up
to 0.8 GtCO2 yr1 (46% of the total residential heating emissions
without the use of HPs).
−1
−2
−3
0
1
2
3
2030 2040 2050
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
−1
−2
−3
0
1
2
3
2030 2040 2050
GHG emissions (GtCO
2
e yr
–1
)
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
−1
−2
−3
0
1
2
3
2030 2040 2050
GHG emissions (GtCO
2
e yr
–1
)
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
−1
−2
−3
0
1
2
3
2030 2040 2050
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
−1
−2
−3
0
1
2
3
2030 2040 2050
GHG emissions (GtCO
2
e yr
–1
)
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
−1
−2
−3
0
1
2
3
2030 2040 2050
GHG emissions (GtCO
2
e yr
–1
)
−20
−10
0
10
20
Cumulative GHG (GtCO
2
e)
Current technological trajectory End-use without power policies 2 °C policy scenario
Passenger cars
GHG emissions (GtCO
2
e yr
–1
)
Household heating
GHG emissions (GtCO
2
e yr
–1
)
2015–2050 2015–2050 2015–2050
2015–2050 2015–2050 2015–2050
1% 5% 19% 1% 12% 50% 1% 12%
50%
7% 11% 16% 12% 23% 35% 12% 23% 35%
a c e
b d f
Car/heating system
production
Battery
production
Fossil fuel
combustion
Fossil fuel
production
Use-phase
electricity
Net change in GHG emissions: median and uncertainty range
(due to uncertain future average efficiencies of EVs/HPs)
Projected market share of
EVs/HPs
12%
Fig. 6 | Changes in global GHG emissions from EVs and HPs. af, Indirect GHG emissions from use-phase electricity generation (orange), compared with
avoided direct GHG emissions from fossil fuel combustion (dark purple) and indirect GHG emissions from fossil fuel production (light purple) that would
result if the same demand were fulfilled with average new fossil-fuel-based cars (a, c, and e) and heating systems (b, d and f). The GHG emissions from
producing cars and heating systems are shown in dark blue (battery production in light blue). Grey dots indicate the overall net change in global GHG
emissions from using EVs and HPs. Ranges around the median estimate illustrate the possible range of net changes under lower and higher average use-
phase efficiencies of EVs and HPs. Percentages show the global market share of EVs/HPs. Projections under the current technological trajectory (a,b), the
2 °C policy scenario (c,d), and the end-use without power policies scenario (e,f) are shown.
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles NaTurE SuSTaiNabiliTy
As EVs and HPs replace fossil-fuel-based technologies over time,
production emissions are projected to grow from around 25% of
the total road transport emissions in 2015 to 35–38% in 2050, and
from 1% of the total heating emissions in 2015 to 2–9% in 2050
(Supplementary Fig. 4). This is due to reduced use-phase emis-
sions from electricity and increased production emissions, which
are currently around 30% higher for EVs than for petrol cars (at the
average global electricity mix) and 15 times higher for HPs than for
fossil boilers (mainly from the leakage of refrigerant liquid). A full
decarbonization of household energy use therefore remains infeasi-
ble without also reducing the embodied emissions from producing
and recycling technologies and required materials (such as steel),
beyond the decarbonization of the electricity input.
Owing to the delay between (relatively higher) production emis-
sions and (relatively lower) use-phase emissions, a rapid techno-
logical transition towards EVs and HPs could temporarily increase
emissions in individual regions, compared with the production and
use of fossil-fuel-based technologies—even if EVs and HPs cause
lower emissions over their whole life cycles35. However, we find that
in all three scenarios, temporary emission increases from EV and
HP production are limited to regions accounting for less than 7%
of the global transport demand and 4% of the global heat demand
(Supplementary Table 9). In almost all regions, such temporary
increases are outweighed by emission reductions in subsequent
years. Even in the end-use without power policies scenario, EVs
and HPs would therefore reduce cumulative emissions from 2015
to 2050 in regions accounting for 96% of road transport and 97% of
the heating demand.
Discussion
Overall, we find that current and future life-cycle emissions from
EVs and HPs are on average lower than those of new petrol cars
and fossil boilers—not just on the global aggregate but also in most
individual countries. Over time, in increasingly more regions even
the use of inefficient EVs or HPs is less emission intensive than the
most efficient new petrol cars or fossil boilers.
Importantly for policymaking on the national level, given that
the alignment of policymaking across departments is highly com-
plex and not always successful3638, we showed that the risk of imple-
menting incoherent decarbonization policies is low in the case of
EVs and HPs. Even if future end-use electrification is not matched
by rapid power-sector decarbonization, the use of EVs and HPs
almost certainly reduces emissions in most world regions, com-
pared with fossil-fuel-based alternatives.
Our analysis disaggregates global demand into 59 world regions,
a spatial resolution that is considerably higher than in any previous
forward-looking life-cycle study of EVs or HPs. Further research
could focus on the remaining variation within larger simulated
world regions (such as China19 and the United States16,20). Such stud-
ies could also analyse the location-specific impacts of integrating
EVs and HPs into the electricity grid3942, and how this translates
into varying marginal emission intensities over time (compared
with the average emission intensities used in this study)42,43.
Finally, our findings imply (1) that support for high-efficiency
fossil fuel technologies may be justified only in the short term,
when the market uptake of EVs and HPs can still be constrained by
limited production capacities and necessary infrastructure adjust-
ments, and (2) that policymakers in most parts of the world can
go ahead with ambitious end-use electrification policies, without
the need to rely on further power sector decarbonization, while
(3) achievable emission reductions in transport are partly con-
strained by the remaining production emissions.
Methods
GHG emission intensities. For estimating current and future emission intensities
of electricity generation, passenger road transport and household heating,
we combined estimates from the life-cycle assessment literature with model
projections of future technology uptake and the resulting emission intensities27,31,
inspired by the work in refs. 4448. For both the use and the production of
technologies, we explicitly included the projected emission changes that result
from the changing mix of electricity generation technologies over time. For
all technologies, we included all production and end-of-life emissions. ese
were equally distributed over the entire lifespan for the calculation of emission
intensities (Figs. 25), and allocated to the respective years of production and
disposal for the estimation of absolute emission levels over time (Fig. 6). Note
that we evaluated the emission intensities of technologies rather than those of
households (which in some cases may use a combination of technologies).
Electricity generation. We based all calculations on the region-wide average grid
emission intensities of electricity generation (gCO2e kWh1), which we calculated
from the model-projected levels of total power-sector emissions and electricity
demand in each region and year. As we divide the total GHG emissions by the
total electricity demand (instead of generation), the resulting intensity values
include transmission and distribution losses. Historic data (up to 2012) were
calculated based on data from the International Energy Agency (IEA), while
relative future changes of these historic values were projected by E3ME-FTT. We
included indirect emissions from the extraction and processing of fossil fuels,
the construction of power-generation technologies (including the necessary
infrastructure and supply chain emissions) and methane emissions (all on the basis
of the most likely estimates from the IPCC’s Fifth Assessment Report33), as well as
indirect emissions from biomass use49. The resulting life-cycle emission intensities
per year and region are given in Supplementary Table 2.
EVs. For all cars, we subdivided GHG emissions into use-phase emissions (from
driving the car), and production and end-of-life emissions. We calculated use-
phase emissions as the product of the car’s electricity use and the emission intensity
of electricity generation in each region (as described above). Ranges of current and
future electricity use per vehicle-kilometre were based on estimates by Cox etal.50
for 2015 (median, 0.19 kWh km1; 5th–95th percentile range, 0.13–0.24 kWh km1)
and 2040 (median, 0.15 kWh km1; 5th–95th percentile range, 0.10–0.19 kWh km1,
on the basis of the ‘most likely automation’ scenario), including auxiliary power
demand and charging losses. These values were based on a review of currently
available EVs, and calibrated to match empirical energy use under real-world
driving conditions. We linearly interpolated the efficiency ranges between 2015
and 2040, and linearly extrapolated this trend to 2050. Relative improvements
compared with 2015 equal around 12% until 2030 and 24% until 2050.
Production and end-of-life emissions were further subdivided into emissions
from electricity required for the production process, and non-electricity emissions.
Electricity requirements (excluding the battery) were obtained from EcoInvent51
(v.3.5), adding up the electricity inputs of the foreground process (the production
of the car) and of all background processes (the production of parts and materials,
transport, mining and so on) (Supplementary Methods 1). We determined the
electricity emissions by multiplying the amount of required electricity by the
projected GHG intensity of electricity generation in the country where the car is
driven, thereby abstracting from the import and export of cars (and car parts).
For the production of medium-sized EVs (curb weight of 1,500 kg), electricity
requirements (excluding the battery) were estimated at 6,900 kWh (0.046 kWh km1,
assuming an average lifetime of 150,000 km)51. Emissions from other sources in the
car production (excluding the battery) were set at 4,700 kgCO2e (31 gCO2e km1)51.
For the battery production, non-electricity emissions were estimated at
3,200 kgCO2e (21.3 gCO2e km1), and battery cell electricity requirements at
5,000 kWh (0.034 kWh km1)50. The latter was estimated to linearly decrease
to 3,400 kWh (0.023 kWh km1) in 204050, and we further linearly extrapolated
this trend to 2050. As electricity requirements and embodied emissions of the
production processes can be subject to uncertainty, we included a sensitivity
analysis for a range of life-cycle parameters (Supplementary Figs. 5 and 6).
Petrol cars. For use-phase emissions, we first calculated tank-to-wheel emissions of
cars on the basis of the distributions of manufacturer-rated intensities (without any
blend of biofuels) of all liquid-fuel cars (petrol and diesel, including non-plug-in
hybrids) that are sold in a given region and year—on the basis of empirical data at
the start of the simulation, and projected into the future by E3M3-FTT. Real-world
fuel use and resulting use-phase CO2 emissions of petrol cars are widely recognized
to exceed official manufacturer ratings, by an average margin of 10–40% (on the
basis of empirical studies in Europe, the United States and China)5256. We therefore
adjusted all manufacturer ratings by the central estimate of 25%, consistent
with the adjustment calculations by the US Environmental Protection Agency56.
For obtaining well-to-wheel emissions, we added upstream emissions from the
extraction and processing of fuels (26% of tank-to-wheel emissions for petrol, and
28% for diesel)5759. Emissions from car production and end-of-life were subdivided
into emissions from electricity required for the production process (including
background processes) and non-electricity emissions. The electricity requirements
for producing a medium-sized car (curb weight 1,600 kg) were estimated at
9,200 kWh (0.061 kWh km1), and emissions from other sources at 5,900 kgCO2e
(40 gCO2e km1)51.
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles
NaTurE SuSTaiNabiliTy
HPs. We differentiated between use-phase emissions (from heating), and
production and end-of-life emissions. We calculated use-phase emissions as
the product of HP point-of-use conversion efficiencies (that is, the ratio of heat
delivered to the electricity consumed over the season), and the region-specific
intensities in electricity generation. The average efficiency was set to 300% in 2015
(range: 200–600%), on the basis of the IEA Energy Technology Systems Analysis
Programme expert ranges given for the most common types of HPs (air-to-air,
air-to-water and ground-source)32. The same literature source estimated that future
efficiencies of HPs will improve by 30–50% until 2030 and 40–60% until 2050. As
HPs are a relatively mature technology, we based our calculations on the lower-
bound estimates (30% efficiency improvement until 2030, 40% until 2050). We
linearly interpolated between 2015 and 2050, yielding average efficiencies of 390%
in 2030 (range: 260–780%) and 420% in 2050 (range: 280–840%).
For the production and end-of-life stage of HPs, we estimated emissions from
non-electricity sources at 830 kgCO2e per kW of installed capacity51. Of these
emissions, 750 kgCO2e stem from the leakage of refrigerant liquids over the entire
life cycle, all included here in the production emissions. We converted the impacts
into the functional unit of gCO2e kWhth1, assuming an average technical lifetime
of 20 yr (ref. 60) with 2,000 operating hours per year61, yielding non-electricity
emissions of 20.8 gCO2e kWhth1 (including leakage). Electricity requirements for
the production of HPs (including background processes) were set at 65 kWh per
kW of installed capacity (0.002 kWh kWhth1)51.
Fossil fuel heating systems. We based our calculation of use-phase emissions on
the distribution of intensities of all decentral residential fossil-fuel-based heating
systems (oil, gas and coal) being sold in a respective region and year, simulated
until 2050 by E3ME-FTT (‘Distributions of petrol cars and fossil boilers’). We
assumed conversion efficiencies of 75% for oil and gas heating systems, 86% for
advanced oil systems and 90% for advanced gas systems62. We combined these
with IPCC emission factors to obtain emission intensities per technology. We
added upstream emissions from the extraction and processing of heating oil
(equivalent to 28% of direct emissions, on the basis of the estimate for diesel57,
which is chemically near-equivalent to heating oil), gas (23% of direct emissions63)
and coal (6% of direct emissions64). For the production, we based our calculations
on EcoInvent (v.3.5) estimates for gas and oil boilers51, which constitute the large
majority of global sales. The electricity requirements (including background
processes) are 37 kWh per kW of installed capacity (0.001 kWh kWhth1, on the
basis of the same lifetimes and operating hours as for HPs), and emissions of other
sources are 30 kgCO2e kW1 (0.8 gCO2e kWhth1)51.
Distributions of petrol cars and fossil boilers. We estimated the ranges of
emission intensities from empirically measured and projected sales in the
respective year and country (Supplementary Tables 4 and 5). For cars, the
distribution of current sales was derived from detailed market data on vehicle sales
(years 2004–2012), which we compiled by matching sales data to manufacturer
data for thousands of individual vehicle models currently on the market in 18
countries, and we extrapolated these values for countries where data is missing28,29.
Distributions of future sales (2013–2050) were projected by E3ME-FTT
(see ‘Integrated assessment model’), on the basis of the market data and simulated
future consumer choices. For some regions (mainly in Africa; see Supplementary
Table 1), vehicle sales were assumed to equal global averages, owing to the
unavailability of empirical data. For heating systems, current and future sales were
simulated by E3ME-FTT (from 2015 to 2050), according to the available data
on fuel use and technology stocks (years 1990–2014)30,65. Both for cars and for
boilers, we then calculated the mean and standard deviation of emission intensities
(including upstream emissions) of all sales in a respective region, for each year
until 2050, according to our simulations (Supplementary Tables 6–8). The intensity
of each technology type was thereby weighted by the number of model-projected
sales in each world region. Emissions from the production of technologies were
added as a constant. This way, future changes in the range of emission intensities
are not an exogenous input, but are endogenously projected by the model, on the
basis of a gradually changing technology composition in the context of different
policy assumptions.
Net changes in GHG emissions. We estimated the net changes in overall emissions
for each world region in each year. First, we calculated the emissions from EVs
and HPs, on the basis of their model-projected region-specific market shares and
average use-phase emission intensities (‘Scenarios of technology uptake’). Emissions
from the production phase were fully allocated to the year in which a car or heating
system is produced, and end-of-life emissions to the year of its disposal (assuming
average lifetimes of 10 yr for cars and 20 yr for heating systems) (see Supplementary
Methods 2 for the relative shares). Second, we subtracted avoided emissions that
otherwise would have been emitted by new petrol cars or fossil boilers, if they
would have been used to fulfil the same service demand, also on the basis of the
projected average intensities of sales in each region (without the blend of biofuels).
The use of region-specific intensities results in relatively smaller net savings in
regions where the average efficiency of new petrol cars or fossil boilers is relatively
higher and relatively larger net savings in regions where the average efficiency
is relatively lower. Results depend on the assumed reference point: while many
combinations are possible, what matters for region-wide effects is the sum over
all individual choices of cars and heating systems within one region in any given
year. While the mean efficiencies in each region can change over time, we assumed
that the structure of all sales remains distributed (that is, that people would not
suddenly all buy economic small-engine cars). Cumulative net changes can then be
approximated on the basis of the region-specific means of distributed intensities.
Global changes in emissions equal the sum of all region-specific estimates.
Scenarios of technology uptake. We used E3ME-FTT model projections of future
technology diffusion and fuel use in three scenarios: (1) current technological
trajectory, (2) 2 °C policy scenario and (3) end-use without power policies. These
scenarios were chosen so that they allowed us to simulate the emission trade-
offs from electrification as realistically as possible, given (1) what is likely from a
current perspective, (2) what would be likely in a hypothetical case of ambitious
climate policies around the globe and (3) a worst-case scenario in which end-use
electrification is not matched by power-sector decarbonization. The first two
scenarios were based on recent modelling studies26,27,31, and detailed descriptions
of the underlying policy assumptions are available in ref. 27. All policies included
in the scenarios are designed to match as closely as possible real-world policy
instruments (for example, energy taxes, vehicle taxes, feed-in tariffs, subsidies,
direct regulation or efficiency standards).
Current technological trajectory. As a result of the path-dependent simulation
nature of E3ME-FTT, the model projects a baseline trajectory in which
technological change already takes place without the implementation of additional
policies. To differentiate from baselines without any technological change, we
refer to it as the current technological trajectory, in which several low-carbon
technologies (such as solar photovoltaics, EVs or HPs) already diffuse to some
extent, following the trajectory observed in historical data, while other technology
types (such as low-efficiency petrol cars or coal and oil heating systems) are
projected to decline in market shares, also observed in the data. The scenario
implicitly includes current policies in the transport and heating sectors, given that
they already had a measurable impact on empirically observed technology uptake
in our historic datasets. For the heating sector, we further assumed that the average
insulation efficiency of buildings gradually increases over time (Supplementary
Methods 3). For the power sector, we explicitly included existing policy schemes,
such as the EU ETS.
2 °C policy scenario. We imposed sets of sector-specific policies to achieve a
projected trajectory of global emissions that is consistent with a 75% probability
of not exceeding 2 °C of global warming by the end of the century. Policies are
implemented in or after 2020. In electricity generation, transport and heating,
they are defined so that they incentivize the uptake of low-carbon technologies
(for example, subsidies or feed-in tariffs), disincentivize the use of fossil fuels
(for example, carbon taxes) or regulate the use of fossil fuel technologies (for
example, efficiency standards or a phase-out of coal power plants). In electricity
generation, the main policies are carbon pricing, subsidies for renewables and
nuclear, feed-in tariffs (for wind and solar), a ban on the construction of new
coal power plants, and increased capacities for electricity storage. In passenger
road transport, the main policies are fuel efficiency standards for newly sold
petrol cars; a gradual phase-out of older, low-efficiency petrol cars; a gradually
increasing fuel tax; a purchase tax for vehicles proportional to their rated emission
intensity; procurement programmes for EVs where they are not available yet; and
an increasing biofuel mandate (reaching up to 10–30% in 2050; region-specific
mandates extrapolate IEA projections). In household heating, the main policies
are a tax on the residential use of fossil fuels (oil, gas and coal); subsidies on the
upfront purchase costs of renewable heating technologies (HPs, solar thermal and
modern biomass), which start in 2020 and are linearly phased out after 2030; and
more stringent building regulations, implying that a large fraction of houses are
retrofitted to passive house properties. More details can be obtained from refs. 26,27.
End-use without power policies. We combined the power sector trajectory from
scenario 1 with the road transport and heating trajectories from scenario 2, making
the scenario assumption that policymakers would implement policies to push EVs
and HPs while not pursuing any further decarbonization of electricity generation.
No policies were imposed on any other sectors. Although such a combination of
policies is unlikely in the real world, the scenario serves as a worst-case analysis.
Integrated assessment model. E3ME-FTT-GENIE is a simulation-based
integrated assessment model that combines bottom-up representations of the
power, transport and heating sectors with a macroeconometric representation of
the global economy, for 59 regions covering the globe (Supplementary Table 1)26.
Future technology transformation models. The future technology transformation
(FTT) family of models project the uptake of energy technologies in the future
until 2050, by extending the current trajectory of technological change with a
diffusion algorithm, which is calibrated on datasets of technology uptake in recent
history (up to 2012 for power and transport, 2014 for heating) (Supplementary
Tables 4 and 5). Each FTT model is based on a bottom-up description of
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles NaTurE SuSTaiNabiliTy
heterogeneous agents who own or operate technologies that produce certain
societal services (such as electricity generation, road transport and household
heating), and who consider replacing such technologies according to lifetimes
and contexts. As such, it is both a model of choice and one of technology vintage
(or technology fleets). Replacement, or technological change, takes place at rates
determined by the survival in time of technology units and/or the financing
schedule. We assume that agents make comparisons between technology options
that they individually see as available in their respective national markets, which
we structure by pairwise comparisons of distributed preferences. The model
is a discrete choice model in which choice options are weighted by their own
popularity, a method that generates endogenous S-shaped technology diffusion
curves66. The technological trajectory is not based on economy-wide optimization,
but endogenously evolves from the sum of individual choices of heterogeneous
agents with bounded rationality. FTT models are characterized by strong path-
dependence of projected technology diffusion (equivalent to strong autocorrelation
in time), as it is typically found in technology transitions67,68, and for that
reason, these models provide a good representation of the inertia embedded in
technological systems. They are thus well suited to analysing existing technological
trajectories as observed in recent historical data. A description of how future
demand for transport and heating is determined is given in Supplementary
Methods 3. Further descriptions of the individual FTT models are in refs. 29,30,65,6971.
E3ME model. The FTT models are part of E3ME (hard-coupled in the same
computer code), which represents relationships between macroeconomic quantities
in a top-down aggregate perspective through a chosen set of econometric
relationships that are regressed on the past 45 yr of data and are projected 35 yr
into the future (until 2050). The macroeconomics in the model determine the total
demand and trade for manufactured products, services and energy carriers, output
and employment for 43 economic sectors, 24 fuel users and 12 fuels. The model
is path-dependent, such that different policy scenarios generate different techno-
economic and environmental trajectories that diverge from each other over time.
Using the what-if mode of impact assessment, policies are chosen, and the resulting
outcomes can be projected. Meeting policy objectives (such as emissions targets) is
not achieved by means of maximizing or minimizing some target function (such as
welfare or costs). Instead, the model is run iteratively until the target would be met
with a chosen set of policies. The model is regularly used in policy analyses and
impact assessments for the European Commission and elsewhere72,73. See ref. 26 for
a detailed description of the integrated model, and ref. 74 for the E3ME manual.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The main data that support the findings of this study are available as supplementar y
tables. Additional data are available from the corresponding authors upon request.
Code availability
The computer code used to generate the results that are reported in this study are
available from the corresponding authors on reasonable request.
Received: 21 March 2019; Accepted: 12 February 2020;
Published: xx xx xxxx
References
1. de Coninck, H. etal. in Special Report on Global Warming of 1.5 °C (eds
Masson-Delmotte, V. etal.) 313–443 (IPCC, WMO, 2018).
2. International Energy Agency Global EV Outlook 2017 (IEA/OECD, 2017).
3. Clarke, L. E. etal. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. etal.) 413–510 (IPCC, Cambridge Univ. Press, 2014);
https://doi.org/10.1017/CBO9781107415416.012
4. Kennedy, C. Key threshold for electricity emissions. Nat. Clim. Change 5,
179–181 (2015).
5. Rogelj, J. etal. in Special Report on Global Warming of 1.5 °C
(eds Masson-Delmotte, V. etal.) 93–174 (IPCC, WMO, 2018).
6. International Energy Agency CO2 Emissions from Fuel Combustion
(OECD/IEA, 2017).
7. Energy Agenda—Towards a Low-Carbon Energy Supply (Ministry of Economic
Aairs of the Netherlands, 2017); https://go.nature.com/385jsT9
8. Proposal for a Directive of the European Parliament and of the Council on the
Promotion of the Use of Energy from Renewable Sources—Analysis of the Final
Compromise Text with a View to Agreement (Council of the European Union,
2018); https://go.nature.com/37XxTbO
9. Cox, B., Mutel, C. L., Bauer, C., Mendoza Beltran, A. & van Vuuren, D. P.
Uncertain environmental footprint of current and future battery electric
vehicles. Environ. Sci. Technol. 52, 4989–4995 (2018).
10. Mattinen, M. K., Nissinen, A., Hyysalo, S. & Juntunen, J. K. Energy use and
greenhouse gas emissions of air-source heat pump and innovative ground-
source air heat pump in a cold climate. J. Ind. Ecol. 19, 61–70 (2014).
11. McGee, P. Electric cars’ green image blackens beneath the bonnet. Financial
Times (8 November 2017); https://go.nature.com/3cf8YUf
12. Sinn, H.-W. Are electric vehicles really so climate friendly? e Guardian
(25 November 2019); https://go.nature.com/396bqup
13. Mercure, J.-F., Pollitt, H., Bassi, A. M., Viñuales, J. E. & Edwards, N. R.
Modelling complex systems of heterogeneous agents to better design
sustainability transitions policy. Glob. Environ. Change 37, 102–115 (2016).
14. iel, C., Perujo, A. & Mercier, A. Cost and CO2 aspects of future vehicle
options in Europe under new energy policy scenarios. Energy Policy 38,
7142–7151 (2010).
15. Miotti, M., Supran, G. J., Kim, E. J. & Trancik, J. E. Personal vehicles
evaluated against climate change mitigation targets. Environ. Sci. Technol. 50,
10795–10804 (2016).
16. Onat, N. C., Kucukvar, M. & Tatari, O. Conventional, hybrid, plug-in hybrid
or electric vehicles? State-based comparative carbon and energy footprint
analysis in the United States. Appl. Energy 150, 36–49 (2015).
17. Bauer, C., Hofer, J., Althaus, H.-J., Del Duce, A. & Simons, A. e
environmental performance of current and future passenger vehicles: life
cycle assessment based on a novel scenario analysis framework. Appl. Energy
157, 871–883 (2015).
18. Jochem, P., Babrowski, S. & Fichtner, W. Assessing CO2 emissions of electric
vehicles in Germany in 2030. Transp. Res. A 78, 68–83 (2015).
19. Wu, Y. etal. Energy consumption and CO2 emission impacts of vehicle
electrication in three developed regions of China. Energy Policy 48,
537–550 (2012).
20. Archsmith, J., Kendall, A. & Rapson, D. From cradle to junkyard: assessing
the life cycle greenhouse gas benets of electric vehicles. Res. Transp. Econ.
52, 72–90 (2015).
21. Hawkins, T. R., Gausen, O. M. & Strømman, A. H. Environmental impacts
of hybrid and electric vehicles—a review. Int. J. Life Cycle Assess. 17,
997–1014 (2012).
22. Woo, J., Choi, H. & Ahn, J. Well-to-wheel analysis of greenhouse gas
emissions for electric vehicles based on electricity generation mix: a global
perspective. Transp. Res. D 51, 340–350 (2017).
23. Saner, D. etal. Is it only CO2 that matters? A life cycle perspective on shallow
geothermal systems. Renew. Sustain. Energy Rev. 14, 1798–1813 (2010).
24. Kikuchi, E., Bristow, D. & Kennedy, C. A. Evaluation of region-specic
residential energy systems for GHG reductions: case studies in Canadian
cities. Energy Policy 37, 1257–1266 (2009).
25. Mendoza Beltran, A. etal. When the background matters: using scenarios
from integrated assessment models in prospective life cycle assessment.
J. Ind. Ecol. 24, 64–79 (2020).
26. Mercure, J.-F. etal. Environmental impact assessment for climate change policy
with the simulation-based integrated assessment model E3ME-FTT-GENIE.
Energy Strategy Rev. 20, 195–208 (2018).
27. Mercure, J.-F. etal. Macroeconomic impact of stranded fossil fuel assets.
Nat. Clim. Change 8, 588–593 (2018).
28. Mercure, J.-F. & Lam, A. e eectiveness of policy on consumer choices for
private road passenger transport emissions reductions in six major
economies. Environ. Res. Lett. 10, 064008 (2015).
29. Mercure, J.-F., Lam, A., Billington, S. & Pollitt, H. Integrated assessment
modelling as a positive science: private passenger road transport policies to
meet a climate target well below 2 °C. Climatic Change 151, 109–129 (2018).
30. Knobloch, F., Pollitt, H., Chewpreecha, U., Daioglou, V. & Mercure, J.-F.
Simulating the deep decarbonisation of residential heating for limiting global
warming to 1.5 °C. Energy Ec. 12, 521–550 (2019).
31. Holden, P. B. etal. Climate–carbon cycle uncertainties and the Paris
Agreement. Nat. Clim. Change 8, 609–613 (2018).
32. Heat Pumps Technology Brief (Energy Technology Systems Analysis
Programme (ETSAP) of the International Energy Agency (IEA) and
International Renewable Energy Agency (IRENA), 2013).
33. Schlömer, S. etal. in Climate Change 2014: Mitigation of Climate Change
(eds. Edenhofer, O. etal.) 1329–1356 (IPCC, Cambridge Univ. Press, 2014).
34. Ciez, R. E. & Whitacre, J. F. Examining dierent recycling processes for
lithium-ion batteries. Nat. Sustain. 2, 148–156 (2019).
35. Dale, M. & Benson, S. M. Energy balance of the global photovoltaic (PV)
industry—is the PV industry a net electricity producer? Environ. Sci. Technol.
47, 3482–3489 (2013).
36. Liu, J. etal. Systems integration for global sustainability. Science 347,
1258832 (2015).
37. Jordan, A. & Lenschow, A. Environmental policy integration: a state of the art
review. Environ. Policy Gov. 20, 147–158 (2010).
38. Sterner, T. etal. Policy design for the Anthropocene. Nat. Sustain. 2,
14–21 (2019).
39. Muratori, M. Impact of uncoordinated plug-in electric vehicle charging on
residential power demand. Nat. Energy 3, 193–201 (2018).
40. Richardson, D. B. Electric vehicles and the electric grid: a review of modeling
approaches, impacts, and renewable energy integration. Renew. Sustain.
Energy Rev. 19, 247–254 (2013).
NATURE SUSTAINABILITY | www.nature.com/natsustain
Articles
NaTurE SuSTaiNabiliTy
41. Fischer, D. & Madani, H. On heat pumps in smart grids: a review.
Renew. Sustain. Energy Rev. 70, 342–357 (2017).
42. Chen, X. etal. Impacts of eet types and charging modes for electric vehicles
on emissions under dierent penetrations of wind power. Nat. Energy 3,
413–421 (2018).
43. Tamayao, M. A. M., Michalek, J. J., Hendrickson, C. & Azevedo, I. M. L.
Regional variability and uncertainty of electric vehicle life cycle CO2 emissions
across the United States. Environ. Sci. Technol. 49, 8844–8855 (2015).
44. Hertwich, E. G. etal. Integrated life-cycle assessment of electricity-supply
scenarios conrms global environmental benet of low-carbon technologies.
Proc. Natl Acad. Sci. USA 112, 6277–6282 (2015).
45. Gibon, T., Arvesen, A. & Hertwich, E. G. Life cycle assessment demonstrates
environmental co-benets and trade-os of low-carbon electricity supply
options. Renew. Sustain. Energy Rev. 76, 1283–1290 (2017).
46. Pehl, M. etal. Understanding future emissions from low-carbon power
systems by integration of life-cycle assessment and integrated energy
modelling. Nat. Energy 2, 939–945 (2017).
47. Pauliuk, S., Arvesen, A., Stadler, K. & Hertwich, E. G. Industrial ecology in
integrated assessment models. Nat. Clim. Change 7, 13–20 (2017).
48. Gibon, T. etal. A methodology for integrated, multiregional life cycle
assessment scenarios under large-scale technological change. Environ. Sci.
Tec hnol. 49, 11218–11226 (2015).
49. Creutzig, F. etal. Bioenergy and climate change mitigation: an assessment.
GCB Bioenergy 7, 916–944 (2015).
50. Cox, B., Mutel, C. L., Bauer, C., Mendoza Beltran, A. & van Vuuren, D. P.
Uncertain environmental footprint of current and future battery electric
vehicles. Environ. Sci. Technol. 52, 4989–4995 (2018).
51. Wernet, G. etal. e ecoinvent database version 3 (part I): overview and
methodology. Int. J. Life Cycle Assess. 21, 1218–1230 (2016).
52. Zhang, S. etal. Real-world fuel consumption and CO2 emissions by
driving conditions for light-duty passenger vehicles in China. Energy 69,
247–257 (2014).
53. Duarte, G. O., Gonçalves, G. A. & Farias, T. L. Analysis of fuel consumption
and pollutant emissions of regulated and alternative driving cycles based on
real-world measurements. Transp. Res. D 44, 43–54 (2016).
54. Tietge, U., Mock, P., Franco, V. & Zacharof, N. From laboratory to road:
modeling the divergence between ocial and real-world fuel consumption
and CO2 emission values in the German passenger car market for the years
2001–2014. Energy Policy 103, 212–222 (2017).
55. Fontaras, G., Zacharof, N. G. & Ciuo, B. Fuel consumption and CO2
emissions from passenger cars in Europe—laboratory versus real-world
emissions. Prog. Energy Combust. Sci. 60, 97–131 (2017).
56. Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel
Economy Trends—1975 rough 2014 (US Environmental Protection Agency,
2018); https://go.nature.com/2HXxrja
57. Solid and Gaseous Bioenergy Pathways: Input Values and GHG Emissions
(Joint Research Centre of the European Commission, 2014); https://go.nature.
com/397sRLk
58. Well-to-Wheels Analysis of Future Automotive Fuels and Powertrains in the
European Context (Joint Research Centre of the European Commission,
2014); https://go.nature.com/394f2xd
59. Upstream Emissions of Fossil Fuel Feedstocks for Transport Fuels Consumed in
the European Union (International Council on Clean Transportation (ICCT),
2014); https://go.nature.com/3cd7Lgp
60. International Energy Agency. Technology Roadmap—Energy-Ecient
Buildings: Heating and Cooling Equipment (IEA/OECD, 2011).
61. European Commission. Commission Delegated Regulation (EU) No 811/2013
of 18 February 2013 supplementing Directive 2010/30/EU of the European
Parliament and of the Council with regard to the energy labelling of space
heaters, combination heaters, packages of space heater, temperature control
and solar device and packages of combination heater, temperature control
and solar device. O. J. Eur. Union L 239/1, 1–81 (2013).
62. Space Heating and Cooling—Technology Brief R02 (IEA Energy Technology
Systems Analysis Program, 2012).
63. Hauck, M., Steinmann, Z. J. N., Laurenzi, I. J., Karuppiah, R. &
Huijbregts, M. A. J. How to quantify uncertainty and variability in life
cycle assessment: the case of greenhouse gas emissions of gas power
generation in the US. Environ. Res. Lett. 9, 074005 (2014).
64. Steinmann, Z. J. N., Hauck, M., Karuppiah, R., Laurenzi, I. J. &
Huijbregts, M. A. J. A methodology for separating uncertainty and variability
in the life cycle greenhouse gas emissions of coal-fueled power generation in
the USA. Int. J. Life Cycle Assess. 19, 1146–1155 (2014).
65. Knobloch, F., Mercure, J.-F., Pollitt, H., Chewpreecha, U. & Lewney, R. in
Technical Study on the Macroeconomics of Energy and Climate Policies
(European Commission, DG Energy, 2017); https://go.nature.com/3cdndJl
66. Mercure, J.-F. Fashion, fads and the popularity of choices: micro-
foundations for diusion consumer theory. Struct. Change Econ. Dyn. 46,
194–207 (2018).
67. Rogers, E. M. Diusion of Innovations (Simon and Schuster, 2010).
68. Wilson, C. Up-scaling, formative phases, and learning in the historical
diusion of energy technologies. Energy Policy 50, 81–94 (2012).
69. Mercure, J.-F. FTT:Power: a global model of the power sector with induced
technological change and natural resource depletion. Energy Policy 48,
799–811 (2012).
70. Mercure, J.-F. etal. e dynamics of technology diusion and the impacts of
climate policy instruments in the decarbonisation of the global electricity
se ctor. Energy Policy 73, 686–700 (2014).
71. Knobloch, F., Huijbregts, M. A. J. & Mercure, J.-F. Modelling the eectiveness
of climate policies: how important is loss aversion by consumers? Renew.
Sustain. Energy Rev. 116, 109419 (2019).
72. Cambridge Econometrics in Final Report for the European Commission
(DG Energy, 2013); https://go.nature.com/2PvVFoK
73. Mercure, J.-F. etal. in Study on the Macroeconomics of Energy and Climate
Policies (European Commission, DG Energy, 2016).
74. E3ME Manual, Version 6 (Cambridge Econometrics, 2014); https://go.nature.
com/2T1FIsO
Acknowledgements
The authors acknowledge funding from the EPSRC (J.-F.M., fellowship no. EP/K007254/1),
the Newton Fund (J.-F.M. and P.S., EPSRC grant nos. EP/N002504/1 and ES/N013174/1),
the ERC (M.A.J.H. and S.V.H., grant no. 62002139 ERC – CoG SIZE 647224), Horizon
2020 (J.-F.M., F.K. and H.P.; Sim4Nexus project no. 689150) and the European Commission
(J.-F.M., H.P., F.K. and U.C.; DG ENERGY contract no. ENER/A4/2015-436/SER/
S12.716128). F.K. acknowledges participants of the CIRED summer school in Paris (2018)
for valuable discussions.
Author contributions
F.K. designed the research and wrote the manuscript, with contributions from all authors.
S.V.H. and F.K. performed the life-cycle analysis, with contributions from M.A.J.H. F.K.,
J.-F.M., U.C. and H.P. ran the model simulations. U.C. and H.P. managed E3ME. J.-F.M.
and A.L. developed FTT:Transport. F.K. and J.-F.M. developed FTT:Heat. J.-F.M. and P.S.
developed FTT:Power.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41893-020-0488-7.
Correspondence and requests for materials should be addressed to F.K.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
NATURE SUSTAINABILITY | www.nature.com/natsustain
... In the analysis of current and future emissions from EV and heat pumps (HP) in 59 regions of the world by Knobloch et al. (2020) current and future emissions of EV and HP were found to be lower in most of the regions. Moreover, in the future, even the most inefficient EV will have lower life-cycle emissions than the best and most efficient gasoline vehicles in many regions of the world (Knobloch et al., 2020). ...
... In the analysis of current and future emissions from EV and heat pumps (HP) in 59 regions of the world by Knobloch et al. (2020) current and future emissions of EV and HP were found to be lower in most of the regions. Moreover, in the future, even the most inefficient EV will have lower life-cycle emissions than the best and most efficient gasoline vehicles in many regions of the world (Knobloch et al., 2020). This finding is further substantiated by Hoekstra (2019), who states that the potential reduction of GHG by battery-electric vehicles (BEV) is underestimated by scientific literature. ...
... He argues that lifecycle GHG emissions of diesel cars is 244 g/km versus that of BEV with 95 g/km, and can be brought further down to 10 g/km by using renewable electricity. Moreover, Knobloch et al. (2020) found that it is safe to issue policies aimed at end-use electrification even if policies for the power industry are limited or complex, which means that having electrified transportation and heating is more beneficial even if the power sector is not decarbonised. Further decarbonisation of electricity generation will bring even more benefits and will help lower atmospheric anthropogenic emissions. ...
Article
Full-text available
Air pollution from the combustion of fossil fuels has adverse health impacts and is linked to cardiovascular disease, strokes, acute respiratory disease and cancer, predominantly in urban centres around the world. Increasing use of renewables for power generation has been seen to bring about the benefits of cleaner air in regions and countries that have high shares of renewable energy installations. This research aims to estimate current NOx, SOx, PM2.5, and PM10 emissions from the energy sector and identify the trends in development of these emissions during a global energy transition towards 100% renewable energy. In this energy transition, total emissions from the global energy sector are projected to drop by almost 92% in 2050 compared to 2015, with annual premature deaths from energy sector-related air pollution reducing by about 97% from 5.2 million deaths in 2015 to 150 thousand deaths by 2050. Annual damage costs are forecast to drop drastically by 88.5% from about 4600 b€ in 2015 to 529 b€ by 2050. This research shows that defossilisation and a shift to renewable forms of energy enables a massive reduction in air pollutant emissions that are directly harmful to human health, resulting in immense health and economic benefits in terms of avoided fatalities and saved health expenditures across the world.
... Dincer, Rosen és Zamfirescu [25] szerint az elektromos autók az elektromos áram forrásának függvényében minden alternatívánál környezetkímélőbbek lehetnek -tegyük hozzá azonban, hogy ez a 2010 előtti elektromos és egyéb megoldásokra vonatkozik. Knobloch et al. [26] rámutatnak, hogy az elektromos gépkocsik használata hosszú távon kevesebb CO 2 kibocsátással jár -akkor is, ha a megtermelt elektromos energia részben fosszilis -, mint a hagyományos meghajtású gépkocsik CO 2 kibocsátása. ...
Article
Full-text available
Az autóiparban és a fogyasztók között egyre nagyobb népszerűségnek örvendenek az elektromos autók; részben azok környezetkímélő jellemzőinek eredményeként. A tanulmány célja, hogy bemutassa az elektromos autók fogyasztói megítélését a környezetvédelmi szempontok tükrében. A tanulmány első felében a szerzők hét jellemző alapján rávilágítanak az elektromos autók környezeti hatásaira: rámutatunk, hogy bár az elektromos autó üzemeltetése során nem szennyezi a környezetet lokálisan, az üzemeltetéshez szükséges akkumulátorok legyártása, azok töltése és megsemmisítése további kérdéseket vet fel a környezetszennyezéssel kapcsolatban. A második részben saját kérdőíves kutatásunk alapján vizsgáljuk a válaszadók hozzáállását az elektromos autókhoz. A kérdőíves adatok alapján elmondható, hogy a fogyasztók továbbra is drágának tartják az elektromos autókat és tisztában vannak vele, hogy az elektromos autók kis mértékben orvosolnak egyes környezetszennyezési kérdéseket. Abstract. In the first half of the paper, the authors highlight the environmental impacts of electric cars based on seven characteristics: for example, we point out that although the operation of an electric car does not pollute the environment locally, the production, charging and disposal of the batteries needed to run it raises further questions about pollution.
... Undoubtedly, EVs have minimal climate impact as opposed to vehicles that run on combustion engines (Knobloch et al., 2020). Along with governmental subsidies and technological progress, this has resulted in the massive production of electric cars (Deng, Bae, Denlinger & Miller, 2020). ...
Conference Paper
Full-text available
The purpose of this study is to make an Analysis of FinTech usage in the banking industry and to highlight the Opportunities and Challenges in Central and Eastern European countries in adopting FinTech. Using a comparative analysis, the most progressive countries regarding the development of fintech companies in Central and Eastern European (CEE) region are: Czech Republic, Slovenia and Poland. The main challenges that banks in CEE countries may bring from cooperation with fintech companies are: data security remains as a main challenge, difficulty in hiring qualified personnel to make the collaboration effective, regulatory issues, banks are becoming more dependent on financial technology solutions. While the main benefits are: bank customers would benefit from the opportunity to transact using cutting-edge technologies, saving time, effort, and money in the process, joint investment in technology, innovation, in terms of high transaction volumes with a low operating cost. Fintech companies can also benefit from these collaborations such as: in a pure peer-to-peer lending model the fintech platform does not take any risk, financial markets are highly standardized and low-cost, they can gain access to a certain market as well as the customers within that region. However, they also have challenges in the CEE market: Cybersecurity risks, lack of support system for fintech innovation, lack of a clear fintech strategy by regulators and policy makers, emigration of local talent or “brain drain” and smaller domestic market. Keywords: Fintech, CEE countries, Banks, Opportunities, Challenges, Sustainabilit
... Although achieving this goal will depend on decarbonisation of the power sector (or hydrogen supply), deployment of BEVs already saves emissions even in countries where the power supply is dominated by coal as a result of their high energy efficiency compared to ICEs. The emissions saving will continue to increase as the power sector is decarbonised (Knobloch, 2020). ...
Technical Report
The Breakthrough Agenda was launched by 45 world leaders at COP26 and is a commitment to work together this decade to accelerate innovation and deployment of clean technologies, making them accessible and affordable for all this decade. To kick start this Agenda, countries endorsed Breakthrough goals to make clean technologies and sustainable practices more affordable, accessible and attractive than their alternatives by 2030 in the power, road transport, steel, hydrogen and agriculture sectors. The Breakthrough Agenda establishes an annual cycle to track developments towards these goals, identify where further coordinated international action is urgently needed to accelerate progress and then galvanise public and private international action behind these specific priorities in order to make these transitions quicker, cheaper, and easier for all. To initiate this cycle, world leaders tasked the IEA, IRENA and the UN Climate Change High Level Champions to develop an annual Breakthrough Agenda report to provide an independent evidence base and expert recommendations for where stronger international collaboration is needed. This document, the 2022 Breakthrough Agenda Report, is the first of these annual reports. It provides an assessment of progress towards each Breakthrough goal and a framework for tracking progress in the future, a pathway of coordinated international actions through to 2030 and a set of specific recommendations on the most urgent and high impact opportunities to strengthen international collaboration that can accelerate progress across each Breakthrough sector.
Article
Lithium-ion batteries (LIBs) have been gathering increasing attention worldwide as they are being widely used in portable devices and implemented in electric vehicles. With the increasing volume of LIBs poured into the market, the recycling of LIBs is becoming essential because the elements being currently used in LIBs, such as, Li and Co, have limited deposits in the world. In this paper, various recycling approaches, which are now prevalent or have the potential to become dominant in the near future are reviewed. Recycled materials reuse performance is included to exhibit the feasibility of these recycling technologies. Furthermore, emerging cathode chemistries are also introduced and possible recycling strategies for them are discussed.
Article
Manufacturing companies are expected to make decisions that achieve not only the goals of the company but also the goals of society. Each company’s decisions affect the material flow and demand of other companies. Therefore, each company can play a role in strategic management by predicting in advance the impact of its own and other companies’ decisions on the achievement of social goals. To support such strategic management, this study proposes a life cycle simulation method that can estimate the impact of strategic decisions by considering social goals. The target is a connected life cycle systems (CoLSys) consisting of multiple product life cycle systems and interactions, in which the interactions are operated according to the life cycle system of each product. A decision-making model is included in the proposed method, and changes in the interaction settings are made in each product life cycle system to achieve predefined social and individual goals. To show the effectiveness of the proposed method, a case study was conducted for a CoLSys consisting of six products: electric vehicles, gasoline vehicles, hybrid vehicles, home batteries, battery charging stands, and photovoltaic power generation systems. In the case study, the social goal was decarbonization by 2050 and the individual goal was increasing profits. The simulation results confirmed that the decision-making model would result in greater reductions in CO 2 emissions, including a faster transition from gasoline vehicles to electric vehicles. Moreover, we confirmed that the decision-making model contributed to balancing the achievement of social goals with the benefits of individual systems while adjusting the intensity of the interactions. However, it was found that decarbonization cannot be achieved by 2050 if only the assumed products and interactions are applied in the case study.
Article
In Complexity Economics for Environmental Governance, Jean-François Mercure reframes environmental policy and provides a rigorous methodology necessary to tackle the complexity of environmental policy and the transition to sustainability. The book offers a detailed account of the deficiencies of environmental economics and then develops a theory of innovation and macroeconomics based on complexity theory. It also develops a new foundation for evidence-based policy-making using a Risk-Opportunity Analysis applied to the sustainability transition. This multidisciplinary work was developed in partnership with prominent natural scientists and economists as well as active policy-makers with the aim to revolutionize thinking in the face of the full complexity of the sustainability transition, and to show how it can best be governed to minimize its distributional impacts. The book should be read by academics and policy-makers seeking new ways to think about environmental policy-making.
Chapter
To accelerate any electric vehicle or electric motor a high power with high energy density-based energy storage system is required. Secondary batteries (Li-ion) (energy density of 130–250 Wh kg⁻¹ and power density of <1200 W kg⁻¹) and electrochemical capacitors (energy density: <15 Wh kg⁻¹ and power density: >20,000 W kg⁻¹) are incapable to fulfill the requirement of high energy density and high-power density in a single system. For low power consumption devices (laptops, smartphones, tablets, power backup systems, etc.) the secondary batteries are suitable and acceptable but not for high power consumption applications (e-vehicles, bikes, power tools, etc.). To overcome this, researchers look forward to making a new device by which both high energy density and power density can be achieved. A hybrid energy storage system (HESS) is the coupling of two or more energy storage technologies in a single device. In HESS a battery type of electrode is used in which the redox process is followed. On the other side capacitor type of electrode material is used in which a double layer is formed during the process. HESS is the alternative and trade between supercapacitors and batteries. HESS devices show sufficient energy and power densities, self-discharge rate, efficiency, lifetime, etc. Lithium-ion, sodium-ion, potassium-ion, etc., based batteries, capacitors, and fuel cells are frequently used as HESS. An arrangement of transition-metal and carbon-based materials are a well-known combination of hybrid systems. These systems offer possibilities to develop highly-efficient electrodes. Lithium-ion-based hybrid batteries are already commercialized for the e-vehicles by the Nissan motor corporation, Tesla Model S and X, BMW iX3, etc. In this chapter, the Na-ion and Li-ion-based hybrid energy storage devices will be discussed. The used electrode materials for hybrid energy storage systems and some basic understanding of electrochemical energy storage devices will be discussed.
Article
Full-text available
Reliable decarbonisation policies can only be developed with a thorough understanding of how consumers choose between energy technologies. Current energy models assume optimal consumer decisions which may result in expectations of the effectiveness of climate policies that are far too optimistic. Prospect Theory, on the other hand, aims to model real-life choices, based on empirical observations that losses have a relatively larger influence on decisions than gains, relative to a reference point. Here, we show for the first time how loss aversion can be included into a global energy model with high spatial resolution, using heating technology uptake as a case study. We simulate the future heating technology diffusion for 59 world regions covering the globe, with and without the consideration of loss aversion. We find that ignoring the implications of loss aversion overestimates the market uptake of renewables, in individual countries as well as on the global level. As a consequence , loss aversion results in higher projected CO 2 emissions by households, and the need for much stronger policy instruments for achieving decarbonisation targets. In the case of residential heating, a carbon tax of 200 €/tCO 2 is projected to reduce overall emission levels to a similar extent than a carbon tax of 100 €/tCO 2 without the consideration of loss aversion. Even for similar degrees of decarbonisation, accounting for loss aversion implies substantial changes in the underlying technology composition: technology choices become subject to a 'conservative shift' towards low-carbon technologies which are relatively less efficient, but already more established in local markets.
Article
Full-text available
Finding scalable lithium-ion battery recycling processes is important as gigawatt hours of batteries are deployed in electric vehicles. Governing bodies have taken notice and have begun to enact recycling targets. While several battery recycling processes exist, the greenhouse gas emissions impacts and economic prospects of these processes differ, and could vary by specific battery chemistry. Here we use an attributional life-cycle analysis, and process-based cost models, to examine the greenhouse gas emissions, energy inputs and costs associated with producing and recycling lithium-ion cells with three common cathode chemistries: lithium nickel manganese cobalt oxide (NMC-622), lithium nickel cobalt aluminium oxide and lithium iron phosphate. We compare three recycling processes: pyrometallurgical and hydrometallurgical recycling processes, which reduce cells to elemental products, and direct cathode recycling, which recovers and reconditions ceramic powder cathode material for use in subsequent batteries—retaining a substantial fraction of the energy embodied in the material from their primal manufacturing process. While pyrometallurgical and hydrometallurgical processes do not significantly reduce life-cycle greenhouse gas emissions, direct cathode recycling has the potential to reduce emissions and be economically competitive. Recycling policies should incentivize battery collection and emissions reductions through energetically efficient recycling processes. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
Article
Full-text available
Today, more than ever, ‘Spaceship Earth’ is an apt metaphor as we chart the boundaries for a safe planet¹. Social scientists both analyse why society courts disaster by approaching or even overstepping these boundaries and try to design suitable policies to avoid these perils. Because the threats of transgressing planetary boundaries are global, long-run, uncertain and interconnected, they must be analysed together to avoid conflicts and take advantage of synergies. To obtain policies that are effective at both international and local levels requires careful analysis of the underlying mechanisms across scientific disciplines and approaches, and must take politics into account. In this Perspective, we examine the complexities of designing policies that can keep Earth within the biophysical limits favourable to human life.
Article
Full-text available
Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies.
Article
Full-text available
Transport generates a large and growing component of global greenhouse gas emissions contributing to climate change. Effective transport emissions reduction policies are needed in order to reach a climate target well below 2 ∘C. Representations of technology evolution in current integrated assessment models (IAM) make use of systems optimisations that may not always provide sufficient insight on consumer response to realistic policy packages for extensive use in policy-making. Here, we introduce FTT: transport, an evolutionary technology diffusion simulation model for road transport technology, as an IAM sub-component, which features sufficiently realistic features of consumers and of existing technological trajectories that enables to simulate the impact of detailed climate policies in private passenger road transport. Integrated to the simulation-based macroeconometric IAM E3ME-FTT, a plausible scenario of transport decarbonisation is given, defined by a detailed transport policy package, that reaches sufficient emissions reductions to achieve the 2 ∘C target of the Paris Agreement. Electronic supplementary material The online version of this article (10.1007/s10584-018-2262-7) contains supplementary material, which is available to authorized users.
Article
Full-text available
The Paris Agreement aims to address the gap between existing climate policies and policies consistent with “holding the increase in global average temperature to well below 2 C”. The feasibility of meeting the target has been questioned both in terms of the possible requirement for negative emissions and ongoing debate on the sensitivity of the climate– carbon-cycle system. Using a sequence of ensembles of a fully dynamic three-dimensional climate–carbon-cycle model, forced by emissions from an integrated assessment model of regional-level climate policy, economy, and technological transformation, we show that a reasonable interpretation of the Paris Agreement is still technically achievable. Specifically, limiting peak (decadal) warming to less than 1.7 °C, or end-of- century warming to less than 1.54 °C, occurs in 50% of our simulations in a policy scenario without net negative emis- sions or excessive stringency in any policy domain. We evalu- ate two mitigation scenarios, with 200 gigatonnes of carbon and 307 gigatonnes of carbon post-2017 emissions respec- tively, quantifying the spatio-temporal variability of warming, precipitation, ocean acidification and marine productivity. Under rapid decarbonization decadal variability dominates the mean response in critical regions, with significant implications for decision-making, demanding impact methodologies that address non-linear spatio-temporal responses. Ignoring carbon-cycle feedback uncertainties (which can explain 47% of peak warming uncertainty) becomes unreasonable under strong mitigation conditions.
Article
Full-text available
Several major economies rely heavily on fossil fuel production and exports, yet current low-carbon technology diffusion, energy efficiency and climate policy may be substantially reducing global demand for fossil fuels1,2,3,4. This trend is inconsistent with observed investment in new fossil fuel ventures1,2, which could become stranded as a result. Here, we use an integrated global economy–environment simulation model to study the macroeconomic impact of stranded fossil fuel assets (SFFA). Our analysis suggests that part of the SFFA would occur as a result of an already ongoing technological trajectory, irrespective of whether or not new climate policies are adopted; the loss would be amplified if new climate policies to reach the 2 °C target of the Paris Agreement are adopted and/or if low-cost producers (some OPEC countries) maintain their level of production (‘sell out’) despite declining demand; the magnitude of the loss from SFFA may amount to a discounted global wealth loss of US$1–4 trillion; and there are clear distributional impacts, with winners (for example, net importers such as China or the EU) and losers (for example, Russia, the United States or Canada, which could see their fossil fuel industries nearly shut down), although the two effects would largely offset each other at the level of aggregate global GDP.
Article
Full-text available
Current Chinese policy promotes the development of both electricity-propelled vehicles and carbon-free sources of power. Concern has been expressed that electric vehicles on average may emit more CO2 and conventional pollutants in China. Here, we explore the environmental implications of investments in different types of electric vehicle (public buses, taxis and private light-duty vehicles) and different modes (fast or slow) for charging under a range of different wind penetration levels. To do this, we take Beijing in 2020 as a case study and employ hourly simulation of vehicle charging behaviour and power system operation. Assuming the slow-charging option, we find that investments in electric private light-duty vehicles can result in an effective reduction in the emission of CO2 at several levels of wind penetration. The fast-charging option, however, is counter-productive. Electrifying buses and taxis offers the most effective option to reduce emissions of NOx, a major precursor for air pollution.
Article
A high degree of consensus exists in the climate sciences over the role that human interference with the atmosphere is playing in changing the climate. Following the Paris Agreement, a similar consensus exists in the policy community over the urgency of policy solutions to the climate problem. The context for climate policy is thus moving from agenda setting, which has now been mostly established, to impact assessment, in which we identify policy pathways to implement the Paris Agreement. Most integrated assessment models currently used to address the economic and technical feasibility of avoiding climate change are based on engineering perspectives with a normative systems optimisation philosophy, suitable for agenda setting, but unsuitable to assess the socio-economic impacts of a realistic baskets of climate policies. Here, we introduce a fully descriptive, simulation-based integrated assessment model designed specifically to assess policies, formed by the combination of (1) a highly disaggregated macro-econometric simulation of the global economy based on time series regressions (E3ME), (2) a family of bottom-up evolutionary simulations of technology diffusion based on cross-sectional discrete choice models (FTT), and (3) a carbon cycle and atmosphere circulation model of intermediate complexity (GENIE-1). We use this combined model to create a detailed global and sectoral policy map and scenario that sets the economy on a pathway that achieves the goals of the Paris Agreement with >66% probability of not exceeding 2°C of global warming. We propose a blueprint for a new role for integrated assessment models in this upcoming policy assessment context.
Knowledge acquisition by consumers is a key process in the diffusion of innovations. However, in standard theories of the representative agent, agents do not learn and innovations are adopted instantaneously. Here, we show that in a discrete choice model where utility-maximising agents with heterogenous preferences learn about products through peers, their stock of knowledge on products becomes heterogenous, fads and fashions arise, and transitivity in aggregate preferences is lost. Non-equilibrium path-dependent dynamics emerge, the representative agent exhibits behavioural rules different than individual agents, and aggregate utility cannot be optimised. Instead, an evolutionary theory of product innovation and diffusion emerges.