Quantifying the potential for climate change mitigation of consumption options

Abstract

Around two-thirds of global GHG emissions are directly and indirectly linked to household consumption, with a global average of about 6 tCO2eq/cap. The average per capita carbon footprint of North America and Europe amount to 13.4 and 7.5 tCO2eq/cap, respectively, while that of Africa and the Middle East to 1.7 tCO2eq/cap on average. Changes in consumption patterns to low-carbon alternatives therefore present a great and urgently required potential for emission reductions.
In this paper, we synthesize emission mitigation potentials across the consumption domains of food, housing, transport and other consumption.

We systematically screened 6,990 records in the Web of Science Core Collections and Scopus. Searches 21 were restricted to 1) reviews of lifecycle assessment studies and 2) multiregional input-output studies of household consumption, published after 2011 in English. We selected against pre-determined eligibility criteria and quantitatively synthesized findings from 53 studies in a meta-review. We identified 771 original options, which we summarized and presented in 61 consumption options with a positive mitigation potential. We used a fixed-effects model to explore the role of contextual factors (geographical, technical and socio-demographic factors) for the outcome variable (mitigation potential per capita) within consumption options.

We establish consumption options with a high mitigation potential measured in tons of CO2eq/capita/yr. For transport, the options with the highest mitigation potential include living car-free, shifting to a battery electric vehicle, and reducing flying by a long return flight with a median reduction potential of more than 1.7 tCO2eq/cap. In the context of food, the highest carbon savings come from dietary changes, particularly an adoption of vegan diet with an average and median mitigation potential of 0.9 and 0.8 tCO2eq/cap, respectively. Shifting to renewable electricity and refurbishment and renovation are the options with the highest mitigation potential in the housing domain, with medians at 1.6 and 0.9 tCO2eq/cap, respectively. We find that the top 10 consumption options together yield an average mitigation potential of 9.2 tCO2eq/cap, indicating substantial contributions towards achieving the 1.5-2°C target, particularly in high-income context.

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Environ. Res. Lett. 15 (2020) 093001 https://doi.org/10.1088/1748-9326/ab8589
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TOPICAL REVIEW
Quantifying the potential for climate change mitigation of
consumption options
Diana Ivanova1, John Barrett1, Dominik Wiedenhofer2, Biljana Macura3, Max Callaghan1,4
and Felix Creutzig4,5
1School of Earth and Environment, University of Leeds, Leeds, United Kingdom
2Institute of Social Ecology, University of Natural Resources and Life Sciences, Vienna, Austria
3Stockholm Environment Institute, Linn´
egatan 87D, Box 24218, Stockholm 10451, Sweden
4Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany
5Technical University Berlin, Str. des 17. Junis 135, 10623 Berlin, Germany
E-mail: d.ivanova@leeds.ac.uk
Keywords: sustainable consumption, carbon footprint, mitigation potential, food, transport, housing, climate change
Supplementary material for this article is available online
Abstract
Background. Around two-thirds of global GHG emissions are directly and indirectly linked to
household consumption, with a global average of about 6 tCO2eq/cap. The average per capita
carbon footprint of North America and Europe amount to 13.4 and 7.5 tCO2eq/cap, respectively,
while that of Africa and the Middle East—to 1.7 tCO2eq/cap on average. Changes in consumption
patterns to low-carbon alternatives therefore present a great and urgently required potential for
emission reductions. In this paper, we synthesize emission mitigation potentials across the
consumption domains of food, housing, transport and other consumption.
Methods. We systematically screened 6990 records in the Web of Science Core Collections and
Scopus. Searches were restricted to (1) reviews of lifecycle assessment studies and (2) multiregional
input-output studies of household consumption, published after 2011 in English. We selected
against pre-determined eligibility criteria and quantitatively synthesized findings from 53 studies
in a meta-review. We identified 771 original options, which we summarized and presented in 61
consumption options with a positive mitigation potential. We used a fixed-effects model to explore
the role of contextual factors (geographical, technical and socio-demographic factors) for the
outcome variable (mitigation potential per capita) within consumption options.
Results and discussion. We establish consumption options with a high mitigation potential
measured in tons of CO2eq/capita/yr. For transport, the options with the highest mitigation
potential include living car-free, shifting to a battery electric vehicle, and reducing flying by a long
return flight with a median reduction potential of more than 1.7 tCO2eq/cap. In the context of
food, the highest carbon savings come from dietary changes, particularly an adoption of vegan diet
with an average and median mitigation potential of 0.9 and 0.8 tCO2eq/cap, respectively. Shifting
to renewable electricity and refurbishment and renovation are the options with the highest
mitigation potential in the housing domain, with medians at 1.6 and 0.9 tCO2eq/cap, respectively.
We find that the top ten consumption options together yield an average mitigation potential of 9.2
tCO2eq/cap, indicating substantial contributions towards achieving the 1.5 ◦C–2 ◦C target,
particularly in high-income context.
1. Background
1.1. The need for demand reductions
Global greenhouse gas (GHG) emissions (carbon
footprints) have been steadily rising, with faster, siz-
able and immediate CO2emissions declines needed
to limit cumulative emissions and reach net zero
emissions in 2050 [1]. Annual GHG emissions must
decrease by 45% percent of their 2010-levels by 2030,
and reach net-zero by 2050 to limit temperature
changes to 1.5 ◦C above preindustrial levels. The
potential impacts and risks are substantially lower
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
for a 1.5 ◦C global warming compared with a 2 ◦C,
including climate-related risks and threats regarding
various ecosystems and human welfare [1]. Global
GHG emissions amounted to 6.3 tCO2eq/cap in
2011 [2]; however, these are highly unequally dis-
tributed across income groups and countries [3–8].
For example, the average per capita carbon footprint
of North America and Europe amount to 13.4 and
7.5 tCO2eq/cap, respectively, while that of Africa and
the Middle East—to 1.7 tCO2eq/cap on average (SM
figure 1). For a population of 8.5 billion by 2030
[9], emissions need to decrease to an average of ~2.8
tCO2eq/cap by 2030, to comply with a pathway of
limiting climate change to 1.5 ◦C of global warming.
This is broadly in line with other estimates of per cap-
ita carbon budgets [10–12].
The exact carbon budget for limiting global
warming to 1.5 ◦C is influenced by uncertainty about
earth system dynamics, as well as the scale and
speed of adoption of negative emission technologies.
Almost all of the IPCC scenarios currently assume
large-scale adoption of negative emission technolo-
gies at massive scales [14–16], which are potentially
associated with strong adverse economic and envir-
onmental consequences [17], energy constraints (e.g.
expanding carbon) [18] and moral hazards because
they tempt policy makers to delay mitigation action
now [16].
Energy end-use is the least efficient part of
the global energy system with the largest improve-
ment potential, where appropriate scaling down of
the global energy demand allows for feasible de-
carbonization without betting on controversial neg-
ative emission technologies or geoengineering. While
technological solutions that decarbonize energy
supply or capture carbon have to make a significant
mitigation contribution, changing consumption
offers more flexibility for reducing carbon intensity
in the energy supply sector and limit the related
supply-side risks [19]. Mitigation scenarios relying
more heavily on reduction in the demand of energy
services are clearly associated with the lowest mitiga-
tion and adaptation challenges [16,20] and provide
a range of co-benefits.
1.2. Challenging consumption
Behavior, everyday life and cultural norms around
consumption have a crucial influence on energy
use and embodied emissions, with a high mitiga-
tion potential in various consumption domains [19,
21,22]. 65% of global GHG emissions, and 50%–
80% of land, water and material use, can be directly
and indirectly linked to household consumption [3].
Income is a major driver of household carbon foot-
prints [5,7,8,23,24], directly affecting purchas-
ing power of households. Changes in household con-
sumption patterns to low-carbon alternatives, such
as transport model shifts, home energy reduction
and dietary shifts, thus present a great mitigation
potential.
Importantly, in the last decade, so-called multire-
gional input-output models (MRIO) have enabled
the systematic analysis of global production and con-
sumption using consistent accounts of global GHG
emissions, and taking into account the scale and com-
plexity of international trade and supply chains [25–
27]. Consumption estimates derived through MRIOs
were the first to fully allocate global emissions to
national household consumption (as well as gov-
ernment activities and investments) without double-
counting or omitting emissions, thus overcoming
a long-standing limitation of single-regional input-
output approaches and lifecycle assessment (LCA)
studies [28,29]. However, understanding options for
change also requires bottom-up detailed information
and insights going down to the product-level—which
is a challenge for MRIOs as they offer a quite limited
product detail. In this context, LCAs are relevant due
to their process-specific and highly detailed nature.
Here we argue that a combination of bottom-up and
top-down approaches provides a robust base for the
review of the mitigation potentials of consumption
options.
In this paper, we systematically review the liter-
ature on mitigation potentials across various con-
sumption domains, including food, housing, and
transport, focusing on academic publications since
2011 to ensure relevance of derived estimates. While
prior studies address some of these concerns (for a
non-comprehensive list of studies see [11,17,30–
32]), we conduct meta-review including the more
recent evidence. Therefore, we provide a richer and
more updated evidence base to inform about mitiga-
tion potentials of changes in consumption practices,
policies and infrastructure.
For the purpose of this paper, we do not cap-
ture mitigation potential associated with other aven-
ues towards social change [22], such as community
action and engagement [33,34], policies and incent-
ives, political engagement and non-violent civil dis-
obedience [35] or reductions in overall working time
and re-definitions of paid labour [24], which all are
highly relevant for challenging societal norms around
consumption and tackling climate change. Supply
chain actors play a key role for climate change mitiga-
tion, having direct agency over the majority of energy
and emissions along supply chains [36,37]. Simil-
arly, structural change by governments, ending fossil-
fuel support, and providing low-carbon infrastruc-
tures, is crucial to enable climate change mitigation
[38–40]. We also do not review system-wide effects
and potential for income rebound effects [41–43].
Our focus on consumption options should not be
interpreted as passing the mitigation responsibility to
consumers [44]. Still, a change in consumption prac-
tices is needed for reaching net-zero carbon emissions
[1,45].
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
1.3. Research questions
Primary question: What is the mitigation potential of
household-level consumption options within mobility,
housing and food sectors, when considering GHG emis-
sions along the whole lifecycle?
The primary question consists of the following
question components:
Population (P): Household consumption of food,
mobility and housing
Intervention (I): Consumption options within each
end-use sector
Comparator (C): Average per capita carbon footprints
of food, mobility and housing
Outcome (O): Annual carbon savings measured in per
capita CO2-equivalent reductions
Study types: LCA review studies with quantitative
synthesis of data, MRIO studies of household con-
sumption, consumption scenario studies
We focus on household consumption associ-
ated with the three end-use sectors of food, trans-
port and housing as they are highly relevant in
terms of consumption-based GHG emissions [3,46],
energy [47] and other resource use [3] with some of
the highest potential for consumption intervention
[30,48].
Secondary question: What factors may explain dif-
ferences in carbon savings associated with each con-
sumption option across studies and contexts?
We aimed to capture sources of heterogen-
eity across studies, including system boundary [49],
methodological specificities, socio-economic, urban-
rural and geographical context among others.
2. Methods and search results
The review followed the Collaboration for Environ-
mental Evidence Guidelines [50] and it conformed
to ROSES reporting standards [51]. It was conduc-
ted according to peer-reviewed protocol [52] that
was submitted to Environmental Research Letters
in March 2019 and approved in April 2019. The
approved protocol is openly available online [52].
2.1. Deviations from the protocol (outline)
The following changes were made from the final pub-
lished protocol [52]: first, we applied machine learn-
ing in the article screening process; second, we dis-
cussed the variation among studies in a qualitative
manner in text rather than using the CEESAT tool for
critical assessment (which was not suitable to assess
non-review studies).
2.2. Searches for literature
Searches were performed on Web of Science Core
Collections (WoSCC) and Scopus to identify relevant
peer-reviewed studies published after 2011, using the
University of Leeds subscription. The searches were
done on titles, keywords and abstracts in English.
The search string was composed of three sub-
strings: the GHG emission (X), study type (review) (Y)
and consumption domain (Z) sub-string (table 1). The
sub-strings were connected with the Boolean oper-
ator ‘AND’ as follows: X AND Y AND Z. We based
the GHG emission sub-string (X) on prior similar
searches [53,54]. The consumption domain sub-string
(Z) captured the consumption domains of trans-
port, food, housing and other consumption (gen-
eral), and specific consumption options (interven-
tions) within these domains. The sub-strings in each
domain-specific cell were connected with Boolean
operator ‘OR’ to form the consumption domain sub-
string (Z). To test comprehensiveness of the search,
we used a list of benchmark papers (see the protocol
for details).
A search on WoSCC (conducted on 24 May
2019) yielded 5638 records and on Scopus addi-
tional 1352 records (see the supplementary materi-
als for search queries), totaling 6990 records. The res-
ults of both searches were combined into a ‘Scoping
Review Helper’ library where exact duplicates were
removed. Figure 1provides more detailed overview
of the search and screening process of the review.
2.3. Article screening and eligibility criteria
Article screening was done first at the title and
abstract level, and then on full text level (figure
1). The title and abstract screening was supported
by machine learning. Table 2provides an overview
the eligibility criteria according to the PICO frame-
work (see the supplementary information for more
details).
Having reviewed the first 991 records (15% of
unique records) drawn randomly from the total num-
ber of records, we started an iterative process where
at each iteration, we (1) trained a machine learning
model with the already screened documents; (2) fit-
ted this model on the unseen documents; and (3)
assigned the next set of documents for review by
selecting the documents predicted to be most rel-
evant. We went through four iterations of machine
learning prioritized screening, (see figure 2(a) and
each had decreasing proportions of relevant docu-
ments in the set of reviewed records. The first iter-
ation of 250 documents contained 38% of relevant
records, while the last iteration of 100 documents—
only 3% relevant documents. We screened a final ran-
dom sample of 100 documents, and used this sample
to generate an estimate of the number of relevant doc-
uments remaining using the Agresti-Coull confidence
interval. Figure 2(b) shows the minimum recall at dif-
ferent levels of uncertainty.
After titles and abstract screening, we considered
228 relevant records at full-text (figure 1). In addi-
tion, nine pre-screened articles were added separately,
which were considered relevant but were not found
through the original search. Six of these additions
were not published at the time of the original search.
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Search
Records identified through Web of Science
Core Collections (n = 5,638)
Records identified through Scopus
(n = 2,837)
Records after exact duplicates
removed (n =6,990)
Screening
Records after title and abstract
screening (n = 228)
Articles retrieved at full text
(n =215)
Articles after full text screening
(n =44)
Duplicates
(n =1,485)
Duplicates
(n = 228)
Excluded records
(n = 6,534)
Irretrievable full texts
(Not accessible, n = 13)
Excluded full texts, with reasons
(n = 169)
Excluded on:
•No quantitative assessment of
mitigation potential (intervention)
(n = 110)
•Assessment only by functional
unit (outcome) (n = 45)
•Study type (n = 14)
Pre-screened articles
from other sources
(n =9)
Articles included after full text
screening (n =53)
Initial consistency
check
(n = 100)
Records after duplicates removed
through Jaccard similarity index
(n =6,762)
Random-record
screening
(n = 891)
Machine learning
prioritized
screening (n = 700)
Final random
sample
(n = 100)
Figure 1. Flow diagram—adapted from the ROSES flow diagram for systematic reviews [13]. See the supplementary data
extraction for more detail about excluded articles and the supplemenary materials (https://stacks.iop.org/ERL/15/
093001/mmedia) for details on the methods.
We applied the inclusion and exclusion criteria (table
2) and a final set of 53 articles were considered eli-
gible at full text. See the supplementary materials and
extraction sheet for more details on the procedure.
We used software for evidence synthesis ‘Scoping
Review Helper’ (developed by MCC Berlin), for man-
aging search results, removing duplicates, screening
records, extracting data and conducting synthesis. We
also designed search queries through managing top-
ics iteratively, and refined the inclusion criteria dur-
ing the screening process.
2.4. Data extraction and synthesis
We extracted meta-data from each reviewed study,
including title, author team, year of publication and
data collection, consumption option and domain,
geographical context, method, system boundary, car-
bon metric and GHGs included from the eligible
studies. We further extracted the study quantitat-
ive findings, e.g. average, standard deviation, num-
ber of studies reviewed, min-max range, absolute
and relative carbon savings, contextual carbon foot-
print calculations. Missing or unclear information
was requested directly from authors. We recal-
culated the mitigation potential of consumption
options in tons CO2equivalents per capita where
needed in order to improve comparability across
studies.
The baselines considered in the reviewed studies
are associated with large uncertainties and different
assumptions (e.g. average baseline vs high-carbon
baseline). At the same time, the baselines are key
for the calculation of mitigation potentials and may
largely affect the order of consumption options on the
graph. In such cases results should be interpreted with
caution.
4

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
2.5. Data synthesis and potential effect
modifiers/reasons for heterogeneity
Included literature is characterized by a large vari-
ation in methods, internal validity of studies, cover-
age of different GHGs, location and timeframe, sys-
tem boundary, assumptions about uptake rate [57]
and other potential sources of heterogeneity. We dis-
cussed heterogeneity along with the narrative syn-
thesis of study findings. Where data allowed, we con-
sidered the effect modifiers in quantitative synthesis.
We used a fixed-effects model to explore the relation-
ship between predictors (various geographical, tech-
nical and socio-demographic factors) and outcome
variables (mitigation potential per capita) across
consumption options as a way to explain the vari-
ation in mitigation potential. Using the fixed-effect
approach, we control for factors invariant across mit-
igation options, which we could not include directly
in our model.
3. Review results
Figures 3–6depict the mitigation potential ranges of
various consumption options in the domains of food,
transport, housing and other consumption. Positive
values are associated with positive mitigation poten-
tial, with the options ordered by medians.
3.1. Transport
The highest mitigation potential of reviewed options
is found in the domain of transport (figure 3), which
is also associated with a substantial carbon footprint
in most world regions (SM figure 1). The consump-
tion options with the highest mitigation potential
advocate reduction in car and air travel, as well as a
shift toward less carbon intensive fuel sources, means
and modes of transportation.
There is substantial mitigation potential in redu-
cing air travel for those who fly. One less flight (long
return) may reduce between 4.5 and 0.7 (mean of
1.9) tCO2eq/cap, while taking One less flight (medium
return)—between 1.5 and 0.2 (0.6) tCO2eq/cap. The
two options have a median reduction potential of 1.7
and 0.6 tCO2eq/cap, respectively. Yet, the number of
trips per passenger in 2018 amounted to 2.0 in the
United States and to 3.6 and 4.8 in wealthy European
countries such as Luxembourg and Norway, with the
numbers projected to increase rapidly [64]. Other
studies exploring partial reductions in air travel (Less
transport by air) find an average reduction potential
of 0.8 tCO2eq/cap. The overall mitigation potentials
strongly depend on income, as high-income house-
holds fly much more [4,5,65].
Reducing car travel is associated with substantial
mitigation potential. Living car-free has the highest
median mitigation potential across all of the reviewed
options at 2.0 tCO2eq/cap, with a range between
3.6 and 0.6 tCO2eq/cap. Assumptions around vehicle
and fuel characteristics as well as travel distance are
key for the estimated mitigation potential, with the
maximum value in our sample being associated with
giving up an SUV [30]. Partial car reductions, cap-
tured by the options of Less car transport, Shift to
active transport and Shift to public transport in our
sample, have an average mitigation potential between
0.6 and 1.0 tCO2eq/cap. These options are generally
limited to replacing short and urban car trips with
alternative transportation modes or reducing leisure
trips [43,66–68], which constitute a relatively small
portion of all travel and its embodied emissions [58,
69,70]. Yet, active and public transport alternatives
have much lower carbon intensities per travel km
[58,71,72]. Active and public transport are char-
acterized by average carbon intensities at 0.00 and
0.09 kgCO2eq km−1, while individualized motorized
transport at 0.23 kgCO2eq km−1[58]. Telecommut-
ing practices reduce commute emissions between 1.4
and 0.1 (mean of 0.4) tCO2eq/cap, while Car-pooling
and car-sharing and Fuel efficient driv ing have an aver-
age carbon savings of 0.3 tCO2eq/cap. The practice
of ride-hailing, or receiving transportation from an
unlicensed taxi service, may result in an increase in
emissions as a result of ‘deadheading’, the travelled
miles without a passenger between hired rides [73].
For example, a non-pooled ride-hailing trip generates
47% greater emissions per mile compared to a private
car trip of an average fuel efficiency [73]. The num-
ber of passenger sharing the trip makes a substantial
difference in terms of mitigation potential, as well as
the type of trip that is displaced (e.g. private driving,
public transit, walking). Thus, the shift from public
transport to active transport [43] offers only marginal
mitigation potential per capita (figure 3).
The differences in assumed travelled distance
explain why options for reducing car travel alto-
gether may show lower mitigation potential com-
pared to a shift to alternatives of internal combus-
tion engine vehicles (ICEV). The Shift to battery elec-
tric vehicle (BEV) from ICEV has mitigation poten-
tial between 5.4 and −1.9 tCO2eq/cap, with an aver-
age and median of 2.0 tCO2eq/cap. Carbon reduc-
tion potential varies between 3.1 and −0.2 (mean of
0.7) tCO2eq/cap for (plug-in) hybrid electric vehicles
(PHEV/HEV), and between 5.8 and −3.4 (mean of 0)
tCO2eq/cap for fuel cell vehicles (FCV). The carbon
intensity of the electricity mix (widely varying across
countries [61]) is crucial for the GWP of BEVs [61,
74–77], where the electricity mix alone was found to
explain almost 70% of the variability in LCA results
[77]. Furthermore, while modelling studies are often
based on the average grid carbon intensity, the mar-
ginal emissions factor may be substantially higher if
additional demand is met by fossil-fuel thermal plants
[61,77], e.g. 35% higher in the UK [61]. Fuel con-
sumption is the most influential factor affecting the
GWP of ICEV, HEV and PHEV [75]. PHEV have a
similar electricity consumption to that of BEV when
driving electric [76]. Strong coal-dependence (when
5

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 2. Screening progress (a) and probable recall (b). In (a), each bar represents a set of screening decisions, with the width
showing the number of documents and the height showing the percentage of them that was relevant. The first bar represents the
991 documents screened at random. The subsequent bars represent four sets of machine learning prioritised documents, a
random sample of 100 documents, and remaining unseen documents. The random sample is used to generate the errorbar, the
Agresti-Coull confidence interval. (b) shows the probability distribution of the minimum level of recall.
Table 1. A summary of the sub-string X, Y and Z terms. The sub-strings are shown as formatted for Web of Science search. See the
supplementary material for Scopus formatting.
Sub-string X GHG emission (((atmospheric OR anthropogenic OR effect∗OR emission∗OR footprint∗OR mitigat∗
OR sav∗OR reduc∗OR budget∗OR impact∗OR decreas∗) AND (carbon OR CO2 OR
CH4 OR methane OR N2O OR nitrous oxide OR ‘greenhouse gas∗’ OR GHG OR GHGs))
OR (climat∗AND (action∗OR chang∗OR warm∗OR shift∗)) OR ‘global warming’ OR
‘emission reduction∗’ OR (mitigation AND (action∗OR potential∗)) NOT (catalyst∗OR
distill∗OR chemicals OR super-critical OR foaming OR pore OR nanotube∗))
Sub-string Y Study type ((lifecycle OR life-cycle OR ‘life cycle’ OR LCA OR embodied OR indirect OR embedded
OR ‘supply chain’ OR ‘impact assessment∗’) AND (review∗OR meta-aggrega∗OR meta-
analys∗OR metaggrega∗OR metaanalys∗OR meta-stud∗OR metastud∗OR overview∗
OR ‘systematic map’ OR synthesis OR (meta AND (stud∗OR analys∗OR aggrega∗))) OR
(((multiregional OR multi-regional OR ‘multi regional’) AND (input-output OR ‘input
output’)) OR MRIO))
Sub-string Z-term (1) General (2) Transport (3) Food (4) Housing
Consumption
domains
(consum∗OR
lifestyle∗OR
demand∗OR
waste∗)
((airplane∗OR
automobile∗OR bicycl∗
OR bik∗OR bus∗OR
car∗OR commut∗OR
cycl∗OR ∗diesel OR
driv∗OR engine∗OR
flight∗OR fly∗OR
fuel∗OR gasoline OR
‘liquefied petroleum
gas’ OR LPG OR ker-
osene OR metro OR
mobil∗OR plane∗OR
ride∗OR subway OR
touris∗OR train∗OR
transit OR transport∗
OR travel∗OR under-
ground OR vehicle∗))
(beef OR beverage∗
OR ‘calor∗intake’ OR
cereal∗OR cheese OR
chicken OR dairy OR
diet∗OR egg∗OR
fertilizer∗OR fish OR
food OR fruit∗OR
grain∗OR meat OR
milk OR plant∗OR
pork OR restaurant OR
sugar OR vegetable∗
OR yoghurt)
(‘air condition∗’
OR apartment∗OR
appliance∗OR boiler∗
OR cement OR clay OR
concrete OR construct∗
OR cool∗OR dwelling∗
OR electronic∗OR
energy OR ‘floor space’
OR heat∗OR hemp
OR home∗OR hous∗
OR light∗OR ‘living
space’ OR metal∗OR
refrig∗OR rent∗OR
room OR sand OR shel-
ter OR ‘solar panel∗’
OR stone OR timber
OR window∗OR ‘white
good∗’ OR wood)
Consumption
interventions
(decreas∗OR
durab∗OR eco∗
OR efficien∗OR
green∗OR longet-
ivity OR natural
OR maintain∗OR
recycl∗OR reduc∗
OR renewabl∗
OR repair∗OR
reus∗OR ‘second
hand’ OR second-
hand OR shar∗OR
sufficien∗)
(‘light weight’ OR
electric∗OR hybrid∗
OR telecommut∗OR
telework∗OR walk∗)
(‘eat less’ OR compost∗
OR flexitarian OR
local OR organic OR
season∗OR vegan OR
vegetarian)
(cohous∗OR co-hous∗
OR downsize∗OR
insulat∗OR refurbish∗
OR renovat∗OR
retrofit∗OR ((tem-
perature OR thermal)
AND (preference OR
comfort OR set-point∗
OR ‘set point∗’ OR
setting)))
6

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 3. Annual mitigation potential of consumption options for transport measured in tCO2eq/cap. The figure is based on a
sample of 23 review articles and 16 consumption options. Negative values (in the red area) represent the potential for backfire.
The dots represent single reviewed studies and the x–s—the average mitigation potential within the same consumption option.
The 25th percentile, median and 75th percentile are noted with lines, with the options ordered by medians. The supplementary
spreadsheet sheet contains an overview of all options. For transport, we adopted the estimate of 15 000 km per passenger per year
in the OECD [61], 1000 km in China [62] and 24 000 km in the USA [62,63] for studies which do not specify annual travel.
the proportion of coal electricity is 20% or larger)
78] eliminates any potential GHG savings with the
shift to FCV. The main advantage of a FCV com-
pared to a BEV is the higher range and quick refilling
of the tank [76,78]; yet, the necessary H2filling sta-
tion infrastructure is currently lacking [76]. We noted
substantial differences in the system boundary and
modelling approaches, which may also influence the
mitigation ranges.
Energy and material efficiency (e.g. more efficient
combustion engine, lightweight materials, improved
fuel economy, cleaner fuels) [74,79–82] brings a
reduction between 1.46 and 0.01 (mean of 0.3)
tCO2eq/cap. Yet, there has been a clear trend of
increased number of vehicles [68], travelled dis-
tance per person [61] and increased mass of light-
duty vehicles [81], which offset efficiency improve-
ments with transport emissions still on the rise
[68]. Differences in ranges may be explained by
assumptions about recycling rates and material sub-
stitution factors, vehicle lifetime, class and drive cycle
and other factors [79,81].
We could not evaluate annual mitigation potential
from biofuels, as most studies communicate mitig-
ation potential in terms of functional unit (e.g. per
MJ of fuel), without further discussions of travelled
distance and vehicle efficiency. There are large uncer-
tainties around the mitigation potentials of biofuels
due to inconsistencies in scope definition (e.g. sys-
tem boundary and functional unit), assumptions (e.g.
impacts of infrastructure and coproduction), tech-
nological choices, and data sources [83]. If system
boundaries are expanded to include indirect LUC,
physical land constraints from food and feed, and
biodiversity conservation as well as the temporal
effects on natural carbon stocks, biofuels are revealed
as less attractive if not detrimental option for climate
change mitigation [84,85].
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Table 2. Eligibility criteria. See the supplementary materials for more details on the inclusion and exclusion criteria.
Inclusion criteria Exclusion criteria
Eligible population/setting No geographical restriction and focus on
household consumption
Mitigation potential not directly linked to
households (e.g. government spending)
Eligible intervention: Con-
sumption options by con-
sumption domain
•Direct reduction—consumption reduc-
tion, shift between consumption cat-
egories, and curtailment. Examples
include living car-free or avoiding
flights (transport) [30], consuming
fewer calories (food) [55] and conserve
energy at home (housing) [56]
•Indirect reduction—changes in con-
sumption patterns, changes in use
behavior and changes in disposal pat-
terns. Examples include carpooling
(transport), sharing of food surplus
(food), or equipment maintenance
(housing) [57]
•Direct improvement—purchases of
products that are more efficient in use
or produced more efficiently. Examples
include opting for electric vehicles
(transport) [58], plant-based diet
(food) [30,55] and renewable energy
(housing) [30].
•Indirect improvement—changes in
disposal behavior. Examples include
recycling batteries (transport), food
packaging (food), electrical appliances
(housing).
Mitigation options beyond the adopted
framework [59] were out of scope. This
includes macro-economic or industrial
energy efficiency measures and techno-
logical solutions, producer incentives or
other options on the supply side; popula-
tion [11] measures; mitigation potential of
policies
Outcome: Mitigation poten-
tial and lifecycle emissions
Mitigation potential assessed through
annual carbon savings in kilograms/tons
CO2-equivalents per capita, converting
GHGs (e.g. CO2, CH4, N2O, SF6) to equi-
valent amounts of CO2(e.g. GWP100).
Focus only on direct emissions [57] (e.g.
well-to-wheel LCAs) or carbon intensit-
ies in functional units with no estimate
of consumption; system-wide effects and
potential for income rebound effects [41–
43]. Consumption activities with high car-
bon intensity [3,60] should be considered
to avoid rebound.
Study types Supply chain lifecycle GHG emissions
through LCA review studies and MRIO
studies, physical trade flow or hybrid mod-
elling studies, studies on re-designing of
consumption.
Systematic maps and reviews with only
narrative synthesis; mitigation assessment
through regression coefficients.
3.2. Food
Figure 4provides an overview of various consump-
tion options in the food domain. The majority of
reviewed studies covered the potential GHG reduc-
tion associated with a change of diet and a reduction
in food waste.
The mitigation potential associated with a diet
change involving a reduction in the amount of
animal products consumed varies between 2.1 and
0.4 tCO2eq/cap (mean of 0.9 tCO2eq/cap) for a Vegan
diet, between 1.5 and 0.01 (0.5) for a Vegetarian diet,
and between 2.0 and −0.1 (0.6) for Mediterranean
and similar diet—e.g. Atlantic and New Nordic. The
three types of diets have median mitigation potential
of 0.9, 0.5 and 0.4 tCO2eq/cap, respectively. Adopting
more Sustainable diet or a Shift to lower carbon meats is
also associated with sizable reductions, with an aver-
age annual reduction of 0.5 tCO2eq/cap. The carbon
intensity per calorie/kg of primary product is sub-
stantially lower for vegetal foods compared to rumin-
ants, non-ruminants and dairy [11,86–88], with
meat producing more emissions per unit of energy
due to energy losses at each trophic level [89]. Emis-
sions associated with land use change (LUC) are also
most significant for meat-intensive diets [90], due to
increases in pasture land and arable land for growing
feed. Nutrition guidelines diets optimized with regards
to health guidelines (generally including a reduction
in the red meat intake and increase in plant-based
foods) are associated with more moderate potential
reductions between 1.3 and 0.01 tCO2eq/cap (mean
of 0.3 tCO2eq/cap).
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Improved cooking equipment is associated with
strong mitigation potential amounting to a mean and
a median of 0.6 tCO2eq/cap. Cooking methods, fuels,
choice of food and cook-ware, use and management
of the cook-ware as well as storage time and space are
all relevant factors [91,92].
Other options for carbon footprint reductions in
the food domain focus on the production methods,
transportation, seasonality and processing of food
products. Organic food have lower emissions com-
pared to conventionally produced food, with an aver-
age annual mitigation potential of 0.5 tCO2eq/cap
and a median of 0.4 tCO2eq/cap. This mitigation
potentials is primarily attributable to the increased
soil carbon storage and reductions of fertilizers and
other agro-chemicals [93–95]. Yet, increases in GHG
emissions from organic food for the same diet are
not uncommon [93,94,96], due to lower crop
and livestock yields of organic agriculture and the
potential increase in production and associated LUC
[93]. Opting for Regional and local food and Sea-
sonal and fresh food involves average reductions of
0.4 and 0.2 tCO2eq/cap. One of the advantages of
producing and consuming food in its natural sea-
son is that it does not require high-energy input
from artificial heating or lighting [92,97], thus
reducing the embodied GHG emissions. Producing
and consuming locally may reduce emissions from
transportation and abate impact displacement over-
all [92], provided there are not large increases in
energy requirements (e.g. in the case of heated green-
house production or through the use of fertilizer [98,
99]). Regional production requiring the use of heat-
ing systems (e.g. fresh vegetables in the beginning of
the growing season) may be associated with higher
emissions compared to even substantial long-distance
transport emissions from production sites without
heating [100].
We also note substantial mitigation potential
associated with the reduction in consumed food
and waste. Food sufficiency—implying a reduction
in the overall food intake—and Food waste reduc-
tion options mitigate an average of 0.3 tCO2eq/cap
and a median of 0.1 tCO2eq/cap. Food waste studies
generally make a distinction between avoidable and
potentially avoidable waste, which are said to amount
to 80% [101] of all food waste. Food waste manage-
ment of unavoidable food waste is associated with
more modest average mitigation potential of 0.03
tCO2eq/cap.
There are large uncertainties [93,102–105] asso-
ciated with environmental (e.g. emissions arising
from biological processes, LUC and highly integrated
production such as beef and dairy), nutritional data
(e.g. consumption and waste, weighting factors for
gender and age). Impact assessment studies gener-
ally do not consider emissions associated with LUC
[102], which is estimated to contribute between 9%
and 33% of the total livestock emissions (primarily
attributable to feed imports) [93,102]. Furthermore,
even though food is a basic good (see SM figure 2),
the distribution of diets and their embodied GHG
impacts is largely unequal [106]. For example, 20% of
diets with the highest carbon contribution in the USA
account for more than 45% of the total food-related
emissions, mostly linked to meat consumption [106].
3.3. Housing
The methodological differences were particularly
strong for the reviewed studies in the housing
domain, where mitigation potential was quantified
per kWh of energy use, kg of primary material [107],
embodied and operational energy per m2of living
space, unit of fuel, thermal insulation per surface unit
[108] and others.
The mitigation options with the highest potential
on average include purchasing Renewable electricity
and Producing own renewable electricity with average
values of 1.5 (ranging between 2.5 and 0.3) and 1.3
(ranging between 4.8 and 0.1) tCO2eq/cap (figure 5).
The two options have median mitigation potential
of 1.6 and 0.6 tCO2eq/cap, respectively. The mitiga-
tion potential of adopting renewable technologies is
dependent on the energy source [109] and a wide
range of contextual factors [110]—e.g. type of electri-
city to manufacture renewable technologies, location
(affecting the amount of energy that can be produced
in the use phase), and the way technologies are used
and maintained [110].
Other effective infrastructure-related options
associated with space heating include Refurbish-
ment and renovation, opting for Heat pump and
Renewable-based heating, which offer an average mit-
igation potential of 0.9, 0.8 and 0.7 tCO2eq/cap,
respectively. The shift to a Passive house is asso-
ciated with an average reduction potential of 0.5
tCO2eq/cap (based on estimates by three studies),
excluding GHG emissions associated with changes in
infrastructure. The carbon intensity of materials and
sources [66,109], infrastructure [66] and geograph-
ical differences in energy and heating requirements
and temperature tolerance [58] are all key factors
for the absolute mitigation potential associated with
these options. The reviewed mitigation potential of
Smart metering varies between 1.1 and 0 tCO2eq/cap,
with an average of 0.2 tCO2eq/cap. Smart meter-
ing improves household awareness of their energy
consumption and support energy reduction activit-
ies (e.g. it may encourage retrofitting of houses or
change of appliances and equipment) [111]. These
indirect effects are generally not captured in pilot
studies [110]. Factors such as climate differences,
dwelling type and share of renewables in the local
grid are of crucial importance for the carbon savings
potential [111].
Less living space and co-housing—which includes
options such as smaller living space (and hence less
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 4. Annual mitigation potential of consumption options for other consumption measured in tCO2eq/cap. The figure is
based on a sample of ten review articles and nine consumption options. Negative values (in the red area) represent the potential
for backfire. The dots represent single reviewed studies and the x–s—the average mitigation potential within the same
consumption option (options ordered by averages). The 25th percentile, median and 75th percentile are noted with lines, with the
options ordered by medians. The supplementary spreadsheet contains an overview of all options.
heating and construction), collective living with oth-
ers and renting out guest rooms for other people
to live in—offer carbon reductions of up to 1.0
tCO2eq/cap, and an average of 0.3 tCO2eq/cap. When
people live together, they tend to share space heat-
ing, cooling, lighting and the structure of the com-
mon living space, appliances, tools and equipment
[24,112,113]. While these estimates of household
economies of scale from shared living are only limited
to the housing domains, sharing within households
extends to other types of consumption (e.g. shar-
ing food and cooking together) [113]. Furthermore,
the energy use reductions associated with an addi-
tional household member tend to be lower for large
households compared to small households [113].
As building size is the most important factor for
home energy consumption, downsizing may substan-
tially reduce housing-related emissions and energy
use [114]. However, there are significant structural
(e.g. lack of adequate alternatives), psychological (e.g.
attachment) and security barriers (e.g. loss of own-
ership) related to downsizing [114].Other behavioral
interventions such as Hot water saving and Lowering
room temperature by 1 ◦C–3 ◦C bring about an aver-
age saving of 0.3 and 0.1 tCO2eq/cap, respectively.
3.4. Other consumption
Finally, other consumption options with substan-
tial mitigation potential include not having a pet
and sharing and consumption of services instead of
goods with median mitigation potential around 0.3
tCO2eq/cap (figure 6). The service/sharing economy
includes options such as opting for local, non-market
and community services, share and repair. Strategies
encouraging sharing include adequate design and
infrastructure for durability, recyclability, reuse and
product longevity [82] and incentives for multi-
household living [58], grassroots initiatives and
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 5. Annual mitigation potential of consumption options for food measured in tCO2eq/cap. The figure is based on a sample
of 32 review articles and 19 consumption options. Negative values (in the red area) represent the potential for backfire. The dots
represent single reviewed studies and the x–s—the average mitigation potential within the same consumption option. The 25th
percentile, median and 75th percentile are noted with lines, with the options ordered by medians. The supplementary spreadsheet
contains an overview of all options.
downsizing [33,115]. Yet, studies also warn that peer-
to-peer strategies do not necessarily translate into car-
bon footprint reductions due to extra income and
induced consumption [116].
4. Discussion and conclusions
4.1. Mitigation potential of consumption options
One contribution of this study is the systematic provi-
sion of mitigation ranges across various consumption
domains and the harmonization of results from dif-
ferent methodologies, scopes and assumptions within
the same framework (figure 7). The top consumption
options (by medians) include substantial changes in
car travel (living car-free, shifting to electric vehicles
and public transport), air travel reductions, use of
renewable electricity and more sustainable heating
(renewable-based heating and heat pump), refur-
bishment and renovation, a shift to a plant-based
diet and improved cooking equipment. The top ten
consumption options together (accounting for the
overlap of car travel alternatives) yield an average
annual mitigation potential of 9.2 tCO2eq/cap. While
crudely estimated, this indicates a substantial mitig-
ation potential of already available low-carbon con-
sumption options towards achieving the 1.5 ◦C–2 ◦C
target.
Across world regions, the average consumption-
based carbon footprints vary between 1.9 and 0.4
tCO2eq/cap for food, 4.6 and 0.2 tCO2eq/cap for
transport, 3.7 and 0.5 tCO2eq/cap for housing, and
3.16 and 0.4 tCO2eq/cap for other consumption [2,
3] (see SM figure 1). United States and Australia stand
out with the highest average per capita carbon foot-
prints in our model: with 2.2 and 2.5 tCO2eq/cap
for food, 4.7 and 5.5 tCO2eq/cap for transport, 5.8
and 4.3 tCO2eq/cap for housing, and 4.0 and 3.9
tCO2eq/cap for other consumption, respectively.
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 6. Annual mitigation potential of consumption options for housing measured in tCO2eq/cap. The figure is based on a
sample of 13 review articles and 17 consumption options. Negative values (in the red area) represent the potential for backfire.
The dots represent single reviewed studies and the x–s—the average mitigation potential within the same consumption option
(options ordered by averages). The 25th percentile, median and 75th percentile are noted with lines, with the options ordered by
medians. The supplementary spreadsheet sheet contains an overview of all options.
Yet, the carbon allowances according to the climate
targets by 2050 are substantially lower: 0.4
tCO2eq/cap to food, 0.2 to shelter, 0.7 to travel, 0.4 to
goods and 0.4 to services, amounting to a total of 2.1
tCO2eq/cap [11].
The interconnected nature of these strategies need
to be recognized in order to adequately respond in
mitigating climate change. For example, studies warn
about the potential increase in LUC-emissions with
the shift to organic; yet, if this shift occurs in parallel
to shifts in diets and better food waste management,
the conversion of natural or semi-natural vegetation
to cropland may be reduced substantially (figure 4).
Furthermore, co-benefits associated with upscaling
these mitigation options have also been widely dis-
cussed [68,71,117].
4.2. Limitations
This review is limited to the English language liter-
ature published since 2011. More relevant evidence
could be captured if the scope is extended to other
languages, e.g. capturing more evidence from non-
OECD countries. Moreover, although we used a very
comprehensive set of search terms, there is a risk that
we missed literature that did not list them in their
title, abstract or key words. Furthermore, as we did
not perform an extensive search for the other con-
sumption domain, we may have omitted key options
and potentials. We may have also missed relevant
research through the adoption of machine learning
and the focus on peer-reviewed literature. Includ-
ing grey literature (such as theses and governmental
reports) would decrease susceptibility to publication
bias and resulting inclination of peer-reviews literat-
ure towards more ‘positive’ results.
The included studies often do not report suf-
ficient methodological details in order to judge
rigor of the primary data included. The studies
differ largely in assessment method and method-
ological choices, system boundary, and modelling
12

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Figure 7. A summary of all reviewed consumption options, excluding inner values. Negative values (in the red area) represent the
potential for backfire. The x-s represent the average mitigation potential within the same consumption option (options ordered
by medians). The supplementary spreadsheet contains an overview of all options.
assumptions. For example, most food-related LCAs
adopt a system boundary at the farm gate or retail
gate [102] (thus, suffering from truncation errors),
and exclude consumer losses, impacts associated with
the consumption and end-of-life stages, and LUC.
LCA reviews generally do not publish an a-priori
protocol, conduct a comprehensive and transpar-
ent search for studies or discuss an explicit set of
13

Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Global
North America
Australia/New Zealand
Asia
Africa/Middle East
LCA
LCA/IO/hybrid
Unspecified method
Specified energy mix
Specified renewable energy mix
Specified location/region
Specified socio-demographics
Global
North America
Australia/New Zealand
Asia
Africa/Middle East
LCA
LCA/IO/hybrid
Unspecified method
Specified energy mix
Specified renewable energy mix
Specified location/region
Specified socio-demographics
-4 -2 0 2 4 -4 -2 0 2 4
Food Transport
Housing Other consumption
EU/Europe excluded
EU/Europe excluded
IO excluded
IO excluded
Adjusted R2
Adjusted R2
0.32 0.48
0.75 0.55
Figure 8. Factors contributing to differences in mitigation potential within consumption options. The coefficients are based on
fixed-effects linear model using clustered standard errors by mitigation options. The dependent variable is the annual carbon
mitigation potential in tCO2eq per capita.
inclusion/exclusion criteria. Studies lack transpar-
ency with regards to critical appraisal and data extrac-
tion, and rarely evaluate the heterogeneity statistic-
ally (see the CEESAT tool [118]). IO studies generally
disregard end-of-life stages, LUC emissions and the
effects on natural carbon stocks.
Most studies do not consider feedback effects
in the global supply chains (e.g. the wider adop-
tion of vegetarian diets is expected to influence
the supply chains of hotels, restaurants, supermar-
kets). Furthermore, the reviewed studies generally
disregard embodied emissions in the new infrastruc-
ture needed for the upscale of low-carbon prac-
tices, e.g. the infrastructure of renewables, and the
associated costs. Large-scale investments in energy-
intensive industries and infrastructure have been
shown to counter-balance and even outweigh the
sectoral carbon efficiency gains, especially in fast-
developing countries [119,120]. Prior analysis of
GHG emissions from existing and proposed infra-
structure suggests that a cost-effective strategy to
reduce committed emissions is to target the early
retirement of electricity and industry infrastructure
in the presence of affordable low-carbon alternatives
[121]. Finally, other environmental indicators such as
resource use and scarcity may differ substantially in
their implications and prioritization of consumption
options [55,99].
A major obstacle with regards to external valid-
ity (applicability to our research question) is that
LCA reviews, in particular, communicate mitigation
potential by various functional units [103] without
providing the context of scale. We particularly
excluded a number of housing-related LCA reviews
as mitigation potential is solely communicated in
terms of functional units. This makes the comparison
with other environmental assessments (using differ-
ent methodologies) and carbon targets/budgets very
difficult.
4.3. Modifier effects
Considerations about default-option are critical for
the assessment of mitigation potential. While some
studies present mitigation potential compared to
averages, others compare to ‘high carbon’ consump-
tion patterns [122]. Furthermore, there is a large
uncertainty associated with basic assumptions about
human behavior and public acceptability of demand-
side mitigation options [82]. While we depict absolute
reduction potential of various mitigation options—
e.g. shift all car travel to public transport—partial
adoptions may also be adopted, with relative reduc-
tion potential easily calculated as a proportion of the
ranges discussed in this paper.
Geographical context and other location, impact
assessment method, energy mix and carbon intens-
ity and socio-demographics specifications were eval-
uated in the fixed-effects model as potential factors
that influence the mitigation potential ranges within
consumption options (figure 8).
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
Table 3. A summary of the consumption options with the highest mitigation potential and ways to influence the infrastructural,
institutional and behavioral carbon lock-ins associated with them.
Consumption options
with high mitigation
potentials
Overcoming infrastruc-
tural lock-in
Overcoming institutional
lock-in
Overcoming behavioral
lock-in
Dietary shift (e.g. vegan,
vegetarian)
Change land use prac-
tices; Remove investment
infrastructure supporting
unsustainable and extract-
ive industries
Remove unsustainable sub-
sidies in agriculture, e.g. for
meat and dairy; Offer support
for alternatives.; Encourage
just transition for animal
farmers; Better availability of
low-carbon options in super-
markets, restaurants, schools,
etc; Coordinated efforts of
health organizations and gov-
ernment [89]; Ban advertising
of high-carbon meats and
other high-carbon items.
Encourage low-carbon
shared meals [127] and
diets; Feedbacks for change
in social norms and tra-
ditions around food con-
sumption [127], e.g. vegan
food as default; Decouple
veganism/vegetarianism
from a particular social
identity
Transport mode shift (e.g.
active, public transport),
car-free
More public transport
infrastructure develop-
ments for urban and long-
distance travel, e.g. cycling
lanes, buses, trains; More
bike spaces on public trans-
port
Parking and zoning restric-
tions, e.g. car-free zones and
days; Vehicle and fuel tax
increases and toll charges;
Make driving less conveni-
ent in urban areas; Enforce
stricter air pollution stand-
ards; Ban car advertising
Raising awareness about
co-benefits associated with
active travel [58]; Social
feedback with the visibility
of cycling [127]; Decouple
car travel from a particular
social identity; Improve
drivers awareness of cyclers
and safety
Reduction in overall travel
demand
More compact urban
spaces and diverse land
use [17]
Allow for flexible working
schemes and telecommuting;
Halt air travel expansion; Ban
flight advertising
Carpooling and carsharing;
Encourage telecommut-
ing, moving into denser
settlements
Upscaling of electric
vehicles
Decarbonize the grid and
meet potential additional
capacity through renew-
ables; Provision of charging
infrastructure
Sustained policy support, e.g.
free public charging, tax and
fee deductions, subsidies for
low-income buyers; Enforce
stricter air pollution standards
Tackle charging time
acceptance, range anxiety
[61,67,75]
Renewable-based heating
and electricity
Infrastructure investment
in renewables
Halt fossil fuel expansion/use
and support upscaling of
renewables; Incentivize
decentralized electricity gen-
eration, particularly for low-
income households; Enforce
stricter air pollution stand-
ards; Encourage just trans-
itions for fossil fuel workers;
Fossil fuel divestment
Raise public awareness and
target NIMBY concerns
Refurbishment and renova-
tion
Energy efficient construc-
tion and equipment
Enforce building standards;
Encourage investment by
dwelling owners and land-
lords in the fabric of the
building and energy effi-
ciency as well as broader
home improvements [115];
Encourage just transitions,
e.g. consideration of fuel
poverty; Remove inefficiency
of listed building
Public awareness around
economic and environ-
mental benefits; Reconcile
investment incentives with
householders’ images of
home comfort [115]
For food, mitigation potential estimates from
North America, Australia and New Zealand of dietary
changes are higher compared to EU estimates, while
estimates from Asia are lower. In the context of food,
LCA-based results were slightly higher compared to
IO-based results. Methodological, geographic and
socio-demographic factors explain 32% of the dif-
ferences in mitigation potential within food-related
options. Other potential modifier effects include the
accounting of food and cooking losses [123] and LUC
[102]; the magnitude of change/reduction of calories
[90] and the share imported by air [11]; nutritional
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Environ. Res. Lett. 15 (2020) 093001 D Ivanova et al
guidelines [124]; the consideration of rebound effects
and knock-on savings from food waste reduction
or dietary change including avoided shopping and
storage [101]; and other social and behavioral char-
acteristics [103–105].
For transport, mitigation potential estimates
from Australia and New Zealand are significantly
higher than the European ones, while those from
Asia are lower. IO-based estimates are substantially
lower than the hybrid estimates in reviewed stud-
ies. Geographic and methodological factors attrib-
ute to 48% of the differences in mitigation potential
within transport-related options. Additional mod-
ifiers include fuel and vehicle characteristics [58,
74–76], travel distance and occupancy rate [58,65],
energy chain and infrastructure [125], driving [125],
income group [65] as well as additional technical and
behavioral factors [125].
The geographic location, methodology and
energy mix are significant for the mitigation poten-
tial ranges within housing options, attributing to 75%
of the within options variance (figure 8). The loca-
tion factor includes contextual factors influencing the
supply of energy, e.g. the location of solar panels dur-
ing use [110] and geographical differences in energy
and heating requirements [58]. Additional modifier
effects include the backup electricity mix, dwelling
size, type and lifetime assumption, and additional
social and infrastructural influences.
4.4. Policy recommendations
Finally, we selected the top ranking consumption
options and synthesized respective policy recom-
mendations from the literature. Table 3communic-
ates a list with the options with the highest mitigation
potential and potential actions towards overcoming
the main infrastructural, institutional and behavioral
carbon lock-ins [126]. While the table is informed by
the reviewed literature, it should be noted that we did
not conduct a systematic search specifically on target-
ing actions towards overcoming carbon lock-ins.
4.5. Concluding remarks
In times of a climate emergency, research and policy
urgently needs to move beyond focusing on the effi-
ciency of production and use of goods and services.
The explicit consideration of the absolute scale of
consumption and its implications for climate change
and well-being is ever more relevant. There is a need
for an open discussion about the overall scale of
resource use and emissions and sustainable consump-
tion corridors [128] towards remaining within plan-
etary boundaries and satisfying human needs [34].
We conducted a comprehensive literature review
to summarize and compare the reported GHG ranges
of various consumption options, critically appraise
results and uncertainties, clarify the methodological
issues and modifier effects, and identify knowledge
gaps to inform future research and policy. The
priorities in terms of consumption options may dif-
fer substantially depending on income, geographic
location, energy context, other factors and carbon
lock-ins. Still, consumption is intimately connected
to issues of climate change, well-being and sustainab-
ility, and thus needs critical attention.
We find that the large majority of the house-
hold carbon footprints can be mitigation with already
available low-carbon consumption options. Challen-
ging current patterns of consumption and the soci-
etal dynamics through a critical assessment of infra-
structural, institutional and behavioral lock-ins and
potential rebound effects, therefore, needs to become
a priority for successful climate change mitigation.
Acknowledgments
DI and JB received funding from the UKRI
Energy Programme under the Centre for Research
into Energy Demand Solutions [EPSRC award
EP/R035288/1]. DW received funding from the
European Research Council (ERC) under the
European Union’s Horizon 2020 research and innov-
ation programme (MAT_STOCKS, Grant agreement
No. 741950). MC received a PhD scholarship from
the Heinrich Böll foundation.
Data availability
The data that support the findings of this study are
openly available in the supplementary spreadsheet.
Please cite this article and its digital object identifier
(DOI) when making use of the data.
ORCID iDs
Diana Ivanova https://orcid.org/0000-0002-3890-
481X
John Barrett https://orcid.org/0000-0002-4285-
6849
Dominik Wiedenhofer https://orcid.org/0000-
0001-7418-3477
Biljana Macura https://orcid.org/0000-0002-
4253-1390
Max Callaghan https://orcid.org/0000-0001-8292-
8758
Felix Creutzig https://orcid.org/0000-0002-5710-
3348
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