Demand-side approaches for limiting global warming to 1.5 °C

Abstract

The Paris Climate Agreement defined an ambition of limiting global warming to 1.5 °C above preindustrial levels. This has triggered research on stringent emission reduction targets and corresponding mitigation pathways across energy economy and societal systems. Driven by methodological considerations, supply side and carbon dioxide removal options feature prominently in the emerging pathway literature, while much less attention has been given to the role of demand-side approaches. This special issue addresses this gap, and aims to broaden and strengthen the knowledge base in this key research and policy area. This editorial paper synthesizes the special issue’s contributions horizontally through three shared themes we identify: policy interventions, demand-side measures, and methodological approaches. The review of articles is supplemented by insights from other relevant literature. Overall, our paper underlines that stringent demand-side policy portfolios are required to drive the pace and direction of deep decarbonization pathways and keep the 1.5 °C target within reach. It confirms that insufficient attention has been paid to demand-side measures, which are found to be inextricably linked to supply-side decarbonization and able to complement supply-side measures. The paper also shows that there is an abundance of demand-side measures to limit warming to 1.5 °C, but it warns that not all of these options are “seen” or captured by current quantitative tools or progress indicators, and some remain insufficiently represented in the current policy discourse. Based on the set of papers presented in the special issue, we conclude that demand-side mitigation in line with the 1.5 °C goal is possible; however, it remains enormously challenging and dependent on both innovative technologies and policies, and behavioral change. Limiting warming to 1.5 °C requires, more than ever, a plurality of methods and integrated behavioral and technology approaches to better support policymaking and resulting policy interventions.

Full text

EDITORIAL
Demand-side approaches for limiting global warming to 1.5 °C
Luis Mundaca &Diana Ürge-Vorsatz &
Charlie Wilson
Received: 20 July 2018 /Accepted: 6 August 2018
#The Author(s) 2018
Abstract The Paris Climate Agreement defined an am-
bition of limiting global warming to 1.5 °C above pre-
industriallevels. This has triggered research on stringent
emission reduction targets and corresponding mitigation
pathways across energy economy and societal systems.
Driven by methodological considerations, supply side
and carbon dioxide removal options feature prominently
in the emerging pathway literature, while much less
attention has been given to the role of demand-side
approaches. This special issue addresses this gap, and
aims to broaden and strengthen the knowledge base in
this key research and policy area. This editorial paper
synthesizes the special issue’s contributions horizontally
through three shared themes we identify: policy inter-
ventions, demand-side measures, and methodological
approaches. The review of articles is supplemented by
insights from other relevant literature. Overall, our paper
underlines that stringent demand-side policy portfolios
are required to drive the pace and direction of deep
decarbonization pathways and keep the 1.5 °C target
within reach. It confirms that insufficient attention has
been paid to demand-side measures, which are found to
be inextricably linked to supply-side decarbonization
and able to complement supply-side measures. The
paper also shows that there is an abundance of
demand-side measures to limit warming to 1.5 °C, but
it warns that not all of these options are Bseen^or
captured by current quantitative tools or progress indi-
cators, and someremain insufficiently represented in the
current policy discourse. Based on the set of papers
presented in the special issue, we conclude that
demand-side mitigation in line with the 1.5 °C goal is
possible; however, it remains enormously challenging
and dependent on both innovative technologies and
policies, and behavioral change. Limiting warming to
1.5 °C requires, more than ever, a plurality of methods
and integrated behavioral and technology approaches to
better support policymaking and resulting policy
interventions.
Keywords Behavioral change .Demand-side
approaches .Energy efficiency.Climate change
mitigation .Low-carbon energy technologies .Paris
Climate Agreement .Mitigation pathways .Policy
instruments .1.5 °C target
Introduction
The Paris Climate Agreement set an ambitious target of
limiting global warming to 1.5 °C above preindustrial
levels. However, national pledges to reduce emissions,
so-called Nationally Determined Contributions (NDCs),
Energy Efficiency
https://doi.org/10.1007/s12053-018-9722-9
L. Mundaca (*)
International Institute for Industrial Environmental Economics,
Lund University, Lund, Sweden
e-mail: Luis.Mundaca@iiiee.lu.se
D. Ürge-Vorsatz
Centre for Climate Change and Sustainable Energy Policy, Central
European University, Budapest, Hungary
C. Wilson
Tyndall Centre for Climate Change Research, University of East
Anglia, Norwich, UK

are insufficient to meet this goal, meaning wide-
reaching policy action is urgently needed (Rogelj et al.
2016a;UNEP2017). Carbon-budget studies that link
cumulative CO
2
emissions to global mean temperature
rise clearly show that if the target is to be met, emissions
need to peak in the very near term and decline to net zero
by mid-century following the implementation of deep
decarbonization measures (e.g., Kriegler et al. 2018;
Rogelj et al. 2015a). Available emission budgets defined
by the 2 °C target were already challenging; the 1.5 °C
goal and the lack of rigorous global action has made this
all the harder to achieve: with each year that passes, the
remaining budget shrinks by another ~ 40 GtCO
2
(GCP
2016)(Fig.1). Although carbon budget estimates re-
main uncertain, the urgent need for decisive mitigation
policies remains (Michaelowa et al. 2018;Millaretal.
2017; Xu and Ramanathan 2017). The challenge set by
the Paris Agreement is enormous (Rockström et al.
2016) and requires profound social as well as techno-
logical change (Geels et al. 2017; Turnheim et al. 2015).
Meeting this ambitious target will need a marked depar-
ture from historical rates of change and current policy
(Knutti et al. 2016;vanSluisveldetal.2015). Against
this backdrop, there is a vital need for robust scientific
research to inform policymakers of mitigation actions
across the entire energy system, building on NDC
commitments.
The current scientific evidence base on long-term
system transformation to meet the 1.5 °C ambition is
dominated by global scenarios based on Integrated
Assessment Models (IAMs),
1
which emphasize
supply-side technologies and carbon dioxide remov-
al (CDR) options.
2
From a methodological point of
view, an important reason for the dominance of
IAMs in the 1.5 °C literature is their unique ability
to link mitigation strategies and technology portfo-
lios to cumulative emission budgets and, conse-
quently, warming outcomes. As a result, they have
become Bgatekeepers^of research in the domain.
IAMs outcomes tend to heavily rely on CDR and
storage technologies, particularly bioenergy with
carbon capture and storage (BECCS) (Clarke et al.
2014;Fussetal.2014; Millar et al. 2017;Minx
et al. 2017). These options are even more apparent
when energy-intensive and temperature-overshoot
pathways are analyzed (Kriegler et al. 2017).
In comparison, demand-side options tend to be
neglected in IAMs (Kriegler et al., 2018), even
though deep decarbonization pathways rely on low-
energy demand or low-energy intensity (Rogelj et al.
2015a). Modeling studies consistently show that
demand-side measures play a critical role in meeting
ambitious mitigation targets (Clarke et al. 2014;
Riahi et al. 2015). Creutzig et al. (2016)arguethat
a better understanding and integration of demand-
side mitigation strategies may help to reduce or
remove reliance on large-scale CDR options or
(even more controversial) solar radiation manage-
ment technologies. Moreover, demand-side portfoli-
os have potentially wider benefits and encompass
fewer risks than supply-side options: they are close-
ly associated with synergistic co-benefits for health,
pollution, security, equity, living standards, and sys-
tem costs (von Stechow et al. 2016; Ürge-Vorsatz
et al. 2016). Furthermore, demand-side options re-
duce risks by introducing greater flexibility into the
choice of energy-system transitions (Lucon et al.
2014). These aspects raise important questions about
the options, measures, strategies, sectors, services,
timing, and feasibility of demand-side mitigation
pathways to limit warming to 1.5 °C. Furthermore,
we do not yet fully understand the implications of
stringent, demand-side policy instruments in the
1.5 °C context. Emerging literature stresses the im-
portance of demand-side measures and call for sub-
stantial new research advances in this area (e.g., on
policy issues, modeling, mitigation costs) (Creutzig
et al. 2016,2018; Grubler et al. 2018).
Purpose of the special issue and editorial
The purpose of this special issue is to broaden and
strengthen the evidence base on the role of demand-
side policies, measures, and corresponding mitigation
pathways to limit global warming to 1.5 °C. First, it
addresses the knowledge gap regarding how demand-
side mitigation options can contribute to achieving the
1.5 °C goal. Second, it aims to raise the profile of
demand-side options in the current policy and academic
discourse focusing on the 1.5 °C goal. Third, it seeks to
bring together diverse research communities through a
1
Two main types of IAMs are identified in the literature (Weyant
2017): Detailed Process IAMs provide disaggregated and detailed
regional and sectoral mitigation analyses, while Cost-Benefit IAMs
generate a more aggregated view of impacts and climate mitigation
economics by region or sector.
2
For a detailed review of CDR approaches see Minx et al. (2018).
Energy Efficiency

collection of deep decarbonization studies that provide a
starting point for cross-disciplinary discussions of
demand-side approaches.
Energy Efficiency has consistently addressed climate
mitigation and demand-side issues since its inception.
The journal provides critical analyses of demand-side
issues in the transition to more sustainable energy sys-
tems. This special issue is consistent with this practice in
bringing together a collection of articles that explore
demand-side approaches and address the following re-
search questions: Are deep decarbonization pathways in
the transport sector compatible with the 1.5 °C target?
How can 1.5 °C pathways be pursued through (radical)
changes in household consumption? What is the role of
potentially disruptive low-carbon innovation to limit
warming to 1.5 °C? What is the gap between national,
bottom-up studies, and European (aggregated) 1.5 °C
scenarios? Which policy options will encourage deep
decarbonization pathways in the building sector? What
is the role of the sectoral policies for triggering an
immediate peak in global emissions to keep the 1.5 °C
target within reach? To what extent can minimum per-
formance standards complement carbon pricing? Will
policies to promote energy efficiency help or hinder in
achieving the 1.5 °C climate target? What are the im-
pacts of carbon pricing and energy-efficiency policies
on electricity supply and demand in the USA? What
policy interventions would foster near-zero carbon
emissions in the residential heating sector? Taken as a
whole, this special issue reflects the emerging knowl-
edge on demand-side options and provides policy-
relevant insights to complement the existing IAM
literature.
These articles scrutinize a wide variety of demand-
side topics that underpin the Paris Agreement and the
1.5 °C discourse. This breadth of vision is very much
consistent with Energy Efficiency’s philosophy. In this
editorial, we introduce the special issue’scontributions
Bhorizontally^through three shared themes: policy in-
terventions, demand-side measures, and methodological
approaches. For each theme, we summarize the key
insights from each article, supplemented by insights
from other relevant literature on 1.5 °C mitigation and/
or demand-side measures. In the final section, we draw
out the lessons learnt and present our concluding
thoughts as editors of this special issue.
Stringency and the role of demand-side policies
to limit global warming to 1.5 °C
As a whole, the set of papers in this special issue
emphasize the importance of ambitious and coordinated
policy efforts to drive the speed and direction of a 1.5 °C
transition. A wide variety of policy instruments are
assessed or modeled. Following the sectoral policy in-
terventions contained in Bruckner et al. (2014),
Fischedick et al. (2014), Lucon et al. (2014), and Sims
Limiting warming in 2100 based on
cumulative global CO
2
emission budget from 2011-2100
median [interquarle range]
1140 GtCO
2
[1110-1150]
790 GtCO
2
[470-1085]
365 GtCO
2
[275-425]
2
o
C
(>50%) 2
o
C
(>66%) 1.5
o
C
(>50%)
~200 GtCO
2
used up
2011-2016
2011
median
median
2100
Fig. 1 Cumulative emission budgets for limiting warming to 2 °C
and 1.5 °C. Notes: Areas of boxes are proportional to cumulative
total CO
2
emission budgets from 2011 to 2100 consistent with
limiting warming to 2 °C and 1.5 °C (with 50–66% and > 66%
probability) based on data from Rogelj et al. (2015b). Historical
annual emissions of ~ 40 GtCO
2
from 2011 to 2016 are estimated
from GCP (2016). Note that cumulative emission budgets vary
depending on the methodology and assumptions (Rogelj et al.
2016b). Budgets for limiting warming to 2 °C given in the IPCC
Fifth Assessment Report (Clarke et al. 2014) are slightly higher at
960–1430 GtCO
2
(50–66% probability) and 630–1180 GtCO
2
(>
66% probability). Budgets for limiting warming to 1.5 °C, report-
ed in Rogelj et al. (2015a) are slightly lower at 200–415 GtCO
2
(>
50% probability)
Energy Efficiency

et al. (2014), we highlight various policy insights that
can be derived from the collection of papers as a whole.
Sectoral targets and emission peaks
Ambitious sectoral targets are needed to drive deep
decarbonization pathways. Gota et al. (2018)high-
light the need to set long term, ambitious sectoral-
mitigation targets to decarbonize the transport sec-
tor. This includes targets for absolute greenhouse
gas (GHG) emission reductions, vehicle CO
2
stan-
dards, modal split, technology (e.g., electric vehi-
cles), and renewable energy (e.g., the electrification
of transport and the fuel mix). Establishing very
ambitious targets for the transport sector is critical
to avoid or reduce trips, encourage a change towards
highly efficient travel modes, and improve the per-
formance of vehicles and fuels.
Wachsmuth and Duscha (2018) compare reduc-
tions in energy and carbon intensities across different
end-use sectors, and stress the importance of sectoral
mitigation targets, particularly at the national level.
They assess target-oriented national scenarios and
show that conceivable emission reductions in end-
use sectors can be more stringent than aggregated
(IAM) scenarios suggest.
Méjean et al. (2018) highlight the importance of
global emission peaks across end-use sectors. Their
modeling approach assumes a global carbon price to
meet emission targets, and the authors highlight that
ambitious, near-term sectoral policiesare needed to drive
rapid decarbonization of the electricity, industrial, and
transport sectors. Brown and Li (2018) adopt indicative
targets for the US electricity sector—ranging from
14.2 GtCO
2
(based on population) to 74.3 GtCO
2
(based
on GDP)—until 2040, which they then use as a bench-
mark to analyze the extent to which a combination of
carbon pricing and dedicated demand-side policies can
contribute to meeting the mitigation target.
Building codes
Several papers argue that stringent building codes are a
critical way to implement systems that define minimum
technical conditions for energy performance. An implic-
it and significant assumption in this area is that such
codes can be effectively enforced.
In their modeling approach, Brown and Li (2018)
assume ambitious state building codes will lead to
high-shell thermal efficiencies for single-family
homes, apartments, and mobile homes. Similarly,
Méjean et al. (2018) highlight the importance of
rigorous building standards (including energy label-
ling) in fostering energy-efficiency improvements
andreducingenergydemandintheresidentialsector.
Building codes are also addressed by Wachsmuth and
Duscha (2018), who demonstrate plausible national
emission reductions based on the assumption that
both new buildings and retrofits meet the highest
available thermal-efficiency standards. Consistent
with the available literature (Lucon et al. 2014;Seto
et al. 2016;Ürge-Vorsatzetal.2013), the authors
argue that stricter building codes in the near term
are a necessary condition to avoid carbon lock-in in
the sector. This, in turn, is consistent with Kuramochi
et al. (2018) who identify various key benchmarks to
limit warming to 1.5 °C, including: higher renovation
rates (5% in 2020) and all new builds to be fossil-free
and near-zero energy in 2020.
Chen et al. (2018) devote considerable attention to
ambitious building codes in their modeling of miti-
gation pathways for the Chinese building sector.
Their approach is based on an annual increase in
the efficiency rate of building shells by 1–1.5%.
The authors argue that such improvements could be
comparable to those studied in Germany. Chen et al.
conclude that stringent building codes combined with
technology standards and carbon pricing, are critical
in achieving energy (and resulting cost) savings in
the building sector.
Performance standards
Sonnenschein et al. (2018) offer a new analysis of the
important question of whether energy-efficiency
standards are necessary if economy-wide carbon
pricing is in place. The authors model the effect of
BMinimum Energy Performance Standards^(MEPS)
on the lifecycle costs of four home appliances includ-
ing the CO
2
externality. They found that fairly mod-
est MEPS could achieve the same result as the (con-
siderably more stringent) carbon prices that are evi-
dent today (e.g., equal to or above 100 US$/tCO
2
).
The authors conclude that MEPS for appliances are a
cost-effective, complementary policy to carbon pric-
ing in the long term.
Chen et al. (2018)considerstricterMEPSinvarious
scenarios. The authors assume a 0.75% annual rate of
Energy Efficiency

efficiency improvements for a variety of technologies
associated with heating, cooling, lighting, cooking, and
hot water services. Combined with a carbon tax, they
find that more ambitious performance standards are
central to triggering deep decarbonization pathways in
the Chinese building sector. The authors conclude that
national performance standards should be significantly
improved. Similarly, in the transport sector, Wachsmuth
and Duscha (2018) and Gota et al. (2018) highlight that
stringent fuel-efficiency standards should be put in place
to lay the foundations for a meaningfully contribution to
the 1.5 °C goal.
Behavioral policies
As mitigation pathways require both technical and so-
cial change, both dimensions must be addressed in
combination (Eyre et al., 2018). While most of the
1.5 °C literature has focused on technology policy and
the need to address market failures, growing attention is
being given to behavioral anomalies (e.g. heuristics,
limited attention) and the need to address them with
(innovative) policy interventions (e.g. Bager and
Mundaca 2017; Frederiks et al. 2015;Gillinghamand
Palmer 2014; Pichert and Katsikopoulos 2008).
Sonnenschein et al. (2018) argue that, in addition to
performance standards and carbon pricing, further
(mixes of) policy interventions that address behavior-
al anomalies should be studied (e.g., labelling pro-
grams, green defaults). Likewise, Gota et al. (2018)
stress that current mitigation measures rely heavily on
assumptions of behavioral change. Knobloch et al.
(2018) find that the potential impacts of modeled
policy instruments are highly dependent on assump-
tions of behavioral decision-making (cf. Kolstad et al.
2014; Mundaca et al. 2010; Worrell et al. 2004). The
authors argue that a failure to acknowledge behavioral
issues (e.g., bounded rationality) in modeling can lead
to misguided policy recommendations and thus mis-
leading outcomes. Consistent with the literature ad-
dressing demand-side issues in the domain of climate
mitigation (Creutzig et al. 2016; Lucon et al. 2014;
Sims et al. 2014), Gota et al. underline that not only do
optimistic mitigation scenarios require greater behav-
ioral change but also that mitigation potential due to
behavioral options may be higher than is often as-
sumed in modeling studies (see also Creutzig 2016).
In line with the literature that addresses human behav-
ior, climate mitigation, and modeling tools (e.g.,
Jochem et al. 2000; McCollum et al. 2017; Mundaca
et al. 2010), the papers in the special issue implicitly
(or explicitly) acknowledge the challenges associated
with the parameterization of behavioral change in
modeling tools.
Moberg et al. (2018) devote considerable attention
to behavior and policy. Analyzing different end-use
segments across four European countries in a deep
decarbonization context, the authors conclude that
existing behavioral-oriented policies that address
consumption are limited and rely heavily on self-
governance and nudging. They stress that voluntary
options are insufficient and that only Bforced^policy
scenarios will lead to necessary lifestyle changes.
Their results show that individuals are willing to
embrace climate responsibility; however, (further)
policy interventions—beyond economic incentives
—are needed to encourage behavioral change. The
authors conclude that there is high potential for reg-
ulatory approaches targeted at high emission domains
(e.g., mobility and food).
Carbon pricing and complementary policies
Carbon pricing mechanisms (cap-and-trade schemes
or carbon taxes) are often cited as a key part of the
policy mix in 1.5 °C (or 2 °C) mitigation pathways
(e.g., Bertram et al. 2015;Riahietal.2017;Rogelj
et al. 2018; Rogelj et al. 2013; Stiglitz et al. 2017). In
this particular case, stringent policy takes the form of
carbon prices that are considerably higher than today.
For example, in the building sector, Chen et al.
(2018) model an economy-wide carbon tax ranging
from 70 US$/tCO
2
in 2020, 300 US$/tCO
2
in 2050 to
1265 US$/tCO
2
in 2100. Combined with other policy
assumptions related to ambitious minimum perfor-
mance standards and building codes, the tax regime
accelerates the implementation of mitigation mea-
sures, both on the supply side and in the building
sector. The authors conclude that a combination of
carbon pricing and technology-oriented policies is
needed if the Chinese building sector is to meet the
1.5 °C goal.
Méjean et al. (2018) model emission constraints
and corresponding peaks based on a uniform carbon
price, ranging from nearly 150 US$/tCO
2
to 450
US$/tCO
2
in 2030. Results show that higher carbon
prices are required in high-energy demand scenarios
(e.g., ~ 360 US$/tCO
2
in 2030) than low-energy
Energy Efficiency

demand scenarios (e.g., ~ 250 US$/tCO
2
in 2030).
High-energy demand scenarios are characterized by
slow energy-efficiency improvements in end-use
sectors and energy-intensive lifestyles. On the other
hand, and especially when combined with sector-
specific demand-side management policies, the level
of carbon pricing that is required to reach the tem-
perature target is reduced by between 25 and 50% in
2030. The authors stress that their model implies a
relatively limited set of mitigation technologies
compared to other IAMs. Thus, the carbon price
needed to meet a given mitigation goal is higher
than in other IAMs, which implies higher mitigation
costs.
Modeling deep decarbonization pathways in the res-
idential heating sector, Knobloch et al. (2018) use a
sectoral carbon tax to drive mitigation measures. In this
case, a carbon tax increases the price of fossil fuels
relative to their carbon content. Different scenarios are
modeled using a tax in the range of 50 US$/tCO
2
to 100
US$/tCO
2
in 2020 and from 200 US$/tCO
2
to 400 US$/
tCO
2
in 2050. Carbon taxes are either implemented in
isolation or combined with technology subsidies, a pro-
curement scheme and building codes. Their results show
that it is this combination of policies that is most effec-
tive in driving market uptake of fuel-efficient low-car-
bon technologies.
Brown and Li (2018) analyze mitigation scenarios
based on a carbon tax in the US electricity sector,
either implemented in isolation or combined with
other policies (e.g., MEPS). Carbon prices are rela-
tively lower than those modeled by Chen et al. and
Méjean et al. and range from 10 US$/tCO
2
,20US$/
tCO
2
, and 40 US$/tCO
2
in 2020 to 26 US$/tCO
2,
53
US$/tCO
2
, and 59 US$/tCO
2
in 2040. Results show
that a mix of energy-efficiency policies and a carbon
tax that increases from 10 US$/tCO
2
in 2020 to 27
US$/tCO
2
in 2040 delivers net savings. Interestingly,
a higher tax (ranging from 20 US$/tCO
2
in 2020 to
53 US$/tCO
2
in 2040) is shown to deliver the same
emission reductions and keep the sector within the
1.5 °C target; however costs are much higher in the
absence of complementary demand-side policies.
This emphasizes that increased energy efficiency (or
lower energy intensity) reduces mitigation costs
(Grubler et al. 2018; Luderer et al. 2013;Riahi
et al. 2015;Rogeljetal.2013).
The modeled or optimal carbon prices presented
in this special issue need to be compared with real-
life observations. In 2016, nearly 15% of global
GHG emissions were priced directly via a tax or
emissions trading system (ETS) (World Bank et al.
2017). However, almost three quarters of emissions
currently addressed by carbon pricing mechanisms
are below 10 US$/tCO
2
(World Bank et al. 2017)
,
which is significantly lower than pricing levels con-
sistent with the Paris Climate Agreement: 15–360
US$tCO
2-eq
in 2030, 45–1000 US$tCO
2-eq
in 2050,
and 140–8300US$/tCO
2-eq
in 2100 (Stiglitz et al.
2017). Wachsmuth and Duscha (2018) conclude that
the European Union’s ETS is very unlikely to pro-
vide the economic incentives for decarbonizing the
industrial sector unless substantial reforms are un-
dertaken. The authors argue that although the ETS
innovation fund
3
may offer better incentives, com-
plementary demand-side policies are still urgently
needed across end-use sectors.
Coordinated and effective policy mixes
Finally, ambitious policies often take the form of in-
creasing the stringency of existing measures while si-
multaneously implementing new, bold interventions.
This process needs to be coordinated, and policy evalu-
ation is crucial. In addition to the implementation of
carbon pricing alongside demand-side policies, various
papers underline the need for comprehensive and inte-
grated policy mixes.
Analyzing emission peaks and dynamics across
various sectors, Méjean et al. (2018)discusslow-
energy demand scenarios in terms of making current
policy instruments (e.g., building codes, standards)
more stringent, while at the same time implementing
novel regulatory and market-based interventions (e.g.,
financial products for retrofits, aggressive investment in
public transport, incentives for teleworking)—in addi-
tion to carbon pricing. Policy-driven scenarios show that
a rich mix of stringent sectoral policies, particularly for
transport and industry, are critically needed in the near
term. The authors conclude that this approach helps to
lower energy demand and reduce the level of carbon
pricing needed to meet the temperature target. Similarly,
Chen et al. (2018) highlight the need for a coordinated
policy mix, comprising a carbon tax, ambitious building
codes, and stricter technology standards to drive deep
3
For further information see https://ec.europa.
eu/clima/events/articles/0115_en
Energy Efficiency

decarbonization pathways in the Chinese building
sector. Moberg et al. (2018) conclude that a policy mix
that goes beyond market-based incentives and voluntary
agreements is more likely to drive deep decarbonization
pathways across various end-use segments. The authors
stress the need for command-and-control policy initia-
tives to drive behavioral change and sustainable con-
sumption patterns.
Wachsmuth and Duscha (2018) show that national
mitigation scenarios based on bottom-up exercises
open up opportunities for carbon emission reductions
in end-use sectors that are more stringent than those
observed in more aggregated scenarios. From a pol-
icy perspective, the authors conclude that emissions
can be reduced by targeting specific areas, namely
sufficiency, energy efficiency, electrification, and fu-
el switching. In line with other studies (e.g., Darby
2007; Princen 2003), they argue that sufficiency in
energy services in the building sector can be ad-
dressed via stringent building codes (e.g., that can
approximate passive house standards), retrofitting
targets, MEPS, energy use quotas, or progressive
electricity tariffs (cf. Wilhite and Norgard 2004).
Knobloch et al. (2018) explore policy mixes and
their interactions. Carbon taxes, technology subsi-
dies, a procurement scheme, and building codes are
explored in a variety of scenarios. Assuming coor-
dinated and effective policy efforts, the results
show that policy mixes are more effective than
carbon taxes in isolation to drive the nearly full
decarbonization of the residential-heating segment.
Modeling outcomes show that carbon taxes (from
50 to 200 US$tCO
2-eq
−1
) combined with subsidies
and renewable energy procurement policies can
trigger mitigation measures that deliver near-
complete decarbonization.
In contrast to calls for stringent and coordinated
policy mixes, Patt et al. (2018) argue that demand-side
policies (and measures) could compete with
decarbonization measures. In their Bthought
experiment,^the authors argue that the limited polit-
ical capital for tackling climate change may be used up
by energy-efficiency policies—at the expense of pol-
icy support for renewables and other decarbonization
measures. They also argue that investments in energy
efficiency may crowd out investments in the supply of
low-carbon energy, as both are capital intensive and
compete for the same, limited, pool of finance. More-
over, renewables offer increasingly cost-effective
abatement opportunities due to learning effects, while
the marginal abatement in the cost of energy-
efficiency measures may be lost once the low-
hanging fruits are picked. Patt et al. (2018) conclude
that these political, institutional, and investment bar-
riers to rapid decarbonization—if they materialize in
practice—could mean that demand-side measures do
not contribute as much to the 1.5 °C mitigation target
as the other empirical, analytical, and modeling stud-
ies presented in this special issue suggest.
Sectoral decarbonization pathways
and corresponding measures
This section provides a crosscutting or horizontal over-
view of the demand-side measures resulting from the
policy interventions described in the BStringency and
the role of demand-side policies to limit global warming
to 1.5 °C^section. This overview is grouped by end-use
sector. Where relevant, it includes energy supply issues.
To frame the discussion, we follow the IPCC (Working
Group III) definition of (sectoral) measures, understood
as Btechnologies, processes or practices that contribute
to mitigation, for example [energy efficiency measures],
renewable energy (RE) technologies, waste minimiza-
tion processes, [and] public transport commuting
practices^(Allwood et al. 2014,p.1266).
Buildings
The articles that analyze demand-side measures in the
buildings sector stress the critical need for rapid im-
provements in energy efficiency if the 1.5 °C target is
to remain viable. Consistent with previous IPCC As-
sessment Reports (Levine et al. 2007; Lucon et al.
2014), their findings confirm the value of several miti-
gation measures and opportunities.
Méjean et al. (2018) analyze global emission
peaks intended to meet the 1.5 °C goal and the
resulting dynamics across various sectors, including
the residential sector. First, they show that a post-
2030 peak makes the 1.5 °C goal unachievable but, if
it is reached earlier (in 2020), direct emissions in the
residential sector peak at nearly the same time. This
finding indicates the need for a high degree of policy
coordination between international climate policy
and specific sectoral interventions. From a global
perspective, the study finds that the residential sector
Energy Efficiency

contributes relatively less to an earlier peak than
others. However, these results are sensitive to as-
sumptions about energy-demand patterns and corre-
sponding policies, which critically affect the timing
of peaks.
Wachsmuth and Duscha (2018) analyze the feasi-
bility of 1.5 °C and 2° targets under various mitiga-
tion scenarios for the European Union (EU) and
European countries. In the building sector, national
scenarios (e.g., France, Germany, Italy) show deep
decarbonization pathways resulting in emission re-
ductions ranging from 94 to 100% in 2050. Using a
decomposition analysis, the authors reveal a sub-
stantial difference between these national scenarios
and an aggregated IAM scenario, which only finds
reductions of up to 46%. They attribute this gap to
the reduced contribution of per capita energy use to
emission reductions in the IAM. In contrast, in na-
tional scenarios these contributions are the result of
moderate lifestyles changes and higher levels of
energy efficiency. The study also finds evidence of
a gap between national scenarios and an aggregated
EU bottom-up scenario, which indicates reductions
up to 85%. However, the fact that this gap is con-
siderably smaller than the IAM scenario, illustrates
the more detailed analytical potential of bottom-up
approaches (Lucon et al. 2014). National deep-
decarbonization scenarios include a high level of
electrification in energy end-use across the building
sector, a reduction in fossil carbon intensity due to a
shift to natural gas, a slow increase in house size
(per capita m
2
), and highly efficient appliances and
lighting. Stringent thermal standards for both new
buildings and retrofits also play a critical role.
Chen et al. (2018) analyze the implications of the
1.5 °C target for the building sector in China. Policy-
driven scenarios show that direct emissions need to
peak before 2030 and the sector must approach net-
zero emissions by the end of the century. Key mea-
sures facilitating the transition include massive effi-
ciency improvements in building shells, together
with space heating and cooling. Other contributions
includemuchslowergrowthinenergydemandand
an increase in the supply of decarbonized power.
Wilson et al. (2018) focus on the building sector in
cities in the UK and identify various potentially dis-
ruptive innovations, which are grouped into three
generic strategies: (a) interconnectivity for optimized
usage (e.g., smart appliances), (b) improved thermal
performance (e.g., smart heating controls), and (c)
reduced demand for space and materials (e.g., flexi-
ble use or shared space). Innovations that are per-
ceived by experts to be both disruptive and emissions
reducing include home energy-management systems
(HEMS), the internet of things, and LED lighting
with smart controls. Two innovations are analyzed
in detail to explore potential annual reductions. Esti-
mates range from 0.1 MtCO
2-eq
(for smart appli-
ances) to 2.6 MtCO
2-eq
(for smart heating controls).
The authors argue that these estimates are conserva-
tive, and further research is needed regarding the
potential contribution of HEMS and other disruptive
innovations to the 1.5 °C goal.
Brown and Li (2018) model mitigation pathways
in the USA electricity sector (upstream), which re-
sult in significant efficiency improvements in the
building sector (downstream). Policy-driven mea-
sures include highly efficient air conditioners, refrig-
erators, freezers, geothermal heat pumps, electric
water heaters, dishwashers, air source heat pumps,
and gas and electric clothes dryers. Assuming strin-
gent building codes, shell-thermal efficiencies in
single-family homes, apartments, and mobile homes
also play a key role in emission reductions consis-
tent with the 1.5 °C goal.
Knobloch et al. (2018) focus on deep decarbonization
pathways for residential heating. Their results show that
near-zero decarbonization in 2050 is feasible, provided
that stringent, integrated policy frameworks are put in
place. The study highlights the critical importance of
high-thermal insulation for new houses, extensive
retrofitting of existing buildings, and the expansion of
small-scale renewable energy technologies. Technology
portfolios feature a move away from fossil fuel technol-
ogies. The rapid deployment of ground source heat
pumps, solar thermal and modern biomass is a feature
of all stringent mitigation pathways. An important as-
sumption underlying the study’s results is that the resi-
dential sector converges to an average heating intensity
(e.g., 45 kJ per m
2
per heating degree day by 2050 under
Brapid retrofitting^). The authors also note that their
technology choice decision framework relies upon spe-
cific behavioral characteristics.
Transport and mobility
Transport, as an end-use sector, has often been seen as
Bhard-to-decarbonize^, with a high degree of lock-in to
Energy Efficiency

fossil fuel-powered private vehicles (Creutzig et al.,
2015). Several articles in this special issue challenge
these findings and identify a range of approaches for
deep decarbonization. Wachsmuth and Duscha (2018)
analyze mitigation scenarios and show that emission
reductions of 90–100% are possible by 2050 in various
national scenarios (e.g., France, Germany, the UK).
Their decomposition analysis indicates that carbon
emissions in national scenarios are much lower than
those projected by aggregated EU scenarios based on
bottom-up or an IAM. Reasons for this difference in-
clude, for example, the higher resolution of transport
activity, the greater diffusion of electric vehicles, and the
larger share of biofuels. Specific mitigation measures
characterizing national pathways include, for instance,
the electrification of the sector (including electrically
driven heavy-duty vehicles via trolley tracks), marked
modal shifts, and the use of biofuels or synthetic fuels.
From a global perspective, Méjean et al. (2018)also
identify clear decarbonization pathways for the transport
sector. Bringing forward the global emission peak from
2025 to the present results in an earlier peak in the
transportation sector. This reduces peak emissions by
around 2 GtCO
2
and the stringency and pace of
decarbonization efforts (after the peak). However, un-
like other sectors, their findings reveal that a global
emissions peak in 2020 could lead to a relatively late
peak in direct emissions (around 2035) in the transport
sector. This is mostly driven by lock-in effects due to
existing infrastructure, suboptimal urban planning, and
the coupling of GDP growth with demand for mobility.
On the other hand, emissions decrease at a faster rate
than other end-use sectors after the global peak. The
authors acknowledge that further studies are needed to
better understand these trends.
Gota et al. (2018) provide an extensive analysis of
deep decarbonization pathways in the transport sector.
Theirreviewcoversupto1500low-carbonmeasuresin
81 countries, which are grouped into three main catego-
ries: (a) Bavoid^measures that aim to decrease the need
for transport trips, (b) Bshift^measures that aim to move
trips to more efficient modes, and (c) Bimprove^mea-
sures that aim to increase the fuel efficiency of vehicles.
Their study reveals that two thirds of these measures
address fuel efficiency or decarbonization, while nearly
one third address changes in travel behavior. Using three
low-carbon scenarios (conservative, optimistic, and av-
erage) the authors show that an optimistic scenario
(stringent, intensive measures in the near term,
continuing until 2050) can decrease emission levels to
2.5 GtCO
2
. In this scenario, the transport sector meets
the indicative 2 °C target, and puts it very close to a
sectoral 1.5 °C goal.
Wilson et al. (2018) explore the role of potentially
disruptive innovations in passenger mobility and con-
sider four generic strategies: (a) alternative fuel or vehi-
cle technologies (e.g., electric vehicles), (b) alternative
forms of auto-mobility (e.g., car clubs), (c) alternatives
to auto-mobility (e.g., mobility-as-a-service), and (d)
reduced demand for mobility (e.g., telecommuting).
Innovations in all four categories include both business
model and technological innovations. The authors find
that mobility-as-a-service and electric vehicles are rela-
tively more disruptive. Estimated annual emission re-
ductions for the UK range from a lower bound of 0.04
MtCO
2-eq
(for e-bikes) to an upper bound of 0.9 MtCO
2-
eq
(for car clubs). Consistent with other studies in this
special issue, Wilson et al. highlight the potential for
mobility-related measures to deliver significant emis-
sion reductions.
Industry
Brown and Li (2018) study the situation in the USA, and
model many highly efficient measures within the indus-
trial sector. Such measures include combined heat and
power, electric motors, and specific measures to reduce
energy use by 2030 in bulk chemicals (18%), cement
and refining (23%), pulp and paper (40%), and iron and
steel (57%) subsectors.
Like the building and transport sectors, Wachsmuth
and Duscha (2018) find deeper decarbonization path-
ways in national scenarios than aggregated EU sce-
narios. In national scenarios, emission reductions by
2050 range from 93 to 103%, compared to 61% (in
IMAGE) or 74% (in PRIMES). Their decomposition
reveals that low per capita energy use, high electrifi-
cation, resource efficiency, and increased penetration
of renewable energy fuels (biomass and biogas) all
play an important role in decarbonized national sce-
narios. Reductions in per capita energy use are par-
tially driven by improvements in the manufacturing of
energy-intensive products (e.g., the substitution of
cement clinker by cleaner alternatives, and recycling
in the iron and steel and aluminum subsectors).
Méjean et al. (2018) emphasize the need for industry
to implement robust mitigation measures in the near
term if the 1.5 °C goal is to be achieved. Like the
Energy Efficiency

residential sector, their results show that if a global
emission peak is reached in 2020, direct emissions in
the industrial sector peak at nearly the same time. Like
the transport sector, an earlier global peak (very close to
the present time) reduces both peak industrial emissions
(byapproximately2GtCO
2
,from10to8GtCO
2
)and
the pace of decarbonization efforts after the peak. The
authors acknowledge that their results are sensitive to
assumptions about high- or low-energy demand. Under
low-energy demand, short-term emission reductions are
high due to rapid improvements in energy efficiency,
which, in turn, delay emission peaks in other sectors
(such as transport).
Food and dietary choices
The importance of food consumption and dietary
choices in climate mitigation has been clearly identified
in the literature (see e.g., Hedenus et al. 2014; Weindl
et al. 2017; Wynes and Nicholas 2017). Two articles in
this special issue consider food-related measures and
resulting emissions. Moberg et al. (2018) identify sig-
nificant potential for emission reductions based on a so-
called sustainable diet, which encompasses an increase
in organic, locally produced foods combined with a
vegetarian diet. Wilson et al. (2018) identify various
disruptive innovations related to food, grouped into four
strategies: (a) alternative dietary preferences (e.g., re-
duced meat consumption), (b) urban food production
(e.g., vertical farming), (c) producer–consumer relation-
ships (e.g., food-link schemes), and (d) reduced demand
for food (e.g., food-waste reduction). Scaling up evi-
dence of emission reductions from early adopters to
similar segments of the UK population, they conserva-
tively estimate that consumer-related innovations could
reduce direct and indirect emissions in the agriculture
sector by up to 7.1%.
Supply side and distribution
Various papers analyze the link between end-use sectors
and supply. Confirming findings from other whole-system
analyses of 1.5 °C mitigation, Méjean et al. (2018)con-
clude that an early peak in global emissions entails accel-
erated emission reductions in demand sectors together
with the rapid decarbonization of the electricity sector.
Wachsmuth and Duscha (2018) analyze energy
supply in the context of indirect carbon emissions
that are excluded from end-use sectoral analyses at
the national level, and reveal interdependencies be-
tween electricity demand and supply. The authors
argue that across the EU, and in selected European
countries, there is a critical need for a greater share of
renewable energy sources in the supply mix, which is
identified as an important complement to demand-
side efforts. Their analysis shows that energy-use
reductions make a greater contribution to emission
reductions in national (bottom-up) scenarios than
international or EU mitigation pathways. One impor-
tant reason for this is the exclusion of carbon capture
and storage (CCS) measures in national scenarios,
which means that demand-side options reach their
technical limits in deep decarbonization pathways.
At the same time, the authors acknowledge that na-
tional scenarios only consider mitigation options
close to their market maturity.
SimilarlyChenetal.(2018) emphasize the need for
accelerated low-carbon electrification in the construc-
tion sector in rural and urban areas of China. A com-
mon element is the phasing out of traditional fossil
fuels (coal and petroleum). In rural areas, traditional
biomass is completely replaced by modern fuels in
two or three decades and, under a 1.5 °C scenario,
the fuel mix is nearly identical to that of urban resi-
dential areas. The authors also find that fossil fuel-
based district heating is displaced by geothermal
heating. Solar energy overtakes natural gas and plays
a major role in the hot water and heating segments.
Despite these far-reaching changes, the authors stress
that their projected increases in solar and geothermal
energy are still lower than indicative national targets.
Knobloch et al. (2018) also stress the links between
demand and supply. For instance, improved building
shells combined with solar thermal and ground source
heat pumps are shown to considerably reduce electric-
ity demand (by up to 50% in certain scenarios). How-
ever, scenarios that envisage the complete electrifica-
tion of residential heating would require substantial
additional capacity, equivalent to nearly half of cur-
rently installed global-power capacity. In addition, the
full electrification of the heating sector results in indi-
rect emissions that cancel out up to 80% of direct CO
2
reductions. These results strongly suggest that the
direct electrification of the heating sector might be
ineffective and expensive compared to more efficient,
cost-effective alternatives.
Wilson et al. (2018) analyze various potentially dis-
ruptive innovations thatlie at the interface between end-
Energy Efficiency

users and energy supply. These are categorized as fol-
lows: (a) new service providers (e.g., energy
aggregators, such as when municipalities or market
intermediaries enable energy users to collectively bar-
gain for low-carbon investments), (b) the integration of
consumers into grids (e.g., time-of-use pricing), and (c)
decentralized energy supply (e.g., community energy,
peer-to-peer electricity trading). In line with the litera-
ture that explores potential conflicts between business
models, centralized supply systems, and energy efficien-
cy (see e.g., Bachrach et al. 2004; Blumstein et al. 2005;
Eyre 1997), Wilson et al. underline that consumers
might become less passive and move towards the active
production, organization, and management of small-
scale energy systems. This development is a potentially
disruptive threat to the core business of centralized
networks and utilities.
Brown and Li (2018) highlight synergies between
stringent energy-efficiency policies across end-use sec-
tors, and a carbon tax in the power sector designed to
shift the fuel mix away from coal and towards wind and
solar. They argue that emission reductions and slower
growth in electricity demand are key elements in strin-
gent mitigation pathways. On the demand side, in-
creased energy efficiency can also decrease investment
in installed capacity and fuel expenses, which in turn
significantly lowers utility resource costs.
Methodological approaches
This section explores the different methodological ap-
proaches referred to in this special issue, including their
strengths and limitations. As stated earlier, a significant
challenge for the analysis or evaluation of demand-side
policies and mitigation pathways concerns the existence
of robust, consistent links with the temperature target. In
this respect, the choice of analytical tools and method-
ological approaches is of prime importance. Despite
inherent uncertainties, complexities, and caveats, the
papers in this special issue map out various analytical
avenues that can complement or strengthen existing
knowledge. They are grouped into four main categories.
System modeling (including IAMs)
As noted in the introduction, global integrated assessment
model (IAM) analyses of 1.5 °C mitigation pathways
emphasize the importance of demand-side measures.
Some IAMs specialize in developing a detailed represen-
tation of end-use technologies (Hibino et al. 2003). Other
IAMs run specific studies in which model variants with
more demand-use detail are developed to answer specific
research questions (McCollum et al. 2017). But it is
neither possible nor desirable for IAMs to capture the full
richness of demand-side approaches to mitigation. Mod-
elers face a trade-off between elaboration and elegance
(Held 2005): increased complexity can reduce transpar-
ency and interpretability. This is necessarily so for parsi-
monious and tractable models trying to capture the entire
global energy and land-use systems.
As a result, the resolution of demand-side tech-
nologies and measures in global IAMs is generally
quite coarse. Particularly in models from a macro-
economic tradition, sectoral energy demand is given
as a function of income growth based on historical
calibrations. In other words, energy demand in fu-
ture scenarios is commonly defined exogenously
based on assumed GDP growth and parameterized
income elasticities (together with limited responsive-
ness to changing energy prices). This leaves little
scope for exploring demand-side strategies that
could rapidly and dramatically reduce emissions.
Ultimately, global IAMs can try to capture stringent
demand-side approaches to mitigation in four broad
ways: endogenously or exogenously; and explicitly
or implicitly. Table 1summarizes this 2 × 2 grid and
gives an example of each combination. Exogenous
representations are derived from scenario narratives
or storylines, which IAMs interpret quantitatively.
4
As an example, the potential for demand reduction is
analyzed under the awkward rubric of Blifestyle
change,^which—in model speak—conflates arbitrarily
selected behavioral changes with assumed shifts in so-
cial norms and institutions. Lifestyle change is hard to
model endogenously in global IAMs; but it is also hard
to parameterize or quantify by mapping storylines to
model inputs and assumptions (Schwanitz 2013). Nev-
ertheless, some IAM studies do specifically set out to
analyze the system outcomes of lifestyle change. As an
example, van Sluisveld et al. (2015)findthatassumed
reductions in demand for heating, cooling, residential
floorspace, appliance ownership, and private vehicle
use can reduce global CO
2
emissions by up to 35% by
4
A coherent and internally consistent storyline is what distinguishes a
scenario from a model (where inputs and parameters are set or varied to
test behavior or answer a specific research question).
Energy Efficiency

2050. But such studies tend to lack an underlying nar-
rative that can explain why lifestyles are changing in the
first place (Geels et al. 2016).
In sum, global IAMs have significant limitations. First,
they tend to be anchored on currently available mitigation
options, particularly on the energy supply (although
BECCS is a notable exception). Second, they tend to
have relatively aggregated representations of energy
end-use technologies and the contexts in which they are
deployed, and so have limited capacity to analyze energy-
demand transformation or the emergence of novelty in
energy services. Third, they do not capture the broad
range of socio-technical energy demand drivers, such as
social norms, culture, institutions, and lifestyles
(Schwanitz 2013). Consequently, insights from IAM
analyses are increasingly limited by the failure to reflect
the rapid, real-world transformations that lie outside the
scope of a tractable model (Geels et al. 2016).
Two articles in this special issue build on this basic
insight and aim to provide a better understanding of
the implications of 1.5 °C mitigation pathways at the
sectoral level. Méjean et al. (2018) apply the
IMACLIM-R model to assess the worldwide transi-
tion across demand sectors (transport, residential,
industry). The authors analyze global emission peaks,
and the pace and dynamics of deep decarbonization
pathways. They find that in the short term, ambitious
demand-side policies are crucial in supporting a glob-
al emission peak and maintaining the likelihood of
meeting the 1.5 °C goal. From a national perspective,
Chen et al. (2018) apply an IAM (the GCAM-TU) to
the building sector in China. Both studies confirm the
high degree of uncertainty about future energy use,
which is often driven by complex relationships be-
tween economic development, technological prog-
ress, population growth, behavioral patterns, and pol-
icy assumptions (cf. Kriegler et al. 2014;O’Neilletal.
2017; Riahi et al. 2017).
It is important to acknowledge the findings of an
earlier paper using two IAMs (MESSAGE and RE-
MIND) to explore a range of 1.5 °C scenarios: Bin line
with what was found for 2°C [...] targeted measures to
stimulate energy-efficiency improvements are a key en-
abling factor for achieving a 1.5°C target^(Rogelj et al.
2015a, p. 523). Subsequent analysis using six IAMs and
the BShared Socioeconomic Pathway^(SSP) scenario
framework used in the IPCC reinforced this earlier find-
ing. By adding stringent climate policy assumptions to
Table 1 Ways of analyzing demand-side approaches in global IAMs. Note: Arrow from top left to bottom right shows direction of improved
Bresolution^of energy end-use
Implicit
(represented as being part of a more
general process or phenomenon)
Explicit
(represented as a discrete, idenfiable process
or phenomenon)
Exogenous
(externally
specified)
Feature of scenario narrave interpreted
into modelling assumpons (or used to
interpret modelling results ex post)
e.g., rising GDP drives rising demand for
energy services (Bauer et al. 2017)
Feature of scenario narrave mapped into
specific model input or parameter
e.g., strong policy and innovaon emphasis on
reducing transport-sector emissions implies a
high learning rate for alternave fuel vehicles
and so costs reduce rapidly as a funcon of
experience (McCollum et al. 2016)
Endogenous
(internally
generated)
General relaonship between energy
end-use and internal model variables but
without resolving specific causal
mechanisms
e.g., stringent climate policy implemented
as a high carbon price increases the cost
of energy carriers and so reduces demand
through parameterised price elascity
(Pye et al. 2014)
Specific relaonship between determinant of
energy end-use and internal model variables
based on a specific causal mechanism
e.g., social influence effects reduce perceived
risk of alternave fuel vehicles and so accelerate
adopon rates by making lifecycle costs more
compeve with convenonal vehicles (Pefor
et al. 2017)
Energy Efficiency

each of the five SSP
5
baseline scenarios, a range of 1.5 °C
mitigation pathways were identified (Rogelj et al. 2018).
All such pathways were found to strongly limit energy-
demand growth, although the study also found that:
BEnergy conservation is a common strategy in stringent
mitigation scenarios, but it also has limits^(Rogeljetal.
2018, p. 327). These limits refer to the need for full
decarbonization of the energy supply to reach net-zero
emissions around 2050 or soon thereafter.
A recent IAM study posed the question: Can the
1.5 °C target be met without relying on BECCS, or
indeed, any CCS? The study found that it can, but only
by unprecedented transformation of end-use services to
reduce total global energy demand by around 40% from
2020 to 2050 (Grubler et al. 2018). Importantly, al-
though this study used a global IAM to quantify the
optimal energy supply mix, the analysis of energy-
demand reduction potentials was done Boff model^
using bottom-up estimations of activity levels and ener-
gy intensity for each energy service and end-use sector.
This methodological innovation was necessary to over-
come the inherently limited endogenous representation
of energy demand in the IAM.
Decomposition analyses
Decomposition approaches are also used to explore the
challenges and implications of meeting the 1.5 °C mit-
igation target. Departing from, or building upon, the
IPAT equation (Ehrlich and Holdren 1971;Holdren&
Ehrlich 1974), the Kaya Identity (Yamaji et al. 1991)or
the Logarithmic Mean Divisa Index (Ang and Zhang
2000), studies that apply a decomposition approach pay
particular attention to changing energy and carbon in-
tensities as critical drivers of CO
2
emission reductions.
Patt et al. (2018) reflect on the potential compe-
tition between policies and investments in energy
efficiency on the one hand, and clean energy tech-
nology on the other. As part of this Bthought
experiment^and assuming carbon budgets consis-
tent with a 1.5 °C target, the authors use the Kaya
Identity to assess substitution rates between im-
provements in energy intensity and carbon intensity
from initial values of 1.6 kWh per US$ and 300
gCO
2
per kWh respectively. Like many Kaya Iden-
tity analyses, they do not consider policies designed
to reduce population or economic growth. Based on
several, highly stylized, assumptions regarding
learning, diffusion, and abatement costs, Patt et al.
argue that substantial and unprecedented energy-
efficiency improvements may have only marginal
effects given the timeframe needed to decarbonize
the energy supply.
Wachsmuth and Duscha (2018) use an index decom-
position analysis (IDA) to analyze and compare reduc-
tions in energy and carbon intensities in various strin-
gent mitigation scenarios applied to the EU and selected
European countries. Their approach evaluates ambitious
bottom-up scenarios for France, Germany, Italy, and the
UK, and estimates sectoralmitigation rates, and assesses
them against European scenarios contained in global
1.5 °C (or 2 °C) mitigation pathways. The use of IDA
helps to understand the development of sectoral energy
and carbon intensities and reveals the gap between
national bottom-up studies on the one hand, and Euro-
pean and IAM scenarios generated by the PRIMES and
IMAGE models respectively.
Like any methodology, decomposition approaches
have their limitations. Concerns about collinearity, cau-
sality, and the reliability of significance statistics
(pvalues) have been stressed in the literature on the
drivers of CO
2
emissions (Mundaca and Markandya
2016; Raupach et al. 2007). In addition, an IDA be-
comes mathematically complicated if the dataset in-
cludes zero values (Ang and Zhang 2000). As
Wachsmuth and Duscha correctly point out, this can
be an important issue in CCS scenarios where zero or
negative carbon intensity values are possible. Further-
more, Wachsmuth and Duscha underline the intricacies
of carbon and energy intensities in a 1.5 °C context: the
approximation of independence of these intensities is
valid for marginal changes, while variation in 1.5 °C
mitigation pathways is considerable. It is also important
to take into account sectoral specificities regarding the
(in)dependence of energy and carbon intensities.
Wachsmuth and Duscha note that the assumption of
independence may not hold—even in the short term.
For example, priority dispatch rules in markets may
mean that lower electricity demand may not reduce the
use of renewably generated electricity.
5
The Shared Socioeconomic Pathways (SSP) framework Bprovides a
basis of internally consistent socio-economic assumptions that repre-
sent development along five distinct storylines: development under a
green-growth paradigm (SSP1); a middle-of-the-road development
along historical patterns (SSP2); a regionally heterogeneous develop-
ment (SSP3); a development that results in both geographical and
social inequalities (SSP4); and a development path that is dominated
by high energy demand supplied by extensive fossil-fuel use (SSP5)^
(Rogelj et al. 2018, p. 325).
Energy Efficiency

Bottom-up approaches
In this special issue, we define Bbottom-up^ap-
proaches in broad terms, as disaggregated character-
izations and analyses (including quantitative model-
ing) of end-use sectors, services, or technologies
that aim to provide policy-relevant knowledge. By
their nature, these bottom-up approaches are less
concerned with systemic effects (e.g., on the energy
supply or wider macroeconomy effects) than IAMs.
Gota et al. (2018) use a bottom-up approach to ana-
lyze the extent to which the transport sector can meet a
sector-specific mitigation goal consistent with the 1.5 °C
target. Relying extensively on available mitigation stud-
ies, the authors first translate the 1.5 °C target to an
indicative 2050 sectoral target (equivalent to 2 GtCO
2
).
They then compare this target with mitigation potentials
resulting from the aggregation of bottom-up estimates,
including comparisons with IAM scenarios. Finally,
they compare the sectoral target with low-carbon sce-
narios that are aggregated at national and global levels.
Their evaluation is based on a metanalysis comprising
over 500 bottom-up modeling estimates from 81 coun-
tries that account for nearly 92% of global transport
emissions. The authors acknowledge that there is a high
degree of uncertainty in the transport-mitigation poten-
tial and emphasize that their results must be taken with
due caution. A key area of improvement for compara-
tive studies lies in the consistency of key assumptions
(e.g., growth in transport demand) and the inclusion of
wider mitigation measures, notably those that address
behavioral change.
Brown and Li (2018) use the National Energy
Modeling System (NEMS) model. The authors com-
bine NEMS with various policy-driven scenarios
and an indicative 1.5 °C target for the US electric
sector. The target is derived in three steps: (a) the
adoption of a global carbon budget consistent with a
1.5 °C goal, following estimates presented in Millar
et al. (2017), (b) the estimation of a national 1.5 °C
carbon budget as a percentage of the global carbon
target based on GDP and population, and (c) the
determination of a 1.5 °C target for the electricity
sector as a percentage of total US emissions gener-
ated by the overall power sector in 2016. An impor-
tant methodological element lies in the type of fore-
sight used in the model. Future price increases are
based on the assumption that utilities consistently
engage in integrated resource planning to meet
least-cost operations resulting from future policies
(see also Hourcade et al. 2006;Mundacaetal.2010;
Worrell et al. 2004). This drives carbon emission
and electricity-demand reductions ahead of policy
implementation. The paper notes two important lim-
itations. One relates to the timeframe of the analysis
(until 2040) and the potential underestimation of
mitigation costs if they are not rooted in a much
longer perspective (e.g., until 2100). The authors
also highlight that the analysis does not take into
account leakage or additional emissions that may
arise during deep decarbonization transitions.
Knobloch et al. (2018) use the Future Technology
Transformations model (FTT): Heat model to explore
deep decarbonization pathways for residential heating
given various policy scenarios (e.g., carbon taxes, tech-
nology subsidies). The FTT: Heat model provides a
bottom-up simulation of technology diffusion and aims
to forecast technology portfolios for residential heating
systems up to 2050. Despite the difficulty of parameter-
izing behavioral change in modeling tools, the authors
implement a stylized heterogeneous, decision-making
approach to understanding the choice of household tech-
nology. The study explicitly addresses various limita-
tions and uncertainties, and the authors stress that the
inclusion of behavioral features means that the results are
more realistic and, unlike optimization models, provide
more valuable insights for policymakers. Finally, they
argue for much closer collaboration between behavioral
scientists and modelers.
Wilson et al. (2018) apply a simple quantitative
approach to estimate potential emission reductions
from a set of disruptive low-carbon innovations
(DLCIs) currently available for adoption by con-
sumers. Building upon the method of Dietz et al.
(2009) for quantifying realistically achievable emis-
sion reductions from household measures, the au-
thors follow a four-step approach using the UK as a
case study. These steps are: (a) identify existing early
adopting DLCI niche, (b) quantify emission reduc-
tions based on observed activity by early adopters,
(c) match DLCI niche to equivalent segment of the
UK population, and (d) estimate potential annual
reductions if DLCI niche was scaled up to the UK
population. Based on available behavioral, energy,
and emissions data, the authors estimate emission-
reduction potentials for 11 DLCIs across three do-
mains: mobility (e.g., car clubs), food (e.g., urban
farming), and buildings (e.g., smart heating controls).
Energy Efficiency

They stress that the key assumption in their approach
is that diffusion is limited to population segments that
match the sociodemographic characteristics of early
adopters. However, they argue that evidence from
other diffusion studies suggests that this assumption
is conservative and that the estimated emission re-
ductions represent a lower bound.
Sonnenschein et al. (2018) use a life cycle costs
(LCC) approach to analyze the potential effectiveness
of MEPS. MEPS set minimum levels of efficiency, and
often ban underperforming products from the market
(Lucon et al. 2014). According to the authors, MEPS
are often defined by determining which efficiency re-
quirement minimizes LCC for end users. First, their
approach considers the relationship between annual
units of energy consumption and the market price of
home appliances (e.g., refrigerators, dishwashers) with
different efficiency classes. Then, optimal LCC are cal-
culated based on purchase price, operating costs, and a
high-end estimate of the social costs of carbon (SCC)
emissions (equivalent to 150 US$ per tCO
2
). The latter
is used as a proxy of near-term shadow carbonprices for
1.5 °C scenarios. LCC optima (with and without carbon
prices) are calculated for different appliances and the
price of switching between inefficient and efficient ap-
pliances following the implementation of MEPS incen-
tives is analyzed. Sonnenschein et al. acknowledge that
modeling LCC with SCC may be overly simple and that
various limitations and requirements need to be consid-
ered. These include statistically sound relationships be-
tween high-market prices and efficient products, the
difficulty of forecasting product improvements based
on an ex post market approach, and alternative functions
in the LCC optimization method.
Stakeholder approaches
Methodologies that generate and analyze data collected
from the public, experts, policymakers, and other actors
are an important part of the analytical toolkit. Wilson
et al. (2018) use a survey of low-carbon innovation
experts (firms, investors, market intermediaries,
policymakers, and researchers) to assess potential emis-
sion reductions from selected DLCIs. The survey was
distributed to experts prior to two workshops addressing
innovation, markets, and research needs. It spanned a set
of 40 innovations in four domains: mobility, food,
homes, and energy supply. Experts were asked to score
the potential of these innovations in terms of both
disruptiveness and emission reductions. The authors
acknowledge that their results are based on a small
sample, and emphasize that their findings are illustra-
tive. Nevertheless, experts perceived that potentially
disruptive innovations were either dependent on tech-
nological progress or behavioral change, and that poten-
tial emission reductions were dependent on market
proximity and current market size, rather than the po-
tential for long-term transformation.
Moberg et al. (2018) explore how to achieve 1.5 °C
mitigation pathways via transformations in household
consumption. Combined with other tools (e.g., simula-
tion game to explore emission reductions of 50%), the
authors use in-depth interviews held in four European
countries to evaluate household behavior and responsi-
bility across various domains (food, housing, mobility).
Their approach focused on a single, overarching ques-
tion: BWho do you consider responsible for climate
mitigation?^A content analysis of the data revealed
two main areas of discussion: responsibility and system-
ic barriers to (individual or collective) mitigation ac-
tions. No significant variations were found between
countries but the sample size needs statistical consider-
ations. The results suggest that there is consensus re-
garding individual responsibility in implementing miti-
gation actions, and the role of public policy to steer
behavioral change.
Conclusions
The objective of this special issue is to strengthen the
evidence base on the role of demand-side policies, mea-
sures, and corresponding mitigation pathways to limit
global warming to 1.5 °C. Three themes recur: policies,
measures, and methods. What are the main lessons
learnt? Where does this leave us?
From a policy point of view, this special issue under-
lines the crucial role of demand-side solutions for keep-
ing the 1.5 °C target within reach. It emphasizes the
importance of policy portfolios in driving the pace and
direction of deep decarbonization pathways and reduc-
ing mitigation costs. Overall, authors argue that strin-
gent and well-coordinated policy mixes are required to
remain on a path leading to a 1.5 °C target. Shifts in
investment patterns are critically needed. Sectoral tar-
gets, building codes, performance standards, behavior-
oriented interventions, and carbon pricing are all seen as
important policy options that are already available to
Energy Efficiency

policymakers. Carbon pricing is found to be particularly
important, albeit insufficient to drive mitigation path-
ways compatible with the 1.5 °C goal. End-use behavior
is characterized by non-financial preferences; therefore,
path dependency and a variety of barriersand anomalies
need to be targeted more specifically. In addition, con-
cerns about sufficiency, rebound effects, and changes in
social welfare resulting from (potential) additional ener-
gy use need to be addressed indicating that policy inter-
ventions cannot be reduced to technological innovation,
substitution, or purely market-based approaches.
Meeting the goals of the Paris Agreement is an enor-
mous challenge for societies and policymakers, and
trade-offs go well beyond the technological (supply)
dimension. A key issue is the ongoing management and
evaluation of a diverse portfolio of policies. Experimen-
tation must go hand-in-hand with assessment to improve
the design and implementation of interventions. The pa-
pers presented here show that demand-side mitigation in
line with the 1.5 °C goal is possible; however, it remains
immensely challenging and reliant upon both innovative
technologies and policy, and behavioral change.
This special issue also highlights an abundance of
demand-side measures to limit warming to 1.5 °C. Low-
carbon innovations offer transformative potential by
integrating technologies with new business models.
Some measures are specific to sectors, segments, prod-
ucts, or energy services, while others are generic (nota-
bly technical-efficiency improvements). But not all of
these demand-side measures can be Bseen^or captured
by current quantitative tools or progress indicators, and
some measures remain poorly represented in the litera-
ture and policy discourse. Demand-side measures and
supply-side decarbonization are inextricably linked, and
rapid action is needed on both fronts. This is consistent
with the basic idea that downsizing the energy system
(by tackling ever-rising demand) makes it more feasible
to decarbonize the energy resource mix via renewables
and other measures (Grubler et al. 2018).
A careful analysis cautions against single-solution
approaches (e.g., the potentially adverse effects of heat
pumps on the electricity system, and the crowding out of
renewable energy investments by energy-efficiency in-
vestments). At the same time, measures that address
consumption are very reliant on self-governance. How-
ever, and contrary to prevailing thinking, this special
issue shows that sector-specific deep decarbonization
pathways are possible. Ambitious and sustained imple-
mentation of demand-side measures reduces mitigation
costs and the need for CDR options. Overall, the papers
presented here suggest that there could be a kind of
Btriple dividend^from increased energy efficiency,
encompassing environmental, social, and economic as-
pects. Further research should systematically assess the
multiple co-impacts and welfare effects of demand-side
measures in a 1.5 °C context.
Finally, and from a methodological point of
view, the articles in this series underline the notion
that there is no single best method that can com-
prehensively capture the dynamics, complexities,
and potentials of deep decarbonization pathways.
The range of methodological approaches in this
special issue clearly illustrate that analyzing
demand-side measures requires a plurality of tools,
methods, and datasets that are applied to diverse
sectors, services, and domains. Forward looking,
scenario-based analyses yield different insights de-
pending on the approach or tools used. Comparing
and contrasting these insights underlines the
strengths and weaknesses of particular approaches.
Whole systems models are useful and important
tools for analyzing systemic effects, trade-offs and
interdependencies between demand and supply-side
issues. Global IAMs play an important role as
gatekeepers in 1.5 °C analyses by linking long-
term energy transformation pathways with cumula-
tive emission budgets and global warming out-
comes. These powerful tools can simulate or opti-
mize energy and land-use transitions and are par-
ticularly useful in evaluating resource, supply side
or upstream transformations. On the other hand,
they provide fewer details when assessing services,
final energy demand, or living standards.
An important challenge in modeling studies relates
to the integration, parameterization, and assessment of
behavioral (or lifestyle) changes. In line with the lit-
erature, authors highlight the need for unambiguous
references to norms and values whenever behavioral
change or related policies are analyzed. Furthermore,
given the growing evidence that cognitive, motiva-
tional, and contextual factors affect technology choice
and energy use, much more effort needs to be devoted
to the inclusion of behavioral anomalies and devia-
tions from rational choice theory, which can lead to
systematic differences between decision and experi-
enced utility. The multiple challenges of limiting
warming to 1.5 °C require, more than ever, a plurality
of methods and integrated behavioral and technology
Energy Efficiency

approaches to better support policymaking and
resulting policy interventions.
Acknowledgements The guest editors would like to thank En-
ergy Efficiency Editor-in-Chief Paolo Bertoldi for the opportunity
to host this special issue and his encouragement throughout its
development. We are extremely grateful to the authors for the time
and significant effort they have devoted to preparing their articles.
We would also like to thank all of the reviewers for their careful
and thorough reports. Finally, we would like to thank Journal
Editorial Officer Maria Verna Remellite and Production Coordi-
nator Kenneth Mercullo for their guidance and support during the
entire process.
Funding information This special issue received financial sup-
port from the Swedish Energy Agency (grant no. 38263-1).
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestrict-
ed use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
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