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Energy poverty, a condition whereby people cannot secure adequate home energy services, is gaining prominence in public discourse and on political and policy agendas. As its measurement is operationalised, metrical developments are being socially shaped. A European Union mandate for biennial reporting on energy poverty presents an opportunity to institutionalise new metrics and thus privilege certain measurements as standards. While combining indicators at multiple scales is desirable to measure multi-dimensional aspects, it entails challenges such as database availability, coverage and limited disaggregated resolution. This article converges scholarship on metrics – which problematises the act of measurement – and on energy poverty – which apprehends socio-political and techno-economic particulars. Scholarship on metrics suggests that any basket of indicators risks silencing significant but hard to measure aspects, or unwarrantedly privileging others. State-of-the-art energy poverty scholarship calls for indicators that represent contextualised energy use issues, including energy access and quality, expenditure in relation to income, built environment related aspects and thermal comfort levels, while retaining simplicity and comparability for policy traction. We frame energy poverty metrology as the socially shaped measurement of a varied, multi-dimensional phenomenon within historically bureaucratic and publicly distant energy sectors, and assess the risks and opportunities that must be negotiated. To generate actionable knowledge, we propose an analytical framework with five dimensions of energy poverty metrology, and illustrate it using multi-scalar cases from three European countries. Dimensions include historical trajectories, data flattening, contextualised identification, new representation and policy uptake. We argue that the measurement of energy poverty must be informed by the politics of data and scale in order to institutionalise emerging metrics, while safeguarding against their co-optation for purposes other than the deep and rapid alleviation of energy poverty. This ‘dimensioned’ understanding of metrology can provide leverage to push for decisive action to address the structural underpinnings of domestic energy deprivation.
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European energy poverty metrics: Scales, prospects and limits
Siddharth Sareen
, Harriet Thomson
, Sergio Tirado Herrero
ao Pedro Gouveia
Ingmar Lippert
, Aleksandra Lis
University of Bergen, Norway
University of Birmingham, UK
Autonomous University of Barcelona, Spain
NOVA University Lisbon, Portugal
IT University Copenhagen, Denmark
Adam Mickiewicz University, Poland
article info
Article history:
Received 21 June 2019
Received in revised form
8 November 2019
Accepted 6 January 2020
Energy poverty
Data politics
Energy poverty, a condition whereby people cannot secure adequate home energy services, is gaining
prominence in public discourse and on political and policy agendas. As its measurement is oper-
ationalised, metrical developments are being socially shaped. A European Union mandate for biennial
reporting on energy poverty presents an opportunity to institutionalise new metrics and thus privilege
certain measurements as standards. While combining indicators at multiple scales is desirable to mea-
sure multi-dimensional aspects, it entails challenges such as database availability, coverage and limited
disaggregated resolution. This article converges scholarship on metrics ewhich problematises the act of
measurement eand on energy poverty ewhich apprehends socio-political and techno-economic par-
ticulars. Scholarship on metrics suggests that any basket of indicators risks silencing signicant but hard
to measure aspects, or unwarrantedly privileging others. State-of-the-art energy poverty scholarship
calls for indicators that represent contextualised energy use issues, including energy access and quality,
expenditure in relation to income, built environment related aspects and thermal comfort levels, while
retaining simplicity and comparability for policy traction. We frame energy poverty metrology as the
socially shaped measurement of a varied, multi-dimensional phenomenon within historically bureau-
cratic and publicly distant energy sectors, and assess the risks and opportunities that must be negotiated.
To generate actionable knowledge, we propose an analytical framework with ve dimensions of energy
poverty metrology, and illustrate it using multi-scalar cases from three European countries. Dimensions
include historical trajectories, data attening, contextualised identication, new representation and
policy uptake. We argue that the measurement of energy poverty must be informed by the politics of
data and scale in order to institutionalise emerging metrics, while safeguarding against their co-optation
for purposes other than the deep and rapid alleviation of energy poverty. This dimensionedunder-
standing of metrology can provide leverage to push for decisive action to address the structural un-
derpinnings of domestic energy deprivation.
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1. Introduction: a scalar lens on energy poverty metrics
Over the past decade in Europe, we have come a good way on
addressing energy poverty, a condition whereby people are unable
to secure adequate levels of energy services in the home [1]. The
issue has moved from the margins of academia during the late 20th
century and the 2000s (cf. [2], to one that has garnered attention
from policymakers at the national and European Union (EU) scale.
Pan-European initiatives have taken off and organised efforts to
systematically address energy poverty alleviation as a key priority.
*Corresponding author.
E-mail address: (S. Sareen).
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Global Transitions 2 (2020) 26e36
The challenge of measurement has emerged as one of the key tasks
in pushing forward with this agenda, and must resolve practical
barriers linked with limited databases, coverage and disaggregated
resolution. There are other challenges esuch as lack of political will
by policymakers and implementers, and conditions that limit and
inhibit public engagement ebut the focus here is on metrics, as
improvements on this front are claimed to serve as a basis for
practical action (cf [3e5].
While existing databases of relevant indicators are treated as
comparable across most European countries, these are far from
comprehensive in terms of capturing what energy poverty is. These
databases primarily allow for generic country comparison and are
thus of limited use for national policymakers. Composite indices are
hard to institutionalise both due to questions around how to assign
weightage and ensure transparency and commensurability across
components [6] and because the EU policymaking environment
prefers simplication of metrics [7]. Most European contexts lack
energy poverty indicators that represent contextualised energy use
issues, including energy access and quality, expenditure in relation
to income, built environment related aspects and thermal comfort
levels. Without tracking a greater variety of multi-dimensional at-
tributes, and being able to unpack them at more disaggregated
scales than mainly the national, current efforts risk failing to
actually alleviate energy poverty due to selection biases, perverse
policy effects, regressive cost burden distribution and exclusion of
energy poor people from support schemes through misrecognition
and imprecise targeting. Insufcient data and metrics may direct
excessive attention to the more easily recognisable symptoms of
energy poverty at the expense of obscuring injustices and in-
equalities deeply embedded in domestic energy provision systems.
Having seen global efforts at addressing poverty more broadly
extend over decades with limited effectiveness, it is desirable to
avoid a similar fate for the narrower but nonetheless complex issue
of energy poverty in Europe.
Specic methodologies to measure energy poverty are emerging
rapidly in many EU countries as well as regions and cities [8,9]. In
this paper, we take stock and posit an overview of what reections
and measures must inform and accompany such methodological
innovation to progress energy poverty alleviation. After problem-
atising the act of innovating and instituting indicators at multiple
scales as inevitably fraught with data politics and problematic
tendencies, we frame this process as ve dimensions within an
analytical framework.
These interrelated dimensions capture the full range of multi-
scalar concerns the EU must address as it develops methodolo-
gies and adopts metrics for measuring energy poverty and consti-
tute the core around which the paper is organised. The rst
dimension focuses on the existing pathways of energy poverty
metrics: historical trajectories. This concerns the path dependencies
of the technologies involved in measurement and of the sector
where the problem manifests. The next two dimensions capture
aspects of how metrology (i.e., the study of measurement) is
enacted, namely through data attening and contextualised identi-
cation. The dynamics between these countervailing forces capture
the politics of which actors are able to generate and access data to
develop and implement particular measurements. The last two
dimensions address how metrics are recongured, namely through
new representation and policy uptake. They set focus on what hap-
pens as actors institutionalise metrics (privilege emerging mea-
surements as standards) through national or higher scale policies in
order to track and systematise energy poverty alleviation.
In our conceptual and analytical discussion, we set out the
theoretical problematic of the act of measurement and relate this to
the general issue of measuring energy poverty (Section 2). Based on
a strong emerging literature, we abstract out ve dimensions for
energy poverty metrology research (Section 3). We then move into
an empirical section (Section 4) that illustrates our analytical
framework using multiple cases at varied spatial scales across three
European country contexts. Our application identies numerous
challenges and opportunities in three European country contexts e
Portugal, Spain and the United Kingdom (UK) ewhich offer rich
contrasts but also similarities. The concluding section (Section 5)
reects on the implications for energy poverty metrology research:
the measurement of energy poverty must be informed by the
politics of data and scale in order to institutionalise emerging
metrics while safeguarding against their co-optation for purposes
other than the deep and rapid alleviation of energy poverty.
Reframing energy poverty metrology and illustrating it in terms
of these dimensions requires multiple competencies. We accord-
ingly assembled a set of co-authors with command over state-of-
the-art scholarship on energy poverty, a range of measurement
methodologies and participatory techniques, a reexive under-
standing of metrology, and empirical research experience on how
and for what purposes policies are mobilised by networked actors.
In terms of disciplines, we span environmental engineering, soci-
ology, human geography, environmental science, development
studies, and Science and Technology Studies (STS). Our ambition to
engage and overcome strong disciplinary silos implies a commit-
ment to a widely accessible writing style and to presenting work
with relevance for the interdisciplinary challenge of measuring
energy poverty. While full data coverage and complete represen-
tation of such complex real-world phenomena are impossible, ef-
forts to institute new, improved metrics must be premised on a
critical understanding of data politics to enable energy poverty
alleviation (cf [10]. These collaborative terms of engagement for
actionable knowledge [11] generate theoretically informed, prag-
matically oriented reections on the tenets of energy poverty
2. Data politics and energy poverty metrics
While energy poverty governance relies signicantly on data, it
does not necessarily become more accountable, transparent or
responsible if data choice and processing remain hidden behind the
technocratic curtains. Therefore, this section opens up the black
box of data, drawing on insights developed in the social scientic
eld of STS.
Typically, data are imagined to be something that can be found,
collected or gathered and neutrally processed to represent the
underlying reality. This is a simple realist account; it does not hold if
we turn to data-making (cf. [12], which is a practice, like many
others. The social group of the energy poorcan be constructed
differently by drawing on different data. Our assessment rests on
the assumption that a universal set of metrics is unviable because
STS originates in the empirically driven analysis of how knowledge and tech-
nical solutions in science and technology are historically, socially and culturally
shaped, and social, cultural and political orders are shaped by knowledge and
technologies. The eld, thus, does not accept plans, such as textbooks, scriptsor
standardsaccounts of scientic and technical work as representatives of that work,
but analyses these as prescriptive or performative texts, non-deterministically
related to action and context [55]. Knowledge-production is situated and both
socially and materially shaped. Contexts matter to the formation of the continuum
between data, information, evidence, knowledge and practice [49]. This trajectory
of analyses also nds technologies of representation including scientic, technical
and economic data infrastructures and models to be partially shaping and trans-
forming the realities that they are deemed to represent [109 e111]. Data and met-
adata are analysed in STS, along these lines, as contingent socio-technical effects
[29,112]. Data are political in multiple ways [113 ]. Adding layers of data and algo-
rithms, under these circumstances, does not necessarily add transparency but may
diminish the chance to understand what data actually represent [33].
S. Sareen et al. / Global Transitions 2 (2020) 26e36 27
energy poverty exhibits great context specicity. Indeed, energy
poverty has activated scholarship across Europe but also beyond, in
substantially contrasting countries like Japan [13], Australia [14],
India [15], South Africa [16], Brazil [17] and Mexico [18]. The choice
of data may be shaped by central policy actors as much as lobby
groups and civil society [19]. However, it is not just interest groups
that matter, but also material and human resourceseavailability
of data, algorithms, computing power, data scientists. Within en-
ergy poverty representations, furthermore, temperature condi-
tions, the weather, make a context relevant. Thus, the stability of
the climate matters for assessing energy poverty; however, climate
change is causing more chaotic weather, rendering energy poverty
and its measurement chaotic, too [20]. Data processing presumes
negotiations as well as constructions of data, dealing with un-
certainties and indeterminacies in a messy world.
Constructing the energy poorthrough data, in short, entails the
work of coordinating and relating heterogeneous elements ea
work of assembling. A straightforward issue is the access to rele-
vant data on, for instance, energy-consuming equipment, heating
and cooling, buildings characteristics, and the socio-economic po-
sitions of household members. In current energy infrastructure
transitions, energy data may be held by private owners, e.g., in the
context of smart cities [21]. Even if some data are available and
accessible, it matters whether those data, the producer or owner
can be trusted ewhich is a concern of power relations and legiti-
macy [22e24]. A challenge when measuring a multi-dimensional
problem like energy poverty is to operationalise diverse metrics
at multiple scales (neighbourhood, urban, district, regional, na-
tional) to maximise coverage and minimise biases in representa-
tions of outcomes [13,25]. This means embracing data hybridity and
methodological versatility to systematically align and employ
multiple databases and multi-dimensional indicators. Thus, in
scaling up energy poverty metrics, scales are socio-politically sha-
ped [26]. This constitutes an opportunity to formally bring hybrid
actors, such as civil society organisations, municipalities, regional
authorities, utilities, various national agencies and citizens them-
selves into roles of measurement and monitoring [16]. Such co-
production and polycentric governance of energy poverty metrics
requires keen awareness of the ways in which data infrastructures
for energy poverty recongure relations of accountability (which
actors can hold which others to account) by informing policy-
directed reporting processes in the EU [27].
STS posits that the political is not restricted to the social re-
lations to preexisting data, but also shapes what is insideof data,
how that data is assembled [28]. There is no rawdata that is
antecedent [29]. The making of a datapoint presumes catego-
risation and, often, quantication. Bowker and Star [30] show, for
instance, that the meaning of categories in health policy has been
changing historically; this involved not just different in-
terpretations but also different infrastructures including shifts in
metrology. Verran [31] shows that even quantifying and counting
basic dimensions (like length) depends on cultural, bodily and
material contexts. Metrics and metrologies are socially and mate-
rially shaped, implying implicit and explicit choices over inclusion
and exclusion [32]. Consider the measurement of energy efciency
of buildings, which requires classication according to efciency
related to energy performance. Implicit are questions of what
counts as a building and how its efciency is measured ee.g., its
bearing structure and type of materials, which end uses and
equipment types are included ebut also of how boundaries are
drawn between various groups (e.g., energy classes A/B/C and so
on). Assigning a building to a class of buildings establishes equiv-
alence among its members and renders them commensurable[6].
In practice, any calculation simultaneously stabilises and pri-
oritises certain categories and metrics, which get complexied in
contexts of big data [33e35]. Whilst mathematical rules and formal
ontologies are highly relevant to data construction (which include
calculations and algorithms), within data construction we
encounter also bugs, errors, ignorance and have to assume a range
of problems we are not yet aware of [36]. Therefore data and
numbers cannot be assumed to be fully under control [24,37,38].
We need to recognise that data are often perceived by data users
ee.g., policymakers, implementing agencies, researchers, citizens
eas more trustworthy, if the contingencies, the choices and politics
of inclusions and exclusions involved in the data construction are
rendered invisible. The context is removed; in effect, the remaining
text, the gure, becomes the essence. Typically, a data point is sent
to travel without accounts of these contexts of construction. This
means data travel black-boxed, as immutable mobiles [39]. The less
context can be traced, the more difcult it is to comprehend what
the data mean and to assess them from a position of a to-be-
informed actor.
Thus, within data generation and processing, experts are rarely
the single decisive factor that shapes data; rather, the eventual
formation and content of data depends on the specic, situated and
contextual relations between the actors, technologies and the
environment involved. STS has shown this to be the case for data
that underlie, e.g., climate science or shale gas governance
[24,28,38,40]. The following dilemma ensues: if data come with too
little context, accountability relations are weakened or destroyed,
but providing too much context may be overwhelming and paral-
yse action. Given that data users are differentially equipped with
resources to read, interpret and assess data, there is no universally
optimal balance between context provision and removal. Further-
more, readers themselves, as well as the social and historical
environment in which data get used, are changing over time.
Keeping data infrastructures stable, however, also risks data inertia,
e.g., lock-ins and path dependence [33,41], undermining the
recognition of energy poor actors and their ability to exercise up-
ward accountability.
3. An analytical framework for energy poverty metrology
Data handling (generation, structuring and interpretative
treatment) is a contingent outcome of the intersection of social,
political and technical matters. Its output, namely measurement,
aims to inform action; energy poverty as a multi-dimensional locus
of measurement further complicates these challenges [42]. This
section organises these processes into ve related dimensions and
proposes an analytical framework to scrutinise energy poverty
metrology (Fig. 1).
The evolution of energy poverty metrics displays considerable
contextual variance ewithin Europe, it goes back over three
Fig. 1. An analytical framework with ve dimensions for energy poverty metrology
S. Sareen et al. / Global Transitions 2 (2020) 26e3628
decades in a few countries [43e45] while being at an emergent
stage in research and policy arenas in others (cf [9,46]. Pan-
European policy on the one hand, and national specicities on
the other hand, imply both commonalities and differences in the
metrics being promulgated and instituted, the data that thereby
become available, and how suitable particular metrics and data are
to alleviate energy poverty across various scales and contexts
[47,48]. Thus, situated historical trajectories are a key characteristic
of metrology: any system of measurement has a history of
contestation and can be reopened for future renegotiation [30]. The
attempt to assess energy poverty with universal coverage, typically
at a national scale, involves some degree of data attening and
reduction, which is inevitable for such a multi-faceted phenome-
non but neglects diversity and ignores contextuality. Data can only
be generated by excluding some relations, yet the effect of exclu-
sion is politically shaped [29]. Whilst contexts can be technically
removed at any scale, contexts cannot be recovered at will [38,49].
Correspondingly, metrics risk systematic exclusions of some energy
poor groups, as well as an exaggerated emphasis of energy poverty
amongst other groups (cf [50,51]. This sets up the need for con-
textualised identication, which is a basic requirement in order to
apply principles of afrmative action that cover vulnerable groups
as well as safeguards that prevent adverse selection. Contextualised
identication involves a range of scales and is enacted by various
public, private and civil society actors [52].
Increasingly, energy poverty scholarship is cognisant of the need
to populate energy poverty metrics with a wider range of variables
at multiple scales. For instance, built environment characteristics
like insulation and thermal inertia at disaggregated scales like
neighbourhoods display systematic spatial variation in many cities,
regions, and countries, with consequences for what aspects metrics
must capture to represent energy poverty [53]. Innovative meth-
odologies based on big data sources like smart meters and building
energy certicates are coming to the fore, promoting novel metrics
(e.g., Ref. [54]. Yet, new metrics and analytics are not necessarily
positive, because the newest fads in data analytics can also decrease
transparency and undermine accountability [33,55]. This raises
questions of new representation eas alternative metrics are pro-
posed with claims that they capture hitherto neglected variables, it
is important to negotiate the specic politics of their inclusions and
exclusions. Who develops and deploys these metrics, and based on
what manner of apprehending newly emphasised variables, de-
serves consideration [56]. This process of how energy poverty
metrics evolve shapes policy uptake by particular actors with their
own interests and expertise [57]; see also [58]. Yet, the uptake of
evidence does not guarantee the soundness of the underlying al-
gorithms and data [37,38]. Policy uptake merits express attention to
the legitimacy of metrics: who operationalises and uses emerging
metrics in what precise manner [59], through what formal and
informal power relations these metrics are oriented at energy
poverty alleviation, and whether they risk catering to narrow
These ve dimensions constitute steps of an iterative process
that continually recongures existing measurement frameworks.
Fig. 1 indicates the relationship between historical trajectories of
how energy poverty metrics are shaped, the characteristics
inherent in the enactment of metrology (data attening and con-
textualised identication), and the reconguration of metrics
through new representation and policy uptake. We circumscribe
and subsequently illustrate each dimension.
3.1. Historical trajectories
As an actionable concept, energy eor fuel epoverty has come of
age over the past three decades in Europe. A particular policy
discourse has accompanied this development [9,60]. Energy
poverty has been unevenly recognised over time across Europe, and
both manifests and is addressed to vastly different extents [61].
Specicdenitions and methods for its identication that arose in
one set of contexts, such as the United Kingdom, have at times been
uncritically and incompletely transferred to other contexts where
their suitability is questionable and potentially problematic [8,50].
For instance, an early UK indicator that dened households
spending over 10% of their income on energy services as energy
poor [2], was more recently uncritically adopted by countries as
they began to act on energy poverty, despite their contextual needs
being different from the UK. Awareness of the evolution of energy
poverty metrics, the focus on specic energy services (e.g., space
heating), and their historical trajectory within a given context are
key towards grasping what measurements are privileged as stan-
dards and why, as well as what potentially relevant indicators and
available datasets are currently under-utilised.
3.2. Data attening
Much historical and current energy poverty monitoring takes
place at the national scale, which is also drawn on for pan-
European monitoring purposes. While important for compara-
bility and a nation-wide picture, large-scale monitoring typically
features methodological simplication, small and statistically non-
representative samples at the regional scale, and a reduction of
complexity in the chosen heuristics. It thus leads to a attening of
energy poverty data, missing the opportunity for local action and
regional comparative assessments within a country. While widely-
used databases
provide good coverage of affordability and needs,
the main gaps at the national and pan-European scale concern
detail on (i) the actual nature of energy access, (ii) the degree of
choice and availability of energy carriers, (iii) the type and exi-
bility of the built environment (e.g., limitations to making modi-
cations to the home due to tenancy status), and (iv) everyday
practices and cultural habits [8]. Typically, databases do not contain
metadata that could render the politics of data generation and
transformation built into the provision of data transparent and
accountable. When designing and implementing indicators, max-
imising coverage faces a trade-off with capturing sufcient depth
and detail, especially as these requirements vary by context. For
instance, heating needs are routinely tracked, whereas cooling
needs ehighly relevant in Southern European countries eonly
appear (as a summertime indoor comfort question) in ad hoc SILC
modules in 2007 and 2012.
3.3. Contextualised identication
This dimension of energy poverty metrology exists in dynamic
tension with data attening, as a partial ameliorating strategy. It
encompasses on the doorstepmeasures [52], and is an essential
component of a move from targeting the energy poor in policies to
implementing measures to alleviate energy poverty [62]. As a
complement to the data attening of large databases, identication
can be direct through cross-database identication [63], indirect
through energy poverty proxies such as geographical location [64],
and decentralised at lower scales such as the urban using new and
increasingly available data sources like energy performance cer-
ticates or smart meter registries [62,65,66]. Contextualised iden-
tication encompasses efforts to capture hidden energy poverty
These databases include the EU member statesHousehold Budget Surveys
(HBS), the EU Survey on Income and Living Conditions (SILC), the European Quality
of Life Survey (EQLS), and the Eurobarometer.
S. Sareen et al. / Global Transitions 2 (2020) 26e36 29
that remains invisible in prevalent comparative indicators, such as
under-heating and cooling [67,68], and gaps pinpointed in the
ENGAGER [20] policy brief (e.g., cross-linkages with electrical
safety, health, indoor air quality, and extreme weather impacts). For
instance, heat sources like biomass and butane gas are absent from
Eurostat energy price statistics, but matter in many EU regions [69].
3.4. New representation
Methodological innovation is evident at multiple scales to un-
pack the complexity of energy poverty. This identies additional
relevant variables, measurement techniques, and advances new
quantitative and qualitative bases to track performance on energy
poverty. A key emerging concern is how marginalised actors
contributing to such measurements, especially at lower scales such
as the urban and regional, can nd room for play [19,70]. Can en-
ergy poverty measurement unlock the promise of grassroots
innovation and deliver decentralised modes of monitoring? Tirado
Herrero [50] argues against exact denitionsof energy poverty
and ofcial, single-indicator energy poverty metrics like the UKs
low income-high cost. In contrast, he makes the case for pro-
gressing multiple additional metrics that reect awareness of the
shortcomings of data collection and data parsing methods applied.
These cautions pertain, for instance, to the diversity of household
energy services and needs considered, the numerical trans-
formations involved in the production of indicators and indices
(e.g., income equivalisation, setting of thresholds, weighting fac-
tors, etc.), the subjectivity in survey responses, and contextual
concerns around the socio-demographic, spatial and temporal
representativeness of data[50]: 1018).
3.5. Policy uptake
Finally, energy poverty metrology features the institutionalisa-
tion of measurements. Through relational mobilisation by situated
actors who engage with evolving institutional structures [71], data
may inform and reshape policy, potentially driving the adaptive
implementation of energy poverty alleviation efforts [56,57]. Such
acts of institutional embedment mark the (re-)making of energy
poor (and rich) subjects, as they enshrine in policy and legal doc-
uments the specic forms of metrical recognition that come to
represent energy poverty. Measurements privileged as standards
are deployed as new metrics through policy uptake if they suc-
cessfully legitimate their claim to represent hitherto hidden, rele-
vant energy poverty variables. Policy uptake can translate and
consolidate emerging metrics into databases and systematic re-
positories, aiming at consistent reporting and avoiding fragmen-
tation through polycentric governance [72,73]. This can be
challenging at lower scales with weakly-resourced actors, inade-
quate social capital to shape decision-making, and patchy expertise
[9]. Sustaining such efforts requires central support for initiatives
that seek to overcome path dependency and institutional inertia, as
well as concerted outreach efforts to improve the quantity and
quality of democratic demand.
The next section illustrates how these dimensions can be pro-
ductively deployed in the rapid current move through a design
moment in energy poverty metrology, as new metrics are inno-
vated, operationalised and compete for multi-scalar routinisation
and systematic implementation.
4. Illustrating the ve dimensions of energy poverty
This section presents examples at multiple scales from Portugal,
Spain and the UK to illustrate the analytical framework. While
energy poverty rst surfaced in academic and policy discourse in
the UK within Europe [2], it took nearly a quarter century to gain
similar traction in Spain, and in Portugal explicit policy engagement
and research only began in the late 2010s. This has bearings on the
existence of national denitions on energy poverty, their percola-
tion with limited adaptation across contexts, and the state of
knowledge and data about each context. Until recently, data was
largely held at the national scale with statistical comparability
across the EU [20]. This limited range includes, for instance, na-
tional performance expressed in percentages for indicators such as
householdsinability to maintain adequately warm housing during
the winter or pay their utility bills on time, and the presence of
moisture and rot within the house, as indicators from the EU Survey
on Income and Living Conditions (SILC) illustrate. However, a wider
range of indicators is emerging at regional and urban scales,
opening up new options and questions for energy poverty
metrology. Our examples feature currently silenced concerns and
groups, where data requisite for relatively critical policy in-
terventions are unavailable or underutilised by institutions.
4.1. Historical trajectories
The historical foundations for present day measurement of en-
ergy poverty can be found in the UK, which is recognised as the
leading country in Europe for its measurement expertise and use of
specialist models for estimating theoretical expenditure on energy
[52]. Isherwood and Hancock [43] are credited with being the rst
to dene the issue, but the topic did not gain widespread promi-
nence until the publication of Brenda Boardmans seminal mono-
graph in 1991. Boardman found that income poor households spent
twice as much on fuel, as a proportion of income, than the rest of
the population [2], and determined that households unable to
achieve an adequate level of energy services for 10% of income are
fuel poor.
The stickinessof indicators is evidenced in the way these travel
across countries and locations. The ofcial UK-wide denition be-
tween 2001 and 2013 was based on Boardmans original 10% de-
nition [2] and has been widely transferred to other contexts eboth
European and non-European ebut mostly in an incomplete or
uncritical manner [8]. Englands current ofcialLow Income-High
Cost (LIHC) indicator based on the 2012 UK government-
commissioned Hills [74] review is still regarded as a benchmark
for objectiveexpenditure- and income-based indicators. In the
absence of government-sanctioned indicators in most EU member
states, there have been attempts to put forward the LIHC as leading
indicator for ofcial energy poverty statistics eas an example in
Spain see Romero et al. [75]. The pre-eminence of such indicators is
justied on the alleged superiority of household expenditure and
income data versus consensualindicators [76] that rely on
householdsself-reported assessments of their material and living
conditions. However, as the next dimension unpicks, the use of
expenditure-based models often necessitates a attening of data.
Strong historical contingency and path dependency is also
evident for the World Health Organisations (WHO) [77] recom-
mendation for a safe internal temperature range of 18e24
(depending on room function) for non-vulnerable households and
C for vulnerable groups [78]. Despite forming the basis for
buildings energy performance certication and energy-related
models, including the UKsofcial fuel poverty models, the ori-
gins and applicability of these thresholds is not widely critiqued by
researchers or policymakers. A recent systematic review for Public
Health England stated that
whilst there is strong evidence that cold homes have a harmful
effect on health, and there are good arguments for making
S. Sareen et al. / Global Transitions 2 (2020) 26e3630
recommendations for minimum home temperature thresholds
in winter there is very limited robust evidence on which to
base these recommendations [79]: 6).
This is also evident in the variance of national regulations. In
Portugal, for instance, the buildings energy performance regulation
of 2006 [80]dened as reference indoor temperatures 20 and 25
in heating and cooling seasons respectively. Its downward revision
to 18
C in the heating season in a subsequent update in 2013 [81]
was justied by characterising its predecessor as very ambitious for
a typical average temperature that represented citizen demand for
Standardised temperature thresholds and nominal conditions
used for energy needs calculations inherently fail to recognise
cross-country differences in culture, habits and expectations, oc-
cupancy schedules, consumption patterns [68,82]. They are also
insensitive to ownership and the type of use of heating/cooling
systems, and thus neglect the potential of adaptive comfort while
setting ambitious thermal comfort levels and building energy
needs [83]. By the same token, adaptive standards may consolidate
unhealthy or unequal indoor thermal comfort levels. Thus, the
historically contingent nature of energy poverty metrology renders
existing standards and legacies signicant and inuential.
4.2. Data attening
Cases of data attening and simplication are widespread in
energy poverty metrology, and generally relate to data availability.
Energy poverty rates are typically reported at national scales in the
EU, due to the statistical representativeness of corresponding
datasets like HBS and SILC. First-time calculations and reporting of
energy poverty rates have been systematically conducted at the
national scale (with results sometimes disaggregated at the
regional or NUTS-II scale),
as in Poland [84], Hungary [85] and
Spain [86]. This practice reinforces the idea that domestic energy
deprivation factors are primarily governed at national scales. Such
representation overlooks instances where supra-national and sub-
national scales play key roles in either dening the structural
framework for the emergence of energy poverty or for articulating
governmental action in response to the issue [48]. A prominent
supra-national instance is the EU Directives, which through sub-
sequent Energy Packages formed the current institutional archi-
tecture of the EUsinternal marketfor electricity and natural gas.
At the sub-national scale, state regions and local governments are
often in charge of identifying the energy poor and deploying
remedial measures through social services or local support
schemes. For instance, BarcelonasPunts dAssessorament Energ
(city-district level energy advisory helpdesks for vulnerable con-
sumers) largely serve energy vulnerable citizens, and Hungarys
szocialis t}
uzifa (social fuelwood) programme run by local govern-
ments provides need-based subsidised rewood to households.
Averages based on national and regional datasets obscure the
immense spatial complexity of energy poverty. Where data allow,
sub-regional analyses should be undertaken to potentially uncover
signicant diversity in the drivers and impacts of energy poverty
and identify national hotspots for local action (e.g., Ref. [87]. City-
specic, statistically representative sub-samples populations are
scarce, and the urban scale is oft-neglected in energy poverty sta-
tistics. Yet, urban-scale studies indicate substantially different rates
of incidence across districts and neighbourhoods [88e90] with
vulnerable populations highly concentrated in pockets [91]. These
underrepresented lower spatial scales need to be re-emphasised to
enrich and recalibrate energy poverty metrology.
The tyranny of averagesalso surfaces when considering time-
scales of energy poverty-relevant data collection. Yearly (or even
less granular) point data dominate and remove the temporal
textureof phenomena and experiences. The longstanding headline
energy service to conceptualise energy poverty eseasonal differ-
ences in indoor thermal comfort eis weakly captured by SILC
questions on householdsinability to keep homes adequately warm
in winter. Exemplifying the paucity of seasonally representative
data, the complementary summertime indoor comfort question has
only appeared in two ad hoc SILC modules (2007 and 2012). With
the increased occurrence of extreme weather events like heat
waves and cold spells, and higher average temperatures due to
climate change, higher temporal resolution becomes more impor-
tant, especially in signicantly impacted regions such as Portugal
and Spain [92,93]. Another notable trait is the data attening ten-
dency to use cross-sectional rather than longitudinal analysis [8],
whereby survey questions are asked to a new sample of households
each year, rather than repeating questions to the same sample over
a number of years. This prevents analysis of the entry and exit
points for households experiencing energy poverty.
Temporal simplication is particularly acute in the case of do-
mestic energy prices of natural gas and electricity reported by
Eurostat. Despite being the most authoritative source for
comparing energy prices across EU countries, its energy price sta-
tistics are not keeping pace with rapid developments in the com-
mercialisation of these energy carriers, especially electricity where
time-of-use tariffs are increasingly the norm in parallel with smart
meter deployment. Eurostat offers some level of disaggregation by
consumption level and by composition (taxes versus provision
costs) but conceals the complexities in energy markets that are
simultaneously heavily liberalised and regulated. In Spain and
Portugal, the effective price per electric unit paid by each house-
hold exhibits wide variety. It depends on the individual conditions
of their supply contracts: regulated tariffs versus free-market
contracts; monthly at rates versus constant or daytime/night-time
tariffs; and add-on maintenance and insurance services which are
systematically embedded in free-marketcontracts.
Examples of systematic exclusion in the UK include the Standard
Assessment Procedure approach to energy modelling which ig-
nores cultural diversity [94], the inclusion of disability related
benets as income that results in some disabled people being
articially excluded [95], and more generally, the institutionalised
nature of ofcial surveys that construct the meaning of a household
in a manner that excludes transient populations such as out-of-
town student tenants. On a related note, the reduction of avail-
able pan-EU data (on relevant variables such as damp, rot and
cooling) compared to pre-existing standards is a concerning trend
4.3. Contextualised identication
Contextualised identication refers to methodologies that
bridge gaps left by national and sub-national level indicators, and
enable doorstep identication of individual energy poverty cases to
deliver support or alleviation measures. The prevalent lack of high-
resolution spatial and temporal data makes comprehensive multi-
dimensional indices potentially hard to develop. Contextualised
identication inevitably accompanies data attening, and mani-
fests a tension with it that goes beyond simply lack of sufcient
data. At the root of this tension is the process of decontextualisation
inherent in the technical generation and processing of data. Ver-
satile efforts can distinguish between subject-oriented strategies
NUTS-II refers to the immediate sub-national scale of regions within the
tripartite Nomenclature of Territorial Units for Statistics (NUTS).
S. Sareen et al. / Global Transitions 2 (2020) 26e36 31
(that support households) and object-oriented strategies(that
target the built environment, e.g., to improve housing stock).
From a legislative perspective, energy poorequals vulnerable
consumersas established in EU Directives on common rules for the
internal market in electricity and natural gas.
Member states set
national criteria to identify households as vulnerable. These
criteria vary by country and have little to do with the headline in-
dicators used to monitor the incidence of energy poverty for sta-
tistical purposes, such as the ones proposed by the EU Energy
Poverty Observatory (EPOV). In the UK, the fuel poverty strategy
broadly denes vulnerable consumers as older households,
householders who are disabled or have a long-term illness, and
families with young children (a major population segment), but
households on social benets are considered eligible for various
energy schemes more generally [96]. In Portugal and Spain,
vulnerable consumers are the eligible beneciaries of social elec-
tricity and natural gas tariffs identied through social welfare
systems. As indicated by Tirado et al. [97]; in Spain many of those
identied as eligible electricity social tariff recipients are not in
energy poverty according to headline indicators, and vice versa. The
same holds true in Portugal, surfacing the need for complementary
indicators for better assessment, for instance related to the energy
efciency of building stock.
Examples of subject-orientedidentication strategies at the
urban scale include Barcelona City Councils protocol for detecting
individual energy poverty cases through the local re service,
council housing services and family doctor surgeries. These pre-
existing city-wide networks are mobilised for reaching out to
households that are not reporting themselves to municipal energy
poverty support units [89]. A further elaboration of this approach to
locate and improve the living conditions of individual subjects or
households is the UK Boiler on Prescriptionscheme. This small
pilot project carried out in 2014e2016 enabled family doctors to
prescribeenergy efciency (e.g., double glazing, boilers and
insulation) to patients with Chronic Obstructive Pulmonary Disease
exacerbated by living in cold, damp homes [98]. In Portugal, social
support institutions connected with very poor sections of the
population undertake local identication of vulnerable consumers.
A nation-wide initiative (Support Program 65 eElderly in Security)
aims to support elderly subjects, especially those living remotely in
isolation from active population centres.
Object-oriented strategieshave been applied in Madrid, where
the City Councils MAD-RE (Madrid Recupera) programme targets
building improvement interventions at so-called
Areas Preferentes
de Impulso a la Regeneraci
on Urbana (Priority Areas for Boosting
Urban Regeneration) or APIRUs. These APIRUs have been dened
through multi-dimensional urban vulnerability criteria also applied
in mapping energy poverty in Madrids suburbs [90,91]. Portugal
similarly uses 1,092 Urban Rehabilitation Areas to address building
stock, infrastructures, public buildings and public green spaces
through subsidised renovation projects in pre-identied deprived
urban areas for improved energy efciency. Greg
orio and Seixas
[99] designed and implemented a composite geospatial index to
benchmark the capacity and opportunity for energy renovation in
historic centres.
These practical experiences suggest that national and local
governments rely on their own identication strategies and tools
primarily to contextually identifyparticular cases for energy
poverty intervention. Wilfully or inadvertently, they thus construct
new categories of energy poor subjects by highlighting household
characteristics such as chronic respiratory disease incidence or
residence in a demarcated neighbourhood. Considerable scope
remains to enlarge the basis for contextualised identication,
thereby makingmore (or less) energy poor subjects. For instance,
the use of biomass eparticularly widespread in Portugal among
vulnerable rural populations etypically remains unaccounted for
in energy consumption and expenditure statistics due to its
informalcollection. Alongside biomass, district heating, butane
gas and other social fuels are absent from Eurostat statistics on
energy prices, but constitute important heat sources for large EU
populations and give form to specic, locally-important forms of
energy poverty such as energy degradation[8,69] that merit ex-
press recognition.
4.4. New representation (new methodologies, data and richness)
Data availability can be increased and enriched by tapping new
sources for improved metrics. National cadastres and increasingly
available energy performance certicates can be tremendously rich
sources of information. Cadastres systematically collect details of
ownership, boundaries and real estate value mainly for taxation
purposes. Both datasets contain information about the shape, size,
age of construction and orientation of individual buildings and
dwellings, which can yield detailed energy vulnerability metrics
when combined with socio-economic census variables, as in Mar-
tín-Consuegra [100] for census tracks in Madrid, and Gouveia et al.
[87] for all the 3,092 Portuguese civil parishes. But recongured
forms of representation can also be problematic, as they may
obfuscate important issues.
Innovative forms of representation include new ways of un-
derstanding and (dis)aggregating existing data and metrics. While
indicators often employ the reductionist binary logic that a
household is either energy poor or not based on pre-dened
criteria, the reality of energy poverty is experienced in various
forms and intensities and clashes with such simplied represen-
tation [50]. An easy way to capture the depthof energy poverty
has been through Likert-type scales in questionnaire-based surveys
on indoor thermal comfort (see Refs. [45,101,102]. Expenditure- and
income-based approaches have addressed the issue of depth by
measuring artefacts such as the fuel poverty gapof the LIHC ea
calculation device that quantitatively assesses by how much the
energy needs of an individual household exceed the threshold for
reasonable costs [74]. Additional research has expanded this
assessment by combining theoretical building energy needs and
real energy consumption, as Palma et al. [68] show for Portugal by
identifying regional thermal comfort gaps. Such treatment, along
with recent discussion around introducing a Hidden Energy
Poverty indicator by EPOV,
furthers understanding on problems
like under-heating and under-cooling which are invisible in prev-
alent comparative European indicators.
An exploratory example for new ways of aggregation is a study
of energy poverty indicators in Barcelona based on a 799 household
city sub-sample of the Spanish SILC survey from 2016 [89]. Tradi-
tionally, SILC headline indicators have been regarded as separate
metrics that individually represent different aspects of domestic
energy deprivation. Interpreting SILC indicators through a rela-
tional lens surfaces a composite picture, with vulnerability levels
dened based on the number and typology of energy poverty
conditionsindividual households experience. This ranges from
households with inadequate thermal comfort levels at home to
those that additionally show two or more arrears on utility bills and
one or more energy supply interruption over the past year. This
new form of representation moves towards more accurate
The relevant Directives are (2009/72/EC and 2009/73/EC).
After feedback from EU member states, this indicator has been adjusted to Low
share of energy expenditure in income (M/2).
S. Sareen et al. / Global Transitions 2 (2020) 26e3632
representation of domestic energy deprivation as multi-
dimensional and granular from the perspective of affected house-
holds. The aggregation of indicators, discounting overlaps, consol-
idates and enlarges energy poverty numbers.
Increasing the richness of available data is crucial for bringing
specic forms of energy poverty to the fore, especially for very
vulnerable segments who have thus far been systematically
excluded in quantitative representations. For the rst time, the
Spanish SILC 2016 enables an indicator that remains absent in
practically all conventional data sources including Eurostat and
national datasets: domestic energy supply disconnections due to
inability to pay or consumer indebtedness with utility companies. It
indicates that almost one million people in Spain lost access to their
regular sources of domestic energy during that year [97]. Consistent
numeric representation of such realities, if available, could well
challenge the presumed notion of universal access to modern
energy in the Energy Union.
A vibrant new approach to representing energy poverty is citi-
zen science and crowdsourced data. This pertains to purposive,
non-random datasets collected by non-governmental actors
through grassroots surveys or collaborative open-source mapping.
In Barcelona, two civil society organisations in the struggle for
housing and energy rights ePlatform of People Affected by Mort-
gages and the Alliance against Energy Poverty ecollaboratively put
together a dataset of several hundred affected households that
passed through their collective advisory assemblies over years. This
survey fed into a report on the relationship between mental health
and access to housing and energy written collaboratively with the
Barcelona Public Health Agency (Ag
encia de Salut Pública de Bar-
celona). Analysis of the rst 100 households introduced in the
dataset identied poor mental health conditions (as measured by
the Goldberg-Shapiro scale) in 70% of men and 83% of women at
risk of eviction and/or basic supply disconnections. These per-
centages are substantially higher than the average incidence of
poor mental health in Barcelona e16.5% for men and 20.4% for
women [103]. Thus, such representation can both generate new
data categories and strengthen links across existing variables.
4.5. Policy uptake
EPOV is laying the groundwork for common measurement
frameworks, partially in response to EU legislation that is sched-
uled to introduce an obligation for member states to report peri-
odically on the incidence of energy poverty at the national level.
While its impact is still unclear, the momentum generated by this
EU-wide initiative is worth noting as a window of opportunity for
the uptake of more nuanced metrologies. EPOVsinuence is
evident in the Spanish National Energy Poverty Strategy adoption
of its four headline indicators with slight methodological modi-
cations to make them responsive to its national context [104]. The
EU Cooperation on Science and Technology (COST) Action on Eu-
ropean Energy Poverty likewise enables cross-fertilisation of
policy-relevant insights on energy poverty metrics for coordinated
progress on energy poverty metrology among EU member states.
Most draft National Energy and Climate Plans (NECPs) for 2030
highlight the need to address energy poverty and establish de-
nitions and metrics, including both countries like Portugal where it
is an emerging hot policy topic and those with extensive experience
such as the UK. The Climate Action Network [105], however, calls
for several NECPs to be more ambitious and take up measures to
improve thermal comfort and reduce energy bills. Member states
are deciding on monitoring tools and metrics, with EPOV advo-
cating for multi-indicator approaches. This proposal contrasts with
the former international benchmark ethe UKs fuel poverty single
indicator erst in the form of the 10% indicator and later
substituted by the LIHC methodology. These older denitions pre-
clude alternative understandings of the nature of and factors
behind energy poverty and risk narrow identication of energy
poor subjects, thus invisibilising vulnerable populations who may
be dropped out of support schemes [50]. Metrics depend on pol-
icies for mobilisation, and how identication occurs can have
enormous consequences. For instance, both Spain and Portugal
provide social tariffs discounts to electricity and natural gas con-
sumers. In Spain, the 25e40% social tariff discount only benets
eligible consumers that know about it and have successfully
applied for it. In Portugal, a deep review was undertaken in 2016
(Law 7-A/2016) to ensure that all consumers that are entitled to the
social tariff can benet from it through a simple and automatic
allocation procedure premised on information from the social
welfare system, and the resultant coverage is far more extensive.
Policy can also make existing but hitherto inaccessible data
sources available. A case in point is the information on individual
household consumption patterns collected through smart meters
with very high temporal resolution being rolled out across the EU.
Paradoxically, such data are difcult to access for householders and
usually out of reach for researchers and governmental and non-
governmental actors, who could push for afrmative policy ac-
tion. Given the increasing complexity of the dynamic pricing sys-
tems that are likely to rapidly dominate electricity markets, smart
meter data are being rapidly commodied ea trend referred to as
datacationealgorithmically translating various aspects of life
into computerised data which appear as new valuein shifting
political economies (see Refs. [33,35,106]). Mining household scale
data by combining smart meter and survey techniques has
demonstrated relevance for energy poverty metrics [54,65]. Smart
meter data can help understand daily and seasonal energy con-
sumption and identify energy poor consumer groups. It also
prompts questions relating to what levels of data complexity and
scale are desirable for designing policies and alleviation schemes.
Clarity on access, ownership, requisite resources for data mining,
and privacy issues under the new EU data protection regime is
necessary in order to systematise metrics-relevant utilisation of
these data sources.
5. Institutionalisation without cooption and the politics of
Europe is at a denitive moment when it comes to addressing
the historically intractable and under-attended problem of energy
poverty. The task of its measurement is increasingly embraced by
actors like the European Commission, public and private utilities,
governments at various scales, local and international civil society
outts, and inuential scholars at research and training in-
stitutions. Discussion and contestation is likewise increasing
among differently positioned organisations with competing in-
terests and perspectives. This move from experimentation and
innovation to adoption and routinisation represents a switch from
ad hockeryto deeper institutional embedment. Recent scholarship
on urban sustainability initiatives has emphasised the potential for
catalysis across the spatial scale (e.g. Refs. [19,70], for instance
through the translocal effects of performative practices [107]. A
related strand of scholarship that continues to gather strength, on
This law increased coverage from 200,000 to over 800,000 (14% of all resi-
dential electricity consumers); and from 15,000 to 35,000 (2.57% of all natural gas
consumers). This difference relates to the lack of common eligibility criteria on the
one hand, with the electricity social tariff being broader as it includes an income
criterion, and to the electricity network being larger than for natural gas on the
other hand [100].
S. Sareen et al. / Global Transitions 2 (2020) 26e36 33
polycentric governance, attunes us to pay attention to the promise
and necessity of cross-scalar coordination (e.g., Refs. [72,73].
We note such developments around energy poverty metrology
in multiple policy contexts ethe EU and its member states
constitute one set of key forums, and other global regions are
evolving their own sets of protocols and state-of-the-art: contexts
like India [15], Brazil [17 ] and Mexico [18] are addressing specic
issues within energy poverty such as basic energy access and
exposure to poor air quality. The concurrent trends we have ana-
lysed in different European contexts constitute valuable opportu-
nities for cross-fertilisation. Application of our framework can thus
help scholars identify how energy poverty measurement can be
productively orchestrated at multiple scales and across a range of
cognate sectors and actors, to collaboratively advance their com-
mon ambition of alleviating energy poverty in similar contexts.
Our dimensions show that the institutionalisation of metrics in
data infrastructure as well as through organisational and policy
processes involves unevenness. Current attempts must articulate
and address these power dynamics to avoid lock-inof power dif-
ferentials and unaccountabilities. Failure to do so can enhance the
policy impact of some metrics over others, based not on their
greater relevance for energy poverty measurement, but rather on
who is in charge of these metrics, at what scale, and how they ease
portrayal of advances in energy poverty alleviation. Thus, energy
poverty scholars, policymakers and practitioners must safeguard
openings for progress towards the broad objective of alleviating
energy poverty from co-option by dominant actors for narrower
For energy poverty measurement to enable energy poverty
alleviation, we argue for fostering the sort of discussion and debate
of data politics and problematic tensions that the ve dimensions
illustrate. These identify not only solutions, but also the process for
progress on energy poverty metrology and alleviation. The mobi-
lisation of metrics to address energy poverty must be informed by
an understanding of the quality and politics of data woven into the
metrics, and thus situated within concrete contexts and key policy
ows. As we have shown, such changes are relationally mobilised
by actors at multiple scales, and inected by often inequitable po-
wer relations. A world where regions as prosperous as Europe still
exhibit signicant levels of persistent energy poverty is by deni-
tion an unequal place. Thus, institutionalising new energy poverty
measurements is a deeply political project eaffected actors have
different stakes and inuence. Our dimensions unpack the range of
mobilisation techniques that different actors can and do employ to
inform action on energy poverty through networked relationships
across spatial scales [71]. This insight is essential to grasp and
articulate contextualised data politics, and we hope that future
research will draw on the framework and ve dimensions to extend
this preliminary foray.
In closing, we reect on the import that our initial problem-
atisation of the act of measurement has for measuring energy
poverty in order to alleviate it. As an object that policy acts on,
energy poverty becomes whatever is measured. Its measurement is
thus performative: versions of energy poverty metrics are enacted
in such a way that the logics of their enactment are attuned to its
eventual reduction. However, no measurement is perfect (the map
is not the territory), hence there will always be varieties of energy
poverty that policy action fails to apprehend and/or address as
intended. Therefore, techniques to capture new metrics, while
important, must be informed and accompanied by processes that
ensure public inputs into and scrutinisation of the nature of these
metrics. This can help ensure accurate and robust metrics, as well as
nurture and nourish bottom-up involvement, create an invited
spacewhere citizens feel empowered and welcome to engage (but
see Ref. [108]) and help democratise energy poverty monitoring.
It follows that evolving metrics must be accompanied by
devolving power to transform the energy sector into one that the
public associates with its stakes and concerns and where people
have a say. By cultivating such a process, energy poverty mea-
surement will necessarily become participatory. Policymakers as
well as local and national politicians will nd it easier to be
responsive to clearly articulated public needs and desires; this can
to some extent overcome problems of political will. Civil society
organisations, practitioners and applied researchers can build co-
alitions around specic claims to unlock energy poverty alleviation,
leading to more organised pushes on policy levers for impact. In
sum, measuring energy poverty constitutes an opportunity to
mobilise actors and organisations and shape a more publicly
accountable energy sector. With respect to the objective of energy
poverty alleviation, the participatorily undertaken act of mea-
surement is as vital a component as the metrics themselves.
This article is based upon work from COST Action European
Energy Poverty: Agenda Co-Creation and Knowledge Innovation
(ENGAGER 2017e2021, CA16232) supported by COST (European
Cooperation in Science and Technology Support
from the Trond Mohn Foundation project European Cities as Actors
in Climate and Energy Transformationis also gratefully
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... Since it is such a multifaceted phenomenon, some distortion in the analysis of data at national level is inevitable. However, this methodology based on precise definitions neglects the diversity and context of each community (Sareen et al., 2020). Other institutions have opted for more generalised definitions, without limiting themselves to specific indicators (Tirado Herrero, 2017). ...
... However, there is currently no unified set of indicators. This is partly because establishing a set of indicators risks silencing certain significant aspects but difficult to measure (Sareen et al., 2020). ...
... Qualitative techniques can be more easily adapted to an equality and human rights approach, which would allow for a more realistic identification of vulnerable groups (Luxán Serrano, 2020). It will be necessary to build a methodology that contributes to social change on the basis of both techniques (Bassi Follari, 2014), since the groups classified as being in energy poverty are built on the basis of the data and the way in which these are analysed (Sareen et al., 2020). It is necessary, therefore, to develop databases from local governments that incorporate these issues. ...
Full-text available
Interest in energy poverty has increased in recent years and has made it possible to define the lack of energy resources in households and the importance of energy as a right. The research carried out in this work shows the importance it acquires in the current context, where a large part of the population lives confined to their homes due to the global COVID-19 pandemic and has to face higher energy costs, which affects their health and safety. This paper focuses on showing the need to study and take action on energy-poor households in Andalusia, which has been identified as one of the Spanish communities with the highest level of energy poverty. To this end, the main indicators are calculated for Andalusia. The research is transdisciplinary and has been developed by the Aura team of the University of Seville, which participates in the Solar Decathlon university competition. A high degree of energy vulnerability is concluded, with all the main indicators exceeding the national average. Finally, the conclusions section shows the need to modify the current methodology that defines vulnerable households and develop local databases in territories where the factors that affect energy vulnerability are homogeneous and evolving towards decentralised studies.
... Over the years, many methods and indicators to measure EP have been proposed, but the outcomes of investigations have often led to wide discrepancies for the same country or adjacent areas depending on the methods applied. The indices of EP also vary significantly according to the characteristics of families (composition and size), the demographics of the regions and the habits of the population [8][9][10]. Additionally, disparities in technological and economic conditions under which EP assessments are conducted and the high variability in the quantification of vulnerable consumers lead to anomalous results, and to the emergence of relevant disparities among regions depending on energy sources and consumers' behaviours [2,3,11]. ...
... Nevertheless, in the last decades, several projects and support schemes were developed to address the problem of EP directed mainly to enhance the energy efficiency of buildings and to the provision of households' income support through the recognition of tax credits aimed at partially covering incurred costs. These policies were mostly used by families who were not in conditions of EP, and were claimed only by a share (35%) of the eligible [10]. So, not surprisingly, the effect of these policies has not achieved the expected results. ...
Full-text available
This paper investigates the presence of a causal relationship between energy poverty and income poverty in the EU Member States through a Panel Vector Autoregressive specification, and controlled with a set of explanatory variables collected from the Eurostat energy database and the OECD environment database for 2007–2018. Deepening the nexus between energy poverty and income poverty is a relevant issue for tailoring policies to tackle poverty and improve the well-being of citizens, supporting the policy makers in the allocation of planned funds provided by the Recovery plan, “Next Generation EU”. The results of the panel VAR model estimation and Dumitrescu and Hurlin test suggest that there will be no change in the long-run equilibrium when income poverty remains constant. Moreover, the reduction in energy poverty is expected to have a positive effect in terms of overall economic poverty reduction. Finally, there is evidence that substituting fossil fuels with renewables helps to reduce energy poverty and widespread poverty due to the leverage effect on economic development as well as to support the achievement of some of the 17 Sustainable Development Goals addressed by United Nations.
... In the last two decades, the European Union (EU) has played an important role in coping with the issues of climate change and energy security, but energy poverty still remains a prevalent problem; for example, rough estimates suggest that more than 50 million households in the EU are experiencing energy poverty. 1 According to the Member State Report from the EU Energy Poverty Observatory, in 2018, 6.6% of the EU population were unable to pay utility bills and 7.3% of the EU population were unable to keep home adequately warm. 2 Given the phase-out of fossil power in the presence of global warming, the situation of energy poverty could be even more serious [2]. The issue of energy poverty has recently gained considerable attention in the EU [3][4][5][6][7], and in single countries in the EU, such as France [8,9], Germany [10], Lithuania and Greece [11], Netherlands [12], Spain [13][14][15][16], and the United Kingdom [17]. Energy poverty research has mostly focused on the measurement and impact of energy poverty, however, there remains a scarcity of literature considering the evolutionary dynamics of energy poverty across countries. ...
... Economic growth (pgdp it ) is proxied by real gross domestic product per capita (Chain linked volumes (2010), Urbanization data is extracted from the World Development Indicators [43]; data pertaining to the other variables are taken from Eurostat. 3 In view of data availability, particularly energy poverty, a balanced panel data of 28 countries in the EU from 2005 to 2018 is chosen as the data sample for empirical analysis (see Appendix A for details on the list of countries). 4 Since the growth rate of energy poverty is adopted as a dependent variable, the final time frame of empirical analysis is actually from 2006 to 2018. Fig. 1 shows the boxplots of arrears on utility bills for the 28 EU countries. ...
This study investigates the heterogeneous effects on convergence of energy poverty across the 28 countries in the European Union from 2006 to 2018. Unlike the existing research on convergence, which is mainly based on mean estimators, the Method of Moments Quantile Regression considers quantile distribution, and we employ it to explore the unconditional and conditional β-convergence of energy poverty. The empirical results sufficiently support the existence of both unconditional and conditional β-convergence of energy poverty, and the significance and rate of convergence are heterogeneous with the quantiles. To be specific, convergence significantly exists in countries with higher quantiles of energy poverty, and the convergence rate increases with the quantile of energy poverty. This means that countries with high levels of energy poverty tend to eliminate poverty more quickly than do countries with low levels. It is therefore of great necessity for governments to capture the dynamics of energy poverty, which could be insightful for long-term policy making for energy poverty elimination.
... In this work we approach the study of energy poverty for the 28 countries that constitute the European Union from a multidimensional perspective in 2008 and 2017. We are aware that working on a national scale is a limitation of the study, as stated by Sareen et al. (2020) " . . . current efforts risk failing to actually alleviate energy poverty due to selection biases, perverse policy effects, regressive cost burden distribution and exclusion of energy poor people from support schemes through misrecognition and imprecise targeting "; however, it has been necessary given the availability of data. ...
... The evolution of the measurement of PE must be accompanied by an evolutionary power to transform the energy sector into one that the public associates with their interests, then the measurement of energy poverty will become participatory. In addition, policy-makers will be able to more easily respond to the needs of society Sareen et al. (2020). ...
The objective of this work is to measure the energy poverty in the European Union through the construction of an Energy Poverty Index by means of the multivariant technique of factorial analysis. The index is calculated for the 28 member countries of the European Union in the years 2008 and 2017. Moreover, the effect of distributed generation renewable resources (such as photovoltaic, small hydro or micro wind) on energy poverty is studied. The obtained results show that Bulgaria, Rumania, Greece, Latvia and Lithuania are among the countries that display the highest index. The countries with the lowest index are Denmark, Sweden, Finland, the Netherlands and Slovakia, among others. The distributed generation contributes to reduce energy poverty in all countries. In fact, Ireland, France, Luxembourg, Slovenia, Finland and Sweden, have shown greater capacity than others to respond to changes in the distributed generation.
... This issue, referred to as energy poverty, receives growing policy prominence throughout Europe, particularly given recent fuel price rises [13]. Energy poverty is recognized as multidimensional [76], and multiple indicators are used when attempting to measure this phenomenon [21]. Energy vulnerability is understood as encompassing those that are currently energy poor including those that are at risk of becoming energy poor, but also recognizes the dynamic and temporal nature of energy poverty [14,65]. ...
Full-text available
Stakeholders see great potential in PEDs for energy poverty reduction. • Energy poverty mitigation needs to be included in PEDs from the onset. • PED replication can synergistically address both decarbonization and energy poverty mitigation. • Increasing levels of energy poverty makes PEDs more financially viable as mitigation tools. • More consideration needs to be given to the social dimension in decisions on new PED creation. A B S T R A C T 100 Positive Energy Districts (PEDs) are to be created in Europe by 2025, with a stated goal of urban decar-bonization. These are highly energy efficient residential urban areas, powered entirely through renewables. PED creation is to be guided by principles of quality of life, sustainability, and inclusiveness (specifically focusing on affordability and energy poverty prevention). Although there is research into the decarbonization aspects of PEDs, there has been little focus on the guiding principles, and their potential to reduce energy vulnerability. Using energy vulnerability factors and an energy justice framework, this article examines how the topic of energy vulnerability mitigation is perceived by professional PED stakeholders. Stakeholders from multiple countries were interviewed in order to determine how and to what extent they approached the topic of inclusivity and energy vulnerability. The contribution of this paper to academic research is in helping to frame energy vulnerability in European smart city urban areas, focusing on the perceptions of key stakeholders. This contributes to research on the identification and evaluation of innovations such as PEDs which offer a potential Acronyms: ATELIER, "AmsTErdam BiLbao cItizen drivEn smaRt cities" EU funded smart city project operating in 8 cities.; Banc D'Energia, non-profit social innovation project in Spain, whereby members are given energy advice and a portion of the savings are assigned to the energy vulnerable.; EERA, European Energy Research Alliance, comprising over 250 organisations working towards a climate-neutral Europe by 2050.; Energiewende, German term for the energy transition.; EuroPACE, Home renovation pilot project combining affordable financing with technical assistance.; IEA Annex 83, International Energy Agency group to enhance international cooperation on PED development.; JPI Urban Europe, Joint Programming Unit, European commission instrument to strengthen research and innovation in urban areas which also proposed PEDs.; MakingCity, Horizon2020 project to address and demonstrate PED concepts, enabling better replication of these. Operates in 8 cities.; MaxQDA, software tool for qualitative data analysis, used for interview analysis.; PAH, Platform for those affected by mortgages (Plataforma affectats per la hipoteca) Catalan grass roots movement to assist those evicted through non-payment of rent or mortgages.; PED, Positive Energy District, urban neighbourhood which produces an annual surplus of energy through the use of renewables.; PV, Photovoltaic panels.; REC, Renewable Energy Community, local energy associations often formed by residents which must also help to alleviate energy poverty (according to the EU renewable energy directive). Not fully implemented into national law in all EU member states.; RES, Renewable energy systems such as photovoltaics or wind turbines.; SET Plan 3.2, Strategic Energy Plan from the European Union in which PEDs are proposed.; Sharingcities, smart cities project operating in 89 European cities, addressing urban challenges such as energy use, low carbon transport and buildings, and harnessing data.; SILC, Survey on Income and Living Conditions, conducted yearly (since 2003) in the EU to provide comparable cross-sectional and longitudinal data on income, poverty, social exclusion and living conditions.; SPARCS, "Sustainable energy Positive & zero cARbon CommunitieS", Horizon2020 consortium operating in two lighthouse and five fellow cities.; TransPED, Co-funded by JPI Urban Europe, a 2-year project to develop a governance approach for PED stakeholders operating in 5 cities.; Triangulum, European Smart-district research and development Horizon2020 project coordinated by the Fraunhofer institute. Operated in 6 European cities.. 2 model for an inclusive transition. Furthermore, this article offers a contribution for policymakers, informing PED replication policies with a focus on the synergistic aims of decarbonization and energy vulnerability mitigation.
... Okushima (2017) developed a multidimensional energy poverty index composed of energy costs, income, and energy efficiency of housing for Japan proving the negative impact of energy price escalation on energy poverty, especially among vulnerable households and the elderly. Also, Sareen et al. (2020) reinforce that combining indicators at multiple scales is needed to capture the multi-dimensional aspects of energy poverty. However, challenges like database availability, coverage, and limited disaggregated resolution persist. ...
Full-text available
Using a unique or common measure of energy poverty is very limited for the true clas�sifcation of a household being in energy poverty. Thus, this study proposes a compos�ite indicator, whose weights will be determined from the estimation of two relationships using a robust and stable methodology based on information theory. This work considers two regression models, where the two dependent variables are the gross domestic product and greenhouse gas, and the 12 energy poverty explanatory variables are based on those proposed by Recalde et al. (Energy Pol 133:110869, 2019., for the period 2008–2018. Hence, the study presents a more comprehensive measurement with additional dimensions, weights, and indicators. Probably most impor�tant, in addition to the discussed proposal with a specifc choice of models and variables, this work reveals a promising methodology that can be replicated in any other theoretical confguration. This approach is suitable for the discussion and design of new energy, envi�ronmental and social policies. Findings can be used to assess in advance the efectiveness of energy poverty measures, turning the model into a valuable policy tool.
Conference Paper
Energy developments frequently involve land degradation, human health impacts, impacts on ecosystems, and damage to the social fabric. The uneven distribution of these impacts can lead to situations of spatial inequality, in which regions importing electricity are exempt from the environmental costs of power generation and distribution while the producing regions pay the socioecological costs of overproducing energy that will eventually be consumed far away. While conceptualizing energy injustice (EiJ) has been a prominent topic in the literature in recent years, less attention has been paid to how EiJ can be measured. This paper proposes indices that can measure the spatial characteristics of EiJ from three different angles: evenness, concentration, and centralization. We illustrate the utility of the proposed indices by applying them to the cases of Spain, Denmark, and South Korea. Data from all three show a common EiJ pattern in the form of the rural–urban divide: peripheral regions having more generation than capital cities. In terms of evenness, Denmark exhibited the most equitable distribution of power generation, followed by Spain and South Korea. The number and size of the top producing regions were proportionally greater in South Korea, which potentially indicates less injustice in terms of concentration, compared to Denmark and Spain. Future research should pay more attention to the socioeconomic implications of energy production, particularly for the most affected regions. Our methodology can help policymakers design fairer energy and regional planning policies.
In this paper we identify drivers for energy poverty in Europe using machine learning. The establishment of predictors for energy poverty valid across countries is a call made by many experts, since it could provide a basis to effectively target energy-poor households with adequate policy measures. We apply a “low income, high expenditure” framework to classify households as being at risk of energy poverty, to a dataset from a survey conducted at the household-level in 11 European countries with vastly different economies, cultures, and climates. A gradient boosting classifier is successfully trained on a set of socio-economic features hypothesized as predictors for energy poverty in this diverse set of countries. The classifier’s internal model is analyzed, providing novel insights into the intricacies that underlie energy poverty. We find that besides the main driver - income - floor area and household size can be confirmed as predictors. Our results suggest the presence of universal predictors that are valid across Europe, and contextual ones that are governed by local characteristics. To facilitate advanced research into energy poverty in Europe, we recommend to increase and streamline household data collection efforts, both at the country- and EU-level.
Summer weather conditions in Spain generate many thermal discomfort hours. Consequently, it is difficult for low-income family units to ensure adequate conditions inside dwellings. Natural ventilation could improve the thermal comfort conditions of these users. However, the number of thermal comfort hours could be limited in summer. This study analyses the possibility of improving users’ thermal comfort by applying tolerances to the upper limit of adaptive thermal comfort models. The study is conducted in 10 Spanish cities in the current scenario and in a climate change scenario. The tolerances were those considered by the adaptive standards: 1.2 °C, 1.8 °C and 2.2 °C. The results showed that the use of the upper limit without tolerances could imply a low number of thermal comfort hours in some cities and that the use of the tolerances would improve thermal comfort conditions. The estimated climatic evolution throughout the 21st century could limit the use of natural ventilation even with tolerances in the warmer regions, while in regions with less severity in summer natural ventilation would be an appropriate measure.
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While the crisis of statistics has made it to the headlines, that of mathematical modelling hasn’t. Something can be learned comparing the two, and looking at other instances of production of numbers.Sociology of quantification and post-normal science can help.
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Urban energy transitions are key components of urgently requisite climate change mitigation. Promissory discourse accords smart grids pride of place within them. We employ a living lab to study smart grids as a solution geared towards upscaling and systematisation, investigate their limits as a climate change mitigation solution, and assess them rigorously as urban energy transitions. Our 18 month living lab simulates a household energy management platform in Bergen. Norway’s mitigation focus promotes smart meter roll-out as reducing carbon emissions, by (i) unlocking efficiency gains, and (ii) increasing awareness for demand-side management. We problematise this discourse. Raising awareness encounters intractable challenges for smart grid scalability. Scattered efficiency gains constitute modest increments rather than the substantial change requisite for rapid mitigation. Whereas promissory smart grid discourse overlooks these ground-truthed limits, our findings caution against misplaced expectations concerning mitigation. We contest discursive enthusiasm on smart grids and argue for aligning local and systemic concerns before upscaling to avoid obscuring risks. Scaling up requires understanding and addressing interdependencies and trade-offs across scales. Focus group discussions and surveys with living lab participants who used sub-meter monitors to track real-time household electricity consumption data over an extended period show that technical issues and energy behaviour, as well as political economic and policy structures and factors, pose significant limits to smart grids. Urban strategies for climate change mitigation must be informed by this recognition. Our results indicate that upscaling relies on bottom-up popular acceptance of the salient technical, organisational and standardisation measures, but that measures to improve the democratic legitimacy of and participation in energy transitions remain weak. We highlight limits to smart grids as a standalone urban mitigation solution and call for a sharper focus on accompanying thrust areas for systematisation and scalability, such as renewable energy integration and grid coordination.
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h i g h l i g h t s • High spatial scale multi-faceted method for EP regional vulnerability assessment. • Buildings energy performance gaps and socioeconomic variables are combined. • Tool for hotspots identification for local action and comparative analysis. • Both space heating and cooling problems are highlighted for a Southern EU country. a b s t r a c t Energy poverty is a growing societal challenge that puts the welfare of many European citizens at risk. Several different indicators have been developed with the objective of assessing this phenomenon. The aim of this work is to develop a novel high-resolution spatial scale composite index, focusing on space heating and cooling, to map energy poor regions and identify hotspots for local action. The proposed index (EPVI) combines socioeconomic indicators of the population (AIAM sub-index) with building's characteristics and energy performance (EPG sub-index). The method was tested for all 3092 civil parishes of Portugal and could be potentially replicated at Pan European scale. Results show a higher prevalence of significative EPVIs in the inland region and the islands, particularly in rural civil parishes. Although cooling EPVIs are generally higher, heating may be a more significant issue in terms of energy demand and health hazard. Energy poverty vulnerability assessment at such a disaggregated regional scale could bridge the gap between common overall country analyses and local-scale initiatives targeting vulnerable households. The outcomes of this paper support national and local energy efficiency policies and instruments while fostering better assessments and enabling local actions for tackling this problem.
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While questions of energy and energy transition have become hotly contested, the abstract and fetishized conception of energy that dominates contemporary political debates occludes connections to everyday life. By tracing the activities of Catalan activist network Alianza contra la Pobreza Energética (Alliance against Energy Poverty or APE), this article seeks to excavate the political possibilities opened up by a more everyday energy politics. The article addresses the practice of illegal utilities connections among the urban poor of Catalonia, arguing that this constitutes a form of makeshift urbanism resonant of that conceptualized from within ‘Southern’ cities. These ‘irregular connections’ to urban infrastructure networks are then distinguished from the ‘irregular connections’ formed between people within the collectivized social infrastructure of APE. APE, I argue, translate ‘energy’ as social reproduction, framing their struggle for the right to energy around the right to sustain life with dignity. This, I suggest, is the starting point for a feminist praxis capable of creating new and unruly subjectivities, reconfiguring reproductive relations in more caring and collective directions, and ultimately challenging the violence of the commodity form.
To date, social sciences have devoted little attention to the processes of expert knowledge production related to the exploitation of unconventional hydrocarbon resources. In this article, we examine an epistemic experiment led by the European Commission, the European Science and Technology Network on Unconventional Hydrocarbon Extraction, which was aimed at producing authoritative knowledge claims on shale energy development. By developing the idiom of ‘co-production’, the article provides a more fine-grained understanding of the processes through which competing knowledge claims, forms of epistemic authority, and new energy publics co-evolve in a situation of highly-politicized controversy. Drawing on our first-hand observations as participants representing the social sciences in the expert network, this article provides an in-depth ethnographic account of the struggles of the European Union authorities to manage and delimit the controversy. In this way, the analysis develops our understanding of the challenges in improving the deliberation of shale gas as a transnational energy policy issue.
The European Green Capital (EGC) award has become a familiar feature in a polycentric sustainability governance landscape increasingly characterized by fragmentation and voluntary initiatives. Unclear accountability for translocal connections renders these initiatives at risk of locking unsustainable practices into transitions. Seeking clarity, this paper examines accountability through the lenses of material dislocation and discursive construction in an assessment of Oslo’s (2019) and Lisbon’s (2020) winning EGC entries. How can the EGC distinction better enable substantive urban sustainability, situating claims within wider energy transitions in these capital regions? Within the award’s circumscribed focus on urban centres, do cities account for cognitive and material dislocation through their discursive emphases and telecoupling respectively? Does the EGC catalyse change, brand the capture of low-hanging fruit, or spatially dislocate rather than reduce emissions? We argue that it propagates a focus on optimizing local sustainability effects, while rarely accounting for larger translocal or cross-scalar repercussions. Hence, urban sustainability strategies risk spatially dislocating socio-ecologically unsustainable practices rather than decreasing emissions systemically. Cities need to institute accountability mechanisms that reshape the geographies of responsibility for the systemic and translocal impacts of urban sustainability initiatives, which the EGC could promote by, e.g. including emission indicators for consumption and aviation.