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Redefining fuel poverty: Introducing the temporal equity framework (TEF)

Torran Semple, John Harvey, Grazziela Figueredo, Lucelia Rodrigues, Mark Gillott, Phil Grunewald, Alexander Sullivan, Jan Rosenow

arXiv:2610.11885v1eess.SYcs.SY

TL;DR

The paper asks whether English fuel-poverty definitions can track affordability as energy prices and efficiency change. It compares definitions across scenarios and proposes TEF; the authors report that LILEE understates price-shock impacts and overstates efficiency-upgrade benefits relative to TEF.

  • Problem

    The paper examines whether fuel-poverty definitions can reflect changing energy affordability norms during energy transitions.

    Source: §1 · p. 2, §2.3 · p. 5

  • Method

    The authors compare fuel-poverty definitions across optimistic and pessimistic scenarios and propose TEF, combining a 10% energy-cost clause, a Minimum Income Standard criterion, and continuous depth indicators.

    Source: §1 · p. 2, §3 · p. 5

  • Results

    The authors report that LILEE overstates efficiency-upgrade benefits and trivializes the added energy-cost burden in the pessimistic scenario, while TEF produces more modest, spatially varied reductions and a pronounced response.

    Source: §6.1 · pp. 24–25

  • Takeaways & Limitations

    The authors present TEF’s gap and buffer indicators as tools for assessing entrenchment, household resilience, and geographical shifts in energy equality over time.

    Source: §6.1 · p. 25

    The scenarios are dichotomous, largely theoretical, and specific to Nottingham, limiting generalisability and motivating real-world forecasts and wider-scale estimates.

    Source: §6.3 · p. 27

Abstract

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The definition and quantification of fuel (or energy) poverty remain contentious, particularly within the English policy landscape. The current official definition - Low Income Low Energy Efficiency (LILEE) - systematically underestimates the condition in low- and middle-income households, effectively obfuscating changing energy affordability norms. This paper presents a sensitivity analysis of fuel poverty trajectories in Nottingham, UK, across competing definitions and diverging optimistic domestic efficiency upgrades) and pessimistic (energy price inflation) scenarios. Results demonstrate that LILEE overstates the efficacy of efficiency upgrades while failing to adequately reflect the impact of price shocks. To address these deficiencies, we propose the Temporal Equity Framework (TEF), a budget standard-based approach paired with a proportional energy expenditure indicator that captures shifting affordability norms more equitably. The TEF also introduces continuous expenditure-based depth indicators to evaluate the entrenchment of fuel-poor households (the fuel poverty gap) and the resilience of non-fuel-poor households (the fuel poverty buffer). We argue that responsive definitions which also recognise fuel poverty as a continuum of vulnerability, rather than a static, dichotomous state, are paramount to ensuring the equitability of energy transitions. Further, we suggest that the early identification of fuel poverty and spatial targeting of remedial resources could be markedly improved by adopting the TEF. We conclude that the adoption of a multidimensional budget standard-based definition is essential to facilitate a more accurate and just evaluation of energy transitions in England.

1. Introduction

The paper examines whether England’s fuel-poverty definitions can measure affordability fairly as energy costs and housing efficiency change. It compares competing definitions across economic scenarios and proposes the Temporal Equity Framework (TEF).

1. Introduction

  • Fuel poverty matters to a just energy transition because decarbonising housing should not undermine energy affordability, especially for vulnerable households.The paper frames fairness in terms of the metrics used to evaluate social change.Source: §1 · pp. 1–2
  • England introduced LILEE as its official fuel-poverty definition in 2021, with an objective of tracking progress toward the statutory target while reflecting income, efficiency, and prices.The policy agenda accompanying LILEE primarily focused on structural efficiency programmes and targeted retrofits.Source: §1 · p. 2
  • The authors argue that LILEE is structurally insulated from energy-price shocks and may therefore miss deteriorating affordability conditions.In the pessimistic scenario, LILEE incidence rises by only 3.11 percentage points.Source: §1 · p. 2
  • The paper proposes TEF, combining a tiered affordability indicator with a Minimum Income Standard budget criterion and continuous poverty-depth indicators.The indicators are intended to capture both entrenchment among fuel-poor households and resilience among those not classified as fuel poor.Source: §1 · p. 2
  • The analysis compares five fuel-poverty definitions using the Synthetic Nottingham Homes dataset of 105,570 households across optimistic and pessimistic scenarios.The dataset supplies household-level income, energy-cost, and efficiency variables.Source: §1 · p. 3

2. Background

The background traces changing fuel-poverty definitions, reviews expenditure-based measurement approaches, and links their responsiveness to the evaluation of equitable energy transitions.

2.1. English fuel poverty politics

  • England moved from the absolute 10% definition to LIHC and then LILEE, while Scotland adopted a 10% and Minimum Income Standard definition using both thresholds.The paper describes this policy divergence as largely specific to England.Source: §2.1 · pp. 3–4

2.2. Literature review

  • Fuel-poverty measurement includes expenditure-based, self-reported, and physical approaches, while the paper compares five expenditure-based definitions relevant to English policy.Those definitions span the absolute 10% measure, relative LIHC and LILEE measures, Scottish 10% & MIS, and a proposed disjunctive 10% | MIS variant.Source: §2.2 · pp. 4–5

2.3. Fuel poverty quanti$ications and equitable energy transitions

  • The paper argues that existing English definitions cannot individually track changing affordability norms, motivating TEF’s budget-standard approach and continuous depth indicators.The indicators are intended to identify both entrenched and borderline cases beyond a fuel-poor/not-fuel-poor classification.Source: §2.3 · p. 5

3. The Temporal Equity Framework (TEF)

TEF separates primary fuel-poverty classification from continuous depth indicators. Its classification combines proportional energy costs and a Minimum Income Standard criterion, while its gap and buffer measures describe monetary distance from the 10% threshold.

3. The Temporal Equity Framework (TEF)

  • TEF treats primary classification and fuel-poverty depth as two independent processes.Step 1 classifies households; Step 2 supplies complementary depth indicators.Source: §3 · p. 5

3.1. Step 1: Primary fuel poverty classi$ication

  • The primary classification combines the 10% energy-cost definition with a criterion based on whether income after energy costs can maintain an acceptable standard of living.The budget-standard clause uses 90% of the Minimum Income Standard, a threshold the paper grounds in evidence on material deprivation.Source: §3.1 · p. 6
  • TEF uses OR logic for its 10% and MIS clauses, unlike Scotland’s AND logic, to include households below the MIS threshold even when energy costs fall below 10%.A high-earner cap excludes households above median net income when they meet only the 10% clause, but not when they breach the MIS threshold.Source: §3.1 · p. 7
  • TEF divides proportional energy expenditure into five bands, from not fuel poor below 8% to extreme fuel poverty above 20%.The tiers are intended to distinguish marginal, fuel-poor, severe, and extreme cases for differentiated policy responses.Source: §3.1 · pp. 7–8, Table 2 · p. 8

3.2. Step 2: Fuel poverty depth indicators

  • The depth indicators represent a continuous monetary spectrum: the gap measures the amount required to lift a fuel-poor household out of poverty, while the buffer measures costs a non-fuel-poor household can absorb.Unlike conventional depth measures described in the paper, the buffer also captures resilience among households not classified as fuel poor.Source: §3.2 · pp. 8–9, Figure 2 · p. 9
  • The monetary distance from the 10% threshold is positive for fuel-poor households and negative for non-fuel-poor households.The paper examines its distribution across Nottingham’s small-area geographies to evaluate depth across sensitivity-analysis scenarios.Source: §3.2 · p. 9

4. Methodology and data

The study uses synthetic Nottingham household data to compare fuel-poverty definitions under baseline, retrofit, and energy-price-shock conditions. It defines the comparison measures and scenario assumptions for assessing how classifications respond.

4.1. The Synthetic Nottingham Homes (SNH) dataset

  • The synthetic Nottingham Homes dataset pairs household income, energy costs, and efficiency for comparisons across definitions, while its modelled costs represent required rather than actual expenditure.Income is sampled by LSOA and capped at the 97.5th percentile; dwelling characteristics from EPC records inform required energy costs.Source: §4.1 · pp. 10–11

4.2. Fuel poverty sensitivity analysis: baseline, Scenario 1 and Scenario 2 conditions

  • Scenario 1 assumes all homes reach potential energy efficiency; Scenario 2 applies a 78.38% energy-cost increase and 1.46% income growth, with housing efficiency fixed.Scenario 1 reduces median modelled costs to £1,330/year from the baseline £2,185; behavioural self-rationing could keep actual spending below required-cost estimates in Scenario 2.Source: §4.2 · pp. 12–13

4.3. Fuel poverty de$inition parameters: 10%, LIHC, LILEE*, Scottish (10% & MIS) and TEF (10% | MIS)

  • Analysis applies 10% fuel poverty (required energy costs >10% of net income), LIHC (costs above £2,252/year and residual income below the ~£18,840/year 2021 poverty line), and LILEE* (SAP-based).LILEE* estimates use SAP score alone because energy-rebate information needed to calculate FPEER was unavailable; LILEE’s criterion is FPEER below 69 with residual income below the poverty line after required energy costs.Source: §4.3 · p. 13
  • Because household composition is absent from the synthetic data, the TEF assigns composition probabilistically using Nottingham census proportions and floor area, weighting larger homes toward higher occupancy.The chosen multiplier m=2 balances floor-area sorting with census proportions and suppresses implausible large-family/small-flat pairings.Source: §4.3 · pp. 13–14
  • The TEF’s higher-earner cap excludes households above median income when they meet only the 10% clause, but retains them if they breach the MIS threshold.The analysis assumes median UK net household income of £31,400 for applying the cap.Source: §4.3 · p. 14

5. Results and discussion

Baseline fuel-poverty rates in the Nottingham sample vary substantially across definitions, reflecting that the measures identify different conditions. The section sets up scenario comparisons of unit-level rates and changes.

5.1. Baseline fuel poverty

  • 36.12% of households were fuel poor under capped TEF 10% | MIS, versus 39.47% uncapped; LIHC (10.26%) and LILEE* (18.30%) rates were lower than 10% and comparable to Scottish 10% & MIS.The higher baseline rates reflect Nottingham's local deprivation; the analysis emphasizes relative differences between definitions rather than national-level rates.Source: §5.1 · pp. 14–15, Table 5 · p. 15
  • The Scottish conjunctive 10% & MIS formulation excludes many households classified as fuel poor by the 10% measure, while TEF's disjunctive rule includes households meeting either its capped 10% threshold or MIS criterion.The authors note that the conjunctive rule risks overlooking high energy costs among households just above the MIS threshold.Source: §5.1 · p. 15
  • Figure 3 compares household classifications under LILEE* and 10%, with colored points showing 10% status across LILEE* profile groups.The cross-classification exposes households selectively identified as fuel poor depending on the definition.Source: §5.1 · p. 15, Figure 3 · p. 16
  • In LILC and LIHEE groups, 63.39% and 52.39% respectively meet the 10% definition, while TEF rates are 74.02% and 67.22%, illustrating households missed by relative definitions.The disaggregated comparisons show that low-income households with relatively low running costs can still face high energy burdens.Source: §5.1 · p. 16, Table 6 · p. 16

5.2. Sensitivity analysis results (I): unit-level results

  • The unit-level sensitivity analysis compares Scenario 1 retrofit and Scenario 2 price-shock fuel-poverty rates with baseline, reporting absolute and relative changes.The section identifies Tables 7 and 8 as the scenario result displays; this passage does not provide their numerical outcomes.Source: §5.2 · p. 17

Scenario 1 (the ‘optimistic’ case): unit-level results

Efficiency upgrades reduce fuel poverty across the tested definitions, but energy-price inflation produces sharply different responses, with LILEE changing least. The results also show how the TEF’s capped rate differs from uncapped and 10% classifications.

Scenario 1 (the ‘optimistic’ case): unit-level results

Scenario 2 (the ‘pessimistic’ case): unit-level results

  • In Scenario 2, fuel poverty rose across all definitions, but LILEE increased by only 3.11% in absolute terms.The authors describe this muted response as evidence that LILEE captures price volatility and its consumer burden poorly.Source: Scenario 1 (the ‘optimistic’ case):… · p. 17, Table 8 · p. 18
  • The authors argue that English fuel-poverty rates are statistically destined to stagnate or decline despite worsening economic conditions.This follows from LILEE’s weak response under the pessimistic scenario.Source: Scenario 1 (the ‘optimistic’ case):… · pp. 17–18
  • LIHC rose by over 200% in Scenario 2, yet its absolute rate of 33.35% remained below 10% (78.51%) and TEF (57.34%).The LIHC relative rise may be inflated because the median energy-cost threshold (£2,252/year) was held constant; a scenario-adjusted median was infeasible and would have lowered the rate.Source: Scenario 1 (the ‘optimistic’ case):… · p. 18, Table 8 · p. 18
  • The TEF (10% | MIS) classified 57.34% as fuel poor in Scenario 2, versus about 79% under 10% and uncapped 10% | MIS.The cap excluded 21.33% (~22,500 households) who met the 10% clause but exceeded the high-earner cap and did not meet MIS.Source: Scenario 1 (the ‘optimistic’ case):… · p. 18, Table 8 · p. 18
  • The authors argue that an approximately 79% fuel-poverty rate could make support per beneficiary too low, favoring the capped 10% | MIS as a targeting compromise.They also note that stable LIHC and LILEE rates amid major change may be politically expedient.Source: Scenario 1 (the ‘optimistic’ case):… · p. 18

Five-fold 10% fuel poverty

The five-fold 10% approach divides fuel poverty into ranked tiers, offering more detail on severity and precariousness than a binary classification. The area-level analysis uses TEF (10% | MIS) to compare spatial responses across definitions.

Five-fold 10% fuel poverty

  • The five-fold approach disaggregates 10% fuel poverty into five ordinally ranked tiers, reducing reliance on a single 10% threshold as affordability norms shift.The paper presents the resulting detail as useful for assessing severity and precarity, including among households classified as non-fuel-poor.Source: Five-fold 10% fuel poverty · pp. 18–19, Table 9 · p. 19
  • In Scenario 2, severe cases were 22.52% and extreme cases 26.17%; the binary 10% definition aggregates both as fuel poor.The five-fold version also distinguishes marginal, non-fuel-poor but precarious households.Source: Five-fold 10% fuel poverty · p. 19, Table 9 · p. 19

5.3. Sensitivity analysis results (II): area-level results

  • Because Scottish 10% & MIS and TEF (10% | MIS) have comparable relative responsiveness but different baseline rates, the area-level analysis focuses on TEF.The authors say presenting both would be redundant and use TEF to compare directly with 10%, LIHC, and LILEE.Source: §5.3 · p. 19
  • Figure 4 plots baseline LSOA fuel-poverty rates against Scenario 1 or 2 rates; points on the equality line indicate no change.Top-left positions indicate increases and bottom-right positions reductions relative to baseline.Source: §5.3 · p. 19, Figure 4 · p. 20

Scenario 1 (the ‘optimistic’ case): area-level results

Scenario 1 produced near-universal decreases in LSOA-level fuel-poverty rates, but the size and pattern of reductions varied across areas and definitions.

Scenario 1 (the ‘optimistic’ case): area-level results

  • Scenario 1 produced near-universal LSOA-level reductions across the tested definitions, with the size of the decrease varying by area.The authors suggest that older, hard-to-treat properties may help explain this spatial variation; the analysis treats potential efficiency as building-based, independent of financial capacity.Source: Scenario 1 (the ‘optimistic’ case):… · p. 20, Figure 5 · p. 21
  • LIHC and LILEE showed sharper reductions than 10% and TEF, while 10% and TEF showed sharp decreases in some LSOAs but entrenched poverty in others.The authors consider the latter pattern more realistic where income poverty cannot be overcome by efficiency upgrades alone.Source: Scenario 1 (the ‘optimistic’ case):… · p. 21, Figure 5 · p. 21
  • Figure 5 standardises axes to 0–100, causing LSOAs under relative definitions LIHC and LILEE* to cluster or overlap.This is a comparability caveat for reading the plotted area-level results.Source: Scenario 1 (the ‘optimistic’ case):… · p. 21, Figure 5 · p. 21

Scenario 2 (the ‘pessimistic’ case): area-level results

Under Scenario 2’s assumed energy-cost growth, fuel poverty increased, but the area-level response varied by definition. LILEE* was comparatively inelastic, while 10% and TEF responded substantially.

Scenario 2 (the ‘pessimistic’ case): area-level results

  • Scenario 2 assumes energy costs outgrow household income by 78%, producing a pronounced rise in LSOA-level fuel poverty relative to baseline.The scenario is framed as a pessimistic case mimicking the 2021–2022 crisis.Source: Scenario 2 (the ‘pessimistic’ case):… · p. 22, Figure 6 · p. 22
  • LILEE* showed an almost equality-line-parallel response, while 10% and TEF responded substantially; SAP served as a proxy for FPEER, which the authors say is equivalent in most cases (DECC, 2014).They infer this trend also reflects England’s official definition (BEIS, 2021; DESNZ, 2026).Source: Scenario 2 (the ‘pessimistic’ case):… · pp. 22–23, Figure 6 · p. 22
  • Although the analysis uses Nottingham data, the authors suggest that median-tethered definitions’ limited responsiveness to macroeconomic shocks may transfer internationally.They qualify this by noting the focus on one English city and the need for multiple absolute and relative indicators to capture fuel poverty’s multidimensionality.Source: Scenario 2 (the ‘pessimistic’ case):… · p. 23

Depth indicators: the 10% fuel poverty gap and buffer

The 10% fuel poverty gap and buffer describe depth and resilience across households, rather than only whether they fall above or below a threshold. The examples show sharper vulnerability in the deprived LSOA under price shocks than in the affluent LSOA.

Depth indicators: the 10% fuel poverty gap and buffer

6. Conclusions, policy implications and limitations

The conclusions argue that LILEE misses the effects of changing energy costs, while the TEF combines a budget-standard criterion with tiered energy-affordability measures and depth indicators. The paper recommends reconsidering England’s official definition, while noting limits tied to the Nottingham setting, stylised scenarios, and cost estimates.

6.1. Conclusions

  • TEF classifies households using a disjunctive 10% | MIS criterion and five severity tiers, replacing the binary fuel-poor/not-fuel-poor classification.The framework is intended to capture hidden fuel poverty and support monitoring of marginal populations.Source: §6.1 · p. 25
  • LILEE overstates fuel-poverty reductions from efficiency upgrades and understates the effects of rising energy costs, whereas TEF responds more strongly to price shocks.In the pessimistic scenario, TEF classified approximately 57% of the Nottingham sample as fuel poor.Source: §6.1 · p. 25
  • TEF’s gap and buffer indicators use the 10% threshold to track fuel-poverty entrenchment and resilience across places and over time.The paper presents these as indicators of temporal energy equality.Source: §6.1 · p. 25

6.2. Policy implications

  • The authors recommend reconsidering England’s official definition and adopting TEF’s five-fold classification with complementary depth indicators.They argue this would provide a more representative assessment for evaluating the equity of energy transitions.Source: §6.2 · p. 26
  • Where housing upgrades are constrained by retrofit poverty or hard-to-treat homes, targeted income support should continue alongside remediation.TEF may help target preventative strategies by identifying areas with low resilience to energy-price volatility earlier.Source: §6.2 · p. 26
  • The authors identify relevance beyond England, especially for European countries relying on median-tethered definitions in isolation.They contrast those definitions with TEF’s combined 10% | MIS formulation, which accounts for living standards and cost-of-living pressures.Source: §6.2 · p. 26

6.3. Limitations and further work

  • The estimates are bounded by probabilistically assigned household composition, required rather than actual energy costs, and stylised Nottingham-specific scenarios.Required costs may overestimate actual spending because behavioural self-rationing was not modelled; the authors call for real-world forecasts and broader estimates.Source: §6.3 · p. 27

Appendix

The appendix provides material on Minimum Income Standard (MIS) budgets and the household-composition assumptions used to map Nottingham households to MIS budgets.

Appendix

  • Appendix Table 1 lists MIS budget components by household composition.Source: Appendix · pp. 33–34
  • Appendix Table 2 presents typical Nottingham household composition alongside corresponding MIS composition and assumed annual budgets.The table title specifies an annual MIS budget and assumed AHC.Source: Appendix · p. 33
  • The appendix includes household-composition categories spanning one-person, single-family, and other household types.Source: Appendix · pp. 33–34
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