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arxiv: 2606.17807 · v1 · pith:T5FMTVF2new · submitted 2026-06-16 · 💰 econ.GN · q-fin.EC

Household coping mechanisms under grid failure: Evidence from a high electrification context in Lebanon

Majd Olleik , Haytham M. Dbouk , Anne Neumann , Elsa Bou Gebrael , Sebastian Zwickl-Bernhard This is my paper

Pith reviewed 2026-06-26 21:57 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC
keywords household energy copinggrid failureLebanon electricitybackup powersolar PV adoptiondiesel generatorsunmet demandsocioeconomic status
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The pith

Socioeconomic status shapes which Lebanese households can access backup power and how much electricity demand goes unmet during grid failures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper draws on a survey of 1,000 households to map how families respond when the national grid fails frequently despite near-universal connections. It separates supply-side fixes such as diesel generators and solar-battery systems from demand-side changes like shifting loads or simply using less power. Wealthier households reach solar options more often while others rely on generators or suppress use, and the work shows that self-generated power frequently wastes solar output. The central point is that energy planning must separate met demand from suppressed demand to avoid underestimating needs in unreliable-supply settings.

Core claim

Households facing chronic grid failure combine diesel generators, PV-battery systems, load shifting, and demand suppression; socioeconomic status determines access to these backups and the share of demand that is actually met. Diesel generators stay common, yet a shift toward PV-battery systems appears among higher-income households, accompanied by notable solar curtailment. Consumption profiles vary by backup type, and the data indicate that ignoring suppressed demand leads to incomplete pictures of household electricity needs.

What carries the argument

A 1,000-household survey that records both the adoption of backup technologies and the extent of demand suppression under unreliable supply.

If this is right

  • Wealthier households increasingly choose PV-battery systems over diesel.
  • Decentralized solar generation produces substantial curtailed output.
  • Households reduce overall electricity use, with distinct profiles tied to the backup type they employ.
  • Energy system models must incorporate suppressed demand to avoid underestimating true needs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same income-driven gaps in backup access could appear in other countries that have high grid coverage yet frequent outages.
  • Incorporating these observed consumption profiles into planning tools might reduce over- or under-sizing of future generation capacity.
  • Targeted support for lower-income households to acquire efficient backups could lower overall system inefficiencies.

Load-bearing premise

The survey sample represents the broader Lebanese population and self-reported answers accurately reflect actual technology ownership and consumption levels without major bias.

What would settle it

A direct measurement study of actual electricity consumption and backup ownership across income groups that finds no systematic link between socioeconomic status and either access to backups or levels of unmet demand.

Figures

Figures reproduced from arXiv: 2606.17807 by Anne Neumann, Elsa Bou Gebrael, Haytham M. Dbouk, Majd Olleik, Sebastian Zwickl-Bernhard.

Figure 1
Figure 1. Figure 1: Overview of the research methodology Category Question Expected answer type General information Socioeconomic status (SES) Low/ Medium/ High Number of members in household Numerical Household area (m2 ) Numerical Geographic division Categorical Number of rooms in household Numerical Solar water heater available Yes or no LED lighting percentage Numerical (0-100%) Electricity sources Source, consumption and… view at source ↗
Figure 2
Figure 2. Figure 2: Constituents of the adopted stratification sample size 𝑛 is determined using Yamane’s formula for sample size calculation [46] (Equation 1): 𝑛 = 𝑁 1 + 𝑁(𝑒 2) (1) where 𝑁 is the total population size (1.37 M households in Lebanon), and 𝑒 is the margin of error (3%). Then, the needed sample size is ≈ 850 households. To account for potential errors and biases, the final sample size 𝑛 is rounded up to 1000 hou… view at source ↗
Figure 3
Figure 3. Figure 3: Survey distribution across Lebanon [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Number of surveys in each category Prior to the 2020 crisis, households adopting PV-battery system were doing so due to their techno-economic advantages, despite their high investment costs. Therefore, the share of lithium-ion batteries was initially higher ( [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: PV installation times and battery installation times and categories [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Solar PV capacity distribution case of higher-SES households, limited available space is the dominant constraint, particularly in urban areas ( [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Barriers to household PV adoption [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of monthly supply from diesel generator per household 4.2.1. Load shifting for PV-owners A commonly observed behavioral response to residential solar PV adoption, across both developed and developing countries, is the shifting of electricity demand toward earlier hours of the day, aligning with periods of peak solar generation [38]. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of hours of peak demand by backup supply source Test T-statistic P-value Recommendation PV vs DG -4.034 2.818 ×10−5 Reject 𝐻0 , 𝜇𝑃 𝑉 is significantly smaller than 𝜇𝑛𝑜𝑃 𝑉 Hybrid vs DG -4.040 2.742 ×10−5 Reject 𝐻0 , 𝜇𝑃 𝑉 is significantly smaller than 𝜇𝑛𝑜𝑃 𝑉 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Box plots of yearly unmet demand distributions 4.2.3. Unmet demand As part of adapting to the chronic shortages through reshaping their demand, households often end up suppressing part of it. As reported in [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Heatmaps of appliance ownership and use 4.2.4. Appliance ownership and usage In addition to behavioral adaptations such as load shifting and reduction, households also adjust their electricity consumption through the acquisition and use of appliances [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Representative load profiles for urban, medium SES households in summer 4.3. Macro-level insights Looking forward, planning and policy-making should consider the entrenched household adaptations emerging from decades of chronic outages. This includes understanding the new shape of the demand of households, evaluating the extent of excess PV generation potential that could be used, and assessing how these … view at source ↗
Figure 13
Figure 13. Figure 13: Wasted household PV generation potential PV-only households waste at least 41% of their generation potential on average, amounting to over 2.4 MWh per year per household. This excess can be used in the design of a policy promoting PV-owner exchange with the grid. 4.3.3. Energy poverty Backup technologies impose additional costs on households beyond EDL tariffs. These costs are captured through survey data… view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of annual costs The findings reveal a diversified landscape of generation coping mechanisms, with households relying on three main configurations: diesel generators, solar PV-battery systems, and hybrid combinations. Households installing PV-battery systems are increasingly shifting towards lithium-ion battery technologies reflecting their improving affordability and performance. A striking r… view at source ↗
Figure 15
Figure 15. Figure 15: Daily load profiles in summer A. Representative profiles Olleik et al.: Preprint submitted to Elsevier Page 20 of 24 [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Daily load profiles in winter [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Daily load profiles in spring Olleik et al.: Preprint submitted to Elsevier Page 21 of 24 [PITH_FULL_IMAGE:figures/full_fig_p021_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Daily load profiles in fall Olleik et al.: Preprint submitted to Elsevier Page 22 of 24 [PITH_FULL_IMAGE:figures/full_fig_p022_18.png] view at source ↗
read the original abstract

Despite near-universal electrification in many countries, electricity supply shortages continue to shape household energy use. This paper examines how households adapt to chronic grid failure in high-electrification, high-dependence contexts, using Lebanon as a case study. Drawing on original survey data from 1,000 households, we analyze both supply-side coping mechanisms such as diesel generators and solar photovoltaic (PV)-battery systems, and demand-side adaptations, including load shifting and demand suppression. The results reveal a landscape of household responses, where socioeconomic status plays a central role in determining access to backup solutions and the extent of met demand. While diesel generators remain widespread, a transition toward PV-battery systems is observed, especially among financially capable households. However, decentralized self-generation is associated with inefficiencies, including substantial levels of curtailed solar generation. On the demand side, households exhibit reductions in electricity use, leading to distinct consumption profiles depending on the type of backup system employed. These findings highlight the importance of distinguishing between met and unmet demand when assessing energy needs under unreliable supply. The paper contributes to the literature by providing a quantitative characterization of the interaction between self-generation and demand adaptation in a supply-constrained high-electrification context. It also offers empirical demand profiles that incorporate suppressed consumption, addressing a key gap in electricity system planning. From a policy perspective, the results underscore the need to account for unmet demand, address inequities in access to coping technologies, and reduce inefficiencies in decentralized systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper examines household adaptations to chronic grid electricity failures in Lebanon using original survey data from 1,000 households. It describes supply-side coping via widespread diesel generators and an emerging shift to PV-battery systems (especially among higher-SES households), demand-side adaptations such as load shifting and suppression, and resulting inefficiencies including substantial solar curtailment. The central claim is that socioeconomic status determines access to backups and the gap between met and unmet demand, with implications for distinguishing these in energy planning.

Significance. If the survey is representative and self-reports are reliable, the work supplies quantitative demand profiles that incorporate suppressed consumption and documents inequities in decentralized backup access. This addresses a documented gap in system planning for high-electrification but supply-constrained settings and could inform policy on technology transitions and unmet demand.

major comments (2)
  1. [Survey methodology section] Survey methodology section: No information is provided on the sampling frame, stratification (by region, urban/rural, or SES), response rate, or weighting to match Lebanese population benchmarks. This directly undermines the claim that SES centrally determines access to diesel/PV backups, as selection bias cannot be assessed.
  2. [Results on curtailed solar and unmet demand] Results on curtailed solar and unmet demand (abstract and corresponding results tables): Self-reported measures of curtailed generation and suppressed consumption lack any external validation (e.g., against meter data, utility records, or engineering estimates). This measurement concern is load-bearing for the quantitative characterization of inefficiencies and distinct consumption profiles by backup type.
minor comments (1)
  1. [Abstract] The abstract states 'near-universal electrification' but does not specify the survey year, exact geographic coverage within Lebanon, or how 'high-electrification context' was operationalized.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation of our survey-based findings on household adaptations to grid failure in Lebanon. We address each major comment below.

read point-by-point responses

  1. Referee: [Survey methodology section] Survey methodology section: No information is provided on the sampling frame, stratification (by region, urban/rural, or SES), response rate, or weighting to match Lebanese population benchmarks. This directly undermines the claim that SES centrally determines access to diesel/PV backups, as selection bias cannot be assessed.

    Authors: We agree that the manuscript would be strengthened by explicit documentation of the survey design. In the revised version we will add a dedicated data section subsection describing the sampling frame (household listings from selected districts), stratification by governorate and urban/rural status, achieved response rate, and any post-stratification weighting used to align with available Lebanese demographic benchmarks. These details were collected during fieldwork and can be reported without altering the core results. revision: yes

  2. Referee: [Results on curtailed solar and unmet demand] Results on curtailed solar and unmet demand (abstract and corresponding results tables): Self-reported measures of curtailed generation and suppressed consumption lack any external validation (e.g., against meter data, utility records, or engineering estimates). This measurement concern is load-bearing for the quantitative characterization of inefficiencies and distinct consumption profiles by backup type.

    Authors: We recognize that self-reported curtailment and suppression cannot be cross-validated against meter or utility data in the current Lebanese context, where formal metering has largely collapsed. In the revision we will add an explicit limitations paragraph discussing the reliance on self-reports, potential recall and social-desirability biases, and the consistency of reported patterns with engineering rules of thumb for PV-battery systems. We retain the quantitative estimates as the best available descriptive evidence while qualifying their precision; no external validation data exist for us to incorporate. revision: partial

Circularity Check

0 steps flagged

Empirical survey study with no derivation chain or self-referential reductions

full rationale

The paper is a descriptive empirical analysis drawing on original primary survey data from 1,000 households. It reports observed patterns in backup system adoption, load shifting, demand suppression, and curtailment without any equations, fitted parameters, predictive models, or self-citations that reduce claims to prior inputs by construction. Central findings (SES role in access, transition to PV, inefficiencies) are direct tabulations and comparisons from the collected responses. No load-bearing steps exist that match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Empirical survey study; no mathematical free parameters or invented entities. Relies on standard domain assumptions about survey validity.

axioms (1)
  • domain assumption Survey responses accurately reflect household adoption, consumption, and unmet demand without substantial bias
    Required to interpret met versus unmet demand and technology adoption rates from self-reported data

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