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Record W2615995276 · doi:10.1126/sciadv.1602153

Does basic energy access generate socioeconomic benefits? A field experiment with off-grid solar power in India

2017· article· en· W2615995276 on OpenAlexaff
Michaël Aklin, Patrick Bayer, Santosh Harish, Johannes Urpelainen

Bibliographic record

VenueScience Advances · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsGridSocioeconomic statusField (mathematics)Solar energyPower gridEnergy (signal processing)Solar powerPower (physics)Computer scienceEnvironmental scienceGeographyEnvironmental healthElectrical engineeringPhysicsMedicineEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

This article assesses the socioeconomic effects of solar microgrids. The lack of access to electricity is a major obstacle to the socioeconomic development of more than a billion people. Off-grid solar technologies hold potential as an affordable and clean solution to satisfy basic electricity needs. We conducted a randomized field experiment in India to estimate the causal effect of off-grid solar power on electricity access and broader socioeconomic development of 1281 rural households. Within a year, electrification rates in the treatment group increased by 29 to 36 percentage points. Daily hours of access to electricity increased only by 0.99 to 1.42 hours, and the confidence intervals are wide. Kerosene expenditure on the black market decreased by 47 to 49 rupees per month. Despite these strong electrification and expenditure effects, we found no systematic evidence for changes in savings, spending, business creation, time spent working or studying, or other broader indicators of socioeconomic development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.253
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations159
Published2017
Admission routes1
Has abstractyes

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