MétaCan
Menu
Back to cohort
Record W4402135007 · doi:10.1093/ooec/odae023

Can mass media campaigns increase demand for renewable energy? Experimental evidence from Senegal

2024· article· en· W4402135007 on OpenAlexaff
Aidan Coville, Víctor Orozco-Olvera, Arndt Reichert

Bibliographic record

VenueOxford Open Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsImpact
Fundersnot available
KeywordsRenewable energyEarly adopterMass mediaMargin (machine learning)BusinessInvestment (military)Environmental economicsPrint mediaMarketingAdvertisingEconomicsPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract This paper examines public investment in mass media campaigns aimed at stimulating market-led growth within the renewable energy sector in a developing country setting. We run a randomized field experiment to estimate the impact of a large-scale information campaign on the adoption of renewable energy technology, which includes clips broadcasted on national radio and print materials distributed in rural villages in Senegal. While the radio campaign primarily influences the number of renewable energy products owned by existing users (intensive margin), the combination of radio and print significantly increases the number of new adopters (extensive margin). However, when scaled nationally, we calculate a meaningful advantage of the radio clips over the print materials in terms of the cost per additional sales of solar lamps. The study further introduces an innovative methodological approach for examining nationally broadcasted information campaigns, offering valuable insights for future research and policy evaluations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.332
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOxford Open EconomicsSame topicMedia Influence and PoliticsFrench-language works237,207