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Record W3118170049 · doi:10.1016/j.jue.2020.103313

Market tremors: Shale gas exploration, earthquakes, and their impact on house prices

2020· article· en· W3118170049 on OpenAlexaff
Stephen Gibbons, Stephan Heblich, Christopher Timmins

Bibliographic record

VenueJournal of Urban Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of Toronto
FundersSmithsonian Environmental Research CenterEconomic and Social Research CouncilUniversity of Bristol
KeywordsShale gasHouse priceOil shaleHydraulic fracturingEconomicsPetroleum engineeringGeologyNatural resource economicsMonetary economicsPaleontology

Abstract

fetched live from OpenAlex

Shale gas has grown to become a major new source of energy in countries around the globe. While its importance for energy supply is well recognized, there has also been public concern over potential risks from hydraulic fracturing (‘fracking’). Although commercial development has not yet taken place in the UK, licenses for drilling were issued in 2008, signalling potential future development. This paper examines whether public fears about fracking affect house prices in areas that have been licensed for shale gas exploration. Our estimates suggest differentiated effects. Licensing did not affect house prices but fracking the first well in 2011, which caused two minor earthquakes, did. We find a 3.9–4.7 percent house price decrease in the area where the earthquakes occurred. The earthquakes were too minor to have caused any damage but we find the effect on prices extends to a radius of about 25 km served by local newspapers. This evidence suggests that the earthquakes and newspaper coverage increased awareness of exploration activity and fear of the local consequences.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.038
GPT teacher head0.204
Teacher spread0.166 · 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 designObservational
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

Citations20
Published2020
Admission routes1
Has abstractyes

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