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Record W4407163139 · doi:10.1021/acs.est.4c06086

Inequity of Exposure to Unconventional Natural Gas Development in Northeastern British Columbia, Canada─An Environmental Justice Analysis

2025· article· en· W4407163139 on OpenAlexafffundabout
Miranda Doris, Coreen Daley, Amira Aker, Margaret J. McGregor, Marc-André Verner, Naomi Owens‐Beek, Élyse Caron-Beaudoin, Heather L. MacLean, Marianne Hatzopoulou

Bibliographic record

VenueEnvironmental Science & Technology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsThe Scarborough HospitalUniversity of British ColumbiaUniversité de MontréalCentre hospitalier universitaire de QuébecAssembly of First NationsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoGovernment of Ontario
KeywordsEnvironmental justiceNatural gasNatural (archaeology)Environmental protectionEnvironmental scienceEnvironmental planningGeographyPolitical scienceArchaeologyEngineeringWaste managementLaw

Abstract

fetched live from OpenAlex

In the past two decades, northeastern British Columbia, Canada, has experienced rapid growth in unconventional natural gas production and is home to Indigenous and rural communities. Living near oil and gas production can lead to deteriorated air quality that negatively impacts human health. This study explores whether three oil- and gas-related exposure metrics: modeled concentrations of 12 gases and particles; oil and gas facility-reported emissions; and active wells are disproportionately distributed in areas with higher concentrations of Indigenous people and community socioeconomic vulnerability. We calculated exposure metrics from 2018 to 2020 in geographic dissemination areas (DAs). We used a rural deprivation index that included income, education, employment, and access to amenities to identify areas of high socioeconomic vulnerability. We estimated that DAs with greater than 90% Indigenous population experience 1.2-1.8 times higher median air pollution concentrations than DAs with less than 10% Indigenous population. We estimated that DAs with high community vulnerability experience higher modeled air pollution and higher odds of exposure to facility emissions, with the most vulnerable areas experiencing 11-96 times higher median air pollution concentrations. Overall, these results suggest the presence of environmental injustice in an area that is expected to continue producing a large portion of Canadian natural gas.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.251
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 teacher head, not a consensus.

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

Citations3
Published2025
Admission routes3
Has abstractyes

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