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Record W4391071461 · doi:10.1016/j.rset.2024.100080

Electrification policy impacts on land system in British Columbia, Canada

2024· article· en· W4391071461 on OpenAlexaffabout
N. Arianpoo, Md. Eliasinul Islam, A.S. Wright, Taco Niet

Bibliographic record

VenueRenewable and Sustainable Energy Transition · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsElectrificationNexus (standard)Renewable energySustainabilityGreenhouse gasNatural resource economicsLand useEnergy securityFossil fuelEnvironmental resource managementEnergy policyEnvironmental economicsBusinessEnvironmental scienceEconomicsEngineeringElectricityCivil engineeringEcology

Abstract

fetched live from OpenAlex

British Columbia (BC) is committed to transitioning to a low-carbon energy system to meet its CO2 emission reduction targets, but this shift towards renewable energy sources may have significant implications for land use. This paper investigates the land-use impacts of different electrification pathways and technology choices in BC's energy system using the BC Nexus model. Our analysis highlights the potential increase in land-use requirements associated with transitioning from fossil fuels to renewable energy sources, with the occupied land of the power system potentially increasing up to six times larger than the current total build-up land (depending on the scale of electrification and technology choice). These findings have important implications for policymakers in terms of balancing the trade-offs between energy security, economic development, and environmental sustainability. By understanding the physical footprint of the energy transition, decision-makers can develop more effective climate policies and sustainable development strategies.

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)0.002
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 score0.092
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.002
GPT teacher head0.168
Teacher spread0.165 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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