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Record W4410452250 · doi:10.1016/j.renene.2025.123473

Optimal adoption and cost-effectiveness of rooftop solar and wind turbines for community energy systems under climate change

2025· article· en· W4410452250 on OpenAlexafffundabout
You Wu, Lexuan Zhong

Bibliographic record

VenueRenewable Energy · 2025
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaChina Scholarship CouncilUniversity of Alberta
KeywordsClimate changeWind powerEnvironmental scienceRenewable energyMeteorologyEnvironmental economicsNatural resource economicsEngineeringGeographyEconomicsOceanographyElectrical engineeringGeology

Abstract

fetched live from OpenAlex

Community energy systems represent a promising pathway for urban decarbonization, yet developing locally adapted energy supply strategies remains challenging due to evolving climate conditions, local environmental factors, and technological advancements. Here, this study presents a bottom-up modeling framework that integrates these factors to assess the optimal adoption of community solar and wind resources for community energy systems across eight major Canadian cities under future climate periods ranging from 1°C to 3.5°C. The results indicate that the solar-only and hybrid strategies reduce costs by over 40% from the 1°C to the 3.5°C climate period, while wind-only strategy increase costs by at least 30% in six cities. Furthermore, technological advancements could contribute to an additional 5% to 20% cost reduction across all energy supply strategies. Hybrid supply strategies consistently offer the best balance of economic viability and resilience in current and future periods, while the optimal single-resource strategy remains highly dependent on local solar or wind resource availability.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations5
Published2025
Admission routes3
Has abstractyes

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