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Record W2081129361 · doi:10.1108/ijshe-07-2012-0062

Barriers to energy efficiency and the uptake of green revolving funds in Canadian universities

2015· article· en· W2081129361 on OpenAlexaffabout
John Maiorano, Beth Savan

Bibliographic record

VenueInternational Journal of Sustainability in Higher Education · 2015
Typearticle
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEfficient energy useIncentiveOriginalityInvestment (military)BusinessValue (mathematics)Public relationsFinanceEconomicsPolitical scienceSociologyEngineeringQualitative researchMicroeconomics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to investigate the barriers to the implementation of energy efficiency projects in Canadian universities, including access to capital, bounded rationality, hidden costs, imperfect information, risk and split incentives. Methods to address these barriers are investigated, including evaluating the efficacy of revolving funds. Design/methodology/approach – Senior administrators of 15 Canadian universities were interviewed, making use of both structured and open-ended questions. As university executives and senior technical directors are responsible for investment in energy efficiency at Canadian universities, these individuals were the focus of our study. Findings – The results offer a curious contradiction. While “Access to Capital” was found to be the largest barrier to energy efficiency in Canadian universities, and while respondents agreed that green revolving funds are both an effective method to address these capital funding constraints, and may be an effective method to implement energy conservation projects at their university, only 2 out of the 15 universities interviewed and 7 out of the 98 universities in Canada currently make use of a green revolving fund. A general reluctance at Canadian universities to formalize processes to prioritize energy efficiency limits the associated benefits of mechanisms such as revolving funds to institutionalize energy efficiency and reduce long-term energy use. Practical implications – To provide insights into barriers to energy efficiency in universities and methods to address them, including the efficacy of revolving funds. Originality/value – This research is one of the first to investigate the efficacy of revolving funds to confront barriers to energy efficiency. The findings, implications and recommendations are valuable to organizations, university administrators, researchers and practitioners implementing energy efficiency measures.

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)0.015
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0140.006
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.272
Teacher spread0.261 · 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 designQualitative
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

Citations30
Published2015
Admission routes2
Has abstractyes

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