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Record W4413208389 · doi:10.1016/j.egyr.2025.08.002

A review of environmental assessments of ground-source heat pumps

2025· article· en· W4413208389 on OpenAlexafffund
Seama Koohi‐Fayegh

Bibliographic record

VenueEnergy Reports · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundUniversity of Alberta
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Ground source heat pumps (GSHPs) are considered to be a promising technology to improve the energy efficiency of heating and cooling in buildings. However, sustainability of heat pump technologies and in particular GSHPs can sometimes be questioned. Detailed investigation of the environmental impacts of GSHPs over all stages of their life is needed to evaluate their sustainability. In this article, studies focused partially or in full on the environmental impact assessment of GSHPs, and their integrations with other systems, are reviewed. The focus of the review is on the main findings of such studies to provide a clearer picture of their status, but a summary of data input and methodologies that are used in such studies is also provided to ensure meaningful comparisons can be made among results of various studies, and to guide new studies on environmental assessment of GSHPs. It is found through this review that the main life stage contributing to environmental impacts of GSHPs is operation stage and the main contributor during this stage is electricity generation mix. Improvements in electricity generation source seem to be the most important factor to be considered if a wider adoption of GSHPs is sought. • Environmental impact assessments of ground-source heat pumps (GSHPs) are reviewed, including Life cycle assessments (LCAs). • A summary of data input and methodologies that are commonly used is provided to guide new LCA studies in this area. • Results of LCAs made to compare GSHPs with other heating and cooling technologies are reported and compared where feasible. • Validity of the results and how one might draw conclusions based on the results of LCAs on GSHPs is discussed. • The common results of LCAs and what factors could potentially improve their environmental performance are reported.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.993

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 head0.008
GPT teacher head0.253
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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 routes2
Has abstractyes

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