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Barriers and Enablers Toward Stakeholder Engagement for Net-Zero Neighbourhood Projects

2024· article· en· W4400445333 on OpenAlexaff
Emily Nichols, Qian Li, Alberto Gallotta, Peter Wells

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsWSP (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsNeighbourhood (mathematics)Stakeholder engagementStakeholderZero (linguistics)Net (polyhedron)BusinessZero emissionKnowledge managementProcess managementComputer sciencePolitical sciencePublic relationsEngineeringMathematics

Abstract

fetched live from OpenAlex

Net-zero neighbourhoods offer a promising avenue for decarbonisation in the construction sector, yet challenges in stakeholder engagement hinder their broader adoption. This paper investigates the current barriers, enabling factors, the significance of barriers between stakeholder groups, in order to provide suggestions for accelerating the transition could be made. We employ a mixed-method design for this study, incorporating both a systematic literature review using the Systematic Review Reporting Standards (ROSES) methodology and primary data analysis derived from semi-structured interviews. Guided by the conceptual framework of social-technical systems theory, this paper offers a distinct perspective on existing barriers and provides insights into effective strategies for overcoming them. We suggest that net-zero neighbourhoods are niche innovations within the broader socio-technical system, currently in the first phase of socio-technical transition. We uncover that while stakeholders have unique challenges to address, ultimately, interconnection of all system-actors is required to overcome them. The findings of this paper will inform the implementation of net-zero neighbourhoods in the South Wales region and beyond, providing a unique insight into the current stakeholder engagement challenges and how they can be overcome to accelerate socio-technical transition.

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: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.810

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 head0.049
GPT teacher head0.245
Teacher spread0.195 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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