Barriers and Enablers Toward Stakeholder Engagement for Net-Zero Neighbourhood Projects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".