Embedding net zero practice and managing the decarbonising of built environments: domains of nudging, tugging and mooring change
Bibliographic record
Abstract
Decarbonising the economy has drawn more academic management research attention in recent years. Policy practitioners such as the UK Climate Change Commission (2022) frequently review and report on the net zero transition. Significantly, it is identified that scarce progress has been made on the transition towards the decarbonising of built environments. There is therefore a significant challenge for the built environment such as the retail and distribution trades to reduce both carbon in build and also carbon in use. The immense complexity and related gaps in knowledge on ‘how to do this’ for moderately heated/cooled built environments makes it vital to understand strategic pathway frameworks. This paper contributes to the scientific net zero conversation and the need to embed net zero practice as well as manage the strategic change associated with the decarbonisation challenge. Our findings underline the opportunity for strategically orientating towards the institutional market transition, including technology cycle, collective mobilization, affirmation and innovation. We develop an overarching practice framework of how efforts to embed and manage net zero practice form and reflect domains of pushing (nudges), pulling (tugs) and mooring (tyes) practice in the overall market transitions.
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 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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".