A strategic niche management framework to scale deep energy retrofits
Bibliographic record
Abstract
Mass deployment of deep energy retrofits (DERs) is essential to drive the building stock transition to net zero. However, the rate of DER adoption remains extremely low, as existing financial, technical, regulatory and social systems favour piecemeal retrofit approaches. To address this issue, this paper applies strategic niche management (SNM)—a core theory in transition studies that focuses on creating ‘niches’ to help novel technologies gain momentum—to understand the role ‘transition intermediaries’ play in scaling DER. Specifically, three mass DER initiatives are explored that use an industrialised overcladding approach: Energiesprong in the Netherlands; RetrofitNY in New York, US; and REALIZE-MA, in Massachusetts, US. The study examines how each intermediary has addressed regionally specific barriers and opportunities to industrialised DERs through a systematic, iterative SNM framework of niche formation processes. These include choice of technology, selection, design and scaling up of the experiments, and the breakdown of protection mechanisms. SNM could be used as a practical tool to analyse and inform interventions to accelerate DER adoption in diverse jurisdictions. Furthermore, the importance of intermediaries is vital for catalysing transformations to secure a healthy, resilient, high-performing and environmentally responsible building stock. Practice relevance Mass deployment of DERs is critical to achieve a net zero building stock; however, necessary rates of adoption are hindered by existing systems that favour piecemeal retrofit approaches. SNM—a transitions theory that focuses on creating ‘niches’ to scale up novel technologies—is applied to understand the role of ‘intermediaries’ in facilitating mass DER. The work of Energiesprong, RetrofitNY and REALIZE-MA is examined through a systematic, practice-oriented SNM framework that considers how each agency has addressed regionally specific barriers and opportunities to scale DER primarily through an industrialised overcladding approach. Although the deep retrofit market has yet to mature out of protected niches at scale, SNM can analyse and inform interventions to accelerate DER adoption in diverse jurisdictions, and reveals the importance of intermediaries in managing niches to encourage critical learning processes.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".