Assessing the adoption of sustainable heating technologies in the United Kingdom – A case study of socioeconomically deprived neighbourhoods of Nottingham city
Bibliographic record
Abstract
The transition to sustainable heating technologies is crucial to reduce greenhouse gas emissions, mitigate the impacts of climate change and enable a sustainable and low-carbon society. However, a successful transition will require transformative, and large-scale household behavioural changes, and their acceptance and adoption of new technologies. Through mixed data collected at household level (n = 70) in three of the 10 poorest areas of the UK city of Nottingham (Aspley, Clifton, and St Ann's) we deepen the understanding of people's engagement with their current heating systems, their heating preferences, and views on adopting sustainable heating systems in the future. We find that despite the price increase in fossil fuel-based heating and people's reduction in heating use to reduce costs, getting them to move away from their current systems is very challenging, as most people are unwilling (41.13%) or sceptical (23.01%) about it as these systems are familiar, and generally perceived as more affordable, cost effective and efficient. Moreover, most people (71.43%) are unaware of the government's heating transition plans, but they believe that the adoption of sustainable heating systems should be optional to allow them to evaluate the pros and cons of the systems, and to choose the one that is better for them, that they can afford. Prompting a shift will need more than the common type of financial incentive. There must be first the provision of non-financial incentives to reduce some of the sociotechnical and perceptual barriers to adoption and motivate people to accept and engage in heat decarbonisation as part of a moral responsibility to the environment, and towards current and future generations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".