Sustainable Energy Use Across the UK’s Northern Powergrid
Bibliographic record
Abstract
The United Kingdom (UK) aims to achieve net zero greenhouse gas (GHG) emissions by 2050.Hence, the government is promoting use of heat pumps (HPs) in residences, use of domestic solar photovoltaic (PV) panels, and adoption of electric vehicles (EVs).However, critics cite barriers to adopting these strategies, and current adoption patterns and domestic energy consumption (DC) are unclear.This project uses open data on primary substations (PS) in the Northern Powergrid, which serves the UK's North East.The UK provides a good case study, given its readily available data and the key fact it is undergoing a net zero transition.This paper aims to answer the research questions: 1) In the 2022 data, what were patterns of DC in the Northern Powergrid, and 2) How are DC patterns associated with HP, solar PV, and EV use?Using data from 541 PSs, DC patterns were mapped, and correlation and regression analysis was used to characterise the relationship between number of HPs, solar PV use, number of EVs and DC for the areas served by the PSs.Areas of high DC had higher population and were more affluent.Increasing number of HPs and EVs were statistically significantly associated with higher DC in both correlation (HPs r = 0.8579, EVs r = 0.3246, p < 0.0001) and regression analysis, and higher solar PV use was statistically significantly positively correlated with higher DC (r = 0.6937, p < 0.0001).Residences in more densely-populated and affluent areas are newer and therefore more likely to be suitable for HPs, and EV infrastructure is more likely to be established in these areas.These results support criticisms that the UK's net zero strategy goals may be unattainable, and that increased government intervention is required in order to prevent exacerbating inequities while pursuing this strategy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".