The “four Ds” and support for Local Smart Grids: analysis from national surveys in the UK and Canada
Bibliographic record
Abstract
Abstract Local Smart Grids are emerging during the climate crisis, as governments and industry recognize the need to better integrate intermittent renewable energy, storage, transportation, heating, and smart technologies. Such projects can represent profound changes to the status quo of energy and citizen lifestyles. They are also being associated with the “four Ds,” whereby Local Smart Grids are decarbonizing, decentralizing, digitalizing, and potentially democratizing energy systems. Yet, due to their recent arrival, there is very little social scientific research that has aimed to better understand public views, expectations, and support for this change. We attempt to fill this important gap in the literature through the analysis of two nationally representative surveys in the UK (n = 3034) and Canada (n = 941). This analysis highlights within- and between-country trends, including how the variation in responses regarding the “four Ds,” demographic factors, and other variables may explain the differences we see in terms of support for energy system change in the UK and Canada. Our analysis also shows that there are common elements, including the importance of the decentralization, and especially the democratization of energy in shaping support. We hope that this study will help governments, industry, community groups, and local residents themselves in both countries come together to advance the kind of Local Smart Grids that address climate change and represent a supported, just energy 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".