Socio-political acceptance of smart grid as a tool to mitigate climate change: the case of British Columbia
Bibliographic record
Abstract
Jurisdictions around the world are investing in “smart grid”, which comprises a myriad of technologies that can help optimize the electricity grid. While one motivation for smart grid is to reduce greenhouse gas (GHG) emissions, deployment can also be driven by motivations to reduce electricity costs and strengthen grid reliability. These motivations or “frames” can conflict with one another, e.g. environmental versus economic benefits. This study uses British Columbia (BC), Canada as a case study to explore socio-political acceptance (or public acceptance broadly speaking) of smart grid as a tool to mitigate climate change. BC installed smart meters throughout the province from 2010-2014 while encountering a significant amount of citizen opposition, and is set to release a smart grid plan in 2015. I collected and analyzed data from the BC context via interviews with key stakeholders, media analysis of newspaper articles (from 2006-2012), and a survey of Canadian citizens implemented in 2013 (n = 2930). I find that key stakeholders and media in BC focus more on economic frames than environmental frames (e.g. climate abatement), and news media mention risks more often than benefits. The survey indicates that citizen acceptance of smart meters (one particular smart grid technology) is lower in BC than in Alberta and Ontario, but acceptance increases in all provincial samples when smart meters were framed according to environmental and economic benefits. In summary, the discussion of smart grid deployment in BC is tending to neglect environmental benefits—but an environmental framing might help to stimulate citizen support.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".