Understanding Smart Energy Transitions as a New Source of Distrust:The Perspectives of Hong Kong Citizens on the Risks of Regional Intercity Energy Collaboration in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA)
Bibliographic record
Abstract
Hong Kong has an ambitious carbon neutral goal to meet by 2050. Achieving this goal requires a departure from a traditional city-scale centralised, fossil fuel-based energy infrastructure to a more decentralised, locally-generated renewable energy (RE) while expanding the regional intercity smart grid system to accommodate RE import in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Such energy transitions inevitably bring new social challenges, but how Hong Kong citizens perceive such transitions in the GBA context is not well studied. This study draws on quantitative and qualitative data derived from an online deliberative poll (DP) (N = 174) on smart energy transitions. We have four key findings. Firstly, citizens showed a low level of trust in the national, provincial, and city governments whilst a high level of trust towards the incumbent electricity companies. Secondly, citizens showed distrust to the governments, suspecting that the genuine motives of the governments were to prioritise regional RE import over local RE production. Thirdly, citizens raised concerns over five types of risks (price volatility risks, energy reliability risks, cost overrun risks, data privacy risks, and environmental risks) that contributed to new sources of public distrust in governments’ competence. Fourthly, the public distrust toward multilevel governments was found to be underpinned by demographic factors (age group and family size) and a sociopolitical context of recent social movements against government policies. Our findings suggest that policymakers in the GBA need to give sufficient attention to enhancing public trust, and thereby the policy legitimacy of regional smart energy transitions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".