Risks Governance of Innovative Power Generation Technologies in Saskatchewan: Pathways to a Sustainable Energy Future
Bibliographic record
Abstract
Literature on socio-technical transitions acknowledge that negative risk perception is important to socio-technical transitions. However, beyond acknowledging risk as a potential barrier to the deployment of innovative technologies, this study points out that the acceptance or rejection of innovative technologies due to their associated risks can be a predictor of the socio-technical transition pathway that will be followed. This dissertation uses socio-technical transitions in the electric power generation sector of Saskatchewan as a case study. It finds that existing literature in the field of energy systems transitions fail to fully map the relationship between risk analysis and socio-technical transition pathways. This, the dissertation argues could be a function of the multidimensional and multidisciplinary nature of the concept of risk. This dissertation applies the risk governance framework to understand the multidimensional nature of risk and then map the findings on the multilevel perspective through which it showed how the outcome of citizen’s risk analysis may result in several transition pathways. The analysis in this dissertation is based on data collected from six citizen’s juries held in Saskatchewan in 2017. Through the discussions from these citizen’s jury sessions, this study identified how citizens apply heuristic devices as they analyse their perceived risks in two baseload power generation sources, carbon capture and storage (CCS) and small modular nuclear reactors (SMRs). This dissertation finds that in Saskatchewan, familiarity, experiential knowledge of a technology based on a history of usage and consumption, rather than cost or technical risk, are the strongest factors influencing people’s perception and attitude toward the risk in innovative technologies in the energy sector. These factors are most potent when CCS and SMRs are compared directly. But when compared as part of a portfolio of options including renewable resources such as solar and wind, citizens seem to balance the risks they associate with SMRs with the gains from having renewable resources as part of the grid. Hence, there is a chance that Saskatchewan can advance its energy system transition through either a reproduction, substitution, or a transformation pathway. Clearly, this study not only maps the way risk analysis influence transition pathways, it also provides insight into the difference in the tools experts deploy in analysing risks compared to those that unspecialized citizens use. Through the use of expert witnesses in the citizen’s jury sessions, this dissertation challenges the knowledge deficit model of citizen engagement as it revealed that citizens are more likely to develop confirmation biases when they are exposed to new information that deviates from their previous understanding of a phenomenon based on their lived experience, especially when they do not have full trust in the information sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".