Illuminating practitioner challenges in energy transitions
Bibliographic record
Abstract
Sustainable development (SD) is a concept that can be used to address complex challenges, including energy transitions. SD offers diverse strategies that provide useful direction in navigating tensions, trade-offs and synergies in energy transitions. The purpose of this research was to identify the challenges that energy practitioners are faced with in energy transitions and explore potential solutions. To achieve this purpose, we identified and explored the challenges faced by energy practitioners in Canada. Specifically, we conducted a survey of 34 energy practitioners from across Canada, as well as in-depth interviews with the Energy Futures Lab design team (which is a civil society initiative actively working on the energy transition in Canada). We identified the following challenges faced by energy practitioners in Canada: there is no simple, single solution for energy transitions; energy transitions have potentially conflicting considerations; energy systems have potentially conflicting goals; energy practitioners have different levels of trust and competencies in key actors; energy practitioners need to work across the political spectrum; and the costs and benefits of energy transitions are unevenly distributed. We discuss how the three strategies of SD (i.e., economic choice, political choice, social choice) could be applied to manage the intended and unintended tensions and trade-offs inherent in energy transitions. We conclude that the three SD strategies are not always equally valued by energy practitioners, but they have the potential to be useful in different energy transitions scenarios.
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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 | 0.052 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.035 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".