¨ A Dilemma of Abundance: Governance Challenges of Reconciling Shale Gas Development and Climate Change Mitigation
Bibliographic record
Abstract
Shale gas proponents argue this unconventional fossil fuel offers a “bridge” towards a cleaner energy system by offsetting higher-carbon fuels such as coal. The technical feasibility of reconciling shale gas development with climate action remains contested. However, we here argue that governance challenges are both more pressing and more profound. Reconciling shale gas and climate action requires institutions capable of responding effectively to uncertainty; intervening to mandate emissions reductions and internalize costs to industry; and managing the energy system strategically towards a lower carbon future. Such policy measures prove challenging, particularly in jurisdictions that stand to benefit economically from unconventional fuels. We illustrate this dilemma through a case study of shale gas development in British Columbia, Canada, a global leader on climate policy that is nonetheless struggling to manage gas development for mitigation. The BC case is indicative of the constraints jurisdictions face both to reconcile gas development and climate action, and to manage the industry adequately to achieve social licence and minimize resistance. More broadly, the case attests to the magnitude of change required to transform our energy systems to mitigate climate change.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".