Regional strategic environmental assessment roles and stakes in Arctic oil and gas development
Bibliographic record
Abstract
Canada’s Beaufort Sea-Mackenzie Delta Basin possesses significant energy resource potential. Decisions about energy development, however, are largely project-based and do not always provide an opportunity for more efficient and more effective regional and strategic approaches to development impact assessment and management. As such, there are constant messages about the need for a more regional and strategic form of environmental assessment, practiced above the project level and focused on broader planning-based assessment as a means to sustainability assurance. The problem, however, is that there is no formal system of regional or strategic assessment (R-SEA) in northern Canada, and considerations as to what R-SEA is and what it should deliver are far from consolidated. The role of R-SEA must be better understood and a means found to make it a meaningful component and accepted and worthwhile part of planning, regulation and development decision-making. The purpose of this thesis is to identify stakeholder understandings and expectations about R-SEA, and its potential roles and opportunities in Arctic energy planning and assessment. Data were collected using semi-structured interviews with knowledgeable stakeholders in Arctic oil and gas initiatives, including energy regulators, industry, and energy interest groups, as well as Inuvialuit governments and community boards. Four key themes are identified and discussed: the efficacy of the current approach to environmental assessment for offshore energy development; knowledge of R-SEA; RSEA benefits and risks; and opportunities and challenges to advancing R-SEA in the Inuvialuit Settlement Region (ISR). Only consultation and engagement was seen by most participants as working well within the current EA system in the study area. Many challenges were raised, however, which would indicate a need for a new or revised approach to EA in the study area. There was agreement on the need for a more regional and strategic approach to EA in the ISR, but there was no consensus amongst participants as to the nature and scope of R-SEA and what it is intended to deliver. Though there continues to be much confusion regarding the terminology used, it appears that participants are identifying the same deliverables and advantages, suggesting that they are looking for similar benefits. Challenges to moving such a process forward include leadership, coordination of interests, financial resources, legislated versus voluntary approaches, and human capacity in the ISR region. Future research is needed to address the perceived risks and challenges raised by participants for R SEA to be a worthwhile and effective process.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".