Achieving Efficiency in Environmental Assessment Through Focused Selection of Valued Components
Bibliographic record
Abstract
Some of the largest oil and gas projects in Canada are currently being proposed in British Columbia. Establishing a fulsome and scientifically and socially defensible scope for environmental assessments in the oil and gas sector is a serious challenge for government and proponents. The approach taken by the federal National Energy Board to scope effects assessments on pipelines is quite different than the approach taken by the British Columbia Environmental Assessment Office on other types of oil and gas projects. The NEB has published guidelines for scoping and conducting environmental and socio-economic assessments within its Filing Manual (National Energy Board [NEB] 2014). This manual sets out the expectations for scoping, baseline information, and effects assessments to be submitted as part of approval applications. Proponents are expected to provide all information necessary to meet the guidelines. In British Columbia, the environmental assessment process is dictated by the British Columbia Environmental Assessment Act and includes a negotiated terms of reference for the assessment, called the Application Information Requirements (AIR). The approach to selection of valued components is guided by provincial guidelines (EAO, 2013). The first draft of the AIR is prepared by the proponent and is then amended to address matters raised by federal and provincial agencies, local governments, and representatives of potentially affected First Nations. Through two to three revisions, the scope of assessment is jointly established and then formally issued by the government. While there are valid reasons for the differing federal and provincial approaches to scoping environmental assessments, each of these processes create risks for proponents in terms of project timelines and costs for preparing the environmental assessment. More specifically, the use of generic and negotiated guidelines can result in a number of issues including: • A scope of assessment that is broader than necessary to understand the potential for significant adverse effects • Inclusion of issues that are “near and dear” to a specific regulator or community but has no direct relationship to the effects of the project itself • Selection of valued components that do not allow for defensible quantification of effects or use of directly relevant significance thresholds • Selection of valued components that are only of indirect concern as opposed to focusing the assessment on the true concern. • Double counting of environmental effects • Risks in assessing cumulative effects This paper discusses where and how these risks occur, and provides examples from recent and current environmental assessments for pipelines and facilities in British Columbia. Opportunities to manage the scope of assessment while providing a fulsome, efficient, effective and scientifically/socially defensible assessment are discussed.
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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 | 0.045 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".