Best practices for Strategic Environmental Assessment and application to the Ontario Long-Term Energy Plan
Bibliographic record
Abstract
Research shows that project-level Environmental Assessment (EA) in Ontario is failing to achieve the goals that it was designed to meet, including protection and management of the environment. The practice of Strategic Environmental Assessment (SEA) is emerging internationally and an increasing number of countries and organizations are carrying out SEA either formally or informally. Although there is a considerable amount of debate in terms of standardized SEA methodology, SEA is seen as a proactive tool for incorporating sustainability objectives within Policies, Plans and Programmes (PPPs) and addressing cumulative and long-term effects of of multiple projects and policy decisions. The energy sector is globally a large impact generator in terms of resource exploration, production, consumption and waste disposal. Energy development and policy in Ontario have great implications for sustainable development. Project-level EA is the process followed for developing energy infrastructure. However, decisions regarding energy supply are strategic in nature and cannot be adequately addressed through project-level EA. Therefore, SEA is an important tool used to deal with such decisions in the early stages of the assessment process and can help decision makers make informed choices regarding the long-term sustainability of strategic energy initiatives. This study focuses on identifying best practices criteria for carrying out SEA and investigating the extent to which the Ontario Long-Term Energy Plan conforms to SEA best practices.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".