A Policy Scan of Cumulative Effects Assessment in Support of Renewable Clean Growth Projects in Canada
Bibliographic record
Abstract
In Canada, clean growth has been viewed as an essential strategy for achieving net zero carbon emissions by the year 2050. However, clean growth initiatives can create cumulative effects. Moreover, such initiatives are evolving rapidly and it is unclear if conventional environmental assessments are sufficient. The assessment of cumulative effects of clean growth remains a relatively novel activity, yet is seemingly important given that some of the emerging actions and technologies could themselves yield a variety of unanticipated environment impacts. To support evidence-based policy development for clean growth, we conducted a policy scan at subnational and national levels in Canada supported by targeted scans in other jurisdictions to assess the scope at which cumulative effects are assessed for renewable clean growth projects, and to identify best practices, approaches, and/or methods for assessing the cumulative effects of clean growth. Our policy scan revealed that approaches for assessing cumulative effects of renewable clean growth activities are inadequately developed across Canada. Though we confirmed few existing cumulative effects frameworks in practice, we found a diverse set of cases where cumulative effects have been effectively identified and managed (in Canada and afar) for projects predominately in the natural resource sector. Four policy insights were generated for assessing cumulative effects of renewable clean growth in Canada; (1) adopt a regional approach that considers local context, (2) support the development of valued ecosystem components, (3) conduct rigorous and comprehensive baseline monitoring, and (4) prioritize collaborative governance including with Indigenous governments and communities. Failure to consider cumulative effects during the early phases of renewable clean growth could impede the ability to meet targets and yield the environmental and socio-economic benefits that are promised by the clean growth movement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".