From Paris to Projects Clarifying the Implications of Canada’s Climate Change Mitigation Commitments for the Planning and Assessment of Projects and Strategic Undertakings (Summary Report)
Bibliographic record
Abstract
By signing the Paris Agreement, Canada made a commitment to do our fair share to limit global average temperature rise to “well below 2°C” relative to pre-industrial levels, and to pursue “efforts to limit the increase to 1.5°C.” The federal Impact Assessment Act that is now before Parliament requires consideration of whether assessed undertakings would “hinder or contribute to” meeting Canada’s climate change commitments.So far, however, Canada has done little to define what the Paris Agreement entails for planning, assessment and decision making on projects and other undertakings with significant implications for meeting the Paris commitments. That leaves a serious gap in law, policy and practice between Canada’s commitments and the assessment of major undertakings.Assessments are an important venue for proactive climate change mitigation. They guide decision making on major extractive and infrastructure projects and other undertakings that will entrench existing practices or drive key transitions for many decades. If these assessments are to contribute to meeting our climate change mitigation commitments, we need to understand what meeting those commitments entails – how far we have to go and what we have to do to close the gap between our current efforts and our promised accomplishments.To inform serious efforts to fill that gap, this paper examines• what the Paris Agreement’s temperature goals imply for global and Canadian GHG reduction targets in light of “fair share” principles and feasible pathways;• what is needed to raise Canadian climate change mitigation ambitions to the Paris Agreement level, and ensure sufficiently strengthened and clarified targets, frameworks and applied tools to inform evaluations of particular undertakings; and• how to translate these needs and tools into well-specified and authoritative requirements for effective application under federal assessment law.Our intent has not been to deliver final answers but to establish a reasonably firm working base for comparing what we are doing with what is needed to meet our Paris commitments.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".