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Record W7005047572

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)

2019· article· en· W7005047572 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeClimate change mitigationParliamentGlobal warmingGovernment (linguistics)Greenhouse gasBest practice
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.308
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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