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Record W6977348475 · doi:10.6084/m9.figshare.22240476

Multi-level climate governance: examining impacts and interactions between national and sub-national emissions mitigation policy mixes in Canada

2023· article· en· W6977348475 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsClimate policyContext (archaeology)Policy analysisSubsidyGreenhouse gasClimate changePublic policyPolicy studies

Abstract

fetched live from OpenAlex

Jurisdictions use an assortment of policies to reduce greenhouse gas emissions. Climate policy mixes have often evolved through the ad hoc layering of new policies onto an existing policy mix, rather than deliberate design of a complete policy portfolio. This can lead to unanticipated interactions between policies which can support or undermine policy objectives and is further complicated where climate policy is implemented at multiple jurisdictional levels. In the context of Canada and its four most populous provinces, we examine the development of climate policy mixes across jurisdictional levels between 2000 and 2020 and evaluate policy interactions. We develop an inventory of 184 climate policies, and examine each in terms of instrument type, implementation timing, technological specificity, and expected abatement. We evaluate interactions between overlapping policies both within jurisdictional levels (horizontal) and across jurisdictional levels (vertical) for their impact on emissions abatement using a policy coherence analysis framework. We find that subsidies and R&amp;D funding were the most abundant policies (58%), although pricing and flexible regulation are expected to achieve the most abatement. Sub-national jurisdictions have often acted as policy pilots preceding federal policy implementation. We evaluate 356 policy interactions and find 74% are consistent in adding abatement. Less than 8% have a negative impact by reducing abatement, although vertical interactions between federal and provincial policies were more often negative (11%) than horizontal interactions at the federal (&lt;3%) or provincial (&lt;2%) levels. Although the impact of many interactions is unknown (13%), we generate interaction matrices as a foundational roadmap for future research, and for policy-makers to consider potential interactions when designing and assessing policy effectiveness. <b>Key policy insights</b>Climate policy mixes have expanded and diversified over the period 2000–2020 across jurisdictions in Canada.Sub-national jurisdictions have often acted as policy ‘pilots’ by implementing policy before the adoption of similar national level policy.Climate mitigation policy interactions are predominantly supportive toward achieving additional emissions abatement.Vertical interactions between federal and provincial policy can undermine the additionality of policy effort by sub-national jurisdictions.These findings emphasize the need for better coordination in climate policy mix design between national and sub-national jurisdictions. Climate policy mixes have expanded and diversified over the period 2000–2020 across jurisdictions in Canada. Sub-national jurisdictions have often acted as policy ‘pilots’ by implementing policy before the adoption of similar national level policy. Climate mitigation policy interactions are predominantly supportive toward achieving additional emissions abatement. Vertical interactions between federal and provincial policy can undermine the additionality of policy effort by sub-national jurisdictions. These findings emphasize the need for better coordination in climate policy mix design between national and sub-national jurisdictions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.173
GPT teacher head0.391
Teacher spread0.217 · 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.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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