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Record W4368375959 · doi:10.1007/s10784-023-09599-6

Tempering and enabling ambition: how equity is considered in domestic processes preparing NDCs

2023· article· en· W4368375959 on OpenAlexaffabout
Christian Holz, Guy Cunliffe, Kennedy Mbeva, Pieter Pauw, Harald Winkler

Bibliographic record

VenueInternational Environmental Agreements Politics Law and Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsCarleton University
FundersEnergimyndigheten
KeywordsEquity (law)Public economicsBusinessPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract The considerations of how Nationally Determined Contributions (NDCs) to global climate action under the Paris Agreement are ambitious and fair, or equitable, is expected to guide countries’ decisions with regards to the ambition and priorities of those contributions. This article investigates the equity aspect of the NDCs of four cases (Canada, the EU, Kenya, and South Africa) utilizing a combination of document analysis and expert interviews. It interrogates both the NDC documents themselves and, uniquely, the role of international and domestic equity considerations within the domestic policy processes that led to the formulation of the NDCs. For this, 30 participants and close observers of these processes were interviewed. We find countervailing effects of equity on ambition, with an enabling, or ambition-enhancing, effect resulting from international equity, in that these four Parties show willingness to do more if others do, too. In contrast, tempering effect appears to result from domestic equity concerns, for example with regards to real, perceived, or anticipated adverse distributional impacts of climate action across regions, sectors, and/or societal strata. Political cultures differ across the four case studies, as do the key actors that influence domestic policies and the preparations of NDCs. This paper also demonstrates that research on equity in NDCs can benefit from expanding its scope from the contents of NDC submissions to also examine the underlying decision-making processes, to generate insights that can contribute to future NDCs being both equitable and ambitious.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.073
GPT teacher head0.272
Teacher spread0.199 · 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 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

Citations14
Published2023
Admission routes2
Has abstractyes

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