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Record W4386464887 · doi:10.15173/esr.v26i1.4591

From NDC to national long-term low greenhouse gas emission development strategies compatible with a 2 °C target

2023· article· en· W4386464887 on OpenAlexvenueno aff
Sandrine Mathy

Bibliographic record

VenueEnergy Studies Review · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasTerm (time)Environmental scienceNatural resource economicsPhysicsEconomicsGeologyOceanography

Abstract

fetched live from OpenAlex

Given the lack of collective ambition resulting from the Nationally Determined Contributions (NDCs) to the objective ofthe Paris Agreement, countries must submit revised and more ambitious NDCs. Countries are invited to formulate longtermlow greenhouse gas emission development strategies that should be designed within the context of otherdevelopment goals and co-benefits. This article addresses the issues related to the evaluation of national trajectoriesdeveloped in a cooperative framework aiming at collectively reaching 2°C and based on the integration of developmentpriorities and co-benefits into national trajectories. The national decarbonization trajectories discussed in this articlewere developed as part of the Deep Decarbonization Pathway Project (DDPP) by the 16 major GHG emitting countries.These 16 bottom-up decarbonization strategies are implemented in the POLES model, a partial equilibrium model of theglobal energy sector, which is an appropriate tool to provide a harmonized contextual framework for assessing thesetrajectories. The results make it possible to evaluate the gap between, on the one hand, national DDPP trajectories andNDCs and, on the other hand, national DDPP trajectories and a scenario resulting from a minimization of abatement costs.They allow to feed a discussion on the development of NDCs and the move away from national trajectories of trajectoriesminimizing the overall reduction cost and produced with integrated assessment models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.154
GPT teacher head0.321
Teacher spread0.167 · 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 designNot applicable
Domainnot available
GenreReview

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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