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Record W2049534470 · doi:10.3390/su5062727

The Distributional Impact of Developed Countries’ Climate Change Policies on Senegal: A Macro-Micro CGE Application

2013· article· en· W2049534470 on OpenAlexaff
Dorothée Boccanfuso, Luc Savard, Antonio Estache

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputable general equilibriumNatural resource economicsEconomicsClimate changeSubsidyGreenhouse gasFossil fuelProductivityPovertyConsumption (sociology)AgricultureElectricityAgricultural productivityAgricultural economicsDeveloping countryEconomic growthGeographyMacroeconomicsEcology

Abstract

fetched live from OpenAlex

In this paper, we present a distributional impact analysis of climate change policies envisaged or implemented to reduce greenhouse gas emissions in Senegal. We consider policies implemented in developed countries and their impact on a developing country. Moreover, we simulate the diminishing productivity of agricultural land as a potential result of climate change (CC) for Senegal. This country is exposed to the direct consequences of CC and is vulnerable to changes in world prices of energy, given its lack of substitution capacity. Past researches have shown that countries with this profile will bear the greatest burden of CC and its mitigation policies. Our results reveal slight increases in poverty when the world price of fossil fuels increases and the negative impact is further amplified with decreases in land productivity. However, subsidizing electricity consumption to protect consumers from world price increases in fossil fuels is shown to provide a weak cushion to poverty increase.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.988

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 communication0.0000.000
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 head0.010
GPT teacher head0.279
Teacher spread0.270 · 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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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