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Enregistrement W2888570744 · doi:10.1596/30259

Energy Subsidy Reform Assessment Framework

2018· book· en· W2888570744 sur OpenAlexaff
Andrew Burns, Calvin Djiofack Zebaze, Dinar Prihardini

Notice bibliographique

RevueWorld Bank, Washington, DC eBooks · 2018
Typebook
Langueen
DomaineEnergy
ThématiqueEnergy, Environment, and Transportation Policies
Établissements canadiensMcGill UniversityUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésSubsidyEnergy subsidiesEnergy (signal processing)Public economicsEconomicsBusinessEnvironmental economicsPhysicsEnergy engineeringMarket economy

Résumé

récupéré en direct d'OpenAlex

This note provides guidance on the different economy-wide modeling tools that can be utilized to quantify the economic effects of energy subsidy reform. ESRAF defines an energy subsidy as a deliberate policy action by the government that specifically targets electricity, fuels, or district heating and that has one or more of the following effects: i) It reduces the net cost of energy purchased; ii) It reduces the cost of energy production or delivery; iii) It increases the revenues retained by those engaged in energy production and delivery (energy suppliers). Subsidies are provided through four primary mechanisms: 1) Budgetary transfers of government funds; 2) Government-induced transfers between producers and consumers; 3) Forgone taxes and other government revenues; 4) Under pricing of goods and services. Examples include government control of energy prices that are kept artificially low (referred to as consumer price subsidies hereafter); budgetary transfers to energy suppliers or tax expenditures granted to energy suppliers to keep costs down to benefit consumers, producers, or both; underpricing of goods and services, such as fuels, land, and water used by energy producers; subsidized loans; and shifting of risk burdens, such as the assumption of risks created by energy supply or use through limits on commercial liability. Among the economy wide modeling tools, the main focus of this note is computable general equilibrium (CGE) models. Partial equilibrium models are discussed only briefly The latter models, by carefully mapping the details of energy production technologies including substitution between fuel types and process and efficiency improvements (Bohringer and Rutherford 2008), can generate important insights to shape the design of a reform. However, they tend to have limited or no interaction between the market of interest and the rest of the economy. As a result, they are unable to measure the indirect and dynamic effects that a reform can generate, particularly with respect to energy-consuming sectors, the prices of goods and services that use energy as an intermediate input, and the impact of all of these changes on investment, industrial structure, and household welfare. The rest of the note is organized as follows. It begins with a brief overview of the different types of modeling tools in section two. Existing studies on estimating the effects of energy price subsidy reforms are outlined in annex A. The literature review shows that the bulk of studies use a CGE model for examining the effects of energy subsidy reform. Macrostructural models do this much better and can be used to quickly quantify the likely macroeconomic impacts of a reform, and have the advantage of requiring relatively few data and being easier to work with than CGE models. A guide to using macrostructural models to estimate the short-term effects of energy subsidy reform is presented in section three. The various macrostructural models that are available are included in annex B. Section four presents a guide to using CGE models to estimate the long-term effects of reform. A more detailed discussion of CGE models is included in annex C The feasibility of using any given model will depend heavily on the availability of data, requirements for which are discussed in section five. After briefly touching on empirical studies on energy reform in section six, section seven concludes with some highlights and guidance on the issues to consider when choosing a model to carry out energy price subsidy reform.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,814
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,248
Écart entre enseignants0,235 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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