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Record W2888570744 · doi:10.1596/30259

Energy Subsidy Reform Assessment Framework

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

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2018
Typebook
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsMcGill UniversityUniversity of Manitoba
Fundersnot available
KeywordsSubsidyEnergy subsidiesEnergy (signal processing)Public economicsEconomicsBusinessEnvironmental economicsPhysicsEnergy engineeringMarket economy

Abstract

fetched live from 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.

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), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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