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Record W3043378256

An examination of oil and gas taxation and revenue management in Ghana

2019· dissertation· en· W3043378256 on OpenAlexaboutno aff
Abdallah Ali-Nakyea

Bibliographic record

VenueUpSpace Institutional Repository (University of Pretoria) · 2019
Typedissertation
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueRevenue managementPetroleum engineeringTax revenueBusinessNatural resource economicsEconomicsAccountingPublic economicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Ghana’s discovery of oil and gas in commercial quantities, in 2007, triggered a sense of optimism about the prospects for accelerated development in the country. However, pessimism about oil and gas revenues set in when the country ran into a liquidity crisis and, in 2014, it had to implore the International Monetary Fund (IMF) for assistance. This development has spurred research interest into the taxation and management of revenue accruing from the oil and gas sector. However, to date most research on oil and gas taxation and revenues has been on economic development, with a dearth of focused studies in oil and gas taxation and revenue management from a legal perspective. This study fills this knowledge gap through an examination of oil and gas taxation and revenue management in Ghana. \nThis thesis is qualitative by design, although quantitative data was used to highlight the revenue performance indicators from the oil and gas sector. The tax and revenue management policies and regulations of a number of oil-rich countries were briefly reviewed and compared to Ghana’s oil and gas taxation and revenue management legislation. For comparative purposes, relevant literature, fiscal legislation and data from four oil and gas producing countries - Nigeria, Norway, Canada and the United Kingdom – were also examined in some detail. \nA key finding is the noncompliance of the Government of Ghana with the provisions in the Petroleum Revenue Management Act (PRMA), which potentially makes the occurrence of the “oil curse” in Ghana more likely. A major concern in the findings of this research shows differences in fiscal regime in Ghana relative to the regimes in the countries used for comparative purposes (i.e. the United Kingdom, Norway, Canada and Nigeria). Ghana’s oil and gas tax legislation is currently contained in different laws. This constitutes an unnecessary complication. \nThe research recommends that the Government of Ghana put in place adequate measures, underpinned by appropriate legislation, to enable the retention and investment of its share of oil revenues, and also deal with oil revenue volatility. The Government of Ghana should design a long-term fiscal strategy, based upon high quality, long-term economic and revenue projections, which includes a sensitivity analysis. I also recommend that watchdog entities in the oil and gas environment, such as the Public Interest and Accountability Committee (PIAC) and the Africa Centre for Energy Policy (ACEP), spearhead calls for amendments to the PRMA. These amendments should be in the areas of accounting for the oil and gas revenues, setting up safeguards for the use of the revenues accrued from the oil and gas sector, as well as adhering strictly to the priority areas determined for the allocation of the petroleum revenues. This will allow for robust provisions and safety nets to be enshrined in oil and gas taxation and revenue management laws, to safeguard the revenue inflows for development. This will also constitute a check on the Government of Ghana against increasing its spending of petroleum revenues meant to contribute to the Ghana Stabilization Fund.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

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