Fiscal Policy Reforms and Their Effects on the Economic Viability of Mineral Projects in Ghana
Bibliographic record
Abstract
Mineral sector regulatory and fiscal policies in Ghana have undergone a lot of reforms over the past three decades in an effort to attract the much-needed Foreign Direct Investment (FDI) into the mineral sector and also to maximise the returns from the exploitation of mineral asset to the country. This paper puts in perspective the effect of changes in fiscal policies on the viability of mineral projects and assesses the general risk associated with investing in the mineral industry of Ghana, using the Sikaman Gold Mining (SGM) Project as a test case. Cash flow, sensitivity and risk analyses of the SGM Project under three fiscal regimes namely: PNDCL 153, Act 703, and amendments to Act 703, indicated the second regime as the most economically favourable as it gave the highest NPV and lowest risk. It is recommended that the government should involve the mineral industry players during such reviews to show all-inclusiveness. Furthermore, mineral investors are advised to explore stability and development agreements to protect their investments in the wake of changes in fiscal policies in the mineral industry of Ghana. Future research could consider comparing the current fiscal regime of Ghana with those of the competing countries within the Sub-Saharan African region to assess whether Ghana could continue to pride itself as a preferred investment destination within the sub-region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".