Presentation whilst at Ministry of Finance to stakeholders on Mining Working Group

1. STEVE MACEY
MoFED

2. Mining Revenue Forecasting
Taskforce:
 MoFED (EPRU and RTP)
 NRA (Research)
 MMMR
 State House (SPU)
 Bank of Sierra Leone (Research)

3. FARI Mining Revenue Model
Model Structure
3
Project sub-models
Commodity
prices
Aggregated
revenue
forecasts and
sensitivities
African
Minerals
London Mining
Koidu
Kimberlite
Vimetco SMHL
Sierra Rutile
Project Data Sheets
Provided annually by companies
• By mine
• By category of revenue
Specific fiscal terms applied to each project
Assumptions
Sierra Rutile
Vimetco SMHL
London Mining
African
Minerals
Koidu
Kimberlite
Other
Other
Outputs
& Analysis
Economic
assumptions

4. Inputs
 Project economics
 Mineral price forecasts
 Fiscal Regime/s

5. Sensitivities
 Production, Prices and Costs:
 20% fall in production causes 38% drop in revenue.
 20% fall in mineral prices causes 29% drop in revenue.
 20% increase in costs causes 16% drop in revenue.

6. Project Data
 Production profile by year
 Cost profile by year, broken down into cost breakdown relevant for
fiscal calculations
 Exploration
 Capital by same categories as depreciation
 Operating costs
 Closure and rehabilitation costs
 Sources
 Company development plans, feasibility studies, annual reports,
websites, media
 Government must insist on companies providing data
6

7. Mineral Prices
 Iron Ore Forecasts:
 World Economic Outlook
 Company Information
 Subscription to ‘Platts’

8. Fiscal Regime
 Updating the model with relevant fiscal terms
 Vary between projects
 Need copies of agreements

9. Next Steps
 Company Data
 Price Assumptions
 Regular Meetings