Financial Model Presenta0on. Techno-Economic Assessment & Financial Payment Regime

Transcription

1 Financial Model Presenta0on Techno-Economic Assessment & Financial Payment Regime Deep Seabed Mining Payment Regime Workshop #3: Exploring a Financial Model and Related Topics Wednesday, April 19 Friday, April 21 Grand Copthorne Waterfront, Singapore Kris Van Nijen, GSR NV.

2 Disclaimer Not only model, not industry model, just a model to show interdependencies between variables (Priority #2, ISA, July 2015) Some assumptons are substantated, others are for sake of comparison or simplicity. Model is based on #32 variables introduced in London (2016) during the workshop #2; Indicated values within a tolerance of +/- 25%. Values are subject to future environmental, financial and exploitaton regulatons. The proposed financial payment regime is from a total-cost perspectve. Similar input is requested from other contractors, which would support the Tmely development of a financial payment regime.

3 Methods

4 Techno-economic assessment (TEA) 1. Decide on project boundaries; 2. Decide on realistc development schedule; 3. Determine capital expenditures & operatonal expenditures from a totalcost perspectve; 4. Forecast commodity prices; 5. IdenTfy uncertain model inputs and assign a probability distributons; 6. Develop economic model in Excel; 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model +10,000 simulatons; 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR).

5 1.a Project boundaries of this model Commercially oriented venture (not strategic project!) ü Project needs to show investment potental (IRR) compared to other investment opportunites (HR) ü Step-by-step de-risking strategy to anract investors according to reportng standards ü Focus on being the low-cost producer to increase compettveness ü Timing is everything: E.g. Regulatory delays are detrimental Three million tonnes of (dry) polymetallic nodules (E.g. 4 Mtpa wet) Four-metal operaton (Ni, Cu, Co, Mn) Ver/cally integrated project consortum from seabed to market, as no market will exist for intermediates in the nearby future

6 1.b De-risking: Repor0ng standards

7 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

8 2. Schedule Most contractors would be in this phase Contract with conditons based on draf EIA and plan for feasibility testng.

9 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

10 3. Case study input (1/3) Project /ming and phases Variability Sources Pre-feasibility 4 years - GSR Feasibility 5 years - GSR ConstrucTon 3 years - GSR ProducTon phase-in period 2 years Uniform (1-3y) GSR OperaTonal mine life 25 years (net) Uniform (20-30y) GSR Project producton data Variability Sources Annual tonnes of nodules collected Metal ore content a 3 x 10 6 tpa (dry) or +/- 4 x 10 6 tpa (wet) - GSR N i=1.30% ; Cu = 1.10% ; Co = 0.21% ; Mn = 27.00% - GSR Recovery / Yield Ni =95% ; Cu = 95% ; Co = 85% ; Mn = 90% - [35] Annual tonnes of metal produced Ni = 37,050tpa; Cu = 32,400tpa; Co = 6.375pa; Mn = 769,500tpa - GSR

11 3. Case study input (2/3) Capital expenditures Variability Sources Pre-feasibility 35 x 10 6 USD - GSR Feasibility 325 x 10 6 USD - GSR CollecTon system(s) capex 584 x 10 6 USD Triangular (+/-25%) GSR Surface vessel(s) capex 692 x 10 6 USD Triangular (+/-25%) GSR Processing plant capex x 10 9 USD Triangular (+/-25%) GSR RecapitalizaTon estmates Included - GSR Annual opera/onal expenditures Variability Sources CollecTon system(s) opex Incl. in Surface vessel(s) opex - GSR Surface vessel(s) opex 325 x 10 6 USD Triangular (+/-25%) GSR Processing plant opex 670 x 10 6 USD Triangular (+/-25%) GSR Financial project data Variability Sources Hurdle rate 18% - GSR DepreciaTon schedule 25 years, straight-line Uniform (20-30y) GSR InflaTon commodity prices 0.0% b - GSR InflaTon operatonal costs 0.0% b - GSR

12 3. Case study input (3/3) Regulatory costs Variability Sources ExploraTon license applicaton fee 0.5 x 10 6 USD - ISA ExploraTon license annual fee 47.0 x 10 3 USD - ISA ExploitaTon license applicaton fee 1.0 x 10 6 USD - [24] ExploitaTon annual fee 0.1 x 10 6 USD - [24] Ad-Valorem royalty (light vs full) 2-4% Uniform (0-6%/2-8%) [38] Environmental bond Total cost Incl. in Ad-Valorem - [24] Environmental liability trust fund Total cost Incl. in Ad-Valorem - [24] Seabed sustainability fund Total cost Incl. in Ad-Valorem - [24] Corporate tax rate of the HeurisTc sponsoring State(s) c 25% [Weighted Avg.] Uniform (20-30%) estmate a weight percentages b The cost escalaton is offset by the price escalaton c The corporate tax will be payable in the different countries where the consortum will be operatonal. [E.g. (Pre-)Processing plants in different locatons]

13 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

14 4.a US Historical Infla0on rate ( ) 100% Price defla/on (2016 = 100%) 90% 80% Cumula/ve deflator indices 70% 60% 50% 40% 30% 20% 10% 0% Year Historical InflaTon Rate (used by GSR - based on Consumer Price Index)

15 4.b Deflated commodity 0me-series ( ) 1,800 Polymetallic Nodules Moving Averages [Deflated in USD/tonne] 1,600 1,400 1,200 thirty years representng 1.5 commodity cycles 1, Maximum/Minimum of 10-year moving average, incl. trend y-avg 5y-M_avg 10y-M_avg Linear (1y-avg)

16 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

17 5. Assigning probabili0es and ranges Uniform DistribuTon Triangular DistribuTon Frequency Frequency Difference Difference Example: The probability of a commodity price being high, is the same as it being low. (symmetrical) Example: The probability of the capex being X is highest, while it may be X-5, or X+5, although this probability is much lower.

18 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

19 6. Economic model in Excel

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22 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

23 7. Monte Carlo Risk Analysis [VIDEO]

24 Techno-economic assessment (TEA) 1. Project boundaries 2. RealisTc schedule 3. Capital expenditures & operatonal expenditures from a total-cost perspectve 4. Forecast commodity prices 5. IdenTfy uncertain model inputs and assign a probability distributons 6. Develop economic model in Excel 7. Apply Monte Carle Risk Analysis sofware (Oracle Crystal Ball ) and model 10,000 simulatons 8. Results: Compare Internal Rate of Return (IRR) vs Hurdle Rate (HR)

25 8.a Internal rate of return vs. hurdle rate

26 8.b Impact of discoun0ng [Example Interest rates]

27 8.c Cumula0ve vs. discounted cashflow Project year Cum. Discounted Cashflow (billion USD) Discounted CumulaTve -5-10

28 8.d Cumula0ve discounted cashflow 1 Project year Cum. Discounted Cashflow (billion USD)

29 8.e Opportunity cost & Power of discoun0ng There is always an opportunity cost Investors have alternatve investment opportunites with different risk & return. A higher risk = higher discount rate => major effect on long-term projects. Also income obtained via royaltes (in case of the ISA) needs to be discounted. However, using a social discount rate (SDR). We have used an average SDR according to Drupp et al. (=2.27%)

30 8.f Social discount rate Drupp, Freeman, Groom and Nesje (2013) DiscounTng Disentangled: An expert survey of the longterm social discount rate Grantham Research InsTtute Working paper

31 Results

32 Probability of IRR achieving the HR ForecasTng scenario: 10y Moving average incl. trend Number of trials: 1,000 Displayed: 994 Red: Below 18% IRR Blue: Above 18% IRR Range: 13-23%

33 Financial Payment Regime

34 Financial payment regime: Op0ons (San Diego 2016) Unit-based royalty Ad-valorem-based royalty Profit-based royalty Fixed rate: USD/tonne MulTplied with unit, or in this case producton (+) Very simple, straigh orward, transparent (+) Only measure producton (-) Outcome not associated with fluctuatons of metal prices (-) least economically efficient Fixed percentage: % MulTplied with producton (+) Simple, straigh orward, transparent (+) Determine basket of metals, measure producton, measure grade of mined resource, set-up source for metal prices (+) Outcome associated with fluctuatons of metal prices (-) Increased administraton compared to producton-based royalty IniTal system Fixed percentage: % MulTplied with profit (+) Mostly used in mature industries (+) Outcome associated with fluctuatons of metal prices (-) Requires heavy administraton, cost codes for reportng, auditng of the entre value chain, moving outside the jurisdicton of the ISA (-) not transparent due to possible transfer pricing Afer industry matures

35 Financial payment regime: Formula In which Q = Quantity mined [tonne] PN = Polymetallic Nodules P LMEi = Average price for Ni, Cu and Co on London Metal Exchange [USD tonne -1 ] P CRU = Average price of Mn through Metal Bulletin or CRU [USD tonne -1 ] Ni = Nickel Cu = Copper Co = Cobalt Mn = Manganese

36 Transi0onal ad-valorem royalty (San Diego 2016)

37 Transi0onal ad-valorem royalty (San Diego 2016) A transitonal ad-valorem royalty would incentvize first movers proving the industry, as future capital would be less expensive. [Possibly for a certain period of Tme]

38 Transi0onal ad-valorem royalty Project year Cum. Discounted Cashflow (billion USD) Light Full Given no data is available on the exact discounted break-even point for each contractor, a fixed period should be mentoned in the exploitaton contract.

39 IRR in func0on of ad-valorem rate (Total-cost)

40 Ques0ons? Thank you