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

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.

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.

Methods

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).

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

1.b De-risking: Repor0ng standards

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)

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

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

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 2.415 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

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]

4.a US Historical Infla0on rate (1987-2016) 100% Price defla/on (2016 = 100%) 90% 80% Cumula/ve deflator indices 70% 60% 50% 40% 30% 20% 10% 0% 1985 1990 1995 2000 2005 2010 2015 2020 Year Historical InflaTon Rate (used by GSR - based on Consumer Price Index)

4.b Deflated commodity 0me-series (1987-2016) 1,800 Polymetallic Nodules Moving Averages [Deflated in USD/tonne] 1,600 1,400 1,200 thirty years representng 1.5 commodity cycles 1,000 800 600 400 200 Maximum/Minimum of 10-year moving average, incl. trend 0 1985 1990 1995 2000 2005 2010 2015 2020 1y-avg 5y-M_avg 10y-M_avg Linear (1y-avg)

5. Assigning probabili0es and ranges Uniform DistribuTon Triangular DistribuTon Frequency Frequency -5 0 5 Difference -5 0 5 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.

6. Economic model in Excel

7. Monte Carlo Risk Analysis [VIDEO]

8.a Internal rate of return vs. hurdle rate

8.b Impact of discoun0ng [Example Interest rates]

8.c Cumula0ve vs. discounted cashflow Project year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 25 20 Cum. Discounted Cashflow (billion USD) 15 10 5 0 Discounted CumulaTve -5-10

8.d Cumula0ve discounted cashflow 1 Project year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 0.5 Cum. Discounted Cashflow (billion USD) 0-0.5-1 -1.5

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%)

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

Results

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%

Financial Payment Regime

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

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

Transi0onal ad-valorem royalty (San Diego 2016)

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]

Transi0onal ad-valorem royalty Project year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 1 0.5 Cum. Discounted Cashflow (billion USD) 0-0.5-1 -1.5 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.

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

Ques0ons? Thank you