# World Fund Financial Model European climate tech VC fund targeting startups with Carbon Performance Potential (CPP) of at least 100 MtCO2e/year - Canonical: https://finamodel.com/startups/world-fund - Excel download: https://finamodel.com/startup-models/world-fund.xlsx - Category: Climate/Energy - Model type: VC Fund Waterfall - Funding round: Fund - Funding: $365M - Founded: 2022 - Geography: European technologies with global applicability [DECK slide 5]; LP base >150 individuals & families across Europe [DECK slide 12] - Customer: B2B ## About the company World Fund is a European climate-tech venture fund investing from seed to Series B in companies with at least 100 MtCO2e of annual Carbon Performance Potential. It targets 10–20% ownership, reserves more than 65% of capital for follow-ons, and combines investing with policy and ecosystem access. The LP pitch points to a large decarbonisation opportunity, a team history of more than 70 investments at 8.9x MOIC, and six Fund I deals at 1.4x MOIC. More than 1,650 inbound opportunities and a network of over 150 individuals and families support sourcing and fundraising. The financial model should be a ten-year fund waterfall. Forecast commitments, calls, management fees, portfolio construction, initial and follow-on deployment, ownership, losses, exit proceeds, and distribution timing; then calculate carry and LP DPI, RVPI, TVPI, and net IRR. A corporate operating model would misrepresent the economics. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition - Fund invests in climate tech startups from Seed to Series B - Proprietary Climate Performance Potential (CPP) framework: minimum threshold of 100 MtCO2e annual reduction potential per portfolio company - CPP methodology developed with CRANE, Project Drawdown, and TU Berlin - Target ownership: 10–20% equity with active board roles - Follow-on reserves: >65% of fund reserved for follow-on - Competitive edge: thought leadership (highest-visibility climate tech VC per deck), >1,650 inbound deals since launch, policy influence via Cleantech for Europe, CRANE, European Parliament, German Ministry of Economy - LP platform: >150 HNWIs, >120 interested in active involvement, LPs built >10 unicorns ## Market - €5tr market opportunity from decarbonization transformation - €5.9tr in corporate revenue committed to Net-Zero that needs to be served - Stacked-column chart (slide 4) shows mismatch: Mobility represents 16% of global emissions (2016) but received 61% of climate tech VC (2013–H1 2021); Energy 29% emissions vs. ~9% VC; Food & Agriculture 20% emissions vs. 12% VC; Built Environment 14% emissions vs. ~15% VC; Industry 21% emissions vs. ~4% VC - "The next 1,000 unicorns will be in climate tech" - Larry Fink, Blackrock - Europe: first continent to become net-zero by law ## Revenue model This is a fund, not an operating company. Economics follow standard VC fund structure: - LP capital deployment: Multi-stage Seed to Series B; follow-on reserves >65% - Target ownership per deal: 10–20% ## Traction & metrics - >70 investments by the team historically, 8.9x MOIC - Fund I portfolio: 6 deals executed since 2021, performing at 1.4x MOIC - Portfolio companies: Space Forge, Q_OA, Juicy Marbles, FreshFlow, Treecard, RECUP (exited) - RECUP marked "Exited" - >1,650 inbound deals since launch - LinkedIn monthly reach >1,000,000; Twitter monthly reach >5,000,000; articles published >2,000 - LP base: >150 individuals & influential families; >120 interested in monthly active involvement - LPs built >10 unicorns across focus sectors - LP mix by capital invested: Tech entrepreneurs 41.3%, Climate tech pioneers 23.2%, Mittelstand & DAX 19.5%, Seasoned VCs/PEs 16.0% - Ranked #1 European ESG fund (Preqin and other platforms) ## Unit economics Key observable data points for fund-level modelling: - Current portfolio MOIC: 1.4x on 6 deals - Team historical MOIC: 8.9x on >70 deals - Target ownership: 10–20% - Follow-on reserve ratio: >65% ## Competition / moat - Thesis: highest-visibility climate tech VC in Europe - CPP framework as proprietary deal selection tool (quantified, science-backed) - Co-VC network across all five sectors (Energy, Food/Agtech, Buildings, Industry, Transport) - Team: Best Male + Best Female Investor 2020 (Bundesverband Deutsche Startups) - Policy influence: seats at Cleantech for Europe, European Parliament, German federal ministry, CRANE, GIIN - Comparable funds referenced as past co-investors: BP Ventures, Octopus Ventures, SET Ventures, Sofinnova, S2G Ventures, Astanor, Demeter, 2150, Picus Capital, BMW i Ventures, MANIV ## Team & funding ask / use of funds **Team (slide 9):** - Tim Schumacher - Co-founder/CEO, IPOed Sedo, co-founder/chairman Eyeo (>400m users), >40 personal investments; Best Male Investor 2020 - Daria Saharova - Managing Partner Vito ONE, ex-Seven Ventures & HV Ventures, Jefferies GER; >20 early-stage investments; Best Female Investor 2020 - Danijel Visevic - Director Comms at Project A (>€500m AUM), comms office for Angela Merkel, 60k LinkedIn reach, >5m Twitter reach - Craig Douglas - Principal at SET Ventures, ESG/climate reporting for 50+ funds, physicist & chemist **Funding ask:** Not explicitly stated in deck. No fund size target, close date, or minimum ticket disclosed. **Use of funds:** Multi-stage Seed to Series B climate tech investments across Energy, Food & Agriculture, Buildings, Industry & Manufacturing, Transportation --- ## Recommended financial model - **Archetype + why:** VC Fund Economics model. This is an LP pitch for a climate tech VC fund - the entity raising money is the GP/fund vehicle, not an operating startup. The correct model tracks capital deployment, management fees, portfolio construction, exit proceeds, carry, and LP return metrics (DPI, TVPI, IRR). An operating 3-statement or SaaS model would be wrong here. - **Forecast horizon & granularity:** - 10-year fund life (standard for European early-stage VC), annual granularity - Investment period: years 1–4 (typical Seed–Series B deployment window) - Harvest period: years 5–10 - **Key drivers & assumptions:** - Management fee rate: - Carried interest rate: - Preferred return (hurdle): - Number of portfolio companies: - Average initial check size: - Follow-on reserve ratio: >65% - Target ownership: 10–20% - Portfolio MOIC distribution: Base 3.0x gross / Bull 5.0x / Bear 1.5x is top-quartile; current fund at 1.4x early-stage] - Loss ratio (write-offs): - Average hold period to exit: - Recycling of management fees / early exits: - LP close / capital calls: - **Scenarios (Base / Bull / Bear - which variables flex):** - Bear: Gross fund MOIC 1.5x, higher loss ratio (50%), longer hold periods - tests whether carry is earned and LPs get capital back - Base: Gross fund MOIC 3.0x, 35% loss ratio, 6-year average hold - Bull: Gross fund MOIC 5.0x, 25% loss ratio, 5-year hold, 1–2 breakout unicorn outcomes (consistent with team's 8.9x track record) - **Required sheets / outputs:** 1. **Assumptions** - fund size, fee rates, carry, hurdle, deployment schedule, MOIC distribution 2. **Portfolio Construction** - number of companies, check sizes, ownership %, follow-on schedule 3. **Capital Deployment** - annual capital calls, cumulative deployed vs. committed 4. **P&L / Management Company** - management fees by year, fund expenses, GP economics 5. **Exit Waterfall** - gross proceeds by company, return of capital, preferred return, carry split (GP/LP) 6. **LP Returns** - DPI, RVPI, TVPI, net IRR by scenario 7. **Sensitivity Table** - net IRR vs. gross MOIC and fund size (or management fee rate) ## Frequently asked questions ### Is the World Fund financial model free? Yes. The World Fund model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.