Key Energy and Economic Assumptions

1. MacroEconomic Drivers

Macroeconomic variables set the stage for energy demand and supply. The most frequently used assumptions include:

• GDP Growth Rate: Annual real growth of 2.0%3.5% for developed economies; 4.0%7.0% for emerging markets.
• Population Growth: 0.5%1.2% per year in the OECD region; 1.5%2.5% in AsiaAfrica.
• Inflation: Targeted at 2%1% for most central banks, influencing real energy prices.
• Exchange Rates: Assumed stability within 5% over a fiveyear horizon, unless modeling currencyrisk scenarios.

2. Energy Demand Assumptions

Demand forecasts are calibrated to sectoral activity, technology adoption, and policy targets.

2.1 Residential and Commercial

• Energyintensity decline of 1.0%1.5% per year driven by efficiency standards.
• Electrification of heating and cooling reaching 30% of final demand by 2035 in Europe.

2.2 Industrial

• Processenergy intensity reductions of 0.8%1.2% annually, reflecting bestavailabletechnology upgrades.
• Carbon capture and storage (CCS) deployment in 10% of highemitting processes by 2040.

2.3 Transport

• Vehicle fleet turnover: average replacement every 12years.
• Electric vehicle (EV) market share reaching 25% of new sales by 2030 and 50% by 2045.
• Average fuel economy improvement of 1.2% per year for internalcombustion vehicles.

3. SupplySide Assumptions

Supply projections incorporate resource availability, technology cost trajectories, and geopolitical risk.

3.1 Fossil Fuels

3.2 Renewables

• Solar PV module cost decline of 10%12% per year until 2030, then 5%7% thereafter.
• Onshore wind capital cost reduction of 7%9% per year through 2028.
• Battery storage cost trajectory: $120/kWh in 2024 falling to $55/kWh by 2035.
• Capacity factor assumptions: 20%25% for solar PV, 35%45% for onshore wind, 50%55% for offshore wind.

4. Policy and Regulatory Frameworks

Assumptions about policy shape both demandside behavior and supplyside investment.

• Carbon Pricing: A global carbon price converging to $60$80/tCOe by 2030, with a linear increase to $120/tCOe by 2045.
• Renewable Portfolio Standards (RPS): 30%40% renewable electricity share by 2030 for most OECD jurisdictions.
• Subsidy Phasing: Gradual removal of fossilfuel subsidies at 2%3% of GDP per year.
• Efficiency Mandates: Building envelope standards improving Uvalues by 15% every five years.

5. Technology Adoption Curves

Many models use logistic or Scurve functions to capture the diffusion of new technologies.

• Hydrogen production capacity expected to reach 10GW by 2030 and 100GW by 2045, assuming a 15% annual growth rate after 2027.
• Carbon Capture, Utilization & Storage (CCUS) installations projected to scale to 30MtCO/yr by 2035, following a 12% yearly capacity increase.
• Smartgrid technologies (advanced metering, demand response) achieving 40% penetration in residential customers by 2030.

6. Financial Assumptions

Capital costs, discount rates, and financing structures directly affect project economics.

• Weighted Average Cost of Capital (WACC): 6%8% for utilityscale renewables, 8%10% for commercialscale fossil projects.
• DebttoEquity Ratio: 70% debt for mature generation assets; 50% for emerging technologies.
• Operating & maintenance (O&M) cost inflation set at 2% per year for all assets.
• Tax depreciation schedules: 5year MACRS for solar PV (U.S.), 15year straightline for wind (EU).

7. Sensitivity and Scenario Analysis

Robust modeling always includes alternative pathways to capture uncertainty.

• HighGrowth Scenario: GDP growth +0.5% points, faster EV adoption (40% of new sales by 2030), and a carbon price of $120/tCOe by 2035.
• Stagnation Scenario: GDP growth 0.5% lower than baseline, slower renewables cost decline, and carbon price plateauing at $40/tCOe.
• TechnologyShock Scenario: Breakthrough battery cost of $30/kWh by 2028, leading to 70% of electricity demand served by storageaugmented renewables by 2035.

Scenario outcomes are typically reported as ranges for key metrics such as total emissions, electricity mix, and cumulative investment.

8. Conclusion

The assumptions listed above form the backbone of most integrated assessment models and energysystem forecasts. While they are grounded in historical data and expert judgement, they remain subject to change as technology evolves, policies shift, and macroeconomic conditions fluctuate. Regularly revisiting and calibrating these variables ensures that projections stay relevant and that decisionmakers have a reliable basis for planning the transition to a lowcarbon, resilient energy future.