Historical and future projected costs of capital for ten energy technologies across 176 countries – Scientific Data

Accelerating clean power deployment is essential for meeting global climate goals, but capturing the financial reality of projects remains a persistent modeling challenge. The cost of capital—often expressed as the discount rate or weighted average cost of capital (WACC)—is a crucial input in energy system models that assess technology choices and pathways to decarbonisation. Yet available data are frequently outdated, confidential, or concentrated in a few regions, forcing modelers to rely on generic assumptions that can bias results, especially for capital-intensive clean technologies.

This work provides country-level estimates of the cost of debt, cost of equity, and overall cost of capital for 10 electricity generation technologies across 176 countries from 2015 to 2030, yielding 27,640 data points. Estimates are technologically and geographically granular, with short-term projections informed by electricity market and macroeconomic trajectories. The result is a consistent, transparent foundation for energy system modeling, investment appraisal, and policy design.

Why the cost of capital matters

For renewables and other low-carbon technologies, most costs are incurred upfront, with low operating expenses thereafter. This makes project viability and levelised cost of electricity (LCOE) highly sensitive to financing terms. At a cost of capital of around 8%, financing can represent roughly half of a renewable project’s lifetime costs—several times the share typical of fossil-fuel generation. In many developing economies, the cost of capital can be two to three times higher than in high-income markets, sometimes reported up to 18%, further amplifying LCOE and slowing deployment.

These differences influence the competitiveness of technologies, the pace of the transition, and the equity of outcomes across regions. Accurately representing financing conditions strengthens the relevance of energy system insights and supports better-targeted de-risking policies, concessional finance, and mobilization of private capital.

Scope and technologies

The dataset covers 176 countries, including many developing and least developed economies where empirical information is typically scarce. Ten technologies central to power sector decarbonisation are included:

• Solar photovoltaics (PV)
• Onshore wind
• Offshore wind
• Hydropower
• Biomass
• Natural gas combined-cycle turbines (CCGT)
• CCGT with carbon capture and storage (CCS)
• Geothermal
• Tidal
• Wave

Nuclear and certain emerging technologies (such as green hydrogen) were not included due to distinct risk profiles and data limitations; these merit separate, dedicated analysis.

Methodological overview

The model integrates financial market indicators with electricity sector characteristics to estimate, by country and year, the cost of debt, cost of equity, and WACC for each technology. It draws on established approaches used in the literature, including:

• Elicitation of project finance terms
• Expert and stakeholder surveys
• Back-calculation from competitive procurement outcomes
• Analysis of financial market data and risk premia

In addition to historical estimates from 2015, the model provides short-term projections through 2030 based on plausible macroeconomic and electricity market trajectories, including risk-free rate evolution and sectoral targets. Beyond 2030, uncertainties around market dynamics, policy, and technology learning suggest that explicit scenario analysis is preferable to point estimates.

Key insights

• Geographic coverage and granularity: Estimates span 176 countries with annual resolution, improving representation for developing economies where default assumptions are often used. This reduces bias in modeled pathways and investment comparisons.
• Technology-specific differences: WACC varies not only by country and time, but also by technology, reflecting differences in maturity, perceived risk, and financing familiarity. Applying a single discount rate across technologies can distort cost competitiveness and LCOE estimates.
• Temporal dynamics: Financing terms evolve with macroeconomic conditions, interest rate cycles, technology learning, and policy signals. The dataset captures these shifts, enabling sensitivity analysis and near-term projections.
• Comparisons to generic ranges: Standardized, global discount-rate bands (often used for long-term scenarios) are informative but can mask significant country- and technology-specific variation. More context-specific inputs can sharpen investment signals and policy conclusions.

Implications for policy and investment

High financing costs in developing economies are a major barrier to equitable, rapid decarbonisation. Accurate, open estimates support:

• Targeted de-risking measures (e.g., guarantees, insurance, currency risk tools)
• Efficient concessional finance allocation that mobilizes private capital
• Evidence-based calibration of auction designs, tariffs, and incentives
• Improved benchmarking for national plans and international support

For project developers and investors, transparent WACC estimates inform project screening, sensitivity testing, and risk management. For modelers, they enhance the credibility of scenario outputs and reduce reliance on blanket assumptions.

Data coverage and use

The dataset improves representation across income groups and regions, including many markets with limited prior deployment. This allows energy system models to:

• Use country- and technology-specific discount rates rather than uniform values
• Capture cross-country cost differentials that shape technology choice and deployment pace
• Reflect near-term macro-financial shifts in scenario analysis

Because capital costs can dominate total costs for clean technologies, more realistic financing inputs can meaningfully alter modeled mitigation pathways, reduce estimation error in LCOE, and influence policy prioritization.

Limitations and future work

While the projections to 2030 are grounded in observable trends and stated targets, uncertainties in policy, market structure, and global capital conditions remain. After 2030, scenario-based approaches that link financing conditions to explicit policy and market narratives are advisable. Extending coverage to additional technologies with distinct risk profiles, and incorporating more project-level observations as they become available, would further improve accuracy.

Conclusion

Providing consistent, transparent estimates of the cost of capital for ten energy technologies across 176 countries from 2015 to 2030 addresses a critical data gap in the energy transition. By reflecting country, technology, and temporal differences, these estimates support more accurate modeling, better investment decisions, and more effective policy design—especially in developing economies where financing costs critically shape the pace and affordability of decarbonisation.