How Can Regulation and Subsidy Models Unlock the Next Wave of Renewable Investment?

Regulation and subsidy design now sit at the critical hinge point of the energy transition. In 2023, approximately 507 gigawatts of new renewable power capacity were added globally, almost 50% more than in 2022, with solar alone accounting for roughly three-quarters of these additions. At the same time, new renewable projects commissioned between 2010 and 2023 that undercut fossil fuel generation costs are estimated to have avoided around US$409 billion in fuel expenditure in 2023.  

Yet, this growth relies on increasingly sophisticated policy and subsidy architectures, from the United States' Inflation Reduction Act to Germany's evolving feed-in-tariff regime and India's reverse auction, as well as the viability gap funding model. For energy companies, investors, and regulators, the challenge is no longer whether to subsidise renewables, but how to model the interaction of subsidies, market design, and investor behaviour to drive gigawatt-scale deployment without distorting markets or fiscal positions. 

Why regulation and subsidies are evolving 

Globally, renewables supplied around 30% of electricity in 2023, but still accounted for only about 10% of heat and less than 4% of transport fuels, highlighting the significant progress still needed in the transition. At the same time, investment in renewables across power and end-use sectors reached nearly US$0.5 trillion in 2022, roughly one-third of the annual level that the International Renewable Energy Agency considers necessary to align with 1.5-degree scenarios. This combination of rapid growth and a persistent investment gap is driving a shift away from blunt, open-ended subsidies toward targeted, performance-linked, and fiscally constrained support schemes.  

Cost deflation in renewables makes the design problem more nuanced. Between 2010 and 2023, the global weighted average levelized cost of onshore wind energy decreased to approximately US$0.033 per kilowatt-hour, approximately 67% lower than the average price of new fossil fuel generation capacity in 2023, which stood at nearly US$0.10 per kilowatt-hour. Solar photovoltaics experienced similar declines, supported by a 93% drop in module prices between 2009 and 2023, as well as significant reductions in soft costs. As more projects become cost-competitive, subsidies must increasingly target system integration, storage, grid reinforcement, and risk allocation, rather than simple tariff top-ups.  

Core regulatory instruments to model 

Advanced subsidy modelling must differentiate among several primary policy instruments. 

• Feed-in tariffs: Germany's Renewable Energy Sources Act (EEG) illustrates the long-term impact of guaranteed tariffs combined with grid purchase obligations. The EEG framework, introduced in the early 1990s and refined repeatedly, underpinned decades of wind and solar expansion and is now being adjusted to increase tariffs for rooftop systems up to 750 kilowatts. Rates for smaller systems are being raised from roughly 0.069 to 0.086 euros per kilowatt-hour, and differentiated bonuses are being introduced for full feed-in. For modellers, feed-in tariffs translate into predictable cash flows but create sensitivity to tariff degression schedules and volume caps.  

• Auctions and contracts for difference: India's Solar Energy Corporation of India (SECI) utilises competitive reverse auctions, combined in some tenders with viability gap funding grants per megawatt, to derive efficient tariffs while capping subsidy exposure. In a SECI tender for 440 megawatts of solar energy in Uttar Pradesh, projects at Allahabad and Vijaypur were awarded viability gap funding of around 7.4 to 7.5 million Indian rupees per megawatt. At the same time, a substantial portion of the capacity remained unallocated due to the limited number of bids. This highlights the need to model investor appetite, ceiling tariffs, and grant levels together, rather than in isolation.  

• Tax credits and direct pay: The United States Inflation Reduction Act directs nearly US$400 billion in federal funding to clean energy, primarily through production and investment tax credits, many of which are available as direct pay or transferable instruments. Corporate entities are expected to capture around US$216 billion of these tax credits, which shifts subsidy modelling toward project-level tax capacity, credit monetisation discounts, and the lifespan of credit eligibility.  

Building a rigorous subsidy modelling framework 

For expert stakeholders, a robust modelling framework needs to cover fiscal, system and investor dimensions. 

• Fiscal and macroeconomic lens: Authorities need to quantify the present value of subsidy outlays under different uptake scenarios and stress-test against fuel price paths and demand growth. The IRENA estimate of US$409 billion in global fuel cost savings in 2023 from low-cost renewables provides a valuable anchor for comparison with projected subsidy envelopes. Sensitivity analysis should include exchange rate risks for imported equipment, as well as inflation impacts on capital expenditure and revenue erosion if wholesale prices fall, particularly in cases where variable renewables gain market share.  

• System and integration lens: Subsidies need to reflect not only energy output but system value. Battery storage additions, which grew from 0.1 gigawatt-hours of capacity in 2010 to nearly 96 gigawatt-hours in 2023, illustrate how storage can be incentivised to absorb surplus solar and wind energy and reduce curtailment. Regulators can structure auctions that bundle generation with storage obligations, as seen in SECI tenders that require megawatt-hour-scale storage aligned with solar capacity, and then model the impact on capacity value, reserve margins and network congestion.  

• Investor risk and bankability lens: Levelized cost metrics must be combined with policy-specific risk parameters, such as curtailment rules, grid connection timelines, and balancing responsibilities. The International Energy Agency's forecasts show that solar and wind will account for approximately 96% of new renewable capacity additions by 2028, making investor perception of regulatory stability a dominant variable. Performance-based regulation experiments, including those discussed in recent Deloitte work on funding growth in the United States' power sector, show that aligning utility returns with outcomes such as cost control and reliability can reduce long-term subsidy needs if designed correctly.  

Case study: Germany's evolving EEG framework 

Germany's Energiewende serves as a long-standing example of how tariff design influences the deployment of technology and market integration. Early feed-in tariffs in the 1990s and 2000s offered high, fixed prices and priority dispatch, which spurred rapid build-out but created high surcharge costs for consumers and increasingly frequent negative wholesale prices. From 2014 onward, reforms progressively shifted support toward competitive auctions and, more recently, differentiated tariffs for rooftop systems that reward full feed-in or partial self-consumption, with revised rates for system size bands up to 750 kilowatts.  

For corporate developers of rooftop portfolios in Germany, modelling now needs to capture two paths. Owners can accept a lower base tariff and use part of the electricity on-site, or opt for a higher combined remuneration by feeding 100% of their output to the grid, subject to separate metering. Optimisation involves comparing tariff structures to retail prices, on-site load profiles and tax treatments, with the EEG 2023 changes aimed at overcoming a previous bias toward undersized systems that matched only self-consumption.  

Case study: US corporations under the IRA 

The Inflation Reduction Act reshapes the economics of renewable subsidies for companies operating in the United States. McKinsey estimates that approximately US$370 to US$400 billion of federal funding will flow into clean energy over the next five to ten years, with the bulk of this funding delivered via technology-neutral production and investment tax credits, as well as support for transmission and clean manufacturing. Corporations can benefit from both developing generation and storage, as well as from being off-takers, by utilising tax credit-enhanced power purchase agreements to hedge energy and carbon exposure.  

Subsidy modelling in this context must explicitly value transferability and direct pay features. Entities with limited tax appetite can sell credits, introducing a monetisation discount that should be captured in project financial models alongside compliance requirements such as domestic content or wage standards. At the portfolio level, companies need to integrate IRA incentives with evolving state-level regulations and performance-based utility frameworks that reward cost-efficient grid connections and demand flexibility.  

Case study: SECI auctions and viability gap funding in India 

India's Solar Energy Corporation of India, headquartered in New Delhi, has established itself as a reference point for auction-based subsidy design in emerging markets. In its 440-megawatt solar tender for projects in Uttar Pradesh, SECI combined reverse auctions with viability gap funding to bridge the gap between the discovered tariffs and benchmark project economics. However, only three developers participated in the auction, and more than 300 megawatts of capacity did not receive enough bids to proceed, despite grants of around 7.4 to 7.5 million rupees per megawatt for awarded projects. 

For policymakers and investors, this outcome underscores the importance of modelling non-price factors. Land acquisition complexity, grid evacuation risks and perceived policy stability can all offset the nominal attractiveness of viability gap grants. Developers require integrated models that assess equity returns under delays, curtailments, and currency risk. Meanwhile, authorities should iterate auction parameters to ensure that discovered tariffs and grants are consistent with a robust pipeline, rather than a thin, winner's curse scenario.  

Implications for strategy and policy design 

Regulation and subsidy modelling for renewables is now a strategic capability, not a compliance chore. The combination of rapidly falling technology costs, increasingly granular support schemes, and evolving regulatory models, such as performance-based regulation, requires integrated modelling of policy risk, system value, and fiscal impact. 

Leading players are already embedding these models into their capital allocation and sourcing strategies, using policy scenarios to prioritise geographies, technologies, and contracting structures, such as long-term power purchase agreements and contracts for difference. Governments that couple clear, stable regulation with well-modelled subsidy schemes can capture a disproportionate share of the next wave of renewable investment, while those that rely on static or opaque incentives risk stranded assets, investor pullback and higher long-term system costs.