Machine Learning Reveals Which Policy Levers Really Drive Rooftop Solar Economics in India

Rooftop solar power has long been promoted as India’s cleanest path to household energy independence, yet adoption rates across the country remain stubbornly below what the nation’s abundant sunshine would suggest is possible. A new study published in Environmental and Sustainability Indicators tackles this puzzle with an unusual toolkit: instead of relying on traditional econometrics, researchers Saankho Bardhan, Kshitij Ganesh Ingle, Siddhartha Nayak and Surya Pratap Mehrotra deployed machine learning to quantify exactly how three policy variables—electricity consumed, electricity sold back to the grid, and initial installation cost—shape the financial returns of residential rooftop photovoltaic systems in every Indian state and Union Territory.

The starting point for the analysis is India’s striking geographic advantage. Most of the country receives a photovoltaic power potential of around 4 kilowatt-hours per kilowatt-peak per day, according to World Bank data, translating into roughly 120 units of electricity per kilowatt of installed capacity each month for 300 to 325 sunny days a year. Once panels are mounted, that electricity is essentially free for more than two decades; the world’s first modern solar panel, now sixty years old, still produces power. Yet subsidies, compensation mechanisms and financing schemes introduced by both central and state governments have not delivered adoption anywhere near this technical potential, prompting the authors to ask which policy levers actually matter most.

The challenge is that the key variables interact in complex, nonlinear ways. The effect of a subsidy may depend on the prevailing electricity tariff, and multiple policy measures can combine to produce effects greater or smaller than the sum of their parts. Linear regression coefficients and analytic derivatives from discounted cash-flow equations struggle in this setting: they capture only local marginal effects, assume variables are independent when they are in fact strongly correlated, and break down entirely at the threshold where cumulative discounted cash flow first exceeds the installation cost—the very moment that defines the payback period. Conventional feature-importance measures from tree-based models and popular SHAP explanations also become unreliable when predictors overlap.

To sidestep these pitfalls, the team combined two complementary techniques. Conditional Permutation Importance, or CPI, permutes each feature’s unexplained residual variation rather than the feature itself, preserving the correlation structure among predictors while isolating each variable’s unique contribution to variation in payback period and net benefit. The procedure was repeated twenty times per train-test split across fifty independent splits, with confidence intervals computed from the t-distribution after Shapiro-Wilk tests confirmed the estimates were normally distributed. In parallel, Double Machine Learning, a causal estimation framework introduced by Chernozhukov and colleagues, was used to estimate elasticities: the percentage change in an economic outcome expected from a one percent change in a policy variable, after orthogonalizing that variable against correlated covariates through cross-fitting.

The economic engine beneath the machine learning layer is a deterministic discounted cash-flow model spanning a twenty-five-year system lifetime, consistent with the operational period assumed in India’s national rooftop solar programs and supported by field studies showing mono-crystalline modules still operating after twenty-eight years under Indian conditions. The model assumes a conservative 0.5 percent annual degradation rate—the ceiling mandated for subsidized installations under the PM Surya Ghar: Muft Bijli Yojana—two percent yearly grid tariff escalation, 2,000 rupees per kilowatt in annual maintenance, and an inverter replacement costing 8,000 rupees every eight years. Payback periods were computed with an eight percent discount rate, while net benefits after payback were tallied without discounting. Installation cost data came from vendor consultations across multiple states, and net metering compensation rates were compiled from every state and Union Territory.

The headline finding is that avoided grid-electricity expenditure—the money a household saves by consuming its own solar generation rather than buying from the grid—dominates both net benefit and payback period in India’s non-special states, particularly when installed capacity closely matches household demand. Electricity consumption consistently showed the highest feature importance across the model groups, which used gradient boosting, multi-layer perceptrons and support vector regression depending on predictive performance. Installation cost and subsidies mattered significantly too, especially for payback period, but the recurring savings from avoided purchases accumulate over the entire operating life, while the one-time capital subsidy exerts its influence only at the start of the project and fades in relative importance as cash flows compound.

The picture changes sharply in the so-called special states—the Himalayan territories, the Northeastern states and remote islands including Jammu and Kashmir, Ladakh, and the Andaman and Nicobar Islands—where terrain, fragmented logistics and weak grid connections push installation costs far above national norms. There, installation-cost effects become the more powerful driver of both outcomes, and revenue from exported electricity grows sharply in importance for oversized systems, in some cases exceeding the contribution of self-consumed electricity. The elasticity analysis reinforced this regional split: installation cost elasticities for net benefit were negative everywhere but substantially larger in magnitude in the special states, while positive elasticities for consumption and exported electricity confirmed that both avoided purchases and export revenue raise long-term returns.

These results carry pointed policy implications. Nagaland emerges as an unexpectedly strong performer, with payback periods nearly six years shorter than neighboring Mizoram for comparable systems—thanks largely to well-designed state subsidies that even outperform Gujarat, Kerala and Telangana. Conversely, Jammu and Kashmir and Ladakh illustrate how generous subsidies can be neutralized by very low grid tariffs: when electricity costs under two rupees per unit, the savings from going solar shrink so much that payback often stretches beyond the panel’s twenty-five-year lifetime. In states like West Bengal and Bihar, decent returns are achieved despite the absence of state capital subsidies, simply because higher consumption makes avoided purchases valuable.

The study also sketches a fiscal argument for rethinking subsidy design. Offering electricity at very low tariffs burdens government-owned distribution companies, and in less industrialized territories there are few industrial consumers whose higher tariffs could cross-subsidize the losses. Rajasthan has already piloted the alternative: rather than giving 100 free units monthly to 10.4 million beneficiaries, the state now promotes rooftop systems that generate 150 free units after a modest one-time investment, converting a recurring expenditure into a capital subsidy. For remote island territories with average power purchase costs as high as 23 to 32 rupees per unit, the authors suggest procuring surplus electricity from prosumers below APPC rates—still highly attractive to households, given how influential net metering compensation is in special states.

The authors are candid about limitations: the analysis reflects policy conditions at the time of writing, relies on deterministic assumptions about generation and degradation, and excludes batteries, electric vehicle charging and community solar. The elasticities derived from Double Machine Learning describe the causal structure of the assumed cash-flow model rather than empirically observed systems. Yet the framework’s flexibility is its selling point. Updated tariffs, subsidies or compensation rates can be plugged in to regenerate every result, and the same CPI-plus-DML pipeline can be applied to other emerging economies with heterogeneous policy landscapes. With more than one million installations already completed under the PM Surya Ghar scheme, quantifying which levers genuinely move the economics may determine whether India’s rooftops become the decentralized power plants its sunshine promises.

Subject of Research: Quantifying the influence of electricity tariffs, subsidies and net metering on the economics of residential rooftop solar in India using machine learning

Article Title: Quantifying the influence of policy variables on rooftop solar economics with evidence from India

Article References: Bardhan, S., Ingle, K. G., Nayak, S., & Mehrotra, S. P. (2026). Quantifying the influence of policy variables on rooftop solar economics with evidence from India. Environmental and Sustainability Indicators, 32, Article 101481. https://doi.org/10.1016/j.indic.2026.101481

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101481

Keywords: rooftop solar, India, machine learning, net metering, subsidies, payback period, Double Machine Learning, Conditional Permutation Importance, energy policy, photovoltaics, discounted cash flow, state tariffs