ECONOMIC RETURNS AND BEHAVIORAL BIASES IN SOLAR ENERGY INVESTMENT DECISIONS IN INDIA: A QUANTITATIVE ASSESSMENT USING SECONDARY DATA

Abstract

India's solar power sector has undergone rapid growth in recent years, driven by supportive government policies and declining costs of solar technology. Yet, investment patterns within the industry demonstrate notable deviations from what would be expected based on purely economic or resource-based optimization. This dissertation examines the economic returns and behavioral dimensions influencing solar energy investment decisions in India, utilizing secondary data from 2015 to 2023. The analysis applies quantitative methods to data from authoritative sources including the Ministry of New and Renewable Energy (MNRE), the Central Electricity Authority (CEA), the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), and industry publications. Key findings highlight pronounced geographical clustering—over 60% of utility-scale capacity is located in just four states—as measured by a Herfindahl-Hirschman Index exceeding 1000. These patterns align with herding behavior often observed in investment decision-making. In the residential rooftop segment, the analysis uncovers a significant adoption gap, with only 0.37% of technical potential utilized, and approximately 4% contribution to national solar capacity. This suggests strong behavioral barriers such as loss aversion and status quo bias, supported by survey evidence indicating long payback periods (typically 4–6 years) and very low net metering participation rates (approximately 7%). The study also explores differences in economic returns across solar market segments. Utility-scale projects exhibit the lowest levelized cost of electricity (LCOE), around ₹2.00– ₹3.00/kWh, owing to scale economies, whereas residential projects face higher LCOEs (₹4.00–₹6.00/kWh) and capital costs. Policy mechanisms, including the Production Linked Incentive (PLI) scheme and the expansion of Solar Radiation Resource Assessment (SRRA) infrastructure, are analyzed for their influence on investment behavior and market dynamics. This dissertation concludes that while economic fundamentals drive sectoral growth, behavioral biases significantly shape decision-making, particularly in the distributed solar segment. It recommends behavioral-informed policy strategies—such as simplifying processes, framing benefits more effectively, introducing default options, and offering targeted financial instruments—to enhance adoption and achieve equitable solar energy deployment across India.