Emerging at the forefront of the global energy transition, the Indian government is

implementing renewable energy policies to address the country’s energy crisis, climate

goals and economic well-being. PM KUSUM, an Indian scheme, is part of the

governmental push to increase the use of solar energy in agriculture. Under the policy,

farmers receive financial subsidies and institutional support to install solar-powered

irrigation pumps and decentralized solar panels. However, since its launch in 2019,

there has been limited literature examining the scheme's effectiveness and the factors

influencing its success. With over 5 billion USD allocated to enforcing the policy, it

becomes critical for policymakers to determine state-level characteristics that affect the

scheme’s impact and improve its outcomes.

This research aims to evaluate the effectiveness of PM-KUSUM through empirical

analysis. Two regression models were developed to assess the variation of the

scheme’s impact across Indian states. The first model employs a multiple linear

regression framework to study the relationship between state-wide deployment count of

solar technology under the scheme, and key characteristics such as per-capita income,

urbanization rate, education rate, and installed grid-interactive renewable power. The

second model is a panel data regression that accounts for within-state, over-time

effects. We expect to find that the chosen socio-economic, political, and demographic

predictors influence the deployment count at a statistically significant level.