A technical analysis of the energy substitution effect of Indian manufacturing industries based on trans-log production function

Manufacturing is an important sector of the Indian economy and a significant contributor to the nation’s economic growth and development. This growth is heavily dependent on energy consumption, which has important implications for both economic and environmental sustainability. The study utilizes twenty years of dynamic panel data from Annual Survey of Industry (ASI). The study examines substitutability among labor, capital, and energy using the trans log production function. Ridge regression is used via penalization, such as the penalty parameter (λ), to address the problem of multicollinearity among input variables. Along with that GMM method used to address endogeneity which is persistent in dynamic panel data. The study finds that output elasticity for capital, labor, and energy is positive and increasing returns to scale, indicating output increases more than proportionally in Indian manufacturing industries. Capital is the highest contributor, followed by labour then energy. The elasticity of substitution between capital and labour is positive and very high, indicating the inputs are very easily substituted. The elasticity between capital-energy and labour-energy, on the other hand, is negative, indicating these inputs are complementary, meaning more capital use increases energy consumption, similarly more labour employment raises energy as well. These results show possible solutions for industries to improve output when they face problems like growing energy costs or a lack of workers. They also help policymakers who want to modernize industries and make them more energy efficient.