Socio-Economic Dynamics of Energy: A Micro-Level Regression Approach to Domestic Consumption in India

Abstract

Anticipating energy consumption, especially at the micro level, holds paramount significance for energy planning and the effective implementation of Clean Development Mechanism (CDM) initiatives, which have emerged as integral components of global endeavors. In the contemporary world, the imperative nature of such predictions underscores their pivotal role in facilitating strategic energy management and aligning with the prevailing global emphasis on sustainable practices. Modeling household energy consumption using conventional methods like time series forecasting can prove challenging due to the myriad of influencing factors. This paper introduces a systematic stepwise regression model designed for forecasting domestic energy consumption. The model is constructed based on micro-level household survey data gathered from Kerala, a state situated in the southern part of India. This approach aims to offer a more nuanced and accurate prediction by incorporating granular data specific to individual households, thereby enhancing the efficacy of energy consumption forecasts. Upon scrutinizing the data, the analysis unveils a noteworthy impact of various factors, including socio-economic, demographic, geographic, and family attributes, on the overall energy needs of households. This underscores the intricate interplay of diverse elements in shaping the total household energy requirements. The recognition of these influential variables contributes to a comprehensive understanding of the intricate dynamics governing energy consumption patterns at the household level. The examination of energy requirements within the domestic sector reveals a notable diversity across different expenditure classes. Per capita income level emerges as the foremost explanatory variable influencing the variations in energy requirements. Additionally, the developed models illustrate the impact of per capita land area and residential space within higher income groups, whereas average age and literacy levels serve as significant variables among lower income groups. This nuanced analysis sheds light on the distinct factors driving energy consumption disparities within various socio-economic segments.