A New Composite Indicator for Assessing Energy Poverty Using Normalized Entropy

Using a unique or common measure of energy poverty is very limited for the true classification of a household being in energy poverty. Thus, this study proposes a composite indicator, whose weights will be determined from the estimation of two relationships using a robust and stable methodology based on information theory. This work considers two regression models, where the two dependent variables are the gross domestic product and greenhouse gas, and the 12 energy poverty explanatory variables are based on those proposed by Recalde et al. (Energy Pol 133:110869, 2019. https://doi.org/10.1016/j.enpol.2019.07P05), for the period 2008-2018. Hence, the study presents a more comprehensive measurement with additional dimensions, weights, and indicators. Probably most important, in addition to the discussed proposal with a specific choice of models and variables, this work reveals a promising methodology that can be replicated in any other theoretical configuration. This approach is suitable for the discussion and design of new energy, environmental and social policies. Findings can be used to assess in advance the effectiveness of energy poverty measures, turning the model into a valuable policy tool.

Automated summary

This study proposes a composite indicator to more accurately measure household energy poverty, with weights determined through a robust methodology. The study utilizes two regression models to analyze the impact of factors such as GDP and greenhouse gas emissions on energy poverty, providing valuable insights for the discussion and design of energy, environmental, and social policies.