European Business Schools Librarian's Group
Working Papers,
Copenhagen Business School, Department of Economics
No 9-2023: Persistent and Transient Energy Poverty: A Multi-Level Analysis in Spain
Armin Pourkhanali (), Donya Kholghi (), Manuel Llorca () and Tooraj Jamasb ()
Additional contact information
Armin Pourkhanali: Economics Finance & Marketing, Royal Melbourne Institute of Technology
Donya Kholghi: Department of Mathematics, Institute for Advanced Studies in Basic Sciences
Manuel Llorca: Department of Economics, Copenhagen Business School, Postal: Copenhagen Business School, Department of Economics, Porcelaenshaven 16 A. 1. floor, DK-2000 Frederiksberg, Denmark
Tooraj Jamasb: Department of Economics, Copenhagen Business School, Postal: Copenhagen Business School, Department of Economics, Porcelaenshaven 16 A. 1. floor, DK-2000 Frederiksberg, Denmark
Abstract: This empirical analysis investigates the determinants and dynamics of energy poverty in Spain using a combination of traditional regression models and machine learning techniques. The study identifies significant determinants of energy poverty, including income, housing type, education level, and health conditions. Findings demonstrate that lower income, specific housing types, and lower education levels increase the likelihood of energy poverty. This study also investigates the dynamics of energy poverty, and the results show the coexistence of both transient and persistent aspects of energy poverty. 35% of Spanish households struggled to maintain adequate warmth in their homes during at least one period from 2016 to 2021. While a small portion (5%) experienced chronic energy poverty, indicating their inability to maintain their home adequate warmth throughout the 70% sample period. Finally, the study offers valuable insights into the dynamics and drivers of energy poverty. It underscores both its temporary and persistent characteristics, in addition to the impact of socioeconomic factors.
Keywords: Transient and persistent energy poverty; Self-assessed health; Dynamic random effects probit; Machine learning
Language: English
32 pages, October 17, 2023
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