Autor: Cansino, J.M.; Dugo, V.; Román-Collado, R.; Ribbot, E.

Datos de publicación: Utilities Policy, 2025, vol.95, p. 101947.

https://doi.org/10.1016/j.jup.2025.101947

Abstract

Throughout the European Union (EU), households represented 25.8 % of final energy consumption in 2022. Almost a quarter of this consumption (25.1 %) was electricity (Eurostat, 2024a). If we focus on electricity used for lighting and most household appliances, Spain stands out as the principal consumer, significantly exceeding the EU average (31.4 % compared to 13.6 %). Since the 1990s and until recently, energy consumption in Spanish households has increased (49 %) at a rate that surpasses population growth (24 %), even reaching two times the country’s growth rate (IDAE, 2024). Electricity use for other purposes is also higher in Spain than the EU average, although the difference is less pronounced. This usage includes space heating (7.9 % versus 6.1 %) and cooking (50.9 % versus 50.6 %) (Eurostat, 2024b). This profile highlights the need for a specific research focus on the Spanish case, as household electricity consumption in Spain accounted for a quarter of national electricity use (IDAE, 2011).

Energy consumption within the residential sector or in households (these terms are used interchangeably here) is influenced by climate, population, income, and a broad range of socio-demographic and psychological factors (Frederiks et al., 2015). Given the challenge posed by rising temperatures, the relationship between energy consumption and climate is particularly significant.

As such, consumption patterns vary based on geography and climate. Spain is of additional scientific interest because it has five distinct climate zones. In coastal areas, where summers are typically hot, electricity demand for cooling is high. Conversely, winter heating can constitute significant electricity consumption in mountainous areas. Nonetheless, the existing literature has not yet provided an analysis based on climate zones. Previous analyses of Spanish household electricity consumption have focused solely on administrative geographical areas (such as autonomous communities (ACs), regions, and provinces) rather than climate zones.

According to the Spanish Geographical Association and the National Geographic Institute (Instituto Geográfico Nacional, 2023), accurately representing climatically homogeneous zones on a map of Spain is challenging, as there is no perfect alignment with provincial administrative boundaries. This challenge explains why the literature has not researched the determinants of household electricity consumption based on climatic zones rather than administrative areas (provinces or regions). This study addresses this knowledge gap.

This research aims to identify the primary inhibiting and driving factors of household electricity consumption in Spain (2015–2020), considering the climate zone in which each household is located. A dual methodology has been devised to achieve this objective. First, a temporal and spatial Logarithmic Mean Divisia Index (LMDI) decomposition analysis of Spanish household electricity consumption is conducted (Ang, 2005, 2015; Ang et al., 2015), enabling disaggregation into specific components. Secondly, a panel data model is developed based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) methodology (Dietz and Rosa, 1994), which allows for assessing the impact of selected variables on household electricity consumption. STIRPAT analyses address the need to combine a structural approach with an econometric analysis to provide a more comprehensive evaluation of the factors determining household electricity consumption in Spain. While LMDI allows for a precise decomposition of changes in electricity consumption and attributing these changes to their specific determinants, STIRPAT complements this analysis by statistically modeling the relationship between the target variable and the significant explanatory factors. This methodological combination strengthens the validity and robustness of the results by contrasting findings through different approaches and enhances the applicability of the study by enabling a post hoc analysis of observed changes, as well as an anticipatory evaluation of the factors that could influence future electricity consumption, being consistent with previous studies such as Chai et al. (2018), who also integrated the LMDI methodology with the STIRPAT model. Consequently, integrating both methods provides a more solid and well-founded analytical framework, improving the interpretation of the results and their usefulness for the design of energy policies.

The obtained results provide valuable insights for policymakers and electricity utility companies. Adopting stricter regulations for heating and cooling systems, promoting energy-efficient technologies, and updating building codes that encourage bioclimatic design and rehabilitation are all crucial for reducing household electricity consumption. Improving real-time information on household electricity consumption through mobile applications (apps) could mitigate the rebound effect and encourage setting savings goals. Finally, strategies such as housing rehabilitation, modernization of lighting systems, and the introduction of performance-based incentives could effectively improve energy efficiency and significantly reduce residential electricity consumption.

The article is structured as follows. After the Introduction, Section 2 reviews the literature on both methodologies, which are then described in Section 3. Section 4 details the data used, while the main findings are presented in Section 5. Section 6 discusses these findings, and finally, Section 7 offers a conclusion.

Highlights

• The lower household efficiency drive electricity consumption across all climate zones.
• The inverse of household density increases household’s electricity consumption.
• Hot days rather than cold days significantly affect household’s electricity consumption.
• Considering climate zones enrichs electricity consumption analysis