Determinants of Building-Sector CO2 Emissions in the EU: A Combined Econometric and Machine Learning Approach

Marco Mele; Alberto Costantiello; Fabio Anobile; Angelo Leogrande

doi:10.55845/jos-2026-21101

Authors

Marco Mele Unicusano University
Alberto Costantiello LUM University Giuseppe Degennaro
Fabio Anobile LUM University Giuseppe Degennaro
Angelo Leogrande LUM University Giuseppe Degennaro

DOI:

https://doi.org/10.55845/jos-2026-21101

Keywords:

Building-sector Carbon Emissions, Panel Data Econometrics, Machine Learning Prediction, Environmental and Climatic Drivers, Cluster Analysis

Abstract

The research aims to explore the structural, environmental, and climatic factors that influence carbon dioxide emissions in the building sector across the 27 member states of the European Union between 2005 and 2023. It is evident that higher activity in the primary sector, along with higher forest area, is associated with reduced building sector emissions, while environmental stress, air pollution, and demands for heating and cooling are positively related to building sector emissions. Subsequent analyses show that there are significant differences between EU member states, differentiating between those that are classified under high-emitting patterns, including pollution, inefficient energy, and adverse climatic conditions, and those that are classified under low-emitting patterns, including clean energy sources and favourable environmental conditions.

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