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

Aug 5, 2026

This paper evaluates structural, environmental, and climatic drivers of carbon dioxide emissions from the building sector (CBE) across 27 EU member states from 2005 to 2023. It combines World Bank panel data with econometric models (Random Effects, Fixed Effects, Dynamic Panel GMM, and Weighted Least Squares) and machine learning with clustering. Results show a negative link between agriculture and forest area, while water stress, PM2.5, heating and cooling degree days, and N2O positively and significantly influence CBE. Diagnostics favor Fixed Effects and Weighted Least Squares; GMM is constrained by instrument validity, and KNN yields strong predictive diagnostics. A 10-cluster solution distinguishes low- from high-emission groups, highlighting renewable energy, pollution levels, energy efficiency, and climate as key patterns for EU decarbonization targets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages36

Reading Time1 h 31 min

AccessFree to download

Introduction

Research gap and motivation

Methodological approach and novelty

Study scope and dataset overview

Abstract

Research objectives and countries/time span

Econometric modeling strategy

Machine learning and clustering design

Key findings and policy relevance

Munich Personal RePEc Archive Determinants of Building-Sector CO Emissions in the EU: A Combined Econometric and Machine Learning Approach Mele, Marco and Costantiello, Alberto and Anobile, Fabio and Leogrande, Angelo Unicusano University, LUM University Giuseppe Degennaro, LUM University Giuseppe Degennaro, LUM University Giuseppe Degennaro 12 December 2025 Online at [https://mpra. ub. uni-muenchen. de/127321/](https://mpra. ub. uni-muenchen. de/127321/) [MPRA Paper No. 127321](MPRA Paper No. 127321) , [posted 07 Jan 2026 09:43 UTC](posted 07 Jan 2026 09:43 UTC) Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach Marco Mele, Unicusano University, Rome, [marco.mele@unicusano.it](marco.mele@unicusano.it)[ ](marco.mele@unicusano.it)Alberto Costantiello, LUM University Giuseppe Degennaro, Casamassima, [costantiello@lum.it](costantiello@lum.it)[ ](costantiello@lum.it)Fabio Anobile, LUM University Giuseppe Degennaro, Casamassima, [anobile.phdstudent@lum.it](anobile.phdstudent@lum.it) Angelo Leogrande*, LUM University Giuseppe Degennaro, Casamassima, [leogrande.cultore@lum.it](leogrande.cultore@lum.it) *Corrisponding author Abstract This paper evaluates the structural, environmental, and climatic factors influencing carbon dioxide emissions from the building sector (CBE) in 27 European Union member states from 2005 to 2023. This analysis uses panel data from the World Bank and four econometric models—Random Effects, Fixed Effects, Dynamic Panel GMM, and Weighted Least Squares—coupled with machine learning and clustering to provide a robust analysis of emissions. The econometric models show that all models support a negative relationship between agriculture, forestry, and fishing value added (AFFV) and forest area (FRST), suggesting that a robust rural economy and substantial natural carbon sinks are accompanied by lower emissions in the building sector. On the other hand, water stress (WSTR), PM2.5 pollution, heating and cooling degree days, and nitrous oxide emissions (N2OP) are found to significantly, yet positively, affect CBE. Tests of diagnostic analyses support Fixed Effects and Weighted Least Squares models, whereas results from GMM models are limited by instrument validity violations. In machine learning analysis, K-Nearest Neighbors (KNN) models are found tobe most diagnostic, with all performance metrics being improved, establishing a prominent role for coal electricity, water stress, agricultural intensities, and climatic factors. Subsequently, a solution with 10 clusters, selected using Bayesian Information Criteria and silhouettes, identified a set of environmental and economic characteristics based on differences between low-and high-emission groups. High-emitting groups result from agricultural intensification, pollution, and low energy efficiency, while low-emitting groups are associated with renewable energy, low pollution, and a favorable climate. This analysis, hence, presents a multifaceted assessment of building sector emissions, with climatic, structural, and energy transition patterns as driving factors for meeting decarbonization targets for the European Union. Keywords: Building-sector carbon emissions; Panel data econometrics; Machine learning prediction; Environmental and climatic drivers; Cluster analysis JEL Codes: C33; Q54; Q41; Q56; C38 1. Introduction The building industry is a major area of policy engagement in the European Union's decarbonization plan, accounting for a substantial share of total energy use and greenhouse gas emissions (Gianneloset al., 2023) . Although interest in building energy performance has been growing, knowledge of the structural, environmental, and climatic factors influencing the magnitude of CO₂ emissions in this area remains fragmented. Existing research usually focuses on separate variables—energy efficiency, electricity supply composition, and renovation strategies—without adop

FAQs

Which models are used to analyze EU building-sector CO₂ emissions from 2005 to 2023?

The study applies panel econometric models including Random Effects, Fixed Effects, Dynamic Panel GMM, and Weighted Least Squares, then complements them with machine learning and clustering methods.

What factors show a negative relationship with building-sector CO₂ emissions?

Agriculture, forestry, and fishing value added (AFFV) and forest area (FRST) are associated with lower emissions, indicating a role for rural economic strength and natural carbon sinks.

How are emission patterns grouped and what distinguishes high- from low-emission clusters?

A 10-cluster solution identifies environmental and economic profiles: high-emitting groups align with agricultural intensification, pollution, and low energy efficiency, while low-emitting groups align with renewable energy, low pollution, and a more favorable climate.

Aug 5, 2026

Upload by Valentina