Decarbonizing the Building Sector: The Integrated Role of ESG Indicators

Nicola Magaletti (), Valeria Notarnicola, Mauro Di Molfetta and Angelo Leogrande ()
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Nicola Magaletti: TXT eSolutions [Milano]
Valeria Notarnicola: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Mauro Di Molfetta: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Angelo Leogrande: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro

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Abstract: This work tests the relationship of the building sector's carbon dioxide (CO₂) emissions with a set of environmental, social, and governance (ESG) indicators in an international panel of countries. Using machine learning approaches alongside traditional econometric techniques, the work identifies strong predictors of emissions intensity in the nature of scientific productivity, healthcare infrastructure, and good governance. The findings indicate higher scientific productivity and better government governance are associated with reduced CO₂ emissions from building stocks, while the effectiveness of government and R&D expenditures are found to be associated with higher emission rates, possibly due to the broader urban infrastructures of the developed nations. With the help of clustering as well as permutation-based measures of importance, the work establishes the complex dynamics interlinking development, knowledge creation, and environmental efficiency. The result provides practical indications for policymakers who aim to harmonize the national ESG policies with the targets of decarbonization in the built space.

Keywords: Carbon Emissions ESG Indicators Building Sector Machine Learning Governance Effectiveness JEL Codes: Q56 O44 C55 H52 O38; Carbon Emissions; ESG Indicators; Building Sector; Machine Learning; Governance Effectiveness JEL Codes: Q56; O44; C55; H52; O38 (search for similar items in EconPapers)
Date: 2025-06-20
Note: View the original document on HAL open archive server: https://hal.science/hal-05123559v1
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