Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets.

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Bibliographic Details
Title:	Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets.
Authors:	Marković, Ivana¹ ivana.markovic@eknfak.ni.ac.rs, Ljajić, Adela² adela.ljajic@ivi.ac.rs, Stanković, Jelena Z.¹ jelenas@eknfak.ni.ac.rs.jovica, Košprdić, Miloš² milos.kosprdic@ivi.ac.rs, Stanković, Jovica¹ stankovic@eknfak.ni.ac.rs
Source:	Computer Science & Information Systems. Jun2026, Vol. 23 Issue 3, p969-1000. 32p.
Subjects:	Environmental, social, & governance factors, Random forest algorithms, Machine learning, Knowledge transfer, Emerging markets, Sustainable investing
Abstract:	While ESG (Environmental, Social, and Governance) assessment plays a key role in sustainable finance, data scarcity and noise in emerging economies hinder robust model development. To address this, we propose a model para meter based transfer learning with random forest (MPBTL-RF) approach for domain adaptation situations where source data are not available. The proposed model is evaluated using three traditional learning approaches: Random Forest (RF), eXtreme Gradient Boosting (XGB), and Feedforward Neural Networks (FNN). Cross-validation is used to assess model generalizability, and domain adaptation is tested through indomain and out-of-domain settings. The proposed MPBTL-RF approach achieves competitive performance compared to traditional baselines in scenarios with limited training data, offering time advantages with predictive efficiency and stability. This work demonstrates how machine learning pipelines can adapt to data-constrained, real-world domains, fostering the synergy between AI (Artificial Intelligence) and business. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	While ESG (Environmental, Social, and Governance) assessment plays a key role in sustainable finance, data scarcity and noise in emerging economies hinder robust model development. To address this, we propose a model para meter based transfer learning with random forest (MPBTL-RF) approach for domain adaptation situations where source data are not available. The proposed model is evaluated using three traditional learning approaches: Random Forest (RF), eXtreme Gradient Boosting (XGB), and Feedforward Neural Networks (FNN). Cross-validation is used to assess model generalizability, and domain adaptation is tested through indomain and out-of-domain settings. The proposed MPBTL-RF approach achieves competitive performance compared to traditional baselines in scenarios with limited training data, offering time advantages with predictive efficiency and stability. This work demonstrates how machine learning pipelines can adapt to data-constrained, real-world domains, fostering the synergy between AI (Artificial Intelligence) and business. [ABSTRACT FROM AUTHOR]
ISSN:	18200214
DOI:	10.2298/CSIS250616028M