Applying Artificial Intelligence in Integrating ESG into Economic Forecasting Using Extreme Gradient Boosting (XGBoost)

Manuela-Violeta Tureatca, Valentin Sava and Lidia Musat (Ciobota)
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Manuela-Violeta Tureatca: Dunarea de Jos University of Galati, Romania
Valentin Sava: Dunarea de Jos University of Galati, Romania
Lidia Musat (Ciobota): Valahia University of Targoviste, Romania

Economics and Applied Informatics, 2025, issue 2, 273-282

Abstract: In recent decades, the concept of ESG (Environmental, Social, and Governance) has gained significant importance in the decision-making processes of organizations, influencing investments, financial assessments and economic strategies. The integration of ESG factors into economic forecasts is essential to support sustainable development and to meet the increasing demands of investors and regulators. At the same time, advances in the field of Artificial Intelligence (AI) offer significant potential to improve the processes of their integration into economic models. One of the most promising AI techniques currently used in this context is Extreme Gradient Boosting (XGBoost). XGBoost is a powerful machine learning method that has demonstrated remarkable results in regression and classification problems, having a particular impact in economic forecasting and financial data analysis. The purpose of this application is to explore the use of XGBoost for the integration of ESG indicators into predictive economic models, with a focus on economic risk forecasting and analysis. In particular, it can improve the accuracy of financial forecasts by modeling ESG factors as essential variables that affect the development and stability of financial markets. Integrating these factors through XGBoost can help create more effective tools for economic forecasting, allowing organizations and investors to make more informed decisions.

Keywords: Economic Forecasting; Artificial Intelligence; XGBoost (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2025:i:2:p:273-282

DOI: 10.35219/eai15840409537

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