Predicting Business Failure with the XGBoost Algorithm: The Role of Environmental Risk

Mariano Romero Martínez, Pedro Carmona Ibáñez () and Julián Martínez Vargas
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Mariano Romero Martínez: Department of Accounting, Faculty of Economics, Tarongers Campus, University of Valencia, 46022 Valencia, Spain
Pedro Carmona Ibáñez: Department of Accounting, Faculty of Economics, Tarongers Campus, University of Valencia, 46022 Valencia, Spain
Julián Martínez Vargas: Department of Accounting, Faculty of Economics, Tarongers Campus, University of Valencia, 46022 Valencia, Spain

Sustainability, 2025, vol. 17, issue 11, 1-32

Abstract: This study addresses the increasing emphasis on sustainability and the importance of understanding how environmental risk influences business failure, a factor unexplored in traditional financial prediction models. Environmental risk, or environmental financial exposure, refers to the potential percentage of a company’s revenue at risk due to the environmental damage it causes. Previous research has not sufficiently integrated environmental variables into failure prediction models. This study aims to determine whether environmental risk significantly predicts business failure and how it interacts with conventional financial indicators. Utilizing data from 971 Spanish cooperative companies in 2022, including financial ratios, the VADIS bankruptcy propensity indicator, and the TRUCAM environmental risk score, the study employs the Extreme Gradient Boosting (XGBoost) machine learning algorithm, chosen for its robustness in handling multicollinearity and nonlinear relationships. The methodology involves training and validation samples, cross-validation for hyperparameter tuning, and interpretability techniques such as variable importance analysis and partial dependence plots. Results demonstrate that the variable related to environmental risk (TRUCAM) ranks among the top predictors, alongside liquidity, profitability, and labor costs, with higher TRUCAM values correlating positively with failure risk, underscoring the importance of sustainable cost management. These findings suggest that firms facing substantial environmental risk are more prone to financial distress. By incorporating this environmental variable into a machine learning framework, this work contributes to the interaction between sustainability practices and corporate viability.

Keywords: business failure; environmental risk; prediction models; XGBoost; cooperatives; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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