## 3 Dataset and Variables
### Selection and Definition of Explanatory Variables  
One of the most important factors in creating prediction or classification models for business failure is the use of independent or explanatory variables. This study focuses on economic and financial ratios as key predictors, following the line established by influential works [6; 40; 41] that introduced key concepts and methodologies for predicting business failure using financial ratios. One acknowledged limitation is the lack of a clear theoretical foundation for choosing predictor variables in business failure models. To address this, we rely on comprehensive bibliographic reviews [42; 43], which meticulously analyzed the variables employed in leading studies on business failure prediction. The diversity of methods and variables included in the literature emphasizes the complexity of this problem and how much more research is required to determine which factors are most important and to create more reliable and accurate models. Thus, the selection of variables was systematically derived from existing bibliographic sources, ensuring that the model incorporates predictors supported by empirical evidence within the field. Accordingly, we initially considered all 25 economic and financial variables available in the ORBIS database, as these variables are commonly examined in previous literature. We employed the XGBoost algorithm to identify the key variables that are most strongly associated with business failure. XGBoost is a widely recognized machine learning technique for feature selection [31], known for its high accuracy and efficiency in predictive tasks. It belongs to the family of ensemble methods, specifically gradient boosting, which combines multiple weak learners (typically decision trees) to create a strong predictive model. This feature selection process helps to reduce the number of variables included in the subsequent fsQCA, ensuring a more parsimonious and interpretable model. (For a detailed explanation of the XGBoost algorithm and its application as a feature selection method, readers may refer to a previous study [31] and [[https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)]([https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)) [accessed on 5 April 2024] [44]). Figure 1 illustrates the ten most relevant predictors identified by the XGBoost algorithm, from which we have selected the top six:  
1. SOLL: Indicative of the solvency of the entity, reflecting its capacity to meet payments arising from debts. 2. GEAR: Provides a measure of the entity's level of indebtedness. 3. RTAS and ROA: Representative of the company's profitability in relation to its size. 4. TPR and PPE: Reflect returns per employee. These variables provide insights into the financial health, profitability, and efficiency of the companies, thereby contributing to the prediction of business failure propensity (see Table 3 for details). The selected variables were then completed with the TRUCAM variable, which quantifies the financial impact of a company's environmental footprint. We employed fsQCA to analyze this set of variables, specifically examining the role of environmental risk in contributing to business failure. This analysis focused on identifying causal configurations that incorporate the TRUCAM variable, indicating the potential for environmental risk to influence business failure. The objective is to understand how a company's environmental impact can lead to adverse financial outcomes, using fsQCA to identify the specific combinations of factors that contribute to a high propensity for business failure. Thus, the inclusion of the TRUCAM variable is essential to this investigation. FsQCA is particularly beneficial for examining a restricted number of variables or conditions, as it allows for the examination of complicated causal relationships and exposes how different combinations of conditions can lead to specific outcomes [45].