## 6 Discussion  
This study aimed to explain the causal configurations underlying business failure, with a specific focus on environmental financial exposure--that is, the proportion of a company's revenue at risk due to the environmental damage it causes. By integrating Extreme Gradient Boosting (XGBoost) for feature selection with fuzzy-set qualitative comparative analysis (fsQCA) for identifying causal configurations, our analysis provides an understanding of how traditional financial indicators and environmental risk interact. Prior studies have established a link between high environmental risk and negative financial outcomes, such as decreased profitability and increased operational inefficiencies [12]. Our findings corroborate this, showing that environmental financial exposure (TRUCAM) is a core condition in one of the causal configurations leading to a high propensity for business failure. This directly supports the idea that environmental risk is not just an ethical concern but a tangible financial threat. Likewise, previous research often focused on the independent effects of either environmental risk or financial factors [6; 7; 8]. The use of fsQCA enables a more comprehensive examination. We identify specific combinations of conditions, including environmental risk, that are sufficient for business failure. This reveals the complex interaction between environmental and financial factors. For example, the third solution in Table 6 shows that high environmental risk (TRUCAM), in conjunction with low economic profitability (RTAS) and low returns per employee (TPR and PPE), leads to a high propensity for failure. This configurational approach provides a better understanding than simply correlating environmental risk with negative financial performance. The application of fsQCA revealed multiple causal pathways (equifinality) that lead to business failure [58]. The configurations that explain a high propensity for failure exhibit greater overall consistency and coverage compared to those explaining the robustness analysis. These results support the argument that incorporating environmental financial exposure into traditional financial risk assessments enhances the explanatory power of such models [28]. \begin{table}
\begin{tabular}{c c c} \hline Solution: F.SOLL*-F.GEAR*-F.TPR*-F.PPE*F.TRUCAM & Consistency & Coverage \\ F.SOLL*-F.GEAR*-F.TPR*-F.PPE*F.TRUCAM & 0.944 & 0.120 \\ Total minimum & 0.944 & 0.120 \\ \hline \end{tabular} Notes: Sample size = 38,456. The symbol – indicates the absence of the condition (or low values), and its omission indicates its presence (or high values of the condition). The asterisk symbol (*) represents the logical “and” operator, while the plus symbol (+) represents the logical “or” operator, consistent with standard Boolean algebra. “F.” denotes that variables have been transformed into conditions (fuzzified). LOW_VADISP2 BB: Low Propensity for Failure. The remaining variable definitions are provided in Table 3.  
\end{table} 
Table 9: Sufficiency analysis of conditions for low propensity for failure. Our study focuses on environmental financial exposure or environmental risk. This focus is critical, as recent reports emphasize that environmental risk factors are becoming increasingly integral to assessing corporate performance and sustainability [4,5]. The findings contribute to this ongoing debate by demonstrating that it is not merely the direct costs of environmental damage but the associated financial exposure that significantly raises the risk of business failure. From a methodological perspective, the integration of XGBoost and fsQCA offers a complementary approach. XGBoost ensures that only the most influential predictors are considered, thereby simplifying the model and enhancing interpretability [31]. Meanwhile, fsQCA captures the complexity of the causal relationships, offering a clear representation of how various combinations of financial and environmental conditions can lead to similar outcomes [45,56]. Several limitations should be acknowledged.