4. RESULTS
4.1. Bivariate analysis
First, we want to test whether default risk is different for different levels of physical risk and the rest of the variables. To perform the mean test and variance test, the data has been classified into two groups: greater than and less than the median of the variable. The results of this analysis can be found in Table 5.

The analysis reveals several important insights into factors affecting the PD for companies. While company size was initially thought to be a significant predictor, the data show that it is not statistically significant in influencing PD, suggesting that size alone does not markedly impact default risk.
In contrast, the BTM ratio has a significant relationship with default risk. Companies with higher BTM ratios face higher default risk, indicating that these firms have more probability of financial distress.
Leverage is another critical factor with significant implications for PD. High leverage is strongly correlated with increased default risk, highlighting that firms with larger amounts of debt relative to equity are more susceptible to financial strain and, consequently, a higher PD.
ROA shows significant statistical relevance, with higher ROA associated with a lower PD. This suggests that companies with better asset utilization and higher profitability are better positioned to handle financial distress.
Similarly, asset turnover ratio is a significant factor, with higher turnover correlating with lower default risk. Effective management of assets helps companies maintain a lower risk of default.
CAPEX also has significant implications, with high CAPEX linked to high default risk. Significant investments can create financial strain, particularly if they do not lead to immediate returns.
Regarding emissions, there is no significant relationship with default risk.
The analysis of physical risk scores reveals that the differences observed between risk levels do not appear to be statistically significant. Specifically, the variance and mean of PD across different levels of physical risk scores—whether categorized as low, medium, or high—do not show significant differences. This lack of statistical significance suggests that, based on the current data, physical risk scores may not be a strong predictor of default risk. Consequently, while physical risk scores reflect increasing exposure to physical threats over time, their direct impact on the PD is not clearly supported by the statistical evidence. This indicates that other factors might be more influential in determining default risk, and the role of physical risk scores may require further investigation or refinement to better understand their impact on financial stability.
4.2. Multivariate analysis
The objective of our multivariate analysis is to rigorously investigate the predictive relationships between the dependent variables (including control variables, physical risk and environmental performance and initiatives) and the PD. By employing multivariate regression models, we aim to discern which factors most significantly influence the PD, thus providing valuable insights for risk assessment and management.
4.2.1. Models with control variables
In order to initiate a comprehensive analysis, we first estimated a series of general models incorporating only control variables. This initial approach allows us to identify which control variables exhibit the strongest relationships with the PD for the year 2023. To this end, we experimented with the model described in Table 6.

The results are shown in Table 7 (see Appendix B).
These analyses, drawn from various models, yield important insights. The intercept consistently appears significant across all models, indicating a positive baseline PD in 2023 when all other variables are controlled. This underscores the importance of accounting for inherent baseline risk factors in our models. Additionally, the coefficient for PD2022 is highly significant and positive across all models, demonstrating that the PD from the previous year is a strong predictor of the current year’s default risk, thereby validating the role of historical default probabilities.
In terms of financial metrics, company size in 2023 shows a significant negative relationship with default risk, suggesting that larger firms tend to be more financially stable and less likely to default. The BTM provides mixed results but remains significant in some models, indicating that firms with higher BTM ratios, which suggest lower market valuation relative to book value, face a slightly higher default risk.
Leverage in 2023 is another significant factor, with higher leverage ratios correlating with increased default risk, emphasizing the financial strain associated with higher levels of debt relative to equity. ROA shows a significant negative relationship with default risk, suggesting that more profitable companies are less likely to default. Similarly, asset turnover in 2023 has a significant negative coefficient, underscoring the importance of efficient asset utilization in reducing default risk.
CAPEX in 2023 are significantly associated with a higher PD, reflecting the financial risks tied to substantial investments.
Among the models analyzed, Model 10 stands out with the highest R-squared value of 0.4088. This model not only explains the greatest proportion of variability in PD2023 but also includes all statistically significant variables with clear economic implications. Therefore, we will proceed with Model 10 for the next steps in our analysis.
4.2.2. Models including physical risk
Once Model 10 has been selected, we will proceed to interact this model with the physical risk variables to examine their relationship with the PD. The results of this analysis are presented in Table 8 (see Appendix C).
The analysis conducted on the coefficients for various physical risk categories provides valuable insights into Hypothesis 1: that climate risk increases corporate default risk. However, the findings consistently reveal a negative impact of physical risk on the dependent variable across different models and time horizons, contradicting the initial hypothesis.
Overall, these results present an intriguing contradiction to Hypothesis 1. Instead of supporting the notion that increased climate risk leads to higher corporate default risk, the data suggests an inverse relationship, where increased physical risk levels are associated with a decreased impact on the dependent variable. Thus, in this case, Hypothesis 1 does not hold, highlighting a significant deviation from expected outcomes and indicating the need for further exploration to understand the underlying dynamics at play.
4.2.3. Moderating effects
After analyzing the relationship between physical risk and the probability of default (PD), we will proceed to examine the impact of different variables on physical risk. The first moderating effect we will investigate is that of firm size, to assess whether it moderates the effects of physical risks. The results are presented in Table 9 (see Appendix D).
The results shown in this table indicate that neither the coefficient of the dummy variable for size nor the interaction term are statistically significant. This suggests that the size of the company does not play a relevant role in moderating physical risks. In other words, there is no evidence that the size of the company has a significant impact on its ability to cope with or mitigate associated physical risks.
从这里The next moderating effect to be tested is that of leverage. The results of this analysis are presented in Table 10 (see Appendix E).
According to the observed data in this case, although the interaction term is statistically significant, the physical risk variables are not. Based on the observed data in this case, a noteworthy relationship emerges while the interaction term is statistically significant, the physical risk variables are not. It suggests that there is no effect for companies with low leverage, but there is a different effect for those with high leverage. Therefore, it can be concluded that physical risks do not impact companies with low levels of leverage.
The next moderating effect we will test is that of emissions. The results of this analysis are presented in Table 11 (see Appendix F).
As observed in Table 11, similar to the leverage variable, although the interaction coefficients and the dummy variable created for emissions are statistically significant, the physical risk variables are not. This suggests that the effect differs between companies with low emissions and those with high emissions. It indicates that physical risk is significant for companies with high levels of emissions but does not affect those with low emissions.但这里这段需要重点强调
The next moderating effect to be tested is that of relative emissions. The results of this analysis are presented in Table 12 (see Appendix G).
In the case of Table 12, it can be observed that relative emissions are not significant. Therefore, statistically, they do not appear to affect the PD nor do they help to moderate the effects of physical risks.
After reviewing the effects of emissions, we will proceed to examine the moderating effects of environmental policies. The first aspect to be tested is whether the company has undertaken environmental initiatives or not. The results of this analysis are presented in Table 13 (see Appendix H).
According to the observed data, the moderating effect of environmental initiatives is not statistically significant. Finally, we will test the moderating effect of whether companies have compensation for sustainability or not. The results of this analysis are presented in Table 14 (see Appendix I).
In Table 14, it is observed that both physical risks and compensation for sustainability are statistically significant. Physical risks have a negative sign, while compensation for sustainability has a positive sign, after checking with the test of Wald that the sum of coefficients is different from zero. Therefore, we observe that physical risk reduces default risk for companies without sustainability compensation, and that his reduction is smaller for companies with such compensation. Our Hypothesis 2 is: ‘The effect of physical climate risk on firms’ default risk is mitigated for firms that implement effective environmental policies.’ So far, it appears that only compensation for sustainability influences physical risks. However, as previously discussed, the unexpected sign of physical risks complicates subsequent analyses.
It is important to note that physical risks do not impact entities with low leverage or companies with low emissions. Physical risks only affect companies with high levels of leverage and significant emissions. This distinction highlights the differential impact of physical risks, emphasizing the importance of leverage and emissions as moderating factors.     for the master thesis presentation what should I write for the part database and the methodology