This paper explores the intersection of climate transition risk and credit risk, leveraging a distinctive dataset from an undisclosed Czech bank containing financial and carbon footprint data of Small and Medium Enterprises during and before the 2022 European energy crisis. Utilizing logistic regression, we first develop a conventional credit scoring model based on client-level financial data, identifying 4 key financial predictors of credit default. We then enrich the model by incorporating 11 carbon footprint variables. Our contribution is four-fold. First, we provide empirical evidence showing that Scope 1 emitters are less prone to credit default, while Scope 2 and Scope 3 emitters are more likely to default. These findings align with the economic context of 2022, where the Czech Republic experienced soaring electricity and gas prices due to post-COVID demand and geopolitical tensions. Scope 1 emitters were less affected by these price hikes, either selling these commodities or having fixed prices. Second, we demonstrate that including Scope 2 carbon footprint improves the credit scoring model's accuracy. Third, we identify four significant financial predictors of credit default, supporting previous research. Our approach offers a more robust framework for assessing credit risk in the context of climate change, highlighting the importance of environmental factors in financial risk management. Finally, we leverage newly available climate risk disclosures to show how they can enhance both climate risk mitigation and banks' credit risk analysis, emphasizing the crucial role of energy use.