Credit risk of enterprise carbon emission reduction based on entropy weight TOPSIS-KNN-BPNN model under unbalanced data

Carbon neutralization, as a key strategy to cope with climate change, has an important impact on stakeholders and national sustainable development. In view of this, in order to improve the prediction accuracy of corporate carbon emission reduction credit risk, this paper takes 2,939 listed enterprises from 2003 to 2020 as the research subjects and proposes a corporate carbon emission reduction credit risk early-warning model integrated with entropy weight TOPSIS-KNN-BPNN. The paper applies firstly entropy weight TOPSIS to give a comprehensive score in regards of the credit risk of enterprise carbon emission reduction; then conducts a treatment of Kmeans clustering on the scoring results to effectively obtain the grade interval of credit risk, thus to lay a foundation for the supervised learning of BP neural network; furthermore SMOTE algorithm is introduced to value-interpolating operation for those enterprises with numerically-small samples to generate some new samples, so as to solve the problem of imbalance of samples of various grades of enterprises; finally, through the ablation and multi-model comparative experiments, the predictive performance of the model constructed in this paper is verified. The results show that: first, there is a significant difference in terms of the degree of influence of various carbon emission reduction indicators on enterprises with various credit risk levels, of which the highest degree of influence is found to be the coal carbon emission index, and the lowest degree of influence is from the enterprise carbon emission index; second, the use of the XGBoost algorithm to effectively screen the indexes improves the predicting performance of the model, with an average improvement of 3.55%; third, compared with other models, the predicting accuracy of the model reaches 99.05%, with an average improvement of 17.38%, indicating that the model is feasible and capable of providing technical support for financial institutions performing credit rating.