Increasing the financial institutions’ deployment of capital in energy efficiency investments remains still a challenge. The present article is intended to investigate the benefits of the application of traditional classification methods, such as the ordinal logit, the ordinal probit and the linear discriminant analysis (LDA), as well as machine learning techniques, such as the k-Nearest Neighbors and the Support Vector Machines, in the development of models for predicting the performance of energy efficiency investments. We are dealing with the process of investments identification that can be considered attractive, in terms of fostering green growth, while also having an extremely strong capacity to meet their financial commitments and therefore bridging the gap between investors and project developers. In addition, the deduction of critical comparative insights regarding the application of these five widely used techniques, is anticipated. The validity of the attempt is verified through an illustrative testing procedure on the Energy Efficiency De-risking Project database. The qualitative and technical conclusions obtained demonstrate that machine learning methods moderately outperform traditional methods regarding their predictive accuracy. Finally, findings that confirm and expand the existing underlying research are also reported.

增加金融机构在节能投资中的资金配置仍然是一个挑战。本文旨在研究应用传统分类方法（例如有序logit，有序概率和线性判别分析（LDA））以及机器学习技术（如k最近邻和近邻）的好处。支持向量机，用于预测能效投资绩效的模型。就促进绿色增长而言，我们正在处理被认为具有吸引力的投资过程，同时也具有极强的能力来履行其财务承诺，从而弥合投资者与项目开发商之间的鸿沟。此外，预计将得出关于这五种广泛使用的技术的应用的重要比较见解。通过能源效率降低风险项目数据库中的说明性测试程序，验证了尝试的有效性。获得的定性和技术结论表明，机器学习方法的预测准确性在某种程度上优于传统方法。最后，还报告了证实和扩大现有基础研究的发现。获得的定性和技术结论表明，机器学习方法的预测准确性在某种程度上优于传统方法。最后，还报告了证实和扩大现有基础研究的发现。获得的定性和技术结论表明，机器学习方法的预测准确性在某种程度上优于传统方法。最后，还报告了证实和扩大现有基础研究的发现。