The integration of Artificial Intelligence into sustainable finance represents a transformative paradigm shift in how Environmental, Social, and Governance factors are analyzed, predicted, and incorporated into investment decisions. This review provides a comprehensive taxonomy of AI approaches applicable to sustainable investment decision-making, categorizing methodologies based on their underlying algorithms and their impact on ESG-related financial processes. The proposed AI Taxonomy includes machine learning paradigms -- including supervised, unsupervised, and reinforcement learning -- as well as natural language processing techniques and optimization algorithms, examining their specific applications in ESG score prediction, controversy detection, portfolio management, and sustainability report analysis. By synthesizing findings from the recent literature, a framework emerges on AI-powered sustainable finance that identifies technological applications to overcome ESG data barriers.

翻译：将人工智能融入可持续金融代表了一种变革性的范式转换，它深刻改变了环境、社会和治理因素的分析、预测以及纳入投资决策的方式。本综述全面构建了适用于可持续投资决策的人工智能方法分类体系，根据其底层算法及其对ESG相关金融流程的影响对各类方法进行归类。所提出的AI分类体系涵盖机器学习范式（包括监督学习、非监督学习和强化学习），以及自然语言处理技术和优化算法，并深入探讨了它们在ESG评分预测、争议事件检测、投资组合管理和可持续性报告分析中的具体应用。通过综合近期文献的研究成果，本文提炼出一个关于AI驱动的可持续金融的框架，该框架明确了可用于克服ESG数据障碍的技术应用方案。