[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123540-en":3,"doc-seo-123540-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123540,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Toward green finance - applying Bayesian machine learning in environmental portfolio management","Recent years have accelerated recognition of climate change, environmental sustainability, and climate finance as urgent global priorities. Conventional portfolio optimization often ignores environmental factors, which can lead to sub-optimal investment choices. This paper presents the E-Sharpe Ratio, designed to assess environmental risk-adjusted returns. By integrating this metric with Bayesian machine learning, the framework evaluates stocks and portfolios using both financial and environmental performance measures, improving stock selection and enabling more environmentally aware sustainable investment decisions.","International Journal of Data Science and Analytics [https://doi.org/10.1007/s41060-025-00830-y](https://doi.org/10.1007/s41060-025-00830-y)  \nToward green finance: applying Bayesian machine learning in environmental portfolio management  \nXavier Martínez-Barbero1 · Roberto Cervelló-Royo1 · Jaume Jordán2 · Javier Ribal1  \nReceived: 24 December 2024 / Accepted: 26 May 2025 © The Author(s) 2025  \nAbstract  \nIn recent years, the importance of climate change, environmental sustainability, and climate finance has witnessed a significant surge in recognition and relevance. The pressing global need to address environmental challenges and promote sustainable financial practices has become more pronounced than ever before. Traditional portfolio optimization often overlooks environmental considerations, resulting in sub-optimal investment decisions. In this paper, we propose the E-Sharpe Ratio, a metric tailored for evaluating environmental risk-adjusted returns. By combining this ratio with Bayesian machine learning, our methodology provides a comprehensive framework for assessing stocks and portfolios, accounting for both financial and environmental performance metrics. Our research contributes to the field of environmental and climate finance by bridging financial and environmental considerations, enabling investors to make environmentally-aware decisions, and enhancing the stock selection process underscoring the importance of integrating environmental criteria into modern investment strategies, paving the way for a more sustainable financial future.  \nKeywords Environmental stock selection · Environmental sustainability · Bayesian machine learning · Environmental sharpe Ratio · Sustainable portfolio optimization  \n1 Introduction  \nOver the past two decades, the extensive industrial combustion of coal, oil, and gas, coupled with rampant deforestation, has significantly amplified the concentration of carbon dioxide in the atmosphere. This surge in CO2 levels is a primary contributor to the acceleration of climate change, leading to a range of adverse environmental impacts [46] . Such activities not only elevate greenhouse gas emissions but also disrupt  \nB Xavier Martínez-Barbero[xamarbar@doctor.upv.es](xamarbar@doctor.upv.es)  \nRoberto Cervelló-Royo  \nrocerro@esp.upv.es  \nJaume Jordán  \n[jjordan@dsic.upv.es](jjordan@dsic.upv.es)  \nJavier Ribal  \n[frarisan@upv.es](frarisan@upv.es)  \n1 Faculty of Business Administration and Management, Universitat Politècnica de València, Camino de Vera s/n, 46025 València, Spain  \n2 Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain  \nnatural carbon sinks, further intensifying the global climate crisis. The urgent need to address these issues is crucial for ensuring a sustainable future [34] .  \nAdditionally, the relevance of Environmental, Social, and Governance (ESG) criteria in the financial world has grown substantially, with investors paying closer attention to these factors during their decision-making and investment screening processes [9] . Assets evaluated and selected based on ESG standards are projected to reach approximately $53 trillion by 2025, accounting for over one-third of the global assets under management [11] . Investors are increasingly considering sustainable investment strategies, not only to minimize risks but also to enhance financial performance over time, showcasing a paradigm shift toward responsible and sustainable investing practices [36] . This evolution reflects a broader pursuit of sustainable prosperityan approach to development that seeks to advance human well-being and economic progress within the ecological boundaries of a finite planet [27] .  \nDespite that some studies highlight the lack of a homogeneous ESG standard [10], the increasing transparency and availability ofdata facilitates the application of artificialintelligence techniques in blending financial market insights with  \n1 3 ","cbCailR9awQjPg3U","https://ap.wps.com/l/cbCailR9awQjPg3U","pdf",1400986,1,14,"English","en",105,"# 1 Introduction\n## Climate change, ESG, and the need for integrated sustainability\n## Motivation and contributions: E-Sharpe Ratio and Bayesian machine learning","[{\"question\":\"Why does the paper argue that traditional portfolio optimization is insufficient?\",\"answer\":\"Traditional portfolio optimization often overlooks environmental considerations, which can produce sub-optimal investment decisions for sustainability goals.\"},{\"question\":\"What is the E-Sharpe Ratio introduced in the study?\",\"answer\":\"The E-Sharpe Ratio is a dynamic metric that extends the traditional Sharpe Ratio by incorporating environmental factors to balance financial returns with environmental awareness.\"},{\"question\":\"How does Bayesian machine learning support environmental portfolio management in this work?\",\"answer\":\"Bayesian machine learning models uncertainty probabilistically and updates predictions as new data arrives, enabling improved comparison and stock selection based on both financial and environmental performance.\"}]","Toward green finance - 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