[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122553-en":3,"doc-seo-122553-105":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},122553,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning in ESG Risk Management - CSRD - A literature review","ESG factors anchor sustainability disclosure and risk management in Europe’s pulp and paper industry, where CSRD reporting requirements create both pressure and opportunity for stronger decision support. This degree thesis uses a literature review and qualitative documentary analysis to examine how machine learning can help identify and assess ESG risks in the sector. Three theories—Stakeholder Theory, a risk management framework, and socio-technical system theory—guide the discussion. Findings indicate improvements in reporting accuracy, timeliness, and financial relevance, while enabling new risk detection for carbon emissions, water resources, supply-chain dependencies, and economic distress indicators. Barriers include insufficient data structures, metric misalignment, and ethical concerns.","Machine Learning in ESG Risk Management: Machine Learning for Sustainability Disclosure in EU Pulp and Paper Industry under the CSRD  \nA literature review  \nChau Minh Tran  \nDegree Thesis International Business 2025  \nDegree Thesis  \nChau Minh Tran  \nMachine Learning in ESG Risk Management: Machine Learning for Sustainability Disclosure in EU Pulp and Paper Industry under the CSRD – A literature review.  \nArcada University of Applied Sciences: International Business, 2025.  \nAbstract:  \nThe Environmental, Social, and Governance (ESG) factors are central to the sustainability disclosure practices and risk management in the European pulp and paper industry (PPI) . Especially, under the requirements of the Corporate Sustainability Reporting Directive (CSRD), this thesis examines how Machine Learning (ML) can strengthen ESG Risk Management in resource-intensive sectors. The thesis applies a literature review and qualitative documentary analysis to systematically explore the theoretical potential and applications of ML techniques for identifying and assessing ESG risks in the PPI. The thesis further explores the opportunities and challenges in integrating AI-driven approaches to ESG risk management. The analysis is guided by three central theories: Stakeholder Theory, Risk Management framework (Hopkins), and Socio-technical System theory (STS) . The results demonstrate that ML can significantly improve the accuracy, timeliness, and financial relevance of ESG reporting while also enabling innovative risk identification for carbon emissions, water resources, supply chain dependencies, and economic distress indicators. Nonetheless, the pain points are insufficient data structure, misalignment between environmental and financial metrics, and ethical concerns, which are limiting the full implementation of AI/ML in the industry. The work is limited to a qualitative, conceptual exploration using thematic coding and secondary data, without technical modelling or primary interviews. As the thesis concluded, Machine Learning serves not as a tool for ESG frameworks but as a critical enabler in aiding more reliable, efficient, and forward-looking sustainability reporting in the PPI.  \nKeywords:  \nESG risk management; Machine Learning; CSRD; Pulp and Paper Industry, Sustainability Reporting, Artificial Intelligence.  \nContents  \n1 Introduction ........................................................................................................................ 8  \n1.1 Problem statement ................................................................................................................... 9  \n1.2 Aim of the study ..................................................................................................................... 10  \n1.3 Definitions .............................................................................................................................. 11  \n1.4 Demarcation ........................................................................................................................... 12  \n1.5 Thesis structure ...................................................................................................................... 13  \n2 Literature review .............................................................................................................. 13  \n2.1 ESG Definition in Pulp and Paper Context .............................................................................. 14  \n2.1.1 Environmental (E) in pulp and paper industry ................................................................... 14  \n2.1.2 Social (S) in the pulp and paper industry ........................................................................... 15  \n2.1.3 Governance (G) in pulp and paper industry....................................................................... 16  \n2.2 Machine Learning and Artificial Intelligence .......................................................................... 16  \n2.2.1 AI in ","cbCaikE18ZpVZjCk","https://ap.wps.com/l/cbCaikE18ZpVZjCk","pdf",1571825,1,58,"English","en",105,"# Introduction\n## Problem statement\n## Aim of the study\n## Definitions\n## Demarcation\n## Thesis structure\n# Literature review\n## ESG Definition in Pulp and Paper Context\n## Machine Learning and Artificial Intelligence\n## Theories within ESG Risk Management\n## The shift to Machine Learning and AI advancement in Risk Management\n## Regulatory and Industry Context\n## Overview and summary of the Theoretical Framework\n# Method\n## Choice of method\n## Justification of the choice of method\n## Sources of documentary analysis\n## Analysis of data\n## The limitation of the methodological process\n## Use of AI\n# Results","[{\"question\":\"How does the thesis link machine learning to ESG risk management under CSRD?\",\"answer\":\"It examines how machine learning techniques can identify and assess ESG risks in the pulp and paper industry in response to CSRD sustainability reporting needs.\"},{\"question\":\"Which theories guide the thesis analysis?\",\"answer\":\"The analysis is guided by Stakeholder Theory, a risk management framework (Hopkins), and socio-technical system theory (STS).\"},{\"question\":\"What limitations and challenges are highlighted for implementing AI/ML in the industry?\",\"answer\":\"Key pain points include insufficient data structures, misalignment between environmental and financial metrics, and ethical concerns limiting full AI/ML implementation.\"}]","Machine Learning in ESG Risk Management - 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