Assessing the alignment of corporate ESG disclosures with the UN sustainable development goals: a BERT-based text analysis

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Foilsithe in:Data Technologies and Applications vol. 59, no. 1 (2025), p. 19-40
Príomhchruthaitheoir: Kim, Hyogon
Rannpháirtithe: Lee, Eunmi, Yoo, Donghee
Foilsithe / Cruthaithe:
Emerald Group Publishing Limited
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Rochtain ar líne:Citation/Abstract
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LEADER 00000nab a2200000uu 4500
001 3154310073
003 UK-CbPIL
022 |a 2514-9288 
022 |a 2514-9318 
022 |a 0033-0337 
022 |a 1758-7301 
024 7 |a 10.1108/DTA-01-2024-0065  |2 doi 
035 |a 3154310073 
045 2 |b d20250101  |b d20250331 
084 |a 38174  |2 nlm 
100 1 |a Kim, Hyogon  |u Korea Land and Housing Corporation, Jinju, South Korea 
245 1 |a Assessing the alignment of corporate ESG disclosures with the UN sustainable development goals: a BERT-based text analysis 
260 |b Emerald Group Publishing Limited  |c 2025 
513 |a Journal Article 
520 3 |a PurposeThis study aims to provide measurable information that evaluates a company’s ESG performance based on the conceptual connection between ESG, non-financial elements of a company and the UN Sustainable Development Goals (SDGs) for resolving global issues.Design/methodology/approachA novel data processing method based on the BERT is presented and applied to analyze the changes and characteristics of SDG-related ESG texts from companies’ disclosures over the past decade. Specifically, ESG-related sentences are extracted from 93,277 Form 10-K filings disclosed between 2010 and 2022 and the similarity between these extracted sentences and SDGs statements is calculated through sentence transformers. A classifier is created by fine-tuning FinBERT, a financial domain-specific pre-trained language model, to classify the sentences into eight ESG classes.FindingsThe quantified results obtained from the classifier reveal several implications. First, it is observed that the trend of SDG-related ESG sentences shows a slow and steady increase over the past decade. Second, large-cap companies relatively have a greater amount of SDG-related ESG disclosures than small-cap companies. Third, significant events such as the COVID-19 pandemic greatly impact the changes in disclosure content.Originality/valueThis study presents a novel approach to textual analysis using neural network-based language models such as BERT. The results of this study provide meaningful information and insights for investors in socially responsible investment and sustainable investment and suggest that corporations need a long-term plan regarding ESG disclosures. 
610 4 |a Samsung Electronics Co Ltd 
651 4 |a United States--US 
653 |a Form 10-K 
653 |a Data processing 
653 |a Trends 
653 |a Data mining 
653 |a International organizations 
653 |a Environmental impact 
653 |a Keywords 
653 |a Investors 
653 |a Social responsibility 
653 |a Neural networks 
653 |a Public companies 
653 |a Sustainable development 
653 |a Text analysis 
653 |a Decision making 
653 |a Impact analysis 
653 |a Sentences 
653 |a Environmental social & governance 
653 |a Disclosure 
653 |a Models 
653 |a Companies 
653 |a Sustainability 
653 |a COVID-19 
653 |a Classifiers 
653 |a Global perspective 
653 |a Interlocking directorates 
653 |a Business 
653 |a Textual analysis 
653 |a Pandemics 
653 |a Investments 
653 |a Language modeling 
653 |a Literature Reviews 
653 |a World Problems 
653 |a Predominantly White Institutions 
653 |a Influence of Technology 
653 |a Sex Fairness 
653 |a Developed Nations 
653 |a Language Processing 
700 1 |a Lee, Eunmi  |u Department of Textile and Apparel Management, University of Missouri, Columbia, Missouri, USA 
700 1 |a Yoo, Donghee  |u Department of Management Information Systems (Bus & Econ Res Inst.), Gyeongsang National University, Jinju, South Korea 
773 0 |t Data Technologies and Applications  |g vol. 59, no. 1 (2025), p. 19-40 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3154310073/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3154310073/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3154310073/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch