GX & Decarbonization Research

*Pramod Prabhakar1 and Rajesh Kumar2 *Pramod Prabhakar1 and Rajesh Kumar2

This review comprehensively summarizes environmental impacts of livestock systems (GHG emissions, water pollution, biodiversity loss) and mitigation technologies such as precision feeding, anaerobic digestion, AI, and IoT. It highlights the…

Elena A. FEDOROVA, Anzhelika A. KOVALEVA, Lyudmila I. CHERNIKOVA

This paper applies LDA topic modeling to Russian companies' non-financial reports to construct an industry-standardized social (S) index. It identifies industry differences in social disclosure and the influence of specific topics on market…

Qianxing Chen

This paper traces the evolution of AI models for ESG assessment, from topic models to domain-adaptive Transformers and retrieval-augmented language models. It focuses on multi-label phrase annotation from news, earnings calls, and reports, …

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This paper proposes a method to construct and manage ESG factors using news text data. It explores the use of natural language processing to extract ESG-related information for investment decisions.

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This paper proposes an efficient method for extracting ESG information from corporate websites using machine learning. It reduces cost and time compared to manual collection. The method leverages natural language processing and text classif…

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This paper applies statistical causal inference to analyze the causal effect of ESG evaluation on corporate value. It examines whether improvements in ESG scores truly enhance firm value, assessing the effectiveness of ESG investing.

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Proposes and validates ESG evaluation using large language models. Automatically computes ESG scores from corporate disclosures and news, comparing efficiency and accuracy with traditional methods. Contributes to automation and sophisticati…

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This paper proposes a method to automatically extract ESG-related information from integrated reports using large language models (LLMs). It enables efficient analysis of corporate disclosure data for ESG scoring and evaluation. The approac…

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This paper proposes a method for automatically extracting ESG-related information from integrated reports. It leverages natural language processing and machine learning to efficiently collect and analyze corporate disclosure data, supportin…

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This study applies cosine similarity to analyze the time series of CEO messages from major real estate developers, capturing shifts in management discourse.

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This paper proposes an automatic evaluation method for integrated reports that incorporates interpretability. Using AI to analyze report content, it enhances transparency of assessment, aiming to improve the quality of corporate ESG disclos…

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This paper proposes a method to extract capital conversion information from integrated reports using BERT-based multi-label classification with intensity. It automatically identifies conversions among the six capitals and assesses their int…

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This paper proposes a method to automatically extract and classify entrepreneurial function expressions in top messages of integrated reports using natural language processing. It contributes to improving transparency of corporate disclosur…

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This paper proposes a method for constructing an evaluation model of integrated reports using text mining. It enables quantitative assessment of report quality, aiding companies in improving their disclosures.

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This paper proposes a machine learning method to automatically identify ESG-related pages in integrated reports. Using NLP-based text classification, it enables efficient extraction of disclosure information.

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This study attempts to evaluate integrated reports using text mining. It proposes a method to quantify the disclosure quality of integrated reports through content analysis. Using Japanese corporate data, it offers insights for future discl…

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This paper applies text mining to university integrated reports to analyze the relationship between SDGs descriptions and THE Impact Rankings. It demonstrates how AI methods can extract and visualize SDG-related information from unstructure…

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This study applies text mining to time-series analysis of integrated reports, quantifying changes in corporate ESG disclosure over time. Using natural language processing, it extracts trends and turning points, exploring the evolution of Ja…

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This paper proposes an automatic evaluation method for integrated reports, with a focus on interpretability. Using AI techniques, it analyzes report content and enhances explainability of evaluation results, aiming for practical application…

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This study reports an attempt to extract risk-related sentences from Japanese securities reports. Using natural language processing (NLP), it examines methods to automatically identify ESG-related disclosures, particularly climate risks. Th…