This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 421–440 of 991 papers
統合報告書トップメッセージにおける企業家機能表現の自動抽出・分類
(著者不明)
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…
テキストマイニングを用いた統合報告書の評価モデルの構築法
(著者不明)
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.
機械学習を用いた統合報告書のESG 関連ページの推定
(著者不明)
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.
テキストマイニングを利用した統合報告書評価の試み
(著者不明)
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…
テキストマイニングによる大学の統合報告書分析~SDGs の記述と THE Impact Rankings との関係~
(著者不明)
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…
テキストマイニングによる統合報告書時系列分析
(著者不明)
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…
解釈性を考慮した統合報告書の自動評価
(著者不明)
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…
有価証券報告書からのリスク文抽出の試み
(著者不明)
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…
LLMを用いた情報系企業のESG評価についての一考察
(著者不明)
This study explores the use of LLMs for ESG evaluation of information companies, focusing on extracting and analyzing ESG-related information through natural language processing. It develops evaluation indicators tailored to the characteris…
企業における環境活動の改善案の自動生成
(著者不明)
This paper proposes a method for automatically generating improvement proposals for environmental activities in companies. By leveraging AI technology, it aims to derive efficient and effective environmental improvement measures, expecting …
BERTを用いた有価証券報告書からのESG関連文抽出
(著者不明)
This paper proposes a method using BERT to extract ESG-related sentences from Japanese securities reports, enabling automated ESG disclosure analysis.
テキストマイニングを用いた統合報告書の分析による企業戦略の特徴に関する研究‐大手不動産デベロッパーを対象として‐
(著者不明)
This paper applies text mining to integrated reports of major real estate developers to extract and analyze characteristics of corporate strategy. It quantitatively captures ESG-related trends in the reports and highlights differences withi…
Kokubu, Hiroyuki
This paper proposes a disclosure-based method for measuring non-financial evidence infrastructure maturity in corporate sustainability disclosures. It introduces N4 and N5 scoring axes, ΔN (year-on-year change), four regression-type classif…
Hiroyuki Kokubu
This paper empirically examines the value of reasoning-heavy LLMs for automated ESG narrative scoring, using data from ten Japanese listed firms. Using a four-model consensus design, it finds that the mean score deviation between the reason…
Hiroyuki Kokubu
This paper examines structured GHG disclosure accessibility for 89 major Japanese listed firms using EDINET statutory filings and LLM-assisted extraction from voluntary reports. Only 23 firms had current-year structured GHG tags in EDINET; …
Hao Xu, Xixuan Peng, Xiaodan Jin +2
This study evaluates co-benefits of reducing CO2 and air pollutants from transport. A random forest model predicts PM2.5 concentrations and health benefits are estimated using GEMM. In the best scenario, CO2 emissions peak in 2032 with a 51…
Benneth Oyinna, Zubairu Usman, Aisha Abisoye +2
This study presents a machine learning framework for monitoring and forecasting Nigeria's industrial CO2 emissions to support the 2060 net-zero target. Comparing four models (MLR, Prophet, SVR, Random Forest), MLR performed best (R²=0.978).…
Ibrahim M.
This review paper discusses recent advances in CO2 capture, utilization, and storage (CCUS), with a particular focus on the application of machine learning techniques to optimize processes and the integration of circular economy principles.…
Kai-Robin Lange, José Cassola, Marcelo Lufin +11
This paper applies dynamic topic modeling (RollingLDA) to 1,996 validated news articles from 2011-2025 to analyze public narratives around socio-environmental conflicts related to Chile's energy transition. It identifies twelve topics, reve…
Gyu Jin Pyo, Mi Jin Noh, Yang Sok Kim
This paper applies DirectLiNGAM, a causal structure learning algorithm, to analyze vehicle energy consumption in the EV-ICEV transition market. It identifies causal relationships among factors affecting energy consumption, contributing to e…
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