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 361–380 of 991 papers
Dauda Bola Abdulsalam, Duminda Kuruppuarachchi, Helen Roberts
This study applies NLP to ESG/sustainability reports of S&P 500 firms (2018-2023) to measure TNFD framework adherence. Findings show that TNFD alignment, clear nature-related risk/opportunity identification, and comprehensive reporting are …
Ngone Mirimi, H. Manuere
AI-enabled climate decision-support systems are de facto governance infrastructures with political and accountability consequences, yet they are treated as neutral technical tools. The paper reframes transparency as an enforceable instituti…
Dr. Thanakit Ouanhlee
This study examines the relationship between AI integration in compensation systems and human capital accounting disclosure (HCAD) quality among manufacturing organizations in Thailand. It finds that while AI integration is moderate, disclo…
Adrian Lim, Putri Rahayu
This review synthesizes AI applications across financial time-series forecasting, portfolio construction, and firm-level sustainability analysis, with emphasis on AI-driven ESG rating prediction and disclosure signal extraction. It argues f…
Christie Azour
This paper demonstrates AI's effectiveness in ESG investing. AI processed ESG documents 1,200 times faster than humans, reduced rating divergence by 34%, detected greenwashing with 83% accuracy, identified climate risks 8-14 months earlier,…
Hasan Talaş, E. Gök, Özen Akçakanat +4
This study uses tree-based machine learning on 428 Turkish non-financial firms to show that ESG and governance signals provide statistically significant additional information for predicting ROE beyond traditional financial ratios. Random F…
Cristina Caro-González, Daniel Jato-Espino, Yudith Cardinale
This systematic review of 64 studies on integrating ESG factors into financial risk prediction finds that traditional econometrics still dominate (48%), but ML (39%), NLP (8%) are growing. ML models, especially ensemble methods and neural n…
Chen Xu, Batkhuyag Ganbaatar
This study examines the impact of intelligent manufacturing on ESG performance for Chinese A-share listed companies (2015-2023) using two-way fixed-effects panel regression, PSM-DID, and System GMM. It finds that intelligent manufacturing s…
Jianfeng Zhang, Tiantian Qi
This study proposes an interpretable predictive model for ESG greenwashing in listed companies using XGBoost and SHAP, based on Chinese listed companies data from 2009-2022 with 16 input variables. The one-period-lagged model achieves 86.82…
Xiaoqian Liu, Gazi Md. Nurul Islam, Xuelian Bai
This paper develops a credit evaluation system for new energy enterprises integrating big data (807 wind/solar plants, over 84 million photovoltaic and 570 million wind power records) with AI time-series modeling, game-theoretic weighting, …
Javier Nieves Almanza, Roberto Yoan Castillo Dieguez
This study analyzes the relationship between CSR (measured by ESG scores) and financial performance of Mexican listed firms from 2014-2024 using ANCOVA, random forest, and probit models. Results show a positive association with ROA and ROE,…
M. Rao, Falguni Nayak
This study analyzes the short-term relationship between ESG performance and stock prices of 45 Indian BSE-listed companies across nine industries in the post-BRSR period (FY2023-24 and FY2024-25). Using descriptive statistics, ANOVA, regres…
Sasmita Mishra, Zefree Lazarus Mayaluri, C. Liew +2
Proposes a hybrid deep learning model combining ESG scores and news sentiment for asset return forecasting. Uses TFT, SVR residual correction, and gated fusion to quantify ESG-sentiment interactions, finding regime dependence. A latency-opt…
Xiao-Xing Cai, Wang Yang, Xiyu Ren +3
This paper proposes a human-AI collaborative framework to enhance the trustworthiness and comparability of sustainability (ESG) ratings. The framework consists of STRIDE (principled criteria and scoring) and SR-Delta (discrepancy analysis),…
Zilong Yang
This paper examines how negative ESG news affect short-run trading in China's A-share market (2015-2023). Using a BERT classifier, it identifies 4,873 events and finds abnormal turnover +2.84pp (28.3% relative increase), volume +35%, and id…
Guangxing Hu, Bin Li
This study examines the impact of China's Intelligent Manufacturing Demonstration Projects (IMDPs, 2015-2018) on corporate ESG ratings using a quasi-experimental design with propensity score matching and difference-in-differences. IMDP part…
Soon-Ae Choi
This study employs a systematic review to analyze how AI/ML technologies can mitigate the structural dilemma of high initial investment costs and declining financial performance due to strengthened ESG regulations in the Korean cruise indus…
Kahyun Lee
This paper proposes a data-driven predictive framework that uses machine learning to map financial disclosures to ESG ratings, demonstrating practical applicability in linking disclosure content to scores.
D. Anjaneyalu, M. V. Subramanyam, A. K. N. Rani +3
This paper combines econometric panel-data modeling with machine learning (gradient boosting, explainable AI) to estimate the causal impact of ESG-bond issuance on corporate credit ratings and cost of capital. Findings show ESG-bond issuers…
Giuseppe Pernagallo, F. Quatraro, Eleonora Rubichi
This paper proposes a text-based circularity index using mandatory non-financial disclosures from large Italian companies. By computing cosine similarity between FTSE MIB sustainability reports (2017-2022) and a circular economy vocabulary …
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