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 321–340 of 943 papers
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 …
Benjamin Josef Schüßler, Jakob Prange
This study extends a sentence-level dataset of German ESG reports with crowdsourced readability annotations and evaluates various readability scoring methods. It finds that while LLM prompting can distinguish clear from hard-to-read sentenc…
R. M. Ellahi, J. Qureshi, M. H. Farooqui +2
This study proposes two pathways for generative AI to enhance supply chain sustainability: automated reporting and predictive analytics. PLS-SEM analysis shows predictive analytics is the strongest predictor, with regulatory pressure and su…
Kerstin Forster, Lucas Keil, Victor Wagner +3
This paper develops a machine learning framework to extract ESG indicators from corporate reports, applied to 600 large European firms (2014-2023). It reveals a transparency gap between high and low ESG-rated firms, narrowing over time, and…
Dongxin He, Linhan Jiao, Jingyi Yang +1
This paper examines the impact of carbon information disclosure quality on corporate risk-taking in China using a five-dimensional vectorized text index from ESG, CSR, and sustainability reports of A-share listed firms (2007-2023). Higher-q…
Fahad Ullah Khan, Asad Amin
This review comprehensively surveys FinTech applications (AI, ML, big data, blockchain) in ESG scoring, climate risk assessment, and green bonds. It highlights how digital tools improve efficiency and data coverage while addressing challeng…
Tamás Zelles, Bernadett Domokos, Sándor Remsei
This paper examines how combining narrative techniques with machine learning can enhance decision-making, particularly in accounting and sustainability reporting. It finds that narrative-driven reporting with expert interpretation improves …
Thierry Warin, Christophe Bisson
This paper longitudinally compares scenario knowledge representation across IPCC synthesis reports from AR1 to AR6 using a RAG pipeline. Early reports relied on qualitative conditional reasoning; later reports increasingly used quantitative…
Chong Zhang
This paper proposes a digital management platform for corporate carbon emissions using a cloud-edge-end distributed architecture, integrating IoT and AI for automatic aggregation, precise calculation, and intelligent analysis. It establishe…
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