This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing 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 81–100 of 986 papers
Biswal S.
This paper proposes an AI framework, ClimateBondNet, for assessing the environmental credibility of green bonds. It offers a machine-based means of verifying issuers' environmental claims amid greenwashing concerns. Sitting at the AI x ESG …
Alshareef M.N.
This study examines how AI-enhanced ESG disclosure quality relates to financial performance among Saudi listed companies under Vision 2030. It offers emerging-market evidence on linking AI-driven disclosure assessment to firm value and cost…
(著者不明)
Using 2016–2023 disclosures from Chinese A-share non-financial listed firms, the study builds a board digital cognition index (directors' digital backgrounds, governance language, committee setup, training) and measures ESG strategy executi…
(著者不明)
This systematic literature review examines how risk governance architecture shapes the interaction between AI and ESG-oriented sustainability policies in banking. From 248 records, 20 studies were synthesized, showing AI is mainly applied t…
(著者不明)
Using an unbalanced panel of 5,740 Indian listed firm-years (2012–2024), this study finds AI intensity is positively associated with corporate green innovation; a one-SD rise in AI intensity links to ~1.1% more green innovation. Digital fin…
(著者不明)
A qualitative content analysis of 2021–2024 sustainability, ESG, and annual reports from 23 LSE-listed construction firms identifies four themes: AI-driven environmental innovation, AI in sustainable finance mechanisms, strategic stakeholde…
(著者不明)
A PRISMA-guided review of 127 studies on how ML, DL, NLP, and XAI are transforming ESG rating analysis. It proposes a tripartite framework classifying ESG scores as predicted (29), used (57), or assessed (41), and is the first synthesis of …
(著者不明)
This study compares manual content analysis with AI-assisted semantic evaluation of sustainability disclosure at Saudi Arabia's four fully Shariah-compliant banks (2022–2024), using a hybrid ESG–Shariah framework across five dimensions. Dis…
국립금오공과대학교 수리빅데이터학과 학생, Juhyeon Jeong, Woosol Kim +4
This study constructs a Symbolic Environmental Disclosure Index (SEDI) capturing firms that emphasize environmental keywords and rhetoric while under-reporting quantitative performance, and tests its effect on readability. Using 457 firm-ye…
Amzad Hossain, Ayub Ali
This paper appears to examine how artificial intelligence, mediated by financial system development, drives low-carbon transformation. Authored by researchers at two Bangladeshi universities, it sits at the AI-climate-finance intersection. …
Wasim K. Al-Shattarat, Basiem K. Al-Shattarat, Ruba S. Hamed
This systematic review synthesizes 156 peer-reviewed articles (2001–H1 2026) using the TCCM framework to examine how sustainability accounting practices (SAP) shape ESG reporting quality. SAP strengthen measurement and internal controls and…
Maria M.R.
A bibliometric study mapping the evolution of green finance research using complex networks and machine learning. It visualizes thematic clusters and temporal shifts, revealing the field's knowledge structure and emerging directions. It exe…
(著者不明)
This paper proposes a hybrid ML framework fusing financial indicators with ESG scores, carbon intensity, firm size and audit opinions to predict financial distress among Chinese listed firms. Using XGBoost and Random Forest with oversamplin…
(著者不明)
A modular decision system fuses heterogeneous supply-chain data, applies two-stage emission estimation with time-series residual correction, and solves constrained multi-objective optimization for carbon strategy. On 21,000+ enterprise even…
Eleni F. Tsantsani, Kosmas G. Kosmidis, Leonidas L. Fragidis +1
This paper proposes an Artificial Neural Network (ANN) model using the Levenberg–Marquardt algorithm to predict ESG scores from financial data, evaluated on S&P 500 firms with two rating agency datasets, outperforming prior work. It also in…
Aarush Agarwal, Deepti Mehrotra, Renuka Nagpal +1
This paper applies the best–worst method (BWM), a multi-criteria decision-making technique, to data-driven ESG scoring for sustainable portfolio management. It offers a quantitative framework for weighting ESG criteria and integrating them …
The PLOS One Editors
This editorial note explores using AI to analyze CEO strategy narratives in sustainable management reports and incorporate them into corporate performance prediction. It bridges non-financial sustainability disclosure text with quantitative…
Shixiong Guo, Xinzhi Wang
This paper proposes Auto-LCA, a system architecture for continuous product-level carbon estimation combining automated supply-chain data acquisition, low-latency multilingual NLP for extracting life-cycle attributes from unstructured docume…
Nanning Guo, Ming Li, Arjun Rachana Harish +2
This paper provides a systematic review of authenticity in ESG disclosure, outlining current challenges and future research directions. It emphasizes the role of AI and technology in enhancing disclosure quality and credibility, offering in…
Prima Ayu Novita Junianto, Dedy Iswanto
This systematic literature review (PRISMA, 19 studies) examines AI competency, sustainability assurance quality, and future-ready auditors in emerging markets. It finds that AI, blockchain, and big data transform assurance via continuous au…
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