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 1414 papers
Telukdarie A.
This work addresses automating ESG reporting processes with AI. It targets reducing disclosure burden and improving data quality through automated ESG data collection, aggregation, and report generation. It sits squarely at the AI×ESG inter…
Costache I.
This paper proposes guidelines for applying process mining to greenhouse gas emission management in production processes. It systematizes how event-log analysis can identify emission sources and inefficiencies, supporting Scope 1 and 2 acco…
Archana Digvijay Suryavanshi
This paper reviews the convergence of automation, AI, and ESG/sustainability disclosure in modern accounting and financial reporting. It maps the shift toward real-time data, automated audit trails, and integrated non-financial reporting, a…
Fang Sun
This study builds a bounded three-dimensional discrete nonlinear model linking AI-investment maturity, ESG disclosure quality, and green supply chain collaboration. It separates structural complementarity from organizational responsiveness,…
Jacob Beck, Anna Steinberg, Andreas Dimmelmeier +4
This paper presents a gold-standard dataset of GHG emission metrics extracted from 139 sustainability reports. Using an LLM-powered extraction pipeline with two-stage independent annotation and expert review for discrepancies, it achieves h…
David S.
Proposes an AI-enhanced web crawler to detect emerging trends in sustainable finance in real time. It automates collection and analysis of ESG and climate-related information, potentially advancing disclosure infrastructure. No abstract is …
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…
(著者不明)
This study examines how CEO narcissism affects the quality of TCFD-aligned climate-related financial disclosure among S&P 1500 firms. Using generative NLP measures across the four TCFD pillars, it finds narcissistic CEOs produce weaker, les…
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…
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