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 161–180 of 1673 papers
Naseema N.
An introductory piece framing the integration of sustainable finance and AI. It likely surveys how AI can support ESG data analysis and evaluation, though no abstract is available to confirm findings. Useful as a conceptual entry point to t…
Di Tollo G.D.
This study applies a neural network to predict ESG disclosure behavior among SMEs, a segment with sparse disclosure data. It demonstrates how machine learning can estimate disclosure likelihood where reporting is limited, with potential use…
Guan-Jie Li
Examines how AI is reshaping financial risk management as sustainable finance shifts from principles to hard limits. Reviews AI applications in ESG data integration, climate transition risk quantification, and carbon finance pricing, while …
Iryna Spilnyk, Pavlo Spilnyk
This conceptual study develops the 'From Data to Green Value' framework, positioning AI-enabled accounting as an information-transformation mechanism that converts data assets into reliable, connected financial and non-financial information…
Atia Alam, Myra Nadeem
Using NLP and entropy methods on FTSE non-financial firms' disclosures (2020–2024), the study builds firm-level digital transformation (DT) and ESG measures. Fixed-effects regressions show both exploitative and explorative DT are positively…
Zhyhlei, Iryna
A dataset scoring 24 major Ukrainian firms across six sectors on 37 ESRS-aligned indicators for FY2024–2025 (48 company-years). It links 1,776 coding decisions to source documents, pages and excerpts, and releases Python scripts for text ex…
Mohammad Hatta Fahamsyah, Dian Sulistyorini Wulandari, Muhammad Najamuddin Dwi Miharja +3
Using 2020–2025 panel data from Indonesian Islamic commercial banks, this study tests whether AI readiness and digital infrastructure improve profitability (ROA) via sustainability performance. Disclosure-index measures show both capabiliti…
Swati Dronkar, Sapana Choudhari, Shruti Wankhede +3
CarbonWise is an AI-driven Green Software Engineering platform that analyzes GitHub repositories to estimate energy use and CO₂e emissions from CI runtime, storage, and network activity. It computes a composite sustainability score combinin…
Lujia Yang, Yiheng Lu, Zherui Wang +4
This paper introduces MineClaim, a benchmark for LLM-based verification of ESG claims and greenwashing detection in Australian mining reports. It systematizes the task of matching qualitative ESG claims against supporting evidence, enabling…
Youngju Han
A document-based quantitative content analysis of Netflix's ESG reports (2019-2025) and shareholder letters (2021-2025). It examines how the firm draws ESG responsibility boundaries and exposes a gap in AI-related disclosure. The method off…
Vishwakarma A, Guneja RK
Analyzed 35,040 fifteen-minute meter records (2018) from a Korean steel plant, linking load regimes to reactive energy and power factor via a path model. An MLP forecast reached test R²=0.72 versus 0.32 for seasonal-naive. In-plant measures…
Justina Senam Lotsu, Nana Yaa Damtewaa Anti, Gilbert Yaw Bimpong +1
Haul trucks cause over half of direct mining emissions, making haulage decarbonization central to net-zero goals. This review synthesizes 73 sources (2017-2026) on route optimization, electric/hybrid trucks, predictive maintenance, carbon r…
Geetha N.
This paper argues that sustainable finance can be strengthened by education and awareness-building that fosters responsible use of AI and quantum computing for ESG analysis. It frames these emerging technologies as enablers of more sophisti…
Fawad A.
An empirical study using textual analysis of Chinese A-listed firms to test whether green intellectual capital improves ESG information disclosure quality. It links green intangible assets to higher disclosure levels, illustrating NLP/text …
Wang T.
This paper examines how institutional factors shape corporate ESG disclosure using a machine learning approach. It quantitatively links regulatory, governance, and legal institutions to firms' ESG reporting behavior, offering implications f…
Raman R.
A machine-learning-driven review of net-zero supply chain management (NZS CM) research, organized around alignment with the SDGs. It maps how AI/ML methods support supply-chain decarbonization, emissions visibility, and supplier-level net-z…
Liang-Shun Mo, Yu-Jiang Xie
This study builds an ESG rating prediction model combining financial features with a random forest (RF) algorithm, tested on China's CSI ESG dataset. RF achieved precision 0.8414 and AUC 0.8971, outperforming logistic regression, SVM, and X…
Mengdie Li, Derong Yi, Tao Wu +3
This paper proposes a two-stage carbon quota allocation framework for power system decarbonization. Stage one allocates quotas reflecting heterogeneous consumer low-carbon potential to regulate a secondary carbon market; stage two establish…
唐安宝, Zirui Wang
Using 914 Chinese A-share energy firms (2020–2025), this study examines how digital transformation shapes carbon performance. Digitalization significantly boosts green-patent applications and carbon-disclosure likelihood, but direct effects…
Huynh Long Hung NGUYEN, Alan Megargel, Rajesh Krishna Balan +1
Proposes a replicable data-mining pipeline that uses ESG risk scores to conduct climate transition stress tests on corporate credit risk. It bridges AI/ML methods with ESG evaluation and climate-risk modeling, offering practical implication…
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