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 1001–1008 of 1008 papers
Vibhavari Prasad Mane
This systematic review explores how AI techniques (machine learning, NLP, predictive analytics) enhance ESG measurement, portfolio management, risk analysis, and sustainability disclosure. It also highlights challenges like data quality, al…
Filippo di Pietro, Pilar Giráldez‐Puig, P. Palos-Sanchez +1
This study uses a systematic literature review and bibliometric analysis to examine machine learning applications in climate-related financial challenges. It finds growing AI adoption in emissions forecasting, green investment, and sustaina…
Hind Gatoi, Islam Belhaoua, Kashf Akhtar +3
This study examines whether AI-driven ESG analytics enhance sustainable investment performance. It finds that AI-enhanced high-ESG portfolios achieve higher returns and lower downside risk compared to traditional ESG portfolios. AI-derived …
H. Akula
This paper proposes a novel climate governance framework integrating AI, remote sensing, and trade policy. Using the EU's CBAM as a case, it argues for a universal treaty-based architecture to mitigate adverse impacts on developing countrie…
Junkai Chen
This study analyzes AI information disclosure in ESG reports of listed companies in the US, Europe, and China. It finds that ESG reports are the primary channel, with significant disparities across AI domains (development, application, manu…
Lars Wilhelmi, Christian Bruns, Matthias Schumann
This study investigates the use of Vision Language Models (VLMs) to extract ESG metrics, particularly GHG emission-reduction targets, from corporate sustainability reports. Using Design Science Research Methodology, we developed an extracti…
Kunj Modh, Varun Patel, Vivek Devani
This paper presents ESG Analytics, an AI platform for automated analysis of ESG reports. It uses a Retrieval-Augmented Generation (RAG) approach to extract ESG metrics from text and tables, integrating FinBERT-ESG for classification, Chroma…
Junying Fan, Daojuan Wang, Yuhua Zheng
This paper proposes FinATG, an AI-driven framework for extracting sustainability-related financial information from English texts to support ESG investment decisions in emerging markets. It achieves entity F1 of 88.5 and REL F1 of 80.2 on s…
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