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 261–271 of 271 papers
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…
Yassine Charabi
This paper presents a system-dynamics model quantifying the temporal mismatch between immediate AI infrastructure emissions and delayed system-level savings. Using IEA scenarios, it calculates cumulative carbon debt and analyzes policy leve…
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…
Emmanuel Ohimai Ojo, Prince Michael Akwabeng, Gloria Opoku Darkoh +1
This systematic review examines AI applications in carbon emission monitoring and green investment decisions, analyzing 55 studies from 2021-2025. It finds that machine learning, deep learning, and hybrid algorithms significantly improve em…
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…
Dr.Rachana Saxena, Hashim Khan, Dr.Mohsina Hayat
This paper critically examines how AI is reshaping corporate sustainability reporting and green accounting. It analyzes AI applications such as automated carbon accounting, environmental footprint analysis, real-time monitoring, and AI-assi…
Tianyi Ren
This paper examines how AI-powered carbon footprint tracking can reshape green investment decision-making by delivering near-real-time, high-resolution estimates of Scope 1-3 emissions, especially Scope 3. It synthesizes methods combining N…
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…
Nadia Mansour
This systematic review of 42 studies (2018–2025) examines AI, IoT, and blockchain in sustainable finance. It identifies three application areas: enhanced MRV of environmental impacts, improved physical and transition risk control via predic…
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