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 241–260 of 1055 papers
Gatty M.S.
This paper examines the role of FinTech and AI in advancing green finance. It likely discusses applications such as AI-driven ESG assessment, climate disclosure analysis, and green bond selection. Specific methods and findings are unclear, …
Silpa M.
This paper proposes a federated learning approach to analyze ESG disclosure data from NSE sectoral indices. It aims to improve ESG assessment accuracy while preserving data privacy. The methodology offers technical insights applicable to co…
Moojin Park
This preprint asks whether ESG regulatory events mandating independent third-party assurance produce larger asset-price reactions than mere disclosure expansions. It formalizes a signaling game where verification intensity enters the cost f…
Dominic Ayegba Okoliko, Mehita Iqani, Mehita Iqani
This living repository provides supplementary materials for a multi-phase project analyzing the salience of climate change in electoral discourse in South Africa's 2024 national election and Nigeria's 2023 presidential election, using BERTo…
Newton. Tyler James
AquaContam uses over 4 million samples from 95,223 U.S. water systems to show that ML models learn monitoring processes as much as contamination. Apparent lead-model performance is largely a target-leakage artifact, and monitoring is inequi…
Bushra Ansari, Amjid Khan, Raja Muhammad Usman
This study examines how AI contributes to resilient and sustainable supply chains via multiple case studies. Optimization-driven AI cut emissions or resource waste in 5 of 6 global cases, and predictive analytics/digital twins halved disrup…
Xie M.
This paper empirically analyzes the impact of green finance and financial technology on regional green energy technological innovation using dual machine learning and spatial econometric models. It reveals the effects of financial policy an…
Mejda T.
This paper analyzes the causal impact of regtech adoption on corporate greenwashing using staggered DiD, DDD, and generalized random forests. It explores heterogeneous effects across firms, contributing to the AI×ESG intersection by applyin…
Annu Dhull, D. Abdukhamidov, D. Yusupov +3
This paper proposes a digital twin framework for real-time monitoring and performance optimization of green hydrogen electrolysers. It combines physics-based models with AI prediction to enhance efficiency and stability under fluctuating re…
Yuzhi Wang, Cong Zhang
This study analyzes the causal effect of China's intellectual property pilot policy on low-carbon green energy eco-co-evolution (LCEE) using spatial Durbin DID and double machine learning. The inclusive potential of regional innovation ecos…
Youbi D.
This paper systematically reviews the literature on AI applications in sustainable finance, mapping current research and identifying emerging paths. It analyzes AI's impact on ESG assessment, climate-related disclosure, and investment decis…
Gupta U.
This paper proposes a green procurement policy for AI labs' compute resources, requiring minimum environmental disclosures, energy metering, and emission reduction targets. It addresses the growing energy consumption of AI computing and pro…
Vijeta, Neha Yadav, Meenakshi Rao
This conceptual study proposes integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and weak assurance by applying forensic techniques and emerging technologie…
Vijeta, Neha Yadav, Meenakshi Rao
This conceptual study proposes a framework integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and selective disclosure by applying techniques like fraud risk…
Олег Ивченко, Iryna Ivchenko
This paper proposes an observability approach for real-time monitoring of carbon emissions from distributed models. By visualizing energy consumption and carbon footprint, it provides practical insights for reducing the environmental impact…
Vu Thi Hoai Thu, Dang Thi Kieu Oanh, Trieu Anh Ngoc
This study integrated GEDI LiDAR, Sentinel-1/2, SRTM, and machine learning to estimate aboveground blue-carbon stocks in the Cua Dai estuary, Vietnam. Random Forest performed best, yielding mean AGC of 13.659 Mg C/ha and total 109,472 Mg C.…
C D, VISMAYA, V P, ANAMIKA, JURIYA, FATHIMA +2
Proposes an AI-based framework integrating IoT, cloud, and Random Forest Regression to monitor data center operations, predict heat generation, and recommend optimal heat recovery applications. Aims to improve energy efficiency, reduce cool…
Dariusz Sala, Alla Polyanska, Vladyslaw Psyuk
This paper uses bibliometric analysis of 146 publications to show the evolution of energy transition research from technical aspects to climate change, renewables, and policy. It proposes a Digital-Twin-Oriented Techno-Economic Decision-Sup…
Chethan K Murthy, Prateek Verma, Subhash Mondal
This paper introduces an agentic AI ESG consultant using a privacy-preserving, localized RAG system to automate ESG compliance gap analysis for regulations like CSRD and BRSR. It bridges corporate disclosures and legal statutes via iterativ…
Maria Luiza Xavier de Holanda Cavalcanti, Lúcio Câmara e Silva, Luciano Costa +3
This systematic review analyzes 400 experimental articles (2015-2026) on AI applications in wind energy, highlighting a paradigm shift from RNN/LSTM to attention-based Transformers. It identifies emerging trends like foundation models for t…
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