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 986 papers
Wang H.
This paper proposes an AI-forecasting-based approach to manage cloud computing resources in a carbon-neutral manner, optimizing energy efficiency and reducing carbon emissions to support net-zero targets. It exemplifies the application of A…
Imad Ud Din Durrani, Hassan Raza
This study integrates disclosure tone (positivity, negativity, uncertainty) into machine learning models to enhance financial performance prediction for non-financial firms in Pakistan. Textual data from annual reports were analyzed using a…
Ritwick Boyra, Rajesh Bose, A. Khan +5
This paper proposes CrossChain-VaxTrace, an integrated architecture combining cross-chain blockchain, federated graph neural networks, and edge AI to address vaccine supply chain challenges. It achieves 67% energy savings via PoA consensus …
Marisol Coba-Martínez, Jarniel García-Morales, G. Guerrero-Ramírez +4
Proposes an intelligent control strategy for alkaline water electrolysis to produce green hydrogen on-demand, matching the stoichiometric requirements of methanol synthesis. ANN models with classical and conformable activation functions are…
Nicolas van der Nest
AI-assisted analysis of 30 Singapore Exchange-listed firms' reports reveals a gap between disclosure and implementation in supplier sustainability governance. While Scope 3 disclosure is universal, enforceable supplier controls remain limit…
Chandrani Ray Chowdhury
This chapter reviews generative models for probabilistic forecasting of renewable energy production, addressing the limitations of traditional ML/DL in handling stochastic, nonlinear data. It demonstrates how probabilistic forecasts support…
Jinliang Wang, Xiang Ma, Z. Ding
This paper proposes a method using large language models (LLMs) to automatically extract and analyze ESG disclosure information from corporate sustainability reports. Compared to traditional manual analysis, it enables efficient and compreh…
Y. D. Bao, Yiyi Lu, C. Liu +5
This paper proposes an LSTM-based forecasting framework for data center energy consumption, integrated into a dual-carbon data management pipeline. The model, featuring attention pooling and multi-task PUE prediction, reduces RMSE by up to …
Rosella Castellano, Federico Cini, Annalisa Ferrari +1
This paper proposes a methodological framework to detect overreporting and underreporting in corporate sustainability disclosures by leveraging divergences among ESG rating agencies. It interprets rating gaps as signals, contributing to rel…
Ya-Ting Jhan, Sheng-Feng Hsieh
This paper examines whether ESG narratives are financially informative by using LLM-based summarization and sentiment analysis. It empirically tests the value of ESG disclosure text for investment decisions.
Jiayi Zhu, Lei Yang, Wenhui Zhou
This paper explores integrating AI into remanufacturing supply chains to enhance sustainability and intelligence diffusion. AI-driven demand forecasting and quality control can reduce waste and improve resource efficiency, supporting the tr…
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…
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