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 721–740 of 1437 papers
Damla Yalçıner Çal, Ecir Uğur Küçüksille
This study uses the EDGAR dataset (1970-2023) to forecast GHG emissions with LSTM and Transformer models. While LSTM captures long-term trends, it underperforms a naive benchmark under regime changes. Transformer shows improvement for the l…
Jiachen Shou, Waner Li, Hui Li +5
This study integrates Random Forest and DEMATEL to identify key drivers of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Findings show that R&D expenditure, registered scientific achievements, and energy con…
Shimei Li, Yuqing Li, Jinman Luo +1
This paper proposes a dynamic optimization strategy for carbon emissions in an electrocarbon coupling system using reinforcement learning. The approach enables real-time emission reduction, outperforming static methods. It contributes to sy…
Omar Nassar, Fahimeh Jafari, David King
This paper presents KG-ESG, an AI-driven proof-of-concept that converts public ESG news into structured, queryable knowledge graphs. It processes 1,098 news articles for 450 FTSE 100/250 and ASX 200 companies using NER, sentiment analysis, …
Mai-Phuong Nguyen, Minh Thi Ngoc Trieu
Using a panel of Vietnamese manufacturing firms from 2018-2024, this study finds that digital transformation positively impacts ESG performance, but the effect is weaker for state-owned enterprises. The analysis employs text-mining methods …
Cande Lian, Wentao Zeng, Jiabin Wu +10
This paper proposes FGDSE, an interpretable causal-ensemble framework that predicts EV charging station fault risk under climate stress (extreme heat, heavy precipitation, humidity). Using 25 months of data from 13 stations, it outperforms …
Yuhui Lyu, Fan Zheng, Haodong Ji +1
This study presents a machine learning-driven multiobjective optimization framework for ultrahigh-performance concrete (UHPC) mix design to balance strength, cost, and embodied carbon. Using LightGBM predictive models, single-objective and …
Mahantesh.H.M, Mandeep Narang, R.Padmapriya +3
This paper presents GreenSynapse, a cognitive AI-driven platform integrating neuro-symbolic reasoning, digital twins, and blockchain for sustainable smart cities. It uses reinforcement learning and symbolic reasoning to adaptively control e…
M. Kayakuş, Mustafa Terzioğlu, Dilşad Erdoğan +3
This study applies a multi-method NLP framework to analyze green finance discourse on social media. Using TF-IDF, FinBERT sentiment analysis, LDA topic modeling, and keyword co-occurrence networks on 23,452 X posts, it finds that discourse …
M. Raslan, M. Mostafa, Ahmed S. Abdellah +2
This paper proposes a Demand Side Management strategy using Non-Intrusive Load Monitoring (NILM) to optimize green hydrogen microgrid costs. By identifying and shifting deferrable appliances (dishwasher, washing machine) from evening peak t…
Francesco Smaldone, Benedetta Esposito, Daniela Sica +1
This study profiles employability skills needed for the climate-circularity nexus in agri-food using large-scale text mining and LLM-based classification of job ads. Findings highlight the prominence of transversal skills, growing demand fo…
Sohan Kumar Jha, Nirmala Kushwah
This paper critically examines AI's role in sustainable finance and green banking. It develops an integrated conceptual model linking AI capabilities to ESG integration, climate risk analytics, green credit allocation, and portfolio managem…
Krasteva-Hristova R
This exploratory study analyzes the 2024 UK transparency reports of Deloitte, EY, KPMG, and PwC to assess governance disclosures for AI-assisted professional judgment in audit and sustainability assurance. It develops a seven-dimension AI-J…
Kodela, Kalyan Chakravarthy, Roy, Stabak, Ghorbanzadeh, Shaghayegh +2
This paper proposes the Spatio-Temporal Graph Multi-Agent Carbon Flux Model (STG-MACFM), a learning-driven framework within a GeoAI-enabled digital twin. It uses hierarchical agents, reinforcement learning, and graph attention networks to d…
Gangineni R.
This paper uses a bibliometric review to analyze trends in sustainable marketing and green finance in the age of AI, identifying key research themes and applications of AI in these fields.
Kumar T.
This paper explores the role of AI in sustainable finance, balancing ESG investment strategies, central bank digital currencies, and financial privacy. It likely discusses AI-driven ESG evaluation, optimization of investment decisions, impl…
Twinamatsiko E.
Proposes a machine learning approach to incorporate ESG factors into investment decision-making. Develops a framework for integrating ESG criteria to support responsible and sustainable investments.
Shien T.M.
This paper proposes an AIoT-based waste classification system for solid waste management, contributing to the Sustainable Development Goals (SDGs). It aims to improve efficiency and promote recycling, reducing environmental impact.
Poorvika N.
This paper proposes a framework integrating AI-powered digital twins and zero-trust security for predictive maintenance of net-zero assets, aiming to enhance grid reliability and optimize renewable energy asset operations.
Aman Ali, Hritik Verma, Prateek Maurya +1
This paper reviews traditional carbon footprint methods (LCA, emission factors) and their limitations across sectors (manufacturing, construction, logistics, transport, digital infrastructure), including fixed emission factors and high unce…
Browse by Topic