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 281–300 of 991 papers
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…
Yifan Duan, Si Zheng, Lihuan Li +2
GHGbench provides an open dataset and benchmark for company- and building-level greenhouse-gas prediction. The company track covers 12,000+ firms with Scope 1+2 and Scope 3 disclosures; the building track harmonizes 491,591 records from 13 …
Yanming Guo, Charles Guan, Jin Ma
ExioML is an open-source dataset of environmentally extended multi-regional input-output tables for emission factors, covering 49 regions and 28 years (1995-2022). It includes GPU-accelerated computational tools and two aggregation schemes …
Boaz Mwubahimana, Dingruibo Miao, Jianguo Yan +7
This paper proposes CarbonFormer, a causal spatiotemporal framework for forest aboveground carbon density (ACD) estimation. It overcomes CNN limitations by using an LSTM-based temporal causal decoder and a cross-causal loss function to enfo…
Triana Arias Abelaira, María Jesús Guillén Palomino, Lázaro Rodríguez Ariza +1
This study uses a bibliometric approach to analyze 221 articles on climate risk in finance, focusing on the integration of AI techniques. It finds that NLP and digital transformation are key themes, linking AI innovation policies to greenwa…
Anuj Pal
This paper uses Large Language Models (LLMs) to evaluate corporate ESG disclosure quality by distinguishing symbolic from substantive sustainability communication. It introduces the Operational ESG Disclosure Coding Framework (OEDCF) and co…
Wanting Wang
This study uses machine learning to analyze how negative ESG emotions on social media affect the quality of corporate ESG disclosure, testing whether public pressure drives disclosure improvement.
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