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 201–220 of 274 papers
Jian W.
This paper proposes AI-driven green finance innovation using NLP under the ESG framework. It conceptually discusses the potential of combining AI and finance to promote decarbonization, but lacks specific methods or empirical data.
Bawack R.E.
This paper conducts a bibliometric analysis of AI in sustainable finance and accounting, mapping research trends and proposing a future research agenda.
Xu T.
This paper proposes an AI-driven knowledge graph approach to analyze ESG disclosures at the action level, moving beyond traditional rating methods to reveal granular insights into corporate sustainability practices.
Olawade D.B.
This paper systematically reviews the potential of artificial intelligence (AI) to help achieve net-zero targets. It assesses how AI technologies can contribute to carbon emission reduction, energy efficiency, and renewable energy integrati…
Naveed K.
This study examines the impact of AI adoption on ESG disclosure quality using Chinese firm data. It finds that AI adoption improves disclosure quality, and heterogeneity of sustainability committees strengthens this effect.
Lesor Ikeh
This review analyzes diverse AI applications for climate change mitigation, including energy efficiency, carbon sequestration, weather and renewable forecasting, smart grids, smart manufacturing, intelligent transportation, precision agricu…
Madalina Ana BURDUJA, Dorel Mihai PARASCHIV
This study introduces the AI-Enhanced Decarbonisation Model (AEDM), integrating AI with behavioral and welfare economics for adaptive climate policy. Simulations in Massachusetts and Seoul show that AI-driven policy feedback outperforms sta…
Dr. Honey Gupta, Ms. Shivangi Seth
This paper explores the integration of AI into sustainability reporting and accountability frameworks. It examines AI's role in data acquisition, ESG analytics, assurance, regulatory compliance, and stakeholder engagement. The findings high…
Lin Li, Meng Li
This paper empirically examines the relationship between artificial intelligence (AI) orientation and decarbonization spillovers among Chinese listed firms. It suggests that AI adoption not only reduces firms' own carbon emissions but also …
Mohammad Ridwan, Jeremy Ko, C. Leung +1
This study examines how AI innovation, energy consumption, economic growth, industrialization, and population affect the US ecological footprint from 1996 to 2024 using ARDL and robustness tests. Results show AI innovation reduces ecologica…
Jess B. Kozman, Jack Bashian
This paper emphasizes that AI/ML workflows for low-carbon subsurface energy projects (geothermal, CCS) require high-quality, well-curated geotechnical data adhering to FAIR principles. It highlights that many datasets lack integration and g…
Guangfan Sun, Song Yue, Jianqiang Xiao +1
This study empirically examines how AI enables green innovation in Chinese aviation manufacturing firms. It identifies three channels: technological empowerment, labor structure optimization, and improved resource access. The positive effec…
Aditya Tripathi, M. Machado, L. Spierdijk +1
This systematic literature review synthesizes insights from circular business models, industrial symbiosis, eco-industrial parks, and closed-loop supply chains to inform the development of Hubs for Circularity (H4Cs). It highlights the role…
Xiaoling Yang, Mohd Rahimie Abd Karim, B. Abdullah
Using panel data from China's A-share listed companies from 2014 to 2023, this study empirically shows that AI significantly enhances corporate green competitiveness across strategy, innovation, and practices. The mechanisms include improve…
M. Eraña-Díaz, Juana Enríquez-Urbano, B. Martínez-Bahena +3
This study proposes a two-phase computational framework combining K-Means clustering and a binary ANN classifier to capture heterogeneity in environmental performance across manufacturing units in remanufacturing. Using 1000 synthetic recor…
Jayesh Rane
This paper assesses green AI adoption in industrial systems via SWOT analysis using mixed methods. Findings show organizational willingness, technological readiness, and regulatory standards positively influence adoption (73.4% variance). O…
Fadi Alkaraan, Mahmoud Elmarzouky, V. G. Venkatesh +3
This study uses large-scale UK data (32,273 firms from the UK Innovation Survey, FTSE All-Share data, and annual reports) to show that AI-powered Industry 4.0 technologies enable circular economy strategy practices, boosting ESG and ecosyst…
Dr. Syed Shameel Ahmed Quadri, Dad Ansari, Zonaira Akbar +1
This study empirically examines how AI-driven ESG signal processing enhances green finance intelligence, leading to better sustainable capital allocation and capital market efficiency. Using survey data from 312 financial professionals and …
Yasser Elouargui, Abdellatif Sassioui, M. Chergui +4
This paper introduces ESG-Graph, a lightweight and interpretable graph-based framework for ESG text classification. It leverages a taxonomy based on the European Sustainability Reporting Standards (ESRS) and uses a multi-layer Graph Attenti…
Siqi Sun, Ben Wu, Mali Jin +3
This paper introduces ESG-Bench, a benchmark dataset for evaluating LLMs on ESG report understanding and hallucination mitigation. The dataset includes human-annotated QA pairs with factual support labels. Chain-of-Thought prompting and fin…
Browse by Topic