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 61–80 of 986 papers
Song Dongze
This paper proposes HiCon-ESG, a confidence-aware, hierarchy-regularized learning method for fine-grained classification of ESG disclosure text. By incorporating the hierarchical structure of ESG topics into regularization while accounting …
Burhanuddin
This paper proposes ADWC, a machine-learning conceptual framework for automated digital waste classification integrating metadata across a five-layer architecture. On an assumed ~30TB corpus it projects identifying 38.7% as actionable digit…
Siavash Ghorbany, Ming Hu, Siyuan Yao +2
This study presents EcoSphere, a standalone tool automating building-level carbon assessment by integrating National Structure Inventory data, computer vision, and LLMs on Street View and satellite imagery. It builds archetype-based baselin…
Abdubannonova Khonzodabonu, Worldly Knowledge Publishing Centre
This article assesses the economic effects of AI and intelligent energy management on industrial decarbonisation, comparing Germany with Uzbekistan. It frames machine learning as an economic rather than environmental instrument, decomposing…
Wang C.
This study appears to examine the relationship between artificial intelligence and green finance across European 'intelligent' nations. It likely explores how AI supports green finance expansion or environmental evaluation, but with no abst…
Yi-Ning Ding
A data-mining study that decomposes and predicts the link between ESG ratings and financial performance. It stratifies by industry, region, and time to quantify green innovation. The work offers a quantitative framework for connecting ESG e…
Xiao-Xi Chen, Saira Kharuddin, Jalila Johari
A pilot text-analytics study that detects generative-AI-produced ESG narratives and links them to earnings quality. It flags the risk that AI-generated 'synthetic transparency' distorts ESG assessment and investor judgment, raising a new au…
Bei-Ning Han
Proposes an AI-driven framework integrating large language models with causal inference for ESG disclosure analysis and CSR evaluation. It applies generative AI to parse ESG disclosure text and causal methods to estimate links between evalu…
Llopis, Guillermo
Version 2 of SOMA AI's open dataset of AI-inference emission and resource factors for corporate GHG inventories. It adds embodied-hardware and training-amortisation terms, France and Finland grid rows, a commercial model-class mapping table…
E. Tenakwah, B. Otchere-Ankrah, E. Tenakwah
This narrative review synthesizes literature at the intersection of AI, ESG strategy, and sustainable value creation. It examines how machine learning, predictive analytics, and big data enable resource optimization, sustainability risk ant…
W. Lin
A systematic review of 53 studies at the intersection of AI, supply-chain management and sustainability. It reframes AI not as a set of techniques but as a governed capability system where data, models, decision routines and accountability …
Loso Judijanto
This qualitative literature review examines how AI transforms finance from a transactional/reporting function into strategic intelligence within Indonesia's palm oil supply chain. It identifies opportunities in cash-flow forecasting, risk m…
Pan Li, Meng-Lin Bian, Yi-Zheng Lin
This paper proposes CA-CQL, a budget-conditioned carbon-aware conservative offline RL method for joint inventory replenishment and transportation scheduling. It combines a remaining-budget state, carbon-shaped conservative value learning, a…
M. Arunakumari, Rajesh Mattaparthi, S. Kothuri +3
This paper proposes an AI-based decision framework integrating household occupancy, energy use, renewable generation and environmental quality for carbon-free urban living. Using the open-access Mafate off-grid microgrid dataset from Reunio…
Telukdarie A.
This work addresses automating ESG reporting processes with AI. It targets reducing disclosure burden and improving data quality through automated ESG data collection, aggregation, and report generation. It sits squarely at the AI×ESG inter…
Costache I.
This paper proposes guidelines for applying process mining to greenhouse gas emission management in production processes. It systematizes how event-log analysis can identify emission sources and inefficiencies, supporting Scope 1 and 2 acco…
Archana Digvijay Suryavanshi
This paper reviews the convergence of automation, AI, and ESG/sustainability disclosure in modern accounting and financial reporting. It maps the shift toward real-time data, automated audit trails, and integrated non-financial reporting, a…
Fang Sun
This study builds a bounded three-dimensional discrete nonlinear model linking AI-investment maturity, ESG disclosure quality, and green supply chain collaboration. It separates structural complementarity from organizational responsiveness,…
Jacob Beck, Anna Steinberg, Andreas Dimmelmeier +4
This paper presents a gold-standard dataset of GHG emission metrics extracted from 139 sustainability reports. Using an LLM-powered extraction pipeline with two-stage independent annotation and expert review for discrepancies, it achieves h…
David S.
Proposes an AI-enhanced web crawler to detect emerging trends in sustainable finance in real time. It automates collection and analysis of ESG and climate-related information, potentially advancing disclosure infrastructure. No abstract is …
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