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 1–20 of 986 papers
Hamid Boustanifar, Sasan Mansouri
Seven LLMs from different providers scored S&P 500 earnings call transcripts on thirteen textual measures including climate and political risk. Cross-model rank correlations averaged only 0.52, and disagreement did not predict analyst or ma…
Hamid Raza Malik, Abdul Basit, Naeem A. Nawaz +1
This narrative review synthesizes AI's contributions to climate mitigation across renewable energy optimization, carbon emission tracking, environmental monitoring, and sustainable urban/agriculture development. It argues AI's net climate e…
Ying-Nan Cong, Wen-Ting Zhang, Xiao-Jing Cai +1
This study benchmarks standalone and hybrid deep learning models (Transformer, Informer, TCN, BiGRU, TCN-Transformer, TCN-BiGRU) for forecasting EU-ETS EUA futures prices using high-frequency OHLC data. Recurrent architectures generally out…
(著者不明)
This work applies AI (machine learning and data analytics) to measuring and mitigating carbon footprints. It likely centers on automating Scope 1/2/3 accounting and optimizing emission-reduction measures. With no abstract available, methods…
(著者不明)
This study estimates facility-level greenhouse gas emissions for U.S. hospitals by using machine learning to impute incomplete self-reported data. It addresses the widespread missingness in healthcare emissions reporting, enabling more comp…
Verma M.
This paper examines the time-varying interactions between artificial intelligence and sustainable finance in the post-pandemic era. It aims to capture how AI development and ESG/green finance market dynamics influence each other over time. …
Lawrence Oladeji
SiteFootprint estimates operational carbon emissions and on-site water use of AI training at any location, computing hourly data-centre cooling from real weather and combining it with grid carbon intensity, with 95% uncertainty ranges and p…
Zeeshan, Mohammad
Scope3Scout is a 16-agent multi-agent platform that automates ESG and CSRD supply chain compliance verification across supplier data. It was a TinyFish Accelerator finalist, offering a concrete example of AI-driven disclosure verification i…
E. Jin
This study proposes an integrated UAV–IoT system fusing high-resolution aerial remote sensing with continuous in-situ sensor data for real-time carbon emission monitoring in industrial parks. A case study shows the fused system substantiall…
Joseph J. Kim, Pooja D. Chavan
This study develops an AI tool that automates environmental code checking for infrastructure projects by integrating NLP, LCA, and Envision scoring. A spaCy-based NER model extracts project metadata and material quantities from PDFs, comput…
Jin-Xin Zhu, Guo-Feng Wang, Xin-Yan Wen +1
This paper proposes a carbon-accountable P2P trading framework for multi-microgrids that traces carbon responsibility through bilateral transactions rather than treating carbon as an external penalty. It combines a belief-aware Stackelberg …
Neelam Gupta, Smita Goswami
This chapter reviews how AI—machine learning, NLP, computer vision, and blockchain—can address limitations in carbon accounting, emissions auditing, and corporate climate accountability. It argues AI enables >90% prediction accuracy, sub-ho…
Afonso Henrique Torres Lucas, Julianne Oliveira, ROBERTO HIGINO PEREIRA DA SILVA
Open code and outputs for an experiment that treats sustainability report pages as images: ColQwen2.5 multi-vector embeddings in Qdrant retrieve pages, and a vision-language model extracts Scope 1, 2 (market- and location-based) and 3 emiss…
Jeremiah Osida Onunga, Jared Okello
This systematic desk review (PRISMA-guided) examines how AI can support measurement, verification, and reporting of sustainability indicators in Kenyan public universities as mandatory non-financial reporting (e.g., IPSASB SRS1) approaches …
Vattappoyil Safas, Mohsin Khan
Using Sentence-BERT and an ESG taxonomy, this study analyzes ESG language in 310 Indian mainboard IPO prospectuses. ESG sentences average 11.4% of text, with governance dominating at 75.5% while social and environmental disclosure remain li…
Lauri Kokkinen
This paper proposes a new theme-based model, supported by AI, for analyzing ESG reports. It aims to extract and structure key themes from corporate ESG disclosures for comparable evaluation. By automating qualitative and quantitative analys…
(著者不明)
This work presents automated product carbon footprint (PCF) estimation using low-latency neural language processing across multilingual, real-time supply chains. It applies NLP to supplier text data to automate and accelerate per-product em…
Papan Hao, Madiha Afzal, Nimra Riaz +2
Using a balanced panel of 71 non-financial Pakistan Stock Exchange firms (2015–2024, 710 firm-years), this study examines how ESG disclosure affects corporate performance (ROA/ROE) and how audit quality moderates it. Combining System GMM wi…
Umut Kılıç, İlker Kılıç
A case study applying artificial neural networks to predict energy efficiency and carbon footprint on livestock farms in Turkey. It demonstrates how machine learning can estimate farm-level emissions and environmental performance in the agr…
Ivan Gentile, Motaz Saad, Kianna Kazemi +1
Standard PDF-to-Markdown pipelines silently discard embedded figures, creating a representation gap in RAG. Across 274 ESG reports, 21.7% of 48,896 images are data-bearing yet invisible to text-only retrieval. A vision-language augmentation…
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