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 1055 papers
Bharathi S., G. Poornima
This paper reviews how AI applications in logistics—route and fleet optimization, predictive maintenance, demand forecasting, warehouse automation, and carbon accounting—can reduce emissions and optimize resources. Drawing on secondary sour…
Manktelow, James
With no provider disclosing per-query inference energy or carbon, this paper offers a bounded estimation framework that triangulates public data to derive per-token carbon intensity. Applied to OpenAI's GPT-5 family, it estimates ~1.07–14.8…
Shi J.
Proposes a rule-guided BERT model to automatically assess ESG disclosure quality, validated on Chinese listed firms' annual reports. It combines rule-based cues with a pretrained language model to quantify disclosure quality, sitting at the…
Sitabhra Majumder, Vrinda Venugopalan Nair, Mousomi Ghosh +1
This review explores how agentic AI and data-driven advisory systems can optimise nitrogen use efficiency and enable accurate MRV of soil carbon gains in rice straw-incorporated paddy systems. By combining AI-based precision nitrogen manage…
Mohd Kamil Vakil, Mohamed Yusuf Alkoheji, Shahrukh Ahmad
This paper proposes a Food-System Carbon Data Layer that integrates AI-driven automated LCA digital twins as an uncertainty-aware carbon data layer for urban digital twins. It addresses boundary alignment, uncertainty propagation, semantic …
Farah Nasri, Salim Ben Sassi
This study applies NLP to quantify word frequency in ESG disclosure texts and uses machine learning to predict US stock market risk. It tests whether timely response to shifts in disclosure language improves risk prediction. It offers a met…
Jaehyung Bark, Xiaoying Du
Using 12,814 Chinese A-share firm-year observations (2020–2023), the study builds two text-based indicators of AI strategy and AI governance disclosure from ESG reports. Overall ESG performance—especially the governance pillar—is positively…
Riham Alkabbji
This paper examines how using AI to automate ESG data analysis affects sustainable financial reporting in the digital age. It likely addresses efficiency and accuracy gains in ESG disclosure workflows. No abstract was available, so methodol…
Sohyun Baek, Dayeon Lee
This paper reports the development of an AI-based online tool designed to support the habit-formation process for carbon-neutral behaviors. It combines behavior-change/habit-formation theory with AI-driven personalization and feedback to en…
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 …
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