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 101–120 of 743 papers
S. Shin, Seon-Mi Kim, Seung-Weon Yoo
This study uses text mining to analyze sustainability reports of Korean listed firms, quantitatively assessing ESG disclosure similarity and completeness. Similarity declined over time but rose in 2023 due to postponed mandatory disclosure.…
Marek R. Helinski
This paper presents a generative-AI decision-support framework that optimizes sustainable logistics by integrating EU policy variables (CBAM, EU ETS for maritime, FuelEU, Digital Product Passport, CSRD) into planning equations. Using genera…
R. Purushothaman, R. Alamelu, Sathya Manickam +2
This chapter examines how AI and emerging technologies reshape sustainable supply chains amid ESG goals and tighter regulations. It covers AI-driven design and inventory optimization to cut waste and emissions, plus blockchain for transpare…
(著者不明)
This article presents Nutritics' AI-based technology that automatically matches food items to lifecycle inventory carbon and water data, enabling food service companies to measure and report Scope 3 emissions. It highlights the current lack…
Bamidele Samuel Adelusi, Abel Chukwuemeke Uzoka, Yewande Goodness Hassan +1
Scope 3 emissions are hardest to manage across Tier-2 supplier networks. This study combines LCA with multi-objective genetic algorithms to find Pareto-efficient strategies balancing carbon reduction, compliance cost, and operational contin…
Harish Narne
This paper proposes a cloud-based system that uses AI, IoT sensors, and big data analytics to monitor carbon footprints in real time and generate predictive reports. The scalable infrastructure enables continuous observation of inter-indust…
X.F. Wang
This paper examines how AI can empower sustainable supply chains, addressing efficiency-environmental protection-equity challenges. It identifies key AI capabilities such as demand forecasting, logistics optimization, risk management, suppl…
Yunjing Wang, Jinfang Tian, Siyang Sun +2
Using LLM and text analysis of Chinese listed firms' blockchain patents (2010–2022), this study shows that blockchain innovation reduces fossil energy intensity. Mechanisms include lower internal control costs, stronger external oversight, …
Andreas Froemelt, Thomas Wiedmann
This study employs a two-stage clustering approach (Self-Organising Map and Ward clustering) to analyze household consumption patterns and lifestyle carbon footprints in Sydney and Melbourne. By combining expenditure data with socio-economi…
Azeez Lamidi Olamide, Omolola Badmus
This study proposes a comprehensive evaluation model to improve carbon accounting accuracy across Scope 1, 2, and 3. It integrates machine-learning anomaly detection, uncertainty quantification, and materiality assessment, aligning with GHG…
ZHANG Chunhui^1^ ZHANG Zhen^2^
This paper examines the power generation sector, the first to launch domestic carbon emissions trading. It presents management measures and implementation schemes for emissions trading, highlighting the role of smart meters in emissions acc…
Wei Yigang, Shi Jiawei, Xu Guannan
This study analyzes 1,743 low-carbon policies issued in China from 2000 to 2021 using text mining and policy econometrics within a framework of 'policy objectives—policy themes—policy fluctuations.' It systematically reviews the development…
Zihang Wang, Ling Zhang, Mengmeng Pu
This study develops a machine learning framework to predict project-level embodied carbon emissions in buildings across design stages, using data from 78 projects (426 buildings). The residual-corrected weighted ensemble model achieved the …
Sadhasivam Mohanadas, Leela Prasad Gorrepati
COSMOS-R2 proposes an AI-driven autonomous cloud scaling method using Constrained MDP control, enforcing risk bounds and carbon caps while optimizing resource allocation. It addresses the trade-off between carbon emissions, performance, and…
Bora ÖÇAL
This study estimates carbon footprints in logistics using machine learning on synthetic data generated from variables such as distance, cargo weight, and transport mode. Six algorithms were compared; XGBoost achieved the best performance (R…
Ahmadinia M.
Reports a practical application of a sustainability decision assistant for industrial net zero pathways, demonstrated at Unipart Manufacturing. The tool likely employs AI/ML to help manufacturers evaluate and implement decarbonization optio…
Yang M.
This paper examines how data mining techniques applied to electrified vehicles can support net zero carbon emission targets. It reviews the challenges and opportunities in using data analytics for EV energy management, charging infrastructu…
Zheng Zhi, Guoqing Chen
This paper examines the role of artificial intelligence in ESG disclosure and sustainable finance, arguing for a shift from narrative reporting to data-driven transparency. It highlights how AI can automate extraction, analysis, and reporti…
Capt Yashodhan Mahajan
This paper explores how AI can transform sustainability and audit reporting from compliance-driven processes to intelligence-driven ones. It argues that AI enables deeper analysis, risk assessment, and higher-quality disclosures, offering s…
Dely Ramírez, Jonathan Muñoz Tabora, Ozy D. Melgar‐Dominguez
This study assesses GDP-CO2 decoupling in Honduras (1990-2023) using k-means, PELT, Tapio index, EKC modeling, Granger causality, and Random Forest. An inverted-U EKC is supported, but renewable share has negligible predictive power, sugges…
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