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 321–340 of 403 papers
Ellen Kulset, Charlotte Haugland Sundkvist, Tonny Stenheim
This study uses dictionary-based tone analysis to examine climate disclosures under the mandatory CSRD/ESRS framework. Analyzing firms listed on the Oslo Stock Exchange, it finds that disclosures are generally not neutral: firms use more po…
Isaiah Oluwasegun Owolabi
This paper presents an ESG-as-Code framework that translates ESG regulatory texts (CSRD, SDR, SEC) into machine-executable compliance logic. It demonstrates how containerized AI workflows on Kubernetes-OpenStack enable automated, auditable,…
Abhinav Mahajan
This paper frames corporate climate risk as a multi-domain decision problem and proposes the Climate AI Decision System (CADS) architecture that integrates AI for evidence-based management. It synthesizes literature on climate finance, ML f…
Basaula Pululu Jordan, Xinyu Yang, Y Shi +5
This paper proposes an uncertainty-aware BIM-LCA methodology integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine learning optimization to reduce embodied carbon in building design. Using a case s…
Zaidoon Alhatabat, Diala Ershaid, Nashat Ali Almasria +1
This paper reviews current trends in integrated reporting (IR) within the convergence of ESG, digital transformation, and Industry 5.0. It finds that AI, NLP, and blockchain enhance reporting accuracy and timeliness but introduce ethical an…
Xiyue Cao, Xujiang Qin, Yanqiu Zuo +2
This review comprehensively synthesizes AI applications for carbon emissions management across monitoring (satellite remote sensing, sensor networks, ML), prediction (deep learning, ensemble learning, statistical learning), and reduction (i…
Murali Krishna Pasupuleti
This paper presents a comprehensive socio-technical framework for AI-enabled EV charging optimization, covering modeling, causal inference, ML prediction, trustworthy AI, MLOps, and policy analytics. It emphasizes balancing cost, reliabilit…
Butler T.
This paper proposes an ISO-compliant upper-domain ontology for EU sustainability reporting, leveraging AI to automate and standardize reporting and decision-making. It aims to enhance interoperability and efficiency in sustainability disclo…
Wu J, Zhong S, Lian X +5
This study evaluates coordinated adaptive signal control (ATSC) and autonomous vehicles (AVs) for congestion and carbon reduction using micro-simulation across a real urban corridor and 100 Chinese cities. The coordinated strategy outperfor…
Kumar S.
This paper uses machine learning to analyze big data from scholarly research on sustainable finance, revealing the evolution of research themes and future directions. It provides a comprehensive overview of the field, highlighting key topic…
Akhai S.
This paper discusses how AI and automation can help factories achieve net-zero emissions and sustainable manufacturing. It highlights the role of AI in improving energy efficiency and reducing emissions in the manufacturing sector.
Gourav Kumar Rath, Jesús David G. Palencia, A. Dalai
This review comprehensively assesses thermochemical biomass valorization pathways, emphasizing hydrothermal liquefaction (HTL) for simultaneous biocrude and hydrochar production. It covers upgrading processes and AI/ML applications (Random …
C. Bhargava, Bhavya Tiwari, P. Bhatnagar +10
This paper develops an XGBoost-based framework to predict CO2 adsorption capacity and classify adsorbent materials using process and material parameters. A comprehensive dataset including activated carbon, zeolites, MOFs, and others was use…
Bo Zhang, Xiao‐Min Zou, Xin Wen +4
This review comprehensively synthesizes machine learning (ML) applications for predicting the cycle life of lithium-ion and sodium-ion batteries. It compares supervised, unsupervised, semi-supervised, and deep learning algorithms, highlight…
KUROSAWA, ISAO
This paper presents ARGUS, an end-to-end deep learning pipeline that jointly estimates hypocenter location, centroid moment tensor, and peak ground acceleration from as few as four to eight stations in 17 ms, targeting induced seismicity mo…
A. Mikhaylov, S. Barykin, D. Dinets +9
This paper proposes a Grok-based temporal fusion transformer framework for multi-horizon coastal flood risk forecasting. It uses the optimized Grok algorithm to improve time series analysis accuracy and support strategic adaptation planning…
Said Zulfigarzada, Fatulla Alizade
This review systematically examines AI's role in enhancing the resilience of integrated energy systems for climate-neutral cities. It covers applications in demand forecasting, renewable generation prediction, fault diagnosis, and cyber def…
Vahit Çalışır
This study quantifies CO2 emission changes from the February 2023 Kahramanmaraş earthquakes using AIS port visit data and GNN modeling. It finds a 35.9% increase in per-visit CO2 during the acute disruption phase, with 27,574 tonnes of exce…
Y. Yardımcı, Yasemin Erbil
This review analyzes AI-integrated LCA research in construction. ML and ANN are used to predict energy and carbon, but integration is fragmented due to unstructured data and lack of standards. Focus is on operational energy, neglecting embo…
Anna Szewczyk, J. Dzwierzynska
This systematic review evaluates how AI, Generative Design, and BIM contribute to sustainable development in construction. Using the PRISMA protocol, it synthesizes evidence on algorithmic intelligence supporting UN SDGs (9, 11, 12, 13). Fi…
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