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 501–520 of 716 papers
Khan T.
This paper proposes a conceptual framework for using AI in Scope 3 GHG emissions measurement, reporting, monitoring, and assurance from a critical systems perspective. It examines AI's potential to improve data quality and automation while …
Feiya Luo, Yanyun Nian, Pinqi Rao +2
This study proposes a scalable workflow integrating UAV LiDAR and hyperspectral data for individual tree species classification and carbon stock estimation in urban forests. Using an adaptive feature selection method (ACV-DCC) and RF classi…
Jacob Goldverg, Elvis Rodrigues, Tevfik Kosar
This paper proposes spatiotemporal scheduling methods for data transfers to minimize carbon footprint, leveraging AI/optimization techniques and accounting for temporal and geographical variations in carbon intensity. It contributes to ener…
Ivan Savin (5189054), Mateo López Carel, Eva Schlindwein
This paper uses regression to link topic prevalences extracted from ESG reports to environmental KPIs. It demonstrates how NLP and machine learning can quantitatively assess the relationship between disclosure content and actual environment…
Laurence L. Delina, Johanne Rei R. Castro, Yuet Sang Marie Tung
This chapter explores the use of AI, including a generative AI chatbot, to facilitate just energy transitions for vulnerable communities. It proposes strategies grounded in energy justice, reliable datasets, and participatory decision-makin…
Xavier Martínez-Barbero, Ana Pastor-Merino, Josep Domenech
This paper presents a nationwide dataset of 104,553 Spanish firms, using LLMs to extract energy transition practices (efficiency, decarbonization, renewables) from corporate websites. Aggregated at province, sector, and size levels for 2023…
Susanna Ruth Gruyal
This paper critically analyzes the IMF's proposed International Carbon Price Floor (ICPF) on AI carbon emissions through a law and political economy lens. It argues that market-based efficiency assumptions mask inequities, disadvantaging sm…
Das N.
This paper proposes an end-to-end framework integrating IoT, AI, and Layer-2 blockchain for real-time MRV (Monitoring, Reporting, Verification) and autonomous tokenization of carbon credits in industrial CCUS. AI is employed for automated v…
Agraj Magotra, Md. Rafiqul Islam Rana, F. S. Shishir +1
This paper proposes a machine learning and NLP pipeline for analyzing ESG and sustainability disclosures in the textile and apparel industry, applicable to supply chain disclosure analysis.
Thi-Kien Dao, Trong-The Nguyen
This paper proposes CASO, a framework for carbon-aware VM placement integrating adaptive RBF surrogate model with self-adaptive PSO-DE swarm optimizer. It minimizes carbon emissions, energy, SLA violations, and latency simultaneously under …
Kálita Cristina Araújo, Ana Carolina Fernandes Maciel, Bruno B. F. da Costa
This systematic review (PRISMA-based) examines whether automating CO2 emission calculation with AI in BIM can support low-carbon building design. From 2567 records (2021-2025), 85 studies were classified as Core (BIM+CO2+AI) or Base. 60% qu…
Lee H, Jung J, Roh J
This study proposes a Residual U-Net deep learning model to correct significant wave height forecasts from WAVEWATCH III, validated with real-ship fuel consumption data. The corrected forecasts show improved accuracy up to 7-8 days ahead, e…
Hanno Blankenstein
This paper presents a practical approach combining AI-driven visual analytics with automated drone-based inspection workflows for scalable emissions reduction and energy transformation in energy production. It overcomes limitations of tradi…
Ünal Kızıl, Cafer Türkmen, Yakup Çıkılı +2
This paper presents a low-cost, AI-enhanced electronic nose system for quantifying ammonia (NH₃) volatilization from fertilized soils, which contributes to indirect nitrous oxide (N₂O) emissions. Using machine learning, Gradient Boosting ac…
Zia U. Ahmed, Tek B. Sapkota, Md. Khaled Hossain +3
This study applies AutoML and explainable AI (XAI) to rice production systems in Bangladesh, identifying key yield predictors and estimating greenhouse gas emissions. Machine learning models reveal relationships between weather, soil data, …
Graziano E.A.
This paper provides a comprehensive review of AI applications in sustainable finance, covering ESG scoring, climate risk modeling, greenwashing detection, and related areas. It proposes an integrative framework that synthesizes current appr…
Yong Z.J.
This paper uses machine learning models to analyze the global impact of green finance on greenhouse gas emissions, suggesting that green finance policies contribute to emission reductions.
D. Hall
This essay proposes an AI-powered carbon intelligence platform for the global sports industry to track, predict, and reduce emissions in real time using data from transportation, energy, stadium operations, and supply chains. It argues that…
Kanan Aliyev, Emre Artun, B. Kulga
This study applies machine learning to quantify the economic viability and carbon mitigation potential of CO2 sequestration in shale reservoirs, supporting CCUS deployment decisions.
Wenqi Liao
This dataset examines the link between AI hiring intensity and corporate carbon performance using firm-level panel data. It includes variables on AI exposure, policy adoption, and workforce composition to explore how AI integration shapes f…
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