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 21–40 of 271 papers
Ivan Savin (5189054), Mateo López Carel, Eva Schlindwein
Using computational linguistics on 1,477 ESG reports from STOXX Europe 600, this study identifies 34 main topics (six environmental). It finds that topics like sustainable value chains and renewable energy correlate with improved environmen…
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…
Mengxi Xie
This dissertation combines machine learning (NLP) with econometrics to empirically analyze environmental policies. The first three studies examine the EU's Carbon Border Adjustment Mechanism (CBAM) via meta-analysis, US equity market reacti…
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…
Ahmed G. Abo-Khalil
This paper proposes a unified framework integrating AI-driven renewable forecasting, grid economics, and lifecycle assessment. Using deep learning, it reduces prediction errors by 50% and operational costs by 18.7%. The study includes AI en…
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…
Chris Meniw
This whitepaper analyzes the dual role of AI: optimizing power grids and integrating renewables while increasing energy and carbon footprint from compute infrastructure. It examines deployments in smart grids, industrial optimization, and r…
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.
Kowalik R.
This paper reviews AI applications in photovoltaic-integrated buildings, covering energy forecasting, intelligent control, and net-zero performance. It demonstrates the effectiveness of machine learning and optimization methods. The study e…
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.
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