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 61–80 of 265 papers
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…
Samson Mwakapoma, Bertha Mwaituka, Ally Ngulugulu
This study explores the use of AI in clean cooking technologies in Tanzania to improve system performance and access to carbon credits. Key AI functions include real-time monitoring, predictive maintenance, user behavior analysis, and AI-ba…
Hang Yang, Hongli Diao, Shibin Xia
This paper discusses how AI technologies revolutionize carbon capture, utilization, and storage (CCUS). Machine learning and optimization algorithms enable efficient CO2 capture, storage site selection, and process monitoring. It highlights…
Jiahong Qi, Biying Yu, ZM Xu +15
This paper investigates trade-offs and synergies between educational equity and low-carbon transportation under school enrollment policies. It uses an XGBoost model to identify key determinants of school travel mode choice and a Nested Logi…
Ivan Savin, Mateo López Carel, Eva Schlindwein
This study applies computational linguistics to 1,477 ESG reports from STOXX Europe 600 companies, identifying 34 topics (six environmental). It finds that topics like sustainable value chains and renewable energy are associated with improv…
Shabanpour, Negar, Roche, Stephane, Mellouli, Sehl
This study uses BERTopic to analyze Canadian Reddit posts on individual climate actions, identifying four behavioral domains: energy, transportation, diet, and consumption. Different model configurations provide complementary insights, offe…
Gihun Gil, Minu Baek, Jio Yoo +5
Proposes a conformal prediction-guided approach to optimize carbon emissions in paper-mill energy systems, balancing emission reduction with cost efficiency while quantifying uncertainty.
Guohao Li
This study develops a confidence-graded framework for mangrove restoration planning across the Indo-West Pacific, integrating random forest, MaxEnt, and Delphi-based evidence. High-confidence opportunities are limited to 2,235 km², with eco…
Kharisma Rinandyta, Rizki Atthoriq Hidayat, Surya Hafizh
This study quantifies seagrass blue carbon stock in Teluk Saleh, Indonesia, and projects its dynamics to 2035. Using Sentinel-2 imagery and SVM classification for mapping (2019-2025) and ARIMA for projections, results show significant tempo…
Diptyaroop Maji, Kang Yang, Prashant Shenoy +2
CarbonX is an open-source tool for computational decarbonization that leverages time series foundation models. It enables accurate carbon emission estimation and optimization using AI, providing a scalable solution for decarbonization effor…
Vidya N
This study quantifies sediment organic carbon in mangrove-seagrass ecotones using Sentinel-2 remote sensing with Random Forest classification and sediment coring. Ecotones show intermediate carbon concentrations, storing an estimated 142 ± …
de la Cruz-Azuara J.E.
This paper critically reviews machine learning applications for CO2 geological storage and enhanced oil recovery (EOR) in hydrocarbon reservoirs. It identifies the sim-to-real gap and proposes a roadmap for field-scale deployment, offering …
Browse by Topic