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 521–540 of 716 papers
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 …
Indra Kumar Pandey, Sulekh Kumar, Brajkishor Prasad +3
This study applies ensemble machine learning with ant colony optimization (ACO) to predict carbon emissions from fly ash and slag-based geopolymer concrete. The ACO-enhanced XGBoost model achieved the highest accuracy (R2=0.97), though diff…
Pengfei Xu, Siqi Jia, Y Cao +5
This study integrates percolation theory with spatiotemporal clustering to objectively delineate ground transport carbon emission (TCE) clusters for 323 Chinese cities. Using random forest and interpretable ML, it reveals regional heterogen…
Xuesi Chen, Ilan Mandel, Eren Yıldız +2
Presents MicroGreen, a design-time framework for carbon-aware edge ML systems. It combines component-level carbon models, workload profiling, and environment-aware energy analysis to identify carbon-optimal configurations. A real-world depl…
Addhwaith S Ajith, Aaron John Joy, Vaishnav Biju +2
This paper proposes CarbonCred AI, an AI-driven digital MRV framework for carbon credit analysis and valuation. It aims to enhance efficiency and accuracy in carbon credit measurement, reporting, and verification using artificial intelligen…
J Zhang, Yan Gong, Hairuo Wang +4
This study integrates emergy analysis, carbon footprint accounting, and LSTM neural network modeling to investigate how river–coast connectivity affects coastal ecosystem services and blue carbon in the Yellow River Delta. High-connectivity…
Ni Li
This study quantifies the carbon footprint of Yunnan's cut rose supply chain using LCA, and proposes a low-carbon transformation path combining AI precision agriculture, blockchain traceability, and IoT-enabled collaborative governance. It …
Yan Hao, Yanmei Yuan, Hui Liu
This study proposes a data-driven approach for fertilization behavior recognition and low-carbon decision optimization using multi-source agricultural time-series data. It combines LSTM with attention mechanism for behavior recognition and …
Ning Hu, Zhiyuan Tang, Shuaijia He +3
This paper proposes a multi-agent reinforcement learning based bidding strategy for virtual power plants (VPPs) in the spot market under carbon trading. It develops an AI method for VPPs to optimize bids considering carbon costs, improving …
Yu-Cian Lin, Ying-Chieh Chan
This study develops a multi-objective optimization framework for urban greening that visualizes trade-offs among cost, carbon sequestration, and shading. Using 3D parametric modeling and evolutionary algorithms with localized data from Taiw…
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