GX & Decarbonization Research

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…

Sarah Meier, Ben Balmford, Ville Inkinen

This paper evaluates the additionality of protected areas in Bolivia using a Random Survival Forest model to predict deforestation risk. On average, protected areas reduce deforestation by 0.19 percentage points (68%), but effects are highl…

Simona Bigerna, Tulia Gattone, Cosimo Magazzino

This paper examines structural constraints on energy transition in low- and lower-middle-income countries. It finds that fossil fuel dependence, resource rents, and methane-intensive production reinforce carbon lock-in, while renewable depl…

(著者不明)

This paper explores AI-driven carbon accounting methods to enhance transparency in ESG reporting. It discusses how automation of data collection and analysis can improve accuracy in Scope 1/2/3 calculations and disclosure reliability. The w…

Haizhou Wang, School of Business, University of Chinese Academy of Social Sciences, Beijing, 102488, China

This paper empirically examines how China's carbon emissions trading pilot promotes corporate digital transformation, using dual machine learning and difference-in-differences on panel data of A-share listed firms from 2010-2021. It identif…

Timileyin Abiodun, Nnamdi Nwulu, Peter Olukanmi

This paper proposes a multi-agent AI framework integrated with blockchain-based audit trails to secure industrial greenhouse gas monitoring infrastructure. It enhances data integrity and transparency, supporting credible carbon accounting a…

C. Ghenai

This study assesses the performance of green hydrogen power systems for AI data centers and develops AI-based short-term energy forecasting models. It models an off-grid solar PV/hydrogen fuel cell system and evaluates technical, financial,…

(著者不明)

This scoping review examines the application of artificial intelligence (AI) to forest carbon accounting. It finds that AI methods such as machine learning and remote sensing can improve estimation accuracy and monitoring efficiency, but al…

Habes Ali Khawaldeh, Moath Bani Fayyad, Mohammad Al-Smairan, Wasseem Al Rousan and Omar Alnhoud

This paper designs a fixed-dome biogas plant for a 200-cow dairy farm in Jordan, performing techno-economic and environmental assessment with LSTM-based AI forecasting. Results show a 4-year payback, LCOE ~0.093 USD/kWh, and 28.46 tCO2/year…