GX & Decarbonization Research

Xiyue Cao, Xujiang Qin, Yanqiu Zuo +2

This review comprehensively synthesizes AI applications for carbon emissions management across monitoring (satellite remote sensing, sensor networks, ML), prediction (deep learning, ensemble learning, statistical learning), and reduction (i…

Murali Krishna Pasupuleti

This paper presents a comprehensive socio-technical framework for AI-enabled EV charging optimization, covering modeling, causal inference, ML prediction, trustworthy AI, MLOps, and policy analytics. It emphasizes balancing cost, reliabilit…

Butler T.

This paper proposes an ISO-compliant upper-domain ontology for EU sustainability reporting, leveraging AI to automate and standardize reporting and decision-making. It aims to enhance interoperability and efficiency in sustainability disclo…

Wu J, Zhong S, Lian X +5

This study evaluates coordinated adaptive signal control (ATSC) and autonomous vehicles (AVs) for congestion and carbon reduction using micro-simulation across a real urban corridor and 100 Chinese cities. The coordinated strategy outperfor…

Kumar S.

This paper uses machine learning to analyze big data from scholarly research on sustainable finance, revealing the evolution of research themes and future directions. It provides a comprehensive overview of the field, highlighting key topic…

Akhai S.

This paper discusses how AI and automation can help factories achieve net-zero emissions and sustainable manufacturing. It highlights the role of AI in improving energy efficiency and reducing emissions in the manufacturing sector.

Gourav Kumar Rath, Jesús David G. Palencia, A. Dalai

This review comprehensively assesses thermochemical biomass valorization pathways, emphasizing hydrothermal liquefaction (HTL) for simultaneous biocrude and hydrochar production. It covers upgrading processes and AI/ML applications (Random …

C. Bhargava, Bhavya Tiwari, P. Bhatnagar +10

This paper develops an XGBoost-based framework to predict CO2 adsorption capacity and classify adsorbent materials using process and material parameters. A comprehensive dataset including activated carbon, zeolites, MOFs, and others was use…

Bo Zhang, Xiao‐Min Zou, Xin Wen +4

This review comprehensively synthesizes machine learning (ML) applications for predicting the cycle life of lithium-ion and sodium-ion batteries. It compares supervised, unsupervised, semi-supervised, and deep learning algorithms, highlight…

KUROSAWA, ISAO

This paper presents ARGUS, an end-to-end deep learning pipeline that jointly estimates hypocenter location, centroid moment tensor, and peak ground acceleration from as few as four to eight stations in 17 ms, targeting induced seismicity mo…

A. Mikhaylov, S. Barykin, D. Dinets +9

This paper proposes a Grok-based temporal fusion transformer framework for multi-horizon coastal flood risk forecasting. It uses the optimized Grok algorithm to improve time series analysis accuracy and support strategic adaptation planning…

Said Zulfigarzada, Fatulla Alizade

This review systematically examines AI's role in enhancing the resilience of integrated energy systems for climate-neutral cities. It covers applications in demand forecasting, renewable generation prediction, fault diagnosis, and cyber def…

Vahit Çalışır

This study quantifies CO2 emission changes from the February 2023 Kahramanmaraş earthquakes using AIS port visit data and GNN modeling. It finds a 35.9% increase in per-visit CO2 during the acute disruption phase, with 27,574 tonnes of exce…

Y. Yardımcı, Yasemin Erbil

This review analyzes AI-integrated LCA research in construction. ML and ANN are used to predict energy and carbon, but integration is fragmented due to unstructured data and lack of standards. Focus is on operational energy, neglecting embo…

Anna Szewczyk, J. Dzwierzynska

This systematic review evaluates how AI, Generative Design, and BIM contribute to sustainable development in construction. Using the PRISMA protocol, it synthesizes evidence on algorithmic intelligence supporting UN SDGs (9, 11, 12, 13). Fi…

N. Albelwi

This paper introduces FedCarbon, a federated learning-based building stock modeling system that enables collaborative embodied carbon benchmarking without central data aggregation. Using hierarchical federated aggregation with attention-bas…

N. Albelwi

This paper proposes DT-LCAF, a digital twin-enabled LCA framework for real-time embodied carbon optimization in construction. It integrates BIM, IoT, and ML (graph attention networks and reinforcement learning), validated on proxy data from…

Zhihan Qiu

This paper examines the application of digital technologies (IoT, big data, AI) in ship energy efficiency management to comply with IMO regulations like CII. It analyzes practices in fuel monitoring, route optimization, and power control, i…

Shiwei Zhou, Xinglong Liu, Feng Zhang +1

This paper proposes a genetic algorithm (GA)-based ship path optimization framework that uses AIS data to incorporate wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. Evaluated on real AIS datasets, i…

Doru Coșofreț, Florin Postolache, Adrian Popa +2

This paper proposes a hybrid digital CO2 emission-control technology for maritime transport using physics-informed adaptive speed optimization. It integrates exact optimization (Backtracking, Dynamic Programming) with reinforcement learning…