GX & Decarbonization Research

Y Y Li, Yu Zhang, Xuanang Gui +3

This paper proposes a risk-aware coupled electricity-carbon market trading framework combining safe deep reinforcement learning, knowledge assistance, and Conditional GAN. Domain knowledge from physical market models and rule-based protecti…

João Felipe Freitag, Lucas Kovaleski, Cleomar Reginatto

This paper proposes integrating geoprocessing and AI to support the Sustainable Development Goals under climate change. While no abstract is available, the title suggests the development of methods for monitoring and predicting environmenta…

Johnson O Abiola, Humbulani Simon Phuluwa, David Aborisade +3

This study applies deep reinforcement learning (SAC algorithm) to optimize hybrid hydrogen-battery storage in a renewable microgrid. Using a Markov Decision Process model, it reduces operational costs by 2.0% and achieves smoother power tra…

Nathan Udoinyang, Reuben Daniel, Akarue Blessing Okiemute Okiemute +1

This study evaluates the cost-benefit, energy sustainability, and technological implications of AI adoption in Nigeria's agricultural and waste-to-energy (WTE) systems. Based on survey data from 522 respondents, findings indicate moderate-t…

May, Jacinta, De Matteis, Nicolas

This paper proposes ClimateChem-QX, a quantum-accurate AI pipeline for climate catalyst discovery. Compared to DFT errors up to 861 meV, SQD+Krylov achieves 0.006 meV accuracy. Active learning reduces quantum oracle calls by 35%. Results su…

Mohammed Abdalghafoor, IJETRM Journal

This review comprehensively analyzes how AI-powered Energy Management Systems (EMS) can contribute to sustainable decarbonization. It surveys recent advances in machine learning, deep learning, reinforcement learning, predictive analytics, …

(著者不明)

This study proposes a coupled LSTM model to predict blue carbon sequestration and fishery dynamics in tropical coastal wetlands under climate change. Blue carbon is crucial for climate mitigation, and fisheries support local economies. It d…

Soufiane El Khiam, Lampros Litos

This paper synthesizes industry perspectives on reducing Scope 3 emissions in manufacturing, highlighting challenges such as data collection and supplier engagement, and opportunities through AI-driven tracking and optimization. It connects…

Mahdi IBRAHIM, Fatima HARKOUSS, Pascal BIWOLE

This paper applies a machine learning model to building retrofitting strategies for achieving net zero energy under future climate scenarios. It provides a data-driven framework for evaluating and optimizing retrofit options, demonstrating …

Pattanaporn Chatjuthamard, Pandej Chintrakarn, P. Jiraporn

Using NLP-derived sentiment metrics from earnings calls, this paper analyzes how corporate climate optimism evolves and the role of independent directors. Optimism increases over time; independent directors initially dampen it but effect di…

Mohd Nadeem, Ankit Singh, Shreya Yadav +1

This paper proposes a framework using AI for carbon footprint assessment and green AI lifecycle management, aiming to achieve sustainable AI through methodological contributions.

Yuliana Susilowati, Ayubella Anggraini Leksono, Elsa Rakhmi Dewi +5

This study integrates remote sensing and AI to analyze mangrove dynamics and blue carbon potential in Semarang, Indonesia. By applying machine learning to satellite imagery, it maps mangrove changes and estimates carbon storage, providing a…

Ding Q, Anandarajah G, McDowall W

This study integrates AI-enabled social sensing with energy system modeling to identify least-regret VRE pathways, using China as a case. Unlike cost-optimal baselines, the least-regret strategy requires spatially distributed deployment for…

Venios, Stefanos, Velmachos, Thodoris, Georgiadis, Panagiotis +2

This report presents the development of the HarvRESt cloud platform and dashboard for farm energy management. AI models (LightGBM, XGBoost) provide forecasts for PV, wind, demand, biogas, and battery flexibility. A Self-Consumption Maximisa…

Alshahmy S.

This paper explores how artificial intelligence (AI) can enhance the issuance and monitoring of sustainable finance instruments such as green bonds and sustainability-linked loans. AI can streamline issuance processes, improve tracking and …

Onur Sevli

This paper presents a benchmark dataset (1,000 records, 12 features, 3 balanced classes) for classifying carbon footprint of ML model training, grounded in the Green AI emission formula. It adds log-space Gaussian noise (sigma=0.20) to mimi…

Talemwa Byomutonzi Johansen, Mwema Felix Mwema, Silas Mirau +1

This study proposes a hybrid framework integrating regression, time-series, and machine learning models for land-use carbon accounting, demonstrated via a Tanzanian case study. Random Forest and XGBoost showed lower prediction errors, but r…

Shadfar Davoodi, Mohammad Moosazadeh, Geovanny Branchiny Imasuly +3

This review comprehensively overviews the application of explainable AI (XAI) to subsurface carbon capture, utilization, and storage (CCUS) systems. XAI improves interpretability of complex subsurface processes, enhancing safety and efficie…

G Li, Hongxin Guo, Jian Kang +5

This corrigendum refers to a framework that uses machine learning to optimize multi-scale spatial forms within urban blocks for low-carbon performance. It analyzes the relationship between building morphology and energy consumption, aiding …

Sejal Jain, Pranav Singh, Sachin kushwaha +1

This paper presents CarbonLens, an AI-powered carbon footprint tracking and sustainability analytics platform for personal emissions. It uses React.js, Node.js, Express.js, MongoDB, and Ollama-based LLMs to automatically estimate emissions …