GX & Decarbonization Research

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…

Salinas JVR, Calderon AD, Macayan JV +1

This review comprehensively synthesizes AI techniques (ML, DL, RL, fuzzy logic) applied to solar, wind, and hydropower systems. Deep learning achieves up to 50% error reduction in forecasting, while RL and hybrid AI-physics models enable ad…

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 …

Nivetha Martin, Rajkumar S, Said Broumi

This study proposes an integrated neutrosophic MCDM framework with AI (random forest, SHAP) for selecting Li-ion batteries for BEVs. Unlike simple weighted sum methods, it applies multiple MCDM techniques to determine criterion weights and …

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 …

Jiapeng Dai

This study examines how forward integration into global value chains affects energy poverty, moderated by governance. Using panel data from 57 economies (2005-2022) and estimators including double machine learning, it finds that forward int…

Hasitha Priyashantha, Imasha S. Jayathissa, Janak K. Vidanarachchi +3

This policy paper synthesizes evidence on climate adaptation and mitigation for livestock systems in South Asia, emphasizing climate-smart interventions including precision livestock farming with AI and sensors, traditional knowledge, and r…