GX & Decarbonization Research

Khushi Jatolia

This paper proposes Smart Carbon Tracker, an AI-driven web application for tracking, analyzing, and predicting carbon emissions using machine learning. It combines a React frontend, Node.js backend, MongoDB, and a Python AI module to help u…

Heyuan Wang

This repository implements Carbon Mind, a global spatiotemporal AI system for reconstructing 120-year multiscale changes in the land carbon sink. It includes all analyses and visualizations from the associated manuscript.

Yanhua Zhang, Gongguo Wu, Chujun Zou +4

This study developed the first coupled LSTM framework to predict bidirectional relationships between blue carbon stocks and fishery abundance in tropical coastal wetlands. Using 96 monthly field observations from 15 sites in China (2018-202…

Xiaoyu Yang, Liyong Yan, Haichao Zheng

This paper proposes a method to quantify community carbon offsets using point cloud segmentation of buildings and environment. By extracting vegetation and structures, it estimates carbon storage and emissions, supporting local GHG manageme…

Xuejun Wang, Qibin Han

This paper proposes a BIM-CIM integrated decision-making method combining NSGA-II and energy simulation for low-carbon urban renewal. Experiments show convergence distance decreasing from 165.40 to 105.20 over 50 generations, with clear syn…

Demet Ozturk, Nuh Erdoğan, Reza Vatankhah Barenji +1

Proposes a dynamic knowledge graph framework to assess hosting capacity for low-carbon technologies (solar PV, EVs) in low-voltage distribution networks, enabling efficient grid planning and operation.

Bin Li, Weilong Yang

This paper develops an AI framework combining symbolic regression, reinforcement learning, and LLMs to optimize photothermal performance of low-carbon building materials. The method is validated in urban microclimate simulations, showing si…

Cheng Feng, Fengqi You

This dataset supports the study showing that existing hydropower can provide firm low-carbon capacity for datacenters. It includes SWAT+ models for 81 global regions, machine-learning streamflow predictions using LSTM-Transformer, and a dat…

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…

Shilin Ou, Yifan Xu, Luyao Zhang

This paper introduces SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents in decentralized energy markets. It integrates an LLM-based Planner/Auditor layer to supervise RL agents, revealing a utility-…

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.