GX & Decarbonization Research

Yassine Charabi

This data descriptor presents the Carbon Valley dataset, a probabilistic carbon-accounting framework for AI infrastructure. It includes 1.92M annual records across four deployment pathways (Lift-Off, Base, Headwinds, High Efficiency) spanni…

Adel Elgammal

This study proposes an intelligent control framework integrating machine learning-based multi-objective optimization and robust sliding mode control for a solar-powered direct air capture (DAC) and electrochemical carbon utilization system.…

Xu Cheng

This paper proposes a transient multiphysics hybrid energy storage framework integrating physics-based battery, thermal storage, and supercapacitors with a hybrid physics–deep learning surrogate strategy using PINN and Transformer models. I…

Xue Jiang, Xiaoli Ji

This paper applies machine learning (automated preprocessing, GNN, reinforcement learning, multi-objective optimization) to optimize path selection for green/low-carbon industrial clustering. Under the context of renewable energy integratio…

Waqas Ahmed

This study examines the impact of Big Data Analytics (BDA) and ESG disclosure on corporate budgeting forecast accuracy using a sample of US and EU firms (2015-2024) with a hybrid econometric and machine learning approach. It finds that firm…

Hui Mao, Benyan Tan, Ribesh Khanal +2

This paper uses large language models (LLMs) to analyze the strategic choices of photovoltaic generators, grid enterprises, and government in the marketization of new energy electricity prices. It combines an evolutionary game model with LL…

Yuanshang Ji, Bin He, Sheng Yu +1

This paper proposes a method to predict carbon footprint at the manufacturing stage during product design driven by large language models (LLMs). Using a vibration isolation platform as a case study, it demonstrates the feasibility of quant…

Sonali R. Kanthe, Shruti A. Nikalje, Mahesh S. Hol +3

This paper proposes an AI-powered carbon footprint tracker that helps individuals measure and reduce their environmental impact. The system integrates modules for carbon calculation, an AI-driven green chatbot, AR visualization, and report …

Xiang-Bo Tang, You-Wei Huang, Han Su

This study constructs a machine learning-based prediction model for net carbon sink in Chinese agriculture and analyzes driving factors. Results show that effective irrigated area is the most significant factor, affecting crop carbon absorp…

Ming Li, Jun Wang, Xihang Li +3

This paper proposes a Hybrid Whale Optimization Algorithm (HWOA) for flexible process planning in part manufacturing under low carbon constraints. It aims to reduce carbon emissions while maintaining production efficiency, showcasing AI app…

Sun J, Wenxiang Peng

This study uses double machine learning to evaluate the impact of digital trade (cross-border e-commerce pilot zones) on pollution control and carbon mitigation (PCCM) in 280 Chinese cities (2011-2023). Digital trade significantly improves …

Changyi Weng, Zhenghua Shu, Jueying Qian +2

As China's ETS expands, reliable emission data becomes critical. This study proposes a hybrid anomaly detection framework using the ratio of material-based to flue gas-based emissions, combining Hartigan's dip test and Random Forest. Evalua…

emilywang0525-ui

This release archives custom machine learning code for provincial railway-operation carbon-footprint accounting and projection in China. It uses ensemble model screening, SSP-based future projection, baseline harmonization, and uncertainty …

(著者不明)

This study proposes an integrated approach combining satellite-ground observations, machine learning, and ecological process modeling to improve forest carbon sink accounting. The method enhances the accuracy of carbon budget estimates, sup…

Erlin Jin, Yang Bai, Dongning Feng +2

This study developed the DeepSeek-Python-ArcGIS (DPA) framework for blue carbon storage estimation in Daya Bay mangroves. It used LLM-driven code generation to process Sentinel-2, GEDI LiDAR, and UAV data, achieving 92.13% accuracy in mangr…

Ali Asghar Firoozi, Ali Asghar Firoozi, Ali Asghar Firoozi +1

This paper explores the integration of AI and IoT in energy geotechnics to achieve resilient, low-carbon infrastructure. Specific findings are unavailable, but the title indicates a novel pathway for decarbonizing infrastructure through geo…

Guanghan Song, Xuejiao Miao, Yujie Lu

This paper proposes a framework combining semantic ontology and hybrid intelligence to optimize low-carbon construction. It leverages AI techniques to efficiently reduce carbon emissions in the building process, contributing to a sustainabl…

Bingbing Guo, Yujie Jiao, Fengling Zhang +3

This study proposes a multi-objective optimization framework for concrete in chloride environments, treating compressive strength and chloride diffusivity as constraints while minimizing carbon emissions and cost. Deep neural network (DNN) …

Joanna Rosak-Szyrocka

This paper reviews the role of AI in ESG reporting through a bibliometric analysis of 765 publications from Scopus (2004-2026). Using keyword co-occurrence, Ishikawa diagram, and Pareto-Lorenz analysis, it identifies thematic clusters and k…

Haoyuan Deng, Yihong Zhou, Thomas Morstyn +1

This paper proposes a Supervised Reinforcement Learning (SRL) framework for coordinating Distributed Energy Resources (DERs). It pre-trains a policy via supervised learning on demonstration data and then fine-tunes it using RL in two steps:…