GX & Decarbonization Research

Komal Bamugade and Archana Jadhav

This review systematically examines AI (ML, deep learning, etc.) applications in climate change modeling and prediction, with emphasis on improving carbon footprint accuracy and climate pattern understanding. It identifies challenges like d…

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…

Amayrol Zakaria, Shamila Azman, Khairul Anuar Mat Saad +2

This paper systematically reviews AI-driven decarbonization strategies for maritime ports using PRISMA and bibliometric analysis. It identifies key research trends and highlights AI's role in enhancing operational efficiency and reducing em…

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:…

(著者不明)

This paper constructs a machine learning model to predict net carbon sinks in Chinese agriculture and analyzes driving factors. The model integrates multiple data sources to estimate carbon sequestration and emissions from agricultural acti…

Mahmood Mazin Ali Mahmood, Keng Wai Chan

This systematic review of 41 studies (2012-2025) evaluates AI-driven renewable energy integration in buildings, covering PV, ML prediction, HVAC optimization, and occupancy management. Quantitative findings show 35-64% electricity cost redu…

Yadav A.

This study uses a hybrid ANN-Fuzzy ISM approach to assess drivers influencing net-zero emission adoption in manufacturing supply chains, applying AI to sustainability analysis.

Chao Wu, Michael L. Goulden, James T. Randerson +9

Using satellite data, disturbance modeling, and machine learning, this study provides the first spatially explicit maps of long-term carbon loss probability in global forests under climate scenarios. North American conifer, tropical rainfor…

Qi Li, Ying Zhang, Hao Li +2

This paper proposes a novel HST-Heterogeneous Spatiotemporal Graph Neural Network (GNN) framework to dynamically assess environmental risks and carbon emissions from Land Use, Land-Use Change, and Forestry (LULUCF) during Ultra-High Voltage…

Hakpyeong Kim, Jun‐Ki Choi, Taehoon Hong

This study proposes a graph neural network-based input-output framework to estimate construction supply chain carbon footprints, enhancing the accuracy of Scope 3 emissions accounting with AI.

Vaishali Hirlekar, Balajee Maram

This paper proposes a digital MRV framework integrating machine learning for carbon estimation and blockchain for transparency in blue carbon markets, enhancing trust and traceability of carbon credits.

Mohammed Saber Eltohamy, Mahmoud A. Hassanin, Nabila A. Khodeir +6

This paper reviews the role of artificial intelligence in enabling energy interconnection between low-carbon power systems and electric mobility, focusing on a comparative analysis of China and Egypt. It analyzes AI-driven optimization of e…

Abhishek N. Srivastava, Arun Krishna Vuppaladadiyam, Rakhi Punnadan Koroth +8

This review explores how AI-driven circular economy approaches can transform waste management, reduce GHG emissions, and recover resources. It proposes a three-level CE framework (micro, meso, macro) and discusses challenges in implementati…