GX & Decarbonization Research

N. Albelwi

This paper introduces FedCarbon, a federated learning-based building stock modeling system that enables collaborative embodied carbon benchmarking without central data aggregation. Using hierarchical federated aggregation with attention-bas…

N. Albelwi

This paper proposes DT-LCAF, a digital twin-enabled LCA framework for real-time embodied carbon optimization in construction. It integrates BIM, IoT, and ML (graph attention networks and reinforcement learning), validated on proxy data from…

Zhihan Qiu

This paper examines the application of digital technologies (IoT, big data, AI) in ship energy efficiency management to comply with IMO regulations like CII. It analyzes practices in fuel monitoring, route optimization, and power control, i…

Shiwei Zhou, Xinglong Liu, Feng Zhang +1

This paper proposes a genetic algorithm (GA)-based ship path optimization framework that uses AIS data to incorporate wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. Evaluated on real AIS datasets, i…

Doru Coșofreț, Florin Postolache, Adrian Popa +2

This paper proposes a hybrid digital CO2 emission-control technology for maritime transport using physics-informed adaptive speed optimization. It integrates exact optimization (Backtracking, Dynamic Programming) with reinforcement learning…

Juei-Cheng Jao, Muhammad Hanzla Alvi

This paper examines legal frameworks for Maritime Autonomous Surface Ships (MASS) in the context of maritime decarbonization. It argues that existing conventions designed for crewed vessels create gaps in safety, cybersecurity, and liabilit…

Simin Huang, Lin Yang, Jing Meng +4

This study develops a machine-learning optimization framework to quantify how five enabling technologies (hydrogen, storage, grids, electrified transport, CCUS) shape decarbonization, equity, and resilience goals. It finds investment distri…

Alessandro Sura, EMANUELE DI VENTURA

This study compares stakeholder engagement in EFRAG and ISSB sustainability standard-setting using NLP. Sentiment and topic analysis of comment letters reveals that EFRAG shows balanced sentiment and broader thematic focus, while ISSB empha…

Rakesh Dondapati

This paper develops a Greenwashing Intelligence System (GIS) using multimodal AI (transformer-based NLP, satellite data, emissions data, controversy records, financial disclosures, supply-chain signals) to construct a Narrative Ambition Sco…

A. Gomaa

This systematic review examines how AI transforms ESG assessment, disclosure, and monitoring. It identifies barriers including data fragmentation, algorithmic bias, regulatory inconsistency, and lack of explainability, and proposes a future…

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…

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…

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.

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…

Xuandong Zhang, Yankui Su, Jinjiang Li +1

This study proposes a method using digital trace data (mobile phone signals, POI check-ins, traffic data, etc.) to predict low-carbon urbanization. A hybrid model combining spatiotemporal graph convolutional network and LSTM identifies carb…

Subhodeep Mukherjee, Ruchi Sharma, Rashmiranjan Panigrahi +1

This systematic review examines how digital technologies (IoT, blockchain, AI, big data) enhance circular supply chain (CSC) performance. Four themes emerge: use of digital tech to optimize CSC processes, key industries (manufacturing, elec…

Jasmine Selvakumari Jeya Israel, Aldrin Joan Pandian William, Pradeep Kumar Mishra +1

This study proposes a Bi-LSTM-based approach to optimize emission forecasting, carbon offset allocation, and green fund utilization for steel, cement, and aluminum industries. Bi-LSTM demonstrates superior performance in long-term predictio…

Shamsun Nahar, Florina Rahman, Mahrima Akter Mim

This paper presents an AI-integrated platform combining renewable energy and data analytics for corporate ESG compliance. It uses AI/ML for energy demand forecasting, carbon emission estimation, and automated ESG reporting aligned with inte…

C. Rao, Praveen Kumar Naidu Rayanki, Polepalli Rajeev Meenon +2

This paper proposes a privacy-preserving cross-chain federated blockchain-IoT framework for carbon credit management, integrating RFID-based emission data collection, IBC interoperability, and DP-FL for fraud detection and price prediction.…

Shaena Ulissi, Andrew Dumit, P. James Joyce +4

This paper proposes evaluation criteria for AI-assisted product carbon footprinting (PCF) systems, using mapping and supply chain modeling as case studies. It offers a framework assessing accuracy, data quality, scalability, and other dimen…