GX & Decarbonization Research

Arunodaya Raj Mishra, Pratibha Rani, A. F. Alrasheedi +3

This paper proposes a hybrid picture fuzzy decision-making framework for implementing urban mobility strategies that account for digital carbon footprint. It provides a multi-criteria decision support method for selecting sustainable transp…

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…

Jiawei Wen, Chengxin Pang, Yanxin Wang +1

This study proposes an automated emission factor matching algorithm combining LLMs with semantic retrieval for PCF accounting. Evaluated on eight industrial products using Ecoinvent 3.10, it achieves high precision and low latency, outperfo…

Jayashree Chaudhari1 and Asst. Prof. Sonali Tushar Sambare

This paper proposes an AI-driven green cloud computing framework that reduces data center energy consumption and carbon emissions. By integrating intelligent workload management, dynamic resource allocation, energy-aware scheduling, and ada…

Jayashri Waman, Pratik Pawar, Shruti Dalvi +1

This paper proposes AUMO, an AI-powered Urban Mobility Optimizer that integrates carbon-aware routing with smart carpooling for passenger transportation, addressing a gap in freight-focused literature. Simulation results show reductions in …

Arshia Eslami, Mahsa Ardakani, Amin Roostaee +2

This paper analyzes the energy consumption and carbon emissions of Vision-Language-Action (VLA) model inference for edge robotic systems. Evaluating π0.5, X-VLA, and SmolVLA on an NVIDIA Jetson AGX Orin, it reports task performance, latency…

Yuan Chai, Haytham F. Isleem, P Kumar +2

This paper proposes an AI-driven pipeline integrating IoT telemetry, ERP logs, and grid carbon intensity for near-real-time Scope 1-2 emission tracking. Using attention-based multimodal fusion, an ensemble of Transformer/CNN/BiLSTM, and mul…

Kaiwen Zhao, Bharathan Balaji, Stephen Lee

This paper proposes an LLM-based question-answering framework to extract product carbon footprint information from PDF sustainability reports. It introduces CarbonPDF-QA, an open-source dataset of 1,735 reports with human-annotated Q&A pair…

Heedo Choi, Jee Soo Park, Chul-Hee Lim

This paper proposes an explainable graph neural network (GNN) for urban heat risk assessment under the IPCC AR6 framework. It incorporates spatiotemporal attention to identify vulnerable areas from a climate justice perspective. The work me…

(著者不明)

This paper discusses the use of AI and digital twin technologies for achieving carbon neutrality. Specific methods or case studies are not available from the title alone, but it suggests potential applications.

(著者不明)

This paper analyzes the efficient collection of used home appliances using multi-agent simulation. It explores optimal collection routes and schedules under cooperative and competitive agent scenarios, demonstrating the feasibility of reduc…

(著者不明)

This paper proposes using large language models (LLMs) to assess corporate Scope 3 greenhouse gas emissions, focusing on Category 11 (use of sold products). It demonstrates the effectiveness and challenges of automated LLM-based estimation,…

Lin Sun, Jun Zeng

This study examines how carbon transition risk affects green debt financing spreads for Chinese high-energy-consuming firms. Using panel fixed-effects models, event studies, and machine learning (random forest, XGBoost, neural networks), it…

Liu X.

This study proposes an adaptive recurrent neural network model to predict risks in carbon emission trading markets. The model aims to improve prediction accuracy for market volatility and price fluctuations.

Vidura perera

This paper develops the Adaptive Green Finance Effectiveness Theory (AGFET), an integrated framework explaining how green finance effectiveness is conditional on policy uncertainty, governance, institutional quality, and technological capab…

Aria Dolatabadian, Khalil Kariman

This review compares classical and machine learning (ML) approaches for soil organic matter and carbon analysis. ML techniques like random forests and neural networks improve prediction accuracy and scalability, but the paper concludes that…

SangYoun Kim, T. Woo, Usama Ali +8

This systematic review of 906 publications (2016-2025) categorizes AI applications into flue gas treatment, air quality management, and CO2 capture. It identifies a shift from traditional monitoring to AI-driven surrogate modeling and gener…

Ritik Raj, Survi Sinha, Atreyi Pramanik +1

This paper reviews how AI enhances efficiency, sustainability, and system integration across the hydrogen value chain. AI modeling and optimization improve environmental impact assessment of hydrogen production routes like waste polymer and…

Taedong Lee, Sinjae Kang, D. Utami +1

This study analyzes 634 sustainability reports (2014-2024) from the top 200 Korean firms using Structural Topic Modeling (STM). It identifies 13 topics and a phased shift: CSR and basic environmental management (2014-2018); workplace safety…

Lakshmi Narasimha Prasad Nagaragere, Dr.S. Prabakar

Proposes a vernacular ESG transformation model rooted in South Asian institutional cultures. Using AI-based environmental impact assessment and ESG reporting tools, the study demonstrates through pre/post case studies in family-owned busine…