GX & Decarbonization Research

(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…

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…

S. Kong

This study examines how digital transformation in Korean smart agriculture organizations enhances governance transparency and reduces corruption risks. Using AI, IoT, and blockchain, data-driven decision-making and tamper-proof traceability…

Tingting Li, Ziming Zhao, Zhaoxuan Li +2

This paper proposes a method for routing LLM queries in web services that minimizes carbon footprint while ensuring fairness. It contributes to reducing the environmental impact of AI services and promoting sustainability.

明辉 李

This study proposes a digital twin-driven framework for real-time perception and dynamic optimization of carbon footprints in universities, integrating IoT and energy management systems. It develops a reinforcement learning-based optimizati…

Jun-Hye Baek, Jae Min Lee, Dong‐Seong Kim

This paper proposes a trust framework for certifying individual carbon reduction activities using AI and blockchain. It integrates automated image analysis to validate eco-friendly actions and stores verification results on a blockchain for…

R. Thendral, M. Abinanthida

CarbonSense360 integrates AI and blockchain for end-to-end carbon footprint monitoring, using OCR/NLP for automated data capture, AI for emission forecasting and personalized recommendations, and blockchain for tamper-proof validation. Gami…

Guanqiuyue Chen, Edmund Loi, Ka Ip Chan

This study examines the impact of China's carbon emission trading pilot policy on corporate digital transformation using a multi-period DID approach. Analyzing data from 2009-2020, the authors find that market-based environmental regulation…

Erica Hargety Kimei, Devotha Godfrey Nyambo, Neema Mduma +1

This study proposes an uncertainty-aware deep learning model using a gated recurrent unit (GRU) to forecast dairy cattle greenhouse gas concentrations. It integrates remote sensing and ground sensor data to predict N2O, CH4, CO2 hourly. The…

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…

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…