GX & Decarbonization Research

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…

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…

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…

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…

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…

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…

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…

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.…

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…

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…