GX & Decarbonization Research

Abed Saif Ahmed Alghawli, Ali Raza, Suzan Hassan Bakhit +2

This study proposes a Q-learning-based routing and fault-recovery framework for CCS monitoring WSNs, integrating energy awareness. Evaluated on a 50-node HIL testbed, it reduced latency by 53.6%, improved recovery time by 65.9%, achieved 99…

Man Luo, Yixuan Zhao

Bike-sharing is key for low-carbon urban mobility, but expansion faces cold-start prediction and equitable allocation challenges. FairGIN, a fairness-aware GNN, integrates expansion-simulated training, attention-based knowledge transfer, an…

Ren-Jie Wu, Hesong Jin, Boyang Liu +6

This paper proposes a generative AI approach to automate the mix design of low-carbon, high-ductility engineered cementitious composites (ECC). The AI optimizes material performance and carbon emissions, contributing to decarbonization in c…

Yuxuan Wu, Qianqian Wang, Qihan Zhang +6

This study develops a data-driven framework for sustainable design of rare-earth permanent magnets, using 346 experimental samples and MLP models to predict magnetic properties with high accuracy. SHAP analysis identifies Nd as key, and mul…

Yu Tian, Bangjun Wang

This study analyzes the integration of Green Electricity Certificates (GEC) and Carbon Emissions Trading (CET) in China using AI methods (BERTopic, cosine similarity, Bayesian ERGMs) on 1,137 policy documents. It finds rapid central discour…

Simona Fioretto, Elio Masciari, Enea Vincenzo Napolitano

This paper applies AI techniques to institutional reports from the public sector to extract and analyze ESG information. It demonstrates the potential and challenges of AI-driven ESG assessment, offering new perspectives on utilizing disclo…

Avuzwa Lerotholi, Ibidun Christiana Obagbuwa, Olaperi Okuboyejo

This paper proposes a machine learning and explainable AI (XAI) approach to predict ESG risk scores, enhancing transparency in ESG assessments. It enables investors and companies to understand the drivers of ESG ratings. Originating from So…

Ruosong Hou, Wei Guo, Ziheng Zhao +2

This study proposes a data-model hybrid method for multi-timescale low-carbon economic dispatch of manufacturing-park virtual power plants. It integrates empirical uncertainty scenarios with a production-constrained stochastic program, achi…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper presents an experimental dataset of ultra low-carbon concrete designed for performance prediction and machine learning applications. It supports decarbonization in construction by enabling efficient material design and CO2 reduct…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper provides an experimental dataset of ultra low-carbon concrete for performance prediction and machine learning applications. The dataset supports decarbonization in the construction sector, enabling performance evaluation and opti…

Shuangyang Zhai, Zishan Zhang, Haoyu Hou +2

This study examines whether China's carbon emissions trading (CET) pilot policy affects auditor industry specialization using a staggered difference-in-differences design on A-share firms from 2010 to 2022. Results show that CET exposure in…

Yelmen B.

This study models energy use and GHG emissions in orange production in Turkey using artificial neural networks (ANN). It demonstrates AI application in agriculture for emission prediction and energy efficiency improvement, offering insights…

Shunhao Mai, Zenglu Zhang, Jie Zhu +2

This paper introduces the Disclosure-Emission Gap (DEG) Index, a novel metric to detect corporate carbon washing by quantifying the divergence between disclosed environmental commitments and verified emission performance. Using CarbonBERT-C…

Jinpeng Dai, Fanghui Lu, Riccardo Maddalena +2

This study uses CPO-XGBoost machine learning to predict compressive strength of limestone calcined clay cement (LC3) and optimize low-carbon mix designs. LC3 reduces CO2 emissions in cement production, contributing to construction decarboni…

Zeqi Zhang, Yingjie Li, Danhui Lai +2

This paper proposes a low-carbon operational decision-making method for computing-energy parks using large language models (LLMs) and multi-agent collaboration. A dynamic carbon emission factor (DCEF) captures time-varying carbon signals, a…

Tong Qian, Yang Liu, Yunlin Huang +2

This review examines how AI enables electricity–computing synergy to address data center energy demands and renewable energy variability. It highlights deep learning for renewable forecasting and deep reinforcement learning for flexible wor…

Leyi Yan, Shuangning Li, Sihang Liu

This paper characterizes carbon emissions of AI agent workloads and proposes carbon-aware execution scheduling that exploits deadline flexibility. AgentDecarbonizer, running alongside OpenClaw, estimates task duration and schedules executio…

Wiem Dridi, Saliha Theiri

Using French SBF120 firms from 2018-2024, this study finds that higher carbon emissions are associated with higher audit fees, with effects amplified in concentrated audit markets and regions with high environmental carbon exposure. Carbon …

Jicheng Du

This study develops a machine learning benchmark and life-cycle screening database for low-carbon alkali-activated materials from coal gangue and steel slag. It compiles 237 mixture records, benchmarks Random Forest and XGBoost under variou…

Nhat‐Luong Nhieu, Hoang‐Kha Nguyen

This study develops a T-Spherical Fuzzy-Valued Neutrosophic MEREC-EDAS framework to evaluate low-carbon cooling and energy management technologies for data centers. Based on assessments from 30 experts, nine technologies are ranked against …