GX & Decarbonization Research

Yu Xing, Siyuan Zou, Guohua Liu

This paper proposes a machine learning ensemble framework for carbon price prediction across China's seven regional pilot carbon markets. It integrates price, volume, inter-market, and macroeconomic variables using XGBoost, LightGBM, and Ra…

Zesen Wang, Lijuan Lan, Yonggang Li +1

This paper proposes TriHead-GAN, a Transformer-based GAN with a triple-head discriminator that jointly supervises distributional authenticity, cross-variable dependencies, and temporal smoothness for carbon emission time series generation. …

Xin-song Ma, Ren-wei Zhang

This study proposes a multi-scale adaptive graph convolution model for carbon price prediction, capturing complex price dynamics in carbon markets using deep learning.

Na Du, Baoyi Liu, Ao Gong +4

This paper proposes a deep reinforcement learning approach for optimal dispatch of regional integrated energy systems with carbon capture and trading. The method adapts to dynamic carbon prices, balancing economic and environmental objectiv…

Huan Liu, Shaojun Bai

This study applies multidimensional text analysis to corporate ESG reports to examine whether high ESG performers exhibit distinct linguistic patterns. By leveraging NLP methods, it sheds light on the relationship between disclosure quality…

Zhaoshuai Dang

This dataset comprises carbon price data from four Chinese regional markets (Hubei, Guangdong, Beijing, Shanghai). It is used for multivariate and univariate carbon price forecasting experiments with the AMST-Pyraformer model, an adaptive m…

Ahmed A. A. Abdel-Wareth, Ahmed A. Ahmed, Mohamed O. Taqi +2

A comprehensive review of AI applications (machine learning, sensors, data analytics) in livestock farming for climate resilience and sustainability. Covers GHG mitigation, feed efficiency, disease prevention, and animal welfare, highlighti…

K. Umer, Sajjad Ahmad, I. Raisani +5

This study examines the impact of transparency on the relationship between AI-driven finance and sustainable performance. Using a survey of 252 respondents from financial institutions and corporations, the findings indicate that transparenc…

Marina Nazir, Marina Nazir, Minhas Akbar +2

This paper explores how artificial intelligence and environmental digitalization contribute to mitigating climate risks and promoting sustainable development amid geopolitical uncertainty. It examines the potential of AI-driven climate risk…

Shukai Zhang, Chengzhi Yu, Shuang Liang

This study reframes street-level urban renewal as a coordination problem between vitality and low-carbon performance. Using multisource data and interpretable machine learning, it diagnoses vitality-carbon mismatches in Chengdu, China, find…

Abdul Rehman, Suyang Zhou, Sheeraz Iqbal +3

This paper proposes an Adaptive Quantum-DRL Multi-Objective Optimization (AQ-DRLMO) framework for low-carbon CCHP systems. It integrates quantum-inspired evolutionary algorithms with deep reinforcement learning, achieving 40.08% emission re…

Rency Dayne Duque, John Rein Vinuya, Kristenz Mingoy +2

This study presents Cravely, an AI-integrated digital platform for food waste reduction in the Philippines. It enables surplus food redistribution at discounted prices and uses an AI module to estimate avoided methane emissions. Evaluation …

Human Sciences Research Council

This chapter proposes a dynamic policy formulation system inspired by natural systems, using AI and big data to monitor climate indicators and automatically update policies via rapid feedback loops, avoiding delays of traditional policy pro…

NOUNANGNONHOU CT, DIDAVI KBA, AZA-GNANDJI MR

This paper proposes a hybrid deep learning model (B6) combining CEEMDAN-VMD dual decomposition with a GRU-attention backbone for wind power forecasting. Evaluated on seven wind farms, it reduces MAE and RMSE by 25-35% over baselines for 1h,…

Maataoui, Wijdane El, Abounada, Abdelouahed

This paper proposes replacing the classical virtual synchronous generator (VSG) control with a deep neural network (DNN) for wind power systems. The DNN is trained end-to-end using supervised learning to generate inverter control signals fr…

Yantong Li, Taoyu Chen, Yajie Guo +3

This study uses NLP and XGBoost-SHAP on Sina Weibo data to analyze urban travel behavior changes under extreme heat and oil price shocks. Key findings: heat leads to trip reduction (52.4%) and motorized travel (24.6%), with a transition int…

Siyi Wang

This paper proposes a low-carbon intelligent path planning method using genetic algorithms for machining carbon fiber reinforced polymer aerospace components. Experimental results show a reduction in path length, machining time, energy cons…

Yaping Luo, Jianxian Wu

This paper explores the potential of AI technologies to reduce carbon emissions in the transport sector, focusing on optimization, automation, and data-driven decision-making. It likely examines case studies or models where AI steers transp…

Amal Alharthi, Ahmad Alomari, Fawwaz Alrwabdah +3

This paper examines how green digital technologies (ERP, cloud, IoT, AI, big data analytics) improve ESG disclosure quality for industrial firms listed on the Amman Stock Exchange. Using panel data from 30 firms (2020-2024) and institutiona…

Mohsin Khan, Wendy Ashurst

This paper examines the role of AI in enhancing ESG reporting quality, proposing a sector-specific hybrid governance framework. It finds environmental metrics are more amenable to AI verification, while social and governance disclosures req…