GX & Decarbonization Research

H. Liao, Chang-Yi Kao, K. Ang

This paper introduces a zero-trust orchestration framework for Smart Fabs to comply with the EU's SSbD, CSDDD, and CBAM. Using Professional Proxies and industrial data spaces, it automates compliance verification via federated machine learn…

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…

Han-Teng Liao, Chang-Yi Kao

As the EU CBAM approaches, this paper proposes a RegTech reference architecture using the International Data Spaces (IDSA) framework to reconcile regulatory transparency and data sovereignty in global semiconductor-petrochemical value chain…

Qina Zhu, Shanshan Jiang, A. Du

Using data from 2630 Chinese A-share listed companies (2010-2022), this study demonstrates that AI enhances corporate ESG performance. Based on information processing theory, it identifies three mechanisms: improving green innovation rate, …

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…

Wenze Wu, Mei Sun, Mark Goh

This study constructs firm-level indices of supply chain digitalization (SCD) and corporate green transition (CGT) via textual analysis of Chinese A-share manufacturing firms' annual reports. It finds that SCD significantly promotes CGT, me…

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…

Somasundaram, Deepa, Krishnamoorthy, N., Anand, J. Vijay +3

This paper proposes an AI-driven framework for EV charging station placement integrating LSTM-based spatiotemporal demand forecasting, GA-PSO multi-objective optimization, and deep reinforcement learning for adaptive resilience. Evaluation …

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…