GX & Decarbonization Research

Qingwen Zeng, Hanlin Xu, N. Xu +5

This systematic review synthesizes engineering practices for carbon credits using AI, focusing on carbon price prediction and corporate emission prediction algorithms. It highlights how these data-driven solutions enhance transparency, miti…

Hui Li, Mingyue Chen, Xin Kang

This paper presents a physics-guided generative inverse design method for optimizing low-carbon concrete mixtures. Using multi-objective latent space optimization, it achieves strength targets while reducing CO2 emissions.

Chen Wang, Songyuan Liu, Guoqing Li +5

This study proposes a framework that decomposes power grid projects into 11 standard modules and uses an improved PSO-DRESN model to determine modular carbon quotas with high precision. Empirical analysis of a 500kV PGP reveals a lifecycle …

Solomon Oisasoje Ayo-Odifiri, Andrew Ebekozien, Clinton Aigbavboa +1

This study explores the integration of AI technologies to enhance urban green infrastructure (UGI) for carbon neutrality in smart buildings. Through interviews with 30 stakeholders in three Nigerian cities, it finds that AI effectively pred…

Sara; id_orcid 0000-0003-1053-1641 Tavakoli, Pedro Sampaio, Ali Hassanzadeh

This study develops a three-level modeling framework operationalizing intersectionality to capture how combinations of socio-economic characteristics interact to produce regional energy poverty. Using statistical and machine learning method…

L. Minh Dang, Sufyan Danish, Muhammad Fayaz +4

This paper provides a domain-structured review of AI applications for climate change mitigation and adaptation across seven high-impact domains. It systematically organizes methods, applications, and research gaps, offering a comprehensive …

Murali Krishna Pasupuleti

This paper presents a comprehensive socio-technical framework for AI-enabled EV charging optimization, covering modeling, causal inference, ML prediction, trustworthy AI, MLOps, and policy analytics. It emphasizes balancing cost, reliabilit…

Haixia Li, Qiang Liu, Xiaolin Tang +4

This study uses a random forest model to analyze 15 environmental drivers of GHG emissions from 79 major hydropower reservoirs in China. Reservoir area is the dominant driver, with emissions increasing with area, NH4+ concentration, and cat…

Butler T.

This paper proposes an ISO-compliant upper-domain ontology for EU sustainability reporting, leveraging AI to automate and standardize reporting and decision-making. It aims to enhance interoperability and efficiency in sustainability disclo…

Collin Smith, Huey Xin Loong, Oliver Cook +2

This paper demonstrates that deep convolutional and recurrent neural networks can learn temporal patterns of solar and wind power generation to optimize Power-to-X processes, specifically green ammonia production. The networks predict optim…

Elinda Esa, Abdul Rahman Zahari

This conceptual paper examines the role of board governance in mitigating sustainability washing among Malaysian public listed companies. It proposes a dynamic panel-data methodology using NLP to contrast disclosure volumes with operational…

Chunli Wang, Zhijiao Chu

This paper proposes an explainable temporal attention enhanced backpropagation neural network for carbon asset value assessment and prediction, aiming to improve prediction accuracy of carbon price fluctuations while ensuring model explaina…

Seyed Alireza Mousavian Anaraki, Danilo Croce, Roberta Costa +3

This paper proposes an unsupervised method to align GRI and TCFD frameworks, using LLM-assisted validation for evaluating climate disclosure quality and greenwashing risk. It demonstrates effectiveness on real data and suggests applicabilit…

Weixuan Luo

This paper proposes an AI and digital twin–driven intelligent management and control pathway for green transformation of process equipment in high-emission industries (petrochemical, power, metallurgy) under China's Dual Carbon Goals. It ad…

Ritika Kanwar, Anant Bhatnagar

This paper proposes an agentic AI framework called 'AI Sustainability Copilot' to help individuals reduce their carbon footprint. The AI analyzes behavior patterns and provides personalized recommendations, facilitating a transition to sust…

Mohd ibrahim, Mohammed Abdullah, Mohammed Abdul Bari

This paper proposes a multi-agent AI framework that integrates IoT sensors, satellite imagery, and blockchain for autonomous carbon credit verification. It reduces inefficiencies and fraud risks in manual verification, enhancing transparenc…

Adil Husain Rather, Inam UL HAQ, Irfan RASOOL

This chapter evaluates how global policy frameworks (European Green Deal, Paris Agreement, SDGs) integrate AI to accelerate cleaner energy systems. It highlights predictive analytics, real-time monitoring, and adaptive control for more effe…

Siddique AB

This paper proposes a thermodynamics-informed graph learning approach for predicting CO2 solubility in brines, crucial for dissolution trapping in CCUS. Tested on 3323 records, graph-based descriptors improved relative error but boosted tre…

Wu J, Zhong S, Lian X +5

This study evaluates coordinated adaptive signal control (ATSC) and autonomous vehicles (AVs) for congestion and carbon reduction using micro-simulation across a real urban corridor and 100 Chinese cities. The coordinated strategy outperfor…

Minh Phuoc Bui, Mien Thi Ngoc Nguyen

This study analyzes ESG disclosure patterns in sustainability reports of Vietnamese commercial banks using PhoBERT-based text mining of 25 reports. Findings show disclosure concentrated in governance, especially corporate governance, follow…