This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 721–740 of 1075 papers
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…
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…
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…
Salil Seth, Mohd Irfan Pathan, Lokesh Tomar
This paper applies the BERT model to analyze consumer reviews and social media posts for sentiments on decarbonization, sustainability, and waste management in retail. Offline shoppers are more critical of waste practices, while online shop…
Zhai G.
This paper applies machine learning and GARCH-MIDAS models to assess climate uncertainty in green bonds. It estimates the contribution of climate risk factors to green bond yield spreads. The findings indicate that climate uncertainty signi…
Taneja S.
This paper proposes an AI-driven green finance framework for the chemical industry to accelerate SDG-12 (responsible consumption and production) compliance. It uses AI to analyze sustainability data, automate financing decisions, and evalua…
Bashar A.
This study applies topic modeling to bibliometric data on sustainable finance, identifying emerging themes such as green bonds, ESG investing, and climate risk. It provides a structured overview of research trends for scholars.
Ayodele O.F.
This paper analyzes how the choice of activation function affects the accuracy and interpretability of neural network models that capture the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters. It contributes to A…
Gunasiri B.D.T.S.
This paper proposes an AI-assisted method for ESG disclosure analysis in beach hotels, using NLP and machine learning to extract and assess environmental, social, and governance information from reports. It contributes to automating ESG pra…
Das P.
This paper proposes a machine-learning model to predict solar power generation for a net zero energy building (NZEB). The model improves prediction accuracy, enabling efficient renewable energy use and optimized building energy management, …
Leong W.Y.
This paper proposes a generative AI approach for urban planning to achieve net-zero emissions. It optimizes land use, transportation, and energy systems, enabling efficient urban designs through simulation.
Chhatwal M.
This paper examines how generative AI and digital transformation can contribute to business sustainability, particularly achieving net-zero goals. It explores AI-driven optimization in energy management, supply chain, and emissions tracking…
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