This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open 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 521–540 of 943 papers
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…
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…
Kumar S.
This paper uses machine learning to analyze big data from scholarly research on sustainable finance, revealing the evolution of research themes and future directions. It provides a comprehensive overview of the field, highlighting key topic…
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, …
Akhai S.
This paper discusses how AI and automation can help factories achieve net-zero emissions and sustainable manufacturing. It highlights the role of AI in improving energy efficiency and reducing emissions in the manufacturing sector.
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…
Nikhil Jarunde
This paper proposes a unified framework using LLMs and generative modeling to construct transparent, regulation-ready trading strategies that integrate ESG information. It includes an LLM-powered ESG sentiment engine, automated scenario gen…
Vinuri Nilanika Goonetilleke, Muditha K. Heenkenda, K. Zaniewski
This review synthesizes how indoor mapping technologies (LiDAR, SLAM, deep learning-based floor plan extraction) contribute to reducing greenhouse gas emissions from buildings. Integration with Digital Twins, BIM, GIS, and IoT enables impro…
Abdullah Kursat Merter, Yavuz Selim Balcıoğlu
This study proposes a TabTransformer-based framework to predict environmental outcomes of energy sector M&A using ESG and financial data from 1000 transactions (2018-2023). ESG factors account for over one-third of predictive power, with po…
Bowei Zeng, J. Niu, Gefan Yang +4
This study develops a physics-guided deep learning framework for urban flood prediction, integrating U-Net, Bidirectional LSTM, and multi-head attention with physics-informed loss functions (gradient consistency, spatial smoothness). Using …
Gourav Kumar Rath, Jesús David G. Palencia, A. Dalai
This review comprehensively assesses thermochemical biomass valorization pathways, emphasizing hydrothermal liquefaction (HTL) for simultaneous biocrude and hydrochar production. It covers upgrading processes and AI/ML applications (Random …
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