GX & Decarbonization Research

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 …

C. Bhargava, Bhavya Tiwari, P. Bhatnagar +10

This paper develops an XGBoost-based framework to predict CO2 adsorption capacity and classify adsorbent materials using process and material parameters. A comprehensive dataset including activated carbon, zeolites, MOFs, and others was use…

Ke Ren, Weiyu Zhang, Haoxiang Chang +3

This paper proposes a method for carbon price forecasting that models lead-lag relationships among market participants using dynamic graphs and provides uncertainty intervals, potentially improving prediction accuracy and interpretability.

Xin Cheng, Peisi Xu, Sylvie Van Damme

This study develops a framework integrating text mining (Jieba segmentation, dictionary matching) and image classification of social media data to evaluate cultural ecosystem services (CES) from nature-based solutions (NBS) in urban renewal…

ylm0216

This repository provides a reinforcement learning tool for learning optimal trading strategies in carbon credit markets. v1.0.0 is the initial release, including the foundational environment and agent models.