GX & Decarbonization Research

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 …

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.

Arda Aktaş, Miguel Poblete-Cazenave

This study uses causal forests to estimate heterogeneous effects of residential energy technologies on health and economic outcomes in India. Combining climate policies with a full transition to clean fuels yields larger reductions in sickn…

Hoeser, Thorsten, Bachofer, Felix, Kuenzer, Claudia

DeepOWT is a globally accessible dataset of offshore wind turbine locations derived from Sentinel-1 SAR imagery using deep learning object detection. Version 4.25.1 enriches dense time series with deep learning event predictions (e.g., depl…

D'Césares, Isaac

GreenRisk scores corporate climate-disclosure paragraphs for greenwashing risk on a 0–100 scale using pinned ClimateBERT signal classifiers and a Mamdani fuzzy rule base. Each score ships with an auditable rule-activation trace and a W3C PR…

Tufail Muhammad, Rubab Hafeez, Waqas Bin Khidmat +5

This paper proposes a hypothesis-driven responsible AI framework for interpretable ESG forecasting using RuleFit, enhancing transparency in ESG scoring.

Yulong Geng, Qi Li, Aobo Guo +3

This study proposes a BBE-weighted positive-unlabeled learning framework to assess and predict the photovoltaic development potential and deployment suitability of open-pit mines in China. It contributes to repurposing mine lands for renewa…

R. F. Ghanim, rd Murad, Ali Ahmad Al-Zaqeba

This study systematically reviews prior literature on how artificial intelligence contributes to corporate sustainability, focusing on accounting and ESG perspectives. It finds that AI enhances transparency, accounting reliability, and fina…

L L Sumbu, F G Chong, D B Enggong +8

The Heart2Miss study demonstrates that an AI-powered decentralized triage pathway using handheld POCUS and telehealth significantly reduces patient travel distance and carbon emissions compared to conventional tertiary center diagnostics. A…

Riddhi Mazumdar, Debashish Sakunia, Prof. Biswajita Parida

This paper introduces EcoPersona AI, a segmentation method classifying consumers by Carbon-Footprint Sensitivity (CFS) using ISO 14067-based product carbon footprints and behavioral signals. It generates adaptive personas grounded in VBN th…

Bhatia, Tanvi

This study compares synthetic survey responses generated by LLMs with human responses in the context of renewable energy acceptance research. Using structural equation modeling, it examines structural coherence, contextual uncertainty, and …

Felix Kubler

This paper applies Bayesian optimization to compute optimal carbon taxes in a dynamic heterogeneous-agent economy with climate change. It shows that when the equilibrium manifold has a low-dimensional Negishi-weight parameterization, Bayesi…

Hongzhi Lu, Hongxue Lu

This scoping review systematically examines the dual environmental impacts of AI in ecosystem management: advantages like real-time monitoring and optimized life-cycle assessments versus costs such as high computational energy and e-waste. …

Sampada G. Kulkarni, S. Harshitha, Asish Kumar Yeleti +4

This paper introduces GreenScore, an AI-based sustainability intelligence system for quick-commerce. It uses an ISO 14040/44 compliant LCA scoring engine with five weighted measures (carbon, water, packaging, transport, seasonal sourcing). …