GX & Decarbonization Research

A. Mikhaylov, S. Barykin, D. Dinets +9

This paper proposes a Grok-based temporal fusion transformer framework for multi-horizon coastal flood risk forecasting. It uses the optimized Grok algorithm to improve time series analysis accuracy and support strategic adaptation planning…

Haylee Massaro

This memo argues that expanding generative AI in schools without environmental accountability risks climate injustice for children. It proposes that governments embed climate disclosure, sustainable procurement standards, and child-centered…

Qi Cheng, Licheng Liu, Yao Zhang +3

This work introduces AgroFlux, a first-of-its-kind spatiotemporal benchmark dataset for carbon and nitrogen flux prediction in agricultural ecosystems, integrating simulations from Ecosys and DayCent with observations from flux towers. It e…

Wenwen Liu

This paper proposes the Hierarchical Coupling Carbon Emission Estimation Model (HCCEEM) for datacenter carbon footprint, integrating GNN and physical modeling across four levels from chip to campus. Validated on a 14-month dataset, it achie…

F. Lin, Chin-Chiu Lee, Te-Nien Chien

This study develops an ML-based framework to classify environmental disclosure characteristics of high vs. low ESG performers in Taiwan (2022-2024). Ensemble models (CatBoost) outperform traditional ones, with carbon emissions, energy effic…

Said Zulfigarzada, Fatulla Alizade

This review systematically examines AI's role in enhancing the resilience of integrated energy systems for climate-neutral cities. It covers applications in demand forecasting, renewable generation prediction, fault diagnosis, and cyber def…

Roberto Spacey Martín, Nicola Ranger, T. Schimanski +1

This paper uses large language models (LLMs) to assess adaptation and resilience (A&R) disclosure in S&P 500 sustainability reports. It develops an A&R framework and finds significant gaps, especially regarding risks, metrics, and targets. …

Stefano Dell’Atti, Matteo Foglia, Grazia Onorato

This paper examines how climate risk disclosures affect firm value for large European nonfinancial firms. It develops a firm-level Climate Risk Exposure (CRE) index using NLP to assess narrative disclosures across transition risk, physical …

Meng Yuan, Ma Zhong

This study develops an industry-specific carbon information disclosure quality (CIDQ) framework for Chinese listed petroleum and petrochemical firms, using LLM (DeepSeek-V3.2) to score disclosure texts. The framework comprises 7 primary, 15…

Ning Wang, Fengjuan Wang, Yitian Li +1

This study examines climate risk's impact on organizational resilience of high-carbon Chinese firms using a text-based climate risk index and panel data from 2008-2023. It finds a dual-path financial lock-in effect through financing constra…

Yuchen Du, Daniel Jacome, Jianghao Wang

This study optimizes fine-tuning of the Prithvi Earth Foundation Model using Multidimensional Latin Hypercube Sampling (LHS) for small-scale burn scar identification. LHS outperforms Simple Random Sampling, achieving 0.91 mIoU and retaining…

Murali Krishna Pasupuleti

This study positions AI as an environmental intelligence infrastructure for climate-risk assessment, emissions monitoring, and decision support. Using official data, it proposes a transparent prioritization model ranking major emitters by m…

Claudia Banciu, Adrian Florea

This review examines the convergence of AI and IoT (AIoT) for climate change management in smart cities. A bibliometric analysis of over 3700 articles reveals rapid growth, with waste management and air quality monitoring as leading applica…

Zhanyi Li, Zhanhong Liu, Chengping Zhou +2

This paper proposes an AI-driven framework for smart campus microgrids that integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) for meteorological hazard prediction, a multi-intelligence co-optimization algorithm …

Vahit Çalışır

This study quantifies CO2 emission changes from the February 2023 Kahramanmaraş earthquakes using AIS port visit data and GNN modeling. It finds a 35.9% increase in per-visit CO2 during the acute disruption phase, with 27,574 tonnes of exce…

Yuliang Hu, He Wang, Y. Duan +7

This review systematically analyzes all-solid-waste cementitious materials (ASWs) from industrial waste. It covers activation strategies for precursors like fly ash and slag, synergistic waste design, and machine learning for mix optimizati…

By Marjan Ilbeigi, Mohamed Alnejem, Mozhgan Karimi +4

This study proposes an integrated framework combining Lifecycle Assessment (LCA), Circular Economy (CE) evaluation, Multi-Criteria Decision Analysis (MCDA), and Artificial Neural Network (ANN) modeling to assess bio-based retrofitting strat…

Zeeshan Tariq, A. Bahadori‐Jahromi, Shah Room +1

This study develops multiple ensemble machine learning models with explainable AI (SHAP) to predict compressive and tensile strength of concrete incorporating fly ash and ground granulated blast furnace slag. The optimal hybrid mix (GF4) wi…

Suhib O. A. Amro, S. Naimi, A. Ibrahim

This paper proposes an AI-driven method to jointly optimize scheduling, risk, and sustainability in BIM-enabled construction project management. By explicitly including sustainability as an objective, it enables holistic decision-making tha…

Sachin Venu Jaya, V. Swarnakar, A. Acquaye +2

This study develops an integrated BIM-AI framework to enhance value engineering in construction, focusing on material optimization and resource management for circular economy. Using PRISMA-based literature review and Delft Ladder approach,…