GX & Decarbonization Research

Michelangelo Misuraca, Luca D’Aniello, Maria Spano

This study analyzes renewable energy social representations in Reddit climate discourse (2018-2026, 91,817 comments) using a computational adaptation of the Hierarchical Evocation Method. Combining theory-informed lexical anchoring with dat…

Sharif M, Seker H

This paper proposes Agentic CAMA-DRL, a context-aware multi-agent deep reinforcement learning framework for optimizing charging infrastructure of last-mile delivery e-bikes. Four stakeholder agents coordinate via DQNs conditioned on real-ti…

Vrcelj Z, Sandanayake MS

This paper addresses the gap between AI technical performance and actual project decision changes in construction. Through a structured review of 34 studies, it proposes a Governed AI Project Controls Framework that separates data foundatio…

Bérard, Cedric

This snapshot demonstrates that NAM-classification-filtered portfolio allocation outperforms five naive baselines in expected avoided-loss NPV for climate adaptation of 725,462 substations across 39 OECD countries. At the reference cell, al…

Jianxun Zhao, Mingxuan Huang, Changqing Tian +1

This study optimizes GHG emissions in vegetable handling processes using Deep Deterministic Policy Gradient (DDPG). Sensitivity analysis identifies key parameters, and a Markov decision process model with multi-objective reward design minim…

Bingler J.A.

This paper uses ClimateBert, a specialized language model, to analyze corporate climate risk disclosures, focusing on 'cheap talk' (substantively empty claims) and 'cherry-picking' (selective disclosure of favorable information). It offers …

Zhang Bokai, Chengjie Huang, Xiao Qiang

Using panel data of Chinese listed firms from 2012-2022, this study exploits the establishment of environmental courts as a quasi-natural experiment with a multi-period DID approach. It finds that environmental courts significantly promote …

Xiao Qin, Quan Fang, Yuan Virtanen

This study empirically examines the impact of climate policy uncertainty on corporate sustainability (ESG scores) using Chinese A-share listed firms from 2016 to 2025. Employing a two-way fixed-effects model, it finds that climate policy un…

Jinqi Jiang, Zhijing Wu, guosen zhang +9

WaterMAP is a scalable ML framework for predicting GHG emissions from wastewater treatment plants. Using data from 5155 Chinese WWTPs, it estimates Scope 1/2/3 emissions and suggests 9.6-34.0% reduction potential by 2060.

Mengqi Yuan, Wenfei Zang, Guangchong Chen

Using provincial data from China (2012-2022), this study analyzes the coupling coordination between AI and high-quality energy development (AHCC). Both advanced, but AI lagged and regional disparities persisted, with intensifying spatial au…

Michael Padget, Gregg Furie, Dionne Kringos +2

This paper is the first to systematically evaluate environmental sustainability measures for healthcare using quality measurement criteria. Six measures were assessed, and a minimum set of greenhouse gas emissions (scopes 1 and 2), water us…

Mehrdad Taghipour, Sina Rostami, Muhammad Bilal

This scoping review systematically maps technologies and strategies to reduce energy use and carbon footprints in operating rooms, including HVAC optimization and AI-driven management. It identifies a methodological gap in kinetic energy ha…

Rufaizal Che Mamat, Azuin Ramli, M. N. A. Ghani +3

This study compares fine tree regression and ANFIS for predicting carbon footprints across four stages of residential construction. Using 2000 observations, ANFIS outperformed Rtree in all stages, achieving lower RMSE values, especially in …

Christian‐Oliver Ewald, Yaoyu Li

This study proposes a text-based framework for forecasting EU carbon prices, combining FinBERT sentiment analysis with PCA dimensionality reduction. Using weekly data from 2020-2024, the CNN-LSTM model with PCA inputs achieves the best perf…

Sébastien Houde, Wenjun Wang

Using micro-level data from Chinese firms, this paper shows that AI adoption reduces carbon emission intensity, with stronger effects for large firms, those in AI hubs, and high-carbon sectors. Mechanisms include improved energy management,…

Can Hankendi, Ayse K. Coskun, Benjamin K. Sovacool

This paper goes beyond AI's carbon emissions to analyze the whole-system energy and environmental sustainability of datacenters in Denmark, Germany, and Norway. It evaluates how renewable energy availability and cooling technologies affect …

Paul Foulquier, Ryma Haddad, Ali Assem Mahmoud +4

This paper presents a machine learning approach to accelerate the design of complex and high entropy alloys for protective coatings in low-carbon energy applications (nuclear, high-temperature electrolysis). It introduces the French DIADEM …

Adrian Friday, Abdessalam Elhabbash, Ignatius Ezeani +3

This volume contains the proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, UK. It covers topics such as carbon measurement and reporting, sustainable software engineering, energ…

Yao Lv, Jincheng Mu, Kanglei Du +2

This study proposes a machine learning-driven approach to optimize the mix design of recycled aggregate concrete, maximizing its carbon sequestration potential. It aims to reconcile low-carbon design with carbon fixation, contributing to de…

Ye Duan, Minghan Yang, Zhaowei Hou +3

This study analyzes spatiotemporal carbon emission patterns across 19 Chinese urban agglomerations (2006-2023) using spatial statistics and machine learning (random forest, SHAP). It identifies industrial structure and economic development …