GX & Decarbonization Research

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…

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 …

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.

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…

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 …

ABIR MAH, Paul B

This paper proposes an AI-accelerated MDO framework for hydrogen-electric aircraft that integrates aerodynamics, propulsion, thermal management, and structural mass. Physics-informed neural network surrogates replace high-fidelity solvers w…

Marco Cucculelli, Noemi Giampaoli, Matteo Renghini

This paper operationalizes the EU's Net-Zero Industry Act (NZIA) technology framework at the firm level, using large-scale web scraping to extract unstructured data from corporate websites and map Italian firms' adoption and development of …

Yan Luo

This study proposes an AI-driven ESG scoring framework using Slime Mould Algorithm (SMA) for feature selection and Random Forest Regressor (RFR) for prediction. With rigorous preprocessing, it achieves RMSE 0.3649, MAE 0.2538, and R² 0.9912…

Chenyan Wang, Yingying Jiang

This study proposes a data-driven, AI-assisted design framework for community low-carbon recycling terminals, combining K-means clustering and Kano-AHP to quantify user needs and prioritize design features. The developed terminal and platfo…

Kuanhou Tian, Le Sun

Using Chinese A-share listed firms from 2012-2023, this study finds institutional ownership is positively associated with absolute carbon emissions but negatively with emission intensity, with site visits strengthening monitoring. It highli…

Zixin Ma, Daliang Xu, Bobo Yang +6

Applies machine learning to synergistically optimize carbon emissions and contaminant removal in wastewater treatment plants. Proposes a control method that balances process efficiency and GHG reduction, contributing to carbon neutrality.

Yang Wang, Wenhao Chen, Mengyi Di +3

Proposes a blockchain-enabled game-theoretic carbon trading mechanism for shipping enterprises, combining a Stackelberg game between regulator and firms with a bidding game solved via no-regret learning. Simulations show reduced emissions a…

Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi +2

This study models AI data-center load growth in a three-level Stackelberg-Bayesian game among regulator, ISO, and investors. It quantifies how carbon taxes and subsidies for renewables and second-life batteries reshape investment mix, emiss…

Chuanzhu Sun, Weiguang Zhang, Leixiang Peng +6

IRPVS is the first public dataset providing pixel-level instance segmentation masks for process-specific industrial soiling on rooftop PV systems. It includes 8,012 UAV images from an industrial park in China, annotated with three contamina…

Abdelrehim Awad, Bshair Alharthi, Hiyam Abdulrahim +5

This study proposes an MCDA framework to holistically assess AI-driven data centers in the GCC, comparing environmental impacts with dispersed AI sustainability benefits across sectors. Under business-as-usual, emissions rise substantially …

Huang, Bo, Yan, Yan, Lu, Jianan +1

This paper proposes TASR, a trustworthy LLM-based framework for TCFD-aligned sustainability report analysis. It integrates LLM-based scoring, benchmarking against third-party ESG ratings, and predictive modeling. Experiments on 100 U.S. oil…

Wang K.H.

This paper analyzes the dynamic mutual relationship between AI technology and the green finance market in China, examining whether AI acts as a driver or barrier to green finance development. Using dynamic models, it reveals the interaction…