GX & Decarbonization Research

Patrycja Hąbek, Kjartan Sigurðsson, Małgorzata Radomska

This paper examines the impact of AI on sustainability reporting. It discusses how AI can automate ESG data collection, analysis, and disclosure, enhancing efficiency and accuracy. It also addresses challenges and regulatory implications of…

Dajiang Liu, Tao Yu, Huiying Xu

This paper proposes a GAT-LSTM hybrid model for dynamic carbon footprint accounting in prefabricated steel structure buildings, integrating spatio-temporal data to estimate emissions across the construction process. It overcomes limitations…

Thing-Yuan Chang, Chien-Chih Chen, Chin-Hsien Hsu +2

The food supply chain emits 13.7 billion tons of CO2e annually, yet accurate Product Carbon Footprint (PCF) estimation is inaccessible to many manufacturers. This study proposes a machine learning framework combining Gaussian Mixture Model …

Tarak Barhoumi, Najoua Mezghani

This paper proposes CarbontoScope IA–SFAX, an augmented intelligence system to visualize urban carbon footprints, applied to Sfax, Tunisia. It identifies hidden emission sources, providing data for urban planning and policy. Integrating AI …

Paul Kron

This paper estimates the carbon cost of a large language model (LLM) query using life-cycle analysis and greenhouse gas emissions, calculating an ultimate cost of approximately $0.4 per query (about 10 gCO2eq/query) for future generations. …

Dara Ghahremani

This repository provides a machine-readable corpus of annual reports from European listed banks (2013-2023), along with scripts and an ESG dictionary for text analysis. It supports reproducible ESG disclosure research, including term-densit…

Małgorzata Radomska

This chapter analyzes the relationship between sustainability reporting and AI from a regulatory perspective. ESG disclosure and AI regulation have developed separately, leading to fragmentation and practical challenges. It maps key legisla…

P. Vibin Sri Balaji, Srinivasan R

This study proposes an AI-based framework integrating satellite data and machine learning to identify optimal solar plant sites and forecast 10-year energy output. Using NASA POWER data and Random Forest regression, it achieves high predict…

P. Vibin Sri Balaji, Srinivasan R

This study integrates satellite data and machine learning to identify optimal solar installation sites and forecast 10-year energy output. Using NASA POWER data and physical PV modeling, Random Forest achieves high predictive accuracy (R²≈0…

Jacopo Ghirri, Carlos Rodriguez-Pardo, Lara Aleluia Reis +1

Using deep learning on high-resolution socioeconomic and sectoral data, this study projects EU27 CO2 trajectories to 2030 under current trends. It finds emissions will exceed the 2030 target by 35% (620 Mt shortfall), with only a minority o…

Kaithari D.K.

This review surveys AI systems for real-time monitoring and optimization of ocean-based carbon capture, utilization, and storage (CCUS) networks. It synthesizes how AI enhances efficiency and risk management in carbon capture and storage, a…

Yihsu Chen, Abel Souza, Fargol Nematkhah +1

This paper models power markets where hyperscalers shift LLM inference workloads to modular datacenters co-located with renewables. It reveals that contract reshuffling can undermine emission reductions from cleaner procurement, and that fo…

J. Z. Lin

This study constructs a financial health rating system using Deep Belief Networks (DBN) to classify enterprises into seven tiers. Using 2,486 firm-year observations from 247 textile, apparel, and leather companies listed on Shanghai-Shenzhe…

Pranav Nerurkar

This paper empirically examines the 'green halo effect,' revealing real-world inconsistencies between ESG scores and retail investor sentiment. It analyzes how ESG ratings influence investor perception and behavior, questioning the reliabil…

Alessandra Allini, Alessandro Corrado, Luca Ferri +1

This study examines how abnormal tone in ESG reporting (discretionary narrative unexplained by fundamentals) affects firm value and the value relevance of accounting information, using a sample of 2,002 European listed firms (2017-2024). Us…

Qing Wang

This review systematically explains the concept of circular logistics, integrating circular economy principles into logistics engineering to minimize waste and carbon footprint. It clarifies differences from traditional logistics and presen…

Yuanyang Guo, Liqi Xu, Miaoxi Gu

Using panel data from Chinese cities (2008-2023), this study empirically examines how AI entrepreneurship affects carbon emission intensity. It finds an inverted U-shaped relationship: early expansion increases emissions, but after a thresh…

Jolly Masih, Dinesh Yadav, Sanskriti Singh +3

This study examines whether carbon credits are credible climate solutions or greenwashing, focusing on the gap between institutional narratives and public discourse. Using mixed methods including VADER sentiment analysis, Google Trends, Tal…

Xiaoyuan Feng

This paper empirically examines how corporate digital capabilities affect ESG performance, focusing on the mediating role of low-carbon innovation. Results show digital capabilities significantly boost ESG performance, with low-carbon innov…

B.K. Tripathy, Riya, Hemant Kumar Saini

This chapter explores energy-saving solutions to reduce the carbon footprint of UAVs across their life cycle. It covers AI-based optimization (ML for energy-aware routing, RL for navigation, GANs for flight energy prediction), blockchain fo…