GX & Decarbonization Research

Jiachen Shen, Jian Shi, Hui Zhong +3

This paper applies mechanism-guided graph learning to evaluate carbon border policies (e.g., CBAM) in interconnected European electricity markets, integrating AI methods with climate policy assessment to quantify cross-border policy spillov…

Kollia M

This Perspective proposes the ZERO framework to shift AI data-centre sustainability evaluation upstream, before resource consumption. It combines hard admissibility constraints, workload characterisation, local resource-state assessment, fe…

Zhang Q, Sheng S, Liu T

A two-stage lexicographic framework for geo-distributed AI inference that preserves service while optimizing carbon and water. Trace-driven analysis of 26,392 requests reveals a 21-point carbon-water frontier, quantifying trade-offs. Provid…

Yao, Huaying

This replication package audits corporate climate-target claims against mandatory verified emissions from the EU Transaction Log and US EPA GHGRP. Using only public data, it parses 1,779 claims into six adjudication tiers and documents sens…

Yutong Shen, Wang Yang, Yue Shen

CogDeBias is a multilingual framework integrating LLMs and ML to automatically detect and mitigate six cognitive biases (e.g., confirmation bias, sunk cost fallacy) in corporate annual reports. A bilingual corpus of 1,200 reports (600 Engli…

Antans Sauhats, Diāna Žalostība, Roman Petrichenko +4

This paper proposes an AI-based surrogate modeling framework using artificial neural networks (ANNs) to accelerate long-term power system transition planning. By combining stochastic scenario generation with detailed simulations and ANN sur…

Ma Y.

This study uses interpretable machine learning to identify greenwashing in heavily polluting enterprises and uncover its multidimensional driving mechanisms. By analyzing corporate ESG disclosures and behavioral data, it builds a model that…

Usama Aslam, Vikram Kumar, Muhammad Ahsan Niazi +1

This paper proposes Graph-X, a unified graph-structured deep learning and stochastic optimization framework for day-ahead electricity price forecasting and risk-aware VPP bidding. It models market-clearing behavior by converting bids into p…

Yuhao Gu, Han Lai

This paper explores how FinTech (blockchain, AI, big data) enhances green finance efficiency through environmental transparency, risk mitigation, and green innovation. Empirical results show blockchain improves disclosure authenticity, AI r…

Andreas Poyias, Panayiotis Mourtopallas, Diamanto Platanou +3

This study develops a digital framework for optimizing green hydrogen integration in food industry steam production, which accounts for up to 57% of energy use. LightGBM models trained on PV and weather data achieve high forecasting accurac…

Firat Bolat, Hande Demi̇rel

This study integrates Sentinel-5P TROPOMI satellite observations with AIS data to monitor ship emissions in the Istanbul Strait, estimating annual CO2 of 213,678 t, NOx of 5,970 t, and SOx of 686 t. A correlation of 0.76 between AIS-derived…

Kjartan Sigurðsson

This chapter analyzes AI's transformative role in sustainability reporting, particularly ESG, arguing it represents institutional change rather than mere technological improvement. It examines how blockchain, generative AI, and real-time mo…

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 …

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…

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…

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…

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…