GX & Decarbonization Research

Francesco Sotti, Elettra Giulia Beatrice Bandi, Patrizia Tettamanzi

This study empirically analyzes how digitalization in sustainability reporting affects the textual quality (clarity, conciseness, tone) of non-financial disclosures among Italian listed companies. Against the backdrop of regulatory tighteni…

Ahlam El Ferrad, Aya Klaffa, Mohamed Oudgou +1

A systematic bibliometric review following PRISMA guidelines examines the link between digitalization, AI, and ESG disclosure using Scopus data. It finds 56% annual growth, concentration in China, and underrepresentation of Africa. Digitali…

Esther Curtin, Kerry A. Brown, Elizabeth McGill +3

This cross-sectional study quantified greenhouse gas emissions of 205 AI-generated recipes from a health app using LCA data. Plant-based recipes had lowest emissions, while red meat recipes were over fourfold higher than seafood/poultry. Mo…

Okon Udo Frank, Julius U. Akpabio, Aniefiok Livinus

This study develops AI models (ANN and SVR) to predict CO2 trapping efficiency with high accuracy, validated on 260 data points from a gas flare site in the Niger Delta. The ANN model achieved R2=0.9993, and CO2 mass fraction was the most i…

Caixuan Zhan

This paper develops a two-echelon cross-border supply chain model integrating blockchain, vertical spillover of emission reduction, and consumer low-carbon preference under EU CBAM. Using Stackelberg games, it compares centralized vs. decen…

Guorui Wang, Liang Zhong, Yixuan Zeng

This review systematically synthesizes evolutionary game theory (EGT) fused with AI (deep reinforcement learning, federated learning, blockchain) across three scales: enterprise-level industrial symbiosis, system-level smart energy operatio…

Lu Wang, Chunyan Sun, Xiaoyu Wang

This review surveys AI-enabled low-carbon technologies in the oil and gas industry, covering core needs, applications across key processes, comparisons of AI methods, challenges, and future trends. It serves as a reference for researchers a…

Jiamin Zhang, Yang Qian, Christina W.Y. Wong +1

This study examines how configurations of institutional pressures affect firms' low-carbon behaviors (hard and soft) and the moderating role of AI capability. Using panel data from Chinese A-share listed firms (2007-2022) with cluster analy…

İlker Mert, Hüseyin Yağlı, J. Costa +1

This study combines NLP and stochastic techno-economic modeling to analyze Türkiye's national hydrogen strategy. Baseline LCOH is 4.89 €/kg with electricity price as dominant driver. Proactive policy intervention raises the probability of r…

Oskars Bagants, Raita Rollande, Juris Klonovs

The construction sector accounts for ~34% of global energy-related CO₂ emissions, yet carbon accounting is not integrated into cost estimation and procurement. This study proposes the AI-driven 'EEBOQ' framework, combining LLMs, ontology-ba…

Tuğba Yeğin

This study examines how GenAI-based carbon footprint feedback (AI-CFB) influences consumer purchase intentions for renewable energy-powered products (REPPs). Analyzing 841 Turkish participants with PLS-SEM, it finds AI-CFB as an antecedent …

Chen Song, Zhihao Gao

This paper empirically examines how CEO-chairperson dialect connections affect corporate carbon emissions. Findings show that such connections significantly increase emissions, especially in competitive industries, non-state-owned firms, an…

Wen Xiang, Jianzhong Gao

Using survey data from 10,057 kiwifruit growers in China, this study applies machine learning (random forest, SHAP) to analyze how farmers' digital capabilities affect initial and sustained adoption of low-carbon agricultural technologies. …

Helena M. Ramos, Chetan Rishi, Óscar E. Coronado-Hernández +3

This study proposes an MCDA framework combining machine learning (EL-SVM, decision tree, logistic regression) with AHP to evaluate candidate sites for hybrid renewable energy systems (HRES) integrating solar, wind, hydropower, and storage. …

Zeeshan, Mohammad

Scope3Scout is a 16-agent platform that automates ESG and CSRD supply chain compliance verification using a multi-agent architecture. It automates verification of supplier data and was a TinyFish Accelerator finalist. It demonstrates a nove…

Tao Wu, Xinyu Li, Jie Li +2

This paper proposes a multi-agent system enhanced by large language models (LLMs) to optimize low-carbon energy scheduling under load and renewable uncertainties. It applies AI to energy transition, improving grid operation efficiency and s…

Xin Huang, Yixin Li, Keteng Jiang +3

This paper proposes a low-carbon optimal dispatch strategy that models dynamic carbon emission factors of thermal power units using spatiotemporal attention and absolute position encoding, integrated into a power-carbon coupled scheduling f…

Shankar Subramanian Iyer, Dr Brinitha Raji, Dr. Rajesh Arora +1

This scoping review examines the integration of circular economy (CE) and AI/digital technologies in the energy sector, combining 143 peer-reviewed sources with interviews of 15 industry leaders. It finds that CE offers significant value bu…

LEKBIR, DJAMEL

This study develops an Estimation-to-Compensation framework using double/debiased machine learning and causal forests to estimate installation-level heterogeneous effects of the EU ETS on ~1,900 German installations, validated on Chinese pi…

Mohammadi, Sepehr, Mostafa, Sherif, Jadidi, Zahra

This is a coded dataset and reproducibility package for a PRISMA-informed evidence map of AI and computational methods for carbon decision support in the built environment. It includes a 457-record audited database (335 primary studies, 102…