GX & Decarbonization Research

Lukas Nagel, Rainer Gerstbauer, Levon Harutyunyan +3

This paper proposes a data-provision method to predict carbon footprints across supply chains during the strategic planning stage of product development, aligning with EU climate neutrality goals. Leveraging the Gaia-X data infrastructure, …

Zhouhui Zhang, xinyu Liu, Xin‐Rong Zhang

This paper addresses energy consumption and environmental issues in agricultural cold chains, proposing AI and IoT for energy saving and low-carbon operations. It notes refrigeration accounts for ~80% of energy use and cold transport emits …

Xinyue Hao, Emrah Demir

This study identifies triggers and inhibitors of AI adoption in supply chains using a PRISMA systematic review and thematic analysis under the ESG framework. Environmental triggers include waste and GHG reduction; social triggers include pr…

Afia Saiara Akhi, Mustakim Jahan, Shamima Shammay +1

This systematic review synthesizes recent research on AI-driven optimization for sustainable project and supply chain management, covering predictive analytics, machine learning, digital twins, and multi-objective optimization. It identifie…

Manuel Reif, Christiana Plociennik, Mohammad Hossein Rimaz +2

This paper proposes a Digital Product Passport (DPP) combining Gaia-X principles with the Asset Administration Shell, enabling interoperable access to product data via distributed submodels. Demonstrated through a practical Scope 3 carbon f…

Srikanta Routroy, Prasanta K. Sahu, Prem Chhetri

This editorial introduces a special issue on decarbonizing logistics and supply chains, highlighting strategies such as digital technologies, renewable energy, and circular economy. It emphasizes AI-driven decision support systems for reduc…

Prathaban Durai, Jagannath Nalavade

Proposes a data monitoring framework for aerospace Scope 3 emissions using big data analytics, supporting batch and real-time inputs. Enables airlines to track emissions, set targets, and develop reduction strategies, providing insights for…

Joan van Eeckhout, Miona Dimic, Cecilio Ángulo

Agrixel is a new interoperable geographic standard for agricultural data, leveraging AI and machine learning to integrate externalities like carbon footprint, energy use, and water management. It supports FAIR data principles and aims to en…

Duncan J. Reynolds

This paper reveals that AI-related carbon emissions are largely omitted from NHS mandatory Green Plan reporting. It identifies three gaps: lack of granularity obscuring AI workloads' energy intensity, exclusion of hardware lifecycle emissio…

Duncan J. Reynolds

This paper reveals that AI-related carbon emissions are largely omitted from mandatory NHS Green Plan reporting. Current Scope 1-3 accounting misses emissions from AI workloads' energy intensity, hardware life-cycle (e.g., GPUs), and unproc…

Wang Jingqi, Gao Yan†, Wu Zhiqiang, Li Renjie

This paper proposes a real-time pricing model incorporating load uncertainty and carbon trading in smart grids, maximizing welfare for both supply and demand. Using Q-learning to handle non-convex nonlinear problems, it contributes to renew…

Zhao Sanglin, Zhao Sanglin

This study empirically analyzes the impact of digital transformation (DX) on carbon emission reduction using data from Chinese listed construction enterprises from 2000 to 2021. The results show that DX significantly reduces carbon emission…

Girish K A, Chiranthana Shreya, Nischitha H E +2

This paper presents an autonomous robot using AI and image processing to detect plants and spray fertilizers precisely. It reduces chemical waste and farmer exposure, enhancing sustainability. Experiments demonstrate improved spraying accur…

Yinfeng Chen, Ying Hu

Using panel data from China's carbon-intensive industries (2007-2023) and double debiased machine learning, this study shows that GVC participation and domestic content ratio promote low-carbon innovation. Mediation paths include upgrading …

Wen‐Chi Yang, Wen‐Min Lu, Irene Wei Kiong Ting +1

This study analyzes 37 firms in TSMC's value chain using inverse DEA and chance-constrained DEA to examine ESG initiatives' impact on eco-efficiency. Environmental dimension negatively affects eco-efficiency, while social and governance dim…

Çağdaş Civelek

This study compares Türkiye's agricultural GHG emissions with EU-27 countries and forecasts 2025-2030 emissions using machine learning. Agricultural emissions account for ~12% of total, ranking second in total but ninth lowest in intensity.…

Elena Claudia BADEA (FLOREA), A. Olteanu (Burca), M. Bunea +1

This study proposes an integrated competency framework for auditors in the AI era, expanding roles from financial audit to ESG assurance and AI tool use. Based on a structured literature review of 22 articles (2019-2025), it identifies six …

Hwang S.

This paper proposes an LLM-agent framework to automatically assess IFRS S2-aligned climate disclosures in Korean corporate ESG reports. It aims to efficiently check completeness and consistency, identifying disclosure gaps. Contributes to t…

Naila Rana Andira, Philipp Sander, A. Pehlken

Industry accounts for three-quarters of global GHG emissions, and procurement changes affect product emissions. This paper integrates an event-based architecture with ERP systems to dynamically calculate and visualize material GHG emissions…

Zhufeng Huang

Using Chinese A-share listed firms' green bond issuances from 2016-2024, this study shows that corporate digital transformation (CDT) significantly lowers green bond credit spreads. Text-based CDT index and fixed-effects regressions reveal …