This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 241–260 of 403 papers
Tao Wang, Yang Shen
Using panel data of Chinese A-share listed firms (2013-2023) and double machine learning, this study finds that supply chain digitalization (SCD) significantly improves enterprise green transformation performance (EGTP). Effects are stronge…
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 …
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…
Real-time pricing strategy based on reinforcement learning with load uncertainty*
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…
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 …
Hanwen Zhang
This narrative review examines how AI influences ESG disclosure quality and greenwashing governance. It proposes a three-level framework where AI serves as disclosure mechanism, audit instrument, and promoter of normative behavior. AI can i…
Marco Aurélio dos Santos Bernardes
This study introduces Adaptive Energy Stations (AESs) that integrate marginal grid emissions, fuel life-cycle carbon intensities, wholesale prices, and vehicle constraints into station-level dispatch optimization. A one-week winter CAISO pr…
Anh Bui-Tuyet, Nhat Duy Lai, Quyen Hua-Thi-Ngoc
AI has a dual role in energy transition: productivity gains in heavy industry reduce energy intensity, while data centers surge electricity demand. Using a 23-sector CGE model for Vietnam through 2035, Green AI boosts GDP by +1.79%, while B…
Hussain K. Alagele, Nagham Hussein Neama
This study analyzes the impacts of economic, ESG, and AI factors on energy transition in three OECD countries via skills enhancement, using CS-ARDL on 2022-2026 panel data. Economic factors negatively impact transition, but skills enhanceme…
Guoqun Ma, Zhiyu Qin, Lihao Yao +1
Using China's major grain-producing areas designation as a quasi-natural experiment, this study employs DID and double machine learning to show the policy significantly reduces agricultural carbon emissions and pollution, with crop structur…
Xiao Xiao, Miaomiao Zhang, Yueyi Wei +1
This study proposes a green innovation framework combining permissioned blockchain with adaptive monitoring to reduce waste and decarbonize food supply chains. A 12-month pilot in a Chinese dairy supply chain reduced data transmission by 90…
Ikhlas Laasri, Vaibhav Shrivastava
This review examines the 'Biostimulants 4.0' paradigm, converting agro-industrial waste into high-value biostimulants via nanotechnology and AI. Green extraction methods like enzymatic hydrolysis cut carbon emissions by up to 23.2 times vs.…
Mei Tao, Hongchen Liu
This paper proposes a multi-objective optimization method for runway-taxiway systems to enhance airport surface sustainability. Integrating CTOT compliance, taxiing time, and runway workload balance, the hybrid SSA-SCA-NSGA-II algorithm is …
HUAN Jiajia, LIU Jiawen, LIU Guozhong, WEN Fushuan, CHEN Biyun, GUO Shaoqing
This paper proposes a coordinated dispatching strategy for interconnected data centers to participate in low-carbon power grid operation by leveraging the spatio-temporal flexibility of computing loads. A high-precision dynamic carbon accou…
Aso Abdullah, Abdullah Omar Yassen, Mohammad Talha
Using panel data from 5,378 firms across Asia, Europe, and North America (2015-2024), this study examines how board governance structures affect climate governance effectiveness and transition performance. Independent and gender-diverse boa…
Guillermo Llopis
AI inference services fall under Scope 3 Category 1 under CSRD, yet lack standardized accounting. This paper proposes a four-tier framework from token-based physical estimation to spend-based EEIO, correcting 10-40x overestimates from gener…
Fatima Zahra El-Yazidi, H. Mounir
This paper proposes an AI-based predictive energy management system for a hybrid solar-wind-battery system producing green hydrogen. It combines machine learning for day-ahead solar and wind generation forecasting with a reinforcement learn…
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