This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open 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 61–80 of 217 papers
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…
Meziouni, Reda
This thesis proposes a sovereign AI-driven compliance framework for supply chain due diligence under the German LkSG. It extends the WBCSD PACT protocol with a standardized JSON schema, a deterministic rule engine, an offline Mistral 7B nar…
Cong Doanh Duong
This study demonstrates that AI-based reverse logistics improves net-zero green performance through circular economy practices, but tariff policy uncertainty weakens these effects. Based on a three-wave survey of 209 ISO 14001-certified hig…
Jiayuan Guo, Kai Quan Zhang, Yanlai Wang +1
This study integrates XGBoost with SHAP interpretability to identify carbon emission drivers of agricultural exports using panel data from Central China (1993-2019). The optimized model achieves high predictive accuracy (R²=0.935), revealin…
Sonia Sayari, Nidhal Mgadmi, Majed A Helmi, Hanen Louati, Abdulaziz M Basahel, Hamed Yousef Alshinkity, Sumayya Albalawi
Using US data from 1990-2024, this study examines how green economy, FinTech, and governance affect environmental sustainability. Applying cointegration, error-correction, QVAR, and heatmap diagnostics, it finds that renewable energy, fores…
Maria Aluchna, Leszek Bohdanowicz, Bogumił Kamiński
This study examines how female board representation affects the scope of climate-related disclosure (energy use and GHG emissions) after mandatory NFRD reporting. Using a difference-in-differences design on 72 Warsaw Stock Exchange firms, i…
Xiaoyu Guo, Weisen Wang, Zhanghua Xu +6
This study develops a UAV-LiDAR and machine learning framework for organ-level aboveground carbon (AGC) estimation in bamboo forests. XGBoost and Random Forest achieved highest accuracy (stem R2=0.82, leaf R2=0.73), offering a cost-effectiv…
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