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 81–100 of 923 papers
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…
Tony Newton
This paper addresses the paradox that AI systems providing ecosystem services also consume water through their infrastructure. It proposes the AI–Nature Reciprocity Gate, a local, seasonal, full-scope standard to verify whether nature-based…
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.…
Nisha Shankar, K. Sagar
Proposes a framework integrating AI and blockchain for carbon credit trading tailored to Indian tourism firms. Introduces the Emission Reduction Progress Factor, uses Entropy Optimal Transport for P2P trading, and particle swarm optimizatio…
Xie, Tengyue
An intelligent emergency decision-support system for transnational renewable energy projects is proposed. It fuses structured and textual evidence via conflict-aware Dempster-Shafer and uses cost-sensitive case-based retrieval to diagnose r…
Michael Addai
This study investigates AI adoption as a driver of sustainability reporting among Ghana Stock Exchange-listed firms. It explores how AI transforms reporting practices, highlighting challenges and opportunities for disclosure in an emerging …
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…
Nawal Louzi, Mohammad Q. Al-Jamal, Mahmoud AlJamal +2
This study proposes a framework integrating AnyLogic simulation and graph-based multi-agent deep reinforcement learning (Graph-MAPPO) to optimize pavement maintenance for carbon reduction and renewable energy use. It manages pavement condit…
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…
Dr. Ayalapogu Ratna Raju, T. Akila, Dr.K Umapathy +3
This study proposes a data-centric framework using a Multilayer Perceptron (MLP) to predict carbon emission reduction potential across the lifecycle of residential buildings. Analyzing 1,640 projects, the model classifies reduction levels w…
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…
Vincenzo Barrile, Emanuela Genovese, Clemente Maesano +2
This study integrates machine learning, remote sensing, and ecosystem modeling to assess carbon dynamics in a semi-arid region of Tunisia. It maps land use/land cover, estimates carbon stocks and emissions using InVEST, and simulates future…
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…
Musa Lefagana, H. Suleiman
This study proposes a conceptual model where cloud accounting moderates the relationship between audit quality and sustainability disclosure. It identifies five audit quality dimensions (anomaly detection, cost efficiency, risk assessment, …
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…
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