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 341–360 of 1487 papers
Yang Lu, Joston Gary
This study develops an environmental systems engineering framework for carbon-management decision support in prefabricated construction, integrating production and transport decisions under dynamic energy prices and carbon signals. A tri-ob…
Yuzhi Wang, Cong Zhang
This study analyzes the causal effect of China's intellectual property pilot policy on low-carbon green energy eco-co-evolution (LCEE) using spatial Durbin DID and double machine learning. The inclusive potential of regional innovation ecos…
Junhao Ren, Honglin Gao, Sijie Wang +5
This paper proposes a multi-agent reinforcement learning approach to jointly design a low-carbon peer-to-peer (P2P) energy market and bidding strategies in microgrids. It optimizes economic and environmental outcomes, promoting renewable in…
Will Alpine, Nathan B. Geldner, Holly Alpine +1
This study models AI as a bidirectional productivity amplifier in a global CGE model, finding that AI-driven productivity gains in fossil fuel sectors lead to net CO2 emission increases of 0.47-1.8 Gt annually, outweighing reductions from r…
Joseph Sanderson
Climate change intensifies food safety risks; digital technologies (AI/ML, IoT, blockchain) strengthen risk analysis via monitoring, prediction, and traceability. Three case studies (AI sorting, blockchain supply chain, IoT cold chain) show…
Youbi D.
This paper systematically reviews the literature on AI applications in sustainable finance, mapping current research and identifying emerging paths. It analyzes AI's impact on ESG assessment, climate-related disclosure, and investment decis…
Gupta U.
This paper proposes a green procurement policy for AI labs' compute resources, requiring minimum environmental disclosures, energy metering, and emission reduction targets. It addresses the growing energy consumption of AI computing and pro…
Vijeta, Neha Yadav, Meenakshi Rao
This conceptual study proposes integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and weak assurance by applying forensic techniques and emerging technologie…
Vijeta, Neha Yadav, Meenakshi Rao
This conceptual study proposes a framework integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and selective disclosure by applying techniques like fraud risk…
Shi Yang, Polina Ellina, Kyriakos Christofi +2
This study develops an ESG disclosure quality evaluation framework tailored to the agricultural chemicals industry, based on GRI 2021 Standards and Chinese regulatory requirements. Using qualitative content analysis and AHP, it constructs a…
Lu Gong
This study analyzes how digital monitoring and ESG disclosure affect the assetization efficiency of agricultural carbon sinks. Using data from typical agricultural regions, it demonstrates that high-quality monitoring and ESG disclosure sig…
Ammar Ali Gull, Irfan Haider Shakri, Faizul Haque +1
This study examines the impact of external sustainability assurance and Big-4 providers on GHG emissions using a global sample of 7,968 firm-year observations from 34 countries. It finds a negative association, stronger in stakeholder-orien…
Yu Qiao, Xinwen Deng, Xiaoying Huang +7
This study proposes a UAV-UGV collaborative delivery framework for urban last-mile logistics, developing a multi-objective optimization model considering efficiency, safety, and low-carbon performance. A visual simulation platform using HTM…
Олег Ивченко, Iryna Ivchenko
This paper proposes an observability approach for real-time monitoring of carbon emissions from distributed models. By visualizing energy consumption and carbon footprint, it provides practical insights for reducing the environmental impact…
K. Zhou, Y. H. Cao, H. Yang
Using data from Shanghai and Shenzhen A-share listed companies (2010-2023), this study employs a two-way fixed-effects model to analyze the impact of venture capital (VC) on corporate carbon performance. VC improves carbon performance by al…
Vu Thi Hoai Thu, Dang Thi Kieu Oanh, Trieu Anh Ngoc
This study integrated GEDI LiDAR, Sentinel-1/2, SRTM, and machine learning to estimate aboveground blue-carbon stocks in the Cua Dai estuary, Vietnam. Random Forest performed best, yielding mean AGC of 13.659 Mg C/ha and total 109,472 Mg C.…
Qiusheng Gao, Meichen Yao, Liang Duan
This paper proposes a new paradigm for low-carbon retrofitting of existing wastewater treatment plants by coupling AI fine-tuned operation with dynamic in-situ retrofitting. AI optimizes operations to reduce energy consumption and greenhous…
Fahrettin Pala, Emine Kaya, Esra Nur Akpınar +3
Using panel data from 50 countries (2002-2019), this study examines how AI, renewable energy technology innovation (RETI), and regulatory quality (RQ) affect low-carbon energy transition. AI promotes transition across all income groups, whi…
Xiaojie Lin, Jian Li, Rui Jing +4
This paper reviews the role of AI in transforming energy systems for carbon neutrality. It highlights how data-driven prediction, physics-informed learning, reinforcement learning, digital twins, and generative models enhance renewable fore…
Jiachen Shen, Jian Shi, Hui Zhong +3
This paper applies mechanism-guided graph learning to evaluate carbon border policies (e.g., CBAM) in interconnected European electricity markets, integrating AI methods with climate policy assessment to quantify cross-border policy spillov…
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