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 121–140 of 398 papers
Qing Wang
This review systematically explains the concept of circular logistics, integrating circular economy principles into logistics engineering to minimize waste and carbon footprint. It clarifies differences from traditional logistics and presen…
Yuanyang Guo, Liqi Xu, Miaoxi Gu
Using panel data from Chinese cities (2008-2023), this study empirically examines how AI entrepreneurship affects carbon emission intensity. It finds an inverted U-shaped relationship: early expansion increases emissions, but after a thresh…
Jolly Masih, Dinesh Yadav, Sanskriti Singh +3
This study examines whether carbon credits are credible climate solutions or greenwashing, focusing on the gap between institutional narratives and public discourse. Using mixed methods including VADER sentiment analysis, Google Trends, Tal…
Xiaoyuan Feng
This paper empirically examines how corporate digital capabilities affect ESG performance, focusing on the mediating role of low-carbon innovation. Results show digital capabilities significantly boost ESG performance, with low-carbon innov…
Sanjana Santra, Kishore Kumar, Ankita Sharma
This paper proposes a roadmap for AI-powered circular value chains to achieve carbon neutrality and corporate sustainability in emerging economies. It integrates AI technologies with circular economy principles to enhance resource efficienc…
Arnima Pathak, Monika Sharma, Devendra Kumar
This paper proposes a hybrid process and machine learning framework to predict and classify nitrous oxide emissions in legume-based cropping systems. Using 22 years of field data, the ensemble model achieved high predictive accuracy (R²=0.9…
Michael Bane
This study shows that for computational matrix operations, shifting computation in time and space based on carbon intensity yields far greater carbon savings (up to 354x) than energy reductions from code-level optimizations (up to 51x). A r…
Desmond Bayong, Dejun Zhou, Andrews Osei Agyemang
This study examines the impact of AI adoption on ESG disclosure quality among manufacturing firms in G7 economies using panel data from 2017-2024 (5,600 firm-year observations). Quantile regression reveals a consistently positive relationsh…
L. Lin, X. Yao, J. L. Liang
This paper proposes a method using an integrated Knowledge Graph (KG) and NLP to quantify green finance policy impacts by integrating policy, market, ESG, and carbon data. Validation with DID on China's green finance pilot zones (2016-2023)…
X. L. Wang, X. Xue, Y. Lin +1
Proposes a Graphormer-TCN fusion model to assess market operation of new energy settlement mechanisms in industrial parks. Models transmission topology as a weighted graph, integrating spatial and temporal features to score market operation…
Nugroho Nugroho, Rahmatul Husni
This longitudinal critical discourse analysis compares how National Oil Companies (NOCs) and International Oil Companies (IOCs) responded to evolving climate disclosure regulations from the Paris Agreement to EU Omnibus I (2016-2026). Analy…
Yifei Lu, Jia Lu, L. Jin +2
This paper proposes a dual-channel deep learning framework using mixed-frequency data to forecast region-specific daily industrial carbon emissions with high accuracy. It applies AI to carbon emission monitoring, aiding climate action and e…
Mahmoud Elmarzouky, Sabrina Khemiri, Doaa Shohaieb
This study examines how board gender diversity affects the quality of climate impact disclosure in UK nonfinancial firms. Using a novel text-analysis-based index, it finds that boards with at least four female directors significantly enhanc…
Haibo Du, Youping Zhao
This paper presents a multi-agent simulation framework to explain ESG rating divergence among agencies. Using CSI 300 firms, it shows weight differences alone cause significant divergence (4.19 points), low disclosure quality amplifies it b…
Courtney Bower, Farzin Lotfi-Jam, Jennifer S. Minner +2
This study uses agent-based modeling (ABM) to simulate how building reuse and deconstruction choices affect city-wide embodied carbon emissions in Ithaca, NY. Comparing four scenarios, it finds that maximizing preservation achieves the lowe…
Sensheng Li, Minjun Huang, Fuping Wang +3
Proposes a hybrid ET-MVMD-GRU framework for carbon price forecasting across six Chinese pilot ETSs. Integrates feature selection, multi-scale decomposition, and deep learning, outperforming 10 competing models on MAE, RMSE, and MAPE. Offers…
Murali Krishna Pasupuleti
This monograph proposes an integrated architecture for high-integrity carbon-credit market analytics, combining geospatial observation, topological data analysis, statistical inference, machine learning, and quantum-assisted computation. It…
Kui Hu, Wenhui Chen, Kunpeng Zhang +3
This study uses machine learning to optimize the design of low-carbon asphalt mixtures incorporating recycled concrete aggregate. By incorporating engineering constraints, it proposes mix designs that balance carbon emission reduction with …
Yan Liang
This paper proposes an intelligent management platform architecture for ecological governance under low-carbon constraints. It integrates edge-cloud collaboration, spatiotemporal graph networks, multi-modal anomaly detection, and multi-obje…
Fang Qi
This paper constructs a multi-objective optimization model for low-carbon urban fresh logistics distribution, minimizing total cost and carbon emissions. An improved ant colony algorithm with optimized pheromone update and heuristic weights…
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