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 321–340 of 1414 papers
Pranav Nerurkar
This paper empirically examines the 'green halo effect,' revealing real-world inconsistencies between ESG scores and retail investor sentiment. It analyzes how ESG ratings influence investor perception and behavior, questioning the reliabil…
Alessandra Allini, Alessandro Corrado, Luca Ferri +1
This study examines how abnormal tone in ESG reporting (discretionary narrative unexplained by fundamentals) affects firm value and the value relevance of accounting information, using a sample of 2,002 European listed firms (2017-2024). Us…
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…
B.K. Tripathy, Riya, Hemant Kumar Saini
This chapter explores energy-saving solutions to reduce the carbon footprint of UAVs across their life cycle. It covers AI-based optimization (ML for energy-aware routing, RL for navigation, GANs for flight energy prediction), blockchain fo…
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…
(著者不明)
This paper explores AI-driven circular economy strategies to achieve carbon neutrality in emerging economies. It discusses how AI optimizes resource efficiency and waste reduction, contributing to emission cuts, with implications for policy…
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…
Isaiah Oluwasegun Owolabi
This paper introduces ESG-as-Code, a deterministic rule-based framework that converts ESG regulatory obligations (CSRD, SFDR, SDR, SEC rules) into machine-executable rule logic. It separates probabilistic interpretation from deterministic c…
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…
Yang S.
This paper proposes a hybrid BiLSTM-XGBR model to predict corrosion depth and compressive strength of cement stone in CCUS wells. It applies AI to assess well integrity, contributing to safe and durable CO2 storage.
K. Strang, N. Vajjhala
This single-case study analyzes ESG compliance measurement in financial software engineering projects as a socio-technical system. Applying an open-source ML workflow to 207 archival records, it finds near-unity correlations between social/…
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…
Xinyi Wang
This conceptual paper critically examines generative AI in ESG reporting. It argues that while generative AI enhances efficiency by reducing cost and time in processing text-intensive data and drafting disclosures, it introduces challenges …
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…
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