GX Research Hub · English

GX & Decarbonization Research

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.

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Showing 3601–3620 of 31536 papers

Peer-reviewed🇪🇺 EuropeJournalEnvironmental Impact Assessment Review2018#Carbon AccountingDOI

Measuring greenhouse gas emissions from international air travel of a country's residents methodological development and application for Sweden

Larsson J.

This study develops and applies a method to estimate greenhouse gas emissions from the international air travel of a country's residents, using Sweden as a case. It addresses cross-border aviation emissions that territorial inventories miss…

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Peer-reviewed🌍 GlobalJournalFinancial and credit activity problems of theory and practice2026#ESGDOI

ESG PRACTICES AS AN INSTRUMENT FOR ENSURING BALANCED DEVELOPMENT OF HOSPITALITY INDUSTRY ENTERPRISES

T. Zubekhina, L. Matviichuk, Yuliia Sheiko +2

This study introduces the ESG Balance Index (EBI), a novel measure of proportionality across the three ESG pillars based on normalized Euclidean distance from the disclosure simplex centroid. Using ESRS-coded content analysis of 300 firm-ye…

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CNConferenceProceedings of the 3rd International Conference on Machine Intelligence and Digital Applications2026#AI × ESGDOI

Research on Interpretable Machine Learning Model for Financial Distress Early Warning Integrating ESG Features

(著者不明)

This paper proposes a hybrid ML framework fusing financial indicators with ESG scores, carbon intensity, firm size and audit opinions to predict financial distress among Chinese listed firms. Using XGBoost and Random Forest with oversamplin…

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