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 6181–6200 of 32968 papers

Peer-reviewedJournalRisks2021#ESGDOI

Esg disclosure and portfolio performance

Bermejo Climent R.

This paper examines the relationship between ESG disclosure and portfolio performance. It analyzes how the quality and scope of disclosure affect investment outcomes, suggesting the usefulness of ESG information in investor decision-making.

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Peer-reviewed🇪🇺 EuropeJournalSustainability Switzerland2019#Energy TransitionDOI

The relationship between economic complexity, energy consumption structure and greenhouse gas emission: Heterogeneous panel evidence from the EU countries

Neagu O.

This study examines the impact of economic complexity and energy consumption structure on greenhouse gas emissions using heterogeneous panel data from EU countries. It explores conditions under which economic sophistication contributes to e…

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Peer-reviewedJournalJournal of the Institute of Science and Technology2022#Energy EfficiencyDOI

Reducing Energy Use and Greenhouse Gas Emissions in Greenhouse Tomato Production with Input Optimization Approach: The Case of Antalya Province

Şahin K.

This study evaluates the potential for reducing energy consumption and GHG emissions through input optimization in greenhouse tomato production in Antalya, Turkey. Using data envelopment analysis, it identifies inefficiencies and suggests i…

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Peer-reviewed🌍 GlobalJournalRisks2026#AI × ESGDOI

Predicting Credit Risk with ESG Factors Using XGBoost and Structural Learning in Vague Environments (SLAVE) in Commercial Banks

Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh +3

This study predicts credit risk in commercial banks using machine learning (XGBoost) and a fuzzy rule-based model (SLAVE) on panel data from 40 banks across seven Middle Eastern countries (2014-2023). Regression results show profitability a…

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