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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Topic: #Hydrogen (clear)

Showing 41–60 of 286 papers

Preprint🌍 GlobalZenodo2026#HydrogenDOI

İzmir Green Hydrogen Hub — Digital Twin (code)

YOLADI, Mehmet

This digital twin implementation sizes and dispatches a solar-assisted, grid-connected green hydrogen refueling hub using hourly demand and carbon-aware linear programming. It integrates real-world data from İzmir and the Turkish grid, prov…

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🇺🇸 USADatasetZenodo2026#HydrogenDOI

Evidence ledger and supplementary material for "Emissions and cost outcomes under additionality, temporal matching, and deliverability rules for grid-connected electrolytic hydrogen"

Enam, Tashfique, Shuvo Dip Datta, Technology, Mostakim Hossain Rony +1

This record provides supplementary material and a machine-readable evidence ledger for a systematic review and cross-model evidence-conversion analysis of additionality, temporal matching, and deliverability rules for grid-connected electro…

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🇪🇺 EuropeConference2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)2026#HydrogenDOI

Green Urban Mobility: Optimizing Hydrogen Public Transport from Demand to Energy Supply

Sofia Polymeni, M. Fotopoulou, Georgios Spanos +8

This paper proposes a framework integrating hydrogen demand forecasting and production scheduling for public transport. Using seasonal forecasting and optimization, it predicts daily demand with <3% error and optimizes green hydrogen from P…

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🇪🇺 EuropeDatasetZenodo2026#HydrogenDOI

Supporting data for "Designing Cost-Effective Hydrogen Distribution Networks for Industrial Decarbonisation: A Geo-Referenced Metaheuristic Model"

Pedrero Alegria, Juan, Arrizabalaga, Eneko, DIARCE BELLOSO, GONZALO +2

This dataset supports a geo-referenced metaheuristic model for designing cost-effective hydrogen distribution networks for industrial decarbonisation. It optimizes pipeline and transport configurations based on geographic demand and supply,…

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