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.
Preprint🌍 GlobalCase Studies on Transport Policy2025#EV & TransportDOI
Economic feasibility of a sustainable future: Comparative life cycle cost assessment of electric and internal combustion engine vehicles in the Swedish automotive market
Hakan İnal, Emma Karlsson, Oliver Nåfors +2
This study compares the life cycle cost (LCC) of BEV, PHEV, and ICEV in Sweden in 2024. The Tesla Model Y (BEV) has an LCC of 877,736 SEK, the Volkswagen T-Roc (ICEV) the lowest at 635,222 SEK, and the Volvo XC60 (PHEV) the highest at 1,104…
Peer-reviewed🌍 GlobalJournalZenodo (CERN European Organization for Nuclear Research)2026#EV & TransportDOI
D2.5 Technical requirements and targets integrated for pilots
Shift2Zero Partners
This deliverable documents user-centered requirements elicitation for Shift2Zero innovations, collecting data from 87 interviews, 8 workshops, a survey of 512 participants, and simulations. It defines 181 technical requirements for electric…
Peer-reviewedJournalWorld Electric Vehicle Journal2026#EV & TransportDOI
A Structured Review of Electric Vehicle Sales Research: Multi-Level Driving Factors and Forecasting Pathways over the Past Decade
Guo-Ying Han, Zonglin Li
This review systematically structures EV sales research from 2016-2025, selecting 194 papers from 1518 records. It develops a macro-meso-micro framework for determinants and finds econometric models dominant (54%) but machine learning (18%)…
