This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open 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 1–2 of 2 papers
Toyoshima T, Takaguchi H
This study simulates the potential of EV-based demand response in the Tokyo Station Front District, projecting building stock and electricity demand to 2050. Results show that bidirectional V2B/V2G charging can reduce annual energy costs by…
Kaoru Natsuda, Martin Schröder
This study examines Japan's carbon-neutral automotive policy, detailing the Multi-Pathway Strategy (technology-neutral promotion of various powertrains) and green supply chain networks. It critiques the Tank-to-Wheel method, advocating for …
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