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From Absorbing Idleness to Preventing Functional Regression: The Shift in the Problem of Post-Event Venue Utilization under the Stock-Renovation Model

Zhi-An Wu

Journal of Artificial Intelligence and Information · 2026

Vollständiger Abstract

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The 15th National Games of China were staged without constructing new large venues, with more than 90% of competition venues delivered through the renovation and upgrading of existing facilities. This marks a shift in venue supply from incremental new construction to stock renovation. Existing research on post-event venue utilization treats idleness as the core problem, resting on three presuppositions: that venues are newly added facilities, that the risk is visible and discrete, and that the remedy lies in finding uses. This article terms this the incremental paradigm. Under stock renovation all three presuppositions fail: venues are already embedded in the urban public service network before the event and will not stand idle afterwards. What may instead decline is the elevated functional level produced by renovation, eroded by operating costs, policy support and operating capability. This article terms this functional regression. Its root cause is the rupture between the construction logic of renovation and the operating logic of maintenance, entrenched by the construction-phase bias of support policy. Four types are distinguished: policy-discontinuity, cost-squeeze, demand-mismatch and capability-loss regression. Current evaluation, built around opening hours and visitor numbers, cannot measure the standard at which venues operate. The article proposes functional retention as a reconstructed evaluation dimension.

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Publikationsdaten

Autor:innen
Zhi-An Wu
Quelle
Journal of Artificial Intelligence and Information
Publikation
2026-01-01
Band / Ausgabe
Nicht angegeben
Seiten
Nicht angegeben
ISSN / ISBN
3064-8033
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Zitierfähiger Nachweis

Zhi-An Wu (2026). From Absorbing Idleness to Preventing Functional Regression: The Shift in the Problem of Post-Event Venue Utilization under the Stock-Renovation Model. Journal of Artificial Intelligence and Information. https://doi.org/10.66069/ojspub.1811260818
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