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<p>The COVID-19 pandemic caused unprecedented disruptions in global tourism, revealing critical gaps in system analysis with historical data. Our study addresses this challenge using Grey System Theory (GST), an innovative approach for small-sample, high-uncertainty contexts, to model Iran’s tourism recovery trajectories. Seven key indicators, including inbound arrivals, hotel capacity, and transport modes, are analyzed across three scenarios. The results demonstrate that the GST model using post-pandemic data of 2022 to 2024 achieves the best accuracy, with a mean absolute percentage error below 1% and grey relational degree exceeding 0.99. Practically, in the long-term perspective (2024–2034), Iran’s inbound tourism scales up by 71% overall (Compound Annual Growth Rate ≈ 5.5%). Air arrivals more than double (102%), while land arrivals grow 55% and remain the volume mainstay; water arrivals nearly triple from a small base. On the supply side, hotel stock more than doubles (129%), with rooms up 50% and bed-places up 63% by 2034. Near-term capacity growth is front-loaded: by 2026/2027, hotels rise 31/48%, rooms 17/24%, and bed-places 19/29% versus 2024. This research explores how policymakers can balance short-term recovery with long-term sustainability, addressing a critical theme in current tourism research. The results validate GST’s effectiveness in modeling tourism system relationships with small samples, maintaining robust performance even when historical data patterns are disrupted. The framework successfully captures systemic behaviors and trends that would be inaccessible to conventional econometric methods under such conditions. The proposed methodology makes significant contributions to tourism system analysis by validating GST as a robust tool for modelling, particularly in economies where data reliability is strained by external shocks such as sanctions and pandemics.</p>

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Analyzing post-pandemic tourism recovery: a grey system theory approach with high-accuracy small-sample modeling

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Tooraj Karimi,
  • Mohamad Ahmadian

摘要

The COVID-19 pandemic caused unprecedented disruptions in global tourism, revealing critical gaps in system analysis with historical data. Our study addresses this challenge using Grey System Theory (GST), an innovative approach for small-sample, high-uncertainty contexts, to model Iran’s tourism recovery trajectories. Seven key indicators, including inbound arrivals, hotel capacity, and transport modes, are analyzed across three scenarios. The results demonstrate that the GST model using post-pandemic data of 2022 to 2024 achieves the best accuracy, with a mean absolute percentage error below 1% and grey relational degree exceeding 0.99. Practically, in the long-term perspective (2024–2034), Iran’s inbound tourism scales up by 71% overall (Compound Annual Growth Rate ≈ 5.5%). Air arrivals more than double (102%), while land arrivals grow 55% and remain the volume mainstay; water arrivals nearly triple from a small base. On the supply side, hotel stock more than doubles (129%), with rooms up 50% and bed-places up 63% by 2034. Near-term capacity growth is front-loaded: by 2026/2027, hotels rise 31/48%, rooms 17/24%, and bed-places 19/29% versus 2024. This research explores how policymakers can balance short-term recovery with long-term sustainability, addressing a critical theme in current tourism research. The results validate GST’s effectiveness in modeling tourism system relationships with small samples, maintaining robust performance even when historical data patterns are disrupted. The framework successfully captures systemic behaviors and trends that would be inaccessible to conventional econometric methods under such conditions. The proposed methodology makes significant contributions to tourism system analysis by validating GST as a robust tool for modelling, particularly in economies where data reliability is strained by external shocks such as sanctions and pandemics.