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Does Climate Policy Uncertainty Affect Tourism Demand? Evidence from Time-Varying Causality Tests

Author

Listed:
  • Nicholas Apergis

    (Department of Banking and Financial Management, University of Piraeus, Karaoli & Dimitriou 80, 18534, Piraeus, Greece)

  • Konstantinos Gavriilidis

    (Stirling Management School, University of Stirling, FK9 4LA, Stirling, UK)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

This study examines whether climate policy uncertainty affects the propensity of people to travel. To do so, we employ the Climate Policy Uncertainty (CPU) index and US air travel data to eight regional overseas destinations for the period 2000-2019. Using time-varying causality tests to deal with the structural breaks that exist in the relationship between CPU and US air travel, we find that CPU is a major determinant of air-travel demand to all destinations examined. The results are robust when we control for macroeconomic factors, uncertainty and geopolitical risks. The findings have important implications for destination countries and tourism professionals.

Suggested Citation

  • Nicholas Apergis & Konstantinos Gavriilidis & Rangan Gupta, 2021. "Does Climate Policy Uncertainty Affect Tourism Demand? Evidence from Time-Varying Causality Tests," Working Papers 202186, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202186
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    Cited by:

    1. Xu, Lanting & Hu, Mao, 2025. "Development of tourism market in China: What role will climate policy play?," Finance Research Letters, Elsevier, vol. 85(PC).
    2. Concetta Castiglione & Davide Infante & Marta Zieba, 2025. "Contextual factors and efficiency gains in Italian tourist destinations," Tourism Economics, , vol. 31(5), pages 1006-1030, August.
    3. Lukman Lasisi & Philip C. Omoke & Afees A. Salisu, 2024. "Climate Policy Uncertainty and Stock Market Volatility," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 5(2), pages 1-6.
    4. Lukman A. Lasisi & Franklin N. Ngwu & Mohammed K. Taliat & Abeeb O. Olaniran & Kelechi C. Nnamdi, 2025. "Modelling commodity market volatility with climate policy uncertainty: a GARCH-MIDAS approach," SN Business & Economics, Springer, vol. 5(3), pages 1-21, March.
    5. Mehmet Sahiner & Maher Khasawneh & Burak Korkusuz, 2026. "From pandemic turbulence to recovery: a dynamic assessment of return and volatility spillovers in tourism finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-26, December.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L8 - Industrial Organization - - Industry Studies: Services

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