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<p>This paper uses fractional integration methods t<!--Query ID="Q1" Text="Please check whether the names of all authors are correct." Resolved="yes"-->o obtain comprehensive evidence on the evolution of the number of hot d<!--Query ID="Q2" Text="Please check whether the affiliations of all authors are correct." Resolved="yes"-->ays, defined as those with temperatures above 35&#xa0;°C, in 54 countries from various regions of the world over the period from 1950 to 2022. The variable analysed is a key indicator of global warming, and the chosen modelling approach is most informative about the behaviour of the series as it provides evidence on the possible presence of time<!--Query ID="Q3" Text="Please check whether the information provided by the corresponding authors, especially email addresses, are correct." Resolved="yes"--> trends, on whether or not mean reversion occurs, and on the degree of persistence. In brief, the findings indicate the presence of considerable heterogeneity among the countries studied and highlight the importance of tailored climate policies based on both global and local factors.</p>

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Trends and persistence in the number of hot days: some multi-country evidence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月23日

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  • Guglielmo Maria Caporale,
  • Luis Alberiko Gil-Alana,
  • Nieves Carmona-Gonzalez

摘要

This paper uses fractional integration methods to obtain comprehensive evidence on the evolution of the number of hot days, defined as those with temperatures above 35 °C, in 54 countries from various regions of the world over the period from 1950 to 2022. The variable analysed is a key indicator of global warming, and the chosen modelling approach is most informative about the behaviour of the series as it provides evidence on the possible presence of time trends, on whether or not mean reversion occurs, and on the degree of persistence. In brief, the findings indicate the presence of considerable heterogeneity among the countries studied and highlight the importance of tailored climate policies based on both global and local factors.