Abstract
The present paper endeavours to analyse and provide fresh insights from the dynamic association between tourist arrivals, transportation services, growth and carbon dioxide emanation in the United States. The analysis employs a unique Morlet’s Wavelet method. Precisely, this paper implements Partial and Multiple Wavelet Coherence techniques to the monthly dataset spanning from 2001 to 2017. From the frequency perspective, this research finds remarkable wavelet coherence and vigorous lead and lag associations. The analysis discovers significant progress in variables over frequency and time. The variables display strong but inconsistent associations between them. There exist a strong co-movement among the variables considered, which is not equal across the time scales. The study may help the policymakers and regulars to devise strategies and formulate policies pertaining to tourism development, which can contribute towards environmentally sustainable economic growth.
| Original language | English |
|---|---|
| Pages (from-to) | 2733-2755 |
| Number of pages | 23 |
| Journal | Current Issues in Tourism |
| Volume | 23 |
| Issue number | 21 |
| DOIs | |
| Publication status | Published - 1 Nov 2020 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- CO emission
- partial and multiple wavelet coherence
- Tourism
- transportation
- USA
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