Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: evidence from partial and multiple wavelet coherence

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.

KEYWORDS:

• Tourism
• transportation
• CO₂ emission
• partial and multiple wavelet coherence
• USA

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Kumar, Prashar, and Jana (2019) and Suresh and Tiwari (2018) are the earliest ones to apply wavelet analysis in the area of tourism, and our methodology is adopted from their studies.

2 The reason for opting Morlet wavelet is ‘yields information on the amplitude and phase, both essential to study synchronism between different time-series’ (Aguiar-Conraria & Soares, 2011).

3 https://travel.trade.gov/research/monthly/arrivals/index.asp.

4 Energy Information Administration.

5 https://fred.stlouisfed.org/series/TSITTL.

6 Hylleberg, Engle, Granger, and Yoo (1990) seasonal unit root test results (see Appendix) show no unit root in various frequencies.