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Abstract
The recent literatures indicate that the tourism development (TD) has significant influence over the environmental degradation of both high-tourist-arrival and low-tourist-arrival countries. This study investigates the empirical influence of TD on environmental degradation in a high-tourist-arrival economy (i.e. United States), using the wavelet transform framework. This new methodology enables the decomposition of time-series at different time–frequencies. In this study, we have used maximal overlap discrete wavelet transform (MODWT), wavelet covariance, wavelet correlation, continuous wavelet power spectrum, wavelet coherence spectrum and wavelet-based Granger causality analysis to analyse the relationship between TD and CO2 emission in the United States by using the monthly data from the period of 1996(1) to 2015(3). Results indicate that TD is majorly having the positive influence over CE in short, medium and long run. We find the unidirectional influence of TD on CE in short run, medium and long run in the United States.
Notes
2 Fan and Gençay (Citation2010, p. 1307) documented that “Wavelet analysis circumvents the limitation of the Fourier approach that could only be applied to analyze stationary time series.”
3 For details, see Ramsey and Lampart (Citation1998a, Citation1998b).
4 The HWP is a pair of wavelet filters that are designed to be approximate Hilbert transform of one another.
5 For further details of this approach, see Whitcher and Craigmile (Citation2004) and Whitcher et al. (Citation2005).
6 The Daubechies’ (Citation1992) “least asymmetric wavelet filter LA is a widely used wavelet, because it provides the most accurate time-alignment between wavelet coefficients at various scales and the original time-series, and it is applicable to a wide variety of data types”.