Strategies in Sustainable Tourism, Economic Growth and Clean Energy, Daniel Balsalobre-Lorente,Oana M. Driha,Muhammad Shahbaz, Editör, Springer, London/Berlin , Berlin, ss.15-38, 2020
The effect of tourism development on GHG has been a controversial research topic, and the existing literature fails to provide satisfactory evidence about the impact of tourism on climate change. To the best of our knowledge, this work is the first to study the dynamics of tourism development with several climate-changing substances through time- and regime (state)-varying analysis. Therefore, this article aims at contributing towards a novel analysis of the behaviour of carbon emissions and tourism development in the US following Markov regime-switching VAR (MS-VAR) models. This book chapter will observe the estimates to understand the effect of tourism on air pollution (CO2 emissions) at different regimes/states. The stochastic process generating the unobservable regimes is an ergodic Markov chain with a finite number of states (st = 1……N) which is defined by the transition probabilities. Most of the current studies provide mixed evidence on the relationship between tourism and climate change through time- and regime-invariant parameter estimations. In contrast, MS-VAR model predictions reveal the constant term and other parameter coefficients, which are also subject to change from one regime to another regime, to explore the effects of explanatory variables on CO2 in the US. The explanatory variables of this work are the Number of Tourist Visiting the US, Energy Consumption of Transportation Sector, and Industrial Production. MS-VAR models also monitored seasonality effects. In the estimations, we aim at observing accurately the impact of tourism on CO2 emissions, as well as the effects of industrial production and transportation sector's energy usage on emissions, in the US.