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International tourism, digital infrastructure, and CO2 emissions: fresh evidence from panel quantile regression approach.
- Source :
- Environmental Science & Pollution Research; May2022, Vol. 29 Issue 24, p36273-36280, 8p
- Publication Year :
- 2022
-
Abstract
- The main motivation behind this study is the importance of tourism and ICT industry in the economic development of a country and their potential effects on the country's environmental quality in the digital era. For empirical analysis, the study applies FMOLS, DOLS, and quantile regression techniques for Asian economies. The findings of the study confirmed that tourism and digitalization improve environmental quality in FMOLS and DOLS models. In the basic quantile regression model, the estimates attached to tourism arrival are positive 5<superscript>th</superscript> quantile to 40<superscript>th</superscript> quantile and then turn negative from 60<superscript>th</superscript> quantile and onwards. Likewise, the estimates attached to tourism receipts in the robust quantile regression model are positive from quantile 5<superscript>th</superscript> to quantile 20<superscript>th</superscript> and negative and increasing from quantile 30<superscript>th</superscript> and onwards. Conversely, the estimates of digital infrastructure are insignificant in the basic quantile model at all quantiles except the 95<superscript>th</superscript>. However, the estimated coefficients of digital infrastructure in the robust model are negative and rising from 40<superscript>th</superscript> quantile to 70<superscript>th</superscript> quantile and negative and declining from 80<superscript>th</superscript> quantile to 95<superscript>th</superscript> quantile. In general, we can say that as the tourism and digital sectors grow, the CO<subscript>2</subscript> emissions decline. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09441344
- Volume :
- 29
- Issue :
- 24
- Database :
- Complementary Index
- Journal :
- Environmental Science & Pollution Research
- Publication Type :
- Academic Journal
- Accession number :
- 156759189
- Full Text :
- https://doi.org/10.1007/s11356-021-18138-2