{"title":"The nexus between economic growth, energy use, urbanization, tourism, and carbon dioxide emissions: New insights from Singapore","authors":"Asif Raihan , Almagul Tuspekova","doi":"10.1016/j.samod.2022.100009","DOIUrl":null,"url":null,"abstract":"<div><p>Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO<sub>2</sub>) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO<sub>2</sub> emissions in Singapore. Time series data from 1990 to 2019 were utilized by employing the dynamic ordinary least squares (DOLS) approach. The DOLS findings show that the long-run coefficient of economic growth is negative and significant, indicating that a 1% rise in economic growth will result in a 0.99% reduction in CO<sub>2</sub> emissions. Furthermore, the coefficient of energy use is positive and significant which reveals that an increasing 1% of energy use is linked with a rising of 0.52% CO<sub>2</sub> emissions in the long run. In addition, the long-run coefficient of urbanization is positive and significant, implying that rising urbanization by 1% causes a 1.90% increase in CO<sub>2</sub> emissions. Moreover, the coefficient of tourism is positive and significant, which specifies that an increase in tourism activities by 1% is associated with a 0.45% increase in CO<sub>2</sub> emissions in the long run. The estimated results are robust to alternative estimators such as ordinary least squares (OLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations toward environmental sustainability by establishing strong regulatory policy instruments to reduce environmental degradation.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100009"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000078/pdfft?md5=d695ae3f136fcfcc58ca369792d1148b&pid=1-s2.0-S2667259622000078-main.pdf","citationCount":"41","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainability Analytics and Modeling","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667259622000078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 41
Abstract
Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO2) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO2 emissions in Singapore. Time series data from 1990 to 2019 were utilized by employing the dynamic ordinary least squares (DOLS) approach. The DOLS findings show that the long-run coefficient of economic growth is negative and significant, indicating that a 1% rise in economic growth will result in a 0.99% reduction in CO2 emissions. Furthermore, the coefficient of energy use is positive and significant which reveals that an increasing 1% of energy use is linked with a rising of 0.52% CO2 emissions in the long run. In addition, the long-run coefficient of urbanization is positive and significant, implying that rising urbanization by 1% causes a 1.90% increase in CO2 emissions. Moreover, the coefficient of tourism is positive and significant, which specifies that an increase in tourism activities by 1% is associated with a 0.45% increase in CO2 emissions in the long run. The estimated results are robust to alternative estimators such as ordinary least squares (OLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations toward environmental sustainability by establishing strong regulatory policy instruments to reduce environmental degradation.