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Influencing factors on carbon emissions in China transport industry. A new evidence from quantile regression analysis

期刊

JOURNAL OF CLEANER PRODUCTION
卷 150, 期 -, 页码 175-187

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.02.171

关键词

China's transport industry; Carbon dioxide emission; Quantile estimates

资金

  1. Grant for Collaborative Innovation Center for Energy Economics and Energy Policy [1260-Z0210011]
  2. Xiamen University Flourish Plan Special Funding [1260-Y07200]
  3. Newcastle University Joint Strategic Partnership Fund

向作者/读者索取更多资源

This research examines the impact of GDP per capita (gross domestic product), energy intensity (EI), carbon intensity (CI), and total population on carbon dioxide emissions in China's transport industry using quantile analysis from 1980 to 2010. Obviously the study on carbon dioxide has garnered attention globally due to climate change and its relation to green house gas emissions and several other factors, and considering the alarming pace of industrialization and urbanization in China which has led to rapid economic growth and high energy consumption. Also, the percentage of oil consumption to total oil demand in the transport industry of China was 38.2% for the year 2010 which has significantly raised the emission level of carbon dioxide. In this study, having confirmed stationarity and that there exist a long term relationship among our variables (carbon emission, gross domestic product, energy intensity, carbon intensity, and urbanization), we checked which variable(s) has a greater impact on carbon emission on different quantiles. Our quantile estimates showed how the effects of the independent variables (gross domestic product, energy intensity, carbon intensity, and urbanization) varies across the levels of the dependent variable (carbon emission), the results showed that these effect are not constant across the spectrum of the dependent variable. Unlike the gross domestic product, energy intensity, carbon intensity, and urbanization had an inconsistent effects across the spectrum of carbon emissions. All variables were statistically significant in all the spectrum of carbon emissions except for urbanization, which was only significant at the tail ends of the distribution (urbanization was only significant at 10th percentile and 90th percentile respectively). The results therefore shows clearly that GDP, energy intensity, carbon intensity has a greater impact on carbon emission than urbanization, this makes sense to an extent in real life comparing the fact that China is still in the process of urbanization, so not all cities are urban for now. However, this shows that where urbanization exist, it can influence carbon emissions alongside other factors immensely. (C) 2017 Elsevier Ltd. All rights reserved.

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