An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China
Abstract
:1. Introduction
2. Literature Review
3. Methodology and Data
3.1. Methodologies
3.1.1. Calculation the Carbon Emission in Transportation Sector
3.1.2. Tapio Decoupling Evaluation Model
3.1.3. The Logarithmic Mean Divisia Index (LMDI) Model
3.2. Data Sources
4. Analysis Results
4.1. Economic Output Values and Carbon Emissions of the Transportation Sector
4.2. Decoupling State in the Transportation Sector
4.3. Influencing Factors of Carbon Emissions from the Transportation Sector
5. Conclusions and Policy Implication
5.1. Conclusions
5.2. Policy Implication
- (1)
- Optimizing the industrial structure. According to the study on the influence of industrial structure to transportation energy consumption conducted by Wang et al. [83], with China entering the late stage of industrialization, the influence exerted by adjusting the industrial structure will become more and more obvious in terms of transportation energy consumption. The adjustment of industrial structure in Beijing, Tianjin and Hebei has got great feedback, especially for Tianjin, the cumulative carbon emission caused by industrial structure is −182.66% of the total cumulative carbon emission during 2005–2013. Beijing and Hebei is −33.61% and −12.84% respectively. Therefore, we should continue to optimize and adjust the industrial structure, speed up the adjustment and pace of that industrial structure, vigorously develop high-tech industries, and promote the transformation of industrial structure to become advanced and reasonable;
- (2)
- Optimizing energy structure. We can develop new energy sources and renewable energy to optimize the country’s energy structure, thereby reducing the carbon emissions of the transportation sector. The use of clean energy and reducing dependence on fossil fuels should be encouraged;
- (3)
- Improving energy efficiency. The government should introduce policies and establish financial support systems to promote the development of low-carbon technologies. In order to address the issue of transportation-related carbon emission, we can develop intelligent transportation and introduce networking, cloud computing and other new-generation technology into the transportation sector. Of course, due to the literature and author knowledge limitations, the influencing factors of transportation-related carbon emissions in BTH are not decomposed or detailed enough;
- (4)
- Controlling population growth: Beijing and Tianjin is the economic axis of enclosed Bohai Sea, which attracted migrants from across the country. Therefore, it is necessary to make some policies to control the population. Especially for Tianjin, its growth rate of population is always more than 4%.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Fuel | Fi (tce/t) | Fuel | Fi (tce/t) |
---|---|---|---|
Raw coal | 0.7143 | Diesel oil | 1.4571 |
Gasoline | 1.4714 | Fuel oil | 1.4286 |
Kerosene | 1.4714 | Natural gas | 12.1430 tce/104 m3 |
Variable | Meaning |
---|---|
Ci | Carbon emissions of the ith fuel type |
Ei | The ith fuel consumption |
E | The total primary energy consumption |
G | GDP in the transportation sector |
GDP | The gross domestic product |
P | Population |
fi | It is the carbon emissions of the ith fuel type. That is the carbon emission coefficient factor. |
si | It is the total energy consumption share of the ith fuel type in the transportation sector. That is the energy structure factor. |
e | It is the energy consumption per unit GDP in transportation sector. That is energy intensity factor. |
u | It is the GDP in the transportation sector weight in GDP. That is industrial structure factor. |
g | It is the GDP per capita. That is economic growth factor. |
p | It is the population size factorrelecting the level of GDP per capita. |
Year | Beijing | Tianjin | Hebei |
---|---|---|---|
2005–2006 | 3.33 (Expansive negative decoupling) | 0.36 (Weak decoupling) | 0.13 (Weak decoupling) |
2006–2007 | 1.72 (Expansive negative decoupling) | −0.09 (Strong decoupling) | 0.53 (Weak decoupling) |
2007–2008 | 3.38 (Expansive negative decoupling) | 1.20 (Expansive negative decoupling) | −0.12 (Strong decoupling) |
2008–2009 | 1.28 (Expansive negative decoupling) | 0.89 (Expansive coupling) | −0.48 (Strong decoupling) |
2009–2010 | 0.64 (Weak decoupling) | 0.75 (Weak decoupling) | 0.74 (Weak decoupling) |
2010–2011 | 0.99 (Expansive coupling) | 0.56 (Weak decoupling) | 0.64 (Weak decoupling) |
2011–2012 | 0.28 (Weak decoupling) | 0.30 (Weak decoupling) | 0.21 (Weak decoupling) |
2012–2013 | 0.78 (Weak decoupling) | −1.32 (Strong decoupling) | 0.09 (Weak decoupling) |
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Zhu, X.; Li, R. An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China. Sustainability 2017, 9, 722. https://doi.org/10.3390/su9050722
Zhu X, Li R. An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China. Sustainability. 2017; 9(5):722. https://doi.org/10.3390/su9050722
Chicago/Turabian StyleZhu, Xiaoping, and Rongrong Li. 2017. "An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China" Sustainability 9, no. 5: 722. https://doi.org/10.3390/su9050722