The Spatiotemporal Pattern of Decoupling Transport CO2 Emissions from Economic Growth across 30 Provinces in China
Abstract
:1. Introduction
2. Methods and Data Collection
2.1. Data Description
2.2. Calculation of CO2 Emissions in the Transport Sector
2.3. Tapio Decoupling Model
2.4. Decoupling Indicator in Terms of Per Capita and Decoupling Typology
3. Results and Discussion
3.1. Spatiotemporal Patterns of CO2 Emissions in the Transport Sector
3.1.1. Total Amount and the Energy Structure of Transport CO2 Emissions
3.1.2. Spatiotemporal Changes in Transport CO2 Emissions
3.1.3. Per Capita Transport CO2 Emissions
3.1.4. CO2 Emissions Intensity in the Transport Sector (kg/104 yuan)
3.2. Analysis of the Decoupling between Transport CO2 Emissions and Economic Growth
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Fuel | Fi (tce/t) |
---|---|
Raw coal | 0.7143 |
Cleaned coal | 0.9000 |
Coke | 0.9714 |
Crude oil | 1.4286 |
Gasoline | 1.4714 |
Kerosene | 1.4714 |
Diesel oil | 1.4571 |
Fuel oil | 1.4286 |
Liquefied petroleum gas (LPG) | 1.7143 |
Natural gas | 1.2000 tce/104 m3 |
Transport CO2 Emissions or Per Capita Transport CO2 Emissions | |||
---|---|---|---|
Decrease ( < 0 or < 0) | Increase ( > 0 or > 0) | ||
GDP or GDP per capita | Growth ( > 0 or > 0) | IIIa falls or or , negative Decoupling: absolute; weak | Ia increases or or , positive Coupling: relative; strong |
IIIb 2 falls or or , negative Decoupling: absolute; strong | Ib falls or or , positive Decoupling: relative; weak | ||
Decline ( < 0 or < 0) | IVa increases or or , positive Coupling: relative; weak | IIa increases or or , negative Coupling: absolute; weak | |
IVb falls or or , positive Decoupling: relative; strong | IIb 2 increases or or , negative Coupling: absolute; strong |
Region | D (Decoupling Index) | DP (Decoupling Index in the Sense of Per Capita) | ||||||
---|---|---|---|---|---|---|---|---|
1995–2000 | 2000–2005 | 2005–2010 | 2010–2015 | 1995–2000 | 2000–2005 | 2005–2010 | 2010–2015 | |
Beijing | 1.19 | 0.55 | 1.12 | 0.60 | 1.23 | 0.51 | 1.18 | 0.46 |
Tianjin | 2.46 | 0.38 | 0.27 | −0.13 | 2.63 | 0.35 | 0.07 | −0.70 |
Hebei | 0.03 | 1.22 | 1.28 | −0.09 | −0.05 | 1.22 | 1.30 | −0.23 |
Shanxi | 0.72 | 0.93 | 1.12 | 0.76 | 0.66 | 0.93 | 1.13 | 0.73 |
Inner Mongolia | −0.44 | 2.44 | 0.64 | −0.24 | −0.55 | 2.45 | 0.63 | −0.30 |
Liaoning | 1.95 | 2.58 | 0.32 | 0.54 | 2.02 | 2.57 | 0.29 | 0.54 |
Jilin | 0.05 | 0.73 | 0.88 | 1.90 | −0.07 | 0.73 | 0.88 | 1.91 |
Heilongjiang | 0.81 | 1.03 | 0.26 | 4.04 | 0.81 | 1.03 | 0.26 | 3.98 |
Shanghai | 1.46 | 1.57 | 0.65 | 0.28 | 1.63 | 1.67 | 0.46 | 0.13 |
Jiangsu | 0.68 | 1.61 | 0.59 | 0.78 | 0.65 | 1.62 | 0.57 | 0.78 |
Zhejiang | 1.13 | 0.86 | 0.66 | 0.82 | 1.15 | 0.86 | 0.62 | 0.81 |
Anhui | −1.48 | 5.33 | 0.65 | 1.78 | −1.45 | 5.48 | 0.66 | 1.83 |
Fujian | 0.48 | 1.55 | 1.05 | 0.58 | 0.42 | 1.58 | 1.05 | 0.55 |
Jiangxi | 2.57 | 1.02 | 0.35 | 1.06 | 2.64 | 1.02 | 0.33 | 1.07 |
Shandong | −0.47 | 8.09 | 0.56 | −0.63 | −0.59 | 8.22 | 0.54 | −0.74 |
Henan | 0.06 | 1.51 | 0.64 | 1.22 | 0.03 | 1.52 | 0.64 | 1.23 |
Hubei | −0.77 | 14.83 | 0.23 | 0.33 | −0.90 | 13.30 | 0.23 | 0.30 |
Hunan | 0.97 | 1.61 | 0.41 | 0.90 | 0.97 | 1.59 | 0.39 | 0.89 |
Guangdong | 1.26 | 0.88 | 0.51 | 0.31 | 1.49 | 0.87 | 0.41 | 0.23 |
Guangxi | 6.82 | 1.19 | 0.62 | 0.34 | 6.43 | 1.20 | 0.63 | 0.28 |
Hainan | 2.96 | 0.82 | 1.31 | −0.07 | 3.75 | 0.80 | 1.33 | −0.17 |
Chongqing | - | 2.18 | 0.54 | 0.69 | - | 2.06 | 0.53 | 0.67 |
Sichuan | 10.99 | 1.17 | 0.73 | −0.04 | 2.25 | 1.16 | 0.74 | −0.08 |
Guizhou | 0.81 | 0.72 | 1.29 | 0.49 | 0.80 | 0.70 | 1.27 | 0.48 |
Yunnan | 0.99 | 3.68 | 0.77 | 0.29 | 0.99 | 3.85 | 0.76 | 0.26 |
Tibet | - | - | - | - | - | - | - | - |
Shaanxi | −0.13 | 1.98 | 0.82 | −0.20 | −0.19 | 2.00 | 0.82 | −0.24 |
Gansu | 1.61 | 0.00 | 0.26 | 1.30 | 1.67 | 0.01 | 0.26 | 1.31 |
Qinghai | 1.88 | 0.26 | 2.02 | 0.43 | 2.06 | 0.21 | 2.06 | 0.38 |
Ningxia | 0.30 | 1.11 | 0.20 | 0.21 | 0.09 | 1.13 | 0.17 | 0.11 |
Xinjiang | 0.45 | 1.41 | 0.32 | 1.59 | 0.21 | 1.44 | 0.24 | 1.71 |
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Zheng, J.; Hu, Y.; Dong, S.; Li, Y. The Spatiotemporal Pattern of Decoupling Transport CO2 Emissions from Economic Growth across 30 Provinces in China. Sustainability 2019, 11, 2564. https://doi.org/10.3390/su11092564
Zheng J, Hu Y, Dong S, Li Y. The Spatiotemporal Pattern of Decoupling Transport CO2 Emissions from Economic Growth across 30 Provinces in China. Sustainability. 2019; 11(9):2564. https://doi.org/10.3390/su11092564
Chicago/Turabian StyleZheng, Ji, Yingjie Hu, Suocheng Dong, and Yu Li. 2019. "The Spatiotemporal Pattern of Decoupling Transport CO2 Emissions from Economic Growth across 30 Provinces in China" Sustainability 11, no. 9: 2564. https://doi.org/10.3390/su11092564