Journal of Geographical Sciences >
Spatiotemporal dynamics of carbon intensity from energy consumption in China
Author: Cheng Yeqing (1976-), PhD and Associate Professor, specialized in economic geography and rural development. E-mail: yqcheng@iga.ac.cn
Received date: 2013-10-27
Accepted date: 2013-12-05
Online published: 2014-04-20
Supported by
Key Research Program of the Chinese Academy of Sciences, No.KZZD-EW-06-03.No.KSZD-EW-Z-021-03.Key Project of Chinese Ministry of Education, No.13JJD790008.National Natural Science Foundation of China, No.41329001.No.41071108
Copyright
The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption in 1997-2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China’s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP (11.72%). Secondly, the trend of Moran’s I indicated that China’s carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China’s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.
CHENG Yeqing , WANG Zheye , YE Xinyue , WEI Yehua Dennis . Spatiotemporal dynamics of carbon intensity from energy consumption in China[J]. Journal of Geographical Sciences, 2014 , 24(4) : 631 -650 . DOI: 10.1007/s11442-014-1110-6
Table 1 Average low-order calorific value and the CO2 emissions coefficient of each kind of fossil energy |
Natural gas | Diesel oil | Coal oil | Gasoline | Fuel oil | Crude oil | Coke | Coal | |
---|---|---|---|---|---|---|---|---|
NCV | 38931 | 42652 | 43070 | 43070 | 41816 | 41816 | 28435 | 20908 |
CEF | 56100 | 74100 | 71500 | 70000 | 77400 | 73300 | 107000 | 95333 |
Figure 1 Carbon emissions and carbon intensity in 1997-2010 |
Figure 2 Moran’s I of carbon intensity in 1997-2010 |
Figure 3 Spatial pattern of China’s carbon intensity in 1997, 2000 and 2010 |
Table 2 The provincial change of carbon intensity in 1997, 2000 and 2010 |
CI | Provinces | ||
---|---|---|---|
1997 | 2000 | 2010 | |
1-3 | Zhejiang, Fujian, Hainan, Guangdong, Guangxi | Jiangsu, Hunan, Hainan, Fujian, Guangdong, Zhejiang, Guangxi | Beijing, Tianjin, Jiangsu, Sichuan, Hubei, Zhejiang, Chongqing, Guangxi, Fujian, Hunan, Hainan, Shanghai, Jiangxi, Guangdong |
3-5 | Shandong, Henan, Jiangsu, Anhui, Hubei, Hunan, Sichuan, Jiangxi, Chongqing, Shanghai | Beijing, Tianjin, Shandong, Shanghai, Henan, Anhui, Heilongjiang, Jiangxi, Hubei, Sichuan, Yunnan, Chongqing | Heilongjiang, Jilin, Liaoning, Shandong, Henan, Anhui, Yunnan |
5-9 | Beijing, Tianjin, Hebei, Qinghai, Shaanxi, Heilongjiang, Yunnan | Jilin, Liaoning, Hebei, Shaanxi, Gansu, Qinghai, Xinjiang, | Gansu, Hebei, Shaanxi, Qinghai, Xinjiang, Guizhou |
9-14 | Inner Mongolia, Xinjiang, Jilin, Liaoning, Gansu, Ningxia | Inner Mongolia, Guizhou, Ningxia | Inner Mongolia, Shanxi |
>14 | Shanxi, Guizhou | Shanxi | Ningxia |
Figure 4 Spatial Moran’s I scattersplots of China’s carbon intensity in 1997, 2000 and 2010 |
Table 3 Spatiotemporal transition matrices in 1997-2010 |
HH | LH | LL | HL | ||
---|---|---|---|---|---|
1997- 2000 | HH | Ⅳ (Heilongjiang, Xinjiang, Jilin, Shanxi, Gansu, Ningxia, Qinghai, Inner Mongolia) | Ⅰ(Shaanxi) | Ⅲ | Ⅱ |
LH | Ⅰ | Ⅳ (Chongqing, Henan, Sichuan, Hunan) | Ⅱ | Ⅲ | |
LL | Ⅲ | Ⅱ (Beijing, Shandong) | Ⅳ (Tianjin, Anhui, Jiangsu, Guangdong, Fujian, Hubei, Shanghai, Guangxi, Jiangxi, Yunnan, Zhejiang, Hainan) | Ⅰ | |
HL | Ⅱ | Ⅲ | Ⅰ | Ⅳ (Liaoning, Hebei, Guizhou) | |
2000- 2010 | HH | Ⅳ (Xinjiang, Gansu, Ningxia, Qinghai, Shanxi) | Ⅰ (Heilongjiang) | Ⅲ (Jilin) | Ⅱ (Inner Mongolia) |
LH | Ⅰ (Shaanxi) | Ⅳ (Chongqing, Henan, Sichuan) | Ⅱ (Beijing, Shandong, Hunan) | Ⅲ | |
LL | Ⅲ | Ⅱ | Ⅳ (Tianjin, Anhui, Jiangsu, Zhejiang, Fujian, Shanghai, Jiangxi, Guangxi, Yunnan, Guangdong, Hubei, Hainan) | Ⅰ | |
HL | Ⅱ | Ⅲ | Ⅰ | Ⅳ (Liaoning, Hebei, Guizhou) | |
1997- 2010 | HH | Ⅳ (Xinjiang, Ningxia, Gansu, Shaanxi, Qinghai) | Ⅰ (Heilongjiang) | Ⅲ (Jilin) | Ⅱ (Inner Mongolia) |
LH | Ⅰ | Ⅳ (Chongqing, Henan, Sichuan) | Ⅱ (Hunan) | Ⅲ | |
LL | Ⅲ | Ⅱ | Ⅳ (Beijing, Zhejiang, Anhui, Hubei, Guangxi, Shanghai, Tianjin, Jiangsu, Guangdong, Jiangxi, Shandong, Yunnan, Fujian, Hainan) | Ⅰ | |
HL | Ⅱ | Ⅲ | Ⅰ | Ⅳ(Liaoning, Hebei, Guizhou) |
Table 4 Estimation and test results of traditional pooled panel data model without spatial interaction |
No fixed effect | Spatial fixed effect | Temporal fixed effect | Two-way fixed effect | |
---|---|---|---|---|
lnTP | 0.000299*** | 0.000285 | 0.000112** | 0.000405** |
lnGDPC | 0.000000*** | -0.000000*** | -0.000000 | -0.000000*** |
lnEI | 2.604854*** | 1.839098*** | 2.991630*** | 2.139974*** |
lnES | 7.678943*** | 7.392118*** | 7.279430*** | 7.007975*** |
lnIS | 0.007099 | 0.069346*** | -0.008369 | 0.036622*** |
lnUR | 0.014439** | 0.025785*** | 0.015482*** | 0.025928*** |
lnFTO | 0.190929** | 0.030146 | 0.187188** | -0.008518 |
lnFDI | -2.735575 | 0.032475 | 7.613159*** | -1.096239 |
R2 | 0.9060 | 0.6944 | 0.9210 | 0.6510 |
δ2 | 1.4227 | 0.3572 | 1.1706 | 0.3115 |
LMlag | 5.9626** | 1.4440 | 9.7850*** | 1.2629 |
R-LMlag | 0.0001 | 2.7390* | 8.0936*** | 5.9562** |
LMerror | 27.718*** | 10.3775*** | 1.7023 | 0.1566 |
R-LMerror | 21.756*** | 11.6724*** | 0.0109 | 4.8499** |
Notes: ***, ** and * denote the significant levels at 1%, 5% and 10%, respectively. |
Table 5 Estimation and test results of two-way fixed effect of the SDM |
Statistic | t | P | Direct effect | t | Indirect effect | t | |
---|---|---|---|---|---|---|---|
lnTP | 0.000301 | 1.366923 | 0.171649 | 0.0003 | 1.4971 | 0.0005 | 1.3281 |
lnGDPC | -0.00000** | -3.97533 | 0.00007 | 0.0000 | -4.1219 | 0.0000 | -2.7592 |
lnEI | 2.095377*** | 18.13572 | 0.00000 | 2.0856 | 18.5869 | -0.3775 | -1.1446 |
lnES | 7.246105*** | 16.41636 | 0.00000 | 7.2272 | 16.6046 | -0.2375 | -0.2021 |
lnIS | 0.033355*** | 2.815624 | 0.004868 | 0.0325 | 2.7874 | -0.1028 | -3.4552 |
lnUR | 0.036719*** | 3.923004 | 0.000087 | 0.0365 | 4.0443 | -0.0132 | -0.8468 |
lnFTO | -0.001210 | -0.02154 | 0.982812 | -0.0037 | -0.0636 | 0.0191 | 0.3077 |
lnFDI | -0.499004 | -0.18946 | 0.84973 | -0.6511 | -0.2427 | -5.3279 | -1.0219 |
W*lnP | 0.000418 | 1.231853 | 0.218004 | R2=0.9808 Corrected R2=0.6751 δ2=0.3157 Wald test spatial lag=31.144***(P=0.000132) LR test spatial lag=29.2358***(P=0.000288) Wald test spatial error=31.5994***(P=0.0001) LR test spatial error=30.6443***(P=0.00016) | |||
W*lnGDPC | 0.00000** | -2.54037 | 0.011074 | ||||
W*lnEI | -0.52500 | -1.56407 | 0.117802 | ||||
W*lnES | -0.78034 | -0.66511 | 0.505978 | ||||
W*lnIS | -0.09924*** | -3.67783 | 0.000235 | ||||
W*lnUR | -0.01547 | -1.08121 | 0.279602 | ||||
W*lnFTO | 0.020459 | 0.371643 | 0.710159 | ||||
W*lnFDI | -4.90060 | -1.01557 | 0.309832 | ||||
W*lnCI | 0.087230 | 1.347382 | 0.177857 |
Notes: ***, ** and * denote the significance levels at 1%, 5% and 10%, respectively. |
The authors have declared that no competing interests exist.
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|
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|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
|
15 |
|
16 |
|
17 |
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
28 |
|
29 |
IEA, 2009. World Energy Outlook. Paris: IEA Publications.
|
30 |
IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Japan: IGES.
|
31 |
IPCC, 2007. Climate Change 2007: Synthesis Report. Summary for Policymakers, 5.
|
32 |
|
33 |
|
34 |
|
35 |
|
36 |
|
37 |
|
38 |
|
39 |
National Bureau of Statistics of China (NBSC), 1998-2011a. China Energy Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
|
40 |
National Bureau of Statistics of China (NBSC), 1998-2011b. China Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
|
41 |
|
42 |
|
43 |
|
44 |
|
45 |
|
46 |
|
47 |
|
48 |
|
49 |
|
50 |
|
51 |
|
52 |
|
53 |
|
54 |
|
55 |
|
56 |
|
57 |
|
58 |
|
59 |
|
60 |
|
61 |
|
62 |
|
63 |
|
64 |
|
65 |
|
66 |
|
67 |
|
68 |
|
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