Journal of Geographical Sciences >
Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China
Author: Su Wensong (1982-), PhD, specialized in urban geography. E-mail: suws.13b@igsnrr.ac.cn
*Corresponding author: Liu Yanyan (1985-), PhD, E-mail: liuyyan@mail2.sysu.edu.cn
Received date: 2017-05-01
Accepted date: 2017-09-01
Online published: 2018-03-30
Supported by
National Natural Science Foundation of China, No.41601151
Guangdong Natural Science Foundation, No.2016A030310149
Copyright
Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation (Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club’ agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.
SU Wensong , Liu Yanyan , WANG Shaojian , ZHAO Yabo , SU Yongxian , LI Shijie . Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China[J]. Journal of Geographical Sciences, 2018 , 28(4) : 495 -513 . DOI: 10.1007/s11442-018-1486-9
Figure 1 Average per capita carbon emissions at the city-level across China between 1992 and 2013 plotted alongside corresponding values for the Gini index |
Figure 2 Contributions of regional inequalities within, and between, groups versus overall values of city-level per capita carbon emissions across China between 1992 and 2013 |
Figure 3 Scatter plots to show city-level per capita carbon emissions (Moran’s I) within a geospatial weight matrix for three selected years |
Table 1 Markov-chain transitional matrices for Chinese city-level per capita carbon emissions between 1992 and 2013 |
n | P (≤ 50%) | L (between 51% and 100%) | D (between 101% and 150%) | R (≥ 150%) | |
---|---|---|---|---|---|
Between 1992 and 2013 | |||||
P | 3,098 | 0.957 | 0.042 | 0.000 | 0.000 |
L | 2,002 | 0.023 | 0.936 | 0.040 | 0.000 |
D | 872 | 0.006 | 0.054 | 0.873 | 0.068 |
R | 1,252 | 0.005 | 0.006 | 0.034 | 0.955 |
1992-2000 | |||||
P | 1,342 | 0.949 | 0.049 | 0.001 | 0.001 |
L | 657 | 0.038 | 0.906 | 0.055 | 0.002 |
D | 323 | 0.015 | 0.065 | 0.848 | 0.071 |
R | 430 | 0.014 | 0.014 | 0.044 | 0.928 |
2001-2013 | |||||
P | 1,756 | 0.964 | 0.036 | 0.000 | 0.000 |
L | 1,345 | 0.016 | 0.951 | 0.033 | 0.000 |
D | 549 | 0.000 | 0.047 | 0.887 | 0.066 |
R | 822 | 0.000 | 0.001 | 0.029 | 0.970 |
Abbreviations: P, low level; L, medium-to-low level; D, medium-to-high level; R, high level. |
Figure 4 Spatial patterns in Chinese per capita carbon emissions at the city-level between 1992 and 2000 (a) and between 2001 and 2013 (b) |
Table 2 Spatial Markov-chain transitional matrices for Chinese city-level per capita carbon emissions between 1992 and 2013 |
Between 1992 and 2000 | Between 2001 and 2013 | ||||||||
---|---|---|---|---|---|---|---|---|---|
P | L | D | R | P | L | D | R | ||
P | P | 0.962 | 0.038 | 0.000 | 0.000 | 0.972 | 0.028 | 0.000 | 0.000 |
L | 0.043 | 0.904 | 0.053 | 0.000 | 0.091 | 0.904 | 0.025 | 0.000 | |
D | 0.057 | 0.096 | 0.836 | 0.011 | 0.000 | 0.112 | 0.864 | 0.024 | |
R | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | |
L | P | 0.913 | 0.087 | 0.000 | 0.000 | 0.946 | 0.054 | 0.000 | 0.000 |
L | 0.027 | 0.926 | 0.047 | 0.000 | 0.042 | 0.942 | 0.016 | 0.000 | |
D | 0.017 | 0.154 | 0.818 | 0.011 | 0.000 | 0.063 | 0.916 | 0.021 | |
R | 0.000 | 0.000 | 0.066 | 0.934 | 0.000 | 0.000 | 0.064 | 0.936 | |
D | P | 0.902 | 0.098 | 0.000 | 0.000 | 0.868 | 0.132 | 0.000 | 0.000 |
L | 0.015 | 0.931 | 0.054 | 0.000 | 0.061 | 0.928 | 0.011 | 0.000 | |
D | 0.000 | 0.139 | 0.813 | 0.038 | 0.000 | 0.088 | 0.884 | 0.018 | |
R | 0.000 | 0.000 | 0.072 | 0.928 | 0.000 | 0.000 | 0.036 | 0.964 | |
R | P | 1.000 | 0.000 | 0.000 | 0.000 | 0.784 | 0.216 | 0.000 | 0.000 |
L | 0.015 | 0.931 | 0.054 | 0.000 | 0.075 | 0.914 | 0.011 | 0.000 | |
D | 0.000 | 0.108 | 0.863 | 0.029 | 0.000 | 0.000 | 0.908 | 0.092 | |
R | 0.000 | 0.000 | 0.013 | 0.987 | 0.000 | 0.000 | 0.007 | 0.993 |
Abbreviations as in Table 1. |
Table 3 LM test results using spatial panel models |
LM test | W1 weight matrix | W3 weight matrix | ||
---|---|---|---|---|
χ2 | p-value | χ2 | p-value | |
No lag | 1675.474 | 0.000 | 1684.328 | 0.000 |
No lag (robust) | 25.856 | 0.000 | 26.732 | 0.002 |
No error | 1671.253 | 0.000 | 1669.529 | 0.000 |
No error (robust) | 18.712 | 0.248 | 14.967 | 0.169 |
Table 4 Estimated results from the dynamic spatial panel data models applied in this study |
Variable | W1 weight matrix | W3 weight matrix | ||||
---|---|---|---|---|---|---|
PLOS | FE | Sys-GMM | PLOS | FE | Sys-GMM | |
ln(CEt‒1(θ)) | 0.864*** | 0.266*** | 0.703*** | 0.864*** | 0.268*** | 0.702*** |
ωln(CE(ρ)) | 1.455*** | 1.622*** | 1.082*** | 1.396*** | 1.624*** | 0.179*** |
ωln(CEt‒1(γ)) | 0.688*** | 0.247*** | 0.726*** | 0.687*** | 0.249*** | 0.724*** |
ln(GDPPC) | 0.578* | 0.642*** | 0.594*** | 0.580 | 0.638*** | 0.596*** |
ln(GDPPC)2 | ‒0.017* | 0.034** | 0.026*** | 0.019 | 0.035** | 0.025*** |
ln(POPD) | 0.142 | ‒0.247** | ‒0.386*** | 0.104 | ‒0.246** | ‒0.388*** |
ln(TC) | ‒0.053*** | ‒0.049*** | ‒0.064*** | ‒0.054*** | ‒0.048*** | ‒0.064*** |
ln(SIND) | 0.099*** | 0.163*** | 0.254*** | 0.104*** | 0.164*** | 0.255*** |
ln(ROAD) | ‒0.067* | ‒0.123*** | ‒0.154*** | ‒0.066** | ‒0.123** | ‒0.155*** |
ln(FAI) | 0.057*** | 0.086*** | 0.066*** | 0.057*** | 0.085*** | 0.067*** |
ln(TO) | ‒0.023* | ‒0.018*** | ‒0.027*** | 0.023* | ‒0.019*** | ‒0.028*** |
Sargan P | 0.675 | 0.663 | ||||
AR(2) P | 0.816 | 0.892 |
Abbreviations: *, significant at the 10% level; **, significant at the 5% level; ***, significant at the 1% level. |
The authors have declared that no competing interests exist.
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
GEA, 2012. Global Energy Assessment: Toward a Sustainable Future. Cambridge, UK: Cambridge University Press.
|
[6] |
|
[7] |
IEA, 2012. World Energy Outlook 2012. Paris: International Energy Agency (IEA).
|
[8] |
IPCC, 2007. Summary for Policymakers of Climate Change: The Physical Science Basis. 2007-06-30. [2017-04-10].
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
/
〈 | 〉 |