Journal of Geographical Sciences ›› 2018, Vol. 28 ›› Issue (4): 495-513.doi: 10.1007/s11442-018-1486-9
• Research Articles • Previous Articles Next Articles
Wensong SU1,2,3(), Yanyan Liu4(
), Shaojian WANG5, Yabo ZHAO6(
), Yongxian SU7, Shijie LI5
Received:
2017-05-01
Accepted:
2017-09-01
Online:
2018-03-30
Published:
2018-03-30
About author:
Author: Su Wensong (1982-), PhD, specialized in urban geography. E-mail:
*Corresponding author: Liu Yanyan (1985-), PhD, E-mail:
Supported by:
Wensong SU, Yanyan Liu, Shaojian WANG, Yabo ZHAO, Yongxian SU, Shijie LI. 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.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
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 |
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 |
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 |
[1] |
Cheng Yeqing, Wang Zheye, Zhang Shouzhiet al., 2013. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China. Acta Geographica Sinica, 68(10): 1418-1431. (in Chinese)
doi: 10.11821/dlxb201310011 |
[2] |
Deng Jixiang, Liu Xiao, Wang Zheng, 2014. Characteristics analysis and factor decomposition based on the regional difference changes in China’s CO2 emission.Journal of Natural Resources, 29(2): 189-200. (in Chinese)
doi: 10.11849/zrzyxb.2014.02.001 |
[3] |
Fan Ying, Zhang Xiaobing, Zhu Lei, 2010. Estimating the macroeconomic cost of CO2 emission abatement in China based on multi-objective programming.Advances in Climate Change Research, 6(2): 130-135. (in Chinese)
doi: 10.1360/972010-1322 |
[4] |
Feng K S, Davis S J, Sun L Xet al., 2015. Drivers of US CO2 emissions 1997-2013.Nature Communications, 6: 7714.
doi: 10.1038/ncomms8714 pmid: 4518269 |
[5] | GEA, 2012. Global Energy Assessment: Toward a Sustainable Future. Cambridge, UK: Cambridge University Press. |
[6] | He Xiaogang, Zhang Yaohui, 2012. Influence factors and environmental Kuznets curve relink effect of Chinese industry’s carbon dioxide emission: Empirical research based on STIRPAT model with industrial dynamic panel data. China Industrial Economy, (1): 26-35. (in Chinese) |
[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] | LeSage J P, Pace R K, 2009. Introduction to Spatial Econometrics, Boca Raton: Taylor and Francis. |
[10] |
Liu Z, Feng K S, Davis S Jet al., 2016. Understanding the energy consumption and greenhouse gas emissions and the implication for achieving climate change mitigation targets.Applied Energy, 184: 737-741.
doi: 10.1016/j.apenergy.2016.10.110 |
[11] |
Liu Z F, He C Y, Zhang Qet al., 2012. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008.Landscape and Urban Planning, 106: 62-72.
doi: 10.1016/j.landurbplan.2012.02.013 |
[12] | Pu Yingxia, Ma Ronghua, Ge Yinget al., 2005. Spatial-temporal dynamics of Jiangsu regional convergence with spatial Markov-chain approach.Acta Geographica Sinica, 60(5): 817-826. (in Chinese) |
[13] | Shao Shuai, Li Xin, Cao Jianhuaet al., 2016. China’s economic policy choices for governing smog pollution based on spatial spillover effects. Economic Research, (9): 73-88. (in Chinese) |
[14] |
Su Yongxian, Chen Xiuzhi, Ye Yuyaoet al., 2013. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries.Acta Geographica Sinica, 68(11): 1513-1526. (in Chinese)
doi: 10.11821/dlxb201311007 |
[15] |
Su Y X, Chen X Z, Li Yet al., 2014. China’s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines.Renewable and Sustainable Energy Reviews, 35: 231-243.
doi: 10.1016/j.rser.2014.04.015 |
[16] | Wang Changjian, Wang Fei, Zhang Hongou, 2016a. The process of energy related carbon emissions and influencing mechanism research in Xinjiang.Acta Ecologica Sinica, 36(8): 2151-2163. (in Chinese) |
[17] |
Wang Changjian, Zhang Xiaolei, Zhang Hongouet al., 2016b. Influencing mechanism of energy-related carbon emissions in Xinjiang based on IO-SDA model.Acta Geographica Sinica, 271(7): 1105-1118. (in Chinese)
doi: 10.1007/s11442-017-1382-8 |
[18] | Wang Lei, 2014. Prediction of carbon emissions in Tianjin based on the input-output model.Ecological Economy, 30(1): 51-56. (in Chinese) |
[19] |
Wang S J, Fang C L, Guan X Let al., 2014a. Urbanization, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces. Applied Energy, 136: 738-749.
doi: 10.1016/j.apenergy.2014.09.059 |
[20] |
Wang S J, Fang C L, Ma H Tet al., 2014b. Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China. Journal of Geographical Sciences, 24(4): 804-822.
doi: 10.1007/s11442-014-1109-z |
[21] |
Wang S J, Fang C L, Wang Y, 2016c. Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data.Renewable & Sustainable Energy Reviews, 55: 505-515.
doi: 10.1016/j.rser.2015.10.140 |
[22] |
Wang Shaojian, Fang Chuanglin, Wang Yanget al., 2013. The directions and mechanisms of regional inequality in Guangdong province.Geographical Research, 32(12): 2244-2256. (in Chinese)
doi: 10.11821/dlyj201312019 |
[23] |
Wang S J, Fang C L, Wang Yet al., 2015a. Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis.Ecological Indicators, 49: 121-131.
doi: 10.1016/j.ecolind.2014.10.004 |
[24] |
Wang S J, Li Q Y, Fang C Let al., 2016d. The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China.Science of the Total Environment, 542: 360-371.
doi: 10.1016/j.scitotenv.2015.10.027 pmid: 26520261 |
[25] |
Wang S J, Liu X P, 2017. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces.Applied Energy, 200(15): 204-214.
doi: 10.1016/j.apenergy.2017.05.085 |
[26] |
Wang S J, Liu X P, Zhou C Set al., 2017. Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities. Applied Energy, 185: 189-200.
doi: 10.1016/j.apenergy.2016.10.052 |
[27] |
Wang Shaojian, Liu Yanyan, Fang Chuanglin, 2015b. Review of energy-related CO2 emission in response to climate change.Progress in Geography, 34(2): 151-164. (in Chinese)
doi: 10.11820/dlkxjz.2015.02.004 |
[28] |
Wang Shaojian, Wang Yang, Zhao Yabo, 2014c.GIS-based multi-scale and multi-mechanism research on regional inequality in Guangdong province.Scientia Geographica Sinica, 30(10): 1184-1192. (in Chinese)
doi: 10.1089/109493102321018141 |
[29] |
Wang Shaojian, Wang Yang, Zhao Yabo, 2015c. Spatial spillover effects and multi-mechanism for regional development in Guangdong province since the 1990s.Acta Geographica Sinica, 70(6): 965-979. (in Chinese)
doi: 10.11821/dlxb201506010 |
[30] |
Wang Y, Li G D, 2017. Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China.Environmental and Planning A, 49(2): 248-251.
doi: 10.1177/0308518X16656374 |
[31] |
Yao C R, Feng K S, Hubacek K, 2014. Driving forces of CO2 emissions in the G20 countries: An index decomposition analysis from 1971 to 2010.Ecological Informatics, 26: 93-100.
doi: 10.1016/j.ecoinf.2014.02.003 |
[32] | Yuan Lu, Pan Jiahua, 2013. Disaggregation of carbon emission drivers in Kaya identity and its limitations with regard to policy implications.Advances in Climate Change Research, 9(3): 210-215. (in Chinese) |
[33] |
Zhang Y, 2009. Structural decomposition analysis of sources of decarbonizing economic development in China: 1992-2006. Ecological Economics, 68(8/9): 2399-2405.
doi: 10.1016/j.ecolecon.2009.03.014 |
[34] | Zhang Zhenghua, Peng Diyun, 2013. Review on the empirical research on the impact factors of China’s carbon dioxide emissions.Ecological Economy, (6): 50-54. (in Chinese) |
[35] | Zheng Changde, Liu Shuai, 2011. Empirical research of carbon emission and economic growth in China based on the spatial econometric analysis.China Population, Resources and Environment, 21(5): 80-86. (in Chinese) |
[36] |
Zhu Yuen, Li Lifen, He Sisiet al., 2016. Peak year prediction of Shanxi Province’s carbon emissions based on IPAT modeling and scenario analysis.Resources Science, 38(12): 2316-2325. (in Chinese)
doi: 10.18402/resci.2016.12.11 |
[1] | LUO Xiang, AO Xinhe, ZHANG Zuo, WAN Qing, LIU Xingjian. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints [J]. Journal of Geographical Sciences, 2020, 30(4): 535-552. |
[2] | Changjian WANG, Fei WANG, Xiaolei ZHANG, Hongou ZHANG. Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis [J]. Journal of Geographical Sciences, 2017, 27(3): 365-384. |
[3] | Chao BAO, Xiaojie CHEN. Spatial econometric analysis on influencing factors of water consumption efficiency in urbanizing China [J]. Journal of Geographical Sciences, 2017, 27(12): 1450-1462. |
[4] | Lei SHEN, Yanzhi *SUN. Review on carbon emissions, energy consumption and low-carbon economy in China from a perspective of global climate change [J]. Journal of Geographical Sciences, 2016, 26(7): 855-870. |
[5] | CHUAI Xiaowei, HUANG Xianjin, WANG Wanjing, WEN Jiqun, CHEN Qiang, PENG Jiawen. Spatial econometric analysis of carbon emissions from energy consumption in China [J]. Journal of Geographical Sciences, 2012, 22(4): 630-642. |
[6] | CHUAI Xiaowei, LAI Li, HUANG Xianjin, ZHAO Rongqin, WANG Wanjing, CHEN Zhigang. Temporospatial changes of carbon footprint based on energy consumption in China [J]. Journal of Geographical Sciences, 2012, 22(1): 110-124 . |
[7] | WEI Benyong, FANG Xiuqi, WANG Yuan. The effects of international trade on Chinese carbon emissions: An empirical analysis [J]. Journal of Geographical Sciences, 2011, 21(2): 301-316. |
|