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Journal of Geographical Sciences    2014, Vol. 24 Issue (4) : 612-630     DOI: 10.1007/s11442-014-1109-z
Research Articles |
Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China
Shaojian WANG1,2(),Chuanglin FANG1,*(),Haitao MA1,Yang WANG3,Jing QIN1,2
1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Guangzhou Institute of Geography, Guangzhou 510070, China
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Abstract  

Global warming has been one of the major concerns behind the world’s high-speed economic growth. How to implement the coordinated development of the carbon footprint and the economy will be the core issue of the world’s economic and social development, as well as the heated debate of the research at home and abroad in recent years. Based on the energy consumption, integrated with the “Top-Down” life cycle approach and geographically weighted regression (GWR) model, this paper analyzed the spatial differences and multi-mechanism of carbon footprint in provincial China in 2010. Firstly, this study calculated the amount of carbon footprint of each province using “Top-Down” life cycle approach and found that there were significant differences of carbon footprint and per capita carbon footprint in provincial China. The provinces with higher carbon footprint, mainly located in northern China, have large economic scales; the provinces with higher per capita carbon footprint are mainly distributed in central cities such as Beijing, Shanghai and energy-rich regions and heavy chemical bases. Secondly, with the aid of GIS and spatial analysis model (GWR model), this paper had unfolded that the expansion of economic scale is the main driver of the rapid growth of carbon footprint. The growth of population and urbanization also acted as promoting factors for the increase of the carbon footprint. Energy structure had no considerable promoting effect for the increase of the carbon footprint. Improving energy efficiency is the most important factor to inhibit the growing carbon footprint. Thirdly, developing low-carbon economies and low-carbon industries, as well as advocating low-carbon city construction and improving carbon efficiency would be the primary approaches to inhibit the rapid growth of carbon footprint. Moderately controlling the economic scale and population size would also be required to alleviate carbon footprint. Meanwhile, environmental protection and construction of low-carbon cities would evoke extensive attention in the process of urbanization.
Keywords carbon footprint      spatial differences      multi-mechanism      GWR model      China     
Fund:National Natural Science Foundation of China, No.41371177.Major Program of National Social Science Foundation of China, No.13&ZD027
Corresponding Authors: Chuanglin FANG     E-mail: 1987wangshaojian@163.com;fangcl@igsnrr.ac.cn
About author: Wang Shaojian (1986?), PhD Candidate, specialized in land use and resources & urban geography. E-mail: 1987wangshaojian@163.com
Issue Date: 09 July 2015
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Shaojian WANG
Chuanglin FANG
Haitao MA
Yang WANG
Jing QIN
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Shaojian WANG,Chuanglin FANG,Haitao MA, et al. Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China[J]. Journal of Geographical Sciences, 2014, 24(4): 612-630.
URL:  
http://www.geogsci.com/EN/10.1007/s11442-014-1109-z     OR     http://www.geogsci.com/EN/Y2014/V24/I4/612
Figure 1  Spatial distribution of total carbon footprint in provincial China in 2010
Provincial region Carbon
footprint (108 t)		 Per capita carbon
footprint (t)		 Provincial
region		 Carbon footprint (108 t) Per capita carbon footprint (t)
Beijing 2.16 11.01 Henan 5.54 5.89
Tianjin 1.46 10.28 Hubei 3.47 6.06
Hebei 6.35 8.84 Hunan 3.80 5.79
Shanxi 4.84 13.55 Guangdong 5.28 5.06
Inner Mongolia 4.77 19.31 Guangxi 2.62 5.69
Liaoning 4.59 8.49 Hainan 0.78 8.99
Jilin 4.38 15.95 Chongqing 1.55 5.37
Heilongjiang 3.25 8.48 Sichuan 3.46 4.30
Shanghai 2.36 10.25 Guizhou 2.56 7.37
Jiangsu 5.81 7.39 Yunnan 2.30 5.00
Zhejiang 3.83 7.04 Shaanxi 2.95 7.90
Anhui 3.06 5.14 Gansu 1.36 5.32
Fujian 2.03 5.50 Qinghai 0.43 7.64
Jiangxi 2.57 5.77 Ningxia 1.03 16.35
Shandong 6.55 6.84 Xinjiang 2.04 9.35
Table 1  Total carbon footprint and per capita carbon footprint in provincial China in 2010
Figure 2  Spatial distribution of per capita carbon footprint in provincial China in 2010
Figure 3  The spatial distribution of five potential determinants in 2010 Notes: energy structure (dominant energy share of total energy consumption), energy efficiency (per unit GDP energy consumption, t/104 yuan), urbanization (urban population/total population), economy factor (per capita GDP, yuan) and population factor (population size, 104 persons)
Sstructure
(%)		 Fefficiency
(t/104 yuan)		 Uurbanization
(%)		 Reconomy
(104 yuan)		 Ppopulation
(104 persons)
Minimum 43.48 0.29 29.9 13119.00 562.67
Mean 77.57 0.95 49.82 33964.10 4432.58
Maximum 95.03 2.62 88.6 76074.00 10430.31
Standard deviation 14.75 0.57 14.29 17343.43 2707.25
Table 2  Descriptive statistics of five factors in 2010
OLS model
Coefficient Standard error t/z-value Pr(>|t|)
CONSTANT 0.04598533 0.07577884 -0.606836 0.5490287
Sstructure 0.1226138 0.2005283 -0.6114537 0.1460138
Fefficiency -0.4224572 0.1843807 2.29122313 0.0299797
Uurbanization 0.0091527 0.1504661 -0.0608201 0.0519423
Reconomy 0.8650346 0.0990884 2.67472845 0.0125450
Ppopulation 0.4778324 0.1223842 7.17277265 0.0000001
Adjusted R-squared: 0.775289, F-statistic: 23.081 on 2 and 27DF, p-value: 5.67195e-009
Table 3  Global regression analyses (OLS model) in 2010
Spatial error model
Coefficient Standard error t/z-value Pr(>|t|)
CONSTANT 0.0153703 0.0589723 -0.2606363 0.1943731
Sstructure 0.0038843 0.1456612 0.02666697 0.0787253
Fefficiency -0.2229032 0.1366329 1.63140212 0.0028055
Uurbanization 0.0069441 0.1119279 0.06204101 0.1505300
Reconomy 0.8142238 0.0931959 1.22563354 0.0000000
Ppopulation 0.4292908 0.0918455 9.02919169 0.0000001
Lambda: 0.787422 LR test value: 8.502896, p-value: 0.0035458, Log likelihood: 27.077155 for error model, AIC: 67.543 (AIC for OLS: 79.231), Robust Lagrange Multiplier test: 2.5314, on 1 DF, p-value: 0.0367
Table 4  Global regression analyses (spatial error model) in 2010
Spatial lag model
Coefficient Standard error t/z-value Pr(>|t|)
CONSTANT 0.06978879 0.0646215 -1.079962 0.2801591
Sstructure 0.2301915 0.1820049 -1.264755 0.2059594
Fefficiency -0.4195918 0.1568021 2.675932 0.0074523
Uurbanization 0.0692413 0.1309372 0.528814 0.0969344
Reconomy 0.8338999 0.0913506 1.465779 0.0000086
Ppopulation 0.5362292 0.1035332 8.076922 0.0000000
Rho: 0.287546 LR test value: 4.866677, p-value: 0.0273802, Log likelihood: 25.259 for lag model, AIC: 56.5181 (AIC for OLS: 79.231)
Robust Lagrange multiplier test:24.328, on 1 DF, p-value: 0.000002354
Table 5  Global regression analyses (spatial lag model) in 2010
Factors R2 Significantly related provinces
p<0.05 (%) + (%) - (%)
Sstructure 0.62 62.4 51.2 7.6
Fefficiency 0.67 67.5 46.8 17.2
Uurbanization 0.56 57.7 55.6 5.9
Recomomy 0.78 77.3 75.6 4.7
Ppopulation 0.82 89.7 88.4 2.5
Table 6  Summary of univariate GWR models for different factors in 2010
Figure 4  The spatial distribution on the local relationship between carbon footprint and five factors at provincial level in 2010
Figure 5  Local R2 derived from mixed GWR model in 2010
The mixed GWR model
Coefficient Standard error t/z-value Pr(>|t|)
CONSTANT 0.0956437 0.0446845 1.255213 0.214675
Sstructure 0.2541348 0.0820820 -1.112547 0.064572
Fefficiency -0.6278633 0.2568357 2.364672 0.036743
Uurbanization 0.1859435 0.1409965 0.867456 0.043675
Recomomy 0.5726534 0.0613432 0.472894 0.002672
Ppopulation 0.7435687 0.0035566 2.047683 0.000000
Table 7  Summary of mixed GWR models for different variables in 2010
Author Carbon footprint (109 tons) Year Reference
Wei Baoren 1.282 2005 Wei, 2007
Liu Qiang 1.51 2005 Liu et al., 2008
Xu Guangyue 1.66 2006 Xu, 2010
Zhao Rongqin 1.647 2007 Zhao et al., 2011
Shi Minjun 6.01 2007 Shi et al., 2012
Chuai Xiaowei 2.05 2008 Chuai et al., 2012
This paper 9.72 2010
Table 8  Comparison of results with other authors
1 Brunsdon C, Fotheringham A S, Charlton M, 1996. Geographically weighted regression: A method for exploring spatial nonstationarity.Geographical Analysis, 28(4): 281-298.
2 Brunsdon C, Fotheringham A S, Charlton M, 1998. Spatial nonstationarity and autoregressive models.Environment and Planning A, 30(6): 957-973.
url: http://dx.doi.org/10.1068/a300957
3 Brunsdon C, Fotheringham A S, Charlton M, 2002. Geographically weighted summary statistics: A framework for localized exploratory data analysis. Computers,Environment and Urban Systems, 26(6): 501-524.
url: http://dx.doi.org/10.1016/S0198-9715(01)00009-6
4 Casler S D, Rose A, 1998. Carbon dioxide emissions in the U.S. economy: A structural decomposition analysis.Environmental and Resource Economics, 11(4): 349-363.
url: http://dx.doi.org/10.1023/A:1008224101980
5 Chang Y F, Lin S J, 1998. Structural decomposition of industrial CO2 emission in Taiwan: An input-output approach.Energy Policy, 26(1): 5-12.
url: http://dx.doi.org/10.1016/S0301-4215(97)00089-X
6 Chen Lin, 2013. A study on the factors affecting carbon emissions in China: Based on input-output model.Sino-Global Energy, 18(1): 17-22. (in Chinese)
7 Chuai X W, Lai L, Huang X Jet al., 2012. Temporospatial changes of carbon footprint based on energy consumption in China.Journal of Geographical Sciences, 22(1): 110-124.
url: http://dx.doi.org/10.1007/s11442-012-0915-4
8 Druckman A, Jackson T, 2009. The carbon footprint of UK households 1990-2004: A socio-economically disaggregated, quasi-multi-regional input-output model.Ecological Economics, 68(7): 2066-2077.
url: http://dx.doi.org/10.1016/j.ecolecon.2009.01.013
9 Du Tingting, Mao Feng, Luo Rui, 2007. The analysis between China’s economic growth and the evolution of the carbon emission.China Population, Resources and Environment, 17(2): 94-99. (in Chinese)
10 Duro J A, Padilla E, 2006. International Inequalities in per capita CO2 emissions: A decomposition methodology by Kaya factors.Energy Economics, 28(2): 170-187.
url: http://dx.doi.org/10.1016/j.eneco.2005.12.004
11 Fan Jie, Li Pingxing, Liang Yutian, 2010. Framework of final consumption oriented research on carbon footprints.Advances in Earth Science, 25(1): 62-70. (in Chinese)
12 Fotheringham A S, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, UK: John Wiley and Sons.
13 Gilbert A, Chakraborty J, 2010. Using geographically weighted regression for environmental justice analysis: Cumulative cancer risks from air toxics in Florida.Social Science Research, 40(1): 273-286.
14 Hong X, 2011. The calculation of carbon emissions of Shandong Province and the comparison with the national average.Energy Procedia, 5: 1514-1518.
url: http://dx.doi.org/10.1016/j.egypro.2011.03.259
15 Hu M G, Li Z J, Wang J Fet al., 2012. Determinants of the incidence of hand, foot, and mouth disease in China using geographically weighted regression models.PLoS ONE, 7(6): e38978.
url: http://dx.doi.org/10.1371/journal.pone.0038978
16 IPCC, 2008. Summary for Policymakers of Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
17 Janssens I A, Freibauer A, Ciais Pet al., 2003. Europe’s terrestrial bio-sphere absorbs 7 to 12% of European anthropogenic CO2 emission. Science, 300: 1538-1542.
url: http://dx.doi.org/10.1126/science.1083592
18 Jeffrey Q, Chambers, Jeremy Iet al., 2007. Hurricane Katrina’s carbon footprint on U.S. gulf coast forest.Science, 318(16): 1107.
url: http://dx.doi.org/10.1126/science.1148913
19 Jiao Wenxian, Chen Xingpeng, Jia Zhao, 2012. Carbon footprint change of energy consumption in Gansu Province and its influencing factors.Resources Sciences, 34(3): 559-565. (in Chinese)
20 Kees V, Kornelis B, 1995. The direct and indirect energy requirements of households in the Nether lands.Energy Policy, 23: 893-910.
url: http://dx.doi.org/10.1016/0301-4215(95)00072-Q
21 Kenny T, Gray N F, 2009. Comparative performance of six carbon footprint models for use in Ireland.Environmental Impact Assessment Review, 29(1): 1-6.
url: http://dx.doi.org/10.1016/j.eiar.2008.06.001
22 Lee K H, 2011. Integrating carbon footprint into supply chain management: The case of Hyundai Motor Company (HMC) in the automobile industry.Journal of Cleaner Production, 19(11): 1216-1223.
url: http://dx.doi.org/10.1016/j.jclepro.2011.03.010
23 Lin Boqiang, Jiang Zhujun, 2009. The prediction and influencing factors analysis of environmental Kuznets curve for CO2 emissions in China.Management World, 4: 27-36. (in Chinese)
24 Liu Hongguang, Liu Weidong, 2009. Decomposition of energy-induced CO2 emissions in industry of China.Progress in Geography, 28(2): 285-292. (in Chinese)
25 Liu Hui, Cheng Shengkui, Zhang Lei, 2002. The international latest research of the impacts of human activities on carbon emissions.Progress in Geography, 21(5): 420-429. (in Chinese)
26 Liu Qiang, Zhuang X, Jiang K Jet al., 2008. Energy and carbon embodied in main exporting goods of China.China Industrial Economy, (8): 46-55. (in Chinese)
27 Liu Yanhua, Ge Quansheng, He Fanneng, 2008. Countermeasures against international pressure of reducing CO2 emissions and analysis on China’s potential of CO2 emission reduction.Acta Geographica Sinica, 63(7): 675-682. (in Chinese)
28 MacMinn C W, Juanes R, 2009. A mathematical model of the footprint of the CO2 plume during and after injection in deep saline aquifer systems.Energy Procedia, 1(1): 3429-3436.
url: http://dx.doi.org/10.1016/j.egypro.2009.02.133
29 Munoz P, Steininger K W, 2010. Austria’s CO2 responsibility and the carbon content of its international trade.Ecological Economics, 69(10): 2003-2019.
url: http://dx.doi.org/10.1016/j.ecolecon.2010.05.017
30 Padgett J P, Steinemann A C, Clarke J H, 2008. A comparison of carbon calculators. Environmental Impact Assessment Review, 28(2/3): 106-115.
url: http://dx.doi.org/10.1016/j.eiar.2007.08.001
31 Peng Junming, Wu Renhai, 2012. Decomposition of Pearl River Delta’s carbon emissions based on LMDI method.China Population, Resources and Environment, 22(2): 69-74. (in Chinese)
32 Piao S L, Fang J Y, Ciais Pet al., 2009. The carbon balance of terrestrial ecosystems in China. Nature, 458: 1099-1014.
33 Piecyk M I, McKinnon A C, 2010. Forecasting the carbon footprint of road freight transport in 2020.Production Economics, 128(1): 31-42.
url: http://dx.doi.org/10.1016/j.ijpe.2009.08.027
34 Pierucci S, 2009. PRES 2007: Carbon footprint and emission minimization, integration and management of energy sources, industrial application and case studies.Energy, 33: 1477-1479.
35 Rawski T G, 2001. What is happening to China’s GDP statistics? China Economic Review, 12(4): 347-354.
url: http://dx.doi.org/10.1016/S1043-951X(01)00062-1
36 Shi Minjun, Wang Yan, Zhang Zhuoyinget al., 2012. Regional carbon footprint and interregional transfer of carbon emissions in China.Acta Geographica Sinica, 67(10): 1327-1338. (in Chinese)
37 Shimada K, Tanaka Y, Gomi Ket al., 2007. Developing a long-term local society design methodology towards a low-carbon economy: An application to Shiga Prefecture in Japan. Energy Policy, 35(6): 4688-4703.
url: http://dx.doi.org/10.1016/j.enpol.2007.03.025
38 Soytasa U, Sari R, Ewing B T, 2007. Energy consumption, income, and carbon emissions in the United States.Ecological Economics, 62(3/4): 482-489.
url: http://dx.doi.org/10.1016/j.ecolecon.2006.07.009
39 Tobler W R, 1970. A computer movie simulating urban growth in the Detroit Region.Economic Geography, 46: 234-240.
url: http://dx.doi.org/10.2307/143141
40 Wackernagel M, Rees W, 1996. Our Ecological Footprint Reducing Human Impact on the Earth. Gabriola Island, BC:New Society Publishers.
41 Wang J, Liu X, Christakos Get al., 2010. Assessing local determinants of neural tube defects in the Heshun Region, Shanxi Province, China.BMC Public Health, 10: 52.
url: http://dx.doi.org/10.1186/1471-2458-10-52
42 Wei Baoren, 2007. Energy Demand and Scenario Analysis of CO2 Emission in China. Beijing: China Environmental Science Press, 75. (in Chinese)
43 Wei Y H D, Yu D L, Cheng X J, 2011. Scale, agglomeration, and regional inequality in provincial China.Tijdschrift voor Economische en Social Geografie, 102(4): 406-425.
url: http://dx.doi.org/10.1111/j.1467-9663.2010.00621.x
44 Wei Yiming, Liu Lancui, Fan Ying et al., 2008. China Energy Report (2008): CO2 Emissions Research. Beijing: Science Press, 31. (in Chinese)
45 Wiedmann T, 2009. A first empirical comparison of energy eootprints embodied in trade -MRIO versus PLUM.Ecological Economics, 68(7): 1975-1990.
url: http://dx.doi.org/10.1016/j.ecolecon.2008.06.023
46 Wright L, Kemp S, Williams I, 2011. ‘Carbon footprinting’: Towards a universally accepted definition.Carbon Management, 2(1): 61-72.
url: http://dx.doi.org/10.4155/cmt.10.39
47 Xu Guangyue, 2010. The research on the relationship for energy consumption, carbon emissions and economic growth in China [D]. Wuhan: Huazhong University of Science and Technology, 64. (in Chinese)
48 Xu Guoquan, Liu Zeyuan, Jiang Zhaohua, 2006. Decomposition model and empirical study of carbon emission for China, 1995-2004.China Population, Resources and Environment, 16(6): 158-161. (in Chinese)
49 Zhang Lei, 2006. A changing pattern of regional CO2 emissions in China.Geographical Research, 25(1): 1-9. (in Chinese)
50 Zhang Zhuoying, Shi Minjun, 2011. Intra-industrial trade and interregional structural isomorphism of manufacturing industry based on China-IRIO 2002.Acta Geographica Sinica, 66(6): 732-740. (in Chinese)
51 Zhao Rongqin, Huang Xianjin, Zhong Taiyang, 2010. Research on carbon emission intensity and carbon footprint of different industrial spaces in China.Acta Geographica Sinica, 65(9): 1048-1057. (in Chinese)
52 Zhao Rongqin, Huang Xianjin, Zhong Taiyanget al., 2011. Carbon footprint of different industrial spaces based on energy consumption in China.Journal of Geographical Sciences, 21(2): 285-300.
url: http://dx.doi.org/10.1007/s11442-011-0845-6
53 Zhu Qin, Peng Xizhe, Lu Zhiminget al., 2009. Factors decomposition and empirical analysis of variations in energy carbon emission in China.Resources Science, 31(12): 2072-2079. (in Chinese)
54 Zou Xiuping, Song Dunjiang, 2013. Analysis on the regional gaps of the carbon emissions and its impact factors in China.Green Economy, (1): 52-55. (in Chinese)
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