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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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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:;
About author: Wang Shaojian (1986?), PhD Candidate, specialized in land use and resources & urban geography. E-mail:
Issue Date: 09 July 2015
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Shaojian WANG
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Cite this article:   
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.
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Figure 1  Spatial distribution of total carbon footprint in provincial China in 2010
Provincial region Carbon
footprint (108 t)
Per capita carbon
footprint (t)
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)
(t/104 yuan)
(104 yuan)
(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
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