Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China

doi:10.1007/s11442-014-1109-z

Abstract

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.

Key words: carbon footprint, spatial differences, multi-mechanism, GWR model, China

Shaojian WANG, Chuanglin FANG, Haitao MA, Yang WANG, Jing QIN. 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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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

	S_structure (%)	F_efficiency (t/10⁴ yuan)	U_urbanization (%)	R_economy (10⁴ yuan)	P_population (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

OLS model
	Coefficient	Standard error	t/z-value	Pr(>\|t\|)
CONSTANT	0.04598533	0.07577884	-0.606836	0.5490287
S_structure	0.1226138	0.2005283	-0.6114537	0.1460138
F_efficiency	-0.4224572	0.1843807	2.29122313	0.0299797
U_urbanization	0.0091527	0.1504661	-0.0608201	0.0519423
R_economy	0.8650346	0.0990884	2.67472845	0.0125450
P_population	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

Spatial error model
	Coefficient	Standard error	t/z-value	Pr(>\|t\|)
CONSTANT	0.0153703	0.0589723	-0.2606363	0.1943731
S_structure	0.0038843	0.1456612	0.02666697	0.0787253
F_efficiency	-0.2229032	0.1366329	1.63140212	0.0028055
U_urbanization	0.0069441	0.1119279	0.06204101	0.1505300
R_economy	0.8142238	0.0931959	1.22563354	0.0000000
P_population	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

Spatial lag model
	Coefficient	Standard error	t/z-value	Pr(>\|t\|)
CONSTANT	0.06978879	0.0646215	-1.079962	0.2801591
S_structure	0.2301915	0.1820049	-1.264755	0.2059594
F_efficiency	-0.4195918	0.1568021	2.675932	0.0074523
U_urbanization	0.0692413	0.1309372	0.528814	0.0969344
R_economy	0.8338999	0.0913506	1.465779	0.0000086
P_population	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