Spatiotemporal dynamics and influencing factors of provincial carbon emissions in China

doi:10.3969/j.issn.1004-9479.2020.03.2018507

Abstract

Abstract:

In this paper, the carbon dioxide emissions from 2000 to 2015 in the provinces of China were calculated, using the natural segment method, the carbon emissions in 2000, 2005, 2010 and 2015 were also classified respectively, to analysis of its spatial differentiation characteristics.Spatial autocorrelation analysis was used to reveal the spatial correlation of carbon emissions between neighboring provinces.On this basis, the LMDI method was used to decompose the factors affecting carbon emissions from the aspects of energy structure, energy intensity, economic development and population scale. The results showed that: In terms of time, China's total carbon emissions showed an overall upward trend, with only a 2% decline in 2014-2015. Except for Beijing, the carbon emissions of other provinces are increasing.In space, high-value carbon emissions have gradually spread from the Bohai Rim and the eastern coastal provinces to individual provinces in the central and western regions. The provincial carbon emissions were mainly characterized by high concentration and low agglomeration. The high concentration and centralization of carbon emissions were concentrated in Liaoning province,Hebei, Shandong, Shanxi and Jiangsu provinces, Beijing and Tianjin had a low concentration area with high carbon emissions. The eastern and central regions are more susceptible to energy structure, energy intensity, economic development and population scale than the western provinces. Economic development is driving the carbon emission. Energy intensity is inhibitory to carbon emissions. The energy structure has a positive and negative inhibitory effect on the carbon emissions of various provinces. Except for Guizhou province, the population scale of other provinces is positively driving carbon emissions.

Key words: carbon emission, spatiotemporal dynamics, spatial autocorrelation, LMDI

摘要：

通过测算全国30个省域2000—2015年的二氧化碳排放量，采用自然段点法，分别对2000年、2005年、2010年和2015年各省碳排放量进行分类，分析其空间分异化特征。采用空间自相关分析法揭示了相邻各省份碳排放量的空间关联性。在此基础上，运用对数均值迪氏分解法，从能源结构、能源强度、经济发展和人口规模等角度，对碳排放影响因素进行无残差分解。结果表明：1）时间上，我国碳排放总量整体呈上升趋势，在2014—2015年仅下降2%。除北京市外，其余各省份的碳排放量呈增长趋势；空间上，高值碳排放由环渤海及东部沿海省份逐步蔓延至中西部个别省份;2）各省域碳排放主要呈现高高集聚和低高集聚的特征，高高集聚稳定集中在辽宁、河北、山东、山西和江苏省，北京市和天津市与高碳排放的省份形成一个低高集聚区域;3）东、中部比西部省份更易受能源结构、能源强度、经济发展和人口规模等因素的影响。经济发展对碳排放是驱动作用，能源强度对碳排放是抑制作用，能源结构对各省份碳排放的影响有正向驱动和负向抑制作用，除贵州省外，其余省份人口规模对碳排放均是正向驱动作用。

关键词: 碳排放, 时空格局, 空间自相关, LMDI

Ying WANG, Yanfen HE. Spatiotemporal dynamics and influencing factors of provincial carbon emissions in China[J]. World Regional Studies, 2020, 29(3): 512-522.

王瑛, 何艳芬. 中国省域二氧化碳排放的时空格局及影响因素[J]. 世界地理研究, 2020, 29(3): 512-522.

Figures/Tables 6

Tab. 1 Standard coal reference and carbon emission factors of various types of energy

能源种类	煤炭	原油	天然气
折标准煤参考系数	0.7143	1.4286	1.3300
碳排放系数	0.7559	0.5857	0.4483

Fig.1 Total carbon emission,emission intensity and per capita carbon emission in China from 2000 to 2015

Fig.2 Carbon emissions of various provinces in the country in 2000 and 2015

Tab.2 Types of carbon emission in China's provinces in 2000（万t）

年份	分类		省份	数量
2000年	超重型	（≧10000）		0
	重型	Ⅰ（6000-9999）	河北、山西、山东、辽宁	13
	重型	Ⅱ（3000-5999）	内蒙古、黑龙江、上海、江苏、浙江、安徽、河南、湖北、广东	13
	一般	Ⅰ（2000-2999）	北京、吉林、湖南、四川、贵州、甘肃、新疆	14
	一般	Ⅱ（1000-1999）	天津、江西、福建、广西、重庆、云南、陕西	14
	轻型	（≦999）	海南、青海、宁夏	3
2005年	超重型	（≧10000）	河北、江苏、辽宁、山东、河南、山西	6
	重型	Ⅰ（6000-9999）	广东、浙江、内蒙古、黑龙江	15
	重型	Ⅱ（3000-5999）	上海、甘肃、贵州、陕西、四川、新疆、云南、安徽、湖北、湖南、吉林	15
	一般	Ⅰ（2000-2999）	北京、福建、天津、广西、江西	7
	一般	Ⅱ（1000-1999）	宁夏、重庆	7
	轻型	（≦999）	海南、青海	2
2010年	超重型	（≧10000）	广东、河北、江苏、辽宁、山东、内蒙古、河南、山西	8
	重型	Ⅰ（6000-9999）	浙江、陕西、四川、新疆、安徽、黑龙江、湖北、湖南	19
	重型	Ⅱ（3000-5999）	福建、上海、天津、甘肃、广西、贵州、宁夏、云南、重庆、吉林、江西	19
	一般	Ⅰ（2000-2999）	北京	2
	一般	Ⅱ（1000-1999）	海南	2
	轻型	（≦999）	青海	1
2015年	超重型	（≧10000）	河北、山西、内蒙古、山东、辽宁、江苏、河南、广东、陕西、新疆	10
	重型	Ⅰ（6000-9999）	吉林、黑龙江、浙江、安徽、湖北、湖南、贵州	17
	重型	Ⅱ（3000-5999）	天津、上海、江西、福建、广西、四川、重庆、云南、甘肃、宁夏	17
	一般	Ⅰ（2000-2999）		2
	一般	Ⅱ（1000-1999）	北京、海南	2
	轻型	（≦999）	青海	1

Tab. 3 Global Moran’s I of provinces carbon emissions from 2000 to 2015

年份	Moran’s I	$Z$ 值	年份	Moran’s I	$Z$ 值
2000	0.2604	3.2610	2008	0.2028	2.6635
2001	0.2701	3.3641	2009	0.1860	2.4677
2002	0.2527	3.1848	2010	0.1906	2.5151
2003	0.2413	3.0590	2011	0.1858	2.4380
2004	0.2355	2.9860	2012	0.1721	2.2939
2005	0.2360	3.0112	2013	0.1608	2.1569
2006	0.2140	2.7758	2014	0.1395	1.9331
2007	0.2090	2.7159	2015	0.1304	1.8425

Tab. 3 Global Moran’s I of provinces carbon emissions from 2000 to 2015

年份	Moran’s I	$Z$ 值	年份	Moran’s I	$Z$ 值
2000	0.2604	3.2610	2008	0.2028	2.6635
2001	0.2701	3.3641	2009	0.1860	2.4677
2002	0.2527	3.1848	2010	0.1906	2.5151
2003	0.2413	3.0590	2011	0.1858	2.4380
2004	0.2355	2.9860	2012	0.1721	2.2939
2005	0.2360	3.0112	2013	0.1608	2.1569
2006	0.2140	2.7758	2014	0.1395	1.9331
2007	0.2090	2.7159	2015	0.1304	1.8425

Fig. 3 Decomposition effects of various influencing factors from 2000 to 2015

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