1990-2014年泛长三角地区能源利用碳排放时空格局及影响因素

doi:10.18306/dlkxjz.2016.12.004

Abstract

Abstract:

In recent years, the spatial and temporal patterns of regional carbon emissions and their influencing factors were a key research focus by researchers from various fields. Taking the Pan-Yangtze River Delta region— one of the most economically developed and fast changing regions in China—as case area, we analyzed the spatiotemporal patterns of carbon emissions in six representative years of 1990, 1995, 2000, 2005, 2010, and 2014. Employing scree plot analysis and a nonlinear polynomial model, we also investigated the major influencing factors of carbon emissions in 2010. Results indicate that the total amount of carbon emissions has increased rapidly, but the spatial pattern has been largely stable since 1990. Sixteen cities of the core area dominated the carbon emissions in the Pan-Yangtze River Delta region, and they accounted for more than 50% of the total at most stages. However, the amount and rate of growth of the carbon emissions of all cities showed significant spatial differences. With 2005 as the turning point, the share of carbon emissions of most peripheral cities declined gradually before, but grew faster than the core cities after this point. The total share of the peripheral cities increased to 47% in 2014 from 33% in 2005. The spatial agglomeration degree first increased and then decreased. The spatial heterogeneity of regional carbon emissions was affected by various factors including economic development and energy consumption. Industrialization, urbanization, and population agglomeration are the major factors of carbon emissions. Fixed asset investment and foreign direct investment also had effects on carbon emissions. The relationship between economic development and carbon emissions shows an inverted U shape. The results indicate the environmental effect of change in economic development pattern and can provide some reference for energy saving and carbon emission reduction policy making.

Key words: carbon emission, spatial heterogeneity, temporal change, influencing factor, Pan-Yangtze River Delta

Jianglong CHEN, Pingxing LI, Jinlong GAO. Spatiotemporal patterns and influencing factors of carbon emissions in the Pan-Yangtze River Delta region, 11990-2014[J].PROGRESS IN GEOGRAPHY, 2016, 35(12): 1472-1482.

Figures/Tables 9

Tab.1

Fig.1

Tab.2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.4

References 35

[1]	邓吉祥, 刘晓, 王铮. 2014. 中国碳排放的区域差异及演变特征分析与因素分解[J]. 自然资源学报, 29(2): 189-200. doi: 10.11849/zrzyxb.2014.02.001
	[Deng J X, Liu X, Wang Z.2014. Characteristics analysis and factor decomposition based on the regional difference changes in China's CO2 emissions[J]. Journal of Natural Resources, 29(2): 189-200.] doi: 10.11849/zrzyxb.2014.02.001
[2]	丁仲礼, 段晓男, 葛全胜, 等. 2009. 2050年大气CO2浓度控制: 各国排放权计算[J]. 中国科学: 地球科学, 39(8): 1009-1027.
	[Ding Z L, Duan X N, Ge Q S, et al.2009. Control of atmospheric CO2 concentrations by 2050: A calculation on the emission rights of different countries[J]. Science in China: Earth Sciences, 52(10): 1447-1469.]
[3]	高长春, 刘贤赵, 李朝奎, 等. 2016. 近20年来中国能源消费碳排放时空格局动态[J]. 地理科学进展, 35(6): 747-757. doi: 10.18306/dlkxjz.2016.06.008
	[Gao C C, Liu X Z, Li C K, et al.2016. Spatiotemporal dynamics of carbon emissions by energy consumption in China from 1995 to 2014[J]. Progress in Geography, 35(6): 747-757.] doi: 10.18306/dlkxjz.2016.06.008
[4]	郝珍珍, 李健. 2013. 我国碳排放增长的驱动因素及贡献度分析[J]. 自然资源学报, 28(10): 1664-1673. doi: 10.11849/zrzyxb.2013.10.002
	[Hao Z Z, Li J.2013. Analysis of China's carbon emission growth: Drive factors and its contribution[J]. Journal of Natural Resources, 28(10): 1664-1673.] doi: 10.11849/zrzyxb.2013.10.002
[5]	黄国华, 刘传江, 赵晓梦. 2016. 长江经济带碳排放现状及未来碳减排[J]. 长江流域资源与环境, 25(4): 638-644. doi: 10.11870/cjlyzyyhj201604014
	[Huang G H, Liu C J, Zhao X M.2016. Status quo of carbon emission and future carbon emission reduction for the Yangtze River Economic Zone[J]. Resources and Environment in the Yangtze Basin, 25(4): 638-644.] doi: 10.11870/cjlyzyyhj201604014
[6]	黄蕊, 王铮, 丁冠群, 等. 2016. 基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J]. 地理研究, 35(4): 781-789. doi: 10.11821/dlyj201604015
	[Huang R, Wang Z, Ding G Q, et al.2016. Trend prediction and analysis of influencing factors of carbon emissions from energy consumption in Jiangsu Province based on STIRPAT model[J]. Geographical Research, 35(4): 781-789.] doi: 10.11821/dlyj201604015
[7]	康文星, 姚利辉, 何介南, 等. 2012. 辽宁省能源消费和碳排放与经济增长的关系[J]. 生态学报, 32(19): 6168-6175. doi: 10.5846/stxb201108111179
	[Kang W X, Yao L H, He J N, et al.2012. The relationship between energy consumption and carbon emissiont with economic growth in Liaoning Province[J]. Acta Ecologica Sinica, 32(19): 6168-6175.] doi: 10.5846/stxb201108111179
[8]	李建豹, 黄贤金. 2015. 基于空间面板模型的碳排放影响因素分析: 以长江经济带为例[J]. 长江流域资源与环境, 24(10): 1665-1671. doi: 10.11870/cjlyzyyhj201510007
	[Li J B, Huang X J.2015. Analysis of influential factors of carbon emission based on spatial panel models: A case study for Yangtze River Economic Belt[J]. Resources and Environment in the Yangtze Basin, 24(10): 1665-1671.] doi: 10.11870/cjlyzyyhj201510007
[9]	李平星, 曹有挥. 2013. 产业转移背景下区域工业碳排放时空格局演变: 以泛长三角为例[J]. 地球科学进展, 28(8): 939-947. doi: 10.11867/j.issn.1001-8166.2013.08.0939
	[Li P X, Cao Y H.2013. Spatial and temporal changes of industrial carbon emissions under regional industrial transfer: The case of Pan-Yangtze River Delta[J]. Advances in Earth Science, 28(8): 939-947.] doi: 10.11867/j.issn.1001-8166.2013.08.0939
[10]	刘贤赵, 高长春, 宋炎, 等. 2017. 湖南市域化石能源消费碳排放时空格局及驱动因素[J]. 生态学报, 37(7)doi: 10.5846/stxb201511212362.
	[Liu X Z, Gao C C, Song Y, et al.2017. Temporal-spatial carbon emission patterns caused by fossil energy consumption at the city level in Hunan Province, China and the factors driving their composition[J]. Acta Ecologica Sinica, 37(7): doi: 10.5846/stxb201511212362.] (in Press)
[11]	刘晓燕, 董锋. 2012. 基于协整检验的江苏省碳排放强度影响因素分析[J]. 华东经济管理, 26(9): 16-19. doi: 10.3969/j.issn.1007-5097.2012.09.004
	[Liu X Y, Dong F.2012. An analysis of influence factors on carbon intensity of Jiangsu Province based on co-integration test[J]. East China Economic Management, 26(9): 16-19.] doi: 10.3969/j.issn.1007-5097.2012.09.004
[12]	陆大道. 2011. 中国地理学的发展与全球变化研究[J]. 地理学报, 66(2): 147-156. doi: 10.3969/j.issn.1003-8477.2003.09.048
	[Lu D D.2011. Development of geographical sciences and research on global change in China[J]. Acta Geographica Sinica, 66(2): 147-156.] doi: 10.3969/j.issn.1003-8477.2003.09.048
[13]	马荣华, 蒲英霞, 马晓冬. 2007. GIS空间关联模式发现[M]. 北京: 科学出版社: 97.
	[Ma R H, Pu Y X, Ma X D.2007. Mining spatial association patterns from GIS database[M]. Beijing, China: Science Press: 97.]
[14]	曲建升, 王琴, 陈发虎, 等. 2010. 我国二氧化碳排放的区域分析[J]. 第四纪研究, 30(3): 466-472. doi: 10.3969/j.issn.1001-7410.2010.03.03
	[Qu J S, Wang Q, Chen F H, et al.2010. Provincial analysis of carbon dioxide emission in China[J]. Quaternary Sciences, 30(3): 466-472.] doi: 10.3969/j.issn.1001-7410.2010.03.03
[15]	舒娱琴. 2012. 中国能源消费碳排放的时空特征[J]. 生态学报, 32(16): 4950-4960. doi: 10.5846/stxb201109071317
	[Shu Y Q.2012. Spatiotemporal characteristics of carbon emissions from energy consumption in China[J]. Acta Ecologica Sinica, 32(16): 4950-4960.] doi: 10.5846/stxb201109071317
[16]	宋德勇, 刘习平. 2013. 中国省际碳排放空间分配研究[J]. 中国人口·资源与环境, 23(5): 7-13. doi: 10.3969/j.issn.1002-2104.2013.05.002
	[Song D Y, Liu X P.2013. Spatial distribution of provincial carbon emissions[J]. China Population, Resources and Environment, 23(5): 7-13.] doi: 10.3969/j.issn.1002-2104.2013.05.002
[17]	王锋, 吴丽华, 杨超. 2010. 中国经济发展中碳排放增长的驱动因素研究[J]. 经济研究, (2): 123-136.
	[Wang F, Wu L H, Yang C.2010. Driving factors for growth of carbon dioxide emissions during economic development in China[J]. Economic Research Journal, (2): 123-136.]
[18]	王喜, 鲁丰先, 秦耀辰, 等. 2016. 河南省碳源碳汇的时空变化研究[J]. 地理科学进展, 35(8): 941-951. doi: 10.18306/dlkxjz.2016.08.003
	[Wang X, Lu F X, Qin Y C, et al.2016. Spatial and temporal changes of carbon sources and sinks in Henan Province[J]. Progress in Geography, 35(8): 941-951.] doi: 10.18306/dlkxjz.2016.08.003
[19]	徐大丰. 2010. 我国碳排放结构的区域差异分析[J]. 江西社会科学, (4): 79-82.
	[Xu D F.2010. An analysis of regional differences of China's carbon emission structure[J]. Jiangxi Social Sciences, (4): 79-82.]
[20]	杨骞, 刘华军. 2012. 中国二氧化碳排放的区域差异分解及影响因素: 基于1995-2009年省际面板数据的研究[J]. 数量经济技术经济研究,(5): 36-49, 148.
	[Yang Q, Liu H J.2012. Regional difference decomposition and influence factors of China's carbon dioxide emissions[J]. The Journal of Quantitative & Technical Economics,(5): 36-49, 148.]
[21]	张豪, 罗亦泳, 张立亭. 2010. 基于遗传支持向量机的城市扩张非线性组合模型[J]. 地理学报, 65(6): 656-664. doi: 10.11821/xb201006003
	[Zhang H, Luo Y Y, Zhang L T.2010. A nonlinear polynomial model for urban expansion incorporating genetic algorithm and support vector machines[J]. Acta Geographica Sinica, 65(6): 656-664.] doi: 10.11821/xb201006003
[22]	张雷. 2003. 经济发展对碳排放的影响[J]. 地理学报, 58(4): 629-637. doi: 10.3321/j.issn:0375-5444.2003.04.019
	[Zhang L.2003. Economic development and its bearing on CO2 emissions[J]. Acta Geographica Sinica, 58(4): 629-637.] doi: 10.3321/j.issn:0375-5444.2003.04.019
[23]	张雷, 黄园淅, 李艳梅, 等. 2010. 中国碳排放区域格局变化与减排途径分析[J]. 资源科学, 32(2): 211-217.
	[Zhang L, Huang Y X, Li Y M, et al.2010. An investigation on spatial changing pattern of CO2 emissions in China[J]. Resources Science, 32(2): 211-217.]
[24]	张秀梅, 李升峰, 黄贤金, 等. 2010. 江苏省1996 年至2007 年碳排放效应及时空格局分析[J]. 资源科学, 32(4): 768-775.
	[Zhang X M, Li S F, Huang X J, et al.2010. Effects of carbon emissions and their spatio-temporal patterns in Jiangsu Province from 1996 to 2007[J]. Resources Science, 32(4): 768-775.]
[25]	赵欣, 龙如银. 2010. 江苏省碳排放现状及因素分解实证分析[J]. 中国人口·资源与环境, 20(7): 25-30. doi: 10.3969/j.issn.1002-2104.2010.07.004
	[Zhao X, Long R Y.2010. The status and decomposition model of carbon emissions for Jiangsu Province[J]. China Population, Resources and Environment, 20(7): 25-30.] doi: 10.3969/j.issn.1002-2104.2010.07.004
[26]	Clarke-Sather A, Qu J S, Wang Q, et al.2011. Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO2 emissions in China 1997-2007[J]. Energy Policy, 39(9): 5420-5428. doi: 10.1016/j.enpol.2011.05.021
[27]	Collobert R, Bengio S, Bengio Y.2002. A parallel mixture of SVMs for very large scale problems[J]. Neural Computation, 14(5): 1105-1114. doi: 10.1162/089976602753633402 pmid: 11972909
[28]	IEA (International Energy Agency). 2013. Key world energy STATISTICS 2013[R]. Paris, France: SOREGRAPH.
[29]	IPCC. 2008. 2006 IPCC guidelines for national greenhouse gas inventories: Volume II[EB/OL]. 2008-7-30[2016-7-30]. .
[30]	Marland G, Boden T A, Andres R J.2010. Global, regional, and national fossil-fuel CO2 emissions[R]. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, doi: 10.3334/CDIAC/00001_V2010.
[31]	Messerli B, Grosjean M, Hofer T, et al.2000. From nature-dominated to human-dominated environmental changes[J]. Quaternary Science Reviews, 19(1-5): 459-479. doi: 10.1016/S0277-3791(99)00075-X
[32]	Song M L, Guo X, Wu K Y, et al.2015. Driving effect analysis of energy-consumption carbon emissions in the Yangtze River Delta region[J]. Journal of Cleaner Production, 103: 620-628. doi: 10.1016/j.jclepro.2014.05.095
[33]	Stern N.2007. The economics of climate change: The stern review[M]. Cambridge, UK: Cambridge University Press.
[34]	Su Y X, Chen X Z, Li Y, et al.2014. China's 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines[J]. Renewable and Sustainable Energy Reviews, 35: 231-243. doi: 10.1016/j.rser.2014.04.015
[35]	Zhang Y, Zhang J Y, Yang Z F, et al.2011. Regional differences in the factors that influence China's energy-related carbon emissions, and potential mitigation strategies[J]. Energy Policy, 39(12): 7712-7718. doi: 10.1016/j.enpol.2011.09.015

能源类型	碳排放量/(kg·C/kg标准煤)	能源类型	碳排放量/(kg·C/kg标准煤)
原煤	0.7559	柴油	0.5714
洗精煤	0.7559	燃料油	0.6185
焦炭	0.8550	液化石油气	0.5042
焦炉煤气	0.3548	炼厂干气	0.4602
其他煤气	0.3548	天然气	0.4483
原油	0.6449	其他石油制品	0.5857
汽油	0.5538	其他焦化产品	0.6449

项目		1990年	1995年	2000年	2005年	2010年	2014年
核心区	总量/万t	8115.51	11206.87	15751.26	30915.65	43018.76	45176.17
核心区	占比/%	58.41	61.16	65.39	66.76	60.41	53.38
外围区	总量/万t	5778.77	7115.84	8335.69	15396.11	28192.70	39461.95
外围区	占比/%	41.59	38.84	34.61	33.24	39.59	46.62

变量	OLS逐步回归模型				Stacking 法非线性组合模型
变量	模型I		模型II	模型III	估计值	标准误	95%置信区间
地区生产总值	0.586^**				-0.291	0.181	-0.657~0.074
工业增加值			0.071		0.001	0.000	0.001~0.001
重工业增加值			0.526^***		0.318	0.244	-0.173~0.809
能源消费总量		0.927^***			1.182	0.229	0.722~1.642
外商直接投资		0.104^**			0.002	0.113	-0.226~0.229
固定资产投资	0.480^**	0.026			0.322	0.095	0.130~0.514
常住人口总量	0.699^**		0.406^**		-0.222	0.073	-0.370~-0.074
城镇化率			1.099^**		-0.061	0.016	-0.093~-0.028
调整R²	0.681	0.798	0.719		0.963

Spatiotemporal patterns and influencing factors of carbon emissions in the Pan-Yangtze River Delta region, 11990-2014

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 9

References 35

Related Articles 15

Metrics

Comments

Recommended 0

[1]	YANG Tao, CHEN Hai, LIU Di, ZHANG Hang, SHI Qinqin. Spatiotemporal change of rural community resilience in loess hilly-gully region and influencing factors: A case study of Gaoqu Township in Mizhi County, Shannxi Province [J]. PROGRESS IN GEOGRAPHY, 2021, 40(2): 245-256.
[2]	JIANG Wanbei, LIU Weidong, LIU Zhigao, HAN Mengyao. Inequality and driving forces of energy-related CO₂ emissions intensity in China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(9): 1425-1435.
[3]	WANG Bo, WU Tianhang, FENG Shuyi. Comparative study of the impact of local government land transfer intervention on regional industrial carbon emissions:An example of eight economic zones [J]. PROGRESS IN GEOGRAPHY, 2020, 39(9): 1436-1446.
[4]	FU Zhanhui, MEI Lin, ZHENG Rumin, WANG Tongtong. Spatial differentiation mechanism of urban female employment rate in Northeast China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1308-1318.
[5]	LUO Qing, WANG Bingbing, FAN Xinsheng, LI Xiaojiian. Spatio-temporal change of population distribution and its influencing factors in a poor mountainous county: A case study of Song County, Henan Province [J]. PROGRESS IN GEOGRAPHY, 2020, 39(7): 1073-1084.
[6]	CHENG Hanbei, ZOU You, LIN Sainan, LI Zhigang. Review of recent research on the impacts of residential mobility on health [J]. PROGRESS IN GEOGRAPHY, 2020, 39(7): 1210-1223.
[7]	ZHANG Jinping, LIN Dan, ZHOU Xiangli, YU Zhenxin, SONG Wei, CHENG Yeqing. Spatial difference of multidimensional poverty and its influencing factors in the rural areas of Hainan Province [J]. PROGRESS IN GEOGRAPHY, 2020, 39(6): 1013-1023.
[8]	DING Jianjun, WANG Zhang, LIU Yanhong, YU Fangwei. Measurement of economic resilience of contiguous poverty-stricken areas in China and influencing factor analysis [J]. PROGRESS IN GEOGRAPHY, 2020, 39(6): 924-937.
[9]	JIN Gui, HU Yihang, CHEN Biyuan. Measurement of poverty reduction effectiveness at the city scale in the Yangtze River Economic Belt and its spatiotemporal change [J]. PROGRESS IN GEOGRAPHY, 2020, 39(6): 972-981.
[10]	HU Xiaofang, LI Xiaoya, WANG Tianyu, ZHAO Hongmin, YANG Shuo, DENG Lei, LI Jingwang. Spatial agglomeration pattern of homestay inn and influencing factorsbased on the comparison of Hangzhou, Huzhou, and Enshi cities [J]. PROGRESS IN GEOGRAPHY, 2020, 39(10): 1698-1707.
[11]	CHENG Chaonan, HU Yang, ZHAO Ming. Progress and prospect of the spatiotemporal change and ecosystem services evaluation of urban green space pattern [J]. PROGRESS IN GEOGRAPHY, 2020, 39(10): 1770-1782.
[12]	CAO Zhihong, HAO Jinmin, XING Hongping. Spatial-temporal change of Chinese resident food consumption carbon emissions and its driving mechanism [J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 91-99.
[13]	YANG Kui,ZHANG Yu,ZHAO Xiaofeng,WEN Qi,ZHONG Taiyang. Temporal and spatial characteristics and influencing factors of structural efficiency of rural land use [J]. PROGRESS IN GEOGRAPHY, 2019, 38(9): 1393-1402.
[14]	CHEN Yangfen, LIU Yu, WANG Guogang. Rural development in metropolitan areas and implications for rural vitalization strategy [J]. PROGRESS IN GEOGRAPHY, 2019, 38(9): 1403-1411.
[15]	Xujun DAI, Aili LIU. Progress of connotation, dimension, and influencing factors of place identity [J]. PROGRESS IN GEOGRAPHY, 2019, 38(5): 662-674.