中国能源碳足迹时空格局演化及脱钩效应

doi:10.11821/dlxb202101016

摘要/Abstract

摘要：

利用DMSP-OLS夜间灯光数据和碳排放统计数据,构建碳排放面板数据模型,模拟了2000—2013年中国的碳排放量。运用探索性时空数据分析（ESTDA）框架体系,从时空交互视角分析2001—2013年碳足迹的空间格局和时空依赖动态演化;利用改进的Tapio脱钩模型对3个时间段336个地级单元环境碳负荷与经济增长之间的脱钩效应进行综合分析。研究表明：① 2000—2013年,中国的碳排放在时空演变上既表现出稳中有进的总体特征,也存在快速增长的阶段特征。② 碳足迹和碳赤字均呈逐年增长趋势,年均增长率分别为4.82%和5.72%;碳足迹和碳赤字整体北方大于南方,不同的行政单元尺度下碳足迹和碳赤字空间异质性特征明显。各地级单元碳足迹变异系数逐步增大,存在极为显著的空间自相关特征。③ LISA时间路径相对长度北方大于南方,且呈由沿海地区向中西部地区递增的趋势;LISA时间路径弯曲度整体上则由沿海地区向内陆地区递减。④ 综合脱钩指数整体以弱脱钩型为主,但弱脱钩型城市数量持续减少,扩张连接、扩张负脱钩区域数量逐渐增多且向中西部及东北地区聚集分布;全国平均脱钩弹性值逐步增长,变异系数持续下降。

关键词: 能源碳足迹, 碳排放, 夜间灯光, 脱钩效应, 时空动态, 探索性时空数据分析

Abstract:

The global environment issue resulting from carbon emissions has aroused worldwide concern for governments, the public and scientific communities. A precise measurement of the time-resolved and spatial distribution characteristics of carbon dioxide (CO₂) and carbon footprint as well as its long-period evolution mechanism, can help clarify the relationship between environmental carbon load and economic growth, and are critical references to the formulation of scientific carbon emission reduction targets with reasonable and differential emission reduction policies. In this study, the mainland of China is taken as the research object. According to the quantitative correlations between DMSP-OLS nighttime light image data and carbon emission statistics, the carbon emission panel data model was simulated for China's carbon emissions in the period 2000-2013, and then the spatiotemporal evolving trend and spatial distribution characteristics of carbon emissions in the 14-year research period were discussed using Theil-Sen Median trend analysis and Mann-Kendall test method. Based on the framework of exploratory spatial-temporal data analysis (ESTDA), the spatial pattern and spatiotemporal dynamic evolution of carbon footprint from 2001 to 2013 were analyzed from the perspective of spatiotemporal interaction. In the three periods, the decoupling effect between environmental carbon load and economic growth of 336 prefecture-level cities were analyzed using the improved Tapio decoupling model. The results show that the overall carbon emissions in China had been on the rise from 2000 to 2013, in which the stable-slow rise type was dominant. China's carbon footprint and carbon deficit increased year by year, and the central and western regions became the focus of the growth of carbon footprint and carbon deficit from 2001 to 2013. At different administrative city scales, the spatial distribution pattern of carbon footprint and carbon deficit show obvious administrative orientated and spatial zonal differentiation characteristics. The annual average of global Moran's I index of each level unit is 0.491, which indicates that there is a significant spatial auto-correlation feature in the carbon footprint of China's prefecture-level units. The relative length of the LISA time path is greater in the north than in the south, and it tends to increase from the coastal areas to the central and western regions. The curvature of LISA time path decreases from coastal areas to inland areas on the whole. The curvature of northeast and central regions is higher, while that of eastern and western regions is lower. There is a different trend of the decoupling effect of environmental carbon load in China. Meanwhile the expansion-connection and expansion of negative decoupling regions continuously increased and spatially agglomerated, presenting an "E"-shaped distribution pattern from the north to the south. The national average decoupling elastic value is gradually increasing, while the coefficient of variation continues to decline, and the decoupling type has a significant evolution trend. Therefore, the unbalanced trend of economic growth and carbon emissions in China will continue for a certain period.

Key words: carbon footprint, carbon emissions, nighttime light, decoupling effect, spatio-temporal dynamics, exploratory spatio-temporal data analysis

潘竟虎, 张永年. 中国能源碳足迹时空格局演化及脱钩效应[J]. 地理学报, 2021, 76(1): 206-222.

PAN Jinghu, ZHANG Yongnian. Spatiotemporal patterns of energy carbon footprint and decoupling effect in China[J]. Acta Geographica Sinica, 2021, 76(1): 206-222.

图/表 12

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

表2

参考文献 42

[1]	IPCC. Climate Change 2013: The Physical Science Basis Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambrigde: Cambridge University Press, 2013.
[2]	Wang Shaojian, Huang Yongyuan. Spatial spillover effect and driving forces of carbon emission intensity at city level in China. Acta Geographica Sinica, 2019,74(6):1131-1148. doi: 10.11821/dlxb201906005
	[ 王少剑, 黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素. 地理学报, 2019,74(6):1131-1148.]
[3]	The petroleum corporation of BP. The BP statistical review of world energy published in 2019, (2019-06-20). https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
	[ BP石油公司. BP世界能源统计年鉴, (2019-06-20) https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.]
[4]	Lu W W, Chen C, Su M R, et al. Urban energy consumption and related carbon emission estimation: A study at the sector scale. Frontiers of Earth Science, 2013,7(4):480-486.
[5]	Zhou X, Zhang M, Zhou M H, et al. A comparative study on decoupling relationship and influence factors between China's regional economic development and industrial energy-related carbon emissions. Journal of Cleaner Production, 2017,142:783-800.
[6]	Zhang Y J, Da Y B. The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renewable and Sustainable Energy Reviews, 2015,41:1255-1266.
[7]	Xiao H, Ma Z, Zhang P, et al. Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects. Natural Hazards, 2019,99(3):1365-1380.
[8]	Zhou Di, Wu Zewen. Potentialities and paths of Chinese industrial carbon emission reduction. China Environmental Science, 2019,39(3):1306-1314.
	[ 周迪, 吴泽文. 中国工业碳减排潜力与路径研究. 中国环境科学, 2019,39(3):1306-1314.]
[9]	Wang Wei, Lin Jianyi, Cui Shenghui, et al. An overview of carbon footprint analysis. Environmental Science & Technology, 2010,33(7):71-78.
	[ 王微, 林剑艺, 崔胜辉, 等. 碳足迹分析方法研究综述. 环境科学与技术, 2010,33(7):71-78.]
[10]	Lu Junyu, Huang Xianjin, Chen Yi, et al. Spatiotemporal changes of carbon footprint based on energy consumption in China. Geographical Research, 2013,32(2):326-336.
	[ 卢俊宇, 黄贤金, 陈逸, 等. 基于能源消费的中国省级区域碳足迹时空演变分析. 地理研究, 2013,32(2):326-336.]
[11]	Zhang Qifeng, Fang Kai, Xu Ming, et al. Review of carbon footprint research based on input-output analysis. Journal of Natural Resources, 2018,33(4):696-708.
	[ 张琦峰, 方恺, 徐明, 等. 基于投入产出分析的碳足迹研究进展. 自然资源学报, 2018,33(4):696-708.]
[12]	Rees W E. Ecological footprints and appropriated carrying capacity: What urban economics leaves out. Environment and Urbanization, 1992,4(2):121-130.
[13]	Wiedmann T, Minx J. A definition of carbon footprint. Journal of the Royal Society of Medicine, 2007,92(4):193-195.
[14]	Mancini M S, Galli A, Niccolucci V, et al. Ecological footprint: Refining the carbon footprint calculation. Ecological Indicators, 2016,61:390-403. doi: 10.1016/j.ecolind.2015.09.040
[15]	Röös E, Karlsson H. Effect of eating seasonal on the carbon footprint of Swedish vegetable consumption. Journal of Cleaner Production, 2013,59:63-72. doi: 10.1016/j.jclepro.2013.06.035
[16]	Alvarez S, Sosa M, Rubio A. Product and corporate carbon footprint using the compound method based on financial accounts: The case of Osorio wind farms. Applied Energy, 2015,139:196-204.
[17]	Kenny T, Gray N F. A preliminary survey of household and personal carbon dioxide emissions in Ireland. Environment International, 2009,35(2):259-272.
[18]	Steen-Olsen K, Wood R, Hertwich E G. The carbon footprint of Norwegian household consumption 1999-2012. Journal of Industrial Ecology, 2016,20(3):582-592.
[19]	Adewale C, Reganold J P, Higgins S, et al. Agricultural carbon footprint is farm specific: Case study of two organic farms. Journal of Cleaner Production, 2019,229:795-805.
[20]	Cadarso M Á, Gómez N, López L A, et al. Quantifying Spanish tourism's carbon footprint: The contributions of residents and visitors: A longitudinal study. Journal of Sustainable Tourism, 2015,23(6):922-946. doi: 10.1080/09669582.2015.1008497
[21]	Chen S Q, Chen B. Network environ perspective for urban metabolism and carbon emissions: A case study of Vienna, Austria. Environmental Science & Technology, 2012,46(8):4498-4506.
[22]	Harris N L, Brown S, Hagen S C, et al. Baseline map of carbon emissions from deforestation in tropical regions. Science, 2012,336(6088):1573-1576. pmid: 22723420
[23]	Wolfram P, Wiedmann T, Diesendorf M. Carbon footprint scenarios for renewable electricity in Australia. Journal of Cleaner Production, 2016,124:236-245.
[24]	Zhao Rongqin, Huang Xianjin, Zhong Taiyang. Research on carbon emission intensity and carbon footprint of different industrial spaces in China. Acta Geographica Sinica, 2010,65(9):1048-1057.
	[ 赵荣钦, 黄贤金, 钟太洋. 中国不同产业空间的碳排放强度与碳足迹分析. 地理学报, 2010,65(9):1048-1057.]
[25]	Zhao Y, Zhang Q, Li F Y. Patterns and drivers of household carbon footprint of the herdsmen in the typical steppe region of Inner Mongolia, China: A case study in Xilinhot City. Journal of Cleaner Production, 2019,232:408-416.
[26]	Zhang Yuehan, Zhang Pingyu. Energy consumption carbon footprint of metropolitan district of Changchun and Jilin, China. Scientia Geographica Sinica, 2012,32(9):1099-1105.
	[ 张约翰, 张平宇. 长吉都市区能源碳足迹测度及影响因素研究. 地理科学, 2012,32(9):1099-1105.]
[27]	Pan Jinghu, Li Junfeng. Estimate and spatio-temporal dynamics of electricity consumption in China based on DMSP/OLS images. Geographical Research, 2016,35(4):627-638.
	[ 潘竟虎, 李俊峰. 基于夜间灯光影像的中国电力消耗量估算及时空动态. 地理研究, 2016,35(4):627-638.]
[28]	Zhuo Li, Zhang Xiaofan, Zheng Jing, et al. An EVI-based method to reduce saturation of DMSP/OLS nighttime light data. Acta Geographica Sinica, 2015,70(8):1339-1350.
	[ 卓莉, 张晓帆, 郑璟, 等. 基于EVI指数的DMSP-OLS夜间灯光数据去饱和方法. 地理学报, 2015,70(8):1339-1350.]
[29]	Zhang Yongnian, Pan Jinghu. Spatio-temporal simulation and differentiation pattern of carbon emissions in China based on DMSP/OLS nighttime light data. China Environmental Science, 2019,39(4):1436-1446.
	[ 张永年, 潘竟虎. 基于DMSP/OLS数据的中国碳排放时空模拟与分异格局. 中国环境科学, 2019,39(4):1436-1446.]
[30]	Chen Qiang. Advanced Econometrics and Stata Applications. Beijing: Higher Education Press, 2014.
	[ 陈强. 高级计量经济学及Stata应用. 北京: 高等教育出版社, 2014.]
[31]	Xu Jianjun, Xiong Deping, Wang Haohan. Panel cointegration test and causality anlysis of the relationship between financial development and foreign trade in China. Economic Geography, 2008,28(5):784-789.
	[ 徐建军, 熊德平, 汪浩瀚. 中国金融发展与外贸关系的面板协整检验和因果分析. 经济地理, 2008,28(5):784-789.]
[32]	Wu Wenjia, Jiang Jinliang, Gao Quanzhou, et al. Spatiotemporal patterns of carbon emission and carbon footprint in China during 2001-2009. Acta Ecologica Sinica, 2014,34(22):6722-6733.
	[ 吴文佳, 蒋金亮, 高全洲, 等. 2001—2009年中国碳排放与碳足迹时空格局. 生态学报, 2014,34(22):6722-6733.]
[33]	Jiang Jinliang, Xu Jiangang, Wu Wenjia, et al. Patterns and dynamics of China's human-nature carbon source-sink system. Journal of Natural Resources, 2014,29(5):757-768.
	[ 蒋金亮, 徐建刚, 吴文佳, 等. 中国人—地碳源汇系统空间格局演变及其特征分析. 自然资源学报, 2014,29(5):757-768.]
[34]	Rey S J, Janikas M V. STARS: Space-time analysis of regional systems. Geographical Analysis, 2006,38(1):67-86.
[35]	Gao Changchun, Liu Xianzhao, Li Chaokui, et al. Spatiotemporal dynamics of carbon emissions by energy consumption in China from 1995 to 2014. Progress in Geography, 2016,35(6):747-757.
	[ 高长春, 刘贤赵, 李朝奎, 等. 近20年来中国能源消费碳排放时空格局动态. 地理科学进展, 2016,35(6):747-757.]
[36]	Wang Junhua, Li Xia. The effect of sector decoupling between China's industrial economic growth and carbon dioxide emissions. Economic Geography, 2015,35(5):105-110.
	[ 王君华, 李霞. 中国工业行业经济增长与CO₂排放的脱钩效应. 经济地理, 2015,35(5):105-110.]
[37]	Tian Yun, Zhang Junbiao, Li Bo. Agricultural carbon emissions in China: Calculation, spatial-temporal comparison and decoupling effects. Resources Science, 2012,34(11):2097-2105.
	[ 田云, 张俊飚, 李波. 中国农业碳排放研究: 测算、时空比较及脱钩效应. 资源科学, 2012,34(11):2097-2105.]
[38]	Wu Hong, Gu Shuzhong, Guan Xingliang, et al. Analysis on relationship between carbon emissions from fossil energy consumption and economic growth in China. Journal of Natural Resources, 2013,28(3):381-390. doi: 10.11849/zrzyxb.2013.03.003
	[ 武红, 谷树忠, 关兴良, 等. 中国化石能源消费碳排放与经济增长关系研究. 自然资源学报, 2013,28(3):381-390.]
[39]	Zhong Taiyang, Huang Xianjin, Wang Baiyuan. On the degrees of decoupling and re-coupling of economic growth and expansion of construction land in China from 2002 to 2007. Journal of Natural Resources, 2010,25(1):18-31. doi: 10.11849/zrzyxb.2010.01.003
	[ 钟太洋, 黄贤金, 王柏源. 经济增长与建设用地扩张的脱钩分析. 自然资源学报, 2010,25(1):18-31.]
[40]	Zhao Guimei, Zhao Guiqin, Chen Lizhen, et al. Research on spatial and temporal evolution of carbon emission intensity and its transition mechanism in China. China Population, Resources and Environment, 2017,27(10):84-93.
	[ 赵桂梅, 赵桂芹, 陈丽珍, 等. 中国碳排放强度的时空演进及跃迁机制. 中国人口·资源与环境, 2017,27(10):84-93.]
[41]	Zhu Wenbo, Li Shuangcheng, Zhu Lianqi. Ecosystem service footprint flow and the influencing factors within provinces, China. Geographical Research, 2019,38(2):337-347.
	[ 朱文博, 李双成, 朱连奇. 中国省域生态系统服务足迹流动及其影响因素. 地理研究, 2019,38(2):337-347.]
[42]	Guan D, Shan Y, Liu Z. CO₂ emissions from China's lime industry. Applied Energy, 2016,166:245-252.

t/t+1	HH(深压型)	LH(洼地型)	LL(浅压型)	HL(极化型)
HH(深压型)	类型0(0.251)	类型I(0.007)	类型III(0.002)	类型II(0.002)
LH(洼地型)	类型I(0.009)	类型0(0.121)	类型II(0.013)	类型III(0.000)
LL(浅压型)	类型III(0.003)	类型II(0.019)	类型0(0.514)	类型I(0.005)
HL(极化型)	类型II(0.004)	类型III(0.000)	类型I(0.003)	类型0(0.047)