中国居民消费碳排放影响因素的时空异质性

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摘要研究我国居民消费间接碳排放的阶段性和区域性特征,对于制定社会经济新常态发展下的碳减排策略具有重要的现实意义.利用投入产出法和结构分解分析法,核算了2002~2017年中国居民消费间接碳排放水平,量化了影响因素对间接碳排放的贡献;利用地理加权回归模型定量描述了省域间接碳排放的时空分异特征.结果显示:2002~2017年中国居民消费间接碳排放呈现先增长后下降的趋势,“食品”类和“居住”类消费是其主要来源,占比42%~48%.新常态下,“直接碳排放强度”、“生产技术”和“消费倾向”因素的抑制作用显著加强,大大抵消了“收入规模”、“人口”及“消费结构”因素对碳排放的促进作用,促使碳排放降低了145MtC.省域间接碳排放从东至西呈现递减的分布特征,表现出一定的集聚性.碳排放的影响因素具有空间异质性,“生产技术”与碳排放呈现负相关关系,其他因素呈现正相关关系.其中,“人口”、“收入规模”及“直接碳排放强度”对碳排放的影响程度较为突出.

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	彭璐璐
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关键词 ：间接碳排放, 居民消费, 时空异质性, 影响因素

Abstract：It is important to formulate a carbon reduction strategies under the phased and regional characteristics of indirect household consumption carbon emissions (IHCE) in China, especially the new social-economic development circumstance. With the input-output analysis (IOA) and the structural decomposition analysis (SDA), the IHCE of China from 2002 to 2017 was calculated and the contributions of different influencing factors was quantified. Based on which, a geographically weighted regression model (GWR) was used to quantitatively describe spatial-temporal differentiation characteristics of IHCE in provinces. The results showed that the IHCE increased first and then decreased from 2002 to 2017. In that period, food and residence consumption were the main sources of emissions, which accounted 42~48% of contribution. In the new social-economic development circumstance, the negative effects with the direct carbon emission intensity, the production technology and the consumption tendency were significantly strengthened, which greatly offset the positive effects with the income scale, the population and the consumption structure, and the net effect was a decline of 145MtC of the total IHCE. The provincial IHCE showed a decreasing from east to west as well as an agglomeration phenomenon. Furthermore, the influencing factors of carbon emissions showed a spatial heterogeneity. The production technology factor were negatively correlated with IHCE. Other factors were positively correlated with IHCE among which, the population, income scale, and direct carbon emission intensity factors had more prominent impacts on carbon emissions.

Key words： indirect carbon emissions household consumption spatial-temporal heterogeneity influencing factors

收稿日期: 2020-06-02

PACS:

X24

基金资助:国家自然科学基金资助重点项目（71533004）；国家林业局“948”引进项目（2011-4-79）

通讯作者: 王震,副教授,wangzhen@bjfu.edu.cn E-mail: wangzhen@bjfu.edu.cn

作者简介: 彭璐璐(1993-),女,吉林白城人,北京林业大学环境科学与工程学院硕士研究生,研究方向为产业生态学.

引用本文:

彭璐璐, 李楠, 郑智远, 李锋, 王震. 中国居民消费碳排放影响因素的时空异质性[J]. 中国环境科学, 2021, 41(1): 463-472. PENG Lu-lu, LI Nan, ZHENG Zhi-yuan, LI Feng, WANG Zhen. Spatial-temporal heterogeneity of carbon emissions and influencing factors on household consumption of China. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(1): 463-472.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2021/V41/I1/463

[1]	IPCC. Climate change 2013:the physical science basis[R]. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambrigde and New York:Cambrigde University Press, 2013.
[2]	IPCC. 2006IPCC guidelines for national greenhouse gas inventories[M]. Japan:IGES, 2006.
[3]	Hertwich E, Peters G. Carbon footprint of nations:a global, trade-linked analysis[J]. Environment Science and Technology, 2009, 43(16):6414-6420.
[4]	Guan D, Hubacek K, Weber C L, et al. The drivers of Chinese CO2 emissions from 1980 to 2030[J]. Global Environmental Change, 2008, 18(4):626-634.
[5]	Liu L C, Wu G, Wang J N, et al. China's carbon emissions from urban and rural households during 1992-2007[J]. Journal of Cleaner Production, 2011,19:1754-1762.
[6]	Zhang Y J, Bian X J, Tan W P, et al. The indirect energy consumption and CO2 emission caused by household consumption in China:an analysis based on the input-output method[J]. Journal of Cleaner Production, 2017,163:69-83.
[7]	Wu S, Lei Y, Li S. CO2 emissions from household consumption at the provincial level and interprovincial transfer in China[J]. Environment International, 2019,210:93-104.
[8]	史琴琴,鲁丰先,陈海,等.中原经济区城镇居民消费间接碳排放时空格局及其影响因素[J]. 资源科学, 2018,40(6):1297-1306. Shi Q Q, Lu F X, Chen H, et al. Temporal-spatial patterns and factors affecting indirect carbon emissions from urban consumption in the Central Plains Economic Region[J]. Resources Science, 2018,40(6):1297-1306.
[9]	Grubb M, Sha F, Spencer T, et al. A review of Chinese CO2 emission projections to 2030:the role of economic structure and policy[J]. Climate Policy, 2015,15:S7-S39.
[10]	程铁军,王曼,王济干,等.经济新常态背景下江苏省碳排放驱动效应研究[J]. 科技管理研究, 2019,39(23):242-249. Cheng T J, Wang M, Wang J G, et al. Research on driving factors of carbon emissions in Jiangsu province under the background of China's new normal[J]. Science and Technology Management Research, 2019,23:242-249.
[11]	杨传明.新旧常态下不同来源技术进步对中国产业碳强度的影响[J]. 技术经济, 2019,38(2):112-120. Yang C M. Influence of technological progress from different sources on China's industrial carbon intensity under new and old normal periods[J]. Technology Economics, 2019,38(2):112-120.
[12]	Yu S, Zheng S, Li X, et al. China can peak its energy-related carbon emissions before 2025:Evidence from industry restructuring[J]. Energy Economic, 2018,73:91-107.
[13]	Wang Y N, Zhao M J, Chen W. Spatial effect of factors affecting household CO2 emissions at the provincial level in China:a geographically weighted regression model[J]. Carbon Management, 2018,9:187-200.
[14]	Yuan R, Rodrigues J F D, Behrens P. Driving forces of household carbon emissions in China:A spatial decomposition analysis[J]. Journal of Cleaner Production, 2019,233:932-945.
[15]	Duarte R, Mainar-Causape A J, Sanchez-Choliz J. Domestic GHG emissions and the responsibility of households in Spain:looking for regional differences[J]. Applied Economics, 2017,49:5397-5411.
[16]	米红,张田田,任正委,等.城镇化进程中家庭CO2排放的驱动因素分析[J]. 中国环境科学, 2016,36(10):3183-3192. Mi H, Zhang T T, Ren Z W, et al. Driving factors of China's household CO2 emissions in the process of urbanization[J]. China Environmental Science, 2016,36(10):3183-3192.
[17]	王会娟,夏炎.中国居民消费碳排放的影响因素及发展路径分析[J]. 中国管理科学, 2017,25(8):1-10. Wang H J. Xia Y. Influence factors vs. developments of China's household carbon emissions[J]. Chinese Journal of Management Science, 2017,25(8):1-10.
[18]	范建双,周琳.中国城乡居民生活消费碳排放变化的比较研究[J]. 中国环境科学, 2018,38(11):4369-4383. Fan J S, Zhou L. A comparative study on the changes of residential living consumption carbon emissions in urban and rural China[J]. China Environmental Science, 2018,38(11):4369-4383.
[19]	Song K, Qu S, Taiebat M, et al. Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households[J]. Environment International, 2019,133:105137.
[20]	万文玉,赵雪雁,王伟军,等.我国农村居民生活能源碳排放的时空特征分析[J]. 生态学报, 2017,37(19):6390-6401. Wan W Y, Zhao X Y, Wang W J, et al. Analysis of spatio temporal patterns of carbon emission from energy consumption by rural residents in China[J]. Acta Ecologica Sinica, 2017,37(19):6390-6401.
[21]	崔盼盼,张艳平,张丽君,等.中国省域隐含碳排放及其驱动机理时空演变分析[J]. 自然资源学报, 2018,33(5):879-892. Cui P P, Zhang Y P, Zhang L J, et al. Analysis on the spatial and temporal evolution of indirect carbon emissions and its driving mechanism in China[J]. Journal of Natural Resources, 2018,33(5):879-892.
[22]	Wang Y, Yang G, Dong Y, et al. The scale, structure and influencing factors of total carbon emissions from households in 30 provinces of China-based on the extended stirpat model[J]. Energies, 2018, 11(5):1105-1125.
[23]	杜威.基于LMDI分解模型的中国居民生活间接碳排放特征分析[J]. 中国人口·资源与环境, 2016,26(S2):5-9. Du W. Analysis of characteristics of Chinese residents' indirect carbon emissions in daily life based on LMDI decomposition model[J]. China population, resources and environment, 2016,26(S2):5-9.
[24]	Liu X Y, Wang X, Song J N, et al. Indirect carbon emissions of urban households in China:Patterns, determinants and inequality[J]. Journal of Cleaner Production, 2019,241:118335.
[25]	曾晓莹,邱荣祖,林丹婷,等.中国交通碳排放及影响因素时空异质性[J]. 中国环境科学, 2020,40(10):4304-4313. Zeng X Y, Qiu R Z, Lin D T, et al. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China[J]. China Environmental Science, 2020,40(10):4304-4313.
[26]	胡艳兴,潘竟虎,李真,等.中国省域能源消费碳排放时空异质性的EOF和GWR分析[J]. 环境科学学报, 2016,36(5):1866-1874. Hu Y X, Pan J H, Li Z, et al. Spatial-temporal analysis of provincial carbon emissions in China from 1997 to 2012 with EOF and GWR models[J]. Acta Scientiae Circumstantiae, 2016,36(5):1866-1874.
[27]	王悦,李锋,孙晓.城市家庭消费碳排放研究进展[J]. 资源科学, 2019,41(7):1201-1212. Wang Y, Li F, Sun X. Progress of research on carbon emissions of urban household consumption[J]. Resources Science, 2019,41(7):1201-1212.
[28]	Zhang Y. Structural decomposition analysis of sources of decarbonizing economic development in China; 1992~2006[J]. Ecological Economics, 2009,68(8/9):2399-2405.
[29]	李景华.SDA模型的加权平均分解法及在中国第三产业经济发展分析中的应用[J]. 系统工程, 2004,(9):69-73. Li J H. A weighted average decomposition method of SDA model and its application in Chinese tertiary industry development[J]. Systems Engineering, 2004,(9):69-73.
[30]	国家统计局.中国统计年鉴[M]. 北京:中国统计出版社, 2012-2017. National Bureau of Statistics of China. China statistical yearbook[M]. Beijing:China Statistics Press, 2012-2017.
[31]	省级温室气体清单编制指南编写组.省级温室气体清单编制指南[M]. 2010. Writing Group of the Provincial Greenhouse Gas Inventories. The provincial greenhouse gas inventories[M]. 2010.
[32]	Mi Z, Meng J, Green F, et al. China's "exported carbon" peak:patterns, drivers, and implications[J]. Geophysical Research Letters, 2018,45:4309-4318.
[33]	苑立波.直接消耗系数矩阵特征值与经济增长的关系研究[J]. 统计与决策, 2014,(12):21-23. Yuan L B. Research on the relationship between the eigenvalue of direct consumption coefficient matrix and economic growth[J]. Statistics and Decision, 2014,(12):21-23.