闽三角地区碳排放时空差异及影响因素研究

黄琳琳; 王远; 张晨; 黄逸敏

PDF(765 KB)

中国环境科学 ›› 2020, Vol. 40 ›› Issue (5) : 2312-2320.

环境影响评价与管理

闽三角地区碳排放时空差异及影响因素研究

黄琳琳^1,2, 王远^1,2,3, 张晨^1,2, 黄逸敏^1,2

作者信息 +

A spatial-temporal decomposition analysis of CO₂ emissions in Fujian Southeast Triangle Region

HUANG Lin-lin^1,2, WANG Yuan^1,2,3, ZHANG Chen^1,2, HUANG Yi-min^1,2

Author information +

文章历史 +

摘要

以闽三角地区为研究对象，以2005~2017年为研究期，构建城市尺度的碳排放清单，应用对数平均迪氏指数分解方法从时间维度的纵向比较和典型年份城市横向比较两个维度开展了驱动因素的分解分析及评价，探讨了闽三角碳排放变化影响因素的时空差异.结果显示：研究期内闽三角CO₂排放增长较快，从2005年的74.08Mt增加到2017年169.48Mt，增幅为128.75%.其中，泉州贡献最大，占比为67.93%.碳排放变化趋势分析来看，产业结构和经济增长为导致闽三角地区碳排放量增长的主要因素，累计贡献度分别为30.38%和12.21%，能源结构为抑制碳排放的重要影响因素，累计贡献度为-45.76%.时空差异上看，能源结构效应在研究期内均表现为抑制效应，最大贡献率为52.95%；而产业结构效应均表现为促进效应，最大贡献率为33.85%.在研究期内，漳州市碳减排力度最大，最大净减排148.27Mt.而泉州市经济增长和产业结构效应贡献率较大，未来仍具有较大的减排空间.厦门市经济增长和产业结构效应贡献率均低于参考值，且在研究期内变动幅度较小，碳减排压力较低.研究结果深化了闽三角地区碳排放的时空格局及影响因素的科学认识，为闽三角地区及相似城市群的减排治理提供了有益借鉴.

Abstract

In the present study, the carbon emissions inventory at city level is first constructed for Fujian Southeast Triangle Region during the period 2005~2017. The temporal and spatial differences of CO₂ emissions changes and main driving factors of emissions are explored by using the Logarithmic Mean Divisia Index Method. Empirical results show that:During the period 2005~2017, the CO₂ emissions has increased rapidly, from 74.08 Mt in 2005 to 169.48 Mt in 2017, an increase of 128.75%. Among them, Quanzhou City is the top carbon emitter, representing high to 67.93%. In terms of the emissions change trend, the industrial structure and economic growth are the main factors leading to the increase in carbon emissions in Fujian Southeast Triangle Region, contributing to 30.38% and 12.21%, respectively. While the energy structure is an important factor in controlling carbon emissions, contributing to -45.76%. In terms of temporal and spatial differences, the energy structure effect exhibits an inhibitory effect, with a maximum contribution rate of 52.95%. While the industrial structure effect exhibits a promotion effect, with a maximum contribution rate of 33.85%. During the study period, there exists the largest reduction amount of carbon emissions in Zhangzhou City, with a maximum net emission reduction of 148.27 Mt. The contribution rates of economic growth and industrial structure effects in Quanzhou City are relatively high, indicating that there is still a great potential for emissions reduction in the future. The contribution rates of economic growth and industrial structure effects in Xiamen City are both lower than the reference value, and their fluctuations are relatively small, showing that the pressure of carbon emissions reduction is rather low. These results have deepened the scientific understanding of the spatial and temporal pattern and driving factors of carbon emissions in Fujian Southeast Triangle Region, and also provided some valuable recommendations for the government to abate emissions in this region or similar urban agglomerations.

导出引用

黄琳琳, 王远, 张晨, 黄逸敏. 闽三角地区碳排放时空差异及影响因素研究[J]. 中国环境科学. 2020, 40(5): 2312-2320

HUANG Lin-lin, WANG Yuan, ZHANG Chen, HUANG Yi-min. A spatial-temporal decomposition analysis of CO₂ emissions in Fujian Southeast Triangle Region[J]. China Environmental Science. 2020, 40(5): 2312-2320

中图分类号： X32

参考文献

[1] 国务院印发《"十三五"控制温室气体排放工作方案》[J]. 印刷杂志, 2016,(12):68-68. The State Council issued the "Thirteenth Five-Year Plan of Work on Controlling Greenhouse Gas Emissions"[J]. Printing Field, 2016,(12):68-68.
[2] Ang B W, Zhang F Q, Choi K-H. Factorizing changes in energy and environmental indicators through decomposition[J]. Energy, 1998, 23(6):489-495.
[3] Shigetomi Y, Matsumoto K, Ogawa Y, et al. Driving forces underlying sub-national carbon dioxide emissions within the household sector and implications for the Paris Agreement targets in Japan[J]. Applied Energy, 2018,228:2321-2332.
[4] Cheng H, Mao C, Mandant S, et al. Minimizing the total costs of urban transit systems can reduce greenhouse gas emissions:The case of San Francisco[J]. Transport Policy, 2018,66.
[5] 徐军委.基于LMDI的我国二氧化碳排放影响因素研究[D]. 北京:中国矿业大学(北京), 2013. Xu J W. Study on the factors of China's carbon dioxide emissions based on LMDI[D]. Beijing:China University of Mining and Technology (Beijing), 2013.
[6] 曹俊文,曾康.基于LMDI和M-R分解模型的长江经济带碳排放驱动效应研究[J]. 环境污染与防治, 2019,41(8):992-998. Cao J W, Zeng K. Study on carbon emission driving effect in Yangtze River Economic Belt based on LMDI and M-R decomposition models[J]. Environmental Pollution Control, 2019,41(8):992-998.
[7] 赵玉焕,李浩,刘娅,等.京津冀CO₂排放的时空差异及影响因素研究[J]. 资源科学, 2018,40(1):207-215. Zhao Y H, Li H, Liu Y, et al. Identifying driving forces of CO₂ emissions in Beijing-Tianjin-Hebei region from temporal and spatial angles[J]. Resources Science, 2018,40(1):207-215.
[8] 刘汉初,樊杰,曾瑜皙,等.中国高耗能产业碳排放强度的时空差异及其影响因素分析[J]. 生态学报, 2019,(22):1-13. Liu H C, Fan J, Zeng Y X, et al. Spatio-temporal differences in carbon intensity in high-energy-intensive industry and its influence factors in China.Acta Ecologica Sinica, 2019,39(22):8357-8369.
[9] 谢欣秀.珠三角碳强度预测与减排路径分析[D]. 广州:华南理工大学, 2018. Xie X X. The prediction of carbon intensity and analysis of carbon abatement in the Pearl River Delta[D]. Guangzhou:South China University of Technology, 2018.
[10] Zhang M, Bai C Y, Zhou M. Decomposition analysis for assessing the progress in decoupling relationship between coal consumption and economic growth in China[J]. Resources Conservation and Recycling, 2018,129:454-462.
[11] He S B, Yan T, Zhou H R. Decomposition and spatial clustering analysis of China's SO₂ Emissions[J]. Procedia Computer Science, 2016,91:1064-1072.
[12] Wang M, Feng C. Decomposition of energy-related CO₂ emissions in China:An empirical analysis based on provincial panel data of three sectors[J]. Applied Energy, 2017,190:772-787.
[13] Bai C Q, Chen Y B, Yi X, et al. Decoupling and decomposition analysis of transportation carbon emissions at the provincial level in China:perspective from the 11th and 12th Five-Year Plan periods[J]. Environmental Science and Pollution Reserach, 2019,26(15):15039-15056.
[14] Ang B W, Xu X Y, Su B. Multi-country comparisons of energy performance:The index decomposition analysis approach[J]. Energy Economics, 2015,47:68-76
[15] Ang B W, Su B, Wang H. A spatial-temporal decomposition approach to performance assessment in energy and emissions[J]. Energy Economics, 2016,60:112-121.
[16] Qi Y W. Environmental analysis of carbon emission decoupling and decomposition in regional economic growth in China[J]. Ekoloji, 2018,27(106):1443-1453.
[17] Liu J, Zhang S H, Wagner F. Exploring the driving forces of energy consumption and environmental pollution in China's cement industry at the provincial level[J]. Journal of Cleaner Production, 2018,184:274-285.
[18] Cazcarro I, Martin-retortillo M, Serrano A. Reallocating regional water apparent productivity in the long term:methodological contributions and application for Spain[J]. Reginal Environmental Change, 2019,19(5):1455-1468.
[19] Tian Y, Zhang J B, He Y Y. Research on spatial-temporal characteristics and driving factor of agricultural carbon emissions in China[J]. Journal of Integrative Agriculture, 2014,13(6):1393-1403.
[20] Chen K L, Liu X Q, Ding L, et al. Spatial characteristics and driving factors of provincial wastewater discharge in China[J]. International Journal of Environmental Research Public Health, 2016,13(12):1221-1239.
[21] Chen L, Yang Z F. A spatio-temporal decomposition analysis of energy-related CO₂ emission growth in China[J]. Journal of Cleaner Production, 2015,103:49-60.
[22] Roman-colladoa R, Morales-carrion A V. Towards a sustainable growth in Latin America:A multiregional spatial decomposition analysis of the driving forces behind CO₂ emissions changes[J]. Energy Policy, 2018,115:273-280.
[23] 陈谈强.中国对外开放展现新格局——国务院召开长江、珠江三角洲和闽南厦漳泉三角地区座谈会追记[J]. 瞭望周刊, 1985,(8):9-11+3. Chen T Q. China's opening to the outside world shows a new pattern——The state council holds the symposium on the Yangtze River, Pearl River Delta and Xiamen-Zhangquan Delta[J]. Outlook Weekly, 1985,(8):9-11+3.
[24] 福建省统计局.福建统计年鉴[J]. 北京:中国统计出版社:2005~2018. Fujian Provincial Statistics Bureau. Fujian statistical yearbook[J]. Beijing:China Statistics Press:2005~2018.
[25] 泉州市统计局.泉州统计年鉴[J]. 北京:中国统计出版社:2005~2018. Quanzhou Statistics Bureau. Quanzhou statistical yearbook[J]. Beijing:China Statistics Press:2005~2018.
[26] 厦门市统计局.厦门经济特区统计年鉴[J]. 北京:中国统计出版社:2005~2018. Xiamen Statistics Bureau. Xiamen Statistical Yearbook[J]. Beijing:China Statistics Press:2005~2018.
[27] 漳州市统计局.漳州统计年鉴[J]. 北京:中国统计出版社:2005~2018. Zhangzhou Statistics Bureau. Zhangzhou Statistical Yearbook[J]. Beijing:China Statistics Press:2005~2018.
[28] 蔡博峰,朱松丽,于胜民,等.《IPCC 2006年国家温室气体清单指南2019修订版》解读[J]. 环境工程, 2019,37(8):1-11. Cai B F, Zhu S L, Yu S M, et al. The interpretation of 2019 refinement to the 2006 IPCC guidelines for national greenhouse gas inventory[J]. Environmental Engineering, 2019,37(8):1-11.
[29] Li L, Shan Y, Lei Y, et al. Decoupling of economic growth and emissions in China's cities:A case study of the central Plains urban agglomeration[J]. Applied Energy, 2019,244:36-45.
[30] Shan Y, Guan D, Liu J, et al. Methodology and applications of city level CO₂ emission accounts in China[J]. Journal of Cleaner Production, 2017,161:1215-1225.
[31] Xu X, Huo H, Liu J, et al. Patterns of CO₂ emissions in 18 central Chinese cities from 2000 to 2014[J]. Journal of Cleaner Production, 2018,172:529-540.
[32] Kaya Y. Impact of carbon dioxide emission on GNP growth:interpretation of proposed scenarios[R]. Prias:IPCC Energy and Industry Subgroup, 1989.
[33] 袁家海,胡兆光.中国"十一五"能源强度目标——政策演化与进展评估[C]. 长春:中国电机工程学会动能经济专业委员会清洁能源与低碳发展学术研讨会, 2010:1-15. Yuan J H, Hu Z G. China's "Eleventh Five-Year" energy intensity goals-Policy evolution and progress evaluation[C]. Changchun:Seminar on Clean Energy and Low Carbon Development of the Professional Committee of Kinetic Energy Economics of the Chinese Society of Electrical Engineering, 2010:1-15.
[34] 宋雅琴,古德丹."十一五规划"开局节能、减排指标"失灵"的制度分析[J]. 中国软科学, 2007,(9):25-32+87. Song Y Q, Gu D D. An institutional analysis on failure to achieve energy conservation target and pollutant emission control target at the Beginning Stage of the 11th Five-Year Plan[J]. China Soft Science, 2007,(9):25-32+87.
[35] Wang Y, Zhang C, Lu A, et al. A disaggregated analysis of the environmental Kuznets curve for industrial CO₂ emissions in China[J]. Applied Energy, 2017,190:172-180.
[36] Wang Y, Zhang X, Kubota J, et al. A semi-parametric panel data analysis on the urbanization-carbon emissions nexus for OECD countries[J]. Renewable and Sustainable Energy Reviews, 2015,48:704-709.
[37] 王远,王春春,赵静,等.江苏省SO₂排放环境库茨涅茨关系研究——基于半参数面板数据模型分析[J]. 中国环境科学, 2016,36(10):3143-3149. Wang Y, Wang C C, Zhao J, et al. Investigating the environmental Kuznets curve hypothesis for SO₂ emissions in Jiangsu Province:A semiparametric panel data analysis[J]. China Environmental Science, 2016,36(10):3143-3149.