A spatial-temporal decomposition analysis of CO2 emissions in Fujian Southeast Triangle Region
HUANG Lin-lin1,2, WANG Yuan1,2,3, ZHANG Chen1,2, HUANG Yi-min1,2
1. Fujian Provincial Key Laboratory for Subtropical Resources and Environment, Fujian Normal University, Fuzhou 350007, China; 2. School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China; 3. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
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.
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.
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.
