Abstract
Carbon emissions in the transportation sector are of great concern, since they are the third leading contributor to China’s carbon emissions. This research examines the decoupling relationship between economic outputs and carbon emissions of 11 provinces in 2000–2016 by focusing on Yangtze River Economic Belt (YREB), which is the densest traffic and economic corridor in China. Although many studies have focused on the decoupling state and its driving forces between economic outputs and carbon emissions, few studies further addressed the microscale analysis for decoupling drivers. This paper reveals the characteristic, inequality contribution ratio, and dynamic evolution of the drivers by integrating Dagum’s Gini ratio with kernel density estimation in YREB. Results are as follows: (1) The decoupling states presented weak decoupling state at the whole belt in the majority of the latter observed sub-periods. The decoupling states at the provincial level turned more satisfactory during the four observed sub-periods, especially for Shanghai and Zhejiang. (2) The energy intensity (EI) effect is the predominant driver for promoting the decoupling state in the vast majority of provinces, whereas value added per capita effect is the major role for inhibiting the decoupling state. (3) During the four observed sub-periods, the Gini inequality and transvariation intensity of the EI effect between sub-regions are the main sources of the provincial differences in YREB. The driving force of EI effect is increasing, but the provincial differences are expanding in the upstream and downstream regions by analyzing its dynamic evolution. Understanding the temporal and spatial microscale inequality of the decoupling drivers provides governments with differentiated and forward-looking suggestions towards coordinating regional economic growth and carbon emissions reduction.
Similar content being viewed by others
References
Ang BW (2015) LMDI decomposition approach: A guide for implementation. Energy Policy 86:233–238. https://doi.org/10.1016/j.enpol.2015.07.007
Chai J, Liang T, Lai K, Zhang Z, Wang S (2018) The future natural gas consumption in China: based on the LMDI-STIRPAT-PLSR framework and scenario analysis. Energy Policy 119:215–225. https://doi.org/10.1016/j.enpol.2018.04.049
Chinese National Development and Reform Commission Climate Office (2011). Guidelines for the preparation of provincial greenhouse gas inventories (Document 1041). Beijing
Dagum C (1997) A new approach to the decomposition of the Gini income inequality ratio. Empir Econ 22(4):515–531. https://doi.org/10.1007/bf01205777
Dharmani BC (2015) The Gram-Charlier a series based extended rule-of-thumb for bandwidth selection in univariate and multivariate Kernel density estimations. Comput Sci (4):1–30
Dong B, Zhang M, Mu H, Su X (2016) Study on decoupling analysis between energy consumption and economic growth in Liaoning Province. Energy Policy 97:414–420. https://doi.org/10.1016/j.enpol.2016.07.054
El Heda K, Louani D (2018) Optimal bandwidth selection in kernel density estimation for continuous time dependent processes. Stat Probabil Lett 138:9–19. https://doi.org/10.1016/j.spl.2018.02.001
Engo J (2018) Decomposing the decoupling of CO2 emissions from economic growth in Cameroon. Environ Sci Pollut Res 25:35451–35463. https://doi.org/10.1007/s11356-018-3511-z
Finel N, Tapio P (2012) Decoupling transport CO2 from GDP. Finland Future Research Centre FFRC ebook
Frosini BV (2012) Approximation and decomposition of Gini, Pietra–Ricci and Theil inequality measures. Empir Econ 43(1):175–197. https://doi.org/10.1007/s00181-011-0464-1
Gao J, Wang J, Zhao J (2012) Decoupling of transportation energy consumption from transportation industry growth in China. Procedia-Social Behav Sci 43:33–42. https://doi.org/10.1016/j.sbspro.2012.04.075
Grand MC (2016) Carbon emission targets and decoupling indicators. Ecol Indic 67:649–656. https://doi.org/10.1016/j.ecolind.2016.03.042
Han X, Xu Y, Kumar A, Lu X (2018) Decoupling analysis of transportation carbon emissions and economic growth in China. Environ Prog Sustain 37(5):1696–1704. https://doi.org/10.1002/ep.12857
IPCC (2006) IPCC guidelines for national greenhouse gas inventories. In: Intergovernmental Panel on Climate Change. NGGIP Publications, IGES, Hayama
Kristan M, Leonardis A (2014) Online discriminative kernel density estimator with Gaussian kernels. IEEE Trans Cybernetics 44(3):355–365. https://doi.org/10.1109/TCYB.2013.2255983
Leal PA, Marques AC, Fuinhas JA (2019) Decoupling economic growth from GHG emissions: decomposition analysis by sectoral factors for Australia. Econ Anal Policy 62:12–26. https://doi.org/10.1016/j.eap.2018.11.003
Lu S, Jiang H, Liu Y, Huang S (2017) Regional disparities and influencing factors of average CO2 emissions from transportation industry in Yangtze River Economic Belt. Transp Res D Transp Environ 57:112–123. https://doi.org/10.1016/j.trd.2017.09.005
Marques AC, Fuinhas JA, Leal PA (2018) The impact of economic growth on CO2 emissions in Australia: the environmental Kuznets curve and the decoupling index. Environ Sci Pol Res 25(27):27283–27296. https://doi.org/10.1007/s11356-018-2768-6
Mussard S, Richard P (2012) Linking Yitzhaki’s and Dagum’s Gini decompositions. Appl Econ 44(23):2997–3010. https://doi.org/10.1080/00036846.2011.568410
NBSC (2017a) China Energy Statistics Yearbook (2001-2017). China Statistics Press (in Chinese), Beijing
NBSC (2017b) China Statistical Yearbooks (2001-2017). China Statistical Press (in Chinese), Beijing
Schandl H, Hatfield-Dodds S, Wiedmann T, Geschke A, Cai Y, West J, Newth D, Baynes T, Lenzen M, Owen A (2016) Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions. J Clean Prod 132:45–56. https://doi.org/10.1016/j.jclepro.2015.06.100
Silverman BW (1986) Density estimation for statistics and data analysis (1st ed.), Chapman & Hall/CRC Monographs on Statistics & Applied Probability. CRC
Su B, Ang BW (2016) Multi-region comparisons of emission performance: the structural decomposition analysis approach. Ecol Indic 67:78–87. https://doi.org/10.1016/j.ecolind.2016.02.020
Tapio P (2005) Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transp Policy 12(2):137–151. https://doi.org/10.1016/j.tranpol.2005.01.001
UNEP (2018) Frontiers 2018/19: emerging issues of environmental concern: UN Environment Programme
Wang Q, Zambom AZ (2019) Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation. J Stat Comput Sim 89(9):1740–1759. https://doi.org/10.1080/00949655.2019.1597099
Wang Y, Xie T, Yang S (2017) Carbon emission and its decoupling research of transportation in Jiangsu Province. J Clean Prod 142:907–914. https://doi.org/10.1016/j.jclepro.2016.09.052
Wang Y, Zhou Y, Zhu L, Zhang F, Zhang Y (2018) Influencing factors and decoupling elasticity of China’s transportation carbon emissions. Energies 11(5):1157. https://doi.org/10.3390/en11051157
Worrell E, Price L, Martin N, Farla J, Schaeffer R (1997) Energy intensity in the iron and steel industry: a comparison of physical and economic indicators. Energy Policy 25(7–9):727–744. https://doi.org/10.1016/S0301-4215(97)00064-5
Wu Y, Chau KW, Lu W, Shen L, Shuai C, Chen J (2018) Decoupling relationship between economic output and carbon emission in the Chinese construction industry. Environ Impact Asses 71:60–69. https://doi.org/10.1016/j.eiar.2018.04.001
Wu Y, Tam VWY, Shuai C, Shen L, Zhang Y, Liao S (2019) Decoupling China’s economic growth from carbon emissions: empirical studies from 30 Chinese provinces (2001–2015). Sci Total Environ 656:576–588. https://doi.org/10.1016/j.scitotenv.2018.11.384
Yang L, Yang Y (2019) Evaluation of eco-efficiency in China from 1978 to 2016: based on a modified ecological footprint model. Sci Total Environ 662(20):581–590. https://doi.org/10.1016/j.scitotenv.2019.01.225
Zhang C, Xu J, Zhang L, Pang Q (2018) Driving effect of spatial-temporal difference in water resource consumption in the Yangtze River Economic Zone. Resour Sci 40(11):2247–2259. https://doi.org/10.18402/resci.2018.11.11
Zhao Y, Li H, Zhang Z, Zhang Y, Wang S, Liu Y (2017) Decomposition and scenario analysis of CO2 emissions in China’s power industry: based on LMDI method. Nat Hazards 86(2):645–668. https://doi.org/10.1007/s11069-016-2710-5
Funding
This study was funded by Humanities and Social Science Fund of ministry of Education of China (grant number: 20YJC790104); the Fundamental Research Funds for the Central Universities (grant number: CZB19020049); the National Natural Science Foundation of China (grant number: 41701610); and Humanities and Social Science Fund of Ministry of Education of China (grant number: 19YJAZH068).
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Muhammad Shahbaz
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 22 kb)
Rights and permissions
About this article
Cite this article
Zhang, L., Chen, D., Peng, S. et al. Carbon emissions in the transportation sector of Yangtze River Economic Belt: decoupling drivers and inequality. Environ Sci Pollut Res 27, 21098–21108 (2020). https://doi.org/10.1007/s11356-020-08479-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-08479-9