Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Yuwen ZHENG, Yifang ZHENG, Guannan HE, Jie SONG

PDF(3014 KB)
PDF(3014 KB)
Front. Eng ›› 2022, Vol. 9 ›› Issue (3) : 486-498. DOI: 10.1007/s42524-022-0215-3
RESEARCH ARTICLE
RESEARCH ARTICLE

Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Author information +
History +

Abstract

Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (i.e., emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.

Graphical abstract

Keywords

electricity consumption carbon emission measurement / LMDI model / decoupling model / data driven

Cite this article

Download citation ▾
Yuwen ZHENG, Yifang ZHENG, Guannan HE, Jie SONG. Decomposition and decoupling analysis of electricity consumption carbon emissions in China. Front. Eng, 2022, 9(3): 486‒498 https://doi.org/10.1007/s42524-022-0215-3

References

[1]
Abokyi, E Appiah-Konadu, P Tangato, K F Abokyi, F ( 2021). Electricity consumption and carbon dioxide emissions: The role of trade openness and manufacturing sub-sector output in Ghana. Energy and Climate Change, 2: 100026
CrossRef Google scholar
[2]
Andersson, F Karpestam, P ( 2013). CO2 emissions and economic activity: Short- and long-run economic determinants of scale, energy intensity and carbon intensity. Energy Policy, 61: 1285– 1294
CrossRef Google scholar
[3]
Andreoni, V Galmarini, S ( 2012). Decoupling economic growth from carbon dioxide emissions: A decomposition analysis of Italian energy consumption. Energy, 44( 1): 682– 691
CrossRef Google scholar
[4]
Chen G ( 2011). Trends in US carbon intensity indicator and enlightenment to China. Sino-Global Energy, 16( 2): 17– 22 (in Chinese)
[5]
Chen, L Cai, W Ma, M ( 2020). Decoupling or delusion? Mapping carbon emission per capita based on the human development index in Southwest China. Science of the Total Environment, 741: 138722
CrossRef Pubmed Google scholar
[6]
Feng, K Davis, S J Sun, L Li, X Guan, D Liu, W Liu, Z Hubacek, K ( 2013). Outsourcing CO2 within China. Proceedings of the National Academy of Sciences of the United States of America, 110( 28): 11654– 11659
CrossRef Pubmed Google scholar
[7]
Howard, B Waite, M Modi, V ( 2017). Current and near-term GHG emissions factors from electricity production for New York State and New York City. Applied Energy, 187: 255– 271
CrossRef Google scholar
[8]
Jiang, Q Khattak, S I Rahman, Z U ( 2021). Measuring the simultaneous effects of electricity consumption and production on carbon dioxide emissions (CO2e) in China: New evidence from an EKC-based assessment. Energy, 229: 120616
CrossRef Google scholar
[9]
Kucukvar, M Cansev, B Egilmez, G Onat, N C Samadi, H ( 2016). Energy–climate–manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries. Applied Energy, 184: 889– 904
CrossRef Google scholar
[10]
Lin, J Pan, D Davis, S J Zhang, Q He, K Wang, C Streets, D G Wuebbles, D J Guan, D ( 2014). China’s international trade and air pollution in the United States. Proceedings of the National Academy of Sciences of the United States of America, 111( 5): 1736– 1741
CrossRef Pubmed Google scholar
[11]
Liu, M Zhang, X Zhang, M Feng, Y Liu, L Wen, J Liu, L ( 2021). Influencing factors of carbon emissions in transportation industry based on C–D function and LMDI decomposition model: China as an example. Environmental Impact Assessment Review, 90: 106623
CrossRef Google scholar
[12]
Liu, Z Feng, K Hubacek, K Liang, S Anadon, L D Zhang, C Guan, D ( 2015a). Four system boundaries for carbon accounts. Ecological Modelling, 318: 118– 125
CrossRef Google scholar
[13]
Liu, Z Guan, D Wei, W Davis, S J Ciais, P Bai, J Peng, S Zhang, Q Hubacek, K Marland, G Andres, R J Crawford-Brown, D Lin, J Zhao, H Hong, C Boden, T A Feng, K Peters, G P Xi, F Liu, J Li, Y Zhao, Y Zeng, N He, K ( 2015b). Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 524( 7565): 335– 338
CrossRef Pubmed Google scholar
[14]
Lu, Q Yang, H Huang, X Chuai, X Wu, C ( 2015). Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China. Energy, 82: 414– 425
CrossRef Google scholar
[15]
Ma, M Cai, W ( 2019). Do commercial building sector-derived carbon emissions decouple from the economic growth in tertiary industry? A case study of four municipalities in China. Science of the Total Environment, 650( Part 1): 822– 834
CrossRef Pubmed Google scholar
[16]
Mi, Z Meng, J Zheng, H Shan, Y Wei, Y M Guan, D ( 2018). A multi-regional input–output table mapping China’s economic outputs and interdependencies in 2012. Scientific Data, 5( 1): 180155
CrossRef Pubmed Google scholar
[17]
Quick, J C ( 2014). Carbon dioxide emission tallies for 210 US coal-fired power plants: A comparison of two accounting methods. Journal of the Air & Waste Management Association, 64( 1): 73– 79
CrossRef Pubmed Google scholar
[18]
Shan, Y Liu, J Liu, Z Xu, X Shao, S Wang, P Guan, D ( 2016). New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors. Applied Energy, 184: 742– 750
CrossRef Google scholar
[19]
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. Transport Policy, 12( 2): 137– 151
CrossRef Google scholar
[20]
Wang, Q Hang, Y Zhou, P Wang, Y ( 2016). Decoupling and attribution analysis of industrial carbon emissions in Taiwan. Energy, 113: 728– 738
CrossRef Google scholar
[21]
Xi, X Han, F Xie, Y Yang, L Yan, H Luo, C Sun, L ( 2021). The key factors influencing the decline of carbon emission intensity in low-carbon cities and countermeasure research: A case of Fuzhou, Jiangxi. IOP Conference Series: Earth and Environmental Science, 769( 2): 022040
CrossRef Google scholar
[22]
Yousuf, I Ghumman, A R Hashmi, H N Kamal, M A ( 2014). Carbon emissions from power sector in Pakistan and opportunities to mitigate those. Renewable & Sustainable Energy Reviews, 34: 71– 77
CrossRef Google scholar
[23]
Zhang, X Jiang, Q Khattak, S I Ahmad, M Rahman, Z U ( 2021). Achieving sustainability and energy efficiency goals: Assessing the impact of hydroelectric and renewable electricity generation on carbon dioxide emission in China. Energy Policy, 155: 112332
CrossRef Google scholar
[24]
Zhang, X Zhang, H Zhao, C Yuan, J ( 2019). Carbon emission intensity of electricity generation in Belt and Road Initiative countries: A benchmarking analysis. Environmental Science and Pollution Research International, 26( 15): 15057– 15068
CrossRef Pubmed Google scholar
[25]
Zhang, Y J Da, Y B ( 2015). The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renewable & Sustainable Energy Reviews, 41: 1255– 1266
CrossRef Google scholar
[26]
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. Natural Hazards, 86( 2): 645– 668
CrossRef Google scholar
[27]
Zhou, Z Liu, J Zeng, H Xu, M Li, S ( 2022). Carbon performance evaluation model from the perspective of circular economy — The case of Chinese thermal power enterprise. Frontiers of Engineering Management, 9( 2): 297– 311
CrossRef Google scholar

RIGHTS & PERMISSIONS

2022 Higher Education Press
AI Summary AI Mindmap
PDF(3014 KB)

Accesses

Citations

Detail

Sections
Recommended

/