IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v86y2020ics0140988320300189.html
   My bibliography  Save this article

Changing trends of the elasticity of China's carbon emission intensity to industry structure and energy efficiency

Author

Listed:
  • Wang, Feng
  • Sun, Xiaoyu
  • Reiner, David M.
  • Wu, Min

Abstract

In this article, the calculation model of carbon intensity elasticity based on an input-output table is used to measure the elasticity of China's carbon intensity with respect to development of industries, intermediate input coefficients, and energy efficiency during 1990–2015. The industrial differences of the elasticity in 2015 are compared horizontally, and changing trends of the elasticity during 1990–2015 are analyzed in the vertical direction. The main research results imply that: first, in China's 28 subdivided industries, the development of seven industries will increase the national carbon intensity, while the development of 21 industries will decrease the national carbon intensity. The driving forces of some industries show a growing trend year by year; second, lowering industrial intermediate input coefficients by raising the technological level and management level will lead to a significant decline in national carbon intensity; third, the national carbon intensity will reduce by 0.36%, 0.119%, and 0.04% respectively, if the coal using efficiency in electricity and heat industry, coke using efficiency in metal smelting and processing industry, and the diesel using efficiency in transport and post industry increases by 1%; fourth, during 1990–2015, the elasticity of national carbon intensity with respect to the degree of residential coal saving drastically decreased and the elasticity of that with respect to the degree of refined oil saving significantly increased, yet the elasticity of that with respect to the degree of natural gas saving was relatively stable.

Suggested Citation

  • Wang, Feng & Sun, Xiaoyu & Reiner, David M. & Wu, Min, 2020. "Changing trends of the elasticity of China's carbon emission intensity to industry structure and energy efficiency," Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300189
    DOI: 10.1016/j.eneco.2020.104679
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988320300189
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2020.104679?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach," Energy, Elsevier, vol. 73(C), pages 741-750.
    2. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    3. Vieira, Nathália Duarte Braz & Nogueira, Luiz Augusto Horta & Haddad, Jamil, 2018. "An assessment of CO2 emissions avoided by energy-efficiency programs: A general methodology and a case study in Brazil," Energy, Elsevier, vol. 142(C), pages 702-715.
    4. Du, Kerui & Xie, Chunping & Ouyang, Xiaoling, 2017. "A comparison of carbon dioxide (CO2) emission trends among provinces in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 19-25.
    5. Wang, Chunhua, 2007. "Decomposing energy productivity change: A distance function approach," Energy, Elsevier, vol. 32(8), pages 1326-1333.
    6. Gu, Gaoxiang & Wang, Zheng, 2018. "China’s carbon emissions abatement under industrial restructuring by investment restriction," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 133-144.
    7. Wang, Yuan & Zhang, Chen & Lu, Aitong & Li, Li & He, Yanmin & ToJo, Junji & Zhu, Xiaodong, 2017. "A disaggregated analysis of the environmental Kuznets curve for industrial CO2 emissions in China," Applied Energy, Elsevier, vol. 190(C), pages 172-180.
    8. Brown, Marilyn A. & Kim, Gyungwon & Smith, Alexander M. & Southworth, Katie, 2017. "Exploring the impact of energy efficiency as a carbon mitigation strategy in the U.S," Energy Policy, Elsevier, vol. 109(C), pages 249-259.
    9. Su, Bin & Ang, B.W. & Li, Yingzhu, 2017. "Input-output and structural decomposition analysis of Singapore's carbon emissions," Energy Policy, Elsevier, vol. 105(C), pages 484-492.
    10. Bye, Brita & Fæhn, Taran & Rosnes, Orvika, 2018. "Residential energy efficiency policies: Costs, emissions and rebound effects," Energy, Elsevier, vol. 143(C), pages 191-201.
    11. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    12. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    13. Zhang, Pingdan & Yuan, Haoming & Bai, Fuli & Tian, Xin & Shi, Feng, 2018. "How do carbon dioxide emissions respond to industrial structural transitions? Empirical results from the northeastern provinces of China," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 145-154.
    14. Zhou, P. & Ang, B.W., 2008. "Decomposition of aggregate CO2 emissions: A production-theoretical approach," Energy Economics, Elsevier, vol. 30(3), pages 1054-1067, May.
    15. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    16. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    17. Kim, Kyunam & Kim, Yeonbae, 2012. "International comparison of industrial CO2 emission trends and the energy efficiency paradox utilizing production-based decomposition," Energy Economics, Elsevier, vol. 34(5), pages 1724-1741.
    18. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "Dynamic carbon emission performance of Chinese airlines: A global Malmquist index analysis," Journal of Air Transport Management, Elsevier, vol. 65(C), pages 99-109.
    19. Özbuğday, Fatih Cemil & Erbas, Bahar Celikkol, 2015. "How effective are energy efficiency and renewable energy in curbing CO2 emissions in the long run? A heterogeneous panel data analysis," Energy, Elsevier, vol. 82(C), pages 734-745.
    20. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    21. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    22. Sun, Licheng & Wang, Qunwei & Zhang, Jijian, 2017. "Inter-industrial Carbon Emission Transfers in China: Economic Effect and Optimization Strategy," Ecological Economics, Elsevier, vol. 132(C), pages 55-62.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Wei & You, Jianmin & Lin, Weiwen, 2021. "Internet plus and China industrial system's low-carbon development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Shi, Huiting & Chai, Jian & Lu, Quanying & Zheng, Jiali & Wang, Shouyang, 2022. "The impact of China's low-carbon transition on economy, society and energy in 2030 based on CO2 emissions drivers," Energy, Elsevier, vol. 239(PD).
    3. Wang, Bo & Zhao, Jun & Dong, Kangyin & Jiang, Qingzhe, 2022. "High-quality energy development in China: Comprehensive assessment and its impact on CO2 emissions," Energy Economics, Elsevier, vol. 110(C).
    4. Ge Huang & Wei Pan & Cheng Hu & Wu-Lin Pan & Wan-Qiang Dai, 2021. "Energy Utilization Efficiency of China Considering Carbon Emissions—Based on Provincial Panel Data," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    5. Xu, Xiaojing & Xu, Runguo, 2023. "Role of green financing and stability in the development of green resources in China," Resources Policy, Elsevier, vol. 85(PA).
    6. Decun Wu & Jinping Liu, 2020. "Spatial and Temporal Evaluation of Ecological Footprint Intensity of Jiangsu Province at the County-Level Scale," IJERPH, MDPI, vol. 17(21), pages 1-23, October.
    7. Yulan Lv & Yumeng Pang & Buhari Doğan, 2022. "The role of Chinese fiscal decentralization in the governance of carbon emissions: perspectives from spatial effects decomposition and its heterogeneity," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 635-668, June.
    8. Xu, Renjing & Xu, Bin, 2022. "Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach," Energy, Elsevier, vol. 243(C).
    9. Ying Sun & Long Qian & Zhi Liu, 2022. "The carbon emissions level of China’s service industry: an analysis of characteristics and influencing factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 13557-13582, December.
    10. Lin, Boqiang & Wang, Chonghao, 2023. "Does industrial relocation affect regional carbon intensity? Evidence from China's secondary industry," Energy Policy, Elsevier, vol. 173(C).
    11. Huo, Xiaolin & Jiang, Dayan & Qiu, Zhigang & Yang, Sijie, 2022. "The impacts of dual carbon goals on asset prices in China," Journal of Asian Economics, Elsevier, vol. 83(C).
    12. Zhong, Mei-Rui & Cao, Meng-Yuan & Zou, Han, 2022. "The carbon reduction effect of ICT: A perspective of factor substitution," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    13. Dongsheng Yan & Pingxing Li, 2023. "Can Regional Integration Reduce Urban Carbon Emission? An Empirical Study Based on the Yangtze River Delta, China," IJERPH, MDPI, vol. 20(2), pages 1-25, January.
    14. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    15. Zhang, Dongyang & Mohsin, Muhammad & Taghizadeh-Hesary, Farhad, 2022. "Does green finance counteract the climate change mitigation: Asymmetric effect of renewable energy investment and R&D," Energy Economics, Elsevier, vol. 113(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    2. Song, Yi & Huang, Jian-Bai & Feng, Chao, 2018. "Decomposition of energy-related CO2 emissions in China's iron and steel industry: A comprehensive decomposition framework," Resources Policy, Elsevier, vol. 59(C), pages 103-116.
    3. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    4. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    5. Zhang, Wei & Wang, Nan, 2021. "Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment," Energy, Elsevier, vol. 221(C).
    6. Li, Ding & Gao, Ming & Hou, Wenxuan & Song, Malin & Chen, Jiandong, 2020. "A modified and improved method to measure economy-wide carbon rebound effects based on the PDA-MMI approach," Energy Policy, Elsevier, vol. 147(C).
    7. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    8. Du, Kerui & Li, Jianglong, 2019. "Towards a green world: How do green technology innovations affect total-factor carbon productivity," Energy Policy, Elsevier, vol. 131(C), pages 240-250.
    9. Wang, Miao & Feng, Chao, 2020. "The impacts of technological gap and scale economy on the low-carbon development of China's industries: An extended decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    10. Wang, Miao & Feng, Chao, 2018. "Using an extended logarithmic mean Divisia index approach to assess the roles of economic factors on industrial CO2 emissions of China," Energy Economics, Elsevier, vol. 76(C), pages 101-114.
    11. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    12. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    13. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    14. Lizhan Cao & Hui Wang, 2022. "The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
    15. Feng Dong & Xinqi Gao & Jingyun Li & Yuanqing Zhang & Yajie Liu, 2018. "Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches," IJERPH, MDPI, vol. 15(12), pages 1-28, December.
    16. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    17. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, vol. 9(5), pages 1-13, April.
    18. Azam, Muhammad & Younes, Ben Zaied & Hunjra, Ahmed Imran & Hussain, Nazim, 2022. "Integrated Spatial-Temporal decomposition analysis for life cycle assessment of carbon emission intensity change in various regions of China," Resources Policy, Elsevier, vol. 79(C).
    19. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 813-833.
    20. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

    More about this item

    Keywords

    Carbon intensity; Elasticity; Development of industries; Energy efficiency; Intermediate input coefficient;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.