IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i19p8016-d420859.html
   My bibliography  Save this article

Exploring the Effects of Industrial Structure, Technology, and Energy Efficiency on China’s Carbon Intensity and Their Contributions to Carbon Intensity Target

Author

Listed:
  • Feng Wang

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China)

  • Min Wu

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China)

  • Jiachen Hong

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China)

Abstract

To achieve the national carbon intensity (NCI) target, China should adopt effective mitigation measures. This paper aims to examine the effects of key mitigation measures on NCI. Using the input-output table in 2017, this paper establishes the elasticity model of NCI to investigate the effects of industrial development, intermediate input coefficients, energy efficiency, and residential energy saving on NCI, and further evaluates the contributions of key measures on achieving NCI target. The results are shown as follows. First, the development of seven sectors will promote the increase of NCI while that of 21 sectors will reduce NCI. Second, NCI will decrease significantly with the descending of intermediate input coefficients of sectors, especially electricity production and supply. Third, improving energy efficiency and residential energy saving degree could reduce NCI, but the latter has limited contribution. Fourth, the development of all sectors will reduce NCI by 10.11% in 2017–2022 if sectors could continue the historical development trends. Fifth, assuming that sectors with rising intermediate input coefficients would keep their coefficients unchanged in the predicting period and sectors with descending coefficients would continue the historical descending trend, the improvement of technology and management of all sectors will reduce NCI by 14.02% in 2017–2022.

Suggested Citation

  • Feng Wang & Min Wu & Jiachen Hong, 2020. "Exploring the Effects of Industrial Structure, Technology, and Energy Efficiency on China’s Carbon Intensity and Their Contributions to Carbon Intensity Target," Sustainability, MDPI, vol. 12(19), pages 1-24, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8016-:d:420859
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/19/8016/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/19/8016/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Shiwei & Zheng, Shuhong & Li, Xia & Li, Longxi, 2018. "China can peak its energy-related carbon emissions before 2025: Evidence from industry restructuring," Energy Economics, Elsevier, vol. 73(C), pages 91-107.
    2. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    3. Luigi Aldieri & Concetto Paolo Vinci, 2018. "Green Economy and Sustainable Development: The Economic Impact of Innovation on Employment," Sustainability, MDPI, vol. 10(10), pages 1-11, October.
    4. Wu, Rui & Dai, Hancheng & Geng, Yong & Xie, Yang & Tian, Xu, 2019. "Impacts of export restructuring on national economy and CO2 emissions: A general equilibrium analysis for China," Applied Energy, Elsevier, vol. 248(C), pages 64-78.
    5. Tian, Xin & Chang, Miao & Tanikawa, Hiroki & Shi, Feng & Imura, Hidefumi, 2013. "Structural decomposition analysis of the carbonization process in Beijing: A regional explanation of rapid increasing carbon dioxide emission in China," Energy Policy, Elsevier, vol. 53(C), pages 279-286.
    6. Yang, Lin & Yang, Yuantao & Zhang, Xian & Tang, Kai, 2018. "Whether China's industrial sectors make efforts to reduce CO2 emissions from production? - A decomposed decoupling analysis," Energy, Elsevier, vol. 160(C), pages 796-809.
    7. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    8. Akbostancı, Elif & Tunç, Gül İpek & Türüt-Aşık, Serap, 2018. "Drivers of fuel based carbon dioxide emissions: The case of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2599-2608.
    9. Yan, Junna & Zhao, Tao & Kang, Jidong, 2016. "Sensitivity analysis of technology and supply change for CO2 emission intensity of energy-intensive industries based on input–output model," Applied Energy, Elsevier, vol. 171(C), pages 456-467.
    10. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    11. Zhang, Yue-Jun & Peng, Yu-Lu & Ma, Chao-Qun & Shen, Bo, 2017. "Can environmental innovation facilitate carbon emissions reduction? Evidence from China," Energy Policy, Elsevier, vol. 100(C), pages 18-28.
    Full references (including those not matched with items on IDEAS)

    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. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Zhang, Lixiao & Yang, Min & Zhang, Pengpeng & Hao, Yan & Lu, Zhongming & Shi, Zhimin, 2021. "De-coal process in urban China: What can we learn from Beijing's experience?," Energy, Elsevier, vol. 230(C).
    3. Mingyue Wang & Yu Liu & Yawen Liu & Shunxiang Yang & Lingyu Yang, 2018. "Assessing Multiple Pathways for Achieving China’s National Emissions Reduction Target," Sustainability, MDPI, vol. 10(7), pages 1-16, June.
    4. Wang, Wei-Zheng & Liu, Lan-Cui & Liao, Hua & Wei, Yi-Ming, 2021. "Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries," Energy Policy, Elsevier, vol. 151(C).
    5. Weidong Chen & Ruoyu Yang, 2018. "Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    6. Hua Liao & Celio Andrade & Julio Lumbreras & Jing Tian, 2018. "CO2 Emissions in Beijing: Sectoral Linkages and Demand Drivers," CEEP-BIT Working Papers 113, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    7. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    8. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    9. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    10. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    11. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    12. Ying Han & Baoling Jin & Xiaoyuan Qi & Huasen Zhou, 2021. "Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    13. Tan, Feifei & Lu, Zhaohua, 2015. "Current status and future choices of regional sectors-energy-related CO2 emissions: The third economic growth pole of China," Applied Energy, Elsevier, vol. 159(C), pages 237-251.
    14. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Le, TN-Lan & Leyva-de la Hiz, Dante I., 2021. "Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    15. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. Chen, B. & Li, J.S. & Zhou, S.L. & Yang, Q. & Chen, G.Q., 2018. "GHG emissions embodied in Macao's internal energy consumption and external trade: Driving forces via decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4100-4106.
    17. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    18. Khan, Ali Nawaz & En, Xie & Raza, Muhammad Yousaf & Khan, Naseer Abbas & Ali, Ahsan, 2020. "Sectorial study of technological progress and CO2 emission: Insights from a developing economy," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    19. Rong Yuan & Tao Zhao & Jing Xu, 2017. "A subsystem input–output decomposition analysis of CO2 emissions in the service sectors: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(6), pages 2181-2198, December.
    20. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.

    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:gam:jsusta:v:12:y:2020:i:19:p:8016-:d:420859. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.