IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10305-d1182872.html
   My bibliography  Save this article

The Optimal Path for China to Achieve the “Dual Carbon” Target from the Perspective of Energy Structure Optimization

Author

Listed:
  • Qi Jiang

    (School of Mining, Liaoning Technical University, Fuxin 123008, China)

  • Zhigang Yin

    (School of Mining, Liaoning Technical University, Fuxin 123008, China)

Abstract

Exploring the path of energy structure optimization to reduce carbon emissions and achieve a carbon peak has important policy implications for achieving the “Dual Carbon” target. To this end, this paper explores the optimal path for China to achieve the “dual carbon” target from the perspective of energy structure optimization in three steps: (1) we forecast China’s carbon emissions and carbon intensity during 2024–2035 based on a combined forecasting model; (2) we simulate the development of energy consumption and carbon emissions under the “economic development scenario-energy structure scenario” with the help of Markov chain forecasting model; (3) we construct a multi-attribute decision model to account for the above elements as variables to calculate a composite index to analyze the optimal path for China to achieve “Dual Carbon” target under different decision preferences. It is found that (1) potential negative effects caused by COVID-19 are not as serious as reported; (2) only the scenario with low-speed economic growth and effective policies guiding, which doesn’t follow laws of social development, can contribute to reaching carbon peaking by 2030 while maintaining a high-quality carbon intensity; (3) the optimal path that scenario with middle-speed economic growth and strict cost control is a sub-optimal choice subject to realities; (4) technologies innovations in green or low-carbon fields are needed to accelerate energy consumption structure optimization.

Suggested Citation

  • Qi Jiang & Zhigang Yin, 2023. "The Optimal Path for China to Achieve the “Dual Carbon” Target from the Perspective of Energy Structure Optimization," Sustainability, MDPI, vol. 15(13), pages 1-32, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10305-:d:1182872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10305/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/10305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bakirtas, Tahsin & Akpolat, Ahmet Gokce, 2018. "The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries," Energy, Elsevier, vol. 147(C), pages 110-121.
    2. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    3. Liu, Wei & Li, Hong, 2011. "Improving energy consumption structure: A comprehensive assessment of fossil energy subsidies reform in China," Energy Policy, Elsevier, vol. 39(7), pages 4134-4143, July.
    4. Elzen, Michel den & Fekete, Hanna & Höhne, Niklas & Admiraal, Annemiek & Forsell, Nicklas & Hof, Andries F. & Olivier, Jos G.J. & Roelfsema, Mark & van Soest, Heleen, 2016. "Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?," Energy Policy, Elsevier, vol. 89(C), pages 224-236.
    5. Xu, Lei & Chen, Nengcheng & Chen, Zeqiang, 2017. "Will China make a difference in its carbon intensity reduction targets by 2020 and 2030?," Applied Energy, Elsevier, vol. 203(C), pages 874-882.
    6. Hao, Yan & Zhang, Menghui & Zhang, Yan & Fu, Chenling & Lu, Zhongming, 2018. "Multi-scale analysis of the energy metabolic processes in the Beijing–Tianjin–Hebei (Jing-Jin-Ji) urban agglomeration," Ecological Modelling, Elsevier, vol. 369(C), pages 66-76.
    7. Agovino, Massimiliano & Bartoletto, Silvana & Garofalo, Antonio, 2019. "Modelling the relationship between energy intensity and GDP for European countries: An historical perspective (1800–2000)," Energy Economics, Elsevier, vol. 82(C), pages 114-134.
    8. Feng, Taiwen & Sun, Linyan & Zhang, Ying, 2009. "The relationship between energy consumption structure, economic structure and energy intensity in China," Energy Policy, Elsevier, vol. 37(12), pages 5475-5483, December.
    9. Yi, Wen-Jing & Zou, Le-Le & Guo, Jie & Wang, Kai & Wei, Yi-Ming, 2011. "How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development," Energy Policy, Elsevier, vol. 39(5), pages 2407-2415, May.
    10. Aydin, Celil & Esen, Ömer, 2018. "Does the level of energy intensity matter in the effect of energy consumption on the growth of transition economies? Evidence from dynamic panel threshold analysis," Energy Economics, Elsevier, vol. 69(C), pages 185-195.
    11. Zhu, Bangzhu & Wang, Kefan & Chevallier, Julien & Wang, Ping & Wei, Yi-Ming, 2015. "Can China achieve its carbon intensity target by 2020 while sustaining economic growth?," Ecological Economics, Elsevier, vol. 119(C), pages 209-216.
    12. Jia, Zhijie & Lin, Boqiang, 2021. "How to achieve the first step of the carbon-neutrality 2060 target in China: The coal substitution perspective," Energy, Elsevier, vol. 233(C).
    13. Mahalingam, Brinda & Orman, Wafa Hakim, 2018. "GDP and energy consumption: A panel analysis of the US," Applied Energy, Elsevier, vol. 213(C), pages 208-218.
    14. Green, Fergus & Stern, Nicholas, 2016. "China’s changing economy: implications for its carbon dioxide emissions," LSE Research Online Documents on Economics 65483, London School of Economics and Political Science, LSE Library.
    15. Fallahi, Firouz, 2011. "Causal relationship between energy consumption (EC) and GDP: A Markov-switching (MS) causality," Energy, Elsevier, vol. 36(7), pages 4165-4170.
    16. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    17. Cynthia Rosenzweig & William Solecki & Stephen A. Hammer & Shagun Mehrotra, 2010. "Cities lead the way in climate–change action," Nature, Nature, vol. 467(7318), pages 909-911, October.
    18. Hoefnagels, Ric & Resch, Gustav & Junginger, Martin & Faaij, André, 2014. "International and domestic uses of solid biofuels under different renewable energy support scenarios in the European Union," Applied Energy, Elsevier, vol. 131(C), pages 139-157.
    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. Kangping Wang & Pengjiang Ge & Naixin Duan & Jili Wang & Jinli Lv & Meng Liu & Bin Wang, 2023. "The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm," Energies, MDPI, vol. 16(19), pages 1-18, September.

    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. Wang, Juan & Hu, Mingming & Tukker, Arnold & Rodrigues, João F.D., 2019. "The impact of regional convergence in energy-intensive industries on China's CO2 emissions and emission goals," Energy Economics, Elsevier, vol. 80(C), pages 512-523.
    2. Wei, Zixiang & Han, Botang & Pan, Xiuzhen & Shahbaz, Muhammad & Zafar, Muhammad Wasif, 2020. "Effects of diversified openness channels on the total-factor energy efficiency in China's manufacturing sub-sectors: Evidence from trade and FDI spillovers," Energy Economics, Elsevier, vol. 90(C).
    3. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2020. "Who shapes China's carbon intensity and how? A demand-side decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    4. Luo, Yulong & Zeng, Weiliang & Wang, Yueqiang & Li, Danzhou & Hu, Xianbiao & Zhang, Hua, 2021. "A hybrid approach for examining the drivers of energy consumption in Shanghai," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. repec:aud:audfin:v:21:y:2019:i:50:p:75 is not listed on IDEAS
    6. Ravetti, Chiara & Cambini, Carlo, 2021. "Energy Use Beyond GDP: A Dynamic Panel Analysis with Different Development Indicators," Working Papers 10-2021, Copenhagen Business School, Department of Economics.
    7. Saldivia, Mauricio & Kristjanpoller, Werner & Olson, Josephine E., 2020. "Energy consumption and GDP revisited: A new panel data approach with wavelet decomposition," Applied Energy, Elsevier, vol. 272(C).
    8. Zhang, Xi & Geng, Yong & Shao, Shuai & Dong, Huijuan & Wu, Rui & Yao, Tianli & Song, Jiekun, 2020. "How to achieve China’s CO2 emission reduction targets by provincial efforts? – An analysis based on generalized Divisia index and dynamic scenario simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    9. Ying Wang & Peipei Shang & Lichun He & Yingchun Zhang & Dandan Liu, 2018. "Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?," Energies, MDPI, vol. 11(10), pages 1-32, October.
    10. Paul-Razvan Șerban & Monica Dumitrașcu & Bianca Mitrică & Ines Grigorescu & Irena Mocanu & Gheorghe Kucsicsa & Alexandra Vrînceanu & Cristina Dumitrică, 2020. "The Estimation of Regional Energy Consumption Based on the Energy Consumption Rate at National Level. Case Study: The Romanian Danube Valley," Energies, MDPI, vol. 13(16), pages 1-18, August.
    11. Liu, Jinpeng & Niu, Dongxiao & Song, Xiaohua, 2013. "The energy supply and demand pattern of China: A review of evolution and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 220-228.
    12. Wang, Jie & Xiong, Yiling & Tian, Xin & Liu, Shangwei & Li, Jiashuo & Tanikawa, Hiroki, 2018. "Stagnating CO2 emissions with in-depth socioeconomic transition in Beijing," Applied Energy, Elsevier, vol. 228(C), pages 1714-1725.
    13. Ping Wang & Bangzhu Zhu, 2016. "Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    14. Zhu, Bangzhu & Ye, Shunxin & Jiang, Minxing & Wang, Ping & Wu, Zhanchi & Xie, Rui & Chevallier, Julien & Wei, Yi-Ming, 2019. "Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach," Applied Energy, Elsevier, vol. 233, pages 196-207.
    15. Yulan Lv & Wei Chen & Jianquan Cheng, 2019. "Direct and Indirect Effects of Urbanization on Energy Intensity in Chinese Cities: A Regional Heterogeneity Analysis," Sustainability, MDPI, vol. 11(11), pages 1-20, June.
    16. Zhu, Hongtao & Cao, Shuang & Su, Zimeng & Zhuang, Yang, 2024. "China's future energy vision: Multi-scenario simulation based on energy consumption structure under dual carbon targets," Energy, Elsevier, vol. 301(C).
    17. Ning, Yadong & Chen, Kunkun & Zhang, Boya & Ding, Tao & Guo, Fei & Zhang, Ming, 2020. "Energy conservation and emission reduction path selection in China: A simulation based on Bi-Level multi-objective optimization model," Energy Policy, Elsevier, vol. 137(C).
    18. Wang, Yajie & Yu, Huan & Zhang, Hongda & Chen, Tianyu, 2021. "Non-linear analysis of effects of energy consumption on economic growth in China: Role of real exchange rate," Economic Modelling, Elsevier, vol. 104(C).
    19. Koščak Kolin, Sonja & Karasalihović Sedlar, Daria & Kurevija, Tomislav, 2021. "Relationship between electricity and economic growth for long-term periods: New possibilities for energy prediction," Energy, Elsevier, vol. 228(C).
    20. Xu, Lei & Chen, Nengcheng & Chen, Zeqiang, 2017. "Will China make a difference in its carbon intensity reduction targets by 2020 and 2030?," Applied Energy, Elsevier, vol. 203(C), pages 874-882.
    21. Stefan Dragos Cirstea & Andreea Cirstea & Irimie Emil Popa & Gabriel Radu, 2019. "The Role of Bioenergy in Transition to a Sustainable Bioeconomy – Study on EU Countries," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 21(50), pages 1-75, February.

    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:15:y:2023:i:13:p:10305-:d:1182872. 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.