IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v70y2018icp45-60.html
   My bibliography  Save this article

Achieving China's energy and climate policy targets in 2030 under multiple uncertainties

Author

Listed:
  • Duan, Hongbo
  • Mo, Jianlei
  • Fan, Ying
  • Wang, Shouyang

Abstract

The stringency of China's energy and climate targets in 2030 and the policy needed to realize these targets are full of controversy, mainly as a result of multiple future uncertainties. This study has developed a stochastic energy-economy-environment integrated model, to assess China's energy and climate targets in 2030, with a particular focus on the carbon intensity reduction, carbon emission peaking, and non-fossil energy development. The probabilities of realizing the targets are obtained, and the nexus among different targets is explored. It's argued that carbon emission management and policy-making should be implemented from the perspective of risk management, and policy makers can take corresponding policy measures based on the degree of confidence required under multiple future uncertainties. It is found that the probabilities of realizing carbon emission-peaking target and non-fossil energy target are low, with the business-as-usual efforts, and additional policies may still be needed. More specific, carbon pricing plays a major role in curbing and peaking carbon emissions, while the policy mix of carbon pricing and non-fossil energy subsidies can peak the carbon emission with relatively low cost compared to the single carbon pricing policy. It is also found that the carbon intensity reduction target is most likely to be attained, followed by the carbon-peaking target, and then the non-fossil energy target, given the same policy efforts. This indicates that, China may not deliberately increase carbon emissions rapidly over the next decade to make the carbon emission peak as high as possible; otherwise, it may be difficult to achieve the non-fossil energy target.

Suggested Citation

  • Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," Energy Economics, Elsevier, vol. 70(C), pages 45-60.
  • Handle: RePEc:eee:eneeco:v:70:y:2018:i:c:p:45-60
    DOI: 10.1016/j.eneco.2017.12.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988317304425
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2017.12.022?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. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    2. Golub, Alexander & Narita, Daiju & Schmidt, Matthias G.W., 2011. "Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches," Sustainable Development Papers 99638, Fondazione Eni Enrico Mattei (FEEM).
    3. Fergus Green & Nicholas Stern, 2017. "China's changing economy: implications for its carbon dioxide emissions," Climate Policy, Taylor & Francis Journals, vol. 17(4), pages 423-442, May.
    4. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    5. Qiang Liu & Alun Gu & Fei Teng & Ranping Song & Yi Chen, 2017. "Peaking China’s CO 2 Emissions: Trends to 2030 and Mitigation Potential," Energies, MDPI, vol. 10(2), pages 1-22, February.
    6. Mo, Jian-Lei & Schleich, Joachim & Zhu, Lei & Fan, Ying, 2015. "Delaying the introduction of emissions trading systems—Implications for power plant investment and operation from a multi-stage decision model," Energy Economics, Elsevier, vol. 52(PB), pages 255-264.
    7. 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.
    8. Elmar Kriegler & John Weyant & Geoffrey Blanford & Volker Krey & Leon Clarke & Jae Edmonds & Allen Fawcett & Gunnar Luderer & Keywan Riahi & Richard Richels & Steven Rose & Massimo Tavoni & Detlef Vuu, 2014. "The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies," Climatic Change, Springer, vol. 123(3), pages 353-367, April.
    9. Joseph Aldy & William Pizer & Massimo Tavoni & Lara Aleluia Reis & Keigo Akimoto & Geoffrey Blanford & Carlo Carraro & Leon E. Clarke & James Edmonds & Gokul C. Iyer & Haewon C. McJeon & Richard Riche, 2016. "Economic tools to promote transparency and comparability in the Paris Agreement," Nature Climate Change, Nature, vol. 6(11), pages 1000-1004, November.
    10. Zhang, Da & Rausch, Sebastian & Karplus, Valerie J. & Zhang, Xiliang, 2013. "Quantifying regional economic impacts of CO2 intensity targets in China," Energy Economics, Elsevier, vol. 40(C), pages 687-701.
    11. Grimaud, André & Lafforgue, Gilles & Magné, Bertrand, 2011. "Climate change mitigation options and directed technical change: A decentralized equilibrium analysis," Resource and Energy Economics, Elsevier, vol. 33(4), pages 938-962.
    12. Stephan Lewandowsky & James Risbey & Michael Smithson & Ben Newell, 2014. "Scientific uncertainty and climate change: Part II. Uncertainty and mitigation," Climatic Change, Springer, vol. 124(1), pages 39-52, May.
    13. Zhou, Nan & Fridley, David & Khanna, Nina Zheng & Ke, Jing & McNeil, Michael & Levine, Mark, 2013. "China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model," Energy Policy, Elsevier, vol. 53(C), pages 51-62.
    14. Alex Y. Lo, 2012. "Carbon emissions trading in China," Nature Climate Change, Nature, vol. 2(11), pages 765-766, November.
    15. Fei Wang & Liqiu Zhao & Zhong Zhao, 2017. "China’s family planning policies and their labor market consequences," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(1), pages 31-68, January.
    16. Duan, Hong-Bo & Fan, Ying & Zhu, Lei, 2013. "What’s the most cost-effective policy of CO2 targeted reduction: An application of aggregated economic technological model with CCS?," Applied Energy, Elsevier, vol. 112(C), pages 866-875.
    17. Mo, Jian-Lei & Agnolucci, Paolo & Jiang, Mao-Rong & Fan, Ying, 2016. "The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment," Energy Policy, Elsevier, vol. 89(C), pages 271-283.
    18. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    19. Leonardo Barreto & Ger Klaassen, 2004. "Emission trading and the role of learning-by-doing spillovers in the "bottom-up" energy-system ERIS model," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 70-95.
    20. Kumbaroglu, Gürkan & Karali, Nihan & ArIkan, YIldIz, 2008. "CO2, GDP and RET: An aggregate economic equilibrium analysis for Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2694-2708, July.
    21. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    22. Wang, Can & Lin, Jie & Cai, Wenjia & Liao, Hua, 2014. "China׳s carbon mitigation strategies: Enough?," Energy Policy, Elsevier, vol. 73(C), pages 47-56.
    23. Cui, Lian-Biao & Fan, Ying & Zhu, Lei & Bi, Qing-Hua, 2014. "How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target?," Applied Energy, Elsevier, vol. 136(C), pages 1043-1052.
    24. Duan, Hong-Bo & Zhang, Gu-Peng & Zhu, Lei & Fan, Ying & Wang, Shou-Yang, 2016. "How will diffusion of PV solar contribute to China׳s emissions-peaking and climate responses?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1076-1085.
    25. Wang, Tao & Watson, Jim, 2010. "Scenario analysis of China's emissions pathways in the 21st century for low carbon transition," Energy Policy, Elsevier, vol. 38(7), pages 3537-3546, July.
    26. Duan, Hong-Bo & Zhu, Lei & Fan, Ying, 2014. "Optimal carbon taxes in carbon-constrained China: A logistic-induced energy economic hybrid model," Energy, Elsevier, vol. 69(C), pages 345-356.
    27. Zhaolin Hu & Jing Cao & L. Jeff Hong, 2012. "Robust Simulation of Global Warming Policies Using the DICE Model," Management Science, INFORMS, vol. 58(12), pages 2190-2206, December.
    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. Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
    2. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Research on the peak of CO2 emissions in the developing world: Current progress and future prospect," Applied Energy, Elsevier, vol. 235(C), pages 186-203.
    3. Yang, Lin & Lv, Haodong & Wei, Ning & Li, Yiming & Zhang, Xian, 2023. "Dynamic optimization of carbon capture technology deployment targeting carbon neutrality, cost efficiency and water stress: Evidence from China's electric power sector," Energy Economics, Elsevier, vol. 125(C).
    4. Hongbo Duan, Lei Zhu, Gürkan Kumbaroglu, and Ying Fan, 2016. "Regional Opportunities for China To Go Low-Carbon: Results from the REEC Model," The Energy Journal, International Association for Energy Economics, vol. 0(China Spe).
    5. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    6. Mu, Yaqian & Wang, Can & Cai, Wenjia, 2018. "The economic impact of China's INDC: Distinguishing the roles of the renewable energy quota and the carbon market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2955-2966.
    7. Zhang, Yanfang & Gao, Qi & Wei, Jinpeng & Shi, Xunpeng & Zhou, Dequn, 2023. "Can China's energy-consumption permit trading scheme achieve the “Porter” effect? Evidence from an estimated DSGE model," Energy Policy, Elsevier, vol. 180(C).
    8. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.
    9. Chen, Huadong & Wang, Can & Cai, Wenjia & Wang, Jianhui, 2018. "Simulating the impact of investment preference on low-carbon transition in power sector," Applied Energy, Elsevier, vol. 217(C), pages 440-455.
    10. 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.
    11. Cui, Lianbiao & Li, Rongjing & Song, Malin & Zhu, Lei, 2019. "Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments?," Energy Economics, Elsevier, vol. 83(C), pages 61-73.
    12. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2020. "Adjusting energy consumption structure to achieve China's CO2 emissions peak," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    13. Paul Lehmann & Patrik Söderholm, 2018. "Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 475-505, October.
    14. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    15. Cheng, Beibei & Dai, Hancheng & Wang, Peng & Xie, Yang & Chen, Li & Zhao, Daiqing & Masui, Toshihiko, 2016. "Impacts of low-carbon power policy on carbon mitigation in Guangdong Province, China," Energy Policy, Elsevier, vol. 88(C), pages 515-527.
    16. Duan, Hong-Bo & Zhu, Lei & Fan, Ying, 2014. "Optimal carbon taxes in carbon-constrained China: A logistic-induced energy economic hybrid model," Energy, Elsevier, vol. 69(C), pages 345-356.
    17. Tu, Qiang & Mo, Jianlei & Betz, Regina & Cui, Lianbiao & Fan, Ying & Liu, Yu, 2020. "Achieving grid parity of solar PV power in China- The role of Tradable Green Certificate," Energy Policy, Elsevier, vol. 144(C).
    18. Cai, Liya & Luo, Ji & Wang, Minghui & Guo, Jianfeng & Duan, Jinglin & Li, Jingtao & Li, Shuo & Liu, Liting & Ren, Dangpei, 2023. "Pathways for municipalities to achieve carbon emission peak and carbon neutrality: A study based on the LEAP model," Energy, Elsevier, vol. 262(PB).
    19. Lucas Bretschger & Matthias Leuthard & Alena Miftakhova, 2024. "Boosting Sluggish Climate Policy: Endogenous Substitution, Learning, and Energy Efficiency Improvements," CER-ETH Economics working paper series 24/391, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    20. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.

    More about this item

    Keywords

    Integrated assessment model; Uncertainty; INDC target; China; Carbon emission peaking; Carbon pricing; Renewable energy subsidy;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    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:70:y:2018:i:c:p:45-60. 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.