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Planning China’s non-deterministic energy system (2021–2060) to achieve carbon neutrality

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  • Ma, Y.
  • Li, Y.P.
  • Huang, G.H.

Abstract

Long-term energy system planning at the national level is critical for achieving carbon neutrality. However, the multiple uncertainties inherent in energy system pose enormous challenges to the planning process. In this study, a flexible chance-constrained programming (FCP) method is developed to tackle uncertainties presented as random probability distribution and fuzzy information, which can also balance the trade-off between system objective and constraint-violation risk. Then, considering carbon capture, utilization and storage (CCUS) technology and forest carbon sinks, a China’s non-deterministic energy planning model is formulated to achieve carbon neutrality under the minimization of system cost. Results under 72 scenarios associated with different CCUS installation rates and risk-response attitudes disclose that (i) in order to realize the national carbon neutrality goal, China should gradually reduce the use of coal and oil and increase the use of renewable energy (accounting for [75.3, 78.6]% by 2060); (ii) China should vigorously increase wind power and photovoltaic power, phase down coal-fired power installed capacity but retain 144.0 GW by 2060 to ensure stable electricity supply; (iii) China should reach peak CO2 emissions for its energy system by 2025 to gain time and release the urgency for carbon neutrality; (iv) the synergistic effect of carbon neutrality is also beneficial to the abatement of pollutants emissions and the improvement of air quality. The results can provide policy recommendations for the planning of China’s carbon neutral energy system during 2021–2060.

Suggested Citation

  • Ma, Y. & Li, Y.P. & Huang, G.H., 2023. "Planning China’s non-deterministic energy system (2021–2060) to achieve carbon neutrality," Applied Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000375
    DOI: 10.1016/j.apenergy.2023.120673
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