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Optimization of Preventive Maintenance Timing of Highway Bridges Considering China’s “Dual Carbon” Target

Author

Listed:
  • Lunyou Pei

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Bing Wang

    (School of Civil & Environmental Engineering and Geography Science, Ningbo University, Ningbo 315211, China)

  • Ying Liu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Xiaoling Liu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

Abstract

The dual carbon target is a two-stage carbon reduction goal proposed by China, while the bridge maintenance strategy does not consider the need for sustainable development. Therefore, this article studies the optimization of bridge maintenance timing under China’s dual carbon goals. Firstly, this paper aims to minimize the total cost of maintenance and carbon emissions, considering the continuous effects of carbon pricing and emissions in the context of the dual carbon goals. The CHINAGEM-E model is employed to predict carbon prices, and a preventive maintenance decision-making method for highway bridges is established. Secondly, based on the theory of material residual strength, a degradation model for the technical condition of highway bridges is constructed. Finally, an in-depth case analysis of an in-service highway bridge is conducted to derive optimal maintenance solutions under three scenarios. In comparison to scenarios considering only maintenance costs or those based on benchmark carbon prices, the comprehensive maintenance cost under the dual carbon targets is the highest. In the total maintenance cost, carbon emission costs constitute over 50%, emphasizing the need for increased attention to carbon emission cost studies in future maintenance research. The methodology proposed in this paper is the first to connect carbon prices with the timing of preventive maintenance for bridges, providing a more scientific and sustainable basis for future highway bridge maintenance decisions.

Suggested Citation

  • Lunyou Pei & Bing Wang & Ying Liu & Xiaoling Liu, 2023. "Optimization of Preventive Maintenance Timing of Highway Bridges Considering China’s “Dual Carbon” Target," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16388-:d:1289875
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    References listed on IDEAS

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    1. Shenghao Feng & Xiujian Peng & Philip Adams, 2021. "Energy and Economic Implications of Carbon Neutrality in China -- A Dynamic General Equilibrium Analysis," Centre of Policy Studies/IMPACT Centre Working Papers g-318, Victoria University, Centre of Policy Studies/IMPACT Centre.
    2. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    3. Huimin Bi & Hao Xiao & Kejuan Sun, 2019. "The Impact of Carbon Market and Carbon Tax on Green Growth Pathway in China: A Dynamic CGE Model Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(6), pages 1312-1325, May.
    4. Ignacio J. Navarro & Víctor Yepes & José V. Martí, 2018. "Life Cycle Cost Assessment of Preventive Strategies Applied to Prestressed Concrete Bridges Exposed to Chlorides," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
    5. A. Indermühle & T. F. Stocker & F. Joos & H. Fischer & H. J. Smith & M. Wahlen & B. Deck & D. Mastroianni & J. Tschumi & T. Blunier & R. Meyer & B. Stauffer, 1999. "Holocene carbon-cycle dynamics based on CO2 trapped in ice at Taylor Dome, Antarctica," Nature, Nature, vol. 398(6723), pages 121-126, March.
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