IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8388-d1152657.html
   My bibliography  Save this article

Container Shipping Optimization under Different Carbon Emission Policies: A Case Study

Author

Listed:
  • Xiangang Lan

    (School of Management, Jinan University, Guangzhou 510632, China)

  • Xiaode Zuo

    (School of Management, Jinan University, Guangzhou 510632, China)

  • Qin Tao

    (Faculty of Business, City University of Macau, Macau, China)

Abstract

Climate change is a major environmental issue facing humanity today, and the International Maritime Organization has accelerated the formulation of greenhouse gas emission policies. This study considers different carbon emission policies to construct an optimization model for container shipping, design an improved Whale Swarm Algorithm to solve related issues, and use the marginal carbon abatement cost method to analyze the deep-seated reasons for the optimization of liner shipping according to different carbon emission policies, thereby revealing the underlying reasons of emission-reduction decisions. The conclusions reveal that both kinds of carbon emission policies will reduce the profits of companies, the average speed of shipping, and carbon emissions. The carbon tax model has the greatest impact on the profits of shipping companies, and carbon cap-and-trade is easier to obtain support from enterprises. Sensitivity analysis shows that the implementation of carbon cap-and-trade or a carbon tax policy is closely and complexly related to the carbon trading price, carbon tax rate, fuel price, and ship size, and there is uncertainty.

Suggested Citation

  • Xiangang Lan & Xiaode Zuo & Qin Tao, 2023. "Container Shipping Optimization under Different Carbon Emission Policies: A Case Study," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8388-:d:1152657
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8388/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8388/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Shuaian & Wang, Xinchang, 2016. "A polynomial-time algorithm for sailing speed optimization with containership resource sharing," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 394-405.
    2. Joseph E. Aldy & William A. Pizer, 2015. "The Competitiveness Impacts of Climate Change Mitigation Policies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(4), pages 565-595.
    3. Xing, Hui & Spence, Stephen & Chen, Hua, 2020. "A comprehensive review on countermeasures for CO2 emissions from ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Roar Adland & Haiying Jia, 2018. "Dynamic speed choice in bulk shipping," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 20(2), pages 253-266, June.
    5. Xiangang Lan & Qin Tao & Xincheng Wu, 2023. "Liner-Shipping Network Design with Emission Control Areas: A Real Case Study," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    6. K Fagerholt & G Laporte & I Norstad, 2010. "Reducing fuel emissions by optimizing speed on shipping routes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 523-529, March.
    7. Christos Kontovas & Harilaos N. Psaraftis, 2011. "Reduction of emissions along the maritime intermodal container chain: operational models and policies," Maritime Policy & Management, Taylor & Francis Journals, vol. 38(4), pages 451-469, March.
    8. Hualong Yang & Xuefei Ma & Yuwei Xing, 2017. "Trends in CO 2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications," Energies, MDPI, vol. 10(7), pages 1-17, July.
    9. Wang, Yadong & Meng, Qiang & Du, Yuquan, 2015. "Liner container seasonal shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 141-161.
    10. Lee, Tsung-Chen & Chang, Young-Tae & Lee, Paul T.W., 2013. "Economy-wide impact analysis of a carbon tax on international container shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 87-102.
    11. Christian Va Karsten & Stefan Ropke & David Pisinger, 2018. "Simultaneous Optimization of Container Ship Sailing Speed and Container Routing with Transit Time Restrictions," Transportation Science, INFORMS, vol. 52(4), pages 769-787, August.
    12. Guericke, Stefan & Tierney, Kevin, 2015. "Liner shipping cargo allocation with service levels and speed optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 40-60.
    13. Wang, Shuaian & Meng, Qiang, 2012. "Sailing speed optimization for container ships in a liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 701-714.
    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. Hua Pan & Huimin Zhu & Minmin Teng, 2023. "Low-Carbon Transformation Strategy for Blockchain-Based Power Supply Chain," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

    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. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    2. Wang, Yadong & Gu, Yuyun & Wang, Tingsong & Zhang, Jun, 2022. "A risk-averse approach for joint contract selection and slot allocation in liner container shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    3. Meng, Qiang & Du, Yuquan & Wang, Yadong, 2016. "Shipping log data based container ship fuel efficiency modeling," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 207-229.
    4. He, Qie & Zhang, Xiaochen & Nip, Kameng, 2017. "Speed optimization over a path with heterogeneous arc costs," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 198-214.
    5. Shuaian Wang & Dan Zhuge & Lu Zhen & Chung-Yee Lee, 2021. "Liner Shipping Service Planning Under Sulfur Emission Regulations," Transportation Science, INFORMS, vol. 55(2), pages 491-509, March.
    6. Wang, Shuaian & Qu, Xiaobo & Yang, Ying, 2015. "Estimation of the perceived value of transit time for containerized cargoes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 298-308.
    7. Zhijia Tan & Yadong Wang & Qiang Meng & Zhixue Liu, 2018. "Joint Ship Schedule Design and Sailing Speed Optimization for a Single Inland Shipping Service with Uncertain Dam Transit Time," Service Science, INFORMS, vol. 52(6), pages 1570-1588, December.
    8. Yadong Wang & Qiang Meng & Haibo Kuang, 2019. "Intercontinental Liner Shipping Service Design," Transportation Science, INFORMS, vol. 53(2), pages 344-364, March.
    9. Wang, Shuaian, 2016. "Fundamental properties and pseudo-polynomial-time algorithm for network containership sailing speed optimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 46-55.
    10. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Laporte, Gilbert, 2020. "Green technology adoption for fleet deployment in a shipping network," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 388-410.
    11. De, Arijit & Choudhary, Alok & Turkay, Metin & Tiwari, Manoj K., 2021. "Bunkering policies for a fuel bunker management problem for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 289(3), pages 927-939.
    12. Sun, Qinghe & Li, Wei & Meng, Qiang, 2024. "Single-leg shipping revenue management for expedited services with ambiguous elasticity in transit-time-sensitive demand," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    13. Koza, David Franz, 2019. "Liner shipping service scheduling and cargo allocation," European Journal of Operational Research, Elsevier, vol. 275(3), pages 897-915.
    14. Mallidis, Ioannis & Iakovou, Eleftherios & Dekker, Rommert & Vlachos, Dimitrios, 2018. "The impact of slow steaming on the carriers’ and shippers’ costs: The case of a global logistics network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 18-39.
    15. Zhao, Shuaiqi & Yang, Hualong & Zheng, Jianfeng & Li, Dechang, 2024. "A two-step approach for deploying heterogeneous vessels and designing reliable schedule in liner shipping services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    16. Xiangang Lan & Qin Tao & Xincheng Wu, 2023. "Liner-Shipping Network Design with Emission Control Areas: A Real Case Study," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    17. Fukasawa, Ricardo & He, Qie & Song, Yongjia, 2016. "A disjunctive convex programming approach to the pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 61-79.
    18. Li, Chen & Qi, Xiangtong & Song, Dongping, 2016. "Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 762-788.
    19. Xin Wen & Qiong Chen & Yu-Qi Yin & Yui-yip Lau, 2023. "Green Vessel Scheduling with Weather Impact and Emission Control Area Consideration," Mathematics, MDPI, vol. 11(24), pages 1-25, December.
    20. Wang, Yadong & Meng, Qiang & Jia, Peng, 2019. "Optimal port call adjustment for liner container shipping routes," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 107-128.

    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:10:p:8388-:d:1152657. 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.