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Choose clean energy or green technology? Empirical evidence from global ships

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  • Bai, Xiwen
  • Hou, Yao
  • Yang, Dong

Abstract

On January 1st, 2020, the International Maritime Organization (IMO) implemented a new regulation for a 0.50% global sulphur cap for marine fuels, which was a dramatic decrease from the previous emissions cap of 3.5%. The new regulation will have an enormous impact on the shipping market. At present, there are different feasible schemes for reducing sulphur emissions from ships. Shipowners need to consider the economic cost, energy feasibility, and other relevant factors of different schemes before making decisions. This paper empirically explores the factors that affect shipowners' energy choices. Based on the new emerging individual ship dynamic data, Automatic Identification System (AIS), and other relevant databases, we apply various data mining methods and a threshold discrete choice model combined with an oversampling technique to conduct quantitative measurements and statistical analyses of factors for each ship type that affect the shipowners' choices. Three groups of indicators, including ship characteristics, shipowner characteristics, and market conditions, are considered in our analysis. In the model, we also address the heterogeneity of the carriers towards environmental awareness. This study provides important practical implications for responding to the new emissions regulations among maritime and maritime-related industries and policymakers.

Suggested Citation

  • Bai, Xiwen & Hou, Yao & Yang, Dong, 2021. "Choose clean energy or green technology? Empirical evidence from global ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:transe:v:151:y:2021:i:c:s1366554521001320
    DOI: 10.1016/j.tre.2021.102364
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    References listed on IDEAS

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    Cited by:

    1. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    2. Shao, Shuai & Tan, Zhijia & Wang, Tingsong & Liu, Zhiyuan, 2023. "Configuration design of the emission control areas for coastal ships: A Stackelberg game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    3. Peng, Wenhao & Bai, Xiwen, 2022. "Prospects for improving shipping companies’ profit margins by quantifying operational strategies and market focus approach through AIS data," Transport Policy, Elsevier, vol. 128(C), pages 138-152.
    4. Li, Huanhuan & Jiao, Hang & Yang, Zaili, 2023. "AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    5. Bai, Xiwen & Cheng, Liangqi & Iris, Çağatay, 2022. "Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).

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