The Impact of Bunker Risk Management on CO2 Emissions in Maritime Transportation Under ECA Regulation

  • Yewen Gu
  • Stein W. Wallace
  • Xin Wang
Part of the Springer Optimization and Its Applications book series (SOIA, volume 129)


The shipping industry carries over 90% of the world’s trade, and is hence a major contributor to CO2 and other airborne emissions. As a global effort to reduce air pollution from ships, the implementation of the ECA (Emission Control Areas) regulations has given rise to the wide usage of cleaner fuels. This has led to an increased emphasis on the management and risk control of maritime bunker costs for many shipping companies. In this paper, we provide a novel view on the relationship between bunker risk management and CO2 emissions. In particular, we investigate how different actions taken in bunker risk management, based on different risk aversions and fuel hedging strategies, impact a shipping company’s CO2 emissions. We use a stochastic programming model and perform various comparison tests in a case study based on a major liner company. Our results show that a shipping company’s risk attitude on bunker costs has impacts on its CO2 emissions. We also demonstrate that, by properly designing its hedging strategies, a shipping company can sometimes achieve noticeable CO2 reduction with little financial sacrifice.



The authors acknowledge the financial support from the project “Green Shipping Under Uncertainty (GREENSHIPRISK)” partly funded by the Research Council of Norway under grant number 233985.


  1. 1.
    Alizadeh, A.H., Kavussanos, M.G., Menachof, D.A.: Hedging against bunker price fluctuations using petroleum futures contracts: constant versus time-varying hedge ratios. Appl. Econ. 36(12), 1337–1353 (2004)Google Scholar
  2. 2.
    Andersson, H., Fagerholt, K., Hobbesland, K.: Integrated maritime fleet deployment and speed optimization: case study from RoRo shipping. Comput. Oper. Res. 55, 233–240 (2015)Google Scholar
  3. 3.
    Cames, M., Graichen, J., Siemons, A., Cook, V.: Emission reduction targets for international aviation and shipping (2015). Accessed 25 June 2016
  4. 4.
    Cariou, P.: Is slow steaming a sustainable means of reducing co2 emissions from container shipping? Transp. Res. D 16(3), 260–264 (2011)Google Scholar
  5. 5.
    Clarkson: Clarkson Research Services website (2015). Accessed 11 Jan 2016
  6. 6.
    Corbett, J.J., Wang, H., Winebrake, J.J.: The effectiveness and costs of speed reductions on emissions from international shipping. Transp. Res. D 14(8), 593–598 (2009)Google Scholar
  7. 7.
    De, A., Mamanduru, V.K.R., Gunasekaran, A., Subramanian, N., Tiwari, M.K.: Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization. Comput. Ind. Eng. 96, 201–215 (2016)Google Scholar
  8. 8.
    Doudnikoff, M., Lacoste, R.: Effect of a speed reduction of containerships in response to higher energy costs in sulphur emission control areas. Transp. Res. D 28, 51–61 (2014)Google Scholar
  9. 9.
    Fagerholt, K., Gausel, N.T., Rakke, J.G., Psaraftis, H.N.: Maritime routing and speed optimization with emission control areas. Transp. Res. C 52, 57–73 (2015)Google Scholar
  10. 10.
    Gencer, M., Unal, G.: Crude oil price modelling with Lévy process. Int. J. Econ. Finance Stud. 4(2), 139–148 (2012)Google Scholar
  11. 11.
    Gu, Y., Wallace, S.W., Wang, X.: Integrated maritime bunker management with stochastic fuel prices and new emission regulations. Working paper 13/16, Department of Business and Management Science, Norwegian School of Economics (2016)Google Scholar
  12. 12.
    Høyland, K., Kaut, M., Wallace, S.W.: A heuristic for moment-matching scenario generation. Comput. Optim. Appl. 24(2), 169–185 (2003)Google Scholar
  13. 13.
    ICS: International Chamber of Shipping website (2015). Accessed 05 Feb 2016
  14. 14.
    Kaut, M., Wallace, S.W.: Evaluation of scenario-generation methods for stochastic programming. Pac. J. Optim. 3(2), 257–271 (2007)Google Scholar
  15. 15.
    Kontovas, C.A.: The green ship routing and scheduling problem (GSRSP): a conceptual approach. Transp. Res. D 31, 61–69 (2014)Google Scholar
  16. 16.
    Krichene, N.: Crude oil prices: trends and forecast. Working paper WP/08/133, International Monetary Fund (2008)Google Scholar
  17. 17.
    Lindstad, H., Asbjørnslett, B.E., Strømman, A.H.: Reductions in greenhouse gas emissions and cost by shipping at lower speeds. Energy Policy 39(6), 3456–3464 (2011)CrossRefGoogle Scholar
  18. 18.
    Maloni, M., Paul, J.A., Gligor, D.M.: Slow steaming impacts on Ocean carriers and shippers. Marit. Econ. Logist. 15(2), 157–171 (2013)CrossRefGoogle Scholar
  19. 19.
    Menachof, D.A., Dicer, G.N.: Risk management methods for the liner shipping industry: the case of the bunker adjustment factor. Marit. Policy Manag. 28(2), 141–155 (2001)CrossRefGoogle Scholar
  20. 20.
    Norstad, I., Fagerholt, K., Laporte, G.: Tramp ship routing and scheduling with speed optimization. Transp. Res. C 19(5), 853–865 (2011)CrossRefGoogle Scholar
  21. 21.
    Pedrielli, G., Lee, L.H., Ng, S.H.: Optimal bunkering contract in a buyer-seller supply chain under price and consumption uncertainty. Transp. Res. E 77, 77–94 (2015)CrossRefGoogle Scholar
  22. 22.
    Psaraftis, H.N., Kontovas, C.A.: CO2 emission statistics for the world commercial fleet. WMU J. Marit. Aff. 8(1), 1–25 (2009)CrossRefGoogle Scholar
  23. 23.
    Qi, X., Song, D.-P.: Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transp. Res. E 18(4), 26–31 (2012)Google Scholar
  24. 24.
    Rockafellar, R.T., Uryasev, S.: Conditional value-at-risk for general loss distributions. J. Bank. Financ. 26(7), 1443–1471 (2002)CrossRefGoogle Scholar
  25. 25.
    Tai, H.-H., Lin, D.-Y.: Comparing the unit emissions of daily frequency and slow steaming strategies on trunk route deployment in international container shipping. Transp. Res. D 21, 26–31 (2013)CrossRefGoogle Scholar
  26. 26.
    Wang, X., Teo, C.C.: Integrated hedging and network planning for container shipping’s bunker fuel management. Marit. Econ. Logist. 15(2), 172–196 (2013)CrossRefGoogle Scholar
  27. 27.
    Wong, E.Y., Tai, A.H., Lau, H.Y., Raman, M.: An utility-based decision support sustainability model in slow steaming maritime operations. Transp. Res. E 78, 57–69 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Business and Management ScienceNorwegian School of EconomicsBergenNorway
  2. 2.Department of Industrial Economics and Technology ManagementNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations