Modelling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation
Abstract
The maritime transport industry continues to draw international attention on significant Greenhouse Gas emissions. The introduction of emissions taxes aims to control and reduce emissions. The uncertainty of carbon tax policy affects shipping companies’ fleet planning and increases costs. We formulate the fleet planning problem under carbon tax policy uncertainty a multi-stage stochastic integer-programming model for the liner shipping companies. We develop a scenario tree to represent the structure of the carbon tax stochastic dynamics, and seek the optimal planning, which is adaptive to the policy uncertainty. Non-anticipativity constraint is applied to ensure the feasibility of the decisions in the dynamic environment. For the sake of comparison, the Perfect Information (PI) model is introduced as well. Based on a liner shipping application of our model, we find that under the policy uncertainty, companies charter more ships when exposed to high carbon tax risk, and spend more on fleet operation; meanwhile the CO2 emission volume will be reduced.
Keyword : carbon emission, carbon tax, policy uncertainty, maritime shipping, fleet mix strategy, stochastic programming
How to Cite
Zhu, M., Chen, M., & Kristal, M. (2018). Modelling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation. Transport, 33(3), 707-717. https://doi.org/10.3846/transport.2018.1579
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Avi-Yonah, R. S.; Uhlmann, D. M. 2009. Combating global climate change: why a carbon tax is a better response to global warming than cap and trade, Stanford Environmental Law Journal 28(3): 3–50.
Bakkehaug, R.; Eidem, E. S.; Fagerholt, K.; Hvattum, L. M. 2014. A stochastic programming formulation for strategic fleet renewal in shipping, Transportation Research Part E: Logistics and Transportation Review 72: 60–76. https://doi.org/10.1016/j.tre.2014.09.01
Bang, H.-S.; Kang, H.-W.; Martin, J.; Woo, S.-H. 2012. The impact of operational and strategic management on liner shipping efficiency: a two-stage DEA approach, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 39(7): 653–672. https://doi.org/10.1080/03088839.2012.740165
Birge, J. R.; Louveaux. 2011. Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer. 485 p. https://doi.org/10.1007/978-1-4614-0237-4
Cariou, P.; Wolff, P.-C. 2013. Chartering practices in liner shipping, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 40(4): 323–338. https://doi.org/10.1080/03088839.2013.781280
Clarkson Research Services Ltd. 2014. Container Intelligence Quarterly: Second Quarter 2014. Available from Internet: http://www.crsl.com
Dentcheva, D.; Ruszczynski, A. 2003. Optimization with stochastic dominance constraints, SIAM Journal on Optimization 14(2): 548–566. https://doi.org/10.1137/S1052623402420528
Ermolieva, T.; Ermoliev, Y.; Fischer, G.; Jonas, M.; Makowski, M.; Wagner, F. 2010. Carbon emission trading and carbon taxes under uncertainties, in M. Jonas, Z. Nahorski, S. Nilsson, T. Whiter (Eds.). Greenhouse Gas Inventories. Springer, 277–289. https://doi.org/10.1007/978-94-007-1670-4_16
Fagerholt, K.; Gausel, N. T.; Rakke, J. G.; Psaraftis, H. N. 2015. Maritime routing and speed optimization with emission control areas, Transportation Research Part C: Emerging Technologies 52: 57–73. https://doi.org/10.1016/j.trc.2014.12.010
Fagerholt, K.; Johnsen, T. A. V.; Lindstad, H. 2009. Fleet deployment in liner shipping: a case study, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 36(5): 397–409. https://doi.org/10.1080/03088830903187143
Franc, P.; Sutto, L. 2014. Impact analysis on shipping lines and European ports of a cap- and-trade system on CO2 emissions in maritime transport, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 41(1): 61–78. https://doi.org/10.1080/03088839.2013.782440
Hammoudeh, S.; Nguyen, D. K.; Sousa, R. M. 2014. Energy prices and CO2 emission allowance prices: a quantile regression approach, Energy Policy 70: 201–206. https://doi.org/10.1016/j.enpol.2014.03.026
He, Y.; Wang, L.; Wang, J. 2012. Cap-and-trade vs. carbon taxes: a quantitative comparison from a generation expansion planning perspective, Computers & Industrial Engineering 63(3): 708–716. https://doi.org/10.1016/j.cie.2011.10.005
ICS. 2009. Greenhouse Gas Emissions and Market Based Instruments: MBI Analysis Report. International Chamber of Shipping (ICS). Available from Internet: http://www.ics-shipping.org
IMO. 2009. Second IMO GHG Study 2009. International Maritime Organization (IMO). 240 p. Available from Internet: http://www.imo.org/en/OurWork/Environment/Pollution-Prevention/AirPollution/Documents/SecondIMOGHGS-tudy2009.pdf
Kim, J.-G.; Kim, H.-J.; Lee, P. T.-W. 2013. Optimising containership speed and fleet size under a carbon tax and an emission trading scheme, International Journal of Shipping and Transport Logistics 5(6): 571–590. https://doi.org/10.1504/IJSTL.2013.056835
King, A. J.; Wallace, S. W. 2012. Modeling with Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer. 173 p. https://doi.org/10.1007/978-0-387-87817-1
Lee, T.-C.; Chang, Y.-T.; Lee, P. T. W. 2013. Economy-wide impact analysis of a carbon tax on international container shipping, Transportation Research Part A: Policy and Practice 58: 87–102. https://doi.org/10.1016/j.tra.2013.10.002
Mason, R.; Nair, R. 2013. Strategic flexibility capabilities in the container liner shipping sector, Production Planning & Control: the Management of Operations 24(7): 640–651. https://doi.org/10.1080/09537287.2012.659873
Meng, Q.; Wang, T.; Wang, S. 2015. Multi-period liner ship fleet planning with dependent uncertain container shipment demand, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 42(1): 43–67. https://doi.org/10.1080/03088839.2013.865848
Meng, Q.; Wang, T. 2011. A scenario-based dynamic programming model for multi-period liner ship fleet planning, Transportation Research Part E: Logistics and Transportation Review 47(4): 401–13. https://doi.org/10.1016/j.tre.2010.12.005
Meng, Q.; Wang, T. 2010. A chance constrained programming model for short-term liner ship fleet planning problems, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 37(4): 329–346. https://doi.org/10.1080/03088839.2010.486635
Pantuso, G.; Fagerholt, K.; Hvattum, L. M. 2014. A survey on maritime fleet size and mix problems, European Journal of Operational Research 235(2): 341–349. https://doi.org/10.1016/j.ejor.2013.04.058
Psaraftis, H. N. 2012. Market-based measures for greenhouse gas emissions from ships: a review, WMU Journal of Maritime Affairs 11(2): 211–232. https://doi.org/10.1007/s13437-012-0030-5
Ramseur, J. L.; Parker, L. 2010. Carbon Tax and Greenhouse Gas Control: Options and Considerations for Congress. BiblioGov. 60 p.
Reinelt, P. S.; Keith, D. W. 2007. Carbon capture retrofits and the cost of regulatory uncertainty, The Energy Journal 28(4):101–127. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol28-No4-5
Seaspan Corporation. 2013. FORM 20-F: Annual and Transition Report (Foreign Private Issuer). 295 p. Available from Internet: http://www.seaspancorp.com/wp-content/uploads/2014/10/SSW_2013_Annual_Report_20-F.pdf
Strand, J. 2013. Strategic climate policy with offsets and incomplete abatement: carbon taxes versus cap-and-trade, Journal of Environmental Economics and Management 66(2): 202–218. http://doi.org/10.1016/j.jeem.2013.03.002
UNCTAD. 2013. Review of Maritime Transport 2013. United Nations Conference on Trade and Development (UNCTAD). 204 p. Available from internet: http://unctad.org/en/PublicationsLibrary/rmt2013_en.pdf
Yu, Y. 2009. Stochastic Ship Fleet Routing with Inventory Limits. PhD Dissertation. University of Edinburgh, UK 130 p. Available from Internet: https://www.era.lib.ed.ac.uk/handle/1842/4640
Bakkehaug, R.; Eidem, E. S.; Fagerholt, K.; Hvattum, L. M. 2014. A stochastic programming formulation for strategic fleet renewal in shipping, Transportation Research Part E: Logistics and Transportation Review 72: 60–76. https://doi.org/10.1016/j.tre.2014.09.01
Bang, H.-S.; Kang, H.-W.; Martin, J.; Woo, S.-H. 2012. The impact of operational and strategic management on liner shipping efficiency: a two-stage DEA approach, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 39(7): 653–672. https://doi.org/10.1080/03088839.2012.740165
Birge, J. R.; Louveaux. 2011. Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer. 485 p. https://doi.org/10.1007/978-1-4614-0237-4
Cariou, P.; Wolff, P.-C. 2013. Chartering practices in liner shipping, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 40(4): 323–338. https://doi.org/10.1080/03088839.2013.781280
Clarkson Research Services Ltd. 2014. Container Intelligence Quarterly: Second Quarter 2014. Available from Internet: http://www.crsl.com
Dentcheva, D.; Ruszczynski, A. 2003. Optimization with stochastic dominance constraints, SIAM Journal on Optimization 14(2): 548–566. https://doi.org/10.1137/S1052623402420528
Ermolieva, T.; Ermoliev, Y.; Fischer, G.; Jonas, M.; Makowski, M.; Wagner, F. 2010. Carbon emission trading and carbon taxes under uncertainties, in M. Jonas, Z. Nahorski, S. Nilsson, T. Whiter (Eds.). Greenhouse Gas Inventories. Springer, 277–289. https://doi.org/10.1007/978-94-007-1670-4_16
Fagerholt, K.; Gausel, N. T.; Rakke, J. G.; Psaraftis, H. N. 2015. Maritime routing and speed optimization with emission control areas, Transportation Research Part C: Emerging Technologies 52: 57–73. https://doi.org/10.1016/j.trc.2014.12.010
Fagerholt, K.; Johnsen, T. A. V.; Lindstad, H. 2009. Fleet deployment in liner shipping: a case study, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 36(5): 397–409. https://doi.org/10.1080/03088830903187143
Franc, P.; Sutto, L. 2014. Impact analysis on shipping lines and European ports of a cap- and-trade system on CO2 emissions in maritime transport, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 41(1): 61–78. https://doi.org/10.1080/03088839.2013.782440
Hammoudeh, S.; Nguyen, D. K.; Sousa, R. M. 2014. Energy prices and CO2 emission allowance prices: a quantile regression approach, Energy Policy 70: 201–206. https://doi.org/10.1016/j.enpol.2014.03.026
He, Y.; Wang, L.; Wang, J. 2012. Cap-and-trade vs. carbon taxes: a quantitative comparison from a generation expansion planning perspective, Computers & Industrial Engineering 63(3): 708–716. https://doi.org/10.1016/j.cie.2011.10.005
ICS. 2009. Greenhouse Gas Emissions and Market Based Instruments: MBI Analysis Report. International Chamber of Shipping (ICS). Available from Internet: http://www.ics-shipping.org
IMO. 2009. Second IMO GHG Study 2009. International Maritime Organization (IMO). 240 p. Available from Internet: http://www.imo.org/en/OurWork/Environment/Pollution-Prevention/AirPollution/Documents/SecondIMOGHGS-tudy2009.pdf
Kim, J.-G.; Kim, H.-J.; Lee, P. T.-W. 2013. Optimising containership speed and fleet size under a carbon tax and an emission trading scheme, International Journal of Shipping and Transport Logistics 5(6): 571–590. https://doi.org/10.1504/IJSTL.2013.056835
King, A. J.; Wallace, S. W. 2012. Modeling with Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer. 173 p. https://doi.org/10.1007/978-0-387-87817-1
Lee, T.-C.; Chang, Y.-T.; Lee, P. T. W. 2013. Economy-wide impact analysis of a carbon tax on international container shipping, Transportation Research Part A: Policy and Practice 58: 87–102. https://doi.org/10.1016/j.tra.2013.10.002
Mason, R.; Nair, R. 2013. Strategic flexibility capabilities in the container liner shipping sector, Production Planning & Control: the Management of Operations 24(7): 640–651. https://doi.org/10.1080/09537287.2012.659873
Meng, Q.; Wang, T.; Wang, S. 2015. Multi-period liner ship fleet planning with dependent uncertain container shipment demand, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 42(1): 43–67. https://doi.org/10.1080/03088839.2013.865848
Meng, Q.; Wang, T. 2011. A scenario-based dynamic programming model for multi-period liner ship fleet planning, Transportation Research Part E: Logistics and Transportation Review 47(4): 401–13. https://doi.org/10.1016/j.tre.2010.12.005
Meng, Q.; Wang, T. 2010. A chance constrained programming model for short-term liner ship fleet planning problems, Maritime Policy & Management: the Flagship Journal of International Shipping and Port Research 37(4): 329–346. https://doi.org/10.1080/03088839.2010.486635
Pantuso, G.; Fagerholt, K.; Hvattum, L. M. 2014. A survey on maritime fleet size and mix problems, European Journal of Operational Research 235(2): 341–349. https://doi.org/10.1016/j.ejor.2013.04.058
Psaraftis, H. N. 2012. Market-based measures for greenhouse gas emissions from ships: a review, WMU Journal of Maritime Affairs 11(2): 211–232. https://doi.org/10.1007/s13437-012-0030-5
Ramseur, J. L.; Parker, L. 2010. Carbon Tax and Greenhouse Gas Control: Options and Considerations for Congress. BiblioGov. 60 p.
Reinelt, P. S.; Keith, D. W. 2007. Carbon capture retrofits and the cost of regulatory uncertainty, The Energy Journal 28(4):101–127. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol28-No4-5
Seaspan Corporation. 2013. FORM 20-F: Annual and Transition Report (Foreign Private Issuer). 295 p. Available from Internet: http://www.seaspancorp.com/wp-content/uploads/2014/10/SSW_2013_Annual_Report_20-F.pdf
Strand, J. 2013. Strategic climate policy with offsets and incomplete abatement: carbon taxes versus cap-and-trade, Journal of Environmental Economics and Management 66(2): 202–218. http://doi.org/10.1016/j.jeem.2013.03.002
UNCTAD. 2013. Review of Maritime Transport 2013. United Nations Conference on Trade and Development (UNCTAD). 204 p. Available from internet: http://unctad.org/en/PublicationsLibrary/rmt2013_en.pdf
Yu, Y. 2009. Stochastic Ship Fleet Routing with Inventory Limits. PhD Dissertation. University of Edinburgh, UK 130 p. Available from Internet: https://www.era.lib.ed.ac.uk/handle/1842/4640