UNDERSTANDING THE THERMAL COAL MOVEMENTS FROM COLOMBIA TO CHILE THROUGH THE PANAMA CANAL USING LOGIT MODELS- LOOKING AHEAD

Javier  Ho; Paul  Bernal

doi:10.46754/jml.2022.08.001

Authors

Javier Ho Panama Canal Authority
Paul Bernal Panama Canal Authority

DOI:

https://doi.org/10.46754/jml.2022.08.001

Keywords:

Panama Canal, Cape Horn, Magellan Strait, Logit Model, Thermal Coal, Panamax Plus

Abstract

This study attempted to specify logit models for bulkers transporting mostly thermal coal from the East Coast of Colombia to Chile through the Panama Canal compared to the alternative route. The preliminary proposed predictors for the logit models included voyage cost variables and Canal's attributes. For the route choice of coal from the East Coast of Colombia to Chile, voyage cost factors such as Panama Canal cost, distance difference between Panama versus alternative route, post arrival of vessel to the next port and the maximum transit draft were important factors in this choice, as well as Panama Canal attributes such as vessel arrivals at the Panama Canal and the Panamax Plus requirement to transit the neopanamax locks. The route choice involved the Panama Canal and Cape Horn/Magellan Strait in the Southern tip of South America. This study analyzed coal traffic between October 1, 2016, and September 30, 2020, and briefly discussed the future of coal movements through Panama, given Chile's long term plans to generate electricity using renewanable energy sources and hydrogen. This paper is a contribution to the discrete choice literature and attempted to provide insights into route choice factors involving the Panama Canal, proposing new preliminary explanatory variables to better understand route choices that may apply in future Panama Canal studies. The study will be a contribution to the universal maritime coal transportation literature, and it is a continuation on research related to the Panama canal, particularly on route choices using AIS information.

References

Adland, R., & Jia, H. (2018). Dynamic speed choice in bulk shipping. Maritime Economics & Logistics, 20(2), 253- 266. https://doi.org/10.1057/s41278- 016-0002-3 DOI: https://doi.org/10.1057/s41278-016-0002-3

Bai, X., & Siu J. (2019). A destination choice model for very large gas carriers (VLGC) loading from US gulf. Energy, 174, 1267- 1275. https://doi. org/10.1016/j.energy.2019.02.148 DOI: https://doi.org/10.1016/j.energy.2019.02.148

Berndt, E., & Wood, D. (1975). Technology, prices and the derived demand for energy. The Review of Economics and Statistics, 57(3), 259- 268. https://doi. org/10.2307/1923910 DOI: https://doi.org/10.2307/1923910

Brantes, R., & Cantallopts, J. (2020). In¬forme de actualización del consumo energético de la minería del cobre al año 2019. Comisión Chilena del Co¬bre (Cochilco). Registro Propiedad Intelectual © N° 2020-A-9342. DEPP 16/2020. October 2020. https://www. cochilco.cl/Mercadoper cent20deper cent20Metales/Informeper cent20de¬per cent20Consumoper cent20deper cent20Energper centC3per centADa¬percent202019.pdf

Breen, B.,Vega, A., & Feo-Valero, M. (2015). An empirical analysis of mode and route choice for International Freight Transport in Ireland. Working Paper 262587, Galway: National University of Ireland, Socio-Economic Marine Research Unit. DOI: 0.22004/ ag.econ.262587

Chang, M., & Lu, P. (2013). A Multinomial Logit Model of mode and arrival time choices for planned special events. Journal of the Eastern Asia Society for Transportation Studies, 10, 710-727. https://doi.org/10.11175/ easts.10.710

Coordinador Eléctrico Nacional of Chile (n.d.), Reports, statistics and frequently used platforms. Accessed on April 21, 2021, from https://www.coordinador.cl/ reportes-y-estadisticas/#Estadisticas.

Fan, L., Wilson, W., & Dahl, B. (2012). Impacts of new routes and ports on spatial competition for containerized imports into the United States. Maritime Policy & Management, 39(5), 1-23. https://doi.org/10.1080/03088839.201 2.705027 DOI: https://doi.org/10.1080/03088839.2012.705027

Fiorini, M., Capata, A., & Bloisi, D. (2016). AIS Data Visualization for Maritime Spatial Planning (MSP). International Journal of e-Navigation and Maritime Economy, 5, 45-60. https://doi. org/10.1016/j.enavi.2016.12.004 DOI: https://doi.org/10.1016/j.enavi.2016.12.004

Guoqiang, S., & Jiahui, W. (2012). A Freight Mode Choice Analysis using a Binary Logit Model and GIS: The case of cereal grains transportation in the United States. Journal of Transportation Technologies, 2, 175-188. http://dx.doi. org/10.4236/jtts.2012.22019 DOI: https://doi.org/10.4236/jtts.2012.22019

Ho, J. & Bernal, P. (2018b). Elastic or not elastic? Attempting to estimate an aggregate demand function for the dry bulkers at the Panama Canal. Presented at the International Association of Maritime Economist Conference (IAME) in Mombasa, Kenya. September 2018.

Ho, J. & Bernal, P. (2019). Panama Canal vs alternative routes: Estimating a logit model for grains. Maritime Business Review, 5(1), 99-120. http://dx.doi. org/10.1108/mabr-07-2019-0025 DOI: https://doi.org/10.1108/MABR-07-2019-0025

Hussain, D., Mohammed, A., Salman, A., Rahmat, R., & Borhan, M. (2017). Analysis of transportation mode choice using a comparison of artificial neural network and multinomial logit model. ARPN Journal of Engineering and Applied Sciences, 12(5), 1483-1493.

Itoh, H., Tiwari, P., & Doh, M. (2002). An Analysis of cargo transportation behavior in Kita Kanto (Japan). International Journal of Transport Economics, 29(3), 319- 335. http:// worldcat.org/issn/03918440

Kanamoto, K., Murong, L., Nakashima, N., & Shibasaki, R. (2021). Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carrier. Maritime Economics & Logistics, 23, 211- 236. https://doi.org/10.1057/ s41278-020-00171-6 DOI: https://doi.org/10.1057/s41278-020-00171-6

Lee, E., Mokashi, A., Moon, S., & Kim, G. (2019). The maturity of Automatic Identification Systems (AIS) and its implications for innovation. Journal of Marine Science and Engineering, 7(9), 287. https://doi.org/10.3390/ jmse7090287 DOI: https://doi.org/10.3390/jmse7090287

Manssour, A., Alhodairi, A. M., & Rahmat, R. (2013). Modeling a Multinomial Logit Model of intercity travel mode choice behavior for all trips in Lybia. International Journal of Civil and Environmental Engineering, 7(9), 636- 645. doi.org/10.5281/zenodo.1087592

Manssour, A., Alhodairi, A. M., & Rahmat, R. (2013). Modeling of intercity transport mode choice behavior in Lybia: A binary logit model for business trips by private car and intercity bus. Australian Journal of Basic and Applied Sciences, 7(1), 302-311.

Mao, S., Tu, E., Zhang, G., Rachmawati, L., Rajabally & E., Huang, G. (2018). An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining. In: Cao J., Cambria E., Lendasse A., Miche Y., Vong C. (Eds.), Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, 9. Cham: Springer, https://doi.org/10.1007/978- 3-319-57421-9_2

Martinez, C., Adams, S., & Dresner, M. (2016). East Coast vs. West Coast: The impact of the Panama Canal’s expansion on the routing of Asian imports into the United States. Transportation Research Part E: Logistic and Transportation Review, 91(C), 274-289. https://doi. org/10.1016/j.tre.2016.04.012 DOI: https://doi.org/10.1016/j.tre.2016.04.012

Mason, C., & Rowlands, A. (1938). Panama Canal traffic. Economic Geography, 14(4), 325-337. https://doi. org/10.2307/141526 DOI: https://doi.org/10.2307/141526

Nathan Associates (2012). Update and development of the dry bulk market segment study. Arlington, Virginia. Nathan Associates. Study for the Panama Canal Authority.

Pham, T., Kim, K., & Yeo, G. (2018). The Panama Canal expansion and its impact on East–West liner shipping route selection. Sustainability, 10(12), 4353. https://doi.org/10.3390/su10124353 DOI: https://doi.org/10.3390/su10124353

Regianni, A., Nijkamp, P., & Tsang, W.F. (1997). European Freight Transport Analysis using Neural Networks and Logit Models (No. 97-032/3). Tinbergen Institute discussion paper, 5-9.

Schøyen, H., & Bråthen, S. (2011). The Northern Sea Route versus the Suez Canal: Cases from bulk shipping. Journal of Transport Geography, 19(4), 977-983. https://doi.org/10.1016/j. jtrangeo.2011.03.003 DOI: https://doi.org/10.1016/j.jtrangeo.2011.03.003

Serry, A. (2017). The Automatic Identification System (AIS): A data source for studying maritime traffic: The case of the Adriatic Sea. Book of Proceedings, 7th International Maritime Science Conference (IMSC), Solin, Croatia, April 20-21, 2017.

Shibasaki, R., Azuma, T., & Yoshida, T. (2016). Route choice of containership on a global scale and model development: Focusing on the Suez Canal. International Journal of Transport Economics, 43(3), 263- 288. http://worldcat.org/issn/03918440

Shibasaki, R., Azuma, T., Yoshida, T., Teranishi, H., & Abe, M. (2017). Global route choice and its modelling of dry bulks carriers based on vessel movement database: Focusing on the Suez Canal. Research in Transportation Business & Management, 25, 51-65. https://doi. org/10.1016/j.rtbm.2017.08.003 DOI: https://doi.org/10.1016/j.rtbm.2017.08.003

Shibasaki, R., Kanamoto, K., & Suzuki, T. (2019). Estimating global pattern of LNG supply chain: A port-based approach by vessel movement database. Maritime Policy & Management, 47(3), 1-29. DO I:10.1080/03088839.2019.1657974 DOI: https://doi.org/10.1080/03088839.2019.1657974

Surbakti, M., & Bombongan, C. (2017). Characteristics of modal choice preference between bus and train from Medan to Kuala Namu Airport. IOP Conference Series: Material Science and Engineering, 180(1), 012143. DOI:10.1088/1757- 899X/180/1/012143 DOI: https://doi.org/10.1088/1757-899X/180/1/012143

Svanberg, M., Santén, V., Hörteborn, A., & Holm, H. (2019). AIS in maritime research. Marine Policy, 106, 103520. https://doi.org/10.1016/j. marpol.2019.103520. DOI: https://doi.org/10.1016/j.marpol.2019.103520

Sytsmas, T., & Wilson, W. (2021). Estimating the Demand for Railroad and Barge Movements of Corn in the Upper Mississippi River Valley. Research in Agricultural & Applied Economics. Cooperative Agreement Number Agreement 18-TMTSD-TN-0012, with the Agricultural Marketing Service (AMS) of the U.S. Department of Agriculture (USDA). https://doi. o r g / 1 0 . 2 2 0 0 4 / a g . e c o n . 3 0 8 9 3 8 . https://ageconsearch.umn.edu/ record/308938.

Tixerant, M., Guyader, D., Gourmelon, F., & Queffelec, B. (2018). How can Automatic Identification System (AIS) data be used for Maritime Spatial Planning? Ocean & Coastal Management, 166, 18-30. DOI:10.1016/j. ocecoaman.2018.05.005 DOI: https://doi.org/10.1016/j.ocecoaman.2018.05.005

Tu, E., Zhang, G., Rachmawati, L., Rajabally, & E., Huang, G.B. (2017). Exploiting AIS data for intelligent marine navigation: a comprehensive survey from data to methodology. IEEE Transactions on Intelligent Transportation Systems, 99, 1-24. 10.1109/TITS.2017.2724551

Ungo, R., & Sabonge, R. (2012). A competitive analysis of Panama Canal routes. Maritime Policy & Management, 39(6), 555- 570. https://doi.org/10.108 0/03088839.2012.728727 DOI: https://doi.org/10.1080/03088839.2012.728727

Tewalt, S., Finkelman, R., Torres, I., & Simoni, F. (2006). World Coal Quality Inventory: Colombia. United States Geological Service (USGS). Chapter 5. Open File Report 2006-1241. https://pubs.usgs.gov/of/2006/1241/ Chapterper cent205-Colombia.pdf

Wen, C., & Huang, J. (2007). A Discrete Choice Model of ocean carrier choice. Journal of the Eastern Asia Society for Transportation Studies, 7, 795- 807. https://doi.org/10.11175/ easts.7.795

Wilson, W., & Ho, J. (2018). Panama Canal. In Blonigen, B., & Wilson, W. (Eds.), Handbook of International Trade and Transportation (pp. 628-657). U.K.: Edward Elgar Publishing. 628-657. https://doi. org/10.4337/9781785366154.00032 DOI: https://doi.org/10.4337/9781785366154.00032

Wojcik, S. (2017). The determinants of Travel Mode Choice: The case of Lodz. Bulletin of Geography. Socio-economics Series, 44(44), 93-101. doi:10.2478/bog-2019-0018 DOI: https://doi.org/10.2478/bog-2019-0018

World Coal. Accessed on March 3, 2022, from https://www.worldcoal.com/ coal/10022011/coal_in_colombia/

Wright, D., Janzen, C., Bochenek, R., Austin, J., & Page, E. (2019). Marine observing applications using AIS: Automatic Identification System. Frontier in Marine Science, 6, 1-7. https://doi. org/10.3389/fmars.2019.00537 DOI: https://doi.org/10.3389/fmars.2019.00537

Wu, L., Xu, Y., Wang, Q., Wang, F., & Xu, Z. (2017). Mapping Global Shipping Density from AIS Data. The Journal of Navigation, 70(1), 67-81. DOI: https:// doi.org/10.1017/S0373463316000345 DOI: https://doi.org/10.1017/S0373463316000345

Wu, J., & Xu, D. (2021). Research on alternative passage of Suez Canal. E3S Web of Conferences, 283, 01017. Presented in the 2021 3rd International Conference on Civil, Architecture and Urban Engineering (ICCAUE 2021). DOI: https://doi.org/10.1051/e3sconf/202128301017