Determining Productivity of Container Ports and Climate-Change Factors using a Hybrid of Malmquist Productivity Index and Artificial Neural Network
Keywords:
Productivity, Malmquist Productivity Index, Climate Change Factors, Artificial Neural Network, Container PortsAbstract
Assessing port productivity enables each port to understand its competitive advantages and disadvantages. Hence, the objectives of this research were: (1) to assess the productivity levels of ten container ports in Malaysia, and (2) to determine important climate change factors on the productivity levels of container ports. The findings show that Port of Tanjung Pelepas exhibits the highest productivity index, followed by Port Klang and Port of Penang. Further results also revealed that the productivity of container ports was most impacted by sea level, followed by precipitation level, wind speed, and temperature.
References
Container port productivity. (2022, December). Retrieved November 13, 2023, from https://www.pc.gov.au/inquiries/completed/maritime-logistics/report/maritime-logistics-technical-paper.
David. (2022, June 27). Port Klang and Port of Tanjung Pelepas are among the world’s busiest. New Straits Times. Retrieved November 13, 2023, from https://www.nst.com.my/news/nation/2022/06/808625/port-klang-and-port-tanjung-pelepas-among-worlds-busiest.
Gavurova, B., Kocisova, K., Behun, M., & Tarhanicova, M. (2018). Environmental performance in OECD countries: A non-radial DEA approach. Acta Montanistica Slovaca, 23(2), 206–215.
Honma, S., & Hu, J. L. (2014). Industry-level total-factor energy efficiency in developed countries: A Japan-centered analysis. Applied Energy, 119, 67–78.
Jebali, E., Essid, H., & Khraief, N. (2017). The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach. Energy, 134, 991–1000.
Kasman, A., & Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade, and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44, 97–103.
Liddle, B., & Lung, S. (2010). Age-structure, urbanization, and climate change in developed countries: Revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Population and Environment.
Moghanlo, S., Alavinejad, M., Oskoei, V., Saleh, H. N., Mohammadi, A. A., Mohammadi, H., & DerakhshanNejad, Z. (2021). Using artificial neural networks to model the impacts of climate change on dust phenomenon in the Zanjan region, northwest Iran. Urban Climate, 35, 100750.
Ng, A. S. F., & Lee, C. X. (2007). Productivity analysis of container ports in Malaysia: a DEA approach. Journal of the Eastern Asia Society for Transportation Studies, 7, 2940-2952.
Suzuki, S., & Nijkamp, P. (2016). An evaluation of energy-environment-economic efficiency for EU, APEC, and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis. Energy Policy, 88, 100–112.
Ritchie, H. & Roser, M. (2019). CO₂ and Greenhouse Gas Emissions - Our World in Data. Retrieved from https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions.
Song, M. L., Zhang, L. L., Liu, W., & Fisher, R. (2013). Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data. Applied Energy, 112, 1049–1055.
Sherman, F. (2019). Different Ways to Increase Income Per Capita. Retrieved from https://bizfluent.com/13654154/different-ways-to-increase-income-per-capita.
Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural networks. Wireless Personal Communications, 102, 1645-1656.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Noor Aisyah Rashif, Zalina Zahid, Siti Aida Sheikh Hussein
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
