Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Carlos D. Paternina-Arboleda (), Dayana Agudelo-Castañeda, Stefan Voß and Shubhendu Das
Additional contact information
Carlos D. Paternina-Arboleda: Fowler College of Business, Department of Management Information Systems, San Diego State University, San Diego, CA 92182, USA
Dayana Agudelo-Castañeda: Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla 081007, Colombia
Stefan Voß: Institute of Information Systems, University of Hamburg, 20146 Hamburg, Germany
Shubhendu Das: Computational Science Master Program, San Diego State University, San Diego, CA 92182, USA

Sustainability, 2023, vol. 15, issue 16, 1-18

Abstract: Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO 2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO 2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.

Keywords: port emissions; AutoML TPOT; linear regression; non-linear regression; interaction effects (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/16/12171/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/16/12171/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:16:p:12171-:d:1213539

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().