Tiered prediction models for port vessel emissions inventories

Philip Cammin (), Jingjing Yu () and Stefan Voß ()
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Philip Cammin: University of Hamburg
Jingjing Yu: University of Hamburg
Stefan Voß: University of Hamburg

Flexible Services and Manufacturing Journal, 2023, vol. 35, issue 1, No 6, 142-169

Abstract: Abstract Albeit its importance, a large number of port authorities do not provide continuous or publicly available air emissions inventories (EIs) and thereby obscure the emissions contribution of ports. This is caused by, e.g., the economic effort generated by obtaining data. Therefore, the performance of abatement measures is not monitored and projected, which is specifically disadvantageous concerning top contributors such as container ships. To mitigate this issue, in this paper we propose port vessel EI prediction models by exploring the combination of different machine-learning algorithms, data from the one-off application of an activity-based bottom-up methodology and vessel-characteristics data. The results for this specific case show that prediction models enable acceptable trade-offs between the prediction performance and data requirements, promoting the creation of EIs.

Keywords: Air emissions; Inventory; Green ports; Machine learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10696-022-09468-5

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