Data-driven investigation of ship fuel consumption integrating causal inference and hierarchical analysis

Cao, W, Wang, X, Zhang, W, Li, H ORCID: 0000-0002-4293-4763, Fang, S and Yang, Z ORCID: 0000-0003-1385-493X (2026) Data-driven investigation of ship fuel consumption integrating causal inference and hierarchical analysis. Transportation Research Part D Transport and Environment, 159. ISSN 1361-9209

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Abstract

This study develops a data-driven analytical framework that integrates causal inference with hierarchical analysis to reveal the underlying mechanisms influencing ship fuel consumption (SFC). The framework combines multi-source data fusion, direct linear non-gaussian acyclic model-based causal discovery, and double machine learning with causal forests model to estimate Average Treatment Effects (ATEs), followed by interpretive structural modelling for hierarchical decomposition. Experimental results under the expanded DAG-derived adjustment specification indicate that Daily sailing hours has the strongest positive conditional effect on SFC (ATE = 2.058), followed by Main engine RPM with a positive but more uncertain effect estimate (ATE = 0.268). The hierarchical analysis further organises the directional-dependence network into interpretable structural levels, thereby illustrating possible multi-level transmission patterns among operational and environmental factors. This framework provides an exploratory and graph-informed analytical basis for interpreting directional dependencies and observed-covariate conditional effect patterns in SFC, offering cautious decision support for data-driven energy management. The source code is publicly available at: https://github.com/AdvMarTech/ship_fuel_consum_causalinfer.

Item Type:	Article
Uncontrolled Keywords:	Maritime transport; Maritime decarbonisation; Ship fuel consumption; Causal inference; Machine learning; Explainable artificial intelligence; 3509 Transportation, Logistics and Supply Chains; 33 Built Environment and Design; 35 Commerce, Management, Tourism and Services; 3304 Urban and Regional Planning; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); 0502 Environmental Science and Management; 1205 Urban and Regional Planning; 1507 Transportation and Freight Services; Logistics & Transportation; 3304 Urban and regional planning; 3509 Transportation, logistics and supply chains
Subjects:	H Social Sciences > HE Transportation and Communications T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering and Built Environment
Publisher:	Elsevier BV
Date of acceptance:	22 June 2026
Date of first compliant Open Access:	4 September 2026
Date Deposited:	04 Sep 2026 10:59
Last Modified:	04 Sep 2026 10:59
DOI or ID number:	10.1016/j.trd.2026.105491
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29325

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