ScholarWorks@중앙대학교

초록

This study aims to compare the predictive performance of various models using a small-sample maritime time series dataset and to identify the key drivers of carbon dioxide (CO2) emissions in the shipping sector through principal component analysis (PCA)-based variable contribution analysis. The dataset comprises 25 years of annual maritime cargo volumes, ship specifications, and vessel counts, with CO2 emissions as the dependent variable. After dimensionality reduction via PCA, five predictive models —Linear Regression, Lasso, Ridge, Random Forest, and XGBoost—were applied. Model performance was evaluated using holdout validation, Leave-One-Out Cross Validation (LOOCV), and 5-fold Time Series Split cross-validation. The results show that Ridge regression achieved the highest prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 1.44%, while Lasso and Linear models also demonstrated stable performance. In contrast, tree-based models suffered from overfitting in the small-sample environment, leading to performance degradation. The variable contribution analysis revealed that Gross Tonnage (TT), Container Tonnage (CT), and the number of container vessels (CF_NO) were the most influential predictors, followed by indicators related to LNG/LPG carriers and bulk carriers. This study provides a methodological framework for model selection and variable interpretation in small-sample time series contexts, contributing to both academic research and practical policy-making for carbon emission reduction in the maritime industry.

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