Abstract
Port environmental problems have gradually become the primary concern of port authorities. The future trend of port carbon emissions is crucial to port authorities and managers in formulating regulations and optimizing operation schedules. Owing to the limitations of current prediction methods and the complex social–environmental impact, the estimation results of port carbon emissions have insufficient accuracy to support port development in the future. In this work, the stochastic impacts by regression on population, affluence, and technology (STIRPAT)–long short-term memory (LSTM)–autoregressive integrated moving average with explanatory variable (ARIMAX) integrated model is proposed for the estimation of the carbon emission of Port of Los Angeles to improve the reliability of emission prediction. Macroeconomic indicators that affect port throughput are selected using the principal component analysis—multiple linear regression model. The chosen indicators are then combined with long-term historical port throughput data as the input of the multivariate autoregressive integrated moving average (ARIMAX) model to predict port throughput. Indicators related to port carbon emissions are verified by the STIRPAT model. The LSTM–ARIMAX integrated model is then applied to estimate the emission tendency, which can be useful in developing corresponding carbon reduction strategies and further understanding port emissions. Results show that the proposed method can significantly improve the estimation accuracy for port emission by 11% compared with existing techniques. Energy conservation strategies are also put forward to assist port authorities in achieving the peak clipping of port carbon emission.