Integrated Carbon Emission Estimation Method and Energy Conservation Analysis: The Port of Los Angles Case Study

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