Decarbonization pathways and key emission drivers in ports: A scenario-based study of Shanghai Port

Ports are critical points in the global logistics chain and are crucial for China to achieve its 2030 carbon peak goal. It is necessary to assess ports’ current carbon emission levels and predict future trends to formulate effective emission reduction strategies. However, differences among ports make it challenging to conduct a systematic assessment and prediction. Establishing a systematic port carbon emission analysis framework is important. An extended stochastic impacts by regression on population, affluence, and technology (STIRPAT)-Tapio-Monte Carlo modeling framework is developed to analyze port-related carbon emissions. The Shanghai Port is used as a case study. The model identifies key emission drivers and projects static and dynamic carbon emission trajectories. The results show the following. (1) The number of berths of special container terminals and the number of terminal companies in coastal ports are the dominant factors affecting peak emissions in static and dynamic forecasts, with average variance contribution rates of 78.428%, 49.45% and 49.56%, respectively. (2) In the static simulation, Shanghai Port’s mean peak time is 2027, with peak carbon emissions of 4.17 million tons and a peak probability of 3.7%. (3) In the dynamic simulation, the average peak years are 2028.56 and 2028.57, with peak carbon emissions of 3.97 million tons and peak probabilities of 46.26% and 47.12%. Recommendations regarding technical upgrades, organizational optimization, and market incentives are provided for governments and port enterprises. The proposed framework contributes to the global discourse on low-carbon port development and provides a decision-support tool for emission management in maritime transport systems.