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

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-10-01 DOI:10.1016/j.scs.2025.106877

Rong Shi , Yue Chen , Shuxia Yang , Xiaopeng Guo , Xiongfei Wang

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Abstract

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.

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港口脱碳路径及关键排放驱动因素——以上海港为例

港口是全球物流链的关键点，对中国实现2030年碳峰值目标至关重要。有必要评估港口当前的碳排放水平，预测未来的趋势，制定有效的减排策略。然而，港口之间的差异使得进行系统的评估和预测具有挑战性。建立系统的港口碳排放分析框架十分重要。建立了扩展的人口、富裕和技术回归随机影响(STIRPAT)-Tapio-Monte Carlo模型框架，用于分析港口相关碳排放。本文以上海港为例进行了研究。该模型确定了关键的排放驱动因素，并预测了静态和动态碳排放轨迹。结果显示如下。(1)静态和动态预测中，沿海港口专用集装箱码头泊位数量和码头公司数量是影响峰值排放的主导因素，平均方差贡献率分别为78.428%、49.45%和49.56%。(2)静态模拟中，上海港平均峰值时间为2027年，峰值碳排放量为417万吨，峰值概率为3.7%。(3)在动态模拟中，平均峰值年为2028.56年和2028.57年，峰值碳排放量为397万吨，峰值概率分别为46.26%和47.12%。为政府和港口企业提供了技术升级、组织优化、市场激励等方面的建议。拟议的框架有助于全球低碳港口发展的讨论，并为海运系统的排放管理提供决策支持工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;