Rong Shi , Yue Chen , Shuxia Yang , Xiaopeng Guo , Xiongfei Wang
{"title":"港口脱碳路径及关键排放驱动因素——以上海港为例","authors":"Rong Shi , Yue Chen , Shuxia Yang , Xiaopeng Guo , Xiongfei Wang","doi":"10.1016/j.scs.2025.106877","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"133 ","pages":"Article 106877"},"PeriodicalIF":12.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decarbonization pathways and key emission drivers in ports: A scenario-based study of Shanghai Port\",\"authors\":\"Rong Shi , Yue Chen , Shuxia Yang , Xiaopeng Guo , Xiongfei Wang\",\"doi\":\"10.1016/j.scs.2025.106877\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"133 \",\"pages\":\"Article 106877\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670725007504\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725007504","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
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.
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;