福建省交通碳排放核算及驱动因素分析
Carbon Emission Accounting and Influencing Factors Analysis of Transportation in Fujian Province
DOI: 10.12677/mos.2026.153043, PDF,   
作者: 化 蕾:福建省公安厅交通管理总队,福建 福州;魏健洛, 丰明洁, 邵 悦, 孙政浩:上海理工大学交通系统工程系,上海
关键词: 福建省交通碳排放能源消耗核算方法低碳政策Fujian Province Transportation Carbon Emissions Energy Consumption Accounting Method Low-Carbon Policy
摘要: 城市交通低碳转型是实现双碳目标的关键环节。以福建省为研究对象,本文基于2010~2024年交通运输周转量与汽车保有量数据,避开对宏观能源统计的单一依赖,采用基于活动水平的自下而上法核算碳排放,并结合LMDI分解与Tapio模型,揭示排放演变机理及其与经济增长的耦合特征。研究表明:福建省交通碳排放呈长期刚性增长–短期震荡回调–需求修复期强劲反弹的演进特征,2020年受外部环境突发因素冲击短暂回落,但2024年随客货运需求集中释放显著回升,并创下历史新高;结构上呈现道路主导、私人激增、水运托底特征,私人小汽车保有量的爆发式增长是驱动排放攀升的首要内生动力,而水路运输凭借低能耗强度发挥了重要的减排缓冲作用;驱动因素方面,经济增长效应是绝对的主导性增排因素,能源强度效应则是核心的抑制性减排因素;脱钩分析显示,行业整体处于弱脱钩阶段,但在2024年出现向扩张性连接演变的复钩风险,提示规模扩张带来的增排压力依然严峻。基于此,提出深化公转铁/水运输结构调整、加速全域电动化进程、构建数智化监测预警体系以及完善政策市场双轮驱动机制等建议,以促进国家生态文明试验区交通领域的稳健减排与高质量发展,对同类沿海省份具有参考意义。
Abstract: The low-carbon transition of urban transportation is a critical link in achieving carbon peaking and carbon neutrality goals. Taking Fujian Province as the research object, this paper calculates carbon emissions based on the activity level (turnover volume and vehicle population) from 2010 to 2024, adopting a bottom-up approach to overcome the limitations of macro-energy statistics. Combined with the LMDI decomposition method and the Tapio decoupling model, the study reveals the evolution mechanism of emissions and their coupling characteristics with economic growth. The results indicate that: (1) Fujian’s transport carbon emissions exhibit a trajectory of long-term rigid growth, short-term fluctuation, and strong post-pandemic rebound. After a brief decline in 2020 due to COVID-19, emissions rebounded significantly in 2024 driven by the release of passenger and freight demand, hitting a historical high. (2) Structurally, the emissions are characterized by road dominance, surging private traffic, and water transport underpinning. The explosive growth of private vehicle ownership is the primary endogenous driver of emission growth, while water transport plays a crucial buffering role in emission reduction due to its low energy intensity. (3) Regarding driving factors, the economic growth effect is the dominant positive driver, while the energy intensity effect acts as the core negative inhibitor. (4) The decoupling analysis shows that the industry is generally in a weak decoupling stage. However, a risk of re-coupling (shifting towards expansive coupling) emerged in 2024, signaling severe pressure from scale-driven emissions. Based on these findings, the paper proposes suggestions such as deepening the modal shift from road to rail/water, accelerating comprehensive electrification, building a digital monitoring and early warning system, and perfecting the dual-drive mechanism of policy and market. These insights aim to promote robust emission reduction and high-quality development in the transportation sector of the National Ecological Civilization Experimental Zone, offering a reference for similar coastal provinces.
文章引用:化蕾, 魏健洛, 丰明洁, 邵悦, 孙政浩. 福建省交通碳排放核算及驱动因素分析[J]. 建模与仿真, 2026, 15(3): 56-70. https://doi.org/10.12677/mos.2026.153043

参考文献

[1] 陈思茹, 张帅, 袁长伟. 中国交通运输经济发展与碳排放效率评价[J]. 中国公路学报, 2019, 32(1): 154-161.
[2] Ritchie, H. and Roser, M. (2020) CO₂ Emissions.
https://ourworldindata.org/co2-emissions
[3] Meng, C., Du, X., Zhu, M., Ren, Y. and Fang, K. (2023) The Static and Dynamic Carbon Emission Efficiency of Transport Industry in China. Energy, 274, Article ID: 127297. [Google Scholar] [CrossRef
[4] 杨晓光, 朱际宸, 彭晴, 等. 面向预约出行的车路联网与协同交通控制: 前沿与展望[J]. 上海理工大学学报, 2023, 45(4): 307-320, 331.
[5] 冉茂平, 邓须红, 关佳希, 等. 基于LCA的道路基础设施碳排放核算与低碳减排技术综述[J/OL]. 交通运输工程学报: 1-16. 2025-09-22.[CrossRef
[6] 左大杰, 赵亮, 熊巧, 等. 交通碳排放研究综述: 核算方法、影响因素及作用机理[J]. 交通运输工程与信息学报, 2024, 22(1): 111-127.
[7] 张晔, 宋国华, 尹航, 等. 综合交通运输系统碳排放预测的不确定性分析[J]. 交通运输工程与信息学报, 2023, 21(1): 64-79.
[8] 包含, 王耿, 晏长根, 等. 公路建设碳排放核算与岩土工程低碳措施及碳补偿研究综述[J]. 中国公路学报, 2025, 38(1): 46-72.
[9] 乔亚宁, 文霞, Gao, Yang-Ming, 等. LCA分配方法对道路生命周期碳排放核算的影响及不确定性分析[J/OL]. 交通运输工程学报: 1-18. 2025-09-15.[CrossRef
[10] 单肖年, 胡颖, 寇泷丹, 等. 碳达峰目标下城市公共交通系统低碳转型路径[J]. 交通运输工程与信息学报, 2023, 21(3): 1-12.
[11] 赵靖, 杨晓光, 章程. 交通设计技术发展与对策建议[J]. 前瞻科技, 2023, 2(3): 45-57.
[12] 陈涛, 李晓阳, 陈斌. “双碳”目标下交通运输业碳排放脱钩效应与峰值预测[J]. 交通运输工程学报, 2024, 24(4): 104-116.
[13] 喻洁, 达亚彬, 欧阳斌. 基于LMDI分解方法的中国交通运输行业碳排放变化分析[J]. 中国公路学报, 2015, 28(10): 112-119.
[14] 杨青, 郑衍迪, 汪金美, 等. 交通碳排放与经济发展“协调-脱钩”的危机转化[J/OL]. 环境科学: 1-27. 2025-09-15.[CrossRef
[15] Wu, C., He, X. and Dou, Y. (2019) Regional Disparity and Driving Forces of CO2 Emissions: Evidence from China’s Domestic Aviation Transport Sector. Journal of Transport Geography, 76, 71-82. [Google Scholar] [CrossRef
[16] Hou, L., Wang, Y., Hu, L., Wang, Y., Li, Y. and Zheng, Y. (2023) Economic Growth and Carbon Emissions Analysis Based on Tapio-EKC Coupled Integration and Scenario Simulation: A Case Study of China’s Transportation Industry. Environment, Development and Sustainability, 26, 18855-18881. [Google Scholar] [CrossRef
[17] 葛青秀, 杨萍果, 蒲英霞. 中国省域交通运输碳排放脱钩状态及其驱动因素[J]. 环境科学, 2025, 46(4): 2009-2019.
[18] 邵志国, 李可心, 李梦笛. “双碳”背景下中国交通运输业碳排放驱动因素及脱钩效应[J]. 中国环境科学, 2025, 45(1): 571-582.
[19] 唐文斌, 王惟政, 穆孟等. 电气化情景下高速公路建设期碳排放核算及减排潜力量化研究[J]. 公路交通科技, 2025, 42(5): 206-214.
[20] 张燕, 石晶华, 戴菲. 中国机场航空碳排放时空特征、影响因素及脱钩效应[J/OL]. 环境科学: 1-18. 2025-09-15.[CrossRef
[21] 蔡文媛, 陈洁, 高郭平. 上海市浦东新区碳排放时空格局及驱动机制[J/OL]. 环境科学: 1-16. 2025-09-15.[CrossRef