A hybrid method for assessing the city emission status toward carbon peak

• Published in: Urban climate 2024-05, Vol.55, p.101927, Article 101927
• Main Authors: Ruan, Jianhui, Qin, Ziyu, Qian, Shuangyue, Li, Mingyu, Jia, Min, Zhang, Li, Zhang, Zhe, Zhu, Shuying, Jiang, Hanying, Cai, Bofeng, Wang, Shouyang, Tang, Ling
• Format: Article
• Language: English
• Subjects: Carbon peak; City emissions; Emission stages; Long-term series
• ISSN: 2212-0955; 2212-0955
• DOI: 10.1016/j.uclim.2024.101927

Achieving carbon peak in cities is of great significance for global climate change mitigation, especially in China. However, existing methods for assessing carbon peaks fall short of capturing the emission status of cities due to their inherent limitations in applicability and accuracy. To bridge this knowledge gap, we proposed a hybrid statistics-based method by coupling the Mann–Kendall test, autoregressive integral moving average model, and Chow test, aiming to explore the emission stages (before, during, or after carbon peak) in a long-term emission series of cities. With the use of this method, we assessed the emission status of 20 representative cities worldwide, and the results show that 9 cities peaked their emissions as of 2020, mainly due to significant carbon intensity reduction. Furthermore, the applicability of the proposed method was confirmed by applying it at the province, sector, and country levels. Compared with existing carbon assessment methods, this method can be used to evaluate the carbon peak status directly and precisely and offers applicability, stability, and accuracy advantages. This method, therefore, provides a high-performance approach for cities and other emission entities globally to explore their emission status and design effective strategies for addressing climate issues. [Display omitted] •A feasible definition of the carbon peak and associated processes were proposed.•A hybrid statistics-based carbon peak assessment method has been developed.•This method could support the emission status assessment in multiple scenarios.•It shows higher applicability, stability, and accuracy than existing methods.