Peaking in pieces? Decoding the carbon peak dynamics of Chinese cities

Abstract

Understanding the diverse carbon peaking pathways of different cities are critically important for achieving China's dual carbon goals. To address data limitations, this study introduces a novel Data Alignment Technique to compile the most comprehensive historical carbon emission dataset of 329 Chinese cities from 1992 to 2023. The dynamics of carbon peak—including trajectories, timelines, and driving factors are systematically analysed by integrating machine learning techniques and econometrics models. The findings include: (1) The carbon peaking process at the city level in China was fragmented and asynchronous, as evidenced by the distribution of 213 Non-Peak Cities (64.74 %), 84 Pre-Peak Cities (25.53 %), and 32 Post-Peak Cities (9.73 %). (2) Peak Cities have formed nine distinct carbon peak clusters across the country, primarily driven by the spillover effects of technological innovation and regulatory policies. (3) Emissions in Peak Cities were reduced in stages: first, by energy structure optimization (1995–1999), thereafter, by technological innovation (2005–2019), and recently, by policy regulation and industrial restructuring (2020−2022). (4) The marginal effects of green technology, policy regulation and industrial structure adjustment on carbon exhibited the greatest variability among Non-Peak Cities, with only half of them achieving emission reduction benefits from these measures. These findings underscore the urgent need for coordinated, yet differentiated, climate efforts, particularly to facilitate technological diffusion and enhance policy synergy within the city agglomerations.