Driving mechanisms and peak prediction of industrial carbon emissions in the Yangtze River Delta Region from a comprehensive model perspective

As a primary source of CO₂ emissions, the industrial sector's emission reduction pathways and peaking schedules hold significant importance for global low-carbon transition and the achievement of national dual-carbon goals. However, existing research lacks a systematic characterization of driving factors, economic decoupling relationships, and peak predictions for industrial CO 2 emissions (CEI) in typical economic development regions. Moreover, current scenario analyses fail to adequately account for regional economic disparities and policy intervention intensity. Based on this, this paper focuses on the three provinces and one municipality of the Yangtze River Delta Region (YRDR), constructing an integrated “LMDI-Tapio-STIRPAT-Ridge” assessment model to systematically examine the driving mechanisms-decoupling status-carbon emission of CEI from 2000 to 2022. The peaking time and emission levels of CEI are simulated under nine scenarios (S1-S9) through scenario analysis. Key findings include: (1) CEI in the YRDR increased at an average annual rate of 6.25%, with pronounced intra-regional disparities. Economic scale was the dominant driver, while energy intensity acted as the main mitigating factor but was insufficient to offset growth- and structure-driven emission pressures. (2) The YRDR was predominantly characterized by weak decoupling, with only Shanghai achieving phased strong decoupling, whereas Jiangsu, Zhejiang, and Anhui remained constrained by structural and developmental pressures. (3) The timing of CEI peaks exhibited strong scenario dependence: only scenario S3 peaked early in 2025, while all other scenarios peaked around 2030. Scenario-based proactive peak governance indicates that energy structure optimization delivers deeper and more durable emission reductions than efficiency improvements alone.