Climate & Environmentarticle2026-09-03

Land Transition Pathways Govern Carbon Storage Dynamics in an Olympic Host Region: A PLUS–InVEST Simulation in Yanqing District

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Abstract

Mega-events can accelerate land–use/land cover change (LULCC) through infrastructure development and ecological interventions, but the carbon consequences of different land transition pathways remain unclear. Using Yanqing District, a host region of the Beijing 2022 Winter Olympics, as a case study, this study investigates LULC transitions, carbon storage dynamics, and future land management. A coupled PLUS–InVEST framework integrating transition attribution and trajectory analysis was applied to 10-m LULC datasets (2018, 2021, and 2024) to reconstruct historical transitions and simulate four land management scenarios for 2035. Results indicated that total carbon storage increased continuously from 41.19 × 106 t C in 2018 to 42.14 × 106 t C in 2024 despite concurrent urban expansion. This increase resulted from distinct transition processes across the two periods: cropland-to-rangeland conversion contributed most to carbon gains during 2018–2021, whereas rangeland-to-trees conversion dominated gains during 2021–2024. Pixel-level trajectory analysis further revealed that these dominant transitions rarely formed a continuous restoration sequence at the same locations, with rangeland–rangeland–trees trajectories accounting for 76.2% of trajectories leading to trees-class gains. Future simulations showed that the Urban Development Scenario would reduce carbon storage by 5.6%, whereas the Ecological Protection Scenario would increase carbon storage by 2.3% relative to the 2024 baseline. These results indicate that regional carbon dynamics depend not only on LULC composition but also on transition pathways, providing insights into how transition pathways can inform ecological restoration and sustainable land management.

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Authors: Min Wang, Hui Zhang, Quan Zhou

Institutions: University of Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, Capital Normal University