The Nexus of UG-ESs in the Chinese Loess Plateau using CL-CA and Ecological Assessment Models

Abstract

Urban growth (UG) in the Chinese Loess Plateau has increased rapidly, impacting regional ecosystem services (ESs). Accurate evaluations of UG-ESs have become a challenge for the development of simulation models. To address long-term spatiotemporal dependencies in grid neighborhood interactions, this study enhances land-use simulation accuracy using a method combining machine learning algorithms and cellular automata (CL-CA) to model competitive relationship between urban growth and other land-use types during 2000-2050, and then, ESs supply was simulated with ecological assessment models under three landuse scenarios: business as usual, ecological priority, and economic priority. Key findings include: (1) Growth rates and land-use patterns are influenced by policies and natural environments, with an expected urban growth rate of 3%-6%, shifting from leapfrog to marginal growth post-2030; (2) Forests and grasslands primarily impact ESs; and (3) The EP scenario optimally balances sustainable urban development and ES enhancement.