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March 30, 2026Open Access

Spatio-temporal evolution and influencing factors of landscape ecological risk in the Beijing-Tianjin-Hebei region

Key Points

Assess the spatio-temporal evolution and influencing factors of landscape ecological risk in the BTH region.
Analyzed multi-temporal land use data from 2000 to 2020.
Applied the GMOP-PLUS model to predict land use changes under three scenarios.
Constructed a landscape ecological risk assessment model including disturbance and vulnerability indices.
Used Geodetector and Geographically Weighted Regression to identify key influencing factors.
Construction land expanded while cropland and water bodies decreased from 2000 to 2020.
Risk index rose from 0.226 in 2000 to 0.252 in 2020 with high-risk areas in urban agglomerations.
The EP scenario provides better ecological land protection by 2030.
Digital Elevation Model and annual average temperature were identified as major drivers.

Abstract

As one of China’s most critical urban agglomerations, the Beijing-Tianjin-Hebei (BTH) region faces growing landscape fragmentation and ecological risks under intense human pressure. A scientifically grounded assessment of its landscape ecological risk is therefore vital for optimizing land resource allocation.Using multi-temporal land use data from 2000 to 2020, this study applied the GMOP-PLUS model to simulate land use changes in 2030 under three scenarios: Natural Development (ND), Ecological Protection (EP), and Economic Development (ED). We constructed a landscape ecological risk assessment model integrating landscape disturbance and vulnerability indices to reveal its spatiotemporal evolution. Furthermore, by combining Geodetector and Geographically Weighted Regression (GWR) models, we quantitatively identified the dominant influencing factors and their spatial heterogeneity, providing a scientific basis for targeted ecological risk zoning and prevention. The main findings are: (1) From 2000 to 2020, construction land expanded markedly in the BTH region, while cropland and water bodies shrank. By 2030, the EP scenario performs best in protecting ecological land. (2) Landscape ecological risk shows a “higher in the northwest, lower in the southeast” pattern. The risk index increased from 0.226 in 2000 to 0.252 in 2020, with high-risk areas concentrated in the Beijing-Tianjin-Tangshan urban agglomeration, displaying clear spatial clustering. By 2030, the EP scenario demonstrates stronger long-term risk control. (3) Digital Elevation Model (DEM) and annual average temperature emerged as core drivers, with most factor interactions showing nonlinear enhancement. The interaction q-value between DEM and annual average temperature reached 0.695. (4) Dominant factors such as GDP, DEM, and population density exhibited significant spatial heterogeneity, with their effects closely tied to regional characteristics. This study offers a scientific reference for coordinated development and zonal ecological risk management in the BTH region.

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