Work

A Bayesian network-based study on the evolution of ecosystem service trade-offs intensity mechanisms in the Nansihu Basin, China

Year: 2025

Type: article

Abstract: Understanding the mechanisms that drive the trade-off intensity of ecosystem services in basins is critical to achieving sustainable regional development. First, this study quantifies ecosystem services using the InVEST model and then calculates the trade-off intensity using the root mean square error (RMSE). Second, a Bayesian network model is combined to analyze the dynamics of the drivers of trade-off intensity over multiple time periods. Finally, a probabilistic model of Bayesian networks is used to simulate areas within the basin that are more likely to have high trade-off intensities. The results show a significant increase in grain production but a decrease in water conservation, water purification, habitat quality and carbon storage. Bayesian inference identified precipitation, the proportion of cropland and the NDVI as dominant drivers. Spatial optimization simulations highlighted Heze and Zaozhuang city as priority regions, suggesting integrated land–water management strategies to reconcile agricultural and ecological objectives.

Source: Geocarto International

DOI: 10.1080/10106049.2025.2567905

Authors: Cong Luo, Cheng Li, Jie Zhao, Jinlong Guo, Zhongjian Mei

Institutions: China University of Mining and Technology, Jiangsu Normal University

Language: English

Topic: Land Use and Ecosystem Services

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Clean water and sanitation, Climate action

Open Access status: gold

Estimated APC paid: $2,390