Ecosystem service trade-offs and synergies relationships and their driving factor analysis based on the Bayesian belief Network: A case study of the Yellow River Basin

Abstract

A systematic understanding of shifts in the temporal and spatial distributions of ecosystem services (ESs) in the Yellow River Basin and the trade-offs and synergies between these services and their driving variables is essential for effective management of the environment and for enhancing human well-being. This study quantitatively assessed the carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, combinded Bayesian Belief Network (BBN) and ESs, determined the crucial elements influencing the supply of ESs via sensitivity analysis, identified synergies and trade-offs between ESs via probabilistic reasoning, and explored the driving processes via scenario analysis. The following are the key findings of this research. (1) Geographically, the Yellow River Basin, SR, CS, and WY exhibited higher levels in southern regions and lower levels in northern regions, whereas HQ showed higher levels in the northwestern region and lower levels in the southeastern region. Both WY and CS showed annual increases in the time series. (2) With respect to trade-offs and synergistic interactions, HQ, WY, and CS had a synergistic connection, whereas SR and HQ, CS, WY had a trade-off connection. (3) According to the BBN sensitivity analysis, the normalized difference vegetation index (NDVI), temperature, precipitation, and land use (LU) were the main variable nodes affecting ESs. (4) The results of the scenario analysis revealed that LU was the primary driver of the four ESs In addition, precipitation and NDVI primarily drove the synergistic and trade-off relationships between HQ, CS, and WY. This study combined BBN and ESs to develop a new method for investigating ES functions and established a solid scientific foundation for ES management and macroeconomic growth planning.