Analysis of trade-off and synergy of ecosystem services and driving forces in urban agglomerations in Northern China

A thorough comprehension of the trade-offs/synergies (TOS) among ecosystem services (ESs) and their underlying drivers are vital for promoting regional sustainable development and making informed decisions. In this study, we explored the spatial and temporal dynamics of six ESs and their driving factors at both the grid and county scales in the Central-southern Liaoning urban agglomeration (CSLUA) utilizing various analytical techniques such as Pearson correlation analysis, geographically weighted regression, self-organized mapping analysis, and Redundancy Analysis. Our results revealed that: (1) Between 2000 and 2020, water yield (WY), soil retention (SR), landscape aesthetics (LA), and food production (FP) exhibited an overall upward trend, while habitat quality (HQ) and carbon storage (CS) demonstrated a decreasing trend; (2) From grid scale to county scale, we found concentration effects of TOS relationships was weaken, mainly between supply services (WY, FP) and other services. The significant relationship between some ES pairs has become neutral. The geospatial heterogeneity of TOS relationships for all ESs was also weakened at county scale. More details of geographical features can be captured at grid scale than at county scale. (3) At both scales of analysis, we identified five ES bundles with different ecological functional characteristics and spatial patterns. We found that the Potential High-value Synergistic Bundle would shift to High-value Synergistic Bundle. Although the areas of the LA bundle increased from the grid scale to the county scale, there is less continuity. (4) The direction of the influence of the driving factor on ES is not constant in time, and the degree of the drivers' impacts on the six ESs varies on the two scales. But NDVI was the main driver for most of the ESs. This study contributes to expanding our understanding of the relationships between ESs at different scales and could inform sustainable ecosystem management at multiple scales.