How to Bridge the Gap? integrating ecosystem service flows to optimize resource allocation in rapidly Urbanizing regions

• There is an obvious spatial mismatch of ecosystem services supply–demand. • Ecosystem service flows effectively mitigated this mismatch, their regulatory capacity is limited. • Priority areas can better reduce this mismatch, but they need to integrate more ecological resources. Rapid urbanization has exacerbated the mismatch of ecosystem service (ES) supply–demand. Although ecosystem service flows (ESFs) are expected to mitigate this mismatch by optimizing resource allocation. However, limited by the current knowledge of ESs dynamic process, few studies have focused on the complex relationships between the ESs supply-flow-demand chain and natural resource configuration. Therefore, this study took the Wuhan urban agglomeration (WUA) as a case study to construct a natural resources optimization framework based on the ESs supply-flow-demand process. The results showed a significant spatial supply–demand mismatch of three typical ESs in the WUA from 2000 to 2020. The surplus areas were mainly distributed in ecological areas, while the deficit areas were concentrated in construction land, with Wuhan city exhibiting the largest deficit. The ESFs effectively alleviated ESs supply–demand mismatch in the WUA by covering over 90% of the food production deficit, 65% of the carbon sequestration deficit, and 4% of the freshwater deficit in all periods. However, the rapid expansion of ESs deficits had weakened the mitigation effects of ESFs. The ESs priority areas, identified through the ESFs, provided over 16% of the ESs supply and 32% of the ESFs, significantly mitigating the ESs supply–demand mismatch. However, the priority areas have expanded in response to growing human demand, and more high-quality ecological resources will need to be integrated in the future to guarantee the sustainability of ESs. In conclusion, this study provides reliable references for regional ecological resource management and optimization through the simulation of the ESs dynamic process.