Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services uri icon


  • Conclusions IM-LUDAS proved itself to be an advanced empirical model that can recreate essential features of complex H-E systems and assess multi-dimensional trade-offs in ecosystem services.
  • Context Recent conceptual developments in ecosystem services research have revealed the need to elucidate the complex and unintended relationships between humans and the environment if we are to better understand and manage ecosystem services in practice.
  • Methods We constructed an agent-based model and empirically calibrated it for a semi-arid region in Northeast China, and examined ecosystem service trade-offs derived from the Sloping Land Conversion Program (SLCP), which is based on payment for ecosystem services. This paper describes our model, named Inner Mongolia Land Use Dynamic Simulator (IM-LUDAS), using the overview, design concepts, and details + decision (ODD + D) protocol and demonstrates the capabilities of IM-LUDAS through simulations.
  • Objectives This study aimed to develop a model that spatially represents a complex human-environment (H-E) system consisting of heterogeneous social-ecological components and feedback mechanisms at multiple scales, in order to assess multi-dimensional (spatial, temporal, and social) trade-offs in ecosystem services.
  • Results IM-LUDAS represented typical characteristics of complex H-E systems, such as secondary and cross-scale feedback loops, time lags, and threshold change, revealing the following results: tree plantations expanded by the SLCP facilitated vegetation and soil restoration and household change toward off-farm livelihoods, as expected by the government; conversely, the program caused further land degradation outside the implementation plots; moreover, the livelihood changes were not large enough to compensate for income deterioration by policy-induced reduction in cropland.

publication date

  • 2017
  • 2017