Ecological Indicators (Nov 2023)

Regional ecological risk assessment based on multi-scenario simulation of land use changes and ecosystem service values in Inner Mongolia, China

  • Li Na,
  • Yangling Zhao,
  • Chen-Chieh Feng,
  • Luo Guo

Journal volume & issue
Vol. 155
p. 111013

Abstract

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Regional ecological risk assessment (ERA) is an important means towards sustainable regional development. Yet, existing ERA approaches often fall short of characterizing ecological processes as dynamic processes involving uncertainty. Using Inner Mongolia, a high ecological risk area in China, this study employed a framework for regional ERA that considers ecosystem service value (ESV) and four scenarios of future land use patterns to understand its regional ecological risk. The spatially explicit risk assessment method was implemented by a model combining multi-criteria evaluation (MCE), cellular automata (CA), and Markov chain to predict future land use/cover of 2030, an adjustment-based equivalence factor method to assess the ESV in each scenario, and Sharpe Index to assess regional ecological risk. The results show the following. First, the overall ESV increases from 2020 to 2030 under all but the socio-economic development (SED) scenarios considered in this study. Second, the overall regional ecological risk of Inner Mongolia is high in the central region and low in the east, north, and west regions. The low ecological risk areas account for the largest areal proportion in the ecosystem services protection (ESP) and natural development (ND) scenarios, while the medium ecological risk areas are the largest under SED. However, some low ecological risk areas are converted into medium ecological risk in ecological and socioeconomic balance scenario (ESB) scenario. Third, NDVI is the driving factor with the strongest explanatory power for the regional ecological risk in Inner Mongolia, followed by mean annual precipitation and temperature. The present study applied a framework for regional ERA that explicitly considers spatial heterogeneity and uncertainty, which can be useful for guiding decision-making for ecological risk control and the planning of the land use of ecosystems.

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