Ecosystem Health Scenario Prediction and Ecological Efficiency Analysis via the Stochastic Impacts by Regression on Population, Affluence, and Technology Model of the Manas River Basin

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Systematically assessing the regional ecosystem health (EH) and accurately simulating and predicting the trend are important for ecological civilization. At present, research on EH scenario simulation and prediction methods is insufficient. In this study, the carbon footprint, water footprint, ecological footprint, landscape pattern, and response status were employed to construct an EH assessment framework, and the pressure–state–response model was applied to assess the EH of Manas River Basin. An extended stochastic impacts by regression on population, affluence, and technology EH scenario prediction model was established. The EH over the next 15 years was predicted under a natural development (ND) scenario, an economic and urban priority development (ED) scenario, and a low-carbon ecological protection (LEP) scenario. In conjunction with the slack-based measure model, past and future ecological efficiency were comprehensively analyzed. The results indicated the following: (a) In 2000 to 2020, the EH exhibited the following U-shaped change trend: healthy → subhealthy → diseased → subhealthy. As of 2020, the overall EH remained relatively fragile. (b) In 2021 to 2035, the increase in the EH under the 3 development scenarios could be ranked in the order of LEP scenario > ND scenario > ED scenario. The EH under the LEP scenario was markedly greater than that under the ED scenario, but the increase in the EH was not significant compared with that under the ND scenario. (c) In 2000 to 2020, ecological efficiency increased annually. The redundancies of EH consumption and ecological footprint decreased. The redundancies of water resources and carbon emissions increased. The LEP scenario could reduce EH depletion while maintaining an increase in ecological efficiency. This study provides systematic model reference for EH assessment and scenario simulation prediction.

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