Spatiotemporal simulation of sustainable development based on ecosystem services under climate change

2.6. Analysis of spatial agglomeration We utilize Moran’s I index [47, 48] to evaluate spatial autocorrelation and create hot spot maps to visually represent the spatial distribution of ESs. The formula for the Getis-Ord Gi* statistic is as follows [49, 50]: G∗ i ¼ Pn j¼1 wijxj �X Pn j¼1 wij S ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ½n Pn j¼1 w2 ij ð Pn j¼1 wijÞ2� n 1 r ð9Þ Here, xj represents the observed value, wij denotes the spatial weight, �X is the mean of the observed values, and S stands for the standard deviation. This method reveals the patterns of aggregation or dispersion of ESs across different spatial and temporal scales, providing a scien- tific basis for future land use and ecological conservation strategies. 2.7. Calculation of SDG based on various types of ESs Due to the differing units of measurement for the indicators of various ESs, it is not feasible to perform additive calculations directly in the experiment. To eliminate the errors caused by these discrepancies, normalization was applied. In 2018, Wood et al. [51] proposed the con- nection between ESs and SDGs, highlighting how different ecosystem indicators support vari- ous SDG [52], as shown in Fig 2. Habitat quality supports 10 SDGs with a total of 29 targets, Fig 2. Correspondence of support for SDGs across ESs categories. https://doi.org/10.1371/journal.pone.0316605.g002 PLOS ONE Sustainable development simulation under climate change: A spatiotemporal ecosystem services approach

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