Regression models.

Land use and land cover in cities experience complicated changes in response to rapid urbanization, requiring significant impact on ecosystem service value. However, traditional valuation methods tend to ignore artificial ecosystem. This study develops a novel Unit Value (UV) method to evaluate urban ecosystem service values with both natural and artificial ecosystems considered comprehensively. This method is used to reveal the spatial-temporal evolution of ESV in Wuhan from 1996 to 2018. Additionally, Pearson’s correlation between ESVs and driving factors is studied by using a multiple regression model. The results show that: (1) Ecosystem service values are observed to increase by 20.94% because of land use and land cover increases in woodland ((32.21%) and man-made wetland (61.73%) in 1996–2018, respectively. (2) ESVs declined in central districts and increased in suburban districts, with a clear imbalance between “high central areas” and “low suburban areas”. (3) Human activities play a more important role in urban ecosystem compared to natural environmental factors, especially agriculture, per capita GDP, and population. This study provides a distinctive method for the spatiotemporal evaluation of urban ecosystem service values, establishing an insightful basis for urban sustainable development.