Forecast urban ecosystem services to track climate change: Combining machine learning and emergy spatial analysis

It is important to simulate urban ecosystem services and provide guidance for their conservation and management under complex scenarios. We developed software for the rapid computation of ecosystem services based on emergy spatial analysis, machine learning, and the DLUCP model to predict the precipitation based on climate change and land use changes in five Chinese cities. The LSTM model is used to predict monthly precipitation for the cities based on historical data of 720 months. We also utilized the land use change prediction model based on the idea of distribution to improve the accuracy of future land use changes in China. The results show that based on the predicted values in 2025, an increase in ES is observed for Beijing, Wuhan, and Chongqing in 2020–2025 under both SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, while the ES in Guangzhou and Shenzhen decreased. Furthermore, among the three types of ecosystems, the woodland ecosystem contributed the most to the total ES. The change in the woodland area had the greatest impact. Therefore, it is recommended that ecosystem conservation and restoration should focus on woodland ecosystem, and proactively address climate change to help achieve carbon peak and neutrality goals. The accuracy and applicability of the software are also tested.