Evaluating the potential impacts of land use changes on ecosystem service value under multiple scenarios in support of SDG reporting: A case study of the Wuhan urban agglomeration

Terrestrial ecosystem services can offer various kinds of benefits for human life and production and play a critical role in the attainment of the Sustainable Development Goals (SDGs). The quantitative evaluation and spatial mapping of ecosystem services can offer useful information and knowledge for the natural resource protection and implementation of the SDGs. In our study, we investigated the ecosystem service value (ESV) from 2015 to 2035 under three alternative scenarios and examined their contributions to the SDGs in the Wuhan urban agglomeration. The spatial allocation algorithm, which integrates random forest regression and the CLUE-S model, was used to project the spatial distribution of land use from 2015 to 2035 under the natural increase scenario (NIS), economic development scenario (EDS), and ecological protection scenario (EPS). These simulation results generated the indicators of SDG 15.1.1 and 15.3.1. Then, the equivalent coefficients table method was leveraged to evaluate the ESV changes that resulted from land use changes, which could directly contribute to the SDG 15.9 target. The results indicated that the EDS had the lowest value for SDG 15.1.1 and the highest value for SDG 15.3.1, while the EPS had the highest value for SDG 15.1.1 and a moderate value for SDG 15.3.1. The total ESV increased from 2015 to 2035 under NIS and EPS, with gains of 5.46 and 8.86 billion CNY, respectively. In contrast, the total ESV decreased under EDS, with a loss of 0.33 billion CNY. In addition, it was found that the land degradation corresponding to SDG 15.3.1 would result in ESV losses of 8.40, 12.24, and 4.88 billion CNY under NIS, EDS, and EPS, respectively. This research developed an integrated framework to assess the potential impacts of land use changes on the ESV under three future scenarios and could offer foundational knowledge for urban development and realization of the SDGs. • Predict future patterns of land use by coupling random forest and CLUE-S model. • Evaluate ecosystem service value (ESV) using equivalent coefficients table method. • Calculate SDG 15.1.1 and 15.3.1, and link the ESV with other SDGs targets. • Analyze implications for SDGs reporting and urban sustainable development.