Soil wind erosion is a global problem that leads to increasingly serious regional land degradation, where the need for windbreak and sand fixation services (WSFS) is substantial. Inner Mongolia plays an important role in global semiarid and arid areas and the severe land degradation resulting from soil wind erosion warrants an urgent solution. However, the mechanism of influence of various driving factors on windbreak and sand fixation services is still not fully studied. In this paper, the revised wind erosion equation (RWEQ) model was used to synthesize the monthly spatiotemporal dynamics of soil wind erosion modulus (SWEM) and WSFS in Inner Mongolia from January 2000 to February 2020 from a semi-monthly scale. The influencing factors of WSFS were examined from both natural and anthropogenic aspects. Results show that over the past 20 years, the average SWEM in Inner Mongolia was 118.06 t ha−1 yr−1, the areas with severe wind erosion were mainly concentrated in the desert areas in the southwest of Inner Mongolia, and the forests in the northeast suffered less soil wind erosion. Meanwhile, the average WSFS was 181.11 × 108 t yr−1, with the high-value areas mainly located in major deserts, sandy land, and the area bordering Mongolia in the north and the low-value areas mainly located in the densely forested northeast and the Gobi Desert in the northwest. Both SWEM and WSFS showed a clear downward trend and a certain periodicity over the past 20 years. WSFS showed two peaks a year (April and October). Among the natural factors, precipitation and NDVI showed a significant correlation with WSFS and were identified as the main driving factors of WSFS, whereas temperature had no significant correlation. Among the anthropogenic factors, farming and animal husbandry intensity and GDP showed a positive correlation with WSFS, whereas population showed a negative correlation. These four types of factors were identified as socio-economic factors that drive WSFS. Meanwhile, WSFS did not show any significant correlation with the administrative area. Land use change contributed to a large proportion of WSFS change, thereby suggesting that the intensity of human activities is another central driver of WSFS.

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A high rate of urbanization comes with high environmental costs, leading to reductions in biodiversity and ecosystem services (BES). How to maximize the efficiency and representation of BES in cities is of utmost urgency. However, in the process of spatial prioritization identification, it remains unclear whether a preference for ecosystem services (ES) promotes or detracts from biodiversity. In this study, a Marxan‐based spatial conservation prioritization framework is provided to achieve a win–win situation for ES provision and biodiversity protection. Using Hohhot city, China as a study case, it sets up different weighting scenarios with species and five ES to determine the optimal protected area network for each possible combination. At the same time, it tests the conservation costs, protected features, and spatial overlap of different scenarios with existing protected areas to quantify their conservation efficiency. We found that (1) closed deciduous broadleaved forests, closed evergreen needle‐leaved forests, and deciduous shrublands could support both high biodiversity and abundant ES at altitudes of 1600–2000 m. (2) Although a positive association is found between ES and biodiversity, there is some spatial variation. The geographical overlap rate with biodiversity prioritization was only 29.72% when only ES were considered. (3) Conservation of ecological hotspots by increasing the weight of ES can reduce conservation expenses by 0.69%–20.32% compared to meeting solely biodiversity targets. Our analyses highlight the need for an appropriate weighting of ES in decisions seeking to identify protected areas. This study provides methodological support for the integration of ES and biodiversity, facilitating more comprehensive conservation planning decisions.