Rapid urban expansion has profound impacts on global biodiversity through habitat conversion, degradation, fragmentation, and species extinction. However, how future urban expansion will affect global biodiversity needs to be better understood. We contribute to filling this knowledge gap by combining spatially explicit projections of urban expansion under shared socioeconomic pathways (SSPs) with datasets on habitat and terrestrial biodiversity (amphibians, mammals, and birds). Overall, future urban expansion will lead to 11–33 million hectares of natural habitat loss by 2100 under the SSP scenarios and will disproportionately cause large natural habitat fragmentation. The urban expansion within the current key biodiversity priority areas is projected to be higher (e.g., 37–44% higher in the WWF’s Global 200) than the global average. Moreover, the urban land conversion will reduce local within-site species richness by 34% and species abundance by 52% per 1 km grid cell, and 7–9 species may be lost per 10 km cell. Our study suggests an urgent need to develop a sustainable urban development pathway to balance urban expansion and biodiversity conservation.

Protected areas are the cornerstones of conservation efforts to mitigate the anthropogenic pressures driving biodiversity loss. Nations aim to protect 30% of Earth’s land and water by 2030, yet the effectiveness of protected areas remains unclear. Here we analyze the performance of over 160,000 protected areas in resisting habitat loss at different spatial and temporal scales, using high-resolution data. We find that 1.14 million km² of habitat, equivalent to three times the size of Japan, across 73% of protected areas, had been altered between 2003 and 2019. These protected areas experienced habitat loss due to the expansion of built-up land, cropland, pastureland, or deforestation. Larger and stricter protected areas generally had lower rates of habitat loss. While most protected areas effectively halted the expansion of built-up areas, they were less successful in preventing deforestation and agricultural conversion. Protected areas were 33% more effective in reducing habitat loss compared to unprotected areas, though their ability to mitigate nearby human pressures was limited and varied spatially. Our findings indicate that, beyond establishing new protected areas, there is an urgent need to enhance the effectiveness of existing ones to better prevent habitat loss and achieve the post-2020 global biodiversity goals.

A healthy urban environment is considered as an important issue for the amenity and equity of migrants. China has one of the largest internal population movements in the world, and the environmental health of its migrants becomes a growing concern. Based on the 1‰ microdata from the 2015 1% population sample survey, this study uses the spatial visualization and spatial econometric interaction model to reveal intercity population migration patterns and the role of environmental health in China. The results are as follows. First, the main direction of population migration is toward economically developed high class cities, especially the eastern coast where the intercity population migration is most active. However, these major destinations are not necessarily the healthiest areas for the environment. Second, environmentally friendly cities are mainly located in the southern region. Among them, the areas with less serious atmospheric pollution are mainly distributed in the south, climate comfort zones are mainly located in the southeastern region, but areas with more urban green space are mainly distributed in the northwestern region. Third, compared with socioeconomic factors, environmental health factors have not yet become a major driver of population migration. Migrants often place higher value on income than on environmental health. The government should focus not only on the public service wellbeing of migrant workers, but also on their environmental health vulnerability.

How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km², showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km². The scale of urban village blocks in 2020 decreased to 1.02 km², 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: “urban village–blue–green space”, “urban village–real estate”, and “urban village–municipal facilities”.