The assessment of coastal vulnerability to erosion is urgently needed due to increasing coastal erosion globally. Based on the coastal characteristics of the Xiamen artificial coastline, which accounts for more than 80% of the coastline in this area, this study provides an integrated approach based on a multi-criteria index. The evaluation index system of the local coastal vulnerability to the erosion of Xiamen includes 12 indexes based on natural (coastal characteristics, coastal forcing), and socio-economic factors (coastal infrastructure, disaster reduction). The spatial differentiation characteristics of the coastal vulnerability to erosion along the Xiamen coast (2018) have been quantitatively assessed with the aid of GIS (Geographic Information System) and RS (Remote Sensing) technology. The results show that the very high vulnerability, high vulnerability, medium vulnerability, low vulnerability and very low vulnerability areas of coastal erosion accounted for 4.6%, 30.5%, 51.6%, 12.5% and 0.8% of the Xiamen coast, respectively. The coastal vulnerability to erosion classes of artificial coasts is significantly higher than those of natural coasts. This difference is mainly controlled by the coastal slope and coastal buffer ability. The results of the evaluation are basically consistent with the present situation. The rationality of the index system and the applicability of the theoretical method are well explained. The evaluation model constructed in this study can be extended to other areas with high ratios of artificial coasts.

Island infrastructure material stock (MS) analysis reveals resource flow and accumulation, providing policymakers with insights for managing resources and planning. This study comprehensively analyzed the MS of infrastructure on Miao Island using a bottom-up approach and multi-source data. Key findings include the following: In 2020, the total in-use MS was 249.8 kt, with stone, gravel, and sand accounting for 80%. Transportation infrastructure held the largest share (40.8%). Over the past 40 years, Miao Island’s infrastructure MS increased by 256%, with a net addition of 179.6 kt. The three distinct development periods were closely linked to China’s reform and local stimulus plans. Miao Island has a lower construction intensity than urban areas, with a per capita MS growth rate of 10.12 t/yr. Despite this, future waste remains a challenge to the island’s capacity. Given the constraints of limited investment due to population decline and the aging of in-use stock, island infrastructure should conform to local resource availability and long-term plans. Priority should be given to environmental compatibility, resilience to resource disruptions, and establishing a circular system to support sustainable island development.

Water shortages and groundwater depletion are critical issues in the world, leading to unsustainable agricultural production and adverse ecological impacts. Here, the new Multifactor-Quantitative joint Prediction Model (MQPM) is developed to quantitatively predict the Groundwater Storage Anomalies (GWSA), which includes an annual multifactor module and a monthly quantitative module. The correlative coefficients from two modules between simulated GWSA and observed GWSA reach up to 0.98 and 0.87, respectively. Taking Beijing as an example, results show that GWSA trends before South-to-North Water Diversion (SNWD) (2005–2014) and after SNWD (2015–2018) are at a rate of −3.00 × 10⁸ m³/yr and 1.95 × 10⁸ m³/yr, respectively, which reflects the effectiveness of water diversion. Additionally, the predicted results show that GWSA from the multifactor module will increase to 45.97 × 10⁸ m³ by 2028. The quantitative module designs four scenarios under different climate changes and policies, from which the predicted GWSA with values ranging from 28.49 × 10⁸ m³ to 63.06 × 10⁸ m³. For the latter module, the groundwater level will recover to ∼8.9 m up to 2028, combining multiple favorable conditions. Finally, factors consisting of water diversion, climate change, and water-saving policies have a vital influence on groundwater variations, and contributions of these factors to the GWSA account for 46%, 27%, and 27%, respectively.