Optimising node-based strategies for enhanced carbon sequestration in mining cities

Continuous exploitation of mineral resources has had a significant impact on natural ecosystems. This makes it imperative to address problematic nodes in order to restore and improve ecosystem functions within mining areas. However, only a few studies have systematically discussed how to achieve regional carbon sequestration benefits at the regional level, using optimising node-based strategies and taking the impacts of external risk into account. This study develops 'structure-risk' node optimisation strategies that could be employed to improve regional carbon sequestration using ecological network theory. Using Xinzhou City, in Shanxi Province, as a case study, the approach ranks carbon nodes by linking node topology features to the landscape risk index. Our results show that from 2000 to 2020, the network exhibited stronger cohesion (clustering: 0.34 to 0.37; degree: 4.80 to 5.27) but reduced efficiency (closeness: 28.70 to 24.87; path length: 0.49 to 0.52) and key node importance (eigenvector: 2.98 to 2.72). Ecological nodes vary in correlation with 'structure' and 'risk', and thus require targeted optimisation. Node topology positively correlates with carbon sequestration (p < 0.001) and negatively with risk (p < 0.001), with key-linked nodes being crucial and high-risk areas offering limited storage potential. Subsequently, we delineate the optimisation sequence of these nodes to facilitate the restoration of urban areas impacted by mining. This study contributes to enhancing understanding of the intricate interplay between ecological networks and carbon sequestration dynamics under risk interference, thus providing valuable insights for sustainable urban transformation in mining regions.