Coordinating ecosystem service supply and demand equilibrium and utilizing machine learning to dynamically construct an ecological security pattern (ESP) can help better understand the impact of urban development on ecological processes, which can be used as a theoretical reference in coupling economic growth and environmental protection. Here, the ESP of the Changsha–Zhuzhou–Xiangtan urban agglomeration was constructed, which made use of the Bayesian network model to dynamically identify the ecological sources. The ecological corridor and ecological strategy points were identified using the minimum cumulative resistance model and circuit theory. The ESP was constructed by combining seven ecological sources, “two horizontal and three vertical” ecological corridors, and 37 ecological strategy points. Our results found spatial decoupling between the supply and demand of ecosystem services (ES) and the degradation in areas with high demand for ES. The ecological sources and ecological corridors of the urban agglomeration were mainly situated in forestlands and water areas. The terrestrial ecological corridor was distributed along the outer periphery of the urban agglomeration, while the aquatic ecological corridor ran from north to south throughout the entire region. The ecological strategic points were mainly concentrated along the boundaries of the built-up area and the intersection between construction land and ecological land. Finally, the ecological sources were found primarily on existing ecological protection zones, which supports the usefulness of machine learning in predicting ecological sources and may provide new insights in developing urban ESP.

Under the backdrop of achieving carbon neutrality and accelerating urbanization, China’s forests face unprecedented pressures. This study explored the spatiotemporal characteristics of forest loss in the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR). The dynamic mechanism of forest loss caused by fire, logging, construction, and pollution was also analyzed using spatial database development, polygon superposition analysis, grid system construction, and coordinate system calculation. The results show that the forest loss in the UAMRYR experienced three stages: continuous acceleration (1990–2010), peak (2010–2015), and slight decline (2015–2020). Rapid urban expansion is the primary cause of forest loss, and the three metropolitan areas had the fastest urban expansion and the most severe forest loss. Due to the success of afforestation efforts, the forest loss caused by fire, logging, and pollution was restored by 80%, while most of the forest losses caused by construction are permanent. Given the current forest loss trends, large expanses of forests in the UAMRYR are at risk of being destroyed and causing serious damage to the region’s ecological environment. Forest losses can be significantly reduced by guiding the rational expansion of cities, supporting afforestation for urban construction projects, strengthening forest fire risk investigation, and implementing ecological reconstruction of polluted areas.

Improving regional innovation efficiency is the key to developing an innovative country. Exploring the spatio-temporal evolution characteristics of regional innovation efficiency is crucial in the formulation of regional policies and the choice of innovation models. This study used the superdata envelopment analysis method with undesirable outputs in evaluating the innovation efficiency of Chinese provinces. To assess the spatial spillover effects of innovation factors, the spatial autocorrelation and spatial Durbin model were adopted to characterize the spatio-temporal evolution, spatial correlation, and mechanisms of innovation efficiency. The highlights of the results are as follows: (1) The time-series changes in innovation efficiency showed a general trend from declining to increasing. (2) There were pronounced regional differences in innovation efficiency. The innovation efficiencies at the provincial level evolved from being decentralized to concentrated. The innovation efficiency was relatively stable in the eastern region and increased significantly in the central and western regions. The east–center–west evolution pattern gradually weakened. (3) The innovative efficiency exhibited spatial dependence, and the spatial agglomeration continued to increase. The extent of hot spots expanded, while cold spots shrunk slightly. (4) The scientific research environment, entrepreneurial environment, labor quality, and market environment were the essential elements that improved innovation efficiency. The impact of the different factors on innovation efficiency at different periods exhibited significant spatial heterogeneity.