Africa’s PM_2.5 pollution has become a security hazard, but the understanding of the varying effects of urbanization on driven mechanisms of PM_2.5 concentrations under the rapid urbanization remains largely insufficient. Compared with the direct impact, the spillover effect of urbanization on PM_2.5 concentrations in adjacent regions was underestimated. Urbanization is highly multi-dimensional phenomenon and previous studies have rarely distinguished the different driving influence and interactions of multi-dimensional urbanization on PM_2.5 concentrations in Africa. This study combined grid and administrative units to explore the spatio-temporal change, spatial dependence patterns, and evolution trend of PM_2.5 concentrations and multi-dimensional urbanization in Africa. The differential influence and interaction effects of multi-dimensional urbanization on PM_2.5 concentrations under Africa’s rapid urbanization was further analyzed. The results show that the positive spatial dependence of PM_2.5 concentrations gradually increased over the study period 2000–2018. The areas with PM_2.5 concentrations exceeding 35 μg/m³ increased by 2.2%, and 36.78% of the African continent had an increasing trend in Theil–Sen index. Urbanization was found to be the main driving factor causing PM_2.5 concentrations changes, and economic urbanization had a stronger influence on air quality than land urbanization or population urbanization. Compared with the direct effect, the spillover effect of urbanization on PM_2.5 concentrations in two adjacent regions was stronger, particularly in terms of economic urbanization. The spatial distribution of PM_2.5 concentrations resulted from the interaction of multi-dimensional urbanization. The interaction of urbanization of any two different dimensions exhibited a nonlinear enhancement effect on PM_2.5 concentrations. Given the differential impact of multi-dimensional urbanization on PM_2.5 concentrations inside and outside the region, this research provides support for the cross-regional joint control strategies of air pollution in Africa. The findings also indicate that PM_2.5 pollution control should not only focus on urban economic development strategies but should be an optimized integration of multiple mitigation strategies, such as improving residents’ lifestyles, optimizing land spatial structure, and upgrading the industrial structure.

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.

The Xin’an River ecological compensation mechanism is China’s first horizontal compensation mechanism to improve the ecological environment of the downstream areas and increase the fiscal revenues of the upstream areas. Although the amount invested in the program is increasing every year, few studies have been conducted to evaluate the impact of the program. Based on the Levinsohn–Petrin method and a comprehensive dataset of Chinese industrial companies from 2009 to 2015, we employed the k-nearest neighbor propensity score matching–difference-in-difference (PSM-DID) to explore the impact of the Xin’an River Ecological Compensation Gambling Agreement (ECGA) on company technological progress and its drive mechanism. The empirical results show that ECGA inhibits the technological progress of upstream industrial companies and has more pronounced inhibitory effects for heavy-polluting companies. ECGA indirectly inhibits the technological progress in upstream companies by affecting profitability, scale, human capital, foreign direct investment, and management efficiency.

The normalized difference vegetation index (NDVI) is a useful indicator to characterize vegetation development and land use which can effectively monitor changes in ecological environments. As an important area for ecological balance and safety in China, understanding the dynamic changes in land cover and vegetation of the Yangtze River Basin would be crucial in developing effective policies and strategies to protect its natural environment while promoting sustainable growth. Based on MODIS-NDVI data and meteorological data from 2000 to 2019, the temporal and spatial distribution of vegetation coverage in the Yangtze River Basin during the past 20 years were characterized, and the impacts of human activities and climate change were quantitatively evaluated. We drew the following research conclusions: (1) From 2000 to 2019, the vegetation cover of the Yangtze River Basin presented a fluctuating inter-annual growth trend. Except for the Taihu Lake sub-basin, the vegetation cover in other sub-basins showed an upward trend. (2) The vegetation cover exhibited a spatial distribution pattern of “high in the middle and low in the east and west”, with the multi-year average value of NDVI being 0.5153. (3) Areas with improved vegetation cover were significantly larger than the areas with degraded foliage. The central region has stronger overall trend of change than the east, and the east is stronger than the west. These vegetation cover changes are largely related to anthropogenic activities. (4) Vegetation cover changes due to precipitation and temperature exhibited significant spatial heterogeneity. While both temperature and precipitation influenced vegetation cover, the temperature was the leading climate factor in the area. (5) Anthropogenic and climate factors jointly promoted the change of vegetation cover in the Yangtze River Basin. Human activities contributed 79.29%, while climate change contributed 20.71%. This study could be used in subsequent studies analyzing the influencing factors affecting long-term vegetation cover in large-scale watersheds.