Different types of poverty traps can reinforce each other, and consequently, multiple traps pose challenges for poverty alleviation, especially in rural areas. There remains a lack of understanding of social and ecological poverty traps and their relationship with one another. We considered the Selinco area in the Tibetan highlands of China and investigated social and ecological poverty, for an understanding of the poverty trap through the land-human relationship lens. We found that ecological and social poverty differ between various townships due to their poor ecological and social environs. We affirmed reinforcing feedback between ecological and social poverty, and that the relative importance of social and ecological poverty differs at various thresholds. These results advance our understanding of different poverty traps and their feedback process. The findings inform the policy-making process when addressing the land-human relationship to improve ecosystem stability and promote social equity to foster sustainable development.
It is of great significance to study the interactive relationship between urban transportation and land use for promoting the healthy and sustainable development of cities. Taking Jinan, China, as an example, this study explored the interactive relationship between street centrality (SC) and land use intensity (LUI) in the main urban area of Jinan by using the spatial three-stage least squares method. The results showed that the closeness centrality showed an obvious “core-edge” pattern, which gradually decreased from the central urban area to the edge area. Both the betweenness centrality and the straightness centrality showed a multi-center structure. The commercial land intensity (CLUI) showed the characteristics of multi-core spatial distribution, while the residential land intensity (RLUI) and public service land intensity (PLUI) showed the characteristics of spatial distribution with the coexistence of large and small cores. There was an interactive relationship between SC and LUI. The closeness centrality and straightness centrality had positive effects on LUI, and LUI had a positive effect on closeness centrality and straightness centrality. The betweenness centrality had a negative impact on LUI, and LUI also had a negative impact on betweenness centrality. Moreover, good location factors and good traffic conditions were conducive to improving the closeness and straightness centrality of the regional traffic network. Good location factors, good traffic conditions and high population density were conducive to improving regional LUI.
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