urban green
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2022 ◽  
Vol 219 ◽  
pp. 104319
Davide Geneletti ◽  
Chiara Cortinovis ◽  
Linda Zardo

2022 ◽  
Vol 114 ◽  
pp. 105970
Md. Julfikar Ali ◽  
Mohidur Rahaman ◽  
Sk. Iqbal Hossain

2022 ◽  
Yang Liu ◽  
Ting-Ting Huang ◽  
Xi Zheng

AbstractAccelerated urbanization and population growth lead to the fragmentation of urban green space and loss of biodiversity. There are few studies on the integration of structural and functional connectivity to solve this problem. Our study aims to draw up a methodology to synthesize two methods of connectivity evaluation, accordingly, to construct an urban green infrastructure (UGI) network which is of great significance to maintain the stability of the urban ecosystem. Taking Beijing as a study area, we first used Morphological Spatial Pattern Analysis (MSPA) to identify the source patches, then combined with the graph theory-based landscape metrics to discuss the effect of different diffusion distances on the regional landscape connectivity and classify the importance level of the source patches. Finally, we used both least-cost path (LCP) and circuit theory to construct network and identify pinch areas in corridors for network optimization. The results show that (1) the landscape connectivity of the study area is obviously polarized. Source patches in mountain and hilly areas have good ecological bases and large areas, and the density of corridors is relatively high, which makes a large contribution to the overall landscape connectivity; Source patches in plain areas are severely fragmented, and there are only a small number of potential corridors connecting urban areas and suburban areas. (2) The UGI network is composed of 70 source patches and 148 potential corridors. The diffusion distance that is most beneficial to improve landscape connectivity is 20–25 km. (3) 6 pinch areas that are of great significance for improving the connectivity of the landscape present the coexistence of high migration resistance and large optimization potential, and urgently need to be restored first. This study provides a method to combine the structural and the functional analysis to construct a UGI network and formulate more scientifical protection strategies for planning departments.

Clean Energy ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 905-916
Hongliang Wu ◽  
Ling Wang ◽  
Daoxin Peng ◽  
Benjie Liu

Abstract With the acceleration of urbanization, cities are the main targets for carbon neutrality and urban energy is the terminal of energy consumption and the integration point of various energy systems. Therefore, there is a need to promote the development of urban green energy and achieve low input and high output to achieve a low-carbon economy in cities. Previous studies have not considered the input–output efficiency of urban green-energy development. This study fills this gap. Based on the economic–energy–environmental framework, an input–output efficiency-evaluation index system for urban green-energy development was constructed. Based on improved data-envelopment analysis, a comparative evaluation of the input–output efficiency of green-energy development was carried out in 30 provinces in China in 2019. Considering the differences in regions, the development of urban green energy in different provinces was classified. From the perspective of a low-carbon economy, economic growth factors and environmental constraint factors were set. Together with the generalized Divisia index approach, the input–output efficiency optimization directions of urban green-energy development were obtained. The results showed that the input–output efficiencies of urban green-energy development in Jiangsu, Zhejiang, Fujian, Inner Mongolia, Ningxia and other provinces and cities were relatively high. Provinces with faster economic development and higher environmental carrying capacity have advantages after optimization and will become pilot areas for the development of urban green energy. This research provides a reference for the development of urban green energy in various provinces from the input and output perspective.

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