Frontiers in Environmental Science (Aug 2024)
Spatial characteristics and optimization of urban living space carbon suitability index (ULS-CSI) in Tianjin, China
Abstract
The global climate crisis is escalating, and urban living Space (ULS) is a significant contributor to carbon emissions. How to improve the carbon suitability of ULS while promoting social and economic development is a global issue. This study aims to develop an evaluation system for comparing and analyzing carbon suitability inequality and spatial differences in different areas. To achieve this goal, an urban living space carbon suitability index (ULS-CSI) based on spatial organizational index (SOI) has been proposed. The ULS-CSI was calculated at the area scale in Tianjin using information from the Tianjin Land Use Database in 2021. The carbon emissions coefficient method was used to calculate the urban living space carbon emissions (ULSCE). Moran’I and LISA analysis were used to quantify the spatial differences of ULS-CSI. The results showed that the residential living area (RLA) carbon emissions was the highest at the area scale, with carbon emissions of 1.14 × 1011 kg, accounting for 33.74%. The green space leisure area (GLA) carbon absorption was the highest at the area scale, with carbon absorption of 5.76 × 105 kg, accounting for 32.33%. SOI in different areas have spatial heterogeneity as the SOI such as building area, road network density and land use characteristics are significantly different in different areas. Areas with superior CSI were primarily situated in Heping, Hexi, Nankai, and Beichen, accounting for 83.90%. Conversely, areas under the basic CSI threshold included Xiqing, Jinnan, and Dongli, accounting for 16.10%. Spatial characteristics of ULS-CSI in Tianjin portrayed a significant spatial positive correlation, indicating the highest autocorrelation degree of CSI at 500 m, with a Moran ’I value of 0.1733. Although these findings reflect the spatial characteristics of ULS-CSI and the SOI affecting the ULS-CSI at area scale, more perfect data are needed to reflect the complexity of structural factors affecting ULS-CSI at area scale. This study is helpful for urban planning to develop differentiated carbon reduction strategies and promote low-carbon and healthy urban development.
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