Unveiling the Impact of Urbanization on Net Primary Productivity: Insights from the Yangtze River Delta Urban Agglomeration

被引:1
|
作者
Gao, Jing [1 ]
Liu, Min [2 ,3 ]
Wang, Xiaoping [4 ]
机构
[1] Northwest Normal Univ, Sch Educ, Lanzhou 730070, Peoples R China
[2] East China Normal Univ, Sch Ecol & Environm Sci, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200241, Peoples R China
[3] Minist Nat Resources, Technol Innovat Ctr Land Spatial Ecorestorat Metro, Shanghai 200241, Peoples R China
[4] Northwest A&F Univ, Coll Nat Resources & Environm, State Key Lab Soil Eros & Dryland Farming Loess Pl, Xianyang 712100, Peoples R China
基金
中国国家自然科学基金;
关键词
NPP; urbanization; spatial dependence; spillover effects; urban agglomeration; LAND-USE CHANGE; ECOSYSTEM SERVICES; CLIMATE-CHANGE; DRIVING FORCES; CARBON STORAGE; LAKE BASIN; CHINA; TERRESTRIAL; PATTERNS; NPP;
D O I
10.3390/land13040562
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran's I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems.
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页数:18
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