What causal drivers influence carbon storage in Shanghai, China's urban and peri-urban forests?

Yao, Xin; Zhao, Min; Escobedo, Francisco J

doi:https://doi.org/10.3390/su9040577

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Acceso Abierto

What causal drivers influence carbon storage in Shanghai, China's urban and peri-urban forests?

https://repository.urosario.edu.co/handle/10336/21465
https://doi.org/10.3390/su9040577

Autores

Yao, Xin

Zhao, Min

Escobedo, Francisco J

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2017

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Abstract

Studies have documented many biophysical factors that are correlated with urban forest carbon storage. This urban forest function is also increasingly being promoted as a nature-based solution for cities. While urbanization affects both the structure and function of urban forest ecosystems, quantitative analyses of specific casual drivers of carbon storage in urban versus periurban forests are scarce. To address this lack of information, we used field data of random plots located along an urban to rural gradient in Shanghai, China, region-specific biomass equations, and path analysis of commonly studied urban forest socioeconomic and ecological drivers to analyze their effects on above ground tree carbon storage. An urbanization index was also developed to quantitatively differentiate urban from peri-urban sites along the transect. Results show that in both urban and peri-urban forests, percent tree and shrub cover had a significant and positive effect on tree and shrub carbon, but tree and shrub density had an even greater effect. Further, tree and shrub species diversity had no effects on carbon storage, while the effects of species composition on tree and shrub carbon in urban forests was different from those in peri-urban areas. Peri-urban forests also exhibited a significant effect of percent tree and shrub cover on tree and shrub species diversity. This approach, using a path analysis of field and plot data and site-specific dendrometric and urbanization information, can be used to quantitatively identify little explored causal dependences between drivers and ecosystem services without relying exclusively on spatial land cover data often not available in developing countries. © 2017 by the authors.