What Causal Drivers Influence Carbon Storage in Shanghai, China’s Urban and Peri-Urban Forests?

Xin Yao, Min Zhao and Francisco J. Escobedo
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Xin Yao: College of Life and Environmental Sciences, Shanghai Normal University, Shanghai 200234, China
Min Zhao: Urban Ecology and Envrionmental Center, Shanghai Normal University, Shanghai 200234, China
Francisco J. Escobedo: Biology Program, Facultad de Ciencias Naturalesy Matemáticas, Universidad del Rosario, location, Bogotá 111221492, Colombia

Sustainability, 2017, vol. 9, issue 4, 1-17

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 peri-urban 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.

Keywords: urban to rural gradient; climate change; nature-based solutions; path analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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