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Assessing Lisbon Trees’ Carbon Storage Quantity, Density, and Value Using Open Data and Allometric Equations

NOVA Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
Author to whom correspondence should be addressed.
Information 2019, 10(4), 133;
Received: 22 February 2019 / Revised: 2 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
Urban population has grown exponentially in recent years, leading to an increase of CO2 emissions and consequently contributing on a large scale to climate change. Urban trees are fundamental to mitigating CO2 emissions as they incorporate carbon in their biomass. It becomes necessary to understand and measure urban tree carbon storage. In this paper is studied the potential of open data to measure the quantity, density, and value of carbon stored by the seven most represented urban trees in the city of Lisbon. To compute carbon storage, the seven most represented urban tree species were selected from an open database acquired from an open data portal of the city of Lisbon. Through allometric equations, it was possible to compute the trees’ biomass and calculate carbon storage quantity, density, and value. The results showed that the tree species Celtis australis is the species that contributes more to carbon storage. Central parishes of the city of Lisbon present higher-density values of carbon storage when compared with the border parishes despite the first ones presenting low-to-medium values of carbon storage quantity and value. Trees located in streets, present higher values of carbon storage, when compared with trees located in schools and green areas. Finally, the potential usage of this information to build a decision-support dashboard for planning green infrastructures was demonstrated. View Full-Text
Keywords: carbon storage; urban trees; allometric equation; open data carbon storage; urban trees; allometric equation; open data
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Neto, M.C.; Sarmento, P. Assessing Lisbon Trees’ Carbon Storage Quantity, Density, and Value Using Open Data and Allometric Equations. Information 2019, 10, 133.

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