Ítem
Solo Metadatos
Total urban tree carbon storage and waste management emissions estimated using a combination of LiDAR, field measurements and an end-of-life wood approach
Título de la revista
Autores
Speak, Andrew
Escobedo, Francisco J.
Russo, Alessio
Zerbe, Stefan
Fecha
2020
Directores
ISSN de la revista
Título del volumen
Editor
Elsevier Ltd
Buscar en:
Métricas alternativas
Resumen
Abstract
Climate action plans, with goals for carbon neutrality of cities, often rely on estimates of urban forest biomass and related annual carbon sequestration balanced against citywide carbon emissions. For these estimates to be successful, there is a need both for accurate quantification of urban tree populations and structure, and consideration of the net carbon sequestered when the fate of wood waste is factored in. This study provides a novel approach to providing a full city tree inventory for the city of Meran in northern Italy, using a combination of Light Detection and Ranging (LiDAR) and field techniques. Allometric equations, and the i-Tree application quantified the carbon storage in Meran as 8923 and 9213 Mg respectively, with an average carbon storage of 13.5 t/ha (5.47 kg C/m2). The percentage of traffic emissions sequestered annually is 0.61% falling to 0.17% when all emissions are considered. Differences between end-of-life wood management techniques were revealed, with burning with energy recovery for electricity being the most efficient with a carbon emissions/input ratio of 0.5. Landfill was the least efficient with a ratio of 121.9. The fate of this end-of-life wood has significant implications for carbon budget calculations in cities worldwide. © 2020 Elsevier Ltd
Palabras clave
Keywords
Budget control , Lithium compounds , Optical radar , Urban growth , Waste management , Wood , Accurate quantifications , Allometric equations , Carbon neutralities , Carbon sequestration , Field measurement , Light detection and ranging , Missing value imputation , Urban trees , Forestry , Carbon sequestration , I-tree , IPCC waste model , Lidar , Missing value imputation , Urban tree growth , Urban tree inventory
