A geographic carbon emission estimating framework on the city scale

Gengzhe Wang (Corresponding author), Qi Han, Bauke de Vries

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Uittreksel

To facilitate sustainable carbon management on the city scale, estimating carbon emissions is necessary for determining carbon reduction targets. Although many studies have focused on mitigating GHG emission through industrial sector optimization and energy efficiency improvement, limited research was conducted to reduce carbon emission based on spatial planning. This paper proposes a comprehensive carbon emission estimation framework based on GIS technology with open data. It presents the spatial distribution of carbon emission on the city scale. The sectors of carbon emissions are mainly attributed to buildings, transportation, vegetation, and residence. Statistic data was applied to estimate the carbon emission in buildings and residence sectors. Transport carbon emission was calculated through a bottom-up method with the support of the logistic regression model and spatial microsimulation. Carbon sequestration of vegetation was estimated by remote sensing technology. Eindhoven was employed as a case study to verify the feasibility of the framework. The results clearly illustrate the carbon balance in association with land use patterns. The new framework can be used to analyze the impact of urban spatial planning on carbon emission. The estimation results can also apply to urban form components (land use, buildings types, and road network) optimization and environmental assessment.

Originele taal-2Engels
Artikelnummer118793
Aantal pagina's12
TijdschriftJournal of Cleaner Production
Volume244
Vroegere onlinedatum10 okt 2019
DOI's
StatusGepubliceerd - 20 jan 2020

Vingerafdruk

carbon emission
Carbon
spatial planning
land use
Land use
city
Carbon emissions
vegetation
carbon balance
carbon
environmental assessment
energy efficiency
carbon sequestration
Planning
logistics
GIS
spatial distribution
remote sensing
Geographic information systems
Spatial distribution

Citeer dit

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abstract = "To facilitate sustainable carbon management on the city scale, estimating carbon emissions is necessary for determining carbon reduction targets. Although many studies have focused on mitigating GHG emission through industrial sector optimization and energy efficiency improvement, limited research was conducted to reduce carbon emission based on spatial planning. This paper proposes a comprehensive carbon emission estimation framework based on GIS technology with open data. It presents the spatial distribution of carbon emission on the city scale. The sectors of carbon emissions are mainly attributed to buildings, transportation, vegetation, and residence. Statistic data was applied to estimate the carbon emission in buildings and residence sectors. Transport carbon emission was calculated through a bottom-up method with the support of the logistic regression model and spatial microsimulation. Carbon sequestration of vegetation was estimated by remote sensing technology. Eindhoven was employed as a case study to verify the feasibility of the framework. The results clearly illustrate the carbon balance in association with land use patterns. The new framework can be used to analyze the impact of urban spatial planning on carbon emission. The estimation results can also apply to urban form components (land use, buildings types, and road network) optimization and environmental assessment.",
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A geographic carbon emission estimating framework on the city scale. / Wang, Gengzhe (Corresponding author); Han, Qi; de Vries, Bauke.

In: Journal of Cleaner Production, Vol. 244, 118793, 20.01.2020.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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AU - de Vries, Bauke

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N2 - To facilitate sustainable carbon management on the city scale, estimating carbon emissions is necessary for determining carbon reduction targets. Although many studies have focused on mitigating GHG emission through industrial sector optimization and energy efficiency improvement, limited research was conducted to reduce carbon emission based on spatial planning. This paper proposes a comprehensive carbon emission estimation framework based on GIS technology with open data. It presents the spatial distribution of carbon emission on the city scale. The sectors of carbon emissions are mainly attributed to buildings, transportation, vegetation, and residence. Statistic data was applied to estimate the carbon emission in buildings and residence sectors. Transport carbon emission was calculated through a bottom-up method with the support of the logistic regression model and spatial microsimulation. Carbon sequestration of vegetation was estimated by remote sensing technology. Eindhoven was employed as a case study to verify the feasibility of the framework. The results clearly illustrate the carbon balance in association with land use patterns. The new framework can be used to analyze the impact of urban spatial planning on carbon emission. The estimation results can also apply to urban form components (land use, buildings types, and road network) optimization and environmental assessment.

AB - To facilitate sustainable carbon management on the city scale, estimating carbon emissions is necessary for determining carbon reduction targets. Although many studies have focused on mitigating GHG emission through industrial sector optimization and energy efficiency improvement, limited research was conducted to reduce carbon emission based on spatial planning. This paper proposes a comprehensive carbon emission estimation framework based on GIS technology with open data. It presents the spatial distribution of carbon emission on the city scale. The sectors of carbon emissions are mainly attributed to buildings, transportation, vegetation, and residence. Statistic data was applied to estimate the carbon emission in buildings and residence sectors. Transport carbon emission was calculated through a bottom-up method with the support of the logistic regression model and spatial microsimulation. Carbon sequestration of vegetation was estimated by remote sensing technology. Eindhoven was employed as a case study to verify the feasibility of the framework. The results clearly illustrate the carbon balance in association with land use patterns. The new framework can be used to analyze the impact of urban spatial planning on carbon emission. The estimation results can also apply to urban form components (land use, buildings types, and road network) optimization and environmental assessment.

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