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- Publisher Website: 10.1016/j.jclepro.2021.128792
- Scopus: eid_2-s2.0-85114129370
- WOS: WOS:000706246700002
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Article: The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review
Title | The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review |
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Authors | |
Issue Date | 2021 |
Citation | Journal of Cleaner Production, 2021, v. 319, p. 128792 How to Cite? |
Abstract | Cities produce over 70% of global carbon emissions and are thus crucial in driving climate change. Urban carbon emissions may continue to increase especially in those less-developed countries and regions which are still under rapid urban development. Policymakers need to find ways to effectively control and reduce carbon emissions. Thus, spatial modeling methods to map and predict urban carbon emissions have been developed to meet these needs. This paper examines the progress of the spatial modeling of carbon emissions and the relationship between urban form and carbon emissions in China by reviewing more than 100 peer-reviewed journal articles in the Scopus database. The latest prediction methods and techniques are described in the paper. Their advantages and limitations are then discussed. Urban forms have a significant influence on carbon emissions and have been applied in spatial modeling studies in other countries. However, this review has identified the lack of urban form data and high-resolution inventories from existing studies in China. Future developments in the spatial modeling in China should therefore have a fine spatial resolution and incorporate open and high-quality urban form data, including urban morphology and land use/land cover. |
Persistent Identifier | http://hdl.handle.net/10722/310503 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cai, M | - |
dc.contributor.author | Shi, Y | - |
dc.contributor.author | Ren, C | - |
dc.contributor.author | Yoshida, T | - |
dc.contributor.author | Yamagata, Y | - |
dc.contributor.author | Ding, C | - |
dc.contributor.author | Zhou, N | - |
dc.date.accessioned | 2022-02-07T07:57:36Z | - |
dc.date.available | 2022-02-07T07:57:36Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Journal of Cleaner Production, 2021, v. 319, p. 128792 | - |
dc.identifier.uri | http://hdl.handle.net/10722/310503 | - |
dc.description.abstract | Cities produce over 70% of global carbon emissions and are thus crucial in driving climate change. Urban carbon emissions may continue to increase especially in those less-developed countries and regions which are still under rapid urban development. Policymakers need to find ways to effectively control and reduce carbon emissions. Thus, spatial modeling methods to map and predict urban carbon emissions have been developed to meet these needs. This paper examines the progress of the spatial modeling of carbon emissions and the relationship between urban form and carbon emissions in China by reviewing more than 100 peer-reviewed journal articles in the Scopus database. The latest prediction methods and techniques are described in the paper. Their advantages and limitations are then discussed. Urban forms have a significant influence on carbon emissions and have been applied in spatial modeling studies in other countries. However, this review has identified the lack of urban form data and high-resolution inventories from existing studies in China. Future developments in the spatial modeling in China should therefore have a fine spatial resolution and incorporate open and high-quality urban form data, including urban morphology and land use/land cover. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Cleaner Production | - |
dc.title | The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review | - |
dc.type | Article | - |
dc.identifier.email | Ren, C: renchao@hku.hk | - |
dc.identifier.authority | Ren, C=rp02447 | - |
dc.identifier.doi | 10.1016/j.jclepro.2021.128792 | - |
dc.identifier.scopus | eid_2-s2.0-85114129370 | - |
dc.identifier.hkuros | 331602 | - |
dc.identifier.volume | 319 | - |
dc.identifier.spage | 128792 | - |
dc.identifier.epage | 128792 | - |
dc.identifier.isi | WOS:000706246700002 | - |