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- Publisher Website: 10.1016/j.jclepro.2017.03.083
- Scopus: eid_2-s2.0-85016512323
- WOS: WOS:000399624000036
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Article: Identification of the numerical patterns behind the leading counties in the U.S. local green building markets using data mining
Title | Identification of the numerical patterns behind the leading counties in the U.S. local green building markets using data mining |
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Authors | |
Keywords | Clustering Classification Greedy forward search Green building market Influential feature analysis |
Issue Date | 2017 |
Citation | Journal of Cleaner Production, 2017, v. 151, p. 406-418 How to Cite? |
Abstract | © 2017 Elsevier Ltd The U.S. is reported to have one of the most developed green building markets. The country wide success must start from the success of different local markets. So how many important local green building markets are there in the U.S. and why those areas became important? The answers can provide useful implications for developing countries like China to better promote their green building markets. To explore the question in a numerical way, this study therefore collected the data of 17,636 green building projects in the U.S., clustered them into 39 important regions, and analyzed the numerical patterns behind based on 82 different features covering demography, economy, education, climate and policy. Non-linear machine learning algorithms help find that economic factors and educational factors are one of the most influential features. Discoveries were also implemented in China to suggest 20 areas to be the focus of developing Chinese green building market. |
Persistent Identifier | http://hdl.handle.net/10722/287057 |
ISSN | 2023 Impact Factor: 9.7 2023 SCImago Journal Rankings: 2.058 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ma, Jun | - |
dc.contributor.author | Cheng, Jack C.P. | - |
dc.date.accessioned | 2020-09-07T11:46:23Z | - |
dc.date.available | 2020-09-07T11:46:23Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Journal of Cleaner Production, 2017, v. 151, p. 406-418 | - |
dc.identifier.issn | 0959-6526 | - |
dc.identifier.uri | http://hdl.handle.net/10722/287057 | - |
dc.description.abstract | © 2017 Elsevier Ltd The U.S. is reported to have one of the most developed green building markets. The country wide success must start from the success of different local markets. So how many important local green building markets are there in the U.S. and why those areas became important? The answers can provide useful implications for developing countries like China to better promote their green building markets. To explore the question in a numerical way, this study therefore collected the data of 17,636 green building projects in the U.S., clustered them into 39 important regions, and analyzed the numerical patterns behind based on 82 different features covering demography, economy, education, climate and policy. Non-linear machine learning algorithms help find that economic factors and educational factors are one of the most influential features. Discoveries were also implemented in China to suggest 20 areas to be the focus of developing Chinese green building market. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Cleaner Production | - |
dc.subject | Clustering | - |
dc.subject | Classification | - |
dc.subject | Greedy forward search | - |
dc.subject | Green building market | - |
dc.subject | Influential feature analysis | - |
dc.title | Identification of the numerical patterns behind the leading counties in the U.S. local green building markets using data mining | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jclepro.2017.03.083 | - |
dc.identifier.scopus | eid_2-s2.0-85016512323 | - |
dc.identifier.volume | 151 | - |
dc.identifier.spage | 406 | - |
dc.identifier.epage | 418 | - |
dc.identifier.isi | WOS:000399624000036 | - |
dc.identifier.issnl | 0959-6526 | - |