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250725s2025 xx |||||o 00| ||eng c |
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|a 10.1016/j.wasman.2025.115026
|2 doi
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|a pubmed25n1527.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Chen, Yiwei
|e verfasserin
|4 aut
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|a Spatiotemporal evolution, driving factors and generation patterns of construction and demolition waste
|b A multi-scale analysis based on provinces and cities in China
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|c 2025
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 10.08.2025
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|a Date Revised 10.08.2025
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2025 Elsevier Ltd. All rights reserved.
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|a Construction and demolition waste generation (CDWG) reduction is a key issue in urban waste management. The complex distribution and regional disparities of CDWG exacerbate the challenges of reduction. Existing research mainly focuses on macro-level national or regional scales, neglecting multi-scale interactions and detailed analysis at city scale. To address this gap, this study proposes a multi-scale STDP framework to systematically analyze the spatiotemporal evolution, driving mechanisms, and generation patterns of CDWG. Using China as a case and applying spatial econometrics and machine learning models, the main findings are as follows: (1) CDWG distribution consistent with Hu-Line, and the gap between two sides of the line gradually widened; (2) CDWG consistently shows spatial autocorrelation, with an "increase-decrease-recovery" fluctuation in aggregation, and city-scale correlation is always lower than at provincial scale; (3) Construction industry development and neighborhood influence are key drivers at provincial scale, while real estate market activity, housing demand, and urban economic level are major drivers at city level. Additionally, CDWG generation patterns for 31 provinces and 351 cities in China are identified, providing direct insights for governmental reduction management. The main theoretical contribution is to reveal that CDWG is driven by multi-scale spatial coupling, and its generation mechanism is the interaction of multiple factors at different geographical scales. The innovation lies in: (1) presenting the finer-grained evolution characteristics and driving factors of CDWG at city scales for the first time and (2) introducing a quantitative indicator to measure the impact of spatial spillover effects from neighboring regions on CDWG
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|a Journal Article
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|a Construction and demolition waste generation
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|a Machine learning model
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|a Multi-scale
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|a Spatial econometric model
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| 650 |
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|a Spatiotemporal evolution
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| 700 |
1 |
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|a Xie, Qiu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Feng, Yingbin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Yuxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Yi
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Tong
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 205(2025) vom: 24. Aug., Seite 115026
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnas
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| 773 |
1 |
8 |
|g volume:205
|g year:2025
|g day:24
|g month:08
|g pages:115026
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| 856 |
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|u http://dx.doi.org/10.1016/j.wasman.2025.115026
|3 Volltext
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