Role of land use in China’s urban energy consumption: based on a deep clustering network and decomposition analysis

Wei Fan, Chunxia Zhu, Lijun Fu, Charbel Jose Chiappetta Jabbour, Zhiyang Shen and Malin Song ()
Additional contact information
Wei Fan: Southwestern University of Finance and Economics
Chunxia Zhu: Southwestern University of Finance and Economics
Lijun Fu: Southwestern University of Finance and Economics
Charbel Jose Chiappetta Jabbour: NEOMA Business School
Malin Song: Anhui University of Finance and Economics

Annals of Operations Research, 2024, vol. 339, issue 1, No 31, 835-859

Abstract: Abstract Land use can affect energy consumption by changing the economic and social structure of cities. Thus, the optimization of land use patterns is key to promoting energy sustainability. In this study, we explored the spatiotemporal evolution of China’s urban energy consumption and its driving factors from the role of land use, with the application of high-precision nighttime light images and land use data acquired from remote sensing satellites. A deep clustering network in deep learning was used for clustering analysis of urban energy consumption. The results indicated that the economic and structural effects of land use were the primary driving factors of the increasing urban energy consumption, whereas the decrease in the energy intensity (caused by technological progress) restrained the growth rate of energy consumption. With the exception of economically developed cities, generally, the contribution of the population size to the temporal increase in energy consumption was relatively small. The spatial difference in urban energy consumption was mainly due to the between-group differences among the diversified cluster groups, which were strongly influenced by land urbanization and population size. These conclusions can help the Chinese government formulate differentiated urban energy policies.

Keywords: Urban energy consumption; Land use; Driving factors; Deep clustering network; Decomposition analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05277-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-023-05277-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05277-7

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().