Local Full-Sector Land Uses Influenced by Multiregional Demand and Supply: The Case of Beijing

Land is the foundation of human survival and well-being. It is important to investigate the land uses of economic sectors and recognize critical supply chain activities influencing land uses. However, the full-sector land uses and relevant multiregional supply chain activities have not been well cha...

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Bibliographic Details
Main Authors: Xuechun Yang, Xiaohui Lu, Nan Li, Chengdong Wang, Wei Xie, Qiumeng Zhong, Sai Liang
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2023-01-01
Series:Ecosystem Health and Sustainability
Online Access:https://spj.science.org/doi/10.34133/ehs.0075
Description
Summary:Land is the foundation of human survival and well-being. It is important to investigate the land uses of economic sectors and recognize critical supply chain activities influencing land uses. However, the full-sector land uses and relevant multiregional supply chain activities have not been well characterized. This study constructs a new accounting framework based on road network, point of interest data, and remote sensing data to estimate sector-specific land uses. The accounting framework is applied to Beijing, China. The multiregional supply chain activities influencing full-sector land uses in Beijing are revealed based on environmentally extended multiregional input–output model. Results show that the largest nonagricultural land users are the wholesale and retail trades sector and the accommodation and catering sector. Land uses of these sectors are substantially driven by the final demand for the construction sector and enabled by the primary inputs of the finance sector. Moreover, the final demands of Henan and Zhejiang as well as the primary inputs of Jiangsu and Hebei are critical impetus. Optimizing consumption behavior of final consumers and product allocation of primary suppliers can help improve land management. This accounting framework can be further applied flexibly to studies on larger spatial scales and time-series studies.
ISSN:2332-8878