Spatio-Temporal Dynamics of Carbon Storage in Rapidly Urbanizing Shenzhen, China: Insights and Predictions

Chunxiao Wang (), Mingqian Li, Xuefei Wang, Mengting Deng, Yulian Wu and Wuyang Hong
Additional contact information
Chunxiao Wang: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Mingqian Li: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Xuefei Wang: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Mengting Deng: Shenzhen Longhua District Development Research Institute, Shenzhen 518000, China
Yulian Wu: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Wuyang Hong: School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Land, 2024, vol. 13, issue 10, 1-20

Abstract: Rapid urbanization in developing countries leads to significant land-use and land-cover change (LULCC), which contributes to increased carbon dioxide (CO 2 ) emissions and the degradation of carbon storage. Studying spatio-temporal changes in carbon storage is crucial for guiding sustainable urban development toward carbon neutrality. This study integrates machine-learning random forest algorithm, CA–Markov, and InVEST models to predict carbon storage distribution in Shenzhen, China, under various scenarios. The findings indicate that, over the past two decades, Shenzhen has experienced significant land-use changes. The transformation from high- to low-carbon-density land uses, particularly the conversion of forestland to construction land, is the primary cause of carbon storage loss. Forestland is mainly influenced by natural factors, such as digital elevation model (DEM) and precipitation, while other land-use and land-cover (LULC) types are predominantly affected by socio-economic and demographic factors. By 2030, carbon storage is projected to vary significantly across different development scenarios, with the greatest decline expected under the natural development scenario (NDS) and the least under the ecological priority scenario (EPS). The RF-CA–Markov model outperforms the traditional CA–Markov model in accurately simulating land use, particularly for small and scattered land-use types. Our conclusions can inform future low-carbon city development and land-use optimization.

Keywords: carbon storage assessment; land-use and land-cover change; random forest; machine learning; multi-scenario simulation of land use (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/10/1566/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/10/1566/ (text/html)

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:gam:jlands:v:13:y:2024:i:10:p:1566-:d:1486647

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().