Monitoring Land-Use Efficiency in China's Yangtze River Economic Belt from 2000 to 2018

被引:10
作者
Wang, Yunchen [1 ,2 ]
Li, Boyan [3 ]
Xu, Lei [4 ]
机构
[1] Shaanxi Inst Geol Survey, Shaanxi Satellite Applicat Technol Ctr Nat Resour, Xian 710054, Peoples R China
[2] Minist Nat Resources, Key Lab Spatial Temporal Big Data Anal & Applicat, Shanghai 200063, Peoples R China
[3] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[4] China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
SDG; 11.3.1; indicator; land-use efficiency; urban agglomeration level; Yangtze River Economic Belt;
D O I
10.3390/land11071009
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring of the indicator Sustainable Development Goal (SDG) 11.3.1 is important for understanding the coordination between land consumption rate (LCR) and population growth rate (PGR). However, the spatiotemporal indicator SDG 11.3.1 changes at the urban agglomeration (UA) level, and the relationship between LCR and PGR in the prefecture-level cities from different UAs remains unclear. In this study, we monitored the spatiotemporal indicator SDG 11.3.1 in the Yangtze River Economic Belt (YREB) and its three major UAs (i.e., Chengdu-Chongqing (CC), the Middle Reaches of the Yangtze River (MRYR), and the Yangtze River Delta (YRD)) for the periods 2000-2010, 2010-2015, and 2015-2018, using the space-time interaction (STI) method and Pearson's method. Our major findings were as follows: (1) Compared with the world average of 1.28 for LCRPGR (i.e., ratio of LCR to PGR), except for the LCRPGR of the YRD (2000-2018) and CC (2000-2010), the LCRPGR of CC, the MRYR, and the YREB was lower than 1.28 during 2000-2018. (2) The gaps in both population and built-up area between the YREB and the three UAs did not narrow, but widened. (3) Compared with the LCRPGR in China, except for the LCRPGR of the YRD (2000-2018) and CC (2000-2010), the LCRPGR of the YREB increased from 1.21 to 1.23 between 2000-2010 and 2010-2015, and then decreased to 1.16 in 2015-2018, indicating that the relationship between LCR and PGR in the YREB is relatively stable. (4) A significant positive relationship (p < 0.001) was found between LCR and PGR in CC, the MRYR, the YRD, and the YREB. We conclude that the indicator SDG 11.3.1 is a helpful tool for evaluating land-use efficiency caused by the LCR and PGR at the UA level. Our results provide information support for promoting sustainable and coordinative development between LCR and PGR.
引用
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页数:13
相关论文
共 36 条
[1]  
[Anonymous], 2017, SUSTAINABLE DEV GOAL
[2]  
[Anonymous], 2016, The sustainable development goals report
[3]  
[Anonymous], 2018, SDG INDICATOR TRAINI
[4]   Hierarchical analysis of landscape urbanization and its impacts on regional sustainability: A case study of the Yangtze River Economic Belt of China [J].
Bian, Hongyan ;
Gao, Jie ;
Wu, Jianguo ;
Sun, Xiao ;
Du, Yu .
JOURNAL OF CLEANER PRODUCTION, 2021, 279
[5]   Identification of urban land use efficiency by indicator-SDG 11.3.1 [J].
Cai, Guoyin ;
Zhang, Jinxi ;
Du, Mingyi ;
Li, Chaopeng ;
Peng, Shu .
PLOS ONE, 2020, 15 (12)
[6]   The Ratio of the Land Consumption Rate to the Population Growth Rate: A Framework for the Achievement of the Spatiotemporal Pattern in Poland and Lithuania [J].
Calka, Beata ;
Orych, Agata ;
Bielecka, Elzbieta ;
Mozuriunaite, Skirmante .
REMOTE SENSING, 2022, 14 (05)
[7]   Impacts of large-scale landscape restoration on spatio-temporal dynamics of ecosystem services in the Chinese Loess Plateau [J].
Chen, Hao ;
Fleskens, Luuk ;
Schild, Johanna ;
Moolenaar, Simon ;
Wang, Fei ;
Ritsema, Coen .
LANDSCAPE ECOLOGY, 2022, 37 (01) :329-346
[8]   Monitoring global land-use efficiency in the context of the UN 2030 Agenda for Sustainable Development [J].
Estoque, Ronald C. ;
Ooba, Makoto ;
Togawa, Takuya ;
Hijioka, Yasuaki ;
Murayama, Yuji .
HABITAT INTERNATIONAL, 2021, 115
[9]   Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach [J].
Ghazaryan, Gohar ;
Rienow, Andreas ;
Oldenburg, Carsten ;
Thonfeld, Frank ;
Trampnau, Birte ;
Sticksel, Sarah ;
Juergens, Carsten .
REMOTE SENSING, 2021, 13 (09)
[10]   Monitoring of Urban Landscape Ecology Dynamics of Islamabad Capital Territory (ICT), Pakistan, Over Four Decades (1976-2016) [J].
Gilani, Hammad ;
Ahmad, Sohail ;
Qazi, Waqas Ahmed ;
Abubakar, Syed Muhammad ;
Khalid, Murtaza .
LAND, 2020, 9 (04)