Assessment of Chinese urban land-use efficiency (SDG11.3.1) utilizing high-precision urban built-up area data

Hao Wang , Yafei Liu , Lianze Sun , Xiaogang Ning , Guangzhe Li

Geography and Sustainability ›› 2025, Vol. 6 ›› Issue (1) : 100210

PDF
Geography and Sustainability ›› 2025, Vol. 6 ›› Issue (1) :100210 DOI: 10.1016/j.geosus.2024.06.007
Research Article
review-article

Assessment of Chinese urban land-use efficiency (SDG11.3.1) utilizing high-precision urban built-up area data

Author information +
History +
PDF

Abstract

Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals (SDGs) is a valuable tool for policymakers in urban planning. This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator’s assessment outcomes. We developed an algorithm to generate accurate urban built-up area data products based on China’s Geographical Condition Monitoring data with a 2 m resolution. The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery. The results indicate: (1) A significant improvement in the accuracy of our urban built-up area data, with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics. The indicator shows a positive correlation with the accuracy level of the built-up area data; (2) From 2015 to 2020, Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth, leading to less optimal land resource utilization. Only in extra-large cities does urban population growth show a relatively balanced pattern. However, urban population growth in other regions and cities of various sizes lags behind land urbanization. Notably, Northeast China and small to medium cities encounter significant challenges in urban population growth. The comprehensive framework developed for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions, yielding more accurate SDG11.3.1 outcomes. Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.

Keywords

SDG11.3.1 / Land-use efficiency / Urban built-up area / Urbanization / Population growth

Cite this article

Download citation ▾
Hao Wang, Yafei Liu, Lianze Sun, Xiaogang Ning, Guangzhe Li. Assessment of Chinese urban land-use efficiency (SDG11.3.1) utilizing high-precision urban built-up area data. Geography and Sustainability, 2025, 6(1): 100210 DOI:10.1016/j.geosus.2024.06.007

登录浏览全文

4963

注册一个新账户 忘记密码

Data availability statement

The dataset of China’s Urban Area (CUD) and Urban Built-up Area (CUBD) generated in this study is accessible to the public for non-commercial research purposes. The dataset is available for download at https://github.com/CSPON2035/China-s-Urban-area-CUD-and-Urban-Built-up-area-CUBD-.

CRediT authorship contribution statement

Hao Wang: Project administration, Writing – original draft, Resources, Writing – review & editing. Yafei Liu: Formal analysis, Writing – original draft, Writing – review & editing, Methodology. Lianze Sun: Data curation, Visualization. Xiaogang Ning: Conceptualization, Supervision, Writing – review & editing. Guangzhe Li: Validation, Visualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research is jointly funded by the National Key Research and Development Program of China (Grant No. 2023YFC3804001), the Natural Resources Planning and Management Project (Grant No. A2417, A2418) and the Fundamental Scientific Research Funds for Central Public Welfare Research Institutes (Grant No. AR2409).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.06.007.

References

[1]

Compilation Committee of China’s Small and Medium-sized City Development Report (CSMCDR), 2010. Annual Report on Development of Small and Medium-Sized Cities in China (2010). Social Sciences Academic Press, Beijing (in Chinese).

[2]

Diriba, D, Hossein, A, Feyera, S, Ketema, A, Fatemeh, T, Tll, S., 2016. Urban sprawl and its impacts on land-use change in Central Ethiopia. Urban For. Urban Green 16, 132-141.

[3]

Dong, Y. N., Han, D. Q.Plot division in the historic area conservation and regeneration practice: Nanjing Xiaoxihu block as an example. Architect 2022; (2), 55-61.

[4]

Du, C. M., 2019. An exploration of the relationship between urban land expansion and population growth of prefecture-level cities in China. Ph.D. thesis, Wuhan Univercity, Wuhan

[5]

Elmqvist, T, Bai, X, Frantzeskaki, N, Griffith, C, Maddox, D, McPhearson, T, Parnell, S, Romero-Lankao, P, Simon, D, Watkins, M., 2018. Urban Planet: Knowledge towards Sustainable Cities. Cambridge University Press

[6]

Estoque, R. C., Ooba, M, Togawa, T, Hijioka, Y, Murayama, Y., 2021. Monitoring global land-use efficiency in the context of the UN 2030 Agenda for sustainable development. Habitat Int., 115, 102403.

[7]

Europeanommission, C.A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons.https://unstats.un.org/unsd/statcom/51st-session/documents/BG-, Item3j-Recommendation-, E.pdf (accessed 16 December 2022).

[8]

Faye, B, Du, G. M., Zhang, R., 2022. Efficiency analysis of land use and the degree of coupling link between population growth and global built-up area in the subregion of West Africa. Land 11(6), 847.

[9]

Feng, J., 2012. Urban-rural Division and Monitoring. Science Press, Beijing

[10]

Françoise, N, Florence, K, Lauren, M., 2016. Indicateurs Nationaux de la Transition Ecologique Vers Undeveloppement Durable 2015–2020. Commissariat General au Developpement Durable

[11]

Fu, B. X., Zhang, J. C., Du, W. J., Wang, P. L., Sun, Z. C., 2021. Effective and novel impervious surface fine mapping method and its application on monitoring urban sustainable development goals. Remote Sens. Technol. Appl., 36(6), 1339-1349.

[12]

Gao, B, Huang, Q, He, C, Sun, Z, Zhang, D., 2016. How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data. Landsc. Urban Plan., 148, 89-98.

[13]

Garouani, A, Mulla, D. J., El Garouani, S, Knight, J., 2017. Analysis of urban growth and sprawl from remote sensing data: case of Fez, Morocco. Int. J. Sustain. Built Environ., 6(1), 160-169.

[14]

Gerten, C, Fina, S, Rusche, K., 2019. The sprawling planet: simplifying the measurement of global urbanization trends. Front. Environ. Sci., 7, 140.

[15]

Ghazaryan, G, Rienow, A, Oldenburg, C, Thonfeld, F, Trampnau, B, Sticksel, S, Jürgens, C., 2021. 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. Remote Sens., 13(9), 1694.

[16]

Huang, H. Y., Jiang, H.Coordination and optimization of transit-oriented development and structure of small and medium-sized cities—a case of Dangyang. Urban. Archi. 2020; (15), 20-22.+76

[17]

Huang, M, Zhang, M, Zhang, B, Wang, W, Zhang, X, Liu, C, Zhang, S. W., Yu, T, Xu, C, Feng, J, Chen, X. X., Ying, S, Sun, L, Zhao, Z. Q., Dong, W, Yi, X. X., Zhang, H, Li, X. W., Wang, G. H., Zheng, L. J., Zhang, J, Chai, X., 2022. Exploration on the concept of “urban built-up area” and method for “urban built-up area delineation”: taking 115 cities as the practice object. City Plan. Rev., 46(5), 17-26.

[18]

Jia, X. H.Study on the coordination of population urbanization and land urbanization in Northeast China. North. Econ. 2020; (9), 56-59.

[19]

Jiang, H. P., Sun, Z. C., Guo, H. D., Weng, Q. H., Du, W. J., Xing, Q, Cai, G. Y., 2021. An assessment of urbanization sustainability in China between 1990 and 2015 using land use efficiency indicators. Npj Urban Sustain., 1(1), 34.

[20]

Jiang, H. P., Sun, Z. C., Guo, H. D., Weng, Q. H., Du, W. J., Xing, Q, Cai, G. Y., 2022. A standardized dataset of built-up areas of China's cities with populations over 300,000 for the period 1990–2015. Big Earth Data 6(1), 103-126.

[21]

Jin, D, Dai, L. L.Temporal and spatial characteristics and driving factors of coordinated development between population urbanization and land urbanization in China. China Land Sci. 2021; (6), 74-84.

[22]

Koroso, N. H., Zevenbergen, J. A., Lengoiboni, M., 2020. Urban land use efficiency in Ethiopia: an assessment of urban land use sustainability in Addis Ababa. Land Use Policy 99, 105081.

[23]

Kuang, W. H., Zhang, S, Li, X. Y., Lu, D. S., 2021. A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018. Earth Syst. Sci. Data 13(1), 63-82.

[24]

Laituri, M, Davis, D, Sternlieb, F, Galvin, K., 2021. SDG Indicator 11.3.1 and secondary cities: an analysis and assessment. ISPRS Int. J. Geo-Inf., 10(11), 713.

[25]

Li, C. P., Cai, G. Y., Sun, Z. C., 2021. Urban land-use efficiency analysis by integrating LCRPGR and additional indicators. Sustainability 13(24), 13518.

[26]

Li, C. P., Cai, G. Y., Du, M. Y., 2021. Big data supported the identification of urban land efficiency in Eurasia by indicator SDG 11.3.1. ISPRS. Int. J. Geo-Inf., 10(2), 64.

[27]

Liu, B. L., Zhu, J. F.The urbanization development of the People's Republic of China in the past 70 years: process, problems and prospects. Res. Econ. Manage. 2019; (11), 3-14.

[28]

Liu, C, Huang, X, Zhu, Z, Chen, H, Tang, X, Gong, J., 2019. Automatic extraction of built-up area from ZY3 multi-view satellite imagery: analysis of 45 global cities. Remote Sens. Environ., 226, 51-73.

[29]

Liu, J. Y., Kuang, W. H., Zhang, Z. X., Xu, X. L., Qin, Y. W., Ning, J, Zhou, W. C., Zhang, S. W., Li, R. D., Yan, C. Z., 2014. Spatio temporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J. Geogr. Sci., 24(2), 195-210.

[30]

Liu, J. Y., Liu, M. L., Tian, H. Q., Zhuang, D. F., Zhang, Z. X., Zhang, W, Tang, X. M., Deng, X. Z., 2005. Spatial and temporal patterns of China's cropland during 1990–2000: an analysis based on Landsat TM data. Remote Sens. Environ., 98(4), 442-456.

[31]

Lloyd, C. T., Sorichetta, A, Tatem, A. J., 2017. High resolution global gridded data for use in population studies. Sci. Data 4, 170001.

[32]

Lu, L. L., Qureshi, S, Li, Q. T., Chen, F, Shu, L., 2022. Monitoring and projecting sustainable transitions in urban land use using remote sensing and scenario-based modelling in a coastal megacity. Ocean Coast. Manage., 224, 106201.

[33]

Ma, J, Sun, Y, Meng, D, Huang, S, Li, N, Zhu, H. 2021. Accuracy assessment of two global gridded population dataset: a case study in China. ICISS 2021: Proceeding of the 4th International Conference on Information Science and Systems, pp.120-125.

[34]

Ma, S, Long, Y., 2019. Identifying spatial cities in China at the community scale. J. Urban Reg. Plan., 11(1), 37-50.

[35]

Marco, Z, Carlotta, F, Luigi, P, Margherita, C, Luca, S., 2015. Long-term urban growth and land use efficiency in Southern Europe: implications for sustainable land management. Sustainability 7(3), 3359-3385.

[36]

McDonald, R. I., Mansur, A. V., Ascensão, F, Colbert, M. L., Crossman, K, Elmqvist, T, Ziter, C., 2020. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat. Sustain., 3(1), 16-24.

[37]

Melchiorri, M, Pesaresi, M, Florczyk, A. J., Corbane, C, Kemper, T., 2019. Principles and applications of the global human settlement layer as baseline for the land use efficiency indicator—SDG 11.3.1. ISPRS Int. J. Geo-Inf., 8(2), 96.

[38]

Ministry of Housing and Urban-Rural Development of the People’s Republic of China (MOHURD), 2011. Code for Classification of Urban Land Use and Planning Standards of Development Land (GB50137 —2011). China Architecture & Building Press, Beijing.

[39]

Ministry of Natural Resources of the People’s Republic of China (MNR), 2020. Specifications For Quality Inspection and Acceptance of Geographic Conditions Monitoring Achievements (GB/T 39613 —2020). China Quality Inspection Press, Beijing.

[40]

Ministry of Natural Resources of the People’s Republic of China (MNR), 2021. Content and Index of Fundamental Geographic Conditions Monitoring (CH\T9029 —2019). Surveying and Mapping Press, Beijing.

[41]

Mudau, N, Mwaniki, D, Tsoeleng, L, Mashalane, M, Beguy, D, Ndugwa, R., 2020. Assessment of SDG indicator 11.3.1 and urban growth trends of major and small cities in South Africa. Sustainability 12(17), 7063.

[42]

Mwaniki, D., 2018. Module 3: indicator 11.3.1 land consumption rate to population growth rate. https://www.unescap.org/sites/default/files/Module%203_Land%20 Consumption%20Rate%20to%20Population%20Growth%20Rate%20for%20 indicator%2011.3.pdf (accessed 16 December 2022).

[43]

Nicolau, R, David, J, Caetano, M, Pereira, J. M., 2018. Ratio of land consumption rate to population growth rate—analysis of different formulations applied to mainland Portugal. ISPRS Int. J. Geo-Inf., 8(1), 10.

[44]

Ning, X. G., Wang, H, Liu, Y. F., Pang, B, Hao, M. H., 2018. High-precision urban boundary extraction and spatial-temporal analysis in China’s prefecture cities from 2000 to 2016. Geomati. Inf. Sci. Wuhan Univ., 43(12), 1916-1926.

[45]

Office of the Leading Group for the 7th National Population Census of the State Council, 2021. Main Data of the 7th National Population Census in 2020. China Statistics Press, Beijing (in Chinese).

[46]

Qi, W, Liu, S. H., Jin, F. J., 2017. Calculation and spatial evolution of population loss in Northeast China. Sci. Geogr. Sin., 37(12), 1795-1804.

[47]

Ratcliffe, M., 2015. A century of delineating a changing landscape: the Census Bureau's urban and rural classification, 1910 to 20doi: 10. Annual Meeting of the Social Science History Association . doi: 10.13140/RG.2.1.4161.4803.

[48]

Ritchie, H., Roser, M., 2018. Urbanization. http://ourworldindata.org/urbanization.

[49]

Schiavina, M, Melchiorri, M, Corbane, C, Florczyk, A. J., Freire, S, Pesaresi, M, Kemper, T., 2019. Multi-scale estimation of land use efficiency (SDG 11.3.1) across 25 years using global open and free data. Sustainability 11(20), 5674.

[50]

Schiavina, M, Melchiorri, M, Freire, S., 2022. European Commission, Joint Research Centre (JRC)

[51]

Schneider, A, Friedl, M. A., Potere, D., 2009. A new map of global urban extent from MODIS satellite data. Environ. Res. lett., 4(4), 044003.

[52]

Sharma, L, Pandey, P. C., Nathawat, M. S., 2012. Assessment of land consumption rate with urban dynamics change using geospatial techniques. J. Land Use Sci., 7(2), 135-148.

[53]

Shao, Z. F., Sumari, N. S., Portnov, A, Ujoh, F, Musakwa, W, Mandela, P. J., 2021. Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data. Geospat. Inf. Sci., 24(2), 241-255.

[54]

Shen, X. Q., Wang, X. D., Zhang, Z, Lu, Z. W., Lv, T. G., 2019. Evaluating the effectiveness of land use plans in containing urban expansion: an integrated view. Land Use Policy 80, 205-213.

[55]

Song, X. D., Liu, P, Zhou, Y. X., 2006. Urban and rural area division: taking Shanghai as an example. Acta Geogr. Sin., 61(8), 787-797.

[56]

Sun, Z. C., Xu, R, Du, W. J., Wang, L, Lu, D. S., 2019. High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine. Remote Sens., 11(7), 752.

[57]

Su, Q. L., Zhou, W. J., Wu, X. Y., Liu, N. N., Pan, X. Q., 2023. Design and implementation of the supervision information system for the implementation of the provincial land and space planning “One Map”—taking Jiangsu province as an example. Inf. Res., 3, 73-78.

[58]

Tian, Z. H., Tang, P, Cheng, X. J., 2023. Construction and application of a framework for monitoringand evaluating early warning mechanisms for provincial-level land spatial planning: taking Hunan province as an example. Land Resour. Herald 20(3), 54-60.

[59]

Tongji University, Tianjin University, Chongqing University, South China University of Technology, Huazhong University of Science and Technology, 2011. Regulatory Detailed Planning. China Architecture & Building Press, Beijing (in Chinese).

[60]

UK Office for National Statistics. 2018. Using Innovative Methods to Report against the Sustainable Development Goals. https://www.gov.uk/government/statistics/using-innovative-methods-to-report-sustainable-development-goal-indicators (accessed 14 December 2022).

[61]

UN-Habitat, 2021. Sustainable Development Goal 11+ Make Cities and Human Settlements Inclusive, Safe, Resilient and Sustainable: A Guide to Assist National and Local Governments to Monitor and Report on SDG Goal 11+ Indicators. Monitoring Framework —Definitions —Metadata —UN-Habitat Technical Support (accessed 16 December 2022).

[62]

United Nations, 2015. Transforming our world: the 2030 Agenda for sustainable development. UN General Assembly.

[63]

Wang, H, Liu, Y. F., Ning, X. G., Zhang, H. C., 2019. Review on remote sensing extraction of urban boundary. Sci. Surv. Mapp., 44(6), 159-165.

[64]

Wang, J. T., Zhang, J. T., 2011. Study on the urban land expansion in the urbanization of China. Mod. Urban Res., 26(8), 12-15.

[65]

Wang, Y. C., Huang, C. L., Feng, Y. Y., Gu, J., 2021. Evaluation of the coordinated relationship between land consumption rate and population growth rate in the Pearl River Delta based on the 2030 Sustainable Development Goals. Remote Sens. Tech. Appl., 36(5), 1168-1177.

[66]

Wang, Y. C., Huang, C. L., Feng, Y. Y., Zhao, M. Y., Gu, J., 2020. Using earth observation for monitoring SDG 11.3.1- ratio of land consumption rate to population growth rate in Mainland China. Remote Sens., 12(3), 357.

[67]

Wang, Q. J., Chen, H. Y., Yin, J. T.Revitalizing the existing land and broadening the development space: exploration and reflection on the transformation of development mode promoted by economizing and intensive land use in Hebi City. Resour. Guide 2023; (21), 22-23.

[68]

Xu, H., 2016. The annual “China Urban Development Report ” has been released, and some urgent problems faced by cities have been paid attention to - taking the pulse and prescribing or “urban diseases ”. Economic Daily, 11. http://paper.ce.cn/ jjrb/html/2016-08/12/content_308805.htm (accessed 1 February 2024).

[69]

Ying, S, Sun, L, Wang, W., 2021. Connotations and spatial delimitation of urban area. Geomat. Inf. Sci. Wuhan Univ., 46(9), 1370-1377.

[70]

Zhai, L, Zhang, N, Hou, W, Feng, Z. X., Qiao, Q. H., Luo, M. H., 2018. From big data to big analysis: a perspective of geographical conditions monitoring. Int. J. Image Data Fusion 9(3), 194-208.

[71]

Zhang, H. C., Ning, X. G., Wang, H, Shao, Z. F., 2018. High accuracy urban expansion monitoring and analysis of China’s provincial capitals from 2000 to 2015 based on high-resolution remote sensing imagery. Acta Geogr. Sin., 73(12), 2345-2363.

[72]

Zhang, J. X., Li, W. S., Zhai, L., 2015. Understanding geographical conditions monitoring: a perspective from China. Int. J. Digit. Earth 8(1), 38-57.

[73]

Zhou, M. L., Lu, L. L., Guo, H. D., Wang, Q. H., Cao, S. S., Zhang, S. C., Li, Q. T., 2021. Urban sprawl and changes in land-use efficiency in the Beijing–Tianjin–Hebei region, China from 2000 to 2020: a spatiotemporal analysis using earth observation data. Remote Sens., 13(15), 2850.

[74]

Zhu, X. L., Jiang, H. P., Sun, Z. C., Zhao, J. W., 2020. Analysis and evaluation of population and land urbanization in China based on SDG 11.3.1. Land Resour. Inf., 11(6), 36-41.

[75]

Zhang, P. H., 2021. Research on the intensive use of rural land in Chongqing under the background of urban and rural coordination: based on the perspective of population migration. Anhui Agri. Sci. Bull., 27(22), 138-143.

PDF

302

Accesses

0

Citation

Detail

Sections
Recommended

/