Differences in cost efficiency of runoff control in different urban built-up phases

Zijing Liu , Haiyuan Yao , Zhengxia Chen , Gaoling Zhang , Qi Liu , Haifeng Jia

Front. Environ. Sci. Eng. ›› 2025, Vol. 19 ›› Issue (12) : 161

PDF (3841KB)
Front. Environ. Sci. Eng. ›› 2025, Vol. 19 ›› Issue (12) : 161 DOI: 10.1007/s11783-025-2081-9
RESEARCH ARTICLE

Differences in cost efficiency of runoff control in different urban built-up phases

Author information +
History +
PDF (3841KB)

Abstract

Rapid urbanization reshapes landscape patterns and intensifies stormwater runoff pressure, yet the shifting cost-effectiveness of green infrastructure across different urban development phases remains poorly quantified. Focusing on Beijing’s 150 km2 urban subcenter, this study quantified 21 block-level landscape metrics, which were distilled via principal component analysis into five landform indicators: dominance, fragmentation, edge, aggregation, and shape. K-means clustering classified each block into constructed, constructing, or unconstructed phases. A life-cycle cost analysis then estimated the bioretention investment required to meet an 80%–85% annual runoff volume control target. The constructing phase, characterized by contiguous impervious surfaces at the urban edge, demands 45% more bioretention investment per unit area than the unconstructed phase and 4% more than the constructed phase. As land transitions from unconstructed to constructed, bioretention costs increase by approximately 109% for agricultural land and 86% for green space, whereas changes for residential and commercial areas remain minimal. These results indicate that uniform runoff control investment policies risk underfunding rapidly developing fringes and overfunding consolidated urban centers. A phase-specific and land use–sensitive investment strategy is therefore necessary to avoid capital inefficiency while achieving runoff control goals. By linking dynamic landscape evolution with infrastructure economics, this study provides a forward-looking tool to guide runoff control investment during urban expansion.

Graphical abstract

Keywords

Urban built-up phases / Landscape pattern / Bioretention facilities / Life cycle cost (LCC) / Runoff control cost-effectiveness / Supplementary investments

Highlight

● Runoff control cost-efficiency varies by urban development phase.

● Quantitative landscape indices delineate urban sprawl and build-up phases.

● Bioretention life-cycle costs for runoff control were calculated.

● Runoff management needs of land types across build-up phases were examined.

● supplementary investments required for urban expansion were identified.

Cite this article

Download citation ▾
Zijing Liu, Haiyuan Yao, Zhengxia Chen, Gaoling Zhang, Qi Liu, Haifeng Jia. Differences in cost efficiency of runoff control in different urban built-up phases. Front. Environ. Sci. Eng., 2025, 19(12): 161 DOI:10.1007/s11783-025-2081-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Abdi H, Williams L J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4): 433–459

[2]

Chan F K S, Griffiths J A, Higgitt D, Xu S Y, Zhu F F, Tang Y T, Xu Y Y, Thorne C R. (2018). “Sponge City” in China—A breakthrough of planning and flood risk management in the urban context. Land Use Policy, 76: 772–778

[3]

Chui T F M, Liu X, Zhan W T. (2016). Assessing cost-effectiveness of specific LID practice designs in response to large storm events. Journal of Hydrology, 533: 353–364

[4]

Dadashpoor H, Azizi P, Moghadasi M. (2019). Land use change, urbanization, and change in landscape pattern in a metropolitan area. Science of the Total Environment, 655: 707–719

[5]

Dou P, Chen Y B. (2017). Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015. International Journal of Remote Sensing, 38(19): 5388–5407

[6]

Ferraro M B. (2024). Fuzzy k-means: history and applications. Econometrics and Statistics, 30: 110–123

[7]

Fu B J, Zhao W W, Chen L D, Liu Z F, Y H. (2005). Eco-hydrological effects of landscape pattern change. Landscape and Ecological Engineering, 1(1): 25–32

[8]

Hunt W F, Davis A P, Traver R G. (2012). Meeting hydrologic and water quality goals through targeted bioretention design. Journal of Environmental Engineering, 138(6): 698–707

[9]

Kim H W, Park Y. (2016). Urban green infrastructure and local flooding: the impact of landscape patterns on peak runoff in four Texas MSAs. Applied Geography, 77: 72–81

[10]

Lafortezza R, Sanesi G. (2019). Nature-based solutions: settling the issue of sustainable urbanization. Environmental Research, 172: 394–398

[11]

Li C L, Liu M, Hu Y M, Zhou R, Wu W, Huang N. (2021). Evaluating the runoff storage supply-demand structure of green infrastructure for urban flood management. Journal of Cleaner Production, 280: 124420

[12]

Li F Z, Chen J Q, Engel B A, Liu Y Z, Wang S Z, Sun H. (2020). Assessing the effectiveness and cost efficiency of green infrastructure practices on surface runoff reduction at an urban watershed in China. Water, 13(1): 24

[13]

Li Y H, Jia L R, Wu W H, Yan J Y, Liu Y S. (2018). Urbanization for rural sustainability: rethinking China’s urbanization strategy. Journal of Cleaner Production, 178: 580–586

[14]

Lippera M C, Khurelbaatar G, Despot D, Kouyi G L, Rizzo A, Friesen J. (2025). Spatial-economic scenarios to increase resilience to urban flooding. Water Research X, 26: 100284

[15]

Liu S C, Pan R H, Chen X, Xue Z H, Zhang Y, Cao Z. (2024). A comprehensive evaluation method of cost-effectiveness of LID facilities in sponge city based on the life cycle. Water Conservation Science and Engineering, 9(2): 84

[16]

Wang Q W, Zhao G Y, Zhao R Z. (2024). Resilient urban expansion: Identifying critical conflict patches by integrating flood risk and land use predictions: A case study of Min Delta Urban Agglomerations in China. International Journal of Disaster Risk Reduction, 100: 104192

[17]

McGarigal K. (2002). Landscape pattern metrics. In: El-Shaarawi A H, Piegorsch W W, eds,

[18]

Miller J D, Kim H, Kjeldsen T R, Packman J, Grebby S, Dearden R. (2014). Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. Journal of Hydrology, 515: 59–70

[19]

Pappalardo V, La Rosa D, Campisano A, La Greca P. (2017). The potential of green infrastructure application in urban runoff control for land use planning: a preliminary evaluation from a southern Italy case study. Ecosystem Services, 26: 345–354

[20]

Peng J, Hu Y N, Liu Y X, Ma J, Zhao S Q. (2018). A new approach for urban-rural fringe identification: integrating impervious surface area and spatial continuous wavelet transform. Landscape and Urban Planning, 175: 72–79

[21]

Salerno F, Gaetano V, Gianni T. (2018). Urbanization and climate change impacts on surface water quality: enhancing the resilience by reducing impervious surfaces. Water Research, 144: 491–502

[22]

Shao Z Y, Li Y X, Gong H F, Chai H X. (2024). From risk control to resilience: developments and trends of urban roads designed as surface flood passages to cope with extreme storms. Frontiers of Environmental Science & Engineering, 18(2): 22

[23]

Singh J S, Roy P S, Murthy M S R, Jha C S. (2010). Application of landscape ecology and remote sensing for assessment, monitoring and conservation of biodiversity. Journal of the Indian Society of Remote Sensing, 38(3): 365–385

[24]

Song J L, Lu Y, Fischer T, Hu K J. (2024). Effects of the urban landscape on heatwave-mortality associations in Hong Kong: comparison of different heatwave definitions. Frontiers of Environmental Science & Engineering, 18(1): 11

[25]

Song X P, Sexton J O, Huang C Q, Channan S, Townshend J R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175: 1–13

[26]

Zhu H R, Yu M M, Zhu J Q, Lu H Z, Cao R J. (2019). Simulation study on effect of permeable pavement on reducing flood risk of urban runoff. International Journal of Transportation Science and Technology, 8(4): 373–382

RIGHTS & PERMISSIONS

Higher Education Press 2025

AI Summary AI Mindmap
PDF (3841KB)

Supplementary files

FSE-25108-OF-LZJ_suppl_1

198

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/