Coupling Coordination Assessment on Sponge City Construction and Its Spatial Pattern in Henan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source
2.3. Coupling Coordination Mechanism
2.4. Evaluation Index System
2.5. Research Methods
2.5.1. Determination of Entropy Weight
2.5.2. Coupling Coordinated Development Model
2.5.3. Spatial Statistical Methods
3. Results
3.1. Analysis of Coordination Degree between Regions
3.2. Evolution of Spatial Pattern of Coordinated Development
3.2.1. Evolution of Regional Differences
3.2.2. Statistical Analysis of Local Space
4. Conclusions and Recommendations
Author Contributions
Funding
Conflicts of Interest
References
- Liu, H.; Jia, Y.; Niu, C. “Sponge city” concept helps solve China’s urban water problems. Environ. Earth Sci. 2017, 76, 473–479. [Google Scholar] [CrossRef]
- Xia, J.; Zhang, Y.; Xiong, L.; He, S.; Wang, L.; Yu, Z. Opportunities and challenges of the Sponge City construction related to urban water issues in China. Sci. China Earth Sci. 2017, 60, 652–658. [Google Scholar] [CrossRef]
- MEP. Report on the State of the Environment in China; Environmental Information Centre, Ministry of Environmental Protection of the Peoples Republic of China: Beijing, China, 2015.
- Li, H.; Ding, L.; Ren, M.; Li, C.; Wang, H. Sponge City Construction in China: A Survey of the Challenges and Opportunities. Water 2017, 9, 594. [Google Scholar] [CrossRef] [Green Version]
- Shao, W.; Zhang, H.; Liu, J.; Yang, G.; Chen, X.; Yang, Z.; Huang, H. Data Integration and its Application in the Sponge City Construction of China. Procedia Eng. 2016, 154, 779–786. [Google Scholar] [CrossRef] [Green Version]
- Chan, F.K.S.; Griffiths, J.A.; Higgitt, D.; Xu, S.; Zhu, F.; Tang, Y.-T.; Thorne, C.R. “Sponge City” in China—A breakthrough of planning and flood risk management in the urban context. Land Use Policy 2018, 76, 772–778. [Google Scholar] [CrossRef]
- Pyke, C.; Warren, M.P.; Johnson, T.; Lagro, J., Jr.; Scharfenberg, J.; Groth, P.; Freed, R.; Schroeer, W.; Main, E. Assessment of low impact development for managing stormwater with changing precipitation due to climate change. Landsc. Urban Plann. 2011, 103, 166–173. [Google Scholar] [CrossRef]
- Griffiths, J. Sustainable urban drainage. In Encyclopedia of Sustainable Technologies; Abraham, M., Ed.; Elsevier: Amsterdam, The Netherlands, 2017. [Google Scholar]
- Morison, P.J.; Brown, R.R. Understanding the nature of publics and local policy commitment to Water Sensitive Urban Design. Landsc. Urban Plann. 2011, 99, 83–92. [Google Scholar] [CrossRef]
- Voyde, E.; Fassman, E.; Simcock, R. Hydrology of an extensive living roof under sub-tropical climate conditions in Auckland, New Zealand. J. Hydrol. 2010, 394, 384–395. [Google Scholar] [CrossRef]
- Hui, L.; Zihao, W.; Xifeng, L. Ecological Sensitivity Analysis of Sponge City Planning Area Based on GIS. Land Resour. Guide 2017, 14, 49–52. [Google Scholar]
- Yu, K.; Li, D.; Yuan, H.; Fu, W.; Qiao, Q.; Wang, S. Theory and Practice of “Sponge City”. City Plan. Rev. 2015, 39, 26–36. [Google Scholar]
- Wang, Y.C.; Gao, J. Seattle stormwater management practice inspired for the development of sponge city. J. Hum. Settl. 2017, 6, 65–70. [Google Scholar]
- Baoxing, Q. The Connotation, Ways and Prospects of Sponge City (LID). Water Supply Drain. 2015, 51, 1–7. [Google Scholar]
- McDaniels, T.; Chang, S.; Cole, D.; Mikawoz, J.; Longstaff, H. Fostering resilien to extreme events within infrastructure systems: Characterizing decision contexts for mitigation and adaptation. Glob. Environ. Chang. 2008, 18, 310–318. [Google Scholar] [CrossRef]
- Ouyang, M.; Duenas-Osorio, L.; Min, X. A three-stage resilience analysis framework for urban infrastructure systems. Struct. Saf. 2012, 36, 23–31. [Google Scholar] [CrossRef]
- Hossain, F.; Arnold, J.; Beighley, E.; Brown, C.; Burian, S.; Chen, J.; Tidwell, V.; Wegner, D. Local-to-regional landscape drivers of extreme weather and climate: Implications for water infrastructure resilience. J. Hydrol. Eng. 2015, 20, 2005–2015. [Google Scholar] [CrossRef] [Green Version]
- Mugume, S.N.; Gomez, D.E.; Fu, G.; Farmani, R.; Butler, D. A global analysis approach for investigating structural resilience in urban drainage systems. Water Res. 2015, 81, 15–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mugume, S.N.; Diao, K.; Astaraie-Imani, M.; Fu, G.; Farmani, R.; Butler, D. Enhancing resilience in urban water systems for future cities. Water Sci. Technol. Water Supply 2015, 15, 1343–1352. [Google Scholar] [CrossRef] [Green Version]
- Willems, P.; Arnbjerg-Nielsen, K.; Olsson, J.; Nguyen, V.T.V. Impact assessment on urban rainfall extremes and urban drainage: Methods and shortcomings. Atmos. Res. 2012, 103, 106–118. [Google Scholar] [CrossRef]
- Karamouz, M.; Hosseinpour, A.; Nazif, S. Improvement of urban drainage system performance under climate change impact:case study. J. Hydrol. Eng. 2011, 16, 395–412. [Google Scholar] [CrossRef]
- Huong, H.T.L.; Pathirana, A. Urbanization and climate change impacts on future urban flooding in Can Tho City, Vietnam. Hydrol. Earth Syst. Sci. 2013, 17, 379–394. [Google Scholar] [CrossRef] [Green Version]
- Dong, X.; Guo, H.; Zeng, S.Y. Enhancing future resilience in urban drainage system: Green versus grey infrastructure. Water Res. 2017, 124, 280–289. [Google Scholar] [CrossRef] [PubMed]
- Liu, W.; Chen, W.; Peng, C. Assessing the effectiveness of green infrastructures on urban flooding reduction: A community scale study. Ecol. Model. 2014, 29, 6–14. [Google Scholar] [CrossRef]
- Liu, W.; Chen, W.; Peng, C. Influences of setting sizes and combination of green infrastructures on community’s stormwater runoff reduction. Ecol. Model. 2015, 318, 236–244. [Google Scholar] [CrossRef]
- Kamil, P.; Daniel, S.; Sabina, K. The temporal variability of a rainfall synthetic hyetograph for the dimensioning of stormwater retention tanks in small urban catchments. J. Hydrol. 2017, 549, 501–511. [Google Scholar]
- Eckart, K.; Mcphee, Z.; Bolisetti, T. Performance and implementation of low impact development—A review. Sci. Total Environ. 2017, 608, 413–432. [Google Scholar] [CrossRef]
- Kong, F.; Ban, Y.; Yin, H.; James, P.; Dronova, I. Modeling stormwater management at the city district level in response to changes in land use and low impact development. Environ. Model. Softw. 2017, 95, 132–142. [Google Scholar] [CrossRef]
- Fletcher, T.D.; Shuster, W.; Hunt, W.F.; Ashley, R.; Butler, D.; Arthur, S.; Trowsdale, S.; Barraud, S.; Semadeni-Davies, A.; Bertrand-Krajewski, J.-L.; et al. SUDS, LID, BMPs, WSUD and more—The evolution and application of terminology surrounding urban drainage. Urban Water J. 2016, 12, 525–542. [Google Scholar] [CrossRef]
- Mei, C.; Liu, J.H.; Wang, H.; Yang, Z.Y.; Ding, X.Y.; Shao, W.W. Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed. Sci. Total Environ. 2018, 639, 1394–1407. [Google Scholar] [CrossRef]
- Hu, M.; Sayama, T.; Zhang, X.; Tanaka, K.; Takara, K.; Yang, H. Evaluation of low impact development approach for mitigating flood inundation at a watershed scale in China. J. Environ. Manag. 2017, 193, 430–438. [Google Scholar] [CrossRef]
- Jia, H.; Yu, S.L.; Qin, H. Low impact development and sponge city construction for urban stormwater management. Front. Environ. Sci. Eng. 2017, 11, 20. [Google Scholar] [CrossRef] [Green Version]
- Manocha, N.; Babovic, V. Development and valuation of adaptation pathways for storm water management infrastructure. Environ. Sci. Policy 2017, 77, 86–97. [Google Scholar] [CrossRef]
- Yike, W.; Qianhu, C. Study on Sustainable Stormwater Management Policies from the International Perspective: Based on the Comparison of the United States, the United Kingdom and China. Urban Plan. Int. 2020, 1–15. [Google Scholar] [CrossRef]
- Zhao, J.; Fonseca, C.; Zeerak, R. Stormwater Utility Fees and Credits: A Funding Strategy for Sustainability. Sustainability 2019, 11, 1913. [Google Scholar] [CrossRef] [Green Version]
- Ding, L.; Ren, X.; Gu, R.; Che, Y. Implementation of the “sponge city” development plan in China: An evaluation of public willingness to pay for the life-cycle maintenance of its facilities. Cities 2019, 93, 13–30. [Google Scholar] [CrossRef]
- Ma, Y.; Jiang, Y.; Swallow, S. China’s sponge city development for urban water resilience and sustainability: A policy discussion. Sci. Total Environ. 2020, 729, 139078. [Google Scholar] [CrossRef] [PubMed]
- Xiong, J.X.; Wang, W.H.; He, S.H.; Yin, Y.; Tang, C.F. Spatio-temporal Pattern and Influencing Factor of Coupling Coordination of Tourism Urbanization System in the Dongting Lake Region. Sci. Geogr. Sin. 2020, 40, 1532–1542. [Google Scholar]
- Sun, Y.; Deng, L.; Pan, S.-Y.; Chiang, P.-C.; Sable, S.S.; Shah, K.J. Integration of Green and Gray Infrastructures for Sponge City: Water and Energy Nexus. Water Energy 2020, 3, 29–40. [Google Scholar] [CrossRef]
- Woznicki, S.A.; Hondula, K.L.; Jarnagin, S.T. Effectiveness of landscape-based green infrastructure for stormwater management in suburban catchments. Hydrol. Process 2018, 32, 2346–2361. [Google Scholar] [CrossRef] [Green Version]
- Alves, A.; Gersonius, B.; Sanchez, A.; Vojinovic, Z.; Kapelan, Z. Multi-criteria Approach for Selection of Green and Grey Infrastructure to Reduce Flood Risk and Increase CO-benefits. Water Resour. Manag. 2018, 32, 2505–2522. [Google Scholar] [CrossRef]
- Shannon, C.E. A mathematical theory of communication. ACM SIGMOBILE Mobile. Comput. Commun. Rev. 2001, 5, 3–55. [Google Scholar] [CrossRef]
- Wu, S.S.; Fu, Y.; Shen, H.; Liu, F. Using ranked weights and Shannon entropy to modify regional sustainable society index. Sustain. Cities Soc. 2018, 41, 443–448. [Google Scholar] [CrossRef]
- Jiaqi, Z.; Yijin, W.; Yong, G.; Chenghao, W.; Kung, H. Comprehensive evaluation of ecological security in poor areas based on grey relational model—A case study of enshi poor area. Geogr. Res. 2014, 33, 1457–1466. [Google Scholar]
- Zheng, S.; Fu, Y.; Lai, K.K.; Liang, L. An improvement to multipie criteria ABC inventory classification using Shannon entropy. J. Syst. Sci. Complex. 2017, 30, 857–865. [Google Scholar] [CrossRef]
- Wu, J.; Sun, J.; Liang, L. DEA cross-efficiency aggregation method based upon Shannon entropy. Int. J. Prod. Res. 2012, 50, 6726–6736. [Google Scholar] [CrossRef]
- Li, M.; Fengjun, J.; Yi, L. Analysis on the coupling pattern and industrial structure of China’s economy and environmental pollution. J. Geogr. 2012, 67, 1299–1307. [Google Scholar]
- Xia, W.; Zhaolin, G.; Jinyuan, L.; Hu, M. Multi-level gray method for evaluation of tourism resources development potential—Taking Laozi mountain scenic spot as an example. Geogr. Res. 2007, 3, 625–635. [Google Scholar]
- Zheng Dong New District. Available online: http://www.zhengdong.gov.cn/sitesources/zhengdong/page_pc/index.html (accessed on 25 November 2020).
Target Layer | System Layer | Indicator Layer | Index |
---|---|---|---|
Coupling Coordination Evaluation Index of Sponge City Construction in Research Area (A) | “gray” infrastructure construction (B1) | Road area (10,000 square meters) X1 | Negative |
Drainage pipe length (km) X2 | Positive | ||
Road length (km) X4 | Negative | ||
Sewage treatment rate (%) X5 | Positive | ||
Number of bridges (seat) X8 | Positive | ||
“Green” infrastructure construction (B2) | Green area (hectare) X3 | Positive | |
Per capita park green area (m2) X6 | Positive | ||
Park area (hectare) X7 | Positive | ||
Green coverage area (hectare) X10 | Positive | ||
The level of economic development (C1) | GDP(Billion) X11 | Positive | |
Garden green land fixed investment (ten thousand yuan) X12 | Positive | ||
Drainage fixed investment (ten thousand yuan) X13 | Positive | ||
Urban population (10,000 people) X14 | Positive | ||
Built-up area (square kilometers) X15 | Negative | ||
Urbanization rate (%) X16 | Positive | ||
The actual investment in the construction of municipal public facilities is in place (10,000 yuan) X17 | Positive |
Coupling Coordination | Coordination Level | S1 > S2 | S2 > S1 |
---|---|---|---|
0 < D ≤ 0.3 | Low coupling coordination phase | Economic development lags behind | Infrastructure lag |
0.3 < D ≤ 0.5 | Moderate coupling coordination phase | Economic development lags behind | Infrastructure lag |
0.5 < D ≤ 0.8 | Highly coupled coordination phase | Economic development lags behind | Infrastructure lag |
0.8 < D ≤ 1 | Extreme coupling coordination phase | Economic development lags behind | Infrastructure lag |
Region | Coupling Coordination Degree in 2013 | Coupling Coordination Degree in 2017 | ||||||
---|---|---|---|---|---|---|---|---|
S1 | S2 | D | Coordination Level | S1 | S2 | D | Coordination Level | |
Zhengzhou City | 0.623386 | 0.825219 | 0.846899 | Extreme coordination | 0.669786 | 0.816205 | 0.859872 | Extreme coordination |
Kaifeng City | 0.322134 | 0.273311 | 0.54472 | Highly coordinated | 0.372635 | 0.27094 | 0.563689 | Highly coordinated |
Luoyang City | 0.465014 | 0.334487 | 0.628002 | Highly coordinated | 0.502356 | 0.337348 | 0.641612 | Highly coordinated |
Pingdingshan City | 0.41014 | 0.332901 | 0.607872 | Highly coordinated | 0.434414 | 0.291147 | 0.596354 | Highly coordinated |
Anyang City | 0.434528 | 0.270151 | 0.585337 | Highly coordinated | 0.43343 | 0.2893 | 0.595068 | Highly coordinated |
Hebi City | 0.404502 | 0.262699 | 0.570946 | Highly coordinated | 0.436042 | 0.287656 | 0.595114 | Highly coordinated |
Xinxiang City | 0.40586 | 0.270464 | 0.575601 | Highly coordinated | 0.35694 | 0.278649 | 0.561582 | Highly coordinated |
Jiaozuo City | 0.43791 | 0.30888 | 0.606448 | Highly coordinated | 0.500011 | 0.310613 | 0.627769 | Highly coordinated |
Puyang City | 0.42392 | 0.246459 | 0.568535 | Highly coordinated | 0.455427 | 0.239409 | 0.574632 | Highly coordinated |
Xuchang City | 0.445089 | 0.269307 | 0.588401 | Highly coordinated | 0.460815 | 0.336353 | 0.627452 | Highly coordinated |
Luohe City | 0.447582 | 0.25596 | 0.581783 | Highly coordinated | 0.465954 | 0.288366 | 0.605441 | Highly coordinated |
Sanmenxia City | 0.451144 | 0.271231 | 0.591444 | Highly coordinated | 0.439332 | 0.28157 | 0.593055 | Highly coordinated |
Nanyang City | 0.600006 | 0.329184 | 0.666651 | Highly coordinated | 0.429884 | 0.345018 | 0.620581 | Highly coordinated |
Shangqiu City | 0.32222 | 0.279107 | 0.547622 | Highly coordinated | 0.360593 | 0.328569 | 0.586693 | Highly coordinated |
Xinyang City | 0.467129 | 0.324367 | 0.623905 | Highly coordinated | 0.381195 | 0.266289 | 0.564449 | Highly coordinated |
Zhoukou City | 0.344228 | 0.225794 | 0.528007 | Highly coordinated | 0.392165 | 0.222019 | 0.543206 | Highly coordinated |
Zhumadian City | 0.384815 | 0.299556 | 0.582683 | Highly coordinated | 0.482803 | 0.315774 | 0.624866 | Highly coordinated |
Jiyuan City | 0.436586 | 0.338317 | 0.619938 | Highly coordinated | 0.462085 | 0.294883 | 0.607565 | Highly coordinated |
Region | Infrastructure Construction Index | Economic Development Index | Relative Development | Coupling Coordination | Relative Development Type |
---|---|---|---|---|---|
Zhengzhou City | 0.633578115 | 0.774055169 | 0.822082425 | 0.8364244 | Infrastructure lag |
Kaifeng City | 0.396918457 | 0.279733272 | 1.42961227 | 0.5761015 | Lag in economic development |
Luoyang City | 0.483916433 | 0.333859849 | 1.449215794 | 0.6338987 | Lag in economic development |
Pingdingshan City | 0.430533325 | 0.313778336 | 1.383605691 | 0.6057436 | Lag in economic development |
Anyang City | 0.441702908 | 0.291403433 | 1.517672072 | 0.598873 | Lag in economic development |
Hebi City | 0.429776229 | 0.282747714 | 1.518187027 | 0.5899238 | Lag in economic development |
Xinxiang City | 0.399777152 | 0.293814553 | 1.365102599 | 0.5850529 | Lag in economic development |
Jiaozuo City | 0.474586257 | 0.34826711 | 1.396870733 | 0.6358566 | Lag in economic development |
Puyang City | 0.456965847 | 0.25831484 | 1.774071396 | 0.5858622 | Lag in economic development |
Xuchang City | 0.450395427 | 0.291419111 | 1.557051753 | 0.6014466 | Lag in economic development |
Luohe City | 0.466232093 | 0.284559279 | 1.641295553 | 0.603342 | Lag in economic development |
Sanmenxia City | 0.42761795 | 0.292404637 | 1.466660645 | 0.5935525 | Lag in economic development |
Nanyang City | 0.530638566 | 0.366931643 | 1.461614198 | 0.6622117 | Lag in economic development |
Shangqiu City | 0.329256604 | 0.296774658 | 1.109013939 | 0.5584797 | Lag in economic development |
Xinyang City | 0.43525355 | 0.292081501 | 1.493643174 | 0.5966539 | Lag in economic development |
Zhoukou City | 0.405418306 | 0.243993113 | 1.673831676 | 0.5598729 | Lag in economic development |
Zhumadian City | 0.419954597 | 0.280990821 | 1.501264572 | 0.5854403 | Lag in economic development |
Jiyuan City | 0.450710664 | 0.324022045 | 1.396122648 | 0.6179675 | Lag in economic development |
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Wang, K.; Zhang, L.; Zhang, L.; Cheng, S. Coupling Coordination Assessment on Sponge City Construction and Its Spatial Pattern in Henan Province, China. Water 2020, 12, 3482. https://doi.org/10.3390/w12123482
Wang K, Zhang L, Zhang L, Cheng S. Coupling Coordination Assessment on Sponge City Construction and Its Spatial Pattern in Henan Province, China. Water. 2020; 12(12):3482. https://doi.org/10.3390/w12123482
Chicago/Turabian StyleWang, Kun, Lijun Zhang, Lulu Zhang, and Shujuan Cheng. 2020. "Coupling Coordination Assessment on Sponge City Construction and Its Spatial Pattern in Henan Province, China" Water 12, no. 12: 3482. https://doi.org/10.3390/w12123482