Influence of Community Factors on Water Saving in a Mega City after Implementing the Progressive Price Schemes
Abstract
:1. Introduction
2. Material and Methods
2.1. Data Collection
2.2. PPS in Shanghai
2.3. Statistical Analysis
3. Results and Discussion
3.1. Overview of the Dataset
3.1.1. Parameters of the 14 Investigated Communities
3.1.2. Relationship between the Number of Residents and Water Consumption
3.1.3. Changes in Water Consumption before and after PPS Implementation
3.2. Results of the PCA
3.3. Relationship between Household Water Use and Location Factors
3.4. Effect of the Progressive Price Scheme on Water Use
3.5. Improvement and Promotion for PPS
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Goonetilleke, A.; Vithanage, M. Water Resources Management: Innovation and Challenges in a Changing World. Water 2017, 9, 281. [Google Scholar] [CrossRef]
- Irfan, M.; Kazmi, S.J.H.; Arsalan, M.H. Sustainable Harnessing of the Surface Water Resources for Karachi: A Geographic Review. Arab. J. Geosci. 2018, 11, 24. [Google Scholar] [CrossRef]
- Chu, J.; Wang, C.; Chen, J.; Wang, H. Agent-Based Residential Water Use Behavior Simulation and Policy Implications: A Case-Study in Beijing City. Water Resour. Manag. 2009, 23, 3267–3295. [Google Scholar] [CrossRef]
- Cominola, A.; Giuliani, M.; Piga, D.; Castelletti, A.; Rizzoli, A.E. Benefits and Challenges of Using Smart Meters for Advancing Residential Water Demand Modeling and Management: A Review. Environ. Model. Softw. 2015, 72, 198–214. [Google Scholar] [CrossRef][Green Version]
- Seelen, L.M.S.; Flaim, G.; Jennings, E.; De Senerpont Domis, L.N. Saving Water for the Future: Public Awareness of Water Usage and Water Quality. J. Environ. Manag. 2019, 242, 246–257. [Google Scholar] [CrossRef]
- Xue, P.; Hong, T.; Dong, B.; Mak, C. A Preliminary Investigation of Water Usage Behavior in Single-Family Homes. Build. Simul. 2017, 10, 949–962. [Google Scholar] [CrossRef][Green Version]
- Rees, P.; Clark, S.; Nawaz, R. Household Forecasts for the Planning of Long-Term Domestic Water Demand: Application to London and the Thames Valley. Popul. Space Place 2020, 26. [Google Scholar] [CrossRef]
- Suárez-Varela, M. Modeling Residential Water Demand: An Approach Based on Household Demand Systems. J. Environ. Manag. 2020, 261, 109921. [Google Scholar] [CrossRef]
- Chen, X.; Li, F.; Li, X.; Hu, Y.; Hu, P. Evaluating and Mapping Water Supply and Demand for Sustainable Urban Ecosystem Management in Shenzhen, China. J. Clean. Prod. 2020, 251, 119754. [Google Scholar] [CrossRef]
- Lu, S.; Gao, X.; Li, W.; Jiang, S.; Huang, L. A Study on the Spatial and Temporal Variability of the Urban Residential Water Consumption and Its Influencing Factors in the Major Cities of China. Habitat Int. 2018, 78, 29–40. [Google Scholar] [CrossRef]
- Torres López, S.; de los Angeles Barrionuevo, M.; Rodríguez-Labajos, B. Water Accounts in Decision-Making Processes of Urban Water Management: Benefits, Limitations and Implications in a Real Implementation. Sustain. Cities Soc. 2019, 50, 101676. [Google Scholar] [CrossRef]
- Wang, H.; Xie, J.; Li, H. Water Pricing with Household Surveys: A Study of Acceptability and Willingness to Pay in Chongqing, China. China Econ. Rev. 2010, 21, 136–149. [Google Scholar] [CrossRef]
- Polebitski, A.S.; Palmer, R.N. Seasonal Residential Water Demand Forecasting for Census Tracts. J. Water Resour. Plan. Manag. 2010, 136, 27–36. [Google Scholar] [CrossRef]
- Otaki, Y.; Otaki, M.; Aramaki, T. Combined Methods for Quantifying End-Uses of Residential Indoor Water Consumption. Environ. Process. 2017, 4, 33–47. [Google Scholar] [CrossRef]
- Voskamp, I.M.; Sutton, N.B.; Stremke, S.; Rijnaarts, H.H.M. A Systematic Review of Factors Influencing Spatiotemporal Variability in Urban Water and Energy Consumption. J. Clean. Prod. 2020, 256, 120310. [Google Scholar] [CrossRef]
- Zheng, J.; Kamal, M.A. The Effect of Household Income on Residential Wastewater Output: Evidence from Urban China. Util. Policy 2020, 63, 101000. [Google Scholar] [CrossRef]
- Taştan, H. Estimation of Dynamic Water Demand Function: The Case of Istanbul. Urban Water J. 2018, 15, 75–82. [Google Scholar] [CrossRef]
- Richter, C.P.; Stamminger, R. Water Consumption in the Kitchen—A Case Study in Four European Countries. Water Resour Manag. 2012, 26, 1639–1649. [Google Scholar] [CrossRef]
- Seidl, C.; Wheeler, S.A.; Zuo, A. High Turbidity: Water Valuation and Accounting in the Murray-Darling Basin. Agric. Water Manag. 2020, 230, 105929. [Google Scholar] [CrossRef]
- Kotagama, H.; Zekri, S.; Al Harthi, R.; Boughanmi, H. Demand Function Estimate for Residential Water in Oman. Int. J. Water Resour. Dev. 2017, 33, 907–916. [Google Scholar] [CrossRef]
- Rajeevan, U.; Mishra, B.K. Sustainable Management of the Groundwater Resource of Jaffna, Sri Lanka with the Participation of Households: Insights from a Study on Household Water Consumption and Management. Groundw. Sustain. Dev. 2020, 10, 100280. [Google Scholar] [CrossRef]
- Mayol, A. Social and Nonlinear Tariffs on Drinking Water: Cui Bono? Empirical Evidence from a Natural Experiment in France. Rev. D’économie Polit. 2017, 127, 1161. [Google Scholar] [CrossRef]
- Mostafavi, N.; Shojaei, H.R.; Beheshtian, A.; Hoque, S. Residential Water Consumption Modeling in the Integrated Urban Metabolism Analysis Tool (IUMAT). Resour. Conserv. Recycl. 2018, 131, 64–74. [Google Scholar] [CrossRef]
- Makki, A.A.; Stewart, R.A.; Panuwatwanich, K.; Beal, C. Revealing the Determinants of Shower Water End Use Consumption: Enabling Better Targeted Urban Water Conservation Strategies. J. Clean. Prod. 2013, 60, 129–146. [Google Scholar] [CrossRef][Green Version]
- Barraqué, B.O.; Laigneau, P.; Formiga-Johnsson, R.M. The Rise and Fall of the French Agences de l’Eau: From German-Type Subsidiarität to State Control. Water Econ. Policy 2018, 4, 1850013. [Google Scholar] [CrossRef]
- De Brito, P.L.C.; de Azevedo, J.P.S. Charging for Water Use in Brazil: State of the Art and Challenges. Water Resour. Manag. 2020, 34, 1213–1229. [Google Scholar] [CrossRef]
- Wang, Y.; Tian, K.; Wang, H.; Zhang, B. Regional Differences in Citizens’ Water Behaviors: A Comparative Study of Typical Cities Based on AMOS. Water Policy 2019, 21, 742–757. [Google Scholar] [CrossRef]
- Tong, Y.; Fan, L.; Niu, H. Water Conservation Awareness and Practices in Households Receiving Improved Water Supply: A Gender-Based Analysis. J. Clean. Prod. 2017, 141, 947–955. [Google Scholar] [CrossRef]
- Wang, C.-H.; Dong, H. Responding to the Drought: A Spatial Statistical Approach to Investigating Residential Water Consumption in Fresno, California. Sustainability 2017, 9, 240. [Google Scholar] [CrossRef][Green Version]
- Kazor, K.; Holloway, R.W.; Cath, T.Y.; Hering, A.S. Comparison of Linear and Nonlinear Dimension Reduction Techniques for Automated Process Monitoring of a Decentralized Wastewater Treatment Facility. Stoch Environ. Res. Risk Assess. 2016, 30, 1527–1544. [Google Scholar] [CrossRef]
- Snelder, T.H.; Booker, D.J. Natural flow regime classifications are sensitive to definition procedures. River Res. Appl. 2013, 29, 822–838. [Google Scholar] [CrossRef]
- Zheng, H.; Zhou, W.; Zhang, L.; Li, X.; Cheng, J.; Ding, Z.; Xu, Y.; Hu, W. Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis. IJERPH 2020, 17, 2983. [Google Scholar] [CrossRef] [PubMed]
- Tamura, M.; Tsujita, S. A Study on the Number of Principal Components and Sensitivity of Fault Detection Using PCA. Comput. Chem. Eng. 2007, 31, 1035–1046. [Google Scholar] [CrossRef]
- Villarreal, E.L.; Dixon, A. Analysis of a Rainwater Collection System for Domestic Water Supply in Ringdansen, Norrköping, Sweden. Build. Environ. 2005, 40, 1174–1184. [Google Scholar] [CrossRef]
- Jessoe, K.; Papineau, M.; Rapson, D. Utilities Included: Split Incentives in Commercial Electricity Contracts. Energy J. 2020, 41. [Google Scholar] [CrossRef][Green Version]
- Melvin, J. The Split Incentives Energy Efficiency Problem: Evidence of Underinvestment by Landlords. Energy Policy 2018, 115, 342–352. [Google Scholar] [CrossRef]
- Wichman, C.J.; Taylor, L.O.; von Haefen, R.H. Conservation Policies: Who Responds to Price and Who Responds to Prescription? J. Environ. Econ. Manag. 2016, 79, 114–134. [Google Scholar] [CrossRef][Green Version]
- Mayol, A.; Porcher, S. Tarifs discriminants et monopoles de l’eau potable: Une analyse de la réaction des consommateurs face aux distorsions du signal-prix. Rev. Économique 2019, 70, 461. [Google Scholar] [CrossRef]
- Tortajada, C.; González-Gómez, F.; Biswas, A.K.; Buurman, J. Water Demand Management Strategies for Water-Scarce Cities: The Case of Spain. Sustain. Cities Soc. 2019, 45, 649–656. [Google Scholar] [CrossRef]
- Mahmood, B.; Sharma, S. Affordability of Household Water and Wastewater Charges in Manukau City: A Case Study. In WIT Transactions on Ecology and the Environment; WIT Press: Boston, MA, USA, 2009; pp. 313–324. [Google Scholar]
- Reynaud, A.; Romano, G. Advances in the Economic Analysis of Residential Water Use: An Introduction. Water 2018, 10, 1162. [Google Scholar] [CrossRef][Green Version]
- Chen, J.; Hao, Q.; Stephens, M. Assessing Housing Affordability in Post-Reform China: A Case Study of Shanghai. Hous. Stud. 2010, 25, 877–901. [Google Scholar] [CrossRef]
- Park, H.; Lee, D.K. Is Water Pricing Policy Adequate to Reduce Water Demand for Drought Mitigation in Korea? Water 2019, 11, 1256. [Google Scholar] [CrossRef][Green Version]
- Grafton, R.Q.; Ward, M.B.; To, H.; Kompas, T. Determinants of Residential Water Consumption: Evidence and Analysis from a 10-Country Household Survey: Determinants of Residential Water Consumption. Water Resour. Res. 2011, 47. [Google Scholar] [CrossRef][Green Version]
Name | Abbreviation | Description |
---|---|---|
Housing price | price | Housing prices can reflect the consumption power of residents. |
Age of the building (until 2017) | age | The newness of the community indirectly represents the level of living facilities in the community. |
Plot ratio | PR | Plot ratio refers to the ratio of the total building area above the ground to the area of land available for construction. The floor area ratio directly relates to the comfort of living. The smaller the plot ratio is, the better the community environment is considered to be. |
Management fees | MF | The high management fee indicates that the overall environment of the community is good. |
Total number of households | ToHouse | The number of houses in the community is large, and the population is also relatively large; large communities with large populations can often receive attention from the government. |
Number of parking sites | parking | The number of parking sites is one of the criteria for measuring the quality of the community. |
Number of parking sites per household | CPH | CPH is a measure of the parking space for one household, reflecting the comfort level of parking. |
Greening area ratio | green% | The higher the greening rate and the lower the building density, the more comfortable the residents. |
Ratio of unoccupied houses on the rental market to the total number of households | WL | The ratio indicates the houses waiting to be rented; WL is calculated based on the ratio of the number of residents whose water consumption is 0 m3 in the monthly water bill to the total number of households in the community. When WL is high, the number of houses waiting to be rented out is large, and the number of residents in the community is relatively small. |
Average water consumption per household in the community | water | The total community consumption is divided by the number of houses occupied in the community. |
Community | Prices (10 Thousand CNY/m2) | Age (year) | PR | MF (CNY/m2) | Parking (per) | ToHouse (households) | CPH | WL (%) | Green (%) | Water (m3/month/household) |
---|---|---|---|---|---|---|---|---|---|---|
S1 | 7.1 | 10 | 2.3 | 1.5 | 150 | 1540 | 0.10 | 6.43 | 30 | 20.73 |
S2 | 7.4 | 14 | 3.5 | 1 | 100 | 239 | 0.42 | 4.18 | 38 | 29.31 |
S3 | 7.1 | 18 | 1.8 | 2.7 | 120 | 659 | 0.18 | 6.07 | 41 | 25.44 |
S4 | 8.8 | 11 | 2.9 | 2.5 | 807 | 670 | 1.20 | 9.70 | 20 | 32.20 |
S5 | 7.9 | 7 | 2.6 | 2.3 | 527 | 776 | 0.68 | 8.25 | 50 | 28.29 |
S6 | 7.9 | 12 | 2.5 | 1.8 | 1200 | 1126 | 1.07 | 9.59 | 35 | 25.66 |
S7 | 9.1 | 10 | 2.5 | 4 | 700 | 993 | 0.70 | 14.20 | 46 | 33.55 |
S8 | 7.6 | 9 | 2.1 | 1 | 500 | 1160 | 0.43 | 29.31 | 42 | 33.08 |
S9 | 11.5 | 10 | 3.5 | 5 | 248 | 452 | 0.55 | 72.35 | 29 | 20.32 |
S10 | 13.9 | 11 | 4.0 | 3 | 500 | 708 | 0.71 | 66.81 | 45 | 26.25 |
S11 | 9.8 | 9 | 4.6 | 4 | 496 | 1465 | 0.34 | 23.89 | 30 | 26.25 |
S12 | 9.9 | 13 | 3.8 | 2.5 | 400 | 1053 | 0.38 | 16.90 | 40 | 30.47 |
S13 | 7.2 | 27 | 3.4 | 1.35 | 200 | 1151 | 0.17 | 6.43 | 32 | 18.02 |
S14 | 3.8 | 21 | 1.7 | 0.45 | 300 | 1156 | 0.26 | 2.00 | 35 | 13.82 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Han, S.; Zhou, J.; Liu, Z.; Zhang, L.; Huang, X. Influence of Community Factors on Water Saving in a Mega City after Implementing the Progressive Price Schemes. Water 2021, 13, 1097. https://doi.org/10.3390/w13081097
Han S, Zhou J, Liu Z, Zhang L, Huang X. Influence of Community Factors on Water Saving in a Mega City after Implementing the Progressive Price Schemes. Water. 2021; 13(8):1097. https://doi.org/10.3390/w13081097
Chicago/Turabian StyleHan, Shaohong, Jizhi Zhou, Zeyuan Liu, Lijian Zhang, and Xin Huang. 2021. "Influence of Community Factors on Water Saving in a Mega City after Implementing the Progressive Price Schemes" Water 13, no. 8: 1097. https://doi.org/10.3390/w13081097