Skip to main content

Advertisement

Log in

Quantification of the Driving Factors of Water Use in the Productive Sector Change Using Various Decomposition Methods

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The water use in the productive sector in developing regions increases with quick socioeconomic development. This study is a quantitative analysis of the factors affecting changes in water use of the productive sector. Using Guangdong province as a case study, the driving factors of changes in water use in the productive sector are summarized as population, affluence, structure and technology factors on the basis of the impact = population × affluence × technology (IPAT) model (GDP is expressed at constant prices). Then the Laspeyres, the logarithmic mean Divisia index (LMDI), and the Shapley value decomposition model were adopted to determine the appropriate method and quantify the relative contribution of the driving factors. The results showed that the LMDI decomposition model was preferable for this case due to its accuracy, easy to use and expression. Affluence factor and population factor induce positive variation of water use of the productive sector, while structure factor and technology factor induce negative variation of water use of the productive sector. We also determined that water restriction policies helped to curb the increasing trend in water use of the productive sector, but also hinder economic growth to a certain extent. And we suggest that the future direction of water saving in the study area should focus on industrial restructuring. These findings have significant policy implications for water use in developing countries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aidam PW (2015) The impact of water-pricing policy on the demand for water resources by farmers in Ghana. Agric Water Manag 158:10–16

    Article  Google Scholar 

  • Ameyaw EE, Chan AP (2015) Evaluation and ranking of risk factors in public–private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach. Expert Syst Appl 42(12):5102–5116

    Article  Google Scholar 

  • Ang BW, Choi KH (1997) Decomposition of aggregate energy and gas emission intensities for industry: a refined Divisia index method. Energy J:59–73

  • Ang BW (2004) Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy 32(9):1131–1139

    Article  Google Scholar 

  • Ang BW, Xu XY, Su B (2015) Multi-country comparisons of energy performance: the index decomposition analysis approach. Energy Econ 47:68–76

    Article  Google Scholar 

  • Bai M, Zhou S, Zhao M, Yu J (2017) Water use efficiency improvement against a backdrop of expanding city agglomeration in developing countries: A case study on industrial and agricultural water use in the Bohai Bay Region of China. Water 9(2):89

    Article  Google Scholar 

  • Bao C, Chen X (2015) The driving effects of urbanization on economic growth and water use change in China: A provincial-level analysis in 1997–2011. J Geogr Sci 25(5):530–544

    Article  Google Scholar 

  • Eliasson J (2015) The rising pressure of global water shortages. Nature News 517(7532):6

    Article  Google Scholar 

  • García-Montoya M, Sengupta D, Nápoles-Rivera F, Ponce-Ortega JM, El-Halwagi MM (2016) Environmental and economic analysis for the optimal reuse of water in a residential complex. J Clean Prod 130:82–91

    Article  Google Scholar 

  • Ghiassi M, Zimbra DK, Saidane H (2008) Urban water demand forecasting with a dynamic artificial neural network model. J Water Resour Plan Manag 134(2):138–146

    Article  Google Scholar 

  • Commoner B (2014) The closing circle: nature, man, and technology. Knopf

  • Haque MM, Egodawatta P, Rahman A, Goonetilleke A (2015) Assessing the significance of climate and community factors on urban water demand. Int J Sustain Built Environ 4(2):222–230

    Article  Google Scholar 

  • Hsiao CR, Raghavan TES (1993) Shapley value for multichoice cooperative games, I. Games and Economic Behavior 5(2):240–256

    Article  Google Scholar 

  • Huong TTL (2016) Water Resource for Economic Development in Vietnam and Implications for Developing Countries. Global Journal of Management and Business Research

  • Kahil MT, Albiac J, Dinar A, Calvo E, Esteban E, Avella L, Garcia-Molla M (2016) Improving the performance of water policies: Evidence from drought in Spain. Water 8(2):34

    Article  Google Scholar 

  • Katz D (2015) Water use and economic growth: reconsidering the Environmental Kuznets Curve relationship. J Clean Prod 88:205–213

    Article  Google Scholar 

  • Laspeyres K (1871) IX. Die Berechnung einer mittleren Waarenpreissteigerung. Jahrbücher für Nationalökonomie und Statistik 16(1):296–318

    Article  Google Scholar 

  • Lavee D, Danieli Y, Beniad G, Shvartzman T, Ash T (2013) Examining the effectiveness of residential water demand-side management policies in Israel. Water Policy 15(4):585–597

    Article  Google Scholar 

  • Lü S, Wang F, Yu Y, Zhong H, Xu S (2018) Analysis of dynamic evolution and driving factors behind water consumption in China. Water Sci Technol Water Supply 18(3):1093–1102

    Article  Google Scholar 

  • Lu Z, Yang Y, Jian W (2014) Factor decomposition of carbon productivity chang in china's main industries: based on the laspeyres decomposition method. Energy Procedia 61:1893–1896

    Article  Google Scholar 

  • Malthus TR (1878) An essay on the principle of population: Or, a view of its past and present effects on human happiness, with an inquiry into our prospects respecting the future removal or mitigation of the evils which it occasions. Reeves and Turner, London

    Google Scholar 

  • Qin Y, Curmi E, Kopec GM, Allwood JM, Richards KS (2015) China's energy-water nexus–assessment of the energy sector's compliance with the “3 Red Lines” industrial water policy. Energy Policy 82:131–143

    Article  Google Scholar 

  • Rockström J, Falkenmark M (2015) Agriculture: increase water harvesting in Africa. Nature News 519(7543):283

    Article  Google Scholar 

  • Shang, Y., Lu, S., Gong, J., Shang, L., Li, X., Wei, Y., Shi, H. (2017a). Hierarchical prediction of industrial water demand based on refined Laspeyres decomposition analysis. Water Science and Technology, wst2017432

  • Shang Y, Lu S, Shang L, Li X, Shi H, Li W (2017b) Decomposition of industrial water use from 2003 to 2012 in Tianjin, China. Technol Forecast Soc Chang 116:53–61

    Article  Google Scholar 

  • Shang Y, Lu S, Shang L, Li X, Wei Y, Lei X, Wang H (2016) Decomposition methods for analyzing changes of industrial water use. J Hydrol 543:808–817

    Article  Google Scholar 

  • Shorrocks AF (2013) Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. J Econ Inequal 11(1):99–126

    Article  Google Scholar 

  • Sun S, Fang C (2018) Water use trend analysis: A non-parametric method for the environmental Kuznets curve detection. J Clean Prod 172:497–507

    Article  Google Scholar 

  • Sun S, Wang Y, Engel BA, Wu P (2016) Effects of virtual water flow on regional water resources stress: a case study of grain in China. Sci Total Environ 550:871–879

    Article  Google Scholar 

  • Veiga LBE, Magrini A (2013) The Brazilian water resources management policy: Fifteen years of success and challenges. Water Resour Manag 27(7):2287–2302

    Article  Google Scholar 

  • Veldkamp TI, Wada Y, de Moel H, Kummu M, Eisner S, Aerts JC, Ward PJ (2015) Changing mechanism of global water scarcity events: Impacts of socioeconomic changes and inter-annual hydro-climatic variability. Glob Environ Chang 32:18–29

    Article  Google Scholar 

  • Wang C, Wang F, Zhang X, Yang Y, Su Y, Ye Y, Zhang H (2017) Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang. Renew Sust Energ Rev 67:51–61

    Article  Google Scholar 

  • Wang Z, Deng X, Li X, Zhou Q, Yan H (2015) Impact analysis of government investment on water projects in the arid Gansu Province of China. Physics and Chemistry of the Earth, Parts A/B/C 79:54–66

    Article  Google Scholar 

  • Worland SC, Steinschneider S, Hornberger GM (2018) Drivers of Variability in Public-Supply Water Use Across the Contiguous United States. Water Resour Res 54(3):1868–1889

    Article  Google Scholar 

  • Yao L, Zhang H, Zhang C, Zhang W (2018) Driving effects of spatial differences of water consumption based on LMDI model construction and data description. Clust Comput:1–20

  • York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46(3):351–365

    Article  Google Scholar 

  • Zhang DQ, Jinadasa KBSN, Gersberg RM, Liu Y, Ng WJ, Tan SK (2014) Application of constructed wetlands for wastewater treatment in developing countries–a review of recent developments (2000–2013). J Environ Manag 141:116–131

    Article  Google Scholar 

  • Zhao C, Chen B (2014) Driving force analysis of the agricultural water footprint in China based on the LMDI method. Environ Sci Technol 48(21):12723–12731

    Article  Google Scholar 

  • Zuo Q, Jin R, Ma J, Cui G (2014) China pursues a strict water resources management system. Environ Earth Sci 72(6):2219–2222

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to all reviewers for their valuable comments. This study was financially supported by the National Key R&D Program of China (2017YFC0405900) and the National Natural Science Foundation of China (Grants 51861125203, 91547202 and 51479216).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaohong Chen.

Ethics declarations

Conflict of Interest

The authors declared that they have no conflicts of interest to this work.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, J., Chen, X. Quantification of the Driving Factors of Water Use in the Productive Sector Change Using Various Decomposition Methods. Water Resour Manage 33, 4105–4121 (2019). https://doi.org/10.1007/s11269-019-02338-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-019-02338-0

Keywords

Navigation