IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i1p135-d88533.html
   My bibliography  Save this article

Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City

Author

Listed:
  • Jiefeng Kang

    (Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jianyi Lin

    (Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Xiaofeng Zhao

    (Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Shengnan Zhao

    (School of Resource and Environmental Science, Chifeng University, Chifeng 024000, China)

  • Limin Kou

    (Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

Abstract

Decomposition of the urban water footprint can provide insight for water management. In this paper, a new decomposition method based on the log-mean Divisia index model (LMDI) was developed to analyze the driving forces of water footprint changes, attributable to food consumption. Compared to previous studies, this new approach can distinguish between various factors relating to urban and rural residents. The water footprint of food consumption in Xiamen City, from 2001 to 2012, was calculated. Following this, the driving forces of water footprint change were broken down into considerations of the population, the structure of food consumption, the level of food consumption, water intensity, and the population rate. Research shows that between 2001 and 2012, the water footprint of food consumption in Xiamen increased by 675.53 Mm 3 , with a growth rate of 88.69%. Population effects were the leading contributors to this change, accounting for 87.97% of the total growth. The food consumption structure also had a considerable effect on this increase. Here, the urban area represented 94.96% of the water footprint increase, driven by the effect of the food consumption structure. Water intensity and the urban/rural population rate had a weak positive cumulative effect. The effects of the urban/rural population rate on the water footprint change in urban and rural areas, however, were individually significant. The level of food consumption was the only negative factor. In terms of food categories, meat and grain had the greatest effects during the study period. Controlling the urban population, promoting a healthy and less water-intensive diet, reducing food waste, and improving agriculture efficiency, are all elements of an effective approach for mitigating the growth of the water footprint.

Suggested Citation

  • Jiefeng Kang & Jianyi Lin & Xiaofeng Zhao & Shengnan Zhao & Limin Kou, 2017. "Decomposition of the Urban Water Footprint of Food Consumption: A Case Study of Xiamen City," Sustainability, MDPI, vol. 9(1), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:135-:d:88533
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/1/135/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/1/135/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    2. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    3. Yifan Gu & Yi Li & Hongtao Wang & Fengting Li, 2014. "Gray Water Footprint: Taking Quality, Quantity, and Time Effect into Consideration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3871-3874, September.
    4. Willa Paterson & Richard Rushforth & Benjamin L. Ruddell & Megan Konar & Ikechukwu C. Ahams & Jorge Gironás & Ana Mijic & Alfonso Mejia, 2015. "Water Footprint of Cities: A Review and Suggestions for Future Research," Sustainability, MDPI, vol. 7(7), pages 1-30, June.
    5. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    6. Ang, B.W. & Huang, H.C. & Mu, A.R., 2009. "Properties and linkages of some index decomposition analysis methods," Energy Policy, Elsevier, vol. 37(11), pages 4624-4632, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gang Liu & Lu Shi & Kevin W. Li, 2018. "Equitable Allocation of Blue and Green Water Footprints Based on Land-Use Types: A Case Study of the Yangtze River Economic Belt," Sustainability, MDPI, vol. 10(10), pages 1-27, October.
    2. Guojing Li & Xinru Han & Qiyou Luo & Wenbo Zhu & Jing Zhao, 2021. "A Study on the Relationship between Income Change and the Water Footprint of Food Consumption in Urban China," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    3. Ruogu Huang & Xiangyang Li & Yang Liu & Yaohao Tang & Jianyi Lin, 2021. "Decomposition of Water Footprint of Food Consumption in Typical East Chinese Cities," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    4. Changfeng Shi & Hang Yuan & Qinghua Pang & Yangyang Zhang, 2020. "Research on the Decoupling of Water Resources Utilization and Agricultural Economic Development in Gansu Province from the Perspective of Water Footprint," IJERPH, MDPI, vol. 17(16), pages 1-16, August.
    5. Xiaomei Yan & Shenghui Cui & Lilai Xu & Jianyi Lin & Ghaffar Ali, 2018. "Carbon Footprints of Urban Residential Buildings: A Household Survey-Based Approach," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    6. Gang Liu & Weiqian Wang & Kevin W. Li, 2019. "Water Footprint Allocation under Equity and Efficiency Considerations: A Case Study of the Yangtze River Economic Belt in China," IJERPH, MDPI, vol. 16(5), pages 1-24, March.
    7. Cristian Silviu Banacu & Mihail Busu & Raluca Ignat & Carmen Lenuta Trica, 2019. "Entrepreneurial Innovation Impact on Recycling Municipal Waste. A Panel Data Analysis at the EU Level," Sustainability, MDPI, vol. 11(18), pages 1-13, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Chunbo, 2014. "A multi-fuel, multi-sector and multi-region approach to index decomposition: An application to China's energy consumption 1995–2010," Energy Economics, Elsevier, vol. 42(C), pages 9-16.
    2. Huang, Yun-Hsun, 2020. "Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data," Energy, Elsevier, vol. 213(C).
    3. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    4. Suvajit Banerjee, 2019. "Addressing the Drivers of Carbon Emissions Embodied in Indian Exports: An Index Decomposition Analysis," Foreign Trade Review, , vol. 54(4), pages 300-333, November.
    5. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    6. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Combining physical and economic output data to analyse energy and CO2 emissions trends in industry," Energy Policy, Elsevier, vol. 49(C), pages 422-429.
    7. Patiño, Lourdes Isabel & Alcántara, Vicent & Padilla, Emilio, 2021. "Driving forces of CO2 emissions and energy intensity in Colombia," Energy Policy, Elsevier, vol. 151(C).
    8. Sobrino, Natalia & Monzon, Andres, 2014. "The impact of the economic crisis and policy actions on GHG emissions from road transport in Spain," Energy Policy, Elsevier, vol. 74(C), pages 486-498.
    9. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    10. González, Domingo & Martínez, Manuel, 2012. "Changes in CO2 emission intensities in the Mexican industry," Energy Policy, Elsevier, vol. 51(C), pages 149-163.
    11. Hu, Junfeng & Kahrl, Fredrich & Yan, Qingyou & Wang, Xiaoya, 2012. "The impact of China's differential electricity pricing policy on power sector CO2 emissions," Energy Policy, Elsevier, vol. 45(C), pages 412-419.
    12. Román-Collado, Rocío & Morales-Carrión, Any Viviana, 2018. "Towards a sustainable growth in Latin America: A multiregional spatial decomposition analysis of the driving forces behind CO2 emissions changes," Energy Policy, Elsevier, vol. 115(C), pages 273-280.
    13. Mariana Conte Grand, 2018. "Desacople y Descomposición del Consumo Final de Energía en Argentina," CEMA Working Papers: Serie Documentos de Trabajo. 678, Universidad del CEMA.
    14. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    15. Ang, B.W. & Wang, H., 2015. "Index decomposition analysis with multidimensional and multilevel energy data," Energy Economics, Elsevier, vol. 51(C), pages 67-76.
    16. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    17. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    18. Xuankai Deng & Yanhua Yu & Yanfang Liu, 2015. "Effect of Construction Land Expansion on Energy-Related Carbon Emissions: Empirical Analysis of China and Its Provinces from 2001 to 2011," Energies, MDPI, vol. 8(6), pages 1-22, June.
    19. Baležentis, Alvydas & Baležentis, Tomas & Streimikiene, Dalia, 2011. "The energy intensity in Lithuania during 1995–2009: A LMDI approach," Energy Policy, Elsevier, vol. 39(11), pages 7322-7334.
    20. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:135-:d:88533. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.