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Identifying Spatial Driving Factors of Energy and Water Consumption in the Context of Urban Transformation

Author

Listed:
  • I-Chun Chen

    (Department of Land Resources, Chinese Culture University, Taipei 11114, Taiwan)

  • Kuang-Ly Cheng

    (Department of Environmental Engineering, National Cheng Kung University, Tainan 701401, Taiwan)

  • Hwong-Wen Ma

    (Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, Taiwan)

  • Cathy C.W. Hung

    (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

Abstract

Urban energy and water consumption varies substantially across spatial and temporal scales, which can be attributed to changes of socio-economic variables, especially for a city undergoing urban transformation. Understanding these variations in variables related to resource consumptions would be beneficial to regional resource utilization planning and policy implementation. A geographically weighted regression method with modified procedures was used to explore and visualize the relationships between socio-economic factors and spatial non-stationarity of urban resource consumption to enhance the reliability of predicted results, taking Taichung city with 29 districts as an example. The results indicate that there is a strong positive correlation between socio-economic context and domestic resource consumption, but that there are relatively weak correlations for industrial and agricultural resource consumption. In 2015, domestic water and energy consumption was driven by the number of enterprises followed by population and average income level (depending on the target districts and sectors). Domestic resource consumption is projected to increase by approximately 84% between 2015 and 2050. Again, the number of enterprises outperforms other factors to be the dominant variable responsible for the increase in resource consumption. Spatial regression analysis of non-stationarity resource consumption and its associated variables offers useful information that is helpful for targeting hotspots of dominant resource consumers and intervention measures.

Suggested Citation

  • I-Chun Chen & Kuang-Ly Cheng & Hwong-Wen Ma & Cathy C.W. Hung, 2021. "Identifying Spatial Driving Factors of Energy and Water Consumption in the Context of Urban Transformation," Sustainability, MDPI, vol. 13(19), pages 1-18, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10503-:d:640463
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    References listed on IDEAS

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    1. Chuanglin Fang & Haimeng Liu & Guangdong Li & Dongqi Sun & Zhuang Miao, 2015. "Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models," Sustainability, MDPI, vol. 7(11), pages 1-23, November.
    2. Rafael Laurenti & Jagdeep Singh & Björn Frostell & Rajib Sinha & Claudia R. Binder, 2018. "The Socio-Economic Embeddedness of the Circular Economy: An Integrative Framework," Sustainability, MDPI, vol. 10(7), pages 1-10, June.
    3. Asian Development Bank (ADB) & Asian Development Bank (ADB) & Asian Development Bank (ADB) & Asian Development Bank (ADB), 2014. "Urban Metabolism of Six Asian Cities," ADB Reports RPT146817-2, Asian Development Bank (ADB).
    4. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    5. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    6. Patricia Romero-Lankao & Timon McPhearson & Debra J. Davidson, 2017. "The food-energy-water nexus and urban complexity," Nature Climate Change, Nature, vol. 7(4), pages 233-235, April.
    7. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    8. Wa’el A. Hussien & Fayyaz A. Memon & Dragan A. Savic, 2016. "Assessing and Modelling the Influence of Household Characteristics on Per Capita Water Consumption," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2931-2955, July.
    9. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    10. Huebner, Gesche M. & Hamilton, Ian & Chalabi, Zaid & Shipworth, David & Oreszczyn, Tadj, 2015. "Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes," Applied Energy, Elsevier, vol. 159(C), pages 589-600.
    11. James Keirstead & Aruna Sivakumar, 2012. "Using Activity‐Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions," Journal of Industrial Ecology, Yale University, vol. 16(6), pages 889-900, December.
    12. Shu‐Li Huang & Chia‐Wen Chen, 2009. "Urbanization and Socioeconomic Metabolism in Taipei," Journal of Industrial Ecology, Yale University, vol. 13(1), pages 75-93, February.
    13. Camaren Peter & Mark Swilling, 2014. "Linking Complexity and Sustainability Theories: Implications for Modeling Sustainability Transitions," Sustainability, MDPI, vol. 6(3), pages 1-29, March.
    14. Berkhout, Peter H. G. & Muskens, Jos C. & W. Velthuijsen, Jan, 2000. "Defining the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 425-432, June.
    15. Monica Di Donato & Pedro L. Lomas & Óscar Carpintero, 2015. "Metabolism and Environmental Impacts of Household Consumption: A Review on the Assessment, Methodology, and Drivers," Journal of Industrial Ecology, Yale University, vol. 19(5), pages 904-916, October.
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