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

I-Chun Chen, Kuang-Ly Cheng, Hwong-Wen Ma and Cathy C.W. Hung
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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

Sustainability, 2021, vol. 13, issue 19, 1-18

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.

Keywords: geographic weighted regression; socio-economic variable; spatial non-stationarity; resource consumption; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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