Spatial heterogeneity of factors affecting GHG emission intensity in urban water supply and wastewater treatment systems in China

Abstract

The water supply and treatment in urban water systems (UWS) consume massive energy, quantitative estimate of the intensity of their accompanying GHG (Greenhouse Gas) emissions is important to clarify the major source of the GHG for UWS. Herein, on the city scale, the intensity of energy consumption and GHG emissions of the water supply and wastewater treatment systems were calculated, based on the operating data of 5972 water or wastewater treatment plants in China. Results showed that the water supply system consumed 38.6 billion kWh of electrical energy and accounted for 26.8 Mt CO2-eq GHG emissions, accordingly of 14.4 billion kWh and 21.9 Mt CO2-eq for the wastewater treatment system. For wastewater treatment, significant spatial autocorrelation was observed for the intensities of energy consumption and GHG emission, and the clustering areas highly matched with the topography, especially the N2O and CH4 emissions high-high clustering in the Loess Plateau and Inner Mongolia Plateau. Screened by multi-scale geographically weighted regression, the annual precipitation was found to be the main factor influencing the intensities, with a higher regression coefficient than social factors (e.g., GDP and population density). And a clear spatial heterogeneity was observed with the regression coefficient distributed higher in the northwest than in the southeast, with a gap of 2.3–7.5 times. This study provides systematic estimation results of energy consumption and GHG emissions intensity in urban water supply and wastewater treatment systems on the city scale in China, quantifies regional differences in energy consumption and GHG emission intensity of UWS, and revealed the inequality of UWS in regions with different basic conditions while facing low-carbon transformation. Our findings provide a reference for the formulation of regionally targeted UWS low-carbon transition policies.