Assessment of the Energy Use for Water Supply in Beijing

Liu, Jiahong; Wang, Dong; Xiang, Chenyao; Xia, Lin; Zhang, Kun; Luan, Qinghua

doi:10.1016/j.egypro.2018.09.122

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…However, a series of measures aimed at improving water resources and protecting urban water bodies subsequently led to a steady expansion of the UBS area. The implementation of the South-North Water Diversion Project, which began in late 2014, significantly contributed to the increase in UBS area [76].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

Chen,

Zhen,

et al. 2023

Remote Sensing

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With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents’ well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

Chen,

Zhen,

et al. 2023

Remote Sensing

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“…The operation power consumption of stormwater pumping station for discharge of stormwater can be calculated by Equation (1) [20] :…”

Section: The Carbon Emissions From Hydrological Cycle Process In Trad...mentioning

confidence: 99%

“…The carbon emission reduction benefits of Sponge City can be calculated by reducing carbon emissions handling the same quantity of water through the corresponding traditional facilities. The power consumption of the treatment and supply of 1m 3 tap water is taken as 0.65kWh [20] .…”

Section: Accounting For Carbon Emission Reduction Benefits In Sponge ...mentioning

confidence: 99%

Carbon Reduction Assessment of Drainage System in Sponge City- A Case Study in Dongying, China

Jiahong¹,

Liu²,

Wang³

et al. 2020

Preprint

Self Cite

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“…In other regions of the world, drinking water resources are also decreasing. In turn, the energy consumption by water utilities and costs of water production are constantly increasing [8]. The water and wastewater sector in the EU is responsible for about 3.5% of electric energy consumption [9].…”

Section: Introductionmentioning

confidence: 99%

Issues in Implementation of EU Regulations in Terms of Evaluation of Water Losses: Towards Energy Efficiency Optimization in Water Supply Systems

Ociepa-Kubicka,

Deska,

Ociepa

2024

Energies

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The water and sewage sector is responsible for approximately 3.5% of energy consumption in the European Union (EU). Leaks causing water losses in water distribution systems (WDSs) are responsible for approximately 24% of water consumption in the EU, which contributes to additional energy losses and emissions of greenhouse gases (GHGs). The implementation of the Directive of the European Parliament and the EU Council on the quality of drinking water (Directive (EU) 2020/2184) introduces the obligation to report water losses by large water utilities in EU Member States. The reported indicator will be the infrastructure leakage index (ILI) which is the ratio between current annual real loss (CARL) and unavoidable annual real loss (UARL). The paper presents a comparative analysis of selected water loss performance indicators calculated for 12 Polish WDSs. Results show that values of calculated indicators were diverse. The overestimation of both the reported value of operating pressure and total length of service connections may lead to the overestimation of UARL and thus to the underestimation of ILI. Obtaining a satisfactory, but incorrect, value of ILI may result in the abandonment of activities aimed at water loss reduction. Water losses in water distribution systems (WDSs) contribute to a significant increase in both energy consumption and GHG emissions. Total approximated electrical energy related to CARL consumed in 2021 by eleven utilities (except for one company) amounted to 3.276 GWh and total approximated carbon emissions amounted to 2807.84 MgCO2eq. In the case of four WDSs, reduction of ILI to the value of 1.5 may reduce GHG emissions by 31–54%. It can be concluded that the implementation of Directive (EU) 2020/2184 will require unification of methodology for calculation of parameters used in ILI evaluation in all EU Member States.

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Assessment of the Energy Use for Water Supply in Beijing

Cited by 9 publications

References 7 publications

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

Carbon Reduction Assessment of Drainage System in Sponge City- A Case Study in Dongying, China

Issues in Implementation of EU Regulations in Terms of Evaluation of Water Losses: Towards Energy Efficiency Optimization in Water Supply Systems

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