Analysis and Comprehensive Evaluation of Water Use Efficiency in China

Zhang, Wenge; Du, Xianzeng; Huang, Anqi; Yin, Huijuan

doi:10.3390/w11122620

Cited by 26 publications

(20 citation statements)

References 18 publications

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“…The average for students born in China is lower still at 16 L/p/day, very close to the value of 15 L/p/day suggested as the minimum quantity of water to survive [54]. More importantly, it is lower by 86% than the 104 L/p/day (38 m 3 /year) suggested when assessing water efficiency measures adopted for China [74]. Overall, it can be seen that 115 students out of 121 (i.e., 95%) underestimated their water use compared to the national average of their country of birth (i.e., China).…”

Section: Q4-how Much Water Do You Think You Use Per Daysupporting

confidence: 56%

Sustainable Water Use Practices: Understanding and Awareness of Masters Level Students

Hunt

Shahab

2021

Sustainability

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Present water consumption in the UK is unsustainable, with increasing societal and environmental pressures driving water stress. Personal use of water is a significant contributor to water demand and considering the number of universities and students in the UK the water use practices therein cannot be ignored. Therefore, this paper aims to deepen the theme of water consumption in the United Kingdom from the point of view of use practices for students. The originality in this research lies in better understanding whether UK Masters level students have a basic awareness of personal water consumption and water pricing and whether they have a preferred approach to reducing their water consumption—through a behavioral change, or through adoption of technologies. Through use of a questionnaire approach applied to five cohorts (2017 to 2021) of Masters level students, the level of understanding and awareness towards their own domestic water use both now and in the future was demonstrated. Key findings suggest that Masters students underestimated their water use by 76% compared to the average UK national range and that there was an overall preference to adopt water saving technologies rather than changing user behavior (40% vs. 27%). The study concludes that it is important to approach water conservation from an SPT perspective in order to achieve meaningful change in water use practices. Qualitative and quantitative research is analyzed in light of theoretical models (i.e., Social Practice and Attitude Behavior Framework ABC) in order to make recommendations for greater societal prominence for this issue through media and education.

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Section: Q4-how Much Water Do You Think You Use Per Daysupporting

confidence: 56%

Sustainable Water Use Practices: Understanding and Awareness of Masters Level Students

Hunt

Shahab

2021

Sustainability

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“…Therefore, based on the existing literature (Deng et al 2016;Fang et al 2017;Zhou et al 2018a;Wang et al 2018;Hu and Jiao, 2019;Ding et al 2019a;Zhang et al 2019;Zhang et al 2020a) and the principles of data accessibility, observability and representativeness, this paper finally takes GWRUE as the dependent variable and selects the influencing factors of GWRUE spatiotemporal differentiation as the independent variables. The factors influencing GWRUE are selected as shown in Table 1.…”

Section: Influencing Factors Of Gwruementioning

confidence: 99%

“…In recent years, many scholars in China and abroad have studied WRUE and its driving factors by using a variety of methods. Although there are many methods to evaluate WRUE (Fang et al 2017;Guo et al 2018;Zhang et al 2019Hai et al 2020;Zhang et al 2020a;Cao et al 2021), the data envelopment analysis (DEA) model is widely used to measure relative WRUE. For example, two-stage DEA was applied to assess the WRUE in Gansu Province (Ren et al 2017), a DEA model considering undesirable output and based on the Malmquist-Luenberger productivity index was used in China (Song et al 2018), and a DEA model with Seiford's linear transformation method was used in the 30 provinces of China (Wang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Green water resource utilization efficiency in urban agglomerations: measurement, spatiotemporal variations and influencing factors

hu¹,

La²,

Yuan³

2021

Preprint

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Green development is the coordinated development of the economy, society and environment and has become a mainstream development model. This paper evaluates the green water resource utilization efficiency (GWRUE) of 38 regions in the four-city area in central China during 2010–2019 using a super-slacks-based measure (super-SBM) DEA model considering unexpected output. Then, the spatiotemporal variations in GWRUE are analyzed by the standard deviational ellipse method, and the geographical detector method is employed to reveal the dominant impacts and interaction impacts on GWRUE spatiotemporal variations. The results show that (1) from 2010 to 2019, the GWRUE in the four-city area in central China was low, and the difference among regions was obvious, showing a downward trend. (2) From 2010 to 2019, the spatial gravity center of GWRUE experienced a change process from northeast to southwest, and its moving speed showed a “waveform” rising trend. Moreover, the SDE range of each characteristic time point showed a decreasing trend, indicating that the spatial variations in GWRUE tended to be agglomerated. (3) From 2010 to 2019, the influence of each factor on the spatial variations in GWRUE was different each year. In addition, the two-way interactions between different influencing factors were mainly manifested as bivariate enhancement relationships and nonlinear enhancement relationships and were especially affected by multiple factors that produce a nonlinear enhancement interaction. This study can provide a practical basis for realizing water ecological civilization construction and high-quality development in the four-city area in central China.

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“…Given the fact that the importance of the projection is dependent on the goal to be achieved (e.g., exploratory data analysis, density estimation, regression, classification, de-correlating variables), various PIs can be defined for each application (clustering analysis and detection [11,12], classification [13][14][15][16][17], regression analysis [18,19] and density estimation [20], leading to different indices for different projections (among these indices one can find entropy [21], variance, robust scale estimator [22], skewness [23], L1-norm [24]). One of the most popular PI is the variance of the projected data, defined by the largest Principal Components (PCs) of the PCA.…”

Section: Projection Pursuit Algorithmsmentioning

confidence: 99%

Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA

Machidon

Ciobanu

et al. 2020

Remote Sensing

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Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Projection Pursuit (PP) based algorithms were shown to be efficient solutions for performing dimensionality reduction on large datasets by searching low-dimensional projections of the data where meaningful structures are exposed. However, PP faces computational difficulties in dealing with very large datasets—which are common in hyperspectral imaging, thus raising the challenge for implementing such algorithms using the latest High Performance Computing approaches. In this paper, a PP-based geometrical approximated Principal Component Analysis algorithm (gaPCA) for hyperspectral image analysis is implemented and assessed on multi-core Central Processing Units (CPUs), Graphics Processing Units (GPUs) and multi-core CPUs using Single Instruction, Multiple Data (SIMD) AVX2 (Advanced Vector eXtensions) intrinsics, which provide significant improvements in performance and energy usage over the single-core implementation. Thus, this paper presents a cross-platform and cross-language perspective, having several implementations of the gaPCA algorithm in Matlab, Python, C++ and GPU implementations based on NVIDIA Compute Unified Device Architecture (CUDA). The evaluation of the proposed solutions is performed with respect to the execution time and energy consumption. The experimental evaluation has shown not only the advantage of using CUDA programming in implementing the gaPCA algorithm on a GPU in terms of performance and energy consumption, but also significant benefits in implementing it on the multi-core CPU using AVX2 intrinsics.

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Analysis and Comprehensive Evaluation of Water Use Efficiency in China

Cited by 26 publications

References 18 publications

Sustainable Water Use Practices: Understanding and Awareness of Masters Level Students

Sustainable Water Use Practices: Understanding and Awareness of Masters Level Students

Green water resource utilization efficiency in urban agglomerations: measurement, spatiotemporal variations and influencing factors

Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA

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