Threshold of wastewater treatment improvement costs - applying a hybrid dynamic DEA analysis

Liu, Fan‐peng; Teng, Xiangyu; Chiu, Yung‐ho

doi:10.1080/1573062x.2021.1930068

Cited by 1 publication

(1 citation statement)

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“…The diversity of research subjects shows the richness of water resources research and provides a scientific basis for further improving water efficiency. In addition, for efficiency measurement, some scholars usually use quantitative methods, analytical hierarchy process (AHP), ratio analysis and data envelopment analysis (DEA) [9][10]. Two-stage model evaluation with game cross-efficiency is also a valid model for the GEIWR evaluation [11].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution of the Green Efficiency of Industrial Water Resources and Its Influencing Factors in the Middle Reaches of the Yangtze River of China

Lv¹,

Geng²,

Zhang³

et al. 2023

Pol. J. Environ. Stud.

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Exploring ways to improve the green efficiency of industry water resources (GEIWR) is an inevitable requirement for achieving harmonious and sustainable industrial development and water use. Taking the urban agglomerations in the middle reaches of the Yangtze River (UAMRYR) as an example, this paper constructed an industrial green water resource efficiency index system. Based on the slack based model (SBM), the spatiotemporal evolution characteristics of the GEIWR during 2006-2019 were analyzed, and its spatial differentiation characteristics were investigated by exploratory spatial data analysis (ESDA), Besides, a spatial Tobit model was constructed to reveal its influencing factors. The results are as follows: (1) Temporally, the GEIWR ranged between 0.525 and 0.745 from 2006 to 2019 with an overall flat change, showing two distinct phases of change. Spatially, the difference varied markedly, with the GEIWR showing a pattern of "high in the south and low in the north" from 2006-2010 and "high in the centre and low around" from 2014-2019. ( 2) GEIWR global Moran's I value between 0.003 and 0.462, showing positive correlation characteristics, but the coefficient gradually decreased and the spatial correlation decreased; The local autocorrelation aggregation region changed significantly, the spatial aggregation effect was apparent before 2014, the concentration area decreased in 2019, the spatial correlation decreased, the spatial disequilibrium appeared. (3) The economic and industrial structure and the level of science and education had a significant effect on GEIWR and the economic benefits of industrial water resources showed an inverse inhibitory effect, while the natural resource endowment and degree of industrialization had no significant impact on GEIWR. Thence,

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Section: Introductionmentioning

confidence: 99%