| Title |
Benchmarking the efficiency of the Chilean water and sewerage companies: a double-bootstrap approach
|
|---|---|
| Published in |
Environmental Science and Pollution Research, January 2018
|
| DOI | 10.1007/s11356-017-1149-x |
| Pubmed ID | |
| Authors |
María Molinos-Senante, Guillermo Donoso, Ramon Sala-Garrido, Andrés Villegas |
| Abstract |
Benchmarking the efficiency of water companies is essential to set water tariffs and to promote their sustainability. In doing so, most of the previous studies have applied conventional data envelopment analysis (DEA) models. However, it is a deterministic method that does not allow to identify environmental factors influencing efficiency scores. To overcome this limitation, this paper evaluates the efficiency of a sample of Chilean water and sewerage companies applying a double-bootstrap DEA model. Results evidenced that the ranking of water and sewerage companies changes notably whether efficiency scores are computed applying conventional or double-bootstrap DEA models. Moreover, it was found that the percentage of non-revenue water and customer density are factors influencing the efficiency of Chilean water and sewerage companies. This paper illustrates the importance of using a robust and reliable method to increase the relevance of benchmarking tools. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 24% |
| Student > Master | 8 | 17% |
| Professor | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Student > Bachelor | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 14 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 8 | 17% |
| Engineering | 5 | 11% |
| Decision Sciences | 3 | 7% |
| Business, Management and Accounting | 3 | 7% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 8 | 17% |
| Unknown | 17 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 June 2018.
All research outputs
#16,223,992
of 23,911,072 outputs
Outputs from Environmental Science and Pollution Research
#3,738
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Outputs of similar age
#277,593
of 448,531 outputs
Outputs of similar age from Environmental Science and Pollution Research
#93
of 226 outputs
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