*4.1. Estimated Energy Efficiency*

The energy efficiency of each DWTP was estimated following the methodological approach described in Section 2.1., i.e., by employing Equation (1). According to the energy efficiency scores

calculated with the original data, 6 of 146 (4.1%) DWTPs were energy efficient (Figure 1). These DWTPs formed the best practice frontier, as they used the minimum quantity of energy given their efficiency of pollutant removal compared with the other DWTPs evaluated. The mean energy efficiency of the DWTPs assessed was 0.38, meaning that on average they could reduce the energy consumed by 62% while retaining output production if they were operated as energy-efficient facilities. The average energy efficiency seems to be low, but this result is consistent with those of previous studies. Molinos-Senante and Guzmán [10] and Hernández-Sancho et al. [28] reported average energy efficiencies of 0.45 and 0.31 for samples of Chilean DWTPs and Spanish WWTPs, respectively. The results of this study confirmed in general that the water treatment facilities had notable room for energy saving. Moreover, the sample of DWTPs evaluated was very heterogeneous in terms of energy efficiency (Figure 1); almost one-third (42 of 146) of the facilities had energy efficiency scores <0.2, indicating dramatically poor energy performance and thus much improvement potential.

**Figure 1.** Original energy efficiency scores of drinking water treatment plants.

As reported in Section 2.2, to compute bias-corrected energy efficiency scores and bootstrap 95% confidence intervals, 2000 bootstrap samples were generated. The original and bias-corrected bootstrap energy efficiency estimates are compared in Figure 2. To facilitate comparison, only results for the 30 DWTPs with the highest original energy efficiency scores are presented. Detailed information for all DWTPs evaluated is provided in the Supplementary Materials. As expected from a theoretical point of view [12,16,29], the bias signs were negative (bias-corrected energy efficiency scores were lower than original scores) for the 146 DWTPs assessed. The average bias-corrected energy efficiency score was 0.28, meaning that DWTPs could conserve 71% of the energy currently used while maintaining output generation if they were operated as energy-efficient facilities. The difference between the bias-corrected and original energy efficiency scores reflects the limitations of the traditional DEA model, which does not integrate data variability in efficiency assessment. Although the difference in average scores was not large, it altered the ranking of DWTPs (Figure 2). For example, DWTP 144 was ranked first based on the original energy efficiency score (1), but 15th of the 30 DWTPs listed in Figure 2 based on the bias-corrected score. Under both methodological approaches, DWTP 141 was ranked first, showing that it was the most energy efficient facility in the sample. It is one of the largest facilities evaluated and it employs rapid gravity filters to treat raw water. To verify that the DWTP ranking differed statistically according to the DEA model used, the non-parametric Mann–Whitney test was performed. The *p* value was <0.01, reflecting a strongly significant difference in ranking.

**Figure 2.** Ranking of drinking water treatment plants based on original and bias-corrected energy efficiency scores.

The lower and upper bounds are the maximum and minimum energy efficiency scores computed, considering the 2000 bootstrap samples generated. In other words, the difference between the lower and upper bounds represented the variability in energy efficiency for each DWTP evaluated. For DWTPs with the lowest energy efficiency scores, the gaps between the upper and lower bounds were small (minimum, 0.008), reflecting almost no variability (Figure 3). By contrast, the gaps between maximum and minimum energy efficiency scores was large (maximum, 0.75) for DWTPs with the highest original energy efficiency scores. This finding showed the importance of considering data variability in efficiency assessment employing the DEA approach, to provide more reliable and robust energy efficiency estimations to support decision-making. Such integration of variability is essential when the purpose of the analysis is to rank facilities based on performance (Figure 3). In the context of regulated water industries, this issue is very relevant, as benchmarking is used to set water tariffs in several water regulation models.

**Figure 3.** Lower and upper bounds of energy efficiency for each drinking water treatment plant.

## *4.2. Determinants of Energy Efficiency*

To improve the energy efficiency of DWTPs, not only inefficient facilities, but also the factors that influence energy efficiency, must be identified. This is the main advantage of the double-bootstrap technique. In the second stage of analysis, regression was conducted with four variables to identify factors influencing DWTP energy efficiency. Table 2 shows the bias-corrected coefficients of the regressed variables, with standard errors and *p* values. From a statistical point of view, a variable influences the energy efficiency of DWTPs at the 95% significance level if its *p* value is ≤0.05.


**Table 2.** Results of bootstrap regression.

\* Significant at 1% level; \*\* Significant at 5% level.

The DWTP age positively influenced energy efficiency (Table 2). Hence, older water treatment facilities exhibited a better energy performance than did younger facilities. The oldest facility analyzed had been operating for 52 years, and the sample contained a non-minor number of DWTPs that were more than 25 years old, in which old equipment had been replaced with newer, more efficient systems. This finding reflects the importance of proper equipment maintenance and the continuous incorporation of processes to improve the energy efficiency of DWTPs [10].

As in the case of WWTPs [15], the water treatment facilities presented economies of scale regarding energy use (Table 2). Larger DWTPs had significantly higher energy efficiency scores than did smaller facilities. This information is essential for the planning of new DWTPs, given the current tendency to decentralize urban water treatment facilities to increase redundancy in the case of an unplanned event (e.g., earthquake, volcanic eruption, or hurricane). However, from economic and environmental perspectives, larger DWTPs, i.e., centralized systems, are more favorable because per-unit energy use decreases with increasing capacity.

The quality of raw water, and thus the treatment intensity required to produce drinking water, sometimes depends on the water source. The DWTPs evaluated treated surface water, groundwater, and mixed water (Table 1). Following [12], the raw water source variable was integrated into the regression analysis as three dummy variables. The results showed that the source of raw water did not affect DWTP energy efficiency. This finding was consistent with the definition of the outputs considered in energy efficiency estimation, which included the concentrations of pollutants in DWTP influents and effluents. Usually, the use of groundwater requires more energy for water pumping, as well as the depth, which depends on water availability. However, in this case study, to guarantee homogeneity to the greatest extent possible, energy use for groundwater pumping was not considered in the analysis.

Several studies [24,30,31] have focused on the comparison of the performance of public and private water companies. In Chile, 98% of urban customers are supplied by private water companies [22], which operate under two regimes: i) Fully private water companies following the English and Welsh model, and ii) concessionary water companies following the French model [32] (The difference between water company types is the concession term (perpetuity for fully private companies and 30 years for concessionary water companies). Thus, DWTP ownership was integrated in the analysis as two artificial dummy variables. DWTP ownership did not impact energy efficiency (Table 2). This finding was consistent with Sowby's [33] finding that energy efficiency did not differ significantly between public and private water companies.

For WWTPs, which have been studied more widely than DWTPs, results regarding the influence of treatment technologies on energy efficiency were inconclusive [34]. Thus, we investigated whether the technology used (i.e., pressure filtration or rapid gravity filtration) was a determinant factor for DWTP energy efficiency. The average energy efficiency score for water treatment facilities using pressure filtration was 0.351, and that for those employing rapid gravity filtration was 0.408; thus, the latter facilities exhibited significantly better energy performance (Table 2).

From the perspective of cleaner production, the findings of this study demonstrate the importance of adequate maintenance and equipment replacement to ensure DWTP energy efficiency. Moreover, the role of the technology in this efficiency has been revealed. This issue is fundamental for decision-making, especially in the context of the UN's Sustainable Development Goals. The achievement of Goal 6 ("by 2030, achieve universal and equitable access to safe and affordable drinking water for all") will involve the construction and operation of new DWTPs. The technological factor must be taken into account to reduce the energy requirements of these new facilities.
