• Energy Research

The management of municipal solid waste sector is crucial for a sustainable circular economy. Waste utilities are expected to provide high quality solid waste services at an affordable price. The efficient management of solid waste requires its assessment from an economic and environmental perspective, i.e., eco-efficiency assessment. Although the reduction of unsorted waste incurs an economic cost, its positive externalities are huge for the well-being of society, the environment, and people. Our study quantifies the marginal cost of reducing any unsorted waste using stochastic frontier analysis techniques which allow us to estimate the eco-efficiency of the waste sector. Our empirical approach focuses on the municipal solid waste collection and recycling services provided by several waste utilities in Chile. The results indicate that substantial eco-inefficiency in the sector exists, since the average eco-efficiency score is roughly 0.5 which means that the municipalities could approximately halve their operational costs and unsorted waste to produce the same level of output. The average marginal cost of reducing unsorted waste is 32.28 Chilean pesos per ton, although notable differences are revealed among the waste utilities evaluated. The results provided by this study are of great interest to stakeholders to promote sustainable management solutions and resource efficient solid waste services.

In monopoly services that provide drinking water, it is of paramount importance to evaluate the total factor productivity (TFP) change of water companies. Most of the previous studies have computed the Malmquist productivity index (MPI) by applying non-parametric methods. By contrast, following a pioneering approach, in this study, we estimated the MPI using a parametric method that allows us to decompose TFP change into a larger number of drivers, including exogenous and quality of service variables. An empirical application for the Chilean water industry over 2007-2015 was conducted. We found that productivity change estimates were variable across years, differentiating a first period (2007/11) in which productivity declined and a second period (2011/15) in which TFP notably improved. In both periods, scale efficiency change and input mixed effect were the main drivers of productivity change, illustrating the importance of operation scale in water companies' performance. The decomposition of the TFP change in a large number of drivers is essential to propose incentives and measures to promote productivity across time.

Wastewater treatment plants (WWTPs) are energy intensive facilities. Controlling energy use in WWTPs could bring substantial benefits to people and environment. Understanding how energy efficient the wastewater treatment process is and what drives efficiency would allow treating wastewater in a more sustainable way. In this study, we employed the efficiency analysis trees approach, that combines machine learning and linear programming techniques, to estimate energy efficiency of wastewater treatment process. The findings indicated that considerable energy inefficiency among WWTPs in Chile existed. The mean energy efficiency was 0.287 suggesting that energy use should cut reduce by 71.3 % to treat the same volume of wastewater. This was equivalent to a reduction in energy use by 0.40 kWh/m3 on average. Moreover, only 4 out of 203 assessed WWTPs (1.97 %) were identified as energy efficient. It was also found that the age of treatment plant and type of secondary technology played an important role in explaining energy efficiency variations among WWTPs.

The water industry plays an important role in reducing greenhouse gas (GHG) emissions and therefore, moving to a low-carbon urban water cycle is of great importance. However, traditional performance assessment of water companies ignores GHG emissions. To overcome this limitation and to compare productivity change estimations of water companies excluding and including GHG emissions, this study computes the Luenberger productivity indicator (LPI) and the Malmquist-Luenberger productivity index (MLPI), respectively. Moreover, in a second stage, we investigate the impact of exogenous variables on environmentally sensitive productivity change estimations. The empirical application conducted for a sample of water and sewerage companies in England and Wales over the period 201-2019 has illustrated that when GHG emissions were considered in the assessment (i.e., MLPI estimations), average productivity decreased. By contrast, when productivity estimation ignored GHG emissions (i.e., LPI), average productivity increased. Hence, it is concluded that the inclusion of GHG emissions in productivity analysis impacted the results. This finding is very relevant from a policy perspective as it illustrates the importance of considering GHG emissions when evaluating the performance of water companies for regulatory purposes.

Analyzing costs and greenhouse gas (GHG) emissions could be of great importance for the water utilities to supply water services in a healthy and sustainable manner. In this study, we measured the eco-efficiency of several water utilities in England and Wales by incorporating GHG as an undesirable output. For the first time, we evaluated the eco-efficiency of the water production process using robust cross-efficiency data envelopment analysis (DEA) techniques. The further use of clustering and regression techniques allowed us to better understand the drivers of eco-efficiency. The results showed that the mean eco-efficiency of the water sector was 0.748, which indicates that costs and GHG emissions could be reduced by 25.2% to generate the same level of output. Large water companies with high energy costs and levels of GHG emissions belonged to the less eco-efficient group. Environmental factors related to density, topography, and treatment complexity further impacted eco-efficiency. Finally, we linked our results to the regulatory cycle and discuss some policy implications.

Understanding water-energy nexus in the provision of drinking water services is a challenge which has outstanding relevance in the current climatic emergency. Environmental efficiency and eco-efficiency assessment of water companies are two useful tools to address this challenge. In this study, we estimated hyperbolic and enhanced hyperbolic distance functions to compute the potential reduction in greenhouse gas (GHG) emissions and energy costs in the provision of drinking water. The empirical application focused on the English and Welsh water companies over 2011-2019. Average environmental efficiency and eco-efficiency scores were 0.920 and 0.962, respectively which indicates that water companies performed well but there is room for improvement. Moreover, due to the economies of scale, the cost of reducing GHG emissions was higher for water and sewerage companies than for water only companies. The results and conclusions of this study allow better understanding of the relationship between the provision of drinking water, energy costs and GHG emissions.

ABSTRACT Within the global climate change framework, enhancing energy efficiency presents a significant challenge for water utilities. Drinking water treatment is energy-intensive, involving several physicochemical processes to remove multiple pollutants from raw water. This study employs artificial neural networks (ANNs) and decision tree methods to gain a deeper understanding of the water–energy nexus in drinking water treatment processes. The energy efficiency of a sample of Chilean drinking water treatment plants (DWTPs) was estimated, resulting in an average score of 0.343. This indicates that on average, DWTPs could potentially save 65.7% of their current energy consumption if they were operating at an efficient level while producing the same quantity and quality of drinking water. The main source of raw water and the technology for treating water have been identified as critical factors influencing energy efficiency. Specifically, using surface water for producing drinking water, energy efficiency can increase to 0.514, whereas using groundwater would regress energy efficiency to 0.240. The use of predictive tools such as ANNs provides relevant information to support decision-making processes for a transition toward a sustainable urban water cycle.