Transient and Persistent Energy Efficiency in the Wastewater Sector based on Economic Foundations.

Date01 November 2020
AuthorLongo, Stefano

    Energy used to supply water to consumers is a source of demand that is set to grow rapidly over the coming decades. At European level, the data show values of around 90 kWh/year/person (EEA, 2014), or that for each person a 10 W light bulb is burning 24/7. Today, 4% of the global electricity consumption is used in the water sector, and over the period to 2040, this amount is projected to more than double (IEA, 2016). Around 30% of the electricity consumed for water, is used for wastewater treatment (EBC, 2016). Hence, energy-related considerations in the wastewater sector will receive increasing attention from an environmental and economic point of view, as also announced by the European Commission in the proposal for a revision of the European Union drinking water directive (EC, 2018). Assessing and improving energy efficiency is a valuable means to address these challenges (Huntington and Smith, 2011).

    Recently, the first methodology specifically tailored to estimate energy efficiency at wastewater treatment plants (WWTPs) has been described (Longo et al., 2019). The methodology, for the first time, provides engineers, wastewater operators and decision-makers a method to obtain standardized and comparable efficiency information. However, a widespread concern in the wastewater sector is the extent to which efficiency estimates ignore external influences on performance (Guerrini et al., 2016). A development in Data Envelopment Analysis (DEA) application is the Robust Energy Efficiency DEA (REED), a methodology to systematize the inclusion of exogenous factors and to robustly estimate efficiency of WWTP (Longo et al., 2018). Although DEA is attractive as it easily handles multiple inputs/outputs and it does not require the specification of a functional form to define an efficiency frontier, a major drawback is that it attributes all deviations from the frontier to inefficiency. Yet, deviations from the frontier may be due to a number of factors other than inefficiency such as omitted variables and measurement errors. Stochastic Frontier Analysis (SFA) represents an interesting framework to overcome the above limitations since it is able to separate the inefficient component from the statistical noise due to data errors and omitted variables (Boyd, 2008).

    From an economic point of view, there exist a number of reasons why econometric techniques such as SFA can be useful apart from providing sound efficiency indicators. Even within a relatively non-competitive industry, such as the water industry, there may be public policy questions that could be considered to improve the operation. In particular, the ability to accommodate formal statistical testing makes econometric techniques such as SFA more attractive for both regulators and operators (Leth-Petersen, 2002). For example, knowing economic relationships between pollutants removed from water and energy demand is fundamental to understand/predict the trade-off between pollution control and energy footprint.

    At micro-level, there are numerous critical questions that would benefit from this sort of analysis. For example, a key strategic question may be whether or not merging two adjacent WWTPs makes sense. Although there are multiple reasons for considering plants centralization, one of the key questions to answer is whether it will result in cost savings through economies of scale. Moreover, by using specific SFA models and panel data, wherein each plant is observed at different points of time, it is possible to examine whether inefficiency has been persistent over time or whether plant inefficiency is time-varying (Tsionas and Kumbhakar, 2014; Filippini and Greene, 2016). Distinguishing between persistent and transient inefficiency seems to be essential to deduce appropriate energy diagnosis and design useful energy efficiency strategies for WWTPs. Otherwise, water utilities may wrongly decide to invest in new equipment and infrastructure, while inefficiency arises from some applications of wrong operational strategies due to e.g. error in management of sludge age and return sludge, too infrequent sampling or inadequate evaluation of monitoring data, or vice versa. In order to answer to these questions, proper measure of efficiency and the effects of efficiency determinants are important.

    Therefore, this study applies a SFA approach for energy demand modelling to determine energy efficiency in the wastewater sector based on economic foundations, for the first time to the best of our knowledge. An energy demand (1) frontier function for the wastewater sector is estimated using a panel data of 183 Swiss WWTPs for the period 2001 to 2015, explicitly controlling for technology, size and the removal of the main contaminants from wastewater, as well as for both observed and unobserved heterogeneity. Once these effects are controlled for, it is possible to better estimate a measure of energy efficiency for each plant and to take corrective energy-saving measures.

    The objective of this paper is then to investigate how overall inefficiency of WWTPs can be decomposed into persistent and transient inefficiency. Moreover, in order to find out whether accounting for unobserved heterogeneity in the model significantly influences the results, the energy efficiency estimates obtained from three panel data models are compared, and context-specific considerations on the different models are done to explore their best use. In addition, the impact of technical progress on energy efficiency is studied in order to estimate the margin for improvements. Finally, to determine whether the analysed WWTPs are operated at optimal load and size, economies of output and scale are estimated.

    The paper is organized as follows. Section 2 presents an econometric model for WWTPs energy demand and discusses the empirical specifications for estimating the level of efficiency in the use of energy. Section 3 describes data and the variables used in the model. The results of estimations and discussions are presented in section 4 and Section 5 concludes.


    Wastewater treatment is a process used to produce an effluent that can be returned to the water cycle with minimal impact on the environment. The treatment process takes place in a WWTP, which is organized in different unit operations grouped together to provide various levels of treatment known as preliminary, primary, secondary, tertiary and sludge treatment (Metcalf and Eddy, 2003). Based on the function of the plant, WWTPs can produce different outputs, e.g. pumping wastewater, producing an effluent free of contaminants such as solids, chemical oxygen demand (COD), nitrogen (N), phosphorus (P) and pathogens, processing the sludge produced during treatment, recovering of energy and materials. The resources used for treatment process are the inputs, being electricity one of the main inputs in all the cases.

    From an economic perspective, WWTPs energy efficiency can be discussed using the microeconomics theory of production framework. In this context, the production function can be described by a mathematical representation of a WWTP that converts input(s) (e.g. electricity) into output(s) (e.g. COD and other nutrients removed from wastewater). Considering the input-oriented nature of the problem, in this study we focus on WWTPs whose objective is to produce a given level of outputs with the minimum possible level of input. Apart from desired outputs, WWTPs can produce also undesirable outputs such as greenhouse gases emitted during treatment (e.g. C[O.sub.2], C[H.sub.4], and [N.sub.2]O). Although efficiency analysis (especially in non-parametric context) have been extended to include undesired outputs into account (Fare et al., 2004; Kuosmanen, 2005), those have been excluded from the present study as they belong to broader environmental analysis such as Life Cycle Assessment (Corominas et al., 2013).

    WWTPs may be characterized by operating under particularly heterogeneous environment, e.g. under highly heterogeneous topography. Variation in operating environment that manifests as variation in energy use, if not controlled for, may be misinterpreted as efficiency differences. It is however virtually impossible to observe (or measure) all relevant aspects that may affect energy use at WWTPs. Thus, unless unobserved heterogeneity is properly taken into account the estimated inefficiencies are likely to be biased. This has been a pervasive problem in cross-sectional analysis (Arellano, 2003). If, however, panel data are available, this limitation can be overcome. Utilizing information on both the intertemporal dynamics and the individuality of the entities being investigated, panel data permit to control for the effects of unobserved variables (Hsiao, 2014).

    Another important advantage of using panel data over cross-section is that it is possible to think of the inefficiency term as comprised by two components: persistent (i.e. time-invariant) and transient (i.e. time-varying) (Tsionas and Kumbhakar, 2014; Filippini and Greene, 2016). The persistent component is determined by the presence of structural problems such as inefficient equipment or design limitations that do not allow the plant to minimize the use of energy, and the transient component may be caused by the presence of non-systematic difficulties that can be solved in the short term such as adaption of wrong operational strategies due to e.g. too infrequent sampling.

    The simplest frontier model that accounts for the stochastic effects, and extended for panel data, can be written as:

    [mathematical expression not reproducible] (1)

    where E denotes the energy consumption, X stands for the vector of explanatory variables influencing energy demand, including plant characteristics and exogenous factors, [beta] is the vector of coefficients and [alpha] is the regression constant. Subscripts i and t stand for WWTP and time...

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT