Reducing CO2 emissions from drinking water treatment plants: A shadow price approach

Applied Energy xxx (2016) xxx–xxx

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Reducing CO2 emissions from drinking water treatment plants: A shadow price approach María Molinos-Senante a,b,c,⇑, Catalina Guzmán a,c a

Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile Facultad de Arquitectura e Instituto de Estudios Urbanos, Pontificia Universidad Católica de Chile, El Comendador 1916, Santiago, Chile c Centro de Desarrollo Urbano Sustentable CONICYT/FONDAP/15110020, Av. Vicuña Mackenna 4860, Santiago, Chile b

h i g h l i g h t s
The shadow price of CO2 for drinking water treatment plants is estimated.
The average shadow price of CO2 is 5.7% of the price of the drinking water.
Factors affecting shadow price of CO2 are examined.

a r t i c l e

i n f o

Article history: Received 29 July 2016 Received in revised form 13 September 2016 Accepted 24 September 2016 Available online xxxx Keywords: Drinking water treatment Shadow price Directional distance function Greenhouse gas emissions Marginal abatement cost

a b s t r a c t The water industry is currently facing the challenge to reduce its carbon footprint. Although the majority of previous studies have focused on wastewater treatment energy issues, a non-negligible quantity of energy is consumed in drinking water treatment plants (DWTPs). To develop environmental policies aimed to reduce CO2 emissions, it is essential to estimate the shadow price of CO2 because this can provide information about the marginal abatement cost of CO2. This paper computes the shadow price of CO2 for a sample of Chilean DWTPs using directional distance function estimation. The potential reduction of CO2 emissions for each DWTP is also calculated. Finally, applying a non-parametric hypothesis test, factors that affect CO2 shadow prices are investigated. The results indicated that the Chilean DWTPs evaluated have notable room to reduce CO2 emissions. Moreover, the average shadow price of CO2 for DWTPs is 5.7% of the drinking water price. The methodology and results of this study are of great interest for water companies and policy makers to introduce incentives for promoting the transition towards an urban water cycle with a low carbon footprint. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Water and energy resources are intrinsically linked through a co-dependent and complex relation that is often referred to as the water-energy nexus [1]. Water is used for many processes in electricity generation, and energy is required in the urban water sector [2]. In developed countries, the water sector is one major contributor to municipal energy use; water and wastewater treatment and transport are responsible for up to 44% of a city’s energy cost [3]. This use of energy by water utilities contributes significantly to an increased carbon footprint with an estimated 45 million tons of greenhouse gases (GHGs) annually emitted into the atmosphere in the United States [4]. Moreover, as populations ⇑ Corresponding author at: Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile. E-mail address: [email protected] (M. Molinos-Senante).

grow and environmental requirements become more stringent, demand for electricity by drinking water and wastewater treatment facilities is expected to grow substantially [5]. The urban water cycle involves several activities that might be split into two main services, namely, drinking water supply and wastewater treatment collection and treatment. A literature review has demonstrated that most of the previous studies on the topics of energy use, energy efficiency and development of low carbon technologies have focused mainly on wastewater treatment, while energy issues related to drinking water have been much less investigated [6]. Nevertheless, some papers have assessed the environmental life cycle of the urban water cycle involving both water supply and wastewater treatment services. A review conducted by Loubet et al. [7] demonstrated that the electricity consumed in wastewater treatment plants (WWTPs) is larger than that consumed in drinking water treatment plants (DWTPs); however, the electricity consumed in DWTPs is not neg-

http://dx.doi.org/10.1016/j.apenergy.2016.09.065 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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M. Molinos-Senante, C. Guzmán / Applied Energy xxx (2016) xxx–xxx

ligible. Thus, according to the 10 studies reviewed by Loubet et al. [7], DWTPs consumed an average of 0.26 kWh/m3 with a maximum value of 0.64 kWh/m3 reported by Lemos et al. [8]. These figures show that energy issues in DWTPs deserve to be investigated. Recent studies have evidenced that the nexus between energy and water provides cross-cutting opportunities to mitigate urban energy and water demand pressure [9,10]. Moreover, synergistic approaches are critical for helping decision makers better understand the interrelationships between energy and water [11]. From a policy perspective, some governments have already realized the important role that the urban water industry might play in the reduction of GHG emissions. For example, in Quebec and British Columbia, the water industry is already subject to a carbon tax [12]. The United Kingdom (UK) Environmental Agency, in its report ‘‘Evidence. A low carbon water industry in 2050” (UKEA, 2009), stated that the price of carbon needs to be internalized in every aspect of the water industry’s activities. In this context, the estimation of the shadow price of CO2 plays an essential role given that it reflects the opportunity costs for pollution abatement, which can be used for measuring the difficulty level of reducing CO2 emissions [13]. Based on the pioneering methodological approach developed by Färe et al. [14], several applications have been carried out to estimate the shadow price of a variety of pollutants such as NOx, SO2, CO2, BOD, SS, N and P that result from several industries. A summary of the existing studies on this topic is provided by Zhou et al. [15]. The water industry has not ignored this area of research. Thus, since the pioneering paper by Hernández-Sancho et al. [16], several empirical applications have been developed to estimate the shadow price of pollutants removed from wastewater [17,18]. In the framework of CO2 emissions, Molinos-Senante et al. [12] computed the shadow price of CO2 for a sample of 25 Spanish WWTPs. However, as was reported previously, within the urban water cycle, the electricity consumption and therefore CO2 emissions of DWTPs are not negligible. Against this background, the objectives of this paper are threefold. The first one is to estimate the shadow price of CO2 associated with electricity consumption in DWTPs. To do so, the methodological approach proposed by Färe et al. [19] based on the directional output distance function was applied. The second objective is to calculate potential reduction of CO2 emissions. This information is essential for identify best managerial practices and for improve the environmental sustainability of DWTPs. The third objective of this paper is to explore factors that might affect CO2 shadow prices. This issue is fundamental for planning new DWTPs or updating the existing ones. The empirical application focused on a sample of 36 Chilean DWTPs. Chile presents an interesting case within the context of this research since it is a middle income country which has achieved almost universal access to urban water services. Given that Latin America could be described as being situated at a medium level in terms of coverage and quality of water services, including environmental issues, water authorities in other low and middle income countries can learn some lessons from the Chilean case. This paper contributes to the current strand of literatures by estimating, for the first time, the shadow prices of CO2 emissions associated with energy consumption in DWTPs. In the framework of the urban water industry, the water-energy nexus should be addressed from a multidisciplinary point of view. However, to the best of our knowledge, only Molinos-Senante et al. [12] computed the shadow price of CO2 emissions from WWTPs. It should be noted that urban water cycle involves two services namely, drinking water supply and wastewater treatment. Actually, in many countries both services are provided by different types of water companies namely, water only companies and water and sewerage companies. Hence, the shadow prices of CO2 estimated by Molinos-Senante et al. [12], who focused on wastewater, are

not useful to develop energy and environmental policies to regulate drinking water supply service. This study introduces a pioneering and novel approach in the framework of drinking water supply service to link energy and water policies. Moreover, this study identifies factors affecting the shadow price of CO2 emissions from DWTPs. This issue is relevant for many low and middle income countries where the access to drinking water supply is not universal. Unicef and WHO [20] reported that in 2015, 663 million people still lack improved drinking water services. Hence, in order to meet the Sustainable Millennium Development Goals, new DWTPs should be constructed. In this context, this paper contributes to support the decision making for implementing new and sustainable DWTPs. From a policy perspective, the results of this study are expected to be pertinent to water managers and policy makers because the estimation of the shadow price of CO2 and reduction potential is fundamental to support environmental policy issues. In this context, it should be noted that the water industry is regulated in many countries, and therefore, policy makers (i.e., water regulators) have the capacity to introduce efficient incentives to reduce the carbon footprint of the water industry. Given that the shadow prices can be interpreted as the marginal abatement costs [21], these costs can be used to fix carbon tax rates and to ascertain an initial market price to reduce GHG emissions in the water industry. Hence, economic incentives for reducing CO2 emissions in the water industry, such as carbon tax or trading system, can be implemented by water regulators. 2. Methodology 2.1. Directional output distance function A literature review conducted by Zhou et al. [15] indicated that the shadow price of undesirable or bad outputs can be estimated using two alternative distance functions, namely, radial output/ input distance functions [14] and directional output distance functions [19]. While both approaches have been widely applied, the directional distance function provides a more useful and flexible approach to assess the environmental performance of productive processes [22]. Hence, in this study, shadow prices of CO2 were estimated using the directional distance function. First, the directional distance function is introduced; then, the function is used to derive shadow prices. Suppose that a production process employs a vector of inputs x ¼ ðx1 ; . . . ; xN Þ 2 RNþ to produce a vector of desirable outputs denoted by y ¼ ðy1 ; . . . ; yM Þ 2 RM þ and a vector of undesirable outputs denoted by b ¼ ðb1 ; . . . ; bJ Þ 2 RJþ . Eq. (1) describes the production technology [23]:

PðxÞ ¼ fðy; bÞ : x can produceðy; bÞg

ð1Þ

In addition to the standard assumptions of convex, compact, and freely disposal inputs, the following assumptions should be imposed on the output set. First, it is assumed that the undesirable outputs are produced jointly with the desirable outputs. Second, desirable and undesirable outputs are assumed to be weakly disposable. Third, it is assumed that desirable outputs by themselves are freely disposable [24]. Taking into account the above assumptions, the directional output distance function is formally defined as [19] !
 Do ðx; y; b; g y ; g b Þ ¼ max b : ðy þ bg y ; b  bg b Þ 2 PðxÞ

ð2Þ

J where g ¼ ðg y ; g b Þ 2 RM þ XRþ is a directional vector that specifies the direction of the output vector. The directional distance function describes the simultaneous maximum expansion of desirable outputs and contraction of undesirable outputs that is feasible for

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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any given production technology. Given the production technology PðxÞ and g ¼ ðg y ; g b Þ > 0, the directional output distance function expands the desirable output y and contracts the undesirable output b along the g direction until it reaches the production frontier. Moreover, the directional output distance function describes inefficiency. b ¼ 0 means that the producer is located on the frontier and is therefore efficient. By contrast, b > 0 means that the producer is inefficient. According to Färe et al. [25], the directional output distance !

function has the following properties: (i) Do is concave and non!

negative for feasible output vectors; (ii) Do is monotonic and freely !

disposable in desirable outputs and inputs; (iii) Do is weakly dis!

posable in undesirable outputs and; (iv) Do satisfies the translation property: !

!

Do ðx; y þ ag y ; b  ag b ; gÞ ¼ Do ðx; y; b; gÞ  a

ð3Þ

where a is a scalar. This property indicates that if an undesirable output is contracted by ag b and a desirable output is expanded by ag y , the resulting value of the directional distance function will be more efficient by the amount a [25]. 2.2. Shadow price of undesirable output To derive the shadow price of an undesirable output, the duality relationship between the directional output distance function and the revenue function is employed. According to Färe et al. [19], the revenue function is defined as

Rðx; p; qÞ ¼ maxfpy  qb : ðy; bÞ 2 PðxÞg

ð4Þ

y;b

where p ¼ ðp1 ; . . . ; pM Þ 2 RM þ represents desirable output prices and q ¼ ðq1 ; . . . ; qJ Þ 2 RJþ represents undesirable output prices. The revenue function describes the largest feasible revenue obtainable represented as the sum of the positive revenue generated by desirable outputs and negative revenue produced by undesirable outputs. Given a feasible directional vector g ¼ ðg y ; g b Þ, the revenue function (Eq. (4)) can be written as !

Given the market price of the mth desirable output denoted by pm , the shadow price of the jth undesirable output is

0

Rðx; p; qÞ  ðpy  qbÞ pg y þ qg b

ð6Þ

The directional distance function in terms of maximal revenue function is related, as shown in Eq. (7): !

Do ðx; y; b; gÞ 6 minp;q

Rðx; p; qÞ  ðpy  qbÞ pg y þ qg b

!

¼ a0 þ

p ry Do ðx; y; b; gÞ ¼ pg y  qg b !

q rb Do ðx; y; b; gÞ ¼ pg y  qg b

N X

M X

n¼1

m¼1

an xnk þ

bm ymk þ

J X

cj bjk þ

j¼1

N X N 1X ann0 xnk xn0 k 2 n¼1 n0 ¼1

M X M N X M X 1X 1 XX þ bmm0 ymk ym0 k þ cjj0 bjk bj0 k þ dnm xnk ymk 2 m¼1m0 ¼1 2 j¼1 0 n¼1 m¼1 J

J

j ¼1

þ

J N X X

J M X X

n¼1 j¼1

m¼1 j¼1

gnj xnk bjk þ

lmj ymk bjk

ð11Þ

To calculate the parameters a, b, c, d, g and l of the quadratic function, the following linear program was solved (Eq. (12)), where the objective was to minimize the sum of deviations of the directional-distance function value from the frontier of the production technology:

Minimise

K ! X ½ Do ðxk ; yk ; bk ; 1; 1Þ  0 k¼1

ð7Þ s:t:

Assuming that directional distance function and the revenue function are both differentiable, the first-order condition with respect to desirable and undesirable outputs is shown in Eqs. (8) and (9): !

ð10Þ

Do ¼ ðxk ; yk ; bk ; 1; 1Þ

ð5Þ

!

1 ! @Do ðx; y; b; gÞA @ym

There are two methodological approaches to estimate the directional distance function, namely, non-parametric and parametric methods. However, in the framework of shadow price estimation, the main drawback of non-parametric methods is that the distance function is not differentiable, and therefore, it is not well-suited to derive shadow prices [25]. By contrast, parametric estimation preassumes a specific functional form for the directional distance function, and then unknown parameters are estimated using linear programming [26]. As was illustrated by Zhou et al. [15], to ease the theoretical restrictions imposed on the estimation, the parametric approach is the most widely used in empirical applications to estimate the shadow price of undesirable outputs. Following previous studies, the directional distance function was estimated parametrically. The quadratic form was used to parameterize the directional output because it satisfies the translation property and is twice differentiable [27,21]. Following Färe et al. [19], the directional vector g ¼ ð1; 1Þ was chosen, where the first M components are equal to one and the next J components are also equal to one to make the parameterization parsimonious. Assuming that there are k ¼ 1; . . . ; K producers (DWTPs in this case study), n ¼ 1; . . . ; N inputs, m ¼ 1; . . . ; M desirable outputs and j ¼ 1; . . . ; J undesirable outputs, then the quadratic directional-distance function for the k-th producer is as follows:

Rðx; p; qÞ P ðpy  qbÞ þ p  Do ðx; y; b; gÞ  g y þ q  Do ðx; y; b; gÞ  g b

Do ðx; y; b; gÞ 6

,

2.3. Empirical specifications

!

The left side of Eq. (5) determines the maximal feasible revenue, while the right side equals observer revenue plus the revenue gained by technical efficiency improvement. By rearranging Eq. (5), the following is obtained:

!

@Do ðx; y; b; gÞ qj ¼ pm  @ @bj

ð8Þ

!

ðiÞ Do ðxk ; yk ; bk ; 1; 1Þ P 0;

k ¼ 1; . . . ; K

!

ðiiÞ

@ Do ðxk ; yk ; bk ; 1; 1Þ P 0; @bj

j ¼ 1; . . . ; J; k ¼ 1; . . . ; K

!

ð9Þ

ðiiiÞ

@ Do ðxk ; yk ; bk ; 1; 1Þ 6 0; @ym

m ¼ 1; . . . ; M; k ¼ 1; . . . ; K

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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M. Molinos-Senante, C. Guzmán / Applied Energy xxx (2016) xxx–xxx !

ðivÞ

@ Do ðx; yk ; bk ; 1; 1Þ P 0; @xn

J J M M X X X X bm  cj ¼ 1; bmm0  lmj ¼ 0;

ðvÞ

m¼1 J X

M X

j0 ¼1

m¼1

cjj0 

M X m¼1

m0 ¼1

j¼1

among the groups of DWTPs. The facilities were grouped based on the different factors investigated, i.e., on factors that might affect CO2eq shadow prices. Intuitively, the Mann-Whitney test is similar to the traditional one-way analysis of variance (ANOVA). However, it does not assume a normal distribution, unlike ANOVA. Therefore, for our case study, the Mann-Whitney test was more suitable. If the p-value of the non-parametric test is smaller than 0.05, the null hypothesis can be rejected, which means that the shadow price of CO2eq for the groups of DWTPs is significantly different with the usual level of confidence of 95%.

n ¼ 1; . . . ; N

m ¼ 1; . . . ; M

j¼1

lmj ¼ 0; j ¼ 1; . . . ; J

dnm 

J X

3. Data and variables

gnj ¼ 0; n ¼ 1; . . . ; N

j¼1 0

ðviÞann0 ¼ an0 n n – n ;

0

bmm0 ¼ bm0 m m – m ;

cjj0 ¼ cj0 j j – j

0

ð12Þ

The restriction (i) imposes feasibility, which means that each producer is located either on or below the boundary. Restrictions (ii) and (iii) impose the monotonicity requirement in undesirable and desirable outputs. Positive monotonicity constraints were also imposed on the inputs for the mean level of input usage (iv). This means that at the mean level of inputs, an increase in input usage holding desirable and undesirable outputs constant causes the directional output distance function to increase [19]. The restrictions in (v) are due to the translation property. The restrictions in (vi) ensure symmetry conditions. It should be noted that to compute the shadow price of CO2eq data were normalized by dividing the input and outputs by their average values, as suggested by Färe et al. [19]. Hence, the convergence problem was overcome. 2.4. Potential reduction of CO2 emissions The estimation of the directional distance function enables measuring the feasible reduction potentials of CO2eq emissions [13]:

Dbi ¼ bi  ðbi  bi g b Þ

ð13Þ

where bi is the quantity of CO2eq emissions by the DWTP i, bi is the estimated technical inefficiency score of the DWTP i and g b is the directional vector for the undesirable output. Dbi describes the maximum attainable amount of CO2eq emissions for the DWTP i when production is fully efficient. Given that the volume of drinking water produced by the DWTPs evaluated is relatively heterogeneous (Table 1), the scale of CO2eq emissions is also considerably heterogeneous. This makes it difficult to compare each DWTP’s relative ability to reduce CO2eq emissions based on its desirable output. To overcome such a difficulty, the scale of potential CO2eq emissions should be divided by the real observed emissions for each DWTP. This gives the percentage of potential CO2eq savings for each DWTP that can be used to compare across DWTPS. 2.5. Factors affecting the shadow price of CO2 emissions According to previous studies, there are two main methodological approaches to determine factors that affect shadow prices, namely, regression analysis [28] and hypothesis testing [12]. In our case study, most of the potential factors investigated are qualitative; therefore, it is not feasible to introduce all of them as dummy variables. Hence, the hypothesis test approach was applied to identify factors that affect the shadow price of CO2eq. In doing so, non-parametric tests (Mann-Whitney or Kruskal-Wallis test for three groups of more) were performed to test the following null hypothesis: there are no differences in the shadow prices of CO2eq

This section provides the definitions of inputs, desirable and undesirable outputs and the DWTPs used in this study. Analogously to WWTPs, which have been widely studied, DWTPs can be considered as carrying out a productive process in which a desirable output (drinking water with a certain quality) is obtained from inputs (operational and maintenance costs), and GHGs are also emitted. Accordingly, in this study, the input considered is the operational and maintenance cost of each DWTP expressed in CLP1 per year. Ideally, it would be desirable to have this cost disaggregated in several cost items, such as staff costs, material costs and waste management costs. Unfortunately, this information was not available. Nevertheless, given that the aim of this paper is to estimate the shadow prices of CO2 and not an efficiency score for each variable, it is not relevant to have all cost items as total operational and maintenance costs. Regarding the selection of the desirable output, intuitively, it should be the volume of drinking water generated in the DWTPs. However, the operational and maintenance costs and the emission of GHGs from the DWTPs depend on the drinking water quality. In this context, previous studies [29,30] defined a quality-adjusted variable as the desirable output. The construction of this qualityadjusted output is defined as follows. The Chilean water industry regulator, the ‘‘Superintendencia de Servicios Sanitarios (SISS)”, defines and measures an indicator of drinking water quality based on a series of parameters regulated by the Chilean norm NCh 409 for drinking water. This norm is based on the guidelines for drinking water quality published periodically by the World Health Organization. Moreover, it takes into account the special conditions of the raw water in some Chilean regions [31]. Considering the degree of compliance of the Chilean norm for drinking water, the SISS develops a synthetic indicator of drinking water quality, which integrates the following parameters: bacteriology, turbidity, free residual chlorine and critical and non-critical parameters. Critical and non-critical parameters depend on the source of raw water and are not the same for all DWTPs. The synthetic indicator estimated by the SISS ranges between 0% and 100%. A value of 100% indicates that the drinking water fulfils the drinking water quality norm for all parameters. In this study, the desirable quality-adjusted desirable output ðQADOÞ for each DWTP is defined using Eq. (13), where V is the volume of drinking water produced (m3/year) adjusted by the synthetic indicator of drinking water quality ðSIDWQ Þ.

QADO ¼ V  SIDWQ

ð14Þ

The undesirable output considered is the indirect emission of GHG expressed in kg of CO2 equivalents (CO2eq) per year. GHG emissions are categorized as direct and indirect GHG emissions. Direct emissions are the emissions released to the atmosphere as a direct result of an activity or a series of activities at a facility level. Indirect GHG emissions are the ones released to the atmosphere 1

On 1st June, US$1 was CLP689.81 and €1 was CLP768.25.

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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M. Molinos-Senante, C. Guzmán / Applied Energy xxx (2016) xxx–xxx Table 1 Descriptive statistics of DWTPs assessed.

Average Std. Dev. Minimum Maximum

Energy consumption (kWh/m3)

Volume of water (103 m3/year)

SIDWQ (%)

Input Operational and maintenance costs (103 CLP/year)

Desirable Output Volume of water ⁄ SIDWQ (103 m3/year)

Undesirable Output GHG emissions (kgCO2eq/year)

0.1767 0.2137 0.0048 0.7178

17,883 62,199 31 359,455

98.42 2.85 88.54 100.00

179,166 296,324 15 1,333,152

17,600 1775 27 359,455

109,004 165,263 2986 806,782

from the indirect consumption of an energy commodity [32]. Based on IPCC Guidelines [33], direct emissions have not been considered in this empirical application. Hence, GHG emissions were quantified based on the energy consumed by the DWTPs and the Chilean national electrical production mixes. In this sense, in Chile, there are two main national electrical production mixes, namely, SIC and SING. The SIC is the Central Interconnected System, which serves the central part of the country and represents approximately 75% of the total installed capacity and 93% of the population. The SING is the Large North Interconnected System and serves the north of the country (I and II regions); it represents approximately 23% of the total capacity (Central [34]). Given that both systems have different energy production mixtures, their GHG emissions expressed as CO2eq per kWh of produced electricity is also different. Specifically, for 2014, the average value for the SIC was 0.360 tCO2eq/MWh, while it was 0.790 tCO2eq/MWh for the SING [35]. Hence, based on the localization of the DWTPs, the CO2eq emission associated to the SIC or SING was applied. The sample used in this case study consists of 36 Chilean DWTPs located across the country. The volume of water treated in each of these DWTPs varies between 31,000 m3/year and 359,500,000 m3/year. All facilities carry out water treatment using conventional physical-chemical processes, i.e., none of the DWTPs apply reverse osmosis to treat the water. Nevertheless, the specific unitary processes in all DWTPs are not exactly the same. For example, only some DWTPs use a coagulation-flocculation process, and only some of the facilities have a catch basin. The statistical information was supplied for the year 2014 by the SISS through the transparency of information process, which involves requesting certain information from the SISS, which has the legal duty to provide it. Table 1 describes statistics for the variables used in this empirical application. 4. Results and discussion 4.1. Shadow prices of CO2eq emissions The parameter estimates for the quadratic form of the directional distance function (Eq. (11)) were obtained by solving the linear programming (Eq. (12)) using GAMS (General Algebraic Modeling System with CPLEX solver). According Eq. (10), to compute the shadow price of CO2eq for each DWTP, it is required to ascribe a reference price for the desirable output, i.e., the drinking water produced by each DWTP. Following Molinos-Senante et al. [36], water-billing data taken from SISS’s webpage was used as a reference price of the drinking water, which ranges between 0.44 €/m3 and 1.82 €/m3. Moreover, the shadow price of CO2eq can be expressed as a percentage of the price of the drinking water. The shadow price of CO2eq for each of the 36 DWTPs was computed using the previously described methodology. Table 2 shows that the shadow price of the CO2eq emitted by the DWTPs is relatively constant because the minimum value is 5.54% of the price of the drinking water, while the maximum value is 8.56%. The average value, i.e. the shadow price of CO2eq for the drinking water industry, is 5.71%, and the standard deviation is 0.51%. This

demonstrates the low variability of the shadow price of the CO2eq among DWTPs when it is expressed as a percentage of the price of the drinking water. The value of 5.71% means that if the water regulator introduces a carbon tax to the water industry and therefore, water companies have to pay for the emission of GHG, they would have to pay in average the 5.71% of the sales value of one cubic meter of drinking water for each kg of CO2eq emitted in DWTPs. By considering the price of drinking water, the variability of the shadow price of CO2eq increases notably because the minimum value is 0.025 €/kg, while the maximum value is 0.104 €/kg. The average value for the 36 DWTPs assessed is 0.040 €/kg with a standard deviation of 0.020 €/kg. This divergence in the shadow prices is due to the large variability in the water prices for the Chilean cities. The shadow price of an undesirable output is interpreted as the opportunity cost of reducing the desirable output by one unit once inefficiencies in the production have been eliminated [37]. However, in the framework of water utilities, the UK Environmental Agency [38] and Hernández-Sancho et al. [16], among others, considered that if the current pollution levels are optimal, then the shadow price of CO2eq is interpreted as the environmental costs of using energy to treat the water. According to this approach, the shadow price of CO2eq of 0.040 €/kg means that on average, each kg of CO2eq emitted as a result of treating raw water involves an environmental cost of 0.040 € (5.7% of the price of the drinking water). The UK Environmental Agency [38] estimated the shadow price of GHGs, expressed as CO2eq, associated with the urban water cycle. According to their methodology, the shadow price depends on the year that the carbon is emitted. It was reported that for 2007, the shadow price of CO2eq was 25.5 £/tCO2eq (0.033 €/ KgCO2eq), and for 2014, it should be 29.4 £/tCO2eq (0.038 €/ KgCO2eq). In the framework of WWTPs, Molinos-Senante et al. [12] estimated that the average shadow price of CO2eq for a sample of 25 Spanish WWTPs was 0.88 €/KgCO2eq. Hence, the results of this study are consistent (are of the same order of magnitude) with previous research on this topic because the mean value for the 36 DWTPs analysed was computed as 0.040 €/m3. 4.2. Potential reduction of CO2 emissions Fig. 1 shows the potential reduction ratio of CO2eq emissions for the 36 DWTPs evaluated. A large divergence across DWTPs was found. Thus, there are three facilities that are efficient and act as references for the other DWTPs; therefore, they do not have the potential to reduce CO2eq emissions. By contrast, 15 of the 36 DWTPs (42%) could reduce more than 75% of CO2eq emissions if they produced at this most efficient level. 4.3. Factors affecting the shadow price of CO2 emissions From a policy and managerial perspectives, identifying the factors affecting the shadow price of CO2eq emissions is essential for support decision-making in order to promote the transition towards sustainable and low-carbon-footprint drinking water

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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Table 2 Shadow price of CO2eq expressed in % of the drinking water price and expressed in €/kg. DWTP

Shadow price (%)

Shadow price (€/kg)

Energy consumption (kWh/m3)

Volume of drinking water (m3/year)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

8.56 6.45 5.56 5.59 5.66 5.70 5.68 5.58 5.65 5.59 5.64 5.70 5.56 5.65 5.60 5.55 5.74 5.61 5.54 5.56 5.56 5.55 5.55 5.55 5.56 5.56 5.55 5.63 5.66 5.75 5.56 5.55 5.55 5.54 5.58 5.54

0.0379 0.0285 0.0246 0.0248 0.1029 0.1037 0.1032 0.0312 0.0316 0.0313 0.0316 0.0319 0.0312 0.0438 0.0483 0.0478 0.0472 0.0263 0.0397 0.0398 0.0398 0.0397 0.0397 0.0397 0.0398 0.0398 0.0315 0.0320 0.0322 0.0327 0.0316 0.0315 0.0315 0.0284 0.0286 0.0284

0.006 0.010 0.053 0.011 0.032 0.033 0.014 0.022 0.028 0.016 0.011 0.005 0.010 0.005 0.013 0.029 0.298 0.532 0.044 0.154 0.068 0.068 0.131 0.222 0.156 0.085 0.642 0.653 0.267 0.244 0.340 0.718 0.415 0.282 0.573 0.171

359,454,940 124,580,137 1,861,988 8,007,871 7,718,164 10,116,219 14,387,527 4,805,179 11,932,496 6,623,541 14,990,813 28,038,677 3,846,102 19,640,674 9,290,835 662,313 3,541,785 723,461 198,612 757,673 988,509 452,662 261,838 200,998 609,702 988,561 133,702 787,667 2,437,155 4,580,542 358,271 92,782 163,667 30,624 410,717 113,641

100

Potenal Reducon (%)

90 80 70 60 50 40 30 20 10 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

DWTP Fig. 1. Potential reduction of CO2eq emissions for each DWTP.

treatment. To investigate the determinants of the CO2eq shadow prices of DWTPs, potential explanatory variables were selected taking into account the available statistical information and the features of the DWTPs [39]. Hence, we explored the possibility that the shadow prices of CO2eq may be affected by the following factors: (i) size of the DWTP; (ii) year of construction of the DWTP; (iii) source of raw water; (iv) company managing the DWTP; (v) presence of catch basin; and (vi) use of coagulation-flocculation process. The size or capacity of the DWTP was expressed as the volume of drinking water produced annually. This variable informs about potential economies of scale in the shadow price of CO2eq. To investigate whether the size of DWTPs affects its CO2eq shadow price,

the facilities were categorized into five groups: (i) less than 500,000 m3/year; (ii) between 500,000 m3/year and 1,000,000 m3/ year; (iii) between 1,000,000 m3/year and 10,000,000 m3/year; (iv) between 10,000,000 m3/year and 100,000,000 m3/year; and (v) more than 100,000,000 m3/year. Fig. 2 shows that as the capacity of the DWTPs increases, its shadow price of CO2eq also rises. This means that the opportunity costs of CO2eq emissions are higher for the largest DWTPs than for the smallest facilities. Thus, the group of DWTPs with the largest size present an average shadow price of CO2eq of 7.51%, while for the group of smallest DWTPs the average shadow price is 5.55%. Moreover, the Kruskal-Wallis test led us to confirm that the differences between the DWTP groups are statistically significant (Table 3).

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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M. Molinos-Senante, C. Guzmán / Applied Energy xxx (2016) xxx–xxx

6.6

6.2 6.0 5.8 5.6 5.4 5.2 5.0

1

2

3

4

Source of water

Fig. 2. Average shadow price of CO2eq expressed as % of the drinking water price for DWTPs grouped based on the capacity, year built and main source of raw water.

The next explanatory factor investigated is the year in which the DWTP was built as a measure of the age of the facility. It should be noted that 1990 and 2000 were two significant milestones for the Chilean water industry. On the one hand, in 1990 the Chilean Government created the water and sewerage industry regulator (Chilean Law 18902). Hence, before 1990 the DWTPs in Chile were operated by public companies without any regulation. On the other hand, based on the Strategic Plan developed by the SISS from 1998 to 2000 and Law 19549, the organization of the SISS was modified to its current state. Moreover, from 1998 to 2000, a significant part of the capital of the main Chilean water companies was privatized [40]. Hence, DWTPs were categorized into three groups: (i) plants built before 1990; (ii) plants built between 1990 and 2000; and (iii) plants built after 2000. Fig. 2 and Table 3 show that the age of the DWTPs does not affect the shadow price of CO2eq. In Chile, DWTPs are supplied by groundwater, surface water, and both simultaneously. Hence, eight of the 36 facilities evaluated treat exclusively groundwater, 10 of the 36 facilities use exclusively surface water, while half of the DWTPs assessed use mixed raw water (groundwater and surface water in different percentage). Accordingly, the DWTPs were classified into three groups: (i) groundwater; (ii) surface water; and (iii) mixed water. Fig. 2 illustrates that on average, the shadow price of CO2eq emissions from DWTP treatment of surface water is the largest. However, the p-value of the Kruskal-Wallis test is larger than 0.05, which means that the differences among the three groups are not statistically significant. In other words, the source of raw water does not affect the shadow price of CO2eq emissions from the analysed DWTPs. The management of DWTPs is essential for establishing their performance. Hence, it was investigated whether the shadow price of CO2eq is affected by the company that manages the DWTP. The 36 facilities evaluated in this paper are operated by 10 different water companies. DWTPs were grouped according to the water company that operates them. Hence, 10 groups of DWTPs were composed. Fig. 3 shows the average shadow price of CO2eq for each group of DWTPs. It is illustrated that there is one water company for which its average shadow price of CO2eq is notably larger than for the other water companies. Thus, for this water company, the average shadow price of CO2eq is 6.54% of the drinking water price,

5

6

7

8

9

10

Water companies Fig. 3. Average shadow price of CO2eq expressed as % of the drinking water price for DWTPs grouped based on the managing water company.

6.1 6.0 5.9 5.8 5.7

Yes

Year of built

5.6 5.5 5.4 5.3 5.2

No

Capacity (m3/year)

Average shadow price (%)

0.0

No

Mixed

Surface

Groundwater

Y>=2000

Y<=1990

1990
1.0

6.4

Yes

2.0

V>100*106

3.0

1*106
4.0

10*106
5.0

V<= 5*105

6.0

Average shadow price of CO2eq (%)

7.0

5*105
Average shadow price of CO2eq (%)

8.0

Catch basin

Coagulaon-Flocculaon

Fig. 4. Average shadow price of CO2eq expressed as % of the drinking water price for DWTPs grouped based on the presence or absence of catch basins and coagulationflocculation process.

while the minimum value corresponding to another water company is 5.55%. The differences in the average shadow price of CO2eq among water companies are statistically significant (Table 3). In the framework of WWTPs, Molinos-Senante et al. [12] concluded that the shadow price of CO2eq is affected by the sewage sludge treatment technology. In other words, the shadow price of CO2eq is influenced by the technology of the facility. In the context of DWTPs, the quantity of sludge produced might be considered negligible. However, there are two ‘‘technological” factors that vary across the DWTPs and that might affect to the shadow price of CO2eq, namely, the presence or absence of one or more catch basins in the DWTPs and the existence of a coagulation-flocculation process for treating the raw water. Table 3 illustrates that both the presence of catch basin(s) and the coagulation-flocculation process significantly affect the shadow price of CO2eq. Fig. 4 shows that the average shadow price of CO2eq is larger for the DWTPs that have catch basins and carry out a coagulationflocculation process. The assessment carried out in this second-stage analysis allows water regulators and company managers to identify the factors

Table 3 p-value of the Mann-Whitney and Kruskal-Wallis hypothesis tests.

p-value

Size

Year of built

Source of water

Company

Catch basin

Coagulation-flocculation

<0.001

0.249

0.168

0.028

0.040

<0.001

Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065

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M. Molinos-Senante, C. Guzmán / Applied Energy xxx (2016) xxx–xxx

affecting the shadow price of CO2 emissions from DWTPs. Hence, it provides sound scientific baseline data to develop policies to improve the environmental and economic performance of DWTPs. In particular, our study illustrates that the size of the plants and the presence of coagulation-flocculation process affects the shadow price of CO2. Given than the largest DWTPs present the highest shadow prices of CO2, if the water regulator were to introduce a trading system for the water industry, these plants should be the first ones to be part of the market. The differences in shadow prices of CO2 for DWTPs with and without coagulation-flocculation also involve policy implications. Thus, the potential trading system should take into account the different shadow price of CO2 for DWTPs with coagulation-flocculation process when making its initial permit allocations. 5. Conclusions The desire to promote the transition towards a sustainable urban water cycle with a low carbon footprint has increased notably in recent years. In the framework of the water industry, most previous studies have focused on wastewater treatment energy issues. Nevertheless, the energy consumed by DWTPs is not negligible; therefore, their energy issues deserve to be investigated. To achieve the ambitious challenge of a low carbon urban water cycle, policy makers should introduce efficient and effective policies. In this context, the estimation of the shadow price of CO2 is essential to support environmental policy issues. Given that the shadow price is interpreted as the marginal abatement cost, it can be used to fix carbon tax rates and to ascertain an initial market price for CO2 emissions. To contribute on this topic, this paper computed for the first time the shadow price of CO2 for a sample of DWTPs. In doing so, the directional distance function was estimated, which also enabled us to calculate the potential reduction of CO2 emissions at the DWTP level. Finally, by applying a nonparametric hypothesis test, the factors affecting CO2 shadow prices were investigated. The empirical application developed for a sample of Chilean DWTPs provided the following primary results. First, the average shadow price of CO2 is 5.7% of the price of the drinking water, which is approximately 0.040 €/kg of CO2. This result is consistent with the values reported by the UK Environmental Agency [38] for the whole urban water cycle. Second, the DWTPs that were evaluated have notable room to reduce CO2 emissions because, on average, they could save 58.9% of current CO2 emissions. Third, there are four factors that significantly affect the shadow price of CO2: the size of the DWTP, the water company that operates the DWTP and the presence of a catch basin and the use of a coagulationflocculation process. From a policy perspective, the shadow price of CO2 emissions from DWTPs can be interpreted as the economic value of the negative externalities associated to energy consumption. Hence, this information is essential to compare the economic feasibility of a set of technologies that consume different quantities of energy. Usually, this information is ignored in the economic assessment, which focuses on financial assessment without integrating the value of negative externalities. Moreover, the results of this paper illustrate that an important challenge for the Chilean water industry is to reduce energy use in water treatment. To achieve this goal, the water regulator should introduce incentives for water companies to promote energy efficiency improvements. The reduction of the carbon footprint of DWTPs will have positive effects on water companies that will reduce their energy bill. More importantly, these reductions will impact society as a whole because carbon reductions are needed to facilitate the transition towards a more sustainable urban water cycle with a low carbon footprint.

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Please cite this article in press as: Molinos-Senante M, Guzmán C. Reducing CO2 emissions from drinking water treatment plants: A shadow price approach. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.09.065