Ecological Engineering 60 (2013) 172–182
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Emergy evaluation of water treatment processes Damien Arbault a,b , Benedetto Rugani a , Ligia Tiruta-Barna b,∗ , Enrico Benetto a a Public Research Centre Henri Tudor (CRPHT)/Resource Centre for Environmental Technologies (CRTE), 6A, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg b Université de Toulouse INSA, UPS, INP, LISBP, INRA UMR792, CNRS UMR5504, 135 av. de Rangueil, 31077 Toulouse, France
a r t i c l e
i n f o
Article history: Received 16 February 2013 Received in revised form 21 June 2013 Accepted 6 July 2013 Keywords: Ecological performance Emergy evaluation Life cycle assessment (LCA) Unit emergy value (UEV) Water treatment
a b s t r a c t The emergy evaluation (EmE) method is acknowledged to be a holistic approach to account for the primary (solar) energy that generates the renewable and non-renewable resource ﬂows used up by human activities. This paper examines its application and robustness, using four water treatment plants (WTPs) as case studies. We obtained an average unit emergy value for potable water of 1.06 (±0.15) E12 sej/m3 , which is in accordance with existing literature. Chemicals and electricity were the most important manmade inputs; infrastructure, when accounted for, had a signiﬁcant but lesser contribution. The application of several emergy-based indicators allowed comparing the ecological performance of water production with other types of resource extraction. These indices showed that WTPs are rather blind to economic markets and they exerted a low pressure on local non-renewable resources. A critical analysis of current EmE procedure highlighted the relative low accuracy of the method compared to Life-Cycle Assessment (LCA), when man-made inputs are predominant, as well as the complementary goals and scopes of the two methods. Methodological improvements in the classiﬁcation and treatment of the emergy associated with man-made inputs are necessary to make EmE indicators more straightforward and robust. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Society as a whole is far from relying on natural resources in a sustainable way. Individuals and businesses must share the collective effort to reduce the pressure on resources. Appropriate tools and indicators are therefore needed to assess that pressure and provide decision-makers with an estimate of the distance-to-target between the current condition of stress and a more sustainable relationship with the natural environment (Moldan et al., 2012). Among the available environmental assessment tools, emergy evaluation (EmE) is a resource-oriented method that compares all resources on the basis of the solar-driven natural processes that contributed to their formation (Odum, 1996). The EmE associated with an activity or a territory embraces a holistic picture of the studied human system embedded within a surrounding natural and economic environment and the global Earth system. It highlights the need for an activity to adjust to the local and global ecosystems that support it, instead of focusing on the local and relative efﬁciency of technological processes. The cumulative direct and indirect solar energy used up by natural systems to form a resource contributes to its emergy value,
∗ Corresponding author. Tel.: +33 (0)5 61 55 97 88; fax: +33 (0)5 61 55 97 60. E-mail address: [email protected]
(L. Tiruta-Barna). 0925-8574/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecoleng.2013.07.046
expressed in solar emjoules (sej; i.e. equivalents of solar energy). The Transformity of a resource is the ratio of emergy value to its available energy content (or exergy), expressed in sej/J. Speciﬁc emergy of a resource or a product is deﬁned as its emergy value per unit mass (sej/g), while the more general term unit emergy value (UEV) is typically used when the denominator involves also other relevant physical units (e.g. volume). Average UEVs have been estimated for a wide variety of natural resources, including fossil fuels, mineral ores and renewable resources (Brown and Bardi, 2001; Odum, 1996, 2000; Odum et al., 2000). The emergy value associated with a natural resource accounts for the direct and indirect goods and services provided by the geobiosphere only. Concerning man-made products, each transformation step in their life cycle requires additional inputs, which are either natural resources already transformed by upstream human activities, or direct human interventions through labor and services (L&S). L&S are also fueled by extracted and imported natural (renewable and non-renewable) resources. Accordingly, EmE enables accounting for the various forms of energy, materials and services ultimately consumed by a human activity with the sej unit. To assist decision-making, emergy-based indicators (Brown and Ulgiati, 1997; Odum, 1996; Ridolﬁ and Bastianoni, 2008; Ulgiati and Brown, 1998) aggregate EmE results into metrics that aim at describing the integration of the production system within its surrounding human and natural environment (section 2.2).
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EmE has been applied during the last 30 years to coupled natural-human systems of various types and sizes. The emergy evaluation of nations (e.g. Brown and McClanahan, 1996; Chen and Chen, 2006; Pereira and Ortega, 2012; Siche et al., 2008), states, provinces (e.g. Liu et al., 2008; Pulselli et al., 2008; Zhao et al., 2005) and regions (e.g. Campbell and Garmestani, 2012) inform us on the local natural (and imported) resources used up to fuel these economies. Also, EmE has been applied to analyze the production of various commodities, e.g. in agriculture and farming (Castellini et al., 2006; La Rosa et al., 2008; Lefroy and Rydberg, 2003; Liu et al., 2008; Lu et al., 2009; Ortega et al., 2002; Zhang et al., 2012), forestry (Tilley and Swank, 2003), aquaculture (Lima et al., 2012), energy production (Baral and Bakshi, 2010; Brown and Ulgiati, 2002; Brown et al., 2012; Ciotola et al., 2011; Lapp, 1991; Paoli et al., 2008; Yang et al., 2010), building materials (Brown and Buranakarn, 2003; Buranakarn, 1998; Meillaud et al., 2005; Pulselli et al., 2007), recycling in industry (Giannetti et al., 2013; Mu et al., 2012, 2011), ecological conservation or restoration (Dang and Liu, 2012; Dong et al., 2012; Lu et al., 2007, 2011). EmE results for these analyses (i.e. emergy-based indicators, UEVs and transformities of the products) have been used as benchmarks to assess the ecological performance of water treatment. The focus of this paper is on the production of potable water. Few past studies refer speciﬁcally to potable water production plants (e.g. Odum et al., 1987). The ﬁrst most comprehensive survey is given by Buenﬁl (2001), who compared different household technologies with tap water from several municipal treatment plants in Florida. Then, Pulselli et al. (2011a) tracked the UEV of freshwater along a water course, from raw resource to water on tap, and Rugani et al. (2011a) compared ancient and modern aqueduct systems in the city of Siena, Italy. A common conclusion of those studies is that man-made inputs at the factory level make a large contribution to the ﬁnal UEV of tap water. Case studies on contemporary potable water production plants (Buenﬁl, 2001; Pulselli et al., 2011a) provide ranges of 6.9 E5–6.9 E6 sej/g, and 1.4 E5–1.4 E6 sej/J (adjusted to the 9.44 baseline, as explained in Section 2.4). Potable water is thus a man-made product with a high transformity relative to its speciﬁc emergy. Such particularity is due to the low exergy content of water, compared to the other types of man-made goods. Water Treatment Plants (WTPs) rely on a single local, renewable resource (freshwater), and a diverse set of man-made products and services. Local, non-renewable resources used up are apparently negligible (Rugani et al., 2011a). Such a situation can also be found in various other commodities, such as wind and solar electricity production, and organic farming (see, e.g. Brown et al., 2012; Ciotola et al., 2011; Lu et al., 2009). Therefore, it seems critical to estimate the UEV of raw freshwater consistently. The water cycle (and the use of water in human activities) has been widely studied in EmE: it shapes landscapes and ecosystems, which can be used for many different activities. Freshwater-related EmEs cover a very large spectrum of situations, including dam proposals (Brown and McClanahan, 1996; Kang and Park, 2002), the overview of the Cache river basin (Odum et al., 1998) and water treatment via natural or artiﬁcial wetlands (Carey et al., 2011; Cohen and Brown, 2007; Duan et al., 2011; Martin, 2002) reﬂecting different aims. The most common objective of EmEs related to freshwater is to value this natural asset, i.e. its contribution to a regional or national public welfare (Chen and Chen, 2009; Chen et al., 2009; Lv and Wu, 2009; Tilley and Brown, 2006), its relationship with land occupation (Huang et al., 2007) and ecosystem services (Huang et al., 2011; Odum and Odum, 2000; Watanabe and Ortega, 2011). EmE of the global water cycle was the subject of several studies (e.g. Buenﬁl, 2001; Campbell, 2003; Campbell et al., 2013; Watanabe
and Ortega, 2011). EmE was also proposed for a method to assess the full cost recovery of water management in a watershed (Brown et al., 2010). The aim of this study was to compare the outcomes of EmE associated with four WTPs located in France, in particular focusing on the UEV of the potable water produced (considering the actual quality level) and on a selection of emergy-based indicators. A particular emphasis was given to man-made inputs that are necessary to run the plant, and the computation of their emergy value. The importance of infrastructure to the overall performance of the WTPs is also investigated. Additionally, results of EmE are compared to Life Cycle Assessment (LCA) results for the same plants (Igos et al., 2013a, 2013b), in order to highlight differences and complementarities of both environmental assessment methods. The ﬁnal goal of the paper was to provide new UEVs of drinking water quantiﬁed in a consistent manner along with a critical analysis of the EmE application, highlighting weak points of the method and including recommendations on how to deal with them.
2. Methodology and data collection 2.1. Energy system diagram According to the EmE methodology (Odum, 1996), an energy systems diagram of the WTPs is presented in Fig. 1. The left-hand side of the diagram shows the contribution of the surrounding environment in delivering the freshwater from a river. Geothermal heat runs geological processes that shape the landscape. Rainwater collected within the watershed is stored in soil moisture and then either evaporates or converges into streams and rivers. On the right-hand side, man-made inputs (fuels, electricity, chemicals, infrastructure materials and L&S) are used in the WTP to transform the freshwater into a product (potable water) valuable for humans. The distribution system was excluded from the system boundary, because speciﬁc data were not available, the scope of the analysis being the potable water production at the plant. Man-made inputs are the ‘feedback’ (F) from the larger economy (i.e. purchased resources and human services), while raw freshwater is the only local, renewable input (R). Local, nonrenewable resources (N) were not used up in the potable water production systems investigated. Moreover, one could argue that land occupation of the site by the plant may hamper soil regeneration and could be counted as an N input. However, this was considered negligible in most of the studies presenting a similar situation (see the Supplementary Information material, hereafter SI, Table S8). In the present case studies, preliminary calculations showed that this emergy contribution was much smaller than any other input (SI, section S3), and therefore it was disregarded. The emergy value associated with each input was calculated by weighting its quantity (in physical units) with the corresponding UEV. When several R ﬂows are feeding the system, only the input with the highest emergy value should be counted to avoid double-counting (Odum, 1996) in the case they are all co-products of the same generating processes and are supporting local, natural processes. Only the highest contributor to R can thus be summed with all other (N and F) inputs (which are not co-products of any local process). By deﬁnition, the emergy associated with the process outputs is Y (Brown and Ulgiati, 2002; Odum, 1996). When inputs are not co-products, Y is equal to the total emergy value of inputs.
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Geo. Geo. Heaat He
1.1 1. 19
F Stream Str Water
Win Wi nd
3.1 3. 15
L&S 1.9 1. 90
Fig. 1. Energy diagram of potable water production. Figures (in E18 sej/yr) are related to Site A.
2.2. Emergy-based indicators The aggregation of emergy inputs in the three categories R, N and F and their further combination can enable the calculation of the following indicators: • The emergy yield ratio (EYR = Y/F) of a process is the emergy associated with the process output (Y) divided by the sum of the emergy inputs from the human economy (F). According to the literature (Brown and Ulgiati, 1997; Campbell and Garmestani, 2012; Odum, 1996; Ridolﬁ and Bastianoni, 2008; Ulgiati and Brown, 1998) the EYR represents the energetic beneﬁts gained by the human society for its investment in utilizing local, natural resources. The higher the EYR, the greater the net energetic beneﬁt to the society. • The environmental loading ratio (ELR = (F + N)/R) compares the sum of the emergy associated with local, non-renewable resources and imported resources to the emergy carried by local, renewable resources absorbed by the system. A high ELR often indicates a high intensity of nonrenewable resource use, or a high technological level accompanied by a high level of environmental stress on the local environment (Brown and Ulgiati, 1997; Ridolﬁ and Bastianoni, 2008; Ulgiati and Brown, 1998). • The emergy investment ratio (EIR = F/(R + N)) describes the ‘investment’ made by the surrounding economy (i.e. F) into the process to exploit local resources (R and N). It indicates the matching of resources of the studied system with the inputs from the technosphere that encompasses it (Ridolﬁ and Bastianoni, 2008). A high EIR would thus denote a system in which human investments are artiﬁcially high, and consequently likely to be affected by ﬂuctuations in the economy. A low EIR would indicate a system beneﬁcial for the surrounding economy and likely to ‘receive’ more investments – which would increase the EIR. We may conclude that in the long run, the EIR of a process tend to match the EIR value of the region in which it is embedded. • %R represents the contribution of renewable input to the process output (R/Y). Processes showing a higher value of this indicator are likely to be more sustainable. • The emergy sustainability index (ESI = EYR/ELR) indicates the ecological sustainability of the activity, indicated by the ratio of the net beneﬁt to the society to the pressure on local renewable resources (Brown and Ulgiati, 1997).
• Also, the UEV of the output of a system can be considered as an efﬁciency indicator, as stated by, e.g. Brown et al. (2012), ‘UEVs are inversely related to the system efﬁciency on the scale of the biosphere’. In other terms, a lower UEV means a more efﬁcient overall use of resources by the coupled human-natural system. These indicators were applied in the present research to analyze the environmental sustainability of four potable water production systems. When benchmarking them to the various activities mentioned in Section 1, EmE results needed to be ﬁrst homogenized, in order to wipe out the variability of formulations of emergy-based indicators (see SI, Section S5 for all the calculation details). Since N is null in our case studies (i.e. no local, non-renewable resource like groundwater is used up), the indicators could be further simpliﬁed as follows: • • • •
EYR = 1 + R/F ELR = EIR = F/R %R = 1/(1 + F/R) ESI = EYR/ELR = (1 + R/F) × R/F
Noticeably, each indicator became a function of R/F only; consequently they would deliver the same ranking of the studied WTPs. 2.3. Data collection from LCA studies and comparison of EmE and LCA The four WTPs (hereafter Site 1, Site 2, Site A and Site B) are all currently operating in France. They were comprehensively studied using the LCA methodology (European Commission, 2010; ISO, 2006). Sites 1 and 2 (Igos et al., 2013a) are plants located in the Paris area, which get raw water from the Seine River. The other two sites, i.e. A and B (Igos et al., 2013b), are new plants located in Brittany, taking raw water from local streams. Noteworthy, streams in Brittany are more polluted than the Seine River, and require a heavier treatment process. Detailed information on the life cycle inventory data and economic inputs for the plants are provided in the SI, Sections S1 and S2. The main difference on the life cycle inventory between the four WTP datasets is that A and B included infrastructure materials, while 1 and 2 did not. Input data (i.e. energy and material consumptions, expenditures of man-made goods and services, etc.) were
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calculated for the production of 1 m3 of potable water collected on the sites. Detailed LCA results were available and used for comparison with the EmE results. The scope and the accuracy of the results, as well as the divergences in interpretations, are presented in Section 3.2. 2.4. Unit emergy values (UEVs) A UEV is assigned to each inventory input to calculate its corresponding emergy value. Previous literature studies refer to different baselines (i.e. the sum of annual independent emergy inputs to the geobiosphere, i.e. solar radiation, tidal energy and geothermal heat, which is used as the reference to quantify the transformity of natural resources; see in Brown and Ulgiati, 2010; Campbell, 2001; Campbell et al., 2005). In the present study, inputs and results are expressed with respect to the 9.44 E24 sej/yr baseline. This baseline was chosen because of its extensive use in the literature, supported by the consideration that there is currently no agreement on the choice of the reference baseline. Emergy values referred to another baseline in their original publications were converted using a simple ratio. Most of the UEVs were retrieved from the recently developed UEVs database (Tilley et al., 2012); Tables S4 and S5 in the SI report the original publication in which those UEVs are included. For some chemicals, we did not ﬁnd any appropriate UEV in the available literature; thus, we used their Solar Energy Demand (SED, Rugani et al., 2011b) as a proxy. The discussion section presents the limitation of their usage for EmE. The complete data collection and elaboration procedure is disclosed in the SI, Section S2. Speciﬁc UEVs for the freshwater used in the plants have been calculated (see Section 2.4.1). Electricity mix was another important input whose UEV has been reﬁned according to national speciﬁcities (Section 2.4.2). The emergy values of human L&S were also speciﬁcally calculated, using national data, from emergy-money ratios available in the literature (Section 2.4.3). 2.4.1. Local freshwater UEVs Natural energy ﬂows shape the landscape of a catchment area and concentrate rainwater into streams and rivers (see, e.g. Brown et al., 2010; Chen and Chen, 2009). Wind and rain are co-products of atmospheric processes driven by solar radiation. Therefore, only the highest contributor among them was counted in this study. Rain conveys two forms of available energy, namely chemical free energy (chemical exergy) and geopotential energy (physical exergy). Geothermal heat was not accounted for, since its past geologic contribution is already reﬂected in the geopotential energy of rainwater when it reaches the ground. Spring water from aquifers were disregarded is this study: although it can be considered as an input independent from rain at the short time scale, its contribution (in sej) is approximately 20 times smaller in the watershed studied in Pulselli et al. (2011a), which landscape rather favors the occurrence of springs. We noticed that the Seine watershed and Brittany are gently sloped, which favors inﬁltration and communication between deep and shallow aquifers rather than the occurrence of spring water. This lead us to assume that spring water in the studied watershed were relatively less important than for the Arno River basin studied in Pulselli et al. (2011a); consequently, the emergy value of spring water would amount for around 1% of the emergy value of rainfall. The UEV of freshwater should be calculated at the point of uptake. Since it was unknown for Sites A and B, we considered the whole watershed for the calculation, i.e. the UEV of freshwater at the estuary. Calculation details for the Seine River (near Site 1) are provided in the SI (Table S6). Table S7
Table 1 Calculation of the French electricity mix UEV. Production type
UEV (E4 sej/J) b
Nuclear Hydropower Hard coal Natural gas Oil
78.50% 10.94% 4.47% 3.18% 1.01%
Nuclear Hydroc Coalc Methanec Oilc
a b c
4.90 5.87 16.2 16.0 18.7 5.91
Ecoinvent v2.2 (2010), process #676. Campbell and Ohrt (2009), assumed without labor and services. Brown and Ulgiati (2002), excl. labor and services.
displays the local characteristics of each river basin. The emergy value associated with the river was further divided by its annual ﬂow to retrieve the freshwater’s UEV. The resulting UEVs, used in Section 3 and ranging from 9.9 E11 to 1.4 E12 sej/m3 (SI, Section S4), are of the same order of magnitude than those estimated in Pulselli et al. (2011a). 2.4.2. French electricity mix UEV No speciﬁc UEV for the French electricity mix was available in the literature. Hence, we used results from Brown and Ulgiati (2002) for electricity production systems in Italy and further adapted the share of production types to the French mix (Table 1). Nuclear power plants are the most relevant electricity production sources in France, for which we retrieved an UEV of 4.90 E4 sej/J from Campbell and Ohrt (2009). This value was calculated for nuclear electricity production from Minnesota. We assumed that this was calculated excluding L&S, i.e. inputs ‘from the economy’ in the mentioned paper only consider material and energy inputs for the maintenance of the power plants and the preparation of the combustible. 2.4.3. Human L&S UEVs Emergy accounts for both natural and man-made energy forms. While physical units are used to calculate the emergy value of a natural resource, the emergy associated with human labor and services is approximated using its economic price and the concept of the emergy-money ratio (EMR; Odum, 1996). The latter, expressed in sej/D , is the ratio between the emergy budget of a nation and its economic activity, represented by its Gross Domestic Product (GDP). It indicates the amount of emergy embodied in the monetary unit. Though 1D of different forms of L&S may have different emergy values, EMR remains the best available proxy to translate L&S costs into emergy terms. The National Environmental Accounting Database (NEAD, Sweeney et al., 2007) provided us with an EMR value of 2.8 E12 sej/$ for France in the year 2000, based on the 15.83 baseline. Using a 0.924 D /$ conversion ratio for the year 2000 (INSEE, 2012) and adjusting to the 9.44 baseline, the resulting French EMR was set to 1.81 E12 sej/D and used here to convert the L&S inventory inputs into emergy terms. 3. Results and discussion 3.1. Emergy analysis of ﬂows Emergy inventory calculations are provided in Tables 2–5. The contribution of the local renewable resource (freshwater stream, R) to the total emergy of the plant ranges between 4.1 E18 (Site A) and 20.6 E18 sej/yr (Site 2). When compared in terms of m3 produced (the size of the plants and the annual amounts of treated water are quite different among the four cases), the variations are due to slightly different UEVs associated with freshwater streams (4.01–5.87 E11 sej/m3 ) and water input/output ratios
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Table 2 Emergy table for Site 1. Items Renewable resources (R) Seine River water at Site 1 Purchased energy (F) Electricity mix, France (w/o L&S) Diesel Purchased materials (F) Activated carbon Regenerated activated carbon Acrylic acid Al2 SO4 NaOCl, 15% Labor and services (F) Purchased inputs and labor Coal ﬂy ash, with services Material transport (truck) Output Potable water
4.18E+04 4.18E+04 2.02E+04 2.08E+05 1.03E+04
kg kg kg kg kg
1.56E+13 8.54E+12 3.55E+12 1.18E+12 2.59E+12
6.51E+17 3.57E+17 7.17E+16 2.45E+17 2.67E+16
6% 3% 0.6% 2% 0.2%
4.10E+05 4.17E+04 2.82E+05
D kg tkm
1.81E+12 1.40E+13 6.61E+11
7.41E+17 5.84E+17 1.86E+17
6% 5% 2%
(1.02–1.16 m3 /m3 , see Tables 2–5). Man-made inputs (F) range between 8.8 and 17.0 E11 sej/m3 (see Table 6), and %R is between 22% and 40%. Accordingly, a variability score can be assessed in terms of standard deviation (i.e. weighted on the total production), obtaining an average UEV for drinking water production in France equal to 1.06 (± 0.15) E12 sej/m3 (excluding. infrastructure). In potable water production, consumption of energy and chemicals is mostly determined by the quality (for the user) of the raw water. A more intensive consumption of F inputs can be observed for Sites A and B, which translates into higher output UEVs. However, we did not ﬁnd a suitable emergy-based explanation to discriminate among polluted and non-polluted resources – we could only notice that more polluted resources needed more emergy to be treated. However, the water resources for Sites A and B are much more polluted than the resources for Sites 1 and 2. Transformities do not provide additional information, since the speciﬁc exergy of water (in J/g) is calculated using the concentration of water in the river, which is an indicator of freshwater purity but not of its quality for drinking purposes: for instance, pure water with a small amount of highly toxic compound may be ‘purer’ (i.e. present a higher concentration of water), but less potable, than bottled mineral sparkling water or orange juice.
Table 6 shows the results from calculating the emergy indicators for all the WTPs. The highest EYR is observed for Site 2, i.e. this plant shows the highest efﬁciency in converting local resources into valuable goods for the larger economic system. Sites A and B need more technology-intensive processes to treat the more polluted resource, which translates into higher ELRs. EIR is 15–60 times lower than the national value of 37.13 (Sweeney et al., 2007), which denotes an activity that is not sensitive to economic stress. Indeed, production of potable water runs independently from the economic context as it supplies a fundamental resource to society. The size of the plant, i.e. its production capacity, does not seem inﬂuential. However, this conclusion should be counterchecked through a larger survey of WTPs. The accounting for infrastructure noticeably increases F, thereby decreasing the measured performance, as shown in the results for Sites A and B. In Fig. 2, the UEVs of potable water output show a similar ranking between the treatment sites. Sites 1 and 2 are the most efﬁcient, since the UEVs of their potable water outputs are the lowest. These sites provide potable water with the lowest requirements of direct and indirect solar energy captured by the geobiosphere. Chemicals are the main man-made inputs (F), covering 40–55% of the total emergy value of F (except for Site 1), followed by L&S (24–31%)
Table 3 Emergy table for Site 2. Items Renewable resources (R) Seine River water at Site 2 Purchased energy (F) Electricity mix, France (w/o L&S) Purchased materials (F) Activated carbon Regenerated activated carbon Acrylic acid Al2 SO4 Cl2 gas Lime H3 PO4 , 85% Caustic soda H2 SO4 Labor and services (F) Purchased inputs and labor Material transport (truck) Output Potable water
1.52E+05 9.78E+04 6.07E+03 8.79E+05 4.91E+04 2.63E+05 3.06E+03 4.09E+05 2.43E+05
kg kg kg kg kg kg kg kg kg
1.56E+13 8.54E+12 3.55E+12 1.18E+12 6.67E+12 1.00E+12 6.20E+12 1.46E+12 4.15E+11
2.37E+18 8.35E+17 2.16E+16 1.04E+18 3.27E+17 2.63E+17 1.90E+16 5.98E+17 1.01E+17
7% 2% 0.06% 3% 0.9% 0.7% 0.05% 2% 0.3%
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Table 4 Emergy table for Site A. Items Renewable resources (R) Freshwater at Site A Purchased energy (F) Electricity mix, France (w/o L&S) Purchased materials (F) Activated carbon CO2 liquid FeCl3 , 40% Lime KMnO4 Caustic soda NaOCl, 15% H2 SO4 Infrastructure (F) Em-building surface Em-building volume Concrete Copper Glass Plastic (PVC) Steel Material transport (truck) Excavation Labor and services (F) Purchased inputs and labor Output (w/o infra) Potable water Output (w/infra) Potable water
4.20E+04 1.76E+05 5.43E+05 3.95E+05 3.00E+03 8.36E+03 1.34E+04 1.96E+03
kg kg kg kg kg kg kg kg
1.56E+13 9.48E+11 3.01E+12 1.00E+12 8.24E+13 1.46E+12 2.59E+12 4.15E+11
6.55E+17 1.66E+17 1.64E+18 3.95E+17 2.47E+17 1.22E+16 3.46E+16 8.12E+14
7% 2% 17% 4% 3% 0.1% 0.4% 0%
6% 1% 15% 4% 2% 0.1% 0.3% 0%
6.31E+07 6.34E+07 2.26E+08 1.28E+08 6.69E+06 1.88E+09 3.34E+10 4.25E+05 3.34E+08
mm2 cm3 cm3 mg mg mg mg tkm cm3
4.47E+09 6.38E+08 3.54E+09 2.00E+06 2.12E+06 5.85E+06 4.13E+06 6.61E+11 7.30E+05
2.82E+17 4.05E+16 8.02E+17 2.57E+14 1.42E+13 1.10E+16 1.38E+17 2.81E+17 2.44E+14
and electricity (12–31%, except for Site 1 where it covers 43% of F). Fossil fuels are directly used only on site 1 and their contribution is marginal (1%). Infrastructure, when accounted for, covers a signiﬁcant 11–20% of the total F. Noteworthy, Sites 1 and 2 require less L&S and chemicals than Sites A and B per m3 potable water produced. All the results are comparable in magnitude to the results on potable water production reported in the literature (Buenﬁl, 2001; Pulselli et al., 2011a), with UEVs ranging between 0.69 and 6.80
% w/o infra
3% 0.4% 7% 0% 0% 0.1% 1.2% 3% 0% 20%
E12 sej/m3 (SI, Section 6). The WTPs studied in Buenﬁl (2001) use raw freshwater with very different UEVs, which explains the higher variability of the results. EmE studies of other types of human activities, such as agricultural systems, energy extraction and industrial manufacturing, showed a relatively high disparity of results (see SI, Section 5 for details on data and comparative tables). The relative closeness of the results obtained for the studied WTPs does not reveal signiﬁcant differences between these case studies, in terms of economical-ecological competitiveness. Drinking water
Table 5 Emergy table for Site B. Items Renewable resources (R) Freshwater at Site B Purchased energy (F) Electricity mix, France (w/o L&S) Purchased materials (F) Activated carbon CO2 liquid FeCl3 , 40% Lime KMnO4 Caustic soda NaOCl, 15% H2 SO4 Infrastructure (F) Em-building surface Concrete Copper Glass Plastic (PVC) Steel Material transport (truck) Labor and services (F) Purchased inputs and labor Output (w/o infra) Potable water Output (w/infra) Potable water
% w/o infra
4.51E+04 2.58E+05 2.32E+05 4.23E+05 1.83E+04 5.67E+03 7.53E+03 8.81E+02
kg kg kg kg kg kg kg kg
1.56E+13 9.48E+11 3.01E+12 1.00E+12 8.24E+13 1.46E+12 2.59E+12 4.15E+11
7.03E+17 2.45E+17 6.97E+17 4.23E+17 1.50E+18 8.28E+15 1.95E+16 3.66E+14
7% 2% 7% 4% 14% 0.1% 0.2% 0%
6% 2% 6% 4% 13% 0.1% 0.2% 0%
6.66E+07 9.96E+07 1.16E+08 7.09E+06 2.46E+09 1.85E+10 6.83E+04
mm2 cm3 mg mg mg mg tkm
4.47E+09 3.54E+09 2.00E+06 2.12E+06 5.85E+06 4.13E+06 6.61E+11
2.98E+17 3.53E+17 2.33E+14 1.50E+13 1.44E+16 7.63E+16 4.52E+16
3% 3% 0% 0% 0.1% 0.7% 0.4% 19%
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Table 6 Comparison of emergy-based indicators for the four water treatment plants.
R (sej/yr) N (sej/yr) F (sej/yr) Y (sej/yr) EYR ELR = EIR %R ESI = EYR/ELR Potable water produced (m3 /yr) UEV (sej/m3)
Site A w/o infra
Site B w/o infra
Site A w/infra
Site B w/infra
6.51E+18 0 5.13E+18 1.16E+19 2.27 0.79 55.9% 2.88 1.16E+07 1.00E+12
2.18E+19 0 1.33E+19 3.51E+19 2.64 0.61 62.2% 4.34 3.53E+07 9.93E+11
3.43E+18 0 6.24E+18 9.67E+18 1.55 1.82 35.5% 0.85 8.36E+06 1.16E+12
3.92E+18 0 6.48E+18 1.04E+19 1.60 1.65 37.7% 0.97 7.80E+06 1.33E+12
3.43E+18 0 7.80E+18 1.12E+19 1.44 2.27 30.6% 0.63 8.36E+06 1.34E+12
3.92E+18 0 7.27E+18 1.12E+19 1.54 1.85 35.0% 0.83 7.80E+06 1.43E+12
Fig. 2. Contribution of each type of input (feedback and raw water ﬂows) to the unit emergy value (UEV) calculated for the 4 plants and for Sites A and B including infrastructure items. N.B. On-site use of fossil fuels barely visible for Site 1 and not present for the others.
production lies amongst the studied activities with the lowest EYR (Fig. 3), meaning that this sector provides a low net contribution to the larger economic system. Indeed, potable water is a necessity and is not expected to be a primary energy. This sector does not provide an energetic return on investment. The ELR of this sector is also relatively low (Fig. 4) compared to other sectors, which denotes a low level of environmental stress on the environment. EIR of drinking water production shows a high variability. The same situation is observed for vegetal and animal products. The return on investment of the larger system to the local activity is thus averagely efﬁcient. The combination of a relatively low EYR and a low ELR leads to an average ranking of potable water production in
terms of overall sustainability. %R and ESI in drinking water production are also average when compared to the other activities. Note that %R only relates to the use of resources that are both renewable and local. Finally, the speciﬁc emergy (sej/g) of potable water is much lower than other products, while its transformity is among the highest ones: a gram of potable water needs less indirect solar energy to be produced as compared to other products, while a joule of potable water (exergy) apparently requires more transformation of primary solar energy to be produced (see SI, Section 5). The identiﬁcation of available UEVs for chemicals in the emergy literature was critical for our case studies, due to the high number of reagents and their diversity. Their UEVs (or proxies) range between 4.15 E11 sej/kg for sulfuric acid and 8.24 E13 sej/kg for potassium permanganate. UEVs of lime, caustic soda and gaseous chlorine were retrieved from Campbell and Ohrt (2009), not referenced in the online database (Tilley et al., 2012). The UEV of other chemicals remain not available in the existing literature to our knowledge. Indeed, this can be considered as a practical limitation of emergybased accounting. The UEV of activated carbon and regenerated activated carbon were computed speciﬁcally for this study (see SI, Section S2). For the other chemicals, we used an updated value of their SED (Rugani et al., 2011b) as a proxy (SI, Section 2). Both UEVs and SEDs refer to the indirect amount of solar energy required to make a product, but the latter are computed following the rationale of LCA for allocation between co-products, which does not match the emergy algebra; however, they rely on a high level of detail in the network of industrial processes, which makes them more accurately calculated than UEVs. In the near future, the software SCALE (Marvuglia et al., 2013), currently under development, may provide equally accurate UEVs for such products, while respecting the emergy algebra. Fig. 2 also highlights the importance of electricity consumption in Site 1 (0.90 kWh per m3 of produced water, vs. 0.53–0.67 for the other sites). Sites 1 and 2 also have lower L&S costs (0.033–0.054 D /m3 ) compared to Sites A and B (0.126–0.142 D /m3 ), which employ a more complicated treatment ELR (log scale)
8.00 EYR Min EYR Median EYR Max
ELR Median ELR Max
2.00 1.00E-01 Electricity Ren Energy
0.00 Ren EnergyDrinking Water Vegetal
Fig. 3. Comparison of environmental yield ratio (EYR) scores for the production of various types of man-made products (see SI, Section 5).
Fig. 4. Comparison of environmental loading ratio (ELR) scores for the production of various types of man-made products (see SI, Section 5).
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process. The emergy contribution of L&S is usually approximated by the economic cost of purchase. Since the highest expenditures are for energy and reagents, it may be relevant to further decompose these expenses, considering the actual labor in the supply chain, the assets, the speculation, etc. Typical UEVs of a year of human labor could not be found for the French context. In a country of similar level of industrialization, Italy, they range between 5.3 E15 and 2.8 E17 sej/yr (Brown and Ulgiati, 2002; Pulselli et al., 2007, 2008, 2011a; Rugani et al., 2011a). With an average value of 5.00 E16 sej/yr, an annual production of 7.80 E6 m3 potable water/yr and 4.5 full-time equivalent workers to run the Site B plant (personal communication with the company), the total L&S input is worth 2.89 E10 sej/m3 . This value is rather close to the 4.58 E10 sej/m3 found using the monetary approach (0.0253 D /m3 of labor, Table S3). This result-checking somehow validates the assumption that economic inputs are mostly composed of human labor, although more in-depth analyses should deﬁnitely be carried out. 3.2. Comparison with life cycle assessment In Fig. 5 (and SI, Section 7), the EmE results are compared to LCA results (Igos et al., 2013a, 2013b). LCA results were computed using the ReCiPe method (Goedkoop et al., 2009). Inputs from technosphere used in the infrastructure processes represent 6–7% of impacts on resource depletion, which is lower than results of EmE. Concerning the impacts of chemicals, there is no clear conclusion unanimously emerging: though both methods indicate a higher impact on sites A and B, EmE and LCA would rank Sites 1 and 2 differently. Also, EmE shows the contribution of electricity to the output emergy value is very similar between Sites 2 and B, while in LCA the impacts on resources from electricity is quite higher in Site B than in Site 2. Therefore, detailed results seem contradictory while overall results are analogous. The main reason is that impacts on resource depletion in LCIA are computed from accessible resources only, while the UEV of natural resources are computed from both accessible and inaccessible stocks. In other words, LCA takes into account the notion of scarcity of a resource, which denotes a user-side point of view, while the UEV of a resource is not calculated based on its potential utility for a user (and thus its rate of consumption), but it is rather based on a donor-side approach. The rationale of both approaches is thus complementary on this point. Another major difference between EmE and LCA lies in the inventory of inputs: while resources used up for the provision of L&S are fully accounted for in EmE, they are partially included in LCA because only non-economic inputs are considered (energy and materials for transportation and sludge disposal). Human labor is at present outside the scope of LCA (Rugani et al., 2012). The relative importance of L&S for LCA results (Fig. 5) is due to the fossil fuels consumed by transporting chemicals and sludge. LCA also considers impacts on ecosystems and human health, while traditional emergy accounting focuses on resource use – there exist however some attempts to consider pollution effects in EmE (Liu et al., 2012; Ulgiati and Brown, 2002). Finally, EmE also includes the use of the renewable resource to be transformed by the activity, and compares it with the other inputs using emergy-based indicators. LCA follows a different goal, i.e. comparing resource consumption and pollution for the production of a similar functional unit. The present analysis outlines several strengths of EmE, as compared to LCA. First, EmE provides a more holistic assessment of resource use by an activity, considering: (1) the ‘natural’ value of a resource (deﬁned as the solar energy necessary to regenerate it): this rationale is disregarded in current impact assessment methods (Ingwersen, 2011; Raugei et al., 2013); (2) human labor directly and indirectly required (through purchased services and products) to produce potable water, or any other man-made good.
Second, this framework places the human activity within its local, natural context. For potable water production, the main local natural resource used up is freshwater, while energy and reagents are imported inputs. Third, emergy-based indicators rank different activities that produce a variety of man-made goods, as shown in Figs. 4 and 5 and SI (Section 5). In contrast, LCA can only compare two technological solutions for an identical output (the functional unit), though with a much ﬁner analysis of environmental impacts and a depletion-oriented approach for natural resource consumption assessment (which emphasizes scarcity). Therefore, EmE and LCA show insightful complementary features (Raugei et al., 2013). EmE also proves to be useful for territorial analysis issues. Watershed management obviously considers the provision of potable water as a priority, but a river provides other ecosystem services such as ﬂood regulation, pollutant ﬁltration, habitat for ﬁsheries, communication roads, local climate regulation, etc. (Wilson and Carpenter, 1999). In terms of decision-making, these ecosystem services should be handled altogether (Jewitt, 2002); in emergy terms, they all are co-products of the same ecosystem (Pulselli et al., 2011b; Rugani et al., 2013). In this connection, EmE offers a physical common denominator (rather than an economic framework) for a multi-user approach to resource management (Agostinho et al., 2010; Brown and McClanahan, 1996; Brown et al., 2010; Cohen and Brown, 2007; Huang et al., 2007; Tilley and Swank, 2003). 3.3. Uncertainty and limitations This study revealed some current limitations of the EmE methodology as it is most often performed. Major issues are clearly related the high uncertainty of the results, which originate from two aspects. First, as illustrated by the emergy value of chemicals, available UEVs in the literature can be scarce and some useful ones are not gathered in the database(s) under construction. UEVs of industrial products require a consistent effort to be reﬁned, if absent from published scientiﬁc and transparent work. In most cases, only generic or proxy UEVs could be identiﬁed. For example, different types of steel are aggregated into a single material, as well as plastics, transport systems, etc. (Tables S4 and S5). Critical UEVs like those for chemicals in our case studies had to be approximated by their SEDs. Using UEVs retrieved from literature requires a high but subjective level of attention, because of data may include L&S and the choice of baseline needs to be checked systematically. The phase of UEVs selection in EmE is one of the most critical issues of the methodology (Hau and Bakshi, 2004; Raugei et al., 2013). Despite the 40 years of development of the method, the standardization of EmE is not as mature as for LCA (e.g. ISO 14040); to our opinion, it is certainly one of the reason why EmE is much less popular than LCA, despite the ground-breaking paradigm shift it offers. The recent publication of an online UEV database (Tilley et al., 2012) is an important step forward. Practitioners of EmE may also beneﬁt from the recent results on how to use high-resolution LCI database (e.g. Ecoinvent, 2010) to automatically calculate the UEV of manmade products (Marvuglia et al., 2013; Raugei et al., 2013; Rugani and Benetto, 2012; Rugani et al., 2011b; Zhang et al., 2010). Similarly, economic inputs may potentially be further reﬁned, using for instance national input-output tables. The second source of uncertainty is due to the possible omission of natural or man-made inputs. Among possible missing elements in the present study, one can mention the intervention of ecosystem services to treat pollution (Liu et al., 2012; Ulgiati and Brown, 2002), the contribution of sunlight and wind on the WTP sites to provide a healthy working environment (Brown et al., 2012), the contribution of knowledge and technology (Odum, 1996), the
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Fig. 5. Comparison of emergy (i.e. UEVs) and life cycle assessment (LCA) results for the four water treatment plants; for LCA, the ReCiPe method is used; ecopoints are the aggregated single score of impacts on human health, ecosystems and resource depletion.
contribution of aquifers to the freshwater stream (Pulselli et al., 2011a), which participates to the longer water cycle (and thus the formation of the river) but not necessarily to the short-run river ﬂow, and also the transformation and control of the river by human activities, which ensures WTPs a stable provision of raw freshwater. In principle, all these omitted elements could be eventually evaluated within the emergy framework, but site-speciﬁc data and detailed regional environmental surveys would be necessary to perform such an extended treatment of information. In the case of the four WTPs, the absence of a standardization procedure on how to select priority items and thus broaden the energy system diagram, as well as the lack of additional spatially explicit information related to the biophysical conditions of the territorial system (e.g. to evaluate the emergy of wind and sunlight) limited our possible investigation on that direction. Developing standardized procedures would be favored by a formal agreement on the mathematical framework, which continues to develop within the emergy community (see, e.g. Le Corre and Truffet, 2012; Li et al., 2010; Patterson, 2012). In particular, systemic methods of handling double-counting (Morandi et al., 2013), though improving, remain theoretical. Besides, Tiruta-Barna and Benetto (2013) demonstrated that the calculation process is inaccurate when used in particular complicated systems because it depends on the level of details in the description of the network. Consensus among researchers may also require the decomposition of F inputs (Brown et al., 2012). For instance, man-made inputs and L&S can potentially be further decomposed into a renewable share (Fr) and a non-renewable one (Fn), as already found in literature, but with a non-standardized procedure (Ciotola et al., 2011; La Rosa et al., 2008; Lima et al., 2012; Lu et al., 2011, 2009; Paoli et al., 2008;
Rugani et al., 2011a; Yang et al., 2010; Zhang et al., 2012). This will inevitably lead us to reconsider (and strengthen) the formulation of emergy-based indicators, whose robustness suffers from a plethora of variants and requires a considerable work to allow comparing human-natural systems of different nature (SI, Section 6). Another important limitation is the inability of EmE to compare the quality (as perceived by the user) of freshwater resources, as demonstrated by the UEVs of the various freshwater streams. But this is also the case in LCA, although pioneering approaches attempt to handle this issue (Igos et al., 2013a). 4. Conclusion This paper applied EmE to assess and compare the resource consumption of four selected water treatment plants located in France. Results show a high stability of emergy-based indicators among these similar industrial systems. Our ﬁndings are comparable to those of other recent studies. Man-made inputs are of primary importance to run the plants, while infrastructure accounts for around 10–20% of the total emergy associated with these inputs. Regarding the operational phase, EmE highlights the need for more imported inputs to treat more polluted raw water, although the UEV of raw freshwater does not reﬂect its level of pollution (i.e. concentration, hazard and recalcitrance to treatment of harmful substances). Water treatment plants run on a single local, renewable resource. But like most industrial plants, they do not use local nonrenewable resources (i.e. N equals zero). Therefore, emergy-based indicators become correlated (i.e. each one can be expressed as a monotone function of R/F) and thus rank the four plants identically.
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The predominance of man-made inputs (F) and the inherent low accuracy of their UEV suggest that the lack of a clear and deﬁned standardization of the method in emergy still provides users with little guidance in the choice of those UEVs. However, UEVs of manmade products could be reﬁned by adopting life-cycle perspective and datasets, including the whole production chain within the technosphere. A formal agreement on the procedure for emergy calculation for man-made products needs to be reached, and may inﬂuence the deﬁnition and calculation of emergy-based indicators. These open questions could be partially addressed by applying hybrid emergy-LCA approaches on the same case studies and compare them to the results presented in this paper. Such option may also strengthen the added value of emergy evaluation relative to other resource-oriented, thermodynamic indicators used in LCA, such as CEENE (Dewulf et al., 2007). Acknowledgments This project is supported by the National Research Fund, Luxembourg (Ref 1063711) and the French National Research Fund (project EVALEAU ANR-08-ECOT-006-00 0894C0238). The authors thank the two reviewers of the manuscript for their valuable comments and suggestions to improve the general quality of our work. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.ecoleng.2013.07.046. References Agostinho, F., Ambrósio, L.A., Ortega, E., 2010. Assessment of a large watershed in Brazil using emergy evaluation and geographical information system. Ecol. Model. 221, 1209–1220. Baral, A., Bakshi, B.R., 2010. Emergy analysis using US economic input–output models with applications to life cycles of gasoline and corn ethanol. Ecol. Model. 221, 1807–1818. Brown, M.T., Bardi, E., 2001. Folio# 3: Emergy of Ecosystems. Handbook of Emergy Evaluation. Center for Environmental Policy, Environmental Engineering Sciences. University of Florida, Gainesville. Brown, M.T., Buranakarn, V., 2003. Emergy indices and ratios for sustainable material cycles and recycle options. Resour. Conserv. Recy. 38, 1–22. Brown, M.T., Martínez, A., Uche, J., 2010. Emergy analysis applied to the estimation of the recovery of costs for water services under the European Water Framework Directive. Ecol. Model. 221, 2123–2132. Brown, M.T., McClanahan, T.R., 1996. Emergy analysis perspectives of Thailand and Mekong River dam proposals. Ecol. Model. 91, 105–130. Brown, M.T., Raugei, M., Ulgiati, S., 2012. On boundaries and investments in emergy synthesis and LCA: a case study on thermal vs. photovoltaic electricity. Ecol. Indic. 15, 227–235. Brown, M.T., Ulgiati, S., 1997. Emergy-based indices and ratios to evaluate sustainability: monitoring economies and technology toward environmentally sound innovation. Ecol. Eng. 9, 51–69. Brown, M.T., Ulgiati, S., 2002. Emergy evaluations and environmental loading of electricity production systems. J. Clean. Prod. 10, 321–334. Brown, M.T., Ulgiati, S., 2010. Updated evaluation of exergy and emergy driving the geobiosphere: a review and reﬁnement of the emergy baseline. Ecol. Model. 221, 2501–2508. Buenﬁl, A.A., 2001. Emergy evaluation of water (PhD dissertation). Department of Environmental Engineering Sciences. University of Florida, Gainesville, USA. Buranakarn, V., 1998. Evaluation of recycling and reuse of building materials using the emergy analysis method (PhD dissertation). Department of Environmental Engineering Sciences, University of Florida, Gainesville. Campbell, D.E., 2001. A revised solar transformity for tidal energy received by the earth and dissipated globally: implications for emergy analysis. In: Brown, M.T. (Ed.), Emergy Synthesis 1: Theory and Applications of the Emergy Methodology. Proceedings of the 1st Biennial Emergy Conference. Center for Environmental Policy. University of Florida, Gainesville, pp. 255–263. Campbell, D.E., 2003. A note on the uncertainty in estimates of transformities based on global water budgets. In: Brown, M.T., Odum, H.T., Tilley, D., Ulgiati, S. (Eds.), Emergy Synthesis 2: Theory and Applications of the Emergy Methodology.
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