Improvements to Emergy Evaluations by Using Life ... - ACS Publications

Critical Review pubs.acs.org/est

Improvements to Emergy Evaluations by Using Life Cycle Assessment Benedetto Rugani*,† and Enrico Benetto† †

Public Research Centre Henri Tudor (CRPHT)/Resource Centre for Environmental Technologies (CRTE) - 66 rue de Luxembourg, P.O. Box 144, L-4002 Esch-sur-Alzette - Luxembourg ABSTRACT: Life Cycle Assessment (LCA) is a widely recognized, multicriteria and standardized tool for environmental assessment of products and processes. As an independent evaluation method, emergy assessment has shown to be a promising and relatively novel tool. The technique has gained wide recognition in the past decade but still faces methodological difficulties which prevent it from being accepted by a broader stakeholder community. This review aims to elucidate the fundamental requirements to possibly improve the Emergy evaluation by using LCA. Despite its capability to compare the amount of resources embodied in production systems, Emergy suffers from its vague accounting procedures and lacks accuracy, reproducibility, and completeness. An improvement of Emergy evaluations can be achieved via (1) technical implementation of Emergy algebra in the Life Cycle Inventory (LCI); (2) selection of consistent Unit Emergy Values (UEVs) as characterization factors for Life Cycle Impact Assessment (LCIA); and (3) expansion of the LCI system boundaries to include supporting systems usually considered by Emergy but excluded in LCA (e.g., ecosystem services and human labor). Whereas Emergy rules must be adapted to life-cycle structures, LCA should enlarge its inventory to give Emergy a broader computational framework. The matrix inversion principle used for LCAs is also proposed as an alternative to consistently account for a large number of resource UEVs.

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via the use of natural resources.11 EME is suggested to be a suitable method of accounting for the use of a wide set of natural resources, and can be used to define guidelines for consumption of resources compatible with their formation times.11,22 Past studies have suggested EME as a promising tool to support environmental management actions and public planning policies because of its ability to characterize the dynamics of territorial systems.12−14,100 Emergy stems from the qualitative and quantitative consideration that all different forms of energy can be hierarchized and measured with the common metric of the solar emjoule (seJ).8,9,15 To convert material and energy items in seJ, EME uses a conversion factor called transformity or Unit Emergy Value (UEV), which is the Emergy amount required to make one unit of a given product or service.7,8 Emergy goes beyond the accounting of the amount of natural resources by evaluating the environmental work needed for their formation. However, EME has been strongly criticized due to its weak methodological framework and lack of overall consistency, which have hampered its acceptance and use by environmental policy authorities.16−20 Among the most significant limitations is the UEVs calculation, which is based on very simplified models (in terms of structure) of the economic systems behind products and processes. In this

INTRODUCTION Life cycle thinking recognizes that all product life cycle stages (e.g., extracting and processing of raw materials, manufacturing, transportation and distribution, use/reuse, recycling, and/or waste management) generate environmental impacts which need to be evaluated and then reduced.1,2 This comprehensive perspective has been the basis for the development and standardization of the Life Cycle Assessment (LCA) methodology.3 Today, LCA is one of the most accepted and used tools for the environmental assessment of products and services.4−6 During the past decade, LCA has become a core element in environmental policy or voluntary actions in the European Union, United States, Japan, Korea, Canada, and Australia, and is increasingly used in booming economies such as India and China.1 Many scientific papers on LCA application case studies and on methodological developments demonstrate the worldwide interest for LCA, both from industry and the sustainability science community.2,6 Outside the LCA community, another environmental assessment method, Emergy evaluation (EME), is gaining international recognition and increasingly being applied.7,8 In the conventional definition, Emergy, spelled with an “m”, represents the amount of total energy used in the entire chain of production and incorporates the solar energy previously required to generate a product and/or to support a system and its level of organization.9 Emergy is indeed the memory of the (solar) energy that has been used in the past or accumulated over time,10 or the memory of the geobiosphere exergy provision (environmental work) related to economic systems © 2012 American Chemical Society

Received: Revised: Accepted: Published: 4701

September 29, 2011 April 9, 2012 April 10, 2012 April 10, 2012 dx.doi.org/10.1021/es203440n | Environ. Sci. Technol. 2012, 46, 4701−4712

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ecological services and that the economic value of pollutant emissions can be converted to Emergy through the Emergy to money ratio,37 i.e., the ratio of Emergy use in a country or a region to its GDP, which tries to measure how much Emergy is associated with the economic wealth of a state.8,9 Despite that the EmLCA approach does highlight the complementary nature of EME and LCA, it does not set any formal integration between the two methods and is debatable due to use of the Emergy to money ratio, which is often perceived as one of the sources of inaccuracy and unreliability for EME.17,39 Later, the concept of Exergy was used as an argument to understand Emergy as an opportunity to include natural capital and ecosystem services contributions in LCA. The Ecological Cumulative Exergy Consumption (ECEC) indicator was built to include the Exergy consumed by ecological processes to produce the raw materials, to dissipate the emissions, and to sustain the operation of industrial processes.38,40,41 A methodology for ECEC computation was developed, and further comparison between ECEC and Emergy highlighted that the two concepts are equivalent if the boundaries of the study, the allocation approach, and the method for combining global energy inputs are identical.40,41 As for LCA, the question of allocation is crucial in the comparison of ECEC with Emergy. Whereas in LCA one allocates inputs and outputs entering a unit process between outputs of different nature (coproducts), Emergy adopts special algebra rules that, in the case of coproducts, assign the complete Emergy input to all the coproducts.8,42 More recently, Zhang et al. attempted to formalize a consistent inclusion of ecosystem services in LCA.43,44 To reduce the gap between LCA and the assessment of natural capital (i.e., ecosystem goods and service essential for human well-being45), an ecologically based LCA approach (Eco-LCA) was defined, both as method and freely available web-software.46 The current Eco-LCA implementation is at the scale of the U.S. economy and uses for the inventory the input−output model of 1997, which is also used for Environmentally Extended Input Output (EEIO) based LCA .47 A hierarchy of metrics was also proposed to understand the role of ecological resources via multiple levels of aggregation, from resource inventory and normalization, to midpoint characterization, up to end-point indicators.44 This model, together with previous ECEC, was then applied to several case studies.48−50 Similar research works have been carried out to analyze the environmental burdens of the different economic sectors using Chinese input−output economic models, considering not only Emergy and Exergy but also emissions and industrial wastes.52 Many similarities between traditional Emergy results and results from Emergy-based hybrid LCA models were found,53 although, for large process networks it still remained difficult to compute the strict Emergy algebra.50 The use of process LCI models for EME calculation has also been proposed. For example, the calculation of Emergy using the LCA software SimaPro54 was suggested by applying UEVs to the LCI results (i.e., amount of resources used up by functional unit).23 More recently, Ingwersen55 ran an uncertainty analysis of UEVs through the use of uncertainty information contained in the Ecoinvent database.56 Furthermore, he used Ecoinvent data for EME of a gold mined production.25 Nevertheless, the LCIbased Emergy framework was restricted solely to the application of UEVs at the foreground level, whereas the background LCI data were not characterized. A step further was taken by Rugani and coauthors (2011), who further

context, it appears that the use of the detailed network models typically considered in LCI may allow improvement of the accuracy of Emergy calculations.21−25 Because a thorough investigation of the benefits and the constraints of the use of LCI models for EME is currently lacking in the scientific literature, we are going to fill this gap with this paper. This Review aims to (i) review the most prominent attempts in literature to combine EME and LCA that are essential to identify the key similarities and differences between the two methods; (ii) define the crucial methodological steps necessary to integrate the EME within the framework of LCA; and (iii) formulate an alternative approach to overcome the inaccuracies of UEVs. We are convinced that consistent EME founded on LCI models could improve the quality of UEVs and provide a step forward to future standardization of the Emergy method. Accordingly, the paper is structured as follows: first, a critical survey on literature studies that attempted to combine EME and LCA is provided, with emphasis on the sources of methodological complementarity and discrepancy between the two methods. A few of the critical shortcomings of EME are then treated in detail along with an overview on the major issues to be covered as fundamental steps to carry out EME using LCI models; finally, a conceptual framework to improve global Emergy values calculation is proposed and a general outlook is provided.

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EMERGY IN LCA: CRITICAL REVIEW OF EARLY ATTEMPTS Joint and Hybrid Analyses. In recent years, complementarities between EME and LCA were emphasized within the Emergy community, encouraging researchers to make contributions toward a novel integrated approach able to link the territory-oriented EME with the product-oriented LCA.24 It is claimed that EME may better evaluate macro-scale systems (e.g., territorial systems) when compared to LCA or energybased approaches that are used to evaluate materials flows but not services or information.26 The combination of EME with LCA has been suggested to be a tool for qualitative and quantitative evaluation of progress toward industrial symbiosis and sustainable production as well as consumption patterns.27 EME can show how to maximize resource use while LCA allows identifying where to reduce pollutant emissions and improve wastes reuse and quantifying the related benefits.17,21,27 In most of the studies presenting the joint use of EME and LCA results,28−35 the approach consisted of the application of ISO 14040 steps3 to the conventional EME, together with the extension of its boundaries using the LCA perspective, “from cradle to grave”. Although this approach gave a more consistent structure to the EME of processes, it did not bring along the use of extensive LCI databases. By using simplified models of economic systems, EME is missing most of the connections and interactions occurring between processes of the system resulting in a less comprehensive data inventory. Consequently, the degree of detail of the analysis in EME is definitively lower than the one of LCA studies.21,22 From 2000 on, works from Bakshi and coauthors have highlighted methodological controversies of Emergy and attempted to reduce the discrepancies between EME and LCA.36−38 A novel Emergy-based LCA approach (EmLCA) was formulated by combining the Emergy calculation for some ecological and economic inputs with an LCA framework. EmLCA showed that an LCI database may be useful for measuring the Emergy content of industrial products and 4702

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Life Cycle Impact Assessment several software tools available using matrix (e.g., SimaPro) or sequential calculation approaches (e.g., Umberto)60 selection of the LCIA models related to given Areas of Protection (AoPs); several environmental impact indicators are applied to evaluate the effects of resource use and/ pollutant emissions (midpoint and/or end-point assessments) Life Cycle Interpretation human-oriented (user side): LCA evaluates environmental impacts related to Areas of Protection (AoP), with a metric based on human utility sensitivity and uncertainty analysis; interpretation of LCIA results; conclusions and recommendations to decision-makers; consistent with goal and scope of the study; iterative process

Life Cycle Inventory collection of data inputs and outputs for all the unit processes included in the lifecycle: inputs from technosphere + resource inputs from biosphere; product outputs from technosphere (coproducts, waste) + pollutant emissions (to soil, to water, to air) several different units (e.g., m3, kg, MJ, kg·km, m2) large LCI databases (including broad technosphere coverage and multioutput processes) (e.g., Ecoinvent, GaBi professional, NREL); systematic update of inventory data);60 quality review procedures “cradle to gate”, or “gate to gate”, or “cradle to grave”; most often LCA of production systems carried out at micro- or mesoscale several allocation criteria (mass, energy, price, etc); cutoff or system expansion

Goal and scope def inition to assess the potential environmental impacts of the life-cycle of a product due to resources consumption and pollutant emissions model of the system life-cycle; choice of system boundary and functional unit, data quality and allocation rules, and LCIA models

LCA (ISO 14040:2006)

Table 1. Life Cycle Assessment (LCA) as Compared to Emergy Evaluation (EME) EME

results from Emergy indices’ are used to support decision making processes related to sustainability and use strategies of resources; the procedure is not standardized

Support to decision-making nature-oriented (donor side): EME accounts for the contribution of natural capital in sustaining economic activities

Emergy (indices) calculation software tool EmSim;51 some simulation models of flow and Emergy dynamics are also available;59 usually, emergy calculations are performed in spreadsheet formats evaluation of natural resource use in terms of a common equivalent unit of reference (e.g., solar emjoule: seJ); calculation of sustainability indicators (e.g., %R, EIR, ELR, empower density);64

usually “cradle to gate” (using LCA terminology) but no transparency (too aggregated inventory); few studies rely on end-of-life phases;63 ability to account for all kinds of production systems, from micro- to macro-scale according to the emergy algebra,8,42 allocation is not allowed in multioutput processes but only in the case of splits

Construction of an Emergy table (spreadsheet format) all product inputs from technosphere + (in principle) all inputs of natural capital (ecosystem provisioning, regulating and supporting services43) + inputs of information (e.g., human labor, genetic diversity61) and services (monetary inputs);7,8 only products and/or coproducts of interest; emissions are usually not of interest several different units (e.g., m3, kg, MJ, kg·km, m2); monetary units (e.g., $) are also very often included no comprehensive databases; NEAD framework for Emergy calculation at national scale;62 often lack of data accuracy, representativeness and completeness

System modeling to evaluate the memory of the geobiosphere exergy provision (environmental work) related to products, services or large-scale systems via the use of natural resources Emergy diagramming based on energy system diagram rules;8 assumptions on system modeling representation, UEVs selection and results calculation (choice of Emergy indices to use)

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implemented a new LCIA indicator based on Emergy concept, i.e. Solar Energy Demand (SED).22 In this case, UEVs of elementary resources are comprehensively implemented at the background level in Ecoinvent, increasing the completeness of the evaluation. Though SED is probably the most meaningful progress toward a consistent inclusion of Emergy in LCA, it still relies on different rules from Emergy with regard to allocation and system boundary definition.22 Basically, the Emergy algebra rules have not been implemented to the more or less complex and comprehensive inventory networks considered in all these studies. LCA Compared to EME. A comparison between LCA and EME, for each phase of analysis, is provided in Table 1. The major differences between the tools are the following. (i) The evaluation perspective: LCA is a “human-oriented or user-side” approach, which evaluates resources directly solicited by human-driven production processes (through market mechanisms) and accounts for, as well, the environmental impacts generated by pollutant emissions; instead, EME adopts a “nature-oriented or donor-side” perspective, by starting from natural systems before and independent of human intervention to evaluate the contribution of natural capital in sustaining economic activities.17,45 Despite the set of natural resources already included in LCI, ecosystems goods and services are mostly not included in the assessment, contrary to EME (Table 1, phases 2 and 3), mainly because of the difficulty to evaluate how much they sustain the economic processes (since they are mostly not directly solicited). (ii) Data quality and requirements: LCA consistently accounts for hundreds of inputs and outputs collected in large LCI databases based on records and models of thousands of technological processes. Data collection and validation follow strict quality rules, including procedures that are systematically updated. In contrast, there is no consistent library of UEVs for EMEs available. The most common data source for EME are the “Emergy Folios”,57 which are, however, not updated and cover only a limited number of economic sectors and goods. (iii) Calculation boundaries: In EME, the number of process outputs is usually limited to the useful product(s). The additional work required to avoid, to dilute, or to fix damages due to emissions and wastes, which are coproducts, is in some case estimated,64 despite not always consistently for all the processes under study.31 EME instead accounts for a larger variety of natural resources than LCA, and allows including, at least in principle, the evaluation of information, human labor, and services.7,8 (iv) Allocation procedures: As already said, Emergy owns a special set of algebra rules that, in the case of multioutput processes, avoid allocation of emergy inputs.8,42 In contrast, LCA uses allocation principles (partition by mass, energy, price, volume, etc.) or system expansion (avoided burdens) and cutoff approaches.5 (v) Scientific recognition and policy support role: LCA represents a standardized method for environmental assessment widely used for product improvement and policy support3 whereas EME is claimed to be a tool to support decision-making processes but, so far, it does not

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have a comprehensive and widely accepted framework.17,58

TOWARD A COMPREHENSIVE EMERGY CALCULATION IN LCA: CHALLENGES Points (ii), (iii), and (iv) above are mainly related to the reliability and consistence of Emergy calculations using LCI data, whereas points (i) and (v) are related to the implementation of an Emergy-based LCIA indicator. To address these points, three methodological issues have to be tackled (Figure 1): 1. Allocation rules 2. Characterization factors 3. System boundary

Figure 1. Required steps for a consistent Emergy Evaluation (EME) via Life Cycle Assessment (LCA).

First, the application or reject of allocation rules for coproducts represents the main source of incompatibility between Emergy and LCA. Second, the development or update of characterization factors corresponds to the revision of current UEVs for future characterization of Emergy in LCIA. Whereas the problem of allocation rules requires a consistent technical interaction between LCI and EME, which has failed until now, the issue of UEVs could be treated within the Emergy method solely, but a synergic use of specific LCA procedures is recommended (see Proposal for a UEVs Calculation Framework). Finally, the issue of system boundary deals with the inclusion of ecosystems services, human labor, and information and mostly depends on the comprehensiveness of LCI rather than on EME. Allocation Rules. A consistent calculation of Emergy using LCI models cannot disregard the specific algebraic rules that distinguish EME from other energy-based analyses.8,42 Being based on “memorization” rather than on conservation logic, EME emphasizes knowing the difference between splits and coproducts.9 In particular, coproducts are “product items showing different physico−chemical characteristics, but which can only be produced jointly”,20 for instance, wheat-grains and wheat-straw from a single wheat plant. Therefore, coproducts have the same Emergy assigned (and different UEV) since individually they cannot be produced without investing the whole Emergy input.8,9,42 The opposite is valid for splits, which are “originating flows showing the same physico−chemical characteristics”,20 e.g. drinking-water from tap that can be used both for drinking and for washing floors. In handling splits and coproducts, another problem arises when the sum of the Emergy assigned to all the coproducts exceeds the Emergy input. This problem is solved by another algebraic rule that recommends avoiding the surplus of Emergy amount by not accounting for coproducts twice and feedback loops.8,42 4704



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Figure 2. Simplified diagram of flows interaction in Emergy Evaluation (EME) via Life Cycle Assessment (LCA). Materials production includes all agricultural and industrial activities.

processes have been derived and included in commercial software packages. In other words, the user should be able to manage the existing unit processes as they were in their unallocated form, which is apparently possible only by manipulating upstream the allocated versions, i.e. by reassigning the 100% of inputs to the original coproduct. Then, in order to implement the emergy rules, all the possible paths connecting together the unit processes have to be studied in order to avoid double counting. Li et al.79 demonstrated the feasibility of using matrix systems for UEVs calculation in compliance to the Emergy algebra. A type of preconditioning is however mandatory to structure the matrix equations in a way that accounts for configurations of feedback, splits, and coproducts. If the preconditioning is not handled properly, the calculation of UEVs results in large errors due to double counting.79 Despite its promises, the applicability of this method to large LCI models is not verified since the “preconditioning” was applied only to very simple models.79 Apart from this attempt, in the published literature Emergy algebra does not seem to be consistently applied and is not operational in the case of very detailed networks including hundreds of loops and feedbacks. Furthermore, the application of Emergy rules is very sensitive to the level of detail of the system under study: the decision of whether multifunctional processes deliver coproducts or splits is not always obvious either.17 To account for the Emergy of a system output in LCA, the only contributions to be included are those of the independent inputs (coming from different production systems): the problem is to demonstrate whether those inputs are actually independent or, instead, are originated from coproducts. Therefore, a detailed survey of the system investigated is necessary (i) to properly identify the origin of each pathway, and (ii) to understand the relationships between the processes within the system and the contributions of external (independent) processes.8,67,80 How to properly integrate Emergy rules in LCI networks remains an open field of research and application. Very recently, Marvuglia et al.121 have implemented an algorithm to calculate the Emergy of a product resulting from a complex network of processes. The algorithm has been successfully applied to a simple case study of flat glass production (described by a 7 × 7 matrix) and represents a variant of the original track-summing method.8 The

The discussion on how to handle, improve, or modify allocation rules in Emergy is still very controversial. Several attempts have been made to clarify the meaning of Emergy coproduction,65,66 to identify alternative allocation rules interpretations,67 or even to provide a new formal definition of Emergy algebra.68 In this context, EME has been compared in depth with other energy-based methods. For example, energy analysis, conversely to EME, does not have rules for manipulating byproducts: it performs a necessary but transparent manipulation to remove byproducts preventing, what energy analysis considers, double counting. In the end, every compartment follows a logic of conservation.42,69 In the comparison between Emergy and Exergy or thermo-economic analyses,16,20,70−72 it is stressed that exergy is a property of mass and energy flows, calculated in function of the thermodynamic state of the matter. Consequently, the exergy-based methods are derived from the mass and energy conservative balance and do not suffer of particular coproducts or splits problems. In LCI databases, such as Ecoinvent, standard allocation rules are defined.22,73 Based on the conservation principle, Ecoinvent applies allocation factors to each individual input and output of a multioutput process. Then, only allocated unit processes (for each coproduct) are entered in the database,74 i.e. multioutput processes and related coproducts are no longer considered. Several allocation criteria are available (e.g., by mass, energy, price, volume), depending on the type of multifunctional process and on the available information.73−75 The final goal of the allocation procedure is to assign to a given product of a multioutput process only those inputs and outputs (and related environmental impacts) which “belongs to” (are really solicited by) that product, following a conservation logic. From a practical point of view, the use of allocation is necessary for calculating LCI results by matrix inversion.76,77 Without allocation, the multioutput processes make the inventory matrix no longer square and it becomes impossible to compute LCI results without approximation.76,77 The implementation of the Emergy algebra into a LCI model is a challenging issue because of the existing allocation in the model and of its complexity, including hundreds of splits and loops.56,76,78 First, allocation shall be avoided by reconstituting the original multioutput processes, from which the allocated 4705



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resources, on a yearly basis, represents only around 1/4 of the total driving force, while the highest contribution is given by tidal Emergy. Literature studies, rather than overtly confuting the baseline concept, provide a number of baseline values where components (i.e., sun, tide, geo) are weighted differently.8,81,86,87 Sciubba identified several uncertainty issues behind the Emergy calculation of these three primary inputs to the geobiosphere, highlighting the potential ability of Exergy to compute the real “cost” of each component to the biosphere.16 Because UEVs are calculated through a sort of pyramidal process starting from the baseline, it is not surprising to find large inconsistencies in Emergy results, in particular when comparing systems that use different baselines at different times and spatial scale.87 Furthermore, UEVs calculation is based on rather crude assumptions and results are therefore seldom reproducible,16,17 and cannot be validated a posteriori. To this respect, Emergy and LCA share the same limitations. However, due to its “memorization” (and nonconservative) character, Emergy is intuitively even more dependent on the degree of detail at which the system under study is described than LCA. Emergy calculation strictly depends on the path followed and the models chosen. In LCA there is no notion of “path” but nevertheless the results still depend on the degree of detail of the studied system. For instance, the results stemming from the application of allocation rules can be different if one considers a detailed representation of a unit process, where flows to be allocated are correctly specified, or a more simplified one, where all flows are associated to the same “black box” process.80 The LCA method has the great advantage of using structured software tools and large databases that make the operational framework more flexible. In EME, the choice of the baseline in EME may hide mistakes in UEVs estimations, making the results of hundreds of Emergy studies hardly comparable.16,87 Furthermore, no explicit calculation has been ever presented that includes second law concepts. For instance, Odum regards internal heat and solar radiation as equivalent forms of energy,8 which they are not. In the Emergy community, a large effort has been spent to provide a uniform approach and to increase UEVs robustness. For example, the recent National Environmental Accounting Database (NEAD)62 addressed a global formalization of EME (Table 1). However, this framework includes sets of aggregated and unclear data and results are therefore only useful for comparisons at national scale.62 To a limited extend, attempts to increase consistency of UEVs with characterization of uncertainty have been provided.88 This aspect is of particular interest for future implementations of UEV as characterization factors, since the consideration of uncertainty in LCA results is common practice and also mandatory in comparative assertions that are disclosed to the public, based on ISO 14044:2006 standards.55 System Boundary. By definition, EME aims to extensively account for natural processes, both at global (e.g., atmospheric processes, lands uplift, ocean currents circulation) and regional scale (e.g., water runoff, soil erosion, surface wind, precipitations).81,89 Despite this stated aim, the use of the baseline concept does not provide enough accuracy and detail on which processes are actually considered and how. Furthermore, EME attempts to include many items related to human culture and information systems (e.g., knowledge and educational levels, genetic information) that usually depend on a huge and complex network of supporting activities and connections,7,61 which is indeed oversimplified in the current practice.

application of the algorithm to a much more complex network, like the Ecoinvent database, is currently under testing.121 Characterization Factors. Conventional UEVs can be used as characterization factors in LCIA.22,23,25 Only UEVs of primary resources (e.g., water, biomass, atmospheric, fossil, and mineral resources) must be considered for this purpose, since characterization factors are applied to cumulated elementary resource flows obtained in the LCI of a product. Figure 2 shows a simplified conceptual model of Emergy evaluation in LCA. It is based on a rationale similar to one used in Liao et al.119 and Bakshi et al.44 insofar combining the ecosphere with the antroposphere (economy), the latter depending on the former. The rules of Odum’s energy systems diagram8 are applied to describe the flow of resources from the primary storage of Nature (i.e., the “biosphere”) to the technological processes (i.e., the “technosphere”). The produced “biosphere” includes storage tanks of water, biomass, minerals, metals, and fossil resources, which are created and maintained through constant power of three primary sources: solar radiation, deep Earth heat, and tidal energy.8,81 Atmosphere and oceans interact with land to sustain and to make the biosphere resources available. These resources are then used in the technosphere to produce materials, energy carriers, transport systems, infrastructures, and other commodities. Based on Figure 2, several UEVs have to be implemented as characterization factors for LCIA in order to cover all the natural resources available in the LCI databases considered. Then, the total Emergy associated to the technosphere product (in terms of joules of equivalent solar energy) can be calculated by multiplying each UEV for the corresponding amount of resource inventoried (i.e., process of Emergy-life cycle characterization).22,23,25 However, this final calculation is not feasible until Emergy algebra is properly considered in the technosphere model. In the Emergy literature, a large number of UEVs of natural resources is available,22 such as for water flows and storages,82 renewable energy resources such as wind or geothermal heat, biomass resources, soil erosion, different kinds of minerals, and fossil fuels.8,81 A summary of resource flow UEVs was provided by Brown and Ulgiati.83 Recently, UEVs were calculated for a large number of metal ore resources,84 besides this, the calculation for fossil resources of gas, crude oil, and coal was also upgraded.85 The calculation of UEVs values is rooted on the “Baseline” concept.8,81,86,87 This concept states that resource storages and flows are coproducts of the same global Emergy budget (i.e., baseline) that drives all geobiosphere processes (“biosphere” in Figure 2). The UEV of a given resource (e.g., mineral, water, biomass) is quantified by dividing the baseline to the total annual quantity of that resource, estimated by the ratio of the stored quantity and its turnover time.22,81 The Emergy baseline is the sum of solar radiation Emergy, tides Emergy, and geothermal heat Emergy. To assign an Emergy value (i.e., equivalents of joule of solar energy) to these three different sources, a number of balance equations are applied.81 As a result, the Emergy of 1 unit of tidal energy absorbed and of 1 unit of crustal heat is 72 400 and 20 300 times higher, respectively, than the Emergy of 1 unit of solar energy absorbed.86 This means that 72 400 and 20 300 J of solar energy are assumed to make, respectively, 1 J of tidal energy absorbed and 1 J of energy from crustal heat sources. The total annual baseline is then composed by 24% of solar Emergy, 54% of tidal Emergy, and 22% of geothermal Emergy. In other words, the direct solar Emergy contribution to the formation of 4706



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Figure 3. Model of bottom-up approach for the alternative calculation of Unit Emergy Values (UEVs) of resources and life cycle products. The complete “geo-bio-techno-sphere” is assessed.

calculation on the actual knowledge a person has reached along his/her life (e.g., by calculating the Emergy associated to the past years of education). More recently, Abel has been revisiting Odum’s approach, providing a set of transformities for human services based on occupational attainment (e.g., from world poor or underclass to superclass of people) and on a global hierarchical scale.61 However, assigning different Emergy values to different types of work may be incorrect for ethical reasons, since it would correspond to assigning a different “value” to persons. More research should be addressed to justify the differences in human labor, focusing on household needs but without disregarding cultural and information items. To this respect, LCI databases (in particular input−output databases) offer a solid base for the implementation and characterization of the human work impact. An updated UEV of human labor could be obtained based on an LCI of human labor. Accordingly, LCIs of human labor types should be added to existing LCI models of products and processes where the contribution of human labor is likely to be significant (e.g., in agricultural productions, industrial manufacturing, transportation, etc.), with a consistent enlargement of the system boundary as compared to conventional LCAs. Unit processes of different types of human labor could be derived by including both inputs and outputs related to the production of commodities used during household activities (e.g., food and drinks, water for nondrinking uses, means of transport, clothes, household and energy consumptions, land use, wastes production, etc.). By using EME algebra rules, UEVs for human labor are likely to be very high since everything humans eat or use would be fully allocated to the functional unit. In contrast, by using LCA allocation rules, only a part of the consumptions would be assigned to labor, and therefore the significance of human labor is likely to be much lower. So far, no attempts to derive comprehensive data sets for human labor are available in the LCA literature and LCI databases.102 Currently, LCA studies related to human activities mostly concern food, energy consumption, and waste production,103,104 but all these items are considered separately.

Depending on data availability, system boundaries of EME can be very large, including system dynamics at territorial scale,12−14 or more detailed when evaluating patterns at local scale (e.g., ecosystems90 or urban landscapes14,91). The main limitation of using LCI models for EME consists in the limited number of resources currently considered in LCIs.43,44 While EME attempts to evaluate the energy memory associated to information and to ecosystem services (e.g., provisioning and regulating services such as rain, tide, and evapotranspiration, or supporting services such as soil formation and photosynthesis43,89,90,92−94), LCA does not generally account for those inputs because they are not specifically required/solicited by the economic systems, and are assumed to be freely available and not scarce. This approach is indeed stemming from the user-side perspective of LCA as compared to the donor-side perspective of EME. As a result, the combination of EME with existing LCI databases is not a trivial task, because of the complexity of the natural systems behind the missing inputs,95,96 which indeed are proven to sustain human economies and should not be disregarded.21,43 Research is ongoing in the LCA community to deal with this issue.97,98 It is expected that the system boundary of LCI will be enlarged in the near future with inclusion of a meaningful set of ecosystem services.99 Another disregarded input in LCA is the one of human labor, which is usually not considered because of the debatable causal relationship to the functional unit. Despite the fact human labor is supported by a huge indirect flow of resources,100 the pertinence and contribution of human labor to life cycle processes has been depicted to be negligible in the few LCA studies that explicitly assessed human labor.101 Conversely, EME emphasizes the importance of quantifying the environmental support to human work and services.7,8 Together with human culture and information, human labor shows one of the highest UEV.61 The Emergy of human labor can be calculated in different ways; for example, by multiplying the human labor (in currency unit) by the Emergy to money ratio, or by dividing the annual amount of country/region Emergy by the number of people multiplied by their metabolism energy content (i.e., UEV in seJ/J or seJ/h of work).12,13,100 Education levels can also be used to distinguish among types of human labor.8 However, this approach can be misleading, since the Emergy of human labor is obtained by dividing the annual Emergy flow of the nation by the number of people in each category (e.g., school, postcollege learning), rather than concentrating the

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A RESEARCH PROPOSAL TO ADDRESS THE CHALLENGES Allocation rules and system boundary require methodological developments to be tackled separately within EME and LCA, respectively (Figure 1). Regarding allocation rules, a life cyclebased calculation of Emergy has to be implemented into 4707



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available energy flows from β to α. Finally, once the two matrices are completed, UEVs of resources (i.e., natural products in α) can be derived using conventional matrix inversion. This matrix framework is already in use in current LCI databases, e.g. Ecoinvent, when the technosphere matrix is inverted in order to calculate the amount of natural resources (and pollutant emissions) related to a functional unit.56,74,76 The primary-sphere in the bottom-up approach corresponds to the biosphere (i.e., Ecoinvent matrix B) in Ecoinvent, whereas the resource-sphere corresponds to the technosphere (i.e., Ecoinvent matrix A) (Figure 3). Accordingly, the set of resources in α should be at least the same as the set considered in B. Such a framework will provide a consistent calculation of the primary energy fluxes throughout the formation of resources that are finally used to drive the technological processes, i.e. a “cradle to grave” perspective of the entire techno-geobiosphere (Figure 3). Emergy allocation rules and a number of ecosystem services (to improve system boundaries of LCA) must be also included to enrich the bottom-up model. Challenges and Constraints. The main challenge to tackle for the development of the bottom-up approach is the collection of reliable data that could approximate the geobiosphere complexity and the environmental work. The bottom-up based UEVs for nonrenewable resources, such as fossil fuels, minerals, metal ores, and groundwater reserves, should include the past solar radiation, tidal energy (if not negligible), and geothermal heat available energies that were required to generate them along with the corresponding geological ages of formation. Reliable assumptions on how to average turnover times and to report data on a yearly basis must be defined. The flow of energy that supports ecosystems (mostly solar radiation on yearly basis) should be properly assigned to renewable resources such as biomass (e.g., net amount of solar energy converted to plant organic matter through photosynthesis), or surface waters according to the solar energy required for the different water cycle processes.105 Previous studies have already recalculated fossil resource UEVs, i.e. for gas and oil,112 using a bottom-up approach. Those new UEVs consider the biogeochemical efficiency of the natural and geological steps of oil formation. To this respect, only the solar energy that was actually required for photosynthesis of the biomass, which later formed oil and gas resources, was accounted for.112 Nevertheless, this proposal failed to consider important additional parameters, e.g. the contribution of geothermal “treatment” necessary to convert the buried biomass into kerogen to finally obtain oil and gas.85 The bottom-up approach proposed here could improve the UEV consistency, since all calculations are based on the same principles and on the same model. However, this requires interdisciplinary research work between experts from different fields (e.g., geology, natural sciences, hydrology). It is not yet defined which kind of data collection should be pursued, such as the use of average global data and/or specific data representative of an optimal resource-formation condition. Furthermore, the definition of Emergy (for the bottom-up) as based on (embodied) Exergy or Energy is still debated.16,20,112,113 According to the second law of thermodynamics, Exergy or available energy is lost or consumed in all processes, which makes Exergy the ultimate limiting resource for the functioning of all systems and a promising property for the joint analysis of industrial and ecological systems.114,115 Several authors argue that geobiosphere processes are driven by constant Exergy flows that are destroyed when resources are

existing LCI database and software tools. Regarding system boundary, LCI databases shall be enlarged to provide a comprehensive calculation framework for EME. With regard to the characterization factors-step (Figure 1), the conventional matrix-based computational approach used in LCI76 could be adopted for a more consistent quantification of UEVs. Proposal for a UEVs Calculation Framework. In the conventional EME, the accounting procedure usually models the Emergy input associated to local resources (e.g., rain, sun) in such a manner that the Emergy input to a system does not fully contribute to the system development but instead a fraction is exported.58 In this regard, only the equivalent (solar) energy that was actually needed to generate a given resource should be accounted for in the calculation of emergy. The proposed alternative UEVs framework uses a “bottom-up” perspective, as opposed to the “top-down” perspective adopted with the baseline concept (cfr. Figure 2). The bottom-up procedure explicitly acknowledges that the three primary sources contribute differently in time, power, and space to generate resources, factors neglected when using the baseline but essential to estimate the environmental work. Moreover, the bottom-up seems to be a more systematic and aware approach than the one presented in Bastianoni et al.,70 who started from the output and went backward using “partial efficiencies” (i.e., the amount of an input necessary to obtain a unit product). In the new framework, UEVs calculation is no longer referred to the baseline (as a sum of sun, tides, and geo-heat Emergies), but instead the provisions of the three sources are independently quantified among each natural resource storage and flow at a global scale (Figure 3), e.g. the sunlight required for water evaporation,105 the tidal energy implied in the productivity of saltmarsh ecosystems,106 or the geothermal energy needed for coalification processes.85 An effective structure to assign and connect flows (e.g., the portion of solar radiation that annually feeds the compartment of Net Primary Production, NPP, on land) and then to calculate the Emergy associated to average interactions among the resource compartments (e.g., the wood from NPP that is used to produce peat) is provided by the matrix scheme already used in LCI. Previous studies demonstrate the feasibility of using matrix systems to represent the main biosphere connections when assessing ecosystem services.107,108 Proposals to integrate natural geologic features into ecosystem matrices were also formulated.79,109 A framework of interlinked chains to define (in matrix form) environmental processes and combine them with economic processes, very similar to the bottom-up approach, has been also defined by Heijungs in 2001.120 Finally, examples of using matrix systems to calculate UEVs already exist in the Emergy literature.71,72,110 As conceptually illustrated in Figure 3, a square matrix α (here named resource-sphere) and a rectangular matrix β (here named primary-sphere) are defined. First, the flows of primary energy sources (in MJ/yr), listed in the β rows, are assigned to a given number of “natural processes” listed in the β columns, e.g. ≈2 × 1015 MJsunlight/yr are converted by photosynthesis105 to produce ≈6 × 1013 kgC/yr of NPP.111 Then, the same natural processes, now listed in α’s columns, will produce a corresponding number of “natural products”, listed in α’s rows. Natural processes and products may be connected differently in α (e.g., the ≈6 × 1013 kgC/yr of NPP can be linked to a production of a given amount of peat as well as of a hardwood resource) to describe the transfer and exchange of the (annual) 4708


Environmental Science & Technology extracted and used.115−118 Solar radiation and, to a lesser extent, geothermal and tide exergies are the three independent fluxes of Exergy that are destroyed in or on the Earth along with the various natural transformation processes, such as atmospheric absorption, photosynthesis, evaporation, carbon burial, and others.105 The Exergy derived from these sources is thus “embodied” in each resource as available energy that can be extracted once the resource is exploited.116 It is assumed that if the formation time of the resource were included and multiplied by the “embodied Exergy content” of the corresponding unit of resource, then the total “memorized” (i.e., embodied) Exergy of each resource could be accounted for. The “bottom-up” procedure avoids partitioning of the Emergy baseline as well as accounting for the emergy actually sustaining the processes, i.e. without the exported fraction. This will improve the estimation of individual contributions of sun, tides, and geo-heat energies along with the natural processes that produce flows and stocks of resources. Thermodynamic transformations of energy can also be incorporated as biogeochemical processes that provide natural products.112 Therefore, the bottom-up framework can provide a measure of the actual environmental work spent to make available resources at a higher quality than the primary solar, tidal, and geothermal heat exergies, as stated by the Emergy theory.8 Whether the complexity of natural cycles and loops within the network processes becomes excessive for a satisfactory degree of accuracy of UEVs calculation, a solution would be then to aggregate subsystems and attribute to each of them an average or cumulative transformity.

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OUTLOOK

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AUTHOR INFORMATION

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ACKNOWLEDGMENTS

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REFERENCES

Critical Review

This work is supported by the National Research Fund of Luxembourg, and cofunded under the Marie Curie Actions of the European Commission (FP7-COFUND). These are gratefully acknowledged. The authors also thank three anonymous reviewers for their valuable contribution.

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More than ten years ago, Campbell58 said: “The future success of Emergy Analysis as a method for assessing environmental systems will depend on developing accurate and consistent methods for calculating transformities, documenting the uncertainty associated with these calculations, and developing a clear understanding of how to use these factors in determining the Emergy basis for economic and ecological products, services, and system”. The calculation of Emergy using LCI databases and LCA matrix framework will definitively increase the reliability and applicability of the Emergy concept in environmental decision-making. The three main issues to be addressed are the refinement of the UEVs for elementary resources, the implementation of Emergy algebra into LCI, and the expansion of the scope of LCI. Emergy accounts for the environmental work invested in order to obtain a product and therefore can potentially provide insightful information on the long-term sustainability of human processes.25 The integration of Emergy into LCA might deliver a paradigm shift to the latter, by providing an assessment perspective larger than the common anthropocentric one, toward a nature-oriented evaluation of natural resource use.

Corresponding Author

*Tel.: +352 425 991 682; fax: +352 425 991 555; e-mail: [email protected]. Notes

The authors declare no competing financial interest. 4709



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