Emergy evaluation using the calculation software scale: case study, added value and potential improvements
Table S1: UEVs retrieved from the application of the three methods on the man-made products used in WTPs and the production of activated carbon (x 1E11 sej/unit)
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- SI3: Decomposition of technospheric inputs UEV SCALE per resource category
- SI4. Resources disregarded in SEF dataset due to double-counting
- SI5. Application of the EcoLCA tool
- References
Table S1: UEVs retrieved from the application of the three methods on the man-made products used in WTPs and the production of activated carbon (x 1E11 sej/unit).
References: [a]: Arbault et al. (2013). [b]: this study (Table S2). [c]: Odum (1996); with 29.307 E6 J/kg coal. [d]: Bastianoni et al. (2009). [e]: Bastianoni et al. (2005). [f]: Campbell and Ohrt (2009). [g]: Buranakarn (1998); with 1 ton.mile = 907 kg x 1 609 m = 1.4594 tkm. Table S2: Electricity production mix for UCTE and UEVsCONV from literature (excluding human labor and services).
References: [f]: Campbell and Ohrt (2009). [h]: ecoinvent® v2.2 (2010), processes #666-694, 7191, 7199. [i]: Brown and Ulgiati (2002). SI3: Decomposition of technospheric inputs UEVSCALE per resource categoryFigure S2 shows the ratio of SED to the emergy value of SCALE inputs, for each technospheric product of Table S1 and Figure 4, per resource category. It can be observed that chloride-based co-products and liquid oxygen show similar ratios for the most of the resource categories, and that land resources have a very low ratio for most products, contrary to nuclear, renewable energy and water resources. Fossil, metal and mineral resources show more heterogeneous behaviors. A more detailed, per resource decomposition is necessary to deepen the analysis. Figure S2: ratios of SED value to the emergy value of SCALE inputs of technospheric products, per resource category. Table S3 provides the detailed results of SCALE and the SED method applied to the technospheric inputs. Table S3: Decomposition of SCALE output (UEVSCALE), SCALE input (application of rule #2 only) and SED of technospheric inputs. Nfo:Non-renewable fossil resources. Nme: Non-renewable metal resources. Nmi: Non-renewable mineral resources. Nnu: Non-renewable nuclear resources. Ren: Renewable energy resources. Rla: Renewable land resources. Rwa: Renewable water resources. Tot: Total. Results in Msej/kg, except for electricity products (in Msej/kWh). Product labels refer to the nomenclature in the ecoinvent® database.
SI4. Resources disregarded in SEF dataset due to double-countingSCALE relies on the SEF dataset to convert the results of graph search algorithm into emergy values. Some resources are assigned a characterization factor equal to zero: the atmospheric resources listed in ecoinvent® (CO2, Krypton, Xenon) are considered as ground state resources i.e. with no solar energy requirement, while biomass related resources (airborne CO2, soil organic carbon, biomass energy and wood resources) as well as direct solar energy, land transformation and land volume occupation are considered already accounted in land occupation SEFs. Emergy accounting was initially developed with a top down approach: the annual baseline (the Earth energy budget expressed in emergy values) supports all geobiosphere processes, whose outputs are considered as co products of the global system. This is consistent with the specific rationale for allocation (rule #2) in emergy algebra. Rule #4 was thus introduced to deal with double counting issues e.g. of rain and wind, since both resources are driven by the same (atmospheric) processes. Most often, freshwater has the highest emergy value of the renewable resources used up in a coupled natural human system, so that there is no need to account for other renewables. Though often justified, this assumption is by no means a rigorous application of emergy algebra, and should not be implicitly incorporated in an algorithm that claims coping with approximation in conventional emergy evaluation. In addition, UEVs of raw materials suffer from scarce variability and low representativeness: typically, only one value is consistently available from the emergy literature. These UEVs are not homogeneously calculated: for instance, petroleum based resources are evaluated from a description of natural mechanisms involved in their formation (Bastianoni et al., 2009, 2005; Brown et al., 2011), while mineral resources are mostly based on a top down approach, in which the annual emergy budget (baseline) is divided by the regeneration rate of the resource (Rugani et al., 2013). A bottom up approach at the level of the geobiosphere could be implemented to retrieve adequate characterization factors of natural resources in compliance with the emergy algebra within SCALE; such approach would focus on modeling geobiosphere processes and their co products, using material and energy flows, so that the application of emergy algebra would prevent double counting of co produced resources. As a first step, a model development could rely on the framework proposed by Rugani and Benetto (2012), in which the geobiosphere is modeled with an array of static geological and biological processes and driven by the three independent external energy sources (sunlight, tidal potential energy and crustal heat); the graph search algorithm used in SCALE could be once again applied to this matrix framework. For the time being, this is however far from real applications due to difficulties in developing such a comprehensive model and retrieving the necessary data about the static geological and biological processes (Rugani and Benetto, 2012). SI5. Application of the EcoLCA toolThe EcoLCA tool was applied in order to get indications on potentially important emergy values of some ES that are not accounted for in the SCALE-based methodology. The following data were used to simulate the water industry. They are based on Site A information (Igos et al., in press): Table S4: data used in the EcoLCA software.
The quantitative results are not expected to be implemented in EME, but are just presented for interpretation purposes and for discussion on ES accounting in EME. Indeed, there are many discrepancies to remind in both the data used and the application of the method, among which:
It is important to remind that the EcoLCA method does not comply with emergy algebra: it uses matrix algebra operations which are common in the manipulation of Input Output Tables (the Gosh inverse, see Zhang et al., 2010), which rely on a linear relationship between inputs and outputs and a monetary allocation of multi output commodities. It can be concluded that the EcoLCA method is conceptually analogous to the SED method, though the former is applied to IO based LCI and the latter to process based LCI. ReferencesArbault, D., Rugani, B., Tiruta-Barna, L., Benetto, E., 2013. Emergy evaluation of water treatment processes. Ecol. Eng. 60, 172-182 Bastianoni, S., Campbell, D., Susani, L., Tiezzi, E., 2005. The solar transformity of oil and petroleum natural gas. Ecol. Model. 186, 212–220. Bastianoni, S., Campbell, D.E., Ridolfi, R., Pulselli, F.M., 2009. The solar transformity of petroleum fuels. Ecol. Model. 220, 40–50. Brown, M.T., Protano, G., Ulgiati, S., 2011. Assessing geobiosphere work of generating global reserves of coal, crude oil, and natural gas. Ecol. Model. 222, 879–887. Brown, M.T., Ulgiati, S., 2002. Emergy evaluations and environmental loading of electricity production systems. J. Clean. Prod. 10, 321–334. 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, USA. Campbell, D.E., Ohrt, A., 2009. Environmental Accounting Using Emergy: Evaluation of Minnesota. USEPA Project Report. EPA/600/R-09/002. Ecoinvent, 2010. Ecoinvent database v2.2 [WWW Document]. Swiss Cent. Life-Cycle Inven. - Dübendorf Switz. URL http://www.ecoinvent.org/database/ Igos, E., Dalle, A., Tiruta-Barna, Benetto, E., L., Baudin, I., Mery, Y., in press. Life cycle assessment of water treatment: what is the contribution of infrastructure and operation at unit process level? J. Clean. Prod. dx.doi.org/10.1016/j.jclepro.2013.07.061. Marvuglia, A., Benetto, E., Rios, G., Rugani, B., 2013. SCALE: Software for CALculating Emergy based on life cycle inventories. Ecol. Model. 248, 80–91. Odum, H.T., 1996. Environmental accounting: emergy and environmental decision making. John Wiley & Sons Inc. Rugani, B., Benetto, E., 2012. Improvements to Emergy Evaluations by Using Life Cycle Assessment. Environ. Sci. Technol. 46, 4701–4712. Rugani, B., Benetto, E., Arbault, D., Tiruta-Barna, L., 2013. Emergy-based mid-point valuation of ecosystem goods and services for life cycle impact assessment. Rev. Métallurgie 110(4), 249-264. Rugani, B., Huijbregts, M.A.J., Mutel, C., Bastianoni, S., Hellweg, S., 2011. Solar Energy Demand (SED) of Commodity Life Cycles. Environ. Sci. Technol. 45, 5426–5433. Zhang, Y., Baral, A., Bakshi, B.R., 2010. Accounting for Ecosystem Services in Life Cycle Assessment, Part II: Toward an Ecologically Based LCA. Environ. Sci. Technol. 44, 2624–2631. Yüklə 309,17 Kb. Dostları ilə paylaş:
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