http://dl.gi.de/handle/20.500.12116/23835 Thu, 23 Jul 2026 18:30:20 GMT 2026-07-23T18:30:20Z http://dl.gi.de/handle/20.500.12116/26136 A scalable implementation of the track summing algorithm for Emergy calculation with Life Cycle Inventory databases Marvuglia, Antonino; Benetto, Enrico; Rugani, Benedetto; Rios, Gordon Pillmann, W.; Schade, S.; Smits, P. Emergy analysis is an environmental accounting approach that links thermodynamics and systems ecology to evaluate the work made by both natural processes and human activities to make a product or service available. Emergy is a measure of the energy used in the past and thus “memorized“ in the product, including also the energy spent by natural processes up to the main source (the sun). In order to compute thisamount of solar energy (called solar energy equivalent) Emergy Evaluation (EME)uses conversion factors called transformities or Unit Emergy Values (UEVs), which express the amount of Emergy required per unit ofa given product or service. This work aims to develop an operational tool for allowing the calculation of the Emergy associated to each of the commodities involved in a given product’s life cycle along with its related inventoried resources. More specifically, the Emergy was calculated starting from a Life Cycle Inventory (LCI), which represents a list of environmental inputs and outputs (resource extractions and pollutant emissions) related to the production of a specific product. The motivation for our work is linked first of all to the consideration that, while Life Cycle Assessment (LCA) can nowadays avail itself of large LCI databases (such as Ecoinvent) which are constantly updated and extended, consistent libraries of UEVs for Emergy calculations do not exist. As a consequence, a methodology able to link LCI databases and emergy calculations and formalize the latter ones in a matrix form would represent an important step forward for Emergy-based environmental accounting. The case study tackled here deals with a simplified version of the production system of flat glass. We formalized the problem in a matrix-based structure which comes directly from the LCA framework and developed a variant of the track summing algorithm originally due to Tennenbaum (Tennenbaum1988). Two versions of the algorithm were implemented: one in Scala (a general purpose programming language that smoothly integrates features of object-oriented and functional languages) and one in C++. The former is a multi-threaded breadth first search (BFS), the latter follows a depth first search (DFS) and is more efficient in terms of memory usage.The algorithm consisted in calculating Emergy flows separately per Emergy independent sources, then summing the results. Solving the problem at stake took an operation time of 1.37 seconds on a 2.4 GHz Intel Core 2 Duo laptop running Mac OS X. The results were validated using the software Emsim, a free-share Emergy simulatorthat can workwith lifecycle systems using a graph instead of a matrix. However, Emsimdoes not allow a direct link to automatic calculation routines, since it requires the system’s diagram to be drawn by the operator. The promisingresult obtained will enable us to scale-up the method, possibly using the whole Ecoinvent database. This would allow the achievement of a reproducible, consistent, and transparent calculation of Emergy values for thousands of products of a LCI database. Furthermore, the algorithm could be applied case by case to specific product’s life cycles modelled using conventional LCA software tools like Simapro, allowing an exact calculation of the Emergy associated to the studied products and therefore a complete combination of LCA and Emergy perspectivesinenvironmental assessment. Sat, 01 Jan 2011 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/26136 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/26137 Environmental impact assessment of electricity production by photovoltaic system using GEOSS recommendations on interoperability Menard, Lionel; Gschwind, Benoît; Blanc, Isabelle; Beloin-Saint-Pierre, Didier; Wald, Lucien; Blanc, Philippe; Ranchin, Thierry; Hischier, Roland; Gianfranceschi, Simone; Smolders, Steven; Gilles, Marc; Grassin, Cyril Pillmann, W.; Schade, S.; Smits, P. Within the Architecture Implementation Pilot (AIP-3) of GEOSS, we have developed a scenario called “environmental impact assessment of the production, transportation and use of energy for the photovoltaic (PV) sector through Life Cycle Assessment (LCA)”. It aims at providing decision-makers and policy-planners with reliable and geo-localized knowledge of several impacts induced by various technologies of the PV sector. The scenario is implemented in the GEOSS Common Infrastructure (GCI) and benefits from the GEOSS interoperability arrangements. The FP7-co-funded EnerGEO project provides a GEOSS compliant Catalogue Service for the Web (CSW) that permits to discover the Web Processing Service (WPS) allowing computation of the environmental impact. A WebGIS client provided by the FP7-co-funded GENESIS platform allows users to interact with geospatial data and computation processes. This scenario has proven to be an efficient tool to disseminate knowledge on environmental impacts related to PV because of the GEOSS capabilities in interoperability. Sat, 01 Jan 2011 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/26137 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/26138 Linking PCF, LCA and ecodesign – A practical approach for the food sector Schiesser, Philippe; Teixeira, Ricardo; Himeno, Anne; Southwood, Andrew Pillmann, W.; Schade, S.; Smits, P. Product Carbon Footprint (PCF) and Life Cycle Assessment (LCA) for food and agriculture products is becoming more mainstream as more companies adopt and integrate the process. On the one hand, food products have one of the largest shares of carbon emissions. On the other hand, primary production sectors feel the effects of climate change before any others, in price and availability of inputs, in soil and water quality, and in yields. In response, PCF studies are progressively being integrated in companies’ day-to-day activities. Reducing the footprint of products can, however, be costly. First, assessing the impacts of products can be time and resource consuming. For this reason, it’s important to start simple and use screening tools providing insights on hotspots and chain management. In this paper, we discuss how PCF and LCA are being used by companies in the agri-food sector to turn the issue of sustainability around. Instead of being a cost-inducing burden, sustainability can be a profit-driving activity for business. To support this conclusion, we present different improvement scenarios studied for an agri-food company. We show how LCA-oriented changes in ingredients, packaging and energy use in food products can provide companies with win-win improvements to their operations. Sat, 01 Jan 2011 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/26138 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/26140 Stochastic Assessment by Monte Carlo Simulation for LCI applied to steel process chain: The ArcelorMittal Steel Poland S.A. in Krakow, Poland case study Bieda, Boguslaw Pillmann, W.; Schade, S.; Smits, P. The aim of the paper is stochastic approach for LCA/LCI probabilistic conception with uncorrelated input/output data in steel process chain with six processes (including Coke Plant, Iron Blast Furnace, Sintering Plant, BOF, Continuous Steel Casting and Hot Rolling Mill) applied to ArcelorMittal Steel Poland (AMSP) S.A. in Krakow, Poland case study. Uncertainty assessment in LCI is based on a Monte Carlo (MC) simulation with the Excel spreadsheet and CrystalBall® (CB) software was used to develop scenarios for uncertainty inputs. The economic and social criteria and indicators will not further be discussed in this paper. The framework of the study was originally carried out for 2005 data because important statistics are available for this year and also because it represents the data, which are the foundation for the Environmental Impact Report of the AMSP, annually collected (2005) and evaluated. The study comprises the inventory corresponding to the all process stages including the Coke Plant, Iron Blast Furnace, Sintering Plant, BOF, Continuous Steel Casting and Hot Rolling Mill. The complete inventory was integrated by main environmental loads (inputs, outputs): energy and raw materials consumed, wastes produced, and emissions to air, water and soil. The functional unit in this study is defined as “steel process chain includes all activities linked with steel production from Coke Plant and Sinter Plant to Hot Rolling Mill in 2005”. In this study only the following substances: hard coal, blast furnace gas, coke oven gas, natural gas, lubricant oil and the atmospheric emission of sulfur (S), cadmium (Cd), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), chloridric acid (HCL), chromium (Cr) nickel (Ni), sulfur dioxide (SO2), manganese (Mn), cooper (Cu), lead (Pb) have been taken in account. LCA/LCI data are full of uncertain numbers. The benefits of Monte Carlo simulation are saving in time and resources. CB eliminates the need to run, test, and present multiple spreadsheets. Simulation models are generally easier to understand than many analytical approaches. Monte Carlo analysis generates a mean value and upper and lower boundary value for each LCI exchange. The created inventories using the probabilistic approach facilitate the environmental damage estimations for industrial process chains with complex number of industrial processes (e.g. steel production). Consequently, MC analysis is a power full method for quantifying parameter uncertainty in LCA studies Sat, 01 Jan 2011 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/26140 2011-01-01T00:00:00Z