A tool to guide the selection of impact categories for LCA studies by using the representativeness index.

Understanding the environmental profile of a product computed from the Life Cycle Assessment (LCA) framework is sometimes challenging due to the high number of environmental indicators involved. The objective here, in guiding interpretation of LCA results, is to highlight the importance of each impact category for each product alternative studied. For a given product, the proposed methodology identifies the impact categories that are worth focusing on, relatively to a whole set of products from the same cumulated database. The approach extends the analysis of Representativeness Indices (RI) developed by Esnouf et al. (2018). It proposes a new operational tool for calculating RIs at the level of impact categories for a Life Cycle Inventory (LCI) result. Impact categories and LCI results are defined as vectors within a standardized vector space and a procedure is proposed to treat issues coming from the correlation of impact category vectors belonging to the same Life Cycle Impact Assessment (LCIA) method. From the cumulated ecoinvent database, LCI results of the Chinese and the German electricity mixes illustrate the method. Relevant impact categories of the EU-standardized ILCD method are then identified. RI results from all products of a cumulated LCI database were therefore analysed to assess the main tendencies of the impact categories of the ILCD method. This operational approach can then significantly contribute to the interpretation of the LCA results by pointing to the specificities of the inventories analysed and for identifying the main representative impact categories.

[1]  Antoine Esnouf,et al.  Representativeness of environmental impact assessment methods regarding Life Cycle Inventories. , 2017, The Science of the total environment.

[2]  Jean-Francois Le Téno,et al.  Visual data analysis and decision support methods for non-deterministic LCA , 1999 .

[3]  Reinout Heijungs,et al.  Identifying best existing practice for characterization modeling in life cycle impact assessment , 2012, The International Journal of Life Cycle Assessment.

[4]  Gonzalo Guillén-Gosálbez,et al.  Combined use of MILP and multi-linear regression to simplify LCA studies , 2015, Comput. Chem. Eng..

[5]  T. Seager,et al.  Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA) , 2014, The International Journal of Life Cycle Assessment.

[6]  Brian E. Granger,et al.  IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.

[7]  J. Aubin,et al.  Environmental assessment of trout farming in France by life cycle assessment: using bootstrapped principal component analysis to better define system classification , 2015 .

[8]  Gregor Wernet,et al.  The ecoinvent database version 3 (part I): overview and methodology , 2016, The International Journal of Life Cycle Assessment.

[9]  R. Scholz,et al.  Management influence on environmental impacts in an apple production system on Swiss fruit farms: Combining life cycle assessment with statistical risk assessment , 2006 .

[10]  G. Arfken Mathematical Methods for Physicists , 1967 .

[11]  Gonzalo Guillén-Gosálbez,et al.  Statistical analysis of the ecoinvent database to uncover relationships between life cycle impact assessment metrics , 2016 .

[12]  Mark A J Huijbregts,et al.  Resource Footprints are Good Proxies of Environmental Damage , 2017, Environmental science & technology.

[13]  M. Huijbregts,et al.  Is cumulative fossil energy demand a useful indicator for the environmental performance of products? , 2006, Environmental science & technology.

[14]  Gonzalo Guillén-Gosálbez,et al.  On the use of Principal Component Analysis for reducing the number of environmental objectives in multi-objective optimization: Application to the design of chemical supply chains , 2012 .

[15]  Reinout Heijungs,et al.  The computational structure of life cycle assessment , 2002 .

[16]  Christopher L. Mutel,et al.  Brightway: An open source framework for Life Cycle Assessment , 2017, J. Open Source Softw..

[17]  Jeroen B. Guinée,et al.  Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded? , 2018, Environmental science & technology.

[18]  Mark A J Huijbregts,et al.  How Many Environmental Impact Indicators Are Needed in the Evaluation of Product Life Cycles? , 2016, Environmental science & technology.

[19]  Not Indicated,et al.  International Reference Life Cycle Data System (ILCD) Handbook - General guide for Life Cycle Assessment - Detailed guidance , 2010 .

[20]  J. Bare,et al.  Development of normalization factors for Canada and the United States and comparison with European factors. , 2010, The Science of the total environment.

[21]  Zenon Gniazdowski Geometric interpretation of a correlation , 2013 .

[22]  M. Guerci,et al.  How can farming intensification affect the environmental impact of milk production? , 2014, Journal of dairy science.

[23]  Jane C. Bare,et al.  TRACI 2.0: the tool for the reduction and assessment of chemical and other environmental impacts 2.0 , 2011 .

[24]  Lauren Basson,et al.  An integrated approach for the consideration of uncertainty in decision making supported by Life Cycle Assessment , 2007, Environ. Model. Softw..

[25]  Marie de Saxcé,et al.  Assessment and improvement of the appropriateness of an LCI data set on a system level – application to textile manufacturing , 2014, The International Journal of Life Cycle Assessment.