Criteria List for LCA Professional Certification
Prerequisites for testing: Applicants must provide documentation of their readiness for the testing one of the following four ways A degree in LCA at the undergraduate or graduate level (submit copy of diploma) A degree in environmental science or environmental engineering (submit copy of diploma), plus submitting proof of a internship (at least 3 months, shown by a letter from employer) or participation in doing an LCA (LCA study submitted) LCA skills gained through experience: submit an LCA that is either critically reviewed or published in a peer reviewed journal (study submitted: applicant must be one of the listed authors) Proof of an internship (at least 3 months, shown by a letter from employer) or participation in doing an LCA (LCA study submitted) plus college-level class work, in which one has taken the following coursework or classes containing the essential skills of the class work: General Chemistry Statistics Economics Systems analysis or systems dynamics Environmental Toxicology LCA Certified Professional Criteria General 1 Understand basic life cycle concepts 2 Understand different life cycle stages 3 Understand the holistic nature of LCA 4 Understand the concept of reference flow 5 Understand LCA’s applicability to product, process, or system 6 Understand formal definitions of life cycle concepts 7 Be able to describe the main steps for completing a life cycle assessment 8 Be able to explain uses for LCA as a decision-supporting tool 9 Understand the relationship between LCA and other environmental assessment techniques 10 Understand that there are standards available, and that these are incomplete specifications 11 Understand the policy uses and implications of LCA Life Cycle Inventory 12 Understand the concept of the LCA unit process 13 Understand process LCI 14 Understand the mathematical procedures available for inventory calculation and analysis and the advantages and limitations of these. 15 Understand mass & energy balances 16 Understand economic input-out analysis LCA 17 Understand the differences between process & EIO LCA and their hybrids 18 Understand the concepts of the consequential and attributional approaches for LCA 19 Understand the difference between ecosphere and technosphere flows 20 Understand system boundary expansion 21 Understand allocation procedures 22 Understand how to define functional units and select appropriate ones for LCA analyses Life Cycle Impact Assessment 23 Understand the concepts of life cycle impact indicators and fate and transport models 24 Be able to identify and describe environmental impact pathways 25 Understand the concept of environmental relevance 26 Know the content of each damage category and the state of the art on damage indicators. 27 Understand the concepts of environmental impact inventory, midpoint and endpoint indicators 28 Understand the sources of characterization factors for each of the common environmental impact categories 29 Know the present state of the art of impact pathway modeling in different categories 30 Be able to explain characterization factors for impact analysis 31 Understand the relationship between natural science and mid-point impact assessment 32 Understand the relationship between value judgement and endpoint or damage categories 33 Understand the centrality of energy systems in LCA’s 34 Understand concepts of carbon footprints LCA Project Management 35 Be able to write a clear and concise scope statement for LCA projects 36 Be able to develop an LCA project plan including data requirements and timeline 37 Understand the requirements for critical review LCA Data Quality Management 38 Know the types, sources and applicability of data used in LCA 39 Know how to find sources of environmental impact models and methodologies and assess their quality 40 Know how to structure and prioritise data collection for a specific LCA 41 Know how to identify, document and manage information on data quality and uncertainty 42 Know how to identify the sufficiency and appropriateness of the available data 43 Know how to manage situations where the available data are insufficient 44 Be able to cross-check reference to confirm data accuracy 45 Be able to use mass & energy balances for data quality checks 46 Understand the limitations, weaknesses, and uncertainties in current LCA practice 47 Be able to identify errors in data and avoid mistakes in data manipulation 48 Understand how data from different sources can be combined in an LCA analysis and be able to describe the potential errors in this process 49 Know how to document data and data manipulation in a standardised format LCA post-impact calculations 50 Understand weighting 51 Understand the use of normalization in LCA 52 Understand the use of grouping in LCA LCA Modeling/Software 53 Be able to make a screening impact assessment (using software) and know the basic LCIA calculation procedure 54 Be able to perform screening, back of the envelope calculations for LCA’s 55 Be familiar with available lifecycle tools 56 Know the sources of dedicated LCA data and software and how to find out more about these 57 Know how to use stream-lined LCA techniques or LCA screening analyses when appropriate for a specific situation or client Statistics 58 Understand the basics of uncertainty analysis including simulation techniques 59 Understand basic statistical concepts including average, standard deviation, and normal distribution 60 Understand sensitivity analysis 61 Understand the concepts of relative accuracy and continuing uncertainties for characterization factors ISO 62 Know definitions from the ISO 14040 and ISO 14044 standards 63 Know the ISO 14044 basic requirements 64 Know the ISO 14044 requirements on comparative assertion Ethics 65 Understand the difference between professional judgement and personal values 66 Know that it is possible to distinguish good, justifiable practice from unacceptable, unjustifiable practice 67 Understand the elements of the ACLCA Ethics statement 68 Understand the importance of ethical decision and proper disclosure in terms of data limitations