The main results from the inventory and impact assessment components shall be presented in a manner to ensure the proper use of the information, and statements about data quality and value judgments shall be included. Finally, the technical summary shall name any recommendations made and conclusions drawn by the practitioner of the LCA. Third element: Main part For LCA practitioners.  Goal of the study: The reporting of any LCA shall include a clear and concise statement of the following 6 aspects: - Intended application(s) - Method or impact limitations (e.g. Carbon footprinting) - Reasons for carrying out the LCI/LCA study and decision-context - Target audience - Comparative assertions to be disclosed to the public - Commissioner of the LCI/LCA study  Scope of the study The Scope chapter shall identify the analysed system in detail and address the overall approach used to establish the system boundaries


The system boundary determines which life cycle stages and process steps are included in the LCA and which have been left out. The scope chapter should also address data quality requirements/ambitions. Finally the scope chapter includes a description of the

European Commission – JRC, ILCD Handbook: General Guide for LCA (2010)


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting method applied for assessing potential environmental impacts and which impact categories, LCIA methods, normalisation and weighting sets are included. Below is the list of information that shall be report in scope chapter: - Types of final LCA deliverables and intended applications - Function, functional unit, and reference flow - System boundaries and cut-off criteria (completeness) plus system boundaries diagram Full analysis of all operations in a system may be extremely difficult and complex. Therefore, the system boundaries should be made clear to any reader. The reason and potential significance for any exclusion should be provided. - Methodology (LCI modelling framework and handling of multifunctional processes) A full description of the methodology used for a particular LCA needs to be presented


- Methodology (LCI modelling framework and handling of multifunctional processes) A full description of the methodology used for a particular LCA needs to be presented. It is recognized that the methodologies contain assumptions, all of which may influence the overall results. The report shall explicitly identify all assumptions and value judgments and provide a basis for these assumptions. - Data representativeness and appropriateness of LCI data & Types and sources of required data and information The data used in LCAs come from a wide range of sources, which can be of differing quality, variability, and uncertainty. All such issues should be addressed in the report. Data can be gathered from public and private sources. Any such data used in a public study but not disclosed shall be clearly noted. The sources of all public data (for example, specifically referenced textbooks, government reports, or previous LCAs) shall be clearly identified


The sources of all public data (for example, specifically referenced textbooks, government reports, or previous LCAs) shall be clearly identified. When used, public data should be included in the report. To prevent losing information by the way data are presented, the same level of detail used in collection should be maintained in reporting. - Impact assessment methods and factors, normalisation basis and weighting set - Comparisons between (product) systems  Collecting inventory (LCI) data, modelling the system, calculating LCI results The 'Inventory' phase involves data collection and modelling of the system, as well as description and verification of data. This encompasses all data related to environmental (e.g. CO2 emissions) and technical (e.g. consumed intermediate chemicals) quantities for all relevant unit processes within the system boundaries that compose the analysed system


CO2 emissions) and technical (e.g. consumed intermediate chemicals) quantities for all relevant unit processes within the system boundaries that compose the analysed system. Examples of inputs and outputs quantities include inputs of materials, energy, chemicals and 'other' - and outputs of air emissions, water emissions or solid waste. Other types of exchanges or interventions such as radiation or land use should also be included. The data must be related to the reference flow(s) and/or functional unit(s) defined in the scope chapter. Data can be presented in tables and some interpretations can be made already at this stage. The results of the inventory is an LCI which provides information about all inputs and outputs in the form of elementary flows to and from the environment from all the unit processes involved in the study. Below is list of information which shall be report in this part: - Flow Diagram


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting The flow diagram(s) should clearly describe the foreground system and links to the background system, and all major inputs and outputs. Several flow diagrams in different levels of detail may be required to adequately describe the system. The link between the flow diagram(s) and the data should be clearly evident to the reader. - Describing/documenting unit process data collected for the foreground system - Calculated LCI results  Calculating Life Cycle Impact Assessment results (LCIA results) The practitioner needs to document the LCIA results, applying the selected LCIA method and factors, as well - if included for reporting purposes - of the normalised and of the normalised and weighted LCIA results


 Interpretation - Significant issues - Completeness check - Sensitivity check (of achieved accuracy and precision) - Consistency check - Conclusions Any conclusions drawn from the study shall be explicit. They shall be limited to the materials or processes actually examined, appropriate to the variability of the data used in the analyses, and wholly based on the results and methodologies presented in the report. The conclusions should be honest and unbiased, and cover the whole study. - Recommendations Recommendations derived from the conclusions involve interpretations and are thus subjective. Ideally, they should be based solely on the conclusions of the study and incorporate an explicit explanation of the subjective process which form the bases upon which they are founded. The inclusion, and extent, of any recommendations will be determined by the target audience of the LCA. Fourth element: Annex For LCA practitioners


The inclusion, and extent, of any recommendations will be determined by the target audience of the LCA. Fourth element: Annex For LCA practitioners. The annex serves to document elements that would inappropriately interrupt the reading flow of the main part of the report and are also of a more technical nature for reference. It should include:  Questionnaire/ data collection template and raw data  List of all assumptions This should include those assumptions that have been shown to be irrelevant). The important ones are to be considered quantitatively in the sensitivity analysis and quantitatively and qualitatively in the interpretation. The relevant assumptions are also to be documented in the context where they belong, e.g. for processes together with the processes they concern at the relevant place (LCI chapter or scope definition)  Full LCI results


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Fifth element: Confidential report The confidential report shall contain all those data and information that is confidential or proprietary and cannot be made externally available. It shall be made available to the critical reviewers under confidentiality. Provisions: 10.3 Three levels of reporting requirements Fully applicable to all types of deliverables, implicitly differentiated. I) SHALL - The following form and level of reporting shall be done: I.a) The required level of reporting was identified in chapter 6.12. [ISO+] I.b) Use ILCD report template and data set format: The ILCD report template and the ILCD data set format should be used for reporting LCI/LCA studies and data sets, respectively. [ISO+] I.c) Enclose / reference report to data sets: It is recommended to accompany data sets with a LCI/LCA study report


[ISO+] I.c) Enclose / reference report to data sets: It is recommended to accompany data sets with a LCI/LCA study report. I.d) Enclose / reference LCI data sets in report: It is recommended to enclose the modelled LCI data sets to the LCA study report (e.g. as printout and/or via hyperlinks) as far as confidentiality concerns and ownership rights permit this. The full LCI results shall be included in this report. I.e) Use / combine correct level(s) of reporting: These specific levels go back to the three main levels of reporting that have a different set of requirements under ISO 14044:2006 that shall be used: “Reports for internal use”, “Third-party report”, “Report on comparative studies to be disclosed to the public”. In detail: I.f) MAY - Reports for internal use (recommendation only) (10.3.1): [ISO+] I.f.i) Document results and conclusions of the LCA in a complete, accurate and unbiased way


In detail: I.f) MAY - Reports for internal use (recommendation only) (10.3.1): [ISO+] I.f.i) Document results and conclusions of the LCA in a complete, accurate and unbiased way. I.f.ii) Especially regarding inventory data, it is recommended to document the data on the level that it enters the calculations before its unit or property conversion, scaling, etc. (i.e. as “raw data”) to provide appropriate information for reviewers and users. This information may be provided together with calculations such as conversions, scaling factors applied, averaging, extrapolations, etc. I.f.iii) Consider to address some of the requirements to third-party reports or public reports also in internal reports as this will strengthen the robustness and hence reliability of the results. I.g) SHALL - Third-party reports (10.3.2): The third-party report is a reference document for any third party to whom the communication is made


I.g) SHALL - Third-party reports (10.3.2): The third-party report is a reference document for any third party to whom the communication is made. The report can be based on confidential information, while this information itself does not need to be included in the third-party report. It is recommended to meet confidentiality interests by making sensitive and proprietary data and information available only to the critical reviewers under confidentiality as a separate confidential report. [ISO+] I.h) In addition to the requirements on reports for internal use, the following


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Provisions: 10.3 Three levels of reporting requirements components and aspects shall be included in the third-party report208: [ISO!] II) SHALL - Executive summary (for non-technical audience) [ISO+] III) SHALL - Technical summary (for technical audience / LCA experts) [ISO+] IV) SHALL - Main report, with the following aspects: Note that the following items and the [ISO+] and [ISO!] marks do relate to the general structuring and items to be included only; the exact items to be reported are identified in the other Provisions of this document. IV.a) General aspects: IV.a.i) date of report; IV.a.ii) statement that the study has been conducted according to the requirements of ISO 14044:2006 and the ILCD Handbook


IV.a) General aspects: IV.a.i) date of report; IV.a.ii) statement that the study has been conducted according to the requirements of ISO 14044:2006 and the ILCD Handbook. [ISO!] IV.b) Goal of the study: IV.b.i) intended application(s); IV.b.ii) method, assumptions or impact coverage related limitations; [ISO!] IV.b.iii) reasons for carrying out the study and decision-context; IV.b.iv) the target audiences; IV.b.v) statement as to whether the study intends to support comparative assertions intended to be disclosed to the public IV.b.vi) commissioner of the study and other influential actors, including LCA practitioner (internal or external)


[ISO+] IV.c) Scope of the study: IV.c.i) function, including IV.c.i.1) statement of performance characteristics, and IV.c.i.2) any omission of additional functions in comparisons; IV.c.ii) functional unit(s), including IV.c.ii.1) consistency with goal and scope, IV.c.ii.2) definition, IV.c.ii.3) result of performance measurement; IV.c.iii) reference flow(s) IV.c.iv) LCI modelling framework applied, i.e. according to Situation A, B, or C, including [ISO!] IV.c.iv.1) uniform application of the procedures IV.c.v) system boundary, including IV.c.v.1) types of inputs and outputs of the system as elementary flows 208 The parts in italics are directly taken from ISO 14044, chapter 5.2, but removing ISO-internal chapterreferences. A few aspects have been moved to other places, but all are covered.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Provisions: 10.3 Three levels of reporting requirements should be provided, IV.c.v.2) decision criteria on system boundary definition, and on individual or systematic inclusions and exclusions [ISO!] IV.c.v.3) omissions of life cycle stages, activity types, processes, or flows, IV.c.v.4) quantification of energy and material inputs and outputs, and IV.c.v.5) assumptions about electricity production; IV.c.vi) cut-off criteria for initial inclusion of inputs and output, including IV.c.vi.1) description of cut-off criteria and assumptions, IV.c.vi.2) effect of selection on results, IV.c.vi.3) inclusion of mass, energy and environmental cut-off criteria


IV.c.vii) data quality requirements should be included (in addition to the finally achieved quality) IV.c.viii) LCIA scope settings, including IV.c.viii.1) impact categories and category indicators considered, including a rationale for their selection and a reference to their source; IV.c.viii.2) descriptions of or reference to all characterization models, characterization factors and methods used, including all assumptions and limitations; IV.c.viii.3) any differentiations, additions or modifications of original, default LCIA method with justifications [ISO!] IV.c.viii.4) descriptions of or reference to all value-choices used in relation to impact categories, characterization models, characterization factors, normalization, grouping, weighting and, elsewhere in the LCIA, a justification for their use and their influence on the results, conclusions and recommendations; IV.c.viii.5) a statement that the LCIA results are relative expressions and do not predict impacts on category


for their use and their influence on the results, conclusions and recommendations; IV.c.viii.5) a statement that the LCIA results are relative expressions and do not predict impacts on category endpoints, the exceeding of thresholds, safety margins or risks


and, when included as a part of the LCA, also IV.c.viii.6) a description and justification of the definition and description of any new impact categories, category indicators or characterization models used for the LCIA, IV.c.viii.7) a statement and justification of any grouping of the impact categories, IV.c.viii.8) any further procedures that transform the indicator results and a justification of the selected references, weighting factors, etc., IV.c.ix) included comparison between (product) systems IV.c.x) modifications of the initial scope together with their justification should be


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Provisions: 10.3 Three levels of reporting requirements provided IV.d) Life cycle inventory analysis: IV.d.i) data collection procedures; IV.d.ii) qualitative and quantitative description of unit processes, at least of the foreground system; [ISO!] IV.d.iii) references of all publicly accessible data sources (sources for all data used and individual identification for the key processes / systems); [ISO!] IV.d.iv) calculation procedures (preferably including the steps from raw data to foreground system unit process(es)); [ISO!] IV.d.v) validation of data, including IV.d.v.1) data quality assessment, and IV.d.v.2) treatment of missing data; IV.d.vi) sensitivity analysis for refining the system boundary; IV.d.vii) specific substitution or allocation procedures for key multifunctional processes (and products in case the study directly compares multifunctional products), including [ISO!]


boundary; IV.d.vii) specific substitution or allocation procedures for key multifunctional processes (and products in case the study directly compares multifunctional products), including [ISO!] IV.d.vii.1) justification of the specific procedures IV.e) Life cycle impact assessment results calculation, where applicable: IV.e.i) the LCIA procedures, calculations and results of the study; IV.e.ii) limitations of the LCIA results relative to the defined goal and scope of the LCA; IV.e.iii) the relationship of LCIA results to the defined goal and scope; IV.e.iv) the relationship of the LCIA results to the LCI results; IV.e.v) any analysis of the indicator results, for example sensitivity and uncertainty analysis or the use of environmental data, including any implication for the results, and IV.e.vi) data and indicator results reached prior to any normalization, grouping or weighting shall be made available together with the normalized, grouped or weighted results


IV.f) Life cycle interpretation: IV.f.i) the results; IV.f.ii) assumptions and limitations associated with the interpretation of results, both methodology and data related; IV.f.iii) data quality assessment; IV.f.iv) full transparency in terms of value-choices, rationales and expert judgements. IV.g) Critical review, where applicable:


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Provisions: 10.3 Three levels of reporting requirements IV.g.i) name and affiliation of reviewers; IV.g.ii) critical review reports; IV.g.iii) responses to recommendations. V) SHALL - Annex: The annex serves to document elements that would inappropriately interrupt the reading flow of the main part of the report, and are also of a more detailed or tabular technical nature and for reference. It should include: [ISO!] V.a) Questionnaire/ data collection template and raw data, V.b) list of all assumptions (It should include those assumptions that have been shown to be irrelevant), V.c) full LCI results. VI) MAY - Confidential report: If prepared, the confidential report shall contain all those data and information that is confidential or proprietary and cannot be made externally available. It shall however be made available to the critical reviewers under confidentiality


VII) SHALL - Report for comparative studies: Reporting on assertive and non-assertive comparative studies intended to be disclosed to the public, the following additional reporting209 shall by done in addition to the requirements to reports for internal use and third party reports (10.3.3): VII.a) analysis of material and energy flows to justify their inclusion or exclusion; VII.b) assessment of the precision, completeness and representativeness of data used; VII.c) description of the equivalence of the systems being compared in accordance with ISO-chapter 4.2.3.7 and related provisions in this document; [ISO!] VII.d) description of the critical review process; VII.e) an evaluation of the completeness of the LCIA; VII.f) a statement as to whether international acceptance exists for the selected category indicators and a justification for their use; VII.g) an explanation for the scientific and technical validity and environmental relevance of the category indicators used in the study;


category indicators and a justification for their use; VII.g) an explanation for the scientific and technical validity and environmental relevance of the category indicators used in the study; VII.h) the results of the uncertainty and sensitivity analyses; VII.i) evaluation of the significance of the differences found


VIII) Grouping: If grouping is included in the LCA, add the following: VIII.a) the procedures and results used for grouping; VIII.b) a statement that conclusions and recommendations derived from grouping are 209 The parts in italics are directly taken from ISO 14044, chapter 5.3.1, but excluding requirements related to “Grouping”, as grouping of impact indicators is not recommended in the ILCD System. A few aspects have been moved to other places, but all are covered.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting Provisions: 10.3 Three levels of reporting requirements based on value-choices; VIII.c) a justification of the criteria used for normalization and grouping (these can be personal, organizational or national value-choices); VIII.d) the statement that “ISO 14044 does not specify any specific methodology or support the underlying value choices used to group the impact categories”; VIII.e) the statement that “The value-choices and judgements within the grouping procedures are the sole responsibilities of the commissioner of the study (e.g. government, community, organization, etc.)".

ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 10 Reporting


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 11 Critical review 11 Critical review (Refers to ISO 14044:2006 chapter 6) The scope and type of critical review desired should have been defined in the scope phase of an LCA, and the decision on the type of critical review should have been recorded (see chapter 6.11). The critical review is one of key feature in the LCA. Its process shall assure among others whether  the methods used to carry out the LCA are consistent with this guidance document and thereby also with ISO 14040 and 14044:2006,  the methods used to carry out the LCA study are scientifically and technically valid,  the data used are appropriate and reasonable in relation to the goal of the study,  the interpretations reflect the limitations identified and the goal of the study, and  the study report is transparent and consistent


The detailed review requirements regarding what to review and how, and how to report the outcome of the review are given in the separate document "Review scope, methods, and documentation". More details on the minimum required level/type of review for each specific type of deliverables of the LCI/LCA study can be found in the separate document “Review schemes for Life Cycle Assessment (LCA)”. Eligibility of reviewers is addressed in the separate document "Reviewer qualification". For LCA studies directed towards public audiences, an interactive review process at various stages of the LCA can improve the study's credibility. Provisions: 11 Critical review Applicable to Situation A, B, and C, implicitly differentiated. Fully applicable to all types of deliverables, implicitly differentiated. I) SHALL - See chapter 6.11 for key decisions made on the critical review: The scope and type of critical review desired should have been defined in the scope phase of an LCA (see chapter 6.11)


I) SHALL - See chapter 6.11 for key decisions made on the critical review: The scope and type of critical review desired should have been defined in the scope phase of an LCA (see chapter 6.11). The following provisions repeat these key provisions that otherwise have to be applied at this point: [ISO!] I.a) Identify minimum critical review type: Identify along the separate document “Review schemes for Life Cycle Assessment (LCA)” whether a critical review shall be performed and which review type shall be applied as a minimum. This depends on the kind of deliverable of the study, its foreseen decision-context, the kind of intended audience (internal / external / public and technical / nontechnical), and whether a comparison is part of the study. I.b) Select eligible reviewers: If a critical review is to be done, eligible reviewer(s) shall be selected. Eligibility of reviewers is addressed in the separate document "Reviewer qualification"


I.b) Select eligible reviewers: If a critical review is to be done, eligible reviewer(s) shall be selected. Eligibility of reviewers is addressed in the separate document "Reviewer qualification". II) SHALL - Review scope, methods, and documentation: The selected reviewer(s) shall perform the review and report its outcome along the provisions of the separate


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 11 Critical review document "Review scope, methods, and documentation"210. [ISO!] 210 This document was under preparation when the present document has been finalised. Until it has been published under the ILCD Handbook the relevant ISO 14040 and 14044 requirements shall be met as a minimum.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach 12 Annex A: Data quality concept and approach (Refers to aspects of ISO 14044:2006 chapter 4.2.3.6) 12.1 Introduction and overview (Refers to aspects of ISO 14044:2006 chapter 4.2.3.6) The following components and aspects of data quality are used or referenced in various chapters of this document. ISO 14044:2006 lists under “Data quality” a number of aspects such as representativeness, uncertainty / precision, and other directly data quality related aspects, but also aspects such as methodological consistency, data sources used, and reproducibility. In the ILCD Handbook, and to better structure quality indicators and assessment as well as the review of LCI/LCA studies, the concept of data quality is addressed by two complementary approaches: Firstly on data quality in the stricter sense, i.e


aspects that determine the quality of the inventory data and the related LCIA results. Secondly to aspects that relate to data quality documentation and review and to efforts of basic consistency such as nomenclature and terminology. The first approach is named “ILCD data quality indicators” and allows classifying the achieved data quality of LCI data:  Overall data quality - Technological representativeness - Geographical representativeness - Time-related representativeness - Completeness - Precision / uncertainty - Methodological appropriateness and consistency211 In the context of LCA studies, especially including comparisons, this information can then be used to judge in how far the data quality supports conclusions and recommendations from the study. Chapter 12.2 briefly introduces the concepts of these quality aspects as well as of "accuracy" and the difference between "variance" and "variability"


Chapter 12.2 briefly introduces the concepts of these quality aspects as well as of "accuracy" and the difference between "variance" and "variability". The second approach covers aspects that do not reflect the actual data quality itself but are complementary:  Documentation (i.e. providing information of data quality and other aspects as basis for reproducibility)  Review (i.e. assurance of quality)  Nomenclature (i.e. to support data consistency in practice by using e.g. the same elementary flows, units of measurement, etc.) In order to support a quality classification of data sets, the overall data quality (i.e. the integrated “Overall data quality” of the different data quality indicators) and the complementary items are combined to a set of “Overall data set quality”. Given the interest to 211 „Method“ is included as data quality item, as e.g. technological representativeness and the LCI modelling frameworks applied (attributional and consequential) strongly interrelate.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach single out method principles and approaches applied “Method” is additionally used also as criterion for the Overall data set quality. The resulting five criteria can be used to classify data sets212 as being in line with e.g. the different ILCD Handbook requirements, as follows:  (Overall) data quality  Method  Nomenclature  Review  Documentation This includes the possibility to set fixed requirements for data quality e.g. minimum requirements, or classes of quality such as “high quality”. The latter is used related to completeness or data when quantifying cut-offs etc. Chapter 12.3 provides some more details


minimum requirements, or classes of quality such as “high quality”. The latter is used related to completeness or data when quantifying cut-offs etc. Chapter 12.3 provides some more details. On the level of product comparisons, these data set quality aspects can be used to evaluate and document in how far the achieved data quality supports the conclusions and recommendations from studies and in how far the data basis of the study meets requirements regarding reporting, transparency, review, reproducibility, etc. While this chapter mainly focuses on LCI data quality, it is to be highlighted, that on the level of LCIA results and LCA studies, of course also the quality of LCIA methods (and if applied: normalisation basis and weighting set) contribute to the overall quality on that level. Of these the uncertainty of LCIA methods can generally be assumed to have the highest uncertainty


Of these the uncertainty of LCIA methods can generally be assumed to have the highest uncertainty. Before detailing the two approaches of the Overall data quality indicators and the Overall data set quality indicators, the concepts of the main data quality aspects are described in the next subchapter. 12.2 Data quality aspects (Refers to aspects of ISO 14044:2006 chapter 4.2.3.6) Representativeness and appropriateness Representativeness is a key concept in LCA with its three components of technological, geographical and time-related representativeness. Figure 27 illustrates the concepts of the quality aspects completeness and representativeness. Note that these graphics are not meant to be guidance on how to visualise achieved representativeness but only to illustrate the concept behind this. 212 This is helpful when externally communicating in a harmonised and comparable way the achieved quality of data sets and when searching for data of specific quality characteristics e.g


212 This is helpful when externally communicating in a harmonised and comparable way the achieved quality of data sets and when searching for data of specific quality characteristics e.g. in the ILCD Data Network.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Figure 27 The four quality aspects completeness and technological, geographical and timerelated representativeness; illustrative (for precision/uncertainty see Figure 28). The segments’ share of each bar indicates the contribution to the total impacts. The respective left bar depicts the (only theoretically knowable) “true” situation whereas the right bar shows the data used: For this virtual, illustrative example product system, e.g. the “Geographical representativeness” bars show that the major share of the impact is actually caused by processes located in Brazil, Argentina, Japan, Chile, China, the U.S. and so on, whereas the data that was used represents mainly the Brazilian, Japanese and the global average situation


and so on, whereas the data that was used represents mainly the Brazilian, Japanese and the global average situation. When modelling a system, the representativeness of the inventory of a data set is complemented by the appropriateness of the data set in the context of the specific system, where it is used: The representativeness of the inventory characterises in how far the inventory as a whole is depicting the functional unit(s) and/or reference flow(s) of the process or system. The appropriateness now characterises, in how far a data set in a system model represents the truly required process or product. E.g. a "Low carbon steel, XZY" production mix data set for 1995, with the geographical scope UK data set might be highly representative, but when I use this data set in my system model where I would instead need a "High carbon steel, ABC" for the year 2005 with Global average consumption mix, the data set is probably not very representative, i.e. it has limited appropriateness


it has limited appropriateness. Note that the lack of appropriateness is to be judged for the given case and usually any limited appropriateness adds to limited representativeness213. The overall achieved representativeness on system level can be assessed with expert judgement that also takes into account in how far - especially on a system level - the single contributing data sets actually lack full representativeness e.g. for the country mix they state to represent. 213 It can however happen that a process with limited representativeness is actually very appropriate: This would be if in an example only one of five relevant technology routes has been used to model a country-mix data set for a material, but in my product system I need a data set exactly for that used technology route. This is to be verified along the data set documentation. Representativeness Geographical BR AR CL JP CN US ...... ...... ...... True mix BR JP GLO ...... ...... .....


This is to be verified along the data set documentation. Representativeness Geographical BR AR CL JP CN US ...... ...... ...... True mix BR JP GLO ...... ...... ...... Data used Geographical BR AR CL JP CN US ...... ...... ...... True mix BR JP GLO ...... ...... ...... Data used BR AR CL JP CN US ...... ...... ...... True mix BR JP GLO ...... ...... ...... Data used Time-related True mix Data used ...... ...... ...... Time-related True mix Data used ...... ...... ...... Technological C True mix A B D E F Data used A C B ... B Technological C True mix A B D E F Data used A C B ... B Completeness 100 % impact CO2 emissions to air N2O emissions to air True LCI Data used SO2 emissions to air CO2 emissions to air NO3 - emissions to water N2O emissions to air SO2 emissions to air NO3 - emissions to water ... ............... ...... ............... ...... ............... ...... ............... ...... ... CO2 emissions to air N2O emissions to air ... ..


CO2 emissions to air N2O emissions to air ... ... Completeness 100 % impact CO2 emissions to air N2O emissions to air True LCI Data used SO2 emissions to air CO2 emissions to air NO3 - emissions to water N2O emissions to air SO2 emissions to air NO3 - emissions to water ... ............... ...... ............... ...... ............... ...... ............... ...... ... CO2 emissions to air N2O emissions to air ... ...


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Methodological appropriateness and consistency The choice of the method, especially when modelling whole systems' life cycles, typically strongly influences the results (e.g. attributional vs. consequential modelling, as one example). The choice of the most appropriate modelling principles and method approaches and their consistent use is hence important for the appropriateness and reproducibility of the results. Methods are hence necessarily a data quality aspect. Here it relates to the use of the most appropriate methods as identified for the three archetypal goal Situations A, B, and C plus possible adjustments of the "should" requirements within the permissible deviations, as detailed in chapter 6.5.4. Accuracy The term "accuracy" in general refers to the degree of closeness of a measured or calculated quantity to its actual (true) value


Accuracy The term "accuracy" in general refers to the degree of closeness of a measured or calculated quantity to its actual (true) value. This term includes the influence of methods and method assumptions. Accuracy in LCA hence can be used complementary to precision/uncertainty, capturing the technological, geographical and time-related representativeness as well as appropriateness and consistency of methods and their use. In a more condensed way, the 6 named data quality aspects can therefore also be shortened to accuracy, precision/uncertainty and completeness. Precision / uncertainty ISO 14044:2006 defines precision as the “measure of the variability of the data values for each data expressed (e.g. variance)”. ISO 14044:2006 does not define uncertainty, but uses the term in the sense of expressing the quantitative degree of the lack of precision, i.e. its (negative) measure, i.e. for the error


variance)”. ISO 14044:2006 does not define uncertainty, but uses the term in the sense of expressing the quantitative degree of the lack of precision, i.e. its (negative) measure, i.e. for the error. In science and practice of engineering and statistics, precision is also used synonymous with reproducibility, i.e. the degree to which further measurements or calculations done by different experts show the same results. The ISO definition relates to the statistical meaning of stochastic uncertainty (i.e. variance). The errors can be measurement errors but also choice-errors. Accuracy is here hence used complementary to the ISO usage of precision, i.e. accuracy is the combination of representativeness and methodological consistency. Note that lack of representativeness of data is a complementary issue, as not a stochastic uncertainty, but a bias. Figure 28 illustrates the concepts. Illustration of the concepts of precision (i.e. uncertainty) and accuracy (i.e


Figure 28 illustrates the concepts. Illustration of the concepts of precision (i.e. uncertainty) and accuracy (i.e. representativeness plus methodological consistency) High precision, low accuracy. The results are biased. High accuracy, low precision. The results are uncertain. Figure 28 Illustration of the concepts of precision (i.e. uncertainty) and accuracy (i.e. representativeness and methodological consistency)


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Note that the results of LCA calculations can be accurate but not precise, precise but not accurate, neither of it, or both of it. Note also that very good raw data can lead to inaccurate results if the LCI methods combine these data in an inappropriate way. Both aspects need to be addressed therefore. Variance vs. variability It is suggested to differentiate between "variance" as stochastic measure of uncertainty and "variability" to capture processes and systems that have different LCI data under different e.g. operation conditions: E.g. the LCI data of 100 km average goods transport on a country-wise averaged motorway-overland- inner-city mix with a fleet mix of currently operated EURO 0, 1, 2, 3, 4, 5 motors system of all trucks of equal or more than 7.5 t total weight truck at the average e.g. 80 % load factor may have a certain variance


80 % load factor may have a certain variance. The single data e.g. load factor, transport distance, specific emission profile of the truck on a motorway etc. has stochastic uncertainties (measurement errors) that aggregated give the data variance. If the data set is based on many measurements it can be very precise, i.e. have a low variance. The variability would refer to the situation where one uses this data set for different, specific kinds of transport situations with different load factors and specific shares of inner-city transport etc. Transport process data sets are hence very variable and the use of an average transport data set - even though it may have a low variance - cannot be used for a specific transport situation, simply as it is not appropriate due to limited technological representativeness - it lacks accuracy


That means that using this average transport data set for specific transport situations, the given variance does not capture the true error, which is due to lack of accuracy. Note that this differentiation of variance and variability of LCI data sets is not contradicting the ISO 14044:2006 definition of precision (see above), as in ISO variability is explicitly related to the (single) data values that jointly result in the variance. Completeness In addition to accuracy and precision, "completeness" of coverage of all relevant impact categories via the completeness of the inventoried flows can be understood as the third component of data quality214. Integrated view on data quality LCA results can be called valid (“of high overall data quality”) if they are both accurate, precise, AND complete. The weakest of the criteria generally weakens the overall quality of the specific case. This is reflected by the ILCD data quality indicators (see below in chapter 12.3)


The weakest of the criteria generally weakens the overall quality of the specific case. This is reflected by the ILCD data quality indicators (see below in chapter 12.3). In LCA one can hence use the term “validity” to refer to the overall quality of the data (and the results of LCA studies). Procedurally, one can effectively work towards high quality data, but first precisely identifying the technological, geographical and time-related appropriateness, i.e. what the data set should represent. Next, completeness of the related inventory in coverage all to-beincluded and relevant impact categories is aimed at. In quantifying the flows, paying attention to low variance of the values completes the approach. On a system level, the methodological appropriateness and consistency comes into play. 214 A potential overlap of completeness with precision and accuracy can be argued - data quality aspects could also be differentiated in another way


214 A potential overlap of completeness with precision and accuracy can be argued - data quality aspects could also be differentiated in another way. The given differentiation however relates to widely used terms and concepts (including those of ISO 14044, differentiating precision and completeness) and helps to better understand and address the different kinds of aspects, why they are seen to serve their purpose.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Regarding the final LCA results, this is combined with LCIA characterisation factors (and potentially normalisation and weighting sets) that contribute to the overall quality on system level. The required overall quality of the results of LCI/LCA study is determined by the intended applications and hence to be derived from the goal definition. The finally achieved quality determines in comparative studies whether differences between systems can be considered significant and robust. For data sets, the overall LCI data quality determines for which cases the data can be used


For data sets, the overall LCI data quality determines for which cases the data can be used. The overall data quality is hence important information for the evaluation and the interpretation of the results of an LCA: The degree to which the data set‟s overall representativeness, completeness, precision as well as methodological appropriateness and consistency reflects the reality the data set is representing. The quantitative precision of the inventory data is an obvious component, but structural and modelling aspects of both the LCI and – if included - the LCIA play an important and often dominating role. Data and structural gaps and modelling assumptions can lead to biases and hence all strongly affect the accuracy of the results, while they cannot be addressed directly or quantitatively in uncertainty calculation. Uncertainty estimates can therefore always only be approximate


Uncertainty estimates can therefore always only be approximate. They tend to understate not only the true uncertainty but especially do not fully capture the achieved accuracy of the results. Frequent errors: Overly reliance on stochastic data uncertainty calculations It is an increasingly found error to only consider the (known or estimated) quantitative stochastic inventory data uncertainty only and directly use this to demonstrate significance of differences in compared systems. The overall precision and accuracy however needs to also judge the other, structural components, assumptions, method appropriateness and consistency, limited representativeness of data, and the like. If only a partial analysis is done, it shall be clearly stated that the other part is lacking. In addition, it shall be clearly stated how accuracy and precision have been determined if one or both have been quantified


In addition, it shall be clearly stated how accuracy and precision have been determined if one or both have been quantified. It is argued that in practice lack of accuracy is the more relevant problem than stochastic data uncertainty, why especially a lack of addressing the former while reporting the later can be understood as an attempt of misleading the target audience. This is even more so as lack of accuracy typically introduces a bias into the results (see Figure 28). The judgement of the overall data quality can ultimately only be done by expert judgement. Uncertainty calculations and qualitative or quantified accuracy assessment can substantially help but provide supporting, quantitative information only. Working with fixed quality requirements Sometimes the completeness and precision requirements are stated explicitly for the intended application


Working with fixed quality requirements Sometimes the completeness and precision requirements are stated explicitly for the intended application. For an Environmental Product Declaration (EPD), there may thus be precisely defined quantitative requirements to the completeness and precision given by the applied EPD scheme (e.g. “At least 95 % completeness of overall environmental impact and maximum variance on inventory data lower than 10 % for Climate change and Primary energy, 25 % for Acidification, Eutrophication and Summer Smog impact potentials.”). At the same time the qualitative aspects of representativeness are to be addressed. A similar example is the three levels of completeness and precision used for classifying LCI data sets in the ILCD Data Network.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach 12.3 Data quality indicators (Refers to aspects of ISO 14044:2006 chapters 4.2.3.6 and 4.3.2.1) The ILCD data quality indicators relate directly to those key characteristics of LCI data sets that describe their quality215. These are:  technological, geographical and time-related representativeness,  completeness of environmental impacts covered by the inventory,  achieved precision of the data, and  appropriate and consistent application of LCI methodologies (the latter especially on the system level) Table 5 describes the concept of the ILCD data quality indicators / components in more detail


Table 5 Overall inventory data quality (validity) and its main 6 aspects Indicator / component Definition / Comment Chapters Technological representativeness (TeR) "Degree to which the data set reflects the true population of interest regarding technology, including for included background data sets, if any." Comment: i.e. of the technological characteristics including operating conditions. 6.8.2 Geographical representativeness (GR) "Degree to which the data set reflects the true population of interest regarding geography, including for included background data sets, if any." Comment: i.e. of the given location / site, region, country, market, continent, etc. 6.8.3 Time-related representativeness (TiR) "Degree to which the data set reflects the true population of interest regarding time / age of the data, including for included background data sets, if any." Comment: i.e. of the given year (and - if applicable – of intra-annual or intra-daily differences)


of the given year (and - if applicable – of intra-annual or intra-daily differences). 6.8.4 Completeness (C) "Share of (elementary) flows that are quantitatively included in the inventory. Note that for product and waste flows this needs to be judged on a system's level." Comment: i.e. degree of coverage of overall environmental impact, i.e. used cut-off criteria. 6.6.3 Precision / uncertainty (P) "Measure of the variability of the data values for each data expressed (e.g. low variance = high precision). Note that for product and waste flows this needs to be judged on a 6.9.2 215 This is a different approach compared to generic quality indicators that attempt at capturing data quality by proxy-indicators such as type of used data sources that are used to estimate the quality by overlaying an uncertainty factor to each proxy-indicator (e.g. age of data). The approach chosen here better reflects the casespecific relevance of the aspects: E.g


age of data). The approach chosen here better reflects the casespecific relevance of the aspects: E.g. is four years old data fully representative for technologies that change slowly with time (e.g. basic materials industry), while it would be quite outdated for most IT products.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach system's level." Comment: i.e. variance of single data values and unit process inventories. Methodological appropriateness and consistency (M) "The applied LCI methods and methodological choices (e.g. allocation, substitution, etc.) are in line with the goal and scope of the data set, especially its intended applications and decision support context. The methods also have been consistently applied across all data including for included processes, if any." Comment: i.e. correct and consistent application of the recommended LCI modelling framework and LCI method approaches for the given Situation A, B, or C. 6.5.4 Please note that the components “Completeness” and “Precision” can be quantified (e.g. “90 % completeness/cut-off criterion for overall environmental impact” and “+-10 % LCIA results for Climate change216, +-20 % for Acidification, etc.”)


“90 % completeness/cut-off criterion for overall environmental impact” and “+-10 % LCIA results for Climate change216, +-20 % for Acidification, etc.”). The other components are of a qualitative nature and the achieved quality is to be judged semi-quantitatively by experts e.g. during a critical review. The following quality levels of Table 6 and definitions of Table 7 should be used for documenting what has been achieved for the final data and for each of the data quality indicators: Table 6 Quality levels and quality rating for the data quality indicators, and the corresponding definition (for the three representativeness and the methodological appropriateness and consistency criteria) and quantitative completeness and precision / uncertainty ranges in %. Quality level Quality rating Definition Completeness overall environmental impact Precision / uncertainty overall env


Quality level Quality rating Definition Completeness overall environmental impact Precision / uncertainty overall env. impact (relative standard deviation in %)217 Very good "Meets the criterion to a very high degree, having or no relevant need for improvement. This is to be judged in view of the criterion's contribution to the data set's potential overall environmental impact and in comparison to a hypothetical ideal data quality."  95 %  7 % 216 This percentage refers to the stochastic uncertainty of the inventory values only excluding the uncertainty of the LCIA characterisation factors. 217 This does exclude the uncertainty of the LCIA method, the normalisation basis, and the weighting set but only of the LCI results, however in view of the overall environmental impact. For log-normally distributed results, the confidence intervals shall be used that are obtained with the percentages given in the table and under normal distribution.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Good "Meets the criterion to a high degree, having little yet significant need for improvement. This is to be judged in view of the criterion's contribution to the data set's potential overall environmental impact and in comparison to a hypothetical ideal data quality." [85 % to 95 %) (7 % to 10 %] Fair "Meets the criterion to a still sufficient degree, while having the need for improvement. This is to be judged in view of the criterion's contribution to the data set's potential overall environmental impact and in comparison to a hypothetical ideal data quality. " [75 % to 85 %) (10 % to 15 %] Poor "Does not meet the criterion to a sufficient degree, having the need for relevant improvement


" [75 % to 85 %) (10 % to 15 %] Poor "Does not meet the criterion to a sufficient degree, having the need for relevant improvement. This is to be judged in view of the criterion's contribution to the data set's potential overall environmental impact and in comparison to a hypothetical ideal data quality." [50 % to 75 %) (15 % to 25 %] Very poor "Does not at all meet the criterion, having the need for very substantial improvement. This is to be judged in view of the criterion's contribution to the data set's potential overall environmental impact and in comparison to a hypothetical ideal data quality."  50 %  25 % Additional options, not being quality levels: Not evaluated / unknown "This criterion was not judged / reviewed or its quality could not be verified / is unknown." na na Not applicable "This criterion is not applicable to this data set, e.g. its geographical representativeness cannot be evaluated as it is a location-unspecific technology unit process." na na


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach By this way of classifying the achieved overall quality and its components of the developed e.g. unit process or LCI result data set, a structured communication and identification (e.g. sorting/filtering of suitable data e.g. in the ILCD Data Network) is supported. Overall data quality and three data quality levels for LCI data sets In addition to the more differentiated quality levels, for orientation it is useful to label data sets with different levels of overall LCI data quality. The overall quality of the data set can be derived form the quality rating of the various quality indicators / components. As said earlier, the weakest of the quality indicators generally weakens the overall quality of the data set. The overall data quality shall be calculated by summing up the achieved quality rating for each of the quality components


The overall data quality shall be calculated by summing up the achieved quality rating for each of the quality components. The rating of the weakest quality level is counted 5-fold. The sum is divided by the number of applicable quality components plus 4. The Data Quality Rating result is used to identify the corresponding quality level in Table 7. Formula 3 provides the calculation provision: Formula 3 * i X M P C TiR GR TeR DQR w  DQR : Data Quality Rating of the LCI data set; see Table 7  TeR, GR, TiR, C, P, M : see Table 5  Xw : weakest quality level obtained (i.e. highest numeric value) among the data quality indicators  i : number of applicable (i.e. not equal "0") data quality indicators Table 7 Overall quality level of a data set according to the achieved overall data quality rating Overall data quality rating (DQR) Overall data quality level  1.6218 "High quality" >1.6 to 3 "Basic quality" >3 to 4 "Data estimate" See Table 8 and the text below for an example


Table 8 Illustrative example for determining the data quality rating. Illustrated with a location unspecific technology data set (e.g. a diesel electricity generator for a construction site and of a given emission standard) Component Achieved quality level Corresponding quality rating Technological representativeness (TeR) Very good 218 This means that not all quality indicator need to be "very good", but two can be only "good". If more than two are only good, the data set is downgraded to the next quality class.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Geographical representativeness (GR) Not applicable219 Time-related representativeness (TiR) Fair Completeness (C) Good Precision / uncertainty (P) Fair Methodological appropriateness and consistency (M) Good For the example given in Table 8, the overall data quality rating is calculated as: DQR = (TeR+GR+TiR+C+P220+M+3*4) / (5221+4) = (1+0+3+2+3+2+3*4) / 9 = 2.56. Table 7 helps to identify the corresponding overall data quality level "Basic quality" for the overall data quality rating of that virtual example data set. Accuracy, precision and completeness of LCI data, LCIA results and LCA studies including normalisation and weighting Accuracy, precision and completeness of LCI data should be assessed on the system level


This in addition needs to be done in view of the respective LCIA results, per impact category, but disregarding the (additional) uncertainties and limited accuracy of the characterisation factors (and any eventually applied normalisation and weighting factors) as the focus here is on the requirements to the inventory data. Accuracy, precision and completeness of LCIA results would than include also the uncertainty and limited accuracy of the LCIA factors. For LCA studies including normalisation, the respective uncertainty and limited accuracy would be additionally included. In contrast, for the weighting step (same as for methodological choices and other assumptions), an uncertainty calculation is potentially less suitable. Scenario analysis should better suit to capture the additional lack of robustness any specific weighting method introduces


Scenario analysis should better suit to capture the additional lack of robustness any specific weighting method introduces. 12.4 ILCD Handbook compliance criteria (Refers to aspects of ISO 14044:2006 chapters 4.2.3.6 and 4.3.2.1) Overview For structuring the approach of developing ILCD Handbook compliant data and studies as well as product-specific guidance documents or Product Category Rules (PCRs), the ILCD 219 Not applicable as location unspecific technology data set. 220 The second occurrence of the lowest level "fair". In the calculation the lowest level rating is multiplied only once with "5", here for TiR. 221 As "Geographical representativeness" is not applicable here, only five of the otherwise up to six indicators / components are counted.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach compliance is composed of five groups of aspects: Data quality, Method, Nomenclature, Review, and Documentation222. These aspects shall also be used when referring only to selected of the ILCD compliance criteria and reporting this partial compliance in a structured way, e.g. when documenting LCI data sets, using the ILCD reference data set format. The requirements for claiming ILCD compliance for data sets and studies are found in chapter 2.3. Note that exclusively the "Data quality" compliance is further differentiated by different levels of achieved data quality. The other compliance criteria can only either have been achieved or not; there is not further differentiation


The other compliance criteria can only either have been achieved or not; there is not further differentiation. Logic of compliance criteria structure The structure of the ILCD compliance criteria applies the following logic:  Items that directly relate to the inventory data and impact assessment results data are grouped under “Data quality”. These were addressed in the preceding chapter 12.3.  “Method” groups all issues around the appropriateness of applied methods and the consistency of their use. This can be assessed without having relevant interrelationships to the underlying data. Note however, that method consistency is necessarily also part of the “Data quality”, e.g. technological representativeness means something different under attributional and consequential modelling and consistent use of the methods hence affects the overall achieved representativeness especially of LCI results data


 “Nomenclature” is an issue that predominantly relates to the used naming and structuring of elementary flows and other named elements. This ensure that different practitioners can at all consistently work with the data (e.g. that the elementary flow Carbon dioxide is clearly identified by name, CAS number, measured always in the same unit etc.) and that the LCI data can be correctly linked with the LCIA factors. Correct and consistent use of LCA terminology is a second component under “Nomenclature”.  “Review” captures all review aspects.  “Documentation” finally captures several issues: the extent and detail of the documentation as key requirement to support transparency and to ensure that the results can be reproduced. At the same time the documentation is important for the LCA practitioner to know what the data set inventory actually represents and whether it is the appropriate data for his/her systems


At the same time the documentation is important for the LCA practitioner to know what the data set inventory actually represents and whether it is the appropriate data for his/her systems. The form (report, data set) and format (ILCD reference format, ILCD report template etc) completes the documentation information, making sure that the documented information can be electronically exchanged without loss of information etc. Note that the exact coverage of items under each aspect and component depends on the type of LCI/LCA study. E.g. will an unit process LCI data set not include certain aspects that relate exclusively to (product) system modelling, etc. Table 9 gives more details on the compliance criteria. 222 Following the same logic of this set of 5 compliance aspects, also the overall quality of LCIA methods can be described and assessed. More detailed provisions for this are still to be developed.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach Table 9 ILCD compliance of LCI and LCA studies and data sets, direct applications, and derived more specific guidance documents / Product Category Rules (PCR). Compliance aspects, components, brief description and main corresponding chapters (indicative). Aspect Components Description / Comment Main chapters Quality Completeness Details see Table 5, Table 6, and Table 7. Chapter 12.3 Technological representativeness Geographical representativeness Time-related representativeness Precision / uncertainty Methodological appropriateness223 and consistency Method Application of LCI modelling and method provisions of this document Adhering to the provisions for the selection and LCI modelling of the applicable goal situation A, B, or C. Chapter 6.5.4, and referenced chapters


Chapter 6.5.4, and referenced chapters. Application of other method provisions of this document Adhering to the other method provisions of this document. Other chapters with method provisions. Nomenclatu re Correctness and consistency of applied nomenclature Appropriate naming of flows and processes, consistent use of ILCD reference elementary flows, appropriate and consistent use of units, etc. Chapter 7.4.3 and separate document "Nomenclature and other conventions". Correctness and consistency of applied terminology Correct and consistent use of technical terms (LCA and other domains). Key terms of chapter 3, "terms and concepts" boxes throughout the document, and application of the separate terminology. Review Appropriateness of applied review type Selection of the applicable review type. Chapter 11 and separate document "Review schemes for Life Cycle Assessment 223 See text for reason to include “method...” in both data quality and as separate item “Method”


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 12 Annex A: Data quality concept and approach (LCA)". Correctness of applied review scope Correct scope of what is reviewed. Separate document on "Review scope, methods, and documentation". Correctness of applied review methods Correct methods of how to review each of the items within the review scope. Separate document on "Review scope, methods, and documentation". Correctness of the review documentation224 Correct scope, form and extent of what is documented about the final outcome of the review. Separate document on "Review scope, methods, and documentation". Documentat ion Appropriateness of documentation extent Appropriate coverage of what is reported / documented. Chapter 10. Appropriateness of form of documentation Selection of the applicable form(s) of reporting / documentation. Chapter 10.3


Chapter 10. Appropriateness of form of documentation Selection of the applicable form(s) of reporting / documentation. Chapter 10.3. Appropriateness of documentation format Selection and correct use of the data set format or report template, plus review documentation requirements. See separate ILCD data set format and LCA report template (separately available files). 224 The documentation of the review findings belongs to the "Review" part, since it does not relate to the documentation of the object of the data set.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation 13 Annex B: Calculation of CO2 emissions from land transformation Many aspects influence emissions form land transformations. Their combinations result in the native soil carbon stock, varied by three further influence factors:  Native soil carbon stock (factors climate region and soil type (Table 10)),  land use factor (land use type, temperature regime, and moisture regime (Table 11)), and  management factor (specific land management for cropland and for grassland (Table 12 and Table 13)), and the related  input level factor (in variation of the above named land management types, in the same tables). These aspects and resulting factors are derived from the most recent available related IPCC reports and are included in the tables below


These aspects and resulting factors are derived from the most recent available related IPCC reports and are included in the tables below. CO2 emissions from any land transformation can be easily calculated by calculating the difference of the steady-state soil carbon content between the land use before and after transformation. This number is then to be multiplied by 44/12 to convert C-losses stoichiometrically to CO2 emissions. The steadystate carbon stock of each land use is calculated by simple multiplication of its basic soil carbon stock with the loss factors. Formula 4 and Formula 5 serve to calculate the soil organic carbon stock of the initial and final land use. Formula 6 provides the final prescription


Formula 4 and Formula 5 serve to calculate the soil organic carbon stock of the initial and final land use. Formula 6 provides the final prescription. Formula 4 * * * IL LMF LUF SOCn SOCi with  SOCi = Initial soil organic carbon stock of initial land use "1", given in [t/ha]  SOCn = Native soil organic carbon stock (climate region, soil type); Table 10, given in [t/ha]  LUF = Land use factor; Table 11, dimensionless  LMF = Land management factor; Table 12 and Table 13, dimensionless  IL = Input level factor; also Table 12 and Table 13, dimensionless Formula 5 * * * IL LMF LUF SOCn SOCf with  SOCf = Final soil organic carbon stock of land use "2", i.e. after transformation, given in [t/ha] Formula 6 * ) ( SOCf SOCi CO with


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation CO2 = resulting CO2 emissions from soil (given in [t/ha]) as the difference in soil carbon stocks multiplied by the atomic weight of CO2 and divided by the atomic weight of C. Note that this is the total amount of CO2 that has to be allocated to the individual crops and/or crop years after conversion, as detailed in chapter 7.4.4.1. At the end of the tables some example calculations are given


At the end of the tables some example calculations are given. Table 10 Native soil carbon stocks under native vegetation (tonnes C ha-1 in upper 30 cm of soil) (IPCC 2006) Climate Region High activity clay soils Low activity clay soils Sandy soils Spodic soils Volcanic soils Wetland soils Boreal NA Cold temperate, dry NA Cold temperate, moist Warm temperate, dry NA Warm temperate, moist NA Tropical, dry NA Tropical, moist NA Tropical, wet NA Tropical montane NA Table 11 Land use factors (IPCC 2006) Land-use Temperature regime Moisture regime Land use factors (IPCC default) Error (±)225 Long-term cultivated Temperate/Boreal Dry 0.80 9 % Moist 0.69 12 % Tropical Dry 0.58 61 % Moist/Wet 0.48 46 % Tropical montane n/a 0.64 50 % 225 Error = two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis a default, a value based on expert judgement (40 %, 50%, or 90%) is used as a measure of the error


NA denotes „Not Applicable‟, for factor values that constitute reference values or nominal practices for the input or management classes. This error range does not include potential systematic error due to small sample sizes that may not be representative of the true impact for all regions of the world.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation Permanent grassland All 1.00 Paddy rice All Dry and Moist/Wet 1.10 50 % Perennial/Tree Crop All 1.00 50 % Set-aside (< 20 yrs) Temperate/Boreal and Tropical Dry 0.93 11 % Moist/Wet 0.82 17 % Tropical montane n/a 0.88 90 % Table 12 Land management and input level factors for cropland (IPCC 2006) Land management (for cultivated land only) Land-use management Temperature regime Moisture regime Land management and input level factors (IPCC defaults) Error (±)225 Full tillage All Dry and Moist/Wet 1.00 NA Reduced tillage Temperate/Boreal Dry 1.02 6 % Moist 1.08 5 % Tropical Dry 1.09 9 % Moist/Wet 1.15 8 % Tropical montane n/a 1.09 50 % No tillage Temperate/Boreal Dry 1.10 5 % Moist 1.15 4 % Tropical Dry 1.17 8 % Moist/Wet 1.22 7 % Tropical montane n/a 1.16 50 % Input level (for cultivated land only) Low input Temperate/Boreal Dry 0.95 13


Temperate/Boreal Dry 1.10 5 % Moist 1.15 4 % Tropical Dry 1.17 8 % Moist/Wet 1.22 7 % Tropical montane n/a 1.16 50 % Input level (for cultivated land only) Low input Temperate/Boreal Dry 0.95 13 % Moist 0.92 14 % Tropical Dry 0.95 13 % Moist/Wet 0.92 14 % Tropical montane n/a 0.94 50 % Medium input All Dry and 1.00 NA


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation Moist/Wet High input without manure Temperate/Boreal and Tropical Dry 1.04 13 % Moist/Wet 1.11 10 % Tropical montane n/a 1.08 50 % High input with manure Temperate/Boreal and Tropical Dry 1.37 12 % Moist/Wet 1.44 13 % Tropical montane n/a 1.41 50 % Table 13 Land management and input level factors for grassland (IPCC 2006) Land management (for grassland only) Land-use management Temperature regime Land management and input level factors (IPCC defaults) Error (±)225 Nominally managed (non-degraded) All 1.00 NA Moderately degraded Temperate/Boreal 0.95 13 % Tropical 0.97 11 % Tropical Montane 0.96 40 % Severely degraded All 0.70 40 % Improved grassland Temperate/Boreal 1.14 11 % Tropical 1.17 9 % Tropical Montane 1.16 40 % Input level (for improved grass land only) Medium All 1.00 NA High All 1.11 7 % In order to calculate the annual


grassland Temperate/Boreal 1.14 11 % Tropical 1.17 9 % Tropical Montane 1.16 40 % Input level (for improved grass land only) Medium All 1.00 NA High All 1.11 7 % In order to calculate the annual changes in carbon stocks due to land-use change, please refer to the following three illustrative examples226: Example 1: Transformation of "set-aside land" in the UK for "annual crop production" Aspects:  Climate Region of UK: Cold temperature 226 Note: The climate regions, soil types, temperature and moisture regimes, as well and the land use and management adopted in all these examples is for illustrative purposes only.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation Moisture Regime of UK: Moist  Soil type (typical, average, or specific, e.g


this might be): High activity clay soils --> SOCn = 95 t/ha (Table 10)  Land use 1 (before transformation): Set-aside land (< 20 yrs) --> LUF1 = 0.82 (Table 11)  Land use 2 (after transformation): Long-term cultivated crop land --> LUF2 = 0.69 (Table 11)  Land management of land use 1: none (as land use is "set-aside land") --> LMF1 = 1227  Input factor land use 1: none (as land use is "set-aside land") --> IF1 = 1  Land management of land use 2: Full tillage --> LUF2 = 1.00 (Table 12)  Input factor land use 2: High input without manure --> IF2 = 1.11 (Table 12) Factors from the tables and calculations:  Original carbon stock of land use 1= 95 * 0.82 * 1 * 1 = 77.9 tonnes of Carbon per ha  Final carbon stock of land use 2= 95 * 0.69 * 1.00 * 1.11 = 72.8 tonnes of Carbon per ha  Loss in carbon stock = 5.1 tonnes of Carbon per ha Resulting annual CO2 emissions to be attributed to that "annual crop" over the applicable entire time period of use (20 years) = 5.1 * 44 / 12 = 18.7


in carbon stock = 5.1 tonnes of Carbon per ha Resulting annual CO2 emissions to be attributed to that "annual crop" over the applicable entire time period of use (20 years) = 5.1 * 44 / 12 = 18.7 tonnes of CO2 emissions per ha228,229


Example 2: Transformation of forest in Indonesia for annual crop production  Climate Region of Indonesia: Tropical  Moisture Regime of Indonesia: wet  Soil type: Volcanic  Land use 1: Native  Land use 2: Long-term cultivated  Land management and input level of land use 1: none 227 For no use of the land (i.e. fallow, natural forest, etc.), the land management factor and the input factor are both always = 1; these values are not given in the table that only lists factors for managed land (i.e. cropland and grassland). 228 The numbers are given per ha (10,000 m2) and need to be converted to the e.g. kg of harvested crop. 229 These numbers are of course to be complemented with other GHG etc. emissions from machine operation, fertiliser production, etc.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 13 Annex B: Calculation of CO2 emissions from land transformation Land management and input level of land use 2: Reduced tillage, low input  Original carbon stock of land use 1= 130 * 1.00 * 1 * 1 = 130 tonnes of Carbon per ha  Final carbon stock of land use 2 = 130 * 0.48 * 1.15 * 0.92 = 66.0 tonnes of Carbon per ha  Loss in carbon stock = 64.0 tonnes of Carbon per ha230 Resulting annual CO2 emissions to be attributed to that "annual crop" over the applicable entire time period of use (20 years) = 64 * 44 / 12 = 234.67 tonnes of CO2 emissions per ha


Example 3: Transformation of grassland in Canada for annual crop production  Climate Region of Canada: Cold temperate  Moisture Regime of Canada: dry  Soil type: Sandy soils  Land use 1: Permanent grassland  Land use 2: Long-term cultivated  Land management and input level of land use 1: Nominally managed (non-degraded), medium input  Land management and input level of land use 2: Full tillage, high input with manure  Original carbon stock of land use 1 = 34 * 1.00 * 1.00 * 1.00= 34 tonnes of Carbon per ha  Final carbon stock of land use 2 = 34 * 0.80 * 1.00 * 1.37 = 37.3 tonnes of Carbon per ha  Loss in carbon stock = -3.3231 tonnes of Carbon per ha Resulting annual CO2 emissions to be attributed to that "annual crop" over the applicable entire time period of use (20 years) = -3.3 * 44 / 12 = -12.1 tonnes of CO2 emissions per ha, i.e. 12.1 tonnes of CO2 accumulation / binding as soil organic carbon


12.1 tonnes of CO2 accumulation / binding as soil organic carbon. This last example illustrates a land transformation that results in net carbon storage in the soil. Please note that, even though this crop is credited for sequestering Carbon dioxide from the atmosphere to the soil, the temporary nature of this storage may need to be considered in the results interpretation. 230 Note that the Carbon bound in the biomass (i.e. trees) of the natural tropical forest is several times higher. 231 Negative loss, i.e. an accumulation


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery 14 Annex C: Modelling reuse, recycling, and energy recovery (Refers to ISO 14044:2006 chapter 4.3.4.3) 14.1 Introduction and overview (Refers to ISO 14044:2006 chapter 4.3.4.3) Note that this chapter refers to ruse, recycling and recovery from the perspective of the tobe-recycled end-of-life product or waste, i.e. the system that generates it, not from the perspective of a multifunctional recycling or reuse system (e.g. a mixed waste incineration plant). For solving multifunctionality of such multi-waste treatment / recycling processes see chapter 7.2.4.6 for consequential modelling and chapter 7.9 for attributional modelling


For solving multifunctionality of such multi-waste treatment / recycling processes see chapter 7.2.4.6 for consequential modelling and chapter 7.9 for attributional modelling. Terminology “reuse/recycle/recover” and “secondary good” in LCA-context Through the processing of waste and end-of-life products secondary materials, energy resources, parts and complex goods are regained in a form, which allows to use them in subsequent products. There they can replace primary production of the same or another material, energy form, part, or product. Note also that this always involves some form of processing (and be it only the cleaning of refillable bottles or the in-house storage and transport as in case of internal recycling of e.g. polymer production waste). Terms and concepts: Reuse/recycling/recovery and secondary good Methodologically, all the different forms of e.g. reuse, recycling, and recovery of energy are equivalent in LCA. This covers e.g


Terms and concepts: Reuse/recycling/recovery and secondary good Methodologically, all the different forms of e.g. reuse, recycling, and recovery of energy are equivalent in LCA. This covers e.g. reprocessing of production waste, regeneration of nuclear fuels, restoration of buildings, reclaiming or recovering energy, reusing and further using of parts or goods, refitting of parts for other goods, repair, rehash, etc. To ease reading, all these forms are referred to as “reusing/recycling/recovery” in this document, unless specifically differentiated. A common cover term could not be identified and the most widely understood term "recycling" was found incorrect as being too narrow. Note that the terms used here do not imply any legal meaning but relate exclusively to the use in LCA methodology. The product of these processes i.e. the recycled material, recovered energy, or reused or further used part or good etc. is generally referred to as the “secondary good” throughout the text


The product of these processes i.e. the recycled material, recovered energy, or reused or further used part or good etc. is generally referred to as the “secondary good” throughout the text. The terms closed-loop and open-loop recycling (including two sub-types “open loop – same primary route” and “open loop – different primary route”) are detailed in the subchapters 14.3.2.1 and 14.3.2.2. Recycling and multifunctionality Recycling is methodologically a case of multifunctionality, with the product to be recycled having two functions: firstly the function(s) the product is primarily made for and secondly the function of providing secondary resources for use in subsequent life cycles / systems. This fully applies not only to end-of-life products but to all types of waste, as long as any valuable products are recycled from the waste.


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery Frequent errors: Omission or double counting/modelling of recycling An error that still in some cases of LCI and LCA studies can be seen is the omission or double counting of recycling. Care must be taken to ensure consistency in modelling and background data, avoiding e.g. that in case background already considers recycling, the recycling is modelling twice, respectively in case it is not included in the data that it is correctly modelled once. Dispute over the correct way how to model recycling The correct way how to model recycling has been extensively discussed over the past two decades. Many approaches have been suggested. These range from simple cut-offs, i.e


Many approaches have been suggested. These range from simple cut-offs, i.e. assigning all waste management burdens and benefits of having a valuable secondary good to the second system, to a wide range of combinations of how the primary production, the waste pre-treatment, recycling steps and waste land-filling are to be shared between the first and second life cycle (and directly or indirectly the subsequent life cycles). Some of these approaches are more closely derived along the ISO hierarchy. Some (including some that have been developed in pre-ISO times) look at the justice of allocation, trying to provide incentives for an increased use of secondary goods and increased recyclability via the allocation / substitution procedures. It can also be observed that most of the discussions on how to model recycling are in fact discussions on whether to use attributional or consequential modelling in the first place


It can also be observed that most of the discussions on how to model recycling are in fact discussions on whether to use attributional or consequential modelling in the first place. Others relate to the question whether the ISO hierarchy should be generally followed or whether the way how recycling is modelled should be derived from the goal of improving the situation (i.e. to implement incentives that award the use of secondary goods respectively improved recyclability of products). ILCD guidance: Goal-oriented application of the ISO hierarchy It is argued here that the appropriate LCI modelling provisions are to be derived by applying the ISO hierarchy based on the decision-context of the goal of the LCI/LCA study. There is no free choice but the goal limits the options


There is no free choice but the goal limits the options. However, it will also be discussed whether the ILCD approach to recycling provides the appropriate incentives to improve the situation regarding increased use of secondary goods and improved recyclability of products, as for the given case indicated. Terms and concepts: Recycling in ISO 14044:2006 ISO 14044:2006 states that the allocation hierarchy applies also to recycling situations. It is clarified that in cases of recycling the drawing of the system boundary (between the first and subsequent life cycles) needs special attention and justification. In addition (and implicitly referring to those cases where substitution is to be applied) any change in the inherent properties of the secondary good must be taken into account. As allocation criteria (implicitly referring to cases of attributional modelling and where allocation is to be applied) the following ones should be used: Physical properties (e.g


As allocation criteria (implicitly referring to cases of attributional modelling and where allocation is to be applied) the following ones should be used: Physical properties (e.g. mass), economic value (price ratio secondary good to primary production), the number of subsequent uses of the secondary good. Attributional modelling of recycling From the perspective of attributional LCI modelling it is appropriate to assign to both the system that generates the waste or end-of-life product and to the one that uses the secondary good the corresponding share of the inventory (e.g. emissions, consumables etc.).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery Important is that the allocation is done - strictly spoken - not between the first and second life cycle, but between the two co-functions that the reused, recycled or recovered good performs once for the primary product and ones for further products as the secondary good. Note that as a preceding step the true joint process (see Figure 29) needs to be identified for all cases. Consequential modelling of recycling From the perspective of consequential modelling, the modelling is to reflect the consequences of the recycling. This implies that it has to motivate – to the most appropriate degree – both recycling (both quantitatively and qualitatively) and the use of the secondary good (again both quantitatively and qualitatively, e.g. in high value applications, substituting high value primary production)


in high value applications, substituting high value primary production). In the case of consequential modelling, the superseded mix of processes is to be determined and their avoided production is credited. This is detailed in chapter 14.5. Note that also for consequential modelling of reuse/recycling/recovery the true joint process (see Figure 29) needs to be identified. Before developing the guidance for how to model recycling in line with the goal and scope of the LCI/LCA study, the two main different recycling situations (“closed loop” and “open loop”) will be explained. A sub-case of open loop recycling (“same primary route”) is introduced. 14.2 True joint process and true co-product True joint process and co-product - consequential modelling The true joint process of the generated waste or end-of-life product is that process earlier in the life cycle of the analysed system, where the function (e.g


a primary aluminium bar) is technically approximately equivalent to the secondary good produced from the waste or endof-life product (e.g. an aluminium bar produced from aluminium scrap). I.e. in this example, the primary aluminium bar would be the true joint product of the secondary aluminium bar. That means that first the true joint process has to be identified that is understood to produce both the primary and secondary good. Figure 29 illustrates the principle: the true joint process is the process step "M1" that produces a technically about equivalent good "Xj" to the secondary good "Xc" that has been obtained via recycling. The same principle applied for production waste that is recycled. Figure 29 True joint process (M1) and true co-product (Xj) for the secondary good (Xc) obtained from recycling of an end-of-life product, under consequential modelling; schematic. Under attributional modelling, Xj is the co-product of Xa, if the latter has a positive market value, i.e


Under attributional modelling, Xj is the co-product of Xa, if the latter has a positive market value, i.e. is a valuable product. For cases of "open loop - different primary route" recycling (for the concept see chapter 14.3.2.2) the true joint process and co-product is slightly more difficult to be identified: this is, Extraction Part production & Assembly Use Disposal Recycling M1 M2 Material production Xa Xc Xj


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery since the secondary is not of the same type of good as the primary good from which is derived. An example: Heavily soiled postconsumer paper packaging waste is incinerated an electricity produced. Which is the true joint process, as electricity has not been an interim production step from wood to the paper? In such cases, the process that produces a product with the minimum required functional characteristics that would be equivalent to the secondary good (e.g. electricity, as in the above example) should be considered the true joint process. This can mean to go back to the initial resource extraction, i.e. ignoring all further processing steps (except for the transport to the location where the waste is e.g. incinerated, as in this example). Here this could be e.g


ignoring all further processing steps (except for the transport to the location where the waste is e.g. incinerated, as in this example). Here this could be e.g. the round-wood logs delivered to the paper mill, which could be found to be the true joint product and process for the electricity. The logic is the same as before, i.e. to exclude all earlier processing steps that are not required towards obtaining an technical equivalent to the secondary good (here: electricity). In this example these would be all preceding manufacturing steps of the wood including fibre production, papermaking, paper use etc. These are exclusively required for the product of the first life cycle and hence entirely attributed to it; same as all the initial waste treatment steps of the negatively valued waste that are attributed to the first life cycle. However, the production of the e.g. round wood is the basis for both the first life cycle and for the second and further life cycles


However, the production of the e.g. round wood is the basis for both the first life cycle and for the second and further life cycles. True joint process and co-product - attributional modelling The principle for this step is the same as under consequential modelling, with the difference that the final secondary good after recycling is not the co-product for which the true joint process and true co-product are identified: Under attributional modelling, this coproduct is the waste or end-of-life product as it is generated, if its market price is positive ("Xa" in Figure 29). Otherwise, if this market price is negative, the co-product is that valuable good that is directly produced by the process step that is located at the boundary between the first and second life cycle (see Figure 33). I.e. in contrast to consequential modelling, the further steps of recycling etc


I.e. in contrast to consequential modelling, the further steps of recycling etc. are not modelled, but at a maximum the initial treatment steps towards the first valuable product with at least minimum positive market value. Once the true joint process has been identified, the attributional and consequential modelling provisions are applied, as required. For attributional modelling that means that the two-step allocation guidance is applied as for all multifunctional processes. This is detailed in chapter 14.4.1, applying this general approach to waste and end-of-life product reuse, recycling, and recovery


This is detailed in chapter 14.4.1, applying this general approach to waste and end-of-life product reuse, recycling, and recovery. 14.3 Concepts: Closed-loop and open-loop recycling (Refers to aspect of ISO 14044:2006 chapter 4.3.4.3) 14.3.1 Closed-loop recycling (Refers to aspect of ISO 14044:2006 chapter 4.3.4.3) The simplest form of recycling is closed-loop recycling: the secondary good is shunted back to an earlier process in the same system where it directly replaces ("supersedes") input from primary production of the same e.g. material. An example is the recycling and re-melting of runners from a flow injection moulding process, where the recycled high-density polyethylene directly replaces virgin high-density polyethylene in the inlet to the process (“internal production waste recycling”). Another example is the use of refillable 5 l aluminium kegs for packaging of beer


Another example is the use of refillable 5 l aluminium kegs for packaging of beer. The consumer pays a deposit, which ensures that a high percentage of the kegs are returned for refill where they supersede an input of newly produced kegs (“reuse”).


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery Schematically closed-loop recycling is shown in Figure 30. Figure 30 "Closed loop" recycling (schematic): The recycled material, recovered energy, or reused part/product is entering again the supply-chain replacing the input of newly produced materials, energy or parts. In the cases of process-internal recycling this can even happen without any environmentally relevant recycling process (see internal loop leaving and reentering the “Material production” process). There is one variant of recycling that is sometimes interpreted as closed-loop recycling, while it is in fact a form of open-loop recycling: the secondary good is used within the same system, but it is substantially changed during recycling. A prominent example is the incineration of e.g. post-consumer plastic waste with energy recovery in form of e.g. electricity


A prominent example is the incineration of e.g. post-consumer plastic waste with energy recovery in form of e.g. electricity. Even though the analysed system may also use electricity and the recovered energy in form of electricity may be modelled to replace this electricity, the secondary good (i.e. electricity) is a very different product than the original material (i.e. polymer), why such cases belong to “open-loop recycling”. It is to be noted that sometimes it is difficult to differentiate between closed-loop recycling and open-loop recycling: e.g. in the 5 l aluminium keg example the keg-refilling plant will have seen some minor modifications at the time when the used kegs are returned for another refill. Or beer of another producer is filled into the keg, hence it is not resulting in the same product. It is however providing again the same functional unit, why this is easier to be understood as closed-loop recycling from the perspective of the keg


It is however providing again the same functional unit, why this is easier to be understood as closed-loop recycling from the perspective of the keg. In the injection moulding example, the machine may produce some other kind of polyethylene parts, i.e. formally another system. From LCA perspective it can be argued that what matters most, is that the secondary good is providing again the same functional unit, independently whether it is used in the same or another product. I.e. as long as the secondary good is not changing its inherent technical properties and provides the same functional unit, closed-loop recycling best captures the situation. However, to ensure robustness and plausibility of results, as well as applicability in daily practice, there is hence a need for a coherent treatment of closedloop and open-loop cases in any case


However, to ensure robustness and plausibility of results, as well as applicability in daily practice, there is hence a need for a coherent treatment of closedloop and open-loop cases in any case. 14.3.2 Open-loop recycling (Refers to aspect of ISO 14044:2006 chapter 4.3.4.3) A more complex and more common form of recycling is the open-loop recycling, where at least a share of the secondary good is used in different systems. Open-loop recycling is frequent for recyclable materials that often are recycled to the same type of material, but are used for at least somewhat different products (e.g. recycled steel from a soft drink can is used to produce a beer can). Two variants should be differentiated: “Open loop - same primary route” (in ISO 14044:2006 described as "open-loop product system where no changes occur in the inherent properties of the recycled material") and “Open loop – different primary route” (in ISO 14044:2006 "open-loop product systems where the material ..


Extraction Part production & Assembly Use Disposal Recycling, refurbishing ... Material production


ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance First edition 14 Annex C: Modelling reuse, recycling, and energy recovery undergoes a change to its inherent properties"). These imply a somewhat different modelling of the recycling inventory: 14.3.2.1 Open loop - same primary route (Refers to aspect of ISO 14044:2006 chapter 4.3.4.3) The case of closed-loop recycling, in the stricter sense, is not very common as discussed above. From the perspective of the materiality and the potential replacement of primary production, as modelled in consequential modelling, there is however no strict necessity that the secondary good is used for the same product. Important is, that it is replacing the same primary production route. To support this differentiation, a respective sub-type of open-loop recycling is used here that stands between closed-loop and open-loop recycling: “closed loop - same primary route” recycling


To support this differentiation, a respective sub-type of open-loop recycling is used here that stands between closed-loop and open-loop recycling: “closed loop - same primary route” recycling. An example: if steel cans are recycled to steel cans, this would be closed-loop recycling. If steel cans are recycled to tailored blanks for cars, this would be open-loop recycling. But if both the steel cans and the tailored blanks need the same steel basis they are identical regarding their primary route. This would also be applicable if the secondary good would be degraded during the recycling process, as often for e.g. for recycled polymers. Important is hence only that the secondary good effectively substitutes the same primary route, also if it does not replace the same but a lower amount. This situation is therefore called “open loop - same primary route”. Figure 31 illustrates this schematically