Guide for interpreting life cycle assessment result.
Date: 2016-12-22
Institutions: Joint Research Centre
Subjects: data processing, durable goods, ecology, environmental policy, environmental research, product life, research report, user guide

for 
Guide 
assessment result  

interpreting 

life  cycle 

Zampori  L.,  Saouter  E.,  Schau  E.,  Cristobal 
J., Castellani V., Sala S. 

2016  

EUR 28266 EN 

 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and 
knowledge service. It aims to provide evidence-based scientific support to the European policy-making process. 
The scientific output expressed does not imply a policy position of the European Commission. Neither the 
European Commission nor any person acting on behalf of the Commission is responsible for the use which might 
be made of this publication. 

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JRC104415 

EUR 28266 EN 

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ISBN 979-92-79-64114-5 

ISSN 1831-9424 

doi:10.2788/171315 

Luxembourg: Publications Office of the European Union, 2016 

© European Union, 2016 

The  reuse  of  the  document  is  authorised,  provided  the  source  is  acknowledged  and  the  original  meaning  or 
message of the texts are not distorted. The European Commission shall not be held liable for any  consequences 
stemming from the reuse. 

How  to  cite  this  report:  Zampori  L.,  Saouter  E.,  Castellani  V.,  Schau  E.,  Cristobal  J.,  Sala  S.;  Guide  for 
interpreting life cycle assessment result; EUR 28266 EN; doi:10.2788/171315 

All  images  ©  European  Union  2016,  except:  [page  8]  (2015)  Source:  James  and  Galatola;  [page  27]  (2017) 
Source: International Journal of Life Cycle Assessment; Rigamonti et al. 2017 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

Abstract ............................................................................................................... 1 

1 

Introduction .................................................................................................... 2 

2  A practical scheme to interpret LCI and LCIA results ............................................ 4 

3 

Identification of key issues: Hotspots analysis ..................................................... 6 

3.1  The hotspot analysis in the UNEP/SETAC Life Cycle Initiative ........................... 6 

3.2  The hotspot analysis in the Environmental Footprint pilot phase (2013 – 2017) . 7 

3.2.1 

Procedure to identify the most relevant impact categories ........................ 8 

3.2.2 

Procedure to identify the most relevant life cycle stages .......................... 8 

3.2.3 

Procedure to identify the most relevant processes ................................... 9 

3.2.4 

Procedure to identify the most relevant elementary flows ........................ 9 

3.2.5 

Procedure to identify the hotspots ......................................................... 9 

3.2.6 

Dealing with negative numbers ........................................................... 10 

3.2.7 

Specific instructions about aggregating elementary flows ....................... 10 

3.2.8 

Conclusions ...................................................................................... 10 

3.2.9 

Example to identify most relevant contributions and hotspots ................. 12 

3.2.9.1 

Example: most relevant Impact Categories .................................... 12 

3.2.9.2 

Example: most relevant Life Cycle Stages ...................................... 13 

3.2.9.3 

Example: most relevant Processes ................................................ 13 

3.2.9.4 

Example: most relevant Elementary Flows ..................................... 13 

3.2.9.5 

Example: hotspots ...................................................................... 15 

4 

Interpretation on Life Cycle Inventory (LCI) level .............................................. 15 

4.1  Analysis of data sources ............................................................................ 16 

4.1.1 

Analysis of the system boundaries ...................................................... 17 

4.1.2 

Foreground system analysis ............................................................... 18 

4.2  Data quality ............................................................................................ 19 

4.3  Further completeness and consistency checks.............................................. 23 

4.3.1 

Anomaly assessment ......................................................................... 23 

4.3.2 

Cut-off ............................................................................................ 23 

4.3.3 

Long term emissions ......................................................................... 23 

5 

Interpretation on Life Cycle Impact Assessment (LCIA) level ............................... 25 

5.1  Characterization: sensitivity analysis .......................................................... 25 

5.1.1 
Case  study  1  –  WEEE  management:  Toxicity-related  and  Resource-related 
impact categories affecting interpretation of results ........................................... 25 

5.1.2 
Case  study  2  –  Basket  of  Products  Food:  sensitivity  analysis  comparing 
different impact assessment methods .............................................................. 27 

5.1.3 

Conclusions and recommendations ...................................................... 28 

5.2  Characterization: mapped flows and unmapped flows ................................... 28 

 
 
 
 
5.2.1 

Comparison between two different methods ......................................... 28 

5.2.2 

Analysis of uncharacterized flows within an impact category ................... 30 

5.2.3 

Conclusions and recommendations ...................................................... 33 

5.3  Normalization: sensitivity analysis .............................................................. 33 

5.3.1 

Conclusions and recommendations ...................................................... 35 

5.4  Weighting: sensitivity analysis ................................................................... 35 

5.4.1 

Conclusions and recommendations ...................................................... 39 

6  Estimation of uncertainty ................................................................................ 40 

6.1.1 

Conclusions and recommendations ...................................................... 40 

7  Meta-analysis ................................................................................................ 41 

7.1.1 

Conclusions and recommendations ...................................................... 42 

8  Conclusion .................................................................................................... 43 

References ......................................................................................................... 45 

List of abbreviations and definitions ....................................................................... 51 

List of figures ...................................................................................................... 52 

List of tables ....................................................................................................... 53 

 
 
 
 
Abstract  

for 

framework 

Interpretation of the results of a life cycle assessment (LCA) study is a mandatory phase 
of LCA and it is a key aspect in order to derive robust conclusions and recommendations. 
One  of  the  key  aims  of  LCA  is  to  provide  the  decision  makers  with  comprehensive  and 
understandable  information:  this  task  is  achieved  by  a  proper  interpretation  of  the 
results of an LCA study.  
The robust interpretation of a LCA study needs specific guidance to support the decision 
making process, both in policy and business contexts. Existing standards and guidelines 
provide  a 
improving 
interpretation,  but  there 
comprehensiveness  and  practical  aspects  thereof.  This  report  aims  at  providing 
practitioners with a practical guidance on  what are the aspects to be  always taken into 
account  when  interpreting  results  of  an  LCA.  Building  on  this  report,  further  guidance 
needs to be developed to support interpretation of LCA studies both for LCA practitioners 
and LCA users (e.g. decision makers). 
This  report  provides  a  practical  and  schematic  workflow  to  be  followed  during 
interpretation,  starting  from  the  assessment  of  the  hotspots  of  the  baseline  scenario, 
which  is  completed  by  sensitivity,  completeness  and  consistency  checks,  to  be 
performed at the level of the life cycle inventory (LCI) and life cycle impact assessment 
(LCIA).  Additionally,  case  studies  are  reported  to  illustrate  specific  issues  related  to 
interpretation of results.  

is  the  need  of 

1 

 
 
 
 
1  Introduction  

Life  cycle  assessment  (LCA)  is  more  and  more  considered  a  reference  method  for  the 
evaluation  of  supply  chains,  production  and  consumption  systems,  up  to  regions 
(Hellweg and Mila I Canals, 2014). 

Life  cycle  interpretation  is  one  of  the  four  phases  identified  in  the  ISO  14040  and  the 
ISO  14044  standards  (ISO  2006a,b).  In  fact,  according  to  the  ISO  standard 
interpretation  of  Life  Cycle  Inventory  (LCI)  or  Life  Cycle  Impact  Assessment  (LCIA) 
phases according to the goal and scope of the study is a required step of an LCA study. 

ISO  14044  further  specifies  that  interpretation  comprises  the  following  elements:  i)  an 
identification of the significant issues based on the results of the LCI and LCIA phases of 
LCA;  ii)  an  evaluation  that  considers  completeness,  sensitivity  and  consistency  checks; 
iii) conclusions, limitations, and recommendations. 

In  2013,  the  European  Commission  has  put  forward  a  communication  which  aims  at 
harmonising  the  application  of  LCA  for  evaluation  green  products  (Building  the  Single 
Market  for  Green  Products,  EC,  2013a)  calculating  their  Environmental  Footprint  (EF) 
(both Product environmental footprint – PEF, and Organisation environmental footprint – 
OEF). The communication is accompanied by the PEF Guide  (EC, 2013b) which explains 
that  interpretation  of  the  results  of  a  PEF  study  serves  two  purposes:  i)  to  ensure  that 
the performance of the PEF model corresponds to the goals and quality requirements of 
the  study;  in  this  sense,  PEF  interpretation  may  inform  iterative  improvements  of  the 
PEF model until all goals and requirements are met; and ii) to derive robust conclusions 
and  recommendations  from  the  analysis,  for  example  in  support  of  environmental 
improvements.  

The  International  Reference  Life  Cycle  Data  System  (ILCD)  Handbook  (EC,  2010) 
clarifies that interpretation of an LCA has two main purposes that fundamentally differ: i) 
steering the work towards improving the Life Cycle Inventory model to meet the needs 
derived from the study goal; ii) especially for comparative LCA studies (while partly also 
applicable  to  other  types  of  studies)  it  serves  to  derive  robust  conclusions  and,  often, 
recommendations. 

Notwithstanding  that  several  methodological  guidance  exists,  on  the  different  steps  of 
LCA,  so  far,  the  interpretation  phase  has  been  little  systematized,  even  though  its 
importance  is  well  recognized  within  the  LCA  community.  LCA  studies  sometimes 
formulate conclusions and recommendations disregarding the uncertainties and the lack 
of consistency within the LCI and LCIA steps and across the goal and scope definition.  

The need of guidance for the interpretation phase is becoming even  more important as 
LCA is being increasingly recognised and used by various stakeholders. LCA has recently 
become a reference methodology for decision support in the policy context, in which its 
results may impact entire sectors or societies. In the EU context, these recent trends are 
reflected  through  initiatives  and  pilots  related  to  the  above-mentioned  Building  the 
Single Market for Green Products (EC, 2013a) as well as the new inclusion of LCA among 
the  methods  in  the  EU  Better  Regulation  toolbox  (EC,  2015)  which  identified  tools 
relevant  for  conducting  the  impact  assessment  of  policies.  In  this  setting,  robust  and 
sound interpretation of LCA results is a must. 

Several authors have provided evidence that interpretation is one of the key steps of an 
LCA study and give guidance and/or recommendations depending on the purpose of the 
study. Skone (2000) pointed out that the purpose of performing life cycle interpretation 
is  to  determine  the  level  of  confidence  in  the  final  results  and  communicate  them  in  a 
fair, complete, and accurate manner. Initially, it was recognized that interpretation was 
not  one  of  the  hot  topics  in  literature  studies  (Heijungs  et  al.,  2001),  and  authors 
provided  numerical  techniques  for  interpretation.  More  recently  other  authors,  such  as 
Guadreault  et  al.  (2009),  while  recognizing  that  LCA  has  become  an  important 
methodology  for  more  sustainable  process  design,  observed  that  its  application  in  a 
decision-making  context  has  been  limited  by  a  poor  understanding  of  methodological 

2 

 
 
choices and assumptions, therefore they recommend careful interpretation of  results to 
improve  the  quality  of  the  outcome  (i.e.  improve  the  decision-making  process).  This 
view is shared by authors such as Prado Lopez et al. (2014), who have identified the lack 
of  robust  methods  of  interpretation  to  support  decision  makers,  hence,  they  provide  a 
novel  approach  based  on  a  multi-criteria  decision  analytic  method  (stochastic  multi-
attribute  analysis  for  life-cycle  impact  assessment  (SMAA-LCIA))  which  in  their  view 
should  support  both  interpretation  of  results  and  policymakers.  Van  Hoof  et  al.  (2013) 
explained how normalization helps maintain a multi-indicator approach while keeping the 
most relevant indicators, allowing effective decision making. Finally, other authors, such 
as Cellura et al. (2011) and Huang et al. (2013) performed LCA of specific products and 
they pointed out the relevance of  sensitivity analysis to strengthen the reliability of the 
results obtained and draw conclusions to support sector specific guidelines. A structured 
approach  covering  the  LCIA  phase  has  been  proposed  (Castellani  et  al  2017), 
highlighting  the  importance  of  a  systematic  sensitivity  analysis  of  impact  assessment 
models, normalization and weighting set. Regarding normalisation and weighting steps, 
which  are  optional  according  to  ISO  standards,  a  recent  overview  of  approaches, 
strengths  and  limitations  has  been  provided  in  the  context  of  activities  of  the 
UNEP/SETAC Life Cycle Initiative (UNEP/SETAC LCI) by Pizzol et al (2016). 

The  objective  of  this  technical  report  is  to  provide  guidance  on  how  to  perform  an 
interpretation of LCI and LCIA results, based on what are the meaningful aspects to be 
taken into account, common to the majority of LCA studies.  

This technical report aims to be a practical document to be used by LCA practitioners as 
a  checklist  of  the  main  aspects  to  consider  when  interpreting  LCI  and  LCIA  results. 
Therefore, this report makes extensive use of examples, based on real case studies that 
may  help  critically  assess  LCA  results,  ensuring  a  reliable  decision-  and  policy-making 
support. 

A general framework on how to perform a proper interpretation is presented in chapter 
2;  the  key  aspects  will  be  discussed  further  on  in  the  report,  using  case  studies  as  an 
example to guide the reader and the practitioner through “real life” applications. 

Chapter  3  focuses  on  the  identification  of  significant  issues,  namely  the  main 
contributors to the LCIA results (i.e. hotspots, most relevant impact categories, life cycle 
stages, processes and elementary flows). 

Chapter  4  and  5  focus  on  the  evaluations  (sensitivity,  completeness  and  consistency 
checks)  in  relation  to  the  LCI  and  LCIA  phases,  respectively.  Regarding  the  LCI,  the 
report  goes  through  the  analysis  of  the  data  sources,  by  giving  examples  on  how  to 
interpret the system boundaries and the foreground system); it provides an overview on 
data quality and it comprises further completeness and consistency checks (e.g. cut-off, 
long  term  emissions,  anomaly  checks).  When  analysing  the  LCIA  the  report  provides 
insights  on  sensitivity  analysis  regarding  characterization,  normalization  and  weighting. 
When  dealing  with  characterization,  the  attention  of  the  reader  is  drawn  also  on  issues 
such  as:  the  mapping  between  the  inventory  and  the  characterization  models;  and  the 
evaluation of uncharacterized elementary flows.  
Chapter  6  and  7  deal  with  overarching  topics,  such  as  estimation  of  uncertainty  of  the 
results of LCA study and meta-analysis. 

3 

 
 
 
 
2  A practical scheme to interpret LCI and LCIA results 

The  initial  step  of  the  interpretation  phase  is  to  review  information  from  the  first  three 
phases  of  the  LCA  process,  such  as  the  ones  used  to  build  the  study  goals  and  scope, 
quality  assurance  procedures,  reporting  requirements,  results,  assumptions,  external 
involvement (e.g. stakeholders, peer reviews) (Skone, 2000).  

Both  ISO  14044  and  the  ILCD  Handbook  (EC-JRC,  2011a)  propose  a  scheme  with  the 
elements  to  be  considered  in  the  interpretation  phase,  and  their  relation  to  the  other 
phases of the LCA study.  

The elements can be grouped in: 

 

identification  of  significant  issues  (based  on  the  results  of  the  LCI  and  LCIA 
phases) 

the purpose of this first element of interpretation is to analyse and structure the 
results  of  earlier  phases  of  the  LCI/LCA  study  in  order  to  identify  the  significant 
issues. There are two interrelated aspects of significant issues: i) firstly there are 
the  main  contributors  to  the  LCIA  results,  i.e.  most  relevant  life  cycle  stages, 
processes  and  elementary  flows,  and  most  relevant  impact  categories;  ii) 
secondly,  there  are  the  main  choices  that  have  the  potential  to  influence  the 
precision  of  the  final  results  of  the  LCA.  These  can  be  methodological  choices, 
assumptions,  foreground  and  background  data  used  for  deriving  the  process 
inventories,  LCIA  methods  used  for  the  impact  assessment,  as  well  as  the 
optionally used normalisation and weighting factors. 

  evaluation that considers completeness, sensitivity and consistency checks 

Completeness  checks  on  the  inventory  are  performed  in  order  to  determine  the 
degree  to  which  it  is  complete  and  whether  the  cut-off  criteria  have  been  met. 
Sensitivity  checks  have  the  purpose  to  assess  the  reliability  of  the  final  results 
and  of  the  conclusions  and  recommendations  of  the  LCA  study.  The  consistency 
check  is  performed  to  investigate  whether  the  assumptions,  methods  and  data 
have been applied consistently throughout the LCI/LCA study. 

 

conclusions, limitations and recommendations.  

Integrating  the  outcome  of  the  other  elements  of  the  interpretation  phase,  and 
drawing  on  the  main  findings  from  the  earlier  phases  of  the  LCA,  the  final 
element of the interpretation is to draw conclusions and identify limitations of the 
LCA,  and  to  develop  recommendations  for  the  intended  audience  in  accordance 
with the goal definition and the intended applications of the results. 

In  this  report,  a  schematic  workflow  representing  a  practical  approach  to  interpret  LCI 
and LCIA results is proposed in Fig.1, building on both ISO 14044 (ch. 4.5.1.1) and the 
ILCD  Handbook  (ch.  9.1)  (EC-JRC,  2011a).  The  workflow  is  to  be  seen  as  an  iterative 
approach to finally draw robust conclusions and recommendations. 

The scheme should not be seen as substituting the approaches of the ISO 14044 and the 
ILCD Handbook, instead it should be seen as a practical application of the requirements 
laid down in these documents. 

Furthermore,  the  scheme  should  not  be  intended  as  a  comprehensive  list  of  items  to 
check, instead it should be seen as a normal workflow that should be carried out by an 
LCA  practitioner  in  order  to  derive  robust  conclusions  out  of  the  LCA  study.  More 
accurate  and  case-specific  checks  should  be  done  depending  on  the  intended  goal  and 
scope of the study. 

Further  aspects,  such  as  the  appropriateness  of  the  functional  unit,  the  intended  goal, 
the system boundaries and value choices should normally be part of an interpretation, in 
order to identify recommendations and limitations of the study and draw the appropriate 
conclusions. They are not discussed further in this report, but it is recommended that at 

4 

 
 
least,  each  decision  based  on  a  personal  or  a  stakeholder’s  value  shall  always  be 
documented  and  explained  in  the  conclusions  of  the  LCA  study.  This  is  consistent  with 
one of the key aims of LCA, which is to provide the decision makers with comprehensive 
and understandable information in a transparent manner. 

Figure 1 Practical application of requirements in ISO 14044 and ILCD Handbook to interpret LCI and LCIA results. 

5 

 
 
 
 
3  Identification of key issues: Hotspots analysis 

In order to get started with interpretation of results, it is necessary to identify what the 
key  issues  are.  Once  the  key  issues  are  identified  it  is  possible  to  further  evaluate  the 
overall robustness of the LCA study by using for example completeness, consistency and 
sensitivity checks. 

This  chapter  on  hotspot  analysis  gives  an  overview  of  the  overarching  methodological 
framework  of  hotspot  analysis  as  currently  developing  with  the  UNEP/SETAC  Life  Cycle 
Initiative  -  Flagship  Project  3a  and  the  hotspot  analysis  as  tested  in  the  Environmental 
Footprint (EF) pilot phase (2013 – 2017). In the first case, hotspot analysis is regarded 
as  a  subject  on  its  own,  in  the  second  case  it  is  shown  a  practical  way  to  perform  a 
hotspot  analysis  in  an  environmental  footprinting  context:  in  the  EU  Environmental 
Footprint,  it is  more  focused  on  the  interpretation  of  the  screening  study  results  of  the 
average  or  representative  product1.  The  hotspots  in  a  EU  Environmental  Footprint  may 
be, but does not have to, found in a contribution analysis of the characterized results. 

Therefore,  in  this  chapter,  we  first  describe  the  hotspot  analysis  as  outlaid  in  the 
UNEP/SETAC LCI and then the hotspot analysis as performed in the EF pilot phase. 

3.1  The hotspot analysis in the UNEP/SETAC Life Cycle Initiative  

The  UNEP/SETAC  Life  Cycle  Initiative  has  been  running  a  Flagship  project  (nr.  3a)  on 
hotspot analysis, since  2012 until 2017. According to UNEP/SETAC (2016), the benefits 
of hotspots analysis include ensuring:  

  Focus on priority issues (e.g., waste, water, materials of concern)  
  Focus on the right life cycle stage (e.g., material acquisition, manufacturing, use, 

end of life)  

  Focus  on  the  right  actors  (e.g.  producers,  manufactures,  suppliers,  retailers, 

customers) to evaluate, influence and implement solutions  
Implications of trade-offs are understood  

 
  Resources (e.g. time, money) can be effectively allocated to actions.  

Previously, there was not currently a common global approach to hotspots analysis; nor 
has  there  been  sufficient  effort  to  bring  together  or  share  best  practice  amongst  those 
organisations or initiatives currently developing and using these methods. Nor was there 
any  accepted  guidance  on  how  to  translate  and  apply  the  results  of  hotspots  analysis 
into  meaningful  sustainability  information  and  insight  for  use  by  industry,  governments 
and  other  stakeholders.  Therefore,  the  UNEP  SETAC  Life  Cycle  Initiative  established  a 
new  Flagship  Project  to  address  these  and  other  issues.  The  objectives  of  the 
UNEP/SETAC LCI Flagship Project 3 are to produce:  

1.  A  common  methodological  framework  and  global  guidance  for  sustainability  hotspots 

analysis;  

2.  A  protocol  for  the  appropriate  use  and  communication  of  sustainability  information 

derived from hotspots analysis;  

3.  To evaluate and, if possible, implement a range of options to bring together the findings 
from  existing  hotspots  studies  to  provide  a  richer,  global  picture  of  sustainability 
hotspots in the economy and society.  

Phase  1  was  finished  in  December  2014  and  gives  an  overview  of  different  tools  and 
methods  that  can  be  used  in  the  hotspot  analysis.  This  includes  42  hotspots  analysis 

1 In the context of the EF pilot phase, within each Product Environmental Footprint Category Rule 
(PEFCR)  pilot  a  “representative  product”  has  to  be  defined,  which  is  representing  >50%  of  the 
European  market.  The  PEF  screening  study  is  a  PEF  study  conducted  on  the  representative 
product:  one  of  the  aims  of  the  screening  study  is  to  identify  the  hotspots,  most  relevant 
elementary flows, processes and life cycle stages. 

6 

 
 
                                           
methodologies divided into product, sector and national scale of application. Of these, 21 
methodologies  (4  at  the  national-level;  5  at  the  sector-level;  and  12  predominantly 
applied  at  the  product  category-level)  were  shortlisted  and  further  analysed.  Examples 
of  methods  are  EU  EIPRO  and  Global  Protocol  for  Community  Scale  GHG  emissions  on 
the  national  level,  GHG  Protocol  Corporate  Value  Chain  (Scope  3)  Accounting  and 
Reporting Standard and Sustainability Accounting Standards Board Materiality Map™  on 
the  sector  level  and  French  Grenelle  I  and  II  BPX  30-323-0  Product  Lifecycle 
Environmental  Impact  Quantification  Guidance  Standard  and  Water  Quality  Association 
(WQA) Hotspots Analysis on the product level. Please refer to Figure ES 2 in (Barthel et 
al 2014) for the complete list. 

This first phase of the project fed into the second phase of the Flagship project.  

Phase 2 is running from January 2015 – January 2017 and produces the methodological 
framework (UNEP/SETAC, 2016) and the communication guidance2. These are currently 
in a six-weeks public consultation (27. Oct – 15 Dec 2016). The framework provides:  

  a recognized set of guiding principles and practices for hotspots analysis 
  a globally agreed methodological framework for hotspots analysis 
  agreement  on  the  appropriate  use  and  communication  of  product  sustainability 

information derived from hotspots analysis 

Future step will be to work with hotspot analysis for cities and regions, and road testing 
of the methodological framework. 

3.2  The  hotspot  analysis  in  the  Environmental  Footprint  pilot 

phase (2013 – 2017) 

The 2013-2017 Environmental Footprint (EF) pilot phase has three main objectives: 

 
 
 

test the process for developing product- and sector-specific rules; 
test different approaches to verification; 
test  communication  vehicles 
performance to business partners, consumers and other company stakeholders. 

for  communicating 

life  cycle  environmental 

This  is  tested  by  groups  of  organisations  called  Technical  Secretariats  (TS),  who 
volunteered to develop the rules for their product or sector. 

The hotspot analysis as described below were  tested and developed in an iterative way 
on  the  approximatively  25  pilots  participating  in  the  EF  pilot  phase  to  develop  Product 
Environmental  Footprint  (PEF)  Category  Rules  (PEFCR)  or  Organisation  Environmental 
Footprint (OEF) Sector Rules (OEFSR). 

A  hotspot  analysis  is  required  for  the  PEF/OEF  screening  studies  of  the  average  or 
representative product for the PEFCR/OEFSR and for the supporting studies3 (EF study of 
a single product/organisation belonging to the product category/sector) when developing 
PEFCR/OEFSR  (PEFCR Guidance document, (EC, 2016)).  

The  hotspot  analysis in  the  EF  context  aims  to  identify  the  hotspots,  the  most  relevant 
impact categories, life cycle stages, processes  and elementary flows,  as shown in Fig 2 
and it provides a guidance on how to do that. 

2  Communicating  hotspots:  The  effective  use  of  sustainability  information  to  drive  action  and  improve 
performance 
from 
http://www.wrap.org.uk/sites/files/wrap/Hotspots%20Communications%20Guidelines%20v.3.5%207Oct2016.
pptx 
3 The  EF  supporting  studies  are  PEF/OEF  studies  run  by  single  companies,  with  the  aim  of  testing  the  draft 
PEFCRs and OEFSRs. 

7 

 
 
 
 
                                           
Figure 2: Hotspot analysis in the context of the Environmental Footprint pilot (James & Galatola, 2015)  

As shown in the figure, the hotspots (and thereby also the hotspot analysis) is important 
for  the  decision  on  which  life  cycle  stages,  processes  and  elementary  flows  that  are 
hotspots (> 50 % contribution) and which are relevant (> 80 % contribution).  

The hotspots might serve the purpose of "warning" an organisation about the area where 
they should focus their attention in order to improve the environmental performance of a 
product  (PEF)  or  an  organisation  (OEF).  Whilst  traditionally  LCA  practitioners  are 
expected  to  provide  primary/site-specific  data  for  processes  taking  place  in  the 
foreground and secondary data for processes taking place in the background, in PEF/OEF 
the  implementation  of  the  materiality  principle  leads  to  a  different  approach.  The 
processes  most  contributing  to  the  final  results  (in  terms  of  environmental  impacts  or 
savings)  are  those  for  which  the  best  quality  of  data  should  be  provided.  For  the 
processes less relevant it is possible to use data of lower quality. 

3.2.1  Procedure to identify the most relevant impact categories   
Starting  from  the  normalised  and  weighted  results  of  PEF  screening  study,  each 
Technical  Secretariat  is  asked  to  select  the  impact  categories  that  they  consider  more 
relevant in terms of "communication purposes”.  

3.2.2  Procedure to identify the most relevant life cycle stages   

The proposed guideline by the PEFCR Guidance (EC, 2016) is to consider as relevant all 
life  cycle  stages  which  together  contribute  over  80%  (before  normalisation  and 
weighting) to any of the baseline impact categories. This should start from the largest to 
the smallest contributions. In addition, more life cycle stages can be added to the list of 
the most relevant ones.  

In  order 
PEFCRs/OEFSRs, a list of default life cycle stages was identified:  

to  guarantee  a  minimum 

level  of  harmonisation  among  different 

  Raw  material  acquisition  and  pre-processing  (including  production  of  parts  and 

unspecific components); 

8 

 
 
 
 
 
  Production of the main product4 

  Product distribution and storage 

  Use stage scenario (if in scope); 

  End-of-life (including product / part reuse, recovery / recycling; if in scope). 

3.2.3  Procedure to identify the most relevant processes   

Each impact category shall be investigated to identify the most relevant processes. The 
identification of the most relevant processes shall be done at the  whole life cycle level. 
Similar/identical processes taking place in different life cycle stages (e.g. transportation) 
shall be accounted separately. 

In the context of the EF pilot phase processes were defined as most relevant where they 
collectively  contributed  at  least  80%  to  any  impact  category  before  normalisation  and 
weighting. 

In  some  instances,  vertically  aggregated  datasets  may  be  identified  as  representing 
relevant processes.  It may not be obvious which process within an aggregated dataset 
is responsible for contributing to an impact category.   The metadata  accompanying the 
data should be reviewed and used to identify relevant processes. If this is not possible, it 
can  be  decided  whether  to  seek  further  disaggregated  data  or  to  treat  the  aggregated 
dataset as a process for the purposes of identifying relevance5.  

3.2.4  Procedure to identify the most relevant elementary flows 

As far as the most relevant elementary flows is concerned they are the ones contributing 
cumulatively  more  than  80%  to  the  impact  category.  It  can  be  decided  whether  this  is 
calculated at the level of overall life cycle and / or single (most relevant) process level. 
All  elementary  flows  contributing  more  than  5%  to  the  impact  category  shall  be 
considered as relevant. 

Once the most relevant elementary flows have been identified they shall be linked to the 
processes emitting them. 

3.2.5  Procedure to identify the hotspots  

A  hotspot  can  be  identified  at  different  levels  of  granularity: impact  category,  life  cycle 
stage, process or elementary flow. 

In the context of PEF/OEF pilot phase a hotspot is defined as either (James and Galatola, 
2015):  

  OPTION  A:  (1)  life  cycle  stages,  (2)  processes  and  (3)  elementary  flows 
cumulatively  contributing  at  least  50%  to  any  impact  category  before 
normalisation and weighting (from the most contributing in descending order).   

  OPTION B: At least the two most relevant life cycle stages, processes and at least 
two elementary flows (minimum 6).  Additional hotspots may be identified by the 
TS. 

4 If  it  is  not possible  to  differentiate  between  the  production  of  the  main  product  and  ancillary  materials  and 
other life cycle stages, e.g. raw material acquisition and transport, then these life cycle stages can be merged. 
Adequate justifications shall be provided in the screening report and be highlighted in the consultation phase. 
If  an  "aggregated"  life  cycle  stage/process  is  most  relevant  it  implies  that  all  the  processes  included  will  be 
classified as most relevant.  
5 In this last case, if an aggregated dataset is relevant, everything in it is automatically relevant. 

9 

 
 
 
 
                                           
3.2.6  Dealing with negative numbers 

When  identifying  the  percentage  contribution  from  any  process  or  flow  it  is  important 
that  absolute  values  are  used  (i.e.  the  minus  sign is  ignored)  in  calculating  percentage 
contributions. This allows indentifying the relevance of any credits. 

3.2.7  Specific instructions about aggregating elementary flows 

The  toxicity-related  impact  categories  (“Human  toxicity,  cancer  effects”,  “Human 
toxicity,  non-cancer  effects”,  and  “Freshwater  ecotoxicity”)  contain  long  lists  of 
characterization  factors  specified  on  the  elementary  flow  category,  level  3  (also  known 
as  sub-compartment  –  for  example  urban  air,  freshwater,  agricultural  soil).  The  impact 
category  “Particulate  matter”  also  contains  characterization  factors  at  the  level  3 
category.  

It  is  recommended  to  aggregate  the  elementary  flows  on  the  level  2  category  (i.e. 
emissions  to  air,  water,  soil)  in  the  contribution  analysis.  This  is  to  make  the  analysis 
more manageable and because the level 3 category does not give additional information 
when  analyzing  on  the  process  level  (the  processes  already  contain  information  on  the 
location type of the emissions).  

It  is  important  to  model  foreground  data  on  the  level  3  category  for  particulates  and 
toxic substance flows, because the characterization factors can deviate considerably. 

Metal  resource  flows  are  not  specified  per  origin  of  ore  type  in  the  source  files  of  the 
ILCD  recommended  methods.  However,  in  several  background  databases,  metal 
resource flows are differentiated (for example, Silver, Ag 4.6E-5%, Au 1.3E-4%, in ore, 
Silver, Ag 4.2E-3%, Au 1.1E-4%, in ore, Silver, Ag 2.1E-4%, Au 2.1E-4%, in ore, etc.). 
Therefore, the specified flows were added to the ILCD method in LCA software packages 
with  the  same  characterization  factors  as  for  the  unspecified  metals.  When  doing  a 
contribution  analysis  of  the  metal  resource  flows,  it  is  therefore  recommended  to 
aggregate the flows per metal (silver, copper, nickel, etc.). 

There are 5 different fossil fuel related resource flows specified in the source files of the 
ILCD recommended methods (brown coal; 11.9 MJ/kg, crude oil; 42.3 MJ/kg, hard coal; 
26.3 MJ/kg, natural gas; 44.1 MJ/kg, peat; 8.4 MJ/kg). However, in several background 
databases, fossil resource flows are specified with different calorific values (for example, 
Gas, natural, 46.8 MJ per kg, Gas, natural, 36.6 MJ per m3, Gas, natural, 35 MJ per m3, 
Gas,  natural,  30.3  MJ  per  kg,  etc.).  Therefore,  the  specified  flows  were  added  to  the 
ILCD  method  in  LCA  software  packages  with  characterization  factors  related  to  the 
factors  in  the  original  source,  taking  the  different  calorific  value  into  account.  When 
doing a contribution analysis of the fossil resource flows, it is therefore recommended to 
aggregate  the  flows  based  on  the  5  original  flows  (brown  coal,  crude  oil,  hard  coal, 
natural gas and peat). 

3.2.8  Conclusions  

In  table  1  the  requirements  to  define  most  relevant  contributions  during  the  EF  pilot 
phase are summarized as found in the PEFCR Guidance (EC, 2016). 

10 

 
 
 
 
 
 
 
 
Table 1 Summary of requirements to define most relevant contributions and hotspots (James & Galatola, 2015) 

Item 

At what level does relevance need to be 
identified? 

Threshold 

Most relevant impact categories 

Most relevant life cycle stages 

Hotspots 

the 

final 

In 
from 
results, 
normalized  and  weighted  results  but 
deviations possible if justified 

starting 

No  threshold.  Decision  left  to  TS 
but 
stakeholder 
consultation and TAB opinion 

subject 

to 

impact 

For  each 
normalization 
relevant for data needs identification 

category,  before 
and  weighting.  Not 

All  life  cycle  stages  contributing 
cumulatively more than 80% to any 
impact category 

For  each 
normalization and weighting 

impact 

category,  before 

(i) 

life 

cycle 

stages, 
Either 
processes,  and  elementary  flows 
cumulatively  contributing  at  least 
50% to any impact category, or 

(ii)  at  least  the  two  most  relevant 
impact categories, life cycle stages, 
processes  and  at 
two 
elementary  flows  (minimum  6).  
Additional 
be 
identified by the TS 

hotspots  may 

least 

Most relevant processes 

impact 

For  each 
category,  before 
normalization  and  weighting.  Essential 
for data needs identification 

processes 

All 
contributing 
cumulatively more than 80% to any 
impact category 

Most relevant elementary flows 

impact 

For  each 
category,  before 
normalization  and  weighting.  Essential 
for data needs identification 

All  elementary  flows  contributing 
cumulatively more than 80% to any 
impact category and in any case all 
those  contributing  more  than  5% 
individually 

When  assessing  what  is  most  relevant  it  is  also  recommended  to  check  if  what  is 
identified as being non-relevant can be actually considered as such. A practical example 
is given at paragraph 4.3.1. 

11 

 
 
 
 
 
3.2.9  Example to identify most relevant contributions and hotspots  

The  PEFCR  Guidance  (2016)  also  provided  an  example  on  how  to  perform  a  hotspot 
analysis  and  the  identification  of  most  relevant  impact  categories,  life  cycle  stages, 
processes and elementary flows. The example is reported here: it is a fictitious example, 
not based on any specific PEF study results. 

3.2.9.1  Example: most relevant Impact Categories 

Table  2  shows  the  contribution  of  different  impact  categories  based  on  normalised  and 
weighted results 

Table 2 Selection of most relevant Impact Categories (James & Galatola, 2015)  

Impact category 

Unit 

Contribution (%) 

Climate change 

kg CO2 eq 

Ozone depletion 

kg CFC-11 eq 

Human toxicity, cancer effects 

CTUh 

Human toxicity, non-cancer effects  CTUh 

Particulate matter 

kg PM2.5 eq 

Ionizing radiation, human health 

Kbq U235 eq 

Photochemical ozone formation 

kg NMVOC eq 

Acidification 

molc H+ eq 

Terrestrial eutrophication 

molc N eq 

Freshwater eutrophication 

kg P eq 

Marine eutrophication 

kg N eq 

Freshwater ecotoxicity 

CTUe 

Land use 

Water resource depletion 

kg deficit 

m3 water eq 

Mineral, fossil resource depletion 

kg Sb eq 

21.5 

3.0 

8.3 

14.9 

0.1 

0.5 

2.4 

1.5 

1.0 

1.0 

0.1 

0.1 

14.3 

18.6 

12.7 

Based  on  the  normalised  and  weighted  results  the  TS  can  decide  that  the  following 
impact  categories  are  relevant  for  "communication  purposes":  climate  change,  water 
depletion  and land  use.  Where  there  is  deviation  from  the  most  significant  contributors 
to the normalised and equally weighted results, justification shall be provided. 

Once  the  relevant  impact  categories  for  communication  purposes  have  been  selected, 
the TS shall start identifying the most relevant life cycle stage, processes and flows per 
each  impact  category  (all  baseline  EF  impact  categories,  not  only  those  relevant  for 
communication purposes).  

12 

 
 
 
3.2.9.2  Example: most relevant Life Cycle Stages 

Table 3 shows the contribution of different life cycle stages to the climate change impact 
category  (based  on  the  characterised  inventory  results  before  normalisation  and 
weighting). 

Table 3 Selection of most relevant life cycle stages (James & Galatola, 2015)  

Life cycle stage 

Contribution (%) 

Raw material acquisition and pre-processing 

Production of the main product 

Product distribution and storage 

Use stage (if in scope) 

End-of-life 

42.1 

25.2 

16.4 

10.8 

5.5 

The  three  life  cycle  stages  in  purple  will  be  the  ones  identified  as  "most  relevant"  for 
climate  change  as  they  are  contributing  to  more  than  80%.  This  procedure  shall  be 
repeated for all the baseline EF impact categories. 

3.2.9.3  Example: most relevant Processes 

Table  4  shows  the  contribution  of  different  processes  to  the  climate  change  impact 
category  (based  on  the  characterised  inventory  results  before  normalisation  and 
weighting). 

Table 4 Selection of most relevant processes (James & Galatola, 2015) 

Unit process 

Contribution (%) 

Process A 

Process B 

Process C 

Process D 

Process E 

Process F 

Other processes 

8.9 

61.4 

23.4 

2.8 

1.5 

0.9 

0.9 

According to the proposed procedure the processes in orange shall be selected as “most 
relevant”.  Process  E  could  be  added  to  the  as  most  relevant  ones  based  on  specific 
considerations  (e.g.  it  is  under  operational  control,  it  is  of  relevance  for  the  specific 
sector, etc.). This procedure shall be repeated for all the baseline EF impact categories. 

3.2.9.4  Example: most relevant Elementary Flows 

Considering  that  the  selection  of  the  most  relevant  elementary  flows  can  be  done  at 
overall  life  cycle  level  (option  1)  and/or  per  relevant  process  (option  2),  there  are  two 

13 

 
 
 
 
possible  outcomes.  Starting  from  the  inventory  results  provided  in  Table  5,  the  list  of 
most relevant flows are highlighted in blue in Table 6 (at overall life cycle) and Table 7 
(at process level).  
Table 5 Inventory results (climate change, results expressed in gCOeq). (James & Galatola, 2015)  

Inventory flow 

Substance 1 

Substance 2 

Substance 3 

Substance 4 

Substance 5 

Total 

Process A 

Process B 

Process C 

Process D 

Process E 

Process F 

Other processes 

249 

1100 

300 

60 

64 

15 

15 

85 

600 

250 

30 

1 

10 

10 

6 

500 

20 

20 

1 

8 

8 

45 

450 

30 

10 

1 

5 

5 

5 

390 

50 

2700 

430 

1030 

5 

1 

3 

3 

125 

68 

41 

41 

Total 

1803 

986 

563 

546 

497 

4395 

Table 6 Most relevant inventory flows contributing to climate change (based on the inventory results before normalisation 
and weighting) - overall life cycle (option 1). (James & Galatola, 2015) 

Inventory flow 

Substance 1 

Substance 2 

Substance 3 

Substance 4 

Substance 5 

Total 

Process A 

Process B 

Process C 

Process D 

Process E 

Process F 

Other processes 

5.7% 

1.9% 

0.1% 

1.0% 

0.1% 

8.9% 

25.0% 

13.7% 

11.4% 

10.2% 

1.1% 

61.4% 

6.8% 

1.4% 

1.5% 

0.3% 

0.3% 

5.7% 

0.7% 

0.0% 

0.2% 

0.2% 

0.5% 

0.5% 

0.0% 

0.2% 

0.2% 

0.7% 

0.2% 

0.0% 

0.1% 

0.1% 

9.8% 

23.4% 

0.1% 

2.8% 

0.0% 

1.5% 

0.1% 

0.9% 

0.1% 

0.9% 

Total 

41.0% 

22.4% 

12.8% 

12.4% 

11.3% 

Table 7 Most relevant inventory flows contributing to climate change (based on the inventory results before normalisation 
and weighting) – process level (option 2). (James & Galatola, 2015) 

Inventory flow 

Substance 1 

Substance 2 

Substance 3 

Substance 4 

Substance 5 

Total 

Process A 

Process B 

Process C 

64% 

41% 

29% 

22% 

22% 

24% 

2% 

19% 

2% 

12% 

17% 

3% 

1% 

100% 

2% 

100% 

42% 

100% 

14 

 
 
 
 
 
Process D 

Process E 

Process F 

Other processes 

48% 

94% 

37% 

37% 

24% 

1% 

24% 

24% 

16% 

1% 

20% 

20% 

8% 

1% 

12% 

12% 

4% 

100% 

1% 

100% 

7% 

100% 

7% 

100% 

3.2.9.5  Example: hotspots 

In  Tables  8  and  9  the  identified  hotspots  -  based  on  the  two  different  approaches 
available- are presented. 
Table 8 Hotspots based on 50% cumulative contribution (OPTION A). (James & Galatola, 2015) 

Life cycle stages 

Processes 

 
 

 

Hotspots 

Raw material acquisition and pre-processing 
Production of the main product 

Process B 

Elementary flows 

Option 1: Substance 1 and 2  

Option 2:  

 
 
 

Substance 1 and 2 in processes B 
Substance 1 and 5 in process C 
Substance 1 in process E 

Table 9 Hotspots based on top two representatives (OPTION B). (James & Galatola, 2015) 

Hotspots 

Life cycle stages 

Processes 

 
 
 
 

Raw material acquisition and pre-processing 
Production of the main product 
Process B 
Process C 

Elementary flows 

Option 1: Substance 1 and 2 in process B 

Option 2: Substance 1 in process E6 and substance 5 in process C 

4  Interpretation on Life Cycle Inventory (LCI) level 

During  the  life  cycle  inventory  phase,  the  actual  data  collection  and  modelling  of  the 
system  has  to  be  done,  in  line  with  the  goal  definition  and  aiming  at  meeting  the 
requirements  derived  in  the  scope  phase.  The  LCI  results  are  the  input  to  the 
subsequent Life Cycle Impact Assessment (LCIA) phase. 

Interpretation involves evaluations at the level of Life Cycle Inventory, in order to: 

improve the inventory model to meet the needs derived from the study goal, 
 
  perform  a  sensitivity  analysis  to  check  for  limitations  in  the  appropriateness  of 

the life cycle inventory work, 

6  Choosing  the  hotspots  based  on  option  B-2  might  lead  to  anomalies  (like  in  this  fictitious 
example)  where  an  elementary  flow  is  identified  as  hotspot  even  if  its  absolute  value  is  not 
prominent.  

15 

 
 
 
 
 
 
                                           
  understanding  the  underlying  assumptions  when  secondary  datasets  are  used  in 

the model, 
combining the above points to refine the LCA model, 

 
  draw appropriate and robust conclusions.  

Key  aspects  to  evaluate  life  cycle  inventories  are  the  consistency  and  completeness 
thereof. They can be evaluated taking into account different aspects. A sensitivity check 
to evaluate the reliability of the results is also a needed step.  

A  first  aspect  to  be  taken  into  account  is  the  data  source.  Primary  data  are  collected 
directly  by  the  company  performing  the  study,  thus  the  consistency  of  the  data  used 
with  the  goal  and  scope  of  the  study  should  be  more  straightforward.  However,  when 
and LCA study is making use of secondary datasets to model the background system, a 
careful analysis of the consistency of these datasets with the primary data collected, and 
in relation with the goal and scope, shall be made.  

Indeed,  different  modelling  assumptions  in  datasets  aimed  at  representing  the  same 
product  system  can  lead  to  different  results,  affecting  the  reliability  of  the  LCA  study 
(Williams  et  al.,  2009).  An  example  of  the  relevance  of  different  data  sources  and 
consistency has been  discussed in Corrado  et al (2017): this study focuses  on different 
data sources to originate the inventory of arable crops (1 kg of wheat). The choice of the 
secondary  dataset  can  have  a  relevant  influence  on  LCA  results  and  it  is  considered 
among the challenges for a robust LCA (Notarnicola et al. 2017a).  

A second aspect to be taken into account regarding life cycle inventories is the quality of 
the  data  used,  regarding  time,  geographical  and  technological  representativeness, 
precision,  methodological  compliance  and  documentation.  Therefore,  in  this  chapter  a 
paragraph is dedicated to analyzing more in detail this aspect. 

A third relevant aspect in relation to LCIs is the consistency and completeness in relation 
to  single  aspects,  such  as  cut-off,  inclusion/exclusion  of  specific  elementary  flows  (e.g. 
long term emissions). It is not straightforward to judge if an inventory can be considered 
complete. As a general rule, it has always to be kept in mind that the inventory has to 
be  checked  in  its  completeness  in  relation  to  the  goal  and  scope  of  the  study:  for 
example, if the goal of the study is to assess only the climate change impact category, 
the  completeness  of  the  inventory  shall  be  evaluated  against  those  emissions  that  are 
actually contributing to climate change within the system boundaries. It does not lead to 
improved conclusions the compilation of a more detailed inventory, with the inclusion of 
e.g. substances that do not contribute to climate change. 

4.1  Analysis of data sources 

When selecting secondary data sets, it is important to ensure that all data sets used in 
the  modelling  of  the  system  model  are  methodologically  consistent  (i.e.  consistency 
check). The use  of inconsistent data  can lead to an unreliable and distorted LCA study, 
often with wrong conclusion and recommendations drawn. Furthermore, use of datasets 
with  different  level  of  completeness  (i.e.  completeness  checks)  may  also  lead  to 
unreliable conclusions. 

The study from Corrado et al. (2017) is used as a case study to highlight the relevance 
of  different  data  sources  in  an  LCA  study.  Therefore,  the  content  of  this  paragraph  is 
mainly based on this paper. The paper is organized as follows: firstly, the authors report 
an  overview  of  the  system  boundaries  and  underlying  assumptions  adopted  in  building 
secondary  datasets  for  arable  crops  within  three  databases.  Secondly,  they  summarise 
the  approaches  adopted  to  model  the  foreground  system,  highlighting  analogies  and 
differences. Afterwards, they point out the influence that modelling approaches can have 
on the impact assessment. Finally, the combination of these elements, allowed to derive 
some considerations on relevant elements of datasets. 

16 

 
 
 
It  is  focused  on  the  analysis  of  secondary  datasets  for  arable  crops  production,  as 
modelled  in  three  databases  adopted  as  source  of  secondary  data  in  LCA:  Agribalyse  v 
1.2  (Colomb  et  al.,  2015),  Agrifootprint  v  1.0  (Blonk  Agri-footprint  BV,  2014)  and 
Ecoinvent  v  3.1  (Weidema  et  al.,  2013).  For  simplicity,  in  the  remainder  of  the  report 
they are named Agribalyse, Agrifootprint and Ecoinvent respectively.  

The authors performed three types of analysis: 

 

comparison of the system boundaries and underlying assumptions; 

  analysis  of  how  the  foreground  system  is  modelled  in  each  dataset,  focusing  on 
different  aspects,  such  as:  fertilisers  application  and  nutrient  fate;  plant 
protection products application and fate; heavy metals (HMs) input, mass balance 
and fate; irrigation; agricultural operations; land occupation and transformation; 

comparison of the results of the life cycle impact assessment for the  foreground 
system, including the relative contribution of background. 

4.1.1  Analysis of the system boundaries 

The  system  boundaries  and  the  main  underlying  assumptions  adopted  to  model  arable 
crops production in the three databases are reported in Table 10. 

Table 10 Underlying assumptions in relation to the three databases considered by the authors (Corrado et al., 2017) 

Agribalyse 

Agrifootprint  

Ecoinvent 

Data source 

Provided 
technical 
by 
institutes  (e.g.  ARVALIS  – 
Institut du Végétal) 

sources 

(e.g. 
Different 
scientific 
literature,  official 
statistics  such  as  FAOstat, 
Eurostat) 

GL-Pro  project  –  Barrois 
region 
and 
(Nemecek 
Baumgartner, 2006)  

Straw  management  (when 
applicable) 

Partly  removed 
field  

from  the 

Totally  removed  from  the 
field 

Left on the field  

Economic,  mass  and  energy 
allocation  

applicable 

Not 
because 
straw  is  assumed  to  be  left 
on the field 

Allocation of co-products  

(grains and straw)  

Nutrients  from  straw  left 
on the field 

Not  applied  because 
the 
straw  market  was  not  very 
structured  at  the  time  when 
the datasets were developed 

The  fertilising  effect  of  crop 
residues  and  emissions  from 
the residues are allocated to 
the  crop  that  generated  the 
residues  

Not applicable  

Crop 
rotation 
modelling 

(P)  and 

Phosphorou
s 
potassium 
(K) 
and 
emissions 
allocation  

inputs 

Not reported  

P and K fertilisers production 
and  emissions  due  to  their 
application  are  allocated  to 
each  crop  pro  rata  for  the 
crop exports 

17 

The fertilising effects of crop 
residues are allocated to the 
crop  that  generated  it  (only 
for  P  and  K).  The  amount  of 
fertilisers is corrected for the 
amount of nutrients in  crop 
residues. 

The amount of P and K of the 
residues supplied to the field 
through 
residues  were 
allocated  to  the  crop  that 
generates them 

 
 
 
 
 
 
Not reported 

Allocation not performed  

N inputs and 
emissions 
allocation 

the 

organic 

fertiliser 

nitrogen 
The 
available  for  the  crop  to 
which 
is 
applied,  is  allocated  to  that 
crop.  The  remaining  fraction 
to 
contributes 
that 
increasing 
stock  of 
the 
organic matter was allocated 
in  the 
to  all  the  crops 
rotation.  Mineral  nitrogen  is 
completely  allocated  to  the 
crop to which it was applied.  

4.1.2  Foreground system analysis  

The authors  compared  the activities modelled in the foreground system of the datasets 
referring  to  seven  field  activities:  agricultural  operations,  fertilisers  application  and 
nutrients  environmental  fate,  plant  protection  products  (PPP)  application  and  related 
environmental fate, heavy metals (HMs) input and related environmental fate, irrigation 
and land occupation and transformation. The average crop yields reported in the dataset 
presents  small  differences  (wheat  min-max;  barley  min-max,  rapeseed  min-max;  pea 
min-max).  The  95%  confidence  interval  of  the  yields  is  defined  only  in  Agrifootprint 
datasets, whereas it is not reported in Agribalyse and Ecoinvent datasets.  

For completeness, findings and aspects to be taken into account are below reported. The 
below checklist can also be used to build a completeness check of the different datasets 
and it can be used to build a sensitivity analysis based on the parameters that are seen 
to be affecting the final results. 

Agricultural practices modelling  

  Management of agricultural residues 
  Composition of the crop rotation 
  Allocation to the co-products 

Agricultural operations modelling 

  Number of operations  
  Number of passages 
  Machine power  
  Soil texture  
  Airborne emissions due to fuel combustion  
  Emissions of heavy metals due to tires abrasion   

Fertilisers application and nutrients fate modelling  

  The nutrients fate is highly influenced by site-specific conditions 

o  environmental conditions,  
o  soil type,  
o  agricultural management practices  
o 
o  spatial differentiated emissions when modelling agricultural systems  
  Nomenclature (e.g. the paper shows that phosphorus compounds emissions were 
expressed in the datasets with different flows, limiting the possibility of making a 
comparison among inventory data). 

type of fertiliser (Brentrup et al., 2000)  

PPP application and environmental fate modelling  

18 

 
 
 
  application method,  
  weather and soil conditions,  
 
 
  active substances whose use is not anymore authorised   

crop characteristics  
irrigation 

Heavy metals inputs and environmental fate modelling  

  Mass balance  
  Fate  

Irrigation modelling  

Irrigation volumes  

 
  Water flows type 
  Environmental conditions 
  Spatially-differentiated water flows  

Land use transformation modelling  

  Amounts of transformed land are considered.  
  Type of land transformed (e.g. from … to …)  
  CO2 emissions due to land transformation  

4.2  Data quality 

As  data  is  essential  in  all  quantitative  studies  like  LCAs,  so  is  also  their  quality. 
Therefore,  the  data  applied  in  the  life  cycle  inventory  phase  shall  be  assessed  in  the 
interpretation  phase  against  the  goal  and  scope  of  the  study:  is  the  data  quality 
sufficient  for  the  goal  of  the  study?  Is  the  data  consistent?  Can  the  data  be  seen  as 
complete? 

If the answer is yes, then the interpretation can be preceded and conclusions drawn. If 
no,  then  either  the  goal  and  scope  need  to  be  adjusted,  or  probably  in  most  cases 
better,  the  life  cycle  inventory  phase  need  to  be  revisited  in  an  iterative  manner  to 
improve the data quality to the appropriate level. The assessment of the data quality is a 
mandatory reporting element in the interpretation phase. 

ISO  14044  (2006)  defines  data  quality  as  "characteristics  of  data  that  relate  to  their 
ability  to  satisfy  stated  requirements".  The  data  quality  requirements  are  described  in 
the  goal  and  scope  of  the  LCA  study  and  covers  the  following  10  aspects  (ISO14044, 
2006, ch. 4.2.3.6): 

1)  time-related  coverage:  age  of  data  and  the  minimum  length  of  time  over  which 

data should be collected; 

2)  geographical  coverage:  geographical  area  from  which  data  for  unit  processes 

should be collected to satisfy the goal of the study; 

3)  technology coverage: specific technology or technology mix; 

4)  precision:  measure  of  the  variability  of  the  data  values  for  each  data  expressed 

(e.g. variance); 

5)  completeness: percentage of flow that is measured or estimated; 

6)  representativeness:  qualitative  assessment  of  the  degree  to  which  the  data  set 
reflects  the  true  population  of  interest  (i.e.  geographical  coverage,  time  period 
and technology coverage); 

7)  consistency: qualitative assessment of whether the study methodology is applied 

uniformly to the various components of the analysis; 

19 

 
 
 
8)  reproducibility:  qualitative  assessment  of  the  extent  to  which  information  about 
the  methodology  and  data  values  would  allow  an  independent  practitioner  to 
reproduce the results reported in the study; 

9)  sources of the data; 

10)  uncertainty of the information (e.g. data, models and assumptions). 

In the EF methods7, the consistency and  reproducibility is one  of the  main aims of any 
EF study and not evaluated as part of the data quality assessment. Data sources should 
be  ILCD/EF  compliant  and  specified  if  a  PEFCR/OEFSR  is  followed.  Precision  and 
uncertainty are evaluated together in the EF method. Therefore, the EF operates with six 
semi qualitative data quality requirements instead  of 9 in  ISO14044.  (cf.  section 5.6 in 
European Commission, 2013). 

In order to evaluate the appropriateness and the quality of the data used to compile the 
LCI, they have to be scored against certain requirements. As an example the table below 
(Table 11) provides the data quality requirements for a PEF study of intermediate paper 
products (EC 2013a, Schau and Davidsson 2015). 

The  table  12  shows  the  semi-qualitative  data  quality  rating  (DQR)  (based  on  expert 
judgement)  of  the  most  impacting  processes  in  an  early  phase  of  a  PEF  study  of 
intermediate packaging paper.  The data set  DE: Potato starch, at plant was deemed to 
be of too low quality (DQR = 2.67), as it scored poor (4) on  Parameter uncertainty and 
Methodological  Appropriateness  and  Consistency  to  be  used  for  comparative  assertion. 
Therefore, another dataset was searched for that was compliant with the goal and scope 
of the study. 

It is also recommended that the practitioner is aware of the methodological choices used 
to  build  the  datasets  (may  be  evaluated  under  Methodological  Appropriateness  and 
Consistency),  such  as  the  definition  of  the  system  boundaries  between  nature  and 
technosphere and the time-horizons of the emissions (short-term/long term) 

System  boundaries  between  nature  and  technosphere  are  especially  relevant  for  food 
and  agricultural  product  (and  products  made  thereof)  as  for  these  products  the 
technosphere  is  closely  interlinked  with  nature  (Schau  et  al  2008).  This  includes 
modelling of pesticides and fertilizers (De Schryver and Galatola 2016). 

The time-horizons of the emissions are essential, especially for long lived product and for 
end-of-life  processes  (e.g.  landfilling,  waste  handling  of  residues  from  mining  in  raw 
material extraction) (Guo and Murphy, 2012). Also how radioactive waste is modelled is 
essential  to  assess  with  a  time  horizon  in  mind.  Often,  a  distinction  is  made  between 
long  term  and  short  term  emission.  Where  the  boundary  is  set  between  those  (100 
years,  300  years,  50  000  years  etc.)  can  therefore  be  important  and  should  be  taken 
into  account  when  assessing  the  data  quality  for  methodological  appropriateness  and 
consistency (see chapter 4.3.3). 

Database providers publish their modelling principle (see e.g. Baitz et al, 2016), which is 
an essential source for evaluating and assessing the data quality (of background data) in 
addition to the metadata of the LCI data itself. 

Table 11 Data quality rating in an example of intermediate paper products (EC 2013, Schau and Davidsson 2015). 

7 In  the  EF  Guide  the  term  "representativeness”  is  used  instead  of  "coverage”  used  in  ISO  14044  for  the 
geographical  and  time-related data  quality requirement.  The  term  “parameter  uncertainty”  is  used  instead of 
“precision” used in ISO14044 and the term “methodological appropriateness and consistency” is used in the EF 
Guide instead of “consistency” used in ISO14044. 

20 

 
 
 
 
                                           
 
Representativeness 

Data quality elements 

l

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D

Meets the 
criterion to a 
very high 
degree, 
without need 
for 
improvement.   

 E.g. Process is 
same. For 
electricity from 
grid, average 
technology as 
country- specific 
consumption mix.   

Country 
specific data 

 ≤ 3 
year
s old 
data   

 Very good 
completene
ss  (≥ 90 %)   

Full compliance with 
all requirements of 
the PEF guide   

 Very low 
uncertainty (≤ 
7 %)   

g
n
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t
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Q

1 

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Very 
goo
d    

Goo
d    

2 

Meets the 
criterion to a 
high degree, 
with little 
significant 
need for 
improvement.   

E.g. average 
technology as 
country- specific 
consumption mix.   

Central 
Europe, 
North 
Europe, 
representativ
e EU 27 mix,   

 3-5 
year
s old 
data   

Good 
completene
ss (80 % to 
90 %)   

 Fair     3 

Meets the 
criterion to an 
acceptable 
degree, but 
merits 
improvement.   

E.g. average 
technology as 
country- specific 
production mix or 
average 
technology as 
average EU 
consumption mix.   

EU-27 
countries, 
other 
European 
country   

 5-
10 
year
s old 
data   

Fair 
completene
ss (70 % to 
80 %)   

Poor    

4 

Does not meet 
the criterion to 
a sufficient 
degree, but 
rather requires 
improvement.   

E.g. average 
technology as 
country- specific 
consumption mix 
of a group of 
similar products   

Middle east, 
North-
America, 
Japan etc.   

 10-
15 
year
s old 
data   

Poor 
completene
ss (50 % to 
70 %)   

Very 
poor 

5 

Does not meet 
the criterion. 
Substantial 
improvement 
is necessary.   

E.g. other process 
or unknown, not 
available (n.a.)   

Global data 
or unknown   

 ≥ 
15 
year
s old 
data   

Very poor or 
unknown 
completene
ss (< 50 %)   

Attributional 
Process based 
approach AND 
following three 
method 
requirements of the 
PEF guide met: (1) 
Dealing with multi-
functionality; (2) End 
of life modelling; (3) 
System boundary.   
Attribution Process 
based approach 
AND two of the 
following three 
method 
requirements of the 
PEF guide met: (1) 
Dealing with multi-
functionality; (2) End 
of life modelling; (3) 
System boundary.   
Attributional 
Process based 
approach AND one 
of the following 
three method 
requirements of the 
PEF guide met: (1) 
Dealing with multi-
functionality; (2) End 
of life modelling; (3) 
System boundary.   
Attributional 
Process based 
approach BUT: 
None of the 
following three 
method 
requirements of the 
PEF guide met: (1) 
Dealing with multi-
functionality; (2) End 
of life modelling; (3) 
System boundary.   

Low 
uncertainty  
(7 % to 10 %)   

Fair 
uncertainty 
(10 % to 15 
%)   

High 
uncertainty 
(15 % to 25 
%)   

Very high 
uncertainty  
(>25 %)   

Table 12 Data quality rating in an early phase of a PEF study of intermediate packaging paper 

21 

 
 
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Representativeness 

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Data set 

CH: building, hall, steel construction 

CH: disposal, sludge from pulp and paper 
production, 25% water, to sanitary landfill 

CH: disposal, steel, 0% water, to municipal 
incineration 

DE: lignite briquettes, at plant 

DE: maize starch, at plant 

DE: potato starch, at plant 

RER: AKD sizer, in paper production, at plant 

RER: aluminium sulphate, powder, at plant 

RER: building, multi-storey 

RER: electricity, medium voltage, production 
RER, at grid 

RER: facilities, chemical production 

RER: heavy fuel oil, at regional storage 

RER: heavy fuel oil, burned in industrial 
furnace 1MW, non-modulating <u-so> 

RER: industrial wood, hardwood, under bark, 
u=80%, at forest road 

RER: industrial wood, softwood, under bark, 
u=140%, at forest road 

RER: natural gas, high pressure, at consumer 

RER: paper mill, integrated <u-so> 

RER: PEFEmissions-RF <u-so> 

RER: PEFEmissions-VF v.03 <u-so> 

RER: sodium chlorate, powder, at plant 

RER: sodium dithionite, anhydrous, at plant 

RER: sodium hydroxide, 50% in H2O, 
production mix, at plant 
RER: steel, converter, chromium steel 18/8, at 
plant 
RER: steel, electric, chromium steel 18/8, at 
plant 

RER: sulphate pulp, ECF bleached, at plant 

RER: sulphate pulp, unbleached, at plant 

RER: sulphuric acid, liquid, at plant 

RER: transport, freight, rail 

RER: transport, lorry >16t, fleet average 

RER: Water Withdrawals-VF <u-so> 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

3 

3 

3 

3 

3 

3 

3 

3 

3 

2 

3 

3 

3 

3 

3 

3 

3 

3 

2 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

2 

3 

3 

3 

3 

3 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

1 

22 

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a
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3 

3 

3 

3 

3 

4 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

4 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

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1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

4 

1 

1 

1 

1 

3 

2 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

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4 

4 

4 

4 

4 

4 

4 

4 

4 

3 

4 

4 

4 

4 

4 

4 

4 

3 

3 

4 

4 

4 

4 

4 

4 

4 

4 

4 

4 

3 

  Resulting  
y
c
Data  
n
e
t
Quality  
s
i
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Rating  
n
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C
(DQR) 

2.50 

2.50 

2.50 

2.50 

2.50 

2.67 

2.33 

2.33 

2.33 

2.00 

2.33 

2.33 

2.83 

2.33 

2.33 

2.33 

2.33 

2.50 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

2.33 

1.83 

 
 
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
4.3  Further completeness and consistency checks 

In  this  section  some  further  checks  are  presented,  referring  both  to  completeness  and 
consistency checks. 

4.3.1  Anomaly assessment 
An  anomaly  assessment  can  be  done  where,  based  on  previous  experience,  unusual  or 
surprising  deviations  from  expected  or  normal  results  are  observed  and  examined  for 
relevance (Skone, 2000). 
As a practical example, it can be considered the case of LCA studies of mining activities. 
When  the  production  of  e.g.  copper  concentrates  was  assessed  adopting  an  impact 
assessment  method  such  as  ILCD  and  performing  the  hotspot  analysis  as  described  in 
chapter  3  of  this  report,  it  was  found  that  the  identification  of  most  relevant  impact 
categories  did  not  include  Resource  Depletion,  fossil,  mineral.  In  addition,  the  most 
relevant  processes  and  life  cycle  stages  contributing  to  this  impact  category  were  not 
related to the ones involved in the copper extraction. In the case under analysis, it was 
found that elementary flows related to extraction of resources from nature were missing 
from the inventory, because the dataset used in the analysis did not include in its goal 
and scope the assessment of Resource Depletion, fossil, mineral. 
A learning was that it is true that it is important to evaluate what is most relevant (e.g. 
impact  categories,  life  cycle  stages,  processes,  elementary  flows),  however  it  is  also 
important to check if what is found as being non-relevant can be actually considered as 
such.  Hence,  it  is  important  to  evaluate  if  the  LCI  modelled  is  complete  with  regard  to 
the impact categories that the assessor wants to evaluate. 

4.3.2  Cut-off 
ISO  14044  identifies  cut-off  as  the  “specification  of  the  amount  of  material  or  energy 
flow or the level of environmental significance associated with unit processes or  product 
system  to  be  excluded  from  a  study”.  ISO  14044  also  requires  that  the  effect  on  the 
outcome  of the study  of the cut-off  criteria selected shall be assessed and described in 
the final report. 
Cut-off criteria can be based on: 

  Mass: e.g. <5% cumulative mass input 
  Energy: e.g. <5% cumulative energy input 
  Environmental significance: e.g. <5% of the results of the selected impact categories 

It is important to note that the cut-off criteria need to be selected based on the goal and 
scope  of  the  study.  Therefore,  if  the  study  is  intended  to  be  used  for  comparisons  and 
comparative  assertions,  a  sensitivity  check  using  all  the  above  three  criteria  is  a 
mandatory requirement. 

4.3.3  Long term emissions 
Long term emissions are those emissions considered to happen beyond 100 years, up to 
thousands of years (e.g. 60000).  
Not  all  databases  include  long-term  emissions  within  the  system  boundaries.  In  some 
cases,  it  can  be  observed  that  LCA  results,  after  characterization,  normalization  and 
weighting  are  by  far  dominated  by  toxicity-related  impact  categories.  When  this  is  the 
case,  it  is  recommended  to  check  if  the  datasets  used  include  long  term  emissions, 
which  in  terms  of  absolute  amounts  are  well  superior  compared  to  emissions  occurring 
within  100  years.  It  has  also  to  be  noted  that  the  models  to  estimate  long  term 
emissions  are  affected  by  a  high  uncertainty which  has  to  be  taken  into  account  in  the 
LCA study, to avoid biased conclusions and wrong recommendations. 
When  dealing  with  long  term  emissions,  it  is  important  however  not  to  overlook  their 
potential impact, therefore it is recommended to: 

  Assess a baseline scenario, excluding long-term emissions 
  Perform a sensitivity analysis, including long-term emissions in the LCI. 
 

Include  an  analysis  of  the  uncertainty  associated  to  the  inclusion  of  long-term 
emissions. 

23 

 
 
24 

 
 
 
 
5  Interpretation  on  Life  Cycle  Impact  Assessment  (LCIA) 

level 

Life  Cycle  Impact  Assessment  is  the  phase  in  an  LCA  where  the  inputs  and  outputs  of 
elementary flows that have been collected and reported in  the inventory are translated 
into impact indicator results. LCIA has two mandatory steps, which are the classification 
and  the  characterization  of  LCI  results  and  two  optional  steps:  normalisation  and 
weighting. 

Many  elements  can  be  subject  to  interpretation  in  the  LCIA  phase.  In  this  report  the 
focus will be on: 

 
 
 
 

 

the relevance of applying different characterization models, 
the relevance of applying different normalisation sets, 
the relevance of applying different weighting sets, 
the identification of the most sensitive elements in determining the final results, 
conclusions and recommendations, 
the coverage  of characterization factors compared to the inventoried elementary 
flows 

Those points will be illustrated in the following section through illustrative case studies.  

First,  a  case  study  dealing  with  sensitivity  analysis  of  using  different  characterization 
models for toxicity-related and resource-related impact categories will be discussed. 

Second, a case study dealing with using different impact assessment methods. This case 
study will also be used to go through sensitivity analysis in relation to normalisation sets 
and weighting sets.  

Third,  a  case  study  mapping  the  coverage  of  characterization  factors  of  toxicity-related 
categories compared to the inventoried elementary flows will be shown. 

5.1  Characterization: sensitivity analysis 

The  use  of  different  characterization  models  to  assess  a  certain  impact  category,  may 
play  a  relevant  role  in  the  calculation  of  the  final  results  and  the  identification  of 
hotspots, most relevant life cycle stages, processes and elementary flows.  

5.1.1  Case study 1 – WEEE management: Toxicity-related and Resource-
related impact categories affecting interpretation of results  

The first case study is based on a work by Rigamonti et al. (2017); the authors selected 
a  case  study  in  which  LCA  has  been  applied  to  assess  a  waste  electrical  and  electronic 
equipment  (WEEE)  management  system  (Biganzoli  et  al.,  2015).  In  that  study,  the 
assessment  was  carried  out  to  quantify  the  mass  balance  of  the  WEEE  management 
system in Lombardy Region in the year 2011 and to calculate its environmental benefits 
and burdens. 

Firstly,  they  tested  characterization  models  and  characterization  factors  (CFs)  for 
toxicity-related  impact  categories:  human  toxicity  cancer  and  non-cancer  and  eco-
toxicity.  There  is  an  evolving  debate  on  the  robustness  of  these  impact  categories  and 
specifically for what concern impacts due to metals (see e.g. Pizzol et al., 2011). In fact, 
some  specific  features  of  the  metals  (e.g.  essentiality),  as  well  as  elements  affecting 
their fate modelling (e.g. different conditions affecting their bioavailability), are not fully 
captured  by  currently  available  models  applied  in  LCA.  Therefore,  the  authors  ran  a 
sensitivity analysis where updated CFs for metals were tested, along the lines of recent 
literature  (Dong  et  al.,  2014)  aiming  at  identifying  the  different  results  associated  to 
metals  impact,  compared  to  the  ILCD  method  (EC-JRC,  2011),  based  on  the  USEtox 
model (Rosenbaum et al. 2008). 

25 

 
 
 
The total impact obtained by applying Dong et al. 2014 CFs  resulted about three times 
higher than the corresponding obtained by the USEtox model. This is mainly due to the 
Dong et al. 2014 CFs of Copper (Cu) and Zinc (Zn) which are higher than those adopted 
in  the  USEtox  model  when  considering  the  freshwater  archetype  proposed  in  the  paper 
of Dong as default when the location of the emission is not known. 

Secondly, they tested impact assessment models for resource. Models currently available 
differ  both  in  the  modeling  approach,  in  the  perspective  adopted  for  assessing  the 
resources (Dewulf et al., 2015) and, as consequence, in the characterization metric and 
factors adopted (Mancini et al., 2016). Beyond the potential benefit associated to a mass 
based  approach  to  recycling  (e.g.  kg  of  material  recycled),  there  is  indeed  the  need  of 
understanding  to  which  extent  the  recycled  materials  are  contributing  to  the  resource 
depletion impact categories. The authors tested: abiotic depletion potential (ADP) (CML, 
2012),  which  is  focusing  on  potential  depletion  based  on  the  ratio  between  resource 
consumption and availability (either considering ultimate reserves in earth crust, known 
base reserves, or economically viable reserves); ILCD model (EC-JRC, 2011), which is an 
extended  version  of  CML2012  reserve  based  (based  on  CML  algorithm,  few  other 
resources  have  been  added  described  in  Sala  et.  al,  2012);  EDIP97  (Hauschild  and 
Wenzel,  1998),  which  is  comparing  the  resource  with  the  deposits  economically 
exploitable,  without  accounting  for  current  level  of  consumption;  EPS2000  (Steen, 
1999),  which  assesses  the  cost  (as  society’s  willingness  to  pay)  of  substituting  a 
substance by an alternative for future generations affected by current level of depletion; 
Recipe 2008 (Goedkoop et al., 2009), which is looking at marginal increase of extraction 
cost per kg  of  extracted resource, differentiating it by deposit and assuming a discount 
rate  over  an  indefinite  timespan;  the  Anthropogenic  Abiotic  Depletion  Potential  (AADP, 
Schneider  et  al.,  2011),  which  is  accounting  for  the  potential  of  resource  recycling, 
assuming  urban  mining  as  an  additional  source  of  resources.  The  recent  update  of  the 
AADP  (Schneider  et  al.,  2015)  was  also  considered.  It  introduces  the  concepts  of 
"ultimately  extractable  reserves"  represented  by  the  amount  available  in  the  upper 
earth's crust that is ultimately recoverable. A new set of CFs were also tested, developed 
by Mancini et al. (2016) aiming at addressing the benefit associated to the  recovery of 
Critical  Raw  Materials  (CRMs).  The  characterization  model  is  based  on  the  use  and 
adaptation  to  LCA  of  the  supply  risk  indicators  developed  by  the  European  Commission 
(EC,  2014).  Three  sets  of  CFs  are  tested,  based  on  different  assumptions:  (1)  baseline 
option,  the  supply  risk  factors  as  such  –  (SR);  (2)  an  exponential  function  which 
magnifies the differences between the CRMs - (SR)^6; (3) the ratio between supply risk 
and production data (SR/world mine production in 2011), which reflects the  size of the 
market, giving more importance to the materials used in small amounts in products and 
applications, like, e.g., specialty metals, that are often perceived as critical. 

The comparison among the different models was first of all made considering the mineral 
resources present in the inventory of the case study covered by each of them. The ILCD 
method is the most comprehensive one with 31 mineral resources included in its model 
for the resource depletion category. On the contrary, the Supply Risk (3) is the one that 
covers the minor number of substances (i.e. 7).  

A  contribution  analysis  was  also  performed  (Figure  3).  Silver  (Ag)  gives  the  main 
percentage contribution to the indicator in five models (i.e. CML 2012 versions b and c, 
ILCD,  Supply  Risk  3),  and  EDIP97).  Cobalt  (Co)  is  the  main  contributor  in  the  Supply 
Risk  (2),  Copper  (Cu)  in  the  ReCiPe  2008,  Palladium  (Pd)  in  the  EPS  2000  and  AADP 
(2015),  and  Nickel  (Ni)  in  the  AADP  (2011).  In  the  CML  2012  (a)  Tellurium,  Gold  (Au) 
and  Silver  (Ag)  are  the  most  important  substances,  whereas  Aluminium  (Al),  Copper 
(Cu) and Nickel (Ni) are the main contributors in the case of the Supply Risk (3).  

26 

 
 
 
Figure  3  Contribution  analysis  for  the  resource-  related  indicator  calculated  adopting  several  characterization  models 
(Rigamonti et al., 2017) 

5.1.2  Case  study  2  –  Basket  of  Products  Food:  sensitivity  analysis 

comparing different impact assessment methods  

This  case  study  is  based  on  the  application  of  different  life  cycle  impact  assessment 
methods for the evaluation of the environmental impacts associated to food consumption 
in  Europe.  A  process-based  LCA  has  been  performed  for  a  basket  of  products  that 
represent  the  most  relevant  food  product  groups,  selected  by  importance  in  mass  and 
economic  value,  to  depict  the  average  consumption  for  nutrition  of  EU  citizens  in  2010 
(Notarnicola  et  al.  2017b).  The  product  groups  in  the  basket  were:  pig  meat,  beef, 
poultry,  milk,  cheese,  butter,  bread,  sugar,  sunflower  oil,  olive  oil,  potatoes,  oranges, 
apples, mineral water, roasted coffee, beer, pre-prepared dishes. For each product group 
in  the  basket,  an  inventory  model  based  on  a  representative  product  has  been 
developed.  The impact  of  each representative  product  was then multiplied by the mass 
of products in that product group that is consumed in one year by an average EU citizen.  

The  environmental  impact  of  the  average  food  consumption  of  a  European  citizen  has 
been  characterized  using  ILCD  method  for  the  life  cycle  impact  assessment  (EC-JRC, 
2011). The paper by Notarnicola et al. (2017) illustrates the details of the process-based 
LCA  study  on  the  BoP  nutrition  for  EU  27  citizens  and  presents  the  results  of  the 
characterisation  phase.  The  variability  of  the  results  associated  to  methodological 
choices  has  been  already  preliminary  explored  (Castellani  et  al.  2017).  The  present 
report  builds  on  those  results  and  elaborates  the  interpretation  phase  (including  also 
normalisation and weighting). The questions that  these analyses help answering are:  i) 
are the products found to have the highest impact sensitive to changes in LCIA elements 
(characterisation factors, normalization references or the weighting sets)? and ii) which 
are the most contributing impact categories?  

This  case  study  will  also  be  used  in  paragraph  5.2,  5.3  and  5.4  as  a  basis  for  further 
insights on the interpretation of an LCA study. 

For  this  specific  case,  the  authors  performed  a  sensitivity  analysis  on  the  LCIA 
characterisation phase, using ILCD compared with other two methods: the CML-IA, v 4.2 
(Guinée et al. 2002) and the Ecological footprint (EcF) (Wackernagel et al. 2005). CML-
IA was chosen because it is widely used in LCA studies and because it adopts a different 

27 

 
 
 
modelling  approach  compared  to  ILCD  in  several  impact  categories  that  could  be 
relevant  for  food  chains.  For  instance,  for  toxicity-related  impact  categories  CML-IA  is 
based  on  USES-LCA  model  (Van  Zelm  et  al.  2009),  whereas  ILCD  is  applying  USETOX 
model  (Rosenbaum  et  al.  2008);  for  eutrophication,  CML-IA  is  using  RAIN-LCA 
(Huijbregts,  1999)  whereas  ILCD  is  using  different  models  for  terrestrial  and  aquatic 
eutrophication  (respectively  accumulated  exceedance  by  Seppälä  et  al.  2006,  and 
EUtrend  model  as  applied  by  Struijis  et  al.  2009).  The  EF  was  chosen  because  it 
assesses  impacts  in  terms  of  direct  and  indirect  land  use  and  has  been  considered  a 
method  that  may  complement  LCA  (Lee  et  al.  2014)  although  based  on  an  accounting 
framework that has been questioned (Giampietro and Saltelli 2014). The version of EcF 
method  implemented  in  the  study  is  the  one  already  adapted  to  LCA,  and  provided 
within the SimaPro 8.0.5 software package (Prè Consultants, 2015). 

The three methods are  mostly convergent in the identification of the product groups to 
be considered hotspots within the BoP nutrition, i.e. within the food consumption of the 
European citizens. ILCD and CML-IA identify a wider range of hotspots compared to EcF, 
mainly because they cover a wider range of impact categories (e.g. bread and potatoes 
do not emerge as hotspot for EcF, because their main impacts are in impact categories 
not covered by the EcF method). 

The  product  groups  that  emerge  as  hotspots  in  all  the  methods,  even  if  with  slightly 
different level of  contribution, are meat and dairy products and beer  (as representative 
product for alcoholic drinks).  

The impact categories for which beer emerge as hotspot are slightly different among the 
three  methods:  ozone  depletion,  ionizing  radiation  and  resource  depletion  in  ILCD  and 
CML-IA,  photochemical  ozone  formation  in  ILCD,  human  toxicity  and  marine  aquatic 
ecotoxicity in CML-IA and nuclear energy in EcF. The most contributing processes for all 
the impact categories listed before is energy (electricity and heat), as already discussed 
for ILCD. In the case of CML-IA method, toxicity-related impacts are mainly due to direct 
emissions to air coming from the production process of the packaging glass. 

5.1.3  Conclusions and recommendations 

These results reveal that the relative importance of one substance compared to another 
changes  with  the  model.  This  is  because  the  models  differ  in  the  substances  that  they 
include and in the CFs of each substance.  It is recommended to run a sensitivity check 
with  different  characterization  models  to  evaluate  the  robustness  of  the  conclusions 
drawn.  It  is  also  recommended  to  evaluate  the  theory  underlying  the  characterization 
models  chosen  to  evaluate  a  certain  impact  category,  as  this  helps  understanding  the 
results obtained. 

5.2  Characterization: mapped flows and unmapped flows  

This section shows two different levels of checks: the first one focuses on a comparison 
between two different life cycle impact assessment methods (i.e. ILCD vs CML-IA) based 
on  Castellani  et  al.  (2017),  the  second  one,  focuses  on  what  are  the  implications  of 
uncharacterized  flows  for  a  specific  impact  category  (freshwater  ecotoxicity),  based  on 
the case study presented in Notarnicola et al. (2017b). 

5.2.1  Comparison between two different methods 

A check of the inventoried elementary flows that are not characterized at the LCIA phase 
should  always  be  done  to  support  interpretation  of  results.  Indeed,  the  comprehensive 
translation of the inventory into potential environmental impacts, may not occur if some 
of the elementary flows are not covered by the chosen characterization models. Table 13 
reports  an  example  of  the  total  amount  of  elementary  flows  that  are  available  in  an 
inventory but they are not mapped, and characterized, by the methods considered (ILCD 
and  CML-IA),  as  reported  in  Castellani  et  al.  (2017).  The  authors  checked  the  list  of 
uncharacterized  flows  for  the  inventory  of  the  case  study  under  evaluation  and 

28 

 
 
highlighted  the  most  relevant  ones,  either  in  term  of  mass  or  of  known  environmental 
concern.  The  information  obtained  was  then  used  in  support  to  interpretation.  For 
instance, in case the two methods used for characterisation and normalisation give very 
different  results  for  one  impact  category,  the  information  about  uncharacterized  flows 
can help to check if this difference is due to a different characterisation of impacts (i.e. 
to a different way of considering the environmental relevance of some elementary flows) 
or if it is affected by the fact that some flows (e.g. some emissions) are not taken into 
account by one of the methods. 

This may occur also due to improper nomenclature of the elementary flows, which may 
cause  a  mismatch  between  the  elementary  flows  listed  in  the  inventory  and  the 
elementary  flows  to  which  a  characterization  factor  is  available:  for  example,  the 
inventory  may  report  CaCO3,  while  the  characterization  model  may  provide  a  CF  for 
calcite. This mismatch would prevent the characterization of CaCO3. 

Table 13 Example of check of inventoried elementary flows that are not characterized by two impact assessment methods 
(based on Castellani et al. 2017) 

Flows 

Typologies 
of flows 

Total  elementary 
flows 
not 
characterized 
ILCD 

CML-
IA 

raw 
materials  

97 

510 

metals  and 
minerals 
water flows 
Energy 
land use 

emissions  to 
soil  

emissions  to 
water  

83 

227 

167 

310 

emissions  to 
air  

179 

286 

47 

25 
0 
11 

14 
45 

38 
42 

34 

91 
49 

65 

7 
70 
17 

others 
pesticides  to 
soil 
others 
radioactive 
substances 
metals  and 
inorganics 
compounds 
others 
radioactive 
substances 
metal 
inorganics 
compounds 
pesticides 
others 
waste 

and 

Details on the elementary flows not characterized 

ILCD 
Nr flows 

Amount 

Unit 

Amount 

CML-IA 
Nr 
flo
ws 
47 

86.3 

300 
12 
117 

34 
173 

54 
61 

382.5 
1896.4 
1289.8 

n.a. 
0.08 

4.1 
3.85E+06 

79.5 

1.26 
0 
0.01 

n.a. 
0.003 

kg 

(or 

m3 
MJ 
m2 
m2a) 
mixed 
kg 

0.8 
kg 
1.19E+03  Bq 

2.8 

kg 

36 

2.8 

Unit 

kg 

(or 

m3 
MJ 
m2 
m2a) 
mixed 
kg 

kg 
Bq 

kg 

n.a. 
8.99E+04  Bq 

mixed 

213 
73 

n.a. 
9.73E+07 

mixed 
Bq 

0.07 

kg 

56 

0.044 

kg 

5.80E-08 
n.a. 
37.84 

kg 
mixed 
kg 

32 
125 
17 

9.3 
n.a. 
37.84 

kg 
mixed 
kg 

17 
543 

17 
1350 

waste flows 
Overall 
number  of 
elementary 
flows 
not 
characterize
d 
In order to assess which elementary flows available in the inventory were not accounted 
during  the  characterization,  the  authors  performed  an  analysis  of  the  elementary  flows 
not  characterized  when  using  ILCD  and  CML-IA  methods.  In  total,  the  model  of  the 
basket  includes  1730  elementary  flows.  The  ILCD  method  is  not  able  to  cover  543  of 
these elementary flow as emission into air, water, soil or resource used, whereas when 
CML-IA is used to characterize the inventory, 1350 elementary flows out of 1730 are not 

29 

 
 
  
characterized. In Table 1 the list of main typologies of flows is reported. Many pesticides, 
organic  chemicals  and  inorganics  are  not  characterized  by  the  two  methods.  Several 
ionizing substances are not characterized as well as elementary flows related to group of 
substances (such as AOX, PAH, BTEX, VOC, BOD and COD). Missing the CFs means that 
it  is  not  possible  to  estimate  the  extent  to  which  e.g.,  the  toxicity-related  and  ionizing 
radiation-related impact categories are underestimated. 

Apart from the total number of flows covered, the main difference between the two LCIA 
methods is the ability to assign a characterization factor (CF) to the pesticides included 
in  the  inventory.  ILCD  has  no  CFs  for  45  pesticides’  emission  to  soil  (plus  4  pesticides’ 
emissions  to  air),  whereas  CML-IA  for  173  (plus  32  pesticides’  emissions  to  air).  This 
may be the reason why human toxicity impacts appear as relevant in the results of ILCD 
characterization (and related normalization and weighting, as discussed below) and they 
appear  as  less  relevant  in  the  results  of  CML-IA  characterization  and  related 
normalization and weighting.  

5.2.2  Analysis of uncharacterized flows within an impact category 
Based  on  the  study  of  Castellani  et  al.  (2017),  fourteen  impact  categories  (IC) 
recommended  in  PEF  are  calculated.  The  number  of  elementary  flows  (ElFls)  by  ICs 
varies from few flows (eutrophication, acidification, etc..) to thousands (eco and human 
toxicity) (see table 14).  
For most of the ICs, the contributing elementary flows reported in an inventory are well 
identified and their inclusion in the calculation of the final score is expected to be done 
correctly.  For  climate  change  at  mid-point,  104  elementary  flows  with  their  associated 
CFs are derived from the IPCC 2007 report for a 100-year period.  
However, for impact categories like toxicity, the number of elementary flows contributing 
to  the  impact  can  be  very  important  making  possible  the  omission  of  some  EFs  at  the 
inventory  level.  Additionally,  a  high  number  of  flows  reported  in  the  inventory  have  no 
CFs.  If  the  number  of  un-characterized  flows  is  too  high,  this  can  lead  to  a  non-robust 
interpretation of the LCIA results. 

Table  12  shows  the  number  of  elementary  flows  with  associated  a  CF  (i.e.  the  single 
chemical) and the amount of elementary flows with a CF per different compartment (e.g. 
the single chemical in each compartment and sub-compartment) 
Table 14 List of impact categories listed in ILCD with total number of elementary flows contributing to each IC. 

Impact Category 

Climate change 
Ozone Depletion 
Human toxicity non cancer 

Human toxicity cancer 

Particulate matter 
Ionizing radiation Human Health 
Photochemical ozone formation 
Acidification 
Terrestrial eutrophication 
Freshwater eutrophication 
Freshwater ecotoxicity 

fossil  use  &  Resource 

Land use 
Water source depletion  
Mineral, 
depletion 
Marine eutrophication 
Additional IC not used in PEF 
Ionizing radiation Ecosystem (interim) 

ElFl with CFs (all emission 
compartments) 
104 
23 
3434 

4734 

46 
50 
133 
7 
7 
10 
15636 

206 
1180 
181 

14 

62 

30 

Unique ElFls with CFs (single chemical 
/ flows) 
102 
23 
442  
(USEtox 419 org+18 metals) 

607 

 (USEtox 596 org +8 metals) 

24 
29 
133 
7 
7 
5 
2526  
(USEtox 2498 org +21 metals) 
206 
1180 
181 

11 

15 

 
 
 
 
 
 
The  potential  impact  of  un-mapped  (or  uncharacterized)  flows  is  illustrated  via  a  case 
study focusing the freshwater aquatic toxicity impact category using a recent modelling 
of 17 food products (Notarnicola, 2017b). 

For a high proportion of the total mass of un-characterized ElFls, the impact on the final 
IC scores is null. As a matter of fact, 99.98% of the 1887 kg ElFls emitted to the air are 
made of: air, oxygen and hydrogen. Only 0.33 kg (but still 199 flows) are actual made of 
other  type  of  chemicals:  80%  of  those  0.33  kg  are  nitrogen  type  of  chemicals,  7.4% 
hydrogen  chloride,  1.2%  sulphate,  etc.  (table  15).  However,  regarding  the  ElFls  to  soil 
and  water,  the  mass  and  number  of  un-characterized  flow  could  have  a  significant 
impact on the final score.  
Table  15  Type  of  chemicals  (mass  and  number  of  EFs)  not  characterized  per  emission  compartment  (air,  oxygen  and 
hydrogen have been excluded). 

Compartments 

Group of chemicals 

Mass (g)  Number of ElFl 

Air 

Soil 

Water 

Total air 
N-related 

inorganic/salt 
organic 

S - related 
metal/mineral 

Alkenes 
Benzene, Toluene, Ethylbenzene, and Xylene), unspecified ratio 

ozone 
P-related 

ionising  
Aldehydes 

Terpenes 
PAH 

Pesticide 
Acid 

Total soil 
metal/mineral 

inorganic 
oils 

organic 
Pesticide 

S - related 
N-related 

PAH 
Total water 

inorganic/salt 
TOC water 

metal/mineral 
Suspended solid 

inorganic 
organic 

AOX 
Acid 
S - related 
VOC 

31 

330 
238.662 

74.637 
5.693 

3.896 
3.793 

1.530 
0.488 

0.314 
0.304 

0.261 
0.213 

0.102 
0.034 

0.000 
0.000 

800 
328.315 

320.234 
93.496 

48.702 
5.873 

3.777 
0.000 

0.000 
13871 

7549.785 
2840.410 

1998.366 
1257.655 

108.851 
107.546 

2.390 
2.032 
2.008 
0.643 

122 
2 

44 
36 

1 
17 

2 
1 

1 
4 

4 
1 

1 
1 

6 
1 

68 
15 

4 
3 

2 
39 

2 
2 

1 
112 

22 
4 

13 
6 

6 
37 

1 
2 
5 
1 

 
 
N-related 

Propylene 
oils 

PAH 
Sodium Hydroxide 

Salt, unspecified 
Hypochlorite 

ionising  
VOC  

P- related 

0.511 

0.244 
0.079 

0.055 
0.054 

0.015 
0.013 

0.002 
0.001 

0.000 

3 

1 
2 

2 
1 

1 
1 

2 
1 

1 

Looking at the results of the freshwater aquatic toxicity impact categories for all 17 food 
products, 90% of the impact category is driven by 10 out of 734 elementary flows that 
represents only 1,4% of the mass of chemicals entering the aquatic environment (table 
16). Furthermore, it can be seen that:  

  57%  of  the  toxicity  score  are  due  to  3  metals  representing  0,1  kg  of  the  mass. 
However,  there  are  still  more  than  2  kg  of  un-characterized  metals/minerals  in 
the inventory list (see table 15). 

  33%  of  the  impact  are  due  10  pesticides  (35  g),  although  there  are  still  39  un-
characterized pesticides in the inventory list (representing 5 g) (see table 15).  

Table  16  Most  relevant  elementary  flows,  contributing  cumulatively  up  to  90%  to  the  freshwater  ecotoxicity  impact 
category with USEtox. 

Substance 

Chemical 
class 

Emission 
Compartment 

Sub-compartment 

Total impact 
(CTUe) 

% impact 
versus Total 

% 
Cumulated 
impact 

Mass (kg) 

Zinc 
Copper 
Chlorpyrifos 
Copper 
Zinc 
Folpet 
Chlorothalonil 
Chromium 
Cyfluthrin 
Isoproturon 
Cypermethrin 
Chromium 
Prochloraz 
Captan 
Alachlor 
Metolachlor 

metal 
metal 
pesticide 
metal 
metal 
pesticide 
pesticide 
metal 
pesticide 
pesticide 
pesticide 
metal 
pesticide 
pesticide 
pesticide 
pesticide 

Soil 
Soil 
Soil 
Soil 
Water 
Soil 
Soil 
Water 
Soil 
Soil 
Soil 
Soil 
Soil 
Soil 
Soil 
Soil 

agricultural 
agricultural 
agricultural 

agricultural 
agricultural 

agricultural 
agricultural 
agricultural 
agricultural 
agricultural 
agricultural 
agricultural 
agricultural 

931 
902 
833 
351 
139 
134 
111 
105 
85 
81 
70 
51 
45 
39 
38 
30 

21% 
21% 
19% 
8% 
3% 
3% 
3% 
2% 
2% 
2% 
2% 
1% 
1% 
1% 
1% 
1% 

21% 
42% 
61% 
69% 
72% 
75% 
78% 
80% 
82% 
84% 
85% 
87% 
88% 
88% 
89% 
90% 

0.0441 
0.0429 
0.0079 
0.0120 
0.0036 
0.0004 
0.0019 
0.0020 
0.0001 
0.0086 
0.0010 
0.0019 
0.0024 
0.0039 
0.0041 
0.0050 

Among  the  non-characterized  elementary  flows,  in  relation  to  freshwater  ecotoxicity,  it 
can be observed: 

 

it is not known to which extent some of the chemicals may contribute to the IC; 

  a  number  unspecified  chemicals  such  as  ‘Total  organic  matter’  (TOC,  BOD, 
COD…),  PAH,  VOC  that  could  contribute  significantly  to  the  overall  score.  Since 
the quantity of those ElFl can change significantly between products, a default CF 
could be proposed for those flow 

 

some  chemicals  are  known  to  be  contributing  to  toxicity  like  pesticides  and 
metals, still there are significant proportions that are not characterized. 

32 

 
 
 
 
 
Although  it  is  not  possible  to  conclude  if  those  un-characterized  flows  would  have 
changed  the  total  score  significantly,  based  on  the  mass  and  number  of  known-
contributing class of chemicals (metals and pesticides) it is highly recommended that a 
quantification  of  the  uncharacterized  chemicals  is  reported  for  the  freshwater  toxicity 
impact category before drawing final conclusions.  

5.2.3  Conclusions and recommendations 

Results of the LCIA phase can be highly affected by the characterization model chosen, 
as  shown  in  chapter  5.1  Furthermore,  the  evaluation  of  uncharacterized  elementary 
flows  is  of  primary  importance  to  assess  the  completeness  of  the  LCIA  coverage  of  the 
identified  LCI  flows.  Indeed,  a  high  number  of  elementary  flows  inventoried  might  be 
non-characterized by the chosen models, even if their relevance in the assessed impact 
categories  could  be  of  primary  importance.  Therefore,  it  is  highly  recommended  that  a 
quantification  of  the  uncharacterized  elementary  flows  is  reported  before  any 
interpretation  or  conclusion  is  performed  on  the  superiority  of  one  product  versus 
another.  

5.3  Normalization: sensitivity analysis 

As for the characterisation phase, also the choice of the normalisation reference set can 
highly influence results. Castellani et al (2017) have done a sensitivity analysis based on 
the case study of a basket of product on food (see paragraph 5.1.2 for further details). 

The  authors  report  that  as  an  optional  step,  normalisation  and  weighting  could  be 
applied  to  characterized  LCA  results  to  support  the  identification  of  the  most  important 
impact categories. Given that several sets of normalisation and weighting are available, 
the  authors  tested  some  of  them  in  addition  to  the  ILCD  baseline,  with  the  aim  of 
assessing  the  sensitivity  of  the  results  to  the  choice  of  particular  geographical  and 
temporal  reference  systems  (i.e.  normalization  using  EU  or  global  scales)  as  well  as 
perspectives (i.e. weighting). This was investigated by selecting 3 sets of ILCD-compliant 
normalization  references  (Benini  et  al.  2014,  Benini  et  al.,  2015,  Laurent  et  al.,  2013) 
and  2  sets  of  CML-IA  compliant  normalization  references  (EU  25+3,  year  2000  and 
World,  year  2000  -  Guinée  et  al.,  2002),  -reported  in  Table  17.  In  this  section,  the 
findings  of  the  authors  regarding  normalisation  will  be  discussed,  while  the  findings  on 
weighting will be discussed in section 5.4. 
Table 17 Summary of normalization sets used in the sensitivity analysis in Castellani et al. (2016) 

ILCD Impact Category 

Unit 

Climate change 
Ozone depletion 
Human toxicity, cancer effects 
Human toxicity, non-cancer effects 
Particulate matter/Respiratory inorganics  

Ionizing radiation, human health 
Photochemical ozone formation, human health 
Acidification 
Eutrophication terrestrial 
Eutrophication freshwater 
Eutrophication marine 
Land use 
Ecotoxicity freshwater 

Resource depletion water 
Resource depletion, mineral, fossils and 
renewables 

kg CO2 eq. 
kg CFC-11 eq. 
CTUh 
CTUh 
kg PM2.5 eq. 
kBq U235 eq. (to 
air) 
kg NMVOC eq. 
mol H+ eq. 
mol N eq. 
kg P eq. 
kg N eq. 
kg C deficit 
CTUe 
m3 water eq. 

EC-JRC EU27 
(2010),  
per persona 

EC-JRC Global 
(2010 or 2013),  
per personb 

PROSUITE 
Global (2010 or 
2000),  
per personc 

9.22E+03 
2.16E-02 
3.69E-05 
5.33E-04 
3.80E+00 

1.13E+03 
3.17E+01 
4.73E+01 
1.76E+02 
1.48E+00 
1.69E+01 
7.48E+04 
8.74E+03 

8.14E+01 

7.07E+03 
1.22E-02 
1.24E-05 
1.55E-04 
5.07E+00 

2.41E+02 
4.53E+01 
5.61E+01 
1.64E+02 
6.54E+00 
3.04E+01 
5.20E+06 
3.74E+03 

6.89E+01 

8.10E+03 
4.14E-02 
5.42E-05 
1.10E-03 
2.76E+00 

1.33E+03 
5.67E+01 
4.96E+01 
1.15E+02 
6.20E-01 
9.38E+00 
2.36E+05 
6.65E+02 

2.97E+01 

kg Sb eq. 

1.01E-01 

1.93E-01 

3.13E-01 

33 

 
 
An  additional  analysis  was  performed  to  verify  which  one  between  normalization  and 
weighting  is  the  most  sensitive  step  in  the  identification  of  the  most  relevant  impact 
categories.  The  same  weighting  sets  described  before  were  applied  on  the  results  of 
normalization obtained using the two ILCD-compliant global normalization reference sets 
tested before, namely EC-JRC Global (Benini et al., 2015) and PROSUITE Global (Laurent 
et al., 2013). 

The contribution of product groups (Table 18) and of impact categories (Table 19) to the 
total normalized impact of the BoP nutrition per citizen were analyzed. 
Table 18 presents a summary of the sensitivity analysis on product group contribution at 
the  normalization  stage,  comparing  the  product  group  hotspots  according  to  ILCD  and 
CML-IA methods. Values are expressed as percentage of the normalized value per citizen 
for  that  product  group  with  respect  to  the  total  normalized  impact  per  citizen  (total 
impact  of  nutrition  in  one  year  for  an  average  EU-27  citizen).  Equal  weighting  among 
impact  categories  is  assumed.  Results  at  the  normalization  stage  almost  confirm  the 
hotspots  identified  at  the  characterization  stage:  meat  and  dairy  products  are  again 
hotspots  for  all  the  methods,  whereas  other  products  that  emerged  before  are 
considered less contributing at the normalization stage. This means that those products 
generate impacts in impact categories that are considered less relevant compared to the 
others, according to the normalization scheme adopted. For instance, beer appeared to 
be a hotspot for both ILCD and CML-IA at the characterization stage, whereas only CML-
IA method identifies it as a hotspot at the normalization stage. This happens because the 
CML-IA  normalization  references  (EU  25+3  and  World)  for  marine  aquatic  toxicity  -one 
of the impact categories for which beer is a hotspot - are relatively low compared to the 
ones in other LCIA methods.  
Table  18  presents  a  summary  of  the  results  of  the  sensitivity  analysis  at  the 
normalization  stage,  comparing  the  relevance  of  impact  categories  according  to  ILCD 
and  CML-IA  methods.  Results  refer  to  the  entire  BoP.  For  each  impact  category  of  the 
two methods, values are expressed as percentage of the normalized value per citizen for 
that impact category with respect to the total normalized impact per citizen.  

Table  18  Results  of  sensitivity  analysis  on  normalization  sets  at  the  European  and  global  scale,  for  ILCD-  and  CML-IA 
methods (Castellani et al., 2016). The percentages represent the contribution of each product group to the total impacts of 
the BoP per citizen (reference flow). Equal weighting of impact categories is applied. The second column reports the mass of 
each  product  group  in  the basket.  A  color  scale  is  applied  to  the  results  in  each  column  (i.e.  product  group),  from  green 
(lowest contribution), to red (highest contribution). 

Quantity in 
the basket 
(kg/year) 
105.0 
69.8 
4.7 
16.1 
17.4 
69.1 
39.3 
5.3 
5.3 
29.8 
80.1 
15.0 
3.6 
13.7 
41.0 
22.0 

Share of 
total BoP 
mass 
19.4% 
12.9% 
0.6% 
3.0% 
3.2% 
13.0% 
7.3% 
1.0% 
1.0% 
5.5% 
14.8% 
2.8% 
0.7% 
2.5% 
7.6% 
4.2% 

2.9 

0.5% 

Mineral water 
Beer  
Coffee 
Apple 
Oranges 
Potatoes 
Bread 
Olive oil 
Sunflower oil 
Sugar from beet 
Milk 
Cheese 
Butter 
Meat - beef 
Meat - pork 
Meat - poultry 
Pre-prepared 
meal 

EC-JRC EU27 
(2010) 

EC-JRC 
Global (2010 
or 2013) 

PROSUITE 
Global (2010 
or 2000) 

CML 
EU 25+3 
(2000) 

CML World 
(2000) 

1% 
4% 
1% 
0% 
1% 
3% 
3% 
0% 
4% 
3% 
9% 
16% 
8% 
17% 
20% 
8% 

1% 

1% 
7% 
5% 
1% 
2% 
3% 
3% 
1% 
5% 
3% 
8% 
13% 
6% 
16% 
19% 
8% 

1% 

3% 
16% 
5% 
1% 
1% 
3% 
4% 
2% 
3% 
2% 
7% 
10% 
3% 
11% 
18% 
10% 

2% 

3% 
21% 
6% 
1% 
2% 
4% 
5% 
3% 
3% 
1% 
6% 
9% 
2% 
10% 
13% 
7% 

2% 

1% 
4% 
2% 
0% 
1% 
3% 
3% 
1% 
4% 
3% 
9% 
15% 
7% 
18% 
20% 
8% 

1% 

34 

 
 
 
 
100% 

100% 

100% 

100% 

100% 

100% 

Table  19  Results  of  sensitivity  analysis  on  normalization  sets  at  the  European  and  global  scale,  for  ILCD  and  CML-IA 
methods (Castellani et al., 2016). The percentages reflect the relative relevance of impact categories compared to the total 
impacts of the BoP per citizen (reference flow). A colour scale is applied to the results in each column (i.e. impact category), 
from green (lowest contribution), to red (highest contribution). 

EC-
JRC 
EU27 
(2010) 

EC-JRC 
Global 
(2010 
or 
2013) 

PROSUITE 
Global 
(2010  or 
2000) 

1.9% 

1.1% 

1.3% 

Global 
(GWP100a) 

warming 

0.0% 

0.0% 

0.0%  Ozone layer depletion  

6.2% 

8.3% 

2.5% 

44.6% 

69.2% 

12.7% 

Human toxicity 

CML  
EU 
(2000) 

25+3 

CML  World 
(2000) 

7.8% 
0.2% 

10.2% 
0.0% 

5.9% 

12.0% 

2.6% 
0.5% 

0.9% 
1.1% 

1.0% 

0.3% 

7.3% 
8.3% 
3.4% 
8.2% 

2.86% 
4.0% 
0.4% 
2.0% 

2.1% 
0.3% 

0.3% 

- 
- 
Photochemical 
formation 

4.1%  Acidification 
7.5% 
4.8% 
8.7% 

Eutrophication 

ozone 

4.6% 

2.1% 

27.2% 
13.3% 

20.1% 
16.3% 

cancer 

toxicity, 

Climate change 
Ozone depletion 
Human 
effects 
Human  toxicity,  non-cancer 
effects 
Particulate matter 
Ionizing radiation HH 
Photochemical 
formation 
Acidification 
Terrestrial eutrophication 
Freshwater eutrophication 
Marine eutrophication 

ozone 

Freshwater ecotoxicity 

- 
- 
Land use 
Water resource depletion 

5.8% 

6.1% 

45.0% 

- 

- 
2.3% 
6.2% 

- 

- 
0.0% 
3.3% 

- 

- 
0.4% 
10.0% 

Mineral, 
resource depletion 

fossil  & 

ren 

1.8% 

0.4% 

0.4% 

100% 

100% 

100% 

aquatic 

aquatic 

Freshwater 
ecotoxicity. 
Marine 
ecotoxicity 
Terrestrial ecotoxicity 
- 
- 
Abiotic depletion 
Abiotic  depletion  (fossil 
fuels) 

34.0% 

31.4% 

0.2% 

1.1% 

6.8% 
100% 

6.7% 
100% 

5.3.1  Conclusions and recommendations 

Different  normalisation  sets  may  produce  different  results  in  terms  of  identification  of 
most  relevant  impact  categories,  life  cycle  stages  and  processes.  Different  reference 
systems  refer  to  different  situation  which  can  all  be  valid,  depending  e.g.  on  the 
geographical  scope  of  the  study.  It  is  recommended  to  evaluate  different  normalisation 
tests,  which  fit  the  study  at  hand,  to  support  the  conclusions  drawn  with  the  baseline 
scenario or to provide additional insights. 

5.4  Weighting: sensitivity analysis 

Castellani  et  al.  (2016),  investigated  only  for  ILCD-compliant  normalization  sets,  in 
addition to equal weighting, 8 ILCD-compliant weighting sets (reported in Table 20) were 
tested.  The  different  sets  were  selected  aiming  at  covering  several  perspectives  on 
weighting,  namely:  distance  to  target  for  EU  policies  considering  binding  and  non-
binding target at 2020 (Castellani et al., 2017); a previous distance to target set (EDIP) 

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
for Europe for 2005 (Stranddorf et al., 2005); two sets considering planetary boundaries 
(Tuomisto  et  al.,  2012;  Bjørn  and  Hauschild,  2015 8);  a  set  which  gives  relevance  to 
midpoint indicators based on their contribution to impact at the endpoint (Ponsioen and 
Goedkoop,  2015);  a  set  resulting  from  the  combination  of  different  panel-based 
approaches (Huppes et al., 2012).  

When  needed,  available  weighting  sets  have  been  adapted  to  ILCD  impact  categories. 
The majority of these weighting sets build on normalization factors that are specific of a 
geographic scale. Tuomisto et al. (2012) calculated planetary boundaries at global scale, 
whereas  and  Bjørn  and  Hauschild  (2015)  did  it  for  both  global  and  European  scales.  
Huppes  et  al.  (2012)  adopted  a  panel-based  approach  with  no  specific  geographic 
representativeness,  whereas  all  the  other  weighting  sets  used  in  this  work  have  been 
originally  developed,  or  adjusted,  for  applications  at  the  targeting  the  European  scale. 
The  use  of  EU27-consistent  weighting  sets  in  combination  with  global  normalization 
factors may lead to inconsistencies; therefore, the results have to be interpreted in the 
light  of  this  inconsistency.  In  this  work  the  normalized  and  weighted  results  are 
calculated  and  presented  anyway  for  all  combinations  so  to  show  the  effect  of  the 
selection  of  weighting  available  for  practitioners,  rather  than  focussing  on  the  (in-
)consistency  of  their  joint  use  with  normalization  factors.  The  performed  analysis 
quantified the relative contribution by impact category to the overall impacts associated 
with  the  BoP,  as  well  as  the  contribution  by  each  of  the  products.  The  effects  of  the 
different  weighting  sets  tested  on  the  results  of  the  BoP  normalized  to  EC-JRC  EU27 
normalization references are presented in Table 21 and Table 22.  
Table 20 Summary of weighting sets used in the sensitivity analysis by Castellani et al. (2016) 

Distance to target 

Policy targets 

Planetary boundaries 

Castellani 
et al. 2016 
WFsA 

Castellani et 
al. 2016 
WFsB 

EDIP 2003 
(Stranddo
rf et al., 
2005) 

Tuomisto 
et al. 2012 

Damage 
oriented 
Mid-to-
endpoint 

Ponsioen 
& 
Goedkoop 
2016 

Panel-
based 

Huppes et 
al. 2012 

44% 
0% 
1% 

4% 

8% 

0% 

0% 

0% 
0% 
0% 
0% 
19% 
0% 
3% 

19% 

23.2% 
3.6% 
6.5% 

4.1% 

6.6% 

6.5% 

5.4% 

4.2% 
2.3% 
2.3% 
2.3% 
10.2% 
10.9% 
5.1% 

6.9% 

Bjørn & 
Hauschild 
- 
European 
2015 

Bjørn & 
Hauschi
ld – 
Global 
2015 
dimensionless (%) 
26% 
2% 
n.a 
n.a 

25% 
1% 
n.a 
n.a 

n.a 

n.a 

34% 

1% 
1% 
9% 
1% 
25% 
2% 
1% 

n.a 

n.a 

n.a 

48% 

1% 
0% 
2% 
1% 
16% 
0% 
4% 

n.a 

ILCD Impact Category 
Climate change 
Ozone depletion 
Human toxicity, cancer effects 
Human toxicity, non-cancer 
effects 
Particulate matter/Respiratory 
inorganics  
Ionizing radiation, human 
health 
Photochemical ozone 
formation, human health 
Acidification 
Eutrophication terrestrial 
Eutrophication freshwater 
Eutrophication marine 
Land use 
Ecotoxicity freshwater 
Resource depletion water 
Resource depletion, mineral, 
fossils and renewables 

7.1% 
6.4% 
6.9% 

6.2% 

5.4% 
4.9% 
5.2% 

4.7% 

7.4% 

5.6% 

6.1% 

4.6% 

7.8% 

7.2% 
7.0% 
6.2% 
6.9% 
6.4% 
6.1% 
6.1% 

6.1% 

5.9% 

5.5% 
5.3% 
4.7% 
5.2% 
5.3% 
5.1% 
29.6% 

3.0% 

2% 
87% 
2% 

2% 

n.a 

n.a 

2% 

2% 
2% 
1% 
2% 
n.a 
0% 
n.a 

0% 

10% 
8% 
n.a 
n.a 

n.a 

n.a 

n.a 

8% 
28% 
7% 
28% 
6% 
n.a 
5% 

n.a 

8 Bjørn and Hauschild provided to the authors of this report a modified version to be used as weighting method 
in  compliance  with  EU27  ILCD  normalization  factors.  Global  weighting  factors  were  calculated  directly  from 
global normalization factors published by Bjørn and Hauschild 2015 

36 

 
 
 
 
 
 
 
 
                                           
The  contribution  of  specific  products  to  the  overall  impacts  associated  to  the  BoP 
nutrition  is  very  similar  when  using  several  weighting  sets  in  combination  with 
normalization references EU27 (Table 21). In fact, for all the weighting sets tested pork 
meat  and  beef  meat  are  the  highest  contributor  to  the  impacts  (varying  between  15% 
and 22%). According to all sets, cheese is the 3rd largest contributor (14% on average), 
whereas  milk  and  poultry  meat  are  most  frequently  identified  as  4th  and  5th  largest 
contributors  (roughly  8%  each).  On  average,  these  product  groups  account  for  roughly 
70% of the total impacts. Apples, olive oil and pre-prepared meals are those contributing 
the  less,  followed  by  mineral  water  and  oranges.  On  average,  their  cumulative 
contribution is around 5%. 

Table 21 Product groups contribution. Results of several ILCD-compliant weighting sets in combination with normalization 
references  EU27  (Castellani  et  al.,  2016).  A  color  scale  is applied to  the  results  in  each  column (i.e.  product  group),  from 
green (lowest contribution), to red (highest contribution). 

Quant
ity in 
the 
basket 
(kg/ye
ar) 

Share of 
total 
BoP 
mass 

105 

19.40% 

PRODUCT  
GROUPS 
CONTRIB
UTION  
Mineral 
water 

Beer 66 cL 

69.8 

12.90% 

Coffee 

4.7 

0.60% 

Apple 

16.1 

3.00% 

Oranges 

17.4 

3.20% 

Potato 

69.1 

13.00% 

Bread 

39.3 

7.30% 

Olive oil 
Sunflower 
oil 
Sugar 
from beet 

5.3 

1.00% 

5.3 

1.00% 

4% 

29.8 

5.50% 

Milk 

80.1 

14.80% 

Equa
l 
weig
hting 

Castella
ni et al. 
(2016) 
WFsA  

Castell
ani et 
al. 
(2016) 
WFsB  

EDIP20
03 9 

Tuomis
to et 
al. 
(2012) 

Bjorn&
Hausch
ild 
(2015) 
– 
Europe
an  

Bjorn&
Hausch
ild 
(2015) 
– 
global 

Ponsi
oen 
and 
Good
koep 
(2016)  

Hup
pes 
et 
al. 
(201
2) 

1% 

4% 

2% 

0% 

1% 

3% 

3% 

1% 

3% 

9% 

1% 

4% 

2% 

0% 

1% 

3% 

3% 

1% 

4% 

3% 

9% 

3% 

4% 

2% 

1% 

3% 

4% 

3% 

1% 

4% 

4% 

8% 

1% 

4% 

1% 

0% 

0% 

3% 

4% 

0% 

4% 

3% 

9% 

1% 

3% 

2% 

0% 

1% 

2% 

3% 

1% 

3% 

4% 

8% 

1% 

8% 

6% 

1% 

2% 

3% 

3% 

1% 

4% 

2% 

8% 

2% 

8% 

5% 

1% 

2% 

3% 

3% 

1% 

4% 

3% 

8% 

1% 

1% 

7% 

5% 

3% 

2% 

1% 

1% 

1% 

1% 

4% 

3% 

3% 

3% 

1% 

1% 

4% 

4% 

3% 

3% 

8% 

8% 

Cheese 

15 

2.80% 

15% 

15% 

16% 

15% 

13% 

12% 

13% 

14% 

14% 

Butter 
Meat - 
beef 
Meat - 
pork 
Meat - 
poultry 
Pre-
prepared 
meal 

3.6 

0.70% 

7% 

7% 

6% 

8% 

6% 

5% 

5% 

7% 

7% 

13.7 

2.50% 

18% 

18% 

17% 

18% 

22% 

15% 

16% 

16% 

17% 

41 

22 

7.60% 

20% 

20% 

16% 

21% 

21% 

19% 

17% 

18% 

19% 

4.20% 

8% 

8% 

7% 

8% 

9% 

8% 

8% 

8% 

8% 

2.9 

0.50% 

100% 

1% 
100
% 

1% 

1% 

1% 

1% 

1% 

1% 

1% 

100% 

100% 

100% 

100% 

100% 

100% 

100% 

1% 
100
% 

9 (based on Stranddorf et al. 2005) 

37 

 
 
 
 
                                           
Conversely, the identification of the most relevant impact categories contributing to the 
overall  impact  of  the  entire  BoP  nutrition  presents  huge  discrepancies  when  applying 
different  weighting  sets  (Table  22).  This  is  due  to  the  intrinsic  differences  among  the 
weighting  sets,  which  build  on  different  perspectives  and  attribute  different  weights  to 
the impact categories. 

Table 22 Relevance of the different impact category. Results of the application of several ILCD-compliant weighting sets in 
combination  with  normalization  references  EU27  (Castellani  et  al.,  2016).  A  color  scale  is  applied  to  the  results  in  each 
column (i.e. impact category), from green (lowest contribution), to red (highest contribution). 

CONTRIBUTION 
ANALYSIS 

1-1-1 

Castella
ni et al. 
(2016) 
WFsA  

Castella
ni et al. 
(2016) 
WFsB 

EDIP2003 
(based on 
Stranddo
rf et al. 
2005) 

Tuomist
o et al., 
(2012)  

Bjørn & 
Hauschil
d (2015)  

Bjørn & 
Hauschil
d (2015) 
- global  

Ponsioen 
& 
Goedkoo
p (2015) 

Huppe
s et al. 
(2012) 

Climate change 

Ozone depletion 
Human toxicity, 
cancer effects 
Human toxicity, 
non-cancer 
effects 
Particulate 
matter/Respirator
y inorganics  
Ionizing radiation, 
human health 
Photochemical 
ozone formation, 
human health 

Acidification 
Eutrophication 
terrestrial 
Eutrophication 
freshwater 
Eutrophication 
marine 

Land use 
Ecotoxicity 
freshwater 
Resource 
depletion water 
Resource 
depletion, 
mineral, fossils 
and renewables 

1.9% 

2.0% 

1.5% 

0.0% 

0.0% 

0.0% 

6.2% 

6.6% 

5.0% 

2% 

2% 

7% 

3.2% 

21.4% 

26.0% 

20.4% 

8.7% 

0.0% 

0.0% 

0.0% 

0.0% 

0.0% 

n.a. 

n.a. 

n.a. 

2.2% 

8.1% 

44.6
% 

42.5% 

32.3% 

53% 

n.a. 

n.a. 

n.a. 

47.7% 

36.3% 

2.6% 

3.0% 

2.3% 

n.a. 

n.a. 

n.a. 

n.a. 

5.1% 

3.4% 

0.5% 

0.5% 

0.4% 

n.a. 

n.a. 

n.a. 

n.a. 

0.0% 

0.7% 

1.0% 

1.2% 

0.9% 

1% 

0.0% 

16.2% 

26.3% 

0.0% 

1.1% 

7.3% 

8.1% 

6.1% 

9% 

10.1% 

4.7% 

4.2% 

0.3% 

6.1% 

8.3% 

8.9% 

6.7% 

10% 

37.9% 

3.1% 

1.8% 

0.0% 

3.9% 

3.4% 

3.2% 

2.5% 

3% 

3.7% 

13.4% 

4.3% 

0.0% 

1.6% 

8.2% 

8.7% 

6.6% 

11% 

37.4% 

5.5% 

4.4% 

0.0% 

3.8% 

2.3% 

2.2% 

1.9% 

n.a. 

2.3% 

25.7% 

18.8% 

10.6% 

4.6% 

5.8% 

5.5% 

4.6% 

0% 

n.a. 

6.0% 

0.4% 

0.0% 

12.7% 

6.2% 

5.9% 

28.4% 

n.a. 

5.3% 

3.9% 

13.7% 

4.9% 

6.3% 

1.8% 

1.7% 

0.9% 

0% 

n.a. 

n.a. 

n.a. 

8.6% 

2.5% 

TOTAL 

100% 

100% 

100% 

100% 

100% 

100% 

100% 

100% 

100% 

The  discrepancies  in  the  results  suggest  that  it  is  important  to  acknowledge  that 
different  perspectives  lead  to  different  results.  Moreover,  it  is  relatively  straightforward 
to  identify  the  product  groups  impacting  the  most,  as  from  Table  21,  when  keeping 
constant normalization references although varying weighing sets. 

The  authors  also  checked  the  most  sensitive  element  between  normalization  and 
weighting: their findings indicate normalization as the most sensitive choice if compared 

38 

 
 
 
  
to  the  selection  of  a  specific  weighting  set  among  those  considered  in  their  work. 
However,  this  result  may  change  in  case  weighting  methods  building  on  different 
approaches  are  added  to  the  ones  included  in  their  work.  Nevertheless,  the  ranking  of 
products  is  not  significantly  affected  by  changing  normalization  reference  (e.g.  from 
ILCD  EU27  to  ILCD  Global  –  Laurent  et  al.  2013).  This  might  be  due  to  few  impact 
categories  driving  the  results  regardless  of  the  normalization  and  weighting  methods 
selected, or to the fact that all impact categories are strongly correlated. 

5.4.1  Conclusions and recommendations 
Weighting sets are often dependent on different value choices to assign different weights 
to all impact categories. Therefore, different weighting sets lead to significant differences 
of  the  final  conclusions.  It  is  recommended  that  the  decision  about  inclusion  of  which 
weighting  set  to  use  has  been  made  and  documented  in  the  first  scope  definition  and 
shall not be changed later during the study. 

39 

 
 
 
6  Estimation of uncertainty 

Evaluation of the uncertainty of an LCA study is a step which involves both the LCI and 
LCIA  phase,  therefore  it  is  strictly  linked  to  the  content  of  the  previous  chapters. 
Uncertainty in LCA can be defined in different ways (Ciroth et al., 2016). For examples it 
can be meant as describing the variability of the LCA data to determine the significance 
of  the  indicator  results,  being  “the  estimated  amount  or  percentage  by  which  an 
observed or calculated value may differ from the true value” (Skone, 2000). ISO 14044 
defines  uncertainty  analysis  as  systematic  procedure  to  quantify  the  uncertainty 
introduced in the results of a life cycle inventory analysis due to the cumulative effects of 
model imprecision, input uncertainty and data variability.  

The estimation of uncertainty serves three main purposes: 

 
 
 

it supports a better understanding of the results obtained  
it supports the iterative improvement of an LCA study 
it also helps the target audience to assess the robustness and applicability of the 
study results 

The PEF Guide identifies two key sources of uncertainty in PEF studies: 

1.  Stochastic uncertainties for the inventory data 

they  refer  to  statistical  descriptions  of  variance  around  a  mean/average.  For 
normally  distributed  data,  this  variance  is  typically  described  in  terms  of  an 
average and standard deviation.  

2.  Choice-related uncertainties 

They  arise  from  methodological  choices  including  modelling  principles,  system 
boundaries,  allocation  choices,  choice  of  impact  assessment  methods  and  other 
assumptions related to time, technology, geographical aspects, etc. These are not 
amenable  to  statistical  description,  but  rather  can  only  be  characterized  via 
scenario model assessments. 

In  this  report  we  refer  to  uncertainty  analysis  to  describe  the  range  of  likely  outcomes 
based on a set of inputs and it differs from a sensitivity analysis which is linked to how 
sensitive the results are to a change in an input parameter (see previous chapters). 

It has to be kept in mind that all steps involved in a quantitative process are affected by 
uncertainties. Therefore, it is important that the uncertainty is assessed: 

-  At  inventory  level  (e.g.  standard  deviation  associated  to  a  data  point).  An 
example  on  how  to  assess  the  uncertainty  at  inventory  level  was  recently 
provided by Ciroth et al. (2016) 

-  At characterization level (e.g. linked to characterization factors) 
-  At normalisation level (e.g. linked to normalisation factors) 
-  At weighting level (e.g. linked to weighting factors) 

6.1.1  Conclusions and recommendations 
It  is  recommended  that  the  overall  uncertainty  of  an  LCA  study  is  estimated  and 
reported  in  the  final  report.  It  is  also  recommended  to  include  an  evaluation  to  which 
extent the uncertainties identified affect the final conclusions. 

40 

 
 
 
 
 
 
 
 
 
7  Meta-analysis 

The  widespread  development  of  LCA  in  the  last  decades  as  a  decision  supporting  tool 
leads  to  a  huge  amount  of  LCA  studies  in  literature  concerning  all  kind  of  goods  and 
services  (“products”).  When  trying  to  make  sense  of  their  results  that  sometimes  are 
very different, or come up with a conclusion, techniques to combine or re-analyze them 
are needed such as meta-analysis. 

Zumsteg et al., (2012) described the key factors for conducting and reporting systematic 
reviews,  including  meta-analyses10,  in  LCA.  Those  key  factors  are  simplified  in  a  check 
list:  1.  Review  title,  keywords,  and  abstract;  2.  Rationale  for  the  review;  3.  Review 
question  and  objectives;  4.  Description  of  review  protocol;  5.  Findings  and  features  of 
the  individual  studies  in  the  review;  6.  Assessment  of  bias;  7.  Synthesis  methods 
(qualitative and quantitative); 8. Limitations of the review; and 9. Summary of findings 
and conclusions.   

This chapter is not intended to give guidelines on how to perform a systematic review or 
a meta-analysis but to highlight the critical points that the practitioner should take into 
account  when  collecting  and  using  the  LCA  data.  Recalling  the  check  list  previously 
described,  it  is  in  the  findings  and  features  of  the  individual  studies  where  one  of  the 
biggest shortcomings when performing a meta-analysis concerning LCA studies is found. 
Assembling data from the individual studies is complex due to: 

 

the  existing  “flexible”  guidance  in  conducting  LCA  that  leads  to  different 
assumptions  and  methodological  choices  made  in  the  various  LCA  modelling 
exercises.  The  ISO 14040 and 14044 standards provide the framework for LCA. 
This  framework,  however,  leaves  the  individual  practitioner  with  a  range  of 
choices, which can affect the legitimacy of the results of an LCA study.  

 

the lack of homogeneity reporting LCA results.  

As found in De Matos et al., (2015) while doing a meta-analysis of different bioeconomy 
value chains, the most influencing differences among studies mainly relate to:  

 

 

 

 

 

 

The  definition  of  the  system  boundaries  and  the  stages  included  in  the 
study (e.g. even if the same general system boundaries are considered - 
e.g. cradle to gate - some studies may or may not include intermediate 
transport, construction and decommissioning of buildings, etc.).  

The  impact  assessment  methods  used,  as  different  methods  may 
consider, for example, different substances for a given impact category, 
and different characterization factors for the same substance.  

The  definition  of  the  functional  unit  (e.g.  as  the  input,  the  output 
product,  the  agricultural  land  unit,  etc.)  (Cherubini  and  Stromman, 
2011).  

The consideration of direct and indirect land use change (dLUC and iLUC, 
respectively) (Cherubini and Stromman, 2011).  

The technology considered in the process and its maturity level.  

The approach used to model the multifunctional system. For instance, if 
substitution  is  used,  the  reference  system  selected  may  have  a 
significant  influence  on  the  final  LCA  results.  On  the  other  hand,  if 
allocation is used, the selection of the allocation criteria and the relative 

10 The  definition  of  Meta-analysis  given  by  Zumsteg  et  al.,  (2012)  is  “A  melding  of  data  from 
multiple studies, usually involving additional mathematical analysis, with the goal of utilizing this 
synergy  of  information  and  data  size  to  answer  questions  that  cannot  be  answered  by  existing 
individual studies or to improve the certainty or impact of know findings by increasing the sample 
size. Meta-analyses are often performed as part of a systematic review” 

41 

 
 
                                           
contribution of each co-product may considerably influence the results of 
the assessment.  

These  points  are  in  line  with  those  reported  in  other  studies  in  literature.  Wolf  et  al., 
(2015)  conducted  a  meta-analysis  of  LCAs  for  wood  energy  services  (heat,  power  and 
combined  heat  and  power  (CHP))  and  chose  a  set  of  “decisive  parameters”  to  be 
analyzed  arguing  that  those  factors  promote  inconsistent  findings  along  LCA  studies. 
Apart from the previously mentioned, the authors include the following factors (some of 
them specific of the good or service under study): 

 

 

 

 

 

Data  sources  such  as  empirical  data,  literature,  well-known  databases, 
unspecified  databases,  simulators  or  expert  interviews  are  some  of 
them. 

Transportation distances and types 

Feedstock properties (in this case wood) 

Combustion capacity and efficiency and co-combustion rates 

Energy service provided (power, heat, and CHP) 

Schreiber  et  al.,  (2012)  performed  a  meta-analysis  on  LCA  studies  of  electricity 
generation  with  carbon  capture  and  storage  (CCS)  comprising  the  three  main  CCS 
technologies  (post-combustion,  oxyfuel  and  pre-combustion).  In  line  with  previous 
studies,  they  highlighted  the  different  choices  in  the  LCA  methodology  and  parameters 
(specific of the good or service under study) that heavily impact the overall results. Here 
we report those which differ from the already mentioned: 

 

 

 

 

 

 

 

 

Data quality and availability 

Time  horizon  as  most  studies  considered  present  and  future  power 
plants as far as 2050. 

Spatial  representation  –  some  elements  in  the  CCS  process  chain  are 
highly site-specific such as storage sites. 

Life cycle inventory (LCI) – which inputs and outputs are considered. 

Operational valuation and weighting methods  

Power plant efficiency and energy penalty of the capture process 

Carbon dioxide capture efficiency and purity 

Fuel origin and composition 

Moreover,  when  having  a  look  at  the  limitations  of  the  review  and  meta-analysis  some 
studies  just  focus  on  GHG  emissions.  Wolf  et  al.,  (2015)  set  the  CML  midpoint  impact 
category  Global  Warming  as  the  basis  for  the  comparison  of  results  because  it  is 
represented  in  100%  of  the  studies.  They  did  not  consider  other  less-frequent  impact 
categories,  but  yet  important,  such  as  acidification,  eutrophication,  and  particulate 
matter,  owing  to  the  lack  of  results  published  in  the  studies.  On  the  other  hand, 
Schreiber  et al., (2012) not only focused  on GHG but they included other impacts such 
as  acidification  potential,  eutrophication  potential,  photochemical  ozone  creation 
potential and cumulative energy demand. 

As  a  conclusion,  increased  harmonization  and  transparency  in  the  calculation  and 
reporting of LCA data and results is needed (Cristobal et al., 2015).  

7.1.1  Conclusions and recommendations 

The  number  of  LCA  studies  available  is  increasing  and  making  sense  of  all  the  results 
that  can  be  obtained  from  them  is  a  difficult  task.  As  a  conclusion  increased 
harmonization and transparency in the calculation of LCA data and results is needed. It 
is recommended that reporting of LCA data is done in a more consistent and harmonized 

42 

 
 
manner,  to  allow  users  of  LCA  study  a  better  understanding  of  the  results  and 
conclusions drawn. 

8  Conclusion 
One  of  the  key  aims  of  LCA  is  to  provide  the  decision  makers  with  comprehensive  and 
understandable  information:  this  task  is  achieved  by  a  proper  interpretation  of  the 
results of an LCA study.  
This report builds on the requirements provided in relevant international standards (ISO 
14044)  and  guides  (ILCD  Handbook  (EC-JRC,  2011a),  PEF  Guide  (EC,  2013b)  and  it 
proposes  a  practical  approach  on  how  to  perform  a  robust  interpretation  of  an  LCA 
study. It provides a ready-to-use approach for the identification of significant issues, and 
it includes examples of completeness, sensitivity and consistency checks based on real-
case studies, which are used to draw the attention of the reader on how to evaluate the 
results of an LCA study.  
The  scheme  proposed  in  this  report  should  not  be  intended  as  a  comprehensive  list  of 
items  to  check,  instead  it  should  be  seen  as  a  normal  workflow  that  should  be  carried 
out  by  an  LCA  practitioner  in  order  to  derive  robust  conclusions  out  of  the  LCA  study. 
More accurate and case-specific checks should be done depending on the intended goal 
and scope of the study. 
The  report  is  structured  in  order  to  allow  a  practitioner  focus  on  the  two  quantitative 
phases of LCA: the life cycle inventory (LCI) and the life cycle impact assessment (LCIA). 
Regarding  the  LCI,  the  report  goes  through  the  analysis  of  the  data  sources,  by  giving 
examples  on  how  to  interpret  the  system  boundaries  and  the  foreground  system);  it 
provides  an  overview  on  data  quality  and  it  comprises  further  completeness  and 
consistency checks (e.g. cut-off, long term emissions, anomaly checks).  
When  analysing  the  LCIA  the  report  provides  insights  on  sensitivity  analysis  regarding 
characterization,  normalization  and  weighting.  When  dealing  with  characterization,  the 
attention  of  the  reader  is  drawn  also  on  mapping  between  the  inventory  and  the 
characterization  models  and  a  discussion  on  the  evaluation  of  uncharacterized 
elementary flows is also included.  
Finally, an overarching topic, such as meta-analysis was deemed useful to be included as 
this  is  often  used  when  there  is  the  need  of  assessing  the  state  of  the  art  of  the  LCA 
studies on a specific product/ supply chain. Key messages stemming from this report are 
as follows: 

  Results reveal that the relative importance of one substance compared to another 
changes with the characterization model. This is because the models differ in the 
substances  that  they  include  and  in  the  CFs  of  each  substance.  It  is 
recommended to run a sensitivity check with different characterization models to 
evaluate  the  robustness  of  the  conclusions  drawn.  It  is  also  recommended  to 
evaluate the theory underlying the characterization models chosen to evaluate a 
certain impact category, as this helps understanding the results obtained. 

  Results  of  the  LCIA  phase  can  be  highly  affected  by  the  characterization  model 
chosen.  Furthermore,  the  evaluation  of  uncharacterized  elementary  flows  is  of 
primary  importance  to  assess  the  completeness  of  the  LCIA  coverage  of  the 
identified LCI flows. Indeed, a high number of elementary flows inventoried might 
be  non-characterized  by  the  chosen  models,  even  if  their  relevance  in  the 
assessed impact categories could be of primary importance. Therefore, it is highly 
recommended  that  a  quantification  of  the  uncharacterized  elementary  flows  is 
reported  before  any  interpretation  or  conclusion  is  performed  on  the  superiority 
of one product versus another.  

  Different  normalisation  sets  may  produce  different  results 

in  terms  of 
identification of most relevant impact categories, life cycle stages and processes. 
Different  reference  systems  refer  to  different  situation  which  can  all  be  valid, 

43 

 
 
 
 
depending  e.g.  on  the  geographical  scope  of  the  study.  It  is  recommended  to 
evaluate different normalisation tests, which fit the study at hand, to support the 
conclusions drawn with the baseline scenario or to provide additional insights. 
  Weighting sets are often dependent on different value choices to assign different 
weights  to  all  impact  categories.  Therefore,  different  weighting  sets  lead  to 
significant  differences  of  the  final  conclusions.  It  is  recommended  that  the 
decision  about  inclusion  of  which  weighting  set  to  use  has  been  made  and 
documented in the first scope definition and shall not be changed later during the 
study. 
It is recommended that the overall uncertainty of an LCA study is estimated and 
reported  in  the  final  report.  It  is  also  recommended  to  include  an  evaluation  to 
which extent the uncertainties identified affect the final conclusions. 

 

  The  number  of  LCA  studies  available  is  increasing  and  making  sense  of  all  the 
results  that  can  be  obtained  from  them  is  a  difficult  task.  As  a  conclusion 
increased  harmonization  and  transparency  in  the  calculation  of  LCA  data  and 
results is needed. It is recommended that reporting of LCA data is done in a more 
consistent  and  harmonized  manner,  to  allow  users  of  LCA  study  a  better 
understanding of the results and conclusions drawn. 

Further  aspects,  such  as  the  appropriateness  of  the  functional  unit,  the  intended  goal, 
the system boundaries and value choices should normally be part of an interpretation, in 
order to identify recommendations and limitations of the study and draw the appropriate 
conclusions. They are not discussed further in this report, but it is recommended that at 
least,  each  decision  based  on  a  personal  or  a  stakeholder’s  value  shall  always  be 
documented  and  explained  in  the  conclusions  of  the  LCA  study.  This  is  consistent  with 
one of the key aims of LCA, which is to provide the decision makers with comprehensive 
and understandable information. 

44 

 
 
 
 
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List of abbreviations and definitions 
AOX 
BOD 
BTEX   
CFs 
COD 
DQR 
EcF 
EF 
ElFl 
HMs 
IC 
ILCD 
LCA 
LCI 
LCIA 
OEFSR  
PAH 
PEF 
PEFCR  
PPP 
OEF  
TAB 
TS 
UNEP/SETAC LCI 
VOC 
WEEE   

adsorbable organic halogens 
biochemical oxygen demand 
benzene, toluene, xylene isomers 
characterisation factors 
chemical oxygen demand  
data quality requirements 
ecological footprint 
environmental footprint 
elementary flow 
heavy metals  
impact category 
International Reference Life Cycle Data System 
life cycle assessment 
life cycle inventory 
life cycle impact assessment 
organisation environmental footprint sector rules 
polycyclic aromatic hydrocarbons 
product environmental footprint 
product environmental footprint sector rules 
plant protection products 
organisation environmental footprint 
technical advisory board 
technical secretariat 
Unep Setac Life cycle Initiative 
volatile organic compounds 
waste electrical and electronic equipment 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure  1  Practical  application  of  requirements  in  ISO  14044  and  ILCD  Handbook  to 
interpret LCI and LCIA results. ................................................................................ 5 

Figure 2:  Hotspot analysis in the context  of the  Environmental Footprint pilot (James & 
Galatola, 2015) ..................................................................................................... 8 

Figure  3  Contribution  analysis  for  the  resource-  related  indicator  calculated  adopting 
several characterization models (Rigamonti et al., 2017) ......................................... 27 

52 

 
 
 
 
List of tables 

Table  1  Summary  of  requirements  to  define  most  relevant  contributions  and  hotspots 
(James & Galatola, 2015) ..................................................................................... 11 

Table 2 Selection of most relevant Impact Categories (James & Galatola, 2015) ......... 12 

Table 3 Selection of most relevant life cycle stages (James & Galatola, 2015) ............. 13 

Table 4 Selection of most relevant processes (James & Galatola, 2015) ..................... 13 

Table  5  Inventory  results  (climate  change,  results  expressed  in  gCOeq).  (James  & 
Galatola, 2015) ................................................................................................... 14 

Table  6  Most  relevant  inventory  flows  contributing  to  climate  change  (based  on  the 
inventory  results  before  normalisation  and  weighting)  -  overall  life  cycle  (option  1). 
(James & Galatola, 2015) ..................................................................................... 14 

Table  7  Most  relevant  inventory  flows  contributing  to  climate  change  (based  on  the 
inventory results before normalisation and weighting) – process level (option 2). (James 
& Galatola, 2015) ................................................................................................ 14 

Table 8 Hotspots based on 50% cumulative contribution (OPTION A). (James & Galatola, 
2015) ................................................................................................................ 15 

Table  9  Hotspots  based  on  top  two  representatives  (OPTION  B).  (James  &  Galatola, 
2015) ................................................................................................................ 15 

Table  10  Underlying  assumptions  in  relation  to  the  three  databases  considered  by  the 
authors (Corrado et al., 2017) .............................................................................. 17 

Table  11  Data  quality  rating  in  an  example  of  intermediate  paper  products  (EC  2013, 
Schau and Davidsson 2015). ................................................................................ 20 

Table 12  Data quality rating in an early phase of a PEF study of intermediate packaging 
paper ................................................................................................................. 21 

Table 13 Example of check of inventoried elementary flows that are not characterized by 
two impact assessment methods (based on Castellani et al. 2017) ............................ 29 

Table 14  List of impact categories listed in ILCD with total number of  elementary flows 
contributing to each IC. ....................................................................................... 30 

Table  15  Type  of  chemicals  (mass  and  number  of  EFs)  not  characterized  per  emission 
compartment (air, oxygen and hydrogen have been excluded). ................................ 31 

Table  16  Most  relevant  elementary  flows,  contributing  cumulatively  up  to  90%  to  the 
freshwater ecotoxicity impact category with USEtox. ............................................... 32 

Table 17 Summary of normalization sets used in the sensitivity analysis in Castellani et 
al. (2016)........................................................................................................... 33 

Table 18 Results of sensitivity analysis on normalization sets at the European and global 
scale,  for  ILCD-  and  CML-IA  methods  (Castellani  et  al.,  2016).  The  percentages 
represent  the  contribution  of  each  product  group  to  the  total  impacts  of  the  BoP  per 
citizen  (reference  flow).  Equal  weighting  of  impact  categories  is  applied.  The  second 
column reports the mass of each product group in the basket. A color scale is applied to 
the results in each column (i.e. product group), from green (lowest contribution), to red 
(highest contribution). ......................................................................................... 34 

Table 19 Results of sensitivity analysis on normalization sets at the European and global 
scale,  for  ILCD  and  CML-IA  methods  (Castellani  et  al.,  2016).  The  percentages  reflect 
the relative relevance of impact categories compared to the total impacts of the BoP per 
citizen  (reference  flow).  A  colour  scale  is  applied  to  the  results  in  each  column  (i.e. 
impact category), from green (lowest contribution), to red (highest contribution). ...... 35 

53 

 
 
 
Table 20 Summary of weighting sets used in the sensitivity analysis by Castellani et al. 
(2016) ............................................................................................................... 36 

Table  21  Product  groups  contribution.  Results  of  several  ILCD-compliant  weighting  sets 
in combination with normalization references EU27 (Castellani et al., 2016). A color scale 
is  applied  to  the  results  in  each  column  (i.e.  product  group),  from  green  (lowest 
contribution), to red (highest contribution). ............................................................ 37 

Table 22 Relevance of the different impact category. Results of the application of several 
ILCD-compliant  weighting  sets  in  combination  with  normalization  references  EU27 
(Castellani  et  al.,  2016).  A  color  scale  is  applied  to  the  results  in  each  column  (i.e. 
impact category), from green (lowest contribution), to red (highest contribution). ...... 38 

54 

 
 
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