8
F. Scrucca et al.
to complete the complex assessment required according to the PEF guide. Another
important issue identified is the data quality, actually, in the PEF method minimum
quality requirements that go beyond the requirement to simply report quality are
required considering its use as a decision support especially in a policy context. The
result of the review confirms the high consistency of PEF method, concerning the
core criterion analyzed, guidelines and directions given, even reducing flexibility
minimizing the number of choices and decisions that the user would have to make,
allow a high level of reproducibility and comparability between studies [47].
What we are going to do here is to propose our point of view based on our
experience and knowledge.
Comparing standardized Carbon Footprint and Product Environmental Footprint,
the common points are plentiful, first of all both are based on ISO 14040 series, they
consider the wall life cycle of the product. Also, the method to calculate impacts
from GHG emissions in all the case is the IPCC.
The first and more important difference between Carbon Footprint and Product
Environmental Footprint is the number of impact categories, while the CF is focused
on the Climate Change and all the methodology is set to evaluate source and effects
of greenhouse gas emissions, the PEF describes the environmental profile of the
product using a set of indicators. The main objective to have a single indicator is to
simplify the communication to the public, a single number that comprehend a lot of
information would have been very useful, the problem is that Carbon Footprint is
not always able to be the key indicator in many product categories, this is why PEF
has a set of indicators that can be limited in the PEF category rules.
Considering product category rules, they are not mandatory for all the considered
l methodologies, like for example in ISO14067, but they are very important to drive
the practitioner during the analysis. PEF category rules (PEFCR) go further in this
role and suggest also the correct dataset to use when primary data are not available.
A huge work has been made regarding database, the main objective is to furnish
users with data with a high level of quality. These because an LCA database can
be used in a vast number of evaluations, like in product assessments, development of standards, certification and product labelling, product, process, and system
development [57].
In the PEF guide a series of instruction are set to define the dataset quality.
It is based on four criteria: Technological representativeness (TeR), Geographical
representativeness (GeR), Time representativeness (TiP) and Precision (P). The Data
Quality Rating (DQR) result in the average of the categories and is used to identify
the corresponding quality level. The overall data quality of the dataset requires the
evaluation of each single quality indicator. (PEF Guide revised) The data quality
requirements for primary and secondary data are set in PEFCR.
The problem concerning data quality is not new, database contain a vast number
of datasets that allow practitioners to model products in software and their quality
may vary even in the same database, in PEF method each data used must be evaluated
using a data quality matrix while in Carbon Footprint methods, there are not minimum
data quality requirements.
F. Scrucca et al.
to complete the complex assessment required according to the PEF guide. Another
important issue identified is the data quality, actually, in the PEF method minimum
quality requirements that go beyond the requirement to simply report quality are
required considering its use as a decision support especially in a policy context. The
result of the review confirms the high consistency of PEF method, concerning the
core criterion analyzed, guidelines and directions given, even reducing flexibility
minimizing the number of choices and decisions that the user would have to make,
allow a high level of reproducibility and comparability between studies [47].
What we are going to do here is to propose our point of view based on our
experience and knowledge.
Comparing standardized Carbon Footprint and Product Environmental Footprint,
the common points are plentiful, first of all both are based on ISO 14040 series, they
consider the wall life cycle of the product. Also, the method to calculate impacts
from GHG emissions in all the case is the IPCC.
The first and more important difference between Carbon Footprint and Product
Environmental Footprint is the number of impact categories, while the CF is focused
on the Climate Change and all the methodology is set to evaluate source and effects
of greenhouse gas emissions, the PEF describes the environmental profile of the
product using a set of indicators. The main objective to have a single indicator is to
simplify the communication to the public, a single number that comprehend a lot of
information would have been very useful, the problem is that Carbon Footprint is
not always able to be the key indicator in many product categories, this is why PEF
has a set of indicators that can be limited in the PEF category rules.
Considering product category rules, they are not mandatory for all the considered
l methodologies, like for example in ISO14067, but they are very important to drive
the practitioner during the analysis. PEF category rules (PEFCR) go further in this
role and suggest also the correct dataset to use when primary data are not available.
A huge work has been made regarding database, the main objective is to furnish
users with data with a high level of quality. These because an LCA database can
be used in a vast number of evaluations, like in product assessments, development of standards, certification and product labelling, product, process, and system
development [57].
In the PEF guide a series of instruction are set to define the dataset quality.
It is based on four criteria: Technological representativeness (TeR), Geographical
representativeness (GeR), Time representativeness (TiP) and Precision (P). The Data
Quality Rating (DQR) result in the average of the categories and is used to identify
the corresponding quality level. The overall data quality of the dataset requires the
evaluation of each single quality indicator. (PEF Guide revised) The data quality
requirements for primary and secondary data are set in PEFCR.
The problem concerning data quality is not new, database contain a vast number
of datasets that allow practitioners to model products in software and their quality
may vary even in the same database, in PEF method each data used must be evaluated
using a data quality matrix while in Carbon Footprint methods, there are not minimum
data quality requirements.