Actors and innovators in the bioeconomy 217
the University of Oslo in 2017 that focused on mapping organic waste- related
activities in the following subsectors: forestry, aquaculture and seafood
processing, beer brewing, meat processing, dairy, and organic waste processing. This study will be presented in section 11.3.3.
11.3.2.1 Identification strategy of the Norwegian Inventory of Bioeconomy
Entities (NIoBE)
The approach used in this research to identify a target population of active
organisations in Norway included two main stages. The first stage involved
collecting and collating a first estimation of the population. We used three
types of data to open the population. The inclusion rules moved from the
more stringent (the entity is involved in RD&I activities in the area, as in the
example above), to an intermediate level of accuracy (the entity has been
identified by another systematic project), to a more generic association (the
entity is a member of a population that is nominally associated with the
bioeconomy).
This first stage, which is akin to using three nets with different meshes,
was designed to include as many of the true population as possible (i.e. maximise ‘recall’). It is clear, however, that as we progress from the narrow to the
more broad- meshed nets, we risk including considerable bycatch in the form
of entities that are not a part of the true population. It proved difficult to
weed out these ‘false positives’ from our population as the ‘true’ population is
not known. Therefore, a second stage was undertaken. In this stage we set out
to increase precision by using other standardised information to exclude entities that were clearly not part of the population. In particular, we used industrial classifications (NACE, which is the starting point of other studies) and
other firm- level information, such as ‘trade descriptions’ found in financial
data sources. In the following, we briefly present the three components of
our approach before fleshing out the resulting population.
11.3.2.1.1 CONFIRMATION By ACTIVITy
The first inductive stage of the approach establishes a stable foundation. It
identifies Norwegian actors – universities, research institutes and firms – using
data on RD&I activities that are recognised to advance the ‘bioeconomy’. In
this stage, recognised definitions are employed by impartial authorities in
three contexts:
1 The R&D survey that demonstrates that the entity is actively involved in
innovative bioeconomy activities as described above in section 11.3.1.
2 Research and innovation projects funded by the Bionær programme at
the Research Council of Norway as described in Chapter 10.
3 Patenting activity in the bioeconomy area based on the Cooperative
Patent Classification (CPC) (particularly the taggings under y02W,
the University of Oslo in 2017 that focused on mapping organic waste- related
activities in the following subsectors: forestry, aquaculture and seafood
processing, beer brewing, meat processing, dairy, and organic waste processing. This study will be presented in section 11.3.3.
11.3.2.1 Identification strategy of the Norwegian Inventory of Bioeconomy
Entities (NIoBE)
The approach used in this research to identify a target population of active
organisations in Norway included two main stages. The first stage involved
collecting and collating a first estimation of the population. We used three
types of data to open the population. The inclusion rules moved from the
more stringent (the entity is involved in RD&I activities in the area, as in the
example above), to an intermediate level of accuracy (the entity has been
identified by another systematic project), to a more generic association (the
entity is a member of a population that is nominally associated with the
bioeconomy).
This first stage, which is akin to using three nets with different meshes,
was designed to include as many of the true population as possible (i.e. maximise ‘recall’). It is clear, however, that as we progress from the narrow to the
more broad- meshed nets, we risk including considerable bycatch in the form
of entities that are not a part of the true population. It proved difficult to
weed out these ‘false positives’ from our population as the ‘true’ population is
not known. Therefore, a second stage was undertaken. In this stage we set out
to increase precision by using other standardised information to exclude entities that were clearly not part of the population. In particular, we used industrial classifications (NACE, which is the starting point of other studies) and
other firm- level information, such as ‘trade descriptions’ found in financial
data sources. In the following, we briefly present the three components of
our approach before fleshing out the resulting population.
11.3.2.1.1 CONFIRMATION By ACTIVITy
The first inductive stage of the approach establishes a stable foundation. It
identifies Norwegian actors – universities, research institutes and firms – using
data on RD&I activities that are recognised to advance the ‘bioeconomy’. In
this stage, recognised definitions are employed by impartial authorities in
three contexts:
1 The R&D survey that demonstrates that the entity is actively involved in
innovative bioeconomy activities as described above in section 11.3.1.
2 Research and innovation projects funded by the Bionær programme at
the Research Council of Norway as described in Chapter 10.
3 Patenting activity in the bioeconomy area based on the Cooperative
Patent Classification (CPC) (particularly the taggings under y02W,