Actors and innovators in the bioeconomy 213
Consortium, 2018), for example, tries to estimate the boundary of existing
sectors and presents the turnover linked to these estimates. In addition, there
are a number of other national (LUKE Natural Resources Institute Finland,
2018) and sectoral efforts, such as for the cellulose industry (CEPI, 2018), and
cross- sectoral efforts available as data sources for bioenergy and biofuels
(EurObserv’ER, 2018).
Metrics for the bioeconomy are largely based on estimates of how biomass
production (agriculture, forestry, fisheries) is processed/refined in discrete
sectors (food and beverages, paper) to produce organic- based products. The
first problem is that this relationship is not one- to-one. Estimates are used to
allocate subpopulations of established categories (like chemicals and plastics)
to the ‘bioeconomy’, based on various estimates.
The most formalised efforts focusing on the bioeconomy involve measures
of biomass and ‘organic residuals’ or ‘side- streams’. New rules have been
introduced to more accurately account for the generation and treatment of
real resources. In Europe, the more accurate measures of organic waste are in
keeping with the revision of statistics in line with Eurostat WStatR.
However, this data is not linked directly to the firm level (yet). In the case of
Norway, the revision of sector- based estimates (before 2011) to improve data
collection exposed estimation errors of up to 100%. This suggests a need for a
stronger micro- level foundation for accounting in this area.
The problem extends further, notably to our ability to map not only economic activities that produce organic residuals or ‘waste’, but also those that
process them: it is difficult to properly size up the bioeconomy. However,
efforts to link economic activity to biomass, such as those undertaken by
EuroObserv’ER, should be encouraged. Being unable to frame the bioeconomy reliably and accurately in metrics has important consequences. We highlight the difficulties in properly framing and focusing on the role that
innovation plays in the circular ‘bioeconomy’.
11.3 Empirical sections
There is a range of ways to design a procedure that can identify the target
population in these circumstances. As sizing up emerging technologies, industries and sectors is not a new problem, the chapter references the sectoral
systems, transition literature and other current work (e.g. Bugge, Hansen &
Klitkou, 2016; Rotolo, Hicks & Martin, 2015). We also refer to ongoing
work in the SusValueWaste project (see note 1) using project and CV data to
explore empirical ways of getting a handle on the question of knowledge and
competencies. Improving the measurement of an emerging sector or metasector like the bioeconomy boils down to evolving metrics along the following dimensions:
• Coverage of supply and demand side measures for resources, activities
and actors;
Consortium, 2018), for example, tries to estimate the boundary of existing
sectors and presents the turnover linked to these estimates. In addition, there
are a number of other national (LUKE Natural Resources Institute Finland,
2018) and sectoral efforts, such as for the cellulose industry (CEPI, 2018), and
cross- sectoral efforts available as data sources for bioenergy and biofuels
(EurObserv’ER, 2018).
Metrics for the bioeconomy are largely based on estimates of how biomass
production (agriculture, forestry, fisheries) is processed/refined in discrete
sectors (food and beverages, paper) to produce organic- based products. The
first problem is that this relationship is not one- to-one. Estimates are used to
allocate subpopulations of established categories (like chemicals and plastics)
to the ‘bioeconomy’, based on various estimates.
The most formalised efforts focusing on the bioeconomy involve measures
of biomass and ‘organic residuals’ or ‘side- streams’. New rules have been
introduced to more accurately account for the generation and treatment of
real resources. In Europe, the more accurate measures of organic waste are in
keeping with the revision of statistics in line with Eurostat WStatR.
However, this data is not linked directly to the firm level (yet). In the case of
Norway, the revision of sector- based estimates (before 2011) to improve data
collection exposed estimation errors of up to 100%. This suggests a need for a
stronger micro- level foundation for accounting in this area.
The problem extends further, notably to our ability to map not only economic activities that produce organic residuals or ‘waste’, but also those that
process them: it is difficult to properly size up the bioeconomy. However,
efforts to link economic activity to biomass, such as those undertaken by
EuroObserv’ER, should be encouraged. Being unable to frame the bioeconomy reliably and accurately in metrics has important consequences. We highlight the difficulties in properly framing and focusing on the role that
innovation plays in the circular ‘bioeconomy’.
11.3 Empirical sections
There is a range of ways to design a procedure that can identify the target
population in these circumstances. As sizing up emerging technologies, industries and sectors is not a new problem, the chapter references the sectoral
systems, transition literature and other current work (e.g. Bugge, Hansen &
Klitkou, 2016; Rotolo, Hicks & Martin, 2015). We also refer to ongoing
work in the SusValueWaste project (see note 1) using project and CV data to
explore empirical ways of getting a handle on the question of knowledge and
competencies. Improving the measurement of an emerging sector or metasector like the bioeconomy boils down to evolving metrics along the following dimensions:
• Coverage of supply and demand side measures for resources, activities
and actors;