[Paper Review] Collaboration for the Bioeconomy -- Evidence from Innovation Output in Sweden, 1970-2021

Philipp Jonas Kreutzer, Josef Taalbi|arXiv (Cornell University)|Feb 4, 2026

Bioeconomy and Sustainability Development0 citations

TL;DR

The study analyzes Sweden’s forest-based bioeconomy collaboration networks (1970–2021) using literature-based innovation output and network measures, finding that direct ties boost innovation similarly for bioeconomy and non-bioeconomy firms, while indirect ties and brokerage show no clear effects and cognitive proximity has negligible practical relevance.

ABSTRACT

Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike.

Motivation & Objective

• Motivate the role of collaboration in the bioeconomy transition and its potential to spur innovation.
• Construct and analyze a long-run panel linking collaboration networks to actual, commercialized innovation in Sweden’s forest-based bioeconomy.
• Test whether bioeconomy-intensive firms have different collaboration–innovation dynamics than other firms.
• Assess the impact of network properties (direct ties, indirect ties, brokerage) and cognitive proximity on subsequent innovation output.
• Evaluate policy implications for broad collaboration promotion versus optimizing collaboration composition.

Proposed method

• Construct a novel panel dataset using the SWINNO literature-based innovation output database spanning 1970–2021, linking innovations to participating organizations.
• Build undirected collaboration networks from trade-journal data that reference collaborators on commercially developed innovations.
• Operationalize key network measures: direct ties (degree), indirect ties (distance-two neighbors), two-step betweenness (brokerage), and cognitive proximity (normalized Jaccard index of partners’ knowledge bases.
• Identify bioeconomy actors with at least 25% of cumulative innovations in forest-biomass sectors to form a bioeconomy subsample.
• Estimate Poisson panel regressions of annual innovation counts on lagged network measures and cognitive proximity, with year fixed effects; perform instrumental variable checks for endogeneity.

Figure 2 : Panel Composition and Innovation Output Over Time. Number of active firms (panel a), total annual innovations (panel b), mean innovation rate per firm (panel c), and standard deviation (panel d). Bioeconomy firms (dashed) and total firms (solid).

Experimental results

Research questions

• RQ1 Do direct collaboration ties increase subsequent innovation output, and is this effect different for bioeconomy actors?
• RQ2 Do indirect ties or structural brokerage (two-step betweenness) influence innovation output, and how robust are these effects for bioeconomy vs. other firms?
• RQ3 What is the role of cognitive proximity in driving innovation, and does the bioeconomy context shift the optimal proximity level (inverted-U)?

Key findings

• Direct ties have a positive association with subsequent innovation, increasing predicted yearly output for both bioeconomy and non-bioeconomy firms.
• Increasing the number of direct collaborators yields about a 14% average rise in predicted annual innovation output per additional direct tie.
• Indirect ties show no clear, consistently significant effect on innovation output, with high uncertainty as ties increase.
• Two-step brokerage (structural holes) shows no clear association with innovation output and results are uncertain.
• Cognitive proximity has negligible practical relevance, and no strong inverted-U pattern is evident.
• Bioeconomy firms do not exhibit substantially different collaboration mechanisms than other firms; broad collaboration appears more effective than optimizing specific bioeconomy partnerships.

Figure 3 : Sweden’s Innovation Collaboration Network (1970–2021) . Panel a) depicts all collaborations observed in our study period; panel b) depicts component size distributions and panel c) the log-binned degree distribution. Isolated nodes are omitted. Green edges represent bioeconomy collaborati