Towards local bioeconomy: A stepwise framework for high-resolution spatial quantification of forestry residues

• Published in: Renewable & sustainable energy reviews 2020-12, Vol.134, p.110350, Article 110350
• Main Authors: Karan, S.K., Hamelin, L.
• Format: Article
• Language: English
• Subjects: Bioeconomy; Biotechnology; Environmental Sciences; Forest residues; Fossil carbon transition; Life Sciences; Spatial quantification; Theoretical potential; Uncertainty assessment
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2020.110350

In the ambition of a transition from fossil carbon use, forestry residues are attracting considerable attention as a feedstock for the future bioeconomy. However, there is a limited spatially-explicit understanding of their availability. In the present study, this gap has been bridged by developing a generic framework “CamBEE”, for a transparent estimation of aboveground primary forestry residues. CamBEE further includes guidelines, based on standard uncertainty propagation techniques, to quantify the uncertainty of the generated estimates. CamBEE is a four-step procedure relying on open-access spatial data. The framework further provides insights on the appropriate spatial resolution to select. In this study, the proposed framework has been detailed and exemplified through a case study for France. In the case study, primary forestry residues have been spatially quantified at a resolution of 10 m, using spatial and statistical data on forest parameters (net annual increment, factor of basic wood density, biomass expansion factors, etc.). The results for the case study indicate a total theoretical potential of 8.4 Million Mgdry matter year−1 (4.4–13.9 Million Mgdry matter year−1) available in France, the equivalent of 161 PJ year−1. The case study validates that the CamBEE framework can be used for high-resolution spatial quantification of PFRs towards integration in local bioeconomy. •CamBEE: A framework for high-resolution spatial quantification of forestry residues.•Uses open-source spatial data & presents results with uncertainties.•A metric for deciding the spatial resolution for such assessments is provided.•Exemplified results for France reveal 8.4 Million t DM y−1 of forestry residues.