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Purpose <p>Improving the life-cycle assessment (LCA) of forestry products is important for accounting for the environmental impacts of the transition to a sustainable bioeconomy. Performing accurate biogenic carbon accounting, building time-dependent inventories, and using dynamic impact assessment methods is increasingly required from LCA practitioners. This work documents the development and validation of a managed forest database that extends the scope of an existing dynamic carbon flux model (De Rosa et al., 2017).</p> Methods <p>Data was obtained from published forestry inventories and reports, plant trait databases and peer-reviewed literature. Data gaps were filled using aggregation and averages. Results obtained from modelling forest management scenarios with the new database were validated against empirical forest biomass and carbon stock data published in literature. An extensive sensitivity analysis was conducted to assess the degrees of data variability and uncertainty, and how the input parameters influence the model results.</p> Results and discussion <p>The managed forest database enables the modeling of over 280 forest plantations for use in LCAs of biobased products. The database includes data on multiple species in different geographical locations, management practices such as the length of the rotation and harvested woody debris ratios, biomass growth parameters such as mean annual increment, and physical parameters such as basic wood density, carbon factor, below-to-above ground biomass ratio, and biomass conversion and expansion factors, as well as climate zone and forest type information. The results of the sensitivity analysis showed the most influential parameters for accurate carbon flux modelling, and highlighted the inter-dependencies of parameters. This can guide practitioners to make sound choices when using the model as well as in interpreting and evaluating the accurateness of the resulting inventory.</p> Conclusions <p>Using the new database, the model can replicate biomass growth and carbon stocks to a great extent in single-species, even-aged stands, though with limitations for other forest management practices. Users of the database and the model should be aware of the high sensitivity of parameters such as rotation time, mean annual increment, and the biomass conversion and expansion factor, and disclose uncertainties when interpreting the results.</p>

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Extension and validation of forest carbon flux model for dynamic Life Cycle Assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Kíra Lancz,
  • Agneta Ghose,
  • Massimo Pizzol

摘要

Purpose

Improving the life-cycle assessment (LCA) of forestry products is important for accounting for the environmental impacts of the transition to a sustainable bioeconomy. Performing accurate biogenic carbon accounting, building time-dependent inventories, and using dynamic impact assessment methods is increasingly required from LCA practitioners. This work documents the development and validation of a managed forest database that extends the scope of an existing dynamic carbon flux model (De Rosa et al., 2017).

Methods

Data was obtained from published forestry inventories and reports, plant trait databases and peer-reviewed literature. Data gaps were filled using aggregation and averages. Results obtained from modelling forest management scenarios with the new database were validated against empirical forest biomass and carbon stock data published in literature. An extensive sensitivity analysis was conducted to assess the degrees of data variability and uncertainty, and how the input parameters influence the model results.

Results and discussion

The managed forest database enables the modeling of over 280 forest plantations for use in LCAs of biobased products. The database includes data on multiple species in different geographical locations, management practices such as the length of the rotation and harvested woody debris ratios, biomass growth parameters such as mean annual increment, and physical parameters such as basic wood density, carbon factor, below-to-above ground biomass ratio, and biomass conversion and expansion factors, as well as climate zone and forest type information. The results of the sensitivity analysis showed the most influential parameters for accurate carbon flux modelling, and highlighted the inter-dependencies of parameters. This can guide practitioners to make sound choices when using the model as well as in interpreting and evaluating the accurateness of the resulting inventory.

Conclusions

Using the new database, the model can replicate biomass growth and carbon stocks to a great extent in single-species, even-aged stands, though with limitations for other forest management practices. Users of the database and the model should be aware of the high sensitivity of parameters such as rotation time, mean annual increment, and the biomass conversion and expansion factor, and disclose uncertainties when interpreting the results.