Thesis by Ariane Albers*

Low-carbon strategies encourage the use of renewable energy sources based, among others, on biomass. The objective of achieving carbon neutrality is expressed via a perfect balance between the amount of CO2 emitted and the amount captured.

Emissions impacting climate are analyzed via dedicated methodologies, such as life cycle assessment (LCA). However the models used in these approaches are static since they only represent systems in steady-state conditions. But incorporating the time dimension, whereby biogenic carbon (Cbio)a flows are distributed over time, may call into question these low carbon strategies.

The research conducted for this thesis focused on this very issue, via the development of tools to predict dynamic Cbio flows and combine them with different demand models (figure). The differences between the results generated by new dynamic evaluations and those derived from traditional static approaches were then analyzed(1-2). The analysis showed that this new Cbio sequestration and SOC
b dynamics modeling approach provided a more accurate representation of Cbio flows and that its incorporation into climate change models had a significant impact on forecasts.

A prospective energy model combined with a forestry biomass growth model.

This advanced methodology, supporting life cycle analyses, is of particular interest within the context of initiatives to be introduced to tackle climate change.

*Thesis entitled "Incorporation of the time factor in the environmental evaluation of biomass products: Dynamic carbon modeling"

a - Carbon from the Earth’s biosphere
b - Soil organic carbon

(1) A. Albers, P. Collet, D. Lorne, A. Benoist, A. Hélias (2019a). Applied Energy 239, 316-330.
DOI : 10.1016/j.apenergy.2019.01.186

(2) A. Albers, A. Avadi, A. Benoist, P. Collet, A. Hélias (2019c). Science of The Total Environment, 135278.
DOI : 10.1016/j.scitotenv.2019.135278

Scientific contact: pierre.collet@ifpen.fr