Combining LUCC models and environmental models for assessing potentials and risks of land-based negative emission technologies on different scale levels

An important element of Nationally Determined Contributions (NDCs) to reduce greenhouse gas emissions under the Paris Agreement are land-based mitigation technologies (LMTs) to achieve “negative emissions”. The aim is to remove CO2 from the atmosphere and to store the carbon in soils, vegetation biomass, or geological structures. Examples for LMTs are afforestation, reforestation and forest restoration, enhanced soil and land management, and the cultivation and combustion of dedicated energy-plants to generate electricity, in combination with carbon capture and storage technologies (BECCS). A reliable information basis on the potential contribution of these LMTs to climate change mitigation is crucial for defining climate-related policies on the national (e.g. in form of improved NDCs with specific targets and/or realistic strategies for scaling up) and global scale levels (e.g. international climate negotiations). Important factors of this information basis include the feasibility and societal acceptance of specific LMTs, as well as their potential locations, spatial extent and long-term effectiveness to store carbon. While questions of feasibility and acceptance can be tackled from a social science perspective, e.g. through participative scenario development processes, suitable methods for identifying potential locations for different LMTs and for assessing the amount of carbon uptake are spatial simulation models. In our presentation, we introduce an ongoing case study for Burkina Faso conducted as part of the EU funded project LANDMARC where we couple the spatially explicit land-use model LandSHIFT with empirical (IPCC Tier 1 approach) and process-based (LPJ-GUESS) models to determine emissions and uptake of carbon in agricultural and forest systems. Our goal is to investigate future land-use trajectories to identify trade-offs and synergies between food production and the implementation of LMTs, in particular climate-smart agriculture (CSA) and afforestation. We present first simulation results and critically reflect on modelling uncertainties as well as on challenges of using this type of models in a participative scenario context.