Combining environmental footprint models, remote sensing data, and certification data towards an integrated sustainability risk analysis for certification in the case of palm oil

Monitoring the potential impacts of the growing Bioeconomy (BE) is a crucial precondition for the development of viable and sustainable strategies. Potential environmental consequences from resource production for the German Bioeconomy can be assessed with the concept of environmental footprint modelling. Furthermore, remote sensing and sustainability certification are tools that can support risk assessment and mitigation i.e., regarding land use (change), biodiversity, carbon stocks, and water consumption. Thus, they can complement the results of footprint models and produce assessment results with a much higher resolution. Among other things, this can enable the development of strategies for more sustainable production practices in high-risk areas and avoid potential bans of biomass imports from entire countries/regions. The conducted case study on palm oil in this paper shows intersections between indicators used in sustainability certification systems and in footprint modelling considering processes on plantation and mill levels. Local best practices for the sustainable production of biomass are identified through a literature review and are extended by a survey, which evaluates the feasibility and conditions of implementing the selected practices on plantations. The conceptual approach outlined in this paper can be seen as a first step towards an integrated sustainability risk analysis of processes and products used within the BE that might be further developed from this starting point. It takes into account footprint modelling data, the use of sustainability certification systems, and data and results from remote sensing analyses. This will enable low-risk producers of renewable resources, who are located in regions generally flagged as high-risk when using environmental footprint modelling, not to be excluded from market activities but to set best practice examples that can then be expanded into these regions.