Combining Environmental Footprint Models, Remote Sensing Data, and Certification Data towards an Integrated Sustainability Risk Analysis for Certification in the Case of Palm Oil

Abstract

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.

Industry 4.0 quasi-effect between circular economy and sustainability: Palm oil industry

“…Mainly, risk factors at plantations can be reduced through mobile-robots, boosting total farm performance (Azwan et al, 2017). Likewise, Helka et al (2020) argue that remote-sensing can clarify superficial environmental impact analyses. These technologies also help promote clean environments (Anh et al, 2021), especially during transportation from palm oil plantations to palm oil mills, and enhance the traceability of agri-products along the supply chain, such as detecting live loading weights and truck locations during distribution.…”

“…Remote-sensing is one of the I4.0-in-a-CE applications that can collect data without requiring physical contact, particularly with the advancement of satellite imaging technology. Such technology can reduce environmental risk by detecting specific threats to plantation land and water consumption (Helka et al, 2020). Through this application, POI-related issues, including deforestation and associated biodiversity losses, can be traced to prevent the abuse of these elements and improve risk mitigation by identifying fresh-fruit-bunches sources (Mohd Najib et al, 2020).…”

Ecological Footprint as an Indicator of Corporate Environmental Performance—Empirical Evidence from Hungarian SMEs

“…Since the introduction of the concept, the EF has been used to measure environmental sustainability both at a global level and of individual consumption, as well [18][19][20][21][22]. Nonetheless, other indicators could also be used for measuring environmental sustainability [23][24][25], but it is only the EF that indicates the upper limit of growth properly [26]. The GFN started its National Footprint Accounts (NFA) program in 2003 based on Wackernagel's calculations, and, since then, the EF calculation methodology framework is regularly updated [27].…”