Modelling multifunctionality of agricultural product systems in the bioeconomy and assessment in the context of the sustainable development goals

Abstract

Over the last decade, there has been a noticeable increase in the adoption of bioeconomy policy strategies on a global scale. Through the lens of resource economics, this trend can be seen as a carbon transition complementing the ongoing energy transition. While the energy transition primarily focuses on decarbonisation by phasing out fossil fuels, bioeconomies are centred on a shift from non-renewable to renewable carbon, i.e., biomass. In any case, ex-ante modelling assessments of biomass supply and use are required to understand its potential impacts on and contributions to the Sustainable Development Goals (SDGs). Agricultural biomass poses a particular challenge in the modelling of product systems (PS) because of its multifunctional nature and the resulting competition in the markets known as the 4 Fs: Food, Feed, Fibre (material), and Fuel (energy). Current approaches have primarily been divided into modelling of food systems, or of industrial applications of biomass (material and energy), or of cross-sectoral modelling concerning bioenergy and food security. The objective of this thesis is to develop a harmonized modelling approach among all aspects of multifunctionality of agricultural PS along its life cycle, i.e., the potential use of agricultural biomass in the 4 Fs, and the variety of co-products generated throughout its end-of-life. In this sense, this thesis explores the systemic consequences of multifunctionality at each stage of agricultural life cycles by introducing the C-Trans-LCA methodology. The C-Trans-LCA combines material flow analysis (MFA) and consequential life cycle assessment (LCA). A novel feature of the methodology is the introduction of archetypes to define the functional unit based on the most direct utilization of an agricultural resource in the 4 Fs in a specific region. Furthermore, the developed methodology enlarges the understanding of multifunctionality of agricultural PS by defining the classes resource, land, process, and product multifunctionality. It is demonstrated that consequential life cycle inventory modelling is effective in harmonizing the representation of these multifunctionality classes. Moreover, the 4 Fs evolve to the 4 Fs+ modelling the function of carbon dioxide removal via soil organic carbon (SOC) increase when climate smart practices are in place. For the assessment, the SDGs were operationalised for agricultural PS (micro level of bioeconomy) in a set of five life-cycle indicators. The importance of SDG 2 Zero hunger, SDG 12 Responsible consumption and production, and SDG 13 Climate action is highlighted as fundamental requirements for a sustainable bioeconomy. A graphical representation is provided by the “Bioeconomy Compass”. The case study “Grain maize to 4 Fs+ in Baden-Württemberg” showed that for sweetcorn (Food), consumer actions can importantly reduce the carbon footprint by minimizing food waste, whilst for polylactide (PLA, Fibre) optimising production processes to reduce material losses is more effective. Regarding dried grain maize (Feed), the sensitivity analysis resulted in a substantial variation depending on whether the substituted soybean meal came from Brazil or the United States. This result is also relevant for distiller’s dried grains with solubles, a co-product of bioethanol production (Fuel). In a carbon transition scenario 2030, the climate benefits of SOC increase may offset the greenhouse gas emissions of the Food and Feed PS, except when the larger area needed for organic cropping is diverted from PLA production. This occurs because PLA’s marginal supplier (MS) is assumed to be still a fossil counterpart (polyethylene terephthalate, PET) in the middle-term. Alternatively, where the larger area is diverted from bioethanol production, the carbon footprint would be lower. This is due to the fact that bioethanol MS is a combination of a fossil counterpart (gasoline) and electric vehicles. It is assumed that these electric vehicles are supplied by a less carbon-intensive electricity mix by 2030. The lower revenues resulting from lower yields of organic cropping may be compensated by processing grain maize to products of higher added-value. Such a potential is higher for Food rather than Feed products, which are usually directly consumed. The C-Trans-LCA methodology evidenced that resource multifunctionality leads to “competing” functions across the 4 Fs, while “complementary” functions resulting from dependent co-products from multi-output processes and from the cascade and circular use of products increase the palette of co-products of a PS. It was shown that both aspects of multifunctionality can be modelled with a harmonized consequential approach. The identification of MS of dependent co-products and diverted areas proves to be of paramount importance in the modelling given the global behaviour of agricultural markets. Such a finding has implications for contentious debates like "Food vs. Fuel." While using agricultural resources for energy purposes may displace its use as food, in the case study it was evident that a "food vs. food" dynamic exists. This means that reducing food waste could free up productive areas, easing the strain on food security. The modelling of changes in SOC as a complementary function of agricultural PS shall be encouraged to support the participation of producers in voluntary carbon markets and diversify their income. The C-Trans-LCA methodology provides the opportunity to formulate tailored strategies for utilizing the same resource across different markets, leveraging the multifunctionality of renewable carbon in a sustainable carbon transition. This involves preserving it as a resource, mitigating its impact as a greenhouse gas emission, and capitalizing on its potential as a marketable product. This resource economics perspective serves as an overarching umbrella, enabling the modelling and assessment of PS. Under this umbrella, broader bioeconomy visions can seamlessly integrate: biotechnological processes can be modelled in life cycle studies, and biological knowledge and principles can support the definition of indicators for impact assessment. Adopting this life cycle perspective on carbon, strengths the effectiveness of bioeconomies in truly contributing to societal well-being.