How to promote a new and sustainable food consumption model: A fuzzy cognitive map study.

Abstract The amount of food wastage produced at the global level generates high environmental, economic and social costs, such as greenhouse gas emissions, soil degradation, waste generation, consumption of natural resources, as well as economic losses, inequality and poverty. Taking stock of these problems, this paper conducts an empirical investigation in order to identify and recommend the most effective policy actions and private initiatives that might modify the current unsustainable food consumption model, characterizing high income countries, in order to achieve a significant reduction in the amount of food wastage. Specifically, it performs a fuzzy inference simulation by means of a three-step methodology: analyzing the use of language within the food waste reduction/valorization debate to identify system variables; deducing the map of causal-effect relationships among the identified system variables through interviews to a pool of experts; and finally, performing a fuzzy inference simulation, to identify drivers potentially able to discourage current unsustainable consumer behaviors. Among other things, the fuzzy analysis shows how some policy drivers, as "Public food waste rules", "Investments and infrastructure" and "Small-scale farming" are particularly effective in supporting a new and sustainable food consumption model. At the same time, the experts' knowledge allows highlighting the crucial role of "biorefinery" in fostering a transition towards a sustainable consumption model. This is an interesting result, as it points at the ability of biorefineries to enhance a general positive attitude of consumers which, in turn, would translate into more sustainable consumption practices. Highlights • Effective policy drivers and private initiatives to reduce the amount of food waste are identified. • Food waste system variables are elicited by means of discourse analysis. • Fuzzy inference is employed to investigate the causal-effect map derived from experts' knowledge. • Side-effects should be considered in designing policy interventions. [ABSTRACT FROM AUTHOR]