Chinese household food waste and its' climatic burden driven by urbanization: A Bayesian Belief Network modelling for reduction possibilities in the context of global efforts

Consumer food waste usually exceeds food losses when a developing country transitions to a developed one. With this notion, China, which is experiencing socioeconomic transition, is projected to be a future hotspot of global food waste. However, the mechanism of food waste generation is more complex than that of food losses, because various driving factors entangle with each other in a non-linear way. Here, by linking household survey data and reviewed life-cycle-assessment dataset, we quantified food waste in Chinese typical provinces, and developed a Bayesian Belief Network (BBN) model to reveal the mechanism of household food waste generations. We explored the possibilities of food waste reduction based on the Chinese contextualized scenario analysis, and further revealed the association of food waste and food security at global scale. Results show that the average food waste varies among Chinese provinces ranging from 12 to 33 kg cap(-1) yr(-1), with carbon footprint from 30 to 96 kg CO(2)e cap(-1) yr(-1). Animal derived food accounts for 5-18% in weight, but disproportionately for 18-40% of carbon footprint. The accuracy of BBN model is 78%. Sensitivity analysis shows that refrigerator ownership ranks first in determining food waste generations, compared to other factors of income, education, household size, and urbanization levels; and ages of family members. At the global scale, household food waste climbs sharply when food-security status of a certain country rises. China with its barely satisfied food-security status would astonish the world if we followed the global waste trajectory due to its largest population. However, according to our BBN-based scenarios, it is too early to say that China will become a global hotspot of food waste considering its specific socioeconomic and cultural backgrounds in its rapid urbanization period. (C) 2018 Elsevier Ltd. All rights reserved.