Grainger Matthew James, Aramyan Lusine, Piras Simone, Quested Thomas Edward, Righi Simone, Setti Marco, Vittuari Matteo, Stewart Gavin Bruce
School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.
Wageningen Economic Research, Wageningen, The Netherlands.
PLoS One. 2018 Feb 1;13(2):e0192075. doi: 10.1371/journal.pone.0192075. eCollection 2018.
Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.
在发达国家，家庭食物浪费占食物浪费总量的比例最大。因此，减少食物浪费需要了解导致家庭产生食物浪费的社会经济（背景和行为）因素。解决这样一个复杂的问题需要合理的方法，而到目前为止，这些方法受到了大量与废物产生相关的因素、缺乏公认的定义以及可用数据有限的限制。这项工作通过使用最大的可用数据集之一为食物浪费产生的文献做出了贡献，该数据集包括可避免的家庭食物浪费的客观数量数据以及一系列社会经济因素的信息。为了解决问题复杂性的一个方面，实施了用于变量选择的机器学习算法（随机森林和博鲁塔算法），并将其与线性建模、模型选择和平均相结合。模型选择解决了模型结构的不确定性，而这在文献中对食物浪费的评估中通常未被考虑。在最简约模型中选定的家庭食物浪费的主要驱动因素包括家庭规模、挑食者的存在、就业状况、房屋所有权状况以及地方当局。无论模型基于哪组变量运行，结果都表明大家庭是减少食物浪费干预措施的关键目标对象。
