As major drivers of economy, households induce a large share of worldwide environmental impacts. The variability of local consumption patterns and associated environmental impacts needs to be quantified as an important starting point to devise targeted measures aimed at reducing household environmental footprints. The goal of this article is the development and appraisal of a comprehensive regionalized bottom‐up model that assesses realistic environmental profiles for individual households in a specific region. For this purpose, a physically based building energy model, the results of an agent‐based transport simulation, and a data‐driven household consumption model were interlinked within a new probability‐based classification framework and applied to the case of Switzerland. The resulting model predicts the demands in about 400 different consumption areas for each Swiss household by considering its particular circumstances and produces a realistic picture of variability in household environmental footprints. An analysis of the model results on a municipal level reveals per‐capita income, population density, buildings' age, and household structure as possible drivers of municipal carbon footprints. While higher‐emission municipalities are located in rural areas and tend to show higher shares of older buildings, lower‐emission communities have larger proportions of families and can be found in highly populated regions by trend. However, the opposing effects of various variables observed in this analysis confirm the importance of a model that is able to capture regional distinctions. The overall model constitutes a comprehensive information base supporting policymakers in understanding consumption patterns in their region and deriving environmental strategies tailored to their specific population.

中文翻译：

---

基于机器学习的家庭建模：研究消费引起的环境影响的区域性自下而上方法

作为经济的主要驱动力，家庭在全球范围内对环境产生了很大的影响。需要量化当地消费方式的可变性以及相关的环境影响，以此作为制定针对性措施以减少家庭环境足迹的重要起点。本文的目的是开发和评估一个综合的区域性自下而上模型，该模型可以评估特定区域中每个家庭的实际环境概况。为此，在新的基于概率的分类框架内将基于物理的建筑能耗模型，基于代理的交通运输模拟的结果以及由数据驱动的家庭消费模型相互关联，并应用于瑞士。结果模型通过考虑瑞士的特殊情况，预测了每个瑞士家庭在大约400个不同消费区域中的需求，并得出了家庭环境足迹变化的真实画面。在市政一级对模型结果的分析表明，人均收入，人口密度，建筑物的年龄和家庭结构是造成市政碳足迹的可能动力。虽然排放量较高的市政当局位于农村地区，并且倾向于显示较高比例的老式建筑，但排放量较低的社区的家庭比例较高，并且按趋势可以在人口稠密的地区找到。但是，在此分析中观察到的各种变量的相反影响证实了能够捕捉区域差异的模型的重要性。