Global Sensitivity Analysis of Background Life Cycle Inventories.

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

A novel machine-learning approach for evaluating rebounds-associated environmental footprint of households and application to cooperative housing.

Multiple environmental policies aim to increase resource efficiency and reduce consumption of goods and services with high environmental impact. This may lead to cost-savings and, consequently, additional consumption with environmental impacts (rebound effects). In this study, a supervised machine-learning model (i.e. an application of random forest regression) is developed to quantify consumption rebound effects. In contrast to previous approaches, it is a versatile method, which allows to estimate any income-related rebound at household level considering specific household properties and the entire profile of consumption. Socio-economic properties (e.g. income, age group) of the households are used as the independent properties for the regressor to detect the dependent consumption expenses of the households. Thus, this method can be used as a bottom-up study for understanding rebounds and developing targeted measures to prevent or reduce rebound effects. To illustrate the application of the method, it is applied to the case of cooperative housing in Switzerland. In addition to environmental goals, the cooperative aims to provide affordable housing, and the reduced rent increases the disposable income of tenants. The results show that households tend to spend the 'extra' income on housing (e.g. for larger apartments) and travel. For the former, the cooperative already has a policy in place regulating the apartment area permitted per person, which delimits induced environmental impacts. For the latter, households with lower income particularly spend their extra-money on purchase and operation of vehicles, while higher-income groups rather spend it on recreation and package holidays. Travel, housing, clothing and personal care products have highest emissions per Swiss Franc (∼0.3-0.6 kg CO2-eq/CHF). Thus, it is recommended to provide incentives for shifting these expenses to other consumption, to avoid jeopardizing environmental goals. The method was also used for a range of other applications e.g. rebounds due to energy-efficient devices to illustrate its versatility.