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.

What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption.

Both sharing and using open research data have the revolutionary potentials for forwarding scientific advancement. Although previous research gives insight into researchers' drivers and inhibitors for sharing and using open research data, both these drivers and inhibitors have not yet been integrated via a thematic analysis and a theoretical argument is lacking. This study's purpose is to systematically review the literature on individual researchers' drivers and inhibitors for sharing and using open research data. This study systematically analyzed 32 open data studies (published between 2004 and 2019 inclusively) and elicited drivers plus inhibitors for both open research data sharing and use in eleven categories total that are: 'the researcher's background', 'requirements and formal obligations', 'personal drivers and intrinsic motivations', 'facilitating conditions', 'trust', 'expected performance', 'social influence and affiliation', 'effort', 'the researcher's experience and skills', 'legislation and regulation', and 'data characteristics.' This study extensively discusses these categories, along with argues how such categories and factors are connected using a thematic analysis. Also, this study discusses several opportunities for altogether applying, extending, using, and testing theories in open research data studies. With such discussions, an overview of identified categories and factors can be further applied to examine both researchers' drivers and inhibitors in different research disciplines, such as those with low rates of data sharing and use versus disciplines with high rates of data sharing plus use. What's more, this study serves as a first vital step towards developing effective incentives for both open data sharing and use behavior.

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures.

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.