The objectives of open government data initiatives range from enhancing transparency and accountability to increasing innovation and participation. However, there is a lack of knowledge of the extent to which the objectives of open government data initiatives are achieved. This article investigates the relationship between the objectives of open government data initiatives and the benefits delivered. A total of 168 survey responses concerning 156 open government data initiatives at different government levels worldwide suggest that operational and technical benefits are the benefits most often delivered, followed by economic benefits and, finally, societal benefits. Surprisingly, our study suggests that whether an open government data initiative delivers a benefit (e.g. increased openness, trust or innovation) is not significantly affected by having an objective related to the delivery of that benefit. The objectives of state-and national-level open government data initiatives are more often achieved than those of local-and regional-level open government data initiatives.
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.
According to the waste hierarchy, waste prevention is environmentally superior to recycling or recovery, hence its inclusion in government policy. The assessment and prioritization of waste prevention strategies are impeded, inter alia, by ambiguous definitions and the lack of a sound environmental assessment method. In this study, a systematic approach to the environmental assessment of waste prevention activities (WPAs), covering the whole life cycle of products, was developed. The approach combines material flow analysis and life cycle assessment with a sustainable circular system design framework whilst giving special consideration to pivotal factors such as diffusion factor (share of population engaging in WPA), substitutability (degree to which a new product is replaced), effects on use‐phase impacts, and rebound effects. The application of the approach to the case studies of clothing and household furniture in Switzerland revealed lower impact saving potential than assumed initially, due to lack of participation, low substitutability, or high rebounds. For example, reusing clothing locally, instead of exporting it to low‐income countries, as currently done, displayed no or even negative impact savings since secondhand clothing in high‐income countries is often consumed in addition to new clothing. Drastic scenarios for clothes led to only moderate impact reductions of less than 15%, whereas a take‐back scheme for furniture reduced impacts by 70%. Concluding, the four factors (diffusion rate, substitutability, effects on use‐phase impacts, and rebounds) proved crucial in the assessment of waste prevention strategies and the approach presented was able to pinpoint improvement potentials of the waste prevention scenarios investigated.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.