By using information technology, local governments can develop alternative forms of citizen engagement. Civic crowdfunding campaigns supported by online platforms enable citizens to participate financially in social projects and can be matched with government funding. As such, an alternative for subsidies seems to be developing. In this paper, we assess empirically the success of civic crowdfunding campaigns in the Netherlands by using data collected during 2018 from 269 civic crowdfunding projects and local demographic data from the neighborhoods of these projects. The factors—the use of match-funding, the target amount of money, and the theme of the project, as well as the age structure, the province, and the degree of urbanization of the neighborhood of the civic crowdfunding project—turn out to be empirically related to the success of a civic crowdfunding campaign.
Exploratory analyses are an important first step in psychological research, particularly in problem-based research where various variables are often included from multiple theoretical perspectives not studied together in combination before. Notably, exploratory analyses aim to give first insights into how items and variables included in a study relate to each other. Typically, exploratory analyses involve computing bivariate correlations between items and variables and presenting them in a table. While this is suitable for relatively small data sets, such tables can easily become overwhelming when datasets contain a broad set of variables from multiple theories. We propose the Gaussian graphical model as a novel exploratory analyses tool and present a systematic roadmap to apply this model to explore relationships between items and variables in environmental psychology research. We demonstrate the use and value of the Gaussian graphical model to study relationships between a broad set of items and variables that are expected to explain the effectiveness of community energy initiatives in promoting sustainable energy behaviors.
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Charging infrastructure in neighborhoods is essential for inhabitants who use electric vehicles. The development of public charging infrastructure can be complex because of its dependency on local grid conditions, the responsibility to prepare for anticipated fleet growth policies, and the implicit biases that may occur with the allocation of charging resources. How can accessible EV charging be ensured in the future, regardless of energy infrastructure and socio-economic status of the neighborhood? This study aims to represent the decision-making in the allocation of public charging infrastructure and ensure that various key issues are accounted for in the short-term and long-term decision making. The paper first identifies these issues, then describes the decision-making process, and all of these are summarized in a visual overview describing the short-term and long-term decision loop considering various key indicators. A case study area is identified by comparing locally available data sources in the City of Amsterdam for future simulation.