In 2018, the real amount invested in the European Union's energy transition fell short of the funding level required to reach the 2030 climate and energy targets by €179 billion. Citizen-led finance in renewable energy development emerges as an innovative tool to bridge this investment gap. However, in spite of the European Union's ambition to involve local communities for co-driving the low-carbon energy transition, there is no comprehensive analysis quantifying citizens' potential to co-finance and participate in community renewable energy initiatives across Europe. We address this knowledge gap through a representative choice experiment survey that collected responses from 16,235 participants to different hypothetical investment options on renewable energy schemes across all European Union Member States, and estimate the social potential of European citizens to participate and invest in community-administered wind farms. Results from a novel survey-based social simulation indicate that €176 billion could be obtained from citizen-led finance in community-administered wind farm developments, enough to halve the investment gap to achieve a 32% renewable energy share in final energy consumption by 2030. Our analysis substantiates the case for facilitating easily accessible, risk-insured community investment options across Europe to unlock citizens' social potential for investing in community renewable energy.

Many commonly used test statistics are based on a norm measuring the evidence against the null hypothesis. To understand how the choice of a norm affects power properties of tests in high dimensions, we study the consistency sets of p-norm based tests in the prototypical framework of sequence models with unrestricted parameter spaces, the null hypothesis being that all observations have zero mean. The consistency set of a test is here defined as the set of all arrays of alternatives the test is consistent against as the dimension of the parameter space diverges. We characterize the consistency sets of p-norm based tests and find, in particular, that the consistency against an array of alternatives cannot be determined solely in terms of the p-norm of the alternative. Our characterization also reveals an unexpected monotonicity result: namely that the consistency set is strictly increasing in p∈(0,∞), such that tests based on higher p strictly dominate those based on lower p in terms of consistency. This monotonicity allows us to construct novel tests that dominate, with respect to their consistency behavior, all p-norm based tests without sacrificing size.

Corporate venturing (CV) has become a very important business tool for both corporations and start-ups and has received considerable research attention. However, existing studies are ambiguous and incomplete in terms of the optimal set-up, operation and termination of a CV program and the capture of value that is generated for the parent company. Based on the available research, we developed the Corporate Venturing Navigator (CVN) holding two artefacts: 1) the CVN Framework and 2) the CVN Cockpit. The results provide initial guidance on how to improve CV activities and realize their full potential with first application in practice. We highlight the contributions of the CVN in terms of theory and measurement.

In this paper, I evaluate the self-developed Corporate Venturing Navigator (CVN) and its two artefacts, the CVN-Framework and the CVN-Cockpit, by putting the CVN under the microscope in practice. I assess the CVN with practice partners to validate the identified current problem with CV programs, the CVN phases and element characteristics and dynamics that were developed and the CVN’s usability, as well as to consider additional input. The goal is to gain further scientific insight into the creation and operation of corporate venturing with a focus on capturing and measuring value. The results reflect additional expertise in optimizing CV investments, further justifying their existence and usefulness.