Regional development has often been described in economic terms, using economic indicators such as growth in GDP or demographic indicators such as net migration or employment. Some researchers argued that regional development should be understood broader, by including for example social indicators and living environment indicators . Recently, researchers have shown that policies directed towards regional development have broadened as well , but are also still evaluated within specific narratives or frameworks that often constitute the goals of the policy, for example the Keynesian framework favours increasing demand and favours the evaluation of policies aimed at exactly this. This self-constituting practice of an amalgam of related policies has also been referred to by Hall as a policy paradigm. Because policies are often evaluated within these policy paradigms it becomes difficult to decontextualise them, disentangle them and compare policies with each other.In this paper we propose to use a different, more quantitative and comparative method. By applying the above mentioned work from Andy Pike on numerous data sources from EUROSTAT and OECD, researchers from the PREMIUM_EU project developed a new framework that is measuring Regional Development (dubbed “R”) using economic, social and living environment indicators.MethodBy regarding this “R” (and individual indicators) as an outcome of public policies on the local, regional, national and international level and by analysing regional development policies on different levels from 2010 and onwards we believe it is possible to understand the impact of these policies in a more evidenced based sense, regardless of the above mentioned different types of narratives or frameworks.We started our research with an analysis from the OECD on the different types of regional development policies and the relations between different levels of government within countries. Based on this and literature research, we developed a framework with relevant topics for regional development policies and different levels of government.Based on the work of Moritz Schütz presented during the ERSA 2024 conference, we developed and employed a webcrawler to automatically download and summarise policies from municipalities, regional and national authorities and analyse the results of this exercise.Findings/resultsThe webcrawling and -mining exercise in combination with the new set of indicators will offer a much broader and more comprehensive view of the use and necessity of regional development policies. The findings will be discussed in dedicated policy labs with policymakers and researchers from the respective regions.Discussion/conclusionsBoth the new set of indicators and the analysis of the policies are not only innovative, but will also be viewed as speculative. Although we believe that a direct causal relationship between policies and the regional development will be hard to uncover, we do believe that this research will move the field of policy analysis forward, because it is more focused on evidence-based indicators and is based on larger sets of policies.
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The chapter analyses knowledge management paradigms for the understanding and prioritisation of risks (risk assessment), leading to decision- making amongst policy makers. Studies and approaches on knowledge-based risk assessment, and in general risk management, vary depending on perceptions of risk, and these perceptions affect the knowledge scope and, ultimately, affect decisions on policy. Departing from the problems of big data in aviation, the shortcomings of the existing knowledge management paradigms and the problems of data conversion to knowledge in aviation risk management approaches are discussed. The chapter argues that there is a need for transciplinarity and interdisciplinarity for greater understanding of context, deriving from the challenges in the big data era and in aviation policy making. In order to address the challenging dynamic context in aviation, the chapter proposes a strength/knowledge-based inquiry that involves public sector and high-power organisations, in order to gain holistic knowledge and to aid the decision analysis of policy makers.
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In the era of Industry 4.0, data has become a critical driver of innovation and competitiveness in manufacturing. The up-take of technologies such as IoT, AI, and robotics has led to an unprecedented explosion of data, offering transformative opportunities for process optimization, product quality enhancement, predictive maintenance, and customisation. However, realizing these benefits requires effective data collection, storage, and processing, which necessitates robust and adaptable IT infrastructures. This white paper provides a comprehensive overview of four popular data infrastructure paradigms: data lakes, data fabrics, data meshes, and data spaces. Each of these paradigms have their unique focus points and application domains. Metadata serves as a crucial component, enhancing the findability, accessibility, interoperability, and reusability of data. The paper also explores critical building blocks of modern data infrastructures within manufacturing environments, such as OPC UA, brokers, and data catalogues, highlighting their role in enabling effective data sharing, management, and governance.
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