Dr. Giulio Breglia (p), Dr. Federico Fantechi

Natural hazards are increasing in frequency and magnitude, and while prevention and risk reduction phases are increasingly supported by global policy frameworks, the post-disaster phase still presents several challenges for coherent and transferable policy design. Recovery processes are often fragmented, strongly context-dependent, and influenced by institutional capacity, socio-economic conditions, and local reconstruction cultures, making comparison and learning difficult across territories.
This paper aims to provide a data-driven tool for national and local policymakers that learns from previous recovery plans, explicitly taking into account different reconstruction cultures worldwide and their territorial outcomes. Rather than proposing a single optimal recovery model, the approach is grounded in past experiences and seeks to support scenario-based reasoning in the early stages of post-disaster planning.
Our methodology relies on three main stages. First, disasters are categorized in terms of their impacts on human societies, based on past events and observed recovery trajectories. Second, a worldwide text-mining analysis is performed on post-disaster recovery plans produced by both governmental and non-governmental actors, focusing on their effects on regions and other sub-national administrative territories, with particular emphasis on recovery timing and sequencing of interventions. Finally, these results are implemented in an interactive tool that combines regional resilience indicators with hypothetical budget categories and expenditure timing, providing insights into potential impacts across different economic and social dimensions.
The proposed tool is intended to support learning-oriented recovery planning, helping policymakers explore trade-offs, timing effects, and place-based vulnerabilities in post-disaster contexts.

Ms Marina Stanojević (p), Ms. Valentina Ivanić

This paper builds on the concept of the institutional paradox in regional development—situations in which the need for cross-sector coordination is highest precisely where institutional capacities are weakest—and positions smart specialisation as a practical pathway for institutional “leapfrogging” through evidence-based priority setting. Conceptually, the paper treats the academic community as an intermediary and agenda-setter that can anticipate emerging development needs and translate them into policy-relevant knowledge, especially in regions facing coordination failures. Methodologically, it operationalises data-based policy-making through bibliometric mapping, using network, overlay, cluster, and density analyses to reveal how knowledge structures and thematic emphases evolve over time, and how these shifts can inform sustainable urban and regional development strategies.
The aim of this paper is to examine how long-running academic conference outputs can serve as a data source for policy intelligence that supports sustainable urban and regional development and smart specialisation design. Drawing on avaliable papers and topics presented at regional-development-oriented conferences organised by relevant scientific institutions in Serbia, the analysis identifies a clear thematic transition from macro-fiscal and crisis-related agendas to contemporary sustainability-related domains (e.g., circular economy, tourism, gender equality, local development and public sector topics), the emergence of place-sensitive focus (including increased attention to South-East Serbia), and strategic “white spaces” where policy demand is rising faster than institutional recognition (e.g., AI, ESG, circular economy). By translating these empirical patterns into implications for priority setting, stakeholder alignment, and policy sequencing, the paper demonstrates how academic knowledge ecosystems, aligned frameworks and smart specialisation logic, can strengthen data-based policy-making capacity in contexts marked by institutional constraints.

Prof. Anna Lejpras (p), Daniel Nepelski

This study examines how document features influence grant proposal success in competitive research funding. Analyzing proposals submitted to the European call for digital innovation intermediaries, we investigate whether proposal success depends on content alignment with agency priorities and communication quality, measured through document structure, text readability, and linguistic tone. We find that proposals with greater emphasis on conceptual content, higher readability, and less constraining language achieve significantly higher evaluation scores and more favorable funding outcomes. Technological diversification reflecting applicants' capabilities and alignment with policy objectives affects expert scores but not final funding decisions, while visual elements show the opposite pattern, suggesting distinct cognitive stages in the evaluation process. Our findings indicate that how ideas are communicated matters as much as what is communicated, with practical implications for proposal preparation and evaluation design. Improvements in communication quality could yield substantial gains in proposal success.