Conference Agenda

This study examines whether crisis-induced lesson-drawing processes shaped the European Union’s formulation of the Recovery and Resilience Facility (RRF) in response to the COVID-19 pandemic, focusing on the role of policy and political entrepreneurs and the dynamic between inter- and intra- crisis policy learning. The research specifically explores how lessons from a prior similar crisis, the Euro-crisis, influenced the EU’s response to the pandemic. Following the Euro-crisis, the EU’s reliance on mechanisms such as the European Stability Mechanism (ESM) and the implementation of austerity measures triggered significant political and social backlash, especially in debtor countries like Greece and Portugal (Buti, 2020). This experience underscored the need for institutional and policy reform to avoid repeating these missteps.

Initially, the EU’s response to the COVID-19 pandemic reflected focusing on modifying existing mechanisms such as the ESM, the Pandemic Crisis Support, the SURE program, and the flexible use of cohesion funds to address immediate needs (Deverell, 2009; Howarth & Quaglia, 2021; Ladi & Wolff, 2021). However, these measures, while necessary, did not signify a shift in the EU’s overall crisis governance approach but instead functioned as a stabilising response, a buffer. The pandemic’s unprecedented nature created a "permissive context," fostering conditions favourable to political entrepreneurs to re- consider and re- introduce more ambitious ideas, including the idea of common debt.

Anchored in policy learning theory and crisis governance, this paper will examine the inter- and intra- crisis periods of the COVID-19 pandemic, investigating how lessons from the Euro-crisis were learnt by policy entrepreneurs and how the evolving nature of crises, characterised by the slow-burning inter-crisis phase punctuated by the fast-burning intra-crisis, facilitated the reframing of the crisis as a shared European problem, fostering solidarity among Member States (Ladi & Tsarouhas, 2020; Capati, 2024; Capati, 2023; Fabrini & Capati, 2023).

In order to research the core question driving this study: how do policy and political entrepreneurs leverage policy learning within the European Union to induce change, and what role do temporal dynamics in crisis decision-making play in these processes? This study will make use of process-tracing and qualitative document analysis complemented by 10-15 semi-structured interviews with EU policymakers and experts directly involved in the ESM and the RRF’s development.

By coupling insights from policy learning with a focus on the temporal dimensions of crises, this research aims to further the understanding of the mechanisms that lead to significant policy change in times of crisis more broadly as well as what may lead to deeper integration and reform in the context of the EU.

This article investigates whether and how the Association of Southeast Asian Nations (ASEAN) and the African Union (AU) mobilized their imagined futures in response to the COVID-19 crisis. Both regional intergovernmental organizations (ROs) envision utopian futures that promise socioeconomic advancement and prosperity in the regions, respectively articulated in “ASEAN Community Vision 2025” and “Agenda 2063: The Africa We Want.” While these future-oriented visions are optimistic in their goals and abstract in their means, the unprecedented disruptions triggered by the COVID-19 crisis cast doubt on such optimism and required concrete responses. Against this backdrop, this article examines the interplay between the ASEAN and AU’s crisis response and their imagined futures, asking how the imagined futures were mobilized in response to the crisis and to what extent the ASEAN and AU retained the relevance of their imagined futures during this period. Drawing on qualitative document analysis, the study finds that both ROs not only invoked their imagined futures to guide crisis responses, thus demonstrating continued commitment, but also fortified them by integrating lessons learned from the crisis. The empirical analysis identifies a tripartite dynamic underpinning this process: preservation, activation, and recalibration. This threefold process collectively enabled the ROs to maintain continuity with their imagined futures while adapting to new realities. This article contributes to scholarship on international organizations and future studies by theorizing imagined futures as strategic resources that IOs can mobilize and reconfigure in response to crisis.

The Netherlands can be described as technocratic. Trust in expert elites is high and their ability to provide sound advice to the government is undisputed. As such, experts exert a lot of influence over policy decisions. Still, a strong case can also be made for Dutch representative democracy. In line with its strong neo-corporatist tradition and consensus-based governance system, interest groups have an institutionalised voice in political decision-making. In this paper, we investigate this already precarious dichotomy under stress conditions. We look at the government’s decision to close schools during the turbulent first wave of the Covid-19 crisis. After collecting evidence from interviews, the media and official documents, we use process tracing to uncover the causal chain leading to this policy decision. This clarifies the role of interest groups and technical experts in the narrative battle around this policy issue, while revealing new evidence of the broader contest fought between technocracy and representative democracy.

In an era of accelerating technological disruption, this paper advances a future-oriented policy proposal that addresses the urgent need for inclusive, accountable, and sustainable Artificial Intelligence (AI) governance. We propose the adoption of a new Sustainable Development Goal (SDG 18): Responsible and Inclusive AI for Sustainable Development. Grounded in the principles of anticipatory and adaptive governance, this proposal introduces a hybrid policy design framework that synthesizes ethical, risk-based, rights-based, human-centric, environmental (ontocentric), and business-oriented approaches.

The paper positions SDG 18 as a strategic soft-law mechanism to future-proof public policy against the rapidly evolving and systemic impacts of AI. By embedding AI governance within the established SDG framework, the model leverages international legitimacy and coordination to foster equity, transparency, and environmental integrity in technological development. The framework promotes interdisciplinary collaboration, participatory governance, and the use of measurable progress indicators—ensuring that AI-driven innovation remains aligned with long-term sustainability goals.

More specifically, the paper introduces ten interconnected policy targets that align with existing SDGs, including SDG 3 (Health), SDG 4 (Education), SDG 6 (Water and Sanitation), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action). These targets range from equitable access to AI technologies and ethical AI certification, to sector-specific applications in climate resilience, healthcare, disaster prediction, and smart cities. Each target includes suggested indicators and implementation strategies, designed to be adaptable across national contexts.

This work contributes to the field of policy design by offering a multi-dimensional framework that combines high-level normative principles with actionable and flexible tools. It addresses a core challenge in contemporary policy evaluation: how to assess, guide, and adapt governance frameworks for emerging technologies that are fast-moving, cross-sectoral, and value-laden. By drawing from existing SDG implementation mechanisms, the model benefits from an established architecture of monitoring and review, while expanding it with tailored, future-relevant tools for AI oversight.

Importantly, this paper advances the conversation on future-proofing policy architecture, offering concrete strategies for enhancing institutional resilience, designing with uncertainty, and embedding iterative evaluation mechanisms within international frameworks. It argues that the SDG structure—although non-binding—can serve as a powerful scaffolding for global AI governance, particularly where treaty-based mechanisms face political or procedural barriers.

In bridging the domains of AI governance and sustainable development, the proposed SDG 18 model offers a timely and scalable solution for national governments, international organizations, and civil society actors seeking to balance innovation with inclusion and precaution. The paper concludes by identifying pathways for piloting the SDG 18 framework in AI-intensive sectors such as education, healthcare, and climate adaptation—inviting comparative evaluations and collaborative learning.

Ultimately, this contribution illustrates how forward-looking policy design and evaluation can guide emerging technologies toward socially just and ecologically sound outcomes, ensuring that public policy remains relevant, resilient, and responsible in the face of rapid change.