How 15 European countries are working to improve the policymaking process
The persistent gap between scientific knowledge and political decision-making remains a major weakness in European Union (EU) governance. Despite decades of investment in research and innovation, the EU continues to struggle to ensure that its policies are consistently guided by robust evidence. This disconnect weakens both the quality and legitimacy of public decisions, especially in areas where fast, informed action is critical. Recent crises, from the COVID-19 pandemic to the climate emergency and disruptive technological shifts, have only heightened the urgency for more timely, transparent, and evidence-informed policymaking.
In response, the European Commission launched a Mutual Learning Exercise (MLE) aimed at improving how research-based knowledge informs policy decisions across Europe. The initiative focused on strengthening Science-for-Policy (S4P) systems, meaning the structures, people, and processes that support the effective use of scientific evidence in public decision-making.
Current practices and landscape
S4P refers to the structured use of research-based knowledge in support of public decision-making. Its goal is to improve the quality, transparency, and coherence of policy by grounding it in sound scientific evidence. S4P systems are expected to promote innovation, ensure policy coherence across sectors, and reinforce democratic legitimacy. Yet across the EU, these systems are often constrained by structural weaknesses that limit their effectiveness and impact.
Recognising the strategic importance of evidence-informed policy, the MLE was launched in 2024 under the Horizon Europe Policy Support Facility. The initiative involved 15 EU Member States and Associated Countries and aimed to promote mutual exchange, identify transferable good practices, and support the development of coherent S4P systems.
The picture that emerged is one of fragmentation and imbalance. While some countries have established mature advisory structures and foresight capabilities, others still depend on informal networks or ad hoc mechanisms. Common barriers include the absence of professional intermediaries (such as knowledge brokers), limited institutional capacity to absorb and act on evidence, mismatched timelines and expectations between scientific and political processes, and unclear distribution of roles and responsibilities. Policymakers often struggle to interpret or compare diverse sources of expertise, while researchers receive little recognition or support for engaging in policy dialogue.
The shift from linear advice to co-creation
The MLE process included four country visits (Belgium, Spain, the Netherlands, and Poland), each focused on a different dimension of S4P: knowledge sharing, system mapping, evaluation and assessment, and the role of trust. These visits combined expert presentations with participatory workshops involving local stakeholders. They revealed that the difficulties facing S4P systems are not only technical or organisational, but also conceptual and cultural.
One of the most important insights was the need to move beyond a linear model of science communication. Rather than seeing scientific advice as a unidirectional flow of information from researchers to policymakers, participants increasingly viewed S4P as a collaborative, iterative process of co-creation. Scientific evidence is just one of several inputs into policymaking, alongside experience, values, stakeholder interests, and political judgment.
Moreover, evaluation and trust emerged as particularly vulnerable elements. Many countries lack structured methods for assessing the performance of their S4P systems, which hampers learning, improvement, and accountability. At the same time, trust between scientists and policymakers is often implicit, fragile, or undermined by institutional opacity. Without mutual respect, transparency, and clarity of roles, even well-designed mechanisms struggle to deliver meaningful results.
The final report adopts the concept of the S4P ecosystem as a central analytical and operational tool. Unlike linear models based on information transfer, an ecosystem approach recognises the plurality of actors, interests, and relationships involved in evidence-informed policymaking. These systems are inherently complex, and that complexity must be governed, not avoided.
Eight recommendations for better science-informed policies
Building on this shared analysis, the final report outlines eight strategic recommendations to strengthen Science-for-Policy systems across the EU. These are intended as adaptable principles rather than fixed models, allowing each country or institution to tailor them to its own context. The following are the eight recommendations identified to strengthen Science-for-Policy systems across the EU.
1. Governing at the ecosystem level
As already mentioned, Science-for-Policy systems work best when all the parts are connected and coordinated. That means ensuring that researchers, public institutions, advisors, and civil society organizations work together. Clear roles, shared goals, and good communication across these actors are essential to avoid confusion or missed opportunities.
2. Institutionalising collaboration and public engagement
To work well, Science-for-Policy systems need scientists, policymakers, and citizens to collaborate regularly, not occasionally. This means creating stable ways for people to participate in shaping policies, like forums, consultations, or working groups. When people are included, policies are more likely to reflect real needs and gain public trust.
3. Integrating foresight and anticipatory policymaking
Science-for-Policy systems should use tools like trend analysis, future scenarios, and early warning signals to spot challenges in advance and plan with the long term in mind. This helps governments make long-term strategies and prepare policies that are resilient in the face of uncertainty and complexity.
4. Redesigning incentives for policy engagement
Many researchers see limited rewards and few institutional incentives for engaging in policymaking. Academic systems should value science advice and policy impact alongside publications, making engagement a recognised part of research careers.
5. Building capacity for all actors
Researchers, policymakers, and intermediaries all need targeted training to work effectively across disciplines and institutions. Core skills include clear communication, critical use of evidence, systems thinking, and ethical awareness.
6. Enhancing transparency and accountability
Transparency in how evidence is produced, selected, and used is crucial to promote trust in science-policy processes. S4P systems should have clear procedures to manage conflicts of interest, give access to data and methods, and explain how evidence contributes to final decisions. This helps build public trust and keeps institutions accountable.
7. Safeguarding scientific integrity and quality
Policy should rely on research that meets high scientific and ethical standards. To ensure this, S4P systems must check that the evidence is accurate, independent, and produced responsibly. This includes applying strict quality controls, respecting ethical rules, and making scientific methods transparent.
8. Evaluating S4P systems as a whole
To improve over time, S4P systems need to be evaluated as a whole, not just in parts. Looking at how all components work together helps spot weaknesses, measure results, and guide future improvements.
Why businesses should care
The development of stronger Science-for-Policy systems has implications that extend beyond public administration and academia. It has significant implications for businesses, especially those operating in highly regulated and innovation-driven sectors such as pharmaceuticals, energy, digital technology, and advanced manufacturing.
Companies engaged in research and development often generate valuable knowledge that could help inform public decisions. However, without clear and structured connections between science and policy, this expertise risks being overlooked in shaping the regulations, incentives, and frameworks that directly affect these sectors. Improving the science-policy interface opens new opportunities for businesses to contribute meaningfully to policy agendas, not just as subjects of regulation, but as active partners in designing forward-looking strategies. Public-private partnerships, participatory foresight processes, and stakeholder consultations are key tools for making this collaboration effective.
The final report of the Mutual Learning Exercise highlights the importance of inclusive S4P ecosystems, where knowledge from universities, public agencies, think tanks, and industry can interact constructively to support better, more informed governance.
