Institutional level

Summary of institutional impacts and factors that contribute to them


Collective intelligence design features that support these impacts

Increased satisfaction, legitimacy and support for climate policy

  • Facilitation (in-person or automated) to support high-quality deliberation
  • Direct feedback from decision makers to participants about outcomes of the process

Affects trust between institutions and citizenry

  • Transparency about intentions to act on public recommendations
  • Co-design of process

More appropriate and feasible policy programs, with buy-in from local stakeholders

  • Visualizing future impacts through modeling
  • Participatory design of models
  • Commitments to locally relevant actions


Impact reporting on outcomes related to institutions is rare and mostly limited to projects that directly involve government actors or policy discussions. There is evidence that when these processes are well designed and enable good quality deliberation, participants experience high levels of satisfaction with outcomes, even if recommendations do not fully reflect their own personal views. Evaluations of climate assemblies have also shown that they are perceived to have high legitimacy by the public, which is especially important for countries where trust in institutions is declining. This suggests that if decision-makers use collective intelligence methods they can retain public support even when making difficult choices, giving governments a strong mandate for bold climate action.

Trust is a key factor in ensuring public support of institutional actions. Evidence about the impact of participation on trust is mixed and can be affected by the willingness of institutions to take public recommendations on board. Deliberative Polling® in the United States has shown that even though participation significantly increases trust in both local and state-level government, the baseline levels of trust remain low (between 40-45 percent). Other collective intelligence methods have been more successful at increasing support for institutions. For example, a study of marine and coastal citizen science projects demonstrated that almost 90 percent of participants increased their support for marine science, and official coastal restoration or management policies. 


There is some evidence that collective intelligence processes lead to decisions and plans that are more feasible and appropriate in the long term because they are based on realistic assessments of diverse priorities and needs. For example, when stakeholders came together to discuss land management strategies in Zimbabwe using participatory modeling, community members reported that the process helped them understand the systemic effects of various behaviors. This led to changes in local land use policies and commitments from groups including local Chiefs and village heads to take the collective action necessary to improve outcomes for everyone in the long run. The Urban Heat Island Mapping project is another good example where working with local communities to collect granular data about extreme heat has led to locally tailored adaptation planning by municipalities in cities from Honolulu to Cincinnati. A pilot initiative by the UNDP Mozambique Accelerator Lab on collaborative mapping to increase resilience to climate-related extreme weather showed that the local government’s responsiveness increased substantially when the authorities accepted to participate in data collection alongside local communities.

In some cases, collective intelligence initiatives can be steered towards a direct contribution to plans or decisions sanctioned by law, but that national or local governments do not have the resources to invest in. This, for example, is the case of initiatives developed by the UNDP Accelerator Labs in the Maldives and Bolivia.