Towards Global Transdisciplinary Research: Lessons Learned from the Belmont Forum

Abstract:

The Belmont Forum was initiated in 2009 by a sub group of the International Group of Funding Agencies for Global Change Research. The Belmont Challenge embodies the Forum’s central goal: to deliver knowledge needed for societies to take action to mitigate and adapt to detrimental environmental change. This is fostered through collaboration among scientists across the globe and by stressing the importance of co-production of knowledge associated with coupled natural and social systems. The Belmont Challenge was clearly aligned with other collective thinking processes within the research community and key knowledge users from intergovernmental bodies. Convergence of these efforts gave rise to the S&T Alliance for Global Sustainability and its initiative – Future Earth.

Collaborative Research Actions, consisting primarily of multilateral research calls to address topics relevant to Future Earth, have been the main tool developed to address the Belmont Challenge and some early lessons emerged. First, obstacles faced by the Belmont Forum are similar to those met by scientists collaborating across traditional boundaries. Building shared languages and interests between various disciplines and across global cultures, remains difficult; this results in a persistent underestimation of the transformation required to move knowledge creation towards a truly global inter- and transdisciplinary science. Second, the diversity of organizations, cultures and practices within the Belmont Forum is the main source of its creativity and its challenges. While some convergence is needed to build coherent strategies and work efficiently together, diversity is necessary to design actions suitable for all partners regardless of their national research system and science-policy priorities. Finding the right trade-offs is a learning process that Future Earth is also facing; thus both initiatives are not only linked through funding relations but also through strongly intertwined learning curves.