| dc.description.abstract | To address the pressing issues human overpopulation has created on earth, such as climate change and poverty, the United Nations (UN) introduced the Sustainable Development Goals (SDGs) as a global agenda to guide the world onto a more sustainable trajectory. To achieve the goals, the UN and scholars urge for local action, support, and collaboration of scientific research institutes. Additionally, embedding this structural transformation locally, requires new knowledge to address region specific challenges. Important in this process is to gain insights into the current knowledge base of regions, to find opportunities for new knowledge development. This study therefore aims to explain differences among European regions in complex knowledge production on SDG related research, specifically looking at the knowledge complexity of a region, it’s scientific relatedness to the SDGs, and several regional characteristics based on the SDG indicators. These concepts are drawn from literature on Evolutionary Economic Geography that argues that complex knowledge production is influenced by mechanisms of path- and place dependency. Using the CWTS wos_2113 database, scientific publications are retrieved that represent a region’s knowledge base. Data from the STRINGS project is used to identify SDG related publications. Findings show that North-Western Europe produces the most complex knowledge and has the highest relatedness to the SDGs. Following from this, four regression models are estimated, to find relationships between the variables. These models include data from before the introduction of the SDGs, 2010-2014, hereafter, 2015-2020, and with and without the inclusion of the regional characteristic variables, as these are only selected for a limited number of SDGs. The findings show that the SDGs are not equally well explained by the different variables, suggesting that there is not one model that fits all the SDGs. Nevertheless, for most SDGs a positive relationship is discovered between the knowledge complexity of regions and their scientific relatedness to the SDGs. This indicates that path- and place-dependent mechanisms also apply to the SDGs and proximity advantages should be considered. In addition, for the regional characteristic variables no general conclusion is drawn, as the indicators vary per SDG. However, surprising to see is that several SDGs show a negative relationship with the SDG research share, indicating that there is a misalignment between research priorities and societal needs. This study thus provides several promising insights and recommendations to expand the knowledge base of regions through research and collaboration, thereby aiming to accelerate the sustainability transition. | |
