TERRANOVA, “The European Landscape Learning Initiative: Past and Future Environments and Energy Regimes shaping Policy Tools,” is an EU Horizon 2020 Marie Skล‚odowska-Curie project which started in April 2019.

TERRANOVA is a consortium of high-profile universities, institutions with acknowledged heritage, landscape and planning expertise, civil society organisations and SMEs, located in Sweden, Norway, Denmark, Germany, France, Belgium and the Netherlands.

The project aims at improving our diachronic long-term understanding of landscape histories and land use strategies in Europe in the Holocene and Anthropocene. Previously identified socio-cultural transitions and the effects of natural forcings will be critically assessed in a new intellectual interdisciplinary arena created by the TERRANOVA project. Regional and continental syntheses will be used to anchor a new generation of landscape and climate change models which include the effects of past human actions and generate scenarios for landscape management and rewilding. Ultimately this project will contribute to identifying major previous shifts in resource use and energy regimes and provide options for the future transition to a low carbon society. There is a consensus that the intensity of management and impacts of land management on natural systems today is unprecedented. This leads on to consideration of themes of sustainability and societal impact upon landscapes in the 21st century. From this perspective knowledge of past energy regimes and landscape interactions are essential components in understanding the present transition to a low carbon society.

ELO’s role will be to document pathways and perceptions of future landscape change contextualised in the long-term landscape history of Europe. In order to do so, the role of perception in decision-making for landscape planning will be quantified. Along with this, ELO will be highly involved in communication and dissemination activities.





TERRANOVA will run 1-4-2019 – 31-3-2023. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 813904.

H2020 MSCA ITN 2018


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