Abstract

Forests are expected to provide multiple ecosystem services and mitigate climate change whilst also being adapted to the impacts of climate change. This thesis aims to analyse these competing expectations placed on forests in Sweden and how to tailor forest management locally to meet them by (i) applying machine learning to analyse forest conflicts in daily media from 2012 to 2022 and (ii) collaborating with local forest stakeholders to co-produce locally-tailored forest management pathways in two study areas in Sweden. The results showed that media coverage of forest conflicts has increased over time and that the conflicts concerned why and for whom forests should be managed. The co-production processes additionally highlighted expectations of how forests should be managed. Overall, the local stakeholders wanted to diversify forest management to enable more multifunctional and climate-smart forests, whilst they also stressed several conditions that may enable or disable its implementation in practice, depending on how they are handled. To adapt forest management to climate change impacts, the stakeholders emphasised the value of learning from past experiences and continuously improving management in line with an adaptive forest management approach. To limit climate change, they argued that it is necessary to consider climate change mitigation holistically and in conjunction with climate change adaptation and forests’ provision of ecosystem services. By collaborating with local stakeholders and combining their context-based local knowledge with forest science, this thesis developed a broader and pluralistic understanding of forest management while enabling collaborative learning. In summary, this thesis highlights competing expectations placed on forests in Sweden and the value of co-production processes to tailor forest management to local socio-ecological contexts in collaboration with local stakeholders.

Keywords

sustainable forest management; climate-smart forestry; ecosystem services; media analysis; machine learning; co-production processes; local knowledge; forest policy; forest stakeholders; scenario modelling

Published in

Acta Universitatis Agriculturae Sueciae
2023, number: 2023:19
ISBN: 978-91-8046-090-3, eISBN: 978-91-8046-091-0
Publisher: Swedish University of Agricultural Sciences

SLU Authors

• Hallberg-Sramek, Isabella

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

Associated SLU-program

SLU Future Forests


UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.54612/a.6os9e6ei21

Permanent link to this page (URI)

https://res.slu.se/id/publ/120847