To obtain net climate benefits from intensified forest utilization in the short-term, the substitution impact of wood harvesting and increased carbon stocks of wood-based products should be higher than the forest carbon stock loss. However, the product specific substitution impacts change dynamically over time along with technological development, and long-term climate change mitigation strategies need to consider this, as well as the market forces. We identified future pathways enabling globally market viable environments for wood utilization scenarios in Finland resulting in net climate benefits in 2050. We I) indicated substitution impacts in 2050 applying future-oriented parameter modification based on LCA, and developed quantitative target scenarios by altering wood-based product portfolios to achieve required substitution, and II) identified pathways enabling scenarios by applying participatory backcasting. In the scenarios, a major shift from primary energy use to high added-value products including textiles, chemicals, composites and advanced biofuels, or long-lifetime construction products was needed and more lifecycles for wood products by recycling were required. The actions enabling market viable environment focused on the global level policies and consumer perceptions, while actions locally were limited and current production technology development oriented. We recommend multi-target backcasting together with LCA to analyse more synergies and trade-offs.
Highlights
- Wood-based substitution benefits are likely to decrease over time
- Carbon residence has more importance in the future as an indicator
- Energy use of wood should be reduced to increase climate benefits
- Global agreement is needed to fund clean technologies and reduce fossil ones
Reference:
Janni Kunttu, Elias Hurmekoski, Tanja Myllyviita, Venla Wallius, Antti Kilpeläinen, Teppo Hujala, Pekka Leskinen, Lauri Hetemäki, Henrik Heräjärvi. 2021. Targeting net climate benefits by wood utilization in Finland: Participatory backcasting combined with quantitative scenario exploration. Futures, Volume 134, 102833.
https://doi.org/10.1016/j.futures.2021.102833