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Ecosystem services case study report
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering. (Environmental Management and Assessment)ORCID iD: 0000-0001-6417-4497
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering. (Environmental Management and Assessment)ORCID iD: 0000-0002-1640-8946
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2018 (English)Report (Other academic)
Abstract [en]

Forests are important for climate change mitigation by providing bioenergy feedstock to substitute fossil fuels, as well as for carbon storage, while they are also important for other ecosystem services. The REEEM project targets to gain a comprehensive understanding of the system-wide implications of energy strategies in support of transitions to a competitive low-carbon EU society. The current REEEM case study of Lithuania aims to i) analyse the forest biomass potential for Lithuania using the LEcA tool, assessing impacts on ecosystem services of alternative forest management strategies, and compare to energy pathways including forest bioenergy feedstock as a RES; and ii) develop and discuss linking between the energy assessment model that project the energy pathways, in this case the MESSAGE model, and the LEcA tool to enable iterations and information exchange.

Two alternative pathways were run with the MESSAGE model of the use of forest bioenergy feedstock in the development of the Lithuanian energy sector, Biomass Low and Biomass High. This initiated the simulation of forest growth and management of Lithuania with the LEcA tool for 2015-2050 and beyond, applying a Business-As-Usual (BAU) forest management strategy. Based on these results, a second, more intensive (INT) forest management strategy was developed and applied in order to increase the yields. From the output of the simulations of both strategies, the development of five ecosystem services was assessed: forest bioenergy feedstock, industrial wood, carbon storage in the forest, recreation area and habitat supporting biodiversity. For bioenergy feedstock, environmental restrictions and transport distances for harvest residues were considered. In addition, different assumptions about the use of forest compartments for bioenergy purposes were tested.

The estimations of bioenergy feedstock were comparable with the empirical data. However concerning logging residues, the transport distances affecting economy and climate impacts need more considerations, as those may become more pronounced in the future. It could also be concluded that the assumptions concerning the allocation of different forest compartments as bioenergy feedstock would highly influence the results. In the comparison with energy pathways, though, assumptions based on empirical data came much closer than assumptions following forestry manuals. When comparing results with the energy pathways, it was still difficult to estimate with any precision the bioenergy feedstock availability. Looking at the overall situation applying allocation assumptions based on empirical data, the results indicated that during the period up to around 2040 supply and demand may not be so far apart from each other with Strategy BAU, and the supply exceeded the demand with Strategy INT. However, closer to year 2050 when the energy pathways projected a much higher use of forest biomass, it may be more difficult to meet the demands with either of the forest management strategies.

With Strategy INT, the overall yield was around 10% higher than with Strategy BAU, with the highest yields in the beginning of the period. However, the yields were not timing well with the energy pathways, since the major increase would be needed after around year 2040. Still, these results served to illustrate that when increasing the yields above a certain threshold, the resources may be exhausted in the long run. As well, comparing strategies BAU and INT, it could be shown that there are trade-offs to be expected between bioenergy feedstock and industrial wood on the one hand, and carbon storage, recreation and habitat supporting biodiversity on the other hand.

The LEcA tool can simulate forest growth and management with modest data requirements, which allows for exploring forest management strategies across the whole landscape. The GIS-based approach to the bioenergy feedstock problem, using data that in this context has a high geographical resolution, gives more detailed and localised information than what would be possible in more lumped approaches. The possibilities to spatially allocate and as well aggregate spatially explicit information makes the LEcA tool suitable for flexible model linking. Not only impacts can be assessed, but for instance constraints can be formulated for the assessed ecosystem services, so that they should not go below a certain value in any time period, which could also be fed back to the energy model.

For linking between the energy model and the LEcA tool, the first steps of information exchange were recognized and tested. The energy pathways created by the MESSAGE model initiated the forest management strategy BAU, from which the results concerning bioenergy feedstock yield was fed back and compared with the pathways. From this comparison, the second forest management strategy INT was developed, targeting higher yields. In future work these first steps will be further developed, preparing for full linkage between models. The results from the ecosystem service assessment will be fed back to the energy model for informed adjustments concerning a sustainable production of forest bioenergy feedstock. In this way, the links between energy assessment and ecosystem services could be strengthened in a more integrated assessment, targeting to inform energy policy and to increase the sustainability of forest bioenergy options.

Place, publisher, year, edition, pages
REEEM Consortium , 2018. , p. 29
National Category
Environmental Sciences
Research subject
Land and Water Resources Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223733OAI: oai:DiVA.org:kth-223733DiVA, id: diva2:1186951
Funder
EU, Horizon 2020StandUp
Note

QC 20180302

Available from: 2018-03-01 Created: 2018-03-01 Last updated: 2018-03-02Bibliographically approved

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