DOI: 10.5061/dryad.p8cz8w9t5
title: Data from: More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions

description: To reach the Paris Agreement, societies need to increase the global
 terrestrial carbon sink. There are many climate change mitigation
 solutions (CCMS) for forests, including increasing bioenergy, bioeconomy
 and protection. Bioenergy and bioeconomy solutions use climate-smart,
 intensive management to generate high quantities of bioenergy and
 bioproducts. Protection of (semi-)natural forests is a major component of
 ‘natural climate solution’ (NCS) since forests store carbon in standing
 biomass and soil. Furthermore, protected forests provide more habitat for
 biodiversity and non-wood ecosystem services (ES). We investigated the
 impacts of different CCMS and climate scenarios, jointly or in isolation,
 on future wood ES, non-wood ES, and regulating ES for a major wood
 provider for the international market. Specifically, we projected future
 ES given by three CCMS scenarios for Sweden 2020-2100. In the long term,
 fulfilling the increasing wood demand through bioenergy and bioeconomy
 solutions will decrease ES multifunctionality, but the increased stand age
 and wood stocks induced by rising greenhouse gas (GHG) concentrations will
 partially offset these negative effects. Adopting bioenergy and bioeconomy
 solutions will have a greater negative impact on ES supply than adopting
 NCS. Bioenergy or bioeconomy solutions, as well as increasing GHG
 emissions, will reduce synergies and increase trade-offs in ES. NCS, by
 contrast, increases the supply of multiple ES in synergy, even
 transforming current ES trade-offs into future synergies. Moreover, NCS
 can be considered an adaptation measure to offset negative climate change
 effects on the future supplies of non-wood ES. In boreal countries around
 the world, forestry strategies that integrate NCS more deeply are crucial
 to ensure a synergistic supply of multiple ES.
description: For the Swedish boreal and hemiboreal zone we projected forest
 dynamics, management and ES levels for all the combinations of four
 scenarios implementing different CCMS (<em>Current
 Policy</em>, <em>Bioenergy</em>,
 <em>Bioeconomy</em> and <em>Set-aside</em>
 scenario) and three climate scenarios (Constant Climate, RCP4.5 and
 RCP8.5). We projected the levels of ES on 29,892 plots of the Swedish
 National Forest Inventory (NFI), which represent all productive forest in
 Sweden (the 23 million ha producing ≥1 m<sup>3</sup> of wood
 ha<sup>-1</sup> yr<sup>-1</sup>,<sup>
 </sup>corresponding to 1.4% of the global boreal biome), including
 production and protected land (Fridman et al., 2014). The projections were
 initialized with observed levels for wood ES and the model-predicted
 levels for<strong> </strong>non-wood ES based on data from
 2008-2012 (‘2010’ henceforth). Projections were made for the period 2010
 to 2100; results were analyzed from 2020, the year of the first GHG
 mitigation target of the <em>Current Policy</em>
 scenario. Forest dynamics and management were projected
 with the Heureka system (http://www.slu.se/en/sha, Wikström et al., 2011).
 The Heureka core contains a set of empirical growth and yield models that
 simulate the development of the tree layer in five-year time steps,
 including models for stand establishment, diameter and height growth,
 ingrowth, and mortality. Climate change modifies tree growth based on the
 process-based vegetation model BIOMASS, indirectly implemented as an
 approximation model. Decomposition is modeled by the dynamic soil carbon
 Q-model, a cohort-based decomposition model that follows the mass loss of
 litter over time for different litter compartments. The Heureka
 application PlanWise allows for determining the optimal combination of
 management strategies that meet user-defined objectives and constraints.
 For each NFI plot and time step, a large number of management activities
 (such as thinning and clear felling) are simulated, that in sequence
 constitute different treatment schedules. In a harvest scheduling model
 (Johnson & Scheurman, 1977) the optimal treatment schedule for
 each plot is selected based on an objective function and possible
 constraints using a built-in optimization tool based on the ZIMPL
 optimization modeling language. References
 Fridman, J., Holm, S., Nilsson, M., Nilsson, P., Hedström
 Ringvall, A., & Ståhl, G. (2014). Adapting national forest
 inventories to changing requirements-the case of the Swedish national
 forest inventory at the turn of the 20th century. <em>Silva
 Fennica</em>, 48, 1095. Johnson, K.N., Scheurman,
 H.L. (1977). Techniques for prescribing optimal timber harvest and
 investment under different objectives-discussion and synthesis.
 <em>Forest Science</em>, 18, 1–30.
 Wikström, P., Edenius, L., Elfving, B., Eriksson, L. O., Lämås,
 T., Sonesson, J., Öhman, K., Wallerman, J., Waller, C., &
 Klintebäck, F. (2011). <em>The Heureka forestry decision support
 system : an overview</em>.
 http://mcfns.net/index.php/Journal/article/view/MCFNS.3-87
description: R programming language for statistical computing (R Core Team,
 2019) or a text editor. R Core Team. (2019). R: A
 language and environment for statistical computing. R Foundation for
 Statistical Computing. https://www.Rproject.org/