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Data from: More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions


Mazziotta, Adriano et al. (2023), Data from: More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions, Dryad, Dataset,


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.


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 (Current Policy, Bioenergy, Bioeconomy and Set-aside 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 m3 of wood ha-1 yr-1, 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 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 Current Policy scenario.

Forest dynamics and management were projected with the Heureka system (, 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.


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. Silva Fennica, 48, 1095.

Johnson, K.N., Scheurman, H.L. (1977). Techniques for prescribing optimal timber harvest and investment under different objectives-discussion and synthesis. Forest Science, 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). The Heureka forestry decision support system : an overview.

Usage notes

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.


Svenska Forskningsrådet Formas, Award: 2016-02109

Kungl. Skogs- och Lantbruksakademiens

Academy of Finland, Award: UNITE 337653