Trade-offs in using European forests to meet climate objectives.

The Paris Agreement promotes forest management as a pathway towards halting climate warming through the reduction of carbon dioxide (CO2) emissions1. However, the climate benefits from carbon sequestration through forest management may be reinforced, counteracted or even offset by concurrent management-induced changes in surface albedo, land-surface roughness, emissions of biogenic volatile organic compounds, transpiration and sensible heat flux2-4. Consequently, forest management could offset CO2 emissions without halting global temperature rise. It therefore remains to be confirmed whether commonly proposed sustainable European forest-management portfolios would comply with the Paris Agreement-that is, whether they can reduce the growth rate of atmospheric CO2, reduce the radiative imbalance at the top of the atmosphere, and neither increase the near-surface air temperature nor decrease precipitation by the end of the twenty-first century. Here we show that the portfolio made up of management systems that locally maximize the carbon sink through carbon sequestration, wood use and product and energy substitution reduces the growth rate of atmospheric CO2, but does not meet any of the other criteria. The portfolios that maximize the carbon sink or forest albedo pass only one-different in each case-criterion. Managing the European forests with the objective of reducing near-surface air temperature, on the other hand, will also reduce the atmospheric CO2 growth rate, thus meeting two of the four criteria. Trade-off are thus unavoidable when using European forests to meet climate objectives. Furthermore, our results demonstrate that if present-day forest cover is sustained, the additional climate benefits achieved through forest management would be modest and local, rather than global. On the basis of these findings, we argue that Europe should not rely on forest management to mitigate climate change. The modest climate effects from changes in forest management imply, however, that if adaptation to future climate were to require large-scale changes in species composition and silvicultural systems over Europe5,6, the forests could be adapted to climate change with neither positive nor negative  climate effects.

Following the Paris Agreement, the European Union and its 28 Member States committed to a 40% domestic reduction in greenhouse gas emissions by 2030 compared to 1990. About seventy five percent of this reduction is expected to come from emission reductions, and the remaining 25% from land use, land use change and forestry7. The commitment to reduce the domestic greenhouse gas emissions through forestry is in turn reflected in national strategies for energy, climate change, and forestry8–10 of several European countries. These strategies typically focus on enhancing forestry-based sinks and reservoirs and developing neutral or negative emissions approaches based on woody biomass. Furthermore, European forest owners who reported to have experienced climate change, indicated that this experience influenced their management decisions11. Hence, climate change and the Paris Agreement are already shaping forest management decisions. Despite the fact that it is explicitly mentioned in both the Kyoto Protocol12 and the Paris Agreement1 little is known about the climate effects of forest management including the effects of human-induced tree species changes and silvicultural systems3,13,14.

This study searches for spatially-explicit forest management portfolios for Europe that comply with the Paris Agreement up to the turn of the 21st-century. Compliance requires that forest management jointly reduces the growth rate of atmospheric CO2 (Art. 4 and 5) and the radiative imbalance at the top of the atmosphere (Art. 2). Furthermore, forest management compliant with the Paris Agreement should neither increase the near-surface air temperature (hereafter referred to as air temperature) nor decrease precipitation since changing the climate of the terrestrial biosphere would make adaptation to climate change (Art. 7) even more difficult (see Methods “Operationalizing the Paris Agreement”).

Simulation experiments which combine vegetation modelling, climate modelling, vegetation-climate feedbacks, and life cycle analysis were used to quantify the CO2 emissions, radiative imbalance at the top of the atmosphere, near-surface air temperature, and precipitation of three spatially-explicit forest management portfolios in Europe. Each portfolio came with its own objective: maximise the forest carbon sink, maximise forest albedo, or reduce near-surface air temperature.

All portfolios started from the same 2010 species and age-class distribution. Once an individual forest reached maturity, six scenarios were explored: (i) refrain from harvesting; (ii) harvest, replant the same species and apply the same silvicultural strategy as before; (iii) harvest, replant the same species, and thin prior to the final felling; (iv) harvest, change to the most common deciduous species in that region and thin prior to the final felling; (v) harvest, change to the most common deciduous species in that region and manage it as a coppice; and (vi) harvest, change to the most common conifer species in that region and thin prior to the final felling. Subsequently, portfolios were constructed by selecting the best-performing management scenario –out of six– for each of the three objectives and for each grid cell in the European domain.

Contrary to previous land-use simulation experiments, our portfolios simulate a realistic rate of change for tree species distribution and silvicultural systems because changes were only implemented following a harvest or stand-replacing mortality. Management changes were, thus, dictated by forest growth and human choices within natural constraints, rather than through externally prescribed harvest volumes or through strictly natural succession.

A management portfolio that maximises the carbon sink15,16 reflects the widely-held view that the net climate effect of forest management is dominated by decreasing the growth rate of atmospheric CO2 through forest-based carbon sequestration, carbon storage in wood products, and material and energy substitution. Implementing the sink-maximising portfolio would –compared to business-as-usual– require converting 475,000 km2 of deciduous forest in central and southern Europe into coniferous forest whereas 266,000 km2 of previously coniferous forests in northern and central Europe would have to be converted to deciduous forests (Fig. 1; Extended Data Table 1; see “Drivers of changes in forest management”).

Figure 1

Surface areas (x 10,000 km2) under forest management by the year 2100 for portfolios that target maximising the carbon sink, continue present-day management, and reduce the near-surface air temperature. Forest management distinguishes between tree species composition and silvicultural systems. The inset presents the mean values for all of Europe. Regional difference are shown for three geographical regions, each shown in a different shade of grey.

Extended Data Table 1

Changes in surface area (km2) by 2100 for six different forest management portfolios over Europe. Note that the total surface area of forests was held constant at 2,000,000 km2 between 2010 and 2100, for reasons described in “Simulation experiment”.

Change in surface area (km2)	Business as usual (BAU)	Maximise carbon sink	Maximise albedo	Minimise carbon sink	Minimise albedo	Reduce near-surface temperature
Deciduous to conifers	0	475,000	30,000	6,000	516,000	41,000
Conifers to deciduous	0	266,000	590,000	236,000	26,000	534,000
Net increase conifers	0	209,000	-560,000	-230,000	490,000	-493,000
Net increase thin and fell	0	-280,000	-330,000	-390,000	-230,000	-680,000
Net increase coppice	0	-20,000	130,000	-130,000	-210,000	600,000
Net increase unmanaged	0	300,000	200,000	520,000	440,000	80,000

A sink-maximising portfolio would come with a 12 % lower wood harvest but could offset an additional 8.1 Pg C of fossil fuel emissions (Table 1) between 2010 and 2100 compared with a business-as-usual management portfolio that continues the present-day forest management portfolio into the future. This increase in the projected carbon savings is similar to estimates by the forestry sector16, and could be achieved by optimising the balance between forest-based sequestration (8.2 Pg C) on the one hand and product-based sinks and substitution (-0.3 Pg C), energy-based substitution (0.2 Pg C), and savings in the exploitation and production emissions (0.05 Pg C) on the other. Accounting for ocean uptake of atmospheric CO2 (see Methods "Life cycle analysis") results in a cumulated net reduction of the atmospheric CO2 concentration of 4.3 Pg C in 2100, which translates into a 2 ppm decrease in atmospheric CO2 compared with business-as-usual (Table 1). Owing to the changes in tree species and silvicultural systems required to realize this 2 ppm draw-down, the ~0.002 W m-2 decrease in the radiative imbalance at the top of the atmosphere from the stronger carbon sink17 is neutralized by unintended but unavoidable changes in surface albedo (-0.001) and cloud cover (-0.1%). The carbon-based portfolio has a small negative effect on precipitation (-2 mm) and no effect on air temperature (Table 1).

Table 1

Biogeochemical and biophysical effects in 2100 for four different forest management portfolios over Europe. The business as usual simulation which served as a control, was repeated three times with slightly different initial atmospheric conditions (see Methods “Equilibrium climate for the management portfolios”). The variability between these three repetitions was considered the minimal model noise of the climate model. The reported noise was taken to be the definition of one standard deviation. TOA denotes the radiative imbalance at the top of the atmosphere.

Variable name (units)	Business as usual (BAU)	Maximise carbon sink	Maximise albedo	Reduce near-surface temperature
Global average TOA (W m-2)	4.31 ± 0.01	4.31	4.33	4.32
Δ2100-2010 CO2 sink & avoided emissions (Pg C)	4.7	12.8	5.0	8.1
Δ2100-2010 net cumulated atmospheric CO2 (Pg C)	2.7	7.0	2.8	4.5
Atmospheric CO2 (ppm)	934.6	932.6	934.6	933.8
Near surface temperature (K)	283.84 ± <0.001	283.84	283.83	283.81
Annual precipitation (mm)	734.7 ± 0.1	732.6	730.0	730.9
Summer precipitation (mm)	166.1 ± 0.1	165.2	163.7	165.0
Wood harvest (Tg C y-1)	203.2	179.5	144.5	151.6
Surface albedo (-)	0.113 ± <0.0001	0.113	0.128	0.126
Evapotranspiration (mm)	555.5 ± 0.1	552.8	546.4	549.2
Latent heat (W m-2)	44.35 ± <0.01	44.13	43.60	43.82
Sensible heat (W m-2)	26.67 ± <0.01	26.82	27.28	27.00
Total cloud cover (%)	46.8 ± <0. 1	46.7	46.7	46.6

A temperature-based portfolio reflects the idea that management-induced changes in surface properties may redistribute the heat away from the surface resulting in a local cooling of the land surface18 that can be beneficial for organisms living there. Implementing such a portfolio requires converting 493,000 km2 of coniferous forests to deciduous forests (of which 65% would be in Scandinavia) and coppicing an additional 600,000 km2 of deciduous forests (Fig. 1; Extended Data Table 1; “Description of the changes in forest management”). Such changes in forest management would, however, reduce the wood harvest by 25 % compared to business as usual (Table 1). By 2100 these changes would result in a cumulative net reduction of the atmospheric CO2 concentration of 1.8 Pg, which is equivalent to a 0.9 ppm reduction of atmospheric CO2 compared with business as usual (Table 1).

The combined biogeochemical and biophysical effects of this portfolio come without a significant effect on the radiative imbalance at the top of the atmosphere but could contribute to a 0.3 K cooling over Scandinavia, while having much less effect on temperature over the rest of Europe (Fig. 2A). Following a large-scale transition to deciduous species, cooling of the air temperature was projected to occur in winter and spring only (Extended Data Fig. 1). In spring, air temperature cooling from an increase in surface albedo due to decreased snow masking by deciduous canopies would be partly compensated by warming from a decrease in turbulent fluxes due to the absence of leaves until bud break later in spring (Fig. 2B). The simulation experiment thus confirms the role of transpiration in determining air temperature, even at high latitudes19.

Figure 2

Changes in, and main drivers of, near-surface air temperature (ΔT; K) in February and March by the turn of the 21st-century for a forest management portfolio that reduces the near-surface air temperature. (a) Spatially explicit changes in near-surface air temperature (K) in February and March. (b) Drivers of the changes in springtime near-surface air temperature for 0.5 degree latitudinal bands. In subplot (a) temperature changes less than 1.96 times the standard deviations are shown in white. Where, the standard deviation represents the minimal noise of LMDzORCAN (see Methods “Equilibrium climate for the management portfolios”). The change in near-surface air temperature (T) due to changes in atmospheric emissivity (ε) is written as ΔT. By analogy ΔT is the change in air temperature due to change in the ground heat flux, ΔT due to changes in turbulent fluxes, ΔT due to changes in shortwave incoming radiation which in this simulation experiment is a proxy for cloud cover, ΔT due to changes in surface albedo, and ΔT due to changes in atmospheric circulation.

Extended Data Figure 1

Drivers of the changes in mean bimonthly near-surface air temperature (ΔT; K) for 0.5 degree latitudinal bands. The change in near-surface air temperature (T) due to changes in atmospheric emissivity ε is written as ΔT|ε. By analogy ΔT|G is the change in air temperature due to change in the ground heat flux, ΔT|LE+H due to changes in turbulent fluxes, ΔT|R due to changes in shortwave incoming radiation (which in this simulation experiment is a proxy for cloud cover), ΔT|α due to changes in surface albedo, and ΔT|circ due to changes in atmospheric circulation. Although all the components contribute to the near-surface air temperature, changes in emissivity always result in a cooling and changes in shortwave incoming radiation always result in warming. Consequently, emissivity and incoming shortwave radiation cannot explain the seasonal variation in the changes in near-surface air temperature. The other components are in some months positively correlated with near-surface air temperature whereas they are negatively correlated for other months, excluding them from being the main driver of changes in near-surface air temperature. Suggesting the net effect is the outcome of the interplay between the different components.

A portfolio that maximises the albedo20 reflects the view that managing the forest albedo would reduce the radiative imbalance at the top of the atmosphere while maintaining the forest carbon sink. Our simulations confirm that an albedo-maximising portfolio would decrease wood harvest by 30 % and realize cumulated net emission savings of up to 2.8 Pg C which is comparable to the savings expected from the business-as-usual portfolio. However, the increase in surface albedo that can be realized through the albedo-based portfolio (+0.015) would be compensated by decreases in cloud cover (-0.1%) and, therefore, come without a significant effect on the radiative imbalance at the top of the atmosphere and a small negative effect on air temperature (-0.01 K; Table 1).

Furthermore, all portfolios reduced the mean annual precipitation by 2.1 to 4.7 mm compared to the business as usual portfolio. Reductions were evenly spread across the seasons and consistent with the decrease in cloud cover and evapotranspiration (Table 1). Hence, none of the tested forest management portfolios meet all four criteria set for compliance with the Paris Agreement. Maximising the carbon sink, and maximising the forest albedo both meet one out of four criteria. Managing the European forests with the objective to reduce air temperature results in reducing air temperature and the CO2 growth rate, thus meeting two of the four criteria.

To our knowledge, this study is the first to quantify the capacity of forest management to comply with the Paris Agreement while addressing both biogeochemical and biophysical effects; hence, its results could not be compared to previous reports. The small temperature effects, compared to those found in global afforestation and deforestation studies21–24, are thought to be the consequence of a realistic 90-year long period over which management changes were implemented, and the limited global land area for which portfolios were tested, i.e., ~7% of the global total of managed forest14. Although a global implementation of carbon-based forest management is likely to enhance the carbon sink of the forest sector globally15, the combined biogeochemical and biophysical effects cannot be extrapolated from Europe to the global scale, due to biome-specific land-atmosphere interactions25,26. A global implementation of locally optimised forest management portfolios would lead to larger areas with near-surface cooling. Given that air temperature cooling was found to quickly saturate with the fractional change in species composition (Extended Data Fig. 2), the magnitude of the cooling is not expected to change substantially following a large-scale implementation, unless ocean feedbacks19,22, cloud feedbacks through species-specific biogenic volatile organic compound emissions27, and changes in the North Atlantic Oscillation28, which were not fully accounted for in this study, are among the key drivers.

Extended Data Figure 2

Relationship between changes in springtime near-surface air temperature (K) and changes in fractional cover of deciduous forest (km2) for 0.5 degree latitudinal bands over Europe. Locations where the tree species were maintained between 2010 and 2100 (i.e. Δ deciduous area on the X-axis equals zero) could experience similar air temperature changes as neighbouring locations where one species was converted into another, especially in Scandinavia, suggesting advection of heat and moisture. Nevertheless, at lower latitudes the spatial scale of this advection was limited to a few pixels (e.g., Fig. 2A) corresponding to a range of 50 to 200 km. Furthermore, the temperature effect quickly saturated with the fractional cover change and showed a strong dependence on geographical location91. Whether the apparent geographical dependency was the outcome of climatic differences and/or differences between northern and southern European deciduous species could not be established by the experimental setup used in this study.

Our results demonstrate –based on a single model– that in the absence of carbon capture and storage the additional climate benefits through sustainable forest management will be modest and local rather than global. Hence, we suggest that the primary role of forest management in Europe in the coming decades is not in protecting the climate but in adapting the forest cover to future climate5 in order to sustain the provision of wood, as well as ecological, social, and cultural services29 while avoiding positive climate feedbacks from fire, wind, pests and drought disturbances30. Even if adaptation would require large-scale changes in species composition and silvicultural system over Europe5,6, our results imply that these changes themselves are likely to have little impact on the climate.

Optimized forest management portfolio’s for Europe to maximize the surface albedo under (a) RCP8.5 and (b) RCP4.5. The numbers in the legend relate to (i) refrain from harvesting; (ii) harvest, replant the same species and apply the same silvicultural strategy as before; (iii) harvest, replant the same species, and thin prior to the final felling; (iv) harvest, change to the most common deciduous species in that region and thin prior to the final felling; (v) harvest, change to the most common deciduous species in that region and manage it as a coppice; and (vi) harvest, change to the most common conifer species in that region and thin prior to the final felling.

Extended Data Figure 3

Setup of the simulation experiments following the description in “Simulation experiment”. Simulations with ORCHIDEE-CAN are shown in black, and simulations with LMDzORCAN are shown in red. Blue boxes show intermediate calculations making use of simulation results (see Methods “Spatially optimised management portfolios” and “Equilibrium climate for the management portfolios”). The labels of the different simulations shown in this figure are the same labels as those used to run and archive the simulations. Note that in the main text the results of BBESTT2M were presented as “reduced near-surface air temperature”, BESTALBEDO as “maximise surface albedo”, BWORSTALBEDO as “minimise surface albedo, BBESTLCA as “maximise C-sink”, BWORSTLCA as “minimise C-sink”, and BWAC as “business as usual”. BWAC, BWAC-P1 and BWAC-P2 were used to calculate the minimal model noise as explained in Methods “Simulation experiment”.

Supplementary Material

Extended Data Information is linked to the online version of the paper at www.nature.com/nature.

Extended Data Table 2

Biogeochemical and biophysical effects in 2100 for two additional –compared to Table 1– forest management portfolios over Europe.

Variable name (units)	Minimise carbon sink	Minimise albedo
TOA (W m-2)	4.32	4.32
Δ2100-2010 CO2 sink & avoided emissions (Pg C)	0.7	10.5
Δ2100-2010 net cumulated atmospheric CO2 (Pg C)	0.5	5.7
Atmospheric CO2 (ppm)	935.7	933.2
Near surface temperature (K)	283.85	283.86
Annual precipitation (mm)	733.1	734.2
Summer precipitation (mm)	164.0	165.4
Wood harvest (Tg C y-1)	122.9	176.2
Surface albedo (-)	0.119	0.107
Evapotranspiration (mm)	550.0	553.9
Latent heat (W m-2)	43.90	44.23
Sensible heat (W m-2)	27.12	26.81
Total cloud cover (%)	46.8	46.8

Extended Data Table 3

Key characteristics of the individual model runs in the simulation experiment. The model runs are presented in the same order as Extended Data Fig. 3. For each model run the time period is described by its start year, end year and the length of the simulation in years (Years) together with the simulation used to initialize key characteristics of the biosphere (Initial state). The atmospheric CO2, CH4, N2O, CFC11, and CFC12 concentrations at the end of the simulation were reported and their values were extracted from Refs. 86 and 95. For the portfolio model runs atmospheric CO2 concentrations were adjusted for the simulated carbon sink after discounting for ocean uptake as outlined in “Atmospheric composition” and “Life cycle analysis”. Climate and other forcing agents including sea surface temperature, sea ice extent, and atmospheric aerosol concentrations were retrieved from the RCP8.5 simulation with the IPSL-CM5 model59 as part of the AR5 model inter-comparison. In this study, forest management consisted of two activities: species changes (Species) and a silvicultural treatment (Silviculture). For historical model runs a forest management reconstruction was used47, and a single year indicates the reconstruction for that specific year was used. For future simulations, species distribution and/or silvicultural management was either fixed to the 2010 distribution or was changed towards deciduous or conifers for the species and/or conservation, high-stand, or coppice for the silvicultural system (see Methods “Forest cover and forest silvicultural reconstruction”, “Future species”, and “Future silviculture”). The labels of the different simulations shown in this table are the same labels as those used to run and archive the simulations. Note that in the main text the results of BBESTT2M were presented as “reduced near-surface air temperature”, BESTALBEDO as “maximise surface albedo”, BWORSTALBEDO as “minimise surface albedo, BBESTLCA as “maximise C-sink”, BWORSTLCA as “minimise C-sink”, and BWAC as “business as usual”. BWAC, BWAC-P1 and BWAC-P2 were used to calculate the minimal model noise as explained in “Simulation experiment”.

Simulation label	Period	Years	Initial state	Climate	CO2 (ppm)	CH4 (ppb)	N2O (ppb)	CFC11 (ppt)	CFC12 (ppt)	Other	Species	Silviculture
SPIN-UP
SPIN1	1600/1600	260	n.a.	1901/1920	277.9	n.a.	n.a.	n.a.	n.a.	n.a.	1600	1600
SPIN2	1600/1600	40	SPIN1	1901/1920	277.9	n.a.	n.a.	n.a.	n.a.	n.a.	1600	1600
TRANSIENT SIMULATION
TRANS1	1601/1900	300	SPIN2	1901/1930	295.8	n.a.	n.a.	n.a.	n.a.	n.a.	Recon.	Recon.
TRANS2	1901/2010	110	TRANS1	1901/2010	395.8	n.a.	n.a.	n.a.	n.a.	n.a.	Recon.	Recon.
FOREST MANAGEMENT SCENARIOS
CWAC	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	2010	2010
CWA1	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	2010	Conser.
CWA2	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	2010	Thin&F.
CWC2	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Coni.	Thin&F.
CWD2	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Deci.	Thin&F.
CWD3	2011/2100	90	TRANS2	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Deci.	Coppice
BWAC	2101/2101	10	CWAC	n.a.	934.6	3751	435	26	167	RCP8.5	2010	2010
BWA1	2101/2101	10	CWA1	n.a.	934.6	3751	435	26	167	RCP8.5	2010	Conser.
BWA2	2101/2101	10	CWA2	n.a.	934.6	3751	435	26	167	RCP8.5	2010	Thin&F.
BWC2	2101/2101	10	CWC2	n.a.	934.6	3751	435	26	167	RCP8.5	Coni.	Thin&F.
BWD2	2101/2101	10	CWD2	n.a.	934.6	3751	435	26	167	RCP8.5	Deci.	Thin&F.
BWD3	2101/2101	10	CWD3	n.a.	934.6	3751	435	26	167	RCP8.5	Deci.	Coppice
EQUILIBRIUM CLIMATE FOR THE MANAGEMENT PORTFOLIOS
CBESTT2M	2011/2100	90	Optimised	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Optim.	Optim.
CBESTLCA	2011/2100	90	Optimised	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Optim.	Optim.
CWORSTLCA	2011/2100	90	Optimised	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Optim.	Optim.
CBESTALBEDO	2011/2100	90	Optimised	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Optim.	Optim.
CWORSTALBEDO	2011/2100	90	Optimised	RCP8.5	935.8	n.a.	n.a.	n.a.	n.a.	n.a.	Optim.	Optim.
BWAC	2101/2101	20	CWAC	n.a.	934.6	3751	435	26	167	RCP8.5	2010	2010
BWAC-P1	2101/2101	20	CWAC	n.a.	934.6	3751	435	26	167	RCP8.5	2010	2010
BWAC-P2	2101/2101	20	CWAC	n.a.	934.6	3751	435	26	167	RCP8.5	2010	2010
BBESTT2M	2101/2101	20	CBESTT2M	n.a.	933.8	3751	435	26	167	RCP8.5	2010	2010
BBESTLCA	2101/2101	20	CBESTLCA	n.a.	932.6	3751	435	26	167	RCP8.5	2010	2010
BWORSTLCA	2101/2101	20	CWORSTLCA	n.a.	935.7	3751	435	26	167	RCP8.5	2010	2010
BBESTALBEDO	2101/2101	20	CBESTALBEDO	n.a.	934.6	3751	435	26	167	RCP8.5	2010	2010
BWORSTALBEDO	2101/2101	20	CWORSTALBEDO	n.a.	933.2	3751	435	26	167	RCP8.5	2010	2010

Extended Data Table 4

Emission coefficients, conversion factors, and assumptions used in the European wide life cycle analysis.

Component	Unit	Value	Source
Carbon in biomass	g g-1	0.5	Assumed
Transport distance roundwood	Km	100	Assumed
Transport distance fuelwood	Km	50	Assumed
Transport by truck	kg CO2 tkm-1	1.12	Ref. 100
Mechanized harvest	kg CO2 ha-1	233	Ref. 101
Mechanized planting	kg CO2 ha-1	93	Ref. 101
Mechanized thinning	kg CO2 ha-1	69	Ref. 101
Product substitution	kg CO2 kg-1 CO2	1.1	Ref. 101
Energy density of biomass	GJ t-1	19.3	Ref. 102
Conversion efficiency	%	34	Ref. 103
Energy from biomass-based electricity	GJ, t-1	6.6	Energy density of biomass multiplied with the conversion efficiency
Emissions from biomass-based electricity	kg CO2 kg-1 CO2	1.05	Assuming that drying consumes 0.05 kg CO2 kg-1 CO2 and burning or gasifying woody biomass produces 1 kg CO2 kg-1 CO2

Extended Data Table 5

Country based CO2 emission factors (g CO2 eq kWh-1) for the current non-renewable electricity mix of energy production based on ref. 100.

Country	Emission factor
Albania, Belarus, Kosovo, Macedonia, Moldova,	1020
Montenegro & Ukraine
Andorra, France & Monaco	810
Austria & Liechtenstein	777
Belgium	687
Bosnia & Herzegovina	1017
Bulgaria	981
Croatia	812
Cyprus, Iceland & Malta	868
Czech Republic	1010
Denmark	904
Estonia	1014
Finland	853
Germany	927
Greece	894
Hungary	780
Ireland	766
Italy	744
Latvia	615
Lithuania	591
Luxembourg	614
Netherlands	748
Norway	641
Poland	1000
Portugal	840
Romania	907
Serbia	1012
Slovakia	842
Slovenia	982
Spain	797
Sweden	857
Switzerland	628
United Kingdom	854