Carbon costs and benefits of France’s biomass energy production targets

Different management styles

At the national scale, 63% of the French forest area was found to be actively managed, 11% unexploitable, 15% harvest-delayed and 11% overstocked; however, large regional differences exist (Fig. 1a for management approaches per silvicultural ecoregion). Most unexploitable forests are located in mountainous areas such as Corsica, the Alps (up to 53.3% of the plots of this region), and the Pyrenees. Harvest-delayed and overstocked forests are relatively evenly distributed across the country, which is consistent with the fact that the drivers for these management approaches are likely to be more related with ownership than with topography or climate. Overall, actively managed forests are the dominant management approach and vary from 27% of the plots in the Corsican mountains to 90% in Les Landes de Gascogne (Fig. 1a).

The type of management also varies with species (Fig. 1b) [4]. Indeed, 86%, 77% and 68% of the French surface cover dominated by Maritime pine (Pinus pinaster), spruce (Picea abies) and fir (Abies alba), respectively, are being actively managed. They are the most managed softwood species in the country. Among the species described in our study, the lowest percentage area being actively managed is the 45% of Scots pine (Pinus sylvestris) dominated stands. For the hardwood species, large percentages of oak (Quercus petraea, 77%), chestnut (Castanea sativa, 77%) and hornbeam (Carpinus betulus, 72%) forests are also actively managed.

Whether ‘overstocked’ or ‘harvest-delayed’ forests are more frequent varies from one species to another and from one region to another. For Quercus robur, Fagus sylvatica, Carpinus betulus, and Pseudotsuga menziesii, the most frequent type of suboptimal management is ‘overstocked’, meaning that they are likely to be harvested but that the more regular tending of stands is skipped. In contrast, Fraxinus excelsior, and three conifer species mainly growing in the southeast of France, Pinus halepensis, Pinus laricio and Pinus nigra, are primarily ‘harvest-delayed’, suggesting that many stands will never be harvested. Finally, where the management is suboptimal for Quercus pubescens, Quercus ilex, Castanea sativa, and Pinus sylvestris, this is mostly explained by difficulties with their marketability.

Meeting the bioenergy target

The French national objectives for energy production from forest biomass foresee an increase from 9.7 Mtoe in 2015 to 11.6–12.4 Mtoe per year in 2026 [3]. Once the target is reached, the plan does not specify whether the 2026 level has to be maintained or whether the annual rate of increase is expected to be sustained over the following decade(s). This lack of clarity in the planning horizon was accounted for by considering two energy production targets: one that was labelled as ‘basic’ and the other that was labelled ‘ambitious’. The basic target prescribes an increase of 1.9 Mtoe between 2016 and 2026. Following 2026, the target for bioenergy production is then held constant at 11.6 Mtoe per year. The ambitious target prescribes an annual increase of 0.25 Mtoe per year between 2016 and 2040.

Applying a BaU scenario to the forest and wood-use chain increases [3] wood-based biofuel production by 9% by 2040 as an effect of age class structure (Additional file 1). Bioenergy production is thus projected to increase under a BaU scenario, but would still fall 14 to 19% short of meeting the national energy production targeted range in 2025 (Fig. 2; Additional file 1). Meeting the bioenergy target hence requires forest management to be intensified. The intensification scenarios considered in this study match the observed management approaches. Hence, three intensification scenarios were studied (Table 3): thinning currently overstocked stands (labelled as scenario O in Table 3), harvesting currently harvest-delayed stands (labelled as scenario D in Table 3), and decreasing the harvest diameter of actively managed stands (labelled as scenario M in Table 3).

Scenario code	Scenario name	Number of simulations	Management of
			Actively managed plots	Overstocked plots	Harvest-delayed plots	Unexploitable plots
‘BaU’	Reference	1	Harvest at current clearcut diameter; thinning at current thinning ratio	Harvest at current clearcut diameter; no thinning	No harvest; no thinning	No harvest; no thinning
‘Ov’	Extra mobilization of overstocked forests	10 scenarios applied to 10 to 100% of the overstocked forests	Harvest at current clearcut diameter; thinning at current thinning ratio	Harvest at current clearcut diameter; thinning at current thinning ratio	No harvest; no thinning	No harvest; no thinning
‘D’	Extra mobilization of harvest-delayed forests	10 scenarios applied to 10 to 100% of harvest-delayed forests	Harvest at current clearcut diameter; thinning at current thinning ratio	Harvest at current clearcut diameter; no thinning	Harvest; no thinning	No harvest; no thinning
‘M’	Extra mobilization of actively managed forests	10 scenarios reducing the clearcut diameter by 1 to 10 cm	Clearcut diameter lowered with respect to current clearcut diameter; thinning at current thinning ratio	Harvest at current clearcut diameter; no thinning	No harvest; no thinning	No harvest; no thinning
‘A’	Extra mobilization of all forests	10 scenarios combining respectively the 1st, 2nd, 3rd, 4th, …, 10th scenario of ‘O’, ‘D’ and ‘M’	Clearcut diameter lowered by 1-10 cm with respect to current clearcut diameter; thinning at current thinning ratio	Harvest at current clearcut diameter; thinning at current thinning ratio	Harvest; no thinning	No harvest; no thinning

Over the 30 years of the simulation, the intensification scenarios mobilize an extra 29, 112 or 119 Mm³ depending on whether the overstocked, the harvest-delayed, or the actively managed forests are targeted, respectively (Additional file 2: Figure S1). Implementing scenarios that target overstocked forests, provides thinning wood, which is mainly directed to pulp and biofuel. As such it would increase the energy production compared to the reference scenario but would still fall short of reaching the 2026 target (Fig. 2), which would only be reached 3 years later. Note that targeting overstocked forest should not be interpreted as a policy to enhance stand production, because an increase in thinning in the growth and harvest simulator does not lead to an increase of the total biomass production in the simulator.

The two other intensification strategies meet the least ambitious target by 2026, but follow largely different approaches: (a) mobilizing 40% of the harvest-delayed stands, or (b) shortening the rotation length to reduce the clearcut diameter of actively managed stands by an average of 5 cm. Obviously the target could be met by various combinations of all three intensification scenarios, for example, shortening the rotation length to reduce the clearcut diameter of actively managed stands by an average of 2 cm, mobilizing 20% of the harvest-delayed stands, and at the same time thinning 20% of the overstocked stands (Fig. 2).

The most ambitious bioenergy target can also be reached by 2026, for example, by decreasing the harvest diameter by 7 cm and thus shortening the rotation length of actively managed forests, or by mobilizing 60% of the harvest-delayed stands. For the latter, the transient character of mobilizing harvest-delayed stands would result in no longer meeting the target after 2030 even though the bioenergy production exceeded the yearly target in the previous decade (Fig. 2a).

Note that these scenarios do not allow to sustain the target after 2026. In order to reach the most ambitious target sustainably over 2026–2040, it is necessary to combine more intensive scenarios—such as the mobilization of 100% of harvest-delayed stands or a 10 cm reduction of clearcut diameters of actively managed stands—with the possibility for a delayed use of the biomass several years after harvest took place (Fig. 2b). While storing wood may not be realistic, this possibility for a delayed use of biomass can be interpreted more realistically as a proxy for a more gradual implementation of the intensification scenarios.

Carbon balance of intensified management scenarios

Depending on the intensification scenario, the properties of the mobilized biomass are projected to differ in terms of tree species and the relative contributions of thinnings versus clearcuts to the total harvest. Consequently, different intensification scenarios result in different amounts of wood products and different levels of energy production.

Although the simulation shows that management intensification could help France meet its target for wood-based bioenergy production, none of these intensification scenarios would result in a higher atmospheric CO₂ sequestration than the reference scenario by 2040 (Fig. 3a). Intensification does indeed affect the three components of the forest sector carbon balance: the in situ carbon stock (Fig. 3b), the carbon stock in the wood products pool (Fig. 3d), and carbon emissions avoided by substitution (Table 4; Fig. 3c).

Wood products	Lifetime expectancy		Substitution coefficient
	Value (years)	References	tC/tC	Reference
Timber	50	[92]	1.2	FCBA (pers.comm.)
Paper (44% of pulp and paper)	4		0	FCBA (pers.comm.)
Pulp (56% of pulp and paper)			0.53	FCBA (pers.comm.)
Energy	1.7	[97]	0.5	In substitution to: gas at 81%, oil at 15%, coal at 3%, electricity at 0.4% and GPL at 0.4% [98]

All three scenarios affect the carbon pools in the same direction but with different intensities. Increasing harvest decreases the carbon stored in the forest, and increases both the carbon stored in the wood products pool and in the apparent stock from energy and product substitution.

In the first years following intensification of the management of harvest-delayed and actively-managed stands, the in situ carbon stock will decrease rapidly and the decrease will not be compensated for by the increase in wood-based carbon stock and energy through product substitutions. The result is a decrease in carbon sequestration of 31 t C/year from harvest-delayed stands and 20 t C/year from actively-managed stands compared to BaU by 2025 (Fig. 3a). The mobilization of harvest-delayed forests yields the largest and fastest decrease in the in situ carbon stock with the loss of 75% of the carbon stored under the BaU scenario by 2040. The gain in the apparent stock from substitution would be 106 Tg C by 2040 (Fig. 3c), thus compensating for only 52% of the carbon stock lost in the forest. Increased carbon stock in wood products would compensate for 21% of the carbon stock lost in the forest.

The intensification scenario that foresees a shortening of the average rotation lengths of actively managed forests results in a different carbon balance. The changes in wood product pools and substitution are very close to the ones obtained for the harvest-delayed strategy; however, the change in the in situ carbon stock shows a faster recovery of the stock. Hence, by 2040 the most intense scenario reducing harvest diameters by 10 cm, decreases carbon stock in the forest to 52% of BaU. Nevertheless, this loss is compensated for by the gain in carbon stock in wood products and the apparent storage from substitution. For this intensification scenario, the net carbon balance in 2040 comes close to that of BaU (Fig. 3a).

Intensifying the management of overstocked forests will reduce the in situ carbon stock (Fig. 3b). This reduction will be compensated for neither by carbon storage in wood products (Fig. 3c) nor by the substitution of fossil fuel-based energy provision by wood-based energy provision (Table 4; Fig. 3d). Consequently, the carbon benefit decreases in proportion to the extra mobilization (Fig. 3a)—a relationship that was also observed in Finland for a range of harvest ratios applied at the national level [15]. Compared to other intensification strategies, thinning overstocked stands yields a better carbon balance than scenarios targeting mature forest in the first decade because it causes less of a reduction in their in situ carbon stock (Fig. 3a, b). In the long term, however, because there is no feedback of thinning on stand-level biomass production, limited feedback of thinning on mortality (see “Methods”) and because of low substitution and low life expectancies of wood-products derived from thinning (Table 4; Fig. 3c, d), thinning overstocked stands becomes less beneficial than intensifying the management of actively managed stands and by 2045 it even becomes less beneficial than intensifying the management of harvest-delayed stands (Additional file 3: Figure S2).

Transient biomass mobilization

Intensification scenarios targeting harvest-delayed and actively managed stands rely heavily on biomass that has accumulated over the past decades to century that is progressively being mobilized. The transient increase in harvest is evident in Figs. 2a and 3b from the bell-shaped trajectories. Once this stock has been consumed, a new equilibrium will emerge between harvest and growth. Projections for a longer time period (Additional file 3: Figure S2) show the new equilibrium that will be reached after the legacy biomass stock is consumed. Note that this new harvest equilibrium is above the BaU level.

In the first years of management intensification, the mobilization of accumulated biomass in the actively managed and harvest-delayed forests make the energy they produce much less carbon-efficient than the energy produced under the BaU scenario (Fig. 4a). This carbon efficiency gap persists until 2045 and 2055 respectively, when previously harvested stands will have regrown and the substitution benefits have accumulated (Fig. 3d).

Different management styles require different policies

Close to 75% of French forests are privately owned and substantial differences in management approaches are observed (Fig. 1a, b), i.e., unexploitable, harvest-delayed, overstocked and actively managed forest (Table 2). Over 60% of private owners own less than 15% of the forest because their individual holding is less than 4 ha [16]. French law does not require a formal management plan for small holdings [17]. Hence, one consequence of the fragmented ownership is that a formal management plan been made for only 48% of the French forest area [18] compared to 66% at the European scale [19].

Despite such management diversity, we are not aware of studies that differentiate their intensification scenarios based on the current management. Recent studies in France with nationwide management scenarios [6, 20] uniformly increased harvest rates for species and climate-specific study units. Yet, differences in management approach may partly reflect underlying differences in the forest owners’ attitudes and expectations such that policies targeting forest under one specific management approach will not necessarily succeed in mobilizing wood from the other management approaches [21, 22].

A policy subsidizing thinning or incentivizing the development of management plans would most likely target the overstocked forests whose owners are motivated by financial profits as is reflected by the fact that they do harvest eventually [23, 24]. These owners, however, generally lack the skills or motivation to plan for forest thinning and other management measures. A policy targeting downstream parts of the wood-sector, for example through end-product price subsidies, would most likely stimulate the mobilization of the forests that are actively managed, because these owners have a management plan and could adapt their planning to the market.

Targeting harvest-delayed forests and their owners could be more challenging as owners appear not to be motivated by financial gains through wood selling as reflected by the fact that they did not seize the opportunity to harvest their forest. Mobilizing wood from harvest-delayed forests will likely require developing a strategy based on information, regrouping of small holdings [25, 26], guidance and services. Finally, biomass mobilization from unexploitable forests—not explored in this study—will require large-scale development of infrastructure [27] such as extraction rides, bridges, and/or investments in harvesting methods previously unused in France, such as cable harvesting [28].

Carbon costs of energy production

The carbon balances described above all mobilize different amounts of wood, produce different amounts of energy, and retain different amounts of carbon in the forest. Given that wood harvest and subsequent bioenergy production differ between scenarios, comparing the carbon balance of scenarios goes back to comparing different levels of service provision. Let us consider that the challenge for future forest management is not simply to produce as much energy as possible, nor to produce the largest sink as possible, but instead to serve society at the lowest possible carbon cost. The optimal intensification scenario is then the scenario that produces bioenergy at the lowest carbon cost. Hence, this trade-off was calculated by normalizing the carbon balance by the total energy produced, defining the carbon efficiency of biomass energy, following an approach similar to the previously defined relative carbon indicator [29] and carbon neutrality factor [30, 31].

For BaU the carbon efficiency of biomass energy production decreases reflecting the forest age dynamics and decrease in in situ carbon sequestration as stands reach maturity. Although BaU and decreasing the harvest diameter by 10 cm (M-10) will have the same overall carbon balance by 2040 (Fig. 3a), their carbon efficiencies of biomass energy production differ: 1.04 Mt C sequestered per Mtoe of energy produced for M-10 versus 1.31 Mt C sequestered per Mtoe produced under BaU (Fig. 4). Similarly, mobilizing 20% of the harvest-delayed forests (D-2), decreasing the harvest diameter by 2 cm (M-2), and mobilizing 70% of the overstocked forests (O_v-7) all produce between 290 and 292 Mm³ of wood over 30 years. Nevertheless these three intensification scenarios have very different carbon efficiencies in 2040, with 1.21 Mt C sequestered per Mtoe for D-2, 1.24 Mt C sequestered per Mtoe for M-2, and 1.12 Mt C sequestered per Mtoe energy produced for O_v-7. Figure 4 compares these figures to the 1.31 Mt C sequestered for each Mtoe of biomass energy produced under the reference scenario.

Differences between the carbon sequestration potentials per unit of energy produced by the different management strategies thus reveals that not all units of bioenergy have the same carbon mitigation potential which itself varies with time (Fig. 4) in line with previous studies. Zanchi et al. [31] compared the carbon balance of energy biomass from increased removals in a managed forest, both from increased fellings and from extracting residues. They found that the carbon benefits of the different strategies after 100 years vary widely from the increased fellings, for which the carbon balance is more negative than the reference, to the use of residues, for which emissions are 76–85% lower than the reference scenario. Similar conclusions were drawn by McKechnie et al. [32], who compared the carbon balance of four sources of biomass energy and their fossil fuel alternatives. Over 100 years, using residues for energy showed larger carbon benefits than using standing trees, whatever the amount of fossil fuel replaced.

Carbon parity time of intensified management scenarios

Only after 2040 is wood energy produced under intensified scenarios for actively managed forests or in harvest-delayed forest likely to result in lower atmospheric CO₂ emissions compared to the BaU (Fig. 3a, 4, Additional file 3: Figure S2). Producing energy from increasing harvest in actively managed stands has a carbon cost over 25 years peaking in 2025 with 0.6 less t C sequestered per ton of oil equivalent energy produced compared with BaU. From 2035 onwards, energy production comes with carbon benefits with respect to BaU management (Fig. 4a), but it will take another 10 years before the debt accumulated in the first 25 years is compensated for and the benefits can be observed in the atmosphere (Fig. 4b).

For energy produced from harvest-delayed stands, the carbon cost per unit of energy produced peaks in 2025 with 0.7 additional t C emitted per toe produced compared with BaU, the carbon benefits would emerge in 2040 (Fig. 4a) and it would take another 15 years for the additional emissions to be compensated for (Fig. 4b). The longer time frame needed to realize the benefits for harvest-delayed intensification is due to the fact that the decrease in the in situ carbon stock is larger when targeting harvest-delayed rather than actively managed stands whereas both scenarios result in similar substitution benefits (Fig. 3b).

In turn, for the strategy targeting overstocked forests, directing thinning to energy production instead of directing harvest to timber has a carbon cost that is apparent in the crossing with other intensification strategies (Fig. 4) since before 2040 the energy produced from overstocked forests is already more expensive in terms of carbon emissions than the energy from actively managed and harvest-delayed stands. Indeed, energy production from targeting the overstocked forest would never result in additional carbon sequestration compared to the BaU scenario. This feature comes from the weak correlation in the inventory data between stand density and mortality: mortality is only proportional to standing volume (not shown). Accordingly, the intuition that thinning overstocked forests partly replaces natural mortality with harvest is not quantitatively borne out in our empirical model.

When targeting mature forests, carbon benefits take time to materialize (Figs. 3, 4; [33] given the combination of factors playing on two scales. On the one hand, at short time scales, recently clearcut stands have low biological production [34], not all biomass removed is transferred to wood product pools due to management operation losses [35] and to the use of wood for short-lived products, and compared to fossil fuel, a larger amount of wood-based carbon is needed to produce the same amount of energy. On the other hand, at a longer time scale, shortening the rotation length increases the overall biological production due to a larger proportion of younger forests becoming more productive than the older forests they are replacing [34] and substitution benefits accumulate over time. The long-term processes hence pay back the carbon debt generated from the short-term ones. The relative weights of these different processes determine if and when intensified harvest will yield carbon benefits.

The carbon debt and parity time have been the object of an extensive body of research, often focused on the single energy use of forest harvest and using a large variety of assumptions and methodologies leading to an even larger range of estimated parity times. As shown by Lamers and Junginger [36], Mitchell et al. [37], Holtsmark [38], or Bentsen et al. [39], the parity time depends mainly on the assumptions on the initial state of the forest, the forest growth rate and management practice. The assumptions used in the present study are sought to be as realistic as possible within the French context.

Here the carbon parity time is analysed at the national scale with a forest inventory-based dynamic forest model that considers the full wood products transformation chain and thus accounts for: multiple harvests, as suggested by Holtsmark [38]; for cascading and end-of-life of wood products, as highlighted by Geng et al. [40]; and the growth in the reference scenario, as advocated by Holtsmark [41]. Our finding that the carbon parity time is longer when targeting harvest-delayed forest than when targeting actively managed forests is consistent with the energy-only sensitivity analysis carried out by Laganière et al. [42] who find parity times longer for stands with slower growing trees. Our estimates of carbon parity times are in the lower range of the values reviewed by Bentsen [39] and Lamers and Junginger [36]. This must be the consequence of the high fraction of harvest used for long-lived timber products when most reviewed studies only consider the use of harvest for energy, and of the high level of cascading in the wood-use chain that leads to the same unit of harvest substituting for multiple alternative products.

In our opinion the search for the carbon parity time of increased harvest per unit energy is highly relevant for policymakers because it effectively considers the carbon stocks in the atmosphere, the in situ carbon stocks as well as the production of wood-based energy. Following these considerations, the carbon parity time of intensifying actively managed forest is 15 years lower than the carbon parity time of targeting harvest-delayed ones (Additional file 3: Figure S2). Targeting currently managed forests and intensifying their management thus appears as a more promising approach. Indeed, this approach could be seen as the forest-based equivalent of a concept known in agricultural science as land sparing [43–46]. The land-sparing concept separates forest for wood and fibre production from forest for conservation. With proper management high production levels can be realised in the managed parts of the forest, enabling the protection of the remaining forest. Note however, that forest production may depend on biodiversity [47], which suggests that land-sparing approaches should be carefully designed to qualify as truly sustainable forest management [46].

To the contrary, once a forest has been left to accumulate a substantial biomass stock, as is the case for the harvest-delayed forests in France, going back to a typical management approach comes at a very high carbon cost. If it is considered essential that the harvest-delayed forests are taken back into management, management that avoids the sudden release of the carbon stock (‘conservation management’) should be developed. Taking harvest-delayed forests back into production would relate to a land-sharing approach, which is centred around the integration of diverse functions, including biodiversity conservation, and wood production, in the same forest [46].

Limitations

Uneven-aged stands and stands for which no stand structure was reported in the inventory each cover 5% of French forests. Respectively 36 and 40% of these stands are classified as unexploitable or harvest-delayed but when management applies, these stands are respectively managed as even-aged high stands and coppice. This assumption is expected to have little impact on the growth and harvest estimates due to the low area covered.

A source of the model lack of fit, for all variables that were evaluated (Additional file 4: Figure S3, Additional file 5: Figure S4), is the statistical weight applied to individual plots when upscaling the plot data to the regional and subsequently national level. The National Forest Inventory agency (NFI) acknowledges that in a few homogeneous regions, they take smaller samples [48]. Accordingly, this undersampling is corrected by applying statistical weight to the plots located in these regions [49]. These statistical weights, however, are not publicly available. Estimates of standing volumes referred to as observations were extracted from an NFI report [50] and thus make use of these weights, whereas the simulated standing volume applied equal weights to all plots to derive the per unit area variables that are then upscaled with the reported areas for each species per region.

The growth and harvest simulator, which is the methodological basis of the study, was set up for single-species stands (see “Methods”—Growth and harvest simulator), hence, mixed stands in the inventory data were treated as homogeneous stands of the dominant tree species in the mixture. This homogenization of assigning mixed stands to just one species leads to an overestimation of the harvest of the dominant species, offset by an underestimation of the harvest of all other species (Additional file 4: Figure S3e). Species mixing is implicitly represented in the stand growth as the volume increment growth equation was derived from whole plot growth estimates.

The growth and harvest simulator, being an inventory-based empirical model, assumes the recent growing conditions are maintained and focuses on the interplay between forest management and the three carbon pools of the forestry sector, i.e., the in situ carbon stock in forests, the carbon stock in wood products and the apparent carbon stock in product and energy substitution, and their changes with age and species distributions. As such, the effect of recent disturbances on in situ carbon stocks are assumed to remain constant but neither future effects of climate change on forest growth [51] nor the feedback of management on soil nutrients [52, 53] were accounted for.

Previous studies suggest a minor effect of forest management on soil carbon, with the exception of whole tree—including stump—removal [54]. In the context of climate change, Schelhaas et al. [55] describes the increase of extreme events in the recent years (1950–2000). Under future climate, it is unlikely that the volume of wood burnt in forest fire would increase drastically because of investments in fire prevention and fighting. There is a risk that the frequency and intensity of damages from storms, pests and diseases increases in the coming decades. While these limitations are unlikely to impair our conclusions for the first 30 years of simulation, the assumption of constant growing conditions cannot be expected to remain valid beyond that. Climate change itself, as well as its expected effects on forest growth, are still the object of large uncertainties which, for time horizons over 50 years, dominate the variance of the forest sector carbon balance [56]. The interaction of management and the climatic response of forests is beyond the scope of this study and was therefore not considered in the growth and harvest simulator.

This study assumed that the extra wood mobilized in each intensification scenario will be directed in the same ratios as today to timber, pulp and paper, and energy production hence ignoring the socioeconomic dynamics of the forest sector. This approach reflects the assumption that over the next 30 years, economic or political change will be sufficiently smooth to avoid high quality wood being used for energy production rather than in construction. This assumption effectively constrains the potential bioenergy production from French forests since increasing the harvest of mature trees only increases energy production along the wood processing chain of the timber and pulp wood products through increased sawing residues and end-of-life recycling and burning. Thinning thus contributes more strongly to energy production than clearcuts because the quality and dimensions of the thinned wood are, at present, not high enough for direct use in timber production [57, 58]. Accounting for changes in wood demand, wood price, technological efficiency, consumer behaviour, substitution potential of products and energy calls for complex socioeconomic scenarios and modelling of feedback between processes that fall out of the scope of this study.

Methods overview

A growth and harvest simulator was developed for the purpose of this study (see “Methods”—Growth and harvest simulator). The initial conditions of the forest and the growth equations of the simulator were parameterized by making use of the French National Forest Inventory Data (hereafter called NFI) (Fig. 5) for the years 2008–2012 [50].

Each plot of the IFN dataset was assumed to be homogeneous in species and stand structure, be it high stand or coppice. Density, fertility and exploitability indices were estimated from the IFN data (see “Characterizing stands using the French National Inventory data” section), used as proxies for the current management state (see “Management approaches” section), and used to build the growth and harvest simulator based on empirical growth relationships derived for volume and diameter (see “Methods”—Growth and harvest simulator).

The simulator assumes that the wood market is supply-driven: stands are harvested when they are mature, irrespective of the demand and wood price. Such a simplification is justified by the relative inelasticity of wood supply [75] and obviates the need to include wood demand or wood price scenarios. The simulated harvest at the first model time-step, 2015, was calibrated to match the nationwide total harvest per species [50], by adjusting the thresholds used to define the different management approaches, i.e., harvest diameter and thinning ratio (see “Methods” Calibration). By combining the descriptions of management and growth, and assuming constant environmental conditions, the evolution of French forest was simulated for a set of diverse but realistic management scenarios (Fig. 5). Furthermore, the wood-use chain was reconstructed from wood industry data. Forest growth, wood use, lifetime, and substitution potential of all wood products were accounted for in the carbon balance of the set of management scenarios which represented different intensification approaches.

The growth and harvest simulator was ran for a Business as Usual (BaU) scenario for all of France between 2010 and 2115 with 2040–2115 results solely used to determine the carbon parity time (see “Discussion”—Carbon parity time of intensified management scenarios). Subsequently, simulated annual harvests were fed into the French wood-use chain to project the stock of wood products as well as the energy production and emissions from wood use.

In this study, BaU represents a scenario in which no political action is taken, and no economic incentive exists, to increase wood harvest above present-day levels and the transformation industry is able to adapt its capacity to absorb the variation in wood supply. The BaU scenario thus assumed that no change in practice occurs, either in harvesting levels or in wood use. In the future, forests are thus assumed to be managed as they are today (see “Discussion”—Different management, different policy) and the harvested wood is assumed to be transformed in the same proportions into timber, pulp, and paper and energy with the same transformation efficiencies as today—whatever the simulated wood supply (see “Methods”—Wood use modelling). Changes to this assumption would require socioeconomic hypotheses that would impair the understanding of the processes driving the carbon balance and fall beyond the scope of this study.

Characterizing stands using the French National Inventory data

Every year a tenth of a 1 × 2 km grid over France is visited by IFN, resulting in the inventory of around 6500 to 7000 forest plots a year. On each inventory point, four circular plots of decreasing radius are established to measure and observe different variables with an appropriate level of detail [49]. Each stand is described in terms of the physical properties of the terrain, e.g., topography and soil properties, and its vegetation characteristics. Mainly tree-level and stand-level measurements were used in this study.

This study explicitly analysed the data of the 14 tree species which comprise 85% of the total area of forest in France (Table 1). Inventory plots for which the main species was not one of these 14 selected species, were pooled in the categories ‘Other hardwood’ or ‘Other softwood’. Some further simplification was made when upscaling the IFN data from the plot to the regional or national level. The IFN methodology under-samples a few homogeneous regions [49]. When upscaling the data, the effect of this sampling bias needs to be corrected by weighting plots by a statistical weight that describes their representativeness in terms of forest area. These statistical weights, however, are not publicly available [76]. Hence, an equal weight for all plots was used when averaging the variables within each species-region combination. The NFI-reported areas of each species-region combination is then used to upscale the data to the national scale.

Management approaches

Plots were classified as one of four management approaches based on their physical and biological characteristics: quadratic mean diameter, fertility index, relative density index, and exploitability index. The use of the exploitability index as a physical constraint builds on the assumption that the stands which were easier to harvest and thin, are more likely to have been harvested. However, depending on the wood price, targeting stands with a lower exploitability index could be justifiable. Price dependency may have contributed to the observation that P. pinaster stands were managed irrespective of their exploitability indices. Harvest and thinning of all other species started with the exploitability index set as ‘difficult to access’.

Discontinuity in the statistical distribution of the diameter was used to determine the expected clearcut diameter for each species and each fertility class. The discontinuity was identified using a segmented regression approach. The rationale for this is the assumption that for a given species, under idealized management and an even age-class distribution, all diameter classes below the harvest diameter would have a similar number of stands whereas no stand would have a mean diameter above the harvest diameter. Subsequently, the clearcut diameters calculated with this method served as a prior and were adjusted during calibration of the harvested wood volume per species, as reported by IFN for the year 2010 [50]. Also, for each species, a density threshold was set based on DI distributions with a segmented regression approach similar to the one used for the diameters. Subsequently, the final density threshold was determined through calibration of the harvested wood volume per species, as reported by IFN for the year 2010 [50].

Each plot is assigned to only one of the four management approaches by following an assignment order. First, plots with too low an exploitability index were classified as unexploitable. Then, the remaining plots with diameters above the calibrated expected quadratic mean diameter were referred to as ‘harvest-delayed’ and assumed not to be harvested for ownership reasons, for example, fragmentation of properties or disinterest of the owner or for conservation. Under present management, or Business as Usual, these plots are neither thinned nor harvested. Then, plots with densities above the final DI threshold were referred to as overstocked. In a Business as Usual scenario, these plots will not be thinned but will be harvested. Finally, the remaining forest with diameters, density and exploitability not matching any of the above criteria, were classified as actively managed. For P. pinaster and Picea abies stands, the harvest statistics were best matched when no threshold was set either on stocking density or on exploitability index.

Growth and harvest simulator

Wood-use modelling

Wood use substantially contributes to the net carbon balance of French forests, because different wood uses come with different lifetimes, shares of wood reuse and potential substitution of fossil fuel emissions [87]. Therefore, wood flows from the forest to the final product including recycling and the products’ end-of-life were reconstructed. To simplify the scheme, recycling was assumed to occur only once. Data were extracted from different literature sources [16, 88–96] for the year 2010 and reconciled into a unified scheme (Fig. 6).

The wood product scheme starts by assigning harvest to either the lumber or the pulp and energy pathway. The proportions of soft and hardwood going down the timber, pulp and energy pathways were calibrated to match the reported national production of 18.6, 10.4 and 28.3 Mm³ including self-consumption energy-wood, respectively [90]. For hardwood, 16%, 14% and 70% of the harvest, were directed to the timber, pulp and industry, and energy pathways, respectively. For softwood, 53%, 23% and 24% of the harvest were directed to the timber, pulp and industry, and energy pathways, respectively. The assignment of harvest from different management practices to timber or pulp and energy was also adjusted to match the reported national values, assuming that harvest from high stand clearcut is preferentially used as timber, whereas the wood from thinning and coppicing is preferentially used in the production of pulp and energy. For softwood, all clearcut and 26% of thinnings were directed to timber, and 74% of thinnings were directed to pulp and paper. For hardwood, all thinnings and 43% of clearcuts were directed down the pulp and energy pathway, and 57% of the clearcuts were directed to timber.

Carbon balance

The carbon balance is calculated by summing all components of the life cycle of wood: sequestration in the forest, carbon storage in wood products, carbon emission at the end of the product’s life and the avoided emissions from substitution. Each wood use is assigned an average life expectancy and substitution potential (Table 4). The BaU scenario is used as the reference for substitution calculation hence assuming that literature-based substitution coefficients used were derived for reference and scenario conditions consistent with the wood harvest and wood-use practices implicitly described in our scenarios. The avoided emissions under each scenario were calculated by multiplying the extra wood products with respect to BaU by their respective substitution coefficients.

Carbon storage in wood products is only accounted for in the first wood use (timber end-product, pulp and paper end-product or energy) and the carbon contained in the products is emitted progressively at each time-step following an exponential decay function. However, for substitution effects, consecutive wood uses are accounted for by multiplying their respective volumes with product-specific substitution coefficients all supposed to occur at time of harvest. This assumption is considered to have a minor effect on the results since most consecutive wood uses occur in a time lapse below the 5-year time-step of the forest growth and harvest simulator. This time compression approach leads to a small overestimation of substitution potential of harvest directed to timber with the anticipation of the substitution benefits from product recycling.