The Complexity of Incorporating Carbon Social Returns in Farm Afforestation: A Microsimulation Approach

There is a growing literature on the use of microsimulation models for agriculture, forestry and land use change (Richardson et al., 2014; Rahman, 2018; Montaud et al., 2020; Boulanger et al., 2020), much of which addresses issues related to productivity and incomes (O’Donoghue, 2017). However, consistent with an increasing global focus on sustainability, there is also increasing interest in combining analyses of both economic and environmental impacts (Ramilan et al., 2011). A sub-field of agricultural microsimulation addresses issues associated with land use change from agriculture to forestry (Phimmavong and Keenan, 2020), while a previous article in this journal described the simulation of private returns for the farm-level land use change to forestry, looking at the impact of afforestation on individual farms (Ryan and O’Donoghue, 2019). This paper extends the literature by incorporating carbon emissions in a microsimulation of the conversion of agricultural land to forestry, enabling the distribution of wider public good returns to be modelled in addition to the private return - a representation of the social value.

A theme that links much of the rural land use focused microsimulation literature is the impact on the environment (Hynes and O’Donoghue, 2014). Much of the environmental microsimulation literature (in particular GHG emissions), looks at the household sector particularly in relation to carbon taxes (Symons et al., 1994; Cornwell and Creedy, 1996; Bach et al., 2002; Jacobsen et al., 2003; Serret and Johnstone, 2006; Ysé and Nick, 2006; Rosas-Flores et al., 2017; Romero et al., 2018; Berry, 2019; O’Malley et al., 2020). A number of papers have considered the impact of environmental pollution in agriculture, such as nitrate pollution in the case of Doole et al. (2013), while Hynes et al. (2009, p. 2013) and O’Donoghue et al. (2019); Carauta et al. (2017) have developed microsimulation models to examine agricultural GHG emissions.

Given the centrality of forestry and agro-forestry as a greenhouse gas mitigation (GHG) strategy, an obvious extension of the market-analysis microsimulation literature for forestry is the incorporation of environmental extensions as in the case of the wider agri-microsimulation field. However, forest carbon modelling is complex; carbon (C) is sequestered in living trees and in harvested wood products (HWP), while needles/leaves and deadwood decompose and contribute to carbon stored in soils. Bateman and Lovett (2000) model forest carbon sequestration while the impact of forest management regimes on carbon sequestration is modelled by (Sedjo, 2001; Kooten et al., 2009; Im et al., 2007; Yemshanov et al., 2015). However, such analyses focus solely on forestry. In this paper, we are interested in microsimulation modelling of carbon sequestration in forests from the perspective of the social (private + public) return to a land use change from agriculture.

According to OECD (2016) and UN (2019) projections, the growing demand for food will lead to greater competition for land between agriculture and forestry, with a consequent requirement for greater understanding of economic and environmental consequences of both land uses. When forests replace livestock, there is a reduction in GHG emissions. The transition from agriculture to forestry therefore, is particularly important as it involves increased forest carbon sequestration and also helps to mitigate agricultural greenhouse gas (GHG) emissions. An indirect cost of the land use change to forestry is the loss of flexibility of land use as a result of the legal requirement to replant harvested forests. While such non-pecuniary factors have been shown to have a negative impact on the decision to plant agricultural land (McDonagh et al., 2011; Duesberg et al., 2013; Duesberg et al., 2014; Pannell and Claassen, 2020), along with other utility-related factors such as leisure (Baker et al., 2017), the relativity of forest and agricultural incomes has been shown to be a strong driver of planting decisions (Ryan et al., 2018).

Pannell (2008) asserts that policy mechanisms to encourage such environmental changes should take the relativity of private and public returns into account. However, due largely to the modelling complexity involved, there is little literature that addresses both private and public returns in microsimulation analyses of farm afforestation (new planting). Yet, given the growing importance of carbon sequestration in public policy, particularly in relation to the justification and design of instruments to promote afforestation and in relation to the challenge of mitigating on-farm and animal related emissions, it is temporally relevant to extend the private returns model to incorporate the impact of carbon sequestration. The objective of this paper is to address this gap by extending the Ryan and O’Donoghue (2019) private planting returns microsimulation framework to incorporate the social return to farm afforestation replacing an agricultural (livestock) system.

The definition of social return can be quite broad, incorporating all monetary and non-monetary aspects. The private return to a landowner incorporates the life-cycle monetary impact of moving from agriculture to forestry. However, this clearly ignores significant public good impacts in relation to carbon sequestration. There are of course, other aspects of the return including the private return to other value chain actors such as timber mills, and other ecosystem services such as biodiversity or water quality. To keep the analysis tractable but useful, we adopt a narrower definition of social return to a land use change, i.e. the combination of the private return to the landowner plus the value of the net carbon sequestration of the land use change.¹

We also use a narrow geographical scope, focusing nationally rather than globally. Although not without criticism, carbon accounting and responsibilities for management are downscaled to the national scale, thus the national approach in this paper is consistent with the policy objectives. On this basis, results should be interpreted as being the net implications for a country in terms of its economic return to a land use change plus a change in its carbon balance. This analysis considers the impact of marginal changes to a single hectare on different types of land. As a result, the consequential global implications and substitution effects are hard to determine as alternative protein may come from different sources or different agricultural systems with different carbon footprints. While an interesting question, it is beyond the capacity of this paper. We believe however, that the policy usefulness of a land use change model is enhanced by adding the public good return to the private return, even if more complex and more comprehensive approaches are possible.

This paper describes in detail the complex elements associated with modelling the life-cycle private and social (incorporating carbon) impact of planting one hectare of forest, substituting for one hectare of an existing agricultural enterprise (in a given year). While the analysis of these changes is in itself interesting and important from a policy perspective, there is a need to properly document and assess the individual parametric assumptions within the model. The time from initial planting to the final release of the carbon stored in harvested wood products can be hundreds of years. Therefore, there are no available data for the entire life-cycle, necessitating a modelling approach. Given the lengthy nature of the timber growing process, there is considerable variability in the assumptions used in different studies. As a result, it is important in a modelling paper to consider the sensitivity of microsimulation results to these parameters. The paper also contributes to the literature by clearly documenting the modelling process and data used in this novel model in order to facilitate replicability. In addition, the robustness and sensitivity of the model are tested against a range of assumptions that may vary over such a long period as a forest life-cycle.

The paper aims to chronicle the elements needed to simulate the life-cycle impact of a potential land use change involved in the conversion of agricultural land to forestry, taking the social value of carbon into account. As there is considerable heterogeneity in agricultural and forestry returns in terms of the private (profit) and the public good impact in terms of carbon, we utilise a microsimulation model to simulate the income and carbon implications of the farm afforestation land use change on a cross-section of farm micro units. This model builds on existing literature that considers the distribution of private returns to afforestation on farms (Ryan and O’Donoghue, 2019) by calculating the public good impact in addition to the private good impact.

Figure 1 describes the structure of the microsimulation model. As the focus of the model is understanding the differential return to different land use types, relative to the return from the original agricultural land use, the model starts with the distribution of land and its potential in terms of soil and other environmental factors such as rainfall, altitude and slope, that influence the productivity of land. An important starting premise is that different land uses may have different productivity on the same soil types. For example, some soil types are more productive under forests than agriculture, whereas within agriculture, some soils are more productive for crops than for animals. There is significant heterogeneity in these environmental factors, even in a small country like Ireland and as a result it is essential to consider the returns to land use change across the range of environmental conditions.

Figure 1

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As State approval is required both to plant forests and to harvest timber, with a legal requirement to replant following harvesting, and given the difficulties in returning forest-land to pasture or tillage, this analysis models a permanent land use change from agriculture to forest. Starting at the top panel of figure 1, the characteristics of the private and social return to agricultural uses are described. Different environmental characteristics are optimal for different farm systems (dairy, cattle, sheep, tillage etc.). However, the return to each of these systems is quite different. Similarly, the environmental footprint in relation to carbon emissions from animals (methane) and pasture or crops (nitrous oxide emissions from fertiliser and nutrient application) and greenhouse gas emissions from other inputs such as energy are quite different, depending on the farm system, the nature of production and the level of intensity and efficiency on individual farm systems. Given this heterogeneity, the starting position for the model is the nationally representative distribution of Irish farms in the Teagasc National Farm Survey (NFS),² which captures most of the diversity of farms. Subsidies vary by policy intervention, farm size, location of the farm and historical production levels. A carbon emissions model is employed to simulate the carbon emissions from each of these farms. For most crops, the production cycle is annual, as crops are planted and harvested annually, or follow an annual growth curve in the case of permanent pasture. As a result, the NFS uses an annual accounting framework.

However, forests have longer growth cycles than agriculture, spanning from tree planting to maturity and potential harvesting. The length of this cycle depends on the environmental context, productivity or yield class, tree species and the management regime of the forest in terms of thinning and harvesting. As the volume of timber increases over time, so too does the carbon stored or sequestered (described later in more detail). As planting, thinning and harvesting tend to be undertaken by contractors rather than foresters, there is not as much heterogeneity in management efficiency between forests as there is between farms. As in the case of agriculture, forest incomes depend to a considerable degree on subsidies.

The incorporation of carbon sequestration in a microsimulation model is challenging in relation to the assumptions used. A criticism of the literature is that there is quite a wide variety of assumptions used in carbon calculations (Asada et al., 2020), which can have a considerable impact on the results. Additionally, these assumptions are often poorly documented. This paper sets out to document the data sources, coefficients, assumptions and model validations of a microsimulation of a land use change to substitute an agricultural enterprise with a forest as is commonly incentivised in Western European countries such as Ireland, UK and Belgium. In this analysis, we use Irish data and coefficients. All modelling is undertaken using STATA.

Microsimulation modelling of private and social income requires the estimation of the following components:

• Forest income is comprised of both afforestation subsidies and market income arising from timber sales as a function of the characteristics of different land types and alternative land uses. The estimation of timber sales requires forest growth data as a component of a forest bio-economic model of costs and market income.

• Farm micro-data are necessary to estimate the agricultural market and subsidy income foregone on planting in order to model the carbon emissions of different farms.

• Quantification of the sequestration of forest carbon and the agricultural emissions displaced from the superseded enterprise, dependent on soil variability and the superseded agricultural enterprise.

• Future valuation of carbon.

2.2. The Public Good (carbon) Impact of Farm Afforestation

There are three components of the public good impact: forest carbon storage (sequestration), displacement of agricultural GHG emissions from the planted land and the valuation of the sequestered/emitted carbon.

Six carbon pools contribute to gains (carbon sequestered) and losses (to the atmosphere) in different ways, with each varying by the soil attributes of the land on which planting occurs:

• There is a slow increase in soil organic carbon (SOC) in mineral soils over time, whereas organic (peat) soils lose carbon on drainage and planting

• Carbon is stored incrementally in forest biomass over time, (depending on the tree species planted and on the soil and climatic conditions of the planted land) in:

  1. above ground biomass (>7cm) and

  2. below -ground biomass (roots >5cm), until the wood is either harvested (removed), decomposes or is combusted.

• Decomposing material such as

  1. dead organic matter (DOM) litter (decaying needles/leaves, branches <7cm diameter) and

  2. wood from dead trees (above/below ground >7cm) can decompose and add to SOC over time

• Wood removed as harvested wood products (HWP) (products in current use from domestic harvests) continue to store carbon until they either decompose (rot) or are combusted.⁴ The half-life of the carbon stored depends importantly on the nature of the harvested wood products and on the disposal and decay rate at end of life for the wood products.

A visualisation of total carbon storage over time is presented for these pools for thinned and unthinned forests and for a range of forest yield classes in Figure 2. Yield Class is a measure of forest site productivity and is reported as the average annual timber production over the life-cycle of a forest measured in cubic metres per hectare per year ( $\text{Missing left or extra right}$ ).

Figure 2

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The agricultural GHGs displaced on planting are (a) methane ${\displaystyle \left(C{H}_4\right)}$ emissions from enteric fermentation in ruminant animals and manure management, (b) direct and indirect nitrous oxide( ${\displaystyle N_2\mathrm{O}}$ ) emissions for each livestock sector i.e. manure management, fertiliser application and dung/urine deposition, along with fertiliser emissions from tillage. Carbon dioxide ( ${\displaystyle C{O}_2}$ ) emissions from fuel and electricity are included for both agriculture and forestry.

The final component of estimating the public good return to farm afforestation is the application of a carbon value to the carbon sequestered/emitted. The concept of the social cost of carbon (SCC) relates to the economic cost caused by an additional tonne of carbon (or equivalent) emissions (Nordhaus, 2014). The cost of additional ${\displaystyle C{O}_2}$ in the atmosphere varies with the level of GHG emissions and as a result, Smith and Braathen (2015) highlight specific assumptions that need definition with regard to SCC and the current and future levels of GHG concentrations, against which its effects are to be measured. The SCC estimates the monetary value of the incremental impact of an additional tonne of emissions. Abstracting from the complexity of these elements of the SCC and from tax, regulation and inconsistent assumptions across sectors, the SCC would equal the carbon price, i.e. the marginal cost of emissions reduction (the present value of the damage caused per unit of emissions) (Nordhaus, 2014).

There is significant variation within the literature with regard to the social value of carbon (NESC, 2018), both in terms of the discount rate and the price of carbon. Stern (2007) valued carbon at $85 / ${\displaystyle tC{O}_2}$ . A report from the Interagency Working Group (IWG) on Social Cost of greenhouse gases estimated a carbon price of between $36 and $42 per ${\displaystyle tC{O}_2}$ in 2007, using a 3% discount rate (IWG, 2013). On the other hand, (Moore and Diaz, 2015) attribute a lower discount rate as a result of damages caused by climate change and report a SCC value of $220 per tonne of additional ${\displaystyle C{O}_2}$ (Moore and Diaz, 2015). While the Irish conventional discount rate for forest investments is 5% (Clinch, 1999), this paper includes scenario analysis of the impact of a range of discount rates on the carbon value.

As one of the rationales for this analysis is the examination of the social return to the land use change from agriculture to forestry as a mitigation measure for agricultural GHG emissions, we require an indication of the substitution effect in physical terms. For this purpose, we utilise the carbon value recommended by the Irish government as the shadow price of carbon for non-ETS sectors. Given the uncertainty around carbon valuation, we utilise the shadow prices for different years i.e. 2019 (€20), 2020 (€32), 2030 (€100), 2040 (€163) (DPER, 2019).

In estimating the social return to land use change, the value of forest carbon sequestered and GHGs emitted by agriculture and the relative private returns are included as follows:

${\displaystyle \begin{array}{}\text{(9)}& NPVSocialIncome{}_{Afforestationofagriculturalland}& =NetPrivateForestIncome-\text{(10)}& & AgriculturalNetMarketIncome+\text{(11)}& & ValueofNetCarbonSequesteredbyForests\text{(12)}& & +ValueofGHGsEmittedbyDisplacedAgriculture\end{array}}$

In order to compare (a) forest returns over different rotation lengths and (b) annual agricultural returns with long-term forest NPVs, an annual equivalised of NPV is required (Herbohn et al., 2013).⁵

The primary data source in this paper is the Teagasc NFS, which collects detailed information from a representative sample of farms and is Ireland’s input to the EU Farm Accountancy Data Network (FADN). Using data for 2015, agricultural cost and revenue streams are generated for each of six agricultural systems (dairy, cattle rearing, cattle other, sheep, tillage and mixed livestock) on six NFS soil codes.⁹ C-ForBES links these agricultural data with the forest yield classes (Edwards and Christie, 1981) using the classification described earlier and forest management costs and revenues in Teagasc FIVE (Teagasc, 2012). Table 3 describes the average agricultural gross margin (including subsidies) per hectare for the livestock farm systems by soil type and corresponding yield class. There is a broad correlation between soil code and the gross margin per hectare as the highest gross margins are achieved on dairy and tillage farming (particularly on the less limiting soil codes), while cattle and sheep farming have lower gross margins per hectare.

Table 2 reports the average return per hectare from planting Sitka spruce (SS) for a range of yield classes, reflecting the opportunity cost of agricultural income foregone. The impact of better quality land as represented by high yield class is clearly evident.

In this section, the results of the carbon sequestration analysis and are presented and discussed in the context of the modelling assumptions used for forest carbon pools and validation against other analyses. For simplicity purposes, ‘No Thin’ scenarios are presented, unless stated otherwise. Next we present the private and social returns to the land use change with sensitivity analyses around the parameters used and lastly, the distributional analysis is presented.

5.1. Carbon Sequestration

First, we illustrate the accumulation and loss of carbon over a 200 year period for Sitka spruce yield class 18 (no thin) in Figure 3. The largest increase in carbon is evident in the above ground livewood, particularly in the early years, with an acute loss of carbon at the point of harvesting, when timber is removed from the forest. The lower rate of sequestration in the below ground livewood reflects the 80/20 above/below ground ratio.

Figure 3

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At clearfell (final harvest), the above ground biomass declines to zero, while the below ground biomass transfers slowly to the DOM pool. There is an immediate decline in carbon in HWP at harvest, relative to above ground livewood, as just over a third of the livewood is used for energy (Knaggs and O’Driscoll, 2017) and is immediately oxidised. Harvesting (roundwood) losses are also incurred during the harvesting process.

In contrast to the no thin scenario presented in Figure 3, on thinning (periodic removal of trees), there is an initial drop in above ground biomass, followed by a subsequent increase in growth (and carbon sequestration) due to the greater availability of light, moisture and nutrients for the remaining trees. As each thinning occurs, livewood carbon declines and the cumulative carbon in HWP increases, albeit declining if the biomass is combusted (oxidised). An important difference between thinned and unthinned forests is that harvest losses at thinning can be substantial, particularly in the case of first thinning.

In this paper, we describe in detail the development of a microsimulation model to simulate the life-cycle impact of the incorporation of carbon emissions in the conversion of agricultural land to forestry, enabling the distribution of wider social returns to be modelled in addition to the private return. The paper is novel in that it brings together the literature on land use change, which focuses mainly on the social return to land at an aggregate spatial level, and farm level simulation analysis that focuses on private returns. Given the importance of afforestation as a driver of carbon sequestration and a mitigation measure for agricultural emissions, there is a policy imperative to combine both dimensions.

Reflecting the negative and positive externalities associated respectively with agriculture in terms of carbon emissions and with forestry in terms of carbon sequestration, the private returns for both land uses were modelled along with a measure of the value of the public good provision in terms of carbon sequestration. These reflect respectively the return from the market and the returns to society, accounting for carbon emissions and sequestration.

The analysis highlights that unthinned livewood is the largest forest carbon pool while the HWP pool is the largest non-forest pool and is perhaps where the greatest advances could be made in relation to the substitution of long-lived sawnwood products for concrete and steel, however the modelling of the substitution effect of replacing concrete and steel is beyond the farm level focus of this paper but is the subject of future work.It also highlights the influence of soil quality on forest carbon sequestration and on livestock emissions. In considering the distributional impact of the private and social returns to agriculture, this paper finds significant heterogeneity between the private and social return across farms, with the incorporation of a value for carbon resulting in many more farms with positive social return than private returns.

While there is a large literature on ecosystem service provision from forests (Temperli et al., 2020; Jönsson et al., 2020; Dunford et al., 2015) the contribution of this paper focuses more narrowly on (a) the relative contribution of an afforestation-focused land use change from agriculture, (b) the incorporation of this land use change within a microsimulation framework to enable the heterogeneity of impacts to be considered and (c) the incorporation of both market and non-market implications. Each of these issues have been considered separately, but not together. An advantage of a microsimulation approach is the capacity to integrate analysis in different dimensions and then to run simulations to draw cross-cutting conclusions. This study examines one specific ecosystem service in quantifying social returns, as this is the dominant focus of current policy discussions. Future research could broaden the scope of this study to incorporate other forest ecosystem services, however the incorporation of wider value chain considerations is beyond the scope of this study.

Recognising the variety of assumptions that are used in modelling land use change to forestry and their impacts on net sequestration (Soimakallio et al., 2021), all assumptions used in what is a complex analytical problem were documented, components were validated against other analyses and the sensitivity of results to different assumptions was tested. In comparing the growth curves from the UK-based Edwards and Christie (1981) stand-level yield models, with more recent growth curves based on Irish forest inventory data and single-tree plots (Teagasc, 2021), we see that the end point in terms of tree biomass is similar, but the growth occurs earlier in the Irish curves. We found that our harvested wood products simulation based on the Irish NIR is similar to an alternative method used by Bateman and Lovett (2000). However, we note that differences in assumptions in relation to harvest losses and the allocation of harvested wood to energy can result in sizeable impacts on carbon storage.

Given the variety of assumptions used in this type of inter-disciplinary analysis, there is merit in a more coordinated approach, both nationally and internationally in relation to model assumptions. The IPCC makes assumptions at a particular level, but there is a need for greater consistency in more detailed assumptions. For example, at national level, agreement around what happens at the point of harvest in terms of losses at international level, greater consistency in terms of the harvested wood value chain and its relationship with the characteristics of the trees in the forest, would both be beneficial to modellers working in this complex field. The US government Interagency Working Group (IWG, 2013) on the Social Cost of Carbon (Nordhaus, 2014) provides an influential example of such a coordinated approach.

There are limitations in relation to the modelling approach employed here that relate largely to the availability of forestry activity data, as forest data collection was historically focused on the science of timber rather than carbon production. The analysis does not take into account the accumulation of carbon in soils over very long time horizons. The focus on Sitka spruce forests is also a limitation of this analysis, and relates to the robustness of production and economic SS data, compared to other conifer and broadleaf species. Thus, this analysis ignores the carbon contribution of broadleaf species planted as areas of biodiversity enhancement within Irish forests. However, these limitations do not affect the qualitative conclusions of this analysis.

There are currently no policy levers to convert carbon prices to either monetary benefits to forest owners or to a cost for farmers, thus there is a challenge to develop policy instruments for the land use sector such as the EU Emissions Trading Scheme (ETS) for climate change, in order to address undesirable trade-offs between agriculture and forestry as carbon prices rise, or to avoid pressure to reduce harvesting to maintain carbon in livewood. Ex-ante analyses using a multi-dimensional models such as the model described here can play an important role in testing such scenarios to assist in future policy development.

Finally, this paper contributes to the growing field of microsimulation modelling for both environmental policy analysis and agricultural policy analysis. Utilising farm survey data that is collected uniformly in many countries, whether it be the European FADN or OECD data (for countries such as the USA, Australia, New Zealand), and utilising information in relation to afforestation that is available as part of national carbon accounting frameworks, this methodology is replicable and scalable to other countries. It thus has the capacity to be utilised for ex-ante analyses of afforestation initiatives associated with GHG reduction and increased carbon sequestration.

In this paper, we utilise the Edwards and Christie (1981) static forest yield models, also used by the Forestry Commission (Britain) CARBINE model (Thompson and Matthews, 1989) and in the Forest Greenhouse Gases Model of the UK NEVO (Natural Environment Valuation Online) tool (Binner et al., 2019). The carbon valuation model developed by Bateman and Lovett (2000) combines data from Edwards and Christie (1981) yield models with carbon storage data, to estimate functional relationships for carbon storage in livewood on a per hectare per annum basis. The carbon accounting framework in NIR (2018) uses the CARBWARE single-tree model model (Black, 2016) along with the stand-based Edwards and Christie (1981) FORCARB model (Black et al., 2012). In more recent analyses by Black (such as NIR (2019) and the Teagasc Forest Carbon Tool (Teagasc, 2021), the Carbon Budget Model (Kurz et al., 2009) is parameterised for Irish forest carbon estimations.

Figure C1 compares C-ForBES livewood (above ground) carbon estimation (based on Edwards and Christie (1981) yield models and NIR (2018) accounting rules, against those produced in the Teagasc Forest Carbon Tool (Teagasc, 2021), using the Carbon Budget Model (CBM) (Kurz et al., 2009) and National Forest Inventory activity data (DAFM, 2018). The resulting carbon estimations for accumulated carbon dioxide over one rotation is quite similar in both models, albeit differing in relation to the estimation of early growth. Growth prior to age of first thinning is not recorded in Edwards and Christie (1981), thus differences arise between carbon models in relation to imputation of early growth and are sensitive to the data or assumptions employed.

Modelling carbon for different purposes can lead to differences in approaches. For example, C-ForBES looks at returns to planting on a hectare basis, (before conversion to volume after thinning and harvest) and has a longitudinal focus. The NIR approach on the other hand takes a cross-sectional perspective on biomass volumes (rather than hectares) and models livewood and wood products separately, using different data sources. Thus, C-ForBES follows the wood-flows from livewood to HWP and wood energy using the Knaggs and O’Driscoll (2017) approach, making particular assumptions about forest rotation and harvest losses etc. The NIR approach meanwhile, derives livewood (and DOM) sequestration from National Forest Inventory (NFI) data and calculates HWP liberation on the basis of separate roundwood use totals (the volume of the domestic harvest in cubic metres). Due to heterogeneity in forest management practices and differing perspectives on optimal harvest rotation lengths, it can prove difficult to reconcile the resulting outputs.

The literature on harvested wood products remains relatively sensitive to assumptions such as the degree of losses of trees/logs to oxidation during harvesting process can have a considerable impact on the proportion of wood that ends up as wood products. In addition, a critical assumption in relation to life-cycle carbon sequestration, is the proportion of the harvested wood that is used for energy. When wood is combusted for energy, the carbon is immediately released into the atmosphere, while other wood products (e.g. sawnwood and wood-based panels) store the carbon for considerable periods of time. Thus assumptions in relation to both harvest losses and allocations to wood energy can impact on the domestic harvested wood inputs to the HWP pool. Figures C2 and C3 present sensitivity analyses of Harvested Wood Flow Carbon Liberation for different models, depending on whether a proportion (34%) of wood is used for energy.

Figure C2 compares the NIR (2018) approach with the C-ForBES approach, plotting the carbon liberation curves for the HWP categories (SW: sawnwood, WBP: wood-based panels and PPB: paper and paperboard. Three scenarios are presented, namely (a) NIR (2018) approach (b) C-ForBES harvest losses from Teagasc FIVE and (c) C-ForBES harvest losses plus 34% allocation of harvested wood to energy, based on Wood Flow (Knaggs and O’Driscoll, 2017).

Comparing the carbon liberation curves for different uses (SW, WBP and PPB harvested wood product categories) we see that the impact of harvest losses is quite small, while the impact of the wood energy use is considerable. In all cases, the Paper and Paperboard HWP is negligible. As expected, sawnwood stores the greatest amount of carbon over time. There is little difference between the sawnwood curves for NIR (2018) (SW) and C-ForBES (SW HL). Similarly, there is little difference between the liberation curves for WBP (NIR) and WBP HL (C-ForBES) harvest losses. However, the reduction of the harvested wood input to HWP as a result of the 34% allocation to wood energy, pulls the C-ForBES carbon liberation curves (SW HL Energy, WBP HL Energy) well below the NIR curves in the earlier years, with the curves converging only towards the end of the 200 year period.

The Bateman and Lovett (2000) approach differs to the previous methods as it is calculated on a per hectare basis and by estimating equations for livewood sequestration, emissions incurred in thinning/harvesting and carbon storage/release from HWP. Comparing Bateman and Lovett (2000) with the NIR (2018) in Table C1 and plotting the curve in Figure C3, we find that the carbon profile for SW and PPB are very similar (they are indistinguishable), when harvest losses and energy are excluded. However, the profile is lower for wood-based panels in NIR (2018) than in Bateman and Lovett (2000).

The science behind forest carbon accounting and modelling is complex and values change over time as the science evolves. One such example is the choice of the biomass expansion factors (BEF), which is the ratio of total tree biomass to merchantable timber volume which changed quite substantially between the 2012 National Inventory Report and the 2015/2018 reports and for thin and no-thin forests. The earlier report assumes a constant BEF of 1.64, whilst the latter uses a minimum of 1.68, with variation over time. Table C2 compares results for annual equivalised tCO ₂ using different BEF coefficients, showing both the greater tCO ₂ for the later coefficients and the change in the ratio between thin and no thin forests.

Figure C1

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Figure C2

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Figure C3

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Author details

1. Mary Ryan

   Teagasc, Athenry, Co. Galway, Ireland

   For correspondence

   Mary.ryan@teagasc.ie

   Competing interests

   No competing interests reported

   "This ORCID iD identifies the author of this article:" 0000-0001-8395-6953

2. Cathal O’Donoghue

   National University of Ireland, Galway, Ireland

   Competing interests

   No competing interests reported

   "This ORCID iD identifies the author of this article:" 0000-0003-3713-5366