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Klima-, Energi- og Forsyningsudvalget 2023-24
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university of copenhagen

d e pa rt m e n t o f g e o s c i e n c e s a n d

n at u r a l r e s o u r c e m a n a g e m e n t

–

Forest Carbon Pool Projections 2024

Thomas Nord-Larsen, Prescott Huntley Brownell II, and Vivian Kvist Johannsen

IGN Report

April 2024

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Title

Forest Carbon Pool Projections 2024

Authors

Thomas Nord-Larsen, Prescott Huntley Brownell II, and

Vivian Kvist Johannsen

Citation

Nord-Larsen, Thomas, Brownell II, Prescott Huntley and Johannsen,

Vivian Kvist (2024): Forest Carbon Pool Projections 2024, IGN Report,

April 2024. Department of Geosciences and Natural Resource

Management, University of Copenhagen, Frederiksberg.

p. ill.

Publisher

Department of Geosciences and Natural Resource Management

University of Copenhagen

Rolighedsvej 23

DK-1958 Frederiksberg C

www.ign.ku.dk

Responsible under press law

Vivian Kvist Johannsen

External/internal review

Professor Henrik Meilby, University of Copenhagen

Associate professor Niclas Scott Bentsen, University of Copenhagen

ISBN

978-87-7903-927-8 (web)

Cover layout

Jette Alsing Larsen

Cover photo

Thomas Nord-Larsen

Published

This report is only published at

www.ign.ku.dk

Citation allowed with clear source indication

Written permission is required if you wish to use the name of the

institute and/or part of this report for sales and advertising purposes

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Preface

The large carbon pools of the forests have a relatively significant importance for the Land Use,

Land-Use Change, and Forestry (LULUCF) segment of the Danish emissions inventories and

thereby the overall climate accounting. In order to navigate towards the objectives of the Danish

climate goals, it is therefore necessary to know what emissions are expected from the forests.

Department of Geoscience and Natural Resource Management at University of Copenhagen has

previously made various projections of the forest carbon pools in different contexts and with

different assumptions to provide estimates of forest greenhouse gas emissions.

As a result of the diversity in the data used and the underlying assumptions, the projections have led

to different results. The models have moreover been aimed at a long-term projection but have had

difficulties in the short term in describing the actual development. There is thus a need for a

renewed projection, which shows the expected development in the carbon pools up to 2025 and

2030 and reflects the recent years' development. Consequently, this project was initiated on an

assumption that a simplified projection of forest carbon pools focusing on 2025 and 2030 and

linking to previous projections for the years up to 2040 would produce sufficiently accurate

estimates. However, during the project it proved difficult to simplify calculations while still

incorporating known changes in forest area, age and species distribution, and in forest management

practices on areas set aside for biodiversity protection.

Because of issues arising from simplification of the projections it was decided to take on a, for

Denmark, novel projection tool called

EFISCEN-space.

The EFISCEN (European Forest

Information SCENario) model, specifically the EFISCEN-space variant, is a spatially explicit forest

model developed to assess the future development of forests at regional to European scales. It

simulates forest growth and dynamics based on inventory data and user-defined management rules,

allowing for the analysis of different forest management and policy scenarios. The model accounts

for various factors such as age class distribution, volume, increment, and forest management

practices, making it a useful tool for predicting forest growth, timber production, and carbon

sequestration under various scenarios. The "space" component in EFISCEN-Space enhances the

model by incorporating spatially explicit information (i.e. plot locations), enabling more detailed

analyses of spatial patterns and processes in forest ecosystems. The foundation for setting up the

model was made on a study visit to Wageningen in November 2023.

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Similarities and deviations from previous projections are described and justified in this report, and it

is explained why the chosen projection method is expected to provide a more accurate projection

towards and beyond 2030. The deliverable also includes a brief description of alternative projection

models, based on preliminary work with a larger project about forecasts for the forests'

contributions to climate and climate accounts.

The results of this study / project are partly based on the EFISCEN-Space model. We acknowledge

the use of the EFISCEN-Space model as developed by Stichting Wageningen Research, Wageningen

Environmental Research and Wageningen University, Department of Environmental Sciences since

2013.

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Content

1.1

INTRODUCTION .......................................................................................................... 7

Aim ...................................................................................................................................................................... 8

2.1

2.2

2.3

CARBON POOL PROJECTION................................................................................... 9

Previous projections of Danish forest resources and carbon pools ................................................................ 9

International projection models ...................................................................................................................... 11

EFISCEN-Space model .................................................................................................................................... 13

3.1

3.2

3.3

MATERIALS ............................................................................................................... 15

The Danish National Forest Inventory ........................................................................................................... 15

National Inventory data 2018-2022 ................................................................................................................. 16

Projections of Danish forest carbon pools and emissions with the EFISCEN-Space model system ......... 17

Growth model ................................................................................................................................................ 18

Harvest probability ........................................................................................................................................ 19

Designation of areas for nature protection ..................................................................................................... 20

Mortality ........................................................................................................................................................ 25

Afforestation .................................................................................................................................................. 26

Ingrowth and reforestation ............................................................................................................................. 27

Dead wood ..................................................................................................................................................... 28

Litter .............................................................................................................................................................. 28

Harvested wood products (HWP) .................................................................................................................. 28

3.3.1

3.3.2

3.3.3

3.3.4

3.3.5

3.3.6

3.3.7

3.3.8

3.3.9

4.1

RESULTS ................................................................................................................... 29

Effects of forest management and programme settings ................................................................................ 31

5.1

DISCUSSION ............................................................................................................. 35

Forest carbon projections ................................................................................................................................ 35

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5.1.1

5.1.2

5.2

5.2.1

5.3

5.3.1

5.4

5.5

5.6

Forest growth models .................................................................................................................................... 38

Uncertainties .................................................................................................................................................. 38

Connection with greenhouse gas reporting .................................................................................................... 41

Comparison to previous projections .............................................................................................................. 43

Forest carbon projection methods .................................................................................................................. 46

Simple projections ......................................................................................................................................... 46

Future development ......................................................................................................................................... 47

Assessing actual climate effects ....................................................................................................................... 49

Concerns Related to the Discontinuation of the National Forest Inventory................................................ 50

REFERENCES .................................................................................................................. 53

APPENDIX ................................................................................................................. 55

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Introduction

Forests play a pivotal role in the global climate system, acting as significant carbon sinks that

absorb and store carbon dioxide (CO

) from the atmosphere, thus mitigating the impacts of climate

change. The ability of forests to sequester carbon makes them invaluable in the fight against rising

global temperatures and the associated adverse effects on ecosystems and human societies.

However, this capacity is not static and it is influenced by a myriad of factors including forest

management practices, land-use changes, and natural disturbances, all of which can transform

forests from carbon sinks to sources of emissions.

In this context, projections of forest carbon stocks and emissions become crucial. They offer a

window into the future, enabling us to anticipate how different scenarios – ranging from business-

as-usual to wide spread management for nature conservation – might play out in terms of forest

health, biomass, and carbon sequestration capabilities. Such foresight is invaluable for policymakers

and environmental planners as it provides a scientifically grounded basis for formulating strategies

and policies aimed at climate change mitigation.

By understanding potential future states of forest ecosystems, decision-makers can better design

policies that not only protect these critical natural resources but also optimize their role in

sequestering carbon. This is essential for meeting international climate targets, such as those set by

the Paris Agreement, and for developing national strategies that align with sustainable development

goals. Thus, forest carbon stock and emissions projections are not just academic exercises; they are

essential tools for guiding global efforts towards a more sustainable and climate-resilient future.

This report presents a projection of forest carbon stocks and related emissions, offering insights into

the dynamic interplay between forest ecosystems and atmospheric carbon levels. Our findings aim

to inform policymakers, environmental scientists, and forest managers, providing a scientific basis

for sustainable forest management practices and climate change mitigation strategies. Through

comprehensive data analysis and modelling, this report underscores the role of forests in global

carbon cycling and the importance of informed decision-making in preserving these natural

resources for future generations.

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1.1

Aim

The aim of this report is to make forest carbon projections to provide estimates of CO

emissions

from Danish forests, including the five principal forest carbon pools as well as harvested wood

products. The aim is further perform a first evaluation of model results in comparison to observed

historical emissions.

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2.1

Carbon pool projection

Previous projections of Danish forest resources and carbon pools

Previous projections of forest resources, carbon pools, and greenhouse gas (GHG) emissions were

based on Markov chain models. Markov chain models are a type of stochastic model that can be

used to project changes in the age class distribution over time. These models involve transition and

conversion probabilities that describe the likelihood of the forest moving from one condition (e.g.,

age class or species composition) to another in a given time period. The probability that the forest

area is transferred to the subsequent age class after a given period is termed the transition

probability whereas the net flow to or from the species classes is termed the conversion probability.

Early projections [1-3] were based the observed species and age-class distribution from

questionnaire surveys conducted by Statistics Denmark. In these studies, 10-year transition

probabilities were derived from the changes in species and age-class distributions observed from

consecutive surveys. For each species class, the aggregated probability that the forest area was

harvested at any given point in time was modelled from the observed area transition and the area

weighted site class in each county, using a logistic function. When applying the model, areas

transferred to the subsequent age-class were estimated as the conditional probability of surviving

into the next age-class (the transition probability) while areas transferred to the youngest age-class

was estimated as one minus the transition probability. The conversion probability was assumed to

be 0. Afforestation was assumed to always enter into the youngest age-class. The development of

forest growing stocks and harvest volumes were modelled from a mathematical formulation of

existing yield tables for the most common Danish forest tree species.

In later projection models, the transition probabilities were modelled from transfers between age-

classes observed on the permanent plots of the Danish National Forest Inventory (DNFI) [4-7]. In

the most recent projection of forest carbon emissions, the survival probability model was estimated

from data collected between 2002-2020 [4], reflecting the management of Danish forest land during

this period. The model used forest age, forest type (deciduous, coniferous, or Christmas

trees/ornamental greenery), and region (Jutland or the Islands) to predict the likelihood of a forest

area progressing to the subsequent age class. Here, growing stocks and harvested volumes were

estimated from observed growing stocks of the sample plots, rather than being modelled from yield

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tables. The model furthermore included explicit modelling of afforestation and changes to forest

management resulting from the setting aside areas for biodiversity protection.

The Danish Forest Inventory data pose some challenges to the modelling using Markov chain

models as the data is much more detailed and comprehensive than the data from the earlier

assessments, reflecting the actual state of the forest to a much larger degree. Consequently, albeit

being used for more than three decades, modelling forest development from transition probabilities

has some inbuilt shortcomings in relation to contemporary forest management practices:

1) According to the DNFI data, large part of the forest is being managed according to other

principles than the clear-cutting system prescribed in the model, where harvested areas are

always transferred to the first age-class. Although the model could principally be built to

represent transfers to other age-classes, the complexity of the model increases dramatically.

2) To be operational, in the Markov-chain model each forest plot is represented by one

dominant species with one stand age in the model. However, a large proportion of the forest

area includes mixtures of tree species having different ages. Those forests are not

represented particularly well by the species and age-class specific model.

3) The transition probabilities have traditionally been modelled using different forms of

logistic models with a log-linear combination of parameters. Seemingly, the actual patterns

deviate from the model with a shape that is difficult to reproduce. Moreover, transitions at

stand level are rare occurrences and the data available is insufficient to capture the actual

system behaviour.

4) Age used in the models to determine harvesting probability is not the principal harvesting

criteria, which is rather being determined by tree size that is closer related to the resulting

forest products.

To summarize, the previous approach to forest carbon projections has become increasingly

inadequate owing to changes in contemporary forest management and forest structure. To alleviate

the problems with the Markov chain models, a shift in modelling approach towards projections

based on the growth and transition probabilities related to the individual tree, rather than the

regional or national species and age class distribution, is required. When the original models were

developed, this approach was not possible, as no model yet existed that could make such individual

tree projections on a national scale, and long-term data of sufficient resolution was not available.

However, the accumulation of Danish NFI data over two decades now represents a rich time series

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of data making it possible to estimate these individual tree probabilities, and continued

advancements in computing power have enabled a new generation of models that is capable of

handling such tasks. To select an appropriate model for these projections, a Europe-wide analysis

was conducted with several criteria to identify an appropriate platform for the Danish forest carbon

projections.

2.2

International projection models

Forest projection tools are essential for understanding the dynamics of forest ecosystems, predicting

future changes, and aiding in sustainable forest management and policy making. Consequently, a

wealth of mostly local or national forest projection models have been developed but they are rarely

distributed outside their region of origin. However, a number of forest projection models have been

designed for and used at larger scales and across different climatic and geographical regions. Three

commonly used tools in this domain are EFISCEN, EFISCEN-space, EUREKA, and CBM. Each of

these models has unique features and applications.

EFISCEN (European Forest Information SCENario model) and EFISCEN-space

EFISCEN [8, 9] is a large-scale forest model that projects forest development at regional to

European scales based on national forest inventory data and scenarios of forest management. It

focuses on the volume and biomass of forest stands, considering different tree species, age classes,

and management regimes.

The first versions of the EFISCEN model [8] were built much like the earlier Danish projection

models relying on Markov chains to model age-class distribution development. More recently, the

modelling concept has been changed to rely on single tree observations in the novel EFISCEN-

Space model [9]. This model relies on individual tree observations from forest inventory data

typically from the sample plots of national forest inventories to project forest growth, thinning, and

felling. By making changes to the individual tree harvesting probabilities or climate attributes, the

model can analyse various scenarios related to forest management, climate change, and policy

impacts. Such changes may be applied locally, allowing differentiated treatments according to local

conditions. Outputs include timber volume, biomass, carbon storage, and potential wood supply.

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EUREKA (European Forest Ecosystem Research Network)

EUREKA is not a single model but rather a network that facilitates the use and development of

various forest models and tools across Europe. It aims to support research, policy-making, and

sustainable forest management by providing a platform for sharing knowledge and methodologies.

The collaborative platform integrates different forest modelling approaches and tools and supports a

wide range of research themes from forest growth and yield to biodiversity and other ecosystem

services. The emphasis on collaboration across different research spheres facilitates data exchange,

methodological standardization, and the application of best practices in forest modelling.

EUREKA mainly supports research and academic studies on forest ecosystems and their

management. The model network has however been used for policy support and decision-making

through the integration of various modelling tools and approaches.

CBM (Carbon Budget Model of the Canadian Forest Sector)

The CBM is a forest carbon accounting model developed by the Canadian Forest Service. It is used

to estimate carbon stocks and stock changes in forest biomass, dead organic matter, and soil carbon

pools under different land-use scenarios and management practices.

The CBM offers detailed accounting of carbon fluxes in forest ecosystems, including emissions

from disturbances like fires, harvesting, and natural disturbances. The model can be applied at

various scales from stand-level to national inventories and supports scenario analysis for forest

management, land-use change, and climate change impacts.

The CBM is tailored for national and sub-national greenhouse gas reporting and carbon accounting.

Furthermore, the model offers potential for making research on forest carbon dynamics and the

impact of management practices on carbon sequestration.

Each of the three tools presented above, offers detailed projections based on inventory data and

contribute to a comprehensive understanding of forest carbon dynamics hereby enabling valid

projections for forest carbon stocks and emissions. However, when selecting the most appropriate

model, we set up a set of criteria including that the candidate model should:

1) have been successfully tested under European conditions and initialised using data from

more than one country,

2) be freely available. This excluded proprietary models designed for country-specific

circumstances or data sources as they would likely require further effort to implement,

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3) be able to project forest development under different management scenarios, enabling the

consideration of planned management changes in the state forests,

4) be capable of handling mixed stands and growth and development of unmanaged forests.

5) make use of the multi-decade dataset from the Danish NFI, and

6) enable the use of Danish-specific volume and biomass functions.

Based on these criteria and the time available for this study, we chose to use the EFISCEN-Space

model.

2.3

EFISCEN-Space model

The EFISCEN-Space model [9] represents a state-of-the-art, spatially explicit model designed for

comprehensive simulations of forest dynamics, management interventions, and policy scenarios.

Developed collaboratively by European forestry research institutions, EFISCEN-Space integrates

advanced ecological, economic, and social components to provide a nuanced understanding of the

intricate interplay between forests and anthropogenic influences.

At its core, EFISCEN-Space utilizes a dynamic, individual-tree-based approach to simulate forest

stand development over time from national forest inventory sample plots (Figure 2.1). The model

captures the growth and mortality of individual trees, considering factors such as tree species, age,

and environmental conditions. By employing a spatially explicit grid, EFISCEN-Space enables

detailed assessments of forest dynamics at regional and national scales, allowing for a more

accurate representation of diverse ecosystems.

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Figure 2.1. The EFISCEN-Space matrix model (from [10]). The modelling of tree increment includes a large set of

geographically explicit factors such as climate and soil conditions that enables adaptation to local growing conditions.

EFISCEN-Space's temporal dynamics are driven by a combination of ecological processes,

including natural disturbances such as wildfires, storms, and insect outbreaks. The model

furthermore enables the integration of climate data to project the impact of future climate scenarios

on forest growth and composition. Additionally, land-use changes, reflecting both societal and

economic influences, are incorporated to assess the consequences of evolving human interventions

on forest landscapes.

One of EFISCEN-space's notable features is its ability to simulate various forest management

scenarios. The model incorporates parameters related to thinning, harvesting, and regeneration,

allowing for the exploration of different management strategies and their implications on forest

structure and composition. This functionality is crucial for evaluating trade-offs between competing

objectives, such as maximizing timber yield while maintaining ecological integrity.

EFISCEN-Space's versatility extends to its capacity for simulating multiple ecosystem services.

Beyond timber production, the model enables assessment of biodiversity, carbon sequestration, and

water regulation, providing a comprehensive perspective on the multifaceted contributions of

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forests to society. This holistic approach facilitates the development of policies that prioritize

sustainability and balance diverse societal needs.

In its inaugural years, EFISCEN-Space has demonstrated its utility in informing forest management

practices and policy decisions. Ongoing technical refinements, calibration efforts, and validation

exercises continue to enhance the model's accuracy and reliability. As EFISCEN-Space evolves, it

stands as a powerful tool for addressing the complex challenges associated with sustainable forest

management, contributing to the advancement of resilient and adaptive strategies for European

forests in the face of changing environmental conditions.

3.1

Materials

The Danish National Forest Inventory

The EFISCEN-Space model at its core is developed and initiated from national forest inventory

data. The Danish National Forest Inventory is based on a nationwide 2 x 2 km grid [11]. In each of

the grid cells, a cluster consisting of four sample plots is placed in the corners of a 200 x 200-meter

square. All clusters are measured over a five-year period, with one-fifth of the sample plots evenly

distributed across the country being measured each year. One-third of the groups are permanent and

are located in the southwest corner of the grid cells. These are re-measured for each five-year

rotation of the forest statistics measurements. Two-thirds of the groups are temporary and are

randomly moved within the respective 2 x 2 km cell in the grid for each repetition of the five-year

rotation. With particular reference to the present study, the permanent plots may be used for

assessing growth, probabilities of natural mortality and ingrowth, and management activities related

to harvest of trees and planting of trees.

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Figure 3.1. Design of the Danish NFI [11].

The sample plots in the forest statistics are circular and have a radius of 15 meters. In total, there are

approximately 43,000 sample plots in the network, with only forest-covered sample plots being

measured over a five-year period. The forest-covered sample plots are identified before each

measurement season based on the latest aerial photos (typically less than one year old). In the forest,

each individual sample plot is located with high geographical precision, allowing for accurate

remeasurement of permanent sample plots as well as linkage with other geographical registry

information. In each sample plot, measurements of many variables are taken, including

measurements of tree size, age, and species, quantity of deadwood, and thickness of the litter layer

(forest floor branches, leaves, etc.), which are among the key factors for estimating forest carbon

pools.

3.2

National Inventory data 2018-2022

The National Forest Inventory data from the latest rotation of measurements is of particular

importance to the carbon pool projections as it forms the baseline and starting point [12]. The

measurements totalled 9.693 sample plots within clusters for which at least one of the sample plots

had forest cover (Table 3.1). Of the total amount of sample plots, 33 % were within permanent

clusters and in most cases remeasured from earlier rotations.

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Table 3.1. Number of measured clusters and sample plots in the five-year rotation 2018-2022.

Year

2018

2019

2020

2021

2022

Total

Clusters

Total

2.191

2.186

2.190

2.175

2.207

10.949

Forest

903

844

887

883

879

4.396

Plots

Total

8.586

8.597

8.569

8.528

8.643

42.923

Forest

2.018

1.896

1.886

1.951

1.942

9.693

During the measurements in the 2018-2022 cycle, 114,426 trees were measured for diameter in the

NFI. The diameter distributions for broadleaves follows a log-linear pattern in which there are many

more small trees than large. This pattern is expected but to some extent influenced by the sample

plot design, where larger trees have a higher probability of being measured.

Figure 3.2. Diameter distribution for broadleaves and conifers based on the 2018-2022 rotation of measurements with

the Dansh NFI. Note the log-scale on the y-axis.

3.3

Projections of Danish forest carbon pools and emissions with the EFISCEN-

Space model system

In EFISCEN-space, the plot specific diameter measurements and species registrations are expanded

to a per hectare diameter distribution used to initialize the projection. Owing to the design of the

Danish National Forest Inventory, where trees with a diameter at breast height of less than 10 cm

are only measured in the inner 3.5 m radius circle, including plots with only partial forest cover

would result in diameter distributions lacking smaller trees. Therefore, we only included plots

where the plot centre had forest cover in the simulations.

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While the National Forest Inventory data was used for initializing the EFISCEN-Space model, it

was also used for training the individual modelling components including harvest and natural

mortality probability models.

Although we adopted the core EFISCEN-Space model unchanged, the model was executed through

a SAS (Statistical Analysis Software) script that enabled the model to run repeatedly in 5-year

cycles (Figure 3.3). This enabled us to add forest area to the model runs every 5 years to account for

afforestation, as well as apply changing management scenarios over time as described for the state

forest below. The SAS script further allows the application of Danish volume and biomass

calculations to the raw output of plot development from the EFISCEN-Space model. A brief

description of key model components and specific setup for the projections in this report follows.

Figure 3.3. Brief description of the application of the EFISCEN-Space model for carbon pool projections in Denmark.

3.3.1

Growth model

EFISCEN-Space utilizes an individual-tree-based growth model to capture the dynamic

development of forest stands over time. EFISCEN-Space uses a Gompertz model, describing a

sigmoid growth pattern [13]. The Gompertz model is defined as:

��

(

��

) =

�� ∙ ��

−��∙��

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Where D(t) represents the diameter of a tree at time t, A is the upper asymptote, which represents

the maximum achievable diameter, b and c are parameters that influence the shape of the curve, and

e is the base of the natural logarithm. The Gompertz model and its derivatives have been widely

applied in forestry and ecology to understand and predict tree growth. The model is flexible and can

be adjusted to fit different species and environmental conditions.

The derivative of the Gompertz model describes the rate of change in diameter (or diameter growth)

at any given point in time. The model is estimated from repeated NFI tree measurements of 2.3

million trees across Europe and considers factors such as tree species, age, competition among

trees, and local environmental conditions such as temperature and precipitation to simulate the

annual tree diameter growth [13]. At the time of the present study, the growth model had not been

fitted including Danish data and the simulations relied on the breadth of data collected across

Europe.

Figure 3.4. Examples of forest growth curves with the derivative of the Gompertz model. Note that the modelled growth

is specific for each plot location, species, stand conditions, and diameter class. In this case, modelling is made for a

stand basal area of 20 sq. m and for a tree that resembles the stand quadratic diameter tree. Also note that the growth

pattern her for Betula sp. and Quercus robur are very similar and that the two growth curves cannot be distinguished in

the graph.

3.3.2

Harvest probability

EFISCEN-Space integrates a detailed harvest probability model to simulate the impact of forest

management interventions. To this end, a matrix specifies annual species and diameter class-

specific harvest probabilities. These may be user specified, modelled, or simply extracted from

repeated NFI measurements to reflect observed patterns (Figure 3.7). Harvest probabilities may be

specified for individual plots reflecting e.g. geographical differences or may be generic across all

parts of the country.

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Harvest probability is commonly affected by tree species, size, and age as well as by overall

management objectives and intensity. The harvest model incorporates the impact of these activities

on individual trees, assessing their susceptibility to removal based on size, species, and management

intensity. The harvest probabilities may be defined based on management rules, estimated based on

statistical analysis or may simply be included as a species and size class specific harvest probability

observed from repeated NFI measurements.

For this report, we extracted national harvesting probabilities from the repeated measurements in

the Danish NFI (2002-2022) to represent the historical harvesting probabilities for each species

(Figure 3.5). The harvesting probabilities show some erratic and much fluctuating patterns,

reflecting the often-few observations especially in large diameter classes. In this study, we opted to

use the observed probabilities directly when making the projections. This is something we could

have modelled to produce smooth curves, but we opted to keep the projections as close to actual

data as possible.

Figure 3.5. Observed annual harvest probability curves of the most common broadleaf and conifer species and species

groups in the Danish forests. Harvest probabilities for large diameter classes, depending on species, are based on

expert judgement owing to the lack of observations in the data.

3.3.3

Designation of areas for nature protection

Different probability matrices may be applied to individual plots to account for different

management scenarios. In this way, the model may account for differences in ownership, regulatory

constraints, and conservation objectives, which affect the likelihood of harvesting in specific areas.

For the relatively short period of the projections, the future harvest will largely be determined by

the current forest structure and trees already present in the forest, and it is unlikely that overall

management priorities will change in the private sector. When projecting future forest

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management, we therefore applied the assumption that management and hence harvesting

probabilities in privately-owned forests will remain similar over the projection period.

However, the case is different for the state-owned forest, where under current policy there will be

significant changes in the management of much of the forest area. The plans to designate large parts

of the forest to be unmanaged (or managed without wood production) should be accounted for in

the model, as the conversion of these areas will occur during the period of our projections. This

includes areas planned to be set-aside as Unmanaged Forest (Urørt

skov)

as well as areas designated

to be Nature National Parks (Naturnationalparker). After consultation with the Danish Nature

Agency, a future management matrix was developed for the designated areas within the state

forests. This enabled us to specify harvesting probabilities for designated areas for the next 25 years

while accounting for the differing timelines for the transition of the designated areas between the

western and eastern part of the country (Figure 3.6).

First, areas owned by the Nature Agency were identified on a map and each NFI plot part of the

projection was assigned a category according to the specific designation. For this projection, we

identified five different categories including 1) Managed forest (i.e. not designated for nature

protection), 2) Nature National Park, east, 3) Nature National Park, west, 4) Unmanaged Forest,

east, and 5) Unmanaged Forest, west. Differences in harvest probabilities between east and west

Denmark are driven by different lengths of the transition period in the two parts of the country.

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Figure 3.6. Regions identified by the Nature Agency to designate differing timelines for the transition of the state forests

to Nature National Parks and Unmanaged Forest between the western (blue, typically 25 years transition) and eastern

(green, typically 6 years transition).

For the entire projection period, we assumed that areas of the state forest that have not been

designated for nature protection will continue with the national historical harvesting probabilities as

earlier described. This differs from the projections made in relation to Climate projections 2023 [4]

in which a 20 pct. decrease in harvesting levels in the state forests was assumed as part the frozen

policy scenario. However, part if the effect of reduced harvesting in the state forests will be

observable in the harvesting probabilities. For the designated areas, we have implemented a matrix

as described below (Table 3.2).

In the first five-year period of the projection (from 2022 to 2027), we assumed that exotic species,

largely understood as species exotic to Denmark and Northern Europe, of 40 cm or more in

diameter at breast height (DBH) will be harvested in all set aside areas. Otherwise, we assumed

harvesting according to the historical probabilities for both the exotic conifers and all other species,

taking into account that some harvest will be made to create a desired forest structure prior to the

setting aside (e.g. gap creation, removal of undesired species, or altering understorey light

conditions).

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In the eastern part of the country (Figure 3.6), we assume harvesting of all exotics irrespective of

their diameter in the following five-year period (2028 to 2032) and onwards. In this region, we

furthermore assume that all other harvesting ceases.

In the western part of the country, we assume a similar pattern, but the conversion period is 25

rather than 5 years. Hence, in the following five-year period (2028 to 2032) and onwards, we here

maintain a 40 cm target diameter for the exotic species and otherwise assumes continued harvesting

according to the historical probabilities. This regime continues until the final run beginning in 2043,

when all non-native conifers of all diameters are also removed in the western part of the country.

Harvest of native tree species (including Norway spruce, larch, mountain pine, and silver fir native

to northern Europe) is expected to gradually cease in the initiation phase. We therefore set the

harvesting probabilities to one quarter of the probabilities observed in the normal harvesting regime.

The harvesting ceases completely after the conversion is completed (after year 2026 in eastern

Denmark and after year 2032 in western Denmark). We are aware that in some areas, actual harvest

strategies may much different from the above described e.g. aiming at creating gaps in the forest

canopy or removing undesired tree species in specific areas. However, such specific modelling was

not possible within the current project although the EFISCEN-Space model would in principle

allow for it.

Deforestation within areas designated for nature protection

Setting-aside forest for both Nature National Parks and Unmanaged Forest has modelling

implications beyond merely the effect on harvesting probabilities described above. The

management required to prepare forest areas to be set-aside for biodiversity protection can be

extensive and commonly involves a variety of actions such as restoring natural hydrological

conditions or historical landscapes, conducting harvests to create a favourable forest structure,

veteranization of trees, and the introduction of grazing by larger animals such as cows and horses.

These measures likely heavily impact the forest carbon pools but are equally difficult to describe in

a modelling context. There is also a general lack of data regarding the potential future development

of these forest types in Denmark.

In this case, we opted to simulate the loss of forest owing to restoration of hydrological conditions

by converting 20 pct. of the Norway spruce dominated forest plots (in the state forest areas to be

set-aside) to non-forest during the conversion period (i.e. 5 years in the eastern part of the country

and 25 years in the western part of the country).

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We furthermore assumed that 50 pct. of

Pinus mugo

and

Abies alba

dominated forest will be

converted to Atlantic heathland as part of the conversion (Table 3.2).

In the simulation, plots corresponding to the 20 and 50 pct. of the forest area respectively were

removed at random from the simulations and the trees on the plots were considered harvested,

simulating that the area was clearcut prior to flooding or other restoration of the landscape.

Table 3.2. Modelling the transition of areas designated for biodiversity conservation as Nature National parks and

Unmanaged Forests within areas owned by the Nature Agency. Areas not designated for nature conservation are

assumed to be managed according to the observed harvesting probabilities in the NFI and similar to forest not owned

by the Nature Agency. The table has been developed in close dialogue with the Danish Nature Agency. Grey shaded

areas show species where harvesting probability is reduced to ¼ of normal harvest probabilities during the conversion

period.

Eastern

Denmark*

Conversion

(years)

Western

Denmark*

Conversion

(years)

Deforestation

Harvesting

probability

after

conversion

(%)

100

Target

diameter

prior to

conversion

(cm)

Abies. sp.

Larix sp.

Picea abies

Picea

sitchensis

Pseudotsuga

menziesii

Pinus

sylvestris

Pinus nigra

and mugo

Other Pinus

Other

conifers

Betula sp.

Castanea

sativa

Fagus

sylvatica

Robinia

pseudoacacia

Quercus

robur and

petraea

Long-lived

broadleaves

Short-lived

broadleaves

Harvesting

probability

after

conversion

(%)

100

Target

diameter prior

to conversion

(cm)

* According to the map in Figure 3.6.

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3.3.4

Mortality

The mortality component of EFISCEN-Space accounts for natural causes of tree death. In a setup

reflecting current management practices, a matrix specifies species and diameter class-specific

mortality probabilities much in the same way as the harvest probabilities. These may be user

specified, modelled, or simply extracted from repeated NFI measurements to reflect observed

patterns (Figure 3.7). Mortality matrices may be specified for individual plots reflecting e.g.

geographical differences or may be generic across all parts of the country.

To simulate the development of the Danish forests, natural mortalities were extracted from repeated

measurements in the Danish NFI (2002-2022) and used to derive historical annual mortalities for

each species (Figure 3.7). These mortalities reflect current forest practices and can be considered to

reflect minor changes in abiotic factors likely occurring for a relatively short projection period. In

initial runs of the model, we opted to use the observed mortalities from the Danish NFI as the basis

for our projections. These were manually adjusted by expert opinion where there were a limited

number of observations for mortalities of a given species and diameter.

Figure 3.7. Observed mortality curves of the most common broadleaf and conifer species and species groups in the

Danish forests. Mortalities for large diameter classes, depending on species, are based on expert judgement owing to

the lack of observations in the data.

Later adjustments of the underlying assumptions of forest management on set aside areas for

biodiversity protection in the areas owned by the Nature Agency is likely to alter the mortality

owing to low or absent harvest affecting stocking severely within the projection period. To

accommodate the likely increase in competition resulting from reduce harvesting, we opted to use

the inbuild dynamic mortality functions in the EFISCEN-Space model [14] in the final simulations.

These models include density parameters and the result of one-sided competition from trees larger

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than the subject trees and may therefore better accommodate simulations with altered basic

conditions.

3.3.5

Afforestation

In the EFISCEN-Space model, afforestation can be simulated implicitly in the model, using the

included ingrowth module to produce growth on empty plots. However, to accommodate a more

realistic and data driven representation of afforestation, we simulated the afforestation through

imputation of sample plots corresponding to the desired afforestation area at the end of every 5-year

rotation of the model. Imputed plots were selected at random from a sample of 388 reference

afforestation plots less than 10 years old identified from the NFI data.

The selection of imputation samples is conducted via unrestricted simple random sampling with

replacement from the reference afforestation plots. The number of sampled plots correspond to the

anticipated afforestation divided by the area represented by each NFI sample plot (~100 ha). The

imputation involves allocating these selected plots onto non-forested NFI sample plots of the 2018-

2022 rotation of measurements. Imputation was only allowed on plot locations where afforestation

is desired according to municipality plans. The imputation process accounts for both spatial

allocation and, if relevant, temporal dynamics, ensuring a robust representation of afforestation

scenarios in the model.

Historical afforestation levels were determined as the sum of afforestation (both regular forest and

Christmas trees) subtracted the annual deforestation reported in the national inventory report [15].

Historical afforestation from the national greenhouse gas inventory differs from then estimates

obtained in the National Forest Inventory [12] owing to statistical uncertainty as well as due to a

need in the inventory reporting to match the forest area with other land-use types. Hence, the

national greenhouse gas inventory to a larger degree relies on cadastral records rather than actual

observations leading to slightly different forest definitions in the two inventories. For simulations

onwards, we used a frozen policy scenario for the afforestation supplied by the Environmental

Protection Agency (Table 3.3).

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Table 3.3. Historical and projected afforestation levels based on a frozen policy approach. For the historical

afforestation, we collected all private and public afforestation in the column “Private/Total” as the figures are derived

from the land-use matrix underlying the emissions reporting in which the ownership is unknown.

Year

Public

Private/Total

Climate

forest fund

Year

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

100

480

2046

2047

270

210

2570

880

Public

Private

Climate

forest fund

700

660

870

820

1000

1200

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

280

300

4110

3372

1537

4641

364

2115

678

1107

1130

1248

1800

4887

1485

2000

3.3.6

Ingrowth and reforestation

The data from the NFI includes areas that have been recently harvested and are temporarily

unstocked which are likely to become re-stocked with trees. Furthermore, EFISCEN-Space

produces empty plots when the simulation results in all trees on a plot becoming dead or harvested.

As EFISCEN-Space utilizes an observed diameter distribution to produce the projection, plots with

no trees cannot be projected into the future unless the plot is populated with new trees.

The problem is similar to introducing afforestation as afforested plots initially have no trees to be

projected into the future. An option would be to use a similar approach as for afforestation,

populating the plots using imputation from known reforested plots, or otherwise specifying the

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number of replanted trees of a certain species. As it is difficult to make realistic assumptions on

future species composition reflecting local conditions, we opted for the default EFISCEN-Space

process which regrows the species most recently present on the sample plot. In cases where there

are no trees present on the plot at the initialization of the model, the model re-populates the plot

with a “short-lived broadleaves” species group (such as rowan, birch, and aspen).

3.3.7

Dead wood

The EFISCEN-space model outputs species and diameter distributions of trees dying in the

projection period. Although it would be possible to estimate the inflow of dead wood to the carbon

pool, the outflow in terms of degrading wood is largely unknown. As the dead wood pool is

relatively minor to the other forest biomass pools, we considered it outside the scope of this project

to attempt advanced modelling of this aspect of forest carbon dynamics. We therefore assumed an

unchanged level of dead wood, well aware that the activities in the set aside forests will likely

increase the amount of dead wood locally, but also expecting that the overall effect on the forest

carbon pool will be relatively minor.

3.3.8

Litter

The YASSO-model tailored for modelling soil carbon pool development may run within EFISCEN-

space providing estimates of forest litter carbon pool development. However, we found that the time

was too limited to test the results for Danish conditions. Instead, we opted to use a constant litter

pool owing to the short projection horizon.

3.3.9

Harvested wood products (HWP)

The EFISCEN-space model outputs species and diameter distribution of harvested trees. In this

projection, we estimated the biomass in harvested trees in the same way as estimating carbon stocks

in live biomass using species specific d/h-functions and national biomass functions [16].

Recognizing that contemporary forest management often involves harvesting of the entire above

ground biomass (including branches), we estimated biomass in harvested timber from the projected

above ground biomass and the share of timber (46.8 pct. for conifers, 14.1 pct. for broadleaves) in

the national harvest statistics reported by Statistics Denmark. The inflow of biomass in HWP was

subsequently calculated from the cutting yield observed in Danish sawmills (42.3 pct. for conifers,

42.4 pct. for broadleaves). As for previous projections, we did not make assumptions on exported or

imported quantities of round-wood, which are not reported in the sourcing country while the sawn

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volumes are part of the HWP pool. For the period 2020-2023, imported and exported amounts were

reported at 224,000 and 137,000 tons, respectively. Given that these figures are reported in kg’s by

the importing party with an unknown moisture content, the uncertainty involved in making more

detailed assumptions was evaluated to be prohibitively high.

Emissions from HWP were estimated using the methodology developed for the national greenhouse

gas reporting using the inflow of biomass from the EFISCEN-space model and previously

determined product half-lives to determine the outflow from this pool.

Results

The model predictions of forest carbon stocks in above-ground biomass, below-ground biomass,

and total biomass showed a continuous uptake of CO

in the forests during the entire projection

(Figure 4.1). Mortality started slightly higher than in the later parts of the projections but declined in

the first simulation period to around 1.5 mi. tons CO

-eqv.

Harvesting levels peaked at ~6 mi. tons CO

-eqv. during the first five-year simulation cycle and

hereafter declined to an ultimate low at ~4 mi. tons CO

-eqv. During the remainder of the

simulations, harvest levels are projected to increase, reaching a level similar to the levels observed

in the first cycle of simulations. The harvest fluctuates across the years, likely owing to spikes in the

harvest probabilities as well as in the diameter distribution. However, the projections show a cyclic

pattern which is largely caused by the way the EFISCEN-Space is set up. As the model does not

allow harvesting of the same plot twice in each 5-year run, a random parameter assigns a time for

harvesting each plot. This time (from the beginning of each cycle) is repeated on each rotation of

the model causing a cyclic pattern. This pattern is further exaggerated by the five-year rotation of

the model, where afforestation is added to the forest area and deforestation is simulated by

harvesting and removing plots at the beginning of each rotation.

The resulting projected emissions indicated a continuous uptake in the forest, that was relatively

low in the first 5-year period owing to the larger harvests projected in this period. Later, emissions

projections total an average CO

-uptake ranging between 3 and 3.5 mi. CO

-eqv. slightly increasing

as a result of the increasing forest area.

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Figure 4.1. Carbon stocks, mortality, harvest, and emissions expressed in CO

-eq. Projections are made from NFI data

collected in 2018-2022 forming the 2022 base line.

In general, the broadleaves delivered a net-uptake resulting in negative emissions with the largest

uptake in beech and long-lived broadleaves such as sycamore maple and cherry (Figure 4.2).

Oppositely, many of the conifer species had near zero emissions and in some notable cases even

substantial emissions for Sitka spruce and Norway spruce.

Figure 4.2. CO

emissions distributed to species and species groups Left: broadleaves, Right: conifers.

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The levels and fluctuations of CO

emissions are comparable with historically reported emissions

[15] replicating the current peak in emissions and subsequently declining to a credible net uptake of

1.5-3.0 mi. tons CO

in above and below ground biomass (Figure 4.3). The overall fluctuations are

at a similar magnitude to what is observed in the reported figures, but the annual fluctuations seem

somewhat more erratic owing to the previously described harvesting patterns in EFISCEN-space.

However, it should be noted that in the emissions reporting, figures are smoothened as 5-year

averages and reporting of individual years, such as in the projected numbers in (Figure 4.3) would

be expected to show more variation.

Figure 4.3. Reported ([15], full line) and projected (dashed line) emissions from above-ground (green), below-ground

(blue), and total biomass (red). Left: Projection from a NFI 2022 baseline, Right: Projection from a NFI 2012 baseline.

4.1

Effects of forest management and programme settings

To illustrate the effect of changes to the model input, we changed a number of parameters in the

model settings to illustrate 1) the choice of static vs. dynamic mortalities in the model, 2) the effect

of setting aside forest for biodiversity protection, and 3) the effect of afforestation.

Static vs. dynamic mortalities

In the basic setting, mortalities were projected using the dynamic functions included in the

EFISCEN-Space model and estimated on a pan-European dataset. Realizing that the underlying

model was estimated absent of Danish data, we intended to analyse how this choice affected the

results, by making the projections with a static mortality observed directly from Danish NFI data

(Figure 3.7). Our analyses show that the static and dynamic mortalities produce quite different

mortality levels (Figure 4.4) reflecting that the static model does not adjust to altered forest

conditions e.g. when forest is designated for biodiversity conservation or standing stocks are altered

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owing to projected harvesting. However, differences in mortalities arising from the choice of model

had little effect on overall emissions EFISCEN-Space (Figure 4.4), reflecting the relative minor

importance of dead wood in the overall carbon budget.

Figure 4.4. Carbon stocks, harvest, mortality, and associated emissions for a scenario using a static mortality function

(dashed lines) with the reference scenario using the dynamic mortality function (solid lines) (Figure 4.1).

Setting aside forest for biodiversity protection

When simulating the effect of setting aside forest for biodiversity protection, we made a

counterfactual setting of the model to harvest trees in accordance with the historical probabilities,

although realizing that these observations to some extent includes ongoing conversion of set aside

forests. The simulations indicate a very minor change in carbon pool development and associated

emissions from biomass carbon pools compared to the basic settings (Figure 4.5, Table 4.1). In the

first, 5-year period, harvesting levels were similar for the counterfactual and the reference scenario.

This is likely the result of contrasting effects. On the one hand, reduced harvesting of particularly

native broadleaves increased carbon pools on parts of the forest area, while deforestation and

removal of exotic conifers as the result of nature restoration leads to reduction of carbon pools in

other parts. The setting aside of forest for biodiversity, however, significantly altered the harvesting

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levels in later rotations of the simulations reflecting that no harvesting is conducted on the ~75.000

hectares set aside.

Figure 4.5. Projected forest carbon pools, harvesting, mortality, and associated emissions from a scenario assuming no

designation of forest for biodiversity conservation (dashed lines) compared with the reference scenario (solid lines)

(Figure 4.1).

Afforestation

To isolate the effect afforestation, we maintained all model settings to be similar to the basic setting

(Figure 4.2) including the setting aside forest for biodiversity protection. The simulations resulted in

a total forest loss of 4,566 ha during the simulations owing to the deforestation occurring on the set

aside forest. Compared to the standard settings, the resulting forest area at the end of the simulations

was 28,565 ha or 4.3 pct. lower. Considering that the afforestation will have comparably low

biomass, it is no real surprise that the no afforestation scenario only had slightly lower carbon pools

and hence also slightly higher emissions than the standard scenario (Figure 4.5, Table 4.1).

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Figure 4.6. Carbon stocks, harvests, mortality, and associated emissions for a scenario with no afforestation (dashed

lines) and a comparison with the reference scenario (solid lines) (Figure 4.1).

Table 4.1. Forest area, carbon stocks, and emissions for the reference scenario and for the two scenarios where no

areas is set aside for biodiversity conservation and where no afforestation is carried out. Figures are provided for 5-

year averages with the initial year (2022) as the overall reference.

Forest area

Scenario

Reference

year

2022

2023-2027

2028-2032

2033-2037

2038-2042

2043-2047

2022

2023-2027

2028-2032

2033-2037

2038-2042

2043-2047

2022

2023-2027

2028-2032

2033-2037

2038-2042

2043-2047

642,976

656,222

667,556

667,825

666,975

642,976

658,876

671,060

671,754

642,976

640,640

640,003

639,153

638,410

Biomass carbon stocks Carbon emissions

Harvest

Mortality

Above

Below

Above

Below

Above

Below

Above

Below

ground

ground ground

ground

1,000 tCO

-eq

138,339

30,441

141,330

31,005

-913

-170

4,354

1,000

1,419

361

149,438

32,703

-2,220

-481

3,484

805

1,250

299

162,200

35,501

-2,636

-576

3,670

853

1,243

287

175,630

38,425

-2,720

-590

3,899

909

1,271

286

188,995

41,300

-2,597

-555

4,144

967

1,304

287

138,339

30,441

141,251

30,997

-881

-167

4,384

1,004

1,420

361

149,139

32,667

-2,093

-455

3,732

860

1,251

300

160,614

35,184

-2,361

-519

3,974

918

1,231

285

172,544

37,785

-2,394

-520

4,206

977

1,245

281

184,339

40,326

-2,304

-490

4,420

1,030

1,265

279

138,339

30,441

141,330

31,005

-913

-170

4,354

1,000

1,419

361

149,404

32,706

-2,191

-476

3,482

805

1,245

298

161,474

35,350

-2,492

-545

3,645

849

1,227

283

174,173

38,106

-2,532

-551

3,829

893

1,244

279

186,732

40,825

-2,432

-519

4,055

949

1,269

279

No setting aside for

biodiversity

No afforestation

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5.1

Discussion

Forest carbon projections

The projection indicated in increasing carbon pools corresponding to the levels observed in recent

years [12, 17, 18]. The increase in forest carbon pools was less in the beginning of the projections,

corresponding to an initial peak in emissions in the first five-year period, likely as a result of several

different factors.

Firstly, as evidenced in national reporting on forest statistics [12, 17, 18], the age- and diameter

distribution of trees in Danish forests is skewed (Figure 5.1) with large quantities of mature trees

with a high probability of being harvested according to the historical harvesting probabilities used

in the projection (3.3.2 Harvest probability). This is particularly pronounced for species such as

beech, where prices have been low for several decades, resulting in a build-up of the volume of

mature trees. Consequently, according to national forest statistics (recalculation of figures presented

in [12]), more than 1/3 of the CO

-eq in beech is found in trees with a diameter (measured at breast

height) of more than 60 cm, which would generally be considered mature. Similarly, about 1/4 of

the CO

-eq in Sitka spruce is found in mature trees with a breast height diameter of more than 40

cm.

Recent increase in prices of both broadleaf and conifer timber as well as a favourable market for

forest fuels have resulted in increased harvest levels reflected both in our projections and in the

reported harvesting levels from Statistics Denmark [19]. Also, the conversion of areas set aside for

biodiversity protection in the state forests, which involves clearing of exotic tree species as

simulated in the projection, albeit to a minor degree affects overall harvesting levels and carbon

pools (Figure 4.5).

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Figure 5.1. Carbon pools in above and below ground biomass for broadleaves and conifers. The red vertical line

indicates approximate maturity for most species in the two categories.

In an attempt to compare the harvesting levels reported from Statistics Denmark [19] to our

projected harvesting, we converted the volumes reported from Statistics Denmark to CO

equivalents, using a basic density of 0.55 ton biomass/m

for broadleaves and 0.38 ton biomass/m

or conifers, a carbon density of 0.47 gC/g, and a conversion from carbon to CO

of 44/12. Realizing

that we have no knowledge on the extracted fraction of the total harvest reported to Statistics

Denmark, we compared the reported harvesting expressed in CO

-equivalents to our projected

above-ground biomass. Our results indicated that the projected harvesting is similar to that reported

by Statistics Denmark in recent years, indicating a similar trend (Figure 5.2) albeit a slightly higher

peak in the harvests in the coming years. It should be noted that we expect the projected harvests to

be systematically higher than the values reported by Statistics Danmark, since the projected values

include all above ground parts of the tree, whereas only parts of these are expected to be extracted

and reported to Statistics Denmark.

Whether the projected peak in harvest levels and therefore also in emissions will be observed in the

coming years depends largely on the future price structure of wood products and bioenergy, which

is not reflected in the model. A special challenge to this end is that the NFI data used as the baseline

for the projections was collected during 2018-2022, meaning that some of the trees measured in

2018 and 2019 may have been harvested during the increased harvest in the last years of this period.

Hence, some of the peak observed may in fact reflect harvesting that has already commenced. To

what extent is not possible to say.

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Figure 5.2. Comparison of projected and reported (Statistics Denmark) harvesting levels expressed in CO

-equivalents.

The recalculating of reported volumes to CO

-equivalents is highly uncertain and the ratio of extracted (and sold)

volumes to total harvest volumes is unknown. Therefore, the direct comparison on projected and reported volumes is

uncertain.

Owing to the large amount of mature beech the model projects increased harvest levels in beech and

hence less uptake of CO

in the coming years. Another important finding is that we project a

significant net uptake of carbon in oak. This is presumably due to the extensive use of oak in

afforestation projects in recent decades [12], ensuring a large net uptake in the young forests a long

way from maturity and final felling.

Interestingly the model projects only limited net uptakes in conifers and even periodic emissions

from Sitka spruce and Norway spruce, owing to annual harvests and mortality exceeding increment.

This may have several reasons including good prices on softwood timber in recent years, increasing

the harvest probabilities and therefore also the predicted harvest levels. However, increasing health

problems for these two species in particular caused by extended periods of drought during the

summer, pest such as bark beetles and aphids, and windthrow may also have impacted both the

mortality of the species but also the forest owner’s decision to harvest the two species earlier.

In general, the model provided credible projections of forest carbon pool development and

associated emissions. In particular, the model projected similar patterns of emissions as have been

observed from the national estimates based on forest inventory data.

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5.1.1

Forest growth models

In the current project, the EFISCEN-space model was for the first time parameterized to Danish

conditions, including the setting of harvest and mortality probabilities, and adjusting to local

conditions such as expected afforestation and specific management of forest designated to

protection of biodiversity. During the project, we further attempted to reparametrize the underlying

growth model with observations of tree growth from the Danish National Forest Inventory.

However, we found that despite of the rich data available (about 70,000 trees with repeated

measurements were included), the modelling seemed less robust compared to the in-build growth

model relying on 2.3 mi. trees with repeated measurements observed from a wide range of

geographical conditions across Europe [13].

To enhance the accuracy of EFISCEN-Space model predictions for Danish forest biomass pools,

reparametrizing the model with Danish data is essential. However, as a simple fitting of the models

with Danish data proved insufficient, this entails a full recalibration of the growth models

underlying EFISCEN-Space with the pan-European and Danish data. Such an effort includes also

validation of the models using independent datasets and sensitivity analysis to ensure the reliability

of the reparametrized model. Such an effort was not possible within the current project.

When reviewing the growth patterns simulated in the current version of the EFISCEN-Space model,

we found that some of the models produces simulations inconsistent with current knowledge of tree

growth, such as unlikely late peaking growth or even growth not peaking at all and excessive

growth levels under low or high competition. A likely reason is that although the underlying data

collected from National Forest Inventories across most of Europe has an impressive breadth and

depth, the vast majority originates from forests managed according to some similar standards. This

results in well behaved functional forms under standard conditions but less so when conditions

deviate from the normal. We speculate that data from forest experiments, typically including

deviating forest management and long time series could substantially improve the growth functions

in EFISCEN-Space.

5.1.2

Uncertainties

The projection of forest carbon pools entails a wealth of uncertainties of which we may here only

describe a few.

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Input data

The input data is measured with a very low uncertainty but represents a sample of the Danish forest

area. Earlier studies have demonstrated that the uncertainty of forest carbon pool estimates are small

(0.9 pct.) but also demonstrated that even a proportionally small uncertainty may have a large

effects when applied to large pools. With more than 160 mi. tons CO

-eq stored in the biomass, the

uncertainty expressed as the standard error is around 1.5 mi. tons CO

-eq. As the uncertainty of

projections presented here will always be larger than the direct estimates from actual measurements

a numerically substantial uncertainty should be expected. In particular when considering that

emissions are calculated as the difference between two subsequent and uncertain estimates of forest

carbon pools.

Natural catastrophes

The model used in this study assumes harvesting probabilities and natural mortalities to follow

some previously observed patterns. However, climate change is projected to result in warmer

summers with more frequent droughts, winters with more precipitation and more frequent flooding,

as well as more frequent and heavier storms. It is thus likely that mortality patterns will change

during the projection period as it has been observed in southern and central Europe.

Specifically for Denmark and building on historical observations, it is far from unlikely that we will

see catastrophic windthrow one or more times during the projection period. In the largest-ever

windthrow observed in Denmark, 3.6 million cubic meters of wood were windthrown

corresponding to a similar number expressed in ton CO

-eq. Such a windthrow would significantly

alter the reported emissions from the LULUCF-sector depending on assement method for reporting.

Changed growing conditions and climate change

As stated in the methods section, we opted to use the currently available growth models obtained

from repeated measurements of trees on National Forest Inventory sample plots. However, climate

change is currently altering the conditions for forest growth in Europe [20] and may impact also the

growth of Danish forests. Effects of climate change are expected to be more elaborate in extreme

latitudes and altitudes. Some tree species may increase vitality and growth at higher boreal latitudes

or higher altitudes and the opposite at lower dry and warm locations [21, 22]. Regional growth

trends are less clear in areas currently better suited for tree growth and recent studies report overall

increasing growth trends for European trees [23] and forests [20]. However, despite this general

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pattern, severe drought events and generally changed precipitation patterns in some regions result in

declining vitality and growth of some of the most abundant European forest tree species [24-27].

The cumulative effect of changed temperature and precipitation patterns in Denmark is unknown,

but the EFISCEN-Space model holds the possibility to alter the underlying growth drivers by

changing model factors to simulate growing conditions currently native to other parts of Europe.

However, with the relatively short projection scope used in this study (25 years), we found that

climate change and its effect on forest growth during this period would likely be moderate and

opted to use current climate conditions in the model.

Human behaviour

In the projections, we have assumed that human behaviour related to harvesting of trees follows

historical patterns. This is a far from likely assumption when realizing that societal changes may

heavily affect the way we use the forests. As an example, a change in the Chinese market for beech

wood in combination with heavy windthrow in central Europe caused an abrupt decline in demand

and more than halved the price of beech wood around year 2000. The prices have so far not

recovered entirely and the change in prices has for more than 20 years reduced the harvesting of

beech substantially. As such the harvest is currently around half of what was reported to Statistics

Denmark in 1990-1999 and only on third of the reported figures in 1960-1970. Oppositely, the

breakout of the war in Ukraine in February 2022 caused a massive increase in energy prices causing

an enormous demand for firewood. Although the price change was only temporal and therefore had

limited impact, the harvesting of firewood went up in the forest likely having an impact on forest

carbon pools.

The forests have many other functions aside from their climate change mitigation potential. Aside

from the changes in price and market dynamics, their treatment may be affected by other desires

regarding the services they provide. A notable example is the setting aside forest for biodiversity

protection. As also reflected in this study, the Danish government in 2020 decided to set aside

75,000 ha of forest for conservation purposes. This drastically changed the forest management on

more than 10 pct. of the forest area. Future political goals therefore have the potential to introduce

even more drastic changes to future forest management and hence to the development of the forest

carbon pools and their climate change mitigation potential.

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Carbon pool development on set aside forest areas

Albeit we succeeded in differentiating management of the forest resource according to geographical

data on set aside forest in eastern and western Denmark, very little knowledge is available on the

actual implications of the management on forest carbon pools.

We assumed that a proportion of the area will be deforested but have no actual knowledge if the

area is correct or whether the areas will grow into forest again. In particular, a portion of the set

aside area will be rewetted and although we have assumed that this will not impact emissions of

other green house gasses such as nitrous oxide and methane, we know that rewetting might affect

the emissions of these very potent climate gasses.

We have assumed limited harvesting of the areas but have no knowledge on the possible reduction

in forest biomass owing to the introduction of large grazers (horses and cattle) in particular in the

nature national parks. It is likely that the animals will both damage trees and hence affect the

present carbon pools while also hampering regeneration of the forest and in time cause substantial

deforestation

in sensu

climate reporting.

5.2

Connection with greenhouse gas reporting

To enable comparison of reported and projected values possible, we entered the projection results

into the reporting tool routinely used for making emissions estimates based on carbon pool

estimates from the national forest inventory. The reporting tool calculates emissions as the average

annual differences between 5-year moving averages of the forest carbon pool estimates to alleviate

significant inter-annual fluctuations in emissions resulting from overlapping cycles of the national

forest inventory [28]. Consequently, the reporting tool is well suited to flatten the cyclic emission

pattern resulting from the EFISCEN-space model repetition of harvesting cycles, which is an

artefact of the model setup rather than reflecting overall model trends.

When merging reported and projected emissions it should be noted that there are prominent

methodological differences in the calculations of carbon pools between the reporting on one hand

and the projection on the other. Firstly, the height of individual trees that is used in the tree biomass

estimation is largely estimated from local diameter/height regression specific to the individual plots

[11]. When making the calculations from the EFISCEN-Space output it is not possible to produce

localised diameter/height regressions and we used a set of general, albeit species specific, equations

estimated from the national forest inventory data. Secondly, to allow for the scaling also of small

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trees in plots covering more than one land use, we included only sample plots with the plot centre

covered by forest in the projections. To accommodate for the difference in statistical design, we

used a consistent estimator assuming full forest cover of included plots and zero cover for plots not

included. However, although the estimator is consistent, one should not expect numerically

identical results. For the initialization of the EFISCEN-space model, we estimated the above ground

biomass carbon pool at 37,728.71 kt C whereas the estimate provided from the usual calculation

used for the reporting estimated 36,206.40 kt C – a difference of 4 pct.

The difference between the most recent reported value of forest carbon stocks and the first year of

the projection makes coupling of the reporting and projection difficult as the difference will result

in technical emissions or uptake, not resulting from forest growth or management but from a basic

difference in statistical design. We therefore made a technical correction to the reporting tool by

subtracting the difference between observed and projected carbon pools in year 2022 that

constitutes the last year in the reporting and first year of the projection (37,728.71 kt C - 36,206.40

kt C ~1,500 kt C for above ground biomass) respectively, hereby alleviating the effect of the

difference in sampling scheme between the NFI data and the data used in the projections. This

correction has no implications for the projected emissions except for the first year of the

projections, where observed and projected carbon pools are linked. The observed pattern now

shows fluctuations that are not different in magnitude from historical observations (Figure 5.3).

Figure 5.3. Observed (solid lines) and projected (dashed lines) emissions connected by the normal reporting tool.

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5.2.1

Comparison to previous projections

The methods applied in this projection differ largely from previous projections in many aspects.

Previous projections relied on an age-class based approach in which the underlying assumption in

both model estimation and application was the harvesting of entire forest stands rather than

individual trees. Oppositely, EFISCEN-Space relies on an individual tree-based approach, in which

individual tree development is projected into the future. Here, the model estimation and application

relied on individual tree observations on national forest inventory plots. Based on an analysis of the

National Forest Inventory data, we found that the approach used in this study is much closer to the

actual management of the Danish forests and also that the model makes a more direct use of the

available data.

In this study, we compared the

Forest Carbon Pool Projections 2022

(KF22) with the current

Forest Carbon Pool Projections 2024

(KF24) (Figure 5.4). The differences between methods, and

not least in the underlying models, are expected to result in differences between previous and

present projections. However, there are also differences in the underlying frozen policy scenario,

that should be observed when comparing the two projections. Such differences include differences

in the size and composition of afforestation, the pace of implementation of set-aside forest for

biodiversity protection, as well as the expected composition and growth of forest regeneration.

The KF22 projected a decline in forest stocks at the onset of the projections (dashed red line in

Figure 5.4) owing to an initial increase in harvesting. This was presumably caused by a skewed age

class distribution, in particular for beech where low prices for decades had resulted in a buildup of

large resources of mature trees. Notably, the current projections (KF24) predict a similar initial

increase in harvests similar to but not of the same magnitude as projected in KF22. Oppositely, the

recovery of net carbon uptake is faster in the current projections, seemingly owing to the use of a

tree-based approach rather than the previous approach assuming harvest of entire stands. The

observed difference may likely be attributed to the modelling of individual trees, that allow for a

gradual turnover of the stand as is normal practice in particular in beech in Denmark, but likely also

in the stability of the underlying modelling framework. In previous modelling efforts, the Markov

chain models were built upon statistical modelling of the chance of forest transition from one age-

class to the next and the associated chance that the forest is converted to the youngest age-class (age

0). Owing to the scarce number of incidents where such conversion took place, and the common

conversion of forest to entirely different age-classes made such modelling difficult and uncertain.

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This in turn likely results in less certain projections of emissions from forests and is likely

responsible for much of the difference observed between current and previous projections.

Collectively, when comparing the 5-year moving averages produced by the climate reporting tool

this leads to differences in emissions projections from live biomass between KF22 and KF24

totalling 1.7 mi. tCO

-eq/yr in 2030 and 3.1 mi. tCO

-eq/yr in 2040 (Figure 5.4, Table 5.1 labelled

“MA”). Since the moving averages in the climate reporting tool utilize data from two consecutive 5-

year periods, the resulting emissions are affected by previous reported/projected emissions. If

instead considering the periodic averages (i.e. average emissions of 2023-2027, 2028-2032, 2033-

2037, 2038-2042, and 2043-2047) differences in emissions totalled 2.5 mi. tCO

-eq/yr in 2030 and

3.2 mi. tCO

-eq/yr in 2040 (Table 5.1 labelled “PA”). In particular the peak in emissions in the first

5-year period of the projection (2023-2027) affects the moving average the following years and

hence the 2030 estimate. Subsequently, the stable level of emissions from 2028 and onwards leads

to lesser differences between the moving and periodic averages.

As the project evolving around

Forest Carbon Pool Projections 2024

was at the initiation meant to

entail only a simple projection, we opted to assume no change in the dead wood and litter layer

carbon pools. This assumption was based on the relatively minor size of these two pools and their

commonly slow change, collectively resulting in only minor contribution to the annual emissions.

As a consequence of this assumption, it is not meaningful to compare emissions from dead wood

and litter between the

Forest Carbon Pool Projections 2022

and

Forest Carbon Pool Projections

2024.

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Figure 5.4. Comparison of projections of forest emissions. Here we compare the KF22 (total emissions, dashed red

line) projection and the present KF24 projection (from 2022, solid red line). Prior to 2022 (marked with a vertical

dashed line), lines show the reported emissions.

Table 5.1. Comparison of emissions from live biomass (above and below ground) for the forest carbon pool projections

Klimafremskrivning 2022 and Klimafremskrivning 2024. Climate projections 2024 (MA) represents the moving

averages depicted in Figure 5.4; Climate projections 2024 (PA) represents simple 5-year averages of the emissions

depicted in Figure 4.1.

Year

Climate projections 2022

Above

ground

Below

ground

Total

Climate projections 2024 (MA)

Above

ground

Below

Total

ground

1,000 tCO

-eq

Climate projections 2024 (PA)

Above Below

ground ground

Total

2025

2030

2035

2040

2045

119

-181

-305

-124

-247

-35

-51

-14

-40

207

-216

-357

-138

-287

-1,815

-1,645

-2,552

-2,682

-2,700

-352

-281

-533

-566

-570

-2,167

-1,925

-3,085

-3,247

-3,269

-913

-2,220

-2,636

-2,720

-2,597

-170

-481

-576

-590

-555

-1,084

-2,701

-3,212

-3,310

-3,152

Although not relying on species and age-class specific treatment of the forest but rather on

individual tree projections from a mere scaling of observed diameter and species distributions

observed on inventory plots, the data used in the previous

Forest carbon pool projection 2022

and

the current

Forest carbon pool projection 2024

are the same: data from the national forest

inventory. Nonetheless, the projections produced here differ largely from recent projections.

Importantly, however, is that both methods entail uncertainties. As explained in section 5.1.2 on

uncertainties, the uncertainty (standard error) of the biomass carbon pool estimate is around 0.9 %

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of the total, resulting in an uncertainty of 1.5 mi. tCO

eq. As the emissions are calculated as

differences between pools, the associated uncertainty may be estimated as the sum of the

uncertainties of the two carbon pool estimates minus their covariance. Although we do not know the

covariance, the uncertainty is likely to be even larger than the uncertainty related the estimate of

carbon pools; i.e. larger than 1.5 mi. tCO

eq. Consequently, although the differences in carbon

emission projections may seem large, they are likely not statistically different.

5.3

5.3.1

Forest carbon projection methods

Simple projections

Initially, the assignment for this task was to produce a simple projection of forest carbon pools and

the associated emissions. It should be noted that even apparently simple projections require

underlying assumptions, which must be valid in order to justify the projections. From the beginning

of the project, it was appreciated that a simple method that captures the current diversity in forest

structure and developments in forest management does not exist. Nonetheless, to demonstrate the

possible application of a very simple projection method, we made an analysis of carbon pools and

emissions from the mere reported values from the forest inventory and the emissions reporting for

the UNFCCC. Furthermore, we intend to demonstrate that even a very simple method for projecting

carbon emissions produce estimates with significant uncertainty and hence that results should be

interpreted with care, when developing policies upon the estimates.

Projections were made using the Autoreg procedure in SAS with a 2nd order autocorrelation

(AR(2)) model estimated through maximum likelihood. The model fits a linear regression model to

the time series data, where each data point is predicted based on a linear combination of its two

most recent past values. The coefficients of this linear combination are estimated using maximum

likelihood, and the model is then used to forecast future values in the time series based on this

learned pattern.

The forecasting of the carbon pools shows an increasing trend and a relatively narrow confidence

interval (Figure 5.5). This is well in line with previous studies that the uncertainty of the live

biomass estimates is around 0.9 pct. and hence a narrow confidence interval is expected. However,

this estimate does not include uncertainties related to e.g. future afforestation, age distribution of the

forest and resulting changes to the harvest levels, or uncertainties related to changes in forest

management on set-aside areas.

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Figure 5.5. Examples of forecasting carbon pools in living biomass using an autoregressive model on reported carbon

stocks from the Danish NFI.

When applying the autoregressive model to reported emissions, confidence intervals became very

large (Figure 5.6), owing to the large variations resulting from estimating differences between large

pools with a relatively small change (~1-2 pct.) even if the pools are estimated with a small standard

error. The figures illustrate the effect of even small uncertainties in the carbon pool estimates when

considering annual differences that make up the emissions estimates. This effect should also be kept

in mind when evaluating the more complex projections made with the EFISCEN-Space model.

Figure 5.6. Examples of simple projections using an autoregressive model (lag=2) on reported emissions data from

above-ground biomass and total biomass.

5.4

Future development

This project has made it possible to implement a new modelling framework that has the potential to

continuously refine the projection of forest carbon and other resources in the Danish forests.

However, the short timeframe of this project did not allow for implementation of all the capabilities

of the EFISCEN-Space model.

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A roadmap of future development and testing is envisioned, both to further take advantage of the

capabilities of EFISCEN-Space and to complement the existing input data from the NFI with

auxiliary data e.g. from long-term experiments and remote sensing. An example is to further

develop new Danish growth increment functions based on data from the NFI that could be used

within the EFISCEN-Space model. These were in fact developed during this project but have not

been adequately tested to include in this report.

Some specific additions to the modelling framework should be considered in future editions. Firstly,

our analyses do not take into account possible effects of changing forest management on dead wood

carbon pools. Especially when analysing various scenarios of setting aside forest for biodiversity

protection where increasing dead wood carbon pools is expected, the current projection system

would fall short of the target. However, such an analysis would to some extent be possible, as the

model provides projections on natural mortality and hence the contribution to dead wood pools.

This would, however, require knowledge on the oxidation of the dead wood pool, commonly

expressed in half-lives of the biomass [29, 30]. Albeit this could be an important extension of our

analyses, we expect that the effect on overall carbon pools would be limited owing to the size of the

dead wood relative to the live carbon pool and the 25-year time perspective in our analyses in which

it is unlikely that any significant build-up of dead wood would occur.

In addition to the pool estimates, the current modelling framework does not take into account

emissions of other greenhouse gasses such as methane or nitrous oxide from the soil. A prominent

feature of current trends in closer-to-nature forest management and the setting aside forest land for

biodiversity protection is the reversion to natural hydrological conditions by ceased maintenance

and even destruction of ditches and drainage pipes. Inhibited soil drainage eventually leads to

wetter conditions in forest soils and to the formation of intermediate or permanently wet soils that

may affect emissions of CO

, methane, and nitrous oxide. As the latter two greenhouse gasses have

a high global warming potential, this may have significant impact on the climate effect of changed

forest management practises, as even small proportions of wet soils contribute substantially to the

emissions of methane and nitrous oxide [31].

As we have little knowledge on the actual rewetted area resulting from e.g. the setting aside of

forest for biodiversity protection in our scenarios, we referred from analyses on the consequences

on emissions of other climate gasses in our study. However, the EFISCEN-Space model has been

coupled with the YASSO soil carbon model in a way that enables outputs and plot data from

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EFISCEN-Space to serve as inputs to YASSO (i.e., biomass inputs from mortality, harvesting, and

litterfall). Such development could enable soil carbon projection that reacts in a dynamic way to

changes in modelling future climate scenarios or forest management.

The EFISCEN-Space model is itself under continuous testing and development with a dedicated

team of software developers and forest scientists. It is planned to add functionality to e.g., explicitly

simulate plot level development under future climate scenarios as well as to consider inputs to

Harvested Wood Product (HWP) pool development from forest harvesting.

5.5

Assessing actual climate effects

In the context of assessing the climate impact of forestry activities, it is imperative to adopt a

comprehensive approach that goes beyond merely accounting of direct emissions associated with

changes in forest carbon pools as is the basis of this report. Traditional metrics including the climate

reporting often focus on the immediate carbon fluxes resulting from forest management practices,

such as carbon sequestration and emissions related to deforestation or afforestation. However, this

narrow perspective overlooks the broader climate benefits derived from the utilization of forest

products and the substitution of these products for more carbon-intensive materials and energy

sources [29, 30].

Wood harvested from forests serves as a critical input for a variety of products and energy solutions

that play a significant role in the transition towards a green economy. When wood products replace

materials that are more carbon-intensive to produce, such as concrete, steel, or plastic, there is a net

reduction in greenhouse gas emissions. This substitution effect extends to the energy sector, where

biomass sourced from sustainably managed forests can displace fossil fuels, further contributing to

the reduction of greenhouse gas emissions. The processing of wood into products and energy is

generally less energy-intensive compared to the manufacturing processes for other materials. This

results in lower emissions from the production phase, enhancing the overall climate benefit of using

wood.

It's essential to recognize that the energy requirements and emissions associated with the processing

of wood are significantly offset by the carbon storage in wood products and the substitution

benefits. However, our analyses do not account for the possible effects of forest products in total

societal emissions to a large degree occurring outside the forests. As an example, designating forest

areas for nature protection obviously results in a decline in the wood production after the

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conversion and hence in time the inflow of wood to the HWP pool. This will result in increased net-

emissions from the HWP pool, since only nationally produced wood is accounted for in the pool

while part of the HWP pool is continuously being oxidized. This effect is included in our model, as

HWP are being projected in the simulations.

While the direct emissions from changes in forest carbon pools provide valuable information on the

immediate impacts of forest management, they fail to capture the full climate effect of forestry

activities. The reduction in wood production resulting from designating forests to biodiversity

protection further results in increased emissions from related sectors such as the energy (relying

more on fossil resources rather than bioenergy), building (relying more on fossil-expensive

materials such as concrete and steel), and transport (transporting wood from larger distances)

sectors. These emissions are however not accounted for in the LULUCF sector and hence also not

in our model. Ignoring the substitution effects of wood products and biomass energy overlooks a

crucial component of the forest's role in climate mitigation. Furthermore, emerging practices such

as Bioenergy with Carbon Capture and Storage (BECCS) present additional opportunities to

enhance the climate benefits of using wood for energy by potentially reducing the carbon debt

associated with biomass energy use.

The developments to the EFISCEN-Space model presented in this study, allows for expansion of

the scope including scenario analyses, not only of direct emissions but including the entire systemic

emissions related to changes in forest management, wood production, and the utilization of wood.

This comprehensive perspective is essential for accurately assessing the potential contribution of

forestry to climate change mitigation and for informing policies and practices that maximize the

climate benefits of forest resources.

5.6

Concerns Related to the Discontinuation of the National Forest Inventory

The methodologies employed in this report are fundamentally dependent on the comprehensive data

provided by the National Forest Inventory (NFI). The NFI has been instrumental in supplying the

foundational data necessary for initiating our projections and crafting the underlying models that

inform our analyses. Recently, the Environmental Protection Agency decided to discontinue the

NFI in its current form. This decision poses profound challenges and raises significant concerns

regarding our capacity to accurately monitor, report, and project the climate effects of the nation's

forests within the Land Use, Land-Use Change, and Forestry (LULUCF) sector. This section delves

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into the critical implications of this discontinuation, focusing on its impact on climate reporting, the

feasibility of forest climate effect projections, and the integrity of future projections due to the

potential loss of continuous data series.

Impact on Climate Reporting for the LULUCF Sector

Adherence to the Intergovernmental Panel on Climate Change (IPCC) guidelines for greenhouse

gas inventory reporting, especially emissions and removals in the LULUCF sector [32], has been

underpinned by the accurate and comprehensive field measurements conducted by the NFI. The

cessation of the NFI's operations in 2024 threatens Denmark's compliance with these international

reporting standards, as remote sensing, in isolation, lacks the capability to capture the nuanced

biophysical parameters essential for thorough LULUCF accounting. This transition jeopardizes the

integrity of Denmark's climate commitments by potentially compromising the credibility of its

reported data.

The potential impact of the decision to discontinue the NFI in its current form is particularly

surprising given the role that forests play in Danish and EU strategies for climate change mitigation.

The Danish government has decided to afforest 250,000 ha with the explicit aim to gain climate

neutrality and in time even net negative emissions. Afforestation is furthermore a pivotal part of

European Green Deal and the EU ambition to be the first climate-neutral continent. Given the

apparent role of forests in climate change mitigation, it is remarkable that Denmark so chooses to

discard of the only available tool for analysing the impact of political initiatives on forest carbon

emissions.

Implications for Forest Climate Effect Projections

The ability to project the climate effects of forests is indispensable for informing climate policies

and strategies at both national and international levels. Such projections are heavily reliant on

robust historical and present-day data regarding forest composition, growth rates, and carbon

sequestration capacities. The continuity of data series provided by the NFI has been invaluable for

understanding the dynamics of forest ecosystems, particularly in the context of changing

management and evolving climate conditions. This longitudinal data has enabled a nuanced

understanding of trends, the assessment of forest management practices, and the formulation of

informed policy decisions. With the termination of the NFI, this continuity is at risk, creating a

significant knowledge gap in our understanding of how forest ecosystems respond to environmental

changes. The resultant data discontinuity will severely hamper future ecological and climate

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

projections, detracting from the effectiveness of research initiatives and policy formulations.

Specifically, the discontinuation of the NFI disrupts the flow of this critical data, thereby impeding

the generation of reliable and accurate forest climate effect projections. This impediment

significantly undermines Denmark's strategic planning for climate change mitigation and

adaptation, diminishing the nation's contribution to global climate objectives.

The discontinuation of the National Forest Inventory presents significant obstacles to Denmark's

climate reporting capabilities, forest management strategies, and scientific research endeavours. It

undermines the nation's ability to fulfil its international reporting obligations, generate precise forest

climate effect projections, and maintain vital long-term ecological data series. In light of these

challenges, it is critical to reassess the decision to discontinue the NFI or to develop an alternative

solution that ensures the continuation of comprehensive, field-based forest monitoring practices in

line with IPCC guidelines.

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

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KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

Appendix

Table 6.1. Projected carbon pools in above and below ground biomass distributed to total pools and pools in the

afforestation made during the simulations (i.e. not including afforestation prior to the initiation of simulations in 2022).

Year

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

Forest

Area

642.976

656.222

667.556

667.825

666.975

Total

Forest less than 30 year

Above ground Below ground Above ground Below ground

biomass

1,000 t CO

-eq

138.339

30.441

139.673

30.705

140.505

30.848

141.339

31.004

142.225

31.176

142.905

31.293

144.694

31.672

230

147.186

32.214

286

149.548

32.718

339

151.755

33.210

402

154.005

33.699

466

157.011

34.368

686

159.769

34.975

799

162.340

35.540

912

164.699

36.044

1.034

167.184

36.580

1.169

170.217

37.252

1.311

173.112

37.888

1.443

175.779

38.463

1.592

178.256

38.994

1.753

100

180.786

39.530

1.884

108

183.767

40.193

2.036

117

186.660

40.808

2.204

126

189.303

41.375

2.335

133

191.473

41.820

2.504

143

193.770

42.304

2.676

153

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

Table 6.2. Projected carbon pool contribution to harvested wood products.

Year

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

Total

515

619

636

654

689

454

466

512

545

569

444

498

539

587

584

499

515

581

598

615

503

556

596

679

652

Sawn timber Panels

1,000 t CO

-eq

386

464

477

491

517

340

349

384

409

427

333

373

404

441

438

375

386

435

448

461

377

417

447

509

489

Paper

129

155

159

164

172

113

116

128

136

142

111

124

135

147

146

125

129

145

149

154

126

139

149

170

163

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

Table 6.3. Emissions reported from previous inventories and in the previous (KF22) and present (KF24) projection

based on the 5-year moving averages of forest carbon pools used in the reporting tool applied for annual reporting to

the UNFCCC. The year 2022 separates reported and projected values from KF24.

Climate projection 2022

Above

Below

Dead

ground

wood

biomass

Reorted values/Climate projection 2024

Total

Above

Below

Dead Soil

Total

ground

wood

biomass

1,000 t CO

-eq

-72

-1.646

-1.006

-406

-222

-1

-29

-18

-16

-418

-251

-84

-2.498

-1.498

Year

Soil

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

Year

Climate projection 2022

Above

Below

Dead

ground

wood

biomass

Soil

Reorted values/Climate projection 2024

Total

Above

Below

Dead Soil

Total

ground

wood

biomass

1,000 t CO

-eq

-1.006

-1.026

-991

-955

-919

-882

-797

-796

-967

-1.590

-1.624

-1.786

-2.430

-2.598

-2.311

-2.478

-2.381

-1.680

-1.347

-907

-1.225

-1.113

-1.933

-2.188

-2.413

-1.892

-1.815

-1.153

-917

-1.008

-1.340

-1.645

-1.907

-2.219

-2.460

-2.511

-2.552

-2.582

-222

-227

-215

-203

-191

-179

-157

-156

-194

-328

-337

-377

-525

-560

-505

-547

-515

-356

-272

-178

-240

-200

-357

-452

-439

-346

-352

-225

-156

-181

-205

-281

-345

-403

-471

-522

-533

-545

-18

-251

-18

-251

-18

-251

-18

-251

-19

-255

-8

-240

-224

-208

-191

-165

-164

-163

-19

-163

-56

-166

-73

-356

-74

-524

-86

-723

-111

-896

-82 -1.048

-102 -1.007

-122 -1.044

-134

-990

-137

-999

-137

-775

-110

-687

-84

-730

-57

-560

-14

-357

-11

-356

-6

-246

-1.498

-1.526

-1.454

-1.380

-1.305

-1.229

-1.084

-1.082

-1.309

-2.078

-2.142

-2.384

-3.383

-3.757

-3.625

-4.031

-4.027

-3.146

-2.785

-2.209

-2.601

-2.224

-3.087

-3.454

-3.469

-2.608

-2.534

-1.630

-1.073

-1.189

-1.546

-1.925

-2.252

-2.622

-2.931

-3.033

-3.085

-3.126

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

-1.114

-1.135

-1.027

-862

-151

119

-1

-61

-121

-181

-206

-231

-256

-281

-305

-269

-203

-192

-171

-120

-10

-35

-38

-42

-45

-48

-51

-44

-133

-166

-195

-225

-241

-298

-252

-206

-160

-114

-68

-65

-63

-60

-58

-55

-56

-756

-580

-392

-152

122

150

138

126

113

101

-2.206

-2.072

-1.784

-1.359

-226

-43

-94

-144

-195

-220

-245

-270

-295

-320

-283

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

Year

Climate projection 2022

Above

Below

Dead

ground

wood

biomass

-233

-197

-160

-124

-149

-173

-198

-223

-247

-246

-244

-37

-29

-22

-14

-19

-24

-30

-35

-40

-39

-38

-56

-57

-55

-53

-51

-49

-47

-45

-44

Soil

Reorted values/Climate projection 2024

Total

Above

Below

Dead Soil

Total

ground

wood

biomass

1,000 t CO

-eq

-246

-209

-172

-134

-167

-199

-232

-264

-297

-291

-286

-2.628

-2.635

-2.662

-2.682

-2.706

-2.715

-2.705

-2.704

-2.700

-2.638

-2.592

-546

-557

-559

-566

-568

-574

-575

-573

-570

-568

-551

-3.175

-3.192

-3.221

-3.247

-3.274

-3.289

-3.279

-3.278

-3.269

-3.206

-3.143

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

KEF, Alm.del - 2023-24 - Bilag 266: Orientering om ny skovfremskrivning, fra klima-, energi- og forsyningsministeren

university of copenhagen

d e pa r t m e n t o f g e o s c i e n c e s a n d

n at u r a l r e s o u r c e m a n a g e m e n t

rolighedsvej 23

dk - 1958 frederiksberg

tel. +45 35 33 15 00

[email protected]