Unce ain ies ela ed o clima e change and o es managemen wi h
implica ions on clima e egula ion in Finland
Ja i Vauhkonen
⇑
, Tuula Packalen
Na u al Resou ces Ins i u e Finland (Luke), Bioeconomy and En i onmen uni , Yliopis oka u 6, FI-80100 Joensuu, Finland
a icle in o
A icle his o y:
Recei ed 6 Oc obe 2017
Recei ed in e ised o m 8 Decembe 2017
Accep ed 19 Feb ua y 2018
A ailable online 3 Ma ch 2018
abs ac
Fo es s play an impo an ole in one o he mos impo an ecosys em se ices, clima e egula ion. In
o de o mi iga e clima e change, a ious in e na ional ag eemen s aim a dec easing emissions h ough
Land-Use, Land-Use Change and Fo es y (LULUCF) ac i i ies. In a legisla i e p oposal by he Eu opean
Union, emissions om o es s a e accoun ed o in ela ion o an es ima e o a e age emissions o a
ange o yea s in he pas . Howe e , di e en o es s uc u es, managemen ac i i ies, g ow h a ia ions
and impac s o changing clima e may esul in conside ably di e en u u e emissions. We assessed he
magni ude o po en ial unce ain ies due o changing clima e and o es managemen o he p ojec ions
o ca bon s ocked in abo e- and belowg ound o es biomass in Finland un il 2050. We used an a ea-
based ma ix model, which was de eloped o inco po a e clima e-induced ee g ow h as a ime-
inhomogeneous Ma ko chain. The po en ial amoun s o bo h he ca bon s o ed and ex ac ed a ied
conside ably depending on he le el and alloca ion o u u e ha es s. I ealized, clima e- o
managemen -induced g ow h imp o emen s could inc ease he ca bon s ocks by up o one hi d in
he end o he simula ed pe iod. P ojec ions based solely on business-as-usual ansi ions and ha es s
could he e o e lead o ine icien decisions ega ding u u e ca bon s ocks and ha es ing possibili ies.
Ó2018 The Au ho s. Published by Else ie B.V. This is an open access a icle unde he CC BY license (h p://
c ea i ecommons.o g/licenses/by/4.0/).
1. In oduc ion
Clima e egula ion is one o he mos impo an ecosys em se -
ices (Cos anza e al., 2017), o which o es s and o es y play an
impo an ole. On one hand, ca bon accumula es h ough g ow h
o ees in o o es g owing s ock. On he o he hand, Land-Use,
Land-Use Change and Fo es y (LULUCF) ac i i ies impac ca bon
s ocks. Fi s , sus ainable land-use and o es managemen can con-
se e o inc ease o es ca bon s ocks. Second, ha es ed wood-
based p oduc s and hei bi-p oduc s can eplace ossil-based
p oduc s, ma e ials and ene gy. The complexi y o ade-o s
be ween ees le g owing o clima e egula ion se ice o hose
ha es ed o p o isioning se ices – and consequen ly o clima e
egula ion as di e se p oduc s – place challenges o decision mak-
ing ega ding LULUCF ac i i ies and hei egula ion.
The issues ela ed o LULUCF a e e lec ed by egula ion mea-
su es in a ious in e na ional ag eemen s unde he Uni ed
Na ions F amewo k Con en ion on Clima e Change (UNFCCC). In
line wi h he Pa is Ag eemen (UNFCCC, 2017), he Eu opean Com-
mission (EC) has p esen ed a legisla i e p oposal (EC, 2016) o se a
binding commi men o each membe s a e o ensu e ha
accoun ed emissions om land use a e en i ely compensa ed by
an equi alen emo al o CO
2
om he a mosphe e h ough ac ion
in he sec o , known as ‘‘no debi ule”. In he p oposed ules, emis-
sions om o es s a e accoun ed o in ela ion o a so called
na ional o es e e ence le el. The o es e e ence le el is an es i-
ma e o he a e age annual ne emissions o emo als esul ing
om managed o es land wi hin he e i o y o a membe s a e.
The a e age alues a e calcula ed o a ange o yea s in he pas ,
e e ed o as a e e ence pe iod, which is a poli ical decision ha
applies o all coun ies.
In p ac ice, he pas o es g ow h, ha es s and, consequen ly,
ne emissions may a y a lo be ween yea s wi hin a coun y and
he a ia ion pa e n o e ime is no he same in all coun ies.
Fi s , he e is a ia ion o sinks due o he g ow h a ia ion o ees
(Mäkinen e al., 2002). Second, he e is in e annual a ia ion o
emissions (Ande sson e al., 2007), due o he ma ke luc ua ion,
o example. In addi ion, clima ic a ia ion a ec s he ee g ow h
and he impac s o changing clima e a e assumed o a y be ween
egions depending on hei ecological condi ions (Cha u e al.,
2017) and managemen (Hen onen e al., 2017). Fu he mo e, glo-
bal ma ke u bulence accele a es luc ua ion in oundwood ma -
ke in Eu ope (Packalen e al., 2017) and he impac s o global
h ps://doi.o g/10.1016/j.ecose .2018.02.011
2212-0416/Ó2018 The Au ho s. Published by Else ie B.V.
This is an open access a icle unde he CC BY license (h p://c ea i ecommons.o g/licenses/by/4.0/).
⇑
Co esponding au ho .
E-mail add ess: [email p o ec ed] (J. Vauhkonen).
Ecosys em Se ices 33 (2018) 213–224
Con en s lis s a ailable a ScienceDi ec
Ecosys em Se ices
jou nal homepage: www.else ie .com/loca e/ecose
changes a e expec ed o ealize a di e en phase in di e en coun-
ies depending on hei socio-economic si ua ion. Consequen ly,
he use o he same e e ence pe iod o all coun ies may penalize
o bene i a coun y depending on how well he u u e o es
esou ce and ma ke up u n and down u n coincide wi h each
o he compa ed o he na ional e e ence alue.
Fo es esou ce p ojec ions ha e been used o model u u e bio-
mass supplies a he Eu opean and na ional le els. The e is a la ge
a ia ion in da a and models used o he na ional p ojec ions ai-
lo ed o local condi ions and in o ma ion needs (Ba ei o e al.,
2016). Fo example, sus ainabili y and he p o isioning o o he
ecosys em se ices han biomass-based p oduc s a e aken in o
accoun as a se ies o ecological, economic and echnical con-
s ain s, which limi he a ailabili y o accessibili y o o es s o
wood supply (Albe di e al., 2016). The Eu opean s udies a e o en
summed up om na ional p ojec ions (Schelhaas e al., 2017) ca -
ied ou using a pan-Eu opean da a se and a gene ic model ha
canno ully accoun o di e ences be ween coun ies. Conse-
quen ly, he esul s om he s udies summed up o he Eu opean
le el o en show a la ge a ia ion, usually claimed o be a esul
o inhe en unce ain ies ela ed o da a (e.g., Re enmaie e al.,
2010; Ben sen and Felby, 2012) and biomass es ima ion models
(Neumann e al., 2016). E en i con ex -dependen changes in
u u e land-use, o es managemen , and clima e ob iously ha e
implica ions ha p opaga e he p ojec ions as unce ain ies, s ud-
ies add essing hese aspec s a e missing (Ba ei o e al., 2016;
Schelhaas e al., 2017).
Acco ding o he legisla i e p oposal o EC (2016), he u u e ca -
bon pools o es ima ing he emissions should be p ojec ed assum-
ing a ‘‘con inua ion o cu en o es managemen p ac ice and
in ensi y” o make he emission accoun ing compa able be ween
o he sec o s and membe s a es, bu also enable accoun ing o
coun y-speci ic o es y dynamics. Based on his p inciple, G assi
and Pilli (2017) desc ibed a simula ion amewo k, which is (a)
pa ame e ized by he p e ailing o es age-class s uc u e, inc e-
men s, and business-as-usual ha es ing p ac ices and in ensi y;
and (b) used o p ojec he ca bon pools a e he e e ence pe iod,
assuming ha ha es s a e con inued in a simila magni ude as in
he e e ence pe iod, bu ela i e o he de elopmen o biomass
a ailable o wood supply ( o de ails, see especially Box 1 in
G assi and Pilli, 2017). Howe e , because o he mul iple ac o s
causing a ia ion o he u u e scena ios, as e iewed abo e, he
membe s a es would mos likely bene i om he assessmen o
u u e o es ca bon sink unce ain ies when nego ia ing on he
na ional o es e e ence le el. Bayesian in e ence echniques such
as Ma ko chain models (e.g., Nabuu s e al., 2000; Thü ig and
Schelhaas, 2006; E iksson e al., 2007; Ve ke k e al., 2011) may be
applicable o quan i ying unce ain ies (c ., Smi h and Ma shall,
2008), due o he po en ial o lexibly a y he assump ions ela ed
o u u e scena ios (see also Vauhkonen and Packalen, 2017).
The aim o his s udy is o es a Ma ko chain model o assess-
ing he deg ee o unce ain ies in he p ojec ions o ca bon s ocked
in abo e- and belowg ound o es biomass in changing clima e and
in he con ex o LULUCF egula ion in Eu ope. The main objec i e
is o de elop me hodology o inco po a e ee g ow h a ia ion
and impac s o changing clima e in o an a ea-based, Ma ko chain
model de eloped o p ojec ing di e en managemen scena ios
(Vauhkonen and Packalen, 2017). The seconda y objec i e is o
apply he me hodology o quan i y he unce ain ies ela ed o o -
es ca bon, and consequen ly, o he selec ion o na ional o es e -
e ence le el in he LULUCF egula ion o Finland. Ou analyses
undamen ally co e wo ecosys em se ices: oundwood ha es s
as a p o isioning se ice and he ela ed e ec s on he ca bon
ex ac ed and s o ed in he emaining g owing s ock as a egula-
ion se ice.
2. Ma e ial and me hods
2.1. O e iew
As he Ma ko chain model, we used . 2.0. o he Eu opean
Fo es y Dynamics Model (EFDM), which is implemen ed in he
R s a is ical modeling en i onmen (R Co e Team, 2016) and
can be downloaded om he EFDM p ojec eposi o y (FISE,
2017) as open sou ce unde he Eu opean Union Public License
(EUPL). The EFDM is an a ea-based ma ix model, in which he
ma ices ep esen o es a eas classi ied acco ding o ecological
and socio-economic ac o s. The EFDM simula es he de elop-
men o he o es a ea dis ibu ion as a p oduc o i s ini ial
s a e, p opo ions o a eas expec ed o be managed acco ding
o di e en sil icul u al p ac ices, and he co esponding ansi-
ion p obabili ies. The ansi ion p obabili ies a e condi ioned
on he ac i i ies, which can bo h di e be ween ac o s such as
si e ype, species, owne , and o he ac o s ei he a ec ing he
o es dynamics o needed o epo ing. As elabo a ed by
Si kiä (2012) and Packalen e al. (2014), he e is a ansi ion
ma ix pe ac o combina ion and pe ac i i y. The ini ial s a e
and ac i i y and ansi ion p obabili y ma ices can be de i ed
h ough a simple classi ica ion and agg ega ion ou ine
om Na ional Fo es In en o y (NFI) plo da a, i he u u e
de elopmen is assumed o ollow ha ealized in he pas .
G ow h models o simula o s modi ying he pai wise obse a-
ions can be used o de i e ansi ion p obabili ies unde chang-
ing clima e.
The EFDM was pa ame e ized o he cu en clima e using
ansi ion and ac i i y p obabili ies de i ed om pe manen NFI
plo s as desc ibed in de ail in he open-access a icle by
Vauhkonen and Packalen (2017). The amewo k was ex ended o
include e ec s o clima e change and con e he ou pu s o ca -
bon. The analyses ca ied ou he e aim a quan i ying he deg ee
o unce ain y occu ing, when decisions a e made acco ding o
he ansi ion p obabili ies obse ed in he pas , bu changes o
hese ansi ions occu due o he clima e o managemen imp o -
ing he g ow h. The gene al amewo k and especially hese
changes a e desc ibed below, bu ega ding de ails o he pa ame-
e s and hei e ec s o he ou pu , he eade is e e ed o he
pape by Vauhkonen and Packalen (2017).
The simula ions we e ca ied ou in 5-yea pe iods, which co -
espond o he measu emen in e al in he pe manen plo da a.
Al oge he eigh pe iods we e simula ed, i.e., he las yea o sim-
ula ions is a ound 2050, depending on he ini ial measu emen
yea . I was assumed ha he land-use es ic ions de e mined
he sil icul u al sys em applied. The de elopmen o o es s wi h-
ou es ic ions was simula ed acco ding o e en-aged manage-
men and age and olume as he axes o he ma ices. Fo es s
wi h es ic ions on wood supply we e simula ed acco ding o
an une en-aged managemen , whe e inal ellings we e eplaced
wi h hinnings om abo e and he simula ions we e based on
s em numbe and olume ma ices. Only he na u al p ocesses
we e simula ed o o es s no a ailable o wood supply. The
de i a ion o he ini ial da a and ansi ion p obabili ies co e-
sponding o obse a ions made om he pe manen NFI plo s
a e desc ibed in Sec ions 2.2 and 2.3.1. Fo he unce ain y assess-
men , expec ed e ec s o clima e and adap ing he ansi ion
p obabili ies due o hese changes we e modeled using ‘‘a ansi-
ion p obabili y d i e ”, as desc ibed in Sec ion 2.3.2. Th ee di -
e en ha es ing a ge s and wo al e na i e alloca ions o he
ha es s we e applied, as desc ibed in Sec ion 2.4. The ha es
decisions we e based on oundwood olume (measu ed in m
3
),
bu ansla ed o ca bon ( onnes) using ou pu coe icien s as
desc ibed in Sec ion 2.5.
214 J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224
2.2. Ini ial s a e space
The sample plo da a om he 11 h Finnish Na ional Fo es
In en o y (NFI11; al oge he 51,827 o es plo s measu ed in
2009–2013) we e used o he es ima e he ini ial dis ibu ion o
o es in an a ea o al oge he 21.28 mill ha on p oduc i e and
poo ly p oduc i e o es land in Finland. The g owing s ock was
2,234 mill m
3
, which is app oxima ely 95% o he en i e g owing
s ock in Finland, and only a eas wi h low impo ance o o es y
we e excluded. Fo he analyses, he o es s we e classi ied o
ma ices wi h axes co esponding o ei he age and olume o s em
numbe and olume unde e en-aged o une en-aged manage-
men , espec i ely. To p oduce an adequa e amoun o obse a-
ions o he es ima ion o he ansi ion p obabili ies, he class
limi s o bo h he olume and s em numbe we e de i ed as he
alues o he 10 h, 20 h, ..., 90 h and 95 h quan iles o he pai -
wise obse a ions made om he pe manen NFI plo s. The age
classes we e de ined as 0, 5, 10, .. ., 120, 120+ yea s, he class in e -
al o i e yea s co esponding o bo h he measu emen s and he
simula ion s ep used in he analyses. The class limi s o he con-
inuous a iables a e p esen ed as Appendix A. These ma ices
we e de i ed sepa a ely applying he ollowing, s a ic land-use
classes: (i) known land-use es ic ions: o es s a ailable, o es s
wi h es ic ions on a ailabili y, and o es s no a ailable o wood
supply; (ii) o es owne ship: p i a e, public + o he ; (iii) si e e il-
i y: al oge he , i e ca ego ies co esponding o ou axa ion
classes adi ionally used in Finland + i h class including all
poo ly p oduc i e o es land; (i ) dominan species: pine, sp uce,
deciduous ees.
2.3. T ansi ion p obabili ies
2.3.1. Cu en clima e
The ansi ion p obabili ies co esponding o he cu en cli-
ma e we e de i ed using pai wise obse a ions om pe manen
plo s o NFI11 (al oge he , 11,987 plo s), which we e measu ed
app oxima ely i e yea s ea lie in he p e ious in en o y
(NFI10). Posi i e di e ences in he o al olumes on plo s wi h
no ea men s based on da a ha could be ma ched wi h ce ain y
be ween he wo subsequen in en o ies we e eco ded as he
pai wise da a. The es ima ed ansi ions he e o e included only
g ow h and no po en ial educ ions due o calami ies o na u al
dis u bances, o example.
The ansi ions due o managemen ac i i ies we e based on
simula ions o hei expec ed de elopmen . The o es s a ec ed
by inal ellings we e o ced o ansi o he beginning o he
e en-aged o a ion. A hinning simula o was implemen ed o
de i e pai wise obse a ions be o e and a e he ea men s.
The hinnings ook place in he beginning o each simula ion
pe iod and he g ow h o he hinned o es s in ha pe iod was
simula ed applying he ansi ion p obabili ies o o es s no
managed.
2.3.2. Adap ing he ansi ion p obabili ies acco ding o he expec ed
clima e change
We used he ollowing wo k low, called ‘‘ ansi ion p obabil-
i y d i e ”, o include he e ec s o clima e-induced o es
g ow h in he Ma ko chain model. In ou d i e , a g ow h
end wi h s ochas ic a ia ion was modeled in ou s eps, he
i s wo o which a e ela ed o p edic ing inc eased CO
2
and
empe a u e unde clima e change scena ios and he la e
wo s eps o using hese alues o p edic ing he esul ing
g ow h inc emen .
1. CO
2
. G eenhouse gas (GHG) emissions esul ing om clima e
change we e expec ed o de elop acco ding o he Rep esen a-
i e Concen a ion Pa hway (RCP) scena ios. We used h ee ou
o he ou scena ios, igno ing RCP6.0, as i s e ec s wi h espec
o o es g ow h we e p ac ically simila o hose o RCP4.5
based on ou modeling app oach. In he scena ios RCP2.6,
RCP4.5, and RCP8.5, he le el o ambien CO
2
is expec ed o ise
om he cu en le el o 350 ppm o 443, 487, and 541 ppm,
espec i ely, by 2050 (Meinshausen e al., 2011).
2. Tempe a u e. The Finnish Me eo ological Ins i u e has p o-
jec ed he annual mean su ace ai empe a u es in Finland o
inc ease by 2040–2069 depending on he le el o GHG emis-
sions ealized in he di e en RCPs. The expec ed changes in
he annual mean empe a u es, ela i e o 1981–2010, a e
exp essed as no mal dis ibu ions wi h pa ame e s o he di -
e en RCPs (Ruos eenoja e al., 2016). The alue ep esen ing
he change in empe a u e by 2050 was ob ained as a andom
alue om he RCP-speci ic dis ibu ions. The empe a u e co -
esponding o 10%, 50%, and 90% alues o he dis ibu ions
we e compu ed o he g ow h end modeling.
3. G ow h end. Ma ala e al. (2005, 2006) used p edic ions
ob ained om a physiological g ow h model, FinnFo , o
desc ibe he impac s o ele a ed empe a u e and CO
2
on ee
g ow h. The models p esen ed gi e a a io (o Rela i e Scena io
E ec , RSE
) o he olume g ow h unde he changing clima e
o ha unde he cu en clima e. Sepa a e models, con olled
by s and densi y, compe i ion, si e e ili y and cu en empe -
a u e sum, a e p esen ed o Sco s pine, No way sp uce and sil-
e bi ch. To implemen he models in con en ional g ow h
simula ions, Ma ala e al. (2005, 2006) also exp ess RSE
as a
shape e ec SE, which is a a io o he heigh and olume
g ow h, and p esen eg ession models o SE. The models o
Ma ala e al. (2005, 2006) a e a ailable only o mine al soils;
howe e , Nuu inen e al. (2006) modeled SE also o pea lands.
We used he CO
2
and empe a u e alues ob ained acco ding o
he desc ip ion abo e in he models o de i e RSE
V
and used
his a io as he g ow h end o ou analyses. We compu ed
RSE
o bo h mine al and pea lands using he models o
Ma ala e al. (2006), bu adjus ed he RSE
alues o pea lands
acco ding o he a io o SE o mine al soil (Ma ala e al., 2006)
o SE o pea lands (Nuu inen e al., 2006).
4. S ochas ic annual a ia ion in ee g ow h. In addi ion o he
g ow h end, we wan ed o include he a ia ions in ee
g ow h as obse ed in he pas g ow h se ies (c ., Hen onen
e al., 2017). We used he au o eg essi e mo ing-a e age mod-
els i o a end-clea ed g ow h-index se ies obse ed om Fin-
land om 1890 o 1988 (Pasanen, 1998), which ake in o
accoun he posi i e au oco ela ion be ween successi e yea s
and he posi i e c oss-co ela ion be ween he g ow hs o di -
e en ee species.
To ob ain se ies o g ow h ends wi h s ochas ic a ia ion,
he p e ious ou s eps we e epea ed 1000 imes o each RCP
and plo . The ini ial and clima e-induced olume alues we e
classi ied o olume classes using class limi s desc ibed abo e
(see also Appendix A). Conside ing each o he simula ion pe iods
sepa a ely, i was compu ed how many imes o he 1000 d aws
he plo s wi h he enhanced g ow h inc eased o a highe olume
class han wi h he ini ial ansi ion p obabili ies. Mul iplying
his p opo ion wi h he a ea ep esen ed by a plo ga e he
o al a ea es ima e ha was expec ed o ansi as e unde he
gi en RCP. These a ea p opo ions we e ansla ed o ansi ion
p obabili ies simila o hose based on he ini ial pai wise NFI
obse a ions (Sec ion 2.3.1). In he EFDM simula ions, he ini ial
J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224 215
ansi ion p obabili ies ( he single p obabili y ma ix de i ed
om he NFI obse a ions) we e used o compu a ions
ep esen ing he cu en clima e, and RCP and simula ion pe iod
speci ic ansi ions ob ained using he d i e o hose ep esen -
ing clima e change.
2.4. Ha es scena ios and managemen ac i i ies
We p ojec ed he de elopmen o he ca bon s ock assuming
h ee di e en ha es ing le els o oundwood: Business-As-
Usual (BAU), Na ional Fo es P og amme (NFP) and Non-
Declining Volume (NDV). The BAU and NFP scena ios we e based
on ixed ha es ing le els o 61.75 mill m
3
/a and 76 mill m
3
/a,
espec i ely, which we e ob ained om he Na ional Fo es S a -
egy o Finland (Minis y o Ag icul u e and Fo es y, 2015)by
adjus ing he p oposed le els o he 95% amoun o g owing s ock
conside ed he e. The NDV scena io had an adap i e le el o ha -
es s ha was de e mined as he le el, which did no dec ease
he olume o he g owing s ock.
The alloca ion o ha es s o di e en ypes o o es s was
de e mined acco ding o ac i i y p obabili ies, which gi e he p o-
po ions o managemen ac i i ies in he da a classi ied acco ding
o he ac o combina ions. Two al e na i e app oaches we e
es ed as he alloca ion o he u u e ha es s. Fi s , a business-
as-usual alloca ion (A
BAU
) was ob ained as he p opo ion o a eas
wi h no managemen , hinning o inal elling ealized du ing he
mos ecen i e-yea pe iod, i.e. based on obse a ions om he
pe manen plo s be ween he wo subsequen in en o ies. Second,
a schoolbook-alloca ion (A
SB
) was ob ained as he p opo ion o
a eas, which we e ma ked in he ield wi h a need o be ha es ed
wi hin he nex i e yea s s ic ly acco ding o he ins uc ions o
o es managemen (Y jölä, 2002). Fig. 1 depic s he di e ences
in he wo al e na i e ha es alloca ions.
To ul ill he ha es ing objec i es, he ac i i y p obabili ies
based on bo h al e na i e ha es alloca ions we e i e a ed o
yield a ha es ing d ain o oundwood co esponding o BAU,
NFP, o NDV scena ios (Vauhkonen and Packalen, 2017). The
ac i i y p obabili ies used in all analyses we e compu ed
assuming g ow h a es o he cu en clima e, i.e., using ansi-
ion p obabili ies de i ed om he pai wise NFI obse a ions.
‘‘Unce ain y” in he impac analyses he e o e o igina es om
po en ial clima e o managemen induced addi ional g ow h
ha occu s on op o he g ow h ha is expec ed based on
he ansi ions obse ed a he ime when making he ha es -
ing decisions.
2.5. Ou pu coe icien s
Coe icien s o he mean alues o a ea classes we e de e -
mined o quan i y he imbe asso men d ain and ca bon s ocked
in o ex ac ed om he o es . Simila o Vauhkonen and Packalen
(2017), we de i ed he imbe asso men d ain by compu ing he
ela i e p opo ion o log- and pulpwood p opo ions o he en i e
olume sepa a ely o inal ellings and hinnings, using all NFI
plo s o hose plo s o which he hinnings we e simula ed,
espec i ely. The biomass in componen s (s em, b anches, oliage,
s ump, oo s) was calcula ed o each plo (Repola, 2008, 2009).
To ob ain he ca bon con en , he biomasses we e mul iplied by
species-speci ic expansion ac o s (a ound 0.5; see Table 1 o
Pukkala, 2014).
3. Resul s
3.1. E ec s o clima e-induced addi ional ee g ow h o he ansi ion
p obabili ies
The ansi ion p obabili ies ha we e adap ed o he changing
clima e di e ed be ween he RCPs as expec ed. Fig. 2 depic s hese
di e ences as he p opo ion o a ea ha is expec ed o ansi as-
e han wi h he p obabili ies de i ed om he pai wise NFI
obse a ions. Fig. 2 indica es ha he a ea expec ed o ansi as-
e inc eased acco ding o he ime s eps o he simula ion, bu his
p opo ion a ied acco ding o si e cha ac e is ics such as e ili y.
When he sou ce and a ge classes o he as e ansi ions a e
examined a he class-le el (Fig. 3), i can be seen ha a e age
ansi ions we e usually equal o hose based on he ini ial pai -
wise obse a ions. Howe e , he e we e mo e equen ly jumps
o mo e han one class han was obse ed in he case o he an-
si ion p obabili ies de i ed om he pai wise obse a ions. The
esul s abo e a e based on using RCP4.5 as an example scena io.
Wi h espec o o he RCPs, he esul s did no essen ially di e
excep o he magni udes o clima e-induced changes.
The con e sion o a eas o ca bon using he ou pu coe icien s
yielded a de elopmen pa e n ha can be assumed o ealis ically
mimic he ca bon dynamics in bo eal o es . Among indi idual ca -
bon (o biomass) componen s, s em o oliage g ew mos o leas
apidly, espec i ely. The di e ences be ween si e ypes we e in
he o de o 5.7–8 pe cen age poin s, species 8.5–11 pp, and geo-
g aphic a eas 8.4–10.7 pp o he componen g ow h, and clima e
change ampli ied hese igu es. Howe e , he main di e ences
Fig. 1. P opo ions o managemen ac i i ies in he business-as-usual (A
BAU
, le ) o schoolbook (A
SB
, igh ) ha es alloca ion. Re e o Appendix A o he de ini ion o olume
classes.
216 J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224
we e ela ed o age-size dis ibu ions: Fig. 4 shows ha clima e-
induced p edic ions had simila ends han hose de i ed om
he pai wise NFI obse a ions. Especially he g ow h o he leas
ma u e o es s was ampli ied by he clima e-adap ed ansi ion
p obabili ies.
3.2. De elopmen o he ca bon s ock and he ela ed unce ain ies due
o clima e and managemen
Figs. 5–6 p esen he esul s o he de elopmen o ca bon
s ocks and d ains, when he ansi ion p obabili ies desc ibed in
Sec ion 3.1 we e applied oge he wi h he ac i i y p obabili ies
o ha es s. The di e ence be ween Figs. 5 and 6 is ha in he o -
me , he ac i i y p obabili ies (Sec ion 2.4) we e i e a ed in e e y
simula ion s ep o yield he desi ed ha es goal, i.e., an equal ol-
ume o oundwood was ha es ed in e e y simula ion pe iod. In
Fig. 6, he ac i i y p obabili ies we e ixed o he le el ha yielded
he ha es goal in he beginning o he simula ions, i.e., an equal
p opo ion o a ea was always ha es ed, whe eas he olume ha -
es ed a e he i s pe iod depended on he (in-)g ow h o o es
a ea o he speci ic class. In all cases, he managemen decisions
we e made acco ding o he ansi ion and ac i i y p obabili ies
de i ed om he o iginal pai wise NFI obse a ions, which is illus-
a ed using g ey ba s in Figs. 5–6. The lines in he igu es depic
al e na i e cou ses o de elopmen , which we e ob ained by
eplacing ei he ansi ion p obabili ies wi h hose induced by cli-
ma e (Sec ion 3.1) o business-as-usual ha es alloca ion wi h
ha based on schoolbook (Sec ion 2.4) o applying bo h o hese
changes.
3.2.1. Ca bon dynamics unde cu en clima e and business-as-usual
managemen
Based on he business-as-usual ansi ions and ac i i ies, wi h
ac i i y p opo ions i e a ed o yield he desi ed ha es goal
(Fig. 5), ha es ing 61.75 mill m
3
o oundwood pe yea (BAU sce-
na io) inc eased he ca bon s ock om 810 o 1065 mill onnes
(31%) by he end o he simula ion. Ha es ing 76 mill m
3
acco d-
ing o he NFP scena io i s sligh ly inc eased he s ock, bu ended
up o educe he s ock o 757 mill onnes (7%) in he end o he
simula ion. The equi emen o no decline he olume esul ed
in a linea ly educing amoun o ha es s and, subsequen ly, ca -
bon ex ac ed.
The esul s di e ed conside ably, i p opo ions o a ea yielding
he desi ed ha es le el we e ixed in he beginning o he simu-
la ions (Fig. 6). The di e ences we e also mos p onounced wi h
he BAU ha es ing scena io, whe e he le el o ha es s inc eased
by ime. This was because o highe p opo ion o o es ha
ma u ed due o low ini ial ha es s and he ixed p opo ion o his
a ea o be ha es ed acco ding o he ac i i y p obabili ies. As a
esul o inc easing he ha es s, howe e , he g owing s ock
dec eased. Wi h he NFP scena io, he de elopmen was ai ly sim-
ila as desc ibed in he p e ious pa ag aph. Wi h he NDV scena io,
he g owing s ock dec eased unlike when he ac i i y p obabili ies
we e con inuously i e a ed du ing he simula ions, whe eas he
ha es le els educed less han when i e a ed.
3.2.2. Le el o unce ain ies due o clima e and managemen
Bo h he imp o ed g ow h and he change o ha es alloca ion
om A
BAU
o A
SB
inc eased he ha es ing possibili ies in all scena -
ios desc ibed abo e. Compa ed o business-as-usual, he clima e
change alone esul ed o 5–33% highe ca bon s ock in abo e-
and belowg ound o es biomass in addi ion o ob aining 8–20%
highe ha es d ain. The a o emen ioned le els depended on he
ha es ing goals and i he ac i i y p obabili ies we e i e a ed du -
ing he simula ions (Figs. 5–6).
A compa ison o he clima e- and managemen induced e ec s
shown in Figs. 5–6 indica es ha he impac s o clima e change
we e pa ly ela ed o hose p oduced by he di e en ha es allo-
ca ions. Al hough he same amoun o oundwood was ha es ed
in A
BAU
and A
SB
, he amoun o o al ca bon (including all compo-
nen s in addi ion o ee s ems) a ec ed was smalle in A
SB
;an
obse a ion ha is u he examined in he nex sec ion. The com-
Fig. 2. The p opo ion o a ea ha was expec ed o ansi as e based on he
ansi ion p obabili ies adap ed o expec ed clima e (RCP4.5) compa ed o hose
based on pai wise NFI obse a ions. The end lines ep esen he a ea p opo ions
ob ained using he median alue o he clima e-induced empe a u e dis ibu ion,
while he e ical lines show he a ia ion be ween he 10% and 90% alues o he
dis ibu ion. The legend e e s o dominan species in di e en si e e ili y classes –
he i h class including all poo ly p oduc i e o es land was omi ed om he
igu e.
Fig. 3. T ansi ions om he ini ial (Class#, 0) o he subsequen olume class
(Class#, 1) in he ini ial pai wise obse a ions (g ey symbols) and hose adap ed
o clima e-induced g ow h (black symbols; ansi ions adap ed o RCP4.5 a e
shown). The hick ho izon al lines depic he median, he bo om and op o he
boxes he in e qua ile ange be ween he 25 h and 75 h pe cen iles, he whiske s
he lowes da a wi hin 1.5 imes he in e qua ile ange, and ci cles he da a no
included in ca ego ies abo e.
J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224 217

bined e ec s o clima e and managemen we e mo e p onounced
in he case, whe e he ac i i y p obabili ies we e ixed in he begin-
ning o simula ions, o which eason he ex below mainly e e s
o Fig. 6. In i , a co esponding inc emen in he g owing s ock
could be obse ed due o clima e change (abou 8% using he
expec ed alues o RCP4.5) as i he ha es alloca ion was changed
om A
BAU
o A
SB
(7–9% depending on he ha es a ge ). In addi-
ion, i bo h he clima e change and he change in ha es alloca-
ion occu ed, i inc eased he amoun o ca bon ha es ed o
app oxima ely he same le el as wi h clima e change alone by
he end o he simula ion, bu wi h a comple ely di e en de elop-
men pa e n du ing he simula ion pe iod (see Fig. 6).
3.2.3. Unce ain ies ela ed o he dynamics o indi idual ca bon
componen s
A dec easing amoun o he o al ca bon ex ac ed could be
obse ed based on Fig. 5 (e.g., lowe le panel), e en i he ac i i y
p obabili ies we e i e a ed o yield he desi ed ha es goals. This
obse a ion and also he conside able di e ences in o al ca bon
s ocks and d ains depending on he ha es alloca ion (Fig. 6)
migh no seem in ui i e, when conside ed as o al ca bon, bu
can be be e easoned when he analyses a e b oken down o
he le el o indi idual ca bon componen s (Fig. 7). Al hough he
ha es decisions a e based on he amoun o oundwood, he o al
ca bon a ec ed by he ha es s includes oliage, b anches, s ump,
Fig. 4. The mean annual inc emen o he o al biomass in he NFI plo s as a unc ion o age – e e o Appendix A o he de ini ion o he age classes. The hick line depic s he
inc emen in he ini ial pai wise obse a ions and he hin lines he inc emen s o he eigh simula ion pe iods, when he pai wise obse a ions we e adap ed o he RCP2.6
(le ), RCP4.5 (middle), and RCP8.5 ( igh ) clima e scena ios.
Fig. 5. The de elopmen o o al ca bon du ing he eigh simula ion s eps, when he ac i i y p obabili ies we e i e a ed be ween he indi idual s eps o ob ain ha es ing goals
BAU (le column), NFP (middle column), and NDV ( igh column). The g ey ba s depic he de elopmen based on business-as-usual ha es alloca ion (A
BAU
) and ansi ion
p obabili ies de i ed om he pai wise NFI obse a ions. The black ‘‘e o ba s” a e based on he same ansi ion p obabili ies, bu schoolbook-alloca ion (A
SB
) o he ha es s.
The lines abo e he ba s depic he de elopmen , when he ansi ion p obabili ies a e adap ed o he clima e: he ed, black and g een lines e e o RCP2.6, RCP4.5, and
RCP8.5 scena ios. The solid o b oken lines e e o he use o A
BAU
o A
SB
, espec i ely, and he e a e h ee lines pe clima e scena io and ha es alloca ion, depic ing he 10%,
50% and 90% alues o he expec ed empe a u e dis ibu ion.
218 J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224
and oo s o he ees ha es ed. The p opo ion o he di e en
componen s a ec ed is ela ed o he di e en alloca ion o he
ha es s (Fig. 1): in A
SB
, mos o he d ain is ob ained om e-
gene a ion ha es s, which a e essen ially inal ellings o e en-
aged o es s. The ca bon ha es ed in A
SB
he e o e o igina es om
mo e ma u ed o es s, whe e a highe p opo ion o he o al ca -
bon is s o ed in he s em wood. Thus, e en i bo h ha es alloca-
ions yielded he same le el o oundwood, he le el o o al
ca bon ex ac ed a ied due o he di e en p opo ion o ca bon
componen s in he o es s subjec o he ha es s.
4. Discussion
4.1. Modeling clima e-induced o es g ow h a ia ions using a ea-
based Ma ko chain models
To accoun o he e ec s o clima e change, some ea lie simu-
la ion s udies ca ied ou in Finland (Nuu inen e al., 2006; Kallio
e al., 2013) ha e used he models o Ma ala e al. (2005, 2006)
o p edic how inc easing annual mean empe a u e and ambien
CO
2
a ec he o es g ow h. When inco po a ing hese e ec s o
he o es de elopmen scena ios, he s udies men ioned abo e
ha e conside ed ei he immedia e o g adual inc ease in he
g ow h, nei he o which is ealis ic acco ding o he g ow h pa -
e ns obse ed in he pas (Pasanen, 1998; Hen onen e al.,
2017). Al e na i e app oaches conside ing he s ochas ici y o
g ow h ha e also been p esen ed (Pukkala and Kellomäki, 2012),
bu addi ional conside a ions we e needed o implemen hese
e ec s wi h ma ix models ha assume he s a iona i y o he
ansi ions.
Ou app oach used he RCPs and subsequen clima e p ojec-
ions calib a ed o Finland (Ruos eenoja e al., 2016) o de i e
p obabili y dis ibu ions o inc ease in CO
2
and annual mean em-
pe a u e. The dis ibu ions we e sampled o p o ide CO
2
and em-
pe a u e alues o be used as p edic o s o he g ow h e ec
(Ma ala e al., 2005, 2006). By means o sampling and adding he
ob ained g ow h end wi h s ochas ic a ia ion, we we e able o
accoun o he unce ain ies ela ed o he RCP p edic ions.
Finally, o be applicable in ou simula ions, he g ow h se ies we e
no used as such, bu as classi ied o espec i e olume classes. The
classi ica ion s ep undamen ally ‘‘smoo hs” he g ow h se ies, as
he addi ional g ow h modeled o e e y plo is no ans e ed
o he p ojec ions as such. Ins ead, he a ea ac ion ep esen ed
by he plo is di ided acco ding o he p obabili y o he plo o
ansi om he o iginal olume class o a highe class. The p oba-
bili y depends on bo h he dis ance o he ini ial olume alue o
he class limi and he magni ude o he ela i e scena io e ec ,
which u he depends on ac o s such as si e ype and ee compe-
i ion (Ma ala e al., 2005, 2006). In p inciple, his ype o p oba-
bilis ic app oach could mode a e he model-based p edic ions o
clima e-induced g ow h a es, which may o he wise seem o e ly
op imis ic (c ., Pukkala, 2017b). Pukkala (2017b) came o his con-
clusion using an al e na i e app oach, which p edic s ee su i al
a es in addi ion o s aigh o wa d changes in he clima e-
p oduc ion ela ionship due o he ele a ed empe a u e. The
mean annual clima e-induced inc emen s o s em olume we e
in he o de o 3.5–12.5%, bu as much as 30% du ing he simula-
ion pe iod (Pukkala, 2017b). In ou s udy, he mean annual inc e-
men o he o al biomass (compu ed as an a e age o e he age
classes in Fig. 4) was 6.8% and was expec ed o inc ease o 8.4–
9.4% in he las simula ion pe iod depending on which RCP was
assumed. Also he esul s o he de elopmen scena ios, when
compa ed in he end o he simula ions, a e undamen ally in line
wi h Pukkala (2017b). E en hough he igu es a e o e all di icul
o compa e due o di e en biomass componen s and compu a ion
Fig. 6. The de elopmen o o al ca bon du ing he eigh simula ion s eps, when he ac i i y p obabili ies we e ixed in he beginning o he simula ion. Re e o he cap ion o
Fig. 5 o he in e p e a ion o he image.
J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224 219
me hods (in Pukkala, 2017b, he igu es we e based on maximizing
he ne p esen alue wi h a ying in e es a es), he app oach
es ed he e is conside ed p omising and o wa an es s wi h
o he applica ions han hose aiming a Ma ko chain models.
Adap ing he ansi ion p obabili ies o he changed clima e
sligh ly changes he concep o he EFDM, which is o a high
deg ee buil upon he Ma ko ian p ope y, whe e he u u e s a e
depends only on he p esen s a e, no on e en s ha p eceded i .
This p ope y is no a ec ed, bu he e is a equi emen o es i-
ma e mul iple ansi ion ma ices and apply each ma ix sepa-
a ely wi hin he gi en ime s eps. Concep ually, he changes
esul o a ime-inhomogeneous Ma ko chain, which is much less
applied o e en s udied han he heo y o homogeneous Ma ko
chains. Ma ko chain models wi h andom o condi ional ansi-
ions ha e been es ed in o he applica ions han o es y
(Shamshad e al., 2005; Meidani and Ghanem, 2013). To da e,
Liéna d and S igul (2016) a e appa en ly he only ones o
desc ibe ime-inhomogeneous Ma ko chains applied o o es
p ojec ions. Thei model was ope a ed a he o es o pa ch le el,
which is no di ec ly compa able o ou analyses because o la ge
a eas ep esen ed by he ma ix cells and he use o ansi ions
condi ional o managemen ac i i ies in ou pape . The e o e,
he wo k on modeling he ansi ion p obabili ies desc ibed he e
may p o ide an in e es ing con ibu ion as an applica ion o ime-
inhomogeneous Ma ko chain models wi h ac i i y-condi ional
ansi ions.
Fig. 7. The ca bon d ain p esen ed in he lowe ow o Fig. 6 b oken down o indi idual (biomass) componen s. The columns ep esen he ha es ing goals BAU (le ), NFP
(middle), and NDV ( igh ). The ba s con ain he p opo ions o s em, oliage, b anches, s ump, and oo s, espec i ely, om bo om o op, and sepa a ely o inal- ellings
(dashed ba s) and hinnings. The wo ba s o each simula ion pe iod show a compa ison o A
BAU
s. A
SB
wi h ansi ion p obabili ies de i ed om he pai wise NFI
obse a ions ( i s ow); o ini ial ansi ion p obabili ies s. hose assuming he ealiza ion o he 90% alue o he expec ed empe a u e dis ibu ion o RCP8.5 unde A
BAU
and A
SB
(second and hi d ow, espec i ely).
220 J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224
4.2. Limi a ions o ou esul s and u he unce ain ies ela ed o
p ojec ing u u e ca bon balance
The de elopmen o he o es esou ces was p ojec ed un il
a ound 2050. Acco ding o sensi i i y analyses based on his o ical
da a o compa isons o scena io p ojec ions (Nabuu s e al., 2000;
Vauhkonen and Packalen, 2017), simila ma ix model p ojec ions
as applied he e could be used o pe iods o up o 50–60 yea s.
E en i he p ojec ions could hus ha e been made o a sligh ly
longe pe iod and also he clima e scena ios ex end beyond 2050,
he end in many scena ios changes app oxima ely a ound 2050.
Ye , nume ical p edic ions o he GHG concen a ions a e p o-
ided only o yea s 2050 and 2100 (Meinshausen e al., 2011).
Because he p ojec ions we e composed o sequences o 5-yea
pe iods, we eel ha excessi e assump ions would ha e been
ela ed only o modeling he clima e end beyond 2050, o which
eason he p ojec ions we e no ex ended u he .
By ‘unce ain ies’ in ou analyses, we essen ially e e o u u e
de elopmen ha canno be p edic ed using ansi ion p obabili-
ies de i ed om he pai wise obse a ions based on measu e-
men s. Ou ocus was pa icula ly in he e ec s o clima e- and
managemen -induced addi ional ne g ow h o he ca bon s ocked
in abo e- and belowg ound o es biomass. Howe e , bo h he
emphases on ne g ow h and o es biomass also p oduce limi a-
ions owa ds he in e p e abili y o ou esul s, which is discussed
below wi h espec o ea lie li e a u e.
4.2.1. Nega i e impac s o clima e change
Clima e wa ming likely a ec s no only o es g ow h, bu also
heal h: isks o na u al dis u bances and e en calami ies inc ease.
I models o occu ence and deg ee o damages we e a ailable,
hose could be used as addi ional ac i i y and ansi ion p obabil-
i ies, espec i ely, in ou model, and he e o e also he nega i e
impac s could easily be assessed wi h espec o he u u e scena -
ios. Howe e , e en hough con inen -speci ic indica ions on he
inc ease o bo h bio ic and abio ic damages due o clima e wa m-
ing ha e been p esen ed (e.g., Seidl e al., 2017), he e a e no
nume ical es ima es a ailable o be used as p obabili ies. Using a
simila Ma ko chain app oach han in his s udy, he ela ed
e ec s need o modeled indi ec ly unless he damages a e speci i-
cally ela ed o he main axes o he ma ices (e.g., olume and
age). Al hough his can be done in he EFDM ia ou pu coe i-
cien s, hei use may add u he unce ain ies o he p ojec ions.
The use o models de eloped by Ma ala e al. (2005, 2006) in he
way desc ibed in he p e ious sec ions allows di ec modeling o
olume inc ease as a unc ion o CO
2
, empe a u e, and o es -
speci ic cha ac e is ics. Excep o hose models, we a e no awa e
o any o he clima e-adap i e models o Finland ha could be
in eg a ed o p ac ical simula ion sys ems making use o o es
da a collec ed o ex ensi e a eas.
Liéna d and S igul (2016), who used a Ma ko chain based
app oach, ound a di e gence o ±5% be ween he de elopmen
scena ios o mean biomass o ha dwood o es s o Quebec,
Canada, by he beginning o 2090. Howe e , hey assumed he
inc easing CO
2
and empe a u e o a ec mo e on i e a es han
g ow h enhancemen s. In Finland, s udies in eg a ing u u e isks
in o es p ojec ions ha e conside ed especially s o m- ela ed
damages (e.g., Reye e al., 2017). Also pes a acks o pa hogen
in ec ions may be expec ed o inc ease, bu hese a e mo e speci ic
in e ms o occu ence a eas and species, and may he e o e
equi e e y delica e species-speci ic modeling (e.g., Ne alainen
e al., 2015). Al ahahleh e al. (2016), using a o es ecosys em
model ha p edic s e-gene a ion, g ow h, and mo ali y acco ding
o empe a u e sum, ee compe i ion, and soil, ni ogen, and
ambien ligh and CO
2
a ailabili y, concluded ha clima e wa m-
ing a ec ed he ees in no he n and sou he n Finland indi e -
en ly. They elabo a ed hese indings wi h discussion on join
clima e wa ming, si e-speci ic wa e holding capaci y, and
species-speci ic esponses o hese phenomena. As men ioned
abo e, ou analyses did no accoun o ei he bio ic o abio ic
damages o dis u bances excep o added g ow h. We acknowl-
edge ha including only posi i e e ec s o clima e change may
be simplis ic, bu he discussion abo e also sugges s he complex-
i y o conside ing all possible clima e- ela ed impac s.
4.2.2. O he componen s o ca bon balance han biomass
The analysis p esen ed in Sec ion 3.2.3 explains he a ia ions
in ca bon dynamics, when all ca bon (o biomass) componen s
a e included as he o al ca bon. Al hough we acknowledge ha
he possibili y o u ilize all hese componen s especially om hin-
nings can be ques ioned, we ound his analysis bene icial om
wo aspec s. Fi s , an idea on he compu a ional unce ain ies
in ol ed is p o ided: he accu acy o biomass models and con e -
sion ac o s ega ding he di e en componen s may a y (c .,
Neumann e al., 2016). Second, i he componen s a e no ex ac ed
and used o biomass-based p oduc s, hose p o ide he li e and
deb is ha accumula es as dead o ganic ma e and a ec s he soil
ca bon.
Ou analyses a e no comple e wi h espec o he o al ca bon
balance o o es s, as we did no explici ly include he ca bon
seques e ed in he o es soil and p oduc s. Rega ding soil ca bon,
howe e , mainly he ini ializa ion o he ca bon pools is p oblem-
a ic, whe eas simula ing he decomposi ion can be based on exis -
ing soil ca bon models (c ., Pukkala, 2014, 2017a; Akujä i e al.,
2016). Howe e , all a o emen ioned s udies assume ha he cu -
en clima e p e ails and no models simila o hose applied o li -
ing biomass in Sec ion 2.3.2 can be ound om he li e a u e o
p edic ing i he decomposi ion should be assumed o accele a e
o slow down in he wa ming clima e. The dynamics o hese pools
could be es ima ed by means o coe icien s (Pukkala, 2014;
Heinonen e al., 2017), bu when no clima e-adap i e models o
he decomposi ion exis , he changes in he soil ca bon would only
be ela ed o he a ying amoun o ha es s. Howe e , he e ec s
o ha es ing o hese s ocks can, o a ce ain deg ee, be deduced
om he ea lie s udies (Pukkala, 2014, 2017a; Zubiza e a-
Ge endiain e al., 2016). In he simula ions o he de elopmen o
biomass, soil, and p oduc pools unde ou managemen scena ios
(Pukkala, 2017a), he soil ca bon a ied much less han ca bon
s ocked in biomass and p oduc s. In hose simula ions, a as
decomposi ion o all pools was s a ed a e a ha es , esul ing
o a nega i e o al ca bon budge in he sho e m ( h ee o i e
decades), bu a posi i e budge in he longe e m due o he
seques a ion in he g owing ees and ha es ed p oduc s. Thus,
acco ding o Pukkala (2017a), he conclusions depend on he ime
ho izon and also on how much weigh is se o he subs i u ion
e ec s, i.e., he educ ion o consump ion o ossil uels due o
wood-based p oduc s (see also Pukkala, 2014). The examples
abo e illus a e he complexi y and assump ions equi ed o model
he ca bon balance beyond he li ing biomass s ocks, in changing
clima e and in he na ional scale.
Due o ocusing on he ca bon s ocked in abo e- and below-
g ound biomass, ou s udy canno be di ec ly compa ed o hose
epo ed ea lie . O e all, he mul i ude o s udies and di e en
app oaches indica es he challenges in he ela ed modeling ask.
Ea lie ca bon balance s udies o p o ide ins uc ions o o es
managemen (Pukkala, 2014, 2017a; Zubiza e a-Ge endiain
e al., 2016) we e ocused on single o es s ands o small o es
p ope ies (up o a ound 1000 ha). Heinonen e al. (2017) used
NFI11 da a and conside ed he ca bon balance o en i e Finland,
bu assumed di e en ha es alloca ion and no clima e change.
The s udy by Al ahahleh e al. (2016) elied on se e al assump ions
behind he o es ecosys em model used. Compa ed o ha , he
J. Vauhkonen, T. Packalen / Ecosys em Se ices 33 (2018) 213–224 221