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energies
Article
Expected Global Warming Impacts on the Spatial
Distribution and Productivity for 2050 of Five Species
of Trees Used in the Wood Energy Supply Chain
in France
Emmanuel Garbolino 1, * , Warren Daniel 2and Guillermo Hinojos Mendoza 3
1MINES ParisTech / Paris Sciences et Lettres PSL UniversitéParis, Centre for research on Risks and
Crises (CRC), 1 rue Claude Daunesse, CS 10207, 06904 Sophia Antipolis CEDEX, France
2Warren DANIEL, Plant and Ecosystems (PLECO), University of Antwerp, Campus Drie Eiken - C 0.13,
Universiteitsplein 1, BE-2610 Wilrijk, Belgium; warren.daniel@uantwerpen.be
3Universidad Autónoma de Chihuahua, Facultad de Zootecnia y Ecología, Periférico Francisco R. Almada
Km. 1, Chihuahua 31000, Mexico; ghinojos@asessc.net
*Correspondence: emmanuel.garbolino@mines-paristech.fr; Tel.: +33-493-957-475
Received: 30 October 2018; Accepted: 29 November 2018; Published: 2 December 2018
Abstract:
The development of collective and industrial energy systems, based on wood biomass,
knows a significant increase since the end of the 90’s in France, with more than 6000 power plants and
heating plants developed currently. Because these systems are built for a minimal duration of 30 years,
it is relevant to assess the availability of wood resources according to the potential impacts of global
warming on five tree species mainly used in such a supply chain. The assessment of the potential
spatial distribution of the suitable areas of these trees in 2050, by using the IPCC (Intergovernmental
Panel on Climate Change) RCP6.0 scenario (Representative Concentration Pathway), shows an
average decrease of 22% of the plots in comparison with the current situation. The results also point
out that mountain areas would maintain a high probability of the development of four tree species.
The assessment of the Net Primary Productivity (NPP) underlines a potential decrease for 93% of the
plots in 2050, and an increase of this parameter in mountain areas. According to these assumptions,
the proposed ecosystem based methodology can be considered as a prospective approach to support
stakeholders’ decisions for the development of the wood energy supply chain.
Keywords: biomass; climate change; impact; ecosystems; supply chain; sustainability
1. Context and Problematic
Wood biomass for energy systems, in order to produce heat or electricity, represents around 40%
of the renewable energy production in France [
1
]. More than 6000 wood based energy installations
have been established since the beginning of the 2000s in France [
2
] for the collective and industrial
energy production. The aim of accelerating the development of the wood-energy sector for heating
or electricity is to ensure France’s commitments to reduce its greenhouse gas emissions for energy
production. This dynamic relies on a regulatory and tax incentive context and the considerable increase
in forest areas (+6 million ha in 100 years, reference [
3
] Figure 1). Currently, this increase of forest
coverage is induced by the abandonment of a part of rural activities, mainly located in hills and
mountains areas, and by the development of forestry in specific territories. The origins of this rural
abandonment are mainly the transformation of the French rural economic model into an industrial
economic model, since the 1850, and the impacts of the two world wars on the rural population that
induced territorial iniquities between rural and urbanized areas [
4
]. The post-war baby-boom also
favored the migration of the rural population to urban areas in order to earn higher salaries.
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Energies 2018,11, 3372 2 of 17
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rural population that induced territorial iniquities between rural and urbanized areas [4]. The
post-war baby-boom also favored the migration of the rural population to urban areas in order to
earn higher salaries.
Figure 1. Afforestation rate of French territories in 1908 (left) and in 2014 (right), (IGN, 2018,
modified).
This increase of forest areas is essential to ensure the sustainability of combustion systems that
are usually planned to operate for a minimum of 25 to 30 years. According to this situation, it seems
relevant to assess the vegetation dynamics and productivity trends for 2050 in order to support the
sustainability of the wood energy supply chain by taking into account the potential effect of climate
change on the wood resource. The fifth report of the Intergovernmental Panel on Climate Change [5]
introduced scenarios of increasing the annual average temperature between 1.4 °C and 3.1 °C
(baseline 2.2 °C) over a period of 100 years. These scenarios show also heterogeneous variations of
precipitations for the future decades according to the different territories. These two parameters of
temperatures and precipitations are the main parameters that drive plants’ distribution at regional,
national and continental scales [6–8]. This climate change should affect the nature and structure of
ecosystems (species composition) and, in the same time, the functioning of ecosystems. In this
context, some researchers estimated the potential impact of global warming on ecosystem services,
like biomass production for bioenergy [9–12], in Europe and America.
We propose a prospective approach to assess the potential impact of global warming towards
2050 on vegetation dynamics of five tree species, commonly used for energy purposes, and the Net
Primary Productivity (NPP). This approach is applied at the scale of France in order to provide the
main trends of the expected location of the most suitable areas for the development of these species.
The following five tree species have been selected according to their use in the economy of the wood
supply chain for energy systems: Fagus sylvatica L. (beech), Populus nigra L. (Italian poplar), Abies alba
Mill. (Silver fir), Picea excelsa (Lam.) Lk. (spruce), and Pinus silvestris L. (Scots pine). Other species
could be considered, but we want to focus our study on an example of some spontaneous species in
France that are well represented and abundant on the territory. Our aim is also to raise the
awareness of the stakeholders on the vulnerability due to climate change of these trees and the forest
ecosystems where they grow, especially in natural areas where human impacts are minimal
(abandoned areas and protected zones).
Figure 1.
Afforestation rate of French territories in 1908 (left) and in 2014 (right), (IGN, 2018, modified).
This increase of forest areas is essential to ensure the sustainability of combustion systems that
are usually planned to operate for a minimum of 25 to 30 years. According to this situation, it seems
relevant to assess the vegetation dynamics and productivity trends for 2050 in order to support the
sustainability of the wood energy supply chain by taking into account the potential effect of climate
change on the wood resource. The fifth report of the Intergovernmental Panel on Climate Change [
5
]
introduced scenarios of increasing the annual average temperature between 1.4
◦
C and 3.1
◦
C (baseline
2.2
◦
C) over a period of 100 years. These scenarios show also heterogeneous variations of precipitations
for the future decades according to the different territories. These two parameters of temperatures
and precipitations are the main parameters that drive plants’ distribution at regional, national and
continental scales [
6
–
8
]. This climate change should affect the nature and structure of ecosystems
(species composition) and, in the same time, the functioning of ecosystems. In this context, some
researchers estimated the potential impact of global warming on ecosystem services, like biomass
production for bioenergy [9–12], in Europe and America.
We propose a prospective approach to assess the potential impact of global warming towards
2050 on vegetation dynamics of five tree species, commonly used for energy purposes, and the Net
Primary Productivity (NPP). This approach is applied at the scale of France in order to provide the
main trends of the expected location of the most suitable areas for the development of these species.
The following five tree species have been selected according to their use in the economy of the wood
supply chain for energy systems: Fagus sylvatica L. (beech), Populus nigra L. (Italian poplar), Abies alba
Mill. (Silver fir), Picea excelsa (Lam.) Lk. (spruce), and Pinus silvestris L. (Scots pine). Other species
could be considered, but we want to focus our study on an example of some spontaneous species in
France that are well represented and abundant on the territory. Our aim is also to raise the awareness
of the stakeholders on the vulnerability due to climate change of these trees and the forest ecosystems
where they grow, especially in natural areas where human impacts are minimal (abandoned areas and
protected zones).
2. Material and Methods
The assessment of the potential impacts of global warming on the biomass resource requires
studying the potential distribution of species and the evolution of the NPP. The developed methodology
combines two complementary models:
Energies 2018,11, 3372 3 of 17
1.
A model to assess the spatial distribution of the most suitable areas, for the current (2015) and
future (2050) climate situations, related to the climatic behavior of the selected tree species; and
2.
A model of the Net Primary Productivity (NPP) proposed by Leith [
13
] to assess its variation
towards 2050 and for the identification of the potential risk on biomass availability.
We integrate the results of these two models into an index allowing the estimation of the most
suitable areas for the development of the five tree species. This index, proposed by [
9
] and named the
Biomass Development Index (BDI), aims to help decision makers to estimate the potential development
and use of the forest resource on a territory. Applied for the development of the wood energy supply
chain, this index gives information to the different stakeholders to optimize the supply chain by
minimizing wood supply risk.
2.1. Model of Suitable Areas Assessment for Plant Growth
Reference [
14
] developed a probabilistic calibration to quantify the relations of 1874 plants
(herbs, shrubs, and trees species) with 72 climatic variables over a period of 50 years in France.
The current probabilistic calibration encompasses more than 4000 plants and climatic variables
(monthly averages for 30 years of day and night average and extreme temperatures, amount of
freezing days, amount of rainy days, and amount of the precipitations) [
15
,
16
]. The results of this
probabilistic calibration provide a set of 4000 plants able to indicate climatic variables. This set of
bio-indicators represents the fundamental basis for modeling the spatial distribution of suitable areas
for vegetation on French territory.
The characterization of the climatic behavior of a taxon is based on a probabilistic model taking
into account three main ecological assumptions:
•
The effect of an ecological factor on a plant’s frequency follows a unimodal trend, defining an
optimum frequency of plant occurrences in a portion of the range of a climatic variable;
•
the effect of an environmental factor on a plant is gradual, even if the distribution of the plant in
the range of the climatic variable is intermittent; and
•
a plant is a better indicator of an environmental factor if its occurrences are concentrated in a
specific portion of the range of the climatic variable. In other words, if two plants are distributed
in the same range of a climatic variable, the most indicative one shows the highest frequencies at
one or more levels of the range.
The validation of this probabilistic calibration is based on the calculation of the difference between
the observed measures of climate variables and the estimated values by the plants inside the climatic
plots. The result of this validation gives an average accuracy of 75% of the bio-indicators of the climatic
parameters in France.
The use of the inverse relation allows estimating the probability of occurrence of a plant in the
climatic plots. The algorithm selects all the climatic plots that match the climatic range of a plant for all
the climatic variables. The next step is the calculation of the average probability of occurrence of a plant
into a climatic plot, according to the probability of occurrence of a plant into specific values of climatic
variables. This calculation allows identifying the suitable areas for each plant by the calculation of the
average probability of occurrence of a plant into the climatic plots. This algorithm assesses the potential
distribution of suitable areas for a plant in the territory for the current climate and for the climatic
scenario estimated for 2050 and provided by the IPCC members (RCP6.0 scenario—Representative
Concentration Pathway). The climatic variables provided by the IPCC and integrated in our model
are the monthly average of day and night temperatures and the monthly amount of precipitations.
The comparison between the potential spatial distribution of suitable areas for these two periods
enables the identification of the potential consequences of the climate change on the spatial distribution
of the suitable areas of trees and forests for the future.
Energies 2018,11, 3372 4 of 17
One of the most innovative points of this research is to take into account the behavior of a
species when it is abundant: This parameter of abundance of a species allows estimating the spatial
distribution of dense coverage of the trees at the scale of France. This methodology has been evaluated
in a previous publication at a local scale [
9
,
15
,
16
] and needs to be evaluated at a national scale. The
validation step compares the potential spatial distribution of the suitable areas for the tree species,
according to the current climate, with the map of distribution of each species provided by the IFN
(National Forest Inventory). The IFN data are not implemented into the vegetation database and, for
this reason, they can be considered as independent data.
Figure 2shows the maps of the most suitable area of the five tree species (with three levels of
probabilities) and it presents the map of their observed distribution according to the IFN data. A simple
observation of these maps shows that the observed distribution of the species is mainly located in
the high level of probabilities of occurrence of the suitable areas, which confirms the relevance of
the model.
Energies 2018, 11, x FOR PEER REVIEW 4 of 17
for these two periods enables the identification of the potential consequences of the climate change
on the spatial distribution of the suitable areas of trees and forests for the future.
One of the most innovative points of this research is to take into account the behavior of a
species when it is abundant: This parameter of abundance of a species allows estimating the spatial
distribution of dense coverage of the trees at the scale of France. This methodology has been
evaluated in a previous publication at a local scale [9,15,16] and needs to be evaluated at a national
scale. The validation step compares the potential spatial distribution of the suitable areas for the tree
species, according to the current climate, with the map of distribution of each species provided by
the IFN (National Forest Inventory). The IFN data are not implemented into the vegetation database
and, for this reason, they can be considered as independent data.
Figure 2 shows the maps of the most suitable area of the five tree species (with three levels of
probabilities) and it presents the map of their observed distribution according to the IFN data. A
simple observation of these maps shows that the observed distribution of the species is mainly
located in the high level of probabilities of occurrence of the suitable areas, which confirms the
relevance of the model.
(a) Abies alba Mill. (Silver fir)
(b) Fagus sylvatica L. (Beech)
(c) Picea excelsa (Lam.) Lk. (Spruce)
Figure 2. Cont.
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(d) Pinus silvestris L. (Scots pine)
(e) Populus nigra L. (Italian poplar)
Legend
High probability of occurrence of suitable areas.
Average probability of occurrence of suitable areas.
Low probability of occurrence of suitable areas.
Observed distribution of tree species.
Figure 2. Comparison of the maps of the potential suitable areas of five tree species with their
observed distribution provided by the IFN.
Table 1 shows the quantitative results of this comparison: It points out that the observed
populations of these tree species are mainly distributed into the high values of probabilities (form
68% to 91%, with an average of 78% of the IFN plots), and between 9% and 32% in the average level
of probabilities (with an average of 20% of the IFN plots).
Table 1. Quantitative comparison of the observed distribution of the five tree species with the
potential distribution of their suitable areas in France for the current climate.
Abies alba Amount of IFN plots= 3931
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 29 1
average proba 371 9
high proba 3531 90
Fagus sylvatica Amount of IFN plots= 6825
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 31 0
average proba 592 9
high proba 6202 91
Picea excelsa Amount of IFN plots= 4378
Proba classes IFN plots in proba. classes % IFN plots in proba. classes
Figure 2.
Comparison of the maps of the potential suitable areas of five tree species with their observed
distribution provided by the IFN.
Table 1shows the quantitative results of this comparison: It points out that the observed
populations of these tree species are mainly distributed into the high values of probabilities (form 68%
to 91%, with an average of 78% of the IFN plots), and between 9% and 32% in the average level of
probabilities (with an average of 20% of the IFN plots).
Table 1.
Quantitative comparison of the observed distribution of the five tree species with the potential
distribution of their suitable areas in France for the current climate.
Abies alba Amount of IFN plots= 3931
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 29 1
average proba 371 9
high proba 3531 90
Fagus sylvatica Amount of IFN plots= 6825
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 31 0
average proba 592 9
high proba 6202 91
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Table 1. Cont.
Picea excelsa Amount of IFN plots= 4378
Proba classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 148 3
average proba 1144 26
high proba 3086 70
Pinus silvestris Amount of IFN plots= 3217
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 57 2
average proba 823 26
high proba 2337 73
Populus nigra Amount of IFN plots= 1962
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 0 0
average proba 632 32
high proba 1330 68
Only 1 to 2% of the IFN plots’ distribution is located in the low probabilities of occurrence of the
suitable areas for the five species. It is important to explain that for some of the populations located
into a low level of probabilities, or into areas without probabilities of occurrence, are the most often tree
plantations, like it is the case for Spruce (Picea excelsa (Lam.) Lk.) that was introduced in Brittany after
1750 [
17
] and this tree has difficulties to develop in some areas of this territory [
18
]. This validation
step has demonstrated that the methodology to assess the potential suitable areas of the species with
climatic data is relevant. This methodology can be applied with the RCP 6.0 scenario provided in the
last IPCC report [
5
] to assess the possible consequences of global warming on the distribution of the
suitable areas for these five species and for the NPP. We selected the RCP 6.0 scenario because this
scenario is considered as an average scenario.
The first part of the results deal with the climate change impact on the species distribution in
France. This scale allows identifying gradients of probabilities of occurrence of suitable areas for the
five tree species for the current climate and for 2050.
Figure 3presents the maps of the potential distribution of the suitable areas for the five trees
in 2015 and 2050. The first observation of these maps shows a potential contraction of the spatial
distribution of the suitable areas for each species in 2050. This phenomenon affects especially the levels
of high probabilities of occurrence, except for the Italian poplar that may have an increase of plots in
the level of high probabilities of occurrence.
Energies 2018, 11, x FOR PEER REVIEW 6 of 17
low proba 148 3
average proba 1144 26
high proba 3086 70
Pinus silvestris Amount of IFN plots= 3217
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 57 2
average proba 823 26
high proba 2337 73
Populus nigra Amount of IFN plots= 1962
Proba. classes IFN plots in proba. classes % IFN plots in proba. classes
low proba 0 0
average proba 632 32
high proba 1330 68
Only 1 to 2% of the IFN plots’ distribution is located in the low probabilities of occurrence of the
suitable areas for the five species. It is important to explain that for some of the populations located
into a low level of probabilities, or into areas without probabilities of occurrence, are the most often
tree plantations, like it is the case for Spruce (Picea excelsa (Lam.) Lk.) that was introduced in Brittany
after 1750 [17] and this tree has difficulties to develop in some areas of this territory [18]. This
validation step has demonstrated that the methodology to assess the potential suitable areas of the
species with climatic data is relevant. This methodology can be applied with the RCP 6.0 scenario
provided in the last IPCC report [5] to assess the possible consequences of global warming on the
distribution of the suitable areas for these five species and for the NPP. We selected the RCP 6.0
scenario because this scenario is considered as an average scenario.
The first part of the results deal with the climate change impact on the species distribution in
France. This scale allows identifying gradients of probabilities of occurrence of suitable areas for the
five tree species for the current climate and for 2050.
Figure 3 presents the maps of the potential distribution of the suitable areas for the five trees in
2015 and 2050. The first observation of these maps shows a potential contraction of the spatial
distribution of the suitable areas for each species in 2050. This phenomenon affects especially the
levels of high probabilities of occurrence, except for the Italian poplar that may have an increase of
plots in the level of high probabilities of occurrence.
Abies alba Mill. (Silver fir)
2015 2050
Fagus sylvatica L. (Beech)
Figure 3. Cont.
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2015 2050
Picea excelsa (Lam.) Lk. (Spruce)
2015 2050
Pinus silvestris L. (Scots pine)
2015 2050
Populus nigra L. (Italian poplar)
2015 2050
Legend
High probability of occurrence of suitable areas.
Average probability of occurrence of suitable areas.
Low probability of occurrence of suitable areas.
Figure 3. Potential distribution of suitable areas for the five species in 2015 and 2050.
Figure 4 and Table 2 underline these results by the use of statistics about the amount of plots of
potential suitable areas in 2015 and 2050 for each species.
Figure 3. Potential distribution of suitable areas for the five species in 2015 and 2050.
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Figure 4and Table 2underline these results by the use of statistics about the amount of plots of
potential suitable areas in 2015 and 2050 for each species.
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Figure 4. Amount of plots for the potential distribution of the suitable areas of the five tree species in
2015 and 2050.
Table 2 shows an average 22% loss of plots of potential suitable areas for the five species in 2050.
There are some differences of the potential distribution of suitable area between the species:
• Silver fir may have a decrease of 29% of the total amount of its suitable areas, with a decrease of
61% in high probable areas and a decrease of 64% in average probabilities. There could be an
increase of 63% of low probabilities to find suitable areas for this tree. This result underlines a
risk of reduction of the areas of this tree and a potential risk of stress in the locations where this
species is observed currently and should not find a high level of probabilities of suitable areas
in 2050;
• Scots pine and beech may have a low decrease of their amount of suitable areas (−11% and −7%).
However, this decrease would affect only high probabilities to find suitable areas for these taxa.
A potential increase of the average of low probabilities could be possible in the future. These
results attest a risk for these trees in terms of stress in the areas where the probabilities to find a
suitable environment may decrease in the future;
• Spruce may have a significant decrease (−53%) of its probabilities to find suitable areas in 2050,
especially for high and average probabilities (−58% and −71%, respectively), which could
underline a high risk for this species to expand and to grow in the half part of its current
distribution; and
• Italian poplar could have a little decrease (−8%) of the potential suitable areas, but this reduction
should affect only low and average probabilities. This tree may have an increase of its high
probabilities (+11%) to find suitable areas that may compensate the decline of some
probabilities of occurrence.
Table 2. Estimated amount of plots for suitable areas of the five tree species in 2015 and 2050 (diff =
difference between 2050 and 2015; diff % is the percentage of this difference).
Abies alba 2015 2050 diff diff %
all classes 2015 643,868 455,297 −188,571 −29
low proba 173,813 283,478 109,665 63
average proba 353,171 126,359 −226,812 −64
high proba 116,884 45,460 −71,424 −61
Fagus sylvatica 2015 2050 diff diff %
all classes 2015 734,287 650,278 −84,009 −11
Figure 4.
Amount of plots for the potential distribution of the suitable areas of the five tree species in
2015 and 2050.
Table 2.
Estimated amount of plots for suitable areas of the five tree species in 2015 and 2050
(diff = difference between 2050 and 2015; diff % is the percentage of this difference).
Abies alba 2015 2050 diff diff %
all classes 2015 643,868 455,297 −188,571 −29
low proba 173,813 283,478 109,665 63
average proba 353,171 126,359 −226,812 −64
high proba 116,884 45,460 −71,424 −61
Fagus sylvatica 2015 2050 diff diff %
all classes 2015 734,287 650,278 −84,009 −11
low proba 152,052 166,362 14,310 9
average proba 341,095 390,912 49,817 15
high proba 241,140 93,004 −148,136 −61
Picea excelsa 2015 2050 diff diff %
all classes 2015 465,780 217,597 −248,183 −53
low proba 224,312 136,329 −87,983 −39
average proba 159,495 46 859 −112,636 −71
high proba 81,973 34,409 −47,564 −58
Pinus silvestris 2015 2050 diff diff %
all classes 2015 740,099 689,576 −50,523 −7
low proba 168,819 190,661 21,842 13
average proba 362,532 410,182 47,650 13
high proba 208,748 88,733 −120,015 −57
Populus nigra 2015 2050 diff diff %
all classes 2015 738,069 679,247 −58,822 −8
low proba 41,393 29,481 −11,912 −29
average proba 264,039 168,343 −95,696 −36
high proba 432,637 481,423 48,786 11
Table 2shows an average 22% loss of plots of potential suitable areas for the five species in 2050.
There are some differences of the potential distribution of suitable area between the species:
Energies 2018,11, 3372 9 of 17
•
Silver fir may have a decrease of 29% of the total amount of its suitable areas, with a decrease of
61% in high probable areas and a decrease of 64% in average probabilities. There could be an
increase of 63% of low probabilities to find suitable areas for this tree. This result underlines a
risk of reduction of the areas of this tree and a potential risk of stress in the locations where this
species is observed currently and should not find a high level of probabilities of suitable areas
in 2050;
•
Scots pine and beech may have a low decrease of their amount of suitable areas (
−
11% and
−
7%).
However, this decrease would affect only high probabilities to find suitable areas for these taxa.
A potential increase of the average of low probabilities could be possible in the future. These
results attest a risk for these trees in terms of stress in the areas where the probabilities to find a
suitable environment may decrease in the future;
•
Spruce may have a significant decrease (
−
53%) of its probabilities to find suitable areas in
2050, especially for high and average probabilities (
−
58% and
−
71%, respectively), which could
underline a high risk for this species to expand and to grow in the half part of its current
distribution; and
•
Italian poplar could have a little decrease (
−
8%) of the potential suitable areas, but this reduction
should affect only low and average probabilities. This tree may have an increase of its high
probabilities (+11%) to find suitable areas that may compensate the decline of some probabilities
of occurrence.
The second part of the results deals with the potential impact of global warming on the evolution
of NPP.
2.2. Model of Net Primary Productivity Assessment
The net primary productivity (NPP) of an ecosystem is the production of biomass that all
photosynthetic organisms of this ecosystem produce per unit area and per unit time. It depends
on the nature of exploited ligneous species, and environmental factors (nutrition, water, and energy
conditions related to climate and soil. The main determining physical factors are temperature and
water availability. In the early 70 s, Lieth proposed a model of climatic NPP integrating this pair of
variables: The Miami Model [
13
]. This model was the first global scale empirical model of terrestrial
ecosystems’ productivity where NPP was considered as a function of the annual mean temperature,
T (in degrees Celsius), and annual mean precipitation, P (in mm).
The model equation is:
NPP = min (NPPT, NPPP) (1)
NPPT= 3000 ×(1 + e(1.315 −0.119 ×T))−1(2)
NPPP= 3000 ×(−e(−0.000664 ×P)) (3)
Thanks to the Miami Model equation, temperature, and rainfall data, we estimated the current
NPP and the NPP for 2050 with global warming scenarios provided by [4].
For the current climate, Figure 5shows that the highest values of NPP are mainly located in the
hills and mountains of a large part of the territory. It also shows that low and average values are more
located in the plains and hills of the Mediterranean area, a large part of the oceanic area, and in the
North and North East areas of the country.
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NPP
2015 2050
NPP difference between 2015 and 2050
Figure 5. Maps of NPP estimated for 2015 and for 2050 (in white: Low values; in light blue: Average
values; in dark blue: High values), and map of the difference of NPP (2050-2015: In white: Decrease
of NPP; in blue: Increase of NPP).
For 2050, according to the RCP6.0 scenario, a decrease of high levels of NPP could occur, but an
increase of NPP could be observed in mountain areas, as it is shown with the calculation of the
difference between the 2015 and 2050 values of NPP.
Table 3 underlines the potential decrease of NPP for almost 93% of the plots in France in 2050,
and a potential increase of NPP only for 7% of the plots, mainly located in hills and mountain areas.
Table 3. Statistics of the potential evolution of the NPP between 2015 and 2050 (NPP is expressed in
g/m
2
/year).
NPP amount of plots = 795,616
Classes of
difference plots % of plots
[−121, −1] 738,465 92.8
[−1, 1] 453 0.1
[1, 180] 56,698 7.1
3. Results and Discussion on the Potential Development and Use of the Five Tree Species for
Energy Purpose within 2050
The identification of the territories that would be suitable for the development of the wood
energy supply chain is based on the use of a spatial index, named the Biomass Development Index –
BDI [9]. This index combines the probabilities of occurrence of the suitable areas for the five tree
Figure 5.
Maps of NPP estimated for 2015 and for 2050 (in white: Low values; in light blue: Average
values; in dark blue: High values), and map of the difference of NPP (2050-2015: In white: Decrease of
NPP; in blue: Increase of NPP).
For 2050, according to the RCP6.0 scenario, a decrease of high levels of NPP could occur, but
an increase of NPP could be observed in mountain areas, as it is shown with the calculation of the
difference between the 2015 and 2050 values of NPP.
Table 3underlines the potential decrease of NPP for almost 93% of the plots in France in 2050,
and a potential increase of NPP only for 7% of the plots, mainly located in hills and mountain areas.
Table 3.
Statistics of the potential evolution of the NPP between 2015 and 2050 (NPP is expressed in
g/m2/year).
NPP Amount of Plots = 795,616
Classes of difference plots % of plots
[−121, −1] 738,465 92.8
[−1, 1] 453 0.1
[1, 180] 56,698 7.1
3. Results and Discussion on the Potential Development and Use of the Five Tree Species for
Energy Purpose within 2050
The identification of the territories that would be suitable for the development of the wood energy
supply chain is based on the use of a spatial index, named the Biomass Development Index – BDI [
9
].
This index combines the probabilities of occurrence of the suitable areas for the five tree species and
the level of NPP. In this frame, the best areas for the development of the supply chain in the future will
be the one where the probabilities of occurrence of trees are the highest, and where the NPP is also at
the maximum.
Energies 2018,11, 3372 11 of 17
The spatial index of biomass development is based on this formula:
BDI = PVT ×NPP (4)
where:
P
VT
= probability of occurrence of the suitable areas for the five tree species. This variable is
discretized into the following modalities: 1 (low probability), 2 (medium probability), and 3 (high
probability).
NPP = the values are discretized into three classes: 1 (low NPP), 2 (medium NPP), and 3 (high
NPP).
Figure 6presents the maps of BDI for the five tree species selected in our research.
Energies 2018, 11, x FOR PEER REVIEW 11 of 17
species and the level of NPP. In this frame, the best areas for the development of the supply chain in
the future will be the one where the probabilities of occurrence of trees are the highest, and where
the NPP is also at the maximum.
The spatial index of biomass development is based on this formula:
BDI = PVT × NPP (4)
Where:
PVT = probability of occurrence of the suitable areas for the five tree species. This variable is
discretized into the following modalities: 1 (low probability), 2 (medium probability), and 3 (high
probability).
NPP = the values are discretized into three classes: 1 (low NPP), 2 (medium NPP), and 3 (high
NPP).
Figure 6 presents the maps of BDI for the five tree species selected in our research.
Abies alba Mill. (Silver fir) Fagus sylvatica L. (Beech)
Picea excelsa (Lam.) Lk. (Spruce) Pinus silvestris L. (Scots pine)
Populus nigra L. (Italian poplar)
Figure 6. Cont.
Energies 2018,11, 3372 12 of 17
Energies 2018, 11, x FOR PEER REVIEW 12 of 17
Legend
BDI values Interpretation
1 Areas with a very low probability of forest development and productivity.
2 Areas with a low probability of forest development and productivity.
3 Areas with a slightly low probability of forest development and productivity.
4 Areas with a medium probability of forest development and productivity.
6 Areas with a high probability of forest development and productivity.
9 Areas with a very high probability forest of development and productivity.
Figure 6. Maps of BDI estimated for 2050 related to Fagus sylvatica L. (beech), Populus nigra L. (Italian
poplar), Abies alba Mill. (Silver fir), Picea excelsa (Lam.) Lk. (spruce), and Pinus silvestris L. (Scots pine).
The maps of BDI show for Abies alba Mill. (Silver fir), Fagus sylvatica L. (beech), Picea excelsa
(Lam.) Lk. (spruce), and Pinus silvestris L. (Scots pine) that the best suitable areas in 2050 for their
development and the development of the wood energy supply chain (classes 6 and 9 of the BDI)
should be mainly located in the hills and mountains in the Alps, Massif Central, and the Pyrenees.
The lowest classes of the BDI should be distributed in the plains, hills, base of mountains, and
valleys of other territories of France. For Populus nigra L. (Italian poplar), the situation should be
different with almost the loss of areas with high and very high probabilities of development and
productivity of forest (classes 6 and 9 of the BDI). Only classes 3 and 4 of the BDI (areas with a
slightly low or a medium probability of forest development and productivity) should be well
represented in 2050, especially in mountain areas with a BDI equal to 4.
Table 4 gives the amount and percentages of plots according to the BDI classes and for each tree
species. For each percentage, we calculate the uncertainty (δ%) of the potential distribution of the
suitable areas by considering the accuracy level of the climatic calibration for each species. When δ%
is not mentioned, it means that this parameter is negligible.
Table 4. Statistics of the potential BDI of the five tree species in 2050.
BDI Values
Abies alba Fagus sylvatica Picea excelsa Pinus silvestris Populus nigra
plots % δ% plots % δ% plots % δ% plots % δ% plots % δ%
1 283
,
478 62 ± 12 478
,
056 74 ± 25 136
,
210 63 ± 7 189
,
093 27 ± 8 10
,
469 2
2 124
,
027 27 ± 5 111
,
565 17 ± 6 35
,
422 16 ± 2 408
,
326 59 ±
18
242
,
894 36 ± 7
3 5077 1
14
,
962 2±1 287 <1
47
,
927 7±2 415 789 61 ± 12
4 90 <1 39 <1 342 <1 17 <1 333 <1
Figure 6.
Maps of BDI estimated for 2050 related to Fagus sylvatica L. (beech), Populus nigra L. (Italian
poplar), Abies alba Mill. (Silver fir), Picea excelsa (Lam.) Lk. (spruce), and Pinus silvestris L. (Scots pine).
The maps of BDI show for Abies alba Mill. (Silver fir), Fagus sylvatica L. (beech), Picea excelsa (Lam.)
Lk. (spruce), and Pinus silvestris L. (Scots pine) that the best suitable areas in 2050 for their development
and the development of the wood energy supply chain (classes 6 and 9 of the BDI) should be mainly
located in the hills and mountains in the Alps, Massif Central, and the Pyrenees. The lowest classes of
the BDI should be distributed in the plains, hills, base of mountains, and valleys of other territories of
France. For Populus nigra L. (Italian poplar), the situation should be different with almost the loss of
areas with high and very high probabilities of development and productivity of forest (classes 6 and 9
of the BDI). Only classes 3 and 4 of the BDI (areas with a slightly low or a medium probability of forest
development and productivity) should be well represented in 2050, especially in mountain areas with
a BDI equal to 4.
Table 4gives the amount and percentages of plots according to the BDI classes and for each tree
species. For each percentage, we calculate the uncertainty (
δ
%) of the potential distribution of the
suitable areas by considering the accuracy level of the climatic calibration for each species. When
δ
% is
not mentioned, it means that this parameter is negligible.
Table 4. Statistics of the potential BDI of the five tree species in 2050.
BDI
Values
Abies alba Fagus sylvatica Picea excelsa Pinus silvestris Populus nigra
plots % δ% plots % δ% plots % δ% plots % δ% plots % δ%
1
283,478
62 ±12
478,056
74 ±25
136,210
63 ±7
189,093
27 ±8 10,469 2
2
124,027
27 ±5
111,565
17 ±6 35,422 16 ±2
408,326
59 ±18
242,894
36 ±7
3 5077 1 14,962 2 ±1 287 <1 47,927 7 ±2
415,789
61 ±12
4 90 <1 39 <1 342 <1 17 <1 333 <1
6 2494 1 4543 1 11,127 5 ±1 1 153 <1 9604 1
9 40,131 9 ±2 41,113 6 ±2 34,209 16 ±2 43,060 6 ±2 158 <1
Energies 2018,11, 3372 13 of 17
These statistics underline that:
•
Abies alba Mill. (Silver fir), Fagus sylvatica L. (Beech), and Pinus silvestris L. (Scots pine): These three
species should have around 90% of their plots distributed in very low and low BDI classes. They
would have, respectively, 10%, 7%, and 6% of their plots that should be located into the highest
classes of the BDI in 2050 (high and very high probabilities of development and productivity of
forest);
•
Picea excelsa (Lam.) Lk. (Spruce): This tree should have around 80% of its plots distributed in very
low and low BDI classes in 2050 and 21% of its plots distributed into high and very high levels of
BDI; and
•
Populus nigra L. (Italian poplar): This tree should have around 95% of its plots located into areas
with a slightly low or a medium probability of development and productivity of forest (BDI levels
3 and 4) in 2050. Unlike the other four tree species, Italian poplar should have less than 2% of
its plots located into high levels of BDI in 2050, which would represent a significant issue for the
development and use of such a species for energy purposes.
The results have pointed that global warming, established with the RCP6.0 IPCC scenario, may
have a significant effect for both trees’ spatial distribution and NPP towards 2050. They show the
potential decrease of suitable areas for the five tree species in 2050, with an average plot loss of 22%.
The model of NPP also underlines a potential decrease of NPP for almost 93% of the plots in the
whole country.
Other studies [
10
–
12
] have assessed the potential consequences of global warming on bioenergy
crops in Europe and North America. The methodology of this research is based on the use of
characterization of bioclimatic envelops of each species, and the calculation of the areas that would be
suitable or not suitable for their growth according to climate data.
In our study, we also considered other elements for the purposes of determining the potentiality
of the development of the supply chain: Firstly, the climatic characterization of plants in France is
based on a probabilistic calibration, which is a very accurate methodology to perform bio-indicators of
climatic variables. The model to assess the suitable areas of each species has been validated with data
from the National Forest Inventory. Secondly, we also estimated the evolution of the NPP in order to
assess the ecosystem productivity. We have identified a potential decrease of NPP in almost 93% of the
whole country. Other studies have shown a decrease of NPP in the case of drought and heat waves
for the current climate [
19
–
22
] and for the future [
23
]. Reference [
24
] gave a comprehensive review of
observed and potential impacts of climate change on growth, productivity, and suitability of species
for both current and future climate on forest ecosystems through Europe. These authors mentioned the
current decline of radial growth of beech in France and in Spain and the migration of some tree species
that need to find suitable areas for their growth. According to the scenarios of climate warming, they
underline the interest to take into account CO
2
atmospheric concentration in the models to predict the
NPP in Europe, even if some uncertainties remain on the capability of tree species to use this increase
of CO
2
resource for their growth. In the case of constant CO
2
concentration, many places may have a
decrease of NPP due to the global warming, but, if this parameter is integrated into the model, a lot
of forest areas may have an increase of their NPP, apart some places from the south of the European
continent where water resources limit their growth. Tree species’ response to global warming for the
spatial distribution of their suitable areas shows a potential shift in Northern latitudes or in higher
altitudes, and a probable reduction in Central and Southern Europe towards 2100. Reference [
25
]
argued that the growth of silver fir and scots pine will be altered within the end of the century due to
the effect of warm and dry conditions in the North-East part of Spain. The results of their projections
for these two species show a potential dieback and contraction of the spatial distribution in the most
thermophilous areas of their study. This result questions our model and our results on the probable
similarities of the future ecological situations for these two species in France in 2050.
Energies 2018,11, 3372 14 of 17
More specifically, in France, regarding the 2003 heat wave episode, reference [
26
] argued that
this phenomenon induced an increase of foliage loss and branch mortality for some tree species,
like scots pine. Other studies attested the role of drought in the decrease of radial growth for silver
fir [
27
] and beech [
28
]. These studies underline the role of temperature increase and precipitation
decrease on the reduction of NPP and the increase of the death rate of trees. Reference [
29
] also
published a study based on the use of models estimating the annual carbon storage in forests under
different management scenarios according to the current climate and the scenario of climate change
for 2050 (RCP 8.5). The results show that in France, independently of the forest management technics,
climate change will induce a significant decrease of forest productivity in comparison with the current
climatic situation. Reference [
30
] focused their study on the Mediterranean area showing the potential
impacts of global warming on the reduction of stem growth of some oaks, even if the concentration of
atmospheric CO
2
will increase. The authors also mention the uncertainty related to this parameter and
to the acclimation process that the trees may know within the end of the 21st century.
The current development of collective energy systems based on wood biomass in France will
have to face this phenomenon (wood biomass production reduction) if these trends will be observed
in the future. However, in some parts of the French territory, especially in mountain areas, these tree
species will be able to grow well in the future. This potential shift of suitable areas in mountain areas
for these five species must be considered for the development of the supply chain in order to establish
new management planning of forest resources. Researches on supply chain optimization for the use
of wood as a fuel are mainly based on the integration of different parameters, like the distance from
the wood resource to the road network or the cost of transportation and processes, and some physical
parameters of the territory, like the slopes that constrain the accessibility to the resource [
31
–
38
]. These
parameters (transportation costs, distances, slopes
. . .
) can have a significant impact on the price of
the wood biomass delivered to the collective and industrial energy systems. For this reason, these
parameters are often implemented into a set of models (based on linear or nonlinear programming,
heuristic approaches, multicriteria decision analysis etc.) dedicated to the optimization of the supply
chain according to the market of wood biomass. These models integrate also the availability and
accessibility of the wood biomass, which is a key factor to ensure a sustainable business model. They
can estimate the distance thresholds to consider the wood supply to the heating and power plants to
minimize the transportation costs.
However, these models do not explicitly consider the potential shift of suitable areas for the tree
species and/or the potential variation of their NPP through 2050. Therefore, it seems to be relevant
to take into consideration the estimation of the spatial variation of suitable areas for trees’ growth
to anticipate the potential effects of global warming on wood resource availability and accessibility.
Variations of distances from resources to energy systems and wood availability in a few decades can
have impacts on the market of wood resources. In this context, our results can contribute to optimize
the supply chain and help stakeholders to select the best areas to establish the energy systems by
considering the parameters that influence the cost of biomass, like the distances between the wood
resource, the biomass platform, and energy systems.
A last point must be mentioned: The spatial distribution of natural protected areas. Many of
the National and Regional Parks and other Natura 2000 sites are located in French mountain areas.
This fact may induce a reduction of the potential availability of the resource in the future for an
energy purpose.
4. Conclusions and Perspectives
The short and mid-term availability of the wood resource, which favors a short economic cycle
and needs to assess local energy needs, is strategic for the industry and municipal boards because
energy systems based on wood resources are developed for at least 30 years.
This study shows the necessity to evaluate the potential impact of climate change on the
availability of the wood resource to optimize the development of the wood energy supply chain
Energies 2018,11, 3372 15 of 17
in France. Our results estimate a potential reduction of suitable areas for the five species for 2050,
with a significant shift of these areas. These assumptions, if they will be observed in the future, allow
identifying the spatial distribution of areas where these species may know constraints or opportunities
to survive and to grow.
The problem of wood resources must also be related to the increase of the vulnerability of forests
to wildfires due to the impact of climate warming [
15
,
16
,
39
]. This hazard may increase in frequency
and spatial representation in the future. The development of biomass extraction for the wood energy
supply chain should represent a way, among others, to reduce the amount of fuel in natural areas and
help the territory to be more resilient. The potential impacts of climate change on forest areas thus
require developing adaptation strategies based on forest management techniques and decisions that
must be elaborated by involving researchers, forest managers, industries, institutions, and territorial
administrations [
40
]. According to this author and to our opinion, the adaptation of forest management
to global warming impacts based on multidisciplinary researches needs to be developed and expanded.
The presented approach here follows this aim because it can help decision makers and
stakeholders to ensure the ecological transition of the energy production of the French territory
with a prospective frame. These results could be integrated into a global risk assessment approach
with a prospective dimension in order to support the sustainability of the wood energy supply chain.
The results mainly claim to a potential contraction of suitable areas and, in this frame, it should be
also interesting to assess the potential colonization of invasive species of trees and their effects on both
biodiversity and ecosystem services in the perspective of the development of wood energy systems.
The corollary of this research is the need to develop management strategies to adapt forests to global
warming and ensure a sustainable use of the wood resource.
Author Contributions:
E.G. contributed to the general redaction of the paper, the model of plants probable
distribution, the definition of the Biomass Development Index (BDI) and the statistical analysis of all data.
W.D. contributed to the general redaction of the paper and the model Net Primary Productivity (NPP). G.H.M.
contributed to the general redaction of the paper and the statistical analysis of all data.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
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