Climate effects from biomass and other energy sources
Main Report
Main Report
1 July 2013
REPORT
Most of the energy sources that are connected to non-fossil fuels are often considered climate-neutral, despite the fact that a large proportion of global emissions of greenhouse gases are not related to the use of fossil fuels. Therefore it is
crucial to the selection of the right measures to tackle climate change, that the
overall climate impact of each energy source is calculated and assessed on an as
uniform, transparent and scientifically based basis as possible. This task is already underway in the scientific world and in international political institutions
such as the EU, and it is the intention of CONCITO with this report to further
qualify and disseminate these calculations. The report includes scenario calculations for a wide range of biofuels that show that virtually none of the bio based
energy sources are climate neutral using the assumptions applied in the study.
In addition, several of the energy sources have a climate impact, which is on par
with or higher than the fossil fuels they are intended to replace.
Author:
Torben Chrintz
Annex report by:
Jannick H. Schmidt and
Miguel Brandão from
2.-0 LCA consultants
The report is funded by:
Fiolstræde 17B · DK-1171 København K
Tel: +45 29 89 67 00 · [email protected] · www.concito.dk
Climate effects from biomass and other energy sources – Main Report
Contents
1. Introduction ................................................................................................. 3
2. LCA analyses of energy sources ................................................................... 5
3. Emission of greenhouse gases and the carbon cycle ................................... 7
4. Method ........................................................................................................ 11
4.1. Functional unit ..................................................................................... 11
4.2. Uncertainties ........................................................................................12
4.3. Consequential approach to modelling .................................................13
4.4. Cut-off criteria ......................................................................................14
4.5. Methodology for quantifying greenhouse gas emissions .....................14
5. Method for modelling Land Use Change (LUC) and biogenic carbon .......14
5.1. Time dependent emission of CO2......................................................... 15
5.2. Indirect changes in land use (iLUC).....................................................19
6. Characteristics for the individual biofuels ..................................................21
6.1. Wood pellets .........................................................................................21
6.2. Wood chips .......................................................................................... 25
6.3. Straw.................................................................................................... 25
6.4. Biogas .................................................................................................. 26
6.5. Bioethanol ........................................................................................... 28
6.6. Biodiesel .............................................................................................. 29
6.7. Coal, gas, oil, wind and solar power .................................................... 29
7. Results .........................................................................................................31
7.1. Electricity based on cultivated wood ................................................... 33
7.2. Electricity based on residual products from forestry and agriculture 37
7.3. Electricity based on biogas .................................................................. 40
7.4. Electricity based on coal, gas, wind and solar ..................................... 44
7.5. Liquid fuels: Biodiesel ......................................................................... 46
7.6. Liquid fuels: Bioethanol ...................................................................... 49
7.7. Liquid fuels: Diesel and petrol............................................................. 52
7.8. Comparaison with literature review from JRC ................................... 54
8. Discussion and conclusion......................................................................... 55
9. References .................................................................................................. 58
10. Annex 1: LCA screening of biofuels ...........................................................61
11. Annex 2: Conclusions from JRC ................................................................61
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Climate effects from biomass and other energy sources – Main Report
1. Introduction
The burning of fossil fuels emits greenhouse gases in different amounts per unit
of energy. Coal emits the most, followed by oil and then gas, which emits approximately half of what coal emits. In both Danish, European and international
climate policy, all renewable energy sources and most types of biomass are considered CO2-neutral, although this is not necessarily the case in practice
(Ref/2/). This can lead to climate policy decisions, which ultimately will have an
insufficient effect in terms of reducing emissions of greenhouse gases to the atmosphere.
An example of an assessment that has led to the wrong instruments being implemented and to bad investments is the use of biodiesel in the EU, where the
production and use of biodiesel have been promoted by building an industry
and infrastructure, only in order to subsequently acknowledge that biodiesel
does not have the anticipated climate benefit when the impact of all sources of
the production are included in the calculations.
This report demonstrates that this phenomenon is likely not to be limited to
biodiesel, but will also be applicable to a number of alternative and renewable
energy sources that today are considered CO2-neutral or climate neutral.
Despite the potentially serious consequences of miscalculating the effect of the
individual energy sources, there is no recognized ranking of climate impacts
available in Denmark today. With this report CONCITO intends to contribute to
the build-up of such a recognized ranking. This report calculates the climate impact from a variety of sources of energy based on uniform and transparent
methods with open and well-defined assumptions and with the intention that all
available, relevant and significant sources are included, including the so-called
indirect Land Use Change (iLUC)1.
The objective of this report can be summarized as follows:
 To determine the extent to which biofuels are CO2-neutral.
 To identify factors with particularly significant effects on greenhouse gas
emissions from biofuels.
In this context it should be mentioned that the European Commission's Joint Research Centre (JRC)
(ref/21/) points to three additional important factors that have not been included in the calculations in
annex 1. One is that bioenergy could potentially displace timber, which according to the JRC could increase CO2 emissions from bioenergy considerably (a view which is also described in (ref/24/). The
second is that bioenergy does not necessarily only displace fossil fuels, but also other forms of energy
such as solar, wind and nuclear power (due to fixed support frameworks / tax exemptions for renewable
energy and fixed targets for shares of renewable energy), and that the future reference for emissions
from fossil fuels can be both higher (e.g. tar sands) or lower (better efficiency, more gas, CCS etc.), when
looking to the future. Moreover, the potential impacts of changes in the albedo, and uncertainties regarding future cultivation practices/climate effects in forests and on arable land can also go both ways.
1
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
To undertake an indicative comparison of selected biofuels and their fossil alternatives.
It should be emphasized that the purpose is not to make a general ranking of
biofuels - but the identification of the above factors of considerable significance,
as well as the indicative comparison with fossil fuels can be used to identify the
conditions under which certain biofuels will have greater or lesser emission levels than others – conditional on a number of assumptions. Scenarios and results
are not necessarily linked to Denmark, but must be seen in an international context.
The analyzed energy sources are:
 Cultivated wood (heartwood + residues) represented by wood pellets of
various raw materials
 Forest residues represented by wood chips
 Straw
 Biogas from manure, maize and organic municipal waste
 First and second generation bioethanol
 Biodiesel
 Wind energy
 Solar power from photovoltaic cells
 Fossil fuels.
The calculations are carried out in a manner that allows the timing of greenhouse gas emissions to be weighted by using different time horizons. Results are
shown for time horizons of respectively 20 and 100 years. Detailed calculations
are mainly carried out by Jannick Schmidt2 and Miguel Brandão3 from 2.0 LCA
Consultants. Their background report with description of methods, scientific
method and detailed calculations are attached as annex 1. JRC (Ref/21/) has
just completed a study of a similar nature, focusing on bioenergy from forestry
and based on a critical review of existing literature, and their conclusions are attached as annex 2.
Jannick H Schmidt has been the CEO of 2.-0 LCA Consultants since 2008. He is an engineer by training with a degree in environmental planning from 2002 from Aalborg University. He obtained his PhD
in 2007 with a study of the life cycle assessment of rapeseed oil and palm oil with special focus on system boundaries, modelling and land use change. Besides managing 2.-0 LCA Consultants, he is an associate professor at Aalborg University. Jannick’s main expertise and experience is within life cycle assessments of agricultural products, biofuels, plastics, basic metals and waste systems, and the development of LCA methods for LCIA modelling, indirect land use change (iLUC), LCA databases, LCIA methods for biodiversity and input-output based LCA .
3 Miguel Brandão has worked on life cycle assessments of land use systems since 2006 as part of his
PhD and subsequently at the Joint Research Centre (JRC) of the European Commission. He has developed methods to quantify indirect land use changes (iLUC) and to assess the impacts of land use in
LCA. His focus is on the demand of land, especially the consequences of using land for food, feed, fuel,
timber and carbon sinks on the economy, ecosystems and climate. He is also currently working on developing guidelines for consequential LCA and for sustainability assessments. At present, Miguel is an
associate editor on LCA of the Journal of Industrial Ecology, and is a member of the Steering Committee of SETAC Europe LCA.
2
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It is relevant to include in the calculation many more scenarios with varying assumptions than those selected for the present study, but unfortunately this has
not been within the allocated scope of this work. The selected scenarios and assumptions are thus those that CONCITO found relevant and timely when the
work was initiated in late 2011, and which form a good basis for further calculations. It should be noted that the very precise figures presented in the results are
only valid for the given methodological and data-related assumptions. The
methodological uncertainty is generally considered to be relatively small, while
on the data side greater uncertainties exist (e.g. the identification of affected regions, yields, cultivation practices, etc.). As a reference case, we have prioritized
references with specific mathematical calculations rather than references based
solely on conceptual models.
The report reflects the professional judgment of the CONCITO secretariat based
on dialogue with members of CONCITO and other stakeholders. As the members of such a broad organization as CONCITO will not always be able to academically or politically agree, CONCITO’s members can in no way be held accountable for the final conclusions and recommendations of the report.
2. LCA analyses of energy sources
Among experts within Life Cycle Assessments (LCA) there is an increasingly
prevalent view that the term "CO2-neutral energy sources" rarely adequately
corresponds to reality, and that it is inappropriate that the total greenhouse gas
emissions related to the various energy sources are not included in the prioritization of the energy and climate policy instruments (e.g. Ref/2,21/).
As early as November 2011 CONCITO issued a report (Ref/1/), which, supported
by newer LCA analyzes and the EU Scientific Committee (Ref/2,3,4/), showed
that energy produced from certain types of biomass, including wood pellets,
from a climate point of view can be worse than the continued use of fossil fuels,
despite the fact that biomass is politically and commercially regarded as CO2
neutral and constitutes a key instrument in the Danish Energy Agreement of
March 2012. Since then, a number of analyzes and research results have been
published on the same subject, which generally supports these conclusions (see
below). Neverthelss in the Danish debate, there continues to be disagreement on
this point.
In Norway, CO2 neutrality and the use of Norwegian timber resources has been
an important topic of debate for a long time, and to settle this dispute the Norwegian directorate for climate and pollution was asked to put together a report
to be used as a basis for political reference (Ref/5/). The model used in the
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Climate effects from biomass and other energy sources – Main Report
analysis was developed by the Norwegian Institute for Forest and Landscape.
The report concluded that wood from Norwegian forests is not CO2 neutral, and
came to the conclusion that even in the most optimistic scenario with storage of
carbon in wood products, displacement of steel structures for buildings with
wood and use of residual products for energy purposes, the climate related payback period was 90 years. This entails that it will take at least 90 years before
wood from Norwegian forests can be considered CO2-neutral (see Chapter 3).
For wood used solely for energy production the payback period will be considerably longer. The report states that:
“Recently, several studies have been undertaken that consider the payback period for logging in Nordic countries (boreal forest). We have compared our results with the results presented by Holtsmark (2010b), Zanchi et al. (2010) and
Rørstad et al. (2010). Holtsmark and Zanchi et al. show that it takes from 80
years to over a century (depending on what the wood is used for) before the
CO2 content of the atmosphere is lower than before the tree was cut down, and
at least 150 years at permanently increased logging.”4
Following the publication of the CONCITO report in 2011 (Ref/1/), it was argued in the Danish debate that the poor climate performance was limited to boreal forests, and the case for wood from Central European temperate forests
would be markedly different: Therefore, in this report we have made calculations on different forest types with different rotation times and climatic conditions. We have not made scenarios that are representative of the boreal forests.
Factors that emissions from bioenergy are related to include:
 Changes in the amount of carbon tied up in trees and soil
 Changes in growth rate
 Use of machinery, pesticides and fertilizers
 Nitrous oxide emissions from the soil and leaching of nitrogen
 Displacement of other crops that instead have to be grown elsewhere in
the world (iLUC)
 Processing of crops for energy purposes, as well as production and construction of productions facilities.
All this may result in cases such as for example biodiesel or biogas produced
with a large supplement of maize, where it may be that the maize wholly or
partly cancels out any climate benefit, and thus makes the technology less attractive from a climate point of view.
For solar panels and wind turbines, for example, the major contributions to the
climate impact are from the production of the solar cells and wind turbines,
4
Translation from Norwegian
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Climate effects from biomass and other energy sources – Main Report
which are energy-intensive processes. This means that when new solar cells and
wind turbines are put into service, they have a CO2 debt that it takes time to recover. Thus, even solar panels and wind turbines account for a non-negligible
emission of greenhouse gases per kWh, and therefore cannot be considered CO2
neutral when all sources are included. However, it should be noted that greenhouse gas emissions from wind and solar power are generally substantially
lower than emissions from other energy sources.
3. Emission of greenhouse gases and the carbon cycle
According to the IPCC 2007 report (Ref/6/), emissions of CO2 from fossil fuels
in 2004 amounted to 56% of human emissions of greenhouse gases, while 44%
come from other sources, including CO2 from deforestation and drainage of soil
etc., see figure 1.
Figure 1. Breakdown of emissions of greenhouse gases according to the IPCC. From
Ref/7/
In the debate on the reduction of greenhouse gas emissions, emissions related to
fossil fuels take up much space, while the remaining 44% of emissions caused by
non-fossil sources takes up much less (given that the figures are from 2004, the
relative importance of emissions from the energy sector has since then increased, as growth in emissions from fossil fuels has been significantly higher
than from changes in land use). This could imply that, in seeking to reduce fossil
emissions, non-fossil emissions could be increased by substituting fossil fuels
with biomass-based fuels5.
As an example of this, The Finish Forestry Research Institute (Ref/22/, referrenced in Ref/21/) has estimated that Finland’s CO2 emissions will de facto increase if Finland is to achieve the EU reduction
target by 2020 with a given level of biomass – not because it would reduce carbon sequestration in Finish forests, but because it as a consequence would reduce the growth rate.
5
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It is thus clear from figure 1 (agriculture + forestry), that the demand for biomass, whether for food, fiber or energy, gives rise to just over 30% of global anthropogenic emissions of greenhouse gases6. Nevertheless, biomass is in most
contexts a priori considered CO2 neutral, which according to Ref/2/ is partly attributable to an unintended fallacy in the negotiations that set the stage for the
Kyoto agreement.
In order to better understand when something should be regarded as an emission, it is relevant in this context to consider the carbon cycle in a little more detail. The world's carbon cycle is shown in a simplified form in figure 2.
Figure 2. The carbon cycle. Red figures indicate anthropogenic emissions, yellow figures depict the natural cycle, while figures in parentheses are stored carbon. All
figures are in billion tones C (Gt). Source: U.S. Department of Energy, Office of Science.
When considering first the stored carbon, it appears that:
 The atmosphere contains 800 Gt C
 Plant biomass on land contains 550 Gt C
 The soil (surphase near) contains 2,300 Gt C
6
According to ref/28/ the LUC effect today is approximately 10 % of total CO2 emissions.
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Climate effects from biomass and other energy sources – Main Report


The subsoil contains 10,000 Gt C
The oceans and ocean floor contains a total of 44,000 Gt C.
It is important to note that plant biomass has a relatively large stock in relation
to the content in the atmosphere, and that the annual anthropogenic (manmade) emissions are relatively small (9 Gt) relative to the total stock of plant
biomass. It should also be noted that the stock in the upper layer of the ground
is very large, more than four times greater than that which is stored in the plant
biomass. This means that there are potentially relatively large quantities that
can be released from these stocks to the atmosphere if the plant biomass and the
surphase soil is managed inappropriately. Conversely, there are also opportunities to increase the large stocks in the biomass and in the soil through agriculture and nature management, and thereby increase the uptake of CO2 from the
atmosphere.
When considering the yellow numbers in figure 2, i.e. the “natural” carbon cycle
without the anthropogenic contribution, it appears that:
 The oceans absorb and emit 90 Gt C / year
 The terrestrial plants via photosynthesis absorb 120 Gt C / year that are
distributed in plants and in the soil, and which in turn is released by
plant respiration (60 Gt C / year) and by microbial respiration and decomposition in and on the soil (60 Gt C / years).
It is important here to note that the annual turnover in the plants and soil is
more than ten times larger than the anthropogenic emission, and that the storage in biomass and in the upper strata is approximately 300 times greater than
the annual anthropogenic emissions. Changes to this system in the form of e.g.
reduced storage will therefore be able to create an imbalance in the system and
a new equilibrium with a higher concentration of CO2 in the atmosphere.
Finally, the red numbers in the figure, as mentioned, constitute the anthropogenic emission. It is apparent here that:




The total emission of C is 9 Gt / year (today closer to 10 Gt C
(Ref/9,28/))
2 of the 9 Gt are absorbed by the oceans
3 of the 9 Gt are absorbed by plants and the soil
The remaining 4 Gt C accumulate in the atmosphere.
This net uptake of approximately 3 Gt C in the terrestrial plants /soils is apart
from the current stocks an essential element in the discussion about whether the
use of biomass is beneficial to the climate or not. In fact, this is the most uncertain of the indicated balances, and is often attributed to the “missing carbon
sink” (Ref/9/). This is because the amount is calculated as a residual amount
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Climate effects from biomass and other energy sources – Main Report
when the other sections have been calculated, as it is very difficult to measure a
change in the terrestrial carbon stock of less than 1%, which indeed has posed
significant challenges in the inventories of carbon storage in the biosphere and
the surphase soil that are made under the Kyoto Protocol in the countries covered.
However, there seems to be a certain level of consensus that this uptake mostly
occurs in the northern hemisphere and particularly in the forests as a result of
increased CO2 concentrations in the atmosphere and/or due to longer growing
seasons (Ref/9/). Recently published analyzes based on satellite measurements
confirm this (Ref/16,28/).
The base scenario is thus not that an increase in forests can be logged in a CO2neutral manner, since growth is a prerequisite in most projections and models
(Ref/9/). If the growth is logged and burned, future atmospheric CO2 concentratiosn will therfore be higher than that predicted by the models. If the extra
absorption in the oceans or on land is reduced, the rate of increase of CO2 concentration in the atmosphere will therefore increase. This distribution is described later in the report in relation to the Bern carbon cycle.
A frequently advanced argument that a given removal of wood for energy purposes from a forrest is CO2-neutral with reference to the fact that only a small
part of the increase is taken away, is therefore not valid. A given emission of
CO2 resulting from a reduction in growth or a reduction in standing stock, has
the same immediate impact on atmospheric greenhouse gas content as an
equivalent emission from burning fossil fuels, although there is an aspect of
time to be considered. This is described later in this report.
The uptake of carbon, especially in the northern hemisphere is thus of the same
order of magnitude as the emissions from the global use of coal, namely 3 Gt C
in uptake (4.1 Gt C according to recent figures, Ref/28/) against an emission
rate of 3.7 Gt C from coal.
In this report and in annex 1, the calculations are therefore performed by modelling changes in the uptake and storage over time for a given action, and not
in relation to a given static equilibrium (see below).
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4. Method
4.1. Functional unit
LCA analyzes operate with the concept of “functional units”, i.e. the unit to
which the calculations refer. The functional unit of the different fuels and forms
of energy in this analysis are shown in table 1.
Table 1: Definition of functional units
Electricity based on:
Functional unit
Wood pellets (3 different sources)
Wood chips
Straw
Biogas (manure, maize, organic waste)
1 kWh electricity at the power plant
Coal
Natural gas
Wind power
Solar power (photovoltaic)
Motor fuels based on:
Functional unit
Rapeseed biodiesel
Biodiesel from palm oil
Bioethanol, 1st generation (wheat and maize)
1 MJ fuel combusted in an engine
Bioethanol, 2nd generation
Motor diesel (mineral)
Motor petrol (mineral)
It should be noted that not all technologies for electricity production can be substituted 1:1. For example, wind turbines only produce electricity when the wind
blows, while gas for example can be adjusted to fit the demand. Therefore, some
technologies would need to be complemented with ‘adjustment capacity’ in order to be absolutely comparable with others. Adjustment capacity can be either
flexible production, storage or flexible consumption.
A specific functional unit for heat has not been included, since relative emissions will generally be comparable to the functional unit for electricity. Both
coal and gas can thus be used for heat production alone, as can biomass, and the
uncertainties will therefore mainly consist in slightly different electrical efficiency levels for the individual fuels.
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4.2. Uncertainties
In general, the LCA model used as a basis for this report offers considerable accuracy in the calculations under the given assumptions, while the accuracy of
the assumptions and the applied data is smaller. The concepts of accuracy and
precision in the LCA are described in more detail in Ref/11/.
Uncertainty about assumptions and data includes:
 The applied yields on a wheat field, which are average values for a given
area
 For the production of biodiesel and bioethanol it is assumed that the raw
material is cultivated in Denmark (high efficiency in agriculture), while
the displaced raw materials (feed from oil mills and bioethanol production) are assumed to be cultivated in low-productive countries. This can
be argued to overestimate the benefits of by-products.
 Methane emissions from biogas installations can vary from 1% to 4%,
where the model applies 1% as standard value.
 The modelled method for upgrading biogas (a choice of several different).
 The assumption that an increased demand for woody biomass does not
increase net deforestation and does not affect cultivated arable land.
 Finally, there are of course uncertainties associated to the applied iLUC
calculations and their assumptions (see annex 1 and its references for
more detailed descriptions of the uncertainties in the iLUC calculations.).
Because of the uncertainties linked to the assumptions and data applied in the
calculations, the results are compared to other studies that have used other data
and other models for similar calculations as an additional independent quality
assurance. In this regard, particular attention is given to a critical literature review undertaken by the European Commission's Joint Research Centre (JRC,
Ref/21/). JRC estimates that the uncertainties in the calculations they use are
limited, but will increase if e.g. albedo effects are included (reflection of light).
The very precise quantities in the results in Chapter 7 and in annex 1 are therefore only valid under the given assumptions for method and data. The uncertainty in the method is generally considered to be relatively small, while there
on the data-side are greater uncertainties (e.g. the identification of the affected
regions, yields, cultivation practices, etc.). The examples for pellets and chips
are, however, largely represented by the possible extremes, so that specific calculations are likely to fall within the modelled scenarios using the same system
delimitations.
Finally, it should be highlighted that the uncertainties of this and similar studies, ceteris paribus, are significantly smaller than the uncertainty of not calculat-
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Climate effects from biomass and other energy sources – Main Report
ing the climate impact and simply defining emissions as zero, which in several
contexts is the case today.
4.3. Consequential approach to modelling
The study models the effects of a change in demand for the functional unit (see
table 1), where the functional unit is provided by various product systems in different scenarios. The approach to this is the so-called consequential approach to
modelling in life cycle assessment. The arguments for applying this approach,
the theoretical foundation, and the practical implementation of the strategy is
described in Ref/10,11,12/. This approach is also recommended in Ref/21/ (see
Annex 2) in order to include as many effects in the calculation as possible.
It is important to note that the effects are modelled for an additional production
and not an existing production. For example, existing wood pellets can be made
wholly or partly from residues from sawmills, but here an additional production
of pellets is modelled assuming that residues from sawmills in practice are fully
utilized or that they will be in near future (see Chapter 6 for reasoning and
documentation).
It is also important to note that the modelling assumes that there are no
changes in biomass productivity or production structure. To the extent that additional demand results in changes in the production of biomass, which leads to
enhanced productivity without increasing the consumption of input materials,
this could lead to a reduction of the climate impact, which is not included in the
calculations. Such changes could for example occur by a shift in Denmark to cultivating grain crops with a higher share of straw without changing grain yield, or
by the introduction of fast growing nurse trees in forestry, which can be utilized
for biomass production without compromising the existing production of timber. Similarly, at the international level, there is scope for the cultivation of additional biomass on former agricultural land that is now going into lowproductivity systems because of low soil fertility, but which could be remedied
with the right tree species and fertilization.
This modelling concept and why it has been selected are important to keep in
mind in order to understand the calculations. The modelled scenarios are thus
not based on how a specific product is made, but on how it will be replaced in
the market when it is used or purchased on the basis of current systems for
biomass production.
As a more practical example, the model approach might be compared to the effect of the purchase of e.g. Norwegian electricity from hydropower. As hydro
power in Norway is almost fully developed, an additional demand for it will not
increase production of hydroelectric power, but simply push other users out of
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Climate effects from biomass and other energy sources – Main Report
the market. The marginal production of electricity will therefore not change,
and hence neither will the overall emission of greenhouse gases.
4.4. Cut-off criteria
Cut-off criteria determine what is included in the calculations and what is not.
In general, all relevant sources are attempted included in the calculations (but
see footnote 1), including capital goods, i.e. production of machinery, buildings,
infrastructure etc., but not services such as auditing, marketing, legal services,
cleaning services, research and development, laboratories, office equipment etc.
Capital goods are, however, omitted for a few production processes in the modelled systems, cf. annex 1. This is, however, not considered to have crucial significance for the results.
4.5. Methodology for quantifying greenhouse gas emissions
In general, the “Global Warming Potentials” of the international climate panel,
IPCC, are applied in a 100-year perspective (GWP100) in the main scenarios,
while sensitivity analyzes are undertaken using a 20-year perspective (GWP20).
GWP is expressed in kg CO2 equivalents (CO2e). No distinction is made between fossil carbon from oil, coal and gas, and biogenic carbon from biomass.
The temporal effect is calculated consistently cf. the GWP principle using the socalled 'Bern Carbon Cycle', which is described in detail in the following chapter.
It should be noted that most CO2 footprint accounts and LCA’s do not distinguish between the point in time of absorption and emission of CO2. In particular for products and fuels based on forestry products this can have a significant
impact on greenhouse gas emissions. Therefore, this is taken into account in the
present study.
5. Method for modelling Land Use Change (LUC) and biogenic carbon
A significant proportion of the greenhouse gas emissions related to the use of
biomass can be linked to iLUC (indirect Land Use Change), and the phase the
biogenic carbon is in at a given time (e.g. as CO2 in the atmosphere or as C in
plant material or soil). This chapter describes the scientific and practical background for how this is modelled. All calculations and methods are, with few exceptions, in accordance with the international ISO14044 standard for LCA, and
with the general rules for consequential LCA. See Annex 1 for a description of
exceptions.
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Climate effects from biomass and other energy sources – Main Report
5.1. Time dependent emission of CO2
Often, when considering emissions of CO2 and other greenhouse gases, no distinction is made regarding when a given emission takes place. Nevertheless,
there are certain political targets for the maximum acceptable human-induced
temperature increase (currently 2 degrees Celsius, corresponding to an atmospheric CO2 concentration of 450 ppm), which require a certain maximum emission and concentration of greenhouse gases at a given time. At the same time,
there is scientificly a (uncertain) threshold for when the concentration of greenhouse gases in the atmosphere and the temperature rises are so high that selfreinforcing effects occur, and climate change, therefore, to some extent can “run
wild” and be extra difficult to counter. Suspension of emissions of greenhouse
gases, therefore, has a positive effect e.g. by reducing the warming effect here
and now, by winning time e.g. for technological advances and for adaptation to
climate change, and by extending the time frame for the transfer of carbon from
the fast (biogenic) carbon cycle to the slow (fossil, limestone) (Ref/9/).
Therefore, given the policy goals to reduce emissions of greenhouse gases by
2020 or 2050, it is the actual emission and warming that is interesting and not
whether measures have ben undertaken that could potentially reduce emissions
e.g. by 2100, in part because then the threshold could be surpassed. Therefore,
it is relevant to also consider greenhouse gas impacts over shorter time periods
than 100 years (GWP100), e.g. over 20 years (GWP20). For example, if by a
concrete reduction targets for emissions of CO2 in 2030, the intention is that by
that time a corresponding lower concentration of CO2 in the atmosphere should
be achieved, the warming effect should also be considered in a 20-year perspective and not solely in a 100-year perspective (e.g. Ref/22/). On this basis, the
IPCC as a standard applies 20-year, 100-year and 500-year perspectives
(Ref/17/), although the Kyoto Protocol for practical and administrative reasons
primarily focuses on a 100-year perspective.
Normally, the burning of biomass is considered CO2-neutral because the CO2
released in the burning process has been absorbed from the atmosphere previously. During the period between the absorbtion in the growth phase and the
discharge during decomposition, the CO2 is out of the atmosphere and thus
does not contribute to the greenhouse effect. Therefore the length of this period
is relevant. This period is the longest in forests with large time differences between absorption and emission, but may also be considerable e.g. for straw and
other crop residues (trimmings in forests, etc.), where there is a difference between whether the CO2 is released by natural degradation over time or immediately upon combustion. (Ref/1,2,3,4,14,15,18/).
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Climate effects from biomass and other energy sources – Main Report
The effect of increased deforestation by iLUC is modelled by an accelerated deforestation, but not as an increase in net deforestation, and may in this regard
be considered a "best case" for CO2 emissions from biomass since the amount of
forest that remain in 100 years is the same in the applied model irrespective of
an increase in demand for biomass. When the assumption is here described as
"best-case" it is because the literature contains an argument that increased demand for certain types of biomass for energy a priori will have dramatic effects
on natural forests (and arable land) i.a. due to the price effects of the creation of
a global market for biomass for energy purposes, and since it will not be feasible
to prevent this through regulation (Ref/19/).
How ILUC is modelled is described further later, and in more details in annex 1.
As described in Chapter 3 on the carbon cycle, approximately half of the emitted
carbon accumulates in the atmosphere, while the rest is absorbed in the oceans
and in biomass or in the soil. This allocation, however, does not happen immediately, but is a balance that adjusts over time, where CO2 is slowly absorbed
from the atmosphere until an equilibrium is reached. This can be described by
Bern's carbon cycle, which is described in mathematical details in annex 1, and
graphically illustrated in figure 3.
As apparent from figure 3, the emission of 1 kg of CO2 will result in a net remainder of 0.5 kg CO2 in the atmosphere after about 30 years, and more than
0.2 kg after 500 years. Thus, the concentration of CO2 in the atmosphere at a
given point in time can be calculated from the immediate release of 1 kg of CO2
from biomass or fossil fuels.
The effect of accelerating or delaying the emission of a given quantity of CO2
can also be calculated using Bern’s carbon cycle by shifting the pulse to the right
(delays emissions) or to the left (accelerates emissions) in the figure. This is illustrated in figure 4.
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Climate effects from biomass and other energy sources – Main Report
Fraction of CO2 pulse remaining in atmosphere over time
1.20
1.00
Fraction
0.80
0.60
0.40
0.20
0.00
0
100
200
300
400
500
Time (t), years
Figure 3: Fraction of a CO2 pulse emitted in year 0 remaining in
the atmosphere at a given point in time (from annex 1).
Fraction of CO2 pulse remaining in atmosphere over time
1.20
1.00
Fraction
0.80
0.60
0.40
0.20
0.00
0
100
200
300
400
500
Time (t), years
Dt
Fraction of CO2 pulse remaining in atmosphere over time
1.20
1.00
Fraction
0.80
0.60
0.40
0.20
0.00
TH= 100
0
Dt
200
300
400
500
Time (t), years
Figure 4. The effect on the emitted CO2 remaining in the atmosphere of
delaying a given CO2 emission. See annex 1 for the mathematical background.
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Climate effects from biomass and other energy sources – Main Report
The effect on the climate does not solely dependent on the concentration in a
given year, but also on how long the CO2 has been present, which can be calculated as the integral of the share of CO2 emitted over time (the area under the
curves in figure 1 and 2). The Figure show that CO2 in the atmosphere within a
100-year horizon (the shaded area) decreases with a delay of emissions of approximately 30 years (t).
Taking into account the effect of a delay of one year in a 100-year perspective
(GWP100), 1 kg of CO2 emitted in year 0 will have the same climate impact as
0.9924 kg CO2e emitted in year 1. The delay thus has a CO2 advantage of
0.00761 kg CO2e.
Considering emissions in a 20-year perspective, the significance of the delay effect is that 1 kg of CO2 emitted in year 0 corresponds to an emission of 0.9586
kg CO2e in year 1, representing a CO2 advantage of 0.0414 kg CO2e by delaying
emissions.
The effect of delay is thus more than five times as strong when considering the
emissions in a 20-year perspective rather than a 100-year perspective, and
therefore CO2 emissions from the burning of biomass usually also increase considerably the shorter the time frame selected for consideration.
With a specific reduction target 20 years into the future, it is important from a
scientific point of view to assess the measures applied to reach the reduction
target within a corresponding time frame, if the reduction target is to reflect a de
facto reduction of CO2 in the atmosphere by the stipulated target year.
Conversely, objectively speaking the requirement for the selection of measures
should be that they have a significant impact in both the short term and in the
longer term so that it is not an either-or, but a both-and. Consequently, it is difficult to argue that a given climate target has been achieved after 20 years if the
measures undertaken for achieving this goal subsequently results in a sharp increase in CO2 concentration. It is similarly difficult to argue for a 100-year objective if the planet in the meantime experiences irreversible and uncontrollable
climate change.
Accordingly, the two degree treshhold is only within reach if emissions of CO2
physically begin to decrease relatively dramatically within a very short period of
time.
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Climate effects from biomass and other energy sources – Main Report
5.2. Indirect changes in land use (iLUC)
Deforestation and changes in land use (Indirect Land Use Change, iLUC) is a
consequence of the demand for land (in the following only for land), whether it
is for the production of biomass, food, urban development, infrastructure or
other (annex 1).
The model considers land as a capacity for the production of biomass and it
considers the market in which land is demanded to be a global market, since
crops can be cultivated in different regions of the world, and are traded globally.
An increased demand for land can globally be met by:
Land that can be used for forestry (but not agriculture)
 Expansion of plantation area (typically change from secondary or primary
forest to cultivated forest)
 Displacement of consumption, someone reducing their consumption.
Land that can be used for agricultural cultivation
 Expansion of arable land area (typically deforestation)
 Intensification of existing agriculture
 Displacement of consumption, someone (typically the poor) reduces their
consumption due to higher prices, so that others can use the biomass.
Displacement of consumption is in the long term considered to be of minor importance because higher prices will lead to increased production by deforestation or intensification. In the iLUC model an intensification of existing cultivated forests is not included in the calculations. This delimitation is due to a
lack of global statistics from which to quantify the intensification.
The model distinguished between five types of relevant markets for land:
1. Extensive woodlands: Not suitable for more intensive forestry (e.g. logging
and replanting) e.g. because it is too hilly, too remote or because the woodlands are located on very infertile soils that make intensive forestry uneconomical. Wood in extensive forest areas is typically logged after natural regrowth of mixed species.
2. Intensive forest: Suitable for intensive forestry (e.g. clearcuts, reforestation,
monocultures, etc.), but not suitable for arable crops because the land cannot be cultivated, for example because it is too rocky. Forests grown in intensive forest areas can be managed as intensive or extensive forestry. Intensive forest land can also be used for other land use, for example for grazing.
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Climate effects from biomass and other energy sources – Main Report
3. Arable land: Can be used for cultivating annual or perennial crops, for intensive or extensive forestry, and for grazing.
4. Grasslands: Often too dry/barren for forestry and traditional crops. Grasslands are therefore often used for grazing.
5. Other areas: Not suitable for production of biomass. Barren lands, deserts,
ice sheets, high mountains etc.
The model simulates the types of changes that occur in the global land use,
when demand for a product changes. Land, i.e. the capacity for production of
biomass, is counted as area x time weighted against productivity (the unit is Net
Primary Production (NPP), measured as kg carbon used for this). Some very fertile areas in for the example the tropics can have a potentially very high production per hectare (high NPP), while other areas have a low productivity and
therefore require more land to produce the same amount of carbon. The model
simulates, which areas comply with a given increase in demand with a certain
share, including the proportion that can be produced by intensification of existing areas given the existing technologies for intensification. The principle is
shown in figure 5.
Existing land
Output
Land already in use
Inputs
None
Emissions
None
Flow
x
-
Land use changes
Output
Flow
Expansion
a3
Ressource inputs from nature
b1
Transformation from…
Transformation to…
b2
Emissions
e.g. CO2
b3…
Intensification
Output
Flow
Intensification
a4
Inputs from technosphere
Diesel for traction
c1
N-Fertiliser, as N
c2
Emissions
e.g. N2O, CO2
c3...
Social/hunger effects
Output
Crop displacement
Inputs
None
Emissions
None
Unit
kg NPP0
Flow
y
Unit
kg NPP0
ha
ha
Land tenure market LCA activity
Output
Flow
Land tenure, NPP 0 as kg C
a1 = a3 + a4
Inputs from technosphere
Land already in use
a2 = 0
Expansion
a3
Intensification
a4
a5 = 0
Crop displacement
Unit
kg NPP0
kg NPP0
kg NPP0
kg NPP0
kg NPP0
Wheat LCA activity (1 ha yr)
Output
Wheat
Inputs from technoesphere
Diesel for traction
N-Fertiliser, as N
Land tenure, NPP 0 as kg C
Emissions
CO2 fossil (diesel combustion)
N2O
Resources
CO2 from air
Flow Unit
7200 kg
4921
161
7000
MJ
kg
kg
365
4.3
kg
kg
10300
kg
wheat
kg
Unit
kg NPP0
MJ
kg
kg
Unit
kg NPP0
-
Figure 3 Illustration of the principle of land consequences and its inputs and outputs, here described by a given wheat production. Only two of these are relevant
when modelling iLUC, LUC and intensification. These two activities are linked to
emissions. The sum of these emissions is called iLUC emissions. See annex 1 for details.
An increased demand for example for wheat will thus lead to a certain change in
the use of arable land, and a certain intensification of existing farmland. Both
the incorporation of new land and the intensification increase the emission of
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Climate effects from biomass and other energy sources – Main Report
greenhouse gases, and this emission is coupled to the production of wheat.
There are several different models for calculating iLUC, and in annex 1 the different models and their principles for calculation and differences are briefly described.
6. Characteristics for the individual biofuels
An important prerequisite for the calculations are the material properties assigned to the individual fuels. Material properties of the fuels used in this report
are shown in table 2.
Table 2: Material properties for the relevant fuels used in the report. See annex 1 for
references.
Fuel / material
Density wood
Dry matter
Lower heating value
(kg dry matter /m3)
(%)
Eucalyptus
0.51
50%
-
Loblolly pinewood
0.42
50%
-
Pinewood
0.42
50%
-
Wood pellets
-
90%
17.5 MJ / kg
Wood chips
-
75%
13.6 MJ / kg
Straw
-
85%
14.5 MJ / kg
Maize silage
-
33%
-
Organic municipal waste
-
40%
5.10 MJ / kg
Purified biogas (96% methane)
-
-
34.50 MJ/Nm3
Diesel
-
-
43.0 MJ / kg
Rapeseed biodiesel
-
-
37.0 MJ / kg
Biodiesel from palm oil
-
-
37.0 MJ / kg
Petrol
43.0 MJ / kg
Bioethanol
-
-
27.0 MJ / kg
Coal
-
-
24.4 MJ / kg
6.1. Wood pellets
Wood pellets are produced from plantation wood or as a residual product from
sawmills. Since residues are today fully exploited (Ref/20/, annex 1), an increase in demand will be met by increased production/logging of wood. The
principle is shown in figure 6. Thus, it is assumed that all wood for wood pellets
is cultivated wood. In the next section where wood chips are analyzed, it is assumed that chips are produced entirely from residual products, which would alternatively have remained in the forest. In this way, the two principal ways in
which timber can be produced are included in the assessment.
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Climate effects from biomass and other energy sources – Main Report
System boundary
Saw mill
Sawn wood
Saw dust / wood chips
are constrained by
demand for sawn wood
Forest plantation
Dependant by-product:
Saw dust / wood chips
Wood
Wood pellet production
Wood pellets
Power plant
Electricity
Figure 4: System delimitation for the production of electricity based on wood
pellets, see annex 1 for details
Wood pellets are the fastest growing source of woody biomass in the EU, and
the most likely supplier for Denmark is currently the Baltic States (see annex 1,
Ref/20/, figure 7). Therefore, production of wood pellets from a climate zone
corresponding to the Baltics is included as a scenario. The largest exporters to
the EU are Canada, the U.S. and Russia, while the future major exporters are
expected to be the U.S. and Brazil, based on a production from whole trees of
Loblolly Pine and Eucalyptus (Ref/20,29/, annex 1, figure 8). Therefore, pellets
from the U.S. and Brazil are also included as scenarios.
Figure 5: Import of wood pellets to Denmark from different countries. From Ref/20/
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Climate effects from biomass and other energy sources – Main Report
Figure 6: Expected growth in wood pellet production from different sources. As
shown, growth in the use of residual products is expected to be limited after 2013,
while by far the largest growth is from pine in the U.S. and in eucalyptus from
Brazil. From Ref/20/.
It is important to point out that the scenarios focus solely on the climate effects
of wood pellets and not, for example, on impacts on biodiversity (where the effect can be very considerable for example for Brazilian eucalyptus cultivated in
savanna or former rainforest areas). An increased demand for biomass in the
model does not increase the net change from non-cultivated forest to cultivated
forest, but simply brings this change forward.
Selected characteristics of the three scenarios are shown in Table 3, and more
are shown in annex 1.
Table 3: Selected characteristics for the three wood pellet scenarios, see annex 1 for
details
Scenario
Eucalyptus, Brazil
Rotation time (years)
Annual growth
(commersial wood/round
wood) m3/ha
6
50
Loblolly Pinewood,
South East U.S.
12
14
Pinewood, Latvia
63
6
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Climate effects from biomass and other energy sources – Main Report
Annex 1 shows the emissions associated with the production and transportation
of wood pellets, and for each scenario time-dependent carbon balances are established for carbon both above and below ground. These balances are included
in the final modelling.
The main assumptions for the modelling of wood pellets are:
 All parts of the logged lumper (trunks) are used for wood pellets, and
not, for example for timber products.
 80% of residues (both residues above and below ground) are assumed to
be consumed (thinnings, branches, cut etc.).
 Rotation time for Eucalyptus in Brazil, Loblolly Pinewood in the U.S.
and Pinewood in Latvia is 6, 12 and 63 years respectively.
 Drying of wood pellets is based on biomass (wood pellets).
 iLUC only includes an accelerated transformation of primary and secondary forests to plantations - no net transformation is assumed.
 iLUC is modelled as if the land concerned is exclusively land not suitable
for arable land. If arable land was included, iLUC effects would increase
by more than a factor of 2, with significantly higher emissions
It is important to emphasise that it is not specific forests in the Baltic countries,
the U.S. and Brazil, which are modelled, but areas with growth, rotation times,
temperatures and humidity, etc., which are characteristic of the climate zones
that are in these countries. The examples thus to a greater extent represent climate zones (temperate, subtropical and tropical) rather than specific countries,
and some countries may therefore have specific regulatory requirements regarding forest management, which are not reflected in the assumptions. Thus, it is
important to keep in mind that the assumptions underlying the calculations and
the results are the expression of a likely marginal production.
An example of where legislation and management practices may have important
implications for the modelling is the proportion of residues above and below
ground respectively that is removed and used for biofuel. Generally, it is assumed that 80% of residues both above and below ground are harvested. This is
a very high figure. However, this assumption does not significantly influence the
results; if the proportion of harvested residues in the soil changed from 80% to
0% GHG emissions are reduced by 1-3%, and if all residues (both above and below ground) are left in the forest, the result increases by 2-6%. The reason that
the results are not going in the same direction, is that the effect is not linear.
When residues are not harvested, yields are reduced, and iLUC increases, and
meanwhile the effect of the biomass left behind in the forest can lead to a net increase in the carbon pool (if it is decomposed more slowly than growth).
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Climate effects from biomass and other energy sources – Main Report
6.2. Wood chips
Wood chips are assumed in the model to be produced from residual products
(thinning wood etc.) from timber production. Thereby wood chip production
does not increase demand for cultivated wood. This is obviously an optimistic
scenario, which lasts only as long as demand is less than the amount of residual
wood, and as long as it can be ensured that it is de facto not decidedly cultivated
wood that is affected. Details surrounding the production and use of wood chips
are shown in annex 1.
The main assumptions for wood chips are:
 That they are based solely on residual products, and that the alternative
to using them is that they are left on the forest floor. If wood chips become a primary product, i.e. if trees are logged for the purpose of making
wood chips, the emissions from forest cultivation will be similar to wood
pellets without drying.
 That as they are solely a residual product, iLUC is not included in the
calculations. This, however, also entails that demand is assumed to be
less than the available amount of residual wood. Therefore, this scenario
can reasonably be expected not to be a fair representation of some regions with a current high residue utilization rate, and of the long term.
As for other fuels, it is important that the applied assumptions are kept in mind
when regarding the calculations. For instance, Ref/21/ discribes the export of
wood chips made from roots from rubber trees from Africa to Europe, where the
argument from the buyer's side has been that the roots were simply burned off
as waste treatment, and the chips was therefore made of residues and consequently CO2-neutral and sustainable. It is claimed, however, that the burning of
the roots was the basis for charcoal for cooking by locals, and that the price of
charcoal, therefore, has doubled in the area with the result that it has had negative social consequences and that logging of local natural forest has probably increased. Based on a consequential modelling, the chips, therefore, give rise to
significant CO2 emissions because it displaces local charcoal and consequently
can increase deforestation. On the other hand, this could well be resolved by replacing charcoal for cooking purposes with other more efficient sources of energy.
6.3. Straw
Straw, as wood chips, is assumed to be a residual product that is available in
sufficient quantities, and increased demand for straw, therefore, does not affect
total cereal production in the model. This is of course continget upon the assumption that demand does not surpass the available quantity of straw.
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Climate effects from biomass and other energy sources – Main Report
Straw is characterized by a very short “rotation time” (< 1 year), and the model
calculates the CO2 balances of the alternative to incineration, which is to plough
the straw into the soil and calculate the subsequent natural decomposition and
fertilization effect, i.e. that the straw left in the soil displaces other fertilizers in
the form of Nitrogen, Phosphorus, and Potassium. Further details about the
models and degradation constants are described in annex 1.
The most important assumptions are:
 That straw is solely a residual product where the alternative is to plough
the straw into the soil. If the straw would have been harvested for other
purposes (feed, bedding, other energy), that means the straw is no
longer an available resource, and thus the secondary effect of gathering
straw becomes that the production of alternative feed, bedding or other
energy must be increased, and that CO2 emissions thereby also increases
 That as straw is solely a residual product, iLUC is not included in the calculations.
6.4. Biogas
Biogas can not with currently available technologies be produced technically or
economically meaningfully on slurry alone, since the energy content relative to
the volume is too low in the raw slurry. Therefore, organic matter is added to the
slurry, and as most organic residues from industry are already used today, readily available organic residues are a scarce commodity.
With plans for a greatly increased biogas production in Denmark, the addition
of organic matter from energy crops, i.e. crops that are cultivated for the purpose of energy production, has been politically sanctioned. Thus, it is permitted
to use e.g. maize for biogas production, and towards 2017, biogas can consist of
gas from 70% maize (70% of the gas comes from the maize, and 30% from
slurry), while in 2018, it may consist of 48% gas from maize. Maize is already to
a limited extent used in biogas production in Denmark. According to the Danish
Energy Authority (Ref/25/), “many, perhaps most new plants” are expected to
depend on being able to use energy crops to a not inconsiderable extent, until it
becomes clear whether alternative sources of additive biomass such as straw,
landscaping biomass or municipal waste can wholly or in part substitute energy
crops. As the future of the alternative biomass sources is still economically and
technically insecure, the scenarios for the use of these have not been modelled,
and with the political decision that 50% of the slurry should be gasified by 2020
there is limited time to develop the alternatives if the plants are to be in function
within the period.
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Climate effects from biomass and other energy sources – Main Report
An alternative of gasification of clean household waste, i.e. without the simultaneous gasification of slurry, has been included in the calculations. This could be
a scenario in for example Copenhagen or in other countries.
Accordingly, the overall climate effects of the following three scenarios are modelled in the study:
• 70 % maize / 30 % slurry
• 48 % maize / 52 % slurry
• 100 % organic household waste.
The biogas is assumed to be upgraded (adjusted for CO2 etc.) for the natural gas
grid, and the emissions related to the production and upgrading of biogas are
shown in annex 1, including estimates of methane and nitrous oxide emissions
from the processes. Other forms of upgrading, e.g. hydrogenation etc., are not
included in this assessment. The establishment and operation of any low pressure biogas grid as an alternative to upgrading the natural gas grid is also not
included in the present calculations.
Emissions related to the productionen of maize are also described in annex 1,
including iLUC from displacement of food and feed production.
The emissions and energy benefits related to biogasification of organic municipal waste is modelled and described in annex 1, and here the displacement that
occurs of organic municipal waste from waste incineration is also included. The
reason is that the energy is already today exploited from part of the organic
waste divided between an electricity production and a heat production, and with
biogasification this energy production disappears and must then be replaced by
something else.
Main assumptions in the modelling are:
 iLUC is solely related to the production of maize
 The leakage of methane from biogas installations is set at 1%, which is a
low estimate (referenced as from 1 -> 4% in the literature), and at 2%
from upgrading to the natural gas grid
 Biogas produced from municipal waste is modelled as a displacement of
waste incineration (electricity and heat).
Regarding the latter point, it may seem rather unreasonable to compare electricity to a biogas instalation, which displaces both electricity and heat, whichcombined will have a higher efficiency rate. In the modelling, however, relatively low
efficiency rates are applied for old waste incinerators, and if instead new biogas
installations were compared with new waste incineration installations, the overall efficiency rates would be about 2-3 times higher for waste incineration in relation to biogasification of pure household waste according to Ref/26/. Applying
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Climate effects from biomass and other energy sources – Main Report
this scenario instead, the total emissions from biogasification of pure municipal
waste including heat production would rise compared to the estimated figures.
The example illustrates that it is possible to construct many combinations and
scenarios, of which we have only selected three in this report.
6.5. Bioethanol
Emissions associated with the production of bioethanol are modelled for both
production of first and second generation bioethanol. First generation bioethanol is modelled with maize/wheat as raw product, while second generation bioethanol is based on straw. In both cases it is assumed biogas is produced from
the waste products that cannot be used for animal feed, and that feed production (DDGS and C5 molasses) displaces other feed production and therefore has
a displaced iLUC effect. The feed products are assumed to displace soybeans
from Brazil and barley from Ukraine, which constitute the marginal supplies of
protein feed and energy feed, respectively. There are certain uncertainties associated with this (see annex 1). Energy and protein breakdown is shown in table
4.
The main assumptions in the model for first generation bioethanol are:
 The marginal resource for wheat and maize are crops grown in Denmark. This may lead to an underestimation of emissions of greenhouse
gases (see annex 1 for details)
 The by-products (feed) displaces Ukrainian barley and Brazilian soy
 Other by-products are used in biogas installations, and the gain from
this is attributed to bioethanol and not biogas.
 iLUC is, as for other crops, modelled as a combination of intensification
and acceleration of deforestation.
For the second generation bioethanol the following assumptions are applied:
 The point of departure has been the IBUS system (Integrated Biomass
Utilisation System), which is considered “best-case” (see annex 1)
 The by-product C5 molasses is used as feed and displaces energy and
protein from the Ukraine and Brazil. Uncertainties associated herewith
are described in annex 1 and its references: Food Values of C5 molasses
is not, as for other feed products, based on official food value tables. It
has not been possible to verify the feed value of C5 molasses, and results
related to displacements by C5 molasses should be interpreted critically.
 Other residues are used for biogas. The alternative use of straw is to
plough it into the soil - if straw is not an available resource and would be
used for something else (food, bedding, energy), the emissions from bioethanol will increase significantly.
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Climate effects from biomass and other energy sources – Main Report


It should be noted that the alternative use of straw can be modelled as
ploughing it into the soil only as long as the demand for straw does not
exceed the available amount. Therefore, the results may be inaccurate in
some regions with high utilization rate of straw, and in the long term.
ILUC is not included for straw. On the contrary, avoided iLUC due to C5
molasses is included.
Table 4: Protein and energy allocation in different feed products. DM is dry matter
content. From Annex 1.
Source
DM
Protein, crude
Energy
%
(MJ net/kg dm)
85.0%
(% of DM)
10.8%
Marginal source of feed protein: Soybean meal
87.4%
53.5%
10.9
By-product: Rapeseed meal
88.9%
35.0%
9.31
90.6%
17.0%
6.49
By-product: DDGS (wheat)
90.0%
32.0%
8.45
By-product: DDGS (maize)
89.0%
29.2%
10.0
70.0%
5.9%
8.05
Marginal source of feed energy: Barley
By-product: Palm kernel meal
By-product: C5 molasses
8.68
Modelling and assumptions are described in annex 1.
6.6. Biodiesel
Two scenarios have been modelled for biodiesel: Biodiesel produced from palm
oil and biodiesel produced from rapeseed. Rapeseed is modelled as produced in
Denmark, while palm oil is modelled as produced in Malaysia. See Annex 1 for
details.
The main assumptions are:
 By-products displace energy and protein feed from Ukraine and Brazil
 ILUC is, as for other crops such as maize, modelled as a combination of
intensification and acceleration of deforestation.
6.7. Coal, gas, oil, wind and solar power
The primary purpose of this report is to estimate the emissions of greenhouse
gases from the use of different biofuels under a given set of assumptions, and to
compare these with the alternatives. Therefore, scenarios for heat and electricity
based on coal, natural gas, wind power and solar power have also been created.
The calculations for coal and natural gas are based on average data from electricity production in Scandinavia and based on the average electrical efficiency
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Climate effects from biomass and other energy sources – Main Report
rate of approximately 41%. New gas-fired CHP plants may, however, have significantly higher electrical efficiency rates.
The calculations for wind turbines are based on a 2 MW offshore wind turbine
on Middelgrunden, but excluding transmission lines. The scenario expresses a
representative mix of land and offshore wind power, and is based on existing
LCI data in the softeware programme SimaPro (from the ecoinvent database).
Large offshore wind turbines will initally cause a greater emissions of greenhouse gases in the establishment phase, but, in turn, they produce more during
the operational phase, so emissions per unit of energy will be comparable.
Calculations of photovoltaic electricity are made under Danish conditions with a
3kWp roof-mounted PV system. These have an estimated production of
850kWh/kWp and a life expectancy of 30 years, which, however, is not necessarily true in practice. Again, the example is based on existing LCI data in the
software program SimaPro (from the ecoinvent database).
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Climate effects from biomass and other energy sources – Main Report
7. Results
In the following, the results for each main group of biofuels are presented and
interpreted, see list below. Within each main group of biofuels, results are displayed for the selected biofuels included in the present study. It is important to
emphasize that each outcome for a biofuels represents one scenario, which is
based on a number of concrete data and assumptions. Therefore, the results
shown are generally only valid for these specific data and assumptions. For example, changes in cultivation practices for forests (e.g. share of residuals harvested or introduction of nurse trees for biomass production) will also change
the results. Therefore, the results cannot be used as a final “values” for what the
greenhouse gas emission are for a certain type of biofuel. Within each type of
biofuel there can be large variation in greenhouse gas emissions depending on
how a specific system is pieced together.
Further, it should be emphasised that the purpose of the present study is:
 To clarify whether biofuels are CO2 neutral.
 To identify factors of particular importance to greenhouse gas emissions
from biofuels.
 To undertake an indicative comparison of the analyzed biofuels and their
fossil alternatives.
It should also be highlighted that the purpose is not to make a general ranking
of biofuels - but the identification of certain issues of great influence, as well as
the indicative comparison with fossil fuels can be used to identify the conditions
under which certain biofuels will cause highler or lower levels of emissions than
others.
Results are shown for the following main groups of biofuels:
Electricity
 Cultivated wood (illustrated by various scenarios for wood pellets)
 Residues from forestry and agriculture (illustrated by various scenarios
for wood chips and straw)
 Biogas (illustrated by biogas produced from various raw materials)
 Other than biofuels (coal, gas, wind and solar power).
Liquid fuels
 Biodiesel (illustrated by biodiesel based on rapeseed oil and palm oil)
 Bioethanol (illustrated by first generation bioethanol from wheat and
maize and second generation bioethanol from straw)
 Other than biofuels (mineral diesel and petrol).
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Climate effects from biomass and other energy sources – Main Report
Regarding the analyzes of electricity, it must be noted that the focus has been
solely on electricity produced by condencing, i.e. 100% electricity and no cogeneration with heat). The reason for this is that a marginal change in demand
for electricity will always affect condensing, since electricity production that is
co-generated with heat, can generally be characterized as a dependent product
of heat generation.
All results are shown divided into three categories of contribution to the total
emission of greenhouse gases:



iLUC (red in the figures): Indirect land use changes.
Accelerated/delayed emissions and uptake (green in the figures):
This concerns the effect caused by the timing of CO2 emissions and absorbtion
Process emissions (blue in the figures): This relates to everything
else. It covers, inter alia, emissions from the use of diesel, electricity, fertilizer etc.
Since the overall results are divided into the above three categories, it is easy to
get an overview of the level the greenhouse gas emissions, if e.g. iLUC or the
time-dependent CO2 emissions and absorbtions are disregarded.
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Climate effects from biomass and other energy sources – Main Report
7.1. Electricity based on cultivated wood
The following results are based on a calculation of greenhouse gas emissions
from cultivated wood in three representative climatic zones, Latvia, Southeast
U.S. and Brazil. All harvested wood is assumed used for wood pellets. The harvested wood includes heartwood and 80% of the residues from logging - both
80% of the residues above the ground and 80% of residues in the soil.
All scenarios in this category are shown for wood pellets. If the electricity is produced from wood chips made from cultivated wood, the results will be slightly
different. About the same as what is saved by using wood chips instead of pellets
is lost again due to a lower efficiency rate in the power plant. The accuracy of
data is not strong enough to determine whether one is better than the other.
Particularly important assumptions in the modelling are:
Modelling of forest management: Forest management is modelled by the
so-called ‘stand-level approach’, where it is assumed that a change in demand
for wood is met by logging a corresponding volume of timber, followed by replanting and growth of new trees. Another approach, which is not used in this
study, is the so-called ‘landscape level approach’ (Ref/30/). A ‘landscape level
approach’ assumes that the forest is at equilibrium with continuous inputs of
CO2 uptake and outputs of logged wood and decomposition of dead wood. Since
a ‘landscape level approach’ represents an average of a larger forest area, and
because it does not include the effect of requesting an additional quantity of
wood, this approach is not adequate for use in the applied consequential approach to modelling of this LCA study.
Indirect land use changes (iLUC): When there is a demand for cultivated
wood, this requires a certain area during a certain period of time. This is expressed in hectares years (area times time). It is assumed that it is the demand
for land suitable for forestry that constitutes the reason why uncultivated forest
is converted into cultivated forest. If 1 hectare is demanded for 1 year, then the
indirect impacts on land use change are modelled as an acceleration of conversion of uncultivated forest to cultivated forest by one year. Resulting CO2 emissions are estimated based on the average carbon stock in secondary forest (assumed that this type of uncultivated forest is converted to cultivated forest) and
in cultivated forest. These data do not relate to the specific affected forest area
but to the indirect effects caused by demanding more land for forestry. The assumption here is that the land used for forestry cannot be used for agriculture
(and is thus not part of the market for agricultural land). If this was not the case,
iLUC emissions would be significantly larger. In addition, it is assumed that a
change in the demand for forest land does not to affect yields elsewhere (intensification). Intensification can be seen as a substitute for land. This delimitation
has been made more due to a lack of data in time series of forest yields globally,
than due to an argument that intensification is not happening or will not happen. However, it should be noted that intensification is not necessarily CO2 neu-
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Climate effects from biomass and other energy sources – Main Report
tral – particularly not if it is obtained by fertilization with Nitrogen fertilizer or
by drainage of organic soils.
It should be noted that there are also other methods of evaluating the effect of
land use changes (LUC):


Direct effects involving 1/20th of the historical land use changes that
have occurred in the specific cultivated plot over the last 20 years. This
approach is used for example in PAS2050. Since this approach does not
take indirect effects into account, it is not considered appropriate to fulfill the purpose of the present study. As an example, the use of Danish
wood would not cause any LUC, since there have not been any significant changes in land use over the past 20 years in Denmark. It will also
not be taken into account that the Danish forestry would only be affected
in a very small scale since the cultivated forest area cannot be changed
due to changes in the demand for wood, or only to a very small extent.
Economic general or partial equilibrium models. These models are based
on globale economic models and prise elasticities. Since this approach
uses price elasticities there is an imbalance between production and demand (which is generally assumed to go together in LCA). Moreover,
price elasticities are generally only applicable within relatively short time
periods (0-3 years), and therefore it is questionable how valid results will
be in the long run. Since the purpose of this screening is to provide decision support for the long term, the short-term validity of price elasticities
is considered to be problematic. Therefore the economic models are not
considered suitable to fulfill the purpose of the present study.
Table 5: Main assumption for biofuels based on cultivated wood.
Representative climate
zone and specie
Assumptions for modelling
Rotation time, years
Annual growth,
m3/ha/year (commodity
timber, roundwood)
Eucalyptus,
Brazil
Loblolly Pinewood,
S.E. U.S.
Pinewood,
Latvia
6
50
12
14
63
6
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Climate effects from biomass and other energy sources – Main Report
Figure 7: Emission of greenhouse gases from power production from the modelled
scenarios for cultivated wood measured as CO2e in a 100-year and a 20-year perspective.
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Climate effects from biomass and other energy sources – Main Report
Comments on the results:
 The shorter the time horizon for GWP, the greater the greenhouse gas
emissions.
 The longer the rotation time and the cooler the climate, the greater the
greenhouse gas emissions.
 Transportation is not unimportant (the main process emissions = the
blue portion of the bars, are made up of transport). Therefore, process
emissions are significant for Brazil and the U.S., but less so for Latvia.
 In principle, the contribution of iLUC should/could be the same for all
scenarios, as harvest should/could be proportional to the potential net
primary production that determines iLUC. The differences can be explained by different utilization of the potential yields in the various regions and data uncertainties in yields (heartwood + residues).
 Emissions from 1 kWh of electricity based on coal and gas approximately
account for 1 kg CO2e and 0.6 kg CO2e respectively (both GWP100 and
GWP20). Therefore, it is evident from the results that under the given
assumptions, the emissions from electricity based on cultivated wood are
less and up to the same level as natural gas for GWP100, and at the same
level as natural gas and up to twice that of coal for GWP20. Uncertainties in the data input, however, entails that the above comparison is associated with large uncertainties. However, it can reasonably be concluded that energy production from cultivated wood is not CO2-neutral
and that in some circumstances it may be associated with higher emissions than the fossil alternatives, it replaces. This result is consistent
with the findings of several other studies, for example Ref/21/ which is
also reproduced in annex 2.
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Climate effects from biomass and other energy sources – Main Report
7.2. Electricity based on residual products from forestry and agriculture
The results in the following are based on a calculation of GHG emissions from
residues from forestry and agriculture. Residues from forestry are analyzed for
the same representative climate zones as cultivated wood, Latvia, Southeast U.S.
and Brazil, and agricultural residues (straw) are analyzed for Denmark.
An important assumption for all the scenarios in this category is that residues
are available, and that an increased demand will translate into a reduced
amount of residues left in the forest/on the field and mixed into the soil over
time. If the assumption that the residues are not fully utilized does not hold,
then a change in demand is likely to “switch” the scenario into the category of
cultivated wood instead: If restudies from timber are demanded, and such residues are fully exhausted, then the marginal effect is that additional wood needs
to be cultivated. There is, however, also a third option in an increased production of these residues, e.g. through selective breeding of cereals with higher
straw yields, which is not considered here.
The results in this category can only be used where there is a guarantee that the
residues are not fully utilized - both here and now, and into the future. If these
results are used as decision support for an energy system over the next 10 years,
it must be ensured that the residues are not fully utilized the next 10 years, otherwise the assumptions underlying the calculations are not representative.
All scenarios in this category are shown for wood chips. If electricity is produced
from wood pellets, but still from residues, then the results will change slightly.
Roughly the same as that is used extra in pelletizing, is regained by a higher efficiency rate in the power plant. The accuracy of data is not strong enough to say
whether one is better than the other.
Particularly important assumptions in the modelling are:
Modelling of residual products: Degradation times for residues from forestry and agriculture are calculated using the model RothC. The model has the
advantage that it is based on a few input data (carbon inputs, soil type and climate), and that it can be applied for all countries. The drawback is that differences in factors such as soil type, and distribution of precipitation over the year
etc. in the individual countries is not taken into account in the model. However,
the model shows a clear trend of longer degradation times in cooler climates.
Indirect land use changes (iLUC): When residual products are demanded,
there is no iLUC.
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Climate effects from biomass and other energy sources – Main Report
Figure 8: Emission of greenhouse gases from power production from the modelled
scenarios for residual products from forestry and agriculture measured as CO2e in
a 100-year and a 20-year perspective. See annex 1 for details.
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Climate effects from biomass and other energy sources – Main Report
Comments on the results:
 The shorter the time horizon for GWP, the greater the greenhouse gas
emissions – the difference is, however, considerably smaller than for cultivated wood.
 Since straw generally has a shorter degradation time than residues from
forestry, greenhouse gas emissions from electricity based on straw are
lower than electricity from forest residues.
 The cooler the climate, the greater the greater the greenhouse gas emissions (the green portion of the bars).
 Transportation is not unimportant (the main process emissions = the
blue portion of the bars, are made up of transport). Therefore, process
emissions are significant for Brazil and the U.S., but less so for Latvia,
and least so for Danish supply of straw.
 If residues are fully utilized within the time frame of when the LCA is
used as decision support, the are prerequisites for the results are no
longer present, and it is more correct to apply the results for cultivated
wood.
 Overall, greenhouse gas emissions from residues are lower than for cultivated wood.
 Emissions from 1 kWh of electricity based on coal and gas are approximately 1 kg CO2e and 0.6 kg CO2e, respectively (both GWP100 and
GWP20). Therefore, it is apparent from the modelling that under the
given assumptions, the emissions from electricity based on residues are
lower and up to the same level as natural gas. Uncertainties in the data
input, however, entails that the above comparison is associated with
large uncertainties. However, it can reasonably be concluded that energy
production based on residual products is not CO2 neutral, and that
greenhouse gas emissions for residues are likely to be somewhat lower
than for natural gas. The results indicate with the higest certainty, that
straw and forestry residues from warm climates have lower emissions
than natural gas.
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Climate effects from biomass and other energy sources – Main Report
7.3. Electricity based on biogas
The following results are based on a calculation of greenhouse gas emissions
from electricity based on biogas from manure (calculated as cattle manure),
maize silage and organic household waste in Denmark.
Particularly important assumptions in the modelling are:
Results for biogas opgraded to the natural gas grid: All results are for
biogas that is upgraded to natural gas quality (96% methane). This leads to
higher greenhouse gas emissions than direct use of raw biogas. Results for the
use of raw biogas in the production of electricity are included in the comments
on the results.
Results for biogas from agriculture are a mix of slurry and maize:
Results for electricity from biogas from agriculture are a mix of biogas from
slurry and maize silage. It is possible to use other substrates than maize silage
and other ratios between manure and maize silage than the analyzed. These are,
however, not included in the present LCA screening. Isolated results for biogas
from slurry and maize silage respectively, are included in the comments on the
results.
Scenario for biogas from organic household waste: An important assumption for the calculations for the scenario with organic household waste is
that increased biogasification entails correspondingly reduced waste incineration. Hereby, the heat and electricity that is recovered by incineration is lost,
and this must be compensated by marginal electricity and heat supply. It may
seem distorting to the results that fuel (organic waste) is removed from the incineration installations that produce both electricity and heat to a usage where
only electricity is produced. The reason for the choice of modelling is the functional unit of the present study, which only includes electricity. Since the overall
efficiency rate of heat production (which is co-generated with electricity) is
much higher than for pure electricity production, this, overall, means less for
the result.7
Modelling of slurry as a residual product from animal production:
An important assumption in the calculations for slurry, which is a residue, is
that it is not fully utilized for energy purposes and that an increased demand
will translate into a reduced amount of residue that is spread on agricultural
land without prior gasification. If the assumption that the residues are not fully
utilized does not hold, a change in demand shifts to another energy source.
The reason that the efficiency rate for heat is higher is: Heating is co-generated with electric power,
typically 0.6 MJ heat and 0.3 MJ electricity per input of fuel (values are for illustration only). I.e. 1 MJ
heat requires 1.66 MJ fuel. In addition to this, 1 MJ heat is co-generated with 0.5 MJ electricity. This
displaces alternative production of (marginal) electricity. Marginal electricity is generally always generated by condensing, where the efficiency rate is approximately 40% (value for illustration only). I.e. the
displaced 0.5 MJ electricity are related to the displaced 1.25 MJ fuel. This amounts to a total fuel consumption of 1.66 MJ - 25.1 MJ = 0.41 MJ per MJ heat produced. This corresponds to an overall efficiency rate of ~ 240%.
7
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Climate effects from biomass and other energy sources – Main Report
Therefore, the results for biogas from manure can only be used where there is a
guarantee that the residues are not fully utilized - both here and now, and also
into the future. If these results are used as decision support for an energy system over the next 10 years, it must be ensured that the residues are not fully
utilized the next 10 years, otherwise the assumptions underlying the calculations are not representative. It should be noted that the animal production is
not changed because the slurry is biogasified.
Indirect land use changes (iLUC): Maize silage is the onle of the raw materials for biogas production that is related to iLUC.
When land is demanden for the cultivation of raw materials, it requires a certain
area for a given period of time. This is expressed in hectare years. It is assumed
that it is the demand for land suitable for agriculture (crop farming) that causes
uncultivated forest to be converted into agricultural land and, existing cultivation to be intensified by an increased application of fertilizers etc. If 1 hectare
year is demanded for 1 year, the indirect impacts on land use change are modelled partly as acceleration of conversion of uncultivated forest to farmland by a
year, and partly as application of extra fertilizer and associated emissions to existing cultivated land. CO2 emissions from land use change are calculated using
the average carbon stock in secondary forest (assumed that this type of uncultivated forest is converted into agriculture) and in agriculture.
It should be noted that other ways to evaluate the effect of land use changes
(LUC) are also available:
 Direct effects involving 1/20th of the historical land use changes that
have occurred in the specific cultivated plot over the last 20 years. This
approach is used e.g. in PAS2050. Since this approach does not take indirect effects into account, it is not considered appropriate to fulfill the
purpose of the present study. As an example, the use of Danish agricultural land would not cause any LUC, since there have not been any significant changes in land use over the past 20 years in Denmark. It will
also not be taken into account that the Danish agriculture would only be
affected in a very small scale since the areable land area cannot be
changed due to changes in the demand for agricultural land, or only to a
very small extent.
 Economic general or partial equilibrium models. These models are based
on globale economic models and prise elasticities. Since this approach
uses price elasticities there is an imbalance between production and demand (which is generally assumed to go together in LCA). Moreover,
price elasticities are generally only applicable within relatively short time
periods (0-3 years), and therefore it is questionable how valid results will
be in the long run. Since the purpose of this screening is to provide decision support for the long term, the short-term validity of price elasticities
is considered to be problematic. Therefore the economic models are not
considered suitable to fulfill the purpose of the present study.8
Price elasticities are generally valid only in the short term (0-3 years). In the long term it can be argued that the price elasticity is approaching 100%, i.e. full elsticity between demand and production.
8
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Climate effects from biomass and other energy sources – Main Report
Figure 9: Emission of greenhouse gases from power production from the modelled
scenarios for biogas measured as CO2e in a 100-year and a 20-year perspective. Net
emissions are the summation of the negative and positive values and are shown
numerically above the pillars. See annex 1 for details.
The reason for this is that it is the marginal productions costs that determines the cost, and not variations in demand.
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Climate effects from biomass and other energy sources – Main Report
Comments on the results:
 The shorter the time horizon for GWP, the greater the greenhouse gas
emissions (at a large share of maize)
 Process emissions (the blue bars) are dominated by methane emissions
from biogas production and upgrading. In the scenario with biogas from
manure, this is negative. This is due the fact that more methane emissions are avoided from storage of manure than is emitted from biogas
production and upgrading.
 The results shown for manure and maize silage consist of two different
mixes. Greenhouse gas emissions from 1 kWh of electricity to increase
bio-gasification of manure alone is -0.651 kg CO2e/kWh electricity,
while for gasification of maize silage alone it is 1.13 kg CO2e/kWh electricity (GWP100). It can therefore be concluded that biogasification of
manure is a good idea, while biogasification of maize silage is associated
with high levels of greenhouse gas emissions.
 The results shown are for upgraded biogas: 0.592 kg CO2e/kWh 70%
maize/30% slurry and 0.201 kg CO2e/kWh 48% maize/52% slurry
(GWP100). Greenhouse gas emission from 1 kWh of electricity from
electricity production based on raw biogas is 0.401 kg CO2e/kWh 70%
maize/30% slurry, and 0.0103 kg CO2e/kWh 48% maize/52% slurry
(GWP100). Thus, there is a saving potential of approximately 190 g
CO2e/kWh by using raw biogas instead of upgraded biogas - given the
applied assumptions concerning the upgrading of biogas. On the other
hand, upgrading offers other advantages in terms of storage in the natural gas grid and future usage also for motor fuel.
 Emissions from 1 kWh of electricity based on coal and gas are approximate respectively. 1 kg CO2e and 0.6 kg CO2e (both GWP100 and
GWP20). Therefore, it is evident that under the given assumptions, the
emissions from electricity from biogas is on a par with electricity from
natural gas (although it is lower than natural gas at mixes with much
manure and little maize) by GWP100. At GWP20 biogas is on a par with
electricity from coal (although it is lower than natural gas at mixes with
much manure and little maize). Uncertainties in the data input, however,
entail that the above comparison is associated with very large uncertainties. Therefore, it can not be concluded that biogas is on par with natural
gas and coal for respectively GWP100 and GWP20. Instead, it can be
concluded that biogas under certain conditions may be associated with
greenhouse gas emission levels that are up to the same level as fossil fuels. The uncertainties have as an implication that emissions from biogas
can be both larger and smaller than shown in the results. Calculations
are based on low values of methane loss from biogas production, and
emission levels will be sensitive to these losses.
 The above comparison is very sensitive to the amount of slurry and the
amount of maize used as raw material. Therefore, if there are available
alternatives to maize, that are not fully utilized, greenhouse gas emissions can be significantly reduced. Examples could be straw and other
residual products that are not currently fully utilized, provided these are
not taken from existing usage.
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Climate effects from biomass and other energy sources – Main Report
7.4. Electricity based on coal, gas, wind and solar
The results in the following are based on a calculation of greenhouse gas emissions from electricity based on coal, natural gas, wind, and solar in Denmark.
Unlike the biofuel scenarios, these scenarios are based directly on the data
available in the ecoinvent LCA database. In addition to the burning of coal and
gas in itself, the data here includes manufacturing, maintenance and disposal of
productions stock, as well as infrastructure.
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Climate effects from biomass and other energy sources – Main Report
Figure 10: Emission of greenhouse gases from production of electricity from the
modelled scenarios for coal, gas, wind and solar measured as CO2e in a 100-year
and a 20-year perspective. See annex 1 for details.
Comments on the results:
 Timeframe for GWP has no significant influence.
 Coal, gas, wind and solar are not associated with ILUC and timedependent CO2 emissions. Therefore, there are no green and red contributions to the pillars in the results.
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Climate effects from biomass and other energy sources – Main Report
7.5. Liquid fuels: Biodiesel
The results in the following are based on a calculation of GHG emissions from
the production and burning of biodiesel based on palm oil and rapeseed oil.
Particularly important assumptions in the modelling are:
Data on biodiesel production: Data for production of biodiesel from vegetable oil is represented by data for normal refining (neutralization, bleaching,
deodorisation) of vegetable oil for food etc. purposes. This assumptiom is considered to have minimal effect on the results.9
Geography for raw materials and by-producs: Raw material for rapeseed
biodiesel, i.e. rapeseed is assumed produced in Denmark. A by-product of rapeseed production is rapeseed cake. Rapeseed cake is used for animal feed, and
hereby displaces marginal protein (Brazilian soy meal) and feed energy (Ukrainian barley). It should be noted that the Danish agricultural production is not
flexible - in the sense that increased rapeseed production can only be achieved
by displacement of other crops, e.g. barley or wheat. Hereby, the marginal influence of Danish rape is for Ukrainian barley, and greenhouse gas emissions will
increase. This means that the calculated emissions for rapeseed biodiesel can be
underestimated.
Indirect land use changes (iLUC): See description of iLUC in the result section on biogas.
Strong data is available for ‘normal’ refining and there is no immediate access to the same strong data
for transesterification. Refining generally means very little for the LCA results for vegetable oils.
9
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Climate effects from biomass and other energy sources – Main Report
Figure 11: Emission of greenhouse gases from production and combustion of biodiesel from rapeseed oil and biodiesel from palme oil measured as CO2e in a 100year and a 20-year perspective. See annex 1 for details.
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Climate effects from biomass and other energy sources – Main Report
Comments on the results:
 The shorter the time horizon for GWP, the greater the greenhouse
gas emissions. The difference is partly due to the importance of the
time horizon for ILUC (red part of the result columns), and partly
because palm oil is associated with considerable methane emissions.
GWP20 for methane is substantially greater than GWP100. The latter is categorized as process emissions (blue part of the result columns).
 Process emissions (the blue columns) are dominated by emissions of
N2O from the soil, the production of nitrogen fertilizers, and for
palm oil of methane emissions from wastewater treatment.
 Differences in iLUC for biodiesel based on rapeseed oil and palm oil
are caused by rapeseed oil being associated with significantly more
by-products (oil cake) than palm oil. The oil cake displaces Brazilian
soy meal and Ukrainian barley.
 Emissions from the production and combustion of 1 MJ diesel is 8688 g CO2e (both GWP100 and GWP20). Therefore, it is apparent
that under the given assumptions, the greenhouse gas emissions for
all scenarios except rapeseed biodiesel in GWP100, are higher than
diesel. Rapeseed biodiesel by GWP100 is related to about the same
amount of greenhouse gas emissions as diesel. Uncertainties in the
data inputs are limited compared to other biofuels analyzed in this
LCA screening. Uncertainties for iLUC, however, are relatively large.
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Climate effects from biomass and other energy sources – Main Report
7.6. Liquid fuels: Bioethanol
The results in the following are based on a calculation of greenhouse gas emissions from the production and combustion of first and second generation bioethanol. First generation bioethanol is analyzed using respectively wheat and
maize as raw material, and second generation bioethanol is analyzed using
straw as raw material.
Particularly important assumptions in the modelling are:
Geography for raw materials and by-products: Raw materials, i.e. wheat,
maize and straw, are assumed produced respectively in Denmark, Europe and
Denmark. By-products from bioethanol production are DDGS (for first generation) and C5 molasses (for second generation). Both are used for animal feed,
and hereby displace marginal protein (Brazilian soy meal) and feed energy
(Ukrainian barley). The effcient Danish production of raw materials, and the
displaced more inefficient Brazilian and Ukrainian production of marginal feed
protein and feed energy, entail that emissions are relatively low - especially for
second generation bioethanol, which shows negative values. It should be noted
that the Danish agricultural production is not flexible - in the sense that increased wheat production can only be achieved by displacement of other crops,
such as barley. This makes the marginal influence of Danish wheat to Ukrainian
barley, and greenhouse gas emissions will increase. Results for bioethanol,
where the marginal raw material is Ukrainian barley are included in the comments on the results.
Feed values: Data for feed values of C5 molasses is not based on official feed
tables. Therefore, the uncertainty in these data must be considered significant.
Limits to the utilization of DDGS: The global production of bioethanol has
been and is growing, and several researchers highlight that a limit for how much
DDGS can be mixed into animal feed will be reached within a few years. When
or if this becomes the case, then the marginal effect of DDGS si no longer displacement of animal feed, but something else - most likely it will be used as a
form of biofuels, including upgrading to bioethanol or utilization for biogas by
other methods. It is not certain that this application will have the same large
displacement effects as the use as animal feed, and therefore greenhouse gas
emissions may be greater in the long term.
Indirect land use changes (iLUC): See description of iLUC in the result section on biogas.
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Climate effects from biomass and other energy sources – Main Report
Figure 12: Emission of greenhouse gases from production and combustion of first
and second generation bioethanol measured as CO2e in a 100-year and a 20-year
perspective. Net emissions are the summation of the negative and positive values
and are shown numerically above the pillars. See annex 1 for details.
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Climate effects from biomass and other energy sources – Main Report
Comments on the results:
 The timeframe for GWP is of less importance than for other biofuels.
 Process emissions (the blue columns) are dominated by emissions of
N2O from the soil (fertilization) and the production of nitrogen fertilizers (both positive contributions in Denmark and displaced contributions in Brazil and Ukraine).
 As mentioned above, the modelling of the Danish wheat probably does
not represent the marginal effect of demanding wheat in Denmark.
Therefore, the scenario for first generation bioethanol from wheat is also
analyzed with a commodity input consisting of Ukrainian barley. This
analysis changes greenhouse gas emissions (GWP100) from 0.035 kg to
0.329 kg CO2e/MJ. The results are therefore very sensitive to the marginal impact of crops (including their productivity - the higher the productivity the lower the emissions). It should also be noted that wheat in
Denmark is associated with particularly low greenhouse gas emissions as
part of the straw is used for energy purposes, and because nitrogen utilization in Danish agriculture is very high.
 Emissions from the production and combustion of 1 MJ petrol is 92-99 g
CO2e (both GWP100 and GWP20). Therefore, it is apparent that under
the given assumptions, greenhouse gas emissions for all scenarios are
less than petrol. However, it should be noted that the Danish wheat and
European maize does probably not correspond to the marginal effect of
demanding such crops - and above is explained that this can have considerable impact on the results. Further, uncertainties in data inputs entail that the above comparison is associated with very large uncertainties.
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Climate effects from biomass and other energy sources – Main Report
7.7. Liquid fuels: Diesel and petrol
The results in the following are based on a calculation of greenhouse gas emissions from the production and combustion of diesel and petrol.
Unlike the biofuel scenarios, these scenarios are based directly on data available
in the ecoinvent LCA database. In addition to the combustion of diesel and petrol in itself, the data here includes manufacture, maintenance and disposal of
production stock, as well as infrastructure.
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Climate effects from biomass and other energy sources – Main Report
Figure 13: Emission of greenhouse gases from production and combustion of diesel
og petrol measured as CO2e in a 100-year and a 20-year perspective. See annex 1 for
details.
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Climate effects from biomass and other energy sources – Main Report
7.8. Comparaison with literature review from JRC
As mentioned, the European Commission's Joint Research Centre (JRC) recently published a report on the same subject (Ref/21/) based on a critical review of existing literature. The concluding chapter of this report is attached in
annex 2.
JRC in their report undertake a qualitative comparison of emissions from various types of biomass for energy production, based on existing literature. This is
illustrated in Table 6.
Table 6: Qualitative comparaison of emissions of greenhouse gases from different
bio based energy sources in relation to the fossil alternatives. From Ref/21/.
As apparent from the table, there is consistency between JRC’s qualitative table
and the results calculated in this model for the relevant products. Accordingly,
JRC points out that for whole trees from temperate and boreal climate zones
used in energy production there is only a climate benefit over the very long term
(centuries), while there is only a slight advantage in energy production on residues/thinnings in a 50-year perspective. The use of industrial residues (sawdust, etc.) is an absolute advantage, provided that this utilization does not displace other uses (MDF products and the like). New plantings on marginal lands
also provide a definite advantage, but here it should be noted that Ref/19/ based
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Climate effects from biomass and other energy sources – Main Report
on an economic modelling finds that biomass production on marginal soils is
unrealistic and virtually impossible to carry out in practise, and that this would
ultimately result in substantial interferences with the natural forests and productive farmland. In any case such production requires strong political will, and
global regulation and control.
8. Discussion and conclusion
This report calculates the climate impact from a variety of different sources at
an overall level with particular focus on energy from biomass. The purpose of
this study is:



To clarify whether biofuels are CO2 neutral.
To identify factors of particular importance to the greenhouse gas
emissions from biofuels.
To carry out an indicative comparison of the analyzed biofuels and
their fossil alternatives.
The results show that virtually none of the energy sources using the selected assumptions and calculation methods are CO2 neutral, and that several of them depending on the chosen time horizon - may have an emission level that is on
par with or greater than the fossil energy sources they replace. In this regard, it
should be emphasized that the calculated results are based on data inputs with
substantial uncertainties, as well as a number of methodological choices. Therefore, the results in this report cannot be used to conclude whether certain biofuels are better or worse than the fossil fuels they are intended to replace, unless
the applied assumptions are representative. If compared to other similar studies
(see e.g. Table 6 and Annex 2), the results, however, largely coincide.
The calculations show that biomass produced on residueal products and straw
has lower emissions than biomass produced on whole trees and commersial
crops, and that energy based on wood from climate zones with short rotation
times are more beneficial to the climate than energy made from wood from climate zones with long rotation times.
For biogas, the calculations show that biogas based wholly or partially on energy
crops will increase the climate impact compared to biogas based solely on manure. The calculations also show that the climate impact of biogas is sensitive to
the amount of leakage of methane from the installations, which therefore should
be a major concern.
Finally, the calculations show that wind and in part also solar power, generally
result in less strain on the climate than electricity from most types of biomass.
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Climate effects from biomass and other energy sources – Main Report
For most biofuels, the selected time horizon for calculating greenhouse gas
emissions (GWP) has significant importance. Generally, a shorter time horizon
implies greater greenhouse gas emissions measured as GWP.
For the liquid fuels, the same applies as for the solid fuels and for biogas; biofuels based on waste products (manure) means lower greenhouse gas emissions
than biofuels from cultivated crops/forestry products.
Finally, ILUC effects cannot reasonably be disregarded, and they can have a significant impact on the total climate impact.
This report focuses exclusively on the climate effects of different types of energy.
Sustainability as a concept usually encompases wider factors than solely the
emission of greenhouse gases. For example, biodiversity poses a particular challenge to biomass because it requires so much land, and also often relies on large
amounts of water and additives. Accordingly, in more general LCAs of biomass
for energy purposes, the impact on biodiversity is often one of the most critical
parameters when the overall environmental impact is weighted. In addition, the
use of agricultural land for bioenergy can affect food production and food prices,
which can have significant social consequences.
The discussion about climate impacts and the alleged CO2-neutrality of biomass
has long been debated within the scientific community. It has by now gained
more general political interest and timeliness, partly from a criterion to ensure
that the selected instruments have the expected climatic effects, but also from
an investment point of view where investments also in the long run should
prove to be the right ones, and prove to be economically viable, and not made on
partially false pretenses as it has been the case with the biodiesel industry in
Europe.
The evaluation of sustainability and climate impacts of biomass for energy purposes initiated by the Danish Government is an example of this rising political
interest. Anorther example is magazines like The Economist in an article and
editorial (Ref/24/) expressing even highly critical views of the alleged positive
climate impact of the use of biomass for energy purposes, including economic
security and political sense of reason in the related investments and instruments.
This more critical focus on biomass stresses that in the long run it is in everyone's interest that the climate impacts are determined as accurately as possible.
The uncertainties in the data of the present study are significant. Looking ahead,
it is recommended that these uncertainties are reduced. This in particular applies to: modelling of ILUC, assessment of potentials for the use of residuals,
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Climate effects from biomass and other energy sources – Main Report
identification of the marginally affected suppliers of raw materials for biofuels,
i.e. where crops are grown and which regions forestry is affected in.
Finally, it is important also to be aware of the time horizon applied. As shown in
the results, many types of biomass pose particular challenges when it comes to
climate impacts over shorter time horizons of less than 50 years. This is evident
from the results with a time horizon of 20 years in this report. In practice this
means that some biofuels will increase the concentration of greenhouse gases in
the atmosphere in the short term. The present study has not focused on determining the appropriate timeframe to be applied. Based on the results, it is recommended to operate with more than one timeframe in analyzes of the impact
on greenhouse gas emissions from biofuels.
Since the current political task is to reduce emissions of greenhouse gases here
and now with a relatively large annual reduction, this will be extremely difficult
to achieve using biomass from crops with other than very short rotation times,
especially if the use of biomass results in a reduction in the growth many forests
outside the tropics have today. The challenge here is that the use of biomass
must boost net growth and sequestration in the short term, which will be a considerable challenge when other sustainability parameters simultaneously have
to be taken into account.
It is the intention of CONCITO with this report and the methodology described
to have contributed to a more nuanced view of the climate impact of biomass even if the result is that Denmark's objective of a 40% reduction in emissions of
greenhouse gases by 2020 compared to 1990 de facto will be significantly
harder to achieve if the biomass is not considered to be climate neutral.
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9. References
/1/ Reducerer brug af biomasse atmosfærens indhold af CO2?, CONCITO, november 2011.
/2/ Opinion of the EEA Scientific Commitee on Greenhouse Gas Accounting in Relation to Bioenergy; European Environment Agency, Scientific
Committee, 15 September 2011.
/3/ McKechnie et al.: Forrest Bioenergy or Forrest Carbon? Assesing TradeOffs in Greenhouse Gas Mitigation with Wood-Based Fuels; Environ.
Sci. Technol. 2011, 45, 789–795
/4/ Cherubini, F. m.fl: CO2 emissions from biomass combustion for bioenergy: atmospheric decay and contribution to global warming; GCB
Bioenergy (2011) 3, 413–426
/5/ Skog som biomasseressurs, Klima og Forurensningsdirektoratet, Norge,
2011.
/6/ Climate Change 2007: Synthesis Report, Summary for Policymakers.
Intergovernmental Panel on Climate Change (IPCC), 2007
/7/ Klimaændringer 2007: Synteserapport
Sammendrag for Beslutningstagere DMIs oversættelse af IPCC (Ref/6/)
/8/ Critical issues in estimating ILUC emissions
Outcomes of an expert consultation
9-10 November 2010, Ispra (Italy), EU Joint Research Center.
/9/ David Archer : The Global Carbon Cycle, Princeton University Press, 2010
/10/ Weidema B P, Ekvall T, Heijungs R (2009), Guidelines for applications of
deepened and broadened LCA. Deliverable D18 of work package 5 of the CALCAS project. http://fr1.estis.net/includes/file.asp?site=calcas&file=7F2938F909CD-409F-9D70-767169EC8AA9
/11/ Chrintz T and Schmidt J H (2012), Carbon Footprint - den ideelle opgørelse
og
anvendelse.
CONCITO,
København. Accessed
October
2012:
http://concito.dk/files/dokumenter/artikler/rapport_gcfr_endelig.pdf
/12/ Schmidt J H and Dalgaard R (2012), National and farm level carbon footprint of milk ‐ Methodology and results for Danish and Swedish milk 2005 at
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Climate effects from biomass and other energy sources – Main Report
farm gate. Arla Foods, Aarhus,
http://www.lca-net.com/ArlaMain
Denmark.
Accessed
October
2012:
/13/ Ruedi Müller-Wenk & Miguel Brandão (2010): Climatic impact of land use
in LCA — carbon transfers between vegetation/soil and air, Int. J. Life Cycle Assess (2010) 15:172 – 182
/14/ THE GHG EMISSIONS INTENSITY OF BIOENERGY,
Does bioenergy have a role to play in reducing Europe’s GHG emissions?
Institute for European Environmental Policy. October 2012.
/15/ Fritsche U. et al (2012), Sustainability Criteria and Indicators for Solid Bioenergy from Forests, IINAS & JRC.
/16/ XU, L et al (2013): Temperature and vegetation seasonality diminishment
over northern lands. Nature Climate Change doi:10.1038/nclimate1836
/17/ IPCC, 2007:
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch2s2-10-2.html
/18/ Haberl, H. (2013) Net land-atmosphere flows of biogenic carbon related to
bioenergy: towards an understanding of systemic
feedbacks. GCB Bioenergy (2013), doi: 10.1111/gcbb.12071
/19/ David K. Bryngelssonn & Kristian Lindgren (2013): Why large-scale bioenergy production on marginal land is unfeasible: A conceptual partial equilibrium analysis. Elsevier, Energy Policy April 2013, Pages 454–466
/20/ Maurizio Cocchi et al. (2011): Golbal Wood Pellet Industri Market and
Trade Study. IEA Bioenergy Task 40.
/21/ Alessandro Agostini et al. : Carbon accounting of forest bioenergy, Conclusions and recommendations from a critical literature review, JRC, 2013.
/22/ UNEP Synthesis Report (2011): Near-term Climate Protection and
Clean Air Benefits: Actions for Controlling Short-Lived Climate Forcers
/23/ Kallio, M. And O. Salminen (2012). “Impacts of the Increased Production
of Wood Based Bioenergy on the Carbon Balance Projections for Finland.” 20th
Biomass conference and exhibition proceedings.
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/24/ The Economist, 2013 :
http://www.economist.com/news/business/21575771-environmental-lunacyeurope-fuel-future
/25/ Energistyrelsen : Begrænsning for brug af majs og andre energiafgrøder til
production af biogas, Notat, 26/9-2012.
/26/ Tore Hulgaard Rambøll, Dakofakonference : AFFALDETS
ENERGIRESSOURCE OG ANVENDELSEN AF NYE TEKNOLOGIER :
http://www.dakofa.dk/Aktiviteter/konferencer_seminarer/120522/Materiale/
Affaldets%20energiressource%20og%20anvendelsen%20af%20nye%20teknologier
%20(Tore%20Hulgaard).pdf
/27/ Davide Tonini & Thomas Astrup (2012): LCA of biomass-based energy systems: A case study for Denmark; Applied Energy (99).
/28/C. Le Quéré et al : The global carbon budget 1959–2011, Earth Syst. Sci.
Data Discuss., 5, 1107–1157, 2012
/29/ Colnes, A. et al: Biomass Supply and Carbon Accounting for Southeastern
Forests, Biomass Energy Resource Center, February 2012.
/30/ Gerrit J, Jonker G, Junginger M And Faaij A (2013), Carbon payback period and carbon offset parity point of wood pellet production in the Southeastern United States. GCB Bioenergy (2013), doi: 10.1111/gcbb.12056
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10. Annex 1: LCA screening of biofuels
11. Annex 2: Conclusions from JRC
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