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A Critical Analysis of the Carbon Neutrality Assumption in
Life Cycle Assessment of Forest Bioenergy Systems
Journal: Environmental Reviews
Manuscript ID er-2017-0060.R1
Manuscript Type: Review
Date Submitted by the Author: 18-Oct-2017
Complete List of Authors: Liu, Weiguo; Northwest Agriculture and Forestry University, College of Forest Yu, Zhen; Iowa State University Xie, Xinfeng; Michigan Technological University von Gadow , Klaus ; Georg-August University Göttingen Peng, Changhui; Northwest Agriculture and Forestry University, College of
Forest; University of Quebec at Montreal
Keyword: carbon neutral assumption, bioenergy, life cycle assessment, climate change impact, forest biomass
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A Critical Analysis of the Carbon Neutrality Assumption 1
in Life Cycle Assessment of Forest Bioenergy Systems 2
Weiguo Liua, Zhen Yu
b,c, Xinfeng Xie
d, Klaus von Gadow
e,f, Changhui Peng
a,g* 3
4
Affiliations and Addresses: 5
a Center for Ecological Forecasting and Global Change, College of Forestry, Northwest 6
Agriculture and Forestry University, Yangling, Shaanxi 712100, China. 7
b College of Earth Sciences,
Chengdu University of Technology, Chengdu 610059, China 8
c Department of Ecology, Evolution, and Organismal
Biology (EEOB), Iowa State University, 9
Ames, IA 50011, U.S. 10
d School of Forest Resources and Environmental Science, Michigan Technological University, 11
Houghton, MI 49931, U.S. 12
e Burckhardt Institute, Georg-August University Göttingen, Göttingen, Germany. 13
f Department of Forest and Wood Science, University of Stellenbosch, South Africa. 14
g Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at 15
Montreal, C.P. 8888, Succ. Centre-Ville, Montreal, Canada H3C3P8. 16
* Corresponding author: 17
Name: Changhui Peng 18
Address: College of Forestry, Northwest Agriculture and Forestry University, Yangling, Shaanxi 19
712100, China. 20
Email: [email protected]. 21
Word Count: 5669 words without Abstract and References. 22
Figure Number: 3. 23
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A Critical Analysis of the Carbon Neutrality Assumption in Life Cycle Assessment of 24
Forest Bioenergy Systems 25
26
Abstract: This study presents a critical analysis regarding the assumption of carbon neutrality in 27
life cycle assessment (LCA) models which aim to assessing climate change impacts of bioenergy 28
usage. We identified a complex of problems in the carbon neutrality assumption, especially 29
regarding bioenergy derived from forest residues. In this study, we summarized several issues 30
related to carbon neutral assumptions, with particular emphasis on possible carbon accounting 31
errors at the product level. We analyzed errors in estimating emissions in the supply chain, direct 32
and indirect emissions due to forest residue extraction, biogenic CO2 emission from biomass 33
combustion for energy, and other effects related to forest residue extraction. Various modeling 34
approaches are discussed in detail. We concluded that there is a need to correct accounting errors 35
when estimating climate change impacts and proposed possible remedies. To accurately assess 36
climate change impacts of bioenergy use, greater efforts are required to improve forest carbon 37
cycle modeling, especially to identify and correct pitfalls associated with LCA accounting, forest 38
residue extraction effects on forest fire risk and biodiversity. Uncertainties in accounting carbon 39
emissions in LCA are also highlighted, and associated risks are discussed. 40
Keywords: forest biomass, carbon neutral assumption, bioenergy, life cycle assessment, climate 41
change impact. 42
43
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1 Introduction 44
The promise of low-carbon energies in mitigating global climate change has prompted numerous 45
research efforts, especially regarding forest residue. Biomass was considered as one of the most 46
attractive and promising sources of energy because of carbon neutral assumption. The 47
assumption has been widely recognized and accepted in life cycle assessment (LCA) because the 48
biogenic CO2 emission is eventually absorbed by biomass regrowth through photosynthesis 49
(Raguskas et al. 2006; UN FAO 2008; Zeman and Keith 2008). Some LCA studies even reported 50
biomass as carbon negative if forests are managed sustainably (Lehmann 2007; Mathews 2008). 51
Based on these premises, current climate policies by the Intergovernmental Panel on Climate 52
Change (IPCC), the European Commission and the American Clean Energy and Security Act 53
seem to ignore carbon emissions resulting from biomass utilization. 54
The guidelines compiled by the IPCC allege that carbon emissions from bioenergy should 55
not be included in national greenhouse gas (GHG) inventories (Gytarsky et al. 2006), because the 56
emissions had already been fully accounted for in the Agriculture, Forestry and Other Land-Use 57
(AFOLU) sector. The American Clean Energy and Security Act, being one of the most informed 58
and evidence-driven energy bill, excludes biomass emissions when accounting GHG emissions 59
from energy use (Waxman and Markey 2009). Similarly, in the new climate policies of the 60
European Union, biogenic CO2 is treated as zero climate impact, although the emissions due to 61
land use change are included (Pallemaerts 2010). Therefore, bioenergy has been frequently 62
referred to as a carbon neutral energy resource in LCA and advocated by governments to 63
substitute fossil fuels to mitigate global warming. 64
Due to the carbon neutral assumption of bioenergy, most of the LCA studies conducted on 65
bioenergy systems treated the climate change impacts of carbon emissions from biomass 66
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utilization as negligible. A survey conducted by Johnson revealed that only one of 25 researchers 67
included the climate change impacts of forest biomass in identifying a carbon footprint of solid 68
biomass fuel (Johnson 2009). After reviewing 94 LCA studies conducted on bioenergy systems, 69
Cherubini and Strømman found that only one single case study had included an accounting of 70
biogenic CO2 emissions (Cherubini and Strømman 2011). Shonnard et al. reviewed 74 LCA 71
studies in the Pan American region, found that most of these studies presumed carbon neutrality 72
(Shonnard et al. 2015). Based on the assumption of carbon neutrality, popular environmental 73
analysis tools like SimaPro, GaBi and openLCA usually exclude GHG emissions from biomass 74
combustion in their calculations (Foster 2001; Frischknecht et al. 2007; Pachauri and Reisinger 75
2007). This assumption is practical because it greatly simplifies the analysis of carbon footprints 76
in biomass utilization by systematically reducing GHG emissions in a bioenergy system. 77
1.1 Towards a More Comprehensive Estimation System 78
The carbon neutral assumption is widely accepted. However, no common definition of carbon 79
neutral assumption can be found. Several assertions on carbon neutrality are dominant in 80
literatures: i) the use of biomass for energy is naturally carbon neutral and no carbon emissions 81
should be accounted for any products from biomass ( UNFCCC 1998; Gytarsky et al. 2006), ii) 82
the use of biomass for energy is carbon neutral because the carbon emissions from biomass 83
combustion are compensated by the biomass regrowth (Ragauskas et al. 2006; UN FAO 2008; 84
Zeman and Keith 2008), iii) the use of biomass for energy has zero net carbon emissions in its 85
life cycle, including biomass cultivation, harvest, and even transportation and conversion (Gan 86
2007; Miner 2010; Repo 2015). However, those statements are problematic when applied in 87
LCA models. 88
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During the past decade, a number of studies have emerged reporting deficiencies of the 89
carbon neutral assumption. Problems that require research involve, for example, biomass 90
harvesting effects on land use change, and CO2 emission as a one-time pulse that could stay in 91
the atmosphere for years (Johnson 2009; Searchinger et al. 2009). Land use change could cause 92
significant carbon emissions. Many researchers suggest to take carbon emissions from land use 93
change into account (Searchinger et al. 2009; McKechnie et al. 2010; Newell and Vos 2012; 94
Koellner et al. 2013). Searchinger et al. (2008) found that the growth of switchgrass on cropland 95
could increase GHG emissions by 50%. The emissions from land use change could also have the 96
possibility to offset the carbon savings from biofuel (Lapola et al. 2010). In the meanwhile, the 97
IPCC also provides a detailed guideline for estimating carbon balance from land use change 98
(Gytarsky et al. 2006). Although the incorporation of emissions from land use change into LCA 99
is complex, Liu et al. (2017) considered the carbon change due to biomass utilization in LCA and 100
found that biofuels still had advantages in terms of GHG emissions in most of the scenarios. 101
The global warming potential (GWP) of biogenic CO2 should also be accounted for in the 102
estimation of climate change impacts of bioenergy systems. This is because biogenic CO2 from 103
biomass combustion is a one-time pulse emission and requires years to be compensated by 104
biomass regrowth (Cherubini and Strømman 2011). In recent years, several studies have tried to 105
develop a standard method to calculate the GWP of biogenic CO2 emission (Cherubini and 106
Strømman 2011; Cherubini et al. 2011; Pingoud et al. 2012; Bright et al. 2012; Guest et al. 2013). 107
The GWP of biogenic CO2 emissions (GWPbio) was based on the relative radiative forcing and 108
calculated according to its estimated persistence in the atmosphere. When the rotation length was 109
100 years, GWPbio values were estimated to range between 0.34 and 0.62 for a 100-year time 110
horizon. However, Cherubini et al. suggested that GWPbio was negligible for very short rotation 111
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lengths (Cherubini et al. 2011), as those in perennial grass systems. Pingoud et al., however, 112
encouraged the collection of waste wood for bioenergy because its GWPbio could be as low as -1 113
(Pingoud et al. 2012). However, whole tree harvesting for bioenergy is not very common, and 114
this harvest strategy could cause high GWPbio. If a forest is harvested primarily for long-lived 115
wood product, the GWPbio of forest residue could be lower (Liu et al. 2017). 116
These findings provide a rationale for correcting problems related to the carbon neutral 117
assumption. Critical analyses by several researchers are useful in creating a new basis for 118
developing a more comprehensive estimation system for accounting emissions from biomass 119
utilization. This system could provide a fair comparison of GHG emissions from bioenergy and 120
fossil fuel, which is essential for accurate estimation of carbon emissions from biomass 121
utilization, especially regarding forest biomass utilization (Chum et al. 2011; Zetterberg and 122
Chen 2015). 123
1.2 Objectives 124
This study presents a critical assessment of common assumptions about carbon neutrality and 125
reviews current literature aiming. Our objectives are: i) to identify and summarize deficiencies of 126
LCA for bioenergy system in current carbon emissions accounting practices, and ii) to 127
recapitulate and propose remedy approaches for those deficiencies. We also identify future 128
research needs to improve the standards of life cycle carbon emissions accounting in forest 129
biomass utilization. Section 2 summarizes necessary adjustments of the carbon neutral 130
assumption, while Section 3 focuses on research needs that are required to improve the accuracy 131
of estimating the impacts of forest residue-derived bioenergy. Section 4 discusses uncertainties 132
in assessing environmental impacts, while Section 5 presents a summary of faulty carbon neutral 133
assumptions and possible remedies. Fig. 1 shows the systematic framework of this paper. 134
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135
Insert Fig. 1 136
137
2 Erroneous Assumptions and Possible Remedies 138
2.1 Accounting Errors 139
According to the guidelines compiled by the IPCC, emissions from biomass combustion are 140
already fully accounted for in the Agriculture, Forestry and Other Land-Use (AFOLU) sector 141
(Gytarsky et al. 2006). Thus, CO2 emissions from biomass combustion are presumed carbon 142
neutral and should not be included in a national GHG inventory. Hence, this approach is 143
developed at the national level and may be acceptable for national GHG inventories, such as the 144
emissions inventory conducted by the US Environmental Protection Agency (US EPA 2013). 145
When a specific bioenergy product is analyzed, however, treating carbon emissions from 146
biomass combustion as neutral is misleading because the LCA results always indicate less GHG 147
emissions from bioenergy products than from fossil fuel (Fantozzi and Buratti 2010). Therefore, 148
the carbon neutral assumption in LCA studies represents an unfair comparison between 149
bioenergy products and fossil fuels (Liu et al. 2017). 150
The guidelines in the Kyoto Protocol developed by the United National Framework 151
Convention on Climate Change (UNFCCC) follows the same philosophy (UNFCCC 1998). 152
Under the Kyoto Protocol rules, countries individually report emissions from biomass harvests 153
and combustion. Because biomass is accounted as land use emissions, the same carbon emissions 154
in the final combustion should be ignored to avoid double accounting (Haberl et al. 2012). But 155
there is no limit to land use emissions from developing countries (Searchinger et al. 2009; 156
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UNFCCC 2002). Thus, the carbon neutral assumption is problematic when biomass is imported 157
from a developing country and burned in a developed country (Marland 2010). Therefore, the 158
carbon neutral assumption in this protocol should be treated with caution, and the accounting 159
system should be modified for consistency. The accounting system at national level is 160
problematic when applied to LCA. New guidelines should be developed to improve their 161
suitability in accounting carbon emissions in LCA. 162
2.2 Calculating GWPbio 163
In LCA studies, the biogenic CO2 emissions are usually assumed to have no climate change 164
impacts. Recently, researchers became aware that the biogenic CO2, especially from forest 165
biomass, may have positive GWP because it is emitted by a one-time combustion and requires 166
years to be compensated by regrowth (Cherubini et al. 2011; Pingoud et al. 2012; Bright et al. 167
2012; Guest et al. 2013). Following this philosophy, an approach to calculate GWPbio was 168
proposed based on the IPCC guidelines, which estimates the decay of biogenic CO2 based on a 169
carbon cycle model and a forest growth model (Myhre et al. 2012; Joos et al. 2013). The fraction 170
of fossil-derived CO2 emissions in the atmosphere y(t) at time t is calculated based on the 171
Bern2.5CC carbon cycle model: 172
���� = �� +����/���
���1�
where � and � are estimated empirical parameters. If the initial emissions are biogenic CO2, the 173
decay could be faster due to the regrowth of biomass. Assuming a normalized biogenic CO2 174
removal rate by biomass regrowth is ����, then the absolute global warming potential (AGWP) 175
of biogenic CO2 is calculated as follows: 176
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��������� = � � �![���� − � ���$���� − �$��
�%�$]%�
'
��2�
and the GWPbio is then defined as: 177
�������� = ����������)*!
= + � �![���� − + ���$���� − �$��� %�$]%�'
�+ � �!����%�'�
= + [���� − + ���$���� − �$��� %�$]%�'
�+ ����%�'�
�3�
178
Insert Fig.2 179
180
Fig. 2 presents the Bern2.5CC carbon cycle model, in which CO2 uptake by both the oceans and 181
the terrestrial biosphere are considered. The decay of biogenic CO2 emissions simulated by 182
different researchers is shown in Fig. 2 (The parameter configurations can be found in the 183
accompanying Supporting Information). Cherubini et al. found that the GWPbio could be as low 184
as 0.43 when the rotation was 100 years (Cherubini et al. 2011). In simulations presented by 185
Bright et al, the biogenic CO2 emissions from a low productivity forest (rotation length=100 186
years) could have positive climate change impacts with GWPbio values equaling 0.44 (Bright et 187
al. 2012). When emissions by decomposition of the remaining forest residue were considered, 188
the GWPbio was 0.58 if 25% aboveground biomass was harvested (Guest et al. 2013). In a recent 189
study, emissions by decomposition were estimated when biomass was not harvested, and carbon 190
storage in woody products was considered (Liu et al. 2017). By including these two factors, 191
biogenic CO2 could decay faster (Fig. 2) and generate less climate change impacts (GWPbio 192
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ranged between 0.21-0.32). The application of GWPbio in LCA is straightforward (Eq. 4). Liu et 193
al. (2017) incorporated GWPbio in five LCA studies using this equation and found that total GHG 194
emissions would be very high for low energy efficient bioenergy products, such as biopower and 195
ethanol. 196
�-����./ = �-�0�11/ + ����� ∙ �-���(4)
where �-����./ is total life cycle GHG emissions of bioenergy product, �-�0�11/ and �-��� 197
are GHG and biogenic CO2 emissions from fossil fuel and biomass during the production of a 198
bioenergy product. 199
Several other methods are also available to assess climate change impacts of biogenic CO2 200
emissions. When considering the decay of biogenic CO2, Pingoud et al. proposed a method to 201
calculate GWPbio by the reduced atmospheric CO2 concentration due to displaced emissions in 202
energy and material substitution and carbon sequestration in biomass products (Pingoud et al. 203
2012). They found that the collection of wood waste after the decommissioning of long-lived 204
woody products could produce a GWPbio as low as -1. Zetterberg and Chen developed three 205
metrics (emissions, radiative forcing, and temperature) to assess climate change impacts of 206
bioenergy systems (Zetterberg and Chen 2015). Due to the regrowth of biomass, radiative 207
forcing and temperature are more suitable for estimating bioenergy effects. According to their 208
results, the utilization of forest residue had significant positive climate change impacts but was 209
still better than fossil gas and coal. 210
2.3 Emissions in the Supply Chain 211
Lacking a commonly recognized definition, several publications define carbon neutral as the 212
production and combustion of bioenergy that has no climate change impact (Gan 2007; Miner 213
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2010; Repo 2015). However, early LCA studies noticed that fossil-fueled biomass handling 214
systems and conversion processes are producing high GHG emissions (Ulgiati 2001; Hill et al. 215
2006). The LCA studies dealing with forest biomass to bioenergy should include harvest, 216
transportation and conversion effects (Yoshioka et al. 2005; Lindholm et al. 2010). Klein et al. 217
reviewed the studies on the forest biomass supply chain and found that GHG emissions from 218
logging sites to refinery facilities varied between 6.3-67.1 kg CO2 eq/m3 (Klein et al. 2015). 219
Though the emissions are very low in comparison with the carbon stored in biomass (0.8-9%), 220
neglecting this portion of total emissions causes incomplete accounting in LCA. 221
Although the use of bioenergy has positive climate change impacts when considering the 222
emissions in the supply chain, many studies suggest that bioenergy still has a huge potential to 223
substitute fossil fuels and reduce GHG emissions (Hsu et al. 2010; Valente et al. 2011; Hsu 2012; 224
Liu 2015). When emissions in the biomass supply chain and conversion systems are considered, 225
the overall life cycle emissions are highly dependent on the specific conversion technology (Liu 226
2015). If forest residue was used to produce liquid fuels by fast pyrolysis, GHG emissions could 227
be reduced by 56-77% compared to fossil fuels (Hsu 2012). Ethanol produced from woody 228
biomass had 43-57% lower GHG emissions than petroleum-derived gasoline (Hsu et al. 2010). 229
When combusted in a heating plant, one cubic meter solid biomass over bark had only 13 kg CO2 230
eq GHG emissions from the forest logging site to the heating plant (Valente et al. 2011). 231
2.4 Effects of Land Use Change and Forest Carbon Change 232
Land use change due to biomass utilization is another reason why the carbon neutral assumption 233
may be questioned. Land use change includes direct (dLUC) and indirect land use change 234
(iLUC). The emissions could occur when a unit of land shifts from one category to another (i.e. 235
dLUC). The emissions from dLUC are well recognized by researchers. The IPCC has a well-236
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developed methodology to account for the emissions from dLUC (Gytarsky et al. 2006). The 237
United Nations Environment Programme-Society of Environmental Toxicology and Chemistry 238
(UNEP-SETAC) Life Cycle Initiative has also developed a framework to integrate land use 239
impacts into LCA ( i Canals et al. 2007a; Koellner 2013). 240
Indirect land use change causes carbon emissions indirectly because dLUC of one site 241
may invoke land category shifts in other sites (Serchinger et al. 2008). Inspired by Fargione et al. 242
(2008) and Searchinger et al. (2008), researchers have been aware that the emissions from iLUC 243
should not be ignored when analyzing the climate change impacts of bioenergy. Some efforts 244
have been made to account for this portion of carbon emissions (Baral and Malins 2016). 245
However, to integrate iLUC in LCA is not trivial. 246
Forest biomass harvesting causes carbon changes. Schlamadinger et al. developed a model 247
to calculate carbon storage changes after residue harvesting and revealed a significant reduction 248
of carbon storage in forest soils (Schlamadinger et al. 1995). The Yasso model is an example of 249
how the soil carbon dynamics after residue harvesting can be simulated (Palosuo et al. 2001; 250
Repo et al. 2011, 2012). These studies have shown how the removal of residues reduces soil 251
carbon storage due to a declining carbon input to litter and soil. This reduction could be offset 252
after a certain length of time because the residue, if not harvested, will be decomposed and will 253
produce emissions anyway (Repo et al. 2012). Much work has been done to incorporate forest 254
carbon changes into LCA, which illustrates the importance to consider forest carbon changes in 255
LCA studies (Perez-Garcia et al. 2007; McKechnie et al. 2010; Liu et al. 2017). It has been 256
shown that life cycle emissions associated with forest carbon changes could temporarily exceed 257
the emissions from substituted fossil fuels. Liu et al. (2017) adapted the approach of McKechnie 258
et al. (2010) for five bioenergy products and found significant increases in life cycle emissions. 259
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2.5 Loss of Carbon Sequestration 260
If a forest is not harvested, it will continue to grow and sequestrate carbon for a long time 261
(Haberl et al. 2012). The carbon neutral assumption fails to account for the extra carbon 262
sequestration if a forest is left undisturbed. Cherubini et al. studied the effect by different forest 263
management practices in a boreal forest and found distinct benefits of carbon sequestration if 264
harvest events are postponed (Cherubini et al. 2011). Because an old-growth forest usually 265
cannot sequestrate more carbon and even turn to a carbon source, no carbon loss could be 266
expected when the old-growth forest is harvested. Yet, a suitable approach to incorporate this 267
amount of carbon sequestration is lacking in common LCA studies. Thus, we proposed an 268
approach to address this issue. Fig. 3 illustrates the estimated growth of a boreal forest under 269
different scenarios of harvest frequencies (See Supplemental Information for detail). 270
271
Insert Fig. 3. 272
273
Assuming a 100-year rotation in a boreal forest (µ=100), and a final carbon accumulation of 274
C100 (tC/ha). If the forest is harvested every 20 years (µ=20), the final carbon accumulation is 275
C20 (tC/ha) and total energy output within the 100 years is E20 (MJ/ha). The emissions of 276
bioenergy (GHGe: g CO2 eq/FU) are calculated as follows: 277
�-�4 = �-�5)6 + 7ρ9:�;��� − ;<��=<� (5)
where �-�5)6 is the result and FU is the function unit (MJ) of a traditional LCA study, ρ is the 278
percentage of forest biomass used for bioenergy, and 7 is the coefficient to convert carbon 279
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emissions (tC/ha) to GHG emissions (g CO2 eq). This example shows a way to address some 280
issues in standardized carbon accounting. It also shows that a simple LCA is not suitable when 281
the full complexity of forest carbon dynamics need to be considered (McKechnie et al. 2010). 282
2.6 Negative Effect of Forest Residue Removal 283
Bioenergy from forest biomass could be a good substitute for fossil fuels. However, the elaborate 284
extraction of forest biomass can have negative effects beyond GHG emissions, such as 285
increasing soil erosion, reducing soil water retention ability, disturbing soil carbon dynamics, 286
reducing the growth of subsequent forest stand and reducing biodiversity. Many studies have 287
shown the importance of biomass cover in preventing soil erosion (Farrish et al. 1993; Lal 1996). 288
Loss of soil increases when logging residue and humus layers which could act as mulch and 289
prevent runoff are removed (Edeso et al. 1999). The removal of residues for bioenergy may alter 290
erosion rates and increase soil loss, which in turn leads to a degradation of stream water quality 291
(Elliot et al. 2010; Schulze et al. 2012). Undoubtedly, the removal of residue can alter soil carbon 292
dynamics. Edwards and Ross-Todd compared clear-cutting with and without residue removal, 293
and found significantly higher soil respiration rates after residue removal due to higher soil 294
temperatures (Edwards and Ross-Todd 1983). The residue removal for bioenergy also reduced 295
the carbon input to litter and soil in forestland and increased carbon debt (Repo et al. 2012). The 296
residue removal also had a negative effect on the subsequent forest stand. A meta-analysis 297
conducted by Achat et al. (2015) indicated that the tree growth could be reduced by 3%-7% in a 298
certain period up to 33 years. Moreover, the extraction of forest residue for bioenergy may have 299
a negative effect on species richness in a forest because of a range of species habitat in decaying 300
wood (Trømborg et al. 2011). Haberl et al.’s (2007) study revealed that residue removal possibly 301
contributed to biodiversity loss in forest ecosystems. Therefore, the Renewable Energy Directive 302
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of the European Commission prohibits the extraction of biomass from land with high 303
biodiversity (European Commission 2009). 304
3 Research Needs 305
3.1 Incorporating Forest Carbon Cycles in LCA 306
To accurately assess climate change impacts of forest residue utilization for bioenergy, several 307
essential modifications need to be incorporated in LCA. Important improvements relate to forest 308
carbon change, continuous forest growth without harvest, time-dependent impacts of biogenic 309
CO2 emissions, and decomposition of forest residues. Currently, there is no standard available 310
which considers these issues. The UNEP-SETAC Life Cycle Initiative has developed a 311
framework for accounting the impacts of land use change (i Canals et al. 2007a). However, land 312
use change should not be triggered if a harvested forest stand is allowed to regrow. Although the 313
land use may remain unchanged, carbon emissions could nevertheless occur because of disturbed 314
soil carbon dynamics. 315
The amount of carbon sequestrated by an unharvested forest should be considered as a 316
negative effect of forest biomass utilization. It is thus necessary to create a “no use” reference 317
scenario based on the guidance provided by the ILCD Handbook (JRC-IES 2010). Following this 318
guideline, LCA needs to be integrated with a dynamic forest carbon cycle model (Helin et al. 319
2013). However, the challenge remains to incorporate LCA with a complex dynamic model 320
(McKechnie et al. 2010). The implementation of time-dependent impacts of biogenic CO2 321
emissions is not very difficult. Many studies have advocated using GWPbio as a multiplier of 322
biogenic CO2 emissions (Pingoud et al. 2012; Bright et al. 2012; Guest et al. 2013; Liu et al. 323
2017). The ISO standards, even the newly developed draft standards 14067, fail to include this 324
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time-dependent factor (ISO 2012). Therefore, a standard with consideration of those issues needs 325
to be developed to make the accounting method complete and consistent. 326
3.2 Soil Carbon Dynamics 327
Among the terrestrial carbon pools, soil carbon represents the largest biotic carbon pool, 328
particularly in boreal forests (Post et al. 1990; Pan et al. 2011). Forest biomass harvest can 329
remarkably reduce soil carbon storage (Nave et al. 2010). McKechnie et al. (2010) estimated 330
forest carbon change after residue removal using FORCARB-ON. The total life cycle GHG 331
emissions of bioenergy were significantly increased when considering forest carbon change. 332
Thus, soil carbon dynamics should not be ignored when assessing climate change impacts of 333
bioenergy products from forest biomass. Besides forest carbon change, Repo et al. (2011) and 334
Liu et al. (2017) suggested that forest residue emitted CO2 anyway by decomposition if not 335
harvested, and these emissions should be subtracted from the total GHG emissions. 336
With the awareness of carbon emissions due to disturbed soil carbon dynamics, many 337
studies have attempted to correct the associated accounting errors in LCA. However, there is no 338
commonly accepted model to simulate soil carbon dynamics with a specific focus on addressing 339
impacts of forest residue removal. This is due to the challenges in capturing the effects of 340
varying tree species, soil properties and soil disturbance types (Walmsley and Godbold 2010; 341
Thiffault et al. 2011; Wall 2012). The models used in previous studies are usually site- or region 342
specific. For example, Yasso07 was developed to assess the decomposition of soil organic 343
matters and is more suitable in boreal forests (Tuomi et al. 2009), while FORCARB-ON is a 344
regional model which is particularly suitable for Ontario in Canada (Chen et al. 2008). The 345
CENTURY model (Parton et al., 2001) was developed by the Natural Resource Ecology 346
Laboratory of Colorado State University and has much wider applications. However, it is 347
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initially used to simulate soil carbon dynamics of cropland and grassland. Therefore, a 348
generalized modeling system with a broad application potential is required that can be applied in 349
a variety of situations to assess soil carbon dynamics and forest carbon change due to forest 350
residue removal. 351
3.3 Bioenergy and Wildfire 352
Forest fires often represent a major environmental disturbance and a significant driving force of 353
carbon emissions to the atmosphere (Denman et al. 2007; US EPA 2013). The US GHG 354
inventory estimated that the carbon released from forest fires in the US was 66.2 Tg/yr in 2011 355
(US EPA 2013). Globally, forest fires, including wildfires in open savannahs, emitted about 3-8% 356
of total terrestrial net primary production (Andreae and Merlet 2001; Denman et al. 2007). The 357
burning of wood in forests also causes high proportions of CH4 to be emitted, because of 358
anaerobic combustion conditions (Kim and Tanaka 2003). About 14.2 Tg CO2 eq of CH4 was 359
emitted into the atmosphere by forest fires in the US in 2011 (US EPA 2013). As large amounts 360
of carbon emissions can be accounted to forest fires, the shift of fire regime can significantly 361
influence the atmospheric CO2 concentration, e.g. the increase of CO2 in 1998 was mainly 362
attributed to wildfires (Yurganov et al. 2005). 363
Fire prevention has thus a high potential to reduce atmospheric CO2. To prevent forest 364
fires, the harvest of forest residue for biofuel is usually thought to be an effective management 365
practice, especially in forests with a high fuel accumulation due to fire suppression (Elliot et al. 366
2010). If the fire prevention activities are improved, biofuel derived CO2 emissions from the 367
residues collected for reducing the fire risk can be considered as carbon neutral. This particular 368
emission reducing practice is feasible when the forest carbon sink is already weak (Hudiburg et 369
al. 2011). The frequency and intensity of fires can change a forest from carbon sink to carbon 370
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source, and the extraction of residue can thus reduce emissions when the forest fire risk is high 371
(Hudiburg et al. 2011). However, no model appears to be available to deal with the effect of 372
residue collection on forest fires, and no generally recognized approach is known for identifying 373
the timing of residue collection for emission reductions. Some significant studies have been 374
conducted to predict the behavior of forest fire. The Fire Weather Index (FWI) System proposed 375
by Van Wagner (1987) is used universally to estimate fire danger in a generalized fuel type 376
(Moritz et al., 2009). Although this system is a good indicator of fire occurrence and is validated 377
worldwide (Urbieta et al. 2015), it excludes the effect of biomass accumulation (Moritz et al., 378
2009). Malamud et al. (2005) also analyzed the behavior of wildfire and developed a burned area 379
model. It is a regional specific model and requires more validations for wider application. 380
Therefore, a suitable modeling approach is required to address the risk and behavior of forest 381
fires in response to residue collection. 382
3.4 Bioenergy and Biodiversity 383
The use of bioenergy in mitigating climate change is often conflicting with biodiversity 384
conservation goals (Felton et al. 2016). In forest management practice, many researchers have 385
provided evidence that the extraction of forest residue has negative effects on biodiversity 386
(Jonsell 2007; Bouget 2012; Driscoll et al. 2012; Ranius et al. 2014). Many dead wood 387
associated species, especially fungi and beetles, depend on residues for breeding, foraging and 388
basking (Joly et al. 2015). The extraction of forest residue removes important habitat structures 389
for saproxylic species. However, much uncertainty still remains regarding the particular impact 390
intensity of forest residue removal on biodiversity. Greater emphasis on biodiversity would 391
reduce the supply of woody biomass (Hänninen and Kallio 2007), and studies on the trade-off 392
between bioenergy and biodiversity are essential to evaluate the effects of a specific climate 393
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change mitigation practice (Joly et al. 2015). Miettinen et al. (2014) and Verkerk et al. (2014) 394
simulated the impacts of different harvest regimes with particular consideration of biodiversity 395
and bioenergy. They presented general ideas about a trade-off strategy, but failed to provide 396
specific suggestions. It is essential to understand the interactions between forest residue removal 397
and biodiversity as biofuels have less GHG emissions than fossil fuels. 398
4 Uncertainties 399
Considerable uncertainties are recognized in modeling forest carbon dynamics, natural 400
disturbance effects and climate change impacts, as well as effects of technology, markets and 401
policy. Denman et al. asserted that both forest carbon cycle models and atmospheric models 402
represent major sources of uncertainty (Denman et al. 2007), and there is also great uncertainty 403
in modeling the dynamics of old-growth forests (Trømborg et al. 2011). Tuomi et al. (2009) and 404
Repo et al. (2011) reported considerable uncertainties in the Yasso model, especially regarding 405
humification and decomposition rates. Forest fire is a primary natural disturbance that influences 406
carbon emissions from forests (Kasischke et al. 1995; Bedia et al. 2014). Because of the 407
uncertainty of fire frequency and intensity, especially regarding shifting fire regimes under 408
climate change, no single model is applicable for all situations (Spittlehouse and Stewart 2004; 409
Denman et al. 2007). Improvement of conversion technology will have a high potential to reduce 410
both fossil and biogenic CO2 emissions, although the uncertainties of such improvement effects 411
are difficult to evaluate (Mathews 2008; Spatari et al. 2010). Moreover, the policies related to 412
biomass utilization and changes of the bioenergy market also involve significant uncertainties 413
(Korhaliller 2010; Guest et al. 2013). We must accept that most of the uncertainties are 414
inevitable and can only be reduced by very large datasets or more efficient inventory methods 415
(Shalaby and Tateishi 2007; Elliot et al. 2010; Repo et al. 2011). 416
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Uncertainty is inherent at any step in the carbon emission accounting of forest biomass 417
utilization. To assess the effects and ranges of uncertainty, sensitivity analysis and Monte Carlo 418
simulation are common approaches (Koellner et al. 2013). Most of the relevant studies provide 419
means by assuming normal distributions. For example, i Canals et al. reported an uncertainty of 420
up to 50% when studying the change of soil organic carbon (i Canals et al. 2007b). Monte Carlo 421
simulation is an accepted approach for estimating uncertainty (Koellner et al. 2013; Liu 2015). 422
Liu (2015) conducted Monte Carlo simulation for GHG emissions from forest biomass to liquid 423
fuels. He concluded that the highest possible GHG emissions were still lower than emissions 424
from fossil fuels. Monte Carlo simulation is also supported by several environmental analysis 425
tools, such as SimaPro and Gabi (Aktas and Bilec 2012; Bieda 2014). The available distribution 426
types are uniform, triangular, normal and lognormal distributions. In the case of unknown 427
distributions, sensitivity analysis is also widely used to interpret the effects of uncertainty (Chum 428
et al. 2011; Röder et al. 2015). The minimum and maximum values of parameters in the 429
accounting system were associated with the range of uncertainty. In Chum et al.’s (2011) study, 430
the life cycle emissions range for bioenergy was significantly lower than emissions from fossil 431
fuels. Röder et al. (2015) analyzed the uncertainty of life cycle emissions from pellet-to-432
electricity system and obtained ranges of emission savings in comparison to fossil fuel. 433
5 Discussion and Conclusions 434
For most climate change mitigation strategies, bioenergy is assumed to be an attractive substitute 435
for fossil fuels. Forest residue is especially important due to its high availability (Berndes et al. 436
2003). However, there is a reason to be skeptical of the bioenergy boom that is largely based on 437
the carbon neutral assumption which is accepted even by popular environmental analysis tools 438
and current mainstream LCA studies. Regarding forest residue derived bioenergy, researchers 439
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have increasingly expressed concerns that the carbon neutral assumption may not hold for four 440
reasons: 1) the national level accounting guidelines may not fit product level assessments and 441
provide unfair comparisons between bioenergy and fossil fuels; 2) biogenic CO2 emissions 442
represent a one-time pulse to the atmosphere and stay in the atmosphere for years and thus have 443
important climate change impacts; 3) fossil-fueled mechanical activities from forest residue 444
collection to biomass conversion produce accountable amounts of GHG emissions which need to 445
be assessed; 4) extraction of forest residues may have positive climate change impacts due to 446
emissions from land use change or effects on the forest carbon cycle. Moreover, harvesting of 447
forest residue may have negative effects on ecosystem services of forests related to soil erosion, 448
water retention, soil carbon storage and biodiversity maintenance. 449
Although much evidence has been collected, additional efforts are still needed to 450
accurately and fully account the climate change impacts of bioenergy use. Numerous models are 451
available for estimating forest growth and soil carbon dynamics. However, these processes are 452
highly complex, and there is no single model that satisfies all scenarios. It should be possible to 453
improve the estimation of climate change impacts of bioenergy use. However, no guideline or 454
standard has been developed to make LCA compatible with more complex models. Forest fire is 455
a primary natural disturbance causing emissions in forests, and residue extraction for bioenergy 456
can reduce those emissions. Thus, the behavior of forest fires and the relationship to forest 457
residue extraction require intensive study. The negative effects of forest residue extraction also 458
need to be investigated to prevent depletion of forest ecosystem services. 459
Considerable uncertainty exists in assessing the climate change impacts of bioenergy use, 460
with regard to forest carbon modeling, natural disturbance effects, specific applications of 461
technology and changes of policy. Uncertainty is inevitable, but efforts should be made to reduce 462
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uncertainty by using appropriate statistical analysis tools, collating larger datasets and improving 463
modeling approaches. With limited information, the effects of uncertainty can be evaluated using 464
Monte Carlo simulation and sensitivity analysis. Dealing with uncertainty should be a major 465
focus in future studies. 466
Acknowledgements 467
This research is supported by the Doctoral Scientific Research Foundation of Northwest 468
Agriculture and Forestry University (2452017241) and the Qian Ren Program. 469
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Figure Captions 768
Fig. 1. The systematic framework of the critical analysis of carbon neutral assumption. 769
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Fig. 3. Cumulative carbon accumulation of a boreal forest under different harvest frequency 771
scenarios. µ is the rotation length. 772
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Fig. 1. The systematic framework of the critical analysis of carbon neutral assumption.
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Fig. 2. The decay of CO2 in the atmosphere over time, simulated in five different studies.
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Fig. 3. Cumulative carbon accumulation of a boreal forest under different harvest frequency
scenarios. µ is the rotation length.
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