A Critical Analysis of the Carbon Neutrality Assumption in ... · Draft 1 A Critical Analysis of the Carbon Neutrality Assumption 2 in Life Cycle Assessment of Forest Bioenergy Systems

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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

https://mc06.manuscriptcentral.com/er-pubs

Environmental Reviews

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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

Fig. 2. The decay of CO2 in the atmosphere over time, simulated in five different studies. 770

Fig. 3. Cumulative carbon accumulation of a boreal forest under different harvest frequency 771

scenarios. µ is the rotation length. 772

773

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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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A Critical Analysis of the Carbon Neutrality Assumption in ... · Draft 1 A Critical Analysis of the Carbon Neutrality Assumption 2 in Life Cycle Assessment of Forest Bioenergy Systems