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Toward a More Comprehensive Greenhouse Gas Emissions Assessment of Biofuels: The Case of Forest-Based Fischer−Tropsch Diesel Production in Finland Sampo Soimakallio* VTT Technical Research Centre of Finland, P.O. BOX 1000, Espoo FIN-02044 VTT, Finland S Supporting Information *

ABSTRACT: Increasing the use of biofuels influences atmospheric greenhouse gas concentrations. Although widely recognized, uncertainties related to the particular impacts are typically ignored or only partly considered. In this paper, various sources of uncertainty related to the GHG emission savings of biofuels are considered comprehensively and transparently through scenario analysis and stochastic simulation. Technology and feedstock production chain-specific factors, market-mediated factors and climate policy time frame issues are reflected using as a case study Fischer−Tropsch diesel derived from boreal forest biomass in Finland. This case study shows that the GHG emission savings may be positive or negative in many of the cases studied, and are subject to significant uncertainties, which are mainly determined by market-mediated factors related to fossil diesel substitution. Regardless of the considerable uncertainties, some robust conclusions could be drawn; it was likely of achieving some sort of but unlikely of achieving significant savings in the GHG emissions within the 100 year time frame in many cases. Logging residues (branches) performed better than stumps and living stem wood in terms of the GHG emission savings, which could be increased mainly by blocking carbon leakage. Forest carbon stock changes also significantly contributed to the GHG emission savings.

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managed forests and agricultural residues.1 In Europe, the five countries that have the largest forest biomass potentials (Sweden, Germany, France, Finland, and Italy) represent about 62% of the European forest biomass potential.8 In the European Union, wood energy currently accounts for approximately 50% of total renewable energy consumption.8 Although the particular share is estimated to decrease, wood energy consumption is estimated to double by 2030.8 Managed forests are one of the most complex bioenergy systems to be assessed. Temporary and relative losses in terrestrial and soil carbon stocks of forests due to wood harvesting are critical issues influencing the GHG emissions of wood-based biofuels (e.g., ref9). The particular GHG emissions depend both on the development of carbon stock in the reference system without the studied biofuel system (baseline), on the time frame applied for the GHG emissions, and on the related warming impact.10 Forest carbon stock development is dynamic, relatively long-term, and uncertain, and there is a lack of a single scientifically superior time frame to account for climate impacts.

INTRODUCTION The impacts of increasing production and use of biofuels on atmospheric greenhouse gas (GHG) concentrations are widely discussed.1 These impacts are due to life cycle GHG emissions of biofuels, loss of additional carbon sequestration related to the land use of feed stock production, and avoidance of fossil fuel GHG emissions.2 The last two of these factors are not emissions per se, but are a similar type of impact from the atmospheric point of view, and are later jointly referred to as GHG emissions. Some studies have concluded that the life cycle GHG emissions of biofuels are typically lower or even significantly lower compared to those of fossil reference fuels.3,4,1 On the contrary, some others have found higher or even significantly higher GHG emissions for some biofuels compared to fossil reference fuels (see references, e.g., in ref 5). This is due in particular to significant nitrous oxide (N2O) emissions (e.g., ref 6) and consideration of so-called indirect (market-mediated) land-use changes (iLUC) (e.g., ref7). Cellulosic biofuels are typically estimated to emit less GHGs over their life cycle compared with biofuels derived from agricultural crops, due to a lower requirement for fertilizers, lower fossil energy consumption, and lower land-use change impacts.1 The feedstock potential estimations for biofuels rely to an increasing extent on cellulosic feedstock which does not directly compete with food and feed production, that is, biomass derived from © 2014 American Chemical Society

Received: Revised: Accepted: Published: 3031

July 13, 2013 February 1, 2014 February 4, 2014 February 4, 2014 dx.doi.org/10.1021/es405792j | Environ. Sci. Technol. 2014, 48, 3031−3038

Environmental Science & Technology

Article

Figure 1. Illustration of the energy and material flows (solid arrows) and information flows (broken arrows) within the system.

sources of uncertainty are examined reflecting the technology and feedstock production chain specific factors, marketmediated factors, and climate policy time frame issues. The possibility of drawing robust conclusions is reflected when the uncertainties are comprehensively considered. Fischer− Tropsch (FT) diesel derived from boreal forest biomass in Finland is used as a case study for the following reasons. The particular technology is of potential relevance for future bioenergy supply,18 and there is high absolute unused potential of forest biomass in Finland, corresponding over 10% of the potential in the EU-27.8 Furthermore, there is a specific complexity related to the GHG dynamics of, in particular, forest biomass cycle.

Life cycle assessment (LCA) is a methodological framework for estimating and assessing the environmental impacts related to the life cycle of a product system,11 including biofuels.12 Any LCA is subject to uncertainties, which are due to (1) methodological choices such as the spatial and temporal system boundary, functional unit, and allocation procedure, (2) parameters such as inaccurate or outdated measurements or lack of data, and (3) models such as the loss of spatial and temporal dimension when accounting for emissions and the derivation and application of characterization factors. Although uncertainties may be significant and the importance of including them in LCA has long been recognized,13 they are often still ignored in LCA studies.11 However, some dimensions of uncertainty related to the GHG emissions of biofuels have been considered. For example, the importance of system boundary setting has been widely discussed in the context of indirect land-use changes (iLUC)7 for biofuels, but to some extent also in the context of military expenditures related to the supply of fossil fuels.14 A common assumption that each unit of bioenergy replaces an energy-equivalent quantity of fossil energy has recently been criticized e.g., ref 15). Wang et al.4 have shown the impact on GHG emissions of various allocation methods between biofuels and coproducts. Parameter uncertainty has been recognized and analyzed to some extent (for certain key parameters), for example by Soimakallio et al.,16 Plevin et al.,7 and Mullins et al.17 Modeling uncertainty and sensitivity related to accounting of carbon stock changes is raised for example by Pingoud et al.9 The rallying point for all the above-mentioned examples is that they consider a certain type of uncertainty while ignoring the rest related to the subject. In order to avoid misleading conclusions, a comprehensive consideration of the uncertainties when assessing the climate change mitigation benefits of various measures has been emphasized by Plevin et al.12 The aim of this paper is to study the GHG emission savings of increasing the use of biofuels by considering the related uncertainties comprehensively and transparently. Various

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MATERIALS AND METHODS As the consequences of increased biofuel production are of interest in this paper, so-called consequential life cycle assessment (CLCA) is a suitable method to be applied. CLCA can be defined as a method that aims to describe how environmentally relevant physical flows would have been or would be changed in response to possible decisions that would have been or would be made.11 Ideally in CLCA, all the parameters that are influenced by the decision are taken into account using suitable marginal data11 and system boundaries wide enough to avoid generally known allocation problems.19,20 It should be noted that the parameters influenced are not necessarily within the system boundaries, determined on “cradle to grave” basis, of the product system investigated. Any future-oriented CLCA is subjected to significant inherent uncertainties due to a number of possible options affecting the market response to a studied change.12 Considering the complexity and extent of market-mediated impacts, the modeling of consequences would require the use of complicated and large models, such as partial and general equilibrium models describing, for example, the energy, land use, and economy systems. The fundamental problem of such models is the large number (e.g., thousands) of assumptions 3032

dx.doi.org/10.1021/es405792j | Environ. Sci. Technol. 2014, 48, 3031−3038


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Table 1. Parameter Assumptions Used to Conduct Probability Distributionsa index

a

assumption

iD, RM, LGR iD, RM, STU iD, RM, STE iE, RM, LGR iE, RM, STU iE, RM, STE iB iE, FT iM iD, constr. FT iE, constr. FT iD, constr. FM iD, constr. TL iP sD ED, WTT eD, comb., CO2

diesel fuel in logging residues harvesting and transportation diesel fuel in stumps harvesting and transportation diesel fuel in stem wood harvesting and transportation electricity in logging residues crushing electricity in stumps crushing electricity in stem wood crushing biomass in FT processing electricity in FT processing methanol in FT processing diesel fuel in construction of FT diesel plant electricity in construction of FT diesel plant diesel fuel in construction and maintenance of forest machinery diesel fuel in construction of long distance transportation lorry increased peat utilization due to raw material competition substitution of diesel oil emission factor for diesel fuel supply (well-to-tank) CO2 emission factor for diesel fuel combustion

unit

2.5 percentile

97.5 percentile

MJdiesel/GJchip MJdiesel/GJchip MJdiesel/GJchip kWh/GJchip kWh/GJchip kWh/GJchip GJchip/GJFT kWh/GJFT kg/GJFT MJdiesel/GJchip kWh/GJchip MJdiesel/GJchip MJdiesel/GJchip GJpeat/GJchip GJdiesel oil/GJFT kg CO2-eq./GJdiesel kg/GJdiesel fuel

8.88 21.44 19.09 0.10 0.10 0.22 0.93 49.38 0.22 0.10 2.77 0.85 1.06 0 0.25 14.32 68

35.68 49.64 46.13 0.28 0.15 0.32 1.13 60.33 0.24 0.30 8.31 2.54 3.18 0.1 1.6 118.78 73.3

eD, comb., CH4

CH4 emission factor for diesel fuel combustion

kg/GJdiesel fuel

0.002

0.009

eD, comb., N2O

N2O emission factor for diesel fuel combustion

kg/GJdiesel fuel

0.016

0.063

eE eP eB, comb CO2

emission factor for consumed electricity production emission factor for peat utilization CO2 emission factor for wood combustion

g/kWhe g/MJchip kg CO2/GJchip

0 95 108.50

900 119 110.70

eM GCH4, 20

emission factor for methanol additive GWP-20 (CH4)

g/kgmethanol

215.36 46.80

646.08 97.20

GCH4, 100

GWP-100 (CH4)

16.25

33.75

GN2O, 20

GWP-20 (N2O)

187.85

390.15

GN2O, 100

GWP-100 (N2O)

193.70

402.30

eCO2, N‑fert.

CO2 emissions from nitrogen compensation fertilization

g/GJchip

0

962.5

eCH4, N‑fert.

CH4 emissions from nitrogen compensation fertilization

g/GJchip

0

0.8

eN2O, N‑fert.

N2O emissions from nitrogen compensation fertilization

g/GJchip

0

3.2

Eash eCH4, storage

emissions from ash recirculation CH4 emissions from chip storage

g CO2-eq./GJchip g/GJchip

0 0

55.6 233.3

eN2O, storage

N2O emissions from chip storage

g/GJchip

GB, LGR, 20

forest carbon stock changes, logging residues (WFCO2, bio(20))

GB, LGR, 100

forest carbon stock changes, logging residues (WFCO2, bio(100))

0.12

0.31

GB, STU, 20

forest carbon stock changes, stumps (WFCO2, bio(20))

0.66

0.82

GB, STU, 100

forest carbon stock changes, stumps (WFCO2, bio(100))

0.33

0.46

0

4.7

0.37

0.60

GB, STE‑20, 20

forest carbon stock changes, stem wood (20 a) (WFCO2, bio(20))

1.84

1.96

GB, STE‑20, 100


0.56

2.01

GB, STE‑100, 20


0.94

1.01

GB, STE‑100, 100


0.19

0.72

The indexes given for the assumptions are the same as the factors in the calculation equation presented in the SI.

market-mediated effects of the raw material and electricity requirement and of biofuel, based on the key findings from earlier research (e.g., refs 12, 22−24). A transparent model (illustration presented in Figure 1) was constructed with a limited number of input parameters. This was possible by constructing some of the input parameters with a reasonable uncertainty range from the outcomes of previous studies and modeling work based on a number of further parameters. In this way some of the sensitivity related to the parameter assumptions is included in parameter uncertainty in order to significantly reduce the required number of individual scenarios. In this paper, CO2, CH4, and N2O emissions are considered. Small amounts of GHG precursors, indirect GHGs, and other

required which typically decreases the transparency of the results.21 At the same time, the descriptions of individual product systems are typically relatively rough, resulting in the possible disregarding of some key parameters.12 These imperfections increase the risk that inadequate assumptions or other errors significantly affect the final CLCA results,11 and that the cause and effect relationship is lost due to the lack of transparency. In this paper, a streamlined CLCA is used. It was assumed that the parameters changed in response to increased biofuel production are those key ones (1) directly related to the biofuel studied and the substituted fossil fuel production chain, (2) indirectly related to the capital goods, and (3) induced to the 3033



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expressed in terms of the functional unit, which was selected to be 1 MJ of biofuel produced annually. The GHG emission savings from replacing fossil fuels by biofuels is measured in relative terms, in other words in comparison to the GHG emissions from fossil diesel fuel. All the parameter assumptions used in the model are presented in Table 1, and the exact equation of the model and the reasoning and references for the parameter setting are presented in the SI. The uncertainty analysis was carried out using a Monte Carlo simulation, in which probabilistic analogy is used to solve deterministic problems of uncertainty. First, a probability distribution is determined for each of the input parameters. Then, a deterministic computation of the input parameters is performed for a selected number (in this case 10,000) of the samples from each distribution. Finally, the results are aggregated as probability distributions. As an empirical basis for conducting probability distributions for various parameters was lacking, uniform distribution was selected for all the input parameters. In addition, the sensitivity of the model to the different probability distributions − normal, uniform, and triangular − was explored and presented in the SI. The central 95% confidence interval values were set for each of the parameters as shown in Table 1, and the central value for triangular distribution was set to the midpoint of the range. The variables were chosen so that there would be no correlations between them in the model. Calculations were carried out using MS Excel software and its add-in @Risk, which is a spreadsheet-based application suite for a Monte Carlo simulation. The contribution of a single variable to the uncertainty of relative GHG emission savings (the width of the result distributions) was measured using Spearman’s rank correlations (ρ) between each of the variables and the result values. Spearman’s rank correlation is a nonparametric measure of statistical dependence between two variables (−1 ≤ ρ ≤ 1). It has the advantage over the common Pearson correlation that it does not require the dependence between the quantities to be linear but only monotonic. If the value of ρ is 1 or −1 for a specific variable, this means that the result value is fully determined by the uncertainty of the particular variable. ρ values between −1 and 0 (0 and 1) means that the increase in the value of the variable decreases (increases) the value of the result. Results for Spearman’s rank correlations are presented in the SI. The contribution of the different variables to the GHG emission savings (to illustrate the location of the result distributions) was measured in absolute terms deterministically using mean values determined for each of the variables.

GHGs that might be released within the system studied are excluded, as they are assumed to make an insignificant contribution to the overall results. When countries report their annual GHG emissions to the UNFCCC and the Kyoto Protocol, non-CO2 GHGs are converted to CO2-equivalents by using so-called global warming potentials (GWP) over 100 years derived from the IPCC.25 GWP-100 factors are also very typically used in LCA studies in order to characterize the GHG impacts. However, there is no scientific basis for using 100-year time frame. In fact, ambitious climate change mitigation targets require rapid reductions in the atmospheric radiative forcing and GHG emissions also emphasizing the use of shorter time f rames. IPCC provides the GWP factors with 20-, 100-, and 500-year time frames. Considering the political will to limit the global mean temperature increase to below the 2 °C target,26 the GWP factors over 20- and 100-year time frames were applied in this paper. FT diesel production was assumed to take place in a largescale production plant16 located in Finland in an economically viable but unspecified place. The process concept which minimizes the biomass feedstock requirement through integration into a pulp and paper mill27 was considered to be the most economically viable option. The excess heat generated in the FT process is utilized in the pulp and paper mill, and this credit is taken into account in the energy and material balances of such a concept.27 For comparison, an integrated process concept which minimizes electricity consumption but uses more biomass and a stand-alone process without utilization of excess heat were considered and presented in the SI. Besides logging residues (branches, tops), stumps and stem wood, increasingly used as raw materials for bioenergy in Finland,28 were also considered as feedstock. Logging residues (referred as LGR) and stumps (referred as STU) are generated in parallel with stem wood (referred as STE) harvested at final felling.29,30 In this paper, stand ages at final felling equaling 20 years (STE20) and 100 years (STE-100) were applied. The GHG emissions resulting from raw material supply were assumed to be generated from changes in forest carbon stocks due to raw material harvesting, the possible use of nitrogen fertilization to compensate nutrient losses, diesel fuel consumption in harvesting, transportation, machinery construction and maintenance, electricity consumption in crushing to forest chips, and CH4 and N2O emissions from chip storage (Figure 1). A small proportion of the wood was assumed to come from reallocations from other end use purposes, thus not influencing the emissions of wood supply but increasing peat harvesting and combustion through market-mediated impacts (Figure 1). FT diesel processing emissions were assumed to be generated from electricity and methanol consumption of FT processing and from the diesel fuel and electricity consumption of the FT processing plant construction (Figure 1). FT diesel entry on the market was assumed to have impacts on fuel prices and thus also on fossil diesel consumption. Considering the GHG emission parameters that are assumed to be influenced by the decision to introduce FT diesel production in Finland, a transparent calculation model was created with only 28 simultaneously affecting input parameters (Table 1). Individual scenarios were studied for different feedstock sources and time frames to account for climate impacts, as they were considered to be factors that may be influenced by normative choices. Additional scenarios for various FT diesel processing concepts were also considered and presented in the SI. All the input and output flows were

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RESULTS AND DISCUSSION The results indicated that in most of the cases studied, the GHG emission savings of FT diesel derived from boreal forest replacing fossil diesel were positive or negative (Figure 2). The GHG emission savings were highly uncertain in all the cases studied. The range width for the central 95% confidence intervals varied from some 150 to almost 250 percentage points for time horizons of both 20 years and 100 years (Figure 2). The upper range exceeded even 100% GHG emission savings for some of the cases, whereas the lower end was less than −200% for stem wood at the age of 20 years at final felling (GWP-20). The slight possibility for over 100% GHG emission savings compared to fossil diesel unit was due to the fact that one unit of FT diesel may result in a saving of more than one 3034



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impact of forest carbon stock changes over the shorter time frame (Table 2). Also, the weight of the emissions from chip storage was more than double when a 20-year time horizon was used compared to a 100-year time horizon (Table 2) due to the higher GWP of CH4 over the shorter time frame (Table 1). Forest carbon stock changes and substitution of diesel oil were the two main contributors to the GHG emission savings, while the impact of the other variables was clearly lower (Table 2). Additional analysis presented in the SI showed that the different selection of FT processing concept did not reduce the uncertainty nor improve the GHG emission savings. In addition, the impact on the results of selection between normal, uniform, and triangular probability distributions was only minor (SI Figure S3), confirming similar conclusions drawn by Plevin et al.7 in their case study for iLUC emissions. Furthermore, the impact of single variables on the uncertainty of the relative GHG emission savings measured by Spearman’s rank correlations was dominated significantly by the substitution factor of diesel oil. Emissions from marginal diesel fuel supply (well-to-tank) and forest carbon stock changes were also important factors in explaining the uncertainty of the results (SI Table S6). The contribution of the other parameters to the overall uncertainty of the results was significantly lower, but not altogether totally negligible. The results presented in this paper showed that the GHG emission savings of FT diesel derived from boreal forest were subjected to significant uncertainties, much higher than was previously estimated by Soimakallio et al.6 in which the key uncertainties considered here were ignored. This emphasizes the conclusion drawn by Creutzig et al.22 that the more comprehensive the consideration of consequential effects, the more uncertain is the results. Furthermore, the overall uncertainty presented in this paper was mainly determined by only a few key variables, in particular the substitution factor of diesel oil, which is, furthermore, closely connected with the factors that cannot directly be influenced by technological and feedstock choices in biofuel production, but are related to

Figure 2. Mean values and central 95% confidence intervals (error bars) for the relative GHG emission savings; positive (negative) value indicates reduction (growth) in GHG emissions. Cumulative probabilities for the 0% and 50% emission savings are shown in italics and underlined italics, respectively.

unit of fossil diesel (factor for substitution of diesel oil ranged up to 1.6). Considering a 20-year time horizon for climate impact assessment, both logging residues and stumps indicated over 50% probability of achieving the positive GHG emission savings (Figure 2). Considering a 100-year time horizon for climate impact assessment, logging residues, stumps and stem wood at the age of 100 years at final felling resulted in a greater than 50% probability of positive GHG emission savings. However, the probability of achieving significant, here 50%, GHG emission savings was less than 50% in all the cases studied (Figure 2). The GHG emission savings were some 25−50 percentage points lower when a 20-year time horizon was applied compared to a 100-year time horizon in climate impact assessment (Figure 2). This was mainly due to the higher

Table 2. Deterministic Absolute Unit GHG Emission Impact Results (g CO2-eq./MJ) Calculated with Mean Values of the Parameters and Grouped by Nine Different Factors; Negative (Positive) Value Indicates Reduction (Growth) in the GHG Emissions

GWP-20 logging residues (LGR) stumps (STU) stem wood 20 years old (STE20) stem wood 100 years old (STE100) GWP-100 logging residues (LGR) stumps (STU) stem wood 20 years old (STE20) stem wood 100 years old (STE100)

chip storage

forest carbon stock changes

methanol additive

increased peat utilization

substitution of diesel oil

total GHG emission impact

diesel fuel consumption

electricity consumption

nitrogen compensation fertilization

4

27

1

0

9

52

0

6

−138

−39

6 5

27 27

1 1

0 0

9 9

79 204

0 0

6 6

−138 −138

−10 114

5

27

1

0

9

105

0

6

−138

15

4

27

1

0

4

23

0

6

−138

−74

6 5

27 27

1 1

0 0

4 4

43 138

0 0

6 6

−138 −138

−52 43

5

27

1

0

4

49

0

6

−138

−46

ash recirculation

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order to reduce parameter uncertainty. Wherever possible, allocation should be avoided in CLCA, but in practice it is very difficult as regards the determination of single input parameters, their input parameters and so on. The influence of allocation on all of the input parameters and the results was not studied, but it would have probably increased the sensitivity of some of the parameters such as emissions from marginal diesel fuel production and consumption, and thus also that of the results. However, most of this sensitivity is probably included in the ranges used for various parameters in this study. The assumption that all the input parameters in the model were independent of each other is not the case in practice. There are certainly stronger and weaker correlations between some of the parameters. The fundamental problem is that, without adequate data, it is very difficult to determine whether the correlation between certain parameters would be positive or negative, strong or weak, linear or nonlinear. The influence of this simplification on the results remains to some extent unclear. According to the rough correlation analysis presented in the SI, the consideration of correlations could slightly increase the GHG emission savings but this is unlikely to be dramatic. Significantly more data and analysis would be required to gain a comprehensive understanding of the impacts of correlations on the results. The speculative nature of many of the important assumptions, may however, make it difficult. Significant reduction in the uncertainty related to the marketmediated factors may require more ambitious international climate policy to block up the carbon leakage possibilities. This case example showed that, with the given uncertainty of the variables, FT diesel production f rom boreal forests cannot presumably contribute to the climate change mitigation in accordance with what would be necessary during the upcoming decades to achieve the 2 °C target, i.e., a reduction in the GHG emissions by at least 50−85% by 2050 from their levels in 2000.31 Even blocking up the carbon leakage to achieve a substitution factor of 1 is not enough to change this conclusion. It should also be noted that consideration of continuous production of biofuel instead of annual production, as in this paper, would result in even higher GHG emission impacts per given functional unit (1 MJ of biofuel), due to the fact that the impact of forest carbon stock change would be higher in continuous harvesting compared to a single harvest.9 However, this does not mean that biomass derived f rom boreal forests should not be used to produce FT diesel, but the trade-of f between longterm sustainability and short-term climate change mitigation targets should be identif ied. In the long run, the climate benefits of forest biomass utilization probably exceed the benefits of not harvesting, as the carbon accumulation in the forest would saturate and the natural disturbances such as forest fires and insect damage could cause losses of forest carbon stocks. Naturally, also other environmental, economic, and social aspects related to biomass use are vital for decision-making, but were out of the scope of this paper. Regardless of the major uncertainties involved in the variables that need to be considered when assessing the GHG emission savings of biofuels, it is possible to make some robust conclusions, but only if the uncertainty is comprehensively included, as claimed by Plevin et al.12 Ignoring the uncertainty should not be an option, as it greatly increases the possibility of misleading conclusions. The conclusion of the uncertain GHG emission savings can be generalized to other types of biofuels as well. Biofuels derived from agricultural crops competing for land resource with, for example, food and

market-mediated factors. Considering the highly uncertain nature and remarkable significance of the substitution factor of diesel oil, it is reasonable to consider the cumulative probability of achieving the GHG emission savings (Figure 3, up) or the

Figure 3. Cumulative probability (y-axis) for the positive (0%) GHG emission savings (up) and the 50% GHG emission savings (down) as a function of the substitution factor of diesel oil (x-axis). The other parameter setting is as presented in Table 1

significant (here at least 50%) GHG emission savings (Figure 3, down) as a function of the substitution factor of diesel oil. Such results show how large the substitution effect should be in order to have a reasonable probability of achieving the given GHG emission savings while keeping the other uncertainties unchanged. The results indicate that there is a high (more than 50%) probability of achieving some sort of GHG emission savings even with the lower than 1 substitution factors except for stem wood at the age of 20 years at final felling or with only a 20-year cumulative warming integration time applied (Figure 3, up). However when the GHG emission savings requirement is increased to 50%, only logging residues with a 100-year cumulative warming integration time applied passed the criteria with more than a 50% probability with substitution factors less than 1 but higher than 0.9 (Figure 3, down). In other words, if the substitution factor is lower than 0.9, none of the cases studied was likely to result in a 50% GHG emission savings. In many cases, the probability distributions of the variables could have been chosen differently due to a lack of empirical data and the highly speculative nature of the future-oriented parameters. However, it is not clear in which of the variables and to what extent the uncertainty could be reduced by improved normative choices so as to reduce the parameter sensitivity, and by providing more reliable and exact data in 3036



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feed production are subject to significant uncertainties due to market-mediated iLUC,7,17 and all types of biofuels including those derived from waste and residues encounter the significant uncertainties of fossil fuel substitution.32,33,15,34,35 The appropriateness and effectiveness of the regulative requirements set for the GHG emission performance of biofuels in the EU36 and the U.S.37 can be criticized due to the significant uncertainties involved in the objective assessment procedure and the exclusion of the key uncertainties.

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ASSOCIATED CONTENT

S Supporting Information *

The calculation equation of the model, explanations of the parameter assumptions, additional scenarios, results and discussion. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: + 358 20 722 6767; fax: +358 20 722 7604; e-mail: sampo.soimakallio@vtt.fi. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The author acknowledges the ECOSUS project (257174) of the Academy of Finland, VTT Technical Research Centre of Finland and Maj and Tor Nessling Foundation for financing. The author is also grateful to the anonymous reviewers for their valuable comments on this paper.

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REFERENCES

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birch according to stand age for boreal forests. For. Ecol. Manage. 2004, 188 (1), 211−224. (31) IPCC Climate Change 2007. Mitigation of Climate Change, 2007. (32) Hochman, G.; Rajagopal, D.; Zilberman, D. The effect of biofuels on crude oil markets. AgBioForum 2010, 13 (2), 112−118. (33) Chen, X.; Khanna, M. The market-mediated effects of low carbon fuel policies. AgBioForum 2012, 15 (1), 1−17. (34) Rajagopal, D.; Hochman, G.; Zilberman, D. Indirect fuel use change (IFUC) and the lifecycle environmental impact of biofuel policies. Energy Policy 2011, 39 (1), 228−233. (35) Thompson, W.; Whistance, J.; Meyer, S. Effects of US biofuel policies on US and world petroleum product markets with consequences for greenhouse gas emissions. Energy Policy 2011, 39 (9), 5509−5518. (36) Renewable Energy Directive 2009/28/EC; The European Commission, 2009. (37) EISA Public Law 11-140 - Energy Independence and Security Act of 2007; The United States, 2007.

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NOTE ADDED AFTER ASAP PUBLICATION This article was published ASAP on February 14, 2014, with minor errors in Table 1. The corrected version was published ASAP on February 18, 2014.

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