Life Cycle Assessment of biogas production by monofermentation of energy crops and injection into the natural gas grid

biomass and bioenergy 34 (2010) 54–66

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Life Cycle Assessment of biogas production by monofermentation of energy crops and injection into the natural gas grid Colin Jury*, Enrico Benetto, Daniel Koster, Bianca Schmitt, Joe¨lle Welfring CRP Henri Tudor/CRTE, 66 rue de Luxembourg, L-4002 Esch/Alzette, Luxembourg

article info

abstract

Article history:

The use of renewable energy is a possible solution to reduce the contribution to climate

Received 2 March 2009

change of human activities. Nevertheless, there is much controversy about the non-

Received in revised form

climate related environmental impacts of renewable energy as compared to fossil energy.

27 July 2009

The aim of this study is to assess a new technology of biomethane production by mono-

Accepted 25 September 2009

fermentation of cultivated crops. Based on the results of an attributional Life Cycle

Available online 29 October 2009

Assessment (LCA), the contribution to climate change of biomethane production and injection into the grid is 30–40% (500a time horizon) or 10–20% (100a) lower than the

Keywords:

contribution of natural gas importation. The reduction depends mainly on the biogas yield,

Life Cycle Assessment

the amount of readily available nitrogen in the digestate and the type of agricultural

Monofermentation

practices. Nevertheless, the natural gas definitively generates far lower ecosystem quality

Crop fermentation

and human health damages than the biomethane production. Farming activities have the

Anaerobic digestion

most important contribution to the damages mainly because of land occupation and the

Biogas

use of fertilizer. The main improvement opportunities highlighted are: the increase of

Biomethane

biogas yield, the choice of good agricultural practices and the cultivation of winter or

LCA

summer crops exclusively. Future research should include the emission and sequestration of CO2 from soil. The ripple effects related to the total increase of farming area and the consequences of farming activities on the food production chain should be addressed as well. To this aim, the switch to consequential LCA is a critical challenge, from both the methodological and application point of view, to support decision-making. ª 2009 Elsevier Ltd. All rights reserved.

1.

Introduction and objectives

In the last decade, the concerns about the effects of climate change, the increasing demand and the price fluctuation of fossil fuels have been more and more present on the international policy agenda. The use of renewable energy sources is often suggested to be a possible solution to lower the contribution to climate change and the dependency from fossil fuels. Luxembourg is particularly concerned by this debate because it has the highest energy consumption per habitant in

Europe, which in addition is almost entirely imported from other European countries [1]. Moreover, Luxembourg has committed itself to an increased share of electricity produced from renewable sources [2] from 2.1% in 1997 to 5.7% by 2010 and to a 28% cut in greenhouse gas emissions by 2008–2012 as compared to the level in 1990 under the Kyoto Protocol [3]. Given the limited potential of other renewable energy sources (wind, photovoltaic, hydroelectricity), the use of biomass and especially biogas is an interesting possibility for Luxembourg to reach these ambitious objectives.

* Corresponding author. Tel.: þ352 42 59 91 629; fax: þ352 42 59 91 555. E-mail address: [email protected] (C. Jury). 0961-9534/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2009.09.011

biomass and bioenergy 34 (2010) 54–66

Historically, biogas has been produced mainly by anaerobic digestion of animal excreta, food industry waste, sewage sludge and municipal green waste [4,5]. Very few studies about monofermentation of energy crops have been published (e.g. [6,7] cited in [8]) and this technology is hardly discussed at international conferences and workshops on biofuels (e.g. [9–11]). The monofermentation of a substrate having a high share of dry matter, such as energy crops, could indeed be an effective production route since the biogas yield depends on the volatile solids (organic dry matter) content, which is directly linked to the dry matter proportion in the substrate. This is indeed one among the parameters, e.g. the residence time in the digester, the microbial consortia, etc., which are of primary importance to determine the biogas yield. Significant drawbacks of energy produced from cultivated crops are, however, highlighted in literature. Most of the fuels produced from energy crops have an overall environmental impact 1.5–4 times higher than conventional fuels, for the same function delivered [12]. The CO2 neutral nature of fuels produced from energy crops, allowing a reduced contribution to climate change, is often a strong argument in favour of renewable energies, but it is not always as significant as expected. Based on this state of the art, in 2006 the Luxembourg’s Ministry of Research has funded a 2-years study aiming at the development of a biotechnologically and environmentally optimised monofermentation technology of energy crops. The study investigated the physical process parameters at pilot scale and the microbiology of the fermentation process. Farming of energy crops was studied at the real scale (on field) as well, including the recycling of methanization residues (digestate) as fertilizers. The use phase of the biogas was not included in the study. Instead, the biogas was considered to be injected into the natural gas grid, which allows a variety of possible uses. In relation to the ongoing Environmental Technology Verification (ETV) schemes [13] and to the European policies, the development of a new technology of biogas production followed by injection into the grid shall include an assessment of the environmental performances of the whole system as compared to a conventional one. To this aim, an energy efficiency analysis and a Life Cycle Assessment (LCA) of the overall biogas production, cleaning, upgrading and injection in grid system as compared to the imported natural gas were carried out based on the field data and information gathered. This paper is aimed at presenting the results obtained from the LCA study, as first screening evaluation of the environmental pertinence of the monofermentation technology with respect to the Luxembourg’s commitments and needs, and at identifying improvement opportunities. In addition, this paper presents a comprehensive application of the state of the art LCA methodology to the environmental verification of an innovative technology, which could support further elaboration and development of ETV schemes.

2.

Scope of the study

The LCA is of the attributional type [14], i.e. it describes the environmental impact related to the future (steady state) operation of a system for biogas production and injection into

55

the grid and to the conventional natural gas system. The consequences of the introduction into the market of such a production and injection system (and technologies), which would be considered in a consequential LCA [11,15,16], are not addressed in this study. For instance, in case the production of energy crops replaces the production of food on the same arable area, the additional production or importation of food is likely to have non-negligible environmental impacts, which shall be considered. Despite its relevance, consequential LCA is still an emerging and not well established research topic, and therefore the attributional approach was preferred. The LCA is performed according to the ISO 14040–44 standards, using the software Umberto 5.5 [17] and the database Ecoinvent 2.0 [18] for background data. The following sections describe the LCA methods and the results obtained, according to the scheme provided by the ISO standards, including: the definition of the functions and functional units, the setting of the system boundaries, the Life Cycle Inventory (including a description of the processes and of multi-functionality), the Life Cycle Impact Assessment (presenting and discussing the results) and finally the interpretation, mainly involving sensitivity analysis.

2.1.

Function and functional unit

The main function of the system studied is the production of biogas fulfilling the quality standard of the technical guideline DVGW G260 [19]. To this aim, the gas is purified and upgraded by adding Liquefied Petroleum Gas (LPG: 95% butane, 5% propane). The functional unit is 1 MJ injected into the natural gas grid. The use phase is not considered. In addition to the biogas, the monofermentation process outputs are digestate (95 %wg) and silage juice (5 %wg). Silage juice is considered to be blended with raw digestate. Part of this mixture, still called digestate, is then supposed to be recycled as organic fertilizer spread on field, up to 170 kg ha1 a1 according to the current Luxembourg’s regulation [20,21]. The economic value of the surplus digestate, i.e. whether it is waste (negative value), neutral product (zero value) or a co-product (positive value) is unknown at the moment because it does not exist yet. If the digestate has a positive economic value, a multi-functionality problem arises, i.e. the LCI data have to be split up between the biogas injected and the digestate sold. This problem is discussed in Section 3.2 and further addressed in Section 5 (sensitivity analysis). In the baseline scenario, it is assumed that the surplus digestate is given free of charge to the farmers, i.e. it is a neutral product that does not affect the assessment.

2.2.

Boundaries

The product system studied includes four main steps: farming, digestion, purification & upgrading and transportation (Fig. 1). These steps are further detailed in the Life Cycle Inventory (LCI). The data related to the production and delivery life cycles of raw materials and energy as well as to waste treatment processes are adapted from the Ecoinvent 2.0. This implicitly sets the cradle-to-grave boundaries, i.e. the number of data entries of the inventory. The environmental burdens related to the storage of biomass for silage, to the addition of an odorant to biogas, as well as to the

56

biomass and bioenergy 34 (2010) 54–66

Surplus digestate Foreground processes and emissions

Digestat

Fermentation

Purification and upgrading

Biogas to natural gas grid

Includes

Includes

Includes

Includes

Fresh Matter transport by truck

Digester

Heat & electricity production

Transport by pipeline

Farming

FM

Includes Seed production and transport Reduced tillage Harvesting

Heat & electricity production

Digestate transport by truck

Methane losses

Methane losses

Biogas losses

LPG production

Chemical fertilizers production and emissions Soil emissions System boundary

Background processes and emissions Complex process

Transport phase

Fig. 1 – Biogas production and injection in grid.

infrastructures for the biogas purification and LPG injection are not included in the study. These steps are usually neglected in literature studies [22]. Indeed, they are likely to generate negligible environmental impacts as compared to the other operations due to the large material and energy flows handled during their lifetime. Air emissions resulting from soil degradation and biodiversity losses due to land transformation are only partly considered. Past use of the land is considered only in sensitivity analysis in order to evaluate the significance of soil carbon emissions [23]. Possible indirect and ripple environmental effects, as well as economic and social impacts, are not considered in attributional LCA. Finally, the uptake of heavy

metals or other elements by crops and their release to the environment by digestate are not included in the LCI due to lack of data and information.

3.

Life Cycle Inventory (LCI) analysis

3.1. Inventory of the biogas production and injection system 3.1.1.

Crop farming

The main data and related sources considered are listed in Table 1. Eight crops are cultivated, i.e. four couples of winter

Table 1 – Farming operations data. Baseline scenario

Sources

Cultivated area Productivity (two crops are considered) Dry Matter (DM) content of crops

1 ha 80 t Fresh Matter (FM) ha1 a1 24 %wg of FM

Digestate spreading

170 kg ha1 a1 of organic nitrogen

– [24] Measurement following the German standard DIN 38414-S2 Luxemburg regulation [20,21]

Crop composition (kg t1 FM) Nitrogen Phosphorus Potassium

4 0.6 4

Elementary analysis of the combustion gas Average of data from [67] Average of data from [67]

Digestate composition (kg t1 FM) Nitrogen Phosphorus Potassium

4.6 0.7 4.6

Calculated from crops content Calculated from crops content Calculated from crops content

57

biomass and bioenergy 34 (2010) 54–66

and summer crops. The rotation of the couples on the same area avoids the use of pesticides and herbicides [24] and supports the conservation or the increase of organic matter content in soil [23]. For sake of simplicity, it is assumed that the land has already been cultivated with the same agricultural practices since 20 years and therefore the carbon soil content has become stable, i.e. on a yearly basis the carbon uptake offsets the carbon emission [25]. Nevertheless, the influence of carbon emissions from soil may be significant. According to [23] the land use change from forest land to agricultural land leads to a non-equilibrated carbon balance and a net carbon emission of 750 kg ha1 a1 during 20 years. This is considered to be a direct land use change, which is retained for the calculation of carbon footprint according to the PAS 2050 specification [26]. However, the PAS 2050 recommends to not consider the likely sequestration of atmospheric carbon following an indirect change of land use, e.g. the shift from conventional farming to improved farming (reduced labour, organic matter spreading, crop rotation, etc.), which should occur according to several authors [23,27,28]. Thus, with respect to the PAS 2050 and for sake of simplicity, the indirect land use change is not considered and the soil carbon balance is assumed to be stable. The influence of a direct land use change emission is studied in sensitivity analysis (Table 2).

Seed and sowing, tillage, harvesting. Seed production, tillage and harvesting operations are based on the Ecoinvent 2.0 datasets of organic production of maize for summer crops and rye for winter crops [29]. Simplified tillage operation and seeds from intensive farming are considered. It is assumed that the grains are transported for 100 km. The environmental impacts related to the nitrogen and phosphorus emissions via soil erosion, which depend on the farming operation, are based on equations from [30]. Use of nitrogen chemical fertilizer. The cultivation of summer crops and then of winter crops on the same hectare of land over 1 year demands 403 kg of NO3NH4 fertilizer. This is calculated considering the crops’ need and the nutrient contribution by digestate and soil. The contribution of digestate is based on the amount of digestate spread (37 t ha1 a1), and on nutrient losses (as NH3, NO3, N2O, NOx and PO4) after spreading. Nutrient losses are calculated according to the equations for organic fertilizers from [30]. The soil is assumed to be composed mainly of clay and silt [31], i.e. having organic matter content higher than 2% [32,33]. This type of soil provides at least 70 kg of organic N according to [34]. The emissions from digestate are based on equations for manure from [30] following two main assumptions. First, the fraction of N as ammonium in digestate is set to 65%, which is an

Table 2 – Sensitivity analysis scenarios. Scenario 1 2 3 4

5

6

7

8

9 10 11 12

13

14 15

Description Higher legal amount of organic nitrogen as compared to the baseline scenario Emission of fossil carbon from soil Lower proportion of direct available nitrogen as compared to the baseline scenario Nitrogen effectiveness changes due to the decrease of direct available nitrogen Higher proportion of direct available nitrogen as compared to the baseline scenario Nitrogen effectiveness changes due to the decrease of direct available nitrogen Biogas productivity is 20% lower than in the baseline biogas productivity scenario due to a shorter time of digestion Digestate production changes due to the decrease of biogas productivity Biogas productivity is 20% lower than in the average biogas productivity scenario due to a shorter time of digestion Digestate production changes due to the decrease of biogas productivity Biogas productivity is higher in the average biogas productivity scenario than in the baseline biogas productivity scenario Digestate production changes due to the increase of biogas productivity Biogas productivity is 20% lower than in the scenario of maximum biogas productivity due to a shorter time of digestion Digestate production changes due to the increase of biogas productivity Maximum biogas productivity is higher than in the baseline scenario Digestate production changes due to the increase of biogas productivity Loss of methane during purification Loss of methane during purification Good agricultural practices could reduce to 20% the NH3 losses as compared to the worst case scenario Nitrogen effectiveness increases due to the decrease of NH3 losses Bad agricultural practices lead to 100% of NH3 losses. This is the worst case scenario Nitrogen effectiveness decrease due to the increase of NH3 losses Higher energy needed to purify biogas as compared to the baseline scenario Lower energy needed to purify biogas as compared to the baseline scenario

Value Baseline scenario

Unit

200 750 50 76 80

170 0 65 69 65

kg Nt ha1 kg Carbon ha1 year1 %wg of total nitrogen %wg of total nitrogen %wg of total nitrogen

60 374

69 467

%wg of total nitrogen Nm3 kg1 DM

89 412

86 467

%wg of FM input to the digester Nm3 kg1 DM

88 515

86 467

%wg of FM input to the digester Nm3 kg1 DM

85 504

86 467

%wg of FM input to the digester Nm3 kg1 DM

85 630 81 3 0 20

86 467 86 1 1 60

%wg of FM input to the digester Nm3 kg1 DM %wg of FM input to the digester %v of purified biogas %v of purified biogas %wg of maximal NH3 losses

83 100

69 60

%wg of total nitrogen %wg of maximal NH3 losses

54 5 2

69 3 3

%wg of total nitrogen % of energy in purified biogas % of energy in purified biogas

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biomass and bioenergy 34 (2010) 54–66

average of literature data for digestate from manure fermentation [35,36]. No information is available about digestate from monofermentation of energy crops. In the sensitivity analysis, the influence of such an assumption on final results is investigated. Secondly, the saturation deficit of the air is set to 4.04. This is an average value calculated for 80 kg of digestate spread in October and 90 kg spread in June fulfilling the requirements of [37], and complying to the maximal legal amount of organic nitrogen fertilisation (170 kg ha1 a1 from [20,21]). The equation used in Ecoinvent to calculate ammonia emissions does not consider good agricultural practices and thus reduced nitrogen losses [30]. According to [8,38], good agricultural practices could reduce ammonia emission by 80%. As a result, three cases of agricultural practices with different NH3 losses and nitrogen efficiency are studied: good practice (20% of ammonia losses leading to 83% N efficiency); baseline practice (60% of ammonia losses, 69% N efficiency), bad practice (100% of ammonia losses, 54% N efficiency). The N efficiencies are calculated assuming that the crops use the whole amount of organic nitrogen. The value for the baseline scenario (69%) is lower than the values proposed by [34] for cow manure (75–80%) but it lies within the range observed for pig manure (60–70%).

Use of phosphorus and potassium chemical fertilizer. The culture of 1 ha of summer crops and then of winter crops over 1 year needs 106 kg of triple superphosphate (P2O5), i.e. 51 kg of P, and 302 kg of potassium chloride (K2O), i.e. 181 kg of K. Phosphorus emissions to water are calculated using the equations from [30]. The cumulated losses are lower than 1%, similarly to the losses observed for the spreading of cattle manure or derived compost (0%) but differently from the case of pig or cow manure (15%) and poultry manure (35%) [34]. Losses of potassium are not considered, like in [30,34].

3.1.2.

Crop digestion

Before the digestion, the preparation of energy crops in silage leads to 5 %wg loss of FM in silage juice [39]. After the digestion, this juice is mixed with digestate to be spread onto the agricultural land. Digestion tests at laboratory-scale on eight types of crops grown over 2 years led to an average biogas yield of 467 Nm3 t1 of DM, with 60%v of CH4 and 40%v of CO2 [24]. This value is close to 400 Nm3 t1 of DM, which is the value used for cereal in Ecoinvent 2.0 [38]. By using data for few relevant crops (maize, barley, triticale, rye and hemp) from [40], biogas yield can lie between 519 and 630 Nm3 t1 of DM. Normal basis is used, which corresponds to a temperature T ¼ 273.15 K and a pressure varying from 1013 hPa [24,38] to 1023 hPa [41]. However, pressure difference of around 1% is assumed to be negligible. By combining these results with other information from [41,42], an average biogas yield of 515 Nm3 t1 of DM is estimated. All these values should be considered as upper estimations because they are obtained under (optimal) laboratory conditions. It is proven that in case of residence time lower than the time needed for complete degradation in full-scale plant (typically 28 days), the biogas yield can decrease by 20% [8,40]. As a result, in the baseline scenario the biogas yield is set to 467 Nm3 t1 of DM, which is

the value observed in this study. Likely variations of the yield, in line with the results from [43], are studied in sensitivity analysis (Table 2). The production of digestate depends on the biogas yield and does influence the nutrients concentration and thus the legal amount of digestate that can be spread. The amount of digestate spread determines the amount of chemical fertilizer to be added as well as the results of the allocation of LCI data in case of multi-functionality. In the baseline scenario, the total production of digestate is 66 t a1 considering 1 ha of land cultivated and the addition of silage juice. Digestate is stored in closed tanks in order to reduce gaseous losses to the atmosphere. Losses during the digestion could sum up to 1% [22] or 0.75% [38], respectively. For our calculation, 1% losses are considered. Further likely values are studied in sensitivity analysis (Table 2). Based on the energy consumption data from [39], electric energy, heat and diesel consumptions are estimated to 5.15 kWh t1 of FM, 131 MJ t1 of FM and 0.536 L t1 of FM respectively.

3.1.3.

Biogas purification & upgrading

The biogas is supposed to be injected into the low-pressure gas network (0.8 MPa). In order to comply with the DVGW260 standard [19], the methane content is increased from 60% to 96% through a water scrubber, which involves compression up to 0.8 MPa. 0.03705 Nm3 of LPG per Nm3 of purified biogas are added as well. The electricity demand for the biogas purification and compression up to 25 MPa corresponds to 5% of the energy content in the biogas produced [44,45]. This value is confirmed by [46], which considers that the demand for compression up to 20 MPa is 3–6% of the energy content in the cleaned biogas. The energy needed to compress up to 0.8 MPa and to purify raw biogas is estimated to be 3% of the energy content of purified biogas. This value is the differential between the energy demand to compress up to 25 MPa (5%) and the energy needed to compress the purified biogas (96% CH4) from 0.8 MPa to 25 MPa (2%). The latter is calculated under isentropic conditions, considering correction factors related to non-isentropic conditions and to the compression engine efficiency. However, alternative values of electricity consumption are considered in sensitivity analysis (Table 2). Methane losses during digestion and purification can range from 1% to 4% of purified biogas [22]. However, in most of the cases the losses during purification only do not exceed 2% [22,46–48]. Purification technology is rapidly evolving and lower losses are expected in the next future. For these reasons, in the baseline scenario methane losses are set to 1% of the purified biogas. Other values are studied in sensitivity analysis (Table 2).

3.1.4.

Transport of crops, digestate and biogas

Energy crops and digestate are transported by truck (28 t) considering a distance between crop farming and biogas production of 5 km [39]. The travels of empty trucks from the origin site to the farming site before and after the transportation of crops and digestate are included. For the transportation of the purified and upgraded biogas from the production site to the distribution network, a pipeline over a distance of 5 km is considered.

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biomass and bioenergy 34 (2010) 54–66

As already mentioned digestate is partly reused in the system as fertilizer and partly exported to other systems. Currently monofermentation is not a mature technology and the surplus digestate is likely to not have a market value. This means that digestate is likely to be a neutral product, i.e. to be given free of charge to farmers as fertilizer and soil amendment. In this case, the emissions related to the production of digestate are included in the LCI of the present study, while the transport and spreading of digestate are excluded. This is usually referred to as the ‘‘cut off’’ approach. In future market situations, the surplus digestate could be considered to be a co-product having a positive economic value or waste. If it is a co-product, the economic value arises because of the lower heavy metal concentrations as compared to sewage sludge or wood ashes that are considered as waste following the landfarming disposal regulation [49,50]. The environmental burdens of the biogas production system are indeed related to the production of digestate as well. The way to address this multi-functionality problem depends on the type of LCA: attributional or consequential. Several approaches are debated in the LCA community and no unique solution does exist [50–52]. In case of attributional LCA, a first possible solution is to consider an economic allocation of burdens. The ratio of economic (market) values of the amounts of digestate and the biogas produced is used to split the environmental burdens of the studied system between the two final products. According to [53], the cost of biogas production from energy crops is estimated to be 0.075 V kWh1. Assuming a net gain of 0.005 V kWh1, the retail price has been set to 0.08 V kWh1. Concerning the value of digestate, two cases are considered: 1) ‘‘allocation-chemical’’. The economic value of digestate is calculated from the amount of chemical fertilizers having the same nutrient efficiency and from their price: K2O: 0.25 V kg1, P2O5: 0.35 V kg1, NH3NH4: 0.22 V kg1 [54]. As a conservative assumption, bad agricultural practices are considered during the spreading of digestate corresponding to N efficiency of 54%. As previously mentioned, P and K efficiencies are nearly 100%. The economic value of digestate is 5.68 V t1. Soil amendment is an additional functionality of digestate as compared to chemical fertilizers which unfortunately could not be taken into account in the calculation. 2) ‘‘allocation-chemical & compost’’. In this case, the economic value of digestate is based on the value of an equivalent mixture of compost and chemical fertilizers. The nutrient content of digestate and compost are very different [55–58], therefore an equivalency ratio for each nutrient has been calculated. The lowest ratio, which is for P, is retained. Then, chemical fertilizers are added to compost to reach the same nutrient contribution than digestate. The nutrient efficiency coefficients for compost are 0.2 for N; 0.6 for P; 1 for K [58]. The economic value of digestate is then calculated from the respective value of compost [59] and chemical fertilizers. An alternative solution of the multi-functionality problem is provided by the ‘‘system expansion’’ approach, which consists in subtracting from the LCI of the biogas production

a MJeq / MJeq in injected gas

Multi-functionality

Non-renewable

Other s renewable

Biomass

2 .5 2 .0 1 .5 1 .0 0 .5 0 .0

Biogas Non-renewable

b

Natural gas Others renewable

Biomass

2.5

MJeq /MJ in injected gas

3.2.

2.0 1.5 1.0 0.5 0.0

Biogas

Natural gas

Fig. 2 – Energy efficiency: (a) Cumulative Exergy Demand (CExD); (b) Cumulative Energy Demand (CED).

system the LCI of the system(s) which has been displaced by digestate. This approach is suggested by the ISO standards and is often considered in the practice [60,61], despite some authors advise restricting its applicability to consequential LCA (see e.g. [14,52]). Furthermore, it is debatable whether the credit arising from the displacement should be somehow split up between the biogas production and injection scenario and the farming system using surplus digestate. Indeed, both systems are contributing to the recovery of digestate. For the sake of simplicity, in this study the credit is fully attributed to the biogas system. Finally, digestate could be considered, despite its nutrient content, as waste. In this case the landfarming is a ‘‘treatment process’’ of the digestate waste and therefore the whole environmental burdens related to the production of biogas and digestate and the landfarming are attributed to the biogas system.

3.3. Inventory of the natural gas system (reference system) The generic Ecoinvent module for high pressure natural gas importation to Luxembourg was updated and improved by considering context specific information and data. Currently, 60% of the natural gas is imported from the south of Belgium and 40% from Germany. The gas from Belgium comes from

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biomass and bioenergy 34 (2010) 54–66

Fig. 3 – Climate change with time horizon 100 and 500 years (midpoint impact): (a) process contribution to climate change (IPCC 500a), considering CO2 sequestration and without use of gas (combustion); (b) process contribution to climate change (IPCC 100a), considering CO2 sequestration and without use of gas (combustion); (c) process contribution to climate change (IPCC 500a), considering CO2 sequestration as well as the use of gas (combustion); (d) process contribution to climate change (IPCC 100a), considering CO2 sequestration as well as the use of gas (combustion).

Norway (66%) and Qatar (34%), which replaced Algeria in the recent past. Half of the German gas is imported from Norway and the other half from Russia [62]. A specific Ecoinvent dataset for Qatar was created, based on the Ecoinvent dataset for Algeria. The transportation of gas by freight ship was supposed to occur through the Suez channel: the overall distance is 3 times higher than from Algeria.

4.

Life Cycle Impact Assessment (LCIA)

4.1.

Methods

A combination of assessment methods was considered to have a comprehensive view of the environmental impacts and damages generated. Cumulative Energy Demand (CED) and Cumulative Exergy Demand (CExD) provide a valuable evaluation of the energy efficiency (from a life cycle perspective) of the biogas system compared to the natural gas system [63]. CED represents the overall energy consumed over the life cycle in order to inject 1 MJ of biogas into the grid and it is mainly related to the consumption of energetic raw materials. CExD represents the useful energy (exergy) consumed over the life cycle, including also non-energetic raw material consumptions (e.g. chemical energy from ore). The unit of CED and CExD is the MJ equivalent (MJ_eq). Climate change impact represents a well-known environmental problem and a prominent issue in the debate on energy production from biomass sources. The climate change

impact was assessed using the characterisation factors provided by the International Panel of Climate Change (IPCC), also known as Global Warming Potentials (GWP), at the 100- and 500-years time horizon. In addition to CED, CExD and climate change, the evaluation of midpoint impacts together with endpoint damages using the Impact2002þ methodology [64] provides a complementary and comprehensive evaluation of the overall environmental burdens generated. Midpoint impacts describe well known environmental problems (like acidification or ecotoxicity), whereas endpoint damages provides a consistent and concise view of all the effects generated on human health, resources and ecosystem quality. Endpoint damages were alternatively assessed using the Ecoindicator 99 method [65] and the results are addressed in sensitivity analysis. In EI99, climate change effects on human health are included in the human health damage assessment, considering a 100-years time horizon. Instead, the I2002 method suggests using the 500-years time horizon and the effects of climate change are not included in the endpoint damage assessment.

4.2.

Cumulative energy and exergy demands

The CExD and CED results are quite similar (Fig. 2a, b), i.e. the energy (exergy) demand is dominated by energetic raw materials. The ratio between the energy (exergy) demand and the energy in the gas injected into the grid depends on whether the solar energy embodied in the biogas (and coming from the biomass) is considered or not. In case it is considered, the energy demand of the biogas system is twice higher the

biomass and bioenergy 34 (2010) 54–66

61

change balance remains favourable to biogas as compared to natural gas regardless of the time range (Fig. 3c, d). Farming has always the largest contribution to the climate change (52% according to IPCC 500a), compared to fermentation (20%), purification (14%) and LPG combustion (13%). The contribution of farming is mainly due to the life cycle of mineral fertilizer and N2O emissions caused by organic and chemical fertilisation. However it has to be noticed that biogenic CO2 and methane emissions of digestate spread on field are not considered. On the other hand, the biogenic methane losses from fermentation and purification are modelled and have a negligible contribution to the climate change impact (3%).

4.4.

Fig. 4 – Endpoint results (I2002D v 2.1): (a) endpoint categories (EQ: ecosystem quality; R: resources; HH: human health; CC: climate change); (b) single score (weighting factors: EQ: 0.4; HH: 0.4; R: 0.2).

one of the natural gas system. In the other case, the biogas energy demand is twice lower.

4.3.

Climate change (IPCC 100a and IPCC 500a)

The scope of the study systems did not include the use phase of the injected biogas. Therefore, in the assessment of climate change, the CO2 uptake during plant growth and converted to biomethane is actually accounted as sequestrated CO2. Considering the IPCC 500a characterisation factors, biogas has a benefic effect on climate change since it stores CO2 (Fig. 3a). Considering the IPCC 100a factors, the greenhouse gas emissions of the biogas system are higher than CO2 sequestration, even including the CO2 stored in biogas (Fig. 3b). However, the storage of CO2 into the biogas is an artificial effect since the gas is likely to be burned short time after the injection. In order to calculate the CO2 from biogas combustion, the emissions from the LPG combustion are included whereas the biogenic CO2 emissions are considered to be offset by the CO2 sequestration during plant growth. Regarding the CO2 emissions from natural gas combustion, since it is assumed that the biogas and the natural gas have the same composition, they are equivalent to the CO2 emissions from LPG (fossil) and biogas (biogenic) combustion. Even by including gas combustion in the assessment, the climate

Midpoint impacts

Farming activities have the highest contribution to most of the midpoint impacts. They are responsible for 90% of aquatic acidification and 94% of aquatic eutrophication impacts of the biogas system, which are respectively one and two orders of magnitude higher than the impacts generated by natural gas. Land occupation related to farming accounts for 78% of the overall ecosystem quality midpoint impact of the biogas system. Ammonia emissions from farming are the main contributor to terrestrial acidification and eutrophication effects and to respiratory effects due to inorganic substances and human toxicity. Farming has also the largest contribution to the consumption of non-renewable resources (40%), followed by purification & upgrading (33%) and fermentation (25%). The main resources used are natural gas and crude oil. The consumption of nonrenewable resources by the natural gas system is more than twice higher than in the baseline biogas scenario.

4.5.

Endpoint damages

The effects highlighted at the midpoint level are further evaluated at the endpoint level, except for aquatic acidification and aquatic eutrophication because of the modelling uncertainties [64]. Damages are further normalized and expressed as points, which represent the number of EU inhabitant equivalent generating over 1 year the same damage than the biogas system. Normalized endpoint damages calculated using I2002 are represented in Fig. 4a. The results observed at the midpoint level are basically confirmed, i.e. biogas has a higher contribution to human health and ecosystem quality damages than the natural gas and a lower contribution to climate change and resources consumption. The damage results can be further aggregated into a single score by considering a weighting scheme, which shall be defined by identifying the decision makers’ preferences. As this type of weighting could not be elaborated in the framework of this study, two approaches were used: 1) The weighting factors (Human Health (HH): 40%; Ecosystem Quality (EQ): 40%; Resources (R): 20%) considered in EI99 [65] were applied to I2002 (Fig. 4b). The single score of natural gas is almost 3 times lower than the score of the baseline biogas scenario. For the latter, the main contribution to the damage is from HH (45%) and EQ (41%). However, the

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Fig. 5 – Mixtri2 results:(1) area where natural gas is preferable; (2) area where biogas is preferable. The inner triangle includes the most likely weighting sets (a) based on I2002 damages and (b) based on EI99 damages.

weighting of HH implicitly considers that in EI99 this damage category includes effects of climate change, which is not the case in I2002. 2) The use of the Mixtri2 [66] tool, presenting all the possible weighting sets (Fig. 5a). The weightings higher than 20% and lower than 80% (outside the inner triangle in Fig. 5a) are rather unrealistic and were excluded. Based on the inner triangle results, there are few likely weighting sets where biogas is preferable to natural gas. Biogas becomes more environmentally friendly only if the weight of resources depletion is around 55%. Regardless of the chosen approach, the single score from I2002 covers only part of the environmental problems since climate change impact is not included. In addition, the increasing demand of fossil fuel and the debate on climate change effects may lead in the next future to a higher weighting of resource depletion damage. In order to evaluate further these issues, the EI99 method was used as well. EI99 uses different damage factors for resources and ecosystem

quality and different normalisation factors than I2002. Furthermore, climate change effects on human health are assessed. The damage generated by land transformation was excluded from this study since it is proven to potentially overestimate EQ damages [12]. Using EI99, the area where biogas is preferable is wider than with I2002 but it is still smaller than the area where natural gas is preferable (Fig. 5b). As a result, the environmental performance of the biogas system does not seem to be significantly affected by the valuation method. However, the environmental performance is likely to be more affected by few key parameters, which are further identified and assessed in sensitivity analysis.

5. Life cycle interpretation: sensitivity analysis Fifteen scenarios were studied by varying a number of parameters which are likely to have a significant influence on the biogas systems (Table 2). After recalculation of the LCIA

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Fig. 6 – Single score damage (weighting: EQ: 0.4; HH: 0.4; R: 0.2) versus climate change: (a) single score damage based on I2002 and not including CC versus climate change (IPCC 500); (b) single score damage based on EI99 and including CC versus climate change (IPCC 100).

Digestate as waste Economic-chemical

results for each scenario, three main parameters showing a strong influence on the single score damage appeared, viz. in order of importance: (1) the biogas yield; (2) the amount of readily available nitrogen (NH4), which leads to ammonia emissions during spreading, and, in turn, to N efficiency and thus to the amount of chemical fertilizer to be used; (3) the agricultural practices, mainly affecting the ammonia emissions and thus the N efficiency. The most important parameters affecting climate change are: (1) the biogas yield, (2) the amount of nitrogen from organic fertilizer which is legally allowed for spreading, and (3) the amount of readily available nitrogen (NH4). An increased amount of organic nitrogen spread has a significant influence only in the case it is coupled with very good or very bad agricultural practices. The leakage of biogas and methane during biogas production and biogas purification respectively as well as the energy needed for purification do not show a significant influence on the results. Within the 100a time horizon and assuming ideal combustion, the land transformation from forest soil to arable land could offset the positive effect of atmospheric CO2 sequestration by crops because of the soil CO2 emissions. At the 500a time horizon, the land transformation leads to a 30% increase of climate change impact, reducing the advantage of the biogas compared to the natural gas from 36% to 17%. However, the switch from bad agricultural practices to good agricultural practices would have a positive effect due to CO2 sequestration. Nevertheless, if crops for biogas production are cultivated on areas where also food crops are allowed, the increase of the total area of arable land to satisfy the food demand and the related CO2 emissions from soil as well as the damage on ecosystem quality should be included in the assessment. The single score damage and the climate change impact of all these scenarios are summarized in Fig. 6a, b. Single score damage is calculated using the I2002 and EI99 methods. Climate change is assessed at 100a (I2002) and 500a (EI99) time horizon. The biogas system generates a single score damage 70–100% (EI99) and 100–250% (I2002) higher than natural gas. The difference between biogas and natural gas is lower using System expansion Economic-compost

Single score

R

HH

EQ

CC (IPCC 500a) -25

-15

-5 5 15 25 35 45 55 Variation in % as compared to the baseline scenario

65

75

Fig. 7 – Percentage variation of the single score damage (I2002D with weighting: HH: 0.4; EQ: 0.4; R: 0.2) as compared to the baseline case by considering different solutions to address multi-functionality as discussed in Section 3.2. The single score does not include climate change.

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EI99 because the characterisation of the damage to resources is far more important than in I2002, which gives the biogas system an advantage. Furthermore, the integration of climate change effects into the human health damage in EI99 gives the biogas an additional advantage (5–10%). For most of the biogas scenarios, the contribution to climate change is reduced by 30–40% (500a) or 10–20% (100a) as compared to natural gas. The influence of the approach to deal with multi-functionality, as discussed in Section 3.2, is shown in Fig. 7. As expected, the ‘‘system expansion’’ approach gives the biogas system a significant advantage, with a reduction of 11% of the single score damage with respect to the baseline case. The different results obtained for the two economic allocation cases are due to the higher price of compost than of chemical fertilizers, which increases the economic value of digestate. If the surplus digestate is considered as waste, the single score damage increases by 45%.

6.

soil, to the total increase of farming area and to the consequences on the food production chain (i.e. land competition, additional food importation, price increase, etc.) shall be included in the assessment. Whether the implementation at large scale of the monofermentation technology could definitively contribute to the fulfilling of the Luxembourg’s objectives in the framework of the Kyoto protocol or not should be addressed by a consequential LCA study in order to properly include all the impacts generated.

Acknowledgements The funding from the Luxembourgish Ministry of Research is gratefully acknowledged. We would like to thank Prof. Manfred Greger (University of Luxembourg), Mr. Vo¨lker Hu¨fner (IGLux) and Dr. Philippe Delfosse (CRP Gabriel Lipmann) for the information and data provided to the study.

Conclusion references

The results presented show that the biogas from the monofermentation of energy crops is hardly competitive to the natural gas regarding the damage on human health and on ecosystem quality. However, the biogas is competitive regarding the climate change effect, the damage on resources and the fossil energy and exergy demands. Regardless of the weighting of the damage categories, the biogas from monofermentation of energy crops is definitively not more environmentally friendly than natural gas. The study shows, however, that the biogas production could be optimised through the implementation of good agricultural practices, by the use of the digestate as well as by the increase of the biogas yield, e.g. considering higher digestion time. Anyway, these improvements are unlikely to lower the environmental impacts of the biogas to the level of the natural gas. The main contributor to the damage on ecosystem quality is the use of land over the year. A possible improvement could be the cultivation of winter or summer crops exclusively, which will certainly lower the land use. The best ratio between the land occupation, the productivity of DM per hectare and the use of fertilizer shall be retained. Apart from the farming activities, the biogas yield is the main parameter to be considered to improve the environmental performance of the biogas system. An increase of the biogas yield would dramatically reduce the land use and the fertilizer demand per functional unit. Further efforts are therefore needed to develop and optimize the monofermentation technology to obtain higher biogas yields per unit of silage mass, without increasing the energy demand of the process. The development of ETV schemes related to biomethane production technologies should not miss to address these points in order to be effective and practical. The present LCA was aimed at evaluating the specific monofermentation technology at the pilot scale. It is important to bear in mind that the development of such biogas production technology to a large scale would have several effects on the economy and the environment. Additional environmental impacts and damages engendered, in particular related to the emission and the sequestration of CO2 from

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