Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy

Journal of Cleaner Production xxx (2015) 1e12

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Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy Martina Boschiero a, *, Francesco Cherubini b, Carla Nati c, Stefan Zerbe a
5, I-39100 Bolzano, Italy Faculty of Science and Technology, Free University of Bozen-Bolzano, piazza Universita Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway c Trees and Timber Institute, National Research Council of Italy (IVALSA-CNR), via Madonna del Piano 10, I-50019 Sesto Fiorentino, FI, Italy a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 December 2014 Received in revised form 28 July 2015 Accepted 22 September 2015 Available online xxx

In the alpine Autonomous Province of Bolzano (NE Italy), about 40% of the biomass used for bioenergy production is currently imported. This share is expected to further increase in the near future owing to growing renewable energy needs. The residual biomass harvestable from the local agronomic sector, mostly based on the cultivation of apple, is a promising option to supply relatively cheap bioenergy feedstock. In this study, we investigate the use of woody residues from apple orchards (apple orchard's woody residues, AWRs) for the production of bioenergy using the life cycle assessment (LCA) methodology. The system boundaries include the harvesting and chipping of AWRs, their transport to the energy plant and conversion into heat and power in a gasification unit. The life cycle inventory (LCI) data rely on field measurements for AWRs harvesting and chipping operations, as well as for their chemical and energy characterization. In the life cycle impact assessment (LCIA) phase, we consider various environmental impact categories like climate change, acidification, fossil depletion, and others. We benchmark the outcomes with two alternative reference systems based on fossils fuels. Our results show that the energy production using AWRs generally presents better environmental indicators than the reference systems, although some trade-offs exist. For instance, whereas the bioenergy system saves up to about 85% of greenhouse gas (GHG) emissions and about 95% of nonrenewable resources, it is usually associated with higher toxicity impact potentials. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Agricultural residues CHP Gasification Environmental sustainability Climate change Apple orchards

1. Introduction Although mitigation of climate change is a global challenge, active protection of the climate and of the local environment is closely linked to a sustainable energy policy. The Alpine Regions show high levels of awareness regarding climate change. The Climate Action Plan, issued in 2009, requires the development of specific energy strategies based on local and regional conditions and resources (Permanent Secretariat of the Alpine Convention, 2011; Provincia Autonoma di Bolzano, 2011). A common action is to incentivize sustainable local bioenergy chains and promote efficient energy conversion technologies.

* Corresponding author. Tel.: þ39 0471 017628, þ39 328 2074364 (mobile). E-mail addresses: [email protected], [email protected] (M. Boschiero).

In the Autonomous Province of Bolzano (NE Italy) about 40% of wood used today for bioenergy production is imported, especially from Austria and southern Germany (Provincia Autonoma di Bolzano, 2011). The intention of the Province is to increase the bioenergy use from local biomass sources, in order to reduce the Province's dependency on external energy supplies, trigger innovation and boost employment in marginal lands (Provincia Autonoma di Bolzano, 2011). In this area, the additional biomass recovery from local forest is limited by the economic profitability of forest operations (Provincia Autonoma di Bolzano, 2009), and by concerns regarding the negative effects that an excessive forest exploitation could cause on ecosystem services, such as the protection of human beings and infrastructures against natural hazards (e.g. rockfall, landslides and avalanches), cultural, recreational and aesthetic services, etc. (Provincia Autonoma di Bolzano, 2008; Radtke et al., 2014).

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Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094

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The local agronomic sector, largely based on the cultivation of apple (Malus domestica), represents a promising potential option of alternative biomass that is underexploited at the moment. Orchards require annual and cycling cultural operations (i.e. annual pruning and tree removals at the end of the fruit producing cycle) which produce woody biomass material such as branches, trunks and rootstocks. Currently, the trunks of cut trees are used as firewood to feed house-stoves or boilers in the farmers' houses. Pruning residues are grinded and left on the field, whereas rootstocks are sent to compost plants or to the landfill. The exploitation of this residual biomass stream (apple woody residues, AWRs) for energy purposes could represent an option for the diversification of farmer's incomes, besides the possibility to limit the fossil fuel use (Brassard et al., 2014). Understanding the environmental impacts of bioenergy chains based on woody biomasses has been an important focus of research in the last years. Life cycle assessment (LCA) is an important tool for assessing environmental burdens associated with bioenergy production, “by identifying energy and materials used as well as waste and emissions released to the environment” (Cherubini and Strømman, 2011). LCA is increasingly adopted by legislative documents, including the European Directive 2009/28/ CE (European Commission, 2009). Several papers investigated the greenhouse gas (GHG) emissions and energy balances of different bioenergy chains based on forest wood and residues (Cowie et al., 2006; Daystar et al., 2012; Guest et al., 2011; Jungmeier et al., 2002; Valente et al., 2011), or from fast growing species like short rotation coppice (Ericsson et al., 2014; Fantozzi and Buratti, 2010; Njakou Djomo et al., 2013; Sandilands et al., 2009). Many papers also tested the environmental performance of bioenergy based on agricultural residues (mainly cereal straws) (Cherubini and Ulgiati, 2010; Giuntoli et al., 2013; Khatiwada and Silveira, 2011; Luo et al., 2009; Nguyen et al., 2013; Sastre et al., 2014; Whitman et al., 2011; Yang and Chen, 2014). However, woody agricultural residues, such as woody residues from fruit cultivation, have so far received comparable little attention. Some authors (Bessou et al., 2013; Cerutti et al., 2014) reviewed several LCA studies of fruit production systems achieving important insights on how to perform the analysis, but focussing on the main fruit product and not explicitly considering the life cycle of woody residues. Some papers assessed technological, economical and fuel characterization aspects of agricultural woody residues (Magagnotti et al., 2013; Picchi et al., 2013; Spinelli et al., 2012; zquez-Martí et al., 2011a, 2011b, 2011c), but we are Vela aware of only one published LCA study investigating a bioenergy chain based on woody residues from apple cultivation (Boschiero et al., 2015). In addition to the well-known LCA methodological issues (Cherubini, 2010; Gnansounou et al., 2008), LCA results can also be very dependent on local aspects and case-specific conditions, which makes the case for undertaking region-specific LCA case studies (Finnveden et al., 2009; Hellweg and Mila i Canals, 2014). The aim of this study is to assess the environmental performance of using AWRs as bioenergy feedstock in the Bolzano Province. The system produces heat and power from orchards residues, using primary data for the agricultural operations, and it is scaled to the annual volumes of residues produced in the entire Province. The main objectives of the study can thus be summarized as follows: 1) to collect and provide site-specific data of the field operations for the life cycle inventory analysis; 2) to assess the GHG emissions and other environmental impacts derived from the whole value chain; and 3) to compare the innovative AWRs bioenergy chain with two reference system based on fossilfuels.

2. Materials and methods 2.1. Goal and scope definition The primary goal of this study is to carry out an analysis of the life cycle environmental impacts of the electricity and heat produced in a hypothetical gasification-CHP plant, fed with AWRs. This study aims to answer the following questions: i) what are the impacts generated by the assessed bioenergy system in the whole Province?, ii) what are the main processes of the bioenergy chain that contribute to the impact categories considered?, iii) does the bioenergy system produce environmental benefits when compared to reference systems mainly based on fossil fuels? 2.2. Functional unit The functional unit of the analysis is one operational year. In one year, 50.9 GWhel y1 (gigawatt hour electricity per year) of electricity and 79.4 GWhth y1 (gigawatt hour heat per year) of heat are produced from gasifying the AWRs of the Province in the CHP plant described below. In order to facilitate the comparison with other energy sources and other studies in the literature, we also show the results on a per unit basis, i.e. per megajoule of useful energy (MJue), per megajoule of electricity (MJel) and per megajoule of heat (MJth) produced. 2.3. System description and data inventory This study examines one hypothetical bioenergy chain based on a gasification-CHP plant using AWRs as feedstock. Two reference systems are considered for comparison. The systems are schematized in Fig. 1, and they are described in the following sub-sections. Data obtained from direct field measurements are used for the majority of the field foreground processes. Literature data are used to complement the field data, and the Ecoinvent v2.0 database (Swiss Centre For Life Cycle Inventories, 2007) is used for the background processes. 2.3.1. Bioenergy system Field measurements were conducted in winter 2013 in selected apple orchard sites to sample biomass yields, composition, and process operations. The pruning residues potentially available in the whole Province are estimated to be about 19,261 tonnes of dry weight (tdw) per year, and the wood from removed trees are estimated to be on average 18,052 tdw per year (see Prando et al., 2014a for more details). Table 1 shows the potential yields and the results of the chemical and energetic analysis of the biomass. Boschiero et al. (2015) show that there are two main different options on how agricultural residues can be assessed in LCA. Essentially, they can be considered either as by-product or as coproduct. In the first case, only the downstream processes should be included in the assessment. On the other hand, when agricultural residues are interpreted as co-products, the impacts deriving from up-stream processes (i.e. the crop cultivation phases) are also to be accounted for. In this study, we consider AWRs as by-products, following common practice in the published studies and as explicitly suggested by Baumann and Tillman (2004). The bioenergy system starts with the biomass harvesting (Fig. 1a). After the pruning operation, residues are collected and chipped immediately with a shredder (Spinelli et al., 2012). Field trials were conducted to assess the diesel consumption of the shredder coupled with a dump bin for chipping and harvesting the prunings. The trials took place in March 2014, on a surface of 20 ha in the land-tenure of the Laimburg Research Centre for Agriculture and Forestry (46 220 5900 N, 11 170 1800 E). Detailed information

Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094

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Fig. 1. Foreground system boundaries for the modelled systems producing the same amount of energy. The bioenergy chain based on AWRs is represented in the upper part (a), whereas the two reference systems are depicted in the lower part, separated with a dotted line (b). The reference systems differ only in the electricity production process.

Table 1 Average values of potential biomass yields, composition and energy content used in the assessment. Figures are expresses on a dry weight (dw) basis. Biomass type

Pruning residues Trunks Rootstocks

Yields tdw/ha

tdw/y

1.03 24.1 4.9

19,261 18,052 3646

Moisture (%)

C (%)

N (%)

P (%)

K (%)

Ashes (%)

HHV (MJ/kg)

51 43.9 43.9

48.06 46.05 e

0.6 0.4 e

0.1 e e

0.4 e e

4.1 3.8 e

19.03 19.5 e

about the experimental design are available in Magagnotti et al. (2013). The main findings are summarized in Table 2. From our field experiment, we found that there is an average harvesting loss of 40%. This figure is higher if compared to the findings of other studies carried out on apple pruning residues. For example, Grella et al. (2013) found an average loss of prunings of 21% and Magagnotti et al. (2013) measured losses of only

8%. However, in the latter study, authors found that the losses of different fruit pruning residues could even be up to 62% (Magagnotti et al., 2013). This discrepancy is maybe due to the use of different harvesting machines and by different pruning management occurring in the orchards. Moreover, field conditions can be also a factor influencing the final results (Spinelli et al., 2012).

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Table 2 Measured data and assumptions related to the management and transport of AWRs, for both the bioenergy system and the reference systems, which are schematized in Fig. 1. Parameter

Pruning residues management Tractor power (kW) Implement type Diesel consumption (l/ha) Harvest loss (%) Biomass transport Distance (km) Type of transport Loading capacity (t) Type of biomass

Systems Bioenergy

Reference

82 Shredder þ bin 7.9 40

45 Chipper 6.5 e

55 Lorry 32 Chips

5 Tractor þ trailer 8 Logs

Once the shredder-bin is full, the load is emptied into an agricultural trailer and transported to the CHP plant, where it will be stored and dried before being gasified. Considering the area of interest, the average distance between the orchards and the CHP plant is estimated to be of 55 km (Provincia Autonoma di Bolzano, 2009). According to Tagliavini et al. (2007) and Tonon et al. (2007), the harvesting of pruning residues does not significantly affect the nutrient balance of the ecosystem. We assume that the amount of additional N, P and K synthetic fertilizers required to compensate the nutrient removals is identical to the nutrient content of the collected biomass, as determined by the chemical analysis shown in Table 1. We then compute the associated emissions of ammonia, nitrate, nitrous and nitrogen oxides following the RSB GHG Calculation Methodology v.2.0 (Faist et al., 2011). Generally, apple trees are substituted with new trees of different varieties after 20 years of life, according to the market requests. The trees are sawed at about 15 cm above the surface, and the rootstocks are pulled out with a small scraper. The trees are chipped by a mobile chipper, fuelled by diesel. The chipper fills up a lorry-bin of 32 tons, which forwards the material to the CHP plant. A loss of biomass of 2% during the transportation and the stocking phase is considered (Guest et al., 2011). The average distance is the same as for the pruning residues. According to our laboratory measurements, AWRs enter into the CHP plant with an average moisture content of 47%. We assume a drying step before their gasification that decreases the moisture to 20%. This drying process is completely supported by the heat overproduced during the gasification stage (Ståhl et al., 2004). Gasification technology is chosen because it is considered the best available bio-CHP technology for small scale energy plants due to high power to-heating ratios and overall efficiencies (Guest et al., 2011; Prando et al., 2014b). We model a biomass integrated gasification combined cycle (BIGCC) plant, using a circulating fluidized bed (CFB) gasifier (Worley and Yale, 2012). This technology is well established all over Europe (Brown, 2011), and IGCC has maybe the highest potential of all the possible gasification applications (Kwant and Knoef, 2004). The energy efficiencies of our BIGCC are estimated from Guest et al. (2011), which are based €rnamo on the performance of a real functioning plant in Va (Sweden) (Ståhl et al., 2004). Assuming an overall efficiency of 87% and a capacity factor of 0.51 (Guest et al., 2011), the estimated electrical and heat power of the plant are 11.4 MWel (megawatt electricity) and 17.8 MWth (megawatt heat), respectively. Table 3 shows the main parameters of the CHP conversion plant. All the other data and references used to model the BIGCC are reported in appendix Tables A1eA4.

A recent study (Prando et al., 2014a) shows that the ash content of apple wood is 3 times higher than that of forest wood and that, the combustion of apple wood in a pellet boiler emits 2.5 times higher particulate matter (PM) than the forest wood. We have considered these updated figures in our study (see Table A1). Concerning the rootstock, we assume to discharge them to a sanitary landfill because the roots trap soil and stones making this substrate unsuitable for gasification plants (Worley and Yale, 2012). 2.3.2. Reference systems As shown in Fig. 1b, we consider two different reference systems, which provide the same amount of electricity and heat of the bioenergy system. The reference systems include the existing biomass management and use of AWRs in the Province. In the present situation, pruning residues are grinded and left on the soil. The diesel consumption of this operation was measured during the field trials mentioned in the previous Section 2.3.1, and found to be 6.5 l ha1 (see Table 2). The trunks of removed trees are used as firewood in house devices. Farmers generally use a chainsaw to cut the trunks and the thicker branches, and move the biomass with a small tractor coupled with a trailer, until the farmer's house. Following a survey conducted in this Province (Provincia Autonoma di Bolzano, 2009), we use a wood heater with a nominal capacity of 6 kWhth, which represents the local widespread domestic device typology. The remaining fraction of heat required to equalize the heat production in the bioenergy system is assumed to derive from the burning of natural gas, the most common source of domestic heat in the area. The rootstocks are assumed to be sent to a sanitary landfill whose operations are modelled using the Ecoinvent v2.0 database (Swiss Centre For Life Cycle Inventories, 2007). The two reference systems differ on the electricity production source. In one case, electricity is produced from coal (R_c), whereas in the other case the electricity derives from the natural gas (R_ng). These two options are selected in order to represent the worst and the best cases in terms of fossil alternatives (Bird et al., 2011). These processes are simply modelled using default data in Ecoinvent v2.0 database (“Electricity, hard coal, at power plant/IT U”, and “Electricity, natural gas, at power plant/IT U”) (Swiss Centre For Life Cycle Inventories, 2007). 2.4. Allocation We allocate the environmental impacts to electricity and heat according to their economic (EC), energy (EN)- or exergy (EX)-basis. The economic allocation is based on the subsidies that the CHP plant gains from the electricity and heat selling. In Italy, bioelectricity and bioheat are subsidized according to the Directive 2004/8/EC (European Commission, 2004). In this case, the bioelectricity is subsidized by the feed-in-tariff of 0.209 V per kilowatt hour of net electricity (kWhel) delivered to the grid. In order to support the use of cogenerated heat, the European regulation (European Commission, 2004) provides a further incentive, based on the cogenerated electricity. In this specific case, the incentive is 0.04 V/kWhel. The Italian Regulatory Authority for Electricity and Gas (AEEG) gives a further heat valorization bonus of 0.057 V per kilowatt hour of heat produced (kWhth) (AEEG, 2013). Thus, we attribute a specific partitioning value of 0.0826 V/kWhth to the heat, which is calculated as follows:

½ð0:04V=kWhel *Enel Þ þ ð0:057V=kWhth *Enth Þ=Enth where Enel represents the electricity produced by the CHP plant (50.9 GWhel y1), and Enth is the heat produced by the same CHP plant, amounting at 79.4 GWhth y1.

Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094

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Table 3 Technical specification of the BIGCCeCHP plant assessed. Parameter

Unit

Value

Reference

Electrical power Heat power Input biomass energy Biomass input flow-rate Cold gas efficiency (CGE) Overall electrical efficiency Overall thermal efficiency Electricity produced Heat produced Capacity factor Steam temperature Initial water temperature

MWel MWth GWh y1 tdw h1

11.42 17.79 149.8 6.25 0.92 0.34 0.53 50.9 79.4 0.51 200 5

Own calculation Own calculation Own calculation Own calculation Tonini and Astrup (2012) Estimated from Guest et al. (2011) Estimated from Guest et al. (2011) Own calculation Own calculation Guest et al. (2011) Worley and Yale (2012) Guest et al. (2011)

GWhel y1 GWhth y1  

C C

Concerning the exergy-based allocation, we calculate the exergy partitioning factors using the Carnot principle, as described in Njakou Djomo et al. (2013). Partitioning coefficients are calculated following the equations reported by Cherubini et al. (2011a). The specific allocation factors used in this study are available in Table 4. 2.5. Impact assessment The midpoint ReCiPe Hierarchist impact assessment method (Goedkoop et al., 2009) is used in the characterization phase. We present the results for the following impact categories: climate change potential (CCP), photochemical oxidation formation (POCP), ozone depletion (ODep), particulate matter formation (PMF), terrestrial acidification (TA), freshwater eutrophication (FWEu), freshwater and terrestrial ecotoxicity (FWE and TE, respectively), human toxicity (HTP), and fossil depletion (FDep). Bioenergy systems are usually characterized by fast CO2 emissions from biomass combustion and slow CO2 uptake by vegetation regrowth. This yields a non-zero climate forcing even if the net CO2 fluxes sum up to zero over time. As recently summarized by the IPCC 5th Assessment Report (Bruckner et al., 2014; Myhre et al., 2013), the climate impacts associated with this temporary forcing can be assessed by assuming specific characterization factors for CO2 emissions from biomass combustion. In our study, we adopt a GWP factor of 0 for CO2 emissions from combustion of pruning residues because they are annually produced, whereas a factor equal to 0.08 is used for the removed trees. This value corresponds to the GWP factor value with a time horizon of 100 years for a biomass feedstock with a rotation period of 20 years (Cherubini et al., 2011b). 3. Results and discussion Results of the bioenergy system are shown in Fig. 2 and Table 5. Fig. 2 shows the total impact per year and the main contribution stages. Table 5 shows the results per unit of energy output using different allocation methods. The comparison with the two reference systems is shown in Fig. 3 and Table 6.

3.1. Bioenergy system 3.1.1. Climate change potential (CCP) CO2 emissions from biomass combustion are the main contributor to CCP, representing approximately 40% of the total impact (Fig. 2). The next main contributor is the gasification of AWRs (26%), mainly because of the operational air emissions of the BIGCCeCHP plant. The emissions derived from the additional fertilization (14.8%) and from the phase of chipping and harvesting pruning residues (7.1%) are the third and fourth largest contributors, respectively; while the emissions from all the other processes makes up the remaining fraction (12.2%). More than 55% of the CCP is due to fossil CO2, derived from the fossil fuels consumption during chipping and harvesting operations (31%), CHP plant running (30%), additional fertilization production and spreading (21.6%), and biomass transportation (10%). The BIGCCeCHP operational air emissions are dominated instead by dinitrogen monoxide (N2O), which is the second larger stressor (37%). Fossil methane (CH4) emissions account for about 2.2% of the total CCP and, as for CO2, it is mainly generated during the running of the CHP plant (38%), additional fertilization production and spreading (26.3%), chipping and harvesting operations (18%) and biomass transportation (9%). The electricity production from AWRs gasification resulted in a total GHG emission ranging from 14.9 g CO2eq MJ1 el (EN) to 23.7 g CO2eq MJ1 el (EC), and the heat production scored from 9.4 g CO2eq 1 MJ1 th (EC) to 15 g CO2eq MJth (EN) (Table 5). When excluding the contributions from CO2 emissions from biomass combustion, they 1 range from 8.95 g CO2eq MJ1 el (EN) to 14.2 g CO2eq MJel (EC) for electricity production, and from 5.6 g CO2eq MJ1 (EC) to 8.9 g CO2eq th MJ1 th (EN) for heat production. There are no studies based on fruit residues that can be directly used as a benchmark for our findings. However, we find that these results are generally in line with those reported by other bioenergy studies (which do not usually consider the contributions from CO2 emissions from biomass combustion), with some exceptions. For instance, GHG emissions of electricity and heat production from woodchips in cogenerating plants range between 15 and 30 g CO2eq MJ1 ue in Cherubini et al. (2009). The life cycle

Table 4 Specific values and partitioning coefficients (ai) to be used in the different partitioning allocation procedures. Coproducts

Allocation Economic (EC)

Electricity Heat

Energy (EN)

Exergy (EX)

V/Unit

Value

ai (%)

MJ/Unit

Value

ai (%)

MJex/Unit

Value

ai (%)

kWhel kWhth

0.21 0.08

61.87 38.13

MJel MJth

1 1

39 61

MJel MJth

1 0.41

60.9 39.1

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Fig. 2. Breakdown of the impacts associated with the entire bioenergy chain based on AWRs into contributing system processes and contributing impact categories. Figures reported next to the bars refer to the absolute value of that impact expressed per functional unit. “PRs” means pruning residues and “CTs” means cut trees.

Table 5 1 1 Absolute environmental impact scores per MJ of useful energy (MJ1 ue ), MJ of electricity (MJel ) and MJ of heat produced (MJth ) for each allocation methods applied (EC ¼ economic based allocation, EN ¼ energy-base allocation, EX ¼ exergy-base allocation). Useful energy (MJ1 ue )

Impact category Name Climate change (CCP) Ozone depletion (OZDep) Photochemical oxidant formation (PCOP) Particulate matter formation (PMF) Terrestrial acidification (TA) Freshwater eutrophication (FWEu) Terrestrial ecotoxicity (TE) Freshwater ecotoxicity (FWE) Human toxicity (HTP) Fossil depletion (FDep)

Unit 2

10 kg CO2eq 1010 kg CFC-11eq 104 kg NMVOC 105 kgPM10eq 104 kg SO2eq 106 kg Peq 106 kg 1.4-DBeq 104 kg 1.4-DBeq 102 kg 1.4-DBeq 103 kg oileq

1.51 6.22 1.75 4.93 1.99 2.21 6.63 2.76 4.82 1.88

CCP of forest wood bioelectricity production was found to range between 1.4 and 25 g CO2eq MJ1 el , in an extensive review of biopower LCAs (CHP included) (Warner et al., 2010). On the other hand, the CCP of our AWRs bioenergy chain is about two times that of the gasification chains in Guest et al. (2011), based on forest wood. By contrast, the gasification chain assessed by Njakou Djomo et al. (2013), based on short rotation woody crops, presents a CCP of about 42 CO2eq MJ1 el , that is almost two times the CCP calculated in our study according to the exergy allocation. The discrepancy is maybe due to the different characteristics of the CHP conversion plant or different system boundaries and modelling assumptions. For example, Guest et al. (2011) did not include the emissions derived by a possible additional fertilization due to residual biomass harvesting, and Njakou Djomo et al.

Electricity (MJ1 el )

Heat (MJ1 th )

EC

EX

EN

EC

EX

EN

2.37 9.78 2.76 7.79 3.16 3.50 10.49 4.36 7.61 2.95

2.34 9.63 2.72 7.67 3.11 3.44 10.33 4.29 7.49 2.91

1.49 6.17 1.74 4.91 1.99 2.21 6.61 2.75 4.80 1.86

0.94 3.87 1.09 3.08 1.25 1.38 4.15 1.72 3.01 1.17

0.96 3.97 1.12 3.16 1.28 1.42 4.25 1.77 3.09 1.20

1.50 6.19 1.75 4.93 2.00 2.21 6.64 2.76 4.81 1.87

(2013) included the emissions generated during the cultivation phase of the woody crops and the emissions from direct land-use change. 3.1.2. Other impact categories As shown in Fig. 2, the CHP operational emissions represent the main stressors in the majority of the impact categories. About 90% of TE comes from the direct CHP operational emissions. This is due to zinc, phosphorous and copper air emissions, which, together, are responsible for about the 83.4% of the total TE. More than 66.5% of the POCP is caused by direct CHP operational emissions, largely due to nitrogen oxides (NOx, accounting for the 49.7%), non-methane volatile organic

Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094

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Fig. 3. Contribution analysis of the environmental impacts from the alternative bioenergy system (AWRs_CHP) and reference systems based on coal (R_c) and on natural gas (R_ng). The x-axis represents the fraction of environmental impacts normalized by the system that is contributing the most to given impact category. Figures reported beside the bars refer to impacts occurring per functional unit.

Table 6 Net environmental impacts (bioenergy system-reference system) of the bioenergy system in comparison with the reference systems based on coal (R_c) and natural gas (R_ng). Positive values mean that the bioenergy system score worse than the reference ones. Savings

R-c

R-ng

Impact category

Unit

Unit/a

%

Unit/a

%

CCP OZDep POCP PMF TA FWEu TE FWE HTP FDep

106 kg CO2eq 101 kg CFC-11eq 104 kg NMVOC 104 kg PM10 eq 104 kg SO2eq 102 kg Peq 103 kg 1.4-DBeq 104 kg 1.4-DBeq 106 kg 1.4-DBeq 105 kg oileq

57.65 13.40 14.84 15.74 25.96 368.18 þ1.42 43.58 7.56 165.17

89 82 64 87 73 97 þ84 77 25 95

38.69 52.42 3.22 7.46 þ2.05 þ3.89 þ1.40 þ7.89 þ15.23 160.71

85 95 28 76 þ28 þ60 þ82 þ156 þ207 95

compounds (NMVOC, responsible for the 46.3%). The additional fertilization required to reintegrate the nutrients exported by harvesting pruning residues is responsible for about 19% of this impact, due to N2O and NOx emissions formation during the fertilizer utilization (80%) and production (20%). Together, the operations of sawing, harvesting and chipping the AWRs account for the 12% of the POCP, mainly derived from the NOx emission from diesel consumption.

CHP operational emissions and additional fertilization dominates also the PMF, amounting to 61.53% and 22.2%, respectively. The main stressors are NOx, ammonia, SO2 and N2O. Similarly, CHP operational emissions represent the main contributor also for FWEu impact (56%), followed by additional fertilization (26%). Phosphate water emissions constitute the main stressor (99.7%) and derive for 51.3% by auxiliary compounds and resources used during the gasification process (such as, liquid oxygen, electricity, dolomite, urea, sodium chloride, lubricating oil, steam, organic chemicals, etc.) and for 25.8% by additional fertilization phosphorous water emissions. The largest part of OZDep impact (about 96%) originates from two stressors, i.e. bromotrifluoro-methane (halon 1301) and bromochlorodifluoro-methane (halon 1211). These chemicals are used as a gaseous fire-suppression agent (Guest et al., 2011), and they are mostly used in the background crude oil production process. Processes involving a considerable use of diesel, such as pruning residues harvesting and chipping, cut trees sawing and chipping, biomass transportation and the additional fertilization phase, cover about 73% of this impact. The CHP operation phase is responsible for about 19.5% of the total OZDep, because of the abundant crude oil requirement of the auxiliary compounds and resources needed for the gasification. FDep presents obviously the highest scores in whichever process consumes the most fossil fuels (Fig. 2). The main fossil

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M. Boschiero et al. / Journal of Cleaner Production xxx (2015) 1e12

resources that are depleted are: oil (55.2%), natural gas (26.9%) and coal (17.7%). The disposal of AWRs ashes in sanitary landfills dominates the FWE, causing the 85.5% of this category. Heavy metal ions, such as manganese, nickel, zinc, and vanadium are the major stressors creating this impact. A similar result is showed by HTP. For this impact the bioashes disposal phase represents the main contributor, for about 65.3%, due to manganese and arsenic water emissions. Direct CHP operating emissions give rise to the 31.7% of HTP, caused essentially (80%) by air phosphorous emissions. The comparison with other bioenergy studies is difficult due to several factors: available LCAs usually focus on GWP and CED, different impact assessments methods are used, and the results are reported as damage or are normalized, as could be found in Fantozzi and Buratti (2010) and in Neupane et al. (2011). 3.2. Systems comparison Fig. 3 shows the contribution analysis for the three energy systems considered, which are disaggregated in different subprocesses. The sub-processes composing the reference systems are: existing management of AWRs (“AWRs ex. manag.”, which include the phase of comminuting the pruning residues and leaving them on the orchard, sawing the cut trees and transport them to houses), “CTs house stoves” (which implies the cut trees (CTs) burning in typical local house stoves and the disposal of ashes to a landfill), “heat, natural gas” burning (in order to make the reference system producing the same amount of heat than the bioenergy system), and electricity from coal (“El. coal”) or from natural gas (“El. natural gas”). The bioenergy system is divided into: alternative management of AWRs (“AWRs alt. manag.”, which includes the phases of chipping and harvesting pruning residues, sawing, chipping and harvesting cut trees and the transport of the biomass to the CHP plant), “additional fertilization” and “CHP”. The latter includes the CHP operating emissions, the CHP buildings, as well as the bioashes disposal. Landfill rootstocks disposal phase and the impacts from CO2 emissions from biomass combustion are included in the three systems. The x-axis (Fig. 3) represents the fraction of environmental impacts normalized by the system that is contributing the most to a given impact category. We find that a large fraction of the impacts from the R_c system is caused by the electricity production from coal, whereas the natural gas and house stoves burning processes are the main contributors for the impacts of the R_ng system. The comparative analysis shows the general better performances of the bioenergy system over the reference systems, but some important trade-offs exist. Although the AWRs-based system has lower GHG emissions and lower scores in several impact categories, it shows higher figures for TE. These two impacts increase approximately by 84% respect the R_c and by about 82% compared to the R_ng system (Table 6). TE in the AWRs_CHP is mainly caused by emissions generated from disposal of bioashes. Since the gasification implies the use of both pruning residues and cut trees, there is a higher production of bioashes than in the reference systems, where only cut trees are burnt. This makes a remarkable difference in the contribution of this sub-process among the systems. When compared to the reference system with the electricity production from natural gas, the bioenergy system has higher potential impacts in the TA, FWE,d FWEu and HTP categories.

It is interesting to note how the direct emissions from the house stoves influence negatively the PMF. The domestic stoves emit higher NOx, NMVOC and PM if compared to the CHP plant. Contrarily to what happens for CHP plants, domestic appliances are not provided with efficient flue gas treatments which can abate these compounds (Caserini et al., 2010; Provincia Autonoma di Bolzano, 2009). This result is in agreement with the findings of Caserini et al. (2010) where a comparative LCA on domestic devices versus centralized biomass combustion technologies is performed. They found that a shift of domestic consumption towards centralized district heating plants, as well as the substitution of old domestic devices with high-efficiency and lowemitting ones, can have relevant gains in terms of GHG emissions and a substantial decrease in toxic emissions, such as PM, N2O, NOx and VOC. Sometimes, when comparing different systems, it could be interesting to perform the analysis in a more aggregated level, thus translating the different environmental impacts of different midpoint categories into a single unit (Nguyen et al., 2013). When performing the analysis at endpoint level following the ReCiPe Hierarchist method, single score, the bioenergy system shows lower figures in all the three endpoint categories (Human health, Ecosystem and Resources) than the two reference systems. For all the endpoint categories, the systems could be ordered as follows: R_c > R_ng > AWRs_CHP. 4. Conclusions The LCA depicted in this study shows that the exploitation of AWRs in a gasification-CHP plant in the alpine Province of Bolzano is an effective option for reducing environmental concerns related to fossil resources. Switching the energy production from fossil fuels to AWRs ensures a considerable reduction of GHG emissions (up to about 58 kt CO2eq per year) and non-renewable energy consumption (up to 16.5 kt oileq per year). This study also shows that gasifying AWRs in CHP plants instead of burning them in lowefficient and air-polluting house-stoves leads to a decrease in particulate matter formation. However, we find that some trade-offs exist. For example, the AWRs bioenergy chain has higher impacts for toxicity potentials than the reference systems. Disposal of post-combustion ashes is the process with the highest contribution to the toxicity impacts. Possible alternative uses of the ashes are therefore attractive to mitigate these concerns. The application of ashes as a soil quality improver is an alternative that can lead to a decreasing use of fertilizers, although the feasibility of this option can be hampered by the high metal contents of the ashes. Not included in this analysis, sustainability implies also economic and social issues. These aspects should be considered in conjunction with the environmental profile of this study to provide policy makers with the required level of information about the sustainability of the AWRs-bioenergy chain in rural mountain areas. Acknowledgements Authors would like to sincerely acknowledge Markus Kelderer and Claudio Casera of the Laimburg Research Centre for the extraordinary support in data retrieval on apple orchard management. M.B., S.Z. would like to thank the Autonomous Province of Bolzano for financial support. F.C. acknowledges the support of the Norwegian Research Council through the CenBio project.

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M. Boschiero et al. / Journal of Cleaner Production xxx (2015) 1e12

Appendixes

Table A1 Data and processes used to model the BIGCC plant. Process

Value

Unit

BIGCC plant Inputs from technosphere BIGCC, wood, building

1

p

0.05

p

BIGCC, wood, common components for heat þ electricity

0.05

p

BIGCC, wood, components for electricity only

0.05

p

Lubricating oil, at plant/RER U Ammonia, liquid, at regional storehouse/CH U Chemicals organic, at plant/GLO U Chlorine, liquid, production mix, at plant/RER U Sodium chloride, powder, at plant/RER U Water, decarbonized, at plant/RER U Urea, as N, at regional storehouse/RER U Dolomite, at plant/RER U Oxygen, liquid, at plant/RER U M_Steam, for chemical processes, at plant/RER U Electricity, low voltage, at grid/IT U Emissions to air Nitrogen oxides Particulates, unspecified

3.35 7.86 5.5 315 9.93 755 25.7 39.1 1270 744 268

t kg t kg t t t t t t MWh

24.4 471

t kg

43.8 13.8 26.2 12.9 3.43 58.8 873 29.7 0.959 0.48 873 57.5 56.1 0.671 173 3.8 0.038 21.1 29.7 28.8 48 125 6.9 0.288 22.4 346 164 1.25 16.3 5.75 288 10.5 23.9 0.007 288 15 288

kg kg t t t kg kg t kg kg kg kg t kg kg kg kg kg mg kg kg kg mg kg t kg kg t t kg kg kg kg kg kg kg kg

Carbon monoxide, fossil Methane, fossil NMVOC, non-methane volatile organic compounds, unspecified origin Sulphur dioxide Dinitrogen monoxide Acetaldehyde Hydrocarbons, aliphatic, alkanes, unspecified Hydrocarbons, aliphatic, unsaturated Arsenic Benzo(a)pyrene Benzene Bromine Calcium Cadmium Chlorine Chromium Chromium VI Copper Dioxin, 2,3,7,8 tetrachlorodibenzo-pBenzene, ethylFluorine Formaldehyde Benzene, hexachloroMercury Potassium Magnesium Manganese Sodium Ammonia Nickel Phosphorus PAH, polycyclic aromatic hydrocarbons Lead Phenol, pentachloroToluene m-Xylene Zinc Waste to treatment Disposal, used mineral oil, 10% water, to hazardous waste incineration/CH U Disposal, municipal solid waste, 22.9% water, to municipal incineration/CH U Treatment, sewage, to wastewater treatment, class 2/CH U Disposal, wood ash mixture, pure, 0% water, to sanitary landfill/CH U

3.15 3.15 755 1070

t t m3 t

Disposal, wood untreated, 20% water, to sanitary landfill/CH U

4370

t

Reference

Modified from Ecoinvent: Cogen unit 6400 kWth, wood burning, building/CH/I U Modified from Ecoinvent: Cogen unit 6400 kWth, wood burning, common components for heat þ electricity/CH/I U Modified from Ecoinvent: Cogen unit 6400 kWth, wood burning, components for electricity only/CH/I U Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Worley and Yale, 2012 Worley and Yale, 2012 Worley and Yale, 2012 Worley and Yale, 2012 Mann and Spath, 1997 Mann and Spath, 1997, corrected by a factor of 2.5, from Prando et al., 2014a Mann and Spath, 1997 Mann and Spath, 1997 Mann and Spath, 1997 Mann and Spath, 1997 Guest et al., 2011 Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Worley and Yale, 2012, adjusted by the factor of 3, from Prando et al., 2014a Own measurements

Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094

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M. Boschiero et al. / Journal of Cleaner Production xxx (2015) 1e12

Table A2 Data and processes used to model the process called “BIGCC, wood, building”. Process

Value

BIGCC, wood, building Inputs from technosphere Alkyd paint, white, 60% in solvent, at plant/RER U Concrete, normal, at plant/CH U Excavation, hydraulic digger/RER U Glued laminated timber, indoor use, at plant/RER U Gravel, crushed, at mine/CH U Reinforcing steel, at plant/RER U Rock wool, at plant/CH U Sawn timber, softwood, raw, air dried, u ¼ 20%, at plant/RER U Steel, low-alloyed, at plant/RER U Transport, lorry 20e28 t, fleet average/CH U Transport, freight, rail/CH U Waste to treatment Disposal, building, mineral wool, to sorting plant/CH U Disposal, building, reinforced concrete, to sorting plant/CH U Disposal, building, paint on wood, to final disposal/CH U Disposal, building, waste wood, untreated, to final disposal/CH U Disposal, building, concrete gravel, to sorting plant/CH U

Unit

1

Reference

p

1.39 473 16,400 37.2 429 64.4 26.6 307 424.5 79,300 53,600 26.6 3330 1.39 27 429

t m3 m3 m3 t t kg m3 t tkm tkm

Calculated from Ecoinvent Mann and Spath, 1997 Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent Mann and Spath, 1997 Calculated from Ecoinvent Calculated from Ecoinvent

kg t t t t

Calculated Calculated Calculated Calculated Calculated

from from from from from

Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent

from from from from from from from from from from from

Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent Ecoinvent

Table A3 Data and processes used to model the process called “BIGCC, wood, common components for heat þ electricity”. Process

Value

BIGCC, wood, common components for heat þ electricity Inputs from technosphere Electricity, low voltage, at grid/CH U Heat, light fuel oil, at boiler 100 kW condensing, non-modulating/CH U Aluminium, primary, at plant/RER U Chromium steel 18/8, at plant/RER U Concrete, normal, at plant/CH U Copper, at regional storage/RER U Polyethylene, HDPE, granulate, at plant/RER U Rock wool, at plant/CH U Steel, low-alloyed, at plant/RER U Transport, lorry 20e28 t, fleet average/CH U Transport, freight, rail/CH U Waste to treatment Disposal, polyethylene, 0.4% water, to municipal incineration/CH U Disposal, mineral wool, 0% water, to inert material landfill/CH U Disposal, concrete, 5% water, to inert material landfill/CH U

Unit

1

Reference

p

1.63 631 654 940 31.7 654 695 184 30.7 14,500 40,300 695 184 69.7

MWh MJ kg kg m3 kg kg kg t tkm tkm

Calculated Calculated Calculated Calculated Calculated Calculated Calculated Calculated Calculated Calculated Calculated

kg kg t

Calculated from Ecoinvent Calculated from Ecoinvent Calculated from Ecoinvent

Table A4 Data and processes used to model the process called “BIGCC, wood, common components for electricity only”. Process BIGC, wood, components for electricity only Inputs from technosphere Generator 200 kWe/RER/I U Control cabinet cogen unit 160 kWe/RER/I U Transport, lorry 20e28 t, fleet average/CH U Transport, freight, rail/CH U

Value 1 2.04 6.13 204 409

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Figure

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Please cite this article in press as: Boschiero, M., et al., Life cycle assessment of bioenergy production from orchards woody residues in Northern Italy, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.09.094