Life Cycle Assessment of Wood Chips from Residual Biomass: A Case Study

The LCA consists of four steps, briefly described in the following [6, 7]:

- Goal and scope definition, that includes a description of the intended use of the study and of the product system in terms of system boundaries, functional unit, allocation procedures, impact categories selected and methodology of impact assessment, etc.;

- Life Cycle Inventory (LCI) analysis, that involves data collection and calculation procedures to quantify the resources consumption, the air, water and soil emissions, and the waste production;

- Life Cycle Impact Assessment (LCIA), that is aimed at evaluating the significance of potential environmental impacts using the LCI result;

- Life cycle interpretation, which is the final step of the LCA procedure, in which the results of a LCI and/or a LCIA are summarized and discussed as a basis for conclusions, recommendations and decision-making in accordance with the goal and scope definition.

2.1 Goal and scope of study

The study analyzes the process chain of residual biomass of forest origin, to be used for energy valorization. It aims to analyze, according to the life-cycle approach, the energy and environmental impacts related to the production of wood chips from the residual biomass.

Wood chips are obtained from a treatment process of forest residues, called chipping, in which the residues are reduced to flakes generating a homogeneous material with a higher density than the residue as is, allowing for easier transport, handling and increased payload of the transport vehicle.

The function of the product system analyzed is the production of wood chips, so the selected functional unit (quantified performance of a product system for use as a reference unit) is 1 kg of wood chips. The system boundaries (unit processes that are part of a product system) are chosen with a "from cradle to gate" approach. They include the stages of residual biomass harvesting (indirect and direct drag), biomass chipping, collection and transport of the wood chips to the energy valorization plant.

The life cycle of all inputs used in the different stages of the production chain are included in the analysis (e.g., diesel used by the tractor for harvesting). The life cycle of the machinery used in the various process units is not included in the study because, considering a useful life of the machinery of more than 10 years, its contribution to the total impacts of the functional unit can be considered negligible. In addition, since residual biomass can be considered as a waste, the “zero burden” principle is applied.

The impact categories selected for the calculation of the energy and environmental performance are shown in the Table 1.

Table 1. The impact categories selected

Primary energy consumption is estimated by applying the Cumulative Energy Demand (CED) method, which allows for estimating the consumption of direct and indirect primary energy sources (both renewable and non-renewable) of a product system during the entire life cycle [8]. The environmental impacts are calculated by using the ILCD 2011 Midpoint method [9, 10].

Secondary data, that are average data representing the life cycle of each input, are modeled by using datasets found in the ecoinvent database [11]. Ecoinvent is a database used to assess the life cycle environmental impacts of products and services. It is an archive covering a wide range of sectors globally and regionally [12]. In detail, data related to the Italian context were used.

2.2 Life Cycle Inventory analysis

The LCI analysis involves the data collection and processing for each unit process of the wood chips supply chain.

Figure 1 shows the qualitative flow chart of the examined unit processes.

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Figure 1. Flow chart

2.2.1 Biomass collection

Harvested biomass belongs to the residual type of forest origin, consisting of necromass, that is the dead and decomposing portion of a tree. The biomasses that belong to the residual type of forest origin are (Figure 2):

- R1. Hard or brittle branches with diameters greater than 5 mm;

- R2. Flexible branches with diameters less than 5 mm;

- R3. Needles, i.e., the green leaves of the branch;

- R4. Pine cones of various sizes.

Biomass samples were analyzed in order to identify their elemental composition, moisture content and heating value (Table 2).

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Figure 2. Types of biomass used

Table 2. Characteristics of biomass used

Residual biomass of forest origin is collected in the forest using a tractor and a winch (a machine used in forestry sites to move plant masses placed in positions that cannot be reached by self-propelled vehicles) (Figure 3).

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Figure 3. Forestry tractor with a winch

The methods of collection and drag are:

- Indirect drag: the tractor remains stationary and the wood collection is carried out by means of the winch;

- Direct drag: an activity in which the load (biomass) moves together with the towing vehicle (tractor and winch).

With reference to the indirect drag of the residual biomass (use of the tractor equipped with a winch), a productivity of 1.35 t/h was calculated for the tractor equipped with a forestry winch [13], while and an average consumption of 4.6 l/h of diesel as fuel was estimated [14] (Table 3).

In order to examine the movement of the biomass to the storage site (direct drag), an average drag distance of 1 km was assumed [15].

Table 3. Data on drag with tractor and winch

With reference to the secondary data, the life cycle of the diesel used in the process (lower calorific value of 44.4 MJ/kg), including the emissions generated by the harvesting process with tractor and winch, was modeled by using the dataset “Diesel, burned in building machine processing”. The direct drag of the residual biomass from the collection site to the temporary storage was modeled using the dataset “Transport, tractor and trailer, agricultural processing” [11].

2.2.2 Chipping

The chipping process is carried out with a semi-industrial chipper, which has an average fuel consumption (diesel) of 3.632 l/t [16]. The diesel supply chain and use was modeled by using the dataset “Diesel, burned in building machine” [11].

The final product (wood chips) goes directly to the means of transport that will be used for the transport to the energy plant.

Figure 4 shows the self-propelled chipper and tractor with trailer.

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Figure 4. Self-propelled chipper (in front) and tractor with trailer (behind)

2.2.3 Transport to the plant

The transport of the wood chips to the plant can take place with a tractor equipped with a trailer when the distances are less than 10 km, or with a truck when the distances are greater. The efficiency of the transport process depends on three key variables [17]: the distance travelled, the form in which the biomass is transported (logs, branches, fresh wood chips) and the means of transport used.

To model the transport phase of chips to the biomass energy valorization plant, different scenario were analyzed considering different means of transport and supply distances, as shown in Table 4.

Table 4. Transport scenarios from the temporary storage to the energy plant

The impacts generated by the transport process with tractor were modeled using the dataset “Transport, tractors and trailers, agricultural”. To evaluate the transport by truck, the dataset “Transport, freight, lorry 7.5-16 metric ton, EURO 4” was considered [11].

2.3 Impact analysis and interpretation of results

In the impact analysis, a base case was defined as a reference for conducting the sensitivity analyses. In the base scenario, the transport phase takes place with a tractor and trailer over a distance of 10 km. The results concern the total impacts associated with the supply chain (from the collection of biomass to the transport of wood chips to the energy plant) (Table 5).

Table 5. The total impacts of the selected functional unit

A dominance analysis (Figure 5) identified that the direct drag step affects the examined energy and environmental impacts with percentages ranging from about 0.5% to 6.81%.

The steps that contribute most to the impact categories are the indirect drag and the chipping, with percentages ranging from about 33% to 49% each, depending on the impact category. Some exceptions occur for the following impact categories, for which the transport step is the main responsible: human toxicity (cancer and non-cancer effects) (about 38.5% and 83%, respectively), freshwater eutrophication (about 50%), renewable energy consumption (about 68%), mineral, fossil & ren resource depletion (about 52.5%).

The sensitivity analysis was conducted only with reference to the primary energy consumption, total (PE_t), renewable (PE_r), non-renewable (PE_nr) and climate change (CC).

In detail, the following scenarios were analyzed:

• Base scenario: the transport phase takes place with a tractor and trailer over a distance of 10 km;

• Scenario 2: the variation with respect to the base scenario consists in the use of a truck during the transport phase over a distance of 50 km;

• Scenario 3: the variation from the base scenario consists in the use of a truck during the transport phase over a distance of 100 km.

The results relating to the sensitivity analysis are presented below (Table 6).

The impacts associated with the transport phase generally increase as the distance increases. In detail, scenario 2 and 3 have a greater impact on CC (about 3 and 6 times more, respectively) than the base case. Similar variations are observed for PE_t.

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Figure 5. Dominance analysis of the supply chain stages

Table 6. Results of the sensitivity analysis: Impacts associated to the transport stage

A dominance analysis relating to Scenario 2 (Figure 6) shows that the contribution of the transport step to CC is approximately 30%, while in the base scenario it is about 13%.

Similarly, the transport step contributes to PE_t with a percentage equal to about 33%, with respect to the base case in which it is about 13%.

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Figure 6. Dominance analysis: Impacts associated with the transport phase in the Scenario 2

A dominance analysis relating to Scenario 3 (Figure 7) shows that the contribution of transport on CC and PE_t is approximately 47% and 49%, respectively.

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Figure 7. Dominance analysis: Impacts associated with the transport phase in the Scenario 3

This work is developed within the research activities of the project BIOmasses Circular Holistic Economy Approach to Energy equipments (BIO-CHEAPER) funded within the Italian call PRIN 2017 Research Projects of National Relevant Interest Prot. 20175TXJER.