This study presents two case studies of 100 GWh of forest biomass supply: Rovaniemi in northern Finland and Mikkeli in south-eastern Finland. The study evaluates the effects of local biomass availability and road network properties on the greenhouse gas (GHG) emissions of these two supply chains. The local forest biomass availability around the case study locations, truck transportation distances, and road network properties were analyzed by GIS methods to produce accurate and site-dependent data for the transportation emission calculations. The GHG emissions were then assessed by LCA methods. The total transportation distance to Rovaniemi was 22% larger than to Mikkeli, but the transportation derived GHG emissions were 31% larger. The results highlight the fact that local conditions should always be taken into account when assessing the sustainability of biomass-based energy production.
The sustainability of biomass production for energy purposes is dependent on many factors. These include, for example, land use change and its further consequences, possible changes in the carbon stocks of the soil, changes in the biomass production capacity of the area in question, energy consumption of the biomass supply chain, and the efficiency of converting the biomass to the desired energy carriers or heat or electricity. One thing that significantly affects the energy consumption and emissions of the biomass supply chain is the local availability of suitable biomass. It is common sense that production facilities that utilize a certain bulk raw material, such as biomass, should be located near or in the middle of the raw material sources to avoid unnecessary transportation of unrefined and cheap material. The practical availability and supply chain emissions however depend also very much on the local transportation possibilities of raw material in question.
In case of forest-based biomass, such as chipped forest residues or small diameter energy wood, the first step of the transportation chain is almost always truck transportation. Normally, when transportation distances are under 100 km or so, the only method is truck transportation, as the costs of unloading and reloading the cargo would be higher than the possible savings achieved through transportation by train or ship [
The European Union recommends its member states to implement sustainability criteria on biomass production for energy purposes in their national legislation. The national criteria should require a 35% reduction of greenhouse gases emitted from biomass-based energy production when compared to EU’s fossil energy mix. The GHG saving requirements for biomass utilizing new installations should eventually rise up to 60% in 2018 [
The Renewable Energy Directive also states that the emissions from transport and distribution will include emissions from the transport and storage of raw and semi-finished materials and from the storage and distribution of finished materials. It does not however give any instructions or more strictly defined guidelines on how these transportation emissions should be calculated. Often in studies concerning biomass energy use, the transportation distances and transportation-derived emissions are based on estimated average distances and collection areas [
When the same type of biomass from similarly managed sources is used and the biomass is similarly collected and processed to be utilised as energy, the largest differences in supply chain emissions come from the transportation activities. Transportation emissions rely on several factors, including, for example, choice of transportation method (road, railway and waterway, and their combinations), type of transportation equipment that is used (e.g., specific large volume trucks for forest fuels may be used), emission characteristics of the equipment that is used, load weight restrictions that may lead to inefficient use of cargo space, used motor fuels or origin of electricity, weather conditions and properties of the transportation network.
The purpose of this study was to assess this difference between two cases in Finland that utilise the same amount of the same raw material, 100 GWh/a of small diameter energy wood. Even though forest-based biomass from adequately managed forests in Finland should easily fulfill today’s sustainability criteria [
The assessment of actual local conditions and supply chain emissions may become increasingly important in the future, if the sustainability requirements for biomass become stricter. Also, the same raw material, for example, forest residue or small diameter energy wood chips produced in Finland, can be utilised locally or transported longer distances by truck, train, or waterway transportation, resulting in different supply chain emissions. Thus, an emission figure representing national or regional averages, for example, forest chips made in EU as in [
The quality and accuracy of a life cycle assessment is dependent on the input data. By utilising location- and case-dependent biomass availability and transportation emission data, the accuracy of sustainability assessments will be significantly improved. The same method of using geographical information systems (GIS) based local raw material availability assessment and transportation network analysis as basis for emission calculations or LCA studies can also be used in other applications besides biomass supply studies. Basically any supply chain where the transported material is spread around a larger geographical area could be assessed using the same methods, for example, waste collection activities.
The assessment of emissions deriving from biomass transportation was based on a three-step procedure: biomass availability assessment, analysis of transportation network properties, and emission calculations.
First, the availability of biomass needed to be accurately assessed, taking into account the scale of biomass supply. For smaller supply areas, such as in the two cases of this study where driving distances are under 100 km, the local availability needs to be taken into account on a detailed level in order to obtain site-specific results and reveal the possible differences due to location. For very large supply areas covering hundreds of kilometers, the local differences in biomass availability tend to level out, and the accuracy of biomass availability data in terms of exact geographical location does not have to be as accurate as when studying local scale supply chains.
In this study the geographical reference area, Finland, was divided into a geographical 2 km × 2 km grid. The midpoints of each square of the grid were then given a figure which represents the share of productive forest land within each square with a total area of 4 km², see Figure
Biomass supply point grid system.
For each 4 km² square the share of biomass producing forest area was calculated by means of raster analysis with GRASS GIS software. The forest area that was taken into account in this study was forest land in the growth categories of forestry land in [
The availability estimations of biomass for each point in the grid were based on the national forest inventory data produced by Finnish Forest Research Institute in 2008 [
The heating value that was used in the calculations was
Secondly, after the biomass availability was assessed and the geographical locations of supply points and their biomass resources were in place, the information had to be linked to the transportation methods and transportation network that will be utilized. This requires accurate and site-specific information on transportation networks. In the two cases of this study, only road transportation by trucks was analyzed. Truck transportation is the dominating method in regional scale supply of biomass in Finland. In larger scale supply chains when the supply area is significantly larger also train and waterway transportation can be used where suitable conditions exist. The aim of this study was however only to evaluate the effects of differences in local availability of biomass and road network properties in the total transportation-derived emissions, and not to optimize or analyze different combinations of transportation methods.
In the two cases of this study, when the amount of biomass is only 100 GWh/a, the transportation would also in practice be done solely by truck. The supply points, that is, the midpoints of the 2 km × 2 km grid were linked directly to the nearest road. The longest road transportation distances to single supply points needed to supply the 100 GWh of biomass to the demand points were 45 km in Mikkeli and 54 km in Rovaniemi, which represented the maximum driving radius’ along the road network around the demand points. The supply points which were located inside the 45 km and 54 km driving radius’ in Mikkeli and Rovaniemi, respectively, but more than 1 km from the nearest road, were excluded from the calculations due to the fact that they probably would not be economically viable for energy use due to increasing forest transportation, that is, forwarding costs. This selection criteria ruled out 1% of the supply points in Mikkeli (6 out 968 supply points) and 9% (114 out of 1247 supply points) in Rovaniemi.
The distances traveled on each road type to transport the desired 100 GWh of biomass to the demand points were calculated with ArcGIS software. The road properties were acquired from the national road and street database of Finland, Digiroad [
In real forest biomass transportations, the truck routes would be planned so that a truck would pick up the remainings of one roadside storage and then move on to the next one to achieve a full truckload whenever suitable roadside storages would be situated near each other. This is however not always possible or economical, and in some cases, the trucks have to drive with only a partial load to the plant. In this study the driving distances and emissions were adjusted by a factor so that, for example, for a 50% full truck load only 50% of the distance and emissions were accounted for. This was done because the truck routes were calculated directly from the supply points to the plants without taking into account the possibility to visit multiple storages during the same trip. For example, if a supply point had enough biomass to fill 1.5 truckloads, the distances and emissions were multiplied by a factor of 1.5. This results in the calculated total driving distances and transportation-related emissions to be slightly less than what they would be in real life, because the partially full truckloads are assumed to be driven only partial distances. This is however compensated by the fact that the driving routes from each supply point are optimized to be as short as possible. Also, whenever multiple stops at supply points are arranged to achieve full truckloads, the driving distances become longer.
The roads that were used were categorized into three different road types from the Digiroad road classification: small roads (forest roads and roads within a city, average speed 27 km/h), regional roads (paved roads, average speed 70 km/h), and motorway (large paved roads, average speed 82 km/h). The road classification from the Digiroad classes into these three categories was done according to the maximum allowed speed for each road segment. Roads with speed limits of 50 km/h or under were classified as small roads, over 50 km/h but under 80 km/h were classified as regional roads, and roads with a speed limit of 80 km/h or higher were classified as motorways. The classification into these three categories was done because the LCA dataset [
In this study, whenever a biomass supply point was needed to obtain the desired amount of biomass to the plant, all biomass from the same supply point was considered to be collected at once. This lead to the situation that the total amount of biomass collected was not exactly 100 GWh, but it was 99.0 GWh and 98.1 GWh for Mikkeli and Rovaniemi, respectively. To obtain an exact amount of 100 GWh of biomass for the transportation and emission calculations, these amounts were adjusted to be 100 GWh by a correcting factor. The correcting factor was obtained by calculating a km/MWh figure for all three road types for both case studies, and then multiplying that figure to obtain the kilometers that were driven on each road type to supply the 100 GWh of biomass to the plants.
The emissions from the transportation activities were calculated with GaBi professional LCA software. The truck type used for the calculations was a 34–40 ton total capacity, 27 ton payload Euro 4 emission level truck [
However, the focus of this study was to assess the possible differences in transportation-derived emissions that are due to road properties and local biomass availability around the plants, and not to calculate exact equipment-specific emission figures for the truck transportations.
The emissions calculation for diesel oil production and supply was based on an LCA dataset for diesel oil at refinery in EU-15 countries [
Forwarding the biomass to the roadside from within the forest was excluded from the calculations as well as the chipping of wood. Also, the building of roads and other infrastructure and manufacturing of the trucks and other transportation-related equipment were excluded from emission calculations. Only emissions of truck transportation due to fuel consumption and lubrication and emissions deriving from the production and supply of the diesel oil used in the trucks were included in the assessment. The emission calculations included empty returns of the trucks.
This work included two case studies with the same annual demand of the same type of forest-based biomass: 100 GWh of forest chips made from small diameter energy wood. The transportation endpoint locations were Rovaniemi, located in northern Finland just south of the arctic circle (latitude: 66° 30′ 0′′ N), and Mikkeli, located in south-eastern Finland (61° 41′ 0′′ N), see Figure
Case study locations and their supply areas.
There is a large woody biomass utilizing powerplant in Mikkeli, and a large biomass powerplant is under planning phase to be built in Rovaniemi, so both cases represented realistic scenarios of biomass supply.
Small diameter energy wood was chosen for this supply study, because it is the forest biomass fraction that has the largest potential for growing use in the future in Finland [
The transportation distances per road type needed to supply of 100 GWh of small diameter energy wood chips to the two case study locations are presented in Figure
Total transported distances to the two case study locations.
The total truck transportation distance was ca. 74 700 km to Mikkeli and ca. 90 800 km to Rovaniemi. The total distance was 22% larger in Rovaniemi case. The average driving distance from a supply point to the endpoint was 67 km in Mikkeli and 71 km in Rovaniemi.
The transportation-induced GHG emissions are presented in Figure
Transportation-derived GHG emissions.
The transportation of 100 GWh of wood chips made of small diameter energy wood produced 154 and 202 tons of CO2-eq. to Mikkeli and Rovaniemi, respectively. The transportation derived emissions were 31% larger for Rovaniemi, even though the total driving distance was only 22% larger. This resulted from the fact that the quality of roads and density of road network around Rovaniemi region is poorer than around Mikkeli. To transport 100 GWh of small diameter energy wood chips to Rovaniemi from the surrounding areas, a distance of 30 600 km needs to be driven on small roads, which are mainly gravel forest roads. In Mikkeli’s case, the distance covered on small roads is only 14 300 km.
This study assessed the emissions deriving from truck transportation of 100 GWh of wood chips made of small diameter energy wood from the surrounding forest areas to two different demand points in Finland. The other demand point was Rovaniemi in northern Finland near the polar circle (latitude: 66° 30′ 0′′ N), and the other was Mikkeli in south-eastern Finland (61° 41′ 0′′ N). The main purpose of this study was to evaluate and demonstrate the possible differences in GHG emissions of biomass supply chains that are caused by local conditions in biomass availability and road network.
The two case studies of this work were quite similar, both used the same raw material and same transportation methods; the supply areas were located around the plants, and the case locations were only ca. 550 km apart. The only differences were that in northern Finland (Rovaniemi case) the forests are not as dense, and the road network is poorer. The total driving distance to Rovaniemi was only 22% longer, but the GHG emissions from transportation were 31% larger. This was due to the fact that the share of driving on small roads, which consumes more fuel, was 34% in Rovaniemi’s case in comparison to 19% in Mikkeli’s case. This shows that whenever the sustainability of a biomass production chain is assessed, the local conditions should be taken into account as accurately as possible, including the properties of the transportation network that is used.
One can not make accurate comparisons of biomass supply chains’ emissions by relying on average transportation distances or biomass availability figures. The same kind of biomass-based end products (e.g., electricity, heat, or biodiesel) may have significantly different total GHG emissions deriving from the production chain because the production facilities are in different locations and the local conditions are different. A case study made by Metsäteho Oy [
The differences in supply chains’ emissions lead to the question that how should these differences in sustainability be taken into account in decision making concerning renewable energy production from biomass? Should the same end product be treated as less sustainable, if it is produced in a location where the local conditions are worse?
These location- and case-dependent sustainability issues are likely to become an increasingly discussed topic as international biomass trading grows, forest biomass procurement from further supply areas to, for example, central Europe increases, and competition for the same biomass sources grows.