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A spatial analysis of woodfuel based on WISDOM GIS methodology: Multiscale approach in Northern Spain
Applied Energy 144 (2015) 193–203
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A spatial analysis of woodfuel based on WISDOM GIS methodology: Multiscale approach in Northern Spain Sandra Sánchez-García a,⇑, Elena Canga a, Eduardo Tolosana b, Juan Majada a a b
CETEMAS, Forest and Wood Technology Research Centre, Sustainable Forest Management Area, Finca Experimental ‘‘La Mata’’ s/n, 33820 Grado, Asturias, Spain E.T.S.I. Montes, Technic University of Madrid, Ciudad Universitaria, s/n, 28040 Madrid, Spain
h i g h l i g h t s A multiscale GIS analysis of woodfuel supply/demand. Template for scale or scenario dependant integration of data into WISDOM methodology. Pixel level calculation of all data enables operations between rasterized maps. Shows benefits of analytical GIS tools in bioenergy system management and planning.
a r t i c l e
i n f o
Article history: Received 7 January 2014 Received in revised form 26 January 2015 Accepted 27 January 2015 Available online 27 February 2015 Keywords: Woodfuel Accessibility Multiscale GIS Logistics Wood-fired power plant
a b s t r a c t Given the complexity of generating energy from biomass, the need has arisen for support tools to assist in balancing energy and forestry policies which are sufficiently flexible to address the issues of planning and management at small and large scales. The present study aims to adapt WISDOM GIS methodology for application in the autonomous region of Asturias (Northern Spain) and thereby, by creating a geodatabase, to contribute to a support tool for investigating the potential of woodfuel. This will aid the public administration by providing information on woodfuel supply and demand at the regional, municipality and site-specific level, and thus assist in decision making in terms of formulating new energy strategies. In terms of supply (t year 1), in this work, woodfuel from forest area is defined as the crown fraction only (branches and leaves), although in the case of Eucalyptus spp., bark is also included as it is remains on-site following extraction of eucalypts for the pulp industry. Non-Forest Direct Supply was calculated on the basis of the relevant categories from an agricultural land use inventory and average woodfuel productivity. In addition, physical and legal constraints related to accessibility of the woodfuel were considered, the former applying restriction filters with values weighted depending on the interaction between slope map, road networks and centres of population, and the latter considering legal limitations in protected areas. In addition, unused waste from the wood processing industry was included. To calculate total woodfuel demand (t year 1) for energy generation (heat and electricity), both the residential and industrial sector were taken into account. All data was georeferenced through Geographic Information Systems (GISs), which allow operations between raster maps to be performed to generate numeric and spatial results focusing on logistics and biomass strategies, depending on the inputs data and scale employed for each scenario considered. In addition, the application of the methodology at the site-specific level illustrates the practical implementation at the small scale of the geodatabase created, by evaluating the woodfuel available to feed, in case 1, a wood-fired power plant in a specific proposed location and, in case 2, this plant combined with a second plant in a different municipality, both cases also taking into consideration industrial demand. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction
⇑ Corresponding author. Tel.: +34 985754725. E-mail address: [email protected] (S. Sánchez-García). http://dx.doi.org/10.1016/j.apenergy.2015.01.099 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
Current key challenges in high population areas such as Europe are to meet an ever increasing energy demand and ensure both the security and sustainability of energy supply. Biomass, whether
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it is from agricultural or forest sources, plays an ever more important role in meeting these challenges and is key to achieving CO2 emission targets in relation to climate change. Recently, the Biomass Panel of the RHC-Platform established the implementation actions for 2014–2020 [1], according to the European Strategic Research Priorities for Biomass Technology published in April 2012 [2]. One goal is that by 2020, biomass supply should double compared to current levels ensuring that adequate feedstock is available at competitive prices. This promotion of biomass generation and use, not only addresses the issue of energy security, but has the added value of creating jobs in the sector and particularly in rural areas. Furthermore, Directive 2009/28/CE of the European Parliament and Council, related to promoting the use of energy from renewable sources, established that each Member State of the European Union (EU) was to develop an action plan for renewable energies for the period 2011–2020 in order to achieve the objectives set out in the Directive. The use of Geographic Information System (GIS) framework may be considered one of the best tools to enable and facilitate this policy challenge [3]. Developments using GIS technology have revolutionised the possibilities available for improving knowledge of existing bioenergy systems (supply and demand) through spatial analysis, and to quantify their potential availability and the practicalities of meeting demand taking into account the various constraints and limitations existing in an area that impact on the viability of exploitation. This information allows the identification of supply opportunities and new markets which can be opened and are compatible with forest, energy and environmental strategies [4]. Integration of data such as forest inventories with GIS data allows the description of the spatial distribution of these resources and can be combined with data relating to consumption to support energy planning policies. In order to be in a position to make decisions about how limited resources are used and managed within an existing area, it is preliminarily necessary to understand how effectively resource conservation is addressed at present in any given area [5]. What is more, GIS support tools enable multiscale approaches to be implemented which facilitate and verify decision making processes. For example, Thomas [6] analysed the spatial supply and demand relationships for biomass energy potential for England and Sacchelli et al. [7], working in the Italian Alps, created a spatial model to quantify the potential amount of woodfuel from the forestry sector at several scales. One of the many GIS tools developed in recent years is WISDOM, the result of a collaboration between FAO, and the Centre for Ecosystem Research of the National Autonomous University of Mexico (UNAM). WISDOM (Woodfuel Integrated Supply/Demand Overview Mapping) is based on certain key characteristics of wood energy systems: geographic specificity, heterogeneity of woodfuel supply sources and provides the user with great flexibility to adapt the methodology to specific scenarios and scales. A detailed description of the potential and specific application of the methodology has been published by Drigo et al. [8] and Masera et al. [9]. Many case studies have been published applying WISDOM at different spatial scales, such as for countries [10,11] or cities [12]. In Spain, the National Renewable Energies Action Plan [13], specifies that renewable energies (REs) should account for 20% of total energy consumption by 2020. In 2009, the base year, REs accounted for 9.4% of primary energy in Spain, 3.9% of this coming from biomass. In parallel with and to complement this, the Spanish Renewable Energies Plan, estimated the energy input from woody biomass for 2015 as 1582 ktep, and for 2020 as 2081 ktep. Furthermore, woodfuels coming indirectly from woody biomass (i.e. from the wood processing industry) were expected to reach 1679 ktep and 1702 ktep respectively in the same year [14]. In recent years, in Asturias, an autonomous region in Northern Spain, there has been an increase in biomass use to generate
energy, enhanced by subsidies for renewable energy use from the Regional Government, which was expected to subsidize in 2013 roughly 200 biomass and geothermal energy projects. During recent years, regional biomass R + D, studies of productivity and cost in biomass logging operations [15,16] have been conducted as well as estimations of biomass quantity for different forest species [17–19]. Recently, in addition, the Government of Asturias has commissioned CETEMAS to carry out WISDOM-Asturias. In this study, a qualitative and quantitative biomass supply and demand evaluation at region, municipality, and site specific levels was developed using the WISDOM GIS methodology and its results mapped through ArcMap GIS software [20]. The current study, in addition, provides results both before and after applying filters which take into account physical and legal accessibility constraints, similar to other authors [7,21]. In practice, however, the final objective is to adapt the WISDOM GIS methodology for a specific region, in this case in the north of Spain, to have all this information available as a support tool to investigate the potential of woodfuel at the regional and municipality level. Furthermore, we aim to show how this methodology can aid the decision making of the public administration to, for example, evaluate the suitability of proposed wood-fired power plants at a site-specific level, as in the work of Viana et al. [22] or to accurately ascertain productions costs so as to promote the use of energy woody biomass. This is important given that woody biomass production costs vary with factors such as the scale of demand, the location of the processing plants and, when considering multi-sources of biomass, rotation periods, production technologies and human accessibility to the biomass resources [23,24]. The work uses data currently available and due to the large quantity and the multitude of formats, as well as the difficulties of analysing it in a uniform manner, this study had to consider specific ways of calculating annual woodfuel at pixel level to adapt the methodology. This work will therefore serve as a template of how to integrate data into the methodology for future applications at different scales and scenarios in other areas. 2. Material and methods 2.1. Multiscale structure approach GISs are scale dependent, and it is necessary to consider the appropriate spatial scale required in each case. In this study data from Asturias – an autonomous region in the north of Spain – was analysed through WISDOM GIS methodology at the pixel level (50 50 m) and then scaled-up to make inferences about supply of and demand (oven dry tonnes) for woodfuel at various spatial levels: regional, municipality and site-specific. Such a small pixel size was used due to the reduced size of many forest plots in the region and enables flexibility at the site specific level, for example consideration of the spatial variation of stands from year to year based on silvicultural planning, or, as in the example provided here, evaluate the woodfuel available to feed future wood-fired power plants. The analysis was structured in three main modules according to the WISDOM GIS methodology [25] namely Supply, Demand and Integration, and followed the FAO (2004) [26] classification of by-product woodfuel as a biofuel source (Fig. 1). 2.2. Application of WISDOM GIS methodology at the regional and municipality level 2.2.1. Supply module To analyse and provide a spatial representation of the supply sources of available woodfuel, different aspects have to be taken
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Fig. 1. Classification of biofuel source used. Adapted from UBET (Unified Bioenergy Terminology).
into consideration, including the forestry, agriculture and wood processing industries. The analytical steps of Supply module are described below. 2.2.1.1. Direct by-products (Direct Supply). Annual Total Direct Supply (i.e. from both forest and non-forest areas) were calculated in dry basis, the former based on information from the 22 strata of the 3rd National Forest Inventory (NFI3) [27] and the latter from the relevant categories of the Spanish Agricultural Plots Identification System (SIGPAC). Biomass stock in the forest (oven dry tonnes per hectare, t ha 1) was calculated for the different tree fractions (stem, bark, branches, leaves and root) for all species identified in the NFI3, using tree biomass equations [28]. Then the Mean Annual Increment (MAI) of each fraction, in dray basis (t ha 1 year 1) was calculated (assuming a direct positive relationship with annual increment in volume (m3 ha 1 year 1)) and used to calculate productivity of woodfuel (t year 1) (both above – and below ground). The MAI relating to the crown (branches and leaves) was used as the proxy of annual available woodfuel (t ha 1 year 1) from forest area, since currently this is the only tree fraction destined for energy use. The one exception was in relation to Eucalyptus spp., where bark was also included in the annual available woodfuel (taken as 12% of stem biomass) since this by-product remains in the forest when the stems are removed for use in the pulp industry – by far the most common use of eucalypts in the region. To map Direct Supply from forest area (t year 1), this information was integrated with that of the Spanish Forest Map (MFE), which classifies land using the same 22 strata as the NFI3, and the results rasterized. Non-Forest Direct Supply (oven dry tonnes per year, t year 1) was calculated on the basis of the relevant categories from the Spanish Agricultural Plots Identification System (SIGPAC) and average woodfuel productivity (t ha 1 year 1) for each category was calculated based on results from previous works [29–31]. This information was then integrated with the Spanish Forest Map and rasterized (t year 1). A Total Direct Supply map was then created by summing the rasters of both maps (forest and non-forest areas), and considered as Level 1 accessibility. Next, different physical and legal access filters were considered. To develop the physical restriction filters, the Cost Weighted tool (Spatial Analyst toolbar/Distance, in ArcGis 9.2 software) [20] was used, which produces an output raster whereby each pixel is assigned a weighted value representing its ease and practicality of access. Applying this filter to the Level 1 map resulted in the Level 2 map (physical access constraints). The shape input (Distance to) was a map using roads and population centres as reference
elements, and the raster input (Cost raster), a slope map composed of a Digital Elevation Model (DEM) with a resolution of 50 50 m obtained from the Asturias Regional Cartography Services. Zones with a steepness of over 60% being assigned a value of 100% physical restriction since at this degree of slope the forestry harvesting system employed in the region (ground-based) is unable to function adequately, although in other future applications this condition could be modified depending on type of harvesting system, the degree of mechanization and/or machines to be used. For the legal restrictions filter, designation as a protected area such as National Park, Natural Park, Site of Community Importance (SCI), Special Protection Area (SPA), and restrictions related to logging operations and commercial enterprise in an area were taken into account, i.e. a weighting depending on the percentage of each pixel subject to legal restriction (i.e. no restriction in any part of the pixel = 0%, all the pixel restricted = 100%). These filters were then applied to the Level 2 map to provide the Level 3 map (physical and legal access constraints). 2.2.1.2. Indirect by-products from processing industry (Indirect Supply). Calculation of Indirect Supply was based on a study of waste from 57% of the companies involved in the wood processing industry in Asturias [32]. The study classified waste material (species, and type of waste) and the end for which the material was currently destined (biomass, recycling, landfill, sale or given away to the public). The Lower Heat Value (LHV) for the material in the latter three categories, which could in fact be used as biomass, was then calculated. Since the moisture content was assumed to vary (from 20% to 50%) depending on species and type of waste, all data were converted to oven dry tonnes and the location of each company mapped and rasterized along with its woodfuel values (t year 1). 2.2.1.3. Total Supply. Finally, the Total Supply (oven dry tonnes per year, t year 1) at pixel level map was created by summing the rasters of the Total Direct and Indirect Supply maps, considering two different scenarios: Total Supply ‘‘Global’’, where all types of Supply were considered and Total Supply ‘‘Commercial’’, where Direct Supply from non-forest area was excluded due to the current low financial returns on the removal of these type of woodfuel. Fig. 2 shows the information required for this module and the final results of each step to calculate Total Supply, with the different levels of accessibility. 2.2.2. Demand module To calculate demand for woodfuel, energy generation (heat and electricity) in both the residential and industrial sector was considered in oven dry tonnes.
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Fig. 2. Flowchart of main analytical steps of Supply module.
Regarding domestic woodfuel consumption (t year 1), in the residential sector, socio-demographic variables from the 2001 Population Census (INE 2001) were used: i.e. location, whether rural or urban, and size (m2) of each home using wood for heating (the data did not distinguish between categories), as well as the average wood heating requirements of Asturias (kW h m 2 year 1), the Average Lower Heating Value LHV (kW h t 1) and the boiler energy coefficient (%). Woodfuel consumption in the residential sector in terms of new heating (i.e. systems installed after the 2001 census and until 2010 for which subsidies were given) was also included in the calculation, using the location of each new installation and its consumption (t year 1). Similarly, woodfuel consumption (t year 1) in the industrial sector was calculated, using the values from the 8 most important enterprises using woodfuel for energy. In both cases figures were obtained from the Asturian Energy Foundation (FAEN). The results were mapped and a Total Demand (oven dry tonnes per year, t year 1) at pixel level map created as the sum of the rasters of Residential and Industrial Demand. Fig. 3 shows the information required for this module and the final results of each step to calculate Total Demand.
Fig. 3. Flowchart of main analytical steps of Demand module.
2.2.3. Integration module The integration is done through the combination of the variables related to woodfuel consumption and supply that have been systematized for each minimum administrative unit of analysis [10]. i.e. The Integration module is the balance left after subtracting the total consumption of woodfuel for a specific area from the potential sustainable woodfuel available for energy. The main scope of this module was to analyse the relevant interactions between supply and demand of woodfuel in different scenarios depending on the type of inputs data employed. The integration module was achieved considering two scenarios; first, the combination of the variables related to Total Supply and Total Demand, which were systematized for each pixel, resulted in a final amount which was named ‘‘Global Balance’’. And secondly, a scenario where woodfuel from the non-forest area (which is currently unprofitable to collect) was excluded from the supply calculation to provide a calculation of woodfuel with real current commercial viability. The balance between this adapted supply and Total Demand was named ‘‘Commercial Balance’’. 2.3. Application of WISDOM GIS methodology at the site-specific level With reference to the long-term feasibility of the sustainable utilization of woodfuel, further studies and discussions need to be carried out. There are many applications and different scenarios for which to use the database described in this work. This study considers the evaluation of the availability of woodfuel supply to an area within which the demand of a wood-fired plant would be satisfied taking into account the stability of woodfuel supply. Specifically, an analysis of the biomass available, at Level 3 access, for the various types of forest biomass (chestnut, pine, eucalypts and other species) for a proposed new wood-fired power plant was conducted. The proposed facility would have an installed power of 20 MW, an annual consumption of approximately 91200 t year 1 (dry basis) and would be located at a specified point in the municipality of Villaviciosa. In order to examine the integration of this plant at a local level, firstly, only the demand and supply within a 50 km radius of the location of the power plant was considered. To calculate the balance (for each species) between supply and demand, Direct Supply was calculated based on figures for clear cutting permits approved
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in the year 2008 (Forestry Planning and Management Service, Department of the Environment and Fisheries, Principality of Asturias). Furthermore, with specific respect to eucalypts this balance was calculated taking into account the fact that one of the pulp mills of the largest pulp company in Spain (ENCE) is located in Asturias and consumes a large quantity of Eucalyptus spp. woodfuel, and that the company sometimes brings in supplies from neighbouring regions. For this reason, two different demand radii were considered in relation to the ENCE paper mill, one of 75 km and a second of 100 km, which were superimposed on the supply radius of the proposed power plant in order to identify the areas of overlap, and these were then discounted in the calculations of available woodfuel at the local level. Secondly, the data were examined under the condition of a second proposed wood-fired power plant being located at a specified point in the municipality of Salas (100 km by road from the first plant), with the same consumption as that in Villaviciosa, and the influence of this new demand on the balance at local level was evaluated. 3. Results and discussion From this analysis of supply and demand, various results, considering oven dry tonnes, were generated, a selection of which are discussed below. GISs are scale dependent, and it is necessary to consider the appropriate spatial scale required in each case. For this study different scales were considered: regional, municipality and the level of a specifically defined small area. At this latter scale, with ownership categories such as private, forest industries or state, it is possible to take into consideration the spatial variation of stand locations from year to year based on silvicultural planning. The pixel size used in this work (0.25 ha), enables considerable flexibility in terms of the translation of the data to different scales. Asturias has considerable untapped resources in terms of woodfuel: there are 49,600,575 oven dry tonnes of above-ground biomass stock, and 72,606,863 t if the root fraction is also considered. And to date little advantage of this is being taken. This work provides valuable information, both in terms of the site-specific application and the value of the methodology for future planning and decision making. The analysis reveals interesting possibilities for the development of the biomass energy sector in the region. Case studies like this [9,33,34], could be promoted by local governments in order to evaluate all possible synergies realizable in the sector. Moreover, in this study a detailed accessibility filter was applied, and the results are compiled of different values depending on the type of restriction considered. 3.1. Results at the regional and municipality level 3.1.1. Supply module The potential productivity of woodfuel in forest area (i.e. branches and leaves, and for Eucalyptus spp., bark) considering Level 1 accessibility, is 895,847 t year 1, a considerable amount that could be destined to renewable energy sources (heat and electricity generation). This productivity increases to 1,097,386 t year 1 if woodfuel from non-forest area is also considered and to 1,139,883 t year 1 when adding woodfuel from the wood processing industry, i.e. Total Global Supply. These data are reduced by around 60% when physical and legal constraints (Level 3) are taken into consideration, as can be seen in Table 1, which shows the results of the Supply module at regional level. The current scenario in Asturias is one where woodfuel mainly comes from the forest area, thus the data for woodfuel from forest
Table 1 Productivity of biomass above ground and below ground and woodfuel at regional level (Supply module), depending on different levels of accessibility and considering two types of scenario (‘‘Global and Commercial’’). (t year Direct by-products of woodfuel from forest area
1
)
Above ground Below ground Level 1: crown Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
2,646,565 3,319,522 895,847 580,005
Direct by-products of woodfuel from non-forest area
Fruit tree and vineyard Shrub Total
36,519 165,020 201,539
Direct by-products of woodfuel from forest and non-forest area
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
1,097,386
Indirect by-products of woodfuel from wood processing industry
Primary wood processing Secondary wood processing Total
30,762 11,734
Global Total Supply
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
1,139,883
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
936,549
Commercial Total Supply
546,965
684,533 640,130
42,497
727,030 682,627
622,502 589,463
area alone (Commercial Total Supply), a value of 589,463 t year 1, is of particular note. The theoretical potential of woodfuel can be viewed as thematic maps of its spatial distribution (t year 1) at pixel level and the information contained in these maps can be used to identify municipalities or areas based on various criteria since multitude of scenarios can be considered by combining the different information, or at a later date including new data. More significantly, the maps can be displayed for each species at different levels of accessibility, and future planning decisions can be based on this information; an illustration of the flexibility of the database. Fig. 4 shows the productivity of woodfuel in forest area (t year 1) at regional level with Level 3 accessibility and Table 2 presents the statistical parameters of these results (t year 1) and also in (t ha 1 year 1). The results from the current work show that potential woodfuel from the wood processing industry (Indirect Supply) is 42,497 t year 1, and four municipalities (Siero, Mieres, Vegadeo and Valdés) provide 74% of this total (31,430 t year 1). Fig. 5 shows the distribution by municipality of this potential woodfuel. These results prove that the amount of woodfuel potentially available from these sources is significant. Furthermore according to CETEMAS [32], currently more than 90% of waste from wood processing industries is destined for recycling, landfill, sold or given away to the public or thrown away despite the fact that it could be used as woodfuel. Regarding the Total Global Supply, the municipalities with the highest values are Tineo (41,198 t year 1), Valdés (40,766 t year 1) and Villaviciosa (32,957 t year 1), whereas municipalities such as, Santo Adriano, Sariego, Yermes y Tameza and Degaña have less
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Fig. 4. Productivity of woodfuel in forest area.
Table 2 Statistical parameters: productivity of woodfuel in forest area. Productivity of woodfuel in forest area (Level 3) Pixel count
Min
Max
Sum
Avg
Std Dev
Units
4,243,041
0 0
57,813.59 6.28
546,965.79 524
1269.06 1.21
4618.17 1.06
(t year 1) (t ha 1 year
Fig. 5. Indirect Supply.
1
)
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than 1000 t year 1. Fig. 6 shows the Global Total Supply (t year at municipality level with Level 3 accessibility.
1
)
3.1.2. Demand module In Asturias demand for woodfuel in the residential sector, with a value of 33,009 t year 1, demonstrates the extensive traditional use of woodfuel, especially in rural areas (where it accounts for approximately 70% of the total residential demand), providing support for the current trend towards exploitation of forest residues in the area.
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Fig. 7 shows residential results at the municipality level. Some municipalities such as Ponga, Mieres, Oviedo, Villayón and Gozón have woodfuel consumption of over 1000 t year 1. Demand in the industrial sector is 218,063 t year 1. Most of this is concentrated in a single company (ENCE; pulp mill), located in Navia, in the west of Asturias. ENCE, the most important pulp mill in Spain, has three plants in Spain with a total of 180 MW of installed power. The mill in Navia, which accounts for 77 MW of this, has a biomass boiler which burns residues of eucalypts cuttings and other products such as black liquor, bark and chips from industries. In this study, only companies that have benefited from
Fig. 6. Total Global Supply.
Fig. 7. Total residential woodfuel consumption.
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Table 3 Woodfuel consumption at regional level (Demand module). (t year Residential sector Domestic woodfuel consumption
New heating woodfuel consumption Total residential woodfuel consumption Industrial sector Total industrial woodfuel Total Demand
Urban area Rural area Total
1
5671 23,853 29,524 3486 33,009 218,063 251,072
)
subsidies and grants for the installation of biomass boiler are included in the data. Other minor consumers of woodfuel, for example, cement factories or traditional activities, such as bakeries with wood ovens, are not included, which shows the need to improve and complement the database for this module. Table 3 shows the results of the Demand module at regional level; residential and industrial woodfuel consumption. 3.1.3. Integration module The fact that all the data inputs used a standardized measurement (oven dry tonnes per year, t year 1) enables the WISDOM
Fig. 8. Global Balance and Commercial Balance.
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GIS methodology to be used to examine different scenarios by comparing or adding and subtracting data between maps. Interpretation of the statistics related to the convergence of supply and demand requires special consideration due to possible competition for the same biomass resource, which can generate an excess of exploitation, and consequently disequilibrium. For example there may be significant competition in some areas due to the presence of woodfuel consuming industries, an important factor to consider when evaluating the possibility of installing new woodfired power plants [22]. As an illustration, the balance at municipality level shows that Navia and Tineo have a woodfuel shortage of approximately 20,000 t year 1, primarily as a consequence of the location of the pulp mill in Navia, which accounts for 80% of the Total Industrial Demand in the region (see Fig. 8). The municipalities with the highest surplus are Villaviciosa, Valdés, Salas, Pravia and Siero for Global Balance and Villaviciosa, Valdés and Salas in the case of Commercial Balance. Table 4 shows the results of the Integration module in Asturias; depending on different levels of accessibility and considering two types of scenario (‘‘Global and Commercial’’). 3.2. Results at the site-specific level Using the municipality of Villaviciosa as a case study, given that it is one of the municipalities with a large surplus, the Total Commercial Supply (t year 1) at Level 3 access in relation to each type of forest biomass within a 50 km radius of the specific location of the hypothetical wood-fired power plant can be estimated (Table 5). Although Table 5 gives data on the potential woodfuel available on a yearly basis, this cannot be used directly to project costs for industrial use since the true price of wood, and associated costs, fluctuates in relation to the market. The data, therefore, need to be modified on the basis of the current volumes of the clear cuttings made in the area, which indicate that true volumes are below those potentially available. Table 6 shows the Direct Supply (t year 1) for the principal species in the area within a 50 km radius around a hypothetical power plant located at a specified point in the municipality of Villaviciosa. In addition, the quantity of woodfuel (t year 1) from clear cuttings is included. These data refer to the year 2008, however the trend
Table 4 Balance at regional level with different levels of accessibility (Integration module). (t year
Table 6 Residual woodfuel from clear cuttings in Asturias (2008). Species
Direct Supply (Level 3) (t year 1)
Residual woodfuel from clear cuttings (2008) (t year 1)
Chestnut Pine Eucalipts Others
76,766.39 5886.88 117,775.07 42,778.6
6339.59 1429.05 62,448.61 11,810.84
Table 7 Available woodfuel (balance between supply and demand) for proposed installation of a wood-fired power plant at one (case 1) or two (case 2) specified locations. Species
Total Supply (t year 1)
Balance (case 1) (t year 1)
Balance (case 2) (t year 1)
Chestnut Pine Eucalyptus (R = 75 km) Eucalyptus (R = 100 km) Others
12,074.89 15,817.60 62,448.61
5052.92 8795.64 34,208.64 20,106.77 4788.87
4320.70 5749.43 27,719.46 20,072.74 5702.37
11,810.84
for the residual woodfuel from clear cuttings to be well below the potential woodfuel available (Direct Supply) is proved (Forestry Planning and Management service, Department of the Environment and Fisheries, Principality of Asturias). The final balances (available woodfuel), by species, are shown in Table 7, based on the proposed siting of a wood-fired power plant in the municipality of Villaviciosa (case 1) and on the installation of two proposed plants (one in Villaviciosa the other in the municipality of Salas; case 2). In addition, and only in the case of eucalypts, the available woodfuel data was calculated considering either a 75 km or a 100 km demand radius for the already functioning ENCE paper mill in the municipality of Navia. Moreover, in Table 7 Total Supply is shown as the sum of Direct Supply from clear cuttings and Indirect Supply. In the most favourable hypothesis, (case 1) there would be more than 50,000 t year 1 on the market. In case 2, there would be more than 40,000 t year 1. Nonetheless, to cover the potential demand of this new wood-fired plant the volume of clear cuttings in the region would need to be increased. The distribution of Eucalyptus spp. biomass across the whole region of Asturias is depicted in Fig. 9, which also shows two demand radii of ENCE (in blue) and the intersection of these with the demand radius (in red) of the two proposed power plants (Villaviciosa in the east, and Salas in the west).
1
)
Global Balance
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
888,761 475,958 431,555
Commercial Balance
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
685,477 371,430 338,391
Table 5 Total Commercial Supply for each type of woofuel within a 50 km radius of specified location (Villaviciosa). Species
Total Commercial Supply (t year
Chestnut Pine Eucalyptus Others
85,501 20,275 117,775 42,778
1
) Fig. 9. Distribution of Eucaliptus spp. biomass showing ENCE demand radii of 75 and 100 km (blue) and demand radius of 50 km (red) for the two proposed woodfired power plants (Salas on the left and Villaviciosa on the right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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These power plant locations were not designed according to local needs of energy or according to local woodfuel availability, but rather this study was conducted to provide information which would assist local or regional government decisions, e.g. the impact on generation potential of new projects which would have implications for overlapping of feedstock [6,22].
4. Conclusions In this study, a regional analysis of available woodfuel based on Geographic Information Systems and the balance between supply and demand was conducted. Accessible supply of woodfuel from forest area and non-forest area, and supply from the wood processing industry was calculated to provide Total Supply. Moreover, Total Demand was calculated considering residential and industrial demand. Thus, information related to bioenergy systems was able to be integrated into a geodatabase. This is beneficial in improving knowledge of bioenergy systems in a region as a whole, while quantifying the potential and available woodfuel in the area in order to optimize logistics and energy strategies so as to increase the viability of biomass applications in the local area. An original contribution of this study is the fact that results can be shown at different scales (region, municipality or adapted to a specific area) and the database can be supplemented with new information or existing data updated in relation to a specific area or target, enabling a more detailed analysis of how supply and demand converge. In each of these cases a database specific to the various target calculation modules can be used. The productivity of woodfuel (over dry tonnes) in the forest area, considering Level 1 accessibility, is 895,847 t year 1. This productivity rises to 1,097,386 t year 1 adding woodfuel from nonforest area and 1,139,883 t year 1 when woodfuel from the wood processing industry is also considered. The residential sector in Asturias has a woodfuel consumption of 33,009 t year 1 and the industrial sector, 218,063 t year 1. The study shows the Global Balance of woodfuel to be 888,810 t year 1 and the Commercial Balance, 685,477 t year 1. In addition, a case study was carried out to quantify the available woodfuel around a theoretical location for a proposed wood-fired power plant (with a 50 km supply radius). In this specific area, the balance (supply minus demand) shows that more than 50,000 t year 1 (dry basis) of woodfuel would be available for energy use. This local balance also considers the demand from another proposed new fired-power plant, and in this case more than 40,000 t year 1 (dry basis) would be available. In both cases a detailed analysis of the overlap between supply and demand radii was performed, also incorporating the eucalipts demand from the biggest industrial consumer in the area, the ENCE pulp mill, considering 75 and 100 km demand area radii. The range of applications reviewed in this paper clearly testify to the significant amount of potential woodfuel in Asturias and the benefits of using WISDOM GIS methodology and other analytical GIS tools to aid in the management and planning of bioenergy supply. As has been stated by other authors [24, 35], the quantity and heterogeneity of input data available nowadays is a great development, but tools capable of integrating the, sometimes diverse, data in order to arrive at a structured analysis of the information are essential. The WISDOM GIS methodology employed in this work proves to be a valuable tool in these terms, and will be even more powerful in the future when the database is extended and complemented. While this work focuses, of necessity, on a specific region and site, the steps followed and information incorporated can be adapted to reflect the particular needs of any area, at any scale. This approach of calculating the supply and demand of woodfuel at
the pixel level opens up a massive variety of possibilities of adapting the WISDOM GIS methodology and the uses to which it can be put by various agencies, from individual proprietors to public and national administrations. Acknowledgements The authors are grateful to the Plan for Science, Technology and Innovation (PCTI) and the Forest Service of the Principality of Asturias for funding the research project ‘‘WISDOM-ASTURIAS’’. Desarrollo de una herramienta de gestión para la Biomasa del Principado de Asturias’’ and we acknowledge receipt of the Severo Ochoa Asturian fellowship (subsidised by the Government of the Principality of Asturias) awarded to SSG. Also special thanks are due to the technical assistance of Miguel Trossero, Marcos Martín, Noelia Flores and Alba Fanjul and to Ronnie Lendrum for providing support with the English. References [1] RHC-Platform. Renewable heating & cooling. European technology platform. Biomass technology roadmap. European technology platform on renewable heating and cooling; 2014. [accessed 14.11.14]. [2] RHC-Platform. Renewable heating & cooling. European technology platform. Strategic Research priorities for biomass technology. European technology platform on renewable heating and cooling; 2012. [accessed 20.11.14]. [3] Randolph J. Environmental land use planning and management, vol. 3. Washington DC: Island; 2004. pp. 36–52. [4] Höhn J, Lehtonen E, Rasi S, Rintala J. A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland. Appl Energy 2014;113:1–10. [5] Mellino S, Ripa M, Zucaro A, Ulgiati S. An energy-GIS approach to the evaluation of renewable resource flows: a case study of Campania Region, Italy. Ecol Model 2014;271:103–12. [6] Thomas A, Bond A, Hiscock K. A GIS based assessment of bioenergy potential in England within existing energy systems. Biomass Bioenergy 2013;55:107–21. [7] Sacchelli S, De Meo I, Paletto A. Bioenergy production and forest multifunctionality: a trade-off analysis using multiscale GIS model in a case study in Italy. Appl Energy 2012;104:10–20. [8] Drigo R, Masera OR, Trossero MA. Woodfuel Integrated Supply/Demand Overview Mapping – WISDOM: a geographical representation of woodfuel priority area. FAO Forestry Department; Unasylva; 2002. [accessed 19.01.15]. [9] Masera O, Drigo R, Trossero MA. A methodological approach for assessing woodfuel sustainability and support wood energy planning. FAO Wood Energy Programme; 2003. [accessed 19.01.15]. [10] Drigo R, Veselic Zˇ. Woodfuel Integrated Supply/Demand Overview Mapping (WISDOM) – Slovenia – Spatial woodfuel production and consumption analysis. FAO Wood Energy Programme and Slovenia Forest Service; 2006. [accessed 19.01.15]. [11] Drigo R. WISDOM – East Africa – Spatial woodfuel production and consumption analysis of selected African countries. FAO Wood Energy Programme; 2006. [accessed 19.01.15]. [12] Drigo R, Salbitano F. WISDOM for cities. Analysis of wood energy and urbanization aspects using WISDOM methodology. FAO Forestry Department. Urban forestry – Wood energy; 2008. [accessed 19.01.15]. [13] Renewable energies action plan (PANER 2011–2020). MITYC, Ministerio de Industria, Turismo y Comercio (Ministry of Industry, Tourism and Trade). IDAE, Instituto para la Diversificación y Ahorro de la Energía (Institute for Diversification and Saving of Energy); 2010. [accessed 19.01.15]. [14] Spanish Renewable Energies Plan (PER 2011–2020). MITYC, Ministerio de Industria, Turismo y Comercio (Ministry of Industry, Tourism and Trade). IDAE, Instituto para la Diversificación y Ahorro de la Energía (Institute for Diversification and Saving of Energy); 2011. [accessed 19.01.15]. [15] Canga E, Prada M, Majada, J. Modelización de la biomas arbórea y evaluación de rendimientos y costes en una clara de Pinus pinaster para la obtención de biomasa en Asturias. Actas del V Congreso Forestal Español (2009). Montes y sociedad: Saber qué hacer. REF.: 5CFE01-633. Editors: S.E.C.F.-Junta de Castilla y León. Ávila, Spain. p. 14. Personal communication. [accessed 25.02.15].
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A spatial analysis of woodfuel based on WISDOM GIS methodology: Multiscale approach in Northern Spain Sandra Sánchez-García a,⇑, Elena Canga a, Eduardo Tolosana b, Juan Majada a a b
CETEMAS, Forest and Wood Technology Research Centre, Sustainable Forest Management Area, Finca Experimental ‘‘La Mata’’ s/n, 33820 Grado, Asturias, Spain E.T.S.I. Montes, Technic University of Madrid, Ciudad Universitaria, s/n, 28040 Madrid, Spain
h i g h l i g h t s A multiscale GIS analysis of woodfuel supply/demand. Template for scale or scenario dependant integration of data into WISDOM methodology. Pixel level calculation of all data enables operations between rasterized maps. Shows benefits of analytical GIS tools in bioenergy system management and planning.
a r t i c l e
i n f o
Article history: Received 7 January 2014 Received in revised form 26 January 2015 Accepted 27 January 2015 Available online 27 February 2015 Keywords: Woodfuel Accessibility Multiscale GIS Logistics Wood-fired power plant
a b s t r a c t Given the complexity of generating energy from biomass, the need has arisen for support tools to assist in balancing energy and forestry policies which are sufficiently flexible to address the issues of planning and management at small and large scales. The present study aims to adapt WISDOM GIS methodology for application in the autonomous region of Asturias (Northern Spain) and thereby, by creating a geodatabase, to contribute to a support tool for investigating the potential of woodfuel. This will aid the public administration by providing information on woodfuel supply and demand at the regional, municipality and site-specific level, and thus assist in decision making in terms of formulating new energy strategies. In terms of supply (t year 1), in this work, woodfuel from forest area is defined as the crown fraction only (branches and leaves), although in the case of Eucalyptus spp., bark is also included as it is remains on-site following extraction of eucalypts for the pulp industry. Non-Forest Direct Supply was calculated on the basis of the relevant categories from an agricultural land use inventory and average woodfuel productivity. In addition, physical and legal constraints related to accessibility of the woodfuel were considered, the former applying restriction filters with values weighted depending on the interaction between slope map, road networks and centres of population, and the latter considering legal limitations in protected areas. In addition, unused waste from the wood processing industry was included. To calculate total woodfuel demand (t year 1) for energy generation (heat and electricity), both the residential and industrial sector were taken into account. All data was georeferenced through Geographic Information Systems (GISs), which allow operations between raster maps to be performed to generate numeric and spatial results focusing on logistics and biomass strategies, depending on the inputs data and scale employed for each scenario considered. In addition, the application of the methodology at the site-specific level illustrates the practical implementation at the small scale of the geodatabase created, by evaluating the woodfuel available to feed, in case 1, a wood-fired power plant in a specific proposed location and, in case 2, this plant combined with a second plant in a different municipality, both cases also taking into consideration industrial demand. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction
⇑ Corresponding author. Tel.: +34 985754725. E-mail address: [email protected] (S. Sánchez-García). http://dx.doi.org/10.1016/j.apenergy.2015.01.099 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
Current key challenges in high population areas such as Europe are to meet an ever increasing energy demand and ensure both the security and sustainability of energy supply. Biomass, whether
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it is from agricultural or forest sources, plays an ever more important role in meeting these challenges and is key to achieving CO2 emission targets in relation to climate change. Recently, the Biomass Panel of the RHC-Platform established the implementation actions for 2014–2020 [1], according to the European Strategic Research Priorities for Biomass Technology published in April 2012 [2]. One goal is that by 2020, biomass supply should double compared to current levels ensuring that adequate feedstock is available at competitive prices. This promotion of biomass generation and use, not only addresses the issue of energy security, but has the added value of creating jobs in the sector and particularly in rural areas. Furthermore, Directive 2009/28/CE of the European Parliament and Council, related to promoting the use of energy from renewable sources, established that each Member State of the European Union (EU) was to develop an action plan for renewable energies for the period 2011–2020 in order to achieve the objectives set out in the Directive. The use of Geographic Information System (GIS) framework may be considered one of the best tools to enable and facilitate this policy challenge [3]. Developments using GIS technology have revolutionised the possibilities available for improving knowledge of existing bioenergy systems (supply and demand) through spatial analysis, and to quantify their potential availability and the practicalities of meeting demand taking into account the various constraints and limitations existing in an area that impact on the viability of exploitation. This information allows the identification of supply opportunities and new markets which can be opened and are compatible with forest, energy and environmental strategies [4]. Integration of data such as forest inventories with GIS data allows the description of the spatial distribution of these resources and can be combined with data relating to consumption to support energy planning policies. In order to be in a position to make decisions about how limited resources are used and managed within an existing area, it is preliminarily necessary to understand how effectively resource conservation is addressed at present in any given area [5]. What is more, GIS support tools enable multiscale approaches to be implemented which facilitate and verify decision making processes. For example, Thomas [6] analysed the spatial supply and demand relationships for biomass energy potential for England and Sacchelli et al. [7], working in the Italian Alps, created a spatial model to quantify the potential amount of woodfuel from the forestry sector at several scales. One of the many GIS tools developed in recent years is WISDOM, the result of a collaboration between FAO, and the Centre for Ecosystem Research of the National Autonomous University of Mexico (UNAM). WISDOM (Woodfuel Integrated Supply/Demand Overview Mapping) is based on certain key characteristics of wood energy systems: geographic specificity, heterogeneity of woodfuel supply sources and provides the user with great flexibility to adapt the methodology to specific scenarios and scales. A detailed description of the potential and specific application of the methodology has been published by Drigo et al. [8] and Masera et al. [9]. Many case studies have been published applying WISDOM at different spatial scales, such as for countries [10,11] or cities [12]. In Spain, the National Renewable Energies Action Plan [13], specifies that renewable energies (REs) should account for 20% of total energy consumption by 2020. In 2009, the base year, REs accounted for 9.4% of primary energy in Spain, 3.9% of this coming from biomass. In parallel with and to complement this, the Spanish Renewable Energies Plan, estimated the energy input from woody biomass for 2015 as 1582 ktep, and for 2020 as 2081 ktep. Furthermore, woodfuels coming indirectly from woody biomass (i.e. from the wood processing industry) were expected to reach 1679 ktep and 1702 ktep respectively in the same year [14]. In recent years, in Asturias, an autonomous region in Northern Spain, there has been an increase in biomass use to generate
energy, enhanced by subsidies for renewable energy use from the Regional Government, which was expected to subsidize in 2013 roughly 200 biomass and geothermal energy projects. During recent years, regional biomass R + D, studies of productivity and cost in biomass logging operations [15,16] have been conducted as well as estimations of biomass quantity for different forest species [17–19]. Recently, in addition, the Government of Asturias has commissioned CETEMAS to carry out WISDOM-Asturias. In this study, a qualitative and quantitative biomass supply and demand evaluation at region, municipality, and site specific levels was developed using the WISDOM GIS methodology and its results mapped through ArcMap GIS software [20]. The current study, in addition, provides results both before and after applying filters which take into account physical and legal accessibility constraints, similar to other authors [7,21]. In practice, however, the final objective is to adapt the WISDOM GIS methodology for a specific region, in this case in the north of Spain, to have all this information available as a support tool to investigate the potential of woodfuel at the regional and municipality level. Furthermore, we aim to show how this methodology can aid the decision making of the public administration to, for example, evaluate the suitability of proposed wood-fired power plants at a site-specific level, as in the work of Viana et al. [22] or to accurately ascertain productions costs so as to promote the use of energy woody biomass. This is important given that woody biomass production costs vary with factors such as the scale of demand, the location of the processing plants and, when considering multi-sources of biomass, rotation periods, production technologies and human accessibility to the biomass resources [23,24]. The work uses data currently available and due to the large quantity and the multitude of formats, as well as the difficulties of analysing it in a uniform manner, this study had to consider specific ways of calculating annual woodfuel at pixel level to adapt the methodology. This work will therefore serve as a template of how to integrate data into the methodology for future applications at different scales and scenarios in other areas. 2. Material and methods 2.1. Multiscale structure approach GISs are scale dependent, and it is necessary to consider the appropriate spatial scale required in each case. In this study data from Asturias – an autonomous region in the north of Spain – was analysed through WISDOM GIS methodology at the pixel level (50 50 m) and then scaled-up to make inferences about supply of and demand (oven dry tonnes) for woodfuel at various spatial levels: regional, municipality and site-specific. Such a small pixel size was used due to the reduced size of many forest plots in the region and enables flexibility at the site specific level, for example consideration of the spatial variation of stands from year to year based on silvicultural planning, or, as in the example provided here, evaluate the woodfuel available to feed future wood-fired power plants. The analysis was structured in three main modules according to the WISDOM GIS methodology [25] namely Supply, Demand and Integration, and followed the FAO (2004) [26] classification of by-product woodfuel as a biofuel source (Fig. 1). 2.2. Application of WISDOM GIS methodology at the regional and municipality level 2.2.1. Supply module To analyse and provide a spatial representation of the supply sources of available woodfuel, different aspects have to be taken
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Fig. 1. Classification of biofuel source used. Adapted from UBET (Unified Bioenergy Terminology).
into consideration, including the forestry, agriculture and wood processing industries. The analytical steps of Supply module are described below. 2.2.1.1. Direct by-products (Direct Supply). Annual Total Direct Supply (i.e. from both forest and non-forest areas) were calculated in dry basis, the former based on information from the 22 strata of the 3rd National Forest Inventory (NFI3) [27] and the latter from the relevant categories of the Spanish Agricultural Plots Identification System (SIGPAC). Biomass stock in the forest (oven dry tonnes per hectare, t ha 1) was calculated for the different tree fractions (stem, bark, branches, leaves and root) for all species identified in the NFI3, using tree biomass equations [28]. Then the Mean Annual Increment (MAI) of each fraction, in dray basis (t ha 1 year 1) was calculated (assuming a direct positive relationship with annual increment in volume (m3 ha 1 year 1)) and used to calculate productivity of woodfuel (t year 1) (both above – and below ground). The MAI relating to the crown (branches and leaves) was used as the proxy of annual available woodfuel (t ha 1 year 1) from forest area, since currently this is the only tree fraction destined for energy use. The one exception was in relation to Eucalyptus spp., where bark was also included in the annual available woodfuel (taken as 12% of stem biomass) since this by-product remains in the forest when the stems are removed for use in the pulp industry – by far the most common use of eucalypts in the region. To map Direct Supply from forest area (t year 1), this information was integrated with that of the Spanish Forest Map (MFE), which classifies land using the same 22 strata as the NFI3, and the results rasterized. Non-Forest Direct Supply (oven dry tonnes per year, t year 1) was calculated on the basis of the relevant categories from the Spanish Agricultural Plots Identification System (SIGPAC) and average woodfuel productivity (t ha 1 year 1) for each category was calculated based on results from previous works [29–31]. This information was then integrated with the Spanish Forest Map and rasterized (t year 1). A Total Direct Supply map was then created by summing the rasters of both maps (forest and non-forest areas), and considered as Level 1 accessibility. Next, different physical and legal access filters were considered. To develop the physical restriction filters, the Cost Weighted tool (Spatial Analyst toolbar/Distance, in ArcGis 9.2 software) [20] was used, which produces an output raster whereby each pixel is assigned a weighted value representing its ease and practicality of access. Applying this filter to the Level 1 map resulted in the Level 2 map (physical access constraints). The shape input (Distance to) was a map using roads and population centres as reference
elements, and the raster input (Cost raster), a slope map composed of a Digital Elevation Model (DEM) with a resolution of 50 50 m obtained from the Asturias Regional Cartography Services. Zones with a steepness of over 60% being assigned a value of 100% physical restriction since at this degree of slope the forestry harvesting system employed in the region (ground-based) is unable to function adequately, although in other future applications this condition could be modified depending on type of harvesting system, the degree of mechanization and/or machines to be used. For the legal restrictions filter, designation as a protected area such as National Park, Natural Park, Site of Community Importance (SCI), Special Protection Area (SPA), and restrictions related to logging operations and commercial enterprise in an area were taken into account, i.e. a weighting depending on the percentage of each pixel subject to legal restriction (i.e. no restriction in any part of the pixel = 0%, all the pixel restricted = 100%). These filters were then applied to the Level 2 map to provide the Level 3 map (physical and legal access constraints). 2.2.1.2. Indirect by-products from processing industry (Indirect Supply). Calculation of Indirect Supply was based on a study of waste from 57% of the companies involved in the wood processing industry in Asturias [32]. The study classified waste material (species, and type of waste) and the end for which the material was currently destined (biomass, recycling, landfill, sale or given away to the public). The Lower Heat Value (LHV) for the material in the latter three categories, which could in fact be used as biomass, was then calculated. Since the moisture content was assumed to vary (from 20% to 50%) depending on species and type of waste, all data were converted to oven dry tonnes and the location of each company mapped and rasterized along with its woodfuel values (t year 1). 2.2.1.3. Total Supply. Finally, the Total Supply (oven dry tonnes per year, t year 1) at pixel level map was created by summing the rasters of the Total Direct and Indirect Supply maps, considering two different scenarios: Total Supply ‘‘Global’’, where all types of Supply were considered and Total Supply ‘‘Commercial’’, where Direct Supply from non-forest area was excluded due to the current low financial returns on the removal of these type of woodfuel. Fig. 2 shows the information required for this module and the final results of each step to calculate Total Supply, with the different levels of accessibility. 2.2.2. Demand module To calculate demand for woodfuel, energy generation (heat and electricity) in both the residential and industrial sector was considered in oven dry tonnes.
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Fig. 2. Flowchart of main analytical steps of Supply module.
Regarding domestic woodfuel consumption (t year 1), in the residential sector, socio-demographic variables from the 2001 Population Census (INE 2001) were used: i.e. location, whether rural or urban, and size (m2) of each home using wood for heating (the data did not distinguish between categories), as well as the average wood heating requirements of Asturias (kW h m 2 year 1), the Average Lower Heating Value LHV (kW h t 1) and the boiler energy coefficient (%). Woodfuel consumption in the residential sector in terms of new heating (i.e. systems installed after the 2001 census and until 2010 for which subsidies were given) was also included in the calculation, using the location of each new installation and its consumption (t year 1). Similarly, woodfuel consumption (t year 1) in the industrial sector was calculated, using the values from the 8 most important enterprises using woodfuel for energy. In both cases figures were obtained from the Asturian Energy Foundation (FAEN). The results were mapped and a Total Demand (oven dry tonnes per year, t year 1) at pixel level map created as the sum of the rasters of Residential and Industrial Demand. Fig. 3 shows the information required for this module and the final results of each step to calculate Total Demand.
Fig. 3. Flowchart of main analytical steps of Demand module.
2.2.3. Integration module The integration is done through the combination of the variables related to woodfuel consumption and supply that have been systematized for each minimum administrative unit of analysis [10]. i.e. The Integration module is the balance left after subtracting the total consumption of woodfuel for a specific area from the potential sustainable woodfuel available for energy. The main scope of this module was to analyse the relevant interactions between supply and demand of woodfuel in different scenarios depending on the type of inputs data employed. The integration module was achieved considering two scenarios; first, the combination of the variables related to Total Supply and Total Demand, which were systematized for each pixel, resulted in a final amount which was named ‘‘Global Balance’’. And secondly, a scenario where woodfuel from the non-forest area (which is currently unprofitable to collect) was excluded from the supply calculation to provide a calculation of woodfuel with real current commercial viability. The balance between this adapted supply and Total Demand was named ‘‘Commercial Balance’’. 2.3. Application of WISDOM GIS methodology at the site-specific level With reference to the long-term feasibility of the sustainable utilization of woodfuel, further studies and discussions need to be carried out. There are many applications and different scenarios for which to use the database described in this work. This study considers the evaluation of the availability of woodfuel supply to an area within which the demand of a wood-fired plant would be satisfied taking into account the stability of woodfuel supply. Specifically, an analysis of the biomass available, at Level 3 access, for the various types of forest biomass (chestnut, pine, eucalypts and other species) for a proposed new wood-fired power plant was conducted. The proposed facility would have an installed power of 20 MW, an annual consumption of approximately 91200 t year 1 (dry basis) and would be located at a specified point in the municipality of Villaviciosa. In order to examine the integration of this plant at a local level, firstly, only the demand and supply within a 50 km radius of the location of the power plant was considered. To calculate the balance (for each species) between supply and demand, Direct Supply was calculated based on figures for clear cutting permits approved
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in the year 2008 (Forestry Planning and Management Service, Department of the Environment and Fisheries, Principality of Asturias). Furthermore, with specific respect to eucalypts this balance was calculated taking into account the fact that one of the pulp mills of the largest pulp company in Spain (ENCE) is located in Asturias and consumes a large quantity of Eucalyptus spp. woodfuel, and that the company sometimes brings in supplies from neighbouring regions. For this reason, two different demand radii were considered in relation to the ENCE paper mill, one of 75 km and a second of 100 km, which were superimposed on the supply radius of the proposed power plant in order to identify the areas of overlap, and these were then discounted in the calculations of available woodfuel at the local level. Secondly, the data were examined under the condition of a second proposed wood-fired power plant being located at a specified point in the municipality of Salas (100 km by road from the first plant), with the same consumption as that in Villaviciosa, and the influence of this new demand on the balance at local level was evaluated. 3. Results and discussion From this analysis of supply and demand, various results, considering oven dry tonnes, were generated, a selection of which are discussed below. GISs are scale dependent, and it is necessary to consider the appropriate spatial scale required in each case. For this study different scales were considered: regional, municipality and the level of a specifically defined small area. At this latter scale, with ownership categories such as private, forest industries or state, it is possible to take into consideration the spatial variation of stand locations from year to year based on silvicultural planning. The pixel size used in this work (0.25 ha), enables considerable flexibility in terms of the translation of the data to different scales. Asturias has considerable untapped resources in terms of woodfuel: there are 49,600,575 oven dry tonnes of above-ground biomass stock, and 72,606,863 t if the root fraction is also considered. And to date little advantage of this is being taken. This work provides valuable information, both in terms of the site-specific application and the value of the methodology for future planning and decision making. The analysis reveals interesting possibilities for the development of the biomass energy sector in the region. Case studies like this [9,33,34], could be promoted by local governments in order to evaluate all possible synergies realizable in the sector. Moreover, in this study a detailed accessibility filter was applied, and the results are compiled of different values depending on the type of restriction considered. 3.1. Results at the regional and municipality level 3.1.1. Supply module The potential productivity of woodfuel in forest area (i.e. branches and leaves, and for Eucalyptus spp., bark) considering Level 1 accessibility, is 895,847 t year 1, a considerable amount that could be destined to renewable energy sources (heat and electricity generation). This productivity increases to 1,097,386 t year 1 if woodfuel from non-forest area is also considered and to 1,139,883 t year 1 when adding woodfuel from the wood processing industry, i.e. Total Global Supply. These data are reduced by around 60% when physical and legal constraints (Level 3) are taken into consideration, as can be seen in Table 1, which shows the results of the Supply module at regional level. The current scenario in Asturias is one where woodfuel mainly comes from the forest area, thus the data for woodfuel from forest
Table 1 Productivity of biomass above ground and below ground and woodfuel at regional level (Supply module), depending on different levels of accessibility and considering two types of scenario (‘‘Global and Commercial’’). (t year Direct by-products of woodfuel from forest area
1
)
Above ground Below ground Level 1: crown Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
2,646,565 3,319,522 895,847 580,005
Direct by-products of woodfuel from non-forest area
Fruit tree and vineyard Shrub Total
36,519 165,020 201,539
Direct by-products of woodfuel from forest and non-forest area
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
1,097,386
Indirect by-products of woodfuel from wood processing industry
Primary wood processing Secondary wood processing Total
30,762 11,734
Global Total Supply
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
1,139,883
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
936,549
Commercial Total Supply
546,965
684,533 640,130
42,497
727,030 682,627
622,502 589,463
area alone (Commercial Total Supply), a value of 589,463 t year 1, is of particular note. The theoretical potential of woodfuel can be viewed as thematic maps of its spatial distribution (t year 1) at pixel level and the information contained in these maps can be used to identify municipalities or areas based on various criteria since multitude of scenarios can be considered by combining the different information, or at a later date including new data. More significantly, the maps can be displayed for each species at different levels of accessibility, and future planning decisions can be based on this information; an illustration of the flexibility of the database. Fig. 4 shows the productivity of woodfuel in forest area (t year 1) at regional level with Level 3 accessibility and Table 2 presents the statistical parameters of these results (t year 1) and also in (t ha 1 year 1). The results from the current work show that potential woodfuel from the wood processing industry (Indirect Supply) is 42,497 t year 1, and four municipalities (Siero, Mieres, Vegadeo and Valdés) provide 74% of this total (31,430 t year 1). Fig. 5 shows the distribution by municipality of this potential woodfuel. These results prove that the amount of woodfuel potentially available from these sources is significant. Furthermore according to CETEMAS [32], currently more than 90% of waste from wood processing industries is destined for recycling, landfill, sold or given away to the public or thrown away despite the fact that it could be used as woodfuel. Regarding the Total Global Supply, the municipalities with the highest values are Tineo (41,198 t year 1), Valdés (40,766 t year 1) and Villaviciosa (32,957 t year 1), whereas municipalities such as, Santo Adriano, Sariego, Yermes y Tameza and Degaña have less
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Fig. 4. Productivity of woodfuel in forest area.
Table 2 Statistical parameters: productivity of woodfuel in forest area. Productivity of woodfuel in forest area (Level 3) Pixel count
Min
Max
Sum
Avg
Std Dev
Units
4,243,041
0 0
57,813.59 6.28
546,965.79 524
1269.06 1.21
4618.17 1.06
(t year 1) (t ha 1 year
Fig. 5. Indirect Supply.
1
)
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than 1000 t year 1. Fig. 6 shows the Global Total Supply (t year at municipality level with Level 3 accessibility.
1
)
3.1.2. Demand module In Asturias demand for woodfuel in the residential sector, with a value of 33,009 t year 1, demonstrates the extensive traditional use of woodfuel, especially in rural areas (where it accounts for approximately 70% of the total residential demand), providing support for the current trend towards exploitation of forest residues in the area.
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Fig. 7 shows residential results at the municipality level. Some municipalities such as Ponga, Mieres, Oviedo, Villayón and Gozón have woodfuel consumption of over 1000 t year 1. Demand in the industrial sector is 218,063 t year 1. Most of this is concentrated in a single company (ENCE; pulp mill), located in Navia, in the west of Asturias. ENCE, the most important pulp mill in Spain, has three plants in Spain with a total of 180 MW of installed power. The mill in Navia, which accounts for 77 MW of this, has a biomass boiler which burns residues of eucalypts cuttings and other products such as black liquor, bark and chips from industries. In this study, only companies that have benefited from
Fig. 6. Total Global Supply.
Fig. 7. Total residential woodfuel consumption.
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Table 3 Woodfuel consumption at regional level (Demand module). (t year Residential sector Domestic woodfuel consumption
New heating woodfuel consumption Total residential woodfuel consumption Industrial sector Total industrial woodfuel Total Demand
Urban area Rural area Total
1
5671 23,853 29,524 3486 33,009 218,063 251,072
)
subsidies and grants for the installation of biomass boiler are included in the data. Other minor consumers of woodfuel, for example, cement factories or traditional activities, such as bakeries with wood ovens, are not included, which shows the need to improve and complement the database for this module. Table 3 shows the results of the Demand module at regional level; residential and industrial woodfuel consumption. 3.1.3. Integration module The fact that all the data inputs used a standardized measurement (oven dry tonnes per year, t year 1) enables the WISDOM
Fig. 8. Global Balance and Commercial Balance.
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GIS methodology to be used to examine different scenarios by comparing or adding and subtracting data between maps. Interpretation of the statistics related to the convergence of supply and demand requires special consideration due to possible competition for the same biomass resource, which can generate an excess of exploitation, and consequently disequilibrium. For example there may be significant competition in some areas due to the presence of woodfuel consuming industries, an important factor to consider when evaluating the possibility of installing new woodfired power plants [22]. As an illustration, the balance at municipality level shows that Navia and Tineo have a woodfuel shortage of approximately 20,000 t year 1, primarily as a consequence of the location of the pulp mill in Navia, which accounts for 80% of the Total Industrial Demand in the region (see Fig. 8). The municipalities with the highest surplus are Villaviciosa, Valdés, Salas, Pravia and Siero for Global Balance and Villaviciosa, Valdés and Salas in the case of Commercial Balance. Table 4 shows the results of the Integration module in Asturias; depending on different levels of accessibility and considering two types of scenario (‘‘Global and Commercial’’). 3.2. Results at the site-specific level Using the municipality of Villaviciosa as a case study, given that it is one of the municipalities with a large surplus, the Total Commercial Supply (t year 1) at Level 3 access in relation to each type of forest biomass within a 50 km radius of the specific location of the hypothetical wood-fired power plant can be estimated (Table 5). Although Table 5 gives data on the potential woodfuel available on a yearly basis, this cannot be used directly to project costs for industrial use since the true price of wood, and associated costs, fluctuates in relation to the market. The data, therefore, need to be modified on the basis of the current volumes of the clear cuttings made in the area, which indicate that true volumes are below those potentially available. Table 6 shows the Direct Supply (t year 1) for the principal species in the area within a 50 km radius around a hypothetical power plant located at a specified point in the municipality of Villaviciosa. In addition, the quantity of woodfuel (t year 1) from clear cuttings is included. These data refer to the year 2008, however the trend
Table 4 Balance at regional level with different levels of accessibility (Integration module). (t year
Table 6 Residual woodfuel from clear cuttings in Asturias (2008). Species
Direct Supply (Level 3) (t year 1)
Residual woodfuel from clear cuttings (2008) (t year 1)
Chestnut Pine Eucalipts Others
76,766.39 5886.88 117,775.07 42,778.6
6339.59 1429.05 62,448.61 11,810.84
Table 7 Available woodfuel (balance between supply and demand) for proposed installation of a wood-fired power plant at one (case 1) or two (case 2) specified locations. Species
Total Supply (t year 1)
Balance (case 1) (t year 1)
Balance (case 2) (t year 1)
Chestnut Pine Eucalyptus (R = 75 km) Eucalyptus (R = 100 km) Others
12,074.89 15,817.60 62,448.61
5052.92 8795.64 34,208.64 20,106.77 4788.87
4320.70 5749.43 27,719.46 20,072.74 5702.37
11,810.84
for the residual woodfuel from clear cuttings to be well below the potential woodfuel available (Direct Supply) is proved (Forestry Planning and Management service, Department of the Environment and Fisheries, Principality of Asturias). The final balances (available woodfuel), by species, are shown in Table 7, based on the proposed siting of a wood-fired power plant in the municipality of Villaviciosa (case 1) and on the installation of two proposed plants (one in Villaviciosa the other in the municipality of Salas; case 2). In addition, and only in the case of eucalypts, the available woodfuel data was calculated considering either a 75 km or a 100 km demand radius for the already functioning ENCE paper mill in the municipality of Navia. Moreover, in Table 7 Total Supply is shown as the sum of Direct Supply from clear cuttings and Indirect Supply. In the most favourable hypothesis, (case 1) there would be more than 50,000 t year 1 on the market. In case 2, there would be more than 40,000 t year 1. Nonetheless, to cover the potential demand of this new wood-fired plant the volume of clear cuttings in the region would need to be increased. The distribution of Eucalyptus spp. biomass across the whole region of Asturias is depicted in Fig. 9, which also shows two demand radii of ENCE (in blue) and the intersection of these with the demand radius (in red) of the two proposed power plants (Villaviciosa in the east, and Salas in the west).
1
)
Global Balance
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
888,761 475,958 431,555
Commercial Balance
Level 1: Total Direct Supply Level 2: Physically accessible Supply Level 3: Physically and legally accessible Supply
685,477 371,430 338,391
Table 5 Total Commercial Supply for each type of woofuel within a 50 km radius of specified location (Villaviciosa). Species
Total Commercial Supply (t year
Chestnut Pine Eucalyptus Others
85,501 20,275 117,775 42,778
1
) Fig. 9. Distribution of Eucaliptus spp. biomass showing ENCE demand radii of 75 and 100 km (blue) and demand radius of 50 km (red) for the two proposed woodfired power plants (Salas on the left and Villaviciosa on the right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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These power plant locations were not designed according to local needs of energy or according to local woodfuel availability, but rather this study was conducted to provide information which would assist local or regional government decisions, e.g. the impact on generation potential of new projects which would have implications for overlapping of feedstock [6,22].
4. Conclusions In this study, a regional analysis of available woodfuel based on Geographic Information Systems and the balance between supply and demand was conducted. Accessible supply of woodfuel from forest area and non-forest area, and supply from the wood processing industry was calculated to provide Total Supply. Moreover, Total Demand was calculated considering residential and industrial demand. Thus, information related to bioenergy systems was able to be integrated into a geodatabase. This is beneficial in improving knowledge of bioenergy systems in a region as a whole, while quantifying the potential and available woodfuel in the area in order to optimize logistics and energy strategies so as to increase the viability of biomass applications in the local area. An original contribution of this study is the fact that results can be shown at different scales (region, municipality or adapted to a specific area) and the database can be supplemented with new information or existing data updated in relation to a specific area or target, enabling a more detailed analysis of how supply and demand converge. In each of these cases a database specific to the various target calculation modules can be used. The productivity of woodfuel (over dry tonnes) in the forest area, considering Level 1 accessibility, is 895,847 t year 1. This productivity rises to 1,097,386 t year 1 adding woodfuel from nonforest area and 1,139,883 t year 1 when woodfuel from the wood processing industry is also considered. The residential sector in Asturias has a woodfuel consumption of 33,009 t year 1 and the industrial sector, 218,063 t year 1. The study shows the Global Balance of woodfuel to be 888,810 t year 1 and the Commercial Balance, 685,477 t year 1. In addition, a case study was carried out to quantify the available woodfuel around a theoretical location for a proposed wood-fired power plant (with a 50 km supply radius). In this specific area, the balance (supply minus demand) shows that more than 50,000 t year 1 (dry basis) of woodfuel would be available for energy use. This local balance also considers the demand from another proposed new fired-power plant, and in this case more than 40,000 t year 1 (dry basis) would be available. In both cases a detailed analysis of the overlap between supply and demand radii was performed, also incorporating the eucalipts demand from the biggest industrial consumer in the area, the ENCE pulp mill, considering 75 and 100 km demand area radii. The range of applications reviewed in this paper clearly testify to the significant amount of potential woodfuel in Asturias and the benefits of using WISDOM GIS methodology and other analytical GIS tools to aid in the management and planning of bioenergy supply. As has been stated by other authors [24, 35], the quantity and heterogeneity of input data available nowadays is a great development, but tools capable of integrating the, sometimes diverse, data in order to arrive at a structured analysis of the information are essential. The WISDOM GIS methodology employed in this work proves to be a valuable tool in these terms, and will be even more powerful in the future when the database is extended and complemented. While this work focuses, of necessity, on a specific region and site, the steps followed and information incorporated can be adapted to reflect the particular needs of any area, at any scale. This approach of calculating the supply and demand of woodfuel at
the pixel level opens up a massive variety of possibilities of adapting the WISDOM GIS methodology and the uses to which it can be put by various agencies, from individual proprietors to public and national administrations. Acknowledgements The authors are grateful to the Plan for Science, Technology and Innovation (PCTI) and the Forest Service of the Principality of Asturias for funding the research project ‘‘WISDOM-ASTURIAS’’. Desarrollo de una herramienta de gestión para la Biomasa del Principado de Asturias’’ and we acknowledge receipt of the Severo Ochoa Asturian fellowship (subsidised by the Government of the Principality of Asturias) awarded to SSG. Also special thanks are due to the technical assistance of Miguel Trossero, Marcos Martín, Noelia Flores and Alba Fanjul and to Ronnie Lendrum for providing support with the English. References [1] RHC-Platform. Renewable heating & cooling. European technology platform. Biomass technology roadmap. European technology platform on renewable heating and cooling; 2014.
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[25] Masera OR. Ghilardi A, Drigo R, Trossero M. WISDOM: a GIS-based supply demand mapping tool for woodfuel management. Biomass Bioenergy 2006;30:618–37. [26] Unified Bioenergy Terminology (UBET). FAO Wood Energy Programme; 2004.