Fuel wood - Demand

General description

The usage of fuel wood as an energy source is largely varying across the Alps and high-resolution data on energy consumption is scarce. Hence, this indicator is based on the fuelwood consumption at the NUTS2 level. Building area from the open-street-map data, together with the heating degree days are used to disaggregate the amount of fuelwood required at municipality level.

Input data

GIS-data

Building: distribution and basal area of buildings. Source: http://download.geofabrik.de/
Heating degree daysat the NUTS2 and LAU2 level.Source: https://www.eea.europa.eu/data-and-maps/indicators/heating-degree-days

Statistical data

Fuelwood consumption per country: The quantity of fuelwood consumed in the countries of the alpine space. Sources: Schmidl et al.,2010; Bundesamt für Umwelt BAFU.
Data needed for the disaggregation at the NUTS2 level: the datasets used for the disaggregation of national level data to the NUTS2 level. Sources: Istat, Households using fuelwood among Italian regions; Statistik Austria, Energy statistics: Energy use of households; Eymann et al,2014; Blank et al.,1999 ;www.energee-watch.eu;

Calculation steps

(1) Calculate building area per NUTS2

The total building area for every NUTS2 is calculated.

(2) Calculate the fuelwood consumption per square meter in the NUTS2 regions

The fuelwood consumption at the NUTS2 level is divided by the building area per NUTS2, which gives the fuelwood consumption per square meter in each NUTS2 region.

(3) Calculate fuelwood consumption at the LAU2 level

To obtain the fuelwood consumption per municipality the result of the previous step is multiplied with the building area of each LAU2 polygon. This result is then related to the average heating degree days at the LAU2 and NUTS2 level.

(4) Calculate temperature correction factor

The disaggregation of the previous step is refined by considering a temperature correction factor for every LAU2. This factor is calculated by first calculating a raster layer with the heating degree days for the Alpine area. From this raster layer, the maximum value for every LAU2 is extracted (Because this value most likely corresponds to the situation in the valley bottoms where the buildings are usually located, the mean function would also include all the values in the mountains where no buildings are) and then these maxima for all LAU2s within a NUTS2 region are averaged. For every LAU2 this value on NUTS2 level is divided with the maximum value within the LAU2, extracted previously. This factor is then multiplied with the results of steps 3 for every LAU2.

In the LAU2s which have a cooler climate the demand is increased. On the other hand, LAUs with a higher maximum of the yearly average temperature will have a smaller temperature correction factor and therefore also a reduced demand.

Fuel wood

References:

Schmidl, C., Friedl, G., Haslinger, W., Humel, S., Schwabl, M., Voglauer, B. 2010: Wooden Biofuels in Europe - Quantities and Corrosion Relevant Characteristics. 18th European Biomass Conference and Exhibition.

Bundesamt für Umwelt BAFU 2014: Jahrbuch Wald und Holz 2014.

Eymann, L., Rohrer, J, Stucki,M. 2014. Energieverbrauch der Schweizer Kantone: Endenergieverbrauch und Mittelabfluss durch den Energie-Import. Forschungsgruppe Erneuerbare Energie, ZHAW Wädenswil.

Blank, P., Wickert, B., Obermeier, A., Friedrich, R. 1999: Erstellung eines Emissionskatasters für Feuerungsanlagen in Haushalt und Kleinverbrauch. Umweltforschungsplan des Bundesministers für Umwelt, Naturschutz und Reaktorsicherheit Luftreinhaltung - Forschungsberich.

Table of Contents

Fuel wood - Demand

General description

Input data

Calculation steps