Difference between revisions of "Assessing Wood Fuel Supply Potentials"

From energypedia
***** (***** | *****)
m
***** (***** | *****)
m
Line 29: Line 29:
 
#development of the integration module
 
#development of the integration module
 
#identification of woodfuel hot spots.
 
#identification of woodfuel hot spots.
 +
 +
  
 
At national level, the WISDOM approach has been implemented in Mexico, Senegal and Slovenia. At the subregional level, WISDOM has been implemented in the eastern and central African countries covered by the Africover Programme (Burundi, Democratic Republic of the Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, the Sudan, United Republic of Tanzania and Uganda) and in the countries of Southeast Asia (Cambodia, Malaysia, Lao People’s Democratic Republic, Thailand, Viet Nam and China, Yunnan Province)
 
At national level, the WISDOM approach has been implemented in Mexico, Senegal and Slovenia. At the subregional level, WISDOM has been implemented in the eastern and central African countries covered by the Africover Programme (Burundi, Democratic Republic of the Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, the Sudan, United Republic of Tanzania and Uganda) and in the countries of Southeast Asia (Cambodia, Malaysia, Lao People’s Democratic Republic, Thailand, Viet Nam and China, Yunnan Province)
Line 35: Line 37:
  
 
|}
 
|}
 +
 +
  
 
<font size="2"></font>
 
<font size="2"></font>
Line 41: Line 45:
  
 
<font size="2">As a consequence, forest resource assessments have to be complemented by legal, regulatory, institutional, and socio-economic studies that analyse the framework conditions.&nbsp;</font>
 
<font size="2">As a consequence, forest resource assessments have to be complemented by legal, regulatory, institutional, and socio-economic studies that analyse the framework conditions.&nbsp;</font>
 +
  
  
Line 47: Line 52:
 
<u><font size="2">The most important factors necessary to estimate woodfuel supply include:</font></u>
 
<u><font size="2">The most important factors necessary to estimate woodfuel supply include:</font></u>
  
<font size="2">'''Forest' '''''area''' is a land area of more than 0.5 hectares, with a tree canopy cover of more than 10%, which is not primarily under agricultural or other specific non-forest land use. In the case of young forests or regions where tree growth stunted by climate, the trees should be capable of reaching a height of 5 m in situ, and of meeting the canopy cover requirement. Forest land may include grassland, shrub land, tree land, wetland, and/or barren land.</font>
+
<font size="2">'Forest' '''area''' is a land area of more than 0.5 hectares, with a tree canopy cover of more than 10%, which is not primarily under agricultural or other specific non-forest land use. In the case of young forests or regions where tree growth stunted by climate, the trees should be capable of reaching a height of 5 m in situ, and of meeting the canopy cover requirement. Forest land may include grassland, shrub land, tree land, wetland, and/or barren land.</font>
  
 
<font size="2">'''Mean stock density''' '''per hectare''' is the average total volume of wood in cubic metres per hectare</font>
 
<font size="2">'''Mean stock density''' '''per hectare''' is the average total volume of wood in cubic metres per hectare</font>
Line 60: Line 65:
  
 
<font size="2">'''Harvest/Cutting fraction''' is the volume harvested after the percentage of harvesting losses have been deducted. Harvesting losses can amount to 10% of the allowable cut.</font>
 
<font size="2">'''Harvest/Cutting fraction''' is the volume harvested after the percentage of harvesting losses have been deducted. Harvesting losses can amount to 10% of the allowable cut.</font>
 +
 +
<font size="2"></font>
  
 
Table 5 provides a summary of these factors as well as how to use them to estimate wood supplies.
 
Table 5 provides a summary of these factors as well as how to use them to estimate wood supplies.
  
{| border="1" cellpadding="2" cellspacing="0" width="100%"
+
{| cellpadding="2" cellspacing="0" border="1" width="100%"
 
|-
 
|-
 
| '''Table 5: Estimating actual and potential wood supplies'''
 
| '''Table 5: Estimating actual and potential wood supplies'''
 
|-
 
|-
 
| width="540" |  
 
| width="540" |  
{| border="1" cellpadding="5" cellspacing="0" width="100%"
+
{| cellpadding="5" cellspacing="0" border="1" width="100%"
 
|-
 
|-
 
| colspan="2" | '''Supply factors'''
 
| colspan="2" | '''Supply factors'''
Line 123: Line 130:
  
 
<font size="2"><span id="1227183625767S" style="display: none">&nbsp;</span></font>
 
<font size="2"><span id="1227183625767S" style="display: none">&nbsp;</span></font>
 +
 +
  
 
= Characteristics of Woodfuel Supply Figures<br/> =
 
= Characteristics of Woodfuel Supply Figures<br/> =
Line 132: Line 141:
  
  
{| class="FCK__ShowTableBorders" border="0" cellpadding="0" cellspacing="0" width="100%"
+
{| class="FCK__ShowTableBorders" cellpadding="0" cellspacing="0" border="0" width="100%"
 
|-
 
|-
 
|  
 
|  
{| border="1" cellpadding="0" cellspacing="0"
+
{| cellpadding="0" cellspacing="0" border="1"
 
|-
 
|-
 
| colspan="3" valign="top" width="300" |  
 
| colspan="3" valign="top" width="300" |  
Line 141: Line 150:
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''&nbsp;'''
 
'''&nbsp;'''
  
Line 151: Line 160:
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>LPG</span>'''
 
'''<span>LPG</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>560</span>
 
<span>560</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>45</span>
 
<span>45</span>
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>Gasoline(petrol)</span>'''
 
'''<span>Gasoline(petrol)</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>720</span>
 
<span>720</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>44</span>
 
<span>44</span>
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>Kerosene</span>'''
 
'''<span>Kerosene</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>806</span>
 
<span>806</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>43</span>
 
<span>43</span>
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>Wood (oven dried)</span>'''
 
'''<span>Wood (oven dried)</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>650-750</span>
 
<span>650-750</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>18-19</span>
 
<span>18-19</span>
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>Wood, (30% moisture)</span>'''
 
'''<span>Wood, (30% moisture)</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>650-750</span>
 
<span>650-750</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>12-13</span>
 
<span>12-13</span>
  
 
|-
 
|-
| valign="top" width="130" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="130" |  
 
'''<span>Charcoal</span>'''
 
'''<span>Charcoal</span>'''
  
| valign="top" width="76" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="76" |  
 
<span>180</span>
 
<span>180</span>
  
| valign="top" width="95" nowrap="nowrap" |  
+
| nowrap="nowrap" valign="top" width="95" |  
 
<span>30</span>
 
<span>30</span>
  
Line 217: Line 226:
  
 
|  
 
|  
{| border="1" cellpadding="0" cellspacing="0"
+
{| cellpadding="0" cellspacing="0" border="1"
 
|-
 
|-
 
| colspan="2" valign="top" width="177" |  
 
| colspan="2" valign="top" width="177" |  
Line 282: Line 291:
 
|}
 
|}
  
&nbsp;Table 2 shows characteristics of woodfuel&nbsp;compared to other fuels. Table 3 depicts the influence that wood moisture has on calorific value.
+
Table 2 shows characteristics of woodfuel&nbsp;compared to other fuels. Table 3 depicts the influence that wood moisture has on calorific value.
  
  
  
{| border="1" cellpadding="0" cellspacing="0"
+
{| style="width: 100%" cellpadding="0" cellspacing="0" border="1"
 
|-
 
|-
 
| valign="top" width="550" |  
 
| valign="top" width="550" |  
Line 310: Line 319:
 
Table 4 shows a calculation for finding the equivalent&nbsp;weight&nbsp;value of of LPG for one stere of wood.
 
Table 4 shows a calculation for finding the equivalent&nbsp;weight&nbsp;value of of LPG for one stere of wood.
  
<br/><font size="2">To harmonize definitions and conversion factors for adequate data collection and estimation, the FAO has published a ‘Unified Bioenergy Terminology’ located at:</font>
+
<br/><font size="2">-> To harmonize definitions and conversion factors for adequate data collection and estimation, the FAO has published a ‘Unified Bioenergy Terminology’ located </font>[http://www.fao.org/DOCREP/007/j4504E/j4504E00.HTM here]
 +
 
 +
<font size="2">When estimating actual or potential wood supplies, an important distinction has to be made between:</font>
 +
 
 +
#<font size="2">clear felling (often limited to plantations) and</font>
 +
#<font size="2">sustainable harvesting.</font>
 +
 
 +
<font size="2">The calculation is straightforward (see Table 5 on the previous page).&nbsp;</font>
 +
 
 +
 
 +
 
 +
= References<br/> =
  
[http://www.fao.org/DOCREP/007/j4504E/j4504E00.HTM http://www.fao.org/DOCREP/007/j4504E/j4504E00.HTM]
+
This article was originally published by [http://www.gtz.de/en/themen/12941.htm GIZ HERA]. It is basically based on experiences, lessons learned and information gathered by GIZ cook stove projects. You can find more information about the authors and experts of the original “Cooking Energy Compendium” in the [[Imprint_-_GIZ_HERA_Cooking_Energy_Compendium|Imprint]].
  
<font size="2">When estimating actual or potential wood supplies, an important distinction has to be made between (i) clear felling (often limited to plantations) and (ii) sustainable harvesting. The calculation is straightforward (see Table 5 on the previous page).&nbsp;</font>
+
<references />
  
  

Revision as of 16:09, 1 August 2012

--> Back to Overview GIZ HERA Cooking Energy Compendium

Supply Assessment

Sound information on baseline forest resources is a precondition for shaping woodfuel supply strategies on national and/or sub-national levels. Theoretically, there are two main sources for woodfuel information: forestry services and energy agencies. Their approaches differ significantly. Analyzing information from these sources is challenging as there are often discrepancies in the reported values: definitions are seldom consistent; measurement units are different; conversion factors needed are not always available etc. Deficiencies of data, coupled with the failure to prioritize forest energy at policy level, frequently result in the absence of legislation on sectoral wood energy. 

The process of collecting and verifying facts and figures is a laborious, costly and time-consuming undertaking, requiring properly trained and qualified personnel.

To alleviate these constraints, FAO published a guide outlining simple and rapid methods to verify existing data, to fill gaps in the information chain, and to conduct more reliable surveys

- > A guide for woodfuel surveys EC-FAO PARTNERSHIP PROGRAMME (2000 - 2002)


The FAO has developed and implemented the ‘Woodfuel Integrated Supply/Demand Overview Mapping (WISDOM) methodology as a tool to support national wood energy planning. This is a GIS-based tool that allows the user to understand, in detail, the current spatial patterns of biomass demand and supply in a country, and to assess the sustainability of woodfuel as a renewable and widespread energy source. The methodology has been expanded to investigate the scope of urban woodfuel supply, which identifies the extent to which supply zones encroach into rural areas and forests. (The term “urban woodsheds” is analogous with the familiar geographic concept of watersheds.) 

WISDOM for Cities. Analysis of wood energy and urbanization using WISDOM methodology


WISDOM analysis involves five main steps:

  1. selection of the spatial base
  2. development of the demand module
  3. development of the supply module
  4. development of the integration module
  5. identification of woodfuel hot spots.


At national level, the WISDOM approach has been implemented in Mexico, Senegal and Slovenia. At the subregional level, WISDOM has been implemented in the eastern and central African countries covered by the Africover Programme (Burundi, Democratic Republic of the Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, the Sudan, United Republic of Tanzania and Uganda) and in the countries of Southeast Asia (Cambodia, Malaysia, Lao People’s Democratic Republic, Thailand, Viet Nam and China, Yunnan Province)

Further information can be found here


Woodfuel problems are not always simply a gap between demand and supply. They are increasingly regarded as a reflection of more systemic, and often locally site-specific deficiencies in land tenure, urban energy markets, and fiscal and incentive policies and in misallocation of forests and cropland.

As a consequence, forest resource assessments have to be complemented by legal, regulatory, institutional, and socio-economic studies that analyse the framework conditions. 


Factors for Estimating Woodfuel Supply

The most important factors necessary to estimate woodfuel supply include:

'Forest' area is a land area of more than 0.5 hectares, with a tree canopy cover of more than 10%, which is not primarily under agricultural or other specific non-forest land use. In the case of young forests or regions where tree growth stunted by climate, the trees should be capable of reaching a height of 5 m in situ, and of meeting the canopy cover requirement. Forest land may include grassland, shrub land, tree land, wetland, and/or barren land.

Mean stock density per hectare is the average total volume of wood in cubic metres per hectare

Mean annual increment (MAI) is the total increase in volume (of wood) of a stand per hectare per year. Except for plantations, the MAI is often estimated as 2.5 percent of the forest stock density.

Allowable cut is the amount of wood that may be harvested annually (or over a given period) per hectare, according to the governing rules. In overexploited stands this figure is often significantly below the MAI, in order to rehabilitate the stand.

Accessible area fraction is the share of forest area accessible for exploitation. Generally, parts of a forest are subject to legal restrictions (protected areas, ownership rights etc.) or are physically inaccessible or economically non-viable, and are therefore not considered for exploitation. On a regional level, a factor of 40-50% is often applied, depending on the population density.

Fuelwood fraction is the share of wood volume destined for woodfuel production. Forest owners are eager to increase their profits by selling their produce as lumber or poles etc. as these often achieve much higher prices on the market than fuelwood.

Harvest/Cutting fraction is the volume harvested after the percentage of harvesting losses have been deducted. Harvesting losses can amount to 10% of the allowable cut.

Table 5 provides a summary of these factors as well as how to use them to estimate wood supplies.

Table 5: Estimating actual and potential wood supplies
Supply factors Hypothetical Data Units
A Forest Area 1000 ha
D Mean stock density 30 m3 /ha
MAI Mean annual increment 0.75 m3/ha/yr
AC Allowable Cut 0.5 m3/ha/yr
AF Accessible area fraction 0.8  
FF Fuelwood fraction 1.0  
HF Harvest/Cutting fraction 0.9  

Example of stock and yield estimation:

Clear felling:                     A x D x FF x HF   (for clear felling , 100% of land accessibility is assumed)

=1000ha x 30 m3/ha x 1.0 x 0.9 = 27 000 m3

Sustainable harvesting: A x AC x FA x FF x HF

=1000ha x 0.5 m3/ha x 0.8 x 1.0 x 0.9 = 360 m3


Characteristics of Woodfuel Supply Figures

Wood is the most widely used resource that provides thermal energy in the world, so high conversion efficiency into energy is a key issue. Utmost care should be taken when using conversion factors, as this is a major cause of serious miscalculation. Foresters in general distinguish between ‘standing stock’ measured in solid cubic meters, and ‘harvested woodfuel’ measured in stacked cubic meters (containing air spaces between the pieces of wood), which are often called stere. A well-piled stacked cubic meter may contain 0.65 m3 solid (e.g. products from plantations) whereas a poorly stacked one may only have 0.33 m3 solid (e.g twisted branches of sahelian shrubs); just half as much as the well-stacked wood.

Energy content is proportional to the dry-weight of wood; so higher density woods have higher calorific values. The reported range in wood densities is between 100 kg/m3  and 1200 kg/m3. Species used as woodfuel are generally from 650 kg/m3 to 750 kg/m3. The moisture content plays a crucial role in determining the calorific value (Table 3). The moisture content of wood is around 50 % (of total weight) when first harvested, whereas air-dried wood contains between 12% to 20% of moisture yielding a calorific value between 14 MJ/kg and 16 MJ/kg. To evaporate one kilogram of water takes about 2.5 MJ. In the case of charcoal, the calorific value is around 30 MJ/kg. In its statistics, the FAO uses a conversion factor of 165 kg of produced charcoal from one cubic meter of fuelwood (see also chapter on charcoal).


Table 2: Density (specific mass) and (net) calorific value (Heat of combustion) of some fuels

 

density (kg/m3)

calorific value (MJ/kg)

LPG

560

45

Gasoline(petrol)

720

44

Kerosene

806

43

Wood (oven dried)

650-750

18-19

Wood, (30% moisture)

650-750

12-13

Charcoal

180

30

NB.: Values are approximate, since fuels vary in composition which affects both the density and calorific value.

Table 3: Influence of wood moisture on calorific value

Moisture content %

MJ/kg

0

19.0

10

16.9

20

14.7

30

12.6

40

10.4

50

8.2

60

6.1

Table 2 shows characteristics of woodfuel compared to other fuels. Table 3 depicts the influence that wood moisture has on calorific value.


Table 4: Calculation for replacement value of one stere of wood by LPG

Assuming a mean value for 1 stere of wood = 0.5m3 and average density of 700kg/m3

Weight of 1 stere of wood : 700 kg/m3 x 0.5 (stere/volume conversion)= 350 kg

Energy content of 1 stere:   350 kg x 13 MJ/kg = 4,550 MJ

 

Assuming energy output of 1 kg of LPG =45MJ (see Table 2) and density of LPG is 0.056Kg/litre

Equivalent weight of LPG for 1 stere of wood =

  • 4,550 MJ ÷45MJ = 101 Kg LPG or in terms of litres of LPG:
  • 101 ÷ 0.056 = 200 litres of LPG

Table 4 shows a calculation for finding the equivalent weight value of of LPG for one stere of wood.


-> To harmonize definitions and conversion factors for adequate data collection and estimation, the FAO has published a ‘Unified Bioenergy Terminology’ located here

When estimating actual or potential wood supplies, an important distinction has to be made between:

  1. clear felling (often limited to plantations) and
  2. sustainable harvesting.

The calculation is straightforward (see Table 5 on the previous page). 


References

This article was originally published by GIZ HERA. It is basically based on experiences, lessons learned and information gathered by GIZ cook stove projects. You can find more information about the authors and experts of the original “Cooking Energy Compendium” in the Imprint.



Top of the page

--> Back to Overview GIZ HERA Cooking Energy Compendium