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Appendix 1: data sources and references.

Notes: Mtoe = million tones of oil equivalent

Energy conversion factors from documents/Conversionfactors_000.xls

1. Natural Gas: Proved Reserves at end 2005

Source: BP Statistical Review of World Energy, 2006


Method: Reserves tabulated in trillion cubic meters Conversion to Mtoe via current production ratio (Mtoe/cubic meters) for each country.

2. Oil: Proved Reserves at end 2005

Source: BP Statistical Review of World Energy, 2006


Method: Direct transcription from BP spreadsheet (Converted to Mtoe)

3. Coal: Proved reserves at end 2005

Source: BP Statistical Review of World Energy, 2006


Method: Reserves tabulated in millions of tons

Conversion to Mtoe via current production ratio (Mtoe/mt coal prod.) for each country.

4. Natural Bitumen, Oil Shale

Source: World Energy Council. 2001. Survey of Energy Resources ser/shale/shale.asp

Method: Oil Shale: Proved recoverable reserves plus estimated additional reserves (Mtoe). Table 3.1 Oil shale: resources, reserves and production at end-1999

Note: Only economically recoverable reserves are used in the calculation since the technology is relatively experimental (and costly) with few examples of actual production-scale implementation.

Source: World Energy Council. 2001. Survey of Energy Resources bitumen/bitumen.asp

Method: Natural Bitumen: Proved amount in place plus estimated additional reserves (Mtoe). Table 4.1 Natural bitumen: resources, reserves and production at end-1999

5. Geothermal Potential Energy

Data Source: Pollack, Henry N.; Hurter, Suzanne J.; Johnson, Jeffrey R. 1993. "Heat flow from the earth's interior Analysis of the global data set." Reviews of Geophysics. vol. 31, no. 3, p. 267-280.

a) Initial screen of potentially exploitable sites from the above database:

- Remove sites where the temperature gradient is larger than 110K/m (volcanoes, etc) (leaving 10703 sites out of 14238).

- Calculate the minimum temperature gradient necessary to produce electricity:

- Depth = dT/TGradient = 150K/TGradient

- If no temperature gradient was given, the following calculation was made:

Depth = 150K*conductivity/heat flow

- If no conductivity value was given: an arbitrary conductivity of 2.5 was chosen (Granite)

- Keep sites with location depths of <6 km (leaving 8040 sites out of 10703)

b) Calculate spacing requirements for exploitation:

- Site observations in mW/m2 (milliwatts/sq. meter) by country

- Calculate total number of observation sites per country

- Divide into area to get sq. km./ site

- Set maximum spacing at 25000 sq. km./site (Spacing procedure incorporates area represented by site)

Calculate a "reasonable" coverage extrapolation area for one sample drilling. Rationale: When mapped, the data points clearly reflect greater drilling frequency in countries that are believed to have more thermal resources. Average heat flow cannot be simply calculated from the sample points and multiplied by national area since this would clearly overestimate the total for countries where sampling is sparse (e.g., Niger). After looking at average area per sample for all the countries, and focusing where coverage is near--total (e.g., US, South Africa), we arrive at 25,000 sq. km. per sample drilling as a reasonable estimate. For countries with smaller sample areas per sample (which we call spacing), we use the actual number, otherwise, we truncate To cut off leading or trailing digits or characters from an item of data without regard to the accuracy of the remaining characters. Truncation occurs when data are converted into a new record with smaller field lengths than the original.  at 25,000).

c) Generate estimated heat (power) flow associated with each site as follows:

- Factors multiplied together to obtain heat energy associated with each site

Heatkw=heatflow/1,000,000 (1 kW = 106 mW): Conversion to kW/m2 Spacing (Area in sq. km.) x 1,000,000 (Conversion to m2)

[1 sq. km. = 103 meters x 103 meters] [After cancellation, heatkw = heatflow x spacing]

- Heatkw totaled across sites for each country to get country kW.

- Conversion to heat potential in annual mtoe:

Heat potential = Heatkw x 24 (hours/day) x 365.25 (days/year) x 8[e.sup.-11] (kWh => mtoe)

d) Assumptions:

Current Capacity Factor: Country--specific capacity factor from Lund, Freeston and Boyd (2005) below Sensitivity ranges:

Low: 10% greater capacity factor (max. 100%)

High: 30% greater capacity factor (max. 100%)

Conversion potential:

Low: 5% of total heat potential

High: 15% of total heat potential; set at current Swiss conversion percent, on the assumption that Swiss exploitation is near the current technical limit]

[Exception for Turkey, which is currently 0.68]

Data Source: Lund, J., D. Freeston and T. Boyd. 2005. "Direct Application of Geothermal Energy: 2005 Worldwide Review," Geothermics 34: 691-727 (Table 1).

e) Final calculation:

Heatmtoe = Capacity Factor x Conversion potential x heat potential

5. Potential Wind Power

Onshore wind potential:

Data Sources:

NASA NASA: see National Aeronautics and Space Administration.
 in full National Aeronautics and Space Administration

Independent U.S.
 Surface meteorology and Solar Energy: Global Data

Monthly Averaged Wind Speed At 50 m Above The Surface Of The Earth (m/s)

 in full Uniform Resource Locator

Address of a resource on the Internet. The resource can be any type of file stored on a server, such as a Web page, a text file, a graphics file, or an application program.

Archer, C. and M. Jacobson (2005) Evaluation of global wind power, Journal of Geophysical Research Journal of Geophysical Research is a publication of the American Geophysical Union. JGR was formerly titled Terrestrial Magnetism from its founding by the AGU's president Louis A. , Vol. 110, D12110, doi:10.1029/2004JD005462.

Urban areas subtracted as they are probably not suitable for large turbine siting. Urban areas GIS data: Global RuralUrban Mapping Project (GRUMP): Urban/Rural Extents Center for International Earth Science Information Network (CIESIN CIESIN Center for International Earth Science Information Network ), Columbia University; International Food Policy Research Institute The International Food Policy Research Institute (IFPRI) was founded in 1975 to develop policy solutions for meeting the food needs of the developing world in a sustainable way.  (IPFRI); the World Bank; and Centro Internacional de Agricultura Tropical (CIAT CIAT Centro Internacional de Agricultura Tropical (Spanish: International Center for Tropical Agriculture, Colombia)
CIAT Chartered Institute of Architectural Technologists (UK) 
);2004. Palisades, NY: CIESIN, Columbia University. Available at

a) Wind speed area calculation:

i) Calculation of wind speed at 80m elevation from NASA 50m

Use of the Shear expression:

v = [v.sub.ref] ln(z/[z.sub.0)/ln([z.sub.ref/[z.sub.0])


v = wind speed at height z above ground level

[v.sub.ref] = reference speed, i.e. a wind speed we already know at height zref

z = height above ground level for the desired velocity, v (i.e., 80 m)

[z.sub.0] = roughness length in the current wind direction.

Roughness lengths may be found in the Reference Manual at

[z.sub.ref] = reference height, i.e. the height where we know the exact wind speed vref

Roughness class = 1.55 and roughness length = 0.055 corresponding to following landscape type: Agricultural land with some houses and 8 meters tall sheltering hedgerows with a distance of approximately 1250 meters

ii) Merge with Archer and Jacobson 80m wind speed data

iii) Spatial interpolation interpolation

In mathematics, estimation of a value between two known data points. A simple example is calculating the mean (see mean, median, and mode) of two population counts made 10 years apart to estimate the population in the fifth year.
 to raster using: Inverse Distance Weighted (IDW IDW Informationsdienst Wissenschaft (German: News service science)
IDW Ideal Weight
IDW Institut der Wirtschaftsprüfer (German: Institute of Auditors )
IDW Inverse Distance Weighting
): Neighbors: 3, at 0.1 degree resolution (approx 11 km at the equator).

iv) Re-classification into 7 wind classes (meters/second): (Archer and Jacobsen (2005)):
      Class 1 < 5.9
5.9 < Class 2 < 6.9
6.9 < Class 3 < 7.5
7.5 < Class 4 < 8.1
8.1 < Class 5 < 8.4
8.4 < Class 6 < 9.4
9.4 < Class 7

v) Intersection with the country land area to get total area by wind classes in each country

vi) Re-projection to equal area projection One in which equal areas on the ground are represented by equal areas on the map.  to get the km2 of each wind class for each country

Steps in calculation of global wind potential:

b) Drop areas that are wind class < 3.0 (not feasible)

c) Assumption: technically feasible rotor density:

High: Assume technical feasibility at current wind rotor density (rotors/sq. km.) for Germany (which has the most installations: 17,574 as of 2005).

Low: 60% of Germany's current density

Source: German Wind Energy Institute, Statistics end of 2005

d) Calculation of power production per standard wind rotor (POSR POSR Pre-Operation Safety Review
POSR Point Of Sale Replenishment
POSR Peacetime Operating Stock Requirement
POSR Point of Sale Reservation

Source: Wind Energy Reference Manual, Danish Wind Industry Association

Wind power input: 0.5*1.225*(average wind speed per power class cubed) [Watts/[m.sup.2]]

[The formula for the power per m2 in Watts = 0.5 * 1.225 * v3, where v is the wind speed in m/s.]

Adjustment for Weibull distribution of wind speed: Multiply by 2 [Watts/[m.sup.2]]

Expected power output: Multiply by 0.3 [Watts/[m.sup.2]]

Convert to yearly kWh: Multiply by (24 x 365.25/1000) [kWh/[m.sup.2]/year]

e) Assumption: Standard 80m wind rotor swept area: Multiply by 4656 [m.sup.2] [kWh/[m.sup.2]/year]

f) Calculate total power output:

Total power output (kWh/year) = POSR x technically feasible rotor density x feasible area

g) Adjustment for power losses (10%) = 0.90 x Total power output [Due to wind turbine wakes, blade soiling, Operations & Management]

h) Convert to mtoe: Total adjusted power output x [8e.sup.-11]

Offshore wind potential:

Most of the offshore wind locations are between 5 to 15 meters in depth and a few kilometres to 15km from the shore. For each country we estimate the total area within its EEZ EEZ Exclusive Economic Zone  that is potentially suitable for offshore turbine installations.

Data Sources:

Wind speed at 80m elevation using same method as above for onshore; and Two--minute gridded global relief for both ocean and land areas (combined bathymetry ba·thym·e·try  
The measurement of the depth of bodies of water.

 and topography) in the ETOPO2v2 (2006), NOAA NOAA
National Oceanic and Atmospheric Administration

Noun 1. NOAA - an agency in the Department of Commerce that maps the oceans and conserves their living resources; predicts changes to the earth's environment;

EEZ: Exclusive economic zones: The Global Maritime Boundaries Database

Coastline: SRTM SRTM Shuttle Radar Topography Mission
SRTM Security Requirements Traceability Matrix
SRTM Software Requirements Traceability Matrix
SRTM System Requirements Traceability Matrix
SRTM Security Requirements Tractability Matrix
SRTM Static Root of Trust for Measurement
 Water Body Dataset (SWBD SWBD Switchboard
SWBD SRTM (Shuttle Radar Topography Mission) Water Body Dataset

Coastal delimitation based on the digital coastlines from the SRTM Water Body Dataset (shuttle radar topography mission The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained digital elevation models on a near-global scale from 56 °S to 60 °N, to generate the most complete high-resolution digital topographic database of Earth to date. ), 90m resolution.

a) Wind power calculation: same as steps b) through h) above.

6. Potential Solar Power

Data Source: NASA Surface Meteorology and Solar Energy: Global Data

Site URL:

a) Average Monthly Insolation (AMI):

Monthly Averaged Insolation Incident On A Horizontal Surface (kWh/m2/day)

Monthly average for July 1983-June 1993

b) Assumptions:

Low: Solar PV collectors cover 0.05% of total land area

High: Solar PV collectors cover 0.18% of total land area

High estimate is equivalent to the country area share assumed in estimates for Germany in: Bundesministerium fur Umwelt, Naturschutz und Reaktorsicherheit (2004), Okologisch optimierter Ausbau der Nutzung erneuerbarer Energien in Deutschland, Berlin. (English summary available at this site)

c) Assumed conversion efficiency range (insolar => PV):

Low: 15%

High: 20%

Sources for conversion efficiency estimate: - U.S. National Renewable Energy Laboratory. 2006. "High Performance Photovoltaic Project--Overview."

- Green, M.A. 1998. "Photovoltaic Solar Energy Conversion: An Update." Australian Academy of Technological Sciences and Engineering, ATSE ATSE Application Timesharing Software Engineering
ATSE Army Training Support Center (US Army)
ATSE Army Topographic Support Establishment (Australia)
ATSE Army Test Program Set Support Environment
 Focus No 102, May/June.

d) Annual Energy Generation (AEG--mtoe/year):

APG APG Assists Per Game (basketball)
APG Assists Per Game (hockey statistic)
APG Aberdeen Proving Ground
APG Automated Password Generator
APG Asia Pacific Group on Money Laundering
 = 0.15 or 0.20 (conversion efficiency) x [8e.sup.-11] (mtoe/kWh) x AMI (kWh/[m.sup.2/day)] x 365.25 (days/year) x .001 (% of land area) x land area (sq. km.) x 106 ([m.sup.2]/sq. km.)

7. Potential Hydro Power

Data Source: World Energy Council. 2001. Survey of Energy Resources--Hydro: Technically--Exploitable Capability (TEC--TWh/year) ser/hydro/hydro.asp

a) Conversion to mtoe/year: TEC (mtoe/year) = 0.08 (mtoe/TWh) x TEC (TWh/year)

8. C[O.sub.2] Storage Potential

Data Source: Hendriks, Chris, Graus, Wina, and Frank van Bergen. 2004. "Global Carbon Dioxide Storage Potential and Costs." Ecofys / Netherlands Institute of Applied Geoscience. EEP--02001. Table 20. C[O.sub.2] storage potentials for the 18 world regions.

a) Initial Estimates, 18 World Regions: USA, Central America, South America, Northern Africa,Western Africa, Eastern Africa, Southern Africa, Western Europe, Eastern Europe, Former Soviet Union, Middle East, Southern Asia, Eastern Asia, South East. Asia, Oceania, Japan, Greenland

"Best Estimates" chosen from [Low, Best, High] for C[O.sub.2] capture potential in four classes: Oil fields, natural gas fields This list of natural gas fields includes major fields of the past and present.

N.B. Some of the items listed are basins or projects that comprise many fields (e.g. Sakhalin has three fields: Chayvo, Odoptu, and Arkutun-Dagi).
, coal fields, and saline aquifers (mostly offshore)

b) Country allocation of regional totals:

Country shares for natural gas, oil and coal fields calculated as:

Sum natural gas, oil and coal reserves by region from Sections 1-3 above

Compute country shares by region for natural gas, oil and coal

Apply country shares to "Best" estimated C[O.sub.2] capture potentials by region

c) Country shares for saline aquifer capture potential:

Sum extended economic zone (EEZ) areas by region

Compute country EEZ shares by region

Apply country shares to "Best" estimated C[O.sub.2] saline aquifer capture potentials by region.

d) Multiply country shares by "Best" regional totals to obtain country C[O.sub.2] capture potential for natural gas, oil and coal fields, and for saline aquifers. Sum to obtain total C[O.sub.2] capture potential (units: Gigatonnes)

e) Calculate storage as multiple of annual country C[O.sub.2] emissions: Annual C[O.sub.2] Emissions (including land use)--thousand tonnes/year

Data Source: World Resources Institute--CAIT Database, 2006.

Ratio: Total C[O.sub.2] capture potential (Gt) x [10.sup.6] (kt/Gt)/Annual C[O.sub.2] emissions (kt)

9. Employment Vulnerability to a Global C[O.sub.2] Shadow Price Shock

a) Calculation of Sectoral Emissions Intensities:

i) Sectoral Emissions by IEA IEA International Energy Agency
IEA International Environmental Agreements
IEA International Association for the Evaluation of Educational Achievement
IEA Institute of Economic Affairs
IEA Inferred from Electronic Annotation
IEA International Ergonomics Association
 Industry Classification, 2002.

Data Source: International Energy Agency C[O.sub.2] Emissions Database

ii) GDP GDP (guanosine diphosphate): see guanine.  Breakdown by Sector, 2002.

Data Source: GDP and its breakdown at constant 1990 prices in US Dollars

UN National Accounts Main Aggregates Database at:

UN/IEA Sector Concordance: Developed by the authors

b) Calculation of Sectoral Employment Shares:

Data Source: ILO ILO
International Labor Organization

Noun 1. ILO - the United Nations agency concerned with the interests of labor
International Labor Organization, International Labour Organization
 LABORSTA Online Database: Paid Employment by Economic Activity at:, Yearly Data.

ILO/UN/IEA Sector Concordance: Developed by authors

Sector shares of paid employment computed by year Utilized shares: Complete set (six composite sectors) only; most recent year.

c) Computation of Employment Vulnerability Index:

Weighted Combination of Sectoral C[O.sub.2] Emissions Intensities (2002; exception Estonia (1998))

Weights are sector employment shares from b)

10. Sequestration Potential via Reduced Deforestation

Data Source: World Resources Institute--CAIT Database, 2006

CED (Capacitance Electronic Disc) An earlier videodisc technology from RCA that was released in 1981 and abandoned five years later. Like phonograph records, the analog disc contained grooves that a stylus rode over. : Annual C[O.sub.2] emissions attributable to deforestation and land clearing (ktonnes/yr)

CEG (Continuous Edge Graphics) A VGA RAMDAC chip from Edsun Labs that adds anti-aliasing on the fly. It can also calculate intermediate shades, thus providing thousands of colors on an 8-bit board that normally generates only 256 colors. : Total annual C[O.sub.2] emissions (ktonnes/yr).

a) Calculation of sequestration potential index: CED / CEG

11. Sea Level Impact

Data Source: DECRG Spatial Analysis Unit and computations from the project, "A Comparative Analysis of the Impacts of Sea-Level Rise in Developing Countries", 2007, Forthcoming Policy Research Working Paper, World Bank: Washington, DC.

SRTM :Shuttle Radar Topography Mission:

Population: Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro

Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3):

Population Grids. Palisades, Columbia University.


Agriculture: PAGE Global Agricultural Extent version 2, IFPRI IFPRI International Food Policy Research Institute

a) Impact index: Average % impacted for population, GDP and agriculture at 1- and 3-meter SLR (1) (Scalable Linear Recording) A line of magnetic tape drives from Tandberg Data that evolved from the QIC Data Cartridge format. See QIC.

(2) (Single Lens Reflex) A camera that uses the same lens for viewing and shooting.

b) Assumptions: Sensitivity range: maximum 50km inland impact zone

Low: 1-meter impact

Intermediate: 2-meter impact

High: 3-meter impact

12. Climate-Induced Damage Index

Data source: EM-DAT: Emergency Disasters Database CRED (Centre for Research on the Epidemiology of Disasters) Universite Catholique de Louvain, Brussels, Belgium

Indicators: Deaths (D), population rendered homeless (H) and population affected (A)

For disasters in weather--related categories: Drought, Extreme Temperature, Flood, Wild Fire and Wind Storm Time period: 1960-2002

b) Assumption: Weights placed on impacts: 1000 for deaths, 10 for homeless and 1 for "affected".

c) Calculation: Damage Index = (1000*D + 10*H + A) / Population 1980 (midpoint population)

13. Biogas From Livestock Manure a) Data on live animals:

Data source: FAOSTAT FAOSTAT FAO Statistical Databases (United Nations)  database, (2005): Cattle, buffalo, sheep, pigs, chickens, ducks Stocks&Domain=Production&servlet=1&hasbulk=&version=ext&language=EN

b) Assumption: Manure generation by species, daily dry dung production

Source: Woods, J., and D.O. Hall. 1994. "Bioenergy for development--Technical and environmental dimensions." FAO, Environment and energy paper No. 13.

Dung production/animal/day: Cattle 3 kg, buffalo 4 kg, sheep 0.5 kg, pigs 0.6 kg, chickens 0.1 kg, ducks 0.1 kg

c) Assumptions (from Woods and Hall): 50% of dung is potentially harvestable; 25% of potentially harvestable dung is recoverable;

Therefore, 12.5% (1/4 of 1/2) of total production is recoverable.

d) Conversion to energy (from Woods and Hall): Vester Hjermitslev plant, Denmark, has a digester capacity of 1,500 m3 (approx. 50 tons manure per day) designed to produce 3,500 m3/day biogas.

e) Assumption: Energy content assumption for biogas: 24.4 MJ/m3

Source: Wheeldon, Ian, Caners, Chris and Kunal Karan. 2005. "Anaerobic anaerobic /an·aer·o·bic/ (an?ah-ro´bik)
1. lacking molecular oxygen.

2. growing, living, or occurring in the absence of molecular oxygen; pertaining to an anaerobe.
 Digester Produced Biogas and Solid Oxide Fuel Cells: An Alternative Energy Source for Ontario Wastewater Treatment Facilities." Conference presentation, BIOCAP Canada.

f) Calculation of Biogas energy potential, based on Vester Hjermitslev and BIOCAP Canada: Biogasmtoe: 0.125 x (total dung (tonnes)/50) x 3500 (m3/50 tons) x 24.4 (MJ/[m.sup.3]) x [1.6e.sup.-10] (mtoe/MJ)

15. Land Ethanol Potential--Sugar Crops

Sources: Sugar crop yields: Table 2-2. Ethanol and Biodiesel Yield per Acre from Selected Crops, in Lester R. Brown, Plan B 2.0: Rescuing a Planet Under Stress and a Civilization in Trouble (NY: W.W. Norton & Co., 2006). Table 2.2 compiled by Earth Policy Institute from the following sources: FAO, U.N. Food and Agriculture Organization (FAO), FAOSTAT Statistics Database, at, updated 14 July 2005; Manitoba Department of Energy, Science, and Technology, "Ethanol FAQ (Frequently Asked Questions) A group of commonly asked questions about a subject along with the answers. Vendors often display them on their Web sites for use as troubleshooting guidelines. ," Energy Development Initiative Web site, ethanolfaq.html, viewed 5 August 2005; Renewable Fuels Association The Renewable Fuels Association (RFA) is an American lobbying organization which promotes policies, regulations, and research and development initiatives that will lead to the increased production and use of ethanol fuel. , Renewable Fuels Association, Homegrown Homeland for the Ethanol Industry Outlook 2005 (Washington, DC: 2005), pp. 2, 14--15; Nandini Nimbkar and Anil Rajvanshi, "Sweet Sorghum Ideal for Biofuel," Seed World, vol. 14, no. 8 (November 2003); Ellen I. Burnes et al., Ethanol in California: A Feasibility Framework (Modesto, CA: Great Valley Center, 2004), p. 18; Berg, op. cit. note 43; DOE, Biofuels from Switchgrass switchgrass

see panicumvirgatum.
: Greener Energy Pastures (Oak Ridge, TN: Oak Ridge National Laboratory, 1998).

a) Sugar crops: Calculation of Ethanol Energy Potential (EEP--in Mtoe) per '000 hectares

Sugar Crop Yields in gallons of ethanol/acre (higher than any other rated farm crop (e.g. corn, cassava cassava (kəsä`və) or manioc (măn`ēŏk), name for many species of the genus Manihot of the family Euphorbiaceae (spurge family). ):
Sugar beet (France)      714
Sugar cane (Brazil)      662
Sweet Sorghum (India)    374

a.1) Conversion to gasoline equivalent (gal./acre): Multiply by 0.67 (Source: Brown, op. cit.)

a.2) Conversion to energy equivalent (MJ/acre): Multiply by 130.88 (MJ/gallon)

Energy in 1 gallon of gasoline: 130.88 MJ

Source: U.S. Department of Energy energy_calculator.html#oilcalc

a.3) Conversion to MJ/hectare: Divide by 0.405 (1 acre = 0.405 hectares)

a.4) Conversion to Mtoe/hectare: Multiply by 1.6e-10 (Mtoe/MJ)

a.5) Conversion to Mtoe/1000 hectares: Multiply by 1000

Final calculation: EEP EEP Export Enhancement Program
EEP Ecosystem Enhancement Program
EEP Early Entrance Program (University of Washington)
EEP Equal Error Protection
EEP Einstein Equivalence Principle
EEP Emergency Evacuation Plan
 = Crop Yield x 0.67 x 130.88 / 0.405 x 1.6e-10 x 1000

b) Area suitable for cultivation of each sugar crop, by country:

Data Source: FAO--Global Agro-Ecological Zone Assessment

Datasets: Suitability for 27 crops under rain--fed conditions

Mixed-input case employed: Determination of total suitable land (TSL TSL Texas State Library
TSL Transport Layer Security
TSL (website)
TSL Teen Second Life (website)
TSL The Svedberg Laboratory (Uppsala, Sweden) 
) and total suitable land under forest ecosystems (TSLFE) as follows:

a) Determine all land very suitable and suitable at high level of inputs;

b) Of the balance of land after a), determine all land very suitable, suitable or moderately suitable at intermediate level of inputs, and

c) Of the balance of land after a) and b), determine all suitable land (i.e. "very suitable, suitable, moderately or marginally suitable land") at low level of inputs;

d) Of the balance of land after a), determine all land very suitable, suitable, moderately suitable or marginally suitable land in areas dominantly under forest ecosystems.

Suitable land for production (SLP (Service Location Protocol) An IETF standard used to announce and discover services such as printers and file shares on an IP network. Apple used SLP prior to Mac OS 10.2, but migrated to its Bonjour technology. SLP is also used in SIP-based IP telephony applications. ) ('000 hectares): [TSL]--[TSLFE]

c) Final calculation of ethanol energy potential from 10% suitable land for each sugar crop in each country:

Potential (Mtoe) = 0.10 x [Ethanol Energy Potential (/'000 hectares)] x SLP

16: Land Ethanol Potential--Switchgrass

a) Assumption: Ethanol Yield from Switchgrass (EYS EYS Energy Search, Inc. (former stock symbol)
EYS Electrical Y Seal
): 2800 liters/hectare

Source: "Switchgrass : a living solar battery for the prairies"

Roger Samson Ecological Agriculture Projects McGill University (Macdonald Campus), Ste-Anne-de-Bellevue, Quebec

Yield in gallons per acre: EYS / 3.79 (gallons/liter) / 0.405 (hectares/acre) x 0.67 (gasoline equivalent of ethanol) = 1222.19 (gallons/liter)

Convert to Mtoe/'000 hectares: (same as steps a.2) to a.5) above) = 1222.19 / 0.405 x 1.6e-10 x 1000

b) Calculation of tallgrass savanna area (TSA--'000 hectares) in each country:

Data Source: WWF See Windows Workflow Foundation.  International: Terrestrial Ecoregions of the World

Area data provided to DECRG in GIS format by WWF International

Agricultural land masked out.

Vegetation class used for tallgrass savanna: "Temperate Grasslands, Savannas and Shrublands"

Urban areas masked out based on GRUMP (see sources for 5. above).

c) Final calculation of switchgrass ethanol potential production (Mtoe) on 10% of total savanna for each country:

Potential (Mtoe) = 0.10 x [Tallgrass savanna area ('000 hectares)] x [Ethanol Yield from Switchgrass (Mtoe/'000 hectares)]

17. Land Biodiesel Potential--Jatropha Curcas

a) Assumption: Biodiesel Yield from Jatropha: 2000 liters of biodiesel/hectare

Source: Worldwatch Institute, German Federal Ministry of Food, Agriculture and Consumer Protection. 2006. "Biofuels for Transportation: Global Potential and Implications for Sustainable Agriculture and Energy in the 21st Century." Washington, D.C., June 7.

b) Calculation of potential cultivation area (sq. km.) in each country:

Data Source: WWF International: Terrestrial Ecoregions of the World

Area data provided to DECRG in GIS format by WWF International

Agricultural land masked out.

Vegetation classes used:

"Deserts and Xeric xer·ic  
Of, characterized by, or adapted to an extremely dry habitat.

xeri·cal·ly adv.
 Shrublands"--Non-montane shrublands only

"Tropical-Subtropical Grasslands, Savannas and Shrublands"

Urban areas masked out based on GRUMP (see sources for 5. above).

c) Assumption: Energy content of biodiesel: 34 MJ/liter

Source: Oak Ridge National Laboratory, Bioenergy Feedstock Development Programs

d) Final calculation: Jatropha biodiesel potential (Mtoe) from 10% of potential cultivable land:

Potential (Mtoe) = 0.10 x [Potential area (sq km)] x 100 (hectares/sq km) x 2000 (liters/hectare) x 34 (MJ/liter) x [1.6e.sup.-10] (Mtoe/MJ)
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Author:Buys, Piet; Deichmann, Uwe; Meisner, Craig; That, Thao Ton; Wheeler, David
Publication:Country Stakes In Climate Change Negotiations: Two Dimensions of Vulnerability
Date:Aug 1, 2007
Previous Article:8. Summary and conclusions.
Next Article:Appendix 2: country level estimates.

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