Technical Notes on use of PoleStar for the OECD
Environmental Outlook
Eric Kemp-Benedict
March 23, 2001
INTRODUCTION............................................................................................................. 2
FUEL DEMAND ............................................................................................................... 2
CO2 EMISSIONS .............................................................................................................. 3
SOX EMISSIONS .............................................................................................................. 4
BOD AND NITROGEN LOADING................................................................................ 4
TIMBER PRODUCTION ................................................................................................ 6
AGRICULTURAL DEMAND AND SUPPLY............................................................... 8
AGRICULTURAL INPUTS ............................................................................................ 9
LAND USE CHANGES .................................................................................................. 10
MUNICIPAL WASTES.................................................................................................. 11
DOMESTIC AND INDUSTRIAL WATER USE ........................................................ 11
REFERENCES ................................................................................................................ 13iIntroduction
These Notes summarize the assumptions and sources used for developing the Reference
scenario in PoleStar.

Fuel Demand
Fuel demand in the scenario is driven by output from the JOBS model. JOBS estimates
economic transfers between different economic sectors within an I-O framework. Thesetransfers include those between energy-producing and energy-consuming sectors, which
are taken for the purposes of this analysis as proxies for the transfer of fuel. The fuelproducing sectors tracked in JOBS are natural gas, electricity, coal, crude oil andpetroleum. Within PoleStar, trends in household biomass consumption and district heat
are also estimated. Biomass consumption is estimated based on a cross-sectional analysis,using IEA energy data, of per capita biomass consumption against income. District heatconsumption per capita is held at base-year levels in all regions.
The raw JOBS output requires manipulation for entry into PoleStar. The JOBS output
used in the PoleStar analysis is expressed in nominal dollar terms. Because values are in
nominal terms, trends in raw output do not correspond directly to physical flows.
However, JOBS also estimates prices for goods paid for by different consuming sectors,
allowing an estimate of transfers in real terms (after first correcting for changes in
efficiency of resource use in consuming sectors). As a result, using JOBS it is possible toestimate trends in physical fuel consumption, as indices calculated relative toconsumption levels in the baseyear.
The sectors for which energy demand is reported in PoleStar analysis are more aggregate
than those in JOBS. The correspondence is given in the following table:
PoleStar Sector
Households
Agriculture
Services
Industrial Subsector• Light Industry• Chemical• Iron & Steel• Non-ferrous Metals• Wood Products• Paper & pulp
• Transport Equip• Construction• Stone, Glass, Clay

JOBS Sector(s)Households
Rice, OthCrops, Livestock, Fisheries, Forestry
Service, Water, TradeTran, Dwellings
Corresponding Industrial Subsectors• Meat, OthFood• Chemicals• I_S• NonFer• WoodProd• PPP• MotorVehi, OtherManu• Construc• Minerals

2Energy use by energy transformation sectors is also estimated. Three energy
transformation sectors are included: oil refining, electricity generation and district heat.
Of these, two are treated in JOBS: oil refining and electricity generation. Furthermore, in
JOBS, within electricity generation a subset of feedstock fuels is considered: petroleum,coal and natural gas. In PoleStar, other sources of electricity are included: nuclear power,hydroelectric and renewables. For the Reference scenario, trends in refining activity anduse of petroleum, coal and natural gas are derived from JOBS output. Real output fromthe oil refining sector drives refinery production in PoleStar, while real transfers from therefining, coal production and natural gas distribution sectors to the electricity generation
sectors are used to generate trends in use of these fuels for electricity production. Trends
in electricity generation from nuclear, hydroelectric and renewable plants are based on
separate analyses from IEA (IEA, 2000). Fuel shares for district heat production are heldat base-year levels.
For the PoleStar analysis, trends in real supplies from energy sectors to other sectors wereapplied to base-year consumption estimates based on IEA energy statistics (IEA, 1999).
The PoleStar current accounts are broadly consistent with those of IEA. However, a few
important differences should be noted:
1. In PoleStar, stock changes are subsumed into overall production values.
2. Within electricity generation slightly different accounting approach is used thanthat of the IEA for renewable feedstock fuels. Geothermal, solar and other nonbiomass renewables are combined into a single category, whose conversion
efficiency is set to 100%. In contrast, IEA uses a 10% efficiency for geothermal.
3. In principle, in the baseyear, conversion efficiencies can be calculated from thedata provided by IEA (1999). However, the IEA's published figures for industrial
consumption of coal and oil include fuels used by auto-producers of electricity
and district heat. The IEA convention of including this consumption as an energy
demand produces high apparent efficiencies of thermal electric generation plants
and district heat plants. In PoleStar, energy conversion efficiencies are set using
assumptions drawn from Lazarus et al., 1993 and other sources, and totalindustrial oil and coal consumption are adjusted so that total fuel consumption byindustry and energy conversion sectors matches the IEA total.
4. Energy consumption for transport is not treated separately in the PoleStar
analysis. Within JOBS, fuel consumption for transport comprises part of theenergy use of two JOBS sectors: Households and TradeTran. It is difficult todisentangle the contribution of transport to total fuel consumption in these sectors.
For the PoleStar analysis, energy use in transport is added to that of theHousehold sector.

CO2 Emissions
Carbon emissions are estimated from energy consumption data and emissions factors
based on IPCC (1995b). Carbon dioxide emissions are estimated for fossil fuelcombustion and feedstock use.

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Carbon emission intensities are applied to fuel consumption in all of the PoleStar sectors:
Households, Transport (included with Households for this analysis), Services, Industry(exclusive of Energy Conversion), Agriculture and Energy Conversion. Industrial process
emissions are not included.

SOx Emissions
Data on sulphur emissions for the baseyear are based on Posch et al. (1996) andKuylenstierna (1998). Sulphur emission intensities are applied to fuel consumption in all
sectors. Additionally, industrial process emissions for Nonferrous Metals sector are
included.
In the industrial sector, a gradual reduction of sulphur emission factors for fuelcombustion and process emissions is assumed in most regions over the course of the
scenario, reflecting the fact that these can be affected by end-of-pipe cleaning
technologies and fuel switching. In all regions emission coefficients converge linearly toa value of 0.65 for coal, 0 for crude oil (used as a feedstock in the Chemical sector) and
0.0005 for petroleum, all other fuels staying at the baseyear value. The values would
converge completely in 2050; otherwise they change linearly with time. Some regions
start out with emission coefficients below these target values. In that case, the emission
coefficient remains at the baseyear value.

BOD and Nitrogen Loading
Households and Services
Sources of domestic pollution include pollution from both the household and service
sectors. For this analysis, pollutant generation is represented by biochemical oxygen
demand (BOD). Estimates of BOD are constructed separately for urban and rural areas.
Loadings are estimated by multiplying population by an emission factor, and then taking
treatment into account. Separate emission factors are used for sewered and unsewered
waste. A fraction of sewered urban wastes is assumed to be treated to some degree, whilein rural areas it is assumed that the population is not connected to central sewer systems,
and no waste is treated. BOD emissions are determined by the size of urban and rural
populations and by regional GDP per capita, as described below.
Data on BOD loading factors (kg BOD/person) are scarce, but they are known to vary
regionally and by sewered and unsewered population. Values for the United States andrepresentative values for developing countries are shown in Table 1. In this study, theUnited States value is used for OECD regions and the developing countries value is used
for non-OECD regions.

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Table 1. Typical loading rates for untreated domestic sewage

RegionUnited States
Developing Countries**

BOD [BOD5](kg/capita/year)
Sewered Population
54.9 [45.5]*
23.7 [19.7]

BOD [BOD5](kg/capita/year)
Unsewered Population
11.0 [9.1] ***8.3 [6.9]*Chapra 1997**WHO 1982***Assumed unsewered population contributes 80% less BOD than sewered population in industrializedcountries, compared to 65% in developing countries, because of higher standards in septic tanks.

The average BOD removed with each degree of treatment (primary, secondary, tertiary)
was applied to average wastewater treatment levels in four of the regions in this analysis,
to obtain the average percent of BOD removed by region (shown in Table 2).
Table 2. Average Percent of BOD Removed During Wastewater Treatment

RegionN AmerW Europe
MexicoCent + E Europe% BOD Removed
81.3
82.6
77.8
58.4

The relationship between treatment level and income (GDP per capita) assumed in theanalysis is as follows: below an income level of around $2400, it is assumed that only
primary treatment is used, in which 25% BOD is removed. Above an income of $7,700,
treatment levels are assumed to remain at 80%, close to the current average values in theregions shown in the figure.
Data for the percentage of the urban population connected to sewerage and thepercentage of sewered wastewater that is treated are based on data from the Global Urban
Observatory (UNCHS, 1998). The percentage of the urban population connected to
sewerage and the percentage of wastewater treated increase with income, based on
average city-level data from the Global Urban Observatory (UNCHS, 1998).
Livestock Production
Estimates for water pollution from livestock wastes, in the form of nitrogen (N) andbiochemical oxygen demand (BOD), are calculated in PoleStar for the current accounts
and the Reference scenario. Loading factors per unit of feed energy requirements are
estimated for the baseyear using data on livestock populations and milk production fromthe FAOSTAT database, and the loading factors from WHO (1982). It is assumed that
only livestock in feedlots contribute significantly to water pollutant loads. For the policy
shock scenarios, relative trends for waste generation by livestock compared to Reference
levels are estimated from relative trends in real transfers of feedgrain to the livestock
sector in JOBS, which serves as a proxy for energy needs of lot-fed animals.

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Estimated BOD emissions are the sum of BOD that is associated with the wastes directly,
as well as oxygen consumed in the oxidation of nitrogen (NBOD). The loading for
NBOD is calculated from the nitrogen loading using the relationship:
NBOD = 4.57 × N
Fertilizer
Following the IPCC guidelines (IPCC/OECD/IEA, 1996), we assume that 30% of thenitrogen applied is lost to leaching and runoff. Note that this value can vary over a broad
range, from as little as 10% to as much as 80% (IPCC/OECD/IEA, 1996).
Nitrogen in manufactured fertilizers is typically in the form of ammonium or nitrate salts.
Since nitrate is already oxidized, it does not contribute to oxygen demand. For thisanalysis it is assumed that fertilizers do not contribute to oxygen demand (in contrast to
the nitrogen contained in livestock wastes).
Industry
Estimation of biochemical oxygen demand (BOD) pollution contributed bymanufacturing is based on a study by the World Bank's Development Research Group(Hettige et al., 1997), which examined cross-country data from 12 countries, including
both developing and industrialized countries. Consistent with the approach of Hettige etal., pollution generation is expressed as the product of an activity level—industrial value
added for the industrial sector—and an intensity. An abatement factor is applied to thetotal generation to estimate the final load:

Total Industrial Pollution = Industrial Value Added × Pollution Intensity ×  1 -% Abatement 
100

The data of Hettige et al. (1997) suggest that aggregate manufacturing intensities decline
steadily from around 5.5 kg/million $/day at an annual income per capita level of $500 toaround 4.0 kg/million $/day at an income level of $5,000 and remain relatively steady,showing only a slight increase, for incomes above that level. For this study it is assumed
that aggregate BOD intensities are 5.5 kg/million $/day for GDP/capita below $500, 4.0
kg/million $/day for GDP/capita above $5,000 and decline steadily for GDP/capita
between $500 and $5,000. Abatement levels are also assumed to vary with income,following the tabulated values reported in Hettige et al. (1997).

Timber Production
Three categories of forest products are considered — fuelwood, paper and pulp and an
aggregate category containing all other wood products. Requirements for fuelwood are
driven by requirements for biomass developed in the energy analysis. Requirements forpaper and pulp and for other wood products are driven by value added in the paper andpulp and the wood products sub-sectors in JOBS.

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Wood products are designated as being either primary or secondary. Primary products aredifferent kinds of roundwood, that is, felled trees from which the twigs, branches andleaves have been removed. Primary products (such as sawlogs, veneer logs, pulpwood,chips) are used to produce secondary wood products (such as sawnlogs, veneer, paper,
pulp and plywood). Wood products are grouped into three categories: paper and pulp,
fuelwood and all other wood products. The last category represents all remaining
secondary wood products, such as sawnwood, plywood, veneer, etc. Baseyear data on
production and trade are taken from the online FAOSTAT database (FAO, 2000).
The analysis proceeds as follows. First, requirements and production of secondary wood
products are developed. Second, the production of secondary products is used todetermine the requirements for primary roundwood products, by first converting
secondary wood production to primary fibre input requirements, and then taking accountof the supply of fibre from sources other than roundwood (e.g., non-wood fibre andrecycled fibre). It is assumed that for each 1% increase in pulp production, there is a 0.2% decrease in the fraction of fibre that comes from roundwood. After first accounting
for trade, regional production of roundwood is then determined. In the Reference
Scenario, production is assumed to expand onto a mix of plantation and forest land. A
fixed fraction of new plantation area is assumed to come from natural forest. Data on
land-use conversion rates are scarce. For this study, we examined data for three aggregate
tropical regions – Africa, Latin America and Asia – reported in FAO (1996b). Based onthese figures, we assumed a fraction of 85% for all regions except the Middle East. For
the Middle East, where natural forest area is scarce, the fraction is 10%
For most regions, it is assumed that importers of secondary wood products in thebaseyear maintain their self-sufficiency ratio in the Reference Scenario, defined as theratio of production to domestic requirements. Most regions that are exporters in thebaseyear maintain their share of total exports in the scenario. However, Western Europe,
an exporter in the baseyear, has no net exports in 2020, consistent with the results fromJOBS.
The fraction from roundwood for paper and pulp is given by the ratio of "pulp for paper"to the total of "pulp for paper", "recovered paper" and "other fibre" from FAOSTAT. For
fuelwood, it is assumed that the fraction from roundwood is 0% for developing regions,and 50% otherwise, on the assumption that in developing regions fuelwood production is
generally obtained by gathering branches and other excess material, rather than by felling
trees.
Wood products are produced from either commercially exploitable forest or plantations.
Data on the area of commercially exploitable forest and plantations are taken from FAOcommissioned Global Fibre Supply Study (FAO, 1998). The annual increase in biomass
on exploitable forest and current plantations are taken from the same source. For anassessment of potential production, the reported value should be reduced, to take into
account the amount of the increment that is left in the forest and uncertainties in thereported increment figures. For this analysis, increments are reduced by 25% to estimate
the increase in roundwood area.

7

Agricultural Demand and Supply
Baseyear data on agricultural demand and supply are taken from the FAOSTAT database(FAO, 1996a). For this study, crop commodities are classified as Wheat and Coarse
Grains, Rice and Other Crops. The livestock commodities are classified as Beef, etc.,
Other Meat, Milk and Fish. The "Beef, etc." category includes animals that are suitablefor grazing, such as cattle, sheep and goats. The "Other Meat" category includes animals
that are not grazed, such as pigs and poultry. Other Meat also includes eggs and animal
by-products. The "Milk" category includes milk products.
Food Demand
Food demand in the analysis is expressed as per capita caloric intake multiplied bypopulation. Total caloric intake is then split into consumption of crop and animalcommodities. In the Reference scenario, dietary patterns adjust with rising incomes.
Shifts in diet include greater food consumption in developing regions, with an increasing
share of calories derived from animal products. In North America and Western Europe,
where caloric intake per capita is currently high (Bender, 1997), it is expected that intake
levels off, or declines, due to saturation effects and health concerns, e.g., with respect tohigh-fat diets. In the other regions, as their average incomes grow, diets tend towards thepattern typical of Europe today.
Livestock Production
In the scenario, patterns of trade in animal products are based on self-sufficiency ratios(SSRs) for the baseyear. The SSR is defined as the amount of food (in tonnes) available
from domestic sources, divided by requirements. In general, SSRs change little over thecourse of the scenario, increasing for regions that were net exporters in the baseyear andstaying at base-year levels for importers. North America, Australia+New Zealand andthe Middle East depart from this pattern. In North America and Australia+New Zealand,
exports increase significantly over the course of the scenario to meet part of the rapidly
growing world demand. In the Middle East, the SSR decreases over the scenario, in
response to increasing pressures placed by livestock on land resources.
To facilitate allocation of feeds to different kinds of livestock in the baseyear, for thisanalysis. livestock were separated into two groups. The first includes animals that canmake use of grazing resources, such as beef and dairy cattle, sheep and goats. Feedrequirements in the scenario are expressed as changes in feed energy requirements per
tonne of meat or milk. Scenario values for OECD regions were based on historicalchanges in the OECD as a whole. Scenario assumptions for developing regions werebased on scenarios developed by Bouwman (1997).
Crop Production
In the Reference scenario, assumptions are made for the demand and productivity of crop
products. The main variables affecting productivity are the yields, cropping intensities
and irrigated area. Changes in these variables are set in the scenario based on values
assumed in Leach (1995) and in the Bending the Curve study (Raskin et al., 1998; Heaps
et al., 1998). Given these assumptions, in the scenario limits on production are assumed
to be determined principally by limits on rainfed crop area. Regional scenario

8

assumptions for domestic production and trade are constrained by the assumption ofcontinuity in trade patterns: i.e., SSRs are consistent with historical trends.
Crop Trade
Trade assumptions are developed in the scenario in several steps. First, the selfsufficiency ratios are assumed to be as in the baseyear in every region. Second, theamount of rainfed crop area is determined that would be required to meet this level ofproduction. This area is then compared to the area of potential cropland (see below). For
importing regions, if the required area is below the limit, then the self-sufficiency ratio
remains as in the baseyear. Otherwise, it is lower than in the baseyear by the amountrequired to stay within the land constraints. Third, total net import requirements are
determined. Finally, production in exporting regions is set so that global demand is met.
In general, regions that are importers in the baseyear remain importers in the scenario,
and the same is true for exporters. The main exceptions are Central and Eastern Europe
and the FSU, as they recover from a recent drop in production and as yields in the FSU,
which today are well below those of Western Europe and North America, increase.
Potentially Cultivable Land
Estimates of potential productivity of rainfed land for 91 developing countries wereprepared by the FAO and the International Institute for Applied Systems Analysis in
Vienna (Alexandratos, 1995; Fischer, 1993). The extent of potentially cultivable land andits distribution in different land-use categories was calculated based on these estimatesand aggregated to 5 regions: Sub-Saharan Africa, Middle East, Latin America, China andSouth and Southeast Asia (Fischer, 1993). For this study, where applicable, estimatedareas of potential cropland in each PoleStar region was developed by assuming the same
distribution of potential cropland under different land uses as in comparable regions fromFischer (1993). For other regions, potential cropland was assumed to be uniformly
distributed under forest and grazing land.
Cropland is assumed to be lost to degradation at a rate comparable to historical rates.
Cropland loss rates are based on regional aggregate rates estimated by Heaps et al. (1998)
using the global soil degradation map prepared for the GLASOD study (Oldeman et al.,
1991; GRID/UNEP, 1991).

Agricultural Inputs
Agricultural Withdrawals
Data for 1995 on withdrawals and available supply are taken from the OECD
environmental compendium for OECD countries (OECD, 1999), and from Shiklomanov(1999) for other countries. For the Reference scenario, agricultural yield assumptions
determine trends in agricultural water use, as described below.
For this analysis, all agricultural withdrawals are assigned to irrigation. In the Reference
scenario, irrigation intensities increase with yields. Based on empirical yield-response
curves (Hexem and Heady, 1978), it is assumed that for each 1% increase in yields,
irrigation intensities increase by 0.25% . As a result, water intensities increase gradually9over the course of the scenario as yields increase, but at a slower rate than the increase inyields.
Fertilizer
In the Reference scenario, the amount of nitrogen fertilizer applied per hectare (theintensity) is calculated in terms of yields, so that the trends in fertilizer use are consistent
with the yield assumptions, based on crop production data from FAOSTAT and fertilizer
application data from FAO's Land and Water Division (FAO, 1999). In the scenarios,
application rates increase with yields, using log-linear regression curves estimated fromcross-sectional data.

Land Use Changes
The principal source for data on land for this analysis is the FAOSTAT database (FAO,
1996a), supplemented by information from other sources. The FAOSTAT databaseprovides figures for crop area, permanent pasture, forest, Protected areas, and protected
forest areas, are based on data from the World Conservation Monitoring Centre (WCMC,
1998a,b). The area of barren land is calculated from the Global Land CoverCharacteristics database version 1.2 (USGS/UNL/JRC, 1999).
In the OECD regions, built environment in the scenario increases at a fixed rate based on
historical trends in the United States, consistent with the similar economic growth in
those regions. In the U.S., between 1982 and 1992 developed land per capita increased at
a rate of about 0.6% per year on average. It is assumed that built environment per capita
increases in all OECD regions at half this rate, 0.3% per year, throughout the scenario.
Built environment per capita in Africa, Mexico and Central + South America converges
towards the OECD average with increasing income, while China, South Asia, East Asia,
the Middle East, Central and Eastern Europe, FSU, ROE and ROW all converge towards
the more compact value for Western Europe. The shares of forest area, barren land and"other" land in the total area converted to built environment are based on the noncultivable land area available under each of these land types in the baseyear. It is assumed
that all non-cultivable land under forest and "other" land is equally suitable forconversion to the built environment. Data on conversion rates of barren land to built land
are not available. It is assumed in the scenario that much of the barren land would be
essentially unusable for the built environment, due to the expense of delivering water inarid areas. Specifically, it is assumed that 10% of barren land in each region is suitablefor conversion to the built environment.
In the Reference scenario, cropland in the developing regions expands into pasture land
and unprotected forest areas in proportion to the amount of potentially cultivable land
under those uses, after losses of potential cropland to degradation and conversion havebeen taken into account. In every region grazing land expands into unprotected forest and"other" land in proportion to the area of non-cultivable land under these uses. Forest
plantations expand largely onto natural forest, with the remainder expanding onto “other”
land. Deforestation rates are estimated by taking all conversions of forest land to otheruses and subtracting from the total area.

10

Municipal Wastes
In the baseyear, waste generation rates in OECD regions, are based on data given in
OECD (1997; 1999). In the remaining regions, waste generation rates in rural and urban
areas are based on the default regional generation rates given in Doorn and Barlaz (1995).
In the scenario, high-income OECD generation rates are, in accordance with
developments over the last decade, assumed to increase at a slightly lower rate than GDP,
while other regions converge toward the average rate in high-income OECD regions as
incomes increase.
For the OECD regions, the waste processing fractions were calculated using data from
OECD (1997; 1999). For other regions the processing fractions are based on the regional
aggregate figures in the Global Urban Indicators Database (UNCHS, 1998), which
provides city-level data and regional averages. Processing fractions are applied to thetotal population in high-income OECD and transitional regions, but to the urban
population alone in developing regions. In the Reference Scenario, recycling trends inNorth America to 2020 are based on anticipated trends in the U.S. to 2020 (Tellus
Institute, 1998). Trends in Western Europe are based on anticipated trends in the EU.
Recycling rates in other regions, which are very low compared to North American andEuropean levels, rise gradually over the course of the scenario, by 10 percentage points
between 1995 and 2020. Landfill fractions decline by the same amount.
Methane emission factors in the Reference Scenario are decomposed into a product of
three factors: the degradable organic carbon content (DOC), the fraction of the DOC thatis dissimilated to gas, and fraction of the gas emitted that is methane, following themethodology adopted for SEI's Greenhouse Gas Scenario System (SEI, 1993). The
degradable organic content converges gradually in all regions toward the baseyear value
for North America. This convergence reflects the increasing importance of paper, which
has a high degradable organic content, in the waste streams of all regions.
Methane recovery in North America, Western Europe and Japan & Korea is close to 10%
in 1995. In the scenario, methane recovery in these regions increase to 60% by 2020,
based on anticipated developments in the U.S. (Tellus Institute, 1998). Methane recovery
in the Australia & New Zealand region is negligible in the baseyear, and is assumed toreach 40% by 2020. Recovery rates in all other regions is assumed to reach 20% by 2020,
based on the assumptions in Tellus Institute (1998).

Domestic and Industrial Water Use
Data for 1995 on water withdrawals and available supply of water are taken from theOECD environmental compendium for OECD countries (OECD, 1999), and from
Shiklomanov (1999) for other countries. These sources provide water withdrawal data for
agricultural, domestic and industrial uses. Industrial withdrawals include bothmanufacturing withdrawals and withdrawals for cooling thermal power plants. Separate
data on manufacturing and energy withdrawals are available in OECD (1999), but not in
Shiklomanov (1999).

11

Domestic Water Use
Domestic water use includes water withdrawals for the household and service sectors.
The measure of activity for domestic withdrawals is regional population. In the Reference
Scenario, in North America and Australia+New Zealand, domestic water intensities are
assumed to decrease by 20% between 1995 and 2050. In Western Europe andJapan+Korea, where current domestic intensities are much less than those in North
America and Australia+New Zealand, domestic water intensities are assumed to remain
at baseyear levels. In non-OECD regions, intensities converge toward average OECDpatterns.
Industrial Water Use
In the scenario analysis, industrial water use is disaggregated into withdrawals forcooling thermal power plants and for manufacturing. Water withdrawals for
manufacturing are further disaggregated by subsector. The measure of activity for
manufacturing withdrawals is subsectoral value added, with the water intensity defined aswithdrawals per unit value added. For thermal cooling, electricity output from thermal
power plants is the measure of activity.
For OECD regions, OECD data on thermal electric cooling were used to allocate totalindustrial withdrawals to manufacturing and energy in OECD regions (OECD, 1999).
Data on withdrawals by manufacturing subsector are scarce. For this study, United States
data on industrial water withdrawals in 1983 (Gleick, 1993) was used to assign relative
intensities to individual manufacturing subsectors, using subsectoral value added data for1983 from UNIDO (1997). The same relative intensities are used for all regions. To
allocate total industrial withdrawals to manufacturing and energy in non-OECD regions,the manufacturing and energy intensities for the North American region were scaled by
the same factor to recover the total industrial withdrawals for the region.
For the Reference Scenario, trends in water intensity are based on trends in the UnitedStates, where disaggregated time-series data are available (Gleick, 1993). Assuming aslowing of the historical trend, intensities in OECD regions are assumed to drop by 10%
between 1995 and 2025, and by 20% between 1995 and 2050 in all sectors.
Manufacturing intensities in the non-OECD regions are assumed to converge toward theaverage for the OECD regions as incomes rise.

12

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