JLCA NEWS ENGLISH EDITION Nov.2014

No.18

JLCA
NEWS LETTER

Life-Cycle Assessment Society of Japan

LIME2

Life-cycle Impact assessment Method based on Endpoint modeling

Chapter 2 :Characterization and Damage Evaluation Methods
2.10
2.11
2.12
2.13

Land use
Resource consumption
Waste
Noise (Road Traffic Noise)LIME2_C2.10-C2.13_2014

Life-cycle Impact assessment Method
based on Endpoint modeling

Chapter 2
Characterization and Damage
Evaluation Methods
2.10 Land use
2.11 Resource consumption
2.12 Waste
2.13 Noise (Road Traffic Noise)
written and edited by
Dr.Norihiro ITSUBO(Professor, Faculty of Environmental and Information Studies,Tokyo City University)
Dr. Atsushi INABA(Professor, Faculty of Engineering, Kogakuin University)
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LIME2_C2.10-C2.13_2014

Chapter Ⅱ

Characterization and Damage Evaluation Methods
Contents
2.10

Land use ....................................................................................................................... 1
2.10.1

What phenomenon is the environmental impact of land use? .............................. 1(1) Causal relationship between land use and environmental impact .............................. 1(2) Endpoints of environmental impact of land use ......................................................... 3
2.10.2

Characterization of land use.................................................................................. 6(1) Characterization factors of existing land use.............................................................. 6(2) Characterization factors of land use under LIME ...................................................... 92.10.3

Damage assessment of land use ............................................................................ 9(1) Basic policy for calculation of damage factors .......................................................... 9(2)

Primary production: damage function for the terrestrial ecosystem........................ 13(3)

Biodiversity: damage functions for terrestrial species............................................. 25(4)

Damage factor of land use ....................................................................................... 36

2.10.4
2.11

Procedure for impact assessment of land use...................................................... 43

Resource consumption ............................................................................................... 47
2.11.1

What phenomenon is the environmental impact of resource consumption? ...... 47(1)

Range and categories of resources dealt with by LCA............................................ 47(2)

Causal relationship of environmental impact concerning resource consumption ... 48(3)

Endpoint of the environmental impact of resource consumption............................ 51

2.11.2

Characterization of resource consumption.......................................................... 53(1)

Existing characterization factors of resource consumption ..................................... 53(2)

Characterization factor of resource consumption under LIME ............................... 57

2.11.3

Damage assessment of resource consumption .................................................... 59(1)

Basic policies for calculation of damage factors and assessment of uncertainty .... 59(2)

Investigation of land use information ...................................................................... 64(3)

Primary production: damage function of mining of underground resources(mineral resources, fossil fuels, stone resources) .................................................... 69(4)

Biodiversity:

damagefunctionofminingofundergroundresources(mineral resources, fossil fuels, stone resources) .................................................... 72

LIME2_C2.10-C2.13_2014(5)

Primary production: damage function of forest resource gathering ........................ 73(6)

Biodiversity: damage function of forest resource gathering.................................... 73(7)

Social Assets: damage function of consumption of mineral resources and fossil
fuels.......................................................................................................................... 74(8)Social assets: damage factor of forest resource consumption.................................. 76(9)

Damage factor of resource consumption ................................................................. 77

2.11.4
2.12

Procedures for impact assessment of resource consumption.............................. 86

Waste.......................................................................................................................... 91
2.12.1

What phenomenon is the environmental impact of waste?................................. 91(1)

Causal relationship between waste and environmental impact ............................... 91(2)

The impact category to which the environmental impact of waste belongs and theendpoints.................................................................................................................. 94

2.12.2

Characterization of waste.................................................................................... 96(1)

Characterization factor of waste under the existing LCA method .......................... 96(2)

Characterization factor of waste under LIME ......................................................... 98

2.12.3

Damage assessment of waste .............................................................................. 98(1)

Basic policy for calculation of damage factor and assessment of uncertainty ........ 99(2)

Arrangement of information on land use that accompanies final disposal of a unit
volume of waste ..................................................................................................... 100(3)Primary production and biodiversity: damage functions of waste ........................ 104(4)

Social assets: damage factor of waste.................................................................... 105(5)

Damage factors of waste........................................................................................ 107

2.12.42.13

Procedures for impact assessment of waste ...................................................... 113

NOISE (ROAD TRAFFIC NOISE)......................................................................... 118
2.13.1

What phenomenon is the environmental impact of noise? ............................... 118(1)

Causal relationship between noise and environmental impact .............................. 118(2)

Endpoints of the environmental impact of noise ................................................... 120

2.13.2

Characterization of noise .................................................................................. 122(1)Characterization factor of noise under the existing LCA method ......................... 122(2)

Characterization factor of noise under LIME ........................................................ 124

2.13.3

Damage assessment of noise............................................................................. 126(1)

Basic policy for calculation of damage factor and assessment of uncertainty ...... 126(2)

Human health: damage function for sleep disturbance and conversation
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LIME2_C2.10-C2.13_2014

disturbance ............................................................................................................. 127(3)

Damage factor of noise .......................................................................................... 141

2.13.4(1)

Procedure for the impact assessment of noise .................................................. 145

Method for the assessment of impact on products, and points that require
attention ................................................................................................................. 145(2)

Relation with other impact assessment.................................................................. 147

LIME2_C2.10-C2.13_2014

Chapter II

Characterization and Damage Evaluation Methods
2.10

Land use

Changes in LIME2• Uncertainty assessment of damage factors was carried out. Uncertainty includes
geographical variability (uncertainty about at what point the land use entered in theinventory is assessed).
• When damage factors for primary production were calculated, the Chikugo model, which
is used for estimation of potential net primary production (NPP) of land, was changed into
an equation more suitable for Japan.
• Assessment cases were added concerning an increase in the extinction risk of vascularplants in the field of land development, which is a basis for the calculation of damage
factors to biodiversity.
2.10.1

What phenomenon is the environmental impact of land use?

Roughly speaking, because most human activities are carried out on land, all of them are land
use activities. Because the area of land and the area of inhabitable land in Japan are small
compared with the population, competition in land use is likely to become a basic cause forvarious environmental problems. Therefore, the viewpoint of land use is extremely
important.
In the framework of LCA, The release of substances generated from human activities into theair, water, and soil (basic flows) is measured (classified) in each impact category, such as
global warming and toxic chemicals. Among the phenomena that are not covered by theother impact categories, those directly or indirectly generated by a change in land surface or
the maintenance of an unnatural state are covered by the impact category of land use – putting
it simply, the destruction and loss of nature that accompany land development.
(1)

Causal relationship between land use and environmental impact

In Japan, if a project is accompanied by land transformation and is large in scale and has
considerable environmental impact, the legislated mechanism of environmental impact
assessment (EIA) is applied, under which the implementing body should survey, predict, andassess the environmental impact of the project beforehand and have the results reflected in the
project as environmental conservation measures and the like (Terada 1999). What are usefulfor grasping the environmental impact of land use are technical guidelines and manuals
concerning environmental assessment and information on existing cases of environmental
assessment. Concretely, such documents and information make it possible to find thefollowing channels for the environmental impact of land use (Figure 2.10-1).
The destruction of land surface gives direct damage to the ecosystem (the extinction ofindividual animals and plants and the disappearance of living and growing spaces due to1

LIME2_C2.10-C2.13_2014changes in land). If surrounding areas are not transformed directly, they receive indirect
impact from drying and changes in the sunshine condition. In addition, if a habitat is divided,
resulting in isolation or reduction, the quality of the ecosystem will decline. In the case ofanimals that move across a wide area, a change in the inhabitation conditions related tofeeding and nesting may have impact. Such indirect impact will continue to exist throughout
the period when land transformation is maintained.

Endpoint impact

Land transformation/
maintenance

If attention is paid to
impact on individuals
Decrease in the numberof individuals
Extinction of individuals

Removal of surface soil and vegetation

Extinction of individual animals/plants
Decline in quality of ecosystem
Disappearance of living/growing spaces
Change in other living/growing conditions

Geographical changes

Decline inenvironmental
capacity

Decline inbreeding rate
Change in feeding condition

Division and isolationof living/growing area

Change in moving condition
Change in breeding condition

Change in the state insurroundings, edge effectDrying, change insunshine condition, etc.

Increase inenvironmental
changes

Impact onlife-maintaining function
Impact on global warming
through change in land use(Change in CO2 sinks, etc.)

Building winds, sunlight disturbance,
radio disturbanceOccurrence of landslide,etc.

Increase in instability of landPrimary production
Terrestrial primary
production

Health impact
Accidental deathSocial assets
Change in important
geographical/geological features

Academic/historicalassetsChange in landscapeLandscaperesources

Change in outflow pattern

Change in land surface

Biodiversity
Composition ofanimal/plant species

Change in groundwaterlevel/quality

Change in physicalenvironment

Contact with nature
Impact on recreation

Contact/recreation
resources

Change in waterbalance

Impact on flood
control

Houses and other private
assets/infrastructural
facilities

Impact of water use

Water resources

Change in infiltration
capacity/evapotranspiration

Change in heat balance

Change inmicroclimate

Impact on heat island
phenomenon throughchange in land use

Figure 2.10 -1: Causal relationships among environmental impacts of land use
As impacts directly received by human society, land transformation may change landscape oracademically or historically important geographical/geological features or give trouble toactivities for having contact with nature or recreation activities. If instability increases in alandslide area, the possibility of occurrence of a disaster will increase. In addition, if
2LIME2_C2.10-C2.13_2014

measures against removal of vegetation are insufficient, rainfall during construction may
cause the inflow of muddy water into surrounding water areas, or a change in the outflow
pattern after construction (at the stage of using land) which has in turn has impact on theecosystem, flood control, and water use through a change in water balance. Moreover, land
use may have impact on microclimate, such as the heat island phenomenon. Althoughforests are expected to contribute to global warming measures as CO2 sinks, theirdisappearance due to land transformation may contribute to an increase in CO2 emissions.
(2)

Endpoints of environmental impact of land use

The environmental impact of land use does not give direct damage to the endpoints.
Therefore, we will outline the endpoints’ relationships with land use.
a

Impact on the ecosystem

Japan’s national biodiversity strategy (Council of Ministers for Global Environment 2002)
roughly divides the biodiversity crises into three from the viewpoint of the current situationand problems of biodiversity. The first crisis is “reduction or disappearance of living or
growing areas through a decrease or extinction of species directly caused by human activities
or development or the destruction, division, or deterioration of the ecosystem.” At present,
many animals and plants in Japan are facing a crisis of extinction. The following are themain causes for a decrease in the number of species: overhunting, illegal digging, excessive
gathering for an ornamental and commercial purpose; destruction of living or growing areas
through development or land use; and worsening of living or growing environments.
With regard to plant species (vascular plants) in Japan, for example, if deforestation is
included in land use, land use accounts for half of the causes for facing a crisis (Figure2.10-2).
In Japan, a basic survey for conservation of natural environments that is generally called the“Green National Census” has been carried out continuously. With regard to vegetation, the
5th survey (Ministry of the Environment, Nature Conservation Bureau 2004) reported thatabout 440,000 ha, 1.28% of the green area (0.26% per year), was transformed during about
five years between the late 1980s and the early 1990s.
In terms of natural grade of vegetation, 2.19% of the secondary grassy plains (plains with highgrass) at a grade of 5, and 1.73% of the secondary forests at a grade of 7 were transformed.
With regard to primary production, the 1st basic survey for conservation of naturalenvironments (in FY1973) (edited by the Environment Agency 1976) examined the “current
amount of vegetation,” the amount of plants in the Kanto region, and the “vegetation
production volume,” the annual amount of organic substances produced by the plants. With
regard to the relation between production volume and the social environment, the relation
with population was analyzed and the result of regression analysis showed that an increase inpopulation density by 1 [person km-2] results in a decrease in the production volume by 50[kg km-2 yr-1]. Based on the result of consideration under LIME, the actual primary
production volume has been estimated to be less than 50% of the potential primary production
volume in Tokyo, Osaka, Kanagawa, and other prefectures.

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LIME2_C2.10-C2.13_2014

Deforestation

Naturaltransition

Land use
Gathering, such as
gardeningMarshdevelopment
Land development
Road construction

Deforestation
Pond development
River development
Coast development
Marsh development
Grass development
Land development
Road construction
Pollution
Gathering, such as gardening

Trampling
Natural transition
Limited production areas

Other

Figure 2.10-2: Causes for decrease in vascular plants
Result of counting of top three items of causes for decrease according to the Red Data Book
(edited by the Environment Agency 2000)b

Human health

Figure 2.10-3 shows the number of dead and missing persons due to recent natural disasters.
Except in 1993 and 1995, when the Southwest-off Hokkaido Earthquake and the Great
Hanshin-Awaji Earthquake occurred, dead and missing persons due to wind and flood
disasters, including avalanche of rock and earth, and snow disasters occupied a large
proportion. For example, around the middle of September 2000, autumn rain front andtyphoon No. 14 caused heavy rain, resulting in many deaths around the Tokai region.
According to the White Paper on Disaster Management (edited by the Cabinet Office 2003),
because Japan not only has many steep mountains, valleys, and cliffs as well as frequent
earthquakes and volcanic activities, but also is prone to be struck by typhoon, heavy rain andheavy snow, sediment disasters such as avalanche of rock and earth, landslide, cliff failure arelikely to occur. Also, because of the recent “changes in land use,” such as progress in theurbanization of forest land, hilly land, and their surroundings, sediment disaster victims
occupy a large proportion of the total number of natural disaster victims.
cInfluence on social assets

If a paddy field in a river basin is transformed into a housing site, the field’s function toreserve and infiltrate rain water will decline. As a result, if the amount of precipitation is thesame, the amount of outflow increases. Because of this, flood has recently been frequently
caused by a small amount of precipitation. This is called urban flood damage.
In addition, progress in urbanization in areas where flood can occur when a river overflows(flooding area, inland water area) means accumulation of assets that may receive damage.
In this situation, it has been pointed out that, in spite of a decrease in the inundated area, theamount of damage has been increasing (Figure 2.10-4).

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LIME2_C2.10-C2.13_2014(Persons)
Wind/flood disaster
Earthquake
Snow disaster
Other

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Notes) The Cabinet Office created this graph based on the Fire Defense Agency’s data. Earthquakes include
tsunami disasters.
Of the deaths in 1995, those due to the Great Hanshin-Awaji Earthquake include 912 indirect deaths.

Figure 2.10-3: Number of dead and missing persons by type of disaster•Reprinted from the White Paper on Disaster Management 2003 (edited by the Cabinet Office 2003).
•Although the proportion of sediment disaster victims was about 80% of the total number of natural disastervictims in 1984, it has been around 50% since then (edited by the Cabinet Office 2003).
Amount of damage[10 billion yen]

Flood damage density[10,000 yen/ha]
Amount of damage per ha ofinundated area (at 1995 price levels)

Flood damage[10,000 ha]

Flood damage density ofgeneral assets

Amount of damage togeneral assets

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

1973

1972

Inundated area of housing
lots, etc.

Figure 2.10-4: Changes in the inundated area and the amount of damage to general assets•Reprinted from “Aiming for Planning of Creation of Sound Water Circulation Systems” (Ministerial Liaison
Council on Creation of Sound Water Circulation Systems 2003) (source: River Bureau of the Ministry of Land,
Infrastructure, Transport and Tourism)•In spite of a decrease in the inundated area, the amount of damage has recently increased, which indicates thatfloods peculiar to urban districts have frequently occurred.

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LIME2_C2.10-C2.13_2014

2.10.2

Characterization of land use(1)

Characterization factors of existing land use

Table 2.10-1 shows a conceivable approach for characterization factors of land use. There isanother approach whereby methods that assess land function and primary production of plants
are classified into the same type, because they assess the life-maintaining function (Udo deHaes et al. 2002).
Among them, the approach that can actually present quantitative and universal impact
assessment factors for the purpose of application to LCA at present is limited to “indexing of
biodiversity or the life-maintaining function.” Table 2.10-2 shows the characteristics of themethods associated with this approach.
Policy(1) Noweighting

Table 2.10-1: Approaches conceivable for impact assessment of land use
OutlineCharacterization is made by simply adding up transformed area and/or maintained
area  time, irrespective of the type of land use. Although this is simple and clear,because transformation and maintenance cannot be integrated, the characterization
result is indicated as two subcategory indices. Alternatively, either transformation or
maintenance is selected.

(2)Classificationof theecosystem andweighting bypanel

The ecosystem is divided into some classes according to quality. If an area istransferred from a class to another class because of land transformation, it is indexed
as inventory – by applying the weight of each class, as the case may be. The panel
method is applied to the weighting.
In Japan, the degree of natural vegetation is well known as a method for classificationof the ecosystem. Tadaki (1998) suggests a quantitative impact assessment method by
the use of the degree of natural vegetation, although this is not intended for application
to LCA. In addition, a report was made about the development and application resultof a method for jointly grasping, for each degree of natural vegetation, the transformed
areas of three categories divided from the viewpoint of the ecological network in order
to assess the impact of natural land transformation for industrial activities on theecosystem (Goto 2001).

(3) Functionof land(assessment
based on two
or more items
or ahierarchicalassessment
item system)

Baitz (1998) suggested characterization factors that take into consideration eight land
functions, such as purification of pollutants and prevention of disasters. Because eachfunction greatly differs among development areas, they cannot be used for general
purposes at present. However, they are expected to become widely applicable in the
future by the use of the geographic information system (GIS).
In Japan, although application of LCA is not intended, there are the following
examples: Tadaki suggested a quantitative (impact) assessment method based on thepanel assessment of 12 environmental conservation functions (1998) and performed
trials about it (2002); Ito et al. (1993) classified green spaces’ environmental
conservation functions into three divisions and six items and created an analysis
support system for Zushi City. This system has been used for setting environmental
conservation objectives and for carrying out the system for environmental impact
assessment of development products under the City’s ordinances.

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LIME2_C2.10-C2.13_2014(4) Indexingof biodiversity
or the life
maintaining
function

As an impact assessment method for biodiversity to apply it to LCA, Müller-Wenk(1998), and Lindeijer et al. (1998) and Köllner (2000) suggested a method whereby
the ratio of endangered species and the density of species generated by land use,
respectively, are used as the index of biodiversity. Based on the early researches by
Müller-Wenk (1998) and Köllner, Goedkoop (2000) suggested a method that useschanges in the density of species as the index. As the assessment index, Weidema etal. (2001) suggested the product of the following three indices: species richness (SR),ecosystem scarcity (ES), and ecosystem vulnerability (EV). By using the combinationof this suggestion (Weidema et al. 2001) and earlier research (Lindeijer et al. 1998),
Lindeijer (2002) mainly examined case studies on construction materials.
As the index of the life maintenance function, Lindeijer et al. (1998, 2002) andWeidema et al. (2001) used net primary production (NPP) (or a similar index).
Under LIME, a method to assess loss of the existing vegetation NPP was developed as
an assessment of impact on primary production, and a method to assess an increase in
the extinction risk was development as an assessment of impact on biodiversity. The
loss and the increase were adopted as damage factors (see 2.10.3).

(5) Landcompetition

Environmental impact means a decrease in remaining land by the use of natural land
for a specific purpose on the assumption that land is a limited resource. Gulée et al.
(2001) suggested land competition as a subcategory, assuming that land competition isan element of the impact of land use.

Table 2.10-2: Characteristics of the suggested methods for quantitative impact
assessment by indexing of biodiversity or the life maintenance function
Characteristics
Commonpoints• As forms of land use, impact is classified into that of transformation (conversion) andthat of maintenance (occupation) to carry out assessment.
• As the main elements that receive impact, attention is paid to biodiversity and the life
maintenance function.
• Among the types of biodiversity, the diversity of species is treated especially.
• The object of assessment of impact on biodiversity is plants.
• The goal is to show the impact assessment factor for each type of land use.

Differences• There are a method whereby the results are shown separately due to limitations in thecalculation method after classification into conversion and occupation and conduct ofassessment (Lindeijer et al. 1998) and a method whereby the results are added up by
introduction of the conception of recovery time (Köllner 2000) (the damage factor ofprimary production developed under LIME falls under the latter).
• There are a method that includes areas that are adjacent to the directly used land andreceive impact (Köllner 2000, Goedkoop et al. 2000) and a method that does not takeneighboring areas into consideration (Lindeijer et al. 1998). The method developed
under LIME falls under the latter.
• As an index for assessment of the life maintenance function (primary production),
Lindeijer et al. (1998) suggested “fNPP,” which does not take into consideration
forests for farm products and lumber because they do not contribute to the natural
ecosystem. In contrast, Weidema et al. (2001), Lindeijer et al. (2002) and themethod developed under LIME adopted NPP, which includes farm products, etc.
• Most of the methods that were suggested overseas and treat biodiversity as the objectof assessment induce an index by weighting land according to the number of vascularplant species that can exist in a certain area (or the inclination of the relationshipbetween the number of species and the area) and multiplying the result by time orarea. Therefore, it is hard to say that the meaning as an endpoint index is notnecessarily clear. The method developed under LIME treats endpoints and theresult is an increment in the extinction risk. This index is clear in both the meaning

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LIME2_C2.10-C2.13_2014

and the calculation standards and is advantageous in that the damage factors are in theimpact category of biotoxicity.
• The area subject to impact assessment differs among cases. Müller-Wenk (1998)
and Köllner (2000) covers Switzerland in principle, Lindeijer et al. (1998) covers theworld, and Goedkoop (2000) covers Europe. LIME generally covers Japan.

Column 2.10-1
Product lifecycle and land use
LCA so far – especially, actual case studies – has hardly considered the aspect of land use. Therefore,
this column will show why land use activities should be incorporated into LCA, taking industrial
products as examples (Figure 2.10-A).
In the case of the development of a mine at the stage of gathering resources, because Japan imports
many minerals and energy resources, land use not only in Japan but also in foreign countries should betaken into consideration as inventory at the stage of gathering resources. Next, lack of facilities for
final disposal of waste at the stage of manufacturing has become an imminent problem.
Because information on land use has not been accumulated in a form usable for LCA, it is difficult tocarry out LCIA of land use through the whole product lifecycle. Under LIME, therefore, consideration
was given not only to the development of an impact assessment method but also to related inventory ofland use in the impact categories of consumption of resources (Section 2.11) and disposal of waste(Section 2.12) as common and typical land use activities. As a result, for the convenience of those
who carry out LCA, assessment factors that contain background data for construction of inventory ofland use have been provided. Some of the LCA databases published recently (after the LCA project)contain information on land use as inventory data (Switzerland ecoinvent).
[Product lifecycle][Land use activities (examples)][Related processes of landuse (examples)]Removal of surface soil

Gathering ofresources

Overseas: gathering of minerals, coal, etc.
Domestic: gathering of soil, stone, etc.

Gathering of ores, etc.
Accumulation of waste
stone and soil

Manufacture

Use of raw fuel storage bases
Use of manufacturing factories* See Section
2.11.

Development of land
Establishment of structures

Distribution

Use of traffic facilities sites
Public use of facilities

Use

Use of product use spaces(in the case of transport machinery, etc.)

Dismantling of structures

Establishment of disposal
sites

Disposal

Use of final disposal facilities(Burying of waste)

Reclamation of spaces
Discontinuance ofdisposal sites

Figure 2.10-A: Land use processes related to product lifecycle8* See Section
2.12.

LIME2_C2.10-C2.13_2014(2)

Characterization factors of land use under LIME

At present, there is no globally-recognized approach as the characterization factor of land use.
Quantitative impact assessment by “indexing of biodiversity or the life maintenance function”
is an attractive approach.
However, it has the following disadvantages as thecharacterization factor: because the objects of assessment are limited to concrete ones, it isdifficult to adapt as a comprehensive index of impact on various elements; calculated values
have uncertainty (especially if maintenance and transformation are integrated); and there is no
globally-recognized approach.
Among the other approaches, the “no weighting” approach cannot take land quality into
consideration, but it is thought to be a comprehensive index that indicates potential impact.
Therefore, calculated values are highly reliable. “Classification of the eco system andweighting by panel” remains semi-quantitative assessment, or the method to put together theclasses depends on subjective assessment. The evaluation of “land function” requires a lotof information and cannot be put to practical use for LCA at present. Although “landcompetition” is a viewpoint to which attention should be paid, the method presented by
Guinée et al. (2001) is the area of land used in the end.
Therefore, under LIME, taking into consideration the comprehensiveness and reliability of theindex for indicating potential impact, we decided that transformation and maintenance should
be divided as subcategories and recommended area as characterization in the case oftransformation, and the simple multiplication of area and time as characterization in the caseof maintenance. Each characterization factor is 1 (for equations, see 2.10.4.)
2.10.3

Damage assessment of land use(1)

Basic policy for calculation of damage factors

Figure 2.10-5 shows the basic framework of damage assessment of land use under LIME.
With regard to the damage factors of land use, the basic policy is the same among the objectsof protection, but the calculation procedure greatly differs among them. Therefore, thissection only shows common bones. Details of damage functions and uncertainty assessment
will be explained in the sections concerning primary production and biodiversity ((2) and (3)below).
Inventory
Land use
activity

Land usecategory

Land use
periodLand use
area

Impact category

Object of protection

Maintenance

In useOcc
Inv time(a )Occ
Inv area(a )Land use(Maintenance /transformation)

Ecosystem

Transformation

Before
(b)

After
(a)

Trans
Inv area( b, a )• Primary production(NPP)• Extinction risk ofvascular plants

Figure 2.10-5: Framework of damage assessment related to land use under LIME

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LIME2_C2.10-C2.13_2014

Column 2.10-2
Life cycle inventory of land use – concepts of transformation, maintenance, and area × time
Before the impact of land use is assessed, it is necessary to determine the form of measuring
land use activities into inventory. We will explain the form by the use of Figure 2.10-B,
picking up one of the related land use processes from Figure 2.10-A in Column 2.10-1 (fordetails, also see Column 2.10-3.)
As the inventory of land use, it is necessary to select a set of the following for each type: (1)area of land use; (2) period (time) of use; (3) form of land use (classification into forest,
construction site, etc.) and activity (transformation, maintenance).
For the purpose hereof, we divided “transformation of land (change or conversion of landuse)” from “maintenance of land (occupation of land).” The former is an act of changing thestate of land division (land use classification), while the latter is an act of keeping thecondition of land to carry out activities for achieving the purpose. Because the period oftransformation is usually far shorter than the period of maintenance, inventory can be
simplified by omitting the measurement of time and paying attention only to changes in theform of land use (division).
Such a form has been usually used for the impact assessment method of land use overseas(Udo de Haes et al. 2002). Because the environmental impact of land use is highly regional,
it seems desirable to become able to include the location of land use in the inventory in the
future (see Column 2.10-6).
In addition, if the ecosystem is restored after being damaged, it seems necessary to include theresult in assessment. In this case, it seems appropriate to include positive restoration
activities in the inventory as transformation that brings about good results. If related positive
and negative processes are measured expressly to the extent possible, the transparency of
LCIA will improve and it will be possible to properly reflect environmental conservation
efforts in the assessment.
Land use inventory[Transformation of land]
Area by form of land use before and after
transformation(Example) Forest → industrial site 2 ha
Industrial site → park 2 ha

[Maintenance of land use]
Area by type of land use × time of use(Example) Industrial site 2 × 15 = 30 ha yr

Type of land use activity
Maintenance of land use(A certain form and quality oflot)Land transformation(Change in form or
quality of lot)

Maintenance of land use(A certain form andquality of lot)

Land transformation(Change in form or
quality of lot)

Maintenance of land use(A certain form andquality of lot)

15 years

Process of land use (actual time axis)

Before land use

Landdevelopment

Establishment ofstructure

Use of facilities (example: 15 years)

Dismantlingof structure

Next land use

Process of land use related to target function unit

Figure 2.10-B: Product lifecycle and related process of land use
10

LIME2_C2.10-C2.13_2014

Column 2.3-10
Points that need attention concerning land use inventory(1) Maintenance: non-separability between area and time
(Udo de Haes et al. 2002; Goedkoop et al. 2000)
Actual product LCA assesses the function performed by a unit of a product, for example.
In this case, if 30,000 units of the product are manufactured in 15 years at an industrial site
(2 ha) in Figure 2.10-B, how much land use area and time are needed for a unit of the
product?
If 2,000 units (30,000/15) are manufactured in a site of 2 ha every year, a unit of the productoccupies 0.001 [ha] (2 [ha] ÷ 2,000 [units]) per year. From another viewpoint, a unit of the
product occupies 1/15,000 [ha] (2 [ha] ÷ 30,000 [units]) for 15 years.
In this way, the number of combinations that relate area and time to function unit is infinite
concerning the process of maintaining land use. However, from the viewpoint of area ×
time, it is fixed at 0.001 [ha yr] under both ways of thinking. Because of this, for the
purpose of the land use inventory under product LCA, maintenance is measured by the unitof area × time.
(2)

Transformation: What process is responsible from the past to the future?
Problem of allocation of transformation(Udo de Haes et al. 2002), (van der Voet 2001), (Guinée et al. 2001)

If a naturally transformed site is used continuously by the same process, is it necessary toinclude the past transformation in the inventory? If so, the amount of land use
transformation should be allocated among the past processes related to the site. In addition,
because land does not disappear, it is necessary to allocate it to future processes. However,this is impossible to carry out.
On the other hand, there is the assertion that it is all right to allocate it only to the initial landtransformation process. Because this view is the same as “consumption of natural
resources should be allocated only to the initial use process, but not to the recycling
process,” it is called “land recycling.” However, if a road site used for transportation of a
product was transformed in the past, is it completely unnecessary to include this in theinventory?
With regard to LCA of land use, there remain many points to be clarified at the stage of
inventory construction.
Table 2.10-3: Category endpoints of land use and objects of calculation of damage
functionObject of Category endpoints
protection

Objects of calculation of damage
function

Human
healthDirect death/injury

Death/injury by disasterSocial asset

Academic &historical assets

Destruction of important
topography and geography, ×
etc.

×

Quantitative
impactassessment is difficult.
Preventive
and
other
measures are taken.
Poor
information

quantitative

11

LIME2_C2.10-C2.13_2014

Landscaperesources
Contact/recreation
resources
Houses and otherprivate assets,
infrastructural
facilities
Water sources

Primary
production

Terrestrial
ecosystem(Aquatic
ecosystem)

Biodiversity

Terrestrial
ecosystem

Landslide, loss of houses by ×flood, inundation, etc.
Fall of level of well water,
decline in river base flow,such as muddiness, etc.
Decline in NPP during landtransformation
Decline in potential NPP
during land use
Decline in potential NPP
during restoration period afterland transformation
Decline in primary productionof phytoplankton
Change in composition ofplant species

Quantitative
impactassessment is difficult.
Poor
information

quantitative

Quantitative
impactassessment is difficult.
Preventive
and
other
measures are taken.

×

Preventive
andmeasures are taken.

Transformation
usually short.

other

time

is

○
○
–
○

Change in composition of ×animal species(Aquatic
ecosystem)

Change in composition of –species in aquatic ecosystem

Global warming

Loss of CO2 sinks, discharge –of methane from soil, etc.

Heat island

Decline in latent heat flux,
increase in accumulated heat –quantity

OtheraLoss/deteriorationoflandscape assets, reduction in ×
viewable range, etc.
Loss/deterioration of contact
with
nature,recreation
resources, non-accessibility, ×
etc.

NPP of each vegetation
No consideration for use ofwater surface
Extinction risk of vascularplantsQuantitative
impactassessment is difficult.
Poor
quantitative
information
No consideration for use ofwater surface
Secondary impact
Classification into othercategories
Secondary impact

Calculation of damage factors according to category of land use

In the inventory of land use, the area of transformed land, the area of occupied land, and theperiod of occupation are grasped for each category of land use (see Column 2.10-2). This
enables the classification system of types of land use to reflect differences in the land use
classification system and available data among countries and regions.
A damage factor is given to each case of transformation or maintenance. In the case oftransformation, a damage factor per unit area is presented for each combination (matrix) oftypes of land use before and after the transformation (however, with regard to the damage
factor for biodiversity, the types of land use before the transformation are not taken into
consideration). With regard to maintenance, a damage factor per unit area and per time ispresented according to type of occupied land use.
b

Approach to damage assessment and object of calculation of damage factors

Under LIME, for the purpose of damage assessment of land use, the assessment method by
the endpoint approach was adopted as in the case of other impact categories. This method isclassified into “(4) Indexing of biodiversity or the life maintaining function” in Table 2.10-1
12

LIME2_C2.10-C2.13_2014

for the purpose of impact assessment of land use.
The category endpoints of land use and the objects of calculation of damage functions under
LIME are as shown in 2.10-3. With regard to the impact on the ecosystem, which isfrequently recognized as an environmental problem as shown in 2.10.1 (2), somecorresponding endpoint indices are included in the objects of calculation.
(2)

Primary production: damage function for the terrestrial ecosystemaBasic view on damage assessment

Because the primary production of plants is expressed by the volume of flow, the frameworkof damage assessment was created based on the following basic view under LIME. With
regard to the maintenance of land use (land occupation), loss of NPP in the occupation period
was calculated on the assumption that damage was no display of potential net primary
production (NPP) (Figure 2.10-6). With regard to land transformation, loss of NPP was
calculated in the period between the transformation of vegetation and the restoration ofpotential vegetation (Figure 2.10-7). This made it possible to add up the damage functionsof transformation and maintenance.
b

Formulation of damage assessment

Figure 2.10-8 (a) shows the definition of the amount of damage concerning the maintenanceof land use. If land use “a” is occupied in a period between to and t1, NPP of potential
vegetation in a natural state (NPPp) is suppressed by NPP of vegetation in the current state(land use) (NPPa). The difference is regarded as lost NPP for the calculation.
In the case of damage assessment of maintenance, comparison with NPPp is always madeirrespective of type of land use before to. Based on this view, the definition was created asshown in the following equation on the assumption that the damage function per unit area andOcctime in the case of maintenance of land use a DFNPP(a ) [t ha-1 yr-1] is the difference between
the potential NPP (NPPp [t ha-1 yr-1]) and the current land use NPP (NPPa [t ha-1 yr-1]):
Occ
DFNPP
(a ) = NPPp － NPPa

(2.10-1)

On the other hand, Figure 2.10-8 (b) shows the definition in the case of land transformation.
If a category of land use “b” is transformed to another category “a” in the period between t-1
and t0, what damage will be caused? If the period between t-1 and t0 is sufficiently short, lossof NPP in the period is small (A in the figure). It can be usually thought that loss of NPP ina land transformation period can be ignored, compared with a maintenance period. In
addition, if the category of land use “a” is maintained in the period between t0 and t1, theamount of damage becomes an object of assessment by land occupation in theabove-mentioned (a). On the other hand, if the land use “a” is given up at the future time oft1, restoration to the potential state “p” is delayed more than restoration from the land use “b”
before the transformation. Therefore, primary production lost due to this delay was deemed
to be the damage from transformation. Thinking in the same way, if giving up occupied landis delayed by one year, the amount of damage in the year is the same as the amount of damage
in the above-mentioned year of occupation.
The definition of the damage fromtransformation – loss by future delay in restoration – is well consistent with the definition of13

LIME2_C2.10-C2.13_2014

the damage from occupation.
As a concrete equation, the damage function per unit area due to transformation from land use
Trans“b” to “a”, DFNPP(b, a ) [t ha-1] was defined as the following equation by the use of the time
(restoration time) (Tb→p, Ta→p [yr]) needed to restore land use b and a to potential naturalvegetation if land use “b” (whose NPP is NPPb) is transformed to land use a (whose NPP is
NPPa):Trans
DFNPP(b, a ) = αa→p ･ (NPPp － NPPa) ･ Ta→p － αb→p ･ (NPPp － NPPb) ･ Tb→p
(2.10-2)

In this equation, ɑ is fixed at 0.5 on the assumption that vegetation is restored linearly.
cFlow of calculation of damage function (by type of vegetation) and basic policy
for uncertainty assessment
The spatial distributions of the NPP of potential natural vegetation (potential NPP) and the
NPP of the current vegetation and land use (current NPP) in Japan were estimated in order tocalculate damage functions. Figure 2.10-9 shows the flow of calculation (calculation
procedure, the applied model, and data). Table 2.10-4 summarizes the causes of uncertainty
considered for the assessment of uncertainty of damage functions and the way of treating
them.
Details of each calculation process are described in “d” to “h” below.
Table 2.10-4: Main causes of uncertainty in the damage functions of land use to primary
production and policies for treating themMain possible causes of uncertainty
Estimation of spatial Parameter of
distribution of
constitutive
potential NPP
equation of(2.10.3 (2) d)
Chikugo model

Policies for treating them in uncertainty assessment
Estimated error in parameter of regression equation for
estimation of proportionality coefficient  related toannual net amount of radiation Rn
Estimated error in parameter of regression equation for
estimation of monthly average water vapor pressure fromtemperature data
Diversity of albedo on ground surface (assuming auniform distribution of 0.5-0.8 when snowing and0.05-0.25 when not snowing)

Estimation of spatial
distribution ofcurrent NPP(2.10.3 (2) e)

Setting of restoration
time of NPP(2.10.3 (2) f)

14

Geographicalvariability

Calculation of frequency distribution of difference
between current and potential NPPs in each mesh (inJapan and in each region)

Representativevalue of NPP foreach type ofvegetation
Transition time

Setting of unit production by normal distribution foreach type of vegetation (Table 2.10-5)
Setting as uniform distribution by reference to various
documents on assumption of secondary transition (Table
2.10-6)

LIME2_C2.10-C2.13_2014

If the landremains natural
Total in the 15 years:

15 years
The difference (450 – 30
= 420 tons) is theamount of damage fromthe maintenance of land
use.

Maintenance
Artificial land use

Total in the 15 years:
15 years

Figure 2.10-6: Image of the view on the calculation of the amount of primary
production damage: maintenance of land use
If the land isnottransformed

If the land istransformed

After theoccupation
periodIf land use is given up,
a long time is needed
for restoring theoriginal productivity.

150
years

The amount of damage
from landtransformation is loss in
the restoration period.

Figure 2.10-7: Image of the view on the calculation of primary production damage: landtransformation

15

LIME2_C2.10-C2.13_2014

Maintenance of the status of land use “a” (period: t0 → t1)

Because there is no transformation,
C is not assessed.

Transformation of land use from “b” to “a” (period: t-1 → t0)
and maintenance of the status “a” (period: t0 → t1)

Impact ofmaintenanceonly:
assessment
of B

Impact of transformation:• A is not assessed.
• Instead, C0 + C1 is assessed.

* Bold lines indicate changes in the status of land use subject to assessment.
t-1: before land use transformation; t0: after land use transformation; t1: when land use is given up;
t2: when NPP is restored to the level before transformation (NPPb);
t3: when NPP is restored the potential level (NPPp)

Figure 2.10-8: Method to assess the amount of primary production damage

16

LIME2_C2.10-C2.13_2014

Climate data
Temperature

Rainfall

5th Basic Survey on
Natural Environment
Conservation

3rd Basic Survey on
Natural Environment
Conservation

Iwaki’s NPP list
(Table 2.10-5)

Chikugo model (d)Adjustment

Adjustment

Restoration time of NPP(Table 2.10-7)*Application for each categoryof vegetation/land use

Adding up foreach categoryof vegetation

Amount of damage from maintenance andtransformation in Japan (standard value)

Figure 2.10-9: Flow of estimation of damage function of land use to primary production
by type of vegetation
Shaded parts indicate factors for which uncertainty of parameter was taken into consideration.
Parts showing the Japanese Islands correspond to geographical changes.

dEstimation of spatial distribution of potential NPPs

When the spatial distribution of potential NPPs in the whole Japan were determined, twotypes of methods were examined: (1) numerical calculation by the Chikugo model (Uchijima
1985; Seino et al. 1985); and (2) the application of the unit production of vegetation (Iwaki
1981) corresponding to the legend of the potential natural vegetation map (Miyawaki et al.
1978). As a result, the former was adopted. Under LIME 2, the Japan Meteorological
17

LIME2_C2.10-C2.13_2014

Agency’s observed normal values and the climate mesh data given by the National Institutefor Agro-Environmental Sciences were used for the calculation by the Chikugo model as inEquation 2.10-3 (for details, see Column 2.10-4).

 



NPP    Rn  0.29  exp  0.216  RDI 2  Rn [t ha–1 yr–1](2.10-3)During the uncertainty assessment, of the parameters that constitute the Chikugo model, thefollowing were considered: the estimated error in the parameter of the regression equation for
estimation of the proportionality coefficient  related to the annual net amount of theradiation Rn, the estimated error in the parameter of the regression equation for estimation of
the monthly average water vapor pressure from temperature data*1, and diversity of albedo on
the ground surface. With regard to the creation of the Chikugo model, it can be thought that
because the proportionality coefficient is calculated by finding Rn through fixing of the valueof albedo (when snowing and when not snowing) and having it regress to actually measured
data of NPP, the proportionality coefficient itself changes if albedo changes. However,assessment was herein made with a uniform distribution of 0.5 to 0.8 when snowing and thatof 0.05 to 0.25 when not snowing on the assumption that the two were separated from eachother.
Table 2.10-5: Representative value of nppi for each type of main vegetation and assumed
standard deviation [t ha-1 yr-1] by Iwaki (1981)

2 Beech forest

6

3
4
5
6
7

Birch forest
Oak forest
Natural conifer forest
Pine forest
Cedar/cypress forest

6
6
8
8
10

NPP (II) (adopted)
NPP
Average Standard i Category
Averagedeviation
20
2.4
16 Deciduous orchard 10
Mulberry
9
17
10
1.2
plantation
9
1.2
18 Dry field
129
1.2
19 Paddy field
11
11
1.3
20 Fallow field
5
14
1.7
21 Bamboo grassland 8
14
1.9
22 Rice plant field
8

8 Deciduous conifer forest

6

10

9 Alpine shrub forest

5iCategory

NPP (I)

1 Evergreen broadleaf forest 8

10 Subtropical shrub forest
8
11 Low-mountain shrub forest 8
Low-mountain deciduous
12
6
forest13 Bamboo forest
10
14 Evergreen orchard
10
15 Tea plantation
7

Amphibious
grassland

1.3
1.5
1.4
0.6
1.0
1.0

1.5

23

0.6
1.0
1.0

Underwater
grassland
25 Special grassland
26 Large plants

1
15

0.1
0.1
1.9

0.8

27 Small plants

1

0.1

1.3
1.3
0.9

28 Artificial grassland 8
29 Urban green space 530 Other
1

1.0
0.6
0.1

24

15

Standard
deviation
1.3

1

1.9

*1 Under LIME 1, a regression equation by statistical processing of average values between 1951 and 1980 as shown in Seino et al. (1985) was used
for setting the monthly average water vapor pressure and the snow index, and a regression equation by statistical processing of data between 1979
and 1982 as shown therein was used for setting the monthly average daily hours. Under LIME 2, a regression equation was formulated based onwater vapor pressure and monthly average temperature data from meteorological observatories and stations all over Japan to estimate theparameter effort, using data between 1971 and 2000, the latest normal year data at the time of the research.

18

LIME2_C2.10-C2.13_2014eEstimation of spatial distribution of current NPPs

The estimation of spatial distribution of current NPPs in the whole Japan was based on themethod to give a current vegetation map that corresponds to each type of vegetation.
n

NPP   npp i  Ai

(2.10-4)i 1

In this equation, nppi is the annual primary production per unit area in the type of vegetation i[t ha-1 yr-1] and Ai is the area [ha] occupied by the type of vegetation i.
nppi is the representative value of NPP for each type of main vegetation by Iwaki (1981)(Table 2.10-5). With regard to forest vegetation, Iwaki (1981) showed two types of NPP:
NPP (I) for main types of adult trees only and NPP (II) that adds shrubs and grasses. We
adopted the latter. In the uncertainty assessment, we checked the sources to which Iwaki(1981) referred to set values of NPP (Environment Agency 1976, Tadaki et al. 1968, Kira
1976), compared each of them with Table 2.10-5, and, with regard to the eight categories
between evergreen broadleaf forest and deciduous conifer forest, supposed a normal
distribution by the use of the latitude shown in Tadaki and Hachiya (1968) or Kira (1976) (onthe assumption that the shown latitude corresponds to 3 σ). With regard to the classificationof vegetation except for these eight categories, the average of the variation coefficients of theeight categories were given as the variation coefficient for each type.
Ai was calculated by the following procedure. The spatial distribution of the current
vegetation was grasped from the natural environment information GIS data set based on theEnvironment Agency’s Third and Fifth Basic Survey on Natural Environment Conservation.
About 900 examples in the surveys were integrated into the 30 examples in Table 2.10-5.
After that, 0.00125º-around mesh data (east-west: about 100 m; north-south: about 150 m)
were prepared and, based on the data, Ai was calculated. When the standard amount ofdamage from transformation was calculated, NPPb was used for the Third Basic Survey on
Natural Environment Conservation (FY1983 - 87), while NPPa was used for the fifth one(FY1993 - 1998).
Under this method, if the type of vegetation is the same, the value of the current NPP is thesame everywhere in Japan. Actually, however, if the type of vegetation is the same, it isthought that the current NPP differs from place to place, depending on climate conditions.
Therefore, we superposed the spatial distribution of normalized difference vegetation indices(NDVI) and multiplied the current NPP by a corrective coefficient, which can be calculatedby dividing the average NDVI of all meshes for each type of vegetation by the NDVI of eachmesh of the vegetation. NDVI is information that can be acquired from satellite image
analysis (spectral reflection characteristics) and indexes differences in chlorophyll
concentration and sunshine condition. Herein, we used data from “East Asia Monthly NDVI
in 1997” (National Institute for Environmental Studies, Center for Global Environmental
Research). The images are shown in Figure 2.10-9. Because introduction of adjustment by
NDVI makes it possible to take into consideration the regionality of the current NPP, areduction in the uncertainty of damage functions can be expected.
f

Setting of restoration time

By reference to the time needed for transition of vegetation (secondary transition) (Hayashi
1990, Japan Institute of Energy 2002, Numata 1977), the time needed for each vegetation/land
use to reach the level of primary production of a forest (restoration time) was set at the19

LIME2_C2.10-C2.13_2014

difference between the time of transition to a forest and the transition time of the vegetation.
By reference to existing research, the restoration time was set at a value calculated on the
supposition of secondary transition (Figure 2.10-9). In addition, thinking that restorationfrom land use that received strong artificial impact would took longer time, we set therestoration period at 150 years. Based on them, under LIME 2, we supposed the range of thetransition time by the use of a uniform distribution as shown in Table 2.10-6.
Table 2.10-6: Transition time, equation for calculation of restoration time andapplication to type of vegetation
Equation for calculation of restoration time
Transition time (years)
Numberof[years](supposed by uniform
typeofFigures in parentheses are restoration time
distribution)
vegetation
established under LIME 1
Main
periodtree

Shrub period

15–45

0

Transition time to main tree period －(15)transition time to shrub period
Transition time to main tree period －transition time to main perennial grass (20)periodTransition time to main tree period －transition time to main annual grass (29)period10–20

Main perennial
2–15
grass periodMainannual
0.5–1.5
grass period
Artificial
use

land

Urban land use(0)

1–8, 13
9–12
21–24, 26

25, 27

0Transition time to main tree period(30)

14–20,
28–30–

50–250(150)30 (see Notes)• The number of type of vegetation was based on Table 2.10-5, etc.
• The representative value of the restoration years from urban land use (applied to building sites and arterial
traffic sites for damage functions of land use division) is five times as large as the transition time to themain tree period. The lower limit was set for the 20 years until the main grass period (by reference toNumata (1977)). The difference between the upper limit and the representative value was set at thesame between the lower limit and the representative value.

Table 2.10-7: Process and results of calculation of damage functions corresponding to
the classification of vegetation (example)#

Type of vegetation

Occ
(# )
NPPp NPPa DFNPP

b#

a#1

2

29

30

31

1
2
3
4
5

Evergreen broadleaf
forest forest
Beech
Birch forest
Oak forest
Natural conifer forest

13
12
10
12
12

20
9
9
9
11

–6
3
1
3
1

1
2
3
4
5

0
0
0
0
0

0
0
0
0
0

129
129
129
129
129

194
194
194
194
194

972
972
972
972
972

18
19
20
21

Dry field
Paddy field
Fallow fieldBamboo grassland

12
13
14
10

12
11
5
8

1
3
9
2

18
19
20
21

-8
-39
-130
-23

-8
-39
-130
-23

121
89
-2
106

186
155
64
171

963
932
841
948

29
30
31

Urban green space
Other
Urban land use

14

5

9

14

1

13

29
30
31

-129
-194
-972

-129
-194
-972

0
-65
-843

65
0
-777

843
777
0

20

LIME2_C2.10-C2.13_2014• The left part shows damage functions of maintenance (unit: t ha-1 yr-1) (#: number of type of vegetation),while the right part shows damage functions of transformation (unit: t ha-1) (b#: vegetation number before
transformation; a#: that after transformation).
• Whereas positive values indicate damage, negative ones indicate benefits.
• The damage function of maintenance is the difference between NPPp calculated by the procedure
specified in “d” and NPPa calculated by the procedure specified in “e”. The calculation of the damage
factor of the “Other” type of vegetation only covered the types of land use “building sites” and “arterial
traffic sites.”• The damage function of transformation was calculated from the damage function of maintenance and therecovery time calculated on the supposition of secondary transition (Table 2.10-6). With regard to thedamage function of transformation related to the “Other” type of vegetation, this table shows both theresult in the case of a restoration time of 30 years (#30) and the result in the case of types of land use thatreceive stronger artificial impact, such as building sites (#31).
• This is not the result of uncertainty assessment but an example that uses mesh averages, etc. to explain thecalculation procedure.

g
Calculation of damage functions corresponding to the classification ofvegetation
We added up the results based on the spatial distribution of vegetation to find the damage
function for primary production due to transformation and maintenance of each type ofvegetation (Table 2.10-7). There are some differences in the estimated potential NPP (NPPp)
for each type of vegetation. For example, although the current NPP (NPPa) of “bamboo
grassland” is not so high, NPPp of the region where bamboo grassland is distributed isrelatively low as well. As a result, the amount of damage to maintenance of bamboo
grassland has been assessed to be relatively small. On the other hand, while “idle farmland”
is distributed in a region where NPPp is relatively high, NPPa was set at a relatively low value.
Because of this, the amount of damage to maintenance has been assessed to be relatively
large.
In addition, although NPPa and NPPp were set for each mesh, it can be thought that the two
are related to each other if they are located in the same point of place. For example,
according to the nationwide distribution map of NPPa and NPPp of cedar (Figure 2.10-10),
there is a correlative relationship between NPPa and NPPp. Because of this, if the
distribution of restoration time from NPPa to NPPp (Ta →p) is supposed to be independent
entirely during uncertainty assessment, the uncertainty of damage functions may beoverestimated.
Under LIME 2, to prevent such overestimation of uncertainty, the geographical distribution of
NPPa and NPPp was considered. Concretely, assessment was carried out by adding up theresults of Monte Carlo calculation for each mesh and including the correlation of thegeographical distribution among the elements of the damage function of maintenance. With
regard to the damage function of transformation, because the fact that the point of place is thesame before and after transformation was not taken into consideration, there are errors in theassessment results, and uncertainty may be overestimated.
hCalculation of damage functions according to the classification of land use forland use statistics
By the use of the results above, a damage function was introduced for each type of land use
classified for land use statistics. Concretely, the spatial distribution of NPPa and NPPp andthe land use dataset of the digital national land information (1/10 fine mesh data: 1987 - 1989)
21

LIME2_C2.10-C2.13_2014

Until 90% accumulation

Potential

Until 50% accumulation

Current NPP

Figure 2.10-10: Example of (correlative) spatial distribution of current NPP and
potential NPP (cedar)

Table 2.10-8: Process and result of calculation of damage functions corresponding totypes of land use (example)
Maintenance

Types of land use after transformation

Orchard14

10

4

Other tree field

14

10

3

Forest12

11

1

Wasteland

11

10

2

Building site

14

1

13

Arterial trafficsite
14
Other site
14

1

13

0

-20

-41

916

71

Dry field

20

0

-21

937

91

Orchard

-15

-35

-56

902

57

Other tree field

-8

-28

-49

909

63

Forest

41

21

0

958

113

Wasteland

22

2

-19

939

94

Building site

-916

-937

-958

0

-845

before1

Paddy field

use

10

Building Other
site
site

Arterial traffic
site
-908
Other site
-71

-929

-950

8

-837

land

12

ForestofDry field

Amount of damage
from transformation Paddy Dry
field field
Type

Amoun
NPP
t
of
Type of land use
NPPa
damag
p
e
Paddy field
13
11
3

6
8
-91
-113
845
0
-1
-1
• The left part shows damage functions of maintenance (unit: t ha yr ), while the right part shows damage
functions of transformation (unit: t ha-1).
• Whereas positive values indicate damage, negative ones indicate benefits.
• It is supposed that the types of vegetation corresponding to “building site” and “arterial traffic site” arealways “other.”• With regard to the damage function of the type of land use “forest,” the vegetation types #1 to #14 inTable 2.10-5 were covered by tabulation. The amount of damage also is not zero if “forest” ismaintained. This is because the type of land use “forest” reflects the inclusion of vegetation for which
NPPa was set at a comparatively low value, such as shrubs. (For details, see 2.10.3 (4) a.)• Because this is an example that uses the mesh average to explain the calculation procedure, it does not thesame as the attached list of factors.

22

LIME2_C2.10-C2.13_2014

were superposed and added up. Although the precision of this calculation (Table 2.10-8) islower than in the case of the classification of vegetation, it is easier for LCA users to use it,
because the creation of an inventory is relatively easier.
When the damage factor was calculated for each type of vegetation (as described in the “g”
above), although the ratio of type of vegetation to type of transition (the item “f”) was fixed at
n to 1, if the land use map is superposed on the vegetation map, various types of vegetation
correspond to a certain type of land use. The restoration time for each type of land use under
LIME 1 (Tb→p, Ta→p) was fixed at the average of the restoration times of the types ofvegetation in all the meshes of a certain type of land use. Under LIME 2, based on the resultof cross tabulation of both, the classification of land use statistics was made corresponding to
the representative types of vegetation, and the transition time of the classification of land use
statistics was deemed to be equal to the transition time of the representative types ofvegetation. However, because the corresponding types of vegetation in forests and waste
land were especially varied, the transition time was selected by using, as the probability
distribution, the relative corresponding ratios of several types of vegetation that fall under“main tree period and shrub period” and “main perennial grass period, main annual grass
period, artificial land use, and urban land use.” With regard to “other sites” also, therestoration period for artificial land use was applied based on the result of tabulation ofseveral corresponding types of vegetation in the same way.
Occ
The damage function of maintenance DFNPP(#was used for NPPp – NPPa and NPPp – NPPb.

No transformation

About an endangered species...

Partially transformed

Transformed area

Transformation resulted in loss of 10
individuals
Number of individuals inJapan: N=2382
Decrease rate: R=0.793
Average remaining life
expectancy: Tb=40.865

Number of individuals inJapan: N1=2372
Decrease rate: R=0.793
Average remaining life
expectancy: Ta=40.852
Number of individuals N
Increment in extinction risk (1/T)

No transformation
Partial transformation

Time* The reciprocal of an average remaining life
expectancy T is the extinction risk.

Figure: Relation between the number ofindividuals N and time t (image)

Figure 2.10-11: Calculation of an increase in the extinction risk (amount of damage) in acase of land transformation

23

LIME2_C2.10-C2.13_2014

Column 2.10-4
Methods to estimate the spatial distribution of NPP
There are various methods to estimate the spatial distribution of NPP.
The method to estimate NPP by the use of climate data (climatological estimation model) was
suggested about 30 years ago. Among the study cases in Japan, the case where Kira (1976)
used the warming index (WI) is well known. In addition to this, there were suggestions thatthe function of the annual average temperature, the annual rainfall, or the annual amount ofvapor exhalation should be estimated.
The MIAMI model by Lieth (1973) was
representative.
On the other hand, Uchijima et al. (1985) took in a theoretical view on the processes related toprimary production and suggested the Chikugo model as a model that takes into consideration
various climatological parameters simultaneously.
Figure 2.10-C shows the flow of calculation of NPP by the use of the Chikugo model under LIME.
Mesh climate value(Rainfall)

Mesh climate value(Solar radiation)

Radiation rate
(definition)

Mesh climate value(Temperature)

Monthly average water vapor pressure(prepared based on normal value data)(Seino et al. 1985)

Extra-terrestrial
radiation

Level of radiation dying(definition)

Annual net radiation (Chang 1970)

Albedo on ground surface
Median under LIME 2(No snow period)(Snow period)(Seino et al. 1985)

Potential heat ofevaporation

Chikugo model(Regression equations based on actually measured data
under IBP project)(Proportionality of each RDI layer
was pointed out.)

Figure 2.10-C: Flow of calculation of NPP by the use of the Chikugo model under LIME
(Japan)The Chikugo model estimates net primary production under a certain year’s climate conditions
from the net amount of radiation from the sun and the level of radiation drying that indicatesthe level of drying in the land (net amount of radiation/rainfall × potential heat ofevaporation). Because of its characteristics, it is used also for assessing the impact of climatechange on the physical production of plants. Moreover, because the model covers forests
24

LIME2_C2.10-C2.13_2014

and other natural vegetation, it can be thought that the calculation result is production
potential under a certain climate condition.
In addition, various estimation methods that use satellite data, whose availability has rapidly
grown, have been suggested (Yamagata et al. 2002). One of the representative methods isthe approach that estimates NPP by multiplying light use efficiency and the amount ofphotosynthetically active radiation. In this method, satellite data are directly used to find theamount of photosynthetically active radiation, for example.
Moreover, various models that incorporate biogeochemical processes have been developed(Cramer et al. 1999). Some of the indirect NPP estimation models even incorporate changes
in vegetation, which makes estimation possible in a hypothetical situation.
Under LIME, to grasp the current NPP, the location situation of each type of vegetation was
grasped based on the current vegetation map and was multiplied by the unit production
volume of each type of vegetation fixed separately from actual measurement, etc. In
addition, because the calculation result can be thought to be production potential under acertain climate condition, the Chikugo model was used for the estimation of the current NPP
during the process of calculating the damage factors in the impact category of land use.
Uchijima et al. (1985) shows two types of equations. Under LIME 2, Equation 2.10-3 wasused for calculating the damage factors in Japan, because it is more suitable to Japan.

(3)

Biodiversity: damage functions for terrestrial speciesaBasic view on damage assessment

The damage function of the extinction risk due to land use indicates how much the extinction
risk of species increases due to land use. Vascular plant species were selected for theassessment, because the diversity of plant species is more important than the diversity of theother species; unlike animals, plants cannot avoid the impact of land use by moving to otherplaces; and available information is limited. The selection of vascular plant species for theassessment of the impact of land use on biodiversity is common throughout the world (see2.10.2 (1)).
According to the red list of vascular plants that the Environment Agency published in 1997,“extinction probability” was used for judgment as to whether species were endangered ones ornot (Column 2.10-5). Under LIME, damage functions were calculated based on theinformation written in the Red Data Book (RDB) (edited by the Environment Agency 2000),along with gathering and arranging a lot of information on local survey results about plant
growth written in documents on environmental impact assessment (EIA) of large-scale
projects accompanied by land transformation.
The assessment scope of the damage functions calculated under LIME is as follows: (1) only
the direct impact of land transformation is dealt with, while the impact of maintenance of landuse, such as loss of potential habitats, is not dealt with; and (2) the scope of assessment islimited to Japan in principle.

25

LIME2_C2.10-C2.13_2014b
Formularization of damage assessment (flow of damage assessment in antransformation case)
What damage is caused by a change in type of land use from “b” to “a”? With regard to theimpact propagation chart shown as Figure 2.10-1, attention is paid to the course throughwhich some individuals of a plant species growing in a transformed area directly die unlessspecial conservation measures are taken. This transformation will decrease the number ofindividuals of the plant species in the area or result in the extinction of the species as a whole.
However, if some individuals are distributed widely in Japan and a group of a sufficientnumber of individuals is continuously maintained in each habitat, this kind of species will not
become extinct for a while. On the other hand, if the number of habitats in Japan is verysmall, including the transformed area, if the species is distributed all over Japan, but eachgroup consists of a small number of individuals, or if the number of individuals is sharply
decreasing in any habitant, the transformation will increase the extension risk of the plant
species without fail.
The damage assessment herein aims to find the degree of an increase in the extinction risk ofvascular plant species in Japan due to a decrease in the number of individuals of species witha high extinction risk as described above – that is, endangered species. The assessment
method is based on existing research carried out in liquefied natural gas (LNG) supply bases(Oka et al. 1999, Oka et al. 2001) and the planned site for Aichi Expo (Matsuda et al. 2003).
Under LIME, an increment in the extinction risk is expressed by difference in the reciprocalof the average extinction time of the species in question. This means that more importanceis placed on the impact of species with shorter average life expectancy than indexing, such as
finding difference in average extinction time (Matsuda 2000). Equation 2.10-5 shows theamount of damage from a transformation case (IER).
IER (IER
b, a )(b,a)= ΣiΔ(1/T)i＝Σi (1/Ti,a－1/Ti,b)(2.10-5)

In this equation, Ti is the average extinction time of the species i. The attached b and aindicate the situation before and after the transformation, respectively. ∆(1/T)i is anincrement in the extinction risk of the species i due to a case of transformation in unit areafrom b to a. Totaling of ∆(1/T)i of all species means giving equal weight to each species.
Under LIME, an increment in the extinction risk expressed by this index is deemed to be an
expected increase in the number of extinct species and is abbreviated as EINES.
Figure 2.10-11 shows an image of calculation about an extinct species due to a transformation
case. IER can be calculated by making such calculation about all the extinct species whose
individuals are lost due to the transformation and adding up the results.
cFlow of calculation of damage function and basic policy for uncertainty
assessment
As shown in Figure 2.10-12, the damage function of the extinction risk due to land use wasset at the representative value of the totals for each type of land use after finding IERs of
several land transformation cases. In addition, Table 2.10-9 summarizes the uncertainty
elements taken into consideration during the uncertainty assessment of the damage function
and the way of treating them.
“d” to “h” below will describe the components of the calculation procedure and the process of26

LIME2_C2.10-C2.13_2014

the calculation.

Regression from result of simulation
model (d)Damage assessment about a land transformation case (example: soil/stone digging)
Information in Red Data Book (Column 2.10-5)(Information of each species’ number of individuals and rank in
the decrease rate → ni, fm)

Environmental impact assessment report(Extraction of the number of growing individuals ofeach species in the habitant)

Statistical models of the number of individuals and the decrease
rate (d) (Table 2.10-10)
Decrease in thenumber ofindividuals of species
A: ∆NA

Decrease in thenumber ofindividuals of species
X: ∆NxAverage remaining
life expectancy ofspecies A: TA,aAverage remaining
life expectancy ofspecies X: Tx,aStatistical model of average remaining life expectancy (d)(Table 2.10-10)

Average remaining
life expectancy ofspecies A: TA,bAverage remaining
life expectancy ofspecies X: Tx,bIncrement in extinction
risk of species A:∆ (1/T)A

Increment in extinction
risk of species X:∆ (1/T)x

Increment in extinction risk in the whole area (f)(Equation 2.10-5)

Increment in extinction risk per unit area (f) UAR = IER ÷ area of habitat(Equation 2.10-6)

Adding up damage assessment results for each case by type of project andadopting the representative value (g)Damage function* Making some adjustments (h)(For results, see Figure 2.10-19, forexample.)Figure 2.10-12: Flow of estimation of damage function of extinction risk due to land use
dCalculation model of average remaining life expectancy

Under LIME, the average life expectancy was calculated by a statistical model that estimates
the number of individuals of endangered species in Japan N and the average decrease rate R
from the number of meshes for the “number of existing individuals” of each of theendangered species written in RDB (edited by the Environment Agency, 2000) ni and thenumber of meshes of the “decrease rate in the number individuals” of each of them fm (seeColumn 2.10-5 and Table 2.10-A) and estimates of the average remaining life expectancy ofthe species T (Table 2.10-10).
This model was obtained based on Matsuda et al. (2003). The concrete procedure was asfollows: first, Rsim and Tsim of 1,146 species were calculated by applying a simulation model
27

LIME2_C2.10-C2.13_2014

by Matsuda (his website) (whose contents are similar to the simulation model used for the redlist judgment explained in Column 2.10-5, but different in some points) after changes weremade in some conditions, such as limiting the targets to the RDB-listed endangered species
about which information necessary for calculation were obtained; next, regression analysis
was carried out about the result and the representative values of parameters (regression
factors) and standard errors were obtained for the equation for the calculation of Rreg and Treg
shown in Table 2.10-10. Under LIME 2, this estimated error was used for setting normal
distribution (although, to be exact, this should have been t distribution, normal distribution
was used because there were 1,146 species and latitude was wide). However, probability
distribution was considered independent between parameters.
Table 2.10-9: Main causes of uncertainty in damage functions of the extinction risk ofland use and policy for dealing with themMain possible causes of uncertainty
Averageremaining life
expectancy
calculation
model(2.10.3 (3) d)
Calculation ofincrement inextension risk ofeach species in
eachenvironment
assessment case(2.10.3 (3) e)

Parameters of regression
equation for averageremaining life expectancyof species T

Applying probability density function for each
evaluation target species to frequency distribution of
average remaining life expectancy based on Monte
Carlo simulation result Tsim, normalizing it by averagevalue and multiplying Treg by the result. (49 species)
Regarding information on the number of individuals ofeach endangered species (example: Table 2.10-A),
setting a number of growing individuals in each meshin RDB according to rank in the number of them andadding up those in all the meshes where the number
was reported (Table 2.10-10). Regarding asarum
satsumense, however, probability distribution wasestablished according to written description in RDB.
(3,158 meshes/species)
The confirmed number of If the confirmed number of individuals is expressedindividuals ∆N in cases
qualitatively in each piece of literature, a flexible value
subject to assessment (data was set by uniform distribution, taking into
form EIA materials)
consideration consistency with qualitative expressionin context. (About 150 points/species)
Calculation of
Geographical variability
The amount of damage per unit area (UAR) in eachdamage function Limitations inassessment case by type of land use (type of project)
by type of land representativeness and the was given as variability distribution by equaluse (2.10.3 (3)number of cases of land
probability of occurrence (for estimation result ofg)transformation subject to
UAR for each case, see Figure 2.10-13). (37 cases)
assessment

28

Probability distribution of
average remaining life
expectancy T (distributionof decrease rate in eachmesh in RDB)
Number of individuals inJapan N
(Indication by rank in
RDB)

Policy for dealing with them during uncertainty
assessment
Estimated error in parameters of regression equation
for decrease rate Rreg (Table 2.10-10) (7 parameters)
Estimated error in parameters of regression equation
for average remaining life expectancy of species T(Table 2.10-10) (3 parameters)

LIME2_C2.10-C2.13_2014

Table 2.10-10: Equation for calculation of average life expectancy used in LIME
(statistical model)
Estimation item

Equation (statistical model)ni4N   log N   N i ,  N i 

Number ofindividuals inJapan (N)i 1 j 1

Herein log-N is lognormal distributioni 1
independent of i,j, N i  3.162  10 .
5

R reg  p 

r F
m

m

m 0

Averagedecrease rate in
decade (R)

 5


F

f

f m  1Herein, mm  m 0

Averageremaining life
expectancy (T) 4 Treg  A  B lnN  / ln 1  Rreg  C ln ni  i1 









Equation parameter, etc.
(Standard error in parenthesis)
ni: number of meshes by “number ofexisting stumps”, i=1: -10 stumps,
i=2: -100 stumps, i=3: -1,000 stumps,
i=4: 1,000 stumps-

p=0.538 (0.001), r0=0.464 (0.002),
r1=0.464 (0.004), r2=0.411 (0.003),
r3=0.171 (0.002), r4= -0.291 (0.002),
r5= -0.541 (0.002)
fm: number of meshes by “change
from the past”
m=0: extinction, m =1: -1/100,
m =2: -1/10, m =3: -1/2, m =4: -1,
m =5:1A=1.891 (1.205),
B=5.806 (0.067),
C=7.572 (0.537)

Column 2.10-5
Judgment and extinction probability of endangered species in the Red List and the Red
Data Book
The Red Data Book (RDB) summarizes the growth condition of endangered wild species andis used as a basic and important material for natural conservation.
Endangered species II

Percentage distribution (%)

Endangered species I

species
species

Mammals
species

species

Birds

species
species

species
Reptilesspecies

species

Amphibians
species

Brackishspecies
water/
freshwater
fishesInsects

species
species

VascularspeciesplantsFigure 2.10-D: Ratio of endangered species to the total number of wild species in Japanby category• Seven categories are shown in this graph among the 13 categories in the “Table of the Number ofSpecies in the Red Data Book and the Red List” in the “2004 White Book on the Environment.”• Endangered species I consists of species on the verge of extinction, while endangered species II
consists of species with an increasing extinction risk.

29

Extinction probability

LIME2_C2.10-C2.13_2014[years]Figure 2.10-E: Example of calculation of cumulative extinction probability (primrose)• The graph is reproduced from RDB of vascular plants (edited by Environment Agency 2000).
• Prepare meshes corresponding to the number of classes reported as existing concerning each typeand set the number of individuals in each mesh according to class. In the case of Table 2.10-A,
information is created for 17 meshes with 316 individuals according to “n3: up to 1,000 stumps.”• A decrease rate is arbitrarily assigned to each mesh that holds a certain number of individuals,
based on the data on the degree of decrease (the information on the number of meshes for each“change from the past” and supposition of hypothetical additional extinction meshes)(corresponding to a change in a decade).
• The result of implementation of this procedure for 100 years is regarded as the conclusion of one
trial. Extinction occurs when the number of individuals in all the meshes becomes zero.

In Japan, the Ministry of the Environment began to review RDB in 1995. The review has
been carried out at two levels: preparation and publication of a red list (list of endangered
species to be included in RDB) about each category (“mammals,” “vascular plants”); andediting of RDBs based on this one after another (the website of the Ministry of theEnvironment’s Biodiversity Center of Japan).
In this assessment, judgment on endangered species was made by dividing the species subject
to the assessment into some categories according to the scale of extinction risk. The
International Union for Conservation of Nature and Natural Resources (IUCN), which edits
RDB on a worldwide level, adopted new categories based on quantitative assessment
standards in 1994 and reviewed the lists (IUCN’s current categories and assessment standards
are the 3.1 version created in 2001. A Japanese version translated by Yahara and Kaneko is
made available to the public by the Japan Wildlife Research Center (http://www.jwrc.or.jp/).
A judgment method based on estimated extinction probability was adopted together with otherquantitative and qualitative judgment methods for the judgment of endangered species ofvascular plants in Japan. By the standards based on the extinction probability, for example,
species whose extinction probability is estimated to be 10% or more 100 years after are judged
to be endangered. This is said to be the first attempt in the world in which quantitative
judgment of degree of risk was made about all the vascular plants in the Japanese islands.
Concretely, the assessment procedure was as follows: calculation of future changes in thenumber of individuals by computer simulation was tried a thousand times by the use of dataon the number of individuals and the decrease rate in each target mesh (about 10-km squarearea) provided by naturalists and others (Table 2.10-A), and extinction probabilities 10, 20,
and 100 years after were estimated based on the number of 1,000 trials resulting in extinction(Figure 2.10-E).
30

LIME2_C2.10-C2.13_2014

These data on the number of individuals and the decrease data in each mesh were derived
based on accumulated survey data for several decades and the results of additional fieldsurveys conducted during two years from FY1994. However, because it was impossible toresearch the habitats of all the target species in the two years, it is said that most of theestimated numbers of individuals may be underestimated.
As described herein, under LIME, the assessment index is ∆(1/T), which is a reduction in thereciprocal of T, the number of years until the extinction of each vascular plant species
calculated based on data shown in RDB and others. If the number of existing individuals isunderestimated, T also is underestimated and ∆T is overestimated due to loss of someindividuals (see the equation for T in Table 2.10-10). Because of this, ∆(1/T) also is highly
likely to be overestimated. However, this point is not reflected in the uncertainty evaluation
under LIME 2.
Table 2.10-A: Example of information written in RDB (in the case of primrose)
Number of meshes by “number of existing individuals”
n1: up to10 n2: up to100 n3: up to1,000 n4: 1,000 or moreindividuals
individuals
individuals
individuals

Unknown

Extinct

12

13

23

60

17

6

Number of meshes by “change from the past”
f1: up to1/100

f2: up to 1/10

f3: up to 1/2

f4: up to 1

f5:1 or more

Unknown

f0: Extinct

8
25
24
10
6
45
13
Source: Revised Red Data Book: Endangered Wild Life in Japan – 8. Plants I (Vascular Plants) (Environment
Agency 2000)e
Calculation of an increment in the extinction risk of each species in eachenvironment assessment case
Under LIME, information on land transformation cases necessary for finding IER was
obtained by gathering and arranging documents on environment impact assessment in the past(see 2.10.1 (1)), and an increment in the extinction risk was calculated for each species whose
growth was confirmed in each case. With regard to the type and number of projects, under
LIME 1, there were 30 projects in total: 16 projects for construction of roads in non-urban
areas, 9 projects for digging soil and stone (area expansion), 4 projects for construction of
final disposal facilities, and 1 project for railway construction. Under LIME 2, information
about 2 road construction projects, 2 projects for digging soil and stone, and 3 projects forconstruction of final disposal facilities was researched and added.
First, arrangement was made about the status of confirmation of RDB-listed species, thescope of research, and the scopes of the projects during a field survey on flora written in eachenvironment impact assessment document (hereinafter, the RDB-listed species confirmed
locally was called the “target species”). With regard to projects before the issuance of RDB,
whether they were listed in RDB was checked only about the species listed as “valuable
vascular plants” in each project. If a species was listed, it was included in the target species.
Next, with regard to each target species, based on data written in RDB and by the use of theabove-described statistical model (Table 2.10-10), the average life expectancy Tb was
calculated from N, the number of individuals in Japan calculated from information written in
RDB, and Ta, the average remaining life expectancy if the number of individuals in the target
31

LIME2_C2.10-C2.13_2014

area for each project (∆N) disappear (N–∆N) in each project was calculated. Then, for eachtarget species, a difference in average remaining life expectancy, ∆T = Tb - Ta, and anincrement in the extinction risk (an increment in the reciprocal of average remaining life
expectancy), ∆(1/T) = 1/Ta – 1/Tb, and log ∆(1/T) were calculated. Among them, ∆(1/T) is
used as the damage index under LIME as described above.
There were differences in the degree of details of description about endangered species(especially, the expression of the number of confirmed species) and the existence inconservation measures. Because of this, under LIME, when basic information for the
calculation of an increment in the extinction risk was arranged, including setting a range(concept) of assessment targets, the extinction risk was calculated after the establishment of a
uniform survey policy. As a result, it is appropriate to interpret the increment in theextinction risk calculated under LIME not as direct evaluation of the impact of a decrease in
the number of individuals caused actually by the implementation of each development project,
but as the size of the potential impact that reflects a location situation where such a project
can be planned.
Under LIME 2, the probability distribution of average remaining life expectancy T, theestimation of the number of individuals in Japan N based on RDB (also see Column 2.10-5),
and the number of confirmed individuals not expressed by specific values in environmental
impact assessment literature were included in the uncertainty assessment. Table 2.10-9
shows details of the setting.
fCalculation of the amount of damage (IER and UAR) in each case

An increment in the extinction risk (IER) in this case was calculated by adding up theincrements in the extinction risk of the species in each of the above-mentioned environmental
impact assessment cases (Equation 2.10-5). Next, an increment in the extinction risk perunit area (UAR) in each case was calculated by dividing IER by A, the area of the extent of
the extinction risk assessment (research extent, etc.).
UAR = IER ÷ A(2.10-6)

Table 2.10-11 shows an example of calculation of IER and UAR.
Table 2.10-11: Example calculation of the amount of damage (increment in theextinction risk)
Increment in the extinction
After transformation
No. ofrisk
confirmed
Averageindividuals No. ofremaininglog(1/in theindividu
T (1/T)
life
T)
project area als
expectancy

Confirmed RDB species

Before transformation

Class

Item

AverageNo. ofDecrea remaining
individual
se rate lifes
expectancy

Vittaria
flexuosa

Antrophyum
230
obovatum

0.856 43.934

1229

43.920849 0.013 6.73E-06-5.17

Scrophulariace Siphonosteg
1396
ae
ia laeta

0.310 143.553

9

1387

143.45173 0.101 4.92E-06-5.31

Cephalanth2801
era falcate

0.583 93.289

1

2800

93.286452 0.002 2.73E-07

-6.56

Total

0.117 1.20E-05

OrchidIER=ΣΔ(1/T)=1.20E-5 [EINES], UAR=IER÷area (280ha) =4.27E-8 [EINES ha-1]

32

LIME2_C2.10-C2.13_2014gCalculation of damage function by type of land use

The increment in the extinction risk per unit area calculated for each case and the target area
were arranged for each type of project as shown in Figure 2.10-13. As a result, because thevalue greatly differed among cases, under LIME 1 it was judged appropriate to show the
representative value as an approximate number based on the median. Under LIME 2, themedian serves as the representative value in the impact categories where uncertainty
assessment was carried out, and the same applies to this damage function.
Soil/stone digging
Final disposal facilities

Soil/stone digging (addition)
Final disposal facilities(addition)

Road construction

Road construction (addition)

Railway construction

90% confidence interval

Assessment area [ha]Figure 2.10-13: Result of calculation of the amount of damage per unit area (UAR) in
each case• The graph shows the average (marker) and the 90% confidence interval obtained as a result of
uncertainty assessment of UAR (IER ÷ assessment area) in each case. For an outline ofuncertainty assessment of IER, see Column 2.10-5 and Table 2.10-9.
• Because UAR is 0 in the case of the digging zone expansion project and the final disposal facilities
construction project (one project each), about which a species subject to extinction risk assessmentin this study were not reported in environmental impact assessment literature, this graph shows it
as IE-10 for convenience sake.
• The “addition” in the legend means a case of environmental impact assessment where UAR was
calculated by gathering and arranging new information under LIME 2.

Moreover, under LIME 2, on the assumption that land transformation shown in the inventory
could not be identified when it is made “somewhere” in Japan, spatial variability was added tothe uncertainty assessment also in this impact category. Concretely, a set of UARs obtained
this time was given as equiprobability distribution. Therefore, the obtained result has awider probability density distribution than the estimated error in the median (representativevalue) as described below.
The obtained damage function has the following characteristics: (1) regarding road
construction, the situation of check of endangered species widely differs among cases, and theextinction risk per unit area differs greatly; (2) regarding the target cases, the potential impact
per unit area of the final disposal facilities construction projects was larger than that of the33

LIME2_C2.10-C2.13_2014

soil and stone digging zone expansion projects; (3) regarding the soil and stone digging zone
expansion projects and the final disposal facilities construction projects, it was thoughtpossible to carry out impact assessment in relation to resource consumption and waste
disposal, other impact categories ; and (4) regarding road construction projects, it was thoughtthat more examination would be necessary when applying to LCA.
h

Adjustment of damage function by standard value

Based on values written in RDB, the yearly decreasing number of individuals of eachendangered species was estimated by multiplying by the estimated number of individuals inJapan. When the average remaining life expectancy before and after the decrease wascalculated only for the yearly decreasing number of individuals and the total increment in theextinction risk of all the target endangered species was calculated by Equation 2.10-5, thetotal increment was 0.553. The annual amount of damage due to land transformation was
0.198 if the result was simply divided by the ratio of answered meshes of decrease causes(excluding deforestation) related to land use (transformation) reported in RDB (Figure2.10-2).
On the other hand, if the amount of damage per unit area in the above-mentioned road
construction cases is multiplied by the annual road construction area and is divided by theratio of answered meshes of decrease cases (ratio of road construction to all the cases related
to land transformation) to find the amount equivalent to annual damage due to causes related
to land transformation, the result was 0.00120, which is extremely different from the above
result.
As a result of examination of the cause, among the plant species observed in theenvironmental impact assessment cases, the number of those belonging to the endangered
species IA (CR), whose extinction risk is higher than the other endangered species, was small,while the number of those belonging to the endangered species IB (EN) and especially theendangered species II (VU) is large. Moreover, an increment in the extinction risk per
decrease of an individual was small concerning most of the UV species. This was thought tobe a cause of the great difference between the two (Figure 2.10-14) (As a characteristic of theindex ∆(1/T), which was selected as the damage index that indicates the extinction risk, asshown above, the impact on species whose average remaining life expectancy is low, such as
the CR species, is regarded to be relatively high).
As a result of examination of geographical bias of the cases and examination of differences
caused by the non-linear nature of the increment in the extinction risk due to a decreasing
number of individuals (the equation for calculation of average remaining life expectancy(Table 2.10-10) is non-linear in terms of the number of individuals. On the other hand,when the decreasing number of individuals is small in each land transformation case,compared with the yearly decreasing number of individuals in Japan), their impact wasthought to be not conspicuous.
In the inventory in usual LCA cases, details of land transformation are thought to be unknown.
Given the standard value based on the nationwide number of individuals grasped in RDB, thedamage function based only on the environmental impact assessment cases gathered this time
may lead to underestimation of the extinction risk. Therefore, the value adjusted throughmultiplication by 164, the ratio between the two, was finally adopted as the damage function.
If the status of growth of endangered species in the transformed land, it is possible to make34

LIME2_C2.10-C2.13_2014

Increment in extinction risk

assessment without applying the adjustment coefficient, by directly calculating the UAR ofthe land by the above-described calculation method.

Per yearPer decreased individual

Per year (target cases)

Per decreased individual (target cases)

Figure 2.10-14: Relation between all RDB-listed species whose average remaining life
expectancy can be calculated and the species confirmed in the target cases (EIA)
In the cases (EIA) covered by LIME 1, the graph shows that, mainly, species whose
increment in the extinction risk is relatively small at the time of decrease of an individual has
been confirmed. Because of this, if the result of calculation only based on the cases subject
to the assessment this time is used as is, underestimation may occur in the application to
LCIA.
Column 2.10-6
Promotion of LCA with consideration for regionality – damage function in accordancewith the location of land use
The impact of land use on the ecosystem greatly differs from region to region. Therefore, it
is favorable to carry out assessment that reflects not only the classification of land use but also
the location of the land, vegetation, and other detailed conditions. However, it would be
difficult to apply this to LCA.
However, if inventory makes it possible to grasp information on the geographical location ofland use, it may be possible to carry out more detailed impact assessment.
Under LIME, the damage function due to the extinction risk was calculated for each mesh(each coordinate) by arranging the distribution of the RDB-listed endangered vascular plant
species about which distribution information is open to the public, using data on them(provided by the Ministry of the Environment’s Biodiversity Center of Japan), and comparing
the distribution with each impact assessment case. For details, see Abe et al. (2003). If
more accurate assessment is needed, it is necessary to carry out a (field) survey on the numberof individuals of endangered species. However, this is beyond the scope of current general
LCA; it is within the territory of the environmental assessment (EIA) of each project.

35

LIME2_C2.10-C2.13_2014

Table 2.10-12: Damage factor for primary production due to land use (maintenance)–2–1
LandUse ( Dcc )(extraction of some types of land use) ( DFNPP( a ) unit: kgDW m yr )Type of land use (a)Paddy field

Dry field

ForestNo. of trials
500
500
500
Average
2.74E-01
1.40E-01
1.04E-01
Median2.55E-01
1.35E-01
1.15E-01
Standard deviation
2.96E-01
3.10E-01
3.39E-01
Dispersion
8.75E-02
9.62E-02
1.15E-01
Skewness
0.37
0.37
-0.09
Kurtosis
3.60
3.62
2.81
Variationcoefficient
1.08
2.22
3.26
10% value-8.50E-02-2.55E-01-3.55E-01
90% value
6.55E-01
5.25E-01
5.35E-01• Positive values indicate damage, whereas negative values indicate benefits.

Building site

Other site

500
1.28E+00
1.28E+00
2.17E-01
4.71E-02
0.08
2.80

500
7.54E-01
7.35E-01
5.21E-01
2.71E-01
-0.11
2.00

0.17
1.01E+00
1.57E+00

0.69
6.50E-02
1.43E+00

Table 2.10-13: Damage factor for primary production due to land use (transformation)–2
LandUse (Trans )(extraction of some types of land use) ( DFNPP(b, a ) (unit: kgDW m )Type of land use before
transformation (b)Type of land use after
transformation (a)No. of trials
Average
MedianStandard deviation
Dispersion
Skewness
Kurtosis
Variation coefficient
10% value
90% value

Paddy field

Paddy field

Dry field

ForestWasteland

ForestBuilding site

Other site

Building site

Arterial traffic
site

50000
-4.05E+00-3.54E+00
4.85E+00
2.35E+01
-0.68
4.62
-1.20
-1.02E+01
1.40E+00

50000
9.17E+01
8.88E+01
4.11E+01
1.69E+03
0.33
2.37
0.45
3.90E+01
1.48E+02

50000
9.13E+00
8.41E+00
1.00E+01
1.01E+02
0.20
2.86
1.10
-3.08E+00
2.28E+01

50000
9.57E+01
9.28E+01
4.08E+01
1.67E+03
0.34
2.35
0.43
4.32E+01
1.52E+0250000
9.26E+01
8.96E+01
4.07E+01
1.66E+03
0.32
2.34
0.44
4.07E+01
1.49E+02

LandUse (Trans )LandUse (Trans )• If the type of land use is in reverse order, apply DFNPP(b, a ) =  DFNPP( a, b ) .
• Positive values indicate damage, whereas negative values indicate benefits.

(4)

Damage factor of land use

Because, in the impact category of land use, the number of types of damage factor for primary
production or the extinction risk is one, the representative value of the damage function serves
as the damage factor as is.
aDamage factor for primary production

With regard to the damage factor for primary production, Tables 2.10-12 and 2.10-13 show
statistical values, Figures 2.10-15 and 2.10-16 show probability density distributions andTable 2.10-14 shows rank correlation. In addition, Figures 2.10-17 and 2.10-18 show
comparison with LIME 1.

36

Probability of occurrence[1/(kgDW/(m2 yr)]

LIME2_C2.10-C2.13_2014

Paddy field
Dry field
ForestWasteland
Building site
Other site

Damage factor [kgDW/(m2 yr)]

Probability of occurrence[1/(kgDW/(m2)]

Figure 2.10-15: Damage factor for primary production due to land use (maintenance)
LandUse ( Dcc )(extract of some types of land use) ( DFNPP(a ) )

Paddy field → forest
Paddy field → building site
Dry field → other site
Forest → building site
Wasteland → arterial trafficsite

Damage factor [kgDW/(m2)]

Figure 2.10-16: Damage factor for primary production due to land use (transformation)
LandUse (Trans )(extract of some types of land use) ( DFNPP(b , a ) )According to Table 2.10-12, the median of the damage factor is positive in any type of landuse. Because of this, if this assessment factor is used, even when a forest is conserved, it isjudged that the forest has damage. Unlike in the case of many other impact categories andobjects of protection, but like the confidence intervals shown in Figure 2.10-17 that uses notlogarithmic but arithmetic scale, the standard deviation of the damage factor is almost at thesame level, except for some intervals. Because of this, the smaller the average (≒ median)of the damage factor, the larger the variation coefficient tends to become. Especially withregard to the types of land use, because there are various types of forests, they include thetypes of vegetation regarded as having small NPP, such as various types of shrubs listed inTable 2.10-5. Because there are many cases (meshes) where the damage factor is judged to
be positive, as a result, the probability density distribution of damage factors is bilaterally
symmetric, centering almost on 0, as shown in Figure 2.10-15.
With regard to the damage functions in the other impact categories under LIME, the amount37

LIME2_C2.10-C2.13_2014of damage is calculated by “multiplication,” while the amount of damage from themaintenance of land use is calculated by “subtraction” from the referential state as shown in
the equation for definition (Equation 2.10-1). Because of this, it is better to understand thatthe difference between the two is about 1 kgDW/(m2 yr) rather than understanding that thedamage factor for a building site is about 10 times that of a forest. Therefore, attention
should be paid to the interpretation of the result. It would be effective to grasp thisquantitatively by carrying out uncertainty assessment under LCA by the use of the probability
density distribution information on damage factors shown in LIME 2.
Table 2.10-14: Example of rank correlation coefficient of damage factor for primary
production due to land use (transformation)
Paddy field

Dry field

ForestOther site

Paddy field
Restoration time forprimary production

Other site

ForestBuilding site
Restoration time forprimary production

Paddy field

Building site

Restoration time forprimary production

ForestRestoration time forprimary production• b: type of land use before transformation; a: type of land use after transformation• Variables with a rank correlation coefficient of 0.05 or more were extracted.

Median (10 to 90% value)

Other site

Right: building site
Left: arterial traffic site

Figure 2.10-17: Comparison of LIME 1 and LIME 2 in the damage factor for primary
production due to land use (maintenance)• Because various types of vegetation (NPPa) correspond to the type of land use “other site,” theconfidence interval has become wider. (See Figure 2.10-15.)• Because, under LIME 2, NPPa for “building site” and “arterial traffic site” is not regarded as 0, but
NPPa of the type of vegetation “other” is used, the result becomes smaller than that of LIME 1 by
0.1 kgDW / (m2 yr).

38

LIME2_C2.10-C2.13_2014b

Damage factor for biodiversity

With regard to the damage factor for an increment in the extinction risk, Table 2.10-15 shows
the statistical amount and Figure 2.10-19 shows the cumulative probability density
distribution. Like LIME 1, the target cases show that the potential impact of the final
disposal facilities construction projects may well be higher than that of the soil and stone
digging zone expansion projects.

Median (10 to 90% value)

Transformation to (from) a construction
site or an arterial traffic site

The confidence interval is wide only in thecase of transformation from a construction
site to an arterial traffic site.

Figure 2.10-18: Comparison of LIME 1 and LIME 2 in the primary production damage
factor due to land use (transformation)
Trans
Table 2.10-15: Damage factors for the extinction risk due to land use ( DFEINES(a ) )(Unit: EINES/m2)

Type of project (a)No. of trials
Average
MedianStandard deviation
Dispersion
Skewness
Kurtosis
Variation coefficient
10% value
90% value
Average standard error

Road construction(Non-urban district)
50,0002.77E-08
3.49E-10
8.48E-07
7.19E-13
125.4
19050.3
30.6
9.07E-12
4.17E-09
3.79E-09

Soil/stone
digging
50,0004.86E-09
8.28E-11
1.62E-08
2.63E-16
4.1
22.0
3.3
4.24E-12
2.65E-09
7.25E-11

Construction of final
disposal facilities
50,0009.97E-10
4.44E-10
1.77E-09
3.14E-18
3.7
20.9
1.8
0.00E+00
3.01E-09
7.92E-12

Other(Default)
50,0001.07E-08
2.22E-10
1.22E-07
1.49E-14
59.6
5,187.9
11.5
6.60E-12
3.46E-09
5.46E-10

Trans• Regarding DFEINES (b, a ) , damage factors have been put together into those related to b, the land use
before transformation.
• Under LIME 2, a variability distribution of UARs in all the cases subject to the assessment is used
for the other types of land use.

39

LIME2_C2.10-C2.13_2014

Road construction
Soil/stone digging
Final disposal
facilities
Other

Figure 2.10-9: Cumulative probability distribution of damage factors

Road (median)
Soil/stone (median)Final disposal (median)Road (average)
Soil/stone (average)
Final disposal (average)

Figure 2.10-20: Cumulative probability distribution of medians and averages of(adjusted) UARs of target cases
The damage factors show wide probability distribution. This is because the large variability
among the UAR cases shown in Figure 2.10-13 was reflected as the governing cause (rankcorrelation coefficient: 0.87 - 0.98). In addition, for reference, Figure 2.10-20 shows theresult of estimation of damage factors as the medians and averages of the cases not by thevariability distribution of the cases but by the bootstrap method. The width of the estimatedmedians is narrower than the variability distribution. With regard to road construction andsoil and stone digging, because there are cases where UAR is relatively large, the difference
between the average and the medial has become large.
With regard to the rank correlation with the variables corresponding to the uncertainty causes
40

LIME2_C2.10-C2.13_2014of each average and median (Tables 2.10-16 and 2.10-17), although it is common for theestimated error between the average and the median to have great influence due to a limited
number of cases subject to assessment, there are differences in the other causes that have great
influence. With regard to the averages, the causes that occupy higher ranks are those related
to species whose extinction risk is high and whose growth was confirmed in cases where an
increment in the extinction risk is high among the cases concerning each type of project, such
as Asarum satsumense (an EN species whose number of individuals discovered through EIA
is large), A.kiusianus (although this is a VU species, the number of meshes whose habitats
were reported in RDB is only four, of which only one mesh has an identified number ofindividuals. In this calculation, the number of individuals in Japan was adjusted upward as
described in the text of RDB), Cymbidium macrorhizon (EN species), and Elaeagnus
matsunoana (although this is a VU species, the number of meshes whose number ofindividuals was reported in RDB is only eight and therefore the regions where it is distributed
are limited). On the other hand, with regard to the medians, many causes related to species
confirmed in two or more cases, such as Euplectes orix (VU species), Calanthe (VU species),
and Prenanthes tanakae (VU species), were extracted. These are species whose growth wasreported widely in Japan. In addition, the tables show that the estimated error in theparameter C (coefficient related to the number of meshes for growth) of the regression
equation that estimates the average remaining life expectancy T of species, an element of
UAR calculation common to each case, has a certain influence.
Although land transformation cases subject to assessment (environmental impact assessment
documents) were added under LIME 2, the damage factors have hardly changed from LIME 1
if consideration is given to great differences among cases in the amount of damage per landarea (UAR). Although this is mainly because the damage functions have been adjusted bystandard values, it is also because the medians of the UARs of the assessment cases were usedas the damage factors under LIME 1. With regard to the forms of land use, therepresentative value of the damage factors for “other (unknown)” is larger than that under
LIME 1 (Figure 2.10-21), and the definition of the damage factors for “other” was madesimpler under LIME 2 (also see the notes to Table 2.10-15).
Table 2.10-16: Rank correlation coefficients of medians of target case UARs
Road construction (18 cases)

Soil/stone digging (11 cases)

Final disposal facilities (7 cases)#1

No. of assessment cases

0.37

No. of assessment cases

No. of assessment cases

0.49

#2

C (parameter of Treg)-0.08

Tsim (Calanthe)

Tsim (Euplectes orix)

-0.18

#3

Tsim (Euplectes orix)-0.07

No. of growing
individuals of Calanthe
in an EIA caseNo. of growing individualsof Euplectes orix at a point
in an EIA case

0.16

#4

Tsim (Polygonaceae)-0.07

N4 (Calanthe)

N4 (Prenanthes tanakae)

-0.11

#5

A (parameter of Treg)-0.04

N4 (Calanthe)

N4 (Prenanthes tanakae)

-0.11

#6

N3 (Euplectes orix)-0.04

Tsim (Euplectes orix)

C (parameter of Treg)

-0.11

#7

N3 (Euplectes orix)-0.03

C (parameter of Treg)

N4 (Prenanthes tanakae)

-0.10

0.34
-0.1
7
0.15
-0.1
4
-0.1
4
-0.1
3
-0.1
2

• No. of assessment cases: Actually, this indicates the ranking of 1,000 bootstrap samples prepared
beforehand (because the ranking is arranged by the use of the averages of re-sampled cases, it is
not necessarily appropriate for examining rank correlation of medians).
• A and C are parameters of the regression equation for the calculation of T, the average remaining

41

LIME2_C2.10-C2.13_2014

life expectancy of species. Nx is the estimated number of individuals in meshes with the rank x in
the number of growing individuals (see Table 2.10-10).
• Tsim corresponds to uncertainty caused by the distribution of rates of variability in the number ofindividuals (fm) when T is estimated by simulation.

Table 2.10-17: Rank correlation coefficients of averages of target case UARs
Road construction (18 cases)

Soil/stone digging (11 cases)

Final disposal facilities (7 cases)#1

No. of assessment cases

0.78

No. of assessment cases

0.88

No. of assessment cases

0.85

#2

Tsim (Asarum satsumense)-0.20

Tsim (Cymbidium
macrorhizon)-0.20

No. of growing individuals of
Elaeagnus matsunoana in an
EIA case

0.18#3N3 (A. kiusianus)-0.17

N2 (Cymbidium
macrorhizon)-0.09

No. of points of growth ofHeterotropa savatieri in an
EIA case

0.14#4

N (Asarum satsumense)-0.12

N2 (Cymbidium
macrorhizon)-0.08

N3 (Elaeagnus matsunoana)

-0.10

#5

Tsim (A. kiusianus)-0.08

N2 (Cymbidium
macrorhizon)-0.08

N3 (Elaeagnus matsunoana)

-0.09

#6

N2 (A. kiusianus)-0.05

N2 (Cymbidium
macrorhizon)-0.08

N3 (Elaeagnus matsunoana)

-0.09

#7

A (parameter of Treg)-0.04

N2 (Cymbidium
macrorhizon)-0.08

Tsim (Elaeagnus matsunoana)-0.08

Median (20 to 80% value)• No. of assessment cases: Actually, this indicates the ranking of 1,000 bootstrap samples prepared
beforehand (ranking arranged by the use of the averages of re-sampled cases).
• A is a parameter of the regression equation for the calculation of T, the average remaining life
expectancy of species. Nx is the estimated number of individuals in meshes with the rank x in thenumber of growing individuals (see Table 2.10-10). N is direct assessment of the number ofindividuals in Japan, not adding up of Nx.
• Tsim corresponds to uncertainty caused by the distribution of rates of variability in the number ofindividuals (fm) when T is estimated by simulation.

Other (unknown)
Final disposal facilities

Soil/stone
digging

Road construction(non-urban district)Figure 2.10-21: Comparison between LIME 1 and LIME 2 (damage factors forbiodiversity)

42

LIME2_C2.10-C2.13_2014

2.10.4

Procedure for impact assessment of land use

Figure 2.10-5 shows the framework of damage assessment of land use under LIME.
Concrete procedures for the characterization and damage assessment of land use are as
described below.
Occ
The inventory of land use is expressed by the occupation time Inv time(a ) [yr] and theOccoccupation area, Inv area(a ) [m2] in the form of land use a and the area of transformation fromTrans
b to a, Inv area(b, a ) [m2].

During the procedure for characterization, with regard to occupation and maintenance each, acategory indicator CI is calculated by the use of the inventory data and the characterization
factor CFOcc (a) or CFTrans (b,a) as follows:
Trans
Land transformation: CI Trans  b ,a Inv area
( b, a )Occ
Occ
Land occupation: CI Occ  a Inv area
( a )  Inv time(a )(2.10-7)(2.10-8)

Actually, however, because difference in the degree of impact due to difference in the form ofland use is ignored and CF is fixed at 1, it is all right to simply add up the areas (× time)entered in the inventory.
The procedure for assessment of damage to the object of protection in primary production iscarried out as follows: regarding occupation, the amount of damage is calculated by
Occ
multiplying the occupation area and time by DFNPP(a ) , the damage function corresponding
to the type of current land use; regarding transformation, the amount of damage is calculatedTransby multiplying the area of land transformation by DFNPP(b, a ) , the damage function
corresponding to the type of land use before and after the transformation (before
transformation b; after transformation a). The total of the two amounts of damage is theamount of damage to the object of protection. The damage index of primary production
DINPP is expressed by the following equation:
Occ
Occ
Occ
Trans
Trans
DI NPP  a DFNPP( a )  Invarea(a )  Invtime(a )  b,a DFNPP(b, a )  Invarea(b, a )

(2.10-9)
The assessment of damage to the object of biodiversity protection is carried out concerningTrans
transformation. The amount of damage is calculated through multiplication by DFEINES(a ) ,
the damage function corresponding to the type of land use after transformation. The damage
index of the extinction risk DIEINES is expressed by the following equation:
DI EINES 

 DF
b,a

Trans
EINES

Trans
( a )  Inv area
(b , a )(2.10-10)

Based on these damage factors of these types of land use, LIME provides the damage factor
per resource consumption or amount of final disposal of waste in other impact categories ofresource consumption and waste disposal. Even if the area of land use is not calculated in
43

LIME2_C2.10-C2.13_2014

the inventory, it is possible to carry out impact assessment based on resource consumption or
the amount of final waste disposal. By contrast, if both the area of land transformation andthe amount of final waste disposal are entered in the inventory as basic flow, because theamounts of damage are counted double if the damage factors for primary production andbiodiversity in both impact categories are applied simultaneously, it is necessary to apply only
one of them.
In the case of integration, the resultant factors from monetary conversion or
Occ
non-dimensionalization of impact on biodiversity or primary production, IFNPP(a ) ，Trans
IFNPP(b , a )Trans
and IFEINES(a ) , are used.

The single index SI can be gained by the use of these

integration factors and the inventory of land use – that is, the area of land
Trans
Occ
Occ
transformation/occupation, Inv area(b, a ) or Inv area(a ) , and the occupation time Invtime(a ) .
SI 

 IF (a)  Inv (a)  Inv (a)   IF
a

Occ
NPP

Occ
area

Occ
time

b, a

Trans
NPP

Trans
(b, a)  Invarea(b, a) 

 IF
b, a

Trans
EINES

Trans
(a)  Invarea(b, a)(2.10-11)
The characterization factors CFOcc (a) and CFTrans (b,a) are attached hereto as A1, and the
Occ
Trans
Trans
damage factors DFNPP(a ) , DFNPP(b, a ) , and DFEINES(a ) are attached hereto as A2. The
Occ
Trans
Trans
integration factors IFNPP (a ) , IFNPP (b, a ) , and IFEINES (a ) are attached hereto as A3.
Acknowledgment
When we developed the damage functions of land use, we received a lot of valuable advice on
assessment related to primary production from Prof. Yasuoka Yoshifumi of the Institute ofIndustrial Science, the University of Tokyo (now, Director of the National Institute forEnvironmental Studies), and Associate Prof. Tsunekawa Atsushi of the Graduate School of
Agricultural and Life Sciences, the University of Tokyo (now, professor of the Arid Land
Research Center, Tottori University). During the assessment related to the extinction risk,
we received great and detailed guidance from Prof. Matsuda Yunosuke of the Ocean ResearchInstitute, the University of Tokyo (now, professor of the Graduate School of Environment andInformation Science, Yokohama National University). However, we made changes in thesimulation model for calculating the extinction probability, estimated the parameters of thestatistical model of average remaining life expectancy by regression analysis, using the
calculation results, and introduced the adjustment by standard values according to our own
demand and judgment. With regard to the Chikugo model, we received guidance from Mr.
Seino Hiroshi, Director of the National Agriculture and Bio-oriented Research Organization.
Prof. Yahara Tetsukazu of the Kyushu University gave us cooperation concerning published
RDB data. We would like to thank all of them for their support.

[References for Section 2.10]
Baitz (1998): Method to integrate land use in life cycle assessment. University of Stuttgart, Stuttgart
Cramer W, et al (1999): Comparing global models of terrestrial net primary productivity (NPP) – overview and44

LIME2_C2.10-C2.13_2014

key results, Global Change Biology, 5 (Suppl.1): 1 - 15
Currie D J, Paquin V (1987): Large-scale biogeographical patterns of species richness of trees, Nature，29: 326 327
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Guinée J B, et al (2001): Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards, Kluwer
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IPCC (2002): The IPCC Data Distribution Centre,
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Köllner T (2000): Species－pool effect potentials (SPEP) as a yardstick to evaluate land－use impacts on
biodiversity. Journal of Cleaner production, 8: 293 – 311
Lindeijer E, et al (1998): Biodiversity and Life Support Indicators for Land－use Impacts in LCA. RWS DWW(Dutch Ministry of Transport, Public Works and Water Management)
Lindeijer E, et al (2002): Improving and testing a land use methodology for LCA - Including case－studies onbricks, concrete and wood. RWS DWW (Dutch Ministry of Transport, Public Works and Water Management)
Matsuda H, Serizawa S, Ueda K, Kato T, Yahara T (2003): Extinction Risk Assessment of Vascular Plants in the2005 World Exposition, Japan. Chemosphere, 53 (4): 325 – 336
Müller─Wenk R (1998): Land Use - The Main Threat to Species - How to Include Land Use in LCA. (IWÖ
Diskussionbeitrag Nr.64) IWÖ －HSG
Oka T, Matsuda H, Kadono Y (2001): Ecological Risk-Benefit Analysis of a Wetland Development Based on
Risk Assessment Using “Expected Loss of Biodiversity”, Risk Analysis, 21 (6): 1011 – 1023
Seino H, Uchijima Z (1992): Global distribution of net primary productivity of terrestrial vegetation, Journal of
Agricultural Meteorology, 48 (1): 39 – 48
Uchijima Z, Seino H (1985): Agroclimatic Evaluation of Net Primary Productivity of Natural Vegetations (1)Chikugo Model for Evaluating Net Primary Productivity, Journal of Agricultural Meteorology, 40 (4): 343 – 352
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2.11

Resource consumption

Changes under LIME 2• Uncertainty assessment of damage factors was carried out. The uncertainty of the damage
factors for biodiversity and primary production included geographical variability(assessment of the uncertainty about at what point the resource gathering entered in theinventory), and the uncertainty of the damage factors for social assets included temporalvariability.
• The parameters used for the calculation of the area of land used for the mining of mineral
resources were updated through a review of the base data, using information on the gradeof each mine and the stripping ratio for typical metals. With regard to uranium, such data
were collected and arranged and then added to the list of damage factors.
• The parameters for the calculation of the damage factor for social assets (user cost) were
updated based on databases and literature research covering recent years.
• Because the damage functions for the impact category of land use were used as some of thedamage functions of biodiversity and primary production, a review of the impact categoryof land use has been reflected in LIME 2.

2.11.1

What phenomenon is the environmental impact of resource consumption?

(1)

Range and categories of resources dealt with by LCA

Generally, the word “resources” is used widely. For example, there are such expressions as
human resources and tourism resources. However, what is measured under LCA is mainly
the flow of substances or energy. Therefore, it is appropriate to assume the following
resources from the viewpoint of LCIA: (1) fossil fuels; (2) mineral resources; (3) stone
resources (for the purpose hereof, the resources listed as (1) to (3) are classified as exhaustible
resources, which may sometimes be written as non-biological resources); (4) biologicalresources (forest resources, aquatic resources, and other recyclable resources); (5) water
resources; (6) land (space) and solar energy (resources that are virtually inexhaustible but
have quantitative limitations).
For the purpose hereof, (2) mineral resources are used for extracting specific ingredients (suchas metals and fertilizers), while (3) stone resources are used mainly for purposes that use theproperties of stone itself.
Although stone resources are quantitatively larger than fossil fuels and mineral resources, itcannot be said that they are socially inexhaustible, given that the minable amount is virtuallylimited due to legal regulations for the protection of nature.
If physical or technical depletion of resources is a problem for LCA, considering the resources
listed in (1) to (6) above, in that order, as the scope of assessment seems to correspond to theinventory analyses carried out so far. Although “since the dawn of history, there has been noreport about resources (mineral resources) becoming impossible to supply due to depletion”(Nishiyama 1993), because there are global problems, such as the destruction of tropicalforests (forest resources) and indiscriminate fishing of aquatic resources, it is an important
challenge to incorporate the assessment of the impact of consumption of “renewable
resources” and “aquatic resources” into the LCIA method. With regard to land, not
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LIME2_C2.10-C2.13_2014

depletion (quantitative decrease) but deterioration (qualitative decrease) or competition may
become a problem (see 2.10 “Land use” also).
(2)

Causal relationship of environmental impact concerning resource consumption

First of all, the impact of resource consumption as a problem for LCA is a decrease in theamount of resources usable for future generations and the impact of the decrease. There isthe view that this is a problem concerning resources and energy and is not an environmental
problem in a narrow sense. Therefore, we will try to show the causal relationship of the
impact of resource consumption after arranging the relationship between resource
consumption, environmental impact and LCA.
Nishiyama (1993) pointed out that the uniqueness of resources is “limitedness” and“maldistribution,” which have caused the longstanding problem of resource depletion, various
kinds of social anxiety, and wars.
By reference to Fukami et al. (1996), what is recognized as “resources problems” at present
can be roughly divided into “physical depletion,” “problems related to stable supply (such as
artificial interruption),” and “environmental problems that accompany supply and resource
consumption.”
For the purpose of LCA, assessment is frequently made by calculating the input of resourcesand energy as the basic flow of input and setting impact categories that focus on resources andenergy themselves. This is a general practice derived from the history of development of
LCA. Assessment has frequently focused on the below-described reserves, which justmeans that the impact of resource consumption on “physical depletion” has been treated as aproblem to be picked up for LCA from the viewpoint of scarcity of resources or usability inthe future – that is, “limitedness of resources.” This can be said to be the viewpoint ofsustainability, which includes the viewpoint of intergenerational, and sometimes regional,
equity.
However, “depletion” is a far longer-term problem than “stable supply.” If the possibility of
impact on society is taken into consideration, it can be thought that the “problem of stablesupply” will first become an actual problem. For example, there may be a situation where
Japan cannot secure sufficient supply of a certain kind of resource even if the resource is
supplied well in neighboring countries. However, although the problem of stable supply iscaused by a political factor or such a factor as a natural disaster, the impact of the problem
spreads or arises within the market economy (see Column 2.11-1).
Given thesecharacteristics, it is hard to think that the viewpoint concerning the problem of stable supply
should be assessed by LCIA as an environmental impact.
Another important viewpoint is the environment impact that accompanies the supply andconsumption of resources. Given that some kinds of pollution caused by the development ofmining in Japan gave serious damage to the mining industry in many cases, it seems clear that
it is important to assess this problem by LCA. However, whether this should be directly set
up as an impact category for an LCIA method system is another problem.
For example, emissions of carbon dioxide due to combustion of fossil fuels – that is,“resource consumption” – are usually dealt with in the impact category of global warming(climate change). If this is dealt with in the impact category of “resource consumption,” an48

LIME2_C2.10-C2.13_2014

extremely large number of environmental burdens must be dealt with in the impact categoryof “resource consumption.” To put it another way, it is possible to have the view that all theenvironmental problems related to this aspect should be dealt with in impact categories otherthan resource consumption. In other words, what interaction with the environment should be
included in the impact category of resource consumption is the problem concerning thedivision between the border of input/output items established by the LCI side and the borderpresupposed by the LCIA side.
Idealistically, it is desirable for LCA researchers or LCI databases to supply information on
all the environmental burdens to be analyzed in connection with the consumption and supplyof resources (such as harmful drainage or changes in the ground surface due to mining).
Because this will make it sufficient to connect them with the impact category of “toxic
chemicals” or “land use” at the stage of LICA, it is all right to deal only with the problems ofdepletion and rarity in the impact category of “resource consumption” in the LCIA method
system. This seems to become the clearest arrangement.
However, if existing LCI databases do not provide full information that can be connected tothe environmental impact presupposed as important under a certain LCIA method system, therealistic solution is that the LCIA method system side should connect with information
provided from the LCI databases by positively incorporating these categories. How thisportion is treated during damage assessment under LIME will be described in 2.11.3.
Based on what has been described above, Figure 2.11-1 shows the environmental impacts
related to resource consumption that may be taken into consideration during LCIA.
Column 2.11-1
A case of assessment of the impact of interruption in the stable supply of resources
In Japan, the impact of interruption in the stable supply of resources was assessed in the“Research on the Impact of Interruption in the Supply of Main Mineral Resources in theJapanese Economy” (March 2002), which the Mining and Materials Processing Institute ofJapan carried out by commission from The Research Institute of Economy, Trade andIndustry, based on discussions at the “Mineral Resources Policy Platform.” The research
dealt with copper, cobalt, and tantalum and consisted of a review of a supply interruption
scenario based on a survey on supply-demand trends, simulation based on a multilateral
general equilibrium model, and a price spread analysis that used the inter-industry relations
table for analysis of resources.
The impact of supply interruption that was considered in the research was the spread fromprice impact and supply volume limitation to a decrease in corporate competitiveness. The
main measures discussed in it focused on the supply side, such as mining and storage, andeconomic and social changes in the world as a factor for supply interruption (at least in the
short term (Kojima 2002)).

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LIME2_C2.10-C2.13_2014

Environmental burdens due
to gathering and extractionof resources (examples)

Air pollutants

Human health(Toxic chemicals)

Water pollutants

Ecosystem(Biotoxicity)

Gathering of natural resources

Relaxation

Extraction of resources(Concentration,refining, etc.)

Reduction

Impacts on the areas ofprotection through otherimpact categories(examples)

Mining waste
Direct destruction ofground surface
Soil erosion

Ecosystem((7) Land use)

Increase
Endpoint impact(1) Decrease inamount of resourcesstock* (3) El Serafy’s user
cost corresponds tothis depletion

Increase in royalty(Rent for rarity)(4) Decrease in gradeof resources stock(5) Increase in amount ofenergy and other
input/output elements due
to resource gathering(6) Increase in related
environmental burdens

Disappearance ofresource stock
Increase in cost ofenvironmental
measures

Increase in mining
cost

Long-term increase in prices of natural resources(Increase in short-term supply interruption is alsopresupposed)

Increase in resources exploration
Improvement in efficiency oftechnology for gathering and extracting
resources

Social assets

Decrease in resource
consumption demandby use of existing
capitalRelaxation

Decrease in economic
welfare for future
generations(Decrease in
consumable amount)

Shift to technologies forsaving resources and using
alternative resources((2) Backstop)

Expansion of recycling use of waste,etc.
Response by the
supply side

Response by the demandsideTechnological development / capital formation(As a result of current generations’ resource
consumption)

Figure 2.11-1: Causal relationships of resource consumption(1) to (7) in the figure are numbers used for reference in 2.11.2 (1).

50

LIME2_C2.10-C2.13_2014(3)

Endpoint of the environmental impact of resource consumptiona.

Impact of resource gathering on human health and the ecosystem

According to the Environment Agency (1990), there are the following three main
environmental impacts of underground resource gathering: “(1) direct destruction of groundsurface; (2) water pollution, air pollution, and soil erosion due to mining and refining; and (3)production of a large quantity of mining waste.”With regard to (1), open-pit mining destroys a wider area of ground surface than does
underground mining and requires the storage of a large quantity of overburden. Because
deposits of iron ore and bauxite expand widely and are located relatively near to groundsurface, they are frequently mined in open pits. In the case of other metal ores also, if alarge-scale and low-grade deposit lies near the ground surface, open-pit mining is
economically advantageous (Craig et al. 2001; Kesler 1994; Pipkin et al. 2004).
With regard to (2), especially if high-sulfur coal or pyrite is mined, drainage from the acidmine may continue for hundreds of years. This not only influences pH but also has great
impact on aquatic organisms through leaching out of iron, zinc, copper, and other toxic metals(Pipkin et al. 2004).
With regard to (3), waste other than overburden is produced. For example, if lead, zinc,copper, or other sulfide is extracted from ores (crude ones), the flotation method is used andproduces residues (tailings), whose disposal requires a sediment pond.
Including these impacts and those that may arise unless appropriate measures are taken, manypotential impacts have been pointed out as shown in Table 2.11-1.
Table 2.11-1: Potential environmental impacts of the mining industry
Impacts on the natural environment

Pollution impacts• Destruction of natural habitats (growing/habitat
environment) in mining sites and waste disposal
sites• Destruction of neighboring habitats as a result ofdrainage and discharge• Destruction of neighboring habitats due to a rushof settlers• Changes in river forms and river ecology due tochanges in silt and flow
• Changes in groundwater surface• Changes in land form
• Deterioration of land due to inappropriate
restoration after closing• Unstableness of land
• Risk of accidents in structures and dams
• Abandoned equipment, factories, and buildings• Drainage from mining sites (including drainage
from acid mines and drainage piped up in mines)• Outflow of sediment from mining sites• Pollution by mining on riverbed• Outflow from processing of minerals• Outflow of polluted water from sites• Leakage of oil and fuel• Soil pollution by leakage of processed residues
and chemicals• Leaching out of pollutants from tailings anddisposal areas• Air pollution by disposal of minerals• Discharge of fine particles from sites nearresidential areas or habitats• Discharge of methane from mines

Translated from UNEP (2000); however, the description of impacts related to working environments wasomitted.

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LIME2_C2.10-C2.13_2014b.

Impact of resource stock depletion and deterioration on social assets

If attention is given to the process through which the depletion and deterioration of resource
stock decreases the availability of resources for future generations, it can be thought that theendpoint of the impact is economic damage to future generations. Concretely, how should
this be understood?
If the supply of oil suddenly stops, society will be confused and receive a great influence.
However, long-term exhaustion of resources differs from a sudden accident. If the price ofan exhaustible resource gradually increases over a long time, search for the resource may
become eager and the amount of deposit may increase to keep up with economic demand. In
addition, the development of resources substitutable for the resource may be promoted, withthe result that the depletion of the resource may be delayed. In this case, it can be predictedthat the price of the resource will not exceed the prices of the substitutable resources(backstop). Moreover, if recycling is promoted thoroughly, the problem as to whether or not
the underground deposit of a resource may be depleted will become unimportant.
According to the theory of resource economics, it is necessary to consider user cost in
addition to the actual costs necessary for mining, such as the personnel cost. If an
exhaustible resource is mined at present, it will become impossible to mine it in the future.
The opportunity cost lost due to this (impact on future profits) is the user cost. It can bethought that the marginal user cost is the royalties that the miner pays to the owner of theresource (rent for rarity). From the viewpoint of maximization of the current value ofresource stock, the most favorable approach is to fix output for each period so that themarginal user cost will increase at a rate equal to the interest rate (Hotelling’s Rule).
However, the current prices of resources do not have such upward trends caused by rarity(according to an analysis by researchers of the US Geological Survey, the prices of mineral
resources decreased as a whole in the 20th century) (Sullivan et al. 2000). Some researchers
have pointed out that the price of a resource may rise sharply when depletion becomes
imminent (Yasui 2000; Daly et al. 2003). In addition, if miners actually take user cost into
consideration, the stock of a resource for future generations will still decrease. There is theview that thinking of the intergenerational allocation of exhaustible resources as a utilitarian
optimum problem has a bias favorable to the current generation, because future generations’utility is discounted (Washida 1992).
In addition to such factors related to rarity, it can be thought that the mining of a resource
might increase mining cost due to a reduction in the grade of the remaining stock or thenecessity for mining it at more remote places.
With regard to the problem of resource depletion, many experts in the fields of resource
geology and resource economics in Japan have the following view: although mineral
resources can be mined (no depletion of the mineral resources) if a decrease in the grade ofores (target metals) is allowed, the amount of energy necessary for concentration and refining
and the amount of waste produced through the processes will become huge, with the resultthat the resources will not be economical (Tachimi 1986; Sato 1992; Nishiyama 1996).
For example, Sato (1992) dealt with the problem of mineral resources in the 21st century.
He concretely discussed “the relationship with energy, the most important element forthinking about future mineral resources whose grade will become lower,” and predicted that if
inexpensive energy can be supplied sufficiently, the depletion of mineral resources that will
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LIME2_C2.10-C2.13_2014

threaten human civilization will not physically occur in the 21st century.
In the next section, when the characterization factors of resource consumption are discussed,
assessment indexes will be explained from the viewpoint of rarity. At present, there is noindex or method that has been widely agreed upon for the calculation of the amount of impactof resource consumption caused from rarity (from the standpoint that the amount has not been
fairly (or sufficiently) evaluated at a simple market price).
2.11.2

Characterization of resource consumption(1)

Existing characterization factors of resource consumption

The characterization factors of resource consumption are based on the following three
viewpoints:(1) Assessment from the viewpoint of rarity, focusing on the amount of resources(2) Assessment from the viewpoint of rarity (or “value” of resources), focusing onenergy or cost(3) Assessment from the viewpoint of environmental impact, focusing on resource
gathering (including relevant acts of altering nature)a.
Resource depletion: the characterization factor that focuses on the amount of
resources (corresponding to Figure 2.11-1 (1))
Both minerals and fossil fuels are deposited underground and their amounts are limited. The
characterization factor that focuses on this limitedness consists of indexes that combine thefollowing: (1) ultimate resources (U); (2) minable reserves (R); and (3) production volume (P)(for the terms that represent the amount of resources, see Column 2.11-2).
Guinée et al. (1995) suggested that the abiotic depletion potential (ADP) should be used as thecharacterization factor that focuses on the amount of abiotic resources.
ADP i ＝ ( P i / U i 2 ) / ( PSb / USb 2 )(2.11-1)

Pi and Ui are the production volume of the abiotic species i [kg yr-1] and the resources of i [kg],respectively. Psb and Usb are P and U of the standard substance (antimony), respectively.
Guinée et al. call U “ultimate reserve.” This is the value calculated by audaciously regarding
the amount existing in the crust with a certain thickness (10 km). Guinée et al. (2001) firstrecommended this as the characterization factor of abiotic resources (After that, the value of
ADP was revised) (van Oers et al. 2002). In the Japanese language, because “resources”
may not be confused with “the amount of resources” as the name of the impact category of
LCIA or the target element of impact assessment, “reserves” sometimes means “the amountof reserves” in this text.
On the other hand, it is impossible for humankind to mine all the ultimate reserves. Because
assessment should be made based on the profitable amount of deposit, if the minable amountis used as the base, characterization factors that include R and P, such as 1/R and P/R2,
deserve to be considered.

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LIME2_C2.10-C2.13_2014

Column 2.11-2
Terms that represent the amount of resources
Various terms are used for representing the amount of resources on earth. For example, they
can be divided according to the degree of geological knowledge or economy as shown in
Table 2.11-A. Minable reserves refer to the amount of economically minable reserves of theexisting resources. If the economically minable amount in the near future is added to theexisting amount, this is called “reserve base.” Reserves change when a new mine is
discovered or technology progresses.
On the other hand, the ultimately minable amount of resources, including unidentified
resources, is called “ultimate resources.” In the case of mineral resources, ultimate resources
have been estimated by the use of global chemical knowledge, such as crustal abundance.
Table 2.11-A: Classification of ultimate resources
Identified mineral resources
Identified
Inference
Close
Rough
estimate estimate
Economical

Reserves

Inferred reserves

Marginally
Subeconomic economic

Marginal reserves

Inferredmarginal
reserves

Potential mineral resources
Degree of certainty
PurelyHypotheticalphysicalSubeconomic• Extracted from the classification table of ultimate resources in Nishiyama (1996); some terms altered according to the text.
• The horizontal axis indicates geographical certainty, while the vertical axis indicates economy. There is an attempt tosystemize this classification method by adding the amount of waste and the available amount (amount of stock, stockpiles)
as a row (Nishiyama 1996). In addition to geological reliability and economy, legal and environmental limitations must be
considered as an axis that is excluded from reserves and included in resources (Craig et al. 2001).

Any of them has the advantage of being able to consider many kinds of resources. On theother hand, the (minable) reserves R changes, depending on the amount of newly discoveredresources. Because of this, it has been pointed out that although the minable years (R/P) offour kinds of metals, such as gold, were less than 20 years in 1970, it has been pointed outthat they increased by 20% to 110% in 1990 (on the other hand, although the minable years offour kinds of metals, such as zinc, were 20 to 40 years in 1970, they decreased by 19% orremained the same) (Nishiyama et al. 1993). In addition, the production volume P, whose
exponential increase often causes anxiety as a factor for resource depletion, changes as amatter of course. Therefore, it is necessary to note that these indexes change according totechnology and resource price. Because it has been pointed out for these reasons that the
number of minable years cannot be used as a standard for resource depletion (Shouji et al.
1997), it is difficult to select an index that can be recognized widely as the characterization
factor that focuses on the amount of resources.
In addition to the above-described static indexes, the following indexes have been
recommended, although their use as characterization factors was not originally recommended:
the increase rate of reserves; the increase rate of life cycle efficiency with the progress intechnology; the increase rate of final demand; and the current “degree of gap” of the mining
speed from the “limit of sustainability” calculated based on the recycling rate, the54

LIME2_C2.10-C2.13_2014

improvement rate, etc. (Matsuhashi 1996).
With regard to the trends after the development of LIME 1 in Japan, the Ministry of theEnvironment calls the harmonic average of the “acceleration of resource depletion” in termsof volume of iron and the “market price” (normalized by iron) the “resource depletion
characterization factor” in the Ministry of the Environment’s product environment
information system (eco sele), and attention has paid to the use of it for providing information
through a radar chart that consists of the following three axes: global warming; resource
consumption; and the use and management of chemicals. Although the market price is the“parameter indicating resource consumption,” there are many cases where correlation withresource depletion cannot be kept only by the price. Therefore, the Ministry of theEnvironment considered using the characteristic coefficient as the parameter indicating
resource consumption by adjusting it with the value of “acceleration of resource depletion”
into which the impact on the basis of resource consumption is incorporated (ProductEnvironment Information System Secretariat 2005).
“Resource depletion acceleration” is an index recommended by the Ecomaterials Center of
the National Institute for Materials Science. Its details are written in a report (National
Institute for Materials Science 2004) and can be outlined as follows: The resource depletion
velocity V, the general assessment index (assessment function), isV [1 / t ] ＝

Consumption speed Di M/t  - reproduction speed M/t Resource stock (reserves) Ri M (2.11-2)

If the resource i is used at a certain speed for n years, how much is V accelerated? If V isdifferentiated and minute terms are ignored, the resource depletion acceleration Ci can becalculated as follows:
Ci 

K 
n 
 1  
Ri   i (2.11-3)

In this equation, λi is the number of durable years (Ri ÷ Di) and K is a constant. The
characterization factor is the result of the following equation, which normalizes the factor interms of iron:Ci resource depletion acceleration in terms of volume of iron  

RFe 
n 
 1  
Ri   i 


n 
1 

  Fe (2.11-4)
Although n depends on product, it is fixed at “100 years, the number of years used for
discussions about sustainability for the time being.”b.

Resource depletion: characterization factor that focuses on energy and cost

Energy and cost are necessary for mining minerals and fossil fuels. Moreover, because
mining is carried out at mineral deposits where minerals of high purity can be mined easily,
mining and refining efforts to gain desirable resources increase, resulting in an increase in
consumed energy and a decrease in the grade of gained resources. In addition, because fossil
fuels themselves have the aspect of energy, the amount of heat generation [MJ kg-1] seems to55

LIME2_C2.10-C2.13_2014

be the clearest index for considering the aspect of energy.
Finnveden (1996) used exergy (theoretical maximum value of energy that can be extracted
externally when heat energy under certain temperature and pressure conditions is transferred
to other conditions; also called “available energy”) to calculate the characterization factors ofsome kinds of minerals and fossil fuels. Although the value of exergy is highly reliable,
there is doubt that the value of a kind of mineral can be measured by exergy. This is because
the rarity of resources is not taken into consideration at all.
Column 2.11-3
Weak sustainability and strong sustainability
The methods for quantitative assessment of resource consumption include methods whereby
some canonicity related to sustainability is introduced and simple assessment indexes aredefined. Like the case explained in 2.11.2 (1), one of such methods assumes a certain
backstop resource as a substitute and uses the price of the resource for assessment.
In such a case, one of the issues is to what extent the substitution of the consumed resource
should be considered (allowed). Kanamori et al. (2002) think that, under “weaksustainability,” “if an environmental resource is depleted and degraded, it can be substitutedby another form of resource (human, economic).” On the other hand, they think that, under“strong sustainability,” “the bad effect of the depletion and degradation of the ecosystem on
human society (especially, the poor and future generations) is difficult to compensate by
artificial means and therefore resources should be used without giving damage to the physicalstructure and functions of the ecosystem, for the benefit of both intergeneration fairness andintra-generation fairness.”
It can be thought that many methods explained in this section (user cost and surplus energy)
are based on “weak sustainability.”
Steen (1999) recommended the cost of mining [amount of money kg-1] as the characterization
factor. Steen suggested that a substitute resource that can be used sustainably (as backstop)should be assumed (corresponding to Figure 2.11-1 (2)) and that the total of the cost of
producing the substitute resource and the social cost accompanying the production shouldused as the weighted factor for the resource. As examples of substitute resources, crude oil
is substituted by canola oil, and coal is substituted by charcoal. On the other hand, under
LIME, the below-described El Serafy’s “user cost” (Figure 2.11-1 (3)) has been adopted forassessing the social assets that are protected against resource consumption. This isassessment on the assumption of loose substitutability (weak sustainability) between natural
resources and other assets (see Column 2.11-3).
Müller-Wenk (1998) suggested an assessment method based on “surplus energy” forgathering resources – mainly, mineral resources. Surplus energy indicates the loss of energy
due to future generations’ use of lower-grade resources (Figure 2.11-1 (4)) as a result of thecurrent generation’s giving priority to the mining of higher-grade minerals according toeconomic principles (a part of Figure 2.11-1 (5)). Moreover, Müller-Wenk (1998) increased
the current inventory (environmental burden and consumption of energy resources) ofgathering resources by the amount of surplus energy (“hypothetical environmental impact”).
As a result, Müller-Wenk concluded that this additional environmental burden (Figure 2.11-1(6)) was small.
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LIME2_C2.10-C2.13_2014

On the other hand, although Goedkoop et al. (2000) based their approach on Müller-Wenk’s
research, they pointed out that Müller-Wenk’s calculation process included arbitrary setting ofparameter values. They adopted excess energy [MJ kg-1] itself as the damage index(therefore, with regard to the impact assessment result by Eco-indicator 99, excess energy andthe amount of consumed energy in the inventory should not be compared or added simply)
and recalculated the assessment coefficient, including fossil fuels. For example, the excessenergy of fossil fuels is assessed on the assumption of substitution with shale oil (alternative
resources may change with the subjectivity of evaluation/selection of a viewpoint).
Clearly, excess energy can be used as an index, for it can assess the impact of resource
consumption on human society. In addition, as described above, Japanese experts inresource geology and resource economics used the results of analysis by Page et al. (1975)
and had similar discussions (Nishiyama 1993; Washida 1992; Tachimi 1986; Nishiyama
1996). At present, however, basic data available for assessment are insufficient and it isdifficult to offer highly precise characterization factors concerning many kinds of resources.
Moreover, it is extremely difficult to create a framework for systematic assessment of the
impact, combining the impact on the current generation with that on future generations.
c.
Resource gathering: the characterization factor that focuses on the scale ofalteration to nature that accompanies resource gathering
The World Resource Institute (WRI) (1997) and Harada et al. (2001) presented and estimatedthe total material requirement (TMR) of substances related to the acquisition of unit amountsof minerals and fossil fuels. In addition, under LIME, the area of land use for mining ofeach kind of mineral or fossil fuel was calculated during damage assessment (Figure 2.11-1(7)). It may be possible to make these indexes relative and use them as characterization
factors, regarding them as substitute indexes of nature alteration.
(2)

Characterization factor of resource consumption under LIME

From the viewpoint of the “resource and energy problem” that concerns a decrease in theavailability of resources for future generations, it may be possible to use indexes focusing on
the amount of resources, such as the number of minable years, or adopt the above-describedexcess energy or the amount of damage to social assets for the purpose of damage calculation.
Because mining from underground is common to both mineral resources and energy resources(fossil fuels), it seems desirable that the characterization factor should be applicable to both.
However, the amount of energy resources (fossil fuels) is limited in terms of humankind’s
temporal axis, and energy resources disappear after use (burning). On the other hand, manykinds of mineral resources can be recycled from products and it may become possible toextract mineral resources from low-grade deposits by additional input of energy. Because ofthis great difference between the two, there is the view that it may be necessary to adopt acharacterization factor which takes such difference into consideration.
The metal weights (annual domestic consumption of natural resources) were calculated based on thewebsite of the Ecomaterials Center of the National Institute for Materials Science(http://www.nims.go.jp/ecomaterial/center/ecoinfo/), “Mining Industry Handbook FY2001” (ResearchInstitute of Economy, Trade and Industry 2001), “Trade Trends Database 2001” (Ministry of Economy,
Trade and Industry 2001), etc.

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LIME2_C2.10-C2.13_2014

Table 2.11-2: Possible characterization factors compared and examined under LIME 1
Viewpoint forassessment
Type ofexhaustible resource

Amountofresource

Mineral resources (metals)

1/R

Fossil fuels

P/R2

Alteration to nature due to mining(substitute index)

Specificity of energy
resources

TMR (WRI (1997); Harada et al.
(2001))Heat value
Area of land alteration

Table 2.11-3: Domestic consumption of natural resources (metal weight) × ranking ofresults of possible characterization factors and percentage distribution of total
Ranking

TMR-Harada et al. [%] Land alteration [%]Metal weight [%]Under LIME 1, the following, which were specified in Table 2.11-2, were examined as
possible characterization factors related directly to resource exhaustion: (1) heat value [MJ
kg-1]; (2) 1/R (normalized by antimony); (3) P/R2 (normalized by antimony); (4) TMR-WRI(1997) [t t-1] (value set based on “hidden flow” and others during this research); (5)TMR-Harada et al. (2001) [t t-1]; and (6) area of land alteration [ha t-1]. With regard to 19
kinds of metals, Table 2.11-3 shows the ranking of the results of multiplying domestic
consumption of natural resources by the factors (2) to (6) and the ratio of the assessment
result for each kind of metal to the total for the 19 kinds.
With regard to the characterization factors that focus on the amount of resources, thedepletion of such kinds of resources as silver, antimony, gold, and copper was assessed aslarge. Meanwhile, from the viewpoint of alteration to nature due to mining, the impact oniron, copper, and other greatly consumed resources was assessed as large, and the impact ontin, gold, silver, and other metals whose consumption is relatively low also was assessed ascomparatively large.
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LIME2_C2.10-C2.13_2014

As a result of the examination, 1/R (normalized by antimony) was recommended as thecharacterization factor that takes into consideration both mineral resources and energy
resources, and heat value was recommended as the characterization factor that takes into
consideration only energy resources. The former is a characterization factor that focuses on
mining common to minerals and fossil fuels, while the latter is a characterization factor that
focuses on the aspect of fossil fuels as energy.
Indexes based on the amount of resources were adopted because importance was placed on
the following: there are many cases where they were applied to cases in which LCA wascarried out; many kinds of resources were considered through assessment because data
directly necessary for calculation are more available than in the case of TMR and the area ofland alteration (Harada et al. (2001) shows various types of estimation formulas for metal
TMR in case data are not available); and the rarity of resources has been reflected. In
addition, 1/R was adopted so that the kinds of minerals that are not produced in great
quantities cannot be ignored during assessment. If P/R2 is adopted, the characterization
factor for a kind of mineral that is not much produced is great quantities becomes smaller.
On the other hand, although 1/R may underestimate the rarity of the kinds of minerals that are
produced in great quantities, such kinds of minerals were judged to be difficult tounderestimate in practice because they are easy to reuse through recycling.
Under LIME 1, information on values, such as the reserves and production of main mineral
resources, was gained from the Resources and Energy Handbook (Japan Society of Energy
and Resources 1996), the Mineral Resources Database (Mining and Materials Processing
Institute of Japan 2001), etc.
2.11.3

Damage assessment of resource consumption(1)

Basic policies for calculation of damage factors and assessment of uncertainty

Among the resources specified in 2.11.1 (1), the kinds of resources covered by the calculationof damage factors are mineral resources, fossil fuels, stone resources, and biological resources(forest resources). The areas of protection covered by the calculation (Table 2.11-4) are theimpact on social assets from the viewpoint of the finite nature of resources and the impact ofland use at the stage of resource gathering on the ecosystem.
Of the environmental impacts that accompany resource gathering and consumption,
information on the environmental impact at the stage of resource gathering is usually
supposed to be hard to provide from the result of inventory analysis under LCA in Japan. It
can be said that attention has been already paid to the environmental impact of energy
consumption as shown in analyses focusing on CO2 emissions in the case of undergroundresources (Sagisaka et al. 1996; Moriguchi et al. 1998; Narita et al. 2001). However, themain environmental impacts due to resource gathering (see 2.11.1 (3)) cannot be covered onlyby this. Under LIME, the impacts of emission substances related to resource gathering, suchas air pollution and water pollution, were assessed in other impact categories, such as toxic
substances. Therefore, in this impact category, the impacts of the direct destruction ofground surface and the accumulation of mining waste were assessed by the use of the method
to assess the damage to the ecosystem due to land use as described in Section 2.10.
Because biological resources are recyclable, if they are gathered from stock for whichappropriate (sustainable) management has been carried out, the impact to resource depletion
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LIME2_C2.10-C2.13_2014

and the environmental impact of land use can be thought to be relatively small. Therefore,
with regard to forest resources, the inventory side should show the status of sustainable
management at gathering places and it is appropriate to determine a method for calculating
the area of land use according to the division. Under this way of thinking, the scale ofconservation measures, such as forestation, will be explicitly incorporated in the inventory asa negative environmental burden (that is, benefit) (see Column 2.11-4).
Figures 2.11-2 to 2.11-4 show the calculation flow of damage functions and damage factors.
In addition, Tables 2.11-5 to 2.11-7 show outlines of uncertainty assessment. Among theseprocesses, those until the calculation of the area of land use (and the location of use) can be
regarded as operations for creating default (background) information on land use LCI.
Table 2.11-4: Category endpoints of resource consumption and objects of calculation ofdamage functions
Areaofprotection

Category endpoint

Human
health(Endpoint by substances emitted at the time ofresource gathering)SocialassetsPrimary
production

Exhaustible
Disappearance of resource
resources
stock(Endpoint by substances emitted at the time ofresource gathering)
Decline in NPP during
land alteration
Terrestrial ecosystem

Decline in potential NPP
during land use

Object of calculation of damage
functions
Assessor’s consideration in other－ impact categories, such as toxic
chemicals－

User cost
Assessor’s consideration in otherimpact categories
Net primary production ofvegetation(Regarding the mining period as
the period of maintenance of landuse)

Decline in potential NPP
Net primary production ofduring recovery period vegetation
after land alteration(Endpoint by substances emitted at the time ofAssessor’s consideration in other－resource gathering)
impact categories
Change in composition of Extinction risk of vascular plants
Terrestrial ecosystem
plant species
Biodiversity(Regarding the mining period as the period ofAssessor’s consideration in other－maintenance of land use)
impact categories
With regard to primary production and biodiversity, assessment was made through the impact category ofland use (for details, see Table 2.10-3).

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LIME2_C2.10-C2.13_2014

Annualproduction

Use of existing DB
Reference for compiled
materials/
examination of detailed
materials

Reserves

Crude ore grade
Mining thickness
Specific gravity ofore, etc.
*Hidden flow weight

No. of minable years*Height/specificgravity ofaccumulation of(overburden/tailing)

Resource mining country
Location of mine

IUCN
redlistAdjustment formula for overseas
application of the augmentation factor
for extinction risk due to domestic
land alteration (Equation 2.11-6)

Augmentation factor for extinction
risk per domestic stone mining area
(2.10)

Interest rate

Result of global calculation by
Chikugo Model (2.10.3 (2) i)

NPP before
alteration(NPPp)

Simple formula for calculating area ofland alteration (Equation 2.11-5)

Expected return in unit
amount(calculated by price)

Use of existing DB, etc.

El Serafy’s assessment formula(Equation 2.11-7)

Consumption of unit amount of underground resources (mineral resources, fossil fuels)

Area of land alteration(2.11.3 (2) a)

Occupation
periodDepletion cost of resource
stock (user cost)(2.11.3 (7))

Increment in
extinction risk
(2.11.3 (4))

Loss of NPP due tooccupation(2.11.3 (3))

Social assets

Biodiversity

Primary production

Recovery period

Loss of NPP due toalteration(2.11.3 (3))

Damagefunction

Damage factor
DF

Figure 2.11-2: Calculation flow of damage factor of resource consumption (part 1:
undergrounds resources – fossil fuels and mineral resources)• The shadowed parts are factors with consideration for uncertainty of parameter. Bold dotted lines arefactors with consideration for variability.
• In this figure, however, parameters that can be determined within the damage function if a mine isdetermined are surrounded by bold dotted lines, while parameters that are expressed by probability
distribution even if a mine is determined are shadowed. Even in the latter case, however, theestablished probability distribution shows not estimated errors in parameters based on sample data, butdata variability.
• If only a half of the inside of a frame is shadowed, treatment differs according to the kind of resource.

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LIME2_C2.10-C2.13_2014

Consumption of unit amount of stone resources
Assessment document onenvironmental impact ofstone mining
Areas of land alteration(2.11.3 (2) b)
Augmentation factor
for extinction risk
per domestic stonemining area
(2.10)(2.11.3 (3))(2.11.3 (4))

NPP damage
function per
domestic stonemining area
(2.10)

Occupation period

Increment in
extinction risk

Loss of NPP due tooccupation

Biodiversity

Primary production

Loss of NPP due toalteration

Damagefunction

Damage factor
DF

Figure 2.11-3: Calculation flow of the damage factor of resource consumption(part 2: underground resources – stone resources)
The shadowed parts are factors with consideration for uncertainty of parameter.
factors with consideration for variability.

Bold dotted lines are

Consumption of unit amount of wood products (Column 2.11-4)
Conversion rate of wood products into logs
Wood consumption(Weight) = (volume) ×

Wood volume per unit forest area in each country

Volumedensity
Breakdown and ratios of countries supplying wood toJapan

Area of land alteration (by country)(2.11.3 (2) c)

Result of global
calculation by
Chikugo Model

Adjustment formula for overseas
application of augmentation factor for
extinction risk due to domestic land
alteration (Equation 2.11-6)
Ratio of artificial forest origin(2.11.3 (5))(2.11.3 (6))

62Forest distribution among
countries

Forest NPP by country

Recovery period

Increment in extinction
risk

Loss of NPP due toalteration

Biodiversity

Primary production

Damage function

Damage factor
DFLIME2_C2.10-C2.13_2014

Figure 2-11-4: Calculation flow the damage factor of resource consumption (part 3:
forest resources)
The shadowed parts are factors with consideration for uncertainty of parameter.
factors with consideration for variability.

Bold dotted lines are

Table 2.11-5: Main causes of uncertainty and policies for uncertainty assessment
concerning damage functions of resource consumption (part 1: fossil fuels, metal
resources)
Main possible causes of uncertainty
Area of land use Grade, hidden flow
for mining ofunit amount of
resources (2.11.
3 (2) a)Mining thickness

Damagefunction ofprimary
production(amount ofdamage toprimary
production per
land area)(2.11.3 (3))
Damagefunction of
extinction risk
(amount ofdamage toextinction risk
per land area)(2.11.3 (4))
Damagefunction ofsocial assets(2.11.3 (7))

Policies for uncertainty assessment
Set up the grade and hidden flow (calculated values) for eachmine as a variability distribution by using production volume,
etc. for weighting the appearance frequency. If the values
for each mine are unclear, set up a lognormal distribution,using the geometric standard deviation as the largest value ofthe identified metals.
Regarding iron, aluminum, and coal, set up a variability
distribution from research materials.
Regarding othermetals, set a lognormal distribution from the geometricstandard deviation of iron.

Potential NPP(NPPp)

If a mine has been located, refer to the NPPp of the mesh towhich the location belongs. If the location is unknown, use
the spatial distribution of the NPPp of the whole producing
country as the uncertainty distribution. After that, create avariability distribution of potential NPPp of each country ormine from Japan’s import rate, etc.

Number of years ofmaintenance ofland use
Recovery time ofprimary production
Number ofendangered species
in each country

Set up a uniform distribution from the relation between theamount of deposit and the production volume in iron ore
mines.
Set up the same uncertainty distribution as that of primary
production damage functions of land use in 2.10.3.
Assess variability in each country, using, as the index, thenumber of endangered species in the IUCN Red List divided
by the area of national land and using Japan’s import rate as
the probability of appearance.

Application ofdamage factors tooverseas

Assess the uncertainty of the correction rate for application of
the damage factor of domestic stone mining (2.10.3) from theestimated errors in the parameters of the regression equation
used for calculation of the correction rate.
Uncertainty distribution of the values in 2005 calculated fromtime-series prediction (smoothing) based on past data

Price
Production volume
Reserves

Each year’s variability in the difference (ratio) between
reassessed reserves in past years calculated backward fromthe latest estimated reserves and each year’s production
volume and the reserves in past years specified in statistics
for past years. Regarding crude oil, however, set up aprobability distribution of estimated ultimate reserves
directly.

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LIME2_C2.10-C2.13_2014

Table 2.11-6: Main causes of uncertainty and policies for uncertainty assessment
concerning damage functions of resource consumption (part 2: stone resources)
Main possible causes of uncertainty

Policies for uncertainty assessment

Area of land use for mining of unit amount of
resources (2.11. 3 (2) b)

Set a variability distribution based on values
calculated from information specified inenvironmental impact assessment documents.
Use the damage factor of land use for alterationof forests into building sites and maintenance ofbuilding sites in 2.10.3.
Use the damage factor of land use for stonemining in 2.10.3.

Damage function of primary production (amount ofdamage to primary production per land area)(2.11.3 (3))
Damage function of extinction risk (amount ofdamage to extinction risk per land area) (2.11.3 (4))

Table 2.11-7: Main causes of uncertainty and policies for uncertainty assessment
concerning damage functions of resource consumption (part 3: forest resources)
Main possible causes of uncertainty

Policies for uncertainty assessment

Area of land
use forresource
gathering(2.11.3 (2) c)

Volume density ofwood
Variability ofwood volume perunit forest area

Damagefunction ofprimary
production(2.11.3 (5))

Potential NPP(NPPp)
Recovery time forprimary
production
Number ofendangered species
in each country
Overseasapplication ofdamage factor

Apply the value by type of tree specified in IPCC GPG-LULUCF(Penman et al. 2003) by equal probability.
Set up the variability of domestic wood volume (relative ratio to thewood volume in Japan specified in FAO (2002) if the area isdeemed to be the relative probability of appearance) by age class(8th age class or higher) and by type of forest (natural or artificial).
(Data in Japan were applied to Japan and each country and thewood volume in each country is multiplied by the relative ratio.)
Variability of wood volume in each Set up the frequency
country (FAO 2002)
of appearance in each
Set up a spatial distribution of NPPp in the country from Japan’s
import ratio of woodforest regions in the producing country.
(and the domestic
Set up a uniform distribution in eachratio).
vegetation zone.

Damagefunction of
extinction risk
(2.11.3 (6))

Ratio of wood
produced from
artificial forests

64

See Table 2.11-5.

Assess the uncertainty of the correction
rate when the damage factor of domestic
deforestation, using estimated errors in theparameters of the regression equation
used for calculation of the correction rate.
Set up a ratio of wood produced from
artificial forests in each country, using theactual ratio of wood produced from
artificial forests in the countries where theratio can be grasped, or the ratio of thearea of artificial forests to the total area offorests (FAO 2000) in the other countries.

LIME2_C2.10-C2.13_2014(2)

Investigation of land use informationa.

Calculation of the area of land use for mining of mineral resources and coalLand use for mining of minerals and coal seems to have the following elements: (1) mining
zones themselves; (2) places for accumulation of overburden produced from mining; (3)places for accumulation of tailing (waste stone) produced through ore concentration and coalpreparation; and (4) zones where ore concentration or coal preparation is carried out. Their
scale and degree greatly differ according to the mining method and among deposits (mines).
Under LIME, element (4) was not taken into consideration.
Importance was placed on comprehensive assessment of many kinds of minerals, and aformula for rough calculation of the area of land use was introduced as shown in Equation
2.11-5. Figure 2.11-5 shows a frame format of relations among terms.
Figure 2.11-5: Frame format of land use for mining of mineral resources
Ore (raw ore)

(Overburden)(Concentrate)(Tailings)

DepositA  mi  mi 

1 1 1 1 1
1 1 
    mi  Ooi   mi  Oti   
Ci Gi Di 
go Ho
gt Ht a
1 1 1
1 1
   mi  Oi  
Ci Gi Di
g H

b
A:
mi:

(2.11-5)cArea of land use [m2]Metal weight of mineral i [t]65LIME2_C2.10-C2.13_2014

Ci :
Gi:
Di:

Grade of ore of mineral i
Specific gravity of ore of mineral i
Thickness of mining of mineral i [m] (not depth but thickness of ore
body)
Ooi: Weight ratio between metal of mineral i and overburden [t/tmetal]
Oti: Weight ratio between metal of mineral i and tailings, etc. [t/tmetal]
Oi: Weight ratio between metal of mineral i and (overburden + tailings, etc.)(hidden flow) [t/tmetal]g:
Density of overburden, tailings, etc. [tm-3] (go: overburden; gt: tailings,
etc.)
H: Height of accumulation of overburden, tailings, etc. [m] (Ho:overburden; Ht: tailings, etc.)
The first term indicates the area of the mining site, and the (parenthesized) second term
indicates the area of the site where overburden and tailings are accumulated. Although thefirst term is simply deducted by grade, the recovery rate of metals from ores does not reach
100% in reality. Moreover, although ores are accumulated as a horizontal layer in the frame
format, the form of deposit varies in reality. In addition, the stripping ratio becomes larger
as the deposit is deeper, because it is necessary to secure a stable pit slope. Therefore, it ishard to dig the ground vertically. These are causes for underestimation. Althoughoverburden and tailings are frequently accumulated at different places and by different
methods, Ho = Ht and go = gt are assumed for convenience of application of the hidden flow
ratio available as information necessary for calculation (Oi).
All the terms are covered in the case of the open-pit mining method (the portion “a” of
Equation 2.11-5). When the underground mining method is adopted, the area of the altered
ground surface of the mining site itself is relatively small (although there is the possibility thata cave-in or a decline in the groundwater level may occur), and the area of accumulation ofwaste soil and stone seems important (only the portion “c” of Equation 2.11-5). On the other
hand, when the strip mining (opencast mining) method is adopted, because stripped soil andwaste stone are buried back in the mined ground behind the mining site, the area of alterationis regarded as the same as the mining zone (only the portion “b” of Equation 2.11-5). Under
LIME, however, the portion “a” of Equation 2.11-5 was basically applied to all cases, because
of insufficient information.
Under LIME 1, assessment was made concerning main metals (aluminum, chrome, copper,
iron, lead, manganese, mercury, molybdenum, nickel, tungsten, tin, zinc, gold, silver,
vanadium, antimony, titanium, and niobium).*2 The grade was gained from Chapman et al.
(1983) (same as Eco-indicator 99 (Goedkoop et al. 2000)) and the hidden flow ratio was
gained from WRI (1997). It was assumed that the thickness of mining was 20 m, the densityof waste soil and stone was 1.75 tm-3, and the height of accumulation of waste soil and stone
was 50 m (two 25-m benches were piled up). The resultant land alteration area per unit
amount of resources was multiplied by the annual consumption of natural resources in Japanto calculate the annual area of land alteration. A follow-up survey (detailed survey) was
carried out concerning copper, aluminum, iron and coal, which greatly contributed to land
alteration, in order to review the grade, the hidden flow ratio, and other parameters. With
regard to aluminum (bauxite), given that the main mining method was the opencast method,*2 With regard to oil and natural gas (excluding tar sand and oil shale), assessment was made on the assumption that theland use area was zero, because solid matters were extracted not by removing surface and underground soil and stone but
from wells (oil and gas wells), and therefore the land alteration area per amount of resources seemed small enough.

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LIME2_C2.10-C2.13_2014

and taking into consideration the method of disposing of red mud, the land use area wasreassessed.
The annual consumption of natural resources in Japan was calculated based on the total
amount of imported natural resources specified by the Ecomaterials Center (website), theMining Industry Handbook (Research Institute of Economy, Trade and Industry 2001) or the
Trade Trends Database (Ministry of Economy, Trade and Industry 2001).
Under LIME 2, with regard to metal resources, a grade was set up for each mine concerning
iron, zinc, lead and copper, based mainly on the mine cost database. The database was usedalso for estimating the hidden flow ratio.*3 Because the grade and the hidden flow ratio wereparameters related with each other, a variability distribution of grades and hidden flows wasestablished as a variability distribution weighted by production in each mine (discrete-type
joint probability function). Figure 2.11-6 shows a concrete example. With regard to ironore, on the assumption that the thickness of ore body is the thickness of mining, a variability
distribution was established from 10 cases among the research report cases (Open-air Mining
Technology Research Committee 1979).
In the same way, with regard to*4*5*6
aluminum/bauxite, uranium and coal also, information on each resource was used toestablish a probability distribution of these parameters. With regard to many other kinds ofmetal resources, information is not sufficient to establish a distribution. Because of this,among the results of adaption of lognormal distribution to the distribution of grades andhidden flows of the above-mentioned four metals, such as iron, the maximum geometricstandard deviation (grade: 3.19; hidden flow ratio: 4.63) was used to set up a probability
density function from lognormal deviation on the assumption that the geometric average was
the same as that under LIME 1. On the assumption that mining thickness follows lognormal
distribution, values under LIME 1 were given to the geometric average and the geometricstandard deviation of 11 iron ore cases. With regard to the specific gravity of minerals, byreference to the Cost Estimation Handbook for the Australian Mining Industry (Noakes andLanz 1993), values were fixed concerning iron ore,*7 bauxite, and coal, and concerning otherores, the probability distribution was established where the value for each kind of rock wasgiven by equal probability.

*3 However, because impurities that seem to be produced as slag during concentration in Japan were excluded from thecalculation, this is consistent with the frame format in Figure 2.11-5. This applies also to iron ores.
*4 With regard to the area of land use for aluminum and bauxite, the grade of Al2O3 and the thickness of bauxite were fixedat values gained from six mines and four mines, respectively (grounds for setting up a parameter under LIME 1) among
the main countries from which Japan is importing them (the import ratio of aluminum ores was used as the probability of
appearance for the variability distribution). With regard to other countries and mines, a uniform distribution wasestablished based on their weighted averages and each mine’s answers to a survey by IAI (1998) to assess the variability
among exporting countries and mines. With regard to the area related to the disposal of red mud, a variability
distribution was established from the area of land use and the volume of red mud disposed of by type of disposal method
throughout the world. The amount of red mud generated per one ton of aluminum was assumed to be the same value(2.25 t/t-Al) as in the case of LIME 1.
*5 With regard to uranium, a variability distribution was established concerning the grades of the mines in Australia andCanada (the probability of appearance is the product of Japan’s import rate by country and each country’s production
ratio).
*6 As for coal, a variability distribution was established by weighting the differences in values of parameters (hidden flow
ratio, thickness of mining) among main importing country by the import rate. Although the value used by WRI for the
calculation of the hidden flow in Japan was used as the hidden flow ratio, a uniform distribution was established between
values in both documents concerning Australia and Canada, where information was gained through a survey under LIME
1 (IKP 2001). With regard to the thickness of mining, in Australia, Canada and US, a probability distribution wasestablished from data on the thickness of coal beds based on surveys conducted under LIME 1; in the other countries, thethickness was assumed to be the same as that in Canada, where the width of the distribution is great.
*7 A uniform distribution was established, using the density of limonite as the minimum value and the value of hematite as
the maximum value.

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LIME2_C2.10-C2.13_2014

Hidden flow ratio [t t-1]

Hidden flow ratio for each mine
Cumulative ratio of production
in mines

Cumulative ratio of production [%][Data for each iron ore mine]

Grade of ore [%]Cumulative probability [%]Calculation of land alteration area by Equation 2.11-5
together with other parameters (mining thickness,
specific gravity of ore)Median: 0.033 m2/t-Fe
Average: 0.038 m2/t-Fe(Weighted average by production ateach mine)

Area of alteration [m2/t-Fe]

Figure 2.11-6: Example calculation of the area of alteration due to mining of mineral
resources (iron)This figure illustrates a case where the special gravity of ore is 5 and the mining thickness is about 250 m.
These parameters vary during actual assessment of uncertainty of damage functions.

b.

Land use for quarrying (domestic)

We arranged the environmental impact assessment documents in nine cases of quarrying(expansion of quarrying zones). In each of the cases, the quarrying period was about 20
years. Although, in some cases, the amount of overburden and waste soil and stone reaches
5 million m3 (9 million tons), they are basically used for soil dressing for planting or
reburying. When they are taken out to the outside, they are used for land development.
Therefore, it was assumed that there was no impact of land use due to overburden and waste
soil and stone produced by quarrying. That is, the amount of damage from land alteration
directly accompanying quarrying and maintenance for 20 years was calculated. On the other
hand, the area of alteration was regarded as the total area of quarrying zones. This has thepossibility of overestimation.
68

LIME2_C2.10-C2.13_2014

The alteration areas divided by the total amount of quarrying were distributed between 0.013
to 0.033 m2 t-1. Under LIME 1, the representative value was 0.02 m2 t-1. Under LIME 2,
we weighted this distribution by the relative ratio of the annual quarrying amount to change it
into a variability distribution.
c.

Land use for gathering forest resources (domestic and overseas)

The volume of wood products was converted into the volume of wood through the division ofwood products by the log conversion rate. The Forestry Agency’s log conversion rate forforest products was adopted. The source of the wood volume per unit forest area was the
Food and Agriculture Organization of the United Nations (FAO 2002). Under LIME 2, thedifferences in the wood volume among countries was used for the variability distribution
where the ratio for each supply country was regarded as the probability of appearance, and aprobability distribution was established concerning the variability of the wood volume perunit forest area.*8
Under LIME 1, volume density was assessed on the assumption that 1 m3 of wood weighs 0.5
t dm (dry matter). Under LIME 2, the table of volume density by type of tree in IPCC
GPG-LULUCF (Penman et al. 2003) was applied in equal probability and used for
uncertainty assessment.
The percentage distribution of supply among producing countries (import or domestic
production) was set up based on the ratio between domestic and foreign materials shown in(Forestry Agency 2002a).
As described above, the process of calculating the area of land use for wood products or thevolume of wood in each supply country was established and applied to the uncertainty
assessment of damage functions.
(3)
Primary production: damage function of mining of underground resources(mineral resources, fossil fuels, stone resources)
With regard to stone resources, as in the case of LIME 1, the damage factor calculated as in
2.10.3 was used as the damage function of primary production per area of land use.
Although the calculation procedure for mineral resources and coal is similar to that under
LIME 1, a part of the procedure was detailed and uncertainty was assessed for each type ofresource as follows (Figure 2.11-7):*8 In Japan, with regard to planned forests (forests covered by regional forest plans under Article 5 of the Forest Act andnational forests covered by the forest plan in each region under Article 7-2 thereof) with an age class of 8 or more (theage of a stand is divided by a certain span (five years); the five years following planting are the age class of 1; the 6th to10th years are the age class of 2), accumulation for unit area was calculated by type of forest (artificial or natural) and age
class, and the ratio with the wood volume per unit forest area shown in FAO (2002) was calculated. On the assumptionthat the area by type of forest (artificial or natural) and age class was the relative probability of appearance, thedistribution of the ratio was applied to Japan and each foreign country as variability distribution of wood volume per unit
deforestation area. However, the area by age class for natural forests only was used as the damage function of
biodiversity.

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LIME2_C2.10-C2.13_2014

CountryMineNPPp[t ha-1 yr-1]

Amount of damage per unit area [t ha-1]

Maintenance

Alteration

Australia

Mount Newman

0

0

0

Australia

Hamersley

0.2

10

15

Australia

Savage River

12.8

640

960

BrazilCarajas

23.6

1,180

1,770

BrazilCapanema

25.0

1,250

1,875

Country

Import ratio(1999)[%]

Australia
53
India
13
Brazil21
13
Other
Weighted average by importratio

Average amount of damage
per unit area in primary
production [t ha-1]

Parameters related to iron ore
Grade
53%
Specific gravity of ore
4.44
Hidden flow ratio
3.16(Equation 2.11-5)(Figure 2.11-5)
Area of land use[m2/t-Fe]

Maintenance

Alteration

206
1,000
1,123
－

309
1,500
1,685
－

Extinction risk
Amountofdamageperαunit area[EINES ha-1]
0.169
4.36×10–7
2.55
1.44×10–6
1.53
1.01×10–6
－
－

428

643

－

0.038

DFNPP (iron) = (428+643) ×(103×10–4) × (0.038×10–3) =4.07×10–3 (kg-NPP/kg-Fe)
DFEINES (iron) = (7.26×10–7) ×
10–4 × (0.038×10–3 ) = 2.76×10–15(EINES/kg-Fe)

7.26×10–7

Figure 2.11-7: Example calculation of damage functions of mineral resources (iron) To illustrate the calculation process, the figure shows the result of calculation by setting up a point
estimate for each parameter. Therefore, this is inconsistent with the list of factors (result of
uncertainty assessment).
 The value of the hidden flow of iron ore was calculated by excluding the blast furnace slag produced
domestically. The import ratio of each country is from the Mining Industry Handbook (ResearchInstitute of Economy, Trade and Industry 2001).

Table 2.11-8: Example calculation of the damage function of forest resources (wood)
Country

Wood volume of forest[m3 ha-1]

Alteration area per wood weight[ha t-1]

(1)

(2)

(1)

(2)

Wood supply ratio(2000)
[%]

US

176

127

0.01140.0157

15.9

Canada155

112

0.01290.0178

14.5

Japan

188

135

0.01060.0148

18.9

Other
130
93
0.0154• Representative value of volume/weight conversion rate[t dm m-3] of wood: 0.5

0.0214

13.6

CountryUS[(1) Primary production][(2) Extinction risk]

AverageforforestNPPp[t ha-1 yr-1]

Recovery
period[yr]

Alteration
damageamount perarea[t ha-1]

Damagefunctionby country

Growthdensityfactor for
endangered
species

Alteration
damageamount per
area[EINES ha-1]

Damagefactorcountry

8.9

35

156

1.77

0.632

8.73×10–7

1.37×10–11–7

5.88×10–12
1.76×10–11

Canada

3.1

82

126

1.62

0.004

3.29×10

Japan

11.4

25

143

1.52

1.000

1.19×10–6

Other

8.0

30

120

1.85

*

5.79×10–6

Average damage factor (unknown country of origin)

1.81

70

*
1.03×10–10

byLIME2_C2.10-C2.13_2014• To illustrate the calculation process, the figure shows the result of calculation by setting up a point
estimate for each parameter. Therefore, this is inconsistent with the list of factors (result of
uncertainty assessment).
• In this table, the unit of damage function is kg-NPP/kg-wood (average for natural and artificial forests)
for primary production and EINES/kg-wood (natural forests) for the extinction risk. The value ofdamage factor is the average for natural and artificial forests also in the case of extinction risk.
• Land alteration area is calculated for both natural and artificial forests in the case of primary
production (1) and for natural forests in the case of the extinction risk (2).
• Wood supply ratio is from the Forestry Agency’s “Annual Report on Forest and Forestry in Japan”(Forestry Agency 2002a). The wood volume in forests was calculated by multiplying the wood
volume in forests (m3 ha-1) in “State of the World’s Forests 2001” FAO (2002) by 0.93, which was
calculated from the value in Japan and the ratio between the accumulation and the area of forests withan age class of 8 years or more (Forestry Agency 2002b).
• The wood volume in forests in the countries that falls under “Other” is the world average, and thealteration damage amount per area in the countries is the average for other supply countries.

First, to assess the damage of land use for mining of resources, the global potential net
primary production (NPP) was estimated by applying the Chikugo model (Uchijima et al.
1985; Seino et al. 1985) as in Japan (see 2.10.3). The values of temperature and rainfall
necessary for calculation were the averages between 1981 and 1990 (IPCC 2002). The
annual net radiation in the model was calculated by applying the relational expression
between temperature and annual net radiation in Seino et al. (1992).
With regard to some iron and copper mines whose locations (latitudes and longitudes) weresurveyed during LIME 1, NPPp at the locations were set up based on the results of the survey(in a form with no uncertainty). On the other hand, with regard to mines whose locations
were not identified and mines for which no location information was found other than the
countries to which they belonged, the probability distribution of NPPp of each of theexporting countries (frequency distribution of NPPp calculated for each mesh in comparison
with the whole world) was set up. A variability distribution was established as theassemblage of them, and Japan’s import ratio by type of resource (with regard to a mine in acountry, the ratio was multiplied by the ratio of the production in the mine to the total
production of the country; therefore, no consideration was given to the mine’s export ratio toJapan) was used as the probability of appearance for each of them.
With regard to the number of years for recovery of primary production, because the degree ofartificial alteration, such as urban land use and mineral resource mining, is large for theuncertainty assessment of land use (2.10.3 (2) f), a uniform distribution of recovery periods
lasting 50 to 250 years was established and applied to cases where soil would be lost for awhile. If a place for mining mineral resources is restored after mining, it seems appropriate
to set up a relatively small number of years for recovery. Such cases were reflected
somewhat in the above-mentioned distribution of a wide range of recovery periods.
Under LIME, the period of mining in a mine is the number of years of land use used fordamage assessment. Under LIME 2, the relation between iron reserves (including not only
confirmed reserves but also estimated reserves) and production volume was clarified based onmaterials arranged about iron ore mines to which the surface mining method was adopted(Open-air Mining Technology Research Committee 1979), and the number of minable years
for each mine was calculated by dividing reserves (estimated at a certain point of time) byproduction volume (in a certain year). After that, on the assumption that the probability
distribution of mining periods ranged from 5.7 years to 223 years and that the maximum value
was 45.7 years, which is the number of minable years that corresponded to the inclination of71

LIME2_C2.10-C2.13_2014

the regression equation as a result of regression analysis of reserves to production volume(under LIME ver. 1, the parameter was 50 years, almost the same value), a triangular
distribution was set up for all underground resources.
(4)
Biodiversity: damage function of mining of underground resources (mineral
resources, fossil fuels, stone resources)
With regard to stone resources, the damage factor of quarrying calculated in 2.10.3 (4) b wasused as the damage function of biodiversity per land use area.
The following three causes were taken into consideration during the uncertainty assessment of
the damage function (Figure 2.11-7) of biodiversity (extinction risk) due to mineral resources
and coal: the variability of the number density of endangered species in each country
according to Japan’s import ratio; the uncertainty of correction rate; and the uncertainty of theincrement in the extinction risk per quarrying area. Details are as follows:
Under LIME 2, consideration was given to how to weight the amount of damage per unit areaaccording to the situation of biodiversity in the exporting countries to Japan, and the result
was incorporated in the calculation as the correction rate (random variable that has
uncertainty) for the amount of damage per area of domestic stone mining (parameter used for
LIME 1). Concretely, with regard to the domestic cases covered by the environmental
impact assessment (EIA) during LIME 1, which included the calculation of an increment in
the extinction risk, a model where the number of endangered species and the increment in theextinction risk were used as the explanatory variable and the objective variable, respectively,
was created through regression analysis of the value of increment in the extinction risk andthe number of endangered vascular plant species,*9 which was reported to exist in each of thedomestic secondary meshes (about 10 km) where the cases were located. In the following
equation, x is the number of endangered species in a mesh, and 4.39 is the average value for x
in all the assessment cases. SE in parentheses is the standard deviation of the regression
parameter. The parameter was deemed to follow a normal distribution and was used as thestandard deviation for the uncertainty assessment of the damage function.
UAR(x) ＝ 6.57E–9 (SE 2.52E-9) × (x － 4.39) + 3.74E–8 (SE 9.48E-9)(2.11-6)
The average of x was 1.50 concerning the assessed stone mining. Therefore, the correction
rate of the increased extinction risk (UAR) to be applied to resource gathering if the numberof endangered species is α times as large was defined as UAR (1.50α) ÷ UAR (1.50) (Figure2.11-8). Although this relation was calculated for Japan only, it was audaciously assumed
that this was applicable to foreign countries, and the value of α was replaced by the ratio
between Japan and resource exporting countries concerning the number of endangered species
divided by the national land area specified in the IUCN Red List (2002).

*9
The numberof endangered species whose locations have been published is limited. Because the growthof endangered species has not been confirmed or has been unknown in Type-0 meshes,assessment was carried out concerning Type-1 and higher meshes.
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LIME2_C2.10-C2.13_2014

12
100%

75%

0%

75%

5
α

6

100%

Correction rate

10
8
6
4
2
0
0

1

2

3

4

7

8

9

10

Figure 2.11-8: Correction rate for an increment in the extinction risk corresponding to
the rate of increase in the number of endangered species(5)

Primary production: damage function of forest resource gatheringWith regard to the unit amount of wood products, the area of land use by type of wood
product and by country as described in (2) c was multiplied by the amount of damage toprimary production per unit due to deforestation calculated from NPP shown in 2.11.3 (4) andthe recovery time of productivity fixed in Table 2.11-9 in order to calculate the amount ofdamage by type of wood product and by country (Table 2.11-8).
With regard to NPP, the variability of forest NPP in each country was set up by the use ofdata on the potential NPP of the grids included in the forest category in each country. With
regard to recovery time, a uniform distribution was adopted, due to lack of information.
Maximum and minimum limits were basically fixed by the use of the recovery periods for theneighboring climate zones (Table 2.11-9).
Table 2.11-9: Establishment of a probability distribution of recovery time
Range of distribution (years)
Recovery time (years)(6)

Tropical
1-19
10Subtropical
10-30
20

Temperate
20-40
30Boreal30-170100

Biodiversity: damage function of forest resource gathering

The calculation flow is the same as that for the damage function of primary production in (5).
With regard to the damage function of wood, on the assumption that it was impossible todistinguish whether or not wood entered in inventory was produced from artificial forests,
Monte Carlo calculation was tried according to the ratio of wood from artificial forests fixed
for each country. When it was assumed that wood was produced from planted trees, theamount of damage was fixed at 0. When it was assumed that wood was not produced from
artificial forests, the amount of damage was calculated as follows:
By the method specified in 2.10.3 (3) h, the damage factor per area of domestic deforestation
was calculated by dividing the estimated increase in the extinction risk due to domestic73

LIME2_C2.10-C2.13_2014

deforestation (result of distribution of the normalization value; no probability density
distribution could be established) into that per area of deforestation, regarding thedeforestation as that of natural forests. The result was applied to Equation 2.11-6 to set up a
correction rate for uncertainty and overseas, which was then used for the calculation of thedamage function of the endangered risk.
(7)
fuels

Social Assets: damage function of consumption of mineral resources and fossila.

Concept of damage function

The Cabinet Office (former Economic Planning Agency) has promoted research anddevelopment about the environment and economy integration account. During the course of
research and development, it has estimated the imputed environmental cost (amount ofdepletion of environmental assets) basically by the maintenance cost assessment method(whereby indirect assessment is made by the use of the estimated cost necessary for
maintaining actual qualitative and quantitative environmental changes).
With regard to the depletion of underground resources, the amount of depletion calculated by
the user cost method by El Serafy (1989) has been estimated as the imputed environmental
cost for the current term. El Serafy’s user cost is the amount of money that becomes
necessary when a part of revenues from gathering of underground resources in every term isinvested in other assets so that the same income (permanent income) can be gained even after
the resource is depleted. The cost is the difference between profits in every term and thepermanent income (Figure 2.11-9). Concretely, the user cost is calculated by Equation
2.11-7 (El Serafy 1989).
User Cost = R － X ＝ ｛ 1 / ( 1 + r ) n ｝･ R

(2.11-7)

In this equation, R is revenue, X is permanent income, n is the number of years of mining, andr is interest rate (for example, 3%).
The above-mentioned environment and economy integration account (Economic Planning
Agency 1998) was calculated by the use of production volume multiplied by price as R (themining cost was assumed to be 0) and minable reserves divided by production volume as n.
The following are points that require attention when values are actually calculated: (1)exponential changes in estimated values due to changes in production volume; (2) greatchanges in estimate results due to changes in interest rate; and (3) the user cost estimated foreach mine and the user cost estimated from the total amount of resources in the whole world –that is, calculated values and their meanings differ between the case where user cost is
calculated for each mine, whose durations differ from each other, and the case where user costis calculated from the total amount of resources in the whole world (Cabinet Office 2001).
Ariyoshi et al. (1996) emphasized theoretical important points concerning this user cost. For
example, (1) although a certain amount of income is guaranteed for the future, this does notmean the sustainability of the resources (from the outset, the object of assessment isexhaustible resources); and (2) it is implicitly assumed that interest will be gained frominvested assets even after the depletion of the resources.
74

LIME2_C2.10-C2.13_2014

Revenue gained actually fromsales of resources R[Before discount (amount in each term)]

End of gathering in t=2 (gathering period n = 3)

Depletion of resource stock (user cost)“True” income is income that can be gained permanently
through management of amount equivalent to user costeven after resource depletion“True” income from resource gathering

Income from interest frommanagement of saved user cost

User cost
R-X
Permanent
income X

Discount by 1/(1 + r)t

Revenue R[After discount (assessment at current value)]
Total
area

User cost saved
during t = 0, 1, 2

Total
area

Total of interest frommanagement from t = 3

Figure 2.11-9: Concept of user cost by El Serafy• Mining begins at t = 0 and ends at t = 2. After that, no revenue can be gained from sales of minedresources.
• To continue to gain revenues after the end of the mining, it is necessary to shift a certain amount ofmoney (“depreciation” of resources = user cost) from revenues to the management of other assets.
• The “true” income is the amount of the permanent income series achieved through the management ofassets (interest). The user cost is calculated as the level where the sales revenue deducted by the user
cost becomes equal to the interest income.

Because El Serafy’s user cost serves as “depreciation” of resource stock from the viewpointof a permanent income series that intends to weaken sustainability (see Column 2.11-3), it
should be deducted afterwards from a part of the value added produced by resource gathering(El Serafy 1997). Therefore, when interpreting the results of damage assessment, it isnecessary to pay attention to the differences between damage assessment by user cost and thatby other damage functions.
b.

Parameters used for calculation of damage functions

Under LIME 1, with regard to minerals and fossil fuels, interest rates of 3% and 5% wereused for the calculation of the user cost (annual depletion cost of exhaustible resources in thewhole world) and the user cost per unit weight [$ kg-1]. As for the parameters used for the
calculation, because price and other conditions may change greatly during a short time,
temporal changes seem to influence the damage factor of this impact category greatly.
Under LIME 2, it was decided that a damage factor should be provided for 2005, the last yearof the project period. Values calculated from time-series prediction (smoothing) based on
past data were used as the parameters for 2005 so that expected values and variability based75

LIME2_C2.10-C2.13_2014

on changes so far could be reflected in the assessment. It can be thought not only that
reserves will simply decrease due to mining but also that there will be an addition (increase)as a result of exploration and other changes as a result of technical and economical causes.
Therefore, with regard to reserves, it was decided, for example, that past years’ reserves
should be calculated backwards from the latest estimate of reserves every year, and eachyear’s production volume and the difference (ratio) of the current year’s reserves in past
years’ statistics should be calculated every year. With regard to the latest reserves also,
because such changes were considered to occur, it was decided that uncertainty should beassessed for convenience and that the latest reserves deducted by the production volume
estimated for each future year should be used as reserves in that year. With regard to crudeoil, however, because the ratio of reserves to the normalization value is great, the alreadyestimated ultimate reserves were used.*10 Although the ultimate reserves of oil differ
according to estimation (Energy Supply-Demand Subcommittee of Advisory Committee for
Natural Resources and Energy 2005), LIME 2 referred to the US Geological Survey’s latest
estimate in 2000 (USGS 2000), also referring to description by OECD/IEA (2004). Because
this estimate was made as a probability distribution and shows the results of interval
estimation, a probability distribution of ultimate reserves was established based on this.
As basic data for setting up parameters, the “Mineral Resources Database 2001” (compiled by
the Resources Economy Committee of the Mining and Materials Processing Institute ofJapan) was referred to concerning metal resources, and changes over time were arranged forabout 20 years from 1980 in principle. In addition, whenever needed, USGS’s “Mineral
Commodity Summaries” (each year’s edition) was referred to concerning the metals forwhich the Database did not provide sufficient information, and “The Red Book Perspective”(OECD/NEA 2006) was referred to concerning uranium. With regard to fossil fuels, “BPStatistical Review of World Energy” (BP 2005) provided by BP (former British Petroleum)
was referred to in the same way (excluding the above-mentioned ultimate reserves of crudeoil). To convert dollars into yen, 110.75 yen/$ was used as the exchange rate of 2005.
(8)

Social assets: damage factor of forest resource consumption

Biological resources (forests) not only serve as places of wood production but also havevarious functions that provide many kinds of environmental services, including
socioeconomic benefits (which are not contained in the market price of wood). Therefore,
during damage assessment, it is necessary to consider not only depletion (simply,
deforestation whose speed exceeds the speed of regeneration) but also the loss of such
multiple functions. However, methodology for assessing the depletion of biologicalresources (forests) as resource stock and the environmental services provided by forests
during LCIA has not been discussed fully and depends on the development of research in the
future.
In Japan, forest functions have been quantitatively assessed by the Forest Agency’s
assessment of the Japanese forests’ function to enhance the public interest, the Science
Council of Japan’s review, and the Mitsubishi Research Institute’s assessment based on thereview.

*10 The reserves of crude oil increased to twice as much as 20 years ago.

If the production volume accumulated during theperiod is added, it increased three times. However, a sharp increase in the reserves in all the OPEC member countries
during 1985-1990 was due to the negotiations about production allocation at that time and was said to have almost no
relation with actual discovery of new reserves (OECD/IEA 2005).

76

LIME2_C2.10-C2.13_2014

The Forest Agency (2000) divided forests’ functions into the following: (1) cultivation ofwater sources; (2) prevention of mountain disasters; (3) conservation of the living
environment; (4) health culture; and (5) production of wood, etc. The Forest Agency
subdivided them and assessed some of the subdivided functions by the replacement cost
method.
On the other hand, the Science Council of Japan divided forests’ multiple functions into thefollowing in its report (Science of Council of Japan 2001): (1) conservation of biodiversity;(2) conservation of the global environment; (3) prevention of sediment disasters and soil
conservation; (4) cultivation of water sources; (5) creation of comfortable environments; (6)health and recreation; (7) culture; and (8) matter production. The Council showed whether
or not quantitative assessment is possible concerning each of the eight divisions. Based on
the results, the Mitsubishi Research Institute assessed some of the functions by the
replacement cost method.
Famous assessment of biological functions on a global scale is Constanza et al. (1997), whomonetarily assessed 17 biological functions of 16 biomes (including two forest biomes:
tropical forests and temperate/boreal forests). The assessed value (per area) of the temperate
forests is far smaller than the Forest Agency’s.
Under LIME 1, based on the results of the assessment of service values by Costanza et al.
(1997), and paying attention to the possibility of overlap with the other areas of protection, we
tried to derive the damage function per unit amount of consumed wood by applying the forest
structure in each country, the number of years for recovery fixed in 2.11.5 (3) b, and theinterest rate fixed in 2.11.3 (7). After that, to assess the depletion of forest stock, we tried to
calculate the amount of growth of forests and the amount of deforestation on a world scale.
However, the damage function could not be calculated, due to insufficient grasp of theamount of deforestation.
(9)

Damage factor of resource consumption

In the impact category of resource consumption, because there is only one kind of endpoint
for the damage factor for biodiversity and social assets, the representative value of the damage
function is used as the damage factor as it is. The damage index for primary production is
the total of the damage factors for land alteration and maintenance. The following are
explanations about the result of the calculation of the damage factor for each area ofprotection:a.

Damage factor for primary production

With regard to some kinds of resources, statistical values (Table 2.11-10) and cumulative
frequency distributions (Figure 2.11-10) were presented herein as examples. With regard to
the damage factor for stone that can be mined only in Japan, the coefficient of variation
became relatively small.
Compared with LIME 1 (Figure 2.11-11), although differences in size among resources havebeen maintained generally, there are many kinds of resources about which the median of thedamage factor under LIME 2 was calculated to be lower than that under LIME 1.
Although, as shown in Table 2.11-11, causes for the uncertainty of the damage factor differ
77

LIME2_C2.10-C2.13_2014

among kinds of resources, causes for the uncertainty were extracted from both the process of
estimating the area of land alteration due to resource gathering and the process of estimating
the amount of damage per land area. Many of the causes are variables that express
geographical variability.
The damage factor for wood is larger than that for copper, for example. This is partly
because of difference in the density of resources. If 1 m3 of rock is gathered from rock witha thickness of 50 m, the altered “area” is only 0.02 m2/m3. On the other hand, if theaccumulated volume of wood is about 100 m3/ha, the area of forest necessary for gathering 1
m3 of wood is 100 m2/m3. The latter is 5,000 times as large as the former. If difference in
specific gravity is taken into consideration, the difference between the two in area per weight
widens severalfold. On the other hand, the two seem to differ from each other in the state of
the ecosystem after resource gathering. If no activities for restoring nature are taken afterrock mining, surface soil will be lost and it will be impossible to expect primary production.
However, forest gathering can be thought to be a land altering act more moderate than rockmining. Therefore, damage assessment requires appropriate differentiation of damage
amount per area of land use. Differences between the two that have been taken into
consideration under the current LIME are limited to the following: although the maintenanceof land use (number of minable years) is included in damage assessment in the case ofunderground resources, only the act of alteration is dealt with in the case of deforestation; andthe time for recovery of primary production volume after gathering is somewhat shorter in thecase of deforestation. As a result, the amount of damage to primary production per unit landarea in the case of underground resources is only about ten times as large as in the case ofwood. In addition, attention should be paid to the fact that the damage factor for woodpresented herein is applicable also to a gathering method whereby a forest becomes bare land
after total deforestation.
Table 2.11-10: Example of statistical values of primary production damage factorsof resource gathering[kg-NPP/kg]

Rock

Iron

Lead

CopperWood

Number of trials

50,00050,00050,00050,00050,000

Average

2.11E-03

6.16E-03

2.02E-01

8.74E-01

2.41E+00

Median1.82E-03

1.43E-03

5.17E-02

3.55E-01

1.61E+00

Standard deviation

1.19E-03

9.97E-03

6.93E-01

1.48E+00

3.25E+00

Dispersion

1.42E-06

9.94E-05

4.80E-01

2.21E+00

1.05E+01

Skewness

1.5

3.1

19.8

5.1

6.9

Kurtosis

5.6

19.4

808.6

57.3

94.8

Coefficient of variation

0.6

1.6

3.4

1.7

1.3

10% value

9.47E-04

0.00E+00

6.26E-04

0.00E+00

3.44E-01

90% value

3.81E-03

1.85E-02

4.37E-01

2.39E+00

4.84E+00

Average standard error

7.27E-03

3.44E-03

4.43E-05

5.01E-06

1.45E-02

78

LIME2_C2.10-C2.13_2014

Wood
Rock
Iron
CopperLead

Figure 2.11-10: Example of prediction result of probability density distribution ofprimary production damage factors of resource gathering

Median (30-70% Values)

The cumulative probability does not become 0% even in the case of 1E-6kgDW/kg, because a trial where
the potential NPP at the point of mining was assessed as 0tDW/ha (therefore, the damage factor is zero) isreflected.

Figure 2.11-11: Comparison between LIME 1 and LIME 2 in primary production
damage factor of underground resources

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LIME2_C2.10-C2.13_2014

Table 2.11-11: Example of rank correlation of primary production damage factors ofunderground resources
Rock(2) Recovery time#
for primary1
production

Iron

0.64

(1) Variability of# land alteration area
0.54
2 ratio at eachmining point(2) Damage factor
for land use# maintenance at
0.33
3 building sites(substitute forresource gathering)#
4(2) Variability ofprimary production
according todifferences amongmines andexporting countries(1) Variability ofgrade and hidden
flow(2) Spatial
distribution ofpotential NPP(Australia)(2) Mining period#
5(2) Recovery time
for primary
production#
6(1) Mining
thickness#
7(2) Spatial
distribution ofpotential NPP(India)Lead

Copper(1) Variability of
0.46 grade and hidden
flow(2) Spatial
distribution of
0.20
potential NPP(Australia)(2) Variability ofprimary production
according to
0.15
differences amongmines and exporting
countries

0.33(2) Spatial
distribution ofpotential NPP(Chile)

0.29

0.32

(1) Variability ofgrade and hidden
flow

0.25

(2) Variability ofprimary production
according to
0.29
0.24
differences amongmines andexporting countries(1) Mining
0.09 (1) Mining thickness -0.29-0.14
thickness(2) Spatial
0.06 distribution of
0.17 (2) Mining period 0.11
potential NPP (US)(2) Recovery time
-0.04 (2) Mining period
0.12 for primary
0.09
production(2) Spatial(2) Recovery time
distribution of
0.03 for primary
0.09
0.03
potential NPP
production(Canada)• The top 7 causes were extracted.
• 1) indicates a cause for uncertainty concerning the estimation of the area of land use for resource
gathering.
• 2) indicates a cause for uncertainty concerning the estimation of the amount of damage per area of landuse. However, for convenience of implementing damage functions, the rank correlation of variabilityof primary production according to difference among mines and exporting countries is underestimated.

Table 2.11-12: Example of statistical values of damage factors for biodiversity[EINES/kg]

Rock

Iron

Lead

CopperWood

Number of trials

50,00050,00050,00050,00050,000

Average

8.50E-14

2.08E-13

9.41E-12

6.31E-11

1.42E-10

Median1.35E-15

1.91E-15

3.76E-14

6.75E-13

1.35E-11

Standard deviation

3.10E-13

1.06E-12

9.04E-11

3.27E-10

1.03E-09

Dispersion

9.58E-26

1.13E-24

8.18E-21

1.07E-19

1.05E-18

Skewness

5.4

10.1

27.5

11.8

125.11

Kurtosis

43.8

169.2

1,067.9

237.8

21,734.4

Coefficient of variation

3.6

5.1

9.6

5.2

7.2

10% value

6.18E-17

0.00E+00

0.00E+00

0.00E+00

0.00E+00

90% value

5.16E-14

8.55E-14

2.70E-12

3.46E-11

2.38E-10

Average standard error

1.38E-15

4.74E-15

4.04E-13

1.46E-12

3.16E-1280

LIME2_C2.10-C2.13_2014

Wood
Rock
Iron
CopperLead

Median (30-70% Values)

Figure 2.11-12: Example of prediction result of cumulative frequency of damage factor
for biodiversity

Figure 2.11-13: Comparison between LIME 1 and LIME 2 in biodiversity damage factor
of underground resources

81

LIME2_C2.10-C2.13_2014

Table 2.11-13: Example of rank correlation of biodiversity damage factors ofunderground resources
Rock(3) Variability of#1 UAR of rockmining cases(1) Variability ofalteration#2 landarea rate by pointof mining(3) Tsim#3 (Calanthe
discolor)Iron(3) Variability of
0.93 UAR of rock mining 0.64
cases(2) Correction rateequation 0.31
0.17 regression(intercept)

N4 (Calanthe#4 (3)
discolor)

0.23(1) Variability ofgrade and hidden
flow

0.19

(2) Variability ofprimary production
according todifference among 0.18
mines / exporting
countries

N4 (Calanthe#5 (3)
discolor)(3)Tsim#6 (Cephalanthera
falcate)Correction rate
-0.0 (2)regression equation
5(slope)(2) Variability ofprimary production-0.0 according to4
difference amongmines / exporting
countries
Variability of-0.0 (1)grade
and hidden
4
flow-0.0 (1) Mining
4
thickness

Lead(3) Variability of
UAR of rockmining cases

0.58(2) Correction rate
regression equation 0.32
(intercept)

Copper(3) Variability of
UAR of rockmining cases(1) Variability ofgrade and hidden
flow

0.77

0.23

(2) Correction rate(2) Correction rate
-0.29 regression equation -0.31 regression equation 0.17
(slope)(intercept)

Mining-0.04 (1)thickness

0.18(1) Mining
thickness-0.13(2) Correction rate
regression equation -0.09(slope)(2) Variability ofprimary production
to-0.15 according-0.04
difference amongmines / exporting
countries(3) Population of
discolor(3) Tsim (Calanthe -0.02 (3) N4 (Calanthe
Tsim (Calanthe -0.03#7 Calanthe-0.02 (3)in a rock mining 0.04 discolor)
discolor)
discolor)case• 1) indicates a cause for uncertainty concerning the estimation of the area of land alteration due to resource
mining.
• 2) indicates a cause for uncertainty and variability in the number density of endangered species in eachcountry by the application of the damage factor of domestic land alteration to overseas. However, forconvenience of implementing damage functions, the rank correlation of variability in the number density ofendangered species is underestimated.
• 3) indicates a cause for uncertainty in the damage actor of domestic land alteration. Tsim (s) indicates
uncertainty caused by the application of the change rate in the number of individuals of the endangered
species s. N4 (Calanthe discolor) indicates the population of Calanthe discolor in a regional mesh. For
details, see 2.10.

b.

Damage factor for biodiversity

Compared with LIME 1 (Figure 2.11-13), trends in size between resources have been
generally stable as a whole. With regard to some kinds of resources, statistical values (Table
2.11-12) and a distribution of cumulative frequency functions (Figure 2.11-12) were presented
herein as examples. Because the damage factors under LIME 2 have wide variability, manyof the damage factors under LIME 1 are within the interval between 30% and 70% around the
median of the damage factors under LIME 2.
This wide variability was caused mainly by the variability in the increased extinction risk
(UAR) per area of land alteration as shown in Table 2.11-13. With regard to resources
imported from overseas, impact has been made also by the uncertainty caused by theapplication of the domestic UAR to overseas (correction rate). If there are many low-grade
mines, such as copper mines, impact is made also by the viability in the grade and the hidden
82

LIME2_C2.10-C2.13_2014

flow. On the other hand, with regard to rock, which is a domestic resource, the coefficient ofvariation has become relatively small.
The damage factor of wood has become larger than that of metal, largely because ofdifference in the area of land use per volume of resources as in the case of primary production(see a. above). Moreover, with regard to the damage factor for biodiversity, it is necessary
to note that the amount of damage per land area (increase in the extinction risk) has beenassessed on basically the same condition as the mining of underground resources – that is, thecondition that all the individuals of (endangered species of) plants growing in land where
wood is gathered should be lost. Because of such limitations, under LIME 2, as describedabove, wood is divided into that from natural forests and that from artificial forests, and anincrease in the extinction risk is deemed to be caused by gathering of wood from naturalforests. As a result, if it is unknown whether wood is from natural or artificial forests (due toinsufficient inventory information), the probability distribution of damage factors has parts
where the damage amount is zero and parts where it is large.

Frequency

Probability

Crude oil (user cost)

Average = 3.17
Median = 2.96

[Yen/kg]
Fit: max extremal value
distribution

Estimate

Figure 2.11-14: Prediction result of the probability density distribution of user costs ofcrude oil
Table 2.11-14: Result of uncertainty analysis of user cost Statistical value of (crude oil)(unit: yen/kg)
Statistical volume
Number of trials
Average
MedianStandard deviation
Dispersion
Kurtosis
Coefficient of variation
10% tile value
90% tile value
Average standard error

Statistical value
50,000
3.17
2.96
1.36
1.86
7.19
0.43
1.64
4.97
0.0061

83

LIME2_C2.10-C2.13_2014

Table 2.11-15: Rank correlation of causes for uncertainty of user cost of oil
Variables
Growth of reserves in the world (excluding the US)
Undiscovered reserves in the world (excluding the US)
Change in oil price
Change in oil production
Undiscovered reserves in the US

Rank correlation
coefficient
-0.61
-0.55
0.50
0.09
-0.02

Median (10-90% Values)• The table shows the top five causes.

Figure 2.11-15: Comparison between LIME 1 and LIME 2 (user cost of resources)c.

Damage factor for social assets

As examples of uncertainty analysis results, Figure 2.11-14 and Tables 2.11-14 and 2.11-15
show a probability density distribution of user costs of oil, statistical values, and rankcorrelation coefficients with variables about which a probability distribution was established.
During the calculation of user costs of oil, the user costs were reviewed by reference to theresults of the estimation of ultimate reserves by USGS (2000) under LIME 2 as describedabove. In this latest estimation, however, because the “growth of reserves” in existing oil
fields was included in addition to cumulative production volume, remaining reserves, andundiscovered minable reserves, this range of estimation was extracted as a cause for
uncertainty.
Comparison in user cost between LIME 1 and LIME 2 indicates that some metals showed agreat change. Among such metals, the user costs of niobium and vanadium were calculated
larger than under LIME 1 because their production was on an upward trend in the second halfof the 1990s. With regard to antimony also, the user cost was calculated larger because of
the recent downward revision of the reserves. On the contrary, because the reserves ofchromium were once estimated to be about a half as much as now, the user cost in LIME 2
was assessed at a smaller amount than before. Although the production or price of somemetals seems to have risen sharply since 2005, such most recent trends have not beenreflected.
84

LIME2_C2.10-C2.13_2014

In addition, there are some metals form which the value, source, and other information shownin the table of resources in the Resources and Energy Handbook used for LIME 1 is different
from the information selected for LIME 2. Such metals include uranium (revised to greater
reserves) and manganese (revised to a lower price).
Column 2.11-4
Way of thinking about the damage factor of deforestation under LIMEAs described in the main text, under LIME, the inventory shows the status of forest
management and it is assumed that the impact of deforestation is small when wood is gatheredfrom stock for which appropriate (sustainable) management has been carried out and that
forestation (on bare land) has a negative environmental burden.
Concretely, the following criteria are used for the classification in the inventory and theapplication of the damage factor. (1) Whether it is wood or a wood product. (2) The
damage factor is used as it is if the forest is not managed or there is no information about
management, while the damage is zero if sustainable management is carried out. If
forestation is carried out after deforestation, but biodiversity is decreasing, it is inappropriate
to regard the damage factor for the extinction risk as zero. In addition, if devastated land isforested for the purpose of nature restoration, a negative damage factor should be applied tothe area of forestation. (3) Whether or not the country of origin is clear? Figure 2.11-A
shows the flow of calculating the area of land use and selecting the damage factor according tothese criteria.
An appendix hereto shows the damage factor in the case of wood volume concerning thecriterion (1). It is assumed that the damage factor for biodiversity is zero if an artificial
forest is deforested. This requires paying attention to the problem of whether any plant
species that can become an object of damage assessment is growing in reality and the issue of
whether the volume of past artificial alteration that might have been made if the artificial
forest had originally been a natural forest should be allocated to the product system that uses
the present artificial forest (Column 2.10-3: Points that need attention concerning land use
inventory). With regard to metals and other mineral resources also, there is a similar
problem in the allocation between primary and secondary metals.
This approach has the following problems and points that require attention. (1) Althoughsustainable management is taken into consideration, no consideration is given to any concrete
method to guarantee it, such as the use of forest certification. (2) If sustainable management
is carried out, any temporary impact is ignored and damage is assumed to be zero. Further
consideration will be necessary in the future concerning primary production, including
consideration for time-dependent changes in NPP according to forest growth and assessment
related to maintenance (occupation) of land use. (3) No consideration is given to thevulnerability of places of production. When the vulnerability is incorporated, it seems
necessary to consider both the biological viewpoint and the socio-economic viewpoint.
Calculation of area of land use and criteria for selectionof damage factor

Inventory information
No management Country of origin is →clear
Wood /
noinformation
Unknown→
volumeSustainable management→

Use of wood volume / area in the country
Use of value estimated from wood volume
Application of 0 as damage factor

85

LIME2_C2.10-C2.13_2014

No management Country of origin is →Wood /
clear
product no information
Unknown→
volumeSustainable management→

Use of wood volume / area and log conversion rate ofwood product in the country

Area of deforestation
Area offorestArea of forestation→

Application of damage factor per area to area→

Application of “negative “ damage factor per area to
area

Use of value estimated from wood product volume
Application of 0 as damage factor

Figure 2.11-A: Flow of determining a method for calculating area of land use
2.11.4

Procedures for impact assessment of resource consumption

Procedures for characterizing resource consumption and assessing damage from it can be
described as follows. It is necessary to note that the impact assessment of resource
consumption is carried out only concerning the amount of gathered natural resources(excluding the amount of recycled resources) in Inv (X), the inventory of the resource species
X. This is important especially for damage assessment. For the inventory of forest
resources, see Column 2.11-4 also.
With regard to mineral resources, it is necessary to clarify whether the inventory has been
constructed in terms of the amount of metals or the amount of ores. Under LIME, the impact
assessment factors have been calculated in terms of not the amount of ores but the amount ofmetals. Therefore, although the assessment factors should be applied after being multipliedby grade if the inventory value is expressed in terms of the amount of ores, this may expand
errors in resultant values from damage assessment.
With regard to the characterization, the category index CI can be gained by Equation 2.11-8
from the viewpoint of the rarity of mineral resources. In addition, fossil fuels can becharacterized not only by Equation 2.11-9, using the amount of energy as the index, but also
by Equation 2.11-8.
Exhaustible resources (mineral resources): CI Depletion   X CF Depl etion ( X )  Inv ( X )(2.11-8)
Fossil Fuels: CI Energy   X CF Energy ( X )  Inv ( X )(2.11-9)

Table 2.11-16: Formula for damage assessment in the impact category of resource
consumption
DI (Safe)
DI (Social assets)
DI (Biodiversity)
DI(Primary
Resources
production)
Fossil fuels † X DFYenDepletion ( X )Mineral resources
Extraction
Extraction
(X )
(X )
 X DFEINE X DFNPP Inv ( X )S
 Inv ( X ) Inv ( X )Stone resources
Forest resources† With regard to oil and natural resources among the fossil fuels, excluding coal, no damage factor is provided
for biodiversity and primary production protected from the impact of land use (assessed as zero). For thereason, see 2.11.3 (2) a *1.

86

LIME2_C2.10-C2.13_2014

Several characterization factors have been suggested so far. Under LIME, the reciprocal of
the amount of minable reserves, 1/R (normalized by antimony), was recommended as thecharacterization factor including both mineral resources and energy resources, CIDepletion, andthe amount of heat generation was recommended as the characterization factor that takes into
consideration energy resources only, CIEnergy.
In addition, the index for endpoint approach – that is, the damage index DI (Safe) – can begained from the damage factor DF (Safe, X) for each resource species Inv (X) and area ofprotection Safe. Although the formula is the same as that in the other impact categories, thedamage factor for the area of protection of social assets is provided only concerning fossil
fuels and mineral resources (Table 2.11-16). These damage indexes mean the amount ofpotential damage from land use for resource gathering in the case of the areas of protection ofprimary production and biodiversity, while they mean the assessed value necessary forinvestments in artificial assets to maintain monetary incomes from natural resource gathering
permanently even after the gathering in the case of the area of protection of social assets.
However, because the damage factor obtained from the setting of the discount rate changes as
one of the characteristics of the user cost method used for the assessment of impact on social
assets, the factor was presented herein in the case of a discount rate of 3% as the default.
The factor used for integration is IFResource (X), the factor that integrates the impacts on social
assets, biodiversity, and primary production. The single index SI can be obtained from Inv(X) and the integration factor IFResource (X) for each resource. The result can be compareddirectly with or added to assessment results in other impact categories.
SI 

 IF

Resource(X)  Inv(X)(2.11-10)

X

The characterization factors CFDepletion (X) and CFEnergy (X) are listed in Appendix A1, thedamage factor DFResource (Safe, X) is listed in Appendix A2, and the integration factor IFResource(X) is listed in Appendix A3.

87

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Part I Volume 2 Chapter 5
Moriguchi Yuichi and Kondo Yoshinori (1998): Journal of Japan Institute of Energy, 77: 1062 – 1069
Yasui Itaru (2000): Will Resource Depletion Occur? – Environmental Prediction and Measures in the 21stCentury, Maruzen
Forestry Agency (2000): Report of Research on the Method for the Assessment of the Public Functions ofForests in FY1999
Planning Division of Forest Agency (2001): Wood Supply and Demand Table 2000, operational material
Forestry Agency (2002a): Annual Report on Forest and Forestry in Japan FY2001 (Annual Report on Trends in
Forests and the Forestry Industry), Japan Forestry Association
Forestry Agency (2002b): Current State of Forest Resources, values as of March 31, 2002
Open-air Mining Technology Research Committee (1979): “Research on Open-air Mining Technology
Overseas,” Journal of Mining and Metallurgical Institute of Japan, 95 (1098)
Washida Toyoaki (1992): Economic Analysis of the Environment and Energy, Hakuto Shobo Publishing(http://washida.net/book/sosyo/sosyo.html)

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LIME2_C2.10-C2.13_2014

2.12

Waste

Changes under LIME 2• Uncertainty assessment of damage factors was carried out. Uncertainty includes
geographical variability (uncertainty about at what final disposal site the waste entered in
the inventory is buried).
• A review of the fundamental damage functions for the impact category of land use wasreflected in the damage factors to biodiversity and primary production due to waste
landfill.
• As a damage factor to social assets, assessment by user cost was added concerning
reduction in the remaining capacity of final disposal sites.
2.12.1

What phenomenon is the environmental impact of waste?

(1)

Causal relationship between waste and environmental impact

In Japan, recently, various problems regarding waste have drawn social attention. One of thereasons for this is expectations for and friction of a physical approach to a sustainable societythat can be expressed as a recycling-based society. The other reason is anxiety about
environmental destruction due to illegal disposal and the environmental impact of waste
disposal.
The latter brings about the feeling of evasion from waste and waste disposal facilities(intermediate treatment facilities and final disposal sites (landfill disposal sites)). Especially
if the location of waste treatment facilities is separated from the district where waste is
produced, the sense of burden and the sense of unfairness increase in the district where thefacilities exist, and as a result it has been difficult to locate new facilities. Under suchcircumstances, with regard to industrial waste (mainly the law-specified 20 types of waste thatare discharged from agriculture, forestry, fisheries, mining, manufacturing, construction, andother industries), it has been pointed out that insufficient disposal capacity becomes a cause
for illegal disposal, creating a vicious circle (Central Environment Council 2004).
Figure 2.12-1 shows the causal relationships among the environmental impacts related to such
waste. Of course, there are various types and properties of waste, ranging from soil-likewaste to highly toxic waste, and various methods to dispose of waste. In addition, because
various substances are included in waste, while some types of waste can be rendered harmlessor stabilized through treatment, some substances are generated unintentionally.
The figure shows some of the environmental impacts and causes concerning incineration
facilities (Figure 2.12-1 (5)) and landfill disposal sites (Figure 2.12-1 (6)), typical facilities for
disposing of “refuse” (urban refuse) among general waste discharged from homes and offices,
including refuse that may be produced when accidents occur (excluding labor accidents).
The Waste Management Act (Waste Management and Public Cleansing Act) roughlyclassifies waste into the above-mentioned industrial waste and other general waste (refuse andhuman waste discharged from homes, shops, and offices). Disposing entities and disposal
facilities basically differ between the two.
Because of this, under LIME also,characterization factors and damage factors have been presented after classification into thetwo types of waste.
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LIME2_C2.10-C2.13_2014

Life cycle of disposal
facilities, etc.

(5) Example of processes
in incineration facilities

Disposal ofvehiclesConstruction offacilities(3) Intermediate
treatment(Crushing/burning, etc.)[Urban air pollution]

Drainage treatment

Operation ofvehiclesDisposal of exhaust gas(2) Collection/transport

Manufacture ofvehiclesIncineration(1) Discharge of
waste

Example of endpoint
impact or [impact
category] to reach

Waste

Waste treatment
procedure[Global warming]

Drainage[Biotoxicity]

To final disposal
or fusion[Eutrophication]

Sludge(8) Collected
dust/ash(9) Burnt ash

Operation offacilities
Dismantling/
disposal of
facilities(7) Exhaust gas[(14) Land use](6) Example of impact causes related tolandfill sites
Concentration ofBringing in waste
vehicles[Urban air pollution][Noise]Use of space(15) Loss of disposal
capacity(13) Scatteringof ash[Biotoxicity]

Fire[Bad smell](11) Gas
generation[Global warming]

Social assets
Landfill(Improper landfill
management)

Construction offacilities

Decomposition of buried
waste, etc.

Ecosystem

Dumping in sea(limited)(4) Final disposal(Usually, landfill)

Operation offacilities/
Management after
closureDiscontinuance offacilities use of vacant lot

Transfer to leachate

Rainwater

Change in composition of
species(Spread of brackish algae)

Drainage treatment(10) Dischargeof leachate

Obstacle to paddy rice
growth, etc.

Social assets[Eutrophication][Biotoxicity]

Disposal into sea((12) Damage to seepage
control)Spread throughgroundwater[Soil/groundwaterpollution]

Improper disposal
route
Illegal dumping

Figure 2.12-1: Causal relationships of environmental impacts of waste
The parenthesized numbers (1) to (15) are referential numbers for the main text.

a.

Waste incineration facilities (Figure 2.12-1 (5))

With regard to exhaust gas from waste incinerators (Figure 2.12-1 (7)), the Air Pollution
Control Act regulates the emission of sulfur oxide, dust, hydrogen chloride and nitrogen oxide,
while the Act on Special Measures against Dioxins regulates dioxin. According to Japan’s“Dioxin Emission Inventory” (Ministry of the Environment 2007a), although the emission ofdioxin from waste incinerators to the atmosphere was more than 7,000 grams in FY1997, it
greatly decreased to about 200 grams in FY2006.
With regard to unregulated substances, a survey conducted by the (then) Ministry of Health92

LIME2_C2.10-C2.13_2014

and Welfare from 1995 to 1998 found the following facts concerning exhaust gas from urban
waste incineration facilities: “Although many toxic substances were detected, their levels
were far lower than the regulatory levels fixed by Germany/Holland and Kyoto. In addition,some toxic substances (mercury and hydrogen fluoride) almost reached the levels or exceededthem” (Tanaka 2000). After this survey, facilities, such as bag filter dust collectors, were
remodeled in Japan as a measure to reduce the emission of dioxin. Some reports (Kida et al.
2003, Tejima et al. 1994) show cases where the efficiency for removing mercury improved
after the remodeling.
Many toxic substances in exhaust gas move into dust collected by dust collectors (Figure
2.12-1 (8)). Generally, dust is hygroscopic and scatters and contains heavy metals containedin products (Nakamura 1994) more densely than does burnt ash (main ash and residual ash)(Figure 2.12-1 (9)). Dust has been designated by law as specially controlled waste (becauseof its toxicity). Because of this, dust should be disposed of by designated methods, such as
melt-solidification, cement solidification, and chemical treatment (heavy metal fixative
treatment). The Act on Special Measures against Dioxins specifies the amount of dioxin
included in dust and burnt ash related to waste incinerators.
b.

Landfill sites (Figure 2.12-1 (6))

One of the environmental impacts related to landfill is alteration of nature due to constructionof landfill sites. In addition to this, neighboring people are afraid of the following
concerning the construction of a landfill site: (a) the possibility of groundwater pollution fromburied matters; (b) concentration of waste transporting vehicles (air pollution, noise, etc.); (c)salt damage in places into which leachate is discharged (damage to growth of paddy rice,
spread of brackish algae, etc.); and (d) scattering of burnt ash that emits bad smell andcontains toxic substances. As objects of environmental impact assessment, the “Manual ofEnvironmental Impact Assessment of Final Waste Disposal Sites” (Japan Waste Research
Foundation 1999) specifies the general environmental impact causes and acts that accompany
final disposal site projects and the elements that receive impact.
Landfill disposal standards have been established for each type of waste (Nakasugi 1994) andtypes of landfill sites that can accept waste differ according to the type and properties of waste.
With regard to industrial waste, if it is toxic and does not meet the standards, it should be sent
to isolated-type disposal sites. Glass waste and four other specified types of waste whose
properties are stable and which are unlikely to give damage to the living environment throughthe pollution of groundwater by leachate (the “stable five items”) should be sent to inert-type
disposal sites. The other types of waste should be sent to controlled-type disposal sites.
Basically, general waste (refuse) should be disposed of at controlled-type disposal sites.
With regard to the items allowed to be buried in inert-type disposal sites, disposal of shredder
dust, residue after the crushing of automobiles, and the like has been prohibited in the sites
since April 1995, and disposal of waste plaster boards has basically been prohibited in thesites since June 1998. In this way, changes were made with the strengthening ofenvironmental conservation.
The structural and maintenance standards for these disposal sites are specified in the Order
Determining Technical Standards Concerning Final Disposal Sites of Industrial and
Non-Industrial Waste” (the so-called “Joint Order”). For example, it is provided thatdischarged water should meet the drainage standards specified in the Water Pollution Control
Act through the construction of facilities for disposing of leachate generated in93

LIME2_C2.10-C2.13_2014

controlled-type disposal sites.
In 1997, the Ministry of Health and Welfare’s survey found that, among the 1,901 facilities
for final disposal of general waste in Japan, 538 facilities (28%) did not have seepage control
or leachate treatment equipment, excluding the facilities that only treated waste that did not
require the treatment of leachate, such as glass dust. A notification (Ministry of Health andWelfare 1998) was issued to the prefectures to have them give guidance about proper
treatment of municipal final disposal sites for general waste.
In Japan, the final disposal sites have been developed based on the idea of “semiaerobic
landfill structure” mainly for general waste. In this structure, air is supplied in landfill layer
through leachate collection pipes to eliminate leachate promptly, and it widely improves thewater quality (biochemical oxygen demand (BOD), chemical oxygen demand (COD), andtotal nitrogen (TN)) of leachate (Figure 2.12-1 (10)), compared with anaerobic landfill, andfacilitates the decomposition of organic substances in an aerobic area (Generating gases(Figure 2.12-1 (11)) include carbon dioxide (CO2) and methane). In an anaerobic area, if aheavy metal is ionized, it forms hardly-soluble sulfide because of the action of hydrogensulfide and remains within the landfill layer in a comparatively stable form (Landfill System
and Technologies Research Association of Japan 2000, Tanaka 1993). According toresearch reports, although dioxin brought into disposal sites cannot be easily decomposed, isstored for a long time, and is discharged into the environment mainly through leachate (Nomaet al. 2000), it is removed together with suspended solid (SS) in water treatment facilities(Noma et al. 2002).
With an increase in the waste incineration ratio and the strengthening of exhaust gas treatment,
the ratio of combustibles decreased, while the ratio of incineration residue and incombustibles
increased and the amount of calcium and salt included in incineration residue increased.
Because their ions dissolve in water, it has become a challenge to develop measures to treat
salt during water treatment.*11 Moreover, it has been found that a comparatively highdensity of boron is contained (National Institute for Environmental Studies 2001). With
regard to the discharge of unregulated substances from final disposal sites, there are research
reports by Fukui et al. (1994), Nakasugi (1993), and Yasuhara et al. (2000, 2003).
In addition, when campaigns against the construction of facilities became heated, a problem
was presented concerning damage to seepage control, an important component of a disposalsite. In 1998, the Joint Order was revised (Ministry of Health and Welfare 1998) to improve
the safety of seepage control (structure that prevents exchange between water in a landfill site
and water in the surrounding environment) (Figure 2.12-1 (12)).
Tojo et al. (1999) investigated the density of heavy metals in soil around disposal sites.
According to their report, because the density of metals in soil changed around disposal sites
where daily earth cover was not carried out, unless appropriate landfill measures are taken,
burnt ash is highly likely to scatter around (Figure 2.12-1 (13)).
(2)
The impact category to which the environmental impact of waste belongs and theendpoints

11

94

For example, “Special Issue: The Recent Technology or Treatment of Leachate in Final Waste Disposal Sites”(Environmental Conservation Engineering (2002), 31 (8)) and “Special Extra: Technology for Removal of Salt during
Leachate Treatment in Final Disposal Sites” (Journal of Resources and Environment (2002), 38 (2))

LIME2_C2.10-C2.13_2014a.

Substances discharged into the environment from waste disposal facilities

Under LCA, exhaust gas and noise from running vehicles during the process of waste
collection and transport, and exhaust gas and drainage from incineration facilities can bemeasures by inventory analysis, like exhaust gas and noise from running vehicles andmanufacturing factories during the process of distributing products. Therefore, the necessity
for dealing with the impact category of waste independently during impact assessment(damage assessment) seems to be low. In the LCA project, the inventory working group
WG-2 has created a life cycle inventory (LCI) methodology for the processes of recycling anddisposal and has been collecting inventory data.
On the other hand, the same is basically true of methane gas and leachate from landfill
disposal sites. It seems all right to measure them as output to the environment and assessthem in other impact categories, such as global warming (Yamada 2004). However, thisprocess is different from the other processes of the product lifecycle, in that the impact ofdischarge into the environment continues for a long time. This is sometimes presented as amethodological issue under LCA (Hellweg et al. 2003).
Generally, it can be said that the environmental impact of substances discharged into theenvironment during the treatment of waste basically belongs to other impact categories, suchas global warming and toxic chemicals. Therefore, the contents of the preceding clauses
were arranged from the viewpoint of which impact categories can be related to the substancesdischarged during the treatment of waste. The results are shown in Table 2.12-1.
Table 2.12-1: Discharge into the environment from waste treatment facilities from theviewpoint of endpoints and impact categories
Examples of endpoints/impact categories

Examples of discharge into the environment

Impact category of urban air pollution

Exhaust gas from incineration facilities (such as nitrogen oxideand hydrogen chloride)

Impact category of global warming

Impact category of toxic chemicals
Impact category of biotoxicity
Salt damage (damage to paddy rice
growth, changes in structure of species(spread of brackish algae, etc.))

Exhaust gas from incineration facilities (such as carbon
dioxide and dinitrogen monoxide)
Organic decomposition gas from landfill facilities (such as
methane)Exhaust gas from incineration facilities (such as dioxin andmercury)
Water discharged from landfill facilities (for a long time)
Water discharged from landfill facilities (salt)

 The density and volume of discharge may greatly differ among facilities according to the existence,
type, and level of environmental conservation measures. As described in the main text,
environmental conservation measures have been strengthened mainly for newly established facilities.
 Discharge can be divided into two types: discharge due to the inclusion of causative substances inwaste, such as carbon dioxide generated due to incineration of plastics; and discharge incidental totreatment, such as thermal NOx generated due to incineration and carbon dioxide generated due toincineration of fossil fuel for auxiliary heating.
 Implementation of LCA requires clarification of treatment of biomass-derived carbon dioxide
concerning the impact category of global warming (such as incineration of biomass and carbon
dioxide among the cracked gases from landfill facilities).

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LIME2_C2.10-C2.13_2014

b.
(14))

Land alteration due to construction of waste treatment facilities (Figure 2.12-1

A typical case where the construction of treatment facilities became a problem in relation to
the conservation of the ecosystem was a plan for the construction of a marine disposal sitethat accompanied the reclamation of a tideland. According to the “Final Disposal Sites inJapan 2000” (Landfill System and Technologies Research Association 2000), if the total
number of landfill disposal sites are classified by location, 70% of them are located in
mountainous areas, following by 28% located on level land. Because marine ones (2%)
were constructed as disposal sites for major cities that have seaside areas, their sizes are large.
Waste disposal has traditionally been municipalities’ legal obligation, and disposal within thejurisdiction of each municipality has been widely recognized as a political principle.
Because of this, there are many waste disposal facilities (about 1,700 incineration facilities
and about 2,300 landfill disposal sites) (Environmental Sanitation Facilities Development
Association 2000).
Therefore, among the landfill sites for general waste, 60% are small ones with an area of lessthan 1 ha. The average landfill area is 2.24 ha. In terms of landfill capacity, disposal sites
with a capacity of 300,000 m3 occupy 90% and the average landfill capacity is 190,000 m3.
Given these figures, the average landfill depth is 8.5 m (Landfill System and Technologies
Research Association 2000). In other words, the landfill of waste of 1 m3 requires a landfill
area of about 0.12 m2 even if consideration is given only to landfill space.
c.

Loss of space for waste disposal (landfill) (Figure 2.12-1 (15))

As described in b. above, the landfill of waste is accompanied by not only the alteration ofnature but also the problem of insufficient disposal capacity.
Hosoda (1999) pointed out that final disposal sites cannot be created indefinitely and, in thissense, they are exhaustible resources, and considered the application of the framework ofresource economics. The book specifies greatly different points from ordinary exhaustible
resources, regarding recycling technology as backstop technology for final disposal, and statesthat the value of rarity of final disposal sites for general waste, which are managed by the useof tax, has not been commercialized. For the economic value of exhaustible resources, see
2.11.3 (7) also.
2.12.2

Characterization of waste(1)

Characterization factor of waste under the existing LCA method

Under the LCA method in Japan, because of the current environmental problems, there aremany cases where waste is regarded as an impact category, and waste is taken into
consideration in some impact assessment methods. In foreign countries, however, few
overseas impact assessment methods take waste into consideration (Environment Committee
of Industrial Structure Council 2001).
Of the impact assessment methods in Japan, the environmental load point (ELP) method andthe time consumption method characterize the waste-related impact categories by totaling theweights of various types of waste. The subjective assessment of the number of years until
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LIME2_C2.10-C2.13_2014

the occurrence of crisis and the criticality concerning solid waste under the time consumption
method is carried out concerning the problem of depletion of waste disposal sites (Yasui et al.
2000).
In the case where the comparative risk method was applied to the impact assessment of
packaging (Terazono et al. 1999), areas of protection were selected for 15 types ofenvironmental problems and four impacts in a comparative risk workshop. Among them,
the following are specified as environmental loads and problems related to waste: emissionsof waste  the problem of mass production, consumption, and disposal; landfill disposal
volume  large-scale natural development; and incineration disposal volume and landfill
disposal volume  the problem of location of troublesome facilities. They are weighted byrelatively many aspects and stages concerning waste. Weight is the unit for thecharacterization by this method.
When Inaba et al. (1999) made suggestions about the index of environmental capacity
consumption by setting of environmental capacity for each region, they made a suggestion
concerning the space resource consumption that accompanies the landfill of waste. The
number of remaining years (remaining capacity of disposal site ÷ annual waste landfill) was
rejected because it is an index that indicates the degree of tightness of a final waste landfill
disposal site – that is, the impact of brought-in waste on the disposal site itself, not the impactof the construction of a disposal site. A scenario was created where the landfill of waste isregarded as the consumption of space resources and some of the space resources remaining in
the region, such as forests and fields, are allowed as land that can be used for the constructionof a waste disposal site, and the allowed amount was set up as the environmental capacity of
waste landfill.
In addition, there are the following cases; a case where the total annual amount of discharged
waste in Japan was used for normalization (Akimoto et al. 2000); a case of easing the lack ofdisposal sites by reduction of the amount of landfill waste (increase in the number ofremaining years) (Ishizaka et al. 2000); and a case where assessment was carried out in theimpact category of “landfill consumption” from the viewpoint of tightness of landfill sites
under the Distance-to-Target (DtT) method (Hirai et al 2001).
(In these cases,characterization was carried out in terms of weight or volume.)
Based on what have been described above, if waste is selected as an impact category, it can besaid that there are the following choices for characterization among the data items: (a) thegeneration amount of waste (Figure 2.12-1 (1)) or (b) the final disposal (landfill) amount ofwaste (Figure 2.12-1 (4)).
(a) focuses on whether the amount of generated waste is large or small. This can be said tobe the setting of an index that considers social influence, taking into account the social
situation where lack of disposal facilities and new construction of them tend to receive an
objection from local people. However, except for illegal dumping, generated waste is notdirectly discharged into the environment. In addition, if characterization is made at the timeof generation, the number of impact categories to be integrated will increase due to changes in
the form of waste through recycling and incineration and the environmental impacts of theseacts.
On the other hand, (b) leads to landfill, an act of altering nature (land and sea), and is closer toenvironmental impact. Therefore, characterization that takes (b) into consideration isdesirable for the damage-calculating impact assessment method.
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LIME2_C2.10-C2.13_2014

With regard to the range of waste to be covered, if environmental impact proportional to thelandfill amount is dealt with as a problem, there is no reason for limiting it to the “waste”
defined in the Waste Management Act. Construction waste soil and the like should be
included in the waste to be measured. On the other hand, if attention is paid to qualitative
differences (such as stability and innocence) among buried things, it is necessary to
distinguish toxic waste from glass or the like. For example, if the spread of toxic substancesfrom a landfill site is a problem, the impact of glass should be characterized as zero. That is,
the range, classification and characterization of waste to be covered are difficult to determine
unless there is a viewpoint of damage assessment as to what environment impact should befocused on.
(2)

Characterization factor of waste under LIME

Under LIME, as described below, on the assumption that the spread of substances from adisposal facility, such as a landfill site, is measured as the inventory for another impact
category, alteration of nature due to the construction of a landfill site (land use) was deemed
to be the object of assessment. Therefore, the characterization factor of waste is any of thefollowing: (1) landfill weight; (2) landfill volume; and (3) area of landfill (area of land use).
Because the inventory is usually expressed by mass unit, uncertainty increases in order of (1) (2)  (3). (1) does not reflect the quality of waste to be buried. On the other hand, (1)is convenient in that the unit is the same as that of the future target of the final disposal
amount under the basic policies based on existing waste-related statistics, the Basic Plan for
Establishing Recycling-Based Society and the Waste Disposal and the Waste Management
Act. On the other hand, (2) and (3) are consistent with the concepts of the characterizationand damage assessment of land use. (3) is characterized by enabling comparison with otherimpact categories, such as resource consumption and land use. However, because the area ofland use was not recommended as the characterization factor for the impact category of
resource consumption, which leads to land alteration in the same way, there is no reason for
positively selecting (3) for waste.
Therefore, (2) can be recommended as thecharacterization factor of waste. In this case, it is possible to regard (2) as thecharacterization index of loss of landfill space.
Under LIME 1, bulk density (weight  volume conversion factor) at the time of landfill bytype of general waste was set up from Tanaka (2000). If the type is unknown, 0.8163 wasused, which is the landfill waste ratio used by the Ministry of the Environment for calculating
the remaining capacity of a final disposal site for general waste. With regard to industrial
waste, reference was made to values specified in “Waste Handbook” (Japan Society of Waste
Management Experts 1997) and “Guidelines for Disposal of Construction Waste” (Ministryof Health and Welfare 1990). With regard to various types of sludge (other thanconstruction sludge), values converted based on disposal facilities’ scope of permission under
the Waste Management Act were used.
Among the environmental loads at the time of landfill, the volume or area of products, etc.
treated as space consumption was set up as an inventory data item also by WG-2, theinventory workgroup for the LCA project (Japan Environmental Management Association for
Industry 2003).
2.12.3 Damage assessment of waste

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LIME2_C2.10-C2.13_2014(1)

Basic policy for calculation of damage factor and assessment of uncertainty

Wada et al. (1996) arranged the environmental impact of final waste disposal from theviewpoint of LCA. According to the result, they mentioned the following as important
items: (1) the environmental burden that accompanies the operation of equipment used for
landfill in a final disposal site; (2) the environmental burden of substances discharged fromwaste buried in a final disposal site; and (3) the environmental burden of space occupation for
final disposal (environmental resources lost due to occupation of a part of the naturalenvironment). Under LIME, a policy was established to deal with the ecosystem impact
related to the alteration of nature and the occupation of space due to the construction of a final
disposal site, which falls under (3). (1) and (2) are items to be grasped by inventory analysis
and assessed in other impact categories (example: the discharge of methane from buried
things (Yamada 2004) was taken into consideration for the impact category of global
warming).
Table 2.12-2 shows the objects of calculation of damage functions under LIME, and Figure
2.12-2 shows the flow of calculation. Under LIME 2, the impact of loss of disposal space
was added to the objects of assessment by the user cost method as in “2.11 Resource
consumption.”
Table 2.12-2: Category endpoints of waste and objects of calculation of damage
functions
Area ofprotection

Category endpoint

Object of calculation of damage
function

Human
health(Impact of substances discharged at the time ofwaste disposal)－

Assessor’s consideration inother impact categories, such as
toxic chemicals

Landfill space(Exhaustible resources)

User cost－

Assessor’s consideration inother impact categoriesNet primary production ofvegetation(Regarding both periods as theperiod of maintenance of landuse)Net primary production ofvegetation－

Assessor’s consideration inother impact categories

Change in composition ofplant speciesExtinction risk of vascularplants(Impact of substances discharged at the time ofwaste disposal)－

Assessor’s consideration
other impact categories

Social assets

Primary
producton

Loss / tightness of
disposal space(Impact of substances discharged at the time ofwaste disposal)
Decline in NPP during constructionof disposal site
Decline in potential NPP during
Terrestrial
waste landfill and until
ecosystem
discontinuance
Decline in potential NPP duringhypothetical recovery period after
discontinuance(Impact of substances discharged at the time ofwaste disposal)
Terrestrial
ecosystem

Biodiversityin With regard to primary production and biodiversity, assessment was made via the impact category ofland use (for details, see Table 2.10-3).
 The impact of substances discharged at the time of waste disposal is illustrated in Table 2.12-1.

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LIME2_C2.10-C2.13_2014

Final disposal of unit amount of waste (landfill weight)
Weight by type of waste  volume conversion factor(2.12.2 (2), 2.12.3 (2))

Type of waste

Final disposal of unit amount of waste (landfill volume)
Volume-area conversion
factor by size of disposal
site

Assuming that soil
remaining fromconstruction andamount of soil that
accompanies supply
of cover soil are
well-balanced

Assuming that coversoil accounts for 20%
of total volume

Conversion factor from final disposal volume
of waste to necessary area (2.12.3 (2))

Area of land use for final disposal of unit amount of waste

Occupation
periodExtinction risk damage
factor per area of final
disposal site
(2.10.3 (3))

NPP damage factor
per land area
(2.10.3 (2))

Damage function(2.12.3 (3), (4))

Increased extinction
risk

Loss of NPP due tooccupation

Damage factor(2.12.3 (5))

Biodiversity

Primary production

El Serafy’s assessment formula(Equation 2.11-7)

Existence distribution bysize of disposal site

Loss of NPP due toalteration

Price for landfill
disposal of industrial
waste
Landfill volume
Annual landfill
volumeNumber of remaining years

Interest rate

Depletion cost of disposal
site(User cost)Social assets

Figure 2.12-2: Calculation flow of damage factor of waste
Shaded parts indicate causes for which the uncertainty of parameters is taken into consideration. Bold
dotted lines indicate causes for which the variability is taken into consideration.

Table 2.12-3 shows possible causes of uncertainty of damage functions (especially, errors andvariability in the estimation of parameters) and how to treat them during the uncertainty
assessment. Among the parameters considered during the uncertainty assessment, thoseincluded in Figure 2.12-2 are indicated by shading and bold dotted lines.
(2)
Arrangement of information on land use that accompanies final disposal of a unit
volume of waste
In the inventory, the final disposal amount of waste seems to be often expressed by notvolume but weight. Under LIME 2, with regard to conversion from volume to weight bytype of waste, after documents other than those referred to when the characterization factor
was set up (see 2.12.2 (2)) (Sakai et al. 1998, Japan Waste Management Association 1999)
were additionally collected and arranged, and a uniform distribution was established by theuse of the minimum and maximum values specified in the documents concerning the same100

LIME2_C2.10-C2.13_2014

type of waste. According to the result, the maximum value is more than twice as large as theminimum value concerning plastic waste. With regard to cement solidified products, molten
slag and soot dust, no probability distribution was established because the uncertainty related
to them is relatively less than that related to other types of waste. If the type of waste is
unknown, the extent of uncertainty was widened by the use of a triangular distribution whose
upper and lower limits are the minimum and maximum values of all types, respectively.
Next, estimation was made about the state of land use that accompanies final disposal of 1 m3
of waste. By reference to the “Annual Report on Waste Disposal Facilities 1999” (Kankyo
Sangyo Shinbun 2001), final disposal sites for general waste in Japan (2,460 facilities) werearranged in order of landfill area, and the characteristics of the top ten facilities were grasped.
With regard to the facilities other than the top ten, 45 facilities were sampled at regularintervals and their characteristics were grasped.
According to the double logarithmic chart that indicates the relation between landfill area andlandfill area ranking, it was observed that about 1,500 higher-ranked facilities follow thepower law, but the sizes of lower-ranked facilities decrease sharply. A similar tendency wasobserved concerning the relation between landfill volume and landfill area ranking (Figure
2.12-3).
Table 2-12-3: Main causes of uncertainty and policies concerning damage functions ofwaste
Main possible causes of uncertainty
Geographical variability of inventory(unknown as to what disposal site relates tothe landfill amount reported in inventory)
Volume-weight conversion by type of waste
Conversion rate of total volume of landfill
disposal sites and disposal volume of waste
into area
Period of land use as final disposal sites

Policies for uncertainty assessment
Carry out assessment on the assumption that there are2,460 disposal sites in Japan and waste is buried in any ofthem with a probability proportional to their total capacity.
Set up width based on differences among values specified
in different documents.
Assess estimated errors in parameters for regression
equation used for estimation under LIME 1 for eachdisposal site. †1
Set up a period based on the number of years of operationof disposal sites for which landfill is completed (taking also
into consideration estimated errors in parameters).
Separately, consider uncertainty during period between
operation and discontinuance. †1
Use the result of examination of “2.10.3 Damage
assessment of land use.” †2
Use the result of examination of “2.10.3 Damage
assessment of land use.” †2
Set up a price based on several companies’ range of price.
Errors in future estimation (smoothing) based on past data.

NPP damage amount per area of final
disposal site
Extinction risk damage amount per area of
final disposal site
Price for landfill of waste
Number of remaining years of landfill
disposal sites (total landfill volume, annual
landfill volume)†1
Because the geographical variability of the parameters is assessed for the damage functions, both thevariability and the estimation errors are assessed.
†2
Parameters whose geographical variability has already been assessed outside of the damage
functions (damage functions for other impact categories)

101Landfill area [m2]Total capacity (m3)

LIME2_C2.10-C2.13_2014

Landfill area ranking

Landfill area ranking

Figure 2.12-3: Relation between landfill area ranking and landfill area (left) and total
capacity (right)

0.70
0.60

Level
ground0.50

Sea level
Mountain

0.40

Converted site area ÷ total capacity [1/m]Landfill area ÷ total capacity [1/m]

Next, according to the value calculated by dividing the landfill area by the total volume(area-volume ratio) – that is, the result of calculation of the landfill area consumed for
disposal of 1 m3 of waste (necessary landfill area), it was found that, the lower the rank inlandfill area, the larger the necessary landfill area. In addition, from the viewpoint oflocation, the necessary landfill area tends to be larger on level ground than in mountains. On
the assumption that the ratio of the landfill area to the site area of the final waste disposal site
is 0.4 in mountains and 0.7 on level ground (both are from Tanaka (2000)) and 1.0 on the sea
surface, the landfill area was converted to the site area, and a distribution of site area
necessary for landfill of 1 m3 of waste (assumed as altered area) was established. Based on
the result, it was deemed that the value calculated by dividing the area of land use in a final
disposal site by the whole landfill capacity did not depend on location (Figure 2.12-4).
1.60

Level
groundSea level

1.20

Mountain

0.80

0.30
0.20

0.40

0.10
0.00

0.00

0

500

1000 1500 2000
Landfill area ranking

2500

3000

0

500

1000

1500

2000

2500

Landfill area ranking

Figure 2.12-4: Relation between landfill area ranking and volume-area ratio The conversion ratio to site area (landfill area ÷ site area) is 1.0 for sea level, 0.7 for level ground, and 0.4 for mountain.

Based on the results above, an estimation equation was formulated concerning the whole
landfill capacity and the converted site area divided by the whole landfill capacity in relation
to landfill area ranking (Table 2.12-4).
On the assumption that the final waste disposal was allocated in proportion to the whole
landfill capacity of each disposal site in Japan under LIME 1, the allocation rate to eachdisposal site was calculated based on the result of Table 2.12-4. Each disposal site’s
converted site area divided by the whole landfill capacity (area conversion rate) was
multiplied by the allocation rate (Figure 2.12-5), and the results were totaled to calculate thenecessary site area for final disposal of 1 m3 of waste in Japan. The necessary site area was

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LIME2_C2.10-C2.13_2014

calculated to be 0.20 m2/m3. *12 Under LIME 2, as described in (1) above, on the assumptionthat it was unclear in what disposal site waste was buried, this allocation rate was assessed as
the probability of occurrence of in what disposal site the waste entered in the inventory as thatto be finally disposed of was buried. Figure 2.12-5 also shows the range of the area
conversion rate corresponding to the probability of occurrence.
Moreover, consideration was given to cover soil, which accounted for about 20% of thequantity of disposal (on the basis of volume).
In addition, under LIME 1, it was assumed that the period of land use by a final waste
disposal site was 30 years, which consisted of 2 years for construction, 18 years forcompletion of landfill, and 10 years for discontinuance. Under LIME 2, with regard to theperiod until the completion of landfill, by reference to the results of the Ministry of theEnvironment’s survey on the actual condition of general waste disposal (the FY1998 version,
which is similar to the FY1999 annual report on facilities referred to under LIME 1), a
distribution of the number of operating years concerning about 200 disposal sites where
landfill was completed and the ending year of landfill was identified was established by sizeof landfill capacity. In addition, with regard to the period until discontinuance, by reference
to Tanaka (2000), a uniform distribution between 5 years (no regulation on COD, T-N) and
15 years (regulations on COD, T-N exist; landfill sites for organic substances) wasestablished.
Table 2.12-4: Estimates of each disposal site’s whole landfill capacity and its converted
site area divided by the whole landfill capacity
Landfill area
Whole landfill capacity
ranking
1-10

11-100101-2000

2001-2460

converted site area ÷ whole landfill
capacity

Values specified in facilities’ annual reports were referred to in detail because of large
impact on the whole.
Regression analysis is conducted fromsampling data (Figure 2.12-3) and volume
was estimated from ranking.
Log (capacity) = α－β (log (ranking)－C)
In this equation, α = 5.372 (SE = 0.063),
β = 0.939 (SE = 0.073), C=2.345

Same as above. α = 3.370 (SE = 0.139),
β =17.964 (SE = 4.428), C=3.350

Application of data in the right of Figure
2.12-4 at equal probability(LIME 1: 0.20)
Application of data in the right of Figure
2.12-4 at equal probability(LIME 1: 0.30)
Application of data in the right of Figure
2.12-4 at equal probability(LIME 1: 0.50)

 Under LIME 2, a normal distribution based on standard errors was applied to the parameters of theregression equation for calculation of whole landfill capacity
 Under LIME 1, the amount calculated by dividing the converted site area by the whole landfill
capacity is almost equal to the median of the distribution of data.

12

With regard to final disposal sites for industrial waste, it was confirmed that distributions of the whole capacity and thearea-volume ratio of the 16 inert-type facilities and the 10 controlled-type facilities in Chiba Prefecture are similar to
those of the final disposal sites for general waste in Japan.

103

Cumulative ratio

Area conversion rate
(Converted site area ÷ whole capacity m2/m3)

Cumulative ratio of whole capacity [%](Total capacity of disposal sites in Japan: 100%)

LIME2_C2.10-C2.13_2014

95% value
5% value
Area conversion rate

80% value
Upper limit value
Lower limit value

Landfill area ranking

Figure 2.12-5: Landfill area ranking, cumulative ratio of whole capacity, and area
conversion rate
The cumulative ratio of the whole capacity of each disposal site is used as the allocation ratio (LIME 1) oras the probability of occurrence in each disposal site corresponded to the inventory (LIME 2).

(3)

Primary production and biodiversity: damage functions of waste

Trans
Occ
The damage function of primary production ( DFNPP = DFNPP+ DFNPP) and the damage functionof biodiversity ( DFEINES ) that accompany the final disposal of a unit amount (1 kg) of waste –that is, the amount of damage – was calculated by the following equation:
Trans
DFNPP
[kgDW/kg] = area of land use [m2]× amount of damage to primary production due to alterationof unit land area [kgDW/m2](2.12-1)

2

Occ[kgDW/kg] = area of land use [m ]DFNPP

× amount of damage to primary production due to maintenanceof unit land area [kgDW/(m2・year)] × number of years ofmaintenance of land use for landfill disposal site [year](2.12-2)

2

DFEINES [EINES/kg] = area of land use [m ]× amount of damage to biodiversity due to alteration of unit
land area [EIENS/m2](2.12-3)

In this equation, based on (2) above, the area of land use can be calculated as follows:
Area of land use [m2] = weight-volume conversion factor
by type of waste [m3/kg] × (1 + cover soil [m3/m3])
× volume-area conversion rate [m2/m3](2.12-4)

The result of the damage assessment for the impact category of land use was used for thedamage amount of land use per unit area. With regard to primary production (2.10.3 (2)),
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LIME2_C2.10-C2.13_2014

because relatively many final disposal sites are located in mountains, and NPP is low as aresult of removal of vegetation from landfill spaces in final disposal sites, a probability
density distribution of the damage factor of alteration “forest  construction site” (Table
2.10-13) is applied to the construction of final disposal sites, while a probability density
distribution of the damage factor of maintenance of “construction sites” (Table 2.10-12) isapplied to the period until discontinuance. A probability density distribution of the damage
factor of construction of “final disposal sites” (Table 2.10-15) is applied to biodiversity(2.10.3 (3)). As an example, Figure 2.12-6 shows the process of calculating the primary
production damage factor of maintenance of land use for final disposal of bottles and ceramic
ware (Equations 2.12-4 and 2.12-2).
(4)

Social assets: damage factor of waste

El Serafy’s equation for calculation of user cost (2.11-7) was applied as in the case of the
impact category of resource consumption (2.11.3 (7)), and user cost per unit landfill amountof waste was calculated as follows:
User cost = {1/(1+r)n}  R(2.12-5)

In this equation, r is the interest rate, n is the remaining capacity of the disposal site, and R is
the final disposal price (per volume).
The number of remaining years is the whole landfill capacity divided by annual landfill
volume and was calculated for the whole Japan by type of waste (general or industrial) as in
the case of the number of remaining years of a disposal site published in the Ministry of theEnvironment’s White Paper (Ministry of the Environment 2007b). As described above, inJapan, waste is roughly divided into general and industrial waste by law. Although some
final waste disposal sites accept both types of waste, permission from final disposal facilities
is basically divided between the two. Therefore, a disposal site’s capacity (number ofremaining years) for general waste is regarded as different from that for industrial waste. On
the other hand, disposal sites for industrial waste are divided into inert-type andcontrolled-type ones, and burying of some types of waste is prohibited in inert-type disposalsites. However, they are not divided herein and landfill capacities were simply totaled,regardless of type of industrial waste or disposal sites. With regard to general waste
especially, although some prefectures have many municipalities with no final disposal sites(Ministry of the Environment 2007b), it is not necessarily transported far. However, this is
not taken into consideration. Values published by the Ministry of the Environment (Ministryof the Environment 2005a, 2005b, 2005c) were referred to for whole landfill capacity andannual landfill volume, and extrapolated widely based on changes until FY2003 to makeassessment as of FY2005.
It can be thought that there is no nationwide standard unit price for disposal of industrial
waste (National Federation of Industrial Waste Management Associations 2003). Herein,
the results of a survey on the final disposal commission in the Kanto region (Kanto Regional
Council; in the case of inert-type landfill sites, 8,000 yen/m3 in the prefecture of the lowest
limit and 15,000 yen/m3 in the prefecture of the highest limit; in the case of controlled-type
landfill sites, 20,000 yen/m3 and 35,000 yen/m3 respectively; excluding public or
public-related ones) (Construction Research Institute 2005) were referred to, and a uniform
distribution was established between 8,000 to 35,000 yen/ m3. Because final disposal sites
for general waste were usually managed by municipalities, there is no market price.
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LIME2_C2.10-C2.13_2014

Bulk density of bottles andceramic ware

Landfill weight of bottles and ceramic ware:
1 kg
Representative
value

Landfill volume [m3]= Landfill weight [kg]  10-3
 (1  1.8 [t/m3])
 (1.2 [m3/m3]= 6.67E-4 [m3]* Reciprocal is weight  volume conversion
factor (characterization factor)

Cumulative probability

Area of land use per landfill volume
Converted site area ÷ whole capacity(variability)

Representative
value

Area of land use [m2]= Landfill volume [m3]  0.2= 6.67E-4  0.2= 1.33E-4 [m2]* Reflecting differences among landfill
disposal sites

Area of land use for landfill of 1 kg of bottles and ceramic
ware [m2/kg]

Number of years of land use as landfill disposal sites

Frequency

Probability in eachsectionPrediction (50,000 times): land
use maintenance period
Representative value
30 years

Amount of activity for land use
maintenance[m2·year]= Area of land use [m2]
 Maintenance period [years]= 1.33E-4 [m2]  30 [years]= 4.00E-3 [m2·year]

Years

Amount of damage to primary production
due to land use maintenance as landfill
disposal sites
Probability ofoccurrence

Representativevalue
1.3 kgDW/(m2·year)

Amount of damage to primary production due
to land use maintenance [kgDW]= Amount of activity for land use maintenance[m2·year]  Amount of damage due tomaintenance per unit area [kgDW/m2·year]= 4.00E-3 [m2·year] 1.3 [kgDW/m2·year]= 5.20E-3 [kgDW]Land use (maintenance: building site) damage
factor (kgDW/m2·year)

Damage function to primary production due to land
use maintenance for landfill of bottles and ceramic
ware [kgDW/kg]

Frequency

Probability in eachsectionPrediction (50,000 times): NPP damage function(bottles/ceramic ware, maintenance)

Figure 2.12-6: Example of process of calculating the damage function of waste (landfillof bottles/ceramic ware, land use maintenance)“Representative value” in the figure is a value shown for illustrating the calculation process andcorresponds to the median, etc. of each parameter.

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LIME2_C2.10-C2.13_2014

However, it can be thought that if construction, maintenance, and other costs are used, thevalue of the resource of “disposal space” cannot be reflected in assessment. Because
disposal sites for general waste seem to have basically the same structure as controlled-type
disposal sites for industrial waste, the same price range as that for controlled-type disposalsites was applied.
A damage factor is basically provided per weight for each type of waste, such as “burnt ash”
and “waste plastics.” Therefore, the weight-volume conversion factor and the probability
density distribution described in 2.12.3 (2) were applied to convert the user cost per volume
into that per type.
(5)

Damage factors of waste

This section shows the result of calculation of the damage factor of waste for each area ofprotection. Under LIME, in addition to specific types, such as “glass and ceramic ware” and“burnt ash,” waste for which information on specific types cannot be gained is called“unknown,” and waste for which landfill volume has been grasped is called “common.”Under LIME 2, the damage factor of the “unknown” type contains uncertainty in theconversion of landfill weight into volume. Because of this, if landfill volume (which isthought to be compressed more than volume at the time of discharge, if waste has large
openings) is known, it is desirable to apply the impact assessment factor for the “common”
type preferentially. On the other hand, if only the landfill weight of general or industrial
waste is known, but the specific type is unknown, it is necessary to apply the “unknown”
type.
a.

Damage factor to primary production

Figure 2.12-7 shows an example cumulative frequency distribution of damage factors toprimary production, and Table 2.12-5 shows example statistical amounts. In addition,
because the rank correlation between causes of uncertainty and damage factors is relatively
similar irrespective of type of waste or final disposal site for the purpose of the constructionof damage functions, Table 2.12-6 shows cases where types of general waste are unknown.
The results of assessment of the uncertainty of damage factors contain geographicalvariability, and the below-described confidence interval indicates the range of estimation ofdamage factors in the case where waste is buried in different disposal sites. Therefore, it iswrong to interpret that, if the confidence interval of the damage factor for “plastics” greatly
overlaps with that for “glass” in the results of assessment of the uncertainty of the damage
factors specified under LIME 2, there is no significant difference in the amount of damage
between the two when they are buried in the “same” final disposal site.
According to Table 2.12-6, main causes of uncertainty are parameters for calculation of areaof land use that accompanies landfill. The variable that has the largest rank correlation
factor is the variable that selects the final disposal site at which the waste included in theinventory is buried. This corresponds to the difference in land area necessary for the samelandfill volume among land disposal sites.
In addition, because specific types of general waste to be buried are unknown under thisclassification, the uncertainty in the conversion of landfill weight in the inventory into volume
was selected as second place. Third place was a parameter related to the calculation ofprimary production damage amount per area. The number of years for recovery of primary107

LIME2_C2.10-C2.13_2014

productivity after discontinuance of a disposal site was selected as the parameter.
Table 2.12-5: Statistical amounts of damage factors of waste landfill (primary
production, biodiversity)
Social assets

Primary production

Biodiversity (extinction risk)

Division

General waste

Industrial waste

Type

CommonCommon Unknown Common

Unit

kgDW/m3 kgDW/kg

kgDW/m3 kgDW/kg EINES/m3 EINES/kg EINES/m3 EINES/kg

No. of trials

50,00050,000

Average

4.48E+01 5.31E-02

MedianUnknown

50,000General waste

Unknown Common

Unknown

50,00050,00050,00050,0004.48E+01 5.42E-02

3.33E-10

3.94E-13

3.33E-10

4.00E-13

2.85E+01 2.98E-02

2.85E+01 2.95E-02

9.18E-11

9.66E-14

9.18E-11

9.54E-14

Standard error

5.39E+01 7.59E-02

5.39E+01 8.15E-02

9.79E-10

1.33E-12

9.79E-10

1.35E-12

Dispersion

2.90E+03 5.77E-03

2.90E+03 6.64E-03

9.59E-19

1.76E-24

9.59E-19

1.82E-24

Skewness

3.0

4.6

3.0

5.3

10.0

13.6

10.0

14.1

Kurtosis

14.1

36.5

14.1

51.6

169.1

313.9

169.1

346.4

Variationcoefficient

1.20

1.43

1.20

1.51

2.94

3.36

2.94

3.38

10% value

6.53E+00 6.75E-03

6.53E+00 6.62E-03

0.00E+00 0.00E+00 0.00E+00 0.00E+00

90% value

9.93E+01 1.19E-01

9.93E+01 1.22E-01

7.50E-10

8.43E-13

7.50E-10

8.57E-13

Averagestandard error

2.41E-01

2.41E-01 3.65E-04

4.38E-12

5.93E-15

4.38E-12

6.04E-15

3.40E-04

50,000Industrial waste

The “unknown type” is applied to cases where landfill weight is known, but the specific type (such as burnt
residue) of general or industrial waste is unknown. The “common” type is preferentially applied to cases
where landfill volume is known. For details, see the main text of 2.12.3 (5).

Table 2.12-6: Example of rank correlation of causes of uncertainty (general waste(unknown type))
Primary production damage factor (kgDW/kg)#

Variable

Biodiversity damage factor (EINES/kg)
Rank# Variablecorrelation

Rank
correlation

1(1)
Probabilityofinventory’s
correspondence to disposal site

0.59

1

(2) Example of assessment of increased
extinction risk#

0.70

2

(1) Weight-volume conversion factor for
general waste (unknown type)

-0.37

2

(1)
Probabilityofinventory’s
correspondence to disposal site

0.34(3) Number of years for recovery of
3 primary productivity after discontinuanceof disposal site

0.27

3

(1) Weight-volume conversion factor for
general waste (unknown type)-0.20(1) Volume-area conversion factor(application to area of about 101 to 2,000)

0.18(1) Volume-area conversion factor
4 (application to area of about 101 to
2,000)

0.10

4

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LIME2_C2.10-C2.13_2014

5(1) Volume-area conversion factor(application to area of about 11 to 100)

0.16

6

(3) NPPp-NPPa (a:building site) (seeEquation 2.10-2)

0.11(1) Intercept of regression equation for
7 conversion of landfill area ranking tovolume (landfill area: about 11 to 100)

0.05

8

(1) Number of years until discontinuanceafter closure of disposal site

0.04

9

(1) Number of operating years of disposalsite (mid-size)

0.04

5

(1) Volume-area conversion factor(application to area of about 11 to 100)

(2) Uncertainty caused by distribution of
6 variability in the number of individualsof Cephalanthera falcata(2) Number of individuals of
7 Cephalanthera falcate at a point of EIA
example(1) Intercept of regression equation for
8 conversion of landfill area ranking tovolume (landfill area: about 11 to 100)(2) Parameter C of regression equation
9 for estimation of number of years until
extinction of species

0.09

-0.03

0.03

0.02

-0.02

 Top nine variables were selected for each factor.
 The factors with (1) are causes of uncertainty in estimation of land area per final disposal amount.
“Probability of inventory’s correspondence to disposal site” is geographical variability that assumes
the disposal site where the final disposal amount specified in the inventory is buried for the purpose of
calculation of area of land alteration and corresponds to difference by size of disposal site (arearanking). “Volume-area conversion factor” indicates a change in examples of disposal sites withsimilar size.
 The causes with (2) are parameters for estimation of biodiversity damage amount per land area.
“Example of assessment of increased extinction risk#” corresponds to difference in examples of thedegree of increased extinction risk per unit area caused by land alteration for construction of a final
disposal site. For details of all the parameters, see Section 2.10.
 The cause with (3) are parameters for estimation of primary production damage amount per land area.
For details, see Section 2.10.

General waste (common) L
General waste (unknown type) kg
Industrial waste (waste plastics) kg
General waste (burnt ash) kg[kgPP/kg, L] Final waste disposal amountFigure 2.12-7: Cumulative probability density distribution of primary production
damage factors
The “unknown type” is applied to cases where landfill weight is known, but the specific type (such as burnt
residue) of general or industrial waste is unknown. The “common” type is preferentially applied to cases
where landfill volume is known. For details, see the main text of 2.12.3 (5).

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LIME2_C2.10-C2.13_2014

Median (10~90% value)

Figure 2.12-8 shows the result of comparison between the damage factors under LIME 1 andthose under LIME 2. Some types of waste relatively changed as a result of a review of theconversion factor from landfill weight to volume. In addition, the weight-volume
conversion factor is the only parameter whose value changes according to type of waste.
The variation coefficients of the damage factors are almost the same irrespective of type ofwaste. In other words, as a damage factor becomes larger, the width of the confidence
interval tends to become larger.

Figure: 2.12-8: Comparison between LIME 1 and LIME 2(primary production damage factor)b.

Damage factors to biodiversity

Figure 2.12-9 shows an example of the cumulative frequency distribution of biodiversity
damage factors, and Table 2.12-5 shows an example of statistical amounts. Table 2.12-6
shows the rank correlation between causes of uncertainty and damage factors as in the case of
primary production.
Unlike the case of primary production, the main cause of uncertainty is a change in theincreased extinction risk per unit area (UAR) that accompanies land alteration for
construction of a final disposal site. As shown in 2.10.3 (3) (Figure 2.10-13), because UAR
greatly differs among examples of construction of a final disposal site, and because all the
final disposal amounts entered in the inventory are regarded as having been buried in a single
disposal site that cannot be identified from among many disposal sites in Japan, it can beunderstood that the impact assessment result of multiplication of the inventory by the damage
factor has a large width.
Figure 2.12-10 shows the result of comparison between the damage factors under LIME 1 andthose under LIME 2. Changes in the trends between LIME 1 and LIME 2 are the same as in
the case of primary production.

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LIME2_C2.10-C2.13_2014

General waste (common) L
General Waste (unknown type) kg
Industrial waste (waste plastics) kg
General waste (burnt ash) kg[kgEINES/kg, L] Final waste disposal amountFigure 2.12-9: Cumulative probability density distribution of damage factors tobiodiversity(10~90% values)

The “unknown type” is applied to cases where landfill weight is known, but the specific type (such as burnt
residue) of general or industrial waste is unknown. The “common” type is preferentially applied to cases
where landfill volume is known. For details, see the main text of 2.12.3 (5).

Figure 2.12-10: Comparison between LIME 1 and LIME 2 (damage factors tobiodiversity)c.

Damage factors to social assets

Table 2.12-7 shows the gained statistical amounts, and Figure 2.12-11 shows the frequency
distribution. As described above, the damage assessment of waste landfill under LIME isbased on damage per volume, which has been converted into damage per weight. Therefore,
if the type of waste to be buried is unknown, because the conversion from weight to volume
has a certain range, the histogram for the damage factor per weight is horizontally wider thanthat for the damage factor per volume.
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LIME2_C2.10-C2.13_2014

Table 2.12-7: Statistical amounts of user costs of waste landfill
Division

General waste

Industrial waste

Type

CommonCommon

Unit

3

Yen/m

No. of trials

Unknown

Unknown

Yen/kg3

Yen/mYen/kg50,00050,00050,00050,000

Average

1.77E+04

2.50E+01

1.55E+04

2.26E+01

Median1.76E+04

2.17E+01

1.52E+04

1.83E+01

Standard error

2.90E+03

1.27E+01

6.51E+03

1.77E+01

Dispersion

8.42E+06

1.61E+02

4.24E+07

3.13E+02

Variation coefficient

0.16

0.51

0.42

0.78

10% value

1.38E+04

1.32E+01

7.21E+03

7.85E+00

90% value

2.16E+04

4.08E+01

2.46E+04

4.08E+01

Average standard error

1.30E+01

5.68E-02

2.91E+01

7.91E-02

General waste (unknown type) kg

General waste (common) L
Industrial waste (common) L

Frequency

Probability

Industrial waste (unknown type) kg

Figure 2.12-11: Histogram of user costs of final waste disposal

The “unknown type” is applied to cases where landfill weight is known, but the specific type (such as
burnt residue) of general or industrial waste is unknown. The “common” type is preferentiallyapplied to cases where landfill volume is known. For details, see the main text of 2.12.3 (5).

Table 2.12-8: Rank correlation of user costs of final waste disposal
Industrial waste (unknown type)
Variable

General waste (unknown type)
Rank
correlation

Weight-volume conversion factor of-0.63
industrial waste (unknown type)
Final disposal price of industrial waste
0.63
Final disposal amount of industrial waste
0.27
in Japan
Remaining capacity of final disposal-0.04
sites for industrial waste in Japan
This table only shows the top four variables.

112

Variable
Weight-volume conversion factor ofindustrial waste (unknown type)
Final disposal price of industrial waste(controlled-type disposal site)
Remaining capacity of final disposalsites for general waste in Japan
Final disposal amount of general waste in
Japan

Rank
correlation

-0.91
0.37
-0.09
0.05

LIME2_C2.10-C2.13_2014

The distribution for industrial waste is wider than that for general waste because, as shown in
the rank correlation (Table 2.12-8), the range of disposal prices was set up by mixing ofinert-type disposal sites and controlled-type ones by disposal prices and because uncertainty
was large in the estimation of the landfill disposal amount in 2005, when the amount was
sharply decreasing.
2.12.4

Procedures for impact assessment of waste

Concrete procedures for characterization and damage assessment of waste are as follows. It
is necessary to note that impact assessment of waste only covers the final disposal amount in
the inventory of the waste species X, Inv (X). In other words, the amount of waste reduced
or made into resources through intermediate treatment after the generation/discharge of waste(after disposal of LCA-assessed products after use) is not included. Of course, if treatment
residue is buried, the amount of the residue is included. For the treatment of constructionwaste soil, which is excluded from the types of waste defined by law, see Column 2.12-1 also.
Column 2.12-1:
Land use that accompanies the disposal of construction waste soil
Although construction waste soil does not fall under the category of waste under the existing
law, it is generated and disposed of in large quantities (Aoyama 1993). In addition,according to the result of an LCIA case study about buildings (Ii et al. 2002), it was found that
construction waste soil can have important influence on LCIA that takes into consideration theimpact on land use.
The purposes and disposal sites of construction waste soil were not necessarily clear in thelatest construction byproduct statistical materials as of the survey. Under LIME, thepercentage distribution of purposes and disposal sites were grasped from actual values in
FY1995 (Committee for the Promotion of Recycling of Construction Byproduct 1997). Of
the purposes, inter-construction use, recycling facilities, raising of farmland, landfill of vacantlots after mining, and cover soil of waste disposal sites were thought to lead to the generationof new land use, and marine landfill, valley landfill, and inland water landfill were connectedto the impact category of land use. However, according to reported cases of landfill ofvacant lots after mining (Sakuma 2002), it is necessary to consider further how to incorporatesuch landfill into the assessment method.
With regard to marine landfill, the ratio between landfill volume and area was calculated to be
0.08 m2/m3 after the collection and arrangement of materials related to the marine landfill
projects about which the Ministry of the Environment issued opinions in the past in view thatthe area of landfill exceeded 50 ha. In addition, with regard to valley and inland water
landfill, the ratio was assumed to be the same as the value for waste disposal sites calculated
in 2.12.3 (2).
Based on the above-described results, the area of land use per 1 m3 of waste soil taken out wascalculated to be 0.05 m2/m3 as the default value if the final disposal method and the amount ofdisposal of generated waste soil are unknown.

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LIME2_C2.10-C2.13_2014

In addition, the landfill disposal standards prohibit the landfill of waste acid and alkali andprovide that waste oil (excluding tar pitch) should be burnt by the use of incinerationequipment. Therefore, if the amount of such waste is included in the amount of disposal in
the inventory, it can be said that retroaction is insufficient for impact assessment. It is also
necessary to note that although decomposed matters (such as organic sludge) require coversoil unless they are burnt to 15% or less by ignition loss, cover soil that accompanies thelandfill of industrial waste is uniformly included in the assessment factors provided by LIME
concerning all types.
With regard to characterization, landfill weights are converted to volumes after landfill
according to type of waste by Equation 2.12-6 to calculate the category index CI from theviewpoint of alteration of nature due to landfill. If volumes after landfill are alreadycalculated during the inventory, it is all right to total them simply.
(2.12-6)
In addition, the index for the endpoint approach – that is, the damage index DI (safe) – can begained from Inv (X) of each type of waste and the damage factor DF (Safe, X) for each area ofprotection Safe. This enables damage assessment of biodiversity, primary production, andsocial assets. With regard to common areas of protection, it is possible to make comparison
and integration with the amounts of damage generated through different impact categories.
DI ( Safe)  DF

Landfill( Safe，X )  Inv ( X )(2.12-7)

X

The factor IFLandfill (X), into which the impact on biodiversity, primary production, and social
assets has been integrated, is used for integration. The single index SI can be gained fromeach waste’s Inv (X) and the integration factor IFLandfill (X). The result can be directlycompared with or added to assessment results in other impact categories.
SI 

 IF
X

Landfill(X)  Inv(X)(2.12-8)

The characterization factor CFLandfill (X) is listed in Appendix A1, the damage factor DFLandfill(Safe, X) is listed in Appendix A2, and the integration factor IFLandfill (X) is listed in Appendix
A3.

114

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EcoBalance 2002, p.367-370
Aoyama S (1993): Problem of Landfill of Massively Generated Waste, Journal of the Japan Society of Waste
Management Experts, 4 (1): 17-22
Akimoto T, Suzuki M (2000): Review of the Life Cycle Impact Assessment Method for Buildings, The FourthInternational Conference on EcoBalance
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Ministry of the Environment (2005a): State of Discharge and Disposal of Industrial Waste (results in FY2003)
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Landfill System and Technologies Research Association of Japan (supervised by Hanashima M, Furuichi T)(2000): Final Disposal Sites in Japan 2000, Kankyo Sangyo Shinbun
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Japan Environmental Management Association for Industry (2003): Collection of Reports on Research andDevelopment of Product Life Cycle Environmental Assessment Technology
Waste Prevention and Recycling Subcommittee, Environment Committee, Industrial Structure Council (2001):
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Fukui H, Awaji N, Ito T (1994): Organic Compounds in Seeping Water in Mixed Collection Waste Landfills,
Journal of the Japan Society of Waste Management Experts, 5 (3): 118-125
Hosoda E (1999): Economics of Goods and Bads, Toyo Keizai Inc.
Yasui I, Sakamura H, Nakazawa K, Okawa T, Chiku K (2000): Impact Analysis of Beverage Containers by theTime Consumption Method, The Fourth International Conference on EcoBalance
Yasuhara A (2000): Chemical Ingredients Included in Seeping Water from Waste Disposal Sites, Journal of theJapan Society of Civil Engineers, 85 (1): 77-80
Yasuhara A et al. (2003): Inorganic Ingredients, Oxygenated Compounds and Nitrogen-Containing compounds
in Seeping Water and Treated Water in Waste Landfill Sites (results of a survey in FY2002), Collection of
Lectures at the 14th Workshop of the Japan Society of Waste Management Experts, p. 1141-1143
Yamada M (2004): Waste Disposal Activities and Greenhouse Gas, Journal of Japan Waste Management
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2.13

Noise (Road Traffic Noise) Although traffic noise – especially, road traffic noise – is important all over Japan, it wasan environmental problem that was not considered to be an impact category (no damage
function was developed) under LIME 1.
 Under LIME 2, the damage function of road traffic noise in Japan was developed and“noise” was added as a new impact category.
2.13.1

What phenomenon is the environmental impact of noise?

(1)

Causal relationship between noise and environmental impact

Noise is generally defined as undesirable sound. Meanwhile, sound is closely connectedwith everyday life as conversation, music, and other means to convey information. This is
one of the causes that makes the noise problem complicated (Yamamoto et al. 1988). The
types of noise covered by this section, such as road noise, aircraft noise, Shinkansen railway
noise, factory noise, and construction noise, are regarded by almost all people as loud andunpleasant at any place (Kabuto 2005). Figure 2.13-1 shows the pathway of theenvironmental impact of noise.
The energy that sound generates per second is expressed as watt (W). The energy of soundthat passes a unit area every second when spreading through the air [W/m2] is called soundintensity. A change in the atmospheric pressure due to sound is called sound pressure, andsound intensity is proportionate to the square of sound pressure.
Activities that causenoise
Traffic(Car, railway,
aircraft, etc.)
Construction
Factory/Workplace
Everyday life

Endpoint impact
Heath impact

Social assets

Primary
production

Biodiversity

Direct impact

Agriculture
production(Livestock)

Plants do nothave hearing
ability

Astonishment,
flight, etc. ofanimals(individuals)(Loss of hearing,
disturbance of
conversation, etc.)

Indirect impact
Generation of noise(Sleep disturbance,
physiologicalimpact, etc.)

Spread of noise(in the air, etc.)(Annoyance, etc.)

Amenity

Impact on animal
individuals (change
in composition of
species)

Overall impact

Figure -2.13-1: Causal relationship between noise and environmental impact

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LIME2_C2.10-C2.13_2014

It is known that human beings’ sensibility (response) to sound intensity is proportionate torelative changes in sound intensity (stimulus) (Weber–Fechner law: ∆E = ∆S/S). Because ofthis, the magnitude of sense to sound is proportionate to the logarithm of sound intensity.
Therefore, the level of sound pressure or intensity is often expressed with a logarithm basedon the threshold of hearing, by the use of the unit of dB (decibel). Sound output also isexpressed by logarithms (acoustic power level, PWL).
Moreover, if the sound pressure level is the same, loudness for people changes if frequency
differs (Figure 2.13-2). Therefore, noise intensity (noise level, LA) is measured after adding
some weighting (usually, the weighting called A-weighting) to the sound pressure level.
Although in the past a noise level was expressed with “phon” or “dB (A),” it is now expressed
uniformly with “dB” under the Measurement Act and the like. Notwithstanding differencesin units, values are the same. Usually, the level of sound pressure is equal to the level ofsound intensity.
Japan’s Third Basic Environmental Plan (Ministry of the Environment 2006), which wasapproved by the Cabinet in April 2006, dealt with traffic noise in a part concerning the current
situation and issues of urban air pollution and states that “the situation of noise damage insurrounding areas due to traffic is generally severe. In areas surrounding highways inparticular, the environmental standards concerning automobile noise have continued to be
hard to achieve. With regard to the Shinkansen railways also, there are many portions where
the environmental standards have still not been satisfied.”Sound pressure level [dB]

New standard
Robinson-Dadson (old standard)
Fletcher-Munson

Frequency [Hz]Figure 2.13-2: Examples of equal loudness level contours (40 phon)• A contour that joins levels of sound intensity so that sensuous loudness can become the sameeven if there are changes in the frequency of pure sound (sound with a single frequency
element) is called an equal-loudness level contour. The dB level that has the same loudness at
the frequency of 1 kHz is expressed with the unit of phon. The above figure (40 phon) shows
contours indicating the same loudness as pure sound with a sound pressure level of 40 dB at thefrequency of 1 kHz.
• Under Japan’s leadership, the international standard was completely revised by a newequal-loudness level contour based on the results of research by an international joint researchgroup that provided data that accounted for 40% of the total (ISO 226: 2003). As a result of

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the research, it became clear that A-weighting (based on Fletcher-Munson’s equal-loudness
contour with 40 phon), which is used for noise level, corresponds well to the new
equal-loudness level contour.
Source: Citation from and summary of the press release by the National Institute of Advanced
Industrial Science and Technology (on October 22, 2003) entitled “Entire Revision of the International
Standard ISO 226 concerning Equal Loudness Contours”(2)

Endpoints of the environmental impact of noise

There are various impacts of noise on human beings. Osada (2001) roughly classified theminto the following three categories: (1) sensual influence, disturbance of conversation, andloss of hearing ability as direct and peculiar impacts; (2) emotional influence, disturbance ofsleep/rest, disturbance of work, and physical influence (circulatory, digestive, and othersystems) as indirect and non-peculiar impacts (impacts of stress not peculiar to noise; and (3)annoyance (general sense of damage) and behavioral reaction as general impacts (Figures
2.13-3 and 2.13-4).
Japan’s environmental standards for noise (1998) set up standard values at equivalent noiselevel (temporal average of noise energy) according to purpose of land use, taking into
consideration the degree of annoyance according to noise level, considering disturbance ofsleep and conversation as endpoints and distinguishing between nighttime and daytime. The
standard values for areas facing roads are different from those for the other areas (generalareas), and notifications about the environmental standards for aircraft noise and Shinkansen
railway noise have been given separately (Table 2.13-1).
Subsequently, the World Health Organization (WHO) internationally published guidelines onnoise (Berglund et al. 1999). To prevent disturbance of nighttime sleep, the guidelines fix
the indoor guideline value at 30 dB and the outdoor guideline value for a bedroom with thewindow opened at 45 dB. During the daytime, it fixes the indoor guideline value at 35 dB
from the viewpoint of disturbance of conversation and fixed the outdoor guideline value forresidential districts at 50 to 55 dB according to the degree of annoyance (although theguidelines show the maximum noise level, only equivalent noise levels are quoted herein inharmony with the environmental standards in Japan).
Cerebrum
Ears
Noise

Loss ofhearing ability(Audible field)
Sensual damage
Disturbance ofhearing(Other parts)
Emotional damage
Disturbance ofsleep/rest
Disturbance of mental
work

Annoyance

Direct impact
Indirect impact
Overall impact(Body)
Physical impact

Figure 2.13-3: How impact is caused by noise
Source: Osada (2001)

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Behavioral
response

LIME2_C2.10-C2.13_2014
Intellectual, emotional,
cognitive response

Sense/perceptionof soundBrain
neocortex

ThalamusAcoustic
pathway

Hypothalamus

Emotional
response Pituitary
gland

Cochlear nerve

Thyroid grandReticular
formation
Pupil

Sound
AuriclePhysicalresponse

Centrifugal
pathways

CardiorespiratoryorgansSex gland

Digestive
organsCentripetal
pathways

Adrenal
gland

Skin

Figure 2.13-4: Pathways of mental and physical impacts of noise
Source: Some words were added to Osada (2001).

Table 2.13-1: Environmental standards for noise in Japan (unit: dB)

Type of area

Time zone

AA (area requiring quiet)
A (exclusively residential area)
B (mainly residential area)†

General area

Area facing road*

Area near highway *

Day

Night

Day

Night

Day

Night

≦50

≦40

≦55

≦45

≦60

≦55

≦65

≦60

≦70

≦65

C (area where houses are mixed≦60
with commerce/industry)≦50†

Time zone: Day = 6 a.m. to 10 p.m.; Night = 10 p.m. to 6 a.m. of the next day* An area facing a road is an area where road traffic noise governs. A highway is a national expressway,
an ordinary national road, a prefectural road, or a section of a municipal road with four or more lanes.
An area near a highway is within 15 m (up to two lanes) or 20 m (two or more lanes) (theDirector-General of the Air Quality Bureau of the Ministry of the Environment’s notification to eachprefectural governor entitled “Revision of the Environmental Standards for Noise” (September 30,
1998, Kantaiki No. 257)).
• These values have been set based on indoor guideline values under the following procedure (Central

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Environment Council 1998): environmental standards in an area falling under A (exclusively
residential) that faces a road (example: nighttime): indoor guideline value for impact on sleep [35 dB(general area), 40 dB (area facing road)] + 10 dB (building soundproof performance with window
opened) + 5 dB (further correction for an exclusively residential area that faces a road) = 55 dB(however, indoor guideline value + 25 dB for a space near a highway). The indoor guideline value for
impact on conversation is 45 dB.
Source: “Environmental Standards for Noise” (the Ministry of the Environment’s Notice No. 64 of
September 30, 1998)

Moreover, the guidelines mention physical health impact, although it is not specified as a
guideline value. For example, the guidelines point out that the impact of long-term exposure
to aircraft or road traffic noise of 65 to 70 dB at a 24-hour equivalent noise level on thecirculatory system has been proved and that although an increase in the risk of suffering
ischemic heart disease is small, the exposure population is large. According to a recent
review (Babisch 2006), although epidemiological research on the health impact of traffic
noise has been carried out mainly in Europe, in Japan also a large-scale study was carried out
around the Kadena and Futenma airfields by Okinawa Prefecture (1999). According to theresults, an increase in the ratio of babies born with a substandard weight, a decline inschoolchildren’s long-term memory, and an increase in the ratio of people with mental or
physical disease (which was found by a Todai Health Index (THT) survey) were detected atsignificant levels.
The auditory pathway from the external auditory canal to the auditory area of the brain cortex
via the auditory nerve is similar among mammals. According to various reports on theimpact of sound on laboratory animals, although in many cases a short-term load was
imposed, the impact of sound on animals tended to depend on the amount of load energy(Honma 1982). Concretely, environmental impacts include not only animals’ astonishment
at sudden noise of construction but also flight from the point of impact, which may influence
foraging and breeding. Forman & Alexander (1998) describes that, with regard to theecological impact of roads and vehicles on a group of individuals, the impact of traffic
disturbance is far greater than that of direct road kill (traffic accidents) and traffic noise would
be the most important factor. They also report that, in forests and prairies near highways, thegrowth density of birds is low and the richness of species has been decreasing, and introduce
oscines (songbirds) sensitive to a considerably low noise level. In addition, it has beenreported that, although, unlike wildlife, livestock cannot escape, cow milking was influenced
in some cases (Mito Construction Office of Japan Highway Public Corporation 2000).
2.13.2

Characterization of noise(1)

Characterization factor of noise under the existing LCA method

With regard to the geographical and temporal characteristics of noise impact, for example, if anoise from a machine in a factory is so loud that it is impossible to make conversation near
the machine, it is quiet around the border of the factory premises, and noise is notaccumulated in the air like carbon dioxide. In addition, from the viewpoint of the amount ofenvironmental load, the “noise level” to be measured under regulations is like the density ofpollutants in the air in the case of air pollution – that is, not the quantity of flow but thequantity of state. Because of this, it is hard to put noise impact in harmony with the flow
accumulation method of LCA.
Table 2.13-2 shows the development of treatment of noise under LCIA so far.
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SETAC’s

LIME2_C2.10-C2.13_2014

early report on LCIA (Fava et al. 1993) described the following points concerning noiseproblems in life cycle impact assessment:• The present LCA cases do not usually include noise data.
• Although noise is one of the important environmental problems, the impact is local.
Moreover, measures are basically taken at each site (such as factory noise).
• It is hard to imagine any index that can significantly indicate all the processes related tolife cycle.
• Traffic noise (which has an area extent) is an exception. If these are possible, theyshould be included in the objects of LCA.
Table 2.13-2: Development of treatment of noise by LCA (examples of research cases)
Year

Case

1992

CML Guide (Heijungs et al. 1992): Sound energy (Pa2s) was recommended forcharacterization.

1993

SETAC LCIA Report (Fava et al. 1993): See the main text.

1999

EPS2000 method (Steen 1999): “Nuisance” was included in health impact and was
defined (it was not WTP of noise itself).

1999, 2002

Müller-Wenk (1999, 2002): They suggested a damage assessment method for road traffic
noise, and published the results of assessment conducted in Switzerland (University
Research Institute Report (1999), Swiss Agency for the Environment (2002)).

2001

CML Guide (Guinée et al. 2002): Pa2s continued to be recommended for characterization.
Using Müller-Wenk (1999) as the starting point, they showed expectations for assessmentof impact on human health and other endpoints.

2002

SETAC 2nd WG on LCIA Report (Udo de Haes et al. 2002): This report mentioned EPS
only briefly.

2003

UNEP/SETAC Life Cycle Initiative “LCIA Definition Study” Report (Jolliet et al. 2003):
Traffic noise was positioned as a midpoint category.

2003

JEPIX method: “EIP/noise km” was presented by the Distance To Target method (traffic
volume and environmental standards) (Miyazaki et al. 2003).

2004

Attention was paid to Müller-Wenk (2002) in a review by Pennington et al. (2004).

2004

EDIP2003 method: Although a report for researchers (Potting&Hauschild 2004) reviewed
noise, guidelines for practical persons (Hauschild&Potting 2005) did not mention it. *EDIP97 mentioned occupational exposure.

2006

Arjen Meijer (2006): Road traffic noise was included in a housing LCA case study(damage assessment).

• Noise is not included in the other LICA method systems not mentioned in this table, such as
Eco-indicator 99, IMPACT 2002+, TRACI, and LIME 1.
• Of the LCIA method systems mentioned in this table, EPS and EDIP 2003 also did not provide any
practically assessable method.

While no suggestion was given of an operational assessment method that can be said to beavailable practically, Müller-Wenk suggested a damage-calculating-type assessment method
in 1999 and 2002. As described in CML Guide (Guinée et al. 2002) and UNEP/SETAC Life
Cycle Initiative’s LCIA Definition Study Report (Jolliet et al. 2003) thereafter, the method
has drawn attention as a method that enables impact assessment of noise under LCA. The
frameworks and some important parameters of the damage function developed under LIME 2
also are based on Müller-Wenk’s research.
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So far, the LCIA method systems that include noise in the impact categories have been
extremely limited in effect, and the number of assessment cases by the systems seems small.
However, some recent LCA case studies have dealt with noise impact. In LCA of houses,
Meijer (2006) assessed noise in addition to air pollutants during research for including roadtraffic damage to human health in the assessment.
(2)

Characterization factor of noise under LIMESound energy has been suggested as an index for characterization. In addition, JEPIX(Miyazaki et al. 2003) has introduced a virtual unit called “noise-kilometer.” This is basedon the Swiss Agency for the Environment’s material and is the total of passenger cars’ annual
mileage, and trucks and other large vehicles’ annual mileage multiplied tenfold on theassumption that the noise of ten passenger cars is equivalent to the noise of one truck. Thisis similar to sound energy in that attention is paid to the energy of generated noise.
Under LIME 2, as described in 2.13.3 below, a damage function for calculation of healthimpact (DALY index as in the case of the other impact categories) was developed based on
vehicle-km in Japan, assuming disturbance of sleep and conversation as endpoints. From theviewpoint of characterization, it is important to give priority to the reduction of uncertainty
through comparison among the impact categories rather than the comparability with the otherimpact categories and to select a midpoint index that can comprehensively and conceptuallyconnect with as many endpoints as possible. In addition, under an LCIA system, it isdesirable that the characterization factor should be consistent with the damage factor.
Based on what has been described above, under LIME 2, the energy of generated noise wasused as the midpoint index, a part of the damage factor was picked out, and the noise energy
to vehicles’ mileage volume [J/vehicle-km] was suggested as the characterization factor.
For example, the characterization factor for small vehicles was calculated. First, the running
time was assumed as the reciprocal of driving speed, and the noise energy generated per 1
vehicle-km was calculated for each road section as follows:
Noise power of a small vehicle [W] × running time [seconds/km]
指定したスタイルは使われていません。.1)(エラー!

Next, the weighted average of the result was calculated by the frequency of appearance in thetarget time zone (daytime, nighttime, or 24-hour) for each road section to find thecharacterization factor. The frequency of appearance was made proportionate to the current
traffic volume [vehicle-km] (of small vehicles, for example) for each road section. Although
the noise power becomes larger with faster running speed, because the running time for unit
distance becomes shorter, the difference in noise energy caused by the difference in runningspeed becomes smaller. Because the model of noise power used herein in particular makes
the noise energy stable irrespective of speed in an unsteady running section, the value does
not change according to the frequency of appearance (on what road vehicles are running).
That is, this characterization factor is hardly reflected in geographical variability (differencein the roadside situation).
Table 2.13-3 shows the calculated characterization factor. Large vehicles’ characterization
factor is about 7 times as high as passenger cars’, a little smaller than 10 times in JEPIX.
The reason for this seems to be a difference between Japan and Switzerland in the passenger
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LIME2_C2.10-C2.13_2014

cars and large vehicles (trucks) assumed in their noise power models.
Because noise power is sound power weighted with the above-described A-weighting throughfrequency, strictly speaking, the characterization factor’s midpoint index is different from thesound energy suggested by CML, but seems consistent with JEPIX’s “noise-kilometer” in thispoint.
To make it closer to the damage factor, it is possible to carry out an assessment by the use of
the increment in the roadside noise level (in the case of air pollution, the increment in thedensity of air pollutants) or the amount of noise energy to which a human body is exposed(intake fraction) as the next step to the generation of noise energy. In the former case, if theratio of large vehicles’ assessment factor to small vehicles’ is calculated, the result is 3.9
times in the daytime and 2.7 times in the night time. Therefore, the difference in vehicle
type becomes a little narrower than in the case of noise energy (4.9 times). On the other
hand, because, unlike the increment in the noise level or the damage factor, thecharacterization factor can hardly express the difference in environmental impact between thedaytime and the nighttime, it is inappropriate to use the characterization factor for comparing
scenarios with different driving time zones. Because of this, information on thecharacterization factor for each time zone was not disclosed.
The used model parameters, such as the equation for calculating noise power and data on
speed and traffic volume, are the same as those for the damage function. For details, see
2.13.3 below.
Table 2.13-3: Characterization factor of vehicle noiseTime zone

Driving category, vehicle type

Characterization factor[J/vehicle km]

Unknown

Vehicle noise (small vehicle)

0.65

Unknown

Vehicle noise (small vehicle; passenger car)0.61

Unknown

Vehicle noise (small vehicle; small freight car)0.80

Unknown

Vehicle noise (large vehicle)

3.21

Unknown

Vehicle noise (large vehicle; midsize car)2.17

Unknown

Vehicle noise (large vehicle; large car)4.23

Unknown

Vehicle noise (unknown type)

1.14• Vehicle type is based on ASJ RTN-Model 2003 (Road Traffic Noise Research Committee, Acoustic
Society of Japan 2004). Concrete definitions are as follows:• A passenger car is a vehicle whose number plates starts with 3, 5, 7, or 4 (van) and whose capacity is
up to 10.
• A small vehicle has a number plate starting with 4 (excluding van) or 6, exceeds 50 cc indisplacement volume, and is up to 4.7 m in length.
• A midsize vehicle is a freight car (usually, two-axle) whose number plate starts with 1 or 2 and whose
length exceeds 4.7 m, excluding a large vehicle, or a midsize bus with a capacity of 11 to 29.
• A large vehicle a freight car (usually, three-axle) with a number plate starting with 1 or 2 (large plate)or 9 or 0 and with a gross vehicle weight of 8 or more tons or a maximum load capacity of 5 or more
tons, a large bus with a capacity of 30 or more, or a large special vehicle.
• Special-purpose vehicles with a classification number starting with 8 are classified according to theactual condition.
• Mini vehicles are included in vehicles with a classification number standing with 4 or 5.
• Two-wheel automobiles and motorized bicycles are included in small freight cars.

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LIME2_C2.10-C2.13_2014

2.13.3

Damage assessment of noise(1)

Basic policy for calculation of damage factor and assessment of uncertainty

Taking application to LCA into consideration, Müller-Wenk (1999, 2002, 2004) showed aframework for the damage function of road traffic noise by DALY and calculated theassessment factor in the case of Switzerland. Under LIME 2, based on this framework, thecalculation procedure was improved and damage function was established based on data andmodels in Japan.
Figure 2.13.5 shows the structure of the damage function. Vehicle-kilometrage by vehicle
type (large or small vehicle) and time zone (daytime or nighttime) was assumed to be theinventory data item that serves as the starting point for the damage function. Although
Müller-Wenk also used such classification into four in total, in the impact assessment
category of noise (environmental assessment, etc.) also, it is usual to divide vehicles into large
and small ones, which differ from each other in noise power level, and make a daytime
assessment and a nighttime assessment separately.
Table 2.13-4: Category endpoints of noise and objects of calculation of damage function
Areaof
Category endpoint
protection

Directspecific
impactHumanhealthIndirect
unspecificimpactOverall
impactAmenity
Impact on
livestock
Impact on
Biodiversity
wildlife
Socialassets†Object of calculation of damage function

Sensual impact

Hearing disturbance

Loss of hearing
abilityEmotional impact
Sleep/rest
disturbanceWork disturbanceThe impact was judged to be very slight as
health damage by DALY index.
Conversation disturbance was selected as arepresentative endpoint.
It was thought that usual road traffic noise
does not have sufficiently serious impact to beassessed by DALY index.
Qualitative assessment is difficult.
Sleep disturbance was selected as arepresentative endpoint.
Qualitative assessment is difficult.
Qualitative assessment is difficult. In a pastcase (de Hollander 1999), although grounds
for calculation were not confirmed, DALY
impact of noise on ischemia may be relatively
small.

Physical impact(Physiologicalimpact)

AnnoyanceBehavioral response

Loss of quietness
Decrease in amountof milking, etc.
Astonishment,
flight,etc.

Qualitative assessment is difficult.

Qualitative assessment is difficult.

Qualitative assessment is difficult.

Secondary impact† With regard to external diseconomy assessment of noise, many cases were assessed by the hedonic
approach. The hedonic approach measures the amount of damage as a decline in the asset value ofland. However, this decline can be thought to be final result of various impacts on human health, etc.
(however, it is possible to think that there is no perceived impact). Because of this, it was not includedin the category endpoints herein, in order to prevent double accounting.

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LIME2_C2.10-C2.13_2014

Inventory (amount of activity)
Traffic volume vehicles/hour(annual average)
Noise emissions [J: joule]

Increase in noise level (∆LAeq)

Increase in the number of
influenced persons

vehicle-kilometrage(day/night) (small vehicles/large vehicles)

Fate analysis (allocation of amount of activity and noise generation/
propagation)• Calculation of increment in roadside noise level by additional 1,000
vehicle-km• Assumption that noise level is annually increased by 1,000 vehicle-km indomestic highway networks• Assessment by average equivalent noise level (LAeq) (energy average)
Exposure analysis (exposure population distribution)• Assumption that (both) roadsides within 50 m from roads receive impact
from the roads.
Impact analysis (dose-response relationship)• Dose-response straight line based on social survey of sleep disturbance andconversation disturbanceDamage analysis (assessment of seriousness and period of health impact)• Seriousness factor as health impact of sleep disturbance and conversation
disturbanceDamage factor [DALY/1,000 vehicle-km]

Figure 2.13-5: Flow of calculating the damage factor of noise (structure of damage
function)
The underlined parts are elements about which uncertainty or variability was taken into account (see Table
2.13-5).

As in the case of Müller-Wenk, nighttime sleep disturbance and daytime conversation
disturbance were selected as the objects of damage assessment (Table 2.13-4). As describedabove, they were taken into consideration when establishing the environmental standards fornoise in Japan. Therefore, they can be thought to be representative endpoints at present.
The damage function can be calculated as follows: first, a certain vehicle-kilometrage(additional traffic volume) is allocated as an increment in the current traffic volume of theroad covered by the impact assessment, to calculate the amount of increase ∆LAeq in theroadside noise level (fate analysis); next, the population (exposure population) is calculated at
the noise level that can be assessed concerning the road (exposure analysis); after that, thenumber of cases where the exposure population receive additional impact due to ∆LAeq is
calculated by the use of the dose-response relationship (impact analysis); and finally, thenumber is converted into DALY to calculate the damage function (damage analysis).
The underlined parts in Figure 2.13-5 are main elements about which probability distribution
was considered during uncertainty analysis. An uncertainty distribution was established for
all the analysis processes (Table 2.13-5).
(2)
Human health: damage function for sleep disturbance and conversation
disturbancea.

Fate analysis

Figure 2.13-6 shows the procedure for fate analysis of the damage function developed under
LIME 2.
The basic concept of Müller-Wenk’s fate analysis procedure is the allocation of marginal127

LIME2_C2.10-C2.13_2014

analysis of an additional vehicle to all road networks according to vehicle-time ratio.
However, this procedure was thought to be problematic.*13 Therefore, in this research,
additional traffic volume was allocated according to vehicle-km in each road section to
calculate ∆LAeq.
Nationally major roads covered by the Road Traffic Census (Column 2.13-1) were selected as
roads for impact assessment. They were divided by type of road (national highway,
national-government-ruled road, etc.) or by type of roadside (DID, other urban area, level area,
mountainous area), and ∆LAeq was calculated for each road category if additional road volume
was allocated among road categories (addition to current traffic volume). ∆LAeq was
calculated for four types of inventory categories according to vehicle type (large or small
vehicle) and by time zone*14 (daytime: 6:00 to 22:00; nighttime: after that).
Table 2.13-5: Main causes of uncertainty and policies concerning the damage function ofnoise
Main possible causes of uncertainty

Policies for uncertainty assessment

Geographical variability (What roadsection had the automobile traffic volume
shown in the inventory in reality?)

Establish a running density distribution according totraffic volume. Consider population density according
to roadside form (assessment by road section and byroadside type) (Figure 2.13-10).

(If the distinction between daytime andnighttime is unclear in the inventory)

Establish a running probability density distribution
during daytime and nighttime according to traffic
volume.

(If the distinction between small and large
vehicles is unclear in the inventory)

Establish a running probability density distribution ofsmall and large vehicles according to traffic volume.

Difference in noise power level amongsmall vehicles and among large vehicles

Establish a probability density distribution ofparameters of the formula for estimation of noise
power level (Figure 2.13-A).

Exposure population

Establish a uniform distribution of differences among
estimation cases as possible width of roadsidepopulation estimate.

Dose-response relationship (D-R) of noise
impactEstablish a normal distribution of parameters, while
regarding the standard error of estimated D-R byregression analysis as standard deviation.

Degree of weight (DW) of noise impact
on healthEstablish a uniform distribution of DW width, which
differs among studies.

13

14

Calculation was made for each road section in Japan by the method used for the 1999 report according to traffic volume,
traveling speed, and road width. The resultant ∆LAeq considerably differed among road networks (four-digit difference,etc.). In addition, with regard to the method used for the 2002 report, even if the total vehicle-kilometrage and the totalroad extension are the same, because ∆LAeq in each road section decreases as the number of road sections for calculation
increases (each assessment section becomes shorter), the assessment method is physically meaningless.
For the purpose of the Road Traffic Census, daytime (weekday) is from 7 a.m. to 7 p.m. (12 hours). On the other hand,for the purpose of this damage function, daytime is from 6:00 to 22:00 (16 hours) as in the case of the environmental
standards. Because of this, the traffic volumes in the daytime of 12 hours and in the nighttime of 12 hours shown in the
Road Traffic Census were converted into the traffic volume in the daytime of 16 hours and in the nighttime of 8 hours.

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Current traffic volume (vehicles/hour)
Road categories (about 30):
• By type of road(National expressway - municipal general road)• By type of roadside(Densely-inhabited district (DID), other urban
area, level area, mountainous area)

Virtual increase in traffic volume
1,000 vehicle-km/year (total for target roads)• By vehicle type (small vehicles/large
vehicles)• By time zone (daytime/nighttime)
Allocation according to vehicle-km/weekday
for each road category

Current traffic volume (vehicles/hour) +additional vehicles/hour
Roadside noise level for each road category (2 cases: current situation vs. whentraffic volume increases)
National total of ∆LAeq in each road section at the time of additional 1,000
vehicle-km/year∆LAeq of road networks covered by assessment (weighted average by roadextension as representative value)

Figure 2.13-6: Fate analysis procedure under LIME 2
Column 2.13-1:
Road Traffic Census
The Road Traffic Census (the official title is the National Road and Street Traffic Census) is
conducted by the Ministry of Land, Infrastructure, Transport and Tourism with thecooperation of the relevant government offices, the prefectures, the ordinance-designatedcities, the expressway companies, etc. The purpose is to grasp the actual situation of thenational roads and road traffic through investigation of road conditions, traffic volume,
traveling speed, starting and ending points of automobiles, purposes of driving, etc. The
results serve as basic data for road planning and management and for establishment of various
road measures. The Road Traffic Census was conducted every three to five years from
FY1928. It has been conducted every five years since FY1980. In the middle year (thethird year) of the five-year period, a supplementary survey is conducted, which consists onlyof a general traffic volume survey.
The total weekday value for each of the above-mentioned road categories specified in the1999 Road Traffic Census was used as current traffic value.* 15 The additional traffic
volume (standard volume for damage factor) was fixed at +1000 vehicle-km/year (in Japan),
as a result of trial calculation at several levels as volume within the extent that ∆LAeq increases
almost linearly according to the additional volume.
Figure 2.13-7 shows an outline of the calculation of the roadside noise level, the corecalculation part of the fate analysis. ASJ RTN-Model 2003 by the Acoustical Society ofJapan (2004) was used for the calculation of the generation and transmission of noise. The
model is widely used for environmental assessment, etc. in Japan (for details, see Column

15

A 12-hour survey or a 24-hour survey is used as the traffic volume survey for the Road Traffic Census. With regard to
the 12-hour survey section, traffic volume for 24 hours was set based on the daytime-nighttime ratio for each of the fourtypes of vehicles in the 24-hour survey section.

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LIME2_C2.10-C2.13_2014

2.13-2).

Herein, the equivalent noise level (LAeq) (at an assumed location of roadside*16)

d: Distancesame before and after increase in traffic volume

Ex.: Target road is straight(Calculation by conversion into 1 lane for both up and down lines)

Simplified analysis model shown in ASJ RTN-Model 2003 (under simple conditions)

L Aeq =L WA － 10 log d－ 10 log V + 10 log N + 10 log (3.6/2T)

Power level of sound source(ASJ RTN-Model 2003 Power Level Model)

National Road Traffic Census(FY1999)

Steady running mode:Small vehicles: LWA = 46.7 + 30 log VLarge vehicles: LWA = 53.2 + 30 log V

By type of road, by type of roadside
V: Velocity (km/h)
N: No. of vehicles per T (seconds)

Unsteady running mode:Small vehicles: LWA = 82.3 + 10 log VLarge vehicles: LWA = 88.8 + 10 log V(Mainly) expressway(Mainly) general road

Figure 2.13-7: Calculation of noise level for each road section during fate analysis

Small vehicle (Swiss, Müller-Wenk)

Large vehicle (Swiss, Müller-Wenk)

Small vehicle (Japan,, this research)

Large

1.E-04vehicle ,(Japan,this∆LAeq (nighttime)

1E-05

1E-06
1E-07
1E-08

1E-09
1E-09

1E-08

1E-07
1E-06
ΔLAeq (daytime)

1E-05

Figure 2.13-8: Distribution of ∆LAeq, increment in noise level when additional traffic
volume of 1,000 vehicle-km is allocated among road sections
16

Actually, even if the traffic conditions (including traffic volume and running speed) are the same, the noise level alongroadsides differs, depending on road width, planting zones, sound insulation walls, etc. In addition, the degree of
decrement differs among road sections according to the existence of roadside buildings, etc. However, the object of thefate analysis for the damage function is not accurate noise level LAeq but the increment in noise level ∆LAeq, which does
not depend on the roadside situation or the distance from the road within the extent that the noise from the road governs.
However, depending on the content of the impact analysis described below, it may be necessary to grasp a detailed
exposure situation by the exposure analysis conducted as the next analysis step.

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LIME2_C2.10-C2.13_2014

was calculated by “the simplified calculation method of LAeq under simple conditions”
concerning traffic volume (N) before and after an increase in traffic volume (current traffic
volume and after allocation of additional traffic volume), and ∆LAeq was calculated marginallyas the difference between the two.
Figure 2.13-8 shows the result of the calculation by the fate analysis (∆LAeq for each roadcategory). As shown in the figure, the result was far smaller than Müller-Wenk’s result in
Switzerland. Because the fate analysis procedure is not the same, they cannot be compared
simply, but the reason can be explained as follows:• The traffic volume of each road in Switzerland is smaller than that in Japan (especiallyduring the nighttime). Because noise energy is proportionate to traffic volume, and theunit of noise level is decibel (logarithm), the noise level increases logarithmically withrespect to traffic volume. Because of this, fate analysis depends on marginal analysis(increase in noise level by an additional increase in traffic volume), and therefore the noiselevel in Switzerland is larger than that in Japan.
• “Large vehicles” in Switzerland are larger than those in Japan (differences between thelarge vehicle (truck) and the small vehicle (passenger car) used at the noise power level bytype of vehicle are larger in Switzerland than in Japan).
Under this calculation method, a slight difference arises in ∆LAeq among road sections becauseof differences in the mix rate of large vehicles. If ∆LAeq is (dLAeq /dN) × ∆N:
LAeq dLAeq
dN N 

Evehiclecategory
N 10
N 10




 E%
N ln10 El arg evehicle  E smallvehicle
N ln10(エ ラー!

指定したスタイルは使われていません。-2)
In this equation, E is the noise energy of all the vehicles in a certain vehicle category, and ∆Ncorresponds to an increase in the number of vehicles in the category. If an additional traffic
volume of 1,000 vehicle-km is allocated according to vehicle-km in each road category, ∆N/N
becomes the same among the road categories, because E% differs. In an example ofcalculation by the use of the following representative values, the average of ∆LAeq (weighted
average weighted by road extension) was calculated for both daytime and the nighttime andfor both large and small vehicles and was used.
In uncertainty analysis, the parameter (difference in noise power level among vehicles) of themodel used for the geographical variability and calculation of automobile traffic volume(inventory) was taken into consideration as a cause of uncertainty. The former is explained
below (as for the latter, see Column 2.13-2).
As described above, ∆LAeq differs among the road categories. In the nighttime especially, adifference of about tenfold arises. In addition, although ∆LAeq is calculated for each roadcategory in this review, if it is calculated more closely for each road section, the variability
may become larger. If ∆LAeq differs among the road categories (sections), the difference inthe distribution of roadside houses, etc. among them becomes a source of uncertainty at thestage of combination with impact analysis. In reality, as shown in Figure 2.13-9, ∆LAeq(inclination of curve) becomes smaller with greater traffic volume (strictly speaking, the two
are not on the same curve, because of differences in the mix rate of large vehicles, speed, etc.).
The figure also shows that traffic volume becomes larger with higher density of roadsidepopulation.
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LIME2_C2.10-C2.13_2014

Daytime roadside noise level [dB]

85
80
DID

75

Other urban areas
Mountain areas

70

Level areas

65
0

10000

20000

30000

40000

50000

60000

70000

Traffic volume (vehicles / daytime of 16 hours)[Simple total of large and small vehicles]
Note) Noise level does not include the effect of sound insulation walls, but is a calculation result under simple conditions.

Figure 2.13-9: Relation between traffic volume and noise level in each road category(image)∆LAeq (daytime small) × exposure population

Assessment of geographicalvariability

Main local road in level area

General national road(government-run) DID

Main local road DID

General prefecturalroad in level area

Frequency

Probability

General national road (government-run) in level area

General prefecturalroad DID
Mountain area/level area[dB persons/1,000
vehicle-km]

Not simple ∆LAeq, but ∆LAeq
multiplied by the adjustment
factor for population density byform of roadside is calculated.

DID (densely-inhabited
district)∆LAeq (nighttime large) × exposure population
National expressway and general national road in mountain area

General national road (government-run) DID

Main local road DID

Mountain area/level area[dB persons/1,000
vehicle-km]

DID (densely-inhabited
district)

Frequency

Probability

General national road (government-run) in level area
National expressway in level area

It is unknown where the vehicles
reported in the inventory run.

[Persons/km2] → use of ratio
DID:
6,682
Other urban areas:
1,325
Level areas:
463
Mountain areas:
17(Source: (Draft) Guidelines forAssessment of Road Investments)* Probability of occurrence ineach road section is proportionate
to traffic volume [vehicle-km]

Figure 2.13-10: Setup of probability of occurrence of ∆LAeq for each road category withconsideration for population density ratio
Because of this, under LIME 2, as shown in Figure 2.13-10, assessment was made of theuncertainty (variability) of the case where the vehicle-km reported in the inventory is runningon any of the road categories. The probability of occurrence for each road category was
assumed to be proportionate to the current traffic volume. To take into considerationdifferences in roadside population density, ∆LAeq was adjusted according to the ratio of
population density by form of roadside.*17 As the population density by form of roadside,17

On the assumption that an annual increase of 1,000 vehicle-km was shown in each road category, the increase was132

LIME2_C2.10-C2.13_2014

the relative ratio of the values shown in the “(Draft) Guidelines for Assessment of Road
Investments” (Review Committee on Guidelines for Assessment of Road Investments
1998)*18 was used.
In other words, for the purpose of calculation by the use of representative values, thekilometrage corresponding to the inventory was “thinly and widely allocated all over Japan,”while, for the purpose of uncertainty assessment, it was modeled as “running to a specificplace.” This can be said to be a basis for justifying the limitation of exposure assessment tothe areas facing the target roads (if the noise covered by the assessment spreads all over Japan,
it is possible to think that the background noise level also increases).
b.

Exposure analysis

Exposure analysis can be defined as the process of finding a distribution of exposure
population by noise level (and changes in the distribution). What information is needed as
an output of exposure analysis can be mutually determined with the contents of the analysis
processes before and after the exposure analysis.
With regard to the calculation of the number of additional impact cases (the number of impact
cases added when the noise level increases by ∆LAeq due to an increase in the traffic volume)that can be found through the integration of fate analysis and exposure analysis to impact
analysis, because the noise level is comparatively low when the response rate for thedose-response relationship fixed by the impact analysis described in c. below becomes 0%, nothreshold level was set. In addition, the dose-response relationship was assumed to be linear.
Therefore, the relationship was calculated by multiplying the population exposed to the noisein the assessed road section by ∆LAeq and the inclination of the D-R straight line.*19
Therefore, the exposure analysis by this damage function only requires the calculation of the
population exposed to the roads covered by the impact assessment. Under LIME 2, theroadside population (13.3 million) was used as the exposure population. The roadsidepopulation was calculated by multiplying the estimated number of roadside houses shown in areference for the Central Environment Council’s report (1998) (4,673,000 houses within 50 mfrom the sides of the roads) (Figure 2.13-11) by the average number of household members

18

19

divided by 365 days and by a factor representing road extension. The result was converted into an increase in traffic
volume during the daytime of 16 hours and the nighttime of 8 hours to calculate ∆LAeq (in the actual calculation, it was
assumed that there as an increase of 0.01 vehicle-km/year in the case of small vehicles and 0.1 vehicle-km/year in thecase of large vehicles, and the result was converted into a value per 1,000 vehicle-km).
To calculate
population-adjusted ∆LAeq (value by which the exposure population in Japan should be multiplied), this was multiplied by
the ratio of roadside population in each road category calculated from the ratio of population density by form of roadside
to total population.
The value is population density calculated from the populations and areas of the categories specified in the FY1997
Annual Report on City Planning (national values). Correspondence between the categories specified in the Census andthose in the Annual Report on City Planning is as follows: densely-inhabited district, urban area, city planning area, andothers.
Because Müller-Wenk set a threshold value for the dose-response relationship, the failure to assume any threshold value
for the impact analysis under LIME 2 becomes a cause for overestimation. On the other hand, Müller-Wenk included
the result of estimation of noise levels for other (general) areas than areas facing (main) roads (including noise sources
other than those from the roads) in the objects of assessment for exposure analysis. Because fate analysis allocates
additional traffic volume only concerning highways, overestimation is highly likely to occur if the population exposed tothe additional traffic volume includes the population of general areas where sound sources not from the highways may
become the main cause (because noise level is logarithmic, it is inappropriate to simply add ∆LAeq to the noise level ofanother sound source). Under LIME 2, the extent of exposure population is both roadsides within 50 m as in the case of
the assessment for the environmental standards (Ministry of the Environment “II. Regional Assessment (Areas Facing
Roads)” 2000), which is conveniently simple.

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LIME2_C2.10-C2.13_2014(2.85) calculated based on the 1995 National Census.

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Column 2.13-2:
ASJ RTN-Model 2003
1) Basic equation for calculation of the noise power level
Under ASJ RTN-Model 2003, the basic equation for a calculation model of noise power level
LWA [dB] is the function of velocity by type of vehicle, V [km/h].
LWA = a + b log10V + C(1)
The parameters of the basic equation indicate the steady state and the unsteady state
concerning 4-type classification and 2-type classification.
Table 2.13-A: Value of the constant (a)Unsteady running
section
Passenger car
82.0
Small
82.3
vehicleSmall freight car
83.2
Midsize vehicle
87.1
Large
88.8
vehicleLarge vehicle
90.0
Source: Acoustical Society of Japan (2004)
Vehicle type

A-weighting noise power level

Unsteady running section

Steadysection
46.7
53.2

running
46.4
47.6
51.5
54.4

Large vehicle, steadyLarge vehicle, unsteady
Small vehicle, steady

Steady running section
Small vehicle, unsteady

Running velocity (km/h)

Figure 2.13-A: Difference in noise power level and establishment of an uncertainty
distribution of parameters
Source: prepared based on Acoustical Society of Japan (2004)

The 2-type classification (large and small vehicles) was applied for the damage function. A
model of steady state and a model of unsteady state were used for expressways and generalroads, respectively. However, even in the case of expressways, if the running velocity islower than the scope of application of stable state in a road section (by type of road and by
type of roadside), unsteady state was applied. C is a correction term representing theproperties of the road surface, the road inclination, etc. Under LIME 2, C was fixed at 0.
The rush-hour average travelling velocity [km/h] for each of the above-mentioned roads
shown in the Road Traffic Census was used as the running velocity.
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In reality, noise power level differs among vehicles. Basically, it seems to become higher as
the size of vehicle is larger. If the size is the same, the level may differ according to type of
vehicle. To assess the uncertainty of this, the range of the parameter a was set up as an
uncertainty distribution by the use of the parameter of the power level of the 4-typeclassification.
2) Analysis formula for equivalent noise level under simple conditions
To calculate the equivalent noise level LAeq,T, in principle, ASJ RTN-Model 2003 performs
energy integration of temporal changes (unit pattern) in the noise of an automobile at theprediction point for the calculation of noise transmission by traffic lane and by type of vehicle.
However, if it is all right to ignore sound diffraction and ground surface effects because theroad is straight, LAeq,T can be calculated analytically. Because the latter calculation method
was used under LIME 2, the simplified calculation method of LAeq,T under simple conditions
shown in the Acoustical Society of Japan’s reference 3 (2004) is excerpted and summarized
below.
LAeq,T can be expressed as the following equation by the use of the single noise exposure level
LAE (NT is the number of automobiles passing at the target time T [s]):N
LAeq ,T  LAE  10log10 T(2)
T
If LWA is calculated when a non-directional point sound source moves at a certain velocity ona straight and infinitely-long road and is substituted in the above equation, the relational
expression between LAeq,T and LWA can be gained as shown in the equation below, which isshown also in Figure 2.13-7 (d is the distance from the position of the traffic lane):
3 .6
LAeq ,T  LWA  10log10 d  10log10V  10log10 N T  10log10(3)2T
If the power level model in 1) above is substituted, the following equations can be derived
concerning unsteady running and steady running, respectively:
3 .6
LAeq ,T    10log10 d  10log10 N T  10log10
(4)
2T
3.6
LAeq ,T    10log10 d  20log10V  10log10 N T  10log10(5)2TThis process includes the following causes of uncertainty in addition to those mentioned infootnote *7.
• The roads about which exposure population is assessed have been limited. If ∆LAeq is
calculated by allocating additional traffic volume among the roads that constitute theassessed road networks, because ∆LAeq becomes larger with a smaller number of roads,
limitations on the number of roads covered by the assessment do not necessarily lead tounderestimation or overestimation. Differences in assessment results depend on
differences between the state of distribution of houses along the roads covered by theassessment and that along the other roads.
• An estimated population along the roads covered by the assessment (Matsuhashi andMoriguchi 2000) pointed out that, although the method used in the Central Environment
Council’s report seems simple and appropriate for the estimation of exposure populationin Japan, the actual state of population distribution in each region may not be reflected
accurately. In addition, the roads covered by the estimation have been limited to the136LIME2_C2.10-C2.13_2014

sections that exist in the use districts.
• The distribution ratio of exposure population (number of houses, etc.) by noise level(however, this does not pertain to the review, because no threshold value has substantiallybeen fixed and a linear dose-response relationship has been used.)• The number of houses was converted into population by the use of the national average
number of household members.
DaytimeNighttime(Ministry of the Environment’s noise map DB system; 1994 Road Traffic Census, within use districts)

Figure 2.13-11: Number of houses exposed to excessive noise level (number of houses by
distance from roadside; unit: 1,000 houses)
Source: prepared based on a reference for the Central Environment Council’s report (1998)

In the uncertainty analysis, the roadside population was estimated with a certain range.
Concretely, because the roadside population differs by about 20% between that based on theabove-mentioned reference to the Central Environment Council’s report (result of estimationby the “Noise Map DB System”) and that based on local governments’ reports on spatialassessment (KCS Corp. 2003), the range was set up as a uniform distribution.
c.

Impact analysis (dose-response relationship)

Under LIME 2, we referred to the “Comprehensive Research on the Assessment of Noise
Impact” (Institute of Noise Control Engineering of Japan 2001, 2003), which was conductedby the Ministry of the Environment from FY2000 as a social survey on noise impact of roadtraffic (Column 2.13-3), rearranged the survey results by type of survey respondent, andestablished a dose-response relationship (occurrence ratio of sleep/conversation disturbance tothe noise level outside a building) necessary for the damage function.
Under LIME 2, a survey was conducted, which included items necessary for the judgment
below in addition to the noise level in houses (outdoors). The number of responses to thesurvey was 534. Responses were regarded as having answered “there was disturbance” ifthey met all the following conditions: “sleep disturbed”*20 and “conversation (telephone)
20

With regard to sleep disturbance, the research report (Institute of Noise Control Engineering of Japan 2001-2003)

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Conversation disturbance [%]disturbed” were given as answers to the question “nuisance from noise (multiple answers
allowed)”; “very loud” was given as an answer as the degree of the noise from a road
(automobiles); and “road” is given as an answer to the question “the loudest annoying noise.”Results were summarized by 5-dB interval of noise level. The ratio of respondents
answering “there is disturbance” was calculated for each interval. With regard to the result,linear dose-response relationships were established by regression analysis as shown in Figures
2.13-12 and 2.13-13.
Ratio of respondents
answering “there is
disturbance”
Regression line

Noise level LAeq (daytime)

Sleep disturbance [%]Figure 2.13-12: Dose-response relationship and basic data (daytime)

Ratio of respondents
answering “there is
disturbance”
Regression line

Noise level LAeq (nighttime)

Figure 2.13-13: Dose-response relationship and basic data (nighttime)
The answer to the question about the degree of noise was limited to “very loud,” because DW(degree of weight) of Müller-Wenk is used for the damage analysis described in d. below andis applied to similar annoyance. Although Müller-Wenk included the frequency of
disturbance as a condition for summary, the frequency is unknown and is not taken into
consideration in the data used for LIME 2.
There is a certain ratio of respondents giving an answer to the effect that there is disturbanceeven at a low noise level,*21 and under LIME 2, no threshold value was substantially fixed.
Because of this, the exposure population covered by the impact assessment may have been

21

describes as follows: “Trends in the ratio of respondents answering “receiving sleep disturbance” differ from year to year.
Throughout the three years, there were differences in response among districts. There is the impact of noise from a sideroad in districts where the noise level of a highway is low.”This may be caused by something other than road traffic noise or by insufficient reflection of unexpected loud noises,
because the assessment index at the noise level is a temporal average (also see footnote 8).

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overestimated, while the inclination of the dose-response relationship may have been
underestimated. The function form of the dose-response relationship will be discussed in
2.13.3 (3) b, together with comparison with the results of other noise impact studies.
In the uncertainty analysis, attention was paid to the inclination of the dose-response line, andan uncertainty distribution was established by a normal distribution by adopting the standard
error in the parameter (inclination) estimated by the above-described regression analysis(daytime: 0.80%/5dB to an inclination of 3.21%/5dB; nighttime: 0.62%/5dB to an inclinationof 3.96%/5dB).
d.

Damage analysis (assessment of loss of life expectancy)

Under LIME, neither age weighting nor time discount were conducted in the assessment of
the damage amount by use of DALY (Itsubo et al. 2005). Moreover, because premature
death is not assessed for the damage function, the damage amount (damage factor) can becalculated by “number of impact cases × DW (degree of weight, disturbance weighting factor)
× disturbance period.”
DALY-based research on burden of disease in the world (Murray 1996) does not show DW
corresponding to noise impact. This is an important issue concerning the calculation of thenoise damage function.
Under LIME 2, DWs of sleep disturbance and conversation disturbance were fixed based on
the results of questionnaires to doctors shown in Müller-Wenk’s report in 2002 (Figure2.13-14). Through the questionnaires, explanation was given to 64 doctors about theconcrete impacts of sleep and conversation disturbance. Moreover, the existing values of
DW (in addition to the list that covers the range between 0 and 1 for various types of healthconditions, with regard to sleep disturbance, for example, similar cases, such as benign
prostate hypertrophy (DW: (1-) 0.96), which is a nighttime symptom, and light to moderateasthma (DW: (1-) 0.97)) were shown and, after that, the doctors were directly asked about thevalue of DW in cases where such impacts were generated at least several times a week or
every day. Valid responses were obtained from 41 doctors. Under LIME 2, F-test (5%)
was carried out by the use of the average value calculated after the exclusion of outlier
responses (Table 2.13-6).
Rejection(conversation)

Rejection(sleep)Sleep
disturbance
Conversation
disturbanceFigure 2.13-14: Questionnaire result (prepared from Müller-Wenk (2002))

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Table 2.13-6: Setting up of DW of conversation and sleep disturbance due to noise
DW/caseConversation disturbanceSleep disturbance
Grounds for setting up

Müller-Wenk
Müller-Wenk (2002)(2002)+F test 5% rejection
0.033
0.028
0.055
0.045
41 doctors responded numerically.

deHollander(1999)
0.01 (annoyance)
0.01
Unknown

As another research case, de Hollader et al. (1999) shows a value, although the grounds forsetting up could not be confirmed. In the uncertainty analysis, a uniform distribution wasestablished, using the value as the lower limit and the above-mentioned average value as theupper limit.
As described above, DW of noise impact depends on overseas assessment cases under LIME
2. In the field of noise impact assessment in Japan, Kabuto (2005) shows the opinion that it
is possible to apply Müller-Wenk’s method to Japan, but expert meetings and consensus
meetings are necessary to discuss to what extent the two impacts should be regarded asburdens of disease. Recently, attention has been drawn to the assessment of health risks ofcardiac and other diseases due to traffic noise as cited in 2.13.1 (2). The reflection of thismay be a future problem.
Column 2.13-3:
Social survey of noise impact (dose-response relationships)
A social survey is an effective method often used for the impact assessment of noise. In
many social surveys, inhabitants’ awareness of noise impact and relative elements areinvestigated and analyzed in combination with the actual measurement and estimation of noiselevels outside houses.
Schuitz (1978) is famous study about the relation between traffic noise and inhabitants’awareness based on the reanalysis of results shown in 11 documents about the ratio of
inhabitants answering “highly annoyed.”
The environmental standards for noise in Japan have been established on the basis of the“indoor guideline values” that should be maintained to prevent impact on sleep andconversation and, at the same time, take into consideration such a dose-response relationshipconcerning annoyance. The Central Environment Council’s report (1998) states that theresults of Schultz (1978) have generally been supported through the process of accumulationof study and after that as scientific knowledge on noise impact and cites the results of studies
by Finegold (1994) and others. Finegold (1994) separately examined aircraft noise,
automobile noise, and railway noise and asserted that response differs according to noise
source.
TNO, an independent research institute in Holland, has created an international data archive ofsocial surveys on noise (Yano 2005). Position papers issued by the European Commission(2002, 2004) concerning the dose-response relationship about annoyance and sleep due tonoise were based on the reports of surveys commissioned to TNO (2000, 2002).

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 Questionnaire survey (interview, questionnaire)• Existence of sleep and conversation disturbance• Degree of disturbance/annoyance (ex. 5 levels)• Frequency of disturbance (how often during aweek)• Other relevant causes and respondents’ properties
 Noise level outside buildings• Field measurement• Estimation
House map

Results of analysis of composed survey results
Source: Ouis (2002)

Figure 2.13-B: Image of noise society survey (left) and dose-response relationships basedon studies after Schultz (1978) (right)
In the field of LCIA, Müller-Wenk (2002) calculated the dose-response relationship based ondata from a noise social survey conducted on about 2,000 people in Switzerland in 1991,
regarding responses of “frequently disturbed (every day or once or more every two or threedays)” and “highly annoyed” as cases where “there is impact,” and applying a linear equation
to daytime noise level sections and nighttime-converted ones.
The “Comprehensive Research on the Assessment of Noise Impact” referred to under LIME 2
has been conducted by the Ministry of the Environment since FY2000 to enrich knowledge on
impact on sleep and annoyance as a material for making appropriate scientific judgment about
environmental standards. The FY2000 to FY2002 versions (commissioned to the Institute of
Noise Control Engineering of Japan) include the following three types of surveys along
highways; (1) survey of domestic and overseas documents; (2) social survey (questionnaire
survey (visiting interview) and noise exposure survey (field measurement, etc.) (FY2000: 342
samples in Osaka Prefecture, Kanagawa Prefecture, Shizuoka Prefecture, Nagoya City, andHyogo Prefecture; FY2001: 344 samples in Kanagawa Prefecture, Chiba City, and Osaka
Prefecture; FY2002: 370 samples in Kanagawa Prefecture, Chiba City, and Nagoya City));and (3) sleep impact survey (measurement of arousal response from body motion).
With regard to (2), research reports (Institute of Noise Control Engineering of Japan 2001,
2003) state that the degree of satisfaction about regional quietness corresponds to the daytime
noise level and that there is a clear dose-response relationship between the sense of being a
victim (annoyed, noisy) due to sound from a road and the daytime noise level.
e.

Result of calculation of damage function

Table 2.13-7 shows the result of calculation of the damage function in addition to the result ofeach of the above-described processes.
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Table 2.13-7: Result of calculation of the damage function(if uncertainty is not considered)
Daytime (6:00 to 22:00)[Conversation
disturbance]

Nighttime (22:00 to
6:00)[Sleep disturbance]

Vehicle type

SmallvehicleLargevehicleSmallvehicleLargevehicle(1) ΔLAeq (μ dB/1,000 vehicle-km)

0.0065

0.023

0.032

0.078

(2) Population exposed to noise from road(3) Inclination of D-R(Increase in the number of impact cases as aresult of an increase in the noise level)(4=1×2×3)
Number of additional impact cases as aresult of increase of 1,000 vehicle-km

13.3 million
0.64%dB
0.00055(5) Weighting of disorder (DW) during ayear(6=4×5) damage factor (DALY/1,000
vehicle-km)

0.00200.79%dB
0.00340.028
1.6E-50.0082
0.045

5.6E-5

1.5E-4

3.7E-4

Because this table indicates the process of calculation, the values differ from representative values for
uncertainty assessment.

(3)

Damage factor of noisea.

Result of uncertainty analysis of damage factor

As described above, Monte Carlo analysis was conducted with consideration for thefollowing: geographical viability as to in what road category the automobile traffic volume
shown in the inventory ran; the width of the estimated roadside population; the standard error
in D-R estimated by regression analysis; and DW, which differs among studies. Details of
the establishment of these distributions are as described concerning each of the elements of
the above-mentioned damage factor.
Figure 2.13-15 shows a probability density distribution of the damage factor in cases where
the distinction between the daytime and the nighttime or between small and large vehicles is
unknown (these probabilities are set from the current traffic volume). If the causes that mostinfluence the viability of the result are examined based on the rank correlation coefficient(Table 2.13-8), the highest influential cause is differences in the traffic and roadside situations
according to road category, the second highest is the difference between the daytime and thenighttime, and the third highest is the difference between small and large vehicles.
Given that it seems difficult to add, to the inventory, geographical information that
distinguishes where vehicles run, it is possible to reconfirm that the division of the damage
factor between the nighttime and the daytime and between small and large vehicles isreasonable for (efficient) improvement of the reliability of noise impact assessment by LCA.
Table 2-13-9 shows the statistical value of the damage factor classified between the nighttime
and the daytime and between small and large vehicles. The table shows that the variation
factor for large vehicles is larger than that for small vehicles. The table also shows that if
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vehicle type and time zone can be distinguished, the variation factor is reduced from 3.2 toaround 1.5 or 2. The main cause of uncertainty by vehicle type and time zone is roadsections where vehicles run (geographical variability).

Frequency

Probability

DF_unkown vehicle type or time zone

DALY/1,000 vehicle-km

Figure 2.13-15: Result of uncertainty analysis (if vehicle type or time zone is unknown)
Table 2.13-8: Noise damage factor(rank correlation in case of unknown vehicle type or time zone)
Variable

Rank correlation
coefficientRoad category (daytime, small)

0.54

Selection of vehicle type and time zone
DW (conversation disturbance)
Road category (daytime, large)
D-R (conversation disturbance)
Road category (nighttime, small)

0.45
0.15
0.14
0.12
0.09

Noise PWL small vehicle (passenger/small freight)

0.05

DW (sleep disturbance)

0.04

Road category (nighttime, large)

0.04

Estimated roadside population

0.03

Noise PWL large vehicle (midsize, large)

0.03

D-R (sleep disturbance)

0.02

Selection between daytime and nighttime (small vehicle)

0.01• Limited to cases where the rank correlation coefficient is 0.01 or more.
• Fluctuation is high among road categories (roadside situations).
• The table shows that the distinctions between the daytime and the nighttime and between
small and large vehicles (selection of vehicle type and time zone) are also important.
• Large daytime traffic volume is reflected in the table.

Table 2.13-9: Statistical value of damage factor (examples)143

LIME2_C2.10-C2.13_2014

Running category

SmallvehicleDaytimeLargevehicleDaytimeSmallvehicleNighttimeLargevehicleNighttime

Unknown
vehicletype/time zone

No. of trials

50,00050,00050,00050,00050,000

Average

1.35E-05

4.53E-05

1.28E-04

2.78E-04

4.64E-05

Median7.14E-06

1.89E-05

7.01E-05

7.88E-05

1.07E-05

Standard deviation

1.73E-05

7.11E-05

1.61E-04

5.25E-04

1.46E-04

Dispersion

2.98E-10

5.06E-09

2.59E-08

2.76E-07

2.13E-08

Variation factor

1.28

1.57

1.26

1.89

3.15

10% value

5.61E-07

6.78E-07

5.88E-06

1.75E-06

7.26E-07

90% value

3.60E-05

1.22E-04

3.26E-04

7.75E-04

9.79E-05

Average standard error

7.72E-08

3.18E-07

7.19E-07

2.35E-06

6.53E-07b.

Comparison with existing studies on the damage factor

The calculation result of the damage factor (representative value) is smaller than those shownin Müller-Wenk (2002) (especially, those for the nighttime and large vehicles). This isbecause of the following reasons: (1) difference in the above-mentioned fate analysis (becauseof marginal assessment, ∆LAeq is smaller in Japan, where traffic volume is relatively large(especially during nighttime); moreover, the power level of large vehicles compared withsmall vehicles is larger in the case of the Swiss model); and (2) difference in the linear
dose-response relationship (D-R) (the inclination is more gradual in this review).
With regard to (2), TNO (2002) compared the result concerning sleep disturbance under
LIME 2 with the D-R for sleep disturbance created based on the archive on the results ofstudies conducted in several countries.
Figure 2.13-16 shows that the inclination set up by Müller-Wenk (2002) is far steeper thanthat for “A little sleep disturbed” by TNO and that the inclination in this review is closely
similar to that for “Highly sleep disturbed” (consistent with the definition used for theapplication of DW to the result of D-R in Müller-Wenk (2002)) (however, it is smaller in thecase of 60 dB or more*22). Therefore, it can be thought that the inclination of the D-R set up
under LIME 2 is not so greatly underestimated.
c.
Verification by comparison with “spatial assessment” (spatially explicit
assessment)
In the fate analysis, the damage function developed under LIME 2 was calculated by using theresult of summation of information on road categories with different traffic conditions inJapan according to type of road and form of roadside and applying the formula for calculating
the noise level under simple conditions, regarding the road as a line at infinity. Moreover,∆LAeq, the resultant increment in the noise level, was assumed to be the national uniform
average, and impact analysis was conducted with regard to the result of multiplication of theincrement by the exposure population in Japan. Although the uncertainty analysis took into
consideration differences in ∆LAeq among the road categories, consideration of roadside22

If a quadratic function is applied to the data used for this review by the least-squares method, parameters are estimated asconvexes not downward but upward. Because of this, it was decided that the dose-response relationship should remain
linear.

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population density has been limited to the differentiation of four types. As described above,the damage function is a very rough estimate of the state of exposure to noise.

Sleep disturbed [%]In Japan, new environmental standards for noise were promulgated in 1998 to change theindex of noise level to equivalent noise level (LAeq), and shifted the local governments’assessment of the environmental standards for noise from so-called “point assessment” to“spatial assessment.” During the spatial assessment, the results of investigation of thelocation and form of a building are usually included in the Geographic Information System(GIS) and are combined with the results of prediction of noise levels (from the noise level on
the side of a road (actually measured value) to the attenuated noise level in the building).*23

Noise level [dB] (nighttime)
Müller-Wenk (frequently disturbed & very annoyed) (Switzerland)
TNO (Highly sleep disturbed) (many countries)
TNO (Sleep disturbed) (many countries)
TNO (A Little Sleep disturbed) (many countries)
This research (Sleep disturbed; often annoyed at sound from road) (recount of Ministry of the Environment’s survey
result)Figure 2.13-16: Comparison of dose-response relationships in sleep disturbance
When LIME was developed, cooperation was obtained from the local governments thatwidely carried out spatial assessment.*24 This made it possible to conduct fate analysis andexposure analysis minutely in the districts covered by the spatial assessment and compare theresults with those of the assessment based on rough count like the damage function for LIME.
Although the results are not described in detail herein due to space limitations, if the traffic
volume of large vehicles increases in the target area, the number of cases where additional
impact occurs (damage factor) is smaller in the case of spatial assessment. On the other
hand, the contrary result was gained in the case of small vehicles. When the cause was
examined, the following two facts were found:
1) Gap due to fate analysis: It was found that the integration of road sections influences the23
24

For example, see the special feature in “Noise Control,” 29 (2), the April 2005 issue.
The borrowed data do not include noise measurement results (actual measurement values). The results of the review
described below are mere results of estimation by model calculation based on traffic volume. Because of this, theresults of the estimation are inconsistent with the results of assessment of the local governments’ environmental
standards.

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result. That is, the mix rate of large vehicles through recalculation of traffic volume on avehicle-km basis for the preservation of traffic volume before and after the integration, q, is
not the same as the q calculated by weighted average on a distance basis. However, from theviewpoint of fate analysis, which requires overlapping with the distribution of roadsideresidences, the environmental impact result does not change before and after the integration in
the case where q is calculated by weighted average on a distance basis. To what extent sucha gap occurs depends on the strength of the correlation between the traffic volume in eachroad section in the area covered by the assessment and the mix rate of large vehicles.
The Road Traffic Census was used to analyze the relation between the traffic volume in eachroad section and the mix rate of large vehicles in the districts covered by the spatialassessment and in Japan. According to the result, in the districts covered by the spatialassessment, the mix rate of large vehicles tends to be higher with higher traffic volume in thecase of a long general road. In Japan, it does not necessary tend to be higher in the case of along general national or regional road. This seems to stem from distortion due to the totaling
being smaller than in the districts covered by the spatial assessment.
2) Gap due to exposure analysis: To examine the cause of a gap due to exposure analysis,
clarification was made about the relation of the extension of a road section and the number ofroadside houses with the mix rate of large vehicles in the districts covered by the spatialassessment. According to the result, in the districts covered by the assessment, the mix rateof large vehicles is smaller from the viewpoint of the number of exposed houses. The reason
for this seems to be that not the roadside average but the housing average is at least a cause
for a small increment in the noise level ∆LAeq. However, this has not been verified at thenational level.
2.13.4

Procedure for the impact assessment of noise(1)
Method for the assessment of impact on products, and points that require
attentiona.

Procedure for LCIA of general products, and points that require attention

If various environmental impacts were integrally assessed concerning general products, etc., it
is expected that the application of the damage factor of noise shown in 2.13.3 and theintegration factor based on them will improve the underestimation of the environmental
impacts during the stage of transportation in particular. However, because the damage
function is used only for the assessment of automobile traffic, attention is necessary if modal
shift or the like is included in the comparison among the objects of assessment.
It cannot be expected that ordinary inventory analysis will include generated noise energy as adata category. Therefore, LIME 2 shows the impact assessment factor per vehicle-km by
type of automobile and by time zone for running. Because running distance is notenvironmental emission, it can be thought that it is often not included in the final result of theinventory analysis. However, with regard to the existing LCA software, for example, thetransportation means should be selected and the loading ratio and the transportation distanceshould be inputted. If estimation is made about such existing information, it is possible tograsp vehicle-km by transportation means.*25
25

However, if vehicle-km is presented in the result of the inventory analysis together with emissions of greenhouse gas andemissions of air pollutants, it may become difficult to know whether emissions of greenhouse gas, etc. due to automobile146

LIME2_C2.10-C2.13_2014

The calculation procedure for impact assessment is as shown in the equation below. In thisequation, vehicle_type is type of vehicle (running category) and time is time zone for running(daytime (6:00 to 22:00), nighttime (22:00 to 6:00), or unknown). Inv is vehicle-km by typeof vehicle and by time zone. However, the characterization factor is the same irrespective oftime zone. With regard of type of vehicle, the 2-type classification (small and large
vehicles) or the 4-type classification (or unknown) is applied to the characterization factor,
while the 2-type classification (or unknown) is applied to the damage factor and theintegration factor. Details of the definition of each type are referred in the notes to Table
2.13-3. In the case of ordinary freight transportation, if the 2-type classification is adopted,
the damage factor for large vehicles is basically applied, excluding the application of thedamage factor for small vehicles to small freight vehicles. From the viewpoint of damage
assessment, it is more important to grasp difference in time zone for running than to grasp
vehicle types as shown in (3).
Characterization:
CI Noise   vehicle _ type CF Noise ( vehicle _ type )  Inv ( vehicle _ type )(エラー!

指定したスタイルは使われていません。-3)
Damage assessment:
DI DALY  vehicle _ type ,time DF Noise ( vehicle _ type, time )  Inv ( vehicle _ type, time ) ( エ ラー! 指定したスタイルは使われていません。-4)Integration:
IF  vehicle _ type ,time IF Noise ( vehicle _ type, time )  Inv ( vehicle _ type, time )(エラー!
指定したスタイルは使われていません。-5)b.
Adaptability to parts of automobiles, automobile parts, etc. (assessment ofdifference in quietness)
For the purpose of the damage function, vehicle-km is assumed to be the unit of the inventory,
and differences in the properties of vehicles have been defined according to roughclassification, such as large and small vehicles. Because of this, the procedure described in a.
above can be used for including noise in the LCA of automobiles, also taking into
consideration other impact categories, but cannot be applied to the assessment of difference inquietness.
Logically, if the noise power level (expression of relation with velocity during steady andunsteady running) of the specific vehicle type to be assessed can be used and some damage
functions can be rearranged, the damage factor of the vehicle type can be calculated.
If the noise power level increases by 10 dB (n dB), for example, the increment in the noise
running are included in the above-mentioned value.
point.

Therefore, it seems desirable to specify the way of dealing with this147

LIME2_C2.10-C2.13_2014

level (therefore, the damage factor also) increases tenfold (10n/10 times). If this relation isused, it seems possible to reflect the improvement of quietness in the assessment in the formof by how many dB the noise power level is reduced from before.
To improve the applicability to such purposes of use, it is necessary to express a damage
function as the product of the part used for the calculation of the noise energy amount
generated from vehicle-km and the part used for the calculation of the damage amount fromthe noise energy amount (Ii et al. 2009). The characterization factor under LIME 2 falls
under the former. Because this arrangement can make it possible to express the inventory by
the unit of not vehicle-km but noise energy amount, it is advantageous from the viewpoint ofconformity with the definition of the basic flows – that is, the input and output flows of
substances and energy.
(2)

Relation with other impact assessment

Because the damage function has been developed to be applied to the LCIA of products, even
if road traffic noise is assessed, it cannot be used for the LCIA of buildings (that receive
noise) and roads. However, it may be possible to use some elements of the damage function.
a.

Buildings (assessment from the viewpoint of noise receivers)

The assessment of buildings from the viewpoint of the noise receiving side may include not
only differences in location conditions but also sound insulation and other conditions related
to noise. However, the damage function has been formulated from the viewpoint of thenoise-generating side and therefore cannot be applied as is. Meijer et al. (2006) is a casestudy that incorporated noise damage in the LCIA of buildings.
b.

Roads

Because the damage factor under LIME 2 indicates the national average damage, it isinappropriate to apply it to the LCIA of the construction of a specific road. Therefore, it
seems necessary to conduct fate and exposure analysis in the road network to be assessed.
When government-subsidized public works are carried out in Japan, cost-benefit analysis isrequired. As described in the “(Draft) Guidelines for Assessment of Road Investments
Volume 1” (Review Committee on Guidelines for Assessment of Road Investment 1998), a
proposal has already been made about the method for assessment of noise impact for the
purpose of the cost-benefit analysis of road construction. The noise impact assessment
described in the (Draft) Guidelines is qualitatively based on the economic assessment method(hedonic method) concerning impact assessment and damage assessment. On the other hand,
although fate and exposure analysis is conducted based on changes in the traffic situation of
the road network before and after road construction, the basic concept seems similar to that ofthe damage function.

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