JLCA NEWS ENGLISH EDITION SEP.2015

No.19

JLCA
NEWS LETTER

LIME2

Life-cycle Impact assessment Method based on Endpoint modeling

Chapter 3 – Integration of Environmental Impacts

Life-Cycle Assessment Society of Japan

LIME2
Life-cycle Impact assessment Method
based on Endpoint modeling

Chapter III
Integration of Environmental Impacts

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)

LIME2_C3- Integration of Environmental Impacts

Chapter III

Integration of Environmental Impacts
Contents
3.1 Characteristics of and main approaches to the integration of environmental impacts ........ 1
3.1.1

Usefulness of the integration of environmental impacts ....................................... 1

3.1.2

Main methods for the integration of environmental impacts ................................ 2

3.1.3

Problems in the integration of environmental impacts ......................................... 7

3.1.4

Important issues concerning research on integration ............................................ 8

3.1.5

Purpose of research in LIME 2 ............................................................................. 9

3.2 Environmental economic assessment and the conjoint analysis ....................................... 11
3.2.1

Characteristics of the main environmental economic assessment methods ........ 11

3.2.2

Method for environmental assessment using conjoint analysis .......................... 15

3.2.3

Question format for conjoint analysis ................................................................. 17

3.2.4

Field survey using conjoint analysis ................................................................... 20

3.2.5

Analysis of the questionnaire results .................................................................. 22

3.2.6

Uncertainty of the preference strength using the random parameter logit model ...
............................................................................................................................. 26

3.3 Integration of environmental impacts by conjoint analysis .............................................. 28
3.3.1

Adopted question format and survey method ..................................................... 28

3.3.2

Survey methods ................................................................................................... 29

3.3.3

Outline of the sampling ....................................................................................... 31

3.3.4

Sampling procedure ............................................................................................ 32

3.3.5

Preliminary survey (calculation of normalization values) .................................. 33

3.3.6

Preparation of the questionnaire ......................................................................... 36

3.3.7

Door-to-door interview survey............................................................................ 43

3.3.8

Calculation results ............................................................................................... 44

3.3.9

Uncertainty analysis of integration factors ......................................................... 50

3.3.10 Integration procedure .......................................................................................... 56
3.4 Conclusion......................................................................................................................... 58

LIME2_C3- Integration of Environmental Impacts

Chapter III
Integration of Environmental Impacts
3.1 Characteristics of and main approaches to the integration ofenvironmental impacts
Main changes for and characteristics of LIME 2
 Random sampling was adopted to carry out a lower-biased survey.
 Weighting factors representative of the Japanese people’s environmental views were gained
through a nationwide door-to-door survey covering 1,000 people.
 It was verified that the weighting factors are statistically significant.
 Differences among individuals in terms of their environmental values were quantified by
the random parameter logit model (RPL).
 The integration factors were calculated using weighting factors that reflect the above
description.
 The representative values of the integration factors were renewed and the amount ofstatistics was calculated. This made it possible to carry out an uncertainty analysis of the
LCIA results during the interpretation of an LCA case assessment.
3.1.1

Usefulness of the integration of environmental impacts
There are various environmental problems around us, including the following: regional
problems, such as air pollution and water pollution, which have been regarded asproblems in Japan since the high-growth era; global problems, such as global warming
and ozone layer destruction; and the problem of resources depletion, which may occur
in the future due to resources consumption, which is the fundamental factor behind theabove-mentioned problems. When products and systems are planned in order toimprove the environment, usually one or several of these environmental problems are
identified and attempts are made to reduce their effects. However, even if a product has
an enormous effect on an environmental problem, the product may worsen anotherenvironmental problem.
LIME makes it possible to obtain the results of characterization and damage assessment
at the stage preceding the integration. The results of characterization can be collected
for each category indicator, and the results of damage assessment can be collected foreach endpoint. Because the results of such a calculation can be obtained for several
items, if a trade-off relationship occurs, the comprehensive judgment is left toresearchers and other persons concerned. If the integrated environmental impacts can be
expressed by a single index after the comparison and measurement of various
environmental problems, it is possible to devise measures for reducing such1

LIME2_C3- Integration of Environmental Impacts

environmental impacts efficiently in an easy-to-understand way.
The use of LCA results is divided into internal use and external use. In the case ofinternal use, those who receive reports on LCA results are expected to be productdevelopers, supervisors, and decision makers. In the case of external use, general
consumers and interested parties are expected to receive such reports. To prevent a
company from unfairly taking advantage of the interests of other companies, ISO 14044(2000) prohibits the imposition of some limits on the use of integration (comparison of
the results of integration between the company’s product and the rival companies’
products and external announcement of the results (which is called comparative
assertion)) (see the next section). On the other hand, there is no limitation on uses otherthan for comparative assertion, such as informing the interested persons within thecompany of the results of integration (which falls under internal use) and externally
announcing the results of a comparison between the company’s products by integration.
Such integration is allowed under ISO because integration that does not cause a
trade-off relationship has been recognized as being easy for general consumers with nospecial environmental knowledge to interpret the results and is excellent as acommunication tool. Recently, integration methods have been highly evaluated as ameans of providing information to consumers. For example, the integration results havebeen published in environmental reports, and environmental accounting has been used
for other assessment tools.
In summary, the integration of environmental impacts seems to have the following
advantages:
1) Because the results are expressed in a single index, no trade-off relationship occurs.
2) Because interpretation is easy, it is useful as a means of communicating
information through environmental reports.
3) It is highly applicable to other environmental assessment tools, such asenvironmental accounting and environmental efficiency.
3.1.2

Main methods for the integration of environmental impacts
Paying attention to the advantages of integration methods as described in the preceding
section, various types of research and development have been carried out so far. In
Chapter I, Section 1.2, the theme-oriented methods and the damage-oriented methods
were identified as the main integration methods for LCIA and a comparison was madebetween them. In this section, integration methods will be explained with the addition ofsubstance comparison methods. Table 3.1-1 summarizes the characteristics of eachapproach.
Under the substance comparison method, a weighting is applied to each of thesubstances of concern. MIPS (Schmidt-Bleek 1993), CED (VDI-Richtlinien 1997),
Ecological Footprint (Wackernagel 1996), Eco-scarcity (Müller-Wenk 1994, and JEPIX(Miyazaki 2003) come under this type of method. Substance comparison methods canbe roughly divided into alternative methods and distance-to-target (DtT) methods.

2

LIME2_C3- Integration of Environmental Impacts

Table
3.1-1: Classificationand characteristics
of main integration
methodssoproposedso farTable 3.1-1:
Classificationand characteristics
of main integration
methods proposedfar

Substancecomparison
methodProcedure until
integration

Main approach

Main method

InventorySingle index

Alternative
methods,(Resource
consumption,energyconsumption,area of usedland), DtT
methods

MIPS (Schmidt-Breek
1993)
CED (VDI-Richtlinien
1997)
Eco-scarcity (MüllerWenk 1994)
Ecological Footprint(Wackernagel 1996)
JEPIX (Miyazaki 2003)• No consideration isto the actual• Assessment method is simple. given
environmental impact.
• Development of the method is • Highly arbitrary andrelatively easy.
manipulable (DtT method).
• The concept is easy to• Natural scientificunderstand.
knowledge on• The meaning of theenvironmental impacts is
assessment results is easy to
abstracted.
understand (alternative index). • Not according to ISO
standards.

Eco-indicator’95(Goedkoop 1995)
Nagata (1994)
Itsubo (1997)
Matsuno (1998)
Yasui (1997)
Lee (1998)• No consideration is
given to the actual
environmental impact.
• Highly consistent with ISO
standards.
• Natural scientific• Concept is relatively easy to knowledge is not fullyreflected.
understand.
• Development of the method is • Lack of transparency of
the weighting.
relatively easy.
• Little information for theweighting.

Themeoriented
methodInventoryImpact categories(Characterization) DtT methods
Panel methods

Normalization
Single index

Damageoriented
methodInventoryImpact categories
Endpoints
Single index

Eco-indicator’99
Panel methods (Goedkoop 2000)
Economic
EPS (Steen 2000)
assessment
ExternE (EC 1998)
methods
LIME

Advantage• Assessment method is highly
transparent.
• A distinction can be madebetween the field of naturalsciences and the field of social
sciences.
• Natural scientific knowledge
is incorporated as much as
possible.
• The number of items forweighting can be minimized.

Problem/issues• A great effort is required
for the development of theassessment method.
• Assessed substances arelimited.
• Difficult to understandthe details of theassessment method.

Alternative methods are based on the assumption that it is difficult to assess
environmental impacts in reality and the assumption that energy consumption and thetotal volume of used substances indirectly expresses environmental impacts. Under
MIPS, the alternative index to environmental impacts is the total volume of raw
materials used throughout the lifecycle of the product in question. Under CED, the total
volume of the energy uses is adopted as an alternative index to environmental impacts.
Under the Ecological Footprint method, the area of land required for dealing with theenvironmental impacts is expressed as an alternative index and was used for assessing
the sustainability of modern society.
The Eco-scarcity method was developed by Müller-Wenk as a method for theassessment of corporate eco-balance. This method comes under the category of the DtT
method, whereby a comparison is made between the current level and the predeterminedtarget level for each substance and the greater the difference between them means agreater environmental impact. This approach has been widely adopted for theme
oriented methods (see Reference Column 3.1-1). Because the current levels (such as thelevel of the environmental density of a substance) and the target levels (such as the levelof environmental standards) are assumed to differ from place to place, European
countries have developed their versions of the Eco-scarcity method based on
environmental standards. JEPIX is a Japanese version of the Eco-scarcity method.

3

LIME2_C3- Integration of Environmental Impacts

The substance comparison methods have the following advantages: assessment results(alternative indexes) are easy to understand; the concept is simple and easy tounderstand; and it is easy to develop integration factors. On the other hand, they havethe following problems: no analysis or assessment is carried out concerning actual
environmental impacts; this makes it impossible to verify the accuracy of the results ofenvironmental impacts; the methods are not in accordance with ISO standards(characterization and other essential processes are not carried out); and natural scientificknowledge on the rise in temperatures due to warming, health impacts, and otheraspects is often abstracted from the assessment. Due to these problems, they are notmuch used for case studies of the LCIA. However, they can be used to set up standards
for the environment performance of companies.
Under themeoriented methods, the potential amount of environmental impact isassessed for each environmental problem, such as global warming, and single indexes
are gained through weighting among the environmental problems. Such methods
include Eco-indicator 95 (Goedkoop 1995), EDIP (Hauschild 1997), and thosedeveloped by Huppes (1997), Walz (1997), Lindeijer (1997), Nagata (1995), Itsubo(2000b), Matsuno (1998), and Yasui (1998). These methods can be roughly classified
into panel methods and DtT methods.
Under the panel methods, the degree of importance of each environmental problem isestimated from samples or by an expert panel to gain a weighting factor for the impact
categories. Nagata directly questions a specific group of respondents (such as students,
industrial associations, and LCA-related persons) about the degree of importance of
impact categories. Yasui obtains the weighting factors by questioning respondents about
the grace period until a crisis situation and the degree of seriousness of the impact
categories. Huppes calculated weighting factors among impact categories throughdiscussions by a panel of policy-related persons. Waltz and Lindeijer calculated it
through discussions by a panel of environmental experts.
Eco-indicator 95, EDIP, Matsuno, and Itsubo adopted DtT methods (see Column 3.1-1).
Although the basic concept of weighting is the same as in the case of DtT methods ofthe substance comparison type, these types of DtT methods are different from thesubstance comparison type in that the weighting factors are developed among
environmental problems (impact categories) instead of among substances. To make acomparison among impact categories, non-dimensionalization is made throughnormalization before weighting. Equation 3.1-1 is a calculation equation for impact
assessment based on a general DtT method.
SI  CI Impact  NV

ImpactImpact CI Impact
NV Impact Impact Impact  NV Impact  WT Impact 

(3.1.1)In this equation, SI is a single index (non-dimensional). CIimpact, NVimpact, Timpact, andWimpact are the characterization results, the normal value, the target value, and theweighting factor in an impact category for the respective Impact. The normal value isused for not only for normalization, but also the calculation of the weighting factor as
the actual value.
Because, under a theme oriented method, integration is based on the results ofcharacterization, consistency with international standards is high. Moreover, this type of4

LIME2_C3- Integration of Environmental Impacts

method has the following advantages: the concept of integration is easy to understandand it is relatively easy to develop a weighting factor. However it has the following
problems: the number of items to be compared is large (ten or more items); a heavy
burden is placed on the respondents (panel method); the weighting factors gained fromquestionnaire results are not examined in terms of statistical significance (panel
method); there is little information for determining the weighting; and the transparencyof the weighting is low (for details, see Section 1.2).
Damage-oriented methods compare and integrate the damage expected to be given to an
endpoint by the environmental impact (for details, see Section 1.2). LIME belongs tothis type of method as with Eco-indicator 99 (Goedkoop 1999), EPS (Steen 1999), andExtern E (EC 1998). These methods can be roughly classified into panel methods andeconomic assessment methods.
Under a panel method, experts and general consumers assess environmental impacts
through questionnaires and group discussions. Under Eco-indicator 99, an integration
factor was gained from a weighting derived from experts through a comparison among
the three predefined areas of protection (human health, soundness of ecosystems, andresources). This type of method is characterized by setting a weighting for eachenvironmental concept (hierarchism, egalitarianism, and individualism). On the other
hand, because the number of the samples is small, it is impossible to ensure therepresentativeness of the research results.
Economic assessment methods express environmental impact using the monetaryamount. Many discussions have been held concerning the environmental impact for the
purpose of environmental economics. Under this type of method, knowledge gained inthis field is used for LCIA. Environmental economics theories regarding environmental
assessment will be described in Section 3.2. Under EPS and Extern E, the willingness topay gained from the contingent valuation method (CVM) or the like is referred to for
the integration of LCIA. Because assessment results are expressed in monetary amounts,
they are easy to understand and can be used for cost effectiveness analysis. Therefore,
they can be said to be an excellent indicator in its application. On the other hand, theeconomic assessment of health losses and ecosystems decline as environmental impacts
are still under development. It has been pointed out that the economic assessment ofhealth has ethical problems.
The advantages of damage-oriented methods include the following: making it possible
to distinguish special categories in the parts based on natural scientific knowledge (until
damage assessment of endpoint) with those in the parts based on social scientificanalysis (from an endpoint to a single index); improving transparency throughclarification of additional assessment items (types of diseases and species of living
things); and reduction in the respondents’ burden due to the small number ofcomparison items (see Section 1.2). On the other hand, research has still not been
sufficiently developed to fully assess endpoint damage, which serves as a precondition
for integration, thus there are the following problems: it takes considerable effort todevelop an assessment method; and the assessable range (of substances and endpoints)
may be limited. Moreover, because the assessment method is complicated, it takes a lotof time for the users to understand the details of the methodology.

5

LIME2_C3- Integration of Environmental Impacts

Column 3.1-1:
Distance to Target methods
Distance to Target (DtT) methods are based on the assumption that the difference between thetarget value and the actual value shows the environmental impact. In the second half of the
1990s, these methods drew considerable attention and many methods were proposed. Figure
3.1-A shows the concept of weighting under the DtT methods. The greater the difference
between the predetermined target value and the current environmental impact, the higher thesetting of the weighting factor. Because the national government’s environmental standards
and emissions reduction level are used for setting the target values, the concept of weighting iseasy to understand. The setting of the target value is not based on an individual’s subjective
view but on information authorized by the national government. Therefore, the introduction of
a subjective view by the executing agency can be avoided. Moreover, because the parameters
Figure
3.1-A: Conceptof thetwo
weighting
DtTandmethods
used forweightingare only– thefactors
targetundervalue
the actual value –, it is relatively easy to
すべての影響領域を横断した共通の目標を設定したうえで
It is desirable to express the difference between the target andset thecurrent
weightingfactor.
environmental impacts as a weighting factor (a). However, because the method developer separately sets a target for each
impact category (b), the actual weighting factors become different from those in (a).

(a)

(b)

Environmental
impactEnvironmental
impactCurrent amount of environmental impact

Current amount of environmental impact
Weighting factor

Weighting factor

1.5
Kyoto Protocol
Environmental
standards(Japan)

4

3

3

0

5

20
Target1

Montreal
ProtocolCarcinogenic
unit risk

Globalwarming

Ozone layer
depletionAir
pollution

Toxic
chemicals

Globalwarming

Ozone layer
depletionAir
pollution

Toxic
chemicals

Figure 3.1-A: Concept of the weighting factors under DtT methods
It is desirable to express the difference between the target and current environmental impacts as a weighting factor (a).
However, because the method developer separately sets a target for each impact category (b), the actual weighting factors
become different from those in (a).

However, DtT methods have some problems. For example, the weighting factor for global
warming greatly differs between the target level of the Kyoto Protocol (in Japan, a reductionof 6% from the emissions level in 1990) and a level that hardly causes any impact fromwarming (in the case of the former, because the weighting factor becomes almost 1, it is often
not adopted). In the case of eutrophication, because environmental standards have been
established for each lake in Japan, there are eutrophicated lakes and oligotrophic lakes. The
weighting factor for eutrophication greatly differs, depending on each lake’s target and current
levels. For example, if the worst case is applied, the weighting factor will become very large.
In this way, there are many possible target levels. A target level that a method developerconsiders appropriate is applied from among the levels to determine the weighting factor. Thismeans that setting the weighting factor is highly arbitrary. To avoid such arbitrariness as much
6

LIME2_C3- Integration of Environmental Impacts

as possible, it is desirable to set a common target (for example, conservation of 90% of theforests in Japan, a reduction of deaths to 100 or less, etc.) and discuss at what level the target
for the impact category should be set. However, with regard to most of the DtT methods, there
has been no discussion about the equivalence among the target levels set for impact categories.
In addition, the assessment equation differs among the DtT methods. For example, under the
Eco-scarcity method, the weighting factor is calculated by dividing the current level by thesquare of the target level (Equation 3.1-A).

 Inv.  N  2 NS SSI   Inv.S  IFS     Inv.S  2      S  TS  S  N S  TS  SS 

(3.1-A)On the other hand, under many of the theme oriented methods, the results of characterization
are normalized (by dividing them by the current level) and multiplied by the ratio of thecurrent level and the target level (therefore, the results of the equation are calculated by
dividing the results of the characterization by the reciprocal of the target level; Equation
3.1-1). Therefore, the Eco-scarcity method has a stronger focus on substances whose target
level is severer. Because the assumption is that the difference between the current and target
levels is regarded as the environmental impact, it is impossible to determine which calculation
methods are more realistic.
Due to the situation described above, recent LCIA research has produced few methods thathave been developed based on this approach.
3.1.3

Problems in the integration of environmental impacts
Method development research and case studies concerning integration have alreadybeen considered to a significant extent, whether in Japan or overseas. Notwithstanding,
ISO 14044 divides each of the steps constituting LCIA into essential elements (whichmust be carried out) and optional elements (which may or may not be carried out
according to each user’s purpose) and regards their integration as an optional element.
Whether expressed or not, the integration of various environmental impacts means thatthe weighting of aspects is influenced by environmental changes, such as human health,plants, biodiversity, farm products, the water industry, etc. Comparison of these cannot
be resolved through knowledge based on the natural sciences, but is determinedaccording to how the assessors and users subjectively or collectively understand theenvironment. If their cultural, academic, or economic backgrounds differ, the sense ofvalue concerning the environment will differ. Since the population for weighting differs
among the integration methods proposed so far (in Europe, Japan, etc.), the LCA results
were often not consistent, depending on the integration method. Moreover, even if the
population to determine the weighting was the same, the results sometimes differed,depending on the integration method used (Itsubo 2000a).
Because of this, there is the fear of malicious use, such as by a company handling theweighting factors so that the assessment results for its product will become better thanthe products of rival companies, or a disclosure of the results of assessment by acompany using methods that favorably assess its product. To restrict such use, ISO
7

LIME2_C3- Integration of Environmental Impacts

14044 has made integration an optional element and has prohibited the use of
integration for comparative assertions.
It is true that there are users who assume a negative attitude towards integration becauseof what has been described above. However, there are many companies that use
integration, placing greater importance on easy-to-understand assessment results and awide range of application than only on complicated ethical, social, and economic factors.
Many pioneer companies carry out assessment through the use of their own weighting
factors or existing integration factors and publish the results through environmental
reports or websites. Examples of use of LIME by companies are shown in Tables 0.1-2
and 0.1-4. According to these tables, LIME is much used for not only the LCA of
products as before, but also for corporate assessment, environmental accounting, andenvironmental efficiency. These use the single index in LIME as a communication tool.
The steps for LCIA, including their integration, have both advantages and disadvantages.
In the future, an increase is expected in the number of companies that use theadvantages of integration, such as ease of interpretation and greater applicability of theassessment results. It is essential to develop an integration method that can cope withsuch general-purpose use.
3.1.4

Important issues concerning research on integration
As described so far, there are various types of approaches to the integration ofenvironmental impacts. Each of the approaches has both advantages and disadvantages.
Moreover, the existing research on integration not only has the problems described in
the preceding section, but also confronts the important issues described below.
(1) Representativeness of value judgments
Many integration methods were developed on the assumption that they would be used
for general purposes irrespective of the products or users. Therefore, it is necessary toconfirm that the value judgments are representative of the population.
CVM and conjoint analysis, which are usually used for environmental economics, arebased on the fusion of economics and inferential statistics. In the case of inferential
statistics, a survey is carried out using samples randomly chosen from a certain
population (for example, the Japanese people) and a statistical model is applied to thesurvey results to infer the social preferences of the population by mathematical analysis(Figure 3.1-1). The inference results are inspected to verify not only that they arestatistically significant, but also that the statistical model used for the regression to the
population has representativeness as a social preference. If the statistical model passes
this inspection, it will be used for decision-making for cost-benefit analysis, etc.
Much of the research on the integration of LCA was not conducted in accordance withthe process shown in Figure 3.1-1 in the past. Finding a weighting factor that isrepresentative of a population requires various efforts – not only using inferential
statistical theories, but also securing a sufficient number of samples and determining
whether the respondents have understood the questionnaire. If the mean value is basedon a penal or questionnaire for a population with a size of several dozen people, but noinspection is carried out, the mean value should be limited to use within the group of
samples, but cannot be used in other groups.

8

LIME2_C3- Integration
Environmental
Impacts
Figure 3.1-1: Procedure for the calculation of social preferences representativeof ofthepopulation
using inferential statistics

Society members
Inspection

Society

Preference
Regression

Preference

Social preference

Preference

Preference
Random sampling

Utility function

Maximum likelihood
estimation method

Figure 3.1-1: Procedure for the calculation of social preferences representative of the population
using inferential statistics(2) Individual differences in the weighting
Value judgments differ among the individuals constituting a group. However, thedifferences are not so great that a trend cannot be ascertained. Their value judgments are
distributed within a certain range. In the integration methods so far, including LIME 1,
weighting factors representative of the population were inferred, but without taking into
consideration individual differences regarding value judgments. Showing how widely
individual differences are dispersed is desirable for securing transparency of theweighting factors.
3.1.5

Purpose of research in LIME 2
In LIME 1, research was carried out to apply conjoint analysis, which has drawn
attention to the integration of LCIA in the fields of market research and environmental
economics. As a result, it has been possible to develop statistically significant
integration factors. However, because the following problems were not solved due tolimitations on the research costs and other factors at this stage, it is difficult to say thatthe representativeness of environmental ideas in Japan has been fully secured:1) Because the target of the research was 400 people in the Kanto region, the research
results cannot be said to indicate the sense of environmental values of the Japanesepeople.

9

LIME2_C3- Integration of Environmental Impacts

2) Because the mall intercept*1 procedure was used for the research, the possibility ofa bias at the stage of sampling cannot be denied.
3) Although the sense of environmental value differs among individuals, noconsideration has been given to the range of variation.
In LIME 2, the problems were solved and consideration was given to developing
integration factors with a higher social consensus.
Figure 3.1-2 shows the survey procedure. Firstly, a questionnaire was prepared andpretests were carried out using the questionnaire. The pretests were carried out twice.
They used a random sampling method like the main survey. The issues to be resolved tocarry out the main survey smoothly were identified and consideration was given tosolving these issues. Although some individuals refused to disclose their basic resident
register status to protect personal information, there seems to have been no special
problems in the implementation of the main survey, since statistically significant results
were acquired. The main survey was carried out as a nationwide random survey with
about one thousand samples to calculate the weighting factors for the areas ofprotection.
To achieve the goal, the main survey focused especially on the following:1) Development of weighting factors that are highly representative and can be used forgeneral purposes2)
Calculationof theamountof statistics
concerning
Figure 3.1-2: Procedure forenvironmental
economicassessment
under
LIME 2 the weighting factors

Setting of the survey target
and preliminary survey

Standard values for selection were calculated
concerning five items (four areas of protection plus tax)
and the results were used as the referential values.

Preparation of aquestionnaire

Consisting of explanations, questions (for analysis), andquestions about the attributes of the respondents

Pretests

Carried out twice
With a size of 100 people; carried out in the Kanto region

Confirmed that assessment results were appropriate

Main survey

Analysis of the results anddetermination of theassessed value

With a size of 1,000 people; carried out throughout Japan

Adoption of a random parameter logit modelCalculation of both the representative values and theamount of the statistics

Figure 3.1-2: Procedure for environmental economic assessment under LIME 2

1

10

The mall intercept procedure involves requesting people on-the-street to cooperate in a questionnaire survey. Mallintercepts can secure relatively many samples in a short period. However, if there are many people who do not cooperate
in the questionnaire survey, a bias may arise due to the small proportion of cooperative people.

LIME2_C3- Integration of Environmental Impacts(1) Development of weighting factors that are highly representative and can beused for general purposes
To ensure the representativeness of the environmental view of the population (in thisresearch, the Japanese people), it was necessary to (1) adopt an appropriate sampling
method and (2) secure a certain number of samples.
In this case, the random sampling method, which can prevent the occurrence of a bias at
the stage of sampling, was adopted, since the weighting factors that are representative of
the population are extremely important in the use the integration factors for general
purposes. Moreover, to improve the representativeness of the Japanese people’s
environmental views, interviews will be held with about 1,000 respondents selected
randomly from throughout Japan to develop integration factors based on an analysis ofinterview results.
(2) Calculation of the extent of the statistics for the weighting factors
Under LIME 1, with regard to weighting factors, only the representative values were
calculated. Under LIME 2, the variability of the weighting factors was also calculated.
RPL was adopted for this analysis. Moreover, the amount of statistics for integration
factors was calculated through the use of the analysis results and the amount of statistics
for damage factors. The Monte Carlo method was used for this analysis.
In Section 3.2, explanations will be given concerning the general procedures andanalysis methods for the environmental economic assessment and conjoint analysis.
Section 3.3 will describe the survey method for LIME 2 and survey results.

3.2 Environmental economic assessment and the conjoint analysis
3.2.1

Characteristics of the main environmental economic assessment methods
From the viewpoint of a company’s design of a product, environmental information on
the product is an element of the product function and requires consideration of thevarious aspects, such as cost and safety, before decision-making. Therefore, it isdesirable that environmental information should be simple and clear. From thisviewpoint, closer attention has been paid to the integration methods than other methods
related to LCIA. Because of the usability of the assessment results and the ease ofinterpretation, expectations for the development of economic assessment methods havebeen growing.
These environmental economic assessment methods can be classified as shown in
Figure 3.2-1 (Washida 1999a). Firstly, they can be classified into methods for assessing
the value of the environment independent (irrespective) of individual preferences(preference-independent type) and methods for assessing the value of the environment
dependent on individual preferences (preference-dependent type).
The preference-independent assessment methods include the replacement cost method
and the dose-response method. Under the replacement cost method, the target
environment is replaced with the cost required to produce an alternative to the11

LIME2_C3- Integration of Environmental Impacts

environment as the environmental value. For example, if the water-retaining function of
a forest is replaced with a dam, the cost of constructing and maintaining the dam is thevalue of the forest’s water-retaining function. Under the dose-response method, if acertain value is created as a result of a change in the environment, the newly gained
value is regarded as the lost environmental value. Although the preference-independent
assessment methods can result in a relatively easy consideration due to the use of costs
and produce highly reliable results, it is hard for them to be socially accepted due toignorance of the environmental preference of individuals. In particular, if the target hascomplicated and various functions, such as in an ecosystem, there is a trend towards
denial of the existence of an alternative economic behavior itself. Therefore,
preference-independent assessment methods have not been used much recently.
The preference-dependent assessment methods are divided into the revealed preference
methods and the stated preference methods. The former indirectly determines individual
preferences from the actual monetary amount paid by them, while the latter directly ask
individuals about their preferences concerning the environment.
The revealed preference methods are divided into the travel cost method and thehedonic price method. The travel cost method assesses the value of the target
environment from expenditures paid by people visiting there. The hedonic pricing
method assesses the values of various living environments from the prices of real estate,such as land and houses. These revealed preference methods are advantageous in thatthe results are highly reliable, since an individual’s sense of value is inferred from theamount actually paid as in the case of the preference-independent assessment methods.
On the other hand, they lack flexibility in their assessment, since they can only assess
whether what an individual actually paid can apply to an alternative act, and therefore
the range of assessable objects is limited.
The stated preference methods are excellent in that they can assess not only theavailable values, but also unavailable values, such as inheritance value and existencevalue. Recently, the stated preference methods have been used for environmental
economic assessment more frequently than the revealed preference methods.
The typical stated preference methods are CVM and conjoint analysis. CVM measuresthe environmental values by directly asking individuals about their willingness to pay(WTP) and willingness to accept (WTA) concerning the environment or by counting the
population concerned with the distribution. CVM is the method most frequently used
for the assessment of ecosystems, including their existence value, because, dependingon the assessor’s design of the questionnaires, it can inform the respondents of thecharacteristics of the environment to be assessed and is highly flexible. Various
methods have been devised to identify the respondents’ real willingness to pay, andthere are guidelines for how to prepare such questionnaires (Federal Register 1993).
Table 3.2-1 shows the main cases of environmental assessment using CVM (Kuriyama
2000a). As shown in this table, CVM has already been used for various purposes. The
US has not only been using the results of assessment by CVM for cost-benefit analysis,
but has also been making efforts to introduce them into the real world. For example, thecourts are using it as grounds for the calculation of compensation costs.

12

LIME2_C3- Integration of Environmental Impacts

Environmental
economicassessment methods

Preferenceindependent
assessment methods

Replacement cost method

Dose-response method

Preference-dependent
assessment methods

Revealedpreference
methods

Travel cost method

Hedonic price method

Statedpreference
methods

Contingent valuation
method (CVM)
Conjoint analysis

FigureClassificationof environmental
assessment
methods 2000a)
Table 3.2-1: Main casesof 3.2-1:
environmental
economicassessment inJapan (Kuriyama
according to environmental economics (Washida 1999)

Table 3.2-1: Main cases of environmental economic assessment in Japan (Kuriyama 2000a)
Area of assessment

Assessed value

Area of counting

Sources

Forestry ecosystem

Conservation of YakushimaIsland, a world natural heritage248.3 billion yen

Whole Japan

Kuriyama (2000b)

River environment

Natural environment in thelower reaches of the YoshinoRiver

262.8 billion yen

Whole Japan

Washida (1999c)

Wetland landscapeLandscape of the Kushiro
Wetland14.8 billion yen

Hokkaido

Kuriyama (1998)

Conservation ofwater source forests

Protection of the water source
forest in the upper reaches of
Yokohama City

0.7 billion yen

Yokohama City

Yoshida (1996)

Conservation oftidelands

Conservation of Fujimae
Tideland296 billion yen

Whole Japan

Washida (1998)

Global warming
measures

Social impact of warming
measures779.5 billion yen

Whole Japan

Iwakura (2000)

Conservation of
farmlandConservation of farmland allover Japan

4,100 billion yen

Whole Japan

Yoshida (1997),
Yoshida (1999)

Recycled products

Recycled value of water
purifiers24.5 billion yen

Purchasers

Kuriyama (1999)

Although conjoint analysis directly asks individuals about their opinions as with CVM,
it differs greatly from CVM in that it can assess differences in the degree of preference
among various attributes of the target environment (Figure 3.2-2).

13

LIME2_C3- Integration of Environmental Impacts

If the natural environment, the object of assessment, is regarded as having a single
attribute, CVM will be used. However, it is often better to regard the naturalenvironment as having many attributes. For example, when the value of a wetland isassessed, analysis that takes into consideration trade-off relationships among various
elements (attributes), such as the cost of conserving a natural landscape, organism
species, and a wetland, is often advantageous to find the direction of policy
decision-making. For such a purpose, it is possible to demonstrate the characteristics ofconjoint analysis.
The purpose of integration under LIME is to establish a single index, weighting
between four areas of protection. CVM has the following advantages: (1) because it has
substantial results, the analysis results can be relatively easily verified; and (2) it is easy
for respondents to provide answers because it is based on a paired comparison between
the price and the object of assessment. However, if CVM is adopted, it is necessary toassess each of the four items separately. Moreover, although weighting among the items
can be gained afterwards from the results of CVM assessment, it is not direct a“weighting” among the areas of protection. Under ISO 14044, integration is calledweighting. This seems to express that weighting is made among the items that have tobe evaluated when a single index is established. Therefore, conjoint analysis is moreconsistent with the framework of ISO than CVM, because the former can carry out
weighting among the target items and assess the partial utility of each item (Figure
3.2-3). Because of this, under LIME, conjoint analysis was used for calculating
integration factors from weighting among the areas of protection.
AnDifference
explanation
will begiven
concerning
Figure 3.2-2:
betweenCVM
and below
conjoint
analysis the general methods for conjoint
analysis (questioning, methods for analysis of the survey results, methods for theinspection of the analysis results).

Environment (ecosystem)
LandscapePurification function
Recreation

CVM
Entire utility

Conjoint analysis
Fixing of CO2

Partial utility

Biodiversity

Figure 3.2-2: Difference between CVM and conjoint analysis

14

Figure 3.2-3: Difference between CVM and conjoint analysis
Conjoint analysis, which assesses partial availability, is consistent with integration under LCIA.
LIME2_C3- Integration of Environmental Impacts

Environment(human society, ecosystem)
Human healthSocial assets

CVM

Conjoint analysis

Entire utility

Biodiversity
Primary
production

Partial utility
Equivalent to weighting

Consistent with weighting under LCIA
Figure 3.2-3: Difference between CVM and conjoint analysis
Conjoint analysis, which assesses partial availability, is consistent with integration under LCIA.

3.2.2

Method for environmental assessment using conjoint analysis

Figure 3.2-4: Generalprocedure
environmental
economicassessment
by conjoint
analysis using
Figure 3.2-4
showsforthe
procedures forenvironmental
economicassessment

conjoint analysis. The same processes were applied to the calculation of the weighting
factors under LIME 2 (Figure 3.1-2).

Setting of the assessment target and the preliminary survey

Preparation of the questionnaires

PretestMain survey

Analysis of the results and determination of the assessed value
Figure 3.2-4: General procedure for environmental economic assessment by conjoint analysis

15

LIME2_C3- Integration of Environmental Impacts(1) Setting of the assessment target and the preliminary survey
Definitions are established concerning the environment as the assessment target,
environmental attributes (which are elements of the environment and are assessed byconjoint analysis), and changes in the status of the environmental attributes. To define
these, it is important to collect information to present a highly realistic profile (a bundleof information on each attribute).
(2) Preparation of a questionnaire
A profile is designed from information gathered beforehand. When a questionnaire isprepared, consideration is given so that respondents can correctively understand thecontents of the profile, a clear explanation of each attribute is included, and questions
are added to gain information on the characteristics of the respondents (yearly income,sex, age, etc.). If the respondents cannot understand the contents of the questionnaire or
their views cannot be reflected, the survey results will not pass inspection, and highly
representative results cannot be gained. Therefore, the preparation of the questionnaire
is the most important factor in gaining results with a high social consensus. The
questioning method will be described in 3.2.3 “Question format for conjoint analysis.”(3) Pretests
Pretests must be carried out several times to check whether the respondents could
correctly understand the prepared questionnaire and provide answers that reflect theirenvironmental views. This questionnaire survey is carried out with a smaller sample
than for the main survey.

(4) Main survey
The main survey is carried out after revision and the completion of the questionnaire
based on the results of the pretests. The questionnaire survey can be carried out byinterview, telephone, mail, the Internet, or other means. The characteristics vary
according to the survey method. Such considerations are taken into account whenselecting the most appropriate means for the main survey. For the details of the survey
methods, see 3.2.4 “Field survey using conjoint analysis.”(5) Determination of the assessed value
The acquired results are analyzed by applying the conditional logit model (CL) to therandom utility model. The preference strength of each attribute is estimated using the
maximum likelihood estimation method. Details of the analysis method will be
described in 3.2.5 “Analysis of the questionnaire results.” If the attributes include amonetary attribute, the WTP of the environmental attribute can be gained from the ratio
between the environmental attribute and the monetary attribute. In addition,
examination is carried out to determine whether the estimated preference strength isappropriate as the representative value.

16

LIME2_C3- Integration of Environmental Impacts

3.2.3

Question format for conjoint analysis (see Washida, Kuriyama, Takeuchi 1999b)

Figure3.2-5: analysis
Example ofcan
a question
for choice
Conjoint
be divided
intoconjoint
the following three types according to the methodof questioning: 1) choice; 2) rating; and 3) ranking. At present, the choice type has beenfrequently used. Each of the types is explained below.
Question: Which car will you purchase, A, B, C, or D?
Car name
Attribute

A

B

C

D

Car type

Coupe

Coupe

Sedan

Wagon

Size (passenger
capacity)

4

5

6

7

Fuel costs (km/L)

13

18

11

13

Enginedisplacement

2000 cc

1500 cc

2500 cc2000 cc

Safety (5 ranks)

3

4

5

4

Price

2 million yen

1.8 million yen¥3 million¥2.5 million

E

Select none.
Attributes

ProfileLevel

Figure 3.2-5: Example of a question for choice conjoint(1) Choice-based conjoint analysis
Choice-based conjoint analysis is also called a choice experiment. This type selects themost desirable one from among the offered choices. This decision-making process issimilar to an ordinary purchase behavior – that is, the purchase of a product that seems
the best option. This question format is more familiar to general consumers than rating
conjoint and ranking conjoint analysis, both of which will be explained later. Because itis easy for consumers to answer questions and there is no significant bias, choice-based
conjoint analysis has been frequently used now.
Figure 3.2-5 shows a simple example of questions for a choice conjoint survey. In thecase of this example, the respondents are asked to select “the car they most want topurchase.” “Car type,” “Size,” “Fuel cost,” “Engine displacement,” “Safety,” and“Price” are elements characteristic of the cars to be assessed and are called attributes.
The combination of the values of the attributes differs among the choices, A, B, C, andD (each of them is called a profile). For example, the profile A’s fuel costs are 13 km/L
and the price is 2 million yen, while the profile B’s fuel costs are 18 km/L and the priceis 1.8 million yen. Such values of attributes are called levels. For example, with regard
to the attribute of the “Size (passenger capacity),” four levels have been set, ranging
from 4 to 7. A profile is established by various combinations of levels of each attribute.
In this case, if four levels have been set for each of the other attributes, 45 profiles canbe established in total. When a questionnaire is prepared in reality, profiles are designed
so that significant results from the analysis can be gained by as few questions as
possible.
From among the products about which several levels of attributes are combined, if a17

LIME2_C3- Integration of Environmental Impacts

respondent selects “C” for example, it can be inferred that the respondent places more
importance on the sedan type, safety, and engine displacement than on other attributes.
If the answers to questions about other combinations of profiles are statisticallyanalyzed, it can be importance the respondents place on which attributes can be
determined. The results are the preference strength of each attribute, which corresponds
to the weighting. If such a result can be obtained, the car function that consumers pay
attention to can be identified, which is important information for car developers in orderto make a decision on what parts R&D expenses should be invested in more. From thisviewpoint, conjoint analysis theory was established in the field of computational
psychology, and conjoint analysis has been widely used as a methodology for market
research.
Another important point is how many attributes should be used for the choice-based
conjoint analysis. If the number of attributes is excessive, it is difficult to answer thequestions, making it impossible to obtain significant results. Because Miller (1956)
shows that it is difficult for human beings to deal with six or more items of data
simultaneously from the psychological viewpoint, it seems appropriate to use six or
fewer attributes for the conjoint analysis. Because LIME compares four items among
the endpoints,* 2 it satisfies this requirement. However, since the theme-oriented
methods compare ten or more items among impact categories, it seems difficult to use
choice conjoint analysis.
(2) Rating conjoint
Under this method, each respondent to the questionnaire gives a grade (or a subjective
probability of purchase) to each of the offered choices. In the case of cars for example, agrade is given to the profile of each car (choice), ranging from 1 to 100 points (Figure
Figure 3.2-6:3.2-6).
Examplea question
rating3.2-6,
conjoint
Asofshownin theforFigure
a grade may be given in the form of purchaseprobability. If a regression analysis is computed with the grade as the explained variable
and with the levels of the attributes of the choice as explanatory variables, it is possible
to calculate a weight for each attribute.
Question: In the case of the following cars, what is the possibility of your purchasing it?
Answer the probability of your purchase as a percentage.

Car type: coupe

Passenger capacity: 4
Fuel costs: 13 km/L

Purchase probability%

Displacement: 2000 cc
Safety (1 to 5): 3
Price: 2 million yen

Figure 3.2-6: Example of a question for rating conjoint
2

18

In reality, profiles have been designed using a combination of levels of the five attributes in total – four environmental
attributes and the tax attribute.

LIME2_C3- Integration of Environmental Impacts

Question: Which car do you like better, A or B? How much do you like it? Choose between 1 and 9.

Product A

Product B

Car type: coupe

Car type: coupe

Passenger capacity: 4

Passenger capacity: 5

Fuel cost: 13 km/L

Fuel cost: 18 km/L

Displacement: 2000 cc

Displacement: 1500 cc

Safety (1 to 5): 3

Safety (1 to 5): 4

Price: 2 million yen

Price: 1.8 million yen

I like A far better.

1

2

I like B far better.

I like both equally.

3

4

5

6

7

8

9

Figure 3.2-7: Example of a question for pair-wise rating

In addition, there is rating called pair-wise rating, whereby respondents have two
opposite choices and then answer which and how much they like. Unlike in the case of
Figure 3.2-6, a comparison is made between a pair (Figure 3.2-7). For example, the car
A and the car B are specified on the left and right. Choose 1 if you like car A very much.
Choose 9 if you like car B very much. Choose 5 if you cannot choose either. In this way,choose the degree of likability between 1 and 9.
Because this type of analysis uses “partial profiles,” which only show the attributes thatdiffer between both choices, it is possible to set many attributes. However, attention
should be paid to the fact that price and other important attributes tend to beunderestimated if there are many attributes (Pinnell 1994). In addition, there is criticism
that, because it is rare that only partial profiles are actually used for consumption
behavior, the practicability of its use in decision-making may be reduced (Green 1991).
(3) Ranking conjoint
In the case of ranking conjoint, questionnaire respondents rank the choices according todesirability (without rating them). To take cars as an example, respondents arerequested to rank the profiles A to D according to their desirability (Figure 3.2-8).
Ranking conjoint is divided into the full-ranking type and partial-ranking type.
While only the best one is chosen in the case of a choice conjoint analysis, several
choices are taken into consideration in the case of a ranking conjoint analysis. Therefore,
even if the number of respondents surveyed in the samples is small, it is possible to gaina relatively significant result. On the other hand, because ranking is a judgment that isdifferent from ordinary consumption behavior, the burden on the respondents may behigher than in the case of choice conjoint analysis.
19

Figure 3.2-8: Example of a question for ranking conjoint
LIME2_C3- Integration of Environmental Impacts

Question: Rank the products A to D according to how much you like them.

Car name

A

B

C

D

Car type

Coupe

Coupe

Sedan

Wagon

Size (passenger
capacity)

4

5

6

7

Fuel costs (km/L)

13

18

11

13

Enginedisplacement

2000 cc

1500 cc

2500 cc2000 cc

Safety (5 ranks)

3

4

5

4

Price

2 million yen

1.8 million yen

3 million yen

2.5 million yen

AttributeYour ranking(1 to 4)
Figure 3.2-8: Example of a question for ranking conjoint

3.2.4

Field survey using conjoint analysis
As is known in the field of social survey methods, questionnaire surveys are carried out
by mail, direct interviews, telephone, visiting, via computer, the Internet, etc. Eachmeans has advantages and disadvantages.
A survey by mail, for example, can increase the number of samples while saving on cost.
However, if many persons do not respond to the questionnaires, it takes a lot of trouble,
such as sending postcards to request a response from them. In addition, if thequestionnaires are responded to only by persons interested in the survey or the object of
the survey, a bias may arise in the group of respondents. Moreover, if the contents of thesurvey are difficult to understand and respondents do not read important explanations,the result of the assessment may be inaccurate.
Although surveys by telephone require payment for the operators, the personnel costs
are relatively low. In addition, because respondents can immediately answer questions
while at home, it does not seem to be much trouble. However, because information canoften be communicated only by means of paper, other devices are required in order tocommunicate information plainly so that the respondents can communicate theiropinions appropriately.
Interviews are the most suitable for communicating the contents of a survey plainly andsecure appropriate samples and answers. However, they have the following problems:
the survey costs are high due to the personnel costs and contact costs; regional biases
may arise, depending on where interviews are held; and the respondents may be biased
due to their impression of the interviewers.

20

LIME2_C3- Integration of Environmental Impacts

Because each survey method has certain advantages and disadvantages, it is desirable toselect the most appropriate means, depending on the purpose of the survey.
It is also important to determine the population of the survey. Samples should be
randomly selected in principle. The method of the survey can greatly change dependingon whether the population is only Japanese people or only the residents in an area
closely related to the object of the assessment. Because the determination of the
population varies according to the object of the assessment, it is essential to define thepopulation appropriately and select the samples randomly.
Column 3.2-1:
Respondents who carry out weighting – general consumers and experts
The integration of LCA so far – especially, the integration of environmental impacts using thepanel method – is divided into integration based on the results of questionnaire surveys ofgeneral consumers (Yasui 1998), integration based on the results of survey questionnaires ofexperts – especially academic experts (for example, Eco-indicator 99 (Goedkoop 2000)), andintegration based on the results of questionnaires to various groups, such as student groups
and industrial groups (Nagata 1998).
If the experience and knowledge of experts are necessary in carrying out weighting properly,
the weighting of the experts is adopted. For example, in the case of a disability weighting for
DALY, it is necessary to assess the QOL (quality of life) of more than 100 symptoms. If
general consumers who do not know the actual situation of many diseases are given
appropriate explanations about all the symptoms, it is almost impossible to carry out adisability weighting while reflecting their own opinions accurately.
On the other hand, if it is assumed that the population selected for the weighting consists ofgeneral consumers, the target of the questionnaire survey is not the experts but the general
consumers. In the case of environmental economic assessment, since the social value isassessed according to the general consumers’ willingness to pay, the target of thequestionnaire is general consumers. In the case of market research, an assessment can be
carried out by limiting it to car purchasers for example. However, when the social value ofenvironmental elements in Japan is assessed, there is no economic room for carrying out aquestionnaire survey for all Japanese people. Because of this, theories of inferential statistics
are used to ensure a statistically significant number of samples to provide accuracy of thesurvey. In this way, the population differs according the purpose of the assessment.
The development of methods for the integration of LCIA is often aimed at general purpose
use. Therefore, it is desirable that the population to be weighted for integration should be theJapanese people. If ten or more impact categories, such as global warming and acid rain, aredirectly compared, because it can be assumed that general consumers do not fully understandthe actual situation of all the environmental problems, it can be considered that discussions
should be conducted by a panel of experts. However, it cannot be said that the judgment ofexperts reflects the view of the population (for example, the Japanese people’s view).
The damage-oriented methods compare between entities, such as human beings and plants.
Although it is difficult to understand the contents of some attributes, such as biodiversity, it
seems possible for general consumers to understand them if appropriate information is21

LIME2_C3- Integration of Environmental Impacts

provided. In the case of integration in LIME, because importance is placed on the acquisitionof indexes with a high level of social consensus, the respondents to a questionnaire survey aresamples from the Japanese people – that is, general consumers. It has been confirmed that,even if the target of the questionnaire is actually general consumers, the statistical significanceof the analysis results can be verified (see 3.3.5).
3.2.5

Analysis of the questionnaire results
In the case of a choice conjoint analysis, the analysis is carried out using data obtained
from respondents through a questionnaire survey – that is, their selection of choices in
the questionnaire (discrete data). Therefore, choice conjoint analysis uses a discrete
selection model, such as a logit model, which is excellent in that it is consistent withrandom utility theory and can be discussed in terms of welfare economics.
According to random utility theory, utility is considered to change probabilistically.
Given that human beings do not necessarily take action strictly according to economic
rationality in the real world (Hagiwara 1999), the view that utility changes
probabilistically is not unnatural in the case of such a behavioral analysis model. An
explanation will be given about a measurement model for the choice conjoint analysis
below. For details, see Ben-Akiva and Lerman (1985) and McFadden (1974).
Figure 3.2-9 shows the procedures for estimating and examining parameters by the useof an estimation model. The procedure starts with the identification of a utility function
form, the selection of characteristic variables, and the application of the maximum
likelihood estimation method for inferring the preference strength of the environmental
attributes. Next, a likelihood ratio test is carried out concerning the estimated volume toverify the accuracy of the analysis results. If the results are statistically significant,integration factors can be gained from the results. Each of the procedures will be
described in detail below.

1. Identification of utility functions

Selection of the attributes

Selection of specific variables

Formularization of the likelihood function
2. Calculation of the estimate by the maximum
likelihood estimation method

3. Examination

4. Conclusion from the results

Calculation of the valueof the t and t-test

Calculation of the maximum likelihood
estimate β (Newton-Raphson method)

Calculation of the likelihood ratio andverification of the consistency of the modelCalculation of
the weighting factors

Figure 3.2-9: Procedures for estimating and examining the questionnaire results

22

LIME2_C3- Integration of Environmental Impacts(1) Identification of a utility function and the calculation of profile selection
probability
First, the random utility function is defined as a utility function (Equation 3.2-1).
U j  V j x j , p j    j(3.2-1)

When an individual n selects profile j, utility is Uj. j is the profile number selected by
the respondent. V is the observable fixed term of utility and refers to the portion that
does not change probabilistically. ε stands for the unobservable element that changes
due to lack of information on the characteristics of choices, the attributes of individuals,
etc., and is expressed as a probability term. xj is the attribute vector of profile j (thepassenger capacity of 4, the fuel cost of 13 L, etc. in the case of Figure 3.2-5). Pj is theprice of the profile j.
V j   a a ja(3.2-2)

The fixed term of utility V can be expressed as the sum of products of attribute a’s
preference strength βa and each attribute’s level xaj as in the above equation. In otherwords, if it is in a linear shape (which is called a main-effect model), the fixed term V in
the Equation 3.2-2 can be expressed by separating the monetary attribute as anotherterm as shown in the following Equation 3.2-3:
V j x, p     a xaj   p p ja(3.2-3)

Typical utility functions include not only linear ones like the above one, but also
log-linear ones and ones in the form of CES (constant elasticity of substitution). Amongthese, the above-described linear utility function is used the most frequently because of
its simplicity.
Pj, the probability of respondents selecting profile j from among group c of profiles (= 1,2, …, m), is expressed as Pr, the probability of Uj becoming higher than other products
k’s utility Uk.
Pj  PrU j  U k , k  C 

 PrV j  Vk   k   j , k  C (3.2-4)

If the error term in Equation 3.2-4 follows type-1 extreme value distribution (Gumbel
distribution), probability Pj can be gained from the following Equation 3.2-5 (which iscalled a logit model):

Pj 

e

V j

 e

Vi

(3.2-5)i

In this equation, λ is the scale parameter, which is usually standardized at 1.
The Gumbel distribution is expressed by a distribution function as in the following
equation:
23

LIME2_C3- Integration of Environmental Impacts

F    exp[ e  w   ],   0(3.2-6)

Such a Gumbel distribution is used because the error term is approximate to the usuallyused normal distribution and is easier to handle for analysis than the normal
distribution.
(2) Estimation of the preference strength using the maximum likelihood
estimation method
The Equation 3.2-5 expresses the probability of each sample’s selection of a specificprofile. Parameter βa can be estimated by having the equation correspond to the resultsof a questionnaire survey and applying the maximum likelihood estimation method. To
explain the method to analyze the results of a survey, it is assumed that the survey has
been conducted on one hundred persons. Table 3.2-2 shows an image of the results. In
this case, the first sample shows the selection of the second profile from among three
profiles, and the second sample shows the selection of the first profile. In addition, thetable shows the probability of each sample’s selection according to the survey results.
Table 3.2-2: Examples of answers gained through a questionnaire survey and the probabilityof each sample
Thetheprobabilityof sample 1’s selection of the second profile is P21.3 , and the probability
providinganswerof sample 2’s selection of the first profile is P12.3 .
Table 3.2-2: Examples of answers gained through a questionnaire survey and the probability of eachsample providing the answer
Sample

1

2

3

4

5

6

‥

99

100

Answer

2

1

2

3

1

2

‥

1

2

Probability

P12.3

P21.3

P32.3

P43.3

P51.3

P62.3

‥

P991.3

P1002.3

Pni.3: probability of the sample n’s selecting the ith profile from among the three profiles

If the maximum likelihood estimation method is applied, it can be considered that thequestionnaire survey resulted as shown in Table 3.2-2 because the joint probability ofcreating that selection pattern is high. Moreover, the method asserts that the value of βathat maximizes the joint probability of creating the pattern is the estimate ˆa ofdesirable βa. The probability of gaining the combination of survey results shown in
Table 3.2-2 can be expressed as the product of the probabilities of the samples’ answers.
100

L*   Pi.n3(3.2-7)n 1

The Equation 3.2-7 is called the likelihood function. The maximum likelihood estimate
ˆ can be found by calculating ˆ that maximizes the log likelihood function L =1naa

L*, a natural logarithm that substitutes L*. This is because ˆa that maximizes L* is thesame as ˆ that maximizes 1n L*, and the logarithm makes the analysis easier.
a24

LIME2_C3- Integration of Environmental Impacts



 


 V j  100  exp    a  a , j  
e 

 
L  ln L*   ln Pi.n3   ln  3
  ln  3  a

 n1


Vi
n 1
n 1
e

exp



 
a a a,i   i 1 
i

1



100

100

(3.2-8)Equation 3.2-8 proves that the unknown parameter vector βa is generally a convex
function. Therefore, the βa that makes L the largest – that is, the maximum likelihood
estimation method ˆa – is the solution to simultaneous equations whereby the resultsof the differentiation of Equation 3.2-8 in terms of Ba is zero. A numerical calculation
method is applied to this solution method. The typical and frequently-used numericalcalculation method is the Newton-Raphson method. For details, see Polak (1971).
The resultant ˆa is the preference strength of each of the attributes that constitute a
profile. In the case of the integration of LCIA – that is, if a profile consists of a
combination of endpoints –, ˆa is just the social preference strength of the endpoints.
(3) Examination of the estimates
It is necessary to examine whether the estimates obtained from the maximum likelihood
examination method can be used for indicating a society’s (the population’s) preference.
There are various examination methods. Below, explanations will be given of the t-test
and likelihood ratios, both of which are typical examination methods and were used forassessment under LIME.
A t-value is the estimate ˆa divided by its estimated standard deviation

ta 

̂ a
va

va .

(3.2-9)If the absolute value of ta is equal to or larger than 1.96 (2.576), null hypothesis ˆa = 0
can be rejected at a significant level of 5% (1%). Therefore, if ta ≥ 1.96 (2.576), thecorresponding a is regarded as a factor that influences the choice probability at aconfidence level of 95% (99%). In other words, it is possible to prove its statisticalsignificance. On the other hand, if ta ≤ 1.96 (2.576), because hypothesis ˆa = 0 cannot
be rejected, it is desirable to estimate the parameters again, excluding attribute a.
ρ2 is called a likelihood ratio or McFadden's coefficient of determination. Like thesquare of the coefficient of correlation, ρ2 is between 0 and 1. If ρ2 is nearer to 1, it ismore consistent with the model – in this case, the logit model. However, unlike thecoefficient of correlation used for regression analysis, it is acceptable to judge that theconsistency of ρ2 is sufficiently high if ρ2 is between 0.2 and 0.4.
Display of these examination results together with estimates gained by statisticalanalysis is an extremely important requirement for showing the representativeness ofthe society’s preference strength.
25

LIME2_C3- Integration of Environmental Impacts

3.2.6

Uncertainty of the preference strength using the random parameter logit modelThis section explains the analysis through the use of the random parameter logit model(RPL). RPL can be regarded as a model developed as a result of the improvement ofproblems in CL. Any analysis that uses this model makes it possible to achieve a levelof explanatory inference for the results, taking into consideration the uncertainty of thepreference strength.
Firstly, explanation is given regarding the problems in CL.
(1) Problems in the conditional logit model (CL)
Although CL is an analysis model widely used in the field of environmental assessment,
it has two significant problems: IIA and the homogeneity of utility.
Firstly, an explanation is given regarding IIA. The above-described choice probability
ratio of CL, Pj/Ph, can be expressed by Equation 3.2-10:

eVj
eVh
eVj

/

Ph  eVk  eVk eVhPjk(3.2-10)kThis equation shows that choices other than j and h do not influence the choiceprobability (Train 2003). This characteristic is called independence from irrelevantalternatives (IIA). Because the red bus/blue bus problem is often used as an example for
IIA, a brief explanation is given regarding this problem.
Suppose that when a person chooses a transportation means, there are two choices: theperson’s own car or a bus (red bus). If the person’s preference is equally divided
between both modes, the preference probability is fifty-fifty. Suppose that there isanother choice: a blue bus. Because these buses differ only in their color, the preferenceis equally divided between the red or the blue bus. In this case, the probability ofchoosing a bus does not change and is equally divided between both buses. That is, theprobability is one-fourth for each of them. However, if CL is applied, because of
independence from irrelevant alternatives (IIA), the choice probability for each of thethree choices becomes one-third. In this case, the buses are overestimated. Generally, if
CL is applied, the choice probability for similar choices is overestimated (Train 2003).
On the other hand, IIA also has advantages. Due to independence from other irrelevant
choices, in the case of a subset from which some unselected choices are excluded,
factors can be estimated while maintaining consistency (Train 2003). This characteristic
is widely used for the discrete hedonic method and the discrete travel cost method.
Brief explanation should be given also to the other problem – homogeneity of utility. In
the case of CL, which is based on random utility theory, homogeneity of utility is
assumed among individuals. However, because it is appropriate to think that utility
differs among individuals, it is a little too hypothetical to assume homogeneity of utility.
As described above, there are two great problems in CL– IIA and homogeneity of utility.
To overcome the problems – that is, to ease IIA and take into consideration
26

LIME2_C3- Integration of Environmental Impacts

heterogeneity of utility –, various constructive models have been developed so far.
Below, explanation will be given to a typical construct model, RPL.
(2) Outline of the random parameter logit model
RPL* 3 is a model whereby β, a factor to be inferred, is supposed to fluctuate
probabilistically. This can be considered according to the case mentioned in this paper.
For example, if probabilistic variation (distribution) is allowed for the health damage
factor of the attribute variables, this means that utility to health damage differs among
the respondents – in other words, factor βi differs for each individual i and is distributed
according to some conditions. This indicates that the assumption of the homogeneity ofutility in CL has been eased and the heterogeneity of utility among individuals has beentaken into consideration as mentioned above. Moreover, this model can ease IIA of CL(Train 2003). To certify this, Equation 3.2-11 below can be used to find the choiceprobability rate. As pointed out in Kuriyama and Shoji (2005), because a term (thedenominator of Pji) contains all the choices, it is clear that IIA has been eased. This
makes it possible to say that RPL is a developmental model that can solve the problems
in CL. Probability in RPL can be formulized as follows. This is expressed by integral
calculation that uses logit probability Pji and the density function of arbitrary
distribution f (β).

 Pji   Pji   f   d*

(3.2-11)In this equation,
Pji*: probability of an individual i’s selecting choice j
Θ: parameter of probability density function for β (average or dispersion)Pji 

exp (V ji ) exp(V )kikFor the purpose of RPL, continuous distribution is assumed for f (β) distribution. The
main types are normal distribution, log-normal distribution, and triangular distribution.
Log-normal distribution is often used if surveyors can assume signs of the variables
beforehand.
Because integral calculation of choice probability like the above equation is difficult,this integration is approximated for estimation by the following simulation method(Train 2003).*4 In this case, choice probability is formulized as the following SP(simulated probability):*5
SPji 

3

4

5

1
Pji  r R r(3.2-12)This is also often called a mixed logit model. For details of various models for improving problems in conditional logit
model, including explanation of this model, see Kuriyama and Shoji (2005). In addition, for details of this model, see thecareful explanation in Train (2003).
What is used for the sampling of β is random draws, which simply carry out random sampling, or Halton draws, whichhave been defined by prime numbers (Train 2003).
For details of the nature of the SP, see Train (2003).

27

LIME2_C3- Integration of Environmental Impacts

R: number of times of sampling
Br: the rth random sampling from the density function f (β)
This is used to assume the following long likelihood function (SLL) and estimate aparameter Θ that specifies a distribution that maximizes the function (Train 2003).
SLL   d ji ln SPji i(3.2-13)j

In this equation, dji is a dummy variable that becomes 1 if individual i selects the choicej.
This calculation makes it possible to gain a distribution function for which differencesin preference strength among individuals are quantified.

3.3 Integration of environmental impacts by conjoint analysis
3.3.1

Adopted question format and survey method
As described in the preceding section, there are three types of question formats forconjoint analysis (choice, rating, and ranking). The rating type is divided into thecomplete profile type (see Figure 3.2-6) and the pair-wise type (see Figure 3.2-7).
In the case of the complete profile rating type, the fixing of purchase probability and thearrangement of profile are not usually carried out by respondents when they purchasegoods. Therefore, it is difficult for them to answer the questions, resulting in many
invalid answers. The protected goods to be assessed this time are inferred to be moredifficult to understand than functions that consumers can actually understand using theirfive senses. Therefore, if this approach is applied to this research, a sufficient number ofvalid answers may not be gained.
Because the pair-wise rating type makes it possible to show a partial profile that only
includes some of the attributes, it is effective when the number of attributes is large.
However, there is a criticism that such a question format, which only shows a partial
profile, is unrealistic (Green 1991). Choice from scales of desirability under thisapproach is not made during ordinary consumption. It is hard to say that this approach isappropriate for profiles whose attributes are difficult to understand.
It can be said that choice conjoint is a highly realistic question format, for choice of themost favorable from among two or more articles of goods is extremely similar to daily
consumption behavior. In addition, the number of attributes for this research is five in
total – four attributes to be protected plus tax. The number is at an acceptable level forthe use of choice conjoint. On the other hand, in the case of ranking conjoint, ranking all
choices is different from ordinary consumption behavior and may increase respondents’burden.
Responding to the discussions described above, a decision was made to adopt choiceconjoint, giving priority to maximum reduction of respondents’ burden.

28

LIME2_C3- Integration of Environmental Impacts

As described in 3.2.4 “Field survey using conjoint analysis,” there are various survey
means, such as mail, direct interviews, telephone, visiting, via computers, and theInternet. Under LIME, because the environment to be assessed is abstract unlike
products on the market, interviews were adopted, placing importance on a careful
explanation of the object of assessment so that any misunderstanding on the part of the
respondents can be avoided. Although meetings of residents in the suburbs of Tokyo
were held because of the limited survey costs under LIME 1, it was decided to adopt anationwide random sampling survey and a visit to target households for LIME 2.
3.3.2

Survey methods
The ultimate goal of this research is to gain integration factors for general-purpose use
by measuring the social preference for the endpoints of the four items set under LIME.
Figure 3.3-1 shows the survey procedure, which roughly consists of the following four
processes:
1) Sampling: sampling of target households. Nationwide random sampling was
adopted.
2) Preparation of questionnaire: A questionnaire is prepared for the purpose ofinterviews. A questionnaire refers sheets that display questions and information that
respondents should know before answering the questions and include explanations
about environmental attributes, profiles for conjoint analysis, and questions about
the respondents’ attributes.
3) Interview survey: Surveyors visit target households, explain the contents of thequestionnaire, and receive answers. In the interview survey, surveyors familiar withthe contents of the questionnaire visited the respondents’ houses, explained thecontents of the questionnaire in detail, and received answers.
4) Calculation: The answers are statistically analyzed to calculate weighting factors.
The results are used for calculating integration factors.
One of the main purposes of the integration under LIME 2 is to disclose a list offactors for uncertainty analysis by LCA. During the main survey, the amount ofstatistics of the weighting factors also was calculated. The results were used forcarrying out uncertainty analysis of the integration factors, and a list was made also
about the results of the uncertainty analysis.
The contents of each stage shown in Figure 3.3-1 will be described in detail below.

29

LIME2_C3- Integration of Environmental Impacts

Figure 3.3-1: The survey procedure

The survey roughly consists of sampling, the preparation of a questionnaire, an interview survey, and the calculation(1) Sampling(2) Preparation of the questionnaire
Calculation of the annual
environmental load

Sampling of spots

Determination ofdamage factors

Acquisition of a basic
resident register

Calculation of the normalization value

Sampling of households

Establishment of the questionnaire
Explanation to the surveyors(3) Interview
surveyPretestInterview survey
Arrangement of the raw data

(4) Calculation

Statistical analysis
WTP, calculation of the weighting factors
Calculation of the integration factors
Calculation of the amount of statistics of theintegration factors

Main survey

Creation of a list of integration factors
Figure 3.3-1: The survey procedure
The survey roughly consists of sampling, the preparation of a questionnaire, an interview survey, and the calculation.

30

LIME2_C3- Integration of Environmental Impacts

3.3.3

Outline of the sampling
The sampling was carried out as follows:
1)
2)
3)
4)
5)
6)

Sampling method: two-stage random sampling
Population: households throughout Japan
List used for sampling: basic resident register
Planned number of samples: 2,040 samples (households)
Number of spots: 147 spots *6
Number of samples per spot: 14 samples (households)

The two-stage random sampling method was adopted for the main survey. Under thismethod, spots were selected before choosing samples (households). After the selectionof spots, samples (households) were chosen. Because the spots are selected beforehand,sampling becomes easier using the basic resident registers. Consideration was given not
to making the number of collected samples less than 1,000 (assuming that the collection
rate would be about 50%), and about 2,000 samples were taken.
To take about 2,000 samples, the number of spots was fixed at 180. However, to protect
personal information, some local governments have recently not cooperated in perusing
their basic resident registers. As a result, households were chosen at 147 spots under thejurisdiction of local governments that approved perusal of them. Based on the estimatednumber of samples (about 2,000), it was decided that 14 samples would be taken at eachof the spots (2040 ÷ 147 = 13.9). Figure 3.3-2 shows the method of taking samples for
Figure 3.3-2: Sampling method for the main survey
the main survey. The sampling will be described in detail below.
Two-stage random sampling was adopted. Spots were sampled first. Households were sampled based on basic resident registers with thecooperation of the local governments in whose jurisdiction the sampled spots were located.

Spot sampling

Japan

Household sampling

14 households per spot

Basic resident register●Prefecture ▲City ■Town

Proportionate samplingaccording to the numberof households

Basic resident register●Prefecture ▲City ■Town

Population

Basic resident register●Prefecture ▲City ■Town

Sampling: 180 spots
Approved spots: 147 spots

Spot

Number of survey spots
The number of sampled
households is adjusted to 147
spots × 14 households→ 2,000 households.

Survey target (households)

Figure 3.3-2: Sampling method for the main survey
Two-stage random sampling was adopted. Spots were sampled first. Households were sampled based on basic resident
registers with the cooperation of the local governments in whose jurisdiction the sampled spots were located.
6

Although applications for perusal of basic resident registers were filed at 180 spots, applications were refused at 33 spots
due to recent restrictions on the perusal of them. As a result, target households were chosen at 147 spots.

31

LIME2_C3- Integration of Environmental Impacts

3.3.4

Sampling procedure(1) Sampling of survey spots (first-stage sampling)
Probability proportionate sampling was applied to choose 180 spots according to thenumber of households among the population (as of April 1, 2005). The following is theconcrete calculation method:
Sampling interval = number of household in population ÷ number of survey spots
This number is defined as sampling interval for determining spots. A smaller number
than the number of intervals was designated by random number selection and was
defined as the starting number. Sampling intervals and a start number were set from thenumber of households in municipal databases, and survey spots were determined. The
Figure 3.3-3: Relationship between the number of spots sampled in each prefecture and the population
arrangement
sequenceof itmunicipalities
wasofintheaccordance
thereflectmunicipal
codes inBecausethe relationshipis correlative,
can be said that the results
spot sampling forwith
this survey
the population
distribution
inJapan.
FY2005.
18
Tokyo

Number of sampled spots

16
14
y = 1E-06x
R2 = 0.9038

Osaka

12
Aichi
Saitama

10
Hyogo

8
Fukuoka

Kanagawa
Hokkaido

6

Chiba

4
2
0
0.0E+00

2.0E+06

4.0E+06

6.0E+06

8.0E+06

1.0E+07

1.2E+07

1.4E+07

Population (number of persons)Figure 3.3-3: Relationship between the number of spots sampled in each prefecture and the
population
Because the relationship is correlative, it can be said that the results of the spot sampling for this survey reflect
the population distribution in Japan.

(2) Sampling of the target households (second-stage sampling)
An interval was set at “five households” to make it possible to sample target households
within the extent of each survey spot (designated by town, district, house number, etc.)
and avoid sampling of neighboring households or sampling only from the sameapartment house, and households were sampled from basic resident registers by thesystematic sampling method.
32

LIME2_C3- Integration of Environmental Impacts

It has been assumed that the main survey is based on the sampling of households andthat the purpose of the question format is to find the amount of willing to pay perhousehold. Therefore, it is desirable that the respondents should be household
representatives – that is, the heads of the households. However, because the heads of thehouseholds are not shown in basic resident registers and cannot be identified accurately,
it was assumed for convenience that the oldest persons of the households are the heads.
(3) Spot sampling result
Figure 3.3-3 shows the results of the sampling of municipalities (first stage sampling).
In this way, the population of each prefecture was taken into consideration in thissampling, which has made clear a correlative relation between the number of times ofsampling and the population of each prefecture.
(4) Determination of the target households
Surveyors visited the sampled households after sending requests. In principle, requests
for response were made to the heads of the households. However, to prevent a decreasein the collection rate, when heads were absent or had difficulties in making a response,
requests were made to household members aged between 20 and 59 as the household
representatives.
3.3.5

Preliminary survey (calculation of normalization values)
When a profile is designed, it is necessary to show quantitative information on each
attribute. Under choice conjoint, which was adopted for this research, a profile consistsof the current amounts of environmental impacts in principle. Profiles in which theamount of environment impact of a certain attribute is changed to a certain extent areused for comparison with the current profile. Therefore, as a preliminary survey,
consideration was given to the preparation of a profile that shows the current situationof environmental impacts.
Here, the result of calculation of the environmental impact on each area of protectionthat accompanies the annual economic activities in Japan was used as the current profile.
Under LCA, the amount of environmental impact generated through the environmentalload in a specific area during a certain period (for example, one year) is called anormalization value, which is used for the normalization of LCIA. Normalization is one
step that corresponds to an added element of LCIA. This refers to
non-dimensionalization by dividing the result of the characterization for each impact
category by the normalization value and is frequently used as a preliminary process forweighting.

 CFImpactN

ImpactCINV Impact

 CF

X

ImpactImpact X  Inv  X 

 X  AnnualEnvironmentalLoad  X 

(3.3-1)X

In this equation, NImpact is the result of normalization of the impact category Impact.
CIImpact is the result of the characterization – that is, a category indicator. In the case of
global warming, for example, this can be gained from the sum of products of theamount of emissions Inv (X) of a greenhouse effect gas X and the characterization factor
33

LIME2_C3- Integration of Environmental Impacts

CFGlobalWaming (X) (global warming index). NVImpact is the normalization value of the
impact category Impact and is calculated using the sum of the products of the annual
environmental load Anual Environmental Load (X) of the substance of concern X andthe corresponding characterization factor CFImpact (X).
Normalization has so far been carried out mainly concerning the result of
characterization in each impact category. This is normalization carried out using atheme oriented method (Equation 3.3-1). In the case of a damage-oriented method of
LCIA, including LIME, because the result of the damage assessment is gained for eacharea of protection, a normalization value also is calculated for each area of protection.
The normalization value was gained using the sum of the products of the annual
environmental load in Japan for each substance of concern Annual Environmental Load
(X) and the corresponding damage factor DF (Safe, X).
NV Safe  DF Safe, X  AnnualEnvironmentalLoad X (3.3-2)

IImpact

In this equation, NV (Safe) is the normalization value (annual potential damage) of thearea of protection Safe. DF (Safe, X) refers to the damage factor (potential damage tothe area of protection Safe due to the environmental load per unit of the substance X1).
Table 3.3-1: Substances of concern included in the calculation of the normalization value and theTable 3.3-1: Substances of concern included in the calculation of the normalization value and the relevant impact categories and areas ofrelevant impact categories
and areas of protection
protection
Areas ofprotection

Impact category
Global warming

Human health

HCFC-21, HCFC-22, HCFC123, HCFC-124, HCFC-133, HCFC-141b, HCFC-142b, HCFC-225, CH3Br, CCl4, 111-TCE, CFC-11, CFC-12, CFC-113,
CFC-114, CFC-115, Halon1301

Photochemical oxidants

NMVOCsUrban air pollution

PM2.5, PM10, SO2 (primary, secondary), Nox (point source primary, radiation source primary, secondary)Toxic chemicals(Including heavy
metals)80 substances, including arsenic, benzene, cadmium, hexavalent chrome, nickel, nickel compound, 2,3,7,8-TCDD, CCL4, 1,1,2-trichloroethane, methyl
chloride, trichloroethylene, dichloromethane, 1,2-dichloroethane, 1.1-dichloroethane, formaldehyde, acetaldehyde, 1,3-butadiene, acrylonitrile,
ethylene oxide, PCBs, 1,4-dioxane, and chloroform

Indoor air pollution

Formaldehyde, NOx, SO2, PM10, PM2.5
85 substances, including arsenic, benzene, cadmium, hexavalent chrome, nickel, 2,3,7,8-TCDD, 1,1,2-trichloroethane, methyl chloride,trichloroethylene, dichloromethane, 1,2-dichloroethane, 1.1-dichloroethane, cyanogen compound, lead, copper, 1,1,1-TCE, tetrachloroethylene, 1,3dichloropropene, thiuram, simazine, thiobencarb, selenium, fluorine, and boron

Land use

Land transformation (for each form of land use)

Waste
Ozone layer depletion
Oxidization
Photochemical oxidant
Land use
Resource consumption
Waste

Global warming
Ozone layer depletion

Social assets

24 substances, including limestone, rock, gravel, coal, oil, natural gas, aluminum, copper, iron, lead, molybdenum, nickel, tungsten, tin, zinc, silver,
gold, and wood
Total waste volume
HCFC-21, HCFC-22, HCFC-123, HCFC-124, HCFC-133, HCFC-141b, HCFC-142b, HCFC-225, CH3Br, CCl4, 111-TCE, CFC-11, CFC-12, CFC-113,
CFC-114, CFC-115, Halon1301
NOx, SO2, NH3, HCl
NMVOCsLand transformation (for each form of land use), land maintenance (9 types)
24 substances, including limestone, rock, gravel, coal, oil, natural gas, aluminum, copper, iron, lead, molybdenum, nickel, tungsten, tin, zinc, silver,
gold, and wood
Total waste volume

CO2, CH4, N2O, CFCs (5 substances), HCFCs (6 substances), HFCs, CCl 4, Halon 1301, PFCs, SF6
HCFC-21, HCFC-22, HCFC123, HCFC-124, HCFC-133, HCFC-141b, HCFC-142b, HCFC-225, CH3Br, CCl4, 111-TCE, CFC-11, CFC-12, CFC-113,
CFC-114, CFC-115, Halon1301

Oxidization

NOx, SO2, NH3, HCl

Eutrophication

Total nitrogen, total phosphorus

Photochemical oxidant

NMVOCsResource consumption

23 substances, including limestone, rock, gravel, coal, oil, natural gas, aluminum, copper, iron, lead, molybdenum, nickel, tungsten, tin, zinc, silver,
and gold (excluding wood)

Waste

34

Car noise

Eco toxicity

Resource consumption

Primary
production

CO2, CH4, N2O, CFCs (5 substances), HCFCs (6 substances), HFCs, CCl 4, Halon 1301, PFCs, SF6

Ozone layer depletion

Noise

Biodiversity

Target substances

Total waste volume

LIME2_C3- Integration of Environmental Impacts

Table
3.3-2:
Results
calculation ofof normalization
Table
3.3-2:
Results
of ofthethecalculation
normalizationvalues
valuesbe obtained
productsofofthe
the annual
annual environmentalload
andandthe thedamagefactor.
These Thesecan becan
obtained
fromfrom
the thesumsumof ofproducts
environmentalload
damagefactor.

Area of protection

Human healthSocial assets

Primary production

Biodiversity

Impact category (↓), unit (→)

DALY

Yen

kg

EINES

Global warming

1.68E+05

7.77E+11

Ozone layer depletion

7.95E+03

5.34E+08

1.71E+09

Oxidization

3.53E+11

1.87E+09

Eutrophication

4.56E+10

Photochemical ozone

1.29E+04

Urban air pollution

2.68E+05

Toxic chemicals

3.11E+04

Indoor air pollution

8.38E+04

5.02E+10

6.66E+09

Eco toxicity

3.70E-02

Land use
Resource
consumption

Non-metals, metals, fossil fuels

5.12E+10

3.27E-01

1.21E+10

7.07E-03

1.08E+11

5.49E-01

7.53E+11

1.70E+09

6.17E-03

2.95E+12

Biological resources
Waste
Noise

6.89E+4

Normalization value (total)

6.40E+5

4.92E+12

1.83E+11

9.27E-1

Reference LIME1

4.80E+5

2.61E+12

1.98E+11

7.90E-1

With regard to data on annual emissions, if there are documents that record annual
emissions in Japan, these were cited. In the case of substances for which no documents
exist, the annual environmental load was calculated with reference to existing
documents. Table 3.3-1 shows the substances of concern used for the calculation of thenormalization value and the relevant impact categories and areas of protection. Table
3.3-2 shows the results of normalization values calculated by Equation 3.3-2.
The annual amount of potential environmental impacts of economic activities in Japan
can be gained from the normalization values. In addition, this construction makes it
possible to extract impact categories and substances of concern. Therefore, comparedwith the normalization values in LIME 1, the values for human health and social assets
are greater. The value for human health became greater because the amount of damage
due to indoor air pollution and noise was added. The value for social assets became
greater due to the addition of the impact of land loss resulting from waste landfill andthe review of user cost due to the consumption of fossil fuels and mineral resources.
Appendix 3 shows the interpretation of the results in Table 3.3-2.
the calculation of normalization values are used as basic data on the profiles constituting
the questionnaire (for details of the use method, see 3.3.4). Questionnaires by CVM
often use photographs and images to facilitate the understanding of the survey contents
for the respondents. With regard to the environmental attributes in this research, it isdifficult to show photographs as in the case of a natural landscape. Consideration wasgiven to make the explanations of the attributes simple and clear, for example, byattaching graphs showing the results of normalization values.
35

LIME2_C3- Integration of Environmental Impacts

3.3.6

Preparation of the questionnaire(1) Construction of the questionnaire
Because environmental value judgments differ from daily consumption behavior,
respondents often have trouble in answering the questionnaire. Therefore, preparation ofa questionnaire that enables the respondents to understand the contents of the questions
easily is essential for gaining highly reliable survey results. Among others, aquestionnaire for conjoint analysis, which requires simultaneous comparison among two
or more environmental attributes is troublesome for the respondents. To reduce theburden respondents as much as possible, attention was paid to the following when aquestionnaire was prepared for this research:
1) Provision of information before questioning for conjoint analysis to facilitate theunderstanding of the respondents concerning the contents of the environmental
attributes (the areas of protection in the case of LIME)
2) If explanations are redundant, it is difficult to convey the message of thequestionnaire to the respondents. Because of this, the panel method has been
adopted for the questionnaire. The surveyors show a panel to the respondents tofacilitate their understanding of important points.
3) The questionnaire consists of the following four parts:• Background• Explanations and simple questions about environmental attributes (areas ofprotection)• Questions for conjoint analysis• Questions about personal attributes
Concrete The concrete contents of the panel are described in Appendix 2.
The main contents of each part are described below.
(2) Background
This part places importance on having the respondents recognize this survey as aquestionnaire survey about environmental problems and understand that environmental
problems are varied and environmental impacts vary according to which measures aretaken. In addition, with regard to the conjoint analysis to be carried out during thisresearch, profiles are presented as a hypothetical combination of information on theareas of protection. To make it easy to understand the contents, the profiles have been
related to the central government’s environmental policies. Figure 3.3-4 shows the firstpage of the questionnaire panel used for the main survey.
Next, the following panel was presented (Figure 3.3-5). The respondents were informedthat there were four environmental attributes and subsequent panel sheets would give
explanations and questions about them so that the respondents could have the full
picture of the questionnaire.

36

LIME2_C3- Integration of Environmental Impacts[Panel 1]
To all of you who are requested to answer the questions
Thank you for your cooperation in this questionnaire.
Today’s Japan has various environmental problems, such as the destruction ofnature, waste problems, and air and water pollution. As a result, damage has
occurred to important human health and rich natural environments.
The purpose of this questionnaire is to investigate how much importance you
give to environmental problems.
The results of the questionnaire will be used as a guide for the environmental
activities of industries. Your cooperation will greatly influence Japan’senvironmental policies. We would appreciate it if you could give us yourcooperation.
Figure 3.3-4: Questionnaire panel (page 1)
A brief explanation is given about the contents of the questions for the main survey.

[Panel 2]
Environmental problems have various impacts. Research so far have found thatthey have impacts on the following:(1) Human health(2) Social assets(3) Biodiversity(4) Plant growth
Below, explanations will be given about the current situation of theseenvironmental impacts. After that, we would like you to answer the questions.
Figure 3.3-5: Questionnaire panel (page 2)
The environmental attributes covered by the main survey are presented.

(3) Explanations about environmental attributes (areas of protection)
Next, an explanation is given about the contents of each area of protection and thecurrent situation of environmental impacts. Based on this information, a question about
whether the environmental impact is serious or not is asked so that the respondents cangive preliminary consideration to answering the questions that will be asked for the
purpose of conjoint analysis.
To take an example from human health, the panel consists of the following: 1)
explanation of the environmental attribute; 2) summary of the current situation of theenvironmental impact; 3) visual illustration of the situation; and 4) questions about thesituation.
37

LIME2_C3- Integration of Environmental Impacts

The panel starts with an introduction to the environmental attribute (Figure 3.3-6). The
introduction states that impact on human health is related to various environmental
problems, such as climate warming and air pollution, and that the following panels will
explain how much the impact has occurred through environmental problems.
Next, the point is displayed about information on the amount of damage to humanhealth (Figure 3.3-7). The point makes it possible to imagine how much environmental
problems impact on human health and explains that the loss of life expectancy is used as
an index for understanding of the amount of damage. Because the information is usedalso for the questionnaire for conjoint analysis, it is extremely important for the
respondents to understand the information.
To explain the reliability of what has been described above, details of the results and thegrounds for the assessment are displayed (Figure 3.3-8). To take an example fromhuman health, details can be described as follows. The result can be gained by dividing
the normalization value of 640,000 years (total amount of impact on human healthcaused by the Japanese people’s economic activities for a year) by the total populationof Japan. The result shown in 3.3.5 is used as the normalization value.
[Panel 3]
Impact on human health
Pollutants discharged through our daily life activities causes various
environmental problems, such as global warming and air pollution, and havebecome a factor that causes damage to human health.
If the current situation continues, environmental problems are expected to havean impact on human health.
Human health 1/4
Figure 3.3-6: Questionnaire panel (page 3)
Explanation about an environmental attribute (human health)[Panel 4]
Impact of environmental problems on human healthYour life expectancy has been falling by about two days every year.
If this situation continues for 50 years, the life expectancy of the Japanesepeople, including you and other people dear to you, will decrease by threemonths.
Human health 2/4
Figure 3.3-7: Questionnaire panel (page 4)
Summary of the current situation of the environmental impact on an environmental attribute (human health)38

LIME2_C3- Integration of Environmental Impacts[Panel 5]
The following are grounds for the assessment:
Health loss from economic activities in Japan and their contribution toenvironmental problems
Noise
11%
Global warming
26%

Indoor air pollution
13%
Toxic substances5%
Total life expectancy lost
due to environmental
problems in a year
Urban airpollution
42%

Population of Japan ⇒Total:
640,000
years

640,000 years
About 120 million people

Ozone layer depletion
1%
Photochemical oxidant2%= loss of about 2 days per person

Human health 3/4
Figure 3.3-8: Questionnaire panel (page 5)
Grounds for the assessment of the environmental impact on an environmental attribute (human health)[Panel 6]
Question 1
What do you think Japan’s environmental policy for human health should be in
the future? Choose one of the following:
1. It should be treated as a very important problem.
2. It is necessary to treat it equally to other important issues.
3. Priority should be placed on other important issues.
4. I don’t know.
Human health 4/4
Figure 3.3-9: Questionnaire panel (page 6)
Question about the respondent’s personal impression of the current situation of the environmental impact on theenvironmental attribute (human health)39

LIME2_C3- Integration of Environmental Impacts

Moreover, a question is asked about the respondent’s impression of the results (Figure
3.3-9). Although their answers do not directly influence the results of the conjoint
analysis, asking a simple question makes it possible for the respondents to make theirown answers based on the information provided so far, leading them to indicate theirviews about the later questions for the conjoint analysis.
These four panels complete a serious from the provision of information on theenvironmental impact on human health to questions about the impact. Other panels of
the same construction have been prepared for other environmental attributes, such associal assets, primary production, and biodiversity.

(4) Questions for the conjoint analysis
A summary explanation about each area of protection is followed by questions for the
conjoint analysis. The panel shown in Figure 3.3-10 was used to explain thatquestioning would continue through the use of a profile that consists of five attributes –the above-described four attributes and tax*7. *8 The concept of the construction of a
profile will be explained later.
Before the questioning for the conjoint analysis, the Figure 3.3-10 Questionnaire panel
instructs the respondents to consider the combination of four environmental attributes
and tax.
[Panel 20]
The combination of the above-mentioned four issues is considered in relation
with tax expenditures.
Issue

Policy target/standard

Loss of life expectancy per person

2 daysLoss of social assets per person

20,000 yen

Disappearance of species of organisms

One species newly disappeared

Inhibition of plant growth

200 million tons

Additional tax (per household, per year)

Addition of 10,000 yen per year

Figure 3.3-10: Questionnaire panel (page 20)
Before the questioning for the conjoint analysis, the respondents are instructed to consider the combination of four
environmental attributes and tax.
7

8

40

The monetary attribute can be either a fund or tax. The normalization value used as quantitative information on
environmental attributes is the amount of damage per year. If economic activities remain the same in the future, the sameamount of damage will continue to occur. Therefore, in the case of the monetary attribute, the scenario of paying eachyear is more consistent with the information indicated by environmental attributes than the scenario of paying only onceand seems to be able to avoid any misunderstanding by the respondents. Therefore, the scenario of paying anenvironmental tax has been adopted for the monetary attribute.
Consideration was given to constituting a profile of only four environmental attributes. However, when assuming
environmental policy, it is easier for the respondents to examine the trade-off between the improvement of environmental
attributes and the cost of the improvement than to compare only among the environmental attributes. Therefore, a profile
is constituted of five attributes in total – the four environmental attributes and a monetary attribute.

LIME2_C3- Integration of Environmental Impacts[Panel 21]
Example of the questionnaire
Issue
Loss of life
expectancy perpersonPolicy 1
Half(1.5 months in 50
years)

Policy 2
None(No loss of life
expectancy)

Loss of social
assets per person

1/4(Loss of 500,000
yen in 50 years)

Disappearance ofspecies oforganisms

Half(Disappearance of
25 species in 50
years)

Inhibition of plant
growth1/4(Loss of 1.3% of theforests in Japan in
50 years)

Maintaining thecurrent situation(Loss of 2 million
yen in 50 years)
No additional
disappearance(No disappearanceof species in 50
years)
Half(Loss of 2.5% of
the forests in Japanin 50 years)

Additional tax
amount(per year, perhousehold)

Addition of 20,000
yen each year
(1 million yen in 50
years)

Addition of 10,000
yen each year
(500,000 yen in 50
years)

Policy 3
Maintaining thecurrent situation(Loss of 3 months
in 50 years)
Maintaining thecurrent situation(Loss of 2 million
yen in 50 years)
Maintaining thecurrent situation(Disappearance of
50 species in 50
years)
Maintaining thecurrent situation(Loss of 5% of theforests in Japan in
50 years)
No additional
expenditures

Figure 3.3-11: Questionnaire panel (page 21)
An example of a panel for a survey for conjoint analysis. Instruct the respondents to choose the most favorablefrom among several policy profiles.

Next, the panel in Figure 3.3-11 was shown as an example of questionnaire for conjoint
analysis. According to this, the respondents were instructed to choose what they think is
the most desirable from among two or more policy profiles.
The following questionnaire for conjoint analysis was not a panel. A printed sheet ofpaper was handed to each respondent to have them orally answer which profile was themost desirable. Because the time needed to make an answer differed among the
respondents, consideration was given so that they could make an answer at their own
pace.
As shown in Figure 3.3-11, the questionnaire for conjoint analysis shows quantitative
information for each of the attributes that constitute a profile. Four levels were set foreach area of protection, using the normalization value as the standard. Table 3.3-3
shows the levels adopted for the main survey. L.1 corresponds to the current profile. In
this construction, data converted from the normalization value are presented for eacharea of protection, and there is no additional expenditure as tax. L.2 to L.4 assume
scenarios of reducing the environmental impact to a certain level (1/2, 1/4, 0). It is
assumed that environmental tax is an annual increment in the direct and indirect taxes
necessary for reducing the amount of damage to the areas of protection. The level of tax
has been fixed at 5,000 to 20,000 yen as a realistic tax amount.
41

LIME2_C3Integrationof Environmental
Table 3.3-3:
Levelfor each areaImpacts
of protection used for the setting of profiles

Table 3.3-3: Level for each area of protection used for the setting of profiles
L.1 (current)

L.2

L.3

L.4

Human health

Maintaining the current
situation(Loss of 3 months/50
years)

Half(1.5 months/50
years)

1/4(20 days/50 years)

None

Social assets

Maintaining the current
situation(Loss of 2 million
yen/50 years)

Half(1 million yen/50
years)

1/4(500,000 yen/50
years)

None

Primary
production

Maintaining the current
situation(5% of forests inJapan/50 years)

Half(2.5% of forests inJapan/50 years)

1/4(1.3% of forests inJapan/50 years)

Biodiversity

Maintaining the current
situation(Disappearance of 50
species/50 years)

Half(25 species/50
years)

1/4(13 species/50
years)Tax

No tax increase

5,000 yen/year

10,000 yen/year

None

None

20,000 yen/year

Table 3.3-4: Example of the profiles used for the questionnaire for the main survey

Table 3.3-4: Example of the profiles used for the questionnaire for the main survey
Issue

Policy 1

Policy 2

Policy 3

Loss of life
expectancy perpersonHalf(Loss of 1.5 months
in 50 years)

None(No loss of life
expectancy)

Maintaining thecurrent situation(Loss of 3 months in
50 years)

Loss of social assets
per person

Half(Loss of 1 million yen
in 50 years)

Maintaining the current
situation(Loss of 2 million yen
in 50 years)

Maintaining thecurrent situation(Loss of 2 million
yen in 50 years)

Loss of habitats

1/4(Loss of 1.3% of
forests in Japan in 50
years)

Half(Loss of 2.5% of forests
in Japan in 50 years)

Maintaining thecurrent situation(Loss of 5% of
forests in Japan)

Loss of plant species

Half(Disappearance of 25
species in 50 years)

No additional
extinction

Maintaining thecurrent situation(Disappearance of 50
species in 50 years)

Additional tax
amount(per year; perhousehold)

Addition of 10,000
yen each year
(500,000 yen in 50
years)

Addition of 5,000 yen
each year(250,000 yen in 50
years)

No additional
expenditures

A profile can be made through a combination of choices selected arbitrarily from thefour levels in Table 3.3-3. Table 3.3-4 shows an example of a profile. Eight versions of
the profile for the conjoint analysis have been prepared by orthogonal design. Each of
the versions has eight question lists. Therefore, 64 types (8  8) of question lists havebeen prepared as shown in Table 3.3-4, and eight questions consisting of a different
42

LIME2_C3- Integration of Environmental Impacts

combination of profiles are given to each respondent. The current profile is shown as
Policy 3, while hypothetical profiles into which the current profile is altered are shownas Policy 1 and Policy 2. The current profile has been included in all the question lists
so that the respondents will be always conscious of the contrast between the current andhypothetical situations. In addition, to enhance the understanding of the respondents, theprofiles are displayed in terms of not only the annual amount of latent damage, but also
the amount of damage per person and the total amount of damage that can occur if theannual damage continues for 50 years.
(5) Questions about personal attributes
Lastly, questions were asked about the attributes of the respondents themselves. The
yearly income, the number of household members, and the age were confirmed as
attributes that may be related to their environmental views.
3.3.7

Door-to-door interview survey
An interview survey was adopted for this research, since it was difficult for the
respondents to imagine the contents of the environmental attributes to be assessedunlike cars and home electrical appliances, and it was most important to avoid answers
based on a misunderstanding of the objects of assessment.
During the interview survey, the surveyors were required explain the contents of thequestionnaire so that the respondents can accurately understand them. Before the survey,
meetings to provide an explanation were held several times to ensure that the contentsof the questionnaire and the explanation method were understood by the surveyors. The
surveyors prepared a script concerning the contents of the questionnaire and thenexplained the contents to the respondents according to the script. Moreover, pretests
were held to check that the surveyors were able to sufficiently explain the contents sothat the respondents could fully understand them. Table 3.3-5 summarizes theguidelines for the pretests and the main survey.
Pretests The pretests were held twice. In the first pretest, sampling was carried out at
two points in Tokyo and Yamanashi Prefecture to compare differences between urban
and suburban areas. In the second pretest, consideration was given to the feasibility of
random sampling for the whole of the Tokyo Metropolis. The number of samples was
about 100 to 200 due to budgetary restrictions.
The door-to-door interview method was adopted for the pretests and the main survey. It
is a method whereby surveyors directly visit the respondents. Sampling is inefficientunder the meeting method in the suburbs, and uncooperative respondents tend to refuse
to give answers under the mall intercept sampling method. In both methods, respondentbias may occur at the time of sampling. Because of this, the door-to-door interview
method was adopted. Before the visit, a written visiting schedule was sent to the
respondents.
In addition, to effectively provide explanations to the respondents, scripts of the
explanations were distributed to the surveyors beforehand. Moreover, explanatory
meetings were held at 11 places throughout Japan before the main survey so that thesurveyor could fully understand the contents of the questionnaire and the explanation
43

LIME2_C3- Integration of Environmental Impacts

scripts.
Table 3.3-5: Comparison of the methods between the pretests and the main survey
Table 3.3-5: Comparison of the methods between the pretests and the main survey

Pretest (1st)Pretest (2nd)Main survey

Check the validity of thequestionnaires

Check the validity of themethods for sampling andsurveysCalculation of theweighting factors and theintegration factors

Survey year

2004

2005

2006

Target area

2 points in Kanto(Suginami-ku, Tokyo;
Ryuo-cho, Yamanashi)Tokyo (23 wards; cities)

Throughout Japan

Samples(Collectionrate)

100 (unknown)

217 (47%)

1,000 (50%)

Population

Residents aged 20-60 ineach area

Residents aged 20-60 inTokyo

Residents aged 20-60 inmunicipalities

MethodRandom walkRandom sampling

Random sampling

PurposeSampling

MethodInterview method used by the surveyors

Questionnaire

The surveyors directly explain the contents of the questionnaire to the respondents
through the use of panels.

Survey3.3.8

Calculation results
Statistical analysis was carried out based on the answers gained from the interview
survey. After verification, representative values of the weighting factors that indicatesocial preferences were gained. For the purpose of the analysis, RPL was applied to theprobability term of the random utility function to obtain the results of the calculation.
Table 3.3-6 shows the results of 952 samples (147 spots throughout Japan). The
estimate is the result gained by the maximum likelihood estimation method ( ̂ a ), whichindicates the value compared with the standard in the table. In this case, it indicates thatthe utility decreases by 0.215 in the case of one day’s loss of life expectancy. All theestimates are negative. Because the utility decreases when damage increases, it can befound that the sign condition is consistent with common sense. The fact that all theattributes are significant at a level of 5% indicates that the people have foundenvironmental values in the four areas of protection. In addition, because the likelihood
ratio, which indicates PRL’s power of explanation, exceeds 0.2, it can be said that the
goodness of fit is sufficiently high.
Table 3.3-7 shows the results of the calculation of economic values against a unit
amount of damage based on the results of Table 3.3-6. The number of households is49,529, and the population is 128 million (FY2005 census). Assuming that the loss ofsocial assets is equivalent to an economic loss, the weighting factor WF1 for each areaof protection was calculated from the relationship between the estimated value of social

44

LIME2_C3- Integration of Environmental Impacts

assets and that of the other environmental assets. For example, the economic value forone year’s loss of life expectancy was calculated to be about 15 million yen. The
amount of damage can be converted into a monetary value by multiplying WF1 by thedamage factor. The use of this makes it possible to calculate the integration factor IF1(see 3.3.9).
The use of the weighting factor WF1 and the normalization value NV makes it possible
to calculate the nondimensional weighting factor WF2 (Equation 3.3-3). This can begained by multiplying WF1 by the normalization value and normalizing the result so thatthe sum total will become 1.
WF2 Safe 

WF1Safe  NV SafeWF1Safe NV Safe

(3.3-3)

Safe

Table 3.3-6:
Results
an analysisof theanswers
from theinterview
surveyrespondents)
Table
3.3-6:ofResultsof ananalysisofobtained
the answers
obtainedfromthe(952
interview
surveyThe negative estimates indicate that the utility will decrease
if damageor payment occurs.
(952
respondents)

The negative estimates indicate that the utility will decrease if damage or payment occurs.

Area of protection

Estimate

Standard

t-value-2.15E-1

1day/p/h1)-4.88-5.35E-2

10,000JY/p/h2)-2.92-5.48E+0

%/h3)

-6.99

Biodiversity-5.95E-1

1EINES/h4)

-7.03

Tax

-4.00E-5

1JY/h

-23.0

Human healthSocial assets
Primary production

N:952; log likelihood: -6481.5; likelihood ratio:0.22: average annual income: 8.31 MJY; p: person;
h: household; JY: Japanese yen
t-value is significant if it is 2 or more and at a level of 5%.
1) Change in the utility per household if a person loses one day in life expectancy
Table 3.3-7: Results of the calculation of the weighting factors
2) Change in the utility per household if a person suffers an economic loss of 10,000 yen
WF1 is the economic value per unit area of protection and WF2 is the relative importance between the areas of protection based on the annual
3) Change
the damage
utilityvalue
per washouseholdif plantJapandecreases
1% by the damage factor.
damagevalue. Theinannualobtained fromthe sumbiomass
total of theinannualenvironmental
loadbymultiplied4) Change in the utility per household if a species disappears

Table 3.3-7: Results of the calculation of the weighting factors
WF1 is the economic value per unit area of protection and WF2 is the relative importance between the areas of protection
based on the annual damage value. The annual damage value was obtained from the sum total of the annual environmentalload multiplied by the damage factor.

Area of protection

Standard(unit)

Weighting factor (WF1)
Economic value (JY/unit)
LIME 2

LIME 1

Annual damage
value

Weighting factor (WF2)
LIME 2 LIME 1(2003)(conducted in 2003)

Human health

1 DALY (year)

1.47E+7

9.70E+6

9.39E+12

0.26

0.31

Social assets

10,000 (JY)

1.00E+4

1.00E+4

4.92E+12

0.14

0.21

Primary production

1 ton

4.63E+4

2.02E+4

8.48E+12

0.24

0.23

Biodiversity

1 specie

1.42E+13

4.80E+12

1.32E+13

0.37

0.26

Total:

3.60E+13 yen (about 7% of GDP)

45

LIME2_C3- Integration of Environmental Impacts

Column 3.3-1:
Willingness to pay for loss of life expectancy
The economic value of endpoints is used for the economic assessment of environmental
impacts. With regard to human health, other methods have examined the calculation ofwillingness to pay (WTP) for the loss of life expectancy. Table 3.3-A shows the results ofcomparison among the methods. The values in the table are the economic values of loss of life
expectancy per year.
Extern E (EC 2005) takes into consideration time discounting. For reference, it also shows
values without time discounting. Because neither EPS nor LIME 2 adopts time discounting,representative values are about 10 million to 15 million if the values without time discounting
are compared. All the methods mentioned herein have adopted the stated preference method.
Because Extern E and EPS cover Europe, the results gained by these methods seem relatively
similar to those gained by LIME 2.
WTP is supposed to differ according to the social and economic background. Many integrationassessment models extrapolate per capita GDP (Y) for calculating Di, the amount of damage ina specific region, and calculating DWORLD, the amount of damage in the world, including otherregions.

DWORLD   YWORLD Yregion   Di
regionε is income elasticity and is often -1 or between 0 to -1.
However, because discussions about the calculation of the WTP are insufficient during LCIA
Table
3.3-A: seems
Comparison
economic withvalue researchof the loss oflifefinding
expectancy
research, a lotof timeto beconcerning
needed totheproceedforlevel of WTP
that reflects differences in economic conditions among countries and regions.
Table 3.3-A: Comparison concerning the economic value of the loss of life expectancy

MethodExternEBasiceconomicassessment
methodUnit

Representative value
Discount (3%)

VSL(person)1.05 M€(160 million
yen)

VOLY
(year)

50,000€(7.5 million
yen)

CVM

Maximum
value

Minimumvalue

225,000€(34 millionyen)27,240€(4.1 million
yen)

18 million
yen

10 million
yen

No discount74,627€(11 million
yen)

EPS

CVM

YOLL
(year)

85,000€(12.8 million
yen)

LIME

Conjoint
analysis

DALY(year)

14.7 million
yen* VSL: Value of Statistical Life, VOLY: Value of the Life Year, YOLL: Years of Life Lost, DALY: Disability
Adjusted Life Years46

LIME2_C3- Integration of Environmental Impacts

Column 3.3-2:
The total value of the environmental impacts of economic activities in Japan
If the results of conjoint analysis are applied to the results for the normalization value, theeconomic value of the environmental impacts is that generated through Japan’s economic
activities in a year (Table 3.3-7). According to this, the value of the environmental impacts is
estimated to be about 36 trillion yen. This is equivalent to about 7% of Japan’s GDP (520trillion yen (2006)).
Such a test calculation is carried out for other research. Environmental costs were calculated
every five years between 1970 and 1990 under the System of Environmental-Economic
Accounting (SEEA), whose purpose is to grasp the level of deterioration of the environment(environmental pollution) based on the System of National Accounts (SNA), a system formeasuring the volume of economic activities. Although the resultant values differed according
to assessment period, the loss of asset values due to environmental changes is estimated to befour to six trillion yen under any method. This is far smaller than the value gained by LIME 2.
According to SEEA, environmental costs are called imputed environmental costs and refer tothe costs necessary for reducing the environmental load to maintain a specific level. Although
the value of the environment consists of utility value and non-utility value, imputed
environmental costs do not include non-utility value. Note that because the value of theenvironment under LIME includes both utility value and non-utility value, there is a differencein the scope of the calculation.
According to this result, it is clear that biodiversity and human health were highly
weighted. The results indicate that, in Japan and other advanced countries, many people
think that quantitative resources are provided to a satisfactory extent in human society,while the values of qualitative items, such as health, biodiversity, and forests, also are
relatively high.
In addition, the annual amount of damage was estimated to be about 36 trillion yen.
This is equivalent to about 7% of Japan’s GDP. Compared with the results of LIME 1 in
2003, it can be found that the economic values of many areas of protection increased.
This is because of differences in the targets of the sampling, the survey method, and thesurvey period. LIME 1 was carried out mainly in Tokyo due to the limited number ofsamples. Because a nationwide survey is carried out under LIME 2, it can be consideredthat suburban people’s environmental views will greatly influence the results.
Because a door-to-door interview survey has been adopted for LIME 2, the respondentbias is less than in the case of the mall intercept method adopted for LIME 1. The mallintercept method is prone to choosing a certain type of respondent. Because this type of
respondent is relatively cooperative in the survey, the results of the analysis are likely tobe relatively stable, but it is difficult to conclude that the results reflect the population’senvironmental views. Even if random sampling is adopted, respondent bias cannot beignored if the collection rate is low. However, because the collection rate was almost
50% in the main survey, it can be considered that respondent bias was reduced to theminimum. On the other hand, although the possibility of choosing samples that are notrelatively positive about the survey is higher than in the case of other methods, this does
not seem a great problem, since highly explainable results could be obtained.

47

LIME2_C3- Integration of Environmental Impacts

Next, Table 3.3-8 shows a comparison between the results of an estimation under RPL
and the results under CL.*9 With regard to RPL, a normal distribution is assumed for
all the attributes except the tax attribute, and Halton draws are used for the simulation.
Comparison of the calculation results of the weighting factors
TableTable
3.3-8:3.3-8:
Comparisonof the calculation results of the weighting factors(a) conditional logit model; (b) random parameter logit model(a)conditional logit model; (b) random parameter logit model
Figures in parenthesis are the estimated factors of the standard deviation.
Figures in parenthesis are the estimated factors of the standard deviation.

(a) Conditional logit model
Attribute(b) Random parameter logit modelStandard
Factort-valuep-value

Factort-valuep-valueHumanhealth1 day/p/h-1.86E-1-7.90<0.0001-2.15E-1(1.80E-2)-4.88(17.9)<0.0001(<0.0001)

Social assets

10,000yen/p/h-4.75E-2-4.29<0.0001-5.35E-2(6.14E-3)-2.92(11.9)

0.0035(<0.0001)

Primary
production%/h-3.98E+0-9.60<0.0001-5.48E+0(3.21E-1)-6.99(15.9)<0.0001(<0.0001)

Biodiversity

1EINES/h-4.75E-1-11.4<0.0001-5.95E-1(3.64E-2)-7.03(19.2)<0.0001(<0.0001)

1yen/h-3.00E-5-25.3<0.0001-4.00E-5-23.0<0.0001(<0.0001)

Tax

Remarks

Log likelihood: -7921.1
Likelihood ratio: 0.05

Log likelihood: -6481.5
Likelihood ratio: 0.22

The likelihood ratio calculated by RPL improved more significantly than the results of
CL (to 0.05-0.22), which clearly shows improvement in the estimation model’s powerof explanation. The mean value is similar to the result of CL. On the other hand, RPL ischaracterized by the gaining of factors for the estimation of standard deviation, whichcannot be gained by CL. Because all the factors are statistically significant, it can be
confirmed that the results of analysis using RPL are valid. It can be said that RPL,
whereby each attribute variable can quantitatively display the degree of preference
among individuals, is extremely effective for gaining integration factors, including thestatistical values.
Figure 3.3-12 (a) to (d) show the frequency distribution of WF1 of the areas ofprotection, together with the statistical values. With regard to all the areas of protection,
the distribution is symmetrical and is consistent with the normal distribution. Amongthe areas, the variation coefficient of the weighting factor for social assets is high,which indicates that individual difference is relatively large.
Table 3.3-9 shows the correlation matrix among the weighting factors in the areas ofprotection. The important area of protection differs among individuals. According tothis table, although all the correlations are negative, the degree of negativity differs
among the areas of protection.

948

Because examination of the resultant estimate itself and economic assessment made by the use of it have already been
carried out, pay attention to the relative comparison with CL in this table.

Figure 3.3-12 (a): Frequency distribution and statistical values of the weighting factor for humanhealth WF1 (yen/DALY)

LIME2_C3- Integration of Environmental Impacts

Frequency

Probability

Comparison graphDistribution
profileNormaldistribution

Mean

1.47E+7Median1.47E+7Standard
deviation

3.08E+6

Variationcoefficient

0.21

Kurtosis
3
Figure 3.3-12 (b): Frequency distribution and statistical values of the weighting factor for social assets
Fit #1: normal
Data value
WF1 (yen/10,000 yen)
Skewness0
distribution

Figure 3.3-12 (a): Frequency distribution and statistical values of the weighting factor for humanhealth WF1 (yen/DALY)

Frequency

Probability

Comparison graph

Fit #1: normal
distribution

Data value

Distribution
profileNormaldistribution

Mean

1.00E+4Median1.00E+4Standard
deviation

3.43E+3

Variationcoefficient

0.34

Kurtosis

3

Figure 3.3-12 (c): Frequency distribution and statistical values of the weightingSkewness
factor for primary0
production WF1 (yen/kgDW)

Figure 3.3-12 (b): Frequency distribution and statistical values of the weighting factor for social
assets WF1 (yen/10,000 yen)Frequency

Probability

Comparison graph

Fit #1: normal
distribution

Data value

Distribution
profileNormaldistribution

Mean

4.62E+1

Median4.62E+1

Standard
deviation

6.77E+0

Variationcoefficient

0.147

Kurtosis3

Skewness

0

Figure 3.3-12 (c): Frequency distribution and statistical values of the weighting factor for primary
production WF1 (yen/kgDW)

49

Figure 3.3-12 (d): Frequency distribution and statistic values of the weighting factor forbiodiversity WF1 (yen/EINES)
LIME2_C3- Integration of Environmental Impacts

Distribution
profileNormaldistribution

Mean

1.42E+13

Median1.42E+13

Standard
deviation

2.05E+12

Variationcoefficient

0.145

Kurtosis3

Skewness

0Frequency

Probability

Comparison graph

Fit #1: normal
distribution

Data value

Figure 3.3-12 (d): Frequency distribution and statistic values of the weighting factor for biodiversity
WF1 (yen/EINES)
Table 3.3-9: Correlation matrix of the weighting factors

Table 3.3-9: Correlation matrix of the weighting factors
Human health
Human healthSocial assets

Primary
production

Biodiversity-0.13*1-0.12*1-0.05*2-0.13*1-0.13*1

Social assets
Primary
production-0.06*1

Biodiversity*1: Significant at the 1% level; *2: Significant at the 5% level

Examination of the decorrelation is an examination of a hypothesis that there is no correlation between the twovariables. If the correlation is judged to be significant, the hypothesis is rejected and “there is a correlation
between two variables” that has statistical verifability.

3.3.9

Uncertainty analysis of integration factors
If results can be gained concerning not only an estimation of the population, but also thevariation in the estimate, uncertainty analysis of the integration factors can be carriedout by applying variation in the damage factor. Because of this, LIME 2 shows theresults of uncertainty analysis of integration factors using the statistical values of theweighting factor WF1 gained from RPL.
The integration factor IF1 can be gained from the sum of products of the weighting
factor WF1 and the damage factor.*10

10

50

An integration factor can be gained from the sum of products of the damage factor and the weighting factor. The
representative value can be calculated by two methods: 1) calculation is made by the median of the weighting factor WF1
and the median of the damage factor DF; and 2) the median is gained from analysis of the sum of the products of theweighting factor WF1 and the damage factor DF using the Monte Carlo method. Under LIME 2, the median gained by the

LIME2_C3- Integration of Environmental Impacts

IF1

Impact X    DF Impact (Safe, X) WF1(Safe) (3.3-4)Safe

In this equation,
DFImpact (Safe, X): the damage factor of the substance X against the area of protection
Safe through the impact category Impact [unit amount of damage/kg]
WF1 (Safe): economic value [weighting factor] of the damage per unit of the area ofprotection Safe [yen/unit amount of damage]IF1Impact (X): integration factor (version 1) of the substance X [yen/kg]
The integration factor was statistically analyzed by performing the calculation of
Equation 3.3-4 using the Monte Carlo method several times.*11 As an example, Figure
3.3-13 shows the results of calculation of the integration factor for CO2 (global
warming). In the case of CO2, although the impact of warming on health and social
assets is assessed, the figure also shows statistical values, such as the mean and the
median. In addition, it also shows the results of fitting the frequency distribution and thecontinuous function. According to the results of calculations performed 50,000 times,
the lognormal distribution is the most consistent.
Moreover, Figure 3.3-14 shows the sensitivity analysis results of the integration factor
for CO2. Because a rank correlation coefficient is shown for each type of variable used
for the calculation of the integration factor, it is possible to extract variables that
influence the uncertainty of the calculation result.
The integration factor of CO2 is highly correlated with the D-R factor of malaria, theclimatic sensitivity, and the weighting factor for human health. This indicates that theseparameters
greatlyFrequency
influence
the uncertainty
theoffinal
results.factor
Although
Figure 3.3-13:
distribution
and statistic ofvaluesthe integration
for CO 2 many
[yen/kg]otherparameters are used for the calculation of integration factors, improvement of thereliability of the above-mentioned variables seem to make it possible to reduce theuncertainty of integration factors.

Mean

Frequency

Probability

MedianDistribution profile

Lognormal

Number of times
performing the
calculation

50,000Mean

2.77 yen/kg

Median2.33 yen/kg

Standard deviation

1.65

Variation coefficient

0.596

Figure 3.3-13: Frequency distribution and statistic values of
the integration factor for CO2 [yen/kg]

11

method 2) is used as the representative value of the integration factor IF1. Note that the results are not the same.
Under LIME 2, there are three types of integration factors (IF1, IF2, IF3). The normalization value is used for the
calculation of IF2, nondimensional factor (see 3.3.10). With regard to the annual environmental load, which is used forthe calculation of the normal value, because information on uncertainty could not be gained, the representative value of
IF2 is shown without having been analyzed. With regard also to IF3, a factor based on CO2, only the representative value
is shown.

51

LIME2_C3- Integration of Environmental Impacts

Sensitivity graph: IF (Carbon dioxide)
Relative risk malaria
Climate sensitivity*WF_HHDALY/1 case malaria AFR
GCM index used for assessment of
the impact on farm products
MEA_population_2050*WF_SA
AFR_population_2050
Rice price change multiplier
DALY/1 kg cold stress World
DALY/1 kg heat stress World
MEA_GDPpp_2050
ARF_GDPpp_2050
Wheat corn price change multiplier
Economic growth damage mitigation
equation
SAS_population_2050
Relative risk famine risk
Uncertainty of sunken areas
AFR number of beneficiaries ofheat/cold stress 1.5C

Figure 3.3-14: Results of a sensitivity analysis of the integration factor for CO2 [yen/kg]
The horizontal axis indicates the rank correlation coefficient, while the vertical axis indicates variables
with a high rank correlation coefficient.

Column 3.3-3:
The integration factor for CO2 and SCC
Under LIME 2, the integration factor for CO2 (the economic value of environmental impacts
per kg of emitted CO2) was calculated at 2.3 yen/kg (the mean is 2.8 yen). Various institutes
have attempted to calculate the economic value per unit of CO2. In this column, comparison
will be made with other methods.
The excessive amount of economic damage from additional emissions per unit of CO2 iscalled SCC (social cost of carbon) by the IPCC Fourth Report Working Group II (IPCC WG22007). The report mentions SCC as the indicator that attracts the greatest attention fromresearch on the assessment of the entire global impact of CO2, and introduces many SCC
research cases, including Tol (2002), Hope (2005), and Stern (2006).

52

LIME2_C3- Integration of Environmental Impacts

This column compares the integration factor calculated under LIME 2 and theabove-described SCC. Table 3.3-B summarizes the comparison between SCC gained by the
integrated assessment model and the integration factor for LCIA. SCC can be roughly divided
into that of about $30 (Nordhaus, Tol, Hope) and that of nearly or more than $100 (Stern,
EPS). Under LIME 2, the result is similar to the former. Confidence has been examined inmany of these assessments. According to the results using LIME, the confidence interval
seems narrow.
Next, Figure 3.3-A shows a comparison with the structure of Tol’s SSC in terms of the
breakdown of the integration factor. Both have common points: consideration is given to not
only damage, but also benefits; and various items are covered, including health and farm
products. However, they also have differences. The following are the main differences:
1) The scope of the assessment differs. Although LIME 2 does not cover economic impacts
on forestry and water, Tol covers them. On the other hand, LIME 2 covers the impacts ofdisasters and malnutrition on health, while Tol does not cover them.
2) Although damage from infection is the greatest among all types of damage under LIME 2,
Tol suggests that the greatest damage is that from the maldistribution of water resources.
Moreover, the results of a sensitivity analysis were compared to find whether the recognition
concerning important parameters is consistent. Table 3.3-C shows the results of Hope’s
sensitivity analysis for SCC (IPCC WG2 2007). According to the table, as in the case of theresults of LIME 2 (Figure 3.3-14), it can be considered that climate sensitivity and willingness
to pay for matters that cannot be handled in the market (such as human health) are important.
However, items related to value judgments, such as the discount rate and Equity Weighting(adjustment for consideration of the economic gap between advanced countries anddeveloping countries), are frequently not included under LCIA methods, including LIME.

Table 3.3-B: Comparison of the calculation results for the SCC
SCC can be roughly divided into costs based on the integrated assessment model and costs based on the LCIA
method.

SCC (US$/ton CO2)
Model
Mean

Confidence interval(5%, 95%)

DICE (Nordhaus et al. (2000))$6

FUND (Tol (1999))$25-$10, $350

PAGE (Hope (2006))$19$4, $51

ExternE (2005)$11-$0.5, $40(10%, 90%)

Stern report (2006)$85

EPS (2000)$150 (108€)

LIME 2 (2009)$28$17, $47(10%, 90%)53

Figure 3.3-A: Comparison in terms of the scope of the research and the breakdown of the SCC
The breakdown differs between them

LIME2_C3- Integration of Environmental Impacts

100%

Malnutrition
栄養不足
Disaster
災害
Heat/cold stress
熱・寒冷ストレス
Infection
感染症
Ecosystems
生態系
Sea level rise
海面上昇
Cooling
冷房
Heating
暖房
水
Water
林業
Forestry
農業
Agriculture

Malnutrition

80%
60%

Ecosystems

40%

Cooling

20%

Infection

Water
Agriculture

0%
-20%

Heating

Tol

LIME2

-40%
Figure 3.3-A: Comparison in terms of the scope of the research and the breakdown of the SCC
The breakdown differs between them

Table 3.3-C: Results of a sensitivity analysis regarding the SCC (using the
integrated assessment model PAGE)

Table 3.3-C: Results of a sensitivity analysis regarding the SCC(usingandthe
integrated
model
PAGE)
Not only climate sensitivity
non-market
goodsassessment(such as the healthimpact),but also the discount rateand Equity
Weightingand(adjustmentof values
betweenadvanced
and developing
countries)
areNot only climate
sensitivity
non-market
goods
(such
as the countries
health impact),
but also
the discountrate and
important.
Equity Weighting(adjustment of values between advanced countries and developing countries) are important.

Parameter

Sign

Degree ofimportance

Climate sensitivity+

100

Discount rate-

66

Economic value (non-market goods)+

57

Equity Weighting-

50

Atmospheric lifetime-

35

Economic impact (market goods)+

32

IPCC AR4 (2007)

Therefore, to discuss the integration factor for CO2 based on an LCIA method such as the SCC
described in the IPCC, it seems necessary not only to see the values, but also to consider
whether to take into consideration the scope of the research, the breakdown of SCC, theprospects for SCC, and north-south gaps when calculating SCC. In addition, the discount rate
and the like are not taken into consideration when integration factors for LCIA are calculated,
the uncertainty shown in Table 3.3-B is expected to increase relatively if these are taken into
consideration.

54

LIME2_C3- Integration of Environmental Impacts

Column 3.3-4:
Control costs and damage costs
With regard to CO2, the economic indicators can be roughly classified into damage costs andcontrol costs. The integration factors proposed by LIME, Extern E, and EPS are a monetaryconversion of the damage that can occur in a society due to the generation of CO2 and fallunder the category of damage costs. All the SCC mentioned in the previous column also fallunder the category of damage costs. The Ministry of the Environment and the Central
Environment Council announced the control costs necessary for restraining the emission of aunit of CO2. This column explains them.
In October 2004, the Ministry of the Environment (2004) proposed that the carbon tax should
be 2,400 yen/carbon ton (650 yen/t-CO2). The main purpose of the introduction of a carbon
tax is to reduce emissions. The introduction is expected to reduce this greenhouse gas by 43
million tons, a little more than 3.5% on the base year of 1900. If the target is a 6% reduction,
this will require a higher cost.
The Central Environment Council’s Global Environment Committee “Goal Achievement
Scenario Subcommittee’s Interim Report” (2001) shows the relationship between the amountof reduction necessary for realizing the COP3 agreement with the full use of domestic CO2
control technologies and the cost of this reduction. Oka et al. (2002) used the information to
calculate the marginal reduction costs. Assuming that the target amount to be achieved in 2010is 310 million carbon tons, down by 6% on 1990, they fixed the reduction target at 26.7
million tons (98 million t-CO2), deducting a forest sink of 3.7% and a credit of 1.8% under the
Kyoto mechanism. The highest cost of reduction technologies to be introduced for theachievement of the target was fixed at 4,600 to 11,700 yen (t-CO2).
In addition to the damage costs, Extern E also calculated the control costs, since the scope of
the assessment could not be sufficiently assured. In this research, the cost of introducing thetechnologies necessary for complying with the guideline values specified in the Kyoto
Protocol was estimated to be 19 euros. If a stricter target of limiting the temperature rise towithin 2 degrees centigrade is set, further limitations will need to be imposed on CO2emissions. As a result, the cost of limiting CO2 emissions was estimated to increase to 95
euros (14,250 yen) per ton of CO2 reduction.
Table 3.3-D summarizes the control costs.
In this way, the economic value of CO2 changes according to the goal of the control cost. Pay
special attention to the great difference in the marginal reduction costs between the goal ofcomplying with the Kyoto Protocol and the goal of limiting temperature rise within two
degrees centigrade.
On the other hand, there are many problems regarding calculation of the damage costs.
Improvement is required in matters related to value judgments, such as WTP and the discountrate, and natural scientific parameters, such as climate sensitivity.
The control costs will be used as a measure of the effects of cost-benefit analysis. For the
purpose of cost-benefit analysis, a reduction in the damage costs provides a social benefit. The
calculation method, the concept, and the resultant meaning differ according to the control
55

LIME2_C3- Integration of Environmental Impacts

costs and the damage costs. Therefore, it is necessary to use different LCIA methods
according to the purpose of the assessment, taking into consideration the limits of their use.
Table 3.3-D: Comparison between the integration factor for CO2 and the marginal reduction costs(avoidancecosts)
Table 3.3-D: Comparison between the integration
factorfor CO2 and the marginal reduction costs(avoidance costs)
Cost per ton of CO2 reduction
MethodUnit

Damage costs
ExternE
Avoidance costs

Representative
value

Remarks

9€(1,350 yen)

Coastal areas, agriculture, forestry,
energy, water resources, natural
ecosystems, health19€(2,850 yen)If the goal is to comply with the
Kyoto Protocol

95€(14,250 yen )If the goal is to limit the temperaturerise to within 2 degrees centigrade

Ministry of theEnvironment

Avoidance costs

650 yen

Goal: reduction of more than 3.5%
on the base year of 1990

Oka et al.

Avoidance costs

8,000 yen

Goal: reduction of 6% on the baseyear of 1990; cited from the Central
Environment Council

LIME 2

Damage cost

2,330 yen

Health, energy, agriculture, coastalarea

3.3.10Integration procedure

The integration factor can be gained by multiplying the weight factor by the damage
factor. The results of the integration of environmental impacts can be expressed inmonetary units by multiplying the integration factor by LCI.
I1  (Inv(X) DF

Impact Safe

X

Impact(Safe, X)  EV(Safe))  (InvX  IF

1

ImpactImpactX  )(3.3-5)X

In this equation,
I1: result of integration based on economic assessment (external cost) [yen]Inv (X): life cycle inventory of the substance X [kg]DFImpact (Safe, X): the damage factor of the substance X against the area of protection
Safe through the impact category Impact [unit amount of damage/kg]
EV (Safe): economic value of the damage per unit of the area of protection Safe
(economic value conversion factor) [yen/unit amount of damage]IF1Impact (X): integration factor for the substance X (version 1) [yen/kg] *12
The integration factor (IF1Impact (X)) is shown in Appendix A3.
1256

In the appendix, the representative values for damage and integration factors reflect the results of the uncertainty analysis.
To obtain the integration factor, the sum of the products of the damage and weighting factors is calculated using theMonte Carlo method. Therefore, note that, strictly speaking, the median of the integration factor (which is treated as therepresentative value under this method) is different from the sum of the products of the medians of the damage andweighting factors.

LIME2_C3- Integration of Environmental Impacts

The use of the weighting factor WF2 in Table 3.3-7 makes it possible to calculate thenondimensional integration factor IF2. IF2 can be gained by dividing the damage factor
by the normalization value and multiplying the result by the weighting factor. It is thus
possible to integrate LCIA. The use of these factors makes it possible to also express theintegration result as a nondimensional indicator.

(3.3-6)

I2: nondimensional result of the integration of environmental impacts (nondimensional)
NV (Safe): normalization value of the area of protection Safe (annual amount of latent
damage)WF2 (Safe): weighting factor for the area of protection Safe gained as shown in Table
3.3-7 (nondimensional)
IF2Impact (X): integration factor for the substance X (version 2) [kg-1]
C: constant for adjusting the unit of the integration factor (1014)
The integration factor (IF2Impact (X)) is shown in Appendix A3.
Gaining two types of integration factors in this way is a characteristic of conjoint
analysis. If the integration factor IF1 is used to express the result as a monetary value,
the result can be used for various purposes, such as cost-benefit analysis, environmental
efficiency, and environmental accounting. Moreover, the expression of the result as amonetary unit, which is used in people’s everyday life, is one of the indicators mostsuitable for activating environmental communication. However, because research on theestimation of willingness to pay is still immature, assessed values may change greatly inthe future. Repeated discussions mainly by experts are essential to determining theextent to which the results obtained in this exercise can be used for general purposes.
On the other hand, the integration factor IF2 is highly consistent with the weighting
procedure specified in the international standards that use normalization and weighting
factors. In addition, the expression of assessment results by economic indicators may be
avoided, depending on users’ purposes. However, because there is no information on
the uncertainty of the normalization value (NV), which is one of the parameters used forthe calculation of IF2, note that there are no statistical values. Users can selectintegration factors that suit the purpose of their assessment, such as to calculate externalcosts, to carry out integration according to international standards, and to clarify the
relationship with CO2 emissions. *13
13

LIME 1 used two types of weighting methods: conjoint analysis and AHP (Analytic Hierarchy Process) (Itsubo 2005).
The CL used for conjoint analysis is consistent with random utility theory and is excellent in that it can be discussed fromthe viewpoint of welfare economics. Moreover, weighting factors, which can be gained as analysis results, enable theanalysis of statistical significance and verify the power of explanation of the model as a whole.
On the other hand, AHP has been frequently used as a decision-making support tool. Some research has used AHP for the
integration of LCIA. Because the respondents make a pair comparison between elements, the effort required of the
respondents is less than making a comparison between the profiles used in conjoint analysis. However, the analysis
results of the answers are sample averages. Therefore, because it is impossible to verify whether they are representativeof the population, it is unreasonable to use integration factors obtained from AHP for LCIA.
In addition, because AHP uses pair comparison in the questioning, its questioning style is different from that of conjoint
analysis. Because obtaining the answers in two styles when conducting a survey increases the burden on the respondents,

57

LIME2_C3- Integration of Environmental Impacts

In addition, LIME 2 also shows the integration factor (IF3) based on CO2 (=1). The
results of calculation using IF3 is expressed as the amount equivalent to CO2 emissions,as with the Global Warming Potential (GWP).

Appendix A3 shows the integration factor (IF3Impact (X)).
However, note that IF3 is an integration factor and is not as accurate as the GWP.
Table 3.4-1: Main differences between LIME 1 and LIME 2 is in the integration factor calculation
procedures and the results
LIME 1

LIME 2

Survey method

Interview survey(Meeting survey)

Interview survey(Door-to-door survey)

Sampling method

Mall intercept

Random sampling

Number of sample
answers400

952

Survey placeTokyo

Throughout Japan

Matters acquired
due to calculation

Representative value

Representative value andstatistical values

Model

Conditional logit model

Random parameter logit
model

Purpose of improvement
under LIME 2

Development of highly
representative and generalpurpose weighting factors

Calculation of the statisticalvalues of the weighting
factors

3.4 Conclusion
The integration, the final process of LIME, makes it possible to express as a single
index the amount of damage to the areas of protection presented as a result of thedamage assessment through the application of a weighting to the areas of protection.
Therefore, it was necessary to give consideration to obtaining weighting factors for theareas of protection that are representative of social views and are highly persuasive.
Attention was focused on conjoint analysis as a highly feasible method. Althoughconjoint analysis was developed in the fields of computational psychology and market
research, it has recently drawn particular attention as a method of assessing theenvironment from the viewpoint of economics. It was judged that the methodology of
conjoint analysis, which is based on theories of both statistics and economics, satisfies
it may be difficult to obtain stable survey results.
As a result, LIME 2 adopted conjoint analysis, whose method is based on theories of inferential statistics and about which
the representativeness of the population has been verified, and a decision was made not to calculate the weighting factors
using AHP.

58

LIME2_C3- Integration of Environmental Impacts

the purpose of gaining highly representative weighting factors among the areas ofprotection.
Choice-based conjoint analysis was adopted for the conjoint analysis. Under this choiceconjoint method, the most suitable policy draft is selected from among the several
policy drafts specified in the questionnaire. A policy profile was established here,
consisting of five attributes – the four environmental attributes defined as areas ofprotection for the purpose of this research (human health, social assets, biodiversity, andprimary production) and a tax. The normalization value gained from the damage factors
and the annual environmental load were adopted for the current baseline policy draft.
Table 3.4-1 summarizes the integration processes under LIME 1 and LIME 2 and thedifferences in the processes between them. The following summarizes thecharacteristics of LIME 2.
Households were sampled from throughout Japan using random sampling to obtainweighting factors that reflect the Japanese people’s environmental views. Following this,surveyors acquainted with the contents of the survey visited the households andconducted interviews. When explaining the questionnaire, they made efforts to reduceany misunderstanding of its contents and avoid introducing any bias as far as possible,
such as by showing panels clearly explaining the contents and reading the explanatory
script aloud.
A certain number of respondents is necessary for estimating highly accurate weighting
factors. In the main survey, interviews were held with about 1,000 respondents, and astatistical analysis was carried out based on the data. This was the first time such alarge-scale survey for the calculation of weighting factors had been carried out for
research on LCIA and research on environmental economic assessment.
RPL was applied to the responses. The economic value per unit of area of protection
was gained by the maximum likelihood estimation method. All the results concerning
the areas of protection were statistically significant, and it was confirmed that theapplied model’s power of explanation was high. This indicated that the respondents
gave their answers in light of their own environmental views after understanding wellthe explanations about the questionnaire. In addition, not only the estimates, but also thestatistical values were calculated during the survey. This resulted in success in making
visible differences in the environmental values among individuals.
According to the results of the survey, the greatest importance was placed on
biodiversity, followed by human health and primary production, while relatively little
importance was placed on social assets. This is a difference from LIME 1, where thegreatest importance was placed on human health. Moreover, the economic values of theareas of protection increased from those under LIME 1. Although the results between
both cannot be compared unconditionally due to various differences in the survey
period, the class of respondents, the data entered in the questionnaire, etc., the results
are assumed to reflect the recent increase in people’s awareness of the environment andthe effect of carrying out the survey in the suburbs.
Main The main issues and problems concerning the integration of environmental
impacts are dealt with below.
59

LIME2_C3- Integration of Environmental Impacts

If environmental views differ among individuals, areas of protection are weighted
differently. Eco-indicator 99 established weighting factors after classifying people withenvironmental views into three types (hierarchist, egalitarian, and individualist). LIME
did not classify weighting factors according to people’s environmental views or theattributes of individuals. This is because the survey found that the model’s power of
explanation is sufficient even if the utility functions of the population are not classified
according to environmental views. In addition, there is the fear that if a list of factors
classified according to environmental views is made, users might become confused byan increasing number of types. However, this does not deny the possibility of obtaining
more consistent results if environmental views are classified. If the purpose is to obtain
more accurate factors, the classification of environmental views is expected to serve as
an effective approach.
The questionnaire explains the purpose of the survey and the current situation of theenvironmental impacts on each area of protection before asking the questions for the
conjoint analysis. Because the way of assessing the environment by the respondents
may change depending on the explanation, it is desirable to examine the extent to which
the answers may change according to the means of presenting the information.
There are two types of integration factors gained through this analysis (economic
indicators and nondimensional weighting factors). Which to use depends on the purposeof each user. If the purpose is cost-benefit analysis, environmental accounting, or fullcost assessment, it is effective to use integration factors based on economic indicators.
On the other hand, if integration is carried out as provided in ISO-LCA, if the results ofintegration are used for environmental efficiency, or if there is no reason for the positive
use of economic indicators because the purpose is an external announcement,
non-dimensional factors will be applied. At present, the relationship between LCIA andeconomic analysis, such as cost-benefit analysis, has not been sufficiently explored. In
future, research on economic assessment of the environment is expected to develop to
acquire more accurate integration factors and use these for decision-making concerning
the selection of environmentally-oriented products.

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